Variable zone of influence responses by large mammals: Implications for conservation planning
Bibliographic record
Abstract
Abstract Anthropogenic development affects the behavior and distribution of species both by removing and altering habitat in the development footprint and through adjacency effects within a zone of influence (ZOI) around development. Accurate estimates of ZOI are needed to understand and mitigate impacts from human development, but quantitative estimates of ZOI are rare, and most studies that develop them do so only for a single species. We developed an approach to estimate seasonal ZOI for multiple species using remote camera data. Seasonal habitat models formed the basis of the approach that we applied to four large mammals in the Elk Valley, British Columbia, Canada: bighorn sheep ( Ovis canadensis ), elk ( Cervus canadensis ), moose ( Alces alces ), and grizzly bears ( Ursus arctos ). We estimated the ZOI from open‐pit metallurgical coal mines for each species by forcing a categorical distance to mine variable into the top habitat model in each season. Our multispecies approach identified diverse ZOI responses to mining, ranging from strongly positive for bighorn sheep and elk to somewhat negative for grizzly bears and strongly negative for moose, with some seasonal variation. Our results highlight important conservation implications of estimating ZOI for multiple species. Species that exhibit positive ZOI may benefit from targeted on‐site reclamation efforts. Conversely, those that exhibit negative ZOI may benefit from mitigation strategies that address the underlying mechanisms driving the negative response, aiming to minimize ZOI. Additionally, implementing actions to offset adverse impacts could further enhance the overall outcome. Mixed mitigation strategies are more complex to implement but may be needed to prevent ongoing declines in global wildlife populations as governments and industry work toward recent nature‐positive commitments for our planet.
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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) | 0.001 | 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 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".