Species richness and temperature influence mussel biomass: a partitioning approach applied to natural communities
Bibliographic record
Abstract
To increase the generality of biodiversity-ecosystem function theory, studies must be expanded to include real communities in a variety of systems. We modified J. W. Fox's approach to partition the influence of species richness on standing crop biomass (net biodiversity effect) of 21 freshwater mussel communities into trait-independent complementarity, trait-dependent complementarity (species with particular traits dominate without impacting other species), and dominance effects (species with particular traits dominate at the expense of others). Overall, species-rich mussel communities have greater biomass than predicted based on average biomass across the region. This effect is largely due to trait-independent complementarity with less abundant species having higher body condition and reduced metabolic rates in species-rich communities. These measures are positively correlated with spatial and temporal thermal variation, suggesting that use of thermal niches as habitat may be important to species coexistence and performance, and emphasizing that knowledge of species traits and environmental context are important to understanding biodiversity-ecosystem function dynamics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".