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.
|Original language||English (US)|
|Number of pages||8|
|Journal||Limnology And Oceanography Letters|
|State||Published - Apr 2020|
Bibliographical noteFunding Information:
This research was funded by the National Science Foundation (EF‐1638679, EF‐1638554, EF‐1638539, and EF‐1638550). Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
This research was funded by the National Science Foundation (EF-1638679, EF-1638554, EF-1638539, and EF-1638550). Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
© 2019 The Authors. Limnology and Oceanography Letters published by Wiley Periodicals, Inc. on behalf of Association for the Sciences of Limnology and Oceanography.