A systematic review of global publications on clouded leopard ( <i>Neofelis nebulosa</i> ): identifying the publication trends, research gaps, and future directions to strengthen its conservation
Bibliographic record
Abstract
Background Despite global investment in studying, protecting, and managing carnivores, species like the clouded leopard Neofelis nebulosa (Griffith, 1821), renowned for its elusive nature, remain significantly understudied. There is also insufficient knowledge of clouded leopard research trends in spatial and temporal domains. Additionally, thematic areas of research on this species are not clearly known. This gap in information may hinder the development of effective strategies to address key conservation challenges such as habitat loss, poaching, and illegal trade. Methods To bridge these gaps, we systematically reviewed 123 peer-reviewed journal articles published up to December 2022, offering critical insights into the current state of knowledge and identifying future research priorities to inform conservation planning. Results The spatial analysis of clouded leopard research reveals that Thailand ( n = 28) dominates the range countries, while the USA ( n = 26) dominates non-range countries in terms of research efforts. Temporally, research output has shown a significant increase since 2006, peaking in 2016 ( n = 13), with a positive trend in publications (Kendall’s tau = 0.52, P < 0.001). Most studies focused on anatomy and physiology in captive populations ( n = 31) and habitat use and distribution in free-ranging populations ( n = 23). The studies on the impact of climate change on the clouded leopard and its habitat, alongside feeding ecology, remain scant, necessitating the future research in these areas. Our analysis also revealed that the maximum number of publications employed diagnosis and treatment (26%), followed by camera trapping (24.4%). We recommend integrating local ecological knowledge and monitoring technologies to map the clouded leopard’s corridors, connectivity, and bottleneck sites at the landscape level. A higher number of publications addressed habitat loss and illegal trade as the primary threats to clouded leopard conservation. Effective law enforcement, proper land use, land cover planning, and community engagement are crucial for conserving this species. Moreover, clouded leopard range countries are recommended to develop sustainable financial mechanisms and implement the conservation action plan across the country, which can improve conservation outcomes.
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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 | 0.014 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.040 | 0.036 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".