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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".