Adams y Paperno -2012- Stable isotopes and mercury in a model estuarine fish- Multibas

Science of the Total Environment 414 (2012) 445–455
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Stable isotopes and mercury in a model estuarine fish: Multibasin comparisons with
water quality, community structure, and available prey base
Douglas H. Adams ⁎, Richard Paperno
Florida Fish and Wildlife Conservation Commission, Fish and Wildlife Research Institute, 1220 Prospect Ave., No. 285, Melbourne, FL 32901 USA
a r t i c l e
i n f o
Article history:
Received 13 August 2011
Received in revised form 27 September 2011
Accepted 2 October 2011
Available online 21 November 2011
Keywords:
Stable isotope analysis
Mercury
Estuarine food web
Cynoscion nebulosus
Estuarine and marine fishes
Indian River Lagoon, Florida, USA
a b s t r a c t
Stable-isotope ratios (δ13C and δ15N) and mercury in a model predator, and associated prey community assessments were used to make inferences regarding food web relationships and how these relationships are influenced by habitat variability and anthropogenic factors. Although interconnected, the three major basins of
the Indian River Lagoon system on the Atlantic coast of Florida comprise noticeably different available habitat
types with spatially distinct faunal communities and available prey for spotted seatrout, Cynoscion nebulosus,
a model predatory fish species. Water quality, degree of urbanization, human population density, and levels
of nitrogen enrichment clearly differ between these representative estuarine basins. The differences can influence feeding ecology and therefore result in different mercury concentrations and different stable-isotope signatures of spotted seatrout between basins. Mercury concentrations in spotted seatrout were greatest in
Mosquito Lagoon (ML) and least in the Indian River Lagoon proper (IRL), although concentrations were low
for all basins. Spotted seatrout from IRL were carbon-depleted and nitrogen-enriched compared with those
from the other basins; this suggests either that the fish's primary source of carbon in IRL is an algae- or
phytoplankton-based food web or that the pathway through the food web is shorter there. The δ15N values
of IRL spotted seatrout were greater than those in the Banana River Lagoon or ML, suggesting slightly different
trophic positioning of fish in these basins. The greater δ15N values in IRL spotted seatrout may also reflect the
greater human population density and resultant anthropogenic inputs (e.g., observed higher total nitrogen
levels) in IRL compared with the other more pristine basins examined. Understanding species' responses to
broad-scale habitat heterogeneity in estuaries and knowing basin-specific differences in stable isotopes, mercury, prey communities, and comprehensive food web relationships will be useful in the future for long-term
monitoring of impacts of anthropogenic disturbances and of recovery from restoration efforts.
© 2011 Elsevier B.V. All rights reserved.
1. Introduction
Estuaries are complex networks of plant and animal communities
and biogeochemical interactions that function to provide one of the
most productive and vulnerable environments in the world (Kennish,
1990; Bertness, 1999). Coastal estuarine lagoons are becoming increasingly influenced by the impacts of changing human populations
and urbanization through increased nutrient loading (McGlathery
et al., 2007), contaminants and related anthropogenic stressors
(Kennish, 2002). Innovative biochemical techniques (e.g., stable isotope, fatty acid, and contaminant tracer analyses) can provide better
understanding of these influences and assist in the effective monitoring and management of these valuable resources.
Technologies developed and more commonly applied over the
past decade that use isotopic signatures of component elements
can provide insight into the nutrient sources that provide support
⁎ Corresponding author.
E-mail address: [email protected] (D.H. Adams).
0048-9697/$ – see front matter © 2011 Elsevier B.V. All rights reserved.
doi:10.1016/j.scitotenv.2011.10.014
for production in a system (Hullar et al., 1996; Kaldy et al., 2005).
Variations in ratios of stable isotopes (δ 13C and δ 15N) have been
used to reconstruct the diets and food-web pathways of many marine animals (Hobson, 1993; Persic et al., 2004; Reich and Worthy,
2006; Ferraton et al., 2007). Researchers have also used changes in
proportions of these isotopes to make inferences regarding foodweb relationships; to examine how these relationships change as
habitats change (Deegan and Garritt, 1997) or vary with life history
stage (Griffin and Valiela, 2001; Sorensen and Hobson, 2005), and
to identify links between isotopic signatures from marine animals
and their initial sources of primary productivity (e.g., seagrass, salt
marsh halophytes, benthic macroalgae, phytoplankton) (Leal et al.,
2008; Prado et al., 2010). Isotopic signatures may also provide insights regarding water quality or level of point-source and
nonpoint-source inputs in the system (Jones et al., 2001; Medina
et al., 2005; Bannon and Roman, 2008). Understanding the interaction between various habitat components in relation to the functioning of estuarine ecosystems is critical to the implementation
of appropriate management, conservation, and restoration efforts
(Nobriga et al., 2005).
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D.H. Adams, R. Paperno / Science of the Total Environment 414 (2012) 445–455
Mercury contamination is of growing concern within estuarine
systems worldwide. Mercury occurs naturally in the environment, although available concentrations have increased due to anthropogenic
release (Munthe et al., 2007). The majority (~95%) of total mercury in
fish muscle is typically methylmercury. Methylmercury is highly toxic
and considered a mutagen, teratogen, and carcinogen (Eisler, 1987)
and recent work suggests that mercury may cause sub-lethal effects
on estuarine fish populations (Adams et al., 2010). Biogeochemical,
physical and ecological processes regulate the distribution and movement of mercury from sources to estuarine food webs (Chen et al.,
2008) and analyses of mercury can provide additional insight into
fish foraging ecology and trophic dynamics within estuarine ecosystems. Mercury accumulates in fish principally from dietary sources
(Trudel and Rasmussen, 2001; Wang, 2002), and typically increases
with increasing trophic level. Just as the isotopic composition of an
organism reflects its diet (DeNiro and Epstein, 1978), non-essential
elements such as mercury may serve as tracers of specific habitats,
of habitat types, or of the feeding compartments of consumers, and
be complementary to stable isotope results for estuarine fishes.
The Indian River Lagoon, designated an estuary of national significance by the National Estuary Program, supports one of the most
diverse estuarine faunas in North America (Gilmore, 1995). The
expansive multibasin structure of the system (Fig. 1) provides the
unique opportunity to examine differing, but connected biotic and
abiotic conditions that are representative and directly comparable
to other estuarine systems. As in many estuaries, shoreline development and associated habitat loss are threats to habitat quality, quantity, and biodiversity (Gilmore, 1995; Tang et al., 2005). Loss or
degradation of important estuarine habitats may have negative impacts on local communities that include species of economic or ecological importance (Bloomfield and Gillanders, 2005; Lotze et al.,
2006). Since the 1990s, conservation efforts have focused on improving water quality through reduction of point and nonpoint pollution
(Crean et al., 2007; Windsor, 2007). Anthropogenic changes in estuarine water chemistry due to land use patterns have been documented
in several systems (Breuer et al., 1999; Otero et al., 2000; Jeong et al.,
2006), however less attention has been devoted to this in the Indian
River Lagoon system. Concurrent conservation and restoration efforts
have involved protecting critical habitats, removing exotic vegetation,
and reconnecting impounded or fragmented wetlands. These efforts
have resulted in habitat improvements, but new threats, such as the increase in invasive exotic plants and animals (Boudreaux and Walters,
Fig. 1. Location of sampling sites in the Indian River Lagoon basin (oval, double line), Banana River Lagoon basin (oval, dashed line), and Mosquito Lagoon basin (ovals, solid line).
Dark gray shading represents area of human density and urban/commercial development; dark green shading represents 2007 seagrass coverage; light green shading represents
areas of saltmarsh and mangrove habitat.
Data from SJRWMD, 2011.
D.H. Adams, R. Paperno / Science of the Total Environment 414 (2012) 445–455
2006; Irlandi et al., 2007; Paperno et al., 2008) and more frequent toxic
algal blooms (Phlips et al., 2002, 2011; Landsberg et al., 2006), are now
challenging efforts to conserve and restore essential habitat. Recent
technological developments and techniques (e.g., stable-isotope analysis) allow comparisons and monitoring of changes in certain environmental parameters that may result from impairment or restoration
(Jones et al., 2001; Piola et al., 2006; Wozniak et al., 2006).
We explored stable isotope signatures and mercury concentrations in a model predatory fish species within the Indian River lagoon
system, comprised of three distinct basins with divergent habitats
and associated biotic communities. We directly relate our results to
multiple physical, chemical, and biological endpoints in the system
that are readily transferable to other estuarine ecosystems. The spotted seatrout (Cynoscion nebulosus), found throughout the coastal waters of the southeastern United States, was selected as a model
species for this study because of its wide coastal distribution, its
range is usually restricted to its natal estuary and because its life history has been so thoroughly documented. Adult spotted seatrout are
opportunistic carnivores, feeding on a wide array of fish and macroinvertebrates (Appendix A; Darnell, 1958; Tabb, 1961; Patillo et al.,
1997; FWC–FWRI unpubl. data), and the broad range of potential
prey species and known habitat associations for this predator indicate
that optimum foraging is based on the relative abundance of available
potential prey (Gilmore, 2003).
This study used stable-isotope signatures and mercury analyses in
conjunction with comprehensive community composition and abundance data to address the following questions regarding the nekton
(fishes and macroinvertebrates) within a representative multibasin
estuarine system: 1) Are there differences in the isotopic signature
of spotted seatrout, a model predatory fish species, between the
three major basins of the Indian River Lagoon? 2) Are there differences in mercury concentration in spotted seatrout between the
three major basins of the Indian River Lagoon? 3) Are there differences in nekton composition and abundance between the three
major basins of the Indian River Lagoon that would suggest a spatially
varied prey base for spotted seatrout?
447
portions of the northern Indian River Lagoon 50% renewal through inlets can take as long as 230 d.
Sampling sites in the present study were confined to the northern
portion of the Indian River Lagoon system, in east-central Florida, and
were divided into distinct and representative areas in each of the
three major basins (Fig. 1). The Indian River Lagoon proper study
area near Melbourne (IRL) is characterized by a comparatively low
abundance and density of SAV, the highest degree of coastal development, human population density and shoreline hardening, two small,
nontidal estuarine tributaries, and reduced mangrove and adjacent
salt marsh habitat. Although limited SAV does exist within the IRL
study area, seagrass beds in this specific portion of the system are
reduced in size with comparatively very low density compared to
the other basins examined (Figs. 1 and 2; FWC-FWRI, unpublished
data; Virnstein et al., 2007). The Banana River Lagoon study area,
near the Kennedy Space Center (BRL) contains relatively dense and
widespread SAV, reduced shoreline development restricted to several
Kennedy Space Center facilities, no residential population density,
little shoreline hardening, and intermediate amounts of salt marsh
and mangrove habitat. The Mosquito Lagoon study area (ML) contains relatively dense and widespread SAV, no residential population
density, almost no development or shoreline hardening, scattered
mangroves, and the largest amount of adjacent salt marsh habitat
(Fig. 1; FWC–FWRI, unpublished data).
2. Methods
2.1. Study area
The Indian River Lagoon system is an extensive shallow estuarine
lagoon extending from Ponce de Leon Inlet (29° 05′N) south to Jupiter
Inlet (26° 50′N) and comprises three interconnected basins (Indian
River Lagoon proper, Banana River Lagoon, and Mosquito Lagoon)
(Fig. 1). The Indian River Lagoon proper connects to Mosquito Lagoon
through Haulover Canal (a 2 km long artificial canal completed in
1854) and to the Banana River Lagoon at the southern end of Merritt
Island and through the manmade Canaveral Barge Canal, which traverses central Merritt Island. Vegetation along the shorelines and
spoil islands is composed of cordgrasses (Spartina spp.), rushes
(Juncus spp.), glassworts (Salicornia spp.), saltwort (Batis maritima),
and mangroves (Rhizophora mangle, Avicennia germinans,
Laguncularia racemosa), among others, with relative composition, degree of shoreline hardening, and human population density varying
between basins (Adkins et al., 2004) (Fig. 1). All basins have substrates consisting of varying amounts of mud, sand, and shell hash,
along with submerged aquatic vegetation (SAV) consisting of several
species of seagrasses; predominant taxa are shoal grass (Halodule
wrightii), manatee grass (Syringodium filiforme), and widgeon grass
(Ruppia maritima). Channels within basins are generally not vegetated but often hold dense mats of drift algae (Gracilaria spp.). Tides are
microtidal, water levels fluctuate slowly, and water circulation is predominantly wind driven (Pitts, 1989), resulting in slow turnover and
long residence times in all basins. Smith (1993) estimated that in
Fig. 2. Average salinity, temperature (°C), dissolved oxygen (ppm), and percentage
seagrass cover at the Indian River Lagoon basin (IRL), Banana River Lagoon basin
(BRL), and Mosquito Lagoon basin (ML) sites, March–September 2007. Error bars represent ±1 SD.
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D.H. Adams, R. Paperno / Science of the Total Environment 414 (2012) 445–455
2.2. Water quality and nutrient loading data
Site-specific and long-term water quality data were utilized in an effort to provide comprehensive spatial data on the physicochemical
characteristics of the three basins in the study. Site-specific waterquality measurements were recorded with a YSI multiprobe and included water temperature (°C), salinity, pH, conductivity (mmhos cm− 1),
and dissolved oxygen (ppm). Water quality data were collected monthly as part of the Indian River Lagoon Water Quality Monitoring Network,
a cooperative, multiagency program managed by the St. Johns River
Water Management District (SJRWMD) (described in Steward et al.,
2003). Within each basin the long-term water quality stations were
located within approximately 2 nautical miles (nm) of the spotted
seatrout collections, and parameters presented here include turbidity
(ntu), chlorophyll a (μg l− 1), nitrogen Kjeldahl organic (TKNT mg l− 1), and dissolved organic carbon (DOC mg l − 1). Turbidity and
chlorophyll a were determined by laboratory analyses following standard EPA methodology (turbidity EPA 180.1, chlorophyll a SM10200H). Total nitrogen (TN) was estimated by combining TKN-T
values from an unfiltered sample with estimates of NOx (nitrate/nitrite), because working with the NOx species can be difficult as measured concentrations are often below detection levels and the
compounds are subject to rapid biological uptake (M. Lasi, SJRWMD
pers. comm.). Detail regarding these analyses can be found elsewhere
(Phlips et al., 2002, 2010).
2.3. Fish collection methods
Spotted seatrout samples used in the stable-isotope and mercury analyses were collected from the three basins of the Indian River Lagoon system (Fig. 1) from June through September 2007. Experimental research
gill nets were deployed in various seagrass habitats in ML and allowed
to soak for a minimum of 1 h. Monofilament gill net mesh size ranged
from 55 mm to 152 mm stretch mesh, with a depth of approximately
1.8 m and a total length of approximately 198 m. Hook-and-line methods
were used to supplement spotted seatrout collections in ML and were the
primary source of samples for IRL and BRL, gear types proven to be effective in these specific basins (Crabtree and Adams, 1998). Gill-net and
hook-and-line samples were collected during dawn, dusk and nighttime
hours, time periods known to be effective for capture of this species within these basins (Crabtree and Adams, 1998). To estimate fish age we removed, processed, and examined sagittal otoliths according to the
protocols of Murphy and Taylor (1994).
Stratified-random sampling (SRS) was conducted to provide comprehensive spatial data on fishes and macroinvertebrates in all three
basins and to provide prey base information and determine basinspecific community structure for the system. Prey base sampling
was conducted from March through September 2007. This sampling
window was chosen to control for temporal changes in the prey
base and to coincide with a timeframe in which prey signatures
could be effectively determined in the tissue of the predatory spotted
seatrout through stable-isotope analysis (MacNeil et al., 2006;
Buchheister and Latour, 2010). At each sampling location, which
was stratified based on the presence or absence of bottom vegetation
or the presence of a shoreline, a 21.3-m × 1.8-m nylon center-bag
seine (3.2-mm stretched mesh) was deployed in shallow water
(≤1.8 m depth). Individuals were identified in the field to the lowest
practical taxon. All samples were collected during daylight hours and
processed in the field. Only samples that were collected within a
range of 2 nm of the center of spotted seatrout collection sites in
each basin were used for prey base community comparisons to provide greater spatial relevance with regard to foraging areas during
the study period. Although data regarding spotted seatrout movement in this system are limited, conventional tagging and initial
acoustic telemetry data suggest that, although capable of moving
large distances, this species does not often make long-range
migrations in the lagoon during this time frame (Stevens and Sulak,
2001; Tremain et al., 2004; E.A. Reyier, pers. comm., 2011).
2.4. Sample preparation
Spotted seatrout used in the stable-isotope and mercury analyses
were placed on ice upon capture and returned to the laboratory for
further processing. White axial muscle tissue, sampled from the left
dorsal area in the region anterior to the origin of the dorsal fin and
above the lateral line, was carefully dissected using standardized procedures to preclude external contamination (Adams et al., 2003). Tissue samples were placed in sterile polyethylene sample containers
and stored frozen (at − 20 °C, for mercury samples or − 80 °C for stable isotope samples). Tissue samples for stable-isotope analysis were
processed at the University of Florida's Wetland Biogeochemical Laboratory in Gainesville, Florida. Following the methods described by
Harris et al. (2001), samples were lyophilized and finely ground in a
mortar and pestle. Samples were then treated by acid fumigation to
remove carbonates (non-dietary carbon; Riera et al., 1996). Carbon
and nitrogen isotopic ratios were determined using a Costech Model
4010 Elemental Analyzer (Costech Analytical Industries Inc., Valencia,
California, USA) coupled to a Finnigan MAT Delta PlusXL Mass Spectrometer (CF-IRMS, Thermo Finnigan, San Jose, California, USA) via a
Finnigan Conflo III interface. Ratios of C and N stable isotopes (Rsample)
were expressed as per mil (‰) differences from the standard (Rstd,
atmospheric N2 and Pee Dee Belemnite, respectively) using delta notation (δ) as: δsample = [(Rsample/Rstd) − 1]∗ 1000. Isotopic calibration was
accomplished throughout each analysis run using NIST standard
peach leaf (2.9% N, 44% C, δ15N = ~1.9‰, δ13C = ~ − 26.1‰) and resulting values adjusted for isotopic accuracy using international isotopic
standards (IAEA-N1, δ15N = 0.4‰; ANU-Sucrose, δ13C = −10.5‰).
Muscle samples analyzed for total mercury (Hg) were processed
using a Direct Mercury Analyzer (DMA-80, Milestone Inc., Shelton,
Connecticut, USA). This methodology is recognized by the U.S. EPA
(Method 7473) and is based on thermal decomposition of the sample
and collection of the Hg vapor on a gold amalgamator. Analytical accuracy was determined using standard reference materials (DOLT-3,
TORT-4, National Research Council of Canada), blanks, and analysis
of duplicate samples. Details regarding sample processing and standardized analyses have been published elsewhere (Nam et al., 2011).
2.5. Statistical analyses
Abundance estimates and frequency of capture of prey taxa were
summarized as the number of individuals/100 m 2 of area sampled.
Species-richness values were calculated by the rarefaction methods
of Ludwig and Reynolds (1988) and used to compensate for differences in sampling effort between basins. The rarified species richness
values were presented as the expected number of species (E.N.S.(n))
in a sample of N individuals generated for a constant number of individuals for sample sizes n5 to nN–max(ni), where N equals the total
number of animals and max(ni) equals the number of individuals of
the most abundant taxa collected.
To evaluate variation in forage communities between basins, we conducted multivariate analyses on basin data from March 2007 through
September 2007. Pairwise comparisons for differences in species assemblages and physical data between basins were made using analysis of
similarity (ANOSIM). Similarity percentage (SIMPER) was used to determine the similarity of samples and which taxa were responsible for differences between groups. Multidimensional scaling (MDS) was used to
compare community-level differences between basins. Multidimensional scaling analyses were conducted on Bray–Curtis similarities of
species abundance data (log [x +1] transformations) and Euclidean distance for fourth-root-transformed physical data. While Appendix A provides the entire breadth of potential prey taxa, only taxa known to be
prey of spotted seatrout (indicated in Appendix A) were used in the
D.H. Adams, R. Paperno / Science of the Total Environment 414 (2012) 445–455
community comparisons. The multivariate analyses and ordinations
were conducted using PRIMER (Clarke and Warwick, 1994).
We examined differences in total mercury concentration, fish size, and
age via Analysis of Covariance (GLM–ANCOVA) using the General Linear
Model procedure (SAS Version 9.2, SAS Institute Inc.). A Kruskal–Wallis
one-way Analysis of Variance on Ranks (K–W ANOVA) was used to examine differences in fish size and age between basins and seagrass coverage
between basins. Differences in stable isotopes between study areas were
tested using a one-way Analysis of Variance (ANOVA), and, when significant differences were detected (Pb 0.05), a Dunn's multiple range test
was used to identify specific differences.
3. Results
During the study the lowest mean salinity (24.0 ± 0.7) was recorded
in IRL, while the highest mean salinity (40.1 ± 0.4) was recorded in ML
(Fig. 2). Mean temperature ranged from 26.2 ± 0.8 °C in ML to 28.8 ±
0.6 °C in the IRL. Dissolved oxygen (DO) did not vary greatly between
basins, with mean DO ranging from 7.0 ± 0.3 ppm in ML to 7.4 ±
0.4 ppm in the IRL. The percentage of seagrass cover varied between basins and was greatest in BRL (62.5 ± 7.3%) followed by ML (52.2 ± 5.4%)
and IRL (29.7 ± 5.7%) (K–W ANOVA, p b 0.01).
Water quality data varied between basins (Fig. 3). Mean dissolved
organic carbon was greatest in BRL (16.0 ± 0.2 mg l − 1), intermediate
in IRL (10.6 ± 0.3 mg l − 1), and least in ML (8.3 ± 0.2 mg l − 1). Total
Fig. 3. Average dissolved organic carbon (DOC), total nitrogen (TN), chlorophyll a
(Chl a) and turbidity at the Indian River Lagoon basin (IRL), Banana River Lagoon
basin (BRL) and Mosquito Lagoon basin (ML) sites, March–September 2007. Error
bars represent ±1 SD.
449
nitrogen was greatest in IRL (1.4 ± 0.03 mg l − 1), intermediate in BRL
(1.0 ± 0.03 mg l − 1), and least in ML (0.9± 0.04 mg l − 1). Chlorophyll
a was greatest in IRL (5.5 ± 0.7 μg l− 1), intermediate in BRL
(BRL = 3.3 ± 0.3 μg l− 1) and least in ML (2.7± 0.2 μg l− 1). Turbidity
was greatest in ML (4.7± 0.4 ntu), intermediate in IRL (3.9 ± 0.3 ntu),
and least in BRL (3.1± 0.2 ntu).
3.1. Spotted seatrout collection data
A total of 123 mature spotted seatrout were collected for this
study from June through September 2007 (IRL n = 28; BRL n = 50;
ML n = 45). Spotted seatrout from ML were significantly older and
larger than those from the other two basins (K–W ANOVA, p b 0.05).
To account for age- and size-related variation, we removed the oldest
and largest specimens (n = 4 for ML; n = 2 for BRL) and only used age
0–2 fish. When age 0–2 fish were analyzed, there were no significant
differences in fish age among basins (K–W ANOVA, p > 0.05). In subsequent analyses, the age-standardized data set was used to compare
levels of stable isotopes between basins.
3.2. Stable-isotope and mercury results
Carbon isotopic composition in spotted seatrout varied by basin
(Fig. 4). Spotted seatrout from IRL were carbon-depleted compared
with seatrout from the other basins examined. Overall δ 13C values
for spotted seatrout in the system ranged from − 20.2 to −12.4‰.
In IRL δ 13C values (mean δ 13C = −18.2 ± 0.83 SD ‰) were significantly less than those in BRL (mean δ 13C = − 15.3 ± 1.08‰) or ML
(mean δ 13C = − 14.2 ± 1.09‰) (ANOVA, p b 0.001).
Spotted seatrout from IRL were nitrogen-enriched compared with
seatrout from the other basins. Overall δ 15N values for spotted seatrout in the system ranged from 9.7 to 14.8‰. The δ 15N values in IRL
(mean δ 15N = 13.01 ± 1.03‰) were significantly greater than those
in ML (mean δ 15N = 11.4 ± 0.51‰) or BRL (mean δ 15N = 11.1 ±
0.48‰) (ANOVA, p b 0.001). We found no significant size-, age- or
sex-mediated differences in δ 15N or δ 13C in spotted seatrout within
the study area. Total mercury concentrations were significantly greater in ML fish than in fish from other basins when age and size were
accounted for in the model (GLM–ANCOVA, p b 0.05). Overall mean
Fig. 4. Stable-carbon and stable nitrogen isotope values and total mercury concentrations
(mg/kg) of spotted seatrout, Cynoscion nebulosus, from the Indian River Lagoon basin (diamonds), Banana River Lagoon basin (squares) and Mosquito Lagoon basin (circles).
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D.H. Adams, R. Paperno / Science of the Total Environment 414 (2012) 445–455
Table 1
Mean δ13C and δ15N isotope values in spotted seatrout, Cynoscion nebulosus, from waters of the southeastern United States.
δ13C
Matagorda Bay, Texas (n = 8)
Barataria Bay, Louisiana (n = 110)
Barataria Bay, Louisiana, mud bottom (n = 32)
Barataria Bay, Louisiana, artificial reef (n = 57)
Florida Bay, Florida, northeastern (n = 36)
Florida Bay, Florida, southwestern (n = 34)
Indian River Lagoon proper, Florida (n = 28)
Banana River Lagoon, Florida (n = 50)
Mosquito Lagoon, Florida (n = 45)
δ15N
Reference
Tissue
Mean
Standard deviation
Mean
Standard deviation
Muscle
Muscle
Muscle
Muscle
Muscle
Muscle
Muscle
Muscle
Muscle
− 19.40
− 20.73
− 19.51
− 19.93
−12.67
− 12.34
− 18.16
− 15.35
−14.18
0.60
13.50
13.68
14.21
14.11
15.62
12.10
13.01
11.07
11.39
2.20
1.55
1.06
0.84
1.08
1.09
0.99
1.23
1.03
0.48
0.51
Winemiller et al., 2007
MacRae, 2006
Simonsen, 2008
Simonsen, 2008
Evans and Crumley, 2005
Evans and Crumley, 2005
This study
This study
This study
mercury concentrations were greatest in ML fish (0.33 ±0.14 mg/kg),
with lower concentrations in BRL (mean = 0.24 ±0.12 mg/kg) and IRL
(mean = 0.18 ± 0.07 mg/kg) (Fig. 4). Age- and size-adjusted least
squares means for total mercury concentrations were 0.28 mg/kg
for ML fish, 0.26 mg/kg for BRL, and 0.20 mg/kg for IRL. Although significant differences were detected, overall mercury concentrations in
spotted seatrout among basins were relatively low.
BRL was less diverse, particularly in regards to specific seagrassassociated and salt marsh taxa (pigfish, pinfish, sailfin molly, and
sheepshead minnow, Cyprinodon variegatus). Both of these basins
were characterized by rainwater killifish, goldspotted killifish, and a
lower abundance of gerreids (mojarras) relative to the IRL.
3.3. Analyses of prey base communities
Since mercury, carbon and nitrogen are integrated into fish tissue
largely via dietary pathways (Hall et al., 1997; Trudel and Rasmussen,
2001; West et al., 2006), stable-isotope analysis can provide useful information regarding variability in mercury concentrations with regard to these pathways and routes of exposure (Jardine et al.,
2006). Differences in mercury concentrations observed between Indian River Lagoon basins may suggest differences in spotted seatrout
A total of 41,932 fish and macroinvertebrates representing 71 species were collected from 155 sampling events during the study period
(Table 2; Appendix A). In general, the community was found to be dominated by several taxa (rainwater killifish, Lucania parva; silversides,
Menidia spp.; bay anchovy, Anchoa mitchilli; and goldspotted killifish,
Floridichthys carpio). While these taxa dominated the overall catch,
they varied in abundance and frequency of occurrence between basins.
Species richness also varied between basins, with IRL being the most diverse (E.N.S.3400 = 53) and BRL the least diverse (E.N.S.3400 = 24).
Multivariate analyses of the community data indicate that there
were community differences among basins (ANOSIM, R = 0.444,
p b 0.001) (Fig. 5). Pairwise results of the fish community data indicated
that there were significant differences between IRL and both of the
other basins (ANOSIM(IRLvBRL), R = 0.655, p b 0.01; ANOSIM(IRLvML),
R = 0.743, p b 0.01) but not between BRL and ML (ANOSIM(BRLvML),
R = 0.111, p = 0.06). The extent of community dissimilarity between
basins was greater when BRL and ML were compared with IRL (SIMPER,
77.7%–80.1%) than between each other (SIMPER, 50.6%). The taxa most
responsible for distinguishing IRL from the other areas in the SIMPER
analysis were bay anchovy, code gobies (Gobiosoma robustum) and, to
a lesser degree, scaled sardine (Harengula jaguana) and leatherjack
(Oligoplites saurus). Bay anchovy, scaled sardine and leatherjack are all
pelagic fish species. Additionally, lower abundance or absence of taxa
associated with salt marshes (sailfin molly, Poecilia latipinna; goldspotted killifish; Gulf killifish, Fundulus grandis) or seagrass (pinfish,
Lagodon rhomboides; pigfish, Orthopristis chrysoptera; and rainwater
killifish) in IRL contributed to the differences (Appendix A). Although
the composition of the BRL and ML communities was relatively similar,
4. Discussion
Table 2
Summary of catch (total fish and macroinvertebrates) and effort (number of seine
hauls) data by basin and strata (vegetated = seagrass >10% spatial coverage; unvegetated = seagrass b 10% spatial coverage; shoreline = land/water interface).
Strata
Vegetated
Unvegetated
Shoreline
Totals
Indian River
Lagoon
Banana River
Lagoon
Mosquito
Lagoon
Totals
Catch
Hauls
Catch
Hauls
Catch
Hauls
Catch
Hauls
803
378
2297
3478
5
8
20
33
5444
436
5419
11,299
9
4
13
26
12,041
459
14,655
27,155
43
8
45
96
18,288
1273
22,371
41,932
59
20
76
155
Fig. 5. Multidimensional scaling of community and available prey base data. Triangles
represent Indian River Lagoon sites (IRL), diamonds represent Banana River Lagoon
sites (BRL) and circles represent Mosquito Lagoon sites (ML). Stress coefficients of
b0.2 indicate acceptable goodness of fit of sample relationships.
D.H. Adams, R. Paperno / Science of the Total Environment 414 (2012) 445–455
feeding ecology, differential habitat use, differences in available mercury, methylation rates, mercury sources, or a combination of these
factors. Variability in stable isotopes has been used to detect differences in habitat use and feeding ecology of fishes in estuarine systems
(Deegan and Garritt, 1997; Fry, 2002; Litvin and Weinstein, 2004). Of
particular interest are those studies that have used stable isotopes to
examine how trophic ecology changes in natural habitats as compared with disturbed habitats or in response to restoration efforts
(Wozniak et al., 2006; Adams et al., 2009). The enrichment of δ 13C
in spotted seatrout collected in ML and BRL may be related to a greater influence of either seagrass-linked prey items or benthic microalgae as a carbon source in the diet of those spotted seatrout.
Seagrasses and benthic microalgae are both enriched in δ 13C (approximately −14 to − 13 and −17‰, respectively) relative to phytoplankton (−21‰) (Peterson and Fry, 1987; Deegan and Garritt,
1997; Winemiller et al., 2007). There is more available seagrass habitat, greater overall seagrass density and more mangrove–salt marsh
habitat in BRL and ML than in IRL study areas (Figs. 1 and 2 FWC–
FWRI, unpubl. data; Sigua et al., 2000; Steward et al., 2005). This is
clearly reflected in the fish species communities in these basins, dominated in ML and BRL by seagrass and mangrove–salt marsh taxa. The
community differences in the basin may be due, in part, to the difference in availability of mangrove–salt marsh and seagrass habitat.
Stevens et al. (2006) demonstrated that many taxa utilize salt
marsh habitats during high-water periods for food and refuge. Since
salt marsh habitats are lacking at the IRL site, differences in habitat resources may contribute to the geographic differences in the prey
community observed in this study.
In IRL, where there are fewer seagrass and mangrove–salt marsh
influences than in BRL and ML, phytoplankton may be a more important source of carbon to spotted seatrout. Salt marsh habitats
have been shown to be sources of available mercury due to environmental conditions (i.e., low DO, low pH, high DOC, and the presence
of sulfate-reducing bacteria) conducive to its methylation (Campbell
et al., 2004; Chumchal et al., 2008). Since spotted seatrout in the IRL
presumably feed on more pelagic prey species not closely linked to
benthic seagrass and mangrove–salt marsh habitats, they may not
be influenced so directly by these sources of mercury. Preliminary
observations of the stomach contents of spotted seatrout in this region included pelagic fish species (e.g., redfin needlefish [Strongylura
notata] and leatherjack) and epibenthic species not always directly
associated with seagrass or mangrove–salt marsh habitats (e.g.,
silver perch, Bairdiella chrysoura). In addition, the significant enrichment of δ 15N in fish collected in IRL further supports this conclusion; relatively enriched δ 13C isotope values and reduced δ 15N
isotope values suggest use of more benthic-based prey, and reduced
δ 13C isotope values and enriched δ 15N values indicate use of more
planktonic- or pelagic-based prey (Peterson et al., 1985; Currin et
al., 1995; Fry et al., 2008).
Many pelagic and cryptic species (e.g., bay anchovy, scaled sardine
and code goby) common in the IRL and seagrass-related species (e.g.,
pigfish and pinfish) more common in the other two basins are known
to contain very low total mercury concentrations (Adams et al., 2003,
FWC–FWRI, unpublished data). However, some epibenthic prey species that are more commonly encountered in IRL and ML (e.g., silver
perch) do frequently contain comparatively high mercury concentrations (Adams et al., 2003, FWC–FWRI, unpubl. data).
Spotted seatrout from IRL contained the greatest δ 15N values of
the three basins examined. Upper-level consumers typically have
higher δ 15N values than consumers closer to the base of the food
web in a given system (Post, 2002; Jardine et al., 2006; Layman
et al., 2007). Although the δ 15N values of spotted seatrout from IRL
were greater than those from BRL or ML, it is not clear whether
the difference indicates slightly different trophic positioning between these basins. Stable nitrogen isotopes can also be used to determine nutrient loading linkages between land-based sources and
451
adjacent estuarine systems that are indicative of land use (e.g.,
human activity, industrial or residential development) (Cole et al.,
2004; Olsen et al., 2010). Increased δ 15N values in consumers and
producers as a result of increased urbanization have been observed
for other systems (McClelland et al., 1997; McClelland and Valiela,
1998; Martinetto et al., 2006; Yamamuro et al., 2003; Tewfik et al.,
2007). More specifically, estuarine fish δ 15N values were shown to
increase along a gradient of increasing residential development
and increasing human population density (Bannon and Roman,
2008). The greater δ 15N values in spotted seatrout in IRL may also
reflect more intensive development, urbanization, groundwater
seepage, human population density, the presence of adjacent small
riverine/creek systems, or observed greater total nitrogen levels in
IRL (Fig. 3; Lindenberg, 2001; Steward et al., 2003). Exploratory analyses with red drum (Sciaenops ocellatus), another sciaenid from
the Indian River Lagoon system, also found δ 15N in dorsal muscle
was significantly higher in the IRL than in the BRL (U.S. EPA and
FWC–FWRI, unpublished data).
Studies from other estuarine/marine systems found similar isotopic signatures for spotted seatrout (Table 1). In comparison with
past studies, spotted seatrout from IRL were most closely aligned
with systems that had reduced/seasonally present SAV habitats or
areas devoid of SAV. For example, spotted seatrout from unvegetated mud substrate and artificial reef habitats in Louisiana and
from a salt marsh-dominated study site in Matagorda Bay, Texas,
which had only limited seasonal SAV coverage (Winemiller et al.,
2007), had δ 13C and δ 15N values similar to those in fish from IRL
(Table 1). Probable sources of carbon and energy at the base of
the food web in this Texas estuary were salt marsh grasses, phytoplankton, and benthic microalgae (Winemiller et al., 2007). The
IRL sampling area in the present study also lacks comparatively
high abundance of SAV and adjacent mangrove–salt marsh habitats
compared with BRL and ML. It is likely that the dominant aquatic
primary producers in this portion of the lagoon system are phytoplankton or benthic microalgae, which may serve as the principle
sources of carbon and energy in IRL. Isotope signatures in spotted
seatrout from BRL and ML were most closely aligned with southwestern Florida Bay (Evans and Crumley, 2005), an area dominated
by expansive seagrass meadows.
5. Conclusions
Although interconnected, the three basins within the study area
contain noticeably different habitat types with distinctly different
faunal communities and available prey bases for spotted seatrout.
Concurrently, the overall water quality, degree of urbanization,
human population density and associated nitrogen enrichment of
these three basins are clearly different. These differences can influence the feeding ecology, resultant mercury concentrations, and
stable-isotope signatures of spotted seatrout between basins.
While overall concentrations of mercury were relatively low, and
not highly variable, content in fish was greatest in ML and least in
IRL. Carbon and nitrogen isotopic compositions in spotted seatrout
varied with regard to specific basins within the lagoon system. Spotted seatrout from IRL were carbon-depleted and nitrogen-enriched
compared with spotted seatrout from the other basins examined, either suggesting that the primary source of carbon is an algae- or
phytoplankton-based food web or indicating a shorter pathway
through the food web. If the habitat characteristics are examined
more closely, IRL can be characterized by a comparatively low density of SAV, reduced mangrove–salt marsh habitat, a relatively high
degree of coastal development, increased human population density
and more extensive shoreline hardening than the other basins in the
study. The fish and invertebrate communities and prey base for
spotted seatrout also differ from those at the other two basins,
where seagrass- and mangrove–salt marsh-associated species
452
D.H. Adams, R. Paperno / Science of the Total Environment 414 (2012) 445–455
dominate. These factors, coupled with the reduced δ 13C isotope
values and enriched δ 15N isotope values, further indicate a more
planktonic- or pelagic-based food web and greater use of more pelagic prey than seagrass-associated prey by spotted seatrout in the
IRL system. Although we did not quantitatively examine fish stomach contents, the δ 15N values of IRL spotted seatrout were greater
than those in BRL or ML, suggesting the possibility of slightly different trophic positioning of fish from different basins. The greater δ 15N
values in IRL spotted seatrout may also reflect the more intensive
human population density and resultant anthropogenic inputs and
the observed higher total nitrogen levels in these waters compared
to the other more pristine basins examined.
The overall habitat heterogeneity that exists within estuarine systems and the basin-specific differences in stable isotope, mercury and
prey communities observed in this study suggest that small-scale spatial variation may be more common than previously understood for
this system. Monitoring of nutrients, contaminants and ecological
communities needs to consider the complexity of the system in
order to effectively assess system functioning. Future work would
benefit from the inclusion of detailed dietary analyses from spotted
seatrout and expansion to other apex predators, mid-level predators
and lower-level consumers that make use of isotopic signatures
from a broad range of macrophytes, fish and invertebrate prey for a
more comprehensive system-wide analysis.
Acknowledgments
This project was supported in part by funding from the St. Johns
River Water Management District and in part by funding from the Department of the Interior, U.S. Fish and Wildlife Service, Federal Aid for
Sportfish Restoration Project Number F-43.
We thank personnel at the Florida Fish and Wildlife Conservation
Commission's Indian River Field Laboratory and many county, state,
and federal agency volunteers for their assistance in collecting and
processing data for this study. We would also like to thank R. Brockmeyer and M. Lasi at the St. Johns River Water Management District
for their support of the stable-isotope work and for providing the
nutrient data from the study area, and P. Inglett and K. Sharma at
the University of Florida for processing spotted seatrout tissue for
stable-isotope analysis, as well as D. Chagaris and the FWRI
Fisheries-Independent Monitoring Program Gut Lab for data regarding spotted seatrout diet. We appreciate helpful comments from C.
Guenther, G. Onorato, T. Switzer, M. Tyler-Jedlund, B. Crowder, and 2
anonymous reviewers which greatly improved this paper.
Appendix A.
Species abundance and % occurrence for all three sampling areas in the northern Indian River Lagoon system, March–September, 2007. Effort, or the total number of seine hauls, is
labeled 'E'. Taxa are arranged alphabetically. * = observed in the diet of adult spotted seatrout from Florida waters (FWC-FWRI unpublished diet data). E.N.S.3400 represents the
rarified species richness of a sample with 3400 individuals.
Sub-basin
Indian River Lagoon
N (% Occurrence)
E = 33
Achirus lineatus
Albula vulpes
Anchoa hepsetus*
Anchoa mitchilli*
Archosargus probatocephalus
Ariopsis felis
Bairdiella chrysoura*
Brevoortia spp.*
Callinectes sapidus*
Callinectes similis
Caranx hippos
Centropomus undecimalis
Chaetodipterus faber
Chasmodes saburrae
Chilomycterus schoepfii
Citharichthys spilopterus
Ctenogobius boleosoma
Ctenogobius spp.
Cynoscion nebulosus*
Cynoscion spp. *
Cyprinodon variegatus*
Dasyatis sabina*
Diapterus auratus
Elops saurus
Eucinostomus harengulus*
Eucinostomus gula*
Eucinostomus spp.*
Eugerres plumieri *
Evorthodus lyricus
Farfantepenaeus duorarum *
Farfantepenaeus spp.*
2
7
8
1008
29
2
261
2
1
0
2
2
6
8
0
0
0
0
7
1
4
2
486
0
170
13
250
1
0
8
2
Banana River Lagoon
N (% Occurrence)
E = 26
(0.1)
(0.2)
(0.2)
(29.0)
(0.8)
(0.1)
(7.5)
(0.1)
(b 0.1)
.
(0.1)
(0.1)
(0.2)
(0.2)
.
.
.
.
(0.2)
(b 0.1)
(0.1)
(0.1)
(14.0)
.
(4.9)
(0.4)
(7.2)
(b 0.1)
.
(0.2)
(0.1)
0
0
1
30
1
2
8
1
0
0
0
1
1
9
1
0
0
0
4
0
13
3
0
0
71
0
28
0
1
0
0
Mosquito Lagoon
N (% Occurrence)
Totals
E = 96
.
.
(b 0.1)
(0.3)
(b 0.1)
(b 0.1)
(0.1)
(b 0.1)
.
.
.
(b 0.1)
(b 0.1)
(0.1)
(b 0.1)
.
.
.
(b 0.1)
.
(0.1)
(b 0.1)
.
.
(0.6)
.
(0.2)
.
(b 0.1)
.
.
1
0
425
3110
23
5
308
65
13
35
0
0
1
5
2
1
5
3
81
0
645
2
71
1
79
37
381
0
0
74
175
E = 155
(b 0.1)
.
(1.57)
(11.45)
(b 0.1)
(b 0.1)
(1.13)
(0.24)
(0.05)
(0.13)
.
.
(b 0.1)
(b 0.1)
(b 0.1)
(b 0.1)
(b 0.1)
(b 0.1)
(0.30)
.
(2.38)
(b 0.1)
(0.26)
(b 0.1)
(0.29)
(0.14)
(1.4)
.
.
(0.3)
(0.6)
3
7
434
4148
53
9
577
68
14
35
2
3
8
22
3
1
5
3
92
1
662
7
557
1
320
50
659
1
1
82
177
(continued on next page)
D.H. Adams, R. Paperno / Science of the Total Environment 414 (2012) 445–455
453
Appendix
(continued)
(continued)
Sub-basin
Indian River Lagoon
N (% Occurrence)
E = 33
Banana River Lagoon
N (% Occurrence)
E = 26
Mosquito Lagoon
N (% Occurrence)
Totals
E = 96
E = 155
Floridichthys carpio *
Fundulus grandis*
Gambusia holbrooki
Gerres cinereus
Gobiesox strumosus
Gobiosoma robustum *
Gobiosoma spp. *
Harengula jaguana *
Hippocampus zosterae
Hyporhamphus meeki
Hyporhamphus unifasciatus
Lagodon rhomboides*
Leiostomus xanthurus*
Limulus polyphemus
Litopenaeus setiferus*
Lucania parva*
Lutjanus griseus*
Lutjanus synagris*
Membras martinica*
Menidia spp.*
Menticirrhus americanus*
Microgobius gulosus*
Micropogonias undulatus *
Mugil cephalus*
Mugil curema*
Oligoplites saurus*
Opisthonema oglinum
Opsanus tau*
Orthopristis chrysoptera*
Paralichthys albigutta
Poecilia latipinna
Prionotus scitulus*
Sarotherodon melanotheron
Sciaenops ocellatus
Selene vomer
Sphoeroides nephelus
Sphoeroides testudineus
Strongylura marina
Strongylura notata *
Strongylura spp.
Syngnathus louisianae *
Syngnathus scovelli *
Trachinotus carolinus
Trachinotus falcatus*
Trinectes maculatus
10
1
0
1
1
83
66
44
0
2
0
8
0
0
0
41
1
0
2
655
11
40
0
0
7
13
48
1
2
0
0
2
0
4
1
5
1
1
90
0
3
34
5
12
1
(0.3)
(b 0.1)
.
(b 0.1)
(b 0.1)
(2.4)
(1.9)
(1.3)
.
(0.1)
.
(0.2)
.
.
.
(1.2)
(b 0.1)
.
(0.1)
(18.8)
(0.3)
(1.2)
.
.
(0.2)
(0.4)
(1.4)
(b 0.1)
(0.1)
.
.
(0.1)
.
(0.1)
(b 0.1)
(0.1)
(b 0.1)
(b 0.1)
(2.6)
.
(0.1)
(1.0)
(0.1)
(0.3)
(b 0.1)
974
0
0
0
0
9
1
0
1
17
1
1
17
0
0
5155
0
0
0
4452
0
291
0
0
7
6
0
0
0
0
5
0
2
0
0
4
0
1
134
3
2
41
0
0
0
(8.6)
.
.
.
.
(0.1)
(b 0.1)
.
(b 0.1)
(0.2)
(b 0.1)
(b 0.1)
(0.2)
.
.
(45.6)
.
.
.
(39.4)
.
(2.6)
.
.
(0.1)
(0.1)
.
.
.
.
(b 0.1)
.
(b 0.1)
.
.
(b 0.1)
.
(b 0.1)
(1.2)
(b 0.1)
(b 0.1)
(0.4)
.
.
.
3114
109
8
0
0
238
54
229
5
1
1
510
329
1
40
8612
7
2
48
6280
0
1095
6
22
19
6
3
0
283
5
297
0
0
0
0
10
0
0
44
10
2
215
0
2
0
(11.5)
(0.4)
(b 0.1)
.
.
(0.9)
(0.2)
(0.8)
(b 0.1)
(b 0.1)
(b 0.1)
(1.9)
(1.2)
(b 0.1)
(0.1)
(31.7)
(b 0.1)
(b 0.1)
(0.2)
(23.1)
.
(4.0)
(b 0.1)
(0.1)
(0.1)
(b 0.1)
(b 0.1)
.
(1.0)
(b 0.1)
(1.1)
.
.
.
.
(b 0.1)
.
.
(0.2)
(b 0.1)
(b 0.1)
(0.8)
.
(b 0.1)
.
4098
110
8
1
1
330
121
273
6
20
2
519
346
1
40
13,808
8
2
50
11,387
11
1426
6
22
33
25
51
1
285
5
302
2
2
4
1
19
1
2
268
13
7
290
5
14
1
Total no. of individuals
Total no. of species
E.N.S.3400
3478
53
53
100
11,299
34
24
100
27,155
57
42
100
41,932
71
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