Microbial diversity of the glass sponge Vazella pourtalesii in response to anthropogenic activities
Bibliographic record
Abstract
Abstract Establishment of adequate conservation areas represents a challenging but crucial task in the conservation of genetic diversity and biological variability. Anthropogenic pressures on marine ecosystems and organisms are steadily increasing. Whether and to what extent these pressures influence marine genetic biodiversity is only starting to be revealed. Using 16S rRNA gene amplicon sequencing, we analysed the microbial community structure of 33 individuals of the habitat-forming glass sponge Vazella pourtalesii , as well as reference seawater, sediment, and biofilm samples. We assessed how two anthropogenic impacts, i.e. habitat destruction by trawling and artificial substrate provision (moorings made of composite plastic), correspond with in situ V. pourtalesii microbiome variability. In addition, we evaluated the role of two bottom fishery closures in preserving sponge-associated microbial diversity on the Scotian Shelf, Canada. Our results illustrate that V. pourtalesii sponges collected from protected sites within fishery closures contained distinct and taxonomically largely novel microbial communities. At the trawled site we recorded significant quantitative differences in distinct microbial phyla, such as a reduction in Nitrospinae in the four sponges from this site and the environmental references. Individuals of V. pourtalesii growing on the mooring were significantly enriched in Bacteroidetes, Verrucomicrobia and Cyanobacteria in comparison to sponge individuals growing on the natural seabed. Due to a concomitant enrichment of these taxa in the mooring biofilm, we propose that biofilms on artificial substrates may ‘prime’ sponge-associated microbial communities when small sponges settle on such substrates. These observations likely have relevant management implications when considering the increase of artificial substrates in the marine environment, e.g., marine litter, off-shore wind parks, and petroleum platforms.
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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 | 0.000 |
| 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".