More fishers and fewer martens due to cumulative effects of forest management and climate change as evidenced from local knowledge
Bibliographic record
Abstract
BACKGROUND: Monitoring of fur-bearing species populations is relatively rare due to their low densities. In addition to catch data, trappers' experience provides information on the ecology and status of the harvested species. Fisher (Pekania pennanti) and American marten (Martes americana) are mustelids that are sensitive to forest management and therefore considered to be ecological indicators of forest health. Fisher populations have increased in eastern North America since the early 2000s and this could have resulted in a northeastern extension of the species' range and increased overlap with marten's range. Moreover, habitats of both species are subject to natural and anthropogenic disturbances. The objective of this study was to document the knowledge held by local trappers in the northern area of sympatry between fisher and marten to identify factors that could explain variation in populations of the two species and interactions between them. METHOD: Forty-one semi-directed interviews with Indigenous and non-Indigenous trappers in the Abitibi-Témiscamingue region of western Quebec (Canada), at the northern limit of the overlapping ranges of the two mustelid species. RESULTS: Trappers highlighted the lack of exclusivity of marten and fisher to coniferous forests, although marten is more closely associated with them than is fisher. Fisher apparently also takes advantage of open environments, including agroforestry systems. Moreover, climate change increases the frequency of freeze-thaw events that cause the formation of an ice crust on the snow surface, which favors fisher movements. CONCLUSION: The fisher was identified as a competitor and even a predator of the marten. Furthermore, the fisher is less affected than the marten by forest management, and it also seems to benefit from climate change to a greater extent.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".