Using species distribution models to effectively conserve biodiversity into the future
Bibliographic record
Abstract
Canadian biodiversity is especially high in temperate southern regions, where human-dominated land uses are both intensive and widespread. As a result, endangered species are also disproportionately concentrated in these areas. Climate change presents a new threat across most of Canada, including areas of intensive human land use, which creates conditions for substantial shifts in species composition and potential losses of many rare species. Protected areas is one adaptation strategy but, in Canada, parks suffer from severe limitations in their distribution, size, and because they have static boundaries. Land use changes around several protected areas in Canada are leading increasingly to their effective isolation, a trend we demonstrate using high resolution satellite data. Little published research has yet addressed this issue in the Canadian context, although some models now forecast ecological changes in the next century. Adaptation to global change impacts will necessitate refocusing conservation strategies beyond the boundaries of protected areas to include broader landscape perspectives. Necessary responses to these challenges include validated models predicting future biotic responses to global change, expanded biodiversity monitoring across Canada, improvements to the patchwork of federal and provincial legislation protecting species, and preemptive conservation strategies that recognize impending transitions to unprecedented environmental conditions.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".