An introduction to agent‐based models as an accessible surrogate to field‐based research and teaching. Issue 22 (2nd October 2020)

• Record Type: Journal Article
• Authors: Murphy, Kilian J.; Ciuti, Simone; Kane, Adam
• Other Names: Cotner Sehoya guestEditor.; Lortie Christopher J. guestEditor.; Lashley Marcus A. guestEditor.
• Is Part Of: Ecology and evolution. Volume 10:Issue 22(2020)
• Page Start: 12482
• Page End: 12498
• Publication Date: 2020-10-02
• Subjects: accessible resource -- agent‐based model -- computational tools -- ecology -- fieldwork -- inclusive resource -- NetLogo -- open‐source resource -- quantitative methods; Ecology -- Periodicals; Evolution -- Periodicals; 577.05
• DOI: 10.1002/ece3.6848
• Languages: English
• ISSNs: 2045-7758

Abstract

Abstract: There are many barriers to fieldwork including cost, time, and physical ability. Unfortunately, these barriers disproportionately affect minority communities and create a disparity in access to fieldwork in the natural sciences. Travel restrictions, concerns about our carbon footprint, and the global lockdown have extended this barrier to fieldwork across the community and led to increased anxiety about gaps in productivity, especially among graduate students and early‐career researchers. In this paper, we discuss agent‐based modeling as an open‐source, accessible, and inclusive resource to substitute for lost fieldwork during COVID‐19 and for future scenarios of travel restrictions such as climate change and economic downturn. We describe the benefits of Agent‐Based models as a teaching and training resource for students across education levels. We discuss how and why educators and research scientists can implement them with examples from the literature on how agent‐based models can be applied broadly across life science research. We aim to amplify awareness and adoption of this technique to broaden the diversity and size of the agent‐based modeling community in ecology and evolutionary research. Finally, we discuss the challenges facing agent‐based modeling and discuss how quantitative ecology can work in tandem with traditional field ecology to improve both methods. Abstract : The COVID‐19 global lockdown may be an early harbinger for future disruptions to fieldwork under climate change scenarios. Agent‐Based models are a powerful and accessible way to surrogate lost field seasons to continue these projects or begin new ones. This innovative method is improving constantly and is an excellent tool for teaching and research in natural science.