Separating the effect of mechanisms shaping species‐abundance distributions at multiple scales in a subtropical forest
Bibliographic record
Abstract
Species abundance distributions (SADs) play an important role in the current dispute over mechanisms shaping community assembly. Niche theory assumes differential occurrence of species in different habitats while neutral theory emphasizes stochastic events and dispersal. The previous tests of niche and neutral models shaping SADs lead to the claim that SADs are not informative for inferring underlying processes. Using spatial statistical models in a fully mapped 24‐ha subtropical forest in China, we first demonstrate that one can not distinguish between the effect of habitat heterogeneity and dispersal limitation on SADs by inspecting whether the observed SADs fall within 95% confidence intervals of the simulated SADs. Subsequently, we demonstrate that SADs can be used to detect mechanisms shaping SADS by comparing alternative process‐based models using model selection techniques. We found that dispersal limitation explain SADs at smaller spatial scales, while the combination of niche and dispersal limitation explain SADs at larger scales. These processes are linked with the degree of conspecific aggregation, informing further attempts to refine and parameterize the statistical theory of sampling SADs.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".