Recasting the dynamic equilibrium model through a functional lens: the interplay of trait‐based community assembly and climate
Bibliographic record
Abstract
Summary According to the dynamic equilibrium hypothesis ( DEH ), plant species richness is locally controlled by productivity and disturbance. Given that regional conditions widely affect local environmental variables such as soil nutrient availability, the DEH predictions could be improved by considering how climate influences local controls of species richness. Further, a trait‐based approach to community assembly has the potential to reveal a deeper, mechanistic understanding of species richness variation across environments. Here, we bring together DEH and trait‐based community assembly expectations to examine whether and how local relationships between diversity, disturbance and productivity are affected by habitat filtering and regional climate. We specifically tested how gradients of local nutrient availability and disturbance intensity interact with climatic conditions to drive the species richness of grassland communities. Further, we recast the DEH through a functional lens by exploring how disturbance–diversity and nutrient availability–diversity relationships are shaped by the functional space occupied by species in a community and species packing within this functional space. The functional space occupied by co‐occurring species and the way they are functionally packed are quantified using multi‐trait indices calculated with five core plant functional traits. Working with grassland communities spread across differing regional climatic conditions, we used mixed models to test whether the variation in taxonomic and functional metrics corresponded to the DEH predictions as well as to determine the relationship between those metrics. Contrary to the expectations based on the relation between species richness and the functional components considered, taxonomic and functional metrics did not vary in accordance along environmental gradients. Climate strongly interacted with the local environment to modulate local diversity patterns, sometimes even inversing a given trend and falsifying the DEH predictions. Synthesis . Our findings quantitatively highlight the interplay between regional and local environmental gradients in driving community assembly. We demonstrate that, depending on climatic conditions, observed patterns of both taxonomic and functional community composition can be opposite to expected productivity–diversity and disturbance–diversity relationships. This emphasizes the relevance of multifaceted studies of biodiversity and the need for a more systematic quantification of regional controls in community assembly studies.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".