A Participatory Approach for Balancing Accuracy and Complexity in Modeling Resilience and Robustness

Abstract

Robustness and resilience are widely used in the biological sciences and related disciplines to describe how systems respond to change. Robustness is the ability to tolerate change without adapting or moving to another state. Resilience refers to the ability for a system to sustain a perturbation and maintain critical functions. Robustness and resilience transcend levels of biological organization, though they do not scale directly across levels. We live in an era of novel stressors and unprecedented change, including climate change, emerging environmental contaminants, and changes to the Earth's biogeochemical and hydrological cycles. We envision a common framework for developing models to predict the robustness and resilience of biological functions associated with complex systems that can transcend disciplinary boundaries. Conceptual and quantitative models of robustness and resilience must consider cross-scale interactions of potentially infinite complexity, but it is impossible to capture everything within a single model. Here, we discuss the need to balance accuracy and complexity when designing models, data collection, and downstream analyses to study robustness and resilience. We also consider the difficulties in defining the spatiotemporal domain when studying robustness and resilience as an emergent property of a complex system. We suggest a framework for implementing transdisciplinary research on robustness and resilience of biological systems that draws on participatory stakeholder engagement methods from the fields of conservation and natural resources management. Further, we suggest that a common, simplified model development framework for describing complex biological systems will provide new, broadly relevant educational tools. Efficient interdisciplinary collaboration to accurately develop a model of robustness and resilience would enable rapid, context-specific assessment of complex biological systems with benefits for a broad range of societally relevant problems.