Energetic cost of human disturbance on the southern sea otter (<i>Enhydra lutris nereis</i>)
Bibliographic record
Abstract
Abstract With increased human populations and tourism in coastal areas, there is greater potential for disturbance of marine wildlife. Because of their high metabolic rates, sea otters (Enhydra lutris) are at particular risk of increased energetic costs due to human disturbance. We used scan surveys to monitor southern sea otter (E. l. nereis) activity and potential disturbance stimuli over 5 years (2015–2020) at 3 California, USA, study sites: Monterey, Moss Landing, and Morro Bay. We developed a process‐based, hierarchical model of sea otter behavior, which we fit to survey data to examine how activity varies in response to the occurrence of and proximity to disturbance stimuli, while controlling for location, group size, pup‐to‐adult ratio, and presence‐absence of kelp or eelgrass canopy. We combined model results with published estimates of activity‐specific metabolic rates, translating estimated activity change into corresponding energetic costs. We found that effects of disturbance stimuli on sea otter behavior were location specific and varied non‐linearly with distance from disturbance stimuli. Our model results suggest that, on average, the likelihood of a group of sea otters being disturbed is <10% when stimuli are >29 m away, although this threshold varies by location, group size, and several other covariates. Based on the observed frequency and magnitude of disturbance at Cannery row in Monterey, we estimated that energetic costs were increased by 7.2%, 5.4%, and 5.4% for adult males, females, and females with large pups, respectively. We observed similar cost increases at the wildlife platform in Moss Landing (5.8%, 4.4%, and 4.3%) and T‐pier in Morro Bay (5.2%, 4.0%, and 3.9%). Our analyses represent a novel approach for estimating behavioral responses and energetic costs of human disturbance, furthering understanding of how human activities affect sea otters and providing a sound scientific basis for management.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".