Evidence for a stochastic geometry of biodiversity: the effects of species abundance, richness and intraspecific clustering
Bibliographic record
Abstract
Summary Most ecological theories that aim to explain coexistence in megadiverse communities employ a set of three rules to describe the stochastic geometry of biodiversity: (i) individuals exhibit intraspecific clustering; (ii) species abundances vary according to a log‐normal distribution and (iii) the spatial arrangement between species is independent. The first two rules have received strong empirical support, but the third remains largely unexplored. To address this deficiency, we evaluated the independent species arrangement rule in a species‐rich shrubland and its potential drivers, that is, the levels of species richness and intraspecific clustering exhibited by a given species at different scales, and the relative abundance of such species in the community. We found that interspecific associations were rare and that independence was positively related to species richness and intraspecific clustering, but negatively related to relative species abundances. Synthesis . Our results agree with the independent species arrangement rule and they provide empirical support for the stochastic geometry of biodiversity. In the context of species‐rich plant communities, the likelihood of two species encountering is very small. However, our study demonstrated a novel feature of this context, where both intraspecific clustering (due limitations on dispersal) and relative species abundances play fundamental roles in determining the probability of two species encountering and interacting, especially at very fine spatial scales.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".