Body mass mediates spatio-temporal responses of mammals to human frequentation across Italian protected areas
Bibliographic record
Abstract
Protected area (PA) networks are a pivotal tool to fight biodiversity loss, yet they often need to balance the mission of nature conservation with the socio-economic need of giving opportunity for outdoor recreation. Recreation in natural areas is important for human health in an urbanized society, but can prompt behavioural modifications in wild animals. Rarely, however, have these responses being studied across multiple PAs and using standardized methods. We deployed a systematic camera trapping protocol at over 200 sites to sample medium and large mammals in four PAs within the European Natura 2000 network to assess their spatio-temporal responses to human frequentation, proximity to towns, amount of open habitat and topographical variables. By applying multi-species and single-species models for the number of diurnal, crepuscular and nocturnal detections and a multi-species model for nocturnality index, we estimated both species-specific- and meta-community-level effects, finding that increased nocturnality appeared the main strategy that the mammal meta-community used to cope with human disturbance. However, responses in the diurnal, crepuscular and nocturnal site use were mediated by species' body mass, with larger species exhibiting avoidance of humans and smaller species more opportunistic behaviours. Our results show the effectiveness of standardized sampling and provide insights for planning the expansion of PA networks as foreseen by the Kunming-Montreal biodiversity agreement.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".