Aspen Browse Levels in on Yellowstone’s Northern Range:
Current status and temporal changes
Emily Wellington, Montana State University
Nathan Korb, Director of Science, The Nature Conservancy
Duncan Patten, Hydroecologist, Big Sky Institute
Project Duration: September 2011- December 2013; May –June 2016; May-June 2017
Budget: 59,700
Project Summary
Recent literature agrees that intense ungulate herbivory has been the chief cause of Quaking Aspen
(Populus tremuloides) decline on the Northern Range of Yellowstone National Park, but current intensity of browsing
is unknown. Copious literature on interactions between Aspen and ungulate herbivory exists for Yellowstone, but the
recent declines in elk numbers warrant a contemporary inquiry into browsing intensity. This paper looks to quantify
browsing intensity on and relative abundance of first-year sprouts as well as overall stand vigor. Up-to-date
measurements of browse intensity and sprout stage distribution can inform the greater question of how much
browse is being endured currently and by what growth form in the face of the changing ungulate population. 209
transects surveyed in 1997-98 will be revisited in the late spring of 2012 and 2013 and again in 2016 and 2017 to
quantify sprout density, height, growth stage, and browsing intensity. Despite a decline in elk numbers, my first
hypothesis expects no significant difference in browse intensity over the past 15-20 years. This is based on a
secondary assumption that there is no site effect, or, that all the observed changed in browse intensity is due to elk
and not other factors. At current winter elk densities, the aspen stands are effectively saturated with predators such
that small declines in elk numbers are not likely to result in fewer stems consumed. To quantify differences in
browse intensity and other variables, simple t-tests will be used, while MANOVA will test for site effects. My second
hypothesis expects a majority of browsed stems to be of an adult stage and expects the relative frequency of adult
sprouts to increase over time. Because stand conditions are site effects, this hypothesis relies on a paired design to
increase precision. Specifically, flagging stand vigor may be the reason for observing lower annual sprouting density
(regeneration) than has been reported in the recent past. If adult sprouts or ‘shrub aspen’ are indeed now receiving
the brunt of the browse pressure, it may have implications for overall stand persistence as it remains unknown how
resilient these taxed growth forms may be to such damages over the long term. Because overstory recruitment has
largely ceased, the future of aspen stands on Yellowstone Park’s Northern Range will depend exclusively on
weakened sprouts and relic underground root systems.
Introduction
Vegetation responses to ungulate herbivory on and adjacent to protected lands have been a topic of
concern for scientists and managers in recent decades. Human impacts have the ability to alter species’ abundances
and distributions which in turn affect ecosystem functions that exist independent of management boundaries
(Debyle 1985, Despain 1990). Several papers have focused on interactions between Aspen and Elk in the Greater
Yellowstone Ecosystem (“the GYE”; Baker et al. 1997, Despain 1991, Hessl 1999, Houston 1982, Kay 1990, Krebill
1972, Ripple and Larsen 2005, Romme et al. 1995, Singer et al. 1998, Wagner 1993, Kimble 2007), yet the long-term
effects of such interactions remain uncertain. Depredation by elk is cited as the major cause for aspen decline on
Yellowstone National Park’s Northern Range (“the Northern Range”; CITATION), but it is unknown how Aspen has
responded to recent Elk reductions and whether or not increased regeneration may be expected in a future with
fewer elk. The proposed study provides a contemporary assessment of Aspen browse intensity and stage distribution
in and around Yellowstone National Park (“YNP”).
Aspen is important in the GYE for its contributions to biodiversity, nutrient cycling, forage, hydrologic
cycling, and recreation (DeByle 1985, Houston 1982, Bartos and Shepperd 2010). Where declines are significant, the
principal concern is that adult trees are dying and are not being replaced despite consistent regeneration. The Forest
Health group s‘decline’ as the mortality of a stand or clone, though ‘die-back’ refers to death of part of an aspen
genotype existing on a site (Bartos and Shepperd 2010); for my purposes let ‘decline’ include ‘die-back’. Proposed
causes include climate change, fire suppression, and human land use and management; proposed consequences are
uncertain (Loope and Gruell 1973, Schier 1975, Olmstead 1979, Bartos and Mueggler 1981, Hinds 1985, Kay 1990,
Bartos et al. 1994, Romme et al. 1995, Baker et al. 1997, Despain 1986, Ripple and Larsen 2005, Brown et al. 2006,
Kimble 2007).
It has been documented with increasing certainty neither fire suppression nor climatic conditions in the past
century in and of themselves are impacting Aspen recruitment (Despain 1986, Kay 1990, Bartos et al. 2004, Hessl
2002, Korb, personal communication). While fires do produce sprout densities of up to 200,000/ha (Despain 1990),
they are not necessary for regeneration and, in decadent stands, actually accelerate die-back (White et al 1998, Kay
1999). There has been no significant downward trend in precipitation in the past 100 years (NRC 2002) and no
relationship between aspen overstory establishment and climatic fluctuation on the northern range has been found
(Larsen and Ripple 2003). In addition, successful aspen regeneration occurring on xeric scree slopes inaccessible to
ungulates (St. John 1995; Larsen and Ripple 2005, Kimble 2007) and inside all aspen exclosures on the northern range
(Kay 2001; NRC 2002) suggests that climate change has not played a major role in aspen decline.
Meanwhile, researchers continue to find evidence that elk browsing accounts for most of the ungulate
herbivory and hence it is the greatest threat to Aspen communites. Aspen declines can affect changes in species
richness (including presences of beavers, bears, birds, bats, insects, and fungi; Chong 2001, Bailey and Whittham
2002, Beetle 1997, Anderson at al 1980, Smith and McMahon 1981, Turchin et al. 1995). The net effect of heavy
ungulate use sharply reduces the structural and habitat diversity of the understory by largely eliminating shrub and
forb strata and replacing them with graminoid vegetation dominated by two non-native species (Wagner 2006).
The Yellowstone elk population began its rapid growth in 1967 due to management changes favoring
‘natural regulation’ (Singer et al. 1998, Kauffman et al. 2010, ). The most recent large-scale recruitment of Aspen in
YNP was recorded late as 1930 though others put the time frame closer to 1880-1900 (Ripple 2002, Despain 1986;
for an in-depth discussion of elk-wolf interactions and factors such as market hunting, drought, and predator
extermination see Kauffman et al. 2010, Baker et al. 1997, White and Garrott 2005, and Vucetich et al. 2005). With
natural predators back in the park, elk populations have declined significantly and look to be stabilizing (Creel 2005).
Furthermore, an important driver of elk populations, the GYE wolf population, has declined slightly and remained
stable in recent years after peaking in 2003 (Smith 2005). Unlike past inquiries into Aspen stand conditions, the
current proposal assesses aspen after the Yellowstone elk population entered this new period of stability secured by
natural balances.
It is unknown what density of elk results in acceptable use levels such that herbivory does not negatively
impact vegetation communities and impair ecosystem function (Hessl 2002, Patten personal communication). Due to
human impacts, predation may not actually be occurring to its full potential and the extent necessary to restabilize
YNP’s ecosystem. Elk tolerate human presences more readily than wolves do, potentially reducing elk-wolf contact in
such areas (Wagner 2007). Park managers admit uncertainty as to why browse levels are higher within park
boundaries, but it is recognized that browsing has more of an effect now that it did historically (YNP 1997, Bishop et
al 1995).
The proposed study provides data on stand conditions and current browsing. My observations will be
compared it to past studies on the Northern Range (Romme et al. 1995, St. John 1995, Ripple & Larsen 2005, Kimble
2007) to illustrate how the stands have fared overall and if differences in browse intensity (defined as the percentage
of stems browsed over winter) in and outside of the park remain significant. Data from the original 209 transects
were not collected on sprout stage, however, so this proposal collects its own before-and-after sprout stage data.
Observations from 2016-2017 will show if adult sprouts are receiving an increasingly large burden of the browse
pressure, as it remains uncertain whether or not chronic heavy browsing on this growth stage negatively affects the
whole stand (Bailey et al. 1990, Kay and Wagner 1996, Hessl 2002).
Literature Review
Though Aspen is a minor species in the GYE, its limited coverage has disproportionate importance for
biodiversity, hydrology, nutrient cycling, forage, and aesthetics (Debyle 1985, Despain 1986, Bartos and Shepperd
2010). It is typically associated with moist habitats and abundant sunlight although its fundamental niche includes a
broad spectrum of environmental gradients that make it the most widely distributed tree species in North America
(Hansen et al. 2000). The subject of Aspen decline emerged in the late 1920’s and remains a moving target. Though
literature on the species suggests flagging vigor and abundance across a large portion of its range, greater losses
have been documented on marginal sites such as those found in Northern Yellowstone Park (Despain 1986, Romme
et al. 1995, Ripple and Larsen 2000, Brown et al. 2006). Marginal sites are those with combinations of low winter
temperatures, shorter growing season, and reduced rainfall, soil fertility, and solar radiation (Hansen et al. 2000,
Brown 2003).
Eight species of ungulates are found in Yellowstone park: elk (Cervus elaphus), pronghorn antelope
(Antilocapra americana), bison (Bison bison), bighorn sheep (Ovis canadensis), mountain goat (Oreamos
americanus), moose (Alces alces), white-tailed deer (odocoileus virginianus) and mule deer (Odocoileus heminus). In
Northern YNP, Aspen is highly palatable to elk, moose, bison, and deer, and retains nutritional value past the time at
which summer grasses and forbs senesce and cure. Once snowfall accumulates, the species becomes increasingly
important forage until green-up in the late spring (Olmstead 1979, DeByle 1985). Annual surveys of bighorn sheep
indicate that the resident herd on Yellowstone's northern range consists of at least 150-225 animals (YNP 2010).
Mule deer populations fluctuated between 1600 and 2500 from 1987 to 1999 and are suspected of slight declines in
recent years (NRC 2002), and much of their wintering grounds are below the elevational range of Aspen (Wagner
2006). Neither White-tailed deer nor Moose have ever been abundant in YNP, and Aspen does not comprise much
of their diet (YNP 2010, Tyers 2003). Pronghorn numbers have been stable at 205-252 animals since 1995 (NPCA
2006). The 2010 census estimates the summer bison population on the Northern Range at 3900 (YNP 2010), but it
should be noted that although they have a larger body size, bison are primarily grazers year-round and woody
browse species are a minor part of their diet (Boyd 1986, Meagher 1986). Mountain goats, being non-native, were
not present in the park until the 1990’s and the 2008 surveys put the population at 175-225 individuals (YNP 2009).
Park officials have stated that ‘there remains no question that ungulate browsing is the immediate cause of the
decline of aspen on the northern range, but there is considerable uncertainty over why that browsing has a different
influence now than it has had historically'' (YNP 1997).
Aspen generally recovers quickly from damage and outcompetes other species following disturbances
(DeByle 1985). Aspen produces primarily vegetatively, sending up new sprouts each year from extensive
underground root structures (Kaye 1993, Houston 1982). Intact dominant apical meristems produce auxins that
inhibit root sprouting, but death or mechanical damage to large stems stimulate vigorous suckering of new sprouts
(Schier at al. 1985, Eckenwalder 1996). Sprouts grow rapidly and develop their own roots within the first few years,
though they depend upon the parent root system for at least the first 25 years (Zahner and DeByle 1965). The parent
root system remains alive and functioning for 40-50 years (Pregitzer and Friend 1996) after above ground stems have
senesced. When a sprout’s terminal leader is browsed, the closest lateral bud sends up a shoot to take over the role
of apical dominance (Debyle 1985). Despite aspen’s precocious growth habits, heavy browsing can result in negative
height gains or mortality (Korb 2006).
Another effect of high ungulate numbers is increased gnawing and rubbing of the bark, especially in trees
less than 13cm in diameter or 20 years old, that causes infection or mortality (Keigley and Frisina 2008). Year after
year, a lack of height precludes the sprout from being recruited to above the browse zone, defined as 200cm of
height or less (Olmsted 1979; Debyle 1985, Kaye and Wagner 1994). In some cases, stems are browsed down below
the point of last year’s growth, resulting in negative annual growth (Nathan korb, personal communication). As the
number of lateral twigs increases in response to continual browsing, sprouts are visibly hedged and are often
characterized as ‘shrub form’ (Deblye 1985, Despain 1990). For aspens in the GYA where elk presence is a constant
factor, few aspens are recruited into the overstory.
As browse pressure precludes height gains necessary for recruitment, adult sprouts must persist with
unknown longevity in a shrub state. Chronic heavy browsing may gradually reduce the density and overall vigor of
the sprouts and of the underlying aspen root system (Tyers 1981, White et al. 1998). As mature stems senesce
without replacement, the sprout generation inherits an enormous underground root mass from the parent trees (up
to 18 tons/ha; DesRochers and Lieffers 2001). Where aboveground primary productivity is low, as in declining stands,
respiration demands of the root system may place an energetic burden on the remaining cohort and result in
decreased vigor (Bailey et al. 1990, DesRochers 2000, Kay and Wagner 1996, White et al 1998).
While one hypothesis offers that a dwarf or shrub form is normal for the species as an adaptation for years
of heavy browsing (Despain 1990), another study found the growth form not to be long-lived (<15 years, n =22),
which raises doubts as to how resilient the stunted adult sprouts may be to browsing over the long term (Kay and
Wagner 1996). Barmore (1980) and Tyers (1981) synthesized the available literature and cite studies that show good
regeneration in the presence of high elk densities. On the other hand, an examination of regeneration and mean
stem lengths of shrub-aspen clone stands and tree clone stands in YNP found the former to have less than 16% of the
latter. The tree clones had 6.3 times as much regrowth, and stem lengths at the end of growing season were 30% of
those measured in tree clones (total n = 268; Kay and Wagner 2006). Olmstead (1997) pointed out that “aspen
shoots can only be browsed a few times before diffusion of apical dominance makes it unlikely that the growing
shoots will mature into a tree.
The presence of patchy stem regeneration may be evidence of temporary persistence, but long-term stand
persistence and extirpation remain fertile areas of research (Sankey 2008, Barnett and Stolghren 2001, Kaye et all
2003). Due to concern about the persistence of aspen stands on this landscape, questions have been raised as to
whether or not shrub aspen clones comprised only of new and adult sprouts are likely to survive another 20 years of
chronic browsing damage (Bartos 2001, Wagner 2006). Whether or not the browsing pressure has been reduced
across the northern range is yet to be determined, though stabilizing elk populations may make it easier to draw up a
conclusion.
In the past 5 years, elk populations on the northern range appear to have stabilized at an average of 6500
animals (NYCWWG 2006-2010 reports). This number is a fraction of the 19,045 recorded in 1994. Elk populations
soared after YNP adopted a ‘natural regulation’ approach in 1969 in response to public distaste for the live-trapping
and shootings that occurred starting in 1923 in attempts to cull the herd (for more on the ‘Natural regulation’
hypothesis see Despain 1986 and Kay 1993). In the past decade, YNP has averaged densities of 12 elk/km2 (range
8.0-17.9 elk/km2), and the Gallatin portion of the northern range averaged 7.6 elk/km2, with a range of 2.8-14.4
elk/km2 (Lemke 2004). Because of the decrease in the Yellowstone elk population, a contemporary assessment of
browse on the herd’s wintering areas is needed.
The most recent YNP northern range browse data was collected in 1999 by Eric Larsen, who found that only
5% of 57 stands inside the park has stems originating after 1921 (Ripple and Larsen 2000). A 1991 study visited 97
stands on the northern range inside the park to take increment cores and found that aspen <20 cm DBH were
exceedingly rare (only 7 cores were taken, none from trees <10cm DBH; Romme et al. 1995). Data from 1998 showed
an 87% browse rate on the northern range, and results from the more limited 1991 survey (18 stands in Northern
YNP) reported over-winter browse intensities up to 75% (Romme et al. 1995). Browsing impacts on aspen are
generally greater where elk density is high, and the northern range of YNP is known to have the highest elk densities
in the country (White et al. 2003). A survey of 342 quaking aspen clones or “stands” on the Gallatin National Forest
(GNF) taken in 1990 and 1991 was repeated in 2006, resulting in a conclusion that, despite fewer elk, aspen
recruitment has not increased at the landscape scale (Kimble 2007). Although thorough, Kimble’s study was limited
to the Gallatin National Forest (GNF), the findings of which underscore the need for a comprehensive assessment of
Aspen on the entire wintering area used by Northern Yellowstone’s elk.
Study Area
The northern range is located in the valleys of the Yellowstone, Lamar, and Gardiner Rivers, divided by the
park boundary, north of which lies a matrix of national forest and private lands. My study area includes the similar
elk wintering areas of Sunlight and Crandall Creek basins (total 43798 ha) in Shoshone National Forest for
comparative purposes (Figure 1). The northern range is the wintering ground for YNP’s largest elk herd and occupies
an area of approximately 153,000 ha, with approximately 65% within Yellowstone National Park and 35% outside the
park (Houston 1982, Lemke et al. 1998). Mean annual precipitation in Jardine at the northwestern portion of the
study area is 44.5cm at an elevation of 6450. Mean minimum temperature in January, available only for
Gardiner (7 miles away at 4850’elevation), -10°C and mean maximum temperature in July is 30°C. For Sunlight
Basin, station elevation, mean annual precipitation, minimum January temperature, and maximum July
temperature is 6450 feet, 29 cm, 16 degrees, and 81 degees, respectively. In Crandall Creek the corresponding
values are 6600 feet, 41 cm, 5 degrees, and 80 degrees. At Tower Falls, corresponding values are 6727 feet, 42
cm, 0 degrees, and 80 degrees (Regional Climate Center 2010). Climate information is necessary for its
potential as a site effect, and can be analyzed as a covariate.
The 1988 fires that burned ~20% of Park’s northern range and parts of the Shoshone generated
considerable impetus for the opportunity to study responses of vegetation, ungulates, and herbivory. Prolific
publishing produced several papers from which I have derived my sampling design. Overall, researchers found that
high browse intensity explained surprisingly low recruitment of Aspen despite high densities of regeneration
(especially on burned sites). Additionally, all the existing Aspen on the burned study sites had been killed by the fire.
Data collected in 1997-98 field surveys revealed an 89.6% browse intensity (986 of 1100 sprouts observed on
transects) in YNP (Larsen and Ripple 2005).
Figure 1. Map with shaded areas of elk winter ranges and study area (Ripple and Larsen 2005).
Methods
My sampling programs is designed to test two hypotheses based around establishing 209 permanent
sampling points randomly scattered throughout the study area. Findings and methods from other studies in the GYE
are also taken into account (St. John 1995, Romme et al. 1995 , Larsen 2001 , Kay 2001, , Larson 2001, Kimble 2007,
Romme et al. 2005). Random design and a large number of observations were lacking in some previous studies
(Ripple and Larsen 2000, Romme et al. 1995), so this assessment attempts to minimize weakness in this regard.
Aspen presence in the Shoshone NF and in YNP was determined using 1:24,000color infrared (CIR) aerial
photographs from September 1988 while 1995 natural color aerial photography was used for the Gallatin NF portion.
A grid of rectangular cells 1.0 x 1.5cm, corresponding to an area 240m x 360m on the ground, was placed on each
photo and each cell classified for presence or absence of Aspen. A random selection of 100 cells has been made from
only those grid cells containing aspen. CIR photography was used because of the simplicity with which aspen (white
crowns in the late fall CIR photographs) could be differentiated from conifers (red crowns in CIR). All aerial
photography interpretation was done with a scanning stereoscope, with which sufficient detail enabled identification
of individual aspen crowns in poorly stocked stands (Ripple and Larsen 2005).
Field surveys consist of 2 meter x 30 meter transects running from the stand edge toward its center. An
aspen stand is defined as a group of trees that are all within 30 meters of each other. Trees further than 30 meters
from any others are not a part of the stand. Furthermore, a stand must contain least two living aspen trees. If a stand
consists of only two trees, the transect will be located at the midpoint between them and extended towards each
tree. If the stand consists of three or more trees, a random starting point (northernmost, easternmost,
southernmost, or westernmost point) has been chosen and the transect will run from that edge towards the center.
In the case that only one living aspen tree is included in any 2 x 30 meter transect, the transect may be expanded (for
tree counting only) to either 6 x 30 or 30 x 30 meters so as to include at least one more living tree. In the case where
a 240 x 360 grid cell contained several stands, one aspen stand has been randomly chosen within the cell and a single
transect established.
The following information will be collected on each transect:
1) Site characteristics: Elevation, topographic position, slope, aspect, UTMs of transect start.
2) Aspen overstory density (per hectare): Density and diameter at breast height (dbh) of aspen overstory
(>200cm) trees, to be later classified as 1-4cm dbh, 5-9 cm, 10-19, or >20. Aspen of a ‘tree’ form are
defined to be greater than two meters tall and are further differentiated into "replacement" and "nonreplacement" trees (Kay 1990). A replacement tree is greater than two meters tall but less than five cm dbh.
A non-replacement tree is greater than two meters tall and also greater than five cm dbh.
3) Amount of bark stripping: calculated as the percentage of black corky bark scars observed on the bottom
two meters of the tree. The trees will be visually inspected with stripping categorized as low (0-33%),
medium (34%-66%), or high (67%-100%) levels of damage.
4) Percent canopy cover: A percentage of a given tree’s canopy that is covered with live leaves, grouped into
categories of low (0-25%); medium (25%-75%), or high (75%+).
5) Mortality: Standing dead (SD), recent dead (RD; leaves still on tree), and fallen (F) aspen in the transect will
be recorded.
6) Browse Intensity: Count the number of browsed, unbrowsed, and dead stems less than 2 meters tall.
Sprouts greater than 100cm are counted as ‘tall’ sprouts. Where there are several stems growing as a
clump from one base, only the tallest sprout is counted.
7) Stage distribution: visually inspect each sprout for the presence of a bud scar, indicating adult status. Where
possible, note the number of visible bud scars as an estimate of sprout age.
Analysis
Four of the above variables represent categorical types of data: dbh class, presence of tall ramets, bark
scarring percentile group, and canopy cover percentile group. Stands containing tall sprouts will be placed into a
categories of present or absent. Aspen regeneration will be reported as sprouts per hectare, browse intensity will be
reported as % of total live sprouts counted that are browsed, and stage distribution is calculated as the percent of
live sprouts that are over a year old. Because I have assumed that the site differences don’t significantly affect
browse intensity, I have gathered enough data on site characteristics (covariates) to be able to test that assumption.
As applicable, I will use the same techniques given in Larsen and Ripple 2005 to compare estimates of the
data. Their analysis has enough transparency to make future results sufficiently comparable, though because this
proposal focuses on temporal changes, not all tests performed on the 1997-1998 data will be repeated. The original
analysis concerned itself with non-normality in testing between sites, but my study is interested differences between
sampling periods. Summary data can still be compared to past estimates, however. T-tests and F-tests will be used
on data that could have non-normal distributions with sufficiently large sample sizes (> 30 per site) and the risk of
committing a Type I error is similar to the risk associated with normal distributions (Lindquist 1953, Boneau 1960).
For comparing browsing intensity, transects surveyed at different time periods are considered independent
due to the assumption that, at moderate to high densities of elk, site effects cease to play a role in browsing
intensity. The current populations of elk effectively saturate the landscape to dwarf any effect steep slopes, sprout
density, snow depth, or ‘attractiveness’ of a site (i.e. high regeneration, numerous healthy adult trees) may have on
browsing intensity. With summary data I will perform a t-test on the sample means from each field season 1998;
2012; & 2017. T-statistics will also be used to construct confidence intervals. Exploratory testing shows that, given
the sample sizes and standard deviations, tests with p= .05 or 0.01 will detect departures in the range of 4-11%. For
example, given the 1998 sample mean of 87% browse in YNP and a hypothetical 2010 sample mean of 83% in YNP,
the differences are not significant at a 95% Confidence Interval. The confidence interval indicates that one could
expect at least 95% of the time for the true difference of the means to be less than 8.388%.
Sprout stage distribution: For the purposes of these data, observations from each year will be treated as
pairs since the question weighs heavily on before-and-after comparisons. Because the hypothesis anticipates that
stand conditions have changed over time, site effects are anticipated and the pairing thus controls for these effects,
enabling variation within pairs to be largely attributed to other processes. Because stand conditions between sites
are not homogeneous and the correlation within pairs is expected to be large, increased precision associated with
pairing should compensate for the loss of degrees of freedom. This test will also be used on the tall sprouts. In terms
of the total number counted, the percent of total sprouts measuring over 100cm will be compared across field
seasons and between sites.
Multivariate Analysis of Variance (MANOVA) will be used to test for differences in the vectors of means
observed (over time and across space) and for the potential contribution of covariates to the variability in browse
intensity and stage distribution (the focal response variables). Independent predictors in the vector could include elk
population, elk density (if applicable), slope, topographic position, elevation, aspect, number of living adult
overstory trees, canopy cover class, snow depth (if available) and amount of deadfall (or recent fire). However, to
minimize degrees of freedom, only winter elk estimates, slope, and snow depth (correlated with aspect, elevation,
and and topographic position) will be analyzed. SPSS software offers an adjustment for unequal sample sizes when
cells in the MANOVA factorial have different sample sizes. If results are shown to be significant, univariate F-tests for
each variable will be examined. It is worth mentioning that analyses of the 1998 data show that neither elevation nor
aspect affect the densities, heights, or dbh of either sprouts or overstory stems, and neither overstory stem nor
sprout densities differed significantly between mesic and xeric sites (Larsen and Ripple 2005). Therefore such tests
may not bear repeating.
Comparisons with other studies: Data can loosely be compared to estimates of browse intensity reported in
Romme et al. 1995, Korb 2006, and Sankey 2008. Romme et al. includes observations on sprout stage which may be
particularly interesting and easily comparable because the survey protocol is nearly identical. Sprout density and
sprout height were also recorded in their paper. Nathan Korb’s work in the nearby Centennial Valley also provides
summary data on sprout density, DBH, live-dead index, browse intensity, and sprout height. Korb uses Richard
Kiegley’s 2008 guide to quantify extent of browse, however, which is a more subjective and time consuming process
not proposed in my sampling design. Sankey, though quick to parse others’ sampling designs, did not in fact treat
any stems less than 8cm dbh with much attention and classified all such as ‘suckers,’ limiting the usefulness of such
data. However, Sankey’s treatment of multiple regression incorporating topographic position and ANOVA using
landform effects is informative.
Figure 2. Summary data, copied from Ripple and Larsen 2005.
Sample results (using hypothetical estimates of future survey observations):
Gallatin
In comparing future data to 1998’s reported browse intensity of 80%, a 95% confidence interval for the potential
observation of “no difference” is encompasses the range of 71.9%-88.1% (Table 1). This test for no difference with
n=63 and s=23 only detects differences of +/-8.1% between sample means.
Given a 5% increase in the relative frequency of adult sprouts with a standard deviation of 15, the change will be
significant at the α=0.01 level. If data are unpaired, the resulting one-tailed p-value is only .031. If the standard
deviation increases to 20, the change will be significant at the α=0.05 level, which would not be true had the data not
been paired (p=.083; Table 2).
YNP
If the true future value of browse intensity was reported at 87%, the 95% CI for no potential difference is 81.8%92.2% (Table 1). This test for no difference with n=92 and s=18 only detects differences greater than +/- 5.2%. Given
a 5% increase in the relative frequency of adult sprouts with a standard deviation of 15, the change will be significant
at the α=0.01 level. If data are unpaired, the resulting one-tailed p-value is only .014. A 5% change with SD=20 will
still be significant at the α=0.05 level (Table 2).
Sunlight-Crandall
A 95% C.I. test for no change in browsing intensity will only detect differences of 4.75% or greater (Table 1). Given a
5% increase in the relative frequency of adult sprouts with a standard deviation of 15, the change will be significant
at the α=0.01 level, but If data had been unpaired, the resulting one-tailed p-value is only .043. If the standard
deviation increases to 20, the change will be significant at the α=0.05 level, which would not be true had the data not
been paired (p=1.0; Table 2).
Table 1. 95%CI for no change in browse intensity
Site
n
Mean 1998
Mean 2012
SD ‘98
SD’12
range
Gallatin
63
80
80
23
23
+/- 8.1%
S/C
54
82
82
21
21
+/- 8.0%
YNP
92
87
87
18
18
+/- 5.2%
Table 2. Results for a hypothetical 5% increase in relative frequency of adult sprouts
Site
n
p-val if SD =
unpaired p-
p-val if SD =
unpaired p-
p-val if SD =
unpaired p-
10%
value
15%
value
20%
value
Gallatin
63
.000
.003
.005
.032
.030
0.083
S/C
54
.000
.008
.010
.043
.038
0.100
YNP
92
.000
.001
.001
.014
.011
0.047
Inference
In the case of the Gallatin and Sunlight-Crandall datasets, the standard deviations result in a fair bit of width
to the 95% confidence intervals. However, it is unlikely that managers or other scientists are likely to consider a
change of less than 10% to be biologically significant. Therefore, accepting my null even with a change 8% is
reasonable. While no one can speculate on what information managers ultimately use, changes of 8% or less may be
of minimal practical importance. Because the variation within transects is mainly a biological phenomenon and
cannot be effectively controlled by observers, only increasing the sample size could improve the sensitivity of
statistical tests. However, adding 20 transects to either of these sites could not begin to narrow the confidence
interval by 1% on either end, which suggests that the cost of increasing precision are prohibitive. Observing browsing
intensity on replicate transects could conceivably reduce the standard deviation of the data despite the fact that
they are being treated as independent sampled.
In the case of the before-and-after observations on sprout stage distribution, survey data were analyzed as
matched-pairs and pairing did increase precision. Table 2 shows improved confidence in the point estimate if
standard deviations of the differences within means of either variable remain moderately small (~20 or less).
Furthermore, while it may be of interest to scientists, managers may not look at a 5% change as being biologically
significant. Ideally the transects could be periodically re-surveyed every 5 years or so to further reduce the question
of how resilient the heavily browsed adult sprouts truly are over 15 or 20 years. With time-series data, smaller
changes carry more importance.
Timeline
September 2011: Submit, secure funding.
October 2011: Make arrangements for the necessary equipment.
Novmber 2011: Obtain permits for each study area and, if needed, permissions from landowners (send letters). Post
job announcement.
December: Interview field assistant(s); select by Jan 15 if adequate funding comes through.
*in the case of adequate funding, field seasons 2012-2013 will be collapsed into one season, as would seasons 2016-2017.
Without funding, I will be doing all field work myself and it will take two seasons as per Eric Larsen’s experience.
January 2012: Arrange for field vehicle, housing; lock in prices.
February: map/plot logistics associated with survey order- i.e. low -> high elevation, starting at lower latitudes.
May 1-15 – reminder to landowners. Check road conditions; adjust survey logistics to suit.
May 15-30 – ready to start based on leaf-out. Training/review protocol.
May 30-July 30 - Field work. Data entry only with downtime.
August 15- data entry complete
August 30- progress report due to department with preliminary analysis
September - Use thesis credits to begin analysis. Damage control if needed.
Fall 2012 – check funding. Obtain statistical advisor if one is not already involved
September 2015 – secure additional funding if needed.
Novmber 2016: Obtain permits for each study area and, if needed, permissions from landowners (send letters). Post
job announcement.
December: Interview field assistant(s); select by Jan 15 if adequate funding comes through.
January 2016: Arrange for field vehicle, housing; lock in prices.
February: map/plot logistics associated with survey order- i.e. low -> high elevation, starting at lower latitudes.
May 1-15 – reminder to landowners. Check road conditions; adjust survey logistics to suit.
May 15-30 – ready to start based on leaf-out. Orientation.
May 30-July 15 - Field work. Data entry only with downtime.
August 15- data entry complete
August 30- interim report due to department with preliminary analysis
Fall 2016 – Final analysis. Damage control if needed.
Dec 2016 – writing, collaboration.
January – submit for review.
Spring2017 – edits completed, hopefully saccepted for publishing.
Budget
Item
Duration (days)
Rate
tech 1 (incl data
entry also)
May 1- July 15 (50)
16 (5 x 8 hrs or 4 x 10h)
6400
6400
Field technician 2
May 15 – June 15 (
13
2080
2080
Field vehicle
May 15 – June 21
28$/day
1036
1036
Mileage in field
40-80 miles/workday
.60 / mile avg 10$/day
500
500
1000
1000
Overhead (work
comp)
cost 2012
cost 2016
Equipment:
Measuring, batteries, notetaking, 4
GPS units (1 spare)
60 per person
980
180
Bear spray
Quantity: 6
33 ea
198
198
Travel (Bzn to
west)
10
$13.14 @ 20mpg, 189 miles
131.4
131.4
Lodging /
camping
Per diem
For 3, location t.b.d.based on need
15-70/night ; avg 40
2000
2000
If / as needed
15?
500
500
Maps
Quantity: 12 topos
Topos, x 4 sets
338
200
Smaller GIS maps
Also 4 sets.
200
proposal lump sum
30,000
wellington
stipend
communication
For correspondence - mail, cell
phone
30,000
150
150
45513.4
14175.4
Sum = 59688.8
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