inst/examples/estMCExamples.R

In MigConnectivity: Estimate Migratory Connectivity for Migratory Animals

\donttest{
  set.seed(101)
  # Uncertainty in detection ('RMark' estimates) with equal abundances
  # Number of resampling iterations for generating confidence intervals

  nSamplesCMR <- 100
  nSimulationsCMR <- 10
  originPos13 <- matrix(c(rep(seq(-99, -81, 2), each = 10),
                          rep(seq(49, 31, -2), 10)), 100, 2)
  targetPos13 <- matrix(c(rep(seq(-79, -61, 2), each = 10),
                          rep(seq(9, -9, -2), 10)), 100, 2)
  originPosCMR <- rowsum(originPos13, c(rep(1:2, 5, each = 5),
                                        rep(3:4, 5, each = 5))) / 25
  originPosCMR
  targetPosCMR <- rowsum(targetPos13, c(rep(1:2, 5, each = 5),
                                        rep(3:4, 5, each = 5))) / 25
  targetPosCMR

  originDist <- distFromPos(originPosCMR, 'ellipsoid')
  targetDist <- distFromPos(targetPosCMR, 'ellipsoid')
  originRelAbundTrue <- rep(0.25, 4)
  # the second intermediate psi scenario, the "low" level
  psiTrue <- samplePsis[["Low"]]
  trueMC <- calcMC(originDist, targetDist, originRelAbundTrue, psiTrue)
  trueMC

  # Storage matrix for samples
  cmrMCSample <- matrix(NA, nSamplesCMR, nSimulationsCMR)
  summaryCMR <- data.frame(Simulation = 1:nSimulationsCMR, True=trueMC,
                           mean=NA, se=NA, lcl=NA, ucl=NA)
  # Get RMark psi estimates and estimate MC from each
  for (r in 1:nSimulationsCMR) {
    cat("Simulation",r,"of",nSimulationsCMR,"\n")
    # Note: getCMRexample() requires a valid internet connection and that GitHub
    # is accessible
    fm <- getCMRexample(r)
    results <- estMC(originRelAbund = originRelAbundTrue, psi = fm,
                     originDist = originDist, targetDist = targetDist,
                     originSites = 5:8, targetSites = c(3,2,1,4),
                     nSamples = nSamplesCMR, verbose = 0,
                     sampleSize = length(grep('[2-5]', fm$data$data$ch)))
    #sampleSize argument not really needed (big sample sizes)
    cmrMCSample[ , r] <- results$MC$sample
    summaryCMR$mean[r] <- results$MC$mean
    summaryCMR$se[r] <- results$MC$se
    # Calculate confidence intervals using quantiles of sampled MC
    summaryCMR[r, c('lcl', 'ucl')] <- results$MC$simpleCI
  }

  summaryCMR <- transform(summaryCMR, coverage = (True>=lcl & True<=ucl))
  summaryCMR
  summary(summaryCMR)
  biasCMR <- mean(summaryCMR$mean) - trueMC
  biasCMR
  mseCMR <- mean((summaryCMR$mean - trueMC)^2)
  mseCMR
  rmseCMR <- sqrt(mseCMR)
  rmseCMR


  # Simulation of BBS data to quantify uncertainty in relative abundance

  nSamplesAbund <- 700 #1700 are stored
  nSimulationsAbund <- 10
  # Storage matrix for samples
  abundMCSample <- matrix(NA, nSamplesAbund, nSimulationsAbund)
  summaryAbund <- data.frame(Simulation = 1:nSimulationsAbund, True = trueMC,
                             mean = NA, se = NA, lcl = NA, ucl = NA)
  for (r in 1:nSimulationsAbund) {
    cat("Simulation",r,"of",nSimulationsAbund,"\n")
    row0 <- nrow(abundExamples[[r]]) - nSamplesAbund
    results <- estMC(originRelAbund = abundExamples[[r]], psi = psiTrue,
                     originDist = originDist, targetDist = targetDist,
                     row0 = row0, nSamples = nSamplesAbund, verbose = 1)
    abundMCSample[ , r] <- results$MC$sample
    summaryAbund$mean[r] <- results$MC$mean
    summaryAbund$se[r] <- results$MC$se
    # Calculate confidence intervals using quantiles of sampled MC
    summaryAbund[r, c('lcl', 'ucl')] <- results$MC$simpleCI
  }

  summaryAbund <- transform(summaryAbund,
                            coverage = (True >= lcl & True <= ucl))
  summaryAbund
  summary(summaryAbund)
  biasAbund <- mean(summaryAbund$mean) - trueMC
  biasAbund
  mseAbund <- mean((summaryAbund$mean - trueMC)^2)
  mseAbund
  rmseAbund <- sqrt(mseAbund)
  rmseAbund

  # Ovenbird example with GL and GPS data
  data(OVENdata) # Ovenbird

  nSamplesGLGPS <- 100 # Number of bootstrap iterations

  # Estimate MC only, treat all data as geolocator
  GL_mc<-estMC(isGL=TRUE, # Logical vector: light-level geolocator(T)/GPS(F)
               geoBias = OVENdata$geo.bias, #Geolocator location bias
               geoVCov = OVENdata$geo.vcov, # Location covariance matrix
               targetDist = OVENdata$targetDist, # targetSites distance matrix
               originDist = OVENdata$originDist, # originSites distance matrix
               targetSites = OVENdata$targetSites, # Non-breeding target sites
               originSites = OVENdata$originSites, # Breeding origin sites
               originPoints = OVENdata$originPoints, # Capture Locations
               targetPoints = OVENdata$targetPoints, # Device target locations
               originRelAbund = OVENdata$originRelAbund,#Origin relative abund.
               verbose = 1,   # output options
               nSamples = nSamplesGLGPS,# This is set low for example
               resampleProjection = terra::crs(OVENdata$targetSites))

  # Estimate MC and rM, treat all data as is
  Combined<-estMC(isGL=OVENdata$isGL, #Logical vector:light-level GL(T)/GPS(F)
                  geoBias = OVENdata$geo.bias, # Light-level GL location bias
                  geoVCov = OVENdata$geo.vcov, # Location covariance matrix
                  targetDist = OVENdata$targetDist, # Winter distance matrix
                  originDist = OVENdata$originDist, # Breeding distance matrix
                  targetSites = OVENdata$targetSites, # Nonbreeding/target sites
                  originSites = OVENdata$originSites, # Breeding origin sites
                  originPoints = OVENdata$originPoints, # Capture Locations
                  targetPoints = OVENdata$targetPoints, #Device target locations
                  originRelAbund = OVENdata$originRelAbund,#Relative abundance
                  verbose = 1,   # output options
                  calcCorr = TRUE, # estimate rM as well
                  nSamples = nSamplesGLGPS, # This is set low for example
                  approxSigTest = TRUE,
                  resampleProjection = terra::crs(OVENdata$targetSites),
                  originNames = OVENdata$originNames,
                  targetNames = OVENdata$targetNames)

  print(Combined)

  # For treating all data as GPS,
  # Move the latitude of birds with locations that fall offshore - only change
  # Latitude
  int <- sf::st_intersects(OVENdata$targetPoints, OVENdata$targetSites)
  any(lengths(int)<1)
  plot(OVENdata$targetPoints)
  plot(OVENdata$targetSites,add=TRUE)
  tp<-sf::st_coordinates(OVENdata$targetPoints)
  text(tp[,1], tp[,2], label=c(1:39))

  tp[5,2]<- -1899469
  tp[10,2]<- -1927848
  tp[1,2]<- -1927930
  tp[11,2]<- -2026511
  tp[15,2]<- -2021268
  tp[16,2]<- -1976063

  oven_targetPoints<-sf::st_as_sf(as.data.frame(tp),
                                  coords = c("X","Y"),
                                  crs = sf::st_crs(OVENdata$targetPoints))
  inter <- sf::st_intersects(oven_targetPoints, OVENdata$targetSites)
  any(lengths(inter)<1)
  plot(oven_targetPoints,add=TRUE, col = "green")

  # Estimate MC only, treat all data as GPS
  GPS_mc<-estMC(isGL=FALSE, # Logical vector: light-level geolocator(T)/GPS(F)
                targetDist = OVENdata$targetDist, # targetSites distance matrix
                originDist = OVENdata$originDist, # originSites distance matrix
                targetSites = OVENdata$targetSites, # Non-breeding target sites
                originSites = OVENdata$originSites, # Breeding origin sites
                originPoints = OVENdata$originPoints, # Capture Locations
                targetPoints = oven_targetPoints, # Device target locations
                originRelAbund = OVENdata$originRelAbund,#Origin relative abund.
                verbose = 1,   # output options
                nSamples = nSamplesGLGPS) # This is set low for example

  str(GPS_mc, max.level = 2)
  str(Combined, max.level = 2)
  str(GL_mc, max.level = 2)
  plot(Combined, legend = "top", main = "Ovenbird GL and GPS")
  text(1.1, 0.98, cex = 1,
       labels = paste("MC = ", round(Combined$MC$mean, 2), "+/-",
                      round(Combined$MC$se, 2)))


  # Generate probabilistic assignments using intrinsic markers (stable-hydrogen
  # isotopes)
  getCSV <- function(filename) {
    tmp <- tempdir()
    url1 <- paste0('https://github.com/SMBC-NZP/MigConnectivity/blob/master/data-raw/',
                   filename, '?raw=true')
    temp <- paste(tmp, filename, sep = '/')
    utils::download.file(url1, temp, mode = 'wb')
    csv <- read.csv(temp)
    unlink(temp)
    return(csv)
  }

  getRDS <- function(speciesDist) {
    tmp <- tempdir()
    extension <- '.rds'
    filename <- paste0(speciesDist, extension)
    url1 <- paste0(
      'https://github.com/SMBC-NZP/MigConnectivity/blob/master/data-raw/Spatial_Layers/',
                   filename, '?raw=true')
    temp <- paste(tmp, filename, sep = '/')
    utils::download.file(url1, temp, mode = 'wb')
    shp <- readRDS(temp)
    unlink(temp)
    return(shp)
  }
  OVENdist <- getRDS("OVENdist")

  OVENvals <- getCSV("deltaDvalues.csv")

  OVENvals <- OVENvals[grep(x=OVENvals$Sample,"NH", invert = TRUE),]

  originSites <- getRDS("originSites")
  originSites <- sf::st_as_sf(originSites)

  EVER <- length(grep(x=OVENvals$Sample,"EVER"))
  JAM <- length(grep(x=OVENvals$Sample,"JAM"))

  originRelAbund <- matrix(c(EVER,JAM),nrow = 1,byrow = TRUE)
  originRelAbund <- prop.table(originRelAbund,1)

  op <- sf::st_centroid(originSites)

  originPoints <- array(NA,c(EVER+JAM,2), list(NULL, c("x","y")))
  originPoints[grep(x = OVENvals$Sample,"JAM"),1] <- sf::st_coordinates(op)[1,1]
  originPoints[grep(x = OVENvals$Sample,"JAM"),2] <- sf::st_coordinates(op)[1,2]
  originPoints[grep(x = OVENvals$Sample,"EVER"),1] <-sf::st_coordinates(op)[2,1]
  originPoints[grep(x = OVENvals$Sample,"EVER"),2] <-sf::st_coordinates(op)[2,2]

  originPoints <- sf::st_as_sf(data.frame(originPoints),
                               coords = c("x", "y"),
                               crs = sf::st_crs(originSites))
  originDist <- distFromPos(sf::st_coordinates(op))


  iso <- isoAssign(isovalues = OVENvals[,2],
                   isoSTD = 12,       # this value is for demonstration only
                   intercept = -10,   # this value is for demonstration only
                   slope = 0.8,       # this value is for demonstration only
                   odds = NULL,
                   restrict2Likely = TRUE,
                   nSamples = 1000,
                   sppShapefile = OVENdist,
                   assignExtent = c(-179,-60,15,89),
                   element = "Hydrogen",
                   period = "GrowingSeason",#this setting for demonstration only
                   seed = 12345,
                   verbose=1)

  targetSites <- sf::st_as_sf(iso$targetSites)
  targetSites <- sf::st_make_valid(targetSites)
  targetSites <- sf::st_union(targetSites, by_feature = TRUE)

  ovenMC <- estMC(originRelAbund = originRelAbund,
                  targetIntrinsic = iso,
                  originPoints = originPoints,
                  originSites = originSites,
                  originDist = originDist,
                  nSamples = 50, # set very low for example speed
                  verbose = 1,
                  calcCorr = TRUE,
                  alpha = 0.05,
                  approxSigTest = FALSE,
                  isIntrinsic = TRUE,
                  targetSites = targetSites)

  ovenMC
}