Adaptive behavior and connectance of invasive plants mediate community composition in multilayered ecological networks
Bibliographic record
Abstract
Abstract Much evidence has shown that adaptive behavior can greatly modulate the dynamics of food webs, but little is known about how adaptive behaviors of invasive plant species affect community composition in multilayered networks. Following a proven network model, we constructed networks of native communities that are invaded by exotic plant species based on three linkage rules. We examined the effects of both adaptive behavior and network connectance of invasive plant species on the persistence of native species and diversity-invasion success relationship. Results showed that community persistence was mainly affected by the connectance of invasive plant species regardless of adaptive behavior. Given a fixed proportion (F 1 ) of native mutualist species linked to the invasive plant species, community persistence displayed an inverse hump-shaped relationship with the increasing proportion (F 2 ) of native plant species linked to the invasive plant species. Compared to the results without adaptive behavior, the adaptive behavior made most negative diversity-invasion relationship become a nonlinear U-shape at fixed proportion (F 1 ). In addition, the adaptive behavior made most negative diversity-invasion relationship insignificant for some proportion (F 1 ) when proportion (F 2 ) was fixed. It could even reverse this relationship if the invading species was more likely to link to native species already having fewer links than those having higher links. Our results underline the importance of considering adaptive behavior and the network degree of invasive plant species for understanding the effect of invasive species on the structure and composition of ecological networks.
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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 | 0.000 | 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".