Population structure in a continuously distributed coastal marine species, the harbor porpoise, based on microhaplotypes derived from poor‐quality samples
Bibliographic record
Abstract
Abstract Harbor porpoise in the North Pacific are found in coastal waters from southern California to Japan, but population structure is poorly known outside of a few local areas. We used multiplexed amplicon sequencing of 292 loci and genotyped clusters of single nucleotide polymoirphisms as microhaplotypes (N = 271 samples) in addition to mitochondrial (mtDNA) sequence data (N = 413 samples) to examine the genetic structure from samples collected along the Pacific coast and inland waterways from California to southern British Columbia. We confirmed an overall pattern of strong isolation‐by‐distance, suggesting that individual dispersal is restricted. We also found evidence of regions where genetic differences are larger than expected based on geographical distance alone, implying current or historical barriers to gene flow. In particular, the southernmost population in California is genetically distinct (FST = 0.02 [microhaplotypes]; 0.31 [mtDNA]), with both reduced genetic variability and high frequency of an otherwise rare mtDNA haplotype. At the northern end of our study range, we found significant genetic differentiation of samples from the Strait of Georgia, previously identified as a potential biogeographical boundary or secondary contact zone between harbor porpoise populations. Association of microhaplotypes with remotely sensed environmental variables indicated potential local adaptation, especially at the southern end of the species’ range. These results inform conservation and management for this nearshore species, illustrate the value of genomic methods for detecting patterns of genetic structure within a continuously distributed marine species, and highlight the power of microhaplotype genotyping for detecting genetic structure in harbor porpoises despite reliance on poor‐quality samples.
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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 | 0.000 |
| 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".