Hierarchical effects of environmental filters on the functional structure of plant communities: a case study in the French Alps
Bibliographic record
Abstract
Understanding the influence of the environment on the functional structure of ecological communities is essential to predict the response of biodiversity to global change drivers. Ecological theory suggests that multiple environmental factors shape local species assemblages by progressively filtering species from the regional species pool to local communities. These successive filters should influence the various components of community functional structure in different ways. In this paper, we tested the relative influence of multiple environmental filters on various metrics of plant functional trait structure (i.e. ‘community weighted mean trait’ and components of functional trait diversity, i.e. functional richness, evenness and divergence) in 82 vegetation plots in the Guisane Valley, French Alps. For the 211 sampled species we measured traits known to capture key aspects of ecological strategies amongst vascular plant species, i.e. leaf traits, plant height and seed mass (LHS). A comprehensive information theory framework, together with null model based resampling techniques, was used to test the various environmental effects. Particular community components of functional structure responded differently to various environmental gradients, especially concerning the spatial scale at which the environmental factors seem to operate. Environmental factors acting at a large spatial scale (e.g. temperature) were found to predominantly shape community weighted mean trait values, while fine‐scale factors (topography and soil characteristics) mostly influenced functional diversity and the distribution of trait values among the dominant species. Our results emphasize the hierarchical nature of ecological forces shaping local species assemblage: large‐scale environmental filters having a primary effect, i.e. selecting the pool of species adapted to a site, and then filters at finer scales determining species abundances and local species coexistence. This suggests that different components of functional community structure will respond differently to environmental change, so that predicting plant community responses will require a hierarchical multi‐facet approach.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".