Trimming the hedges in a hurricane: Endangered Species lack research on the outcomes of conservation action
Bibliographic record
Abstract
Given widespread biodiversity declines, there is an urgent need to ensure that conservation interventions are working. Yet, evidence regarding the effectiveness of conservation actions is often lacking. Using a case study of 208 terrestrial species listed as Endangered in Canada, we conducted a literature review to collate the evidence base on conservation actions to: 1) explore the outcomes of actions documented for each species; and 2) identify knowledge gaps. Action-oriented research constituted only 2% of all literature across target species, and for 56% of species we found no literature investigating outcomes of conservation actions. Protected areas, habitat creation, artificial shelter, and alternative farming practices were broadly beneficial for most species for which these actions were assessed. Habitat restoration actions were most frequently studied, but almost 38% of these actions were harmful, ineffective, or demonstrated mixed results. The effectiveness of prescribed burns, alternative timber harvesting approaches and vegetation control was examined for the greatest number of species, yet 17-30% of these actions demonstrated negative effects. Our synthesis yielded a dataset of conservation evidence that can be implemented to aid in recovery planning for species at risk, and highlighted alarming gaps in the conservation literature that merit further investigation.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".