Environmental heterogeneity plays a bigger role than diet quality in driving divergent California sea lion population trends
Bibliographic record
Abstract
While the global population of California sea lions (Zalophus californianus) is increasing, regional trends show a decline in the Gulf of California (GoC, Mexico) and an increase in the Channel Islands (CI, U.S.) over the last 40 years. The drivers of these divergent trends remain unclear, but previous pinniped studies suggest that differences in diet quality-rather than prey abundance-may play a role. We therefore examined how California sea lion population trajectories relate to diet quality, specifically looking at diet energy density and diet diversity. Using population and diet data from 1980 to 2020 for sea lions in the GoC and CI, we found no simple relationships between population trajectories and diet quality over time at either the local or regional level. Energy densities of sea lion diets were similar between the two regions, but GoC sea lions consumed a more diverse range of prey (n = 88 vs. 23 main prey taxa) dominated by benthic species and schooling fishes, while CI diets consisted mainly of schooling fishes and squid. We also found that GoC sea lions ate more benthic prey and less schooling fish during the 2014-2016 heatwave-decreasing their overall diet energy density. This shift coincided with a temporary population decline in the CI but had variable effects on GoC populations. Overall, our findings suggest that regional population trends are influenced by complex ecological factors beyond diet quality alone, highlighting the need to consider environmental variability and prey composition when assessing the resilience of sea lion populations to climate-driven changes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".