Ecological and Spatial Influences on the Genetics of Cumacea (Crustacea: Peracarida) in the Northern North Atlantic
Bibliographic record
Abstract
The peracarid taxon Cumacea is an essential indicator of benthic quality in marine ecosystems. This study investigated the influence of environmental (i.e., biological or ecosystemic), climatic (i.e., meteorological or atmospheric), and spatial (i.e., geographic or regional) variables on their genetic variability and adaptability in the Northern North Atlantic, focusing on Icelandic waters. We analyzed partial sequences of the 16S rRNA mitochondrial gene from 62 Cumacea specimens. Using the aPhyloGeo software, we compared these sequences with relevant variables such as latitude (decimal degree) at the end of sampling, wind speed (m/s) at the start of sampling, O 2 concentration (mg/L), and depth (m) at the start of sampling. Our analyses revealed variability in spatial and biological variables, reflecting the diversity of ecological requirements and benthic habitats. The most common Cumacea families, Diastylidae and Leuconidae, suggest adaptations to various marine environments. Phylogeographic analysis showed a divergence between specific genetic sequences and two habitat variables: wind speed (m/s) at the start of sampling and O 2 concentration (mg/L). This observation may indicate the possibility of varying local adaptations in response to these fluctuating conditions. These results reinforce the importance of further research into the relationship between Cumacea genetics and global environmental variables to interpret the evolutionary dynamics and adaptation of these deep-sea organisms. This study sheds much-needed light on the acclimatization of invertebrates to climate change, anthropogenic pressures, and marine habitat management, potentially contributing to the evolution of more effective conservation strategies and policies to protect these vulnerable ecosystems. The aPhyloGeo Python package is freely and publicly available on GitHub and PyPi , providing an invaluable tool for future research.
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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 | 0.000 | 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".