Global monitoring for biodiversity: Uncertainty, risk, and power analyses to support trend change detection
Bibliographic record
Abstract
Global targets aim to reverse biodiversity declines by 2050 but require knowledge of current trends and future projections under policy intervention. First, given uncertainty in measurement of current trends, we propose a risk framework, considering probability and magnitude of decline. While only 11 of 198 systems analyzed (taxonomic groups by country from the Living Planet Database) showed declining abundance with high certainty, 20% of systems had a 70% chance of strong declines. Society needs to decide acceptable risks of biodiversity loss. Second, we calculated statistical power to detect trend change using ~12,000 populations from 62 systems currently showing strong declines. Current trend uncertainty hinders our ability to assess improvements. Trend change is detectable with high certainty in only 14 systems, even if thousands of populations are sampled, and conservation action reduces net declines to zero immediately, on average. We provide potential solutions to improve monitoring of progress toward biodiversity targets.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".