# Estimate psi (transition probabilities between locations in two phases of the annual cycle) Estimation and resampling of uncertainty for psi (transition probabilities between origin sites in one phase of the annual cycle and target sites in another for migratory animals). Data can be from any combination of geolocators (GL), telemetry/GPS, intrinsic markers such as isotopes and genetics, and band/ring reencounter data. ```r estTransition( originSites = NULL, targetSites = NULL, originPoints = NULL, targetPoints = NULL, originAssignment = NULL, targetAssignment = NULL, originNames = NULL, targetNames = NULL, nSamples = 1000, isGL = FALSE, isTelemetry = FALSE, isRaster = FALSE, isProb = FALSE, captured = "origin", geoBias = NULL, geoVCov = NULL, geoBiasOrigin = geoBias, geoVCovOrigin = geoVCov, targetRaster = NULL, originRaster = NULL, banded = NULL, reencountered = NULL, verbose = 0, alpha = 0.05, resampleProjection = "ESRI:102010", nSim = ifelse(any(isRaster & isGL) || any(isRaster & isProb) || any(isGL & isProb), 5000, ifelse(any(isGL), 1000, ifelse(any(isRaster), 10, 1))), maxTries = 300, nBurnin = 5000, nChains = 3, nThin = 1, dataOverlapSetting = c("dummy", "none", "named"), fixedZero = NULL, targetRelAbund = NULL, method = c("bootstrap", "MCMC", "m-out-of-n-bootstrap"), m = NULL, psiPrior = NULL, returnAllInput = TRUE ) estPsi( originSites = NULL, targetSites = NULL, originPoints = NULL, targetPoints = NULL, originAssignment = NULL, targetAssignment = NULL, originNames = NULL, targetNames = NULL, nSamples = 1000, isGL = FALSE, isTelemetry = FALSE, isRaster = FALSE, isProb = FALSE, captured = "origin", geoBias = NULL, geoVCov = NULL, geoBiasOrigin = geoBias, geoVCovOrigin = geoVCov, targetRaster = NULL, originRaster = NULL, banded = NULL, reencountered = NULL, verbose = 0, alpha = 0.05, resampleProjection = "ESRI:102010", nSim = ifelse(any(isRaster & isGL) || any(isRaster & isProb) || any(isGL & isProb), 5000, ifelse(any(isGL), 1000, ifelse(any(isRaster), 10, 1))), maxTries = 300, nBurnin = 5000, nChains = 3, nThin = 1, dataOverlapSetting = c("dummy", "none", "named"), fixedZero = NULL, targetRelAbund = NULL, method = c("bootstrap", "MCMC", "m-out-of-n-bootstrap"), m = NULL, psiPrior = NULL, returnAllInput = TRUE ) ``` ## Arguments - `originSites`: A polygon spatial layer (sf - MULTIPOLYGON) defining the geographic representation of sites in the origin season. - `targetSites`: A polygon spatial layer (sf - MULTIPOLYGON) defining the geographic representation of sites in the target season. - `originPoints`: A `sf` or `SpatialPoints` object, with number of rows or length being the number of animals tracked. Each point indicates the origin location of an animal (or point estimate of same, for GL animals released on target sites). Note that to simplify input of multiple data types both between and for the same animal, if origin points are provided for any animal, they must be provided for all except banding data (can be dummy values), unless `dataOverlapSetting` is set to "none". - `targetPoints`: For GL or telemetry data, a `sf` or `SpatialPoints` object, with length or number of rows number of animals tracked. Each point indicates the point estimate location of an animal in the target season. Note that to simplify input of multiple data types both between and for the same animal, if target points are provided for any animal, they must be provided for all except banding data (can be dummy values), unless `dataOverlapSetting` is set to "none". - `originAssignment`: Assignment of animals to origin season sites. Either an integer vector with length number of animals tracked or a matrix of probabilities with number of animals tracked rows and number of origin sites columns (and rows summing to 1). The latter only applies to animals released in the target sites where there is uncertainty about their origin site, for example from genetic population estimates from the rubias package. Optional, but some combination of these inputs should be defined. Note that if `originAssignment` is a probability table, animals with known origin sites can have 1 in that column and 0s in all others. Also note that if `method` is "MCMC", anything in `originAssignment` and `targetAssignment` will be assumed to represent animals tracked via telemetry, with known origin and target sites. - `targetAssignment`: Assignment of animals to target season sites. Either an integer vector with length number of animals tracked or a matrix of probabilities with number of animals tracked rows and number of target sites columns (and rows summing to 1). The latter only applies to animals released in the origin sites where there is uncertainty about their target site, for example from genetic population estimates from the rubias package. Optional, but some combination of these inputs needs to be defined. Note that if `targetAssignment` is a probability table, animals with known target sites can have 1 in that column and 0s in all others. - `originNames`: Optional, but recommended to keep track. Vector of names for the origin sites. If not provided, the function will either try to get these from another input or provide default names (capital letters). - `targetNames`: Optional, but recommended to keep track. Vector of names for the target sites. If not provided, the function will either try to get these from another input or provide default names (numbers). - `nSamples`: Number of post-burn-in MCMC samples to store (`method` == "MCMC") OR number of bootstrap runs for `method` == "bootstrap". In the latter case, animals are sampled with replacement for each. For all, the purpose is to estimate sampling uncertainty. - `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 or FALSE for each animal in data (except those in `banded`, which are handled separately). For TRUE animals, the model applies `geoBias` and `geoVCov` to `targetPoints` where `captured` == "origin" or "neither" and `geoBiasOrigin` and `geoVCovOrigin` to `originPoints` where `captured` == "target" or "neither". Geolocator data should be entered as `originPoints` and `targetPoints`. - `isTelemetry`: Indicates whether or which animals were tracked with telemetry/GPS (no location uncertainty on either end). Should be either single TRUE or FALSE value, or vector with length of number of animals tracked, with TRUE or FALSE for each animal in data (except those in `banded`, which are handled separately). Telemetry data can be entered as points or using the `targetAssignment` and `originAssignment` arguments. - `isRaster`: Indicates whether or which animals were tracked with intrinsic markers (e.g., genetics or isotopes), with location uncertainty expressed as a raster of probabilities by grid cells, either in `targetRaster` or `originRaster`. Should be either single TRUE or FALSE value, or vector with length of number of animals tracked, with TRUE or FALSE for each animal in data (except those in `banded`, which are handled separately). - `isProb`: Indicates whether or which animals were tracked with intrinsic markers (e.g., genetics or isotopes), with location uncertainty expressed as a probability table, either in `targetAssignment` or `originAssignment`. Should be either single TRUE or FALSE value, or vector with length of number of animals tracked, with TRUE or FALSE for each animal in data (except those in `banded`, which are handled separately). - `captured`: Indicates whether or which animals were captured in the origin sites, the target sites, or neither (another phase of the annual cycle). Location uncertainty will only be applied where the animal was not captured. So this doesn't matter for telemetry data, and is assumed to be "origin" for band return data. Should be either single "origin" (default), "target", or "neither" value, or a character vector with length of number of animals tracked, with "origin", "target", or "neither" for each animal. - `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). - `geoBiasOrigin`: For GL data where `captured`!="origin", vector of length 2 indicating expected bias in longitude and latitude of `originPoints`, in `resampleProjection` units (default meters). - `geoVCovOrigin`: For GL data where `captured`!="origin", 2x2 matrix with expected variance/covariance in longitude and latitude of `targetPoints`, in `resampleProjection` units (default meters). - `targetRaster`: For intrinsic tracking data, the results of `isoAssign` or a similar function of class `intrinsicAssign` or class `RasterBrick`/`RasterStack`, for example from the package `assignR`. In any case, it expresses location uncertainty on target range, through a raster of probabilities by grid cells. - `originRaster`: For intrinsic tracking data, the results of `isoAssign` or a similar function of class `intrinsicAssign` or class `RasterBrick`/`RasterStack`, for example from the package `assignR`. In any case, it expresses location uncertainty on origin range, through a raster of probabilities by grid cells. - `banded`: For band return data, a vector or matrix of the number of released animals from each origin site (including those never reencountered in a target site). If a matrix, the second dimension is taken as the number of age classes of released animals; the model estimates reencounter probability by age class but assumes transition probabilities are the same. Note that this age model is currently implemented only for `method` set to "MCMC", and only when banding data is analyzed alone (no telemetry data). - `reencountered`: For band return data, either a matrix with B rows and W columns or a B x [number of ages] x W array. Number of animals reencountered on each target site (by age class banded as) by origin site they came from. - `verbose`: 0 (default) to 3. 0 prints no output during run (except on convergence for `method` set to "MCMC"). 1 prints an update every 100 samples or bootstraps (or a status bar for "MCMC"). 2 prints an update every sample or bootstrap. 3 also prints the number of draws (for tuning `nSim`). - `alpha`: Level for confidence/credible intervals provided. Default (0.05) gives 95 percent CI. - `resampleProjection`: Projection when sampling from location uncertainty. 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 sites. Ignored unless data are entered using sites and points and/or rasters. - `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. For data combinations, we put the default higher (5000) to allow for more data conflicts. Should be integer > 0. Ignored when `method` is "MCMC". - `maxTries`: Maximum number of times to run a single GL/intrinsic bootstrap before exiting with an error. Default is 300; you may want to make a little higher if your `nSim` is low and `nSamples` is high. Set to NULL to never exit. This parameter was added to prevent setups where some sample points never land on target sites from running indefinitely. - `nBurnin`: For `method` set to "MCMC", `estTransition` runs a `JAGS` multinomial non-Markovian transitions model, for which it needs the number of burn-in samples before beginning to store results. Default 5000. - `nChains`: For `method` set to "MCMC", `estTransition` runs a `JAGS` multinomial non-Markovian transitions model, for which it needs the number of MCMC chains (to test for convergence). Default 3. - `nThin`: For `method` set to "MCMC", `estTransition` runs a `JAGS` multinomial non-Markovian transitions model, for which it needs the thinning rate. Default 1. - `dataOverlapSetting`: When there is more than one type of data, this setting allows the user some flexibility for clarifying which type(s) of data apply to which animals. Setting "dummy" (the default) indicates that there are dummy values within each dataset for the animals that isGL, isTelemetry, etc. don't have that data type (FALSE values). If no animals have a data type, no dummy values are required. If no animals have more than one type of data, the user can simplify processing their data by choosing setting "none" here. In this case, there should be no dummy values, and only the animals with a type of data should be included in that dataset. The third setting ("named") is not yet implemented, but will eventually allow another way to allow animals with more than one type of data with named animals linking records. When there is only one type of data, it is fastest to leave this on the default. Note that banding data entered through `banded` and `reencountered` are assumed to have no overlap with other data types, so none of this applies to those. - `fixedZero`: When the user has a priori reasons to believe one or more transition probabilities are zero, they can indicate those here, and the model will keep them fixed at zero. This argument should be a matrix with two columns (for row and column of the transition probability matrix) and number of transitions being fixed to zero rows. For MCMC modeling, substantial evidence that a transition fixed to zero isn't zero may cause an error. For bootstrap modeling, a warning will come up if any bootstrap runs generate the transition fixed to zero, and the function will quit with an error if a very large number of runs do (> 10 * nSamples). Fixing transitions to zero may also slow down the bootstrap model somewhat. - `targetRelAbund`: When some/all data have location error at origin sites (i.e., GL, raster, or probability table data with captured = "target" or "none"), unless the data were collected in proportion to abundance at target sites, simulation work indicates substantial bias in transition probability estimates can result. However, if these data are resampled in proportion to target site abundance, this bias is removed. This argument allows the user to provide an estimate of relative abundance at the target sites. Either a numeric vector of length [number target sites] that sums to 1, or an mcmc object (such as is produced by `modelCountDataJAGS`) or matrix with at least `nSamples` rows. If there are more than [number target sites] columns, the relevant columns should be labeled "relN[1]" through "relN[number target sites]". - `method`: This important setting lets the user choose the estimation method used: bootstrap or MCMC (Markov chain Monte Carlo). Bootstrap (the default) now works with any and all types of data, whereas MCMC currently only works with banding and telemetry data (enter telemetry data for MCMC using `originAssignment` and `targetAssignment`, not `originPoints` and `targetPoints`). However, MCMC is usually faster (and may be a bit more accurate). The third option, "m-out-of-n-bootstrap", is still under development and should be left alone. - `m`: We read that the m-out-of-n-bootstrap method may improve the coverage of confidence intervals for parameters on or near a boundary (0 or 1 in this case). So we're testing that out. This still under development and not for the end user. In the m-out-of-n-bootstrap, m is the number of samples taken each time (less than the true sample size, n). If the "m-out-of-n-bootstrap" is chosen under `method` but this is left blank, currently the default is n/4, rounded up (no idea if that is reasonable). - `psiPrior`: matrix with same dimensions as psi. Only relevant when `method` is "MCMC". Each row provides a Dirichlet (https://en.wikipedia.org/wiki/Dirichlet_distribution) prior on the transition probabilities from that origin site. The default (NULL) supplies Dirichlet parameters of all 1s, which is a standard uninformative Dirichlet prior. Setting these to other positive numbers is useful when you think a priori that certain transitions are unlikely, but don't want to rule them out altogether using `fixedZero`. - `returnAllInput`: if TRUE (the default) the output includes all of the inputs. If FALSE, only the inputs currently used by another MigConnectivity function are included in the output. Switch this if you're worried about computer memory (and the output will be much slimmer). ## Returns `estTransition` returns a list with the elements: - **`psi`**: List 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. May be preferable to `simpleCI` when `mean` is the best estimate of psi. `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`. * `hpdCI` `1 - alpha` credible interval for psi, estimated using the highest posterior density (HPD) method. * `median` Median estimate of psi matrix. * `point` Simple point estimate of psi matrix, not accounting for sampling error. - **`r`**: List containing estimates of reencounter probabilities at each target site. NULL except when using direct band/ring reencounter data. - **`input`**: List containing the inputs to `estTransition`. - **`BUGSoutput`**: List containing `R2jags` output. Only present when using `method` of "MCMC". ## Examples ```r ############################################################################## # Examples 1 (banding data: first example is based on common tern banding # data; the second is made up data to demonstrate data with two ages) ############################################################################## 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 = 60000, nBurnin = 20000, method = "MCMC") COTE_psi COTE_banded2 <- matrix(rep(COTE_banded, 2), 4, 2) COTE_reencountered2 <- array(c(12, 0, 38, 15, 6, 0, 17, 7, 111, 7, 6, 2, 55, 3, 3, 1, 5, 0, 19, 4, 2, 0, 10, 2, 1123, 40, 41, 7, 660, 20, 20, 3), c(4, 2, 4), dimnames = list(LETTERS[1:4], c("J", "A"), 1:4)) COTE_psi2 <- estTransition(originNames = LETTERS[1:4], targetNames = 1:4, banded = COTE_banded2, reencountered = COTE_reencountered2, verbose = 0, nSamples = 60000, nBurnin = 20000, method = "MCMC") COTE_psi2 ############################################################################## # Example 2 (geolocator and telemetry ovenbirds captured on origin sites) ############################################################################## data(OVENdata) # Ovenbird nSamplesGLGPS <- 100 # Number of bootstrap iterations # Estimate transition probabilities; treat all data as geolocator GL_psi <- estTransition(isGL=TRUE, geoBias = OVENdata$geo.bias, geoVCov = OVENdata$geo.vcov, targetSites = OVENdata$targetSites, originSites = OVENdata$originSites, originPoints = OVENdata$originPoints, targetPoints = OVENdata$targetPoints, verbose = 2, nSamples = nSamplesGLGPS, resampleProjection=sf::st_crs(OVENdata$targetPoints)) # Treat all data as is Combined.psi <- estTransition(isGL=OVENdata$isGL, isTelemetry = !OVENdata$isGL, geoBias = OVENdata$geo.bias, # Light-level GL location bias geoVCov = OVENdata$geo.vcov, # Location covariance matrix targetSites = OVENdata$targetSites, # Nonbreeding/target sites originSites = OVENdata$originSites, # Breeding/origin sites originPoints = OVENdata$originPoints, # Capture Locations targetPoints = OVENdata$targetPoints, #Device target locations verbose = 2, # output options nSamples = nSamplesGLGPS, # This is set low for example resampleProjection = sf::st_crs(OVENdata$targetPoints)) print(Combined.psi) # For treating all data as GPS, # Move the latitude of birds with locations that fall offshore 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$targetPoints) text(tp[,1], tp[,2], label=c(1:39)) tp[5,2] <- 2450000 tp[10,2]<- 2240496 tp[1,2]<- 2240496 tp[11,2]<- 2026511 tp[15,2]<- 2031268 tp[16,2]<- 2031268 oven_targetPoints<-sf::st_as_sf(as.data.frame(tp), coords = c("X","Y"), crs = sf::st_crs(OVENdata$targetPoints)) inter <- sf::st_intersects(oven_targetPoints, OVENdata$targetSites) any(lengths(inter)<1) plot(oven_targetPoints,add=TRUE, col = "green") plot(oven_targetPoints[lengths(inter)<1,],add=TRUE, col = "darkblue") # Treat all data as GPS GPS_psi <- estTransition(isTelemetry = TRUE, targetSites = OVENdata$targetSites, # Non-breeding/target sites originSites = OVENdata$originSites, # Breeding/origin sites originPoints = OVENdata$originPoints, # Capture Locations targetPoints = oven_targetPoints, # Device target locations verbose = 2, # output options nSamples = nSamplesGLGPS) # This is set low for example ############################################################################## # Example 3 (all released origin; some telemetry, some GL, some probability # tables, some both GL and probability tables; data modified from ovenbird # example) ############################################################################## library(VGAM) nAnimals <- 40 isGL <- c(OVENdata$isGL, FALSE) isTelemetry <- c(!OVENdata$isGL, FALSE) isRaster <- rep(FALSE, nAnimals) isProb <- rep(FALSE, nAnimals) targetPoints <- rbind(OVENdata$targetPoints, OVENdata$targetPoints[1,]) targetSites <- OVENdata$targetSites originSites <- OVENdata$originSites resampleProjection <- sf::st_crs(OVENdata$targetPoints) targetNames <- OVENdata$targetNames originNames <- OVENdata$originNames targetAssignment <- array(0, dim = c(nAnimals, 3), dimnames = list(NULL, targetNames)) assignment0 <- unclass(sf::st_intersects(x = targetPoints, y = targetSites, sparse = TRUE)) assignment0[sapply(assignment0, function(x) length(x)==0)] <- 0 assignment0 <- array(unlist(assignment0), nAnimals) for (ani in 1:nAnimals) { if (assignment0[ani]>0) targetAssignment[ani, assignment0[ani]] <- 1 else{ targetAssignment[ani, ] <- rdiric(1, c(15, 1, 1)) isProb[ani] <- TRUE } } targetAssignment isProb nSamplesTry <- 100 # Number of bootstrap iterations originPoints <- rbind(OVENdata$originPoints, OVENdata$originPoints[39,]) system.time(psi3 <- estTransition(isGL = isGL, isRaster = isRaster, isProb = isProb, isTelemetry = isTelemetry, geoBias = OVENdata$geo.bias, geoVCov = OVENdata$geo.vcov, targetPoints = targetPoints, targetAssignment = targetAssignment, targetSites = targetSites, resampleProjection = resampleProjection, nSim = 20000, maxTries = 300, originSites = originSites, originPoints = originPoints, captured = "origin", originNames = OVENdata$originNames, targetNames = OVENdata$targetNames, verbose = 3, nSamples = nSamplesTry)) psi3 nNonBreeding <- nrow(OVENdata$targetSites) plot(psi3, legend = "top", main = paste("OVENlike w/", sum(isGL & !isProb), "GL,", sum(!isGL & isProb), "probs,", sum(isGL & isProb), "both, and", sum(isTelemetry), "GPS")) ############################################################################## # Example 4 (add probability animals released on other end) ############################################################################## nAnimals <- 45 captured <- rep(c("origin", "target"), c(40, 5)) isGL <- c(OVENdata$isGL, rep(FALSE, 6)) isTelemetry <- c(!OVENdata$isGL, rep(FALSE, 6)) isRaster <- rep(FALSE, nAnimals) isProb <- rep(FALSE, nAnimals) targetPoints <- rbind(OVENdata$targetPoints, OVENdata$targetPoints[c(1:3,19,23,31),]) targetAssignment <- array(0, dim = c(nAnimals, 3), dimnames = list(NULL, targetNames)) assignment0 <- unclass(sf::st_intersects(x = targetPoints, y = targetSites, sparse = TRUE)) assignment0[sapply(assignment0, function(x) length(x)==0)] <- 0 assignment0 <- array(unlist(assignment0), nAnimals) for (ani in 1:nAnimals) { if (assignment0[ani]>0) targetAssignment[ani, assignment0[ani]] <- 1 else{ targetAssignment[ani, ] <- rdiric(1, c(15, 1, 1)) isProb[ani] <- TRUE } } targetAssignment isProb originPoints <- rbind(OVENdata$originPoints, OVENdata$originPoints[34:39,]) originPoints <- sf::st_transform(originPoints, crs = resampleProjection) originSites <- sf::st_transform(OVENdata$originSites, crs = resampleProjection) assignment1 <- unclass(sf::st_intersects(x = originPoints, y = originSites, sparse = TRUE)) assignment1[sapply(assignment1, function(x) length(x)==0)] <- 0 assignment1 <- array(unlist(assignment1), nAnimals) nOriginSites <- nrow(originSites) originAssignment <- array(0, dim = c(nAnimals, nOriginSites), dimnames = list(NULL, originNames)) for (ani in 1:40) { originAssignment[ani, assignment1[ani]] <- 1 } for (ani in 41:nAnimals) { originAssignment[ani, ] <- rdiric(1, c(1, 1)) isProb[ani] <- TRUE } originAssignment isProb system.time(psi4 <- estTransition(isGL = isGL, isRaster = isRaster, isProb = isProb, isTelemetry = isTelemetry, geoBias = OVENdata$geo.bias, geoVCov = OVENdata$geo.vcov, targetPoints = targetPoints, targetAssignment = targetAssignment, targetSites = targetSites, resampleProjection = resampleProjection, nSim = 15000, maxTries = 300, originSites = originSites, originAssignment = originAssignment, captured = captured, originNames = OVENdata$originNames, targetNames = OVENdata$targetNames, verbose = 2, nSamples = nSamplesTry, targetRelAbund = c(0.1432, 0.3577, 0.4991))) psi4 plot(psi4, legend = "top", main = paste(sum(isGL & !isProb), "GL,", sum(!isGL & isProb & captured == "origin"), "prob.,", sum(isGL & isProb), "both,", sum(isTelemetry), "GPS (all\ncaptured origin), and", sum(isProb & captured == "target"), "prob. (captured target)")) MC4 <- estStrength(OVENdata$originDist, OVENdata$targetDist, OVENdata$originRelAbund, psi4, sampleSize = nAnimals) MC4 ############################################################################## # Example 5 (all raster, from our OVEN example) ############################################################################## 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") OVENdist <- sf::st_as_sf(OVENdist) OVENdist <- sf::st_transform(OVENdist, 4326) 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$Sample,"EVER"),2]<-sf::st_coordinates(op)[2,2] originPoints <- sf::st_as_sf(data.frame(originPoints), coords = c("x", "y"), crs = sf::st_crs(originSites)) iso <- isoAssign(isovalues = OVENvals[,2], isoSTD = 12, # this value is for demonstration only intercept = -10, # this value is for demonstration only slope = 0.8, # this value is for demonstration only odds = NULL, restrict2Likely = FALSE, nSamples = 1000, sppShapefile = terra::vect(OVENdist), assignExtent = c(-179,-60,15,89), element = "Hydrogen", period = "GrowingSeason",#this setting for demonstration only seed = 12345, verbose=1) nAnimals <- dim(iso$probassign)[3] isGL <-rep(FALSE, nAnimals); isRaster <- rep(TRUE, nAnimals) isProb <- rep(FALSE, nAnimals); isTelemetry <- rep(FALSE, nAnimals) targetSites <- sf::st_as_sf(iso$targetSites) targetSites <- sf::st_make_valid(targetSites) targetSites <- sf::st_union(targetSites, by_feature = TRUE) system.time(psi5 <- estTransition(isGL = isGL, isRaster = isRaster, isProb = isProb, isTelemetry = isTelemetry, targetSites = targetSites, resampleProjection = resampleProjection, targetRaster = iso, originSites = originSites, originPoints = originPoints, captured = rep("origin", nAnimals), verbose = 2, nSamples = nSamplesTry)) psi5 ``` ## See Also `estStrength`, `plot.estMigConnectivity`, `estMC`, `estMantel`

estTransition function