Identifying conservation priorities for an understudied species in decline: Golden cats (Catopuma temminckii) in mainland Tropical Asia
Bibliographic record
Abstract
Identifying conservation priorities for an understudied species can be challenging, as the amount and type of data available to work with are often limited. Here, we demonstrate a flexible workflow for identifying priorities for such data-limited species, focusing on the little-studied Asian golden cat (Catopuma temminckii) in mainland Tropical Asia. Using recent occurrence records, we modeled the golden cat’s expected area of occurrence and identified remaining habitat strongholds (i.e., large intact areas with moderate-to-high expected occurrence). We then classified these strongholds by recent camera-trap survey status (from a literature review) and near-future threat status (based on publicly available forest loss projections and Bayesian Belief Network derived estimates of hunting-induced extirpation risk) to identify conservation priorities. Finally, we projected the species’ expected area of occurrence in the year 2000, approximately three generations prior to today, to define past declines and better evaluate the species’ current conservation status. Lower levels of hunting-induced extirpation risk and higher levels of closed-canopy forest cover were the strongest predictors of recent camera-trap records. Our projections suggest a 68% decline in area with moderate-to-high expected occurrence between 2000 and 2020, with a further 18% decline predicted over the next 20 years. Past and near-future declines were primarily driven by cumulatively increasing levels of hunting-induced extirpation risk, suggesting assessments of conservation status based solely on declines in habitat may underestimate actual population declines. Of the 40 remaining habitat strongholds, 77.5% were seriously threatened by forest loss and hunting. Only 52% of threatened strongholds had at least one site surveyed, compared to 100% of low-to-moderate threat strongholds, thus highlighting an important knowledge gap concerning the species’ current distribution and population status. Our results suggest the golden cat has experienced, and will likely continue to experience, considerable population declines and should be considered for up-listing to a threatened category (i.e., VU/EN) under criteria A2c of the IUCN Red List.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".