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ARTICLE
Coastal and Marine Ecology
Influence of seascape spatial pattern on the trophic niche of
an omnivorous fish
Rolando O. Santos
1,2
| W. Ryan James
1,2,3
| James A. Nelson
4
|
Jennifer S. Rehage
1,3
| Joseph Serafy
5,7
| Simon J. Pittman
6
| Diego Lirman
7
1
Institute of Environment, Florida
International University, Miami,
Florida, USA
2
Department of Biological Sciences,
Florida International University, Miami,
Florida, USA
3
Department of Earth and Environment,
Florida International University, Miami,
Florida, USA
4
Department of Biology, University of
Louisiana, Lafayette, Louisiana, USA
5
NOAA, National Marine Fisheries
Service, Southeast Fisheries Science
Center, Miami, Florida, USA
6
Oxford Seascape Ecology Lab, School of
Geography and the Environment,
University of Oxford, Oxford, UK
7
Rosenstiel School of Marine and
Atmospheric Science, University of
Miami, Miami, Florida, USA
Correspondence
Rolando O. Santos
Email: rsantosc@fiu.edu
Funding information
Florida Education Fund; NOAA;
RECOVER Monitoring and Assessment
Program; U.S. Army Corps of Engineers;
Living Marine Resources Cooperative
Science Center
Handling Editor: Thomas C. Adam
Abstract
Habitat fragmentation of submerged aquatic vegetation (SAV) transforms the
spatial pattern of seascapes by changing both the total area and spatial con-
figuration of the habitat patches. The ecological effects of SAV seascapes are
most often assessed using metrics of biological community composition
(e.g., species and assemblage changes).Weknowconsiderablylessaboutthe
effects of seascape structure on ecological processes such as food web func-
tion and energy flow. Here, we assess the difference in the trophic niche of
Pinfish (Lagodon rhomboides, a generalist omnivore) across a spatial gradient
of SAV from continuous to highly fragmented seascapes in Biscayne Bay
(Miami, Florida, USA). The Bay seascapes are influenced by freshwater man-
agement practices that alter the distribution of SAV habitat and fish species
abundance, diversity, and community assemblage. We combined SAV sea-
scape maps with stable isotope and hypervolume analyses to determine how
trophic niche size and overlap varied with changes in the seascape. We
observed similar resource use across the seascape, but trophic niche size
increased in more fragmented SAV seascapes, suggesting diversification of
trophic roles and energy flow pathways. Pinfish collected from more continu-
ous SAV habitats had smaller trophic niche size and higher trophic levels.
Both trophic response metrics manifested a threshold response that
depended on distinct SAV spatial characteristics (amount vs. spatial configu-
ration) and environmental conditions. Our results suggest that habitat frag-
mentation of SAV seascape structure has ecological implications that could
affect energy flow with cascading consequences for food web stability and
ecosystem functioning.
KEYWORDS
habitat fragmentation, hypervolumes, mixing models, seagrass, seascape ecology, stable
isotopes
Received: 10 September 2021 Accepted: 13 October 2021
DOI: 10.1002/ecs2.3944
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided
the original work is properly cited.
© 2022 The Author(s). Ecosphere published by Wiley Periodicals LLC on behalf of The Ecological Society of America.
Ecosphere. 2022;13:e3944. https://onlinelibrary.wiley.com/r/ecs2 1of18
https://doi.org/10.1002/ecs2.3944
INTRODUCTION
The process of habitat fragmentation involves both the
decline in total habitat amount (i.e., area) and changes to
the spatial configuration of habitat patches (e.g., patch size,
density, connectivity) (Fahrig, 2003; Yeager et al., 2016).
Changes to the amount and spatial configuration of habitat
patches can have significant ecological consequences
(Didham et al., 2012; Smith et al., 2009). For instance, both
patterns of habitat amount and spatial configuration within
landscapes/seascapes (i.e., spatially heterogenous area or
mosaic of patches within a “inhospitable”matrix) can influ-
ence the distribution and abundance of associated species,
and the biodiversity and species assemblages of animal
communities (Boström et al., 2011, 2017; Fahrig, 2003,
2019; Santos et al., 2017; Yeager et al., 2016). These ecologi-
cal consequences of fragmentation can affect species inter-
actions (e.g., competition, predation, facilitation) and
trophic linkages (Araújo et al., 2014; Layman et al., 2007;
Santos, 2014; Smith, Hindell, et al., 2011), which control
food web stability and nutrient flow within ecosystems
(Buelow et al., 2018; Valladares et al., 2012).
In landscape and seascape ecology studies, there is a
theoretical and empirical debate on the relative impor-
tance of habitat amount versus spatial configuration of
patches in influencing population and community met-
rics such as abundance, coexistence, and biodiversity
(Fahrig, 2003; Ryall & Fahrig, 2006; Trzcinski et al., 1999;
Yeager et al., 2016). Various studies have documented
negative impacts from declines (losses) of habitat amount
on species richness and abundance (Fahrig, 2003;
Schmiegelow & Mönkkönen, 2002; Smith, Fahrig, &
Francis, 2011). However, the effects of changes in habitat
spatial configuration on associated species are more vari-
able and, in some cases, even positive (Debinski &
Holt, 2000; Fahrig, 2003). Habitat loss causes negative
impacts by eliminating key resources that influence
reproduction, recruitment, and survival (Fahrig, 2003;
Ryall & Fahrig, 2006). While habitat losses are a required
first step of habitat fragmentation (Didham et al., 2012),
changes in the spatial configuration of habitat patches
may have a positive outcome through the expansion and
diversification of microhabitat types (Horinouchi, 2009;
Horinouchi et al., 2009). Changes to spatial configuration
may also alter animal movement and patch interception
(Awade & Metzger, 2008; Connolly & Hindell, 2006),
reduced competition for resources, and modified dynam-
ics of predator–prey systems (Hovel & Regan, 2008;
Magioli et al., 2019). Still, studies that have explored
impacts of fragmentation on associated fauna have
reported mixed findings (Boström et al., 2011;
Connolly & Hindell, 2006; Fahrig, 2003), and there are
knowledge gaps, especially about fragmentation effects
on trophic dynamics and food webs (Boström et al., 2017;
Ryall & Fahrig, 2006).
A version of the trophic niche can be defined as the
resources a species uses in n-dimensional space
(Elton, 1927; Layman et al., 2007). Thus, this definition
of niche represents the overall trophic role of a species
given a set of resources. The availability and access to
resources can be controlled by both the habitat amount
and spatial configuration (Boström et al., 2011, 2017;
Santos et al., 2017; Yeager et al., 2016). Under the optimal
foraging theory (OFT), the size of the niche (or niche
width/volume) is expected to expand as competition
becomes stronger and preferred resources become scarce,
especially under conditions of habitat loss and fragmenta-
tion (Araújo et al., 2011; MacArthur & Pianka, 1966). The
trophic niche is often assessed using dietary diversity
(e.g., abundance and diversity of prey items in gut con-
tents) (Araújo et al., 2011; Jackson et al., 2011; Layman
et al., 2007). Stable isotope analysis (SIA) has become one
of the most commonly used tools to depict dietary diver-
sity and the dynamics of trophic niche size, and is useful
to compare energy flows and assess dietary diversity
changes due to disturbances and habitat conditions
(James, Lesser, et al., 2020; Layman et al., 2012; Nelson,
Johnson, et al., 2019). Several metrics and statistical tools
based on stable isotope data developed over recent years
could be useful for assessing and testing shifts in commu-
nities’trophic niche position and niche, and intraspecific
diet variability under different habitat conditions
(Bearhop et al., 2004; Jackson et al., 2011; Layman
et al., 2007; Post, 2002b). For instance, metrics that cap-
ture the range and variability of isotopic values in
δ
13
C–δ
15
N two-dimensional space are widely used to
determine changes in niche space and trophic levels
among communities (Jackson et al., 2011; Layman
et al., 2007). However, making inferences directly from
isotope values themselves can be problematic, as both
source variation and variation in both ontogenetic and
geographic consumer resource uses can alter the
resulting niche calculated directly from isotope space
(Fry, 2013; Gorokhova, 2018; Hoeinghaus & Zeug, 2008).
To account for the variability in isotope values of primary
producers or other sources, Bayesian mixing models have
become a popular tool for ecologists to estimate resource
use of consumers (Stock et al., 2018). Recent approaches
have combined Bayesian mixing model estimates of
resource use with a new tool for hypervolume analysis
(Blonder, 2018; Blonder et al., 2014) to quantify and
assess the trophic niche of consumers (James, Lesser,
et al., 2020; Lesser et al., 2020).
Our goal was to understand how the trophic niche
and trophic level of an important generalist marine con-
sumer, pinfish (Lagodon rhomboides), varied in response
2of18 SANTOS ET AL.
to the habitat amount and spatial configuration of sub-
merged aquatic vegetation (SAV) seascapes. We concen-
trated our study in Biscayne Bay (Miami, Florida, USA),
where SAV seascapes are influenced by freshwater man-
agement and where seascape properties can influence
species abundance, diversity, and community assem-
blages (Santos et al., 2018). Pinfish were collected from
two salinity zones of varying levels of anthropogenic
influence in both continuous and fragmented seascapes.
Hypervolumes were generated using resource use calcu-
lated from Bayesian stable isotope mixing models and
trophic level as axes. Following OFT, we hypothesized a
larger trophic niche in fragmented SAV in comparison
with continuous seascapes via increased interindividual
diet variation (i.e., incorporating diet sources with a
wider range of preference) (Lesser et al., 2020). We
hypothesized a higher trophic level of the omnivore fish
species in continuous seascapes, considering that seagrass
habitats could support more complex food webs than less
productive habitat patches (e.g., barren or low-canopy
patches), characteristic of highly fragmented seascapes.
Also, few seascape studies have separated the effects of
habitat amount and spatial configuration on ecological
responses (Bonin et al., 2011; Caley et al., 2001; Healey &
Hovel, 2004), and even fewer studies have identified criti-
cal thresholds where small changes in habitat spatial
properties may lead to large changes in population
responses (Pittman et al., 2004; Salita et al., 2003; Thistle
et al., 2010).
METHODS
Study site and focal species
Our study concentrated on the nearshore habitats of the
central-western section of Biscayne Bay, Miami, Florida,
USA (Figure 1a), a shallow subtropical lagoon adjacent to
the city of Miami (county population 2.5 million) and
downstream of the Florida Everglades. We used pre-
served samples obtained by Santos et al. (2018), a study
that sampled marine fishes and crustaceans on nearshore
SAV seascapes (<500 m from shore) in western Biscayne
Bay, where seagrasses are the dominant benthic macro-
phyte (Lirman et al., 2008, 2014). SAV patches are mostly
composed of the seagrass Thalassia testudinum, with
some patches mixed with Halodule wrightii (seagrass),
rhizophytic macroalgae, and drift macroalgae (Lirman
et al., 2014). There are distinct SAV seascape structures
(i.e., different mosaics and arrangements of SAV patches)
along nearshore habitats of Biscayne Bay, which are the
result of disturbances associated with freshwater manage-
ment activities, macroalgae harmful blooms, and other
structuring processes (e.g., hydrodynamics, sediment
depth, storms) (Santos et al., 2011, 2015, 2020; Stipek
et al., 2020).
SAV seascapes, as well as the fringing mangroves,
provide habitat for a large number of commercially and
recreationally valuable species (e.g., Farfantepenaeus
duorarum,Lutjanus griseus,Lachnolaimus maximus;
Diaz et al., 2001; Faunce & Serafy, 2008; Serafy
et al., 1997, 2003), including the focal species of this
study—pinfish Lagodon rhomboides (Santos et al., 2018).
Pinfish are an omnivorous species, which depends
on seagrass seascapes (Jordan et al., 1997; Levin
et al., 1997). Pinfish are also an important forage species
for SAV predators and prey subsidy for offshore food
webs (Nelson et al., 2013; Reynolds & Bruno, 2013). Pin-
fish are a good candidate for this type of work because
their foraging varies in response to seascape and
seagrass characteristics (Froeschke & Stunz, 2012;
Irlandi & Crawford, 1997; Jordan et al., 1997; Levin
et al., 1997), and they exhibit high site fidelity (40–
100 m, Potthoff & Allen, 2003).
SAV seascape characterization
Categorical characterization of seascapes
We quantified the influence of seascape spatial structures
on L. rhomboides trophic niche across twelve
500 500 m (0.25 km
2
) seascape sample units (SSUs)
(Figure 1a,b). The SSUs were the same as in Santos
et al. (2018), and we sampled six fragmented (FS) and six
continuous (CS) SAV seascapes across two salinity zones
(three of each seascape type in each salinity zone
described below; Figure 1b). The 500 m 500 m SSU
classified as CS had a higher proportion of the benthos
covered by larger SAV patches with lower shape com-
plexity. In contrast, FS SSU had a higher density of
smaller SAV patches with complex shapes and a lower
proportion of the substrate cover by SAV patches. For
more details on the mapping and SSU classification pro-
cedures, see Santos et al. (2018).
To compare the trophic response to habitat amount
and spatial configuration independently, we used per-
centage of the seascape occupied by SAV patches
(PLAND) to quantify habitat cover and a fragmenta-
tion index (Santos et al., 2015) integrating four
measures of configuration to quantify habitat fragmen-
tation: patch density (PD, number of patches per
sampling area), landscape division (LD, probability
two randomly sampled cells are not in the same
patch), area-weighted mean perimeter-to-area ratio
(AWMPAR, mean perimeter-to-area ratio of a class
ECOSPHERE 3of18
weighted by the size of the patch relative to the total
area of that class), and mean radius of gyration
(GYRATE_MN, mean distance from each cell in a
patch to the patch centroid). The fragmentation index
(FragIndex) was as follows:
FragIndex ¼4√PD LD AWMPAR 1=Gyrate_MNðÞ:
All metrics were standardized to produce a FragIndex
ranging from 0 (low fragmentation) to 1 (high fragmen-
tation). The spatial pattern metrics used in the
FragIndex are the most appropriate to assess habitat
fragmentation across different levels of habitat cover
(Hovel & Lipcius, 2002; Salita et al., 2003; Sleeman
et al., 2005).
Sampling stratification
Nutrients and freshwater pulses can influence the prey
composition and availability, and the isotopic content of
primary producers and trophic linkages within estuarine
systems (Nelson et al., 2015; Post, 2002a; Swart
et al., 2013). Thus, the sampling in this study was repli-
cated within two zones that have distinct salinity-
nutrient regimes, as described by previous studies (high
and stable salinity zone and low and variable salinity
zone, Figure 1a; Lirman et al., 2008, 2014, Swart
et al., 2013). This sampling stratification was performed
by randomly selecting three 500 500 m SSU replicates
for each seascape type (CS and FS) within each salinity
zone (Figure 1a). The high and stable salinity zone, an
area with limited input of freshwater from canal
FIGURE 1 (a) Map of the study area and sampling design. The study area was divided into two zones based on salinity regimes: high
and stable salinity zone (north, gray grid) and low and variable salinity zone (south, white grid). (b) SAV seascape map with superimposed
500 500 m grid cells (i.e., seascape sampling unit) (some grid cells in the south were excluded due to cloud cover interference with the
image classification process); seascapes within grids were classified and selected as continuous (dark blue) and fragmented (green) SAV
seascapes. (c) Within each selected grid cell, a 100 m 500 plot was centered. Each plot was divided into five 100 m 100 m distance-to-
shore strata where pinfish samples were obtained across randomly placed sampling replicates (orange triangles)
4of18 SANTOS ET AL.
structures, was characterized by higher and more stable
salinity (wet season mean salinity: 26.6 ppt [4.6 SD],
min–max: 13–36 ppt). In contrast, the low and variable
salinity zone is influenced by pulsed freshwater flows
from canals that create a nearshore environment with
low and variable salinity (wet season mean salinity:
17.1 ppt [8.2 SD], min–max: 2–33 ppt) (Lirman
et al., 2008, 2014), and high nutrient concentrations
(Swart et al., 2013). This sampling design allowed us to
assess the influence of seascape spatial patterns on fish
trophic niche under different salinity-nutrient regimes.
Fish sampling and processing
The collection of pinfish was performed via seine nets
within a 100 m 500 m plot starting at the shore that
was randomly placed within each SSU (average
depth =1.1 m, average depth range =0.8 m; Figure 1c).
The SSU sampling plots were divided into five distance-
to-shore strata (100 m 100 m), where three seine sam-
pling locations were randomly positioned (i.e., n=15
points per plot—three deployment sites per distance-to-
shore stratum) (Figure 1c). At each sampling location, a
center bag seine net (21.3 m long, 1.8 m deep, 3 mm
mesh) swept a bottom area of approximately 210 m
2
to
collect fish samples. The sampling was conducted in
2012, during the wet season (July–October). All fish sam-
ples were taken back to the laboratory and frozen at
20C until they could be processed for isotope analysis.
Ten pinfish were randomly selected across the
distance-to-shore strata for isotope analysis (N=10
individuals 12 sites =120 samples). All pinfish selected
were >90 mm (high and stable salinity zone: fragmented
114.0 mm (90–135) mean (min–max), continuous
122.5 mm (100–170); low and variable salinity zone: frag-
mented 132.3 mm (95–175), continuous 115 mm (90–
155); Appendix S1: Figure S1). Dorsal muscle tissue was
obtained for each fish sample, dried at 50C for 48 h, and
ground for stable isotope analysis. Samples were analyzed
for carbon and nitrogen isotopic content using a CN ana-
lyzer (Euro-EA-Elemental Analyser, Eurovector, Milan,
Italy) connected to a mass spectrometer (Elementar,
Germany). Additionally, basal resources were also col-
lected for C and N stable isotopes at each of the SSUs.
The basal resource collection concentrated on the domi-
nant macrophyte species that constitute most of Biscayne
Bay SAV seascapes (Thalassia testudinum,Halodule
wrightii,Batophora sp., Halimeda sp., red algae drift com-
plex). The basal resource samples were rinsed with
deionized water and spin dried to facilitate tissue isola-
tion (i.e., spin out undesired material such as sand and
mud particles, carbonate epiphytes). Also, epiphytes were
removed either with a small soft brush or using a razor
blade. Similar to the muscle tissue, samples were dried
and ground for stable isotope analysis.
Generating trophic response metrics
Bayesian mixing models
The basal resource contributions, derived from the stable
isotope values, were used to derive trophic response met-
rics (trophic level and trophic niche volume) of pinfish
individuals. The relative contribution of the basal
resources was estimated with Bayesian mixing models.
The basal resources were grouped into seagrass
(Thalassia testudinum,Halodule wrightii), benthic macro-
algae (Penicillus sp., Halimeda sp.), and drift algae
(Laurencia sp. and Hypnea sp. drifting algae complex),
based on isotopic similarity among the macrophyte
groups, and to help with resolution of the mixing models
(Phillips et al., 2014). Epiphytic algae were not collected
for stable isotope analysis in this study, but have been
shown to be an important basal resource in seagrass food
webs (Chasar et al., 2005; Kitting et al., 1984). Therefore,
epiphyte stable isotope values from Chasar et al. (2005)
collected from Biscayne Bay were also included as a
potential basal resource in the mixing models. Using pub-
lished literature values of potential sources collected in
the same study location is a common way to account for
important sources for the mixing model (e.g., Baker
et al., 2021; Plumlee et al., 2020).
Bayesian mixing models were run in R version 3.5.2
(R Core Team, 2017) using the package MixSIAR
(v 3.1.10; Stock et al., 2018) to determine the relative con-
tribution of seagrass, benthic macroalgae, drift algae, and
epiphyte-derived organic matter sources to pinfish indi-
viduals. As part of the models, corrections were made for
the elemental concentration in each source, and the tro-
phic enrichment for each element was C =1.0 0.63
(mean SD) and N =3.0 0.74 (Nelson et al., 2015;
Phillips et al., 2014; Post, 2002b). Each model was run
with a Markov chain Monte Carlo algorithm that con-
sisted of three chains, chain length of 1,000,000, burn-in
of 500,000, and thinness of 500 to ensure model conver-
gence. The average of the mixing models’posterior mean
output was used to represent the contribution distribu-
tion among basal resources.
Trophic response metrics
Two trophic response metrics were developed from the
Bayesian mixing model results: trophic level (i.e., position)
and size (i.e., volume) of the trophic niche. The trophic
level represents the average number of trophic transfers it
ECOSPHERE 5of18
takes for energy to reach that organism (Lindeman, 1942).
The relative trophic level in the food web was calculated
for each pinfish individual using the equation:
TL ¼δ15Ncon Pδ15 Nsfs
Δδ15Nþ1,
where Δδ
15
N=3 and represents the trophic enrichment
factor (Hussey et al., 2014; Nelson et al., 2015;
Post, 2002b), δ15Ncon represents the consumer nitrogen
isotopic value, δ15Nsis the nitrogen isotopic value of each
basal resource, and f
s
is the contribution of each basal
resource to the consumer diet based on the output of the
mixing model (Nelson et al., 2015).
The trophic niche (i.e., niche space) of each SSU pin-
fish population was estimated with the hypervolume R
package (v 2.0.11; Blonder et al., 2014, 2018). Hyper-
volumes were estimated using the z-score for the basal
resource contributions predicted by the mixing models and
related trophic levels. The z-scored basal contribution and
trophic level allow for standardized, comparable axes in n-
dimensional space (Blonder et al., 2014). The z-score
values were calculated based on the following equation:
z¼xij xj
SDj
,
where x
ij
is the individual value for a given axis, xjis the
global mean of that axis, across all the SSUs within each
salinity zone, and SDjis the standard deviation of
that axis.
Hypervolumes were created by simulating 5,000,000
points using a Gaussian kernel density estimation for each
SSU pinfish population. The bandwidth is a vector that is
used to estimate the density of the hypervolume, and the
Silverman estimator is used to estimate the bandwidth
using a quasi-optimal approach based on the variation of
the input data (Blonder et al., 2014). Trophic niche size
was calculated by determining the volume of each hyper-
volume generated. Each hypervolume was remade
100 times using the same parameters as a bootstrapping
method to determine the inherent variability in hyper-
volume size that arises from Gaussian kernel density esti-
mation. A detailed description of the algorithms used to
create the hypervolume and calculate hypervolume met-
rics can be found in Blonder et al. (2014, 2018).
Statistical analyses
Trophic niche overlap
We used the Sorensen overlap to measure and test the
overlap between the niche hypervolume estimated in the
continuous and fragmented seascapes located in both
salinity zones. The Sorensen overlap (SO) was calculated
using the following equation:
SOi¼2Vint
Vcont þVfrag
:
Here, V
int
is the volume of the intersection of the contin-
uous and fragmented seascape hypervolumes for each
respective salinity zone, V
cont
is the volume of the contin-
uous seascape hypervolume, and V
frag
is the volume of
fragmented seascape hypervolume (Blonder et al., 2018).
We created a bootstrap distribution of SO to discern dif-
ferences between the seascape hypervolume overlap in
both salinity zones. Overlap confidence intervals were
generated by creating hypervolumes by randomly sam-
pling 2/3 of the data from the fragmented and 2/3 of the
data from the continuous habitats and calculating the
overlap (Lesser et al., 2020). This process was repeated
100 times for each salinity zone.
Trophic response in continuous and fragmented
seascapes and salinity zones
Generalized linear models (GLMs) were used to exam-
ine and test for differences in the trophic response met-
rics among the seascape types and salinity zones
(interactive effects were not included in the models). A
GLM with a Gaussian error distribution and log link
function was used to characterize the differences in
trophic niche size among the model factors. A GLM
with Gamma error distribution and a log link function
was used for trophic level. The full models were simpli-
fied further by dropping terms, using the delta-AIC
(Akaike information criterion) of less than two units as
a selection criterion.
Threshold response of trophic metrics to spatial
habitat characteristics
The trophic response metrics (trophic niche size and tro-
phic level) were related to values of habitat amount
(PLAND) and spatial configuration (FragIndex) to exam-
ine the magnitude and direction of the trophic response,
and identify distinct threshold responses to quantities of
habitat amount or spatial configuration. The assessment
of the relationship between the trophic response metrics
and the spatial habitat characteristics was performed
with GLMs. All GLMs included a full factorial second-
order polynomial structure and salinity zone as a factor.
Both response metrics were assessed with a GLM with a
Gamma error distribution and log link function. Similar
6of18 SANTOS ET AL.
to the factorial analysis, a delta-AIC greater than or equal
to two was used as a model simplification and selection
criterion.
Residual diagnostics plots were used to assess the
assumptions of all GLMs, and D
2
values, which indi-
cate the amount of deviance accounted for by the
models (i.e., analogous to R
2
), were used to evaluate
the goodness of fit of all selected final models. (Barbosa
et al., 2013, 2016). All statistical analyses were per-
formed in R v 3.5.2 (R Core Team, 2017). The GLMs,
model evaluation, and D
2
calculation were performed
respectively with the stats (R Core Team, 2017),
MuMIn (Barton, 2013, 2019), and modEvA (Barbosa
et al., 2016) R packages.
RESULTS
Trophic niche overlap
The shape and overlap of the pinfish’s hypervolumes
(i.e., trophic niche space) between continuous and frag-
mented seascapes differed across salinity zones (Figure 2).
In the high and stable salinity zone, pinfish from continu-
ous and fragmented seascapes shared an overlap of 0.44
(Figure 2c). Between the two seascape types in this zone,
epiphyte algae showed a higher contribution in fragmen-
ted seascapes, and benthic algae, in continuous seascapes
(FS: benthic algae =0.26, epiphytes =0.62, drift
algae =0.02, seagrass =0.10; CS: benthic algae =0.35,
epiphytes =0.53, drift algae =0.03, seagrass =0.09;
Figures 2a and 3a). In contrast, in the low and variable
salinity zone, there was less overlap (0.22) between the
continuous and fragmented seascapes (Figure 2b,c).
Between the two seascape types in this zone, there was a
higher variation in resource contribution in fragmented
seascapes, with benthic algae, seagrass, and drift algae
showing the higher contribution within this seascape type
(FS: benthic algae =0.06, epiphytes =0.80, drift algae =
0.09, seagrass =0.06; CS: benthic algae =0.04, epiphytes =
0.87, drift algae =0.06, seagrass =0.04; Figure 3b). The
overlap bootstrap distribution suggested no significant dif-
ferences in the trophic niche overlap between seascape
types in the two salinity zones (Figure 2c); however, the
overlap estimates were biased toward lower values in the
low and variable salinity zone.
Trophic response in continuous and
fragmented seascapes and salinity zones
The trophic response of pinfish in continuous versus
in fragmented seascapes varied with the response
metric considered (Figure 4). The model selection
showed that trophic niche size (as measured through
volume of the hypervolume) was best explained by the
seascape type, while for trophic level, the best model
considered both seascape type and salinity zone. Tro-
phic niche size tended to be larger in fragmented sea-
scapes (mean SD, FS =377 488, CS =85 102;
Figure 4a), with higher variation in basal resource use
(Figures 2a and 3a). However, seascape type only
explained 31% of the model deviance, and the differ-
ence between seascape types was not significant
according to the GLM summary table (Table 1a).
Trophic-level response was best explained by a combi-
nation of seascape type and salinity regime zone. In
contrast, the trophic level was significantly higher in
continuous seascapes (mean SD, FS =2.8 0.6,
CS =3.1 0.7), and in the low and variable salinity
zone (mean SD, high and stable =2.5 0.6, low
and variable =3.4 0.4; Table 1b, Figure 4b).
Threshold response of trophic metrics to
spatial habitat characteristics
Trophic niche size and trophic-level response in pin-
fish to both seascape fragmentation and habitat
amount were curvilinear (Figures 5 and 6). The model
selection showed that trophic niche size was best
explained by just the seascape characteristics (seascape
cover and FragIndex). In contrast, for trophic level, the
best model considered the seascape characteristics and
salinity zones (Table 2). As reflected by the D
2
and
fitted values, the fitted response of trophic niche size
was more precise with respect to fragmentation
(Table 2a,b, Figure 5), showing a stronger response in
trophic niche size after intermediate levels of fragmen-
tation (FragIndex < 0.5, Figure 5a). In contrast, tro-
phic niche size showed a weak negative relationship
with habitat cover (Table 2b, D
2
< 0.30) and notably
reflecting a high variance around low-to-intermediate
levels of habitat amount (Figure 5b).
Trophic level showed a quadratic, u-shaped relation-
ship with fragmentation and habitat amount (Table 2c,d,
Figure 6). Concerning both fragmentation and habitat
amount, the trophic level of pinfish was low at intermedi-
ate values, with minimum values occurring at 45% for
PLAND and 0.66 for FragIndex. However, inversely to
the niche volume, higher values of trophic level were
observed at lower and higher values of fragmentation
and habitat amount, respectively. The trophic-level
response type did not vary between salinity regime zone,
but it was relatively higher in low and variable
salinity zone.
ECOSPHERE 7of18
FIGURE 2 Legend on next page.
8of18 SANTOS ET AL.
DISCUSSION
Our study contributes to the limited knowledge of how
SAV spatial characteristics influence the realized trophic
niche of fish populations by incorporating a seascape
ecology approach and stable isotope analysis. The
urgency in understanding the ecological consequences of
spatial habitat transformation is heightened by the rapid
global decline in the extent and quality of coastal SAV
seascapes and growing investment in restoration
(Boström et al., 2017; Pittman et al., 2011; Santos
et al., 2018). Understanding how geographic differences
and changes to the amount and spatial configuration of
SAV habitats influence ecological processes for species
and ecosystem functioning has important implications
for conservation, threat management, and restoration
design (Boström et al., 2017; Hovel, 2003; Yeager
et al., 2016). By combining seascape maps, Bayesian
mixing models, and hypervolume analysis, we showed
that the trophic niche overlapped between pinfish
populations in continuous and fragmented seascapes, but
further expanded to distinct regions of the niche in frag-
mented seascapes. In contrast, the overall trophic level of
pinfish populations was higher in continuous seascapes.
Nevertheless, both trophic response metrics manifested a
threshold response that depended on distinct SAV spatial
characteristics (amount vs. spatial configuration) and
environmental conditions (salinity zones). Thus, our
results suggest that changes to SAV seascape characteris-
tics due to fragmentation processes, as evident in
Biscayne Bay and other coastal regions, can have ecologi-
cal implications that could affect energy flows, food web
stability, and ecosystem functioning.
Trophic niche characteristics between
continuous and fragmented seascapes
Optimal foraging theory (OFT) (Stephens & Krebs, 1986)
and patterns presented by other seascape ecology studies
offer a useful framework to explain the larger trophic
niche and lower trophic level observed in fragmented
FIGURE 2 Pinfish trophic hypervolumes in the continuous (dark blue) and fragmented (light green) seascapes in (a) high and stable
salinity zone and (b) low and variable salinity zone. Note the points in the pair plots are random points generated within the hypervolume.
Trophic hypervolumes are depicted as a series of biplots that represent the relationship between the relative contribution of basal resources
to consumers and the estimated trophic level. Axes are z-scored measuring standard deviations with being the global mean across seascape
sampling units (SSUs). (c) Bootstrap distribution of the degree of overlap between the pinfish continuous and fragmented seascape trophic
niche. Overlap estimate based on Sorensen’s similarity index. TL, trophic level
FIGURE 3 Contrast of basal resource contributions for pinfish in continuous (dark blue) and fragmented (light green) seascapes across
(panel a) high and stable salinity zone and (panel b) low and variable salinity zone. Basal resource contributions estimated from stable
isotope Bayesian mixing models
ECOSPHERE 9of18
SAV seascapes. Under OFT, the trophic niche can
increase as a function of intra- and interspecific competi-
tion, low resource abundance, or the diversity of available
resources (i.e., ecological opportunity) (Araújo
et al., 2011), all conditions favored in fragmented sea-
scapes (Boström et al., 2011, 2017; Horinouchi, 2009;
Macreadie, Hindell, et al., 2010). Pinfish individuals
could highly concentrate on smaller patches associated
with fragmented seascapes (i.e., crowding effect;
Debinski & Holt, 2000, Ewers & Didham, 2006,
Macreadie, Connolly, et al., 2010) since they rely on
dense seagrass patches to increase foraging efficiency and
decrease predation risk (Canion & Heck, 2009; Jordan
et al., 1997; Levin et al., 1997; Santos, 2014). Thus,
crowding effects could increase the trophic niche through
pinfish intraspecific competition, which, in theory, reduces
preferred resources and increases the consumption of less
valuable prey (Araújo et al., 2011, 2014). On the contrary,
pinfish manifested smaller trophic niche size in continu-
ous seascape, which suggests optimal foraging on a
narrower range of energetically favorable prey
resources. Similar results were found in another study
where pinfish decreased niche size in response to
increased ecosystem productivity (Lesser et al., 2020).
Continuous seagrass beds likely have more productivity
available to pinfish allowing more individuals to opti-
mally forage than in fragmented habitats, and thus
decrease the niche size in these habitats.
Multiple aquatic experimental and field studies sup-
port that intraspecific competition among fish species
increases foraging specialization (Araújo et al., 2011,
2014; Kobler et al., 2009; Svanbäck et al., 2011); however,
very few studies have investigated the mechanisms of tro-
phic niche dynamics under the context of SAV seascape
fragmentation. Only three studies, which concentrated
on mangrove creek habitats, have recorded changes to
trophic niche size of marine fish populations due to
hydrologic habitat fragmentation (Araújo et al., 2014;
Layman et al., 2007; L
opez-Rasgado et al., 2016). For
instance, in the Bahamas, a study showed how the tro-
phic niche of Gambusia hubbsi broadened across a gradi-
ent of mangrove creek fragmentation due to predation
FIGURE 4 Pinfish (a) trophic niche size and (b) trophic level
across seascape types and salinity zones. Crossbars show the
generalized linear model fitted values (center bolded line) and
associated 95% confidence interval (upper/lower extension of bar).
Colored points illustrate raw observations in continuous (dark
blue) and fragmented (light green) seascapes. Crossbars are based
on fitted values from the final selected models presented in Table 1
TABLE 1 Summary table of the generalized linear models (GLMs) used to test the difference of (a) trophic niche size and (b) trophic
level across seascape types and salinity zones
Variable Coefficient Estimate SE tp Null deviance Deviance D
2
(a) Size Intercept 5.93 0.49 12.2 2.50E07 19.59 13.49 0.31
Seascape: continuous 1.49 0.69 2.16 5.60E02
(b) Trophic level Intercept 0.861 0.03 28.41 2.45E54 56.84 28.08 0.51
Seascape: continuous 0.093 0.03 3.104 2.40E03
Zone: low and variable salinity 0.321 0.031 10.193 7.61E18
Note: Shown are the coefficient estimates in relation to reference point, standard error of estimates (SE), tstatistics, and pvalues for the null hypothesis of no
difference with the reference point. Significant coefficients are bolded. Also included null deviance, deviance, and D
2
to present a quality of fit of the models.
Based on final GLMs identified from the model selection procedure.
10 of 18 SANTOS ET AL.
release and increase in intraspecific competition in highly
disturbed, fragmented habitats (Araújo et al., 2014).
Despite this limitation of seascape studies on trophic
niche, other studies have observed how the intraspecific
competition of consumers and the abundance of epi-
benthic prey are influenced by seascape properties, habi-
tat complexity, and size (Boström et al., 2011; Connolly &
Hindell, 2006; Livernois et al., 2019; Santos et al., 2018),
all potential drivers of individual specialization and niche
expansion. Thus, the prevalence of ecological causes of
individual specialization that may arise from spatial habi-
tat characteristics sustains the need for focusing future
seascape studies on addressing the trophic niche conse-
quences of seascape fragmentation.
Ecological opportunity (i.e., diversity of available
resources) and predation risk (i.e., likelihood of being
predated) are also other mechanisms that could drive pin-
fish trophic intraspecific variability and niche differences
between continuous and fragmented SAV seascapes
(Araújo et al., 2011). Ecological opportunity could arise
from habitat fragmentation due to the increase in the
amount and diversity of microhabitats that could be uti-
lized by different pinfish individuals (Horinouchi, 2009;
Salita et al., 2003; Santos et al., 2018). For instance, in
Biscayne Bay, the abundance of species that are potential
prey for pinfish and the diversity of the nektonic commu-
nity was positively associated with fragmented SAV sea-
scapes (Santos, 2014; Santos et al., 2018). Also, ecological
opportunity could arise from edge effects (Ries &
Sisk, 2004), which tend to increase from continuous to
fragmented seascapes (Macreadie, Connolly, et al., 2010;
Santos et al., 2015; Yeager et al., 2012). For instance, sev-
eral studies have observed more generalist predatory fishes
foraging at edges and the accumulation of amphipods,
copepods, and isopods (primary consumers in seagrass
habitats and essential food sources for pinfish) at seagrass
edges (Macreadie, Hindell, et al., 2010; Smith et al., 2010;
Smith, Hindell, et al., 2011; Thistle et al., 2010). However,
predation risk tends to increase with fragmentation and
can affect pinfish foraging behavior by increasing their
bias toward large, dense seagrass patches (Froeschke &
Stunz, 2012; Jordan et al., 1997; Santos, 2014). Thus, the
trophic niche size difference observed between the two
SAV seascape types could have been caused by the intra-
variability of behavioral traits (i.e., risk aversion, boldness,
dominance), which control the decision-making process of
foraging and diet selection under predation risk (Araújo
et al., 2011; Catano et al., 2014, 2015).
The difference in trophic level between the fragmen-
ted and continuous SAV seascapes also captured the
changes in pinfish trophic interactions across seascape
types. As we hypothesized, the higher trophic level was
in continuous seascapes, which derived from the other
study observations that dense seagrass patches and con-
nected, continuous seascapes support more complex tro-
phic food web and longer food chains (Nelson
et al., 2015; Nelson, Johnson, et al., 2019; Nelson, Lesser,
et al., 2019; Post, 2002a). Like our study, Layman
et al. (2007) found that the trophic level of a generalist
fish predator was higher in unfragmented mangrove
creeks, which they attributed to an “insertion mecha-
nism”that can affect the relative trophic level of con-
sumers through the extension of intermediate links in a
FIGURE 5 Fit of generalized linear models (GLMs) to assess
the relationships of trophic niche size (volume) with spatial habitat
(a) configuration (fragmentation index) and (b) amount (seascape
cover). See Table 2a,b for more details on the GLMs
ECOSPHERE 11 of 18
particular food chain. Other studies in estuarine ecosys-
tems in the Gulf of Maine and the Gulf of Mexico have
also observed how the trophic level of intermediate
aquatic consumers increases with the availability
(i.e., through measurements of area and connectivity) of
mangrove and marsh habitats that contain higher prey
protein (Nelson et al., 2015; Nelson, Lesser, et al., 2019).
However, despite not measuring seascape-level metrics
directly, another study found that the trophic level of pin-
fish did not differ as a function of seagrass percent-
weighted volume (Lesser et al., 2020). This supports the
notion that the trophic interactions and niche of
seagrass-dependent species such as pinfish may be
influenced by habitat characteristics at multiple spatial
scales, mainly operating at or beyond the seascape scale.
Pinfish display ontogenetic shifts throughout life, and
pinfish earlier in their life cycle are more carnivorous
and have higher trophic levels than mature pinfish
(Barbosa & Taylor, 2020; Stoner, 1980). Because pinfish
display ontogenetic shifts, it is possible the difference in
FIGURE 6 Fit of generalized linear models (GLMs) to assess the relationships of trophic level with spatial habitat (a) configuration
(fragmentation index) and (b) amount (seascape cover) across the salinity zones. See Table 2c,d for more details on the GLMs
TABLE 2 Summary table of the generalized linear models (GLMs) used to assess the threshold response of (a–b) trophic niche size and
(c–d) trophic level as a functional habitat spatial configuration (FragIndex) and amount (seascape cover), respectively
Response
variable Effect Coefficient Estimate SE tpNull deviance Deviance D
2
(a) Size FragIndex Intercept 0.8186 1.6055 0.51 6.21E01 1.31E+06 2.05E+05 0.84
Fragmentation 8.2037 1.6954 4.839 6.83E04
(b) Size Seascape cover Intercept 7.42 0.918 8.083 1.08E05 19.59 13.91 0.28
Cover 0.05 0.02 2.573 2.77E02
(c) Trophic level FragIndex Intercept 1.09515 0.0375 29.203 2.71E55 56.84 23.56 0.59
Fragmentation 0.79349 0.17084 4.645 9.05E06
Fragmentation
2
0.60505 0.17571 3.444 8.00E04
Zone: low and
variable salinity
0.32677 0.03004 10.878 2.01E19
(d) Trophic level Seascape cover Intercept 1.338 0.098 13.643 7.24E26 56.84 16.51 0.71
Cover 0.027 0.005 5.944 2.99E08
Zone: low and
variable salinity
0.376 0.026 1.47E+01 2.52E28
Cover
2
0 0 7.157 8.04E11
Note: Shown are the coefficient estimates, standard error of estimates (SE), tstatistics, and pvalues for the null hypothesis of no difference with the reference
point. Significant coefficients are bolded. Also included null deviance, deviance, and D
2
to present a quality of fit of the models. Based on GLMs identified from
the model selection procedure.
12 of 18 SANTOS ET AL.
trophic level across the seascape types is due to differen-
tial use of seagrass seascape configuration depending on
the life stage. Size-specific shifts in habitat use have been
shown in other nekton species depending on the degree
of fragmentation (James, Topor, & Santos, 2020). How-
ever, in this study pinfish were above the size threshold
of when mature pinfish shift to a more herbivorous diet
(80 mm; Stoner, 1980), and were similar in size across
the different seascape types (Appendix S1: Figure S1).
Therefore, the differences observed in trophic level across
seascape types were likely not due to ontogenetic shifts
based on the size of the pinfish.
Threshold response as a function of spatial
habitat cover and configuration
Robust ecological assessments and effective management
practices require an understanding of both the indepen-
dent and interactive effects of habitat amount and spatial
configuration because restoration strategies may differ
depending on the primary cause of habitat fragmentation
(Lindenmayer & Fischer, 2007). We did not evaluate the
interactive effects of these two spatial habitat compo-
nents due to constraints of our study design; however, we
demonstrated how trophic niche size and trophic level
related distinctly to habitat amount (seascape cover) and
configuration (fragmentation index). More importantly,
the analyses evidenced the existence of critical thresholds
for both indices of habitat amount and spatial
configuration—that is, points of abrupt change in the
relationship between trophic interactions and spatial
properties of SAV seascapes (Francesco Ficetola &
Denoël, 2009). Critical thresholds are valuable informa-
tion to capture signals of significant ecosystem shifts and
provide a better understanding of when and how envi-
ronmental changes will have considerable consequences
on ecological processes and ecosystem dynamics
(Francesco Ficetola & Denoël, 2009; Salita et al., 2003;
With & Crist, 1995). Here, we observed how the trophic
niche size and trophic level of pinfish drastically changed
above intermediate values of fragmentation and habitat
amount. Thus, the critical thresholds of pinfish’s trophic
response could help inform how future impacts of resto-
ration and management activities may affect optimal sea-
scape types and determine whether critical ecological
thresholds could be exceeded in nearshore habitats of
Biscayne Bay.
The critical thresholds identified for trophic niche
size and trophic level could be sustained by previous
empirical observations and expected outcomes from theo-
retical models. The fragmentation index more precisely
described the trophic niche size threshold response than
seascape cover (i.e., highly uncertain response particu-
larly over the lower and intermediate values of seascape
cover). Other seascape studies have shown substantial
effects of spatial habitat configuration on community pat-
terns (e.g., species abundance, density, diversity; Hovel &
Regan, 2008, Bonin et al., 2011, Lirman et al., 2014,
Boström et al., 2017) that, in turn, influence trophic niche
size through intraspecific competition processes. For
example, a study in Biscayne Bay found a stronger non-
linear response of pinfish abundance as a function of a
fragmentation index (Santos, 2014), thus supporting the
hypothesis of pinfish niche expansion due to non-linear
density-dependent effects that influence when consumers
decide to add to their diet alternatives or unfavored
resources to meet energy requirements.
In contrast to the trophic niche size response, the tro-
phic level threshold was best explained by the relative
area of seagrass patches (i.e., habitat amount), findings
that may be supported by how prey diversity, resource
availability, and consumers’food-chain length interact as
a function of SAV habitat size (Post, 2002a). SAV sea-
scape productivity is linked to the seagrass habitat
amount (Ricart et al., 2015). Thus, we should expect
higher trophic levels in continuous seascapes following
hypotheses that predict the expansion of food-chain
length as resource availability increases proportionally
with habitat size and productivity (e.g., productivity, pro-
ductive space, and exploitation ecosystem hypothesis;
Post, 2002a, 2002b). Although studies suggest a threshold
in the resource availability, food-chain length relation-
ship was determined by other factors such as predator–
prey interactions (Post, 2002a, 2002b). For instance, the
abundance and diversity of prey that elevates pinfish tro-
phic level may be maximized above a connectivity thresh-
old offered by continuous seascapes (i.e., percolation
threshold—the level of habitat amount at which the sea-
scape transitions from a connected to a disconnected sys-
tem) that increase coexistence and availability of
intermediate consumers as prey (Caldwell &
Gergel, 2013; With & Crist, 1995; Yeager et al., 2016).
Management implications
The results of our study could provide insight on how
management and restoration activities can influence eco-
logical processes and ecosystem functioning in coastal
environments. For example, our seascape ecology
approach integrated with stable isotope analysis demon-
strated that Biscayne Bay’s water management practices
can influence the energy pathways of important con-
sumers by altering both the spatial structure of SAV habi-
tats and nutrient regimes. The results of the overlap
ECOSPHERE 13 of 18
analysis suggested that high nutrient regimes can interact
with seascape characteristics to increase diversity of food
resources, as illustrated by higher niche separation
between continuous and fragmented seascapes in the low
and variable salinity zone. The interactive effects of nutri-
ent and seascape properties still required further
research, but nutrient enrichment is one of the main
stressors of urban ecosystems, such as Biscayne Bay, asso-
ciated with the modification of bottom-up and top-down
processes that control food web structure, trophic rela-
tionship, and productivity values of coastal ecosystems
(Armitage & Fourqurean, 2009; Baeta et al., 2011; Swart
et al., 2014; Tewfik et al., 2005). Higher trophic level
observed in the low and variable salinity zone, especially
in continuous seascapes, corresponded to higher observa-
tions of pinfish abundance and biomass by Santos
et al. (2018), which suggest a positive effect on pinfish
production by nutrients and a larger spatial extension of
SAV habitats. However, further field and laboratory
experiments in conjunction with mass-balanced models
are required to better assess and understand the func-
tional implication of both habitat fragmentation and
eutrophication.
By implementing stable isotope analysis and hyper-
volume metrics, our study captured for the first time how
distinct properties of SAV habitat amount and spatial
configuration influence the realized trophic niche of a
seagrass omnivore fish species. A recent study demon-
strated the usefulness of hypervolumes and mixing
models to evaluate the functional success of restoration
activities (James, Lesser, et al., 2020), and likewise, our
results demonstrated that by integrating a seascape
approach, managers could evaluate the best spatial attri-
butes of habitat patches that contribute to the restoration
of ecological processes and functioning in SAV habitats.
In addition, our results highlight the importance of tak-
ing into consideration the spatial properties of coastal
habitats and the geographic reference (i.e., location rela-
tive to distinct spatial attributes) when assessing trophic
dynamics that may cause a cascade of unintended ecolog-
ical and ecosystem-level changes. Landscape ecology
studies suggest that the effects of habitat fragmentation
are generally much weaker than the impact of habitat
loss (Fahrig, 2003); however, critical thresholds for
organisms and ecosystem function will vary depending
on the system (Andrén, 1994; Pardini et al., 2010). Also,
as demonstrated in recent seascape ecology studies, the
effect of seagrass habitat configuration on nektonic spe-
cies diversity and density is only apparent in seascapes
with habitat amount levels below or above a certain
threshold (i.e., fragmentation threshold hypothesis;
Trzcinski et al., 1999) (Santos, 2014; Yeager et al., 2016).
Thus, our study should be replicated to depict the
interactive effects of habitat amount and spatial configu-
ration on trophic niche dynamics and to identify general-
ities among trophic guilds and across aquatic systems.
Nevertheless, our study approach and results highlight
the importance of identifying critical habitat thresholds
that could inform management and restoration strategies
on the required amount and configuration of habitat nec-
essary to sustain the persistence or recovery of food web
functions.
ACKNOWLEDGMENTS
Special thanks to James McCullars, Matthew Dilly, and
Amanda Guthrie for help in the field and laboratory.
This research was conducted under NPS permit BISC-
2011-SCI-0028. Funding was provided by NOAA’sEdu-
cational Partnership Program and Living Marine
Resources Cooperative Science Center, the Army Corps
of Engineers, and the RECOVER Monitoring and
Assessment Program (MAP). The doctoral studies of
R.O.S. were supported by the McKnight Doctoral Fel-
lowship (Florida Education Fund). We thank the anon-
ymous reviewers, and the University of Miami’sStable
Isotope Facility and Dr. Leonel Sternberg for providing
technical support and mentorship with respect to the
stable isotope analysis portion of this study. This is
contribution no. 1369 from the Coastlines and Oceans
Division of the Institute of Environment at the Florida
International University.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
DATA AVAILABILITY STATEMENT
Data (James, 2021) are available from Zenodo: https://
doi.org/10.5281/zenodo.5703218.
ORCID
Rolando O. Santos https://orcid.org/0000-0002-3885-
9406
W. Ryan James https://orcid.org/0000-0002-4829-7742
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SUPPORTING INFORMATION
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online version of the article at the publisher’s website.
How to cite this article: Santos, Rolando O., W.
Ryan James, James A. Nelson, Jennifer S. Rehage,
Joseph Serafy, Simon J. Pittman, and
Diego Lirman. 2022. “Influence of Seascape Spatial
Pattern on the Trophic Niche of an Omnivorous
Fish.”Ecosphere 13(2): e3944. https://doi.org/10.
1002/ecs2.3944
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