Tipping Points in Seed Dispersal Mutualism Driven by Environmental Stochasticity
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Bibliographic record
Abstract
.The mechanism of seed dispersal mutualism is fundamental to understanding vegetation diversity and its conservation. In this study, we propose a stochastic model that extends the classical framework of seed dispersal mutualism to explore the effects of environmental stochasticity on mutualistic interactions between seed dispersers and plants. We first provide a comprehensive picture of the long-term dynamics of seed dispersal mutualism in deterministic and stochastic environments. We then analyze the relationship between stochasticity and the probability and time that seed dispersal mutualism tips between stable states. Additionally, we evaluate the extinction risk of seed dispersal mutualism for different population values and accordingly assign extinction warning levels to these values. The analysis reveals that the impact of environmental stochasticity on tipping phenomena is scenario-dependent but follows some interpretable trends. The probability (resp., time) of tipping towards the extinction state typically increases (resp., decreases) monotonically with noise intensity, while the probability of tipping towards the coexistence state typically peaks at intermediate noise intensity. Noise in animal populations contributes to tipping toward the coexistence state, whereas noise in plant populations slows down the tipping toward the coexistence state. Noise-induced changes in warning levels of initial population values are most pronounced near the boundaries of the basin of attraction, but sufficiently loud noise (especially for plant populations) may alter the risk far from these boundaries. These findings provide a theoretical explanation for the effect of environmental stochasticity on multistability transitions in seed dispersal mutualism and can be utilized to study the interplay between other population systems and environmental stochasticity.Keywordsseed dispersal mutualismsenvironmental stochasticitytipping pointcoexistenceearly warningMSC codes34F0592B05
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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) | 0.001 | 0.001 |
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 it