Can linear transportation infrastructure verges constitute a habitat and/or a corridor for vertebrates in temperate ecosystems? A systematic review
Bibliographic record
Abstract
Abstract Background Linear transportation infrastructures (roads, railways, oil and gas pipelines, powerlines and waterways) generate well documented fragmenting effects on species habitats. However, the potential of verges of linear transportation infrastructures (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 constant loss of natural habitats, the opportunities of anthropogenic areas for compensating the loss of biodiversity they generated have to be considered. This paper is the first synthesis of evidence addressing this topic for vertebrates (mammals, birds, amphibians and reptiles) in temperate ecosystems. Methods We conducted a systematic literature survey using two online publication databases, three search engines, specialist websites, and by sending a call for literature to subject experts. We successively screened the articles for relevance on titles, abstracts and full texts using criteria detailed in an a priori protocol. We then used six specific questions to categorize the retained studies and to critically appraise them. These questions encompassed the potential of verges as habitats and corridors for vertebrates, and the effects of landscape and management on these potentialities. We critically appraised all studies to assess their risk of bias and created a database of the studies with low and medium risk of bias. We synthesized results for each specific question in narrative syntheses. Finally, studies that met meta-analysis requirements were used for quantitative syntheses. Results Our initial searches identified 83,565 documents. After critical appraisal, we retained 119 documents that reported 128 studies. Most studies were conducted in Europe (49%) and in the United States of America (22%), and were about mammals (61%) and birds (20%). Results from the narrative synthesis and meta-analyses converged and revealed that the potential of linear transportation infrastructures verges to constitute a habitat for vertebrate species varies according to the infrastructure and the biological group considered. Especially, highway verges may be a refuge for small mammals but seems detrimental to birds. The potential also varied depending on the landscape considered, with urbanisation being related to lower biodiversity hosted by verges. We found a wide variety of verge management practices with few studies on each practice, which prevented us from drawing general conclusions. Likewise, we found too few studies assessing the corridor potential of verges to be able to fully conclude although this potential seems to exist. We did not find any study assessing the effect of landscape context or management on the role of corridor of verges. Conclusions We identified a major knowledge gap regarding the potential of linear transportation infrastructure verges as corridors for vertebrates, and when they exist studies rarely directly measured movements on verges. We thus encourage more research on this topic and the development of protocols that enable direct measures of vertebrate movements. The effect of management practices on the role of habitat of verges also deserves further investigations, and research efforts should be coordinated to focus on one specific practice (e.g. vegetation management).
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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 | 0.017 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".