Type:	Package
Title:	Tools for Conducting and Analyzing Respirometry Experiments
Version:	2.0.2
Date:	2025-04-17
Description:	Provides tools to enable the researcher to more precisely conduct respirometry experiments. Strong emphasis is on aquatic respirometry. Tools focus on helping the researcher setup and conduct experiments. Functions for analysis of resulting respirometry data are also provided. This package provides tools for intermittent, flow-through, and closed respirometry techniques.
Imports:	birk, dplyr, graphics, lubridate, marelac, measurements (≥ 1.1.0), methods, minpack.lm, plyr, rlang, seacarb (≥ 3.1), segmented (≥ 1.0-0), stats, utils
License:	GPL-3
Encoding:	UTF-8
RoxygenNote:	7.3.2
NeedsCompilation:	no
Packaged:	2025-04-18 02:31:42 UTC; matthewbirk
Author:	Matthew A. Birk [aut, cre]
Maintainer:	Matthew A. Birk <matthewabirk@gmail.com>
Repository:	CRAN
Date/Publication:	2025-04-18 08:20:02 UTC

Tools for Conducting Respirometry Experiments

Description

Provides tools to enable the researcher to more precisely conduct respirometry experiments. Strong emphasis is on aquatic respirometry. Tools focus on helping the researcher setup and conduct experiments. Analysis of the resulting data is not a focus since analyses are often specific to a particular setup, and thus are better created by the researcher individually. This package provides tools for intermittent, flow-through, and closed respirometry techniques.

Author(s)

Matthew A. Birk, matthewabirk@gmail.com

Parameters of Q10 Temperature Coefficient

Description

Calculates parameters from Q10 temperature coefficient for chemical or biological systems. This function can be used in two ways. 1. if four of the first five parameters are given (Q10, R1, R2, T1, T2) then the fifth parameter is returned, or 2. if R_vec and T_vec are given, then the best Q10 for those data is returned.

Usage

Q10(Q10, R1, R2, T1, T2, R_vec, T_vec, model = FALSE)

q10(Q10, R1, R2, T1, T2, R_vec, T_vec, model = FALSE)

calc_q10(Q10, R1, R2, T1, T2, R_vec, T_vec, model = FALSE)

Arguments

Details

Q_{10} = (R_2 / R_1) ^ {\frac{10}{T_2 - T_1}}

Author(s)

Matthew A. Birk, matthewabirk@gmail.com

See Also

scale_MO2, calc_E, adj_by_temp

Examples

Q10(R1 = 5, R2 = 10, T1 = 10, T2 = 20) # Returns Q10; = 2
Q10(Q10 = 2.66, R1 = 5, T1 = 10, T2 = 20) # Returns R2; = 13.3

# My species has an MO2 of 9.5 umol/g/h at 10 *C. What MO2 should I expect at 13 *C?
Q10(Q10 = 2, R1 = 9.5, T1 = 10, T2 = 13) # expect ~11.7 umol/g/h at 13 *C.

# I measured MO2 at a spectrum of temperatures. What Q10 value best fits my data?
Q10(R_vec = c(1, 2, 5, NA, 18, 33), T_vec = c(0, 10, 20, 30, 40, 50))

# A 100 g individual at 10 *C has an MO2 of 1270 umol/h. How much
# would a 250 g individual likely consume at 14 *C?
Q10(Q10 = 2, R1 = scale_MO2(mass_1 = 100, MO2_1 = 1270, mass_2 = 250), T1 = 10, T2 = 14)

# Visualize MO2 scaling by mass and temperature:
mass <- seq(10, 200, 10)
temp <- 10:25
base_mass <- 50
base_temp <- 20
base_MO2 <- 750
mo2 <- outer(mass, temp, function(mass, temp){
	scale_MO2(mass_1 = base_mass, mass_2 = mass, MO2_1 = Q10(Q10 = 2, R1 = base_MO2,
	 T1 = base_temp, T2 = temp))
})
persp(mass, temp, mo2, xlab = 'Mass (g)', ylab = 'Temperature (*C)', zlab = 'MO2 (umol / hr)',
 theta = 35, phi = 15, expand = 0.5, ticktype = 'detailed', nticks = 10)

Calculate respiratory quotient

Description

Calculates the respiratory quotient (RQ), or ratio of CO2 produced to O2 consumed between observations. To calculate CO2 produced, either DIC or both pH and TA must be provided.

Usage

RQ(
  o2,
  o2_unit = "percent_a.s.",
  pH = NULL,
  TA = NULL,
  DIC = NULL,
  temp = 25,
  sal = 35,
  atm_pres = 1013.25
)

Arguments

Value

ratio of CO2 produced to O2 consumed.

Note

If you want a rough estimate of RQ, but only have pH measurements, TA can be estimated from salinity using guess_TA.

Author(s)

Matthew A. Birk, matthewabirk@gmail.com

See Also

conv_o2, guess_TA

Examples

o2_observations <- c(21, 18, 14.5, 7)
pH_observations <- c(8.05, 7.98, 7.86, 7.65)
TA_observations <- c(2222, 2219, 2208, 2214)

RQ(o2 = o2_observations, o2_unit = 'kPa', pH = pH_observations,
TA = TA_observations, temp = 20, sal = 33)

DIC_observations <- c(2222, 2250, 2284, 2355)
RQ(o2 = o2_observations, o2_unit = 'kPa', DIC = DIC_observations)

RQ(o2 = o2_observations, o2_unit = 'kPa', pH = pH_observations, TA = 2032)

Predict biological parameters at a new temperature

Description

Predicts the values of any inputted biological parameter (e.g. MO2, Pcrit) at a new temperature based on empirical measurements at a range of temperatures. Data can be fit to a temperature-dependence curve using either Q10 or calc_E. By default, the predicted values are also plotted alongside the inputted data to allow the user to assess the quality of the fit.

Usage

adj_by_temp(
  meas_temp,
  meas_x,
  temp_new,
  method = "Q10",
  Q10,
  E,
  show_coef = FALSE,
  plot_fit = TRUE
)

Arguments

Value

If show_coef = FALSE (default), then a numeric vector of new values of "x" are returned. If show_coef = TRUE, then a list of new values and the temperature-dependency coefficient are returned.

Author(s)

Matthew A. Birk, matthewabirk@gmail.com

See Also

Q10, calc_E

Examples

# I measured Pcrit at four different temperatures. What is the Pcrit at an
# intermediate temperature?
adj_by_temp(meas_temp = c(5, 10, 15, 20), meas_x = c(3.1, 6.3, 7, 8.4), temp_new = 18)

# If requested values exceed the inputted temperature range, a message is reported.
# Biology cannot go on forever like the math can.
adj_by_temp(meas_temp = c(10, 15, 20, 25), meas_x = c(4.8, 6, 12.3, 13.6), temp_new = 0:30)

Calculate E temperature coefficient

Description

An E value is a relatively recent metric to parameterize the temperature-sensitivity of a biological rate (MO2). It is similar conceptually (but not numerically) to Q10.

Usage

calc_E(x, temp)

Arguments

Details

E is the slope of the relationship between -ln(x) and 1/(kB T), where kB is the Boltzmann constant expressed in eV/K.

Author(s)

Matthew A. Birk, matthewabirk@gmail.com

References

Deutsch, Curtis et al. 2015. “Climate Change Tightens a Metabolic Constraint on Marine Habitats.” Science 348(6239): 1132–35.

See Also

Q10, adj_by_temp

Examples

calc_E(x = c(1, 2, 3), temp = c(10, 20, 30))

Calculate metabolic rate

Description

Calculates metabolic rate (MO2) given O2 measurements over time. Oxygen measurements are split into bins and MO2s are calculated from each bin (unless bin_width is set to 0). The bin_width parameter defines the width of the bins in timed intervals (e.g. 15 minutes). Linear regressions are fit through each bin and the calculated MO2 is returned as the slope of the change in O2 over time.

Usage

calc_MO2(
  duration,
  o2,
  o2_unit = "percent_a.s.",
  bin_width,
  vol,
  temp = 25,
  sal = 35,
  atm_pres = 1013.25,
  time,
  pH,
  good_data = TRUE
)

calc_mo2(
  duration,
  o2,
  o2_unit = "percent_a.s.",
  bin_width,
  vol,
  temp = 25,
  sal = 35,
  atm_pres = 1013.25,
  time,
  pH,
  good_data = TRUE
)

Arguments

Value

A data frame is returned:

DUR_MEAN

Mean duration of the bin (minutes).

DUR_RANGE

Range of duration timepoints in the bin.

TIME_MEAN

Exists only if the parameter time has values. Mean timestamp of the bin.

TIME_RANGE

Exists only if the parameter time has values. Range of timestamps in the bin.

TEMP_MEAN

Mean temperature of the bin.

PH_MEAN

Exists only if the parameter pH has values. Mean pH of the bin. Averaged using mean_pH().

O2_MEAN

Mean O2 value of the bin in the unit chosen by o2_unit).

O2_RANGE

Range of O2 values in the bin.

MO2

Metabolic rate (umol O2 / hour).

R2

Coefficient of determination for the linear regression fit to calculate MO2.

N

Number of observations in the bin.

Note

Whole-animal MO2 is returned. If mass-specific MO2 is desired, the output from calc_MO2 can be divided by the animal's mass. No matter what unit of oxygen partial pressure or concentration measurement you put into the function as o2_unit, the output in the MO2 column is always expressed in umol O2 / hour. This is because there is a vast variety of units for which people prefer to report dissolved oxygen levels, but most physiologists are at least unified in reporting metabolic rate as umol O2 per hour. If you prefer to report MO2 as mg O2 per hour, for example, you can always do something like: conv_resp_unit(df$MO2, from = 'umol_O2 / hr', 'mg_O2 / hr') If only beginning and ending O2 observations are known, consider using closed. Both functions will work fine, but closed is simpler.

Author(s)

Matthew A. Birk, matthewabirk@gmail.com

See Also

make_bins, calc_b, closed, scale_MO2, conv_resp_unit

Examples

# get O2 data
file <- system.file('extdata', 'witrox_file.txt', package = 'respirometry')
o2_data <- na.omit(import_witrox(file, split_channels = TRUE)$CH_4)

# calculate MO2
(mo2_5_min <- calc_MO2(duration = o2_data$DURATION, o2 = o2_data$O2,
bin_width = 5, vol = 10, temp = o2_data$TEMP, sal = o2_data$SAL))

# what if measurements from the 10 to 12 minute marks can't be trusted?
bad_data = o2_data$DURATION >= 10 & o2_data$DURATION <= 12
(mo2_5_min <- calc_MO2(duration = o2_data$DURATION, o2 = o2_data$O2,
bin_width = 5, vol = 10, temp = o2_data$TEMP, sal = o2_data$SAL, good_data = !bad_data))

# easily make a Pcrit plot
plot(mo2_5_min$O2_MEAN, mo2_5_min$MO2)

# I want to express MO2 in mg per min instead.
(mo2_5_min$MO2 <- conv_resp_unit(value = mo2_5_min$MO2, from = 'umol_O2 / hr', to = 'mg_O2 / min'))

# just endpoint measurement:
calc_MO2(duration = o2_data$DURATION, o2 = o2_data$O2,
bin_width = Inf, vol = 10, temp = o2_data$TEMP, sal = o2_data$SAL)

# In my trial, observations above 77% air saturation were really noisy, but much less noisy at
# lower O2 values. I want to adjust my bin width based on the PO2 to obtain the best balance of
# resolution and precision throughout the whole trial. Below 77% a.s., use 4 minute bins. Above
# 77% a.s. use 10 minute bins.
bins = data.frame(o2 = c(77, 100), width = c(4, 10))
calc_MO2(duration = o2_data$DURATION, o2 = o2_data$O2,
bin_width = bins, vol = 10, temp = o2_data$TEMP, sal = o2_data$SAL)

Calculate the oxygen supply capacity (alpha)

Description

The oxygen supply capacity (\alpha) is a species- and temperature-specific value quantifying an animal's ability to extract oxygen from the ambient medium to support its metabolism (e.g. umol O2 / g / hr / kPa). This function calculates \alpha based on the single highest \alpha0 (MO2/PO2) value in the dataset. If there are outliers that make this prohibitive, consider setting a threshold MO2 value with mo2_threshold.

Usage

calc_alpha(po2, mo2, avg_top_n = 1, MR = NULL, mo2_threshold = Inf)

Arguments

Value

Returns a list of 1) alpha, 2) a list of the PO2, MO2, and alpha0 value(s) where alpha was reached (the number of observations averaged is set by avg_top_n), and 3) the Pcrit at a metabolic rate of MR.

Author(s)

Matthew A. Birk, matthewabirk@gmail.com

References

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.

See Also

calc_pcrit, plot_pcrit

Examples

mo2_data <- read.csv(system.file('extdata', 'mo2_v_po2.csv', package = 'respirometry'))
calc_alpha(po2 = mo2_data$po2, mo2 = mo2_data$mo2, MR = 1.5) # MR set to 1.5 to capture the 
# Pcrit corresponding to some of the lowest MO2 values recorded (something close to SMR).

# extract the alpha0 values that were averaged together
sapply(calc_alpha(po2 = mo2_data$po2, mo2 = mo2_data$mo2, 
  MR = 1.5, avg_top_n = 3)$alpha_obs, function(i) i[3])

Calculate the metabolic scaling coefficient, b

Description

For most organisms, metabolic rate does not scale linearly, but rather according to a power function: MO2 = b0 * M^b. This function estimates the scaling coefficient, b, and normalization constant, b0, given MO2s from different sized individuals.

Usage

calc_b(mass, MO2, method = "nls", plot = "linear", b0_start = 1)

Arguments

Details

MO2 = b0 * M^b

where b0 is species-specific normalization constant, M is mass and b is the scaling coefficient.

Value

Returns a list of 1) the b value, 2) a vector of b0 values corresponding to the input MO2 values, and 3) an average b0 that can be used for summarizing the relationship with an equation.

Author(s)

Matthew A. Birk, matthewabirk@gmail.com

See Also

scale_MO2, calc_MO2

Examples

# Simple example
mass <- c(1, 10, 100, 1000, 40, 4, 400, 60, 2, 742, 266, 983) # made up values
MO2 <- mass ^ 0.65 + rnorm(n = length(mass)) # make up some data
calc_b(mass = mass, MO2 = MO2)

# How about some mass-specific MO2s?
msMO2 <- mass ^ -0.25 + rnorm(n = length(mass), sd = 0.05)
calc_b(mass = mass, MO2 = msMO2)
calc_b(mass = mass, MO2 = msMO2, plot = "log")

Calculate Pcrit

Description

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.

Usage

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
)

Arguments

Details

Alpha Pcrit

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).

Breakpoint Pcrit

Data are fit to a broken-stick regression using segmented.

LLO Pcrit

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.

NLR Pcrit

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).

Sub_PI Pcrit

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.

Value

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.

Author(s)

Matthew A. Birk, matthewabirk@gmail.com

References

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.

See Also

plot_pcrit, calc_MO2, conv_o2, calc_alpha

Examples

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)

Closed respirometry

Description

Returns the unknown parameter given 3 of 4 parameters to calculate respiration rate in a closed respirometer. This is useful both for basic closed respirometry setups, and also for the closed measurement phase of intermittent respirometry. Note that this is mainly useful for designing respirometry experiments. If you already have a timeseries dataset of oxygen measurements you want to analyze, use calc_MO2 instead.

Usage

closed(MO2, delta_pO2, duration, vol, temp = 25, sal = 35, atm_pres = 1013.25)

Arguments

Note

If there are more than two O2 observations, consider using calc_MO2.

Author(s)

Matthew A. Birk, matthewabirk@gmail.com

See Also

flush_water, calc_MO2

Examples

# I've read in the literature that my animal has an SMR of 200 umol/h. How large of a
# respirometer do I want if I want it to breathe down to 80% air saturation in 30 minutes?
closed(MO2 = 200, delta_pO2 = 100 - 80, duration = 30) # returns respirometer volume

# I've read in the literature that my animal has an SMR of 1000 umol/h. How long will it take to
# breathe down a 50 L respirometer by 10% air saturation?
closed(MO2 = 1000, delta_pO2 = 10, vol = 50) # returns the duration to breathe down the O2

# How does animal size affect how long my measurement periods last?
mass_range <- seq(100, 400, 50)
dur_range <- (closed(MO2 = scale_MO2(mass_1 = 100, MO2_1 = 400, mass_2 = mass_range),
 delta_pO2 = 20, vol = 10))
plot(mass_range, dur_range, type = 'b')

# What is the MO2 if O2 drops 0.44 mg/l in 33 minutes when the respirometer volume is 30 L?
closed(delta_pO2 = conv_o2(o2 = 0.44, from = 'mg_per_l', to = 'percent_a.s.'), duration = 33,
 vol = 30)

Calculate CO2 to add to water

Description

Calculates the moles of CO2 gas to be added to a volume of seawater to achieve the desired pCO2. Useful for ocean acidification experiments where CO2 treatments are desired.

Usage

co2_add(
  goal_pco2,
  start_pH,
  vol,
  temp = 25,
  sal = 35,
  TA = NULL,
  atm_pres = 1013.25
)

Arguments

Value

moles of CO2 gas to be added to the seawater.

Note

It is assumed that all of the CO2 added dissolves and remains in solution. This can be achieved (almost completely) by bubbling CO2 according to Jokiel et al. 2014.

Author(s)

Matthew A. Birk, matthewabirk@gmail.com

References

Jokiel PL, Bahr KD, Rodgers KS. 2014. Low-cost, high-flow mesocosm system for simulating ocean acidification with CO2 gas. Limnol Oceanogr Methods. 12:313–322.

See Also

co2_rate, flush_carb, carb, peri_pump

Examples

# I want the 50 L reservoir to have a pCO2 = 1000 uatm. It currently has a pH of 7.88.
# How many moles of CO2 gas should be added to the water to reach my desired pCO2?
co2_add(goal_pco2 = 1000, start_pH = 7.88, vol = 50)

Calculate CO2 to add to flush reservoir

Description

Calculates the moles of CO2 gas to be added to a seawater reservoir before flushing a respirometer to achieve the desired pCO2 in the respirometer after the flush. Useful for ocean acidification experiments where CO2 treatments are desired.

Usage

co2_flush(
  goal_pco2,
  resp_pH,
  resp_vol,
  flush_pH,
  flush_vol,
  flush_remain = 0,
  temp = 25,
  sal = 35,
  TA = NULL,
  atm_pres = 1013.25
)

Arguments

Value

moles of CO2 gas to be added to the flush reservoir.

Note

It is assummed that the entire reservoir is drained into the respirometer during the flush. It is also assumed that all of the CO2 added dissolves and remains in solution. This can be achieved (almost completely) by bubbling CO2 according to Jokiel et al. 2014.

Author(s)

Matthew A. Birk, matthewabirk@gmail.com

References

Jokiel PL, Bahr KD, Rodgers KS. 2014. Low-cost, high-flow mesocosm system for simulating ocean acidification with CO2 gas. Limnol Oceanogr Methods. 12:313–322.

See Also

co2_add, co2_rate, flush_carb, carb, peri_pump

Examples

# I want the respirometer to have a pCO2 = 1000 uatm. It currently has a pH of 7.6 and is 90 L.
# If I have a 200 L reservoir with pH = 7.9 seawater, how much CO2 do I need
# to add to the flush reservoir?
co2_flush(goal_pco2 = 1000, resp_pH = 7.6, resp_vol = 90, flush_pH = 7.9, flush_vol = 200)

Calculate CO2 to add to a respirometer intake flow

Description

Calculates the moles of CO2 gas to be added to a respirometer intake seawater flow to achieve the desired pCO2 in the respirometer. Useful for ocean acidification experiments where CO2 treatments are desired. Can be used for acclimation before a trial begins or for use with flow-through respirometry.

Usage

co2_rate(
  goal_pco2,
  init_pH,
  flow_rate,
  temp = 25,
  sal = 35,
  TA = NULL,
  atm_pres = 1013.25,
  MO2 = NULL,
  RQ = 1
)

Arguments

Value

moles of CO2 gas to be added to the intake flow per minute.

Note

It is assumed that all of the CO2 added dissolves and remains in solution. This can be achieved (almost completely) by bubbling CO2 according to Jokiel et al. 2014.

Author(s)

Matthew A. Birk, matthewabirk@gmail.com

References

Jokiel PL, Bahr KD, Rodgers KS. 2014. Low-cost, high-flow mesocosm system for simulating ocean acidification with CO2 gas. Limnol Oceanogr Methods. 12:313–322.

See Also

co2_add, flush_carb, carb, peri_pump

Examples

# I want the respirometer to have a pCO2 = 1000 uatm. How much CO2 per minute do I need
# to add to the intake flow if the ambient pH is 8.1 and it is flowing at 3 LPM?
co2_rate(goal_pco2 = 1000, init_pH = 8.1, flow_rate = 3)

Convert between units of ammonia (NH3) / ammonium (NH4+)

Description

Ammonia or nitrogen excretion can be measured in a variety of ways. Convert between different measurements.

Usage

conv_nh4(n_waste, from = "umol_NH4", to = "all")

Arguments

Details

The sum of NH4+ and NH3 species are considered (i.e. TAN). Conversions are based on relationships and values from the package marelac.

Author(s)

Matthew A. Birk, matthewabirk@gmail.com

See Also

predict_nh3, conv_o2

Examples

conv_nh4(n_waste = 100)
conv_nh4(n_waste = 100, from = 'mg_N')
conv_nh4(n_waste = 100, from = 'mg_N', to = 'umol_NH4')

Convert between units of oxygen partial pressure and concentration

Description

Unfortunately, a consensus on the best way to express how much oxygen is in water has not been formed to date. Until then, this function converts between all commonly used forms of dissolved O2 measurements.

Usage

conv_o2(
  o2 = 100,
  from = "percent_a.s.",
  to = "all",
  temp = 25,
  sal = 35,
  atm_pres = 1013.25
)

Arguments

Details

Conversions are based on relationships and values from the package marelac which utilizes saturation values from Weiss 1970.

Author(s)

Matthew A. Birk, matthewabirk@gmail.com

References

Weiss R. 1970. The solubility of nitrogen, oxygen, and argon in water and seawater. Deep-Sea Research. 17:721-735.

Examples

conv_o2(o2 = 50)
conv_o2(o2 = 1:50, from = "umol_per_l", to = "ml_per_l", temp = 10, sal = 0,
	atm_pres = 1100)
conv_o2()[c('mmHg','kPa')]

Convert units related to respirometry

Description

Converts units of measurement that are joined by " / " or " * ". This function expands upon conv_multiunit to incorporate O2 unit conversion and seawater volume-mass conversions.

Usage

conv_resp_unit(
  value,
  from,
  to,
  temp = 25,
  sal = 35,
  atm_pres = 1013.25,
  o2_conc_base = "per_l"
)

Arguments

Details

The O2 units supported by conv_o2 should be appended with "_O2" (e.g. "kPa_O2"; even "percent_o2_O2") and O2 unit concentrations should drop "per_l" or "per_kg" (e.g. "umol_O2"). To designate seawater mass-volume conversion, append the unit with "_seawater" (e.g. "kg_seawater").

Author(s)

Matthew A. Birk, matthewabirk@gmail.com

See Also

conv_multiunit, conv_o2, rho

Examples

# I read that an animal's MO2 is 1.92 ml O2/kg/min. What is this MO2 in umol O2/g/h?
conv_resp_unit(value = 1.92, from = "ml_O2 / kg / min", to = "umol_O2 / g / hr")

# Krogh's diffusion coefficient for oxygen through gills can be expressed as ml O2 / mm2 (gill
# surface area) / um (gill thickness) / torr (seawater pO2 - blood pO2) / minute at a given
# temperature.
# To convert to another unit:
conv_resp_unit(value = 1e-6, from = "ml_O2 / mm2 / um / torr / min",
to = "umol_O2 / cm2 / um / kPa / hr", temp = 20)

# Now, with a knowledge of gill morphometrics, seawater pO2, and blood pO2, I can compare
# gill diffusion with whole animal MO2.

Adjust respirometer volume for bubbles

Description

Given the volume of the respirometer and the volume of bubbles or air space, the moles of O2 in the system are calculated, and the volume of a respirometer holding the same quantity of O2 with only water is returned.

Usage

correct_bubble(resp_vol, bubble_vol, temp = 25, sal = 35, atm_pres = 1013.25)

Arguments

Details

Depending on temperature and salinity, air holds 20,000x as much O2 as water per unit volume, thus small air bubbles in a respirometer can dramatically increase the amount of O2 an organism has to consume to lower the pO2 or aqueous [O2]. Thus air bubbles lead to underestimations of MO[2]. To correct for this in MO2 calculations after measurement, the volume of the respirometer can be increased. This function calculates the volume needed for MO2 calculations as a function of the volume of air space. Caution: allowing air bubbles into a respirometer is not recommended, even with this post-measurement adjustment. A small error in bubble volume estimation can lead to a large error in calculated metabolic rate.

Value

The volume of a respirometer holding an equivalent quantity of O2 filled only with water.

Note

Due to the high concentration of O2 in air, very small errors in bubble volume estimates can lead to very large differences in the volume returned. Only trust the returned value if you are very confident of the accuracy of your bubble volume estimate.

Author(s)

Matthew A. Birk, matthewabirk@gmail.com

See Also

molvol

Examples

correct_bubble(resp_vol = 50, bubble_vol = 10) # a 10 mL bubble makes a huge difference!

correct_bubble(resp_vol = 50, bubble_vol = 1, temp = 10, sal = 0) 
# in calculating MO2, a volume of 63.8 L should be used rather than the true 50 L.

Estimate carbonate chemistry after a flush

Description

Given the seawater pH inside the respirometer and in the flush reservoir, the new carbonate parameters (including pH) in the respirometer after the flush are estimated.

Usage

flush_carb(
  resp_vol,
  flow_rate,
  duration,
  resp_pH,
  flush_pH,
  temp = 25,
  sal = 35,
  TA = NULL,
  atm_pres = 1013.25
)

Arguments

Value

A data frame returned by carb.

Author(s)

Matthew A. Birk, matthewabirk@gmail.com

See Also

carb, flush_water

Examples

flush_carb(resp_vol = 90, flow_rate = 10, duration = 3, resp_pH = 7.8, flush_pH = 8.1)

# What will be the pH in the respirometer after this flush?
flush_carb(resp_vol = 90, flow_rate = 10, duration = 3, resp_pH = 7.8, flush_pH = 8.1)$pH

Estimate dissolved O2 after a flush

Description

Calculate the pO2 or [O2] in a respirometer after a flush. Given 5 of the 6 parameters, the 6th parameter is calculated.

Usage

flush_o2(resp_vol, flow_rate, duration, resp_o2, flush_o2, final_o2)

Arguments

Author(s)

Matthew A. Birk, matthewabirk@gmail.com

See Also

flush_water, flush_carb

Examples

# What will be the pO2 in the respirometer after this flush?
flush_o2(resp_vol = 90, flow_rate = 10, duration = 3, resp_o2 = 15, flush_o2 = 21)

# I want to bring the pO2 back up to 95% air saturation. How long do I need to flush?
flush_o2(resp_vol = 20, flow_rate = 2, resp_o2 = 75, flush_o2 = 99, final_o2 = 95)

Find percent of water exchanged after a flush

Description

Calculate the proportion of water in a respirometer that is new after a flush. Useful for intermittent respirometry. Given 3 of the first 4 parameters, the 4th parameter is calculated.

Usage

flush_water(vol, flow_rate, duration, perc_fresh, plot = FALSE)

Arguments

Author(s)

Matthew A. Birk, matthewabirk@gmail.com

References

Steffensen JF. 1989. Some errors in respirometry of aquatic breathers: How to avoid and correct for them. Fish Physiol Biochem. 6:49–59. Equation 5.

See Also

flush_carb, min_flow

Examples

# What proportion of a 90 L respirometer is exchanged after 20 minutes of flow at 2 LPM?
flush_water(vol = 90, flow_rate = 2, duration = 20)

# Would it be worth it to extend the flush another five minutes? How much would that
# improve the exchange?
flush_water(vol = 90, flow_rate = 2, duration = 20, plot = TRUE)
# Another five minutes would increase exchange by nearly 10%.
# Perhaps that's worth the extra time...

# Visualize flushing
vol = 150
flow_rate = seq(0, 10, by = 0.5)
duration = 0:60
perc_fresh = outer(flow_rate, duration, function(flow_rate, duration){
	flush_water(vol = vol, flow_rate = flow_rate, duration = duration)
})
persp(flow_rate, duration, perc_fresh, xlab = 'Flow rate (LPM)', ylab = 'Duration (min)',
zlab = '% exchange', theta = 45, phi = 15, expand = 0.5, ticktype = 'detailed', nticks = 10)

Calculate goal pH of a flush reservoir to achieve the post-flush goal pCO2

Description

Calculates the pH of a flush reservoir that is needed to achieve the goal pCO2 after the flush reservoir has been drained into the respirometer.

Usage

goal_flush_pH(
  goal_pco2,
  resp_pH,
  resp_vol,
  flush_vol,
  flush_remain = 0,
  temp = 25,
  sal = 35,
  TA = NULL,
  atm_pres = 1013.25
)

Arguments

Value

pH needed in the flush reservoir to achieve the goal pCO2 post-flush (total scale).

Author(s)

Matthew A. Birk, matthewabirk@gmail.com

See Also

co2_rate, flush_carb, carb, peri_pump

Examples

# I want the respirometer to have a pCO2 = 1000 uatm. It currently has a pH of 7.6 and is 90 L.
# If I have a 200 L reservoir which will be drained completely, what do I want
# the pH of the reservoir to be?
goal_flush_pH(goal_pco2 = 1000, resp_pH = 7.6, resp_vol = 90, flush_vol = 200)

Estimate total alkalinity from salinity

Description

Estimate total alkalinity from salinity and temperature of surface seawater according to Lee et al. 2006. Useful when a rough guess of TA is needed because measuring TA is not possible or practical.

Usage

guess_TA(temp = 25, sal = 35, region = NULL, extend = TRUE)

Arguments

Details

(Sub)tropics

temp \ge 20 and 31 \le sal \le 38

Equatorial Upwelling Pacific

temp \ge 18 and 31 \le sal \le 36.5

North Atlantic

0 \le temp \le 20 and 31 \le sal \le 37

North Pacific

temp \le 20 and 31 \le sal \le 35

Southern Ocean

temp \le 20 and 33 \le sal \le 36

Estimates total alkalinity using the equations provided by Lee et al. 2006 (Geophysical Research Letters). While these equations are designed for open ocean environments, they can provide a rough estimate even for coastal environments. For improved estimate accuracy, the geographic region can be provided. The North Pacific region is longitude-dependent so a longitude of 150 °W is assumed which provides a typical value within the range. Only applicable for surface waters, not very accurate for the ocean interior.

Value

An estimate of the total alkalinity (umol / kg). If NA or NaN are returned, confirm the temp and sal values are within acceptable ranges for the region of interest.

Author(s)

Matthew A. Birk, matthewabirk@gmail.com

References

Lee K, Tong LT, Millero FJ, Sabine CL, Dickson AG, Goyet C, Park G-H, Wanninkhof R, Feely RA, Key RM. 2006. Global relationships of total alkalinity with salinity and temperature in surface waters of the world’s oceans. Geophys Res Lett. 33:L19605.

See Also

predict_pH

Examples

guess_TA(temp = 22, sal = 33)
guess_TA(temp = 12, sal = 33, region = "North Atlantic")
guess_TA(temp = 20, sal = 31:35)

guess_TA(sal = 31) # salinity is within bounds
guess_TA(sal = 30) # salinity is outside the bounds and TA is extrapolated
guess_TA(sal = 30, extend = FALSE) # do not extrapolate TA
guess_TA(sal = 25, extend = TRUE) # will not extrapolate with sal > 5 psu out of bounds

Estimate when the O2 level will reach a defined level

Description

Estimates the time at which O2 will reach a defined level assuming a linear change in O2 over time.

Usage

guess_when(past_o2, past_time, goal_o2, plot = TRUE)

Arguments

Value

A prediction of the time when O2 will reach goal_o2. If past_time is numeric, then a numeric value(s) will be returned. If POSIX, then POSIX will be returned.

Note

Viewing the plot can be valuable if the O2 consumption or production is not linear.

Author(s)

Matthew A. Birk, matthewabirk@gmail.com

See Also

predict_pH, predict_nh3

Examples

guess_when(past_o2 = rnorm(n = 10, mean = 100:91), past_time = 1:10, goal_o2 = 75, plot = FALSE)
guess_when(past_o2 = rnorm(n = 10, mean = 100:91, sd = 5), past_time = 1:10, goal_o2 = 75)
# Viewing the plot can be helpful to see how trustworthy the prediction is
# when signal:noise is low.

Import data from Pyro Oxygen Logger

Description

Imports the standard txt file output from Pyroscience's deprecated Pyro Oxygen Logger software and converts the data into one or more data frames. If using the newer Pyroscience Workbench software, use import_pyroscience_workbench instead.

Usage

import_firesting(
  file,
  o2_unit = "percent_a.s.",
  date = "%m/%d/%Y %X",
  overwrite_sal = NULL,
  keep_metadata = FALSE,
  drop_channels = TRUE,
  split_channels = FALSE
)

Arguments

Details

The following FireSting fiber optic O2 transmitters are supported:

• FireStingO2

• FireStingO2 (1st generation)

If you would like support for the Piccolo2, FireStingO2-Mini, TeX4, or any OEM instruments, email me a data file from the device.

Value

A data frame (or list of data frames) is returned.

TIME

Date and time, POSIXlt format.

DURATION

Duration of measurement trial (minutes).

CH_X_O2

Oxygen measurement in desired unit as determined by o2_unit.

CH_X_TEMP

Temperature recorded or defined at beginning of measurement trial.

CH_X_SAL

Salinity (psu).

...

Channel columns (CH_...) are repeated for each channel.

COMMENT

Comments from FireSting file.

If keep_metadata = TRUE, then the following columns are appended to the returned data frame:

ATM_PRES

Atmospheric pressure (mbar).

HUMIDITY

Relative humidity (% RH).

PROBE_TEMP

Probe temperature.

INTERNAL_TEMP

Transmitter internal temperature.

ANALOG_IN

Voltage input from the extension port (mV).

CH_X_PHASE

Phase recorded. Phase is inversely related to O2.

CH_X_INTENSITY

Intensity is an indicator of the quality of the signal. A low intensity warning is produced by the transmitter below 10 mV.

CH_X_AMB_LIGHT

Ambient light on the sensor. Expressed in mV.

If split_channels = TRUE, then "CH_X_" is removed from the column names and multiple data frames are returned in a named list.

Note

Oxygen conversions are estimates based on the marelac package.

Author(s)

Matthew A. Birk, matthewabirk@gmail.com

See Also

import_pyroscience_workbench, import_presens, import_witrox, conv_o2

Examples

## Not run: 
file <- system.file('extdata', 'pyro_oxygen_logger_file.txt', package = 'respirometry')
import_firesting(file, o2_unit = 'umol_per_l')

# I want each channel as a separate data frame.
data_list <- import_firesting(file, split_channels = TRUE)
data_list$CH_3 # here's the channel 3 data frame.

## End(Not run)

Import data from a PreSens O2 transmitter

Description

Imports the standard text file output from most single channel PreSens fiber optic O2 transmitters and converts the data into a data frame.

Usage

import_presens(
  file,
  o2_unit = "percent_a.s.",
  date = "%d/%m/%y",
  sal = 35,
  all_cols = FALSE,
  split_channels = FALSE
)

Arguments

Details

The following PreSens fiber optic O2 transmitters are supported:

• Fibox 4

• Fibox 3

• Fibox 3 trace

• Fibox 3 LCD trace

• Microx TX3

• Microx TX3 trace

• SDR SensorDish Reader

If you would like support for another PreSens O2 meter, email the package maintainer a data file from the device you would like supported. It is very important to note that the PreSens fiber optics O2 transmitters that are supported with this function (except the Fibox 4) DO NOT account for salinity (i.e. they assume salinity = 0 ppt). If the water sample measured was not fresh water, the oxygen concentrations (e.g. mg per liter or umol per liter) are incorrect in the PreSens txt file. This function corrects these O2 concentrations based on the salinity value defined by the sal argument. Absolute partial pressures (i.e. hPa and torr) will also be slightly different due to the slight influence of salinity on water's vapor pressure. This difference is typically ~0.05% of the recorded value.

Value

A data frame is returned.

TIME

Date and time, POSIXct format.

DURATION

Duration of measurement trial (minutes).

O2

Oxygen measurement in desired unit as determined by o2_unit.

PHASE

Phase recorded. Phase is inversely related to O2. Not included in SDR SensorDish Reader files.

AMPLITUDE

Amplitude recorded. Amplitude is an indicator of the quality of the signal. A low amplitude warning is produced by the transmitter below 2500. Not included in SDR SensorDish Reader files.

TEMP

Temperature recorded or defined at beginning of measurement trial.

ATM_PRES

Atmospheric pressure (mbar).

SAL

Salinity (psu).

ERROR_CODE

Error code from transmitter. See PreSens user manual for translation of error code. Not included in SDR SensorDish Reader files.

Note

Oxygen conversions are based on conv_o2 and therefore differ slightly from the conversions provided by PreSens.

Author(s)

Matthew A. Birk, matthewabirk@gmail.com

See Also

import_pyroscience_workbench, import_firesting, import_witrox, conv_o2

Examples

## Not run: 

# Import a Fibox 3 file.
file <- system.file('extdata', 'fibox_3_file.txt', package = 'respirometry')
import_presens(file, o2_unit = 'umol_per_l', sal = 25)

# Import a Fibox 4 file.
file <- system.file('extdata', 'fibox_4_file.csv', package = 'respirometry')
import_presens(file = file, date = '%d-%b-%Y')

# Import an SDR SensorDish Reader file.
file <- system.file('extdata', 'sdr_file.txt', package = 'respirometry')
import_presens(file = file, date = '%d.%m.%y%X')


## End(Not run)

Import data from Pyroscience Workbench

Description

Imports the raw channel data from Pyroscience Workbench output files. This allows "live" analyses while the trial is still running. This does not utilize the ".pyr" file, nor the text file that is created once the trial is finished. This utilizes the raw channel data found within the "ChannelData" folder that the software makes when the trial starts.

Usage

import_pyroscience_workbench(
  folder,
  o2_unit = "percent_a.s.",
  sal = NULL,
  keep_metadata = FALSE,
  split_channels = FALSE,
  merge_close_measurements = "min"
)

Arguments

Value

A data frame (or list of data frames) is returned.

TIME

Date and time, POSIXct format. If split_channels = FALSE (default), then the timestamp is the average of all the measurements that were merged. For details, see merge_close_measurements.

DURATION

Duration of measurement trial (minutes).

CH_X_O2

Oxygen measurement in desired unit as determined by o2_unit.

CH_X_TEMP

Temperature recorded or defined at beginning of measurement trial.

CH_X_SAL

Salinity (psu). Only displayed if sal != NULL.

CH_X_STATUS

Warning or error messages from Pyroscience Workbench file.

...

Channel columns (CH_...) are repeated for each channel.

If keep_metadata = TRUE, then the following columns are appended to the returned data frame:

CH_X_PHASE

Phase recorded. Phase is inversely related to O2.

CH_X_INTENSITY

Intensity is an indicator of the quality of the signal.

CH_X_AMB_LIGHT

Ambient light on the sensor. Expressed in mV.

CH_X_T_STATUS

Warning or error messages from Pyroscience Workbench file's temperature measurement.

CH_X_ATM_PRES

Atmospheric pressure (mbar).

CH_X_P_STATUS

Warning or error messages from Pyroscience Workbench file's atmospheric pressure measurement.

If split_channels = TRUE, then "CH_X_" is removed from the column names and multiple data frames are returned in a named list.

Note

Oxygen conversions are estimates based on the marelac package.

Author(s)

Matthew A. Birk, matthewabirk@gmail.com

See Also

import_presens, import_witrox, conv_o2

Examples

## Not run: 
folder <- system.file('extdata/pyro_wb/', package = 'respirometry')
import_pyroscience_workbench(folder = folder, o2_unit = 'umol_per_l', sal = c(0, 35))

# I want each channel as a separate data frame.
data_list <- import_pyroscience_workbench(folder = folder, split_channels = TRUE)
data_list$CH_2 # here's the channel 2 data frame.

## End(Not run)

Import data from a Loligo Systems Witrox O2 transmitter

Description

Imports the standard txt file output from Loligo Systems Witrox fiber optic O2 transmitters and converts the data into one or more data frames.

Usage

import_witrox(
  file,
  o2_unit = "percent_a.s.",
  date = "%m/%d/%Y %I:%M:%S %p",
  overwrite_sal = NULL,
  drop_channels = TRUE,
  split_channels = FALSE
)

Arguments

Details

The following Loligo Systems fiber optic O2 transmitters are supported:

• Witrox 4

If you would like support for the Witrox 1, email me a data file from this device.

Value

A data frame (or list of data frames) is returned.

TIME

Date and time, POSIXlt format.

DURATION

Duration of measurement trial (minutes).

ATM_PRES

Atmospheric pressure (mbar).

CH_X_PHASE

Phase recorded. Phase is inversely related to O2.

CH_X_TEMP

Temperature recorded or defined at beginning of measurement trial.

CH_X_SAL

Salinity (psu).

CH_X_O2

Oxygen measurement in desired unit as determined by o2_unit.

...

Channel columns (CH_...) are repeated for each channel.

If split_channels = TRUE, then "CH_X_" is removed from the column names and multiple data frames are returned in a list.

Author(s)

Matthew A. Birk, matthewabirk@gmail.com

See Also

import_pyroscience_workbench, import_firesting, import_presens, conv_o2

Examples

## Not run: 
file <- system.file('extdata', 'witrox_file.txt', package = 'respirometry')
import_witrox(file, o2_unit = 'umol_per_l')

# Oops. I forgot to change the salinity value when I calibrated
# the instrument. Override the values in the file for 35 psu.
import_witrox(file, o2_unit = 'umol_per_kg', overwrite_sal = 35)

# I want each channel as a separate data frame.
data_list <- import_witrox(file, split_channels = TRUE)
data_list$CH_3 # here's the channel 3 data frame.

## End(Not run)

Make time binning thresholds for MO2 calculations

Description

The width of time bins seems to be an under-appreciated consideration when calculating metabolic rates if PO2 or time are interesting covariates. The wider the bins, the higher the precision of your calculated MO2 value (more observations to average over), but at a loss of resolution of an interesting covariate. The narrower the bins, the higher the resolution of the PO2 or time covariate, but at a cost of lower precision. For Pcrit trials, I have found good success using bins of 1/10th the trial duration at the highest PO2s (where good precision is important) and 1/100th the trial duration at the lowest PO2s (where good resolution is important). Thus, these are the defaults, but can be changed as desired.

Usage

make_bins(
  o2,
  duration,
  good_data = TRUE,
  min_o2_width = 1/100,
  max_o2_width = 1/10,
  n_thresholds = 10
)

Arguments

Value

A data.frame with n_thresholds rows and two columns is returned. Each row describes the threshold and the duration of observations that will be binned together at or above the corresponding O2 value.

o2

The various O2 thresholds at which bin widths change.

width

The bin width applied to values greater than the corresponding row's O2 value but less than the next greater O2 value.

Author(s)

Matthew A. Birk, matthewabirk@gmail.com

See Also

calc_MO2

Examples

# get O2 data
file <- system.file('extdata', 'witrox_file.txt', package = 'respirometry')
o2_data <- na.omit(import_witrox(file, split_channels = TRUE)$CH_4)

# Total trial duration is 21.783 minutes

make_bins(o2 = o2_data$O2, duration = o2_data$DURATION) # creates the default 10 bins. At the
# highest O2 levels, bin widths are 21.783/10 = 2.1783 mins and at the lowest O2 levels, bin
# widths are 21.783/100 = 0.21783 mins.

bins <- make_bins(o2 = o2_data$O2, duration = o2_data$DURATION, min_o2_width = 1/20,
max_o2_width = 1/3, n_thresholds = 5) # creates 5 bins. At the highest O2 levels, bin widths are
# 21.783/3 = 7.261 mins and at the lowest O2 levels, bin widths are 21.783/20 = 1.089 mins.

(mo2 <- calc_MO2(duration = o2_data$DURATION, o2 = o2_data$O2,
bin_width = bins, vol = 10, temp = o2_data$TEMP, sal = o2_data$SAL))

Maximum MO2 supported by flow rate

Description

Calculates the maximum oxygen consumption rate (MO2) supported by a respirometer with a given flow rate. Useful for ensuring an acclimating animal maintains a normoxic environment.

Usage

max_MO2(
  flow_rate,
  min_pO2 = 90,
  pO2_in = 100,
  temp = 25,
  sal = 35,
  atm_pres = 1013.25
)

max_mo2(
  flow_rate,
  min_pO2 = 90,
  pO2_in = 100,
  temp = 25,
  sal = 35,
  atm_pres = 1013.25
)

Arguments

Value

The maximum whole-animal oxygen consumption rate (umol / hr) that can be sustained.

Note

Keep in mind that most organisms are very stressed upon being placed in a respirometer and their MO2 may be much higher than basal MO2.

Author(s)

Matthew A. Birk, matthewabirk@gmail.com

References

Steffensen JF. 1989. Some errors in respirometry of aquatic breathers: How to avoid and correct for them. Fish Physiol Biochem. 6:49–59. Equation 8.

See Also

min_flow, flush_water

Examples

max_MO2(flow_rate = 1)

# What is the maximum MO2 organism I can place in my respirometer and still maintain at
# least 75% air saturation when the intake fresh water is 1.5 LPM, 10 °C and 90% air saturated?
(max_mo2 <- max_MO2(flow_rate = 1.5, min_pO2 = 75, pO2_in = 90, temp = 10, sal = 0))

# If a 300 g individual has an MO2 of 2000 umol/hr, how big of an animal can I use?
scale_MO2(mass_1 = 300, MO2_1 = 2000, MO2_2 = max_mo2) # I can almost support a 1 kg individual!

Mean pH by [H+]

Description

Calculates mean pH from a vector of pH values by averaging [H+] rather than numerical pH values.

Usage

mean_pH(pH, na.rm = FALSE, ...)

Arguments

Details

Since pH is on a logarithmic scale, averaging pH values directly does not provide the true arithmetic mean of what is likely truly important to the organism, [H+] (however, see Boutilier and Shelton 1980). Thus, the pH values are converted to [H+] then averaged and converted back to a mean pH value.

Author(s)

Matthew A. Birk, matthewabirk@gmail.com

References

Boutilier RG, Shelton G. 1980. The statistical treatment of hydrogen ion concentration and pH. J Exp Biol. 84:335–339.

Examples

mean_pH(c(7, 8)) # 7.26 rather than 7.5!

Minimum flow rate to support MO2

Description

Calculates the minimum flow rate into a respirometer required to maintain a high pO2. Useful for ensuring an acclimating animal maintains a normoxic environment. It can also be used to estimate the flow rate needed for a given pO2 decrease desired for flow-through respirometry.

Usage

min_flow(
  MO2,
  min_pO2 = 90,
  pO2_in = 100,
  temp = 25,
  sal = 35,
  atm_pres = 1013.25
)

Arguments

Value

The flow rate (liters / min) into the respirometer required for the steady state pO2 to be min_pO2.

Note

Keep in mind that most organisms are very stressed upon being placed in a respirometer and their MO2 may be much higher than basal MO2.

Author(s)

Matthew A. Birk, matthewabirk@gmail.com

References

Steffensen JF. 1989. Some errors in respirometry of aquatic breathers: How to avoid and correct for them. Fish Physiol Biochem. 6:49–59. Equation 8.

See Also

max_MO2, flush_water

Examples

min_flow(MO2 = 1000)

# What is the minimum flow rate required to maintain at least 75% air saturation in a
# respirometer with an organism(s) with an oxygen consumption rate of 1000 umol/h
# when the intake fresh water is 10 °C and 90% air saturated?
min_flow(MO2 = 1000, min_pO2 = 75, pO2_in = 90, temp = 10, sal = 0)

Calculate peristaltic pump gaseous flow rate

Description

Given the number of moles of a gas, calculates the liters to run through a peristaltic pump.

Usage

peri_pump(
  mol,
  species = "O2",
  temp = 25,
  reg_pres,
  reg_unit = "psi",
  atm_pres = 1013.25
)

Arguments

Details

Most mass flow controllers are programmed with a "standard condition" something like 0 *C and 1013 mbar for which they account for the pressure and temperature of an incoming gas source. For setups without expensive mass flow controllers, a more affordable alternative is to use a peristaltic pump. These do not account for variations in incoming gas pressure and temperature and thus, it must be calculated to set the peristaltic pump to the correct RPM.

Author(s)

Matthew A. Birk, matthewabirk@gmail.com

See Also

co2_rate, co2_add

Examples

peri_pump(mol = 0.5, species = 'O2', temp = 10, reg_pres = 5, reg_unit = "kPa")
# To flow 0.5 moles of O2, then flow 11.1 L.

Plot Pcrit

Description

Creates a Pcrit plot (the threshold below which oxygen consumption rate can no longer be sustained) based on paired PO2 and MO2 values. Five Pcrit metrics are plotted: the traditional breakpoint metric (broken stick regression, black), the nonlinear regression metric (Marshall et al. 2013, green), the sub-prediction interval metric (Birk et al. 2019, red), the alpha-based Pcrit method (Seibel et al., 2021, blue), and the linear low O2 (LLO) method (Reemeyer & Rees 2019, purple). For details on how the Pcrit values are calculated, see calc_pcrit.

Usage

plot_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,
  showNLRs = FALSE,
  ...
)

Arguments

Details

Alpha Pcrit

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).

Breakpoint Pcrit

Data are fit to a broken-stick regression using segmented.

LLO Pcrit

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.

NLR Pcrit

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).

Sub_PI Pcrit

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.

Value

A plot is created showing the relationship between PO2 and MO2. Based on the method used, the alpha, breakpoint, LLO, NLR, and/or sub-PI Pcrit values are shown in the title and on the plot by inverted triangles.

For breakpoint and sub-PI methods, the broken-stick regression is shown by black lines. The gray bands represent the confidence interval (defaults to 95% but will change with level).

For the sub-PI method, the dashed red curves signify the prediction interval used. Black circles represent oxyregulating observations used in the generation of the prediction interval, while grey circles represent both the oxyconforming observations and those observations outside the IQR threshold (defined by iqr).

For the NLR method, the green curve represents the best fitting NLR function and the green inverted triangle represents the NLR Pcrit (modified by NLR_m)

For the Alpha method, the blue line represents alpha, which was fit based on the blue circle observation(s). If MR is not defined by the user, then the black points are those that were averaged to choose MR. These are the "oxyregulating" observations based on the breakpoint method.

If showNLRs = TRUE, then a second plot is generated which shows all the NLR functions that converged. Vertical lines represent the Pcrit values corresponding to each curve.

Black = Michaelis-Menten

Red = Power

Green = Hyperbola

Blue = Pareto

Cyan = Weibull with intercept.

Author(s)

Matthew A. Birk, matthewabirk@gmail.com

References

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.

See Also

calc_pcrit, calc_alpha

Examples

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)
plot_pcrit(mo2_data = mo2_data)

par(mfrow = c(2, 1))
plot_pcrit(po2 = mo2_data$O2_MEAN, mo2 = mo2_data$MO2, method = 'All', MR = 100, showNLRs = TRUE)

Predict NH3 / NH4+ concentration post-respiration

Description

Predicts the [NH3] and [NH4+] of seawater after a defined amount of oxygen consumption. Ammonotelic animals excrete the ionized form NH4+ (ammonium) but some of these ions dissociate into unionized NH3 (ammonia) which is toxic for most fishes and crustaceans around 0.4-2.0 mg/L (Boyd 2012).

Usage

predict_nh3(
  o2_drop = 10,
  o2_unit = "percent_a.s.",
  o2_nh4_ratio,
  temp = 25,
  sal = 35,
  pH = 8.1,
  atm_pres = 1013.25
)

Arguments

Details

Given a known amount of oxygen consumed and an estimated O2:N ratio, the amount of NH4 produced can be estimated. Production or consumption of ammonium by "background" microbes or conversion of ammonium to nitrite and nitrate is ignored since bacteria in the respirometer are typically sought to be in low levels. The amount of dissociation to produce ammonia is calculated by Kn.

Value

A list containing the predicted NH3, NH4+, and TAN produced in mg/l.

Author(s)

Matthew A. Birk, matthewabirk@gmail.com

References

Boyd C. 2012. Water Quality. In "Aquaculture: Farming Aquatic Animals and Plants". Blackwell Publishing, Ltd.

See Also

conv_o2, conv_nh4, Kn

Examples

predict_nh3(o2_drop = 25, o2_nh4_ratio = 10)

Predict pH post-respiration

Description

Predicts the pH of seawater after a defined amount of oxygen consumption.

Usage

predict_pH(
  start_o2 = 100,
  end_o2,
  start_pH,
  temp = 25,
  sal = 35,
  RQ = 1,
  TA = NULL,
  all_carb = FALSE
)

Arguments

Details

Given a known amount of oxygen consumed and an estimated respiratory quotient (see Q10), the amount of CO2 produced can be estimated. From this CO2 production estimate, the carbonate chemistry of the seawater can be estimated. Atmospheric pressure is assumed.

Value

If all_carb is FALSE, then a list of the predicted pH (total scale) at the end of the measurement and the predicted pCO2 (uatm) are returned. If all_carb is TRUE, then the predicted carbonate chemistry parameters are returned from carb.

Author(s)

Matthew A. Birk, matthewabirk@gmail.com

See Also

carb, guess_TA

Examples

predict_pH(end_o2 = 75, start_pH = 8.1)
predict_pH(start_o2 = 75, end_o2 = 50, start_pH = 7.96, temp = 15, sal = 33, RQ = 0.88)

# I know pH at the end was 7.8, but what was pH at the beginning?
predict_pH(start_o2 = 75, end_o2 = 100, start_pH = 8.013536) # reverse the order

Mass-correct metabolic rate

Description

For most organisms, metabolic rate does not scale linearly, but rather according to a power curve. This function estimates MO2 or size of an individual organism given the MO2 and size of another individual of a different size. To mass-correct your MO2 data, plug in your desired mass in mass_2 and the output from calc_b to the b parameter.

Usage

scale_MO2(mass_1, MO2_1, mass_2, MO2_2, b = 0.75)

scale_mo2(mass_1, MO2_1, mass_2, MO2_2, b = 0.75)

Arguments

Details

(MO2 = b0 * M^b)

where b0 is species-specific normalization constant, M is mass and b is the scaling coefficient which is around 0.75 for many organisms.

For scaling of mass-specific metabolic rates, use something closer to b = -0.25 rather than b = 0.75.

Author(s)

Matthew A. Birk, matthewabirk@gmail.com

See Also

Q10, calc_b

Examples

# I know a species has an SMR of 800 umol O2/h at 200 g.
# What would be a likely SMR for a 300 g individual?
scale_MO2(mass_1 = 200, MO2_1 = 800, mass_2 = 300)

# Some squids have a much higher scaling coefficient:
scale_MO2(mass_1 = 200, MO2_1 = 800, mass_2 = 300, b = 0.92)

# A 100 g individual at 10 *C has an MO2 of 1270 umol/h. How much
# would a 250 g individual likely consume at 14 *C?
Q10(Q10 = 2, R1 = scale_MO2(mass_1 = 100, MO2_1 = 1270, mass_2 = 250), T1 = 10, T2 = 14)

# Now I have data from real animals and I want to mass-correct them all to a 10 g animal.
mass = 2:20 # obviously not real but you get the point
mo2 = c(44.8, 41, 36, 35, 35, 33.5, 34.5, 40, 30, 23, 27, 30, 25.6, 27.8, 28, 24, 27, 28, 20)
desired_mass = 10

b = calc_b(mass = mass, MO2 = mo2)
scale_MO2(mass_1 = mass, MO2_1 = mo2, mass_2 = desired_mass, b = b$b)

plot(mass, mo2, ylab = 'Raw MO2') # before
plot(mass, scale_MO2(mass_1 = mass, MO2_1 = mo2, mass_2 = 10, b = b$b),
ylab = 'Mass-corrected MO2') # after


# Visualize MO2 scaling by mass and temperature:
mass <- seq(10, 200, 10)
temp <- 10:25
base_mass <- 50
base_temp <- 20
base_MO2 <- 750
mo2 <- outer(mass, temp, function(mass, temp){
	scale_MO2(mass_1 = base_mass, mass_2 = mass, MO2_1 = Q10(Q10 = 2, R1 = base_MO2,
	 T1 = base_temp, T2 = temp))
})
persp(mass, temp, mo2, xlab = 'Mass (g)', ylab = 'Temperature (*C)', zlab = 'MO2 (umol / hr)',
 theta = 35, phi = 15, expand = 0.5, ticktype = 'detailed', nticks = 10)