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

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  • Pasquali, S.
  • Soresina, C.
  • Gilioli, G.

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 physiologically-based 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 taking three additional elements into account: 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 effects 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.

Suggested Citation

  • Pasquali, S. & Soresina, C. & Gilioli, G., 2019. "The effects of fecundity, mortality and distribution of the initial condition in phenological models," Ecological Modelling, Elsevier, vol. 402(C), pages 45-58.
    • Handle: RePEc:eee:ecomod:v:402:y:2019:i:c:p:45-58
    DOI: 10.1016/j.ecolmodel.2019.03.019
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    1. Edholm, Christina J. & Tenhumberg, Brigitte & Guiver, Chris & Jin, Yu & Townley, Stuart & Rebarber, Richard, 2018. "Management of invasive insect species using optimal control theory," Ecological Modelling, Elsevier, vol. 381(C), pages 36-45.
    2. Gilioli, Gianni & Pasquali, Sara & Marchesini, Enrico, 2016. "A modelling framework for pest population dynamics and management: An application to the grape berry moth," Ecological Modelling, Elsevier, vol. 320(C), pages 348-357.
    3. Sara Pasquali & Gianni Gilioli & Dirk Janssen & Stephan Winter, 2015. "Optimal Strategies for Interception, Detection, and Eradication in Plant Biosecurity," Risk Analysis, John Wiley & Sons, vol. 35(9), pages 1663-1673, September.
    4. Blum, Moshe & Nestel, David & Cohen, Yafit & Goldshtein, Eitan & Helman, David & Lensky, Itamar M., 2018. "Predicting Heliothis (Helicoverpa armigera) pest population dynamics with an age-structured insect population model driven by satellite data," Ecological Modelling, Elsevier, vol. 369(C), pages 1-12.
    5. Langille, Aaron B. & Arteca, Ellen M. & Ryan, Geraldine D. & Emiljanowicz, Lisa M. & Newman, Jonathan A., 2016. "North American invasion of Spotted-Wing Drosophila (Drosophila suzukii): A mechanistic model of population dynamics," Ecological Modelling, Elsevier, vol. 336(C), pages 70-81.
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    1. Kolpas, Allison & Funk, David H. & Jackson, John K. & Sweeney, Bernard W., 2020. "Phenological modeling of the parthenogenetic mayfly Neocloeon triangulifer (Ephemeroptera: Baetidae) in White Clay Creek," Ecological Modelling, Elsevier, vol. 416(C).
    2. Pasquali, Sara, 2021. "A stage structured demographic model with “no-regression” growth: The case of constant development rate," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    3. Rossini, Luca & Contarini, Mario & Severini, Maurizio & Speranza, Stefano, 2020. "Reformulation of the Distributed Delay Model to describe insect pest populations using count variables," Ecological Modelling, Elsevier, vol. 436(C).
    4. Pasquali, Sara & Trivellato, Barbara, 2023. "A stage structured demographic model with “no-regression” growth: The case of temperature-dependent development rate," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 629(C).
    5. Rossini, Luca & Bono Rosselló, Nicolás & Speranza, Stefano & Garone, Emanuele, 2021. "A general ODE-based model to describe the physiological age structure of ectotherms: Description and application to Drosophila suzukii," Ecological Modelling, Elsevier, vol. 456(C).
    6. Pasquali, S. & Soresina, C. & Marchesini, E., 2022. "Mortality estimate driven by population abundance field data in a stage-structured demographic model. The case of Lobesia botrana," Ecological Modelling, Elsevier, vol. 464(C).

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