The role of validation in optimization models for forest management

Abstract

Abstract Key message A validation convention can be established for forest management optimization models. It consists of (1) the delivery of face validation, (2) performing at least one other validation technique, and (3) an explicit discussion of how the optimization model fulfills the stated purpose. Validation by potential users or external experts is of high importance. Context Optimization modeling has long assisted the management of forest ecosystems, but the credibility of these models has always been debated with criticisms concerning data quality, failures to include relevant processes in the scope of models, and the inclusion of unrealistic assumptions. Validation is widely considered to be crucial to establishing the credibility of models in general, but how to validate optimization models in particular represents a permanent question generally in operations research. Aims We aim to synthesize practical recommendations for the development of validation frameworks in the optimization modeling for forest management. Methods We selected a sample of 46 studies devoted to optimization models to be applied in practice, analysed the contents with respect to validation, and provided a critical review. Results We (1) clarified the meaning and usage of different validation-related terms that are commonly encountered in the literature, (2) identified and categorised the various methods and frameworks that are used to demonstrate model credibility, and (3) derived organizing principles that helped to suggest improvements in validation frameworks. Conclusions A practical validation convention can be established and we suggest the convention to consist of three stages. By providing structured and consistent information about validation processes, researchers in forest management optimization can better demonstrate the credibility of their work to readers and potential users.

Keywords

• Ecological modelling
• Decision support system
• Verification
• Model credibility
• Sensitivity analysis
• Environmental management