Rapid introgression of non‐native alleles following hybridization between a native <i>Anolis</i> lizard species and a cryptic invader across an urban landscape
Bibliographic record
Abstract
Invasive species can impact native populations through competition, predation, habitat alteration, and disease transmission, but also genetically through hybridization. Potential outcomes of hybridization span the continuum from extinction to hybrid speciation and can be further complicated by anthropogenic habitat disturbance. Hybridization between the native green anole lizard (Anolis carolinensis) and a morphologically similar invader (A. porcatus) in south Florida provides an ideal opportunity to study interspecific admixture across a heterogeneous landscape. We used reduced-representation sequencing to describe introgression in this hybrid system and to test for a relationship between urbanization and non-native ancestry. Our findings indicate that hybridization between green anole lineages was probably a limited, historic event, producing a hybrid population characterized by a diverse continuum of ancestry proportions. Genomic cline analyses revealed rapid introgression and disproportionate representation of non-native alleles at many loci and no evidence for reproductive isolation between parental species. Three loci were associated with urban habitat characteristics; urbanization and non-native ancestry were positively correlated, although this relationship did not remain significant when accounting for spatial nonindependence. Ultimately, our study demonstrates the persistence of non-native genetic material even in the absence of ongoing immigration, indicating that selection favouring non-native alleles can override the demographic limitation of low propagule pressure. We also note that not all outcomes of admixture between native and non-native species should be considered intrinsically negative. Hybridization with ecologically robust invaders can lead to adaptive introgression, which may facilitate the long-term survival of native populations otherwise unable to adapt to anthropogenically mediated global change.
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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) | 0.001 | 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".