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

Area of Science:

• Ecology
• Systems Biology
• Environmental Science

Background:

• Robustness and resilience are key concepts describing how biological systems respond to change across organizational levels.
• Current environmental changes (climate change, contaminants) necessitate better predictive tools for biological system stability.
• Existing models often struggle with the complexity and cross-scale interactions inherent in biological systems.

Purpose of the Study:

• To propose a common framework for developing predictive models of robustness and resilience in biological systems.
• To address the challenge of balancing model accuracy and complexity in ecological studies.
• To integrate transdisciplinary research and stakeholder engagement for robust biological system assessment.

Main Methods:

• Conceptual and quantitative modeling approaches for robustness and resilience.
• Discussion on balancing accuracy and complexity in model design and data analysis.
• Framework for transdisciplinary research incorporating stakeholder engagement.

Main Results:

• A need for simplified, common model development frameworks to study robustness and resilience.
• Identification of challenges in defining spatiotemporal domains for emergent properties.
• Potential for broadly relevant educational tools and efficient interdisciplinary collaboration.

Conclusions:

• A unified modeling framework is crucial for predicting biological system responses to novel stressors.
• Transdisciplinary research and stakeholder engagement enhance the assessment of ecological robustness and resilience.
• Developing common models facilitates rapid, context-specific assessments for societal benefit.