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Agent-Based Models as an inclusive and accessible surrogate to field-based studies.
  • Kilian Murphy,
  • Adam Kane
Kilian Murphy
University College Dublin

Corresponding Author:[email protected]

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Adam Kane
University College Dublin
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Barriers to fieldwork exist for many reasons such as physical ability, financial cost, and time availability. Unfortunately, these barriers disproportionately affect minority communities and create a disparity in access to fieldwork experience in the natural science community. Travel restrictions and the global lockdown has extended this barrier to fieldwork across the community and led to increased anxiety about gaps in productivity, especially for 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 future scenarios of travel restrictions such as climate change. We detail the process of model development with a plethora of 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. We also describe the benefits of Agent-Based models as a teaching and training resource for students across education levels. Finally, we discuss the current challenges facing Agent-Based Modeling and discuss how the field of quantitative ecology can work in tandem with traditional field ecology to improve both methods.
29 Jun 2020Submitted to Ecology and Evolution
30 Jun 2020Submission Checks Completed
30 Jun 2020Assigned to Editor
03 Jul 2020Reviewer(s) Assigned
20 Jul 2020Review(s) Completed, Editorial Evaluation Pending
21 Jul 2020Editorial Decision: Revise Minor
17 Aug 20201st Revision Received
18 Aug 2020Submission Checks Completed
18 Aug 2020Assigned to Editor
18 Aug 2020Review(s) Completed, Editorial Evaluation Pending
01 Sep 2020Editorial Decision: Accept