Importance of connectivity for carnivore richness and occupancy in fragmented biodiversity hotspots
Bibliographic record
Abstract
Structural connectivity affects wildlife movement between habitat patches, contributing to the persistence of wildlife populations and their resilience to human-induced and environmental changes. However, its importance to wildlife population persistence remains unclear, particularly in fragmented landscapes, where there are additional co-occurring threats and varying protected area coverage (PAC). Using South American carnivore assemblages and fragmented tropical forests as a case study, we assessed the relative effect of structural connectivity on carnivore persistence in fragmented landscapes after accounting for PAC, and the efficacy of single-species connectivity approaches for protecting the habitat of multiple species. We applied a multiscale Bayesian modeling framework to camera-trapping data from 567 cameras in 23 landscapes in the Tumbesian region of Ecuador and Peru. We tested the landscape-scale effects of habitat amount, connectivity, human density, and protected area status on carnivore richness and mean occupancy and the fine-scale effects of forest cover, distance to roads, and hunting on carnivore site occupancy. In 41,861 camera days of sampling, we obtained 5267 independent detections of 12 carnivores across all landscapes. Connectivity, habitat amount, and PAC had a positive effect on carnivore richness, emphasizing that large and well-connected landscapes of natural habitat with greater PAC sustain more species-rich carnivore communities. Mean site occupancy across the carnivore community was positively associated with forest cover at the fine scale and connectivity at the landscape scale. This last relationship varied by species, with occupancy of forest-dependent mesocarnivores being most positively associated with higher connectivity. Our results highlight that increasing connectivity can improve the persistence of vulnerable carnivore populations, even in landscapes with varied amount of PAC. Furthermore, conservation planning to increase connectivity should take a multispecies approach because single-species approaches are unlikely to meet the needs of diverse communities.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| 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".