Fine-scale winter resource selection by American martens in boreal forests and the effect of snow depth on access to coarse woody debris
Bibliographic record
Abstract
Successful management of boreal forests requires an understanding of the scales at which focal species use resources in their environment. Fine-resolution, within-home-range habitat selection by American martens (Martes americana) has not been well studied in boreal forests, although the importance of downed wood seems almost universal for hunting and resting in winter. We examined winter habitat selection by radio-collared martens while the population was at a 5 y low near Kapuskasing, Ontario, Canada. Fine-resolution models were developed using habitat data collected from snow-tracking 5 resident martens. Resource selection models were compared using an information theoretic approach, and model performance was evaluated by the ability of the models to correctly classify resource use events by martens. Our models suggest that martens selected locations within home ranges that had higher subnivean access to large coarse woody debris (CWD), a medium density of large conifers, and a higher proportion of eastern white cedar (Thuja occidentalis) trees compared to random sites within the home ranges. Subnivean access to CWD decreased with increasing snow depth, and sites used by martens had consistently higher access to CWD compared to random sites within home ranges across the entire range of snow depths measured. Areas used by martens also had lower snow depth than the average within home ranges. Our study illustrates how fine-resolution data can increase the predictability of resource use by martens, and suggests that incorporating sub-stand-level measures such as conserving patches of white cedar and ensuring that CWD remains high through forest succession can increase our ability to successfully manage boreal forests for species such as marten that prefer older stands.
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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".