Thyasirid species composition (Bivalvia: Thyasiridae) and genetic connectivity of Parathyasira equalis (A. E. Verrill & K. J. Bush, 1898) in deep basins of sub-Arctic fjords
Bibliographic record
Abstract
BACKGROUND: Thyasirid bivalves are often recorded as a dominant component of macrobenthic infaunal communities in depositional environments such as fjord basins. Fjord basins comprise patchy soft-bottom habitats bounded by steep walls and sills; however, little is known how this semi-isolated nature of fjords affects benthic populations. Accordingly, data on the composition and population connectivity of thyasirids can provide valuable information on the ecology of these ecosystems. RESULTS: The species composition of thyasirid bivalves has been studied in the basins of three sub-Arctic fjords (Nordland, Northern Norway). Overall, six thyasirid species were recorded: Parathyasira equalis, Parathyasira dunbari, Mendicula ferruginosa, Genaxinus eumyarius, Thyasira sarsii, and Thyasira obsoleta. The species composition remained stable within the basins during the sampling period (2013-2020) and suggested the importance of local reproduction over advection of individuals for population dynamics. Only one species, Parathyasira equalis, was common in all fjords. We have further investigated the population genetics of this species by combining two types of genetic markers: a 579 bp fragment of the cytochrome c oxidase subunit I (COI) gene and 4043 single-nucleotide polymorphisms (SNPs) generated by genotyping-by-sequencing. The latter provided a more in-depth resolution on the population genetics of this species and revealed a weak but significant differentiation of populations within fjords, further indicating limited connectivity between basins. CONCLUSION: Based on our findings, we conclude that limited dispersal between the basin communities results in weakly connected populations and might be an important structuring factor for macrobenthic communities.
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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 | 0.000 |
| 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".