DOI: 10.5061/dryad.15dv41p51
title: Data from: Estimating ungulate migration corridors from sparse movement data

description: Many ungulates migrate between distinct summer and winter ranges, and
 identifying, mapping, and conserving these migration corridors have become
 a focus of local, regional, and global conservation efforts. Brownian
 bridge movement models (BBMMs) are commonly used to empirically identify
 these seasonal migration corridors; however, they require location data
 sampled at relatively frequent intervals to obtain a robust estimate of an
 animal’s movement path. Fitting BBMMs to sparse location data violates the
 assumption of conditional random movement between successive locations,
 overestimating the area (and width) of a migration corridor when creating
 individual and population-level occurrence distributions, and precluding
 the use of low-frequency, or sparse, data in mapping migration corridors.
 In an effort to expand the utility of BBMMs to include sparse global
 positioning system (GPS) data, we propose an alternative approach to model
 migration corridors from sparse GPS data. We demonstrate this method using
 GPS data collected every 2 hours from four mule deer (Odocoileus hemionus)
 and four elk (Cervus canadensis) herds within Wyoming and Idaho. First, we
 used BBMMs to estimate a baseline corridor for the 2-hour data. We then
 subsampled the 2-hour data to one location every 12 hours (a proxy for
 sparse data) and fitted BBMMs to the 12-hour data using a fixed motion
 variance (FMV) value, instead of estimating the Brownian motion variance
 empirically. A range of FMV values was tested to identify the value that
 best approximated the baseline migration corridor. FMV values within a
 species-specific range (mule deer: 400–1,200 m2; elk: 600–1,600 m2)
 successfully delineated migration corridors similar to the 2-hour baseline
 corridors; overall, lower values delineated narrower corridors and higher
 values delineated wider corridors. Optimal FMV values of 800 m2 (mule
 deer) and 1,000 m2 (elk) decreased the inflation of the 12-hour corridors
 relative to the 2-hour corridors from traditional BBMMs. This FMV approach
 thus enables using sparse movement data to approximate realistic migration
 corridor dimensions, providing an important alternative when movement data
 are collected infrequently. This approach greatly expands the number of
 datasets that can be used for migration corridor mapping—a useful tool for
 management and conservation planning across the globe.
description: # Estimating ungulate migration corridors from sparse movement data We
 provide the 2-hour and 12-hour GPS locations for migration sequences (fall
 and spring) for the Atlantic Rim mule deer (*Odocoileus hemionus*), Clarks
 Fork mule deer, Dubois mule deer, and Jackson elk (*Cervus canadensis*)
 herds in Wyoming, USA. The 2-hour GPS data come from GPS collars deployed
 opportunistically on mule deer and elk while on their winter ranges,
 before the start of the spring migration period. GPS collars collected
 location data at 2-hour intervals. First, we manually identified the
 spring and fall migratory periods for each individual by selecting
 migration start and end dates, which coincided with changes in the net
 squared displacement (NSD) curve of each animal-year using the Migration
 Mapper application (Bunnefeld et al. 2011, Merkle et al. 2022). This
 resulted in the 2-hour GPS migration sequences shared here. Second, we
 subsampled the 2-hour data to one location every 12 hours (hereafter,
 12-hour data) as a proxy for sparse data, resulting in the 12-hour GPS
 migration sequences shared here. These two datasets were used in
 downstream analyses to evaluate the migration corridors delineated by
 Brownian bridge movement models (BBMMs; Horne et al. 2007) and fixed
 motion variance (FMV) models. Please see McKee et al. (2024)
 "Estimating ungulate migration corridors from sparse movement
 data", published in *Ecosphere*, for complete methods. ## Description
 of the data and file structure We include two shapefiles: 1)
 GPS2hr_MigrationSeqs shapefile and 2) GPS12hr_MigrationSeqs shapefile.
 These include, respectively, the 2-hour GPS locations and the 12-hour GPS
 locations from the migration sequences. A description of individual data
 columns, which apply to both shapefiles, are below.  * **State:** State
 where herd was collared (Wyoming). * **Herd:** Name of herd (Atlantic Rim,
 Clarks Fork, Dubois, or Jackson). * **SpeciesCommon:** Common name of the
 collared species (Mule Deer or Elk). * **SpeciesScientific:** Scientific
 name of the collared species (*Odocoileus hemionus* or *Cervus
 canadensis*).  * **AID:** Unique animal identification number. *
 **SeasonYr:** Season and year of migration sequence ("fa" or
 "sp" followed by 2-digit year when migration sequence occurred).
 * **Season:** Migration season ("fa" for fall or "sp"
 for spring). * **Year:** 4-digit year when migration sequence occurred. *
 **Timestamp:** Date and time of GPS location, in Mountain Time (MT). *
 **UTM_E**: Easting. * **UTM_N:** Northing. Projection is "NAD83(HARN)
 / UTM zone 12N" / ESPG: 3742 ## Sharing/Access information The
 shapefiles provided here do not contain the complete dataset used in
 analyses. Data for analyses of the Cody elk herd in Wyoming, USA are
 available upon request from Arthur D. Middleton
 ([amiddleton@berkeley.edu](mailto:amiddleton@berkeley.edu)) from
 University of California, Berkeley. Data for analyses of the Tex Creek
 mule deer, Lowman elk, and Northfork elk herds in Idaho, USA are available
 upon request from Idaho Department of Fish and Game, Idaho Fish and
 Wildlife Information System, 600 S Walnut, Boise, ID 83703
 ([idfgdatarequests@idfg.idaho.gov](mailto:idfgdatarequests@idfg.idaho.gov)). ## Code/Software BBMMs were fitted to the provided migration sequences using the BBMM package (Nielson et al. 2013) in the R environment for statistical computing (R Core Team 2019). Code for fitting traditional BBMMS to GPS data from migration sequences, as provided here, can be found at [https://github.com/jmerkle1/Migration-Mapper/blob/main/app4_uds/wmiScripts/CalcBBMM.R](https://github.com/jmerkle1/Migration-Mapper/blob/main/app4_uds/wmiScripts/CalcBBMM.R). In the script, users can provide a FMV value in place of the empirically estimated Brownian motion variance (BMV) value to apply the FMV method described in the manuscript associated with these data. Also available through a user-friendly interface, Migration Mapper, the code will fit BBMM and FMV models to imported GPS data (Merkle et al. 2022).  References:  * Bunnefeld, N., L. Börger, B. Van Moorter, C. M. Rolandsen, H. Dettki, E. J. Solberg, and G. Ericsson. 2011. A model-driven approach to quantify migration patterns: Individual, regional and yearly differences. Journal of Animal Ecology 80:466–476. * Horne, J. S., E. O. Garton, S. M. Krone, and J. S. Lewis. 2007. Analyzing animal movements using Brownian bridges. Ecology 88:2354–2363. * Merkle, J. A., J. Gage, H. Sawyer, B. Lowrey, and M. J. Kauffman. 2022. Migration Mapper: Identifying movement corridors and seasonal ranges for large mammal conservation. Methods in Ecology and Evolution 13:2397–2403. * Nielson, R. M., H. Sawyer, and T. L. McDonald. 2013. BBMM: Brownian bridge movement model. R package version 3.0. R package version 3.0. [https://CRAN.R-project.org/package=BBMM](https://CRAN.R-project.org/package=BBMM).