A capture–recapture framework for combining biologging data with physical captures to decompose and estimate demographic rates: Simulations across life cycles and application to polar bears
Bibliographic record
Abstract
Abstract Estimating demographic rates of wild populations is critical to understanding their dynamics but can be challenging because large amounts of data are required, and parts of the life cycle of individuals may be unobserved. In numerous research programmes, capture–recapture (CR) data and biologging data are collected in parallel. The latter often contain information on individual state (e.g. alive/dead, breeding/non‐breeding), but this information is seldom combined with CR data. Here, we present a Bayesian multi‐event CR framework to combine biologging containing information on individual state with CR data. Because biologging can inform on unobservable life cycle events like early offspring mortality, it can allow decomposing a demographic rate into two more precise demographic rates. We outline the principle of the model using a generic example then use simulations in a range of life cycles (ungulate, seabird, galliform, bear) to assess model performance. We then apply our model to 38 years of CR and biologging data from the Svalbard polar bear ( Ursus maritimus ) population. Simulations indicated that across life cycles, our model recovered most parameters well and often outperformed a standard CR model despite increased complexity. Benefits of including biologging data in terms of parameter estimates were apparent even for low biologger deployment probabilities and increased with deployment probability. Benefits were greater at low recapture probability when the amount of information on breeding status contained in biologging data was increased and possibly in long‐lived species. Benefits also extended to yearly offspring survival rates in the species with extended parental care. When applied to Svalbard polar bears, our model decomposed the traditionally estimated breeding probability into the denning probability and early litter survival probability. We document sharp age‐related changes in survival and reproductive success and find low evidence that denning incurred a future reproduction cost. Finally, we uncover pervasive negative impacts of sea‐ice loss on demographic rates. Our model is applicable to any species where CR and biologging data are simultaneously available—provided that biologging data contain information on individual state. It can increase the benefit‐to‐cost ratio of deploying biologgers and provide new demographic insights.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".