Allometric constraints on carabid diets: interspecific differences in carabid-to-seed mass ratios impact seed choice
Bibliographic record
Abstract
Seed choice in seed-feeding omnivorous carabid beetles (Coleoptera: Carabidae) is influenced by numerous ecological factors, including the chemical and physical properties of seed species. Previous work has shown that seed chemistry can drive seed selection decisions by carabids only when the size and mass of seed species are within certain limits. In a model system composed of eight carabid species and seeds of three brassicaceous weeds, we explored predator-to-seed mass relationships and their impact on seed choice by carabids. We show that carabid-to-seed mass ratio scaling relationships are likely to drive seed choice when seed species of different mass are presented to carabid species varying in body mass. Smaller seed species in the experiments were more preferably chosen by smaller carabid species, and mass of the preferable seed species increased as a function of the body mass of carabid species. The taxon-specific mass of carabid predators in relation to the species-averaged mass of available seeds was the main driver of seed choice decisions in the model system under study. These mass-driven changes in seed preferences suggest that feeding interactions between carabid and seed species in agricultural fields are likely driven by mass-structured dynamics. Given this, the intensity of predation pressure imposed by carabids on seed species in the field may potentially be determined by the match/mismatch between the distribution of seed mass in the weed community and the structure of functional body mass in the carabid community.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".