Establishing Bayesian priors for natural mortality rate in carnivore populations
Bibliographic record
Abstract
ABSTRACT In managed carnivore populations, natural mortality rate (d) is difficult to estimate directly, and context‐specific data are typically weakly informative about it. Nevertheless, natural mortality is potentially an important component of total mortality, particularly if additive to harvest or culling mortality. The natural mortality rate exhibits allometric or life‐history relationships that are invariant across diverse taxonomic groups, and it is valuable to derive estimates on this basis to serve as priors in later Bayesian models, steering parameter uncertainty towards biologically plausible values, and leading to more reliable model predictions and improved management recommendations. We used Bayesian hierarchical modeling and data from the literature to establish informative priors for instantaneous d as predictions scaled from body mass or maximum age. Posterior mean estimates of the scaling parameters of these models were −0.27 (body mass) and −1.07 (maximum age), respectively, conforming to expected values of −0.25 and −1.00. Direct estimates of d from published studies of coyotes (Canis latrans) in southern Texas, fisher (Pekania pennanti) in Sierra Nevada, and slender mongoose (Galerella sanguinea) in the Kalahari Desert were within the credible intervals of predictions for d using both models. We also compared survivorship curves based on model predictions with observed survivorship of red fox (Vulpes vulpes) from a variety of studies in Britain, among which intensity of culling varied markedly. Across all species, there was better support for the d prediction from maximum age, than from body mass. We thus recommend use of maximum age data to establish informative priors for d where possible. Posterior median predictions of d from maximum age were within 0.01–0.14/year of the direct estimates, whereas the differences between direct estimates and predictions from body mass were 0.04–0.27/year. Sensitivity analysis showed trivial effects of between‐sex differences in body mass, and age‐specific mortality, on predictions of d. Differences between body mass and maximum age model predictions were attributed to the relative importance of intrinsic and extrinsic mortality factors in the 2 approaches (i.e., maximum age predictions allowed for extrinsic factors to affect predicted mortality). © 2018 The Wildlife Society.
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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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".