Riparian Areas and Fine‐Scale Forest Cover and Structure Drive Occupancy Patterns of Sympatric Mustelids
Bibliographic record
Abstract
ABSTRACT Boreal and subboreal forests of western North America support diverse mesocarnivore populations with overlapping spatial, temporal, and dietary niches. However, interactions among these species and the factors influencing their co‐occurrence—such as population cycles, landscape changes, harvest mortality, and resource availability—are not well documented. We used 5 years (2015, 2016, 2020, 2021, and 2022) of data from remote cameras and fine‐scale habitat data from Light Detection and Ranging to assess patterns in the spatial co‐occurrence of short‐tailed weasel (Mustela erminea), American mink (Neogale vison), American marten (Martes americana), and fisher (Pekannia pennanti) in central British Columbia, Canada. We used multispecies occupancy models to test hypotheses about the relationships between mesocarnivore co‐occurrence and habitat. Mesocarnivores were more likely to co‐occur at sites closer to riparian zones and at sites with greater complexity of vertical forest structure. Short‐tailed weasel, however, did not co‐occur with other mustelids in riparian zones. Importantly, we found that habitat covariates associated with co‐occurrence were relatively similar over time despite changes in the abundance of predators and prey. Our findings highlight the importance of retaining riparian habitats and forest complexity as part of forest harvesting practices to promote species co‐occurrence.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".