Positive heterospecific interactions can increase long‐term diversity of plant communities more than negative conspecific interactions alone
Bibliographic record
Abstract
Abstract Negative conspecific interactions have been shown to promote diversity in plant communities, as have some heterospecific interactions such as intransitive competition and facilitation. However, it is unclear whether combinations of conspecific and other heterospecific interactions can also promote diversity in plant communities. We therefore investigated the effects of heterospecific plant interaction network architecture with and without conspecific interactions on alpha diversity, beta diversity and long‐term diversity. We simulated long‐term plant community dynamics for theoretical plant interaction scenarios with modular, ring and nested networks of positive or negative heterospecific interactions and conspecific interactions, using a spatially explicit cellular automaton model that accounted for stochastic effects. Throughout the simulations several measures of diversity were recorded. The way that heterospecific interactions affected diversity depended strongly on various characteristics of the architecture of the interaction network. Negative conspecific interactions generally promoted alpha diversity and reduced beta diversity, with a few key exceptions. Positive heterospecific ring interactions that resulted in cyclic appearance and disappearance of species groups led to the greatest long‐term diversity (a measure of the total diversity over time). This study provides new theoretical insights into how the network architecture of heterospecific plant interactions can affect the diversity of plant communities over time and provides the first evidence that heterospecific plant interactions can increase long‐term diversity more than negative conspecific interactions alone. A free Plain Language Summary can be found within the Supporting Information of this article.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".