Trophic interaction models predict interactions across space, not food webs
Bibliographic record
Abstract
Aim: Trophic interactions are central to our understanding of essential ecosystem functions as well as their stability. Predicting these interactions has become increasingly common due to the lack of empirical data on trophic interactions for most taxa in most ecosystems. We aim to determine whether and how accurately we can extrapolate to new communities both in terms of pairwise predator-prey interactions and higher level food web attributes (i.e., species position, food web-level properties).Location: Canada, Europe, Tanzania.Time period: Current.Major taxa studied: Terrestrial vertebratesMethods: We use a trait-based model of pairwise trophic interactions, calibrated independently on four different terrestrial vertebrate food webs (Canadian tundra, Serengeti, alpine south-eastern Pyrenees, and entire Europe) and assess the ability of each calibrated instance of the model to predict alternative food webs. We test how well predictions recover individual predator-prey interactions as well as higher level food web properties across geographical locations.Results: We find that, given enough phylogenetic and environmental similarities between food webs, trait-based models predict most interactions and their absence correctly (AUC > 0.82), even across highly contrasting environments. However, network metrics were less well-predicted than single interactions by our models. Predicted food webs were more connected, less modular, and had higher mean trophic levels than observed. Main conclusions: Theory predicts that the variability observed in food webs can be explained by differences in trait distributions and trait-matching relationships. Trait-based models can predict potential interactions amongst species in an ecosystem when calibrated using food web data from reasonably similar ecosystems. This suggests that food webs vary spatially primarily through changes in trait distributions. These models, however, are less good at predicting system level food web properties. We thus highlight the need for methodological advances to simultaneously address trophic interactions and the structure of food webs across time and space.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".