A framework to support the identification of critical habitat for wide-ranging species at risk under climate change
Bibliographic record
Abstract
Abstract To recover species at risk, it is necessary to identify habitat critical to their recovery. Challenges for species with large ranges (thousands of square kilometres) include delineating management unit boundaries within which habitat use differs from other units, along with assessing any differences among units in amounts of and threats to habitat over time. We developed a reproducible framework to support identification of critical habitat for wide-ranging species at risk. The framework (i) reviews species distribution and life history; (ii) delineates management units across the range; (iii) evaluates and compares current and (iv) potential future habitat and population size and (v) prioritizes areas within management units based on current and future conditions under various scenarios of climate change and land-use. We used Canada Warbler ( Cardellina canadensis ) and Wood Thrush ( Hylocichla mustelina ) in Canada as case studies. Using geographically weighted regression models and cluster analysis to measure spatial variation in model coefficients, we found geographic differences in habitat association only for Canada Warbler. Using other models to predict current habitat amount for each species in different management units, then future habitat amount under land use and climate change, we projected that: (1) Canada Warbler populations would decrease in Alberta but increase in Nova Scotia and (2) Wood Thrush populations would increase under most scenarios run in Quebec, New Brunswick and Nova Scotia, but not in Ontario. By comparing results from future scenarios and spatial prioritization exercises, our framework supports identification of critical habitat in ways that incorporate climate and land-use projections.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".