The effects of fecundity, mortality and distribution of the initial condition in phenological models

Abstract

Pest phenological models describe the cumulative flux of the individuals into each stage of the life cycle of a stage-structured population. Phenological models are widely used tools in pest control decision making. Despite the fact that these models do not provide information on population abundance, they share some advantages with respect to the more sophisticated and complex demographic models. The main advantage is that they do not require data collection to define the initial conditions of model simulation, reducing the effort for field sampling and the high uncertainty affecting sample estimates. Phenological models are often built considering the developmental rate function only. To the aim of adding more realism to phenological models, in this paper we explore the consequences of improving these models taking into consideration three additional elements: the age distribution of individuals which exit from the overwintering phase, the age-and temperature-dependent profile of the fecundity rate function and the consideration of a temperature-dependent mortality rate function. Numerical simulations are performed to investigate the effect of these elements with respect to phenological models considering development rate functions only. To further test the implications of different models formulation, we compare results obtained from different phenological models to the case study of the codling moth (Cydia pomonella) a primary pest of the apple orchard. The results obtained from model comparison are discussed in view of their potential application in pest control decision support.

Comparison of New Kairomone-Based Lures for Cydia pomonella (Lepidoptera: Tortricidae) in Italy and USA

“…However, only monitoring male moths requires a robust correlation with key life history events, i.e., female egg laying and larval eclosion [ 56 ]. Thus, monitoring female populations should be a more accurate approach to predict key events [ 57 , 58 ]. The recent availability of new kairomone-based blends such as the four-component lure with enhanced formulations (PVC dispensers instead of septa) will likely open new approaches to monitor and manage this important pest.…”

Non-linear physiological responses to climate change: the case of Ceratitis capitata distribution and abundance in Europe

“…We described the population dynamics of C. capitata through a process-based model based on the Kolmogorov equation (Lee et al 1976;Weiss 1986;Bergh and Getz 1988;Iannelli 1995;Buffoni and Pasquali 2007;Rafikov et al 2008;Solari and Natiello 2014;Lanzarone et al 2017). This modelling approach has already been applied to investigate the dynamics and the potential distribution of Bemisia tabaci (Gilioli et al 2014), Lobesia botrana (Gilioli et al 2016), Pomacea caniculata (Gilioli et al 2017b, c), Aedes albopictus (Pasquali et al 2020) and the phenology of Cydia pomonella (Pasquali et al 2019). Full mathematical details of the model presented can be found in supplementary material 2.…”

Modelling diapause termination and phenology of the Japanese beetle, Popillia japonica

“…All simulations end at the 31st of December of the same year. The Kolmogorov equation used for simulating pest population dynamics and phenology in sub-model M2 is presented in Gilioli et al (2016Gilioli et al ( , 2021, Pasquali et al (2019Pasquali et al ( , 2020. The full mathematical description of the model presented here can be found in Section S1 of supplementary materials.…”

“…Phenological models are among the most useful tools for predicting the emergence of susceptible life stages (e.g. pre-and post-diapause larvae and adults), thus allowing to schedule monitoring and control actions accordingly (Samietz et al 2007;Pasquali et al 2019). Mechanistic models are able to simulate the life-history strategies of the species and how they are influenced by relevant abiotic (e.g.…”