Landscape limits gene flow and drives population structure in Agassiz’s desert tortoise (Gopherus agassizii)
Bibliographic record
Abstract
Distance, environmental heterogeneity and local adaptation can strongly influence population structure and connectivity. Understanding how these factors shape the genomic landscape of threatened species is a major goal in conservation genomics and wildlife management. Herein, we use thousands (6,859) of single nucleotide polymorphism markers and spatial data from hundreds of individuals (n = 646) to re-evaluate the population structure of Agassiz's desert tortoise (Gopherus agassizii). Analyses resolve from 4 to 8 spatially well-defined clusters across the range. Western, central, and southern populations within the Western Mojave recovery unit are consistent throughout, while analyses sometimes merge other recovery units depending on the level of clustering. Causal modeling consistently associates genetic connectivity with least-cost distance, based on multiple landscape features associated with tortoise habitat, better than geographic distance. Some features include elevation, soil depth, rock volume, precipitation, and vegetation coverage, suggesting that physical, climatic, and biotic landscape features have played a strong evolutionary role restricting gene flow between populations. Further, 12 highly differentiated outlier loci have associated functions that may be involved with neurogenesis, wound healing, lipid metabolism, and possibly vitellogenesis. Together, these findings have important implications for recovery programs, such as translocations, population augmentation, reproduction in captivity and the identification of ecologically important genes, opening new venues for conservation genomics in desert tortoises.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".