Niches and neutral processes contribute to the resource-diversity relationships of stream detritivores
Bibliographic record
Abstract
1. Explaining resource–diversity relationships is a long-standing goal in ecology, and there is currently little consensus as to the relative contributions of neutral versus a variety of proposed niche-related mechanisms. 2. The resource–diversity relationship of insect detritivores was examined in a survey of 25 small, parallel streams flowing into the Bay of Fundy in eastern Canada, with the objective of determining whether neutral processes (sampling effects) could account for the observed patterns. 3. Detritivore taxonomic richness showed a positive, but decelerating relationship with quantity of detritus. Richness also increased with catchment area and with stream permanence. 4. Species distribution patterns were significantly nested, and low resource streams (little detritus) tended to have species with large ranges (i.e. found in many or most streams). 5. Sampling effects could explain only part of the positive relationship between richness and detrital resources, but accounted for the species richness–area relationship. 6. Two mechanisms that could potentially increase niche space as resource abundance increased were rejected: there was no evidence that riparian forest diversity or beta diversity increased with detrital resources. 7. Two niche-related mechanisms were consistent with existing data, but will require further testing. First, flood disturbance may decrease species richness by eliminating species that require benign habitat, and lowering detritus retention, producing a positive correlation between detritivore richness and resources. Second, large wood in streams located in older riparian forest may increase habitat heterogeneity (number of niches) and the retention of organic matter, again leading to a positive relationship between detritivore diversity and detrital resources. 8. It was concluded that the positive ‘productivity–diversity’ relationship for stream detritivores was most likely produced in part by sampling effects, but also by ecological processes (disturbance and succession) that simultaneously influence resource level and niche availability.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".