BioInspired! So What? Now What!

Unfortunately I just got this notification and abstract submission ended July 31. If you are interested, I suggest contacting Josh Lerner (https://eeb.tamu.edu/people/student-roster/lerner/) directly.

We will be convening a special session at the AGU24 conference (https://www.agu.org/annual-meeting) in Washington D.C. the week of December 9-13th. The session, GC:166 Quantifying Resilience Across the Natural and Engineered Sciences, co-organized by Rusty Feagin, Josh Lerner, Astrid Layton, and Murat Erkoc, aims to feature innovative research that utilizes big data approaches, remote sensing, and long-term ecological and engineering datasets to push discussion about generalities may exist across natural and human-designed systems. We encourage you to consider submitting an abstract or attending the session if you will be at the meeting. Please reach out to the session conveners if you have any questions or to initiate discussion. Abstract submission ends Wednesday, July 31st 2024 at 23:59 EDT. Abstract guidelines are available here: https://www.agu.org/annual-meeting/present#abstracts 

Session viewer and abstract submission system is available here: https://agu.confex.com/agu/agu24/prelim.cgi/Home/0  

GC: 166 Quantifying Resilience Across the Natural and Engineered Sciences
The notion of “resilience” has been conceptualized in a variety of ways across natural and engineered systems, giving rise to numerous quantitative frameworks, methods, metrics, and statistical analyses. Resilience can be broadly defined as the ability of a system to withstand and recover from perturbation. Individual attempts to quantify resilience have led to discoveries about system dynamics, tipping points, and alternative stable states. Still, there has been little consensus on generating standardized metrics for quantifying and measuring resilience, so comparing results across systems or disciplines has been challenging. The aim of this session is to synthesize theoretical and empirical work across disciplines and explore the potential for unified resilience metrics to facilitate comparisons across a wide diversity of systems. Particular emphasis will be placed on quantifying system resilience in time series, big data, remote sensing, and long-term datasets in natural and engineered system contexts.