Modeling Pest Population Dynamics

A prevailing concept of IPM centers on the regulation of insect populations, which requires an understanding of population dynamics. Population dynamics is the study of change in insect numbers across space and time; therefore, models simulating population behavior must be dynamic. To understand the dynamics of insect populations, the insect population should be considered as part of a system of interacting components. Thus, a systems approach to modeling insect population dynamics is useful in identifying the important components that influence the distribution and abundance of insect numbers. Fundamental population processes involved in modeling insect population dynamics typically include natality (birth rate), mortality (death rate), and dispersal (movement). These key processes are influenced by a host of biotic and abiotic factors, some of which are common to all insect species while others are unique to a given species. For example, populations and their effective environments change constantly across space and time, and a description of a population at one location and time interval may not adequately represent events in the same population at another time and location. Due to the dynamic behavior of insect populations, issues of the temporal and spatial domain to be modeled must be adequately resolved if proper data are to be collected. Specifying the temporal/spatial framework is important for several reasons as it determines the approach for analyses, the components of the system, and, potentially, the appropriate level of detail needed to represent the system.

A myriad of approaches, ranging from simple and analytical to highly complex and mechanistic, are available for modeling population dynamics. These approaches encompass single-species models (e.g., density dependent/independent, age-structured vs. stage-structured), multispecies models (e.g., Lotka-Volterra competition, continuous and discrete predator-prey), and many other model event characteristics (e.g., temperature dependency, disease effects, spatiotemporal effects) related to ecosystem dynamics. More realistic (although more complicated) characterization of population events assigns probability functions to birthrate, death rate, carrying capacity, and other components of the ecosystem. Stochastic models that incorporate probability functions are often less tractable mathematically and less intuitively understandable than are deterministic models, although such models may supply greater realism in describing actual population events and are thus of greater utility in insect pest management. Current computing technology has removed most hurdles to application of stochastic models to pest management (e.g., nonlinear stochastic models of spatial and temporal dynamics of biological populations are now quite common), although experimental verification/evaluation of these models remains tedious.

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