Habitat, Climate, and Fisher and Marten Distributions
Bibliographic record
Abstract
ABSTRACT Since the mid‐twentieth century, fisher populations (Pekania pennanti) increased in several eastern jurisdictions of North America, particularly in the northern part of the species’ range. Changes in fisher distribution have led to increased overlap with the southern portion of the range of American marten (Martes americana), whose populations may be locally declining. This overlap occurs particularly in habitats undergoing natural and anthropogenic modification. The objective of our study was to determine the respective effects of habitat changes and climatic conditions on fisher and marten populations in Quebec, Canada, based on trapper knowledge. We analyzed annual fisher and marten harvest (number of pelts sold/100 km2) between the 1984–1985 and 2014–2015 trapping seasons using linear mixed models. Fisher harvest increased with the increased abundance of mixed forests >12 m tall, resulting from decades of forest harvesting. Fisher harvest decreased with increasing spring rains, which can affect survival when rearing young. Marten harvest decreased with increasing winter rains, which lower thermoregulation capacity and hamper movements by creating an ice crust on the snowpack, reducing access to subnivean areas. Decline in marten harvest during the 30‐year study period coincided with an increase in fisher harvest, suggesting possible interspecific competition. Results highlight that managers should strive to maintain mixedwood stands taller than 12 m to maintain high quality habitat for fishers. Our study confirms the importance of working with trappers to assess furbearing population trends in response to habitat changes and climatic conditions. © 2019 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".