What drives phylogenetic and trait clustering on islands?
Bibliographic record
Abstract
Abstract Context As one primary catchment-scale impacts of dam constructions on ecosystems, fragmentation, resulting in reduced species richness, altered ecological processes and degraded ecosystem functioning, has been increasing intensely. Objectives Explore the drivers that hinder species co-existence and community assembly would facilitate understanding the fragmentation effect caused by dams. Methods: We hypothesized that habitat filtering and competitive exclusion can simultaneously drive community assembly processes, such that communities on small islands, where competition for limited space and resources is more intense, would be functionally and phylogenetically less clustered than those on large islands. We used ten functional traits and a phylogeny of 76 woody plant species to assess species diversity and similarity within communities across an island area gradient. Results As expected, species were more phylo-functionally similar to one another than expected by chance within islands and this underdispersion grew stronger with island area, indicating that while islands contained clustered communities, habitat filtering and competitive exclusion were both likely occurring. By integrating species abundance distributions with community similarity, we found that the most abundant species were phylo-functionally similar to the least abundant species. Species richness increased with island area, as expected, but the additional species found only on large islands tended to have low abundances, providing opportunities for rare species to persist. Conclusions With habitat filtering narrowing the number of species that can persist, the loss of phylo-functionally closely related rare species on small islands was likely caused by competition or stochastic removals, leading to greater species dissimilarity than on large islands. On large islands, the clustered patterns are likely to be the result of a combination of competitive exclusion caused by resource limitation and from habitat filtering.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".