Using social structure to improve mortality estimates: an example with sperm whales
Bibliographic record
Abstract
Summary Estimates of mortality are fundamental to studies of population ecology and assessments of conservation status. Mortality is frequently estimated using individual identifications by means of mark–recapture methods. These estimates become biased with heterogeneity in identification and especially if patterns of heterogeneity change with time. If animals are social, then survival may be inferred from the identifications of social partners. We produce a likelihood model for estimating mortality using such social data. We show using simulation that this method can produce less biased and more precise estimates of mortality than standard methods when individuals are almost always identified with associates, and when there are time‐varying patterns of heterogeneity in identifiability. The method seems little affected by some change in social affiliations or by growth or decline in population size. SEs and confidence intervals of mortality estimates can be estimated using likelihood methods. We apply the method to data from a population of sperm whales ( P hyseter macrocephalus ) in the eastern C aribbean, obtaining estimates that are more precise and probably less biased than those from other methods. The method should be useful in improving mortality estimates for social 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".