Enforcing a constant day time TC

[Caity Dwyer]

Good afternoon Mike,

I am very new to NicheMapR and am having a bit of trouble with getting my model to represent natural torpor. I want to mimic the results of a study which shows relatively constant day time temperatures followed by a nightly drop into torpor (doi:10.1098/rsbl.2019.0211). I'm not sure how can I adjust different aspects of torpor to more closely match measured results. I've pasted my code below and am currently using the following parameters:

########### Parameters ############

TC <- 41.9                # Core body temperature (°C)
TC_MAX <- 46              # Maximum core body temperature (°C)
TC_MIN <- 30.4            # Minimum core body temperature (°C)
TC_INC <- 0.22            # Increment of core body temperature elevation (°C)
PCTWET <- 1               # Base skin wetness (%)
PCTWET_MAX <- 5           # Maximum skin wetness (%)
PCTWET_INC <- 0.25        # Increments of skin wetness increase (%)
Q10s <- rep(1, length(TAs))        # The effect of body temperature on metabolic rate,
Q10s[TAs >= TC_MAX] <- 2           # The effect starts above TC_MAX
QBASAL <- 10^(-1.461 + 0.669 * log10(AMASS * 1000))   # Basal heat generation (W)
DELTAR <- 5               # Difference between air temperature and breath (°C)
EXTREF <- 25              # O2 extraction efficiency (%)
PANT_INC <- 0.1           # Increment by which panting is increased
PANT_MAX <- 15            # Maximum panting rate
PANT_MULT <- 1            # Multiplier on basal metabolism at maximum panting level
TORPOR <- 1               # Allows body temperature to drop to TC_MIN
TREGMODE <- 2
endoshade <- 0

#############################################

Here is my endoR loop. I am running it through different feather conditions however that is working well, it is the torpor that needs adjusting:

##############################################

results_list <- list()   # store results for each condition level

ptm <- proc.time()

for (i in seq_along(condition_grid)) {

  # Scale feather density and length
  LHAIRD <- condition_grid[i] * LHAIRD_base
  LHAIRV <- condition_grid[i] * LHAIRV_base
  RHOD   <- condition_grid[i] * RHOD_base
  RHOV   <- condition_grid[i] * RHOV_base

  # Run endoR across all microclimate time steps
  endo.out <- lapply(seq_along(TAs), function(x) {

    # Behavioural ptiloerection based on air temperature
    ptilo <- ptilo_func(TAs[x])

    # Adjust feather depth (ptiloerection)
    ZFURD <- ZFURD_base + (LHAIRD - ZFURD_base) * ptilo
    ZFURV <- ZFURV_base + (LHAIRV - ZFURV_base) * ptilo

    # Run endoR with modified insulation
    endoR(
      # Climate variables
      TA = TAs[x], TAREF = TAREFs[x], TSKY = TSKYs[x], TGRD = TGRDs[x],
      VEL = VELs[x], RH = RHs[x], QSOLR = QSOLRs[x], Z = Zs[x], ELEV = ELEV,
      ABSSB = ABSSB,

      # Constants
      TC = TC, TC_MAX = TC_MAX, AMASS = AMASS, SHAPE = SHAPE,
      SHAPE_B = SHAPE_B, SHAPE_B_MAX = SHAPE_B_MAX,
      PCTWET = PCTWET, PCTWET_INC = PCTWET_INC,
      Q10 = Q10s[x], QBASAL = QBASAL, DELTAR = DELTAR,
      DHAIRD = DHAIRD, DHAIRV = DHAIRV,
      REFLD = REFLD, TC_INC = TC_INC, PANT_INC = PANT_INC,
      PANT_MAX = PANT_MAX, EXTREF = EXTREF, UNCURL = UNCURL,
      SAMODE = SAMODE, PANT_MULT = PANT_MULT, TORPOR = TORPOR,  
      SHADE = endoshade, TREGMODE = TREGMODE,

      # Varied Feather Traits
      LHAIRD = LHAIRD,
      LHAIRV = LHAIRV,
      RHOD   = RHOD,
      RHOV   = RHOV,
      ZFURD  = ZFURD,
      ZFURV  = ZFURV
    )
  })
 
  # Convert list of endoR outputs into a single data frame
  endo_df <- do.call(rbind, lapply(endo.out, data.frame))
 
  # Climate
  daynight <- ifelse(Zs >= 90, "night", "day")

  # Extract heat balance (metabolic rate)
  enbal <- endo_df[, grep("enbal", colnames(endo_df))]
  colnames(enbal) <- gsub("enbal.", "", colnames(enbal))
 
  # Thermoregulation output
  treg <- endo_df[, grep("treg", colnames(endo_df))]
  colnames(treg) <- gsub("treg\\.", "", colnames(treg))
 
  # Mass aspects
  masbal <- endo_df[, grep("masbal", colnames(endo_df))]
  colnames(masbal) <- gsub("masbal\\.", "", colnames(masbal))

 
  # Save results for this condition level
  results_list[[i]] <- data.frame(
    condition = condition_grid[i],
    time_step = seq_along(TAs),
    QGEN = enbal$QGEN,                             # Metabolic energy generated
    TCs = treg$TC,                                 # Core temperature
    H2O = masbal$H2OResp_g + masbal$H2OCut_g,      # Water evaporated (g/hour)
    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
    Pant = treg$PANT,                              # Panting multiplier
    daynight = daynight                            # Day or night
  )
}

proc.time() - ptm

results_all <- do.call(rbind, results_list)

ggplot(results_all, aes(x = time_step, y = TCs, colour = factor(condition))) +
  geom_line(size = 1) +
  labs(
    x = "Time step",
    y = "Core Temperature",
    colour = "Feather condition"
  )

#############################################

 

I hope this is enough to explain my problem, and thank you for your time.

All the best,

Caity