Testing the standard neutral model of biodiversity in lake communities
Bibliographic record
Abstract
neutral model describes how local communities are structured if population dynamics are statistically identical among species in a constant, possibly patchy, environment with random speciation. Tests of this model have been restricted largely to terrestrial communities. Here we tested the fit of this neutral model to fish, zooplankton and phytoplankton species–abundance distributions from 30 well‐studied lake communities varying widely in lake size and productivity. We measured the fit of the communities to the neutral model in three ways. All but two zooplankton (7 of 9) and all but three fish (9 of 12) communities were consistent with all three measures of fit. However, all nine phytoplankton communities did not fit the neutral model by at least one measure. This result for phytoplankton communities represents to date the most consistent failure of the standard neutral model to predict the shape of species‐abundance distributions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".