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Published on: September 17, 2019
Comparison of three models predicting developmental milestones given environmental and individual variation
Estella Gilbert1, James A Powell, Jesse A Logan
1Department of Mathematics and Statistics, Utah State University, Logan, Utah 84322-3900, USA.
Phenotypic variability is crucial for evolution. This study introduces the Extended von Foerster model, which better predicts insect phenology by accounting for individual and environmental variability.
Area of Science:
- Ecology and Evolutionary Biology
- Mathematical Biology
- Entomology
Background:
- Phenotypic variability is essential for organismal evolution.
- Environmental temperature directly influences the phenology of poikilothermic (cold-blooded) organisms.
- Existing phenology models may not fully capture individual and environmental variability.
Purpose of the Study:
- To compare existing phenology models (distributed delay and Sharpe and DeMichele) with a newly developed model.
- To introduce the Extended von Foerster model, based on the age-structured McKendrick-von Foerster partial differential model.
- To evaluate each model's capacity for incorporating inter-individual and environmental variability.
Main Methods:
- Theoretical comparison of biological assumptions underlying different phenology models.
- Development of the Extended von Foerster model.
- Validation against laboratory developmental data for mountain pine beetle (Dendroctonus ponderosae).
Main Results:
- The Extended von Foerster model was developed, integrating age-structure and variability.
- Theoretical analysis highlighted differences in how models handle variability.
- The Extended von Foerster model demonstrated the highest correlation between theoretical predictions and observed developmental data under constant temperature conditions.
Conclusions:
- The Extended von Foerster model offers a more accurate theoretical framework for predicting phenology in variable environments.
- Accounting for both individual and environmental variability is critical for accurate phenological modeling.
- This approach has significant implications for understanding insect development and population dynamics.
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