Journal article
Increasing accuracy of lake nutrient predictions in thousands of lakes by leveraging water clarity data
Limnology and oceanography letters, Vol.5(2), pp.228-235
04/2020
DOI: 10.1002/lol2.10134
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.
Details
- Title: Subtitle
- Increasing accuracy of lake nutrient predictions in thousands of lakes by leveraging water clarity data
- Creators
- Tyler Wagner - Pennsylvania State UniversityNoah R. Lottig - University of Wisconsin–MadisonMeridith L. Bartley - Pennsylvania State UniversityEphraim M. Hanks - Pennsylvania State UniversityErin M. Schliep - University of MissouriNathan B. Wikle - Pennsylvania State UniversityKatelyn B. S. King - Michigan State UniversityIan McCullough - Michigan State UniversityJoseph Stachelek - Michigan State UniversityKendra S. Cheruvelil - Michigan State UniversityChristopher T. Filstrup - University of Minnesota, DuluthJean Francois Lapierre - Université de MontréalBoyang Liu - Michigan State UniversityPatricia A. Soranno - Michigan State UniversityPang‐Ning Tan - Michigan State UniversityQi Wang - Michigan State UniversityKatherine Webster - Michigan State UniversityJiayu Zhou - Michigan State University
- Resource Type
- Journal article
- Publication Details
- Limnology and oceanography letters, Vol.5(2), pp.228-235
- DOI
- 10.1002/lol2.10134
- ISSN
- 2378-2242
- eISSN
- 2378-2242
- Publisher
- John Wiley & Sons, Inc
- Number of pages
- 8
- Grant note
- National Science Foundation (EF‐1638679; EF‐1638554; EF‐1638539; EF‐1638550)
- Language
- English
- Date published
- 04/2020
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984446065102771
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