Models are representations of complex phenomena and are used to understand and predict changes in those phenomena. Population dynamics of various organisms, especially insects, are of particular concern as population changes affect human health, production of ecosystem commodities, and the quality of terrestrial and aquatic ecosystems. Hence, development of models to improve our ability to understand and predict changes in insect population abundances has a rich history.
Models take many forms. The simplest are conceptual models that clarify relationships between cause and effect. For example, box-and-arrow diagrams can be used to show which system components interact with each other (e.g., Fig. 1.3). More complex statistical models represent those relationships in quantitative terms (e.g., regression models that depict the relationship between population size and environmental factors; e.g., Figs. 5.3-5.4). Advances in computational technology have led to development of biophysical models that can integrate large datasets to predict responses of insect populations to a variety of interacting environmental variables. Computerized decision-support systems integrate a user interface with component submodels that can be linked in various ways, based on user-provided key words, to provide output that addresses specific questions (e.g., C. Shaw and Eav 1993).
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