{"paper_id":"014abddb-8018-4bc6-a26f-ba6520d19df4","body_text":"1\n1\n2\n3 Environmental heterogeneity plays a bigger role than diet \n4 quality in driving divergent California sea lion population \n5 trends\n6\n7 Diet quality and population trends of sea lions\n8\n9\n10\n11\n12 Ana Lucía Pozas-Franco1, *, David. A. S. Rosen1,¶, Andrew W. Trites1, ¶, Francisco J. García-\n13 Rodríguez2, Claudia J. Hernández-Camacho2, ¶\n14\n15\n16 1Marine Mammal Research Unit, Institute for the Oceans and Fisheries, University of British Columbia, Vancouver \n17 BC, V6T 1Z4, Canada\n18\n19 2Instituto Politécnico Nacional, Centro Interdisciplinario de Ciencias Marinas, La Paz, Baja California Sur, 23096, \n20 México\n21\n22\n23\n24 *Corresponding author\n25 E-mail: apozasfr@sfu.ca\n26\n27 ¶These authors contributed equally to this work and were thesis committee members.  \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n2\n28 Abstract\n29\n30 While the global population of California sea lions (Zalophus californianus) is increasing, regional \n31 trends show a decline in the Gulf of California (GoC, Mexico) and an increase in the Channel \n32 Islands (CI; California, U.S.) over the last 40 years. The drivers of these divergent trends remain \n33 unclear, but previous pinniped studies suggest that differences in diet quality—rather than prey \n34 abundance—may play a role. We therefore conducted an analysis to examine how sea lion \n35 population trajectories relate to diet quality, specifically looking at diet energy density and diet \n36 diversity. Using population and diet data from 1980 to 2020 for sea lions in the GoC and CI, we \n37 found no simple relationships between population trajectories and diet quality over time. Energy \n38 densities of sea lion diets were similar between the two regions, but GoC sea lions consumed a \n39 more diverse range of prey (n = 88 vs. 23 main prey species) dominated by benthic species and \n40 schooling fishes, while CI diets consisted mainly of schooling fishes and squid. We also found that \n41 GoC sea lions ate more benthic prey and less schooling fish during the 2014–2016 heatwave—\n42 decreasing their overall diet energy density, similar to the CI. This shift coincided with a temporary \n43 population decline in the CI but had variable effects on GoC populations. Overall, our findings \n44 suggest that regional population trends are influenced by complex ecological factors beyond diet \n45 quality alone, highlighting the need to consider environmental variability and prey composition \n46 when assessing the resilience of sea lion populations to climate-driven changes.\n47\n48\n49 KEYWORDS: Diet quality, Population decline, Zalophus californianus, Gulf of California, \n50 Channel Islands, Pinniped, Environmental heterogeneity \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n3\n51 1. Introduction\n52\n53 California sea lions (Zalophus californianus ) are widely distributed along the Pacific coast of \n54 North America from British Columbia, Canada to the Gulf of California, Mexico, but only \n55 reproduce on certain islands (rookeries) along the southern coast of California (U.S.), the Mexican \n56 Pacific, and the Gulf of California (1,2). Most of the global population (80%) breeds in California \n57 where numbers increased at an annual rate of 2.9% between 1964–2014 (3). The remainder of the \n58 population (20%) breeds in Mexico, where—with a few exceptions—most rookeries show a \n59 declining trend. In the Gulf of California, sea lion populations experienced on average a 2% decline \n60 per year between 1984–2015 (4,5). \n61\n62 In the southern Pacific coast of the U.S., California sea lions breed almost exclusively at four \n63 rookeries that form part of the Channel Islands which vary in size from 3,000–60,000 individuals \n64 (6). This population has grown steadily since the 1980’s but has experienced temporary population \n65 declines in some years associated with increased sea surface temperatures as seen during the 2012–\n66 2016 marine heatwave (6). The population has since recovered, totaling 111,713 sea lions in 2019 \n67 (6). In contrast, breeding sea lion populations in the Gulf of California, Mexico, are distributed \n68 among 13 rookeries of varying size from 400–6,000 individuals (1,2). Although these rookeries \n69 vary in population growth, most show a declining trajectory since the 1980’s. Only one rookery in \n70 the southernmost Gulf of California, Los Islotes, is considered to have a population that has been \n71 increasing since 1979 (7).\n72\n73 The underlying factors causing a divergence in California sea lion population trajectories in the \n74 Channel Islands compared to the Gulf of California are still unknown (5). Possible contributing \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n4\n75 factors that are known to affect marine mammal species in general include regional differences in \n76 prey availability, pollution (both chemical and noise), disease, biotoxins, fishing gear \n77 entanglements, anthropogenic mortality (disturbance, legal and illegal shooting), and migration \n78 (8–11). Of these contributing factors, regional differences in diets associated with environmental \n79 change have been identified as the most likely contributor to population trajectories in several \n80 species (4). \n81\n82 Previous studies have generally focused on the negative effects that short-term reductions in prey \n83 abundance have on California sea lion numbers at rookeries in the Channel Islands (3,12,13) and \n84 the Gulf of California (7,14–18). Sharp declines in quantities of primary prey species available to \n85 sea lions are known to occur during El Niño events in California when warm water causes prey to \n86 remain at inaccessible depths, leading to increased pup mortality (19). However, El Niño events \n87 do not appear to have a comparable direct effect on the Gulf of California sea lion populations \n88 where the response to warming events depends on location, population size, and regional dynamics \n89 (5). This leads to the question of whether changes in the quality of prey (rather that changes in \n90 quantity) might better explain differences that have occurred in sea lion numbers over a longer \n91 timeframe (20), as suggested for Steller sea lions (21–25).\n92\n93 Diet quality can be assessed in many ways. Two main metrics of diet quality are diet energy density \n94 (an important aspect of the nutritional value of prey species) and diet diversity (i.e., the variety of \n95 species that compose the diet). These diet characteristics can affect the nutritional status of \n96 individuals, their reproduction and survival rates, as well as their susceptibility to disease and \n97 predation (23,24). Based on broad trends observed among different marine mammals, a diet \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n5\n98 dominated by a few energy-rich species would be hypothesized to support a growing population, \n99 while a switch to a more diverse diet of energy-poor species would be expected to cause population \n100 declines (26). It has also been demonstrated that changes in environmental conditions can alter the \n101 diet composition and ‘quality’ of prey species available to predators. However, it is not known \n102 how such differences or changes in diet quality may be influencing the population dynamics of \n103 California sea lions in the Channel Islands compared to the Gulf of California. \n104 To investigate the effect of diet on population trajectories, we used estimates of average diet energy \n105 density, and two measures of diet diversity to quantify diet quality of California sea lions at the \n106 Channel Islands and the Gulf of California rookeries from 1980–2020. We compared the measures \n107 of diet quality between and within the two geographic regions and tested for relationships between \n108 rates of population change and the different measures of diet quality over time. Finally, the effects \n109 of increased sea surface temperatures (2014–2016) on sea lion diet and populations were also \n110 compared between regions. Obtaining a better understanding of the interplay between \n111 environmental changes, diets, and population trajectories is needed to inform policies regarding \n112 the conservation and management of California sea lions in Mexico and the U.S.\n113\n114 2. Materials and Methods\n115\n116 2.1 Population and diet data \n117 This meta-analysis focused on the four California sea lion rookeries in the Channel Islands (San \n118 Miguel, San Clemente, Santa Barbara, and San Nicolas), and 12 of the 13 rookeries along the Gulf \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n6\n119 of California (Fig.1). We omitted San Jorge due to a lack of available data and also had insufficient \n120 data to include the west coast of the Baja California Peninsula. Available population and diet data \n121 for California sea lion rookeries in the Channel Islands and the Gulf of California from 1980–2020 \n122 were gathered from published and unpublished sources (Table S1). All data (population counts \n123 and diet data) used in this study were collected during the sea lion breeding season from May to \n124 August because: 1) most of the data available was from this season, 2) population data from this \n125 season captures the maximum number of sea lions present that year at a rookery by including \n126 newborn pups, and 3) we wanted to avoid the potential confounding effects that might be \n127 introduced by seasonal changes in diet when comparing diets across years and areas. \n128 2.1.1 Diet data\n129 Diet data refers to data on the occurrence of identified prey species from sea lion scat samples \n130 collected at rookeries. Available data was used to assess diet quality, which we characterized using \n131 measures of diet diversity and diet energy density. Both diet characteristics were calculated using \n132 data originally reported as frequency of occurrence of prey species (FO) (27), or as an index of \n133 importance (IIMP) of prey species (Gulf of California data only) (16,28).\n134 To test the relationship between diet quality and population change, we ideally needed matching \n135 diet data for the years with available population data. However, diet data were sparse, unevenly \n136 distributed over time, and only available for certain rookeries and years. Additionally, in some \n137 cases data were reported as a single mean spanning several years (e.g., Santa Barbara Island, 1981–\n138 1995; Table S1). To address these limitations and make use of all available data, various data \n139 processing methods were employed prior to conducting analyses. \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n7\n140 2.1.2 Processing diet data\n141 Only prey species with FO values ≥5% were included in this dataset because: 1) this was the cut-\n142 off available from most of the data, and 2) it served to highlight the main prey items. We also \n143 applied a ≥5% cut-off to IIMP values to maintain consistency across the data (this is the same \n144 IIMP cut-off previously used by Porras-Peters 2008). Note: while some literature uses a cut-off of \n145 IIMP values ≥10% (16), we were able to apply a ≥5% IIMP value cut-off because we had access \n146 to the raw diet data (F.J García-Rodriguez unpubl. data). ‘Non–identified’ species reported in the \n147 IIMP data were deemed not useful for this analysis and were therefore excluded.\n148 To be able to compare diet data between rookeries and years, remaining FO and IIMP values were \n149 standardized to sum to 1.0 (or 100%) within each year of data by dividing each reported value by \n150 the total FO or IIMP for that rookery and year. This yielded modified frequency of occurrence \n151 (MFO; (29) and modified importance index (MIIMP) values which were used from this point \n152 onwards to calculate diet diversity and energy density.  \n153 2.1.3 Calculating diet diversity\n154 Two measures of diet diversity were used: 1) the total number of species recorded in the diet, and \n155 2) diet diversity calculated using the Shannon Index (30) from either MFO or MIIMP values for \n156 individual species, where a higher resulting H-index indicates greater species diversity. Values of \n157 average diet diversity using the Shannon Index were calculated for each rookery and year when \n158 data was available, which were then used in subsequent analyses (Table S2). \n159 2.1.4 Calculating (weighted) diet energy density\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n8\n160 The energy density of each prey species was recorded in kilojoules per gram of wet weight \n161 (kJ/gww) from data obtained in Gleiber et al., 2022 (31). If an energy density value was not \n162 available at the species level, an average energy density value from the species’ family (or in a few \n163 cases a closely related family) was used instead to approximate energy density. Then we calculated \n164 the weighted average diet energy density (to account for relative appearance of each prey species \n165 in the diet) by multiplying the MFO or MIIMP (expressions of proportion in the diet) by the \n166 respective energy density of that prey species (in kJ/gww). Summing these values gave an average \n167 weighted diet energy density value for each rookery and year, which was used in subsequent \n168 analyses (Table S2). \n169 2.1.5 Population data\n170 All sea lion counts from 1980–2018 from the Gulf of California rookeries were obtained from \n171 (17). Counts were made from boat surveys and included numbers for each age and sex class. \n172 Population counts for the Channel Islands were available from 1980–2019 and were sourced from \n173 (3) for 1980–2014, from (32) for 2015, and from (6) for 2016–2019 (Table S1). These counts had \n174 already been corrected for pups that were obscured from vision and for adult females that were \n175 foraging during the census. At times, multiple sources reported population counts for the Channel \n176 Islands, so the source with the higher counts was used if this was because a technique with \n177 presumed greater accuracy was used (i.e., aerial photography counts vs. boat counts). \n178 2.1.6 Calculating population change \n179 To test the relationship between population and diet quality, we calculated rates of population \n180 change for years with available diet data at each rookery. Using regression analysis, we estimated \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n9\n181 population changes immediately associated with single or grouped years with diet data (i.e., year-\n182 rookery grouping) by incorporating population counts from a set number of years before and after \n183 the diet data. Since the range of years incorporated into each rookery-year diet data point varied, \n184 a set of rules were established to define the number of years before and after the diet data span that \n185 were included in the calculation of population change, depending on how many years of diet data \n186 were included for that grouping. Further details on calculating population change provided in the \n187 Supplementary Methods. In general, population trends were estimated from data that spanned the \n188 period of diet data by at least an additional year on either side.  \n189 2.2. Grouping data\n190 2.2.1 Creating Zones and Zone-era groupings\n191 To investigate the relationship between population changes and diet quality, we had to ensure the \n192 independence of the data points on both a temporal (both sequential and non-sequential data) and \n193 geographic scale (closely related rookeries). This involved grouping rookery diet data (energy \n194 density and diet diversity averages) and population data (population change averages over a set of \n195 years) together into non-continuous sets of years into rookery-year groupings (more details in \n196 Supplementary Methods).  Then we created sets of matching population and diet data sets averaged \n197 across related geographic areas (Zones) and non-continuous time periods (eras): Zone-era \n198 groupings. \n199 As previously noted, neither population or diet data was continuous across years, and the timing \n200 of the data was not consistent across rookeries, resulting in the previously described rookery-year \n201 groupings. Values for the eventual ‘Zone-era’ data groupings were created by averaging the \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n10\n202 population change of each rookery-year grouping, and the respective diet quality values (energy \n203 density and diversity) within a geographic Zone (described below) for each set of number of years \n204 or ‘era’ with available data.\n205 This grouping of data from individual rookeries into composite geographic Zones was done for \n206 two main reasons: 1) to prevent potential over-representation of individual rookeries relatively \n207 close to each other that could be considered common ecological units, and 2) to best deal with the \n208 lack of available continuous population and diet data over time by grouping available data, thus \n209 allowing us to make comparisons between different geographic Zones for similar time \n210 periods/eras. Rookeries were grouped into a common Zone if they occurred in a similar geographic \n211 area (<100 km away from each other) and had a similar population trajectory over time, which \n212 were determined by fitting a linear regression to the total population counts of each rookery for all \n213 years with available data (Fig. 2). The average annual population change (percent) was calculated \n214 as the slope divided by the intercept (the predicted first year population) of the regression equation. \n215 Population trajectories were therefore classified as increasing, decreasing, or inconclusive \n216 according to the slope of the linear regression. \n217 Previous studies have partitioned the 13 Gulf of California rookeries into three sub-populations \n218 based on factors such as environmental conditions, genetic structure, and diet (17,33–36). \n219 However, in our study we deliberately excluded factors related to diet, and thus created Zones \n220 based only on similarities in population trajectory and geographic proximity. \n221 Following this methodology, 10 Zones were created: the Channel Island rookeries were grouped \n222 together into Zone 1 (Fig. 1). The available data resulted in two Zone-era groupings: Zone 1 1981–\n223 1995 and Zone 1 2000–2011. The Gulf of California rookeries were grouped into 9 zones (Zones \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n11\n224 2–10; Fig. 1).  Data for the Gulf of California using the available FO data resulted in two era \n225 groupings, 1990–2000 and 2015–2019. This resulted in 9 Zone-era groupings for the GoC using \n226 FO data. The IIMP data yielded three era groupings: 1995–1996, 2002, and 2015–2019 (Table S3). \n227 This resulted in 18 Zone-era groupings for GoC. Hence, this process yielded 11 Zone-era sets of \n228 matched population and FO-based diet quality data for the CI and GoC combined, and an \n229 additional 18 sets of data for the Gulf of California from IIMP-based diet quality. The relationship \n230 between diet quality and population change was then tested on these ‘Zone-era’ groupings.  \n231 Fig. 1. Map of the California sea lion rookeries and designated Zones for this study.  Study \n232 sites included the four rookeries in the Channel Islands (a–d: San Miguel, San Nicolas, Santa \n233 Barbara, San Clemente) designated as Zone 1, and the 13 rookeries along the Gulf of California \n234 (e–q), and their respective Zones (2–10). Circles indicate rookeries within the indicated Zone. \n235 Rookery f (San Jorge) did not have diet data available and was omitted from further analysis. For \n236 reference, the distance between the northernmost rookery of Roca Consagradas (Zone 2), and the \n237 southernmost rookery of Los Islotes (Zone 10) is 823 km.\n238 2.3 Effects of environmental change\n239 Changes in diet quality before and after an event characterized by increased sea surface \n240 temperatures were explored by comparing the change in average diet energy density and diet \n241 diversity in the Gulf of California before and after 2014. Sufficient post-environmental shift diet \n242 data were not available for the Channel Islands post-environmental shift, so only pre-\n243 environmental shift data was used to compare the diet quality between the Gulf of California and \n244 the Channel Islands. \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n12\n245 Prey species from the diet data were grouped into 9 species categories to further describe changes \n246 in diet before and after the environmental shift, and to express the ecological distribution of species \n247 consumed (number of species per category). These categories were assigned based on broad \n248 ecological characteristics similar to previous studies  as per Trites et al., 2007 (37), and included: \n249 benthic species (n = 60 prey species), crustaceans (n = 1), gadids (n = 5), lanternfish (n = 7), \n250 octopus (n = 2), rockfish (n = 5), schooling fishes (n = 21), squids (n = 15), and miscellaneous (n \n251 = 17) (Table S4).  \n252 2.4 Statistical analysis\n253 To test relationships between population changes and diet quality, linear regression models were \n254 fit to the data in R-Studio (version 2022.02.3). Simple linear models were used to test for \n255 relationships between population change and measures of diet diversity and energy density at the \n256 level of ecological Zones using Zone-era data. Additionally, since Zones consisted of varying \n257 population sizes, those regression analyses incorporated median Zone population size as a \n258 weighting factor. The resulting p-values and adjusted R-squared values from all linear models are \n259 reported. All energy density and diet diversity values derived from MFO and MIIMP data were \n260 tested for outliers using Grubb’s and Dixon’s outlier tests in R-Studio using the package “outliers”, \n261 at the Zone-era grouping level. Preliminary analyses revealed no statistically significant outliers \n262 in the data. \n263 Two-sample t-tests assuming unequal variances were conducted using MFO and MIIMP data to \n264 compare diet diversity and energy density within the Gulf of California before and after the 2014 \n265 environmental shift, and when comparing pre-environmental shift diets between the Gulf of \n266 California and the Channel Islands. \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n13\n267 3. Results\n268\n269 3.1 Population trajectories\n270 All four Channel Island rookeries (Zone 1) averaged population increases of 2-6% per year from \n271 1980–2020, except for a population decrease associated with increased sea surface temperatures \n272 in 2014 (Fig. 2). In contrast, the overall population in the Gulf of California decreased over the \n273 same time-period, although trends differed at individual rookeries. While most rookery \n274 populations showed a decline, San Esteban and Los Islotes rookeries (Zones 6 and 10) showed \n275 increasing population trajectories (although the San Esteban population increase was not \n276 statistically significant). As opposed to the Channel Islands, sea lion populations in the Gulf of \n277 California did not seem to be collectively affected by the 2014 warming event (Fig. 2). \n278 Fig. 2. California sea lion population trends within Zones (1980–2020). Data shows total sea \n279 lion counts for each Zone with data from 1980–2020. Rookery names within each Zone are labeled \n280 in the bottom right of each panel. Zones composed of multiple rookeries show the sum of the \n281 population totals in those rookeries. Solid lines represent statistically significant regressions and \n282 dotted lines represent regressions that were not statistically significant. Red data points in Zone 1 \n283 represent population declines after the 2014 warming event (2015–2019); these data were not \n284 included in the overall regression. \n285\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n14\n286 3.2 Diet quality and population changes\n287 Diet diversity (using the Shannon Index) did not correlate significantly with rate of population \n288 change when examined at the level of Zone-eras (p = 0.430 using MFO data and 0.622, using IIMP \n289 data; S3 Fig., left panels). Nor were there significant relationships between diet energy densities \n290 and rates of population change (p = 0.804 using MFO data and p = 0.128 using IIMP data; S3 Fig., \n291 right panels).  \n292 3.3 Diet quality in the Channel Islands vs. the Gulf of California \n293 At the Channel Islands, California sea lions primarily consumed 23 main prey species, whereas in \n294 the Gulf of California, they consumed 88 main species (using MFO >5% data; Figs. 3 and 4). Jack \n295 mackerel, Chub mackerel, Pacific hake, and Californian anchovy coincided as top species \n296 consumed by sea lions in both the Channel Islands and the Gulf of California. \n297 Fig. 3. Prevalence of California sea lion prey species in the Channel Islands. Bars represent \n298 the total number of occurrences (out of 41 possible occurrences, equivalent to years) of each prey \n299 species from frequency of occurrence data from 1980–2011; that is, the total number of years \n300 where each prey species was present in the diet. All 23 species with FO ≥5% are listed. The \n301 species’ common name is listed when available, although some were identified only at the family \n302 or genus level.  \n303 Fig. 4. Prevalence of California sea lion prey species in the Gulf of California. Bars represent \n304 total prey species occurrences in the diet per year (total number of years where each prey species \n305 was present in the diet), from available diet data from 1980–2019 for all Gulf of California \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n15\n306 rookeries (Zones 2–10) from either frequency of occurrence (blue bars) or index of importance \n307 (orange bars). The common name of all 88 species (or species groups) from both MFO and MIIMP \n308 data is listed when available (some species were identified only at the family or genus level). \n309 Sea lions in the Channel Islands ate mainly schooling fishes and squid (36% and 21% of diet \n310 respectively; data from 1981–2011), whereas sea lions in the Gulf of California mainly ate multiple \n311 benthic species (41 species; 47% of diet) and schooling fishes (19 species; 22% of diet; data from \n312 1990–2019; Fig. 5, left panels), with schooling fishes having a higher average energy density than \n313 benthic species (Fig. 6). \n314 Fig. 5. Diet composition by prey species categories before and after 2014 from frequency of \n315 occurrence data. Pie chart slices represent the proportion by each species category. White \n316 numbers represent the number of species in the diet from each category. Diet composition data \n317 from the Channel Islands is from 1980–2011 (no comparable diet data available after 2011). Diet \n318 composition data for the Gulf of California before the 2014 environmental shift is from 1990–2000 \n319 and from 2015–2019 after 2014. \n320 Fig. 6. Average energy density of each prey species category consumed by California sea \n321 lions. Colours correspond to species categories illustrated in Fig. 6. Bars represent average energy \n322 densities (kJ/gww; mean value ± standard error) from all species present in the diet data from each \n323 category ordered from highest (lanternfish) to lowest (octopus) value.\n324 Mean diet energy density for sea lions in the Channel Islands (5.43kJ/gww) was comparable to the \n325 Gulf of California (5.32 kJ/gww) (Table 1 and Fig. 7). Mean diet diversity using the Shannon \n326 Index was lower in the Channel Islands than in the Gulf of California (1.83 vs. 2.04; Table 1 and \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n16\n327 Fig. 7) and had higher variability among the Gulf of California Zones (range: 0.79–3.26) than \n328 among the Channel Islands (range: 1.34–2.35). \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n17\n329 Table 1. Diet quality and population trajectory of each of the Gulf of California Zones and the Channel Islands from frequency \n330 of occurrence data. The mean and standard deviation of the range of the annual weighted diet energy density (kJ/gww) and of the diet \n331 diversity (Shannon Index) for sea lion diets is shown (incorporating individual prey species). ‘n’ represents the number of years with \n332 data points used to calculate the corresponding mean and range values. Population trajectories in bold represent statistical significance. \n333 Zones 2, 8 & 9 are omitted due to lack of available FO data. \nEnergy density\n(kJ/gww)\nDiet diversity \n(Shannon Index)Zone: rookery Population \ntrajectory Mean ±SD Range Mean ±SD Range\nn\n1: San Miguel Increasing 5.43 ±0.94 4.14–7.25 1.79 ±0.94 1.34–2.35 10\n1: San Nicolas Increasing 5.63 ±0.22 5.26–5.94 1.75 ±0.23 1.46–1.96 8\n1: San Clemente Increasing 5.16 ±0.84 3.79–6.05 1.94 ±0.84 1.67–2.21 7\n1: Santa Barbara Increasing 5.48 N/A 1.87 N/A 1\nChannel Islands average Increasing 5.43 ±0.20 3.79–7.27 1.83 ±0.12 1.34–2.35 4\n3: Isla Lobos Decreasing 5.66 ±0.66 5.19–6.13 1.93 ±0.66 1.81–2.04 2\n4: Machos, Cantiles, Granito Decreasing 4.90 ±0.35 4.63–5.39 2.05 ±0.35 0.79–3.26 4\n5: Rasito Decreasing 5.04 ±2.45 4.31–5.77 2.34 ±1.03 2.20–2.48 2\n6: San Esteban Increasing 5.90 ±2.83 5.85–5.96 2.04 ±0.08 1.81–2.27 2\n7: San Pedro Mártir Decreasing 5.32 ±2.50 5.27–5.37 2.14 ±0.07 2.07–2.21 2\n10: Los Islotes Increasing 5.06 ±2.17 4.88–5.48 1.90 ±0.28 1.56–2.40 4\nGulf of California average Decreasing 5.32 ±0.39 4.31–6.13 2.04 ±0.15 0.79–3.26 6\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n18\n335 Fig. 7. Average annual diet diversity and energy density by Zones from frequency of \n336 occurrence data. Diet diversity (top panel) and energy density (bottom panel) values were based \n337 on all available Zone and year groupings from MFO (1981–2019; Zones 2, 8 & 9 are omitted due \n338 to lack of available FO data). Diet diversity values were calculated using the Shannon Index. Box \n339 limits represent the first, mean, and third quantile values, box whiskers represent the range of \n340 values. Bar widths are proportional to the number of data points in each Zone. The circle represents \n341 the energy density value of 7.25 in Zone 1 (San Miguel, 2005) which was considered an outlier \n342 according to Grubb’s test. \n343 The lowest diet diversity (0.79) occurred within Zone 4 (Granito, Cantiles, Machos) in 1996, which \n344 was heavily influenced by the Granito rookery where just one main species, largehead hairtail \n345 (Trichiurus lepturus), was consumed that year. Interestingly, the highest diversity recorded among \n346 all locations (3.26) occurred in 2018 also within Zone 4 where data were exclusively from Granito \n347 and showed that that year, sea lions consumed a total of 31 main species.\n348 The mean diet energy density in the Channel Islands was 5.43 ±0.2 kJ/gww, with a surprisingly \n349 small overall variation considering that two data points had anomalously high or low energy \n350 densities (7.25 kJ/gww in San Miguel, 2005 and 3.79 kJ/gww in San Clemente, 1982). The species \n351 that contributed the most to the average energy density in the diet primarily belonged to the \n352 schooling fishes category (Fig. 6), and included jack mackerel, Pacific mackerel, and northern \n353 anchovy. San Miguel Island in particular had years when sea lion diets had above-average energy \n354 densities (S1 Fig.) with a higher-than-normal contribution from two species of schooling fishes, \n355 Pacific sardine (2002–2005) and herring (2005).\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n19\n356 Although the overall diet energy density for sea lions in the Gulf of California was not statistically \n357 different than for those in the Channel Islands, there was significant variability between years and \n358 within Zones in the Gulf of California, demonstrating differences in the species that contributed \n359 the most to the average diet energy densities. The highest diet energy density in the Gulf of \n360 California in any year occurred in Zone 3 (Isla Lobos, 1995, 6.13 kJ/gww), mainly due to the high \n361 energy density of Pacific anchoveta (a schooling fish) and largehead hairtail (a miscellaneous fish) \n362 (Table 1 and S2 Fig.). However, diets in Zone 6 (San Esteban) had the highest mean energy density \n363 overall (5.90 kJ/gww; 1995 and 1996). For both Zones 6 and 7 (San Pedro Mártir), the overall \n364 energy density of the diet largely reflected a high contribution from lanternfish, followed by \n365 largehead hairtail, Californian anchovy, and chub mackerel (S2 Fig.). Interestingly, in 1996 \n366 lanternfish was largely replaced with other species in the diet in both Zones 6 and 7 with little \n367 effect on the mean diet energy density. Diets of sea lions feeding in Zone 4 (Granito, Cantiles, \n368 Machos) had the lowest mean energy density (4.90 kJ/gww), which included a high proportion of \n369 ‘other’ species outside of the top 17 for most years (1996, 2016, 2018) (S2 Fig.). \n370 3.4 Effect of environmental changes on diet quality in the Gulf of \n371 California \n372\n373 From 2014 to 2016, the Gulf of California experienced unusually high sea surface temperatures. \n374 During this period, the proportions of schooling fish and squid in the diet of California sea lions \n375 decreased significantly, from 27% to 16% and from 7% to 1%, respectively, while the proportion \n376 of benthic species increased from 36% to 56% (Fig. 5). These changes marked a shift from high-\n377 energy schooling fish to lower-energy-density benthic species, resulting in a significant reduction \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n20\n378 in the overall average energy density of their diet to 4.69 ± 0.35 kJ/gww (two-tailed p = 0.045; \n379 Table 2, Fig. 8, bottom panel). \n380\n381 Table 2. Diet quality by geographic area before and after 2014. Total number of diet prey \n382 species, mean diet diversity using the Shannon Index, and mean weighted diet energy density. \nGeographic area & era Number of prey \nspecies in diet\nDiet diversity \n(Shannon Index) \n(mean ±SD)\nEnergy density \n(kJ/gww; mean ±SD)\nChannel Islands before 2014 23 1.83 ±0.12 5.42 ±0.36\nGulf of California before 2014 51 1.92 ±0.49 5.22 ±0.53\nGulf of California after 2014 65 2.30 ±0.57 4.69 ±0.35\n383\n384 Fig. 8. Average diet diversity from the Shannon Index (top panel) and average energy density \n385 (bottom panel) from frequency of occurrence data before and after 2014. Box limits represent \n386 averaged data (first, median and third quantiles ± standard error, ‘x’ represents mean value) from \n387 all Channel Island and Gulf of California groupings before and after 2014 (no data for Channel \n388 Islands after 2014). Diet data is based on rookery-year groupings. No significant differences were \n389 found between mean diversity values between geographic areas before 2014, nor in the Gulf of \n390 California between eras. There was a statistically significant decrease in the average energy density \n391 in the Gulf of California after 2014. \n392\n393 Diet diversity calculated using the Shannon Index showed no significant difference in mean values \n394 before (1.92 ±0.49) and after 2014 in the Gulf of California (2.30 ±0.57; two-tailed p = 0.245; \n395 Table 2, Fig. 8, top panel). However, there was an increase in the number of prey species consumed \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n21\n396 (from 51 to 65 species), and an overall increase in the average number of prey species consumed \n397 per rookery after 2014 (S4 Fig.).  Before 2014, sea lions consumed 9 prey species on average \n398 within rookeries (range 5–15 species), increasing to an average of 16 species per rookery (range \n399 7–26 species) after 2014 (S5 Fig.; data unavailable for Zones 8 and 9). Equally notable was that \n400 ~50% of the species consumed throughout the Gulf of California after 2014 were not present in \n401 the diet prior to this time (S6 Fig.) ––in other words, the sea lions did not simply add 14 more \n402 species to their diet but made a fundamental shift in the species they consumed. Despite the overall \n403 dietary shifts in prey quality observed after 2014, there were no apparent differences in the rates \n404 of population changes between these two eras (two tailed p = 0.984). \n405\n406 4. Discussion\n407 Previous dietary studies on California sea lions in the Gulf of California have tended to focus on \n408 detailing differences in the main prey species consumed, or describing feeding behaviours at  \n409 various rookeries (16,18). Only one study has indirectly explored the broad relationship between \n410 population changes and diet within the Gulf of California, finding no significant relationships \n411 between these variables (17). Our study is the first to assess California sea lion diet quality using \n412 available long-term summer data on a finer geographic scale for the Gulf of California (1990–\n413 2019) compared to the Channel Islands (1980–2014), to explore its relationship to differences in \n414 population trajectories. \n415 Our results demonstrate substantial differences in the diversity and type of prey species consumed \n416 by sea lions in these two areas, but do not show any significant relationships between measures of \n417 diet quality (diet energy density or diet diversity) and long-term rates of population change. Results \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n22\n418 also demonstrate that following the significant increase in sea surface temperatures that occurred \n419 in 2014, sea lions in central regions of the Gulf of California consumed a greater number of prey \n420 species that had an overall lower diet energy density. However, this shift did not result in an overall \n421 population decline throughout the Gulf, as occurred in the Channel Islands. These findings \n422 underscore the importance of considering the environmental heterogeneity at the different regions \n423 throughout the Gulf of California, which can heavily influence California sea lion population \n424 dynamics at local levels. \n425\n426 4.1 The role of diet diversity\n427 An ideal diet for California sea lions is one that allows them to meet their nutritional needs to grow \n428 and reproduce by feeding on sufficiently available prey species. However, our results illustrate \n429 that the exact nature of such a diet appears to vary depending on the characteristics of the \n430 ecosystem, making single indicators such as diet diversity difficult to interpret. Animals may \n431 choose to forage on fewer, energy-rich prey species (a low diversity, high energy density diet), \n432 which may reflect either a high degree of prey selectivity or a less biodiverse ecosystem. In more \n433 diverse ecosystems, foraging on a greater combination of species of different sizes and nutritional \n434 profiles might prove to be an optimal strategy and may even be required to fulfill nutritional \n435 requirements (38). Different marine mammal species have exhibited switches to lower quality diets \n436 (lower energy density prey) during environmental challenges that have been characterized by \n437 either decreases (22,39) or increases (40,41,26)  in diet diversity. \n438 Using the total number of prey species as a measure of diet diversity in our study revealed a striking \n439 difference in diets between regions. The sea lions at the Channel Islands consistently consumed \n440 23 primary prey species during the summer (1981–2011, Fig. 3) while those in the Gulf of \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n23\n441 California consumed 88 primary prey species (1990–2019; Fig. 4) that varied between Zones (S2 \n442 Fig.). There was a lower number of prey species observed in the Channel Islands, despite the fact \n443 that diet diversity in this region using the Shannon Index is highest during summer (20). These \n444 regional differences in the number of prey items may mean that the ideal prey species were not \n445 consistently available for sea lions in the Gulf, or it may alternatively illustrate that there were \n446 more prey options available to facilitate diet adaptability in some Zones. \n447\n448 Overall, the Shannon Index and the total number of species consumed illustrate that the diet has \n449 been relatively consistent over time within the Channel Islands (Fig. 7, Table 1). In comparison, \n450 there was greater variability in diet composition in the Gulf of California both between Zones and \n451 over time within each Zone (S2 Fig.). For example, the number of species consumed per rookery \n452 ranged from 5–26 species between Zones (S5 Fig.), indicating greater resource heterogeneity \n453 and/or apparent dietary flexibility. \n454\n455 4.2 The role of diet energy density \n456 Regardless of its relationship to diet diversity, the energy density of a diet is an important \n457 characteristic to consider when assessing diet quality. As each prey species differs in macronutrient \n458 composition and therefore in energy density (kJ/gww), diets based upon prey species that are more \n459 energy-rich can be considered “higher quality” as they are more likely to meet the nutritional \n460 requirements of individuals, allowing populations to grow. Measuring diet energy density can \n461 provide important insight into the nutritional status of a population and the drivers of population \n462 change. \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n24\n463 On average, the diet energy density across the Gulf of California did not differ significantly from \n464 that of the Channel Islands, such that energy density did not explain broad differences in population \n465 trajectories. Contrary to expectations, we found that regions and periods when the diet had the \n466 highest energy density were not necessarily associated with years of greatest population growth \n467 within Zones. Alternate analyses (i.e., using finer data at the rookery-year scale and using changes \n468 in pup numbers as a more immediate indicator of changes in population demographics) also did \n469 not reveal any relationships between diet energy density and population changes. Even the diets \n470 with the highest energy densities were associated with both increasing and decreasing population \n471 trajectories, indicative of the lack of an overall simple relationship between diets and populations \n472 over time. \n473 The highest mean diet energy density of all Zones (including the Channel Islands) occurred in the \n474 Gulf of California Zone 6 (San Esteban rookery), the largest rookery in terms of population size \n475 in the Gulf, where the sea lion population only showed a potentially increasing trend, while Zone \n476 3 (Isla Lobos) had the second highest diet energy density but a significantly decreasing population. \n477 Furthermore, the only significantly increasing population in the Gulf of California (Zone 10, Los \n478 Islotes rookery) had a mean diet energy density that was comparable to the median diet energy \n479 density for all Zones (Table 1). While there is evidence from other pinniped studies to support the \n480 hypothesized link between diet energy density and population growth (24,40), it is possible that \n481 differences in diet quality in our study regions were not great enough to be the primary population \n482 drivers across the regions, except perhaps in cases with extremely low-quality diets (e.g., the \n483 decreasing population in Zone 4).  \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n25\n484 An important factor to consider when exploring the relationship between populations and diet \n485 quality is that, as demonstrated by our results, the Channel Islands and the Gulf of California are \n486 fundamentally different oceanographic systems with different population and diet dynamics. In \n487 the Channel Islands, most of the diet energy density consistently comes from schooling fishes (S1 \n488 Fig.), whereas there is considerable variability in the diet of sea lions in the Gulf of California, \n489 which is made up of different combinations of benthic species (41 different benthic species in \n490 total) making up their main source of energy from food (S2 Fig.), even though this prey category \n491 has a lower average energy density than schooling fishes. In fact, the greater predictability of the \n492 prey available to sea lions in the Channel Islands than those in the Gulf of California may be a \n493 more important contributor to population dynamics, rather than differences in prey energy density \n494 per se. \n495 The heterogeneity in diet between rookeries throughout the Gulf of California raises questions \n496 about the specific trade-offs and foraging strategies among sea lions breeding at different \n497 rookeries. In the Galápagos Islands, individual foraging strategies of Galápagos sea lions \n498 (Zalophus wollebaeki) influence the coping abilities of their population with evidence  that some \n499 foraging strategies may be more advantageous than others during environmental changes (42). In \n500 contrast to pelagic foragers, benthic foraging Galápagos sea lions appear to be less affected by \n501 increased water temperatures, despite consuming prey that are lower in energy density. Such \n502 environmentally dependent fitness trade-offs could also be at play in the Gulf of California sea \n503 lion populations, which should be explored through further research on individual foraging \n504 strategies.  \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n26\n505 4.3 The effects of environmental changes on diet quality\n506 From 2013–2015, a large-scale phenomenon of increased sea surface temperatures known as “The \n507 Blob” was first documented in Alaska and then traveled south along the Eastern Pacific. In 2015–\n508 2016, The Blob coincided with a strong El Niño event, further intensifying the effect of increased \n509 water temperatures (43). In the Channel Islands, El Niño events are known to cause population \n510 declines (with rapid subsequent recovery), and are associated with a decrease in consumption of \n511 energy-rich schooling fish (13). The Blob’s effects on the Channel Island populations were first \n512 seen in 2013 through increased pup mortality (20). \n513 Unlike the Pacific coast, sea lions within the Gulf of California are not affected in the same way \n514 by warming events in the Pacific Ocean, such as El Niño. Instead, they appear to be affected by \n515 local oceanographic processes within the Gulf’s sub-regions (5). Overall, although the increased \n516 water temperatures in the Gulf after 2014 were characterized by an increase in the mean and range \n517 of diet diversity within the Gulf of California (expressed using the Shannon Index), this change \n518 was not significant (Fig. 8, top panel). However, there was an increase in the total number of \n519 species present in the diet (from 51 to 65 species, Table 2). In addition to the increase in the number \n520 of species consumed, the proportion of each diet species category changed (Fig. 5), as well as the \n521 identity of the species within those categories. For example, around 50% of the species consumed \n522 after 2014 were not present in the diet in previous years. These species consisted mostly of new \n523 benthic and lanternfish species (Figs. 7 and S6). \n524 This increase in diet diversity was accompanied by a significant decrease in the overall average \n525 diet energy density throughout the Gulf of California (Fig. 8, bottom panel). This trend was driven \n526 by central rookeries (Zones 4 and 5) where there was a decrease in the proportion of energy-rich \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n27\n527 schooling fishes after 2014 (Fig. 9). Although there was also an increase after 2014 in the overall \n528 number of high-energy lanternfish species in the diet (from 1 to 6 species; Fig. 5), there was no \n529 significant change in their proportion (6% vs. 5%). This could suggest sea lions attempted to \n530 continue to meet their total energy requirements by increasing the diversity of energy-rich \n531 lanternfish species in their diet. \n532 Fig. 9. Average energy density of California sea lion diets at rookeries in the Gulf of \n533 California before and after 2014 from frequency of occurrence data. Only rookeries within \n534 Zones with matched data before and after 2014 are included in this Fig., their Zone number is \n535 shown in brackets. Gulf of California Zones are ordered geographically from North (Z4) to South \n536 (Z10). Zones that are not shown here lacked data after 2014 and were excluded from this Fig.. \n537\n538 However, this pattern of decreased diet energy density after 2014 (mainly due to a decrease in \n539 energy-rich schooling fishes and lanternfish) was not consistent across all rookeries. A decrease \n540 in diet energy density after 2014 occurred at Rasito (Zone 5), which had a lack of energy-rich \n541 lanternfish and Jack mackerel in 2016 compared to 1996 (S2 Fig.). Conversely, within Zone 10 \n542 (Los Islotes rookery), an increase in lanternfish in 2019 was largely responsible for the overall \n543 increase in diet energy density after 2014 (Fig. 9). \n544 The observed differences in the changes in diet energy density post-2014 among different Zones \n545 could be due to a difference in the availability of prey species in the various regions in the Gulf of \n546 California. In some Zones this could lead to California sea lions having to adopt atypical, lower-\n547 energy diets. For example, the unusual dominance of ‘other’ species in the diet in Los Islotes in \n548 2015 may reflect a loss of ideal primary prey due to increased water temperatures (S2 Fig.), \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n28\n549 resulting in them consuming a higher number of prey species (S5 Fig.) to maintain the same level \n550 of dietary energy density from prey (~5kJ/gww). \n551 Previous studies have demonstrated how acute environmental changes and subsequent prey \n552 availability shifts can affect marine mammal population growth. For example, ringed seals (Phoca \n553 hispida) in western Hudson Bay switched to a more diverse diet that had a lower energy density \n554 due to decreases in the availability of their main prey (sand lance) triggered by changes in the \n555 seasonal breaking up of sea ice (40). As a result, body condition of individual seals was greatly \n556 reduced, and population declines ensued.\n557 In the Channel Islands, models predict that every 1C increase in surface temperature could \n558 decrease the population growth rate of California sea lions in the U.S. by 7% (44). Of note, during \n559 years of increased sea surface temperatures (2013–2015), (20) reported how sea lion diet \n560 composition in the Channel Islands decreased in epipelagic species (schooling fish), and increased \n561 in benthic and demersal species. A similar phenomenon appears to be occurring in the northern \n562 and central regions of the Gulf of California where California sea lion pup birth rates declined as \n563 sea surface temperature anomalies exceeded 1C (17). However, population growth in the southern \n564 regions of the Gulf  was not affected by increased sea surface temperatures (45), although pup \n565 abundance and body condition at Los Islotes did decrease during 2014 and 2015, and adult females \n566 were away from the rookery for longer periods than normal (46). Lactating females appeared to \n567 have had to forage further away from the rookery, which cost them more time and energy. Thus, \n568 warmer sea surface temperatures affected both the diet and foraging behaviour of female California \n569 sea lions in the south (Los Islotes, Zone 10), which may have affected pups during the lactation \n570 period.\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n29\n571 4.4 Environmental heterogeneity and its implications for species \n572 management\n573 The variable diet quality and population trends detailed in our study suggest that sea lions at \n574 different rookeries, even those just within the Gulf of California, cannot be viewed nor managed \n575 as a homogeneous group. The Gulf of California is known to have considerable environmental \n576 heterogeneity (47–50), which may influence both the quality of sea lion diets as seen in our study \n577 and, ultimately, predator-prey dynamics at the rookery scale. It has been suggested, for example, \n578 that compared to the northern and central regions of the Gulf, the greater diversity of prey species \n579 present in the south may buffer rookeries like Los Islotes against detrimental environmental \n580 changes (50,51). Having access to a greater diversity of prey species would allow sea lions to \n581 compensate for prey that may no longer be available. \n582 Prey availability and abundance in the Gulf of California varies by region and is not as consistent \n583 or as predictable as in the California Current System. Such variability may mask the ability to \n584 identify simple relationships between diet and population growth, such as those shown in other \n585 Eastern Pacific ecosystems like the Channel Islands or Alaska (23,32,37). This variability may \n586 also underly differences noted by others in terms of genetic differences between California sea \n587 lions, their foraging areas, and the oceanographic conditions they experience (4,17,34). \n588 Understanding diet and population dynamics in the Gulf of California may therefore require a \n589 more detailed understanding of sea lion prey dynamics, foraging strategies, and localized \n590 oceanographic changes.\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n30\n591 The Mexican government deems the California sea lion sub-populations in need of special \n592 protection, and recognizes the need to recover and conserve the populations in Mexico’s rookeries \n593 (52). Our study highlights the variation in the diets, population trajectories, rookery sizes, and \n594 oceanographic dynamics within the Gulf of California, suggesting that each rookery population \n595 faces different sets of challenges that impact their reproduction and survival rates in different ways. \n596 However, current management and surveillance programs (especially in the central region) do not \n597 seem sufficient to monitor and assess how sea lion numbers are affected. More rigorous monitoring \n598 is needed not only to understand changes in prey species, but also other short-term factors affecting \n599 sea lion numbers such as entanglements in fishing gear, shootings, and contaminants to name a \n600 few (53). Such anthropogenic factors may also contribute to the lack of a direct relationship \n601 between diet and population trends in the northern and central Gulf of California (17). Overall, \n602 understanding the complex dynamics affecting each sub-population in both the short-term and \n603 long-term is essential to effectively manage the protection and conservation of California sea lions \n604 in the Gulf of California.\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n31\n605 Acknowledgements\n606\n607 The present work was part of Ana Lucía Pozas Franco’s Master’s thesis. Funding consisted of an \n608 NSERC Discovery Grant to Dr. David Rosen. Thank you to Dr. Miram Gleiber for sharing key \n609 energy density data unpublished at the time, Dr. Tony Orr for initial input and guidance, Eric Lee \n610 for help categorizing prey species, and Pedro González Espinosa for help in creating the Zone \n611 map. \n612\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n32\n613 References\n614\n615 1. Lowry M, Maravilla-Chávez O. Recent abundance of California sea lions in western Baja \n616 California, Mexico and the United States. 6th Calif Isl Symp. 2005;485–97. \n617 2. Peterson RS, Bartholomew GA. The Natural History and Behavior of the California Sea Lion. \n618 American Society of Mammalogists; 1967. (Special Publication No. 1). \n619 3. 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Physical, chemical and biological oceanography of the Gulf of California. \n755 In: The Gulf of California: Biodiversity and Conservation [Internet]. The University of \n756 Arizona Press; 2010. p. 24–48. Available from: \n757 https://www.academia.edu/33338546/Marine_Mammals_of_the_Gulf_of_California_An_Ov\n758 erview_of_Diversity_and_Conservation_Status\n759 51. Durán-Campos E, Coria-monter E, Monreal-gómez MA, Salas-de-león DA, Mazatlán UA, \n760 Ciencias ID, et al. Impact of \" the Blob \" 2014 and 2019 in the sea surface temperature and \n761 chlorophyll- a levels of the Gulf of California : a satellite-based study. Lat Am J Aquat Res. \n762 2022;50(3):479–91. \n763 52. NOM-059-SEMARNAT. Norma Oficial Mexicana NOM-059-SEMARNAT-2010, \n764 Protección Ambiental-Especies Nativas de México de Flora y Fauna Silvestres-Categorías De \n765 Riesgo y Especificaciones Para Su Inclusión, Exclusión o Cambio-Lista de Especies en Riesgo \n766 Prefacio. 2010. \n767 53. Hernández-Camacho CJ, González-López I, Pelayo-González L, Aurioles-Gamboa D, López-\n768 Greene E, Rosas-Hernández MP. Effective management of the national park Espíritu Santo, \n769 through the governance, planning, and design of an integral strategy for Los Islotes. Socio-\n770 Ecol Stud Nat Prot Areas. 2020;(1):679–704. \n771\n772\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n37\n773 Supplementary Methods\n774\n775\n776 Calculating population change rules\n777 In instances where a rookery had diet data for 3 or more continuous years, population data from \n778 one year before to one year after the diet data years were incorporated into the population change \n779 calculation. If the number of continuous years with diet data was less than three, then the \n780 population change was calculated from two years before and after the interval (or single year) of \n781 diet data. This yielded a single rate of population change over the matching diet data interval. In \n782 cases where population changes using the previously mentioned rules were calculated to be \n783 greater than ±20% (an unrealistic growth rate under normal breeding conditions), four years on \n784 either side of the diet data were incorporated into the calculation to obtain a more realistic rate of \n785 population change.  \n786 Estimating missing population totals\n787 Rookeries and years with available diet data were paired with available population totals. In \n788 some cases, population totals had to be estimated for years that lacked data. For the Channel \n789 Island rookeries, pup counts were available for years with missing population totals, and were \n790 therefore used to estimate totals by extrapolating from the linear relationship between pup counts \n791 and total population counts across all years. For the Gulf of California rookeries, missing \n792 population numbers were estimated by extrapolating from a linear regression performed on all \n793 available population data for individual rookeries because additional years with pup count data \n794 were not available. The regression equation was then used to estimate population numbers for \n795 years lacking counts, and the rate of population change over the period of interest was calculated \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n38\n796 from this mixed data set. Inaccurate extrapolations were avoided by only using regressions that \n797 spanned intervals with actual counts.\n798 In cases where rookeries had one year of diet data and where the associated population data \n799 range (when incorporating the ±2 years rule for population change) overlapped with the \n800 population data range for another year of diet data, and one of those diet data years did not have \n801 +2 years of data after (due to it being the latest population year with data), those years of diet \n802 data were grouped. For example, diet data from the Granito rookery from 2016 and 2018 were \n803 combined into one grouping, and the years 2014–2018 were used to calculate population change \n804 since 2018 was the latest year with population data. \n805\n806 Calculating rookery-year groupings\n807\n808 We matched the available diet data at the rookery level with a rate of population change value \n809 calculated to correspond to the specific year or group of consecutive years with available diet data. \n810 Ideally, continuous diet data for all years and rookeries would have been available, and matching \n811 data groupings would have been strategically chosen. However, diet data were patchy in terms of \n812 both years and rookeries. As a result, sequential data points were compiled from each rookery to \n813 form specific ‘year-rookery groupings’ and were treated as independent data points used in all \n814 subsequent analyses (Table S2). \n815 Most rookeries in the Channel Islands had diet data available over several consecutive years, which \n816 were grouped according to the continuity of the data (e.g., San Miguel 2009–2011). Diet data from \n817 several rookeries in the Channel Islands were already averaged over several years (Table S2). In \n818 these instances, those year-rookery groupings were kept and used when calculating corresponding \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n39\n819 population changes. Within the Gulf of California, most years with diet data were single isolated \n820 years that varied by rookery (e.g., Los Islotes 1990, 2000, 2015 and 2019; Rasito 1996 and 2016, \n821 etc.). In cases where two consecutive years of diet data were available, we averaged the diet data \n822 and grouped them to form one rookery-year grouping (e.g., San Esteban 1995–1996). In cases \n823 where non-continuous years with diet data were close in time such that their population change \n824 calculations overlapped, we also combined them to form a single rookery-year grouping (e.g., Los \n825 Islotes 2015, 2019). \n826 Figure S1. Energy density of diets of California sea lions in the Channel Islands (Zone 1). \n827 Average energy density and energetic content contributions (average weighted energy density) of \n828 the top 17 prey species to the total energetic content of the diet for rookeries and years with \n829 available frequency of occurrence data in the Channel Islands. ‘Other’ category represents all other \n830 species in the diet beyond the top 17. \n831\n832 Figure S2. Energy density of diets of California sea lions in the Gulf of California (Zones 3–\n833 5). Average energy density and energetic content contributions (average weighted energy density) \n834 of the top 17 prey species to the total energetic content of the diet for rookeries and years with \n835 available frequency of occurrence data in the Gulf of California. ‘Other’ category represents all \n836 other species in the diet beyond the top 17. \n837\n838 Figure S3. Population changes and diet quality for California sea lions from frequency of \n839 occurrence and index of importance data. The data represents values from Zone-era groupings. \n840 Diet diversity values were calculated using the Shannon Index from frequency of occurrence data \n841 (top-left panel) and index of importance data (bottom-left panel). Panels on the right include diet \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n40\n842 energy density values calculated using frequency of occurrence (top) and index of importance data \n843 (bottom). Regression analysis of data weighted by rookery population size indicated no statistical \n844 relationships. \n845\n846 Figure S4. Average number of prey species per California sea lion rookery before and after \n847 2014. Bars represent averaged data (mean value ± standard error). Average number of species \n848 is based on frequency of occurrence data. There was no data after 2014 available for the Channel \n849 Islands. \n850\n851 Figure S5. Average number of prey species per California sea lion rookery before and after \n852 2014 from frequency of occurrence data. Blue bars represent each of the Channel Islands before \n853 2014, each rookery with available data in the Gulf of California is shown before 2014 (green bars) \n854 and after 2014 (orange bars). Zone number is shown in brackets. \n855\n856 Figure S6. Total number of prey species consumed by California sea lions in the Gulf of \n857 California before and after 2014 from frequency of occurrence diet. The bar on the left \n858 represents prey species before 2014, the bar on the right represents prey species after 2014. Green \n859 bars show number of species present in both eras, yellow bar shows species only present before \n860 2014, and orange bar shows number of species present only after 2014. \n861\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n41\n862 Table S1. Data sources of meta-analysis of California sea lion population and diet. Details \n863 include the rookery, data year and season, diet index or total population estimate (unless stated), \n864 and Table in source publication where relevant data is found. (FO; frequency of occurrence data, \n865 IIMP; index of importance data).  \nSource Rookeries Year(s) of \ndata Season\nData \ntype/diet \nindex\nTable\nChannel Islands\nSan Miguel 1971–1991 \n(pup counts)\n1992–2014 \nJuly or \nAugust\nPopulation \n(total live)\nTable 2\nSan Nicolas 1991–2008\n2009, 2010 \n(pup counts)\n2011–2014\nJuly Population \n(total live)\nTable 2\nSan Clemente 1981–2014 July or \nAugust\nPopulation \n(total live)\nTable 2\nLowry et al. \n(2017a)\nSanta Barbara 1983–1985 \n(pup counts)\n1986–2008\n2009, 2010 \n(pup counts)\n2011–2014\nJuly Population \n(total live)\nTable 2\nLowry et al. (1991) San Nicolas 1981–1986 \n(grouped)\nJune and \nAugust \nFO Table 1, 2\nLowry and Carretta \n(1999)\nSan Nicolas,\nSanta Barbara,\nSan Clemente\n1981–1995 Multiple \nmonths, \nsummer \n(Santa \nBarbara) \nFO Table 3\nA. Curtis unpubl. \ndata\nSan Nicolas, \nSan Clemente\n1981–1986 Summer FO N/A\nLowry et al. \n(2017b)\nChannel \nIslands \n2015 July Population \n(total counts)\nTable 3 \nLowry et al. (2021) Channel \nIslands\n2016–2019 July or \nAugust\nPopulation \n(total live)\nTable 1\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n42\n866\n867\nSource Rookeries Year(s) of \ndata Season\nData \ntype/diet \nindex\nTable\nOrr et al. (2011) San Miguel 2002–2006 March–\nJuly\nFO Table 3\nS. R. Melin et al. \n(2012)\nSan Miguel 2000–2003, \n2005, 2009– \n2011\nJune–Sept FO Table 5\nS. R. Melin et al. \n(2010)\nSan Miguel  2000, 2001, \n2002, 2004, \n2005 and 2009\nJuly–early \nAugust\nFO Table 1\nGulf of California\nPelayo-González, \nGonzález-\nRodríguez, et al. \n(2021)\n1–13 (not all \nrookeries have \ndata for all \nyears)\n1980–2019 June or \nJuly\nPopulation \n(total)\nRaw data\nGarcía-Rodríguez \n(1995)\n13 1990 February–\nSeptember\nFO Table 4\nSource Rookeries Year(s) of \ndata Season\nData \ntype/diet \nindex\nTable\nGulf of California\nF.J Garcia-\nRodriguez unpubl. \ndata\n5 1995 September FO & IIMP N/A\n3, 4, 6, 9, 10 1995 June\n3, 4, 5, 8, 9, \n10\n1996 May\nF.J Garcia-\nRodriguez unpubl. \ndata, \nCardenas-Palomo \n(2003)\n13 2000 May 2000–\nApril 2001\nFO & IIMP Cuadro 1, \nAnexo 3\nPorras-Peters \n(2004) and Porras-\nPeters et al. (2008)\n1, 3, 5, 7–13 2002 Summer IIMP Figure 4 \n(Appendix \nII)\n13 2015 July\n4 2016 October\nPelayo-González et \nal., (2021)\n5 2016 Unknown\nFO Raw data\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n43\n868 Gulf of California rookeries: 1: Rocas Consagradas, 2: San Jorge (no data), 3: Isla Lobos, 4: \n869 Granito, 5: Cantiles, 6: Machos, 7: El Partido, 8: El Rasito, 9: San Esteban, 10: San Pedro Mártir, \n870 11: San Pedro Nolasco, 12: Farallón de San Ignacio, 13: Los Islotes. \n871 Table S2. Raw paired diet and population data for California sea lions by rookery-year \n872 grouping. \nRookery-year \ngroupings\nZone Index Average \ndiet \ndiversity\nWeighted \nenergy \ndensity \n(kJ/gww)\nPopulation \nchange \nAverage \nenergy \ndensity \n(kJ/gww)\nPup \nchange \n(%)\nSan Miguel \n2000–2006\n1 FO 1.70 5.76 5.03% 5.6 3.2%\nSan Miguel \n2009–2011\n1 FO 2.00 4.68 7.80% 5.1 11.6%\nSan Nicolas \n1981–1986 \n(grouped)\n1 FO 1.92 5.66 -2.92% 5.36 -4.8%\nSan Nicolas \n1981–1986\n1 FO 1.68 5.65 -2.92% 5.36 -4.8%\nSan Nicolas \n1981–1995\n1 FO 1.95 5.46 9.47% 5.85 32.0%\nSanta Barbara \n1981–1995 \n(grouped)\n1 FO 1.87 5.48 9.95% 5.85 24.8%\nSan Clemente \n1981–1995 \n(grouped)\n1 FO 1.81 5.59 4.51% 5.74 7.7%\nSan Clemente \n1981–1986\n1 FO 1.96 5.09 -1.61% 5.50 -1.9%\nLos Islotes 1990 10 FO 2.40 4.59 2.43% 4.7 5.12%\nSan Pedro \nMártir 1995–96\n7 FO 2.14 5.3 -1.53% 4.93 -0.18%\n6 2016 October IIMP\n8 2016 October FO & IIMP \n4 2018 August\n4 2018 July\n5 2018 July\n13 2019 August\nFO \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n44\nRookery-year \ngroupings\nZone Index Average \ndiet \ndiversity\nWeighted \nenergy \ndensity \n(kJ/gww)\nPopulation \nchange \nAverage \nenergy \ndensity \n(kJ/gww)\nPup \nchange \n(%)\nSan Esteban \n1995–96\n6 FO 2.04 5.9 7.29% 5.08 -1.52%\nRasito 1996 5 FO 2.48 5.77 -7.60% 5.15 2.31%\nMachos 1995 4 FO 1.96 5.74 -1.17% 5.29 4.37%\nCantiles 1995–\n96\n4 FO 1.36 4.51 6.15% 5.05 6.76%\nGranito 1995–\n96\n4 FO 0.90 5.34 3.70% 5.15 12.74%\nIsla Lobos \n1995–96\n3 FO 1.93 5.13 -2.03% 5.57 5.16%\nLos Islotes 2000 10 FO 2.03 4.69 7.37% 5.00 3.26%\nRasito 2016 5 FO 2.20 4.31 7.25% 4.31 13.52%\nMachos 2016 4 FO 2.01 4.66 -11.65% 4.92 17.32%\nCantiles 2016 4 FO 2.70 4.42 7.09% 4.58 7.92%\nGranito 2016, \n2018\n4 FO 3.02 4.85 7.09% 5.15 12.71%\nLos Islotes \n2015, 2019\n10 FO 1.59 5.19 -1.18% 5.1 8.44%\nSan Pedro \nMártir 1995–96\n7 IIMP 1.526 5.86 -1.5% 5.58 -0.2%\nSan Esteban \n1995–96\n6 IIMP 1.315 6.29 7.3% 6.48 -1.5%\nRasito 1995–96 5 IIMP 1.682 5.19 -11.2% 5.50 3.9%\nMachos 1995 4 IIMP 1.528 5.94 -1.2% 5.75 4.4%\nCantiles 1995–\n96\n4 IIMP 0.931 4.27 6.1% 4.85 6.8%\nGranito 1995–\n96\n4 IIMP 0.561 5.30 3.7% 5.37 12.7%\nIsla Lobos \n1995–96\n3 IIMP 1.479 5.19 -2.0% 4.47 5.2%\nLos Islotes 2002 10 IIMP 1.848 4.7 4.4% 5.08 -0.2%\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n45\nRookery-year \ngroupings\nZone Index Average \ndiet \ndiversity\nWeighted \nenergy \ndensity \n(kJ/gww)\nPopulation \nchange \nAverage \nenergy \ndensity \n(kJ/gww)\nPup \nchange \n(%)\nSan Esteban \n2002\n6 IIMP 1.503 5.3 9.2% 5.42 16.3%\nRasito 2002 5 IIMP 0.062 5.43 5.5% 4.85 12.9%\nSan Pedro \nMártir 2002\n7 IIMP 0.685 3.10 -0.7% 4.98 -6.4%\nCantiles 2002 4 IIMP 1.096 3.81 -3.6% 3.81 -8.8%\nIsla Lobos 2002 3 IIMP 1.326 4.71 4.5% 5.01 -2.1%\nSan Pedro \nNolasco 2002\n8 IIMP 1.874 4.99 -1.4% 5.11 -2.1%\nPartido 2002 5 IIMP 1.454 6.81 -7.9% 5.29 -3.9%\nRocas \nConsagradas \n2002\n2 IIMP 1.060 5.33 5.4% 4.63 5.4%\nFarallón de San \nIgnacio 2002\n9 IIMP 2.386 6.20 -3.8% 4.94 -5.2%\nLos Islotes 2015 10 IIMP 0.957 4.38 1.3% 5.03 8.2%\nRasito 2016 5 IIMP 1.921 4.17 7.2% 4.31 13.5%\nGranito 2016, \n2018\n4 IIMP 1.300 4.59 6.6% 4.84 12.7%\nCantiles 2016, \n2018\n4 IIMP 1.433 5.15 7.1% 4.90 7.9%\nLos Islotes 2019 10 IIMP 1.684 6.10 -3.7% 5.09 8.6%\n873\n874\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n46\n875 Table S3. Raw paired diet and population data for California sea lions by Zone-era grouping. \nZone-era \ngroupings\nEra Index Average \ndiet \ndiversity\nPopulation \nchange \nWeighted \naverage \nenergy \ndensity \n(kJ/gww)\nMedian \npopulation\nZone 1 – \nChannel Islands\n1981–\n1995\nFO 1.87 2.7% 5.49 9,216\nZone 1 – \nChannel Islands\n2000–\n2011\nFO 1.85 6.4% 5.22 44,720\nZone 3 – Isla \nLobos\n1995–\n1996\nFO 1.93 -2.0% 5.13 2,822\nZone 4 – \nMachos, \nCantiles, Granito\n1995–\n1996\nFO 1.41 2.9% 5.20 1,355\nZone 4 – \nMachos, \nCantiles, Granito\n2016, \n2018\nFO 2.58 0.8% 4.64 696\nZone 5 – Rasito 1996 FO 2.48 -7.6% 5.77 362\nZone 5 – Rasito 2016 FO 2.20 7.2% 4.31 308\nZone 6 – San \nEsteban\n1995–\n1996\nFO 2.04 7.3% 5.90 7,171\nZone 7 – San \nPedro Mártir\n1995–\n1996\nFO 2.14 -1.5% 5.32 1,963\nZone 10 – Los \nIslotes\n1990 FO 2.21 4.9% 4.64 347\nZone 10 – Los \nIslotes\n2015, \n2019\nFO 1.59 -1.2% 5.19 538\nZone 2 – Rocas \nConsagradas\n2002 IIMP 1.06 5% 5.33 839\nZone 3 – Isla \nLobos\n1995–\n1996\nIIMP 1.48 -2% 5.19 2,822\nZone 3 – Isla \nLobos\n2002 IIMP 1.33 4% 4.71 1,897\nZone 4 – \nMachos, \nCantiles, Granito\n1995–\n1996\nIIMP 1.01 3% 5.17 1,355\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n47\nZone-era \ngroupings\nEra Index Average \ndiet \ndiversity\nPopulation \nchange \nWeighted \naverage \nenergy \ndensity \n(kJ/gww)\nMedian \npopulation\nZone 4 – \nMachos, \nCantiles, Granito\n2002 IIMP 1.10 -4% 3.81 1,090\nZone 4 – \nMachos, \nCantiles, Granito\n2016, \n2018\nIIMP 1.20 7% 4.87 729\nZone 5 – Rasito, \nPartido\n1995–\n1996\nIIMP 1.68 -11% 5.19 366\nZone 5 – Rasito, \nPartido\n2002 IIMP 0.76 -1% 6.12 507\nZone 5 – Rasito, \nPartido\n2016 IIMP 1.30 7% 4.17 308\nZone 6 – San \nEsteban \n1995–\n1996\nIIMP 1.32 7% 4.99 7,171\nZone 6 – San \nEsteban \n2002 IIMP 1.45 9% 6.81 6,334\nZone 7 – San \nPedro Mártir \n1995–\n1996\nIIMP 1.53 -2% 5.86 7,171\nZone 7 – San \nPedro Mártir \n2002 IIMP 0.69 -1% 3.10 2,405\nZone 8 – San \nPedro Nolasco \n2002 IIMP 1.87 -1% 4.99 937\nZone 9 – \nFarallón de San \nIgnacio \n2002 IIMP 2.39 -4% 6.20 643\nZone 10 – Los \nIslotes \n2002 IIMP 1.85 4% 4.72 404\nZone 10 – Los \nIslotes \n2015 IIMP 1.92 1% 4.38 538\nZone 10 – Los \nIslotes \n2019 IIMP 1.68 -4% 6.10 659\n876\n877\n878\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n48\n879 Table S4. List of 114 diet prey species (scientific and common name) consumed by California \n880 sea lions in the Gulf of California (MEX) or the Channel Islands (USA) showing their average \n881 energy density, category assigned, and country where data was collected. Some species had \n882 the same scientific name, but different common names depending on the region. Categories: B: \n883 benthic species, C: crustaceans, G: gadids, L: lanternfish, O: octopus, R: rockfish, SF: schooling \n884 fish, S: squid, M: miscellaneous.\nScientific name Common name Energy \nDensity \n(kJ/gww)\nCategory Country\nAbraliopsis affinis Squid 4.40 S MEX\nAbraliopsis species Squids 4.40 S USA\nAnisotremus davidsonii Xantic sargo 4.88 B MEX\nApogon retrosella Barspot cardinalfish 4.70 B MEX\nArgentina sialis North-Pacific argentine 3.57 M MEX\nAtherinops species Topsmelt silverside 6.20 SF MEX\nAtherinopsis californiensis Jack silverside 6.20 M MEX\nAulopus Royal flagfin 4.43 B MEX\nAulopus bajacali Eastern Pacific flagfin 4.43 B MEX\nBalistes polylepis Finescale triggerfish 3.84 B MEX\nBodianus diplotaenia Mexican hogfish 3.84 B MEX\nBrosmophycis marginata Red brotula 3.39 B MEX\nCalamus brachysomus Pacific porgy 7.45 B MEX\nCaulolatilus princeps Ocean whitefish 7.45 B MEX\nCeratoscopelus townsendi Dogtooth lampfish 7.16 L MEX\nCetengraulis mysticetus Pacific anchoveta 6.01 SF MEX\nChromis punctipinnis Blacksmith damselfish 4.68 B USA\nCitharichthys species Flatfish 3.33 B MEX\nClupea pallasii Pacific herring 7.51 SF USA\nCoelorinchus scaphopsis Shoulderspot grenadier 5.10 G MEX\nCololabis saira Pacific saury 7.50 M USA\nCynoscion reticulatus Shorefish 7.99 B MEX\nDecapodiformes Superorder of squids 4.60 S USA\nDiaphus theta California headlightfish 9.88 L MEX\nDiplectrum macroposoma Mexican sand perch 4.50 B MEX\nDiplectrum pacificum Inshore sand perch 4.03 B MEX\nDiplectrum species Sandperch 5.02 B MEX\nDoryteuthis opalescens Opalescent inshore squid 3.70 S USA\nDosidicus gigas Humboldt squid 5.39 S MEX\nEngraulidae Anchovies 6.17 SF MEX\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n49\nScientific name Common name Energy \nDensity \n(kJ/gww)\nCategory Country\nEngraulis mordax Californian anchovy 6.70 SF MEX\nEngraulis mordax Northern anchovy 6.80 SF USA\nGirella nigricans Rudderfish 4.40 M MEX\nGonatopsis borealis Boreopacific armhook \nsquid\n4.20 S USA\nGonatus berryi Berry armhook squid 5.02 S MEX\nGonatus onyx Clawed armhook squid 5.86 S USA\nGonatus species Armhook squid 5.90 S USA\nHaemulidae species Grunt fish 4.88 B MEX\nHaemulon californiensis Yellowspotted grunt 4.88 M MEX\nHaemulon flaviguttatum Greybar grunt 4.88 M MEX\nHaemulon sexfasciatum Scaled-fin grunt 4.88 SF MEX\nHaemulon species Californian salema 4.88 SF MEX\nHaemulopsis leuciscus Raucous grunt 4.88 B MEX\nHaemulopsis species Grunt fish 4.88 B MEX\nHemanthias peruanus Splittail bass 4.50 SF MEX\nHemanthias species Sea bass 4.50 B MEX\nHermosilla azurea Zebra perch 4.40 SF MEX\nHolacanthus passer King angelfish 7.45 B MEX\nIcelinus tenuis Spotfin sculpin 5.82 B MEX\nLepophidium prorates Prowspine cusk eel 3.39 B MEX\nLestidiops species Barracudina 4.30 SF MEX\nLeuroglossus stilbius California smoothtongue 3.90 M USA\nLoliolopsis diomedeae Dart squid 3.75 S MEX\nLycodes cortezianus Bigfin eelpout 7.60 B USA\nMerluccius productus North Pacific hake 4.20 G MEX\nMerluccius productus Pacific Hake 4.20 G USA\nMerluccius species Hake 4.07 G MEX\nMicropogonias ectenes Slender croaker 7.99 B MEX\nMicropogonias species Croaker 7.99 B MEX\nMyctophidae Lanternfish 7.62 L MEX\nNannobrachium species Lanternfish 7.63 L MEX\nOctopus rubescens East Pacific red octopus 3.30 O USA\nOctopus species Octopus 3.40 O USA, MEX\nOegopsida Pelagic squid 4.50 S MEX\nOnychoteuthidae Hooked squid family 5.40 S USA\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n50\nScientific name Common name Energy \nDensity \n(kJ/gww)\nCategory Country\nOnychoteuthis \nborealijaponicus\nBoreal clubhook squid 5.48 S USA\nOphidion scrippsae Basketweave cusk-eel 3.39 B MEX\nOphidion species Cusk-eels 3.39 B MEX\nOphistonema species Herrings 7.47 SF MEX\nOrthopristis reddingi Bronze-striped grunt 4.88 SF MEX\nOxylebius pictus Painted greenling 4.44 B MEX\nParalabrax clathratus Kelp bass 4.45 M MEX\nParalabrax species Rock bass 4.45 B MEX\nParalichthys californicus California halibut 3.81 B MEX\nPhysiculus nematopus Charcoal mora 4.00 G MEX\nPhysiculus species Codling 4.00 G MEX\nPleuroncodes planipes Pelagic red crab (lobster) 6.70 C USA\nPontinus furcirhinus Red scorpionfish 3.19 B MEX\nPontinus species Scorpionfish 3.19 B MEX\nPorichthys notatus Plainfin midshipman 3.36 B MEX\nPorichthys species Midshipman 3.36 B MEX\nPrionotus species Searobin 4.63 B MEX\nPrionotus stephanophrys Lumptail searobin 4.63 B MEX\nPronotogrammus eos Bigeye bass 4.45 B MEX\nPronotogrammus \nmultifasciatus\nThreadfin bass 4.45 B MEX\nSarda lineolata Pacific bonito 7.04 SF MEX\nSardinops caeruleus California pilchard 7.47 SF MEX\nSardinops sagax South American pilchard 7.50 SF MEX\nSardinops sagax Pacific sardine 7.50 SF USA\nScomber japonicus Chub mackerel 6.80 SF MEX\nScomber japonicus Pacific mackerel 6.80 SF USA\nScopelengys tristis Pacific blackchin 7.62 M MEX\nScorpaenidae Scorpionfish 3.19 M MEX\nSebastes exsul Buccaneer rockfish 5.51 R MEX\nSebastes jordani/species Rockfish 5.60 R USA\nSebastes macdonaldi Mexican rockfish 5.51 R MEX\nSebastes species Rockfish 5.60 R MEX\nSelar crumenophthalamus Bigeye scad 6.27 SF MEX\nSerranus aquidens/aequidens Deepwater serrano 4.45 B MEX\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n51\nScientific name Common name Energy \nDensity \n(kJ/gww)\nCategory Country\nSpecieshyraena argentea Pacific barracuda 3.36 M MEX\nStenobrachius leucopsarus Northern lampfish 9.70 L USA\nStrongylura exilis Californian needlefish 6.20 SF MEX\nSymbolophorus californiensis Bigfin lanternfish 7.07 L MEX\nSymphurus fasciolaris Banded tongue fish 4.00 B MEX\nSymphurus species Tongue fish 4.00 B MEX\nSynodus lucioceps California lizardfish 4.43 B MEX\nSynodus species Lizardfish 4.25 B MEX\nTrachurus species Jack mackerel 6.30 SF MEX\nTrachurus symmetricus Pacific jack mackerel 6.27 SF MEX\nTrachurus symmetricus Jack mackerel 6.30 SF USA\nTrichiurus lepturus Largehead hairtail 4.76 M MEX\nTrichiurus nitens Pacific cutlassfish 5.05 M MEX\nTriphoturus mexicanus Mexican lampfish 7.07 L MEX\nZaniolepis species Combfish 7.60 B USA\n885\n886\n887\n888\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. 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It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint \n\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.23.650199doi: bioRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}