Identifying the critical habitat of Canadian vertebrate species at risk
Bibliographic record
Abstract
Identification of critical habitat is central to major conservation laws protecting endangered species in North America and around the world. Yet the actual ecological research that is required to identify which habitats are critical to the survival or recovery of species is rarely discussed and poorly documented. Here we quantitatively assess the information and methods used to identify critical habitat in the recovery strategies of 53 vertebrates at risk in Canada. Of the critical habitat identifications assessed, 17% were based on habitat occupancy information, 28% on habitat characteristics and (or) functions, and 40% assessed habitat suitability by linking functional use and biophysical characteristics. However, only 15% of the recovery strategies we evaluated examined relationships between habitat and population viability, abundance, individual fitness, or survival. Furthermore, the breadth of evidence used to assess critical habitats was weaker among long-lived taxa and did not improve over time. Hence, although any approach used to identify critical habitat is likely to be a step in the right direction in minimally protecting and maintaining habitats supporting critical life-cycle processes, there is a persistent gap between the widely recognized importance of critical habitat and our ability to quantitatively link habitats to population trends and individual fitness.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".