move.contour: Utilization Distribution Contours for dbbmm (Dynamic Brownian...

Description Usage Arguments Details Value Author(s) References

View source: R/move.contour.R


move.contour function that creates the utilization distribution contours based on the bbmm.contour from package BBMM and exports the created contour lines as a SpatialLinesDataFrame or a SpatialPolygonsDataFrame to a set of shapefiles for further analysis in ArcMap (or GIS program of choice).


move.contour(x, range.subset, le, lev, ras, ts, path, name, lines = FALSE,



dbbmm object. Currently, I have not included the ability to manipulate the internal dbbmm object within the move.contour function and thus the values for raster=5 and time.step=0.2 are set).


range of model step segments that the user wants to create the UD contours for. Has to be > the margin based on the dbbmm object as estimates of variance for the first few steps are not estimable depending on how big of a margin is specified. Easiest way to define this range is to use the row number from the input file.


location error value for location.error= used in a typical dbbmm object


level of the UD contour as a vector, c(50, 95) that the user is interested in. Will work with multiple values (e.g., c(50, 95)) and will label those values in the resultant shapefile


raster background size for brownian.bridge.dyn() object


time step for integration of brownian.bridge.dyn() object


file path (e.g., "C:/") using standard R path nomenclature specifying the location that the output shapefiles are to be written.


file name, in quotation's, specifying the output files name which will be written to the path designated above. This does not require a file extension (e.g., 'shp').


allows user to select between outputting a shapefile of contour lines or polygons for use in other programs (e.g., ArcMap). Default is FALSE (will export polygons) mainly because the lines are just lines and don't have any structure associated with them thus ArcMap cannot use zonal statistics on other layers with the lines defining the area. I used R package PBSmappling to do lines to polygons within the function, its not overly efficient but I could not come up with a better way.


coordinate reference system for identifying where the polygons are located. Uses standard CRS structure within quotes (e.g., "+proj=longlat +zone=14 +datum=NAD83").


This function works pretty well, but to date it is currently pretty limited in functionality as I have several parts of the brownian.bridge.dyn() call hard coded in it. As a note, if the time steps are closer together and the extent of the individuals movements is fairly large relative to the movement steps, that the process slows down considerably and you have to shorten range.subset value to a smaller number of segments otherwise memory limits will hit pretty rapidly (this may require some trial and error as I have run >1000 segment steps in some cases and have been able only get to 20-30 in other runs. I should deprecate this function, but some folks are using it for short daily studies so I am leaving it in here until then.

As a solution to this issue, try the other function move.forud which is a function that does the same thing that move.contour() does, but as a for loop that outputs one shapefile at a time, versus doing them in batch. The time it takes to do each method seems to be the same as the slowness of the process is due to the calculations being done by brownian.bridge.dyn().

There is probably some need to restructure how the raster grid estimation is done as right now it estimates values for each cell, so many (1000s) have really small (1.2E-200) values that are effectively zero. Probably would benefit from reducing the 'extent' size in the move object, but I have not gotten around to it yet.


Output is a set of shapefiles, written to the specified path


Bret A. Collier <[email protected]>


Kranstauber, D., R. Kays, S. D. Lapoint, M. Wikelski, and K. Safi. 2012. A dynamic Brownian bridge movement model to estimate utlization distributions for heterogenous animal movement. Journal of Animal Ecology 81: 738-746. DOI: 10.1111/j.1365-2656.2012.01955.x.

Byrne, M. E., J. C. McCoy, J. Hinton, M. J. Chamberlain, and B. A. Collier. 2014. Using dynamic brownian bridge movement modeling to measure temporal patterns of habitat selection. Journal of Animal Ecology, In Press. DOI: 10.1111/1365-2656.12205

bacollier/moveud documentation built on June 4, 2017, 12:12 a.m.