Metabolic chamber example
Now try a sequence of air temperatures for a specified core temperature, mass, fur/feather depth and base skin wetness, with the option to sweat at 0.25 % increments to cool down if needed. Note that the default settings make the environment a black-body one (all surface and air temperatures the same) with low (5%) relative humidity and low wind speed (0.1 m/s), as is often the case in metabolic chamber experiments. This example uses parameters for the Australian budgerigar Melopsittacus undulatus and compares it against data from Weathers & Schoenbaechler (1976).
Weathers, W.W. & Schoenbaechler, D.C. (1976) Regulation of body temperature in the Budgerygah, Melopsittacus undulatus. Australian Journal of Zoology, 24, 39–47.
library(NicheMapR)
# environment
TAs <- seq(0, 50, 1) # air temperature (°C)
VEL <- 0.1 # wind speed (m/s)
vd <- WETAIR(rh = 30, db = 40)$vd # Weathers and Schoenbaechler had 16.7 mm Hg
# above 40 °C = 30% RH at 40 °C
vd_sat <- WETAIR(rh = 100, db = TAs)$vd # Weathers and Schoenbaechler had 16.7 mm Hg
# above 40 °C = 30% RH at 40 °C
exp_rh <- vd/vd_sat * 100
exp_rh[exp_rh > 100] <- 100
exp_rh[TAs < 30] <- 15
hum <- exp_rh
# core temperature
TC <- 38 # core temperature (°C)
TC_MAX <- 43 # maximum core temperature (°C)
TC_INC <- 0.25 # increment by which TC is elevated (°C)
# size and shape
AMASS <- 0.0337 # mass (kg)
SHAPE_B <- 1.1 # start off near to a sphere (-)
SHAPE_B_MAX <- 5 # maximum ratio of length to width/depth
UNCURL <- 0.1 # allows the animal to uncurl to SHAPE_B_MAX, the value being the increment
# SHAPE_B is increased per iteration
SHAPE <- 4 # use ellipsoid geometry
SAMODE <- 1 # use bird surface area relations (2 is mammal, 0 is based on shape specified
# in GEOM)
# feather properties
DHAIRD <- 3e-05 # hair diameter, dorsal (m)
DHAIRV <- 3e-05 # hair diameter, ventral (m)
LHAIRD <- 0.0231 # hair length, dorsal (m)
LHAIRV <- 0.0227 # hair length, ventral (m)
ZFURD <- 0.0059 # fur depth, dorsal (m)
ZFURV <- 0.0057 # fur depth, ventral (m)
RHOD <- 5e+07 # hair density, dorsal (1/m2)
RHOV <- 5e+07 # hair density, ventral (1/m2)
REFLD <- 0.248 # fur reflectivity dorsal (fractional, 0-1)
REFLV <- 0.351 # fur reflectivity ventral (fractional, 0-1)
# physiological responses
PCTWET <- 0.5 # base skin wetness (%)
PCTWET_MAX <- 5 # maximum skin wetness (%)
PCTWET_INC <- 0.25 # intervals by which skin wetness is increased (%)
Q10s <- rep(1, length(TAs))
Q10s[TAs >= TC_MAX] <- 2 # assuming Q10 effect kicks in only after air temp rises above TC_MAX
QBASAL <- 10^(-1.461 + 0.669 * log10(AMASS * 1000)) # basal heat generation (W)
DELTAR <- 5 # offset between air temperature and breath (°C)
EXTREF <- 25 # O2 extraction efficiency (%)
PANT_INC <- 0.1 # turns on panting, the value being the increment by which the panting multiplier
# is increased up to the maximum value, PANT_MAX
PANT_MAX <- 15 # maximum panting rate - multiplier on air flow through the lungs above
# that determined by metabolic rate
PANT_MULT <- 1 # multiplier on basal metabolic rate at maximum panting level
ptm <- proc.time() # start timing
endo.out <- lapply(1:length(TAs), function(x) {
endoR(TA = TAs[x], VEL = VEL, TC = TC, TC_MAX = TC_MAX, RH = hum[x],
AMASS = AMASS, SHAPE = SHAPE, SHAPE_B = SHAPE_B, SHAPE_B_MAX = SHAPE_B_MAX,
PCTWET = PCTWET, PCTWET_INC = PCTWET_INC, PCTWET_MAX = PCTWET_MAX,
Q10 = Q10s[x], QBASAL = QBASAL, DELTAR = DELTAR, DHAIRD = DHAIRD,
DHAIRV = DHAIRV, LHAIRD = LHAIRD, LHAIRV = LHAIRV, ZFURD = ZFURD,
ZFURV = ZFURV, RHOD = RHOD, RHOV = RHOV, REFLD = REFLD,
TC_INC = TC_INC, PANT_INC = PANT_INC, PANT_MAX = PANT_MAX,
EXTREF = EXTREF, UNCURL = UNCURL, SAMODE = SAMODE, PANT_MULT = PANT_MULT)
}) # run endoR across environments
proc.time() - ptm # stop timing## user system elapsed
## 0.18 0.00 0.20# extract the output
endo.out1 <- do.call("rbind", lapply(endo.out, data.frame))
# thermoregulation output
treg <- endo.out1[, grep(pattern = "treg", colnames(endo.out1))]
colnames(treg) <- gsub(colnames(treg), pattern = "treg.", replacement = "")
# morphometric output
morph <- endo.out1[, grep(pattern = "morph", colnames(endo.out1))]
colnames(morph) <- gsub(colnames(morph), pattern = "morph.",
replacement = "")
# heat balance
enbal <- endo.out1[, grep(pattern = "enbal", colnames(endo.out1))]
colnames(enbal) <- gsub(colnames(enbal), pattern = "enbal.",
replacement = "")
# mass aspects
masbal <- endo.out1[, grep(pattern = "masbal", colnames(endo.out1))]
colnames(masbal) <- gsub(colnames(masbal), pattern = "masbal.",
replacement = "")
QGEN <- enbal$QGEN # metabolic rate (W)
H2O <- masbal$H2OResp_g + masbal$H2OCut_g # g/h water evaporated
TFA_D <- treg$TFA_D # dorsal fur surface temperature
TFA_V <- treg$TFA_V # ventral fur surface temperature
TskinD <- treg$TSKIN_D # dorsal skin temperature
TskinV <- treg$TSKIN_V # ventral skin temperature
TCs <- treg$TC # core temperature
par(mfrow = c(2, 2))
par(oma = c(2, 1, 2, 2) + 0.1)
par(mar = c(3, 3, 1.5, 1) + 0.1)
par(mgp = c(2, 1, 0))
plot(QGEN ~ TAs, type = "l", ylab = "metabolic rate, W", xlab = expression("air temperature, " *
~degree * C * ""), ylim = c(0.2, 1.2))
points(Weathers1976Fig1$Tair, Weathers1976Fig1$mlO2gh * 20.1/3600 *
(AMASS * 1000), pch = 16, col = 2)
legend(x = 30, y = 1.2, legend = c("observed", "predicted"),
col = c("red", "black"), lty = c(NA, 1), bty = "n", pch = c(16,
NA))
plot(H2O ~ TAs, type = "l", ylab = "water loss, g/h", xlab = expression("air temperature, " *
~degree * C * ""), ylim = c(0, 1.5))
points(masbal$H2OResp_g ~ TAs, type = "l", lty = 2)
points(masbal$H2OCut_g ~ TAs, type = "l", lty = 2, col = "blue")
legend(x = 3, y = 1.5, legend = c("total (obs)", "total (pred)",
"respiratory", "cutaneous"), col = c("red", "black", "black",
"blue"), lty = c(NA, 1, 2, 2), bty = "n", pch = c(16, NA,
NA, NA))
points(Weathers1976Fig3$Tair, Weathers1976Fig3$mgH2Ogh * AMASS,
pch = 16, col = 2)
plot(TFA_D ~ TAs, type = "l", col = "grey", ylab = expression("feather, skin and core temperature, " *
~degree * C * ""), xlab = expression("air temperature, " *
~degree * C * ""), ylim = c(10, 50))
points(TFA_V ~ TAs, type = "l", col = "grey", lty = 2)
points(TskinD ~ TAs, type = "l", col = "orange")
points(TskinV ~ TAs, type = "l", col = "orange", lty = 2)
points(TCs ~ TAs, type = "l", col = "red")
legend(x = 30, y = 33, legend = c("core (obs)", "core (pred)",
"skin dorsal", "skin ventral", "feathers dorsal", "feathers ventral"),
col = c("red", "red", "orange", "orange", "grey", "grey"),
lty = c(NA, 1, 1, 2, 1, 2), bty = "n", pch = c(16, NA, NA,
NA, NA, NA))
points(Weathers1976Fig2$Tair, Weathers1976Fig2$Tb, pch = 16,
col = 2)
plot(masbal$AIR_L * 1000/60 ~ TAs, ylim = c(0, 250), lty = 1,
xlim = c(-5, 50), ylab = "ml air / min", xlab = expression("air temperature, " *
~degree * C * ""), type = "l")
legend(x = 0, y = 250, legend = c("observed", "predicted"), col = c("red",
"black"), lty = c(NA, 1), bty = "n", pch = c(16, NA))
points(Weathers1976Fig5$breaths_min * (13.2 * AMASS^1.08) * ((Weathers1976Fig5$Tair +
273.15)/273.15) ~ Weathers1976Fig5$Tair, col = 2, pch = 16) # tidal volume allometry from Lasiewski, R. C., and W. A. Calder. 1971, correcting volume according to PV = nRT equation, where V_2 = T_2 * V_1 / T_2, and T_1 is at STP, so 0 °C
