Landscape genetics identifies barriers to Natterjack toad metapopulation dispersal
Bibliographic record
Abstract
Abstract Habitat fragmentation and loss reduce population size and connectivity, which imperils populations. Functional connectivity is key for species persistence in human-modified landscapes. To inform species conservation management, we investigated spatial genetic structure, gene flow and inferred dispersal between twelve breeding sites of the Natterjack toad ( Bufo calamita ); regionally Red-Listed as Endangered in Ireland. Spatial genetic structure was determined using both Bayesian and non-Bayesian clustering analysis of 13 polymorphic microsatellite loci genotyping 247 individuals. We tested the influence of geographic distance, climate, habitat, geographical features, and anthropogenic pressure on pairwise genetic distances between breeding sites using Isolation-by-distance and Isolation-by-resistance based on least-cost path and circuit theory models of functional connectivity. There was clear spatial structuring with genetic distances increasing with geographic distance. Gene flow was best explained by Isolation-by-resistance models with coniferous forestry plantations, bog, marsh, moor and heath, scrub, anthropogenic presence (Human Influence Index) and rivers (riparian density) identified as habitats with high resistance to gene flow while metapopulation connectivity was enhanced by coastal habitats (beaches, sand dunes and salt marshes) and coastal grassland. Despite substantial declines in census numbers over the past 15 years and its regional status as Endangered, the Natterjack toad population in Ireland retains high genetic diversity. If declines continue, maintaining habitat connectivity to prevent genetic erosion by management of coastal grasslands, pond construction and assisted migration through translocation will be increasingly important.
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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 | 0.001 |
| 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) | 0.002 | 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".