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The modeling approach applied in ILCYM

Modeling of insect populations is, for a variety of reasons, more complicated than modeling populations of other organisms. Insects pass through different life-stages before reaching maturity. Within these immature stages they may die and when mature they reproduce. Some species have seasonality (i.e. different life-stages of the insects are only found during specific seasons of the year). Others do not (i.e. populations are heterogeneous in their age-stage structure because of continuous reproduction and overlapping generations), but their development is always strongly temperature driven. The approach used in ILCYM is to define functions describing development (Stinner et al. 1974, Logan et al. 1976; Sharpe and DeMichele 1977; Hilbert and Logan 1983; Régnière 1984; Ikemoto 2005), its variation between individuals in a population (Sharpe et al. 1977; Curry et al. 1978; Wagner et al. 1984), mortality in each immature life-stage of the insect, and senescence time and reproduction frequencies of adults according to temperature. These functions are based on experimental data obtained through temperature experiments.

The reproduction model might include functions for different processes depending on the insect species under study, i.e. changing sex ratio in adults due to temperature, age-dependent reproduction frequencies, temperature-dependent reproduction frequencies, etc. The overall approach is factor-process based, with temperature being the principle driver (factor) of these processes. Insect species that show seasonality generally have an over-wintering stage in which the insect hibernates or diapauses. Various factors might be responsible for reactivating hibernating insects, but often it is not temperature alone. Therefore, ILCYM is more adequate for insect species that do not hibernate and hence do not show seasonality in their development. However, many components of ILCYM might be used for such species as well.

ILCYM compiles the established functions into a general (generic) phenology model that uses rate summation and a cohort up-dating approach for simulating populations. The cohort up-dating algorithm is based on scheme proposed by Curry et al. (1978) that was further described by Wagner et al. (1985) and Logan (1988). In published articles there is not so much discussion on including temperature-induced mortality in immature life-stages and recruitment. However, both are necessary for more realistic simulations and are hence included in ILCYM. Development, reproduction, and survival in insect species describe primarily insect demography; for understanding population dynamics additional knowledge about dispersal and migration as well as other biotic or abiotic factors affecting the insects’ survival are necessary. The cohort up-dating algorithm calculates population number and hence it is more than a phenology model only; it also provides information about the quantitative biology of the insect species under study. However, it needs to be considered that the resulting population increase represents the population growth potential of the species at a given temperature regime. Real population increase depends on additional biotic and abiotc factors affecting populations in a given environment. Including such factors would introduce much more complexity into the algorithm, which is not provided in ILCYM.

The approach for “risk mapping”, i.e. simulating the risk of pest establishment and expansion, in ILCYM differs from the "match climate' approach in which climate match functions seek out the potential exploitation of a non-indigenous invasive species to new areas by comparing the long-term meteorological data for each of a selected location where the species is absent with the location of origin or locations where the species prevails. In ILCYM the risk maps are simulated by using the pests’ process-based phenology models that describe the basic physiological principals of insect species’ growth, i.e. its development, survival and reproduction.

Software Associated

R 3.4.1






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