Dispersal‐driven homogenization of wetland vegetation revealed from local contributions to β‐diversity
Bibliographic record
Abstract
Abstract Questions Within a meta‐community, what determines how local species composition differs from the regional community? How do local conditions and landscape context affect this differentiation in wetland vegetation? Location Fleurieu Peninsula, South Australia. Methods We sampled native vegetation across 26 hydrological gradients in a wetland meta‐community within a heavily cleared agricultural landscape. We used the local contribution to β‐diversity to quantify how species composition at each site differed from the average across all sites. We hypothesized that local contribution to β‐diversity would respond to assembly processes (niche, biological interactions, dispersal) through effects on the species turnover and richness difference components of β‐diversity. We used beta regression to model local contribution to β‐diversity, building a candidate set of 55 models, each incorporating one of the assembly processes. We used standardized regression coefficients to measure effect size, and null models to explore diversity patterns further. Results While variations in among‐site niche dimensions were influential, the strongest control on local contribution to β‐diversity was a negative association with the number of wetlands within 200 m. Null models showed this was because common species were over‐represented in well‐connected sites within the meta‐community, while rare species were under‐represented. Conclusions Our results demonstrate the homogenization of native plant species composition in well‐connected wetlands, consistent with theoretical predictions of declining β‐diversity when connectivity is high. We recommend comparative analysis of local species composition to regional average diversity to evaluate the role of wetland connectivity in homogenization of composition before conservation or restoration priorities are assigned.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".