Spatial and temporal variations in interspecific interaction: impact of a recreational landscape
Bibliographic record
Abstract
Abstract Anthropogenic activities, such as outdoor recreation, have the potential to change complex interactions between wildlife and livestock, with further consequences for the management of both animals, the environment, and disease transmission. We present the interaction amongst wildlife, livestock, and outdoor recreationists as a three-way interaction. Little is known about how recreational activities alter the interaction between herbivores in areas extensively used for recreational purposes. We investigate how hiking activity affects spatio-temporal co-occurrence between domestic sheep (Ovis aries) and red deer (Cervus elaphus). We used camera traps to capture the spatio-temporal distribution of red deer and sheep and used the distance from the hiking path as a proxy of hiking activity. We used generalized linear models to investigate the spatial distribution of sheep and deer. We analysed the activity patterns of sheep and deer and then calculated their coefficients of temporal overlap for each camera trap location. We compared these coefficients in relation to the distance from the hiking path. Finally, we used a generalized linear mixed-model to investigate which factors influence the spatio-temporal succession between deer and sheep. We do not find that sheep and red deer spatially avoid each other. The coefficient of temporal overlap varied with distance from the hiking trail, with stronger temporal co-occurrence at greater distances from the hiking trail. Red deer were more likely to be detected further from the path during the day, which increased the temporal overlap with sheep in these areas. This suggests that hiking pressure influences spatio-temporal interactions between sheep and deer, leading to greater temporal overlap in areas further from the hiking path due to red deer spatial avoidance of hikers. This impact of recreationists on the wildlife and livestock interaction can have consequences for the animals’ welfare, the vegetation they graze, their management, and disease transmission.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".