Collapse - Ronald Feldman Gallery

Transcription

Appendix
by Ballengée et al.
(B. Ballengée with T.Gardner, J. Rudloe, B. Schiering and P. Warny)
2012
26,162 preserved specimens representing 370+ species
or
>2.4% of the known 15,419 species of the Gulf of Mexico
Date: 5 May 2012
Ronald Feldman Fine Arts, New York, NY
Table of contents
SPECIES LIST BY TROPHIC LEVEL………………..……………………………..….2-8
SPECIES LIST BY PHYLOGENETIC ORDER…………...………………………….9-15
SELECT PUBLICATIONS
Specimen list by trophic level
Level
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Reference
Species
Col.1.1
Flower coral species
Col.1.2
Organ pipe coral
Col.1.3
Thin finger coral
Col.1.4
Elkhorn coral
Col.1.5
Frilly lettuce coral
Col.1.6
Scarlet coral species
Col.1.7
Boulder coral species
Col.1.8
Brain coral species
Col.1.9
Cauliflower coral species
Col.1.10 Table coral
Col.1.11 Branch coral
Col.1.12 Brownstem coral
Col.1.13 Lace coral species
Col.1.14 Branching coral species
Col.1.15 Blue ridge coral
Col.1.16 Large-cupped fungal coral
Col.1.17 Rooster tail conch
Col.1.18 West Indian fighting conch
Col.1.19 Florida fighting conch
Col.1.20 Incongruous ark
Col.1.21 Southern quahog
Col.1.22 Stout tagelus
Col.1.23 Common razor clams
Col.1.24 Propeller clam
Col.1.25 Soft shell clams
Col.1.26 Neapolitan triton
Col.1.27 Northern moon snails
Col.1.28 Dwarf razor clams
Col.1.29 Hawkwing conch
Col.1.30 Shark eye sea snail (didyma)
Col.1.31 Graceful fig sea snail
Col.1.32 Florida crown conch
Col.1.33 Lettered olive sea snail
Col.1.34 Florida calico scallop
Col.1.35 White scallop
Col.1.36 Nucleus scallop
Col.1.37 Fairyland snail sea snail
Col.1.38 Pillow stinking sponge
Col.1.39 Florida hermit-crab sponge
Col.1.40 Stinking vase sponge
Col.1.41 Half-naked pen clam
Col.1.42 Atlantic plate limpet
Col.1.43 Yellow sea fan species
Col.1.44 Green sea fan species
Col.1.45 Purple sea fan species
Col.1.46 Spiny oyster
Col.1.47 Red sea lettuce
Col.1.48 Tar balls
Scientific Name
Type
Quantity
Origin
Mussa spp.
Partial specimens
5
Pet-trade
Tubipora musica*
Partial specimens
15
Pet-trade
Porites furcate
Partial specimens
8
Pet-trade
Acropora palmate
Partial specimens
1
Pet-trade
Lobophytum crassum
Partial specimens
3
Pet-trade
Dichocoenia spp.
Partial specimens
1
Pet-trade
Monstastrea spp.
Partial specimens
10
Pet-trade
Diplora spp.
Partial specimens
1
Pet-trade
Pocillopora spp.
Partial specimens
3
Pet-trade
Acropora hyacinthus
Partial specimens
23
Pet-trade
Acropora florida
Partial specimens
1
Pet-trade
Pocillopora verrucosa
Partial specimens
22
Pet-trade
Family stylasteridae
Partial specimens
3
Pet-trade
Madracris spp.
Partial specimens
9
Pet-trade
Heliopora coerula
Partial specimens
4
Pet-trade
Scolymia lacera
Partial specimens
3
Pet-trade
Lobatus gallus
Shells only
1
Wholesale supplier
Strombus pugilis
Shells only
4
Wholesale supplier
Strombus alatus
Shells only
11
Wholesale supplier
Anadara brasiliana
Whole specimens
24
Markets
Mercenaria campechiensis notata
Whole specimens
63
Markets
Tagelus plebeius
Whole specimens
10
Markets
Ensis directus
Whole specimens
9
Markets
Cyrtodaria siliqua
Whole specimens
20
Markets
Mya arenaria
Whole specimens
24
Markets
Cymatium parthenopeum
Whole specimens
4
Markets
Lunatia heros
Whole specimens
6
Markets
Ensis megistus
Whole specimens
93
Markets
Strombus raninus
Shells only
6
Wholesale supplier
Polinices duplicatus
Shells only
51
Wholesale supplier
Ficus gracilis
Shells only
2
Wholesale supplier
Melongena corona
Shells only
6
Wholesale supplier
Oliva sayana
Shells only
40
Wholesale supplier
Argopecten gibbus
Shells only
5
Wholesale supplier
Argopecten irradians
Shells only
11
Wholesale supplier
Argopecten nucleus
Shells only
28
Wholesale supplier
Achatina achatina
Shells only
10
Wholesale supplier
Ircinia strobilina
Partial specimens
1
Wholesale supplier
Pseudospongosorites suberitoides
Whole specimens
34
Wholesale supplier
Ircinia campana
Whole specimens
1
Wholesale supplier
Atrina seminude
Shells only
3
Ecological surveys
Lottia testudinalis
Shells only
19
Wholesale supplier
Gongonia spp.
Whole specimens
1
Wholesale supplier
Gongonia spp.
Whole specimens
1
Wholesale supplier
Gongonia spp.
Whole specimens
3
Wholesale supplier
Spondylus americanus
Partial shells
12
Pet-trade
Halymenia floresia
Partial specimens
25
Markets
N/A
N/A
10
Ecological surveys
Year
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
1999
1999
1999
1999
1999
1999
2000
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2005
2012
2012
2012
2012
2012
2012
2011
1
Col.1.49
Mixed cone sea snail species Conus spp.
Shells only
21
Wholesale supplier
2012
1
1
Col.1.50
Col.1.51
Crassispira spp.
Littoraria spp.
Shells only
Shells only
839
753
Wholesale supplier
Wholesale supplier
2012
2012
1
Col.1.52
Haliotis pourtalesii**
Shells only
51
Markets
2012
1
1
1
Col.1.53
Col.1.54
Col.1.55
Turrid snail species
Yellow sea snails
Pourtale's abalone
(pearlized)
Knobbed triton snail
Nerite snail species
Eastern auger
Cymatium muricinum
Nerita spp.
Terebra dislocata
Shells only
Whole specimens
Shells only
146
613
17
Markets
Markets
Wholesale supplier
2012
2012
2012
1
Col.1.56
White donax or Coquina clam Donax variabilis
Shells only
556
Wholesale supplier
2012
2
Level
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Reference
Species
Col.1.57 Rockweed
Col.1.58 Rough file clam
Col.1.59 Southern quahog
Crown conch (smooth and
Col.1.60
horned forms)
Col.1.61 Skate species egg cases
Col.1.62 Pleated sea squirt
Deep sea hexactinellid
Col.1.63
“glass” sponge species
Col.1.64 Five-holed keyhole urchin
Col.1.65 Black sun coral
Col.1.66 Cat's paw coral
Col.1.67 Finger coral
Col.1.68 Plate coral species
Col.1.69 Purple barnacle
Col.1.70 Gulf fire coral
Col.1.71 Spiral Babylon sea snail
Col.1.72 Endive murex sea snail
Col.1.73 Millipede conch
Col.1.74 Horned starfish
Col.1.75 Brown spiny sea star
Col.1.76 Inflated sea biscuit
Col.1.77 Flat sea biscuit
Knobbed whelk egg case
Col.1.78
(string)
Col.1.79 Knobbed whelk
Col.1.80 Channeled whelk
Col.1.81 Common or Waved whelk
Goose neck barnacles
Col.1.82 species and Shipworm
species with wood
Col.1.83 Giant sea roach
Col.1.84 Cannonball jellyfish
Atlantic horseshoe crab
Col.1.85
(eggs)
Col.1.86 Colorful sea rod
Col.1.87 Brown rock sea cucumber
Col.1.88 Furry sea cucumber
Col.1.89 Five-toothed sea cucumber
Col.1.90 Moon jellyfish
Col.1.91 Black encrusting tunicate
Col.1.92 Mangrove tunicate
Col.1.93 Sea pork species
Primitive deep sea stalked
Col.1.94
barnacle species
Col.1.95 Portuguese man of war
Col.1.96 Mottled sea hare
Col.1.97 Atlantic black sea hare
Col.1.98 Hauff’s alcyonidium
Col.1.99 Lettuce bryozoan
Col.1.100 Gulfweed
Sea lettuce macro-algae
Col.1.101
species
Col.1.102 Pincushion urchin species
Col.1.103 Spotted linckia starfish
Col.1.104 Brown sargassum weed
Col.1.105 Paper scallop
Col.1.106 Gulf mole crabs
Col.1.107 Thin sea lettuce
Col.1.108 Soft spaghettiweed
Scientific Name
Fucus vesiculosus
Lima scabra
Mecenaria campechiensis
Type
Quantity
Origin
Partial specimens
12
Ecological surveys
Shells only
1
Wholesale supplier
Partial Shells
6
Pet-trade
Year
2012
2012
2012
Melongena corona
Shells only
90
Wholesale supplier
2012
Raja spp.
Styela plicata
Whole specimens
Whole specimens
81
11
Ecological surveys
Markets
2001
1999
Scolymastra spp.
Partial specimens
1
Wholesale supplier
2012
Mellita quinquiesperforata
Tubastraea micrantha
Pocillopora palifera*
Acropora humilis*
Acropora spp.
Conchylepes conchylepes*
Millepora alcicornis
Babylonia spirata*
Murex endiva*
Lambis millepeda**
Protoreaster nodosus**
Echinaster spinulosus
Clypeaster rosaceus
Clypeaster subdepressus
Whole specimens
Partial specimens
Partial specimens
Partial specimens
Partial specimens
Partial specimens
Partial specimens
Whole specimens
Shells only
Shells only
Whole specimens
Whole specimens
Whole specimens
Whole specimens
9
3
14
7
2
5
3
84
1
6
27
49
2
24
Wholesale supplier
Pet-trade
Pet-trade
Pet-trade
Pet-trade
Pet-trade
Pet-trade
Markets
Pet-trade
Wholesale supplier
Wholesale supplier
Wholesale supplier
Wholesale supplier
Wholesale supplier
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
Busycon carica
Eggs
159
Ecological surveys
2001
Busycon carica
Busycotypus canaliculatus
Buccinum undatum*
Whole specimens
Whole specimens
Whole specimens
6
10
28
Markets
Markets
Markets
2012
2012
2012
Bankia spp. & Lepas spp.
Whole specimens
>40
Ecological surveys
2012
Bathynomus giganteous
Stomolophus meleagris
Whole specimens
Whole specimens
1
2
Ecological surveys
Ecological surveys
2012
2012
Limulus polyphemus
Eggs
241
Ecological surveys
1999
Diodogorgia nodulifera
Holothuria glaberrima
Astichopus multifudus
Actinopyga agassizii
Aurelia aurito
Botrylloides nigrum
Ecteinascidia turbinata
Amaroucium spp
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Partial specimens
Colonies
Partial colonies
Whole specimens
1
2
2
2
3
4
12
1
Wholesale supplier
Markets
Markets
Ecological surveys
Markets
Markets
Markets
Ecological surveys
2012
2001
1999
2012
1999
2001
2000
2012
Litoscalpellum spp.
Colonies
1
Ecological surveys
2012
Physalia physalis
Aplysia brasiliana
Aplysia morio
Alcyonidium hauffi
Thalamoporella gothica
Sargassum spp.
Whole specimens
Whole specimens
Whole specimens
Partial specimens
Partial specimens
Partial specimens
1
1
1
1
1
12
Ecological surveys
Ecological surveys
Ecological surveys
Ecological surveys
Ecological surveys
Ecological surveys
2012
2012
2012
2000
2000
2000
Ulvaria spp.
Partial specimens
67
Markets
2012
Lytechinus spp.
Linckia multiflora**
Sargassum natans
Euvola papyracea
Emerita brasiliensis
Ulva lactuca
Liagora farinose
Whole specimens
Whole specimens
Partial specimens
Whole specimens
Whole specimens
Partial specimens
Partial specimens
12
3
5
7
116
47
4
Markets
Wholesale supplier
Markets
Markets
Ecological surveys
Ecological surveys
Ecological surveys
2012
2012
2000
2012
2001
2001
2012
3
Level Reference
1
Col.1.109
1
1
1
1
Col.1.110
Col.1.111
Col.1.112
Col.1.113
1
Col.1.114
1
1
1
1
1
1
1
1
1
1
Col.1.115
Col.1.116
Col.1.117
Col.1.118
Col.1.119
Col.1.120
Col.1.121
Col.1.122
Col.1.123
Col.1.124
1
Col.1.125
1
1
1
1
1
Col.1.126
Col.1.127
Col.1.128
Col.1.129
Col.1.130
1
Col.1.131
1
Col.1.132
1
Col.1.133
1
1
Col.1.134
Col.1.135
1
Col.1.136
1
1
Col.1.137
Col.1.138
1
Col.1.139
1
1
Col.1.140
Col.1.141
1
Col.1.142
1
Col.1.143
1
1
1
1
Col.1.144
Col.1.145
Col.1.146
Col.1.147
1
Col.1.148
1
1
1
1
1
1
Col.1.149
Col.1.150
Col.1.151
Col.1.152
Col.1.153
Col.1.154
1
Col.1.155
1
Col.1.156
Species
Florida halemenia red macroalgae
Intricate brown macro-algae
Green encrusting tunicate
Feather duster worm species
Line nemerteans worm
White shrimp with oil staining
with one specimen with
assemtrical eyes
Common Shore Shrimp
Ivory barnacles
Northern brown shrimp
Northern white shrimp
Southern pink shrimp
Malaysian prawn
Pacific giant clam species
Silver lip conch
Tulip sea snail
Asian shore crab
Southern and American
horse mussel species
Variable coquina clams
Bruised nassa
Caribbean carpet anemone
Curlycue anemone
Cassiopea jellyfish
Atlantic horseshoe crab
(early instar)
Basket sea-star
Flamescallop or Red file shell
clam
Brittle Star species
Serpent star species
Sponge spider crab species
& Fire sponges
Green reef crab
Ridged slipper lobster
Deep-water slipper lobster
species
Yellowline arrow crab
West Indian sea urchins
Flat-clawed hermit crab
inside Northern moon snail
shell
Acadian hermit crab inside
Neapolitan triton snail shell
Brown spiny sea star
Mud fiddler crab
Purple marsh crab
Mangrove land crab
Mixed southern gulf
periwinkle species
Caribbean mud fiddler crab
Sea nettle jellyfish species
Pink vase sponge
Boring sponge species
Red beard sponge
Rope sponge species
White shrimp with lesions
and one specimen with
missing eye
"sOil" (marsh sediment mixed
with Macondo oil and
chemical dispersants)
Scientific Name
Type
Quantity
Origin
Year
Halymenia floridana
Partial specimens
3
Ecological surveys
2012
Rosenvingeo intricate
Symplegma viride
Sabellastarte spp.
Lineus spp.
Partial specimens
Whole specimens
Partial specimens
Whole specimens
4
54
5
1
Ecological surveys
Ecological surveys
Pet-trade
Markets
2012
2012
2004
2001
Litopenaeus setiferus
Whole specimens
2
Anonymous shrimper
2012
Palaemonetes vulgaris
Chelonibia patula
Farfantepenaeus aztecus
Penaeus setiferus
Farfantepenaeus duorarum
Macrobrachium rosenbergii
Tridacna spp.*
Strombus lentiginosus*
Turbonilla curta
Hemigrapsus sanguineus
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Shells only
Whole specimens
Whole specimens
Whole specimens
64
67
1
212
5
1
21
6
5
36
Markets
Ecological surveys
Markets
Markets
Ecological surveys
Markets
Pet-trade
Wholesale supplier
Wholesale supplier
Ecological surveys
2001
2001
2001
2012
2012
2001
2012
2012
2012
2006
Modiolus spp.
Whole specimens
56
Ecological surveys
2012
Donax variabilus
Nassarius vibex
Stichodactyla helianthus
Bartholomea annulata
Cotylorhiza tuberculata
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
58
31
1
1
1
Ecological surveys
Ecological surveys
Pet-trade
Pet-trade
Pet-trade
2012
2012
2012
2012
2012
Limulus polyphemus
Whole specimens
1
Pet-trade
2012
Gorgonocephalus eucnemis Whole specimens
1
Pet-trade
2012
Lima scabra
Whole specimens
1
Pet-trade
2012
Ophioderma spp.
Hemipholis spp.
Whole specimens
Whole specimens
1
2
Pet-trade
Pet-trade
2012
2012
Macrocoeloma spp. & TedaniaWhole
ignis specimens
1
Pet-trade
2012
Mithrax sculptus
Scyllarides nodifer
Whole specimens
Whole specimens
1
1
Pet-trade
Ecological surveys
2012
2012
Scyllarides spp.
Whole specimens
1
Ecological surveys
2012
Stenorhynchus seticornis
Tripneustes ventricosus
Whole specimens
Whole specimens
1
3
Ecological surveys
Pet-trade
2012
2012
Pagurus pollicaris inside Lunatia
Whole
heros
specimens
1
Markets
2012
Pagurus acadianus inside Cymatium
Whole specimens
parthenopeum 1
Markets
2012
Uca pugnax
Afrithelphusa monodosa
Ucides cordatus
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Pet-trade
Pet-trade
Pet-trade
Pet-trade
2012
2012
2005
2004
Littorina spp.
Shells only
18,584 Wholesale supplier
2012
Uca rapax
Chrysaora spp.
Niphates digitalis
Cliona spp.
Microciona prolifera
Aplysina spp.
Whole specimens
Whole specimens
Whole specimens
Partial specimens
Partial specimens
Partial specimens
65
3
1
8
1
2
Markets
Ecological surveys
Retail store
Retail store
Retail store
Retail store
2012
2012
2012
2012
2012
2012
Whole specimens
2
Anonymous shrimper
2012
Partial specimens
1
Ecological surveys
2012
1
1
1
1
4
Level
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
Reference
Species
Col.2.1
Lady crab
Col.2.2
Rock crab
Col.2.3
Green crab
Rugose or Purple swimming
Col.2.4
crab
Col.2.5
Common mantis shrimp
Col.2.6
Hogchoker
Col.2.7
Mediterranean sand lance
Col.2.8
Rimspine searobin
Col.2.9
Atlantic sturgeon (juvenile)
Banded coral shrimp or
Col.2.10
Cleaner shrimp-like decapod
Col.2.11 Atlantic guitarfish
Col.2.12 Smooth skate
Col.2.13 Clear-nose skate
Col.2.14 Lesser electric ray
Col.2.15 Red goatfish
Col.2.16 Hickory shad
Col.2.17 Blueback herring
Col.2.18 Balao halfbeak
Col.2.19 Southern eagle ray (juvenile)
Col.2.20 Underworld windowskate
Col.2.21 Masked pufferfish
Col.2.22 Regal angelfish
Col.2.23 Six barred angel (juvenile)
Col.2.24 Sargassum triggerfish
Col.2.25 Gray triggerfish
Col.2.26 Queen parrotfish
Col.2.27 Sharpnose puffer
Col.2.28 Sea lamprey
Col.2.29 Thornbacked boxfish
Col.2.30 Violet goby
Col.2.31 White trevally
Col.2.32 Pygmy filefish
Col.2.33 Long-horned cowfish
Col.2.34 Longsnout seahorse
Col.2.35 Northern pipefish
Col.2.36 Chain pipefish
Col.2.37 Lined seahorse
Col.2.38 Checkered puffer fish
Col.2.39 Striped burrfish
Col.2.40 Spotlight parrotfish
Col.2.41 Redband parrotfish
Col.2.42 Porcupinefish
Col.2.43 Blunthead puffer
Col.2.44 Opossum pipefish
Col.2.45 Gulf pipefish
Col.2.46 Sargassum pipefish
Col.2.47 Atlantic Moonfish
Col.2.48 Lookdown
Col.2.49 Blue-striped Angelfish
Col.2.50 Pearl wrasse
Col.2.51 Yellow tang
Col.2.52 Spotfin jawfish species
Col.2.53 Striped killifish
Col.2.54 Flathead mullet
Col.2.55 Naked goby (female & male)
Col.2.56 Tidewater silverside
Scientific Name
Ovalipes ocellatus
Cancer irroratus
Cancer maenas
Type
Quantity
Origin
Whole specimens
12
Markets
Whole specimens
3
Markets
Whole specimens
10
Markets
Year
2005
2012
2005
Callinectes exasperates
Whole specimens
1
Markets
2005
Squilla empusa
Trinectes maculatus
Gymnammodytes cicerelus*
Peristedion thompsoni
Acipenser oxyrinchus
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
1
2
82
2
1
Markets
Markets
Markets
Ecological surveys
Research institute
Stenopus hispidus
Whole specimens
1
Pet-trade
2012
Ecological surveys
Ecological surveys
Markets
Ecological surveys
Markets
Markets
Markets
Markets
Ecological surveys
Ecological surveys
Pet-trade
Pet-trade
Pet-trade
Pet-trade
Ecological surveys
Pet-trade
Pet-trade
Markets
Pet-trade
Markets
Markets
Pet-trade
Wholesale supplier
Pet-trade
Markets
Markets
Markets
Ecological surveys
Pet-trade
Markets
Markets
Pet-trade
Pet-trade
Markets
Ecological surveys
Ecological surveys
Markets
Markets
Pet-trade
Pet-trade
Pet-trade
Pet-trade
Markets
Ecological surveys
Ecological surveys
Ecological surveys
2012
2012
2005
2012
2012
2012
2012
2012
2012
2012
2005
2005
2005
2005
2012
2005
2005
1999
2005
2012
2012
2005
1980
2005
2012
2012
1999
2012
1990
2012
2012
1990
2005
2000
2011
2002
2005
2012
2003
2004
2003
2004
2000
2011
2011
2011
Rhinobatos lentiginosus
Whole specimens
Malacoraja senta
Whole specimens
Raja eglanteria
Whole specimens
Narcine bancroftii
Whole specimens
Mullas auratus
Whole specimens
Alosa mediocris
Whole specimens
Alosa aestivalis
Whole specimens
Hemiramphus balao
Whole specimens
Myliobatis goodei
Whole specimens
Fenestraja plutonia
Whole specimens
Arothron diadematus*
Whole specimens
Pygoplites diacanthus**
Whole specimens
Pomacanthus sexstriatus** Whole specimens
Xanthichthys ringens
Whole specimens
Balistes capriscus
Whole specimens
Scarus vetula
Whole specimens
Bamthigaster rostrata
Whole specimens
Petromyzon marinus
Whole specimens
Tetrosomus gibbosus**
Whole specimens
Gobioides broussoneti
Whole specimens
Pseudocaranx dentex
Whole specimens
Monacanthus setifer
Whole specimens
Lactoria cornuta
Whole specimens
Hippocampus reidi
Whole specimens
Synganathus fuscus*
Whole specimens
Synganathus louisanae
Whole specimens
Hippocampus erectus
Whole specimens
Sphoeroides testudineus
Whole specimens
Chilomycterus schoepfii
Whole specimens
Sparisoma viride
Whole specimens
Sparisoma aurofrenatum
Whole specimens
Diodon hystrix
Whole specimens
Sphoeroides pachygaster
Whole specimens
Microphis brachyurus lineatus Whole specimens
Syngnathus scovelli
Whole specimens
Syngnathus pelagicus
Whole specimens
Selene setapinnis
Whole specimens
Selene vomer
Whole specimens
Chaetodontoplus septentrionalis**
Whole specimens
Anampses cuvier**
Whole specimens
Zebrasoma flavescens*
Whole specimens
Opistognathus spp.
Whole specimens
Fundulus majalis
Whole specimens
Mugil cephalus
Whole specimens
Gobiosoma bosci
Whole specimens
Menidia peninsulae
Whole specimens
1
1
1
1
7
1
13
1
1
1
1
1
1
1
1
1
1
2
1
15
8
1
1
1
30
15
2
40
2
1
3
1
1
8
9
31
3
3
1
1
2
1
177
12
2
10
2005
2005
2005/2012
2012
2005
5
Level
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
3
3
3
3
3
Reference
Species
Col.2.57 Golden topminnow
Col.2.58 Texas cichlid
Col.2.59 Sailfin molly (female & male)
Col.2.60 Rainwater killifish
Dwarf livebearer or Least
Col.2.61
killifish
Col.2.62 Rough silverside
Col.2.63 Green sunfish
Col.2.64 High hat (juvenile)
Col.2.65 Foureye butterfly fish
Col.2.66 Dwarf seahorse
Col.2.67 Pancake batfish
Col.2.68 Wahoo (larvae)
Col.2.69 Gulf killifish
Col.2.70 Striped Mullet
Col.2.71 Inland silverside
Col.2.72 Atlantic silverside
Col.2.73 Spanish sardine
Col.2.74 Dusky pipefish
Col.2.75 Gulf menhaden
Col.2.76 Fantail mullet
Col.2.77 Mummichog
Col.2.78 Blackwing flyingfish
Col.2.79 American gizzard shad
Col.2.80 Butterfish
Col.2.81 Harvestfish
Col.2.82 Blackcheek tonguefish
Col.2.83 Blue reef chromis damselfish
Col.2.84 Creole wrasse
Col.2.85 Sharksucker
Col.2.86 Atlantic thread herring
Col.2.87 Whitecheek surgeonfish
Col.2.88 Cunner
Col.2.89 Mountain mullet
Col.2.90 Giant or Red hermit crab
Col.2.91 Rosy lobsterette
Red swamp crayfish or
Col.2.92
Louisiana crawfish
Common Australian yabby
Col.2.93
crayfish
Longear sunfish or Creek
Col.2.94
perch
Col.2.95 Yellowtail coris wrasse
Col.2.96 Cownose ray
Caribbean spiny lobster
Col.2.97
(larvae)
Col.2.98 Coral mithrax crab
Col.2.99 Whitetail damselfish
Col.2.100 Lined porcelain crab
Bigclaw pistol or snapping
Col.2.101
shrimp
Col.2.102 Mosquitofish
Armored catfish or
Col.2.103
Plecostomus species
Col.2.104 Bay anchovy
Col.2.105 Birdmouth Wrasse
Col.3.1
Freshwater drum
Col.3.2
Permit
Col.3.3
Florida pomano
Col.3.4
Atlantic needlefish
Col.3.5
Red lionfish
Scientific Name
Fundulus chrysotus
Herichthys cyanoguttatus
Poecilia latipinna
Lucania parva
Type
Quantity
Origin
Whole specimens
1
Ecological surveys
Whole specimens
1
Ecological surveys
Whole specimens
2
Ecological surveys
Whole specimens
5
Ecological surveys
Year
2011
2011
2011
2011
Heterandria formosa
Whole specimens
6
Ecological surveys
2011
Membras martinica
Lepomis cyanellus
Pareques acuminatus
Chaetodon capistratus
Hippocampus zosterae
Halieutichthys aculeatus
Acanthocybium solandri
Fundulus grandis
Mugil cephalus
Menidia beryllina
Menidia menidia
Sardinella aurita
Synganathus floridae
Brevoortia patronus
Mugil trichodon
Fundalulus heteroclitus*
Hirundichthys rondeletii
Dorosoma cepedianum
Peprilus triacanthus
Peprilus paru
Symphurus plagiusa
Chromis cyanea
Clepticus parrae
Echeneis naucrates
Opisthonema oglinum
Acanthurus nigricans*
Tautoglabrus adspersus
Agonostomus moniticola
Petrochirus diogenes
Nephropsis rosea
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
4
3
1
2
2
2
61
8
4
4
816
7
3
15
5
147
3
3
14
7
5
2
1
1
1
1
5
29
1
1
Ecological surveys
Ecological surveys
Pet-trade
Pet-trade
Wholesale supplier
Ecological surveys
Markets
Ecological surveys
Markets
Ecological surveys
Markets
Markets
Ecological surveys
Markets
Markets
Markets
Markets
Markets
Markets
Markets
Markets
Pet-trade
Pet-trade
Ecological surveys
Markets
Pet-trade
Markets
Markets
Ecological surveys
Ecological surveys
2011
2011
2012
2012
2012
2012
2000
2002
2012
2011
2002
2012
2001
2012
2012
2004
2004
2012
2012
2012
2012
2012
2012
2012
2012
2003
2005/2012
2004
2012
2012
Procambarus clarkia
Whole specimens
1
Markets
2012
Cherax destructor**
Whole specimens
1
Markets
2002
Lepomis megalotis
Whole specimens
4
Markets
1999
Coris gaimard**
Rhinoptera bonasus
Whole specimens
Partial specimens
1
1
Pet-trade
Ecological surveys
2003
1996
Panulirus argus
Whole specimens
1
Ecological surveys
1999
Mithrax coryphe
Dascyllus aruanus*
Petrolisthes galathinus
Whole specimens
Whole specimens
Whole specimens
5
1
1
Ecological surveys
Pet-trade
Pet-trade
1999
2003
2012
Alpheus heterochaelis
Whole specimens
1
Ecological surveys
2012
Gambusia affinis
Whole specimens
70
Ecological surveys
2012
Hypostomus ssp.
Whole specimens
1
Pet-trade
2012
Anchoa mitchilli
Gomphosus caeruleus**
Aplodinotus grunniens
Trachinotus falcatus
Trachinotus carolinus
Strongylura marina
Pterois volitans
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
20
1
1
1
2
1
1
Markets
Pet-trade
Ecological surveys
Markets
Markets
Markets
Pet-trade
2000
2004
2011
2012
2012
2005
2012
6
Level
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
Reference
Species
Col.3.6
Atlantic blue-clawed crab
Col.3.7
Three-toed amphiuma
Col.3.8
White perch
Royal dorade or
Col.3.9
Mediterranean gilt-head
seabream
Col.3.10 Rainbow smelt
Col.3.11 Ocellated frogfish
Col.3.12 Winter flounder
Col.3.13 Summer flounder
Col.3.14 Gulf flounder
Col.3.15 Southern flounder
Col.3.16 Windowpane flounder
Col.3.17 Spotted hake
Col.3.18 Spotted pig grunt
Col.3.19 White grunt
Col.3.20 Sailor’s choice grunt
Col.3.21 Puddingwife
Col.3.22 Bigeye
Col.3.23 Short bigeye
Col.3.24 Striped searobin
Col.3.25 Southern kingfish
Col.3.26 Northern kingfish
Col.3.27 Atlantic croaker
3
Col.3.28
Deep-sea lantern fish species Diaphus or Lampanyctus spp. Whole specimens
3
3
3
3
3
3
3
3
3
3
3
3
Col.3.29
Col.3.30
Col.3.31
Col.3.32
Col.3.33
Col.3.34
Col.3.35
Col.3.36
Col.3.37
Col.3.38
Col.3.39
Col.3.40
3
Col.3.41
3
3
Col.3.42
Col.3.43
3
Col.3.44
3
3
3
3
3
3
3
Col.3.45
Col.3.46
Col.3.47
Col.3.48
Col.3.49
Col.3.50
Col.3.51
3
Col.3.52
3
3
3
3
4
4
4
Col.3.53
Col.3.54
Col.3.55
Col.3.56
Col.4.1
Col.4.2
Col.4.3
Pinfish
Sea bream
Yellowfin morjarra
Atlantic saury
Atlantic chub mackerel
Mackerel scad
Bigeye scad
Fat sleeper
Tautog or Blackfish
Chinese softshell turtle
American eel
Shrimp eel
Asian swamp or
Synbranchidae eel species
Common Atlantic octopus
Atlantic Oval Squid
Gulf deep-water octopus
species
Caribbean reef octopus
Brownstripe octopus
Caribbean reef scorpion fish
Chestnut Moray eel
Spanish grunt
Northern sennet
Deepreef scorpionfish
Bonnethead shark or
shovelhead
Blackbelly rosefish
Butter hamlet
Clown leaflip soapfish
Longfin squid
St Pierre’s fish or John Dory
Guachanche barracuda
Yellow-tail snapper
3
Scientific Name
Callinectes sapidus
Amphiuma tridactylum
Morone Americana*
Type
Quantity
Origin
Whole specimens
9
Markets
Whole specimens
1
Pet-trade
Whole specimens
2
Markets
Year
2005
2000
2000
Sparus aurata*
Whole specimens
4
Markets
2012
Osmerus mordax*
Whole specimens
Fowlerichthys ocellatus
Whole specimens
Pseudopleuronectes americanus*
Whole specimens
Paralichthys dentatus
Whole specimens
Paralichthys albigutta
Whole specimens
Paralichthys lethostigma
Whole specimens
Scophthalmus aquosus
Whole specimens
Urophycis regius
Whole specimens
Orthopristis chrysoptera
Whole specimens
Haemulon plumierrii
Whole specimens
Haemulon parra
Whole specimens
Halichoeres poeyi
Whole specimens
Priacanthus arentus
Whole specimens
Pristigenys alta
Whole specimens
Prionotus evolans
Whole specimens
Mentricirrhus americanus
Whole specimens
Mentricirrhus saxitilis
Whole specimens
Micropogonias undulates
Whole specimens
5
1
2
1
1
1
1
3
1
1
1
1
1
1
1
4
1
1
Markets
Ecological surveys
Markets
Markets
Markets
Markets
Markets
Markets
Ecological surveys
Ecological surveys
Ecological surveys
Markets
Markets
Markets
Markets
Markets
Ecological surveys
Markets
2012
2012
2005
2012
2002
2003
2012
2012
2012
2012
2012
2000
1999
2006
2012
2012
2004
2012
3
Markets
2012
Lagodon rhomboids
Archosargus rhomboidalis
Gerres cinereus
Scomberesox saurus
Scomber colias
Decapterus macarellus
Selar crumenophthalmus
Dormitator maculatus
Tautoga onitis
Pelodiscus sinensis**
Anguilla rostrata
Ophichthus gomesii
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
2
1
1
4
2
3
3
1
1
1
3
1
Ecological surveys
Ecological surveys
Ecological surveys
Markets
Markets
Markets
Markets
Markets
Markets
Markets
Markets
Ecological surveys
1999
1999
1999
2012
2012
2012
2012
2012
2012
2012
2012
2012
Monopterus spp.
Whole specimens
3
Markets
2002/2012
Octopus vulgaris
Sepioteuthis sepioidea
Whole specimens
Whole specimens
1
1
Markets
Markets
2012
2000
Benthoctopus spp.
Whole specimens
1
Ecological surveys
2012
Octopus briareus
Whole specimens
Octopus burryi
Whole specimens
Scorpaenodes caribbaeus
Whole specimens
Enchelycore carychroa
Whole specimens
Haemulon macrostomum
Whole specimens
Sphyraena boreali
Whole specimens
Scorpaenodes tredecimspinosus
Whole specimens
1
2
1
1
1
1
12
Ecological surveys
Markets
Pet-trade
Pet-trade
Ecological surveys
Ecological surveys
Pet-trade
2012
2000
2012
2012
2012
1999
2012
Sphyrna tiburo
Whole specimens
1
Ecological surveys
2012
Helicolenus dactylopterus
Hypoplectrus unicolor
Pogonoperca punctate**
Loligo pealei
Zeus faber
Sphyraena guachancho
Ocyurus chrysurus
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
3
1
1
1
1
4
1
Ecological surveys
Pet-trade
Pet-trade
Markets
Markets
Markets
Markets
2012
2004
2004
2012
2012
2000
2012
7
Level
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
5
5
5
5
5
5
5
5
5
5
5
5
5
6
6
6
6
6
Reference
Species
Col.4.4
Cottonwick
Col.4.5
Conger eel
Col.4.6
Gulf toadfish
Col.4.7
Gray snapper
Col.4.8
Red snapper
Col.4.9
Freckled stargazer
Col.4.10 Vermilion snapper
Col.4.11 Barmundi or Asian seabass
Col.4.12 Oyster toadfish
Col.4.13 Bearded brotula
Coney Grouper or Leopard
Col.4.14
Hind
Col.4.15 Spinycheek scorpionfish
Col.4.16 Inshore lizardfish
Deep-sea dagger tooth or
Col.4.17
Pharaoh fish
Col.4.18 Pilot fish
Col.4.19 Black sea bass
Chain catshark or Chain
Col.4.20
dogfish
Col.4.21 Caribbean ocellated moray
Col.4.22 Hourglass moray eel
Col.4.23 Bluefish
Col.4.24 Great northern tilefish
Col.4.25 Wrenchman
Col.4.26 Deep-sea squid species
Col.4.27 Mutton hamlet
Col.4.28 Graysby
Col.4.29 Red hind grouper
Col.4.30 Redfish species
Deep-water needlefish or
Col.5.1
Houndfish
Col.5.2
Blue runner
Col.5.3
Crevalle jack
Col.5.4
Blackfin goosefish
Atlantic cutlassfish or
Col.5.5
Ribbonfish
Col.5.6
Burmese python
Col.5.7
Greater amber jack
Dusky smooth-hound or
Col.5.8
Smooth dogfish shark
Col.5.9
Atlantic Spanish mackerel
Col.5.10 Yellowfin grouper
Col.5.11 Yellowmouth grouper
Col.5.12 Red grouper
Col.5.13 Atlantic striped bass
Col.6.1
Blacktip shark (juvenile)
Col.6.2
Mako shark species
Col.6.3
Atlantic wreckfish
Col.6.4
Wahoo
Mahi-mahi or Common
Col.6.5
dolphinfish
Scientific Name
Haemulor melanurum
Conger oceanicus
Opsanus beta
Lutjanus griseus
Lutjanus campechanus
Gnathagnus egreglus
Rhomboplites aurorubens
Lates calcarifer**
Opsanus tau
Brotula barbata
Type
Quantity
Origin
Whole specimens
3
Markets
Whole specimens
2
Markets
Whole specimens
1
Ecological surveys
Whole specimens
2
Markets
Whole specimens
2
Markets
Whole specimens
1
Ecological surveys
Whole specimens
3
Markets
Whole specimens
1
Markets
Whole specimens
1
Markets
Whole specimens
1
Ecological surveys
Year
2000
2000
2012
2000
2000
2012
2012
2012
2012
2012
Cephalopholis fulva
Whole specimens
1
Markets
2012
Neomerinthe hemingway
Synodus foetens
Whole specimens
Whole specimens
3
1
Ecological surveys
Markets
2012
2000
Anotopterus pharao
Whole specimens
3
Ecological surveys
2012
Naucrates doctor
Centropristis striata
Whole specimens
Whole specimens
3
2
Markets
Markets
2012
2012
Scyliorhinus rotifer
Whole specimens
1
Ecological surveys
2012
Gymnothorax ocellatus
Whole specimens
Muraena clepsydra*
Whole specimens
Pomatomus saltatrix
Whole specimens
Lopholatilus chamaeleonticepsWhole specimens
Pristipomoides aquilonaris
Whole specimens
llex spp.
Whole specimens
Alphestes afer
Whole specimens
Cephalopholis cruentata
Whole specimens
Epinephelus guttatus
Whole specimens
Sebastes spp.
Whole specimens
1
1
6
1
1
1
1
1
1
4
Pet-trade
Pet-trade
Markets
Markets
Markets
Ecological surveys
Markets
Markets
Markets
Markets
2012
2004
1999
2012
2000
2012
2000
2000
2012
2012
Tylosurus crocodilus
Whole specimens
1
Ecological surveys
2012
Caranx crysos
Caranx hippos
Lophius gastrophysus
Whole specimens
Whole specimens
Whole specimens
3
2
1
Markets
Markets
Markets
2012
2012
2012
Trichiurus lepturus
Whole specimens
3
Markets
2012
Python molurus bivittatus
Seriola dumerili
Whole specimens
Whole specimens
1
1
Pet-trade
Markets
2012
2012
Mustelus canis
Whole specimens
1
Markets
2000
Scomberomorus maculatus
Mycteroperca venenosa
Mycteroperca interstitialis
Epinephelus morio
Morone saxatilis
Carcharhinus limbatus
Isurus spp.
Polyprion americanus
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Whole specimens
Partial specimens
Partial specimens
Partial specimens
1
1
1
1
2
1
1
1
1
Markets
Markets
Markets
Markets
Markets
Ecological surveys
Ecological surveys
Markets
Markets
Coryphaena hippurus
Partial specimens
1
Markets
TOTAL
2012
2012
2000
2012
2000
2012
Historic
2012
2012
2012
26,162
* non-native species
** non-native species anecdotally reported but not scientifically confirmed or
species with a high-probability of introduction from the pet-trade or seafood industry
8
Specimen list in phylogenetic order prepared by Bryan Weatherwax
Ichthyology Department, New York State Museum, Albany, NY
Family
Ulvaceae
Liagoraceae
Halymeniaceae
Sargassaceae
Scytosiphonaceae
Rossellidae
Aplysinidae
Clionaidae
Irciniidae Microcionidae
Niphatidae
Suberitidae
Acroporidae
Aiptasiidae
Alcyoniidae Anthothelidae Dendrophylliidae Faviidae
Gorgoniidae Helioporidae
Meandrinidae
Mussidae Pocilloporidae Poritidae Stichodactylidae Tubiporidae
Milleporidae
Physaliidae
Stylasteridae
Cepheidae
Pelagiidae
Stomolophidae
Ulmaridae
Lineidae
Aplysiidae
Babyloniidae
Buccinidae
Conidae
Cypraeidae
Ficidae
Haliotidae
Reference
Col.1.101
Col.1.107
Col.1.108
Col.1.109
Col.1.100
Col.1.104
Col.1.110
Col.1.63
Col.1.154
Col.1.152
Col.1.38
Col.1.40
Col.1.153
Col.1.151
Col.1.39
Col.1.4
Col.1.10
Col.1.11
Col.1.67
Col.1.68
Col.1.129
Col.1.5
Col.1.86
Col.1.65
Col.1.7
Col.1.8
Col.1.43
Col.1.44
Col.1.45
Col.1.15
Col.1.6
Col.1.1
Col.1.16
Col.1.9
Col.1.12
Col.1.66
Col.1.14
Col.1.3
Col.1.128
Col.1.2
Col.1.70
Col.1.95
Col.1.13
Col.1.130
Col.1.150
Col.1.84
Col.1.90
Col.1.113
Col.1.96
Col.1.97
Col.1.71
Col.1.78
Col.1.79
Col.1.80
Col.1.81
Col.1.49
Col.1.47
Col.1.31
Col.1.52
Species
Sea lettuce macro-algae species
Thin sea lettuce
Soft spaghettiweed
Florida halemenia red macro-algae
Gulfweed
Brown sargassum weed
Intricate brown macro-algae
Deep sea hexactinellid “glass” sponge species
Rope sponge species
Boring sponge species
Pillow stinking sponge
Stinking vase sponge
Red beard sponge
Pink vase sponge
Florida hermit-crab sponge
Elkhorn coral
Table coral
Branch coral
Finger coral
Plate coral species
Curlycue anemone
Frilly lettuce coral
Colorful sea rod
Black sun coral
Boulder coral species
Brain coral species
Yellow sea fan species
Green sea fan species
Purple sea fan species
Blue ridge coral
Scarlet coral species
Flower coral species
Large-cupped fungal coral
Cauliflower coral species
Brownstem coral
Cat's paw coral
Branching coral species
Thin finger coral
Caribbean carpet anemone
Organ pipe coral
Gulf fire coral
Portuguese man of war
Lace coral species
Cassiopea jellyfish
Sea nettle jellyfish species
Cannonball jellyfish
Moon jellyfish
Line nemerteans worm
Mottled sea hare
Atlantic black sea hare
Spiral Babylon sea snail
Knobbed whelk egg case (string)
Knobbed whelk
Channeled whelk
Common or Waved whelk
Mixed cone sea snail species
Red sea lettuce
Graceful fig sea snail
Pourtale's abalone (pearlized)
Scientific Name
Ulvaria spp.
Ulva lactuca
Liagora farinose
Halymenia floridana
Sargassum spp.
Sargassum natans
Rosenvingeo intricate
Scolymastra spp.
Aplysina spp.
Cliona spp.
Ircinia strobilina
Ircinia campana
Microciona prolifera
Niphates digitalis
Pseudospongosorites suberitoides
Acropora palmate
Acropora hyacinthus
Acropora florida
Acropora humilis*
Acropora spp.
Bartholomea annulata
Lobophytum crassum
Diodogorgia nodulifera
Tubastraea micrantha
Monstastrea spp.
Diplora spp.
Gongonia spp.
Gongonia spp.
Gongonia spp.
Heliopora coerula
Dichocoenia spp.
Mussa spp.
Scolymia lacera
Pocillopora spp.
Pocillopora verrucosa
Pocillopora palifera*
Madracris spp.
Porites furcate
Stichodactyla helianthus
Tubipora musica*
Millepora alcicornis
Physalia physalis
Family stylasteridae
Cotylorhiza tuberculata
Chrysaora spp.
Stomolophus meleagris
Aurelia aurito
Lineus spp.
Aplysia brasiliana
Aplysia morio
Babylonia spirata*
Busycon carica
Busycon carica
Busycotypus canaliculatus
Buccinum undatum*
Conus spp.
Halymenia floresia
Ficus gracilis
Haliotis pourtalesii**
9
Family
Littorinidae
Reference
Col.1.148
Col.1.51
Lottiidae
Col.1.42
Melongenidae
Col.1.32
Col.1.60
Muricidae
Col.1.72
Nassariidae
Col.1.127
Naticidae
Col.1.27
Col.1.30
Neritidae
Col.1.54
Olividae Col.1.33
Pseudomelatomidae Col.1.50
Pyramidellidae
Col.1.123
Ranellidae
Col.1.26
Col.1.53
Strombidae
Col.1.17
Col.1.18
Col.1.19
Col.1.29
Col.1.73
Col.1.122
Terebridae
Col.1.55
Achatinidae
Col.1.37
Arcidae
Col.1.20
Cardiidae
Col.1.57
Col.1.121
Donacidae
Col.1.56
Col.1.126
Hiatellidae
Col.1.24
Limidae
Col.1.58
Col.1.133
Myidae
Col.1.25
Mytilidae
Col.1.125
Pectinidae
Col.1.34
Col.1.35
Col.1.36
Col.1.105
Pharidae Col.1.23
Col.1.28
Pinnidae
Col.1.41
Solecurtidae
Col.1.22
Spondylidae
Col.1.46
Tellinidae
Col.1.48
Veneridae
Col.1.21
Col.1.59
Loliginidae
Col.3.43
Octopodidae
Col.3.44
Col.3.42
Col.3.45
Col.3.46
Ommastrephidae
Col.4.26
Sabellidae
Col.1.112
Limulidae
Col.1.131
Col.1.85
Penaeidae
Col.1.117
Col.1.118
Col.1.119
Stenopodidae
Col.2.10
Species
Scientific Name
Mixed southern gulf periwinkle species
Littorina spp.
Yellow sea snails
Littoraria spp.
Atlantic plate limpet
Lottia testudinalis
Florida crown conch
Melongena corona
Crown conch (smooth and horned forms)
Melongena corona
Endive murex sea snail
Murex endiva*
Bruised nassa
Nassarius vibex
Northern moon snails
Lunatia heros
Shark eye sea snail (didyma)
Polinices duplicatus
Nerite snail species
Nerita spp.
Lettered olive sea snail
Oliva sayana
Turrid snail species
Crassispira spp.
Tulip sea snail
Turbonilla curta
Neapolitan triton
Cymatium parthenopeum
Knobbed triton snail
Cymatium muricinum
Rooster tail conch
Lobatus gallus
West Indian fighting conch
Strombus pugilis
Florida fighting conch
Strombus alatus
Hawkwing conch
Strombus raninus
Millipede conch
Lambis millepeda**
Silver lip conch
Strombus lentiginosus*
Eastern auger
Terebra dislocata
Fairyland snail sea snail
Achatina achatina
Incongruous ark
Anadara brasiliana
Rockweed
Fucus vesiculosus
Pacific giant clam species
Tridacna spp.*
White donax or Coquina clam
Donax variabilis
Variable coquina clams
Donax variabilus
Propeller clam
Cyrtodaria siliqua
Rough file clam
Lima scabra
Flamescallop or Red file shell clam
Lima scabra
Soft shell clams
Mya arenaria
Southern and American horse mussel species
Modiolus spp.
Florida calico scallop
Argopecten gibbus
White scallop
Argopecten irradians
Nucleus scallop
Argopecten nucleus
Paper scallop
Euvola papyracea
Common razor clams
Ensis directus
Dwarf razor clams
Ensis megistus
Half-naked pen clam
Atrina seminude
Stout tagelus
Tagelus plebeius
Spiny oyster
Spondylus americanus
Tar balls
N/A
Southern quahog
Mercenaria campechiensis notata
Southern quahog
Mecenaria campechiensis
Atlantic Oval Squid
Sepioteuthis sepioidea
Gulf deep-water octopus species
Benthoctopus spp.
Common Atlantic octopus
Octopus vulgaris
Caribbean reef octopus
Octopus briareus
Brownstripe octopus
Octopus burryi
Deep-sea squid species
llex spp.
Feather duster worm species
Sabellastarte spp.
Atlantic horseshoe crab (early instar)
Limulus polyphemus
Atlantic horseshoe crab (eggs)
Limulus polyphemus
Northern brown shrimp
Farfantepenaeus aztecus
Northern white shrimp
Penaeus setiferus
Southern pink shrimp
Farfantepenaeus duorarum
Banded coral shrimp or Cleaner shrimp-like decapod
Stenopus hispidus
10
Family
Palaemonidae
Reference
Col.1.120
Col.1.115
Alpheidae
Col.2.101
Nephropidae Col.2.91
Cambaridae
Col.2.92
Parastacidae
Col.2.93
Palinuridae Col.2.97
Scyllaridae Col.1.138
Col.1.139
Diogenidae Col.2.90
Paguridae Col.1.142
Col.1.143
Porcellanidae Col.2.100
Hippidae Col.1.106
Majidae Col.2.98
Col.1.136
Col.1.137
Inachidae Col.1.140
Cancridae Col.2.2
Col.2.3
Portunidae Col.3.6
Col.2.4
Col.2.1
Varunidae Col.1.124
Potamonautidae Col.1.146
Ocypodidae
Col.1.149
Col.1.145
Ucididae
Col.1.147
Cirolanidae Col.1.83
Squillidae Col.2.5
Chelonibiidae Col.1.116
Lepadidae Col.1.82
Scalpellidae Col.1.94
Balanidae
Col.1.69
Alcyonidiidae Col.1.98
Thalamoporellidae Col.1.99
Echinasteridae
Col.1.75
Col.1.144
Oreasteridae Col.1.74
Ophiactidae Col.1.135
Ophidiasteridae Col.1.103
Ophiodermatidae Col.1.134
Gorgonocephalidae Col.1.132
Clypeasteridae
Col.1.76
Col.1.77
Mellitidae Col.1.64
Toxopneustidae Col.1.102
Col.1.141
Holothuriidae Col.1.87
Col.1.89
Stichopodidae Col.1.88
Perophoridae Col.1.92
Polyclinidae Col.1.93
Styelidae
Col.1.91
Col.1.62
Col.1.111
Petromyzontidae
Col.2.28
Lamnidae
Col.6.2
Scyliorhinidae
Col.4.20
Species
Scientific Name
Malaysian prawn
Macrobrachium rosenbergii
Common Shore Shrimp
Palaemonetes vulgaris
Bigclaw pistol or snapping shrimp
Alpheus heterochaelis
Rosy lobsterette
Nephropsis rosea
Red swamp crayfish or Louisiana crawfish
Procambarus clarkia
Common Australian yabby crayfish
Cherax destructor**
Caribbean spiny lobster (larvae)
Panulirus argus
Ridged slipper lobster
Scyllarides nodifer
Deep-water slipper lobster species
Scyllarides spp.
Giant or Red hermit crab
Petrochirus diogenes
Flat-clawed hermit crab inside Northern moon snailPagurus
shell pollicaris inside Lunatia heros
Acadian hermit crab inside Neapolitan triton snail shell
Pagurus acadianus inside Cymatium parthenopeum
Lined porcelain crab
Petrolisthes galathinus
Gulf mole crabs
Emerita brasiliensis
Coral mithrax crab
Mithrax coryphe
Sponge spider crab species & Fire sponges
Macrocoeloma spp. & Tedania ignis
Green reef crab
Mithrax sculptus
Yellowline arrow crab
Stenorhynchus seticornis
Rock crab
Cancer irroratus
Green crab
Cancer maenas
Atlantic blue-clawed crab
Callinectes sapidus
Rugose or Purple swimming crab
Callinectes exasperates
Lady crab
Ovalipes ocellatus
Asian shore crab
Hemigrapsus sanguineus
Purple marsh crab
Afrithelphusa monodosa
Caribbean mud fiddler crab
Uca rapax
Mud fiddler crab
Uca pugnax
Mangrove land crab
Ucides cordatus
Giant sea roach
Bathynomus giganteous
Common mantis shrimp
Squilla empusa
Ivory barnacles
Chelonibia patula
Goose neck barnacles species and Shipworm species
Bankia
with
spp.
wood
& Lepas spp.
Primitive deep sea stalked barnacle species
Litoscalpellum spp.
Purple barnacle
Conchylepes conchylepes*
Hauff’s alcyonidium
Alcyonidium hauffi
Lettuce bryozoan
Thalamoporella gothica
Horned starfish
Protoreaster nodosus**
Serpent star species
Hemipholis spp.
Spotted linckia starfish
Linckia multiflora**
Brittle Star species
Ophioderma spp.
Basket sea-star
Gorgonocephalus eucnemis
Inflated sea biscuit
Clypeaster rosaceus
Flat sea biscuit
Clypeaster subdepressus
Five-holed keyhole urchin
Mellita quinquiesperforata
Pincushion urchin species
Lytechinus spp.
West Indian sea urchins
Tripneustes ventricosus
Brown rock sea cucumber
Holothuria glaberrima
Five-toothed sea cucumber
Actinopyga agassizii
Furry sea cucumber
Astichopus multifudus
Mangrove tunicate
Ecteinascidia turbinata
Sea pork species
Amaroucium spp
Black encrusting tunicate
Botrylloides nigrum
Pleated sea squirt
Styela plicata
Green encrusting tunicate
Symplegma viride
Sea lamprey
Petromyzon marinus
Mako shark species
Isurus spp.
Chain catshark or Chain dogfish
Scyliorhinus rotifer
11
Family
Triakidae
Carcharhinidae
Sphyrnidae
Narcinidae
Rhinobatidae
Rajidae
Myliobatidae
Acipenseridae
Anguillidae
Muraenidae
Ophichthidae
Congridae
Engraulidae
Clupeidae
Loricariidae
Osmeridae
Synodontidae
Anotopteridae
Myctophidae
Phycidae
Ophidiidae
Batrachoididae
Lophiidae
Antennariidae
Ogcocephalidae
Mugilidae
Atherinopsidae
Exocoetidae
Hemiramphidae
Belonidae
Scomberesocidae
Fundulidae
Poeciliidae
Reference
Col.5.8
Col.6.1
Col.3.52
Col.2.14
Col.2.11
Col.1.61
Col.2.20
Col.2.12
Col.2.13
Col.2.96
Col.2.19
Col.2.9
Col.3.39
Col.4.21
Col.4.22
Col.3.48
Col.3.40
Col.4.5
Col.2.104
Col.2.73
Col.2.75
Col.2.79
Col.2.86
Col.2.16
Col.2.17
Col.2.103
Col.3.10
Col.4.16
Col.4.17
Col.3.56
Col.3.28
Col.3.17
Col.4.13
Col.4.6
Col.4.12
Col.5.4
Col.3.11
Col.2.67
Col.2.70
Col.2.76
Col.2.89
Col.2.54
Col.2.71
Col.2.72
Col.2.56
Col.2.62
Col.2.78
Col.2.18
Col.5.1
Col.3.4
Col.3.32
Col.2.77
Col.2.69
Col.2.53
Col.2.57
Col.2.60
Col.2.102
Col.2.59
Col.2.61
Species
Dusky smooth-hound or Smooth dogfish shark
Blacktip shark (juvenile)
Bonnethead shark or shovelhead
Lesser electric ray
Atlantic guitarfish
Skate species egg cases
Underworld windowskate
Smooth skate
Clear-nose skate
Cownose ray
Southern eagle ray (juvenile)
Atlantic sturgeon (juvenile)
American eel
Caribbean ocellated moray
Hourglass moray eel
Chestnut Moray eel
Shrimp eel
Conger eel
Bay anchovy
Spanish sardine
Gulf menhaden
American gizzard shad
Atlantic thread herring
Hickory shad
Blueback herring
Armored catfish or Plecostomus species
Rainbow smelt
Inshore lizardfish
Deep-sea dagger tooth or Pharaoh fish
Long-finned lantern fish species
Deep-sea lantern fish species
Spotted hake
Bearded brotula
Gulf toadfish
Oyster toadfish
Blackfin goosefish
Ocellated frogfish
Pancake batfish
Striped Mullet
Fantail mullet
Mountain mullet
Flathead mullet
Inland silverside
Atlantic silverside
Tidewater silverside
Rough silverside
Blackwing flyingfish
Balao halfbeak
Deep-water needlefish or Houndfish
Atlantic needlefish
Atlantic saury
Mummichog
Gulf killifish
Striped killifish
Golden topminnow
Rainwater killifish
Mosquitofish
Sailfin molly (female & male)
Dwarf livebearer or Least killifish
Scientific Name
Mustelus canis
Carcharhinus limbatus
Sphyrna tiburo
Narcine bancroftii
Rhinobatos lentiginosus
Raja spp.
Fenestraja plutonia
Malacoraja senta
Raja eglanteria
Rhinoptera bonasus
Myliobatis goodei
Acipenser oxyrinchus
Anguilla rostrata
Gymnothorax ocellatus
Muraena clepsydra*
Enchelycore carychroa
Ophichthus gomesii
Conger oceanicus
Anchoa mitchilli
Sardinella aurita
Brevoortia patronus
Dorosoma cepedianum
Opisthonema oglinum
Alosa mediocris
Alosa aestivalis
Hypostomus ssp.
Osmerus mordax*
Synodus foetens
Anotopterus pharao
Diaphus or Lampanyctus spp.
Diaphus or Lampanyctus spp.
Urophycis regius
Brotula barbata
Opsanus beta
Opsanus tau
Lophius gastrophysus
Fowlerichthys ocellatus
Halieutichthys aculeatus
Mugil cephalus
Mugil trichodon
Agonostomus moniticola
Mugil cephalus
Menidia beryllina
Menidia menidia
Menidia peninsulae
Membras martinica
Hirundichthys rondeletii
Hemiramphus balao
Tylosurus crocodilus
Strongylura marina
Scomberesox saurus
Fundalulus heteroclitus*
Fundulus grandis
Fundulus majalis
Fundulus chrysotus
Lucania parva
Gambusia affinis
Poecilia latipinna
Heterandria formosa
12
Family
Zeidae
Syngnathidae
Synbranchidae
Scorpaenidae
Sebastidae
Triglidae
Peristediidae
Latidae
Moronidae
Polyprionidae
Serranidae
Opistognathidae
Centrarchidae
Priacanthidae
Malacanthidae
Pomatomidae
Coryphaenidae
Echeneidae
Carangidae
Lutjanidae
Gerreidae
Reference
Col.4.1
Col.2.74
Col.2.66
Col.2.44
Col.2.45
Col.2.46
Col.2.34
Col.2.35
Col.2.36
Col.2.37
Col.3.41
Col.4.15
Col.3.47
Col.3.51
Col.3.5
Col.4.30
Col.3.53
Col.3.24
Col.2.8
Col.4.11
Col.5.13
Col.3.8
Col.6.3
Col.5.10
Col.5.11
Col.5.12
Col.4.29
Col.4.27
Col.4.28
Col.4.14
Col.4.19
Col.3.54
Col.3.55
Col.2.52
Col.2.94
Col.2.63
Col.3.22
Col.3.23
Col.4.24
Col.4.23
Col.6.5
Col.2.85
Col.5.7
Col.5.2
Col.5.3
Col.4.18
Col.3.34
Col.3.35
Col.3.2
Col.3.3
Col.2.47
Col.2.48
Col.2.31
Col.4.25
Col.4.7
Col.4.8
Col.4.10
Col.4.3
Col.3.31
Species
St Pierre’s fish or John Dory
Dusky pipefish
Dwarf seahorse
Opossum pipefish
Gulf pipefish
Sargassum pipefish
Longsnout seahorse
Northern pipefish
Chain pipefish
Lined seahorse
Asian swamp or Synbranchidae eel species
Spinycheek scorpionfish
Caribbean reef scorpion fish
Deepreef scorpionfish
Red lionfish
Redfish species
Blackbelly rosefish
Striped searobin
Rimspine searobin
Barmundi or Asian seabass
Atlantic striped bass
White perch
Atlantic wreckfish
Yellowfin grouper
Yellowmouth grouper
Red grouper
Red hind grouper
Mutton hamlet
Graysby
Coney Grouper or Leopard Hind
Black sea bass
Butter hamlet
Clown leaflip soapfish
Spotfin jawfish species
Longear sunfish or Creek perch
Green sunfish
Bigeye
Short bigeye
Great northern tilefish
Bluefish
Mahi-mahi or Common dolphinfish
Sharksucker
Greater amber jack
Blue runner
Crevalle jack
Pilot fish
Mackerel scad
Bigeye scad
Permit
Florida pomano
Atlantic Moonfish
Lookdown
White trevally
Wrenchman
Gray snapper
Red snapper
Vermilion snapper
Yellow-tail snapper
Yellowfin morjarra
Scientific Name
Zeus faber
Synganathus floridae
Hippocampus zosterae
Microphis brachyurus lineatus
Syngnathus scovelli
Syngnathus pelagicus
Hippocampus reidi
Synganathus fuscus*
Synganathus louisanae
Hippocampus erectus
Monopterus spp.
Neomerinthe hemingway
Scorpaenodes caribbaeus
Scorpaenodes tredecimspinosus
Pterois volitans
Sebastes spp.
Helicolenus dactylopterus
Prionotus evolans
Peristedion thompsoni
Lates calcarifer**
Morone saxatilis
Morone Americana*
Polyprion americanus
Mycteroperca venenosa
Epinephelus morio
Epinephelus guttatus
Alphestes afer
Cephalopholis cruentata
Cephalopholis fulva
Centropristis striata
Hypoplectrus unicolor
Pogonoperca punctate**
Opistognathus spp.
Lepomis megalotis
Lepomis cyanellus
Priacanthus arentus
Pristigenys alta
Lopholatilus chamaeleonticeps
Pomatomus saltatrix
Coryphaena hippurus
Echeneis naucrates
Seriola dumerili
Caranx crysos
Caranx hippos
Naucrates doctor
Decapterus macarellus
Selar crumenophthalmus
Trachinotus falcatus
Trachinotus carolinus
Selene setapinnis
Selene vomer
Pseudocaranx dentex
Pristipomoides aquilonaris
Lutjanus griseus
Lutjanus campechanus
Rhomboplites aurorubens
Ocyurus chrysurus
Gerres cinereus
13
Family
Haemulidae
Sparidae
Sciaenidae
Mullidae
Chaetodontidae
Pomacanthidae
Cichlidae
Labridae
Scaridae
Ammodytidae
Uranoscopidae
Eleotridae
Gobiidae
Acanthuridae
Sphyraenidae
Trichiuridae
Scombridae
Stromateidae
Scophthalmidae
Paralichthyidae
Pleuronectidae
Achiridae
Cynoglossidae
Balistidae
Monacanthidae
Ostraciidae
Reference
Col.3.49
Col.4.4
Col.3.18
Col.3.19
Col.3.20
Col.3.9
Col.3.29
Col.3.30
Col.3.25
Col.3.26
Col.3.27
Col.3.1
Col.2.64
Col.2.15
Col.2.65
Col.2.49
Col.2.22
Col.2.23
Col.2.99
Col.2.83
Col.2.58
Col.3.21
Col.3.37
Col.2.105
Col.2.95
Col.2.84
Col.2.50
Col.2.32
Col.2.40
Col.2.41
Col.2.26
Col.2.7
Col.4.9
Col.3.36
Col.2.55
Col.2.30
Col.2.87
Col.2.51
Col.3.50
Col.4.2
Col.5.5
Col.5.9
Col.6.4
Col.3.33
Col.2.68
Col.2.80
Col.2.81
Col.3.16
Col.3.13
Col.3.14
Col.3.15
Col.3.12
Col.2.6
Col.2.82
Col.2.24
Col.2.25
Col.2.32
Col.2.29
Col.2.33
Species
Scientific Name
Spanish grunt
Haemulon macrostomum
Cottonwick
Haemulor melanurum
Spotted pig grunt
Orthopristis chrysoptera
White grunt
Haemulon plumierrii
Sailor’s choice grunt
Haemulon parra
Royal dorade or Mediterranean gilt-head seabream
Sparus aurata*
Pinfish
Lagodon rhomboids
Sea bream
Archosargus rhomboidalis
Southern kingfish
Mentricirrhus americanus
Northern kingfish
Mentricirrhus saxitilis
Atlantic croaker
Micropogonias undulates
Freshwater drum
Aplodinotus grunniens
High hat (juvenile)
Pareques acuminatus
Red goatfish
Mullas auratus
Foureye butterfly fish
Chaetodon capistratus
Blue-striped Angelfish
Chaetodontoplus septentrionalis**
Regal angelfish
Pygoplites diacanthus**
Six barred angel (juvenile)
Pomacanthus sexstriatus**
Whitetail damselfish
Dascyllus aruanus*
Blue reef chromis damselfish
Chromis cyanea
Texas cichlid
Herichthys cyanoguttatus
Puddingwife
Halichoeres poeyi
Tautog or Blackfish
Tautoga onitis
Birdmouth Wrasse
Gomphosus caeruleus**
Yellowtail coris wrasse
Coris gaimard**
Creole wrasse
Clepticus parrae
Pearl wrasse
Anampses cuvier**
Pygmy filefish
Monacanthus setifer
Spotlight parrotfish
Sparisoma viride
Redband parrotfish
Sparisoma aurofrenatum
Queen parrotfish
Scarus vetula
Mediterranean sand lance
Gymnammodytes cicerelus*
Freckled stargazer
Gnathagnus egreglus
Fat sleeper
Dormitator maculatus
Naked goby (female & male)
Gobiosoma bosci
Violet goby
Gobioides broussoneti
Whitecheek surgeonfish
Acanthurus nigricans*
Yellow tang
Zebrasoma flavescens*
Northern sennet
Sphyraena boreali
Guachanche barracuda
Sphyraena guachancho
Atlantic cutlassfish or Ribbonfish
Trichiurus lepturus
Atlantic Spanish mackerel
Scomberomorus maculatus
Wahoo
Atlantic chub mackerel
Scomber colias
Wahoo (larvae)
Butterfish
Peprilus triacanthus
Harvestfish
Peprilus paru
Windowpane flounder
Scophthalmus aquosus
Summer flounder
Paralichthys dentatus
Gulf flounder
Paralichthys albigutta
Southern flounder
Paralichthys lethostigma
Winter flounder
Pseudopleuronectes americanus*
Hogchoker
Trinectes maculatus
Blackcheek tonguefish
Symphurus plagiusa
Sargassum triggerfish
Xanthichthys ringens
Gray triggerfish
Balistes capriscus
Pygmy filefish
Monacanthus setifer
Thornbacked boxfish
Tetrosomus gibbosus**
Long-horned cowfish
Lactoria cornuta
14
Family
Tetraodontidae
Diodontidae
Amphiumidae
Trionychidae
Pythonidae
Reference
Col.2.38
Col.2.43
Col.2.21
Col.2.27
Col.2.39
Col.2.42
Col.3.7
Col.3.38
Col.5.6
Species
Checkered puffer fish
Blunthead puffer
Masked pufferfish
Sharpnose puffer
Striped burrfish
Porcupinefish
Three-toed amphiuma
Chinese softshell turtle
Burmese python
Scientific Name
Sphoeroides testudineus
Sphoeroides pachygaster
Arothron diadematus*
Bamthigaster rostrata
Chilomycterus schoepfii
Diodon hystrix
Amphiuma tridactylum
Pelodiscus sinensis**
Python molurus bivittatus
* non-native species
** non-native species anecdotally reported but not scientifically confirmed or
species with a high-probability of introduction from the pet-trade or seafood industry
15
AQUATIC MICROBIAL ECOLOGY
Aquat Microb Ecol
Vol. 63: 101–109, 2011
doi: 10.3354/ame01482
Published online March 31
OPEN
ACCESS
FEATURE ARTICLE
Effects of COREXIT® EC9500A on bacteria from a
beach oiled by the Deepwater Horizon spill
Leila J. Hamdan1,*, Preston A. Fulmer 2
1
Marine Biogeochemistry Section, Code 6114, U.S. Naval Research Laboratory, Overlook Ave. SW, Washington,
DC 20375, USA
2
Bioenergy and Biofabrication Section, Code 6115, U.S. Naval Research Laboratory, Overlook Ave. SW, Washington,
DC 20375, USA
ABSTRACT: Hydrocarbon-degrading bacteria are
important for controlling the fate of natural and
anthropogenic hydrocarbons in the marine environment. In the wake of the Deepwater Horizon spill in
the Gulf of Mexico, microbial communities will be
important for the natural attenuation of the effects
of the spill. The chemical dispersant COREXIT®
EC9500A was widely deployed during the response to
the Deepwater Horizon incident. Although toxicity
tests confirm that COREXIT® EC9500A does not pose
a significant threat to invertebrate and adult fish populations, there is limited information on its effect on
microbial communities. We determined the composition of the microbial community in oil that had been
freshly deposited on a beach in Louisiana, USA, as a
result of the Deepwater Horizon spill. The metabolic
activity and viability in cultures obtained from oil
samples were determined in the absence and presence of COREXIT® EC9500A at concentrations ranging from 0.001 to 100 mg ml–1. In length heterogeneity
PCR (LH-PCR) fingerprints of oil samples, the most
abundant isolates were those of Vibrio, followed by
hydrocarbon-degrading isolates affiliated with Acinetobacter and Marinobacter. We observed significant
reductions in production and viability of Acinetobacter and Marinobacter in the presence of the dispersant compared to controls. Of the organisms
examined, Marinobacter appears to be the most sensitive to the dispersant, with nearly 100% reduction in
viability and production as a result of exposure to
concentrations of the dispersant likely to be encountered during the response to the spill (1 to 10 mg
ml–1). Significantly, at the same concentration of dispersant, the non-hydrocarbon-degrading Vibrio isolates proliferated. These data suggest that hydrocarbon-degrading bacteria are inhibited by chemical
dispersants, and that the use of dispersants has the
potential to diminish the capacity of the environment
to bioremediate spills.
The mobile offshore drilling unit ‘Deepwater Horizon’ experienced an explosion on April 20, 2010, and
sank 2 d later (Coastal Response Research Center
2010). As a result of the explosion, and failure of a
blowout preventer on the sea floor, crude oil began
leaking from a broken riser pipe into the Gulf of Mexico
at an estimated rate of 19 000 to 70 000 barrels (~3000 to
11 000 m3) of oil d–1 (Labson et al. 2010). This resulted in
the largest oil spill in the coastal waters of the USA.
*Email: leila.hamdan@nrl.navy.mil
© Inter-Research 2011 · www.int-res.com
Oil from the Deepwater Horizon spill washing ashore on
Elmer's Island, Louisiana, USA, in May 2010. Inset: viability
assay for the hydrocarbon-degrading bacterium Acinetobacter venetianus before (left) and after (right) exposure to
COREXIT, revealing inhibition by the chemical dispersant.
Photo: Warren Wood; inset: Leila J. Hamdan
KEY WORDS: Deepwater Horizon · Gulf of Mexico ·
Dispersant · Hydrocarbon degraders · Vibrio ·
COREXIT · Toxicity
Resale or republication not permitted without
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Effects of COREXIT EC9500A on bacteria from a beach oiled by the
Deepwater Horizon spill
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U.S. Naval Research Laboratory,Marine Biogeochemistry Section, Code
6114,Washington,DC,20375
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13. SUPPLEMENTARY NOTES
14. ABSTRACT
Hydrocarbon-degrading bacteria are important for controlling the fate of natural and anthropogenic
hydrocarbons in the marine environment. In the wake of the Deepwater Horizon spill in the Gulf of
Mexico, microbial communities will be important for the natural attenuation of the effects of the spill. The
chemical dispersant COREXIT? EC9500A was widely deployed during the response to the Deepwater
Horizon incident. Although toxicity tests confirm that COREXIT? EC9500A does not pose a significant
threat to invertebrate and adult fish populations there is limited information on its effect on microbial
communities. We determined the composition of the microbial community in oil that had been freshly
deposited on a beach in Louisiana, USA, as a result of the Deepwater Horizon spill. The metabolic activity
and viability in cultures obtained from oil samples were determined in the absence and presence of
COREXIT? EC9500A at concentrations ranging from 0.001 to 100 mg ml?1. In length heterogeneity PCR
(LH-PCR) fingerprints of oil samples, the most abundant isolates were those of Vibrio, followed by
hydrocarbon-degrading isolates affiliated with Acine - to bac ter and Marinobacter. We observed
significant reductions in production and viability of Acineto - bacter and Marinobacter in the presence of
the dis - persant compared to controls. Of the organisms examined, Marinobacter appears to be the most
sensitive to the dispersant, with nearly 100% reduction in viability and production as a result of exposure
to concentrations of the dispersant likely to be encountered during the response to the spill (1 to 10 mg
ml?1). Significantly, at the same concen tration of dispersant the non-hydrocarbon-degrading Vibrio
isolates proliferated. These data suggest that hydro - carbon-degrading bacteria are inhibited by chemical
dispersants, and that the use of dispersants has the potential to diminish the capacity of the environment to
bioremediate spills.
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102
Aquat Microb Ecol 63: 101–109, 2011
Natural seepage of hydrocarbons is an important
source of carbon for benthic environments in the Gulf
of Mexico and is a structuring factor for benthic and
near-bottom microbial communities (National Research Council Committee on Oil in the Sea 2003). In
marine environments, bacteria are the predominant
degraders of hydrocarbons (Leahy & Colwell 1990) and
thus are of great importance in controlling the fate of
natural and anthropogenic seepage. In such environments, microbial communities contain members predisposed to hydrocarbon metabolism (Aharon & Fu
2000, Lanoil et al. 2001). Microbial assemblages associated with hydrocarbon seeps are well characterized in
Gulf of Mexico sediments (Hollaway et al. 1980,
Aharon & Fu 2000, Lanoil et al. 2001, Mills et al. 2003,
Joye et al. 2004, Reed et al. 2006, Lloyd et al. 2010,
Orcutt et al. 2010). The impact of natural seepage on
water-column communities has been relatively less
studied, although microorganisms with the ability to
degrade hydrocarbons are ubiquitously distributed in
waters along continental shelves (Venkateswaran et al.
1991). Because large-scale impacts of hydrocarbons on
Gulf of Mexico beaches are rare, little is known about
the microbial communities which live in proximity to
beached oil.
In the USA, the Environmental Protection Agency
(EPA) maintains a list of chemicals and spill-mitigating
devices which may be deployed during an oil spill in
coastal waters of the USA; this is a part of the National
Contingency Plan (NCP) (US EPA 2010b). NCPapproved dispersants are applied to break up masses
of oil and reduce the formation of surface slicks. This is
done to reduce the incidence of oil coating on populations of birds, mammals and invertebrates. In the wake
of the Deepwater Horizon spill, dispersants were
deployed widely in surface and sub-surface waters
(Coastal Response Research Center 2010). As of early
June 2010, more than 990 000 gallons (~3700 m3) of
dispersant were used in the response. The most
commonly used dispersant was COREXIT® EC9500A
(Nalco). There is limited knowledge regarding the
communities that metabolize oil in the water column
and on coastal beaches. Even less is known about the
impact of dispersants in general — and specifically
COREXIT® EC9500A — on hydrocarbon-degrading
microbial communities in the Gulf of Mexico.
Dispersants have been suggested as a means to
improve microbial biodegradation of oil contamination
in the water column by forming small oil droplets with
high surface-to-volume ratios which increase their
lability to microplankton (Brakstad 2008). However,
this same process is likely to increase the concentration
and lability of spill-related toxic compounds in the
water column, which may, in turn, affect the microbial
hydrocarbon-degrading community (Zahed et al.
2010). Furthermore, the dispersants themselves may
impact hydrocarbon-degrading microorganisms.
Thus, the goal of this study was to understand the
impact of COREXIT® EC9500A (henceforth referred
to as ‘COREXIT’) on bacterial viability and metabolic
function in oiled beach samples from the area affected
by the Deepwater Horizon spill. These samples may be
ideal candidates for such analysis because microbial
communities have been acclimatized to conditions of
high contamination by hydrocarbons, environmental
weathering and possibly in situ exposure to dispersants. Future studies of the impact of dispersants on
microbial communities should also include analysis of
‘pristine’ environmental communities. Studies of both
exposed and pristine communities will be of importance in understanding the long-term affects of the
spill and the use of chemical dispersants in aquatic
environments. Previous studies have documented
clear evidence of the deleterious effect of the dispersant on wildlife and microbial communities (Lindstrom
& Braddock 2002, Couillard et al. 2005, Jung et al.
2009). Due to the unknown effects of the large-scale
use of dispersant on microbial communities in affected
areas, more study is warranted on the effects of the
widespread application of dispersant.
MATERIALS AND METHODS
Sample collection and handling. Beached oil samples were collected on May 22, 2010, from the south
end of the Elmer’s Island Wildlife Refuge (EIWR),
Louisiana, USA, on the seaward facing shore. Personal
accounts from EIWR staff indicate that accumulations of
oil reached the beach mid-day on the previous day.
During sampling, small brown pea-sized floating droplets of oil were observed in the water within 0.5 m of the
high-water mark, and pools of thick oil were deposited
at the high-water mark along the beach. Oil on the
beach appeared in several different forms, related to
how long it had been deposited (EIWR staff personal
account). Oil that had been on the beach longer than
24 h had soaked into the sand. Fresher oil rested on the
surface of the sand. Freshly deposited oil was collected
in cellulose acetate butyrate core liners, capped at both
ends, and later sub-sampled into sterile 50 ml polyethylene tubes. Samples were maintained at ambient
temperature during transport from the field (24 h) and
subsequently held in the dark on ice during shipping to
the laboratory (48 h). The total time between sample
collection and analysis was ~72 h. Because of these
hold times, alterations in the structure of the microbial
community may have occurred; this may have resulted
in the enhancement of some phylotypes, masking of the
appearance of phylotypes accounting for less than 1%
Hamdan & Fulmer: Effects of COREXIT® EC9500A on bacteria
of the population in LH-PCR and culture analysis, or
loss of cultivability of others.
Bacterial abundance. A modification of the method
of Hobbie et al. (1977), described in Hamdan & Jonas
(2006), was used to determine bacterial abundance. Oil
and oil-water (liquid surrounding the oil) was diluted
1:100 with buffered sterile seawater, stained with
0.1%, acridine orange, collected on black polycarbonate filters of pore size 0.2 µm (Osmonics) and observed
at 1000× magnification.
Cultivation conditions. Marine agar (MA) (Difco)
was used as a complete medium for bacterial growth.
Bushnell-Haas (BH) agar (Difco) supplemented with
1% w/v hexadecane (Fisher Scientific) was used as a
selective medium to isolate hydrocarbon degraders.
All media were prepared according to the manufacturer’s specifications. Culturable bacteria were assessed using standard microbial culture techniques.
Approximately 10 µl of oil or oil-water was plated on
MA and BH using a quadrant streak method. Plates
were incubated at 30°C. Colonies were isolated based
on morphology and re-plated to produce pure cultures.
Isolation of genomic DNA and 16S rDNA sequencing. To identify members of the microbial community,
individual colonies from MA and BH plates were
added to 5 ml of marine broth (MB) and incubated for
18 h at 30°C. Genomic DNA was isolated by alkaline
lysis. For this, 2 ml of MB culture was centrifuged at
4000 × g. The medium was removed and cells were
resuspended in 1 ml sterile phosphate-buffered saline
(PBS). Cells were pelleted again at 4000 × g and resuspended in 50 mM Tris-Cl (pH 8.0), 10 mM EDTA, and
1 mg l–1 (1 µg ml–1) RNase A. Cells were then lysed in
a buffer containing 200 mM NaOH and 1% sodium
dodecyl sulfate (SDS), and the solution was neutralized
with 3.0 M potassium acetate. Lysates were centrifuged at 15 000 × g to remove cell debris, and supernatants were collected. DNA was precipitated with isopropyl alcohol and centrifuged at 15 000 × g, washed
with ethanol, and dried at room temperature prior to
resuspension in 10 mM Tris-Cl (pH 8.5). DNA (1 µg)
was used as a template for a PCR as follows: 1 U Failsafe Enzyme mix (Epicentre Biotechnologies), 2× Failsafe Premix E, 2 mM 27F 16s rDNA universal bacterial
primer (5’-AGA GTT TGA TCC TGG CTC AG-3’), and
2 mM 1492R 16s rDNA universal bacterial primer (5’ACG GCT AGC TTG TTA CGA CTT-3’). Reactions
were run as follows: 94°C for 5 min; 20 cycles of 94°C
for 30 s, 50°C for 30 s, and 72°C for 90 s; 72°C for 7 min.
PCR product was visualized on a 1% agarose gel containing ethidium bromide. Bands of ~1500 bp size were
excised and purified using a gel purification kit (Qiagen). Excised PCR products were sequenced by Genewiz and checked for homology to known sequences in
GenBank using the BLASTn algorithm.
103
Length heterogeneity polymerase chain reaction
(LH-PCR). Genomic DNA was extracted from ~500 mg of
oil and oil-water mixtures using the Bio 101 FastDNA®
SPIN kit for soil. DNA was quantitated on a 1% agarose
gel with ethidium bromide and diluted with DEPCtreated water such that ~10 ng of DNA was used as template for LH-PCR. Environmental samples (oil and oilwater) and isolates were analyzed by LH-PCR.
Hamdan et al. (2008) provides a detailed description
of LH-PCR. Briefly, amplification of variable regions
V1 and V2 of the small subunit rRNA gene was performed using the primers 6-FAM-27F (5’-6-FAM-AGA
GTT TGA TCM TGG CTC AG-3’) and 355R (5’-GCTGCC TCC CGT AGG AGT-3’). Controls accompanied
reactions to determine PCR efficiency and calibrate
peak intensity. PCR mixtures consisted of 1 × Gold
buffer, 2.5 mM MgCl2, 0.2 mM dNTPs, 0.5 U AmpliTaq
Gold, 0.5 µM primers, 0.01% BSA, and diethylpyrocarbonate (DEPC)-treated water. PCR was performed on a
GeneAmp System 9700 (Applied Biosystems) with the
following program: 20 to 30 cycles of 95°C (30 s), 48°C
(30 s), 72°C (2 min plus 5 s per cycle) and final extension at 72°C (30 min). PCR product was diluted, mixed
with ILS-600 (Promega) and HiDi formamide and
loaded on an ABI 3130xl Analyzer (Applied Biosystems). LH-PCR was performed on the isolates, and the
resulting amplicons were virtually aligned with LHPCR amplicons from the environmental samples so
that the latter could be attributed to one or more isolates. In this manner, the contribution of isolates to the
environmental community was determined. LH-PCR
was also performed using universal archaeal primers.
No product was obtained with archaeal primers 1HKF
and 589R (see Litchfield et al. 2005 for details); thus,
we conclude that most of the environmental community belongs to the bacterial domain.
Bacterial viability. The effect of COREXIT (Nalco,
batch # SLOE 1184) on bacterial growth was determined
using a Bac Light™ (Invitrogen) Live/Dead Bacterial
Viability assay. COREXIT is a mixture of light petroleum
distillates (10 to 30%), propylene glycol (1 to 5%) butanedioic acid, 2-sulfo-1, 4-bis(2-ethylhexyl) ester,
sodium salt (10 to 30%), in addition to unspecified
amounts of propanol and sorbitan (www.nalco.com,
www.epa.gov). Assays were conducted according to the
manufacturer’s instructions, including standardization
curves for each strain. To determine the toxicity of
COREXIT for isolates, 10-fold serial dilutions of
COREXIT, ranging from 1:10 to 1:100 000, in MB were
added to wells of a 96-well microtitre plate. Approximately 106 colony-forming units (cfu) from a mid-log
phase culture of each isolate (in triplicate) were added to
the wells and incubated at 30°C for 18 h. Cells were pelleted at 3000 × g and resuspended in sterile 0.85% NaCl,
followed by staining using the Bac Light™ kit. Bacterial
104
viability was determined using a FLx800 (BioTek) microplate reader.
To determine the effect of COREXIT on hydrocarbon
utilization by isolates with known hydrocarbondegrading capabilities, we used Bushnell-Haas (BH)
agar supplemented with 1% w/v hexadecane.
COREXIT was added to the medium–hexadecane mix
at ratios of 1:10, 1:25 and 1:50 COREXIT:hexadecane.
Cultures were assayed as above.
Heterotrophic bacterial production. Production was
measured as leucine incorporation (Smith & Azam
1992). Briefly, 0.50 ml aliquots of oil, oil-water and MB
cultures were added to microcentrifuge tubes (3 experimental and 1 abiotic control) that contained [3H-4, 5]–1
L-leucine (154 mCi mmol ) and incubated for 1 h at
25°C. Incubations were terminated by the addition of
100% trichloroacetic acid (TCA). Samples were centrifuged to pellet cells and washed with 5% TCA and
ethanol to remove unincorporated radiolabel. Radioactivity was determined on a Beckman-Coulter LS6500
liquid scintillation counter. MB cultures of isolates containing no COREXIT and containing 1 of 2 dilutions of
COREXIT (final conc. ~1 and 10 mg ml–1 in MB) were
used. These were held for 48 h and sampled at t = 0, 6,
12, 24 and 48 h.
RESULTS
Cell abundance in the oil-water fraction of the
sample was 2.9 × 1011 cells l–1. Measurements of cell
abundance on the oil sample were not possible even
when dispersed, as the sample matrix clogged filters,
making accurate measurements impossible. Heterotrophic secondary production in the oil-water was
2.7 × 109 cells l–1 d–1, indicating a high standing stock of
cells in the stationary phase of growth.
Eight isolates were identified from the environmental samples (oil and oil-water) (Table 1). Most isolates
were members of the class Gammaproteobacteria.
With the exception of the isolates related to Exiguobacterium arabatum and Acinetobacter venetianus, all
matched most closely (> 96%) with environmental
clones obtained from marine or coastal waters. Among
the isolates were 3 known hydrocarbon degraders
(Table 1). A total of 15 LH-PCR amplicons were observed in the samples (Table 1). The Simpson’s Index
(D) was used to estimate bacterial diversity. Diversity
was relatively low in the oil and oil-water samples
(0.15 and 0.20, respectively), and 4 amplicons accounted for the majority of the LH-PCR peak area. The
4 peaks that accounted for the majority of peak abundance corresponded with 3 isolates and 1 unidentified
amplicon (Table 1). Vibrio sp. was highly abundant in
both samples and, alone, accounted for ≥31% of the
peak area in both samples.
Experiments to assess the acute toxicity of COREXIT
for environmental isolates are summarized in Fig. 1.
The addition of COREXIT resulted in varying levels of
reduction in cell viability, and an increase in cell numbers in some cases. A reduction in live cells was seen
for all isolates at all concentrations tested with the
Table 1. Summary of isolates obtained from oil and oil-water samples and LH-PCR (length heterogeneity PCR) analysis of
samples and isolates
Isolate
Phylogenetic group
Nearest
Alignment Amplicon Hydrocarbon Cumulative peak
relative
(%)
length
degrader
abundance (%)
(GenBank
(bp)
Oil-water Oil
Accession no.)
Acinetobacter venetianus
Exiguobacterium arabatum
Marinobacter
hydrocarbonoclasticus
Pseudidiomarina sp.
Pseudoalteromonas sp.
Pseudomonas
pseudoalcaligenes
Shewanella algae
Vibrio sp.
Unmatched amplicon
Unmatched amplicon
Unmatched amplicon
Unmatched amplicon
Unmatched amplicon
Unmatched amplicon
Unmatched amplicon
Gammaproteobacteria
Bacillales
Gammaproteobacteria
AM909651
FM203124
GQ901055
99
99
100
338
359
341
Yes
No
Yes
4
<1
12
4
0
12
Gammaproteobacteria
Gammaproteobacteria
Gammaproteobacteria
EF212001
GQ245921
EU440977
96
99
99
357
344
336
No
No
Yes
<1
17
<1
<1
13
<1
Gammaproteobacteria
Gammaproteobacteria
DQ386137
EU834012
98
99
346
353/354
302
310
312
320
322
327
329
No
Yes
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
5
31
0
5
9
1
10
2
1
10
32
2
3
16
0
3
2
3
105
Fig. 1. Toxicity of COREXIT® EC9500A to isolated strains. Dispersant dilutions are 1:10 to 1:100 000 w/v. Values in parentheses
are the concentration of dispersant in each treatment. Percentages of live cells in dispersant-amended cultures are relative
to the controls. Error bars are ±1 SD
exception of Vibrio sp. Dilutions of COREXIT of 1:10
and 1:100 w/v in Marine broth resulted in near total
cell death for all isolates (≥99%). However, at the
1:1000 dilution, corresponding to the addition of
0.964 mg ml–1 COREXIT, there was an increase in live
cells of Vibrio sp. compared to the control, and a
decrease in live cells (40 to 90%) in all other cultures
(relative to controls).
To address the effects of COREXIT on hydrocarbon
degraders, dilutions of COREXIT were added to minimal medium supplemented with hexadecane as a carbon source. COREXIT was added at the concentrations
suggested by the Environmental Protection Agency
(EPA), i.e. ratios of 1:10, 1:25 and 1:50 COREXIT:hexadecane (US EPA 1995); the results are summarized in
Fig. 2. Such ratios of COREXIT to hydrocarbons may
have been encountered during surface and deepwater application in the response to the spill, but dispersant concentrations may have been significantly
lower within the water column and on affected
beaches. Thus, we suggest that these concentrations
represent the maximum encountered in the environment. As before, the addition of COREXIT was toxic
to all hydrocarbon degraders in a dose-dependent
manner.
A second experiment was conducted to determine
the impact of COREXIT on heterotrophic secondary
production. The results of this experiment are summarized in Fig. 3. At t = 0 h in all 3 cultures, there were
statistically significant differences between treatments
and controls. In the Acinetobacter venetianus and
Marinobacter hydrocarbonoclasticus cultures, addition
of the dispersant moderately stimulated production
compared to the controls. In the Vibrio sp. culture, a
Fig. 2. Toxicity of COREXIT® EC9500A to hydrocarbon
degraders when present at concentrations suggested by the
Environmental Protection Agency (EPA). Dispersant dilutions
are 1:10, 1:25 and 1:50 ratios of COREXIT to hexadecane.
Values in parentheses are the concentration of dispersant in
each treatment. Error bars are ±1 SD
106
8x109
Control
+ COREXIT® EC9500A (1:1000)
+ COREXIT® EC9500A (1:100)
A
6x109
4x109
2x109
Heterotrophic secondary production (cells l–1 d–1)
0
0
8x1010
10
20
30
40
50
DISCUSSION
B
6x1010
4x1010
2x1010
0
0
10
20
30
40
50
10
20
30
40
50
C
3x1010
2x1010
1x1010
0
0
hydrocarbonoclasticus remained similar in all treatments until t = 12 h. After t = 12 h, a biofilm appeared
slightly above the medium in treatments containing
COREXIT, and production declined significantly,
while in the control it remained consistent for the duration of the experiment. In Vibrio sp., production in all
treatments declined during the first 12 h of the experiment. After t = 12 h, a concentration-dependent
decline in production was observed in the dispersant
treatments while the control rebounded. A biofilm was
also observed in COREXIT-amended Vibrio sp. tubes
after t = 12 h.
Time (h)
Fig. 3. Time course experiment tracking heterotrophic bacterial production in 3 cultures: (A) Acinetobacter venetianus,
(B) Marinobacter hydrocarbonoclasticus, and (C) Vibrio sp.
No sample for t = 48 h for A. venetianus + dispersant at 1:100
was available due to evaporative loss. Error bars are ±1 SD
moderate decline in production was observed at t =
0 h. At t = 12 h COREXIT clearly inhibited A. venetianus secondary production compared to the controls;
such inhibition remained significant in the highest
dilution of COREXIT until t = 24 h. Interestingly, in the
1:1000 dispersant dilution, production of A. venetianus
rebounded to the level of the control at t = 24 h and
exceeded the control at t = 48 h. Production by M.
LH-PCR revealed a relatively low-diversity population. This is not surprising given the nature of the samples and that others document low diversity in oil-rich
environments (Orcutt et al. 2010). Experiments to
determine microbial responses to oil contamination
using pristine marine sediments demonstrate rapid
increases in the abundance of hydrocarbon degraders
(Brakstad 2008). Thus, we hypothesized that the environmental samples would be enriched in microorganisms capable of hydrocarbon degradation.
Over 80 bacterial genera have been confirmed to
degrade hydrocarbons (Head et al. 2006, Brakstad
2008). The most commonly observed hydrocarbon
degraders in aquatic environments belong to the Gammaproteobacteria and include the genera Pseudomonas, Acinetobacter, Marinobacter and Alcanivorax
(Atlas 1981, Venkateswaran et al. 1991, Head et al.
2006). Fewer reports indicate that Vibrio spp. are
involved in hydrocarbon degradation (Atlas 1981,
Venkateswaran et al. 1991).
Virtual alignment of LH-PCR amplicons with isolate
amplicons reveals that a total of 17% of peak abundance was associated with 3 known hydrocarbon degraders (Table 1). The most abundant of these was
Marinobacter hydrocarbonoclasticus. The M. hydrocarbonoclasticus isolate exhibited 100% homology with an
isolate obtained from coral tissue, infected with Black
Band Disease, obtained proximate to the Gulf of Mexico (Richardson et al. 2009). M. hydrocarbonoclasticus
can degrade a variety of aliphatic and aromatic hydrocarbons and produce a nondialyzable bioemulsifier
when grown on hydrocarbons (Gauthier et al. 1992).
One of the main features that distinguish the genus
Marinobacter from closely related genera is the ability
of these bacteria to tolerate high levels of salt and to
grow at temperatures up to 45°C. These factors suggest
that the location in which the sample was found — and
high ambient temperatures on the beach at the time of
collection — may have selected for this isolate.
The second most abundant hydrocarbon degrader in
the environmental fingerprint was related 99% to
Acinetobacter venetianus. Other studies have demonstrated that A. venetianus proliferates in oil-degrading
consortia and is capable of metabolizing complex
marine hydrocarbon mixtures (Vaneechoutte et al.
2009). Some strains of A. venetianus produce bioemulsifiers to aid in hydrocarbon metabolism. Pseudomonas
pseudoalcaligenes, a confirmed naphthalene degrader
(Garcia-Valdes et al. 1988), was detected in the LHPCR profile, although its amplicon accounted for less
than 1% of peak abundance.
Vibrio sp. accounted for the majority of LH-PCR peak
abundance. The Vibrio isolate was 99% related to 4
Vibrio phylotypes: Vibrio natriegens, alginolyticus, fluvialis and the pathogenic vulnificus. All GenBank entries
which matched the Vibrio sp. isolate are capable of rapid
growth, with generation times of less than 10 min (Aiyar
et al. 2002). Each is capable of forming biofilms, which
may explain the biofilm observed in the production
tubes. V. fluvialis and V. natriegens have been identified
in hydrocarbon-degrading communities in experimental
seawater mesocosms (Venkateswaran et al. 1991);
however, their role as hydrocarbon degraders is not well
recognized. In experimental studies, V. natriegens
was capable of metabolizing insoluble surfactants at
air–water interfaces (Salter et al. 2009). This ability to
metabolize surfactants may explain why the Vibrio sp.
isolate showed greater viability in the presence of
COREXIT compared to other phylotypes in this study.
At some concentrations, all isolates were susceptible
to impairment as a result of exposure to COREXIT. The
addition of dispersant, at all concentrations, resulted in
a significant reduction in the live:dead cell ratio in all
isolates tested — with the exception of Vibrio sp.,
which, of the organisms examined, appear to be the
most tolerant to COREXIT (Figs. 1 & 2).
Because of the importance of microorganisms to the
natural attenuation of hydrocarbons, toxicity tests
were focused on hydrocarbon-degrading isolates.
COREXIT at concentrations <1 mg ml–1 was capable of
killing at least 60% of cells in cultures of the 2 most
abundant hydrocarbon-degrading isolates observed in
the LH-PCR profile (Fig. 2). This is a significant finding
given that the 1:50 dilution, ~0.2 mg ml–1, is within the
concentration range for COREXIT® EC9500A classified as ‘practically non-toxic’ to the standard toxicity
test organism Menidia beryllina (US EPA 2010a). The
result for M. beryllina was included in a recent Environmental Protection Agency (EPA) report identifying
the environmental hazards associated with the use of
National Contingency Plan (NCP)-approved dispersants during the Deepwater Horizon response. The
results of the current study demonstrate that, at concentrations which do not present a significant hazard
107
to some adult test organisms, dispersants may be
highly toxic to communities directly involved in natural
hydrocarbon bioremediation.
In the second live:dead experiment, in which hexadecane was added to growth media (Fig. 2), we confirm that cell death results from the application of the
dispersant, not from limitation of hydrocarbons. Interestingly, in the presence of hexadecane, Pseudomonas
pseudoalcaligenes exhibited the greatest resistance to
COREXIT toxicity, suggesting that the presence of
hydrocarbons may reduce the impact of COREXIT on
some populations.
In microcosm studies conducted on Alaskan North
Slope crude oil treated with COREXIT® EC9500A, the
dispersant did increase the surface area of crude oil
droplets and enhanced microbial droplet colonization
(Lindstrom & Braddock 2002). However, the dispersants resulted in a negligible increase in biodegradation of oil when compared to non-dispersed oil.
COREXIT can be a highly labile substrate for microbial
growth and metabolism (Zahed et al. 2010), and
increases in carbon mineralization in oil samples may
be attributed to mineralization of the chemical dispersant alone (Lindstrom & Braddock 2002, Brakstad
2008). This may explain the results observed for Acinetobacter venetianus where, after t = 24 h, production in
the highest dilution of COREXIT rebounded to levels
that exceeded the control (Fig. 3). However, in the case
of Marinobacter hydrocarbonoclasticus, no such recovery occurred. In this respect, COREXIT may impart
positive effects on hydrocarbon-degrading cultures
capable of withstanding its initial toxicity. Because
some Vibrio spp. are capable of metabolizing dispersant (Salter et al. 2009), it is reasonable to hypothesize
that the increase in viability in cultures of Vibrio sp. in
the live/dead staining analysis may be due to metabolism of COREXIT. However, even within this group, a
reduction in heterotrophic secondary production was
observed in the presence of COREXIT (Fig. 3),
although the impact on Vibrio sp. was less significant
than that on M. hydrocarbonoclasticus. The high
turnover rates observed for numerous Vibrio spp. may
likewise explain the reduced toxicity of COREXIT over
other isolates.
CONCLUSIONS
Hydrocarbon degradation in the marine environment is dependent on the ability of microorganisms to
utilize hydrocarbons for growth and metabolism. The
results of the current study demonstrate that microbial
populations are susceptible to toxicity from the use of
COREXIT® EC9500A when applied at prescribed concentrations. While the short-term goals of dispersants
108
for counting bacteria by fluorescence microscopy. Appl
may be achieved, this study provides evidence that
Environ Microbiol 33:1225–1228
COREXIT® EC9500A differentially impacts hydrocarHollaway SL, Faw GM, Sizemore RK (1980) The bacterial
➤
bon-degrading microorganisms. Although toxicity
community composition of an active oil field in the northtesting with bacteria is only a minor component of the
western Gulf of Mexico. Mar Pollut Bull 11:153–156
development of new dispersants, these experiments ➤ Joye SB, Boetius A, Orcutt BN, Montoya JP, Schulz HN, Erickson MJ, Lugo SK (2004) The anaerobic oxidation of
demonstrate the importance of understanding the
methane and sulfate reduction in sediments from Gulf of
impact of dispersants on microbial communities, as
Mexico cold seeps. Chem Geol 205:219–238
there is potential to diminish the capacity of the
➤ Jung JH, Yim UH, Han GM, Shim WJ (2009) Biochemical
environment to mitigate spills.
changes in rockfish, Sebastes schlegeli, exposed to disAcknowledgements. We thank W. Wood (NRL) for acquiring
the samples used in this study, P. Gillevet and M. Sikaroodi
(George Mason University) for assistance with LH-PCR analysis, B. Ringeisen (NRL) for helpful discussions and support of
this work, R. Jonas (George Mason University) for comments
on the draft manuscript and L. Tender (NRL) for providing the
dispersant used in these experiments. We acknowledge the
Naval Research Laboratory base program for support of this
project.
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Submitted: August 25, 2010; Accepted: December 9, 2010
Proofs received from author(s): February 18, 2011
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Oil carbon entered the coastal planktonic food web during the Deepwater Horizon oil spill
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2010 Environ. Res. Lett. 5 045301
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IOP PUBLISHING
ENVIRONMENTAL RESEARCH LETTERS
Environ. Res. Lett. 5 (2010) 045301 (6pp)
doi:10.1088/1748-9326/5/4/045301
Oil carbon entered the coastal planktonic
food web during the Deepwater Horizon
oil spill
William M Graham1,2,3 , Robert H Condon1, Ruth H Carmichael1,2,
Isabella D’Ambra1,2, Heather K Patterson1,2, Laura J Linn1 and
Frank J Hernandez Jr1,2
1
2
Dauphin Island Sea Lab, Dauphin Island, AL 36528, USA
Department of Marine Sciences, University of South Alabama, Mobile, AL 36688, USA
E-mail: mgraham@disl.org
Received 27 September 2010
Accepted for publication 1 November 2010
Published 8 November 2010
Online at stacks.iop.org/ERL/5/045301
Abstract
The Deepwater Horizon oil spill was unprecedented in total loading of petroleum hydrocarbons
accidentally released to a marine ecosystem. Controversial application of chemical dispersants
presumably accelerated microbial consumption of oil components, especially in warm Gulf of
Mexico surface waters. We employed δ 13 C as a tracer of oil-derived carbon to resolve two
periods of isotopic carbon depletion in two plankton size classes. Carbon depletion was
coincident with the arrival of surface oil slicks in the far northern Gulf, and demonstrated that
subsurface oil carbon was incorporated into the plankton food web.
Keywords: zooplankton, petroleum hydrocarbon, stable isotope, Gulf of Mexico
S Online supplementary data available from stacks.iop.org/ERL/5/045301/mmedia
present quantitative data collected as a rapid-response effort to
track carbon isotopic signal in two size classes representing a
pathway into the bulk zooplankton community of the northern
Gulf of Mexico. The present study reflects only the initial
steps of a larger and continuing laboratory experimental and
field effort aimed at understanding how oil affects pelagic
communities of the northern Gulf and vice versa effects of the
biological community on the fate of the oil.
1. Introduction
Following the sinking of Deepwater Horizon (DWH) on
22 April 2010, an estimated 780 000 m3 of Sweet Louisiana
Crude (SLC) and 205 000 mT of methane [1] were released
into the northern Gulf of Mexico over an 85 d period. General
agreement exists that ∼25% was directly recovered or burned
at sea, leaving ∼75% to be degraded naturally or with the aid
of chemical dispersants [2]. Recent publications document the
scope of deep subsea oil and methane along the northern Gulf
slope [1, 3, 4], but scant evidence exists for the presence of
subsea oil in warm (>25 ◦ C), shallow shelf waters.
A large pool of isotopically depleted carbon from
dispersed oil and methane is presumably available for
biological consumption via prokaryotic consumers [5].
Isotopic depletion extending into marine zooplankton grazers,
a pathway mediated by the microbial food web [6], is a
good proxy for food web modification by the spill. Here we
2. Methodology
We employed δ 13 C as a tracer of oil-derived carbon
incorporation into the lower marine food web across the middle
and inner continental shelf. During June–August 2010, we
followed two plankton size classes: the nominally 1 μm–
0.2 mm ‘small suspended particulate’ and the >0.2–2 mm
‘mesozooplankton’ fractions, with the former considered likely
food for the latter. The study region, >100 km north of the
DWH well head, had three defined northward pulses of surface
3 Author to whom any correspondence should be addressed.
1748-9326/10/045301+06$30.00
1
© 2010 IOP Publishing Ltd Printed in the UK
Environ. Res. Lett. 5 (2010) 045301
W M Graham et al
Table 1. The location and depth of surface and deep sampling stations in the Gulf of Mexico and reference sites within Mobile Bay, AL,
including mean (±SD) salinity at each depth. n.d. = no data, dash = not deep enough to collect separate Deep sample. Symbols next to
station names are for reference to figure 1.
Surface
Station, symbol
Latitude
Gulf sites
T35,
29.7989
T20, 30.0902
T10,
30.1609
Buoy M (BM), 30.1306
Mobile Bay reference sites
Cedar Point Reef, + 30.3256
Sand Reef, −
30.2772
•
Longitude
Deep
Depth (m)
Mean salinity (psu)
Depth (m)
Mean salinity
−88.2083
−88.2116
−88.1229
−88.1097
1
1
1
1
27 ± 3
25 ± 3
24 ± 3
23 ± 4
33
18
8
15
36 ± 0
33 ± 4
32 ± 3
n.d.
−88.1327
−88.1052
1
1
12 ± 6
18 ± 6
—
—
—
—
oil: two prior to the well’s 15 July shut-in, and one in August
(figures 1(A), (B), movie S1 available at stacks.iop.org/ERL/
5/045301/mmedia). Samples were collected in surface and
bottom waters of the Gulf of Mexico at four shelf and two inner
Mobile Bay reference sites (figure 1(A), table 1).
2.2. Stable isotope analysis
Bulk carbon (C) stable isotope ratios (δ 13 C, ) were measured
by continuous flow isotope ratio mass spectrometry at the
University of Utah Stable Isotope Facility (USA) and on a
Picarro cavity ringdown spectrometer coupled to a Costech
elemental analyzer at Dauphin Island Sea Lab (DISL). Bulk
carbon isotopic composition in organisms reflects both shortterm energy stores (i.e., lipids) and relatively longer turnover
in tissues [7]. Since lipids are isotopically depleted and
do not necessarily reflect time-integrated diet of organisms,
variation in lipid content may introduce bias into stable isotope
analyses. Established mathematical normalization techniques
allow correction of δ 13 C values in lipid-rich samples, but
preserve sample integrity for other analyses [7]. Here,
bulk δ 13 C values in mesozooplankton were lipid-corrected
according to [7], after comparison to C:N in mesozooplankton
samples (figure 2(A). Comparison of δ 13 C values to the C:N
and relative C content (mg l−1 ) in suspended particulates
indicated no correction was needed for the smaller fraction
(figures 2(A) and (B)). C and N content were obtained during
stable isotope analysis.
2.1. Plankton and suspended particle collection
Suspended particulates (1 μm–0.2 mm) were collected using
1.7 l vertical Niskin bottles deployed at target depths of one
meter above the bottom and one meter below the surface at
stations T10, T20, and T35 at two-week intervals from 2 June
to 15 August, 2010. At Buoy M, and the two bay reference
sites, Cedar Point Reef and Sand Reef, only water from one
meter below the surface was collected owing to shallowness of
the water column; these were collected at 1–2 week intervals.
At the Sand Reef reference station, samples were collected
using a 1 l horizontal sampler. Air-filled balloons, released
at depth from bottle ends, were used to avoid surface oil
contamination during sampling. Water was vacuum filtered
(5 psi) onto pre-combusted GF/F filters, and dried to a
constant weight at 60 ◦ C.
Mesozooplankton were collected at the same stations and
with the same timing as above with opening–closing plankton
nets (3.5 m long, 0.25 m diameter), using 333 μm for surface
and bottom and 202 μm for oblique samples at Gulf sites. A
202 μm mesh (1 m long, 0.5 m diameter) ring plankton net
was used at BM and reference sites (mesozooplankton were not
collected at Sand Reef). Samples were rinsed with ultrapure
water, dried at 60 ◦ C, and homogenized by mortar and pestle.
Additional historical pre-spill mesozooplankton samples
from Buoy M and suspended particulate samples from T35,
T20 and Buoy M were collected in May–August of both 2008
and 2009 (all data within each station were pooled as ‘prespill’. Collections were similar to those described for 2010.
These samples were historically analyzed only for carbon (C)
and nitrogen (N) content; no pre-spill stable isotope data exist
for continental shelf stations. All mesozooplankton samples
are being processed for community assemblage changes with
respect to the spill; however, assemblage analysis is beyond the
scope of this study. That said, a cursory review of the samples
for presence of contaminating oil droplets revealed the samples
were clear of both oil and resuspended sediments. In addition,
the zooplankton samples were dominated by organisms typical
of spring–summer assemblages such as calanoid copepods.
2.3. Source crude oil samples
We analyzed δ 13 C of oil in both weathered and fresh condition.
Weathered surface oil was collected in the nearshore waters
off Dauphin Island, Alabama, on 11 June 2010, and stored
in the dark at 5 ◦ C. Prior to carbon stable isotope analysis,
the weathered oil was further dried at 60 ◦ C for 48 h to
remove residual water. This sample was analyzed for δ 13 C
at the University of Utah Stable Isotope Facility. Fresh
Source Oil B (SOB) supplied by BP was collected on
22–23 May 2010, from the riser insertion tube on board
the drill ship Enterprise. Accompanying documentation for
two samples (SOB-20100716-067 and SOB-20100716-130)
reported Nalco EC9323A defoamer was injected topsides, and
subsea injections included methanol with 10 000 ppm VX9831
oxygen scavenger/catalysts solution. Fresh oil was stored
at 5 ◦ C until analysis by saturating a small piece of precombusted GF/F filter with oil and analyzed using a Picarro
cavity ringdown spectrometer coupled to a Costech elemental
analyzer at DISL.
2
W M Graham et al
Figure 1. (A) Sample sites, symbols relating to (D) and (E); box defines area used to calculate oil % coverage in (B) with example of peak oil
coverage 28 June 2010. A full animation of oil movement around these sites can be found in the supplementary data (movie S1 available at
stacks.iop.org/ERL/5/045301/mmedia). (B) Timing of three shoreward pulses of oil, I–III (cf movie S1 available at stacks.iop.org/ERL/5/
045301/mmedia). (C) Daily averaged river discharge into Mobile Bay. (D) δ 13 C values for mesozooplankton fraction (0.2–2 mm). (E) δ 13 C
values for suspended particulate fraction (1 μm–0.2 mm). Both (D) and (E) referenced against δ 13 C values from Mobile Bay and weathered
and fresh SLC oil. Error bars show standard deviation.
3
W M Graham et al
Figure 2. Analysis of C stable isotope ratios. (A) Bulk δ 13 C in mesozooplankton (0.2–2 mm) and suspended particulates (1 μm–0.2 mm)
compared to C:N in surface, deep, and oblique (mesozooplankton only) samples. (B) δ 13 C compared to carbon content in suspended
particulates. (C) Corrected δ 13 C in mesozooplankton and δ 13 C in smaller suspended particulates compared to salinity at surface and deep
sampling locations. (D) Bulk δ 13 C in suspended particulates compared to chlorophyll a (chl a ) in water samples from stations T10, T20, and
T35.
2.4. Chlorophyll a
Claiborne Dam on the Alabama River (# 02429500). There is
an estimated 5–10 d flow-dependent lag between readings at
gauging stations and reference sites in Mobile Bay [9]. Daily
mean discharge is reported in figure 1(C) (not including lags
as it did not change the pattern). Salinity was measured at
Gulf stations T10–T35 using a Sea Bird SBE 25 conductivity–
temperature–depth (CTD) probe and at Buoy M and the two
Mobile Bay reference sites using a handheld YSI 85 salinity
probe.
Whole water samples collected at Stations T10, T20 and T35
(1 m below surface and 1 m above bottom) were stored on ice in
the dark and filtered in the laboratory onto GF/F filters within
2 h of collection. Extracted chlorophyll a was fluorometrically
determined with a Turner Designs fluorometer [8].
2.5. Oil proximity data
Surface layer slick distribution was defined from Geographical
Information System (GIS) data (ftp://satepsanone.nesdis.noaa.
gov/OMS/disasters/DeepwaterHorizon/). Per cent coverage of
oil and distance from the nearest slick to station T20 were
measured by mapping oil layers in ESRI ArcMap v9.3 GIS
software.
2.7. Statistical analyses
Analysis of variance (ANOVA) was used for comparison
of δ 13 C values, C:N, and chl a among sample types. If
ANOVAs were significant, post hoc pairwise comparison of
means using Tukey’s test of variability were performed. All
linear correlations were tested using the Z-test. These analyses
were performed in StatView 5.0.1. An additional three-way
ANOVA with location, sample depth and condition (pre- or
post-spill years) was performed on C:N values for suspended
particulates and mesozooplankton using Minitab 15. All data
were normally distributed and did not require transformation,
and also conformed to the assumption of homogeneity of
variance.
2.6. Freshwater discharge
To determine potential effects of freshwater discharge on
δ 13 C, riverine discharge into Mobile Bay was estimated by
adding daily discharge rates at two US Geological Survey
gauging stations (http://waterdata.usgs.gov/usa/nwis/sw), the
Coffeeville Dam on the Tombigbee River (#02469765) and the
4
W M Graham et al
3. Results and discussion
suspended particles, and some primary consumers in Mobile
Bay and elsewhere [13, 14]. δ 13 C values in mesozooplankton
were not related to salinity (figure 2(C)), and δ 13 C in suspended
particulates showed a weak positive correlation only when
surface and bottom samples were considered together (r =
0.37, P = 0.03). This finding is consistent with the relatively
low discharge to Mobile Bay during most of the sampling
period (figure 1(C)). The similar patterns of δ 13 C depletion in
both mesozooplankton and small suspended fractions, despite
decreasing discharge to Mobile Bay and little or no relationship
to salinity during the period of greatest oil proximity and
coverage in the region, supports an oil-derived C source
mediating this shift as opposed to a freshwater-derived source
from Mobile Bay.
Since phytoplankton were a component of the mixed
small suspended particulate fraction (1 μm–0.2 mm), isotopic
depletion of C in this fraction and subsequently in the
mesozooplankton could result from dominance by isotopically
depleted phytoplankton. Given the range of δ 13 C values typical
in marine phytoplankton (−20 to −24; [15, 16]) and the
lack of evidence for a significant freshwater influence during
the period of depletion, phytoplankton alone are unlikely to
account for the observed depletion (figures 1(D), (E)). The
concentration of chlorophyll pigments extracted from water
samples collected at stations T10 through T35 varied relatively
little during the study period and did not indicate a bloom of
shelf phytoplankton (1.50 ± 1.02 μg l−1 ; figure 2(D)). Chl a
concentration also was not related to δ 13 C of the suspended
particulate fraction ( Freg1,24 = 2.33, P = 0.14). Surface
and bottom samples were not different and were combined
for further analysis (two-way ANOVA with station and depth
as variables: F3,26 = 1.30, P = 0.29) (figure 2), indicting
that a change in phytoplankton abundance made no measurable
contribution to the isotopic depletion of C shown in figure 1.
To elucidate whether the observed δ 13 C depletion was due
to the contaminating presence of oil in the water column or to
the assimilation and incorporation of oil-derived C by resident
biota, we compared C:N for mesozooplankton and suspended
particulate fractions during pre- (2008–2009) and post-oil spill
(2010) years between May and August. The expectation was
that presence of SLC oil on or inside the animals would yield
anomalously high C:N values. For the suspended particulate
fraction, there was no difference in C:N (by weight) between
any of the sampling stations within and across pre- and postspill years (three-way ANOVA with station, pre- and postspill years, and sample depth as variables: F9,89 = 1.46,
P = 0.18) (figure 3). Similarly, there was no significant
difference in mesozooplankton C:N at station BM in pre- and
post-oil spill years (ANOVA: F1,17 = 0.69, P = 0.42), and
while we do not have pre-spill C:N data for stations T35, T20
and T10, post-spill C:N data from these sites were the same
as those at station BM (ANOVA: F1,68 = 2.30, P = 0.09)
(figure 3). Combined, these results suggest that the depleted
C isotope values were not driven by direct oil contamination
in the samples (e.g., oil micro-droplets collected on the filter).
That similar results were found for both mesozooplankton
and suspended particulate fractions suggests oil-derived C was
transferred through the food web.
δ 13 C depletion occurred in each size fraction at middle and
inner shelf stations coincident with two sequential northward
pulses of surface oil slicks from DWH (figures 1(D), (E)).
Relative to early June, an isotopic shift of −1 to −4
(toward weathered and fresh oil, −27.23 ± 0.03 and
−27.34 ± 0.34, respectively) occurred during the peak of
areal coverage of oil over the sites (figures 1(B), (D), (E)).
Recovery from this depletion to the pre-spill baseline was 2–
4 wks. A third pulse of residual oil occurred in late July,
and depleted δ 13 C was observed in mid-August at the furthest
offshore stations. Depletion and recovery cycles on the order
of a few weeks are consistent with published warm water
petroleum hydrocarbon decay timescales [10].
The apparent oil-related δ 13 C depletion occurred in both
fractions and throughout the water column. The pattern
was consistent despite the differences in both δ 13 C and C:N
between the two size fractions. δ 13 C and C:N values differed
between mesozooplankton and suspended particulates, with
mesozooplankton having heavier δ 13 C (−20.44 ± 1.37
compared to −23.19 ± 1.26) and lower C:N (4.8 ± 0.6
compared to 6.9 ± 0.9) than suspended particles (ANOVA:
δ 13 C:F4,84 = 23.29, P < 0.001; C:N:F4,84 = 43.04, P <
0.001; figure 2(A). Comparisons among surface, bottom, and
oblique samples did not differ for either size fraction (Tukey’s
post hoc test: P > 0.05 for all comparisons).
Bulk δ 13 C values were correlated with C:N in surface
(r = −0.64), bottom (r = −0.61), and oblique (r = −0.63)
mesozooplankton samples ( P < 0.01) for all comparisons;
figure 2(A), consistent with a mesozooplankton fraction
(largely composed of animals) and requiring correction for
lipid-related depletion of δ 13 C [7].
In contrast, δ 13 C
in suspended particulates was not correlated with C:N
(figure 2(A)), and was weakly correlated with the relative
C content in the sample only when surface and bottom
fractions were considered together (r = −0.44, P < 0.01;
figure 2(B)). These findings suggest a small particle fraction
of mixed composition, including algal and detrital matter that
did not demand lipid correction despite a higher C:N ratio than
mesozooplankton [7, 11]. The mean correction applied to bulk
δ 13 C in mesozooplankton samples was 1.49 ± 1.73. The
relative shift in sample values can be seen by comparing panels
A and C in figure 2.
3.1. Discounting masking effects or sample contamination
In comparison to reference sites inside Mobile Bay, offshore
depletion of δ 13 C was not related to timing of freshwater
discharge from the Bay, phytoplankton blooms, or direct
contamination of samples with external oil. Corrected δ 13 C
in mesozooplankton and bulk δ 13 C values in suspended
particulates were compared to salinity at each station to detect
potential freshwater influence (figure 2(C)). The hydrology of
Mobile Bay is dominated by freshwater inputs, which lead to
salinity stratification [12] and may convey isotopically light
suspended particles and biota from the upper reaches of the
Bay to the Gulf [13]. δ 13 C in reference oil samples was similar
to δ 13 C typically found in freshwater-derived vegetation,
5
W M Graham et al
the Richard C Shelby Center for Ecosystem-Based Fisheries
Management and the Northern Gulf Institute’s rapid-response
funding from BP’s Gulf Research Initiative. We thank
J Burchfield, M Miller, K Weis, J Hermann, B Dzwonkowski,
K Park, K Robinson, L McCallister and the crew of the R/V
E O Wilson for laboratory, field and logistical support.
References
[1] Adcroft A, Hallberg R, Dunne J P, Samuels B L, Galt J A,
Barker C H and Payton D 2010 Geophys. Res. Lett.
37 L18605
[2] Kerr R A 2010 A lot of oil on the loose, not so much to be
found Science 329 734–5
[3] Camilli R, Reddy C M, Yoerger D R, Van Mooy B A S,
Jakuba M V, Kinsey J C, McIntyre C P, Sylva S P and
Maloney J V 2010 Tracking hydrocarbon plume transport
and biodegradation at Deepwater Horizon Science
330 201–4
[4] Hazen T C et al 2010 Deep-sea oil plume enriches indigenous
oil-degrading bacteria Science 330 204–8
[5] Spies R B and DesMarais D J 1983 Natural isotope study of
trophic enrichment of marine benthic communities by
petroleum seepage Mar. Biol. 73 67–71
[6] Sherr E B and Sherr B F 1991 Planktonic microbes: tiny cells at
the base of the ocean’s food webs Trends Ecol. Evol. 6 50–3
[7] Post D M, Layman C A, Arrington D A, Takimoto G,
Quattrochi J and Montana C G 2007 Getting to the fat of the
matter: models, methods and assumptions for dealing with
lipids in stable isotope analyses Oecologia 152 179–89
[8] Welschmeyer N A 1994 Fluorometric analysis of chlorophyll a
in the presence of chlorophyll b and phaeopigments Limnol.
Oceanogr. 39 1985–92
[9] Schroeder W W 1979 Dispersion and Impact of Mobile River
System Waters in Mobile Bay, Alabama (Water Resources
Research Institute vol 37) (Auburn, AL: Auburn University)
p 48
[10] Atlas R M 1981 Microbial degradation of petroleum
hydrocarbons: an environmental perspective Microbiol. Rev.
45 180–209
[11] Søreide J E, Tamelander T, Hop H, Hobson K A and
Johansen I 2007 Sample preparation effects on stable C and
N isotope values: a comparison of methods in Arctic marine
food web studies Mar. Ecol.-Prog. Ser. 328 17–28
[12] Schroeder W W, Dinnel S P and Wiseman W J Jr 1990 Salinity
stratification in a river-dominated estuary Estuar. Coasts
13 145–54
[13] Goecker M E, Valentine J F, Sklenar S A and Chaplin G I 2009
Influence from hydrological modification on energy and
nutrient transference in a deltaic food web Estuar. Coasts
32 173–87
[14] Michener R H and Schell D M 1994 Stable isotope ratios as
tracers in marine aquatic food webs Stable Isotopes in
Ecology and Environmental Science ed K Lajtha and R
H Michener (Oxford: Blackwell Scientific) pp 138–58
[15] Moncreiff C A and Sullivan M J 2001 Trophic importance of
epiphytic algae in subtropical seagrass beds: evidence from
multiple stable isotope analyses Mar. Ecol. Prog. Ser.
215 93–106
[16] Fry B 2006 Stable Isotope Ecology (New York: Springer) p 308
[17] Azam F, Fenchel T, Field J G, Gray J S, Meyer-Reil L A and
Thingstad F 1983 The ecological role of water-column
microbes in the sea Mar. Ecol. Prog. Ser. 10 257–63
Figure 3. Particulate organic carbon to particulate nitrogen ratios
(C:N) of (A) mesozooplankton (0.2–2 mm) and (B) smaller
suspended particulates (1 μm–0.2 mm) collected at stations T35,
T20, T10 and BM (cf figure 1 and table 1). There was no significant
difference between surface and bottom C:N within each station, thus
data were pooled for all pre- and post-spill analyses (ANOVA:
F1,97 = 0.04, P = 0.85). ND indicates data were not available.
4. Conclusions
Carbon isotopic depletion in mesozooplankton and suspended
particulate samples throughout the water column (figures 1(D)
and (E)) indicates trophic transfer of oil carbon into the
planktonic food web. A similar response found in benthic
communities around natural seeps [5] suggests that carbon
isotopic shifts in the plankton fractions are likely due to
the duration and magnitude of depleted carbon released
into the system. These data provide strong evidence that
labile fractions of the oil extended throughout the shallow
water column during northward slick transport and that
this carbon was processed relatively quickly at least two
trophic levels beyond prokaryotic hydrocarbon consumers
given our understanding of microbial-zooplankton trophic
linkages [6, 17]. Further, this study provides a launching
point for follow up experimental laboratory and field exercises
aimed at understanding the fate and transport of petroleum
hydrocarbons in marine planktonic ecosystems under the
influence of natural or human-mediated chemical dispersion.
Acknowledgments
This work was supported by a grant from the National
Science Foundation through the RAPID program funding for
oil spill research (OCE-1043413). Additional funding and
resources were from the Alabama Department of Conservation
and Natural Resources, Marine Resources Division, and the
National Oceanic and Atmospheric Administration through
6
GEOPHYSICAL RESEARCH LETTERS, VOL. 39, L01605, doi:10.1029/2011GL049505, 2012
Macondo-1 well oil-derived polycyclic aromatic hydrocarbons
in mesozooplankton from the northern Gulf of Mexico
Siddhartha Mitra,1 David G. Kimmel,2,3 Jessica Snyder,2 Kimberly Scalise,1
Benjamin D. McGlaughon,2 Michael R. Roman,4 Ginger L. Jahn,4 James J. Pierson,4
Stephen B. Brandt,5 Joseph P. Montoya,6 Robert J. Rosenbauer,7 Thomas D. Lorenson,7
Florence L. Wong,7 and Pamela L. Campbell8
Received 7 October 2011; revised 7 December 2011; accepted 11 December 2011; published 14 January 2012.
[1] Mesozooplankton (>200 mm) collected in August and
September of 2010 from the northern Gulf of Mexico show
evidence of exposure to polycyclic aromatic hydrocarbons
(PAHs). Multivariate statistical analysis revealed that
distributions of PAHs extracted from mesozooplankton
were related to the oil released from the ruptured British
Petroleum Macondo-1 (M-1) well associated with the R/V
Deepwater Horizon blowout. Mesozooplankton contained
0.03–97.9 ng g 1 of total PAHs and ratios of fluoranthene
to fluoranthene + pyrene less than 0.44, indicating a liquid
fossil fuel source. The distribution of PAHs isolated from
mesozooplankton extracted in this study shows that the
2010 Deepwater Horizon spill may have contributed to
contamination in the northern Gulf of Mexico ecosystem.
Citation: Mitra, S., et al. (2012), Macondo-1 well oil-derived
polycyclic aromatic hydrocarbons in mesozooplankton from the
northern Gulf of Mexico, Geophys. Res. Lett., 39, L01605,
doi:10.1029/2011GL049505.
1. Introduction
[2] An estimated 4.93 million barrels (1 barrel = 42
US gallons) of crude oil were released into the Northern
Gulf of Mexico (nGOM) from the British Petroleum
(BP) Macondo-1 (M-1) site (Federal Interagency Solutions
Group, Oil budget calculator—Deepwater Horizon, 2010,
http://www.restorethegulf.gov/sites/default/files/documents/
pdf/OilBudgetCalc_Full_HQ-Print_111110.pdf), the location of the R/V Deepwater Horizon (DWH) blowout. The
total extent of the surface oil slick, derived from wind, ocean
currents, aerial photography, and satellite imagery, was estimated to be 180,000 km (J. Amos, BP spill was greater
disaster than public knew, 2010, http://mcbi.org/news/PRNorse-Amos-2010.pdf). Chemical evidence of subsurface
oil from the leak was found as far away as 30 km south of the
M-1 well [Diercks et al., 2010]. Oil which is a complex
mixture of hydrocarbons and other chemicals, contains
numerous PAHs [Connell, 1997; National Research Council,
2002]. These PAHs may be used as chemical fingerprints of
specific types of oil released into natural environments
[Blumer, 1976]. For example, PAH distributions, or relative
abundances of low and high molecular weight PAHs, have
been used to fingerprint oil and determine the provenance of
various oil spills in the environment [Bennett et al., 2000;
Stout et al., 2001; Christensen et al., 2004]. Despite extensive efforts at completely delineating the extent of the DWH
oil spill, there have been no studies published to date
addressing whether or not the spill introduced oil-derived
toxic compounds into the pelagic food web of the nGOM.
[3] Mesozooplankton are useful sentinel organisms for
oil-derived pollution [Carls et al., 2006] that serve as food
for early life stages of fish and shrimp. Furthermore, they act
as conduits for the movement of oil-derived contamination
and other persistent organic pollutants through the marine
food web [Clayton et al., 1977; Borgå et al., 2004; Sobek
et al., 2006; Hallanger et al., 2011a, 2011c]. The objective
of this study was to extract and analyze PAHs in mesozooplankton collected in the nGOM after the 2010 DWH spill.
The null hypothesis of this study was that the relative PAH
distributions in mesozooplankton collected throughout the
Gulf of Mexico would not resemble the relative PAH distributions in oil collected from the M-1 well.
2. Methods
Department of Geological Sciences, East Carolina University,
Greenville, North Carolina, USA.
2
Department of Biology, East Carolina University, Greenville, North
Carolina, USA.
3
Institute for Coastal Sciences and Policy, East Carolina University,
Greenville, North Carolina, USA.
4
Horn Point Laboratory, Center for Environmental Science, University
of Maryland, Cambridge, Maryland, USA.
5
Oregon Sea Grant, Oregon State University, Corvallis, Oregon, USA.
6
School of Biology, Georgia Institute of Technology, Atlanta, Georgia,
USA.
7
Pacific Coastal Marine Science Center, U.S. Geological Survey, Menlo
Park, California, USA.
8
Water Resources, National Research Program, U.S. Geological
Survey, Menlo Park, California, USA.
2.1. Mesozooplankton Sampling
[4] The mesozooplankton samples in this study were collected from within 20 km from the M-1 site and at distal
stations grouped around 300 km south of the M-1 site
(Figure 1). Mesozooplankton were sampled with a Multiple
Open and Closing Net Environmental Sampling System
(MOCNESS) [Wiebe et al., 1976]. The MOCNESS had a
1 m2 opening and a mesh size of 200 mm. Five to ten minute
tows were conducted at the surface, and forty to sixty minute
tows were conducted between 500 m and the surface in
August and September of 2010 (auxiliary material, Table S1).1
All mesozooplankton (>200 mm) were collected from the
cod end of the nets and immediately frozen in glass vials.
Copyright 2012 by the American Geophysical Union.
0094-8276/12/2011GL049505
1
Auxiliary materials are available in the HTML. doi:10.1029/
2011GL049505.
1
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MITRA ET AL.: DWH PAHS IN GOM MESOZOOPLANKTON
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Figure 1. Map of sampling area. Oil extent as of May 2010. Numbered symbols represent stations from which samples
were analyzed for PAHs in this study.
This procedure generally resulted in two sets of samples
from each station: one group of mesozooplankton samples
from the surface water and one group integrated across the
top 500 m water depth.
2.2. Oil, Surface Slick, and Seep Samples
[5] A sample of M-1 subsurface oil was provided by B &
B Laboratory, College Station, Texas. The well oil was
obtained by BP from the riser insertion tube aboard the
drillship Discoverer Enterprise on May 21, 2010, and was
absent of any defoamer or dispersant. All samples were
collected, processed, and shipped under standard chain-ofcustody protocols according to methods listed in the USGS
National Field Manual for the Collection of Water-Quality
Data (NFM) (http://pubs.water.usgs.gov/twri9A/) as well as
other USGS standard operation procedures [Wilde et al.,
2010]. Surface slick samples were obtained manually in
pre-cleaned I-Chem jars on May 8. The only available natural seep samples from the Gulf of Mexico were collected in
2002. These had been stored frozen at 20 °C at the USGS
Menlo Park. Santa Barbara Channel samples, which consisted of sludge, seep oil, and produced oil, were collected in
2001, 2005, and 2008 and frozen until they were extracted
for PAHs in this study.
2.3. Polycyclic Aromatic Hydrocarbon (PAH)
Extraction
[6] Less than a gram of each oil or seep sample was
weighed out in pre-cleaned (450 °C for 4h) aluminum foil
tins and transferred to vials to be extracted. Following
determination of the sample weight, two mL of deuterated
PAH surrogate standard (in acetone) were added to each
oil, zooplankton, and seep sample. Then, zooplankton
samples were macerated in a Sentry tissue macerator after
which PAHs were extracted from them using an Accelerated Solvent Extractor (ASE) or sonication using hexane:
acetone (1:1, v:v). All extracts were concentrated by rotaryevaporation followed by a N2 stream. Extracts were purified via silica gel and sodium sulfate chromatography on an
open column. The aromatic fraction was eluted with 75 mL
of 80:20 hexane:methylene chloride. Zooplankton PAH
concentrations are reported on a wet weight basis. Additional details of sample collection and extractions, including extraction recoveries, may be found in the auxiliary
material.
2.4. Data Analysis
[7] Data analyses were conducted using R statistical software version 2.10.1 (©R Foundation for Statistical Computing, 2009). The proportion of each of the 24 PAHs was
calculated in each mesozooplankton extract by dividing
individual PAH concentrations by the total concentration.
We created a data matrix of columns containing proportional
concentrations of each PAH compound and rows representing individual samples. Relationships between the PAH
distributions in mesozooplankton and oil samples were
characterized using a two-stage multivariate analysis. First,
we performed an agglomerative, hierarchical cluster analysis
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of a matrix of distances calculated using the dist.prop function in the R library ade4 [Dray and Dufour, 2007], we
calculated the distances between each row based on the suite
of PAH compounds. The cluster analysis was conducted
using the hclust function using Ward’s linkage. To identify
particular groupings of data, we used the resultant cluster
analysis fit and cut the tree into 4 distinct groups using the
cutree function. Using the resulting 4 groupings from this
analysis, we calculated the average ( SD) proportion of
each PAH within each group. A non-metric multidimensional scaling (NMDS) analysis was performed on the
matrix of calculated distances using the isoMDS function
in the R library MASS [Venables and Ripley, 1999].
3. Results and Discussion
[8] There were no systematic trends in PAH distributions
in nGOM mesozooplankton as a function of radial distance
from the M-1 well or with depth in the water column from
which the organisms were collected. The lack of any difference in PAH distributions in mesozooplankton collected
from surface or deeper (0–500 m) waters is not surprising
given that many species of mesozooplankton exhibit diel
vertical migration patterns [Haney, 1988]. In contrast to oil
spills occurring at the sea surface, petroleum hydrocarbons
originating from the M-1 well were subjected to several
unique environmental processes that may account for the
unique PAH distributions within each cluster group. First,
petroleum was released from the Macondo well at 1.5-km
depth. This resulted in partitioning of hydrocarbons into the
aqueous phase in the absence of atmospheric evaporation
[Reddy et al., 2011]. Furthermore, the dispersants applied to
this spill may have enhanced aqueous dissolution of oil
droplets, affecting overall water column concentrations of
PAHs in a manner in which there was no systematic geographical pattern with distance from the M-1 well.
[9] Cluster analysis suggests that PAH distributions in all
samples analyzed could be divided into four distinct groups
(Figure 2, top). Group A corresponded to mesozooplankton
that were dissimilar to any oil samples analyzed in this
study. The PAHs that were detected in natural seeps in the
Gulf of Mexico (Mississippi Canyon, Figure 1, Station 64)
clustered in Group B along with PAH distributions in produced oil, crude oil and tar balls from several locations in
the Santa Barbara Channel. The distributions of PAHs
in mesozooplankton from the other stations led to their
emplacement in Groups C and D. Distributions of PAHs in
Groups C and D were similar to PAH distributions in from
the DWH surface slick and from the broken M-1 riser pipe,
respectively.
[10] We used a non-parametric multidimensional scaling
analysis on the distance matrix calculated for the cluster
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analysis [Cox and Cox, 1994] to help visualize relationships
in PAH distributions between mesozooplankton, DWH oil,
and seep samples (Figure 2, bottom). For a majority of the
mesozooplankton samples, PAHs detected in the nGOM
mesozooplankton scaled closely to either Cluster C (PAHs
in the DWH surface slicks) or Cluster D (PAHs in oil from
the M-1 riser pipe). The PAHs in the nGOM seep and SBC
samples clustered together, but did not scale closely to DWH
oil-derived PAH distributions (Cluster B). Our results indicate that oil derived from the DWH incident was associated
with mesozooplankton collected as far as 180 km from the
M-1 well. Zooplankton in Cluster A do not have a PAH
signature associated with DWH riser oil or DWH-derived
surface slicks. This observation suggests that the oil release
from the DWH incident, although spatially-extensive, may
have been patchy or that these zooplankton were exposed
to oil, but no longer have a relative PAH distribution that
matches that of the DWH oil.
[11] The PAH distributions detected in some nGOM
mesozooplankton in this study match the PAH distributions
extracted in oil released from the broken riser pipe collected
from the M-1 well and from surface oil slicks originating from
the DWH incident (Figure 3). A two-sample KolmogorovSmirnov goodness-of-fit (KS-GOF) test comparing the mean
PAH distributions revealed no differences in PAH signatures
between Cluster B and C ( p = 0.68), Cluster B and D ( p =
0.45), and Cluster C and D ( p = 0.26). Mesozooplankton
PAH signatures from Cluster A (Figure 3a), although relatively abundant in naphthalene, did not possess the ratios of
fluorene:phenanthrene resembling those in the DWH surface
slicks or oil from the broken riser pipe (Figures 3b and 3c).
Comparisons of these PAH distributions showed significant
differences when compared with the KS-GOF, Cluster A and
B ( p = 0.03), Cluster A and C ( p = 0.004), and Cluster A and
D were not statistically different at a = 0.05, but did have a
low p-value of 0.07. The PAH distributions from the SBC
and the nGOM seep sample had much higher proportions of
1-methyl and 2-methylnapthalene (Figure 3b) than the M-1
samples or the nGOM surface slicks (Figures 3c and 3d). The
dissimilarity in these particular PAHs between leaking oil
from the M-1 well and the natural GOM seep samples can be
explained by chemical heterogeneity resulting from varying
oil source rock, age, and migration history that likely occurs
between the seep location and the M-1 site [Aharon et al.,
1997; Hood et al., 2002].
[12] We are not aware of any existing studies examining
PAH distributions in zooplankton collected from the water
column of the nGOM, therefore we have no means of
comparing PAH body burdens in nGOM zooplankton collected prior to the spill. Distributions of PAHs in fluvial
suspended sediments such as those exported from the
Mississippi River generally contain elevated relative
Figure 2. (top) Agglomerative, hierarchical cluster analysis of PAH distributions in oil from the broken riser pipe at the
M-1 well (DWH.D), Deepwater Horizon surface slick oil (DWH.S), mesozooplankton collected from surface tows (ZP.S)
and tows from 0–500 m water depth in nGOM (ZP.D). A natural seep sample from the nGOM (GMS) and oil samples
derived from the Santa Barbara channel (SBC). The four letters in both plots represent the 4 groups identified in the cluster
analysis (see text for explanations). (bottom) Non-metric multi-dimensional scaling (NMDS) biplot of PAH distributions
in oil from the broken riser pipe at the M-1 well (3, oval), Deepwater Horizon surface slick oil (1,2, circles), nGOM seep
samples (64, squares) and oil samples derived from a variety of locations in the Santa Barbara channel (56–63, hexagons).
Other numbers are representative of mesozooplankton samples (see Figure 1 for collection location). The stress of the
NMDS fit was 0.14. The principal variables structuring the data are the PAH signatures within each group.
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Figure 2
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Figure 3. Mean ratio of individual PAHs to total PAHs (R) within groups identified using the cluster analysis. (a) Mesozooplankton samples only, (b) Gulf of Mexico seeps and Santa Barbara Channel, (c) mesozooplankton samples and surface
slick oil, and (d) mesozooplankton samples and oil from the leaking riser pipe.
abundances of higher molecular weight PAHs [Mitra et al.,
2002] than found in mesozooplankton analyzed in this study.
Thermodynamic modeling of PAH distributions on particles has demonstrated that PAHs are strongly-sorbed onto
Mississippi River and Gulf of Mexico sediments, which
contain a non-trivial fraction of combustion-derived black
carbon [Mitra and Bianchi, 2003]. Furthermore, ratios of
fluoranthene:pyrene in these mesozooplankton samples are
less than 0.5 (Table 1), as is expected in PAH distributions
originating from a petrogenic source [Yunker et al., 2002].
Taken together, this suggests that PAHs from suspended sediments did not contribute to the PAH signal detected in these
mesozooplankton samples and that the PAH distributions in
these samples are petrogenic.
[13] There are numerous factors that may affect PAH body
burdens in zooplankton isolated for this study. Unlike the
elevated levels of PAHs accumulated in mesozooplankton
collected in Port Valdez, Alaska [Carls et al., 2006], the
mesozooplankton PAH concentrations in our study (Table 1)
are similar to that found in zooplankton collected near other
oil spills in temperate and tropical environments globally
[Davenport, 1982; Guzman del Proo et al., 1986]. Several
processes (e.g. exchange with water through passive partitioning, ingestion of contaminated food, and production of
Table 1. Table of PAH Concentrations in Each Hierarchal Cluster Groupa
Cluster
Sample Type
N
Mean
Standard Deviation
A
B
mesozooplankton
Santa Barbara Channel seep samples
nGOM seeps
mesozooplankton
surface slicks
mesozooplankton
M-1 well
19
8
4
7
2
26
1
17.4
1.23E + 06
1.19E + 04
29.0
5.45E + 05
13.8
3.61E + 06
28.9
6.91E + 05
1.15E + 04
26.1
C
D
a
Concentrations are given in ng g
1
19.7
Minimum
Maximum
flu/flu + pyr
1.64
4.96E +
1.60E +
11.1
5.40E +
2.04
3.61E +
120.0
2.61E + 06
2.70E + 04
85.7
5.439 + 05
97.9
3.61E + 06
0.19 0.23
0.25 0.14
0.29 0.11
0.17 0.22
0.41
0.44 0.29
0.44
05
03
05
06
wet weight. N signifies number of samples extracted from within that group.
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fecal pellets and eggs), occurring simultaneously, may affect
the final body burden of PAHs derived from oil spills [Sobek
et al., 2006; Berrojalbiz et al., 2011]. Moreover, all of these
processes may vary as a function of ambient temperature and
seasonality [Hallanger et al., 2011b].
[14] Mesozooplankton species in microcosm experiments
have been shown to excrete PAHs on time scales of days
[Berrojalbiz et al., 2009]. However, we detected the presence of PAHs in mesozooplankton collected in August and
early September 2010, well after the M-1 well was capped
on 15 July, 2010. This was surprising given that mesozooplankton population turnover times may be quite rapid in the
warmer waters of the Gulf of Mexico (e.g. Acartia tonsa has
a generation time of 7 days at 25 °C [Heinle, 1966]). We
offer several possible explanations for the persistence of a
low but DWH-derived PAH signal so long after capping of
the well. First, the mesozooplankton samples in this study
consisted of several species homogenized together; thus,
cross-species metabolism of PAHs may vary resulting in
relatively lower body burdens than seen in many single
species laboratory studies. Second, PAHs from DWH oil
may have remained in the system at significant levels long
after the well was capped. Lastly, mesozooplankton may
have been accumulating PAHs in their bodies and passing
them across generations via eggs, which are relatively lipid
rich compared to individual mesozooplankton. Determining
which of these explanations is responsible for the ng g 1
observed levels of PAHs in nGOM mesozooplankton is
beyond the scope of this study. However, our study
demonstrates that there was a signature distribution of
DWH-derived PAHs in zooplankton.
[15] As of August 2010, the U.S. National Incident
Command Center estimated that 26% of the residual oil
could be found either on or below the surface as light sheen
and weathered tar balls, washed ashore or collected from the
shore, or buried in sand and sediments (Federal Interagency
Solutions Group, Oil budget calculator, 2010). Although a
subsurface oil plume was identified [Camilli et al., 2010]
and its composition recently elucidated [Reddy et al., 2012],
the ultimate fate of the oil and its presence in the ecosystem
has yet to be comprehensively determined. The presence of
this PAH signature in nGOM mesozooplankton samples in
the patterns noted in our study suggests that the spatial and
temporal extent of the 2010 spill in the nGOM was extensive
and patchy. A recent study reported a depleted d 13C isotopic
signature in coastal mesozooplankton collected north of the
M-1 well showing that carbon in oil collected at depth was
incorporated up to two trophic levels above prokaryotic
hydrocarbon consumers and into the planktonic food web
[Graham et al., 2010]. That study, combined with ours,
suggests that the potential for movement of DWH-derived
carbon and PAHs to higher trophic levels, existed after the
well at M-1 had been capped.
[16] Acknowledgments. The authors thank the National Science
Foundation RAPID grants OCE-1043249, OCE-1047736, OCE-1057461,
and the captain and crew of the R/V Oceanus.
[17] The Editor thanks two anonymous reviewers for their assistance in
evaluating this paper.
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7 of 7
Helen K. Whitea,1, Pen-Yuan Hsingb, Walter Choc, Timothy M. Shankc, Erik E. Cordesd, Andrea M. Quattrinid,
Robert K. Nelsone, Richard Camillif, Amanda W. J. Demopoulosg, Christopher R. Germanh, James M. Brooksi,
Harry H. Robertsj, William Sheddk, Christopher M. Reddye, and Charles R. Fisherb
a
Department of Chemistry, Haverford College, Haverford, PA 19041; bDepartment of Biology, Pennsylvania State University, University Park, PA 16802;
Biology Department, eDepartment of Marine Chemistry and Geochemistry, fApplied Ocean Physics and Engineering, and hDepartment of Geology and
Geophysics, Woods Hole Oceanographic Institution, Woods Hole, MA 02543; dBiology Department, Temple University, Philadelphia, PA 19122; gSoutheast
Ecological Science Center, US Geological Survey, Gainesville, FL 32653; iTDI-Brooks International Inc., College Station, TX 77845; jDepartment of Oceanography
and Coastal Sciences, Coastal Studies Institute, Louisiana State University, Baton Rouge, LA 70803; and kBureau of Ocean Energy Management,
US Department of the Interior, New Orleans, LA 70115
c
SPECIAL FEATURE
SCIENCE APPLICATIONS IN
THE DEEPWATER HORIZON
OIL SPILL SPECIAL FEATURE
Impact of the Deepwater Horizon oil spill on a
deep-water coral community in the Gulf of Mexico
To assess the potential impact of the Deepwater Horizon oil spill on
offshore ecosystems, 11 sites hosting deep-water coral communities
were examined 3 to 4 mo after the well was capped. Healthy coral
communities were observed at all sites >20 km from the Macondo
well, including seven sites previously visited in September 2009,
where the corals and communities appeared unchanged. However,
at one site 11 km southwest of the Macondo well, coral colonies
presented widespread signs of stress, including varying degrees of
tissue loss, sclerite enlargement, excess mucous production, bleached
commensal ophiuroids, and covering by brown flocculent material
(floc). On the basis of these criteria the level of impact to individual
colonies was ranked from 0 (least impact) to 4 (greatest impact). Of
the 43 corals imaged at that site, 46% exhibited evidence of impact
on more than half of the colony, whereas nearly a quarter of all of the
corals showed impact to >90% of the colony. Additionally, 53% of
these corals’ ophiuroid associates displayed abnormal color and/or
attachment posture. Analysis of hopanoid petroleum biomarkers isolated from the floc provides strong evidence that this material contained oil from the Macondo well. The presence of recently damaged
and deceased corals beneath the path of a previously documented
plume emanating from the Macondo well provides compelling evidence that the oil impacted deep-water ecosystems. Our findings
underscore the unprecedented nature of the spill in terms of its magnitude, release at depth, and impact to deep-water ecosystems.
hopane
| sterane | Paramuricea | sediment
B
etween October 15 and November 1, 2010, approximately 6
months after the Deepwater Horizon blowout and 3 months
after the Macondo well was capped, nine sites hosting deep-water
coral communities were examined with the remotely operated
vehicle (ROV) Jason II. This effort was part of an ongoing study
funded by the Bureau of Ocean Energy Management (BOEM)
and the National Oceanic and Atmospheric Administration’s
Ocean Exploration and Research program. These sites, located
between 93.60 °W and 87.31 °W and between −27.42 °N and
−29.16 °N (Fig. S1), were >20 km from the Macondo well, ranged
in depth from 290 to 2600 m, and hosted coral communities including scleractinian, gorgonian, and antipatharian corals. At
these sites, no visible evidence of impact to the corals and associated communities was observed (Fig. 1). However, on November 2, 2010, the ROV Jason II investigated an area in lease blocks
Mississippi Canyon (MC) 294 and 338, 11 km to the SW of the
site of the Deepwater Horizon spill. This area was explored because 3D seismic reflectivity data (Fig. S1) suggested there was
a strong likelihood of hard grounds, and hence likely coral substrate present. Its location (28.40N, 88.29W, 1,370 m) also placed
it in the path of a 100-m-thick deep-water plume of neutrally
buoyant water enriched with petroleum hydrocarbons from the
Macondo well that was documented at 1,100 m in June 2010
www.pnas.org/cgi/doi/10.1073/pnas.1118029109
(1, 2). Numerous coral colonies were discovered at this location
and many were partially or completely covered in a brown, flocculent material (hereafter referred to as floc). They showed signs
of recent and ongoing tissue damage (Fig. 2) not observed elsewhere at this time (Fig. 1) or in the previous 10 y of baseline
studies in the Gulf of Mexico (GoM) (3–5). Between December 8
and 14, 2010 additional surveys were performed with the deep
submergence vehicle (DSV) Alvin at MC 294 and a newly discovered site 22 km to the ESE of the Macondo well in MC 388
(1,850 m depth). Visible signs of recent impact or stress were not
evident in the corals imaged at MC 388.
To determine whether the cause of the overall decrease in
coral health at MC 294 was related to the Deepwater Horizon oil
spill, the floc covering the corals and nearby sediment was examined for the presence of petroleum hydrocarbons originating
from the Macondo well. Determining the source of petroleum
hydrocarbons in these samples posed a significant challenge. The
complexity of the petrogeochemical signatures in the GoM environment is considerable (6). Specific crude oils can be differentiated from their source rock groups using biomarkers (molecular fossils), which are highly resistant to abiotic and biotic
processes and have been invaluable tools for characterizing and
fingerprinting crude oils (7). For example, sterane biomarkers are
derived primarily from marine phytoplankton and vary depending
on geologic age. Hopanes, which are another class of biomarkers,
can be used individually or in concert with sterane distributions to
provide even greater certainty in characterizing oils (7). The use
of biomarkers by the petroleum industry and subsequently in
environmental forensics has, however, been performed in much
different environments than the Deepwater Horizon spill, where
oil and gas at 105 °C were released at pressure into 5 °C seawater
at ∼1,400 m depth (2). We used traditional 1D gas chromatography (GC) and comprehensive two-dimensional gas chromatography (GC×GC, as in refs. 8, 9 and 10) to analyze floc and
sediment samples from MC 294. These samples were compared
Author contributions: H.K.W., T.M.S., E.E.C., A.W.J.D., C.R.G., and C.R.F. designed research;
H.K.W., P.-Y.H., W.C., T.M.S., E.E.C., A.M.Q., R.K.N., R.C., A.W.J.D., C.R.G., C.M.R., and C.R.F.
performed research; J.M.B., H.H.R., and W.S. contributed new reagents/analytic tools;
H.K.W., P.-Y.H., W.C., T.M.S., E.E.C., A.M.Q., R.K.N., C.M.R., and C.R.F. analyzed data; and
H.K.W. and C.R.F. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
Data deposition: The octocoral and ophiuroid sequences reported in this paper have been
deposited in the GenBank database (accession nos. JQ241244–52, JQ411462–9 and
JQ771615–JQ771617) and all images have been submitted to the US National Oceanographic Data Center (accession no. 0084636).
1
To whom correspondene should be addressed. E-mail: hwhite@alum.mit.edu.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
1073/pnas.1118029109/-/DCSupplemental.
PNAS Early Edition | 1 of 6
ENVIRONMENTAL
SCIENCES
Edited by Paul G. Falkowski, Rutgers, State University of New Jersey, New Brunswick, NJ, and approved February 28, 2012 (received for review November
1, 2011)
A
0
50 100 Kilometers
E
B
D
C
Fig. 1. Healthy deep-water coral communities observed in November 2010 from various sites >20 km from the Macondo well (shown as a star on map). (A)
Paramuricea sp. type E and Asteroschema sp. at 360 m depth in Garden Banks (GB) 299; (B) Paramuricea sp. type E and Asteroschema sp. at 440 m depth in
Mississippi Canyon (MC) 751; (C) Paramuricea sp. type A and Eumunida picta at 530 m depth in Green Canyon (GC) 354; (D) Paramuricea sp. type E and
Asteroschema sp., along with a brisingid basket star at 360 m depth in Viosca Knoll (VK) 906; (E) P. biscaya and A. clavigerum at 2,300 m depth in Desoto
Canyon (DC) 673. Phylogenetic species identifications of corals and associated ophiuroids are given in Fig. S2.
with oil collected from directly above the broken riser pipe at the
Macondo well (2) and field samples from surface water and salt
marshes in areas oiled by the Deepwater Horizon spill.
Here we report on the analyses of the visible impact to the
gorgonian corals and coral associates at MC 294 based on in situ
video imagery, shipboard microscopic analyses, and petroleum
biomarker analysis of the floc adherent to the coral. In addition,
we compare the petroleum hydrocarbon content and biomarkers
with the surrounding surface and subsurface sediments and
compare the condition of the corals and associates between
November and December 2010 visits.
Results and Discussion
Gorgonian and other corals present at MC 294 are predominantly found in a central area 10 × 12 m in extent, composed
of two adjacent carbonate slabs. Scattered boulders surround this
region over an area of 50 × 50 m, and some of the isolated
boulders host one or two additional coral colonies. The majority
of the colonial corals were Paramuricea biscaya, with one or two
colonies of Swiftia pallida, Paragorgia regalis, Acanthogorgia
aspera, and Clavularia rudis (Fig. S2). The majority of these colonies exhibited signs of stress response, including excessive mucous production and retracted polyps, which have been observed
in corals experimentally exposed to crude oil (11). Impact to the
corals was quantified from close-up images (<1 m away) for 43 of
the 58 coral colonies identified in the central area (Fig. S3) (not
all of the corals could be approached for close-up imaging with
ROV Jason II or DSV Alvin without disturbing other colonies).
The level of impact to individual colonies was ranked from
0 (least impact) to 4 (greatest impact) according to the percentage of the colony exhibiting one or more of the following visual
2 of 6 | www.pnas.org/cgi/doi/10.1073/pnas.1118029109
indications of stress: bare skeleton above the basal region, loose
tissue or heavy mucous hanging from the skeleton, and/or coverage with brown flocculent material (Fig. 3). Eighty-six percent
of the coral colonies imaged in the central area exhibited signs of
impact. Forty-six percent exhibited impact to at least 50% of the
colony (impact level 3 or 4), and 23% of the colonies sustained
impact to more than 90% of the colony (impact level 4).
Between the November and December 2010 research cruises,
changes in condition were assessed for all corals or portions of
colonies for which high-resolution imagery was available from
similar perspectives. Although differences in camera placement
November
2010
December
2010
Fig. 2. Impacted corals at MC 294. Brown flocculent material and tissue loss
is observed on the larger coral, A10, in November and December 2010. Although there is no evidence of recovery on A10, note that the tips of some
branches that were living in November were still living in December
(arrows). Coral A14 in the red box was the only colony showing apparent
signs of recovery from coverage by the floc between visits.
White et al.
10
8
6
23%
23%
3
4
14%
9%
4
2
0
0
1
2
Impact ranking
Fig. 3. Impact assessment for coral colonies (n = 43) where high-quality
images could be obtained from at least the November or December 2010
cruise. The levels of impact are ranked according to the proportion of a coral
exhibiting obvious tissue damage, bare skeleton above basal region, or
covered by brown flocculation: rank 0 (0–1%), rank 1 (<10%), rank 2 (10–
50%), rank 3 (50–90%), rank 4 (>90%). Numbers above bars are percentages
of corals in a rating relative to all assessed colonies.
on the two underwater vehicles, lighting, and quality of images
limited the size of this data set to 18 colonies, neither progression
of the visible damage nor clear evidence of recovery or growth
was apparent in the majority of corals. Possible recovery was
noted for one colony (A14, highlighted by box in Fig. 2). The
relatively light covering of floc over more than 50% (impact
level 3) of this colony in November was ranked as less than 10%
impacted (impact level 1) by the time it was revisited in December, when extended polyps were visible in areas that had been
partially covered with floc in November.
Sampling of a P. biscaya coral (E3) in December enabled microscopic analysis to be made after removal of the floc. Varying
degrees of tissue loss and sclerite enlargement were observed
(Fig. S4). The skeleton was bare and entirely devoid of tissue at
the base and along the main axis of the colony. At increasing
distances from the basal point of attachment, less extensive tissue
loss resulted in the exposure of the calcite skeletal elements that
are normally embedded in the tissue layers and coenenchyme.
These sclerites were still in their normal form of a polyp but
appeared enlarged. The localized alteration of growth form, including excessive secretion of gorgonin and sclerite production to
form granuloma-like structures, has previously been observed in
gorgonians as an acute stress response (12, 13). Near the tips of
some branches, which were not covered by the floc in situ, a few
polyps on this coral appeared normal.
Coral associates at MC 294 included 13 actinarian anemones
and 78 Asteroschema clavigerum (a symbiotic ophiuroid). Of the 52
individual corals examined for coral associates, 25% hosted none,
2% hosted actinarian anemones, and 73% hosted A. clavigerum,
with 70% of the ophiuroids present on P. biscaya, 18% on the
single individual of P. regalis, and 12% on A. aspera. A. clavigerum
is typically tan to red in color (Fig. 1); however, at this site only
47% were tan to red, whereas 44% had distinctly white arms (Fig.
2), and 9% (all hosted by P. biscaya), were bleached almost entirely
white. In November, 27% of the ophiuroids displayed behaviors
other than their normal attached posture of arms tightly coiled
around their coral host (Fig. 1). Between visits, 13% of the
ophiuroids transitioned from tightly to loosely coiled (i.e., Fig. 2).
Two ophiuroids (Fig. S5) transitioned from tightly coiled to a
posture with splayed out arms, a previously undocumented behavior in this species.
The floc samples collected (>72 μm in size) were removed
from the surface of the corals in situ and when filtered were
found to contain dead coral polyp fragments, detached sclerites,
and small brown droplets (Fig. S6). Solvent extracts of all of the
floc examined were dominated by C16 and C18 saturated and
unsaturated fatty acids and cholesterol, which are dominant
lipids in biological tissue. Petroleum residues were also present
and quantified via 1D gas chromatography coupled to a flame
White et al.
SPECIAL FEATURE
ionization detector (GC-FID; Table 1). An unresolved complex
mixture (UCM) with n-alkane carbon range of C15–C42 indicates
the presence of weathered petroleum (e.g., ref. 8; Table 1). Slight
variations in UCM carbon range and distributions of n-alkanes
among samples showed no consistent relationship to the pure
Macondo Well oil (described in ref. 2; Table 1). Rather, it is
evident that the n-alkanes in the samples represent input from
a mixture of sources such as plants, biofilms, and differentially
weathered subsurface hydrocarbons, including some that may
have come from natural seeps. Acoustic mapping cruises performed from late May to August 2010 mapped several natural
gas seeps in near proximity to both the Macondo well and the
sample sites presented here, which could provide additional
sources of subsurface hydrocarbons (14).
Polycyclic aromatic hydrocarbon (PAH) distributions from
coral E3 and sediment sample 4664 0–2 cm show good correspondence to Macondo well oil, with similar relative abundances
of naphthalene, phenanthrene, and their alkylated derivatives as
well as dibenzothiophenes, benzo[a]anthracene, and chrysene.
The remaining coral samples are inconclusive owing to the small
quantity of sample available for analysis, as well as the fact that
these samples have been extensively weathered, as evidenced by
the dominance of biodegradation-resistant chrysene in all extracts.
Petroleum systems in the GoM do not display significant differences in the presence or absence of specific biomarkers; instead,
differences in the relative amounts of biomarkers present have
previously allowed sources to be determined (15, 16). Analysis of
biomarkers such as hopanes is critical because these compounds
are more resistant to biodegradation and water washing than
n-alkanes and PAHs and provide insight into petroleum source
determination (as in ref. 17). At the Macondo well, oil sampled
from above the broken riser pipe (2) contains abundant hopanoids, diasteranes, and steranes (Fig. S7). Hopanoid biomarker
ratios have been calculated for comparison with coral and sediment samples, as well a reference surface water (S1) and two
reference coastal water (M1 and M2) samples (shown in Fig. 1 and
described in ref. 18). These reference samples represent Macondo
well oil that has undergone vertical transport from the seabed to
the ocean surface (∼1,400 m) and subsequent lateral dispersion
over ranges of 1–175 km, respectively (Table 1).
Comparison of the hopanoid portion of the GC×GC chromatographic plane for the Macondo well oil to the S1 and M1
samples indicates a high degree of similarity (Fig. S8 A–C). This
similarity is also seen in the floc from coral B8 (Fig. S8D) and in
the surface sediment sample taken in the immediate vicinity of
the corals (core 4664 0–2 cm; Fig. S8E). Slight but significant
differences in hopanoid biomarker ratios are observed, by contrast, both in comparable core-top sediments collected away
from the impacted corals at the MC 294 site (core 4662 0–2 cm;
Table 1 and Fig. S8F) and at greater depths (2–5 cm and 5–10
cm; Table 1) in the core 4664 sediments. Further, the concentrations of oil present in the uppermost sediments of core 4664
(0–2 cm) are much higher (9.25 mg/g; Table 1) than the deeper
sediments (2–5 cm and 5–10 cm) in the same core, which range
in concentration from 0.02 to 0.03 mg/g (Table 1). They are also
higher than the oil concentrations observed in surface sediments
(0–2 cm) collected away from the impacted corals at the MC 294
site (3.46 mg/g; Table 1), where a bimodal n-alkane distribution
indicative of inputs from mixed sources is observed. Significant
variations in sediment oil concentrations have been previously
documented in the GoM, particularly in areas of known natural
oil seepage such as Green Canyon, where oil concentrations may
be as high as 39.0 mg/g (19). The oil concentration and biomarker data from sediments collected away from the impacted
corals and sediments at depth at MC294, are, however most
consistent with long-term background inputs of oil derived from
petroleum sources that are quite distinct to that present in the
most superficial (hence, recent) core-top sediments and floc
Number of corals
12
ENVIRONMENTAL
SCIENCES
30%
14
Table 1. Oil content, alkane distribution, and hopanoid biomarker ratios of brown flocculent material and sediment samples compared
with Macondo Well oil
Sample
Source oil
Macondo well‡
Surface water samples{
1 km from well (S1)
175 km from well (M1)
175 km from well (M2)
Flocculent material samples
MC 294 coral (B8)
MC 294 coral (F6)
MC 294 coral (A5)
MC 294 coral (E3)
Sediment samples
MC 294 4662 0–2 cm
MC 294 4664 0–2 cm
MC 294 4664 2–5 cm
MC 294 4664 5–10 cm
Oil content*
(mg/g extract)
Oil content*
(mg/g dry weight
sediment)
UCM n-alkane
carbon range
C17-n-alkane/
pristane
C18-n-alkane/
phytane
Carbon Preference
Index (CPI)†
Ts/(Ts+Tm)
C29-Ts/NH
NA§
NA
NA
1.71
2.33
0.86
0.59
0.49
NA
NA
NA
NA
NA
NA
C12–C40
C16–C40
C16–C40
2.21
1.89
1.89
2.66
2.72
2.72
0.81
0.83
0.83
0.59
0.59
0.58
0.50
0.50
0.51
310
8.0
74
73
NDjj
ND
ND
ND
C15–C40
C17–C34
C16–C41
C15–C42
2.42
2.41
2.43
1.34
2.69
2.21
1.87
2.28
1.15
1.09
0.38
1.22
0.60
0.58
0.59
0.58
0.43
0.48
0.45
0.48
630
570
68
120
3.46
9.25
0.03
0.02
C15–C37
C17–C42
C16–C42
C11–C42
0.42
0.44
0.56
0.80
0.47
0.29
0.81
1.21
1.12
0.99
1.31
1.37
0.57
0.59
0.52
0.54
0.46
0.50
0.38
0.42
Abbreviations for biomarkers: C29-Ts, 18α(H),21β(H)-30-norneohopane; NH, 17α(H),21β(H)-30-norhopane; Tm, 17α(H)-22,29,30-trinorhopane; Ts, 18α(H)22,29,30-trinorneohopane.
*Oil content was calculated by integration of the UCM observed via GC-FID.
†
CPI = Σ(odd numbered alkane abundances from n-C23 to n-C35)/Σ(even numbered alkane abundances from n-C22 to n-C34).
‡
Described in ref. 2.
§
Not applicable to sample as pure oil was collected.
{
Described in ref. 18.
jj
Not determined due to collection protocol of flocculent onto filters, which did not allow for dry weights of the flocculent material to be taken post collection
and before extraction.
samples collected from site MC 294. Similarly, a comparison of
the sterane portion of the GC×GC chromatographic plane for
the Macondo well oil and floc from the coral samples also shows
significant differences, particularly in the relative distributions of
DiaC29βα-20S, C27αββ-20R, and C27αββ-20S in steranes (e.g., B8,
Fig. S9B). Although preferential loss of steranes and diasteranes
relative to hopanoids would not be expected from traditional
biodegradation sequences, this trend has been observed previously for oil that undergoes severe weathering in energetic and
aerobic conditions (20). This could result from either biodegradation or chemical and physical processes arising from the precipitation of the wax component of oil at the low temperatures
present in the deep GoM (21, 22). Wax formation may have
resulted from turbulent mixing of the well’s hot source-jet fluids
with the surrounding cold seawater, fractionating constituent
hydrocarbons according to their molecular characteristics (1, 2).
Nevertheless, the data from the hopanoids, which have a greater
fidelity, confirm the presence of Macondo well oil in the floc and
surrounding surface sediment samples. The constant, albeit relatively low level, input of hydrocarbons from natural seepage in
the GoM may also complicate these biomarker ratios (14).
Conclusions
Observations of recently damaged corals and the presence of
Macondo well oil on corals indicates impact at a depth of 1,370
m, 11 km from the site of the blowout. This finding provides
insight into the extent of the impact of the spill, which is significantly complicated by physical mixing processes (23) and fractionation of the oil constituents (24). Because deep-water corals
are sessile and release mucous that may trap material from the
water column, these corals may provide a more sensitive indicator
of the impact from petroleum hydrocarbons than marine sediment cores and may record impacts from water masses passing
through a community, even if no deposition to the sediment
occurs. Deep-water colonial corals exhibit extreme longevity as
sessile adults (hundreds to thousands of years; 25–27) and typi4 of 6 | www.pnas.org/cgi/doi/10.1073/pnas.1118029109
cally inhabit areas exposed to a moderate current regime (28).
The presence of a deep-water coral community dominated by
recently impacted, visibly unhealthy, and recently dead individuals (as evidenced by skeletons free of encrusting organisms),
together with ophiuroid symbionts with unhealthy color and
atypical posture, provides evidence of a recent waterborne impact. Although the spatial and temporal proximity of this impact
to the Deepwater Horizon oil spill might be coincidental, the
normal longevity of deep-water corals and the lack of visual evidence of impact to deep-water corals elsewhere in the GoM
suggest that this may not be the case. Importantly, even though
there are multiple inputs of oil to the GoM, the use of hopanoid
biomarker compositions and ratios in the floc collected from the
surface of corals allows us to establish a connection to the oil spill
even though other biomarkers for characterizing oil in these
environments (e.g., PAHs and sterane biomarker ratios) are affected by severe weathering (20) and, hence, are not robust under
the conditions of this spill.
The data suggest the Deepwater Horizon oil spill impacted
a community of deep-water corals near the Macondo well. The
numerous apparently healthy deep-water coral communities in
other parts of the GoM may indicate that the localized impact in
MC 294 found to date, is not part of a much larger, acute, GoMwide event. However, life in deep-water coral ecosystems is
known to operate at a slow pace. Consequently it is too early to
fully evaluate the footprint and long-term effects of acute and
subacute exposure to potential waterborne contaminants resulting from the Deepwater Horizon oil spill.
Materials and Methods
Discovery. Areas for exploration were chosen according to examination of 3D
seismic data in the BOEM database. Areas of high reflectivity and bathymetric
relief were targeted for visual examination, and during the ROV dive,
onboard sonar was used to find exposed carbonates that might host corals.
Image Analyses. A down-looking mosaic (as in ref. 29) was constructed from
379 partially overlapping images, taken 3 m above the seafloor using
White et al.
Coral and Invertebrate Associate Collection. Corals and their associate
ophiuroids were collected by the ROV Jason II and DSV Alvin in October and
December 2010, respectively. The manipulator claws were modified with
a cutting blade to aid in the collection of host corals and attached ophiuroid
associates. Individuals were collected into temperature-insulated bioboxes
on the sea floor and processed immediately after recovery of the vehicles.
Approximately 2 to 3 cm of a coral branch or arms of individual ophiuroids
were subsampled and frozen at −80 °C or in 70% ethanol for shore-based
morphological and genetic analyses. Voucher specimens were either preserved in 95% ethanol or dried.
Microscopic Examination. Tissue necrosis and the presence of bare skeleton
were documented on a Leica S6D microscope with an attached Nikon
D300 camera.
Octocoral Identification. Octocorals were identified to the lowest possible
taxon using molecular barcodes and morphological characters (following
refs. 30–32). DNA was extracted from frozen or preserved (95% ethanol)
specimens using the Qiagen DNeasy kit. The 5′ end of the mitochondrial msh
gene and the COI+igr region were PCR amplified (33). Sequences were
edited, combined with related sequences from GenBank, and aligned by
ClustalW, resulting in a 1,430-bp region. Bayesian phylogenetic inference
was conducted using the GTR+G+I model (MrBayes v3; number of generations = 2,000,000; sample frequency = 100; burnin = 5,000). Because the
COI+igr region has not been previously amplified for many octocorals, these
regions were coded as missing data for the appropriate GenBank specimens.
Sediment and Floc Collection. Floc was collected at depth through 1.5-m-long
precleaned tubing into a 4-L carboy, where it was collected onto two 15-cm
diameter precombusted glass fiber filters (GF/C, >1 μm) mounted between
two layers of Nytex (63 μm). The majority of the floc did not sorb to the filters
and remained suspended in the seawater collected in the carboy. Once
onboard ship, this material was immediately filtered onto 47-mm-diameter
precombusted GF/F filters (0.7 μm), which were then placed in combusted foil
and frozen at –20 °C before further analysis. Push cores (6.35 cm diameter)
were used to collect sediment samples. Immediately after reaching the ship’s
deck, the cores were sectioned as 0–2 cm, 2–5 cm, and 5–10 cm intervals into
combusted glass jars and then frozen at −20 °C until analysis. Macondo well
reference oil was collected directly above the well on June 21, 2010 (as in ref.
2). This sample is the reference Macondo well oil with which samples are
compared throughout the study. Other samples (described in ref. 18) were
also obtained on May 31, 2010 from a saltmarsh ≈175 km from the spill near
Cocodrie Louisiana (29.29 °N; −90.49 °W) as a 2-cm-diameter droplet of surface oil (M1) and from a scraping of Spartina alterniflora saltmarsh grass
taken within meters of the droplet (M2). Another sample (S1) was collected
on June 20, 2010 with the R/V Endeavor from a 1-cm-thick layer of oil floating
on the surface water at 28.74 °N, 88.38 °W (18). All samples were placed in
combusted glass jars and frozen until further analysis.
Oil Analysis. Samples were solvent extracted and purified with fully activated
silica gel. Extracts were analyzed for hydrocarbons via GC-FID, gas chromatography–mass spectrometry (GC-MS), and comprehensive GC×GC. For
quantification and identification, GC×GC was coupled to an FID (GC×GCFID), and identities of biomarkers were confirmed by coupling with MS
(GC×GC-MS). SI Materials and Methods provides a complete discussion of
these analyses.
Ophiuroid Identification. Ophiuroids were identified to the lowest possible
taxon using morphological and genetic data. Morphological examination of
type and voucher specimens was conducted at the Smithsonian Institution
(Washington, DC), and DNA from voucher specimens was obtained for
ACKNOWLEDGMENTS. We thank the crew and captains of the R/V Atlantis
and R/V Ron Brown; the pilots and crew of DSV Alvin and ROV Jason II; and
J. Abbassi, C. Carmichael, O. Chegwidden, D. Cowart, C. Doughty, T. Enderlein, P. Etnoyer, J. Frometa, K. Halanych, K. Reuter, M. Rittinghouse, A. Sen,
C. Sheline, K. Stamler and J. Thoma for their contributions to this work.
This work was supported by Bureau of Ocean Energy Management Contract
1435-01-05-CT-39187 (to TDI-Brooks), the National Oceanic and Atmospheric
Administration Office of Ocean Exploration, the Census of Marine Life (ChEss
Project), USGS-Terrestrial, Water, and Marine Environments Program through
the BOEM/Outer Continental Shelf, Northeast Gulf of Mexico Deep Offshore
Reef Ecology, Lophelia II Study and National Science Foundation RAPID
Grants OCE-1045131 (to H.K.W.), OCE-1045083 and OCE-1064041 (to C.R.F.),
OCE-1043976 (to C.M.R.), OCE-1045025 (to R.C.), OCE-1045329 (to T.M.S.),
OCE-1044289 (to C.R.G.), and OCE-1045079 (to E.E.C.).
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White et al.
SPECIAL FEATURE
genetic comparison. DNA was extracted from frozen or preserved (95%
ethanol) ophiuroids living on MC 294 corals using the Qiagen DNeasy kit.
A fragment of the mitochondrial 16S rRNA (16S) gene was amplified with
the universal primers 16SarL and 16SbrH, sequenced, and aligned using
SEQUENCHER 4.8 (Gene Codes Corporation) and previously described
methods (34). Phylogenetic inference (and subsequent species identification)
was conducted using parsimony (PAUP* 4.0b10), neighboring-joining distance-based and Bayesian approaches associated with Geneious (version 5).
ENVIRONMENTAL
SCIENCES
a Nikon E995 camera in pressure housing mounted on the ROV Jason II.
Individual coral colonies were labeled (Fig. S3). Close-up images of individual
corals from a side-looking perspective were obtained from frame grabs using a dedicated NDSF/AIVL Adimec 2000 HDTV digital video camera mounted on the ROV Jason II vehicle frame and the starboard manipulator of DSV
Alvin. Close-up imagery was used for assessment of impact to all corals that
could be approached by ROV Jason II or DSV Alvin without damaging other
corals. Bare skeleton above the coral’s basal region, obviously damaged
tissue (strands of mucous or loose tissue hanging from the skeleton), and
areas covered by floc were scored as impacted. Levels of impact were
broadly binned into five categories according to the percentage of the imaged portion of the colony showing impact: rank 0, 0–1% of the colony
impacted; rank 1, 1–10% of the colony impacted; rank 2, 10–50% of the
colony impacted; rank 3, 50–90% of the colony impacted; or rank 4, >90% of
the colony impacted. In three cases in which the ranking category changed
between the November and December visits, reexamination of images and
the original video did not substantiate significant changes in the corals, and
the higher-quality images obtained from DSV Alvin in December were used
for the overall rankings (Fig. 3). All images can be accessed through the US
National Oceanographic Data Center (accession no. 0084636).
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petroleum biomarkers. J Chromatogr A 926:275–290.
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dispersion of naphthenic, asphaltenic, and waxy crude oils. Int Oil Spill Conf631–636.
22. Daling PS, Faksness LG, Hansen AB, Stout SA (2002) Improved and standardized
methodology for oil spill fingerprinting. Environ Forensics 3:263–278.
23. Valentine DL, et al. (2012) Dynamic autoinoculation and the microbial ecology of a deep
water hydrocarbon irruption. Proc Natl Acad Sci USA, 10.1073/pnas.1108820109.
24. Ryerson TB, et al. (2012) Chemical data quantify Deepwater Horizon hydrocarbon
flow rate and environmental distribution. Proc Natl Acad Sci USA, 10.1073/
pnas.1110564109.
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habitat-forming gorgonian (Primnoa resedaeformis) from the Gulf of Alaska. Hydrobiologia 471:101–110.
26. Roark EB, Guilderson TP, Dunbar RB, Fallon SJ, Mucciarone DA (2009) Extreme longevity in proteinaceous deep-sea corals. Proc Natl Acad Sci USA 106:5204–5208.
27. Prouty NG, Roark EB, Buster NA, Ross SW (2011) Growth-rate and age distribution of
deep-sea black corals in the Gulf of Mexico. Mar Ecol Prog Ser 423:101–115.
28. Roberts JM, Wheeler AJ, Freiwald A (2006) Reefs of the deep: The biology and geology of cold-water coral ecosystems. Science 312:543–547.
29. Pizarro O, Singh H (2003) Toward large-area mosaicing for underwater scientific
applications. IEEE J Oceanic Eng 28:651–672.
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Gorgonaria). Steenstrupia (Cph) 1:1–10.
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Die Familie Paramuriceidae (Cnidaria, Anthozoa). Meteor Forsch-Ergebnisse 27:5–75.
32. Sanchez J (2005) Systematics of the bubblegum corals (Cnidaria: Octocorallia: Paragorgiidae) with description of new species from New Zealand and the Eastern Pacific.
Zootaxa 1014:1–72.
33. McFadden CS, et al. (2011) Limitations of mitochondrial gene barcoding in Octocorallia. Mol Ecol Resour 11:19–31.
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ophiuroid species on North Atlantic Seamounts. Mar Ecol (Berl) 31:121–143.
White et al.
Environmental Toxicology and Chemistry, Vol. 24, No. 5, pp. 1219–1227, 2005
q 2005 SETAC
Printed in the USA
0730-7268/05 $12.00 1 .00
COMPARATIVE TOXICITY OF TWO OIL DISPERSANTS, SUPERDISPERSANT-25
AND COREXIT 9527, TO A RANGE OF COASTAL SPECIES
ALAN SCARLETT,† TAMARA S. GALLOWAY,*‡ MARTIN CANTY,‡ EMMA L. SMITH,† JOHANNA NILSSON,‡
and STEVEN J. ROWLAND†
†School of Earth, Ocean, and Environmental Sciences, and ‡School of Biological Sciences, University of Plymouth,
Drake Circus, Plymouth, PL4 8AA United Kingdom
( Received 2 July 2004; Accepted 5 November 2004)
Abstract—The acute toxicity of the oil dispersant Corexit 9527 reported in the literature is highly variable. No peer-reviewed data
exist for Superdispersant-25 (SD-25). This study compares the toxicity of the two dispersants to a range of marine species representing
different phyla occupying a wide range of niches: The marine sediment-dwelling amphipod Corophium volutator (Pallas), the
common mussel Mytilus edulis (L.), the symbiotic snakelocks anemone Anemonia viridis (Forskål), and the seagrass Zostera marina
(L.). Organisms were exposed to static dispersant concentrations for 48-h and median lethal concentration (LC50), median effect
concentration (EC50), and lowest-observable-effect concentration (LOEC) values obtained. The sublethal effects of 48-h exposures
and the ability of species to recover for up to 72 h after exposure were quantified relative to the 48-h endpoints. Results indicated
that the anemone lethality test was the most sensitive with LOECs of 20 ppm followed by mussel feeding rate, seagrass photosynthetic
index and amphipod lethality, with mussel lethality being the least sensitive with LOECs of 250 ppm for both dispersants. The
results were consistent with current theory that dispersants act physically and irreversibly on the respiratory organs and reversibly,
depending on exposure time, on the nervous system. Superdispersant-25 was found overall to be less toxic than Corexit 9527 and
its sublethal effects more likely to be reversible following short-term exposure.
Keywords—Dispersants
Anemonia viridis
Corophium volutator
Mytilus edulis
Zostera marina
es of Warren Spring Laboratory Specification LR 448(OP) and
has been approved as a type-2, as well as a type-3, dispersant
under test qualification CSR 4600/8902798 (Oil Slick Dispersants Ltd. product profile, cited May 5, 2004; http://
www.croftpark.co.uk/osd-products.html). Superdispersant-25
has been tested by the Center for Environment, Fisheries, and
Aquaculture Science and has been found to be of low toxicity
to Crangon crangon (brown shrimp) for use at sea and on
beaches, and Patella vulgata (common limpet) for use on
rocky shores; it is licensed under the Ministry of Agriculture,
Fisheries, and Food, Food and Environment Protection Act 53/
98 (Oil Slick Dispersants Ltd. product profile, cited May 5,
2004; www.croftpark.co.uk/osd-products.html). Corexit 9527
has been tested extensively in the laboratory and used on oil
spills since 1978 [4]. A considerable number of toxicity reports
exist concerning a wide variety of species, reviewed by
George-Ares and Clark [3]. Thus Corexit 9527 provides a
useful comparative toxicant for the study of SD-25.
The use of dispersants within enclosed bodies of water may
pose a threat to a diverse range of species. This study compares
the toxicity of SD-25 with that of Corexit 9527 to the marine
sediment-dwelling amphipod Corophium volutator (Pallas),
the blue mussel Mytilus edulis (L), the symbiotic snakelocks
anemone Anemonia viridis (Forskål), and the seagrass Zostera
marina (L.). The mudshrimp C. volutator is distributed widely
around the coasts of western Europe and northeast America,
and is significant in structuring and sustaining the ecology of
near-shore sediment communities [5,6]. Corophium volutator
is now used commonly as a European acute toxicity test organism [7–12]. Amphipods occupying a similar niche exist in
other regions, e.g., Ampelisca abdita (Mills) also are used for
toxicity testing. Blue mussels mainly occur on exposed rocky
INTRODUCTION
Faced with the prospect of an oil spill coming ashore or
passing over reefs, decisions have to be made swiftly as to
how best to deal with the situation. One option is to use chemical dispersants to break up the slick into a large number of
small droplets. Once broken up, the slick poses less of a physical risk to seabirds or marine mammals but may transfer oil
into the water column and possibly to the benthos. Within
estuaries, inlets, enclosed bays, or shallow water reefs, the
concentration of the dispersants alone may be sufficient to
cause toxic effects. In the United Kingdom, dispersants cannot
be used in water less than 20 m deep or within one nautical
mile of such [1] without the permission of the Department for
Environment Food and Rural Affairs; similar rules relating to
sensitive habitats such as coral reefs and mangroves exist in
tropical regions [2]. Hence, the option to use dispersants within
estuaries, inlets, and shallow water does exist and it is in such
circumstances that difficult decisions on how best to protect
the environment and commercial operations have to be made.
The handling of large volumes of dispersant under difficult
conditions may result in accidental release of potentially toxic
chemicals into the sea. Research into the toxicity of dispersants
has been reported widely [3,4] and companies continue to
improve the efficiency of the chemicals and reduce their toxicity. In the United Kingdom, the oil dispersant Superdispersant-25 (SD-25) is now the Maritime and Coastguard Agency’s
main stockpiled chemical for spraying onto oil slicks at sea.
No data exist within peer-reviewed literature for SD-25. However, SD-25 in association with oil meets all the relevant claus* To whom correspondence may be addressed
(tamara.galloway@plymouth.ac.uk).
1219
1220
Environ. Toxicol. Chem. 24, 2005
shores and are distributed widely from the Arctic to the Mediterranean with related species distributed worldwide. Mussels
have been used for long-term monitoring projects such as the
Global Mussel Watch [13], field surveys [14], and Scope for
Growth studies [15], allowing a large body of knowledge to
accumulate regarding their acute and sublethal responses to
stressors. Snakelocks anemones occur on intertidal rocky
shores and are associated closely with seagrass beds [16]. As
symbionts possessing zooxanthellae, anemones have been used
as surrogate organisms for the study of coral organisms (e.g.,
[17,18]). Eelgrass, Z. marina, is a marine angiosperm with a
worldwide distribution and is protected strictly under the Berne
Convention [19]. North Atlantic populations have suffered
great losses during the last century [20] and, as a consequence,
Z. marina is now deemed to be scarce. Eelgrass plants grow
well under laboratory conditions and have proved to be a
robust test species if chlorophyll fast fluorescence transient is
used to measure plant health [21]. Taken together, these species
represent a broad range of organisms that might be affected
by dispersant use. Some degree of extrapolation to other similar species can be justified from the toxicity data obtained.
The aim of this study was to compare the acute and sublethal toxicities of the two dispersants, Superdispersant-25 and
Corexit 9527, following 48-h static exposures. The extent of
recovery of each species was documented for up to 72 h with
regard to changes in the no-observable-effect concentration
(NOEC) and LOEC values. Behavioral responses of test animals also were recorded.
MATERIALS AND METHODS
Collection and maintenance of organisms
Corophium volutator and sediment were collected from an
intertidal area of the Avon estuary near Aveton Gifford, South
Devon, United Kingdom (ordinance survey grid reference: SX
683 467). Amphipods were sieved from the upper 5 cm of
sediment and transported back to the laboratory within 1 h,
where they were placed in 5-L culture tanks lined with fieldcollected, sieved (,300-mm) sediment. The tanks were filled
with filtered seawater 25 6 1 practical salinity units (psu),
which was aerated and maintained at 15 6 18C with a 12:12h light:dark cycle. The animals were fed weekly with five drops
standard aquarium invertebrate food (Waterlife Invert Food,
Waterlife Research Industries, Longford, UK; Liquifry Marine,
Interpret Ltd., Dorking, UK; Roti-Rich, Florida Aqua Farms,
Dade City, FL, USA; and dried algae) and the water was replaced 24 h after feeding. Amphipods were maintained under
the above conditions for one to two weeks after removal from
the field to acclimate them to experimental conditions.
Mussels were collected from Port Quin, on the North Cornwall coast, United Kingdom (ordinance survey grid reference:
SW 972 905). Organisms of 30-mm 6 5 mm length were
removed carefully from the rock by cutting the byssal threads
and transported back to the laboratory within 2 h. Any epifauna
and epiphytes were removed from the mussels, which then
were placed in 20-L tanks filled with filtered 34 6 1 psu
seawater. Other conditions were as above.
Anemones were collected from Jennycliff Bay, Plymouth
Sound on the South Devon coast, United Kingdom (ordinance
survey grid reference: SX 491 523) and transported back to
the laboratory within 1 h. Maintenance conditions were same
as for the mussels.
Eelgrass was collected from the Yealm estuary, South Devon, United Kingdom (ordinance survey grid reference: SX 530
A. Scarlett et al.
476) and transported to the laboratory within 1 h. The outer
leaves were removed so that the three youngest leaves remained. These leaves were wiped with paper to remove epiphytes and shortened to a maximum length of 300 mm above
the sheath. Roots were standardized to three rhizome segments.
The plants were placed in 20-L tanks filled with filtered 34 6
1 psu seawater without any sediment or additional nutrient.
Other conditions were as above.
Preparation of test solutions
Corexit 9527 was supplied gratis from the U.S. Minerals
Management Service and SD-25 was obtained from the U.K.
Maritime and Coastguard Agency. Nominal exposure concentrations were prepared by direct syringe injection or pipetting
of dispersant into seawater adjusted to the required salinity for
the toxicity tests as appropriate (25 6 1 psu for Corophium,
34 6 1 psu for other spp.) and vortex mixing for 3 min at
high speed.
Rationale and general test conditions
Toxicity tests of dispersants have been performed using
short-term, static, continuous flow-through and spiked declining flow-through exposures. Exposure conditions have a considerable influence on the reported toxicity values with spiked
declining exposures giving much higher LC50 values, approximately 3 to 23 times greater, than continuous exposures
[3]. Here we used static exposures of 48-h duration noting that
the length of exposure does not necessarily produce an incipient LC50 for any of the species tested during the 48-h exposure and longer exposure, therefore, may result in lower
LC50 values. As well as acute toxicity, the ability of organisms
to recover in clean seawater for up to 72 h was assessed relative
to the 48-h endpoints. The species-specific sublethal endpoints
for each test are described in the following paragraphs.
Test vessels (2-L Pyrex beakers) were maintained at 15 6
18C with a 12:12 h light:dark cycle. Beakers were sealed loosely with Parafilmt M (Pechiney Plastic Packaging, Menasha,
WI, USA) and aerated via a glass Pasteur pipette. Dissolved
oxygen, pH, temperature, and salinity were measured after 0,
24, and 48 h in one replicate from each treatment. Dissolved
oxygen was measured in all replicates at the beginning and
end of the experiment. Three replicate vessels per treatment
were used in all tests except the mussel feeding-rate bioassay
in which nine replicate vessels per treatment were used. Test
results were analyzed statistically using one-way analysis of
variance after checking for normality and homogeneity of variances; 48-h LC50 values were derived using the trimmed
Spearman-Kärber method. The EC50 values were obtained
from regression of log-transformed data.
Corophium volutator
In order to obtain a concentration-response relationship,
nominal exposure concentrations of 0, 50, 125, 175, 213, 250,
375, and 500 ppm Corexit 9527 and SD-25 were prepared as
above. Sieved sediment (,300 mm), approximately 160 ml,
was placed in the test vessels to a depth of 15 mm and 1 L
of test solution added and allowed to settle for 2 h before 20
amphipods (size range 3–7 mm) were introduced via a plastic
Pasteur pipette to each test vessel, a total of 60 amphipods per
treatment. Beakers were inspected for amphipod activity and
behavior after 18, 24, and 42 h. At the end of the exposure
period of 48 h, the sediment was sieved (300 mm) and the
number of live, dead, and missing individuals recorded. Miss-
Comparative toxicity of Superdispersant-25 and Corexit 9527
ing individuals were assumed to have died. Surviving amphipods were placed in clean sediment and seawater for a 24h recovery period to assess their ability to burrow.
Mytilus edulis
Nominal exposure concentrations of 0, 80, 130, 200, 250,
and 320 ppm Corexit 9527 and SD-25 were prepared as above.
Three mussels, length 30 mm 6 5 mm, were placed in the test
vessels with 1 L of test solution, a total of nine mussels per
treatment. At the end of the exposure period, the mussels were
categorized as: Closed, open but able to close when stimulated,
or open but failed to close when stimulated. Mussels in the
former two categories were placed in clean seawater for a
further 72 h. Open mussels that failed to respond to stimuli
were considered dead and were excluded from the recovery
trial. Following the recovery period of 72 h, mussels were
reassessed. An additional test was performed with a single
toxicant treatment of 50 ppm for both dispersants plus controls
using only one mussel per test vessel but with nine replicates
per treatment. At the end of the 48-h exposure period, the
feeding rate of the mussels was assessed based on methodology
described by Donkin et al. [22]. Mussels were placed individually in 400-ml glass beakers containing 350 ml of clean seawater. After a 10-min acclimation period with slow vortex
mixing, 500 ml of Isochrysis sp. algal solution was added to
give approximately 2 3 104 cells ml21. A 20-ml water sample
was removed immediately from all the beakers upon the addition of the algae and retained in vials for algae enumeration.
Further samples were taken after 15 and 30 min. Algal particles
were analyzed using a Beckman Z2 Coulter particle count and
size analyzer (Beckman Coulter, Wycombe, UK), which was
set to count particles between 3 to 10.43 mm. From the loss
of algal particles during the 30-min period, the feeding rates
of the mussels were determined.
Anemonia viridis
Scoping tests had indicated that anemones were highly sensitive to both dispersants; therefore, a lower concentration
range of 0, 10, 20, 40, 80, and 160 ppm was used followed
by a 24-h recovery period. One anemone per test vessel was
exposed to 1 L of test solution, a total of three anemones per
treatment. At the end of the 48-h exposure period, the anemones were classified as having extended or retracted tentacles.
The tentacles were stimulated gently with a glass rod and their
response recorded. Anemones that were insensitive to stimuli
were classed as moribund because they would be unable to
feed or defend themselves from predation and, therefore, were
unlikely to survive in their natural environment. The anemones
then were placed in clean seawater for a 24-h recovery period
and reassessed. A confirmatory test was performed using three
anemones per test vessel at 20 and 30 ppm, a total of nine
anemones per treatment. The recovery period was extended to
72 h to test if further recovery was possible. The effect of the
dispersants on the density of zooxanthallae within the tentacles
of A. viridis also was assessed using anemones exposed to the
0 to 160 ppm concentration range following 48-h exposure
and 24-h recovery. Zooxanthallae were separated from the host
material after homogenization of weighed tentacles in a Wheaton glass homogenizer in 1 ml of buffer solution (50 nM HEPES, 1 nM ethylenediaminetetraacetate, pH 7.4). Whole homogenate was centrifuged for 3 min at 735 G (Eppendorf
5415C, Eppendorf UK, Cambridge, UK). The supernatant was
discarded and the insoluble plant material retained and resus-
1221
pended in buffer. Centrifugation was repeated until samples
were free of host tissue. The resulting pellet was resuspended
in buffer to give a cell density of about 1 3 106 cells ml21.
Zooxanthallae numbers were determined by replicate counts
(n 5 5) and converted to zooxanthallae per wet weight of
tentacle.
Zostera marina
Because mortality is not a practical parameter for a plant
toxicity test, the photosynthetic efficiency of eelgrass was examined using the chlorophyll fast fluorescence JIP transient
(from 50 ms to 1 s; J is a characteristic step at 2 ms, I is an
intermediate step at 30 ms, and P is maximum fluorescence)
measurements [23,24]. Three plants per exposure vessel were
exposed to a concentration range of 0, 80, 130, 200, 320, and
500 ppm dispersant, a total of nine plants per treatment, followed by a 24-h recovery period in clean seawater. Plants were
dark-adapted for 5 min using a clip system before illumination
with 660 nm light from a light-emitting diode source built into
the fluorometer sensor. Fluorescence measurements at F0 (50
ms), F300ms Fj (2 ms), and Fm (tFmax) were recorded using a
Handy PEA plant efficiency analyzer (Hansatech Instruments,
Kings Lynn, Norfolk, UK) after 24 and 48 h. The Biolyzerq
software (Ronald Maldonado-Rodriguez, Bioenergetics Laboratory, University of Geneva, Geneva, Switzerland, 2002)
was used to load the full fluorescence transients and to calculate the JIP parameters according to the equations of the
JIP-test [23,24]. The effect of the dispersants on the plant’s
photosynthetic apparatus was assessed by statistical comparison of the main parameters: Yield of primary photochemistry
expressed in ratio of variable to maximal fluorescence (Fv/
Fm), performance index (PI), and the area between the fluorescence curve and the maximal fluorescence intensity. Ten
measurements per exposure vessel were taken at random points
on the leaves avoiding any necrotic lesions (minimum of 10
mm distance). The plants then were placed in clean seawater
and further measurements recorded after 24 h.
RESULTS
Physical conditions
Dissolved oxygen was above 70% saturation, pH in the
range 7.8 to 8.2, salinity 25 6 1 psu (Corophium test) or 34
6 1 psu (other spp. tests), and temperature 15 6 18C during
the exposure period in all test vessels.
Corophium volutator
No activity was observed after 18 h at nominal dispersant
concentrations at or below 125 ppm, or within the controls.
At dispersant concentrations of 175 ppm and above, individuals were showing signs of stress: Either crawling on the sediment surface or swimming erratically. The activity caused
greater turbidity within treatments compared to the controls.
After 24 h, several moribund individuals were visible in exposure treatments of 375 and 500 ppm for both Corexit 9527
and Superdispersant-25. After 42 h of exposure, moribund
individuals also were visible at and above 175 ppm for both
dispersants, although this clearly was greater within the Corexit 9527 treatments. By the end of the experiment, little activity was observed, resulting in a reduction in turbidity, especially at the higher concentrations.
Mortality was less than 1% within the controls and 100%
at the highest nominal concentration of 500 ppm for both
Corexit 9527 and SD-25 (Fig. 1). Although there was a sharp
1222
A. Scarlett et al.
Table 2. Responses of the mussel Mytilus edulis after 48-h dispersant
exposures (48 h) and a 72-h recovery period (Rec.) Mussels from all
concentrations were pooled (Superdispersant-25 [SD-25] n 5 45 and
Corexit 9527 n 5 45; Control n 5 9 and classified as closed (closed),
open and responsive to stimuli (active), or open and unresponsive to
stimuli (dead)
Closed (%)
Control
SD-25
Corexit 9527
a
Fig. 1. Mortality (mean 6 standard error) of the mudshrimp Corophium volutator following a 48-h static exposure to the oil dispersants
Corexit 9527 and Superdispersant-25 (each treatment contained three
replicates of 20 amphipods).
Active (%)
Dead (%)
48 h
Rec.
48 h
Rec.
48 h
Rec.a
0
44
67
0
58
51
100
36
11
100
0
0
0
20
22
0
42
49
Dead mussels from the 48-h exposure were excluded from the recovery vessels but are recorded in the recovery column.
close promptly when touched) were able to close very slowly
and then remain closed. Therefore, only those that absolutely
failed to close when touched were deemed to have died (Fig.
2a). At the lowest concentration of 80 ppm, the bivalves exposed to Corexit 9527 experienced about 20% mortality but
variation was high and significant mortality ( p , 0.05) only
occurred at 250 ppm for Corexit 9527–and SD-25–exposed
organisms (Table 1). High variation within treatments occurred
at all treatment levels and reliable LC50 values could not be
calculated. Following 72 h in clean seawater, the number of
dead mussels had increased, but NOEC and LOEC values were
unchanged (Fig. 2b). Sublethal effects were assessed at dispersant concentrations of 50 ppm for 48 h using a feeding rate
bioassay (n 5 9). Two mussels within the Corexit 9527 exposure died and were omitted from the bioassay. Feeding rates
were reduced significantly ( p , 0.05) for both dispersant exposures with SD-25 rates reduced to 9.8% of controls and
Corexit 9527–exposed mussels only 2.6% (Fig. 3). Corexitexposed mussels had feeding rates significantly ( p , 0.05)
less than that of SD-25–exposed organisms.
fall in survival of C. volutator exposed to 175 ppm Corexit
9527, a more gradual decline was observed within the SD-25
treatments. The LC50 (48-h) values of 159 (95% confidence
limits 145–173 ppm) and 260 ppm (95% confidence limits
240–282 ppm) were calculated for Corexit 9527 and SD-25,
respectively (Table 1). The NOEC was 125 ppm for both dispersants. Mortality of Corexit 9527 exposed amphipods was
significantly ( p , 0.05) greater than those exposed to SD-25.
All of the surviving individuals exposed to SD-25 concentrations up to 175 ppm were able to swim normally and were
able to rebury in clean sediment, although swimming activity
still was observed 3 h following introduction into the clean
seawater; all survived the recovery period. Survivors from
Corexit 9527 also were able to rebury and survive the recovery
period with exposures up to 125 ppm. However, above 175
ppm, 100% of amphipods failed to recover from Corexit 9527.
Anemonia viridis
Mytilus edulis
The anemones were highly sensitive to both dispersants
with 55% of SD-25–exposed and 100% of Corexit 9527–exposed organisms insensitive to stimuli at 20 ppm (Fig. 4a). At
40 ppm for both dispersants, all organisms had retracted tentacles that failed to respond to stimuli and were assessed to
be moribund. Above 80 ppm, anemone tissue was starting to
decompose. The concentration range between no effect and
Control mussels appeared active and healthy (i.e., open and
responsive to stimuli) throughout the test period but all dispersant-exposed organisms were classified as either closed or
dead at the end of the 72-h recovery period (Table 2). Assessment of mortality after 48 h was problematic because some
mussels that initially appeared dead (i.e., open and failing to
Table 1. Comparison of toxicity estimates for Superdispersant-25 and Corexit 9527 with four marine species
Superdispersant-25 concn. (ppm)
Corexit 9527 concn. (ppm)
Species
Anemonia viridis
Corophium volutator
Mytilus edulis
M. edulis
Zostera marina
Test
No response to
stimuli
Mortality
Mortality
Feeding rate
JIP-testf PIg
NOECa
LOECb
10
20
125
200
,50
,80
175
250
50
80
LC50 or
EC50c
15d
159
—
NA
55
NOEC 5 no-observed-effect-concentration.
LOEC 5 lowest-observed-effect-concentration.
c LC50 5 median lethal concentration; EC50 5 median effect concentration.
d Interpolated from data only, not derived from model.
e NA 5 not applicable.
f JIP-test 5 measurements acquired from the fast fluorescence transient.
g PI 5 performance index.
a
b
Confidence
limits
NAe
145–173
—
28–150
NOEC
LOEC
LC50 or
EC50
10
20
20a
125
200
,50
,80
175
250
50
80
260
—
NA
386
Confidence
limits
NA
240–282
—
339–439
1223
Fig. 2. Mortality (mean 6 standard error) of the mussel Mytilus edulis
following a 48-h static exposure (a) and a further 72-h recovery period
(b) to the oil dispersants Corexit 9527 and Superdispersant-25 (each
treatment contained three replicates of three mussels).
100% mortality was too small to calculate LC50 values using
the Spearman-Kärber method. However, interpolation of the
data indicated EC50 values of about 20 ppm for SD-25 and
about 15 ppm for Corexit 9527. The NOEC and LOEC (48h) values were 10 ppm and 20 ppm, respectively for both
dispersants (Table 1). Following recovery in clean seawater,
Fig. 4. Percentage of moribund Anemonia viridis (mean 6 standard
error), following a 48-h static exposure (a) and a further 24-h recovery
period (b) to the oil dispersants Corexit 9527 and Superdispersant25 (the 20- and 30-ppm treatments contained three replicates of three
anemones; all other treatments contained three replicates of one anemone).
all of the anemones previously exposed to 30 ppm SD-25 and
below were able to respond to stimuli, as did all organisms
previously exposed to Corexit 9527 at 20 ppm (Fig. 4b); however, 89% of the anemones previously exposed to 30 ppm
Corexit 9527 failed to respond to stimuli (Fig. 4b). No significant differences were observed in zooxanthallae densities.
Zostera marina
Fig. 3. Feeding rates (mean 6 standard error) of the mussel Mytilus
edulis following a 48-h static exposure of 50 ppm to the oil dispersants
Corexit 9527 and Superdispersant-25 (each treatment contained nine
replicates of one mussel).
All of the main parameters, Fv/Fm, PI, and area, were reduced at the lowest exposure of 80 ppm after 24 and 48 h for
both dispersants (Fig. 5); the NOEC for both dispersants was
,80 ppm. The PI was the most-sensitive parameter giving 48h EC50 values of 386 ppm and 55 ppm for SD-25 and Corexit
9527, respectively. At the lowest exposure concentration there
was no significant difference between the dispersants; however, at 130 ppm and above, Corexit was significantly ( p ,
0.05) more toxic for all main parameters. Leaves of the Corexit-exposed plants turned brown at 200 ppm and the outer
leaves started to become detached, leaving only the more protected inner leaf from which to take measurements. As well
as the main parameter values obtained from the PEA, the JIPtest allows calculation of various bio-physical expressions,
such as specific fluctuations and yields, and phenomenological
fluctuations. To visualize the effect of the dispersants on the
1224
A. Scarlett et al.
Fig. 5. Photosynthetic parameters (Fv/Fm), performance index (PI), and area (mean 6 standard error), measured in the leaves of Zostera marina
after 24- and 48-h exposure, and 24-h recovery in clean seawater, to the oil dispersants Corexit 9527 and Superdispersant-25 (each treatment
contained three replicates of three plants from which 10 measurements were taken).
eelgrass leaves, pipeline models have been calculated and
drawn on the basis of experimental signals from the 200-ppm
concentrations and compared to control leaves (Fig. 6). The
SD-25–exposed plants were able to recover slightly after 24
h in clean seawater, e.g., mean PI values rose from 0.88 (standard error [SE] 0.01) after 48 h at 80 ppm exposure to 1.07
(SE 0.04) following recovery. Corexit-exposed plants failed
to recover, e.g., mean PI values were 0.84 (SE 0.01) after the
48-h exposure to 80 ppm and fell to 0.76 (SE 0.07) at the end
of the 24-h recovery period (Fig. 4).
DISCUSSION AND CONCLUSION
This is, to our knowledge, the first report of toxicity data
for SD-25 that reveals its acute toxicity to a range of marine
Fig. 6. Comparison of membrane models of the Photosystem II (PSII)
apparatus in control plants (top), and 48-h exposure to 200 ppm Superdispersant-25 (middle) and Corexit 9527 (bottom). The models
represent the specific activities expressed as fluctuation per reaction
center (RC). The relative magnitude of each activity or fluctuation is
shown by the width of the corresponding arrow. Absorption flux
(ABS) is proportional to the concentration of antenna chlorophyll and
the average antenna size is given as ABS/RC. This expresses the total
absorption flux of PSII antennae chlorophyll divided by the number
of active, in the sense of primary quinone (QA)-reducing, reaction
centers. The absorption and trapping by PSII units with a heat sink
center (non-QA–reducing) is indicated as the hatched parts of the
arrows ABS/RC and energy flux for trapping (TRo/RC). The antenna
belonging to the PSII units with heat sink centers is drawn in black
and the antenna that belongs to the active centers is drawn in white.
The degree of stress, thus, is indicated by the reduction in ABS/RC
and TRo/RC leading to a greater dissipation of energy in the form of
heat (Dlo/RC) and a decrease in the useful energy available to the
plant (ETo/RC).
species to be significantly lower ( p , 0.05), in the majority
of tests, or equivalent (mussel mortality and anemone tests)
to the widely used dispersant Corexit 9527. Dispersants contain surfactants, which may be nonionic or anionic, dissolved,
or suspended in solvents. Corexit 9527 is a mixture of both
nonionic (48%) and anionic (35%) surfactants in an aqueous
solvent containing ethylene glycol monobutyl ether (17%); the
surfactants include ethoxylated sorbitan mono- and trioleates
(nonionic) and sodium dioctyl sulfoccinate (anionic) [25], but
little is known about the constituents of SD-25 except that it
is a blend of glycol and glycol ether solvents, combined with
nonionic and anionic surfactants [26]. Dispersants are thought
to act physically and irreversibly on the respiratory organs and
reversibly, depending on exposure time, on the nervous system
1225
[27]. Surfactants can bind to and disrupt cellular phospholipid
bilayers altering the transmembrane sodium gradient [28].
A review of Corexit 9527 toxicity data by George-Ares and
Clark [3] covering 28 reports and 37 aquatic species, found
no apparent trend in the reported sensitivity between taxa.
Toxicity (24–96–h LC50 or EC50) ranged from 1.6 to .1,000
ppm. Amphipod LC50 values varied between 3 ppm for Allorchestes compressa [29] and .175 ppm for Boekosimus sp.
[30], but no data for C. volutator were reported. Mollusc
(adults) LC50 values were in the range 33.8 ppm for the sand
snail Polinices conicus [29] to 2,500 ppm for the scallop Argopecten irradians [31]; however, no data for Mytilus edulis
were reported. The class Anthozoa (anemones and true corals)
were not represented in the reviewed papers and the only seagrass was Thalassia testudinum with a 96-h LC50 of 200 ppm
[32]. In general, the reviewed data show that Corexit 9527 is
of low acute toxicity to most species, although embryo and
larval stages are more sensitive. The results from this study
are consistent with the literature data but suggest an order of
sensitivity, based on NOEC and LOEC values, of: Anthozoa
. macrophyte . crustacean . mollusc for both Corexit 9527
and SD-25, although the order of sensitivity partly is due to
the differences in test methodology and endpoints.
Both dispersants caused a sharp increase in mortality over
a narrow concentration range; this was most pronounced for
Corexit 9527. Therefore, it is of more use to refer to NOEC
or LOEC than LC50 values. The NOEC values for A. viridis
were much lower than that observed for the other species tested
(Table 1) and the sensitivity of the anemones was similar to
that of the embryo or larval stages of molluscs and fish [33,34].
Of the anemones with extended tentacles that were unresponsive to stimuli following 48-h dispersant exposure of 20 ppm
and, therefore, classed as moribund (Fig. 4a), all were able to
recover when placed in clean seawater (Fig. 4b). All of the
SD-25–exposed anemones also were able to recover from the
30-ppm exposure, but this was true of only 11% of Corexitexposed anemones. The ability of the organisms to recover
implies that the dispersants act reversibly on the neural receptors at low concentrations but cause irreversible membrane
damage at higher concentrations, consistent with reported surfactant toxicity modes of action [27,28]. No effect on zooxanthallae density was found and the cells appeared undamaged
even when their host tissue was damaged severely. It is likely
that the host tissue protected the plant cells and that, during
short-term exposure, the algae do not migrate from their hosts
as happens with coral bleaching episodes. Anemones have
been used as a surrogate organism for symbiotic coral assemblages [17,18], although it is not known if the skeletal cup of
reef-building corals would afford the same protection for the
zooxanthallae as the anemone.
The M. edulis mortality data were very variable, but Corexit
9527 clearly was more toxic at low concentrations (Fig. 2a).
The total percentage of SD-25–exposed mussels that were
closed at the end of the 48-h exposure period increased from
44 to 58%, but Corexit-exposed closed mussels decreased from
67 to 51% with a corresponding rise in the percentage dead
(Table 2). A very pronounced effect was observed in the feeding rates of mussels exposed to 50 ppm of both dispersants,
with Corexit-exposed organisms’ filtration rates reduced so
much that they were not significantly greater than system
blanks (Fig. 3). Although the highly reduced filtration rates
could be a behavioral response to the dispersants, the mussels’
failure to open within the 72-h recovery period following the
1226
48-h concentration-response test (Table 2) suggests that the
mussels may have been suffering physiological damage as they
would be expected to recover from nonspecific narcosis. Although there was no sublethal component to the C. volutator
test, it was observed that many individuals failed to burrow
quickly when exposed to $175 ppm dispersants. It was impossible to tell if it was the swimming amphipods that died
later, although these likely had a greater exposure than those
that burrowed rapidly into the sediment. Briggs et al. [8] used
turbidity caused by stressed Corophium as a measure of toxicity and found a good correlation between turbidity during
the first 24 h of exposure and mortality after 10 d. The failure
to burrow and increased swimming activity observed at moderate contamination levels, are most likely an escape behavioral response rather than a physiological response because
they occur with a range of toxicants [8,35]. The dispersant
concentrations in this study that caused the greatest turbidity
were within the confidence limits of the calculated LC50 values
(Table 1). Under test conditions where escape was impossible,
the behavioral response not to burrow probably proved to be
detrimental, but in the wild it may enable the organisms to
escape from localized areas of high concentration.
Both dispersants disrupted the Photosystem II (PSII) apparatus, known to be a primary casualty during stress conditions [36], within the leaves of Z. marina and this was found
to be largely irreversible, especially for Corexit 9527 (Fig. 5).
The PI is a standard composite of several JIP-test parameters
and has been shown to be a highly sensitive measure that is
correlated strongly with other measurements of plant health
[24]. Indeed, at concentrations $200 ppm Corexit 9527, it was
evident by visual inspection that the leaves were damaged
severely and the outer leaves were becoming detached. Leaves
of Z. marina possess a thin cuticle [37] that may afford a
degree of protection from the dispersants, although it is clear
that 24 h was sufficient for photosynthesis to be affected. Algae
and spores with less protection likely are more sensitive, as
found by Singer et al. [34], exposing zoospores of giant kelp,
Macrocystis pyrifera, to Corexit 9527.
Extrapolating the results from this and other studies suggests that the most sensitive organisms to dispersants are those
with the least protective tissue or shell. As well as the spore,
embryo, and larval stages, it is likely that unprotected organisms such as nudibranchs and seaslugs would be vulnerable
and, therefore, extra care may need to be taken where rare or
endangered species are present. Under current dispersant-use
guidelines that exist in the United Kingdom and elsewhere
[1,2], dispersant concentrations are unlikely to be sufficiently
high to cause harm to aquatic organisms within open waters.
Measured concentrations at sea are rare but Bocard et al. [38]
reported a maximum concentration of 13 ppm. However, if
dispersants are used or spilled accidentally within estuaries,
enclosed bays, or shallow coral reefs, it may be possible for
concentrations to become elevated long enough to cause mortality of sensitive species and stress to more tolerant animals
and plants. Any harm to aquatic species as a result of dispersant
use must be measured against the potential harm that may arise
from the toxic or physical effects of oil. The difficult decision
of when, where, and under what conditions to use dispersants
will remain as long as oil is transported via the sea. Spill
response agencies are better able to make such decisions when
provided with relevant ecotoxicological data. To our knowledge, these are the first reported results concerning the toxicity
of Superdispersant-25; it has shown that this dispersant gen-
A. Scarlett et al.
erally is of lower toxicity than Corexit 9527 to a range of
coastal species, although equivalent in terms of NOEC and
LOEC values; also, at maximum concentrations likely to be
found at sea, any sublethal effects upon organisms are more
likely to be reversible.
Acknowledgement—This study was financed by the Maritime and
Coastguard Agency, the Department for Environment Food and Rural
Affairs, the Department of Trade and Industry, and Minerals Management Service Grant RP 480. The Photosynthesis Membrane model
was derived using Biolyzert software provided by R. MaldonadoRodriguez, Bioenergetics Laboratory, University of Geneva, Geneva,
Switzerland.
REFERENCES
1. Maritime and Coastguard Agency. 2002. Contingency Planning
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Departments of aBiological Sciences, cEnvironmental Sciences, and eOceanography and Coastal Sciences, and dCoastal Studies Institute, Louisiana State
University, Baton Rouge, LA 70803; bDepartment of Chemistry and Biochemistry, Texas State University, San Marcos, TX 78666; and fDepartment of Biological
Sciences, Clemson University, Clemson, SC 29634
Edited by Paul G. Falkowski, Rutgers, The State University of New Jersey, New Brunswick, NJ, and approved September 1, 2011 (received for review
June 13, 2011)
The biological consequences of the Deepwater Horizon oil spill are
unknown, especially for resident organisms. Here, we report
results from a field study tracking the effects of contaminating
oil across space and time in resident killifish during the first 4
mo of the spill event. Remote sensing and analytical chemistry
identified exposures, which were linked to effects in fish characterized by genome expression and associated gill immunohistochemistry, despite very low concentrations of hydrocarbons
remaining in water and tissues. Divergence in genome expression
coincides with contaminating oil and is consistent with genome
responses that are predictive of exposure to hydrocarbon-like
chemicals and indicative of physiological and reproductive impairment. Oil-contaminated waters are also associated with aberrant
protein expression in gill tissues of larval and adult fish. These data
suggest that heavily weathered crude oil from the spill imparts
significant biological impacts in sensitive Louisiana marshes, some
of which remain for over 2 mo following initial exposures.
ecological genomics
toxicogenomics
| ecotoxicology | microarray | RNA-seq |
F
ollowing the Deepwater Horizon (DWH) drilling disaster on
April 20, 2011, in the Gulf of Mexico, acute oiling and the
resulting mortality of marine wildlife were evident. In contrast,
the sublethal effects, critically important for predicting longterm population-level impacts of oil pollution (1), have not
been well described following the DWH disaster. Here, we report the results of a 4-mo field study monitoring the biological
effects of oil exposure on fish resident in Gulf of Mexico coastal
marsh habitats.
Gulf killifish (Fundulus grandis) were used as our model species because they are among the most abundant vertebrate animals in Gulf of Mexico-exposed marshes (2–4). Furthermore, the
Atlantic-distributed sister species to F. grandis (Fundulus heteroclitus) has a narrow home range and high site fidelity, especially
during the summer (5, 6), and, among fishes, it is relatively
sensitive to the toxic effects of organic pollutants (7). Although
home range and toxicology studies are lacking for F. grandis, we
infer that F. grandis is also relatively sensitive to pollutants and
exhibits high site fidelity, such that the biology of this species is
likely affected primarily by the local environment, given the recent shared ancestry of F. grandis with F. heteroclitus (8) and
similar physiology, life history, and habitat (9–13). We sampled
from populations resident in Gulf of Mexico-exposed marshes
before oil landfall (May 1–9, 2010), during the peak of oil
landfall (June 28–30, 2010), and after much of the surface oil was
no longer apparent 2 mo later (August 30–September 1, 2010) at
six field sites from Barataria Bay, Louisiana, east to Mobile Bay,
Alabama (Fig. 1 and Dataset S1).
Results and Discussion
Remote sensing and analytical chemistry were used to characterize exposure to DWH oil, where remote sensing data are
www.pnas.org/cgi/doi/10.1073/pnas.1109545108
spatially and temporally comprehensive but of low resolution
and chemistry data are of high resolution but patchy in space and
time. Ocean surface oil was remotely detected through the
analysis of images from synthetic aperture radar (SAR) (14).
Proximity of the nearest oil slick to each field site (e.g., Fig. S1)
was measured for each day that SAR data were available, from
May 11 through August 13, 2010, to approximate the location,
timing, and duration of coastal oiling (Fig. 1C). Although surface
oil came close to many of our field sites in mid-June, only the
Grande Terre (GT) site was directly oiled (Fig. 1 B and C).
Although the GT site had been clearly contaminated with crude
oil for several weeks before our sampling (Fig. 1C and Fig. S2)
and retained much oil in sediments (Dataset S2), only trace
concentrations of oil components were detected in subsurface
water samples collected from the GT site on June 28, 2010, and
tissues did not carry abnormally high burdens of oil constituents
at any site or time point (Dataset S2). Despite a low chemical
signal for oil in the water column and tissues at the time of
sampling, we detected significant biological effects associated
with the GT site postoil.
We sampled multiple tissues from adult Gulf killifish (average
weight of 3.5 g) from each of six field sites for each of three time
points [only the first two time points for the Mobile Bay (MB)
site] spanning the first 4 mo of the spill event (Fig. 1C). We
compared biological responses across time (before, at the peak,
and after oiling) and across space (oiled sites and sites not oiled)
and integrated responses at the molecular level using genome
expression profiling with complimentary protein expression and
tissue morphology. Genome expression profiles, using microarrays and RNAseq, were characterized for livers because the
organ is internal and integrates xenobiotic effects from multiple
routes of entry (gill, intestine, and skin), and because liver is the
primary tissue for metabolism of toxic oil constituents. Tissue
morphology and expression of CYP1A protein, a common biomarker for exposure to select polycyclic aromatic hydrocarbons
(PAHs), was characterized for gills, the organ that provides the
greatest surface area in direct contact with the surrounding
aquatic environment. In addition, we exposed developing
embryos to field-collected water samples to document bioavailability and bioactivity of oil contaminants for this sensitive
early life stage.
Author contributions: A.W. and F.G. designed research; A.W., B.D., C.B., T.I.G., S.M., C.P.,
V.R., J.L.R., N.W., R.B.W. and F.G. performed research; C.D.R. contributed new reagents/
analytic tools; A.W., B.D., C.B., T.I.G., S.M., C.P., V.R., N.W., R.B.W. and F.G. analyzed data;
and A.W., B.D., C.B., and F.G. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
Data deposition: Microarray data have been deposited to ArrayExpress (accession no.
E-MTAB-663).
1
To whom correspondence should be addressed. E-mail: andreww@lsu.edu.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
1073/pnas.1109545108/-/DCSupplemental.
SPECIAL FEATURE
Andrew Whiteheada,1, Benjamin Dubanskya, Charlotte Bodiniera, Tzintzuni I. Garciab, Scott Milesc, Chet Pilleyd,
Vandana Raghunathane, Jennifer L. Roacha, Nan Walkere, Ronald B. Walterb, Charles D. Ricef, and Fernando Galveza
ENVIRONMENTAL
SCIENCES
Genomic and physiological footprint of the Deepwater
Horizon oil spill on resident marsh fishes
Fig. 1. Location of field study sites and incidence of oil contamination. (A) Location of field sampling sites, which include Grand Terre (GT), Bay St. Louis (BSL),
Belle Fontaine Point (BFP), Bayou La Batre (BLB), Mobile Bay (MB), and Fort Morgan (FMA). Color coding is consistent with other figures. The red star indicates
the DWH spill site. (B) Photograph (by A.W.) of the GT field site on June 28, 2010, showing contaminating oil and minnow traps in the marsh. (C) Proximity of
nearest surface oil to each field site was determined by SAR, where rows are field sites and columns are days. Light gray represents no data, and black
represents the nearest surface oil at a distance of >4 km; the increasing intensity of red indicates closer proximity of oil. Three field sampling trips are
highlighted (blue boxes). BSL; BFP; FMA.
The oiling of the GT site at the end of June 2010 is associated
with a clear functional genomic footprint. Of the 3,296 genes
included in our analysis, expression of 1,600 and 1,257 genes
varied among field sites and throughout the time course, respectively (P < 0.01) (Dataset S3). For the 646 genes that varied
in expression only among sites (no significant time effect or siteby-time interaction), site variation followed a pattern of population isolation by distance, which is consistent with neutral
evolutionary divergence (Fig. 2A) and population genetic expectations (15). Most importantly, 1,500 genes indicated a pattern of
site-dependent time course expression (significant interaction,
false discovery rate <0.01), where the trajectory of genome expression through time was divergent at the GT site compared
with all other sites (Fig. 2 B and C), particularly at the second
time point, which coincides with oil contamination (Fig. 1C).
Previous studies have identified genes that are transcriptionally responsive to planar polychlorinated biphenyl (PCB) exposures in killifish (16). Planar PCBs, dioxins, and PAHs (the
primary toxic constituents in crude oil) are all mechanistically
related insofar as they exert biological effects, in whole or in part,
through aryl-hydrocarbon receptor (AHR) signaling pathways;
indeed, morpholino knockdown of the AHR is protective of the
toxic effects of PAHs and PCBs in killifish (17), and exposures to
PCBs and PAHs induce common genome expression responses
in flounder (18). Of the genes that were transcriptionally responsive to PCB exposures (16), 380 were included in the current
analysis. Expression of this subset of genes is predictive of
transcriptional divergence in fish from the GT site coincident
with oil contamination compared with other field sites (Fig. S3),
especially for the top 10% of PCB-responsive genes (Fig. 2D).
Transcriptional activation of these planar PCB-responsive genes
in developing killifish embryos is predictive of induction of developmental abnormalities, decreased hatching success, and decreased embryonic and larval survival (16, 19). This set of genes
includes members of the canonical battery of genes that are
transcriptionally induced by ligand-activated AHR signaling,
such as cytochrome P450s, cytochrome B5, and UDP-glucuronosyltransferase (Fig. 2F, set 1), for which increased transcription is particularly diagnostic of exposure to select hydrocarbons
(20). Indeed, many genes that are transcriptionally induced or
repressed by AHR activators (dioxins, PCBs, and PAHs) show
induction or repression at the GT site coincident with crude oil
contamination (Fig. 2F, set 1). An independent measure of genome expression, RNAseq, also indicates AHR activation in GT
fish from June 28, 2010, compared with reference RNA (e.g., upregulation of cytochrome P450s, UDP-glucuronosyltransferase
(UGT), and AHR itself; Fig. 2E). In parallel, up-regulation of
CYP1A protein was detected in gills from GT fish sampled
postoil and in early life-stage fish following controlled exposures
to GT waters (Figs. 3 and 4). These data appear to be diagnostic
of exposure to the toxic constituents in contaminating oil (PAHs)
at a sufficient concentration and duration to induce biological
responses in resident fish. Sustained activation of the CYP1A
gene (Figs. 2F and 3) was predictive of persistent exposure to
sublethal concentrations of crude oil components and negative
population-level impacts in fish, sea otters, and harlequin ducks
following the Exxon Valdez oil spill (reviewed in 1), although
PAH toxicity may be mediated through AHR-independent
pathways as well (21).
Transcriptional responses in other sets of coexpressed genes
offer insights into the potential biological consequences of contaminating oil exposure at the GT site. Several gene ontology
(GO) categories were enriched in the subset of genes that
showed GT-specific expression divergence coincident with siteand time-specific oil contamination (Dataset S4). GO enrichment indicates activation of the ubiquitin-proteasome system
(Fig. 2F, set 2), which, among diverse functions, is important for
cellular responses to stress, cell cycle regulation, regulation of
DNA repair, apoptosis, and immune responses (22). The AHR
protein itself plays a role as a unique ligand-dependent E3
ubiquitin ligase that targets sex steroid (estrogen and androgen)
receptor proteins for proteasomal destruction, thereby impairing
Whitehead et al.
SPECIAL FEATURE
normal cellular responses to sex hormones in reproductive tissues, and this response can be activated by planar PAHs (23).
Significant down-regulation of transcripts for egg envelope proteins zona pellucida (ZP3 and ZP4) and choriogenin (ChgHm
and ChgH) that we detect at the GT site coincident with oil
exposure (Fig. 2F, set 1) may be linked to this AHR-dependent
proteolytic pathway because their transcription is estrogen-dependent (24, 25) and is down-regulated by exposure to PAHs in
fish (25–27). In corroboration, RNAseq detects dramatically
down-regulated ZP, ChgH, and vitellogenin transcripts in GT
fish (Fig. 2E). Although the transcriptional response that we
detect is in male fish, these proteins are synthesized in male livers
(reviewed in 25, 27) and down-regulation is consistent with
antiestrogenic effects from exposure to PAHs (28). Possible
impacts on reproduction merit attention because water only
from the GT site induced CYP1A protein in the gills of developing killifish (Fig. 3) at low concentrations of total aromatics
and alkanes (Dataset S2) and more than 2 mo after initial oiling,
indicating persistent bioavailability of PAHs. Marsh contamination with DWH oil coincided with the spawning season for many
marsh animals, including killifish (29), and reproductive effects are
predictive of long-term population-level impacts from oil spills (1).
Controlled exposures of developing killifish to water collected
from GT on June 28 and August 30, 2010, induced CYP1A protein expression in larval gills relative to fish exposed to GT water
preoil and exposed to Bayou La Batre (BLB) site water that was
not oiled (Fig. 3). This response is consistent with the location and
timing of oil contamination, and it indicates that the remaining oil
constituents dissolved at very low concentrations at GT after
landfall (Dataset S2) were bioavailable and bioactive to developing fish. Although exposures to PAHs stereotypically induce
cardiovascular system abnormalities in developing fish at relatively high concentrations (e.g., 21), none were observed in these
animals. However, even very low-concentration exposures during
development, insufficient to induce cardiovascular abnormalities
Whitehead et al.
in embryos, can impair cardiac performance in adulthood (30).
The adult fish sampled in situ from the oil-contaminated GT site
showed divergent regulation of several genes involved in blood
vessel morphogenesis and heme metabolism coincident with oil
contamination (Fig. 2F, set 3). Multigeneration field studies are
necessary to confirm cardiovascular effects from DWH oil contamination of marshes that coincided with spawning.
Fig. 3. CYP1A protein expression (dark red staining) in larval killifish gills
(24 d postfertilization) exposed to waters collected from GT (oiled) and BLB
(not oiled) during development. (Magnification 40×, scale bars = 10 μm.)
CYP1A expression is elevated in the lamellae of larvae exposed during development to waters collected from GT postoil (trips 2 and 3) compared with
background levels of CYP1A expression in larvae exposed to GT water preoil
(trip 1), compared with CYP1A in fish exposed to waters collected from BLB
(which was not directly oiled), and compared with CYP1A in fish reared in
laboratory control water. Nuclei were stained using hematoxylin (blue).
Analytical chemistry of exposure waters is reported in Dataset S2.
ENVIRONMENTAL
SCIENCES
Fig. 2. Genome expression between field sites and across time. Field sites include Grand Terre (GT), Bay St. Louis (BSL), Belle Fontaine Point (BFP), Bayou La
Batre (BLB), Mobile Bay (MB), and Fort Morgan (FMA). GT was the only site to be directly oiled, which occurred between the first and second sampling times
(Fig. 1 and Dataset S2). (A) For genes that vary only among sites (no expression change with time or interaction), pairwise site-specific transcriptome divergence along principal component (PC) 1, as a function of pairwise geographical distance, shows a pattern consistent with isolation by distance. (B) Trajectory of genome expression responses through time for each of six field sites from the preoil sample time (dot at base of arrow) through the peak-oil sample
time (middle dot), to the latest postevent sample time (dot at head of arrow) following PC analysis of genes showing statistically significant main effects (site
and time) and interaction terms. (C) Divergence along PC1 is isolated, where bars for each site from left to right represent sampling times from the earliest to
the latest. (D) Expression divergence along PC1 for the subset of genes that is dose-responsive to PCB exposure (top 10% of PCB-responsive genes). (E) RNAseq
data showing genes up- and down-regulated (x axis positive and negative, respectively) in fish from GT sample time 2 (coincident with oil) compared with
reference RNA, where select genes are identified. (Inset) Genes are dramatically down-regulated at GT (detailed RNAseq data are presented in Dataset S5). (F)
Expression levels for specific genes (rows) and treatments (columns), where cell color indicates up-regulation (yellow) or down-regulation (blue) scaled
according to site-specific expression level at the preoil sample time, for genes with divergent expression at the GT site. Genes are grouped into functional
categories, and scale bars indicate N-fold up- or down-regulation.
Coastal salt marsh habitats are dynamic and stressful, where
changes in environmental parameters, such as temperature,
hypoxia, and salinity, can continuously challenge resident wildlife. Regulation of ion transport in fish is particularly important
for facilitating homeostasis in response to the salinity fluctuations that are common in estuaries. We found altered regulation
of multiple ion transport genes in fish from the GT site coincident with oil contamination (Fig. 2F, set 4). For example, Vtype proton ATPases are up-regulated and Na+,K+-ATPase
subunits and tight-junction proteins are down-regulated, coincident with oiling at the GT site, in the absence of substantial
changes in environmental salinity (Dataset S2). Other genes
important for osmotic regulation in killifish (31) are also divergently down-regulated at the GT site, including type II
iodothyronine deiodinase (DIO2), transcription factor jun-B
(JUNB), and arginase 2 (ARG2). In corroboration, RNAseq
data show down-regulation of DIO2, JUNB, and ARG2 in GT
fish compared with reference fish (Fig. 2E). Although the physiological consequences of oil exposures are typically studied in
isolation, it is reasonable to predict that exposure to oil may
compromise the ability of resident organisms to adjust physiologically to natural stressors.
Induction of CYP1A protein expression is a hallmark of AHR
signaling pathway activation, making it a sensitive biomarker of
exposure to select planar PAHs and other hydrocarbons (20).
Although the liver is the key organ for CYP1A-mediated metabolism of these substrates, gill tissues represent the most
proximate site of exposure to PAHs. As a result of direct contact
with the environment and the nature of the gill as a transport
epithelium, the gill may be a more sensitive indicator of exposure
to contaminants than the liver (32). CYP1A protein was markedly elevated in GT fish postoil compared with GT fish preoil
and compared with fish from other field sites that were not directly oiled (Fig. 4). CYP1A induction was localized predominantly to pillar cells of the gill lamellae and within
undifferentiated cells underlying the interlamellar region, which
may have contributed to the filamental and lamellar hyperplasia
observed during trips 2 and 3, as well as the gross proliferation of
the interlamellar region observed during trip 2 in GT fish (Fig.
4). These effects imply a decrease in the effective surface area of
the gill, a tissue that supports critical physiological functions,
such as ion homeostasis, respiratory gas exchange, systemic acidbase regulation, and nitrogenous waste excretion (33). Currently,
the degree to which oil-induced effects may interact with commonly encountered challenges, such as fluctuations in hypoxia
and salinity, to compromise physiological resilience is unclear.
By integrating remote sensing and in situ chemical measures of
exposure, and linking these with integrated measures of biological effect (genome expression and tissue morphology), we
provide evidence that links biological impacts with exposure to
contaminating oil from the DWH spill within coastal marsh
habitats. Although body burdens of toxins are not high, consistent with reports indicating that seafood from the Gulf of Mexico
is safe for consumption (34), this does not mean that negative
biological impacts are absent. Our data reveal biologically relevant
sublethal exposures causing alterations in genome expression and
tissue morphology suggestive of physiological impairment persisting for over 2 mo after initial exposures. Sublethal effects were
predictive of deleterious population-level impacts that persisted
over long periods of time in aquatic species following the Exxon
Valdez spill (1) and must be a focus of long-term research in
the Gulf of Mexico, especially because high concentrations of
hydrocarbons in sediments (Dataset S2) may provide a persistent
source of exposures to organisms resident in Louisiana marshes.
Methods
The locations (latitude and longitude) of our field sampling sites and dates for
sampling at each site are summarized in Dataset S1. Gulf killifish (F. grandis)
Fig. 4. CYP1A protein expression in adult killifish gills (dark red staining)
sampled in situ from all sampling times (columns) and locations (rows).
Locations include Grand Terre (GT), Bay St. Louis (BSL), Belle Fontaine Point
(BFP), Bayou La Batre (BLB), Mobile Bay (MB), and Fort Morgan (FMA).
(Magnification 40×, scale bars = 10 μm.) The MB site was only sampled on
trips 1 and 2, and gills from trip 1 at the BLB site were not available for
processing. Fish gills from the GT site during trips 2 and 3 showed high CYP1A
expression and an elevated incidence of hyperplasia of the lamellae and
interlamellar space on the gill filaments coincident with oil contamination.
CYP1A protein was elevated at the GT site postoil (trips 2 and 3) compared
with GT preoil (trip 1) as well as with other field sites, none of which were
directly oiled. Nuclei were stained using hematoxylin (blue). Exact site locations and sampling dates are reported in Dataset S1.
were caught by minnow trap, and tissues were excised immediately. Liver
was preserved in RNAlater (Ambion, Inc.) for genome expression (microarray
and RNAseq) analysis. Gill tissues were fixed in situ in buffered zinc-based
formalin Z-Fix (Anatech LTD). Succinct methods follow, and more detailed
methods are available online.
Satellite imagery (SAR) was analyzed to provide estimation of the timing,
location, and duration of coastal oil contamination. The calculated distance
from each field sampling site to the nearest oil slick was calculated from the
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geographical barriers (e.g., Fig. S1).
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for environmental estrogens. Aquat Toxicol 77:348–358.
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rerio) following exposure to produced water components. Aquat Toxicol 90:277–291.
27. Sanchez BC, Carter B, Hammers HR, Sepúlveda MS (2011) Transcriptional response of
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Rouge, LA).
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40. Rice CD, Schlenk D, Ainsworth J, Goksoyr A (1998) Cross-reactivity of monoclonal
antibodies against peptide 277-294 of rainbow trout CYP1A1 with hepatic CYP1A
among fish. Mar Environ Res 46:87–91.
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SPECIAL FEATURE
1. Peterson CH, et al. (2003) Long-term ecosystem response to the Exxon Valdez oil spill.
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deltaic salt marshes undergoing submergence. Mar Ecol Prog Ser 96:147–157.
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adjacent shallow nonvegetated areas of the Galveston Bay Estuary, Texas (USA). Mar
Ecol Prog Ser 193:217–239.
4. Subrahmanyam CB, Coultas CL (1980) Studies on the animal communities in 2 North
Florida salt marshes. 3. Seasonal fluctuations of fish and macroinvertebrates. Bull Mar
Sci 30:790–818.
5. Lotrich VA (1975) Summer home range and movements of Fundulus heteroclitus
(Pisces: Cyprinodontidae) in a tidal creek. Ecology 56:191–198.
6. Teo SLH, Able KW (2003) Habitat use and movement of the mummichog (Fundulus
heteroclitus) in a restored salt marsh. Estuaries 26:720–730.
7. Van Veld PA, Nacci DE (2008) Toxicity resistance. The Toxicology of Fishes, eds Di
Giulio RT, Hinton DE (Taylor and Francis, Boca Raton, FL), pp 597–641.
8. Whitehead A (2010) The evolutionary radiation of diverse osmotolerant physiologies
in killifish (Fundulus sp.). Evolution 64:2070–2085.
9. Able KW, Hata D (1984) Reproductive behavior in the Fundulus heteroclitus-F. grandis
complex. Copeia (4):820–825.
10. Kneib RT (1997) The role of tidal marshes in the ecology of estuarine nekton.
Oceanography and Marine Biology: An Annual Review 35:163–220.
11. Nordlie FG (2006) Physicochemical environments and tolerances of cyprinodontoid
fishes found in estuaries and salt marshes of eastern North America. Reviews in Fish
Biology and Fisheries 16:51–106.
12. Rozas LP, Lasalle MW (1990) A comparison of the diets of Gulf killifish, Fundulus grandis
Baird and Girard, entering and leaving a Mississippi brackish marsh. Estuaries 13:332–336.
13. Weisberg SB, Lotrich VA (1982) The importance of an infrequently flooded intertidal
marsh surface as an energy source for the mummichog Fundulus heteroclitus: An
experimental approach. Mar Biol 66:307–310.
14. Brekke C, Solberg AHS (2005) Oil spill detection by satellite remote sensing. Remote
Sensing of Environment 95:1–13.
15. Williams DA, Brown SD, Crawford DL (2008) Contemporary and historical influences
on the genetic structure of the estuarine-dependent Gulf killifish Fundulus grandis.
Mar Ecol Prog Ser 373:111–121.
16. Whitehead A, Pilcher W, Champlin D, Nacci D (2011) Common mechanism underlies
repeated evolution of extreme pollution tolerance. Proc R Soc B, 10.1098/rspb.2011.0847.
17. Clark BW, Matson CW, Jung D, Di Giulio RT (2010) AHR2 mediates cardiac teratogenesis of polycyclic aromatic hydrocarbons and PCB-126 in Atlantic killifish (Fundulus
heteroclitus). Aquat Toxicol 99:232–240.
18. Williams TD, et al. (2008) Transcriptomic responses of European flounder (Platichthys
flesus) to model toxicants. Aquat Toxicol 90:83–91.
19. Whitehead A, Triant DA, Champlin D, Nacci D (2010) Comparative transcriptomics
implicates mechanisms of evolved pollution tolerance in a killifish population. Mol
Ecol 19:5186–5203.
20. Varanasi U (1989) Metabolism of Polycyclic Aromatic Hydrocarbons in the Aquatic
Environment (CRC, Boca Raton, FL), p 341.
ACKNOWLEDGMENTS. K. Carman helped facilitate early field studies. The
authors thank R. Brennan, D. Roberts, E. McCulloch, Y. Meng, A. Rivera,
C. Elkins, H. Graber, R. Turner, D. Crawford, and M. Oleksiak, for technical
assistance. Funding was from the National Science Foundation (Grants DEB1048206 and DEB-1120512 to A.W., Grant EF-0723771 to A.W. and F.G., and
Grant DEB-1048241 to R.B.W.), the National Institutes of Health (R15ES016905-01 to C.D.R.), and the Gulf of Mexico Research Initiative (A.W.,
F.G., and N.W.).
each differentially expressed target using a negative binomial method with
P values adjusted by the Benjamini–Hochberg procedure.
Gill tissues were sampled from all field sites for morphological analysis and
immunohistochemical analysis of CYP1A protein expression. Gill tissues from
the first and second gill arches were sectioned along the longitudinal axis at
a thickness of 4 μm and probed with mAb C10-7 against fish CYP1A (40).
Sections were counterprobed using the Vectastain ABC immunoperoxidase
system (Vector Laboratories), utilizing the ImmPACT Nova RED peroxidase
substrate kit (Vector Laboratories) to visualize the CYP1A protein in red.
Tissue sections were counterstained with Vector Hematoxylin QS (Vector
Laboratories).
F. grandis embryos obtained from parents not exposed to oil (collected
from Cocodrie, LA) were exposed to water samples from the GT and BLB
sites collected subsurface on the dates indicated in Dataset S1. Following
fertilization, 20 embryos were randomly transferred in triplicate to one of
the six field-collected waters (2 field sites × 3 time points) at 3 h postfertilization. Embryos were also exposed to a laboratory control consisting
of artificial 17 parts per thousand (ppt) water. Larvae were sampled at 24
d postfertilization and fixed in Z-Fix solution. Sectioning and staining were
as described in the previous section.
ENVIRONMENTAL
SCIENCES
Analytical chemistry of water, tissue, and sediment samples was performed
to offer detailed characterization of exposure to contaminating oil (data
reported in Dataset S2). Sample dates and locations are summarized in
Dataset S1. All sample extracts were analyzed using GC interfaced to an MS
detector system. Spectral data were processed by Chemstation Software
(Agilent Technologies), and analyte concentrations were calculated based
on the internal standard method.
Genome expression across sites and time was characterized using custom
oligonucleotide microarrays. Genome expression was measured in liver tissues from five replicate individual male fish per site-time treatment (5 biological replicates) hybridized in a loop design, including a dye swap. Data
were lowess-normalized and then mixed model-normalized using linear
mixed models to account for fixed (dye) effects and random (array) effects.
Normalized data were then analyzed using mixed model ANOVA, with
“site” [Grand Terre (GT), Bay St. Louis (BSL), Belle Fontaine Point (BFP),
Bayou La Batre (BLB), Mobile Bay (MB), and Fort Morgan (FMA)] and
“sampling time” (sampling trips 1, 2, and 3) (Dataset S1) as main effects,
including an interaction (site-by-time) term. The false discovery rate was
estimated using Q-value (35). Principal components analysis was performed
using MeV (36). GO enrichment was tested using DAVID (37).
For RNAseq, transcript abundance was compared between liver mRNA
from three replicate fish (RNA was not pooled) from the GT site from June 28,
2010, and mRNA from two control samples. All RNA samples were sequenced
on the Illumina Gene Analyzer platform (Expression Analysis, Inc.). Following
quality control filtering, quantitative transcript abundance analysis was
performed by mapping sequence reads to target sequences (6,810 unique F.
heteroclitus target EST sequences, Dataset S5) using the Bowtie short read
alignment software (38). A custom Perl script determined the number of
fragments mapped to each target sequence. The Bioconductor package
DESeq (version 2.8) (39) was used to determine the statistical significance of
BP Sees a Return to Grandeur as Gulf Fishermen Reel From Disaster | Bridge The Gulf Project
4/30/12 10:38 PM
BP Sees a Return to Grandeur as Gulf Fishermen Reel From Disaster
Fri, 2012-04-27 13:22
The second memorial of the nation’s worst oil catastrophe has come and gone, forever linked to Earth Day and seared into the psyches
of millions of Gulf residents and fishermen. In recent weeks, the media has unleashed a torrent of stories about the devastating
impacts (http://www.southernstudies.org/2012/04/troubled-waters-gulf-communities-still-reeling-two-years-into-bp-disaster.html) of the nation’s worst oil spill
disaster; deaths, disease and deformities in the fisheries (http://www.tampabay.com/news/environment/water/oil-from-deepwater-horizon-spill-stillcausing-damage-in-gulf-2-years/1225134) ; a two-year record-setting die off (http://www.pbs.org/newshour/rundown/2012/04/baby-dolphin-die-offscontinue-in-the-gulf.html ) in dolphin populations; medical emergencies and family health crises in coastal communities
(http://www.huffingtonpost.com/2012/04/20/gulf-oil-spill-anniversary-children_n_1438959.html) ; and ongoing Congressional wrangling over tens of
millions of dollars in fines needed to save and rebuild (http://www.nola.com/opinions/index.ssf/2012/03/resolve_to_pass_the_restore_ac.html) the
rapidly disappearing Gulf coast.
But it won’t be long before these stories fade from the consciousness of a nation once riveted to the volcanic well spewing out
Louisiana crude a mile below the sea. Instead we will see more stories like this one BP published in the Alabama Press-Register last
week: After Two Years, The Grandeur of the Gulf is Returning. (http://blog.al.com/press-registercommentary/2012/04/bp_exec_after_two_years_grande.html)
"These days, we don’t see oily sheens and miles of orange containment boom; we see sparkling water and clean sand, dotted
with deck chairs and beach towels. On the horizon, we don’t see an armada of ships skimming oil; we see fishing vessels at work
gathering the day’s catch. And, in the skies and on the ground, we don’t see planes and large cleanup crews; we see birds and
other wildlife at play.
"But one thing is clear: Many of the dire predictions for the Gulf, made in the days and weeks after the accident, have not turned
out to be true. Indeed, after two years of hard work alongside local, state and federal officials, the scientific community and the
people of the region, substantial progress has been made. And the grandeur of the Gulf is steadily returning."
You can expect the media and the airwaves to be clogged with happy talk about the Gulf in the months ahead. We all wish it were true,
but the facts—and perceptions of those toiling in the fisheries—just don't support it. After reading BP’s latest polemic, veteran Alaska
marine toxicologist and author Riki Ott (http://www.rikiott.com/) remembered Exxon's tactics after the Valdez disaster in an email this
week;
"Reminds me of when the state of Alaska officials under gag order NOT to speak about impacts complained about Exxon's
conclusion that just b/c wildlife are "cavorting" on beaches, everything is "hunky-dory" in Prince William Sound. Exxon flew in 3 or
4 British "scientists" (biostitutes) to fly-over Prince William Sound and visit beaches in spring 1990. Exxon sponsored them to go
to our state capital and tell all 60 legislators about the "remarkable recovery of Prince William Sound.'"
BP is sponsoring a $500 million scientific initiative (http://www.gulfresearchinitiative.org/) to investigate the impacts of the
Gulf oil catastrophe, but it's way too early to be waving the victory flag. Fishermen in the Gulf of Mexico still are reeling after the
Deepwater Horizon blew up, and "grandeur" isn't exactly the term they're using down there. Here's how Louisiana's Plaquemines Parish
charter fisherman Ryan Lambert described it in the Times Picayunne (http://www.nola.com/news/gulf-oilspill/index.ssf/2012/04/fishing_guides_say_their_busin.html) this week:
"'The oil may have stopped flowing, but the spill still goes on down here every day,' said Lambert, owner and operator of Cajun
Fishing Adventures, a sprawling lodge and charter business in Buras. 'My fishing business is still down 50 to 60 percent, we're
still finding oil and tar balls on the beach and in the marsh, people still think the fish are polluted (http://www.nola.com/news/gulf-oilspill/index.ssf/2012/04/2_years_after_gulf_oil_spill_l_1.html) , and now we can't find speckled trout in nearly the numbers we had before the
spill. So don't tell me the disaster is over. Maybe for BP it is. Maybe for the oil business people it is. But for me and other charter
businesses, it's never stopped.'"
Shrimp with no eyes and tumors caught in Barataria Bay, April, 2012.
http://bridgethegulfproject.org/node/633
Photo by Xuan Chen
Page 1 of 4
4/30/12 10:38 PM
So which is it, gasping for air or returning to grandeur? My bet is most people outside the Gulf will believe the latter as BP continues its
PR assault on the national airwaves. Although thousands of fishermen are struggling, their stories are easily overpowered by
the massive economic and political forces aligned against them. This Sen. David Vitter (R-LA) missive to his constituents pretty much
sums up where the Powers That Be stand in the Gulf:
"The good news is that I don’t think anyone would have predicted that the Gulf would have rebounded to where it is today. That
goes for our tourism industry, which is thriving, and of course our Gulf seafood, which is as safe and delicious as ever…”
Yes, that's the same seafood that NRDC's Miriam Rotkin-Ellman and Gina Solomon reported on in Environmental Health
Perspectives (http://ehp03.niehs.nih.gov/article/info%3Adoi%2F10.1289%2Fehp.1104539R) last year; the very same seafood that can expose
vulnerable populations like children and pregnant women to up to 10,000 times the allowable levels of cancer-causing polycyclic
aromatic hydrocarbons (PAHs). You can read Miriam's excellent Gulf health update here
(http://switchboard.nrdc.org/blogs/mrotkinellman/bp_oil_disaster_two_years_late.html) .
Of course, it's no surprise that Sen. Vitter ignores this. The oil and gas industry ranks numero uno on his list of top campaign
contributors (http://www.opensecrets.org/politicians/summary.php?cid=n00009659#ind) . Most of his colleagues down there float in the same
boat. But that's not the boat Gulf fishermen are working in, the ones pulling up tumor and oil-encrusted shrimp
(http://www.aljazeera.com/indepth/features/2012/04/201241682318260912.html) . It's not the reality residents who still witness record numbers of
dolphins (http://www.pbs.org/newshour/rundown/2012/04/baby-dolphin-die-offs-continue-in-the-gulf.html) washing ashore on beaches littered with tar
balls after every storm.
As the carnage continues, BP and other oil industry behemoths are busier than ever drilling in the Gulf, dragging their mammoth
platforms into deeper water where they can jam cement-reinforced pipelines into even more remote high-pressure subsea oil and gas
deposits. Oil slicks are routinely reported (http://onwingsofcare.org/protection-a-preservation/gulf-of-mexico-oil-spill-2010/gulf-2012/244-shell-macondomars-ursa-green-canyon-gulf-oil-wings-care.html) in drilling areas offshore. Can the disaster happen again?
You bet. Lured by the high prices and increased global demand, more and more rigs are at work, while Congress still has passed no
new drilling safety laws. NRDC's David Pettit reports (http://switchboard.nrdc.org/blogs/dpettit/what_if_another_bp_deepwater_h.html) that while
some new federal drilling regulations are in place, we may be setting ourselves up for a "repeat performance." For instance, in
an Orlando Sentinel article, (http://articles.orlandosentinel.com/2012-04-17/news/os-bp-gulf-spill-anniversary-20120414_1_deepwater-horizon-rig-gulf-ofmexico-oil-bob-dudley) Pettit questioned why the industry doesn't redesign the critical blowout preventers that failed in the first place.
"You can have 10 of them, and if they are all subject to being jammed by a pipe doing something that we know actually
happened [once before], then you're not much safer."
Requiring twice as many safety mechanisms that failed once before doesn’t exactly inspire confidence. But that’s life in our oil-addicted,
rush-to-drill world. Our country’s energy policy is stuck on dig, extricate and burn. The petrochemical industry will become more
dangerous as we rush to extract harder to find deposits of crude in places like the harsh environs of the Arctic, where Shell Oil plans
(http://switchboard.nrdc.org/blogs/rkistner/arctic_oil_drilling_threatens.html) to start drilling this summer. Have we learned nothing from the historic BP
blowout?
But there is some good news; disasters can change attitudes, even in the oil-dominated Gulf of Mexico. The BP debacle blew a hole
through Gulf residents' confidence in the oil industry and changed perspectives on the importance of environmental
protections, according to a recent University of New Hampshire study reported in Science Daily
(http://www.sciencedaily.com/releases/2012/04/120412105227.htm) :
"If disasters teach any lessons, then experience with the Gulf oil spill might be expected to alter opinions about the need for
environmental protection. About one-fourth of our respondents said that as a result of the spill, their views on other environmental
issues such as global warming or protecting wildlife had changed," said Lawrence Hamilton, professor of sociology at the
University of New Hampshire. This proportion rose to 35 percent among those most affected economically by the spill. People
reporting changed views also expressed greater concern about sea level rise due to climate change, more support for a
moratorium on deepwater drilling, and were more likely to favor alternative energy rather than increased oil exploration."
Shrimp with tumors bought in New Orleans grocery store. Photo by Mac MacKenzie.
Changes in attitudes can lead to positive change in the oil patch. But there's also another way to get Big Oil to change its behavior: jail
Page 2 of 4
4/30/12 10:38 PM
time. Oil industry experts say fines and penalties do little to reduce reckless decisions and risky policies that threaten the lives and
livelihoods of fishermen and workers. Pro Publica reporter Abrahm Lustgarten (http://www.propublica.org/site/author/Abrahm_Lustgarten/) laid it
all out succinctly in this New York Times op-ed (http://www.nytimes.com/2012/04/20/opinion/a-stain-that-wont-wash-away.html?
_r=1&partner=rss&emc=rss) :
"What the gulf spill has taught us is that no matter how bad the disaster (and the environmental impact), the potential
consequences have never been large enough to dissuade BP from placing profits ahead of prudence. That might change if a real
person was forced to take responsibility — or if the government brought down one of the biggest hammers
(http://www.propublica.org/article/epa-officials-weighing-sanctions-against-bps-us-operations) [4] in its arsenal and banned the company from
future federal oil leases and permits altogether. Fines just don't matter."
Although low-level prosecutions (http://www.usatoday.com/money/industries/energy/story/2012-04-24/bp-oil-spill-arrest-justice-department/54504158/1)
have just been announced, nobody expects many BP execs to get rolled up by the Department of Justice anytime soon. But this
catastrophe also wasn’t an ordinary industrial accident. It coated the coasts of four states with oil and has damaged the lives of
countless residents. Some fishermen in the Gulf say they still relish the thought of former BP CEO Tony Hayward
(http://www.usatoday.com/money/industries/energy/story/2012-04-24/bp-oil-spill-arrest-justice-department/54504158/1) caught in the wheels of American
justice, a fanciful dream as they struggle to rebuild their lives.
While BP fights a long war in the courts, the battle over public opinion remains hot and intense. Despite the million dollar ad
campaigns and the political rhetoric about the Gulf returning to normal, many—especially in the fishing community—are facing a new
reality. Their lives on the water have changed in ways no one comprehends, in ways many fear will never be the same.
The grandeur of the Gulf will return. But it’s up to people along its shores—and to all of us—to decide what kind of future lies in store; a
marine environment stressed by an oil-soaked sea or a healthy ocean preserved for distant generations. The critical decisions are still
to come.
Rocky Kistner
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missed a fortnight which has a painful appropriate foot.
Page 4 of 4
PLoS ONE: Response of Coastal Fishes to the Gulf of Mexico O...
http://www.plosone.org/article/info:doi/10.1371/journal.pone.0...
RESEARCH ARTICLE
Response of Coastal Fishes to the Gulf of Mexico Oil
Disaster
F. Joel Fodrie1*, Kenneth L. Heck Jr.2
1 Institute of Marine Sciences and Department of Marine Sciences, University of North Carolina at
Chapel Hill, Morehead City, North Carolina, United States of America, 2 Dauphin Island Sea Lab and
Department of Marine Sciences, University of South Alabama, Dauphin Island, Alabama, United
States of America
Abstract
The ecosystem-level impacts of the Deepwater Horizon disaster have been largely unpredictable
due to the unique setting and magnitude of this spill. We used a five-year (2006–2010) data set
within the oil-affected region to explore acute consequences for early-stage survival of fish
species inhabiting seagrass nursery habitat. Although many of these species spawned during
spring-summer, and produced larvae vulnerable to oil-polluted water, overall and speciesby-species catch rates were high in 2010 after the spill (1,989±220 fishes km-towed−1 [µ ±
1SE]) relative to the previous four years (1,080±43 fishes km-towed−1). Also, several exploited
species were characterized by notably higher juvenile catch rates during 2010 following
large-scale fisheries closures in the northern Gulf, although overall statistical results for the
effects of fishery closures on assemblage-wide CPUE data were ambiguous. We conclude that
immediate, catastrophic losses of 2010 cohorts were largely avoided, and that no shifts in
species composition occurred following the spill. The potential long-term impacts facing fishes as
a result of chronic exposure and delayed, indirect effects now require attention.
Citation: Fodrie FJ, Heck KL Jr (2011) Response of Coastal Fishes to the Gulf of Mexico Oil Disaster. PLoS ONE 6(7):
e21609. doi:10.1371/journal.pone.0021609
Editor: Steven J. Bograd, National Oceanic and Atmospheric Administration/National Marine Fisheries Service/Southwest
Fisheries Science Center, United States of America
Received: March 7, 2011; Accepted: June 2, 2011; Published: July 6, 2011
Copyright: © 2011 Fodrie, Heck. This is an open-access article distributed under the terms of the Creative Commons
Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original
author and source are credited.
Funding: The authors acknowledge support from the National Marine Fisheries Service, National Oceanic and
Atmospheric Administration Marine Fisheries Initiative and Northern Gulf Institute. The funders had no role in study
design, data collection and analyses, decision to publish, or preparation of the manuscript.
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Competing interests: The authors have declared that no competing interests exist.
* E-mail: jfodrie@unc.edu
I NTRODUCTION
Prevailing models of ecological impacts resulting from oil pollution are being revised after the April
2010 release of ~4.4 million barrels [1] of oil into the northern Gulf of Mexico (GOM). In part, this is
a legacy of the Exxon Valdez accident as a watershed environmental catastrophe, and the extensive
research on acute and chronic impacts of the resulting inshore oil pollution [2]. Unlike the 0.25–0.5
million barrels released by the Valdez [2], however, the Deepwater Horizon (DH) disaster
hemorrhaged oil into the open ocean at 1500 m depth over a protracted 84-day period [1]. As a
critical step toward new model development applicable for detecting impacts of the DH spill,
rigorous observational data at organismal through community levels are needed to guide
ecosystem-based toxicology.
We have already learned that a significant fraction of the oil released into the GOM from the
Macondo well did not rise to the surface, and this has implications for the ecosystem-level responses
we should anticipate. Rather, oil was emulsified at the well head due to turbulent mixing, reduced
buoyancy at depth, and addition of Corexit 9500 dispersant. Subsequently, mid-water hydrocarbon
plumes [3] have been observed with stimulation of petroleum-degrading bacteria [4]. With this now
understood, we revisit some early concerns regarding impacts for nearshore fisheries [5].
During the DH spill, near-surface waters lacked any reliable refuge from oil pollution, as
slicks/sheens occurred at the immediate surface and oil was emulsified throughout the water
column. For many fishes, including commercially valuable snappers (Lutjanidae) and groupers
(Serranidae), spawning occurs during the spring or summer (table S1), and eggs, larvae and
post-larvae would have relied upon near-surface waters overlaying the continental shelf during the
DH spill [6]–[7]. Furthermore, eggs/larvae and oil can be transported by the same hydrodynamic
and atmospheric processes, enhancing the probability of oil encounters for many species. Because
the population ecology of marine species with bipartite life histories is disproportionately affected by
the health and survival of early life stages [8], understanding how eggs, larvae and newly-settled
juveniles coped with the DH spill is essential for quantifying ecosystem responses.
We hypothesized that the strength of juvenile cohorts spawned on the northern GOM continental
shelf during May–September 2010 in the northern GOM would be negatively affected by
egg/larval-oil interactions. Oiled seawater contains toxic compounds such as polycyclic aromatic
hydrocarbons (PAHs) which, even after weathering, can result in genetic damage, physical
deformities and altered developmental timing for fish eggs/larvae [9]–[10]. These effects may be
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induced at very low (~1 ppb PAHs) levels of exposure when persistent over days to weeks
[11]–[12] - timescales relevant for larval development and descriptive of the DH spill. Additionally,
emulsified oil droplets could mechanically damage the feeding and breathing apparatus of relatively
fragile larvae and further decrease individual fitness. Unfortunately, observing egg/larval mortality,
growth or migration in situ is an enduring challenge for biological oceanographers, as eggs/larvae
are simply too dilute and experience relatively high instantaneous mortality, even in undisturbed
systems [13].
In the absence of direct observations on eggs and larvae, juvenile abundance data provide valuable
indices of the acute, population-level responses of young fishes to the spill. Although indirect
evidence [14], early juvenile abundances are the integrated products of early life-history processes
such as fertilization, larval growth/mortality, and settlement [6]–[8]. Therefore, effects of oil
pollution on early life stages should be detectable in time series data as shifts in the abundance of
recently settled juvenile fishes. We tested these predictions using 2006–2010 survey data collected
from the Chandeleur Islands, LA, to Saint Joseph Bay, FL (Fig. 1), representing most of the
nearshore region directly impacted by oil. In contrast to the difficulties of surveying marine larvae,
quantitative measures of juvenile abundances are tractable due to the tendency of settled fish to
aggregate in specialized nursery habitats [15]. In the northern GOM, many fish species, such as
those in the drum (Sciaenidae), snapper and grouper families have juveniles that are routinely
collected from shallow-water seagrass meadows they use as primary nurseries [16].
Figure 1. Sampling region and study sites.
Map of juvenile fish sampling stations, divided among four survey areas: Chandeleur
Islands (blue circles), Gulf Islands (green circles), Grand Bay (orange circles) and
Florida Bays (red circles). 1. Chandeleur Is., LA; 2. Ship Is., MS; 3. Horn Is., MS; 4. Petit Bois
Is., MS; 5. Dauphin Is., AL; 6. Grand Bature Shoal, AL; 7. Point Aux Pines, AL, 8. Big Lagoon, FL;
9. Pensacola Bay, FL; 10. Choctawhatchee Bay, FL; 11. St. Andrew Sound, FL; 12. St. Joseph
Bay, FL. The spread of surface oil during the 84-day spill is also shown (brown shading). Image
at lower right shows juvenile gray snapper (L. griseus), spotted seatrout (C. nebulosus) and
pipefish (Syngnathus spp.).
doi:10.1371/journal.pone.0021609.g001
Our dataset consisted of 853 individual trawl samples taken between July 15 and October 31 of
2006–2010 within seagrass meadows of the northern GOM (tables S2, S3). We collected 167,740
individual fishes representing 86 taxa, and examined catch-per-unit-effort (CPUE) data for all
species pooled together, as well as separately for each of the 20 most abundant species. We also
tested for post-spill community-level shifts in seagrass-associated fish assemblages using
multivariate analyses [17]. We recognized that not all species were at equal risk for oil exposure
due to variation in spawning timing and larval distributions (tables S1, S4). Furthermore, some
species may have experienced release from fishing pressure due to large-scale fishery closures [18]
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during the spill (table S5), perhaps enhancing their larval production during the summer spawning
season. Therefore, we also considered how these factors affected species-specific CPUEs during
2010. In all analyses, comparisons among years were considered as a proxy for the effects of oil
disturbance (2006–2009 as undisturbed, 2010 as disturbed).
R ESULTS
Within the oil-affected GOM, a five-year survey of seagrass-associated fish communities did not
indicate reductions in juvenile abundances following the spill. Rather, of the twenty most commonly
collected fish species, twelve were characterized by statistically higher catch rates in 2010 relative
to 2006–20009 (α = 0.05; Table 1). Among the remaining eight taxa, pre- and post-spill catch rates
were statistically indistinguishable. Across our entire study region, CPUE increased from 1,080±43
fishes km-towed−1 (µ ± 1SE) during 2006–2009 to 1,989±220 fishes km-towed−1 in 2010. When
resolved among four geographical areas (Chandeleur Islands, Gulf Islands, Grand Bay, Florida Bays;
Fig. 1), overall catch rates of juvenile fishes, as well as CPUE of the most abundant species, pinfish
(Lagodon rhomboides), were consistently higher during 2010 than in 2008 or 2009, and in some
areas were higher in 2010 than all previous years (Fig. 2A–B; fig. S1, S2, S3; table S6).
Figure 2. Catch rates of juvenile fishes, 2006–2010.
Catch rates among years and sampling areas (Chandeleur Islands, Gulf Islands,
Grand Bay and Florida Bays) for: (A) all fishes pooled; (B) pinfish (L. rhomboides),
(C) gray snapper (L. griseus), and (D) spotted seatrout (C. nebulosus). CPUE data in panels B–D
are presented on a log scale, and all data are shown as means of trawl samples (µ + 1SE).
Table 1. Relative frequencies and CPUE data for abundant fishes collected
during sampling in seagrass meadows of the northern GOM.
doi:10.1371/journal.pone.0021609.t001
The species composition of juvenile fish assemblages was unaltered in each sampling area during
the months following the DH disaster (Fig. 3). Prior to the spill, similarities among individual trawl
samples (SIMPER) ranged from 50.3% at the Chandeleur Islands to 52.9% within Florida Bays
(table S7). By comparison, similarity percentages between pre- (2006–2009) and post-spill (2010)
samples were correspondingly high, ranging from 43.4% within Grand Bay to 50.8% at the
Chandeleur Islands. Furthermore, pinfish, silver perch (Bairdiella chyrsoura), mojarras
(Eucinostomus spp.), pigfish (Orthopristis chrysoptera) and spotted seatrout (Cynoscion nebulosus)
drove similarity patterns both before and after the spill (table S7). Species richness (S, up 15%,
p<0.001), diversity (ES(20), up 11%, p = 0.006; H′, up 18%, p<0.001) and evenness (J′, up 11%,
p = 0.003) among trawl samples were all slightly elevated during 2010 relative to 2006–2009
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averages (table S8; fig. S4), indicating that high CPUEs in 2010 were broad based.
Figure 3. Community composition of seagrass-associated fish communities,
2006–2010.
Multi-dimensional scaling plots for seagrass-associated fish communities prior to
(2006–2009; colored symbols) and following (2010; black circles) the DH spill. Data for (A)
Chandeleur Islands, (B) Gulf Islands, (C) Grand Bay and (D) Florida Bays are presented
separately. Each datum represents a single trawl sample.
When averaged across species, there was little statistical evidence that either exposure risk or
release from fishing pressure significantly affected CPUEs during 2010. When comparing 2010 CPUE
data against pre-spill catch rates, we did observe that fishes characterized by moderate (spring
spawning, nearshore larvae) or high risk (spring-summer spawning, larvae distributed across the
continental shelf) exhibited decreases in CPUE following the spill at the Chandeleur Islands and
Grand Bay (Fig. 4A). However, no statistically significant differences were found as a function of
egg/larval risk (F4,848 = 1.410, p = 0.242) or sampling areas (F3,849 = 0.999, p = 0.440; table S9).
Similarly, release from fishing pressure on spawning fishes could be implicated, although not
proven, as a determinant of post-spill CPUEs. Along the Chandeleur and Gulf Islands, increases in
catch rates during 2010 relative to 2006–2009 were 800% and 950% higher, respectively (Fig. 4B),
for species identified in state and federal management plans than for species not harvested by
fishermen (table S5). No similar patterns were recorded within Grand Bay or Florida Bays, however,
and effects of fishing pressure (F1,851 = 1.510, p = 0.223) and area (F3,849 = 1.397, p = 0.225) on
CPUE responses were not significant.
Figure 4. Larval risk and fishery closure impacts.
Effects of (A) egg/larval vulnerability and (B) harvest pressure on the responses of
fishes to the DH spill. Response of individual species calculated as the ratio of 2010
versus 2006–2009 CPUE data. Data are presented on a log scale as group means (µ
+ 1SE), with ratios >1 indicating that 2010 catch rates were elevated relative to 2006–2010
data.
D ISCUSSION
Collectively, no significant, acute impacts on the strength of juvenile cohorts within seagrass
habitats were detected following the DH disaster. This was true for all species examined, bolstering
our confidence in the conclusion that ecosystem-level injuries were not severe for this community of
fishes. Unfortunately, our assessment cannot be compared to the most analogous spill, the IXTOC 1
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blowout [5], due to a paucity of formal scientific investigation following that accident (The 1979
IXTOC I blowout at 3600 m depth, 80-km north of the Yucatán Peninsula, was a ~3.5-million-barrel
spill.). The most parsimonious explanation for our data is that these fishes were resilient to the spill,
possibly due to the retention of a large proportion of spilled oil at depth. As such, these data add to
a developing literature [3]–[4] in which the acute impacts of the spill may be concentrated in the
deep ocean rather than shallow-water, coastal ecosystems that were the focus of early concern [5].
For instance, gray snapper (Lutjanus griseus) larvae were abundant in surface waters (0–25-m
deep) over the continental shelf from July through September [19], and were among the most likely
individuals to have contacted oil-polluted water. Still, catch rates of gray snapper juveniles following
the spill were high relative to the four previous years (up 82%, Fig. 2C; area * pre/post spill context
interaction p<0.001, table S6).
When averaged across species - and in some cases across species with vastly different life histories
- there were no statistically significant differences in the response of fished or unfished species to
the spill (or their responses to subsequent management actions; i.e., fishery closures). Still, there
were notable patterns suggesting that certain species may have been released from harvest
pressure during 2010, subsequently enhancing spawning activity and post-spill cohort sizes despite
any potentially negative oil impacts. For example, spotted seatrout spawn during summer [20], but
many mature individuals are typically removed by recreational fishers before reproducing. Following
the fishery closures in 2010, we recorded order-of-magnitude higher juvenile abundances of spotted
seatrout at the Chandeleur and Gulf Islands, as well as elevated catch rates throughout our survey
region (Fig. 2D; area, pre/post spill context and 2-way interaction p<0.001, table S6).
Consistent with the patterns observed in the species-by-species catch data and analyses of ‘risk” or
‘fishing” effects, there were no significant post-spill shifts in community composition and structure,
nor were there changes in any of several biodiversity measures. While natural recruitment
variability can make it difficult to detect population-level impacts for any one species following
large-scale disturbance [14], our whole-community analyses and results are likely robust against
these concerns.
Several other factors could have contributed to the high catch rates of seagrass-associated fishes in
2010 despite large-scale oil pollution. For instance, fishes may be uniquely buffered against oil
pollution due to their mobility or foraging ecology [21]–[22]. Also, the major predators of fish
eggs/larvae (e.g., gelatinous zooplankton) may have been impacted by the spill, thereby reducing
natural mortality rates for coastal fishes [23]. Regardless of the mechanism(s) involved, thus far the
potential for 2010 cohorts to support regional fisheries over the next several years has persisted
despite the spill. This information is critical for projecting the mode and tempo of ecological and
economic recovery in the oil-affected GOM, as well as guiding future conservation/restoration
activities to mitigate oil-spill injuries.
While these data provide reason for early optimism, attention should now turn to possible chronic
effects of oil exposure on fishes as well as delayed indirect effects cascading through the post-spill
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GOM. Fish may suffer growth, survival or reproductive penalties years after exposure to oil [24], or
experience altered migratory behaviors [25]. Oil sequestered in sediments may also affect species
laying benthic eggs for several years [26]. More broadly, ecosystems experiencing large-scale
disturbance can carry or build instabilities over protracted periods that can eventually result in
delayed collapses of fisheries stocks [27].
Improved threat assessment for energy exploration [28] and process-oriented studies of ecosystem
responses will be long-term initiatives resulting from the DH spill. In the short term, however,
observational data collected over relevant spatial and temporal scales are invaluable for guiding and
evaluating targeted studies of oil toxicology [29]. For fish species experiencing multiple stressors
such as habitat degradation [30] harvest pressure [31], climate change [16], and now oil pollution,
rigorous baseline survey data and new syntheses are needed to enact effective ecosystem-based
management.
M ATERIALS
AND
M ETHODS
Sampling
We analyzed changes in northern Gulf of Mexico (GOM) seagrass-associated fish communities during
the last 5 years by comparing survey data obtained either prior to (2006–2009) or immediately
following the Deepwater Horizon disaster (2010). The survey region extended approximately 340
km along the coastline, including a significant portion of the inshore area most affected by oil (Fig.
1.). Each year, we made repeated sampling trips to 12 sites, extending from the Chandeleur
Islands, LA, to St. Joseph Bay, FL (29.68–30.72°N, 85.30–89.10°W). Sampling occurred within
mixed seagrass meadows that serve as primary nursery habitat for many juvenile fishes that have
recently settled from the water column following a 5–45 day larval period [6], [16]. Our samples
were collected from seagrass mosaics that included turtle grass (Thalassia testudinum), shoal grass
(Halodule wrightii), widgeon grass (Ruppia maritima), and manatee grass (Syringodium filiforme),
along with scattered unvegetated patches (table S3).
During each year, trawls were conducted from July 15 through October 31 in order to record the
abundances and composition of fishes during the period when seagrass meadows are utilized as
primary nurseries by recently settled juveniles (refer to table S1 for reproductive seasons of
common fishes). Fishes were collected using a 5-m otter trawl (2.0-cm body mesh; 0.6-cm bag
mesh; 0.3×0.7-m doors) with conventional 4-seam balloon design including float and lead lines but
without tickler chains. Trawls consisted of 3.9±0.1 (µ ± 1SE) minute tows behind small (<7 m)
research vessels traveling at 3.3+0.1 kilometers hour−1. Overall, 853 samples were taken (table
S2), and the trawl covered a linear distance of 184.7 kilometers during our sampling. These trawls
occurred in depths of 0.5–2.5-m, and were conducted during daylight hours. During our surveys,
species were enumerated in the field unless species-level identifications were not easily made.
Unidentified specimens were transported to the lab where meristics were used by at least two
different technicians to identify each individual. In cases in which species could not be identified,
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specimens were classified to the lowest taxonomic level possible. Typically, fishes were 20–100 mm
long (standard length), indicative of recently-spawned juveniles. However, we did not record
individual sizes for all species, and, for pipefishes (Syngnathus spp.) and hard-headed catfish (Arius
felis), we did observe that a small proportion of our catch included reproductive adults. For four
species: gray snapper (50.5±0.8 mm [µ ± SE]), lane snapper (Lutjanus synagris; 55.7±0.7 mm),
spotted seatrout (60.8±1.1 mm) and gag grouper (Mycteroperca microlepis; 157.5±3.2 mm); we
did record the sizes of all individuals throughout our surveys. Based on our own otolith analyses
(Fodrie unpublished) and published reports of first-year growth among these four species (age-1
sizes: gray snapper ~109 mm; lane snapper ~140 mm; spotted seatrout ~127 mm; gag grouper
>198 mm), we calculated that >96% of individuals were captured in the same year as they were
spawned (including 2010).
Once enumerated, fishes were entered in to an Excel database, and abundance data were converted
into catch-per-unit-effort (CPUE) data based on the linear distance over with each trawl occurred.
All statistical analyses were applied to these CPUE data. Our complete CPUE dataset is included as a
separate appendix in our supporting information (dataset S1). This study was carried out in strict
accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of
the National Institutes of Health. Our sampling protocol was approved by the Committee on the
Ethics of Animal Experiments of the University of North Carolina at Chapel Hill (Permit Number:
10-114.0).
Statistical analyses
We investigated differences in the catch rates of seagrass-associated fishes (all species pooled as
well as the 20 most abundant species individually) by unpaired t-tests comparing pre- (2006–2010)
and post-spill (2010) data (Table 1), as well as 2-way ANOVAs in which sampling area (Chandeleur
Islands, Gulf Islands, Grand Bay, Florida Bays) and context (preversus post-spill) were fixed
factors (table S6). Regions were defined by basic geomorphology and location, local water clarity,
local salinity, and local seagrass composition [32]. Because variances were stable among groups, no
data transformations were required prior to analyses.
We analyzed similarities and differences in fish communities among years (2006–2009 versus 2010)
within each sampling area (each area considered separately) using non-metric multidimensional
scaling (MDS), based on Bray-Curtis similarity indices among all individual trawl samples (4th
root-transformed data). Pairwise comparisons between trawl samples across years were conducted
with analysis of similarity (ANOSIM) and similarity (or dissimilarity) percentages (SIMPER) using
PRIMER 5.2.2 software (PRIMER-E Ltd; [33]).
We also examined patterns of species diversity among regions and years by computing the following
measures for each trawl sample: S, number of species collected; ES(20), species richness rarefied to
20 individuals; H′, Shannon-Weiner diversity index (loge); and J′, Pielou's evenness measure
(PRIMER 5.2.2 software). We investigated differences in community diversity via 2-way ANOVAs in
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which sampling area (Chandeleur Islands, Gulf Islands, Grand Bay, Florida Bays) and context (preversus post-spill) were fixed factors. Because variances were stable among groups, no data
transformations were required prior to analyses.
These approaches are proscribed in earlier syntheses for detecting environmental impacts [17].
Critiques of employing parametric testing to detect ecosystem injury exist due to interannual
variability and reduced statistical power [14], although those concerns have focused on analyses
involving single species.
We determined the relative probability for oil-larvae encounters (‘risk’) for the twenty most
commonly captured fishes, and used these data to explore how individual species responded
differently to large-scale oil pollution in the northern GOM. Information on the seasonal timing of
spawning and distribution of larvae from shore to the outer margin of the continental shelf was
collected from the literature (tables S1 and S4), and used to define 4 levels of risk (in addition to an
‘unknown’ [n = 4] category containing species for which no data were available). ‘Low’ risk species
(n = 6) included those in which larvae remained inside estuaries, either in the plankton or as
benthic egg masses, regardless of spawning season. ‘Moderate-Low’ risk species (n = 4) were
defined by having either 1) larvae distributed in estuaries as well as across the continental shelf, or
2) larvae distributed across the continental shelf, but not likely during the spill period (i.e., April–
September). Only two ‘Moderate’ risk species were identified: pigfish (Orthopristis chrysoptera)
spawn throughout summer, and have larvae distributed within nearshore waters; while flounder
(Paralichthys spp.) have larvae distributed across the continental shelf, with a protracted spawning
that extends into April or May (i.e., some overlap with the oil spill). ‘High’ risk species (n = 4)
spawn offshore and have larvae distributed across the continental shelf. Furthermore, spawning
data indicates that these species would have produced larvae sometime during the DH spill
(April–Sept in our classification scheme). Based on these risk guilds, we examined the response of
fishes to the spill by calculating the ratio of 2010 CPUE data (averaged) to 2006–2009 CPUE data
(averaged) for each species. Following these calculations, ratios >1 indicate that average 2010
catch rates were higher than during the previous 4 years, while ratios <1 indicate that average
2010 catch rates were lower than during the previous 4 years. Using each species as a replicate
measure, we used ‘risk’ and region (Chandeleur Islands, Gulf Islands, Grand Bay, Florida Bays) as
fixed factors in a 2-way ANOVA that compared 2010 CPUE: 2006–2009 CPUE trends. Because
variances were stable among groups, no data transformations were required prior to analyses.
Similarly, we determined whether species were likely to have experienced significant release from
harvest pressure following large-scale closures in the northern GOM, and examined how this may
have affected CPUE data in 2010. For each of the twenty most commonly caught fish, we designated
species as ‘fished’ if they were included in any state or federal management plan as of Dec 31, 2010
(table S5), or identified as <1% (by biomass) of bycatch in shrimp trawl fisheries within the
northern GOM (table S5). As before, we examined the response of fishes to the spill by calculating
the ratio of 2010 CPUE data (averaged) to 2006–2009 CPUE data (averaged) for each species. Using
each species as a replicate measure, we used ‘fishing pressure’ (with fished species including
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species that are targeted or captured as incidental bycatch at significant levels) and region
(Chandeleur Islands, Gulf Islands, Grand Bay, Florida Bays) as fixed factors in a 2-way ANOVA that
compared 2010 CPUE: 2006–2009 CPUE trends. Because variances were stable among groups, no
data transformations were required prior to analyses.
All univariate tests were conducted using StatView 5.0.1 software (SAS Institute Inc.). Because
each statistical analysis applied to separate and easily distinguishable hypotheses, we made no
corrections to experiment-wise alpha for any of the univariate (total fishes CPUE, individual fishes
CPUE, risk guilds, harvest guilds, diversity) or multivariate (ANOSIM) tests we conducted [34].
S UPPORTING I NFORMATION
Figure S1.
Catch rates of all fishes, pooled together, among sampling areas prior to (2006–2009) and following
(2010) the Deepwater Horizon disaster.
(DOCX)
Figure S2.
Catch rates of individual species, among sampling areas prior to (2006–2009) and following (2010)
the Deepwater Horizon disaster. Data are presented for the 20 most abundant species.
(DOCX)
Figure S3.
Catch rates among sampling areas and years for the 20 most abundant species collected during
trawl surveys.
(DOCX)
Figure S4.
Diversity measures for seagrass-associated fish communities within sampling areas affected by the
Deepwater Horizon disaster.
(DOCX)
Table S1.
Summary table for CPUE data (fish kilometer-towed−1) of fishes prior to (2006–2009) and following
(2010) the DH disaster.
(DOCX)
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Table S2.
Distribution of trawl samples among sampling areas (Chandeleur Islands, Gulf Islands, Grand Bay,
Florida Bays) and years (2006–2010).
(DOCX)
Table S3.
Quantitative description of seagrass habitats sampled throughout the northern Gulf of Mexico during
2006–2010.
(DOCX)
Table S4.
Information used to determine the likelihood of larvae contacting oiled water during the summer of
2010.
(DOCX)
Table S5.
Summary table for the management status of the 20 most abundant fishes collected during our
survey program.
(DOCX)
Table S6.
Summary table of the effects of sampling area and year (context: preversus post-spill) on the
catch rates of the 20 most abundant fishes collected during surveys in northern Gulf of Mexico
seagrass meadows.
(DOCX)
Table S7.
Comparisons of community structure between catch data prior to (2006–2009) or immediately
following (2010) the Deepwater Horizon disaster (ANOSIM and SIMPER).
(DOCX)
Table S8.
Summary table of the effects of sampling area and year (context: preversus post-spill) on the
diversity (S, ES(20), H′, and J′) of trawl samples collected within northern Gulf of Mexico seagrass
meadows.
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(DOCX)
Table S9.
Summary table of the effects of sampling area, larval risk and harvest pressure on the change in
catch rates of individual species for pre- (2006–2009) and post-spill (2010) data.
(DOCX)
Dataset S1.
Complete CPUE data obtained for 2006–2009 trawl surveys within seagrass meadows of the
northern Gulf of Mexico.
(XLSX)
A CKNOWLEDGMENTS
We are extremely grateful to the students and technicians who aided in the field, especially C.
Baillie, M. Brodeur, J. Myers, O. Rhoades and S. Williams. B. Raines supplied the image of juvenile
fishes in Fig. 1. Constructive comments and support were provided by S. Powers, C. Peterson, J.
Kenworthy, and 2 anonymous reviewers.
A UTHOR C ONTRIBUTIONS
Conceived and designed the experiments: FJF KLH. Performed the experiments: FJF KLH. Analyzed
the data: FJF. Wrote the paper: FJF KLH.
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All site content, except where otherwise noted, is licensed under a Creative Commons Attribution License.
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Roundtable
Gulf of Mexico Oil Blowout
Increases Risks to Globally
Threatened Species
Claudio Campagna, Frederick T. Short, Beth A. Polidoro, Roger McManus, Bruce B. Collette,
Nicolas J. Pilcher, Yvonne Sadovy de MitchesoN, Simon N. Stuart, and Kent E. Carpenter
Fourteen marine species in the Gulf of Mexico are protected by the US Endangered Species Act, the Marine Mammal Protection Act, and the
Migratory Bird Treaty Act. As the British Petroleum oil spill recovery and remediation proceed, species internationally recognized as having an
elevated risk of extinction should also receive priority for protection and restoration efforts, whether or not they have specific legal protection.
Forty additional marine species—unprotected by any federal laws—occur in the Gulf and are listed as threatened on the International Union
for Conservation of Nature’s (IUCN) Red List. The Red List assessment process scientifically evaluates species’ global status and is therefore a key
mechanism for transboundary impact assessments and for coordinating international conservation action. Environmental impact assessments
conducted for future offshore oil and gas development should incorporate available data on globally threatened species, including species on the
IUCN Red List. This consideration is particularly important because US Natural Resource Damage Assessments may not account for injury to
highly migratory, globally threatened species.
Keywords: IUCN Red List, Gulf of Mexico, oil spill, threatened species
A
primary concern following the British Petroleum
Deepwater Horizon oil well blowout and the resulting
oil pollution in the Gulf of Mexico is the damage to marine
plants and animals—especially to those already considered
vulnerable. Several US federal and state statutes protect
coastal and marine species of special concern found in the
Gulf of Mexico, including 14 marine species protected by
the US Endangered Species Act (ESA), the Marine Mammal
Protection Act, and the Migratory Bird Treaty Act. Additionally, species protected by Mexican and Cuban laws must be
considered.
The International Union for Conservation of Nature
(IUCN) Red List of Threatened Species (IUCN 2010) results
from a rigorous scientific process to assess the relative
extinction risks of species globally, using widely accepted
standards (Mace et al. 2008, Hoffmann et al. 2010). As such,
the IUCN Red List categories and criteria are the most
respected international system for classifying global extinction risk at the species level (De Grammont and Cuarón
2006, Rodrigues et al. 2006, Carpenter et al. 2008, Mace et al.
2008, Schipper et al. 2008). In addition to the 14 species
protected by US law, the IUCN Red List identifies a further
39 species in the Gulf (table 1) as belonging to one of three
threatened categories: critically endangered, endangered, or
vulnerable (IUCN 2001). All species in Red List threatened
categories have an elevated risk of extinction, and we argue
they should receive priority for protection and restoration
efforts in the Gulf, whether or not they have specific legal
protection from any government entity in the region. The
Gulf oil blowout is likely to worsen the threat status of some
of these species as more of the spill’s impacts manifest.
United States law requires restoration to prevent conditions of natural resources damaged by the oil pollution, and
restoration is overseen by NOAA’s (the National Oceanic
and Atmospheric Administration) Natural Resource Damage Assessment (NRDA; NOAA 2010a). The primary legal
authority for assessing damages and providing for recovery of coastal and marine species is the Oil Pollution Act,
which is implemented by the NRDA. Under the Damage
Assessment Remediation and Restoration Program, NRDA
trustees determine whether injury to public trust resources
has occurred, as well as the type, amount, and methods of
restoration needed.
According to the most recent revision of the Mexican list
of threatened and protected species (NOM 2002, 2006), all
marine mammals and marine turtles are accorded some
degree of protection status in Mexico (e.g., classified as in
danger of extinction, as threatened, or under special protection). Other than mammals and turtles, only three species are
protected in Mexico: subspecies of two seabirds present in
BioScience 61: 393–397. ISSN 0006-3568, electronic ISSN 1525-3244. © 2011 by American Institute of Biological Sciences. All rights reserved. Request
permission to photocopy or reproduce article content at the University of California Press’s Rights and Permissions Web site at www.ucpressjournals.com/
reprintinfo.asp. doi:10.1525/bio.2011.61.5.8
www.biosciencemag.org May 2011 / Vol. 61 No. 5 • BioScience 393
Roundtable
Table 1. Marine species in International Union for Conservation of Nature threatened Red List categories (critically
endangered, endangered, or vulnerable) that have a distribution directly overlapping the area of the oil spill, or that are
found in the greater Gulf region extending from Texas to Miami, Florida.
Red List category
species name
Common name
Protection
status
Red List category
species name
Lepidochelys kempii
Kemp’s ridley turtle
ESA-E
Epinephelus flavolimbatus
Yellowedge grouper
Eretmochelys imbricata
Hawksbill turtle
ESA-E
Epinephelus niveatus
Snowy grouper
Dermochelys coriacea
Leatherback turtle
ESA-E
Yellowmouth grouper
Thunnus thynnus
Atlantic bluefin tuna,
western stock
Lachnolaimus maximus
Hogfish
Alopias superciliosus
Bigeye thresher shark
Alopias vulpinus
Common thresher shark
Carcharhinus longimanus
Oceanic whitetip shark
Carcharhinus obscurus
Dusky shark
Carcharhinus plumbeus
Sandbar shark
Carcharhinus signatus
Night shark
Centrophorus granulosus
Gulper shark
Cetorhinus maximus
Basking shark
Carcharodon carcharias
Great white shark
Isurus oxyrinchus
Shortfin mako
Isurus paucus
Longfin mako
Carcharias taurus
Sand tiger shark
Odontaspis ferox
Small-tooth sand tiger shark
Rhincodon typus
Whale shark
Sphyrna zygaena
Smooth hammerhead
Squalus acanthias
Spiny dogfish
Gymnura altavela
Butterfly ray
Scalloped hammerhead shark
Agaricia lamarcki
Lamarck’s sheet coral
Great hammerhead shark
Montastraea franksi
Montastraea coral
Boulder star coral
Dendrogyra cylindrus
Pillar coral
Mountainous star coral
Dichocoenia stokesii
Elliptical star coral
Mycetophyllia ferox
Rough cactus coral
Oculina varicose
Large ivory coral
Halophila baillonii
Clover seagrass
Critically endangered
Speckled hind
Epinephelus itajara
Atlantic goliath grouper
Epinephelus nigritus
Warsaw grouper
Pristis pectinata
Smalltooth sawfish
Pristis perotteti
Largetooth sawfish
Narcine bancroftii
Lesser electric ray
Acropora cervicornis
Staghorn coral
ESA-T
Acropora palmate
Elkhorn coral
ESA-T
ESA-E
Endangered
Balaenoptera borealis
Serving whale
ESA-E, MMPA
Balaenoptera musculus
Blue whale
ESA-E, MMPA
Balaenoptera physalus
Finback whale
ESA-E, MMPA
Pterodroma caribbaea
Jamaica petrel
Pterodroma hasitata
Black-capped petrel
MBTA
Caretta caretta
Loggerhead turtle
ESA-T
Chelonia mydas
Green turtle
ESA-E, ESA-T
(by range)
Sphyrna mokarran
Montastraea annularis
Montastraea faveolata
Vulnerable
Trichechus manatus
Physeter macrocephalus
Protection
status
Vulnerable (continued)
Epinephelus drummondhayi
Sphyrna lewini
Common name
Manatee
Sperm whale
ESA-E, MMPA
ESA-E, MMPA
ESA-E, endangered under the Endangered Species Act (ESA); ESA-T, threatened under the ESA; MBTA, listed on the Migratory Bird Treaty Act; MMPA,
listed on the Marine Mammal Protection Act.
Source: IUCN 2010. See the supplementary table online at dx.doi.org/10.1525/bio.2011.61.5.8.
the Gulf of Mexico (Pelecanus occidentalis and Oceanodroma
leucorhoa) and the smalltooth sawfish (Pristis pectinata). No
species-level protection occurs in Cuba comparable to the
US ESA, but there are laws protecting biodiversity (e.g., Ley
No. 81 Del Medio Ambiente; Ministerio De Ciencia, Tecnologia Y Medio Ambiente Resolucion No. 111/96).
The Gulf of Mexico has exceptionally high marine biodiversity, with 15,419 recorded species, of which 10% (1511)
are endemic (Felder and Camp 2009). This diversity is partly
attributable to the Gulf’s geographic position within the
transition zone between temperate and tropical waters. Some
threatened species in the Gulf (e.g., whale shark, Rhincodon
typus; loggerhead turtle, Caretta caretta) occur globally but
394 BioScience • May 2011 / Vol. 61 No. 5
have significant populations, spawning aggregations, or nesting sites in the Gulf region. Therefore, greater threats in this
region may have implications for the species’ global survival.
Other species (e.g., Kemp’s ridley turtle, Lepidochelys kempii;
the western Atlantic population of bluefin tuna, Thunnus
thynnus) breed only in the Gulf, and oil spill damage exacerbates previously existing threats to these species.
IUCN Red List assessments are being expanded to evaluate
more marine species (http://sci.odu.edu/gmsa/ ), including
some in the Gulf of Mexico. The IUCN has assessed 322
species in the Gulf of Mexico to date, 53 of which are in
threatened categories (table 1); an additional 29 are listed
as near threatened (see the supplementary table online
www.biosciencemag.org
Roundtable
at dx.doi.org/10.1525/bio.2011.61.5.8). The IUCN assessments include all Gulf marine mammals (5 of 28 species
threatened), sea turtles (all 5 species threatened), seagrasses
(2 of 9 threatened or near threatened), mangroves (0 of 6
threatened), reef-building corals (11 of 60 threatened or
near threatened), wrasses (1 of 20 threatened), sharks and
rays (43 of 131 threatened or near threatened), seabirds (3
of 40 threatened or near threatened), and groupers (11 of 22
threatened or near threatened). Groupers are of particular
concern; three species are classified as critically endangered
on the Red List and the Atlantic goliath grouper (Epinephelus itajara) is listed as near extinction.
An oil spill of this magnitude threatens many species
already listed under IUCN threatened categories—more
species than are currently protected by the ESA. In 1996, the
IUCN assessed the western stock of the Atlantic bluefin tuna
as critically endangered, and the Convention on Biological
Diversity recently petitioned the US Department of Commerce to protect the species under the ESA (CBD 2010).
There are two spawning populations of bluefin tuna, one
in the Gulf of Mexico and the other in the Mediterranean
Sea. Although there is extensive mixing of the populations
on both sides of the Atlantic, particularly on the feeding
grounds off the eastern coast of North America, individuals hatched in the Gulf of Mexico return there to spawn
(spawning site fidelity). Peak spawning in the Gulf occurs
from mid-April to June, unfortunately during the period
of the British Petroleum oil spill in 2010. Like tuna, many
other species in threatened Red List categories—that are not
protected by the ESA—require protection and remediation
from the oil spill.
Seagrasses are a unique group of 72 undersea flowering
plants found in coastal seas globally. In the Gulf of Mexico,
there are nine seagrass species, and these plants provide
crucial structural habitat and nursery grounds for many
recreationally and commercially important fish and invertebrates (including Gulf pink shrimp and brown shrimp),
as well as waterfowl. Some seagrasses, as indicated by their
common names (e.g., turtle grass and manatee grass) are the
primary food for already threatened species of sea turtles and
manatees. The seagrass habitat, and the proliferation of the
species it supports, may be at risk as a result of the oil spill;
three diminutive seagrass species of the genus Halophila are
most threatened. Halophila baillonii is listed as vulnerable
and Halophila engelmanni is listed as near threatened on the
Red List (Short et al. 2011), and Halophila johnsonii is listed
on the ESA. The limited global distributions of these species, restricted primarily to Gulf and Florida waters in the
cases of H. engelmanni and H. johnsonii, mean their risk of
global extinction could be elevated by the oil spill. Halophila
baillonii, already rapidly declining in the Caribbean, is the
fourth most threatened seagrass species in the world. Potential
damages to these seagrasses from the oil pollution in the Gulf
should be assessed, and recovery actions for these species
should be aided by funding available from the Oil Pollution
Act and other sources.
www.biosciencemag.org The whale shark is listed as vulnerable on the IUCN Red
List but is not protected by the ESA. Found worldwide in
tropical and warm temperate waters, many individuals
aggregate in the Gulf of Mexico in the summer months. The
whale shark is the largest fish in the world; it feeds almost
entirely on plankton, crustaceans, and small fishes. It is one
of only three filter-feeding species of shark, sieving zooplankton as small as 1 millimeter in diameter through the
fine mesh of its gill rakers. The shark’s feeding behavior puts
it at high risk from the oil itself and the oil dispersants used
in the Gulf. Although relatively little is known about the biology of the whale shark, populations will probably be slow to
recover from disturbances given the species’ estimated long
life span, slow reproductive rate (Pauly 2002), and naturally
low abundance outside of mating aggregations.
The Kemp’s ridley sea turtle is listed as critically endangered on the Red List and is also protected by the ESA.
This turtle nests exclusively in the Gulf and is the rarest sea
turtle in the world. Of the threatened marine species that
frequent the Gulf, only the Kemp’s ridley depends on Gulf
shores for nesting, and most of its young develop in Gulf
waters. Although it appears that the 2010 hatchlings did not
encounter the spill directly, the timing of the oil spill could
not have been worse for this species, clashing as it did with
the turtles’ key reproductive period. The vast majority of
sea turtles found dead since the spill were Kemp’s ridleys
(NOAA 2010b). The Kemp’s ridley was just on the road
to recovery after a population collapse a few decades ago
that drove it near extinction; the species now faces a new
environmental hurdle.
The West Indian manatee (Trichechus manatus) is listed
as vulnerable by the IUCN and is considered endangered
under US law. Manatees are found in the Gulf and around
the coastline of Florida, in the range of the oil spill. Manatees
may be affected by air quality and oil at the surface, which
they encounter as they emerge to breathe; oil irritating their
skin and eyes; the consumption of seagrass species—their
primary food—that are covered in oil; and chemical oil
dispersants that may also have a toxic effect. The Florida
manatee (Trichechus manatus latirostris), a subspecies of the
West Indian manatee, is additionally threatened by loss of
habitat, entanglement with fishing gear, and increased boating activity, as well as extreme cold temperatures that killed
10% of the population during the winter of 2009–2010. The
Florida manatee subspecies was listed as endangered in 2008
by the IUCN.
The trends in species declines are clearly worrying, particularly because the Gulf was already a system affected by
various risk factors before the oil blowout occurred. How
can we adequately address the threats of oil and gas development and steward the Gulf ’s biological diversity? Priorities
at this stage must focus on species with high commercial
value, species critical to the integrity of coastal and marine
ecosystems in the Gulf, species with populations in decline
before the blowout, and species now recognized as in greater
danger of extinction. Because marine species in particular
May 2011 / Vol. 61 No. 5 • BioScience 395
Roundtable
may be underrepresented by the ESA (Wilcove and Master
2005), the ongoing NRDA in the Gulf of Mexico—as well as
environmental impact assessments conducted for offshore
oil and gas development—should include available data on
globally threatened species, including the expanding species
data sets on the IUCN Red List. Species information on the
Red List can serve as a standardized mechanism to identify
and coordinate conservation and mitigation priorities, especially for highly migratory and transboundary species. The
US Department of the Interior must reevaluate the “low
risk” status currently attributed to pollution from routine
operations of oil and gas development, as well the potential impacts of catastrophic events such as oil spills, in its
compliance with the National Environmental Policy Act, the
ESA, and other applicable domestic and international laws.
Species identified as threatened with extinction on the
IUCN Red List may become even more threatened as a result
of the oil spill. Such elevations in threatened status are part
of the spill’s impacts and as such are damages that must be
recognized and compensated. The six threatened grouper
species on the Red List that occur in the Gulf, for example,
currently receive no protection under the ESA or Mexican
law, despite their status as a major food resource in the
region and a high-value restaurant menu item.
Gulf-occurring animals and plants protected by the ESA
(and other US laws) should be priorities for federal damage assessments; as such, these laws should help restore the
natural resources injured by the release of oil or hazardous
substances. Although the methodology of assessment and
the names of threatened categories may differ among laws,
assessments, and criteria, the IUCN Red List is a highly credible source of species requiring particular attention—both
for damage assessment and for special consideration for
future regulations of oil and gas development. As a result
of the rapid increase in IUCN assessments during the last
few years, we now know there are many threatened marine
species in the Gulf that are not currently protected by US
law (table 1). Threatened species not yet listed in national
legislation should nevertheless be the subject of damage
assessments, targeted research, and monitoring, as well as
recovery efforts when needed.
Although understanding has improved of the medium- and
long-term impacts from oil pollution on animal and plant
physiologies, there is still much we do not know. Globally,
countries must improve risk assessments of offshore oil and
gas development, and seek to expand and regularize damage
and impact assessments, domestically and internationally.
These impacts must be systematically considered to establish
priorities for research and monitoring that will best ensure
effective species and system recovery. Although the research
agenda should focus on the United States’ immediate needs,
its development should also support similar efforts in other
regions of the world in identifying species of priority concern.
The IUCN Red List is continually improved and revised under
strict standards and criteria, and its value in assessing the global
conservation status of biological diversity steadily expands.
396 BioScience • May 2011 / Vol. 61 No. 5
Acknowledgments
The majority of marine species assessments conducted
through the International Union for Conservation of Nature
(IUCN) Species Survival Commission are made through
the Global Marine Species Assessment, with core funding
provided by Tom Haas and the New Hampshire Charitable Foundation. We thank numerous partners who helped
compile information, including BirdLife International;
SeagrassNet; the Groupers and Wrasses, Tunas and Billfishes,
Sharks, and Marine Turtles IUCN Species Specialist Groups;
Jonnell Sanciangco and Suzanne Livingstone (Global Marine
Species Assessment); and Cynthia Taylor (Sirenia Red List
Authority Focal Point). Thanks to Cathy Short for editing
the manuscript. This article is Jackson Estuarine Laboratory
contribution no. 498.
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Roundtable
Claudio Campagna (ccampagna@wcs.org) is with the Wildlife Conservation
Society in New York, New York. Frederick T. Short is with the Department
of Natural Resources and the Environment, University of New Hampshire,
Jackson Estuarine Laboratory, in Durham. Beth A. Polidoro, Roger McManus,
and Kent E. Carpenter are with the Global Marine Species Assessment,
Marine Biodiversity Unit, International Union for Conservation of Nature
(IUCN) Species Programme, Department of Biological Sciences, at Old
Dominion University, in Norfolk, Virginia. Roger McManus is also with the
Global Marine Species Assessment, IUCN Species Survival Commission, Perry
Institute for Marine Science, in Jupiter, Florida. Bruce B. Collette is with the
National Marine Fisheries Service Systematics Laboratory, at the National
www.biosciencemag.org Museum of Natural History, in Washington, DC. Nicolas J. Pilcher is with
the Marine Research Foundation in Sabah, Malaysia. Yvonne Sadovy de
Mitcheson is with the School of Biological Sciences, University of Hong Kong,
in China. Simon N. Stuart is with the IUCN Species Survival Commission, at
the United Nations Environment Programme World Conservation Monitoring
Centre, in Cambridge, United Kingdom; the Department of Biology and Biochemistry, University of Bath, in the United Kingdom; the Al Ain Wildlife Park
and Resort, in Abu Dhabi, United Arab Emirates; and Conservation International, in Arlington, Virginia. Campagna, Short, Polidoro, Collette, Pilcher,
Sadovy, and Carpenter are also with the IUCN Species Survival Commission
Marine Conservation Subcommittee in Gland, Switzerland.
May 2011 / Vol. 61 No. 5 • BioScience 397
LETTER
Underestimating the damage: interpreting cetacean carcass
recoveries in the context of the Deepwater Horizon/BP incident
Rob Williams1 , Shane Gero2 , Lars Bejder3 , John Calambokidis4 , Scott D. Kraus5 , David Lusseau6 ,
Andrew J. Read7 , & Jooke Robbins8
1
Marine Mammal Research Unit, University of British Columbia, Vancouver, Canada
Department of Biology, Dalhousie University, Halifax, Canada
3
Centre for Fish and Fisheries Research, Cetacean Research Unit, Murdoch University, Western Australia
4
Cascadia Research Collective, Olympia, WA, USA
5
New England Aquarium, Boston, MA, USA
6
School of Biology, Aberdeen University, Aberdeen, Scotland, UK
7
Nicholas School of the Environment, Duke University, Beaufort, NC, USA
8
Humpback Whale Studies Program, Provincetown Center for Coastal Studies, Provincetown, MA, USA
2
Keywords
Anthropogenic impacts; dolphin; Deepwater
Horizon; Gulf of Mexico; mortality; oil;
strandings.
Correspondence
Rob Williams, Current address: Sea Mammal
Research Unit, Scottish Oceans Institute,
St Andrews Fife KY16 8LB. Tel: +44 (0)1334
462630; Fax: +44 (0)1334 463443.
E-mail: rmcw@st-andrews.ac.uk
Received
23 September 2010
Accepted
15 February 2011
Editor
Leah Gerber
doi: 10.1111/j.1755-263X.2011.00168.x
Abstract
Evaluating impacts of human activities on marine ecosystems is difficult when
effects occur out of plain sight. Oil spill severity is often measured by the number of marine birds and mammals killed, but only a small fraction of carcasses
are recovered. The Deepwater Horizon/BP oil spill in the Gulf of Mexico was
the largest in the U.S. history, but some reports implied modest environmental
impacts, in part because of a relatively low number (101) of observed marine mammal mortalities. We estimate historical carcass-detection rates for 14
cetacean species in the northern Gulf of Mexico that have estimates of abundance, survival rates, and stranding records. This preliminary analysis suggests
that carcasses are recovered, on an average, from only 2% (range: 0–6.2%)
of cetacean deaths. Thus, the true death toll could be 50 times the number
of carcasses recovered, given no additional information. We discuss caveats to
this estimate, but present it as a counterpoint to illustrate the magnitude of
misrepresentation implicit in presenting observed carcass counts without similar qualification. We urge methodological development to develop appropriate
multipliers. Analytical methods are required to account explicitly for low probability of carcass recovery from cryptic mortality events (e.g., oil spills, ship
strikes, bycatch in unmonitored fisheries and acoustic trauma).
Introduction
The Deepwater Horizon/BP oil spill in the Gulf of Mexico
was not only the largest in the US history (Machlis &
McNutt 2010) but was also the first to release oil at
the sea floor (over 1.5 km below sea level) and to involve the widespread use of dispersants below the surface
(Mascarelli 2010). However, many media reports have
suggested that the spill caused only modest environmental impacts (Grunwald 2010; Walsh 2010), in part
because of a low number of observed wildlife mortalities, especially marine mammals (Unified Area Command from the U.S. Fish and Wildlife Service and the
228
National Oceanic and Atmospheric Association 2010).
Not surprisingly, perhaps, this spill has been compared
to other acute environmental disasters, such as the 1989
Exxon Valdez oil spill (EVOS). In the case of EVOS, the
mortality of sea otters became emblematic of environmental impact, as well as a contentious effort to agree
on compensation (Ehrenfeld 1990; Estes 1991). In contrast, the Deepwater Horizon/BP event has not left such
an iconic symbol of devastation. As of November 7,
2010, “only” 101 cetacean (whale, dolphin, and porpoise)
carcasses 1 had been detected across the Northern Gulf
1
http://www.nmfs.noaa.gov/pr/health/oilspill.htm
Conservation Letters 4 (2011) 228–233
c 2011 Wiley Periodicals, Inc.
Copyright and Photocopying: R. Williams et al.
of Mexico. The critical issue is, therefore, how to interpret this relatively low number of carcass recoveries
in terms of impact to populations. The Gulf of Mexico
is a semienclosed subtropical sea that forms essentially
one ecosystem with many demographically independent
cetacean populations (Mullin & Fulling 2004). Some of
these cetacean populations, such as killer whales (Orcinus orca), false killer whales (Pseudorca crassidens), melonheaded whales (Peponocephala electra), and several beaked
whale species, appear to be quite small, are poorly studied, or are found in the pelagic realm where they could
have been exposed to oil and yet never strand. Small, genetically isolated populations of bottlenose dolphins (Tursiops truncatus)could have experienced substantial losses
either inshore or offshore.
In an ideal world, one would simply compare postspill to prespill abundance estimates. But, it is rare to
have good knowledge of long-term trends in wildlife
abundance (Bonebrake et al. 2010). Abundance of many
marine mammal populations has been monitored for
decades, but the low precision of most cetacean abundance estimates would prevent us from detecting all but
the most catastrophic declines using conventional nullhypothesis testing (Taylor et al. 2007b). As a result, it
would not be very informative to compare pre- and postspill abundance estimates for populations of cetaceans in
the northern Gulf of Mexico. An alternative approach is
to count the number of carcasses recovered, acknowledging that these recoveries were subject to a number
of processes (e.g., sinking, decaying, scavenging, drifting)
that reduce detection probability, and then adjusting the
counts upward to estimate total mortality. This is the approach that is commonly taken to estimate the effects of
power lines on bird mortality, for example, in which it
has been shown in one instance that carcass counts underestimate total mortality by 32% (Ponce et al. 2010).
This also appears to be the approach being taken to assess impacts of the Deepwater Horizon Incident on whales
and dolphins, with the important caveat that the carcass counts appear to be presented at face value, with
no attempt to extrapolate to total mortality (Unified Area
Command from the U.S. Fish and Wildlife Service and
the National Oceanic and Atmospheric Association 2010;
Grunwald 2010; Walsh 2010).
Cetacean carcasses do not necessarily strand along
coastlines or remain afloat long enough to be detected at
sea. The probability of detecting the death of a marine
mammal depends on a wide range of physical and biological factors, including: behavioral responses prior to
death, proximity of the carcass to shore (or at-sea observers), decomposition rates and processes, water temperature, wind regime, and local currents (Epperly et al.
1996). Cetaceans subject to natural predation would ob-
Low probability of cetacean carcass recovery
viously leave no carcass at all. Shore recoveries may be
very site-specific, such that the likelihood of a carcass
drifting to shore varies with the geography of the coastline itself (Faerber & Baird 2010). As such, “oiled” carcasses detected subsequent to the Deepwater Horizon/BP
event are expected to represent a small fraction of total
mortality in the Northern Gulf of Mexico.
Given the magnitude of the spill and complexity of the
response, quantifying the ecological impacts will take a
long time. To contribute to this effort, we examined historical data from the Northern Gulf of Mexico to evaluate whether cetacean carcass counts in this region have
previously been reliable indicators of mortality, and may
therefore accurately represent deaths caused by the Deepwater Horizon/BP event.
Methods
We estimated historical carcass-detection rates for 14
species of cetaceans in the northern Gulf of Mexico for
which species-specific estimates of abundance (Waring
et al. 2009a, b; Mullin & Fulling 2004) species-level adultsurvival rates (Taylor et al. 2007a), and stranding records
exist (Waring et al. 2009a, b). Estimates of mortality were
generated for each species by multiplying recent abundance estimates by the species-specific mortality rate. An
annual carcass-recovery rate was then estimated by dividing the mean number of observed strandings each year by
our estimate of annual mortality (Table 1). First, an overall pooled carcass-recovery rate was calculated across all
cetacean species (n = 14) in the Gulf of Mexico for which
data was available by using the expected number of
deaths across all species and the total number of observed
carcasses across all species. Next, species-specific carcassrecovery rates were calculated using only species-specific
values and a mean (n = 14) of those was taken across
species. Species-specific carcass-recovery estimates were
not generated for Bryde’s whale (Balaenoptera brydei),
bottlenose dolphins (Tursiops sp.), and Fraser’s dolphins
(Lagenodelphis hosei) due to uncertainties in their abundance and/or population structure (Waring et al. 2009b).
No attempt was made to estimate carcass-recovery rates
in the two taxonomic groups that are not identified to
species in the field during raw data collection: Kogia
(a pooled estimate for two species, dwarf and pygmy
sperm whales), or mesoplodonts (a pooled estimate for
a genus of similar-looking beaked whales) (Waring et al.
2009b).
Results
Our analysis suggests that an average of 4,474 individual
cetaceans died annually between 2003 and 2007 from all
Conservation Letters 4 (2011) 228–233 Copyright and Photocopying: 229
R. Williams et al.
Table 1 Population parameters and illustrative species-specific carcass-recovery rates for 14 species from the Gulf of Mexico.
Northern Gulf of
Mexico population
Sperm whale
Cuvier’s beaked whale
Atlantic spotted dolphind
Pantropical spotted dolphin
Striped dolphin
Spinner dolphin
Rough-toothed dolphind
Clymene dolphin
Killer whale
False killer whale
Pygmy killer whale
Melon-headed whale
Risso’s dolphin
Short-finned pilot whale
Average of all species
Pooled across all species (n = 14)
Population
estimatea
CVa
Adultsurvival rateb
Estimated annual
mortalityc
Mean observed
annual strandingsa
Carcass-detection
rate (%)
1665
65
37611
34067
3325
1989
2653
6575
49
777
323
2283
1589
716
0.20
0.67
0.28
0.18
0.48
0.48
0.42
0.36
0.77
0.56
0.60
0.76
0.27
0.34
0.986
0.95
0.95
0.95
0.95
0.95
0.95
0.95
0.99
0.99
0.95
0.99
0.95
0.986
23.3
3.3
1880.6
1703.4
166.3
99.5
132.7
328.8
0.5
7.8
16.2
22.8
79.5
10.0
0.8
0.2
2.4
0.8
0.8
1
5.8
0.6
0
0
0.2
1.4
2.8
0.2
93,687
–
–
4,474
17
3.4
6.2
0.13
0.05
0.48
1.0
4.4
0.18
0
0
1.2
6.1
3.5
2.0
2.0
0.4
Population abundance and stranding data (2003–2007) were taken from (Waring et al. 2009b), unless otherwise noted.
Population-level estimates are preferable but generally unavailable, so data taken from (Taylor et al. 2007a).
c
Calculated as the abundance multiplied by mortality rate ( = 1–survival rate).
d
Population abundance and stranding data (2002–2006) were taken from (Waring et al. 2009a).
a
b
natural and anthropogenic causes. However, during that
period, an average of only 17 cetacean carcasses were detected annually along the northern Gulf of Mexico. This
would suggest that the overall pooled rate of carcass recovery for cetaceans in the Gulf of Mexico is approximately 0.4% of the total estimated mortality. Table 1
breaks down the recovery rates by species. Carcasses were
recovered only from a mean of 2.0% (range: 0–6.2%)
of cetacean species deaths along the northern Gulf of
Mexico. The disparity between this value and the overall pooled value likely results from undue influence of
poorly studied and relatively rare species (e.g., Cuvier’s
beaked whale and melon-headed whale; Table 1) with
high estimated carcass-recovery rates that are weighted
equivalently and treated as reliably in this average as estimates from species that are common and well studied.
We have reason to believe that the Cuvier’s beaked whale
recovery rate is positively biased. The original abundance
estimate is thought to be an underestimate by a factor
of 2 to 4, based on the assumption of certain track line
detection (Mullin & Fulling 2004). Our carcass-recovery
rate for deep-diving whales would then be biased high by
a factor of 2–4.
Discussion
Our results indicate that carcass-recovery rates are historically low for cetaceans in the Gulf of Mexico. Studies of other populations show similar recovery rates. In
230
long-term studies of killer whales off the coasts of British
Columbia and Washington State, in which populations
are censused completely every year, carcasses from confirmed deaths of known individuals are recovered only
6% of the time (Fisheries and Oceans Canada 2008). Similarly, low-detection rates have been estimated for carcasses of eastern gray whales (Eschrichtius robustus, <5%,
Heyning & Dahlheim 1990), North Atlantic right whales
(Eubalaena glacialis, 17%, Kraus et al. 2005), and harbor porpoises (Phocoena phocoena, <1%, Moore & Read
2008), all of which occur in near-shore waters. Beached
carcasses of other pelagic marine vertebrates have been
shown to be equally poor indicators of mortality (for example, 7–13% recovery rates for four species of sea turtle, Epperly et al. 1996). As such, raw carcass counts alone
are not reliable indicators of the magnitude of mortality
for these species.
We do not claim to have calculated definitive multipliers for this spill. Instead, our aim is to show plausible ranges for those multipliers, in order to illustrate how
much they would affect our perception of the ecological
damages caused by Deepwater Horizon incident and why
this topic is worthy of additional resources for methodological development. Consider, for example, one sperm
whale being detected as a carcass, and a necropsy identified oiling as a contributing factor in the whale’s death.
If the carcass-detection rate for sperm whales is 3.4%
(Table 1), then it is plausible that 29 sperm whale deaths
represents the best estimate of total mortality, given no
additional information. If, for example, 101 cetacean carcasses were recovered overall, and all deaths were attributed to oiling, the average-recovery rate (2%) would
translate to 5,050 carcasses, given the 101 carcasses detected (Table 1). As the necropsy results emerge, we can
evaluate whether this prediction is high or low, but the
sheer scope for underestimation builds a compelling case,
in our view, for additional work. The vast majority of
carcasses recovered appear to have been bottlenose dolphins.1 As necropsy results emerge and the need for recovery plans debated, we encourage such discussions to
explicitly take into account the probability that the number of dolphins stranded represented something on the
order of only 2% of the number of animals killed. The
potential is high for the spill to have caused catastrophic
impacts on small, localized populations of bottlenose dolphins in the Gulf. We note that coastal and offshore forms
of bottlenose dolphins are found off California, with the
coastal carcass having a 50-fold greater probability of
stranding than an offshore one (Perrin et al. 2010).
Even in the case of EVOS, the large number of observed deaths was acknowledged to represent only a fraction of the total mortality (Estes 1991). Two approaches
were taken to estimate total mortality in Prince William
Sound: (1) a comparison of pre- and postspill population size; and (2) extrapolations from recovered carcasses
to total mortality from a multiplier based on the probability of recovering a carcass (Garshelis 1997). Our estimates of carcass-recovery rates were calculated from
the best available data, but we caution against using historic (i.e., pre-spill) carcass-recovery rates to generate a
simple multiplier to assess total mortality in the Deepwater Horizon/BP Incident. On the one hand, considerable efforts were expended by government agencies and
others to search for marine mammal carcasses after the
spill, which could raise recovery rates above those estimated here. Fortunately, a comparison of pre- and postspill search effort ought to be among the most tractable
factors to account for when calculating carcass-recovery
rates. On the other hand, there are several arguments to
suggest that our carcass-recovery rates are biased high.
First, we estimated the number of carcasses using adultsurvival rate; had we included juvenile and calf mortality, the total number of carcasses would have been
substantially higher and our estimated carcass-recovery
rate substantially lower. The point estimate is strongly influenced by some optimistic values for Cuvier’s beaked
whale and melon-headed whale (Table 1). Abundance of
these elusive species is biased low, due to well-known
difficulties in estimating track line detection probability
(g(0)) for deep-diving species. Some of these cetaceans
represent prey species: our denominators include animals
that would have been preyed upon and not ended up as
carcasses. Given that many cetaceans are highly social,
entire clusters, schools, pods, matrilines, or groups of animals could have been affected (Williams et al. 2009). Although we used recent population estimates, it has yet
to be determined how many animals in each population
were actually exposed to the spill. Finally, the location of
the spill and the subsequent response effort likely affected
the probability of detecting associated deaths. These are
the factors that must be carefully considered as efforts to
assess population impacts continue. We present our historic recovery rates as starting points for discussion, but
caution that incorrect multipliers may result in estimated
mortalities exceeding the number of animals that were
ever in the vicinity of the spill (Parrish & Boersma 1995).
Estimating the correct multipliers will require an interdisciplinary research effort to combine oceanographic and
cetacean habitat modeling to assess exposure risk and
likely deaths caused by exposure. This research is needed,
but currently lacking from research priorities emerging
from the oil spill mitigation and recovery efforts.
The issue of carcass-detection rates is not merely of
academic interest. Our results are directly relevant to assessment of ecological damages caused by the Deepwater Horizon/BP oil spill, but also have global relevance
for litigation and marine conservation policy. Given that
environmental restitution in the United States can be
based on a violation system (Alexander 2010), carcassrecovery rates must be explicitly considered when evaluating the impacts of such disasters. In the case of EVOS,
legal damages placed the value of each sea otter killed
at US$80,000, or the cost of rehabilitating each oiled otter (Estes 1991; Garshelis 1997). In terms of broader recommendations for marine policy, we note that carcass
counts are used in many countries, including the United
States, to monitor human impacts on cetacean populations. The tools that managers use in the United States
to estimate and limit the impacts of human activities on
stocks relies upon “potential biological removal” (PBR),
a calculation that determines how many animals can be
removed from a stock before causing harm. The PBR estimate, under the Marine Mammal Protection Act (MMPA)
depends on reasonably unbiased and precise estimates of
human-caused mortality (Wade 1998). In contrast, the
effects of many human impacts are only witnessed opportunistically, such as a carcass being discovered on a
beach. The issue arises when policymakers, legislators, or
biologists treat these carcass-recovery counts as though
they were complete counts or parameters estimated
from some representative sample, when in fact, they
are opportunistic observations. Our study suggests that
these opportunistic observations should be taken to estimate only the bare minimum number of human-caused
mortalities. This work suggests that carcass counts alone
R. Williams et al.
are unreliable indicators of either natural or anthropogenic sources of mortality. It is vital to develop a
framework that explicitly accounts for the low probability of recovering carcasses, if we are to accurately assess
the sustainability of all cryptic forms of human-caused
mortality.
Human impacts on marine ecosystems and marine
mammals are growing both in type and scale (Kraus &
Rolland 2007; Clausen & York 2008; Duce et al. 2008;
Doney 2010; Hoegh-Guldberg & Bruno 2010; Tittensor
et al. 2010). Establishing the proper spatial and temporal
scales, at which to assess the impacts of acute events, is
further complicated by potential long-term effects and a
lack of basic population-specific information (Bejder et al.
2006). This highlights the need for long-term population
monitoring, such as that mandated by the U.S. MMPA
(Bonebrake et al. 2010). In the first year after the 1989
Exxon Valdez spill, the AT1 group of “transient” killer
whales experienced a 41% loss; there has been no reproduction since the spill (Matkin et al. 2008). Although
the cause of the apparent sterility is unknown, the lesson
serves as an important reminder that immediate death
is not the only factor that can lead to long-term loss
of population viability. The recent disaster in the Gulf
of Mexico provides an important opportunity to assess
whether or not the intensity of monitoring conducted
in the Gulf of Mexico is sufficient to detect even catastrophic effects (Taylor et al. 2007b). If line-transect survey data are found wanting, we see value in exploring new passive acoustic monitoring methods to detect
trends in relative abundance (Marques et al. 2009, RojasBracho et al. 2010). These could be especially useful for
rare or pelagic species, or those for which g(0) estimation is particularly problematic. Accurate assessment of
impacts also must consider how species are likely impacted, whether acutely on contact with oil or over
the longer term through toxicity or habitat degradation
(Lovett 2010; Schrope 2010b). If support for longer term
assessments dwindles as the time passes and public attention moves elsewhere (Schrope 2010a), then chronic
effects will remain unknown. In such cases, only immediately observable effects, such as the number of carcasses,
have and will be used to determine the impact of an
event, and synergistic and lagged effects will not be considered. Our findings suggest that assessments of the impact of anthropogenic events based solely on the numbers
of carcasses recovered are deceptively biased. A better
understanding of carcass-recovery rates and the degree
to which they underestimate actual mortality, is critical
to assessing the true consequences of oil spills and other
human activities known to cause cryptic mortality, such
as ship strikes, certain fisheries interactions, and acoustic
trauma.
232
Acknowledgments
Lynne Barre, Dee Boersma, and Dave Thompson gave
valuable comments at an early stage of the development
of this manuscript. We thank Leah Gerber, Tim Gerrodette and Barb Taylor for their careful reviews.
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