Application of Genomic Offsets to Inform Freshwater Fisheries Management Under Climate Change
Bibliographic record
Abstract
), which has already been negatively impacted by increases in extreme temperature throughout its distribution. A previous study using whole genome resequencing of wild kokanee stocks identified 1412 environmentally associated SNPs and demonstrated genomic offset, a measure of climate vulnerability, to be significantly correlated with higher increases in extreme warm temperatures across much of the species' range in western Canada. Here, we aimed to operationalize this information for fisheries management by first developing a Genotyping-in-Thousands by sequencing (GT-seq) panel populated exclusively with environment associated SNPs. We then evaluated the robustness of the GT-seq panel relative to the signal in the whole genome resequencing baseline and demonstrated a novel application of donor and recipient importance (DI/RI) analysis to inform recreational fisheries stocking decisions. We found that a reduced GT-seq panel of 616 SNPs exhibited a significant positive correlation with those calculated from the full set of 1412 SNPs across the climate change scenarios tested; similar results were obtained when adding new reference populations not included in the original whole genome resequencing baseline. The DI/RI analysis revealed clear spatial trends, with populations situated in the warmest regions of southern interior British Columbia (Canada) having the highest probability for successful translocations to different recipient locations to the north. Similarly, candidate recipient lakes for stocking at the center of the distribution had higher recipient importance values than those located towards the eastern and western range peripheries. Although further refinement is required, pairing targeted genotyping with genomic offset and DI/RI predictions holds great promise for informing freshwater fisheries management moving forward.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".