Variation partitioning as a tool to distinguish between niche and neutral processes
Bibliographic record
Abstract
We assess the potential of different forms of variation partitioning to distinguish between environmental control and dispersal limitation in communities structured by combinations of niche and neutral processes. Simulation data reveal interactions between dispersal limitation, environmental control, and the spatial structure of environmental factors in the detected levels of variance fractions. The degree of dispersal limitation contributes to both the pure environmental and pure spatial variance partitions. This undermines the common practice of interpreting these partitions as direct expressions of niche and neutral processes, respectively. Furthermore, the proportion of variation attributed to environmental variation depends not only on the strength of environmental control, but also on the specific spatial configuration of the environmental variable. This has important implications for the interpretation of empirical studies. In particular, use of these analytical techniques to compare processes governing community structure among different study systems is unwarranted, as the results will reflect not only differences in the strength of the processes of interest, but also the influence of the unique spatial arrangement of the environmental variables in each system.
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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 | 0.013 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".