A landscape genetic analysis of swamp rabbits (<i>Sylvilagus aquaticus</i>) suggests forest canopy cover enhances gene flow in an agricultural matrix
Bibliographic record
Abstract
Habitat loss and fragmentation pose a continued and immediate threat to wildlife and create a persistent need for ecological information at the landscape scale to guide conservation efforts. Landscape features influence population connectivity for many species and genetic analyses can be employed to determine which of these features are most important. Because population connectivity through dispersal is important to the persistence of swamp rabbits (Sylvilagus aquaticus (Bachman, 1837)) at the northern edge of their range, we used a landscape genetic approach to relate gene flow to landscape features that may impact dispersal success. We tested resistance values for attributes of land cover, watercourse corridors, canopy cover, and roads and used causal modeling and redundancy analysis to relate these representations of landscapes to genetic distance for swamp rabbits in southern Illinois, USA. Models that included canopy cover had the strongest correlations with genetic distance and were supported by our methods whereas other models were not. We concluded that high tree canopy cover enhances gene flow and landscape connectivity for swamp rabbits in southern Illinois. Our study provides important empirical evidence that landscape variables may impact the habitat connectivity of swamp rabbits. Preserving dispersal routes for swamp rabbits should focus on improving canopy cover, in both bottomland and upland, to connect suitable habitat.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".