Examples
Utility functions for use in vignette
mlogitMat: Calculates probability matrix based on
exponential decline with distance
mlogitMat <- function(slope, dist) {
preMat <- exp(-slope/mean(dist)*dist)
diag(preMat) <- 0
outMat <- t(apply(preMat, 1, function(x) x/(1 + sum(x))))
diag(outMat) <- 1 - rowSums(outMat)
return(outMat)
}mlogitMC: Crude optimization function for developing
migratory connectivity pattern based on migratory connectivity strength.
Uses same argument (origin.abund) for relative or absolute
abundance.
mlogitMC <- function(slope,
MC.in,
origin.dist,
target.dist,
origin.abund,
sample.size) {
nBreeding <- nrow(origin.dist)
nWintering <- nrow(target.dist)
psi <- mlogitMat(slope, origin.dist)
if (any(psi<0))
return(5*slope^2)
MC <- calcMC(origin.dist,
target.dist,
originRelAbund = origin.abund,
psi,
sampleSize = sample.size)
return((MC.in - MC)^2)
}calcStrengthInd: rM function for individuals
calcStrengthInd <- function(originDist,
targetDist,
locations,
resamp=1000,
verbose = 0) {
nInd <- dim(locations)[1]
originDist2 <- targetDist2 <- matrix(0, nInd, nInd)
for (i in 1:(nInd-1)) {
for (j in (i+1):nInd) {
originDist2[i,j] <- originDist[locations[i,1,1,1], locations[j,1,1,1]]
targetDist2[i,j] <- targetDist[locations[i,2,1,1], locations[j,2,1,1]]
originDist2[j,i] <- originDist[locations[i,1,1,1], locations[j,1,1,1]]
targetDist2[j,i] <- targetDist[locations[i,2,1,1], locations[j,2,1,1]]
}
}
return(ncf::mantel.test(originDist2, targetDist2, resamp=resamp, quiet = !verbose))
} calcPsiMC: Simple approach to estimate psi matrix
(transition probabilities) and MC from simulated or real data (does not
incorporate sampling uncertainty)
calcPsiMC <- function(originDist,
targetDist,
originRelAbund,
locations,
years = 1,
months = 1,
verbose=FALSE) {
nOrigin <- nrow(originDist)
nTarget <- nrow(targetDist)
psiMat <- matrix(0, nOrigin, nTarget)
nInd <- dim(locations)[1]
nYears <- dim(locations)[3]
nMonths <- dim(locations)[4]
for (i in 1:nInd) {
if (i %% 1000 == 0 && verbose) #
cat("Individual", i, "of", nInd, "\n")
originMat <- locations[i, 1, years, months]
targetMat <- locations[i, 2, years, months]
bIndices <- which(!is.na(originMat))
wIndices <- which(!is.na(targetMat))
if (length(bIndices) && length(wIndices))
for (bi in bIndices)
for (wi in wIndices)
psiMat[originMat[bi], targetMat[wi]] <- psiMat[originMat[bi],
targetMat[wi]] + 1
}
psiMat <- apply(psiMat, 2, "/", rowSums(psiMat))
MC <- calcMC(originDist, targetDist, psi = psiMat,
originRelAbund = originRelAbund, sampleSize = nInd)
return(list(psi=psiMat, MC=MC))
}changeLocations: transfers the simulated bird locations
from the true populations to the researcher defined regions
changeLocations <- function(animalLoc,
breedingSiteTrans,
winteringSiteTrans) {
animalLoc[,1,,] <- breedingSiteTrans[animalLoc[,1,,]]
animalLoc[,2,,] <- winteringSiteTrans[animalLoc[,2,,]]
return(animalLoc)
}simLocationError: Simulates location error with defined
bias and variance / covariance matrix
simLocationError <- function(targetPoints,
targetSites,
geoBias,
geoVCov,
projection,
verbose = 0,
nSim = 1000) {
if(!inherits(targetPoints,"sf")){
targetPoints <- sf::st_as_sf(targetPoints)}
nAnimals <- nrow(targetPoints)
geoBias2 <- matrix(rep(geoBias, nSim), nrow=nSim, byrow=TRUE)
target.sample <- rep(NA, nAnimals)
target.point.sample <- matrix(NA, nAnimals, 2)
for(i in 1:nAnimals){
if (verbose > 0)
cat('Animal', i, 'of', nAnimals)
draws <- 0
while (is.na(target.sample[i])) {
draws <- draws + 1
# Sample random point for each bird
# from parametric distribution of NB error
ps <- MASS::mvrnorm(n=nSim,
mu=cbind(sf::st_coordinates(targetPoints)[i,1],
sf::st_coordinates(targetPoints)[i,2]),
Sigma=geoVCov)+
geoBias2
colnames(ps) <- c("Longitude","Latitude")
point.sample <- sf::st_as_sf(
data.frame(ps),
coords = c("Longitude","Latitude"),
crs = sf::st_crs(targetPoints))
# filtered to stay in NB areas (land)
target.sample0 <- unlist(unclass(sf::st_intersects(point.sample,
targetSites)))
target.sample[i]<-target.sample0[!is.na(target.sample0)][1]
}
target.point.sample[i, ]<-sf::st_coordinates(point.sample[!is.na(target.sample0),])[1,]
if (verbose > 0)
cat(' ', draws, 'draws\n')
}
return(list(targetSample = target.sample,
targetPointSample = target.point.sample))
}toPoly: Helper function to convert a string of XY
coordinates of centroids to polygons
toPoly <- function(siteCentroids,
projection.in = 4326,
projection.out = NA,
resolution = NA,
order.by.input = TRUE)
{
# This automatically sets the resolution so that all polygons touch and
# cover the entire surface. Alternatively the user can supply the
# resolution of the cells in the units of the input projection (projection.in)
colnames(siteCentroids) <- c("Longitude","Latitude")
if(is.na(resolution)){
long <- unique(siteCentroids[,1])
lat <- unique(siteCentroids[,2])
if (length(long)>1)
long.res <- abs(long[2]-long[1])
else
long.res <- 1
if (length(lat)>1)
lat.res <- abs(lat[2]-lat[1])
else
lat.res <- 1
resolution <- c(long.res,lat.res)
}
centers <- sf::st_as_sf(data.frame(siteCentroids),
coords = c("Longitude","Latitude"),
crs = projection.in)
polys <- sf::st_as_sf(sf::st_make_grid(centers,
crs = projection.in,
cellsize = resolution,
offset = apply(siteCentroids, 2, min) -
resolution/2))
if(order.by.input){
# Reorders the polygons to match that of the input #
orders <- suppressMessages(unlist(unclass(sf::st_intersects(centers,
polys,
sparse = TRUE))))
polys <- polys[orders,]
}
if(!is.na(projection.out)){
polys <- sf::st_transform(polys, projection.out)
}
return(polys)
}EXAMPLE 1
calcMC - Calculate strength of migratory
connectivity
This function calculates the strength of migratory connectivity between populations during two different time periods within the annual cycle. Below, we illustrate how to calculate the strength of MC between three breeding and non-breeding regions.
To calculate the strength of MC, you will need:
- to define the number of breeding and non-breeding regions
- the distance between the breeding regions and the distance between
the non-breeding regions
- the transition probabilities between each breeding and non-breeding
region (
psi)
- the relative abundance within each of the regions where individuals originate from (here the breeding regions)
- (optional) the total sample size of animals used to estimate the transition probabilities.
Calculate MC between breeding and non-breeding regions
Simple example with three breeding and three non-breeding regions
Define the number of breeding and non-breeding populations
Generate a distance matrix between the different regions
#distances between centroids of three breeding regions
breedDist <- matrix(c(0, 1, 2,
1, 0, 1,
2, 1, 0), nBreeding, nBreeding)
#distances between centroids of three non-breeding regions
nonBreedDist <- matrix(c(0, 5, 10,
5, 0, 5,
10, 5, 0), nNonBreeding, nNonBreeding)
Define the transition probabilities between the breeding and non-breeding regions
#transition probabilities form each breeding to each non-breeding region
psi <- matrix(c(0.4, 0.35, 0.25,
0.3, 0.4, 0.3,
0.2, 0.3, 0.5), nBreeding, nNonBreeding, byrow = TRUE) 
Define the relative abundance within the three breeding regions
#equal relative abundance among the three breeding regions, must sum to 1
relN <- rep(1/nBreeding, nBreeding)
relN
#> [1] 0.3333333 0.3333333 0.3333333
# Define total sample size for psi data
# for small sample corrected version of MC
n <- 250Calculate the strength of migratory connectivity using the inputs above
# Calculate the strength of migratory connectivity
MC <- calcMC(originDist = breedDist,
targetDist = nonBreedDist,
originRelAbund = relN,
psi = psi)
round(MC, 3)
#> [1] 0.05
MC <- calcMC(originDist = breedDist,
targetDist = nonBreedDist,
originRelAbund = relN,
psi = psi,
sampleSize = n)
round(MC, 3)
#> [1] 0.044EXAMPLE 2
estMC - Estimate strength of migratory connectivity
incorporating location and other sampling uncertainty
Estimates of MC may also be influenced by error in individual assignment to regions. Data types vary in location accuracy and precision, which are likely to influence the accuracy with which individuals are assigned to regions.
To estimate MC and include location uncertainty the following data are needed:
- A logical vector indicating the type of device the location
estimates were derived from (GPS = FALSE, Light-level geolocator =
TRUE)
- Location Bias - a vector that has error estimates for both longitude
and latitude
- Location error - a variance, covariance matrix of longitude and
latitude
- Distance matrix between breeding and non-breeding regions
- A shapefile of both breeding and non-breeding regions
- The deployment locations and the ‘unknown’ locations derived from
the tracking devices
- The relative (or absolute) abundance within each region where the birds originate from (deployment regions)
Below is a repeatable example for how to calculate location bias and location error using coordinates derived from light-level geolocators.
# Load in projections
data("projections")
# Define deployment locations (winter) #
captureLocations<-matrix(c(-77.93,18.04, # Jamaica
-80.94,25.13, # Florida
-66.86,17.97), # Puerto Rico
nrow=3, ncol=2, byrow = TRUE)
colnames(captureLocations) <- c("Longitude","Latitude")
# Convert capture locations into points #
CapLocs <- sf::st_as_sf(data.frame(captureLocations),
coords = c("Longitude","Latitude"),
crs = 4326)
# Project Capture locations #
CapLocsM<-sf::st_transform(CapLocs, 'ESRI:54027')
# Retrieve raw non-breeding locations from github
# First grab the identity of the bird so we can loop through the files
# For this example we are only interested in the error
# around non-breeding locations
# here we grab only the birds captured during the non-breeding season
# Using paste0 for vignette formatting purposes
winterBirds <- dget(
"https://raw.githubusercontent.com/SMBC-NZP/MigConnectivity/master/data-raw/GL_NonBreedingFiles/winterBirds.txt"
)
# create empty list to store the location data #
Non_breeding_files <- vector('list',length(winterBirds))
# Get raw location data from Github #
for(i in 1:length(winterBirds)){
Non_breeding_files[[i]] <- dget(paste0("https://raw.githubusercontent.com/",
"SMBC-NZP/MigConnectivity/master/data-raw/",
"GL_NonBreedingFiles/NonBreeding_",
winterBirds[i],".txt"))
}
# Remove locations around spring Equinox and potential migration points
# same NB time frame as Hallworth et al. 2015
# two steps because subset on shapefile doesn't like it in a single step
Non_breeding_files <- lapply(Non_breeding_files,
FUN = function(x){
month <- as.numeric(format(x$Date,format = "%m"))
x[which(month != 3 & month != 4),]})
Jam <- c(1:9) # locations w/in list of winterBirds captured in Jamaica
Fla <- c(10:12) # locations w/in list of winterBirds in Florida
PR <- c(13:16) # locations w/in list of winterBirds in Puerto Rico
# Turn the locations into shapefiles #
NB_GL <- lapply(Non_breeding_files,
FUN = function(x){
sf::st_as_sf(data.frame(x),
coords = c("Longitude",
"Latitude"),
crs = 4326)})
# Project into UTM projection #
NB_GLmeters <- lapply(NB_GL,
FUN = function(x){sf::st_transform(x,'ESRI:54027')})
# Process to determine geolocator bias and variance-covariance in meters #
# generate empty vector to store data #
LongError<-rep(NA,length(winterBirds))
LatError<-rep(NA,length(winterBirds))
# Calculate the error in longitude derived
# from geolocators from the true capture location
LongError[Jam] <- unlist(lapply(NB_GLmeters[Jam],
FUN = function(x){mean(sf::st_coordinates(x)[,1]-
sf::st_coordinates(CapLocsM)[1,1])}))
LongError[Fla] <- unlist(lapply(NB_GLmeters[Fla],
FUN = function(x){mean(sf::st_coordinates(x)[,1]-
sf::st_coordinates(CapLocsM)[2,1])}))
LongError[PR] <- unlist(lapply(NB_GLmeters[PR],
FUN = function(x){mean(sf::st_coordinates(x)[,1]-
sf::st_coordinates(CapLocsM)[3,1])}))
# Calculate the error in latitude derived from
# geolocators from the true capture location
LatError[Jam] <- unlist(lapply(NB_GLmeters[Jam],
FUN = function(x){mean(sf::st_coordinates(x)[,2]-
sf::st_coordinates(CapLocsM)[1,2])}))
LatError[Fla] <- unlist(lapply(NB_GLmeters[Fla],
FUN = function(x){mean(sf::st_coordinates(x)[,2]-
sf::st_coordinates(CapLocsM)[2,2])}))
LatError[PR] <- unlist(lapply(NB_GLmeters[PR],
FUN = function(x){mean(sf::st_coordinates(x)[,2]-
sf::st_coordinates(CapLocsM)[3,2])}))
# Get co-variance matrix for error of
# known non-breeding deployment sites
# lm does multivariate normal models if you give it a matrix dependent variable!
geo.error.model <- lm(cbind(LongError,LatError) ~ 1)
geo.bias <- coef(geo.error.model)
geo.vcov <- vcov(geo.error.model)We measured MC for bootstrapped data of birds tracked from breeding to non-breeding regions using light-level and GPS geolocation when 1) GPS location uncertainty was applied to all individuals, 2) GPS and light-level location uncertainty were applied to individuals with those devices, and 3) light-level location uncertainty was applied to all individuals.
Load location data that accompanies the MigConnectivity
package. The location data are data from breeding Ovenbirds that were
fit with Light-level geolocators or PinPoint-10 GPS tags.
data(OVENdata) # Ovenbird
names(OVENdata)
#> [1] "geo.bias" "geo.vcov" "isGL" "targetPoints"
#> [5] "originPoints" "targetSites" "originSites" "originRelAbund"
#> [9] "originDist" "targetDist" "originNames" "targetNames"The figure below shows the two breeding regions (squares), and the three non-breeding regions (gray scale) used in Cohen et al. (2018) to estimate MC for Ovenbirds tracked with light-level geolocators and PinPoint-10 GPS tags.
library(shape)
library(maps)
library(terra)
# Set the crs to WGS84
originSitesWGS84 <- sf::st_transform(OVENdata$originSites, 4326)
targetSitesWGS84 <- sf::st_transform(OVENdata$targetSites, 4326)
# Create a simple plot of the origin and and target sites
par(mar=c(0,0,0,0))
plot(originSitesWGS84,
xlim=c(terra::ext(targetSitesWGS84)[1],
terra::ext(targetSitesWGS84)[2]),
ylim=c(terra::ext(targetSitesWGS84)[3],
terra::ext(originSitesWGS84)[4]))
plot(targetSitesWGS84,
add = TRUE,
col=c("gray70","gray35","gray10"))
map("world", add=TRUE)
The following code demonstrates how to estimate MC using location data from light-level geolocators and PinPoint-10 GPS tags.
M<-estMC(isGL=OVENdata$isGL, # Logical vector: light-level geolocator(T)/GPS(F)
geoBias = OVENdata$geo.bias, # Light-level geolocator location bias
geoVCov = OVENdata$geo.vcov, #Light-level geolocator covariance matrix
targetDist = OVENdata$targetDist, # Target location distance matrix
originDist = OVENdata$originDist, # Origin location distance matrix
targetSites = OVENdata$targetSites, # Non-breeding / target sites
originSites = OVENdata$originSites, # Breeding / origin sites
targetNames = OVENdata$targetNames, # Names of nonbreeding/target sites
originNames = OVENdata$originNames, # Names of breeding/origin sites
originPoints = OVENdata$originPoints, # Capture Locations
targetPoints = OVENdata$targetPoints, # Target locations from devices
originRelAbund = OVENdata$originRelAbund, # Origin relative abundances
resampleProjection = sf::st_crs(OVENdata$targetPoints),
verbose = 0, # output options - see help ??estMC
nSamples = 10) # This is set low for example
M
#> Migratory Connectivity Estimates
#>
#> Transition probability (psi) estimates (mean):
#> FL Cuba Hisp
#> NH 0.0000 0.2142 0.7858
#> MD 0.3847 0.6153 0.0000
#> +/- SE:
#> FL Cuba Hisp
#> NH 0.0000 0.07902 0.07902
#> MD 0.1552 0.15524 0.00000
#> 95% confidence interval (simple quantile):
#> FL Cuba Hisp
#> NH 0.00000 - 0.0000 0.09642 - 0.3097 0.69031 - 0.9036
#> MD 0.16696 - 0.6194 0.38062 - 0.8330 0.00000 - 0.0000
#>
#> MC estimate (mean): 0.5767 +/- (SE) 0.1133
#> 95% confidence interval (simple quantile): 0.4231 - 0.7613
#>
#> This is a subset of what's available inside this estMigConnectivity output.
#> For more info, try ?estTransition or str(obj_name, max.level = 2).Take a closer look at what is included in the output
str(M, max.level = 2)
#> List of 4
#> $ psi :List of 7
#> ..$ sample : num [1:10, 1:2, 1:3] 0 0 0 0 0 0 0 0 0 0 ...
#> .. ..- attr(*, "dimnames")=List of 3
#> ..$ mean : num [1:2, 1:3] 0 0.385 0.214 0.615 0.786 ...
#> .. ..- attr(*, "dimnames")=List of 2
#> ..$ se : num [1:2, 1:3] 0 0.155 0.079 0.155 0.079 ...
#> .. ..- attr(*, "dimnames")=List of 2
#> ..$ simpleCI: num [1:2, 1:2, 1:3] 0 0 0.167 0.6194 0.0964 ...
#> .. ..- attr(*, "dimnames")=List of 3
#> ..$ bcCI : num [1:2, 1:2, 1:3] 0 0 0.143 0.625 0.087 ...
#> .. ..- attr(*, "dimnames")=List of 3
#> ..$ median : num [1:2, 1:3] 0 0.372 0.234 0.628 0.766 ...
#> .. ..- attr(*, "dimnames")=List of 2
#> ..$ point : num [1:2, 1:3] 0 0.4 0.174 0.6 0.826 ...
#> .. ..- attr(*, "dimnames")=List of 2
#> $ MC :List of 10
#> ..$ sample : num [1:10] 0.793 0.412 0.644 0.653 0.646 ...
#> ..$ mean : num 0.577
#> ..$ se : num 0.113
#> ..$ simpleCI: num [1:2] 0.423 0.761
#> ..$ bcCI : num [1:2] 0.412 0.793
#> ..$ hpdCI : num [1:2] 0.412 0.793
#> ..$ median : num 0.579
#> ..$ point : num 0.621
#> ..$ simpleP : NULL
#> ..$ bcP : NULL
#> $ corr :List of 7
#> ..$ sample : logi [1:10] NA NA NA NA NA NA ...
#> ..$ mean : NULL
#> ..$ se : NULL
#> ..$ simpleCI: NULL
#> ..$ bcCI : NULL
#> ..$ median : NULL
#> ..$ point : NULL
#> $ input:List of 26
#> ..$ originDist : num [1:2, 1:2] 0 581231 581231 0
#> .. ..- attr(*, "dimnames")=List of 2
#> ..$ targetDist : num [1:3, 1:3] 0 852312 1570037 852312 0 ...
#> .. ..- attr(*, "dimnames")=List of 2
#> ..$ originRelAbund : num [1:2, 1] 0.623 0.377
#> ..$ sampleSize : int 39
#> ..$ originSites :Classes 'sf' and 'data.frame': 2 obs. of 1 variable:
#> .. ..- attr(*, "sf_column")= chr "geometry"
#> .. ..- attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..:
#> .. .. ..- attr(*, "names")= chr(0)
#> ..$ targetSites :Classes 'sf' and 'data.frame': 3 obs. of 1 variable:
#> .. ..- attr(*, "sf_column")= chr "geometry"
#> .. ..- attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..:
#> .. .. ..- attr(*, "names")= chr(0)
#> ..$ originPoints :Classes 'sf' and 'data.frame': 39 obs. of 1 variable:
#> .. ..- attr(*, "sf_column")= chr "geometry"
#> .. ..- attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..:
#> .. .. ..- attr(*, "names")= chr(0)
#> ..$ targetPoints :Classes 'sf' and 'data.frame': 39 obs. of 1 variable:
#> .. ..- attr(*, "sf_column")= chr "geometry"
#> .. ..- attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..:
#> .. .. ..- attr(*, "names")= chr(0)
#> ..$ originAssignment : int [1:39] 1 1 1 1 1 1 1 1 1 1 ...
#> ..$ targetAssignment : int [1:39] NA 3 3 3 NA 3 3 2 3 NA ...
#> ..$ originNames : chr [1:2] "NH" "MD"
#> ..$ targetNames : chr [1:3] "FL" "Cuba" "Hisp"
#> ..$ nSamples : num 10
#> ..$ nSim : num 1000
#> ..$ isGL : logi [1:39] TRUE TRUE TRUE TRUE TRUE TRUE ...
#> ..$ geoBias : num [1, 1:2] 2423 265432
#> .. ..- attr(*, "dimnames")=List of 2
#> ..$ geoVCov : num [1:2, 1:2] 5.08e+08 -1.64e+09 -1.64e+09 1.28e+10
#> .. ..- attr(*, "dimnames")=List of 2
#> ..$ row0 : num 0
#> ..$ verbose : num 0
#> ..$ calcCorr : logi FALSE
#> ..$ alpha : num 0.05
#> ..$ approxSigTest : logi FALSE
#> ..$ sigConst : num 0
#> ..$ resampleProjection :List of 2
#> .. ..- attr(*, "class")= chr "crs"
#> ..$ maxTries : num 300
#> ..$ maintainLegacyOutput: logi FALSE
#> - attr(*, "class")= chr [1:3] "estMC" "estPsi" "estMigConnectivity"EXAMPLE 3
simMove Simulates position of animals by individual,
season, year, and month
Calculate the strength of migratory connectivity based on simulation data for 10 years of movement between breeding and non-breeding seasonal ranges. Breeding and non-breeding ranges are equally divided into 100 regions on an ellipsoid globe. See above for details regarding the utility functions.
Simulate data to demonstrate the use of calcMC
set.seed(75)
# Parameters for simulations
nSeasons <- 2 # population with two discrete seasons - breeding / non-breeding
nYears <- 10 # Ten years of data
nMonths <- 4 # Four months within each season
nBreeding <- 100 # Number of populations
nWintering <- 100 # Number of populations Generate the spatial arrangement of breeding and non-breeding populations
breedingPos <- matrix(c(rep(seq(-99,-81,2), each=sqrt(nBreeding)),
rep(seq(49,31,-2), sqrt(nBreeding))), nBreeding, 2)
winteringPos <- matrix(c(rep(seq(-79,-61,2), each=sqrt(nWintering)),
rep(seq(9,-9,-2), sqrt(nWintering))), nWintering, 2)
# calculate distance between populations
breedDist <- distFromPos(breedingPos, 'ellipsoid')
nonbreedDist <- distFromPos(winteringPos, 'ellipsoid') The relative abundance of the study species is needed in each population. Here we generate those data below.
The transition probability (psi) between the breeding and non-breeding populations is calculated below.
# Set up psi matrix
o <- optimize(mlogitMC, MC.in = 0.25, origin.dist = breedDist,
target.dist = nonbreedDist, origin.abund = breedingRelN,
sample.size = sum(breedingN),
interval = c(1, 3), tol = .Machine$double.eps^0.5)#
slope <- o$minimum
psi <- mlogitMat(slope, breedDist)Now that we have all the data needed to calculate the strength of
migratory connectivity we can use the calcMC function in
the MigConnecitivty package to generate a standardized MC
metric.
simMove Simulation of movement between breeding and
non-breeding
Recycle data generated for the calcMC function - see above
sims <- simMove(breedingAbund = breedingN, # Breeding relative abundance
breedingDist = breedDist, # Breeding distance
winteringDist = nonbreedDist, # Non-breeding distance
psi = psi, # Transition probabilities
nYears = nYears, # Number of years
nMonths = nMonths, # Number of months
winMoveRate = 0, # winter movement rate
sumMoveRate = 0, # breeding movement rate
winDispRate = 0, # winter dispersal rate
sumDispRate = 0, # summer disperal rate
natalDispRate = 0, # natal dispersal rate
breedDispRate = 0, # breeding dispersal rate
verbose = 0) # verbose output
str(sims)
#> List of 6
#> $ animalLoc : int [1:50000, 1:2, 1:10, 1:4] 1 1 1 1 1 1 1 1 1 1 ...
#> $ natalDispMat: NULL
#> $ breedDispMat: NULL
#> $ sumMoveMat : NULL
#> $ winDispMat : NULL
#> $ winMoveMat : NULL