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LakeMetabolizer: An R package for estimating lake metabolism from free-water oxygen using diverse statistical models

January 1, 2016

Metabolism is a fundamental process in ecosystems that crosses multiple scales of organization from individual organisms to whole ecosystems. To improve sharing and reuse of published metabolism models, we developed LakeMetabolizer, an R package for estimating lake metabolism from in situ time series of dissolved oxygen, water temperature, and, optionally, additional environmental variables. LakeMetabolizer implements 5 different metabolism models with diverse statistical underpinnings: bookkeeping, ordinary least squares, maximum likelihood, Kalman filter, and Bayesian. Each of these 5 metabolism models can be combined with 1 of 7 models for computing the coefficient of gas exchange across the air–water interface (k). LakeMetabolizer also features a variety of supporting functions that compute conversions and implement calculations commonly applied to raw data prior to estimating metabolism (e.g., oxygen saturation and optical conversion models). These tools have been organized into an R package that contains example data, example use-cases, and function documentation. The release package version is available on the Comprehensive R Archive Network (CRAN), and the full open-source GPL-licensed code is freely available for examination and extension online. With this unified, open-source, and freely available package, we hope to improve access and facilitate the application of metabolism in studies and management of lentic ecosystems.

Publication Year 2016
Title LakeMetabolizer: An R package for estimating lake metabolism from free-water oxygen using diverse statistical models
DOI 10.1080/IW-6.4.883
Authors Luke Winslow, Jacob A. Zwart, Ryan D. Batt, Hilary Dugan, R. Iestyn Woolway, Jessica Corman, Paul C. Hanson, Jordan S. Read
Publication Type Article
Publication Subtype Journal Article
Series Title Inland Waters
Index ID 70194448
Record Source USGS Publications Warehouse
USGS Organization Center for Integrated Data Analytics
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