Per a previous post, I tried increasing the ext value to 5 but still get an error. I don't know how to find a meaningful burst if the issue is that there is too large of a time gap somewhere in the data. This is the post I refer to: https://www.movebank.org/node/69767

In our dataset, locations are scheduled every hour from collars on white-tailed deer.

myVariable <- brownian.bridge.dyn(MyMoveObject, location.error=15, margin=5, window.size=11, raster=5, ext=5)

I would be glad to share our data if that would be helpful in finding a solution. I'm stumped!

If there is a programmatic way to look at the data to help find the large time gap, I would be very interested in that. I am new to R and am still learning much of the basic tools.

Hi, sorry for the late answer. Yes, that is correct, the error "extent is not large enough" due to a large time gap in the data. One solution is to use a move burst to exclude the gap. Another solution is like follows (how to solve typical issus that arrise when calculating a dBBMM is going to be adresses in the new vignette of the Move package in its next update):

The solution is to remove the variance of the segments corresponding to the large time gaps. For this first the variance is calculated with the `brownian.motion.variance.dyn()` function, then the segments corresponding to the large time gaps are set to FALSE, and finally the dBBMM is calculated.

### ----- ###

library(move)

data(leroy)

# creating a gappy data set

leroyWithGap <- leroy[-c(50:500,550:850)]

leroyWithGap_p <- spTransform(leroyWithGap, center=TRUE)

# calculate the dynamic brownian motion variance of the gappy track

dbbv <- brownian.motion.variance.dyn(leroyWithGap_p, location.error=20, window.size=31, margin=11)

# the intended GPS fix rate of leroy was 15min, so we will ignore for example all segments that have a larger time lag than 5hours. The 'dBMvariance' object resulting from the function above, contains the slot '@interest' in which those segments marked as FALSE won't be included in the calculation of the dBBMM. Therefore we set all segments with time lag larger than 300mins to false

dbbv@interest[timeLag(leroyWithGap_p,"mins")>300] <- FALSE

# then we use the 'dBMvariance' object to calculate the dBBMM

dbb.corrected <- brownian.bridge.dyn(dbbv, raster=100, ext=.45,location.error=20)

# now the UD makes more sense

ud.corrected <- getVolumeUD(dbb.corrected)

par(mfrow=c(1,2))

plot(ud.corrected, main="UD")

contour(ud.corrected, levels=c(0.5, 0.95), add=TRUE, lwd=c(0.5, 0.5), lty=c(2,1))

### ----- ###

Best,

Anne

This did end up being a matter of a large gap of time in my data. Once I adjusted by using a moveBurst, it worked with no error.