‘Lost’ taxa and their conservation implications
Bibliographic record
Abstract
Abstract While biological extinctions are predicted to rise sharply during the Anthropocene, extinction declarations are rare, partly due to inherent uncertainties in knowing when the last individual of a species has died. This has led to the growth of a group of ‘lost’ species that have not been observed in decades or even centuries, yet are not declared extinct, and as such possess an uncertain conservation status. The existence of such species may prove increasingly problematic as the extinction crisis worsens, given that their presence may create uncertainty with respect to conservation prioritization efforts and to our understanding of extinction rates. We provide the first assessment of the extent of lost taxa, defined as species that have not been reliably observed in >50 years yet are not declared extinct, for terrestrial vertebrates (amphibians, reptiles, birds and mammals). We reviewed information from IUCN Red List accounts within these Classes using a hybrid code‐based search/manual assessment approach, supplemented with consultation of recent literature. In total, we identify a total of 562 lost species (137 amphibians, 257 reptiles, 38 birds and 130 mammals). Of these, 13% (75 species) are listed as ‘Possibly Extinct’ by the IUCN. Lost species outnumber extinct species for all studied Classes except birds. They were mainly confined to the tropics (92.5%), with distributions being particularly concentrated in ‘mega‐diverse’ countries, as expected. Indonesia (69 species), Mexico (33 species) and Brazil (29 species) possessed the most lost species overall. Our results highlight the prevalence of lost taxa among terrestrial vertebrates and identify ‘hotspots’ for these species where future survey efforts should be prioritized. We suggest minor adjustments to IUCN Red List accounts to allow lost species to be better tracked, including more consistent use of the ‘Possibly Extinct’ marker and wider application of the ‘last seen date’ field.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".