Thesis - School of Environment and Natural Resources

Transcription

Prevalence of Cryptosporidium, Giardia, Salmonella, and
Cephalosporin-Resistant E. coli Strains in Canada goose Feces at Urban
and Peri-Urban Sites in Central Ohio
THESIS
Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in
the Graduate School of The Ohio State University
By
Laura E. Binkley
Graduate Program in Environment and Natural Resources
The Ohio State University
2015
Thesis Committee:
Robert Gates, PhD Advisor
Michael S. Bisesi, PhD
Stephen Matthews, PhD
Copyright by
Laura E. Binkley
2015
ABSTRACT
Large populations of resident geese can pose a pathogen exposure hazard and
disease risk to humans and animals in urban areas. Evidence suggests that waterfowl play
a role in pathogen dissemination and disease transmission to humans, however, more
definitive data are often needed. This exploratory study sought to identify potential
exposure hazards, the first step in risk assessment. This research also discusses doseresponse for protozoan organisms and initiated the exposure assessment process by
measuring environmental variables that may be associated with exposure. A total of 199
Canada goose fecal samples were collected from 5 peri-urban and 7 urban sites
throughout the Greater Columbus, Ohio area. Samples were collected during two time
periods: during 4-11 June, 2013 when geese had just begun their molt, and 16-30 August,
2013 after geese regained flight. Juveniles were distinguished from adults only during the
first sample period. Antigen capture enzyme-linked immunosorbent assay (ELISA) was
used to detect presence of Giardia and Cryptosporidium. Selective media were used to
culture Salmonella and cephalosporin-resistant E. coli strains. Cryptosporidium was the
most prevalent pathogen with 44.7% of samples testing positive. Feces collected from
urban sites during the first period were 1.86 times more likely to be positive for
Cryptosporidium than peri-urban sites (P = 0.10). Forward model selection methods
determined that prevalence was positively associated with human population density
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surrounding collection sites, proportion of each site defined as pasture by the National
Land Cover Database (NLCD), and distance of each site from nearest wastewater
treatment plant (WWTP). Feces collected from urban sites during the second period were
no more likely to be positive for Cryptosporidium than peri-urban sites (P = 1.00, Odds
Ratio= 1.00). Forward stepwise model selection determined that prevalence was
positively associated with human population density within study sites, distance of each
site from nearest livestock farm, and distance of each site from nearest wastewater
treatment plant.
Giardia was present in only 3.5% of samples. None were positive for Giardia in
the first period. Feces collected from urban sites during the second period were 1.9 times
more likely to be positive for Giardia than peri-urban sites (P = 0.74). Prevalence of
cephalosporin-resistant E. coli strains was 10.8%. Only one positive was detected during
the first period. Feces collected from urban sites during the second period were 6.7 times
more likely to be positive for cephalosporin-resistant E.coli than peri-urban sites (P =
0.10). No Salmonella was detected in the fecal samples. All pathogens tested, with the
exception of Salmonella, showed a similar trend where a greater percentage of positives
were detected at urban sites than peri-urban sites.
The exposure potential of goose feces to the human population appears to be high
for Cryptosporidium but low for Giardia and Salmonella. The discovery of
cephalosporin-resistant strains of E.coli in fecal samples poses a potential exposure
hazard because antibiotic-resistant strains are difficult to treat if infection occurs.
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ACKNOWLEDGEMENTS
I want to thank my primary advisor Dr. Robert J. Gates for his time, patience,
guidance, and support as well as for taking me on as an advisee and giving me the chance
to conduct a successful wildlife study.
I also want to thank the highly regarded members of my thesis committee Drs.
Michael S. Bisesi and Dr. Stephen N. Matthews for their additional support and guidance.
I would especially like to thank Dr. Michael S. Bisesi for making this project possible
and allowing me the use of his lab and resources.
I would like to thank Dr. Thomas E. Wittum for offering me his lab, resources,
and insight, so that I could add a whole new and exciting dimension to my project. I also
thank him for his time when helping me with my analysis.
I thank Dr. Gabriel Karns for his time, patience, and guidance, when helping me
carry out my GIS analysis.
Lastly, I would like to thank the Ohio State University Terrestrial Wildlife
Ecology Laboratory for helping to provide me with the necessary resources to carry out
my study.
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VITA
2009.................................................. .B.S. Biology, The Ohio Wesleyan University
2014....................................................MPH at The Ohio State University
2011-2015...........................................SENR Graduate Student at The Ohio State University
PUBLICATIONS
Cunningham, D., DeBarber, A.E., Bir, N., Binkley. L., Merkens, L.S., Steiner, R.D., and
Herman, G.E. (February, 2015). Analysis of Hedgehog Signaling in Cerebellar
Granule Cell Precursors in a Conditional Nsdhl Allele Demonstrates an Essential
Role for Cholesterol in Postnatal CNS Development. Human Molecular Genetic.
First published online February 4, 2015 doi:10.1093/hmg/ddv042
FIELDS OF STUDY
Major Field: Environment and Natural Resources-Wildlife
Public Health- Environmental Health Sciences-Veterinary Preventive Medicine
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TABLE OF CONTENTS
Abstract. ..................................................................................................................... ii
Acknowledgments. .....................................................................................................iv
Vita............................................................................................................................. v
List of Tables ............................................................................................................. ix
List of Figures ........................................................................................................... xi
Chapter 1- Introduction………… .............................................................................. 1
Goose Population Trends in Ohio. ................................................................. 5
Life History .................................................................................................... 6
Migration…………. ........................................................................................7
Resident Populations ...................................................................................... 8
Human Conflicts With Resident Canada Geese ............................................ 10
Protozoa ........................................................................................................ 11
Cryptosporidium ........................................................................................... 13
Giardia ................................................................................................................17
Bacteria ......................................................................................................... 21
Antimicrobial Resistance .............................................................................. 22
Pathogen Transmission Environments. ......................................................... 23
Literature Cited. ............................................................................................ 25
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Chapter 2- Prevalence of Cryptosporidium, Giardia, Salmonella, and
Cephalosporin-Resistant E. coli Strains in Canada goose Feces Urban and
Peri-Urban Sites in Central Ohio. ................................................................ 35
Introduction .................................................................................................. 35
Study Objectives and Hypotheses. ............................................................... 37
Study Sites..................................................................................................... 41
Sample Collection. ........................................................................................ 46
Pathogen Detection ....................................................................................... 51
Immuno Assay. .................................................................................. 51
Bacteria Culture................................................................................52
Data Analysis ................................................................................................ 53
Age, Landscape, and Season. ........................................................... 53
Environmental Factors...................................................................... 55
GIS Analysis ..................................................................................... 56
Results .......................................................................................................... 58
Pathogen Prevalence ........................................................................ 58
Age, Landscape, and Season ............................................................ 58
Discussion .................................................................................................... 69
Pathogen Prevalence .................................................................................... 69
Age, Landscape, and Season ............................................................ 76
Future Research and Management Recommendations. ....................87
Literature Cited ................................................................................ 91
CHAPTER 3- HAZARD IDENTIFICATION SUMMARY .................... 102
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Literature Cited. ............................................................................. 109
BIBLIOGRAPHY ...................................................................................... 112
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LIST OF TABLES
Table 2.1 Land cover categories selected from the National Land Cover Database
Program Legend. .......................................................................................... 42
Table 2.2 Proportion of each land cover type within sites where Canada goose
fecal samples were collected in the greater Columbus area during the molt
and post-molt periods in the year 2013. ....................................................... 43
Table 2.3 Descriptions for sites where Canada goose fecal samples were collected
during the molt and post-molt periods in the year 2013 ............................... 44
Table 2.4 Number of resident Canada goose fecal samples collected per site by
period in the greater Columbus area during the molt and post-molt periods
in the year 2013 ........................................................................................... 49
Table 2.5 Pathogen prevalence in Canada goose fecal samples collected in the
greater Columbus area during the molt and post-molt periods in the year
2013 by period .............................................................................................. 59
Table 2.6 Percentage of Cryptosporidium positives in Canada goose fecal samples
collected in the greater Columbus area during the year 2013 out of number of
samples collected per site for Period 1 and Period 2.................................... 61
Table 2.7 Percentage of Giardia and cephalosporin-resistant E. coli positives in
Canada goose fecal samples collected in the greater Columbus area during
the year 2013 out of number of samples collected per site for period 2 ....... 61
Table 2.8 Summary prevalence for Cryptosporidium, Giardia, and cephalosporinresistant E. coli by age and landscape variables for Canada goose fecal
samples collected in the greater Columbus area during the molt and post
-molt periods in 2013 for Period 1 and Period 2 .......................................... 62
Table 2.9 Environmental variable measurements and odds ratios by site for Period
1 and Period 2 Cryptosporidium data from Canada goose fecal samples
collected in the greater Columbus area during the molt and post-molt
periods in the year 2013 ............................................................................... 65
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Table 2.10 Univariate linear regression results for variable vs. odds ratio during
period 1 and period 2 for Cryptosporidium data collected from Canada
goose fecal samples collected in the greater Columbus area during the
molt and post-molt periods in the year 2013 ................................................ 66
Table 2.11 Linear regression output for Cryptosporidium first and second period
best-fit models from Canada goose fecal samples collected in the greater
Columbus area during the molt and post-molt periods in the year 2013 ... 67
Table 2.12 Prevalence of Giardia and Cryptosporidium in Canada goose fecal
samples among studies ................................................................................. 71
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LIST OF FIGURES
Figure 1.1 Cryptosporidium life cycle with summary (Center for Disease
Control and Prevention. 2013. Cryptosporidium. Parasites.
Retrieved 2014 from: http://www.cdc.gov/parasites/crypto/
biology.html). ......................................................................................... 16
Figure 1.2 Giardia life cycle with summary (Center for Disease Control and
Prevention. 2011.Giardia. Parasites. Retrieved 2014 from:
http://www.cdc.gov/parasites/Giardia/)........................................................20
Figure 2.1 Study sites used for resident Canada goose fecal sample collection
during the molt and post-molt periods in the Greater Columbus area
during the year 2013. ............................................................................... 45
Figure 2.2. Univariate linear regressions for best-fit model variables of
Cryptosporidium odds ratio vs. a.) Period 1 human population
density b.) Period 2 human population density c.) Period 1 proportion
pasture d.) Period 2 distance to livestock farm e.) Period 1 distance to
WWTP f.) Period 2 distance to WWTP in Canada goose fecal samples
collected in the greater Columbus are during the molt and post-molt
periods in the year 2013. ........................................................................ 68
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CHAPTER 1
INTRODUCTION
Resident Canada goose (Branta canadensis maxima) populations have increased
eight-fold in North America over the last 20 years where they concentrate in urban areas
and have close contact with human populations (Clark 2003). Large populations of
resident geese in urban areas can pose an exposure hazard and disease risk to humans and
animals. Potential risks stem from large populations of resident geese that produce large
quantities of fecal matter that accumulates in urban areas used by humans. This raises
concern considering the potential for fecal-oral pathogen transmission via direct or
indirect routes. Canada geese serve as reservoirs and vectors for numerous pathogens
including Giardia, Cryptosporidium, Salmonella, Avian Influenza, Campylobacter jejuni,
Listeria, Legionella, Newcastle Disease, E. coli, and a variety of other pathogens and
microbes (Clark 2003). This thesis addresses prevalence of enteric pathogens including
Cryptosporidium, Giardia, Salmonella, and E. coli in Canada goose fecal samples
collected throughout the Greater Columbus area during the early molt period (4-11 June)
and late post-molt period (16-30 August) in the year 2013. Juveniles were distinguished
from adults during the first period but not the second due to a more fully developed
immune system by August which could have introduced confounding.
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The role of Canada geese in transmission of antimicrobial resistant strains of
bacteria is another human health concern. Of particular interest is the role of Canada
geese in transmission of cephalosprin-resistant E. coli strains. Close contact with
livestock may result in transmission to wildlife populations, thus contributing to the
spread of resistant strains to humans. Evidence in many cases suggests the importance of
waterfowl in pathogen dissemination and pathogen transmission, however quantitative
data are often lacking and more data are needed before any quantitative risk assessments
are possible (Clark 2003).
Thus far, information on the molecular diversity of Canada goose fecal microbiota
is scarce, although Lu (2009) hypothesized that waterfowl gut microbial communities
differ from other birds. Fogarty and Voytek, (2005) found lower levels of the anaerobic
bacterial group, Bacteroides-Prevotella in geese, chickens, and gulls compared to cows,
dogs, horses, humans, deer, and pigs. That microbial composition of waterfowl feces
remains poorly studied is a barrier for developing methods to distinguish goose fecal
sources from other animal fecal sources (Lu et al. 2009). This becomes important for
source-tracking studies to identify causes of contamination in the water supply and food
systems. More studies are needed to better assess what risk of exposure the public may
incur in areas inhabited by geese (Clark 2003). More needs to be understood about
microbial communities and microbial loads in excrement from resident Canada geese.
My central hypothesis was that prevalence of enteric pathogens including
Cryptosporidium, Giardia, Salmonella, and cephalosporin-resistant E. coli strains is
higher in urban than peri-urban areas. This is based on accounts of resident Canada geese
feeding on human refuse and habituation to being hand-fed by humans, which could
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serve as an indirect route of transmission from humans to geese. There simply are more
opportunities for contact between humans and geese in urban areas. This is in addition to
exposure from contact with goose feces in water and grass as well as other wildlife,
domestic animals, and fomites. I also hypothesized that there will be higher pathogen
prevalence during the molt because resident geese often become more localized and
concentrated during the flightless period of their annual molt. My third major hypothesis
is that cephalosporin-resistant E. coli strains will be present in peri-urban Canada goose
fecal samples in the Greater Columbus area. This is thought to come from exposure to
livestock waste where antimicrobial resistance has previously been found (Cole et al.
2005, Wittum et al. 2010, Allen et al. 2011, Seiffert et al. 2013).
Seven urban sites and five peri-urban sites in the Greater Columbus area were
selected for study (Ohio Department of Natural Resources 2012c, Ohio Department of
Natural Resources 2013, and Columbus Metro Parks 2009a). Sites were confirmed as
being either urban or peri-urban based on Geographical Information System (GIS)
analysis in combination with U.S. census data from 2010 and an NLCD overlay (NLCD
2011, Environmental Systems Research Inst. 2011, U.S. Census Bureau 2010, U.S.
Geological Survey 2011).
This research was conducted to assist with hazard identification and
characterization (the first step in the risk assessment process) by determining prevalence
of specific pathogens in Canada goose fecal samples throughout the Greater Columbus
area during the molt and post-molt periods. It also discusses dose-response for protozoan
parasites from previous studies, and initiated the exposure assessment process by
measuring factors such as human population density within study sites, distance to
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potential sources of contamination, and time of year when exposure may be highest.
Future analysis should focus on quantifying dose-response and exposure. This may rely
on molecular methods such as PCR in order to create a dose-response model. In order to
confirm exposure, more variables that may be associated with pathogen exposure should
be measured, surveys should be conducted, behaviors should be observed and analyzed,
and epidemiological databases should be utilized.
My goal was to provide information that will improve understanding of the
ecology of zoonotic pathogens and the transmission pathways between human and animal
populations in addition to determining the effects of urbanization on pathogen
transmission. Ultimately this will facilitate identification of critical control points to
apply preventive measures. Results from this study can be used to guide management
decisions about the most effective ways to reduce exposure and transmission whether by
establishing ecological barriers or baiting with resins that bind bile (cholestyramine) to
prevent proliferation of pathogens (Erlandsen 2005).
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Goose Population Trends in Ohio
The Canada goose, a characteristic species of the Nearctic avifauna, is the most
widely distributed goose in North America (Mowbray et al. 2002). Canada geese thrive in
a broad range of habitats in temperate to low-arctic regions including tundra, boreal
forest, prairies and parklands, high mountain meadows, managed refuges and, of
particular significance here, areas of human habitation (Mowbray et al. 2002). Giant
Canada geese (Branta canadensis maxima) were rarely seen in Midwestern states
including Ohio in the early nineteenth century (Ohio Department of Natural Resources
2012a). This was the result of many years of egg harvesting, over-hunting, and habitat
loss from draining wetlands for crop production (Titchenell and Lynch 2010, Mowbray et
al. 2002). Population levels became so low that recovery programs were initiated.
The Ohio Division of Wildlife initiated a Giant Canada goose restoration program
in 1956 when 10 pairs were released on each of 3 state-owned wetland areas (Ohio
Department of Natural Resources 2012a). Canada geese were nesting in 49 of Ohio’s 88
counties in 1979 with a state population of 18,000 geese (Ohio Department of Natural
Resources 2012a). Recent surveys found goose nests in every county with a population
estimate of 84,000 geese (Ohio Department of Natural Resources 2012a). The Giant
Canada goose is now the most abundant subspecies of Canada goose in Ohio (U.S. Fish
and Wildlife Service 2012). Efforts to re-establish the giant Canada goose in Midwestern
states have been so successful that many of these birds have been translocated from areas
where high populations have become a nuisance to areas outside the original breeding
range of the species (Mowbray 2002). The result of this translocation has been an
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expansion southward of the natural range of giant Canada geese which now includes the
southern and southwestern United States (Nelson and Oetting 1998).
Life History
The resident Canada goose, (Branta canadensis maxima), forages primarily on
grasses, sedges, and berries on breeding grounds and on grasses and agricultural crops on
wintering areas (Mowbray et al. 2002). Additionally, geese are known to feed on human
food such as bread and popcorn in urban areas (Titchenell and Lynch 2010).
Branta canadensis nest individually or semi-colonially, preferring sites on small
islands in tundra lakes and ponds or on margins of lakes and rivers (Mowbray et al.
2002). Breeders form life-long monogamous pair bonds, generally during their second
year (Mowbray et al. 2002). Most begin nesting in substantial numbers as 2-yr-olds and
reach peak breeding in their fourth or fifth year (Mowbray et al. 2002). The nesting
season may start as early as mid-March or as late as June (U.S. Fish and Wildlife Service
2012). Average clutch size for female B. c. maxima is 5.6 eggs (Mobray et al. 2002). The
nest is a large open cup made of dry grasses and sedges, lichens, mosses, or other plant
material typically placed on the ground usually near water. The nest is commonly lined
with down and some contour feathers (Mowbray et al. 2002). Offspring are precocial,
however most remain with their parents throughout the first year of life, traveling
together in large flocks of family groups (Afton and Paulus 1992). Once the goslings
reach one year of age they join other yearlings and move to new areas such as the Arctic
barrens (Hanson 1965).
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Canada geese also molt all of their feathers once each year. Though seemingly
risky, this behavior has proven successful among many Anseriformes (Gates et al. 1993).
Molting usually occurs between June and late July followed by a 4-5 week flightless
period when they shed and re-grow their wing feathers. Most birds resume flight by
August (Mowbray et al. 2002). The rest of the summer is spent preparing for migration
which may start as early as late September or as late as December-January for late
migrants in the Mississippi Valley population (Gates et al. 2001). In general the geese
typically spend summers in northern North America and fly south when cold weather
limits availability of food resources and roosting sites (Mowbray et al. 2002).
Migration
The current study focuses primarily on the population of B. c. maxima in Ohio
which is included in the Mississippi Flyway. The Mississippi Flyway includes all
Canada geese nesting in Mississippi Flyway States (Alabama, Arkansas, Indiana, Illinois,
Iowa, Kentucky, Louisiana, Michigan, Minnesota, Mississippi, Missouri, Ohio,
Tennessee, and Wisconsin), the Canadian Provinces of Saskatchewan, Manitoba, and
Ontario as well as Canada geese nesting south of latitude 50°N in Ontario and 54° N in
Manitoba (U.S. Fish and Wildlife Service 2012).
The general fall migration pattern from the most northerly nesting areas begins in
late August and continues through September with most geese reaching the northern U.S.
by late September to early October. Those wintering farther south arrive at their
wintering grounds by early to mid-November (Mowbray et al. 2002). Spring migration
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begins around late January and peak arrival at breeding ground occurs in April (Mowbray
et al. 2002).
Molt migrations are undertaken mostly by first and second year nonbreeding
geese and adult geese that did not breed or that lose nests or broods. These migrations are
always northward to take advantage of plants in earlier phenological stages of growth and
larger water bodies (Mowbray et al. 2002). Giant Canada geese undertaking molt
migrations from breeding areas in the U.S. and southern Canada migrate in late May to
mid-June to the shores of Hudson and James Bays, between Québec and Manitoba, to
molt (Mowbray et al. 2002). Luukkonen et al. (2008) found that 80% of females in
Michigan whose nests were experimentally destroyed and 51% whose nests failed due to
natural causes undertook molt migrations. Of females with failed nests residing in urban
parks, only 23% migrated north to molt while >65% of females with failed nests in other
areas undertook molt migrations. The geese began to return to their former nesting areas
during late August through early September, with the majority migrating before or during
October (Luukkonen et al. 2008).
Resident Populations
According to the Code of Federal Regulations, the resident Canada goose
population is defined as Canada geese that nest within the lower 48 States and the District
of Columbia in the months of March, April, May, or June, or reside within the lower 48
States and the District of Columbia in the months of April, May, June, July, or August
(50 C.F.R. part 20 2007). This is in contrast to migrant populations that nest in arctic and
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subarctic breeding grounds (Giant Canada goose Committee 1996). Resident Canada
goose populations have increased eight-fold over the past 20 years in North America
(Clark 2003).
Resident geese include two subspecies, the giant Canada goose (B. c. maxima)
and the western Canada goose (B. c. moffitti), and possible hybrids between these and
other subspecies. These populations remain in one area year-round and undertake shortdistance or no migrations during spring and fall (U.S. Fish and Wildlife Service 2012,
Nelson and Oetting 1998). Groepper et al. (2008) found that the mean home range of
resident geese in Nebraska was ~25 km² and the mean maximum distance moved
between study areas of use over the course of 30 months was 13 km. Rutledge et al.
(2013) reported that the mean distance traveled by geese during the molt season (1 Jun-15
July) was 2 km for males and 2.1 km for females. They found that mean distance traveled
per goose was 4.8 km for males and 4.1 km for females during the post-molt (Rutledge et
al. 2013).
Human-modified landscapes provide resident geese with two main habitat
requirements: permanent bodies of freshwater and open areas where they can land, view
their surroundings, and are provided with abundant of vegetation for feeding and nesting
(Titchenell and Lynch 2010). Many urban and suburban areas contain bodies of water
and marshes with islands that provide safe nesting sites for geese (Gosser and Conover
1999). Resident geese have access to growing crops, pastures, lawn, waste grains, and
natural wetland vegetation before, during, and after nesting. They also benefit from
tender new grass growth stimulated by mowing, urban gardens that provide a variety of
native and exotic plants, and human handouts (U.S. Fish and Wildlife Service 2012,
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Conover 1998). Not only is food abundant, but resident geese have few natural predators.
Hunting is restricted in most urban areas where geese can seek refuge to avoid peak
harvest periods (U.S. Fish and Wildlife Service 2012, Nelson and Oetting 1998).
Human Conflicts With Resident Canada Geese
Large populations of resident geese in urban areas pose a potential threat to
environmental and human health. The majority of problems stem from the fact that large
populations of resident geese produce large quantities of fecal matter that accumulate in
urban areas where there is close contact with humans. Resident geese are frequently seen
near picnic areas, in parking lots, and anywhere where there is green vegetation to forage
on. This is cause for concern, considering the potential for fecal-oral pathogen
transmission to humans. Canada geese excrete about one dropping per minute when they
are feeding, producing nearly 907 g/day of feces (Dieter et al. 2001). Studies of urbansuburban populations have implicated Canada goose feces in the eutrophication and
contamination of school yards, parks, and boating and swimming areas, introducing the
possibility of pathogen transmission to humans from direct contact with fecal material or
indirect contact with contaminated water (Conover and Chasko 1985, Feare et al.1999,
Allan et al. 1995).
Canada geese are known to serve as reservoirs and vectors for numerous
pathogens including Giardia, Cryptosporidium, Salmonella, Avian Influenza,
Campylobacter jejuni, Listeria, Legionella, Newcastle Disease, and E. coli, and a variety
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of other pathogens and microbes (Clark 2003). The combination of exotic and domestic
waterfowl along with resident Canada geese in high densities within urban areas is
conducive to transmission of enzootic pathogens, such as Duck Virus Enteritis, and other
zoonotic pathogens such as Avian Influenza. Additionally, coliform bacteria counts
increase dramatically in sewage treatment plants when large numbers of Canada geese
are present and decline when geese are removed (U.S. Fish and Wildlife Service 2012).
The risk of humans contracting infections from Canada goose droppings depends
on: 1) presence of pathogenic organisms in feces, 2) survival of pathogens in feces after
deposition, and 3) deposition frequency and distribution of Canada goose droppings in
areas where humans are likely to contact them (Feare et al. 1999). An additional factor to
consider is the frequency of human contact. This study focuses on addressing the first
factor, presence of pathogenic organisms (Giardia, Cryptosporidium, Salmonella, and
cephalosporin resistant E. coli) in resident Canada goose feces. These pathogens can
survive for long periods in the environment. Giardia can survive for up to two months in
water while Cryptosporidium can survive up to 1 year (Graczyk et al.2008). Salmonella
can survive for weeks in water and years in soil, while E. coli can survive from days to
weeks in the environment (U.S. Environmental Protection Agency 2009).
Protozoa
Protozoan parasites such as Cryptosporidium parvum and Giardia lamblia are
human anthropozoonotic (animal to human transmission/direct zoonoses) pathogens that
inflict considerable morbidity (due to diarrheal disease) on healthy people and can cause
mortality in immunosuppressed individuals (Clark 2003, Graczyk et al. 2008). These
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pathogens are category B biodefense agents on the National Institutes of Health (NIH)
list because they are “moderately easy to disseminate, result in moderate morbidity and
low mortality rates, and require specific enhancements for diagnostic capacity and
enhanced disease surveillance” (National Institute of Allergy and Infectious Disease
2012). Giardia and Cryptosporidium are also the most common enteric parasites of
humans and domestic animals, and are increasingly recognized as parasites of a diverse
range of wildlife species (Hunter and Thompson 2005). The major routes of transmission
include ingestion of contaminated food and/or water and direct contact with infected
people, animals, or contaminated environmental surfaces (Kassa et al. 2004). Cases of
Giardia and Cryptosporidium in the human population tend to peak in early summer
through early fall, coinciding with the summer water-based recreational season (Xiao and
Fayer 2008, Yoder et al. 2012a).
A model of pathogen deposition by waterfowl in water was created by Graczyk et
al. (2008) to facilitate exposure assessment for recreational water users. This model
considers the number of fecal pellets deposited on a 1 x 100 m shore-line segment by a
single visitation of an average flock of waterbirds including gulls, geese, and ducks.
Using this model, researchers concluded that when one considers the average
concentrations of Giardia and Cryptosporidium shed in bird feces, an average waterbird
feces pellet weighing 17.2 g, and 8.6% of birds shedding human pathogens, a single
visitation by an average size flock of waterfowl can introduce approximately 9.3 x 106
infectious Cryptosporidium oocysts and 1.0 x 107 Giardia cysts to the water (Graczyk et
al. 2008, Kirschner et al. 2004). However, this model needs verification from research
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and only addresses exposure for recreational water users and not the human population as
a whole.
The circumstances under which transmission cycles interact to cause zoonotic
transfer of these protozoa is poorly understood, although zoonotic transmission of
Giardia and Cryptosporidium has been documented (Hunter et al. 2005, Graczyk et al.
2008). Evidence of human-to-animal transmission (zooanthroponotic or reverse
zoonoses) is found in the fact that resident geese can acquire Cryptosporidium hominis, (a
human-adapted genotype) oocysts from local garbage and other unhygienic places
(Graczyk et al. 1998, Zhou et al. 2004). Though these forms are not infective to geese, the
geese act as biological vectors. Additionally, geese can become exposed to pathogens
through livestock, domestic pets, and other wildlife.
Cryptosporidium
Cryptosporidium is one of the most frequent causes of waterborne disease among
humans (Centers for Disease Control 2013). The transmissive stage, the oocyst, is
environmentally resilient and therefore ubiquitous in the environment. Oocysts can retain
their infectivity for up to a year in water and can retain viability and infectivity after
freezing (Graczyk et al. 2008, Fayer and Nerad 1996). Cryptosporidium is also highly
chlorine-resistant (Yoder et al., 2012a). As a result, identification, removal, and
inactivation of oocysts in drinking water can be a challenge.
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The genus Cryptosporidium comprises 26 species and over 40 genotypes (Xiao
and Fayer 2008, Ryan et al., 2014a). As of 2011 there were 21 recognized species of
Cryptosporidium with several host groups susceptible to more than one species (Power et
al. 2011). Only C. hominis and C. parvum are of major significance to public health by
causing the majority of human cases (Zhou et al. 2004). However, other species and
genotypes have been associated with human illness as well (Ryan et al. 2014a). For
example, C. meleagridis is generally considered to be an avian pathogen and is
increasingly recognized as an important human pathogen (Appelbee et al. 2005). Birds in
general are known to be susceptible to infection with C. meleagridis, C. baileyi, and C.
galli along with 12 other genotypes (Ryan et al. 2014b). Five of these genotypes are
known as the “goose genotypes I-V.” Only Canada geese become infected by these
genotypes (Ryan et al. 2014b). Geese also act as biological vectors for other species such
as C. parvum (Graczyk et al. 1998). Oral inoculations of Canada geese with infectious C.
parvum oocysts have shown that the oocysts remain infective after passing through the
gastrointestinal tract (Graczyk et al. 1997).
Cryptosporidium parvum is the most widespread cause of cryptosporidiosis in all
mammals, in addition to being the most prevalent zoonotic species (Xiao and Fayer
2008). Two genotypes of Cryptosporidium parvum, the animal-adapted or zoonotic
genotype (also known as C. parvum senso stricto), and the human-adapted genotype (also
known as C. hominis), have been identified with molecular techniques (Xiao and Fayer
2008, Hunter et al. 2005). Both genotypes can produce life-threatening infections in
immunocompromised and immunosuppressed people (Graczyk et al. 1998). Although C.
parvum most often produces a self-limited diarrheal illness, symptoms can be severe and
14
last for several weeks. Kassa et al. (2001) reported that as few as 30 Cryptosporidium
oocysts can cause illness in a healthy adult human (DuPont et al. 1995). The mean
concentration of oocysts found in the average Canada goose fecal sample is 370 ± 197 (±
standard deviation) oocysts/g (Graczyk et al. 1998).
The Cryptosporidium lifecycle is complex (Figure 1.1). Oocysts must be ingested
or inhaled which is followed by excystation during which sporozoites are released and
attach to epithelial cells in the gastrointestinal tract or other tissues (Centers for Disease
Control 2013). Once this is achieved, asexual multiplication and then sexual
multiplication occurs producing microgamonts (male) and macrogamonts (female).
Oocysts develop after macrogamonts are fertilized by the microgamonts. Thick-walled
oocysts are generally excreted from the host while thin-walled oocysts are involved in
autoinfection. Oocysts are infective after excretion (Centers for Disease Control 2013).
Even though cryptosporidiosis has been a Nationally Notifiable Disease since
1995, estimates of prevalence in the human population vary greatly because of low
reporting rates, inconsistent diagnostic methods, and many people have no access to
medical care or do not seek it (Dietz and Roberts 2000, Xiao and Fayer 2008). The
largest outbreak of Cryptosporidium occurred in Milwaukee during spring 1993. An
estimated 403,000 people were affected and over 600 confirmed cases of gastrointestinal
illness were associated with the outbreak. The outbreak was caused by a failure in the
filtration system at a municipal water-treatment plant (MacKenzie et al. 1994). A total of
11,612 laboratory-confirmed cases of cryptosporidiosis were reported to the Centers for
Disease Control during 1995-1998 (Dietz and Roberts 2000). All ages and both sexes
15
Figure 1.1 Cryptosporidium life cycle with summary (Center for Disease Control and
Prevention. 2013. Cryptosporidium. Parasites. Retrieved 2014 from:
http://www.cdc.gov/parasites/crypto/biology.html).
16
were affected. An increase in case reporting was observed in late summer during each
year of surveillance for people <20 years of age (Dietz and Roberts 2000). The greatest
proportion of case patients were in the 0-9 year age group each year (Dietz and Roberts
2000). People younger than age 20 accounted for the highest percentages of cases during
early summer through early fall (Dietz and Roberts 2000). There were 3,505 cases
reported in the USA in 2003, 3,911 in 2004, and 8,269 in 2005 (Yoder and Beach 2007,
Xiao and Fayer 2008). The large increase in reporting in 2005 was primarily attributed to
a single recreational water-associated outbreak (Xiao and Fayer 2008). The most recent
available report from the Centers for Disease Control covers surveillance during 20092010. There were 7,656 confirmed and probable cases of cryptosporidiosis reported in
2009 (2.5 per 100,000 population) and 8,951 confirmed and probable cases in 2010 (2.9
per 100,000 population) (Yoder et al. 2012a).
Giardia
Giardia, a Nationally Notifiable disease since 2002, is the most common human
intestinal parasitic infection reported in the United States (Centers for Disease Control
2014, Centers for Disease Control 2011). The disease is thought to be highly
underreported with an estimated 1.2 million infections occurring annually (Centers for
Disease Control 2014, Adams et al. 2012). The transmissive stage, or cyst, is tolerant to
chlorine disinfection and survivability of the cysts decreases as temperature increases
(Centers for Disease Control 2011, U.S. Environmental Protection Agency 2000). Cysts
can retain their infectivity for up to two months in water (Graczyk et al. 2008).
17
Giardia cysts are common in water and can be acquired by birds from the
environment (Graczyk et al. 2008). Graczyk et al. (2008) and Upcroft et al. (1997) found
that a single avian isolate of Giardia was virulent to mammals, indicating that birds can
serve as reservoir hosts in addition to being biological vectors. The average concentration
of Giardia sp. cysts found in a Canada goose fecal sample was 405 cysts/g (Gatczyk et al.
1998). Genotyping and subtyping data suggest that zoonotic transmission is not as
important in the epidemiology of giardiasis as in cryptosporidiosis (Xiao and Fayer
2008). Most types of Giardia are adapted to a single host species which reduces the
likelihood that zoonotic transmission pathways play a major role in human disease
(Hunter et al. 2005). However, zoonotic transmission has been documented and still
remains poorly understood (Hunter et al. 2005, Xiao and Fayer 2008).
There are currently 6 recognized species of Giardia based on cyst and trophozoite
morphology and host occurrence. These include Giardia agilis (amphibians), Giardia
ardeae (birds) Giardia psittaci (birds) Giardia duodenalis or lamblia (most mammals),
Giardia microti (voles and muskrats), and Giardia muris (rodents) (Xiao and Fayer 2008,
Appelbee et al. 2005). Giardia duodenalis is recognized to have a broader host range than
the other species. There are seven variants of G. duodenalis that are currently recognized,
each with a varying degree of host specificity (Xiao and Fayer 2008). Variants A and B
are often referred to as zoonotic variants because they have the ability to infect a broad
range of mammals, including humans. Variants C and D primarily infect dogs while
variant E infects livestock. The F variant infects cats while the G variant infects rodents
(Appelbee et al. 2005, Xiao and Fayer 2008).
18
Taxonomy has been the most controversial area of Giardia research due to its
wide host range and lack of association with morphological features to measure host
specificity (Thompson and Monis 2011). Molecular tools are only now helping to resolve
this uncertainty (Thompson and Monis 2011). Studies of Giardia in Canada geese have
mostly looked for Giardia spp. or Giardia lamblia. Although Giardia has been clearly
detected in Canada goose fecal samples (Kassa et al. 2001, Roscoe 2001, Kostroff and
Rollender 2001, Ayers et al. 2014, Binkley 2014 unpublished), it is unknown whether
there is a specific isolate that is able to infect and reproduce in Canada geese.
The Giardia lifecycle is relatively complex (Figure 1.2). Once cysts are ingested
by the host, excystation occurs and trophozoites are released into the small intestine
(Centers for Disease Control 2011). Trophozoites then multiply by longitudinal binary
fission and remain in the lumen of the proximal small bowel. Encystation occurs as the
parasites move toward the colon. Cysts are infectious upon excretion by the host (Centers
for Disease Control 2011).
Reporting of giardiasis to the Centers for Disease Control and Prevention
Surveillance Systems during 1998 - 2003 indicated that the total number of cases varied
between 20,075 and 24,226 annually in the USA (Xiao and Fayer 2008). The total
number of reported cases increased slightly from 19,239 for 2006 to 19,794 for 2007 and
decreased slightly to 19,140 in 2008 (Yoder et al. 2010). The total number of cases
increased from 19,403 for 2009 to 19,888 in 2010 (Yoder et al. 2012b). A larger number
of cases were reported for children ages 1-9 (Yoder et al. 2012b).
19
Figure 1.2 Giardia life cycle with summary (Center for Disease Control and Prevention.
2011.Giardia. Parasites. Retrieved 2014 from: http://www.cdc.gov/parasites/Giardia/)
20
Bacteria
Bacterial infections, such as Salmonella and E. coli are also a cause for concern.
Major routes of transmission for these two pathogens include ingestion of contaminated
food or water and direct contact with infected people, animals, or contaminated
environmental surfaces (National Institute of Allergy and Infectious Disease 2011a,
National Institute of Allergy and Infectious Disease 2011b).
Salmonella is a large group of rod-shaped, gram negative bacteria comprising
>2000 known serotypes in the family Enterobacteriaceae (Maier et al. 2009). All
serotypes are pathogenic to humans (Maier et al. 2009). As a gram negative bacterium,
its survival strategy relies on a cell envelope that facilitates interaction with mineral
surfaces and solutes in the environment to obtain nutrients required for metabolism
(Maier et al. 2009). Salmonellosis is the second leading cause of foodborne illness in the
USA. Most common symptoms include nausea, vomiting, abdominal cramps, diarrhea,
fever, and headache (Maier et al. 2009).
E. coli, also a gram-negative rod bacteria, is found in the gastrointestinal tract of
all warm-blooded animals and is usually considered to be harmless, although several
strains are capable of being pathogenic (Maier et al. 2009). These strains include:
enterotoxigenic, enteropathogenic, enteroinvasive, and entercohemorrhagic E .coli (Maier
et al. 2009). Enterohemorrhagic strains can cause renal failure and hemolytic anemia
(Maier et al. 2009).
21
Antimicrobial Resistance
The importance of Canada geese as reservoirs and sources of transmission of drug
resistant strains of bacteria is an important issue for pathogen management.
Antimicrobial-resistant organisms in domestic animals such as poultry, beef, and swine,
are well documented and these animals have been implicated as reservoirs for multidrugresistant foodborne pathogens (Cole et al. 2005). In particular, ESC-R E. coli (ESC-REc), ESC-R Salmonella (ESC-R-Sal) and MDR Acinetobacter spp. have been isolated
from farm, wild, and companion animals (Seiffert et al. 2013). The massive and
indiscriminate use of antibiotics, especially cephalosporins, has contributed to selection
and spread of multi-drug resistant organisms (Seiffert et al. 2013).
Cephalosporins are a group of antibiotics classified by four generations, with later
generations being more resistant to β-lactam destruction. The drug is rendered
ineffective when the β-lactam ring in the molecular structure is destroyed (Merck 20102013). CTX-M extended-spectrum β-lactamases are enzymes produced by bacteria that
are capable of inhibiting the antimicrobial effects of cephalosporin drugs. Extended
spectrum cephalosporins (ESCs) are used to treat serious infections caused by pathogens
such as E. coli, Salmonellaspp. and Acinetobacter spp. (Seiffert et al. 2013). They are the
treatment of choice for invasive gram-negative infections (Wittum et al. 2010). CTX-M
type extended-spectrum β-lactamases from fecal E. coli of both sick and healthy dairy
cattle in Ohio were reported in 2010 (Wittum et al. 2010).
22
Bacterial resistance to β-lactam antibiotics including cephalosporins has risen
dramatically in human pathogens. The use of extended-spectrum cephalosporins in
human health institutions contributes to the emergence of resistance (Bradford et al.
1999). Interaction with waste materials from livestock species may transfer resistant
pathogens and genetic elements to free-ranging wildlife such as Canada geese, therefore
creating an additional environmental reservoir of resistant organisms (Cole et al. 2005).
Pathogen Transmission Environments
The potential for zoonotic transmission is largely influenced by the type of
environment along with the ecology of host organisms within that environment.
Therefore, it is useful to examine the structure of these environments to predict where
transmission may occur. Comparisons of urban and peri-urban environments may reveal
factors that provide the greatest potential for pathogen transmission. Urbanization is a
process where human habitation increases in a particular area along with increased per
capita energy consumption and extensive modification of the landscape. The result is a
system that does not depend principally on local natural resources to persist (McDonnell
et al. 1997).
The greater Columbus area contains a mixture of both urban and peri-urban
environments which provides ideal study conditions to compare pathogen prevalence in
these two environments. The study area allows comparisons of pathogen prevalence in
Canada goose feces between urban resident and peri-urban resident populations. One of
23
the major gaps in knowledge is the effect that urbanization has on zoonotic pathogen
transmission and what pathogens are present that can potentially be transmitted to
humans. It is important to establish what risk factors within the urban environment makes
some sites a higher risk for pathogen exposure than others. For example, a likely risk
factor for exposure to pathogens is high human density. This allows more opportunities
for exposure to occur.
24
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goose Symp. Milwaukee, WI.
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%20conflict.pdf
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2012 from: http://wildlife.ohiodnr.gov/species-and-habitats/species-guideindex/birds/canada-goose
Ohio Department of Natural Resources. 2012c. Big Island Wildlife Area. Wildlife Area
Maps. Retrieved 2012 from:
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Fs/bigisland.pdf
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33
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34
CHAPTER 2
PREVALENCE OF CRYPTOSPORIDIUM, GIARDIA, SALMONELLA, AND
CEPHALOSPORIN-RESISTANT E.COLI STRAINS IN CANADA GOOSE FECES AT
URBAN AND PERI-URBAN SITES IN CENTRAL OHIO
Introduction
Resident Canada goose populations have increased eight-fold over the past 20
years in North America. These populations are concentrated in urban areas (Clark 2003)
resulting in resident geese becoming so numerous in some areas that they are considered
a nuisance (Nelson and Oetting, 1998).
Human-modified landscapes provide these birds with significant advantages. Two
main habitat requirements are easily fulfilled in urban environments, including a
permanent body of freshwater and an open area where they have a good view of their
surroundings and are provided with an abundance of vegetation for feeding and nesting
(Titchenell and Lynch 2010). Because these resident geese are able to remain in areas
associated with human activity and a longer growing season year round, they are
provided with a consistently available source of food (Nelson and Oetting 1998).
35
The major issue is that large populations of resident geese in urban areas pose a
threat to both environmental health, through eutrophication, and human health. The
majority of issues stem from the fact that large populations of resident geese result in
large quantities of fecal matter that accumulate in urban areas where there is close contact
with the human population. Studies of urban-suburban populations have implicated
Canada goose feces in the contamination of school yards, parks, and boating and
swimming areas thus introducing the possibility for pathogen transmission to humans
from either direct or indirect contact (Conover and Chasko 1985, Feare et al.1999, Allan
et al. 1995). Canada geese are known to serve as reservoirs and vectors for numerous
pathogens including Giardia, Cryptosporidium, Salmonella, Avian Influenza,
Campylobacter jejuni, Listeria, Legionella, Newcastle Disease, and E. coli, among other
pathogens and microbes (Clark 2003). This study focuses solely on Giardia,
Cryptosporidium, Salmonella, and cephalosporin resistant E. coli strains.
Cryptosporidium and Giardia cause self-limited diarrheal illness however
symptoms can be severe and last for several weeks. They can also cause life-threatening
infections in immunocompromised and immunosuppressed individuals (Graczyk et al.
1998). Zoonotic transmission of both Giardia and Cryptosporidium has been documented
(Hunter et al. 2005, Graczyk et al. 2008). Evidence of human-to-animal transmission
(zooanthroponotic /reverse zoonoses) can be found in the fact that residential geese can
acquire Cryptosporidium hominis, the human-adapted genotype, oocysts from local
garbage and other unhygienic places (Graczyk et al. 2008).
Presence of Salmonella and antimicrobial-resistant bacteria in Canada goose fecal
samples is also of great concern. Salmonella is one of the leading causes of foodborne
36
illness in the U.S. (Maier et al. 2009). Antimicrobial-resistant bacteria and genetic
elements are thought to be transferred to free-ranging wildlife, such as Canada geese,
through interaction with waste materials from livestock species therefore creating an
additional environmental reservoir of resistant organisms (Cole et al. 2005).
The potential for zoonotic transmission is largely influenced by the type of
environment as well as the ecology of the organisms within that environment. High
densities of humans in urban areas typically results in large-scale modification of the
environment and a tremendous concentration of food, water, energy, materials, sewage,
pollution, and waste in one area (McDonnell et al. 1997). Therefore, urbanization tends
to degrade environmental quality and disrupt natural processes.
Study Objectives and Hypotheses
This was an exploratory study that sought to assist with hazard identification (the
first step in the risk assessment process) by determining prevalence of specific enteric
pathogens in Canada goose fecal samples throughout the Greater Columbus area during
the molt and post-molt period. It also initiated the exposure assessment process by
measuring specific environmental variables that may be associated with exposure. My
central hypothesis was that if greater environmental contamination and more interactions
at the animal-human interface occur in urban locations (McDonnell et al. 1997, Zhou et
al. 2004, Graczyk et al. 2008) while rural sites have less contamination but more
interactions at the wildlife-livestock interface where antimicrobial resistance has been
known to occur (Cole et al. 2005, Wittum et al. 2010, Allen et al. 2011, Seiffert et al.
37
2013). Consequently prevalence of Giardia, Cryptosporidium, and Salmonella will be
higher at urban sites while prevalence of cephalosporin-resistant E. coli strains will be
higher at peri-urban sites. Prevalence of pathogens will be more concentrated at urban
sites during the molt period and will become more evenly distributed during the postmolt period.
This study sought to provide information to explain several major gaps in
knowledge including: 1) The effects of urbanization and landscape factors on zoonotic
pathogen prevalence 2) The prevalence of pathogens and the molecular diversity of
Canada goose fecal microbiota, 3) The presence of antimicrobial-resistant bacteria in the
Canada goose population in the Greater Columbus area. The overall goal was to provide
information that will improve understanding of the ecology of infectious zoonotic
pathogens and the transmission pathways between human and animal populations as well
as to determine the effects of urbanization on pathogen transmission. Ultimately this will
allow critical control points for prevention to be identified. Study objectives and
hypotheses included:
Objective 1. Determine the prevalence of Giardia, Cryptosporidium, Salmonella, and
cephalosporin-resistant E. coli strains in resident Canada goose fecal samples collected
from the Greater Columbus area.
Hypothesis 1. If both Giardia and Cryptosporidium have an outer shell (cyst/oocyst) and
are environmentally robust (Centers for Disease Control 2011, Centers for Disease
Control 2013) while Salmonellaand E. coli have no outer protection, then there will be
38
higher prevalence of Giardia and Cryptosporidium than Salmonella and cephalosporinresistant E. coli strains observed in resident Canada goose fecal samples collected at
various sites throughout the Greater Columbus area.
Objective 2. Examine the effects of age, landscape, and season, on prevalence of the
selected organisms in resident Canada goose fecal samples.
Hypothesis 2a. If juvenile Canada geese lack a fully developed immune system during
the first collection period and have not had enough time to acquire resistance, then
prevalence of pathogens will be higher in juvenile geese than adult geese during the first
collection period.
Hypothesis 2b: If greater environmental contamination and more interactions at the
animal-human interface occur in urban locations (McDonnell et al. 1997, Zhou et al.
2004, Graczyk et al. 2008) while peri-urban sites have less contamination but more
interactions at the wildlife-livestock interface where antimicrobial resistance has been
known to occur (Cole et al. 2005, Wittum et al. 2010, Allen et al. 2011, Seiffert et al.
2013), then the prevalence of Giardia, Cryptosporidium, and Salmonella will be higher at
urban sites while prevalence of cephalosporin-resistant E. coli strains will be higher at
peri-urban sites.
Hypothesis 2c: If geese residing at urban sites are exposed to more indirect and direct
contact with the human population (McDonnell et al. 1997, Zhou et al. 2004, Graczyk et
39
al. 2008), then a greater pathogen prevalence will be observed at urban sites than at periurban sites during the molt period (June) while prevalence will become more even
between urban and peri-urban sites once flight is resumed (August).
Objective 3: Determine the effects of environmental factors on prevalence of the selected
pathogens in Canada goose fecal samples.
Hypothesis 3a: If contact with the human population introduces more opportunities for
pathogen transmission between the two populations (Rolland et al. 1985, McDonnell et
al. 1997, Zhou et al. 2004, Graczyk et al. 2008, Allen et al. 2011), then pathogen
prevalence will be positively associated with human population density and the number
of humans present at each site.
Hypothesis 3b: If antimicrobial resistance is introduced by livestock (Cole et al. 2005,
Wittum et al. 2010, Allen et al. 2011, Seiffert et al. 2013), then sites that contain a greater
proportion of livestock farms or are located in close proximity to a livestock farm will
have a higher prevalence of cephalosporin-resistant E.coli.
Hypothesis 3c. If wastewater treatment plants contain contaminated water, then sites
located in close proximity to wastewater treatment plants will have higher pathogen
prevalence.
40
Hypothesis 3d. If a larger number of geese present at a site provides more opportunities
for horizontal transmission within the Canada goose community, then pathogen
prevalence will be greater at sites with larger Canada goose populations.
Study Sites
Canada geese were studied in 5 peri-urban and 7 urban sites throughout the
Greater Columbus area in Ohio. Sites were selected based on descriptions provided by
the Ohio Department of Natural Resources and Columbus Metro Parks (Ohio Department
of Natural Resources 2012a, Ohio Department of Natural Resources 2013, Columbus
Metro Parks 2009a, Columbus Metro Parks 2009b) as well as general knowledge of the
area. Sites were confirmed as being urban or peri-urban based on GIS analysis in
combination with a National Land Cover Database overlay (Environmental Systems
Research Inst. 2011, NLCD 2011). Land cover categories were based on the NLCD 2011
program legend (Table 2.1). Each site was classified by the land cover category that made
up the greatest percentage of the site (≥35%) (Table 2.2). The grassland, open water,
barren, deciduous forest, evergreen forest, mixed forest, forested wetland, and herbaceous
wetland categories were combined into an “undeveloped” category for linear regressions
and modeling of Cryptosporidium prevalence. Analysis included a 1km radius around
each site (Rodewald and Shustack, 2008). Site descriptions and spatial distribution can be
seen in Table 2.3 and Figure 2.1.
41
Table 2.1 Land cover categories selected from the National Land Cover Database
Program Legend
Land cover category
Description
Grassland
Areas dominated by gramanoid or herbaceous vegetation, generally greater than
80% of total vegetation.
Open Water
Areas of open water, generally with less than 25% cover of vegetation or soil.
Developed Open
Space
Areas with a mixture of some constructed materials, but mostly vegetation.
Impervious surfaces account for less than 20% of total cover.
Developed Low
Intensity
Areas with a mixture of constructed materials and vegetation. Impervious
surfaces account for 20% to 49% percent of total cover.
Developed Medium
Intensity
Areas with a mixture of constructed materials and vegetation. Impervious
surfaces account for 50% to 79% of the total cover.
Barren Land
Areas of bedrock, desert pavement, scarps, talus, slides, volcanic material, glacial
debris, sand dunes, strip mines, gravel pits and other accumulations of earthen
material. Vegetation accounts for less than 15% of total cover.
Deciduous
Forest
Areas dominated by trees generally greater than 5 meters tall, and greater than
20% of total vegetation cover. More than 75% of the tree species shed foliage
simultaneously in response to seasonal change.
Evergreen Forest
20% of total vegetation cover. More than 75% of the tree species maintain their
leaves all year.
Mixed Forest
20% of total vegetation cover. Neither deciduous nor evergreen species are
greater than 75% of total tree cover.
Pasture
Areas of grasses, legumes, or grass-legume mixtures planted for livestock
grazing or the production of seed or hay crops, typically on a perennial cycle.
Pasture/hay vegetation accounts for greater than 20% of total vegetation.
Forested Wetland
Area where forest or shrubland vegetation accounts for greater than 20% of
vegetative cover and the soil or substrate is periodically saturated with or covered
with water.
Emergent Herbaceous
Wetland
Area where perennial herbaceous vegetation accounts for greater than 80% of
vegetative cover and the soil or substrate is periodically saturated with or covered
with water.
42
Table 2.2 Proportion of each land cover type within sites where Canada goose fecal samples were collected in the greater
Columbus area during the molt and post-molt periods in the year 2013
Site
Three
Creeks
Big
Island
Horseshoe
Rd.
ODNR
Morse
Rd.
Bob
Evans
Franklin
Park
% Landcover
Prairie
Oaks
Grassland *
0.00
0.14
0.57
0.20
0.46
0.00
0.00
1.06
Open Water *
18.3
5.16
21.3
0.00
1.43
10.3
0.00
Developed Open Space
4.47
11.4
3.07
12.1
3.47
30.2
Developed Low Intensity
1.69
6.22
2.09
12.3
3.04
Developed Medium Intensity
0.00
7.20
0.00
27.6
Barren *
5.07
0.00
0.00
Decidious Forest *
14.3
37.7
Evergreen Forest *
0.00
Mixed Forest *
Heritage
Scioto
Chadwick
Olentangy
0.00
0.00
0.00
0.00
0.00
0.80
0.00
1.06
3.38
6.91
16.9
15.5
27.7
30.7
4.61
46.3
27.9
21.1
32.0
28.2
23.7
16.20
0.14
12.7
65.2
40.3
51.3
44.1
44.5
75.80
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
8.57
4.35
31.3
0.32
0.00
15.4
0.37
0.00
0.00
0.00
3.56
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.20
0.00
0.00
0.00
0.00
0.00
0.00
Cultivated Pasture/Hay
18.5
1.66
7.02
6.59
8.23
0.00
0.00
5.19
0.00
0.00
0.00
0.00
Cultivated Crops
36.3
11.5
45.9
36.8
51.6
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Forested Wetland *
0.97
15.1
0.14
0.00
0.29
0.00
0.00
0.00
0.00
0.00
0.00
0.000
Emergent Herbaceous
Wetland *
0.40
0.32
11.3
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.000
43
Beekman
*Combined to create an "undeveloped" category for analysis.
Bold Font: Greatest percentage of land cover per site.
1
Table 2.3 Descriptions for sites where Canada goose fecal samples were
collected during the molt and post-molt periods in the year 2013
Site
UTM Coordinates
County
Prairie Oaks
17T0302136 m E
4430395 m N
FranklinMadison
Three Creeks
17S0336515 m E
4417355 m N
Franklin
Big Island
17T0308116 m E
4493327 m N
Marion
Heritage
17T0314148.09 m
E 4437080.22 m N
Franklin
Horseshoe Rd.
17T0328823 m E
4475450 m N
Delaware
Scioto
17T0321386.89 m E
4432626.20 m N
Franklin
Morse Road
17T0332336 m E
4436008 m N
Franklin
Restaurant
17T 0336001 m E
4445931 m N
Delaware
Olentangy
17S 0325942 m E
4429654 m N
Franklin
Franklin Park
17S0333049.58 m E
4425676.04 m N
Franklin
Beekman
17T0325908 m E
4430189 m N
Franklin
Chadwick
17T0326694.84 m N
4430790.06 m N
Franklin
44
Figure 2.1 Study sites used for resident Canada goose fecal sample collection during the
molt and post-molt periods in the Greater Columbus area during the year 2013
= Peri-urban
= Urban
45
Sample Collection
The goal was to sample only resident Canada geese. My study population was
Canada geese that were present consistently at each site during collection periods and had
been observed at the sites at least one week before collection began. Flightless geese were
restricted to sites during the molt period and therefore any pathogens of study would most
likely have been obtained on or near the site where the geese were found
considering these pathogens are self-limiting, meaning they tend to resolve without
treatment, and usually short-lived. I observed that the geese were present at the sites for
at least one week before sample collection, so geese would have likely cleared any
previously acquired pathogens. This assumption cannot be applied to the post-molt period
however, during the second period photos were taken during the observation period in an
attempt to sample from individuals that had been observed at sites the week prior to
collection. As a result my sample population was considered representative of resident
populations in central Ohio. Once the geese were able to resume flight, the population
was likely composed of geese that had been living in the area over the past few months
based on estimates of distance traveled by geese during the post-molt season (4.8 km for
males and 4.1 km for females) (Rutledge et al. 2013).
Additionally, hunting season, which begins in October in Ohio (Ohio Department
of Natural Resources, 2015), and migration had not yet begun. Molt migrants would only
just be starting to return and would not be likely to influence results (Luukkonen et al.
2008).
46
Samples were collected during two separate time periods (period 1 and period 2).
The first period was during June 4-June11 when geese had just begun their molt. The
second occurred August 16-30 when geese had resumed flight. Samples were collected
two separate times at each site within each period to increase validity by attempting to
ensure that results from fecal samples were not specific to a confounding factor that may
have been present on a single day of sampling (ex: wind, temperature). Events that affect
geese, such as flooding between sampling days, could not be controlled. Peri-urban sites
sampled during the first period included Prairie Oaks, Big Island, Horseshoe Road, and
Three Creeks (Table 2.3, Figure 2.1). Urban sites included Beekman Park, Olentangy
River, Morse Road, and a restaurant. Additional samples were collected from the
Franklin Park Conservatory and the Scioto River sites during the first period because
insufficient numbers of samples were collected from adults and juveniles. Additional
samples were needed in case samples from other sites could not be analyzed. The period
2 peri-urban sites did not include the Horseshoe Road or Three Creeks sites because
geese were no longer present. The Heritage site was added as a replacement site for the
Horseshoe Road site because it had similar land cover (≥35% cultivated crops). A
replacement for the Three Creeks site was not found within the Greater Columbus area.
The geese also left the Olentangy River site in the second sampling period. The
Chadwick Arboretum site was used as a replacement and was located less than 1.6 km
from the Olentangy site.
Juveniles were distinguished from adults during the first sampling period based
on small body size, presence of down feathers alone, grey color with little brown
contrast, and lighter colored necks (Sibley, 2000). My goal was to obtain fecal samples
47
from 3 adults and 3 juveniles during each visit in the first period and samples from geese
(both age classes) at each site during the second period with a goal of 96 samples
collected during each period. A total 199 samples were collected and analyzed (Table
2.4). One extra sample was collected from the Prairie Oaks site and 3 samples from the
Chadwick Arboretum site were eliminated because they had insufficient masses of
material to analyze.
Geese were approached slowly after ~15 minutes of standing to allow geese to
become acclimated to my presence. Grazing geese were followed until they defecated.
Droppings were marked with a twig, or the sample was collected immediately. Samples
were collected in 15ml sterile screw cap centrifuge tubes (Corning, Tewksbury, MA,
USA). Attempts to avoid
introduction of any sources of contamination were made by wearing nitrile gloves
and using a sterile tongue depressor.
Samples were stored on ice until returning to the Ohio State University campus
where they were frozen at -80°C until processing. All samples were processed within 9
month after collection. Enteropathogens can survive in fecal samples as long as 12
months when stored at very low temperatures (Dan et al. 1989). Samples were collected
to avoid sampling defecations from the same goose. If the geese were resting, a mental
note was made of their positions and once they moved away, droppings were collected
from where individuals were loafing. Samples were collected from individuals that were
separated by ≥ 0.30 m to avoid collecting from the same goose. Attempts were made to
collect from different family groups during the second visit. If this was not possible,
48
Table 2.4 Number of resident Canada goose fecal samples collected per site by period in
the greater Columbus area during the molt and post-molt periods in the year 2013
Site
Prairie Oaks
Three Creeks
Big Island
Heritage
Horseshoe Road.
Scioto
Morse Road
Restaurant
Olentangy
Franklin Park
Beekman
Chadwick
Total
Period 1
Age
Adult/ Juvenile
Period 2
14
12
12
0
12
6
12
12
13
6
12
0
111
6/8
6/6
6/6
6/6
6/0
6/6
6/6
6/7
5/1
6/6
59/52
13
0
12
12
0
12
12
12
0
0
12
3
88
49
memory of physical markings or individual characteristics were used in order to avoid
sampling from the same individuals. There were seven occasions during the second
period when samples were collected when geese were not present. In this situation,
moisture content of the fecal samples was used to identify fresh fecal deposits. This
means that the samples had to be visibly moist, soft, malleable, and dark in color. Resting
sites were usually identified where droppings and feathers remained and droppings were
collected from fecal deposits that were separated by ≥ 0.30 m. Information was obtained
from bystanders at several urban sites about the time and location where the geese had
last been seen to find fresh droppings (≤ 24 hrs.). However, 11 slightly older (≥ 24 hrs)
and more desiccated droppings were used when necessary.
Data were collected using 6 minute point count surveys to obtain information on
time, date, weather, GPS location, total number of people present before and after
collection, and total numbers of adult and juvenile geese before and after collection.
Nearby landmarks such as paths to or near the water or refuse container were recorded.
Human behaviors that could influence results such as water activities or hand-feeding
geese were recorded along with goose behaviors such as aggression and swimming.
Number and direction of geese landing in or leaving a site during the second period also
were noted .
50
Pathogen Detection
Immuno Assay
Antigen capture enzyme-linked immunosorbent assay (ELISA) was used to detect
presence of Giardia and Cryptosporidium. ProSpecT Giardia microplate assay (Thermo
Fisher Scientific Remel Products, Lenexa, KS, USA) testing for Giardia specific antigen
65 (Giardia lamblia) and ProSpecT Cryptosporidium microplate assay (Thermo Fisher
Scientific Remel Products, Lenexa, KS, USA) testing for Cryptosporidium specific
antigen (not species specific) were used to conduct the assay. Each sample was cut in
half lengthwise on a sterilized cutting board with a sterilized knife. Each half was
weighed to ensure that there were at least 5.00 g of sample. One half was then placed into
a sterile Whirl-Pak bag while the other half of each sample was placed into a 15ml sterile
screw cap centrifuge tube (Corning, Tewksbury, MA, USA). Samples placed in
centrifuge tubes were then stored at 4°C until bacterial culture within 48 hours. The
samples in the Whirl-Pak bags to be used for the assay were left to thaw at 4°C overnight.
Five milliliters of deionized water were added to each bag and each sample was manually
homogenized on the following day. Sterile swabs were inserted into each Whirl-Pak bag
until the tip was covered with a sufficient amount of sample. The swabs were then placed
into 15ml centrifuge tubes filled with 1.00 ml of specimen dilution buffer provided by the
manufacturer. Positive and negative controls were used. Manufacturer’s instructions
were used to conduct the remainder of the assay. Positives and negatives were
determination based on visual interpretation of a color reaction.
51
Bacterial Culture
Methods used for bacterial culture of Salmonella and cephalosporin-resistant E.
coli strains were standard lab practices for the lab of Dr. Thomas E. Wittum at the Ohio
State University and can be found in Mollenkopf et al. (2012). Samples were first
removed from -80°C and allowed to thaw at 4ºC where they were kept for no more than
48 hours before culturing. Four grams of each sample was added into 36ml of
Tetrathionate broth for Salmonella culture. The samples were mixed and left to incubate
for 24-hours at 37°C. One-hundred microliters of sample solution from each sample were
mixed with 10ml of Rappaport-Vassiliadis (RV) enrichment broth the following day.
Samples were then incubated at 42°C for 24 hours. The RV sample solution was
inoculated to XLT-4 agar for each sample with a sterile swab and streaked for isolation.
The plates were incubated for 24 hours at 37°C. Suspect Salmonella were to be
inoculated to MacConkey agar after which they would be streaked for isolation and
allowed to incubate at 37°C for 24 hours. Isolated Salmonella colonies would then be
inoculated to a nutrient slant and allowed to incubate for 24 hours at 37°C. An antisera
test would be performed for final results.
One gram of each fecal sample was added into 9 ml of nutrient broth containing 2
µg/ml of cefotaxime (a third-generation cephalosporin) and allowed to incubate for 24
hours at 37°C to identify cephalosporin-resistant E. coli strains,. Two sets of plates were
prepared. One set consisted of selective MacConkey agar mixed with 4 µg/ml of
cefoxitin (a second-generation cephalosporin). The other consisted of selective
52
MacConkey agar mixed with 4 µg/ml of cefepime (a fourth generation cephalosporin).
Sample solution was inoculated to a MacConkey agar with cefoxitin plate and a
MacConkey agar with cefepime plate using a sterile swab. Samples were then streaked
for isolation and allowed to incubate for 24 hours at 37°C. If phenotypic E. coli was
present, then an isolated colony was inoculated to both a nutrient slant and to Tryptophan
broth and allowed to incubate at 37°C for 24 hours. The nutrient slant was then checked
for growth and the Tryptophan-sample solution was tested using an Indole test for final
verification.
Data Analysis
Age, Landscape, and Season
Each period was examined separately and then compared. For analysis of
cephalosporin-resistant E.coli strains, all sites used for the first period were represented in
the analyses of adult samples. All sites used for the second period, with the exception of
the Chadwick Arboretum site, were also represented. The Chadwick Arboretum samples
were not used due to small mass of fecal samples. No juvenile samples were obtained
from the Big Island, or Beekman Park sites because they were too small to analyze. Forty
peri-urban and 53 urban samples were tested. There were 13 peri-urban and 14 urban
samples that were not analyzed due to desiccation or small sample mass.
Logistic regression was used to examine the effects of age on prevalence of
Cryptosporidium during the first period. An odds ratio was then calculated from logistic
53
regression coefficients. Age differences were not analyzed during the post-molt
collection because age was not distinguished during this period. No statistical tests could
be carried out to examine the effects of age on prevalence of Giardia or cephalosporinresistant E.coli strains during either period due to low numbers of positives and lack of
distinguishing adults from juveniles during the second period.
Logistic regressions were also conducted to determine the effects of landscape on
prevalence of Cryptosporidium. A model that evaluated the effect of landscape alone on
prevalence of Cryptosporidium during the first period was used followed by the same
analysis for the second period. Odds ratios were calculated from the regression
coefficients
Two-tail Fisher’s Exact tests were used to examine presence of Giardia and
cephalosporin-resistant E. coli strains in urban vs. peri-urban sites during the second
period. Fisher’s Exact was used due to low number of positives. Only the second period
could be examined because there were no Giardia and only one cephalosporin-resistant
E.coli strain detected during the first period. An odds ratio was then calculated for each
pathogen during the second period.
Although statistical tests were not performed across the molt and post-molt
periods, prevalence was used to compare Giardia and cephalosporin-resistant E.coli
between periods. For Cryptosporidium data, prevalence, logistic regression results, and
odds ratios were used for comparison. Statistical significance was set at the alpha= 0.10
level for all tests. Statistical programs used included Program R and the Statistical
Package for the Social Sciences (R Core Team 2013, International Business Machines
Corp. 2013).
54
Environmental Factors
Linear regression was used with Cryptosporidium data in order to examine the
relationship between the odds ratio (dependent variable) at each site and seven
independent variables including: population density within study sites, mean number of
geese at each site, distance of each site from nearest livestock farm, distance of each site
from nearest wastewater treatment plant, proportion of site defined by the NLCD as
developed medium intensity land (highest level of development detected in the area),
pasture, and undeveloped space (combined category). Linear regressions were not
conducted for other pathogens because few positives were detected. Variables that
measured the same or similar properties were grouped together into human factors,
livestock factors, and other factors. The human factors group included population density
within study sites, proportion of each site defined by NLCD as developed medium
intensity, and proportion of each site defined by NLCD as undeveloped space. Livestock
factors included proportion of each site defined by NLCD as pasture and distance of each
site from nearest livestock farm. Other factors included distance of each site from nearest
wastewater treatment plant and average number of geese total. Univariate linear
regression analysis was then run on each variable. Collection periods were analyzed
separately. The variable with the lowest P- value within each group during each period,
regardless of statistical significance, was selected for inclusion in a model. Once a model
had been created for each period, forward stepwise selection was used to determine the
55
best model for each period. Multivariate linear regression was then carried out for the
first period best-fit model and again for the second period best-fit model.
The number of humans counted at each site was averaged by sample period.
Human population density within study sites along with proportion of site defined by the
NLCD as developed medium intensity land were factored into the linear regressions run
on the Cryptosporidium data.
Number of adults and juveniles present at each site were averaged together to
produce site averages. Afterward, these averages were grouped by landscape to get an
overall urban and overall peri-urban average of adults and juveniles.
GIS Analysis
GIS analysis was used to calculate values for the seven environmental variables to
examine the effects of these variables on pathogen prevalence. Population density within
study sites was measured with 1km-radius buffers from 2010 U.S. census data at the
census tract level for the following counties: Marion, Madison, Franklin, Delaware, and
Union (U.S. Census Bureau 2010). Human population density was calculated by
multiplying total human population within the census tract by the proportion of the
census tract area within the 1km radius. Where a 1 km-radius surrounding a site
intersected multiple census tracts, the proportion of each census tract area that fell within
the radius was multiplied by the total human density within each census tract. These
values were then added together to obtain the area-weighted sum of human density for
each site.
56
The distance of each site from the nearest livestock farm was determined by
obtaining parcel shape files and land use attributes data from every county containing or
bordering a study site. This included Marion (Burns Marion County Auditor Office
2014), Madison (Madison County Auditor Office 2014), Franklin (Franklin County
Auditor Office 2014), Union (Xiaong Union County Auditor Office 2014), and Delaware
(Delco 2014) counties. The land use attributes used to determine distance to nearest
livestock farm for all counties except Madison county included: livestock farms, dairy
farms, poultry farms, livestock farms qualified Current Agricultural Use Value (CAUV),
dairy farms qualified CUAV, and poultry farms qualified CAUV. The Madison county
data set used a different set of land use codes than the other counties. The “pasture”
attribute was used to determine distance to nearest livestock farm for Madison county.
The distance of each site from the nearest wastewater treatment plant was
measured by using a shape file with attributes that included the location of all wastewater
treatment plants in the area from the Ohio Environmental Protection Agency (Bouder
Ohio Environmental Protection Agency 2014).
Proportion of each site defined by the NLCD as developed medium intensity land,
pasture, and undeveloped space was determined by dividing the number of km made up
by each category at each site and dividing by the 1km radius.
57
Results
Pathogen Prevalence
Overall pathogen prevalence for samples that tested for all pathogens was 55/93
(59%). Cryptosporidium had the highest prevalence in fecal samples 89/199 (44.7%)
followed by cephalosporin-resistant E. coli 10/93 (10.8%) and Giardia 7/199 (3.52%)
(Table 2.5). Prevalence of Cryptosporidium was higher during the first period than the
second period. Prevalence of both cephalosporin-resistant E. coli and Giardia was higher
during the second period than the first period (Table 2.5). No Salmonella was detected in
any fecal samples.
There were no effects of age on prevalence of Cryptosporidium during the first
period (P = 0.77). The odds of detecting a Cryptosporidium positive in adults were 0.89
times the odds of detecting a Cryptosporidium positive in juveniles.
Among 61 samples collected from urban areas during the first period, 35 (57.4%)
were positive for Cryptosporidium, compared to 21 of 50 (42%) samples collected from
peri-urban sites during the first period. Among 51 samples collected from urban areas
during the second period, 21 (41.2%) were positive for Cryptosporidium, compared to 12
of 37 (32.4%) samples collected from peri-urban sites during the second period. The
Morse Road site had the highest percentage of positives detected during both periods
58
Table 2.5 Pathogen prevalence in Canada goose fecal samples collected in the greater
Columbus area during the molt and post-molt periods in the year 2013 by period
Pathogen
Period 1
Number
Positive
Period 2
Number
Positive
N
N
Cryptosporidium
56
111
33
88
Ceph. Resistant
E. coli
1
40
9
53
Giardia
0
111
7
88
Salmonella
0
111
0
88
59
(Table 2.6). Prevalence of Cryptosporidium was higher in urban than peri-urban sites
during the first period (P = 0.10). The odds of detecting a Cryptosporidium positive at an
urban site were 1.86 times the odds of detecting a positive at a peri-urban site during the
first period. There was no difference in prevalence of Cryptosporidium between
landscapes during the second period (P = 1.0). The odds of detecting a Cryptosporidium
positive at an urban site were 1.00 times the odds of detecting a positive at a peri-urban
site during the second period.
No Giardia was detected during the first period. Among 51 samples collected
from urban areas during the second period, 5 (9.8%) were positive for Giardia compared
to 2 of 37 (5.4%) samples collected from peri-urban sites during the second period. The
site with the greatest percentage of positives detected during the second period for both
Giardia and cephalosporin-resistant E.coli was the Morse Road site (Table 2.7). The
Fisher’s Exact analysis for presence of Giardia in urban vs. peri-urban sites during the
second period showed no statistically significant difference with a P- value of 0.74. The
odds of detecting Giardia at an urban site during the second period were 1.9 times the
odds of detecting a positive at a peri-urban site during the second period.
Only one cephalosporin-resistant E. coli strain was detected during the first
period. Among 32 samples collected from urban areas during the second period, 8 (25%)
were positive for cephalosporin-resistant E. coli compared to 1 of 21 (4.8%) samples
collected from peri-urban sites during the second period. There was no difference in
prevalence of cephalosporin-resistant E. coli between urban and peri-urban sites during
the second period (P = 0.11). The odds ratio shows that the odds of detected a
cephalosporin-resistant E. coli positive at an urban site are 6.67 times the odds of
60
Table 2.6 Percentage of Cryptosporidium positives in Canada goose fecal samples
collected in the greater Columbus area during the year 2013 out of number of samples
collected per site for Period 1 and Period 2
Period 1
Period 2
Site
Number
Positive
N
Prairie Oaks
Three Creeks
Big Island
Franklin
Scioto
Beekman
Olentangy
Horseshoe
Morse Road
Restaurant
3
9
3
2
3
6
5
6
10
9
14
12
12
6
6
12
13
12
12
12
Site
Number
Positive
N
Prairie Oaks
Big Island
Scioto
Beekman
Morse Road
Restaurant
Chadwick
Heritage
9
1
0
2
10
7
2
2
13
12
12
12
12
12
3
12
Table 2.7 Percentage of Giardia and cephalosporin-resistant E. coli positives in Canada
goose fecal samples collected in the greater Columbus area during the year 2013 out of
number of samples collected per site for period 2
Giardia
Number
Positive
Site
Prairie Oaks
Big Island
Scioto
Beekman
Morse Road
1
0
0
0
4
Restaurant
Chadwick
Heritage
1
0
1
61
Table 2.8 Summary prevalence for Cryptosporidium, Giardia, and cephalosporin-resistant E. coli by age and landscape variables
for Canada goose fecal samples collected in the greater Columbus area during the molt and post-molt periods in 2013 for Period 1
and Period 2
Period 1
Variable
Period 2
62
Crypto.
Giardia
Resistant
E. coli
Number
Positive
Number
Positive
Number
Positive
N
N
Variable
N
Adult
Juvenile
29
27
59
52
0
0
59
52
1
0
25
15
Peri-urban
Urban
21
35
50
61
0
0
50
61
1
0
19
21
Peri-urban
Urban
1
Crypto.
Giardia
Resistant
E. coli
Number
Positive
Number
Positive
Number
Positive
9
24
N
24
64
1
6
N
24
64
1
8
N
21
32
detecting a positive at a peri-urban site. The only positive detected during the first period
was at a peri-urban site. A summary of positives and negatives for each pathogen during
each period was created (Table 2.8)
Prevalence of Cryptosporidium was slightly higher during the first period (50%)
than the second period (38%). Prevalence of both Giardia and cephalosporin-resistant
E.coli was higher during the second period [Giardia: 7/88 (7.95%), Cephalosporinresistant E. coli: 9/53 (17%)] .
Univariate linear regressions of site-specific odds ratios for Cryptosporidium on
environmental factors (Table 2.9) during the first period show the population density
within study sites variable had the lowest P-value within the human factors group, the
proportion of each site defined by NLCD as pasture variable had the lowest P- value
within the livestock factors group, and the distance of each site from nearest wastewater
treatment plant variable had the lowest P-value within the other factors group (Table
2.10, Figure 2.2a,-d). Forward selection of a model that included the three selected
variables supported a model containing all three variables (Table 2.11).
Population density within study sites was again selected from the human factors
group the second period, and distance of each site from nearest livestock farm variable
was selected from the livestock factors group, and the distance of each site from nearest
wastewater treatment plant variable was selected from the other factors group (Table
63
2.10, Figure 2.2b- f). Forward selection of a model that included the three selected
variables supported a model that contained all three variables (Table 2.11).
There were more humans present at collections sites during period 1 (~18/site)
than in period 2 (~3/site). Estimates of geese present at each sampling site showed that a
greater number of adult geese were present at urban sites on average (~15 adults; ~25
overall on average) than at peri-urban sites (~9 adults on average; ~23 overall on
average) during period 1. Average ratio of adults to juveniles during the first period was
~0.76.
64
Table 2.9. Environmental variable measurements and odds ratios by site for Period 1 and
Period 2 Cryptosporidium data from Canada goose fecal samples collected in the greater
Columbus area during the molt and post-molt periods in the year 2013
Period 1
Site
Odds
Ratio
Proportion
Pop.
Developed
Density Medium
Intensity
Proportion
UnDeveloped
Farm
Dist.
Km
Proportion
Pasture
WWTP
Dist.Km
Mean
No.
Geese
Beekman
Big Island
Bob Evans
1.00
0.33
3.00
2737
62.2
2122
0.44
0.00
0.40
0.00
0.42
0.17
4.27
17.6
11.9
0.00
0.07
0.05
10.1
4.27
4.55
11.0
14.0
25.5
Franklin
Horseshoe
Rd.
ODNR
Morse Rd.
Olentangy
Prairie
Oaks
Scioto
Three
Creeks
0.50
5278
0.51
0.01
8.21
0.00
7.8
19.0
1.00
237
0.00
0.33
4.53
0.08
5.84
15.0
5.00
0.63
0.27
8438
4548
111
0.65
0.76
0.00
0.00
0.03
0.39
3.03
5.33
1.46
0.00
0.00
0.19
5.08
9.8
2.57
26.0
44.0
42.0
1.00
3406
0.13
0.11
4.78
0.00
13.6
24.0
3.00
806
0.07
0.62
14.6
0.02
4.22
19.5
Site
Odds
Ratio
Pop.
Density
Proportion
Developed
Medium
Intensity
Proport
Farm
UnDevelop Dist.
Km
Proportion
WWTP
Hay
Pasture Dist. Km
Beekma 0.20
Big Islan 0.09
2737
62.2
0.44
0.00
0.00
0.42
4.27
17.6
0.00
0.07
10.1
4.27
Bob Eva 1.40
Chadwic 2.00
Heritage 0.20
2122
1285
865
0.40
0.45
0.28
0.17
0.01
0.05
11.9
5.2
2.97
0.05
0.00
0.07
4.55
10.8
8.04
8438
0.65
0.00
3.03
0.00
5.08
111
3406
0.00
0.13
0.39
0.11
1.46
4.78
0.19
0.00
2.57
13.6
ODNR
Morse R 5.00
Prairie
2.25
Oaks
Scioto 0.00
65
Table 2.10 Univariate linear regression results for variable vs. odds ratio during period 1 and period 2 for Cryptosporidium
data collected from Canada goose fecal samples collected in the greater Columbus area during the molt and post-molt
periods in the year 2013
Variable
Period 1
Β
66
Population Density
Developed Medium
Int.
Undeveloped
Pasture
Distance from Farm
in Km.
Distance from WWTP
in Km.
Average Number of
Geese
Std.
Error
R²
Period 2
PValue
β
Std.
Error
PValue
R²
2.87 x 10-4
0.00
0.25
0.14
4.27 x 10-4
0.00
0.46
0.06
1.73
-0.47
-8.54
1.80
2.52
8.71
0.10
0.00
0.11
0.37
0.86
0.36
4.13
-1.97
-0.37
2.46
3.99
10.9
0.32
0.04
0.00
0.14
0.64
0.97
0.01
0.10
0.00
0.92
-0.11
0.12
0.12
0.40
-0.12
0.15
0.07
0.44
-0.18
0.16
0.17
0.31
-8.37 x 10-3
0.05
0.00
0.87
0.01
0.06
0.01
0.82
1
Table 2.11 Linear regression output for Cryptosporidium first and second period best-fit models from Canada goose fecal samples
collected in the greater Columbus area during the molt and post-molt periods in the year 2013
Period 1
Coefficient
Β
(Intercept)
3.90
Population Density
Period 2
Std.
Error
67
P- value
Coefficient
β
1.65
0.06
(Intercept)
2.86
2.32 x 10-4
0.00
0.28
Population Density
4.05 x 10-4
Distance from
WWTP in Km.
-0.35
0.15
0.06
Distance from
WWTP in Km.
-0.25
Pasture
-14.6
10.9
0.23
Distance from
Farm in Km
-0.09
Std.
Error
1.25
0.00
0.08
0.06
0.11
0.09
0.08
0.31
Residual standard error: 1.22 on 6 DF
Residual standard error: 1.09 on 4 DF
Multiple R-squared: 0.60
Multiple R-squared: 0.77
Adjusted R-squared: 0.40
Adjusted R-squared: 0.60
F-statistic: 2.95 on 3 and 6 DF
F-statistic: 4.44 on 3 and 4 DF
P-value: 0.12
P-value: 0.09
2
P- Value
Figure 2.2. Univariate linear regressions for best-fit model variables of Cryptosporidium
odds ratio vs. a.) Period 1 human population density b.) Period 2 human population
density c.) Period 1 proportion pasture d.) Period 2 distance to livestock farm e.) Period 1
distance to WWTP f.) Period 2 distance to WWTP in Canada goose fecal samples
collected in the greater Columbus are during the molt and post-molt periods in the year
2013
Period 1
a.)
c.)
e.)
Period 2
b.)
d.)
f.)
68
Discussion
Pathogen Prevalence
Prevalence of Cryptosporidium was consistent with the hypothesis that parasites
would have the highest prevalence. This indicates that Cryptosporidium was the highest
hazard potential of the pathogens examined. It should be noted that pseudoreplication of
samples during repeat visits to sites could have resulted in an underestimation of
variability of prevalence results, however attempts were made to avoid pseudoreplication
when possible.
Previous studies of Cryptosporidium prevalence in resident Canada geese vary
widely. A study in Northwest Ohio at 16 different urban sites including public parks, golf
courses, an industrial site, cemeteries, and health care facilities, found Cryptosporidium
spp. in 77.8% of fecal samples (Kassa et al. 2001). A second study in Northern Ohio
found a prevalence of 43.9% in fecal samples collected at a wildlife refuge on Lake Erie
(Rea 2013 unpublished, Binkley 2014 unpublished). These results are consistent with the
results found in this study. This may be influenced by the fact that both studies took place
at a similar time and place in addition to using the same methods for Cryptosporidium
detection. A New Jersey study sampled 500 resident geese at various locations including
lakes, rivers, zoos, and parks, and found Cryptosporidium in only 10% of fecal samples
(Roscoe 2001). Zhou et al. (2004) found Cryptosporidium in 49/209 (23.4%) of resident
and migratory Canada goose fecal samples combined in both urban and peri-urban parts
69
of Illinois and Northern Ohio. Kostroff and Rollender (2001) found Cryptosporidium in
81% of samples after examination of 100 Canada goose fecal samples collected from 2
parks in New York, NY. Lastly, Graczyk et al. (1998) detected Cryptosporidium at 78%
of sites they examined near Chesapeake Bay in Maryland however there was no mention
of how many individual samples tested positive. (Table 2.12)
Prevalence of Giardia were not consistent with my hypothesis the protozoa would
have a higher pathogen prevalence. Previous studies on the prevalence of Giardia in
resident Canada geese are scarce, however reports suggest relatively low prevalence.
Kassa et al. (2001) found overall prevalence of Giardia was 16.7% in fecal samples
collected in urban Northern Ohio. Binkley (2014 unpublished) reported 3.5% prevalence
in fecal samples collected at a wildlife refuge in Northern Ohio. Again results from
Binkley (2014 unpublished) agreed with results from this study. Roscoe et al. (2001)
found Giardia in 15% of fecal samples collected in urban and peri-urban sites throughout
New Jersey. However, Kostroff and Rollender (2001) found 74% prevalence in fecal
samples collected from New York, NY. Lastly, a study that collected resident Canada
goose fecal samples from Raleigh, Durham, and Chapel Hill, North Carolina found no
positive fecal samples out of 234 total samples (Ayers et al. 2014) (Table 2.12).
Though there is great variability in existing data on occurrence of both
Cryptosporidium (10%-81% ) and Giardia (0%-74%) in resident Canada goose fecal
samples, three out of the four studies (Table 2.12) that examined both Cryptosporidium
and Giardia in Canada goose fecal samples report a higher percentage of
Cryptosporidium positive samples. The only study that showed a higher prevalence of
70
Table 2.12 Prevalence of Giardia and Cryptosporidium in Canada goose fecal samples
among studies
Study
%Cryptosporidium
%Giardia
Location
Kassa et al.
2001
77.8 (7/18)
16.7 (3/18)
Northern
Ohio/Toledo
Solid phase enzyme immunoassay
(EIA) using monoclonal
antibodies specific for
Cryptosporidium (CSA) and
Giardia (GSA 65) antigens
(ProSpect Microplate Assay kits).
Binkley 2014
(Unpublished)
41.5 (44/106)
2.8 (3/106)
Central Ohio
EIA/Enzyme Linked
Immunosorbent Assay (ELISA)
using monoclonal antibodies
specific for Cryptosporidium
(CSA) and Giardia (GSA 65)
antigens (ProSpect Microplate
Assay kits).
Roscoe 2001
10 (49/500)
15 (75/500)
New Jersey
Direct immunofluorescent
antibody test. Detection reagent is
FITC labeled antiCryptosporidium cell wall and
anti-Giardia cell wall monoclonal
antibodies (Meridian Diagnostics,
Inc). Not species specific.
Zhou et al.
2004
23.4 (49/209)
N/A
Ohio and
Illinois
PCR-restriction fragment length
polymorphism (RFLP) analysis of
the SSU rRNA gene. Testing for
all species.
Ayers et al.
2014
N/A
0 (0/234)
North
Carolina
Enzyme Linked Immunosorbent
Assay (ELISA) ProSpecT®
Giardia EZ Microplate Assay
(Remel Inc., Lenexa, Kan., USA).
(GSA 65).
Kostroff and
Rollender 2001
81 (81/100)
74 (74/100)
New York
Premier Cryptosporidium and
Giardia EIA using monoclonal
antibodies (Meridian Diagnostics,
Inc).
The present
study 20122013
41 (44/107)
2.8 (3/107)
Northern
Ohio
EIZ/Enzyme Linked
Immunosorbent Assay (ELISA)
using monoclonal antibodies
specific for Cryptosporidium
(CSA) and Giardia (GSA 65)
antigens. (ProSpect Microplate
Assay kits).
Rea 2013
(Unpublished)
71
Method
Giardia than Cryptosporidium was Roscoe (2001) study however, even in this case there
was only a 5% difference.
One factor that sets these pathogens apart is that Cryptosporidium can retain
infectivity in water for up to a year while Giardia can only retain infectivity for up to two
months (Graczyk et al., 2008). Cryptosporidium is also highly chlorine-resistant (Yoder
et al. 2012a) while Giardia is considered to be chlorine-tolerant (Centers for Disease
Control 2011) (U.S. Environmental Protection Agency 2000). LeChevallier and Norton
(1995) found that in studies of surface waters, the major route of Giardia and
Cryptosporidium exposure for Canada geese, showed a slightly higher percentage of
Cryptosporidium positives (60.2%) than Giardia positives (53.9%). However, results
from previous studies do not show a difference as significant as the 44.7% positive for
Cryptosporidium and 3.5% positive for Giardia found in this study.
The most likely contributor to such a large difference in prevalence between
Cryptosporidium and Giardia in resident Canada goose fecal samples observed in this
study is a difference in species detection between the ELISA assays. The ELISA assay
used to detect Cryptosporidium was able to detect any species of Cryptosporidium that
was active within the goose at the time of sampling. Conversely, the ELISA assay used to
detect Giardia was only able to detect Giardia specific antigen 65 which means only
Giardia lamblia, the species that can infect humans, could be detected. This is a
limitation for comparisons of prevalence as well as for examination of human exposure to
species of Cryptosporidium that infect humans.
72
However, out of the previous studies mentioned in table 2.12, only Roscoe (2001)
and Kostroff and Rollender (2001) did not look specifically for Giardia lamblia. Kostroff
and Rollender (2001) reported the highest Giardia prevalence by far suggesting that this
result may be related to the lack of species identification. Therefore, prevalence in this
study may have been higher had the ELISA assay not been species specific. Though there
is truth to the finding that Cryptosporidium occurs more frequently in resident Canada
goose fecal samples than Giardia based on previous studies and differences in viability,
the effect is most likely not as dramatic as the difference that was observed in this study.
Prevalence of cephalosporin-resistant E. coli strains, a bacteria, in Canada goose
fecal samples collected during this study was unexpectedly higher than prevalence of
Giardia. It was hypothesized that prevalence of the protozoa would be higher. Total
number of positives detected for both Giardia (7 positives samples) and cephalosporinresistant E. coli (10 positive samples) were low making any conclusions only speculation.
However, Cole et al. (2005) reported that 11% of 46 E. coli isolates found in Canada
goose fecal and cloacal samples collected in Georgia and North Carolina were resistant to
cephalothin (a cephalosporin). This is close to the 10.8% prevalence found in this study.
Because a higher prevalence provides more opportunities for some of those organisms to
be either cephalosporin-resistant E. coli or a specific species of Giardia, it is possible that
overall prevalence of E. coli was simply greater than overall prevalence of Giardia.
Reports of overall E. coli shedding in the Canada goose population are generally higher
than reports of Giardia shedding (Table 2.12). Fallacara et al. (2001) found 67%
prevalence when surveying fecal shedding of E. coli in free-living waterfowl throughout
metropolitan parks in central Ohio. Feare et al. (1999) surveyed 12 parks throughout
73
London, England and found pathogenic E. coli strains in 55% of Canada goose feces. The
majority of studies show that prevalence of Giardia, even though it contains a hardy
outer cyst, is much lower than E.coli prevalence. This study did not test specifically for
E. coli because small sample mass was limiting and presence of resistant bacteria was
considered to be a priority.
A seasonal effect is also possible due to bacterial replication by binary fission
which is faster at warmer temperatures than protozoa (Maier et al. 2009). Bacteria grow
most rapidly in the range of temperatures between 4.4 °C and 60°C and can double in
number in as little as 20 minutes (U.S. Department of Agriculture 2013). Samples were
collected in the summer when bacteria prevalence would be at its peak. Kullas et al.
(2002) found that overall prevalence of E. coli ranged from 2% during the coldest time of
the year to 94% during the warmest months with an average of ~41% in a study that took
place in Fort Collins, Colorado over the course of 1 year (Kullas et al. 2002). In addition
to temperature, the researchers believe that the behavior of the geese may have
contributed to the prevalence patterns. Daily movement patterns of geese during the fall
and winter are often concentrated in areas where they are not likely to come into contact
with habitats contaminated with mammalian sources of E. coli (Kullas et al. 2002). In
contrast, during the spring and summer, geese do not move far from their nests and their
habitat consists of small water impoundments and littoral zones that easily become fouled
(Kullas et al. 2002). When the birds range from their nest area they are more likely to
visit outlying agricultural areas that are surface-treated with manure from local feedlots
and dairies. This may help explain a greater prevalence of E. coli than Giardia as well as
a greater prevalence of Cryptosporidium during period 1 than period 2.
74
Lastly, exposure of geese to an undetected source of contamination, such as a
nearby livestock farm in an urban area, could have resulted in greater exposure to
cephalosporin-resistant E. coli than Giardia. Because geese were able to fly during the
second period when the majority of cephalosporin-resistant E. coli were detected, they
could have received high exposure to antimicrobial-resistant bacteria at an outside site
that was not accompanied with exposure to Giardia. The ability of geese to fly acts as a
limitation here and will be further discussed.
The fact that no Salmonella were detected is consistent with previous studies.
Clark (2003) reported that no Salmonella were isolated in studies by Hussong et al.
(1979), Roscoe (2001), and Fallacara et al. (2001). Prevalence of only 2.5% was found by
Feare et al. (1999), and <0.5% by Converse et al. (1999). It is possible that Salmonella is
simply difficult to culture from Canada geese; however, different methods were carried
out in these studies that produced the same results. Freezing the samples before analysis
may have resulted in die off of a portion of existing Salmonella; however,
enteropathogens can survive in fecal samples for as long as 12 months when stored at
very low temperatures (Dan et al. 1989). Additionally, not all of the previous studies
froze samples before analysis yet they produced similar results. Desiccation could also
have affected survivability of the Salmonella, however most desiccated samples were not
used for this analysis and there were only a total of 11 desiccated samples. Though
Canada geese can serve as vectors of Salmonella, prevalence is very low in the
population and therefore introduces a minimal hazard to the human population in the
Greater Columbus area.
75
The results showed no difference in prevalence of Cryptosporidium between adult
and juvenile populations during the June sampling period (P = 0.77). This finding was
not consistent with hypothesis 2a pathogen prevalence would be higher in adult than
juvenile geese. Roscoe (2001) found that Cryptosporidium was twice as prevalent in
juvenile geese than in adult geese sampled in New Jersey. However, there were many
factors that could have contributed to the unexpected results. The first explanation is that
the sample size was too small to detect an effect. Though the total sample size for the first
period was 111 individuals, only 52 were juveniles and 59 were adults. The study in New
Jersey sampled 500 individuals consisting of 250 juveniles and 250 adults. A second
possibility is that the exact age of the juveniles varied allowing older juveniles to acquire
more resistance than younger juveniles. Wehr and Herman (1954) found that goslings
acquire most parasitic infections during their first week of life. However, this conclusion
was largely based on evidence from worm parasites and Cryptosporidium was not one of
the parasites examined. A third possibility is that some of the goslings were able to
acquire passive immunity from their mothers. Though no studies could be found
examining passive immunity to Cryptosporidiosis in Canada geese, Buckland and Gérard
(2002) showed that female geese that have acquired immunity are capable of giving
passive immunity to their offspring from infections such as spirochetosis and goose
enteritis (Lastly, variation in exposure due to location could have skewed results and
prevented a trend from being observed.
76
There was a difference in prevalence of Cryptosporidium between urban and periurban sites during the first period, supporting hypothesis 2b that if greater environmental
contamination and more interactions at the animal-human interface occur in urban
locations while peri-urban sites have less contamination but more interactions at the
wildlife-livestock interface where antimicrobial resistance has been known to occur, then
the prevalence of Giardia, Cryptosporidium, and Salmonella will be higher at urban sites
while prevalence of cephalosporin-resistant E. coli strains will be higher at peri-urban
sites. The results for Cryptosporidium prevalence during the second period however do
not support hypothesis 2b. This implies that because the geese were able to move freely
between urban and peri-urban sites during the second period and receive exposure from
both urban and peri-urban landscapes, a mixing of pathogens occurred resulted in a more
even distribution of Cryptosporidium between urban and peri-urban sites. However, when
the geese are restricted to sites during the first period and only receive exposure to one
type of landscape, prevalence of Cryptosporidium was higher in geese residing at urban
sites than at peri-urban sites.
It was expected that a greater prevalence of cephalosporin-resistant E. coli would
be detected at peri-urban sites as opposed to urban sites due to exposure to livestock
being treated with cephalosporins. Cole et al. (2005) found that the number of
antimicrobial resistant E.coli isolates recovered from resident Canada goose fecal and
cloacal samples was higher among geese in agricultural areas compared to other land use
types. Additionally, the proportion of isolates resistant to antimicrobial agents was
greater among isolates where interaction with swine waste lagoons was observed, while
little or no resistance was observed among isolates recovered from Canada geese in
77
regions with no known direct contact with liquid wastes (Cole et al. 2005). Allen et al.
(2011) found greater antimicrobial resistance isolates from small mammals trapped on
swine farms when examining E.coli and Salmonella isolates from swine farms,
residential areas, landfills, and natural woodland habitats, throughout Ontario, Canada.
However, study showed that prevalence was higher at urban sites. Proximity to
humans has been associated with higher prevalence of antimicrobial resistance. Rolland
et al. (1985) found that resistant bacteria were detected more frequently in baboons
feeding on human refuse than in animals living in more remote areas with no human
contact in Kenya. This suggests that exposure to anthropogenic sources, such as human
waste, contribute to development and maintenance of antimicrobial resistance in bacteria
of wildlife (Rolland et al. 1985, Allen et al. 2011). It is likely that both factors play a role
in prevalence and distribution of antimicrobial resistant bacteria. The odds of detecting a
positive at an urban site during the second period in this study were unusually high
(6.67). This result is largely due to the fact that 83% of the cephalosporin-resistant E. coli
were discovered at the Morse Road site again suggesting a random exposure or exposure
event.
Because 9 of 10 positives were detected during the second period when flight was
resumed, the geese could have acquired the cephalosporin-resistant E. coli from either an
urban or a peri-urban location. The only positive that was detected during the first period
was at a peri-urban site, however. It appears that both urban and peri-urban sites have the
potential to expose Canada geese to antimicrobial-resistant bacteria. The U.S. Food and
Drug Administration has been working on legislation to limit the use of antibiotics in
78
food animals, however current legislation relies on companies to voluntarily cut back on
use (United States Food and Drug Administration 2014).
The effects of season on pathogen prevalence were most evident with
Cryptosporidium. A difference in prevalence between urban and peri-urban sites was
observed when geese were unable to fly and thus fixed to study sites. No differences were
observed after geese were able to move freely between urban and peri-urban sites. This is
due to the fact that geese were exposed to both landscapes instead of being localized in
one or the other. The pathogens that they acquired could have come from either landscape
resulting in a mixing of pathogens at the study sites. Additionally, prevalence wa
s lower during the second period making any differences more difficult to detect.
Though it was hypothesized that prevalence would become more even between urban and
peri-urban sites once flight was resumed, it was unexpected to see the effect of landscape
entirely diminished. Therefore the effects of regaining flight had a greater impact on
pathogen distribution than expected.
No Giardia and only one cephalosporin-resistant E. coli strain were detected
during the first period while most were detected during the second period. This is most
likely due to the fact that so few positives were detected overall for both organisms.
However, it is important to consider the possibility of a random event or exposure that
was occurred in the second period and not the first. This was supported by the finding
that the majority of both Giardia and cephalosporin-resistant E. coli were detected at the
Morse Road site which suggests that an unknown source of contamination was present at
this site during the second period. Lastly, it is possible that once geese resumed flight
during the second period, they were able to acquire pathogens at other locations
79
throughout the Greater Columbus area that they then brought back with them to the study
sites.
Human factors were analyzed to determine whether presence of humans and
urbanization had an effect on prevalence of Cryptosporidium, with the belief that a higher
human population density would result in higher prevalence of Cryptosporidium. The
statistically significant univariate analysis of the human population density variable for
the first period indicates that this variable played a role in the distribution of
Cryptosporidium (Table 2.10). The Morse Road site had the highest odd ratio during the
first period and was the site with the greatest human population density. Three of four
peri-urban sites had the lowest population densities accompanied with the lowest odds
ratios. The Three Creeks site had an unexpectedly high odds ratio, however it did happen
to be the peri-urban site with the greatest population density of all the peri-urban sites.
Additionally, the presence of portable toilets was observed at this site which could have
provided an extra source of contamination.
The Morse Road site had the highest odds ratio for the second period and was the
site was the site with the greatest human population density as well as the greatest
proportion of developed medium intensity land. Two out of the three peri-urban sites had
the lowest odds ratios combined with the lowest human population densities. However,
the third peri-urban site, Prairie Oaks, had the second highest odds ratio. This was the
only other site where the presence of portable toilets was noted which may imply that the
80
portable toilets serve as a confounding factor. Additionally, the geese could have
obtained pathogens from another site.
There were many more people present on average at sampling sites during first
period sample collection (≈18 per site), when Cryptosporidium prevalence was higher,
than during the second period sample collection. Not only do the geese contribute to
further contamination in these areas, but two-way transmission causes geese to acquire
pathogens from anthropogenic sources which they can then transmit back to the human
population. Zhou et al. (2004) discovered that 10.2% of positive resident Canada goose
fecal specimens collected from Ohio and Illinois had C. parvum and C. hominis; the two
genotypes that can infect humans. These authors believed that the human form of the
pathogen was acquired by several geese from a human-contaminated environment after
determining that the sites where these strains were found were more intensely used than
other sites and were often littered with garbage. Since humans are the predominant source
of C. parvum and C. hominis, it is likely that finding these pathogens in goose feces
suggests mechanical transfer. Similarly, Kullas et al. (2002) isolated E. coli strains
consistent with those associated with human illness from 24.5% of fecal samples during
March-July when non-migratory geese predominate in local goose populations. Since
geese were molting during this time, those located in urban areas were more likely to
come into contact with humans. In urban areas there are simply more opportunities for
pathogen transmission.
Livestock factors were examined to determine whether presence and proximity of
a site to a livestock farm influenced prevalence of Cryptosporidium, with the belief that
sites that sites that contain a greater proportion of livestock farms or were located in close
81
proximity to a livestock farm would have a higher prevalence of Cryptosporidium. The
livestock factors had no significant association with the odds of detecting
Cryptosporidium at sites during either period. (Table 2.10). The proportion of pasture
variable had an opposite effect than what was expected. The site with the greatest
proportion of pasture within a site for the first period, the Prairie Oaks site, had the lowest
odds ratio (Table 2.9, Figure 2.2c). However, the distance to livestock farm variable
showed that the site located furthest from a livestock farm at 17.6 km, the Big Island site,
had the second lowest odds ratio (0.09) while the site with the highest odds ratio (5.00) ,
Morse Road, was the second closest site to a livestock farm at 3.30 km away (Table 2.9).
Other than these associations however, there does not seem to be much of a trend
between odds ratios and distance to livestock farms among sites.
Stronger support for this variable was found in the second period which may be a
result of the change in study sites. The site located closest to a livestock farm, Prairie
Oaks, was the site with the second highest odds ratio. It was also the site with the greatest
proportion of pasture within a site (Table 2.9). This may partially explain why a higher
prevalence than expected was observed at this peri-urban site when the human density
was low and there was no medium intensity developed land. The Morse Road site had the
highest odds ratio and was the third closest site to a livestock farm. The site located
farthest away from a livestock farm, Big Island, had the second lowest odds ratio.
The other factors were analyzed to determine whether proximity to a WWTP or
number of geese present at a site had an effect on prevalence of Cryptosporidium. I
hypothesized that close proximity to a WWTP or high numbers of geese present at a site
would result in a greater prevalence of Cryptosporidium (hypothesis 3c, 3d). No
82
differences were observed when looking at the linear regressions for the other factors vs.
odds ratios for either period.
The average number of geese at each site variable did not have any detectable
effect on prevalence of Cryptosporidium. It is possible that because a portion of
individuals may be immune, the disease cannot be effectively transmitted to these
individuals and therefore the number of individuals would have a small impact on
prevalence. However, this would not explain cases of mechanical transfer. Further
investigation of this topic may provide a more comprehensive explanation.
The linear regression for distance to WWTP vs. odds ratios for the first period
(Table 10, Figure 2.2e) provides little support for the hypothesis (3c). It was unexpected
that the site with the shortest distance to a WWTP, the Prairie Oaks site, would have the
lowest odds ratio. It is possible that management of this site, being a metro-park, was
effective at reducing pathogens during this time.
Examination of the second period linear regression (Table 2.9, Figure 2.2f)
provides more support for the hypothesis than the period 1 regression. The Prairie Oaks
site was located closest to a WWTP and had the second highest odds ratio. The Scioto
site, the farthest site from a WWTP, was the site with the lowest odds ratio. Following
this trend, the third and fourth farthest sites from a WWTP were the sites with the third
and fourth lowest odds ratios.
The site with the highest prevalence of all pathogens is the Morse Road site
(Table 2.6, 2.7). This site had the highest human population density among sites (Table
2.9), was only located only 5.39 km from the Olentangy River (Google Earth 2013), 5.1
km from a WWTP, and 3.03 km from the nearest livestock farm (Table 2.9). Therefore,
83
this site most likely received the strongest influence of all three factors combined. This
supports the results of the multivariate analysis.
The best-fit model’s for both periods show that all three factors play a role in the
variation observed in prevalence of Cryptosporidium in fecal samples observed between
urban and peri-urban sites (Table 2.11). The first period best-fit model produced a low Pvalue and a high coefficient of determination indicating that variation in Cryptosporidium
prevalence is well explained by this model. Within this model, the distance to WWTP
variable had the lowest P-value (Table 2.11). The model selected for the second period
produced an even lower P- value with a higher coefficient of determination. Within the
model, both the human population density and distance to WWTP variables had
statistically significant P-values (Table 2.11).
Because this is an exploratory analysis, even though there are fundamental
differences between the first period and second period data due to sample collection, a
linear regression was run across both periods including the variables that were selected
for both periods. The linear regression was run on odds ratios against human population
density within study sites, distance of each site from nearest livestock farm, proportion of
each site defined by NLCD as pasture, and distance of each site from nearest wastewater
treatment plant. The results of this regression showed a highly significant P-value (P =
0.01) with an F-statistic of 4.1. The coefficient of determination shows that 67% (R2 =
0.67) of the variability in prevalence of Cryptosporidium among sites during both periods
is explained by the model. Consistent with results from the regressions run on period 1
and period 2 separately, the distance to WWTP variable was the strongest explanatory
variable with a P-value of 0.01. These results strongly support the previous results and
84
indicate that the selected variables were significant factors influencing the variability
observed in Cryptosporidium prevalence between urban and peri-urban sites.
Wastewater treatment plants have great potential to serve as a source of
contamination to both human and wildlife populations. The Columbus wastewater
collection system consists of 2,782 miles of sanitary sewers and 167 miles of combined
sewers that collect domestic and industrial wastewater and rainwater from the combined
system (Jackson Pike Wastewater Treatment Plant 2014). There are also 2,537 miles of
storm sewers which discharge untreated storm water to creeks and rivers (Jackson Pike
Wastewater Treatment Plant 2014). When combined sewer overflows (CSOs) occur, they
discharge storm water as well as untreated human and industrial waste, toxic materials,
and debris (U.S. Environmental Protection Agency 2014). All of these sources of
contamination are ultimately combined at the wastewater treatment plants where they are
collected and treated. Treatment includes periods when the wastewater sits out to settle.
Geese often take advantage of this source of open water and not only contribute to further
contamination, but also expose themselves to the contaminants in the wastewater.
Wastewater treatment plants find that coliform bacteria counts increase dramatically
when large numbers of Canada geese are present and decline dramatically when the geese
are removed (U.S. Fish and Wildlife Service 2012).
Though it was hypothesized that contact with livestock waste would result in a
greater prevalence of cephalosporin-resistant E. coli, it is also a significant contributor of
Cryptosporidium. Livestock can have a high prevalence of infection with both
Cryptosporidium and Giardia. During peak shedding, infected animals, particularly
young animals, can excrete 107 Cryptosporidium oocysts and Giardia cysts per gram of
85
feces for several days (Budo-Amoako et al. 2011). Olson et al. (1997) sampled a variety
of livestock species throughout various farms in Canada for Cryptosporidium and found
overall prevalence to be 20% in cattle, 23% in sheep, 11% in swine, and 17% in horse
samples. These results are supported by similar studies (Coklin et al. 2007, Helmy et al.
2013).
Livestock factors play a strong role in the contamination of the watershed which
becomes centralized at the WWTP. Concentrations of Cryptosporidium tend to be higher
in surface waters during periods of high precipitation combined with manure application
on agriculture farms. A study by Hansen et al. (1991) found that seasonal factors,
including runoff of land drainage, may affect oocyst concentrations in rivers by 10-fold,
with concentrations in drier periods being significantly lower than those in wetter periods
(Hansen et al. 1991). Sischo et al. (2000) looked at 11 dairy farms in the northeastern
United States and concluded that the single largest risk factor for detecting
Cryptosporidium in surface water was increased frequency of spreading of manure on
fields. The researchers state that frequent manure spreading was associated with an 8-fold
increase in the odds of detecting oocysts in stream water (Sischo et al. 2000). Numerous
studies all over the world have found similar results (Bodley-Tickell et al. 2002, Hörman
et al. 2004, Wilkes et al. 2009). Not only do geese receive exposure to infected livestock
waste from surface water, but Canada geese have been observed wandering behind
livestock and picking up undigested waste from their feces (Graczyk et al. 1998).
Graczyk et al. 1998 found C. parvum in Canada goose fecal samples on the eastern shore
of Maryland, where land use was 30% agricultural (Maryland Department of Agriculture
2011) with scattered cattle farms. In this case, migratory geese were observed to follow
86
cattle and consume undigested corn from feces (Graczyk et al. 1998). This demonstrates
transmission to Canada geese from livestock.
In Ohio, most farms will apply manure in the spring before planting, then again in
the fall after harvest (Wicks 2014). Small farms, which may not have any manure
storage, may apply daily, but that is overall a minority (Wicks 2014). Total annual
precipitation for the Greater Columbus area during the year 2013 was highest in the
month of June (17.5 cm) which was when the first sampling period took place (National
Oceanic and Atmospheric Administration 2013). Prevalence of Cryptosporidium was
slightly higher during the first period (50%) than the second period which is not only
consistent with observing a greater number of people at each site during this period, but
also agrees with the precipitation patterns and a shorter duration after manure application
on agriculture fields for the first period than the second period. Therefore, surface water
could have contained especially high concentrations of pathogens during this period that
would ultimately be centralized at wastewater treatment plants.
Future Research and Management Recommendations
This study is an exploratory study that deals with hazard identification and
characterization. The next step in a microbial risk assessment is the dose-response
assessment followed by exposure assessment, and finally risk characterization (U.S.
Environmental Protection Agency/U.S. Department of Agriculture 2012). These
assessments are quantitative analyses that would involve examination of the exact
number of cysts, oocysts, or colony forming units per unit of measurement. This would
87
require the use of more molecular methods. For example, it would be useful to use
methods that estimate gene copies or cell equivalents (U.S. Environmental Protection
Agency/U.S. Department of Agriculture 2012). The most important analysis to include
would be identification of the pathogen species and genotypes using PCR or other similar
molecular methods. This will help quantify which pathogens within each fecal sample are
able to infect the human population. Taking into account the various exposure pathways,
this information would then be applied to a dose-response model. This study does initiate
the exposure assessment process by measuring individual environmental variables that
have the potential to influence exposure, such as distance of each site from nearest
livestock farm and WWTP. Future exposure assessment steps should focus on
confirmation of exposure through surveys, observation of behaviors, and use of
epidemiological databases. Information from the initial hazard identification and
characterization, such as the need to focus on the molt period vs. the most-molt period, is
necessary to carry out these next steps.
There are many methods to be considered when it comes to the control and
management of Canada goose populations. Traditional methods include: hunting and
lengthening the hunting season, poisoning or immobilization, repellent chemicals and
grasses (the Alaskan Native Medical Center in Anchorage, Alaska uses grape Kool-Aid
powder with much success), egg sterilization, physical programs including fences around
ponds and overhead placement of lines or grid wires to prevent landing, vegetative or
rock barriers, reduced mowing, sterilization by surgical neutering or oral contraception,
creating single-sex flocks, hazing and scaring techniques, translocation of predators, and
translocation of the geese (Dieter et al. 2001, Smith et al. 1999). One beneficial method
88
that is often overlooked is the use of riparian buffers or alternative feeding areas.
Creating these buffers can draw the geese away from human population centers and
provide the geese with their land requirements thus preventing conflict.
New methods are being experimented with such as induced cholestasis
(interrupting the flow of bile from the liver to the intestine) (Erlandsen 2005). For
example, bile is a major growth factor for the proliferation of Giardia spp. trophozoites in
the small intestine and it stimulates encystment of trophozoites at high concentrations
(Erlandsen 2005). A study on mice found that cholestasis induced through surgery
produced a 3 log reduction in excretion of Giardia cysts within 24 hours of surgery and a
4 log reduction after 3 days (Erlandsen 2005). Cholestasis induced through adding bilebinding resins to the diet was associated with reductions in cyst excretion of 3 logs within
1 day (Erlandsen 2005).The bile-binding diet is much less expensive than surgery and
produces nearly the same reductions in excretion of cysts and therefore should be pursued
further, especially in other organisms.
Another newer method being developed is vaccination. The goal is to vaccinate
mothers so that their offspring acquire passive immunity. Currently most of these studies
focus on livestock species. For example, a recombinant plasmid encoding the 15 kDa
surface sporozoite protein of Cryptosporidium parvum was developed in adult pregnant
goats (Sagodira et al. 1999). The study found that nasal immunization of pregnant goats
with the plasmid led to a transfer of immunity to offspring that resulted in protection
against C. parvum infection (Sagodira et al. 1999). Offspring from vaccinated dams shed
significantly fewer oocysts and over a shorter period of time than did offspring from nonvaccinated dams (Sagodira et al. 1999). Further development of these new pathogen
89
control methods, especially in other species such as B. Canadensis, could have huge
implications for zoonotic pathogen management.
The human population can take preventive measures as well such as making sure
to clean up waste and storing or disposing of it in a way that does not allow access to
Canada goose populations. A simple step such as ensuring that dumpster lids are always
closed properly can prevent geese from feeding on food remnants. Human food handouts
are another issue that attract Canada goose populations. Informative signs and posters can
be used to educate the public about the negative effects of human food handouts. Better
management of livestock waste could be practiced as well. All farms are encouraged to
have a nutrient management plan which gives guidelines specific to that farm as to when
and where (which fields) to apply (Wicks 2014). These plans usually recommend that
manure should not be applied if there is a 50% chance or greater of rainfall of more than
½ inch within 24 hours after application (Natural Resources Conservation Service 2012).
These plans need to be enforced rather than recommended. Similarly, legislation to limit
the use of antimicrobials in livestock, as well as in hospitals, needs to be enforced as
opposed to voluntary.
90
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101
CHAPTER 3
HAZARD IDENTIFICATION SUMMARY
The first step in a pathogen risk assessment is hazard identification and
characterization. This chapter is a qualitative examination that reviews information
related to the epidemiological, surveillance, clinical, and microbial aspects of the hazard.
It seeks to describe the mechanisms involved in causing harm and the pathogen’s ability
or potential to cause harmful effects (U.S. Environmental Protection Agency/U.S.
Department of Agriculture 2012). The stressors examined in this study were enteric
pathogens in fecal samples collected from Canada geese in the greater Columbus, Ohio
area. The adverse health effects examined were cryptosporidiosis, giardiasis,
salmonellosis, and infection with cephalosporin-resistant E. coli.
The National Institute of Health classifies Giardia, Cryptosporidium, Salmonella,
and E. coli as category B biodefense agents, the second highest priority biological agents,
meaning they are “moderately easy to disseminate, result in moderate morbidity rates and
low mortality rates, and require specific enhancements for diagnostic capacity and
enhanced disease surveillance” (National Institute of Allergy and Infectious Disease
2012). They are also all considered to be American Biological Safety Association Risk
102
Group 2 Agents that are associated with human disease, that are rarely serious, and for
which preventive or therapeutic interventions are often available (American Biological
Safety Association 2014). The National Institute of Health has recently classified
antimicrobial resistant bacteria as a category C biodefense agent which includes
“emerging pathogens that could be engineered for mass dissemination in the future
because of availability, ease of production and dissemination, and potential for high
morbidity and mortality rates and major health impact” (National Institute of Allergy and
Infectious Disease 2012). All of these pathogens are nationally notifiable diseases
(Centers for Disease Control 2015). All produce diseases of significance and high levels
of exposure result in an exposure hazard to the human population.
Major routes of transmission for all of these pathogens include ingestion of
contaminated food and/or water and by direct contact with infected people, animals, or
contaminated environmental surfaces (Kassa et al. 2004, National Institute of Allergy and
Infectious Disease, 2011a, National Institute of Allergy and Infectious Disease 2011b).
Canada goose droppings containing these pathogens have the greatest potential for hazard
to human health where there is close contact with the human population such as
recreational sites, near restaurants, or local sources of open water. Canada goose
droppings may also present a greater hazard than other species because of their
widespread distribution and large body size which produces larger droppings that take up
more space and increase the potential for exposure (Feare et al. 1999). Variation in
susceptibility to infection is largely influenced by human behavior in addition to location.
For example, children who play with balls are more likely to contact fecal material with
their hands than adults who do not play in parks. Additionally, children with
103
contaminated hands may be more likely to transfer some of this contamination to the
mouth than are adults (Feare et al., 1999).
Giardia and Cryptosporidium are both persistent pathogens environmentally and
can survive harsh conditions such as freezing and chlorine treatment (Yoder et al. 2012,
Centers for Disease Control 2011, Environmental Protection Agency 2000). Infectivity is
high for both organisms with as few as 30 Cryptosporidium oocysts capable of causing
illness in healthy adults (DuPont et al. 1995, Kassa et al. 2001). A single avian isolate of
Giardia is virulent to mammals (Graczyk et al. 2008, Upcroft et al. 1997). Shedding is
also high in both pathogens. The mean concentration of Cryptosporidium oocysts found
in the average Canada goose fecal sample is 370 ± 197 oocysts/g while the average
concentration of Giardia sp. cysts found in a Canada goose fecal sample is 405 cysts/g
(Gatczyk et al. 1998).
Results from this study showed that 55 of 93 (59%) Canada goose fecal samples
that were tested for all pathogens were positive. Resident Canada goose droppings pose a
minimal exposure hazard to the human population in the Greater Columbus area for
exposure to Giardia lamblia, the only species known to infect humans, with an overall
prevalence of 3.52%. Prevalence in other studies is generally low (Table 2.12). Kostroff
and Rollender (2001) reported a prevalence of 74% in a Canada goose population in New
York but did not test specifically for Giardia lamblia (Kostroff and Rollender, 2001).
The Giardia assay used in this study was specific for the species that can infect humans
so the prevalence of Giardia from this study is more informative about potential hazards
to public health than from Cryptosporidium prevalence.
104
Conversely, results from this study showed that resident Canada goose fecal
samples in the Greater Columbus area have a high potential to expose the human
population to Cryptosporidium with an overall prevalence of 44.7%. Though the
Cryptosporidium assay was not specific for species that can infect humans it is likely that
a significant portion of positives detected were species that are able to infect humans.
Unlike Giardia that tends to be highly host-specific and has only six known species, only
one of which can infect humans, (Hunter et al. 2005, Xiao and Fayer 2008, Appelbee et
al. 2005), there are at least 21 of 26 recognized species of Cryptosporidium that are
capable of infecting multiple species (Xiao and Fayer 2008, Ryan et al. 2014, Power et al.
2011). Though only C. hominis and C. parvum are currently of major significance to
public health by causing the majority of human cases (Zhou et al. 2004), other species
and genotypes, such as C. meleagridis (Appelbee et al. 2005), are increasingly associated
with human illness (Ryan et al. 2014). Additionally, genotyping and subtyping data
suggest that zoonotic transmission is more important in the epidemiology of
cryptosporidiosis than in giardiasis because most types of Giardia are adapted to a single
host species in addition to the fact that only 1 of 6 species of Giardia is capable of
infecting humans (Xiao and Fayer, 2008). Because multiple species of Cryptosporidium
have the potential to infect humans, it is likely that a significant proportion of the total
Cryptosporidium species detected would infect humans.
Results from this, and numerous other studies (Hussong et al. 1979, Converse et
al. 1999, Feare et al.1999, Roscoe 2001, Fallacara et al. 2001), showed that Salmonella in
Canada goose fecal samples in the Greater Columbus area has very low exposure
potential to infect the human population. This study did not detect Salmonella in any of
105
the fecal samples. It appears that although Canada geese can serve as vectors of
Salmonella, prevalence is very low in the population and therefore introduces a minimal
hazard to the human population in the greater Columbus area.
Detection of any antimicrobial-resistant bacteria in wildlife populations represents
a major concern because bacterial strains are very difficult to treat if infection occurs.
This study detected cephalosporin-resistant E. coli in 10.8% of samples in both urban and
peri-urban environments. Though antimicrobial resistance has been documented in
several other wildlife species (Wang et al. 2014), the discovery of cephalosporin-resistant
E. coli in Canada geese is significant because of geese can fly long distances and transmit
antimicrobial-resistant bacteria strains acting as highly efficient vectors.
This study also showed greater overall pathogen prevalence at urban sites 69/199
(35%) compared to peri-urban sites 37/199 (19%). The environmental variables within
the urban landscape that appeared to have the greatest influence on percentage of
Cryptosporidium detected at each site were: distance of site to nearest WWTP, human
population density within study sites, proportion of land defined by the NLCD as pasture,
and distance to nearest livestock farm. The distance to WWTP variable was the only
variable that was significant at the alpha=0.1 level for both periods. Distance to WWTP
was also significant at the alpha=0.05 level for the experimental regression run on the
best fit model between periods.
Wastewater treatment plants collect untreated water as well as human, livestock,
and industrial waste, toxic materials, and debris. Not only do geese contribute
significantly to contamination of wastewater treatment ponds where they are known to
congregate (U.S. Fish and Wildlife Service 2012), but they are also exposed to
106
contaminants in the water that they can then transmit to other locations. It would be
beneficial in some areas to use a grid wire system to prevent geese from landing in ponds,
however this may not be economical for larger ponds. Replacing storm and combined
sewers with sanitary sewers may also be beneficial by preventing contaminants from
reaching the surface water.
High densities of humans in urban areas result in high concentrations of food,
water, energy, materials, sewage, pollution, and waste (McDonnell et al. 1997). Geese
that inhabit these urban areas further contribute to this accumulation by introducing large
quantities of fecal matter. Additionally, they are more likely to be exposed to
anthropogenic waste that they transmit back to the human population or to other
reservoirs including domestic animals, wildlife, and fomites. Deterring geese from
concentrating in high risk urban areas will help eliminate this problem. A successful
strategy that has been used by Suburban Commercial and Residential Animal
Management (SCRAM), a humane wildlife removal organization in central Ohio, has
been the use of border collies that patrol areas where geese tend to congregate. Where
possible, creating riparian buffers and alternative feeding areas can not only be effective
but may also offer other benefits such as water purification through filtration. People can
also work to clean up waste and store it properly to avoid access to geese.
Livestock waste and manure on agricultural fields are a major source of pathogens
that are easily disseminated throughout the environment through surface water. Geese can
acquire these pathogens from surface waters as well as from feeding in agricultural fields
treated with manure. Proper barriers that prevent waste from reaching surface water may
be effective. Creating nutrient management plans for farms is a valuable method to
107
reduce this problem and these plans must be enforced. Similarly, in order to reduce
antimicrobial resistant bacteria in livestock, as well as human populations, legislation to
limit the use of antibiotics need to be enforced as opposed to voluntary.
Future steps should focus on quantitative analysis including dose-response
assessment followed by exposure assessment (U.S. Environmental Protection
Agency/U.S. Department of Agriculture 2012). This would require the use of more
molecular methods such as identification of the pathogen species and genotypes using
PCR. This will help quantify which pathogens within each fecal sample are able to infect
the human population. This study initiates the exposure assessment process by measuring
individual environmental variables that have the potential to influence exposure, such as
distance of each site from nearest livestock farm and WWTP. Future exposure
assessment steps should focus on confirmation of exposure and measurement of
additional environmental variables that may influence exposure. For example, samples
could be taken from multiple wastewater treatment plants. Information from the initial
hazard identification and characterization, such as the need to focus on the molt period
vs. the most-molt period, is necessary to carry out these next steps making this study
necessary.
108
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