One hundred research questions in conservation physiology for generating actionable evidence to inform conservation policy and practice
Bibliographic record
Abstract
Abstract Environmental change and biodiversity loss are but two of the complex challenges facing conservation practitioners and policy makers. Relevant and robust scientific knowledge is critical for providing decision-makers with the actionable evidence needed to inform conservation decisions. In the Anthropocene, science that leads to meaningful improvements in biodiversity conservation, restoration and management is desperately needed. Conservation Physiology has emerged as a discipline that is well-positioned to identify the mechanisms underpinning population declines, predict responses to environmental change and test different in situ and ex situ conservation interventions for diverse taxa and ecosystems. Here we present a consensus list of 10 priority research themes. Within each theme we identify specific research questions (100 in total), answers to which will address conservation problems and should improve the management of biological resources. The themes frame a set of research questions related to the following: (i) adaptation and phenotypic plasticity; (ii) human–induced environmental change; (iii) human–wildlife interactions; (iv) invasive species; (v) methods, biomarkers and monitoring; (vi) policy, engagement and communication; (vii) pollution; (viii) restoration actions; (ix) threatened species; and (x) urban systems. The themes and questions will hopefully guide and inspire researchers while also helping to demonstrate to practitioners and policy makers the many ways in which physiology can help to support their decisions.
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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 | 0.126 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".