Latitudinal variation in climate‐associated genes imperils range edge populations
Bibliographic record
Abstract
The ecological impacts of increasing global temperatures are evident in most ecosystems on Earth, but our understanding of how climatic variation influences natural selection and adaptive resilience across latitudes remains largely unknown. Latitudinal gradients allow testing general ecosystem-level theories relevant to climatic adaptation. We assessed differences in adaptive diversity of populations along a latitudinal region spanning highly variable temperate to subtropical climates. We generated and integrated information from environmental mapping, phenotypic variation and genome-wide data from across the geographical range of the rainbowfish Melanotaenia duboulayi, an emerging aquatic system for studies of climate change. We detected, after controlling for spatial population structure, strong interactions between genotypes and environment associated with variation in stream flow and temperature. Some of these hydroclimate-associated genes were found to interact within functional protein networks that contain genes of adaptive significance for projected future climates in rainbowfish. Hydroclimatic selection was also associated with variation in phenotypic traits, including traits known to affect fitness of rainbowfish exposed to different flow environments. Consistent with predictions from the "climatic variability hypothesis," populations exposed to extremes of important environmental variables showed stronger adaptive divergence and less variation in climate-associated genes compared to populations at the centre of the environmental gradient. Our findings suggest that populations that evolved at environmental range margins and at geographical range edges may be more vulnerable to changing climates, a finding with implications for predicting adaptive resilience and managing biodiversity under climate 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".