Southeastern Naturalist G93 C.T. Peterson, R.D. Grubbs, and A. Mickle 22001177 SOUTHEASTERN NATURALIST 16(1V)o:lG. 9136–, GN1o0. 81 An Investigation of Effects of the Deepwater Horizon Oil Spill on Coastal Fishes in the Florida Big Bend Using Fishery-Independent Surveys and Stable Isotope Analysis Cheston T. Peterson1,*, R. Dean Grubbs2, and Alejandra Mickle3 Abstract - We employed catch data from 2 fishery-independent shark surveys conducted from 2009–2012, as well as stable isotope analysis, to investigate potential effects of the 2010 Deepwater Horizon oil spill on large coastal fishes in Florida’s Big Bend region. The catch-per-unit-effort of 5 indicator species (3 sharks and 2 teleosts) varied significantly among years in only 2 cases. The stable isotope profiles were significantly different among years in 2 of 5 indicator species, but the relative differences were small, and patterns were not consistent among taxa analyzed. Our results provide no evidence that the Deepwater Horizon oil spill had a significant effect on the relative abundances and food-web structure among large coastal fishes in Florida’s Big Bend. Introduction The 2010 Deepwater Horizon (DWH) oil spill released ~4.4 million barrels of oil into the Gulf of Mexico (Crone and Tolstoy 2010, Mariano et al. 2011, Peterson et al. 2012). Given the magnitude of this event, it is important to investigate and monitor the potential effects of such a large introduction of oil on marine fauna through either direct exposure or a series of indirect effects. Fodrie et al. (2014) reviewed published studies examining the effects of oil exposure from the DWH spill on estuarine fishes and reported that, whereas numerous studies showed negative organism-level effects, no measurable negative effects have been found at the population level (e.g., Able et al. 2014, Fodrie and Heck 2010, Moody et al. 2013). More recently, Schaefer et al. (2016) reported minimal effects of DWH oil in nearshore fish assemblages in Mississippi and attributed changes in community assembly to reduced fishing pressure due to fishing closures related to the o il spill. Fishery-independent surveys are useful for delineating habitat use of fishes and connecting patterns of environmental parameters with species distributions (e.g., Drymon et al. 2010, Froeschke et al. 2010). Data from long-term surveys can also be used to investigate potential effects of a disturbance such as an oil spill, which include changes in abundance of common species (Sánchez et al. 2006), assuming data have been collected before and after the event. This potential makes long-term monitoring valuable, and surveys such as the present study also allow for the collection of samples to aid in the investigation of other effects of the disturbance, such as those on trophic structure or physiology. 1Florida State University, 319 Stadium Drive, Tallahassee, FL 32306. 2Florida State University Coastal and Marine Laboratory, 3618 Highway 98, St. Teresa, FL 32358. 3US Fish and Wildlife Service, DWH NRDAR Field Office, 341 Greeno Road North, Suite A, Fairhope, AL 36532.*Corresponding author - cpeterson@bio.fsu.edu. Manuscript Editor: R. Eugene Turner Southeastern Naturalist C.T. Peterson, R.D. Grubbs, and A. Mickle 2017 Vol. 16, No. 1 G94 Stable isotope analysis is a common technique used to study trophic ecology, especially in large species that are logistically challenging to sample, or to investigate the relative trophic structure of several species within a given system. One can use ratios of heavy and light isotopes of carbon (δ13C) and nitrogen (δ15N) to estimate an individual’s trophic level and dietary carbon source relative to other individuals of the same or other species from the same system (Peterson and Fry 1987, Post 2002). This technique provides a relative measure of trophic position over an integrated time scale (as opposed to the temporal “snapshots” of traditional diet analyses) and allows one to trace the flow of energy through the trophic web of an ecological system (Kling et al. 1992, Pinnegar and Polunin 1999). Stable isotope analyses of plankton communities following the DWH were used to investigate the degree to which oil had entered the plankton community in the northern Gulf of Mexico, albeit to contrary conclusions (Fry and Anderson 2014, Graham et al. 2010), and has been employed to investigate effects of spilled oil in seabirds and fishes (Quintana- Rizzo et al. 2015, Sanpera et al. 2008, Tarnecki and Patterson 2015). While trophic shifts inferred from stable isotope analysis related to the DWH oil spill have not been reported in coastal fish communities of the Gulf of Mexico, they have been observed and attributed to the spill in mesopelagic fishes and shrimps (Quintana- Rizzo et al. 2015) and Lutjanus campechanus (Poey) (Red Snapper) occupying offshore reefs in the northern Gulf of Mexico (T arnecki and Patterson 2015). In the present study, we employed data from long-term fishery-independent gillnet and longline surveys to investigate potential changes in abundance and distribution of elasmobranchs and relatively large teleost fishes in Florida’s Big Bend in relation to the 2010 DWH oil spill in the Gulf of Mexico. Although the surface oil slick of the DWH spill did not reach the coastal waters of the Big Bend (Hénaff et al. 2012), evidence exists that some oil was entrained into the Gulf of Mexico Loop Current bringing it to the west Florida continental shelf (Liu et al. 2011, Weisberg et al. 2016) and close to the Big Bend. Anecdotal reports suggested an increase in abundance in the Big Bend region of sharks thought to have been extirpated either from oiled coasts in the northern Gulf of Mexico or offshore habitats exposed to oil entrained in the Gulf of Mexico Loop Current. We compared catch-per-unit-effort (CPUE) across 4 years (before, during, and following the spill) for a set of abundant and widely distributed indicator species (3 species of sharks: Carcharhinus limbatus Müller and Henle [Blacktip Shark], Rhizoprionodon terraenovae Richardson [Atlantic Sharpnose Shark], and Sphyrna tiburo L. [Bonnethead Shark]; 2 species of ariid catfishes: Ariopsis felis L. [Hardhead Catfish] and Bagre marinus Mitchill [Gafftopsail Catfish]) to test for patterns in catch rates before and after the spill and evaluate anecdotal accounts of increased shark abundance in the Big Bend region after the DWH spill. Additionally, we compared stable isotope data for these indicator species across all 4 survey years as a method to elucidate potential long-term, indirect trophic effects that could be related to the spill (e.g., a shift from pelagic to benthic primary production detected through a change in δ13C ratios). Such a shift would be expected to occur if spilled oil altered the proportions of prey supported by different Southeastern Naturalist G95 C.T. Peterson, R.D. Grubbs, and A. Mickle 2017 Vol. 16, No. 1 primary productivity pathways through disproportional effects either on prey directly or indirectly mediated through disruption of the production pathway itself (Quintana-Rizzo et al. 2015, Tarnecki and Patterson 2015). As noted by Sánchez et al. (2006) and Peterson et al. (2003), chains of indirect effects resulting from an oil spill can be influential in population-level changes. Our indicator species are mainly generalist predators occupying a range of trophic levels and feed on species supported by a variety of production pathways (with the exception of Bonnethead Sharks, which specialize on Portunid crabs). Hardhead Catfish, Bonnethead Sharks, and Atlantic Sharpnose Sharks occupy relatively lower trophic levels, feeding on panopeid and portunid crabs, stomatopods, penaeid shrimp, and small teleost fishes. Gafftopsail Catfish and Blacktip Sharks occupy relatively higher trophic levels feeding on a higher proportion of teleost fishes and small elasmobranch, in the case of Blacktip Sharks (Bethea et al. 2004, Cortes 1999, Cortes et al. 1996, Holdridge 2013). We would expect Bonnethead and Atlantic Sharpnose Sharks to feed outside the Big Bend when they emigrate from the system during the winter, and Blacktip Sharks to feed elsewhere in the northeastern GOM during their longshore migration. While we expect that both species of catfishes are less mobile over limited temporal scales, annual movement patterns of these species are poorly understood. These taxa, as generalist predators, are fitting for examining trophic shifts because they feed opportunistically and their diet should be representative of the available prey base. Field Site Description Our survey was conducted along the 300-km stretch of northwest Florida coastline known as the “Big Bend” extending from Apalachee Bay to Anclote Key (Fig. 1). A large, nearly continuous seagrass bed extends throughout this coastline, which is bordered by hard bottom and sponge-reef habitats. We primarily targeted seagrass habitats for the purposes of this survey; however, habitats with sand or mud bottom and occasionally hard bottom or reef were also sampled. Our survey area is divided into 4 regions, based primarily on field logistics and zoogeographic breaks. Beginning with the northwestern-most region, these were: St. Marks, Steinhatchee, Crystal River, and Hernando. The central regions, Steinhatchee and Crystal River, are the most river-influenced, while St. Marks and especially Hernando are less river-influenced. The southernmost region has notably higher salinity and water clarity (Zieman and Zieman 1989), which is thought to culminate in a relatively unique southern faunal zone in the Big Bend (Pe terson 2014). Methods Survey design We employed a spatially balanced, random sampling design in our survey using the function ‘GRTS’ in the ‘spsurvey’ package for the R console (Kincaid and Olsen 2012, Stevens and Olsen 2004) in the Comprehensive R Archive Network site (CRAN-http://cran.r-project.org/). We used this program to generate 140 stations Southeastern Naturalist C.T. Peterson, R.D. Grubbs, and A. Mickle 2017 Vol. 16, No. 1 G96 across our sampling area, at least 40 of which we chose for sampling each year based on sampling coverage, past stations, logistics, habitat type, and bathymetry. We attempted to sample evenly across our entire survey area, and sampled at least 10 stations within each survey region per year. Sampling stations are depicted in Figure 1. Sampling was conducted during the summer months (June–August) from 2009 to 2012. We sampled using an 8-m research vessel specifically outfitted for our gear. We employed 2 gear types: an experimental gillnet and an experimental longline; both were fished concurrently for 1 hour at each station sampled. The experimental gillnet was 3 m deep and 183 m long and consisted of 6 panels 30.5 m in length consisting of 1.3-mm–incremental stretch-mesh sizes ranging from 7.6 cm to 14.0 cm. The net was anchored and marked with a buoy at each end. The experimental longline consisted of an ~1500-m mainline of 4.0-mm monofilament, anchored and marked with buoys at both ends. Each line held 100 gangions composed of four 25-hook sections separated by buoys with a unique hook size in each section. We used 4 sizes of Mustad circle hooks (10/0, 12/0, 14/0, and 16/0) to minimize size-selection bias and allow capture of all possible sharks present from the smallest neonates to the largest adults. Each gangion began with a stainless steel longline snap attached to 2 m of monofilament Figure 1. All stations sampled in the Big Bend from 2009 to 2012, with sampling regions labeled and separated by lines. Dark circles represent longline sets and triangles represent gillnet sets. Southeastern Naturalist G97 C.T. Peterson, R.D. Grubbs, and A. Mickle 2017 Vol. 16, No. 1 (136-kg test for 10/0, 12/0 and 14/0 hooks; 318-kg test for the 16/0 hooks). The monofilament was crimped to an 8/0 stainless steel barrel swivel followed by a 1-m section of 7 x 7 stainless-steel aircraft cable (1.8 mm for 10/0, 12/0, and 14/0 hooks; 2.2 mm for 16/0 hooks). Each gangion was terminated by a circle hook crimped to the steel cable. We baited the hooks with scombrid fishes, Scomber scombrus L. (Atlantic Mackerel) in 2009 and 2010 and Scomberomorus maculatus Mitchill (Spanish Mackerel) in 2011 and 2012. All fishes captured were brought aboard, identified to species and measured. We tagged sharks caught in good condition using nylon dart tags with a unique identification number and contact information for NOAA’s Southeast Fisheries Science Center in Panama City, FL, in the event of a recapture. We collected white muscle biopsies for carbon and nitrogen stable isotope analysis from up to 10 specimens from each species per survey region. Most fishes were caught live and were released. We recorded environmental parameters for each longline and gillnet set. Salinity, temperature (°C), and dissolved oxygen (ppm) were measured at surface, mid-water column, and bottom depths using either YSI 85 or YSI Pro 2030 handheld water-quality meters. Water clarity (cm) was measured using a secchi disc. Maximum and minimum depths (m) were recorded for each longline and gillnet set using on-board sonar. Bottom type was also recorded based on qualitative observation of on-board sonar display and direct observation when poss ible. Sample processing White-muscle biopsies for stable isotope analysis were thawed, cleaned of skin and scales, rinsed in DI water, and dried for ~48 hours at 60 °C to reach a constant mass. We ground samples to a homogenous powder using a mixer mill (SPEX Sample Prep 5100 Mixer Mill; SPEX Sample Prep, Metuchen, NJ). We did not extract lipids from samples from bony fish, following Post’s (2002) recommendations to only extract lipids when the ratio of carbon concentration to nitrogen concentration is over 3.4. However, we did extract lipids and urea from elasmobranch samples (Hussey et al. 2012) following the methods of Folch (1957). Each elasmobranch sample was homogenized in a 2:1 chloroform– methanol solution using a standard orbital shaker (Model T91 Shaker; manufacturer unknown), and centrifuged for 2 minutes. We then decanted the supernatant and added a 1:1 solution of methanol and DI water to the sample as a rinse. Samples were shaken and centrifuged again, the rinse was decanted, and the tissue was dried at 60 °C for 48 hours and again homogenized in a mixer mill. The rinse step in this protocol was found to effectively remove water-soluble, δ15N-depleted urea retained for osmotic balance in elasmobranch tissue (J. Imhoff et al., Florida State University, Tallahassee, FL, 2014 unpubl. data), which has been shown to potentially confound trophic positions inferred from stable isotope analysis results of elasmobranchs (Hussey et al. 2012, Kim and Koch 2011) through the physiological suppression of δ15N values, and therefore also may result in incorrectly inferred relative trophic position. Given the 2 protocols used for sample processing, we standardized untreated samples using Post’s (2002) Southeastern Naturalist C.T. Peterson, R.D. Grubbs, and A. Mickle 2017 Vol. 16, No. 1 G98 lipid-extraction normalization equation, which adjusts stable isotope values of untreated tissue according to the ratio of bulk carbon and nitrogen concentrations. Stable isotope analysis Each sample of ~600 μg of homogenized fish tissue was analyzed using a Carlo Erba Elemental Analyzer (CE Instruments, Wigan, UK) coupled to a continuous flow Thermo Finnigan Delta Plus XP stable isotope ratio mass spectrometer (IRMS; Thermo Fisher Scientific, Waltham, MA). All isotope values are reported in standard δ notation where units are expressed in parts per thousand (‰) differences from the standard: δX = ([Rsample / Rstandard] - 1) * 1000, where X is 13C or 15N, and R is the ratio of the heavy to light isotope of the respective element. PeeDee Belemnite (V-PDB) was used as the reference standard for δ13C, and ambient Air N2 was used as reference standard for δ15N. Precision of the isotopic analysis was ± 0.2‰ or better, and calibration curves created for C and N were based on the repeated analysis of the following 4 different laboratory standard materials: sucrose, phenylalanine, and urea in 2 concentrations (Table 1). We assessed analytical error of the mass spectrometer by duplicating every 12th sample in a run of the instrument. Mean differences between duplicate samples were 0.2‰ (SD = 0.3) for δ15N and 0.1‰ (SD = 0.2) for δ13C. Data analysis We analyzed catch data for each gear type separately due to the species and size-selection differences between gillnet and longlines, and treated each gillnet and longline set as a single statistical unit. To investigate potential effects of the Deepwater Horizon oil spill, we compared catch rates and stable isotope profiles of 5 indicator species that we chose using criteria similar to that of Sánchez et al. (2006). Those selected were generally highly abundant and consistently captured, facilitating comparison of sufficient data collected over the entire survey period throughout the region. We used these indicator species to avoid the potentially misleading conclusions that could result from analyzing data for species captured less frequently or sporadically Due to departures from normality, we compared per-set CPUE across years using Kruskal-Wallis 1-way ANOVA on ranks with a mean-rank adjustment for ties Table 1. Laboratory values and observed values of standard reference materials used for stable isotope analysis. δ15Nair δ13CPDB Standard Lab value Mean SD Lab value Mean SD Sucrose -12.7 -12.7 0.3 0.0 0.0 0.0 Phenylalanine -30.9 -30.7 0.2 2.5 2.2 0.2 V-PDB -25.7 -25.8 0.1 -5.3 -4.9 0.1 Urea 1 -31.8 -31.9 0.2 0.8 0.6 0.2 Urea 2 0.0 0.0 0.0 20.2 20.3 0.1 Southeastern Naturalist G99 C.T. Peterson, R.D. Grubbs, and A. Mickle 2017 Vol. 16, No. 1 (Kruskal and Wallis 1952). In the case of significance (P < 0.05), we conducted a post-hoc multiple comparison test using Dunn’s method. We calculated longline CPUE as catch per 100 hook-hour ([(catch/100 hooks)/hours soaked] * 100) and gillnet CPUE as catch per net-hour. We compared δ13C and δ15N values of each indicator species, as well as environmental parameters, among years using parametric 1-way ANOVA. In the case of significance (P < 0.05), we conducted a post-hoc multiple comparison test using Tukey’s honestly significant difference (HSD) method. Analyses were conducted in the R statistical console (R Core Development Team 2010). Figures were produced in the R console and using Microsoft Excel© (2010). Results We performed 159 paired gillnet and longline sets from 2009 to 2012. Environmental parameters were fairly consistent among years (Table 2). ANOVA revealed statistically significant differences among years in temperature (F3,238 = 16.17, P less than 0.01) and dissolved oxygen (F2,223 = 5.11, P < 0.01), but we did not interpret the differences to be biologically relevant because observed temperatures and levels of DO were well within the ranges tolerated by our indicator species, and differences among sampling years were very small. We also know from our seasonal work using identical fishing methods off the FSU Coastal and Marine Laboratory in St. Teresa, FL, that these species immigrate into this habitat when temperatures reach 16–18 °C and remain in the system until temperatures drop again (R.D. Grubbs and C.T. Peterson, unpubl. data). Five species were dominant overall in terms of relative abundance (in descending order of overall survey abundance): Atlantic Sharpnose Sharks, Hardhead Catfish, Bonnethead Sharks, Blacktip Sharks, and Gafftopsail Catfish. These 5 species combined composed 80.4% of our total catch. Atlantic Sharpnose Sharks and Hardhead Catfish were the 2 most dominant species, making up 30.5% and 27.5% of the total catch, respectively . The total numbers of indicator species caught can be seen in Table 3. We calculated CPUE for the 3 indicator shark species for all maturity classes combined for each gear type (Fig. 2). The gillnet CPUE did not vary significantly among years for any of the 3 indicator shark species (Atlantic Sharpnose: H = 4.98, df = 3, P = Table 2. Mean (± SD) and range of environmental parameters of stations sampled by year. Dissolved oxygen was not measured in 2009. Dissolved Water Water Y ear n Temperature (°C) Salinity oxygen (mg/l) clarity (cm) depth (m) 2009 39 29.4 ± 0.7 27.4 ± 3.2 - 261 ± 106 2.5 ± 1.4 (28.3–30.7) (19.5–31.4) - (110–650) (0.7–8.6) 2010 35 30.8 ± 1.3 27.9 ± 3.6 5.30 ± 0.70 234 ± 105 2.5 ± 1.5 (27.1–33.3) (18.9–34.6) (3.30–6.70) (50–550) (0.7–6.6) 2011 44 30.7 ± 1.0 28.1 ± 2.9 5.90 ± 1.40 254 ± 91 2.4 ± 1.3 (28.4–30.7) (20.7–34.1) (3.50–9.90) (80–450) (0.9–6.1) 2012 40 29.8 ± 1.1 27.0 ± 2.9 5.70 ± 1.30 231 ± 89 2.6 ± 0.9 (27.6–33.1) (20.2–33.0) (2.00–10.3) (50–500) (1.0–5.6) Southeastern Naturalist C.T. Peterson, R.D. Grubbs, and A. Mickle 2017 Vol. 16, No. 1 G100 0.17; Blacktip: H = 7.51, df = 3, P = 0.06; Bonnethead: H = 1.87, df =3, P = 0.60). The longline CPUE did not vary significantly for Blacktip Sharks (H = 7.54, df = 3, P = 0.06), but longline CPUE was significantly different among years for Atlantic Sharpnose Sharks (H = 14.67, df = 3, P < 0.01), with higher catch rates on longlines in 2012 than 2010 (Dunn’s test: P < 0.01). The longline CPUE of Bonnethead Sharks was not calculated because they are only rarely captured using this gear. The CPUE for the 2 species of ariid catfishes did not vary among years in gillnets (Gafftopsail Catfish: H = 1.70, df = 3, P = 0.64; Hardhead Catfish: H = 3.72, df = 3, P = 0.29), or on longlines (Gafftopsail Catfish: H = 3.64, df = 3, P = 0.30; Hardhead Catfish: H = 2.32, df = 3, P = 0.51). We compared stable isotope values (δ13C and δ15N) for each indicator species (all maturity classes pooled) among all 4 years (Fig. 3). The stable isotope values for each species are presented in Tables 1 and 4. The average correction offset of δ13C using Post’s equation was 0.2‰, and the maximum correction offset was 2.6‰. The mean values of both δ13C and δ15N varied significantly among years in Hardhead Catfish (δ13C: F3,128 = 5.82, P < 0.01; δ15N: F3,128 = 2.48, P = 0.05), with 2012 enriched in 13C relative to 2010 (P = 0.04, mean difference = 1.2‰) and 2011 Table 3. Total numbers of indicator species caught by region and survey y ear. Species 2009 2010 2011 2012 (common name) Region GN , LL GN , LL GN , LL GN , LL Carcharhinus limbatus St. Marks 0 , 5 3 , 2 1 , 1 0 , 5 (Blacktip Shark) Steinhatchee 17 , 44 8 , 13 1 , 2 1 , 11 Crystal River 21 , 27 3 , 15 44 , 34 6 , 37 Hernando 2 , 2 0 , 5 1 , 2 0 , 3 Total 40 , 78 14 , 35 47 , 39 7 , 56 Rhizoprionodon terraenovae St. Marks 33 , 46 27 , 22 100 , 48 52 , 71 (Atlantic Sharpnose Shark) Steinhatchee 33 , 48 45 , 11 49 , 30 26 , 57 Crystal River 31 , 38 11 , 21 32 , 48 15 , 80 Hernando 48 , 56 54 , 21 44 , 69 53 , 48 Total 145 , 188 137 , 75 225 , 195 146 , 256 Sphyrna tiburo St. Marks 18 , 0 24 , 0 32 , 0 8 , 1 (Bonnethead Shark) Steinhatchee 46 , 1 88 , 0 29 , 0 16 , 0 Crystal River 36 , 0 9 , 0 11 , 0 19 , 1 Hernando 15 , 2 24 , 0 21 , 1 17 , 0 Total 115 , 3 145 , 0 93 , 1 60 , 2 Arius felis St. Marks 10 , 51 34 , 44 14 , 80 17 , 64 (Hardhead Catfish) Steinhatchee 17 , 45 11 , 67 14 , 80 72 , 83 Crystal River 10 , 23 7 , 40 10 , 50 14 , 55 Hernando 11 , 69 6 , 87 6 , 90 0 , 45 Total 48 , 188 58 , 238 44 , 300 103 , 247 Bagre marinus St. Marks 0 , 3 5 , 14 4 , 23 4 , 11 (Gafftopsail Catfish) Steinhatchee 29 , 15 10 , 15 8 , 13 5 , 20 Crystal River 3 , 3 1 , 8 7 , 12 12 , 12 Hernando 0 , 8 2 , 3 3 , 12 4 , 0 Total 32 , 29 18 , 40 22 , 60 25 , 43 Southeastern Naturalist G101 C.T. Peterson, R.D. Grubbs, and A. Mickle 2017 Vol. 16, No. 1 (P < 0.01, mean difference = 0.7‰), and 2009 enriched in 15N relative to 2010 (P = 0.05, mean difference = 0.8‰). However, the effect sizes of year were relatively low (δ13C: η2 = 0.12; δ15N: η2 = 0.06). Similarly, the mean values of δ15N varied significantly among years in Bonnethead Sharks (F3,113 = 2.96, P = 0.08), with 2009 enriched in15N relative to 2011 (P = 0.04, mean difference = 0.8‰), but the effect size of year was low (η2 = 0.08). The mean stable isotope values did not vary among years in the remaining indicator species (P > 0.05 in all cases). Discussion Our analyses suggest large coastal fishes in Florida’s Big Bend had not been affected by the Deepwater Horizon oil spill as of 2012. The catch rates of the Figure 2. Catch-per-unit-effort of five indicator species (AFEL = Ariopsis felis; BMAR = Bagre marinus; CLIM = Carcharhinus limbatus; RTER = Rhizoprionodon terraenovae; STIB, Sphyrna tiburo) on gillnets (GN) longlines (LL) from 2009 to 2012. Error bars represent ± 1 standard error. Southeastern Naturalist C.T. Peterson, R.D. Grubbs, and A. Mickle 2017 Vol. 16, No. 1 G102 most-abundant species changed little and without consistent pattern from 2009 to 2012. The longline CPUE of Atlantic Sharpnose Sharks was low in 2010, the same year as the DWH oil spill; however, the gillnet CPUE was not significantly different in 2010. These results illustrate the dangers of drawing population-level conclusions based on a single data set or gear type. Based solely on our longline data, one might predict a population-level effect of the oil spill on Atlantic Sharpnose Sharks, but our gillnet data suggest that is not the case. In addition, the oil spill federal stock assessments were conducted for 2 of our 5 indicator species, Atlantic Sharpnose Sharks (http://sedarweb.org/docs/sar/S34_ATSH_SAR.pdf) Figure 3. Stable isotope values, by year, for each indicator species. Error bars represent standard error. Indicator species are represented by shapes: cirlces represent Atlantic Sharpnose Sharks, upside down triangles represent Bonnethead Sharks, squares represent Blacktip Sharks, diamonds represent Hardhead Catfish, and rightside up triangles represent Gafftopsail Catfish. Years are represented by color: white represents 2009, light grey represents 2010, dark grey represents 2011, and black represents 2012. Southeastern Naturalist G103 C.T. Peterson, R.D. Grubbs, and A. Mickle 2017 Vol. 16, No. 1 Table 4. δ 13C and δ15N values of indicator species by year and region. Species 2009 δ13C 2010 δ13C 2011 δ13C 2012 δ13C (common name) Region (n, mean ± SD) (n, mean ± SD) (n, mean ± SD) (n, mean ± SD) Carcharhinus limbatus St. Marks 5, -15.9 ± 0.3 1, -16.7 2, -16.6 ± 0.4 5, -16.6 ± 0.5 (Blacktip Shark) Steinhatchee 14, -15.8 ± 0.5 11, -16.1 ± 0.8 2, -16.1 ± 0.3 3, -17.0 ± 0.4 Crystal River 8, -16.2 ± 1.0 8, -15.7 ± 0.6 11, -16.4 ± 0.8 19, -16.3 ± 1.0 Hernando 4, -14.1 ± 0.6 2, -14.6 ± 0.3 3, -13.6 ± 0.4 2, -13.8 ± 1.4 Total 31, -15.7 ± 0.9 22, -15.9 ± 0.8 18, -15.9 ± 1.2 29, -16.2 ± 1.1 Rhizoprionodon terraenovae St. Marks 20, -16.2 ± 0.4 12, -16.6 ± 0.3 23, -16.7 ± 0.6 15, -16.4 ± 0.7 (Atlantic Sharpnose Shark) Steinhatchee 6, -15.8 ± 0.5 5, -16.4 ± 0.5 23, -16.5 ± 0.5 9, -15.9 ± 0.4 Crystal River 8, -16.5 ± 0.8 11, -16.7 ± 0.6 12, -15.4 ± 1.6 14, -16.5 ± 1.0 Hernando 12, -15.6 ± 0.4 9, -15.3 ± 0.6 15, -14.2 ± 0.9 17, -15.0 ± 0.6 Total 46, -16.0 ± 0.6 37, -16.3 ± 0.7 73, -15.9 ± 1.3 55, -15.9 ± 0.9 Sphyrna tiburo St. Marks 5, -16.7 ± 0.3 2, -17.3 ± 1.1 5, -17.0 ± 0.7 8, -16.6 ± 0.7 (Bonnethead Shark) Steinhatchee 5, -16.8 ± 0.6 7, -16.9 ± 0.5 16, -17.4 ± 0.8 8, -16.6 ± 0.8 Crystal River 5, -16.9 ± 1.0 9, -17.6 ± 1.1 7, -16.7 ± 1.9 11, -17.6 ± 1.0 Hernando 2, -14.7 ± 0.2 9, -15.8 ± 1.0 10, -13.5 ± 0.7 13, -14.9 ± 0.7 Total 17, -16.5 ± 0.9 27, -16.8 ± 1.2 38, -16.2 ± 1.9 40, -16.3 ± 1.3 Arius felis St. Marks 9, -17.5 ± 0.4 10, -18.6 ± 1.0 17, -17.7 ± 1.1 12, -17.1 ± 0.8 (Hardhead Catfish) Steinhatchee 4, -17.5 ± 0.6 3, -17.4 ± 0.7 14, -18.5 ± 1.2 11, -16.2 ± 1.3 Crystal River 2, -16.6 ± 2.1 1, -15.6 9, -18.0 ± 2.1 11, -18.1 ± 1.9 Hernando 4, -17.8 ± 2.0 4, -16.1 ± 0.5 10, -16.6 ± 2.0 11, -14.3 ± 1.0 Total 19, -17.5 ± 1.1 18, -17.6 ± 1.4 50, -17.8 ± 1.6 45, -16.4 ± 1.9 Bagre marinus St. Marks 2, -16.9 ± 0.4 9, -17.6 ± 0.6 13, -17.6 ± 0.5 13, -17.6 ± 0.4 (Gafftopsail Catfish) Steinhatchee 8, -18.4 ± 1.0 5, -17.2 ± 0.7 10, -17.9 ± 0.7 8, -16.3 ± 0.8 Crystal River 2, -18.2 ± 0.3 6, -18.3 ± 0.9 9, -17.8 ± 1.4 10, -18.0 ± 0.5 Hernando 1, -15.7 3, -15.8 ± 0.9 12, -15.7 ± 0.7 1, -15.9 Total 13, -17.9 ± 1.1 23, -17.5 ± 1.0 44, -17.2 ± 1.2 31, -17.3 ± 0.9 Southeastern Naturalist C.T. Peterson, R.D. Grubbs, and A. Mickle 2017 Vol. 16, No. 1 G104 and Bonnethead Sharks (http://sedarweb.org/docs/sar/S34_Bonnethead_SAR.pdf). These assessments included data sets from throughout the US Gulf of Mexico, and though only data through 2011 were analyzed, no changes in stock abundance were detected following the DWH oil spill. An analysis of the stable isotope data revealed no significant trophic shifts in our indicator species, suggesting that there was not a substantial change to the trophic pathways which those species were using; however, some variation in annual mean δ13C and δ15N of each species was evident, particularly in Hardhead Catfish. Production in coastal systems such as the Florida Big Bend is often influenced by river input (Chanton and Lewis 2002, Chasar et al. 2005). The discharge of the Suwannee River, the primary watershed in the Big Bend region, was lowest in 2011 relative to the other 3 years of the survey (US Geological Survey 2013) and highest in 2010, particularly early in the year. Consumers have previously been found to be progressively depleted in 13C and enriched in 15N as functions of the proximity to freshwater input due to the associated increase in dissolved inorganic carbon (Chanton and Lewis 2002). The higher δ13C value of Hardhead Catfish in 2012 may have been a result of low river flow compared to 2011, reflected in a slight shift away from pelagic production (i.e., phytoplankton, 13C-depleted) towards other primary-production sources in the system such as epiphytic algae and seagrasses (13C-enriched). However, there was not a consistent pattern in annual mean stable isotope values between any of the indicator species; thus, it is difficult to determine the causes of inter-annual variation for any taxon. The lack of consistent patterns in stable isotope values between any of the indicator species provide no evidence of a trophic shift affecting these species following the oil spill. It is important to stress that the absence of patterns in our stable isotope data does not demonstrate that there were no effects of the DWH oil spill in this system. Trophic shifts could be masked by spatio-temporal variation in isotopic baselines, which are influenced by environmental conditions that affect primary productivity, such as river flow and strength of anthropogenic effects. The stable isotopes in highly mobile species such as sharks likely reflect foraging in areas outside of the study region, which further confounds interpretations of these data with the potential variability in isotopic baselines. Additionally, the trophic effects may not be evident for extended periods of time following a disturbance, especially in locations at relatively high distances from the site of the perturbation, and establishment of a mechanism or link of indirect effects from a disturbance such as DWH in areas without direct exposure to oil will be extremely difficult. Our analyses focused on predators occupying relatively high trophic levels, and the length of time before indirect effects of the DWH oil spill become evident may be longer for these taxa. Despite these considerations, the relative consistency of catch rates and stable isotope values of our indicator species provide no evidence of an effect of the DWH oil spill on large coastal fishes in Florida’s Big Bend. Our results are not altogether unexpected. Oil from the DWH event did not reach the Big Bend coastline, although oil may have been carried to the west Florida shelf (Hénaff et al. 2012, Liu et al. 2011, Weisberg et al. 2016). In the absence of Southeastern Naturalist G105 C.T. Peterson, R.D. Grubbs, and A. Mickle 2017 Vol. 16, No. 1 direct exposure, the trophic pathways supported by both pelagic and benthic primary production were likely preserved. In previous oil spill events, the effects of oil persisted due to the trapping of oil in the coastal sediment layer. For example, the Exxon-Valdez oil spill had clear effects on some teleost fishes, such as reduced growth in individuals foraging in heavily oiled shorelines and lower survivability due to trapped oil that continued to have an effect on species tied to the sediment at some stage of their life (Heintz et al. 2000, Peterson et al . 2003). In the absence of direct exposure, the effects from persistent oil similar to those observed in the Exxon-Valdez spill are unlikely in the Big Bend. The uncertainty in timelines and pathways of indirect effects illustrates the importance of long-term sampling and monitoring programs, especially in regard to the study of disturbance and pollution events such as the DWH oil spill. Without such studies, it is impossible to investigate the effects of natural or anthropogenic disturbance. By generating a sound ecological comprehension of the potential effects of an oil spill, reaction strategy and mediation techniques could be refined to most efficiently manage risk and damage. Acknowledgments We thank the multitude of volunteers required to conduct our Big Bend shark survey, especially Lisa Hollensead, Matthew Kolmann, Travis Richards, and Johanna Imhoff. We are grateful to Ale Mickle for her work both in the field and in the lab processing stable isotope samples, and the excellent staff of the FSU Coastal and Marine Laboratory. 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