Spatio-seasonal variations in functional trait composition and diversity patterns of marine fish communities in coastal waters
Bibliographic record
Abstract
Despite the consensus that the distribution of functional traits within a community provides insights into community assembly and maintenance mechanisms, few studies have explored spatio-seasonal variations in the functional patterns of marine fish communities. Seven functional traits within the context of 2 distinct groups—habitat use and trophic niche—were selected to assess functional richness (FRic), functional evenness (FEve), and functional dispersion (FDis) across various spatio-seasonal scales. Community-weighted mean redundancy analysis (CWM-RDA) was used to identify the impact of environmental factors on dominant traits. We found seasonal and spatial variations in dominant traits of the fish community, notably influenced by the latitudinal-depth gradient (from shallower stations in the north to deeper stations in the south), east-west (longitudinal) dynamics, and temperature gradient. Latitude was negatively correlated with the CWM values of most functional trait categories. FRic showed more pronounced seasonal variations than other indices, with higher values observed in autumn. Fish assemblages displayed more similarity in functional traits in winter than in other seasons, with lower FRic, higher FEve, and lower FDis. Overall, our findings illustrate that fish assemblages undergo continuous formation and dissolution across different seasons and zones, resulting in various forms of functional diversity patterns.
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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".