Physiology as a tool for at‐risk animal recovery planning: An analysis of Canadian recovery strategies with global recommendations
Bibliographic record
Abstract
Abstract Many government organizations use recovery planning to synthesize threats, propose management strategies, and determine recovery criteria for threatened wildlife. Little is known about the extent to which physiological knowledge has been used in recovery planning, despite its potential to offer key biological information that could aid in recovery success. Using recovery strategies for at‐risk animal species in Canada as a case study, we analyzed the prevalence, purpose, and type of physiological knowledge being used in recovery planning. We found that 73% of strategies contained mention of physiology and that incorporation of physiology has increased since 2006. Of the various types of physiological tools available, reference to stress, immune, thermal, and bioenergetic metrics appeared most frequently. Physiological information was more likely to be found in the background and threat assessment sections compared to action and future research sections, and less likely to be included in strategies for arthropods and birds compared to other taxonomic groups. By synthesizing our results with previous studies, we provide recommendations to encourage the application of physiological tools in recovery planning worldwide, such as increased incorporation of physiology in ongoing threat monitoring, critical habitat assessments, monitoring the success of recovery actions, and modeling responses to future environmental changes.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".