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Contents lists available at ScienceDirect
Ecological Indicators
journal homepage: www.elsevier.com/locate/ecolind
How to choose a biodiversity indicator – Redundancy and complementarity of
biodiversity metrics in a freshwater ecosystem
Belinda Gallardo a,∗ , Stéphanie Gascón b , Xavier Quintana b , Franciso A. Comín a
a
b
Pyrenean Institute of Ecology (CSIC), Zaragoza, Spain
Institute of Aquatic Ecology, University of Girona, Spain
a r t i c l e
i n f o
Article history:
Received 30 July 2010
Received in revised form
21 December 2010
Accepted 23 December 2010
Keywords:
Rarefied richness
Functional diversity
Body-size diversity
Taxonomic distinctness
GAM models
a b s t r a c t
A range of biodiversity metrics are available to assess the ecological integrity of aquatic ecosystems.
However, performance varies considerably among different types of metrics and provides different
information regarding ecosystem conditions, thus making difficult the selection of appropriate metrics for biomonitoring. The present study evaluated the robustness of six biodiversity metrics to assess
environmental change and determine their utility as relevant indicators of ecosystem biodiversity and
functionality. Traditional metrics such as species richness and Shannon diversity were considered along
with alternative metrics such as functional diversity, size diversity and taxonomic distinctness. To that
end, invertebrate assemblages in a river floodplain were used as a case study to evaluate the performance of metrics using Generalized Additive Models (GAM). GAM explained between eight and 49% of
the variability in biodiversity. The regression models exhibited differences in the response of biodiversity
indicators to environmental factors, suggesting that intermediate levels of turbidity and low salinity are
conditions favouring increased biodiversity in the study area. Based on correlations among metrics and
responses to primary environmental factors, it is concluded that Shannon and functional diversity, and
rarefied species richness generated similar information regarding ecosystem conditions (i.e., the metrics
were redundant); while size diversity and distinctness provided useful additional data characterizing
ecosystem quality (i.e., the metrics were complementary). Functional diversity indicated not only number and dominance of species, but also each species functional role in the community, and was therefore
the most informative biodiversity metric. Nevertheless, the use of a combination of metrics, for example
functional and size diversity, and variation in taxonomic distinctness, provides complementary data that
will serve to achieve a more thorough understanding of ecosystem structure and function, and response
to primary environmental influences.
© 2010 Elsevier Ltd. All rights reserved.
1. Introduction
Aquatic environments including wetlands, coastal, and riverine habitats worldwide are subject to extreme human pressures in
the form of extensive regulation, human occupation and pollution
(Tockner and Stanford, 2002; Ward and Tockner, 2001). Furthermore, by 2025, the increased human population and predicted
consequences of climate change (i.e., decreased water variability,
quality and quantity) will lead to further degradation of aquatic
habitats, intensified resource exploitation, rise in pollutant discharge into aquatic ecosystems, and continued proliferation of
invasive species (STRP, 2002). As a consequence, species and habi-
∗ Corresponding author. Present address: Aquatic Ecology Group, Zoology Department, University of Cambridge, Downing Street, CB2 3EJ Cambridge, UK.
Tel.: +44 1223 336617; fax: +44 1223 336676.
E-mail addresses: bg306@cam.ac.uk, galla82@hotmail.com (B. Gallardo).
tat diversity and their functional capacity is expected to decline
(Erwin, 2009). Therefore, it is vital to explore the environmental
processes that support ecosystem biodiversity and functionality in
order to develop management plans and policies to successfully
reduce the negative effects of human impacts (Jansson et al., 2000;
Poff et al., 1997).
Biodiversity metrics have often been applied to evaluate the ecological integrity of aquatic ecosystems. However, the performance
of different types of metrics varies considerably and provides different representations of ecosystem conditions (Wilsey et al., 2005).
For example, species richness and taxonomic diversity are traditional metrics determined by species number and dominance
(Heino et al., 2007). However, the ability of traditional taxonomic metrics to assess human impacts on ecosystems is not
clear (Mouillot et al., 2006). For example, low taxonomic diversity
can reflect either high disturbance or high productivity and biotic
interaction (e.g., competition, predation) (Connell, 1978). Therefore, traditional taxonomic metrics cannot necessarily discriminate
1470-160X/$ – see front matter © 2010 Elsevier Ltd. All rights reserved.
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between natural and human related stressors (Reizopoulou et al.,
1996).
Consequently, alternative metrics have gained attention in
recent years that consider not only the number and dominance of
species, but also their ecological function in the ecosystem, trophic
relationships, or evolutionary relatedness. For instance, functional
diversity is based on the number and dominance of life-history
strategies exhibited by an aquatic community. Habitat conditions
limit the range of life-history strategies capable of supporting survival; therefore functional diversity indicates how an ecosystem
has adapted to natural disturbance and human impacts (Mouillot
et al., 2006; Statzner et al., 2004). Closely related to functional
diversity, diversity in body size provides information regarding
food web energy fluxes, feeding ecology, trophic structure and
niche segregation between species or developmental stages of
species (Badosa et al., 2006; Basset et al., 2004; Brucet et al., 2006;
Gascon et al., 2009b). More recently, taxonomic distinctness, which
assesses the evolutionary relationships among species (Clarke and
Warwick, 1999, 2001) has gained attention among aquatic ecologists. Taxonomic distinctness measures the relatedness among
species assemblages i.e., an assemblage including species belonging
to different families will be more diverse than an assemblage with
the same number of species within one family (Heino et al., 2007). In
contrast to other metrics, taxonomic distinctness reflects long-term
evolutionary adaptation and declines linearly in response to human
impacts but is insensitive to natural habitat differences (Clarke and
Warwick, 1999, 2001). Taxonomic distinctness has been successful
used in marine environments, but scarcely applied in other habitats
(but see Gascon et al., 2009a; Heino et al., 2007; Marchant, 2007).
Biodiversity metrics may respond differently to environmental factors. For example, taxonomic and functional diversity have
often been reported to reflect abiotic constraints (Badosa et al.,
2007; Gallardo et al., 2009a); while size diversity responds to
resource availability (Badosa et al., 2007; Basset et al., 2004;
Gascon et al., 2009b); and taxonomic distinctness experiences a
decline due to eutrophication (Mouillot et al., 2005, 2006). In addition, the relationship among biodiversity metrics is not clear. For
instance, taxonomic and functional diversity have been reported as
highly related (Heino, 2008). Taxonomic diversity and distinctness
exhibited a significant relationship in Mediterranean environments
(Gascon et al., 2009a) but showed different relationship depending
on taxonomic group in Finland lakes (Heino et al., 2005). In fact, as
diversity metrics are addressed individually, it is difficult to know
which metrics represent an inter-relationship, and which would be
their simultaneous response to main environmental drivers. This
is an important consideration, because two biodiversity metrics
that are uncorrelated and show a different response to environmental factors may provide valuable data on the condition of the
aquatic ecosystem (i.e., are complementary) (Gascon et al., 2009a;
Heino, 2005). On the other hand, two highly correlated metrics
that respond similarly to environmental influences may not generate data that characterizes the state of the ecosystem (i.e., are
redundant) (Heino, 2008).
Despite these various considerations, only a few studies that
simultaneously compare the performance of different metrics exist
in aquatic ecosystems (e.g., Gascon et al., 2009b; Heino et al., 2007,
2005; Mouillot et al., 2006). Therefore, further evaluation of the
robustness of different biodiversity metrics to reflect environmental changes is required (Mouillot et al., 2006).
The present study served to investigate and compare the
performance and utility of six biodiversity metrics to detect environmental change, offering the first simultaneous assessment
of metrics that are seldom compared, such as the diversity of
functional traits, body sizes or phylogenetic relatedness. The
study focused on macroinvertebrate assemblages in the ecosystem formed by the main river channel and associated wetlands
of the Ebro River (NE Spain). First, the relationship between the
six metrics was analyzed to identify potential redundancy or complementarity. Second, the performance of the six metrics to detect
environmental change across the aquatic ecosystem was evaluated.
2. Materials and methods
2.1. Sampling procedure
The study area was in the middle section of the Ebro River, where
17 wetlands in its floodplain were selected along a 100-km segment (Fig. 1). Macroinvertebrate assemblages in these wetlands
were sampled in Autumn-2006 and Spring-2007, when according
to previous studies the macroinvertebrate diversity is at its highest (Gallardo et al., 2008). Two to three samples were collected at
the upstream, midstream and downstream ends within each wetland to account for spatial variability, though results were pooled
by wetland for a total of N = 34 samples.
Two-liter water samples were collected at each sampling point
from a depth of 20 cm directly into acid-washed polycarbonate bottles and placed on ice. Samples were filtered the same day through
Whatman® GF/F glass-fiber filters (pre-combusted at 450 ◦ C for 4 h)
to determine the amount of suspended (TSS) and dissolved (TDS)
solids (APHA, 1989), which were used as surrogates for turbidity and salinity, respectively. Filtered water was used for nutrient
determination. Ionic chromatography was used to determine dissolved inorganic nitrogen concentration (DIN), and a continuous
flow analyzer (FLOWSYS-SYSTEA® ) was used to measure the concentration of dissolved organic nitrogen (DON), phosphorus (DOP)
and carbon (DOC) (APHA, 1989). Phytoplankton photosynthetic
pigments (Chl-a) were analyzed using the Spectrophotometric
Method (APHA, 1989). Flood duration (FD) was measured as the
total number of days per year that a wetland is connected with the
river channel by surface pathways. Further details on FD calculation
can be found in Gallardo et al. (2009a).
At various microhabitats within each wetland, invertebrates
were collected with a sweep net (45 cm × 45 cm frame, 500-␮m
sieve) using a 1-min sampling interval covering approximately
0.25 m2 . Samples were preserved in 5% formalin and subsequently
hand-sorted and identified to the lowest practical taxonomic level
(typically genus).
2.2. Biodiversity calculation
Six biodiversity metrics were calculated for this study: Shannon diversity, rarefied richness, functional diversity, size diversity,
average and variation in taxonomic distinctness. Metrics were calculated using R 2.5.1 software (R Development Core Team, 2007),
with the exception of size diversity, and packages used to calculate
each metric are detailed below.
First, Shannon diversity and species richness were chosen
because these metrics are the most widespread metrics to assess
ecosystem biodiversity. Shannon diversity (H ) and rarefied richness (S) were calculated using functions “diversity” and “rarefy”,
respectively, available in the “vegan” package (http://cran.rproject.org/). Rarefied richness was used in place of absolute
richness because the number of species may be affected by the
number of individuals in each sample, i.e., the more organisms in a
sample, the more likely an increase in species (Gotelli and Colwell,
2001).
Functional and size diversity were included in this study because
these metrics are key drivers of important ecosystem processes,
including productivity, stability and recovery (Mouillot et al., 2006).
Traits of species used to calculate functional diversity included
potential body size, life cycle duration, potential number of repro-
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Fig. 1. Study area in the middle sector of the Ebro River. Black dots indicate sampling points where water and macroinvertebrate samples were obtained. Distance from A
to A is approximately 100 km.
duction cycles per year, aquatic stage, reproduction technique,
dissemination strategy (aquatic/aerial, active/passive), resistance
form (eggs, cocoons, diapause, none), respiration technique, locomotion, food source and feeding habits. Further description of traits
and categories within traits can be found in Tachet et al. (2000).
Functional diversity (Hp ) was calculated as the Rao diversity coefficient, using the methodology developed by Champely and Chessel
(2002) and implemented in the “ade4” package (Chessel et al.,
2004). Rao’s diversity index allows the diversity in a set of species
to be measured using trait dissimilarity between species and sites
(Champely and Chessel, 2002).
The body length of at least 20 organisms of each species found in
a sample was measured, biomass calculated using allometric equations, and results extrapolated to the whole sample. Size diversity
() was calculated afterwards using “diversity08” (Quintana et al.,
2008), an open source software that allows sample size diversity to
be measured using a non-parametric method based on the Shannon diversity expression. This methodology has been successfully
applied to assess size diversity in aquatic assemblages (Gascon
et al., 2009b; Ruhi et al., 2009).
Distinctness was calculated by means of two metrics based
on presence–absence data: variation in taxonomic distinctness
(+ ) and average taxonomic distinctness (+ ). The former measures the variance in pairwise path lengths between each pair of
species, reflecting the unevenness of the taxonomic tree (Clarke
and Warwick, 2001). The latter measures the average path length
between two randomly chosen species in a sample. These two
metrics were chosen because they are not always highly related,
suggesting that they reflect different aspects of relatedness. Function “taxondive” available in the “vegan” package was used to
calculate distinctness metrics. The taxonomic levels included in this
study were genus, family, order, class and phylum, and the same
path length was weighted for each taxonomic level (Heino et al.,
2007; Ruhi et al., 2009).
2.3. Statistical analysis
Environmental variables and biodiversity metrics were notnormally distributed (Kolmorov-Smirnov test, P > 0.05) thus
non-parametric analyses were used. First, the relationship between
the six biodiversity metrics was analyzed by means of nonparametric Spearman correlation. The response of biodiversity
metrics to environmental factors was subsequently assessed
using Generalized Additive Models (GAM, Wood, 2008). GAM
was chosen instead of other regression procedures because of
its ability to deal with non-linear relationships between the
response and the set of explanatory variables. The only underlying assumption of GAM is that the functions are additive and
the components smooth (Guisan et al., 2002). This methodology
has been successfully used for species modelling in relationship to environmental factors (e.g., Castella et al., 2001; Gallardo
et al., 2009b). Differences between the two sampling seasons
analyzed were not significant for any of the 6 biodiversity metrics investigated (ANOVA, P > 0.05) so temporal variation was
assumed not to affect the results of the study. Response variables in the models included every biodiversity metric, while
Please cite this article in press as: Gallardo, B., et al., How to choose a biodiversity indicator – Redundancy and complementarity of
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R= 0.97; p< 0.001
Functional div.
Rarefied richness
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R= 0.93; p< 0.001
Size div.
R= 0.98; p< 0.001
R= 0.67; p< 0.001
R= 0.70; p< 0.001
R= 0.90; p< 0.001
R= 0.87; p< 0.001
R= 0.93; p< 0.001
R= 0.69; p< 0.001
R= 0.50; p< 0.001
R= 0.45; p< 0.001
R= 0.54; p< 0.001
R= 0.50; p< 0.001
Var. distinctness
Aver. distinctness
R= 0.71; p< 0.001
Shannon div.
Rarefied richness
Functional div.
Size div.
R= 0.66; p< 0.001
Aver. distinctness
Fig. 2. Scatterplot matrix of the six evaluated metrics, including Spearman correlation (N = 34). To allow comparison, variables were previously standardized. A 1:1 line has
been added to graphics to depict the relationship between Shannon diversity (H ) and the other five-biodiversity metrics. See Table 1 for metric abbreviations.
explanatory descriptors included eight environmental factors
that were previously ln(X + 1) transformed. Explanatory descriptors showed low correlation values (Spearman test, r < 0.7) and
they were considered independent. A quasi-Poisson family was
chosen for every biodiversity metric except taxonomic diversity, for which a Poisson family was selected. The percentage
of deviance explained was used to assess the goodness-of-fit
of the final model. The contribution of each variable to the
final model was tested by evaluating the drop in deviance
explained (drop-contribution) by the model when the variable was removed, and so a high drop-contribution implied a
high variable contribution (Castella et al., 2001; Gallardo et al.,
2009b). GAM analyses were performed with the “mgcv” pack-
age (Wood, 2008) in R 2.5.1 software (R Development Core Team,
2007).
3. Results
3.1. Redundancy and complementarity of biodiversity metrics
The six biodiversity metrics were significantly correlated
(Spearman P < 0.001). The highest correlations were found between
Shannon diversity and rarefied richness, as well as between Shannon diversity and functional diversity. Scatterplots showed an
almost linear relationship in the three metrics (Fig. 2). In contrast,
the lowest correlation values were found between the variation in
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Table 1
Mean (SD) of biodiversity metrics and environmental features in the Ebro river-floodplain wetlands. N = total number of samples.
Biodiversity metrics
Environmental factors
Variable
Abbr.
Range
(N = 34)
Abundance
Absolute richness
Shannon diversity
Rarefied richness
Functional diversity
Size diversity
Variation in tax. distinctness
Average tax. distinctness
Total suspended solids
Total dissolved solids
Chlorophyll-a
Dissolver organic phosphorus
Dissolved organic nitrogen
Dissolved inorganic nitrogen
Dissolved organic carbon
Flood duration
N
R
H
S
Hp
+
+
TSS (mg L−1 )
TDS (mg L−1 )
Chl-a (␮g L−1 )
DOP (␮g L−1 )
DON (mg L−1 )
DIN (mg L−1 )
DOC (mg L−1 )
FD (days y−1 )
3–1274
2–11
0.06–1.93
1.12–5.48
0.82–28.70
0.26–4.03
53.04–1139.93
103.85–881.80
6.67–311.00
800–4056
1.77–64.22
0.09–236.72
0.08–4.31
0.01–8.89
3.53–8.89
1–181
78.35 (431.50)
4.97 (2.39)
0.91 (0.29)
2.91 (0.69)
16.41 (4.82)
2.68 (0.70)
322.03 (234.03)
378.45 (163.28)
62.80 (49.10)
1548.58 (721.02)
19.08 (14.05)
27.37 (28.40)
0.78 (0.84)
2.85 (2.30)
6.58 (3.27)
54.62 (51.22)
taxonomic distinctness and the remainder of the metrics, including average taxonomic distinctness. Fig. 2 indicates that as Shannon
diversity increased, both variation and average taxonomic distinctness scores remained low, whereas size diversity continued to
increase (Table 1).
3.2. Modelling the response of biodiversity metrics to
environmental factors
The Generalized Additive Models (GAM), which correspond
biodiversity metrics to environmental factors, detected significant relationships and showed the importance of physico-chemical
and trophic factors in explaining biodiversity. Goodness-of-fit
of the models ranged from 8% to 49% (Table 2). Shannon
diversity was the metric best explained by environmental
factors, whereas average taxonomic distinctness was poorly modelled.
Physico-chemical factors were most important in explaining
Shannon diversity (Table 2), which peaked at intermediate levels of
turbidity and salinity (Appendix, A–D). In addition, Shannon diversity increased linearly with the trophic factors chlorophyll-a and
organic nitrogen, although the influence of these factors was lower
(Table 2).
Rarefied richness peaked at intermediate levels of turbidity and
was lowest at intermediate levels of chlorophyll-a (Appendix, E–F).
Both factors had a similar explanatory effect on rarefied richness
(Table 2).
Functional diversity peaked at intermediate levels of turbidity
and salinity; and increased with chlorophyll-a and organic nitrogen
(Appendix, G–J).
Size diversity also peaked at intermediate levels of turbidity and
increased linearly with chlorophyll-a; but inconsistent with previous metrics, it decreased with salinity (Appendix, K–M). Body size
of organisms found was remarkably small (body size range from
0.5 to 1.7 × 10−8 g. dry weight), often in the lowest size limit of the
species according to identification keys.
Distinctness metrics were only significantly affected by trophic
factors. Average taxonomic distinctness increased linearly with
chlorophyll-a; while variation in taxonomic distinctness increased
with organic phosphorous (Appendix, N–O).
Overall, turbidity and salinity exhibited the highest drop in
deviance values in every model, i.e., the deviance explained by
the models was dramatically reduced when one of these variables
was removed. The exceptions were distinctness metrics. On the
contrary, dissolved inorganic nitrogen, organic carbon and flood
duration were not included in any model.
4. Discussion
4.1. Redundancy and complementarity of biodiversity metrics
In the present study, the six biodiversity metrics exhibited
high inter-correlation that can be explained by harsh environmental conditions in the Ebro River ecosystem due to extensive
river regulation, human occupation and pollution (Cabezas et al.,
2009; Gallardo et al., 2008). Low Shannon diversity scores support this idea. Congruent with our study, several authors have
found high redundancy among metrics in aquatic ecosystems,
related to severe environmental conditions limiting the species
or traits capable of supporting survival (Beche and Statzner,
2009; Heino, 2008). However, Gascon et al. (2009a) argues that
diversity metrics cannot be considered redundant but complementary when they are sensitive to different environmental factors,
thus metric response to environmental drivers should not be
disregarded.
Among metrics, variation in taxonomic distinctness was least
correlated with all other biodiversity metrics, suggesting the metric provides different information on ecosystem conditions. This
Table 2
Results of GAM models performed between biodiversity metrics (response variable) and 8 environmental variables (N = 34). The % deviance explained by each model is shown
(Total Expl. %). The drop in deviance explained by the model when the descriptor is removed (Drop-contribution) measures the contribution of each variable to the model.
Variable abbreviations are explained in the text and Table 1. –: non-significant variable.
Drop contribution (%)
H
S
Hp
+
+
Total expl. (%)
TSS
TDS
Chl-a
DON
DOP
DIN
DOC
FD
28.8
26.1
25.9
13.7
–
–
21.9
–
29.8
17.6
–
–
18.9
27.1
22.5
10.5
8.7
–
0.4
–
10.1
–
–
–
–
–
–
–
–
13.8
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
49.2
46.9
48.3
43.4
8.7
13.8
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Table 3
The advantages and disadvantages of implementing and interpreting different biodiversity metrics.
Advantages
Shannon diversity and
Rarefied richness
Disadvantages
Implementation: Easy computing
Implementation: Dependent of sampling effort and area, thus un
underestimation of true taxonomic diversity(6,8)
Dependent on taxonomic identification, so time-consuming and
susceptible to many possible taxonomic errors(11)
Interpretation: Do not relate to the ecological role of species and thus
Interpretation: Easy to interpret
Wide use in literature
Comparable with other studies
Functional diversity
Implementation: Independent of taxonomic identification(1)
Independent of sampling effort(2)
Interpretation: Relates to ecosystem functionality (e.g., metabolism,
Size diversity
nutrient cycling, stability, productivity or resilience)(9)
Allow comparison between studies with different species
composition(1)
Implementation: Independent of taxonomic identification
Independent of sampling effort
are difficult to relate to higher scale processes(2)
No clear relationship with human impacts(10)
Highly redundant(7,12)
Implementation. There are not published traits for every species
Interpretation: Difficult due to the wide range of traits (from
respiration to locomotion) included
Limited use in literature
Redundant with taxonomic metrics(7,12)
Implementation: Time consuming as it depends on body-size measure
The lack of formulae relating body length and mass for some species
reduces the reliability of results
Easy to calculate with “diversity 08”
Interpretation: Allow comparison between studies with different
species composition(3)
Relates to ecosystem functionality (e.g., energetic fluxes, species
interaction, food web structure)(3)
Complementary with other metrics(12)
Implementation: Independent of sampling effort
Variation and average
taxonomic distinctness Can be used with simple presence/absence data(4,5)
Implementation: Dependent on taxonomic identification
Interpretation: Difficult to interpret as they reflect evolutionary
characteristics incorporated at the long-term
Interpretation: Relates to phylogenetic diversity
Closely related to ecosystem functionality(4,5)
Declines linearly with degradation but it is insensible to natural
differences in habitat(7,8)
Complementary with other metrics(12)
(1)
Abellan et al. (2006), (2) Bady et al. (2005), (3) Basset et al. (2004), (4 and 5) Clarke and Warwick (1998, 2001), (6) Gotelli and Colwell (2001), (7) Heino (2008), (8 and 9) Mouillot et al.
(2005, 2006), (10) Reizopoulou et al. (1996), (11) Sheppard (1998), (12) present study.
observation agrees with other studies in both freshwater (Gascon
et al., 2009a; Heino et al., 2007) and marine environments (Clarke
and Warwick, 1998; Leonard et al., 2006) and may be because distinctness reflect long-term evolutionary adaptation to ecosystem
conditions, while the other metrics respond to short-term environmental changes.
Scatterplot results indicated that slight differences exist in the
relationship among metrics. If Shannon diversity is used as a reference (Fig. 2), rarefied richness and functional diversity shows
a nearly linear relationship. It is therefore very likely these three
metrics provide similar information regarding ecosystem diversity
(Heino, 2008). Departure from the linear relationship may indicate
metrics that do not exhibit much overlap. In these situations, an
increase in Shannon diversity does not necessarily correspond to a
similar increase in other diversity metrics, and we can conclude that
these specific metrics provide complementary information about
the ecosystem state. For example, size diversity showed relatively
high scores even at low to medium Shannon diversity levels (Fig. 2).
This means that a diverse range of body sizes can be found for
every species, as has been noted before in plankton assemblages
(Brucet et al., 2006). This is surprising as the mean body size of
benthic organisms in the Ebro River-floodplain was generally small
(2–3.4 mg/sample), probably reflecting environmental instability,
high predatory pressure or anthropogenic disturbance. Therefore,
even if the size-range of organisms is generally small, body size is
variable. Among potential factors explaining this fact, fish predation in the floodplain may favour diversification of smaller species
in order to occupy a broader range of niches (Blumenshine et al.,
2000). In contrast, both taxonomic distinctness metrics showed low
scores with increasing Shannon diversity, suggesting high environmental pressure and the absence of a variety of ecological niches
to support different species (Mouillot et al., 2006). Consequently,
as suggested by Heino et al. (2005), cogeneric species might have
adapted to the heterogeneity of the habitat, resulting in lower phylogenetic variability than expected for a given Shannon diversity
score.
4.2. Modelling the response of biodiversity metrics to
environmental factors
GAM models demonstrated that biodiversity metrics were significantly related to habitat characteristics. The deviance explained
by the models ranged from 8% for variation in taxonomic distinctness to 49% for Shannon diversity. These models revealed
differences in the response that the six metrics developed for each
environmental factor.
Shannon and functional diversity exhibited a similar response to
environmental factors. Both metrics peaked at intermediate levels
of turbidity (55 mg/l TSS), decreased with increasing salinity (peaking at concentrations lower than 1000 mg/L), and increased with
increasing chlorophyll-a and organic nitrogen. These factors have
previously been identified as primary influences of macroinvertebrate structure and diversity in the Ebro river floodplain (Gallardo
et al., 2008). Other studies detected a similar significant relationship between functional diversity and habitat factors, including pH
(Heino, 2005), macrophyte cover, moss cover, or lake area (Heino,
2008). Rarefied richness showed a similar response to turbidity
and chlorophyll-a, although the model did not include salinity or
organic nitrogen.
Size diversity was related to turbidity, salinity and chlorophylla, indicating a reduction in organisms’ size ranges at harsher
environmental conditions. However the model explained less than
half the size diversity variance, suggesting ecosystem factors other
than those included here are relevant.
Please cite this article in press as: Gallardo, B., et al., How to choose a biodiversity indicator – Redundancy and complementarity of
biodiversity metrics in a freshwater ecosystem. Ecol. Indicat. (2011), doi:10.1016/j.ecolind.2010.12.019
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Distinctness metrics were not significantly related to abiotic
factors as was turbidity or salinity, yet they were sensitive to
trophic elements as organic phosphorous or chlorophyll-a for
variation and average taxonomic distinctness, respectively. This
result agrees with Mouillot et al. (2005), who found a significant
relationship between average taxonomic distinctness and eutrophication, while variation in taxonomic distinctness was instead
related to salinity. Other authors have also found a weak relationship between distinctness metrics and trophic variables (Heino
et al., 2007; Leonard et al., 2006), which may be explained by
long-term adaptation of populations to environmental changes
instead of short-term changes investigated in this and other
studies.
Regression models support redundancy among Shannon
diversity, rarefied richness and functional diversity, as these
metrics are highly correlated and respond similarly to environmental factors. Therefore, to increase the efficiency of
biomonitoring in floodplain habitats, at least one of these
three metrics should be selected. Furthermore, by demonstrating different responses to main environmental influences,
the complementarity of size diversity and distinctness is
demonstrated.
4.3. How to choose a biodiversity indicator?
The selection of an appropriate biodiversity metric depends on
the objectives of the study, statistical considerations of the data and
the expert experience of the researcher. Results from the present
study should assist in the selection of suitable data metrics. Table 3
summarizes the advantages and disadvantages of implementing
and interpreting every metric addressed in this study. It is concluded that functional diversity is the most versatile metric, as it
provides an indication not only of species number and dominance,
but their functional role in the community (Mouillot et al., 2006).
Functional diversity is independent of sampling effort, requires
low identification effort, is easy to calculate and allows comparisons among sites of different taxonomic composition (Clarke
and Warwick, 2001); three highly desired characteristics when
bio-monitoring aquatic communities on different spatial and/or
temporal scales. Nonetheless the performance of functional diversity using other groups of organisms still needs evaluation. Yet,
Shannon diversity is the most widespread metric used to assess
environmental impacts on ecosystems, which allows a comprehensive comparison with other studies. Nevertheless, the use of a
combination of metrics, for example functional (or Shannon) diversity, size diversity and variation in taxonomic distinctness, provides
complementary information that achieves a more complete understanding of the structure and function of the ecosystem and its
response to main environmental drivers. Ultimately, the most complete understanding of ecosystem diversity derives from different
points of view, which will continue to contribute to our knowledge
of aquatic functionality and aid in future ecosystem monitoring and
management.
Acknowledgements
This study was supported by the Spanish Ministry of Education
(MEC CGL2005-07059-C02-01 and CGL2008-05153-C02-01/BOS)
and the Aragon Government (B061 2005 pre-doctoral grant).
Thanks are extended to people that collaborated in field (M.L.
Dehesa, A. de Frutos), chemical laboratory (M. García, B. Bueno),
taxa identification (J. Sala, D. Boix), and English editing (J. Schultz
from www.writescienceright.com).
7
Appendix A. Supplementary data
Supplementary data associated with this article can be found, in
the online version, at doi:10.1016/j.ecolind.2010.12.019.
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