Shadow under the lamp: Evidence-based assessments reveal local extirpation risk of large Indian civets (Viverra zibetha) concealed by intensive activity in human-dominated landscapes
Bibliographic record
Abstract
Conservation planning requires reliable biodiversity assessments to provide robust evidence. To ensure the long-term effectiveness of management plans, decision-makers must acknowledge the limitations of imperfect evidence and remain aware of the potential biases in species distribution and abundance inference. To illustrate how reliable assessments clarify and reshape conservation strategies, we used large Indian civet ( Viverra zibetha ) and its sympatric species as an example to demonstrate how improved monitoring networks and analytical approaches can enhance our understanding of species status under incomplete information. Large Indian civet has been considered abundant because its camera trap detections ranked top among all meso-carnivores in Bayuelin Nature Reserve, China. However, species distribution modeling revealed a highly restricted range of 58.10 km 2 , where intensive anthropogenic disturbances pose severe threats. Canonical correspondence analysis further suggested that the previous monitoring network must be expanded into disturbed areas beyond reserve boundary for comprehensive assessment. Moreover, by sequentially applying a species distribution model and a spatial capture-recapture model, we estimated a population size of only 11 individuals (95 % CI: 10.28–16.51) for the civet, which is 45 % smaller in density than sympatric leopard cats ( Prionailurus bengalensis ) despite a higher detection rate of the former. By using evidence-based assessments and revealing local extirpation risk of large Indian civets, our research underscores the importance of evidence-based assessments to provide reliable information for conservation planning. Such need exists not only for our study species but also for a wide range of taxa with concerning status, where accurate assessments are hindered by imperfect evidence.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".