Environmental filtering and spatial processes shape the beta diversity of liana communities in a valley savanna in southwest China
Bibliographic record
Abstract
Abstract Questions Lianas contribute substantially to the diversity and function of ecosystems. What is the relative importance of environmental filtering and spatial processes on structuring liana beta diversity at taxonomic, functional and phylogenetic levels? Is there any synergy between these drivers (environmental factors and spatial distance) on shaping these three dimensions of beta diversity in a savanna liana community? Location A dry‐hot valley savanna in Yunnan Province, southwest China. Methods We established 30 20 m × 20 m plots in the savanna to collect data on the distribution of 22 liana species, 19 functional traits, and plot‐level soil nutrients, elevation, and slope. The relative contributions of these environmental factors and spatial distance to liana taxonomic, functional and phylogenetic dissimilarity were analyzed using multiple regression on distance matrices. We also tested which environmental factors influence the beta diversity of liana community using permutational multivariate analysis of variance. Results Both environmental and spatial distances were significantly correlated with taxonomic, functional and phylogenetic dissimilarity. Spatial distance explained more variation in taxonomic beta diversity than environmental factors. But for both nearest‐neighbour functional and phylogenetic distance Dnn’, environment explained relatively more variation than space did. Moreover, the proportion explained by environmental variables was ranked in decreasing order as follows: functional Dnn’, phylogenetic Dnn’, and taxonomic beta diversity. We found soil pH had the highest contribution to taxonomic and functional beta diversity, while soil total nitrogen contributed most to phylogenetic beta diversity. Conclusions This study revealed that liana taxonomic, functional and phylogenetic beta diversity in the studied hot‐dry savanna ecosystem is affected and maintained by both environmental filtering and spatial processes. Moreover, the functional and phylogenetic diversities were more strongly subject to environmental filtering. Our study provides information on the mechanisms underlying liana diversity maintenance in savanna, which is necessary to inform conservation management in this vulnerable ecosystem.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".