Process-based ecological models used to assess organisms’ responses to environmentalconditions 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 usesuch 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, andtheir 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.
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