Individual behaviour and population dynamics: lessons from aphid parasitoids
Bibliographic record
Abstract
Abstract An increasing number of researchers are studying behaviour in the hopes of understanding population dynamics or improving biological control efforts of insect pests by natural enemies. However, it is unclear exactly how behavioural studies will improve our understanding of these population level processes. In this paper we argue that in order to understand population level processes, the problem must be approached from a population biology perspective. A comprehensive understanding of certain behaviours will provide little towards our understanding of host‐natural enemy interactions. For example, using an aphid‐parasitoid model, we examined the effect that a commonly studied behaviour, variance in host selection by aphid parasitoids, has on aphid‐parasitoid population dynamics. Differential host selection does not qualitatively alter classic Nicholson‐Bailey dynamics, but only results in quantitative differences in aphid‐parasitoid population sizes. Irrespective of the degree of aphid instar preference, a large increase in the number of aphids is followed by a large increase in the number of parasitoids, decimating the aphid population. Thus, studying some behaviours, such as variance in host selection, will not contribute substantially to an understanding of aphid‐parasitoid population dynamics.
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 | 0.000 | 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) | 0.002 | 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 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".