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

Abstract

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.

Ecosystem metabolism in salt marsh tidal creeks and ponds : applying triple oxygen isotopes and other gas tracers to novel environments

“…Rates of production for this pond using a different processing approach to the O2 data were reported in Spivak et al (submitted); in that study, daytime GPPO2 was estimated in a model framework by regressing O2 against gas exchange parameters and PAR in order to predict metabolism rates that minimized misfits using ordinary least squares (Winslow et al 2016). Other than relatively small differences due to choices in data processing and gas exchange parameterizations, this approach results in similar NCPO2 (within one standard error) to the rates presented in this manuscript because identical mass balances and data are used.…”

“…The three gas exchange parameterizaitons evaluated in this study were chosen because they are representative of a pond environment with no tidal currents, are functions of readily measureable environmental parameters such as windspeed, and have been applied in other studies as well as modeling toolboxes (Winslow et al 2016). Each parameterization was originally derived following the form:…”

LakeSST: Lake Skin Surface Temperature in French inland water bodies for 1999–2016 from Landsat archives

“…We used the Python packages NumPy (van der Walt et al, 2011), Matplotlib (Hunter, 2007 and Pandas (McKinney, 2010) and the R packages MASS (Venables and Ripley, 2002) and rLakeAnalyzer (Winslow et al, 2016).…”

Networked lake science: how the Global Lake Ecological Observatory Network (GLEON) works to understand, predict, and communicate lake ecosystem response to global change

“…An in-depth description of the state-of-the art in measuring and computing lake metabolism, based on high-frequency free-water gas measurements (Staehr et al 2010), helped catalyze researchers to improve measurements as well as the software commonly used in metabolism calculations (Winslow et al 2016). The use of lake physics indices has become more accessible to ecologists through the development of software (LakeMetabolizer; Read et al 2011) that calculates common metrics of lake physical state, such as the depth of the thermocline, Schmidt stability, and buoyancy frequency.…”