Evaluating Community Evenness as a Function of Environmental
Gradients: Do Trends in Evenness Occur?
Kristin Williams
Montana State University-Department of Ecology
Bozeman, MT
Advisor: Dave Roberts
Montana State University-Department of Ecology
12/11/09
Abstract
Species evenness, the complement of species richness to diversity, refers to the way abundance is
distributed among the species in a community. Many studies have sought to describe the evenness distribution
within communities based on ecologically and statistically-based models (i.e. Geometric, Broken Stick, General
Lognormal, etc…) of species abundance distribution. Very few studies, however, have looked at the
distribution of evenness, as a community structure statistic, across environmental gradients. This is odd, as
evenness is an index of community structure from which general patterns of community structure, regardless of
species composition, might be inferred (Mason et. al., 2002). If communities with strong competitive
interactions between species are more uneven than communities with weak competitive interactions between
species (Cotgreave and Harvey, 1994), it would be reasonable to expect uneven communities to occur under
similar, or at least identifiable, environmental conditions.
The first step in this analysis will be to classify, using existing classifications where applicable and
creating new classifications where necessary (>8,000ft.), the distinct vegetation community types observed in
the Beartooth Mountain study area as detected by 210 sample plots collected during the 2008 and 2009 field
seasons. An evenness statistic will be calculated for each plot using Hill’s ratio. Sample plot evenness values
will then be averaged according to: 1) community type membership, 2) ecosystem type membership, and 3)
lifeform category membership by community. ANOVA analysis will be used to determine whether differences
between ecosystem type units, communities within each ecosystem type unit and lifeform categories between
communities of the same ecosystem type unit are detectable. Regression analysis will be used to determine
whether trends in community and lifeform category evenness distribution are correlated with measured and
modeled environmental gradients.
Table of contents
1) Abstract
2) Introduction
3) Literature Review
a) Concept of Evenness
b) Choosing an Appropriate Evenness Statistic
4) Approach
5) Measurement Methods
a) Vegetation Community Plots
i) Plot Size
ii) Species Occurrence and Abundance Data
iii) Soil Data
iv) Environmental Site Data
v) Material Used
6) Statistical Methods
a) Estimating Total Sample Size
b) Flowchart of Statistical Analyses to be Conducted to Answer Questions of Interest
7) Adequacy of Analysis
8) Timetable
9) Budget
10) Qualifications
11) Implications of Research
Introduction
Biological communities are often described in terms of species diversity, which is generally understood
to be the product of two community components: richness and evenness (Drobner et. al., 1998; Whittaker, 1965;
Tilman, 1996; Stirling and Wilsey, 2001). Species richness refers to the number of different species in a
community large enough to include at least one rare species (Whittaker,1965). Species evenness refers to the
way abundance is distributed among individual species in a community (Whittaker, 1975; Alatalo, 1981).
Communities in which each species present is equally abundant have high evenness and communities where
species differ widely in abundance have low evenness (Smith and Wilson, 1996). Species evenness tells the
biological investigator something about community structure (Bastow, 1991). Intuitively, it seems that
community structure should relate to underlying species competition and facilitation interactions, resource
availability, environmental constraints and/or successional status of the community. Mason et. al. (2002)
concluded that if there are forces (operating via mechanisms such as competition or evolution) that restrict the
ways in which species assemble in communities, there will be patterns of community structure. Most studies to
date have focused on patterns within communities and created models to explain scenarios under which such
structures would be observed given a set of ecological or statistical assumptions. Very few studies, however,
have asked whether observed patterns of vegetation community evenness are associated with large-scale
environmental gradients and, if so, in what ways might these patterns be characterized?
My proposed question of interest is: How is the community structure statistic of evenness distributed
across modeled and measured environmental gradients of the Beartooth Mountain study area in south-central
Montana? I plan on investigating this question from several angles. After calculating an evenness statistic for
each sample plot, I will divide the plots into community type, lifeform type by community and ecosystem type
groups and calculate an average evenness value for each membership scenario. I will use ANOVA analysis to
determine whether or not the averaged evenness values are more different between scenarios than would be
expected if evenness were randomly distributed across scenarios. I will then use regression analysis to look at
the relationships between individual sample evenness statistics and averaged group evenness statistics across
environmental gradients. Discovering underlying relationships, if any exist, between environmental gradients
and community evenness is a step toward developing studies that investigate measureable attributes of evenness
as a changing community diversity component (Pielou, 1965).
The Beartooth Mountain study area is located completely within the boundaries of the Custer National
Forest. It is bordered to the south, southwest and west by the Gallatin National Forest and to the far southwest
by Yellowstone National Park. The southern boundary of the study area is demarcated by the Wyoming state
border. It is an ideal place from which to identify trends in community evenness distribution across
environmental gradients. The study area is primarily comprised of the largest alpine plateau in the lower 48
states, but also contains mid-elevation, forested communities and low elevation grassland/shrubland
communities. As such, a wide range of elevation, slope percent values, aspect values, major geologic units,
daily average temperatures, total precipitation values and annual solar radiation units are represented in the
study area. The alpine ecosystem type unit dominates the southern portion of the study area. The northeast
margin is characterized by steep slopes that quickly descend from the top of the alpine plateau to the sprawling
and significantly more temperate foothill ecosystem below. There is also a distinct moisture gradient present,
ranging from generally wetter conditions in the northwest to generally drier conditions in the southeast.
Geologically, the core of the Beartooth Mountains is dominated by Precambrian granitics and is flanked
to the northeast by a series of Cambrian to Cretaceous sedimentary rocks. The Cretaceous volcanic rocks of the
Slide Mountain Formation occupy the northwestern arm of the study area and the Lindley conglomerate
member of the Paleocene Fort Union formation occurs in a small portion of the southeastern portion of the
study area. Most recently, Pleistocene glaciers receded toward the northeastern boundary of the Beartooth
Mountains leaving cirque basins, glacial troughs, glacial deposits, lateral moraines along major drainages and
outwash till along the northeastern flank in their wake. Major topographic features include: high elevation
alpine turf plateau, timber plateaus, glacial troughs and U-shaped valleys, glacial cirques, stream dissected
mountain slopes, granitic glacial till deposits, outwash till deposits/glacio-fluvial fans and up thrust limestone
blocks, hogbacks and sandstone-shale-limestone dipslope-scarp complexes.
The sample plot data to be used for this analysis was collected during the 2008 and 2009 field seasons
for the purposes of a United States Forest Service (USFS) and Natural Resource Conservation Service (NRCS)
collaborative inventory/mapping project called a Terrestrial Ecological Unit Inventory (TEUI). TEUI’s are an
attempt to produce large scale, consistent and integrated ecosystem inventory, community/habitat type
classification and mapping management tools for all public lands nationwide. While sampling efforts for the
project began in 2004, plots from the2004, 2006 and 2007 field seasons often had incomplete species lists and
will not be used in this analysis.
Literature Review
Concept of Evenness:
Studies of evenness within communities have generally attempted to fit dominance/diversity data to
models, such as the Geometric Series, the Lognormal Distribution, the Broken Stick Model, the Balls and Boxes
Model, and several others (Whittaker, 1965; Cohen, 1968). Resource-apportioning models generally apply over
ecological and evolutionary time. In ecological-time models, species abundances are seen as the result of
competition and other ecological interactions; in evolutionary-time models abundances are seen as innate
properties of the species and a result of co-evolution (Hulbert, 1971). The broken stick, balls and boxes,
lognormal and geometric models are all examples of resource-apportioning models..
The broken stick model assumes that some critical abundance limiting factor in the environment is fixed
at quantity ‘s’ units, that n-1 points could be uniformly randomly distributed between zero and ‘s”, and that the
critical factor is subsequently divided into ‘n’ intervals. The order statistics of interval size are the interval
lengths rearranged in order of increasing size. The model predicts that the ranked average abundances of the
species will be proportional to the expected values of the order statistics of interval size. The biological
interpretation of this model has been to assume that species partition the available, fixed supply of a critical
factor into mutually disjoint, exhaustive subsets (Cohen, 1968). Cohen (1968) also states that if the model is to
have any usefulness, the critical factor must be some measurable dimension of species’ niches.
The balls and boxes model likens the environment to a target of n boxes. Each box represents a
“subniche.” The set of all boxes occupied by balls of a species at the end of the game is that species’ niche.
The ‘n’ species distribute balls into the boxes until each species’ “niche” contains a number of subniches
different from the number of subniches in the niche of each other species. The balls and boxes model predicts
that the ranked average abundances of the species will be proportional to the ranked expected values of the
numbers of balls thrown by each species. The simplified biological interpretation of this model is that the
principle of competitive exclusion is satisfied by letting species niches overlap as long as there is at least one
subniche they do not have in common. This model requires a weaker form of exclusion than the broken-stick
model, but leads to the same predictions of ranked average abundances.
Essentially, the Broken Stick model assumes that only one stick is being broken while the Balls and
Boxes model assumes that there are many sticks being broken, but each species will get a larger portion of one
broken stick and smaller or no portions of the others. Cohen (1968) found that the broken stick model, balls and
boxes model and geometric series model all had the same results even though the assumptions for each are very
different (Cohen, 1968).
According to the Gause theory of competitive exclusion, no two species in a stable community will
occupy the same niche and compete for the same environmental requirements in the same part of intra
community space at the same time (Whittaker, 1965). If this is the case, the models above explain a fictitious
environment. Whittaker (1965) defines competition as “the situation in which, (i.) environmental resources are
limited in amount, (ii.) each species population increases to a maximum determined by the resources available
to it, (iii) amounts available to a given species are affected or determined by the use of these resources by other
species of the community, and (iv) species populations are consequently limited by the presence of other
species, a limitation which is often mutual.” Additionally, each species will occupy the niche space to which it
is best adapted as extensively and densely as competition and environmental limitations will allow and the
fraction of environmental resources utilized along with the fraction of total community production realized by
each species should be closely related to the fraction of niche space occupied (Whittaker, 1965).
While it is very difficult to pinpoint the critical resource/resources or species’ interaction processes
responsible for species niche distribution in a single community, evaluating the distribution of evenness across
ecosystem types, community types, community lifeform categories and environmental gradients may serve to
illustrate more general trends of niche distribution and/or resource utilization. It will be critical to search for
trends between all group memberships and of all group memberships across environmental gradients because
“there is no reason why species-diversity relations for different strata or fractions of the community, subject to
different environmental factors and modes of population limitation, should parallel one another, and they often
do not (Whittaker, 1965).”
Choosing an Appropriate Evenness Statistic:
Many indices have been proposed to calculate evenness. The most popular of which are: the reciprocal
of Simpson’s Index, Shannon’s-Weiner diversity index (H’), Hill’s Ratio (N2/N1) and eveness (J’) Camargo’s
E. Different indices measure different aspects of the partitioning of abundance between species in a
community and particularly differ in their propensity to include or to exclude the relatively rare species (Hill,
1980). Simpson’s index, for example, is sensitive to the abundance of the more plentiful species in a sample
(Whittaker,1965; Hill,1980).
Whittaker (1965) said that the evenness statistic needs to be characterized both
by the dominance concentration and species richness. Camargo (1995) objected to the use of Simpson’s and
Shannon-Wiener’s diversity statistics because of their dubious ecological interpretation and sensitivity to the
relative abundances of dominant species, particularly claiming that Simpson’s (1949) index “should be regarded
as a deficient statistic of species uniformity rather than a statistic of species dominance [evenness] (1995).”
Most people have forsaken J’ because it is mathematically positively correlated with species richness (Alatalo,
1980).
Routledge’s (1983) provided three requirements of a sufficient evenness statistic: 1) it should be
decreased by marginally reducing the abundance of the rarest species in the community, 2) it should also be
decreased by the addition of a further exceedingly rare species to the community, and 3) it should depend
continuously upon the proportional abundance of any species in the community. To date no evenness ratio
meets all three requirements. Most commonly, the 1st and 2nd are met. Hill’s ratio satisfies the first two criteria
and remains stable irrespective of species richness (Alatalo, 1980; Hill, 1973; Tallie, 1979). This is because
Hill’s ratios are calculated using species directly as units, thus escaping the pitfalls of logarithms (Alatalo, 1980;
Camargo, 1995). Additionally, the sampling bias of Hill’s Ratio is much smaller for Hill’s number N1 and N2
because they are not equally sensitive to the exclusion of the rarest species (Camargo,1995). This is important
since species richness is often underestimated resulting in an overestimation of evenness. Hulbert (1971) stated
that “differences in species richness pose no problem to statistical comparisons of species evenness so long as V
indices are used.” Hill’s ratio is a V index (Hulbert, 1971). Based on the conclusions drawn from previous
research pertaining to the applicability, appropriateness and behavior of various evenness indices, for the
purposes of this analysis I will calculate evenness using Hill’s unmodified ratio.
The unmodified Hill’s Ratio: (N2/N1)
p1,p2, p3……pn : these denote the proportional abundances (% cover of each
species to total % cover of the sample plot OR % cover of each species to total %
cover of lifeform category) of n species
N2= 1/Ʃpi² : this is the reciprocal of Simpson’s index and the probability of
interspecific encounter divided by the probability of intraspecific encounter.
N1= expH’, antilogarithmic Shannon’s entropy, where H’= -Ʃpi ln(pi)
No=species richness
Approach
The goal of this project is to use the 2008 and 2009 Beartooth Mountain Study Area TEUI data to: 1)
create a vegetation community type classification key for alpine communities (those >8,000ft.) of the study
area, 2) assign communities below 8,000 ft. to appropriate existing vegetation community classifications and
modify existing classifications where necessary to reflect locally specific variations, and 3) calculate sample
plot evenness, averaged community evenness and averaged ecosystem type evenness in order to answer the
questions of interest. The questions of interest are:
1.)
Does the averaged community evenness vary by ecosystem type unit more than would be expected if
evenness were distributed randomly across ecosystem type units?
Ho1: Average evenness is the same for each of the three broad ecosystem-type units.
Ha1: Average evenness is different for one or all of the three ecosystem types.
2.)
Does the averaged community evenness vary by community type more than would be expected if
evenness were distributed randomly across community types?
Ho2: Average community evenness is the same for all community types.
Ha2: Average community evenness of at least one community type is different.
3.)
Are there differences in averaged lifeform category evenness distribution between community types
of the same broad ecological type unit? Whittaker (1965) stated that “there is no reason why
species-diversity relations for different strata or fractions of the community subject to different
environmental factors and modes of population limitation, should parallel one another; and they
often do not.”
Ho3: Lifeform category evenness will remain constant between community types of the same
broad ecological-type unit.
Ha3: Lifeform category evenness will vary between community types of the same broad
ecological-type units.
4.)
Are observed differences in average evenness of ecosystem type unit and/or community type
associated with one of the measured or modeled environmental variables?
Ho4: Averaged community type evenness and/or averaged ecosystem type evenness are
distributed randomly with respect to measured and modeled environmental variables.
Ha4: Averaged community type evenness and/or averaged ecosystem type evenness are more
significantly associated with one or more of the environmental variables than would be expected
if averaged community and ecosystem evenness distribution were random in relation to all
environmental variables.
Data must be collected in an integrated manner and decisions rules pertaining to sample selection must
be established prior to beginning sampling efforts. Vegetation, soils, landform, surficial geology, and bedrock
geology will be fully described for every sample site used to establish an ecological type and document a map
unit. At each sample site, the plot should be uniform in environment and vegetation, large enough to include
the normal species composition of the stand and have homogenous vegetation throughout the sample plot.
Obvious ecotones, or sites lacking environmental uniformity, will not be suitable for sampling. In addition to
identifying a target GPS point for each sample plot, additional information including: target slope percent,
target aspect value and target community type will be identified. If, upon arriving at the target GPS point, as
identified by latitude, longitude and elevation, it is obvious to the botanist and soil technician that the
community occurring at the target GPS point is an ecotone between two communities, is not representative of
the dominant community type for the specified target slope percent and aspect value, then the field technicians
may move the sample plot to the closest plot center that reflects the dominant community type vegetation for
the target slope and aspect value. If no such representative community can be found, a community sample plot
will not be collected.
Plots will be identified using a stratified sampling plan, where the study area is initially divided into
“ecological units.” Each ecological unit will represent a unique, large-scale, geologic type and topographic
landform combination. All possible combinations are represented by a distinct ecological unit. Within these
units, the plot centers for 405m.² circular plots (11.35m. radius) will be selected to reflect dominant vegetative
community types. Dominant community types within each unit will be identified using aerial photos, satellite
imagery and potential indicated by topographic maps. Plots will be distributed within each ecological unit to
represent the diversity of slopes, elevations, aspects and dominant community types available for that ecological
unit and were also distributed spatially across the study area for each ecological unit to represent the larger
range of environmental gradients.
Two hundred plus vegetation community plots will be collected during the 2008 and 2009 field seasons.
These data will be used for this analysis. Information collected on each of these plots will include: a complete
species list, ocular aboveground cover estimates for each plant species, elevation, slope and aspect. Major
geologic units have already been mapped (Vaseth and Montagne, 1980) and modeled values of relative
effective annual precipitation, total annual precipitation, average daily temperature and units of solar radiation
loading (what units) will be obtained from NRCS models and used in this analysis.
Averaged community type evenness will be calculated by averaging the sample evenness value of all
samples in that community type. Averaged ecosystem type evenness will be calculated by averaging the sample
evenness values of all samples in each ecosystem type. Average lifeform category evenness by community will
be calculated by averaging the sample evenness values of each community type and ecosystem type by lifeform
category. The three broad ecosystem type units represented in the study area are: low elevation
shrubland/grassland communities, mid elevation forest communities and high elevation alpine turf
communities. All community plots with tree cover >10% will be included in the mid-elevation, forested
ecosystem type, grassland/shrubland communities below timberline will be grouped in the low elevation
grassland/shrubland ecosystem unit and grassland/shrubland/turf communities above timberline will be grouped
in the alpine ecosystem unit.
Measurement Methods
Vegetation data will be collected using ocular macroplots and variable-radius plots. As a rule of thumb,
ocular macroplots need to be big enough that enlarging the plot would add no or very few species in order to
accurately estimate community evenness. Variable-radius plots are used to determine density and basal area of
live trees and snags on the plot. Soil pedon data will be collected according to NCSS standards and using codes
and procedures in the NRCS publication Field Book for Describing and Sampling Soils (Shoeneberger et al.,
2002) and sufficient for soil classification. Geologic information will be collected according to the standards in
the Forest Service Manual 2881, surficial geology origin and kind using the terms and definitions listed in the
FRIS Terra data dictionary and the depth to bedrock will be recorded where bedrock encountered. Geomorphic
information is collected to the standards in A Geomorphic Classification System (Haskins et al. 1998). At a
minimum, plot locations will be assigned a classification from the list of hierarchy geomorphology classes in
Appendix E to describe the dominant, active landform-process relationship.
Vegetative Community Plots:
Plot Size
Each sample plot will be a minimum of 405 m.², or a tenth of an acre. Hulbert (1971) states that “as
with species richness, comparisons of species evenness are meaningful only when all collections are adjusted to
a common size and that when other things are equal, larger collections will have more rare species and always a
lower V and V’ value. Plots with greater than 10% tree cover will be considered “tree plots” and subjected to
plot size adjustments in relation to slope percent. Once a plot center is established and it has been determined
whether the community being described is a tree or non-tree dominated community, the margins of the plot are
marked by running out a meter tape to the appropriate radius length and marking the perimeter with flags. The
radius length for all non-tree community plots will be 11.35 m. (405 m.²). In communities with >10% tree
cover, the initial macroplot radius length of 11.35 m. will be increased in length as slope percent increases. The
conversion table is shown below (Macro Plot Radii in m. corrected for slope). For example, if the slope of a
tree-plot is between 18 and 22%, the macroplot radius would be 11.46 m.. This adjustment geometrically
calibrates tree abundance estimates to abundance estimates of forbs, grasses and shrubs. Tree plots will
additionally contain a microplot, also centered at plot center, designed to estimate stand regeneration.
Microplots will also be adjusted in relation to slope percent (Micro Plot Radii in m corrected for slope).
Macro Plot Radii in m.
corrected for slope (1/10 acre,
~405m²)
r
11.35
11.40
11.46
11.52
11.58
11.64
11.67
11.73
11.79
11.85
11.91
11.94
12.01
slope %
0-9
10--17
18-22
23-26
27-30
31-33
34-36
37-39
40-42
43-44
45-47
48-49
50-51
r
12.07
12.13
12.16
12.22
12.28
12.31
12.37
12.43
12.49
12.52
12.58
12.65
12.68
slope %
52-53
54-55
56-57
58-59
60-61
62-63
64-65
66-67
68-69
70
71-72
73-74
75
r
12.74
12.80
12.83
12.89
12.95
12.98
13.04
13.07
13.13
13.19
13.22
13.28
13.32
slope %
76-77
78-79
80
81-82
83
84-85
86
87-88
89
90-91
92
93-94
95
Species Occurrence and Abundance Data
A complete species occurrence list will be collected within the boundaries of the community macroplot.
Abundance of each species will be measured using canopy cover estimates. Since plants show huge plasticity
between individuals and it is often impossible to define an individual, measuring percent cover as an indicator
of biomass production is a useful for plant diversity/dominance measurements (Bastow, 1991). Canopy cover
will be defined as the percentage of ground covered by a vertical projection of the outermost perimeter of the
natural spread of foliage of plants where small openings within the canopy are included in the cover estimate.
Only species, individuals and layers occurring within the plot boundary will be recorded and their cover
included in cover estimates. Cover for each species will be estimated as follows:
• Use 0.1 as “trace” for items present but clearly less than 1 percent cover (1% = 1.1m. radius ).
• Estimate to the nearest 1 percent between 1 and 10 percent cover.
• Estimate to at least the nearest 5 percent between 10 and 30 percent cover.
• Estimate to at least the nearest 10 percent for values exceeding 30 percent cover.
After estimating total cover for each species, cover will be estimated by lifeform categories and
subcategories using the same cover estimation scheme described above. Lifeform categories include: trees,
shrubs, dwarf shrubs, forbs and graminoids. The tree lifeform category will be further divided into strata
subcategories, including: total overstory, total regeneration, main canopy, supercanopy, subcanopy, saplings
and low and high seedlings. Specific tree layer and lifeform category definitions are provided below.
1.) Tree Layers:
Overstory (TO): The overstory layer includes all trees greater than or equal to 5 meters in height that make up
the forest canopy. In dwarf tree stands, the overstory consists of trees that have attained at least half of their
sites specific potential height growth and make up the forest canopy.
Regeneration (TR): The regeneration layer includes all trees less than 5 meters in height. In dwarf tree stands
the regeneration layer includes trees that have attained less than half of their site-specific potential height
growth and are clearly overtopped by the overstory trees.
The overstory will optionally be subdivided into the following sublayers, if they occur, to describe stand
structure in more detail:
Main canopy (TOMC): The dominant and co-dominant overstory trees that receive direct sunlight from above
and make up the majority of the forest canopy.
Supercanopy (TOSP): Scattered overstory trees that clearly rise above the main canopy.
Subcanopy (TOSB): Overstory trees that are clearly overtopped by and separate from the main canopy, but are
larger and taller than the regeneration layer.
The regeneration layer will be subdivided into the following sublayers:
Saplings (TRSA): Regenerating trees greater than 1.4 meters (4.5 feet) in height, or regenerating dwarf trees
greater than 1 meter in height.
High Seedlings (TRhSE): Regenerating trees between .5 meter and 1.4 meters in height.
Low Seedlings(TRlSE): Regenerating trees less than .5 meters in height.
2.) Lifeform categories:
Total vegetation cover. Record the percentage of the ground covered by a vertical projection of the outermost
perimeter of the natural spread of foliage of all vascular plants within the sample unit (plot or transect).
Tree cover. Record the total cover of trees, defined as woody plants that generally have a single main stem,
have more or less definite crowns, and are usually equal to or greater than 5 meters in height at maturity.
Shrub cover. Record the total cover of shrubs, defined as woody plants that generally have several erect,
spreading, or prostrate stems which give it a bushy appearance, and are usually less than 5 meters in height at
maturity.
Dwarf shrub cover. Record the total cover of dwarf shrubs, defined as caespitose, suffrutescent, matted, or
cushion-forming shrubs which are typically less than 50 cm tall at maturity due to genetic and/or environmental
constraints .
Herb cover. Record the total cover of herbs, defined as vascular plants without significant woody tissue above
the ground, with perennating buds borne at or below the ground surface. Includes: forbs, graminoids, ferns, and
fern allies. Herb cover must be equal to or less than the sum of graminoid cover and forb cover.
Graminoid Cover. Record the total cover of graminoids, defined as flowering herbs with relatively long
narrow leaves and inconspicuous flowers with parts reduced to bracts. Include grasses, sedges, rushes, and
arrowgrasses.
Forb Cover: Record the total cover of forbs, defined as spore-bearing herbs or flowering herbs with relatively
broad leaves and/or showy flowers). Include ferns or fern allies.
In addition to estimating the total cover of each species and lifeform category/subcategory, predominant
plant heights for each species and category/subcategory will be recorded. Predominant species height is the
prevailing upper height for each species to the nearest tenth of a meter. Tree height for the overstory and
regeneration layers will be recorded to the nearest meter and tenth of a meter, respectively. Height of overstory
layers will be determined by selecting a representative tree in each and measuring its height using a clinometer
and measuring tape. The representative tree for the overstory layer must be in the main canopy. Predominant
plant height will also be recorded for each optional sublayer. The possibility of overlap between sublayers
requires that overstory and regeneration cover for each tree species be estimated or measured directly, not
calculated by summing the sublayer cover values. First, canopy cover will be estimated by sublayer and then
the estimate of the overlap (if any) between sublayers to will be subtracted to derive the canopy cover of
overstory and regeneration layers.
Soil Data:
A soil pit will be dug near the center of each vegetative community plot in an area that appears to be
representative of the soils supporting vegetative community development. All soil pits will be dug to a depth of
1 m. where possible. If lithic contact is made prior to reaching a depth of 1m., the type of and depth to contact
will be recorded. A high percentage of cobbles and stones in lower layers will not be sufficient reason to stop
digging prior to reaching 1 m. depth. However, in order to be efficient in our data collection efforts, a
maximum time limit of two and a half hours will be set for the digging of each soil pit. If at the end of two and
a half hours, the soil technician has not been able to dig to 1 m., then the depth reached will be recorded along
with a description, both written and sketched, of the conditions making it impossible to reach a meter within the
allotted time. The overriding goal is to reach 1 m. or lithic contact in each soil pit.
Once the pit is dug, the soil horizon layers will be identified and demarcated using nails. Each horizon
will be measured from the top to the bottom and a height will be recorded. Each horizon will also be textured,
colored (either wet or dry but must be indicated) and pHed individually. Textures must include texture
modifiers, such as: percent gravel, cobble, stone, boulder, etc. Additionally, root pore percentage, clay film
percentage, calcium carbonate accumulation and redoximorphic features will be observed and their abundance
estimated for each horizon. Where redoximorphic and/or calcium carbonate features are observed, the depth to
the beginning and end of that feature will be recorded along with color classes specific to the feature.
Once the horizons have been textured, colored, pHed and otherwise described, the soil will be classified
using the NRCS soil classification key and the classification will be recorded. In order to classify the soil, the
soil moisture and temperature regimes must also be determined. A picture of each soil pit, including horizon
demarcation nails and a hanging 1 m. long nylon strap divided into 1/10 m., will be taken and the picture
number recorded. The hanging nylon strap will serve as proof that the target depth of 1 m. was achieved. It is
important to note that all of the above should be done in the field at the soil pit.
Before filling in the pit, the soil technician will collect a soil sample from each horizon. The sample
should be representative of the horizon and should include a sample of any gravel or cobble common to the
horizon. If calcium carbonate accumulations or redoximorphic features were encountered, these features should
also be included in the sample. A sample of the organic horizon will also be collected. These soil samples will
be taken back to the lab and organized into soil boxes. Each soil box will be representative of the sample plot
and each compartment within the box will be representative of a described horizon. The horizons will be
organized in the box from left to right in relation to the depth of the horizon in the soil pit, where the deepest
horizon goes in the leftmost compartment and the organic layer in the rightmost compartment. A total of ten
representative samples will be sent to a lab for analysis. The additional lab analysis will serve as a quality
control measure aimed at confirming the textures, colors, pHs and soil classifications determined by the soil
technician in the field.
Environmental Site Data:
In addition to vegetative community data and soil data, site description and documentation variables will
be recorded. They include: GPS location, Potential Natural Vegetation classification, plot photo number and
description, elevation (as recorded by the GPS), slope (%), aspect (degrees - field declination = 0), horizontal
slope shape and vertical slope shape (diagramed below), slope convexity, slope position and the slope position
modifier. Slope complexity will be described as either simple (S), indicating a slope that is linear, convex or
concave in shape or complex (C), indicating broken, undulating, or patterned slope shape. Slope position will
be described by one of five codes: summit (SU), shoulder (SH), backslope (BS), footslope (FS), toeslope (TS).
Each slope position will be modified by one of the following descriptors: lower (LR), mid (MD) or upper (UP),
where the position modifier indicates whether the plot position is in the lower, middle or upper elevations,
respectively, of the slope position recorded.
Materials Used:
GPS units will be used to record coordinates. A stake will be used to mark plot center and to secure the
meter tape while the radius is flagged. Plots will be photographed from the perimeter toward the staked center
using a digital camera. Slope percent will be measured using a clinometers and will be measured from the
uppermost perimeter slope to the lowermost perimeter slope. Aspect will be recorded using a compass with the
declination set to 0 for consistency purposes. Tree height will be measured using a clinometers and meter tape.
Plants of Wyoming (Dorn, 1977), Plants of Montana (Dorn, 1984) and Plants of the Pacific Northwest
(Hitchcock and Cronquist, 1973). Plants that are unknown will be collected, recorded and pressed in a plant
press. Soil pedon pits will be dug using a steel, Montana Sharpshooter © shovel. A soil sifter will be used to
prepare soil for texturing, a pH indicator kit will be used to pH soils for soil classification, and 5M hydrochloric
acid will be used to test the effervescence of soil. The rest of observation will be made using ocular estimation
techniques described above.
Several vegetation community classifications pertinent to the study area have been completed.
The Shoshone National Forest, neighbor to the Beartooth study area to the south and southeast and similar to
the Beartooth study area in terms of ecosystem type units and vegetation community types likely to present,
recently completed a classification type key. The sample stratification for the Shoshone community
classification was also organized according to TEUI vegetation and NRCS soil sampling protocol and will serve
as the most appropriate pilot study for use in estimating sample size requirements. Other useful community
classifications include: Grassland and Shrubland Habitat Types of Western Montana (Mueggler and Stewart,
1980), Plant Community Classification for the Alpine Vegetation of the Beaverhead National Forest, Montana
(Cooper et al, 1997), Habitat Types of Eastern Idaho and Western Wyoming (Steel et. al., 1963), Habitat Types
of Montana (Phister et. al., 1977), The Alpine Vegetation of the Beartooth Plateau in Relation to Cryopedogenic
Processes and Patterns (Johnson and Billings, 1962). Other alpine classifications describing alpine vegetation
of the northern rocky mountains include: Glacier National Park (Bamberg and Major, 1968), the Big Snowy
Mountains and Fling Creek Mountains (Bamberg and Major, 1968) and Field Guide to the Willows of EastCentral Idaho (Stephen Brunsfeld, 1985).
Statistical Methods
The first analysis to be conducted is the identification and description of alpine (>8,000ft.) vegetation
community types of the Beartooth Mountain study area. The community classification conducted on the
Shoshone National Forest, neighbor to the Beartooth study area to the south and southeast and similar to the
Beartooth study area in terms of ecosystem type units and vegetation community types likely to present, will
serve as a good model for the analysis to be conducted here. The sample stratification for community
classification was conducted using TEUI vegetation and NRCS sampling protocol, as this project proposes to
do. The same statistics that will be calculated for the evenness analysis of the Beartooth Mountain communities
and ecosystem types will first be calculated using data from the Shoshone National Forest TEUI database. By
attaining an estimate of variance for all statistics from a dataset expected to be reasonably similar in community
and ecosystem type and structure, an estimate of minimal necessary plots may be made. The first three
questions of interest will be answered using a one-way ANOVA. ANOVA tests assume that sample cases are
independent, samples come from a normally distributed population and samples have equal variance. If these
assumptions cannot be met using our data set, it may be necessary to use a Kruskal-Wallis ranked sum test
because the assumptions of normal distribution and equal variance are relaxed. The primary statistical test to be
used to model sample, community and ecosystem evenness distribution across measured and modeled
environmental gradients will be multiple linear regressions.
Estimating Necessary Total Sample Size and Sample Community Replication Size:
Calculate Shoshone National Forest community variance using a
distance/dissimilarity matrix, such as Bray-Curtis. Obtain estimates of within and
among community species occurrence and abundance variance. Also calculate the
mean and variance of evenness distribution within and among communities and
ecosystem type units.
Calculate the minimal number of plots
needed to accurately define dominant
community types from Shoshone within
and among community occurrence and
abundance variance.
Calculate the minimal number of
plots needed to recognize
variation in sample, community
and ecosystem type units.
Flowchart of statistical analyses to be conducted to answer questions of interest:
Classify Community Types
Divide
Plots into
Ecosystem Units
ANOVA
High Elevation
Alpine Turf:
Calculate
Average
Evenness
Community
Type “1”:
Calculate
Average
Community
Evenness
ANOVA
ANOVA
Community
Type “2”:
Calculate
Average
Community
Evenness
HIGH ELEVATION
ALPINE
Mid Elevation,
Forested: Calculate
Average Evenness
Community
Type “3”:
Calculate
Average
Community
Evenness
Community
Type “4”:
Calculate
Average
Community
Evenness
MID-ELEVATION
FORESTED
Low
Elevation,
Grassland/
Shrubland:
Calculate
Average
Evenness
Community
Type “5”:
Calculate
Average
Community
Evenness
ANOVA
Community
Type “n”:
Calculate
Average
Community
Evenness
LOW ELEVATION
GRASSLAND/SHRUBLAND
1.) Compare community average evenness within ecosystem types using ANOVA analysis to
see if there are statistically significant differences in evenness between communities that
may be drastically within ecosystem types that may be drastically affecting the differences
that may or may not be observed between ecosystem type average evenness.
2.) Model community average evenness of all ecosystem types across selected environmental
variables using multiple linear regression.
3.) Calculate average lifeform evenness for each community type and repeat steps 1 and 2
above, substituting “average lifeform evenness by community” for “community average
evenness” for all tests. An example of average lifeform evenness by community would be
the average evenness of each lifeform category (i.e. tree species, shrub species, grass species
and forb species) in Pinus flexilis/Leucopoa kingii vegetation community types.
Adequacy of Design
The Shoshone National Forest vegetation community classification and sampling stratification was used
as the pilot study for designing the sample stratification for the Beartooth Mountain vegetation community
classification project. The number of samples per ecological unit is proportional to the percent of the study area
defined as that ecological unit. The sample variance of each community type on the Shoshone National Forest
will be calculated to estimate the number of plots needed to accurately identify dominant community types in
the Beartooth Mountain study area. If enough plots are collected to confidently identify community types, then
an appropriate level of replication should also be met for the evenness analysis. As the alpine ecosystem unit
comprises a larger area of the Beartooth Mountain study area than the mid-elevation forested ecosystem or
grassland/shrubland ecosystem, there will necessarily be higher replication in this ecosystem type. However,
community level replication should be approximately evenly distributed across community types based on the
sample stratification. To confirm that enough plots have been collected at all membership levels, the variance
from the Shoshone pilot study will be calculated for all membership levels and minimal sample sizes will be
determined for each.
Timetable
January
February
March
April
May
June
July
August
September
October
November
December
2008
Preliminary planning phases.
Delineation of ecological
units.
Stratification of Study Area
and Creation of Sample Plan.
Begin Field Work
Field Work
Field Work
Plant Identification
Plant Identification
Plant Identification
Done with Plant Identification
2009
Data Entry
Data Entry
Plan 2009 field season by
checking current plot
completion with overall goals.
Begin Field Work
Field Work
Field Work
Preliminary Data Analysis
Preliminary Data Analysis
Plant Identification
Plant Identification
2010
Done with Plant Identification
Data Entry
Data Entry
Community Classification
Evenness Analysis
Write Thesis Project
Write Thesis Project
Finish Thesis Project
Budget
Amount
Salaries
$1,500 (stipend)
$1,500 (help with plant I.D.)
Benefits
$250
Equipment
Equipment provided by TEUI project:
backpacks, compasses, handlenses, tatums,
clinometers, dbh tape, meter tape, GPS units,
water filters, cameras and necessary software
Travel
$800 (pool vehicle)
$400 (personal vehicle)
Supplies and
$300 (paper)
Services
$150 (write in the rain paper)
$100 (pencils, erasers, pens, markers, etc..)
$150 (copies of data sheets)
Indirect
+ 18% for MSU overhead for offices and
Costs/Overhead
computers
Total
Number of Months
25
1
3
Total
$37,500
$1,500
$750
5
1
FREE
$4,000
$400
$700
$1450
$46,303
Qualifications
Prior to beginning a graduate program studying vegetation ecology at Montana State University, I
accumulated 9 seasons of field and office experience working as a vegetation ecologist/botanist for various
agencies, including: the Forest Service, Idaho Fish and Game and University of Idaho Department of
Agriculture. These positions taught me the importance of complete and consistent data collection, organization
and analysis. I have worked as a crew leader and a crew member and feel confident that I can organize field
efforts, maintain a budget and quality control data collection, database entry and data analysis efforts. As a
graduate student, I have taken several upper division statistics and modeling courses that will enable me to
perform the statistical analysis outlined in this report.
Implications of Research
In the past, the statistic of evenness has been primarily used to describe the distribution of species
abundance within a given community. Researchers have looked for trends between evenness, richness and
production in an attempt to model the underlying relationships between realized niche space and resource
availability/species interactions. While it has remained nearly impossible to pinpoint the critical
resource/resources or species’ interaction processes responsible for species niche distribution in a single
community, the evaluation of evenness distribution across ecosystem types, community types, community
lifeform categories and environmental gradients may serve to illustrate more general trends of niche distribution
and/or resource utilization. If community evenness is consistently lower at certain ranges of given
environmental gradients or in an ecosystem or community type, two conclusions are possible: 1) the resources
available are fairly homogenous and/or 2) one species has effectively competitively excluded other species even
if resources are somewhat heterogeneous. Similarly, if community evenness is consistently higher along
sections of particular environmental gradients or in an ecosystem or community type, it is likely that: 1) the
resources are fairly homogenous, and/or 2) evolution has produced a larger number of competing species, among
which no one has so great a competitive advantage over all others (Whittaker, 1985). The investigation into evenness
distribution between lifeform categories of different communities will further tease apart the dynamics of niche
distribution and possibly provide some insight as to the relative importance of resource availability and/or
competition to securing niche space in different communities.
It is my hope that this project will result in valuable ecological conclusions that will expand the frame of
current community ecology research and possibly provide baseline analysis that will prompt future researchers
to further clarify the usefulness of evenness as a community structure statistic. Valery et. al. (2009) stated that
“changes in evenness deserve increased attention because evenness usually changes more rapidly in response to
human activities than does species richness and because evenness changes have important consequences for
ecosystems long before a species is threatened by extinction.” If this is true, it is important to understand what
changes in evenness may result in species extinction and which changes in evenness could be characterized as
repeated results of succession. In order to understand these dynamics, it is necessary to begin to understand the
dynamics that drive niche realization.
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