Persistence of historical population structure in an endangered species despite near‐complete biome conversion in California's San Joaquin Desert
Bibliographic record
Abstract
Genomic responses to habitat conversion can be rapid, providing wildlife managers with time-limited opportunities to enact recovery efforts that use population connectivity information that reflects predisturbance landscapes. Despite near-complete biome conversion, such opportunities may still exist for the endemic fauna and flora of California's San Joaquin Desert, but comprehensive genetic data sets are lacking for nearly all species in the region. To fill this knowledge gap, we studied the rangewide population structure of the endangered blunt-nosed leopard lizard Gambelia sila, a San Joaquin Desert endemic, using restriction site-associated DNA (RAD), microsatellite and mtDNA data to test whether admixture patterns and estimates of effective migration surfaces (EEMS) can identify land areas with high population connectivity prior to the conversion of native xeric habitats. Clustering and phylogenetic analyses indicate a recent shared history between numerous isolated populations and EEMS reveals latent signals of corridors and barriers to gene flow over areas now replaced by agriculture and urbanization. Conflicting histories between the mtDNA and nuclear genomes are consistent with hybridization with the sister species G. wislizenii, raising important questions about where legal protection should end at the southern range limit of G. sila. Comparative analysis of different data sets also adds to a growing list of advantages in using RAD loci for genetic studies of rare species. We demonstrate how the results of this work can serve as an evolutionary guidance tool for managing endemic, arid-adapted taxa in one of the world's most compromised landscapes.
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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 | 0.001 | 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".