Agent-Based Models as an inclusive and accessible surrogate to
field-based studies.
- Kilian Murphy,
- Adam Kane
Kilian Murphy
University College Dublin
Corresponding Author:kilian.murphy@ucdconnect.ie
Author ProfileAbstract
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