A comparison of stable isotope values in epidermis and blood of nesting leatherback sea turtles in the Northwest Atlantic Ocean

Abstract

Leatherback sea turtles (Dermochelys coriacea) are highly migratory, which poses significant challenges for conservation efforts. This study explored the application of stable isotopes to better understand habitat use and behaviors of Florida’s nesting leatherbacks. Our objectives were to (1) identify potential foraging areas using clustering techniques based on isotopic patterns, (2) monitor changes in 𝛿¹³C and 𝛿¹⁵N isotope values across epidermis and whole blood during the nesting period, and (3) compare isotopic values between tissue types. Our analysis identified distinct isotopic clusters in 𝛿¹³C values, possibly representing specific foraging areas for this population; however, paired studies are needed using both satellite telemetry and stable isotope analysis. Whole blood isotope values remained relatively unchanged across nesting season for sampled individuals, whereas epidermis fluctuated, contrary to expectations of isotopic turnover for these tissues. The isotopic composition of epidermis and whole blood was significantly correlated, allowing for tissue-specific conversion equations. We suggest that studies on leatherbacks should consider the variability in different tissues, as we found blood samples had the least variation in isotopic composition across the nesting season. Together, these results provide new insight into leatherback foraging ecology and support the use of stable isotopes as a growing complementary tool for conservation monitoring.

Introduction

Migration plays a critical role in the life cycles of many marine species (Putman 2018). Sea turtles, like other marine megafauna, have extensive migrations that span various environments throughout different stages of their life cycles. As adults, their foraging grounds are often thousands of kilometers away from nesting sites (Fossette et al. 2010, Bond and James 2017). Additionally, individuals within the same genetic population can have distinct foraging habitats as adults (Robinson et al. 2016, Rider et al. 2024). To date, satellite telemetry has been the primary method used to investigate habitat-use patterns and behavior in sea turtles (Hays and Hawkes 2018). However, satellite telemetry is costly and alternative methods such as genetic (Komoroske et al. 2017) and stable isotope analysis (Figgener et al. 2019) offer valuable, more affordable and less invasive alternatives. In recent years, there has been a marked increase in using stable isotope analysis to better understand ecological behaviors and movement patterns of marine animals (Trueman and St John Glew 2019)

In marine environments, stable isotope composition of animal tissues can serve as an indicator of geographic origin due to naturally occurring environmental gradients. Most studies either use individuals at their foraging, breeding or stopover locations because these sites are known and are often more logistically feasible to sample. Common stable isotope analysis of light elements (C, H, N, O, and S) have been used in a range of taxa to investigate habitat-use and migratory paths (Hobson 2019, Busquets-Vass et al. 2021, Hanson et al. 2022). This approach, using stable isotopes, is possible because the ratios of naturally occurring elements change across large scales (McMahon et al. 2013). The differences in atomic mass of the stable isotopes influence the way isotope ratios change in the environment, body tissues, and metabolic pathways of an animal (Peterson and Fry 1987). Therefore, ratio of stable isotopes in the tissue of the animal can be used to indirectly infer the environment based on the relative enrichment (i.e., greater ratio of the heavy isotopes) or depletion (i.e., greater ratio of the lighter isotope). For carbon, this occurs because the δ¹³C values of primary producers are closely linked to dissolved inorganic carbon, that varies geographically due to differences in temperature, ocean circulation, CO₂ availability, and phytoplankton productivity across latitudinal gradients resulting in predictable spatial variation in δ¹³C values at the base of marine food webs (McMahon et al. 2013, Magozzi et al. 2017).Individuals feeding at the same trophic level exhibit lower carbon isotope (𝛿¹³C) values when occupying higher-latitude habitats (Takai et al. 2000, McMahon et al. 2013). Similarly, 𝛿¹³C values of individuals foraging in offshore (pelagic) regions are lower when compared to those in coastal (neritic) habitats, where inputs from terrestrial ecosystems can elevate 𝛿¹³C values (Burton and Koch 1999).

Variation in 𝛿¹⁵N values in marine primary producers occurs because of different processes by which nitrogen cycles in the environment: nitrogen fixation and denitrification (see Montoya 2007 for review). In primary producers, nitrogen fixation lowers and denitrification increases 𝛿¹⁵N values of the primary producers in the ocean. Inshore ecosystems often exhibit elevated 𝛿¹⁵N values due to anthropogenic or land-derived nitrogen inputs, whereas oceanic ecosystems relying on nitrogen fixation tend to show lower values (Landrum et al. 2011). Coastal upwelling can further elevate 𝛿¹⁵N values, although offshore transport of upwelled nutrients may blur these patterns (Walker and McCarthy 2012). These processes have been used to investigate habitat-use patterns in large scale marine species. In blue whales (Balaenoptera musculus) in the Eastern Pacific Ocean, there were differences in 𝛿¹⁵N values based on foraging location (Busquets-Vass et al. 2021). In Pacific leatherbacks, individual foraging grounds were successfully predicted based on relatively high and low 𝛿¹⁵N values (Seminoff et al. 2012).

Leatherback sea turtles (Dermochelys coriacea) are a useful species for an integrative isotopic study because they have distinct foraging areas, and a strong nest site fidelity to a region where they can be sampled throughout a nesting season and over multiple years. In the Northwest Atlantic Ocean (NWA), adult females typically forage in productive temperate and subpolar regions during the non-breeding period before migrating southward to subtropical and tropical nesting beaches, including those in Florida, where nesting generally occurs between March and July (Eckert et al. 2006, Bond and James 2017). Migration intervals between nesting and foraging grounds are usually two to three years (Caut et al. 2008), and they can nest up to 11 times during a season (Eckert et al. 2006, Plot et al. 2013). During the nesting season, females are thought to function largely as capital breeders, relying on stored energy reserves accumulated prior to migration and exhibiting reduced or limited foraging activity while nesting (Perrault et al. 2012, 2016). While at foraging grounds, they consume exclusively gelatinous zooplankton, which shows isotopic values that reflect regional food webs (Seminoff et al. 2012). Although gelatinous zooplankton is a poor energy source, individuals can consume up to 840 kg/day (Heaslip et al. 2012), which is needed to fuel migration and sustain the largest reproductive output of all reptile species (Perrault et al. 2012, Plot et al. 2013). The NWA nesting population in Florida have shown a steady upward population trajectory with their numbers increasing since standardized nest surveys began in 1979 at 11% per year, with current estimates at 2013 (Stewart et al. 2011, Florida Fish and Wildlife Conservation Commission 2026). Florida is the northernmost nesting extent of the NWA population, making this region potentially important for detecting shifts in nesting distribution and possible range expansion.

Understanding ecological functions of sea turtles using a stable isotope approach requires strategic selection of tissue types. Tissue-specific isotopic turnover, or the time over which a tissue integrates the environmental signal, is crucial for interpreting movement and foraging strategies respectively.Tissue turnover rates provide temporal information for interpreting the time period over which isotopic signals are integrated into biological tissues (Vander Zanden et al. 2015b). In hard-shelled turtles, epidermis tissue has been frequently used to determine foraging area origin, due to its ease of collection and turnover rate that is estimated to reflect the foraging period (Pajuelo et al. 2012, Seminoff et al. 2012, Vander Zanden et al. 2015a, Ceriani et al. 2017). Previous studies in juvenile green sea turtles (Chelonia mydas) noted differences in turnover rates among various tissues. Skin has the longest residence time (83 days for 𝛿¹³C and 67 days for 𝛿¹⁵N) relatively compared to red blood cells (RBCs; 79 days for 𝛿¹³C and 71 days for 𝛿¹⁵N), and whole blood (43 days for 𝛿¹³C and 35 days for 𝛿¹⁵N) (Reich et al. 2008, Seminoff et al. 2009). Conversely, plasma has a comparatively faster isotopic turnover (Seminoff et al. 2007, Reich et al. 2008, Vander Zanden et al. 2015b) and may be valuable for capturing short-term dietary changes across the nesting season. Because epidermis and whole blood have relatively long turnover times, their isotopic composition in nesting leatherbacks is expected to primarily reflect resource use prior to arrival at nesting beaches, especially if collected early in an individual’s nesting season .

Although relative turnover rates in leatherbacks are assumed to be comparable to those of hard-shelled species, leatherbacks exhibit unique physiological traits, such as endothermy (Bostrom et al. 2010, Davenport et al. 2015), larger body size, and distinct metabolic rates, which may influence tissue turnover and isotopic incorporation differently (Thomas and Crowther 2015, Vander Zanden et al. 2015b). Compared to hard-shelled species such as loggerhead sea turtles (Caretta caretta), leatherbacks generally undertake longer migrations between nesting and foraging habitats, often traveling to high-latitude pelagic foraging regions in the North Atlantic Ocean (Bond and James 2017). Both whole blood and epidermis are expected to be stable across the nesting season due to their turnover time, but observations have been inconsistent in sea turtles, including leatherbacks (Caut et al. 2008, Seminoff et al. 2009, Fouda et al. 2024). Physiological processes associated with fasting may also influence stable isotope values in nesting leatherbacks. During periods of fasting or nutritional stress, catabolism of endogenous tissues can alter isotopic composition because lighter isotopes are often preferentially metabolized and excreted, potentially enriching tissues in ¹⁵N (Fuller et al. 2005, Hertz et al. 2015). Because of the duration that leatherbacks spend while at their nesting sites, it is predicted that tissue with relatively faster turnover rate will show a greater change. These physiological effects are important to consider when interpreting stable isotope values in nesting turtles, particularly because most turnover estimates have been derived from hard-shelled species rather than reproductive leatherbacks.

In this study, we used stable isotope analysis of epidermis, whole blood, and RBCs to investigate the habitat-use patterns of nesting leatherback sea turtles in Florida, USA. By analyzing stable carbon (𝛿¹³C) and nitrogen (𝛿¹⁵N) values, we aimed to examine the isotopic variability as a proxy to identify different foraging areas . Furthermore, we assessed isotopic variability in both epidermis and blood across the nesting season to determine whether tissue isotope values remained stable or fluctuated because of potential physiological changes, fasting, or other metabolic processes. Finally, we examined the isotopic relationship between different tissue types to facilitate cross-study comparisons. Understanding these dynamics is important for the application of stable isotopes in ecological monitoring, particularly when using these data to infer long-term habitat use.

Methods
Tissue collection
Samples were collected from nesting leatherbacks on Juno and Jupiter Beaches, Florida, USA from March to July between 2014–2022 (Table 1). Skin and blood sample collection occurred during the nesting fixed action pattern after ~50% of the clutch was deposited (Dutton and Mcdonald 1994). The tissue collection protocol was approved by the University of Florida Institutional Animal Care and Use Committee (Study #202001985) and Florida Fish and Wildlife Conservation Commission (Marine Turtle Permits #16 and #205).

Skin samples were collected from the hind limb during egg deposition using a sterile 6 mm biopsy punch (Thomson et al. 2012). After skin collection, the site was cleaned with a 70% isopropyl alcohol swab. Skin samples were stored in a cryovial with 70% ethanol at room temperature until analysis. Samples were preserved in ethanol because this method has previously been shown to have minimal effects on the stable isotope values of sea turtle epidermis (Barrow et al. 2008)

To collect blood, the venipuncture site was cleaned with a 70% isopropyl alcohol swab before insertion of the needle. Approximately 1 mL of blood was collected from the femoral rete system aseptically using a 20-gauge x 1.5” Monoject (Sherwood Medical, St. Louis, MO, USA) venous collection needle fitted into a BD vacutainer tube holder (Perrault et al. 2012). Red blood cells were isolated by centrifuging whole blood using a Hettich Rotofix 32A Centrifuge (Tuttlingen, Germany) at 4000 g (5000 RPM) for five minutes prior to freezing in an ultralow freezer until analyses.

Stable isotope analysis

Stable isotope analysis for carbon and nitrogen was conducted at the Light Stable Isotope Mass Spec Lab in the Department of Geological Sciences at the University of Florida. Skin samples were processed by first removing the epidermis from the dermis using a scalpel, subsequently rinsing the samples with distilled water, and then cutting each sample into 10–20 smaller pieces. All tissues were dehydrated at 60°C for a minimum of 24 hours. After dehydration, blood samples were homogenized into a fine powder utilizing a pestle.

From each tissue type, a mass ranging from 0.45 to 0.55 mg was weighed into tin capsules. Two instrument combinations were used for measuring elemental compositions (% C and % N) and stable isotope values. For samples collected from 2014–2019, a COSTECH ECS 4010 elemental analyzer interfaced via a Finnigan™-MAT ConFlow III device to a Finnigan™-MAT DeltaPlus XL isotope ratio mass spectrometer was used. Samples from 2020–2022 were measured using Thermo IsoLink elemental analyzer interfaced via a ConFlo IV to a Thermo Delta V Advantage isotope ratio mass spectrometer.

Results are expressed in delta notation using a per mil scale with the standard equation (Coplen 2011):

𝛿X=([] -1)

Where X is ¹³C or ¹⁵N, and R_sample and R_standard are the ratios of heavy to light isotope (¹³C/¹²C and ¹⁵N/¹⁴N) of the element in the sample and standard, respectively. All results are referenced to Vienna Pee-Dee Belemnite for carbon isotope values, and to air for nitrogen isotope values. The reference materials USGS40 (L-glutamic acid with isotopic compositions of 𝛿¹³C = –26.24 ‰ and 𝛿¹⁵N = –4.52 ‰), USGS41 (L-glutamic acid with isotopic compositions of 𝛿¹³C = 37.76 ‰ and 𝛿¹⁵N = 47.57 ‰), and USGS41a (L-glutamic acid with isotopic compositions of 𝛿¹³C = 36.55 ‰ and 𝛿¹⁵N = 47.55 ‰) were used in a linear normalization of results. To examine analytical precision with a matrix-matched working standard, homogenized loggerhead (Caretta caretta) scute keratin was used. The standard deviation of the scute reference material was 0.13 ‰ for 𝛿¹³C values and 0.09 ‰ for 𝛿¹⁵N values (n = 14).

Statistical analysis

All statistical analyses were conducted in R version 4.3.3 (R Core Team, 2024). Results where p-values < 0.05 were considered statistically significant. To evaluate potential differences among epidermis, whole blood, and RBCs, we performed a multivariate permutational analysis of variance (PERMANOVA; Anderson and Walsh 2013) using both δ¹³C and δ¹⁵N values simultaneously. Analyses were conducted using the vegan package (Oksanen et al. 2024) with Euclidean distance matrices and 999 permutations.

To examine if stable isotope values (𝛿¹³C and 𝛿¹⁵N) changed across nesting seasons, PERMANOVA was conducted independently for each tissue type (epidermis and whole blood). Red blood cells were not used in this analysis because this tissue was only collected in one nesting season (2022). We used betadisper function in the vegan package (Oksanen et al. 2026) to assess unequal variance among years. Analyses among years were performed using the pairwiseAdonis package (Arbizu 2017), applying 999 permutations and a Euclidean distance matrix. P-values were adjusted for multiple comparisons using the false discovery rate method. Separate comparisons were conducted for 𝛿¹³C and 𝛿¹⁵N values to test for differences among nesting years within each tissue type.

To assess the clustering patterns of 𝛿¹³C and 𝛿¹⁵N values across different tissue types, a model-based clustering approach was implemented using the mclust package (Scrucca et al. 2023). Gaussian mixture models with one to six clusters were evaluated using Bayesian Information Criterion (BIC) values. For analyses including both δ¹³C and δ¹⁵N values, we selected an EII covariance structure model, which assumes spherical clusters with equal volume and variance among groups. Furthermore, epidermis 𝛿¹³C and 𝛿¹⁵N values from nesting leatherbacks in this study were compared with published data from foraging leatherbacks sampled off northern Florida/southern Georgia (Dodge et al. 2011), Massachusetts (Dodge et al. 2011), Nova Scotia (Wallace et al. 2014), and the Gulf of Mexico (Hsu 2020). As 𝛿¹³C values have been used extensively in marine isotope studies to infer habitat use, we applied the same cluster approach above but with only δ¹³C values. For δ¹³C-only analyses, we selected an E covariance structure model, which assumes equal variance among clusters in a univariate distribution. To ensure each turtle was represented only once per year, we retained the earliest sample when multiple samples existed within the same year and discarded later dates prior to clustering.

We examined in season-temporal variation in isotopic values by focusing on individuals with multiple tissue samples collected across two sampling events within a season. For each individual, we calculated the change in 𝛿¹³C and 𝛿¹⁵N values (represented as Δδ¹³C and Δδ¹⁵N, respectively) between the first and last sampling events, using epidermis and whole blood when paired data were available. The time between two sampling events ranged from 14 to 62 days. We used a set of linear and linear mixed-effects models with isotopic change values (Δδ¹³C or Δδ¹⁵N) as response variables. Models included combinations of nesting interval and sampling year as fixed effects, with and without Turtle ID included as a random intercept to account for repeated sampling of individuals across different seasons. Intercept-only models were also included for comparison. Linear models were fitted using the lm function in R, and mixed-effects models were fitted using the lme4 package in R (Bates et al. 2015) with maximum likelihood estimation. Models were compared using Akaike’s Information Criterion corrected for small sample size (AICc), and residual diagnostics were visually inspected to assess model assumptions.

We developed conversion equations for translating isotope values between tissue types to facilitate inter-study comparisons. We applied Model II regression, specifically Reduced Major Axis (RMA) regression, using the lmodel2 package (Legendre 2024). This method accounts for measurement error in both variables and is appropriate when neither tissue type can be considered strictly independent. Separate RMA regressions were conducted to compare epidermis vs. whole blood for both 𝛿¹³C and 𝛿¹⁵N values using paired samples collected from the same individual on the same sampling date for both first and last nesting events The resulting regression coefficients were then used to generate tissue conversion equations, enabling prediction of isotopic values in one tissue type based on measurements from another.

Finally, to evaluate whether the variability in lipid content differed between tissue types, we compared the variance in C:N ratios of epidermis and whole blood using Levene’s test for homogeneity of variances. The C:N ratio serves as a proxy for lipid content, with higher and more variable values reflecting greater lipid contributions that can bias stable carbon isotope measurements if unaccounted for (Post et al. 2007, Cloyed et al. 2020). Levene’s test was selected because it is robust to deviations from normality and specifically assesses whether two or more groups exhibit equal variance. An RMA regression (see above) was used to look at the relationship between C:N ratio and 𝛿¹³C values. Applying these tests allowed us to determine whether epidermis and whole blood differed in their inherent variability of lipid content and to evaluate how such differences might influence the robustness and interpretation of isotopic comparisons between tissues.

Results
We collected 563 samples from 225 unique individual nesting leatherbacks on Florida beaches between 2014 and 2022 (Table 1). Among these, 111 individuals were sampled more than once for epidermis and 44 individuals for whole blood, allowing for longitudinal comparisons. No individuals were sampled more than once for RBCs.

Across all tissue types, the mean ± standard deviation (SD) in 𝛿¹³C values was –18.03 ± 1.11 ‰ (range: –20.58 to –13.94 ‰), and for 𝛿¹⁵N values was 12.62 ± 1.55 ‰ (range: 8.38 to 17.85‰). Isotope values differed significantly by tissue type, with epidermis exhibiting higher 𝛿¹³C and 𝛿¹⁵N values than both whole blood and RBCs (PERMANOVA; F₂, ₅₆₂ = 163.63, r² = 0.37, p < 0.001; Table 2, Fig 1A). In terms of temporal variation in isotope values, we observed significant differences among years for both epidermis (PERMANOVA; F_8,394 = 5.88, r² = 0.11, p < 0.001) and whole blood (PERMANOVA; F_4,148 = 15.84, r² = 0.31, p < 0.001). Year explained a greater proportion of variation in whole blood isotope values than in epidermis isotope values . Significant pairwise differences among years were detected for 𝛿¹³C and 𝛿¹⁵N values in epidermis and whole blood (p < 0.05, Fig 1B,C). In epidermis, 𝛿¹³C values showed declines in 2016–2017, while 𝛿¹⁵N values were generally higher in 2017–2021 compared to the earliest sampling years (Fig 1B,C). In whole blood, 𝛿¹³C differences across years were limited, whereas 𝛿¹⁵N values peaked in 2017 and 2021, whereas 𝛿¹³C differences across years were limited (Fig 1B,C).

Model-based clustering of epidermis δ¹³C and δ¹⁵N values supported a two-cluster EII model (BIC = −1642, Fig. 2A), which received greater support than the three-cluster (ΔBIC = 12) or no cluster (ΔBIC > 12). Additional cluster models received substantially lower support (ΔBIC > 12). Individuals in the lower 𝛿¹³C value cluster also exhibited reduced 𝛿¹⁵N variability. We also performed clustering using only 𝛿¹³C values, which revealed three clusters (BIC = -712, Fig. 2B) as the better approach with more ecological relevance compared to two clusters (ΔBIC = 6). Additional cluster models received substantially lower support (ΔBIC > 9). When compared with isotopic values from previous studies, these clusters showed partial overlap with isotopic values of leatherbacks from known foraging grounds of Northwest Atlantic (NWA) leatherbacks, including Canada, the South Atlantic Bight, Massachusetts, and the Gulf of Mexico (Fig. 2B). Our study did have δ¹⁵N values that were generally higher than previously reported for NWA leatherbacks.

To assess temporal isotopic dynamics within individuals, we examined changes in 𝛿¹³C and 𝛿¹⁵N values across repeated nesting events. Across the nesting season, changes in isotope values were generally small, with limited evidence of consistent directional trends (Fig 3). Changes in stable isotope (Δ𝛿¹³C and Δ𝛿¹⁵N) values across sampling intervals differed between tissue types. In epidermis tissue, Δ𝛿¹³C values ranged from −3.23‰ to 2.06‰, with a mean of −0.09± 0.84‰ (SD), whereas Δ𝛿¹⁵N values ranged from −1.86‰ to 4.47‰, with a mean of 0.24 ± 1.03‰. In contrast, changes in whole blood between nesting events spanned a a narrower range, with Δ𝛿¹³C values between −0.48‰ and 0.25‰ (mean 0.05 ± 0.13‰) and Δ𝛿¹⁵N values between −0.12‰ and 0.39‰ (mean 0.10 ± 0.09‰). Estimated effects of nesting interval on isotopic change were generally small for both tissues. For epidermis Δδ¹³C values, the nesting interval model was the best supported based on AIC_c whereas models including year were substantially less supported (ΔAICc > 2). Estimated effects of nesting interval were small (β = -0.006 ± 0.005 SE), suggesting limited temporal change across the nesting season. Similarly, epidermis Δδ¹⁵N values showed only weak support for a positive relationship with nesting interval (β = 0.011 ± 0.007 SE). Models including year effects received comparatively less support for Δδ¹⁵N values, and mixed-effects models including Turtle ID as a random intercept provided little improvement in model fit. For whole blood Δδ¹³C values, the model including nesting interval had the best fit among candidate models, whereas models including year were substantially less supported (ΔAIC_c > 5). Estimated effects indicated a weak positive relationship between Δδ¹³C values and nesting interval (β = 0.003 ± 0.001 SE). Similarly, for whole blood Δδ¹⁵N values, the nesting interval model was the best supported, whereas models including year received comparatively little support (ΔAIC_c > 7). Estimated effects suggested a weak positive relationship between Δδ¹⁵N values and nesting interval (β = 0.002 ± 0.001 SE). Across all analyses, year added no additional variation in isotopic change. Although no directional trend in either Δ𝛿¹³C and Δ𝛿¹⁵N was present, there were large differences in residual variation. Epidermis had large variation, while whole blood was more uniform (Fig. 3).

To assess how isotopic values correspond between tissue types, we examined the relationship between epidermis and whole blood isotopic composition. For 𝛿¹³C values, the model explained approximately 41% of the variance between tissues (r² = 0.41), and the slope was significantly different from zero (slope = 1.19, 95% confidence interval = 0.94 – 1.48, p = 0.01). For 𝛿¹⁵N values, the relationship between the tissue types was stronger with 73% of the variation explained by the model (r² = 0.73), and the slope was significantly different from zero (slope = 0.98, 95% confidence interval = 0.88 – 1.10, p < 0.001). These results indicate statistically significant linear relationships between tissues, and the conversion formulas provide a framework for translating isotopic values between tissue types in comparative studies (Fig. 4, Table 3).

Lipid content, as inferred from C:N ratios, are unlikely to affect the interpretation of isotopic results. Epidermis exhibited significantly greater variability in C:N ratios than whole blood (Levene’s test: F_{1, 541} = 24.3, p < 0.001). However, the mean C:N ratio of epidermis (3.08 ± 0.18 SD) was lower than that of whole blood (3.59 ± 0.08 SD), indicating that despite higher variability, lipid levels in epidermis were not elevated overall and were unlikely to bias isotopic interpretations (Fig. 5). The RMA regression showed a significant negative relationship between 𝛿¹³C and C:N ratio across all samples (r² = 0.44, p < 0.001) However, tissue-specific models were weaker (epidermis r² =0.03, p = 0.001; whole blood: r² =0.04, p = 0.006).

Table 1: Epidermis, whole blood, and red blood cell samples collected from nesting leatherback sea turtles (Dermochelys coriacea) in Juno and Jupiter Beaches, Florida, USA during the 2014–2022 nesting seasons. Values in parentheses indicate the number of individuals sampled more than once. Note that some turtles were encountered in multiple nesting seasons and can therefore be represented in more than one year.

[Table]

Table 2: Sample count and mean carbon (𝛿¹³C) and nitrogen (𝛿¹⁵N) isotope values from nesting leatherback sea turtles (Dermochelys coriacea) in Juno and Jupiter Beaches, Florida, USA during the 2014–2022 nesting seasons. Three different tissue types were collected: epidermis, red blood cells, and whole blood. ^a,b Groupings of isotope values from the PERMANOVA, different letters are groups that are significantly different.

[Table]

Table 3: Results of Reduced Major Axis (RMA) regressions comparing 𝛿¹³C and 𝛿¹⁵N values between epidermis (EPI) and whole blood (WB) of nesting leatherback turtles (Dermochelys coriacea) in Juno and Jupiter Beaches, Florida, USA. Reported are regression equations, slope estimates with 95% confidence intervals, intercepts, coefficients of determination (R²), and p-values.

[Table]

[Image]

Figure 1: Stable isotope (𝛿¹³C and 𝛿¹⁵N) values from three tissue types (epidermis, whole blood, and red blood cells) collected from nesting leatherback sea turtles (Dermochelys coriacea) at Juno and Jupiter Beaches, Florida, USA. (A) Values of 𝛿¹³C and 𝛿¹⁵N by tissue type. (B) Yearly boxplots of 𝛿¹³C values. (C) Yearly boxplots of 𝛿¹⁵N values. For the boxplots, the horizontal line denotes the median, boxes span the interquartile range (25th–75th percentiles), whiskers extend to 1.5 × the interquartile range, and points beyond whiskers represent outliers. Letters above boxplots denote post hoc pairwise comparisons among years for epidermis and whole blood separately. Groups sharing at least one letter were not significantly different (p > 0.05), whereas groups with no shared letters were significantly different (p < 0.05).

Figure 1 alt caption: Figure showing stable isotope values of carbon (δ¹³C) and nitrogen (δ¹⁵N) in three tissue types, epidermis, whole blood, and red blood cells, from nesting leatherback sea turtles at Juno and Jupiter Beaches, Florida. Panel A displays δ¹³C and δ¹⁵N values plotted by tissue type. Panel B shows boxplots of yearly δ¹³C values, and Panel C shows boxplots of yearly δ¹⁵N values. Each boxplot has medians, interquartile ranges, whiskers, and outliers. Different letters mark statistically significant differences between epidermis and whole blood. Red blood cells appear only in 2019 and are excluded from the yearly plots.

[Image]

Figure 2: Distribution of 𝛿¹³C and 𝛿¹⁵N stable isotope values from the epidermis of nesting leatherback sea turtles (Dermochelys coriacea) at Juno and Jupiter Beaches, Florida, USA. Individuals were grouped into distinct clusters using Gaussian mixture models: (A) clustering based on both 𝛿¹³C and 𝛿¹⁵N values found two clusters , and (B) clustering based solely on 𝛿¹³C values found three clusters . The colors for each plot represent those respective clusters. We added mean ± SD epidermis values from four known areas based on previous studies (Dodge et al. 2011; Wallace et al. 2014; Hsu 2020).

Figure 2 alt caption: Figure showing δ¹³C and δ¹⁵N values from the epidermis of nesting leatherback sea turtles at Juno and Jupiter Beaches, Florida. Panel A presents two isotopic clusters identified by Gaussian mixture modeling using both δ¹³C and δ¹⁵N values. Panel B shows clusters based only on δ¹³C values. Mean ± SD isotope values from four known foraging areas are included on the graphs for comparison, based on prior studies by Dodge et al. 2011, Wallace et al. 2014, and Hsu 2020. The isotope values from this study are within the same δ¹³C range as previous studies, but overall, the δ¹⁵N values are higher.

[Image]

Figure 3: Change in stable isotope values (represented as Δδ¹³C and Δδ¹⁵N, respectively) between the first and last sampling events of more than 14 days apart for nesting leatherback sea turtles (Dermochelys coriacea) in Juno and Jupiter Beaches, Florida, USA. Isotopic change was calculated by subtracting values from the first encounter from those at the final encounter for each individual, shown separately for epidermis (EPI) and whole blood (WB).

Figure 3 alt caption: Figure showing changes in carbon (Δδ¹³C) and nitrogen (Δδ¹⁵N) stable isotope values between the first and last sampling events for nesting leatherback sea turtles at Juno and Jupiter Beaches, Florida. Changes are plotted separately for epidermis (EPI) and whole blood (WB), with values calculated as the final encounter minus the initial encounter. The axes are different for each of the tissue types, with epidermis being between a change of -2 to 4, and whole blood -0.5 and 0.4.

[Image]

Figure 4: Linear regressions between tissue types (epidermis and whole blood) and isotopic values (𝛿¹³C and 𝛿¹⁵N) for nesting leatherback sea turtles (Dermochelys coriacea) in Juno and Jupiter Beaches, Florida, USA.

Figure 4 alt caption: Figure showing the relationship between whole blood and epidermis for carbon (𝛿¹³C) on the left panel and nitrogen (𝛿¹⁵N) on the right panel. The carbon (𝛿¹³C) has slightly more variation than nitrogen (𝛿¹⁵N) but both have a positive significant relationship. The p-values are 0.01 and 0.001 for carbon (𝛿¹³C) and nitrogen (𝛿¹⁵N), respectively.

[Image]

Figure 5: Relationship between C:N ratio and 𝛿¹³C values in epidermis (EPI) and whole blood (WB) of nesting leatherback turtles (Dermochelys coriacea) at Juno and Jupiter Beaches, Florida, USA.

Figure 5 alt caption: Figure of carbon (𝛿¹³C) on the y-axis by C:N ratio on the x-axis, with tissue type represented by color (gold for epidermis, and blue for whole blood). There is a grouping by tissue with epidermis having lower C:N ratios and higher carbon (𝛿¹³C) values and whole blood having higher C:N ratios and lower carbon (𝛿¹³C) values. There is also more variability for epidermis than whole blood.

Discussion

This study used stable isotope analysis of carbon and nitrogen to investigate how values vary among tissues and through time, and how these patterns can inform future studies of leatherback ecology. . Our goal was to explore variability among individuals and tissues using samples from three tissue types. We identified two distinct isotopic groups using clustering analysis of δ¹³C and δ¹⁵N and three for δ¹³C only in epidermis. These clusters suggest heterogeneity in the isotopic baselines among individuals and are consistent with the idea that leatherbacks forage in a variety of oceanographic regions prior to migration (Robinson et al. 2016, Rider et al. 2024). Across the nesting season, we observed greater variability in epidermis samples compared to whole blood. Additionally, we observed significant relationships between epidermis and blood isotopic values for both 𝛿¹³C and 𝛿¹⁵N values, establishing a basis for cross-tissue comparisons in leatherbacks.

The diversity of habitats and/or large geographic ranges utilized by NWA leatherbacks (Eckert et al. 2006, Bond and James 2017, Sasso et al. 2021) likely contributes to the wide range of 𝛿¹³C and 𝛿¹⁵N values observed in our study. Although cluster analysis of epidermis samples revealed three distinct isotopic groups, we cannot directly relate them to foraging areas without paired satellite tracking data. Using previously published data, one cluster overlapped with isotopic values from the Gulf of Mexico (Fig. 2B). This overlap contrasts with the dominant migratory pattern previously documented for this population, which tends to involve post-nesting movements into the Mid-Atlantic Bight, Gulf of Maine, Scotian Shelf, and pelagic zones such as the New England Seamounts (Eckert et al. 2006, Aoki et al. 2026) . Although no leatherbacks nesting in Florida have been satellite tagged and tracked into the Gulf of Mexico, this area is an important foraging region for leatherbacks migrating from nesting beaches in Central America (Evans et al. 2021). We also observed individuals with isotope values falling outside the ranges previously reported for leatherbacks (Figure 2). These individuals with lower δ¹³C values could be using higher-latitude or offshore pelagic areas not yet sampled for leatherbacks. The discrepancy in 𝛿¹⁵N values between nesting leatherbacks in this study and foraging leatherbacks in the literature is even more striking, with many nesting individuals exhibiting higher δ¹⁵N values than published datasets (e.g. Dodge et al. 2011, Wallace et al. 2014, Hsu 2020). Higher values could reflect the variable use of foraging areas with differing δ¹⁵N baselines or interannual shifts in δ¹⁵N values at the base of the food web. Measuring phenylalanine 𝛿¹⁵N values via compound-specific isotope analysis would separate baseline effects from trophic effects (Chikaraishi et al. 2009, Nielsen et al. 2015). Distinguishing between these processes is essential, as baseline-driven enrichment has different ecological implications than enrichment resulting from feeding higher in the trophic chain.

Interannual variability at the base of the marine food webs may contribute to the differences in 𝛿¹⁵N values between our results and those of other studies. The North Atlantic Oscillation (NAO) is a large-scale atmospheric pattern that shapes climate across the mid- and high latitudes of the Northern Hemisphere (Hurrell et al. 2003). North Atlantic Oscillation fluctuations influence ocean circulation, nutrient supply, and primary productivity, thereby altering the isotopic baselines. Previous work has shown correlations between NAO cycles and bulk δ¹⁵N values in NWA leatherbacks (Hetherington et al. 2018), suggesting that shifts in baseline nitrogen sources move through pelagic food webs and influence consumer isotope values. Recent modeling work shows that the proliferation of pelagic Sargassum has been influenced by the North Atlantic Oscillation with interannual variability in blooms largely sustained by vertical mixing supplying nutrients in the tropical Atlantic (Jouanno et al. 2025). This pattern is consistent with interannual variability in baseline nitrogen sources, as nitrogen fixation in low-latitude regions and related processes can cause decreased δ15N values in lower trophic organisms in Atlantic food webs (Espinasse et al. 2022). Although our study did not directly evaluate oceanographic drivers, the relatively lower δ¹⁵N values observed in some years may reflect broader variability in Atlantic nitrogen cycling and productivity. Future work could integrate isotopic baselines, oceanographic variables, and prey dynamics to test these relationships directly.

We are unable to explain the large variability in the isotopic composition of leatherback turtle epidermis, but not whole blood, between sampling events. However, based on studies of hard-shelled turtles, it is expected that whole blood would exhibit more variability than epidermis due to its relatively faster turnover rate (Reich et al. 2008). Whether these turnover rates apply similarly to adult leatherbacks remains uncertain because of differences in physiology, growth, and reproductive output between taxa. The stability of blood isotope values over the nesting season suggests that additional physiological factors are likely. Leatherbacks exhibit a form of endothermy, whereby they generate heat through the peripheral locomotory muscles (Davenport et al. 2015). Tissue turnover rates are known to differ between ectotherms and endotherms (Vander Zanden et al. 2015b), and leatherbacks may occupy an intermediate position along this continuum. Additionally, inherent variation — from individual differences in physiology even under the same captive conditions — may differ among tissues (Vander Zanden et al. 2012). Our results suggest that leatherback epidermis has higher inherent variability in isotopic composition than whole blood. Therefore, future studies might benefit from prioritizing stable isotope analysis of various blood components (whole blood, red blood cells, and plasma) in leatherbacks.

Lipid content can influence 𝛿¹³C values because they are naturally depleted in 13C relative to proteins and carbohydrates potentially lowering ¹³C values and increasing isotopic variability (Post 2002, Logan et al. 2008). Tissues with high lipid concentrations often exhibit elevated C:N ratios, typically exceeding ~3.5, at which point lipid correction or extraction is advised (Post et al. 2007). In our dataset, epidermis samples consistently had lower C:N ratios than whole blood, suggesting that lipid content in epidermis was relatively low. Previous research suggests that when dermal and epidermal layers are carefully separated, lipid extraction is unnecessary in marine turtle epidermis (Vander Zanden et al. 2014, Turner Tomaszewicz et al. 2017). Whereas the C:N ratio in whole blood in this study was slightly above 3.5, it is unlikely that it affected our results. Although the effects of lipid extraction on sea turtle blood have not been specifically evaluated, studies in other systems have reported minimal effects of lipid extraction on stable isotope values. In the arctic fox (Vulpes lagopus), lipid extraction had minimal impact on mean C:N ratio, 𝛿¹³C, and 𝛿¹⁵N values (Tarroux et al. 2012). We also detected slightly greater variability in epidermal C:N ratios compared to whole blood. Such a relationship has also been reported in green turtles (Chelonia mydas; Vanderklift et al. 2020), where tissues with higher C:N ratios tended to be more ¹³C-depleted. Together, the low C:N values and the 𝛿¹³C–C:N relationship suggest that while lipids may have some influence, their overall contribution to isotopic variability between first and last epidermis samples was minor.

The possibility that fasting during migration or nesting could influence 𝛿¹⁵N values in sea turtles remains largely untested. Studies in other organisms show that tissue catabolism during nutritional stress preferentially removes lighter isotopes (¹⁴N), leaving tissues enriched in ¹⁵N by roughly +0.5 ‰ (Fuller et al. 2005, Hertz et al. 2015). In theory, this enrichment could confound interpretations of diet or habitat use, especially if there are temporal differences in when samples are collected during the nesting season. Leatherbacks in the NWA likely exhibit a capital-breeding strategy, relying on stored reserves throughout the nesting period (Perrault et al. 2012, 2014, 2016; Plot et al. 2013). However, the relatively slow turnover of whole blood and epidermis in leatherbacks makes these tissues unlikely to capture short-term fasting events associated with reproductive migrations. If fasting were driving the observed variation, we would also expect to see a consistent increase in 𝛿¹⁵N values across all nesting females, which was not the case in our dataset with a slope close to 0. Whereas fasting cannot be entirely ruled out as a minor contributing factor, our results indicate it is unlikely to be the primary mechanism behind the 𝛿¹⁵N variability observed here. To disentangle the effects of fasting, compound-specific isotope analysis of amino acids of a faster-turnover tissue (e.g. plasma) could help reveal underlying biochemical pathways and provide deeper insight into the fasting physiology of marine turtles (Whiteman et al. 2019).

While we offer meaningful contributions to the understanding of isotopic variation in leatherbacks, it is important to acknowledge constraints of this study. The absence of plasma samples in our analysis represents a missed opportunity to capture a more immediate reflection of isotopic incorporation. Moreover, our reliance on satellite-telemetry data from previous investigations, rather than direct tagging of the individuals sampled for this study, introduces a potential limitation. The integration of additional markers, alongside stable isotope analysis could enhance the conclusions made in this study. Compound-specific isotopes have been used in other marine systems to study capital versus income breeding strategies (Lübcker et al. 2020) and, along with bulk isotopes, can enhance our understanding of these reproductive strategies. Additionally, the analysis of epibiont community assemblages on individuals may offer a novel approach to distinguishing between in-shore and off-shore movements (Nolte et al. 2020), and data from blood analytes (e.g. hematology, biochemistry) could shed light on how animal health relates to foraging strategy (Perrault et al. 2016, Stacy and Innis 2017). In conjunction with bulk isotopes, these methods could enhance the understanding of leatherback ecology in the NWA region.

This study highlights isotopic variability within NWA leatherbacks and that tissue type could influence interpretation of stable isotope data in sea turtle studies. We found no consistent isotopic shifts across the nesting season, particularly in whole blood, which had less variability among individuals within a nesting season compared to epidermis. Whole blood may be a preferred tissue to collect over epidermis because of the large variability in epidermis among repeated samples from the same individual. Isotope values also differed across nesting seasons, likely reflecting environmental variability driving isotopic baselines. Additionally, the broad range of isotope values and isotopic clustering we found suggests heterogeneity in foraging areas with nesting females. These clustering results should not be interpreted as direct evidence of discrete foraging because clearly defined isoscapes are not currently available for NWA leatherbacks like other species within the region (e.g., Vander Zanden et al. 2015a). Together, these findings provide a framework for future studies integrating stable isotopes, oceanographic data, and animal tracking to better understand ecological variability in this population.

References

Anderson, M. J., and D. C. Walsh. 2013. PERMANOVA, ANOSIM, and the Mantel test in the face of heterogeneous dispersions: what null hypothesis are you testing? Ecological Monographs 83:557–574.

Aoki, D., W. Bishop, A. Page-Karjian, J. Guertin, S. Persaud, G. L. Shillinger, and J. Perrault. 2026. Evaluating the viability and animal welfare of acoustic telemetry to monitor post-nesting presence of leatherback sea turtles (Dermochelys coriacea). Animal Biotelemetry: In press.

Arbizu, P. M. 2017. pairwiseAdonis: Pairwise multilevel comparison using adonis. R package version 0.4.

Barrow, L. M., K. A. Bjorndal, and K. J. Reich. 2008. Effects of preservation method on stable carbon and nitrogen isotope values. Physiological and Biochemical Zoology 81:688–693.

Bates, D., M. Mächler, B. Bolker, and S. Walker. 2015. Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software 67:1–48.

Bond, E. P., and M. C. James. 2017. Pre-nesting movements of leatherback sea turtles, Dermochelys coriacea, in the Western Atlantic. Frontiers in Marine Science 4.

Bostrom, B. L., T. T. Jones, M. Hastings, and D. R. Jones. 2010. Behaviour and physiology: The thermal strategy of leatherback turtles. PLoS One 5:e13925.

Burton, R. K., and P. L. Koch. 1999. Isotopic tracking of foraging and long-distance migration in northeastern Pacific pinnipeds. Oecologia 119:578–585.

Busquets-Vass, G., S. D. Newsome, M. A. Pardo, J. Calambokidis, S. Aguíñiga-García, D. Páez-Rosas, J. Gómez-Gutiérrez, L. M. Enríquez-Paredes, and D. Gendron. 2021. Isotope-based inferences of the seasonal foraging and migratory strategies of blue whales in the eastern Pacific Ocean. Marine Environmental Research 163:105201.

Caut, S., E. Guirlet, E. Angulo, K. Das, and M. Girondot. 2008. Isotope analysis reveals foraging area dichotomy for Atlantic leatherback turtles. PLoS One 3:e1845.

Ceriani, S. A., J. F. Weishampel, L. M. Ehrhart, K. L. Mansfield, and M. B. Wunder. 2017. Foraging and recruitment hotspot dynamics for the largest Atlantic loggerhead turtle rookery. Scientific Reports 7:16894.

Chikaraishi, Y., N. O. Ogawa, Y. Kashiyama, Y. Takano, H. Suga, A. Tomitani, H. Miyashita, H. Kitazato, and N. Ohkouchi. 2009. Determination of aquatic food-web structure based on compound-specific nitrogen isotopic composition of amino acids. Limnology and Oceanography: Methods 7:740–750.

Cloyed, C. S., K. P. DaCosta, M. R. Hodanbosi, and R. H. Carmichael. 2020. The effects of lipid extraction on δ¹³C and δ¹⁵N values and use of lipid-correction models across tissues, taxa and trophic groups. Methods in Ecology and Evolution 11:751–762.

Coplen, T. B. 2011. Guidelines and recommended terms for expression of stable‐isotope‐ratio and gas‐ratio measurement results. Rapid Communications in Mass Spectrometry 25:2538–2560.

Davenport, J., T. T. Jones, T. M. Work, and G. H. Balazs. 2015. Topsy-turvy: Turning the counter-current heat exchange of leatherback turtles upside down. Biology Letters 11:20150592.

Dodge, K. L., J. M. Logan, and M. E. Lutcavage. 2011. Foraging ecology of leatherback sea turtles in the Western North Atlantic determined through multi-tissue stable isotope analyses. Marine Biology 158:2813–2824.

Dutton, P. H., and D. Mcdonald. 1994. Use of pit tags to identify adult leatherbacks. Marine Turtle Newsletter 67:13–14.

Eckert, S. A., D. Bagley, S. Kubis, L. Ehrhart, C. Johnson, K. Stewart, and D. DeFreese. 2006. Internesting and postnesting movements and foraging habitats of leatherback sea turtles (Dermochelys coriacea) nesting in Florida. Chelonian Conservation and Biology 5:239–248.

Espinasse, B., A. Sturbois, S. L. Basedow, P. Hélaouët, D. G. Johns, J. Newton, and C. N. Trueman. 2022. Temporal dynamics in zooplankton δ13C and δ15N isoscapes for the North Atlantic Ocean: Decadal cycles, seasonality, and implications for predator ecology. Frontiers in Ecology and Evolution 10.

Evans, D. R., R. A. Valverde, C. Ordoñez, and R. R. Carthy. 2021. Identification of the Gulf of Mexico as an important high-use habitat for leatherback turtles from Central America. Ecosphere 12:e03722.

Figgener, C., J. Bernardo, and P. T. Plotkin. 2019. Beyond trophic morphology: Stable isotopes reveal ubiquitous versatility in marine turtle trophic ecology. Biological Reviews 94:1947–1973.

Florida Fish and Wildlife Conservation Commission. 2026. Sea Turtle Monitoring (the SNBS and INBS Programs).

Fossette, S., V. J. Hobson, C. Girard, B. Calmettes, P. Gaspar, J.-Y. Georges, and G. C. Hays. 2010. Spatio-temporal foraging patterns of a giant zooplanktivore, the leatherback turtle. Journal of Marine Systems 81:225–234.

Fouda, L., S. R. B. Negus, E. C. Lockley, K. Fairweather, A. Lopes, A. Lopes, S. M. Correia, A. Taxonera, G. Schofield, and C. Eizaguirre. 2024. Productive foraging grounds enhance maternal condition and offspring quality in a capital breeding species. Ecology and Evolution 14:e70137.

Fuller, B. T., J. L. Fuller, N. E. Sage, D. A. Harris, T. C. O’Connell, and R. E. M. Hedges. 2005. Nitrogen balance and δ¹⁵N: Why you’re not what you eat during nutritional stress. Rapid Communications in Mass Spectrometry 19:2497–2506.

Hanson, N., J. Ounsley, S. J. Middlemas, J. Gilbey, and C. D. Todd. 2022. Inferring individual marine migration from otolith ecogeochemical signatures of a wide-ranging fish. Frontiers in Marine Science 9.

Hays, G. C., and L. A. Hawkes. 2018. Satellite tracking sea turtles: opportunities and challenges to address key questions. Frontiers in Marine Science 5.

Heaslip, S. G., S. J. Iverson, W. D. Bowen, and M. C. James. 2012. Jellyfish Support High Energy Intake of Leatherback Sea Turtles (Dermochelys coriacea): Video Evidence from Animal-Borne Cameras. PLOS ONE 7:e33259.

Hertz, E., M. Trudel, M. K. Cox, and A. Mazumder. 2015. Effects of fasting and nutritional restriction on the isotopic ratios of nitrogen and carbon: A meta-analysis. Ecology and Evolution 5:4829–4839.

Hetherington, E. D., J. A. Seminoff, P. H. Dutton, L. C. Robison, B. N. Popp, and C. M. Kurle. 2018. Long-term trends in the foraging ecology and habitat use of an endangered species: an isotopic perspective. Oecologia 188:1273–1285.

Hobson, K. A. 2019. Application of isotopic methods to tracking animal movements. Pages 85–115 in K. A. Hobson and L. I. Wassenaar, editors. Tracking Animal Migration with Stable Isotopes (Second Edition). Academic Press.

Hsu, S. 2020. Using stable isotopes to determine foraging areas of leatherback sea turtles: limitations of the isotope tracking technique in the Western Atlantic Ocean. Master’s thesis, San Jose State University, USA.

Hurrell, J. W., Y. Kushnir, G. Ottersen, and M. Visbeck. 2003. An overview of the North Atlantic oscillation. Geophysical Monograph-American Geophysical Union 134:1–36.

Jouanno, J., S. Berthet, F. Muller-Karger, O. Aumont, and J. Sheinbaum. 2025. An extreme North Atlantic Oscillation event drove the pelagic Sargassum tipping point. Communications Earth & Environment 6:95.

Komoroske, L. M., M. P. Jensen, K. R. Stewart, B. M. Shamblin, and P. H. Dutton. 2017. Advances in the application of genetics in marine turtle biology and conservation. Frontiers in Marine Science 4.

Landrum, J. P., M. A. Altabet, and J. P. Montoya. 2011. Basin-scale distributions of stable nitrogen isotopes in the subtropical North Atlantic Ocean: Contribution of diazotroph nitrogen to particulate organic matter and mesozooplankton. Deep Sea Research Part I: Oceanographic Research Papers 58:615–625.

Legendre, P. 2024. lmodel2: Model II Regression.

Lübcker, N., J. P. Whiteman, R. P. Millar, P. J. N. de Bruyn, and S. D. Newsome. 2020. Fasting affects amino acid nitrogen isotope values: A new tool for identifying nitrogen balance of free-ranging mammals. Oecologia 193:53–65.

Magozzi, S., A. Yool, H. Vander Zanden, M. Wunder, and C. Trueman. 2017. Using ocean models to predict spatial and temporal variation in marine carbon isotopes. Ecosphere 8:e01763.

McMahon, K. W., L. L. Hamady, and S. R. Thorrold. 2013. A review of ecogeochemistry approaches to estimating movements of marine animals. Limnology and Oceanography 58:697–714.

Montoya, J. P. 2007. Natural abundance of ¹⁵N in marine planktonic ecosystems. Stable Isotopes in Ecology and Environmental Science:176.

Nielsen, J. M., B. N. Popp, and M. Winder. 2015. Meta-analysis of amino acid stable nitrogen isotope ratios for estimating trophic position in marine organisms. Oecologia 178:631–642.

Nolte, C. R., M. C. Pfaff, A. M. de Lecea, D. le Gouvello, and R. Nel. 2020. Stable isotopes and epibiont communities reveal foraging habitats of nesting loggerhead turtles in the South West Indian Ocean. Marine Biology 167:162.

Oksanen, J., G. L. Simpson, F. G. Blanchet, R. Kindt, P. Legendre, P. R. Minchin, R. B. O’Hara, P. Solymos, M. H. H. Stevens, E. Szoecs, H. Wagner, M. Barbour, M. Bedward, H. Bengtsson, B. Bolker, D. Borcard, T. Borman, G. Carvalho, M. Chirico, M. D. Caceres, S. Durand, H. B. A. Evangelista, R. FitzJohn, M. Friendly, B. Furneaux, G. Hannigan, I. Heida, M. O. Hill, L. Lahti, C. Martino, D. McGlinn, M.-H. Ouellette, E. R. Cunha, T. Smith, A. Stier, C. J. F. T. Braak, and J. Weedon. 2026, May 25. vegan: Community Ecology Package.

Oksanen, J., G. L. Simpson, F. G. Blanchet, R. Kindt, P. Legendre, P. R. Minchin, R. B. O’Hara, P. Solymos, M. H. H. Stevens, E. Szoecs, H. Wagner, M. Barbour, M. Bedward, B. Bolker, D. Borcard, G. Carvalho, M. Chirico, M. D. Caceres, S. Durand, H. B. A. Evangelista, R. FitzJohn, M. Friendly, B. Furneaux, G. Hannigan, M. O. Hill, L. Lahti, D. McGlinn, M.-H. Ouellette, E. R. Cunha, T. Smith, A. Stier, C. J. F. T. Braak, and J. Weedon. 2024. vegan: Community Ecology Package.

Pajuelo, M., K. A. Bjorndal, K. J. Reich, H. B. Vander Zanden, L. A. Hawkes, and A. B. Bolten. 2012. Assignment of nesting loggerhead turtles to their foraging areas in the Northwest Atlantic using stable isotopes. Ecosphere 3:1–18.

Perrault, J. R., D. L. Miller, E. Eads, C. Johnson, A. Merrill, L. J. Thompson, and J. Wyneken. 2012. Maternal health status correlates with nest success of leatherback sea turtles (Dermochelys coriacea) from Florida. PLoS One 7:e31841.

Perrault, J. R., A. Page-Karjian, and D. L. Miller. 2016. Nesting leatherback sea turtle (Dermochelys coriacea) packed cell volumes indicate decreased foraging during reproduction. Marine Biology 163:232.

Peterson, B. J., and B. Fry. 1987. Stable isotopes in ecosystem studies. Annual review of ecology and systematics 18:293–320.

Plot, V., T. Jenkins, J.-P. Robin, S. Fossette, and J.-Y. Georges. 2013. Leatherback turtles are capital breeders: morphometric and physiological evidence from longitudinal monitoring. Physiological and Biochemical Zoology 86:385–397.

Post, D. M., C. A. Layman, D. A. Arrington, G. Takimoto, J. Quattrochi, and C. G. Montaña. 2007. Getting to the fat of the matter: Models, methods and assumptions for dealing with lipids in stable isotope analyses. Oecologia 152:179–189.

Reich, K. J., K. A. Bjorndal, and C. M. Del Rio. 2008. Effects of growth and tissue type on the kinetics of 13C and 15N incorporation in a rapidly growing ectotherm. Oecologia 155:651–663.

Rider, M. J., L. Avens, H. L. Haas, J. M. Hatch, S. H. Patel, and C. R. Sasso. 2024. Where the leatherbacks roam: Movement behavior analyses reveal novel foraging locations along the Northwest Atlantic shelf. Frontiers in Marine Science 11.

Robinson, N. J., S. J. Morreale, R. Nel, and F. V. Paladino. 2016. Coastal leatherback turtles reveal conservation hotspot. Scientific Reports 6:37851.

Sasso, C. R., P. M. Richards, S. R. Benson, M. Judge, N. F. Putman, D. Snodgrass, and B. A. Stacy. 2021. Leatherback turtles in the Eastern Gulf of Mexico: Foraging and migration behavior during the autumn and winter. Frontiers in Marine Science 8.

Scrucca, L., C. Fraley, T. B. Murphy, and A. E. Raftery. 2023. Model-Based Clustering, Classification, and Density Estimation Using mclust in R. Chapman and Hall/CRC.

Seminoff, J. A., S. R. Benson, K. E. Arthur, T. Eguchi, P. H. Dutton, R. F. Tapilatu, and B. N. Popp. 2012. Stable isotope tracking of endangered sea turtles: Validation with satellite telemetry and δ ¹⁵N analysis of amino acids. PLOS One 7:e37403.

Seminoff, J. A., K. A. Bjorndal, and A. B. Bolten. 2007. Stable carbon and nitrogen isotope discrimination and turnover in pond sliders Trachemys scripta: insights for trophic study of freshwater turtles. Copeia 2007:534–542.

Seminoff, J. A., T. T. Jones, T. Eguchi, M. Hastings, and D. R. Jones. 2009. Stable carbon and nitrogen isotope discrimination in soft tissues of the leatherback turtle (Dermochelys coriacea): Insights for trophic studies of marine turtles. Journal of Experimental Marine Biology and Ecology 381:33–41.

Stacy, N. I., and C. J. Innis. 2017. Clinical pathology. Pages 147–208 in C. A. Manire, T. M. Norton, B. A. Stacy, C. J. Innis, and C. A. Harms, editors. Sea Turtle Health and Rehabilitation. First edition. Ross Publishing, USA.

Stewart, K., M. Sims, A. Meylan, B. Witherington, B. Brost, and L. B. Crowder. 2011. Leatherback nests increasing significantly in Florida, USA; trends assessed over 30 years using multilevel modeling. Ecological Applications 21:263–273.

Takai, N., S. Onaka, Y. Ikeda, A. Yatsu, H. Kidokoro, and W. Sakamoto. 2000. Geographical variations in carbon and nitrogen stable isotope ratios in squid. Journal of the Marine Biological Association of the United Kingdom 80:675–684.

Tarroux, A., J. Bêty, G. Gauthier, and D. Berteaux. 2012. The Marine Side of a Terrestrial Carnivore: Intra-Population Variation in Use of Allochthonous Resources by Arctic Foxes. PLOS ONE 7:e42427.

Thomas, S. M., and T. W. Crowther. 2015. Predicting rates of isotopic turnover across the animal kingdom: a synthesis of existing data. Journal of Animal Ecology 84:861–870.

Thomson, J. A., M. R. Heithaus, D. A. Burkholder, J. J. Vaudo, A. J. Wirsing, and L. M. Dill. 2012. Site specialists, diet generalists? Isotopic variation, site fidelity, and foraging by loggerhead turtles in Shark Bay, Western Australia. Marine Ecology Progress Series 453:213–226.

Trueman, C. N., and K. St John Glew. 2019. Isotopic tracking of marine animal movement. Pages 137–172 in K. A. Hobson and L. I. Wassenaar, editors. Tracking Animal Migration with Stable Isotopes (Second Edition). Academic Press.

Turner Tomaszewicz, C. N., J. A. Seminoff, M. Price, and C. M. Kurle. 2017. Stable isotope discrimination factors and between‐tissue isotope comparisons for bone and skin from captive and wild green sea turtles (Chelonia mydas). Rapid Communications in Mass Spectrometry 31:1903–1914.

Vander Zanden, H. B. 2012. Interpreting sea turtle trophic ecology through stable isotope analysis. University of Florida.

Vander Zanden, H. B., J. B. Pfaller, K. J. Reich, M. Pajuelo, A. B. Bolten, K. L. Williams, M. G. Frick, B. M. Shamblin, C. J. Nairn, and K. A. Bjorndal. 2014. Foraging areas differentially affect reproductive output and interpretation of trends in abundance of loggerhead turtles. Marine Biology 161:585–598.

Vander Zanden, H. B., A. D. Tucker, K. M. Hart, M. M. Lamont, I. Fujisaki, D. S. Addison, K. L. Mansfield, K. F. Phillips, M. B. Wunder, and G. J. Bowen. 2015a. Determining origin in a migratory marine vertebrate: a novel method to integrate stable isotopes and satellite tracking. Ecological Applications 25:320–335.

Vander Zanden, M. J., M. K. Clayton, E. K. Moody, C. T. Solomon, and B. C. Weidel. 2015b. Stable isotope turnover and half-life in animal tissues: a literature synthesis. PLOS One 10:e0116182.

Vanderklift, M. A., R. D. Pillans, N. A. Robson, G. Skrzypek, J. L. Stubbs, and A. D. Tucker. 2020. Comparisons of stable isotope composition among tissues of green turtles. Rapid Communications in Mass Spectrometry 34:e8839.

Walker, B. D., and M. D. McCarthy. 2012. Elemental and isotopic characterization of dissolved and particulate organic matter in a unique California upwelling system: Importance of size and composition in the export of labile material. Limnology and Oceanography 57:1757–1774.

Wallace, B. P., J. Schumacher, J. A. Seminoff, and M. C. James. 2014. Biological and environmental influences on the trophic ecology of leatherback turtles in the northwest Atlantic Ocean. Marine Biology 161:1711–1724.

Whiteman, J. P., E. A. Elliott Smith, A. C. Besser, and S. D. Newsome. 2019. A guide to using compound-specific stable isotope analysis to study the fates of molecules in organisms and ecosystems. Diversity 11:8.