Expression of Concern: Trait variation across biological scales shapes community structure and ecosystem function
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
Trait variation underlies our understanding of the patterns and importance of biodiversity, yet we have a poor understanding of how variation at different levels of biological organization structures communities and ecosystems. Here, we use a mesocosm experiment to test for the effects of a larval dragonfly functional trait on community and ecosystem dynamics by creating artificial populations to mirror within- and between-population trait variation observed in our study area. Specifically, we manipulate variation in activity rate, a key functional trait shaping food webs, across three levels of biological organization: within-populations (differences in trait variation in a population), among-populations (differences in population mean trait values), and among-species (species-level differences of co-occurring dragonflies). We show that differences in activity rate alter prey communities, trophic cascades, and multiple ecosystem processes. However, trait variation among populations had much larger effects than differences between co-occurring species or even the presence of a predator, whereas within-population variation had a relatively minor impact. Interestingly, combined with earlier work in the same system, our study suggests that the relative importance of species vs. individual level differences for ecosystem functioning will depend on the spatial scale considered. Ecological processes, including biodiversity-ecosystem-functioning relationships, cannot be understood without accounting for trait variation across biological scales of organization, including at fine scales.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".