Differentiating between niche and neutral assembly in metacommunities using null models of β‐diversity
Bibliographic record
Abstract
The β‐null deviation measure, developed as a null model for β‐diversity, is increasingly used in empirical studies to detect the underlying structuring mechanisms in communities (e.g. niche versus neutral and stochastic versus deterministic). Despite growing use, the ecological interpretation of the presence/absence and abundance‐based versions of the β‐null diversity measure have not been tested against communities with known assembly mechanisms, and thus have not been validated as an appropriate tool for inferring assembly mechanisms. Using a mechanistic model with known assembly mechanisms, we simulated replicate metacommunities and examined β‐null deviation values 1) across a gradient of niche (species‐sorting) to neutrally structured metacommunities, 2) through time, and 3) we compared the effect of changes in assembly mechanism on the performance of the β‐null deviation measures. The impact of stochasticity on assembly outcomes was also considered. β‐null deviation measures proved to be interpretable as a measure of niche or neutral assembly. However, the presence/absence version of the β‐null deviation measure could not differentiate between niche and neutral metacommunities if demographic stochasticity were present. The abundance‐based β‐null deviation measure was successful in distinguishing between niche and neutral metacommunities and was robust to the presence of stochasticity, changes through time, and changing assembly mechanisms. However, we suggest that it is not robust to changing abundance evenness distributions or sampling of communities, and so its interpretation still requires some care. We encourage the testing of the assumptions behind null models for ecology and care in their application.
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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 | 0.024 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".