Feasibility and pitfalls of<i>ex situ</i>management to mitigate the effects of an environmentally persistent pathogen
Bibliographic record
Abstract
Abstract Ex situ(captive) management can facilitate species recovery, but this approach is invasive and is not universally appropriate. Evidence‐based approaches are critical to determining whetherex situapproaches will be effective, but in many cases biological and ecological data from threatened populations are scarce. We generated a structured set of general biological and social/logistical criteria required for captive management to benefit threatened, free‐ranging populations. We illustrate how these criteria can be applied using a case study where populations are severely threatened, but demographic data are scarce (white‐nose syndrome in Canadian bats). Using (1) population viability modelling (PVA), (2) a survey of Canadian zoos and wildlife rehabilitators, and (3) literature reviews, we identified two of our five initial target species as potential candidates for captive management.PVArevealed that sustainable captive colonies require high adult survivorship relative to free‐ranging populations. Our survey and literature reviews showed that Canadian zoos and wildlife rehabilitators are enthusiastic about bat conservation. However, none could currently maintain the target species, due to limited infrastructure and/or knowledge gaps related to husbandry and reintroduction of captive bats to the wild. Captive management is unlikely to stabilize target populations because released bats risk re‐infection. We conclude thatex situmanagement is not an appropriate tool in our case study, and would represent ineffective use of available conservation resources. However, development of captive husbandry and re‐introduction methods for hibernating, insectivorous bats would contribute to our global capacity to conserve similar species.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".