Open AccessIncreasing accuracy of lake nutrient predictions in thousands of lakes by leveraging water clarity data
Author(s) -
Wagner Tyler,
Lottig Noah R.,
Bartley Meridith L.,
Hanks Ephraim M.,
Schliep Erin M.,
Wikle Nathan B.,
King Katelyn B. S.,
McCullough Ian,
Stachelek Jemma,
Cheruvelil Kendra S.,
Filstrup Christopher T.,
Lapierre Jean Francois,
Liu Boyang,
Soranno Patricia A.,
Tan PangNing,
Wang Qi,
Webster Katherine,
Zhou Jiayu
Publication year - 2020
Publication title -
limnology and oceanography letters
Language(s) - English
Resource type - Journals
ISSN - 2378-2242
DOI - 10.1002/lol2.10134
Subject(s) - nutrient , clarity , environmental science , leverage (statistics) , chlorophyll a , hydrology (agriculture) , ecology , statistics , mathematics , biology , biochemistry , geotechnical engineering , engineering , botany
Abstract 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 possible because water clarity is more commonly measured than lake nutrients. We used a joint‐nutrient model that conditioned predictions of total phosphorus, nitrogen, and chlorophyll a on observed water clarity. Our results demonstrated substantial reductions (8–27%; median = 23%) in prediction error when conditioning on water clarity. These models will provide new opportunities for predicting nutrient concentrations of unsampled lakes across broad spatial scales with reduced uncertainty.