the effects of spring cattle grazing on the …
TRANSCRIPT
THE EFFECTS OF SPRING CATTLE GRAZING ON THE NUTRITIONAL ECOLOGY
OF MULE DEER (ODOCOILEUS HEMIONUS)
IN EASTERN WASHINGTON
By
SARA JANE WAGONER
A thesis submitted in partial fulfillment of the requirements for the degree of
MASTER OF SCIENCES IN NATURAL RESOURCE SCIENCES
WASHINGTON STATE UNIVERSITY Department of Natural Resource Sciences
MAY 2011
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To the Faculty of Washington State University
The members of the Committee appointed to examine the thesis of SARA JANE
WAGONER find it satisfactory and recommend that it be accepted
________________________________ Lisa A. Shipley, Ph.D., Chair
________________________________ Linda H. Hardesty, Ph.D.
________________________________ Kristen A. Johnson, Ph.D.
________________________________ Karen L. Launchbaugh, Ph.D.
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ACKNOWLEDGEMENTS
This project was fully funded and supported by the Washington Department of
Fish and Wildlife (WDFW), and partnered by the Washington Cattlemen’s Association.
My advisor, Dr. Lisa A. Shipley, whom was the brain-child and brawn-child behind this
enormous endeavor, tirelessly offered perspective and assistance when I needed it, and
I am forever indebted. A sincere thank you to my committee members: Linda Hardesty,
Kristen Johnson, and Karen Launchbaugh for contributing their range science expertise
and logistical input to our experimental design. I extend my appreciation to Rachel and
John Cook, for giving me the opportunity to work along side them at Starkey; it gave me
a real grasp on what I was getting into; especially to Rachel, for her professional
guidance, friendship, and assistance towards this project. Thank you Laura Applegate,
Taryn Clark, Becky Greenwood, Ben Maletzke, Tamara Johnstone-Yellin. and Sarah
McCusker for selflessly pitching in, you are truly great friends. And, to the wonderful
WDFW employees, especially to Bob Dice, for taking care of us graduate students, we
are helpless at times. Curt and Jaime Creson, Sam “wise” Huset-Dwinell, Jeremy
Brown, Meghan Camp, Ellen Miller, Elise Olk, Rachel Ambrosen, and Rachel
Grandberg, assisted with the data collections and put up with me, I wouldn’t have. And
to my deer friends, Sydney, Rogue, Cherry, Tea, and Lily, if only you could speak; to
this day, I still dream of counting bites of P. spicata and T. dubius.
To my husband and best friend, Brandon Rogers for all his encouragement, love,
and altruistic computer services. And to my wonderful mother, if not for her, I wouldn’t
be here; especially, for her loving support, hugs, and superb cooking skills.
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THE EFFECTS OF SPRING CATTLE GRAZING ON THE NUTRITIONAL ECOLOGY
OF MULE DEER (ODOCOILEUS HEMIONUS)
IN EASTERN WASHINGTON
Abstract
by Sara Jane Wagoner, M.S. Washington State University
May 2011
Chair: Lisa A. Shipley
In some grassland communities, livestock grazing may reduce residual grass and
promote younger, more nutritious forages. However, no study has yet directly
examined how spring cattle grazing affect the quantity and quality of forage available to
mule deer (Odocoileus hemionus). Therefore, we created 2-sets of 3 plots of paired
grazing treatments using electric fence exclosures within 3 pastures in bluebunch
wheatgrass (Pseudoroegneria spicata) communities in southeastern Washington. In
each grazed/ungrazed plot, we sampled the biomass and nutritional quality of all plants
by species in both spring and fall. We constructed temporary pens in each
grazed/ungrazed plot and measured diet composition selected by 4 tractable mule deer
in each pen using bite count methods, collected representative diets for each deer in
each plot, and analyzed them for digestible nitrogen and protein in spring and fall. In
spring and fall, grazed pastures had 40% less total biomass, and significantly less
perennial grasses and perennial forbs. As a consequence, across seasons, deer
consumed about 40% less perennial and annual forbs, and 30% more perennial
grasses grazed pens. Using Ivlev's selectivity index, we found that deer selected for
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annual forbs and subshrubs and avoided perennial and annual grasses. The dry matter
digestibility and digestible energy of the deer's diets was similar between grazed and
ungrazed plots, but digestible protein was higher in grazed plots in 2 pastures and lower
in the third on than on grazed plots. Deer cropped larger bites (g/bite) in ungrazed plots
than grazed plots in fall, had a higher instantaneous intake (g/min) in ungrazed plots in
one pasture, and had a higher daily dry matter intake (g/day) and daily digestible energy
intake (kJ/day) in ungrazed than grazed plots across pastures and seasons. Because
biomass of forage was reduced up to 50% in grazed plots, while nutritional quality of
forages, including P. spicata increased modestly, if at all, nutritional carrying capacity for
deer was lower in grazed than ungrazed plots in 2 of the 3 pastures. Our results
suggest that moderate spring cattle grazing in dry-stony ecological sites reduced the
amount of digestible nutrients available to mule deer during the year of grazing.
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TABLE OF CONTENTS
ACKNOWLEDGEMENTS ............................................................................................... iii
ABSTRACT .....................................................................................................................iv
LIST OF TABLES.......................................................................................................... viii
LIST OF FIGURES..........................................................................................................ix
INTRODUCTION............................................................................................................. 1
MATERIALS AND METHODS ........................................................................................ 5
Study area............................................................................................................... 5
Experimental design................................................................................................ 7
Treatments .............................................................................................................. 9
Biomass, composition, and nutrition of available forages........................................ 9
Diet composition and foraging behavior of mule deer ........................................... 11
Diet selection of mule deer.................................................................................... 13
Nutritional quality of forages and mule deer diets. ................................................ 14
Nutrient intake of mule deer .................................................................................. 15
Nutritional carrying capacity for mule deer ............................................................ 15
Statistical analyses................................................................................................ 16
RESULTS...................................................................................................................... 17
Preliminary analyses of study sites ....................................................................... 17
Biomass and composition ..................................................................................... 18
Nutritional quality of forages and carrying capacity ............................................... 20
Deer diet composition and selection ..................................................................... 22
Diet quality and nutrient intake of deer .................................................................. 25
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DISCUSSION................................................................................................................ 26
Forage biomass and intake rate of mule deer ....................................................... 27
Diet quality, nutrient intake rate, and carrying capacity ......................................... 30
APPENDICES ............................................................................................................... 80
Appendix A: SPECIES LIST.................................................................................. 81
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LIST OF TABLES
1. Cattle grazing schedule and sampling dates......................................................... 40
2. Changes in biomass and species composition by grazing treatment .................... 41
3. Nutrient of common forbs and grasses.................................................................. 42
4. Diet composition and selection.............................................................................. 43
5. Nutrient content of mule deer diets ....................................................................... 44
6. Deer activity budgets............................................................................................. 45
7. Nutrient intake rates by treatment and season...................................................... 46
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LIST OF FIGURES
1. Study area map ................................................................................................................. 47
2. Average monthly precipitation ......................................................................................... 48
3. Average monthly temperature......................................................................................... 49
4. Diagram of experimental design .................................................................................... 50
5. Pellet group transect layout ............................................................................................. 51
6. Pellet group density by topographical position ............................................................. 52
7. Biomass compostion by pasture ..................................................................................... 53
8. Ratio of live and senescent plant material by treatment ............................................ 54
9. Nutrient quality of P. spicata............................................................................................ 55
10. Nutritional carrying capacity and percent usable biomass by treatment .................. 56
Intake rates and bite size by treatment and season 46
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INTRODUCTION
Semi-arid grasslands, like bluebunch wheatgrass (Pseudoroegnaria spicata)
communities, provide critical habitat for wild herbivores throughout the western United
States. Livestock grazing is one of the most ubiquitous economic uses of public lands
within semi-arid or arid grasslands and shrubsteppe (Sabadell 1982, Wagner 1978).
Hence, land managers often face trade-offs when attempting to provide multiple uses
for both ecological and economic benefit. The availability and quality of forages
determine the nutrient intake, and ultimately influence the nutritional carrying capacity,
of wild herbivores such as mule deer (Odocoileus hemionus; Hobbs and Swift 1985,
McLeod 1997). Therefore, understanding how cattle grazing influences the quantity and
quality of forages available to wild herbivores is an important goal of wildlife managers.
On one hand, forage removed by cattle becomes unavailable to mule deer, at least for
one season, but on the other hand, a number of studies suggest that cattle grazing can
improve the nutritional quality of forages available to wild herbivores.
First, the effect of cattle grazing on above-ground biomass in semi-arid
grasslands varies depending on the timing (i.e., phenological growth stage; Blaisdell
and Pechanec 1949, Clark et al. 2000, Richards 1984), frequency of disturbance (Clark
et al. 1998) and level of utilization of grazing (Ganskopp et al. 2004, Sheley and Svejcar
2009), and fluctuations in annual precipitation (Milchunas and Lauenroth 1993). A
simulated grazing study found that P. spicata clipped at 9 cm in April-May, increased
yield when apical meristems (i.e., growth points) remained intact (Brewer et al. 2007).
However, other studies have found that both spring and fall cattle grazing reduced
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above ground biomass in the year of grazing (Caldwell et al. 1981, Clark et al. 1998,
Milchunas and Lauenroth 1993, Sheley and Svejcar 2009, Westenkow-Wall et al. 1994).
Reductions in forage biomass can, in turn, reduce the bite size a herbivore can crop,
thus its short-term intake rate (Gross et al. 1988, Short 1985). For example, mule deer
cropped smaller bites and increased bite rates as stocking rates of cattle increased in
the Sierra Nevada range (Loft et al. 1987, 1989). Likely as a consequence of reduced
bite sizes, deer spent more time foraging and less time resting in late summer as cattle
stocking rates increased (Kie et al. 1991). A reduction in short-term and long-term
intake rate can influence growth, reproduction, and survival, or reduce the amount of
time wild herbivores have for other important activities (Tollefson et al. 2010).
How much the reduction in biomass caused by cattle grazing affects the
availability of forage for mule deer depends on the extent of dietary overlap between
cattle and mule deer. Because cattle are large grazers and mule deer are small
browsers with higher mass-specific metabolic rates, they would be expected to
consume different diets (Hofmann 1989). Mule deer tend to consume more forbs and
shrubs that are lower in cellulose and higher in digestible energy (DE, kJ/g) than do
cattle, which consume primarily grasses (Demment and Van Soest 1985, Illius and
Gordon 1992). Empirical studies have shown that dietary overlap between mule deer
and cattle can range from 1 – 38% depending on season and location (Hansen and
Reid 1975, Holechek et al. 1982, Julander 1958, Mackie 1981, Stewart et al. 2003,
Willms et al. 1979). However, perennial grasses such as P. spicata, Festuca
idahoensis, and Poa secunda were an important part of the diet of both mule deer and
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domestic ungulates in bunchgrass communities of eastern Oregon (Miller et al. 1981,
Stewart et al. 2003).
Despite reducing forage biomass, cattle grazing may increase the nutritional
quality of diets consumed by mule deer in one of three ways. First, cattle grazing may
reduce the proportion of standing dead biomass in bunchgrasses, which may increase
accessibility of higher quality new growth of bunchgrasses to herbivores (Clark et. al
1998, Cook 2002, Parson et al. 1983, Richards 1984, Rickard et al. 1975, Wilson et al.
1966). Second, grazing may increase quality of grasses by delaying their phenology
(D’Antonio et al. 1999). Some studies have suggested that when grazing removes
apical meristems, it may stimulate re-growth from axillary buds (Mueller and Richards
1986, Richards et al. 1988). As a consequence, if grazing is timed correctly, it may
delay plant development, potentially allowing leaf tissue to cure at a younger, and more
nutritious, phenological stage as soil moisture is depleted (i.e., the forage conditioning
hypothesis; Anderson and Scherzinger 1975, Gordon1988), increasing the nutritional
quality of forage (Wallmo et al. 1977, Willms et al. 1976). Finally, grazing may improve
diet quality for mule deer by promoting forbs and shrubs that are preferred by deer, by
reducing competition with grasses (McNaughton 1983). However, although some
studies suggest that cattle grazing increases forbs (Evans 1986), others indicated that
spring cattle grazing reduced preferred forbs such as Balsamorhiza saggitata (Blaisdell
and Penachec 1949) and Crepis arrabarba (Rickard 1985).
Although a number of studies have examined where deer choose to forage in
response to livestock grazing, their findings have been mixed. Some research has
shown that deer avoid (Austin and Urness 1986, Dusek 1975, Loft et al. 1991,
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Ragotzkie and Bailey 1991, Wallace and Krausman 1987), and others that deer prefer
(Willms et al. 1979, 1981, Yeo et al. 1993), recently-grazed areas. However, these
studies provide no information about the mechanisms involved in these decisions and
are subject to the confounding effects of other habitat features (Ragotzkie and Bailey
1991). Far fewer studies have reliably linked the direct effects of cattle grazing on
forage quality and quantity to diet selection, nutrient intake and nutritional carrying
capacity of mule deer (Austin and Urness 1983, 1986, Findholt et al. 2005, Kie et al.
1991, Willms et al. 1979); and none to our knowledge have been conducted in P.
spicata/F. idahoensis communities. Therefore, we used tractable mule deer in replicated
controlled grazing treatments to examine the influence of spring cattle grazing, as
practically applied in bunchgrass communities, on the nutritional ecology of mule deer
during the first spring and fall after grazing. We hypothesized that spring cattle grazing
at 40% utilization during vegetative and boot-stage of growth in P. spicata/F. idahoensis
communities would, relative to ungrazed controls:
1) Reduce total biomass, and biomass of standing litter and senescent tissue, of
dominant bunchgrasses
2) Increase the nutritional quality of mule deer's diets by
a. Reducing the proportion of senescent to live green perennial grasses
b. Increasing the nutritional quality (e.g., dry matter digestibility, digestible
energy, and digestible protein) of current year's growth of dominant perennial
grasses
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c. Increasing the biomass and proportion of herbaceous forbs and subshrubs in
the pasture and in the deers’ diets in spring and fall
3) Reduce bite size and instantaneous intake, but increase foraging time and daily
digestible protein and energy intake, of mule deer
Finally, we used the quantity and quality of vegetation and nutrient requirements
of mule deer to compare the nutritional carrying capacity of mule deer between grazed
and ungrazed plots.
MATERIALS AND METHODS
Study area
Our study sites were located in south-east Washington, USA, and were largely
dominated by P. spicata and F. idahoensis bunchgrasses, Bromus spp. (exotic bromes
including: Bromus tectorum, B. japonicus ), forbs, and intermittent shrubs within the
steppe cover type (WDFW 2009). This community stretched between the Snake River
to the north, and the foothills of the Blue Mountains to the south, and was considered a
part of the larger contiguous eastern interior grassland in the Columbia Plateau
Ecoregion (Omernik 1987). Our study sites were located within three pastures (1, 2,
and 3) in the Pintler Creek (latitude 46°15’ N, longitude 177°6’ W) and Smoothing Iron
(latitude 46°12’ N, longitude 117°20’W) Wildlife Management Areas (WMA) of the Blue
Mountain Wildlife Area Complex (Fig. 1). Both WMAs had been managed by the
Washington Department of Fish and Wildlife (WDFW) since 2001 (Pintler) and 2003
(Smoothing Iron, WDFW 2009). Previous to WDFW acquisition, both units had
historically been used for grazing by private landowners and operators (WDFW 2009).
Smoothing Iron was known to provide important wintering and calving grounds for elk
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(Cervus elaphus) and important habitat for mule deer, bighorn sheep (Ovis canadensis),
and grassland birds (Fowler 2007, WDFW 2009), and Pintler Creek was purchased to
provide winter habitat for mule deer (WDFW 2007).
Pasture 1 was located at Pintler Creek, with an upper elevation of 762 m and
slopes ranged from 40% to 60% with flat ridges (WDFW 2009). Over half of Pintler
Creek was composed of the dry stony ecological site and 9-15" Precipitation Zone
(NRCS 2004), which averaged 362 mm in annual precipitation, with most occurring
October through June during 1983 and 2011 from a weather station 6 km south of
Pintler Creek at 945 m elevation (latitude 46°11’ N, longitude 177°7’ W, Asotin County
Conservation District 2010, Fig. 2). Summer temperatures were typically hot and dry,
and reach maximum monthly averages between July and August, and low monthly
averages persisted between late November and March between 2008-2011 (Fig. 3,
AgWeatherNet 2011, available online at: http://weather.wsu.edu).
Pastures 2 and 3 were located at Smoothing Iron, 16 km west of pasture 1 at
Pintler Creek, with an upper elevation of 1219 m. The topographic features of the site
included steep slopes, rolling to flat ridges and rocky outcroppings. The nearest
weather station was 6.44 km east of this site (National Weather Service’s Cooperative
Station #450294 Asotin 14 SW; latitude 46°12’ N, longitude 117°15’ W), which
recorded a mean annual precipitation of 397 mm between 1976-2011 (Fig. 2). Average
annual temperatures were lower than Pintler (Fig. 2, National Weather Service 2011,
location: latitude 46°8’ N, longitude 117°8’ W) for years 1948-1981. But, similar to
Pintler Creek, peak average monthly temperatures occurred between July and August,
and lower averages occurred between November and March (Fig. 3). Similarly, the
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predominant ecological site (54 %) was dry stony, but the Precipitation Zone was 15"+
(NRCS 2004).
Although the 3 pastures were similarly dominated by P. spicata on southerly
aspects, pasture 1 located at Pintler Creek, contained more annual grasses and forbs,
pasture 2 was dominated by native perennial grasses, and pasture 3 contained more
subshrubs. North-facing aspects and drainages of the pastures contained heavy
thickets of Physocarpus malvaceus (mallow ninebark) Crataegus douglasii (black
hawthorne), Rosa spp.(rose), Amelanchier alnifolia (serviceberry), and Symphoricarpos
albus (common snowberry). Pastures 1 and 3 had steep slopes and columnar basalt
cliffs, and in some sections creating impassable regions between the upper and lower
topographical positions within the pasture. Pasture 1 was bisected by a perennial creek
between north and south-facing slopes.
Experimental design
We established six sets of paired grazed and ungrazed plots within each of the
three pastures (Fig 4). Plots were selected such that each consisted of P. spicata
communities within dry-stony ecological sites on south facing slopes of 0-20%, which
were located at the upper benchtops of the pastures. To measure relative deer density
on the pastures and on the upper benchtops in summer 2008, one year before our
experiments began, we counted pellet groups of deer and elk on eight 2-m wide belt
transects that ran from the valley bottoms to the top of the hills (Fig. 5). Start points for
the 8 transects originated from a baseline along one edge of the pasture from randomly-
selected points at least 50 m apart. Transects proceeded perpendicular to the baseline
and the slope along a compass-bearing from the baseline to either the ridge or the
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drainage. Pellet groups were defined as 5 or greater fecal pellets of like species (i.e.,
deer, elk, or other) within 0.25-m radius, and recorded for each 50-m segment along the
transect line (Fig. 5). At each 50m segment, Universal Transverse Mercator (UTM)
coordinates were transcribed from a handheld Global Positioning System (GPS)
receiver (Garmin International, Inc., Olathe, Kansas, USA). To examine use of the
hillside by elk and year, we averaged 50-m segments composing the lower, middle and
upper 33% of the slope for each of the 8 transects in each pasture.
To determine the minimum size of experimental plots that could support 4 deer
for 24 h without affecting diet selection, we estimated nutritional carrying capacity by
randomly placing four 0.5-m2 microplots within dry stony ecological sites of upper ridges
in each of the 3 pastures (1,2,3) and clipped all vegetation at 1cm above ground, and
sorted biomass by species. Samples were dried at 100°C for 24 h, and weighed to the
nearest 0.001g on a digital scale for dry matter weight by species. Nutritional quality of
vegetation was estimated by plant species from unpublished data (W. Myers,
Department of Fish and Wildlife, Spokane, WA) collected in similar habitat types. These
data suggested that even with up to 80% utilization by cattle that a 0.4 ha plot would
provide adequate forage for 4 deer for approximately 24 h while meeting daily dry
matter intake (DMI, g/day), digestible dry matter (DMD, %), and digestible energy (DE,
kJ/g) constraints of non-lactating mule deer in spring (Tollefson 2007).
A few days before implementing our spring cattle grazing treatment, we
constructed three exclosures in each pasture made of electrified polywire around each
of the plots designated as ungrazed. Exclosures were placed ≥ 100 m apart and
encompassed 0.8 ha (64 m x 128 m rectangle) that allowed for two 0.4-ha ungrazed
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treatment plots, one for spring measurements and one for fall measurements. A plot
adjacent to each side of the exclosure was left open to the cattle grazing treatment (Fig.
4).
Treatments
Cattle were grazed in each of the three pastures between May and June 2009
(Table 1). Cattle were turned out into the pastures after minimum criteria were met: 1)
soils at normally dry sites were fairly dry and firm; and 2) dominant bunchgrasses (i.e.,
P. spicata and F. idahoensis) had achieved a minimum of 10 cm current annual growth.
For all three pastures, utilization by cattle was targeted at a maximum of 40% of annual
growth of P. spicata and 50% of F. idahoensis by the end of the growing season.
Stocking rates were calculated based on percent effective acreage for each pasture,
number of animal units (AUs), animal unit months (AUMs), and period of grazing. Two
cattle operators were used to apply treatment, one cattle herd was 200 cow-calf pairs
used to graze pasture 1 at Pintler Creek, and the other was 150 cow-calf pairs to graze
pastures 2 and 3 at Smoothing Iron (Table 1). To achieve consistent utilization across
pastures, utilization was estimated using the height-weight method (BLM 1999, WDFW
2009) on 3-7 sites in each pasture and monitored every 4-5 days. When utilization
targets were met, cattle and exclosures were removed within 1-7 days
Biomass, composition, and nutrition of available forages
To estimate the biomass and composition of available treatments, we randomly
selected three of the 6 paired plots per pastures for spring sampling and the other three
for fall sampling. Spring sampling began 1-3 weeks following the spring grazing
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treatment, and fall sampling was completed between October and November 2009
(Table 1). We sampled vegetation biomass In both the grazed and ungrazed 0.4-ha
plots by clipping nine 0.5-m2 microplots systematically distributed in a 3 X 3 array within
each plot placed 16 m distance from the edge of 0.4 ha plot and 16 m equidistant from
the upper-left corner of each microplot. All vegetation was harvested at 1 cm above
ground and sorted by species and color (i.e., brown = senescent tissue, green = live
tissue), dried at 100°C and weighed to 0.001 g to obtain dry matter (DM) weight. In
addition to species identification, we organized vegetation into plant functional groups
(i.e., plant guilds: monocots and dicots, and life-cycle: annual and perennial), as well as
native status relative to our study area (USDA 2011, Appendix A). Furthermore, growth
habits of herbaceous dicots were classified as forbs, subshrubs (i.e., woody prostrate
dicots), and shrubs. Live plant material was defined as plant parts (e.g., leaf, stem) that
contained greater than 20% green tissue, and we did not separate green and senescent
material within an individual plant part. For subsequent analysis of the nutritional quality
of available forages, we collected fresh samples of dominant forbs, subshrubs, and
grasses. Because P.spicata dominated all sites, we collected 3 samples of green and
senescent tissue from each grazed and ungrazed plot in both fall and spring for each
pasture. For other prevalent grasses, forbs and subshrubs, we collected composite
samples from at least 3 different sites within each treatment, pasture and season within
1 week of biomass harvesting. To estimate nutritional quality of rare species, we used
the average nutritional quality for plants within the same functional group, pasture and
season. Forage samples were immediately placed on ice and within 12 h stored in a -
20°C freezer.
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Diet composition and foraging behavior of mule deer
We determined diet selection and nutrient intake rate by mule deer in grazed and
ungrazed plots immediately following biomass sampling in the spring and fall by
observing foraging by tractable mule deer within temporary pens placed on the grazed
and ungrazed plots. For these experiments, we selected four 2-year-old female mule
deer (body mass in spring, X = 61.9± 2.5 and fall, 73.5± 3.1 kg) that had been born and
hand-raised at the Wild Ungulate Facility at Washington State University (WSU), and
had been maintained on a completely-balanced wild herbivore pellet (Wild Herbivore,
Mazuri, St Louis, MO, USA), alfalfa (Medicago sativa), timothy hay (Phleum pretense),
pasture grass (mostly cultivars, Agropyron spp., Dactylis glomerata L., Festuca spp.,
Poa spp.) and fresh willow (Salix spp.) when not participating in foraging trials. Deer
were fitted with Very High Frequency (VHF) radiocollars that were retrofitted with
Actiwatch ™ (Minimitter Co, Inc., Bend, OR) activity sensors, and transported to the site
by horse trailer. Experiments with the deer used in this study were approved by
Washington State University’s Institutional Animal Care and Use Committee (IACUC
protocol # 3775).
A few days before the four deer were placed on the grazed and ungrazed plots in
both spring and fall, we constructed 0.4-ha temporary deer pens using 2-m high mesh
poly fencing suspended on aircraft cable supported by aluminum poles surrounding
each grazed and ungrazed plot. Three of the 4 deer were used in both the spring and
fall sampling periods, but one deer was replaced in fall, because she had become
pregnant before our fall foraging trials. We selected another female of the same age for
our fall foraging trials.
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In both spring and fall, pastures were sampled by the deer in the order in which
they had been grazed by cattle but the order of paired plots within pastures were
selected randomly, as were grazed versus ungrazed plots per pair. The four mule deer
were allowed to forage for 24 h and then moved to the next plot. After deer were placed
in the pen, we allowed a brief acclimation period of 0.5-1 h before we began collecting
data. The same observer measured diet composition and cropping rates during three
foraging trials of 15-20 min each per animal during the 24-h period in each pen. The
observer recorded the plant species of each bite cropped by the deer using a digital
audio recorder. However, we excluded minutes from foraging trials when 1) foraging
activity was not continuous for 3 min or 2) lapses between foraging activity were > 3
min. Within 1-7 days following the foraging trials, the observer collected 10
representative bites of each plant species that were included in ≥ 90% of the diet and of
the size consumed by the deer, dried them at 100°C for 24 h and weighed them to
0.001 g to estimate the bite size taken. Bite sizes were estimated by a single observer,
while simultaneously conducting foraging trials, for each species consumed by deer.
Bite dimensions (e.g., length and number of blades of grasses, size of leaves) for each
species were recorded periodically every minute for each deer during feeding bouts.
Bite dimensions and density were then transcribed and averaged for each plot from
mule deer foraging trials. We estimated bite weights for species that were <1% of the
diet, based on the observed bite density for that species and plant characteristics (i.e.,
growth form, plant part). The observer also collected simulated diets based on the
observed sizes of bites of plants that comprised ≥ 90% of the diet of each deer. The
13
simulated diet samples were immediately placed on ice and placed in a -20°C freezer
for subsequent analysis.
To determine the amount of time the deer spent foraging and participating in
other activities, we recorded deer activities (classified as feeding, walking, bedding, and
standing) using behavioral scan-sampling at 1-min intervals for 6 to 8 h per day in each
plot. We estimated activity during the rest of the day when scan samples were not
available (i.e., mostly at night), we calibrated the activity sensors worn by each animal.
We matched our behavioral observations from scan-sampling for each deer within a
pasture with values recorded on its activity sensor during the observation period (Naylor
and Kie 2004). We then used the mean and standard deviation of the values from the
activity sensor for each behavior to assign behaviors to the animals when scan samples
were not available.
Diet selection of mule deer
We calculated selection of plant functional groups and species using Ivlev’s
selectivity index,
Selectivity Index = ri – pi / ri + pi
where ri is the relative proportion of forage in the diet (%) of and pi is the relative
proportion of available forage (%) in plots (Ivlev 1961). We calculated SI for each of the
36 plots for each functional group and species from % biomass available in plot and %
mass consumed by deer. We then averaged across pastures, seasons and treatments
and constructed a 95% confidence interval to obtain an overall Selectivity Index for that
functional group and species. If the confidence interval for that functional groups or
species was completely above 0, we considered it "selected", and if the confidence
14
interval was completed below 0, the item was "avoided"). Functional groups or species
with confidence intervals that overlapped zero (i.e., the lower confidence interval had a
negative Selectivity Index and the upper confidence interval had a positive Selectivity
Index) were considered to be "used in proportion to availability".
Nutritional quality of forages and mule deer diets.
We prepared the frozen forage samples and mule deer diets for nutritional
analysis by freeze-drying and grinding them in a Wiley mill to pass a 1-mm screen. We
determined the gross energy (GE, kJ/g) content of each simulated diet using a bomb
calorimeter. For both forages and simulated diets, we determined the neutral detergent
fiber (NDF, %), acid detergent fiber (ADF, %), acid detergent lignin (ADL, %), and acid
insoluble ash (AIA, %) from sequential detergent analysis (Goering and Van Soest
1970) with filter bags, sodium sulfite, and alpha amylase (Ankom Fiber Analyzer 200/220,
Ankom Technology, Fairport NY). We determined nitrogen content (%) of food using a
Carbon-Nitrogen TruSpec analyzer (LECO; St. Joseph, MI, USA.) and estimated crude
protein (CP) content as 6.25 times the nitrogen content (Robbins 1993). Finally, we
determined the protein-binding capacity of the diets and major forbs and subshrubs
(woody-prostrate dicots) using the bovine serum albumin precipitation assay of Martin
and Martin (1982). We estimated dry matter digestibility (DMD, %) and digestible
nitrogen (DP, %) using the summative equations of Robbins et al. (1987a, b) developed
and tested in elk and black-tailed deer (O. h. columbianus, Hanley 1984, Hanley et al.
1992, Parker et al. 1999, Robbins 1993).
15
Nutrient intake of mule deer
To estimate instantaneous intake rate (IIR, g/min) of each animal in each plot, we
multiplied the number of bites of each plant taken per minute from our voice recordings
by the average bite weight (g DM) measured for that plant in that pasture, season and
treatment. To determine the daily intake of DM, DP (g) and digestible energy (DE, kJ),
Daily dry matter intake (DMI, g/day) was the product of IIR, the proportion of the day
spent foraging, and min/day. Time spent foraging was determined from our behavioral
observations collected from scan sampling (about 8 daylight h per day), and from values
collected by the activity sensor during periods when scan samples were not available.
Digestible energy intake (DEI, MJ/day) was the product of DMD (%), GE (kJ/day) and
DMI. Finally, digestible protein intake (DPI, g/day) was the product of DP (%) and DMI.
Nutritional carrying capacity for mule deer
We used the biomass available in each plot and nutritional quality (i.e., DMD and
DP) of forage available in each pasture to estimate nutritional carrying capacity by of
mule deer in spring and fall using the FRESH (Forage Resource Evaluation System for
Habitat)-deer model (Hanley et al., in press, available online:
http://cervid.uaa.alaska.edu/deer), This model uses linear programming to determine
the maximum amount of forage biomass (kg/ha) that can be pooled from all available
biomass while satisfying specified nutritional constraintsfor DE and DP for mule and
black-tailed deer, and thus the maximum number of deer-days that an area can support
(Hanley and Rogers 1989, see also Hobbs and Swift 1985). For spring, we specified
minimum constraints for a lactating female mule deer with 1 fawn as 60% DMD and 8%
DP (Hanley et. al, in press, Parker et al. 1999), with an expected DMI of 1860 g/day and
16
metabolizable energy intake (MEI, MJ/day) of 10.27. In fall, when females were not in
peak lactation, we specified a DMD of 52%, a DP of 4.5% (Hanley et al 1992), a DMI of
1500 g/day, and MEI of 8.49 (Hanley et al., in press, Parker et al. 1999).
Statistical analyses
We compared biomass (kg/ha and %), diet composition (%), SI, bite size, bite
rate, IIR, DMI, DEI, DPI, foraging time per day, and nutritional carrying capacity among
grazing treatments, seasons, and pastures. We averaged data from the 9 microplots
and 4 mule deer per pen, resulting in a sample size of 36 plots, which were the
experimental unit. Data were analyzed using the general linear mixed models
procedures (Proc Mixed, ver. 9.2, SAS Inst. Inc., Cary, NC, USA) under a split-
plot experimental design having a two-way treatment structure (pasture and season) in
the whole plot and grazing treatment as the sub-plot factor. The linear model was
defined as;
Y = pasture + season + pasture * season + plot (pasture*season) + treatment +
treatment *pasture + treatment*pasture*season + residual error
where the whole plot experimental units were pastures (1,2,3) and seasons (spring and
fall). Our sub-plots were the grazing treatments (i.e., cattle grazed and ungrazed) with
plot as the unit of replication. Throughout the results, the degrees of freedom of the
numerator for main effects were 2 for pasture and 1 for season and treatment, 2 for
pasture * season, pasture * treatment, and pasture * season * treatment, and 1 for
season * treatment, interactions, and the degrees of freedom for the denominator was
17
12. Because nutritional samples were composited across pens within a pasture, we
used a paired t-test to compared nutritional quality (i.e., fiber constituents, DMD, CP,
and DP) of P. spicata between grazing treatments in fall. Finally, we compared the
density of pellet groups among herbivore species (deer or elk), pasture (1, 2, 3), and
position on hillside (bottom third, middle third, and top third) with all interactions using 3-
way analysis of variance with all interactions.
RESULTS
Preliminary analyses of study sites
. During 2009, Pintler Creek WMA (pasture 1) received 334 mm total annual
precipitation, and Smoothing Iron (pastures 2 and 3) received 380 mm of total annual
precipitation, both 17 – 45 mm less than average over the last 28 years (Fig. 2) .
However, both sites received above average precipitation in March and August (Fig. 2).
Temperature in 2009 was very close to the average at Pintler Creek (Fig. 3).
Temperature data were not available for the Smoothing Iron site for 2009, but previous
data suggest that temperatures averaged about 2-3° C lower at Smoothing Iron than
Pintler Creek (Fig. 3).
One year before our grazing experiments, the density of deer pellets did not differ
among pastures (F = 1.23, P = 0.30), but were higher on the benchtops where our plots
were located than the bottom of the hillsides (F = 7.33, P = 0.001). We found no
pasture * position interaction (Fig. 6, F = 0.08, P = 0.53). The density of elk pellets was
twice as high as deer pellets in pasture 2 (F = 4.05, P = 0.04) and 5 times higher in
18
pasture 3 (F = 20.54, P < 0.0001) , which were both located at the Smoothing Iron study
site. However, no elk pellets were found in pasture 1 at the Pintler study site. In
pastures 2 and 3, the density of elk pellets was lowest at the bottom of the hillside and
highest at the top of the hill (Fig. 6, F = 15.90, P < 0.0001).
Biomass and composition
Although perennial grasses, predominately P. spicata, composed the greatest
proportion of biomass across pastures that were similar in ecological site, aspect, and
slope, the 3 pastures differed in total biomass and composition of plant types (Fig. 7).
Across seasons and treatments, pasture 3 had less total plant biomass than pastures 1
and 2 (F = 5.05, P = 0.03). Pastures 1 and 2 had more senescent brown (F = 16.25, P =
0.0004), but a similar amount of living green biomass (F = 1.06, P = 0.38) than pasture
3. Pasture 1 had a lower proportion of perennial grasses (F = 9.37, P = 0.004), but a
greater proportion of annual forbs (F = 5.35, P = 0.02) and annual grasses (F = 22.40, P
< 0.0001) than pastures 2 and 3. In contrast, pasture 3 had a greater proportion of
perennial forbs (F = 31.81, P < 0.0001) and subshrubs (F = 7.96, P = 0.006) than
pastures 1 and 2. In terms of biomass, pasture 1 had more biomass of annual forbs (F
= 6.18, P = 0.01) than 3. Likewise, pasture 1 had more biomass of annual grasses (F =
25.60, p < 0.001) than 2 and 3. Pasture 1 also had less total biomass of perennial forbs
(F = 7.24, P = 0.007) than 2 and 3 and perennial grasses (F = 5.97, P = 0.02 than
pasture 2.
Across pastures and treatments, total biomass (F = 2.13, P = 0.17) and green
biomass (F = 0.06, P = 0.81) in plots did not differ between seasons, but plots had more
brown biomass in fall than spring (F = 6.99, P = 0.02). Across pastures, plots had a
19
greater proportion (F = 13.09, P = 0.004) and biomass (F = 4.81, P = 0.05) of perennial
forbs in spring than fall, but the proportion of other functional groups remained the same
between seasons (all P's > 0.05).
Grazing influenced both the absolute and the relative biomass of vegetation
within our plots. Across pastures and seasons, grazed plots had substantially less
biomass than ungrazed plots (F = 47.61, P < 0.0001, Fig. 8). In the spring following
grazing, grazed plots ( X = 371.3 ± 64 kg/ha) had about half the biomass of ungrazed
( X = 832.8 ± 64 kg/ha) plots, and by fall, grazed plots ( X = 617.3 ± 64 kg/ha) had about
three-fourths the biomass than ungrazed ( X = 811.6 ± 64 kg/ha) plots (F = 7.90, P =
0.02). Living green biomass was lower in grazed plots in the spring (F = 28.44, P =
0.0002), and senescent biomass was lower in both spring and fall, than in ungrazed
plots (F = 11.06, P = 0.006). However, the proportion of green ( X = 56 ± 4%) and
senescent ( X = 44 ± 4%) biomass did not vary between grazed and ungrazed plots (F =
0.01, P = 0.93)
Grazed plots had a greater proportion of annual forbs (F = 4.62, P = 0.05),
whereas ungrazed plots had a greater proportion of perennial forbs, but only in pasture
2 (F = 5.52, P = 0.02), and a similar proportion of annual grass (F = 0.06, p = 0.81),
perennial grasses (F = 0.31, P = 0.74), and subshrubs (F = 0.12, P = 0.73, Table 2). In
terms of biomass, grazed plots had less perennial grasses (F = 31.43, P < 0.0001)
across all pastures and seasons, less annual grass in pasture 1(F = 4.48, P = 0.03),
and less PF (F = 4.77, P =0.05) in summer than ungrazed plots, but a similar biomass
of annual forbs (F = 0.07, P = 0.79) and subshrubs (F = 2.02, P = 0.18, Table 2, Fig. 7).
Grazed plots had a lower biomass of senescent PF (F = 6.56, P = 0.02), perennial
20
grasses (F = 7.61, P = 0.02) than ungrazed across pastures and seasons, and less
brown subshrubs (F = 6.18, P = 0.007) in pasture 3 only. Grazed also had lower green
biomass of perennial forbs (F = 21.46, P = 0.006) and perennial grasses (F = 10.44, P =
0.007) in spring across pastures, but similar green biomass in fall (all p's > 0.05).
Grazed and ungrazed plots had a similar green and senescent biomass of all other
functional groups (P > 0.05). Of the 3 major perennial grass species, P. spicata (F =
10.97, P = 0.006) and Poa spp. (F = 3.94, P = 0.05) had similar biomass of senescent
between treatments across pastures and seasons, but less green in grazed than
ungrazed in at least one pasture or season (Fig. 4). On the other hand, F. idahoensis
had similar amount of green biomass among treatments, but less senescent biomass on
grazed than ungrazed plots across pastures and seasons (F = 4.90, P = 0.05). The
proportion of green and senescent biomass did not differ among grazing treatments for
any plant functional group across pastures and seasons (all P > 0.08).
Eight of the 66 plant species identified in our plots (Appendix A) had significantly
less biomass and three had a significantly lower proportion, in grazed than ungrazed in
at least in one season or pasture, whereas 1 species had greater biomass in grazed
and 2 species had a greater proportion in grazed than ungrazed (Table 2.)
Nutritional quality of forages and carrying capacity
The nutritional quality of grasses, forbs and subshrubs varied with species. Over
all seasons, pastures and treatments, forbs ranged from 50 - 78% DMD, 3.5 - 17.5%
CP, and -1.3 - 14.0% DP, subshrubs ranged from 45 - 60% DMD, 7.0 - 8.6% CP, and
1.3 - 4.1% DP, and perennial grasses ranged from 63.5 - 73.1% DMD, 3.3 - 15.8% CP,
and -0.8 - 10.8% DP (Table 3). Across pastures and treatments, perennial forbs
21
averaged X = 11.9 ±0.82 in spring and X = 7.71 ±0.17, annual forbs averaged 9.9 ±
1.56 in spring and 5.48 ± 1.03 in fall, and perennial grasses averaged X = 7.67 ± 2.02
in spring and X = 8.22 ± 1.58 in fall. The nutritional quality of P. spicata, the dominant
grass in our study area during the fall did not differ in NDF nor DMD between grazed
and ungrazed plots (all P > 0.25, Fig. 9). However, CP and DP tended to be higher on
grazed than ungrazed pastures (T = 2.28, P = 0.07, Fig. 9).
The FRESH-deer model calculated that the percent of the available biomass that
was “useable” (e.g., met or exceeded minimum DMD and DP requirements for the deer
diets) varied with pasture (F = 7.55, P = 0.008), season (F = 72.62, P < 0.0001), and
grazing treatment (F = 10.85, P = 0.006), and all of the 2-way interactions were
significant (all P's < 0.03, Fig. 10). Across pastures in the fall, the percent useable
biomass was about 35% higher in ungrazed than grazed plots (P = 0.0004), but was
similar between grazing treatments in spring (P = 0.81, Fig. 10). Across seasons,
percent useable biomass was between 42-71% higher in ungrazed than grazed plots in
pastures 1 and 3 (both P's < 0.01), but similar in pasture 2 (P = 0.57, Fig. 6). DMD of
the useable biomass was 2 percentage points higher in spring ( X = 69.8 0.4%) than
fall ( X = 67.1 0.4%, F = 24.51, P < 0.0001), but did not differ significantly in the
ungrazed than grazed plots (F = 3.72, P = 0.08). However, DP limited the useable
biomass in all pastures, seasons, and treatments, thus the useable biomass was
constrained by the minimum DP entered in the model of 8% for spring and 4.5% in fall.
Estimated nutritional carrying capacity (i.e., useable biomass divided by daily
nutrient intake of deer) varied with season (F = 39.58, P < 0.0001), and treatment (F =
85.11, P < 0.0001), with a significant three-way interaction (i.e., pasture * season *
22
treatment, F = 13.23, P = 0.0009). In all plots except grazed plots in pasture 1 and 2,
carrying capacity averaged 8 times higher in fall than spring (all P's < 0.05). Nutritional
carrying capacity was 2 - 3 times greater on ungrazed than grazed plots in pastures 1
and 3 in fall (Fig. 10).
Deer diet composition and selection
Diet composition of mule deer in our study area roughly reflected the relative
availability of functional groups among pastures. Deer consumed a greater proportion
of subshrubs in pasture 3 than pastures 1 and 2 (F = 24.11, P < 0.0001), more annual
forbs in pastures 1 and 2 than pasture 3 (F = 7.37, P = 0.008), and tended to consume
more annual grass (F = 3.58, P = 0.06) and perennial forbs (F = 3.54, P = 0.06) in
pasture 1 than the other pastures. Because non-native plants tended to be annuals,
deer diets in pasture 1 contained a greater proportion of non-native plants such as
Bromus spp., Camelina microcarpa, Centaurea solitantis, and Sisymbrium altissimum
than in pastures 2 and 3 (F = 26.15, P < 0.0001, Table 4).
The composition of deer diets also differed among grazing treatments. On grazed
plots, deer consumed a lower proportion of perennial forbs (F = 7.83, P = 0.02) across
seasons and pastures, and less annual forbs in spring (F = 12.90, P = 0.004) and more
perennial grasses in fall (F = 7.16, P = 0.02) than on ungrazed plots (Table 4). Deer
also consumed a greater proportion of non-native plants in ungrazed than grazed plots
during spring (F = 11.71, P = 0.005). Over all plant functional groups, deer diets
contained a greater proportion of brown, senescent forage on ungrazed plots than
grazed plots in pasture 1 during both spring and fall, and in pasture 3 during fall only
(pasture*season*treatment interaction: F = 9.76, P = 0.003). On the other hand, deer
23
diets in pasture 2 contained less brown, senescent forage in spring on ungrazed than
on grazed plots. Brown, senescent vegetation generally composed < 10% of deer
diets, but exceeded 20% on ungrazed plots in both spring and fall in pasture 1, and both
grazed and ungrazed plots in the fall on pasture 2 (F = 9.76, P = 0.003). Within plant
functional groups, senescent perennial forbs composed a greater proportion of deer
diets in ungrazed than grazed plots fall (F = 9.83, P = 0.009), and deer ate more brown
perennial grasses in ungrazed than grazed plots in pasture 1 in spring (F = 25.61, P <
0.0001). Poa bulbosa bulblets (i.e., plantlets) or tops dominated the senescent
perennial grasses selected by deer, formed a greater proportion of deer's diet in
ungrazed ( X = 7.0 1.4%) than grazed plots ( X = 0.6 1.4%, F = 18.37, P = 0.0002),
and occurred primarily pasture 1 in spring. Balsamorhiza serrata leaves was the most
prevalent brown perennial forb species in the deer's diets, and was higher in ungrazed
( X = 4.3 0.4%) than grazed ( X = 0.5 0.4%, pasture * season * treatment: F = 21.61,
P < 0.0001) plots in fall in pasture 3 (Table 4).
Of the 63 species consumed by deer across seasons and pastures (Appendix A),
only 6 species differed significantly between grazing treatments (Table 4). P. spicata
composed by far the greatest proportion of the deer diets, averaging 40% across all
pastures, seasons, and treatments factors (Table. 4). Furthermore, we found that deer
diets on grazed plots had nearly 50% more P. spicata than ungrazed (F= 13.02, P =
0.004). Similarly, other perennial grass species F. idahoensis (F = 4.29, P = 0.06), and
Sporobolus cryptandrus tended to compose a greater proportion of deer diets on grazed
than ungrazed plots (Table 4). In contrast, Poa bulbosa formed a greater proportion of
the deer diets on ungrazed than grazed plots (F = 16.24, P = 0.002). Two perennial
24
forbs, B. serrata and Cirsium undulatum, were consumed in greater proportion in
ungrazed plots, whereas Lomatium spp. was consumed to a greater extent on grazed
than ungrazed plots (all P’s < 0.05, Table 4)
Deer were highly selective among functional groups and species, and their SI’s
remained relatively constant across treatments, pastures, and seasons. Across
pastures, seasons, and treatments, deer selected for annual forbs and subshrubs (i.e.,
95% confidence intervals around Selectivity Index > 0), whereas deer avoided annual
and perennial grasses (i.e., 95% confidence intervals around Selectivity Index < 0,
Table 4). Deer showed no selection for perennial forbs (i.e., confidence intervals
around Selectivity Index included 0, Table 4). Perennial forbs were selected in fall, but
not selected in spring (F = 5.06, P = 0.03), and were selected in pasture 1, avoided in
pasture 3 and not selected in pasture 2 (F = 37.47, P < 0.001), whereas subshrubs
were selected in pasture 3, avoided in pasture 1, and not selected in pasture 2 (F =
17.24, P = 0.001). Selectivity Indices for the other functional groups did not vary with
season or pasture. However, Selectivity Indices differed between grazing treatments for
some functional groups and species. Although positive in both treatments, the
Selectivity Index for annual forbs was higher in ungrazed than grazed plots in spring (F
= 5.49. P = 0.04). In contrast, the Selectivity Index for perennial grass was negative in
both treatments, but was significantly lower in ungrazed plots in spring (F = 9.03, P =
0.01). Selectivity Indices for annual grasses, perennial forbs, and subshrubs did not
differ among treatments (all P’s > 0.05). Of the plant species that were available and
consumed in enough plots to statistically compare their Selectivity Indices, we found
that 1 species of annual forbs and 2 species of perennial forbs had a higher Selectivity
25
Index in ungrazed than grazed plots, whereas 1 species of perennial forbs and 1
species of perennial grasses (i.e., P. spicata) had a higher Selectivity Index in grazed
than ungrazed plots (all P’s < 0.05, Table 4).
Diet quality and nutrient intake of deer
Overall, nutrient values of the deer's diets were relatively similar across seasons,
pastures, and treatments (Table 5). Fiber, as measured by NDF was 6% higher in
grazed ( X = 53.4 ± 1.3%) than ungrazed ( X = 50.0 ± 1.3%) across pastures and
seasons (F = 5.29, P = 0.04), but did not differ between seasons (F = 1.38, P = 0.26).
In spring, ADF was also higher in grazed than ungrazed plots (season * treatment: F =
5.08, P = 0.04, Table 5). ADL did not vary with pasture, season or treatment (all P's >
0.07). DMD of the deer diets did not differ among grazing treatments (F = 1.41, P =
0.26, but was higher in spring than fall in pastures 1 and 3, but not in pasture 2 (pasture
* season: F = 5.77, P = 0.02) GE was higher in ungrazed plots than grazed plots in
spring but was similar between treatments in fall (season * treatment: F =10.27, P =
0.008), whereas DE did not differ between grazing treatments (F = 1.79, P = 0.21), but
averaged about 10% higher in spring than fall diets across pastures and treatments (F =
26.45, 0.0002). CP was about 17% higher in grazed plots in pastures 2 and 3 than
ungrazed plots, but similar between grazed and ungrazed plots in pasture 1 (pasture *
treatment: F = 4.63, P = 0.03), and was higher in spring than fall in pastures 2 and 3,
but similar in pasture 1(pasture * season: F = 12.77, P = 0.001). Likewise, DP was 25%
higher in grazed than ungrazed plots in pastures 2 and 3 only, but was about 6% lower
in grazed than ungrazed in pasture 1 (pasture * treatment: F = 4.30, P = 0.04, Table 5).
26
In addition, DP was 60 - 200% higher in spring than fall diets in pastures 2 and 3, but
was similar between seasons in pasture 1 (pasture * season: F = 13.06, P = 0.001) .
Our calibrations of data collected from the activity sensors with our behavioral
observations from scan-sampling correctly identified inactive behavior for 87% of the
minutes both were recorded. Deer spent a similar amount of time bedded, foraging and
walking per day across pastures and treatments (all P > 0.13, Table 6). However, deer
spent nearly twice as much of their day walking (F = 13.11, P = 0.004) and 2 h more per
day foraging (F = 13.87, P = 0.003) in fall than spring, and more time standing in grazed
than ungrazed plots across pastures and seasons (F = 5.73, P = 0.04). While foraging,
deer cropped larger bites in ungrazed than grazed plots, but only in fall (season *
treatment interaction: F = 4.51, P = 0.05), and bite size tended to be larger in pasture 2
than pasture 3 (pasture * treatment: F = 3.47, P = 0.06). Bite rate was similar among
pastures, seasons and grazing treatments (all P's > 0.15). Deer had about a 30%
higher IIR in ungrazed ( X = 2.97 ± 0.17 g/min) than grazed plots ( X = 2.18 ± 0.17
g/min) in pasture 3 (pasture * treatment: F = 13.10, P = 0.001, Table 7) across seasons.
Deer achieved approximately 23% higher DMI (F = 5.72, P = 0.03) and DEI (F = 6.09, P
= 0.03) in ungrazed than grazed plots across pastures and seasons, but DPI (F = 0.46,
P = 0.51) did not differ between treatments (Table 7). No measure of activity nor daily
nutrient intake had a treatment by season or pasture interaction.
DISCUSSION
Our data showed that in P. spicata/F. idahonensis plant communities in dry stony
ecological sites, spring cattle grazing, as typically applied by commercial operators and
targeting 40% utilization of P. spicata, influenced diet selection and generally reduced
27
both the estimated nutritional carrying capacity for mule deer in fall and the amount of
DE an individual deer could consume in a day during spring and fall within the year of
grazing . Cattle grazing during the vegetative and boot stages of growth in P. spicata
failed to improve the nutritional quality of the deer's diets enough to offset the loss of
green biomass of perennial and annual grasses and perennial forbs, and the reduction
in the deer's harvesting rates.
Effects of spring cattle grazing on forage biomass and intake rate of mule deer
The fact that spring cattle grazing reduced the biomass of grasses and forbs
during the year of defoliation was not surprising (Clark et al. 1998, Milchunas and
Lauenroth 1993, Sheley and Svejcar 2009, Westenkow-Wall et al. 1994). In our study,
grazing targeted at 40% utilization of P. spicata and 50% utilization of F. idahoensis,
resulted in a 50% reduction in total biomass in spring and 24% by the fall. Across the
arid and semi-arid rangelands of North America, the effects of livestock grazing on the
quantity of forage for wild varies greatly with the type of livestock, the climate and
weather, grazing history, and site productivity (Stohlgren et al. 1999). For example,
although a single defoliation increased annual net primary productivity of grasslands
between 20 and 45% in Yellowstone National Park (Frank et al. 2000), the shortgrass
prairie (Varnamkhasti et al. 1995), and the desert southwest (Loeser et al. 2004), a
global review of livestock grazing suggested that cattle grazing generally decreases
above ground biomass within the first growing season (Milchunas and Lauenroth 1993).
In our study area, cattle reduced both the biomass of perennial grasses, which
were the most abundant plants, and thus composed the greatest proportion of the deer
28
diets, and annual and perennial forbs, both of which included species that were
significantly preferred by deer (Tables 2, 4). Therefore, the forage removed by cattle
directly reduced the amount of forage available to deer. Although deer are considered
browsers and cattle are grazers, Findholt et al. (2005) found a 19% overlap in mule deer
and cattle diets in the summer of bunchgrass rangelands in eastern Oregon, but failed
to find a change in mule deer diets with grazing treatment. However, dietary overlap
between herbivores depends on the relative abundance of forage (Hobbs et al. 1996).
A reduction in overall forage biomass by spring cattle grazing also influenced the
size of bites and the rate at which our mule deer could harvest food in some pastures
and seasons. The relationship between forage biomass (kg/ha) and bite size in
grasslands has been well documented (Chacon and Stobbs, 1976, Gross et al. 1993,
Short 1985), and is primarily driven by forage quality and quantity (Spalinger et al 1988),
including leaf area and height (Owen-Smith and Novellie 1982, Trudell and White 1981).
Bite sizes tended to be 6% larger in the ungrazed than grazed plots, thus IIR averaged
8% higher in ungrazed than grazed plots in some pastures and seasons, and were
within the range found in other studies (Kuzyk and Hudson 2006, Parker et al. 1999,
Table 6). Similarly, Wickstrom et al. (1984) found that bite size and IIR of mule deer
foraging in grasslands in eastern Washington increased to an asymptote of 0.05 g/bite
and 2.22 g/min as plant biomass increased. Furthermore, Willms et al. (1980) found
that mule deer bite weights from burned or clipped treatments in P.spicata communities
in British Columbia declined relative to controls. Likewise, mule deer cropped smaller
bites and had higher cropping rates as stocking rates of cattle increased the Sierra
Nevada (Loft et al. 1987, 1989), presumably because of lower forage availability.
29
Although bite rates, which are a function of cropping, chewing, and searching time
(Shipley et al. 1994), did not differ between grazing treatments, they were within the
range found in other studies (10-13 bites/min, Findholt et al. 2005, and 30 – 50
bites/min, Wickstrom et al. 1984).
Herbivores may be able to compensate for small bites and low IIR by foraging
longer during the day (Bunnell and Gillingham 1985). In Sierra Nevada range, Kie et al.
(1991) observed an increase in foraging in free-ranging mule deer as cattle stocking
rates increased in summer. However, our deer spent the same proportion of the day
foraging in grazed and ungrazed plots, thus their DMI, which was the product of IIR and
foraging time per day, averaged 20% lower in grazed plots than ungrazed plots. Mule
deer DMI’s in our study, averaged between 800 - 1100 g/day and 34-47 g/kg0.75 across
seasons, pastures and treatments, and were somewhat lower than DMI's measured for
mule deer in other studies, such as 680-2400 g/day in Alberta (Kuzyk and Hudson
2006), 500 g/day in canyon forests in Colorado, (Alldredge et al. 1974), and 73-77 kg0.75
in dry meadows to mature forested habitats in Utah (Collins and Urness 1983). These
lower values may reflect the lower energetic requirements of our non-lactating female
deer on summer range. Energy demands, thus DMI, of lactating deer are 2 to 2.5 times
higher than non-lactating deer (Parker et al. 1999, Sadleir 1982).
Alternatively, our relatively low DMI's may reflect the way that we calculated DMI
in our study. DMI was the product of bite size (collected from simulated bites), bite rate
(determined from timed bite counts), and foraging time per day (estimated from 6 – 8 hr
of daytime scan sampling and calibration of 24-hr activity sensor data). Thus, small
errors in any of these measures could compound when calculating DMI. The most likely
30
source of error was in determining daily foraging time, especially in the night when we
only had data from activity sensors. We estimated that deer foraged between 27-30%
of the day regardless of grazing treatment, but foraged 35% longer in fall than spring,
presumably because deer spent more time searching for higher quality forages in fall.
Although our foraging time was similar to that of free-ranging deer in Sierra Nevada
rangeland (32 ±2.2% or 7.68 h/day, Kie. et al. 1991), others have found that mule deer
forage for 40 - 55% of the day in shrub and grassland habitats (Bunnell and Gillingham
1983). For example, Parker et al (1999) found that Sitka black-tailed deer (O. h.
sitkensis) deer spent 11 h of the day foraging in the wet conifer forests of southeastern
Alaska in both summer and winter. However, our foraging behavior measurements may
have been lower than those in other studies because we delineated standing as a
separate behavior, whereas other studies (e.g., Kie et al. 1991) incorporated standing
into other activities. In fact, our deer stood 3% (1 h) longer each day in grazed that
ungrazed plots (Table 5), which requires up to 22% more energy than lying (Fancy and
White 1985). Regardless of whether the absolute value of DMI we measured was
completely consistent with measurements with free-ranging deer in different habitats,
we measured DMI consistently between grazed and ungrazed plots that were
alternately sampled each day, thus provide a reliable index to differences in DMI
between grazed and ungrazed plots.
Effects of spring cattle grazing on diet quality, nutrient intake rate and nutritional
carrying capacity of mule deer
31
Although some studies have shown an increase in the nutritional quality,
primarily CP and DP, in P. spicata following spring grazing (Ganskopp et al. 2004) or
hand clipping (Clark et al 1998, 2000, Pitt 1986), the nutritional quality of diets
consumed by our mule deer did not vary greatly between grazed and ungrazed plots.
Overall, deer diets in our study tended to be relatively high in DMD and DE and low in
CP and DP across pastures, seasons and treatments. Similarly, other studies have
shown that deer tend to select forages that are high in nutrient content and digestibility
(Swift 1948, WIllms et al. 1976), despite high variation in diet composition (Campbell
and Johnson 1983, Wickstrom et al. 1984) and forage quality (Freeland and Janzen
1974, Stewart et al. 2003). A DMD of 60% and DE of 11.3 kJ/g of forages is considered
adequate for lactating females in the spring and 52% and 9.8kJ/g for maintenance in the
fall (Hanley et al. 1992). Deer in our study consumed diets that exceeded both
constraints in spring and fall, and ranged from 60-65% DMD and between 11-12 kJ/g for
DE (Table 5). However, average DP of the diets consumed in the spring was 7.4%,
which was below the minimum required by mule deer when lactating in the spring (8%
DP, Hanley 1984, Hanley et al. in press, Parker et al. 1999). In the fall, dietary DP was
5%, just above that required for maintenance in the fall (4.5%, Hanley et al. in press,
Parker et al. 1999). These results suggest that CP and DP are the most limited nutrients
in P. spicata grasslands on dry stony ecological sites. However, herbivores that are
adapted to low protein environments can successfully recycle urea-N when consuming
low CP forages (Mould and Robbins 1981). In contrast to our study, Parker et al. (1999)
reported that CP levels in summer diets of Sitka black-tailed deer in southeastern
Alaska far exceeded requirements between 29-34%, but digestible energy was limited.
32
In our study, cattle grazing had very modest and inconsistent effects on the
nutritional quality of the diets consumed by our mule deer. Plant fiber, which slows and
reduces digestion, was slightly higher and gross energy was slightly lower in grazed
than ungrazed plots, whereas crude and digestible protein were higher on 2 of the 3
pastures, but equivalent or lower in the third pasture, on grazed than ungrazed plots
(Fig. 9, Table 5). Damiran (2006) found that CP of diets of cattle, elk, and deer feeding
on pastures grazed by cattle in northeastern Oregon tended to be slightly higher than on
ungrazed pastures (P = 0.10). On the other hand, sheep grazing in late spring on a
Artemisia spp.(big sagebrush)/Purshia spp.(bitterbrush)/Poa spp. range in Utah
increased the amount of green herbaceous plants and current annual growth of
bitterbrush in mule deer diets in spring, but neither sheep nor cattle grazing in the area
improved the nutritional quality (CP and in vitro DMD) of mule deer diets (Smith et al.
1979). Similarly, Austin and Urness (1986) found slightly, but not significantly, higher
CP in mule deer's diets on grazed plots in Artemisia-shrubsteppe in Utah. Regardless,
cattle grazing on our sites did not improve nutritional quality of forages enough to offset
the large reduction in plant biomass during the first year after grazing, thus it did not
increase either the number of deer that the plot could support at a minimal nutritional
level (i.e., nutritional carrying capacity) nor the amount of DE or DP the deer could
harvest per day.
One way cattle grazing might be expected to improve nutritional quality of deer
diets is to increase the ratio of live to standing dead tissue of bunchgrasses (Cook 2002,
Rickard et al. 1975, Willms, et al. 1979, Wilson et al. 1966). Livestock grazing may
remove senescent foliage and reproductive culms of graminoids, reducing canopy
33
density, increasing growth response and providing access to new growth (Parsons et al.
1983, Richards 1984). For example, cattle grazing in early spring reduced the standing
dead biomass by 50% in central Washington (Rickard et al.. 1975). In our study,
however, grazed plots had similar proportions of senescent material to living tissue of
perennial grasses in ungrazed plots, because cattle reduced the green biomass of
perennial grasses to a greater extent than the senescent biomass on grazed plots
during the spring (Fig. 8).
Although brown, senescent perennial grasses composed 30-31% of both grazed
and ungrazed plots (Fig. 8), brown perennial grass composed only 1-7% of diets across
grazing treatments. In fact, the bulk of the senescent perennial grass consumed by the
deer was P. bulbosa tops (i.e., plantlets), especially in ungrazed plots in pasture 1.
Although we only measured nutritional quality of the whole plant, these plant parts likely
contained higher DE and CP content than the leaves and reproductive culms. Likewise,
brown B. serrata comprised 4% of the mule deer diets in fall in pasture 3. Perennial
forbs that persist beyond the growing season may be important forages to wild
herbivores later in fall; however, we found that perennial forbs were reduced in grazed
plots across all seasons.
Second, cattle grazing may improve nutritional quality of growing and senescent
perennial grass by delaying or arresting their phenology. However, neither the
nutritional quality of the diets selected by our mule deer (Table 5), nor the nutritional
quality of P. spicata available (Fig. 9) showed more than a very minor improvement on
grazed plots in our study area. Among previous studies, the effect of grazing on
nutritional quality of bunchgrasses has been mixed. In some experiments, when
34
bunchgrasses were defoliated at boot stage, they contained more CP and in vitro DMD
(Pitt 1986, Wambolt et al. 1997, Willms et al. 1980). Likewise, fiber content in F.
idahoensis was lower when grazed in the fall during seed shatter than when ungrazed
or grazed during the vegetative stage in the spring (Dragt and Havstad 1987). Others,
however, have found no differences in CP, fiber or DMD from livestock grazing during
vegetative growth or during seed shatter in bunchgrasses (Bryant 1993, Wambolt et al.
1997, Westenkow-Wall et al. 1994). Furthermore, summer cattle grazing in eastern
Washington actually increased the fiber content and decreased the CP of P. spicata
compared to plants from pastures ungrazed from 2 to 50 years (Thines et al. 2004)
.These results suggest that practical grazing treatments applied in the spring, especially
if not tied closely to phenology for that year, may not be effective at meaningfully
increasing nutritional quality of bunchgrasses or deer diets
Some ecologists suggest that the effects of moderate to heavy cattle grazing on
P. spicata may be unpredictable and unsustainable, because this species has not
evolved with heavy grazing pressure (Mack and Thompson 1982, Meays et al. 2000,
Milchunas and Laurenroth 1993). Results from field studies suggest that the effect of
grazing on growth and quality of bunchgrasses greatly depends on timing. Grazing may
be less harmful to bunchgrasses when they are grazed during early spring when
grasses are growing vegetatively and meristems are near the crown and protected. If
soil moisture is adequate bunchgrasses can rapidly replace lost leaves (Clark et al.
1998, Guinn 1994). However, grazing during the vegetative growth period is less likely
to improve forage quality (Clark et al. 1998). On the other hand, when bunchgrasses
are grazed when the growing points are elevated and vulnerable, replacement tillers
35
must come from axillary buds at the base of the plant, which is often slow to initiate in P.
spicata (Caldwell and Richards 1985) and depends on the stored carbohydrate reserves
and nutrients following defoliation (Caldwell 1984, Hyder 1972). Furthermore, when
bunchgrasses shift from vegetative to reproductive growth they are less able to replace
lost leaves (Guinn 1994), reducing future photosynthetic capacity.
Third, grazing by cattle may increase diet quality for mule deer by reducing grass
and promoting forbs , which are generally more preferred by deer (Table 4) and are
more nutritious than grasses because they contain higher protein and lower fiber (e.g.,
cellulose, Demment and Van Soest 1985) and silica (McNaughten et al.1985), and
potentially lignin as grasses mature (Cook 1972). However, in our study annual and
perennial forbs averaged 10.9 % CP in spring and 6.6% in fall, whereas perennial
grasses averaged about 9.9 % CP in spring and 8.2% in fall (Table 3). However, deer
in our study actually consumed a greater proportion of forbs in ungrazed plots, and
more perennial grass in grazed plots, across seasons and pastures. Likewise, in the
Sierra Nevada Mountains (Winckel 1980) and eastern Utah (Austin and Urness 1986),
mule deer consumed less forbs and more sedges and browses in grazed pastures. The
shift from "selected" forbs to "avoided" grasses in grazed areas in our study may have
occurred either because perennial and annual forbs were less available (Table 2), thus
harder to find and harvest (Austin and Urness 1986, Blaisdell and Pechanec 1949,
Collins and Urness 1983, Holechek et al 1982), or because green perennial and annual
grasses became slightly more nutritious in grazed plots (Fig. 9). Hobbs et al (1983)
found that deer and elk consumed a greater proportion of grasses as crude protein of
those grasses increased, and consumed more forbs and shrubs when crude protein
36
declined in grasses. Regardless, our mule deer seemed to be able to adjust diet
selection between pastures, seasons and grazing treatment to maintain diet quality.
Little work has investigated the effects of livestock grazing on forbs within P.
spicata rangelands. However, in a global review across ecosystems, Stolhgren et al.
(1999) found no consistent effects of cattle grazing on plant diversity. Grazing
treatments in Montana (Dragt and Havstad 1987) and Texas (Ortega 1997) had no
effect on forb abundance or composition, whereas in the Sierra Nevadas, cattle grazing
decreased availability of forbs preferred by mule deer in late summer (Winckel 1980),
and in Arizona, the effect of diversity and abundance of perennial forbs depended on
the intensity of grazing (Loeser 2007). Intermediate grazing intensity had the highest
species richness and native plant diversity, areas where cattle had been removed had
an intermediate level, and high intensity grazing had the lowest level. Therefore, timing
and duration of grazing can dramatically affect the densities of some forb species.
The amount of digestible nutrients an individual deer can harvest each day
(Tollefson et al. 2010) and total amount of digestible nutrients available to mule deer
populations (i.e., nutritional carrying capacity) depends on both the quality and quantity
of forage available (Cook et al. 2004, Hanley and Rogers 1989, Hobbs and Swift 1985).
In our study the biomass available was substantially depleted by cattle grazing, without
a compensatory increase in nutritional quality of forages and diets. Therefore, the
amount of DE our deer could harvest per day was lower in grazed than ungrazed plots,
although DPI was similar. The amount of DE and DP acquired by deer, especially in the
fall, affects chances of pregnancy in wild ungulates (Cook et al 2004, Nicholson et al
1997, Tollefson et al. 2010). Based on experiments from captive mule deer, the 20%
37
lower DEI achieved by mule deer in ungrazed plots could reduce pregnancy rates by up
to 20 percentage points and chance of twinning up to 10 percentage points (Tollefson et
al. 2010). Fall DEI can also influence energy reserves for winter survival, and, in turn,
influence fawn size and survival in spring (Cook 2002, Cook et al. 1996, Hobbs 1989,
Tollefson et al. 2010, Wallmo 1981). DEI is also important during spring to meet the
energy demands of lactation (Cook 2002, Parker et al. 1999, Sadleir 1980, Tollefson et
al 2011). Although wild herbivores often have the behavioral flexibility to increase bite
rates (e.g., through faster searching) and foraging time when nutritious food is less
available (Mould and Robbins 1981), our deer did neither to maintain DEI in grazed
plots.
Beyond the effects of cattle grazing on DEI of individual deer, plots that were
grazed in spring by cattle in our study area provided only 33 - 50% of the nutritional
carrying capacity and useable forage biomass for mule deer in in fall (Fig. 10).
Nutritional carrying capacity for both treatments was much lower in the spring than fall
because of the substantially higher nutritional requirements for lactating animals in the
spring (Hanley and Rogers 1989). Because during the first year after grazing cattle
reduced the biomass without significantly improving nutritional quality of forage or deer's
diets, our data suggest that for the first year following a spring grazing treatment, cattle
grazing may create a trade-off between providing forage for cattle and for deer in
bunchgrass rangelands on dry, stony ecological sites during that year. The extent of
that tradeoff will depend on a number of factors, including grazing intensity (utilization),
season of use and growth stage, frequency of grazing, life form of plant, availability and
location of meristems, carbohydrate reserves, size of root systems, and physical effects
38
of grazing animals on soil, such as trampling (Anderson and Scherzinger 1975,
Anderson et al. 1990, Frisina and Morin 1991, Guinn 1994, Wambolt et al.1997, Brewer
et al. 2007). Because the main limits of cattle grazing on nutritional carrying capacity of
mule deer in our study were almost exclusively through the reduction in plant biomass
during the first year, managers could manipulate utilization and pasture rotation
schedules to achieve the desired trade-off between forage for cattle and for mule deer
consistent with their goals. The NRCS recommends that P. spicata grasslands be
grazed on a 3-year rest rotation schedule (USDA 2011). Assuming that forage
biomass returns to pre-grazing levels during the second and third year after grazing,
any loss in carrying capacity to mule deer during the first year after grazing would be
reduced by one third.. On the other hand, behavior of free-ranging mule deer may also
serve to influence these trade-offs in forage. Some studies found that mule deer
avoided areas used by grazing cattle by selecting steeper slopes and higher elevations,
or used larger home ranges as cattle stocking rates increased (Stewart et al. 2002, Yeo
et al. 1993).
Our pastures varied in overall biomass and composition of annual forbs, annual
grasses and subshrubs (i.e., pastures 1 and 3 vs. pasture 2, Fig. 7), and precipitation
zone, elevation, and elk density (i.e., pasture 1 vs. pastures 2 and 3, Fig. 6), thus
represent a range of P. spicata/F. idahoensis communities located in within Columbia
Plateau Ecoregion (Omernik 1987). However, they were all dominated by perennial
grasses, especially P. spicata (Fig. 7), took place during a relatively average
temperature and precipitation year (Figs. 2, 3), and had a similar mule deer density as
indexed by pellet density (Fig. 6) Under these conditions, spring cattle grazing that
39
resulted in a 25% actual utilization of total plant biomass by the end of the growing
season, generally reduced both the estimated nutritional carrying capacity and DEI of
mule deer, while having minimal effects on the nutritional quality of forage available and
diets consumed by mule deer. Our data suggest that managers can control the extent
of trade-offs between providing forage for cattle and for mule deer by carefully planning
the timing, intensity, and rotation schedules of cattle grazing treatments.
40
Table 1. Spring cattle grazing schedule, animal units (AUs) and animal unit months
(AUMs), stocking rates, and inclusive dates for sampling forages and mule deer
(Odocoileus heminous) diets applied to 3 pastures within the Blue Mountain Wildlife
Area Complex in southeastern Washington during 2009. Stocking rates were calculated
from percent area considered to be effective forage for cattle of the total area in each
pasture.
Cattle grazing
dates
Forage and diet sampling
dates Pasture Area
(ha) AUs AUMs
Stocking
Rate (ha*/
AUM) Start End Spring Fall
1 509 200 300 0.89 Apr-10 May-25 June 1-8 Nov 6-13
2 64 150 55 0.81 Apr-27 May-8 May 17-
24 Oct 7-14
3 292 150 105 1.37 May-8 May-29 June 17-
24 Oct 19-28
* Based on the percent effective area for each pasture
41
Table 2. Plant species composing > 1% of the total biomass of forages across pastures
(1, 2, 3), seasons (S = spring, F = fall) and grazing treatments (G = grazed, UG =
ungrazed), and how they differed in biomass or percent composition among grazing
treatments within the Blue Mountain Wildlife Area Complex in southeastern Washington
during 2009. Numbers in parentheses indicate a significant treatment * pasture
interaction and letters in parentheses indicated a significant treatments * season
interaction ( = 0.05).
Grazing treatment effect
Species by functional type
Biomass
(%) Biomass (kg/ha) Biomass (%)
Annual Forbs 4 G < UG (1) G > UG (1, F)
Centaurea solstitialis 1 G = UG G > UG (1)
Erodium circutatum 1 G = UG G = UG
Annual Grasses 11 G < UG (1) G = UG
Bromus spp 11 G < UG (1) G = UG
Perennial Forbs 9 G < UG (S) G < UG (S)
Astragalus arthurii 1 G < UG G = UG
Balsamorhiza saggitata 2 G < UG (2) G < UG (2)
Balsamorhiza serrata 2 G < UG (3) G = UG
Lomatium spp 2 G < UG G = UG
Lupinis spp 1 G > UG (3) G > UG (3)
Tragopogon dubius 3 G = UG G < UG (S)
Perennial Grasses 74 G < UG G = UG
Aristida purpurea 1 G = UG G = UG
Festuca idahoensis 4 G < UG G = UG
Pseudoroegeneria spicata 60 G < UG (S) G = UG
Poa spp. 7 G = UG G = UG
Poa bulbosa 1 G = UG G < UG (1)
Sporobolus cryptandrus 1 G = UG G = UG
Subshrubs 3 G = UG G = UG
Arenaria congesta 1 G = UG G = UG
Eriogonum heracleoides 1 G = UG G = UG
Opuntia polyacantha 1 G = UG G = UG
42
Table 3. Nutrients, including crude protein (CP), digestible protein (DP) and dry matter
digestibility (DMD), of grasses, forbs and subshrubs during spring and fall averaged
across pastures (1, 2, 3) and grazing treatments within the Blue Mountain Wildlife Area
Complex in southeastern Washington during 2009.
Spring Fall
Species
DMD
(%)
±
s.d.
DP
(%)
±
s.d.
CP
(%)
±
s.d.
DMD
(%)
±
s.d. DP
(%)
±
s.d. CP
(%)
±
s.d.
Annual Forbs
Camelina microcarpa 52.3 (0.8) 3.6 (0.0) 8.1 (0.0) 51.3 (2.2) -0.6 (0.3) 3.5 (0.4)
Centaurea solstitialis 73.5 (0.0) 9.0 (0.0) 13.9 (0.0) 60.6 (0.0) 6.3 (0.0) 11.0 (0.0)
Epilobium spp. a
64.1 (0.8) 1.7 (0.0) 7.6 (0.0) 48.6 (0.8) -1.3 (0.0) 3.8 (0.0)
Erodium circutatum a
78.1 (0.0) 9.2 (0.0) 14.7 (0.0) 78.1 (0.0) 9.2 (0.0) 14.7 (0.0)
Sisymbrium altissimum 73.7 (0.8) 14.0 (0.0) 19.3 (0.0) 64.3 (2.2) 1.3 (0.3) 5.6 (0.4)
Annual Grasses
Bromus spp 72.8 (0.8) 3.2 (0.2) 7.6 (0.2) 70.1 (0.8) 1.1 (0.2) 5.4 (0.2)
Perennial Forbs
Astragalus spp. 60.1 (5.6) 6.3 (1.6) 11.0 (1.7) 50.5 (5.6) 2.8 (1.6) 7.2 (1.7)
Balsamorhiza saggitata 63.4 (2.9) 3.9 (3.0) 8.3 (3.2) 67.6 (2.9) -0.4 (3.0) 3.8 (3.2)
Balsamorhiza serrata 63.4 (5.6) 3.9 (1.6) 8.3 (1.7) 51.0 (4.4) -0.8 (0.1) 3.3 (0.1)
Cirsium undulatum 69.6 (2.7) 3.6 (0.5) 8.1 (0.6) 64.2 (2.2) 0.7 (0.4) 5.0 (0.4)
Erigeron spp. 65.3 (0.8) 0.8 (0.0) 5.1 (0.0) 65.3 (2.2) 0.8 (0.3) 5.1 (0.4)
Lomatium spp 62.2 (2.4) 4.5 (2.0) 9.0 (2.1) 51.3 (2.4) -0.6 (2.0) 3.5 (2.1)
Lupinus spp. 70.8 (0.0) 12.4 (0.0) 17.5 (0.0) 64.3 (0.0) 5.7 (0.0) 10.3 (0.0)
Tragopogon dubius 69.1 (2.7) 6.3 (0.5) 10.9 (0.6) 65.1 (2.2) 1.4 (0.4) 5.7 (0.4)
Perennial Grasses
Festuca idahoensis 71.1 (2.1) 5.8 (1.7) 10.4 (2.0) 65.8 (3.9) -0.8 (1.3) 3.3 (1.4)
Poa spp. 63.5 (2.9) 1.3 (2.4) 5.6 (2.5) 68.0 (4.0) 10.8 (3.3) 15.8 (3.5)
Poa bulbosa 70.2 (3.0) 3.8 (0.4) 8.2 (0.4) 73.1 (3.0) 3.4 (0.4) 7.9 (0.4)
Pseudoroegeneria
spicata 67.2 (0.5) 2.6 (2.0) 6.9 (2.2) 64.9 (2.2) 1.7 (1.0) 6.0 (1.1)
Subshrubs
Antennaria spp. 45.8 (0.8) 2.8 (0.9) 7.2 (0.9) 45.8 (0.8) 2.8 (0.9) 7.2 (0.9)
Arenaria congesta 60.8 (0.8) 2.8 (0.9) 7.2 (0.9) 45.8 (0.8) 2.8 (0.9) 7.2 (0.9)
Eriogonum heracleoidesa
63.4 (2.7) 2.2 (0.4) 8.1 (0.4) 56.3 (3.1) 1.3 (0.2) 7.0 (0.3)
Phlox longifolia 61.2 (0.8) 3.1 (0.9) 7.5 (0.9) 60.4 (0.8) 4.1 (0.9) 8.6 (0.9)
a Protein-binding capacity analysis detected significant amounts of tannins, and reduced digestible protein (DP) by using Robbins et al. (1987a) summative equation.
43
Table 4. Plant species that composed > 1% of the diets of mule deer (Odocoileus
hermionus) across pastures (1, 2, 3), seasons (S = spring, F = fall) and grazing
treatments (G = grazed, UG = ungrazed), their Ivlev’s selectivity index (SI), and how
they differed in percent composition or selection among grazing treatments ( = 0.05)
within the Blue Mountain Wildlife Area Complex in southeastern Washington during
2009. Codes in parentheses indicate where the treatment effect occurred ( = 0.05),
dashes indicate not enough data to compare SI.
Selectivity Index (SI) Grazing treatment effect
Species by functional type Diet (%) Mean 95% CI Diet (%) SI
Annual Forbs 17 0.46 ±0.18 G < UG (S) G < UG (S)
Camelina microcarpa 2 0.26 ±0.33 a G = UG -
Epilobium brachycarpum 4 0.64 ±0.22 G = UG -
Erodium circutatum 1 0.10 ±0.29 a G = UG -
Sisymbrium altissimum 2 0.33 ±0.27 G = UG G < UG (2,3)
Annual Grasses 7 -0.22 ±0.20 G = UG G = UG
Bromus spp 7 -0.22 ±0.20 G = UG -
Perennial Forbs 13 0.14 ±0.18 G < UG G = UG
Astragalus arthurii 2 -0.03 ±0.33 a G = UG -
Balsamorhiza saggitata 1 -0.03 ±0.39 a G = UG G < UG
Balsamorhiza serrata 1 -0.37 ±0.32 G < UG (3,6, F) G = UG
Cirsium undulatum 2 0.82 ±0.15 G < UG (F) -
Lomatium spp 1 -0.67 ±0.16 G > UG (2) G > UG (F)
Tragopogon dubius 3 0.57 ±0.15 G = UG -
Perennial Grasses 51 -0.23 ±0.08 G > UG b G > UG (S)
Festuca idahoensis 2 -0.47 ±0.22 G = UG -
Poa spp. 8 -0.40 ±0.19 G = UG -
Poa bulbosa 1 0.26 ±0.37 G < UG (1, 2, S) -
Pseudoroegeneria spicata 40 -0.26 ±0.09 G > UG G > UG (S)
Sporobolus cryptandrus 1 -0.41 ±0.71 a G > UG, (1) -
Subshrubs 12 0.33 ±0.29 G = UG G = UG
Arenaria congesta 2 -0.30 ±0.36 a G = UG -
Eriogonum heracleoides 10 0.79 ±0.22 G = UG -
Phlox longifolia 1 0.16 ±0.32 a G = UG -
a CI overlap 0, upper and lower selection + and -, b 3-way interaction P < 0.05
44
Table 5. Least squares mean (SE) of the nutritional constituents of mule deer
(Odocoileus hermionus) diets constructed from bite counts in grazing treatments
during fall and spring southeastern Washington in 2009. Nutrients include neutral
detergent fiber (NDF, %), acid detergent fiber (ADF, %), acid detergent lignin
ADL, %), ash insoluble fiber (AIA, %), crude (CP, %) and digestible protein (DP,
%), dry matter digestibility (DMD, %), and gross (GE, kJ/g) and digestible energy
(DE, kJ/g). Asterisks denote significant differences between least squares means
of treatments across seasons and ampersands denote significant differences
between seasons across treatments, and different superscripted letters denote
significant differences in least squares means among season * treatment
interactions ( = 0.05).
Spring Fall
Nutrient Grazed Ungrazed Grazed Ungrazed
Fiber Fraction (DM, %)
NDF 52.2* (1.8) 51.3 (1.8) 54.5* (1.8) 51.3 (1.8)
ADF 31.14A (1.1) 26.1B (1.1) 32.3A (0.8) 31.7A (0.8)
ADL 4.4 (0.3) 4.0 (0.3) 4.7 (0.3) 4.7 (0.3)
AIA 1.0& (0.2) 0.6& (0.2) 2.7 (0.2) 2.7 (0.2)
Protein (DM, %)
CP 13.0A (0.5) 11.5B (0.5) 9.6 C (0.5) 9.8C (0.5)
DP 8.1A (0.5) 6.7B (0.5) 5.0C (0.5) 5.1C (0.5)
Energy (DM, kJ/g)
DE 12.0& (0.2) 12.5& (0.2) 11.1 (0.2) 11.0 (0.2)
GE 18.6B (0.1) 18.8A (0.1) 18.3C (0.1) 18.2C (0.1)
Digestibility (DM, %)
DMD 64.6& (1.0) 66.6& (1.0) 60.5 (1.0) 60.2 (1.0)
45
Table 6. Least squares mean (SE) percent of the day mule deer (Odocoileus
hermionus) spent in different activities by treatment and season across all pastures in
southeastern Washington in 2009. Asterisks denote significant differences between
least squares means of treatments across seasons and ampersands denote
significant differences between seasons across treatments (main effects, =
0.05).
Sping Fall Activity (%)
Grazed Ungrazed Grazed Ungrazed
Bedded 48.7 (2.8) 49.1 (2.8) 49.7 (2.6) 45.4 (2.6)
Feeding 22.2& (2.1) 26.4& (2.1) 32.4 (2.0) 33.2 (2.0)
Standing 19.0* (1.8) 13.3 (1.8) 13.4* (1.7) 12.4 (1.7)
Walking 11.1& (1.0) 9.2& (1.0) 5.1 (1.0) 6.4 (1.0)
46
Table 7. Least squares means (SE) of intake rates of mule deer (Odocoileus
hermionus) by treatment, pastures and seasons in southeastern Washington in
2009. Asterisks denote significant differences between treatments across
seasons (main effects), asterisks with pasture number (1,2,3) indicates a
significant (pasture*treatment interaction for that pasture), and different
superscripted letters denote significant differences in least squares means
among season*treatment interactions ( = 0.05).
Spring Fall
Foraging Variable Grazed Ungrazed Grazed Ungrazed
Bite Size (g/bite) 0.16B (0.01) 0.16B (0.01) 0.16B (0.01) 0.19A (0.01)
Bite Rate (bites/min) 15.33 (1.1) 15.56 (1.1) 15.48 (1.1) 15.72 (1.1)
Instantaneous Intake
Rate (g/min) 2.20*3 (0.14) 2.34 (0.14) 1.99*3 (0.14) 2.20 (0.14)
Dry-matter Intake
(g/day) 706.1* (89.2) 949.77 (89.2) 929.1* (82.6) 1065.1 (82.6)
Digestible Energy
Intake (MJ/day) 8.4* (1.0) 11.7 (1.0) 10.4* (1.0) 11.7 (1.0)
Digestible Protein
Intake (g/day) 58.8 (9.0) 60.1 (9.0) 49.3 (8.4) 56.0 (8.4)
47
Figure 1. Solid lines show the boundaries and individual pastures within the Smoothing
Iron and Pintler Creek Wildlife Management Areas in the Blue Mountain Wildlife Area
complex in southeastern Washington, and numbers indicate pasture location on which
we measured the effects of cattle grazing on nutritional ecology of mule deer
(Odocoileus heminous) in 2009.
0
6km
3
1
2
48
0
10
20
30
40
50
60
70
80
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Pre
cipi
tatio
n (m
m)
2009 SI
Average SI
2009 P
Average P
Figure 2. Average and annual precipitation for Smoothing Iron (SI) and Pintler (P)
Wildlife Management Areas in the Blue Mountain Wildlife Area complex in southeastern
Washington. Solid lines indicate 2009 precipitation and dashed lines are averages
collected from 1983 to 2010 by D. Browne and J. Reeves of Anatone, WA, less than 6
km from Pintler Creek, and from 1976-2010 from Asotin 14 SW cooperative station
operated by National Climate Data Center located 6.4 km east of Smoothing Iron.
49
-10
-5
0
5
10
15
20
25
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Air
Te
mp
era
ture
(°C
)2009 P
Average P
Average SI
Figure 3. Average and annual air temperature for Smoothing Iron (SI) and Pintler (P)
Wildlife Management Areas in the Blue Mountain Wildlife Area complex in southeastern
Washington. The solid line indicates 2009 temperature and dashed lines are averages
collected from 2008-2011 from Washington State University’s AgWeatherNet station 43
and from 1912-1981 from Anatone 2 S cooperative station operated by National Climate
Data Center located at 1088 m elevations and 12 km southeast of Smoothing Iron.
50
Figure 4. Diagram of arrangement of 6 sets of grazed (G, open boxes with hatched
boundaries) and ungrazed (UG, shaded boxes with solid lines) within each of 3 pastures
within the Blue Mountain Wildlife Area complex in southeastern Washington in 2009.
One pair of grazed and ungrazed plots (0.4 ha each) in each of the 3 sets was sampled
in spring (S) and one in fall (F)
Paired plots ≥ 300 m apart
Electric fencing Paired to exclude cattle plots
Open pens, no cattle exclusion
G-S UG-S UG-F G-F
G-S UG-S UG-F G-F
G-F UG-F UG-S G-S
51
Figure 5. Transect layout for determining deer (Odocoileus spp) and elk (Cervus
elaphus) pellet group densities on 3 pastures within the Blue Mountain Wildlife Area
complex in southeastern Washington..
Baseline/Drainage
2 m
50 m
Start PtEnd Pt
Start point
Alternate view
Fence line/Hilltop
52
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Bottom Middle TopTopographic position
Pel
let
grou
p de
nsity
(gr
oups
/m 2 )
1
2
3
1
2
3
Figure 6. The density of deer (Odocoileus spp., closed symbols) and elk (Cervus
elaphus, open symbols) pellets within pastures 1, 2, and 3 relative to position on hill
side (bottom, middle, top) in the Blue Mountain Wildlife Management Complex in
southeastern Washington in 2008.
Deer
Elk
53
0
100
200
300
400
500
600
700
800
900
1 2 3Pasture
Bio
mas
s (k
g/ha
)
SS
PG
PF
AG
AF
Figure 7. Total biomass and composition of plant functional groups, including subshrubs
(SS), perennial grasses (PG), perennial forbs (PF), annual grasses (AG) and annual
forbs (AF) across seasons and grazing treatments within 3 pastures in the Blue
Mountain Wildlife Area complex in southeastern Washington.
54
0
100
200
300
400
500
600
700
800
900
G UG G UG
Senescent
Live
Figure 8. Least squares means of the proportion of live and senescent total vegetation
and perennial grasses in grazed and ungrazed across spring and fall and three pastures
in the Blue Mountains Wildlife Area complex in southeastern Washington in 2009.
Bio
mas
s (k
g/ha
)
Total Perennial Grasses
55
0
20
40
60
80
G UG
0
1
2
3
4
5
6
G UG
0
0.5
1
1.5
G UG
Figure 9. Mean (a) dry-matter digestibility (DMD), (b) crude protein (CP) and (c)
digestible protein (DP) of standing biomass of P. spicata in fall 2009 in grazed and
ungrazed plots averaged across three pastures in the Blue Mountains Wildlife Area
complex in southeastern Washington.
Nut
rient
s (D
MW
, %
) a. Dry matter digestibility
b. Crude protein
c. Digestible protein
56
Figure 10. Least squares means of nutritional carrying capacity for mule deer
(Odocoileus hemionus) and percent of total forage biomass available that on average
meets nutritional requirements for deer in spring and fall across 1 pastures in the Blue
Mountains Wildlife Area complex estimated from the FRESH-deer model (Hanley et al.,
in press). Different letters denote significant differences among season * pasture *
treatment interactions ( = 0.05).
0
50
100
150
200
250
300
350
1 2 3 1 2 3
Nu
triti
on
al C
arr
yin
g C
ap
aci
ty (
de
er/
ha
/da
ys)
G UG
0
10
20
30
40
50
60
70
1 2 3 1 2 3
Pe
rce
nt U
sab
le B
iom
ass
(%
kg
/ha
)
SPRING
SPRING
FALL
FALL
C C C C C C
A
A
B B BC BC
A
B B
C CD CD
DE DE DE DE E E
57
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80
APPENDICES
81
Appendix A. Species list of plant function groups found in deers’ diets and plots in all pastures,
seasons, and treatments. Taxonomy: The PLANTS Database, USDA, NRCS, 2010
(http://plants.usda.gov, accessed 3/12/2011).
Species Common name
Plant
Code
Functional
Group
Native
/Exotic
Achillea millefolium L. common yarrow ACMI2 PF E
Agoseris Raf. agoseris AGOSE PF N
Allium L. wild onion ALLIU PF N
Alyssum alyssoides (L.) L. pale madwort ALAL3 AF E
Antennaria Gaertn. pussytoes ANTEN SS N
Apera interrupta (L.) P. Beauv. dense silkybent APIN AG E
Arenaria congesta Nutt. sandwort ARENA SS N
Arenaria serpyllifolia L. thymeleaf sandwort ARSE2 AF E
Aristida purpurea Nutt. Var.
longiseta (Steud.) Vasey
red threeawn ARPU PG N
Amsinckia menziesii (Lehm.) A
Nelson & J.F. Macbr.
Mensizii fiddleneck AMME AF N
Artemisia dracunculus L. tarragon ARDR4 PF N
Astragalus arthurii M.E. Jones waha milkvetch ASAR8 PF N
Astragalus reventus A. Gray Blue Mountain milkvetch ASRE5 PF N
Balsamorhiza sagittata (Pursh)
Nutt.
arrowleaf balsamroot BASA3 PF N
Balsamorhiza serrata (Pursh) serrate balsamroot BASE2 PF N
Blepharipappus scaber Hook. rough eyelashweed BLSC AF N
Bromus arvensis L. field brome BRAR5 AG E
Bromus briziformis Fisch. & C.A.
Mey.
rattlesnake brome BRBR5 AG N
Bromus inermis Leyss. smooth brome BRIN2 PG E
Bromus tectorum L. cheatgrass BRTE AG E
Calochortus elegans Pursh elegant mariposa lily CAEL PF N
Calochortus macrocarpus
Douglas var. maculosus (A. Nelson
& J.F. Macbr.) A. Nelson J.F. Macbr.
Nez Perce mariposa lily CAMAM PF N
82
Appendix A. Continued
Species Common name
Plant
Code
Functional
Group Native /Exotic
Camelina microcarpa Andrz. ex
DC.
littlepod false flax CAMI2 AF N
Castilleja hispida Benth. harsh Indian paintbrush CAHI9 PF N
Castilleja Mutis ex L. f. Indian paintbrush CASTI2 PF N
Centaurea solstitialis L. yellow star-thistle CESO3 AF E
Chondrilla juncea L. rush skeletonweed CHJU PF E
Cirsium brevifolium Nutt. Palouse thistle CIBR PF N
Cirsium undulatum (Nutt.)
Spreng.
wavyleaf thistle CIUN PF N
Clarkia pulchella Pursh pinkfairies CLPU AF N
Claytonia perfoliata Donn ex
Willd. ssp. perfoliata miner's lettuce CLPEP AF
N
Collomia linearis Nutt. tiny trumpet COLI2 AF N
Dactylis glomerata L. orchardgrass DAGL PG E Draba verna L. spring draba DRVE2 AF N
Epilobium brachycarpum C. Presl tall annual willowherb EPBR3 AF N
Erigeron corymbosus Nutt. longleaf fleabane ERCO5 PF N
Erigeron L. fleabane ERIGE2 PF N
Erigeron pumilus Nutt. shaggy fleabane ERPU2 PF N
Eriogonum heracleoides Nutt. parsnipflower buckwheat ERHE2 SS N
Erodium cicutarium (L.) L'Her. ex
Aiton redstem stork's bill ERCI6 AF E
Festuca idahoensis Elmer Idaho fescue FEID PG N
Galium aparine L. stickywilly GAAP2 AF N
Grindelia squarrosa (Pursh)
Dunal curlycup gumweed GRSQ PF
N
Helianthella uniflora (Nutt.) Torr.
& A. Gray oneflower helianthella HEUN PF
N
Hieracium cynoglossoides Arv.-
Touv.
houndstongue
hawkweed HICY PF
N
83
Appendix A. Continued
Species Common name
Plant
Code
Functional
Group
Native
/Exotic
Hieracium scouleri Hook. Scouler's woollyweed HISC2 PF N
Holosteum umbellatum L. jagged chickweed HOUM AF N
Lactuca serriola L. prickly lettuce LASE AF N
Lagophylla ramosissima Nutt. branched lagophylla LARA AF E Linum lewisii Pursh Lewis flax LILE PF N
Lithophragma parviflorum
(Hook.) Nutt. Ex Torr. & A. Gray
smallflower woodland-
star LIPA5 PF
N
Lithospermum arvense L. annual corn gromwell LIAR4 AF E
Lithospermum ruderale Douglas
ex Lehm. western stoneseed LIRU4 PF
N
Lomatium macrocarpum (Nutt.
Ex Torr. & A. Gray) J.M. Coult. &
Rose
bigseed biscuitroot LOMA3 PF
N
Lomatium Raf. Desert parsley LOMAT PF N
Lomatium triternatum (Pursh)
J.M. Coult. & Rose
nineleaf biscuitroot LOTR2 PF N
Lupinus sericeus Pursh ssp.
asotinensis L. Phillips = Lupinus
garfieldensis C.P. Sm.
Asotin silky lupine,
Garfield lupine
(LUSE3) N
Machaeranthera canescens
(Pursh) A. Gray
hoary tansyaster MACA2 PF N
Medicago sativa L. alfalfa MESA PF E
Microsteris gracilis (Hook.)
Greene
slender phlox MIGR AF E
Myosotis stricta Link ex Roem. &
Schult.
strict forget-me-not MYST2 AF E
Onopordum acanthium L. Scotch cottonthistle ONAC PF E
Opuntia polyacanta Haw, plains pricklypear OPPO SS N
Orthocarpus tenuifolius (Pursh)
Benth.
thinleaved owl's-clover ORTE2 AF N
84
Appendix A. Continued.
Species Common name
Plant
Code
Functional
Group
Native
/Exotic
Penstemon glandulosus Douglas stickystem penstemon PEGL4 PF N
Phlox longifolia Nutt. longleaf phlox PHLO2 SS N
Plantago patagonica Jacq. woolly plantain PLPA2 AF N
Poa bulbosa L. bulbous bluegrass POBU PG N
Poa cusickii Vasey Cusick's bluegrass POCU3 PG N
Poa pratensis L. Kentucky bluegrass POPR PG N
Poa secunda J. Presl Sandberg bluegrass POSE PG N
Polygonum douglasii Greene Douglas' knotweed PODO4 AF N
Potentilla L. cinquefoil POTEN PF N
Potentilla recta L. sulfur cinquefoil PORE5 PF N
Pseudoroegneria spicata (Pursh) A.
Love
bluebunch wheatgrass PSSP6 PG N
Ribes cereum Douglas wax currant RICE Sh N
Rosa woodsii Lindl. Woods' rose ROWO Sh N
Scutellaria angustifolia Pursh spp.
augustifolia
narrowleaf skullcap SCAN3 PF N
Sisymbrium altissimum L. tall tumble mustard SIAL2 AF N
Sporolbolus cryptandrus (Torr.) A.
Gray
sand dropseed SPBR PG N
Stellaria nitens Nutt. shiny chickweed STNI AF E
Symphoricarpos albus (L.) S.F.
Blake
common snowberry SYAL Sh N
Taraxacum officinale F.H. Wigg. common dandelion TAOF PF E
Thlaspi arvense L. field pennycress THAR AF E
Tragopogon dubius Scop. yellow salsify TRDU PF E
Verbascum blattaria L. moth mullein VEBL PF E
Veronica arvensis L. corn speedwell VEAR AF E
Vicia villosa Roth winter vetch VIVI AF E
Vulpia myuros (L.) C.C. Gmel rat-tail fescue VUMY AG E
Zigadenus venenosus S. Watson meadow deathcamas ZIVE PF N