From plant populations to communities: using hierarchical trait environment relationships to reveal within ecosystem filtering
Bibliographic record
Abstract
Abstract Explaining the existence of highly diverse plant communities under strong abiotic filtering is a long-standing challenge in ecology. Hierarchical aspects of abiotic and biotic filters are rarely taken into account and studies focus mainly on community-level aggregated patterns. Because variations in biotic conditions might take place in short abiotic gradient and within the tolerance of species in regional pool, it is likely that biotic filtering will select individuals within species and adjust population characteristics. To challenge this idea, we replicated a diversity gradient in four highly contrasted wetlands with an almost complete species turn-over, sampling individuals in communities irrespective of their taxonomic identities or status. Using hierarchical distributional modelling, we analyzed the variation of the mean and dispersion of functional trait space at the ecosystem, community and species level. We found that the abiotic differences between ecosystems filtered species contrasted in their growth/nutrient conservation trade-off, while within ecosystems community variation were mainly due to the partitioning of canopy and leaf adaptations to light conditions. We found strong species-specific functional and demographic responses of dominant species along the diversity gradient, especially for traits linked to biomass and space occupation. Two contrasted strategies emerged, with species using plasticity to maintain equally dense populations, while others used plasticity to become overwhelmingly abundant when in favorable conditions. Our results demonstrate that within ecosystems, variation in biotic conditions selects individuals within populations, revealing the importance of phenotypic variation for a species to be maintained in more or less diverse communities. Because phenotypic variations are related to demographic responses, it offers a way to link the study of species diversity 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".