A truce with neutral theory: local deterministic factors, species traits and dispersal limitation together determine patterns of diversity in stream invertebrates
Bibliographic record
Abstract
1. Studies seeking to explain local patterns of diversity have typically relied on niche explanations, reflected in correlations with local environmental conditions, or neutral theory, invoking dispersal processes and speciation. 2. We used macroinvertebrate community data from 10 streams that varied independently in local ecological conditions and spatial proximity. Neutral theory predicts that similarity in communities will be negatively associated with distance between sites, while niche theory suggests that community similarity will be positively associated with similarity in local ecological conditions. 3. Similarity in total invertebrate, grazer and predator assemblages showed negative relationships with distance and, for grazers and predators, positive relationships with local ecological conditions. However, the best model predicting community similarity in all three cases included aspects of both local ecological conditions and distance between sites. 4. When assemblages were analysed according to dispersal ability, high-dispersal species were shown to be freely accessing all sites and community similarity was not well predicted by either local ecology or spatial separation. Assemblages of species with low and moderate dispersal ability were best predicted by combined models, including distance between sites and local ecological factors. 5. The results suggest that the perceived dichotomy between neutral and local environmental processes in determining local patterns of diversity may not be useful. Neutral and niche processes structured these communities differentially depending on trophic level and species traits. 6. We emphasize the potential for both dispersal processes and local environmental conditions to explain local patterns of diversity.
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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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".