Landscape partitioning and spatial inferences of competition between black and grizzly bears
Bibliographic record
Abstract
Population effects of competition between large carnivore species may be evident by contrasting actual distributions of putative competitors against predictions of inherent landscape quality for each species. Such comparison can be insightful if covariation with external factors known to influence the occurrence, density, or persistence of each species over space and time can be controlled. We used systematically‐distributed DNA hair‐trap stations to sample the occurrence of black bears ( Ursus americanus ) and grizzly bears ( U. arctos ) across 5496 km 2 in southeastern British Columbia, Canada. We describe interspecific landscape partitioning according to terrain, vegetation and land‐cover variables at 2 spatial scales. We developed multivariate models to predict the potential distribution of each species. At sampling site‐session combinations that detected either species, we then investigated whether the expected or actual occurrence of each influenced the likelihood of detecting the other while controlling for human influence and inherent landscape quality. Black bears were more likely than grizzly bears to occur in gentle, valley bottom terrain with lower proportions of open habitats. Each species also was detected less frequently with the other species than predicted by their respective models; however, the strength of this relationship decreased as landscapes became more characteristic of black bear habitat. As landscapes showed higher inherent potential to support grizzly bears, black bears occurred more than model prediction in areas with higher human access and proximity to major highways but less in national parks. As potential to support black bears increased, grizzly bears occurred more than model prediction only in national parks and less with increasing human access and proximity to major highways. Results suggest that competition is occurring between the species, and that the differential response of each species to human disturbance or excessive mortality may influence the outcome and hence landscape partitioning. Moreover, black bears are more likely to benefit from human encroachment into landscapes of high inherent value for grizzly bears than vice versa. Conservation implications relate to potential mediating effects of habitat and human influence on competitive interactions between the species.
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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 | 0.001 | 0.004 |
| 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 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".