Data from: Estimating ungulate migration corridors from sparse movement data
Data files
Jul 18, 2024 version files 10.48 MB
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GPS12hrs_Locs.zip
1.54 MB
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GPS2hrs_Locs.zip
8.94 MB
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README.md
4.92 KB
Abstract
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.
README: 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) 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).
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. 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.