Predation on Live and Artificial Insect Prey Shows Different Global Latitudinal Patterns
Bibliographic record
Abstract
ABSTRACT Aim Long‐standing theory predicts that the intensity of biotic interactions increases from high to low latitudes. Studies addressing geographic variation in predation on insect prey have often relied on prey models, which lack many characteristics of live prey. Our goals were to explore global latitudinal patterns of predator attack rates on standardised live insect prey and to compare the patterns in predation on live insects with those on plasticine prey models. Location Global forested areas. Time Period 2021–2023. Major Taxa Arthropods, birds. Methods We measured predation rates in 43 forested locations distributed across five continents from 34.1° S to 69.5° N latitude. At each location, we exposed 20 sets of three bait types, one set per tree. Each set included three live fly larvae (maggots), three live fly puparia and three plasticine models of the puparia. We used glue rings to isolate half of the sets from non‐flying predators. Results Arthropod attack rates on plasticine prey decreased linearly from low to high latitudes, whereas attack rates on maggots had a U shaped distribution, with the lowest predation rates at temperate latitudes and the highest rates at tropical and boreal latitudes. This difference emerged from intensive predator attacks on live maggots, but not on plasticine models, in boreal sites. Site‐specific attack rates of arthropod predators on live and plasticine prey were not correlated. In contrast, bird attack rates on live maggots and plasticine models were positively correlated, but did not show significant latitudinal changes. Main Conclusions Latitudinal patterns in predation differ between major groups of predators and between types of prey. Poleward decreases in both arthropod and combined arthropod and bird predation on plasticine models do not mirror patterns of predation on our live prey, the latter likely reflecting real patterns of predation risk better than do patterns of attack on artificial prey.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".