calc_pcrit | R Documentation |
Calculates Pcrit (commonly understood as the threshold below which oxygen consumption rate can no longer be sustained) based on paired PO2 and MO2 values. Five Pcrit metrics are returned using many of the popular techniques for Pcrit calculation: the traditional breakpoint metric (broken stick regression), the nonlinear regression metric (Marshall et al. 2013), the sub-prediction interval metric (Birk et al. 2019), the alpha-based Pcrit method (Seibel et al. 2021), and the linear low O2 (LLO) method (Reemeyer & Rees 2019). To see the Pcrit values plotted, see plot_pcrit
.
calc_pcrit(
po2,
mo2,
mo2_data,
method = "Breakpoint",
avg_top_n = 1,
level = 0.95,
iqr = 1.5,
NLR_m = 0.065,
MR = NULL,
mo2_threshold = Inf,
return_models = FALSE
)
po2 |
a vector of PO2 values. Any unit of measurement should work, but the NLR calculation was optimized using kPa. If the NLR metric is giving you trouble, try converting to kPa using |
mo2 |
a vector of metabolic rate values. Must be the same length and corresponding to |
mo2_data |
for convenience, the output of |
method |
Over the years, many different methods of analysis have been proposed to quantify Pcrit. You must choose one of the following: Alpha, Breakpoint (default), LLO, NLR, Sub_PI, All. If in doubt, try "All". |
avg_top_n |
applies to the |
level |
applies to the |
iqr |
applies to the |
NLR_m |
applies to the |
MR |
applies to the |
mo2_threshold |
applies to the |
return_models |
logical. Should a list of model parameters be returned along with the converged Pcrit values? Default is |
Alpha is calculated from calc_alpha
and the Pcrit corresponding to MR
is returned. This determine's the animal's oxygen supply capacity and calculates the Pcrit at any given metabolic rate of interest. If no MR
is provided, then it defaults to the mean MO2 value from the oxyregulating portion of the curve (as defined by the broken-stick regression).
Data are fit to a broken-stick regression using segmented
.
A subset of observations are chosen only from those with an MO2 < MR
. Then, a linear model is fit through the observations and Pcrit is calculated as the PO2 at which the line reaches MR
.
Data are fit to the following functions: Michaelis-Menten, Power, Hyperbola, Pareto, and Weibull with intercept. Following the method developed by Marshall et al. 2013, the function that best fits the data (smallest AIC) is chosen and the Pcrit is determined as the PO2 at which the slope of the function is NLR_m
(by default = 0.065 following the authors' suggestion).
This metric builds off the Breakpoint
metric and results in a systematically lower Pcrit value. This is useful for applications where it is important to ensure that Pcrit is not being overestimated. It represents a reasonable lower bounded estimate of the Pcrit value for a given trial. Once the Breakpoint
Pcrit is calculated, a 95% prediction interval (can be changed with the level
argument) is calculated around the oxyregulating region (i.e. using PO2 values > breakpoint Pcrit). By default, iqr
provides some filtering of abberant observations to prevent their influence on the calculated prediction interval. Finally, the Sub_PI Pcrit value is returned at the intersection of the oxyconforming line and the lower limit of the oxyregulating prediction interval.
If return_models
is FALSE
(default), a numeric Pcrit value is returned based on method
. If method == "All"
, a named numeric vector of Pcrit values calculated using the Alpha
, Breakpoint
, LLO
, NLR
, and Sub_PI
metrics is returned. If return_models
is TRUE
, then a list of converged Pcrit values, along with breakpoint function parameters, the MR
value used for calculating Pcrit-alpha, a data frame of the "oxyregulating" portion of the curve, and NLR parameters are returned.
Matthew A. Birk, matthewabirk@gmail.com
Birk, Matthew A., K.A.S. Mislan, Karen F. Wishner, and Brad A. Seibel. 2019. “Metabolic Adaptations of the Pelagic Octopod Japetella Diaphana to Oxygen Minimum Zones.” Deep-Sea Research Part I 148: 123–31.
Marshall, Dustin J., Michael Bode, and Craig R. White. 2013. “Estimating Physiological Tolerances - a Comparison of Traditional Approaches to Nonlinear Regression Techniques.” Journal of Experimental Biology 216(12): 2176–82.
Reemeyer, Jessica E., and Bernard B. Rees. 2019. “Standardizing the Determination and Interpretation of Pcrit in Fishes.” Journal of Experimental Biology 222(18): jeb210633.
Seibel, B. A., A. Andres, M. A. Birk, A. L. Burns, C. T. Shaw, A. W. Timpe, C. J. Welsh. 2021. “Oxygen supply capacity breathes new life into the critical oxygen partial pressure (Pcrit).” Journal of Experimental Biology.
plot_pcrit
, calc_MO2
, conv_o2
, calc_alpha
raw_data <- system.file('extdata/pcrit_run/', package = 'respirometry')
o2_data <- import_pyroscience_workbench(folder = raw_data)
mo2_data <- calc_MO2(duration = o2_data$DURATION, o2 = o2_data$CH_1_O2, bin_width = 10, vol = 3)
calc_pcrit(mo2_data = mo2_data)
calc_pcrit(po2 = mo2_data$O2_MEAN, mo2 = mo2_data$MO2, method = 'All', MR = 100)
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