Snow depth, temperature, and residual forage experienced by migrating mule deer during autumn (2011–2020), Wyoming, USA
Data files
Nov 27, 2023 version files 121.55 KB
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Ortega_et_al_2023_Data.xlsx
119.39 KB
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README.md
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Abstract
Growing evidence supports the hypothesis that temperate herbivores surf the green wave of emerging plants during spring migration. Despite the importance of autumn migration, few studies have conceptualized resource tracking of temperate herbivores during this critical season. We adapted the Frost Wave Hypothesis (FWH), which posits that animals pace their autumn migration to reduce exposure to snow but increase acquisition of forage. We tested the FWH in a population of mule deer in Wyoming, USA by tracking the autumn migrations of n = 163 mule deer that moved 15–288 km from summer to winter range. Migrating deer experienced similar amounts of snow but 1.4–2.1 times more residual forage than if they had naïve knowledge of when or how fast to migrate. Importantly, deer balanced exposure to snow and forage in a spatial manner. At the fine scale, deer avoided snow near their mountainous summer ranges and became more risk-prone to snow near winter range. Aligning with their higher tolerance of snow and lingering behavior to acquire residual forage, deer increased stopover use by 1 ± 1 day (95% CI) day for every 10% of their migration completed. Our findings support the prediction that mule deer pace their autumn migration with the onset of snow and residual forage but refine the FWH to include movement behavior en route that is spatially dynamic.
README
Title of Dataset: Snow depth, temperature, and residual forage experienced by migrating mule deer during autumn (2011–2020), Wyoming, USA
This dataset contains the following variables for n = 163 unique adult female mule deer (n = 334 animal-years)
that migrated 15–288 km between their summer and winter ranges in western Wyoming, USA:
- Mean snow depth (m) and residual forage (lbs/acre) experienced by each migratory tactic and their null counterpart at each kilometer along the migratory route between summer and winter range
- Mean snow depth (m) and residual forage (lbs/acre) experienced by empirical deer and their null counterpart throughout the entire autumn migration
- Mean daily minimum temperature (degrees C) and mean daily snow depth (m) on the summer ranges of long-, medium-, and short-distance migrants between July 1 and December 31
- Mean daily NDVI (scaled between 0 and 1) on the summer ranges of long-, medium-, and short-distance migrants between January 1 and December 31
- Mean snow depth (m) and residual forage (lbs/acre), along with the selection ratio of snow depth and residual forage, experienced by long-distance migrants on high-use stopovers and over their entire migration
- Mean snow depth (m) experienced by long-distance migrants and the maximum snow depth (m) at each kilometer along the migratory route between summer and winter range
Description of the Data and file structure
Each sheet in the excel file represents the data used to create the figures in the manuscript. Depending on the sheet, each row in the
excel file may represent the environmental variables experienced by an animal-year or by the migratory tactic at a particular kilometer
along the migratory route from 2011–2020.
Sharing/access Information
Links to other publicly accessible locations of the data: N/A
Was data derived from another source? Yes.
If yes, list source(s):
Sawyer, H., Middleton, A. D., Hayes, M. M., Kauffman, M. J. & Monteith, K. L. The extra mile: ungulate migration distance alters the use of seasonal range and exposure to anthropogenic risk. Ecosphere 7 (2016) doi:10.1002/ecs2.1534.
Methods
Animal capture and handling
From 2014–2020, we captured n = 220 adult female mule deer (>1-yr-old) in the Red Desert via helicopter net-gunning (LaSharr et al., 2022). We outfitted all deer with store-on-board or iridium GPS collars that collected locations every 1–2 hours (Advanced Telemetry Systems, Inc., Isanti, MN, USA; LOTEK Wireless Inc., New Market, Ontario, CAN; Telonics Inc, Mesa, AZ, USA). We used GPS data from a previous study on the Sublette Herd (2011–2013; Sawyer et al., 2016) to analyze movement for an additional n = 27 adult female mule deer (< 1 yr old; n = 66 animal-years) which were outfitted with store-on-board GPS collars that collected locations every 3 hours (Telonics, Mesa, AZ, USA). All animal capture and handling protocols were approved by the Wyoming Game and Fish Department (Chapter 33-937) and an Institutional Animal Care and Use Committee at the University of Wyoming (Protocol 20131111KM00040, 20151204KM00135, 20170215KM00260, 20200302MK00411).
Classification of migratory tactics and delineation of seasonal ranges
We used net squared displacement (Bunnefeld et al., 2011) to determine start and end dates of migration, delineate migratory routes, and calculate net displacement between each GPS location and the start location of autumn migration. We used 95% kernel utilization distributions (Worton, 1989) to delineate summer ranges (end of spring migration–start of autumn migration). Following methods from Sawyer et al. (2016), we classified migratory tactics based on migration distance and where deer spent summer.
Movement rate and stopover use
For each animal-year, we divided migration distance (km; Euclidean distance between first and last locations of autumn migration) by duration to determine hourly and daily rates of movement. We used a 10% utilization distribution from a Brownian bridge movement model (Horne et al., 2007) to delineate high-use stopovers (≥ 3 days of use; Rodgers et al., 2021). Because some animals moved back and forth between adjacent stopovers, we aggregated stopovers that were within a 5-km radius to reduce probability of overestimating stopover use.
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.
- LaSharr, T. N., S. P. H. Dwinnell, B. L. Wagler, H. Sawyer, R. P. Jakopak, A. C. Ortega, L. Wilde, M. J. Kauffman, K. S. Huggler, P. W. Burke, M. Valdez, P. Lionberger, D. G. Brimeyer, B. Scurlock, J. Randall, R. C. Kaiser, M. Thonhoff, G. L. Fralick, and K. L. Monteith. 2022. “Evaluating risks associated with capture and handling of mule deer for individual-based, long-term research.” Journal of Wildlife Management 87: https://doi.org/10.1002/jwmg.22333.
- Rodgers, P. A., H. Sawyer, T. W. Mong, S. Stephens, and M. J. Kauffman. 2021. “Sex-specific migratory behaviors in a temperate ungulate.” Ecosphere 12: https://doi.org/10.1002/ecs2.3424.
- Sawyer, H., A. D. Middleton, M. M. Hayes, M. J. Kauffman, and K. L. Monteith. 2016. “The extra mile: ungulate migration distance alters the use of seasonal range and exposure to anthropogenic risk.” Ecosphere 7: https://doi.org/10.1002/ecs2.1534.
- Worton, B. J. 1989. “Kernel methods for estimating the utilization distribution in home-range studies.” Ecology 70: 164–168.