Testing how antagonistic interactions impact the robustness of plant-pollinator networks
Bibliographic record
Abstract
Given ongoing pollinator declines, it is important to understand the dynamics of linked extinctions of plants driven by pollinator extinctions. Topological robustness models focused on this question suggest relatively high robustness of plant species to pollinator species extinctions. Still, existing robustness models typically assume that all interactions in plant-pollinator networks are positive, which is clearly not always the case. For example, many pollinators remove floral resources without transferring pollen, or even damage floral structures in the case of nectar robbing. Here we introduce antagonistic interactions into plant-pollinator networks and assess the resilience of plant communities to pollinator species losses. Incorporating antagonistic interactions leads to lower network robustness, i.e. an increased rate of plant species loss (as compared to networks with only mutualistic interactions) in empirical plant-pollinator networks. In conjunction with extinction order, the addition of increasingly antagonistic interactions was idiosyncratic in that it did not always magnify the effects of extinction order across the three networks. These results underscore the importance of considering the full spectrum of interaction outcomes when assessing robustness to coextinctions in plant-pollinator networks, as well as other ecological systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".