Trait‐matching models predict pairwise interactions across regions, not food web properties
Bibliographic record
Abstract
Abstract Aim Food webs are essential for understanding how ecosystems function, but empirical data on the interactions that make up these ecological networks are lacking for most taxa in most ecosystems. Trait‐based models can fill these data gaps, but their ability to do so has not been widely tested. We test how well these models can extrapolate to new ecological 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 vertebrates. Methods We train trait‐based models of pairwise trophic interactions on four independent vertebrate food webs (Canadian tundra, Serengeti, alpine south‐eastern Pyrenees and Europe) and evaluate how well these models predict pairwise interactions and network properties of each food web. Results We find that, overall, trait‐based models predict most interactions and their absence correctly. Performance was best for training and testing on the same food web (AUC > 0.90) and declined with environmental and phylogenetic distances with the strongest loss of performance for the tundra‐Serengeti ecosystems (AUC > 0.75). Network metrics were less well‐predicted than single interactions by our models with predicted food webs being more connected, less modular, and with 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. Our finding that trait‐based models can predict many trophic interactions, even in contrasting environments, adds to the growing body of evidence that there are general constraints on interactions and that trait‐based methods can serve as a useful first approximation of food webs in unknown areas. However, food webs are more than the sum of their parts, and predicting network attributes will likely require models that simultaneously predict individual interactions and community constraints.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".