Communicating complex ecological models to non-scientist end users
Authored by Richard M Sibly, Jacob Nabe-Nielsen, Richard A Stillman, Samantha J Cartwright, Katharine M Bowgen, Catherine Collop, Kieran Hyder, Richard Stafford, Robert B Thorpe
Date Published: 2016
DOI: 10.1016/j.ecolmodel.2016.07.012
Sponsors:
European Union
Platforms:
NetLogo
Model Documentation:
Other Narrative
Model Code URLs:
Model code not found
Abstract
Complex computer models are used to predict how ecological systems
respond to changing environmental conditions or management actions.
Communicating these complex models to non-scientists is challenging, but
necessary, because decision-makers and other end users need to
understand, accept, and use the models and their predictions. Despite
the importance of communicating effectively with end users, there is
little guidance available as to how this may be achieved. Here, we
review the challenges typically encountered by modellers attempting to
communicate complex models and their outputs to managers and other
non-scientist end users. We discuss the implications of failing to
communicate effectively in each case. We then suggest a general approach
for communicating with non-scientist end users. We detail the specific
elements to be communicated using the example of individual-based
models, which are widely used in ecology. We demonstrate that despite
their complexity, individual-based models have characteristics that can
facilitate communication with non-scientists. The approach we propose is
based on our experiences and methods used in other fields, but which
until now have not been synthesised or made broadly available to
ecologists. Our aim is to facilitate the process of communicating with
end users of complex models and encourage more modellers to engage in it
by providing a structured approach to the communication process. We
argue that developing measures of the effectiveness of communication
with end users will help increase the impact of complex models in
ecology. Crown Copyright (C) 2016 Published by Elsevier B.V.
Tags
Agent-based model
Decision-Making
Policy
Pesticides
population
systems
Approximate bayesian computation
Individual-based
ecology
Environmental-management
Changing world