When can we trust population trends? Quantifying the effects of sampling interval and duration
Bibliographic record
Abstract
Abstract Species’ population trends are fundamental to conservation, underpinning lUCN red-list classifications, many national lists of threatened species and are also used globally to convey to policy makers the state of nature. Clearly, it’s crucial to quantify how much we can trust population trend data. Yet many studies analyzing large numbers of population time series lack a straightforward way to estimate confidence in each trend. Here we artificially degrade 27,930 waterbird population time series to see how often subsets of the data correctly estimate the direction and magnitude of each population’s true trend. We find you need to sample many years to be confident that there is no significant trend in a population. Conversely, if a significant trend is detected, even from only a small subset of years, this is likely to be representative of the population’s true trend. This means that if a significant decline is detected in a population, it is likely to be correct and conservation action should be taken immediately, but if the trend is insignificant, confidence in this can only be high with many samples. Our full results provide a clear and quantitative way to assign confidence to species trends, and lays the foundation for similar studies of other taxa that can help to add rigor to large-scale population analyses.
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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 | 0.081 | 0.390 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".