A metamodeling framework for extending the application domain of process-based ecological models

Sparks, Adam H. ORCID: https://orcid.org/0000-0002-0061-8359 and Forbes, Gregory A. and Hijmans, Robert and Garrett, Karen A. (2011) A metamodeling framework for extending the application domain of process-based ecological models. Ecosphere, 2 (8).

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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.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Article 90. Creative Commons Attribution License.
Faculty/School / Institute/Centre: No Faculty
Faculty/School / Institute/Centre: No Faculty
Date Deposited: 04 Oct 2016 00:26
Last Modified: 27 Sep 2019 05:01
Uncontrolled Keywords: climate change scenario analysis; data aggregation; ecoinformatics; ecological scaling; metamodels; Phytophthora infestans; plant disease; process-based models; Solanum tuberosum
Fields of Research (2008): 07 Agricultural and Veterinary Sciences > 0701 Agriculture, Land and Farm Management > 070104 Agricultural Spatial Analysis and Modelling
05 Environmental Sciences > 0501 Ecological Applications > 050199 Ecological Applications not elsewhere classified
06 Biological Sciences > 0607 Plant Biology > 060704 Plant Pathology
07 Agricultural and Veterinary Sciences > 0703 Crop and Pasture Production > 070308 Crop and Pasture Protection (Pests, Diseases and Weeds)
Funding Details:
Identification Number or DOI: https://doi.org/10.1890/ES11-00128.1
URI: http://eprints.usq.edu.au/id/eprint/29780

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