Can linear transportation infrastructure verges constitute a habitat and/or a corridor for insects in temperate landscapes? A systematic review
Bibliographic record
Abstract
Abstract Background The role of linear transportation infrastructures (roads, railways, oil and gas pipelines, power lines, rivers and canals) in fragmenting natural habitats is fully acknowledged. Up to now, the potential of linear transportation infrastructures verges (road and railway embankments, strips of grass under power lines or above buried pipelines, or waterway banks) as habitat or corridor for biodiversity, remains controversial. In a context of decreasing natural habitats, the opportunities of anthropogenic areas for contributing to wildlife conservation have to be considered. The present paper is the first synthesis of evidence about the potential of linear transportation infrastructure verges as corridor and/or habitat for insects in temperate landscapes. Methods A systematic literature survey was made using two online publication databases, a search engine and by sending a call for literature to subject experts. Identified articles were successively screened for relevance on titles, abstracts and full texts using criteria detailed in an a priori protocol. We then used six specific questions to categorize and to critically appraise the retained studies. These questions encompassed the potential of verges as habitats and corridors for insects, and the effects of management and landscape context on these potentialities. A user-friendly database was created to sort the studies with low and medium susceptibility to bias. We used these studies to synthesize results of each specific question in a narrative synthesis. Finally, studies that met the meta-analysis requirements were used for a quantitative synthesis. Results Our searches identified 64,206 articles. After critical appraisal, 91 articles that reported 104 studies were included in our review. Almost all of them had “control-impact” design, only two studies used “before-after-control-impact” design, and one study used “before-after” design. In some cases, artificialization of transportation infrastructures lowered insect biodiversity while vegetation restoration had a moderate positive effect; the trend remained unclear for mowing/grazing practices. Urbanization and agriculture in the surroundings tended to lower the biodiversity hosted by verges, while natural and forested areas tended to promote it. No study dealt with the influence of management or surrounding landscape on insect dispersal along the verge. The small number of studies that compared the dispersal along verges and in habitats away from transportation infrastructures, together with the inconsistencies of their results, prevented us from drawing conclusions. Meta-analyses were performed on 709 cases from 34 primary studies that compared biodiversity in verges vs. other habitats. Overall insect species richness did not differ between LTI verges and compared habitats. Globally, insect abundance seemed higher on LTI verges than in compared habitats, a result driven by the higher abundance of pollinators and primary consumers on non-highway road verges than in habitats away from roads. Conclusions A major knowledge gap regarding the potential of linear transportation infrastructure verges as corridors for insects has been identified. Thus, we encourage more research on this topic. Infrastructure practitioners could benefit from our results about linear transportation infrastructure verges as habitat for certain taxa and about the impact of their management practices on insect abundance and species richness.
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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 | 0.015 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".