Plant communities, forage quality, and diet composition on summer ranges of mule deer (2017–2019), Wyoming, USA

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 (<50 km), medium (50–130 km), and long distances (>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 < 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.

Methods
Animal capture and handling

From March 2014–March 2019, we captured n = 162 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 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).

Sampling design

During the summers of 2017, 2018, and 2019, we visited n = 49 summer ranges of short-, medium-, and long-distance migrants. Our sample sites for desert shrublands, foothill shrublands, and montane forests represented n = 19, n = 17, and n = 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).

Plant abundance, richness, and diversity

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.   

Forage quality

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 Artemisia tridentata (Wyoming big sagebrush), Gutierrezia sarothrae (broom snakeweed), and Purshia tridentata (antelope bitterbrush). For summer ranges in foothill shrublands, we collected A. tridentata, Eriogonum umbellatum (sulphurflower buckwheat), Geranium viscosissimum (sticky purple geranium), P. tridentata, and Symphoricarpos occidentalis (western snowberry). For summer ranges in montane forests, we collected A. tridentata, Chamaenerion angustifolium (fireweed), E. umbellatum, Fragaria virginiana (wild strawberry), G. viscosissimum, Iliamna rivularis (streambank wild hollyhock), Potentilla gracilis (slender cinquefoil), Rosa woodsii (Wood’s rose), Spiraea betulifolia (birchleaf spirea), and S. occidentalis. 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 n = 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 >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 (n = 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.

Remotely-sensed metrics of forage quality

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-m2 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.

Diet composition from DNA metabarcoding

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 n = 299 fecal samples and stored them in plastic bags at <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 (<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., Pinus ponderosa [ponderosa pine] 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 >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.

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