Detecting extirpation: A localized approach to a global problem
Bibliographic record
Abstract
Societal Impact Statement The global biodiversity crisis stems from a cascading series of extirpations driving species toward extinction. Addressing this crisis requires methods for early detection of extinction at local scales, where communities can mobilize conservation efforts. We present a method for the detection of species extirpation, harnessing biological specimens, community science, and targeted surveys. Our framework aligns with the International Union for the Conservation of Nature (IUCN) extinction criteria and provides a practical means of testing the hypothesis of extinction. By streamlining the integration of data into a practical framework for inference, our approach overcomes key challenges in inferring local extinction risk, supporting efforts to detect and mitigate biodiversity loss. Summary Extinction results when all local populations have been extirpated. We therefore require systems for the detection of extirpation to contend with this global problem at its source. Through a baseline analysis incorporating half a century of historical collection data with 5 years of data gathered via a community biodiversity project, we identified 10 plant species from Galiano Island, BC, Canada, that had not been seen recently as suitable subjects for this study. These became the target of systematic surveys over an additional 4 years. Using these data, we applied a Bayesian framework to estimate the probability of species' absences within regions of suitable (historical and potential) habitat, in accordance with the IUCN extinction criteria. Six target species were redetected. For the others, we inferred conditional extirpation probabilities based on relaxed (only historical habitat patches) and strict (all potential habitat) interpretations of the IUCN criteria. Under a relaxed interpretation of IUCN criteria, we found a high probability ~95% of extirpation (local extinction) for two species ( Crassula connata and Primula pauciflora ); under strict criteria, we inferred extirpation probabilities of 89.1% and 83.7%. We demonstrate a framework for the detection of species extirpation, harnessing historical collections, community science, and targeted surveys to meet the IUCN extinction criteria with explicitly defined degrees of confidence.
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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) | 0.009 | 0.002 |
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; both teacher heads agree on what is shown here.
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".