Lower marine productivity increases agonistic interactions between sea lions and fur seals in Northern Pacific Patagonia
Bibliographic record
Abstract
Abstract Interspecific interactions are key drivers of individual and population-level fitness in a wide range of animals. However, in marine ecosystems, it is relatively unknown which biotic and abiotic factors impact behavioral interactions between competing species. We assessed the impact of weather, marine productivity, and population structure on the behavioral agonistic interactions between South American fur seals (SAFSs), Arctocephalus australis, and South American sea lions (SASLs), Otaria byronia, in a breeding colony of SAFS. We hypothesized that agonistic interactions between SAFSs and SASLs respond to biotic and abiotic factors such as SAFS population structure, marine productivity, and weather. We found that SASL and SAFS interactions almost always resulted in negative impacts on the social structure or reproductive success of the SAFS colony. SASL adult males initiated stampedes of SAFS and/or abducted and predated SAFS pups. Adult SAFS males abundance and severe weather events were negatively correlated with agonistic interactions between species. However, proxies for lower marine productivity such as higher sea surface temperature and lower catches of demerso-pelagic fish were the most important predictors of more frequent agonistic interactions between SAFS and SASL. Under the current scenario of decline in marine biomass due to global climate change and overfishing, agonistic interactions between competing marine predators could increase and exacerbate the negative impacts of environmental change in these species.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".