DOI: 10.5061/dryad.s1rn8pkjk
title: Plant communities, forage quality, and diet composition on summer ranges of mule deer (2017–2019), Wyoming, USA

description: Many animals track ephemeral peaks in food abundance and quality that
 propagate across landscapes. Migrating ungulates, in particular, track
 waves of newly emerging plants from low-elevation winter ranges to
 high-elevation summer ranges – known as “green-wave surfing.” Because
 plants lose crude protein and gain insoluble fiber with maturation,
 ruminants are expected to exploit peaks in forage quality among individual
 plants (i.e., Forage Maturation Hypothesis). Although ample evidence
 supports the long-standing hypothesis that migratory ungulates surf peaks
 in forage quality during migration, the hypothesis that ungulates track
 peaks in forage quality at a small scale (i.e., microsurf while on summer
 range) remains less known. We studied a partially migratory population of
 mule deer in Wyoming, USA, to understand whether temperate ungulates
 optimize use of high-quality forage as plants grow and senesce on
 disparate summer ranges. Specifically, we evaluated how crude protein,
 digestible energy, and relative abundance changed throughout the growing
 season and whether deer altered their diet to reflect species-specific
 changes in plant phenology. In support of the Forage Maturation
 Hypothesis, forage quality declined as large-scale patterns of phenology
 progressed away from a remotely sensed metric of peak green-up for most
 plant species on the summer ranges of deer that migrated short (&lt;50
 km), medium (50–130 km), and long distances (&gt;130 km). Declining
 rates in forage quality among plant species were heterogeneous, providing
 deer with the phenological diversity required to microsurf. Deer changed
 their diet throughout the growing season and prioritized consumption of
 some plants, including Rosa woodsii and Purshia tridentata, as
 the rank of forage quality increased (P &lt; 0.01). In light of the
 complexities common to studies on foraging behavior, our findings suggest
 that deer may have some potential to microsurf on summer range when
 heterogeneity in resource phenology is prevalent. Moreover, our findings
 validate the accuracy of remote sensing in quantifying peak forage quality
 for plants within sagebrush shrublands and montane habitats.
description: <strong>Animal capture and
 handling</strong> From March 2014–March 2019, we
 captured <em>n</em> = 162 adult female mule deer
 (&gt;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 were programmed to collect locations every 1–2 hours
 (Advanced Telemetry Systems, Inc, Isanti, MN, USA; LOTEK Wireless Inc, New
 Market, Ontario, CAN; Telonics Inc, 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). <strong>Sampling
 design</strong> During the summers of 2017, 2018,
 and 2019, we visited <em>n </em>= 49 summer ranges of short-,
 medium-, and long-distance migrants. Our sample sites for desert
 shrublands, foothill shrublands, and montane forests represented
 <em>n </em>= 19, <em>n </em>= 17, and <em>n
 </em>= 21 mule deer, respectively. For each summer range, we
 collected data on plant communities, forage quality, and diet composition.
 In 2017, we sampled each summer range three times throughout summer (late
 May–late July), and in 2018 and 2019, we sampled each summer range four
 times throughout summer and early autumn (early June–early
 October). <strong>Plant abundance, richness, and
 diversity</strong> We used a 99% Kernel
 Utilization Distribution (Worton, 1989) to delineate summer ranges of each
 deer. We then used systematic random sampling to generate three random
 transects within each summer range, which were separated by at least 200
 meters. We conducted a 25-meter line point intercept (LPI) at each
 transect (Butler & McDonald, 1983) and randomly assigned an
 azimuth to each transect during the first visit. The azimuth of transects
 remained the same for every subsequent visit, which allowed us to evaluate
 changes in plant communities across time. At each transect, we dropped a
 2-mm wide rod every 0.5 meters and identified all plants that intercepted
 the rod to level of genus or species. For each LPI, we calculated species
 richness, the Shannon-Weiner Index of Diversity (Whittaker, 1972), and
 relative abundance of each plant species (number of times a plant species
 intercepted the transect/total number of plants) and averaged these
 metrics within individual summer ranges to reduce pseudoreplication. 
   <strong>Forage quality</strong>
 At each transect, we collected ≥5 g of plants previously known to
 be in the diet of mule deer to determine forage quality (Gill et al.,
 1983; Hansen & Reid, 1975; Kufeld et al., 1973). For summer ranges
 in desert shrublands, we collected <em>Artemisia tridentata
 </em>(Wyoming big sagebrush)<em>,
 </em><em>Gutierrezia sarothrae </em>(broom snakeweed),
 and <em>Purshia tridentata </em>(antelope
 bitterbrush)<em>. </em>For summer ranges in foothill
 shrublands, we collected <em>A. tridentata</em>,
 <em>Eriogonum umbellatum (sulphurflower buckwheat)</em>,
 <em>Geranium viscosissimum </em>(sticky purple
 geranium),<em> P. tridentata</em>, and
 <em>Symphoricarpos occidentalis</em> (western
 snowberry)<em>. </em>For summer ranges in montane forests, we
 collected <em>A. tridentata</em>,<em> Chamaenerion
 angustifolium </em>(fireweed)<em>, E. umbellatum,
 </em><em>Fragaria virginiana</em> (wild strawberry),
 <em>G. viscosissimum, Iliamna rivularis</em> (streambank wild
 hollyhock),<em> </em><em>Potentilla gracilis
 </em>(slender cinquefoil), <em>Rosa woodsii </em>(Wood’s
 rose), <em>Spiraea betulifolia </em>(birchleaf spirea),
 and<em> S. occidentalis</em>. We collected leaves, stems, and
 flowers from perennial forbs, leaves from deciduous shrubs, and the
 current year’s growth from evergreen shrubs. We collected plant samples at
 the same location throughout summer to reduce potential variation in
 abiotic factors (e.g., soil moisture, soil nutrients). In total, we
 collected <em>n </em>= 1218 plant samples from six species of
 perennial forbs, four species of deciduous shrubs, and two species of
 evergreen shrubs. We dried all plant samples at temperatures &gt;20
 °C. We used percent crude protein and digestible energy
 (Mcal/lb) as metrics of forage quality (Van Soest, 1982), which were
 analyzed by the Colorado State University Soil, Water, and Plant Testing
 Lab in Fort Collins, CO, USA, with the Forage Analyses Procedures Manual
 (Undersander et al., 1993). Percent crude protein was calculated from
 total nitrogen content (CP = total N * 6.25), whereas digestible energy
 was calculated from total digestible nutrients (DE = (0.0441 * TDN)/2.2),
 which were calculated from acid detergent fiber (TDN = 88.9 – (0.79 *
 ADF)). We removed outliers (<em>n</em> = 16; 1% of samples)
 for each plant species where forage quality was greater than the mean ± 3
 standard deviations. We averaged forage quality for each plant species and
 day of year for transects within the same summer range to reduce
 psuedoreplication. <strong>Remotely-sensed
 metrics of forage quality</strong> We estimated
 the predicted timing of peak forage quality on each summer range by
 identifying the date of peak Instantaneous Rate of Green-up (IRG) at each
 transect (Aikens et al., 2017; Geremia et al., 2019; Merkle et al., 2016).
 To identify date of peak IRG, we extracted the first derivative of
 double-logistic curves that were fit to a time series of NDVI (MOD09Q1
 satellite array; 250-m<sup>2</sup> spatial resolution, 8-day
 temporal resolution; Bischof et al., 2012). We then calculated the average
 date of peak IRG for each summer range and year.
 <strong>Diet composition from DNA
 metabarcoding</strong> For each visit to a
 transect, we collected fecal samples within a 200-meter radius. We
 prioritized the collection of fresh fecal samples that were less than 24
 hours old. If fresh fecal samples were not available, we collected fecal
 samples that were likely less than a couple of weeks old (i.e., dark on
 the outside, green or yellow on the inside). We did not collect old fecal
 samples that were dry and desiccated and likely more than a couple of
 weeks old. We used nitrile gloves to collect fecal samples to reduce
 potential contamination. In total, we collected <em>n </em>=
 299 fecal samples and stored them in plastic bags at &lt;0 °C for
 approximately 11–28 months before indexing diet composition.
 We used DNA metabarcoding to index diet composition of mule deer
 (Pansu et al., 2018). DNA metabarcoding was conducted by the Jonah
 Ventures Laboratory in Boulder, CO, USA. Genomic DNA from plants were
 extracted with the DNeasy® PowerSoil® HTP 96 Kit (Cat #12955-4) following
 methodology from the manufacturer’s protocol. We used a DNA reference
 database of 766 exact sequence variants (ESVs) to identify plant sequences
 to the lowest taxonomic level. Following previously applied methods (Pansu
 et al., 2018), we excluded plant sequences that had low similarity with
 their ESV (&lt;80%) or did not occur in the Intermountain West. We
 also excluded all coniferous species because of potential contamination
 from wind-dispersed pollen (e.g., <em>Pinus ponderosa
 </em>[ponderosa pine]<em> </em>was found in 67% of
 samples but did not occur in our study area). We averaged the number of
 counts across each plant sequence and only included plant sequences that
 comprised &gt;1% of the average count. We then used the proportion of
 counts to calculate the percent of plant species in each fecal sample. We
 calculated the average percent of each plant species in the diet and day
 of year for transects within the same summer range to reduce
 psuedoreplication.
 <strong>References</strong>
 Aikens, E. O., M. J. Kauffman, J. A. Merkle, S. P. H. Dwinnell,
 G. L. Fralick, and K. L. Monteith. 2017. “The greenscape shapes surfing of
 resource waves in a large migratory herbivore.” <em>Ecology
 Letters</em> <strong>20</strong>: 741–750.
 Bischof, R., L. E. Loe, E. L. Meisingset, B. Zimmermann, B. Van
 Moorter, and A. Mysterud. 2012. “A migratory northern ungulate in the
 pursuit of spring: jumping or surfing the green wave?” <em>The
 American Naturalist</em> <strong>180</strong>:
 407–424. Butler, S. A., and L. L. McDonald. 1983.
 “Unbiased systematic sampling plans for the line intercept method.”
 <em>Journal of Range Management</em>
 <strong>36</strong>: 463–468. Geremia, C.,
 J. A. Merkle, D. R. Eacker, R. L. Wallen, P. J. White, M. Hebblewhite, and
 M. J. Kauffman. 2019. “Migrating bison engineer the green wave.”
 <em>PNAS</em> <strong>116</strong>:
 25707–25713. Gill, R. B., L. H. Carpenter, R. M.
 Bartmann, D. L. Baker, and G. G. Schoonveld. 1983. “Fecal analysis to
 estimate mule deer diets.” <em>Journal of Wildlife
 Management</em> <strong>47</strong>: 902–915.
 Hansen, R. M., and L. D. Reid. 1975. “Diet overlap of deer, elk,
 and cattle in southern Colorado.” <em>Journal of Range
 Management</em> <strong>28</strong>: 43–47.
 Kufeld, R. C., O. C. Wallmo, C. Feddema. 1973. “Foods of the
 Rocky Mountain mule deer.” <em>Rocky Mountain Forest and Range
 Experiment Station, Forest Service, U.S. Department of
 Agriculture</em> <strong>111</strong>: 1–30.
 LaSharr, T. N., S. P. H. Dwinnell, B. L. Wagler, H. Sawyer, R. P.
 Jakopak, A. C.<strong> </strong>Ortega, L. R. 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.” <em>Journal of Wildlife Management</em>
 <strong>87</strong>: e22333.
 https://doi.org/10.1002/jwmg.22333. Merkle, J. A., K.
 L. Monteith, E. O. Aikens, M. M. Hayes, K. R. Hersey, A. D. Middleton, B.
 A.  Oates, H. Sawyer, B. M. Scurlock, and M. J. Kauffman. 2016. “Large
 herbivores surf waves of green-up during spring.” <em>Proceedings of
 the Royal Society B</em> <strong>283</strong>: 20160456.
 https://doi.org/10.1098/rspb.2016.0456. Pansu, J., J.
 A. Guyton, A. B. Potter, J. L. Atkins, J. H. Daskin, B. Wursten, T. R.
 Kartzinel, and R. M. Pringle. 2018. “Trophic ecology of large herbivores
 in a reassembling African ecosystem.” <em>Journal of
 Ecology</em> <strong>107</strong>: 1355–1376.
 Undersander, D., D. R. Mertens, and N. Thiex. 1993. “Forage
 analyses procedures.” <em>National Forage Testing
 Association</em>. Van Soest, P. J. 1982.
 <em>Nutritional ecology of the ruminant.</em> Ithaca, New
 York: Cornell University Press. Whittaker, R.
 H. 1972. “Evolution and measurement of species
 diversity.” <em>Taxon</em> <strong>21</strong>:
 213– 251. Worton, B. J. 1989. “Kernel methods for
 estimating the utilization distribution in home-range studies.”
 <em>Ecology</em> <strong>70</strong>:
 164–168.
description: ## Title of Dataset: Plant communities, forage quality, and diet
 composition on summer ranges of mule deer (2017–2019), Wyoming, USA
 \[Access this dataset on Dryad]
 ([https://doi.org/10.5061/dryad.s1rn8pkjk](https://doi.org/10.5061/dryad.s1rn8pkjk)) This dataset contains data on plant communities, forage quality, and diet composition for *n* = 49 summer ranges of mule deer that migrate short (&lt;50 km), medium (50–130 km), and long distances (&gt;130 km) in western Wyoming, USA, from 2017–2019. Our sample sites for desert shrublands, foothill shrublands, and montane forests represented *n* = 19, *n* = 17, and *n* = 21 mule deer, respectively. --- ## Description of the data and file structure: The excel file includes data used in the analyses and creation of figures in the manuscript. There are 6 different sheets within the excel file, including: 1. **Line_Point_Intercept:** Line-Point intercept (LPI) data from 25-m transects on summer ranges of mule deer in sagebrush shrublands, foothill shrublands, and montane forests. A 2-mm wide rod was dropped every 0.5 meters along the transect and all plants that intercepted the rod were recorded to the level of genus or species (i.e., 50 points per transect). The sheet includes the following columns: * Year: Year of data collection. * Habitat: Habitat of each summer range. * Deer_ID: Unique identifier of a deer. No deer in our study shared the same ID. * Transect_Number: Transect number on the summer range of each deer. * Date: Date data were collected. * Day_of_Year: Day of year data were collected. * Azimuth: Azimuth (degrees) of the 25-m transect. * Observer: Person identifying plants along the LPI. * Recorder: Person recording plants along the LPI. * Point: Point along the 25-m transect (50 points per transect). * Plant_Code: Plant code identifying the plant species that was found along the 25-m transect. Plant codes from the USDA Plant Database ([https://plants.usda.gov/](https://plants.usda.gov/)) were used in the collection of data. Other codes include the following: * CONIFEROUS TREE: Unidentifiable coniferous tree. * NONE: No plant species nor litter were recorded. * HL: Herbaceous litter * UNK FORB: Unknown forb * UNK GRASS: Unknown grass * UNK PLANT: Unknown plant * UNK SEEDLING: Unknown seedling * UNK SHRUB: Unknown shrub * WL: Woody litter *Note: If more than one plant of the same genus or unknown category were found at a transect, then a number was used to distinguish the two species (i.e., POA1 vs. POA2 or UNK FORB1 vs. UNK FORB2).* 2. **Species_Richness_Diversity:** The average species richness and species diversity of plants on the summer ranges of mule deer in sagebrush shrublands, foothill shrublands, and montane forests. The sheet includes the following columns: * Year: Year of data collection. * Habitat: Habitat of summer range. * Deer_ID: Unique identifier of a deer. No deer in our study shared the same ID. * Date: Date data were collected. * Day_of_Year: Day of year data were collected. * Mean_Species_Richness: Total number of unique plant species recorded for each LPI. We calculated mean species richness across transects within the same summer range and on the same day. * Mean_Species_Diversity: We used the Shannon-Weiner Index of Diversity to determine species diversity for each LPI. We then calculated mean species diversity across transects within the same summer range and on the same day. 3. **Relative_Abundance**: The mean relative abundance of plant species found on the summer ranges of mule deer in sagebrush shrublands, foothill shrublands, and montane forests. The sheet includes the following columns: * Year: Year of data collection. * Habitat: Habitat of summer range. * Deer_ID: Unique identifier of a deer. No deer in our study shared the same ID. * Date: Date data were collected. * Day_of_Year: Day of year data were collected. * Date_Peak_IRG: Mean date of peak instantaneous rate of green-up (IRG) for each unique summer range and year. * Days_from_Peak_IRG: Difference in days between the date a summer range was visited (i.e., when data were collected) and the date of peak IRG for that same summer range and year. * Plant_Code: Plant code identifying the plant species that was found along the 25-m transect. Plant codes from the USDA Plant Database ([https://plants.usda.gov/](https://plants.usda.gov/)) were used in the collection of data. * Mean_Relative_Abundance: We determined relative abundance of each plant species (number of times a plant species intercepted the transect/total number of plants) for each LPI and then calculated the mean relative abundance of each plant species across transects within the same summer range and on the same day. 4. **Forage_Quality:** The mean percent crude protein and mean digestible energy (Mcal/lb) for plant species that were collected on the summer ranges of mule deer in desert shrublands, foothill shrublands, and montane forests. The sheet includes the following columns: * Year: Year of data collection. * Habitat: Habitat of summer range. * Deer_ID: Unique identifier of a deer. No deer in our study shared the same ID. * Date: Date data were collected. * Day_of_Year: Day of year data were collected. * Date_Peak_IRG: Mean date of peak instantaneous rate of green-up (IRG) for each unique summer range and year. * Days_from_Peak_IRG: Difference in days between the date a summer range was visited (i.e., when data were collected) and the date of peak IRG for that same summer range and year. * Plant_Species: Plant species for six species of perennial forbs, four species of deciduous shrubs, and two species of evergreen shrubs. * Mean_%Crude_Protein: Percent crude protein of each plant species, which was calculated from total nitrogen content (CP = total N * 6.25). We calculated the mean percent crude protein for each plant species across transects within the same summer range and on the same day. * Mean_Digestible_Energy_Mcal/lb: Digestible energy (Mcal/lb) of each plant species, which was calculated from total digestible nutrients (DE = (0.0441 * TDN)/2.2), which were calculated from acid detergent fiber (TDN = 88.9 – (0.79 * ADF)). We calculated the mean digestible energy for each plant species across transects within the same summer range and on the same day. 5. **Diet_Composition_Day_of_Year:** The mean percent of each plant species found in the diet of mule deer that spent the summer in desert shrublands, foothill shrublands, and montane forests. The sheet includes the following columns: * Year: Year of data collection. * Habitat: Habitat of summer range. * Deer_ID: Unique identifier of a deer. No deer in our study shared the same ID. * Date: Date data were collected. * Day_of_Year: Day of year data were collected. * Plant_Species: Species of plants found in the diet of mule deer (i.e., lowest taxonomic level possible). * Mean_Percent_in_Diet: Mean percent of each plant species found in the diet of mule deer for a particular day of the year. We calculated the average percent in the data across transects within the same summer range and on the same day. 6. **Diet_Composition_Time_of_Season:** The mean percent of each plant species found in the diet of mule deer that spent the summer in desert shrublands, foothill shrublands, and montane forests. The sheet includes the following columns: * Year: Year of data collection. * Habitat: Habitat of summer range. * Deer_ID: Unique identifier of a deer. No deer in our study shared the same ID. * Time_of_Season: The time of season in which data were collected. Time of season was specific to the year of data collection. Specific categories included the early season (late May to late June), mid-season (late June to mid/late July), late season (mid/late July to late August), and end of season (early September to early October). * Plant_Species: Species of plants found in the diet of mule deer (i.e. lowest taxonomic level possible). * Mean_Percent_in_Diet: Mean percent of each plant species found in the diet of mule deer for a particular time of the season. We calculated the average percent in the data across transects within the same summer range and within the same time of season for a given year. ## Sharing/access Information Links to other publicly accessible locations of the data: N/A Was data derived from another source? No. If yes, list source(s): ## Code/Software All statistical analyses were conducted in R version 4.0.5.