Bibliographic record
Abstract
Roads have pervasive impacts on wildlife, including habitat loss and fragmentation, road mortality, habitat pollution and increased human use of habitats surrounding them. However, the effects of roads on interspecific interactions are less understood. Here we provide a synthesis of the existing literature on how species interactions may be disrupted by roads, identify knowledge gaps, and suggest avenues for future research and conservation management. We conducted a systematic search using the Web of Science database for each species interaction (predation, competition, mutualism, parasitism, commensalism and amensalism). These searches yielded 2144 articles, of which 195 were relevant to our topic. Most of these studies focused on predation (50%) or competition (24%), and less frequently on mutualism (17%) or, parasitism (9%). We found no studies on commensalism or amensalism. Studies were biased towards mammals from high-income countries, with most conducted in the USA (34%) or Canada (18%). Our literature review identified several patterns. First, roads disrupt predator-prey relationships, usually with negative impacts on prey populations. Second, new disturbed habitats created in road corridors often benefit more competitive species, such as invasive species, although some native or endangered species can also thrive there. Third, roads degrade mutualistic interactions like seed dispersal and pollination. Fourth, roads can increase parasitism rates, although the intensity of the alteration is species specific. To reduce the negative impacts of roads on interspecific interactions, we suggest the following management actions: (i) verges should be as wide and heterogenous as possible, as this increases microhabitat diversity, thus enhancing ecosystem services like pollination and seed dispersal; (ii) combining different mowing regimes can increase the complexity of the habitat corridor, enabling it to act as a habitat for more species; (iii) the use of de-icing salts should be gradually reduced and replaced with less harmful products or maintenance practices; (iv) wildlife passes should be implemented in groups to reduce animal concentrations inside them; (v) periodic removal of carcasses from the road to reduce the use of this resource by wildlife; and (vi) implementation of traffic-calming schemes could enhance interspecific interactions like pollination and avoid disruption of predator-prey relationships.
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.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; both teacher heads agree on what is shown here.
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".