Combining population genomics with demographic analyses highlights habitat patchiness and larval dispersal as determinants of connectivity in coastal fish species
Bibliographic record
Abstract
Gene flow shapes spatial genetic structure and the potential for local adaptation. Among marine animals with nonmigratory adults, the presence or absence of a pelagic larval stage is thought to be a key determinant in shaping gene flow and the genetic structure of populations. In addition, the spatial distribution of suitable habitats is expected to influence the distribution of biological populations and their connectivity patterns. We used whole genome sequencing to study demographic history and reduced representation (double-digest restriction associated DNA) sequencing data to analyse spatial genetic structure in broadnosed pipefish (Syngnathus typhle). Its main habitat is eelgrass beds, which are patchily distributed along the study area in southern Norway. Demographic connectivity among populations was inferred from long-term (~30-year) population counts that uncovered a rapid decline in spatial correlations in abundance with distance as short as ~2 km. These findings were contrasted with data for two other fish species that have a pelagic larval stage (corkwing wrasse, Symphodus melops; black goby, Gobius niger). For these latter species, we found wider spatial scales of connectivity and weaker genetic isolation-by-distance patterns, except where both species experienced a strong barrier to gene flow, seemingly due to lack of suitable habitat. Our findings verify expectations that a fragmented habitat and absence of a pelagic larval stage promote genetic structure, while presence of a pelagic larvae stage increases demographic connectivity and gene flow, except perhaps over extensive habitat gaps.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".