The interacting influences of competition, composition and diversity determine successional community change
Bibliographic record
Abstract
Abstract Community change is one of the few constants in nature, and the balance of mechanisms influencing this change is central for understanding the structure and functioning of communities and ecosystems. Newly established communities undergo succession and can change in diversity and composition as local environmental change, interspecific interactions and immigration play out over time. Understanding the influence of initial conditions and priority effects (long‐term consequences of the initial community composition and species identity) on community change is critically important for both evaluating ecological theory and predicting restoration outcomes. Here I evaluate how initial experimental conditions in 2012, like initial sown species richness, phylogenetic diversity and early biomass production, along with priority effects caused by the identity of sown species, influence subsequent herbaceous plant community composition and the number of colonizing species after 9 years of uninterrupted natural colonization. I found that sown phylogenetic diversity (measured as mean pairwise distance) indirectly affected the number of colonizing species by increasing biomass production early on and plots with more biomass in 2012 were colonized by fewer species. Individual species influenced the number of colonizing species with taller species reducing the number of colonists and some shorter species subject to a greater number of colonists. Synthesis . Taken together, these results indicate that initial composition influences the number of colonizing species via community‐wide competition. These findings suggest that restoration outcomes can be greatly influenced by decisions about sown species composition and early management practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.000 | 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".