Threats to biodiversity from cumulative human impacts in one of North America's last wildlife frontiers
Bibliographic record
Abstract
Land-use change is the largest proximate threat to biodiversity yet remains one of the most complex to manage. In British Columbia (BC), where large mammals roam extensive tracts of intact habitat, continued land-use development is of global concern. Extant mammal diversity in BC is unrivalled in North America owing, in part, to its unique position at the intersection of alpine, boreal, and temperate biomes. Despite high conservation values, understanding of cumulative ecological impacts from human development is limited. Using cumulative-effects-assessment (CEA) methods, we assessed the current human footprint over 16 regional ecosystems and 7 large mammal species. Using historical and current range estimates of the mammals, we investigated impacts of human land use on species' persistence. For ecosystems, we found that bunchgrass, coastal Douglas fir, and ponderosa pine have been subjected to over 50% land-use conversion, and over 85% of their spatial extent has undergone either direct or estimated indirect impacts. Of the mammals we considered, wolves were the least affected by land conversion, yet all species had reduced ranges compared with historical estimates. We found evidence of a hard trade-off between development and conservation, most clearly for mammals with large distributions and ecosystems with high levels of conversion. Rather than serve as a platform to monitor species decline, we strongly advocate these data be used to inform land-use planning and to assess current conservation efforts. More generally, CEAs offer a robust tool to inform wildlife and habitat conservation at scale.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".