Trait matching and phylogeny as predictors of predator–prey interactions involving ground beetles
Bibliographic record
Abstract
Abstract With global change modifying species assemblages, our success in predicting ecosystem‐level consequences of these new communities will depend, in part, on our ability to understand biotic interactions. Current food web theory considers interactions between numerous species simultaneously, but descriptive models are unable to predict interactions between newly co‐occurring species. Incorporating proxies such as functional traits and phylogeny into models could help infer predator/prey interactions. Here, we used trait matching between predator feeding traits and prey vulnerability traits, along with phylogeny (used as a proxy for chemical defence and other traits difficult to document), to infer predatory interactions using ground beetles as model organisms. A feeding experiment was conducted involving 20 ground beetle and 115 prey species to determine which pair of species did or did not interact. Eight predator and four prey functional traits were measured directly on specimens. Then, using a modelling approach based on the matching‐centrality formalism, we evaluated 511 predictive ecological models that tested different combinations of all predator and prey functional traits, and phylogenetic information. The most parsimonious model accurately predicted 81% of the observed realized and unrealized interactions, using phylogenetic information and the trait‐matches predator biting force/prey cuticular toughness and predator/prey body size ratio. The best trait‐based models predicted correctly >80% which species interact (realized interactions), but predict <58% of which species did not interact (unrealized interactions). Adding a phylogenetic term representing the evolutionary distance within each trophic level increased the ability to predict which species did not interact to >75%. The matching of predator biting force and prey cuticular toughness demonstrated a better predictive power than the commonly used predator/prey body size ratio. Our novel model combining both functional traits and phylogeny extends beyond existing descriptive approaches and could represent a valuable tool to predict consumer/resource interactions of newly introduced species and to resolve cryptic food webs. A plain language summary is available for this article.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".