Insect Life Cycle Modelling (ILCYM) software - a generic platform for developing insect phenology models, population analysis and risk mapping.

Abstract

Insect Life Cycle Modelling (ILCYM) software is an open-source computer-aided tool built on R and Java codes and linked to the uDig platform, which is a basic geographic information system (GIS). The software package consists of three modules: (i) the 'model builder'; (ii) the 'validation and simulations'; and (iii) the 'potential population distribution and risk mapping' module. ILCYM's model builder contains a library of several empirical linear and non-linear models, including the derivations of biophysical models, which have been proposed to define critical temperature effects in insects' development. Several statistical measures are incorporated in this module for estimation of parameters and comparison of models. The validation and simulations module demonstrates the application of the phenology models for estimating and simulating insect population abundance under constant and fluctuating temperatures. Outputs of the simulations are demographic parameters that include: (i) net reproduction rate; (ii) mean generation time; (iii) intrinsic rate of increase; (iv) finite rate of increase; and (v) the doubling time. Through these analyses, the biology and temperature requirements of insects are defined, and the effects of different diets or host plants in insects' demographic are assessed. The ILCYM-GIS component estimates three indices (the establishment risk index (EI), the generation index (GI) and the activity index (AI)) that guide in assessing the potential population distribution and abundance of a particular species. Several functionalities for vector (dbf to shape, raster to points, raster to polygons, extract by points) and raster analysis (merge, cut, mask, aggregate/disaggregate, re-class, describe, raster calculator) are part of the ILCYM-GIS component. Such features improve the manipulation of large datasets and help ILCYM's users in analysing and visualizing the risk assessment maps. The phenology model developed for the potato tuber moth Phthorimaea operculella (Zeller) (Lepidoptera: Gelechiidae) a worldwide pest of potato (Solanum tuberosum L.) is used to demonstrate resulting modelling outputs.

A Temperature-Dependent Phenology Model for Liriomyza huidobrensis (Diptera: Agromyzidae)

“…It is freely available at the website of CIP https://research.cip.cgiar.org/confluence/display/ilcym/Downloads (Sporleder et al 2013, Tonnang et al 2013). Data collected in the life-table studies under constant temperature conditions were arranged in incomplete life-table formats as required by the “model builder” of ILCYM to process, analyze, and develop the phenology model (development time and its variation, development rate, senescence, mortality, total oviposition, and relative oviposition frequency).…”

Temperature-dependent phenology of Plutella xylostella (Lepidoptera: Plutellidae): Simulation and visualization of current and future distributions along the Eastern Afromontane

“…The Insect Life Cycle Modeling (ILCYM version 3.0) software [41] developed by the International Potato Centre, Lima, Peru [29] was used to generate temperature-dependent phenology models. ILCYM is an open-source computer-aided tool built on R codes and Java interface, equipped with modules for building process based and temperature dependent phenology models for insect populations.…”

“…Practically, ILCYM inputs experimental life table data to estimate functions for the species development time, development rate, mortality, senescence and fecundity. The software helps to establish temperature-dependent relationships between the transitions from one stage to another during the life history of an insect [29]. Statistical criteria, such as the Akaike’s information criterion (AIC) [42], which are inbuilt in ILCYM, were used to select the mathematical expression for each life stage of the pest with the best fit.…”

“…The cumulative frequencies of developmental times of each life stage and temperatures were plotted against normalized developmental times. Logit distribution curve [25,29] was considered as the best fit function for the egg, larva, pupa and adult male, whereas a complementary log—log (CLL) distribution curve [25] was considered as the best fit function for the adult female.…”

Predicting the Impact of Temperature Change on the Future Distribution of Maize Stem Borers and Their Natural Enemies along East African Mountain Gradients Using Phenology Models

“…In selected geographical coordinates, minimum and maximum temperature are inferred in the phenology model through a cosine function, which was then applied for direct estimate of the following life table parameters: generation time, net reproduction rate, intrinsic rate of increase, finite rate of increase, and doubling time [26,58] From the expression of individual life table parameter, three risk indices namely establishment index (ERI), this index identifies the geographical areas in which the pest insect may survive; generation index (GI), this index is an estimate of the mean number of generations that a given insect may produce within a given year; and activity index (AI), indicates the decimal power of the estimated population increase within a given year [37] were derived for assessing the potential distribution and abundance of the species. This approach implemented the index interpolator in a sub-module of the Insect Life Cycle Modeling (ILCYM) software [57], and is freely accessible at https://research.cip.cgiar.org/confluence/display/ilcym/Home. Using an index interpolator module, the following steps were considered: the digital elevation model (DEM), defined as co-variables, was inputted into ILCYM; DEM was obtained from the Shuttle Radar Topography Mission (SRTM).…”