Genetic–environment associations explain genetic differentiation and variation between western and eastern North Pacific rhinoceros auklet (<i>Cerorhinca monocerata</i>) breeding colonies
Bibliographic record
Abstract
Abstract Animals are strongly connected to the environments they live in and may become adapted to local environments. Examining genetic–environment associations of key indicator species, like seabirds, provides greater insights into the forces that drive evolution in marine systems. Here we examined a RADseq dataset of 19,213 SNPs for 99 rhinoceros auklets (Cerorhinca monocerata) from five western Pacific and 10 eastern Pacific breeding colonies. We used partial redundancy analyses to identify candidate adaptive loci and to quantify the effects of environmental variation on population genetic structure. We identified 262 candidate adaptive loci, which accounted for 3.0% of the observed genetic variation among western Pacific and eastern Pacific breeding colonies. Genetic variation was more strongly associated with pH and maximum current velocity, than maximum sea surface temperature. Genetic–environment associations explain genetic differences between western and eastern Pacific populations; however, genetic variation within the western and eastern Pacific Ocean populations appears to follow a pattern of isolation‐by‐distance. This study represents a first to quantify the relationship between environmental and genetic variation for this widely distributed marine species and provides greater insights into the evolutionary forces that act on marine species.
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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 | 0.000 |
| 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) | 0.001 | 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".