Long distance dispersal and oceanographic fronts shape the connectivity of the keystone sponge Phakellia ventilabrum in the deep northeast Atlantic
Bibliographic record
Abstract
Little is known about dispersal in deep-sea ecosystems, especially for sponges, which are abundant ecosystem engineers. Understanding patterns of gene flow in deep-sea sponges is essential, especially in areas where rising pressure from anthropogenic activities makes difficult to combine management and conservation. Here, we combined population genomics and oceanographic modelling to understand how Northeast Atlantic populations (Cantabrian Sea to Norway) of the deep-sea sponge Phakellia ventilabrum are connected. The analysis comprised ddRADseq derived SNP datasets of 166 individuals collected from 57 sampling stations from 17 different areas, including two Marine Protected Areas, one Special Area of Conservation and other areas with different levels of protection. The 4,017 neutral SNPs used indicated high connectivity and panmixis amongst the majority of areas (Ireland to Norway), spanning ca. 2,500-km at depths of 99–900 m. This was likely due to the presence of strong ocean currents allowing long-distance larval transport, as supported by our migration analysis and by 3D particle tracking modelling. On the contrary, the Cantabrian Sea and Roscoff (France) samples, the southernmost areas in our study, appeared disconnected from the remaining areas, probably due to prevailing current circulation patterns and topographic features, which might be acting as barriers for gene flow. Despite this major genetic break, our results suggest that all protected areas studied are well-connected with each other. Interestingly, analysis of SNPs under selection replicated results obtained for neutral SNPs. The relatively low genetic diversity observed along the study area, though, highlights the potential fragility of this species to changing climates, which might compromise resilience to future threats.
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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 | 0.001 |
| 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".