Community differentiation on landscapes: drift, migration and speciation
Bibliographic record
Abstract
Theories of the differentiation of ecological communities on landscapes have typically not considered evolutionary dynamics. Here we analytically study the expected differentiation among local communities in a large metacommunity, undergoing speciation, ecological drift and intercommunity dispersal, in the context of neutral theory. We demonstrate that heterogeneity in species diversity and abundance arises among communities when local communities are small and intercommunity migration is infrequent. We propose a new measure to describe community differentiation, defined as the average correlation or the average probability (C st ) that two randomly sampled individuals of the same species within local communities are from the same ancestor. The effects of driving forces (migration, mutation, and ecological drift) are incorporated into the two‐level hierarchical community structure in a finite island model of neutral communities. Community differentiation can increase the effective metacommunity size or the Hubbell's fundamental species diversity in the metacommunity by a factor (1−C st ) −1 . Significant community differentiation arises when C st ≠0. Intercommunity migration promotes species diversity in local communities but reduce species diversity in the metacommunity. In either the finite or infinite island case, one can estimate the number of intercommunity migrants by using multiple local community datasets when the speciation is negligible in the neutral local communities, or by using the metacommunity dataset when the speciation is included in the local neutral communities. These results highlight the significance of the evolutionary mechanisms in generating heterogeneous communities in the absence of complicated ecological processes on large landscapes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".