Plant Invasion Decreases the Likelihood of Community Persistence Through Asymmetric Competition
Bibliographic record
Abstract
Plant invasion is a significant driver of species loss in ecological communities. However, projecting its impact on multispecies coexistence remains a challenge. Here, we conducted pairwise experiments with five native and five non-native species, using the Ricker model to estimate interaction coefficients and population growth rates. We assessed the impact of non-native species on community persistence potential through a structural approach that integrates multispecies interactions and estimates coexistence probabilities. We found that community persistence potential generally declined after invasion, with the feasibility domain (i.e., the probability that all species co-occur simultaneously) becoming more asymmetric as more native species were replaced by non-native ones. Interestingly, non-native species were more likely to be excluded first under random environmental perturbations in communities where they were dominant. Our findings highlight the importance of clarifying species interaction structure under random disturbances in shaping community persistence and suggest tailored invasion management strategies to optimise resource allocation.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".