estMC: Estimate migratory connectivity
In MigConnectivity: Estimate Migratory Connectivity for Migratory Animals
View source: R/estConnectivity.R
estMC R Documentation
Estimate migratory connectivity
Description
Resampling of uncertainty for migratory connectivity strength (MC)
and transition probabilities (psi) from RMark psi matrix estimates or
samples of psi and/or JAGS
relative abundance MCMC samples OR SpatialPoints geolocators and/or GPS
data OR intrinsic markers such as isotopes. NOTE: active development of this
function is ending. We suggest users estimate psi with
estTransition, MC with estStrength, and Mantel
correlations (rM) with estMantel.
Usage
estMC(
originDist,
targetDist = NULL,
originRelAbund,
psi = NULL,
sampleSize = NULL,
originSites = NULL,
targetSites = NULL,
originPoints = NULL,
targetPoints = NULL,
originAssignment = NULL,
targetAssignment = NULL,
originNames = NULL,
targetNames = NULL,
nSamples = 1000,
nSim = ifelse(isTRUE(isIntrinsic), 10, 1000),
isGL = FALSE,
geoBias = NULL,
geoVCov = NULL,
row0 = 0,
verbose = 0,
calcCorr = FALSE,
alpha = 0.05,
approxSigTest = FALSE,
sigConst = 0,
resampleProjection = "ESRI:102010",
maxTries = 300,
targetIntrinsic = NULL,
isIntrinsic = FALSE,
maintainLegacyOutput = FALSE
)
Arguments
originDist
Distances between the B origin sites. Symmetric B by B
matrix
targetDist
Distances between the W target sites. Symmetric W by W
matrix. Optional for intrinsic data
originRelAbund
Relative abundance estimates at B origin sites. Either
a numeric vector of length B that sums to 1 or an mcmc object with
nSamples rows and columns including 'relN[1]' through 'relN[B]'.
Currently, an mcmc object doesn't work with geolocator, GPS, or intrinsic
data
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), or a MARK object with estimates of transition
probabilities. If you are estimating MC from GPS, geolocator, or intrinsic
data, leave this as NULL
sampleSize
Total sample size of animals that psi will be estimated
from. Should be the number of animals released in one of the origin sites
and observed in one of the target sites. Optional, but recommended, unless
you are estimating MC from GPS, geolocator, intrinsic, or direct band return
data (in which case the function can calculate it for you)
originSites
If psi is a MARK object, this must be a numeric
vector indicating which sites are origin. If using GPS, geolocator, or
intrinsic data, this can be the geographic definition of sites in the
release season
targetSites
If psi is a MARK object, this must be a numeric
vector indicating which sites are target. If using GPS, geolocator, or
intrinsic data, this must be the geographic definition of sites in the
non-release season. Optional for intrinsic data; if left out, the function
will use the targetSites defined in targetIntrinsic
originPoints
A POINT sf object, with length number of
animals tracked. Each point indicates the release location of an animal
targetPoints
For GL or GPS data, a POINT sf object, with
length number of animals tracked. Each point indicates the point estimate
location in the non-release season
originAssignment
Assignment of originPoints to release season
sites. Integer vector with length number of animals tracked. Optional,
but if using GL or GPS data, either originAssignment or
originSites and originPoints should be defined
targetAssignment
Optional. Point estimate assignment of
targetPoints to non-release season sites. Integer vector with
length number of animals tracked
originNames
Optional but recommended. Vector of names for the release season sites
targetNames
Optional but recommended. Vector of names for the non-release season
sites
nSamples
Number of times to resample psi and/or
originRelAbund OR number of post-burn-in MCMC samples to store (band
data) OR number of times to resample targetPoints
for intrinsic data OR number of bootstrap runs for GL or GPS data. In
the last two cases, animals are sampled with replacement for each. For all,
the purpose is to estimate sampling uncertainty
nSim
Tuning parameter for GL or intrinsic data. Affects only the
speed; 1000 seems to work well with our GL data and 10 for our intrinsic
data, but your results may vary. Should be integer > 0
isGL
Indicates whether or which animals were tracked with geolocators.
Should be either single TRUE or FALSE value, or vector with length of
number of animals tracked, with TRUE for animals in
targetPoints with geolocators and FALSE for animals with GPS
geoBias
For GL data, vector of length 2 indicating expected bias
in longitude and latitude of targetPoints, in
resampleProjection units (default meters)
geoVCov
For GL data, 2x2 matrix with expected variance/covariance
in longitude and latitude of targetPoints, in
resampleProjection units (default meters)
row0
If originRelAbund is an mcmc object, this can be set
to 0 (default) or any greater integer to specify where to stop ignoring
samples ("burn-in")
verbose
0 (default) to 3. 0 prints no output during run. 1 prints
a line every 100 samples or bootstraps and a summary every 10. 2 prints a
line and summary every sample or bootstrap. 3 also prints the number of
draws (for tuning nSim for GL/intrinsic data only)
calcCorr
In addition to MC, should function also estimate Mantel
correlation between release and non-release locations (GPS or GL data
only)? Default is FALSE
alpha
Level for confidence/credible intervals provided
approxSigTest
Should function compute approximate one-sided
significance tests (p-values) for MC from the bootstrap? Default is
FALSE
sigConst
Value to compare MC to in significance test.
Default is 0
resampleProjection
Projection when sampling from geolocator
bias/error. This projection needs units = m. Default is Equidistant
Conic. The default setting preserves distances around latitude = 0 and
longitude = 0. Other projections may work well, depending on the location
of targetSites. Ignored unless data are geolocator or GPS
maxTries
Maximum number of times to run a single GL/intrinsic
bootstrap before exiting with an error. Default is 300. Set to NULL to
never stop. This parameter was added to prevent GL setups where some
sample points never land on target sites from running indefinitely
targetIntrinsic
For intrinsic tracking data, the results of
isoAssign or a similar function, of class intrinsicAssign
isIntrinsic
Logical indicating whether the animals are tracked via
intrinsic marker (e.g. isotopes) or not. Currently estMC will only estimate
connectivity for all intrinsically marked animals or all extrinsic (e.g.,
bands, GL, or GPS), so isIntrinsic should be a single TRUE or FALSE
maintainLegacyOutput
version 0.4.0 of MigConnectivity
updated the structure of the estimates. If you have legacy code that refers
to elements within a estMigConnectivity object, you can set this
to TRUE to also keep the old structure. Defaults to FALSE
Value
NOTE: Starting with version 0.4.0 of MigConnectivity, we've
updated the structure of MigConnectivityEstimate objects. Below we
describe the updated structure. If parameter maintainLegacyOutput is
set to TRUE, the list will start with the old structure: sampleMC,
samplePsi, pointPsi, pointMC, meanMC,
medianMC, seMC, simpleCI, bcCI, hpdCI,
simpleP, bcP, sampleCorr, pointCorr,
meanCorr, medianCorr, seCorr, simpleCICorr, bcCICorr,
inputSampleSize, alpha, and sigConst.
estMC returns a list with the elements:
psiList containing estimates of transition probabilities:
sample Array of sampled values for psi. nSamples x
[number of origin sites] x [number of target sites]. Provided to allow
the user to compute own summary statistics.
mean Main estimate of psi matrix. [number of origin sites]
x [number of target sites].
se Standard error of psi, estimated from SD of
psi$sample.
simpleCI 1 - alpha confidence interval for psi,
estimated as alpha/2 and 1 - alpha/2 quantiles of
psi$sample.
bcCI Bias-corrected 1 - alpha confidence interval
for psi. Preferable to simpleCI when mean is the
best estimate of psi. simpleCI is preferred when
median is a better estimator. When meanMC==medianMC,
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 psi matrix.
point Simple point estimate of psi matrix, not accounting
for sampling error. NULL when isIntrinsic == TRUE.
MCList containing estimates of migratory connectivity
strength:
sample nSamples sampled values for
MC. Provided to allow the user to compute own summary statistics.
mean Mean of MC$sample. Main estimate of MC,
incorporating parametric uncertainty.
se Standard error of MC, estimated from SD of
MC$sample.
simpleCI Default1 - alpha confidence interval for
MC, estimated as alpha/2 and 1 - alpha/2 quantiles of
MC$sample.
bcCI Bias-corrected 1 - alpha confidence interval
for MC. Preferable to MC$simpleCI when MC$mean is the
best estimate of MC. MC$simpleCI is preferred when
MC$median is a better estimator. When MC$mean==MC$median,
these should be identical. Estimated as the
pnorm(2 * z0 + qnorm(alpha / 2)) and
pnorm(2 * z0 + qnorm(1 - alpha / 2)) quantiles of MC$sample,
where z0 is the proportion of MC$sample < MC$mean.
hpdCI 1 - alpha credible interval for MC,
estimated using the highest posterior density (HPD) method.
median Median of MC, alternate estimator also including
parametric uncertainty.
point Simple point estimate of MC, using the point
estimates of psi and originRelAbund, not accounting
for sampling error. NULL when isIntrinsic == TRUE.
simpleP Approximate p-value for MC, estimated as the
proportion of bootstrap iterations where MC < sigConst (or MC >
sigConst if pointMC < sigConst). Note that if the
proportion is 0, a default value of 0.5 / nSamples is provided,
but this is best interpreted as p < 1 / nSamples. NULL when
approxSigTest==FALSE.
bcP Approximate bias-corrected p-value for MC, estimated as
pnorm(qnorm(simpleP) - 2 * z0), where z0 is the proportion of
sampleMC < meanMC. May be a better approximation of the p-value
than simpleP, but many of the same limitations apply. NULL when
approxSigTest==FALSE.
corrList containing estimates of rM, an alternate measure of
migratory connectivity strength. NULL when calcCorr==FALSE or
!is.null(psi):
sample nBoot sampled values for continuous
correlation. Provided to allow the user to compute own summary
statistics.
mean, se, simpleCI, bcCI, median, point Summary
statistics for continuous correlation bootstraps.
inputList containing the inputs to estMC, or at least
the relevant ones, such as sampleSize.
References
Cohen, E. B., J. A. Hostetler, M. T. Hallworth, C. S. Rushing, T. S. Sillett,
and P. P. Marra. 2018. Quantifying the strength of migratory connectivity.
Methods in Ecology and Evolution 9: 513 - 524.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/2041-210X.12916")}
Cohen, E. B., C. S. Rushing, F. R. Moore, M. T. Hallworth, J. A. Hostetler,
M. Gutierrez Ramirez, and P. P. Marra. 2019. The strength of
migratory connectivity for birds en route to breeding through the Gulf of
Mexico. Ecography 42: 658–669.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/ecog.03974")}
See Also
estStrength, estTransition,
estMantel, calcMC, projections,
isoAssign, plot.estMigConnectivity
Examples
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
MigConnectivity documentation built on May 29, 2024, 8:37 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
View source: R/estConnectivity.R
| estMC | R Documentation |
Estimate migratory connectivity
Description
Resampling of uncertainty for migratory connectivity strength (MC)
and transition probabilities (psi) from RMark psi matrix estimates or
samples of psi and/or JAGS
relative abundance MCMC samples OR SpatialPoints geolocators and/or GPS
data OR intrinsic markers such as isotopes. NOTE: active development of this
function is ending. We suggest users estimate psi with
estTransition, MC with estStrength, and Mantel
correlations (rM) with estMantel.
Usage
estMC(
originDist,
targetDist = NULL,
originRelAbund,
psi = NULL,
sampleSize = NULL,
originSites = NULL,
targetSites = NULL,
originPoints = NULL,
targetPoints = NULL,
originAssignment = NULL,
targetAssignment = NULL,
originNames = NULL,
targetNames = NULL,
nSamples = 1000,
nSim = ifelse(isTRUE(isIntrinsic), 10, 1000),
isGL = FALSE,
geoBias = NULL,
geoVCov = NULL,
row0 = 0,
verbose = 0,
calcCorr = FALSE,
alpha = 0.05,
approxSigTest = FALSE,
sigConst = 0,
resampleProjection = "ESRI:102010",
maxTries = 300,
targetIntrinsic = NULL,
isIntrinsic = FALSE,
maintainLegacyOutput = FALSE
)
Arguments
originDist |
Distances between the B origin sites. Symmetric B by B matrix |
targetDist |
Distances between the W target sites. Symmetric W by W matrix. Optional for intrinsic data |
originRelAbund |
Relative abundance estimates at B origin sites. Either
a numeric vector of length B that sums to 1 or an mcmc object with
|
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), or a MARK object with estimates of transition probabilities. If you are estimating MC from GPS, geolocator, or intrinsic data, leave this as NULL |
sampleSize |
Total sample size of animals that psi will be estimated from. Should be the number of animals released in one of the origin sites and observed in one of the target sites. Optional, but recommended, unless you are estimating MC from GPS, geolocator, intrinsic, or direct band return data (in which case the function can calculate it for you) |
originSites |
If |
targetSites |
If |
originPoints |
A |
targetPoints |
For GL or GPS data, a |
originAssignment |
Assignment of |
targetAssignment |
Optional. Point estimate assignment of
|
originNames |
Optional but recommended. Vector of names for the release season sites |
targetNames |
Optional but recommended. Vector of names for the non-release season sites |
nSamples |
Number of times to resample |
nSim |
Tuning parameter for GL or intrinsic data. Affects only the speed; 1000 seems to work well with our GL data and 10 for our intrinsic data, but your results may vary. Should be integer > 0 |
isGL |
Indicates whether or which animals were tracked with geolocators.
Should be either single TRUE or FALSE value, or vector with length of
number of animals tracked, with TRUE for animals in
|
geoBias |
For GL data, vector of length 2 indicating expected bias
in longitude and latitude of |
geoVCov |
For GL data, 2x2 matrix with expected variance/covariance
in longitude and latitude of |
row0 |
If |
verbose |
0 (default) to 3. 0 prints no output during run. 1 prints a line every 100 samples or bootstraps and a summary every 10. 2 prints a line and summary every sample or bootstrap. 3 also prints the number of draws (for tuning nSim for GL/intrinsic data only) |
calcCorr |
In addition to MC, should function also estimate Mantel correlation between release and non-release locations (GPS or GL data only)? Default is FALSE |
alpha |
Level for confidence/credible intervals provided |
approxSigTest |
Should function compute approximate one-sided significance tests (p-values) for MC from the bootstrap? Default is FALSE |
sigConst |
Value to compare MC to in significance test. Default is 0 |
resampleProjection |
Projection when sampling from geolocator
bias/error. This projection needs units = m. Default is Equidistant
Conic. The default setting preserves distances around latitude = 0 and
longitude = 0. Other projections may work well, depending on the location
of |
maxTries |
Maximum number of times to run a single GL/intrinsic bootstrap before exiting with an error. Default is 300. Set to NULL to never stop. This parameter was added to prevent GL setups where some sample points never land on target sites from running indefinitely |
targetIntrinsic |
For intrinsic tracking data, the results of
|
isIntrinsic |
Logical indicating whether the animals are tracked via intrinsic marker (e.g. isotopes) or not. Currently estMC will only estimate connectivity for all intrinsically marked animals or all extrinsic (e.g., bands, GL, or GPS), so isIntrinsic should be a single TRUE or FALSE |
maintainLegacyOutput |
version 0.4.0 of |
Value
NOTE: Starting with version 0.4.0 of MigConnectivity, we've
updated the structure of MigConnectivityEstimate objects. Below we
describe the updated structure. If parameter maintainLegacyOutput is
set to TRUE, the list will start with the old structure: sampleMC,
samplePsi, pointPsi, pointMC, meanMC,
medianMC, seMC, simpleCI, bcCI, hpdCI,
simpleP, bcP, sampleCorr, pointCorr,
meanCorr, medianCorr, seCorr, simpleCICorr, bcCICorr,
inputSampleSize, alpha, and sigConst.
estMC returns a list with the elements:
psiList containing estimates of transition probabilities:
sampleArray of sampled values for psi.nSamplesx [number of origin sites] x [number of target sites]. Provided to allow the user to compute own summary statistics.meanMain estimate of psi matrix. [number of origin sites] x [number of target sites].seStandard error of psi, estimated from SD ofpsi$sample.simpleCI1 - alphaconfidence interval for psi, estimated asalpha/2and1 - alpha/2quantiles ofpsi$sample.bcCIBias-corrected1 - alphaconfidence interval for psi. Preferable tosimpleCIwhenmeanis the best estimate of psi.simpleCIis preferred whenmedianis a better estimator. WhenmeanMC==medianMC, these should be identical. Estimated as thepnorm(2 * z0 + qnorm(alpha / 2))andpnorm(2 * z0 + qnorm(1 - alpha / 2))quantiles ofsample, where z0 is the proportion ofsample < mean.medianMedian estimate of psi matrix.pointSimple point estimate of psi matrix, not accounting for sampling error. NULL whenisIntrinsic == TRUE.
MCList containing estimates of migratory connectivity strength:
samplenSamplessampled values for MC. Provided to allow the user to compute own summary statistics.meanMean ofMC$sample. Main estimate of MC, incorporating parametric uncertainty.seStandard error of MC, estimated from SD ofMC$sample.simpleCIDefault1 - alphaconfidence interval for MC, estimated asalpha/2and1 - alpha/2quantiles ofMC$sample.bcCIBias-corrected1 - alphaconfidence interval for MC. Preferable toMC$simpleCIwhenMC$meanis the best estimate of MC.MC$simpleCIis preferred whenMC$medianis a better estimator. WhenMC$mean==MC$median, these should be identical. Estimated as thepnorm(2 * z0 + qnorm(alpha / 2))andpnorm(2 * z0 + qnorm(1 - alpha / 2))quantiles ofMC$sample, where z0 is the proportion ofMC$sample < MC$mean.hpdCI1 - alphacredible interval for MC, estimated using the highest posterior density (HPD) method.medianMedian of MC, alternate estimator also including parametric uncertainty.pointSimple point estimate of MC, using the point estimates ofpsiandoriginRelAbund, not accounting for sampling error. NULL whenisIntrinsic == TRUE.simplePApproximate p-value for MC, estimated as the proportion of bootstrap iterations where MC <sigConst(or MC >sigConstifpointMC < sigConst). Note that if the proportion is 0, a default value of 0.5 /nSamplesis provided, but this is best interpreted as p < 1 /nSamples. NULL whenapproxSigTest==FALSE.bcPApproximate bias-corrected p-value for MC, estimated aspnorm(qnorm(simpleP) - 2 * z0), where z0 is the proportion ofsampleMC < meanMC. May be a better approximation of the p-value thansimpleP, but many of the same limitations apply. NULL whenapproxSigTest==FALSE.
corrList containing estimates of rM, an alternate measure of migratory connectivity strength. NULL when
calcCorr==FALSEor!is.null(psi):samplenBootsampled values for continuous correlation. Provided to allow the user to compute own summary statistics.mean, se, simpleCI, bcCI, median, pointSummary statistics for continuous correlation bootstraps.
inputList containing the inputs to
estMC, or at least the relevant ones, such as sampleSize.
References
Cohen, E. B., J. A. Hostetler, M. T. Hallworth, C. S. Rushing, T. S. Sillett, and P. P. Marra. 2018. Quantifying the strength of migratory connectivity. Methods in Ecology and Evolution 9: 513 - 524. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/2041-210X.12916")}
Cohen, E. B., C. S. Rushing, F. R. Moore, M. T. Hallworth, J. A. Hostetler, M. Gutierrez Ramirez, and P. P. Marra. 2019. The strength of migratory connectivity for birds en route to breeding through the Gulf of Mexico. Ecography 42: 658–669. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/ecog.03974")}
See Also
estStrength, estTransition,
estMantel, calcMC, projections,
isoAssign, plot.estMigConnectivity
Examples
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
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.