Uncertainty talk for bio-digital technologies: Expert conceptions of uncertainties in genomic selection for forestry

The burgeoning literature on uncertainty analysis shows the need for accessible and transparent information about the limitations of knowledge associated with predictive models for environmental decision-making. Using qualitative analysis, we examine how experts involved in the development of genomic selection (GS) for Canadian public forestry conifer breeding assess and communicate uncertainty. GS is a bio-digital technology characterized by big data compilation, sophisticated statistical analysis, and high-throughput genome sequencing. While GS applications in forestry have the potential to increase yields, reduce errors, and improve the selection of resilient trees in the face of climate change, our data revealed barriers that impede more comprehensive discussions about uncertainty, including assumptions that uncertainty can (and should) be eliminated through the availability of more data, tacit commitments to the application of GS in commercial forestry operations, deterministic assumptions about linear gene-to-trait outcomes, and difficulties discussing uncertainty in collective settings. Uncertainty talk is uncomfortable as it can be perceived as a threat to applied research goals, but uncertainty talk is also a necessary, productive, and generative way to encourage transdisciplinary and inclusive discussions at early stages of predictive model deployment for environmental applications.