Species richness drove selection of individuals within wetlands based on traits related to acquisition and utilization of light
Bibliographic record
Abstract
Aim: Selection within natural communities has mainly been studied along large abiotic gradient, while the selection of individuals within population should occur locally under the play of biotic filter. To better seize the role of the latter, we postulated that the hierarchal nature of environmental selection and the multiple dimension of species trait space needed to be accounted for. Methods: We replicated a natural species richness gradient (from 2 to 16 species) within four contrasted wetlands (bog, fen, meadow, marsh), sampling functional traits from random individuals in communities. Developing a hierarchical distributional modelling, we analyzed the variation of the mean and dispersion of functional trait space at the ecosystem, community and species levels. Key results: We found that the abiotic differences between wetlands, which shaped a plant productivity gradient, selected species in regards with their leaf nutrient conservation / acquisition strategy. Within ecosystems, plant species richness was a strong driver of trait variation among both communities and species. Among communities, it shaped the selection of individuals according to their space occupation and leaf adaptations to light conditions. Demographically, some species used intraspecific trait variation to maintain equally dense populations, while others used it to become dominant in favorable conditions. Main Conclusions: Within ecosystems, variation in biotic conditions selects individuals along functional dimensions that are independent to the ones selected across ecosystems. Because intraspecific variations of light-related traits are related to demographic responses, it offers a way to link the study of species richness and eco-evolutionary dynamics.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".