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Organized Oral Session

Session: A comparative perspective on integrating ecological forecasting with decision-making

OOS 23-1 - Guidelines for bridging the divide between ecological forecasts and decision-making

Wednesday, August 17, 2022

8:00 AM – 8:15 AM EDT

Location: 520C

Presenting Author(s)

KB

Korryn Bodner

MAP Centre for Urban Health Solutions, St. Michael’s Hospital, Canada

Co-author(s)

CR

Carina Rauen Firkowski

McGill University & University of Toronto, Canada
JB

Joseph R. Bennett, Bennett, PhD

Associate Professor
Carleton University
Ottawa, Ontario, Canada
CB

Cole B. Brookson

University of Alberta, Canada
MD

Michael C. Dietze

Boston University
Boston, Massachusetts, United States
SG

Stephanie Green

University of Alberta, Canada
Josie Hughes

Research Scientist
National Wildlife Research Centre, Environment and Climate Change Canada
Ottawa, Ontario, Canada
JK

Jeremy T. Kerr

Department of Biology, University of Ottawa
Ottawa, Ontario, Canada
MK

Mélodie Kunegel-Lion

Natural Resources Canada
SL

Shawn J. Leroux

Associate Professor
Memorial University of Newfoundland
St John's, Newfoundland and Labrador, Canada
EM

Eliot McIntire

Natural Resources Canada
PM

Péter K. Molnár

University of Toronto, Canada
CS

Craig Simpkins

University of Auckland; Wilfrid Laurier University
E. W. Tekwa

Postdoctoral Fellow
University of British Columbia
Vancouver, British Columbia, Canada
AW

Alexander Watts

Esri Canada
MF

Marie-Josée Fortin

University of Toronto, Ontario, Canada

Background/Question/Methods
As the world continues to experience rapid human-induced changes, decision-makers are often pressured to respond to environmental and social challenges in uncertain circumstances. Forecasting models are important analytical tools that can help decision-makers prepare for these challenges and will be increasingly used to address a range of issues from species conservation to resource management to disease outbreaks. Here, we outline general best practices for creating forecasts in decision-making contexts. These best practices were developed through a working group and are based on the experiences of researchers working in academia, government, and industry.

Results/Conclusions
Our best practices encompass strategies for better model development at the science-policy interface. They range from specific technical practices such as quantifying uncertainties, building adaptable models, and creating reusable code, to soft skill practices such as building diverse teams, developing communication techniques, and addressing biases for different audiences. We also categorize strategies based on the time and resources required to implement them, providing first and next steps for researchers desiring to begin or further invest in forecasting. Lastly, we highlight some of the main external obstacles that can prevent good forecasting practices from being adopted in reality. This talk serves as the foundation for this oral session and will provide the benchmarks to evaluate where the forecasting community has most succeeded in integrating forecasting and decision-making, and where future effort should be focused.