Dominance determines fish community biomass in a temperate seagrass ecosystem
Bibliographic record
Abstract
Biodiversity and ecosystem function are often correlated, but there are multiple hypotheses about the mechanisms underlying this relationship. Ecosystem functions such as primary or secondary production may be maximized by species richness, evenness in species abundances, or the presence or dominance of species with certain traits. Here, we combine surveys of natural fish communities (conducted in July and August 2016) with morphological trait data to examine relationships between biodiversity and ecosystem function (quantified as fish community biomass) across 14 subtidal eelgrass meadows in the Northeast Pacific (54°N, 130°W). We employ both taxonomic and functional trait measures of diversity to investigate whether ecosystem function is best predicted by species diversity (complementarity hypothesis) or by the presence or dominance of species with particular trait values (selection or dominance hypotheses). After controlling for environmental variation, we find that fish community biomass is maximized when taxonomic richness and functional evenness are low, and in communities dominated by species with particular trait values, specifically those associated with benthic habitats and prey capture. While previous work on fish communities has found that species richness is often positively correlated with ecosystem function, our results instead highlight the capacity for regionally prevalent and locally dominant species to drive ecosystem function in moderately diverse communities. We discuss these alternate links between community composition and ecosystem function and consider their divergent implications for ecosystem valuation and conservation prioritization.
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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 | 0.000 |
| 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".