The effect of environmental heterogeneity on species richness depends on community position along the environmental gradient
Bibliographic record
Abstract
Environmental heterogeneity is among the most important factors governing community structure. Besides the widespread evidence supporting positive relationships between richness and environmental heterogeneity, negative and unimodal relationships have also been reported. However, few studies have attempted to test the role of the heterogeneity on species richness after removing the confounding effect of resource availability or environmental severity. Here we constructed an individual-based spatially explicit model incorporating a long-recognized tradeoff between competitive ability and stress-tolerance ability of species. We explored the impact of the level of resource availability (i.e. the position of the community along a gradient of environmental severity) on the heterogeneity-diversity relationship (HDR). The results indicate that the shape of HDR depends on the community position along the environmental gradient: at either end of the gradient of environmental severity, a positive HDR occurred, whereas at the intermediate levels of the gradient, a unimodal HDR emerged. Our exploration demonstrates that resource availability/environmental severity should be considered as a potential factor influencing the shape of the HDR. Our theoretical predictions represent hypotheses in need of further empirical study.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".