Synthesis of academic and traditional ecological knowledge identifies ancient and ongoing hybridization of whitefish species in Beringia
Bibliographic record
Abstract
Abstract Hybridization, the production of offspring mixing genetically distinct ancestries, occurs in nature under diverse circumstances. The prevalence, timing and spatial distribution of hybridization may vary along with other attributes of the hybridizing stocks. Consequently, the effects of hybridization on natural populations are varied; however, they may be classified into broad categories based on their impacts on biodiversity. In this context, hybridization may preserve, reduce or increase biodiversity from population to species levels and from local to global scales. All of these outcomes are of conservation interest and have likely played important historical roles in the evolution and diversification of life. We synthesize knowledge of hybridization in diadromous whitefishes (Salmonidae: Coregoninae) in Beringia, focusing on species near coastal communities of the Chukchi Sea where coregonines are vital wild food resources for rural and Indigenous residents. We draw information from scientific literature, technical reports and interviews conducted with northern Alaska subsistence fishers and fisheries biologists to catalogue the extent of hybridization of whitefishes in Beringia. We find hybridization in Beringian whitefishes has contributed to species diversity in the past and occurs at low prevalence among several lineages, particularly in watersheds where spawning timing and location of multiple species overlaps (e.g. Yukon River Flats). Interestingly and consistent with failed attempts to artificially crossbreed and propagate hybrid whitefishes in the wild, hybrid populations do not appear to be increasing in prevalence or abundance. We propose several mechanisms why hybridization is or is not observed between whitefish species, why ongoing hybridization does not appear to lead to loss of species diversity, and describe methods of identifying hybrid whitefish relying on anatomical traits. Practical implication . Genomic analyses to determine the specific contributions from different lineages to hybrid and unmixed gene pools may serve as a critical baseline and as an ecological monitoring tool given that hybridization is predicted to become more prevalent with habitat disruption and demographic stress under changing environmental conditions. Changes in coregonine abundance, availability and behaviours due to hybridization could have impacts on Indigenous and rural resident harvest opportunities for these fishes, which are vital for sustenance.
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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 | 0.001 |
| 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 | 0.001 |
| 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".