Increasing accuracy of lake nutrient predictions in thousands of lakes by leveraging water clarity data

Aquatic scientists require robust, accurate information about nutrient concentrations and indicators of algal biomass in unsampled lakes in order to understand and predict the effects of global climate and land-use change. Historically, lake and landscape characteristics have been used as predictor variables in regression models to generate nutrient predictions, but often with significant uncertainty. An alternative approach to improve predictions is to leverage the observed relationship between water clarity and nutrients, which is *Correspondence: txw19@psu.edu Author Contribution Statement: All authors contributed to the development of the paper. T.W. and N.R.L. led the writing of the manuscript. E.M.S., E.M.H., N.B.W., and M.L.B. performed the statistical analysis. K.B.S.K., I.M., and J.S. performed database queries and summaries. All coauthors edited and contributed to writing. After the coleads, authors are listed in alphabetical order by groups according to level of contribution. Data Availability Statement: All data are available from the LAGOSNE R package (https://cran.r-project.org/web/packages/LAGOSNE). The code and data required to perform the analysis described in this article are located at https://doi.org/10.5281/zenodo.3484680. Associate editor: Mark Scheuerell Additional Supporting Information may be found in the online version of this article. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.