G'day everyone, I am working on tracking data from penguins and would like to determine the consistency of space use from each of my individuals from one trip to the next. I calculated kerneloverlap between consecutive trips (standard kernel) and wanted to compare the results of the kerneloverlaphr after using the Brownian bridge model. I am not understanding the results I am getting, however, and I was hoping someone could help me understand them. I pretty much get opposite results (in the case of kerneloverlap, the individuals are consistent from one trip to the next, and in the case of kerneloverlaphr, they are not).
I have created a reproducible example (csv attached) where an individual has GPS coordinates for two consecutive trips. I used the following code: test2_kernel<-read.csv("reproducible_example_kerneloverlap2.csv", header=T) # Try the kerneloverlap approach test2_kernel_df<-SpatialPointsDataFrame(coords=test2_kernel[, c(2,3)], data=test2_kernel[, c(1,4)], proj4string = CRS("+proj=utm +zone=55 +south +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs"), bbox = NULL) test2_kernel_BA<-kerneloverlap(test2_kernel_df[,1], grid=200, meth="BA") test2_kernel_BA test2_kernel_UDOI<-kerneloverlap(test2_kernel_df[,1], grid=200, meth="UDOI") test2_kernel_UDOI # Try the kernelbb and then kerneloverlaphr approach test2_kernel$timestamp <-as.POSIXct(strptime(as.character(test2_kernel$timestamp),"%d/%m/%Y %H:%M"), "GMT") test2_coords<-test2_kernel[, c(2,3)] test2_coords_meters<-project(as.matrix(test2_coords), "+proj=utm +zone=55 +south +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs") test2_coords_meters<-as.data.frame(test2_coords_meters) colnames(test2_coords_meters)<-c("Longitude_meters", "Latitude_meters") test2_kernel$Longitude_meters<-test2_coords_meters$Longitude_meters test2_kernel$Latitude_meters<-test2_coords_meters$Latitude_meters test2_ltraj<-as.ltraj(xy = test2_kernel[,c("Longitude_meters","Latitude_meters")], date = test2_kernel$timestamp, id=test2_kernel$id) lik <- liker(test2_ltraj, sig2 = 4, rangesig1 = c(0, 10)) test2_kernelbb<-kernelbb(test2_ltraj, sig1=5, sig2=4, grid = 200) test2_kernelbb_BA<-kerneloverlaphr(test2_kernelbb, grid=200, meth="BA") test2_kernelbb_BA test2_kernelbb_UDOI<-kerneloverlaphr(test2_kernelbb, grid=200, meth="UDOI") test2_kernelbb_UDOI. In the kerneloverlap approach, I am getting high indices, like this: test2_kernel_BA 1st_trip 2nd_trip 1st_trip 0.9998753 0.8604860 2nd_trip 0.8604860 0.9998635 > test2_kernel_UDOI 1st_trip 2nd_trip 1st_trip 1.4040774 0.7812684 2nd_trip 0.7812684 1.3015604 In the kerneloverlap approach, I am getting low indices, like this: test2_kernelbb_BA 1st_trip 2nd_trip 1st_trip 1.0000000 0.1375754 2nd_trip 0.1375754 1.0000000 > test2_kernelbb_UDOI 1st_trip 2nd_trip 1st_trip 6.46508189 0.02059335 2nd_trip 0.02059335 2.75579098 Could anyone tell me what I'm missing please? Your help would be greatly appreciated! Kind regards, Elodie Elodie Camprasse 6/187 Auburn Road Hawthorn, VIC 3122 Australia Email: <mailto:elodie.campra...@gmail.com> elodie.campra...@gmail.com Website: <http://hors-des-sentiers-battus.e-monsite.com/> http://hors-des-sentiers-battus.e-monsite.com/ Mobile: <tel:%28%2B61%29%20049%20794%200793> (+61) 049 794 0793
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