Environmental geometry and heterogeneity–diversity relationships in spatially explicit simulated communities
Bibliographic record
Abstract
Abstract Question How are heterogeneity–diversity relationships ( HDRs ) influenced by spatial structure in environmental variables, sampling grain and the extent of niche differentiation? Methods We developed a spatially explicit simulation model incorporating variable dispersal distances and competition strength on fractal landscapes. By varying the grain used to sample these models, we examined scaling patterns in HDR metrics at fine scales (sampling grain from 100 to 10 000 individuals, sampling extent ca. 260 000 individuals). Results Environmental geometry exerts an important influence on the ecological processes responsible for HDRs . Unique geometric characteristics of individual landscapes can greatly influence emergent community properties; field studies frequently use inadequate sample sizes to account for this phenomenon. Two opposing processes influence spatial scaling of HDRs : variance partitioning, which favours smaller‐grained samples, and mass effects, which favour larger‐grained samples. In assessing HDRs , diversity is more sensitive than species richness, and should be the preferred measure in field studies. The environmental geometry and age of a community interact: compared to high fractal dimension landscapes, low fractal dimension landscapes are slower to develop HDRs , but in the long term their HDRs will be higher. Conclusions Our study demonstrates that, despite the superficial simplicity of the concept, HDRs vary in complex and non‐intuitive ways, and warrant further theoretical and empirical study. More generally, environmental geometry is likely to exert a strong influence on many emergent community processes, but we do not yet have a firm understanding of this relationship.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".