Dynamic Energy Budget approach for modeling growth and reproduction of Neotropical stink bugs

Global warming and other climate change drivers have a significant impact on the life cycle of insects which in turn affect their population dynamics and geographic distribution. Among these insects are stink bugs (Hemiptera: Pentatomidae), which are hemimetabolous agricultural pests that cause major damage on crops in many countries. Their life cycle consists of three stages: egg, nymph, and adult. The nymph grows and molts through a small number of instars until reaching emergence, at which point growth ceases. The Dynamic Energy Budget (DEB) theory provides the framework for modeling the life cycle of stink bugs. Nevertheless, determining the most accurate model for nymphal growth and energy allocation during the adult stage remains a challenge, as the best model is not always evident. For two species of the Pentatomidae family, we parameterized and evaluated four DEB models that differ in nymphal growth and the energy allocation rules in adults: isomorphic or V1-morphic nymphal growth combined with the energy allocation scheme in adults that follows the κ-rule or the scheme that the κ-rule is not operational in adults. Overall, all models fit the data, but those with isomorphic nymphal growth reproduce the growth trajectories and instar duration better. However, with the available data we cannot conclude which rule is most likely the energy allocation in adults. Using these models, we further studied the effects of temperature and food availability on individual dynamics and its life cycle. Simulations show that temperature has a higher impact on the duration of life stages and survival, while food availability affects egg production. When simulating a variable environment in terms of temperature for both a summer and a fall generation, the results reveal that the summer generation has higher feeding and egg production rates in comparison to the fall generation. DEB models are valuable tools for simulating individual under various environmental conditions and when combined with appropriate modeling approaches, they can predict population traits and even the potential distribution of a species.