# Demography of rusty grain beetle in stored bulk wheat: Part II. Mathematical modeling to characterize and predict population dynamics

## Citation

Jian, F., Jayas, D.S., Fields, P.G., White, N.D.G. (2018). Demography of rusty grain beetle in stored bulk wheat: Part II. Mathematical modeling to characterize and predict population dynamics, 47(2), 256-263. http://dx.doi.org/10.1093/ee/nvy002

## Plain language summary

Data collected in Part I of this study were further analyzed by using mathematical modeling methods. Out of the nine unstructured population models tested, no model could fit the insect numbers under all of the tested conditions. This analysis showed that rusty grain beetle, Cryptolestes ferrugineus (Stephens) (Coleoptera: Laemophloeidae), inside small patches (50 ml volume) had different characterization of population dynamics from that inside large patches (18 liter volume) and had different population dynamics when the insect number at the previous time was different. Analysis showed that the first two main factors influencing the population dynamics were the temperature and the previous insect numbers. The total numbers of insects increased with the increase of sum of degree days (temperature and time). However, the degree day model developed based on the constant temperatures could not predict insect numbers under fluctuating temperatures. A newly developed model, which used the result of the unstructured population models, key factor analysis, and the degree day model, could explain about 66% of the insect numbers under fluctuating temperature conditions.

## Abstract

© The Author(s) 2018. Data collected in Part I of this study were further analyzed by using mathematical modeling methods. Out of the nine unstructured population models tested, no model could fit the insect numbers under all of the tested conditions. This analysis showed that Cryptolestes ferrugineus (Stephens) (Coleoptera: Laemophloeidae) inside small patches (50 ml volume) had different characterization of population dynamics from that inside large patches (18 liter volume) and had different population demography when the insect number at the previous time was different.The key factor analysis showed that the first two main factors influencing the population dynamics were the temperature and the previous insect numbers.The total numbers of insects increased with the increase of sum of degree days. However, the degree day model developed based on the constant temperatures could not predict insect numbers under fluctuating temperatures. A newly developed model, which used the result of the unstructured population models, key factor analysis, and the degree day model, could explain about 66% of the insect numbers under fluctuating temperature conditions.