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Oikos 125: 556–565, 2016
doi: 10.1111/oik.02486
© 2015 The Authors. Oikos © 2015 Nordic Society Oikos
Subject Editor: Matthew Bracken. Editor-in-Chief: Dries Bonte. Accepted 5 August 2015
Niche-dependent trophic position distributions among primary,
secondary and tertiary consumers
Ryan J. Woodland, Fiona Y. Warry, Victor Evrard, Rohan H. Clarke, Paul Reich and
Perran L. M. Cook­
R. J. Woodland ([email protected]), F. Y. Warry, V. Evrard and P.L.M. Cook, Water Studies Centre, School of Chemistry, Monash
Univ., Clayton, VIC 3800, Australia. Present address for RJW: Chesapeake Biological Laboratory, Univ. of Maryland Center for Environmental
Science, Solomons, MD 20688, USA. – R. H. Clarke, School of Biological Sciences, Monash Univ., Clayton, VIC 3800, Australia. – FYW
and P. Reich, Arthur Rylah Inst. for Environmental Research, Dept of Environment, Land, Water and Planning, Heidelberg, VIC 3084,
Australia.­
Many consumers display flexible feeding strategies that vary among individuals or populations, through life-history,
or spatiotemporally. Despite the recognized influence of flexible feeding on the structure and dynamics of food webs,
the consequences of these feeding strategies on the actual shape and characteristics of trophic position distributions have
received less attention. We proposed and tested several a priori hypotheses to predict the likely effect of niche-dependent
(e.g. herbivore, secondary consumer) foraging on the shape and statistical properties of consumer trophic position
distributions using natural abundance stable isotope data from a diverse dataset of consumers. We found evidence that
the structural characteristics of consumer trophic position distributions varied as a function of trophic niche. Herbivores
and tertiary consumers tended to be ‘packed’ closely near their mean trophic position, with few individuals realizing
trophic positions markedly higher or lower than the mean. Conversely, secondary consumers often displayed broad trophic
position distributions with many individuals dispersed away from the center of the distribution. We examined the effect
of applying constant versus dynamic isotope trophic fractionation models and found that both models yielded similar
although not identical results. Our findings suggest that trophic level omnivory supports a larger fraction of consumer diet
at intermediate trophic positions than at either the lowest or the highest positions in aquatic food webs. These results suggest that vertical trophic niche declines among higher order consumers despite general evidence that the range of potential
foraging options (i.e. horizontal trophic niche) tends to increase at higher trophic positions. Although further work is
needed to test the generality of these patterns in other ecosystems, proactively examining trophic position distributions and
reporting appropriate measures of central tendency (e.g. arithmetic versus geometric means) will increase the accuracy of
individual trophic studies as well as the applicability of results for meta-analytical food web models.
There is a strong focus in ecology on the theoretical implications and empirical evidence for inter- and intra-species
trophic diversity as a structuring component of food webs
(Fagan 1997, Bolnick et al. 2003, Arim and Marquet 2004,
Schellekens and van Kooten 2012). For example, the prevalence and consequences of omnivory (defined as foraging
across trophic levels) has received particular attention due
to its apparent ubiquity in natural ecosystems (Arim and
Marquet 2004, Thompson et al. 2007) and its potential
stabilizing influence on food web dynamics (Polis and Strong
1996, McCann and Hastings 1997, Holyoak and Sachdev
1998, but see Williams and Martinez 2004). Other flexible
foraging strategies such as intraguild predation, cannibalism,
life-history omnivory, and incidental consumption (among
others) can all alter the realized trophic positions of individuals and, collectively, the trophic position distributions of
populations of consumers (Pimm and Rice 1987, Polis et al.
1989, Polis 1994).
556
One consequence of flexible foraging is that populations
of consumers display a distribution of trophic positions
rather than a single fractional value (Pauly et al. 1998). The
shape of the distribution of trophic positions occupied
by a species or population (hereafter, simply ‘consumer’) is
a critical consideration of trophic niche that is typically overlooked or assumed. The standard approach for calculating
an arithmetic mean and standard deviation of trophic position implicitly assumes samples are drawn from a population
with an underlying normal distribution. This assumption
can make data analysis more tractable; however, constraints
arising from factors such as incidental carnivory, size-based
foraging, and trophic energy attenuation could result in
non-normal trophic position distributions in natural ecosystems. Intraspecific variation in trophic position has been
quantified using measures of variance (Vander Zanden et al.
2000, Bearhop et al. 2004, Gibb and Cunningham 2013)
and coefficients of variation (Edwards et al. 2013), but the
Probability density
(a)
Decreasing
Right skew
Left skew
Skewness
Variance
Variance
Skewness
(b)
Increasing
Decreasing
Increasing
(c)
Range
contribution of directional skewness and the cumulative
effects of trophic variability on consumer trophic position
distributions has received much less attention. To date, there
is very little information available on the likely distribution
of individual trophic positions within a consumer population and how the specific distribution may be influenced by
the consumer’s trophic position.
One of the most widely applied analytical techniques
for estimating the trophic position of a consumer involves
analyzing and comparing the ratio of heavy (15N) to
light (14N) nitrogen stable isotope composition of a consumer’s tissues (i.e. d15N: Cabana and Rasmussen 1996,
Post 2002b, Layman et al. 2012). Stable isotope estimates
of trophic position require specification of an isotopic
fractionation value (Δd15N  d15Nconsumer  d15Nresource; hereafter, ‘Δ’), representing the serial enrichment of d15N across
trophic transfers arising from the interaction of assimilatory, excretory and metabolic processes (Hobson et al. 1993,
McCutchan et al. 2003). In the absence of species-specific
estimates of Δ, constant rates of fractionation (ΔCon) ranging
from 2.5‰ to 3.4‰ per trophic transfer are conventionally assumed (Minagawa and Wada 1984, Vander Zanden
and Rasmussen 2001, Vanderklift and Ponsard 2003). This
assumption has been challenged with evidence that dynamic
fractionation rates (ΔDyn) may be more applicable in certain
circumstances (Robbins et al. 2010, Hussey et al. 2014)
and this has implications for estimates of community-level
trophic position variability and multi-ecosystem comparative analyses. For example: in a given dataset, estimates of
intraspecific variability in trophic position will increase
under the ΔDyn model relative to estimates calculated assuming ΔCon when ΔDyn  ΔCon.
In this study, we use stable isotope data to examine the
shape of trophic position (hereafter, TP) distributions and
assess the relationship between trophic niche and the TP
distribution of consumers in estuarine food webs. Our
hypotheses are based on the expectation that the trophic
niche of a consumer will influence its TP distribution in a
nonlinear fashion (Fig. 1). We reason that the TP distribution of primary consumers will be bounded at the lower end
by the inability to forage at TP  2.0 although individuals
are capable of realizing higher TPs through the incidental
consumption of animal material (e.g. indiscriminant grazing
of mixed assemblage epibionts), potentially leading to positively skewed distributions (Fig. 1b). These characteristics
would lead to a reduction in TP variability and a contraction of individuals toward the center of the TP distribution
and away from the tails (i.e. reduced variance and range;
Fig. 1b–c). Classic omnivores (those that consume plant
and animal matter) and intermediate consumers have a
much broader potential diet (greater variance) and these
taxa could be expected to display less skewed TP distributions characterized by dispersion of individuals away from
the center of the distribution (i.e. increased range). Finally,
the TP of tertiary consumers is bounded at the upper end by
factors such as energy attenuation between trophic transfers
and predator–prey size ratio constraints (Scharf et al. 2000,
Jennings et al. 2002, Barnes et al. 2010). These processes
could lead to a reduction in variance and contraction of
the distribution towards the center and away from the tails
(reduced variance and range; Fig. 1b–c), characterized by a
Primary
consumer
Intermediate
consumer
Tertiary
consumer
Trophic position
Figure 1. Conceptual diagram outlining hypothesized relationships
between consumer trophic position and: (a) shape and dispersion
characteristics of trophic position distributions relative to probability densities; (b) skewness (primary y-axis) and variance (secondary
y-axis); and, (c) range of trophic position distributions. In (b),
dotted line  0.
scattering of individuals realizing lower TPs (i.e. negatively
skewed distributions; Fig. 1b). These ideas will not apply
to every consumer, particularly morphologically adapted
trophic specialists; however, these broad patterns are valid
a priori expectations for aquatic estuarine consumers. Based
on these expectations, we tested the following hypotheses: 1)
TP distributions of primary consumers and tertiary predators will be restricted compared to those of intermediate
consumers; 2) TP distributions of primary consumers and
tertiary predators will be best described by flexible, nonnormal distributions; whereas, TP distributions of intermediate consumers will follow a normal distribution; and,
3) the application of a ΔDyn model in which fractionation
varies inversely with consumer TP will lead to narrower
TP distributions among primary consumers and broader
TP distributions among tertiary consumers, relative to
distributions modeled under ΔCon conditions.
557
Material and methods
In this study, carbon (d13C) and nitrogen (d15N) stable isotope data were used as proxy indicators of trophic niche and
TP. Studies typically use d13C to elucidate primary food
sources at the base of food webs; whereas, d15N is often used
to estimate TP of individual consumers. Consumer d13C
and d15N data were collated from several different surveys
of estuarine fauna and included a wide diversity of invertebrate and vertebrate consumers (Supplementary material
Appendix 1 Table A1). Estuaries are dynamic ecosystems and
consumer food webs are often supported by many autochthonous and allochthonous sources of nutrition. These
conditions can render consumer isotope data more variable
than similar data collected in less variable ecosystems. The
large sample size of our dataset and our standardization and
statistical approach should compensate for the presence of
system-specific idiosyncrasies in consumer–resource stable
isotope relationships; however, this does remain a source of
potential error in our analysis. To avoid potential errors arising from differences in tissue turnover rates, we excluded fast
turnover tissue types (i.e. liver, blood plasma) and focused
on relatively slow (i.e. white muscle, red blood cells, whole
body) turnover tissues. The final combined dataset consisted
of n  4341 invertebrate and vertebrate specimens distributed amongst 65 unique taxonomic groups (57 identified
to species level; Supplementary material Appendix 1 Table
A2). Of the 65 taxa identified, a subset of 47 taxa was further subdivided into age-0 and age-1  age-classes based on
size-at-age information from the literature as well as evidence
of modal length progression from the survey datasets. Invertebrates, birds and those fish for which individual length
data were not available were aggregated at the species level.
This resulted in a total of 90 groups classified at the species,
species-age, or family level (Supplementary material
Appendix 1 Table A2).
Each taxon in the consumer dataset was assigned a trophic
position from the literature (TPLit) with values that ranged
from 2.0 (primary consumer) to 4.5 (tertiary consumer)
at 0.1 TP increments. Assigned TPLit values were based on
diet studies from the primary literature and information summarized in an online ecological database (< www.
fishbase.org/ >). For well-studied fish taxa (e.g. Acanthopagrus
butcheri), multiple estimates of TPLit were often available.
For these taxa, TPLit was calculated as the mean of the available estimates. Sufficient information did not exist to define
age-specific TPLit values for age-0 individuals; however,
ontogenetic increases in TP associated with increasing body
size are a well-established facet of trophic ecology for many
fish species (Werner and Gilliam 1984, Jennings et al. 2002).
In the absence of empirical estimates, age-0 individuals of
the four largest species were assigned TPLit values one half
trophic level below the age-1  age-class (Supplementary
material Appendix 1 Table A2). Conspecifics of all other species were assigned identical TPLit values regardless of assigned
age-class.
Preparation of stable isotope data
Consumer d13C values were mathematically corrected for
lipid content based on sample C:N ratios (Post et al. 2007).
558
All d15N values were centered by subtracting location-specific means from each within-group observation after correcting (if necessary) for the potential influence of resource
(d13C) dependent patterns in observed d15N values (Supplementary material Appendix 1). Briefly, this involved regressing observed d15N values against d13C for each species in
each estuary. If there was a significant relationship (i.e. slope
≠ 0), residuals from the d15N–d13C regression were used in
subsequent analyses, otherwise the individual d15N values of
the species were subtracted from the mean d15N for that species in each estuary. This processes yields a set of residuals for
each species distributed around the species mean for each
estuary. Conspecifics from multiple sites and surveys could
then be combined because all observations had been converted to residuals with m  0. Centered d15N residuals were
then converted to TP ‘equivalents’ using two methods. The
first method assumed a ΔCon  3.4‰ across trophic transfers (Vander Zanden and Rasmussen 2001, Post 2002b);
d15N residuals were divided by this constant fractionation
value. This scaled the d15N residuals to be equivalent to
TP residuals distributed around the mean TP. The second
method assumes Δ is dynamic and inversely related to
the d15N value of a consumer’s food source. We used
an empirical model presented in Hussey et al. (2014) to
model Δ as a function of consumer d15N: ΔDyn  –0.27 
d15NConsumer  5.92‰. These slope and intercept values correspond to the medians of the posterior probability distributions for each parameter derived from a meta-analysis (Hussey
et al. 2014). Several ecosystems with high 15N concentrations
(i.e. baseline primary consumer d15N  11.8–27.6) required
alternative slope (  –0.14) or intercept values (  7.33 or
10.00‰) (Supplementary material Appendix 1). Similar to
the ΔCon method, d15N residuals were divided by the ΔDyn
estimate to convert isotope residuals to TP residuals. Trophic
position distributions calculated using ΔCon and ΔDyn were
analyzed separately and the results used to investigate the
extent to which methodological differences in the treatment
of trophic fractionation influenced the apparent TP distribution of consumers.
Consumer-level distribution characteristics
Skewness, variance and range of consumer TP distributions were identified as potentially informative variables.
Positive skew is indicative of an asymmetrically righttailed distribution (skewness  0); whereas, negative skew
is associated with an asymmetrically left-tailed distribution
(skewness  0). Skewness is therefore useful for examining
the extent and directionality of displacement within the
TP distribution of a consumer. Distribution variance and
range (  maximum – minimum observed values) are common descriptors of distributions and readers can easily consult any standard statistical text for more information on
these metrics. Unbiased estimators of population skewness
(G1) and variance (s; sample variance) were calculated for
the aggregated TP distribution data for each consumer and
age-class (where available).
We used generalized additive models (GAMs) to investigate the influence of consumer trophic position (TPLit) on
the skewness, variance and range of TP distributions. Each
model included an intercept (b0), two parametric linear terms
(bTP  TPLit, bN  Count) and a non-parametric smoothing
spline (sTP  TPLit). Estuary was included as a parametric
class variable in each model to prevent among-estuary differences in isotopic variability from influencing our interpretations of consumer-level TP distributions. This model
structure allowed us to examine the data for a curvilinear
relationship between the response variables and TPLit while
accounting for the possibility of a linear trend. We included
the total number of observations for each consumer (count)
as a covariate to correct for any bias arising from unequal
sample sizes. All response variables were ln-transformed
(following the addition of a constant to remove negative
values) after preliminary GAM results indicated that residuals were Poisson distributed. The models were also run on a
reduced dataset (fish taxa only) to verify that model results
were not dependent on taxon-specific values in high leverage
positions (e.g. low and high TPs).
Trophic position distributions
Consumer TPLit values were rounded to the nearest 0.5 TP
increment (i.e. the nearest half trophic level) to examine the
characteristics of TP distributions at the assemblage-level.
Consumer TP data were aggregated into six TP classes, ranging from primary consumers (TP  2.0) to tertiary consumers (TP  4.5) by 0.5 TP increments. Three theoretical
probability distributions – lognormal, normal, and Gompertz – were fitted to TP distributions within each trophic
class and Akaike’s information criterion, corrected for small
sample size (AICc; Burnham and Anderson 2002), was
used as the model selection criterion. These three distributions were specifically selected because they are often applied
in ecological analyses to describe distributions in nature.
The lognormal distribution has previously been applied in
food web studies to describe the likely TP distribution of
consumers (Gascuel et al. 2011); whereas, TP distributions
are typically treated as normal by default when analyzing
stable isotope or stomach contents data. We included the
Gompertz distribution as a flexible alternative model capable
of describing left-skewed data. Differences in model AICc
values (ΔAICc) and relative model weights (wAICc) were used
to compare and assess model performance (Symonds and
Moussalli 2011).
Initial model fitting indicated that there were very few
differences in model selection results when taxa were analyzed separately for each tissue-type – only four of ten
potential paired model selection criterion values suggested
different outcomes for different tissue types from the same
TP class. Where differences occurred, |ΔAICc| values from
model comparisons were equivocal for all tissue-types (i.e.
|ΔAICc|  0.79), indicating that tissue-specific differences did
not influence model selection results. Similarly, applying
the model fitting analysis to individual consumers or ageclasses yielded model selection criterion patterns that were
remarkably similar to results based on analysis of TP classes
of aggregated taxa (Supplementary material Appendix 2
Table A4, Fig. A1). For this reason, we focus on the model
fitting results for all taxa aggregated into TP classes to maintain the broadest taxonomic coverage possible in our analysis
and discussion. Results from consumer-specific model fitting
are used to provide examples of the dominant patterns.
Data accessibility
Species identifications, stable isotope data and site identifiers
can be found in the Supplementary material.
Results
Taxonomic distribution characteristics and
generalized additive modeling
There was a positive relationship between skewness, variance
and range estimates calculated under the constant and
the dynamic Δ models; however, there was considerable
variability between estimates (Supplementary material
Appendix 3 Table A1, Fig. A2). On average, ΔDyn estimates
yielded lower estimates of skewness, variance and range
at a given TP than the ΔCon estimates. Based on the ΔCon
model, skewness estimates ranged from –3.0 (Contusus richei
[age-0]) to 3.9 (Macquaria colonorum [age-1]); whereas,
ΔDyn skewness estimates ranged from –3.0 (C. richei [age-0])
to 2.0 (Galaxias truttaceus). Minimum variance estimates  0
were estimated for several species under both ΔCon and ΔDyn
conditions, but maximum observed values differed markedly with the ΔCon maximum  1.53 (Galaxias maculatus
[age-1]) and the ΔDyn maximum  0.76 (Tetractenos glaber
[age-1]). Minimum and maximum TP range patterns were
similar to variance, minimum range values  0 were shared
by several species under both ΔCon and ΔDyn conditions,
but maximum TP range was higher under the ΔCon (  3.1,
Galaxias maculatus [age-1]) than the ΔDyn model (  2.5,
Aldrichetta forsteri [age-1]).
There was no evidence of significant linear or nonlinear
terms in the ln(skewness) GAMs (Table 1, Fig. 2a–b);
therefore, we focus on results from ln(variance) and the
ln(range) GAMs. Under both ΔCon and ΔDyn conditions,
ln(variance) increased nonlinearly from primary consumers to omnivorous consumers (TP  2.5) before declining
among tertiary consumers (Fig. 2c–d). The sample size
covariate parameter in the ΔCon model was also significant,
indicating ln(variance) increased slightly with increased
sample size. Similar to variance, there was evidence of
a nonlinear relationship between ln(range) and TPLit
under both constant and dynamic Δ conditions (Table 1,
Fig. 2e–f ). For both fractionation models, ln(range)
increased from primary consumers to secondary consumers then declined among higher TP consumers. The
sample size covariate was significant (and positive) in both
ln(range) models, consistent with the fact that total range
estimates are biased at smaller sample sizes.
The ln(skewness), ln(variance) and ln(range) models were
rerun on a subset of data composed solely of fish samples
to examine the influence of non-fish taxa (i.e. invertebrates,
birds) on the patterns observed between TPLit and the response
variables in the full dataset. The GAM analysis of the subset of fish data (n  62 taxa) yielded qualitatively identical
results to those obtained using the full dataset. There was no
evidence of a linear (parameter |t-statistic|  1.08, p  0.28)
or nonlinear (parameter c2-statistic  3.93, p  0.28)
response of ln(skewness) to TPLit. The significance of the
nonlinear relationships between TPLit and ln(variance) and
559
Table 1. Generalized additive model results relating consumer trophic position distribution skewness, variance and range (Variable)
calculated using constant and dynamic Δd15N models (Method). Included are regression parameter estimates (Estimate) and statistics for
parametric model components (b0  intercept, bN  sample size, bTP  literature-based species trophic positions) and analysis of deviance
statistics for the nonparametric model component (sTP  spline[TP]). Parametric class variable estimates for each estuary not shown.
Parametric components
Variable
Method
Skewness
constant
dynamic
Variance
constant
dynamic
Range
constant
dynamic
Parameter*
b0
bN
bTP, sTP
b0
bN
bTP, sTP
b0
bN
bTP
b0
bN
bTP, sTP
b0
bN
bTP, sTP
b0
bN
bTP, sTP
Nonparametric component
Estimate
t
p
1.40 (0.09)
–0.001 (0.001)
0.02 (0.02)
1.35 (0.11)
–0.001 (0.001)
0.03 (0.03)
0.05 (0.02)
0.001 (0.0002)
–0.007 (0.005)
0.02 (0.01)
0.0002 (0.0001)
0.001 (0.003)
0.29 (0.08)
0.02 (0.001)
–0.03 (0.02)
0.24 (0.07)
0.01 (0.001)
–0.02 (0.02)
15.64
–1.14
0.82
12.84
–0.58
1.08
2.42
2.99
–1.34
1.88
1.79
0.27
3.39
13.58
–1.71
3.72
13.64
–1.04
 0.0001
0.25
0.41
 0.0001
0.56
0.28
0.02
0.003
0.18
0.06
0.07
0.79
0.0008
 0.0001
0.09
0.0002
 0.0001
0.3
c2
Estimate
DF
0.97
3
2.78
0.43
0.97
3
3.93
0.27
0.98
3
12.8
0.005
0.98
3
10.3
0.02
0.99
3
9.3
0.03
0.98
3
13.3
0.004
p
*­ generalized additive model structure: h(x , x )  b  b
N
TP
0
Estuary xEstuary  bN xN  bTP xTP  sTP(xTP).
ln(range) were maintained under both Δ models (parameter
c2-statistic  9.30, p  0.03).
Trophic position distribution modeling
Results from the TP model fitting were generally consistent
for the constant and the dynamic Δ datasets (Table 2,
Fig. 3a). Among the secondary consumers (TP  3.0), the
normal distribution consistently outperformed both the lognormal and Gompertz model as evidenced by relative Akaike
weight values (wAICc; Burnham and Anderson 2002) approaching 1.0. These patterns are typified by an omnivorous mugilid
fish species Aldrichetta forsteri and a carnivorous gobiid fish
species Gobiopterus semivestitus which displayed highly dispersed distributions centered on their mean (Fig. 3b). Model
fitting results for both species supported selection of a normal
distribution regardless of Δ model (wAICc ∼ 1.0). Among secondary consumers with TP class values of 3.5, model fit of
the lognormal (wAICc  0.4) and normal (wAICc  0.6) distributions were similar under ΔCon conditions. Alternatively, the
normal model showed the best model fit under ΔDyn conditions for consumers occupying the TP  3.5 group. At the
taxon-level, age-1  individuals of the sparid Acanthopagrus
butcheri occupied a wide range of estimated TP values and
showed Δ dependent model fitting results (i.e. ΔCon normal wAICc  0.13; ΔDyn normal wAICc  0.77). For tertiary
consumers grouped at TP  4.0 (e.g. Arripis truttaceus; Fig.
3b), the lognormal model provided the best fit (wAICc ∼ 1.0)
for the ΔCon results. The relative performance of the normal
(wAICc  0.44) and lognormal (wAICc  0.56) models were
similar at TP  4.0 under dynamic Δ conditions.
Model fitting results were equivocal among the lowest
(  2.0) and highest (  4.5) TP groupings (Table 2, Fig. 3a).
560
Model performance supported the normal model for primary consumers at TP  2.0 due to highly symmetrical and
relatively invariant TP distributions (e.g. Girella tricuspidata,
Fig. 3b), although the difference in model performance was
negligible under ΔCon. There was some evidence that the
lognormal model outperformed the normal model for TP
group  4.5 under ΔCon fractionation conditions and this
pattern was stronger for the ΔDyn data. For example, model
fitting results for Pomatomus saltatrix (highest order predator
included in study) yielded taxon-level wAICc differences of
0.08 (ΔCon) and 0.30 (ΔDyn). The lognormal and the normal
models were consistently the best performing models – fit
statistics for the Gompertz model were much poorer than
the other models (Table 2).
Discussion
This study tests a core assumption regarding the underlying characteristics of TP distributions using stable isotopes, one of the most widely applied analytical techniques
for estimating TP and associated measures of variability.
Our results indicate that properties of TP distributions
among aquatic consumers are influenced by the functional
trophic niche, although these patterns did not necessarily
follow our a priori expectations. We found evidence that
trophic level omnivory contributed a larger fraction of the
assimilated diet of intermediate consumers relative to herbivores and tertiary consumers. In addition, consumer TP
distributions generally followed normal distributions except
among primary and tertiary consumers where there was
evidence that some consumers may realize lognormal TP
distributions.
Ln-skewness
2.5
∆Constant
(a)
∆Dynamic
2.5
(b)
1.5
1.5
0.5
0.5
Table 2. Relative performance of theoretical distribution fit to trophic
position distributions calculated using constant and dynamic Δd15N
models (Method) for data aggregated into multi-species trophic
position (TP) classes with number of taxa per class (Taxa) and total
observations (n). Model performance presented as relative probability weights based on Akaike’s information criterion corrected for
small sample size (wAICc). Best performing model wAICc in bold.
Parameter estimates of m and s from best fit probability distributions
provided (Estimates).
wAICc*
Ln-variance
–0.5
1.1
(d)
0.1
0.1
0.1
0.1
0.05
0.05
0
1.5
Ln-range
(c)
–0.5
0.6
(e)
0
1.3
(f)
1
1
1
1
0.5
0.5
0
2
3
4
5 2
3
Trophic position
4
5
Method
TP
Taxa
Constant
2.0
2.5
3.0
3.5
4.0
4.5
2.0
2.5
3.0
3.5
4.0
4.5
6
9
26
35
12
3
6
9
24
31
9
3
Dynamic
n
74
664
1518
1396
548
141
71
583
1490
1325
459
141
Estimates†
Lognormal Normal
0.48
 0.001
 0.001
0.40
0.99
0.68
0.09
0.75
 0.001
0.11
0.56
0.85
0.52
1.00
1.00
0.60
0.01
0.32
0.83
0.25
1.00
0.89
0.44
0.15
m
Σ
1.99
2.50
3.00
3.50
1.37
1.50
2.00
0.91
3.00
3.49
1.39
1.50
0.13
0.27
0.25
0.24
0.04
0.04
0.12
0.09
0.16
0.17
0.03
0.05
­*Gompertz model wAICc values are not included due to consistently
poor performance of the Gompertz distribution as evidence by
wAICc  0.08 for all TP classes.
†parameterization of lognormal and normal models is not the
same: mLognormal ≠ mNormal and sLognormal ≠ sNormal. 1) provided for each
model. Model for small sample size (AICc), and relative model
weights (wAICc) provided for each model.
0
Figure 2. Distribution ln-transformed skewness (y-axis; (a), (b)),
variance (y-axis; (c), (d)) and range (y-axis; (e), (f )) of consumer
trophic position equivalents calculated using constant and dynamic
trophic fractionation (Δ) models plotted against literature-derived
estimates of mean trophic position (x-axis). (a) and (b): dotted
line  0. Note: y-axes in (c)–(f ) are given different scales above
and below the break to allow all data to be shown, data below the
break  filled circles, data above the break  open symbols.
Influence of trophic niche on TP distribution
properties
Our analysis supports the hypothesis that there exist identifiable and predictable shifts in the properties of TP distributions in aquatic food webs. Most notably, the transition
from primary to secondary to tertiary consumers entailed
changes in TP variability and range, and a shift in optimal
probability distribution. These results closely follow our a
priori hypotheses (Fig. 1) and underscore the general trend
of broader, more dispersed TP distributions among secondary consumers relative to primary and tertiary consumers.
Also, our method of centering consumer d15N values to sitespecific means reduces the influence of spatial effects on TP
distribution estimates resulting from individuals of the same
taxon feeding in isotopically dissimilar food webs.
Variance and total range are common analytical metrics
in ecological studies and they provide excellent tools for
examining structural aspects of trophic data sets. Here, in
accordance with predictions, curvilinear patterns in both
variance and range indicate that the realized TP of secondary
consumers are more distantly distributed from the centers of
their respective TP distributions than herbivores and tertiary
consumers. The absence of an identifiable skewness trend
shows that this dispersion is not unidirectional, but rather
radiates in both directions along the TP axis. These structural characteristics explain the shift to normally distributed
TP distributions among secondary consumers and support
previous evidence that trophic level omnivory is a central
facet of the trophic ecology of non-herbivorous consumers
(Polis and Strong 1996, Thompson et al. 2007).
Our results do not imply that trophic niche breadth is
lower among primary consumers (herbivores) or tertiary
predators, but rather that the number of trophic transfers separating consumers from basal energy sources (i.e.
realized food chain length, Post 2002a) is more variable
on average for intermediate consumers. From a food web
dynamics perspective, the narrowing of TP distributions
among herbivores and higher-order predators suggests that
either trophic level omnivory is most common among intermediate consumers, or the effect of individual-level foraging
yields more variable realized trophic positions among intermediate consumers. Neither explanation rules out certain
omnivorous behaviors among higher trophic position consumers, particularly size- or age-dependent feeding behaviors (e.g. cannibalism, ontogenetic trophic shifts; Polis and
Strong 1996). One interesting aspect of these findings is that
the broader trophic position distributions among intermediate consumers indicates that functional redundancy in vertical trophic position is maximized at the center of aquatic
food webs. The narrowing of trophic position distributions
as organisms realize higher trophic positions means that the
561
Probability density
0.05
Constant ∆δ15N
Dynamic ∆δ15N
(a)
0.04
0.03
0.02
0.01
Probability density
0
0.05
(b)
I
0.04
0.03
III
II
V
VI
IV
0.02
0.01
0
2.0
2.5
3.5
3.0
Trophic position
4.0
4.5
5.0
Figure 3. Non-linear least squares model fitting results for (a) normal and lognormal distributions fitted to multi-species aggregates
grouped by incremental trophic position classes calculated assuming constant and dynamic trophic fractionation (Δd15N); and (b) best fit
distributions (solid lines) to observed trophic position distribution frequencies for specific consumer taxa calculated assuming constant
Δd15N (overlapping bars with serial darkening from left to right). Species and best fit distribution: I – Girella tricuspidata (normal); II –
Aldrichetta forsteri (normal); III – Gobiopterus semivestitus (normal); IV – Acanthopagrus butcheri (lognormal); V – Macquaria colonorum
(lognormal); VI – Pomatomus saltatrix (lognormal). Dynamic model fitting results for these species were qualitatively similar and are not
included in (b).
potential for functional overlap decreases at higher trophic
positions and that the loss of individual taxa could have
relatively stronger effects on food web dynamics.
Our finding that TP distributions narrow at higher trophic
positions appears counter-intuitive given empirical evidence
that trophic resource bases can increase among higherorder predators due to increasing upper prey size limits set
by predator–prey size ratio constraints (Scharf et al. 2000,
Jennings et al. 2002b, Layman et al. 2005); yet, consideration
of the TP proxy – stable isotopes – provides several explanations for our findings. First, consumer tissue stable isotope
composition reflects the mass-averaged assimilation of all
prey over an interval of time; therefore, larger prey items (in
terms of digestible biomass) will contribute proportionately
more to the isotopic composition of the consumer’s tissues
per individual prey item consumed. This mass-averaging
property of stable isotope composition, combined with the
positive relationship that exists between body size and TP
in aquatic food webs (Vander Zanden et al. 2000, Jennings
et al. 2002b), provides an explanation for why TP distributions of tertiary consumers might contract relative to secondary consumers. From a food web dynamics perspective,
this is a favorable aspect of stable isotope-based estimates of
TP because the estimates are automatically weighted by the
relative contributions of each prey item to the tissue mass of
the consumer. Second, large-bodied consumers often require
longer isotopic equilibration periods due to lower somatic
growth rates and scaling effects of body mass on metabolism (Clarke and Johnston 1999, Woodward et al. 2005). It
562
is possible that the contraction of TP distributions among
larger consumers was influenced by the time-averaging effect
of longer equilibration times; however, there was no evidence of a linear or curvilinear relationship between body
size and TP dispersion (Supplementary material Appendix 4
Fig. A3). Differences in Δ values have been identified among
taxonomic groups and between herbivorous and carnivorous
consumers (Vander Zanden and Rasmussen 2001, Vanderklift and Ponsard 2003, Caut et al. 2009); however, dietary
or taxonomic disparities in the magnitude of Δ would be
expected to have the largest influence on TP distribution
patterns among taxa feeding on both plant and animal matter, or within taxonomically diverse groups. Finally, with the
exception of the single bird species included in this study, we
were unable to consistently sample the adult populations of
the highest-order consumers. Presumably, the adult populations of these consumers are realizing higher TPs on average than the sub-adult contingents (Jennings et al. 2002a).
Under our hypothesized relationship between TP distribution skewness and consumer trophic niche, the absence of
large-bodied piscivores in our dataset would have reduced
our ability to identify negatively skewed distributions.
Static versus dynamic trophic fractionation
There is vigorous debate in the field of stable isotope ecology regarding the application of constant versus dynamic Δ
values (McCutchan et al. 2003, Caut et al. 2010, Perga and
Grey 2010, Hussey et al. 2014). In this study, we evaluated
the application of a previously reported dynamic Δ model
(Hussey et al. 2014) versus a constant Δ model on inferences
regarding the distribution characteristics of TP distributions.
The influence of Δ selection on TP distributions was clearly
apparent, with the alteration of positive and negative offsets
in peak probability density among TP classes. As predicted,
the exponential ΔDyn model results in narrower, less dispersed TP distributions when ΔCon / ΔDyn  1, and broader,
more dispersed TP distributions when ΔCon / ΔDyn  1 for a
given range of stable isotope data. Although this analysis was
not designed to rigorously contribute to the constant versus dynamic trophic fractionation debate, the failure of the
Hussey et al. (2014) model to yield tenable results without
arbitrarily scaling regression parameters at high natural d15N
values provides strong evidence that dynamic TP fractionation was not occurring in the food webs included in our
dataset. Alternatively, we observed reasonable results when
applying the much simpler ΔCon model across the full range
of d15N conditions, consistent with other studies that have
documented stable fractionation values across a gradient
of baseline d15N conditions (Anderson and Cabana 2005,
Bunn et al. 2013).
More generally, results from studies using stable isotopes
to investigate TP variability as an indicator of total or vertical trophic niche diversity (Olsson et al. 2009, Edwards
et al. 2013) will depend on the Δ model selected. Interpreting results relative to previous studies presents the same risk
and requires researchers to be cognizant of any differences in
the underlying Δ model prior to drawing inferences based on
the topical literature. It is important to note that using actual
stable isotope data to explore trophic niche width (Bearhop
et al. 2004, Jackson et al. 2011), rather than deriving
and subsequently analyzing TP distributions, still requires
an implicit decision regarding Δ. For example, direct comparisons of d15N variability across trophic groups or among
multiple populations that differ in their relative positions
in food web d15N space assume constant Δ (Edwards et al.
2013, Woodcock et al. 2013). Whether or not this assumption is appropriate for a given application is an aspect of
stable isotope analysis that requires consideration.
food web applications (Moore and Semmens 2008, Parnell
et al. 2010, Jackson et al. 2011), it seems likely that options
to incorporate probabilistic parameter distributions instead
of explicit parameter estimates will soon be available in other
food web models (e.g. Ecopath with Ecosim, Christensen
and Pauly 1992, limSolve/LIM-MCMC, Kones et al. 2009).
While such applications are technically and computationally feasible, they have apparently not yet been formally
explored.
In conclusion, we found substantial evidence to support
the hypothesis that structural characteristics and probability
distributions of consumer TP distributions vary as a function of trophic niche in estuarine food webs. We advocate
further exploration of these relationships in other ecosystem
types (e.g. marine, freshwater, terrestrial, soil), to test the
generality of our results across ecosystems. Trophic relationships such as predator–prey size ratios, maximum food chain
length, and the prevalence of omnivory can differ markedly
in other ecosystems (Kelly 2000, Cury et al. 2003, Thompson
et al. 2007, Hussey et al. 2014), and structural changes in
these (and other) conditions can be expected to influence
the relationship between consumer trophic niche and the
properties of TP distributions. Careful scrutiny of dispersion
patterns of individuals within groups of consumers should
help inform researchers of the appropriate measures of
central tendency to report when summarizing stable isotopebased estimates of TP. Such scrutiny will also provide food
web ecologists with additional information to more accurately model food web structure and dynamics.­­­­
Consequences for ecological modeling
References
Stable isotope data are increasingly being incorporated into
ecological food web models. Applications of stable isotope data include trophic niche specification during model
construction, constraining model outputs in solution space,
and validation procedures (Baeta et al. 2011, Navarro et al.
2011, Pacella et al. 2013, Lassalle et al. 2014). While our
focus in this study was on stable isotope data, our findings
are also relevant for applications that use other sources of
trophic information to calculate TP values (e.g. stomach contents). To date, most modeling approaches assume consumer
trophic positions are normally distributed and do not allow
for alternative distributions. Conversely, the biomass trophic
spectrum method (Gascuel et al. 2011) is the only modeling
framework that we are aware of that operationally stipulates
lognormal TP distributions, but again, it does not accommodate alternative distributions (EcoTroph; a plug-in module for
Ecopath with Ecosim ver. 6,  www.ecopath.org ). Given
the recent development of Bayesian statistical packages for
Anderson, C. and Cabana, G. 2005. d15N in riverine food webs:
effects of N inputs from agricultural watersheds. – Can. J. Fish.
Aquat. Sci. 62: 333–340.
Arim, M. and Marquet, P. A. 2004. Intraguild predation: a widespread interaction related to species biology. – Ecol. Lett. 7:
557–564.
Baeta, A. et al. 2011. Modelling the effects of eutrophication,
mitigation measures and an extreme flood event on estuarine
benthic food webs. – Ecol. Modell. 222: 1209–1221.
Barnes, C. et al. 2010. Global patterns in predator–prey size relationships reveal size dependency of trophic transfer efficiency.
– Ecology 91: 222–232.
Bearhop, S. et al. 2004. Determining trophic niche width: a novel
approach using stable isotope analysis. – J. Anim. Ecol. 73:
1007–1012.
Bolnick, D. I. et al. 2003. The ecology of individuals: incidence and
implications of individual specialization. – Am. Nat. 161: 1–28.
Bunn, S. E. et al. 2013. Diet-tissue fractionation of d15N by
consumers from streams and rivers. – Limnol. Oceanogr. 58:
765–773.
Acknowledgements – We thank Erinn Richmond, Tom Daniel,
Ashley Herrod, Vera Eate, Keralee Browne, Peter Faber, Daryl
Holland, Miles Jennings, Todd Scicluna and members of the
Victorian Wader Study Group for their help in the field and/or
laboratory. Funding was provided by the Australian Research
Council (LP110100040: RJW, VE, PLMC, PR), Gippsland Lakes
Ministerial Advisory Committee (RJW, FYW, VE, RC, PR,
PLMC), Melbourne Water (FYW, PR), and the Victorian Department of Environment and Primary Industries (FYW, PR). Charles
Todd provided comments on an earlier version of this mansucript.
563
Burnham, K. P. and Anderson, D. R. 2002. Model selection and
multimodel inference: a practical information-theoretic
approach. – Springer.
Cabana, G. and Rasmussen, J. B. 1996. Comparison of aquatic
food chains using nitrogen isotopes. – Proc. Natl Acad. Sci.
USA 93: 10844–10847.
Caut, S. et al. 2009. Variation in discrimination factors (Δ15N and
Δ13C): the effect of diet isotopic values and applications for
diet reconstruction. – J. Appl. Ecol. 46: 443–453.
Caut, S. et al. 2010. Trophic experiments to estimate isotope
discrimination factors. – J. Appl. Ecol. 47: 948–954.
Christensen, V. and Pauly, D. 1992. Ecopath II - a software
for balancing steady-state ecosystem models and calculating
network characteristics. – Ecol. Modell. 61: 169–185.
Clarke, A. and Johnston, N. M. 1999. Scaling of metabolic rate
with body mass and temperature in teleost fish. – J. Anim.
Ecol. 68: 893–905.
Cury, P. et al. 2003. The functioning of marine ecosystems: a
fisheries perspective. – In: Sinclair, M and Valdimarsson, G.
(eds), Responsible fisheries in the marine ecosystem. FAO and
CABI, pp. 103–123.
Edwards, D. P. et al. 2013. Trophic flexibility and the persistence
of understory birds in intensively logged rainforest. – Conserv.
Biol. 27: 1079–1086.
Fagan, W. F. 1997. Omnivory as a stabilizing feature of natural
communities. – Am. Nat. 150: 554–567.
Gascuel, D. et al. 2011. The trophic-level-based ecosystem modelling approach: theoretical overview and practical uses. – ICES
J. Mar. Sci. 68: 1403–1416.
Gibb, H. and Cunningham, S. A. 2013. Restoration of trophic
structure in an assemblage of omnivores, considering a revegetation chronosequence. – J. Appl. Ecol. 50: 449–458.
Hobson, K. A. et al. 1993. Stable nitrogen isotope enrichment in
avian tissues due to fasting and nutritional stress – implications
for isotopic analyses of diet. – Condor 95: 388–394.
Holyoak, M. and Sachdev, S. 1998. Omnivory and the stability of
simple food webs. – Oecologia 117: 413–419.
Hussey, N. E, et al. 2014. Rescaling the trophic structure of marine
food webs. – Ecol. Lett. 17: 239–250.
Jackson, A. L. et al. 2011. Comparing isotopic niche widths among
and within communities: SIBER - stable isotope Bayesian
ellipses in R. – J. Anim. Ecol. 80: 595–602.
Jennings, S. et al. 2002a. Linking size-based and trophic analyses
of benthic community structure. – Mar. Ecol. Prog. Ser. 226:
77–85.
Jennings, S. et al. 2002b. Use of size-based production and stable
isotope analyses to predict trophic transfer efficiencies and
predator–prey body mass ratios in food webs. – Mar. Ecol.
Prog. Ser. 240: 11–20.
Kelly, J. F. 2000. Stable isotopes of carbon and nitrogen in the
study of avian and mammalian trophic ecology. – Can. J. Zool.
78: 1–27.
Kones, J. K. et al. 2009. Are network indices robust indicators
of food web functioning? A Monte Carlo approach. – Ecol.
Modell. 220: 370–382.
Lassalle, G. et al. 2014. An assessment of the trophic structure
of the Bay of Biscay continental shelf food web: comparing
estimates derived from an ecosystem model and isotopic data.
– Prog. Oceanogr. 120: 205–215.
Layman, C. A. et al. 2005. Body size and trophic position in a
diverse tropical food web. – Ecology 86: 2530–2535.
Layman, C. A. et al. 2012. Applying stable isotopes to examine
food-web structure: an overview of analytical tools. – Biol. Rev.
87: 545–562.
McCann, K. and Hastings, A. 1997. Re-evaluating the omnivorystability relationship in food webs. – Proc. R. Soc. B 264:
1249–1254.
564
McCutchan, J. H. et al. 2003. Variation in trophic shift for stable
isotope ratios of carbon, nitrogen and sulfur. – Oikos 102:
378–390.
Minagawa, M. and Wada, E. 1984. Stepwise enrichment of N15
along food chains: further evidence and the relation between
d15N and animal age. – Geochim. Cosmochim. Acta 48:
1135–1140.
Moore, J. W. and Semmens, B. X. 2008. Incorporating uncertainty
and prior information into stable isotope mixing models.
– Ecol. Lett. 11: 470–480.
Navarro, J. et al. 2011. Comparison of ecosystem modelling and
isotopic approach as ecological tools to investigate food webs
in the NW Mediterranean Sea. – J. Exp. Mar. Biol. Ecol. 401:
97–104.
Olsson, K. et al. 2009. Invasions and niche width: does niche
width of an introduced crayfish differ from a native crayfish?
– Freshwater Biol. 54: 1731–1740.
Pacella, S. R. et al. 2013. Incorporation of diet information derived
from Bayesian stable isotope mixing models into massbalanced marine ecosystem models: a case study from the
Marennes-Oleron Estuary, France. – Ecol. Modell. 267:
127–137.
Parnell, A. C. et al. 2010. Source partitioning using stable
isotopes: coping with too much variation. – PloS ONE 5(3):
e9672.
Pauly, D. et al. 1998. Fishing down marine food webs. – Science
279: 860–863.
Perga, M. E. and Grey, J. 2010. Laboratory measures of
isotope discrimination factors: comments on Caut, Angulo
and Courchamp (2008, 2009). – J. Appl. Ecol. 47:
942–947.
Pimm, S. L. and Rice, J. C. 1987. The dynamics of multispecies,
multi-life-stage models of aquatic food webs. – Theor. Popul.
Biol. 32: 303–325.
Polis, G. A. 1994. Food webs, trophic cascades and community
structure. – Aust. J. Ecol. 19: 121–136.
Polis, G. A. and Strong, D. R. 1996. Food web complexity and
community dynamics. – Am. Nat. 147: 813–846.
Polis, G. A. et al. 1989. The ecology and evolution of intraguild
predation: potential competitors that eat each other. – Annu.
Rev. Ecol. Syst. 20: 297–330.
Post, D. M. 2002a. The long and short of food-chain length.
– Trends Ecol. Evol. 17: 269–277.
Post, D. M. 2002b. Using stable isotopes to estimate trophic
position: models, methods and assumptions. – Ecology 83:
703–718.
Post, D. M. et al. 2007. Getting to the fat of the matter:
models, methods and assumptions for dealing with
lipids in stable isotope analyses. – Ocecologia 152:
179–189.
Robbins, C. T. et al. 2010. The impact of protein quality on stable
nitrogen isotope ratio discrimination and assimilated diet
estimation. – Oecologia 162: 571–579.
Scharf, F. S. et al. 2000. Predator size – prey size relationships of
marine fish predators: interspecific variation and effects of
ontogeny and body size on trophic-niche breadth. – Mar. Ecol.
Prog. Ser. 208: 229–248.
Schellekens, T. and van Kooten, T. 2012. Coexistence of two
stage-structured intraguild predators. – J. Theor. Biol. 308:
36–44.
Symonds, M. R. E. and Moussalli, A. 2011. A brief guide to model
selection, multimodel inference and model averaging in behavioural ecology using Akaike’s information criterion. – Behav.
Ecol. Sociobiol. 65: 13–21.
Thompson, R. M. et al. 2007. Trophic levels and trophic tangles:
the prevalence of omnivory in real food webs. – Ecology 88:
612–617.
Vander Zanden, M. J. and Rasmussen, J. B. 2001. Variation
in d15N and d13C trophic fractionation: implications
for aquatic food web studies. – Limnol. Oceanogr. 46:
2061–2066.
Vander Zanden, M. J. et al. 2000. Within- and among-population
variation in the trophic position of a pelagic predator, lake
trout (Salvelinus namaycush). – Can. J. Fish. Aquat. Sci. 57:
725–731.
Vanderklift, M. A. and Ponsard, S. 2003. Sources of variation in
consumer-diet d15N enrichment: a meta-analysis. – Oecologia
136: 169–182.
Werner, E. E. and Gilliam, J. F. 1984. The ontogenetic niche and
species interactions in size-structured populations. – Annu.
Rev. Ecol. Syst. 15: 393–425.
Williams, R. J. and Martinez, N. D. 2004. Limits to trophic levels
and omnivory in complex food webs: theory and data. – Am.
Nat. 163: 458–468.
Woodcock, P. et al. 2013. Impacts of intensive logging on the
trophic organisation of ant communities in a biodiversity
hotspot. – Plos One 8(4): e60756.
Woodward, G. et al. 2005. Body size in ecological networks.
– Trends Ecol. Evol. 20: 402–409.
Supplementary material (Appendix oik-02486 at
< www.oikosjournal.org/appendix/oik-02486 >). Appendix 1–4.
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