Under pressure: the relationship between vertebrate populations and high-intensity cumulative threats in habitats across Canada
Bibliographic record
Abstract
Biodiversity and human pressures are unevenly distributed, and understanding local patterns is key to appropriately directing conservation action. Here, we investigate the relationship between spatially explicit high-intensity cumulative threats (HICTs) and indices of monitored population abundance of Canadian vertebrates, disaggregating patterns among habitats. We found average population declines across habitats and reaffirm the concentration of HICT in southern Canada—particularly in the Mixedwood Plains ecozone. We found that terrestrial vertebrate populations often coincided spatially with proximity to humans, infrastructure, and land cover change, while marine population data commonly overlapped with shipping, exploitation, and pollution pressures. We reveal significant negative associations between monitored population trends and both (i) HICT and the (ii) number of habitats occupied by the species in question—both of which were identified as important predictors. However, marginal R 2 values for our models were small, and thus the proportion of variance that the predictor variables (e.g., threats and habitats) can confidently explain is limited. Our analysis provides novel spatial products and analyses—integrating both marine and terrestrial/freshwater realms, contributing to a growing evidence base for supporting prioritization of conservation action at a national scale. Nevertheless, we provide recommendations on methodological improvements to improve the utility of predictive models.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".