The Bug‐Network ( <scp>BugNet</scp> ): A Global Experimental Network Testing the Effects of Invertebrate Herbivores and Fungal Pathogens on Plant Communities and Ecosystem Function in Open Ecosystems
Bibliographic record
Abstract
Plants are consumed by a variety of organisms, including herbivores and pathogens, which significantly impact plant biomass, diversity, community composition, and ecosystem functioning. While the impacts of vertebrate herbivores are well established, the effects of consumer groups such as insect herbivores, mollusks, and fungal pathogens on plant communities are less clear and remain understudied in many systems. Existing evidence of how they affect plant biomass, diversity, and community composition is mixed, and most studies have focused on individual consumer groups in isolation. However, different consumer groups interact with each other, directly or indirectly, in ways that alter their impacts on plants, and the consequences of these interactions for plant community structure and ecosystem function remain understudied. Further, consumer impacts vary across environmental gradients and likely depend on abiotic conditions such as climate, soil type, or elevation, and biotic conditions such as plant productivity, diversity, or community composition. Existing studies testing the impacts of invertebrate herbivores and fungal pathogens on plant communities differ substantially in methodology, making generalities across large scales difficult. This calls for experimental approaches that implement standardized protocols across many sites. Here, we introduce and report on the methodology of a novel global research network, The Bug-Network (BugNet), that implements standardized consumer-reduction experiments across 5 continents and 18 countries in diverse, herbaceous- or shrub-dominated ecosystems to investigate: (1) the influence of fungal pathogens, insect herbivores, and mollusks on plant diversity and ecosystem functioning, (2) interactions among these consumer groups, and (3) the abiotic and biotic drivers of context-dependent consumer impacts. BugNet aims to advance a predictive understanding of plant-consumer interactions in order to test fundamental ecological hypotheses and improve predictions of global change impacts on biodiversity and ecosystem functioning.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".