A general ODE-based model to describe the physiological age structure of ectotherms: Description and application to Drosophila suzukii

Adapting physics-informed neural networks to improve ODE optimization in mosquito population dynamics

Physics informed neural networks have been gaining popularity due to their unique ability to incorporate physics laws into data-driven models, ensuring that the predictions are not only consistent with empirical data but also align with domain-specific knowledge in the form of physics equations. The integration of physics principles enables the method to require less data while maintaining the robustness of deep learning in modelling complex dynamical systems. However, current PINN frameworks are not sufficiently mature for real-world ODE systems, especially those with extreme multi-scale behavior such as mosquito population dynamical modelling. In this research, we propose a PINN framework with several improvements for forward and inverse problems for ODE systems with a case study application in modelling the dynamics of mosquito populations. The framework tackles the gradient imbalance and stiff problems posed by mosquito ordinary differential equations. The method offers a simple but effective way to resolve the time causality issue in PINNs by gradually expanding the training time domain until it covers entire domain of interest. As part of a robust evaluation, we conduct experiments using simulated data to evaluate the effectiveness of the approach. Preliminary results indicate that physics-informed machine learning holds significant potential for advancing the study of ecological systems.

Effect of temperature on in vitro germination and growth of Colletotrichum fioriniae, a new emerging pathogen of olive fruits

Olive anthracnose induced by different Colletotrichum species causes dramatic losses of fruit yield and oil quality. The increasing incidence of Colletotrichum fioriniae (Colletotrichum acutatum species complex) as causal agent of olive anthracnose in Italy, is endorsing new studies on its biology, ecology, and environmental factors such as temperature. Five isolates from different sampling sites in Lazio region (Central Italy) were studied under controlled laboratory conditions aiming to better understand the differences of thermal development among the isolates and to lay the foundations of a future mathematical model able to describe the key aspects of the pathogen's life cycle. The mycelial growth rate and the conidial germination rate were assessed at seven different constant temperatures (5, 10, 15, 20, 25, 30, and 35°C) and fixed relative humidity (100% RH). The obtained dataset was analysed to estimate the parameters of mathematical functions that connect the mycelial growth rate and the spore germination with the environmental temperature. The parameters set provided as the result of this study constitute a key step forward in the biological knowledge of the species and the basis for future formulations of mathematical models that might be the core of decision support systems in an integrated pest management framework.

A generalized risk assessment index for forecasting insect population under the effect of temperature

Life history traits have been studied under various environmental factors, but the ability to combine them into a simple function to assess pest response to climate is still lacking complete understanding. This study proposed a risk index derived by combining development, mortality, and fertility rates from a stage-structured dynamic mathematical model. The first part presents the theoretical framework behind the risk index. The second part of the study is concerned with the application of the index in two case studies of major economic pest: the brown planthopper (Nilaparvata lugens) and the spotted wing drosophila (Drosophila suzukii), pests of rice crops and soft fruits, respectively. The mathematical calculations provided a single function composed of the main thermal biodemographic rates. This function has a threshold value that determines the possibility of population increase as a function of temperature. The tests carried out on the two pest species showed the capability of the index to describe the range of favourable conditions. With this approach, we were able to identify areas where pests are tolerant to climatic conditions and to project them on a geospatial risk map. The theoretical background developed here provided a tool for understanding the biogeography of Nilaparvata lugens and Drosophila suzukii. It is flexible enough to deal with mathematically simple (N. lugens) and complex (D. Suzukii) case studies of crop insect pests. It produces biologically sound indices that behave like thermal performance curves. These theoretical results also provide a reasonable basis for addressing the challenge of pest management in the context of seasonal weather variations and climate change. This may help to improve monitoring and design management strategies to limit the spread of pests in invaded areas, as some non-invaded areas may be suitable for the species to develop.

Life tables in entomology: A discussion on tables' parameters and the importance of raw data

Life tables are one of the most common tools to describe the biology of insect species and their response to environmental conditions. Although the benefits of life tables are beyond question, we raise some doubts about the completeness of the information reported in life tables. To substantiate these doubts, we consider a case study (Corcyra cephalonica) for which the raw dataset is available. The data suggest that the Gaussian approximation of the development times which is implied by the average and standard error usually reported in life tables does not describe reliably the actual distribution of the data which can be misleading and hide interesting biological aspects. Furthermore, it can be risky when life table data are used to build models to predict the demographic changes of the population. The present study highlights this aspect by comparing the impulse response generated by the raw data and by its Gaussian approximation based on the mean and the standard error. The conclusions of this paper highlight: i) the importance of adding more information to life tables and, ii) the role of raw data to ensure the completeness of this kind of studies. Given the importance of raw data, we also point out the need for further developments of a standard in the community for sharing and analysing data of life tables experiments.