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

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  • Bentley, Chance
  • Anandhi, Aavudai

Abstract

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.

Suggested Citation

  • Bentley, Chance & Anandhi, Aavudai, 2020. "Representing driver-response complexity in ecosystems using an improved conceptual model," Ecological Modelling, Elsevier, vol. 437(C).
  • Handle: RePEc:eee:ecomod:v:437:y:2020:i:c:s0304380020303902
    DOI: 10.1016/j.ecolmodel.2020.109320
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    References listed on IDEAS

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    1. Aavudai Anandhi & Jean L. Steiner & Nathaniel Bailey, 2016. "A system’s approach to assess the exposure of agricultural production to climate change and variability," Climatic Change, Springer, vol. 136(3), pages 647-659, June.
    2. Rammel, Christian & Stagl, Sigrid & Wilfing, Harald, 2007. "Managing complex adaptive systems -- A co-evolutionary perspective on natural resource management," Ecological Economics, Elsevier, vol. 63(1), pages 9-21, June.
    3. Sean Snyder, 2013. "The Simple, the Complicated, and the Complex: Educational Reform Through the Lens of Complexity Theory," OECD Education Working Papers 96, OECD Publishing.
    4. Louise Beveridge & Stephen Whitfield & Andy Challinor, 2018. "Crop modelling: towards locally relevant and climate-informed adaptation," Climatic Change, Springer, vol. 147(3), pages 475-489, April.
    5. Clare Chua Chow & Rakesh Sarin, 2002. "Known, Unknown, and Unknowable Uncertainties," Theory and Decision, Springer, vol. 52(2), pages 127-138, March.
    6. Anandhi, Aavudai, 2017. "CISTA-A: Conceptual model using indicators selected by systems thinking for adaptation strategies in a changing climate: Case study in agro-ecosystems," Ecological Modelling, Elsevier, vol. 345(C), pages 41-55.
    7. Mercure, J.-F. & Paim, M.A. & Bocquillon, P. & Lindner, S. & Salas, P. & Martinelli, P. & Berchin, I.I. & de Andrade Guerra, J.B.S.O & Derani, C. & de Albuquerque Junior, C.L. & Ribeiro, J.M.P. & Knob, 2019. "System complexity and policy integration challenges: The Brazilian Energy- Water-Food Nexus," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 230-243.
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    Cited by:

    1. Khaleel Muhammed & Aavudai Anandhi & Gang Chen, 2022. "Comparing Methods for Estimating Habitat Suitability," Land, MDPI, vol. 11(10), pages 1-19, October.

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