Assessing global patterns in mammalian carnivore occupancy and richness by integrating local camera trap surveys
Bibliographic record
Abstract
Abstract Aim Biodiversity loss is a major driver of ecosystem change, yet the ecological data required to detect and mitigate losses are often lacking. Recently, camera trap surveys have been suggested as a method for sampling local wildlife communities, because these observations can be collated into a global monitoring network. To demonstrate the potential of camera traps for global monitoring, we assembled data from multiple local camera trap surveys to evaluate the interchange between fine‐ and broad‐scale processes impacting mammalian carnivore communities. Location Argentina, Belize, Botswana, Canada, Indonesia, Iran, Madagascar, Nepal, Norway, Senegal, South Africa, and the U.S.A. Methods We gathered camera trap data, totalling > 100,000 trap nights, from across five continents. To analyse local and species‐specific responses to anthropogenic and environmental variables, we fitted multispecies occurrence models to each study area. To analyse global‐level responses, we then fitted a multispecies, multi‐area occurrence model. Results We recorded 4,805 detections of 96 mammalian carnivore species photographed across 1,714 camera stations located in 12 countries. At the global level, our models revealed that carnivore richness and occupancy within study areas was positively associated with prey availability. Occupancy within study areas also tended to increase with greater protection and greater distances to roads. The strength of these relationships, however, differed among countries. Main conclusions We developed a research framework for leveraging global camera trap data to evaluate patterns of mammalian carnivore occurrence and richness across multiple spatial scales. Our research highlights the importance of intact prey populations and protected areas in conserving carnivore communities. Our research also highlights the potential of camera traps for monitoring wildlife communities and provides a case study for how this can be achieved on a global scale. We encourage greater integration and standardization among camera trap studies worldwide, which would help inform effective conservation planning for wildlife populations both locally and globally.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".