Linking Spatial Processes to Life‐History Evolution of Insect Parasitoids
Bibliographic record
Abstract
Understanding the evolutionary transition from solitary to group living in animals is a profound challenge to evolutionary ecologists. A special case is found in insect parasitoids, where a tolerant gregarious larval lifestyle evolved from an intolerant solitary ancestor. The conditions for this transition are generally considered to be very stringent. Recent studies have aimed to identify conditions that facilitate the spread of a gregarious mutant. However, until now, ecological factors have not been included. Host distributions and life-history trade-offs affect the distribution of parasitoids in space and thus should determine the evolution of gregariousness. We add to current theory by using deterministic models to analyze the role of these ecological factors in the evolution of gregariousness. Our results show that gregariousness is facilitated through inversely density-dependent patch exploitation. In contrast, host density dependence in parasitoid distribution and patch exploitation impedes gregariousness. Numerical solutions show that an aggressive gregarious form can more easily invade a solitary population than can a tolerant form. Solitary forms can more easily invade a gregarious, tolerant population than vice versa. We discuss our results in light of exploitation of multitrophic chemical cues by searching parasitoids and aggregative and defensive behavior in herbivorous hosts.
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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 | 0.002 |
| 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) | 0.001 | 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".