Neutrality, niches, and dispersal in a temperate forest understory
Bibliographic record
Abstract
A fundamental goal of ecology is to understand what controls the distribution and abundance of species. Both environmental niches and trade-offs among species in dispersal and competitive ability have traditionally been cited as determinants of plant community composition. More recently, neutral models have shown that communities of species with identical life-history characteristics and no adaptation to environmental niches can form spatial distribution patterns similar to those found in nature, so long as the species have a limited dispersal distance. If there is a strong correlation between geographic distance and change in environmental conditions, however, such spatial patterns can arise through either neutral or niche-based processes. To test these competing theories, we developed a sampling design that decoupled distance and environment in the understory plant communities of an old-growth, temperate forest. We found strong evidence of niche-structuring but almost no support for neutral predictions. Dispersal limitation acted in conjunction with environmental gradients to determine species' distributions, and both functional and phylogenetic constraints appear to contribute to the niche differentiation that structures community assembly. Our results indicate that testing a neutral hypothesis without accounting for environmental gradients will at best cause unexplained variation in plant distributions and may well provide misleading support for neutrality because of a correlation between geographic distance and environment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".