Metabarcoding of larval guts reveals diet diversity in native apex predators—the yellowjacket wasps Vespula vulgaris and Vespula germanica
Bibliographic record
Abstract
Abstract Aculeate wasps are apex predators and play an important role in regulating arthropod populations. Determining what they hunt and how their diet varies with species ecology, geography and demography is essential to value their contributions as pest controllers in natural and human-modified ecosystems. There are many practical advantages in the study of social wasp diets. As central place foragers, hundreds of foragers bring prey back to the same location (the nest) every day; this means wasp diets can be explored by identifying taxa in prey balls collected directly from foragers arriving at the nest. A second advantage is that the colonies tend to contain large numbers of brood: prey can be identified from the gut contents of larvae or from the faeces that remain after metamorphosis. Morphological identification of insect remnants found in prey balls has provided insights into wasp diets but this approach is time-consuming and requires expert taxonomists. Metabarcoding is a more efficient method for describing insect diets; it has been used to identify prey of social wasps by sequencing prey balls, larvae guts and larval faeces. However, because of the ecological impact, data on social wasp diets are currently heavily skewed towards invasive populations. We lack comprehensive studies documenting what social wasps hunt in their native habitats. We use DNA metabarcoding to analyse the prey in over 500 wasp larvae guts from 14 nests of Vespula spp, sampled in their native range, across southern England. We describe the diverse diets of these insects, with the detection of 12 arthropod Orders in the guts of 554 larvae, and explore how diet varies across different locations. These data demonstrate the highly generalist predator role provided by Vespula , highlighting the key ecological roles they play as regulators of a broad range of arthropod taxa in native ecosystems and provide an important baseline for further exploring their ecological and economic value.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".