Nontrophic Interactions, Biodiversity, and Ecosystem Functioning: An Interaction Web Model
Bibliographic record
Abstract
Research into the relationship between biodiversity and ecosystem functioning has mainly focused on the effects of species diversity on ecosystem properties in plant communities and, more recently, in food webs. Although there is growing recognition of the significance of nontrophic interactions in ecology, these interactions are still poorly studied theoretically, and their impact on biodiversity and ecosystem functioning is largely unknown. Existing models of mutualism usually consider only one type of species interaction and do not satisfy mass balance constraints. Here, we present a model of an interaction web that includes both trophic and nontrophic interactions and that respects the principle of mass conservation. Nontrophic interactions are represented in the form of interaction modifications. We use this model to study the relationship between biodiversity and ecosystem properties that emerges from the assembly of entire interaction webs. We show that ecosystem properties such as biomass and production depend not only on species diversity but also on species interactions, in particular on the connectance and magnitude of nontrophic interactions, and that the nature, prevalence, and strength of species interactions in turn depend on species diversity. Nontrophic interactions alter the shape of the relationship between biodiversity and biomass and can profoundly influence ecosystem processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".