reverseTransition: Reverse transition probabilities and origin relative...

In MigConnectivity: Estimate Migratory Connectivity for Migratory Animals

Reverse transition probabilities and origin relative abundance

Description

Reverse transition probabilities (psi; sum to 1 for each origin site) and origin relative abundance (originRelAbund; sum to 1 overall) estimates to calculate or estimate target site to origin site transition probabilities (gamma; sum to 1 for each target site), target site relative abundances (targetRelAbund; sum to 1 overall), and origin/target site combination probabilities (pi; sum to 1 overall). If either psi or originRelAbund is an estimate with sampling uncertainty expressed, this function will propagate that uncertainty to provide true estimates of gamma, targetRelAbund, and pi; otherwise (if both are simple point estimates), it will also provide point estimates.

Usage

reverseTransition(
  psi = NULL,
  originRelAbund = NULL,
  pi = NULL,
  originSites = NULL,
  targetSites = NULL,
  originNames = NULL,
  targetNames = NULL,
  nSamples = 1000,
  row0 = 0,
  alpha = 0.05
)

reversePsiRelAbund(
  psi = NULL,
  originRelAbund = NULL,
  pi = NULL,
  originSites = NULL,
  targetSites = NULL,
  originNames = NULL,
  targetNames = NULL,
  nSamples = 1000,
  row0 = 0,
  alpha = 0.05
)

reverseTransitionRelAbund(
  psi = NULL,
  originRelAbund = NULL,
  pi = NULL,
  originSites = NULL,
  targetSites = NULL,
  originNames = NULL,
  targetNames = NULL,
  nSamples = 1000,
  row0 = 0,
  alpha = 0.05
)

reversePi(
  psi = NULL,
  originRelAbund = NULL,
  pi = NULL,
  originSites = NULL,
  targetSites = NULL,
  originNames = NULL,
  targetNames = NULL,
  nSamples = 1000,
  row0 = 0,
  alpha = 0.05
)

Arguments

psi	Transition probabilities between B origin and W target sites. Either a matrix with B rows and W columns where rows sum to 1, an array with dimensions x, B, and W (with x samples of the transition probability matrix from another model), an 'estPsi' object (result of calling estTransition), or a MARK object with estimates of transition probabilities
originRelAbund	Relative abundance estimates at B origin sites. Either a numeric vector of length B that sums to 1 or an mcmc object with at least nSamples rows and columns including 'relN[1]' through 'relN[B]'
pi	Migratory combination (joint) probabilities. Either a matrix with B rows and W columns where all entries sum to 1, an array with dimensions x, B, and W, or an 'estPi' object (currently only the results of calling this function) Either pi or psi and originRelAbund should be specified.
originSites	If psi is a MARK object, this must be a numeric vector indicating which sites are origin
targetSites	If psi is a MARK object, this must be a numeric vector indicating which sites are target
originNames	Vector of names for the origin sites. If not provided, the function will try to get them from psi
targetNames	Vector of names for the target sites. If not provided, the function will try to get them from psi
nSamples	Number of times to resample psi and/or originRelAbund. The purpose is to estimate sampling uncertainty; higher values here will do so with more precision
row0	If originRelAbund is an mcmc object or array, this can be set to 0 (default) or any greater integer to specify where to stop ignoring samples (additional "burn-in")
alpha	Level for confidence/credible intervals provided. Default (0.05) gives 95 percent CI

Details

Alternatively, can be used to reverse migratory combination (joint) probabilities (pi; sum to 1 overall) to psi, originRelAbund, gamma, and targetRelAbund.

Value

If both psi and originRelAbund are simple point estimates, reversePsiRelAbund returns a list with point estimates of gamma, targetRelAbund, and pi. Otherwise, it returns a list with the elements:

gamma

List containing estimates of reverse transition probabilities:

• sample Array of sampled values for gamma. nSamples x [number of target sites] x [number of origin sites]. Provided to allow the user to compute own summary statistics.

• mean Main estimate of gamma matrix. [number of target sites] x [number of origin sites].

• se Standard error of gamma, estimated from SD of gamma$sample.

• simpleCI 1 - alpha confidence interval for gamma, estimated as alpha/2 and 1 - alpha/2 quantiles of gamma$sample.

• bcCI Bias-corrected 1 - alpha confidence interval for gamma. May be preferable to simpleCI when mean is the best estimate of gamma. simpleCI is preferred when median is a better estimator. When the mean and median are equal, these should be identical. Estimated as the pnorm(2 * z0 + qnorm(alpha / 2)) and pnorm(2 * z0 + qnorm(1 - alpha / 2)) quantiles of sample, where z0 is the proportion of sample < mean.

• median Median estimate of gamma matrix.

• point Simple point estimate of gamma matrix, not accounting for sampling error.

targetRelAbund

List containing estimates of relative abundance at target sites. Items within are the same as within gamma, except for having one fewer dimension.

pi

List containing estimates of origin/target site combination probabilities (sum to 1). Items within are the same as within gamma, except for reversing dimensions (same order as psi).

input

List containing the inputs to reversePsiRelAbund.

If the input is pi instead of psi and originRelAbund, then pi is not an output, but psi and originRelAbund are. Otherwise the same.

Examples

## Example 1: sample psis and relative abundances from Cohen et al. (2018)
## (no uncertainty in psi or relative abundance)
for (i in 1:length(samplePsis)) {
 for (j in 1:length(sampleOriginRelN)){
  cat("For psi:\n")
  print(samplePsis[[i]])
  cat("and origin relative abundance:", sampleOriginRelN[[j]], "\n")
  print(reverseTransition(samplePsis[[i]], sampleOriginRelN[[j]]))
 }
}

## Example 2: Common tern banding example (uncertainty in psi, not relative
## abundance)
# Number of MCMC iterations
ni. <- 1000 # reduced from 70000 for example speed
# Number of iterations to thin from posterior
nt. <- 1
# Number of iterations to discard as burn-in
nb. <- 500 # reduced from 20000 for example speed
# Number of MCMC chains
nc. <- 1 # reduced from 3 for example speed
COTE_banded <- c(10360, 1787, 2495, 336)
COTE_reencountered <- matrix(c(12, 0, 38, 15,
                               111, 7, 6, 2,
                               5, 0, 19, 4,
                               1123, 40, 41, 7),
                             4, 4,
                             dimnames = list(LETTERS[1:4], 1:4))
COTE_psi <- estTransition(originNames = LETTERS[1:4],
                          targetNames = 1:4,
                          banded = COTE_banded,
                          reencountered = COTE_reencountered,
                          verbose = 1,
                          nSamples = (ni. - nb.) / nt. * nc., nBurnin = nb.,
                          nThin = nt., nChains = nc.,
                          method = "MCMC")
COTE_psi
COTE_rev <- reverseTransition(COTE_psi, sampleOriginRelN[[1]],
                               nSamples = 2000)
COTE_rev


## Example 3: Uncertainty in both psi and relative abundance
# Number of populations
nOriginSites <- 3; originNames <- LETTERS[1:nOriginSites]
nTargetSites <- 4; targetNames <- 1:nTargetSites

originRelAbund <- c(1/3, 1/3, 1/3)

psiTrue <- array(0, c(nOriginSites, nTargetSites),
                 list(originNames, targetNames))
psiTrue[1,] <- c(0.22, 0.52, 0.16, 0.10)
psiTrue[2,] <- c(0.41, 0.31, 0.17, 0.11)
psiTrue[3,] <- c(0.10, 0.15, 0.42, 0.33)
rowSums(psiTrue)

rev <- reverseTransition(psiTrue, originRelAbund)

# Simulate abundance data on origin sites
# Number of routes w/i each population (assumed to be balanced)
routePerPop. <- 30 # reduced for example speed
# Number of years
nYears. <- 5 # reduced for example speed
# log(Expected number of birds counted at each route)
alphaPop. <- 1.95
# standard deviation of normal distribution assumed for route/observer random
# effects
sdRoute. <- 0.6
# standard deviation of normal distribution assumed for year random effects
sdYear. <- 0.18
# Number of MCMC iterations
ni. <- 1000 # reduced from 70000 for example speed
# Number of iterations to thin from posterior
nt. <- 1
# Number of iterations to discard as burn-in
nb. <- 500 # reduced from 20000 for example speed
# Number of MCMC chains
nc. <- 1 # reduced from 3 for example speed

sim_data <- simCountData(nStrata = nOriginSites, routesPerStrata = routePerPop.,
                         nYears = nYears., alphaStrat = alphaPop.,
                         sdRoute = sdRoute., sdYear = sdYear.)
# Estimate population-level abundance
out_mcmc <- modelCountDataJAGS(count_data = sim_data, ni = ni., nt = nt.,
                               nb = nb., nc = nc.)

# Simulate movement data
sampleSize <- list(rep(20, nOriginSites), NULL)
captured <- rep("origin", sum(sampleSize[[1]]))
isTelemetry <- rep(TRUE:FALSE, c(sum(sampleSize[[1]]), sum(sampleSize[[2]])))
isProb <- rep(FALSE:TRUE, c(sum(sampleSize[[1]]), sum(sampleSize[[2]])))

# Telemetry data (released origin)
data1 <- simTelemetryData(psi = psiTrue,
                          sampleSize = sampleSize[[1]],
                          captured = "origin")
tt <- data1$targetAssignment
oa <- data1$originAssignment

# Estimate transition probabilities (psi)
est1 <- estTransition(targetAssignment = tt,
                      originAssignment = oa,
                      originNames = originNames,
                      targetNames = targetNames,
                      nSamples = 500, isGL = FALSE,
                      isTelemetry = isTelemetry,
                      isRaster = FALSE,
                      isProb = isProb,
                      captured = captured,
                      nSim = 10, verbose = 0)
# Reverse estimates
rev1 <- reverseTransition(psi = est1, originRelAbund = out_mcmc)
# Compare estimates of gamma, target relative abundance, and pi with calculation
# from true values
rev
rev1

MigConnectivity documentation built on May 29, 2024, 8:37 a.m.