Marten space use and habitat selection in managed coniferous boreal forests of eastern Canada
Bibliographic record
Abstract
Abstract Effects of habitat loss and fragmentation on the behavior of individual organisms may have direct consequences on population viability in altered forest ecosystems. The American marten ( Martes americana ) is a forest specialist considered as one of the most sensitive species to human‐induced disturbances. As some studies have shown that martens cannot tolerate >30–40% clear‐cuts within their home range, we investigated marten space use (home range size and overlap) and habitat selection in landscapes fragmented by 2 different patterns of timber harvesting in the black spruce boreal forest: dispersed‐cut landscapes (10–80 ha cut‐blocks) and clustered‐cut landscapes (50–200 ha cut‐blocks). We installed radio‐collars on female martens and determined 20 winter home ranges (100% minimum convex polygons and 60–90% kernels) in dispersed‐cut ( n = 8) and clustered‐cut ( n = 12) landscapes. Home range size was not related to the proportion of clear‐cuts (i.e., habitat loss), but rather to the proportion of mixedwood stands 70–120 years old. However, female body condition was correlated to habitat condition inside their home ranges (i.e., amount of residual forest and recent clear‐cuts). At the home range scale, we determined that mixedwood forests were also among the most used forest stands and the least used were recent clear‐cuts and forested bogs, using resource selection functions. At the landscape scale, home ranges included more mixedwood forests than random polygons and marten high activity zones were composed of more residual forest and less human‐induced disturbances (clear‐cuts, edges, and roads). These results suggest that mixedwood forests, which occupy approximately 10% of the study area, play a critical role for martens in this conifer‐dominated boreal landscape. We recommend permanent retention or special management considerations for these isolated stands, as harvesting mixedwood often leads to forest composition conversion that would reduce the availability of this highly used habitat. © The Wildlife Society, 2013
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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".