Seascape drivers of <i><scp>M</scp>acrocystis pyrifera</i> population genetic structure in the northeast <scp>P</scp>acific
Bibliographic record
Abstract
At small spatial and temporal scales, genetic differentiation is largely controlled by constraints on gene flow, while genetic diversity across a species' distribution is shaped on longer temporal and spatial scales. We assess the hypothesis that oceanographic transport and other seascape features explain different scales of genetic structure of giant kelp, Macrocystis pyrifera. We followed a hierarchical approach to perform a microsatellite-based analysis of genetic differentiation in Macrocystis across its distribution in the northeast Pacific. We used seascape genetic approaches to identify large-scale biogeographic population clusters and investigate whether they could be explained by oceanographic transport and other environmental drivers. We then modelled population genetic differentiation within clusters as a function of oceanographic transport and other environmental factors. Five geographic clusters were identified: Alaska/Canada, central California, continental Santa Barbara, California Channel Islands and mainland southern California/Baja California peninsula. The strongest break occurred between central and southern California, with mainland Santa Barbara sites forming a transition zone between the two. Breaks between clusters corresponded approximately to previously identified biogeographic breaks, but were not solely explained by oceanographic transport. An isolation-by-environment (IBE) pattern was observed where the northern and southern Channel Islands clustered together, but not with closer mainland sites, despite the greater distance between them. The strongest environmental association with this IBE pattern was observed with light extinction coefficient, which extends suitable habitat to deeper areas. Within clusters, we found support for previous results showing that oceanographic connectivity plays an important role in the population genetic structure of Macrocystis in the Northern hemisphere.
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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 | 0.000 |
| 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".