Nature requires investment: applying Priority Threat Management to support biodiversity and climate targets
Bibliographic record
Abstract
1. Stemming biodiversity loss requires greater investment in conservation and more efficient use of available resources. Prioritizing conservation actions that yield the most biodiversity benefit for the least cost can help maximize return on investment. Actions that have co-benefits for other objectives, such as climate change mitigation, can also help mobilize additional funds for conservation. 2. We used Priority Threat Management to identify actions to secure the greatest number of species groups of conservation concern for the least cost in the Lake Simcoe-Rideau ecoregion, Ontario—one of Canada’s biodiversity crisis ecoregions. We also estimated the carbon sequestration benefits of actions related to land protection and restoration. 3. We found that without additional investment in conservation, 13 of 16 species groups were expected to have <50% probability of persistence in this ecoregion by 2050. Implementing all proposed strategies would yield the greatest biodiversity benefits and secure 12 of the 16 species groups with ≥60% probability of persistence, at a cost of $113 million per year over 27 years. In comparison, investing CA$97 million per year in landowner stewardship, habitat protection, and restoration and regeneration strategies could secure ten species groups and improve the probability of persistence of one additional group from 39% to 55%, an increase of >15%. 4. The habitat protection and restoration strategies also deliver direct carbon benefits of around 11.2Mt in avoided CO2 emissions and 137.6Mt CO2 in potential sequestration, respectively, thus supporting alignment with climate change mitigation targets and delivering co-benefits that may further justify investment. 5. Synthesis and applications. By estimating the costs and demonstrating the expected benefits and potential carbon co-benefits of conservation actions, Priority Threat Management can help maximize return on investment and identify actions that address multiple environmental crises.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.012 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".