Variability in the impact of linear transportation infrastructures on gene flow in French wild ungulate populations
Bibliographic record
Abstract
Abstract Context Linear Transportation Infrastructures (LTIs) are among the largest factors responsible for landscape fragmentation, in turn increasing population isolation. In this context, studies have mainly focused on a single species’ response to barrier elements and mitigation thereof. Yet, the implementation of conservation strategies to restore landscape connectivity may be challenging when multi-specific responses are not measured and fully considered. Objectives We aimed to assess the effect of two different types of LTIs, a fenced highway and a navigation canal on gene flow in three ungulate species in Northeastern France. Methods We genotyped 98 red deer ( C. elaphus ), 120 wild boars ( S. scrofa ) and 140 roe deer ( C. capreolus ) with species-specific microsatellite markers from 3 sampling sites located on either side of both LTIs considered in the study area. We assessed the continuity of gene flow using Bayesian clustering methods and a mapping approach to determine inter-individual genetic dissimilarity in relation to landscape characteristics. Results Our study showed different impacts of LTIs on the gene flow of species belonging a priori to the same functional group. Genetic differentiation among red deer and wild boar sampling units was observed on either side of the highway, but no such differentiation was identified for roe deer. However, no genetic structuring was associated with the presence of the canal in any species. Conclusions The impact of LTIs on gene flow in large species results from the structural characteristics of the infrastructure, and our study shows that mitigation measures should consider species-specific behaviors to facilitate the use of crossing structures and thus ensure gene flow across ILTs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".