DOI: 10.5061/dryad.f4qrfj75k
title: Data from: Individual specialization in a generalist apex predator: The leopard seal

description: Apex predators are typically considered dietary generalists, often masking
 individual variability. However, individual specialization—consistent
 differences in diet and foraging strategies among individuals— is common
 in apex predators. In some species, only a few specialized individuals can
 significantly impact prey populations. Leopard seals (Hydrurga leptonyx)
 are apex predators important to the structure and function of the Southern
 Ocean ecosystem. Leopard seals are broadly described as generalists, but
 little is known about their trophic ecology at the population or
 individual level. We analyzed δ13C and δ15N profiles in whiskers
 (n=46) from 34 leopard seals from an important aggregation in the Western
 Antarctic Peninsula to assess population and individual trophic variation.
 We also evaluated individual consistency across years using repeat samples
 from 7 seals collected over 2-10 years. We compared population and
 individual isotopic niche space and explored drivers of intraspecific
 variation in leopard seal trophic ecology. We find that leopard
 seals have a broad trophic niche (range: 6.96-15.21‰) and are generalists
 at the population level. However, most individuals are specialists (59%
 for δ15N and δ13C), with only a few generalists (13% for δ15N, 6% for
 δ13C). Furthermore, individual seals specialize at different trophic
 levels, resulting in niche partitioning. Most variation in trophic ecology
 is driven by individual specialization, but sex and mass also contribute.
 We also find that some seals specialize over time, consistently foraging
 at the same trophic level, while others switch trophic levels within and
 between years. Long-term specialization by only a few leopard
 seals has likely contributed to the decline of a significant local
 mesopredator colony, the Antarctic fur seal. Our findings show the
 importance of examining individual specialization in leopard seals across
 their range to understand their impact on other prey populations. More
 broadly, this approach should be applied to other apex predator
 populations, as a few specialists can significantly impact ecosystems.
description: <em>Sample
 Collection</em> Leopard seals were
 sampled between January and May from 2013 to 2023 at the U.S. Antarctic
 Marine Living Resources (AMLR) Program ecological monitoring site at Cape
 Shirreff, Livingston Island in the WAP. Leopard seals were sedated
 (Pussini and Goebel, 2015; Krause et al., 2016) to allow for the
 collection of morphometric data (standard length [cm], girths, and mass
 [kg]), life history traits (sex, age class), and whiskers. Whiskers were
 collected from 34 leopard seals (28 females, 6 males). Seven females were
 opportunistically resampled without a full capture (i.e., morphometric
 data were not collected) 1-3 times after their first handling, resulting
 in 12 additional whisker samples. In total, we analyzed 46 whiskers (40
 females, 6 males). A subset of these whiskers (n=18 from field seasons
 2018 and 2019) were previously analyzed by (Charapata et al.,
 2023). <em>Stable Isotope Analysis
 (SIA)</em> Whiskers were wiped with 1:1
 ethanol:methanol solvent, sonicated for 30 mins in distilled water, and
 air dried. Whiskers were measured and sectioned into 0.5-3 mm increments
 (from root to tip) for a targeted weight of ~0.3 mg (Charapata et al.,
 2023). Carbon and nitrogen stable isotope analysis was performed at Baylor
 University using an Elemental Analyzer 4010 Elemental Combustion System
 paired with a Conflow IV interphase (Thermo Scientific) and Thermo Delta V
 Advantage continuous flow Isotope Ratio Mass Spectrometer. Whisker
 nitrogen (ẟ<sup>15</sup>N) and carbon
 (ẟ<sup>13</sup>C) isotope values are expressed in delta
 notation (ẟ) in units of per mil (‰). Additionally, isotope values are
 reported as the ratio of the heavy to light isotope relative to
 international standards--atmospheric nitrogen and Vienna Peedee Belemnite,
 respectively--using the following equation: ẟX  = 
 [(R<sub>sample</sub>/R<sub>standard</sub>) –
 1]*1000 where X is the 13C or 15N and R is the
 corresponding ratio of 13C/12C or 15N/14N. A two-point calibration curve
 for calculating ẟ<sup>15</sup>N and
 ẟ<sup>13</sup>C values of samples was established using
 USGS-40 and USGS-41A international standards. The accuracy and precision
 of isotopic measurements were calculated based on the long-term mean and
 standard deviation (SD) of 244 replicates of an internal lab standard
 (Acetanilide, reported ẟ<sup>13</sup>C =−29.53±0.01‰,
 ẟ<sup>15</sup>N=1.18±0.02‰) measured during each analytical
 run (n=3 replicates/run). The replicate grand averages obtained were very
 close to (ẟ<sup>13</sup>C=−29.42±0.08‰) or within the range
 (ẟ<sup>15</sup>N=1.30±0.17‰) of analytical uncertainty of
 reported values. We measured the atomic C:N ratio for every whisker
 segment with acceptable atomic ratios ranging from 3.0-4.0 (Newsome et
 al., 2009; Kernaléguen et al., 2012; Charapata et al., 2023). Nearly all
 whisker segments had acceptable atomic C:N ratios (3.53±0.14, range:
 2.9-4.0). Twelve whisker segments were excluded for having ratios outside
 this range. <em>Time Stamping
 </em> Leopard seals molt and shed their
 whiskers annually; therefore, whiskers represent growth over a few months
 and up to one year (Rogers et al., 2016). We timestamped whisker segments
 based on leopard seal whisker growth characteristics using the Von
 Bertalanffy growth model (von Bertalanffy, 1938; Rogers et al., 2016)
 following the approach outlined by (Charapata et al., 2023).
 <em>Data Analysis</em>
 All data were tested for normality and homogeneity of variance
 before analysis. Results are reported as mean ± standard deviation (SD)
 unless otherwise stated. We performed all analyses using R (R Core Team,
 2022) with RStudio (Team, 2021) and JMP (SAS).
 <em>Population-Level</em> Population-level analyses included a total of 46 leopard seal whiskers. Each whisker was treated separately based on preliminary data showing inter-annual isotopic variability. We calculated the population-level mean, SD, and range of δ<sup>15</sup>N and δ<sup>13</sup>C values. We used variance component analysis (VCA) to calculate between- and within-individual population variation. Total variance in stable isotopes (“between individuals” variation) indicates variation among individuals in a population, while variance in stable isotopes along the whisker (“within-individual” variation) indicates variation of an individual (Bearhop et al., 2004; Newsome et al., 2009; Hückstädt et al., 2012). We applied the Stable Isotope Bayesian Ellipses in R <em>SIBER</em> package (Jackson et al., 2011) to determine population isotopic niche width. We used the standard ellipse area corrected for small sample sizes (SEAc) for individual whisker(s) as the metric for calculating the population isotopic niche area. We also calculated a population-level SEAc and total area (TA) using the pooled δ<sup>15</sup>N and δ<sup>13</sup>C values from all whisker segments (n = 46 whiskers; 2,198 segments) to compare our results with a previous study on leopard seals (Botta et al., 2018). <em>Individual Specialization</em> We used two approaches to evaluate the isotopic variation at the individual level. First, we calculated individual isotopic niche with SEAc and TA estimates for each individual’s whisker(s) using the δ<sup>15</sup>N and δ<sup>13</sup>C values of the whisker segments; this allowed us to visualize and assess each individual’s range of trophic levels and foraging locations collectively. Next, we calculated δ<sup>15</sup>N and δ<sup>13</sup>C specialization indices for each whisker to describe the variance in δ<sup>15</sup>N and δ<sup>13</sup>C and calculate the degree of individual specialization (Bolnick et al., 2002; Lewis et al., 2022); this allowed us to separately assess the variation in δ<sup>15</sup>N and δ<sup>13</sup>C. The degree of specialization was calculated using the equation: SI = INW/ (INW + BINW) Within the δ<sup>15</sup>N specialist category, some individuals consistently exhibited high δ<sup>15</sup>N values, while others consistently had medium to low values. Therefore, we performed agglomerative hierarchical clustering to determine whether there were subgroups within our δ<sup>15</sup>N specialist isotope data using the ‘agnes’ function in the R package <em>cluster</em> (Kaufman and Rousseeuw, 2009). To determine the optimal number of clusters, we used the Dunn index, which differentiates between sets of clusters that are compact and well separated (Supplementary Figure 1A). We found two distinct clusters (Supplementary Figure 1B): high trophic level specialists (H-Specialist) and medium to low trophic level specialists (ML-Specialist). <em>Trophic Variation & Overlap</em> We examined variation and niche overlap in isotopic signatures as a function of sex, body mass, and degree of individual specialization. We focused these analyses solely on δ<sup>15</sup>N because (1) we were interested in trophic level variability, and (2) leopard seals from Cape Shirreff tend to remain in the near-shore habitat and are primarily coastal foragers (Krause et al., 2015; Kienle et al., 2022b). To investigate variation in δ<sup>15</sup>N, we ran a linear mixed-effects model (LMM; (Pinheiro and Bates, 2000) using the ‘lmer’ function from the <em>lme4</em> package (Bates et al., 2014). This model treated the average δ<sup>15</sup>N values as response variables with sex, mass, the interaction of sex and mass, and δ<sup>15</sup>N specialization category (H-Specialist; ML-Specialist; Intermediate; Generalist) as fixed effects and individual as a random effect. Model selection was performed using the R package <em>MuMIn</em> (Barton and Barton, 2015) based on the smallest Akaike information criterion corrected for sample size (AICc). The model with the lowest AICc had the highest support, and models with ΔAICc &lt; 2 were considered to have substantial support (Anderson and Burnham, 2002; Franklin et al., 2002). Goodness-of-fit for each model was estimated using marginal (R² LMM(m)) and conditional (R² LMM(c)) coefficients of determination, indicating variance explained by fixed effects alone and by both fixed and random effects, respectively (Nakagawa and Schielzeth, 2013). We examined the contribution of each fixed effect of our top models by looking at the estimated coefficients and p-values and then used ANOVAs on each of our top models. Pairwise comparisons were performed using the ‘emmeans’<em> </em>function from the <em>emmeans</em> package with Tukey adjustment for multiple testing δ<sup>15</sup>N and δ<sup>13</sup>C (Lenth and Others, 2022). We also used Spearmen’s correlation to assess the relationship between our continuous variables (average δ<sup>15</sup>N, mass) and the relationship between mass and individual niche width (SEAc). We assessed niche differences and overlap between δ<sup>15</sup>N specialization categories and sexes using the proportion of paired SEAc shared; this was calculated with the ‘maxLikOverlap<em>’</em> function from the <em>SIBER</em> package (Jackson et al., 2011). <em>Between-Year Variability</em> To evaluate between-year variability among repeat individuals (n=7), we used <em>SIBER</em> to visualize data and quantify percent overlap between isotopic niches, assessing the similarity/dissimilarity in isotopic composition between years for each individual. We also used a quadratic discriminant analysis (QDA) to simultaneously evaluate δ<sup>13</sup>C and δ<sup>15</sup>N (Koehler et al., 2019; Smith et al., 2021). QDA is appropriate for analyzing data that are unequally sampled across years and have unequal variance; this allowed us to effectively assign isotope signatures to specific years for each seal. We considered QDA to be unsuccessful in assigning individual isotope data to their respective years if the results were £ 70% (Koehler et al., 2019; Smith et al., 2021), suggesting that the data was too similar to accurately assign it to specific years. <em>Temporal Changes</em> To investigate yearly and monthly trends in δ<sup>15</sup>N data, we used generalized additive models (GAMs) with isotopic signatures of whisker segments as the response variable. Year and month from timestamped whiskers were used as temporal predictors, with individual as a random effect, using the formula: δ<sup>15</sup>N ~ s(Months, k = 10, bs = "cc") + s(Year, k = 10, bs = "tp") + s(Individual.ID, bs = "re"). The “s” represents the smooth functions and “k” represents the number of basis functions used in the smoothing function. For month we used a cyclic spline (bs = 'cc') and for year a thin plate regression spline (bs='tr'; (Wood, 2003). GAMs and corresponding model estimates were conducted using the <em>mgcv </em>and <em>modelbased</em> R packages (Wood, 2017; Makowski et al., 2020). We evaluated isotopic linear temporal fluctuations in significant variables by using a grid approximation, accompanied by CI 95% (Makowski et al., 2020). Because our dataset is overrepresented in some years/months and underrepresented in others, we created a customized prediction grid based on GAM models fitted on the observed data. The conditional expectations generated by the simulated homogeneous dataset (i.e. simulated data for all years and months for all Individual.ID) allowed parameter estimation using the fitted model. The predicted values were used to calculate the first derivative of the response variable and estimate the linear slope of the isotopic signatures to identify the temporal windows where significant linear increase or decrease of isotopic values occurred in time.
description: # Individual specialization in a generalist apex predator: The leopard
 seal
 [https://doi.org/10.5061/dryad.f4qrfj75k](https://doi.org/10.5061/dryad.f4qrfj75k) ## Description of the data and file structure Data for: Individual Specialization in a Generalist Apex Predator: The Leopard Seal The data provided are the raw final bulk stable isotope data (δ¹⁵N and δ¹³C) profiles from whiskers (n = 46) collected from 34 individual leopard seals. All additional metadata and relevant information are included in the manuscript. **Description of the Data and file structure** The spreadsheet contains the following columns: * Seal.ID: Identifies individual seals. If whiskers were collected from the same seal in different years (repeat samples), the Seal.ID includes a suffix to indicate the year of collection. For example, a whisker collected from seal 397 in year 1 is labeled as 397. For subsequent years, the same seal's ID is updated incrementally: 397.1 for year 2, 397.2 for year 3, and 397.3 for year 4. This system ensures that samples from the same individual across multiple years are identified. Finally, if a sample was "not detected" due to low weight or if the C: N ratio fell outside the acceptable range, the data for that segment was excluded. To ensure continuity in timestamping, the excluded segment number was combined with the next valid segment number. For example, if whisker segment #25 was excluded as a "non-detect," the isotopic values for segment #26 were labeled as 25_26. This adjustment preserves the sequence of data and reflects the combined timestamp for analysis. * Year: The year the whisker was collected. - Whisker Segment: Each whisker is divided into segments for isotopic analysis, and this column specifies the segment number. * Isotopic Values (δ¹⁵N and δ¹³C): The stable isotope values for nitrogen (δ¹⁵N) and carbon (δ¹³C) for each segment, expressed in permil (‰). - N and C Weight% %: The weight percentage of nitrogen (N) and carbon (C) in each whisker segment. ## Access information Other publicly accessible locations of the data: * NA Data was derived from the following sources: * NA