THE COMPARATIVE EVIDENCE RELATING TO FUNCTIONAL AND NEUTRAL INTERPRETATIONS OF BIOLOGICAL COMMUNITIES
Bibliographic record
Abstract
Neutral and functional theories provide rival interpretations of community patterns involving distribution, abundance, and diversity. One group of patterns describes the overall properties of species or sites, and derives principally from the frequency distribution of abundance among species. According to neutral theory, these patterns are determined by the number of individuals of novel type appearing each generation in the community, whereas functional theory relates them to the distribution of the extent of niches. A second group of patterns describes the spatial attributes of communities, and derives principally from the decay of similarity in species composition with distance. Neutral theory interprets these patterns as consequences of local dispersal alone, whereas the functional interpretation is that more distant sites are likely to be ecologically different. Neutral theory often provides good predictions of community patterns, yet is at variance with a wide range of experimental results involving the manipulation of environments or communities. One explanation for this discrepancy is that spatially explicit models where selection is generally weak, or where selection acts strongly on only a few species at each site, have essentially the same output as neutral models with respect to the distribution of abundance and the decay of similarity. Detecting a non-neutral signal in survey data requires careful spatial or phylogenetic analysis; we emphasize the potential utility of incorporating phylogenetic information in order to detect functional processes that lead to ecological variation among clades.
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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 | 0.020 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.003 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".