Mortality risks and limits to population growth of fishers
Bibliographic record
Abstract
ABSTRACT Fishers ( Pekania pennanti ) in the west coast states of Washington, Oregon, and California, USA have not recovered from population declines and the United States Fish and Wildlife Service has proposed options for listing them as threatened. Our objectives were to evaluate differences in survival and mortality risk from natural (e.g., predation, disease, injuries, starvation) and human‐linked causes (e.g., rodenticide exposure [toxicants], vehicle strikes). We monitored survival of 232 radio‐collared fishers at a northern and southern study site in the Sierra National Forest, California. We retrieved fisher carcasses, and used necropsy examinations to determine causes of death. We estimated survival and mortality rates using the Kaplan–Meier estimator and nonparametric cumulative incidence functions, respectively, and integrated risk‐specific survival rates into a Leslie matrix to evaluate how population growth (λ) might improve if management can reduce mortality from any of the risks. We determined cause of death for 93 of 121 fishers, and annual survival was 0.69 for all fishers, and 0.72 for female fishers. Mortality rates were 19.5% for predation; 2.5% for disease, injury, and starvation; and 1.9% for toxicant exposure and vehicle strikes. Predation rates were similar between sexes, and relative risk was 51% lower in fall and winter compared to spring and summer. Combined mortality from disease, injuries, starvation, vehicle strikes, and toxicants was 4.4%, and 11 times higher for males than females. We estimated a base λ at the northern site of 0.96, which had the potential to increase to 1.03 or 1.11 if predation were reduced by 25% or 50%, respectively, whereas λ could increase to 0.97 in the absence of deaths from all other risks except predation. Predation was the dominant limiting factor in our study population, and was also the most common mortality risk for fishers in the 3 West Coast states (67%), followed by disease, injury, and starvation (12%) and vehicle strikes (8%). Direct mortality from toxicants appeared limited to fishers in California. Our results identified the need for continued and potential expanded restrictions on habitat disturbance in fisher denning habitats. Research is needed on contact rates between larger predators and fishers, including whether fishers are more vulnerable to attack in open forests or along roads used by coursing predators. © 2015 The Wildlife Society.
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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) | 0.000 | 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".