Recommendations for quantitative uncertainty consideration in ecology and evolution

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. Ecological and evolutionary studies are currently failing to achieve complete and consistent reporting of model-related uncertainty. We identify three key barriers – a focus on parameter-related uncertainty, obscure uncertainty metrics, and limited recognition of uncertainty propagation – which have led to gaps in uncertainty consideration. However, these gaps can be closed. We propose that uncertainty reporting in ecology and evolution can be improved through wider application of existing statistical solutions and by adopting good practice from other scientific fields. Our recommendations include greater consideration of input data and model structure uncertainties, field-specific uncertainty standards for methods and reporting, and increased uncertainty propagation through the use of hierarchical models. Ecological and evolutionary studies are currently failing to achieve complete and consistent reporting of model-related uncertainty. We identify three key barriers – a focus on parameter-related uncertainty, obscure uncertainty metrics, and limited recognition of uncertainty propagation – which have led to gaps in uncertainty consideration. However, these gaps can be closed. We propose that uncertainty reporting in ecology and evolution can be improved through wider application of existing statistical solutions and by adopting good practice from other scientific fields. Our recommendations include greater consideration of input data and model structure uncertainties, field-specific uncertainty standards for methods and reporting, and increased uncertainty propagation throug