Rethinking trophic interactions in agricultural landscapes through tracking secondary feeding
Bibliographic record
Abstract
Abstract Conservation biological control efforts depend on accurately estimating predator roles in crop fields, and knowledge of plant resources generalist predators utilize in agricultural landscapes. Generalist predators move among habitats to feed on insect prey and some predators feed on plants for non‐prey nutrients. Studying predation with molecular gut content analysis (MGCA), provides estimates of within field frequencies of predation on target pests and alternative prey. However, prey DNA takes time to move through the predator digestive system, so a portion of the observed predation likely occurs in adjacent crops or semi‐natural habitats. Therefore, we tested a strategy to estimate recent secondary feeding to help trace predation back to the source habitat. We selected the diamondback moth and three common predators with different mouthparts: Coccinella septempunctata , Geocoris punctipes , and Pardosa spiders, as model organisms for these proof‐of‐concept experiments. We estimated post‐feeding primary and secondary plant DNA detection time and compared it between these three predators using previously designed Lepidoptera primers and newly designed Brassica ‐specific primers. Our results indicate secondary plant feeding detection largely depends on predator mouthparts. While secondary collard DNA detectability half‐life was 5.5 h and remained detectable for up to 30 h for the chewing predator, C. septempunctata , only 4% and 8.3% of G. punctipes and Pardosa spp. individuals, respectively, tested positive for collard DNA. However, more studies are required to confirm this mouthpart‐specific post‐feeding plant DNA detection time. Our feeding trials confirmed the possibility of primary and secondary plant feeding detection for chewing predators. Hence, when crops are not flowering (or anthesis), secondary plant feeding detection can be used to trace chewing predators to source habitats where they consumed sessile prey (e.g., nymphs and sessile adults). Such multitrophic linkage knowledge could unravel the landscape‐wise contribution of predators to pest control services.
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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 | 0.000 | 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) | 0.000 | 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".