Asymmetric interactions and their consequences for vital rates and dynamics: the smaller tea tortrix as a model system
Bibliographic record
Abstract
Asymmetric interactions among conspecifics can have diverse effects on population dynamics including stabilization, generation of cycles, and induction of chaotic fluctuations. A difficult challenge, however, is establishing the link between the impact of asymmetric interactions on life history and the consequences for population dynamics. The smaller tea tortrix, Adoxophyes honmai, is a good example. Larval instars differ dramatically in size and have a tendency for cannibalism, which suggests the potential for strong asymmetric interactions among instars. Yet whether these asymmetries have any role in generating the distinct single-generation cycles observed in the field and laboratory is unclear. Here we report on the development of a new experimental approach to characterize the impact of asymmetric interactions on life history that can be directly embedded into stage-structured population models. The experiments use donor-replacement protocols in which focal individuals are challenged to complete their life cycles in competitive environments where the instar and density of the competitors are held constant. The experimentally derived interaction surface contains all the information about stage-specific interactions and provides a straightforward framework for evaluating alternative ways of abstracting the interactions into traditional models of asymmetric competition. Working with the smaller tea tortrix, we found strong evidence of asymmetric interactions and identified critical "tipping points" in the competitive environment that strongly affected survival but not development. We incorporated the experimentally derived interaction surface into a stage-structured population model and found that despite the strong impact that asymmetric interactions have on tea tortrix life history, they do not scale-up to impact the predicted asymptotic population dynamics. Comparing these dynamics with two abstracted models of stage-structured interactions revealed that while the quantitative details of the emergent dynamics depends on the shape of the interaction surface, the qualitative features, such as the emergence of single-generation cycles and rapid synchronization of development among individuals, are pleasingly robust.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".