Seasonal shifts in the habitat selection patterns of male American Marten (<i>Martes americana</i>) at a fine spatial scale
Bibliographic record
Abstract
Abstract Old-growth forests harbor a large amount of complex structural features that result in a wide array of wildlife habitats. However, intensive forest management is gradually converting old-growth forest into younger, even-aged stands, reducing structural complexity and threatening the persistence of old-growth-dependent species. Maintaining elements of complex stand structure is critical to the conservation of old-growth forest specialists that use different habitat components at different periods of their annual cycle, and it requires a comprehensive understanding of seasonal variation in the habitat needs of these species. However, difficulties in observing free-ranging animals have sometimes limited our ability to assess such variations in habitat requirements, especially for small, elusive species. To address this, we used GPS telemetry collars to describe fine-scale habitat selection patterns of 6 male American Martens (Martes americana) during 2 contrasting periods of the year (snow-free, from mid-April to mid-November; snow-covered, from mid-November to mid-April), an objective formerly hard to achieve using conventional VHF telemetry. We used resource selection functions conducted at the fourth order of selection to compare habitat characteristics found at the sites used by martens (GPS locations, n = 100) to those found on an equal number of available sites (random points, n = 100) within each individual seasonal home range. We conducted vegetation surveys on these 200 sites to describe habitat and built candidate models representing different concurrent hypotheses. Our results showed that proxies of prey availability, predator avoidance, and thermal constraints were the primary factors influencing marten habitat selection during both periods, although their respective importance differed between periods. Martens selected sites with a high density of large-diameter snags (≥30·ha−1), high conifer canopy closure (≥53%), and a dense lateral cover (≥81%) during the snow-free period, but selected sites with a high volume of coarse woody debris (≥64 m3·ha−1) and high conifer canopy closure (≥48%) during the snow-covered period. Our results highlight the importance of contrasting seasonal changes in habitat selection patterns of small carnivores and may help maintain structural attributes in the landscape that are suitable for male American Martens.
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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 | 0.000 |
| 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) | 0.001 | 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 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".