Fisher ( <i>Pekania pennanti</i> ) Populations Exhibit Regional Differences in Cause‐Specific Mortality but Not Survival Rates
Bibliographic record
Abstract
ABSTRACT Mortality causes and survival rates often vary between the geographically disparate populations of a species. Fishers ( Pekania pennanti ) are a mesocarnivore inhabiting forested areas across Canada and the United States of America. Due to their economic and ecological value, fishers have become the focus of many management and conservation efforts. However, a clear understanding of influential demographic parameters and pressures exerted on disparate populations is necessary for such discussions. We conducted a literature review of peer‐reviewed studies investigating fisher cause‐specific mortalities and survival to (a) synthesize the current available knowledge, (b) assess differences in cause‐specific mortalities and the sex‐specific adult survival rates between western fisher populations (i.e., populations from California, Oregon, Washington, or British Columbia) and eastern fisher populations (i.e., elsewhere in their distribution), and (c) identify potential gaps in the literature. We identified 26 studies between 1994–2024 describing cause‐specific mortality ( n = 4), survival rates ( n = 15 studies), or both ( n = 7), with 20 studies assessing western fisher populations. There were significant differences between the cause‐specific mortalities for fishers in the eastern and western populations. Western fishers had higher mortality from predation and lethal toxicant exposure, while eastern fishers had higher mortality from legal harvest. Survival rates of males and females were not significantly different between the eastern and western populations; however, we found that male survival rates in the western populations varied considerably between studies. The geographic concentration of recent research presents a lack of information regarding the species outside of western populations, which may hinder management efforts throughout their range.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".