Abstract
Process-based ecological models used to assess organisms' responses to environmental conditions often need input data at a high temporal resolution, e.g., hourly or daily weather data. Such input data may not be available at a high spatial resolution for large areas, limiting opportunities to use such models. Here we present a metamodeling framework to develop reduced form ecological models that use lower resolution input data than the original process models. We used generalized additive models to create metamodels for an existing model that uses hourly data to predict risk of potato late blight, caused by the plant pathogen Phytophthora infestans. The metamodels used daily or monthly weather data, and their predictions maintained the key features of the original model. This approach can be applied to other complex models, allowing them to be used more widely.
Highlights
There is growing interest in process-based modeling approaches to study the distribution of species (Chuine and Beaubien 2001, Kearney and Porter 2004, Morin et al 2007, Jackson et al 2009, Monahan 2009, Buckley et al 2010), but data requirements for both model development and application have limited the types of questions that can be addressed with these approaches
Metamodel construction and fit The models that considered an interaction of temperature and relative humidity yielded lower Akaike’s Information Criterion (AIC) and Generalized Cross Validation (GCV) scores than the model that did not (Table 1), indicating better fit
We used a metamodel framework to create a new model based on an ecological model that needs high temporal resolution input data, so that it can be applied with low temporal resolution input data that may be available at a relatively high spatial resolution over large extents
Summary
There is growing interest in process-based modeling approaches to study the distribution of species (Chuine and Beaubien 2001, Kearney and Porter 2004, Morin et al 2007, Jackson et al 2009, Monahan 2009, Buckley et al 2010), but data requirements for both model development and application have limited the types of questions that can be addressed with these approaches. Modeling the distribution of species over larger areas is dominated by correlative approaches (Guisan and Thuiller 2005, Elith and Leathwick 2009). This is in part because of the absence of process-based models for many species, and because the large extent - high resolution data sets needed to apply such models often are not available. Process-based ecological models typically require high temporal resolution weather data (e.g., daily or hourly data) This is true for process-based models that capture, for example, the short generation times of microbial and arthropod populations and communities
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