A practical framework for selecting among single-species, community-, and ecosystem-based recovery plans
Bibliographic record
Abstract
Science-based approaches for selecting among single-species, community-, and ecosystem-based recovery plans are needed to conserve imperilled species. Selection of recovery plans has often been based on past success rates with other taxa and systems or on economic cost, but less on the ecology of the system in question. We developed a framework for selecting a recovery plan based on the distributions and ecology of imperilled and nonimperilled species across available habitat types and applied it to fishes in the Sydenham River, Ontario, Canada. We first tested whether distributions of fishes were adequately predicted by habitat features hypothesized to limit the distributions of imperilled fishes versus a broader set of habitat features known to predict fish distributions. We then tested whether imperilled species occurred in similar or disparate habitat types. For the Sydenham River, an ecosystem-based recovery plan was deemed most appropriate because imperilled species occur in disparate habitat types. We lastly provide decision criteria to facilitate applications of our framework to the selection of recovery plans for other species and systems.
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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 | 0.030 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".