View source: R/metab.bookkeep.R

metab.bookkeep | R Documentation |

This model is a simple model based on the assumption that movements in DO during the day are due to NEP and gas exchange. Respiration is estimated from night-time decreases. GPP is calculated from the algebraic manipulation of NEP and R. Based on Cole et al 2000.

metab.bookkeep(do.obs, do.sat, k.gas, z.mix, irr, ...)

`do.obs` |
Vector of dissovled oxygen concentration observations, mg L^-1 |

`do.sat` |
Vector of dissolved oxygen saturation values based on water temperature. Calculate using o2.at.sat |

`k.gas` |
Vector of kGAS values calculated from any of the gas flux models (e.g., k.cole) and converted to kGAS using k600.2.kGAS |

`z.mix` |
Vector of mixed-layer depths in meters. To calculate, see ts.meta.depths |

`irr` |
Integer vector of 1's (daytime) and 0's (nighttime), or numeric vector of irradiance that will be converted to boolean 1's and 0's if "datetime" is passed via |

`...` |
additional arguments to be passed, particularly |

A data.frame with columns corresponding to components of metabolism

- GPP
numeric estimate of Gross Primary Production,

*mg O2 / L / d*- R
numeric estimate of Respiration,

*mg O2 / L / d*- NEP
numeric estimate of Net Ecosystem production,

*mg O2 / L / d*

R. Iestyn Woolway, Hilary Dugan, Luke A Winslow, Ryan Batt, Jordan S Read, GLEON fellows

Cole, Jonathan J., Michael L. Pace, Stephen R. Carpenter, and James F. Kitchell. 2000.
*Persistence of Net Heterotrophy in Lakes during Nutrient Addition and Food Web Manipulations*.
Limnology and Oceanography 45 (8): 1718-1730. doi:10.4319/lo.2000.45.8.1718.

metab.bayesian, metab.mle, metab.kalman

library(rLakeAnalyzer) Sys.setenv(TZ='GMT') doobs = load.ts(system.file('extdata', 'sparkling.doobs', package="LakeMetabolizer")) wtr = load.ts(system.file('extdata', 'sparkling.wtr', package="LakeMetabolizer")) wnd = load.ts(system.file('extdata', 'sparkling.wnd', package="LakeMetabolizer")) #Subset a day mod.date = as.POSIXct('2009-07-08', 'GMT') doobs = doobs[trunc(doobs$datetime, 'day') == mod.date, ] wtr = wtr[trunc(wtr$datetime, 'day') == mod.date, ] wnd = wnd[trunc(wnd$datetime, 'day') == mod.date, ] k.gas = k600.2.kGAS.base(k.cole.base(wnd[,2]), wtr[,3], 'O2') do.sat = o2.at.sat.base(wtr[,3], altitude=300) # Must supply 1 for daytime timesteps and 0 for nighttime timesteps irr = as.integer(is.day(doobs[,1], 45)) metab.bookkeep(doobs[,2], do.sat, k.gas, z.mix=1, irr, datetime=doobs$datetime)

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