Mammal species occupancy in a Honduran cloud forest: A pre- and post-COVID-19 comparison
Bibliographic record
Abstract
Defaunation of medium- and large-bodied mammal species through overharvesting drives local extinctions and impacts key ecosystem services. However, the mechanisms and factors which can drive defaunation rates are incompletely understood. Here, we aimed to assess the impacts of the global COVID-19 pandemic on mammal species probability of use (defined as the probability that a site was occupied by mammal species during our study period) in Cusuco National Park (CNP), a Neotropical cloud forest in north-western Honduras which has been historically impacted by hunting pressures. We also assessed the effects of other covariates on mammal use probability in CNP (namely, distance to roads and elevation). We collected three categories of occupancy data – humans, hunted species, and unhunted species – at the same sites in 2018 and 2019 (pre-COVID period) and 2022 (post-COVID period), and ran multi-season occupancy analyses for each group. We found no association between human probability of use and years. Hunted species probability of use increased between years and with increasing distance to roads. Unhunted species probability of use did not change significantly between years but increased slightly with higher elevations. The significant increase in hunted species use, despite relatively constant levels of human use, suggests that hunting decreased over the COVID-19 pandemic. This may be a result of the largely recreational nature of hunting in CNP, as well as an increased park patrol presence between periods. Our results suggest the COVID-19 pandemic may have had beneficial impacts for hunted species in CNP, and that increasing park patrols during times of decreased hunting may allow hunted species to recover over short time periods.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".