Stable connections in the deep sea: Temporally consistent larval pathways for the deep-sea coral, Lophelia pertusa (=Desmophyllum pertusum) in the Northwest Atlantic Ocean
Bibliographic record
Abstract
Population connectivity facilitates genetic exchange and increases resilience of populations promoting long-term stability. For sessile benthic invertebrates, larval dispersal provides the main mechanism to achieving population connectivity. Biophysical modelling of larval dispersal can quantify dispersal pathways, and when combined with graph theory analysis can elucidate potential connections between populations and evaluate their potential importance to the stability of the entire network. We use these tools to examine the dispersal pathways of the deep-water coral Lophelia pertusa (=Desmophyllum pertusum) over its North American range in the Northwest Atlantic Ocean. We used a high-resolution ocean circulation model in combination with larval parameters for L. pertusa, to project larval dispersal for each season over 14 years from 2005-2018. We then used the strength and number of connections between populations to quantify the importance of each population to the overall connectivity within the network. Larval retention was strongest in the north of the domain and within the Gulf of Mexico. The two dominant transport pathways occurred following the Gulf Stream from Florida north towards the eastern United States and following the Labrador Current travelling southwestwards from the Canadian EEZ with little exchange between northern and southern domains. Our graph theoretic analysis suggested that the populations of L. pertusa in Norfolk Canyon are primarily responsible for exchange between northern and southern populations; however, no northward connections were projected. The results of a cluster analysis based on population connectivity agree well with previous patterns based on estimates of genetic connectivity in the area. The observed dominant connection pathways remained consistent throughout spawning seasons, years, and for biological parameters, emphasizing the utility of connectivity analyses for deep-water species with unresolved life-history traits.
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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 | 0.001 |
| 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".