Mammal species occupancy in a Honduran cloud forest: A pre- and post-COVID-19 comparison
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| 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 it