Prioritizing threat management across terrestrial and freshwater realms for species conservation and recovery
Bibliographic record
Abstract
Abstract The need to manage threats to biodiversity, and to do so cost‐effectively, is urgent. Cross‐realm conservation management is recognized as a cost‐effective approach, but it requires collaboration between agencies and jurisdictions, and local knowledge of anthropogenic threats to biodiversity. With its emphasis on stakeholder engagement and use of structured expert elicitation, Priority Threat Management (PTM) facilitates rapid, cross‐realm planning at the regional scale. We used PTM to identify cost‐effective management strategies with the aim of securing nine ecological groups, comprised of 45 species and one ecological community of conservation concern, across terrestrial and freshwater realms within the Wolastoq|Saint John River watershed in Canada. Under business‐as‐usual, four of nine groups are expected to have >50% probability of persistence over the next 25 years. Investment of $141 million over 25 years in three management strategies could secure seven groups across both realms with >50% probability of persistence. Achieving higher levels of persistence comes at a cost—securing six groups with >60% probability of persistence requires investing $218 million over 25 years in seven strategies. Through a structured, iterative process, whereby stakeholders cooperate to clarify objectives, devise management strategies, and collate data, PTM can support timely and cost‐effective management across multiple realms.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".