Expert solicitation informs conservation planning for a subarctic mammal susceptible to climate change: the collared pika (Ochotona collaris)
Bibliographic record
Abstract
Conservation plans for species at risk are critical for identifying threats and determining means to manage them to permit species persistence. However, the threats faced by many species are rarely fully understood. The collared pika ( Ochotona collaris ) is legally listed as a species at risk in Canada, with climate change as the primary threat. However, the mechanisms by which climate change impacts collared pikas are unclear. To identify threats and priority measures that should be highlighted in a national conservation plan for collared pikas, we solicited 47 experts—representing 427 person-years of experience with pikas—regarding their knowledge and opinions of pika ecology, threats, and priority conservation actions. There was close agreement among respondents regarding the mechanisms that likely threaten collared pikas, including increased summer temperatures (86 % of experts agreed), rain-on-snow events (86 %), rising winter temperatures (74 %), increasing persistence of late spring snow cover (74 %), and shrubification of alpine meadows (71 %). However, experts varied on the adaptive capacity of collared pikas. Hypothetical management scenarios highlighted variability among experts in the best course of action managers should take when pika populations decline, with monitoring and research on the cause of the decline being most supported. Most experts strongly agreed that a management plan should focus on monitoring populations (100 % of experts), enhanced public education regarding climate change (81 %), and research on climate impacts (79 %). Our survey aided the development of a national conservation plan for the species. Where species responses to climate change are uncertain, expert surveys may be useful in addressing knowledge gaps and developing conservation actions, especially when the required research may take years to complete but the concern is more immediate. However, expert surveys are perhaps most valuable if there are strong linkages between the knowledge gained and the development of the conservation plans that they aim to aid.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".