Find New RoadsTM? A Systematic Review on the Impacts of Off-Road Vehicle Activity on Soil, Vegetation, and Wildlife
Bibliographic record
Abstract
The global use of off-road vehicles (ORVs) in natural environments has accelerated rapidly over the last few decades, resulting in significant social and environmental consequences. As the demand, use, and promotion of light-duty ORVs like all-terrain vehicles (ATVs), motorcycles, four-wheel drive trucks and sport utility vehicles (SUVs) increases in remote wilderness, the landscape is becoming fragmented into disorganized and destructive networks of trails and roads. Substantial ecological impacts to a wide range of ecosystem structures and functions will likely result from ORV activity. Applying a global systematic review, we examine 105 publications about plant, soil, and wildlife responses to ORV traffic in different habitats to help guide the direction of future research, monitoring programs, and mitigation efforts. Most studies investigated impacts to animals, followed by soils, then vegetative responses. Soil studies primarily focused on physical impacts to the soil (i.e., compaction, erosion, rut depth), but some studies suggest that soil chemical and biological properties may also be impacted by ORV traffic. The literature on plant responses to ORV activities primarily explored vegetation loss, although impacts on the plant community were also investigated. Animal studies investigated impacts of ORV use on invertebrates, mammals, birds, and to a lesser extent reptiles/amphibians, including population-level, community-level, and behavioral responses. Overall, research on environmental impacts of ORV traffic is biased to coastal and desert ecosystems in the northern hemisphere (primarily in the US), often does not address mechanisms that may produce ecological impacts (e.g., intensity of vehicular disturbance and ecosystem- or species-specific sensitivity to ORV activities), and frequently focused on short-term responses. More research is needed to understand the mechanisms that cause the different responses of soil, plant, and animals to ORVs over the long-term in a broad range of ecosystems to support real-time management and conservation efforts.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".