MR.loops: Calculate Metabolic Rates from Multiple Closed Respirometry...

In rMR: Importing Data from Loligo Systems Software, Calculating Metabolic Rates and Critical Tensions

Description

This function calculates the metabolic rates from multiple closed respirometry loops simultaneously. Requires lots of user input, but is easy to manipulate. Returns list of metabolic rates, as well as the average metabolic rate and the standard deviation of the sample of metabolic rates, as well as biglm objects for each section of data used to calculate MRs.

Usage

MR.loops(data, DO.var.name, time.var.name = "std.time",
             in.DO.meas = "mg/L", out.DO.meas = "mg/L",
             start.idx, stop.idx, syst.vol = 1,
             background.consumption = 0,
             background.indices = NULL,
             temp.C, elevation.m = NULL,
             bar.press = NULL, bar.units = "atm",
             PP.units, time.units = "sec",
             col.vec = c("black","red"),...)

Arguments

data	Must include a time variable in standard POSIXct format. eg "2016-09-25 15:30:00 EST".
DO.var.name	Column name of DO variable, must be entered as character string.
time.var.name	Column name of time variable (which is in POSIXct format) as character. defaults to "std.time" as generated from get.witrox.data().
in.DO.meas	Units of DO measurement entered in the DO variable column: must be one of "mg/L" for miligrams/liter, "PP" for partial pressure, "pct" for saturation percent.
out.DO.meas	Units of DO measurement returned for metabolic rate: must be one of "mg/L" for miligrams/liter, "PP" for partial pressure (units determined by bar.units), "pct" for saturation percent.
start.idx	Character class value or vector matching POSIXct object coding for date time. Each element of the vector represents the start time of a new loop for calculation of metabolic rates.
stop.idx	Character class value or vector matching POSIXct object coding for date time. Each element of the vector represents the stop time of a new loop for calculation of metabolic rates.
syst.vol	System volume in Liters (defaults to 1 L).
background.consumption	Default = 0. If using a one point calibration for background, simply set background.consumption equal to the value of the calculated respiration rate. If using a multi-point calibration, enter a vector of background respiration rates, and enter a corresponding vector for background.indices. CAUTION: The slope must be entered in raw units (i.e. those specified in the input data.frame in the data argument). For example, if the DO.var.name column is recorded in "mg/L", and the POSIXct format goes to the resolution of seconds, background consumption units would need to be entered in mgO2 / sec.
background.indices	If using a multi-point calibration to set the background respiration rate, enter a vector of times for when the respiration rates were calculated. There should be one time point per corresponding value in background.consumption. The background respiration rate is continually factored into all calculations of metabolic rate. The elements of the vector must entered as character strings conforming to the POSIXctformat specified in the time.var.name column.
temp.C	Water temperature in degrees C.
elevation.m	Elevation in m. Only required if bar.press = NULL.
bar.press	barometric pressure in units defined by bar.units argument. Only required if elevation.m = NULL.
bar.units	Units of barometric pressure used as input and in output if DO.meas.out = "PP". Acceptable arguments: "mmHg", "atm", "kpa".
PP.units	Units of barometric pressure used for "PP".
time.units	Denominator for metabolic rate, also displayed as units on X-axis. Acceptable arguments: "hr", "min", "sec".
col.vec	Specifies colors on plot in the following order: 1) scatterplot points, 2) regression lines color.
...	Arguments passed on to internal functions

Value

Returns a list of 2. $MR.summary is of class data.frame with 3 columns: $MR (metabolic rate in user specified units, this is the same as the slope in each linear model), $sd.slope (standard deviation of slopes calculation), $r.square (adjusted r square value from each model). This second object is a list of biglm objects, each one representing a metabolic loop (see McDonnell and Chapman 2016).

Author(s)

Tyler L. Moulton

References

McDonnell, Laura H., and Lauren J. Chapman (2016). "Effects of thermal increase on aerobic capacity and swim performance in a tropical inland fish." Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology 199: 62-70. doi: 10.1016/j.cbpa.2016.05.018.

Roche, Dominique G., et al. (2013). "Finding the best estimates of metabolic rates in a coral reef fish." Journal of Experimental Biology 216.11: 2103-2110. doi: 10.1242/jeb.082925.

See Also

as.POSIXct, strptime, background.resp, Barom.Press, Eq.Ox.conc, biglm,

Examples

## load data ##
data(fishMR)

## create time variable in POSIXct format ##
fishMR$std.time <- as.POSIXct(fishMR$Date.time,
                    format = "%d/%m/%Y %I:%M:%S %p")

## calc background resp rate
bgd.resp <- 
    background.resp(fishMR, "DO.mgL", 
                    start.time = "2015-07-02 16:05:00",
                    end.time = "2015-07-02 16:35:00",
                    ylab = "DO (mg/L)", xlab = "time (min)")

bg.slope.a <- bgd.resp$mat[2]


starts <- c("2015-07-03 01:15:00", "2015-07-03 02:13:00",
            "2015-07-03 03:02:00", "2015-07-03 03:50:00",
            "2015-07-03 04:50:00")

stops <- c("2015-07-03 01:44:00", "2015-07-03 02:35:30",
           "2015-07-03 03:25:00", "2015-07-03 04:16:00",
           "2015-07-03 05:12:00")

metR <- MR.loops(data = fishMR, DO.var.name ="DO.mgL",
                 start.idx = starts, time.units = "hr",
                 stop.idx = stops, time.var.name = "std.time",
                 temp.C = "temp.C", elevation.m = 1180,
                 bar.press = NULL, in.DO.meas = "mg/L", 
                 background.consumption = bg.slope.a,
                 ylim=c(6, 8))

metR$MR.summary


## now lets assume we ran a control loop for background rate
## before and after we ran the MR loops
## let:

bg.slope.b <-bg.slope.a -0.0001 
metRa <- MR.loops(data = fishMR, DO.var.name ="DO.mgL",
                 start.idx = starts, time.units = "hr",
                 stop.idx = stops, time.var.name = "std.time",
                 temp.C = "temp.C", elevation.m = 1180,
                 bar.press = NULL, in.DO.meas = "mg/L", 
                 background.consumption = c(bg.slope.a, bg.slope.b),
                 background.indices = c("2015-07-02 16:20:00",
                                        "2015-07-03 06:00:00"),
                 ylim=c(6, 8))


metRa$MR.summary

# note that the calculated slopes
# diverge as time increases. This is
# because the background respiration
# rate is increasing.

metR$MR.summary-metRa$MR.summary

## This looks great, but you need to check your start and
## stop vectors, otherwise, you could end up with some
## atrocious loops, e.g.:

starts <- c("2015-07-03 01:15:00", "2015-07-03 02:13:00",
            "2015-07-03 03:02:00", "2015-07-03 03:50:00",
            "2015-07-03 04:50:00")

stops <- c("2015-07-03 01:50:00", "2015-07-03 02:35:30",
           "2015-07-03 03:25:00", "2015-07-03 04:16:00",
           "2015-07-03 05:12:00")

metRb <- MR.loops(data = fishMR, DO.var.name ="DO.mgL",
         start.idx = starts,
         stop.idx = stops, time.var.name = "std.time",
         temp.C = "temp.C", elevation.m = 1180,
         bar.press = NULL, in.DO.meas = "mg/L", 
         background.consumption = bg.slope.a,
         ylim=c(6,8))

rMR documentation built on May 2, 2019, 6:51 a.m.