A conceptual framework for the spatial analysis of functional trait diversity
Bibliographic record
Abstract
Functional trait diversity is a popular tool in modern ecology, mainly used to infer assembly processes and ecosystem functioning. Patterns of functional trait diversity are shaped by ecological processes such as environmental filtering, species interactions and dispersal that are inherently spatial, and different processes may operate at different spatial scales. Adding a spatial dimension to the analysis of functional trait diversity may thus increase our ability to infer community assembly processes and to predict change in assembly processes following disturbance or land‐use change. Richness, evenness and divergence of functional traits are commonly used indices of functional trait diversity that are known to respond differently to large‐scale filters related to environmental heterogeneity and dispersal and fine‐scale filters related to species interactions (competition). Recent developments in spatial statistics make it possible to separately quantify large‐scale patterns (variation in local means) and fine‐scale patterns (variation around local means) by decomposing overall spatial autocorrelation quantified by Moran's coefficient into its positive and negative components using Moran eigenvector maps (MEM). We thus propose to identify the spatial signature of multiple ecological processes that are potentially acting at different spatial scales by contrasting positive and negative components of spatial autocorrelation for each of the three indices of functional trait diversity. We illustrate this approach with a case study from riparian plant communities, where we test the effects of disturbance on spatial patterns of functional trait diversity. The fine‐scale pattern of all three indices was increased in the disturbed versus control habitat, suggesting an increase in local scale competition and an overall increase in unexplained variance in the post‐disturbance versus control community. Further research using simulation modeling should focus on establishing the proposed link between community assembly rules and spatial patterns of functional trait diversity to maximize our ability to infer multiple processes from spatial community structure.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".