Natural hybridization reduces vulnerability to climate change
Bibliographic record
Abstract
Abstract Under climate change, species unable to track their niche via range shifts are largely reliant on genetic variation to adapt and persist. Genomic vulnerability predictions are used to identify populations that lack the necessary variation, particularly at climate-relevant genes. However, hybridization as a source of novel adaptive variation is typically ignored in genomic vulnerability studies. We estimated environmental niche models and genomic vulnerability for closely related species of rainbowfish ( Melanotaenia spp.) across an elevational gradient in the Australian Wet Tropics. Hybrid populations between a widespread generalist and several narrow range endemic species exhibited reduced vulnerability to projected climates compared to pure narrow endemics. Overlaps between introgressed and adaptive genomic regions were consistent with a signal of adaptive introgression. Our findings highlight the often-underappreciated conservation value of hybrid populations and indicate that adaptive introgression may contribute to evolutionary rescue of species with narrow environmental ranges.
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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".