Example spatial bobcat data for
These spatial capture-recapture data with multiple mark types are summarized in a list of length 3 containing the following objects:
Enc.Mat is a 42 x (noccas*ntraps) matrix containing observed encounter histories for 42 bobcats across
noccas=187 sampling occasions and
ntraps=30 traps. The first 187 columns correspond to trap 1, the second 187 columns corresopond to trap 2, etc.
trapCoords is a matrix of dimension
ntraps x (2 +
noccas) indicating the Cartesian coordinates and operating occasions for the traps, where rows correspond to trap, the first column the x-coordinate, and the second column the y-coordinate. The last
noccas columns indicate whether or not the trap was operating on each of the occasions, where ‘1’ indciates the trap was operating and ‘0’ indicates the trap was not operating.
studyArea is a 3-column matrix containing the coordinates for the centroids of the contiguous grid of 1023 cells that define the study area and available habitat. Each row corresponds to a grid cell. The first 2 columns indicate the Cartesian x- and y-coordinate for the centroid of each grid cell, and the third column indicates whether the cell is available habitat (=1) or not (=0). The grid cells are 0.65x0.65km resolution.
Bobcats are bilaterially asymmetrical, and sampling was conducted using camera stations consisting of a single camera. Because the left-side cannot be reconciled with the right-side, the two types of “marks” in this case are the pelage patterns on the left- and right-side of each individual. Encounter type 0 corresponds to non-detection, encounter type 1 corresponds to left-sided detection, encounter type 2 corresponds to right-sided detection.
Both-sided encounters were never observed in this dataset, hence the most appropriate
multimark data type is
The first 15 rows of
bobcatSCR$Enc.Mat correspond to individuals for which both the left and right sides were known because they were physically captured for telemetry deployments prior to sampling surveys. The encounter histories for these 15 individuals are therefore known with certainty and should be specified as such using the
known argument in
multimarkClosedSCR (see example below).
These data were obtained from the R package
SPIM (Augustine et al. 2017) and modified by projecting onto a regular rectangular grid consisting of square grid cells (as is required by the spatial capture-recapture models in
We thank B. Augustine and co-authors for making these data publicly available in the
SPIM package (Augustine et al. 2017).
Augustine, B., Royle, J.A., Kelly, M., Satter, C., Alonso, R., Boydston, E. and Crooks, K. 2017. Spatial capture-recapture with partial identity: an application to camera traps. bioRxiv doi: https://doi.org/10.1101/056804
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data(bobcatSCR) #plot the traps and available habitat within the study area plotSpatialData(trapCoords=bobcatSCR$trapCoords,studyArea=bobcatSCR$studyArea) # This example is excluded from testing to reduce package check time # Example uses unrealistically low values for nchain, iter, and burnin # Fit spatial model to tiger data Enc.Mat <- bobcatSCR$Enc.Mat trapCoords <- bobcatSCR$trapCoords studyArea <- bobcatSCR$studyArea # specify known encounter histories known <- c(rep(1,15),rep(0,nrow(Enc.Mat)-15)) # specify prior bounds for sigma2_scr sig_bounds <- c(0.1,max(diff(range(studyArea[,"x"])),diff(range(studyArea[,"y"])))) mmsSCR <- processdataSCR(Enc.Mat,trapCoords,studyArea,known=known) bobcatSCR.dot.type <- multimarkClosedSCR(mms=mmsSCR,iter=200,adapt=100,burnin=100, sigma_bounds=sig_bounds) summary(bobcatSCR.dot.type$mcmc)
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