Population genomics and connectivity of Vazella pourtalesii sponge grounds of the northwest Atlantic with conservation implications of deep sea vulnerable marine ecosystems
Bibliographic record
Abstract
Sponges are key ecosystem engineers that shape, structure and enhance the biodiversity of marine benthic communities globally. Sponge aggregations and reefs are recognized as vulnerable marine ecosystems (or VMEs) due to their susceptibility to damage from bottom-contact fishing gears. Ensuring their long-term sustainability, preservation, and ecosystem functions requires the implementation of sound scientific conservation tools. Here, the genetic diversity, structure, and connectivity of the deep-sea glass sponge, Vazella pourtalesii (Schmidt, 1870), was investigated using 1,102 neutral SNPs obtained in RADseq. This species is distributed across the northwest Atlantic from Florida, USA to Nova Scotia, Canada and we sequenced samples covering this full distribution and provided evidence of strong genetic structure with two distinct clusters: Florida together with the Carolina Shelves and the Scotian Shelf. We estimated moderate levels of diversity with low migration across large distances (> 1000 kms) and high connectivity at smaller scales (< 300 kms). Further, fishing pressure on genetic diversity was evaluated, within two Sponge Conservation Areas (SCAs) on the Scotian Shelf. Those areas have different disturbance histories, and cumulative fishing pressure. Slightly lower levels of genetic diversity were found inside the SCAs, and yet they encompassed a high proportion of the diversity observed within the Scotian Shelf. We provide baseline data for future monitoring of the SCAs, discussing our findings in the light of existing area-based management tools.
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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 | 0.001 |
| 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".