When the aggressiveness degree modifies the intraguild predation magnitude
Bibliographic record
Abstract
In agroecosystems, intraguild predation (IGP) is an ecological interaction that can reduce the effectiveness of biological control programs. Since aggressiveness is correlated with a higher attack rate, it is suspected to influence IGP occurrence. To understand the consequences of increased aggressiveness on the IGP, we used two artificially selected lines of the generalist predator, Nabis americoferus (one aggressive and one docile). We hypothesized that IGP is positively correlated to aggressiveness. Individuals of N. americoferus were tested individually with an intraguild prey (IGPrey), Orius insidiosus, and in the absence or presence of an extraguild prey (XGPrey), third instars of Lygus lineolaris. Firstly, the attack rate of N. americoferus on O. insidiosus was recorded for 15 min and then the IGP after a period of 24 h. We found that aggressive individuals performed a higher attack rate and IGP than docile ones even with the presence of XGPrey. Whereas docile individuals did not display a strong IGP in the absence of XGPrey, it increased significantly when they were introduced. Our results suggest that IGP is positively correlated to a high aggressiveness. Additionally, it seems that docile individuals are more prone to adjust their behavior according to environmental conditions. Finally, the implications of aggressiveness degree for the predator–prey dynamic is discussed.
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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 | 0.001 |
| 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 | 0.000 |
| 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".