Reliability of Indicators of Decline in Abundance
Bibliographic record
Abstract
Although there are many indicators of endangerment (i.e., whether populations or species meet criteria that justify conservation action), their reliability has rarely been tested. Such indicators may fail to identify that a population or species meets criteria for conservation action (false negative) or may incorrectly show that such criteria have been met (false positive). To quantify the rate of both types of error for 20 commonly used indicators of declining abundance (threat indicators), we used receiver operating characteristic curves derived from historical (1938-2007) data for 18 sockeye salmon (Oncorhynchus nerka) populations in the Fraser River, British Columbia, Canada. We retrospectively determined each population's yearly status (reflected by change in abundance over time) on the basis of each indicator. We then compared that population's status in a given year with the status in subsequent years (determined by the magnitude of decline in abundance across those years). For each sockeye population, we calculated how often each indicator of past status matched subsequent status. No single threat indicator provided error-free estimates of status, but indicators that reflected the extent (i.e., magnitude) of past decline in abundance (through comparison of current abundance with some historical baseline abundance) tended to better reflect status in subsequent years than the rate of decline over the previous 3 generations (a widely used indicator). We recommend that when possible, the reliability of various threat indicators be evaluated with empirical analyses before such indicators are used to determine the need for conservation action. These indicators should include estimates from the entire data set to take into account a historical baseline.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".