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Habitat fragmentation of submerged aquatic vegetation (SAV) transforms the spatial pattern of seascapes by changing both the total area and spatial configuration 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). We know considerably less about the effects of seascape structure on ecological processes such as food web function 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 management practices that alter the distribution of SAV habitat and fish species abundance, diversity, and community assemblage. We combined SAV seascape 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 continuous 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 configuration) and environmental conditions. Our results suggest that habitat fragmentation of SAV seascape structure has ecological implications that could affect energy flow with cascading consequences for food web stability and ecosystem functioning.
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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 inhospitablematrix) 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 predatorprey 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-
nitiestrophic 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
Cδ
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
studypinfish 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 ¼4PD 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],
minmax: 1336 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], minmax: 233 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 plotthree 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 (JulyOctober). 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 (90135) mean (minmax), continuous
122.5 mm (100170); low and variable salinity zone: frag-
mented 132.3 mm (95175), 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 modelsposterior 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 pinfishs 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 Sorensens 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-
nismthat 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 (ab) trophic niche size and
(cd) 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
configurationthat 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 pinfishs 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 consumersfood-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
thresholdthe 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 Bays 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 NOAAsEdu-
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 MiamisStable
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 publishers 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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... Environments with predictable pulses in resources would therefore be expected to support a low specialization in space use due to the quantity of resources available for individuals and consequently decreasing the trophic niche size 12,47,[49][50][51] . Conversely, an expansion in the trophic niche can be expected via foraging on different prey resources, which could result from the increase of intra-specific variance in space use or high specialization [52][53][54] . ...
... Snook niches were larger when snook had less specialized space use. This result contradicted our original prediction that higher spatial clustering in space by the consumer (i.e., low space use specialization), due to the concentration of preferable prey resources, would result in smaller trophic niche sizes 50,51,54 . For instance, lower water levels in marshes connected to the SR are associated with a low space use specialization for snook, which rely mostly on freshwater prey subsidies originating from the marshes. ...
... These subsidies increase the amount of energy available 7,57,77 , increasing snook body condition and reproductive output 86 . In other systems, increased production has been shown to lead to decreases in the trophic niche size of consumers 54,101 . Higher availability of prey resources with a high potential of contributing energetically to consumers can reduce species' trophic niche size by buffering intra-and inter-specific competition, and allowing individuals to specialize in common sources of energy 39,46,102 . ...
Article
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Variability in space use among conspecifics can emerge from foraging strategies that track available resources, especially in riverscapes that promote high synchrony between prey pulses and consumers. Projected changes in riverscape hydrological regimes due to water management and climate change accentuate the need to understand the natural variability in animal space use and its implications for population dynamics and ecosystem function. Here, we used long-term tracking of Common Snook (Centropomus undecimalis) movement and trophic dynamics in the Shark River, Everglades National Park from 2012 to 2023 to test how specialization in the space use of individuals (i.e., Eadj) changes seasonally, how it is influenced by yearly hydrological conditions, and its relationship to the between individual trophic niche. Snook exhibited seasonal variability in space use, with maximum individual specialization (high dissimilarity) in the wet season. The degree of individual specialization increased over the years in association with greater marsh flooding duration, which produced important subsidies. Also, there were threshold responses of individual space use specialization as a function of floodplain conditions. Greater specialization in space use results in a decrease in snook trophic niche size. These results show how hydrological regimes in riverscapes influence individual specialization of resource use (both space and prey), providing insight into how forecasted hydroclimatic scenarios may shape habitat selection processes and the trophic dynamics of mobile consumers. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-024-82158-4.
... seagrass) leads to a loss in secondary production unless consumers compensate by altering basal resource use (Deegan and Garritt, 1997;Smith et al., 2008;Lesser et al., 2020). For example, seagrass omnivores increase variation in basal resource use to compensate for lower production (Lesser et al., 2020;Santos et al., 2022). Habitat loss can also alter species interactions by increasing competition for space and resources and changing prey capture efficiency (Hovel and Lipcius, 2001;Fahrig, 2003;Canion and Heck, 2009). ...
... Instead, consumers might concentrate foraging effort into the remnant patches of seagrass, thus increasing competition, with possible negative effects on secondary production (Macreadie et al., 2010). Consumers might also increase movement and home range size or increase variation in resource use in response to lower amounts of basal resources following the die-off in order to meet energetic demands (Schradin et al., 2010;Lesser et al., 2020;Santos et al., 2022). Alternatively, consumers might display shifts in basal resource use immediately following disturbance but display rapid recovery in the timeframe before sampling occurred. ...
... Spatial variation in production, habitat amount, habitat configuration, and environmental factors (e.g. depth, salinity) have all been shown to affect resource use (Livingston, 1984;Deegan and Garritt, 1997;Olin et al., 2012;Nelson et al., 2015;Giraldo et al., 2017;Jankowska et al., 2018;Santos et al., 2022). Our results do not point to these factors influencing basal resource use of seagrass, epiphytes, mangroves, or algae in the wet season as there is little intraspecific spatial variation among the sites sampled, despite broad spatial sampling (Table 3, S1, Figure 4). ...
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Macrophyte foundation species provide both habitat structure and primary production, and loss of these habitats can alter species interactions and lead to changes in energy flow in food webs. Extensive seagrass meadows in Florida Bay have recently experienced a widespread loss of seagrass habitat due to a Thalassia testudinum mass mortality event in 2015 associated with prolonged hypersalinity and bottom-water anoxia. Using stable isotope analysis paired with Bayesian mixing models, we investigated the basal resource use of seven species of seagrass-associated consumers across Florida Bay in areas affected by the 2015 seagrass die-off. Three years after the die-off, basal resource use did not differ for species collected inside and outside the die-off affected areas. Instead, consumers showed seasonal patterns in basal resource use with seagrass the most important in the wet season (58%), while epiphytes were the most important in the dry season (44%). Additionally, intraspecific spatial variability in resource use was lower in the wet season compared to the dry season. We were unable to detect a legacy effect of a major disturbance on the basal resource use of the most common seagrass-associated consumers in Florida Bay.
... E-scapes). IEI values were combined with habitat cover areas within a landscape foraging unit (grid cell with an area that corresponds to the movement range of the consumer; James, Santos, Rehage, et al., 2022) to calculate the HRI. HRI was calculated with the following formula: ...
... Additionally, pink shrimp was the only species with an IEI value >1 for a basal resource that was unaffected by the die-off (algae, , and it is possible that seagrass consumers in Florida Bay had a similar response. Additionally, resource distribution influences variability in resource use and competition between species Santos et al., 2022), as well as consumer movement and distribution (Abrahms et al., 2019(Abrahms et al., , 2021Geary et al., 2020). ...
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Natural and anthropogenic disturbances have led to rapid declines in the amount and quality of available habitat in many ecosystems. Many studies have focused on how habitat loss has affected the composition and configuration of habitats, but there have been fewer studies that investigate how this loss affects ecosystem function. We investigated how a large‐scale seagrass die‐off altered the distribution of energetic resources of three seagrass‐associated consumers with varied resource use patterns. Using long‐term benthic habitat monitoring data and resource use data from Bayesian stable isotope mixing models, we generated energetic resource landscapes ( E‐ scapes) annually between 2007 and 2019. E‐ scapes link the resources being used by a consumer to the habitats that produce those resources to calculate a habitat resource index as a measurement of energetic quality of the landscape. Overall, our results revealed that following the die‐off there was a reduction in trophic function across all species in areas affected by the die‐off event, but the response was species‐specific and dependent on resource use and recovery patterns. This study highlights how habitat loss can lead to changes in ecosystem function. Incorporating changes in ecosystem function into models of habitat loss could improve understanding of how species will respond to future change.
... One of the major criticalities concerning the application of landscape ecology to marine and coastal environment has been the clear identification of habitats boundaries and their mapping. Current advances in marine remote sensing technologies are progressively facilitating this task, especially in intertidal and shallow areas Wedding et al., 2011;Bell and Furman, 2017), with the result that most applications are focused on intertidal/benthic habitat types mainly located in coastal areas, such as salt marshes, seagrasses, coral reefs and macroalgae (Wedding et al., 2011;James et al., 2021;Santos et al., 2022), with only very recent studies going beyond this limit (Swanborn et al., 2022b, a). Although the appropriateness of the transposition of terrestrial methodologies to aquatic ecosystems is still debated (Manderson, 2016;Bell and Furman, 2017), the analysis of seascape structure mostly relies on the use of metrics designed for terrestrial ecosystems (Wedding et al., 2011), which are used to describe characteristics of the habitats mosaics. ...
... influencing the faunal assemblages and a variety of marine ecological processes (Sekund and Pittman, 2017;Abadie et al., 2018;Santos et al., 2018Santos et al., , 2022James et al., 2021). The Venice lagoon does not make an exception in this sense: it is poorly studied from a seascape ecology perspective, but first evidences suggest a link between habitats' spatial patterns, fish assemblages and local fisheries (Scapin et al., 2018(Scapin et al., , 2022. ...
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The relationships between habitat patterns and ecosystem functioning have been widely explored in terrestrial ecosystems, but less in marine and coastal ecosystems, calling for further research in this direction. This work focuses on the mosaic of submerged habitats in the Venice lagoon, Italy. It aims to describe the habitats’ spatial patterns at multiple spatial scales, and to explore their linkages with the ecological status defined according to the EU Water Framework Directive (WFD, 2000/60/EC). The submerged habitats’ mosaic has been analysed by calculating a set of seascape metrics at different spatial scales. These metrics have been linked with the biological quality elements (BQEs) that are monitored in the lagoon in compliance to the WFD. The results show that the habitats’ spatial patterns differ between the areas of the lagoon with marine-like features and the areas which still retain more lagoon characteristics. The similarity between the pattern found in the whole lagoon and those found in marine-like areas suggests a general loss of lagoon characteristics at the lagoon scale. Regarding the ecological status, every BQE seems to be associated with a different habitat configuration at the water body scale. This does not facilitate the joint improvement of the BQEs, as required by the Directive. If we cannot achieve that, at some point we will probably have to choose what to prioritize. On a broader perspective, this calls for a reflection on what lagoon we want for the future, a vision that should be shared and account for the lagoon’s complexity, current trends and challenges.
... Blank ('-') minimum and maximum values for anthropogenic disturbances indicate that variables were not included in the analyses at the respective scale The fragmentation metrics used in this study were types of spatial pattern metrics, which are routinely applied to quantify changes in the composition and configuration of patches in both landscape (McGarigal, Cushman & Regan, 2005;Schindler et al., 2013) and seascape ecology (Sleeman et al., 2005;Wedding et al., 2011;Swadling et al., 2019). The fragmentation metrics selected (Table 1) were patch density (PD), landscape division (LD), area-weighted mean perimeter to area ratio (AWMPAR) and mean radius of gyration (2016) and Santos et al. (2022), but was adapted to include the number of holes metric as follows: ...
... GYRATE), which have been recommended by others(Sleeman et al., 2005;Santos, Lirman & Pittman, 2016;Santos et al., 2022).These metrics quantify four different characteristics of habitat fragmentation which include habitat extent, compactness, subdivision and configuration, and are relative across spatial scales, habitat coverage and aggregation(McGarigal, 2001;McGarigal, Cushman & Regan, 2005;Sleeman et al., 2005;Santos, Lirman & Pittman, 2016).All these metrics became larger with increased fragmentation except GYRATE, which decreased with increased fragmentation.Fragmentation metrics were quantified using the landscapemetrics package in the statistical software R(Hesselbarth et al., 2019). For more information on the behaviour of the fragmentation metrics please seeFigure S1and the formulae used to calculate the metrics with the package landscapemetrics can be found at https://rspatialecology.github.io/landscapemetrics/index.html. ...
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• Although it is well established that human activities are linked to the loss of seagrasses worldwide, the influence of anthropogenic disturbances on the habitat fragmentation of seagrass meadows is less understood. This information is essential to identify how humans are modifying seascapes and what disturbances pose the greatest risk to seagrasses, which is pertinent given the rapid urbanization occurring in coastal areas. • This study examined how the habitat fragmentation of an endangered seagrass Posidonia australis varied in relation to several anthropogenic disturbances (i.e. human population, marine infrastructure, terrestrial run-off and catchment land-usage) within 10 estuaries across 620 km of coastline in New South Wales, Australia. • When comparing between estuaries, the fragmentation of P. australis meadows was significantly greater in estuaries adjacent to highly populated metropolitan centres – generally in the Greater Sydney region. At sites within estuaries, the density of boat moorings was the most important predictor of habitat fragmentation, but there was also evidence of higher fragmentation with increased numbers of jetties and oyster aquaculture leases. • These results suggest that the fragmentation of seagrass meadows will become more pervasive as the human population continues to grow and estuarine development increases. Strategies to mitigate anthropogenic disturbances on seagrass meadow fragmentation could include prohibiting the construction of boat moorings and other artificial structures in areas where seagrasses are present or promoting environmentally friendly designs for marine infrastructure. This knowledge will support ongoing management actions attempting to balance coastal development and the conservation of seagrasses.
... Lower habitat connectivity has also been shown to decrease the conversion of primary production into consumer biomass, lowering overall ecosystem productivity (Cloern et al. 2007). In seagrass sys-tems, pinfish Lagodon rhomboides tend to have larger trophic niches in areas with low seagrass cover (lower productivity) and increased fragmentation, which decrease connectivity between habitat patches (Santos et al. 2022). Thus, decreased seascape connectivity at Dean Creek, resulting from higher marsh edge elevation and lower channel density, could be indicative of lower ecosystem productivity and correspond to larger trophic niches of taxa observed at this site. ...
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Across relatively small spatial scales, differences in seascape structure may influence how species transverse adjacent habitats, with effects on trophic relationships and energy flow dynamics. To understand the effects of seascape structure, we used stable isotope analysis to examine variation in resource use by 2 highly abundant estuarine species, mummichogs Fundulus heteroclitus and grass shrimp Palaemon spp., both of which are known to forage in intertidal areas. We compared the percent contribution of basal resources, trophic position, and trophic niches of mummichogs and grass shrimp from 2 tidal creeks with differing seascape structure located ~10 km apart on Sapelo Island, Georgia, USA. Belle Marsh creekbank edge elevation was on average 0.5 m lower and channel density was 5 times greater than Dean Creek, potentially influencing marsh platform access. Although we found no difference in the contribution of marsh-derived energy to grass shrimp among sites, the contribution of marsh-derived energy to mummichogs was on average 1.9 times higher at Belle Marsh. In addition, both species had higher trophic positions and larger trophic niches at Dean Creek, suggesting a less efficient route of energy transfer to consumer production. There was also little overlap in trophic niche among sites for either species. Our results suggest that species traits, site characteristics, and their interaction may influence resource use by intertidal estuarine consumers. By examining how marsh resource use by estuarine consumers varies across multiple marshes with differing morphologies, we can better predict and quantify how seascape structure may affect secondary productivity of estuarine systems.
... The assessment of widespread and patchy habitat such as CDB requires a seascape approach. It is known that the seascape can directly influence the trophic network and the ecosystem functioning (Boström et al., 2011;Abadie et al., 2018;Santos et al., 2022). In the light of that, a seascape approach should be a potential suitable descriptor to assess the quality of an ecosystem (e.g., marine forests, habitat structure, etc.; Chemineé et al., Thiriet et al., 2014). ...
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Introduction Coastal detrital bottoms (CDB) are one of the most extensive habitats of the continental shelf worldwide, in the upper levels of the circalittoral zone. Hosting a diverse community structured by sediment grain size, trophic interactions and calcified organisms, CDB exhibit important ecological functions. In the Mediterranean Sea, CDB are constituted by recent elements partly provided by adjacent infralittoral and circalittoral ecosystems. Since the 2010s, the offshore extension of many Marine Protected Areas (MPAs) has resulted in the incorporation of vast areas of CDB, raising the issue of their management. The Marine Strategy Framework Directive (MSFD) has embraced the concept of an ecosystem-based approach involving taking into account the functioning of marine habitats and their related ecosystem services. The purpose of this paper is to propose an ecosystem-based quality index (EBQI) tested on CDB from the north-western Mediterranean Sea, focusing mainly on epibenthic assemblages. Methods The first step has been to define a conceptual model of the CDB functioning, including the main trophic compartments and their relative weighting, then to identify appropriate assessment methods and potential descriptors. Twenty-nine sites were sampled along the coast of Provence and French Riviera (Southern France). Study sites were chosen with a view to encompassing a wide range of hydrological conditions and human pressures. Results Very well-preserved sites were found in Provence in areas without trawling and terrigenous inputs, while impacted and low-ES sites were located in the vicinity of urbanized areas. The cover of rhodoliths characterizes the seascape and might be an indicator of the good ES of CDB and reduced human pressure. However, the absence of rhodoliths may also be induced by natural phenomena. Discussion The EBQI designed for CDB proved representative and useful for a functional assessment based on epibenthic assemblages. However, some descriptors have shown their limitations and should be further explored. We highlight here the priority of establishing an index corresponding to a societal demand (e.g., European Directives, Barcelona convention) as a basis for a broad and large-scale assessment, for practical reasons. We stress the need to better apprehend the role of the macro-infauna and to extend this index over a wider geographical scale.
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The relationships between habitat patterns and ecosystem functioning have been widely explored in terrestrial ecosystems, but less in marine and coastal ecosystems, calling for further research in this direction. This work focuses on the mosaic of submerged habitats in the Venice lagoon, Italy. It aims to describe the habitats’ spatial patterns at multiple spatial scales, and to explore their linkages with the ecological status defined according to the EU Water Framework Directive (2000/60/EC). The submerged habitats’ mosaic has been analysed by calculating a set of seascape metrics at different spatial scales. These metrics have been linked with the biological quality elements (BQEs) that define the ecological status in the lagoon. The results show that the habitats’ spatial patterns differ between the areas of the lagoon with marine-like features and the areas which still retain more lagoon characteristics. The similarity between the pattern found in the whole lagoon and those found in marine-like areas suggests a general loss of lagoon characteristics at the lagoon scale. Regarding the ecological status, every BQE seems to be associated with a different habitat configuration at the water body scale. This does not facilitate the joint improvement of the BQEs, as required by the Directive. If we cannot achieve that, at some point we will probably have to choose what to prioritize. On a broader perspective, this calls for a reflection on what lagoon we want for the future, a vision that should be shared and account for the lagoon’s complexity, current trends and challenges.
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Seagrass beds in Florida Bay are home to many ecologically and economically important species. Anthropogenic press perturbation via alterations in hydrology and pulse perturbations such as drought can lead to hypersalinity, hypoxia, and sulfide toxicity, ultimately causing seagrass die-offs. Florida Bay has undergone two large-scale seagrass die-offs, the first in the late 1980s and early 1990s and the second in 2015. Post-die-off events, samples were collected for stable isotope analysis. Using historical (1998–1999) and contemporary (2018) stable isotope data, we examine how food webs in Florida Bay have changed in response to seagrass die-off over time by measuring contributions of basal sources to energy usage and using trophic niche analysis to compare niche size and overlap. We examined three consumer species sampled in both time periods (Orthopristis chrysoptera, Lagodon rhomboides, and Eucinostomus gula) in our study. Seagrass production comprised the majority of source usage in both datasets. However, contemporary consumers had a mean increase of 18% seagrass usage and a mean decrease in epiphyte usage of 7%. The shift in trophic niche from epiphyte usage (green pathway) toward seagrass usage (brown pathway) may indicate that food web browning is occurring in Florida Bay.
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The production and transfer of biomass through trophic relationships is a core ecosystem function. The movement of energy through the food web is mediated by organisms operating in their niche space. For generalists, the size of this niche space is inherently plastic and changes in response to available food sources. Therefore, this relationship between ecosystem productivity and niche size is an important determinant of ecosystem function. Competing theories about the nature of this relationship predict that as productivity increases niche size will either increase as species capitalize on a general increase in resource availability or decrease as it becomes viable to focus on preferred production channels. Here, we test these two competing theoretical frameworks using a novel approach to determine trophic niche size using stable isotope analysis and hypervolume metrics. Resource use is quantified in two generalist fish species at three productivity levels in a seagrass ecosystem. Niche size of both species was inversely related to seagrass productivity, consistent with the hypothesis that increasing productivity allows species to focus on a narrower diet. This pattern describes the relationship between ecosystem production and niche size and provides an empirical ecological explanation for the resource maximization behaviors commonly observed in nature.
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Full-text available
The production and transfer of biomass through trophic relationships is a core ecosystem function. The movement of energy through the food web is mediated by organisms operating in their niche space. For generalists, the size of this niche space is inherently plastic and changes in response to available food sources. Therefore, this relationship between ecosystem productivity and niche size is an important determinant of ecosystem function. Competing theories about the nature of this relationship predict that as productivity increases niche size will either increase as species capitalize on a general increase in resource availability or decrease as it becomes viable to focus on preferred production channels. Here, we test these two competing theoretical frameworks using a novel approach to determine trophic niche size using stable isotope analysis and hypervolume metrics. Resource use is quantified in two generalist fish species at three productivity levels in a seagrass ecosystem. Niche size of both species was inversely related to seagrass productivity, consistent with the hypothesis that increasing productivity allows species to focus on a narrower diet. This pattern describes the relationship between ecosystem production and niche size and provides an empirical ecological explanation for the resource maximization behaviors commonly observed in nature. This article is protected by copyright. All rights reserved.
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Submerged aquatic vegetation (SAV) communities display complex patch dynamics at seascape scales that are presently poorly understood as most studies of disturbance on SAV habitats have focused on changes in biomass at small, quadrat-level scales. In this study, analyses of remote sensing imagery and population modelling were applied to understand SAV patch dynamics and forecast the fate of these important communities in Biscayne Bay, Miami, Florida, US. We evaluated how the proximity of freshwater canals influences seagrass-dominated SAV patch dynamics and, in turn, how patch-size structure influences the stability of seagrass seascapes under different salinity scenarios. Seagrass fragmentation rates were higher in sites adjacent to freshwater canals compared to sites distant from the influences of freshwater deliveries. Furthermore, we documented a clear trend in patch mortality rates with respect to patch size, with the smallest patches (50 m²) undergoing 57% annual mortality on average. The combination of higher fragmentation rates and the higher mortality of smaller seagrass patches in habitats exposed to pulses of low salinity raises concern for the long-term persistence of seagrass meadows in nearshore urban habitats of Biscayne Bay that are presently targets of Everglades restoration. Our model scenarios that simulated high fragmentation rates resulted in SAV population collapses, regardless of SAV recruitment rates. The combined remote sensing and population modelling approach used here provides evaluation and predictive tools that can be used by managers to track seagrass status and stress-response at seascape levels not available previously for the seagrasses of South Florida.
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