Integrated species distribution models predict motorized and non-motorized outdoor recreation across seasons
Bibliographic record
Abstract
Outdoor recreation is occurring at unprecedented levels, yet our knowledge of how to best monitor and analyse outdoor recreation is outdated. Inaccurate and unreliable recreation information affects everything from designing sustainable recreation plans to developing conservation strategies for sensitive or migratory species. We asked: (1) what are patterns of recreation intensity and how do they differ by activity type (motorized, non-motorized) and season; and (2) which variables best explain recreation intensity across space and time and how do relationships with variables differ by activity type and season. In western Canada, a hub for outdoor recreation in all seasons, we collected five unique datasets on recreation use and occurrence (Strava, systematic and incidental aerial surveys, trail counters, cameras) to predict motorized and non-motorized recreation intensity across six seasons. We fit integrated species distribution models (iSDM) to multiple data types including count, presence/absence, and presence-only data to predict the distribution of recreation. We assessed the effects of terrain, vegetation, accessibility, infrastructure, and snow and climate to understand patterns and predict recreation intensity across six seasons: early winter (Nov–Dec), mid-winter (Jan–Feb), late winter (Mar–Apr), spring (May–Jun), summer (Jul–Aug), and fall (Sep–Oct). We found substantial variation of recreation intensity across the study area and seasons, for both activity types. We also found human access (e.g., trails) explained patterns in recreation activities across seasons. Winter recreation occurred at higher elevation relative to non-snow seasons, whereas summer recreation occurred in more steep terrain. By using iSDMs we were able to leverage multiple datasets with disparate spatial or temporal coverages. This work fills important research and knowledge gaps for people managing land-use, conservation and recreation, specifically those working beyond protected and conserved areas where few open data sources exist. Our research suggests recreation intensity is dynamic and static proxies for recreation commonly used in research and monitoring may be insufficient.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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 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".