Habitat distribution influences dispersal and fine‐scale genetic population structure of eastern foxsnakes (<i>Mintonius gloydi</i>) across a fragmented landscape
Bibliographic record
Abstract
Dispersal is a fundamental attribute of species in nature and shapes population dynamics, evolutionary trajectories and genetic variation across spatial and temporal scales. It is increasingly clear that landscape features have large impacts on dispersal patterns. Thus, understanding how individuals and species move through landscapes is essential for predicting impacts of landscape alterations. Information on dispersal patterns, however, is lacking for many taxa, particularly reptiles. Eastern foxsnakes (Mintoinus gloydi) are marsh and prairie specialists that avoid agricultural fields, but they have persisted across a fragmented region in southwestern Ontario and northern Ohio. Here, we combined habitat suitability modelling with population genetic analyses to infer how foxsnakes disperse through a habitat mosaic of natural and altered landscape features. Boundary regions between the eight genetic clusters, identified through assignment tests, were comprised of low suitability habitat (e.g. agricultural fields). Island populations were grouped into a single genetic cluster, and comparatively low F(ST) values between island and mainland populations suggest open water presents less of a barrier than nonsuitable terrestrial habitat. Isolation by resistance and least-cost path analysis produced similar results with matrices of pairwise individual genetic distance significantly more correlated to matrices of resistance values derived from habitat suitability than models with an undifferentiated landscape. Spatial autocorrelation results matched better with assignment results when incorporating resistance values rather than straight-line distances. All analyses used in our study produced similar results suggesting that habitat degradation limits dispersal for foxsnakes, which has had a strong effect on the genetic population structure across this region.
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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) | 0.000 | 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".