Community
To discuss ideas and problems with the NicheMapR community, join the NicheMapR Google Group
Shiny apps
To get a feeling for what the package can do you can play with web-based Shiny apps of different aspects of the package, the most relevant ones to start with being these microclimate and ectotherm models that run using a monthy long-term average climatology for the globe (function ‘micro_global’ in the package). An introduction to the apps can be found here.
Installation
From latest release
To install the latest release of NicheMapR, go to the NicheMapR releases page, download the latest release and do the following, replacing “path to file” and “NicheMapR_x.x.x” accordingly:
Windows:
install.packages("path to file/NicheMapR_x.x.x.zip", repos = NULL,
type = "win.binary")
Mac/Linux:
install.packages("path to file/NicheMapR_x.x.x.tgz", repos = NULL,
type = .Platform$pkgType)
From source via github
To install NicheMapR from source via github you will need the devtools package for R as well as a Fortran compiler for your platform (e.g. Win, Mac, Linux).
Within R, install devtools by typing:
install.packages('devtools')
To install a Fortran compiler:
- for Windows, you need to download and install Rtools
- for Mac, download the relevant gfortran installer for your type of Mac (Intel or arm64-based silicon) and make sure you’ve installed the right version of R as well (the wrong version may still appear to work but won’t compile Fortran)
- for Linux (Ubuntu) you can follow the instructions here
Once you have devtools and a Fortran compiler installed, you can install NicheMapR from the R console by typing:
devtools::install_github('mrke/NicheMapR')
Installing the global climate database
Finally, to install the global climate database (derived from New et al. 2002) do the following, replacing “path” with the location you want to install to (the default is “c:/global_climate”):
library(NicheMapR)
get.global.climate(folder="path")
Example analyses
Simulate microclimate for the typical middle day of each month in Madison, Wisconsin, using the global climate database of New et al. 2002 and plot hourly 5 cm soil temperature:
library(NicheMapR)
loc <- c(-89.40, 43.07)
micro <- micro_global(loc = loc)
soil <- as.data.frame(micro$soil)
plot(soil$D5cm ~ micro$dates, type = 'l')
Simulate microclimate for 2017 in Madison, Wisconsin using the gridMet database for the USA and plot hourly 5 cm soil temperature and soil moisture as well as snow depth:
library(NicheMapR)
loc <- c(-89.40, 43.07)
dstart <- "01/01/2017"
dfinish <- "31/12/2017"
micro <- micro_usa(loc = loc, dstart = dstart, dfinish = dfinish)
metout <- as.data.frame(micro$metout)
soil <- as.data.frame(micro$soil)
soilmoist <- as.data.frame(micro$soilmoist)
plot(soil$D5cm ~ micro$dates, type = 'l')
plot(soilmoist$WC5cm ~ micro$dates, type = 'l')
plot(metout$SNOWDEP ~ micro$dates, type = 'l')
Simulate microclimate for 2017 in Madison, Wisconsin using the NCEP database (this works for any location in the world) and plot hourly 5 cm soil temperature and soil moisture as well as snow depth:
library(NicheMapR)
loc <- c(-89.40, 43.07)
dstart <- "01/01/2017"
dfinish <- "31/12/2017"
micro <- micro_ncep(loc = loc, dstart = dstart, dfinish = dfinish)
metout <- as.data.frame(micro$metout)
soil <- as.data.frame(micro$soil)
soilmoist <- as.data.frame(micro$soilmoist)
plot(soil$D5cm ~ micro$dates, type = 'l')
plot(soilmoist$WC5cm ~ micro$dates, type = 'l')
plot(metout$SNOWDEP ~ micro$dates, type = 'l')
Compute a heat budget for the default ectotherm (Eastern Water Skink, Eulamprus quoyii) in Sydney, Australia for 2017 using the NCEP database and plot the consequences for its body temperature and seasonal activity window:
library(NicheMapR)
loc <- c(150.78, -33.78)
dstart <- "01/01/2017"
dfinish <- "31/12/2017"
micro <- micro_ncep(loc = loc, dstart = dstart, dfinish = dfinish)
metout <- as.data.frame(micro$metout) # aboveground microclimate
# run ectotherm model
ecto <- ectotherm() # run ectotherm model with all default settings (Eastern Water Skink)
# retrieve output
environ <- as.data.frame(ecto$environ) # activity, Tb and environment
# append dates
dates <- micro$dates
############### plot results ######################
# Hourly Tb (black), depth (brown, m) and shade (green, %/10)
with(environ, plot(TC ~ dates, ylab = "", xlab = "month of year", col = 'black', ylim = c(-20, 40), type = "l", yaxt = 'n'))
with(environ, points(SHADE / 10 - 6 ~ dates, type = "l", col = "dark green"))
with(environ, points(DEP - 10 ~ dates, type = "l", col = "brown"))
abline(ecto$T_F_min, 0, lty = 2, col = 'blue')
abline(ecto$T_F_max, 0, lty = 2, col = 'red')
ytick<-seq(15, 40, by = 5)
axis(side = 2, at = ytick, labels = TRUE)
axis(side = 2, at = ytick, labels = FALSE)
mtext(text = seq(0, 100, 20), side = 2, line = 1, at = seq(-6, 4, 2), las = 2)
ytick<-seq(-20, -10, by = 2)
axis(side = 2, at = ytick, labels = FALSE)
mtext(text = rev(seq(0, 100, 20)), side = 2, line = 1, at = seq(-20, -10, 2), las = 2)
abline(h = -10, lty = 2, col = 'grey')
mtext(text = c('body temperature (°C)', 'activity', 'shade (%)', 'depth (cm)'), side = 2, line = 2.5, at = c(30, 9, 0, -15))
text(-0.2, c(ecto$T_F_max + 1, ecto$T_F_min + 1), c('T_F_max', 'T_F_min'), col = c('red', 'blue'), cex = 0.75)
# seasonal activity plot (dark blue = night, light blue = basking, orange = foraging)
forage <- subset(environ, ACT == 2)
bask <- subset(environ, ACT == 1)
night <- subset(metout, ZEN == 90)
day <- subset(metout, ZEN != 90)
with(night, plot(TIME / 60 ~ DOY, ylab = "Hour of Day", xlab = "Day of Year", pch = 15, cex = 0.75, col = 'dark blue'))
# nighttime hours
with(forage, points(TIME ~ DOY, pch = 15, cex = 0.75, col = 'orange')) # foraging Tbs
with(bask, points(TIME ~ DOY, pch = 15, cex = 0.75, col = 'light blue')) # basking Tbs
Compute the full heat, water and Dynamic Energy Budget for the E. quoyii in in Sydney, Australia for 2017, starting as an egg, and plot its growth trajectory as wet mass:
library(NicheMapR)
loc <- c(150.78, -33.78)
dstart <- "01/01/2017"
dfinish <- "31/12/2017"
micro <- micro_ncep(loc = loc, dstart = dstart, dfinish = dfinish)
metout <- as.data.frame(micro$metout) # aboveground microclimate
# run ectotherm model
ecto <- ectotherm(DEB = 1) # run ectotherm model for default parameters (Eastern Water Skink) with DEB model turned on
# retrieve output
debout <- as.data.frame(ecto$debout) # Dynamic Energy Budget model output table
# plot wet mass
plot(debout$WETMASS ~ micro$dates, type = 'l')
E.Hb <- 866.6 # maturity threshold at birth (J) for Eastern Water Skink, needed for next line
abline(v = micro$dates[which(debout$E_H > E.Hb)[1]], lty = 2, col = 'grey') # show when it was born
text(micro$dates[1], max(debout$WETMASS), labels = "embryo", cex = 0.85) # note embryo stage
Source code
To view the code please visit the NicheMapR GitHub account