Conservation physiology can inform threat assessment and recovery planning processes for threatened species
Bibliographic record
Abstract
Conservation physiology has emerged as a discipline with many success stories.Yet, it is unclear how it is currently integrated into the activities of the IUCN and other bodies which undertake international, national, or regional species threat assessments and work with partners to develop recovery plans.Here we argue that conservation physiology has much to offer for the threat assessment process and we outline the ways in which this can be operationalized.For instance, conservation physiology is effective in revealing causal relationships and mechanisms that explain observed patterns, such as population declines.Identifying the causes of population declines is a necessary precursor to the design of actions to reverse or mitigate such threats.Conservation physiology can also identify complex interactions and support modeling activities that consider emerging threats.When a population or species is deemed threatened and recovery plans are needed, physiology can be used to predict how organisms will respond to the conservation intervention and future threats.For example, if a recovery plan was focused on translocation, understanding how to safely translocate organisms would be necessary, as would ensuring that the recipient habitat provides the necessary environmental characteristics to meet the fundamental physiological needs/tolerances of that organism.Our hope is that this paper will clarify ways in which physiological data can make an important contribution to the conservation activities of bodies like the IUCN that are engaged in threat assessment and recovery of endangered organisms.Although we focus on activities at the international scale, these same concepts are relevant and applicable to national and regional bodies.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".