Science of the Total Environment 414 (2012) 445–455 Contents lists available at SciVerse ScienceDirect Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv 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: Doug.Adams@MyFWC.com (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). 446 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. 448 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). 450 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) . 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