Recommendations for quantitative uncertainty consideration in ecology and evolution
Simmonds, Emily Grace; Adjei, Kwaku Peprah; Cretois, Benjamin; Dickel, Lisa; González-Gil, Ricardo; Laverick, Jack H; Mandeville, Caitlin Marie; Mandeville, Elisabeth G.; Ovaskainen, Otso Tapio; Sicacha Parada, Jorge Armando; Skarstein, Emma; O'Hara, Robert
Original version
10.1016/j.tree.2023.10.012Abstract
Correct quantification and reporting of model uncertainties are fundamental to reliable science.
Failing to fully account for uncertainty in scientific work leads to overconfidence and potentially adverse actions. Despite these consequences, many scientific fields do not achieve consistent quantification of all model-related uncertainties.
The factors that drive uncertainty omission are complex, but span methodological challenges to reporting culture and trepidation about uncertainty interpretation.
As ecological and evolutionary models have an increasingly prominent role in informing policy and action, correct uncertainty accounting becomes more vital.
We have many of the tools necessary to close quantitative uncertainty gaps in ecology and evolution. To achieve complete uncertainty consideration, these tools need to be applied more broadly and should be supported by reporting standards.