Planning for success: Why conservation programs need a strategic program for recovering species
Bibliographic record
Abstract
Abstract A substantial amount of money has been spent globally on threatened species management. While the number of threatened species continues to increase, we would expect to observe a portion of those receiving active management to respond positively and recover over time. Management of these recovering species requires a different approach to those which are declining. In particular, recovering species may require active monitoring as the primary management activity, once the threats causing their initial decline have been managed such that populations are stable or increasing. When prioritizing funding actions to improve species persistence (in particular with species prioritization approaches such as cost‐effectiveness rankings), we demonstrate that monitoring species to track their continued improvement would only occur in the (unlikely) scenario of comprehensive program funding. We provide one easily implemented solution to this—the establishment of a separately funded transitional management stream within which recovering or recovered species are prioritized for monitoring from a dedicated monitoring budget. We present a set of criteria to assess recovering species eligible for this management arrangement and demonstrate the successful application of this approach in New South Wales, Australia in the Saving our Species program.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".