Representing driver-response complexity in ecosystems using an improved conceptual model

This conceptual article continues a discussion into the nature of complexity in ecosystems and environmental change through the improved conceptual model (ICM). The ICM developed is useful for reducing the “usability” gap (i.e. between what scientists consider useful information and what users consider usable in decision-making) by improving understanding and representation of complexity in ecosystems, assessment of climate change impacts, and development of adaptation and mitigation strategies. The ICM is demonstrated by applying it to an agroecosystem in the southeastern United States as a case study. It improves Anandhi and Bentley (2018)’s framework by adding indicators, theory (complex adaptive systems theory), a framework (Cynefin framework), and a model (knowledge hierarchy) to the conceptualization. Since this study focused on demonstrating the framework, several simplifying assumptions were made for conciseness and simplicity: selecting precipitation and temperature variables to represent climate, having fewer indicators (frost and wet/dry spells), influencing frost only by minimum temperature, influencing wet/dry spells by daily precipitation, assuming equal likelihood of potential changes in climate scenarios, selecting studies based on impacts, and selecting fewer adaptation strategies. Future studies could use the ICM framework to examine more closely mathematical relationships among climate variables, indicators, prediction uncertainty, and decision making in more realistic scenarios, choosing multiple indicators with detailed information and measured data in each.