Population Genomics Reveals Local Adaptation Related to Temperature Variation in Two Stream Frog Species: Implications for Vulnerability to Climate Warming
Bibliographic record
Abstract
Identifying populations at highest risk from climate change is a critical component of conservation efforts. However, vulnerability assessments are usually applied at the species level, even though intraspecific variation in exposure, sensitivity and adaptive capacity play a crucial role in determining vulnerability. Genomic data can inform intraspecific vulnerability by identifying signatures of local adaptation that reflect population-level variation in sensitivity and adaptive capacity. Here, we address the question of local adaptation to temperature and the genetic basis of thermal tolerance in two stream frogs (Ascaphus truei and A. montanus). Building on previous physiological and temperature data, we used whole-genome resequencing of tadpoles from four sites spanning temperature gradients in each species to test for signatures of local adaptation. To support these analyses, we developed the first annotated reference genome for A. truei. We then expanded the geographic scope of our analysis using targeted capture at an additional 11 sites per species. We found evidence of local adaptation to temperature based on physiological and genomic data in A. montanus and genomic data in A. truei, suggesting similar levels of sensitivity (i.e., susceptibility) among populations regardless of stream temperature. However, invariant thermal tolerances across temperatures in A. truei suggest that populations occupying warmer streams may be most sensitive. We identified high levels of evolutionary potential in both species based on genomic and physiological data. While further integration of these data is needed to comprehensively evaluate spatial variation in vulnerability, this work illustrates the value of genomics in identifying spatial patterns of climate change vulnerability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".