Estimating survival in the Apennine brown bear accounting for uncertainty in age classification
Bibliographic record
Abstract
Abstract For most rare and elusive species, estimating age‐specific survival is a challenging task, although it is an important requirement to understand the drivers of population dynamics, and to inform conservation actions. Apennine brown bears Ursus arctos marsicanus are a small, isolated population under a severe risk of extinction, for which the main demographic mechanisms underlying population dynamics are still unknown, and population trends have not been formally assessed. We present a 12‐year analysis of their survival rates using non‐invasive genetic sampling data collected through four different sampling techniques. By using multi‐event capture–recapture models, we estimated survival probabilities for two broadly defined age classes (cubs and older individuals), even though the age of the majority of sampled bears was unknown. We also applied the Pradel model to provide a preliminary assessment of population trend during the study period. Survival was different between cubs [ ϕ = 0.51, 95% CI (0.22, 0.79)], adult males [ ϕ = 0.85, 95% CI (0.76, 0.91)] and adult females [ ϕ = 0.92, 95% CI (0.87, 0.95)], no temporal variation in survival emerged, suggesting that bear survival remained substantially stable throughout the study period. The Pradel analysis of population trend yielded an estimate of λ = 1.009 [SE = 0.018; 95% CI (0.974, 1.046)]. Our results indicate that, despite the status of full legal protection, the basically stable demography of this relict population is compatible with the observed lack of range expansion, and that a relatively high cub mortality could be among the main factors depressing recruitment and hence population growth.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".