Regional variation in diet may be an underappreciated modulator of mercury uptake in species of concern: A case study using Alligator Gar (Atractosteus spatula)

Regional variation in diet may be an underappreciated modulator of mercury uptake in species of concern: A case study using Alligator Gar (Atractosteus spatula) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Regional variation in diet may be an underappreciated modulator of mercury uptake in species of concern: A case study using Alligator Gar (Atractosteus spatula) Zachary S. Moran, Michael T. Penrose, George P. Cobb, Michael S. Baird, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4009895/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract We compared mercury (Hg) and stable isotopic ratios of nitrogen (δ 15 N) in a long-lived apex predator, Alligator Gar ( Atractosteus spatula ), from a coastal region of the Brazos River exposed to high aerial Hg deposition, to an inland population exposed to moderate Hg deposition, in order to test the relative importance of biomagnification through trophic dynamics and aerial deposition rates in an apex predator. We used generalized linear models (GLMs) to examine the effects of fish size (Length, mm) and Region (Inland versus Coastal) on Hg concentration and δ 15 N. Length had a significant positive effect on both Hg and δ 15 N. However, after accounting for the effect of Length, both Hg and δ 15 N were significantly higher in the Inland population (N = 48; mean ± SE = 0.232 ± 0.020 mg/kg ww and 18.8 ± 0.184‰, respectively) than the Coastal population (N = 45; mean ± SE = 0.143 ± 0.012 mg/kg ww and 16.72 ± 0.291‰, respectively). We further estimated probabilities of Alligator Gar exceeding Hg consumption advisory guidelines used by the World Health Organization (WHO) and the United States Environmental Protection Agency (USEPA). WHO and USEPA exceedance probabilities were 0.414 and 0.048 for Coastal, and 0.835 and 0.276 for Inland populations, respectively. However, WHO and USEPA exceedance probability estimates for fish ≥ 2000 mm climbed to 0.747 and 0.146 for Coastal and ≥ 0.999 and 0.559 for Inland populations, respectively. These results suggest that variation in food web dynamics, and resultant impacts on biomagnification, may be a more important driver of Hg uptake in Alligator Gar, when compared to the role of aerial deposition rates. Our results also demonstrate that Alligator Gar often exceed consumption advisory Hg concentrations, particularly in the largest individuals, and that they likely experience some level of reproductive toxicity because of sublethal Hg exposures. Mercury Alligator Gar Biomagnification δ15N Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Mercury (Hg) in its three forms have long been recognized to have negative health effects on humans and aquatic life, having undergone increased scrutiny since the 1950s (Watanabe and Satoh 1996 ; Driscoll et al. 2013 ). In summary, elemental mercury [Hg(0)] exists as either a liquid or gas and readily combines with other elements to form divalent inorganic mercury [Hg(II)], which naturally cycles in the environment through physical and chemical processes (DeForest et al. 2007 ; Driscoll et al 2013 ). Loadings of inorganic [Hg(II)] into ecosystems are accelerated by atmospheric deposition from anthropogenic hydrocarbon combustion (Jackson 1997 ; Driscoll et al. 2013 ; Bjørklund et al. 2017 ) and become particularly problematic once biochemically transformed into highly toxic and bioavailable methylmercury. (Lockhart et al. 1998 ; Langford and Ferner 1999 ; Selin 2009 ). This transformation of Hg, or specifically methylation, is facilitated by sulfate, iron, and syntrophic reducing anaerobes which utilize specific enzymes to add a methyl (CH 3 ) group to a Hg atom. Methylmercury (MeHg) is generated mostly in the anoxic benthos of aquatic ecosystems, and its lipophilicity allows it to accumulate and bio-magnify rapidly through food webs and spread throughout the biosphere (Depew et al. 2013 ; Lin et al. 2021 ). The toxicological impacts of Hg to fish and aquatic life are systemic, causing damage to the gills, liver, intestines, reproductive organs, and brain (Zulkipli et al. 2021 ). Of particular concern are the damaging impacts of MeHg on fish reproduction, specifically the inhibition of gonadotropin-secreting cells, reduction in gonad size, reduced viability of gametes, and disruption to male and female reproductive function (Crump and Trudeau 2009 ). In humans, consumption of MeHg contaminated fish and shellfish leads to brain, heart, kidney, lung, and immune system damage, and causes developmental problems in the nervous systems of fetuses and young children (Langford and Ferner 1999 ; Wiener et al. 2003 : Selin 2009 ; Bjørklund et al. 2017 ). Anthropogenic activities continue to increase the global atmospheric Hg pool, which has subsequently contaminated much of the world’s waterways (Hammerschmidt and Fitzgerald 2006 ; Pacyna et al. 2010 ; Driscoll et al. 2013 ; Depew et al. 2013 ). In Texas, atmospheric Hg pools are not evenly distributed but are instead concentrated downwind of areas dominated by industrial activity, particularly coal-fired power plants (Menounou and Presley 2002; Drenner et al. 2011 ; NADP 2018). Consequently, aquatic ecosystems within these “hot zones” are observed to be highly contaminated with Hg (Smith et al. 2010 ; Drenner et al. 2011 ), and it is logical to predict that accumulation of Hg in higher trophic level species should be elevated compared to those in less contaminated areas (Wiener et al. 2003 ; Wentz et al. 2014 ). Despite this general supposition, relatively few studies have specifically compared Hg concentrations within higher trophic level species from areas exposed to different Hg loadings by aerial deposition. With this in mind, we set out to test this hypothesis by studying a large, long-lived apex predator, Alligator Gar ( Atractosteus spatula ), in areas predicted to receive high and moderate levels of atmospheric Hg deposition. Alligator Gar are the largest and longest-lived gar species in the world, boasting lengths upwards of 2.4 m, and age estimates to 95 years (Daugherty et al. 2019 ). As important apex predators, they maintain the health and function of ecosystems by removing unhealthy individuals; however, their feeding ecology, in combination with long lifespan, exposes them to potentially high levels of Hg (Scarnecchia 1992 ; Barst et al. 2011 ; Harried et al. 2019 ). In fact, contemporary studies show total Hg (THg) concentrations in their tissues exceed 0.8 parts per million wet weight (mg/kg ww), which is over twice the advised concentration amount in consumable fish tissue by the USEPA (Karimi et al. 2012 ; Harried et al. 2019 ). Such high Hg body burdens likely contributes to poor recruitment of at-risk Alligator Gar populations, which are already declining because of habitat loss from flood control structures and harvest-oriented fishing (Smith et al. 2019 ). Case in point, THg concentrations in Alligator Gar from the Trinity River were higher than those known to cause reproductive impairment in other taxa, and egg THg levels were high enough to contribute to decreased hatch rate, impaired growth and development, and reduced survival of larvae (Harried et al. 2019 ). Furthermore, Alligator Gar are consumed by humans, and it is important to note that a single Alligator Gar can contain over one hundred servings, thus representing a tremendous total mass of Hg being consumed. The meat from a single fish can easily exceed the reference dose for chronic oral exposure to MeHg (0.1 µg mercury / kg body weight / day) for an adult human for an entire year. Accordingly, Hg concentrations in Alligator Gar within “hot zones” should inform policy towards conservation efforts and consumption advisories. This study examines how well Hg accumulation in an apex predator reflects aerial deposition by comparing Hg concentrations in Alligator Gar tissues from areas exposed to high and moderate atmospheric Hg deposition, as defined by the National Atmospheric Deposition Program (NADP 2023). Specifically, the moderate deposition area is the inland Brazos River, proximal to the city of Waco, Texas, USA; and the high deposition area is the coastal Brazos River adjacent to the city of Lake Jackson, Texas, USA. We hypothesized that Alligator Gar in the moderate deposition zone (inland Brazos River) would exhibit lower amounts of Hg in their tissues compared to populations from the higher deposition zone (coastal Brazos River) (Engle et al. 2008 ; Lin et al. 2014 ). Additionally, to evaluate the relative importance of regional variation in food web dynamics and trophic position on biomagnification, we also include an analysis of stable isotopes of nitrogen (δ 15 N) from these respective areas (Smylie et al. 2016 ). Methods Sample Sites Our sample areas were the inland and coastal regions of the Brazos River located near the cities of Waco and Lake Jackson, Texas, USA (Fig. 1 ). The inland Brazos River is characterized by long periods of low-flow with intermittent high-flow periods, while the coastal Brazos River is representative of large tidal-river habitats. Based on imagery data from the National Atmospheric Deposition Program, the middle Brazos River is exposed to moderate amounts of atmospheric Hg compared to high amounts in the lower Brazos River (NADP 2023). The lower Brazos River near Lake Jackson, Texas has high atmospheric Hg due to its proximity to intense industrial activity in Houston, Texas. Collection Methods and Tissue Processing Alligator Gar were collected using experimental multifilament gillnets that were 64 m long and 3.04 m deep with mesh size ranging 88.9-139.7 mm (Bonar et al. 2009 ; Richardson and Flinn 2019). Gillnets were set perpendicular to current, 25–100 m apart, and monitored continuously (Richardson and Flinn 2019). Efforts were made to collect sizes of fish at each site that were representative of the overall population to reduce variability and size bias. Following capture, total length (mm) of each fish was recorded, and Floy® T-Bar and PIT tags with unique identification numbers and contact information were implanted next to, and underneath the dorsal fin, respectively. As Alligator Gar are a species of conservation concern, we elected to use nonlethal muscle biopsies, rather than whole fillets, to reduce unnecessary mortality. We justified this decision based on previous research from Stahl et al. ( 2021 ), who demonstrated a lack of significant differences in Hg concentrations between muscle biopsies and fillets ( p = 0.973). Muscle biopsies were collected by making an incision along the bottom of a scale before using a pair of sharp wire cutters to cut the boundary edges. This disconnected the scale on three sides, which could then be lifted to access the underlying muscle tissue. Scissors and forceps were used to remove and transfer approximately 1 g of muscle tissue into a 2-mL plastic tube. The scale was then realigned to its correct position to promote healing. Following removal, biopsies were immediately stored in a cooler with ice, and fish were safely released. In the lab, muscle biopsies were lyophilized using a VirTis Benchtop Pro with Omnitronics (SP/ATS, Warminster, Pennsylvania, USA) for 24–48 h using automatic manufacturer settings for freeze-drying. Average weight decrease due to water loss during freeze drying was calculated at 77 ± 0.28%. Mercury Analysis Mercury samples were prepared for analysis by first preparing eight calibration standards and one certified reference material (CRM) to run in conjunction with samples. Calibration standards were established at 0, 0.5, 1.0, 2.0, 4.0, 8.0, 10.0, and 20 ng/mL and 0.3 g of DORM-4 Fish Protein CRM was used (National Research Council Canada). Samples were digested by first transferring approximately 0.1 g of lyophilized muscle tissue to a glass test tube and then introducing 1 mL of water and 3 mL of 1:1 nitric acid (HNO 3 ). The digestate mixture was placed into an Environmental Express HotBlock 150 (Charleston, South Carolina, USA) at 95°C for 15 min, after which it was allowed to cool to room temperature before adding 1.5 mL of concentrated HNO 3 and 0.5 mL of hydrochloric acid (HCl). This digestate was returned to the 95°C hot block for a further 30 min before being removed and cooled to room temperature. Lastly, 500 µL of water and 1.5 µL of 30% hydrogen peroxide (H 2 O 2 ) were added to the digestate to oxidatively liberate Hg(II) and aid in digestion during the final incubation period at 95°C for 60 min. After cooling, the digested sample was filtered through a Fisherbrand™ filter (comes pretreated with HCl and neutralized nanopure water) to remove particles and diluted to 10 mL in a 10-mL volumetric flask with nanopure water. At this point, the sample was considered ready for analysis. Mercury analysis was conducted on a CETAC M-8000 QuickTrace cold vapor atomic fluorescence (CVAF) direct mercury analyzer (DMA) equipped with a CETAC ASX-520 autosampler (Faust et al. 2014 ; Teledyne Leeman Labs, Mason, Ohio, USA). Before operation, the DMA was calibrated using the 0, 0.5, 1.0, 2.0, 4.0, 8.0, 10.0, and 20 ng/mL standards with correlation coefficients over 0.997 considered appropriate. Stannous chloride reagent was used to improve the absorbance signal, and a rinse of 98% water, 1% HCl, and 1% HNO 3 was used to clear the system. Continuous argon gas was used to purge the system. To avoid issues with quality control, samples were digested and analyzed in batches of 24. Mercury concentrations within tissue samples were assessed using QuickTrace software and converted to mg/kg of tissue. Samples were corrected based on recovery percentages of DORM-4 which ranged from 88–99%. The limit of detection and limit of quantification from the CETAC M-8000 was calculated as 0.007 mg/kg, and 0.02 mg/kg, respectively. Dry weight concentrations were converted to wet weight using the following equation: Concentration Wet Weight = [(100-% water loss)/100] x Concentration Dry Weight. Stable Isotope Analysis Approximately 1–2 mg of lyophilized muscle was placed into ultra-pure tin capsules for analysis at the Baylor University Stable Isotope Laboratory. Nitrogen isotopes were analyzed using a Thermo-Electron Delta V Advantage Isotope Ratio Mass Spectrometer (IRMS) with a Dual Inlet system (Thermo Scientific). Isotopes were reported as parts per thousand (‰) differences from a corresponding standard as calculated by: δX = [(Rsample/Rstandard) − 1] × 10 3 , where R = 15N/14N. The used standard was nitrogen in air, and the machine was calibrated using certified reference materials (USGS-40 and USGS-41). The standard deviation of the internal laboratory standard (Acetanilide; Indiana University, USA) was 0.16‰ for δ 15 N. Data Analysis Generalized linear models (GLMs) were created in R version 4.2.2 (R Core Team 2023 ) to estimate effects on Hg and δ 15 N from Length, Region (Inland versus Coastal), and potential interactions. When estimating effects on δ 15 N, we only included sizes of fish between the lengths of 1000–1700 mm, because these sizes aligned with the limited number of δ 15 N samples (n = 10) analyzed from the coastal region. Similarly, we estimated effects on Hg with this reduced size range (1000–1700 mm) to aid in direct comparisons to the δ 15 N results, but relied primarily on a second Hg model that included all sizes (Inland n = 48, Coastal n = 45). Data were visualized to assess appropriate error distributions for subsequent analyses. Based on the skewed data distribution (Fig. 2 ), we elected to use a Gamma error distribution with “identity” link. We also log-transformed Length because it exhibited a right-skewed distribution (Fig. 2 ). Akaike Information Criterion for small sample sizes (AICc) was used for model comparison and selection. We observed that models including an interaction term for Region and Length exhibited higher delta-AICc values of 1.83 for the 1000–1700 mm Hg model, 1.44 for the all-sizes Hg model, and 1.64 for the δ 15 N model. Models with interaction terms also exhibited a higher variance inflation than base models. Therefore, we selected the most parsimonious model which included only the main effects of Region and Length (no interaction). Lastly, we used the estimated marginal means (EMMEANS) package (R Core Team 2021) to make post hoc pairwise comparisons of means between Coastal and Inland populations while holding Length constant. We also estimated exceedance probabilities for World Health Organization (WHO, 0.15 mg/kg ww), United States Environmental Protection Agency (USEPA, 0.3 mg/kg ww), and State of Texas (TXDSHS, 0.7 mg/kg ww) consumption advisory guidelines for women of childbearing age and children under the age of 12 with individual fish with [Hg] ≥ consumption advisory. Models were fit using GLMs with the binomial family and “logit” link function. Region and Length were main effects in each model, with Hg concentration (by individual fish) coded as a binary response variable based on each organization’s consumption advisory threshold (0 = below consumption advisory threshold, 1 ≥ consumption advisory). Results A total number of 93 Alligator Gar were captured, with 45 sampled from the inland Brazos River population and 48 from the coastal Brazos River population. Alligator Gar sampled from the inland population were larger on average than the coastal population with mean lengths of 1,462 mm (range = 762–2,348 mm) and 1,278 mm (range: 913- 1,649 mm), respectively (Fig. 3 ). All three GLM models suggested Length and Region significantly affected Hg concentrations and δ 15 N in Alligator Gar (Table 1 ). The main effect of Region indicated Hg concentrations between Inland and Coastal populations were significantly different for both the 1000–1700 mm Hg model (COASTAL = 0.139, 95% CL = 0.113–0.164 vs. INLAND = 0.226, 95% CL = 0.178–0.275; p = 0.002) and the all sizes Hg model (COASTAL = 0.143, 95% CL = 0.118–0.168 vs. INLAND = 0.232, 95% CL = 0.193–0.272; p < 0.001). Similarly, δ 15 N values between the Inland and Coastal populations were significantly different (COASTAL = 16.72, 95% CL = 16.10–17.30 vs. INLAND = 18.81, 95% CL = 18.31–19.20; p < 0.001). Table 1 —. Results of the generalized linear models (GLMs) explaining mercury (Hg) and nitrogen isotopes (δ 15 N) in Alligator Gar from the inland and coastal Brazos River. Model: Response Predictor Coefficient Std. error z p Hg 1000–1700 mm Intercept -1.52 0.64 -2.358 0.0209 log(Length) 0.23 0.09 2.548 0.0128 Region 0.09 0.03 3,230 0.0018 Null deviance: 39.07 on 78 df; Residual deviance 31.26 on 76 df Hg All Sizes Intercept -1.06 0.39 -2.692 0.0085 log(Length) 0.17 0.06 3.005 0.0034 Region 0.09 0.02 3.970 ≤ 0.0001 Null deviance:43.24 on 92 df; Residual deviance 33.16 on 90 df δ 15 N 1000–1700 mm Intercept -25.47 8.28 -3.075 0.0036 log(Length) 5.99 1.26 5.081 ≤ 0.0001 Region 2.11 0.34 6.129 ≤ 0.0001 Null deviance: 0.32 on 46 df; Residual deviance 0.15 on 44 df Alligator gar from Inland populations were significantly more likely to exceed both WHO and USEPA guidelines than Coastal populations (Table 2 , Fig. 5 ). WHO and USEPA exceedance probabilities, after controlling for length (mean = 1367 mm), were 0.414 (95% CL = 0.273–0.570) and 0.048 (0.012–0.176) for Coastal and 0.835 (0.686–0.921) and 0.276 (0.164–0.426) for Inland populations, respectively. However, WHO and USEPA exceedance probability estimates for fish > = 2000 mm climbed to 0.747 (95% CL: 0.471 - ≥0.999) and 0.146 (0.001–0.371) for Coastal and ≥ 0.999 (0.921- ≥0.999) and 0.559 (0.323–0.795) for Inland populations, respectively. Table 2 Summary of generalized linear models (binomial family, link= “logit”) used for estimation of exceedance probabilities for World Health Organization (WHO) and United States Environmental Protection Agency (USEPA) consumption advisory concentrations of Hg in alligator gar. df = degrees of freedom. Model: Response Predictor Coefficient Std. error z p WHO: ≥0.15 mg/kg Hg Intercept -27.36 11.07 -2.494 0.0126 log(Length) 3.78 1.55 2.443 0.0146 Region 1.97 0.52 3.807 ≤ 0.0001 Null deviance: 126.50 on 92 df; Residual deviance: 97.633 on 90 df USEPA: ≥0.30 mg/kg Hg Intercept -25.76 10.04 -2.57 0.0103 log(Length) 3.16 1.39 2.27 0.0234 Region 2.01 0.809 2.48 < 0.0001 Null deviance: 100.96 on 92 df; Residual deviance: 79.46 on 90 df Discussion In contrast to expectations, results of this study demonstrate that Alligator Gar from the inland Brazos River have higher mean Hg muscle concentrations than coastal Alligator Gar. This refutes our initial hypothesis that increased atmospheric Hg deposition around Lake Jackson, Texas coupled with accumulation and biomagnification processes would result in higher Alligator Gar Hg concentrations. Additionally, we observed the inland Alligator Gar population had higher δ 15 N values which suggests the magnitude of Hg biomagnification through trophic levels in this region is likely higher than the coastal region. We conclude that regional variation in food web dynamics is an important modulator of Hg uptake into Alligator Gar, and likely drives the observed elevated Hg body burdens in gar from the inland Brazos River population. While aerial deposition rates are certainly an important factor, they are likely not the main drivers of observed differences in Hg body burdens in this study. Accumulation of Hg into food webs is linked to bioavailability of Hg in the environment, and production of bioavailable MeHg is positively associated with certain biogeochemical conditions conducive to Hg methylation, specifically anoxia, slightly acidic pH, high dissolved nutrients, warm temperatures, and the presence of sulfur reducing bacteria (Hammerschmidt and Fitzgerald 2006 ; Hutcheson et al. 2008 , Farmer et al. 2010 ; Driscoll et al. 2013 ; Chen et al. 2014 ; Smylie et al. 2016 ). Conversely, bioavailable Hg is negatively correlated with salinity, chiefly because higher salinity environments have an abundance of sulfate which when reduced to sulfide can remove Hg from methylation pathways in sulfate reducing bacteria and consequently limit Hg and other cation availability (Compeau and Bartha 1985 ; Devereux et al., 1996 ; Benoit et al. 1999 ; King et al., 1999 ; Fry and Chumchal 2012 ). A further cause for reduced Hg bioavailability is biodilution, a process where nutrient dynamics, primary production, and trophic state (i.e., ecosystem-level characteristics) limit the amount of bioavailable Hg into the food web (Rypel 2010 ). This creates negative correlations between phytoplankton density and Hg burdens in phytoplankton and their consumers, particularly fishes (Chen and Folt 2005 ). Such complexity is difficult to pattern, and we acknowledge such effects on Hg bioavailability from habitat type, geological morphology, phytoplankton density, and water chemistry among our two study regions. These need to be further assessed to understand accumulation into the food web. However, we maintain the idea that the coastal region could have less bioavailable Hg due to biodilution processes or from being immobilized in Hg sulfides. Because of this, primary consumers and benthic foraging species from coastal regions may be less likely to encounter bioavailable Hg while feeding and thus less Hg would accumulate into the basal level of the food web and Alligator Gar prey (Benoit et al. 1999 ; Marziali et al. 2021 ). The primary route of Hg uptake in fish is diet with trophic feeding level and biogeochemical factors governing biomagnification processes into diet items (Hall et al.1997; Zillioux 2015 ). In response, we speculate the reason for dissimilarity of Hg concentrations in Alligator Gar between our two regions is likely linked to different Hg levels in their prey species (Hall et al. 1997 ). For example, studies show Alligator Gar are opportunistic predators on the most abundant prey items available in their environments, with inland Alligator Gar feeding primarily upon Gizzard Shad ( Dorosoma cepedianum ) and buffalo ( Ictiobus sp. ), and coastal Alligator Gar eating mainly Gulf Menhaden ( Brevoortia patronus ) and Striped Mullet ( Mugil cephalus ) (Smith et al. 2019 ; Marsaly et al. 2023 ). In the literature, we observed that Hg concentrations in tissue from these respective prey species appear to reflect those of the Alligator Gar in this study. Case in point, buffalo and Gizzard Shad Hg concentrations from inland freshwater systems reportedly range from 0.28–1.04 mg/kg and 0.08–0.22 mg/kg, respectively, whereas Striped Mullet and Gulf Menhaden Hg from estuarine environments exhibit lower concentrations of 0.049–0.057 mg/kg and 0.023–0.03 mg/kg, respectively (Chalmers et al. 2011 ; Chumchal et al. 2011 ; Fry and Chumchal 2012 ; Gold Quiros 2018 ). While these values do not come from fish in our sample regions, they are from similar type habitats and collectively provide a plausible explanation for why we observed differences in Alligator Gar Hg in our two regions. Biomagnification along with trophic feeding level are other key factors affecting Hg uptake and ones we believe most contributed to regional differences in Hg concentrations of the studied Alligator Gar. A myriad of studies show Hg increases with fish size and age along with their trophic status (Kidd et al. 1995 ; Eagles-Smith et al. 2008; Depew et al. 2013 ; Poste et al. 2015 ; Bradley et al. 2017 ). This is an important consideration because inland Alligator Gar prey upon buffalo that live anywhere from 60 to 112 years (Smallmouth and Bigmouth Buffalo, respectively) which is 10 to 20 times longer than Gizzard Shad, Striped Mullet, and Gulf Menhaden (Lackmann et al. 2019; Long et al. 2023 ). Additionally, buffalos grow considerably larger, and this combined with longer lifespans means the amount of Hg in buffalo ingested by an Alligator Gar could be higher than other prey species. Further supporting our biomagnification hypothesis were our observed δ 15 N values from inland Alligator Gar being ~ 2‰ higher than coastal Alligator Gar. In the literature, we observed δ 15 N values reported in Smallmouth Buffalo ranged from 13–16‰, and Gulf Menhaden and Mullets ranged from 8–14‰ (Akin and Winemiller 2008 ; Lebreton et al. 2011 ; Olsen et al. 2014 ; Coulter et al. 2019 ; Keppeler et al. 2019 ). Again, we acknowledge that these reported values do not come from our sample areas, which is a limitation that could be addressed in future research. However, these patterns do support our supposition that biomagnification of Hg into Alligator Gar is a result of regional diet differences and Hg levels in their prey. Regarding accumulation and biomagnification of Hg into Alligator Gar, we expected Hg to increase with length and age like other fish species (Murphy et al. 2007 ; Barbosa et al. 2011 ; Gewurtz et al. 2011 ; Bradley et al. 2017 ). However, we observed a weak relationship between Hg and length in both the inland and coastal populations. This result was similar to Smylie et al. ( 2016 ), who reported Hg in Longnose Gar ( Lepisosteus osseus ) to have a weak relationship with length. This result could be due to sex-ratios with multiple studies suggesting they are a major factor impacting Hg concentrations. For example, Harried et al. (2021) suggested female Alligator Gar could offload Hg into their eggs after they spawn, similar to other large predators like Goliath Grouper (Malinowski et al. 2021 ), and Smylie et al. ( 2016 ) observed that male Longnose Gar had higher Hg concentrations than females. These are major considerations because female Alligator Gar exhibit sex-specific growth rates, living longer and growing larger than males (Daugherty et al. 2019 ). Simultaneously, a male the same age as a female will be much smaller, however they would have had as much time to bioaccumulate Hg, which could be the reason for higher Hg concentrations in mid-length individuals. Alligator Gar in this study presented Hg concentrations shown to cause reproductive impairment in fish and exceed human consumption limits. Levels of 0.2 mg/kg wet weight have been demonstrated to reduce fish plasma testosterone and 17beta-estradiol, inhibit gonadal development, and reduce spawning success (Drevnick and Sandheinrich 2003 ; Sandheinrich et al. 2011 ). We observed that 36% (34/93) of Alligator Gar in our study exceeded 0.2 mg/kg, with inland region Alligator Gar representing a higher percentage of individuals over this threshold compared to the coastal population (57%, 26/45 versus 17%, 8/48). This indicates that the inland population may be at a greater risk from Hg-induced reproductive impairment (Crump and Trudeau 2009 ). Additionally, we observed 58% (54/93), 12% (11/93), and 0% percent of the fish in this study exceeded the levels set by the WHO (0.15 mg/kg ww), USEPA (0.3 mg/kg ww), and TXDHS (0.7 mg/kg ww), respectively, which illuminates the inconsistency of Hg regulations between world, federal and state agencies. Our exceedance analysis demonstrates that Alligator Gar, especially those > 1600mm, have an increased probability of exceeding the consumption advisories set by these respective agencies. This observation applies most to Alligator Gar from the inland region, with almost all sizes of fish having > 50% chance of exceeding WHO consumption advisories, and fish > 1600 mm were nearly 100% likely to exceed this consumption limit. In response to these observations, we suggest that potential reproductive impairment and risks associated with human consumption (particularly in fish > 1600 mm) should be considered, in addition to conservation concerns, when crafting Alligator Gar fishing regulations. This research creates additional questions that merit further study. First, the literature suggests atmospheric deposition of Hg to be highly impacted by anthropogenic industrial activities, and that increased atmospheric deposition is linked to higher concentrations in aquatic life (Hammerschmidt and Fitzgerald 2006 ; Hutcheson et al. 2008 ). This served as the basis of our primary hypothesis that the coastal region south of Houston, Texas, USA would be exposed to larger amounts of atmospheric Hg deposition than the inland region, as was indicated by the NADP database, which would facilitate higher Hg concentrations in coastal Alligator Gar. This was not the case, as we observed higher Hg concentrations in the inland Alligator Gar, compared to coastal Alligator Gar. In response, we suggest future studies examine the pathways of bioavailable Hg in these two regions. It is possible that the coastal region contains more Hg, however, due to sulfates and different geochemical processes, its availability may be limited. In addition, our observed δ 15 N values gave us reason to theorize that inland Alligator Gar may be feeding at a higher trophic level, meaning that biomagnification is higher inland than at the coast (Smylie et al. 2016 ; Eagles-Smith et al. 2008). However, without baseline values we are unable to definitively make this conclusion, and therefore suggest future studies focus on tracing Hg and δ 15 N transfer to Alligator Gar through the food web, with a focus on contributions from buffalos, Gizzard Shad, Striped Mullet, and Gulf Menhaden. We additionally recommend that future studies consider other factors impacting Hg concentration differences in Alligator Gar, such as sex-ratios, age, and growth. Declarations Author Contribution Z.S.M wrote the main manuscript text, assisted with sample collections, figure preparation, and mercury and istotope analysisM.T.P assisted with mercury analysisG.P.C assisted with mercury analysis and project conceptualizationM.S.B assisted with sample collectionR.S.K assisted with project conseptualization, statistical analysis, sample collection, and figure preparationC.W.M assisted with project conceptualization, and sample collectionAll authors reviewed the manuscript Acknowledgements This research was funded by the C. Gus Glasscock, Jr. Fund for Excellence in Environmental Sciences at Baylor University along with research support from the Center for Reservoir and Aquatic Systems Research (CRASR) and Environmental Science and Biology departments at Baylor University. Special thanks to Texas Parks and Wildlife Department employees who provided research support. The authors would also like to acknowledge the following volunteers: Bianca Possamai, Chad Mansfield, Fallon Bain, and Trevor Troxell. Conflict of Interest: Cole W. Matson is the Editor-in-Chief of Ecotoxicology . The other authors declare no competing interests for this manuscript. References Akin S, Winemiller KO (2008) Body size and trophic position in a temperate estuarine food web. Acta Oecologica 33: 144-153. https://doi.org/10.1016/j.actao.2007.08.002 Barst, BD, Gevertz AK, Chumchal MM, Smith JD, Rainwater TR, Drevnick PE, Hudelson KE, Hart A, Verbeck GF, Roberts AP (2011) Laser ablation ICP-MS co-localization of mercury and immune response in fish. 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DOI: 10.1016/j.envpol.2016.04.007 Stahl LL, Snyder BD, McCarty HB, Cohen TR, Miller KM, Fernandez MB, Healey JC (2021) An evaluation of fish tissue monitoring alternatives for mercury and selenium: Fish muscle biopsy samples versus homogenized whole fillets. Arch Environ Con Tox 81:236-254. https://doi.org/10.1007/s00244-021-00872-w Texas Department of State Health Services (2023). Fishing advisories, bans, and FAWs about bodies of water - seafood and aquatic life. Retrieved from http://dshs.texas.gov/seafood/advisories-bans.aspx US Environmental Protection Agency (2009) The national study of chemical residues in lake fish tissue. EPA 823/R-09/006. Washington, DC. Yako LA, Dettmers JM, Stein RA (1996) Feeding preferences of omnivorous Gizzard Shad as influenced by fish size and zooplankton density. Trans Am Fish Soc. 125:753-759. Wang W (2012) Biodynamics understanding of mercury accumulation in marine and freshwater fish. Watanabe C, Satoh H (1996) Evolution of our understanding of methylmercury as a health threat. Environ Health Perspect. 104: 367-379. https://doi.org/10.1289/ehp.96104s2367 Wentz DA, Brigham ME, Chasar LC, Lutz MA, Krabbenhoft DP (2014) Mercury in the Nation’s streams— Levels, trends, and implications: U.S. Geological Survey Circular 1395, 90 p., http://dx.doi.org/10.3133/cir1395. Wiener JG, Krabbenhoft DP, Heinz GH, Scheuhammer AM (2003) Ecotoxicology of mercury. In Handbook of ecotoxicology. 2nd ed. Edited by Hoffman DJ, Rattner BA, Burton Jr GA, Cairns Jr J. Lewis Publishers, Boca Raton, Fla. pp. 409–463. World Health Organization (2021) Exposure to Mercury: A Major Health Concern, second edition. Zillioux EJ (2015) Mercury in fish: History, sources, pathways, effects, and indicator usage. In: Armon R, Hänninen O (eds) Environ Indicat. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9499-2_42 Zulkipli SZ, Liew HJ, Ando M, Lim LS, Wang M, Sung YY, Mok WJ (2021) A review of mercury pathological effects on organs specific of fishes. Environ Pollut Bioavailab. 33: 76-87. https://doi.org/10.1080/26395940.2021.1920468 Additional Declarations No competing interests reported. Supplementary Files SupplementalTable1.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4009895","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276660625,"identity":"735630a2-07a8-4ad1-bad3-b4d3e4cd6749","order_by":0,"name":"Zachary S. 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Boxplot points are sized by length. Dashed lines represent the World Health Organization (0.15 mg/kg), United States Environmental Protection Agency (0.3 mg/kg), and Texas Department of State Health Services (0.7 mg/kg) advisories for consuming fish and shellfish.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4009895/v1/aa3cfeac510c90f9b3e10e3c.png"},{"id":52186104,"identity":"90601a89-e1a9-44e8-bdf1-b8a7fd9b7ff4","added_by":"auto","created_at":"2024-03-07 18:25:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":364134,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplot comparing stable nitrogen isotope ratios from the inland (black) and coastal (red) regions of the Brazos River, TX. Boxplot points are sized by length.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4009895/v1/1ab2fcf139c2c3062910c623.png"},{"id":52186105,"identity":"8515f241-6ecb-4e28-b472-77db47d83e09","added_by":"auto","created_at":"2024-03-07 18:25:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":140396,"visible":true,"origin":"","legend":"\u003cp\u003eExceedance probabilities for total mercury in alligator gar between 1000 and 2000 mm in total length using World Health Organization (WHO, upper panel) and United States Environmental Protection Agency (USEPA, lower panel) consumption advisory guidelines. Solid lines are mean estimates by Length and Region whereas dotted lines represent 95% confidence intervals.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4009895/v1/fa5d0c9dac1e5a3fbbdf14b8.png"},{"id":70091330,"identity":"98925f6b-5342-48d9-8c53-cd36353878a1","added_by":"auto","created_at":"2024-11-28 09:02:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1297222,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4009895/v1/5a550178-6585-4f4b-a025-c233e5a210b8.pdf"},{"id":52186101,"identity":"ec3f4538-7e45-41b0-9d6e-787bda9907ad","added_by":"auto","created_at":"2024-03-07 18:25:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":64451,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4009895/v1/5523d99be0de4d34a0e8369c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Regional variation in diet may be an underappreciated modulator of mercury uptake in species of concern: A case study using Alligator Gar (Atractosteus spatula)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMercury (Hg) in its three forms have long been recognized to have negative health effects on humans and aquatic life, having undergone increased scrutiny since the 1950s (Watanabe and Satoh \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Driscoll et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In summary, elemental mercury [Hg(0)] exists as either a liquid or gas and readily combines with other elements to form divalent inorganic mercury [Hg(II)], which naturally cycles in the environment through physical and chemical processes (DeForest et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Driscoll et al \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Loadings of inorganic [Hg(II)] into ecosystems are accelerated by atmospheric deposition from anthropogenic hydrocarbon combustion (Jackson \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Driscoll et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Bj\u0026oslash;rklund et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and become particularly problematic once biochemically transformed into highly toxic and bioavailable methylmercury. (Lockhart et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Langford and Ferner \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Selin \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). This transformation of Hg, or specifically methylation, is facilitated by sulfate, iron, and syntrophic reducing anaerobes which utilize specific enzymes to add a methyl (CH\u003csub\u003e3\u003c/sub\u003e) group to a Hg atom. Methylmercury (MeHg) is generated mostly in the anoxic benthos of aquatic ecosystems, and its lipophilicity allows it to accumulate and bio-magnify rapidly through food webs and spread throughout the biosphere (Depew et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Lin et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The toxicological impacts of Hg to fish and aquatic life are systemic, causing damage to the gills, liver, intestines, reproductive organs, and brain (Zulkipli et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Of particular concern are the damaging impacts of MeHg on fish reproduction, specifically the inhibition of gonadotropin-secreting cells, reduction in gonad size, reduced viability of gametes, and disruption to male and female reproductive function (Crump and Trudeau \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In humans, consumption of MeHg contaminated fish and shellfish leads to brain, heart, kidney, lung, and immune system damage, and causes developmental problems in the nervous systems of fetuses and young children (Langford and Ferner \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Wiener et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2003\u003c/span\u003e: Selin \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Bj\u0026oslash;rklund et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnthropogenic activities continue to increase the global atmospheric Hg pool, which has subsequently contaminated much of the world\u0026rsquo;s waterways (Hammerschmidt and Fitzgerald \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Pacyna et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Driscoll et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Depew et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In Texas, atmospheric Hg pools are not evenly distributed but are instead concentrated downwind of areas dominated by industrial activity, particularly coal-fired power plants (Menounou and Presley 2002; Drenner et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; NADP 2018). Consequently, aquatic ecosystems within these \u0026ldquo;hot zones\u0026rdquo; are observed to be highly contaminated with Hg (Smith et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Drenner et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and it is logical to predict that accumulation of Hg in higher trophic level species should be elevated compared to those in less contaminated areas (Wiener et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Wentz et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Despite this general supposition, relatively few studies have specifically compared Hg concentrations within higher trophic level species from areas exposed to different Hg loadings by aerial deposition. With this in mind, we set out to test this hypothesis by studying a large, long-lived apex predator, Alligator Gar (\u003cem\u003eAtractosteus spatula\u003c/em\u003e), in areas predicted to receive high and moderate levels of atmospheric Hg deposition.\u003c/p\u003e \u003cp\u003eAlligator Gar are the largest and longest-lived gar species in the world, boasting lengths upwards of 2.4 m, and age estimates to 95 years (Daugherty et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). As important apex predators, they maintain the health and function of ecosystems by removing unhealthy individuals; however, their feeding ecology, in combination with long lifespan, exposes them to potentially high levels of Hg (Scarnecchia \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Barst et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Harried et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In fact, contemporary studies show total Hg (THg) concentrations in their tissues exceed 0.8 parts per million wet weight (mg/kg ww), which is over twice the advised concentration amount in consumable fish tissue by the USEPA (Karimi et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Harried et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Such high Hg body burdens likely contributes to poor recruitment of at-risk Alligator Gar populations, which are already declining because of habitat loss from flood control structures and harvest-oriented fishing (Smith et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Case in point, THg concentrations in Alligator Gar from the Trinity River were higher than those known to cause reproductive impairment in other taxa, and egg THg levels were high enough to contribute to decreased hatch rate, impaired growth and development, and reduced survival of larvae (Harried et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Furthermore, Alligator Gar are consumed by humans, and it is important to note that a single Alligator Gar can contain over one hundred servings, thus representing a tremendous total mass of Hg being consumed. The meat from a single fish can easily exceed the reference dose for chronic oral exposure to MeHg (0.1 \u0026micro;g mercury / kg body weight / day) for an adult human for an entire year. Accordingly, Hg concentrations in Alligator Gar within \u0026ldquo;hot zones\u0026rdquo; should inform policy towards conservation efforts and consumption advisories.\u003c/p\u003e \u003cp\u003eThis study examines how well Hg accumulation in an apex predator reflects aerial deposition by comparing Hg concentrations in Alligator Gar tissues from areas exposed to high and moderate atmospheric Hg deposition, as defined by the National Atmospheric Deposition Program (NADP 2023). Specifically, the moderate deposition area is the inland Brazos River, proximal to the city of Waco, Texas, USA; and the high deposition area is the coastal Brazos River adjacent to the city of Lake Jackson, Texas, USA. We hypothesized that Alligator Gar in the moderate deposition zone (inland Brazos River) would exhibit lower amounts of Hg in their tissues compared to populations from the higher deposition zone (coastal Brazos River) (Engle et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Lin et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Additionally, to evaluate the relative importance of regional variation in food web dynamics and trophic position on biomagnification, we also include an analysis of stable isotopes of nitrogen (δ\u003csup\u003e15\u003c/sup\u003eN) from these respective areas (Smylie et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e \u003cem\u003eSample Sites\u003c/em\u003e \u003c/p\u003e \u003cp\u003eOur sample areas were the inland and coastal regions of the Brazos River located near the cities of Waco and Lake Jackson, Texas, USA (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The inland Brazos River is characterized by long periods of low-flow with intermittent high-flow periods, while the coastal Brazos River is representative of large tidal-river habitats. Based on imagery data from the National Atmospheric Deposition Program, the middle Brazos River is exposed to moderate amounts of atmospheric Hg compared to high amounts in the lower Brazos River (NADP 2023). The lower Brazos River near Lake Jackson, Texas has high atmospheric Hg due to its proximity to intense industrial activity in Houston, Texas.\u003c/p\u003e \u003cp\u003e \u003cem\u003eCollection Methods and Tissue Processing\u003c/em\u003e \u003c/p\u003e \u003cp\u003eAlligator Gar were collected using experimental multifilament gillnets that were 64 m long and 3.04 m deep with mesh size ranging 88.9-139.7 mm (Bonar et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Richardson and Flinn 2019). Gillnets were set perpendicular to current, 25\u0026ndash;100 m apart, and monitored continuously (Richardson and Flinn 2019). Efforts were made to collect sizes of fish at each site that were representative of the overall population to reduce variability and size bias.\u003c/p\u003e \u003cp\u003eFollowing capture, total length (mm) of each fish was recorded, and Floy\u0026reg; T-Bar and PIT tags with unique identification numbers and contact information were implanted next to, and underneath the dorsal fin, respectively. As Alligator Gar are a species of conservation concern, we elected to use nonlethal muscle biopsies, rather than whole fillets, to reduce unnecessary mortality. We justified this decision based on previous research from Stahl et al. (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), who demonstrated a lack of significant differences in Hg concentrations between muscle biopsies and fillets (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.973).\u003c/p\u003e \u003cp\u003eMuscle biopsies were collected by making an incision along the bottom of a scale before using a pair of sharp wire cutters to cut the boundary edges. This disconnected the scale on three sides, which could then be lifted to access the underlying muscle tissue. Scissors and forceps were used to remove and transfer approximately 1 g of muscle tissue into a 2-mL plastic tube. The scale was then realigned to its correct position to promote healing. Following removal, biopsies were immediately stored in a cooler with ice, and fish were safely released. In the lab, muscle biopsies were lyophilized using a VirTis Benchtop Pro with Omnitronics (SP/ATS, Warminster, Pennsylvania, USA) for 24\u0026ndash;48 h using automatic manufacturer settings for freeze-drying. Average weight decrease due to water loss during freeze drying was calculated at 77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28%.\u003c/p\u003e \u003cp\u003e \u003cem\u003eMercury Analysis\u003c/em\u003e \u003c/p\u003e \u003cp\u003eMercury samples were prepared for analysis by first preparing eight calibration standards and one certified reference material (CRM) to run in conjunction with samples. Calibration standards were established at 0, 0.5, 1.0, 2.0, 4.0, 8.0, 10.0, and 20 ng/mL and 0.3 g of DORM-4 Fish Protein CRM was used (National Research Council Canada). Samples were digested by first transferring approximately 0.1 g of lyophilized muscle tissue to a glass test tube and then introducing 1 mL of water and 3 mL of 1:1 nitric acid (HNO\u003csub\u003e3\u003c/sub\u003e). The digestate mixture was placed into an Environmental Express HotBlock 150 (Charleston, South Carolina, USA) at 95\u0026deg;C for 15 min, after which it was allowed to cool to room temperature before adding 1.5 mL of concentrated HNO\u003csub\u003e3\u003c/sub\u003e and 0.5 mL of hydrochloric acid (HCl). This digestate was returned to the 95\u0026deg;C hot block for a further 30 min before being removed and cooled to room temperature. Lastly, 500 \u0026micro;L of water and 1.5 \u0026micro;L of 30% hydrogen peroxide (H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e) were added to the digestate to oxidatively liberate Hg(II) and aid in digestion during the final incubation period at 95\u0026deg;C for 60 min. After cooling, the digested sample was filtered through a Fisherbrand\u0026trade; filter (comes pretreated with HCl and neutralized nanopure water) to remove particles and diluted to 10 mL in a 10-mL volumetric flask with nanopure water. At this point, the sample was considered ready for analysis.\u003c/p\u003e \u003cp\u003eMercury analysis was conducted on a CETAC M-8000 QuickTrace cold vapor atomic fluorescence (CVAF) direct mercury analyzer (DMA) equipped with a CETAC ASX-520 autosampler (Faust et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Teledyne Leeman Labs, Mason, Ohio, USA). Before operation, the DMA was calibrated using the 0, 0.5, 1.0, 2.0, 4.0, 8.0, 10.0, and 20 ng/mL standards with correlation coefficients over 0.997 considered appropriate. Stannous chloride reagent was used to improve the absorbance signal, and a rinse of 98% water, 1% HCl, and 1% HNO\u003csub\u003e3\u003c/sub\u003e was used to clear the system. Continuous argon gas was used to purge the system. To avoid issues with quality control, samples were digested and analyzed in batches of 24. Mercury concentrations within tissue samples were assessed using QuickTrace software and converted to mg/kg of tissue. Samples were corrected based on recovery percentages of DORM-4 which ranged from 88\u0026ndash;99%. The limit of detection and limit of quantification from the CETAC M-8000 was calculated as 0.007 mg/kg, and 0.02 mg/kg, respectively. Dry weight concentrations were converted to wet weight using the following equation:\u003c/p\u003e \u003cp\u003eConcentration Wet Weight = [(100-% water loss)/100] x Concentration Dry Weight.\u003c/p\u003e \u003cp\u003e \u003cem\u003eStable Isotope Analysis\u003c/em\u003e \u003c/p\u003e \u003cp\u003eApproximately 1\u0026ndash;2 mg of lyophilized muscle was placed into ultra-pure tin capsules for analysis at the Baylor University Stable Isotope Laboratory. Nitrogen isotopes were analyzed using a Thermo-Electron Delta V Advantage Isotope Ratio Mass Spectrometer (IRMS) with a Dual Inlet system (Thermo Scientific). Isotopes were reported as parts per thousand (\u0026permil;) differences from a corresponding standard as calculated by:\u003c/p\u003e \u003cp\u003eδX = [(Rsample/Rstandard)\u0026thinsp;\u0026minus;\u0026thinsp;1] \u0026times; 10\u003csup\u003e3\u003c/sup\u003e, where R\u0026thinsp;=\u0026thinsp;15N/14N.\u003c/p\u003e \u003cp\u003eThe used standard was nitrogen in air, and the machine was calibrated using certified reference materials (USGS-40 and USGS-41). The standard deviation of the internal laboratory standard (Acetanilide; Indiana University, USA) was 0.16\u0026permil; for δ\u003csup\u003e15\u003c/sup\u003eN.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eGeneralized linear models (GLMs) were created in R version 4.2.2 (R Core Team \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) to estimate effects on Hg and δ\u003csup\u003e15\u003c/sup\u003eN from Length, Region (Inland versus Coastal), and potential interactions. When estimating effects on δ\u003csup\u003e15\u003c/sup\u003eN, we only included sizes of fish between the lengths of 1000\u0026ndash;1700 mm, because these sizes aligned with the limited number of δ\u003csup\u003e15\u003c/sup\u003eN samples (n\u0026thinsp;=\u0026thinsp;10) analyzed from the coastal region. Similarly, we estimated effects on Hg with this reduced size range (1000\u0026ndash;1700 mm) to aid in direct comparisons to the δ\u003csup\u003e15\u003c/sup\u003eN results, but relied primarily on a second Hg model that included all sizes (Inland n\u0026thinsp;=\u0026thinsp;48, Coastal n\u0026thinsp;=\u0026thinsp;45).\u003c/p\u003e \u003cp\u003eData were visualized to assess appropriate error distributions for subsequent analyses. Based on the skewed data distribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), we elected to use a Gamma error distribution with \u0026ldquo;identity\u0026rdquo; link. We also log-transformed Length because it exhibited a right-skewed distribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Akaike Information Criterion for small sample sizes (AICc) was used for model comparison and selection. We observed that models including an interaction term for Region and Length exhibited higher delta-AICc values of 1.83 for the 1000\u0026ndash;1700 mm Hg model, 1.44 for the all-sizes Hg model, and 1.64 for the δ\u003csup\u003e15\u003c/sup\u003eN model. Models with interaction terms also exhibited a higher variance inflation than base models. Therefore, we selected the most parsimonious model which included only the main effects of Region and Length (no interaction). Lastly, we used the estimated marginal means (EMMEANS) package (R Core Team 2021) to make post hoc pairwise comparisons of means between Coastal and Inland populations while holding Length constant.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe also estimated exceedance probabilities for World Health Organization (WHO, 0.15 mg/kg ww), United States Environmental Protection Agency (USEPA, 0.3 mg/kg ww), and State of Texas (TXDSHS, 0.7 mg/kg ww) consumption advisory guidelines for women of childbearing age and children under the age of 12 with individual fish with [Hg]\u0026thinsp;\u0026ge;\u0026thinsp;consumption advisory. Models were fit using GLMs with the binomial family and \u0026ldquo;logit\u0026rdquo; link function. Region and Length were main effects in each model, with Hg concentration (by individual fish) coded as a binary response variable based on each organization\u0026rsquo;s consumption advisory threshold (0\u0026thinsp;=\u0026thinsp;below consumption advisory threshold, 1\u0026thinsp;\u0026ge;\u0026thinsp;consumption advisory).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total number of 93 Alligator Gar were captured, with 45 sampled from the inland Brazos River population and 48 from the coastal Brazos River population. Alligator Gar sampled from the inland population were larger on average than the coastal population with mean lengths of 1,462 mm (range\u0026thinsp;=\u0026thinsp;762\u0026ndash;2,348 mm) and 1,278 mm (range: 913- 1,649 mm), respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAll three GLM models suggested Length and Region significantly affected Hg concentrations and δ\u003csup\u003e15\u003c/sup\u003eN in Alligator Gar (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The main effect of Region indicated Hg concentrations between Inland and Coastal populations were significantly different for both the 1000\u0026ndash;1700 mm Hg model (COASTAL\u0026thinsp;=\u0026thinsp;0.139, 95% CL\u0026thinsp;=\u0026thinsp;0.113\u0026ndash;0.164 vs. INLAND\u0026thinsp;=\u0026thinsp;0.226, 95% CL\u0026thinsp;=\u0026thinsp;0.178\u0026ndash;0.275; p\u0026thinsp;=\u0026thinsp;0.002) and the all sizes Hg model (COASTAL\u0026thinsp;=\u0026thinsp;0.143, 95% CL\u0026thinsp;=\u0026thinsp;0.118\u0026ndash;0.168 vs. INLAND\u0026thinsp;=\u0026thinsp;0.232, 95% CL\u0026thinsp;=\u0026thinsp;0.193\u0026ndash;0.272; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, δ\u003csup\u003e15\u003c/sup\u003eN values between the Inland and Coastal populations were significantly different (COASTAL\u0026thinsp;=\u0026thinsp;16.72, 95% CL\u0026thinsp;=\u0026thinsp;16.10\u0026ndash;17.30 vs. INLAND\u0026thinsp;=\u0026thinsp;18.81, 95% CL\u0026thinsp;=\u0026thinsp;18.31\u0026ndash;19.20; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026mdash;. Results of the generalized linear models (GLMs) explaining mercury (Hg) and nitrogen isotopes (δ\u003csup\u003e15\u003c/sup\u003eN) in Alligator Gar from the inland and coastal Brazos River.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel: Response\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ez\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eHg 1000\u0026ndash;1700 mm\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elog(Length)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eNull deviance: 39.07 on 78 df; Residual deviance 31.26 on 76 df\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eHg All Sizes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elog(Length)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eNull deviance:43.24 on 92 df; Residual deviance 33.16 on 90 df\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eδ\u003c/b\u003e\u003csup\u003e\u003cb\u003e15\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eN 1000\u0026ndash;1700 mm\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-25.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elog(Length)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eNull deviance: 0.32 on 46 df; Residual deviance 0.15 on 44 df\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAlligator gar from Inland populations were significantly more likely to exceed both WHO and USEPA guidelines than Coastal populations (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). WHO and USEPA exceedance probabilities, after controlling for length (mean\u0026thinsp;=\u0026thinsp;1367 mm), were 0.414 (95% CL\u0026thinsp;=\u0026thinsp;0.273\u0026ndash;0.570) and 0.048 (0.012\u0026ndash;0.176) for Coastal and 0.835 (0.686\u0026ndash;0.921) and 0.276 (0.164\u0026ndash;0.426) for Inland populations, respectively. However, WHO and USEPA exceedance probability estimates for fish\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;2000 mm climbed to 0.747 (95% CL: 0.471 - \u0026ge;0.999) and 0.146 (0.001\u0026ndash;0.371) for Coastal and \u0026ge;\u0026thinsp;0.999 (0.921- \u0026ge;0.999) and 0.559 (0.323\u0026ndash;0.795) for Inland populations, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of generalized linear models (binomial family, link= \u0026ldquo;logit\u0026rdquo;) used for estimation of exceedance probabilities for World Health Organization (WHO) and United States Environmental Protection Agency (USEPA) consumption advisory concentrations of Hg in alligator gar. df\u0026thinsp;=\u0026thinsp;degrees of freedom.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel: Response\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ez\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWHO: \u0026ge;0.15 mg/kg Hg\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-27.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elog(Length)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eNull deviance: 126.50 on 92 df; Residual deviance: 97.633 on 90 df\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUSEPA: \u0026ge;0.30 mg/kg Hg\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-25.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elog(Length)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0234\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eNull deviance: 100.96 on 92 df; Residual deviance: 79.46 on 90 df\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn contrast to expectations, results of this study demonstrate that Alligator Gar from the inland Brazos River have higher mean Hg muscle concentrations than coastal Alligator Gar. This refutes our initial hypothesis that increased atmospheric Hg deposition around Lake Jackson, Texas coupled with accumulation and biomagnification processes would result in higher Alligator Gar Hg concentrations. Additionally, we observed the inland Alligator Gar population had higher δ\u003csup\u003e15\u003c/sup\u003eN values which suggests the magnitude of Hg biomagnification through trophic levels in this region is likely higher than the coastal region. We conclude that regional variation in food web dynamics is an important modulator of Hg uptake into Alligator Gar, and likely drives the observed elevated Hg body burdens in gar from the inland Brazos River population. While aerial deposition rates are certainly an important factor, they are likely not the main drivers of observed differences in Hg body burdens in this study.\u003c/p\u003e \u003cp\u003eAccumulation of Hg into food webs is linked to bioavailability of Hg in the environment, and production of bioavailable MeHg is positively associated with certain biogeochemical conditions conducive to Hg methylation, specifically anoxia, slightly acidic pH, high dissolved nutrients, warm temperatures, and the presence of sulfur reducing bacteria (Hammerschmidt and Fitzgerald \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Hutcheson et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Farmer et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Driscoll et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Chen et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Smylie et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Conversely, bioavailable Hg is negatively correlated with salinity, chiefly because higher salinity environments have an abundance of sulfate which when reduced to sulfide can remove Hg from methylation pathways in sulfate reducing bacteria and consequently limit Hg and other cation availability (Compeau and Bartha \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Devereux et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Benoit et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; King et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Fry and Chumchal \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). A further cause for reduced Hg bioavailability is biodilution, a process where nutrient dynamics, primary production, and trophic state (i.e., ecosystem-level characteristics) limit the amount of bioavailable Hg into the food web (Rypel \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This creates negative correlations between phytoplankton density and Hg burdens in phytoplankton and their consumers, particularly fishes (Chen and Folt \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Such complexity is difficult to pattern, and we acknowledge such effects on Hg bioavailability from habitat type, geological morphology, phytoplankton density, and water chemistry among our two study regions. These need to be further assessed to understand accumulation into the food web. However, we maintain the idea that the coastal region could have less bioavailable Hg due to biodilution processes or from being immobilized in Hg sulfides. Because of this, primary consumers and benthic foraging species from coastal regions may be less likely to encounter bioavailable Hg while feeding and thus less Hg would accumulate into the basal level of the food web and Alligator Gar prey (Benoit et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Marziali et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe primary route of Hg uptake in fish is diet with trophic feeding level and biogeochemical factors governing biomagnification processes into diet items (Hall et al.1997; Zillioux \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In response, we speculate the reason for dissimilarity of Hg concentrations in Alligator Gar between our two regions is likely linked to different Hg levels in their prey species (Hall et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). For example, studies show Alligator Gar are opportunistic predators on the most abundant prey items available in their environments, with inland Alligator Gar feeding primarily upon Gizzard Shad (\u003cem\u003eDorosoma cepedianum\u003c/em\u003e) and buffalo (\u003cem\u003eIctiobus sp.\u003c/em\u003e), and coastal Alligator Gar eating mainly Gulf Menhaden (\u003cem\u003eBrevoortia patronus\u003c/em\u003e) and Striped Mullet (\u003cem\u003eMugil cephalus\u003c/em\u003e) (Smith et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Marsaly et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In the literature, we observed that Hg concentrations in tissue from these respective prey species appear to reflect those of the Alligator Gar in this study. Case in point, buffalo and Gizzard Shad Hg concentrations from inland freshwater systems reportedly range from 0.28\u0026ndash;1.04 mg/kg and 0.08\u0026ndash;0.22 mg/kg, respectively, whereas Striped Mullet and Gulf Menhaden Hg from estuarine environments exhibit lower concentrations of 0.049\u0026ndash;0.057 mg/kg and 0.023\u0026ndash;0.03 mg/kg, respectively (Chalmers et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Chumchal et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Fry and Chumchal \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Gold Quiros \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). While these values do not come from fish in our sample regions, they are from similar type habitats and collectively provide a plausible explanation for why we observed differences in Alligator Gar Hg in our two regions.\u003c/p\u003e \u003cp\u003e Biomagnification along with trophic feeding level are other key factors affecting Hg uptake and ones we believe most contributed to regional differences in Hg concentrations of the studied Alligator Gar. A myriad of studies show Hg increases with fish size and age along with their trophic status (Kidd et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Eagles-Smith et al. 2008; Depew et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Poste et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Bradley et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This is an important consideration because inland Alligator Gar prey upon buffalo that live anywhere from 60 to 112 years (Smallmouth and Bigmouth Buffalo, respectively) which is 10 to 20 times longer than Gizzard Shad, Striped Mullet, and Gulf Menhaden (Lackmann et al. 2019; Long et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Additionally, buffalos grow considerably larger, and this combined with longer lifespans means the amount of Hg in buffalo ingested by an Alligator Gar could be higher than other prey species. Further supporting our biomagnification hypothesis were our observed δ\u003csup\u003e15\u003c/sup\u003eN values from inland Alligator Gar being ~\u0026thinsp;2\u0026permil; higher than coastal Alligator Gar. In the literature, we observed δ\u003csup\u003e15\u003c/sup\u003eN values reported in Smallmouth Buffalo ranged from 13\u0026ndash;16\u0026permil;, and Gulf Menhaden and Mullets ranged from 8\u0026ndash;14\u0026permil; (Akin and Winemiller \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Lebreton et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Olsen et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Coulter et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Keppeler et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Again, we acknowledge that these reported values do not come from our sample areas, which is a limitation that could be addressed in future research. However, these patterns do support our supposition that biomagnification of Hg into Alligator Gar is a result of regional diet differences and Hg levels in their prey.\u003c/p\u003e \u003cp\u003eRegarding accumulation and biomagnification of Hg into Alligator Gar, we expected Hg to increase with length and age like other fish species (Murphy et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Barbosa et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Gewurtz et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Bradley et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, we observed a weak relationship between Hg and length in both the inland and coastal populations. This result was similar to Smylie et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), who reported Hg in Longnose Gar (\u003cem\u003eLepisosteus osseus\u003c/em\u003e) to have a weak relationship with length. This result could be due to sex-ratios with multiple studies suggesting they are a major factor impacting Hg concentrations. For example, Harried et al. (2021) suggested female Alligator Gar could offload Hg into their eggs after they spawn, similar to other large predators like Goliath Grouper (Malinowski et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and Smylie et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) observed that male Longnose Gar had higher Hg concentrations than females. These are major considerations because female Alligator Gar exhibit sex-specific growth rates, living longer and growing larger than males (Daugherty et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Simultaneously, a male the same age as a female will be much smaller, however they would have had as much time to bioaccumulate Hg, which could be the reason for higher Hg concentrations in mid-length individuals.\u003c/p\u003e \u003cp\u003eAlligator Gar in this study presented Hg concentrations shown to cause reproductive impairment in fish and exceed human consumption limits. Levels of 0.2 mg/kg wet weight have been demonstrated to reduce fish plasma testosterone and 17beta-estradiol, inhibit gonadal development, and reduce spawning success (Drevnick and Sandheinrich \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Sandheinrich et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). We observed that 36% (34/93) of Alligator Gar in our study exceeded 0.2 mg/kg, with inland region Alligator Gar representing a higher percentage of individuals over this threshold compared to the coastal population (57%, 26/45 versus 17%, 8/48). This indicates that the inland population may be at a greater risk from Hg-induced reproductive impairment (Crump and Trudeau \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Additionally, we observed 58% (54/93), 12% (11/93), and 0% percent of the fish in this study exceeded the levels set by the WHO (0.15 mg/kg ww), USEPA (0.3 mg/kg ww), and TXDHS (0.7 mg/kg ww), respectively, which illuminates the inconsistency of Hg regulations between world, federal and state agencies. Our exceedance analysis demonstrates that Alligator Gar, especially those\u0026thinsp;\u0026gt;\u0026thinsp;1600mm, have an increased probability of exceeding the consumption advisories set by these respective agencies. This observation applies most to Alligator Gar from the inland region, with almost all sizes of fish having\u0026thinsp;\u0026gt;\u0026thinsp;50% chance of exceeding WHO consumption advisories, and fish\u0026thinsp;\u0026gt;\u0026thinsp;1600 mm were nearly 100% likely to exceed this consumption limit. In response to these observations, we suggest that potential reproductive impairment and risks associated with human consumption (particularly in fish\u0026thinsp;\u0026gt;\u0026thinsp;1600 mm) should be considered, in addition to conservation concerns, when crafting Alligator Gar fishing regulations.\u003c/p\u003e \u003cp\u003eThis research creates additional questions that merit further study. First, the literature suggests atmospheric deposition of Hg to be highly impacted by anthropogenic industrial activities, and that increased atmospheric deposition is linked to higher concentrations in aquatic life (Hammerschmidt and Fitzgerald \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Hutcheson et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). This served as the basis of our primary hypothesis that the coastal region south of Houston, Texas, USA would be exposed to larger amounts of atmospheric Hg deposition than the inland region, as was indicated by the NADP database, which would facilitate higher Hg concentrations in coastal Alligator Gar. This was not the case, as we observed higher Hg concentrations in the inland Alligator Gar, compared to coastal Alligator Gar. In response, we suggest future studies examine the pathways of bioavailable Hg in these two regions. It is possible that the coastal region contains more Hg, however, due to sulfates and different geochemical processes, its availability may be limited. In addition, our observed δ\u003csup\u003e15\u003c/sup\u003eN values gave us reason to theorize that inland Alligator Gar may be feeding at a higher trophic level, meaning that biomagnification is higher inland than at the coast (Smylie et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Eagles-Smith et al. 2008). However, without baseline values we are unable to definitively make this conclusion, and therefore suggest future studies focus on tracing Hg and δ\u003csup\u003e15\u003c/sup\u003eN transfer to Alligator Gar through the food web, with a focus on contributions from buffalos, Gizzard Shad, Striped Mullet, and Gulf Menhaden. We additionally recommend that future studies consider other factors impacting Hg concentration differences in Alligator Gar, such as sex-ratios, age, and growth.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZ.S.M wrote the main manuscript text, assisted with sample collections, figure preparation, and mercury and istotope analysisM.T.P assisted with mercury analysisG.P.C assisted with mercury analysis and project conceptualizationM.S.B assisted with sample collectionR.S.K assisted with project conseptualization, statistical analysis, sample collection, and figure preparationC.W.M assisted with project conceptualization, and sample collectionAll authors reviewed the manuscript\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis research was funded by the C. Gus Glasscock, Jr. Fund for Excellence in Environmental Sciences at Baylor University along with research support from the Center for Reservoir and Aquatic Systems Research (CRASR) and Environmental Science and Biology departments at Baylor University. Special thanks to Texas Parks and Wildlife Department employees who provided research support. The authors would also like to acknowledge the following volunteers: Bianca Possamai, Chad Mansfield, Fallon Bain, and Trevor Troxell.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConflict of Interest:\u003c/strong\u003e \u003cp\u003eCole W. Matson is the Editor-in-Chief of \u003cem\u003eEcotoxicology\u003c/em\u003e. The other authors declare no competing interests for this manuscript.\u003c/p\u003e \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkin S, Winemiller KO (2008) Body size and trophic position in a temperate estuarine food web. 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Environ Pollut Bioavailab. 33: 76-87. https://doi.org/10.1080/26395940.2021.1920468\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Mercury, Alligator Gar, Biomagnification, δ15N","lastPublishedDoi":"10.21203/rs.3.rs-4009895/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4009895/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe compared mercury (Hg) and stable isotopic ratios of nitrogen (δ\u003csup\u003e15\u003c/sup\u003eN) in a long-lived apex predator, Alligator Gar (\u003cem\u003eAtractosteus spatula\u003c/em\u003e), from a coastal region of the Brazos River exposed to high aerial Hg deposition, to an inland population exposed to moderate Hg deposition, in order to test the relative importance of biomagnification through trophic dynamics and aerial deposition rates in an apex predator. We used generalized linear models (GLMs) to examine the effects of fish size (Length, mm) and Region (Inland versus Coastal) on Hg concentration and δ\u003csup\u003e15\u003c/sup\u003eN. Length had a significant positive effect on both Hg and δ\u003csup\u003e15\u003c/sup\u003eN. However, after accounting for the effect of Length, both Hg and δ\u003csup\u003e15\u003c/sup\u003eN were significantly higher in the Inland population (N\u0026thinsp;=\u0026thinsp;48; mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u0026thinsp;=\u0026thinsp;0.232\u0026thinsp;\u0026plusmn;\u0026thinsp;0.020 mg/kg ww and 18.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.184\u0026permil;, respectively) than the Coastal population (N\u0026thinsp;=\u0026thinsp;45; mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u0026thinsp;=\u0026thinsp;0.143\u0026thinsp;\u0026plusmn;\u0026thinsp;0.012 mg/kg ww and 16.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.291\u0026permil;, respectively). We further estimated probabilities of Alligator Gar exceeding Hg consumption advisory guidelines used by the World Health Organization (WHO) and the United States Environmental Protection Agency (USEPA). WHO and USEPA exceedance probabilities were 0.414 and 0.048 for Coastal, and 0.835 and 0.276 for Inland populations, respectively. However, WHO and USEPA exceedance probability estimates for fish\u0026thinsp;\u0026ge;\u0026thinsp;2000 mm climbed to 0.747 and 0.146 for Coastal and \u0026ge;\u0026thinsp;0.999 and 0.559 for Inland populations, respectively. These results suggest that variation in food web dynamics, and resultant impacts on biomagnification, may be a more important driver of Hg uptake in Alligator Gar, when compared to the role of aerial deposition rates. Our results also demonstrate that Alligator Gar often exceed consumption advisory Hg concentrations, particularly in the largest individuals, and that they likely experience some level of reproductive toxicity because of sublethal Hg exposures.\u003c/p\u003e","manuscriptTitle":"Regional variation in diet may be an underappreciated modulator of mercury uptake in species of concern: A case study using Alligator Gar (Atractosteus spatula)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-07 18:25:14","doi":"10.21203/rs.3.rs-4009895/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"388799f7-9cff-47cc-bb10-fa796b9baa36","owner":[],"postedDate":"March 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-28T08:53:55+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-07 18:25:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4009895","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4009895","identity":"rs-4009895","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}