In cooperation with the National Park Service 

Field-Based Evaluation of Two Herbaceous Plant 

Community Sampling Methods for Long-Term 

Monitoring in Northern Great Plains National Parks 

By Amy J. Symstad, Cody L. Wienk, and Andy Thorstenson 

Open-File Report 2006-1282 

U.S. Department of the Interior 

U.S. Geological Survey 

1
U.S. Department of the Interior 

Gale A. Norton, Secretary 

U.S. Geological Survey 

P. Patrick Leahy, Acting Director 

U.S. Geological Survey, Reston, Virginia 2006 

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Suggested citation:

Symstad, A.J., Wienk, C.L., and Thorstenson, Andy, 2006, Field-based evaluation of two 

herbaceous plant community sampling methods for long-term monitoring in northern Great Plains 

national parks: Helena, MT, U.S. Geological Survey Open-File Report 2006-1282, 38 pages + 3 

appendices. 

Any use of trade, product, or firm names is for descriptive purposes only and does not imply 

endorsement by the U.S. Government. 

Although this report is in the public domain, permission must be secured from the individual 

copyright owners to reproduce any copyrighted material contained within this report. 

2
Contents 

Contents ...............................................................................................................................................................................3

Introduction .........................................................................................................................................................................5

Vegetation in Northern Great Plains Parks................................................................................................................5

Potential Methods for Sampling Plant Community Composition ...........................................................................6

Study Objectives .............................................................................................................................................................8

Methods ...............................................................................................................................................................................8

Study Areas .....................................................................................................................................................................8

Sample Sites....................................................................................................................................................................9

Sampling.........................................................................................................................................................................11

Data Analysis.................................................................................................................................................................12

Results ................................................................................................................................................................................14

Between-Method Comparison of Values Obtained................................................................................................16

Repeatability..................................................................................................................................................................19

Number of Subsamples ...............................................................................................................................................20

Efficiency........................................................................................................................................................................27

Discussion..........................................................................................................................................................................29

Comparison of Values Obtained by Each Method ..................................................................................................30

Repeatability among Observers.................................................................................................................................30

Precision and Number of Subsamples .....................................................................................................................32

Efficiency........................................................................................................................................................................33

Impacts of Sampling on Vegetation ..........................................................................................................................34

Conclusions and Recommendations.............................................................................................................................34

References Cited ..............................................................................................................................................................37

Figures 

Figure 1. Locations of NPS units in the Northern Great Plains I&M Network. The parks used in this 

study are underlined in red.................................................................................................................................. 9

Figure 2. Rank (x-axis)-abundance (y-axis) curves for the 46 plots sampled, by vegetation type. Each 

line-symbol combination within a graph represents a single plot. ............................................................ 15

Figure 3. Relationship between community-level response variable values obtained by the two 

sampling methods. Dashed lines are y=x....................................................................................................... 17

Figure 4. Least squares means and standard errors for log-transformed coefficients of variation 

among subsamples within plots for six response variables, by vegetation type. Different letters 

above symbols within a graph indicate significant (P < 0.05) differences between vegetation types. 

Vegetation type abbreviations follow those in Table 1................................................................................. 21

Figure 5. Species-sample number curves for plots with (A) the lowest and (B) the highest species 

richness from each vegetation type. Solid lines are for the ocular method, dashed lines for the 

point-intercept method. (C) Difference between ocular and point-intercept methods in the number 

3
of species added with an additional subsample. Open symbols are for plots with high species 

richness, closed symbols for plots with low species richness................................................................... 25

Figure 6. Relationship between time to complete each sampling method (ocular = black circles and 

solid line; point-intercept = red triangles and dashed line) and (A) plot species richness or (B) total 

plant cover as measured by the point-intercept method............................................................................. 27

Figure 7. Number of times a method (point-intercept = black bars; ocular = red bars) did not detect a 

species that the other method did, for all 46 plots. ....................................................................................... 28

Figure 8. Photos of plots showing variability in terrain and obstructions to transect tapes. (A)

TPCM03, (B) WPCM09, (C) WPCM12. .............................................................................................................. 32

Figure 9. Suggested modification of transect layout for point-intercept sampling. Red “X” indicates 

permanent marker. Black dots are approximate locations of points, spaced at 1-m intervals. Blue 

boxes are 1-m2 species-occurrence subplots................................................................................................ 35

Tables 

Table 1. Number of sample locations in each vegetation type for each park. ........................................ 10

Table 2. Mean and range (in parentheses) of variables descriptive of vegetation in each vegetation 

type sampled in this study.................................................................................................................................. 14

Table 3. Paired t-test and correlation results for point-intercept vs. ocular sampling methods for six 

community response variables......................................................................................................................... 16

Table 4.Comparison of repeatability for two methods of vegetation sampling based on correlation 

coefficients and paired t-tests.......................................................................................................................... 18

Table 5. Similarity indices and species turnover between first and second sampling for each method 

and double-sampled plot.................................................................................................................................... 19

Table 6. Results of mixed model analysis for effects of sampling method, vegetation type, response 

variable, and their interactions on log-transformed coefficient of variation. .......................................... 20

Tables 7 & 8. Means, standard deviations, coefficients of variation (CV), sample size needed for 

20% precision around mean, and absolute precision attainable with 20 subsamples for the ocular 

(Table 7) and point-intercept (Table 8) methods for six response variables............................................ 22

Table 9. Rate of species added with each subsample for the ocular and point-intercept sampling 

methods, based on species-subsample curves from EstimateS (Colwell 2005) for the most and least 

species-rich plots from each vegetation type. .............................................................................................. 26

Table 10. Standard deviations (mean, minimum, and maximum) of species with 1 and 5% cover from 

ocular sampling method by vegetation type. ................................................................................................. 28

Table 11. Potential cover class scales to use for recording abundance of species in 1000 m2 plot.... 35

4
Field-Based Evaluation of Two Herbaceous Plant 

Community Composition Sampling Methods for LongTerm Monitoring in Northern Great Plains National 

Parks 

By Amy J. Symstad1, Cody L. Wienk2, and Andy Thorstenson2

Introduction 

The Northern Great Plains Inventory & Monitoring (I&M) Network (Network) of the 

National Park Service (NPS) consists of 13 NPS units in North Dakota, South Dakota, Nebraska, 

and eastern Wyoming. The Network is in the planning phase of a long-term program to monitor 

the health of park ecosystems. Plant community composition is one of the “Vital Signs”, or 

indicators, that will be monitored as part of this program for three main reasons. First, plant 

community composition is information-rich; a single sampling protocol can provide information on 

the diversity of native and non-native species, the abundance of individual dominant species, and 

the abundance of groups of plants. Second, plant community composition is of specific 

management concern. The abundance and diversity of exotic plants, both absolute and relative to 

native species, is one of the greatest management concerns in almost all Network parks (Symstad 

2004). Finally, plant community composition reflects the effects of a variety of current or 

anticipated stressors on ecosystem health in the Network parks including invasive exotic plants, 

large ungulate grazing, lack of fire in a fire-adapted system, chemical exotic plant control, nitrogen 

deposition, increased atmospheric carbon dioxide concentrations, and climate change. 

Before the Network begins its Vital Signs monitoring, a detailed plan describing specific 

protocols used for each of the Vital Signs must go through rigorous development and review. The 

pilot study on which we report here is one of the components of this protocol development. The 

goal of the work we report on here was to determine a specific method to use for monitoring plant 

community composition of the herb layer (< 2 m tall). 

Vegetation in Northern Great Plains Parks 

The herb-layer vegetation in the Network parks in which plant community composition will 

be monitored is largely graminoid-dominated, even in areas characterized as shrublands by the 

National Vegetation Classification System. One exception to this generalization is the vegetation 

of black-tailed prairie dog (Cynomys ludovicianus) towns, where forb cover is often greater than 

graminoid cover. In all vegetation types, however, graminoids constitute a major portion of the 

cover. Because of the generally dry conditions in this region (average annual precipitation is 

 

1

 U.S. Geological Survey, Northern Prairie Wildlife Research Center, Black Hills Station 

2

 National Park Service, Northern Great Plains Fire Management Office, Wind Cave National Park 

5
around 38 cm in most parks, but up to 51 cm in the Black Hills parks), herb-layer vegetation is 

rarely over 1 m tall and is often much shorter (<15 cm), particularly in areas where black-tailed 

prairie dogs occur. Total herb-layer cover ranges considerably, with values generally being lowest 

in badlands and under dense pine forest canopy (35-75%) and highest in riparian zones (80-200%). 

In most cases, just one or two species will comprise 50% of the total cover, and the median species 

cover for a typical site (0.1 ha) is less than 1%. These characteristics influence the type of 

sampling that is appropriate for long-term monitoring in these parks. 

Potential Methods for Sampling Plant Community Composition 

In 1959, Daubenmire lamented the lack of standardization of methods used in vegetation 

sampling and analysis (Daubenmire 1959). Not much has changed in the intervening years, despite 

the fact that many investigators have adopted Daubenmire’s described methodology. The main 

reason for this is because the most appropriate method for measuring the relative abundance of 

species in a plant community depends on the objective of the project for which the measurements 

are done, as well as on the type of vegetation being measured. The advantages and disadvantages 

of each method must be weighed carefully when deciding on which method will be used. This is 

especially important when designing a long-term monitoring program because there is a great cost, 

in terms of continuity of data, to changing methods midstream. Also, in contrast to measuring plant 

community composition between experimental treatments, where large differences are often 

expected between the experimental treatments, long-term monitoring must be sensitive to relatively 

subtle changes over time – time in which the people doing the measurements will change. Thus, a 

method must have high repeatability among observers. In addition, the method must be efficient 

enough that a useful amount of data can be collected in a reasonable time, allowing for adequate 

sample sizes. Finally, because the data provided by this monitoring will be used by park staff to 

make resource management decisions, the data must be relevant to those decisions and relatively 

easy to comprehend. Using these guidelines, we evaluated the advantages and disadvantages of the 

four main ways of measuring the abundance of all species within a plant community. This 

evaluation was influenced by a desire to have the resulting methodology compatible with other 

long-term vegetation monitoring programs. 

The four main methods of measuring the abundance of all species in a plant community are 

clipping and sorting biomass, counting stems, calculating frequency of occurrence, and estimating 

cover. We eliminated the first three of these methods from consideration for this program after 

weighing the advantages and disadvantages of all four methods. 

The first method, clipping and sorting of biomass, is a preferred method for sampling plant 

community composition in some circumstances because it reflects the size and abundance of 

individual species. It also represents, to some degree, the amount of energy captured by individual 

species and the plant community as a whole, which is relevant to fire behavior and ecosystem 

processes such as nutrient cycling and support of other trophic levels. However, this method 

requires a lot of time to sort clipped samples in the field, and repeated clipping of the same area 

could affect the values of the variables being monitored. The method also produces a large number 

of samples that require considerable post-field processing (drying and weighing) before any data 

are secured. These issues, as well as logistical problems in transporting a large number of samples 

to sites with adequate drying facilities, make this method untenable for the Network’s long-term 

monitoring program. However, total standing and dead biomass of herbaceous vegetation data may 

be important for some park resource managers because of their relevance to fire fuel loads and 

capacity for supporting large ungulates. If we determine, through further discussions with park 

resource managers, that total herbaceous plant biomass is of sufficient importance, we will develop 

6
a protocol specific to this measurement in the future, possibly by developing biomass-stature 

relationships. 

The second method, counting stems to measure stem density, has the advantage over other 

methods of directly measuring population size. However, stem density does not necessarily 

represent the influence that a species or group of species has on other individuals (i.e., competition) 

or ecosystem processes (e.g., nutrient cycling, energy transfer to other trophic levels) because 

individuals and species differ considerably in their size. In addition, difficulties in determining 

what constitutes an individual stem, particularly when dealing with rhizomatous species and a large 

number of species, makes it likely that counts would be inconsistent among observers. Finally, this 

is probably the most time-consuming method of measuring plant abundance, and the time spent 

counting the stems is also time spent trampling the vegetation being sampled. Thus, we eliminated 

stem density as a possible method for measuring plant community composition. 

The third measurement, frequency, has the reputation of being relatively repeatable among 

observers (Helm and Mead 2004) and can be one of the fastest methods of measuring plant species 

abundance. It is also relatively conservative, or invariable, with respect to variations in 

precipitation, an important consideration for the Great Plains, where precipitation varies 

considerably from year to year. However, in comparison to the other three measurement 

techniques, plant frequency is complicated by the relationship between the size of the sampling 

area used and the measurement obtained (Critchley and Poulton 1998). Larger sample areas yield 

higher frequencies. An optimal sample area for determining differences in frequency is one which 

yields frequencies between 30 and 70% (Elzinga et al. 1998), and this optimal area will vary among 

species because of differences in their abundance. Frequency of dominant species is thus measured 

using smaller sample areas than frequency of rare species. This complication can be somewhat 

overcome by using nested frequency sampling, in which a variety of sample areas are used at each 

location. However, the analysis of the data from this type of sampling can also be complicated. In 

addition, like stem density, frequency does not represent differences in plant species’ size. Finally, 

as a measure of plant abundance, interpreting changes in frequency for management purposes is not 

as intuitive as with other measures of plant abundance. For these reasons, we eliminated frequency 

as the primary method of measuring plant abundance for the Network’s long-term monitoring 

program. 

The remaining method for measuring plant abundance is to measure cover, and there are 

two types of cover that can be measured and many different ways to measure them. Basal cover 

measures the ground-level area occupied by a plant, whereas canopy cover measures the area 

occupied by a plant above ground level. Canopy cover of herb-layer vegetation may vary 

considerably within seasons as plants grow and senesce, as well as among seasons due to variations 

in precipitation and herbivory. Basal cover is generally much more consistent than canopy cover 

within and among seasons, but only for certain types of plants (bunchgrasses, tussocks and trees). 

For other types (sod-forming or rhizomatous grasses, single stemmed forbs, etc.), basal cover is 

quite variable and difficult to measure. Because so many of the species and so much of the cover in 

the vegetation that the Network will work in are comprised of this latter category of species, we 

focused our measurements of individual species on canopy cover. However, we did estimate total 

basal cover (see below). 

The three primary methods for measuring cover are line-intercept, point-intercept, and 

ocular estimates. The line-intercept method is most useful in vegetation with distinct individuals 

and dense canopy covers; it is not useful for diffuse species, such as rhizomatous grasses (Bonham 

1989, Elzinga et al. 1998). Because these species are often dominant in the vegetation that will be 

monitored for this program, we did not consider using this as our primary method. However, it 

may be incorporated into the protocol for measuring the abundance of shrubs. The two remaining 

7
methods for measuring cover both have significant advantages and disadvantages. Ocular cover 

estimates have a reputation for being unrepeatable among observers, though many feel that this can 

be overcome by adequate training (Elzinga et al. 1998). Ocular estimates capture a larger area, and 

therefore more species, in each sample compared to point-intercept methods. The point-intercept 

method is accepted by many as being more repeatable among observers than ocular estimates, but 

recent tests comparing the two have shown this is not necessarily true (Kercher et al. 2003, Helm 

and Mead 2004). Also, this method may be more efficient than ocular cover estimates in that less 

time is needed to adequately capture species composition. In the context of this study, the pointintercept method has the advantage that this is the primary method of the Northern Great Plains 

Fire Effects monitoring program, which has been monitoring plant community composition in 

many of the Network’s 13 parks since 1997 and with which the Network is planning to integrate its 

monitoring. 

Study Objectives 

Because there is no clear advantage of one of these methods over the other, and to ensure 

that the optimal method for the vegetation types occurring in Network parks is chosen for longterm monitoring, we conducted a comparison of the two in a variety of vegetation types over the 

geographic spectrum encompassed by the Network. This comparison focused on herbaceous and 

small (< 2 m tall) shrub vegetation. (Further work will determine whether methods for measuring 

larger woody vegetation currently used by the Fire Effects program are adequate.) For this 

comparison, our specific objectives were: 

Across a variety of broad vegetation categories (riparian herbaceous wetland, grassland, 

prairie dog town, badlands sparse vegetation, ponderosa pine forest/woodland, shrubland, riparian 

forest, and woody draw), 

1. compare point-intercept and ocular estimates of herbaceous and small shrub cover between two 

observer teams for repeatability; 

2. determine the number of subsamples (quadrats in the ocular estimate method or points in the 

point-intercept method) necessary to adequately measure a small number of important plant 

community parameters at a site (e.g., native species cover, exotic species cover, and native 

species richness) using these two methods; and, 

3. compare the efficiency of ocular and point-intercept methods of estimating plant cover. 

Methods 

Study Areas 

We conducted this study at five NPS units in the Network: Agate Fossil Beds National 

Monument (AGFO) in northwestern Nebraska, Devils Tower National Monument (DETO) in 

northeastern Wyoming, Fort Laramie National Historic Site (FOLA) in southeastern Wyoming, 

Theodore Roosevelt National Park (THRO) in west-central North Dakota, and Wind Cave National 

Park (WICA) in southwestern South Dakota (Figure 1). We chose these parks because they 

represent the range of conditions in geography, ecology, and vegetation that occur within the 

Network. 

8
Figure 1. Locations of NPS units in the Northern Great Plains I&M Network. The parks used in 

this study are underlined in red. 

Sample Sites 

Because the sampling design for the long-term monitoring has not yet been determined, we 

could not use sites that will definitely be used in the long term. In addition, differing goals between 

this pilot study and the long-term monitoring require different sampling designs. Whereas the 

long-term monitoring sample design must enable us to make inferences across a whole park from 

the samples, in this study we were determining which sampling method was preferable across the 

variety of vegetation types that occur in the Network. Thus, for this study we based our sampling 

design on vegetation classifications. We allocated the total number of samples across vegetation 

types according to the vegetation’s approximate abundance in the Network (Table 1). We chose 

specific sample sites within each park using a variety of methods based on the size of the park and 

whether previously existing sample sites meeting our criteria were available. Maps of the final 

sample sites and plot GPS coordinates for each park are in Appendix A. 

9
Table 1. Number of sample locations in each vegetation type for each park. 

[Abbreviations for vegetation types used in other tables and figures are in parentheses. Numbers in parentheses 

indicate the number of sites within each park-vegetation type combination that were double sampled for examining 

repeatability.] 

Vegetation Type AGFO DETO FOLA THRO WICA Total 

Riparian herbaceous wetland (HERBRIP) 5 (2) 5 (2) 

Grassland (GRASS) 6 (1) 6 (0) 4 (0) 16 (1) 

Prairie dog town (DOG) 1 (0) 2 (1) 3 (1) 

Ponderosa pine forest/woodland (PIPO) 3 (1) 4 (2) 7 (3) 

Shrubland (SHRUB) 4 (1) 1 (1) 5 (2) 

Woody drawa

1 (0) 1 (0) 

Riparian forest (FORRIP) 1 (1) 1 (0) 1 (0) 3 (1) 

Badlands/Sparse (BAD) 1 (0)b 5 (1) 6 (1) 

Total 11 (3) 5 (2) 8 (0) 10 (2) 12 (4) 46 (11) 

a

characterized as riparian forest in analyses; bcobble river terrace 

At AGFO, we sampled two broad vegetation types – upland native prairie and herbaceous 

riparian vegetation. For prairie sampling sites we simply used six plots previously established by 

the NPS Prairie Cluster Prototype Long-term Ecological Monitoring program (DeBacker et al. 

2004). For herbaceous riparian vegetation (for which no previous sites existed), we randomly 

selected eight sites within the three herbaceous mapped vegetation classes occurring along the 

Niobrara River (Aerial Information Systems 1998a). We eliminated three of these sites because 

they had inappropriate vegetation or because they fell on private land. 

At DETO, we randomly selected sites in three broad vegetation types (prairie dog town, 

ponderosa pine forest/woodland, and riparian forest) based on mapped vegetation classes (Salas 

and Pucherelli 1998), with the restriction that sites must be more than 20 m from a developed trail, 

more than 100 m from a road, and with a slope of less than 35% (based on digital elevation 

models). Our initial pool of sampling sites contained many more points than our desired number 

because we knew that some sites would be discarded after field checking. Thus, we randomly 

selected sites for initial field checking. If we rejected a site in the field (for wrong vegetation, too 

steep of slope, etc.), we field checked the next site on the random list in the desired vegetation type, 

then repeated this process until we had reached the desired number of sites (Table 1). We used this 

method for four of the five sites; the fifth site was a plot previously established by the Fire Effects 

monitoring program using a method identical to that for the other four sites. 

At FOLA, we used a GIS to overlay a coarse grid, with random orientation and start, over a 

map of the park’s vegetation classifications (Aerial Information Systems 1998b). Intersections of 

the lines on the grid constituted the pool of potential sampling sites. The grid size was chosen to 

yield the approximate number of sites we anticipated being able to monitor over the long term at 

this park3

. From this sampling pool of 39 sites, we eliminated those less than 20 m from a 

 

3

 This number is extremely approximate and probably little better than a wild guess. A grid was used because, prior to 

this study, we were planning on using a systematic sampling design within each park. After further investigation into 

various sampling designs, we are now leaning towards using a Generalized Random Tessellation Stratified (GRTS) 

design. 

10
developed trail or less than 100 m from a road. From the remainder, we randomly chose eight sites 

in three broad vegetation types (riparian, disturbed upland, and native prairie) following our desired 

allocation shown in Table 1. If, upon field visitation, we determined that a randomly chosen site 

was unsuitable (i.e., was too close to a road because of mapping errors or was not in the vegetation 

type expected), we used the next nearest grid point in the appropriate vegetation type. 

At THRO, a large park, we restricted our sampling to two focus areas in the South Unit to 

reduce the amount of time spent traveling to sample sites. In the Burning Coal Vein focus area, 

which we defined as south of Scenic Loop Drive and within 1.6 km of the road to the Coal Vein 

trailhead, we sampled badlands sparse vegetation. In the Jones Creek focus area, which we defined 

as within 1.6 km of the intersection of Jones Creek and Scenic Loop Drive, we sampled shrubland 

vegetation and riparian forest vegetation. For both of these focus areas, we used the same method 

of randomly choosing sites based on mapped vegetation classes (Von Loh et al. 2000) and field 

checking described for DETO, with one exception. In the Jones Creek area, we used one shrubland 

site previously established by the Fire Effects program. 

We also limited the geographic scope of areas for sampling at WICA to reduce travel time. 

At this park, we limited sampling sites to be within 1.2 km of a road and used mapped vegetation 

classes (Cogan et al. 1999) to allocate sample sites within this area. We restricted sites in 

grassland, ponderosa pine, and prairie dog town vegetation to those falling on a grid (with random 

orientation and start) overlaid on the park4. We also distributed grassland and prairie dog town 

sites evenly between the eastern and western halves of the park because soil types, and therefore 

vegetation, differ between these two areas. Ponderosa pine sites all occurred in the western half of 

the park because this vegetation is rare in the eastern half. We did not restrict the woody draw and 

shrubland sites to the overlaid grid because of the rarity of these vegetation types (no sites on the 

grid fell in these vegetation types). Instead, we randomly selected sites from the appropriate 

vegetation types. We used the same iterative method of field checking the pool of sample sites 

described for DETO within these vegetation types, except two ponderosa pine sites were plots 

previously established by the Fire Effects program. 

Sampling 

Once a sample site was selected, we established a single 20 m x 50 m plot at that site and 

collected data. Prior to any data collection, we wrote Preliminary Operating Procedures (POPs) for 

establishing and marking plots, sampling plant cover via the two methods (ocular and pointintercept), compiling a plot species list, and dealing with unknown specimens (Appendix B). Field 

crews followed these POPs, with some minor modifications, throughout the study. The general 

procedure we used for each plot follows: 

4. The plot was established and marked following POP 1. Care was taken to avoid trampling of 

vegetation during this process. 

5. We designated a sampling team from the field staff available, ensuring that the team always 

included at least one person with substantial knowledge of the plant species expected and, if 

possible, a person skilled in using a taxonomic key to identify unknown species (often the same 

individual). The composition of the teams was not held constant across plots because we knew 

this would not be the case in the long-term monitoring due to staff abailability varying through 

a given field season. 

6. Sub-teams of two people (rarely one person) were formed. Each of the two 50-m transects that 

comprised a plot’s edge was sampled twice, once by a sub-team using POP 2 (ocular method) 

 

4

 Again, this was in an attempt to increase the chances that the sites would be used in future long-term monitoring. 

11
and once by a sub-team using POP 3 (point-intercept method). For 21 of the 46 plots, the same 

sub-team used both methods on the same transect. In these cases, the sub-team members 

randomly chose which method (ocular or point-intercept) was used first on the transect. For the 

remainder of the plots, the ocular and point-intercept methods were completed by two different 

sub-teams on the same transect. 

7. One person on the team was responsible for compiling a plot species list following POP 4, but 

all team members contributed to this task. 

8. All sampling was done on the same day or on consecutive days to reduce differences due to 

vegetation growth between samples. 

Eleven plots were double-sampled to compare the repeatability of measurements between 

observers for the two cover estimate methods (Table 1). When a plot was designated for double 

sampling, the first team to sample the plot left plot markers in place but removed all tapes. A 

second team relocated and sampled the plot following POPs 2 and 3. If any member of the first 

team was on the second team, care was taken to ensure that those members did not sample the same 

transect both times. The second team did not discuss the plot with the first team prior to the second 

team’s finishing their sampling of that plot. Most double-sampling was completed within 36 hours 

of the original sample, although two second samples occurred 6 days after the original sample. 

Data Analysis 

We concentrated our analyses on six community-level and three species-level response 

variables. The community-level variables were total canopy cover, graminoid canopy cover, 

broadleaf (forb + shrub) canopy cover, percentage of canopy cover that is non-native, bare soil 

ground cover, and number of species for which canopy cover was measured (species richness). The 

species-level variables were the cover of the first- and second-most abundant species in a plot, as 

representatives of dominant species, and the cover of the species with the median cover level in that 

plot, as a representative of the majority of species. 

Except for analyses specifically focusing on repeatability, we used only the values obtained 

in the first sampling of a double-sampled plot. 

We could not use our data to evaluate the accuracy of cover estimates or species richness 

measures by the two sampling methods because the true values were unknown. However, we did 

compare the values obtained for the six community-level response variables between the two 

methods using correlation analyses and paired t-tests. Preliminary analyses showed that our results 

were not affected by whether the same or different sub-teams completed the two sampling methods 

in an individual plot; in an ANOVA in which sampling method, a dichotomous variable denoting 

whether teams were the same or different, and the interaction of these two variables were factors, 

there was no significant effect (P > 0.15) of either the “teams same” variable alone or its interaction 

with sampling method for any of the community-level response variables. Thus, in our analyses 

comparing values between the two sampling methods, we used all plots in one analysis. 

For comparing repeatability among observers for the two methods, we used correlations and 

paired t-tests for the nine response variables at the plot level. Thus, the sample size for these 

analyses was 11. For the species-level variables, the species used for an individual plot were based 

on their abundance measured by the first team. We also compared repeatability of plot-level 

composition by calculating the quantitative Sorenson (Bray-Curtis) and Morisita-Horn similarity 

indices between the two samples of each method for each plot. The quantitative Sorenson index is 

more sensitive to species richness, whereas the Morisita-Horn index is more sensitive to the 

12
abundance of the most abundant species (Magurran 1988). We compared the difference in 

similarity between the two sampling methods using a paired t-test, where each plot was a sample. 

Compositional similarity indices are sensitive to species pseudoturnover, in which one team 

records a species that the other does not. Species pseudoturnover is defined as 

(A + B)/(SA + SB) X 100, 

where A and B are the number of exclusive species found by Team A and Team B, respectively, 

and SA and SB are the total number of species found by Team A and Team B, respectively (Nilsson 

and Nilsson 1985). We calculated species pseudoturnover for each sampling method in each 

double-sampled plot, then used a paired t-test to test for differences in species pseudoturnover 

between sampling methods. 

We used the coefficient of variation (CV = 100 x sample standard deviation/sample mean) 

as a standardized measure of within-plot variability of the six community-level response variables. 

We used a mixed model (SAS Institute Inc. 2004) to evaluate the effects of sampling method, 

response variable, and vegetation type on natural-log-transformed CV values. Transformation was 

necessary to fit the normal-distribution assumption of the mixed model. We used post-hoc tests on 

differences between least-squares means to evaluate differences between factors with significant 

effects in the mixed model. 

To determine the number of subsamples within a plot necessary to adequately measure the 

nine response variables for that site, we used the Power procedure (one-sample confidence interval 

option) in SAS (SAS Institute Inc. 2004). Subsamples in the ocular method were individual 0.5-m2

frames, whereas subsamples in the point-intercept method were 10 consecutive points. Thus, we 

had 20 subsamples at each plot for both methods, from which we calculated the standard deviation 

of three of the six community response variables and all three species-level response variables 

described above. In addition, initial analyses showed that the ocular method detected more species 

than did the point-intercept method. Thus, we were interested in the precision with which the 

ocular method measured the cover of the species missed by the point-intercept method. To do this, 

we calculated the standard deviation of the ocular cover of species with 1% and 5% cover (values 

representing two separate thresholds for species detection) in each plot, using the species with the 

cover closest to these target values when the exact values did not occur. When more than one 

species with an individual target value occurred in a plot, we used the average of the standard 

deviations for these species. 

We used the standard deviations described above as one of the parameters in the Power 

procedure to calculate the number of subsamples necessary to obtain 20% precision about the 

mean, where precision is defined as one-half the width of the specified confidence interval for the 

mean. We varied two other parameters, α and β, in these calculations to provide a range of 

subsample size-statistical power possibilities. α is the probability of committing a type I error 

(saying the true mean is within a given confidence interval when it actually is not), and β is the 

probability of committing a type II error (saying the true mean is not within a given confidence 

interval when it actually is), with the power being the conditional probability that the desired 

precision is achieved, given that the interval includes the true mean. In addition, because the 

resulting subsample numbers were often unrealistically high, we obtained absolute (i.e., 20% cover 

instead of 20 % of the mean cover) precision levels attainable with 20 subsamples for each 

response variable. Finally, we constructed species-subsample number curves (Colwell 2005) for 

the plots with highest and lowest plot species richness in each vegetation type to determine the 

approximate return in greater species capture for each subsample added. 

13
We compared the efficiency of the two methods in two ways. First, we calculated the 

amount of time spent sampling each plot with each sampling method. Second, we used a t-test to 

compare the percentage of species captured by each method. This percentage was the number of 

species encountered in the cover estimate procedure divided by the total number of species found in 

the entire plot via POP 4. We also used linear regression to investigate the relationship between 

time for completion of each method or species capture and plot species richness or total canopy 

cover. 

We used SAS version 9.1 for Windows (SAS Institute Inc. 2004) for all statistical analyses. 

Results 

We encountered 402 species during our sampling. Species encountered at each park are 

listed in Appendix C. To put our results in context with other vegetation sampling protocol 

development efforts in the NPS, we provide summary information on the vegetation types we 

sampled in Table 2. Graminoids (grasses, sedges, and rushes) are by far the most abundant life 

form in all but the prairie dog town and badlands/sparse vegetation types. Species richness per 

1000-m2

 plot ranged from 21 to 92, with the median species cover being quite low. Species rankabundance curves (Figure 2) for each plot, in which the cover of each species in a plot is plotted 

against its rank (species of lowest cover has rank 1), further illustrate the tendency for most species 

to have low cover and for vegetation to be heavily dominated by just a few species in most 

vegetation types. For example, of the 1606 species-plot occurrences, less than 4% had greater than 

20% cover. 

Table 2. Mean and range (in parentheses) of variables descriptive of vegetation in each 

vegetation type sampled in this study. 

[Cover values are those measured by the ocular method, and plot species richness is from the whole plot (1000 m2

) 

species list.] 

Vegetation Type 

Graminoid 

Cover (%) 

Forb Cover 

(%) 

Shrub 

Cover (%) 

Total 

Cover (%) 

Non-native 

Cover (% 

of Total 

Cover) 

Plot 

Species 

Richness 

Cover of an 

individual 

species 

(%) 

Median 

Species 

Cover 

(%) 

Badlands/Sparse 28 

(16 - 36) 

10 

(2 - 19) 

15 

(4 - 33) 

54 

(36 - 70) 

3 

(0 - 14) 

67 

(40 - 88) 

1.28 

(0.02 – 23) 

0.30 

Prairie dog town 34 

(6 - 55) 

75 

(16 - 120) 

5 

(0 - 15) 

104 

(71 - 126) 

15 

(0 - 32) 

50 

(45 - 56) 

2.92 

(0.02 – 39) 

0.31 

Riparian forest 107 

(80 - 136) 

30 

(2 - 57) 

14 

(0 - 46) 

154 

(83 - 201) 

64 

(31 - 86) 

49 

(25 - 78) 

4.84 

(0.02 – 82) 

0.85 

Grassland 78 

(40 - 115) 

17 

(2 - 31) 

9 

(0 - 53) 

104 

(58 - 192) 

22 

(0 - 82) 

58 

(29 - 92) 

2.88 

(0.02 – 76) 

0.44 

Riparian herbaceous 

wetland 

107 

(79 - 141) 

39 

(14 - 55) 

1 

(0 - 6) 

147 

(134 - 159) 

22 

(7 - 37) 

31 

(21 - 42) 

7.36 

(0.02 – 90) 

1.39 

Ponderosa pine 

forest/woodland 

57 

(20 - 79) 

4 

(2 - 7) 

11 

(1 - 43) 

79 

(32 - 125) 

19 

(1 - 50) 

55 

(38 - 69) 

2.18 

(0.02 – 64) 

0.30 

Shrubland 78 

(29 - 121) 

14 

(8 - 21) 

41 

(30 - 54) 

133 

(75 - 160) 

31 

(4 - 55) 

62 

(41 - 85) 

3.35 

(0.02 – 87) 

0.40 

14
Herbaceous Riparian

0 5 10 15 20 25

Cover (%)

0

20

40

60

80

100

Prairie Dog Town

0 10 20 30 40 50

Cover (%)

0

10

20

30

40

50

Badlands/Sparse

0 20 40 60 80

Cover (%)

0

5

10

15

20

25

Forested Riparian

0 10 20 30 40 50 60

Cover (%)

0

20

40

60

80

100

Grassland

0 20 40 60 80

Cover (%)

0

20

40

60

80

100

Ponderosa Pine Forest/Woodland

0 10 20 30 40 50 60

Cover (%)

0

10

20

30

40

50

Shrubland

0 10 20 30 40 50 60 70

Cover (%)

0

20

40

60

80

100

Figure 2. Rank (x-axis)-abundance (y-axis) curves for the 46 plots sampled, by vegetation type. 

Each line-symbol combination within a graph represents a single plot. 

15
Between-Method Comparison of Values Obtained 

The two sampling methods gave significantly different values for five of the six 

community-level response variables (paired t-tests were significant; Table 3). The point-intercept 

method yielded substantially higher values for graminoid and total canopy cover, whereas the 

ocular method yielded substantially higher values for species richness. Differences in values of 

broadleaf cover and bare soil cover were much less substantial, though still statistically significant, 

with the point-intercept method producing higher values for both. The difference between 

proportional non-native cover values was not significant. 

Table 3. Paired t-test and correlation results for point-intercept vs. ocular sampling methods for 

six community response variables. 

[“PI-Ocular Mean” is the mean difference between the ocular and point-intercept value of the variable, with the 

standard error shown in parentheses beneath. df = 45 for all tests.] 

Paired t-test for 

PI-Ocular Mean = 0 

Correlation between 

PI value and Ocular 

value 

Response Variable 

PIOcular 

Mean 

t P r

2 P 

Total canopy cover 32.46 

(3.28) 

 9.90 <0.001 0.84 <0.001 

Graminoid canopy 

cover 

28.97 

(2.94) 

9.87 <0.001 0.78 <0.001 

Broadleaf canopy cover 3.23 

(1.09) 

2.98 0.005 0.96 <0.001 

% cover non-native 1.35 

(0.81) 

1.68 0.100 0.96 <0.001 

Bare soil ground cover 4.71 

(1.34) 

3.51 0.001 0.85 <0.001 

Species richness -14.09 

(1.34) 

-10.48 <0.001 0.63 <0.001 

Although values of the response variables generally differed between the two methods, 

there were strong correlations between them (Table 3). The tightness of this relationship was 

greatest for broadleaf cover and the percentage of cover that’s non-native and smallest for species 

richness (Table 3). Visual inspection of plots of the point-intercept value vs. the ocular value for 

each response variable (Figure 3) suggests that the relationship between values from the two 

methods for four of the community variables deviated from a strict 1:1 fit. Total canopy and 

graminoid cover were consistently higher than a 1:1 line. Bare soil cover tended to be higher than 

the 1:1 line, though less consistently than for total canopy and graminoid cover. Species richness 

was consistently below the 1:1 line, with the deviation being greater at higher levels of species 

richness. 

16
Species Richness

0 20 40 60 80

0

20

40

60

80

Point-Intercep

Ocular

Figure 3. Relationship between community-level response variable values obtained by the two 

sampling methods. Dashed lines are y=x. 

t

17

% Cover Non-native

0 20 40 60 80 100

0

20

40

60

80

100

Bare Soil Cover (%)

0 20 40 60 80 100

0

20

40

60

80

100

Broadleaf Cover (%)

0 20 40 60 80 100 120 140 160

0

20

40

60

80

100

120

140

160

Graminoid Canopy Cover (%)

0 50 100 150 200

0

50

100

150

200

0

50

100

150

200

250

300 Total Canopy Cover (%)

0 50 100 150 200 250 300
18

Table 4. Comparison of repeatability for two methods of vegetation sampling based on correlation coefficients a[For individual methods, t-test results show difference in values between sampling teams, with standard error (SE) in parentheses. For difference shown is the mean difference (SE in parentheses) between methods in between-team differences.] 

Ocular Point-Intercept Meth

Response Variable r2

|Team A – 

Team B| t df P r2

|Team A – 

Team B| t df P

PI – 

Ocular 

Total canopy cover 0.97* 5.32 

(2.23) 

2.39 10 0.038 0.93* 11.18 

(1.66) 

6.74 10 <0.001 5.86 

(2.38) 

Graminoid canopy cover 

0.99* 4.49 

(1.69) 

2.66 10 0.024 0.97* 6.73 

(1.46) 

4.60 10 0.001 2.24 

(2.14) 

Broadleaf canopy cover 

0.99* 2.05 

(0.48) 

4.31 10 0.002 0.99* 3.45 

(0.83) 

4.15 10 0.002 1.41 

(1.00) 

% cover non-native 0.99* 1.99 

(0.61) 

3.25 10 0.009 0.98* 2.37 

(0.68) 

3.51 10 0.006 0.38 

(0.63) 

Bare soil ground cover 

0.96* 3.17 

(1.28) 

2.47 10 0.033 0.96* 4.00 

(0.95) 

4.20 10 0.002 0.83 

(1.05) 

Species richness 0.93* 2.55 

(0.74) 

3.43 10 0.006 0.71* 4.00 

(0.81) 

4.96 10 <0.001 1.46 

(1.05) 

Most abundant species cover 

0.96* 4.96 

(1.00) 

4.96 10 <0.001 0.98* 3.77 

(0.83) 

4.56 10 0.001 -1.19 

(1.67) 

Second-most abundant species cover 

0.72* 4.09 

(1.67) 

2.45 10 0.034 0.70* 5.09 

(1.38) 

3.69 10 0.004 1.00 

(0.79) 

Median cover species cover 

0.73* 0.19 

(0.05) 

3.86 10 0.003 0.28† 1.14 

(0.21) 

5.31 10 <0.001 0.95 

(0.23) 

*P • 0.001; †P =0.095 
Repeatability 

For most community-level variables, correlation between the two teams was high for both 

methods (r

2 ≥ 0.93) and regressions were highly significant (Table 4). Only for species richness did 

correlations between the two teams for the two methods differ substantially (ocular r

2

 = 0.93 vs. 

point-intercept r

2

 = 0.71). However, for both methods there were significant differences between 

the two teams’ values for all community-level response variables (Table 4). These differences were 

similar between methods for all community-level response variables except total canopy cover. For 

this variable, the point-intercept method had significantly greater discrepancies between teams than 

the ocular method did. 

Values of species-level response variables also differed significantly between sample teams 

for both methods (Table 4). These differences were similar between methods for the cover of the 

two most abundant species, but for the cover of median species, the point-intercept method had 

significantly greater discrepancies between teams. For cover of the most abundant individual 

species, correlation between samples for both methods was similar to that for the community-level 

variables (r

2 ≥ 0.96). For cover of the second most abundant species and median species, however, 

correlation was considerably lower, especially for the point-intercept method for the median 

species, for which there was no significant correlation between the two teams’ values (Table 4). 

Compositional similarity tended to be slightly greater for the ocular method than for the 

point-intercept method when measured either with the quantitative Sorenson or the Morisita-Horn 

similarity index (Table 5). Species pseudoturnover was substantial in most plots that were doublesampled, ranging from 6% to 57%, and it was significantly higher with the point-intercept method 

(Table 5). Species pseudoturnover did not vary with total species richness for either method 

(Point-Intercept: r

2

 = 0.003, F1, 9 = 0.03, P = 0.874; Ocular: r

2

 = 0.015, F1, 9 = 0.14, P = 0.716). 

Twenty percent of the species detected by one team but not the other had greater than 1% cover, 

but this did not differ significantly between sampling methods (difference = 6.75 ± 5.91, t = 1.14, 

df = 10, P = 0.280). 

Table 5. Similarity indices and species turnover between first and second sampling for each 

method and double-sampled plot. 

[Mean and standard error of differences between methods (ocular – point-intercept) and results of paired t-test between 

the two sampling methods, are shown at the bottom of the table.] 

Quantitative 

Sorenson Similarity 

Morisita-Horn 

Similarity 

Species 

Pseudoturnover 

Plot Park 

Vegetation 

Type Ocular 

PointIntercept Ocular PointIntercept Ocular PointIntercept 

APR05 AGFO HERBRIP 0.861 0.844 0.989 0.976 33.3 57.1 

APR06 AGFO HERBRIP 0.811 0.847 0.932 0.941 25.5 29.7 

DPCM02 DETO FORRIP 0.920 0.903 0.993 0.981 5.9 16.7 

DPCM03 DETO PIPO 0.877 0.856 0.978 0.973 17.5 19.1 

LTEM04 AGFO GRASS 0.903 0.822 0.991 0.990 20.5 20.0 

TPCM03 THRO BAD 0.721 0.661 0.875 0.854 16.7 30.9 

TPCM06 THRO SHRUB 0.946 0.911 0.997 0.985 19.0 17.6 

WPCM02 WICA DOG 0.903 0.892 0.984 0.985 15.9 25.9 

WPCM09 WICA SHRUB 0.797 0.778 0.942 0.939 19.3 30.7 

19
Table 5, continued. 

Quantitative 

Sorenson Similarity 

Morisita-Horn 

Similarity 

Species 

Pseudoturnover 

Plot Park 

Vegetation 

Type Ocular 

PointIntercept Ocular PointIntercept Ocular PointIntercept 

WPCM12 WICA PIPO 0.763 0.751 0.826 0.812 27.3 18.5 

WPCM13 WICA PIPO 0.827 0.836 0.961 0.970 20.5 33.3 

mean difference 0.021 0.006 -7.12 

SE difference 0.009 0.003 2.73 

df 10 10 10 

t 2.21 1.84 -2.60 

P 0.052 0.095 0.026 

Number of Subsamples 

Sampling method, vegetation type, and response variable, as well as the interaction between 

response variable and vegetation type, significantly affected log-transformed CV values (Table 6). 

Overall, the ocular method had a significantly higher log-transformed CV (least-squares mean ± SE 

= 4.39 ± 0.05) than the point-intercept method (4.29 ± 0.05). The log-transformed CV of median 

species cover did not differ (P > 0.05) among vegetation types. For the other five response 

variables, the only consistent patterns in log-transformed CV among vegetation types were that the 

Badlands/Sparse vegetation type always had the highest log-transformed CV, and the Ponderosa 

Pine Forest/Woodland type had generally high log-transformed CV compared to the others (Figure 

4). Although this analysis does not translate directly into differences in the precision attainable 

with each method in each vegetation type, its results do help explain some of the differences shown 

below. It should be noted that the variability in sample sizes among vegetation types may have 

weakened our ability to detect differences among the vegetation types in the log-transformed CV of 

the response variables. 

Table 6. Results of mixed model analysis for effects of sampling method, vegetation type, 

response variable, and their interactions on log-transformed coefficient of variation. 

Effect 

Numerator 

DF 

Denominator 

DF F P 

Method 1 422 6.07 0.014 

Vegetation type 6 39 7.90 < 0.001 

Response variable 5 422 286.33 < 0.001 

Method x Vegetation type 6 422 0.62 0.717 

Method x Response variable 5 422 1.14 0.340 

Vegetation type x Response variable 30 422 3.59 < 0.001 

Method x Vegetation type x Response variable 30 422 0.29 1.000 

20
Species Richness

BAD DOG FORRIP GRASSHERBRIP PIPO SHRUB

ln(Coefficient of Variation)

2

3

4

5

6

7

a

b

bc c c

bc bc

Canopy Cover

BAD DOG FORRIP GRASSHERBRIP PIPO SHRUB

ln(Coefficient of Variation)

2

3

4

5

6

7

a

b

bc c c c c

Most Abundant Species Cover

BAD DOG FORRIP GRASSHERBRIP PIPO SHRUB

ln(Coefficient of Variation)

2

3

4

5

6

7

a

b

c bc c c

bc

Second-Most Abundant Species Cover

BAD DOG FORRIP GRASSHERBRIP PIPO SHRUB

ln(Coefficient of Variation)

2

3

4

5

6

7

a

b ab ab

b

ab ab

% Non-native Cover

BAD DOG FORRIP GRASSHERBRIP PIPO SHRUB

ln(Coefficient of Variation)

2

3

4

5

6

7

a

b b b

c

b

b

Median Species Cover

BAD DOG FORRIP GRASSHERBRIP PIPO SHRUB

ln(Coefficient of Variation)

2

3

4

5

6

7

(no significant differences)

Figure 4. Least squares means and standard errors for log-transformed coefficients of variation 

among subsamples within plots for six response variables, by vegetation type. Different letters 

above symbols within a graph indicate significant (P < 0.05) differences between vegetation types. 

Vegetation type abbreviations follow those in Table 1. 

For most vegetation types, sampling 20 subsamples with either method was more than 

adequate to obtain 20% precision around the mean in the estimate of total canopy cover with 95% 

21
confidence and 90% power (Tables 7 and 8). For the ponderosa vegetation type, sampling 20 

subsamples was adequate to obtain 20% precision around the mean with slightly lower confidence 

(90%). However, because of low overall cover of vegetation in the badlands/sparse vegetation 

type, as well as the high variability among subsamples, 20 subsamples only attained absolute 

precision of 17-30% cover, which is approximately 30-40% of the mean. 

Twenty percent precision around the mean for the percent of total canopy cover that is nonnative was attainable with 20 subsamples with either method only for the vegetation type with the 

highest value for this variable – forested riparian vegetation. Approximately doubling the number 

of subsamples in either method would attain this precision with either method for the average plot, 

but even this intense of sampling would not provide this precision in most vegetation types (Tables 

7 and 8). The precision obtainable with 20 subsamples ranged from 2% non-native cover in those 

vegetation types with the least non-native vegetation (badlands/sparse and prairie dog town) to 

12% for those with higher non-native cover. Overall, the point-intercept method required slightly 

fewer subsamples than the ocular method to obtain 20% precision around the mean for this 

response variable, but this was not the case for all vegetation types. This may be due to the 

differences in growth form (graminoid vs. forb) of the non-natives that occurred in each vegetation 

type. 

Sampling 20 subsamples was adequate or close to adequate for obtaining 20% precision 

around the mean (95% confidence and 90% power) for species richness for five of the seven 

vegetation types with the point-intercept method and four of the vegetation types with the ocular 

method (Tables 7 and 8). As with total canopy cover, the badlands/sparse and ponderosa pine 

forest vegetation types required more subsamples to attain this precision for species richness 

because of their relatively high CVs. In general, the point-intercept method provided greater 

absolute precision (0.8 species) than the ocular method (2 species) with 20 subsamples, but the 

point-intercept method also recorded fewer species (Table 8 vs. Table 7). 

Twenty percent precision around the mean (95% confidence and 90% power) for cover of 

the most abundant species was not obtainable with either method for any vegetation type (Tables 7 

and 8). The number of subsamples required to attain this precision for this response variable was 

consistently lower for the point-intercept method than for the ocular method across vegetation 

types, except for the prairie dog town type, in which subsample number was approximately equal 

between the two methods. The point-intercept method could obtain absolute precision of 9-15% 

for most abundant species cover across vegetation types (Table 8), whereas the ocular method’s 

absolute precision ranged from 8 to 19% cover (Table 7). 

The number of subsamples required to attain 20% precision around the mean for the cover 

of the second-most abundant species or a species with median cover was very high or astronomical, 

respectively, for either method (Tables 7 and 8). For second-most abundant species cover, 20 

subsamples attained a precision of 7-19% absolute cover, depending on vegetation type, with either 

sampling method. For the point-intercept method, this is 39 to 86% of the mean and for the ocular 

method this is 48 to 102% of the mean. For cover of the median species, the ocular method 

produced a precision of 0.4 to 2 % cover, depending on vegetation type, with 20 subsamples. This 

is 121 to 308% of the mean. The point-intercept method produced a precision of 3 to 6% absolute 

cover, depending on vegetation type, with 20 subsamples. This is 106 to 226% of the mean. 

Tables 7 & 8. Means, standard deviations, coefficients of variation (CV), sample size needed for 

20% precision around mean, and absolute precision attainable with 20 subsamples for the ocular 

(Table 7) and point-intercept (Table 8) methods for six response variables. 

[α and β are the probability of type 1 and type 2 errors, respectively.] 

22
Table 7 ----N for 20% precision---- Prec. w/ 20 subs 

Vegetation Type Mean 

Mean 

SD 

Min 

SD Max SD 

Mean 

CV 

α=0.1 

β=0.2 

α=0.1 

β=0.1 

α=0.05 

β=0.1 

20% of 

Mean 

α=0.1 

β≤0.1 

α=0.05 

β≤0.1 

Total Canopy Cover

BAD 53.69 36.47 22.03 50.11 68.97 40 43 58 10.74 17 21 

DOG 108.28 20.88 17.21 27.96 20.02 6 7 9 21.66 10 12 

FORRIP 154.36 45.94 26.46 65.01 29.86 11 12 16 30.87 22 26 

GRASS 103.54 27.93 14.66 43.34 27.41 9 11 14 20.71 14 16 

HERBRIP 147.31 42.57 35.82 48.02 28.86 10 12 15 29.46 20 24 

PIPO 79.26 32.98 16.99 44.14 46.01 18 20 26 15.85 19 20 

SHRUB 135.06 36.88 30.06 44.68 31.54 11 12 14 27.01 18 21 

Overall 105.58 33.35 14.66 65.01 35.90 12 13 17 21.12 16 19 

Percent Cover Non-native

BAD 2.74 3.91 0.32 18.77 269.03 152 159 221 0.55 2 3 

DOG 14.89 9.49 0.00 18.59 67.20 35 39 52 2.98 5 6 

FORRIP 63.52 20.35 10.70 27.38 36.06 12 13 18 12.70 10 12 

GRASS 22.22 11.21 0.23 30.63 138.81 24 26 35 4.44 6 7 

HERBRIP 21.51 19.83 13.98 28.18 118.77 68 73 100 4.30 10 12 

PIPO 17.99 15.80 1.90 28.47 140.98 63 67 92 3.60 8 9 

SHRUB 17.24 16.64 7.98 32.88 98.82 27 30 40 6.12 8 10 

Overall 22.54 13.25 0.00 32.88 136.16 28 31 42 4.74 7 6 

Species Richness 

BAD 6.58 3.66 2.06 6.21 56.23 28 31 41 1.32 2 3 

DOG 11.92 2.64 2.18 2.99 22.86 4 5 11 2.38 2 2 

FORRIP 7.37 2.64 1.73 3.27 41.18 14 16 21 1.47 2 2 

GRASS 10.10 2.82 1.60 4.37 29.21 10 11 14 2.02 2 2 

HERBRIP 6.06 1.94 1.67 2.26 32.14 12 13 18 1.21 1 2 

PIPO 7.16 3.17 2.08 4.87 45.47 19 22 29 1.43 2 2 

SHRUB 8.45 3.45 2.41 4.23 40.69 13 15 25 1.69 2 2 

Overall 8.43 2.91 1.60 6.21 37.41 17 19 20 1.69 2 2 

Most Abundant Species Cover

BAD 12.34 16.29 8.97 28.45 141.81 132 139 192 2.47 8 10 

DOG 34.30 18.01 10.16 22.85 54.23 26 28 38 6.86 9 11 

FORRIP 60.40 30.71 19.41 38.22 53.88 24 27 36 12.08 15 18 

GRASS 36.87 23.58 10.95 33.71 76.53 36 39 53 7.37 11 14 

HERBRIP 57.55 32.08 10.66 41.33 68.64 28 31 42 11.51 15 19 

PIPO 30.95 24.72 11.13 37.59 95.78 53 57 77 6.19 12 14 

SHRUB 46.23 31.97 21.42 41.10 97.84 41 44 60 9.25 15 18 

Overall 38.22 24.87 8.97 41.33 85.05 37 40 54 7.64 12 14 

Second Abundant Species Cover

BAD 7.83 13.85 8.97 24.96 187.75 230 238 332 1.57 7 8 

DOG 19.29 20.40 10.16 22.41 151.03 88 93 128 3.86 10 12 

FORRIP 23.99 25.09 19.41 33.01 121.48 86 91 125 4.80 12 15 

GRASS 18.82 18.42 10.95 40.44 109.51 76 81 112 3.76 9 11 

HERBRIP 26.57 31.55 10.66 41.36 123.83 109 115 159 5.31 15 18 

PIPO 14.41 15.45 11.13 23.74 149.32 90 95 132 2.88 8 9 

SHRUB 25.75 25.94 21.42 44.74 103.61 80 85 117 5.15 13 15 

Overall 18.75 20.31 8.97 44.74 130.83 92 97 134 3.75 10 12 

Median Species Cover

BAD 0.33 1.26 0.79 1.79 383.52 914 932 1311 0.07 0.6 0.8 

DOG 0.36 1.16 0.90 1.46 347.68 777 794 1116 0.07 0.6 0.7 

FORRIP 0.65 2.16 0.21 4.92 310.17 781 798 1121 0.13 2 2 

GRASS 0.42 1.33 0.26 5.22 320.64 782 799 1123 0.08 0.7 0.8 

HERBRIP 2.45 6.71 0.53 20.94 262.71 536 550 771 0.49 4 4 

PIPO 0.30 0.99 0.46 1.79 337.79 771 787 1106 0.06 0.5 0.6 

SHRUB 0.33 0.81 0.34 1.23 236.95 387 398 557 0.07 0.4 0.5 

Overall 0.62 1.89 0.21 20.94 318.51 704 720 1011 0.12 0.9 2 

23
Table 8 ----N for 20% precision---- Prec. w/ 20 subs 

Vegetation Type Mean 

Mean 

SD 

Min 

SD Max SD 

Mean 

CV 

α=0.1 

β=0.2 

α=0.1 

β=0.1 

α=0.05 

β=0.1 

20% of 

Mean 

α=0.1 

β≤0.1 

α=0.05 

β≤0.1 

Total Canopy Cover

BAD 77.33 52.45 37.19 63.77 69.36 40 43 58 15.47 25 30 

DOG 139.33 36.04 30.17 46.51 26.96 9 10 13 27.87 17 21 

FORRIP 178.50 49.65 40.32 59.77 31.70 10 11 14 35.70 24 28 

GRASS 144.38 37.38 19.22 53.46 27.09 9 10 13 28.88 18 22 

HERBRIP 180.80 43.23 27.31 49.47 23.98 8 9 12 36.16 21 25 

PIPO 107.29 40.93 25.81 61.25 43.30 13 15 23 21.46 20 24 

SHRUB 162.88 39.93 25.98 53.44 24.75 9 10 12 32.58 19 23 

Overall 138.93 41.99 19.22 63.77 35.02 9 10 16 27.79 20 24 

Percent Cover Non-native

BAD 2.69 4.36 0.00 447.21 278.30 194 202 281 0.54 3 3 

DOG 12.77 8.73 0.00 74.55 70.01 40 43 59 2.55 5 5 

FORRIP 63.79 20.48 14.95 48.63 34.39 12 13 18 12.76 10 12 

GRASS 23.06 12.80 0.00 244.44 109.20 28 31 41 4.61 6 8 

HERBRIP 21.39 16.77 12.37 179.86 101.29 51 55 75 4.28 8 10 

PIPO 24.09 15.53 0.00 159.91 84.95 36 39 53 4.82 8 9 

SHRUB 29.54 13.51 6.22 241.27 107.76 20 23 30 5.91 7 8 

Overall 24.70 13.18 0.00 447.21 105.80 26 29 39 4.94 7 8 

Species Richness 

BAD 4.19 2.58 1.63 3.18 62.44 33 36 49 0.84 2 2 

DOG 5.47 1.56 1.10 2.16 28.57 10 11 15 1.09 0.8 0.9 

FORRIP 5.48 1.76 1.33 2.18 36.87 12 13 18 1.10 0.9 1 

GRASS 5.59 1.65 0.92 2.68 30.51 11 12 16 1.12 0.8 1 

HERBRIP 4.57 1.63 0.97 2.15 35.80 14 16 21 0.91 0.8 1 

PIPO 4.14 1.77 0.86 2.39 44.44 18 20 27 0.83 0.9 1 

SHRUB 5.64 1.87 1.48 2.28 33.55 13 14 18 1.13 0.9 2 

Overall 5.06 1.81 0.86 3.18 38.20 14 16 21 1.01 0.9 2 

Most Abundant Species Cover

BAD 16.58 18.95 13.99 29.64 122.74 101 107 147 3.32 9 11 

DOG 39.17 20.96 16.03 27.70 54.47 26 29 39 7.83 10 12 

FORRIP 67.00 25.44 12.09 33.48 42.72 15 17 23 13.40 12 15 

GRASS 49.94 24.73 9.40 35.73 56.49 23 26 34 9.99 12 14 

HERBRIP 68.80 26.41 7.59 42.83 45.43 16 17 23 13.76 11 13 

PIPO 37.71 23.87 12.57 30.86 73.44 35 38 52 7.54 12 14 

SHRUB 48.38 22.43 5.71 42.98 67.22 21 23 31 9.68 11 13 

Overall 46.79 23.66 5.71 42.98 65.81 24 26 36 9.36 12 14 

Second Abundant Species Cover

BAD 9.33 13.52 8.65 19.03 158.24 157 165 239 1.87 7 8 

DOG 24.33 23.11 14.32 30.00 98.24 72 77 105 4.87 11 13 

FORRIP 23.30 25.03 17.74 28.00 115.02 90 96 132 4.66 12 15 

GRASS 25.53 21.30 10.89 37.76 88.78 57 61 84 5.11 10 12 

HERBRIP 29.50 33.32 30.50 36.29 115.19 99 105 145 5.90 16 19 

PIPO 21.14 19.60 11.42 29.20 118.90 69 73 101 4.23 10 12 

SHRUB 30.75 29.15 16.94 40.31 96.62 72 76 105 6.15 14 17 

Overall 23.32 22.54 8.65 40.31 109.45 75 79 109 4.66 11 13 

Median Species Cover

BAD 1.58 4.55 2.24 8.01 338.45 577 591 829 0.32 3 3 

DOG 1.33 4.75 4.47 4.89 366.56 874 891 1253 0.27 3 3 

FORRIP 2.00 5.66 3.08 9.33 297.19 571 585 821 0.40 3 4 

GRASS 2.16 4.86 3.08 10.46 245.61 370 381 533 0.43 3 3 

HERBRIP 4.70 9.02 4.89 18.32 244.61 270 279 390 0.94 5 6 

PIPO 1.86 4.74 3.08 8.13 276.26 471 484 678 0.37 3 3 

SHRUB 1.25 3.27 2.24 4.10 301.11 490 503 706 0.25 2 2 

Overall 2.16 5.19 2.24 18.32 280.59 420 432 604 0.43 3 3 

24
25

The shape of species-subsample curves suggest that adding more subsamples in the pointintercept method would yield fewer additional species than would adding more subsamples in the 

ocular method (Figure 5a, b). For only two of the 14 plots for which we constructed speciessubsample curves did the point-intercept method add species at a greater rate than did the ocular 

method, and this was only at higher subsample numbers (Figure 5c). Only with the ocular method, 

and for only for two plots, did 20 subsamples reach the point where adding more subsamples would 

add no more species (Table 9). For the most diverse plots of each vegetation type, the average 

(over vegetation types) twentieth subsample added 0.53 and 0.78 species with the point-intercept 

and ocular methods, respectively. For the least diverse plots of each vegetation type, the average 

(over vegetation types) twentieth subsample added 0.19 and 0.30 species with the point-intercept 

and ocular methods, respectively. 

Figure 5. Species-sample number curves for plots with (A) the lowest and (B) the highest species 

richness from each vegetation type. Solid lines are for the ocular method, dashed lines for the 

point-intercept method. (C) Difference between ocular and point-intercept methods in the number 

of species added with an additional subsample. Open symbols are for plots with high species 

richness, closed symbols for plots with low species richness. Number of Species Species Added, Ocular - Point-Intercept

0

5

10

15

20

25

30

35

-2

0

2

4

6

8

10

Number of Subsamples

0 5 10 15 20

Number of Subsamples

0 5 10 15 20

Number of Species

0

10

20

30

40

50

60

70 (A) (B)

Grassland

Ponderosa Pine Forest

Badlands/Sparse

Prairie Dog Town

Forested Riparian

Herbaceous Riparian

Shrubland

Number of Subsamples

0 5 10 15 20

(C)
Table 9. Rate of species added with each subsample for the ocular and point-intercept sampling methods, base

subsample curves from EstimateS (Colwell 2005) for the most and least species-rich plots from each vegetation ty [Rate of species added was calculated as the cumulative number of species in subsample i minus the cumulative number of species in s

Badlands/Sparse Prairie Dog Town Forested Riparian Grassland Herbaceous Riparian Ponderosa Pine 

Sample Ocular Point Ocular Point Ocular Point Ocular Point Ocular Point Ocular Point O

Highest Species Richness 

1 6.95 6.68 13.02 3.56 10.95 10.95 13.06 8.00 5.55 4.69 9.40 4.94 12 5.47 4.15 6.28 1.88 7.14 5.45 8.33 3.85 3.01 1.49 5.54 2.42 73 4.59 3.20 4.12 1.38 5.35 3.75 6.15 2.68 2.19 1.01 4.01 1.76 4 4.00 2.72 3.01 1.09 4.25 2.89 4.90 2.06 1.73 0.83 3.07 1.42 55 3.57 2.40 2.37 0.90 3.53 2.38 4.08 1.70 1.44 0.72 2.47 1.21 46 3.24 2.16 1.95 0.77 3.03 2.02 3.49 1.46 1.23 0.65 2.05 1.04 7 2.97 1.97 1.70 0.67 2.68 1.78 3.07 1.30 1.09 0.59 1.79 0.93 8 2.49 1.81 1.36 0.61 2.20 1.58 2.51 1.17 0.89 0.55 1.44 0.83 29 2.40 1.68 1.28 0.55 2.08 1.42 2.36 1.07 0.82 0.51 1.34 0.74 2

10 2.20 1.57 1.15 0.51 1.89 1.28 2.12 0.98 0.73 0.47 1.20 0.67 211 2.00 1.48 1.05 0.46 1.74 1.17 1.93 0.91 0.65 0.45 1.09 0.61 212 1.84 1.39 0.96 0.44 1.60 1.07 1.77 0.85 0.57 0.42 0.98 0.55 13 1.69 1.31 0.89 0.41 1.49 0.97 1.63 0.80 0.52 0.39 0.91 0.50 14 1.55 1.24 0.82 0.39 1.39 0.89 1.50 0.75 0.46 0.36 0.82 0.46 15 1.43 1.18 0.77 0.37 1.30 0.82 1.40 0.71 0.42 0.34 0.77 0.42 16 1.31 1.12 0.72 0.36 1.21 0.74 1.29 0.67 0.39 0.31 0.71 0.39 17 1.21 1.06 0.68 0.34 1.14 0.66 1.21 0.64 0.36 0.29 0.66 0.35 18 1.11 1.01 0.65 0.33 1.07 0.60 1.14 0.60 0.33 0.27 0.62 0.33 19 1.03 0.96 0.62 0.33 1.01 0.54 1.06 0.58 0.32 0.25 0.58 0.30 20 0.95 0.92 0.60 0.32 0.95 0.47 1.00 0.55 0.30 0.23 0.55 0.28 

Lowest Species Richness 

1 5.60 3.96 5.66 4.94 4.60 3.89 4.65 1.40 6.45 6.24 5.70 2.20 2 2.97 2.25 3.99 1.72 1.89 1.43 1.56 0.65 2.81 1.70 3.00 1.11 3 2.37 1.56 2.86 1.25 1.44 0.79 1.26 0.54 1.51 0.78 2.47 0.97 24 2.06 1.15 2.07 1.01 1.12 0.56 1.09 0.45 0.91 0.51 2.07 0.87 25 1.83 0.89 1.50 0.85 0.93 0.46 0.95 0.39 0.60 0.41 1.77 0.78 6 1.64 0.69 1.10 0.72 0.80 0.40 0.84 0.33 0.43 0.36 1.54 0.71 7 1.48 0.56 0.80 0.63 0.71 0.35 0.77 0.29 0.31 0.33 1.36 0.65 8 1.24 0.45 0.57 0.55 0.59 0.32 0.64 0.25 0.24 0.31 1.13 0.61 9 1.16 0.38 0.43 0.49 0.56 0.28 0.61 0.22 0.18 0.28 1.06 0.56 

10 1.06 0.32 0.31 0.44 0.52 0.25 0.55 0.19 0.15 0.27 0.95 0.53 011 0.95 0.28 0.23 0.40 0.49 0.23 0.52 0.18 0.11 0.25 0.86 0.49 012 0.86 0.24 0.16 0.35 0.46 0.21 0.47 0.16 0.08 0.24 0.78 0.46 013 0.79 0.22 0.11 0.32 0.43 0.20 0.45 0.15 0.07 0.22 0.71 0.43 014 0.72 0.20 0.08 0.28 0.41 0.19 0.42 0.13 0.05 0.21 0.65 0.40 015 0.66 0.17 0.06 0.26 0.38 0.17 0.40 0.12 0.04 0.19 0.60 0.37 016 0.60 0.17 0.03 0.23 0.37 0.16 0.38 0.11 0.02 0.18 0.55 0.34 017 0.56 0.15 0.02 0.21 0.35 0.15 0.37 0.10 0.02 0.17 0.50 0.32 018 0.52 0.15 0.01 0.18 0.33 0.15 0.36 0.10 0.01 0.16 0.47 0.29 019 0.48 0.14 0.01 0.16 0.32 0.14 0.36 0.10 0.01 0.15 0.43 0.27 020 0.45 0.14 0.00 0.14 0.30 0.14 0.35 0.09 0.00 0.14 0.40 0.24 0

26
Efficiency 

Sampling time for the point-intercept method was significantly lower than for the ocular 

method (t = 9.50, P < 0.001). On average, it took 2.1 hours (SE = 0.22 hr) longer for a two-person 

team to complete the sampling of two transects with the ocular method than with the pointintercept method. The difference in sampling time between the methods increased with plot 

species richness (r

2

 = 0.26, P < 0.001), but not with total canopy cover (r

2

 = 0.03, P = 0.291). This 

was due to the fact that ocular sampling time increased significantly with plot species richness (r

2

 = 

0.34, P < 0.001; Figure 6a) and total canopy cover (r

2

 = 0.11, P = 0.025; Figure 6b), but pointintercept sampling time did not increase with plot species richness (r

2

 = 0.02, P = 0.344; Figure 6A) 

and only slightly with increasing total canopy cover (r

2

 = 0.09, P = 0.059; Figure 6B). 

Total Plant Cover (PI method)

0 50 100 150 200 250 300

Time to Complete (hours)

0

1

2

3

4

5

6

7

8

Plot Species Richness

0 20 40 60 80 100

Time to Complete (hours)

0

1

2

3

4

5

6

7

8

(A) (B)

Figure 6. Relationship between time to complete each sampling method (ocular = black circles 

and solid line; point-intercept = red triangles and dashed line) and (A) plot species richness or (B)

total plant cover as measured by the point-intercept method.

The two methods also differed significantly in the number of species they captured as a 

percentage of the number of species recorded in the plot walk-through (t = 13.37, df = 45, P < 

0.001). On average, the point-intercept method captured only 44% of the species recorded in the 

plot walk-through, whereas the ocular method captured 68% of a plot’s species. Capture rates did 

not vary by plot species richness or total canopy cover; correlations between capture rate and either 

of these variables were not significant (P > 0.10). Despite the lower number of species captured by 

the point-intercept method, this method was still more efficient than the ocular method in terms of 

the number of species captured per hour (difference = 3.05 ± 0.89 SE; t = 3.43, df = 45, P = 0.001). 

The low species-capture rate of the point-intercept method compared to the ocular method 

is somewhat troubling because the rare species are often of management concern, either as newly 

occurring exotic species or as species more sensitive to adverse environmental conditions (e.g., 

over-grazing). Therefore, we investigated in more detail the difference between the two methods in 

their detection and precision of estimating the cover of these less abundant species. In the entire 

study, there were 1,749 species-plot combinations. The point-intercept method failed to capture 

702 of these that the ocular method did; the average cover of these missed species (as measured by 

the ocular method) was 0.25%. The ocular method failed to capture 141 species-plot combinations; 

the average cover of these missed species (as measured by the point-intercept method) was 0.84%. 

Figure 7 shows the distribution, by cover, of all of these missed species-plot combinations. All of 

27
them had cover of less than 10%, and the vast majority of them from either method had cover of 

less than 0.5%. Based on these results, we calculated the precision with which the ocular method 

estimated the cover of species with 1% and 5% cover (Table 10). The number of subsamples 

required to attain 20% precision (0.2 or 1 % absolute cover) is extremely high; the precision 

obtainable with 20 subsamples is 100-500% of the cover value, depending on vegetation type and 

confidence level (Table 10). 

Cover

0.5 1.0 1.5 2.0 5.0 10.0

Count

0

20

40

60

200

400

600 Point-Intercept did not detect

Ocular did not detect

Figure 7. Number of times a method (point-intercept = black bars; ocular = red bars) did not 

detect a species that the other method did, for all 46 plots. 

Table 10. Standard deviations (mean, minimum, and maximum) of species with 1 and 5% cover 

from ocular sampling method by vegetation type. 

[Sample size needed for 20% precision around these values, and precision attainable with 20 subsamples with the 

ocular method for species with the mean standard deviation in each vegetation type are shown. α and β are the 

probability of type 1 and type 2 errors, respectively.] 

--N for 20% precision-- 

Absolute precision 

w/ 20 subsamples 

Vegetation Type 

Mean 

SD 

Min 

SD 

Max 

SD 

α=0.1 

β=0.2 

α=0.1 

β=0.1 

α=0.05 

β=0.1 

20% of 

Mean 

α=0.1 

β≤0.1 

α=0.05 

β≤0.1 

Species Cover = 1

BAD 2.79 1.69 4.02 556 570 799 0.2 2 2 

DOG 2.16 1.57 2.61 339 349 488 0.2 2 2 

FORRIP 3.76 2.66 4.70 995 1014 1426 0.2 2 5 

GRASS 2.32 0.67 3.80 389 400 349 0.2 2 2 

HERBRIP 3.01 2.43 3.84 644 659 926 0.2 2 2 

PIPO 2.56 1.93 4.02 470 483 677 0.2 2 2 

SHRUB 2.47 1.34 4.02 439 451 632 0.2 2 2 

Overall 2.65 0.67 4.70 503 516 723 0.2 2 2 

28
Table 10, continued. 

--N for 20% precision-- 

Absolute precision 

w/ 20 subsamples 

Vegetation Type 

Mean 

SD 

Min 

SD 

Max 

SD 

α=0.1 

β=0.2 

α=0.1 

β=0.1 

α=0.05 

β=0.1 

20% of 

Mean 

α=0.1 

β≤0.1 

α=0.05 

β≤0.1 

Species Cover = 5

BAD 13.52 5.66 21.24 523 536 752 1.0 10 10 

DOG 5.50 3.07 8.17 94 100 138 1.0 5 5 

FORRIP 15.78 5.27 22.14 706 722 1015 1.0 10 10 

GRASS 7.31 4.19 11.65 161 168 233 1.0 5 5 

HERBRIP 14.82 6.81 21.67 625 640 898 1.0 10 10 

PIPO 12.66 8.33 19.65 460 473 663 1.0 10 10 

SHRUB 9.67 7.03 13.70 274 283 396 1.0 5 10 

Overall 10.83 3.07 22.14 340 351 491 1.0 10 10 

Discussion 

To summarize the results: 

1. The two sampling methods yielded different values for many response variables, particularly 

graminoid cover and species richness. However, the values from the two methods were highly 

correlated. 

2. Using our protocols, both methods were highly repeatable for most community-level response 

variables (species richness with the point-intercept method being the exception) and cover of 

the most abundant species. Cover of less abundant species was less repeatable, and not 

repeatable at all for the median species with the point-intercept method. 

3. The number of subsamples necessary to achieve 20% precision around the mean (an arbitrarily 

chosen level of precision), or, conversely, the precision obtainable with a given number of 

subsamples, varied considerably depending on the type of vegetation and on the response 

variable of interest. Neither method had considerably greater precision than the other, although 

overall the variability among subplots was less with the point-intercept method than with the 

ocular method. 

4. The point-intercept method was significantly faster than the ocular method, and the time it 

required to complete was more consistent across variations in plot species richness and canopy 

cover. 

5. The ocular method captured significantly more (approximately half again as many) species than 

the point-intercept method did. However, the point-intercept method was more efficient in 

terms of species capture per hour. Also, the vast majority of species missed by the pointintercept method had a cover of less than 0.5%. 

All of these factors and more will need to be considered in making the final decision as to 

which method, and the exact form of that method (e.g., number of subsamples) will be used for the 

Network’s long-term plant community monitoring. To make the best decision, these results must 

be taken in their proper context. To that end, we interpret our results and their implications for 

long-term monitoring. 

29
Comparison of Values Obtained by Each Method 

The difference in values obtained for most of the community-level response variables 

between the two methods is not surprising. Our results are consistent with those of two other 

recent studies. Kercher et al. (2003) compared cover values obtained from a line-intercept method 

to those from ocular cover class estimates in herbaceous wetlands, and Miller et al. (2006) 

compared cover values of point-intercept and ocular methods very similar to ours in a variety of 

ecological sites in National Park Service units on the Colorado Plateau. Both of these studies 

found that cover values were consistently lower when measured by the ocular method than when 

measured with an intercept method. Miller et al. (2006) attributed much of this difference between 

methods to the greater cover values obtained for graminoids with the point-intercept method than 

the ocular method, which is also consistent with our results. 

The implications of this difference in values for the two methods are three-fold. First, it 

implies that data from the two methods should not be compared unless one is converted to the 

other. The relatively high correlation coefficients between the two methods that we found for most 

response variables suggest that such a conversion could be quite reliable, with species richness 

being the exception. However, the relationships we obtained in this study may not be equally 

applicable across all types of vegetation. Thus, if data were to be compared between sampling 

methods, we advise that the methods should be carefully calibrated in the vegetation type in which 

the comparison will be done. This is particularly relevant to the two parks in the Network that have 

vegetation monitoring plots established as part of the prototype Long-Term Ecological Monitoring 

program. If the monitoring method used in these plots were switched from the current ocular 

method to the point-intercept method, the two methods should be used in all plots for a few years to 

obtain a clear calibration. 

The second implication is that, when quantitative objectives are set for management 

purposes, these objectives should take into account the method by which those objectives will be 

measured. For example, in our study the ocular method yielded consistently lower graminoid cover 

values than the point-intercept method. Because some of the most abundant non-native species in 

Network parks are grasses (e.g., smooth brome, annual brome species, crested wheatgrass), it could 

be easier to reach a target of, say, 20% cover for one of these species if the measurement is done 

using the ocular method rather than the point-intercept method. 

Finally, it is not clear which method more accurately represents the true cover of vegetation. 

Because the point-intercept method assumes an infinitesimally small point, but actual points used 

in sampling have a diameter, the point-intercept method inevitably over-estimates cover compared 

to the true value (Winkworth 1955). Because we used Daubenmire’s (1959) polygon method, the 

cover values we obtained with the ocular method were also inevitably over-estimates of true cover, 

which would be the amount of vertical light intercepted by leaf tissue. One could conceivably 

evaluate the accuracy of these two methods with model vegetation, but that is beyond the scope of 

this project. Not knowing the accuracy of either method is not necessarily a problem for long-term 

monitoring, however, as long as the sampling method is consistently applied across space and 

through time and interpretation of the information – in setting management goals and evaluating 

management actions, for example – takes into account the peculiarities of the sampling method 

used. 

Repeatability among Observers 

The high repeatability for both methods for most response variables is encouraging. 

Although the differences between the two teams were significant for every response variable with 

both methods, the differences were in most cases a relatively small proportion of the values 

30
recorded. For example, the difference in total canopy cover between the two teams was 4.6% of 

the cover for the ocular method and 9.2% of the cover for the point-intercept method. In 

comparison, these differences are smaller than the level of precision that we obtained for these 

variables (Tables 7 and 8). 

The ocular method seemed to have greater repeatability than the point-intercept method for 

total canopy cover, the cover of the second most abundant species and the cover of the median 

species. The greater difference between teams for total canopy cover for the point-intercept 

method could simply be due to the fact that the point-intercept method produced greater values of 

total canopy cover overall. Similarly, the ocular method’s much lower difference in values 

between teams for median species cover is due to the fact that the cover of the median species was 

much lower in the ocular method (Table 7 vs. Table 8). However, the very low repeatability of the 

median species cover with the point-intercept method suggests that this method is not very good for 

estimating the cover of low-abundance species. We will discuss this topic in more detail in a later 

section. 

We were somewhat surprised at the high repeatability of the ocular method, but our results 

are consistent with Miller et al. (2006), who found no significant difference in repeatability 

between ocular and point-intercept methods. On reflection, we think we can attribute the high 

repeatability of the ocular method to two factors. First, we believe we were able to be much more 

consistent in our estimates of graminoid cover by following the Daubenmire method of estimating 

the cover of an individual plant based on a polygon encompassing the plant’s outer-most points 

than we would have if we had tried to estimate the amount of light intercepted by the foliage of a 

plant, a method used by some in ocular cover estimates. Second, having two people working 

together on each frame made it necessary for those two observers to agree on a number. This most 

likely moderated the tendencies of some individuals to estimate high or low compared to others 

(Klimeš 2003). 

Despite the high repeatability of the ocular method that we found in this study, we are 

somewhat skeptical that this high rate could be sustained over time, particularly in the case where 

there is a large turnover in crew members. To illustrate, during our training at the beginning of the 

season, the first estimates of the cover of an individual grass species in a frame ranged from 2% to 

30%. Although the individuals with the extremely low values were eventually convinced that they 

were underestimating the cover, it is possible that a crew in one year could all be low estimators 

compared to crews in other years. Thus, if the ocular method is the one chosen for long-term 

monitoring, we recommend that a training manual consisting of photographs of real vegetation in a 

sample frame and cover values assigned to each species visible in that frame be produced and used 

in training each year. We would recommend this even if there was little turnover between years. 

In addition, we would recommend establishing a standard deviation among crew members that 

must be attained on training plots before the crew begins sampling real plots to assure consistent 

levels of within-crew variability. 

Repeatability of both methods could probably be improved beyond our rate if two issues are 

addressed. First is the issue of “tape creep”. One plot in badlands vegetation at THRO (TPCM03) 

had particularly low compositional similarity between sampling teams (Table 5). Comparison of 

the data from the two teams and field notes suggest that this was primarily due to variation in the 

placement of the transects from one sample team to the next, in this case because of rough terrain 

(Figure 8a). Two other plots with relatively low similarity between teams, WPCM09 and 

WPCM12 (Table 5), were also on rough terrain and/or had dense woody vegetation near ground 

level that may have made accurate re-placement of the transects difficult (Figure 8b, c). No matter 

which method is used for the long-term monitoring, we highly recommend that a larger number of 

markers be used to designate the location of transects. 

31
(A) 

(C) 

Figure 8. Photos of plots showing variability in terrain and obstructions to transect tapes. (A)

TPCM03, (B) WPCM09, (C) WPCM12.

The second, and more difficult, issue is species identification, which surely caused species 

pseudoturnover. There were definite cases of different identification of an individual species between 

teams – Schizachne purparescens versus Danthonia spicata or Alyssum desertorum versus Lepidium 

densiflorum, for example. Also, there were many cases that suggest that one team was able to identify 

a species whereas the other could not. For at least two of the plots, one team had many more unknown 

species than did the other team. Finally, there seem to be instances in which one team was more 

discriminating in species identification than was the other team, in that the number of species recorded 

by one team but not the other was highly uneven between teams. More training of individuals on 

recording teams and careful attention to always having at least one team member highly skilled in 

plant identification on each plot may reduce this problem of repeatability, but there will always be 

variability in experience and skill levels of observers. One method to increase the likelihood that 

species will be identified the same way from year to year is to have a cumulative list of a plot’s 

previous years’ species in the field when sampling is done. We recommend that previous years’ 

quantitative data not be available, however, in order to avoid influencing team members’ observations. 

Precision and Number of Subsamples 

The ocular method’s slightly higher variability among subsamples within a plot compared to 

the point-intercept method’s made the estimates of the response variables slightly more precise with 

the latter. Although it is difficult to attribute this difference to any one factor, it is possible that a large 

part of it was caused simply by the larger overall cover values obtained with the point-intercept 

method and the larger number of species captured by the ocular method. Whatever the cause of this 

slight difference, it did not substantially affect the precision obtainable by the two methods, as we 

tested them. Both provided precision of 20% around the mean on the estimates for just two of the key 

32
33

community-level response variables (total canopy cover and species richness), and none of the 

species-level response variables. Neither method proved very good at obtaining precise measurements 

of cover for individual species other than the most abundant one. Also, 20 subsamples did not provide 

very precise estimates for any of the response variables in the most variable vegetation type, the 

Badlands/Sparse type. 

In addition to increasing precision, adding subsamples would, in most cases, increase the 

number of species quantitatively sampled by either method. Species-sample number curves show that 

this increase would be greater for the ocular method, but the number of additional species captured by 

adding more subsamples would not be substantial for either method. 

The most straight-forward means for adding subsamples would be to decrease the distance 

between subsamples – frames or points in the ocular and point-intercept methods, respectively. 

Subsamples could instead be added to the interior of the plot. Floyd and Anderson (1987) found that 

sampling more lines generally increased precision more than adding samples to a line. Whether this 

would hold true with our arrangement of lines is unclear because of the potential for encountering 

greater variability in the interior of the plot. Adding subsamples by adding lines in the interior may 

also capture additional species, but field comparisons would be necessary to test this. 

With either method, adding more subsamples would require more sampling time at an 

individual plot. Since a plot contributes just one sample – not the number of subsamples – to the longterm monitoring design, the time added in increasing the precision of that one estimate must be 

weighed against the time that could be spent sampling another plot instead. Also, the precision 

estimates we generated here are useful for understanding the precision of estimates for the status of a 

response variable within a plot at any given time. They do not indicate the precision of the sampling 

methods for measuring trends over time; measurements of year-to-year variability are necessary for 

making these precision estimates. When such data are available, the precision estimates provided here 

may be used for optimizing the number of subsamples within a plot and the number of plots to meet 

status and trends monitoring objectives. 

Efficiency 

The greatest difference between the two sampling methods as we executed them was the 

efficiency, both in terms of the time to complete the sampling and in the number of species captured 

by the method. The point-intercept method was clearly more efficient, and more predictable, in terms 

of time, whereas the ocular method was clearly better at capturing more species quantitatively. 

However, those species captured by the ocular method but missed by the point-intercept method all 

had relatively low abundance. As shown and discussed above, the precision on the estimate of their 

abundance is quite low. Thus, we believe that the greater species capture of the ocular method does 

not make up for the greater amount of time that it takes to complete. 

The amount of time needed to complete the ocular method could be reduced by using cover 

classes instead of estimating cover to the nearest 1%. In this case, the standard practice for data 

analysis is to use the mid-point of the cover class as the datum for a species. Analyses then treat the 

data as continuous. This practice has been questioned, however, because plant abundances generally 

are not distributed evenly around the midpoint of a cover class, and using analytical methods designed 

for continuous data for ordinal data is not legitimate (Podani 2006). It is for these reasons that we did 

not use cover classes in this study and we do not think they should be used for long-term monitoring. 

 The difference in completion time between the two methods is substantial enough to 

significantly impact the number of plots that could be sampled in a given field season. The average 

amount of time to complete the ocular method was 3.8 hours (for two people), whereas it was exactly 

half that (1.9 hours for two people) for the point-intercept method. Thus, a team of four people (the 

usual sample team size) could complete the ocular and point-intercept sampling in 1.9 and 0.95 hours, 

respectively. If plot set-up, tear-down, and compiling the plot-wide species list required an additional 

1.5 hours, the total time for the ocular and point-intercept methods would be 3.4 and 2.4 hours, 

respectively. In a 10-hour day, completing three plots with the point-intercept method would be 
34

doable, whereas it would be impossible with the ocular method since considerable travel time to plots 

is often involved. 

Travel time to plots must be considered in the final decision of which method will be used. In 

many of the parks in this Network, travel time is insubstantial compared to the amount of time spent 

sampling a site. However, at the three largest parks, the amount of time it takes to reach a sampling 

site could easily be greater than the amount of time necessary to do the sampling. Consequently, the 

argument could be made that the benefit of obtaining more or better information at a distant site 

outweighs the cost of extra time at a site to obtain that information. The question in our case, then, is 

whether the information obtained by the ocular method is truly more or better than that obtained with 

the point-intercept method. We address this further in the Conclusions and Recommendations section 

below. 

Impacts of Sampling on Vegetation 

Although we did not quantitatively measure the impacts of the two sampling methods on the 

areas sampled, we did make some observations that are relevant to deciding which method to use for 

long-term sampling. With the point-intercept method, the person recording the data generally sits or 

stands at least a couple of meters from the transect, usually on the outside of the plot. The person 

doing the measurements walks along the transect, generally standing or kneeling (if necessary to 

identify a species) at any given point for a short amount of time (3 to 60 seconds). The area walked on 

in the course of this sampling is thus a fairly continuous line of short-duration impact. With the ocular 

method, since both team members are making the observations, both stand or kneel close to the frame 

that is being sampled, usually one on each side. The amount of time spent at an ocular frame ranged 

from 0 to 78 minutes, with the mean and median being 12 and 10 minutes, respectively. Thus, the 

area impacted with this method was longer duration, but more spatially concentrated, than with the 

point-intercept method. In most cases, the area trampled by sampling was not visible after a few days, 

but in areas with sparse cover and/or steep terrain, or in the wet vegetation of the Herbaceous Riparian 

areas at AGFO, trampling was still visible at least a week after the initial sampling. 

Conclusions and Recommendations 

Based on our results, there is no clear-cut advantage of using one method over the other. 

However, we recommend the point-intercept method for long-term plant community composition 

monitoring in the Northern Great Plains Network for the following reasons. First, it is faster, probably 

has less physical impact on the vegetation, and is as repeatable as the ocular method. Second, the Fire 

Effects program has been using this method in eight of the 12 parks in which plant community 

composition will be monitored; in some parks, this monitoring began in 1998. On the other hand, a 

variation on the ocular method has been used in only two parks in the Network, since 1997. 

Although others have also recommended the point-intercept method over the ocular method for 

its greater efficiency (e.g., Floyd and Anderson 1987), some have argued that the large number of 

species that are missed by this method make it undesirable (Stohlgren et al. 1998). Our results suggest 

that the precision on the cover estimates for the species that are missed by the point-intercept method 

is quite low anyway. However, we are concerned about missing these low-abundance species. 

Consequently, we recommend the following modifications to the protocols we followed in this pilot 

study to increase the amount and quality of information collected on these low-abundance species: 

1. Sample more transects in each plot. Adding five evenly spaced, 20-m transects perpendicular to 

the two 50-m transects (Figure 9) would hopefully capture a greater number of species because the 

coverage of the plot’s area would be more complete. The endpoints of these additional transects 

would be permanently marked to reduce tape creep on the 50 m transects. The distribution of 

points along these transects (i.e., whether the number of points for the whole plot would be greater 

than the 200 we used in this study or if the 200 points would be re-distributed among all transects) 

will depend on whether a larger number of subsamples is desired. If the number of points is kept 
the same, we anticipate that this rearrangement of points would substantially increase neither the 

sampling time nor the amount of trampling of the plot, since the amount of time spent at an 

individual point would not be significantly greater than that spent in the plot-wide species search. 

However, we will test these assumptions on a small number of plots before incorporating this 

change into the protocol. 

2. For all species on the plot-wide species list, assign an abundance-dominance or cover class based 

on their abundance in the whole plot. The Braun-Blanquet (Mueller-Dombois and Ellenberg 

1974) and modified Daubenmire scales (Table 11) are two examples of scales that could be used. 

These ordinal data could be analyzed using ordinal methods, or they could simply be used as 

background information for interpreting trends in the more quantitative data collected with the 

point-intercept method. Because of its slightly greater resolution of the lower cover classes (the 

end of the scale we are most interested in for this part of the protocol), we recommend the BraunBlanquet scale. 

X 

X X X X X 

X X X X 

20m 

50m

Figure 9. Suggested modification of transect layout for point-intercept sampling. Red “X” indicates 

permanent marker. Black dots are approximate locations of points, spaced at 1-m intervals. Blue 

boxes are 1-m2

 species-occurrence subplots. 

Table 11. Potential cover class scales to use for recording abundance of species in 1000 m2 plot. 

Braun-Blanquet modified Daubenmire 

Class Cover-Abundance Class Cover 

r one individual – insignificant cover 1 <1% 

+ few individuals – insignificant cover 2 1-5% 

1 scattered individuals – 1-5% cover 3 6-25% 

2 6-25% cover 4 26-50% 

3 26-50% cover 5 51-75% 

4 51-75% cover 6 76-95% 

5 76-100% cover 7 96-100% 

A substantial modification to address this issue that would take more time but could have 

substantial benefits would be to measure species occurrence in subplots (e.g., 1 m2) within the 0.1 ha 

plot. An example of a potential arrangement of such plots is shown by the blue squares in Figure 9. 

One benefit of adding this type of sampling is greater comparability of species richness values to those 

in most published literature, as well as to those obtained by the Heartland I&M Network and 

potentially other I&M Networks that will be sampling grassland vegetation. In addition, data from 

35
36

these plots could be used for measuring frequency of occurrence or investigating spatial dispersion 

patterns of species within the plots. Finally, by forcing the search of small subplots for individual 

species, it is less likely that species will be missed for the whole-plot species list. The amount of time 

spent on a modification like this, as well as the extra trampling of the vegetation that would occur, 

would have to be tested in the field and weighed against the benefits of obtaining this information. 

For example, Figure 9 shows 10 subplots in a 0.1 ha plot. Although this number is not enough to yield 

20% precision of the estimate of species richness for any but the grassland and prairie dog town 

vegetation types (Table 7), preliminary analyses of data from Scotts Bluff National Monument and 

AGFO suggest that it would be enough to detect a change of 1.5 species at a single site. More data 

from different vegetation types and more thorough analyses are needed before a decision as to whether 

the extra time spent on this effort would be beneficial. 

Additional modifications that could resolve some issues encountered during the course of this 

study include: 

1. Follow an established classification of ground cover. Examples include those used by the Natural 

Resource Conservation Service for rangeland sampling. This will eliminate dissatisfaction 

expressed by crew members with our definition of “rock” as anything larger than a dime, vs. “soil” 

as anything the size of a dime or smaller. 

2. For woody species, record both live and dead material attached to a live plant in the herbaceous 

layer, but distinguish between live and dead material. This will be consistent with current Fire 

Effects monitoring protocols (USDI National Park Service 2003) and will provide useful 

information in the case of shrub die-back. 

Other issues that need to be resolved to complete the protocol for plant community sampling 

include: 

1. Whether biomass and/or structure of herb-layer vegetation (< 2 m height) will be measured. As 

we explained in the Introduction, we would not consider measuring biomass by individual species 

because of the logistical problems. However, because of the relevance of standing biomass to both 

fire behavior and forage availability, some or all parks may want information on the total standing 

biomass in the herb layer. Structure, measured as height or height/density (Robel et al. 1970, 

Benkobi et al. 2000) is of particular relevance to grassland nesting birds and could, with 

calibration, be used as a surrogate for standing biomass. 

2. Shape and distribution of subplots for sampling woody vegetation greater than 2 m tall (trees, 

poles, and saplings). 

3. The amount of heterogeneity allowable within a plot, and whether we should maintain the practice 

of aligning the long edge of a plot parallel to the topographic contours. By following this rule, the 

expectation is that heterogeneity within the plot will be decreased, although the validity of this 

assumption is not clear at the scale of a 20 m x 50 m plot (Keeley and Fotheringham 2006). In 

addition, this does not necessarily provide a representative sample of the park as a whole. This 

issue is particularly relevant in areas where there are steep topographic and vegetation gradients in 

small areas, such as in woody draws or along streams. 

4. The spatial and temporal sampling distribution of plots within and across parks. 
37

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Bonham, C.D., 1989, Measurements for terrestrial vegetation: New York, Wiley. 

Cogan, D., Marriot, H., Von Loh, J., and Pucherelli, M., 1999, USGS-NPS Vegetation Mapping 

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Critchley, C.N.R., and Poulton, S.M.C., 1998, A method to optimize precision and scale in grassland 

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33, p. 43-64. 

DeBacker, M.D., Sasseen, A.N., Becker, C., Rowell, G.A., Thomas, L.P., Boetsch, J.R., and Willson, 

G.D., 2004, Vegetation community monitoring protocol for the Heartland I&M Network and Prairie 

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Helm, D.J., and Mead, B.R., 2004, Reproducibility of vegetation cover estimates in south-central 

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Keeley, J.E., and Fotheringham, C.J., 2006, Plot shape effects on plant species diversity 

measurements: Journal of Vegetation Science, v. 16, p. 249-256. 

Kercher, S.M., Frieswyk, C.B., and Zedler, J.B., 2003, Effects of sampling teams and estimation 

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Klimeš, L., 2003, Scale-dependent variation in visual estimates of grassland plant cover: Journal of 

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Magurran, A.E., 1988., Ecological diversity and its measurement: Princeton, NJ, Princeton University 

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Miller, M.E., Witwicki, D., and Mann, R., 2006, Field-based evaluations of sampling methods for 

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Mueller-Dombois, D., and Ellenberg, H., 1974, Aims and methods of vegetation ecology: New York, 

Wiley and Sons. 

Nilsson, I.N., and Nilsson, S.G., 1985, Experimental estimates of census efficiency and 

pseudoturnover on islands: error trend and between-observer variation when recording vascular 

plants: Journal of Ecology, v. 73, p. 65-70. 

Podani, J., 2006, Braun-Blanquet's legacy and data analysis in vegetation science: Journal of 

Vegetation Science, v. 17, p.113-117. 

Robel, R.J., Briggs, J.N., Dayton, A.D., and Hulbert, L.C., 1970, Relationships between visual 

obstruction measurements and weight of grassland vegetation: Journal of Range Management, v. 

23, p. 295-297. 

Salas, D.E., and Pucherelli, M.J., 1998, USGS-NPS Vegetation Mapping, Devils Tower National 

Monument, Wyoming: Computer Data Systems, Inc., and Bureau of Reclamation. 
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SAS Institute Inc., 2004, SAS OnlineDoc® 9.1.2: Cary, NC, SAS Institute Inc. 

Stohlgren, T.J., Bull, K.A., and Otsuki, Y., 1998, Comparison of rangeland vegetation sampling 

techniques in the central grasslands: Journal of Range Management, v. 51, p.164-172. 

Symstad, A.J., 2004, Interim Report: Development of the Vegetation Component of the National Park 

Service's Northern Great Plains Inventory and Monitoring Network's "Vital Signs" Monitoring Plan: 

U.S. Geological Survey, Northern Prairie Wildlife Research Center, Keystone, SD. 

USDI National Park Service, 2003, Fire Monitoring Handbook: Fire Management Program Center, 

National Interagency Fire Center, Boise, ID. 

Von Loh, Cogan, J.,D., Butler, J.L., Faber-Langendoen, D., Crawford, D., and Pucherelli, M., 2000, 

USGS-NPS Vegetation Mapping Program, Theodore Roosevelt National Park, North Dakota: U.S. 

Department of the Interior, Bureau of Reclamation's Remote Sensing and GIS Group, Denver, CO. 

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Botany, v. 3, p. 68-81. Cited in Higgins, K.F., Jenkins, K.J., Clambey, G.K., Uresk, D.W., Naugle, 

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p. 523-553
39

Appendix A 

Maps and GPS Coordinates of Plots Used in Methods 

Comparison Study 

Notes: All GPS coordinates are in UTM units following the NAD 83 projection, Zone 13T. Maps 

show broad vegetation categories based on vegetation mapping from the National Vegetation 

Classification System. Specific sources of vegetation maps are described in the Methods section of 

the main text under “Sample Sites.” 
40

Agate Fossil Beds National Monument (AGFO) 

Plot Vegetation Type Corner Northing Easting 

APR02 Herbaceous riparian A0 4697797.56 604714.65 

A50 4697751.30 604689.30 

B0 4697808.00 604695.95 

B50 4697762.94 604670.58 

APR03 Herbaceous riparian A0 4697262.29 603619.51 

A50 4697303.54 603588.89 

B0 4697274.44 603635.23 

B50 4697315.10 603605.05 

APR04 Herbaceous riparian A0 4697003.73 602407.59 

A50 4697053.15 602405.54 

B0 4697002.20 602425.71 

B50 4697053.42 602424.52 

APR05 Herbaceous riparian A0 4697113.25 601666.13 

A50 4697121.47 601617.01 

B0 4697093.53 601662.45 

B50 4697101.73 601612.00 

APR06 Herbaceous riparian A0 4697066.92 601348.09 

A50 4697105.51 601379.75 

B0 4697054.64 601364.16 

B50 4697093.22 601394.94 

LTEM03 Grassland A0 4696978.49 604837.81 

A50 4696934.17 604859.32 

B0 4696987.33 604855.82 

B50 4696942.34 604877.31 

LTEM04 Grassland A0 4696446.57 604828.22 

A50 4696414.14 604865.99 

B0 4696461.85 604840.97 

B50 4696428.81 604879.07 

LTEM05 Grassland A0 4696367.29 604868.98 

A50 4696339.51 604910.28 

B0 4696359.37 604863.74 

B50 4696332.05 604905.15 

LTEM07 Grassland A0 4697056.82 604936.26 

A50 4697009.11 604949.95 

B0 4697051.14 604917.24 

B50 4697003.53 604930.91 

LTEM08 Grassland A0 4697519.44 604452.38 

A50 4697472.23 604436.04 

B0 4697527.40 604434.12 

B50 4697480.35 604417.79 

LTEM11 Grassland A0 4696571.18 603477.59 

A50 4696533.17 603444.89 

B0 4696584.30 603461.92 

B50 4696545.62 603429.95 
41
42

Devils Tower National Monument (DETO) 

Plot Vegetation Type Corner Northing Easting 

DPCM01 Prairie dog town A0 4936909.50 523297.31 

A50 4936945.28 523332.40 

B0 4936895.24 523312.59 

B50 4936930.51 523347.24 

DPCM02 Forested riparian A0 4936773.75 523703.78 

A50 4936742.20 523665.82 

B0 4936790.23 523690.53 

B50 4936757.72 523652.98 

DPCM03 Pine woodland A0 4936585.83 521923.11 

A50 4936567.82 521967.06 

B0 4936599.84 521929.72 

B50 4936585.42 521976.28 

DPCM04 Pine woodland A0 4936871.55 521550.77 

A50 4936907.75 521506.79 

B0 4936891.26 521565.18 

B50 4936921.52 521525.05 

DPCM05 Pine woodland A0 4938004.57 522668.91 

A50 4938052.21 522662.35 

B0 4938011.19 522687.62 

B50 4938058.84 522682.34 
43
44

Fort Laramie National Historic Site (FOLA) 

Plot Vegetation Type Corner Northing Easting 

FP01 Forested riparian A0 4673108.24 538504.55 

A50 4673148.29 538475.61 

B0 4673120.36 538517.85 

B50 4673158.74 538492.03 

FP04 Grassland A0 4672906.24 537554.98 

A50 4672894.82 537507.08 

B0 4672886.38 537507.08 

B50 4672873.68 537511.85 

FP07 Grassland A0 4672722 536298 

A50 missing missing 

B0 missing missing 

B50 missing missing 

FP11 Grassland A0 4672589.49 537838.82 

A50 4672592.31 537788.25 

B0 4672568.58 537835.91 

B50 4672572.82 537785.89 

FP16 Sparse vegetation A0 4672292.32 537514.23 

A50 4672323.8 537472.59 

B0 4672275.42 537496.43 

B50 4672309.41 537459.20 

FP18 Grassland A0 4672272.88 538121.66 

A50 4672259.30 538174.53 

B0 4672288.78 538125.58 

B50 4672277.90 538174.68 

FP21 Grassland A0 4672087.77 536862.30 

A50 4672087.97 536909.61 

B0 4672105.87 536858.68 

B50 4672117.01 536907.18 

FP24 Grassland A0 4672004.22 537783.92 

A50 4672003.36 537833.48 

B0 4672022.63 537784.50 

B50 4672023.23 537835.40 
45
46

Theodore Roosevelt National Park (THRO) 

Plot Vegetation Type Corner Northing Easting 

TPCM01 Badlands/Sparse A0 5198413 620406 

A50 5198457 620433 

B0 5198406 620426 

B50 5198448 620450 

TPCM02 Badlands/Sparse A0 5198410 620405 

A50 5198402 620357 

B0 5198390 620411 

B50 5198383 620361 

TPCM03 Badlands/Sparse A0 5198163 620386 

A50 5198128 620419 

B0 5198148 620373 

B50 5198112 620408 

TPCM04 Badlands/Sparse A0 5197763 621985 

A50 5197804 621959 

B0 5197762 621978 

B50 5197802 621950 

TPCM06 Shrubland A0 5202319 615330 

A50 5202314 615381 

B0 5202339 615333 

B50 5202332 615382 

TPCM07 Shrubland A0 5202671 614754 

A50 5202685 614803 

B0 5202687 614751 

B50 5202704 614796 

TPCM08 Shrubland A0 5202548 614889 

A50 5202512 614922 

B0 5202561 614903 

B50 5202526 614938 

TPCM09 Shrubland A0 5203299 614593 

A50 5203337 614564 

B0 5203311 614608 

B50 5203349 614579 

TPCM10 Forested riparian A0 5203356 614494 

A50 5203350 614544 

B0 5203375 614497 

B50 5203370 614546 

TPCM11 Badlands/Sparse A0 5198355 619550 

A50 5198362 619501 

B0 5198377 619552 

B50 5198380 619502 
47
48

Wind Cave National Park (WICA) 

Plot Vegetation Type Corner Northing Easting 

WPCM01 Prairie dog town A0 4821763.64 622949.14 

A50 4821723.68 622979.79 

B0 4821776.44 622964.52 

B50 4821735.86 622994.31 

WPCM02 Prairie dog town A0 4825866.38 629569.93 

A50 4825860.63 629519.34 

B0 4825845.58 629573.36 

B50 4825840.44 629523.19 

WPCM03 Grassland A0 4830670.72 630181.62 

A50 4830623.31 630195.54 

B0 4830677.06 630200.99 

B50 4830629.04 630214.48 

WPCM04 Grassland A0 4824182.01 623689.34 

A50 4824138.63 623663.70 

B0 4824193.61 623672.22 

B50 4824150.24 623647.02 

WPCM05 Grassland A0 4825977.02 631701.87 

A50 4826008.44 631662.69 

B0 4825992.77 631714.56 

B50 4826023.58 631674.96 

WPCM06 Pine woodland A0 4829911.24 622098.86 

A50 4829943.97 622063.62 

B0 4829911.55 622115.75 

B50 4829947.86 622080.44 

WPCM07 Pine woodland A0 4827121.90 621401.09 

A50 4827092.57 621360.90 

B0 4827106.11 621417.84 

B50 4827080.30 621374.55 

WPCM09 Shrubland A0 4828137.13 629691.97 

A50 4828159.57 629650.39 

B0 4828152.79 629700.33 

B50 4828177.01 629658.28 

WPCM10 Forested riparian A0 4826830.31 621819.66 

A50 4826829.36 621767.26 

B0 missing missing 

B50 4826823.39 621865.28 

WPCM11 Grassland A0 4821630.89 620390.52 

A50 4821642.55 620342.62 

B0 4821612 620386 

B50 4821622.79 620337.33 

WPCM12 Pine woodland A0 4827132.69 623396.69 

A50 4827184.52 623395.31 

B0 4827125.20 623378.20 

B50 4827174.09 623379.47 

WPCM13 Pine woodland A0 4832137.17 622454.39 

A50 4832168.29 622493.65 

B0 4832157.27 622445.37 

B50 4832184.76 622482.09 
49
50

Appendix B 

Preliminary Operating Procedures Followed for Field 

Sampling in Methods Comparison Study 
51

Preliminary Operating Procedure (POP) 1: Establishing and Marking 

Permanent Sample Plots 

Version 1.1 (March 2006) 

Description: This POP gives step-by-step instructions for establishing and marking sample plots. It 

provides instructions for marking transect ends and the correct procedure for labeling rebar tags. 

I. Equipment List 

• Park map showing site location 

• GPS unit (1) 

• Clinometer (1) 

• Compass (1) 

• Digital camera 

• Pliers 

• Corner markers: 7” galvanized pole barn nails (4) and 1-3/8” outer-diameter galvanized washers 

(4) OR 24” rebar with one end bent over (4) 

• Hammer 

• Labeled tags 

• Wire 

• 50-m tapes marked for point-intercept (2) 

• 30-m tapes (2) 

• Data Form 1 
52

II. Procedures 

1. Sample sites are located within each park using varying criteria for each park. The plot 

establishment team goes to the designated site using a hand-held GPS receiver in which previously 

designated coordinates, in the UTM NAD83 projection, are stored. This point serves as the A0 

corner of the plot. 

2. The primary sampling unit is a 50 m x 20 m rectangular plot with the 50-m transects running 

parallel to topographic contours. The upslope (A) transect is laid out first. At the site GPS 

coordinates one plot marker is sunk into the ground (nail through a washer) so that approximately 

5 cm of the marker is above the ground level. This is designated corner A0. An azimuth that runs 

parallel to the contours is determined. Since two possible directions (one 180° from the other) are 

possible with this instruction, a coin is flipped to choose which direction will be used. If the area 

is flat, an azimuth is chosen randomly. A 50-m tape is then run out 50 m along this azimuth and a 

second marker into the ground; this is corner A50. An azimuth perpendicular to and heading 

down slope from the first transect line is determined using the 3-4-5 rule of legs and hypotenuse of 

a right triangle and the appropriate tapes. If the area is flat, a coin is flipped to determine which 

direction is “down slope.” The endpoint of the second transect (corner B50 on transect B) is 

established 20 m from A50 on this azimuth, with the third corner marker being driven into the 

ground here. The final corner (B0) is established by running a second 50-m tape parallel to and 20 

m from transect A and driving the fourth marker into the ground at this point. Squareness of all 

corners and distances are checked to ensure that the plot is a 20 m x 50 m rectangle. The area of 

the rectangle for the plot is based on slope distance (the area on the ground) rather than on 

horizontal distance, as this is the truest representation of the area on the ground. Note that all plot 

markers should protrude approximately 5 cm from the ground surface – this low profile reduces 

the chance of vandalism to the markers, excessive interest in the plot by people or wildlife, and 

injury to human or non-human passers-by. 

3. Once all four corner markers are established, brass tags labeled with plot information as shown in 

Figure B1.1 are attached to each marker with wire. 

4. Three photos are taken at the sampling plot. The “Overview” photo is taken from a point in the 

vicinity of the plot so that it captures as much of the plot as possible (with tapes in place) in the 

greater landscape context. The location of the “Transect A” photo is 10 m outside the plot from 

the A0 corner on the line extending Transect A. The photo is directed towards the A50 corner, 

along the transect. The “Transect B” photo is located and directed similarly, except that it is taken 

10 m outside the plot from the B50 corner, looking towards the B0 corner. Photo numbers for 

each photo are recorded on Data Form 1. 

5. Relocation data for each transect are collected. GPS coordinates for all four corners of the plots 

are taken using real-time differential correction for the GPS, which yields approximately 1-m 

accuracy. Metadata are recorded on Data Form 1 “Plot Location and Description Sheet for Plant 

Community Monitoring” (see section III for detailed instructions on Data Form 1). 
DETO PCM10-B50

 NGP Network 

 06/12/05

Park Code Plot 

ID 

Corner 

Name 

Program ID 

Date established 

Figure B1.1. Example tag for plot corner markers. Format shown is for tags on the A0 corner; A50, B0, 

and B50 tags have only the tag (corner) name and establishment date.

III. Collecting and Recording Data 

When a plot is initially established, Data Form 1 “Plot Location and Description Sheet for 

Plant Community Monitoring” is filled out by the field crew involved in the plot installation. The 

intent of the survey is to prompt the field crew to leave a description that others can follow to re-locate 

the plot and to assist in characterizing the structure and composition of the site. Notes about the plots 

should also be noted on this datasheet, including information about disturbance that appears to have 

occurred. 

Park: A unique 4 digit code (example: AGFO Agate Fossil Beds National Monument) 

Park Unit: Management unit or other descriptive location name (example: Carnegie Hill Unit) 

Plot ID: A unique sample unit identification (example: DPCM10, where D stands for Devils Tower 

NM and PCM stands for “Plant Community Monitoring”) 

Establishment date: Include month / day / year (mm/dd/yyyy) 

Established by: Provide the unique initials (example: AJS, Amy J. Symstad) or full names of the 

people that established the plot. 

UTM Zone: UTM zone for UTM coordinates 

UTM N and UTM E: UTM northerly and easterly coordinates, respectively, for each of the four 

corners of the plot. 

NOTE: All GPS work is done using the UTM NAD83 projection!!!! 

53

Description of route to the plot: Describe the location of the plot and a reasonable route to get there 

using permanent landmarks as reference points. Ideally, a crew that has never been to this site would 
54

 (from 0 – 50 m): Recorded to the nearest one degree. Make sure the 

eclination on your compass is correct for your location, so that all compass directions are true. 

mong parks. 

enting notes: If possible, describe the exact location of one or more plot corners with 

spect to landmarks such as trees (e.g., list species, dbh, height, location with respect to corner), large 

slope sighting from Transect A to Transect B. Sight 

lope angle at both transect ends. Specifically, for the A0 to B0 reading, stand at the A0 plot corner. 

 is 

nning and 

nd of transect A (A0 and A50). Slope aspect can be obtained by determining the main direction that 

lot using the provided NVCS 

rminology and definitions. Categories include: 

tly inclined surface at the base of a slope, commonly gentle and almost linear in 

urface profile 

rmediate slope position 

 – the uppermost inclined surface at the top of a slope, 

pically convex in profile 

ge) – linear top of a ridge, hill or mountain; the elevated area between 

o drainage-ways that sheds water 

terrupting a steep slope or cliff face 

tercourse, typically barren and formed of modern alluvium 

rs 

om NVCS. Categories include: 

Permanently flooded – water covers the surface at all times of the year in all years 

be able to find the plot using this description without the aid of a GPS unit. Useful information in this 

description includes a safe parking spot and routes that do NOT work because of hazardous terrain, 

water crossing, etc. 

Azimuth of transects

d

Monumenting: Describe the type of corner markers used (nails, rebar, etc.), since this may vary

a

Other monum

re

rocks, or streams in close vicinity to the corner. 

Slope angle (%): Use the clinometer to measure 

s

Look through the clinometer with your right eye while sighting an object at your eye’s height (e.g., 

another team member holding a card at your eye’s height) located at the B0 plot corner with your left 

eye. Do the same for the A50 to B50 reading. Record each reading in percent (not degrees!), which

usually the right-hand column of numbers in the clinometer. It is important to remember that percent 

slope changes more quickly than degrees slope, e.g., 45 degrees slope = 100 percent slope. 

Slope aspect (deg): Dominant aspect readings are taken at two points in the plot, at the begi

e

water would flow from the observed point. Slope aspect is measured to the nearest degree. 

Terrain shape: Describe or sketch, using transect ends for reference. 

Topographic position: Circle the overall topographic position of the p

te

Level – no slope 

Lower-slope – gen

s

Mid-slope – inte

Upper-slope (high slope, shoulder slope)

ty

Crest (interfluve, summit, rid

tw

Ledge (terrace) – nearly level shelf in

Depression – bottom surface of a basin 

Streambed – bed of single or braided wa

Hydrologic regime: Circle the overall hydrology of the plot using the provided descriptive modifie

fr
55

water persists throughout the growing season in most years; land 

ter table is normally very 

ariable 

ined to a relatively discrete area 

w the soil surface 

rained soils 

erally forming 25-

otes about plot: Describe anything noteworthy, including recent disturbances (bison wallow, fire, 

 that it can be taken from the same location in the future.

Semi-permanently flooded – surface

surface is generally saturated when the water level drops below the surface 

Seasonally/temporarily flooded – surface water is present for extended periods during the growing 

season, but is absent by the end of the growing season in most years; the wa

v

Intermittently flooded – surface water is present during times of increased precipitation, but generally 

dry 

Seep – intermittent, seasonal, or permanent flow of water from a subterranean source that is generally 

conf

Upland – the plot cannot be characterized as a wetland as it either sheds or absorbs water quickly; the 

water table is almost always well belo

Surface water: This is the distance to standing water. Categories to choose from are: (1) in plot; (2) 

<50 m away; and (3) >50 m away. 

Vegetation type: Circle corresponding description, sample categories include: 

Upland Prairie – prairie with well d

Riparian Woodland – open stands of trees with crowns not usually touching, gen

60% cover, located in riparian area 

 Rocky site – prairie area with large amount of exposed rock 

N

windthrow, etc.) 

Photos: List photo numbers and file names, as well as a description of where the “Overview” photo 

was taken from so
56

Preliminary Operating Procedure (POP) 2: Ocular Cover Estimates of Herbaceous and 

Shrub Species 

Version 1.1 (March 2006) 

Description: This POP gives step-by-step instructions for obtaining ocular cover estimates of herbaceous 

and shrub species in 0.5-m2

 quadrats along 50-m transects. Detailed instructions are provided for locating 

the quadrats along transects. This POP describes the procedure for filling in Data Form 2 “Ocular Cover 

Estimate Data Sheet” and Data Form 6 “Unknown Specimen Data Sheet”. 

I. Equipment List 

• Clip boards (2) 

• Data Form 2 (4 pages – 2 for Transect A and 2 for Transect B) 

• Pencils, extra pencil lead, and erasers 

• 0.5-m2 sampling frames (2) 

• 2% cover cards (4) 

• 50-m tapes (2) 

• 30-m tapes (2) 

• Plant press 

• Unknown specimen forms 

• Field guides and identification keys/books as needed 

• Species code list for appropriate park 

• Write-In-Rain blank data sheets (to avoid confusion with regular paper, keep labeled) 

• Metal detector for finding plot corners 
57

II. Procedures 

Site and Quadrat Setup 

Laying out Transects 

Each plot includes two 50-m transects (A and B), which form two edges of the plot. The ends of the 

transects are marked with stakes labeled with metal tags containing pertinent plot and transect 

information. Once all four corners of the plot have been located, a 50-m tape is laid along each 50-m 

edge and a 30-m tape is laid along each 20-m edge of the site. The origin (0m end) of the 50-m tape is 

hooked over the “0” corner marker for its transect. Tapes are stretched as tautly as possible to avoid 

curvature in the line. Curvature in the line makes re-locating quadrats inconsistent. All members of the 

sampling team assist in locating corner markers and setting up transect lines. All are cautious to avoid 

trampling vegetation in the plot, especially on the transects and 1 m into the plot from the transects.

All transects have a “start” (0 m) end and a “finish” (50 m) end, which are indicated by the tags on the 

rebar. It is absolutely crucial to begin at the starting end and to know which transect line you are 

sampling. These two factors relate directly to the location of each quadrat along the length of the transect. 

Sampling teams 

The entire team at the plot is divided into sub-teams. One sub-team consisting of two people is assigned 

to each transect line. The sub-team is responsible for the following: (1) collecting all of the required 

data; (2) collecting and describing any unknown plants encountered and relaying the information to the 

team leader before leaving the site; and (3) ensuring that all equipment, including sampling poles, meter 

tape, data sheets and clip boards, makes it to the next site. The person designated as recorder for a subteam is responsible for recording all data, including times.

Laying out the 0.5-m2 quadrats 

 Herbaceous and shrub species cover data are collected in ten 0.5-m2 quadrats located along each 

transect, spaced 5 m apart (Figure B2.1). Along transect A, quadrats are anchored at 1 m , 6 m, 11 m, 16 

m, ... 46 m. Along transect B, quadrats are anchored at 3 m, 8 m, 13 m, 18 m, ... 48 m. The quadrats 

extend 1 m beyond the anchor (towards the 50 m end of the transect and 0.5 m into the 1000-m2 site. 

Quadrat frames are laid as close to the ground surface as possible, with the inside edge of the frame flush 

with the edge of the tape that is inside the 1000-m2

 site. 
58

A0 

B0 B50 

A50 uphill 

Figure B2.1. 1000 m2 sampling plot (open rectangle), with rebar labels indicating corners, and 0.5 m2 cover 

estimate quadrats.

In summary: 

1. Transect tapes must be pulled as tautly as possible between the two transect ends. 

2. Consult plot records and transect tags to ensure that the "A" and "B" transect lines (recorded on the 

tags around the rebar as "A" and "B") are not reversed. 

3. Always start the tape at the beginning of the transect (recorded on the tags around the corner marker 

as "0" for "0 m"). 

III. Collecting and Recording Data 

Fill in the blanks in the heading of Data Form 2 following the instructions below: 

Park Code: A unique 4-digit code (example: AGFO Agate Fossil Beds National Monument) 

Park Unit: Management unit or other descriptive location name (example: Carnegie Hill Unit) 

Plot ID: A unique sample unit identification (example: PCM10, where PCM stands for “Plant 

Community Monitoring”) 

Observers: The unique initials of the first, middle, and last name, or full name, of each person in the 

team collecting data. If initials of two or more persons are the same, use full names. 

Date: Include month / day / year (mm/dd/yyyy) 

Each data sheet has 10 tables (5 on each side), one for each of the quadrats on the transect. Each of the 

tables is labeled with the quadrat identifier, which indicates the transect (A or B) and the distance from 

the origin of the transect at which the quadrat is anchored. 

For each quadrat: 
59

1. Place the cover frame in the appropriate location (see “Laying out the 0.5-m2 quadrats” above). For 

start time, record the time that the sample frame is set on the ground in the appropriate table on the 

data form. 

2. Search thoroughly for all plant species whose foliage polygon (see # 3 below) or stem overlaps or is 

wholly inside the frame; record their species codes in the appropriate table. Include all species that 

had growth in the current year. Species codes follow NRCS naming conventions; refer to the list of 

codes for species in the park you’re in. If a species cannot be identified, assign it an unknown code 

according to the directions in the “Unknown Specimens” section below. 

3. A foliage polygon for an individual plant is the area within a polygon connecting the outermost points 

of the live leaves (do not include inflorescences) of an individual plant. Figure B2.2 shows some 

examples of foliage polygons. 

4. Using the 2% cards and the marks on the side of the frame to calibrate yourself, estimate, to the 

nearest 1%, the percentage of the area within the frame covered by each species using the individuals’ 

foliage polygons. Overlap of plants of the same species is ignored. Record this in the appropriate row 

and column for that quadrat. For species with cover under 1%, record their cover as “0.5”. For stems 

of plants whose foliage polygon does not intersect the quadrat, or whose foliage is more than 2 m 

above the quadrat, estimate their cover based on their basal area. Record their cover only as the 

area within the frame. See Figure B2.2. Base percent cover estimates on the current year’s growth by 

including living, damaged, and dead material from the current year. Do not adjust the percent for the 

time of year during which the visit was made (i.e., for immature or wilted plants). Sparse plants can 

be difficult to assess, but follow the polygon rule unless it is obvious that the plant(s) have been 

trampled or otherwise recently disturbed and would naturally stand more upright. 

5. Do not count foliage or branches intercepted for woody plants over 2 m tall. (This is because they will 

be sampled using other procedures better suited to large woody vegetation.) If the trunk of a standing 

live woody plant that is over 2 m tall lies either wholly or partially within the frame, record its species 

code followed by “trunk”. Standing dead tree trunks in the quadrat are accounted for in the “Ground 

Cover” estimate. 

6. After recording the cover for all species, the reader should look away from the quadrat for a few 

seconds, then look down at the quadrat again and estimate total plant cover. Meanwhile, the recorder 

sums the values from the individual species to obtain total plant cover. Compare these two values, 

and adjust cover estimates of individual species as (or if) necessary to make the two values agree. 

Total plant cover can be any value equal to or greater than zero. Values over 100% can occur when 

there are multiple layers of vegetation. 

7. Move to the bottom of the table for the quadrat. Estimate, to the nearest 1%, the percentage of the 

ground within the frame covered by each of the cover types listed (veg, bare soil, bare rock, litter, and 

woody debris). Record these values in the appropriate rows. For veg, imagine mowing the quadrat so 

that all that is left is 2 cm of stubble. The percentage of the area of the frame covered by this stubble 

(stems of plants) is the cover of veg. “Rock” is defined as stone larger than a dime (1.8 mm 

diameter). The sum of these five values must be 100%. Do not spend more than 1 minute doing the 

estimates for ground cover. 

8. In the “end time” row of the table, record the time at which all data collection for the quadrat is 

complete. 
60

Figure B2.2. Foliage polygons, indicated in black lines, for different types of plants. Despite the different 

colors, all shapes are intended to be leaves, not flowers. The quadrat frame is indicated in gray, with red 

dashes each being 10 cm in length. Cover of the species as represented here would be: A, 36%; B, 5%; C, 

2%; D, 0.5% (i.e., <1%); and E, 7%.

Unknown Specimens 

Sometimes species determinations of individual plants encountered in sampling are difficult or uncertain. 

When this situation occurs, the area surrounding the quadrat should be searched, looking particularly for a 

specimen of the unknown that is in flower or fruit, or perhaps is a specimen from last year with its 

flowering stalk still intact. These may assist in correctly identifying the species in the quadrat. Most of 

these plants are not entirely “unknown” species. Rather, they are of a species that is known but 

sometimes difficult to distinguish from another given the timing of sampling. For example, small 

Bouteloua curtipendula plants may be confused with Bouteloua gracilis. Both of these species are 

common and often confidently identified during our various sampling procedures. If the evidence does 

not favor one species over another enough, then the next broader taxonomic grouping can be recorded. In 

the above example, “Bouteloua spp.” could be recorded. This name would be sufficient until specieslevel identification is available, e.g., during a subsequent sampling the plant is found in the same location 

in flower, seed, or fruit, aiding correct identification. 

Plants not immediately identified in the quadrat are recorded on the data sheet with an unknown specimen 

code distinguishing it from other unknowns in that plot and other plots in the park. A sample of that 

species is then collected. Because of the long-term nature of the monitoring, collecting outside the plot is 

preferred. If a specimen does not occur outside the plot, a portion of the plant from inside the site can be 

collected, but at no time should the roots be dug up or the entire plant be collected from inside the site. 

Collecting the roots and/or the entire above-ground portion of the plant may affect future sampling events. 

If a camera is available, a photodocument could be taken. 

For each unknown specimen, there is a corresponding Unknown Specimen Data Sheet (Form 6). The 

more detailed a definition of each characteristic, the greater the possibility of a future identification. Use 

the following procedure when filling out the Unknown Record sheet: 

Park: A four letter alpha code unique to a particular park (example: AGFO – Agate Fossil Beds 

National Monument in Nebraska) 
61

Plot and Quadrat/Point: Unique plot ID, and if applicable, quadrat number (m distance along transect) 

or point-intercept point (including transect letter). Indicate whether number refers to quadrat or point 

number. Note that point number is not the same as the distance of the point from the transect anchor. 

Examples: 

PCM5-B38 (quad) = monitoring site 5, transect B, quadrat 38 

PCM5-B38 (point) = monitoring site 5, transect B, point # 38 

Date: Include month (mm) / day (dd) / year (yy) 

Unknown Code: A unique code referring to the plant’s form (e.g., grass, sedge, succulent), taxon 

(genus, family), or some other distinguishing characteristic. 

 

Plant type and General Description: Circle the appropriate category and provide a detailed description 

of the overall appearance 

 

Most Salient Feature: The feature that identifies this plant from all others; a unique characteristic 

 

Leaf Characteristics: Describe the leaf type, leaf margin, leaf surface, petiole, etc. 

Stem Characteristics: Describe the shape, pubescence, markings, and color of the stem, as well as the 

bud characteristics. 

Flower Characteristics: Describe the floral formula, location (axillary or terminal), habit (indeterminate 

or determinate), pubescence, and color. 

General and Microhabitat Characteristics: List other species located in the general vicinity, selecting 

the more conservative species in the area. Describe the microhabitat in which it was found. 

Collected: Circle yes or no, whether a specimen was collected. 

Best Guess: Preliminary guess about species in field. 

Confirmed to be: After consultation of reference books and/or herbarium, the species determined. 
62

Preliminary Operating Procedure (POP) 3: Point-Intercept Cover Estimates of Herbaceous 

and Shrub Species 

Version 1.1 (March 2006) 

Description: This POP gives step-by-step instructions for obtaining cover estimates of herbaceous and 

shrub species using the point-intercept method along 50 m transects. The POP describes the procedure 

for filling in Form 3 “Point-Intercept Data Sheet” and Data Form 6 “Unknown Specimen Data Sheet”. 

I. Equipment List 

• Clip boards (2) 

• Form 3 Point-intercept data sheets (2 sheets for each plot, 1 per transect) 

• Pencils, extra pencil lead, and erasers 

• Sampling poles = 1⁄4 inch diameter collapsible tent poles (2) 

• 50-m tapes (2) 

• 30-m tapes (2) 

• Plant press 

• Unknown specimens forms 

• Field guides and identification keys/books as needed 

• Species code list for appropriate park 

• Write-In-Rain blank data sheets (to avoid confusion with regular paper, keep labeled) 
63

II. Procedures 

Plot Setup 

Laying out Transects 

Each monitoring plot includes two 50-m transects (A and B), which form the edges of the sampling plots. 

The ends of the transects are marked with stakes labeled with metal tags containing pertinent plot and 

transect information. Once all four corners of the sampling plot have been located, a 50-m tape is laid 

along each 50-m edge and a 30-m tape is laid along each 20-m edge of the plot. The origin (0 m end) of 

the 50-m tape is hooked over the “0” corner marker for its transect. Tapes are stretched as tautly as 

possible to avoid curvature in the line. Curvature in the line makes data collection from year to year 

inconsistent. All members of the sampling team assist in locating corner markers and setting up transect 

lines. All are cautious to avoid trampling vegetation in the plot, especially on the transects and 1 m 

on either side of the transects. 

All transects have a “start” (0 m) end and a “finish” (50 m) end, which are indicated by the tags on the 

corner marker. It is absolutely crucial to begin at the starting end and to know which transect line you are 

sampling. 

Sampling teams 

One sampling team, each consisting of a reader and a recorder, is assigned to a transect line. The 

sampling team is responsible for the following: (1) collecting all of the required data; (2) collecting and 

describing any unknown plants encountered and relaying the information to the team leader before 

leaving the site; and (3) ensuring that all equipment, including sampling poles, meter tape, data sheets and 

clip boards, makes it to the next site. The recorder is responsible for recording all data, including start 

and finish times.

In summary: 

1. Transect tapes must be pulled as tautly as possible between the two transect ends. 

2. Consult plot records and transect tags to ensure that the "A" and "B" transect lines (recorded on the 

tags around the rebar as "A" and "B") are not reversed. 

3. Always start the tape at the beginning of the transect (recorded on the tag on the corner marker as "0" 

for "0 m"). 

III. Collecting and Recording Data 

Fill in the information on the top of the first page of the data sheet according to the following directions. 

Park Code: A unique 4-digit code (example: AGFO Agate Fossil Beds National Monument) 

Park Unit: Management unit or other descriptive location name (example: Carnegie Hill Unit) 

Plot ID: A unique sample unit identification (example: DPCM10, where D stands for Devils Tower 

National Monument and PCM stands for “Plant Community Monitoring”) 

Transect Line: Circle A or B as appropriate. Check against the corner marker you are standing next to! 

Date: Include month / day / year (mm/dd/yyyy) 

Start Time: Time that data collection begins. 
64

Reader/Recorder: The unique initials of the first, middle, and last name of each person in the team 

collecting data. If initials of two or more persons are the same, use full names. Reader is the person 

plunking the pole, recorder is the person recording the data. 

Procedure: 

Start at 30 cm from the 0 end of the transect. At each of the 100 evenly spaced, marked points (as 

indicated on the data sheet), gently drop the pole (rigid plumb-bob) to the ground. The pole should be 

plumb to the ground, which on a slope will not be perpendicular to the ground. Record the code for each 

species that touches the pole, starting with the upper-most hit and working down, on the data sheet in the 

appropriate row. Species codes follow NRCS naming conventions; refer to the list of codes for species in 

the park you’re in. Count each species only once at each point-intercept even if the pole touches it more 

than once. If the pole fails to intercept any vegetation, put a dash “—“ in the first column for that row and 

move to the final column labeled “Ground”. 

If a species cannot be identified, record its presence with a unique “unknown” code (see section below on 

unidentifiable specimens) instead of a species code. 

Do not count foliage or branches intercepted for trees or shrubs over 2 m tall, but count all herbaceous 

vegetation no matter its height. (This is because tall woody species will be sampled using other 

procedures.) If the pole intersects the bole of a tree that is over 2 m tall, record “2BOLE” if the tree is 

alive or “2SDED” if the tree is dead. 

For the column labeled “Ground” at each of the marked points, record the substrate (SOIL, ROCK, LIT, 

or WOOD) or species code of what the pole touches where it intersects the ground. A species code 

should be recorded only if the pole touches the base of the plant, as the goal of this measurement is to 

record basal cover of plants vs. substrate cover. “ROCK” is defined as larger than a dime (1.8 mm 

diameter). 

After the last hit is recorded, write down the time in the appropriate place at the top page of the data sheet. 

Unknown Specimens 

Sometimes species determinations of individual plants encountered in sampling are difficult or uncertain. 

When this situation occurs, the area surrounding the point should be searched, looking particularly for a 

specimen of the unknown that is in flower or fruit, or perhaps is a specimen from last year with its 

flowering stalk still intact. These may assist in correctly identifying the species at the point. Most of 

these plants are not entirely “unknown” species. Rather, they are of a species that is known but 

sometimes difficult to distinguish from another given the timing of sampling. For example, small 

Bouteloua curtipendula plants may be confused with Bouteloua gracilis. Both of these species are 

common and often confidently identified during our various sampling procedures. If the evidence does 

not favor one species over another enough, then the next broader taxonomic grouping can be recorded. In 

the above example, “Bouteloua spp.” could be recorded. This name would be sufficient until specieslevel identification is available, e.g., during a subsequent sampling the plant is found in the same location 

in flower, seed, or fruit, aiding correct identification. 

Plants not immediately identified in the plot are recorded on the data sheet with an unknown specimen 

code distinguishing it from other unknowns in that plot and other plots in the park. A sample of that 

species is then collected. Because of the long-term nature of the monitoring, collecting outside the plot is 

preferred. If a specimen does not occur outside the plot, a portion of the plant from inside the plot can be 

collected, but at no time should the roots be dug up or the entire plant be collected from inside the plot. 

Collecting the roots and/or the entire above-ground portion of the plant may affect future sampling events. 

If a camera is available, a photodocument could be taken. 
65

For each unknown specimen, there is a corresponding Unknown Specimen Data Sheet (Form 6). The 

more detailed a definition of each characteristic, the greater the possibility of a future identification. Use 

the following procedure when filling out the Unknown Record sheet: 

Park: A four letter alpha code unique to a particular park (example: AGFO – Agate Fossil Beds 

National Monument in Nebraska) 

Plot and Quadrat/Point: Unique plot ID, and if applicable, quadrat number (m distance along transect) 

or point-intercept point (including transect letter). Indicate whether number refers to quadrat or point 

number. Note that point number is not the same as the distance of the point from the transect anchor. 

Examples: 

PCM5-B38 (quad) = monitoring site 5, transect B, quadrat 38 

PCM5-B38 (point) = monitoring site 5, transect B, point # 38 

Date: Include month (mm) / day (dd) / year (yy) 

Unknown Code: A unique code referring to the plant’s form (e.g., grass, sedge, succulent), taxon 

(genus, family), or some other distinguishing characteristic. 

 

Plant type and General Description: Circle the appropriate category and provide a detailed description 

of the overall appearance 

 

Most Salient Feature: The feature that identifies this plant from all others; a unique characteristic 

 

Leaf Characteristics: Describe the leaf type, leaf margin, leaf surface, petiole, etc. 

Stem Characteristics: Describe the shape, pubescence, markings, and color of the stem, as well as the 

bud characteristics. 

Flower Characteristics: Describe the floral formula, location (axillary or terminal), habit (indeterminate 

or determinate), pubescence, and color. 

General and Microhabitat Characteristics: List other species located in the general vicinity, selecting 

the more conservative species in the area. Describe the microhabitat in which it was found. 

Collected: Circle yes or no, whether a specimen was collected. 

Best Guess: Preliminary guess about species in field. 

Confirmed to be: After consultation of reference books and/or herbarium, the species determined. 
66

Preliminary Operating Procedure (POP) 4: Compiling a Plot Species List 

Version 1.1 (March 2006) 

Description: This POP gives instructions for recording all species encountered within a 1000 m2

 plot. 

The POP describes the procedures for filling in Form 4 “Plot Species List Data Sheet” and Data Form 6 

“Unknown Specimen Data Sheet”. 

I. Equipment List 

• Clip boards 

• Plot Species List Data Sheet (Form 4) 

• Pencils, extra pencil lead, and erasers 

• Field guides and identification keys/books as needed 

• Hand lenses 

II. Procedures 

This POP assumes that a sampling plot has already been set up, with tapes indicating the borders of the 

plot already in place. 

Fill in the information on the top of Form 4 according to the following directions. 

Park Code: A unique 4 digit code (example: AGFO Agate Fossil Beds National Monument) 

Park Unit: Management unit or other descriptive location name (example: Carnegie Hill Unit) 

Plot ID: A unique sample unit identification (example: DPCM10, where D stands for Devils Tower 

National Monument and PCM stands for “Plant Community Monitoring”) 

Date: Include month / day / year (mm/dd/yyyy) 

Start Time: Record the time that searching for species within the plot begins. Usually this is 

immediately after the tapes have been laid out for the plot. 

Finish Time: Record the time that searching for species ends. Usually this is immediately before the 

tapes are rolled up and the team departs from the plot. 

Observers: The unique initials of the first, middle, and last name of each person in the team collecting 

data. If initials of two or more persons are the same, include some other distinguishing initial or full 

name. 

One team member is responsible for recording species on Form 4 for the entire duration of the plot 

sampling. All team members are responsible for reporting species encountered at any time while working 

in the plot to this recorder. 

An initial search of the plot for all vascular plants species whose canopy overlaps or whose stem is rooted 

within the plot is done after the plot tapes are established. The whole team works together on this, with 

more experienced members pointing out new species and their distinguishing characteristics to less 

experienced members as necessary. In addition, special note is made of species within the plot that can be 

confused with others. The recorder records species’ codes and full names on Form 4. The team assists in 
67

assessing the phenological stage of each species, following the definitions of these stages on page 2 of 

Form 4. The stage that the majority of individuals of a species in the plot are in at the time of sampling is 

the one recorded on the form. The team should work in a methodical manner from one end of the plot to 

the other, making sure all areas are covered, with one exception. Team members should avoid walking 

within 1 m of the 50-m tapes to avoid trampling vegetation that will be sampled using other techniques. 

After this initial search, plant cover sampling begins. If any new species are encountered during this 

sampling, the team member that encounters the new species ensures that the Form 4 recorder records this 

species on the list. 

After all data collection in a plot is complete, one person takes responsibility to ensure that species 

occurring on cover data sheets are recorded on the plot species list and that species codes are consistent 

across all data sheets from a plot. This is ideally done immediately after the plot is completed, but it 

should be done no more than three days after the plot is sampled so that questions that arise can be 

answered with information that is fresh in the sampling teams’ memory. 

Directions for dealing with unidentifiable specimens are detailed below. 

Unknown Specimens 

Sometimes species determinations of individual plants encountered in sampling are difficult or uncertain. 

When this situation occurs, the area surrounding the quadrat should be searched, looking particularly for a 

specimen of the unknown that is in flower or fruit, or perhaps is a specimen from last year with its 

flowering stalk still intact. These may assist in correctly identifying the species in the quadrat. Most of 

these plants are not entirely “unknown” species. Rather, they are of a species that is known but 

sometimes difficult to distinguish from another given the timing of sampling. For example, small 

Bouteloua curtipendula plants may be confused with Bouteloua gracilis. Both of these species are 

common and often confidently identified during our various sampling procedures. If the evidence does 

not favor one species over another enough, then the next broader taxonomic grouping can be recorded. In 

the above example, “Bouteloua spp.” could be recorded. This name would be sufficient until specieslevel identification is available, e.g., during a subsequent sampling the plant is found in the same location 

in flower, seed, or fruit, aiding correct identification. 

Plants not immediately identified in the quadrat are recorded on the data sheet with an unknown specimen 

code distinguishing it from other unknowns in that plot and other plots in the park. A sample of that 

species is then collected. Because of the long-term nature of the monitoring, collecting outside the plot is 

preferred. If a specimen does not occur outside the plot, a portion of the plant from inside the site can be 

collected, but at no time should the roots be dug up or the entire plant be collected from inside the site. 

Collecting the roots and/or the entire above-ground portion of the plant may affect future sampling events. 

If a camera is available, a photodocument could be taken. 

For each unknown specimen, there is a corresponding Unknown Specimen Data Sheet (Form 6). The 

more detailed a definition of each characteristic, the greater the possibility of a future identification. Use 

the following procedure when filling out the Unknown Record sheet: 

Park: A four letter alpha code unique to a particular park (example: AGFO – Agate Fossil Beds 

National Monument in Nebraska) 

Plot and Quadrat/Point: Unique plot ID, and if applicable, quadrat number (m distance along transect) 

or point-intercept point (including transect letter). Indicate whether number refers to quadrat or point 

number. Note that point number is not the same as the distance of the point from the transect anchor. 

Examples: 

PCM5-B38 (quad) = monitoring site 5, transect B, quadrat 38 
68

PCM5-B38 (point) = monitoring site 5, transect B, point # 38 

Date: Include month (mm) / day (dd) / year (yy) 

Unknown Code: A unique code referring to the plant’s form (e.g., grass, sedge, succulent), taxon 

(genus, family), or some other distinguishing characteristic. 

 

Plant type and General Description: Circle the appropriate category and provide a detailed description 

of the overall appearance 

 

Most Salient Feature: The feature that identifies this plant from all others; a unique characteristic 

 

Leaf Characteristics: Describe the leaf type, leaf margin, leaf surface, petiole, etc. 

Stem Characteristics: Describe the shape, pubescence, markings, and color of the stem, as well as the 

bud characteristics. 

Flower Characteristics: Describe the floral formula, location (axillary or terminal), habit (indeterminate 

or determinate), pubescence, and color. 

General and Microhabitat Characteristics: List other species located in the general vicinity, selecting 

the more conservative species in the area. Describe the microhabitat in which it was found. 

Collected: Circle yes or no, whether a specimen was collected. 

Best Guess: Preliminary guess about species in field. 

Confirmed to be: After consultation of reference books and/or herbarium, the species determined. 
Appendix C 

Plant Species Encountered During Field Sampling for Methods CompaNomenclature follows the Integrated Taxonomic Information System (www.itis.usda.gov). Variety and subspecies na

provided by the NGPN Inventory & Monitoring program to the methods comparison field crew prior to field samplin
Agate Fossil Beds National Monument (AGFO) 

70

Family Scientific Name Common Name(s) 

Agavaceae Yucca glauca Great Plains yucca, small soapweed, soapweed yucca, 

Spanish bayonet, yucca, beargrass 

Anacardiaceae Rhus trilobata var. trilobata skunkbush sumac Apiaceae Cicuta maculata var. angustifolia water hemlock 

Lomatium orientale oriental desert-parsley Musineon tenuifolium slender wildparsley Pastinaca sativa wild parsnip 

Asclepiadaceae Asclepias incarnata ssp. incarnata swamp milkweed 

Asclepias speciosa showy milkweed Asclepias viridiflora green antelopehorn milkweed, green comet milkweed, 

green milkweed 

Asteraceae Ambrosia trifida blood ragweed, giant ragweed, great ragweed, 

horseweed, perennial ragweed (great), tall ragweed 

Antennaria microphylla littleleaf pussytoes, Rocky Mountain pussytoes, small 

leaf everlasting, smallleaf pussytoes 

Artemisia frigida fringed sagebrush, fringed sagewort, prairie sagewort Bidens cernua bur marigold, nodding beggartick, nodding bur marigold, 

nodding burmarigold, sticktight 

Cirsium arvense Californian thistle, Canada thistle, Canadian thistle, 

creeping thistle, field thistle 

Cirsium canescens Platte thistle, prairie thistle Cirsium flodmanii Flodman thistle, Flodman's thistle Conyza canadensis Canada horseweed, Canadian horseweed, horseweed, 

horseweed fleabane, mares tail 

Erigeron bellidiastrum western fleabane Gutierrezia sarothrae broom snakeweed, broomweed, perennial snakeweed, 

stinkweed, turpentine weed, yellow top 

Helianthus annuus annual sunflower, common sunflower, sunflower, wild 

sunflower 

Helianthus maximiliani Maximilian sunflower Helianthus petiolaris prairie sunflower Heterotheca villosa hairy false goldaster, hairy false goldenaster, hairy 

goldaster, hairy goldenaster 

Hymenopappus filifolius var. polycephalus manyhead hymenopappus Lactuca serriola China lettuce, prickly lettuce, wild lettuce 

Lactuca tatarica var. pulchella blue lettuce, blue wild lettuce, chicory lettuce, Russian 

blue lettuce 

Liatris punctata dotted blazing star, dotted gayfeather 
Agate Fossil Beds National Monument (AGFO) 

71

Family Scientific Name Common Name(s) 

Lygodesmia juncea rush skeletonplant, rush skeletonweed, skeletonplant, 

skeletonweed 

Machaeranthera pinnatifida var. pinnatifida lacy tansyaster Oligoneuron album prairie goldenrod Senecio integerrimus lambstongue groundsel, lambstongue ragwort Senecio riddellii riddell groundsel, Riddell's ragwort, sand groundsel Solidago canadensis var. gilvocanescens Canada goldenrod, common goldenrod Solidago gigantea giant goldenrod Sonchus arvensis ssp. uliginosus sowthistle Symphyotrichum ericoides var. stricticaule white heath aster Symphyotrichum lanceolatum var. hesperium white panicle aster Taraxacum officinale blowball, common dandelion, dandelion, faceclock 

Tetraneuris acaulis var. acaulis stemless hymenoxys, stemless four-nerve daisy Townsendia grandiflora largeflower Townsend daisy Tragopogon dubius goatsbeard, meadow goat's-beard, salsify, Western 

goat's beard, western salsify, wild oysterplant, yellow goat's beard, yellow salsify 

Boraginaceae Cryptantha cana mountain cryptantha 

Cryptantha celosioides buttecandle, minerscandle Cryptantha minima small cryptantha Lappula occidentalis var. occidentalis western stickseed 

Lithospermum incisum fringed gromwell, fringed puccoon, narrowleaf gromwell, 

narrowleaf pucoon 

Brassicaceae Alyssum desertorum desert alyssum, desert madwort 

Arabis holboellii var. collinsii Holboell's rockcress Camelina microcarpa false flax, littlepod false flax, littleseed falseflax, small 

fruited falseflax, smallseed falseflax 

Descurainia pinnata var. intermedia green tansymustard, pinnate tansymustard, 

tansymustard, western tansymustard 

Descurainia sophia flaxweed tansymustard, flixweed, flixweed 

tansymustard, herb sophia 

Draba reptans Carolina draba, Carolina whitlowgrass, creeping draba Erysimum capitatum var. capitatum plains wallflower, prairie rocket, sanddune wallflower, 

western wallflower 

Lepidium densiflorum common pepperweed, miners pepperweed, 

peppergrass, prairie pepperweed 

Lesquerella alpina var. alpina alpine bladderpod 
Agate Fossil Beds National Monument (AGFO) 

72

Family Scientific Name Common Name(s) 

Lesquerella ludoviciana foothill bladderpod, Louisiana bladderpod, silver 

bladderpod 

Sisymbrium altissimum Jim Hill mustard, tall tumblemustard, tumble mustard, 

tumbleweed mustard 

Cactaceae Escobaria vivipara var. vivipara pink pincushioncactus, spinystar, spinystar cactus 

Opuntia fragilis brittle cactus, brittle pricklypear, fragile cactus, jumping 

cactus, little pricklypear 

Opuntia macrorhiza var. macrorhiza twistspine pricklypear, bigroot pricklypear Opuntia polyacantha var. polyacantha Plains pricklypear 

Capparaceae Cleome serrulata bee spiderflower, Rocky Mountain beeplant Caprifoliaceae Symphoricarpos occidentalis western snowberry, wolfberry Caryophyllaceae Arenaria hookeri Hooker's sandwort 

Paronychia depressa spreading nailwort Silene drummondii var. drummondii Drummond's catchfly 

Chenopodiaceae Atriplex subspicata spreading orach 

Chenopodium pratericola desert goosefoot Kochia scoparia fireweed, kochia, Mexican burningbush, Mexican 

fireweed, mock cypress, Summer cypress 

Commelinaceae Tradescantia occidentalis prairie spiderwort, spiderwort, western spiderwort Cyperaceae Carex filifolia threadleaf sedge 

Carex hallii deer sedge Carex pellita wooly sedge Carex praegracilis slim sedge 

Equisetaceae Equisetum laevigatum horsetail, smooth horsetail, smooth scouringrush Euphorbiaceae Euphorbia brachycera horned spurge, Rocky Mountain spurge Fabaceae Astragalus ceramicus var. filifolius painted milkvetch 

Astragalus crassicarpus var. crassicarpus ground-plum, groundplum milkvetch 

Astragalus gracilis slender milkvetch Astragalus laxmannii var. robustior Laxmann's milkvetch, prairie milkvetch Astragalus missouriensis Missouri milkvetch Astragalus sericoleucus silky milkvetch 

Astragalus spatulatus tufted milkvetch Dalea candida var. oligophylla white prairieclover Dalea purpurea Purple prairieclover, violet dalea, violet prairie clover Glycyrrhiza lepidota American licorice, licorice, wild licorice Lathyrus polymorphus ssp. incanus manystem peavine Lupinus argenteus silvery lupine Lupinus plattensis Platte lupine Lupinus pusillus small lupine, rusty lupine 
Agate Fossil Beds National Monument (AGFO) 

73

Family Scientific Name Common Name(s) 

Melilotus officinalis yellow sweet-clover, yellow sweetclover Oxytropis lambertii Lambert crazyweed, Lambert loco, Lambert locoweed, 

purple locoweed, stemless loco 

Oxytropis sericea locoweed, silky crazyweed, silvery oxytrope, white 

crazyweed, white locoweed, white pointloco 

Pediomelum esculentum breadroot scurfpea, Indian breadroot, large Indian 

breadroot 

Psoralidium lanceolatum dune scurfpea, lemon scurfpea, wild lemonweed Psoralidium tenuiflorum slimflower scurfpea Thermopsis rhombifolia goldenpea, prairie thermopsis 

Grossulariaceae Ribes aureum var. villosum golden currant Hydrophyllaceae Ellisia nyctelea Aunt Lucy, ellisia, false babyblueeyes, waterpod 

Phacelia hastata var. hastata silverleaf phacelia 

Iridaceae Iris pseudacorus yellow flag Juncaceae Juncus balticus Baltic rush Lamiaceae Hedeoma drummondii Drummond's false pennyroyal, Drummond's pennyroyal 

Lycopus asper rough water-horehound Mentha arvensis wild mint Nepeta cataria catnip 

Liliaceae Allium textile prairie onion, textile onion, wild onion 

Leucocrinum montanum common starlily, star-lily Zigadenus venenosus var. gramineus meadow deathcamas Linum puberulum Plains flax 

Linaceae Linum rigidum var. rigidum orange flax, stiff flax, stiffstem flax Loasaceae Mentzelia nuda stickleaf mentzelia, bractless blazingstar Onagraceae Calylophus serrulatus halfshrub calylophus, halfshrub sundrop, serrateleaf 

eveningprimrose, yellow sundrops 

Gaura coccinea scarlet beeblossom, scarlet gaura, Scarlet guara Gaura mollis James velvetweed Oenothera albicaulis halfshrub sundrop, white-stem evening-primrose, 

whitest evening-primrose 

Oenothera latifolia mountain evening-primrose 

Orobanchaceae Orobanche fasciculata clustered broomrape, purple broomrape, tufted 

broomrape 

Plantaginaceae Plantago patagonica woolly Indianwheat, woolly plantain Poaceae Achnatherum hymenoides Indian ricegrass 

Agropyron cristatum crested wheatgrass Agrostis stolonifera creeping bentgrass 
Agate Fossil Beds National Monument (AGFO) 

74

Family Scientific Name Common Name(s) 

Andropogon hallii sand bluestem Aristida purpurea Purple three-awn Bouteloua curtipendula sideoats grama Bouteloua gracilis blue grama Bromus inermis awnless brome, smooth brome Bromus japonicus Japanese brome, Japanese chess Bromus tectorum cheat grass, cheatgrass, downy brome, early chess, 

military grass, wild oats 

Calamagrostis stricta ssp. inexpansa slimstem reedgrass Calamovilfa longifolia prairie sandreed Distichlis spicata seashore saltgrass Elymus elymoides ssp. brevifolius squirreltail Elymus trachycaulus ssp. trachycaulus slender wheatgrass Hesperostipa comata ssp. comata needleandthread Koeleria macrantha junegrass, prairie Junegrass Muhlenbergia asperifolia scratchgrass Muhlenbergia pungens sandhill muhly Pascopyrum smithii pubescent wheatgrass, western wheatgrass Poa pratensis Kentucky bluegrass Poa secunda big bluegrass, Sandberg bluegrass Schizachyrium scoparium little bluestem Sporobolus cryptandrus sand dropseed Vulpia octoflora eight-flower six-weeks grass, pullout grass, sixweeks 

fescue, sixweeks grass 

Polemoniaceae Phlox andicola prairie phlox 

Phlox hoodii ssp. hoodii Hood's phlox, spiny phlox 

Polygonaceae Eriogonum annuum annual buckwheat, annual eriogonum, annual wild 

buckwheat, umbrella plant, wild buckwheat 

Eriogonum flavum alpine golden buckwheat, yellow eriogonum 

Polygonum amphibium var. stipulaceum water smartweed Rumex crispus curly dock, narrowleaf dock, sour dock, yellow dock Rumex venosus veiny dock 

Santalaceae Comandra umbellata ssp. pallida bastard toadflax Scrophulariaceae Castilleja sessiliflora downy paintedcup, Great Plains Indian paintbrush, 

Indianpaintbrush 

Penstemon angustifolius var. angustifolius broad-beard beardtongue, broadbeard beardtongue, 

narrowleaf penstemon 

Penstemon eriantherus var. eriantherus fuzzytongue penstemon 

Solanaceae Physalis virginiana Virginia groundcherry 
Agate Fossil Beds National Monument (AGFO) 

75

Family Scientific Name Common Name(s) 

Urticaceae Parietaria pensylvanica Pennsylvania pellitory 

Urtica dioica ssp. gracilis tall nettle, stinging nettle 

Verbenaceae Verbena hastata blue verbena, blue vervain, Simpler's-joy, swamp 

verbena 

Violaceae Viola nuttallii Nuttall violet, Nuttall's violet, yellow prairie violet 
Devils Tower National Monument (DETO) 

76

Family Scientific Name Common Name(s) 

Anacardiaceae Rhus trilobata var. trilobata skunkbush sumac Apiaceae Perideridia gairdneri ssp. borealis common yampah Apocynaceae Apocynum androsaemifolium spreading dogbane Asclepiadaceae Asclepias pumila plains milkweed 

Asclepias verticillata whorled milkweed 

Asteraceae Achillea millefolium var. occidentalis western yarrow 

Ambrosia psilostachya perennial ragweed, western ragweed Antennaria neglecta field pussytoes Antennaria parvifolia little-leaf pussytoes, Rocky Mountain pussytoes, small 

leaf pussytoes, smalleaf pussytoes 

Artemisia frigida fringed sagebrush, fringed sagewort, prairie sagewort Artemisia ludoviciana ssp. ludoviciana cudweed sagewort, gray sagewort, Louisiana sagewort, 

Louisiana wormwood, mugwort wormwood, prairie sage, white sagebrush 

Conyza canadensis Canada horseweed, Canadian horseweed, horseweed, 

horseweed fleabane, mares tail, marestail 

Gutierrezia sarothrae broom snakeweed, broomweed, perennial snakeweed, 

stinkweed, turpentine weed, yellow top 

Hieracium canadense var. canadense yellow hawkweed 

Liatris punctata dotted blazing star, dotted gayfeather Machaeranthera pinnatifida var. pinnatifida lacy tansyaster Oligoneuron album prairie goldenrod Ratibida columnifera prairie coneflower, redspike Mexican hat, upright prairie 

coneflower 

Solidago missouriensis Missouri goldenrod, prairie goldenrod Solidago mollis ashy goldenrod, soft goldenrod, velvety goldenrod, 

woolly goldenrod 

Solidago nemoralis var. longipetiolata dyersweed goldenrod, gray goldenrod 

Symphyotrichum ericoides white heath aster Symphyotrichum laeve var. laeve smooth blue aster, geyer's aster Symphyotrichum oblongifolium aromatic aster Taraxacum officinale blowball, common dandelion, dandelion, faceclock 

Tetraneuris acaulis var. acaulis stemless hymenoxys, stemless four-nerve daisy 

Tragopogon dubius common salsify, goatsbeard, meadow goat's-beard, 

salsify, Western goat's beard, western salsify, wild oysterplant, yellow goat's beard, yellow salsify 

Berberidaceae Mahonia repens trunkee barberry Boraginaceae Cynoglossum officinale common houndstongue, gypsyflower, houndstongue 
Devils Tower National Monument (DETO) 

77

Family Scientific Name Common Name(s) 

Lappula occidentalis var. occidentalis western stickseed Lithospermum incisum fringed gromwell, fringed puccoon, narrowleaf gromwell, 

narrowleaf pucoon, narrowleaf stoneseed 

Brassicaceae Descurainia pinnata green tansymustard, pinnate tansy mustard, pinnate 

tansymustard, tansymustard, western tansymustard 

Sisymbrium altissimum Jim Hill mustard, tall hedge-mustard, tall mustard, tall 

tumblemustard, tumble mustard, tumbleweed mustard 

Campanulaceae Campanula rotundifolia bluebell, bluebell bellflower, roundleaf harebell 

Triodanis perfoliata clasping bellwort, clasping Venus' looking-glass, 

common Venus' lookingglass, roundleaved triodanis, Venus lookingglass 

Caprifoliaceae Symphoricarpos occidentalis western snowberry, wolfberry Caryophyllaceae Cerastium arvense starry chickweed Chenopodiaceae Chenopodium album common lambsquarters, lambsquarters, lambsquarters 

goosefoot, white goosefoot 

Chenopodium pratericola desert goosefoot 

Cupressaceae Juniperus communis var. depressa dwarf juniper 

Juniperus scopulorum Rocky Mountain juniper 

Cyperaceae Carex alopecoidea foxtail sedge 

Carex brevior shortbeak sedge Carex duriuscula needleleaf sedge, spike-rush sedge Carex inops ssp. heliophila sun sedge Carex sprengelii long-beak sedge, Sprengel sedge, Sprengel's sedge 

Euphorbiaceae Euphorbia esula var. uralensis leafy spurge, spurge, wolf's milk Fabaceae Astragalus crassicarpus var. paysonii groundplum milkvetch 

Astragalus missouriensis Missouri milkvetch Lotus unifoliolatus American bird's foot trefoil Medicago lupulina black medic, black medic clover, black medick, hop 

clover, hop medic, nonesuch, yellow trefoil 

Melilotus officinalis yellow sweet-clover, yellow sweetclover Oxytropis sericea locoweed, silky crazyweed, silvery oxytrope, white 

crazyweed, white locoweed, white pointloco 

Pediomelum argophyllum silverleaf Indian breadroot, silverleaf scurfpea 

Pediomelum esculentum breadroot scurfpea, Indian breadroot, large Indian 

breadroot 

Thermopsis rhombifolia goldenpea, prairie thermopsis Vicia americana american vetch 
Devils Tower National Monument (DETO) 

78

Family Scientific Name Common Name(s) 

Fagaceae Quercus macrocarpa bur oak Grossulariaceae Ribes oxyacanthoides ssp. setosum Canadian gooseberry, inland gooseberry, redshoot 

gooseberries 

Juncaceae Juncus compressus roundfruit rush Lamiaceae Hedeoma hispida false pennyroyal, rough false pennyroyal, rough 

pennyroyal 

Monarda fistulosa var. menthifolia mintleaf beebalm, Oswego-tea, wild bergamot, 

wildbergamot beebalm, wildbergamot horsemint 

Liliaceae Zigadenus venenosus var. gramineus meadow deathcamas Linaceae Linum lewisii blue flax, Lewis blue flax, Lewis flax, prairie flax Malvaceae Sphaeralcea coccinea scarlet globemallow, copper mallow, orange 

globemallow, red falsemallow 

Nyctaginaceae Mirabilis linearis linearleaf four-o'clock, narrow-leaf four-o'clock, 

narrowleaf four o clock, narrowleaf four o'clock, narrowleaf four-o'clock 

Oleaceae Fraxinus pennsylvanica green ash Oxalidaceae Oxalis stricta common yellow oxalis, sheep sorrel, sourgrass, toad 

sorrel, upright yellow woodsorrel, yellow woodsorrel 

Pinaceae Pinus ponderosa ponderosa pine, rock pine, western yellow pine Plantaginaceae Plantago patagonica woolly Indianwheat, woolly plantain Poaceae Achnatherum nelsonii ssp. nelsonii Columbian needlegrass 

Agrostis scabra ticklegrass Andropogon gerardii big bluestem, bluejoint, turkeyfoot Aristida purpurea var. longiseta threeawn Bouteloua curtipendula sideoats grama Bromus anomalus nodding brome, nodding bromegrass Bromus inermis var. inermis awnless brome, smooth brome Bromus japonicus Japanese brome, Japanese chess Bromus tectorum cheat grass, cheatgrass, downy brome, early chess, 

military grass, wild oats 

Buchloe dactyloides buffalograss Calamagrostis montanensis plains reedgrass Calamovilfa longifolia prairie sandreed 

Danthonia spicata poverty danthonia, poverty oatgrass, poverty wild oat 

grass 

Elymus canadensis var. canadensis Canada wildrye Elymus glaucus blue wild rye, blue wildrye Elymus trachycaulus slender wheatgrass 
Devils Tower National Monument (DETO) 

79

Family Scientific Name Common Name(s) 

Festuca ovina sheep fescue Hesperostipa comata ssp. comata needleandthread Hesperostipa spartea porcupinegrass Koeleria macrantha junegrass, prairie Junegrass Muhlenbergia racemosa green muhly, marsh muhly Nassella viridula green needlegrass Pascopyrum smithii pubescent wheatgrass, western wheatgrass Phleum pratense common timothy, timothy Piptatherum micranthum little-seed mountain-rice grass, littleseed ricegrass Poa nemoralis var. interior inland bluegrass 

Poa pratensis Kentucky bluegrass Schedonnardus paniculatus tumblegrass Schizachyrium scoparium var. scoparium little bluestem 

Vulpia octoflora var. octoflora eight-flower six-weeks grass, pullout grass, sixweeks 

fescue, sixweeks grass 

Polemoniaceae Collomia linearis narrowleaf mountaintrumpet, slenderleaf collomia, tiny 

trumpet 

Linanthus septentrionalis northern linanthus Phlox alyssifolia alyssumleaf phlox, phlox 

Polygonaceae Polygonum aviculare prostrate knotweed, yard knotweed Primulaceae Androsace occidentalis western rock-jasmine Ranunculaceae Anemone cylindrica cottonweed 

Ceratocephala testiculata curve-seed-butterwort Myosurus minimus tiny mousetail Pulsatilla patens ssp. multifida Pasque flower 

Rosaceae Prunus virginiana var. melanocarpa chokecherry, common chokecherry, Virginia 

chokecherry 

Rubiaceae Galium boreale northern bedstraw Salicaceae Populus deltoides ssp. monilifera Plains cottonwood Scrophulariaceae Collinsia parviflora small-flower blue-eyed mary 

Penstemon gracilis lilac penstemon, slender penstemon 

Penstemon grandiflorus large beardtongue, largeflowered penstemon Verbascum thapsus big taper, common mullein, flannel mullein, great mullein, 

mullein, velvet plant, woolly mullein 

Solanaceae Solanum triflorum cut-leaf nightshade Verbenaceae Verbena bracteata bigbract verbena, bracted vervain, carpet vervain, 

prostrate verbena, prostrate vervain 

Violaceae Viola adunca blue violet, hook violet, hookedspur violet 
Fort Laramie National Historic Site (FOLA)

80

Family Scientific Name Common Name(s) 

Anacardiaceae Toxicodendron rydbergii poison ivy, western poison ivy Asclepiadaceae Asclepias speciosa showy milkweed 

Asclepias viridiflora green milkweed 

Asteraceae Ambrosia psilostachya perennial ragweed, western ragweed 

Artemisia campestris wormwood sagewort Artemisia dracunculus false tarragon, green sagewort, silky wormwood, 

tarragon, wormwood 

Artemisia filifolia sand sagebrush Artemisia frigida fringed sagebrush, fringed sagewort, prairie sagewort Artemisia ludoviciana cudweed sagewort, gray sagewort, Louisiana sagewort, 

Louisiana wormwood, prairie sage, white sagebrush 

Conyza canadensis Canada horseweed, Canadian horseweed, horseweed, 

horseweed fleabane, mares tail, marestail 

Ericameria nauseosa rubber rabbitbrush Erigeron divergens spreading fleabane Grindelia squarrosa curlycup gumweed, curlytop gumweed, gumweed, 

rosinweed, tarweed 

Heterotheca villosa hairy false goldaster, hairy false goldenaster, hairy 

goldaster, hairy goldenaster 

Lactuca serriola China lettuce, prickly lettuce, wild lettuce Lactuca tatarica blue lettuce, blue wild lettuce, chicory lettuce, Russian 

blue lettuce 

Liatris punctata dotted blazing star, dotted gayfeather Logfia arvensis field cottonrose Lygodesmia juncea rush skeleton-plant, rush skeletonplant, rush 

skeletonweed, skeletonplant, skeletonweed 

Machaeranthera tanacetifolia tansyleaf tansyaster Scorzonera laciniata cutleaf vipergrass Senecio riddellii Riddell's grounsel, Riddell's ragwort Symphyotrichum ericoides white heath aster Taraxacum officinale blowball, common dandelion, dandelion, faceclock 

Thelesperma megapotamicum Hopitea, greenthread Tragopogon dubius common salsify, goatsbeard, meadow goat's-beard, 

salsify, Western goat's beard, western salsify, wild oysterplant, yellow goat's beard, yellow salsify 

Boraginaceae Cryptantha cinerea var. jamesii James’ cryptantha 
Fort Laramie National Historic Site (FOLA)

81

Family Scientific Name Common Name(s) 

Cryptantha minima small cryptantha Lappula occidentalis western stickseed Lithospermum incisum fringed gromwell, fringed puccoon, narrowleaf gromwell, 

narrowleaf pucoon, narrowleaf stoneseed 

Lithospermum ruderale western stoneseed 

Brassicaceae Alyssum desertorum desert alyssum, desert madwort 

Camelina microcarpa false flax, littlepod falseflax, littleseed falseflax, small 

fruited falseflax, smallseed falseflax 

Descurainia pinnata green tansymustard, pinnate tansymustard, 

tansymustard, western tansymustard 

Descurainia sophia flaxweed tansymustard, flixweed, flixweed 

tansymustard, herb sophia, herb-sophia 

Lepidium densiflorum common pepperweed, greenflower pepperweed, miner's 

pepperwort, peppergrass, prairie pepperweed 

Sisymbrium altissimum Jim Hill mustard, tall hedge-mustard, tall mustard, tall 

tumblemustard, tumble mustard, tumbleweed mustard 

Cactaceae Escobaria vivipara pink pincushioncactus, spinystar, spinystar cactus 

Opuntia fragilis brittle cactus, brittle pricklypear, fragile cactus, jumping 

cactus, little pricklypear 

Opuntia macrorhiza twistspine pricklypear, bigroot pricklypear Opuntia polyacantha Plains pricklypear 

Caprifoliaceae Symphoricarpos occidentalis western snowberry, wolfberry Chenopodiaceae Kochia scoparia fireweed, kochia, Mexican burningbush, Mexican 

fireweed, Mexican-fireweed, mock cypress, Summer cypress 

Salsola collina spineless Russian thistle 

Commelinaceae Tradescantia occidentalis prairie spiderwort, spiderwort, western spiderwort Convolvulaceae Ipomoea leptophylla bush morningglory Cyperaceae Carex duriuscula needleleaf sedge, spike-rush sedge Euphorbiaceae Chamaesyce glyptosperma ribseed sandmat, ridgeseed spurge 

Fabaceae Glycyrrhiza lepidota American licorice, licorice, wild licorice 

Lupinus pusillus small lupine, rusty lupine Medicago sativa alfalfa Psoralidium tenuiflorum slimflower scurfpea 

Geraniaceae Erodium cicutarium redstem stork's bill Lamiaceae Melilotus officinalis yellow sweet-clover, yellow sweetclover 

Loasaceae Mentzelia nuda stickleaf mentzelia, bractless blazingstar 
Fort Laramie National Historic Site (FOLA)

82

Family Scientific Name Common Name(s) 

Malvaceae Sphaeralcea coccinea scarlet globemallow, copper mallow, orange 

globemallow, red falsemallow 

Nyctaginaceae Mirabilis hirsuta hairy four o'clock 

Mirabilis linearis linearleaf four-o'clock, narrowleaf four-o'clock 

Onagraceae Gaura coccinea scarlet beeblossom, scarlet gaura 

Gaura mollis James velvetweed Oenothera albicaulis halfshrub sundrop, white-stem evening-primrose, 

whitest evening-primrose 

Papaveraceae Argemone polyanthemos prickly poppy Plantaginaceae Plantago patagonica woolly Indianwheat, woolly plantain, woolly plantian Poaceae Achnatherum hymenoides Indian ricegrass 

Agropyron cristatum crested wheatgrass Aristida purpurea purple three-awn Bouteloua gracilis blue grama Bromus inermis awnless brome, smooth brome Bromus japonicus Japanese brome, Japanese chess Bromus tectorum cheat grass, cheatgrass, downy brome, early chess, 

military grass, wild oats 

Calamovilfa longifolia prairie sandreed Distichlis spicata seashore saltgrass Elymus elymoides squirreltail Elymus repens quackgrass 

Hesperostipa comata needleandthread Hordeum pusillum little barley Nassella viridula green needlegrass Pascopyrum smithii pubescent wheatgrass, western wheatgrass Poa pratensis Kentucky bluegrass Sporobolus cryptandrus sand dropseed Vulpia octoflora eight-flower six-weeks grass, pullout grass, sixweeks 

fescue, sixweeks grass 

Polygonaceae Eriogonum annuum annual buckwheat, annual eriogonum, annual wild 

buckwheat, umbrella plant, wild buckwheat 

Salicaceae Populus angustifolia narrowleaf cottonwood 

Populus deltoides ssp. monilifera Plains cottonwood 

Scrophulariaceae Penstemon albidus white penstemon 

Penstemon angustifolius broad-beard beardtongue, narrowleaf penstemon 

Solanaceae Physalis hispida prairie groundcherry 
Fort Laramie National Historic Site (FOLA)

83

Family Scientific Name Common Name(s) 

Physalis longifolia common groundcherry, longleaf groundcherry 

Verbenaceae Phyla cuneifolia wedgeleaf 
Theodore Roosevelt National Park (THRO)

84

Family Scientific Name Common Name(s) 

Amaranthaceae Amaranthus retroflexus redroot pigweed, rough pigweed Anacardiaceae Rhus trilobata skunkbush sumac 

Toxicodendron rydbergii poison ivy, western poison ivy, western poison-ivy 

Apiaceae Lomatium foeniculaceum biscuitroot, carrot-leaf desert-parsley, desert biscuitroot Apocynaceae Apocynum androsaemifolium bitterroot, flytrap dogbane, spreading dogbane Asclepiadaceae Asclepias speciosa showy milkweed 

Asclepias viridiflora green antelopehorn milkweed, green comet milkweed, 

green milkweed 

Asteraceae Achillea millefolium common yarrow 

Ambrosia psilostachya perennial ragweed, western ragweed Anaphalis margaritacea western pearly everlasting Antennaria microphylla pink pussytoes, littleleaf pussytoes Antennaria parvifolia Rocky Mountain pussytoes, smalleaf pussytoes Artemisia campestris ssp. caudata field sagewort Artemisia cana silver sagebrush Artemisia dracunculus false tarragon, green sagewort, silky wormwood, 

tarragon, wormwood 

Artemisia frigida fringed sagebrush, fringed sagewort, prairie sagewort Artemisia longifolia longleaf wormwood Artemisia ludoviciana cudweed sagewort, gray sagewort, Louisiana sagewort, 

Louisiana wormwood, mugwort wormwood, prairie sage, white sagebrush 

Artemisia tridentata big sagebush Cirsium arvense Californian thistle, Canada thistle, Canadian thistle, 

creeping thistle, field thistle 

Cirsium flodmanii Flodman thistle, Flodman's thistle Conyza canadensis Canada horseweed, Canadian horseweed, horseweed, 

horseweed fleabane, mares tail, marestail 

Dyssodia papposa dogbane dyssodia, dogweed, fetid dogweed, fetid 

marigold, prairie dogweed 

Echinacea angustifolia blacksamson, blacksamson echinacea Ericameria nauseosa var. nauseosa rubber rabbitbrush Grindelia squarrosa curlycup gumweed, curlytop gumweed, gumweed, 

rosinweed, tarweed 

Gutierrezia sarothrae broom snakeweed, broomweed, perennial snakeweed, 

stinkweed, turpentine weed, yellow top 

Helianthus annuus annual sunflower, common sunflower, sunflower, wild 

sunflower 
Theodore Roosevelt National Park (THRO)

85

Family Scientific Name Common Name(s) 

Helianthus maximiliani Maximilian sunflower Helianthus pauciflorus stiff sunflower Helianthus petiolaris prairie sunflower Heterotheca villosa hairy false goldaster, hairy false goldenaster, hairy 

goldaster, hairy goldenaster 

Hymenopappus filifolius cutleaf, fine-leaf woollywhite, fineleaf hymenopappus Hymenoxys richardsonii pingue rubberweed Lactuca serriola China lettuce, prickly lettuce, wild lettuce Lactuca tatarica var. pulchella blue lettuce, blue wild lettuce, chicory lettuce, Russian 

blue lettuce 

Liatris punctata dotted blazing star, dotted gayfeather Machaeranthera canescens hoary tansyaster Oligoneuron album prairie goldenrod Packera cana wooly groundsel Packera plattensis prairie groundsel Ratibida columnifera prairie coneflower, redspike Mexican hat, upright prairie 

coneflower 

Solidago canadensis Canada goldenrod, common goldenrod Solidago gigantea giant goldenrod Solidago missouriensis Missouri goldenrod, prairie goldenrod Solidago nemoralis dyersweed goldenrod, gray goldenrod Symphyotrichum ericoides white heath aster Symphyotrichum laeve smooth blue aster, geyer's aster Symphyotrichum lanceolatum white panicle aster Symphyotrichum oblongifolium aromatic aster Taraxacum officinale blowball, common dandelion, dandelion, faceclock 

Tetraneuris acaulis stemless hymenoxys, stemless four-nerve daisy Tragopogon dubius common salsify, goatsbeard, meadow goat's-beard, 

salsify, Western goat's beard, western salsify, wild oysterplant, yellow goat's beard, yellow salsify 

Xanthium strumarium rough cocklebur 

Boraginaceae Cryptantha celosioides buttecandle, minerscandle 

Cynoglossum officinale common houndstongue, gypsy-flower, gypsyflower, 

hound's tongue, houndstongue 

Lappula occidentalis flatspine stickseed Lithospermum incisum fringed gromwell, fringed puccoon, narrowleaf gromwell, 

narrowleaf pucoon, narrowleaf stoneseed 
Theodore Roosevelt National Park (THRO)

86

Family Scientific Name Common Name(s) 

Brassicaceae Alyssum desertorum desert alyssum, desert madwort 

Camelina microcarpa false flax, littlepod falseflax, littleseed falseflax, small 

fruited falseflax, smallseed falseflax 

Descurainia pinnata green tansymustard, pinnate tansy mustard, pinnate 

tansymustard, tansymustard, western tansymustard 

Descurainia sophia flaxweed tansymustard, flixweed, flixweed 

tansymustard, herb sophia, herb-sophia 

Erysimum capitatum plains wallflower, prairie rocket, sanddune wallflower, 

western wallflower 

Erysimum inconspicuum smallflower wallflower Lepidium densiflorum common pepperweed, greenflower pepperweed, miner's 

pepperwort, peppergrass, prairie pepperweed 

Lesquerella alpina alpine bladderpod Lesquerella ludoviciana foothill bladderpod, Louisiana bladderpod, silver 

bladderpod 

Thlaspi arvense fanweed, field pennycress, Frenchweed, pennycress, 

stinkweed 

Cactaceae Opuntia macrorhiza twistspine pricklypear, bigroot pricklypear 

Opuntia polyacantha Plains pricklypear 

Campanulaceae Campanula rotundifolia bluebell, bluebell bellflower, roundleaf harebell Caprifoliaceae Symphoricarpos occidentalis western snowberry, wolfberry Caryophyllaceae Cerastium arvense starry chickweed 

Silene antirrhina catchfly, sleepy campion, sleepy catchfly, sleepy silene 

Chenopodiaceae Atriplex canescens fourwing saltbush 

Atriplex confertifolia spiny saltbush Atriplex nuttallii Nuttall's saltbush Chenopodium album common lambsquarters, lambsquarters, lambsquarters 

goosefoot, white goosefoot 

Chenopodium pratericola desert goosefoot Chenopodium simplex giant-seed goosefoot, mapleleaf goosefoot Endolepis dioica Suckley's endolepis Krascheninnikovia lanata winterfat Salsola kali tumbleweed Sarcobatus vermiculatus black greasewood, greasewood Suaeda moquinii Torrey seepweed 

Cupressaceae Juniperus communis common juniper, dwarf juniper 
Theodore Roosevelt National Park (THRO)

87

Family Scientific Name Common Name(s) 

Juniperus horizontalis creeping juniper Juniperus scopulorum Rocky Mountain juniper 

Cyperaceae Carex brevior shortbeak sedge 

Carex duriuscula needleleaf sedge, spike-rush sedge Carex filifolia threadleaf sedge Carex inops ssp. heliophila sun sedge 

Euphorbiaceae Chamaesyce glyptosperma ribseed sandmat, ridgeseed spurge 

Euphorbia brachycera horned spurge, Rocky Mountain spurge Euphorbia esula leafy spurge, spurge, wolf's milk, wolf's-milk 

Euphorbia spathulata roughpod spurge, warty spurge 

Fabaceae Astragalus agrestis cock's-head, field milkvetch, purple milkvetch 

Astragalus crassicarpus ground-plum, groundplum milkvetch Astragalus gilviflorus plains milkvetch Astragalus laxmannii var. robustior Laxmann's milkvetch, prairie milkvetch Astragalus missouriensis Missouri milkvetch Astragalus purshii woollypod milkvetch Dalea candida white prairieclover Dalea purpurea Purple prairieclover, violet dalea, violet prairie clover Glycyrrhiza lepidota American licorice, licorice, wild licorice Hedysarum boreale sweetvetch Lotus unifoliolatus American bird's-foot trefoil Medicago lupulina black medic, black medic clover, black medick, hop 

clover, hop medic, nonesuch, yellow trefoil 

Melilotus officinalis yellow sweet-clover, yellow sweetclover Oxytropis lambertii Lambert crazyweed, Lambert locoweed, purple loco, 

purple locoweed, stemless loco, whitepoint locoweed 

Oxytropis sericea locoweed, silky crazyweed, silvery oxytrope, white 

crazyweed, white locoweed, white pointloco 

Pediomelum esculentum breadroot scurfpea, Indian breadroot, large Indian 

breadroot 

Thermopsis rhombifolia goldenpea, prairie thermopsis Vicia americana American deervetch, American vetch 

Grossulariaceae Ribes aureum golden currant 

Ribes oxyacanthoides Canadian gooseberry 

Lamiaceae Hedeoma drummondii Drummond's false pennyroyal, Drummond's 

falsepennyroyal 

Hedeoma hispida rough false pennyroyal 
Theodore Roosevelt National Park (THRO)

88

Family Scientific Name Common Name(s) 

Monarda fistulosa mintleaf beebalm, Oswego-tea, wild bergamot, 

wildbergamot beebalm, wildbergamot horsemint 

Liliaceae Allium textile prairie onion, textile onion, wild onion 

Maianthemum stellatum starry false lily of the valley 

Linaceae Linum lewisii blue flax, Lewis blue flax, Lewis flax, prairie flax 

Linum rigidum orange flax, stiff flax, stiffstem flax 

Malvaceae Sphaeralcea coccinea copper mallow, orange globemallow, red falsemallow, 

scarlet globemallow 

Nyctaginaceae Mirabilis hirsuta hairy four o'clock 

Mirabilis linearis linearleaf four-o'clock, narrowleaf four-o'clock 

Oleaceae Fraxinus pennsylvanica green ash Onagraceae Gaura coccinea scarlet beeblossom, scarlet gaura 

Oenethera biennis common evening-primrose Oenothera caespitosa tufted evening-primrose 

Plantaginaceae Plantago patagonica woolly Indianwheat, woolly plantain Poaceae Achnatherum hymenoides Indian ricegrass 

Andropogon gerardii big bluestem, bluejoint, turkeyfoot Bouteloua curtipendula sideoats grama Bouteloua gracilis blue grama Bromus inermis awnless brome, smooth brome Bromus japonicus Japanese brome, Japanese chess Bromus pubescens hairy woodland brome Bromus tectorum cheat grass, cheatgrass, downy brome, early chess, 

military grass, wild oats 

Calamovilfa longifolia prairie sandreed Danthonia spicata poverty danthonia, poverty oatgrass, poverty wild oat 

grass 

Distichlis spicata seashore saltgrass Elymus canadensis Canada wildrye Elymus elymoides squirreltail Elymus repens quackgrass 

Elymus trachycaulus slender wheatgrass Hesperostipa comata ssp. comata needleandthread 

Koeleria macrantha junegrass, prairie Junegrass Muhlenbergia cuspidata plains muhly Muhlenbergia racemosa green muhly, marsh muhly 

Nassella viridula green needlegrass Pascopyrum smithii pubescent wheatgrass, western wheatgrass 
Theodore Roosevelt National Park (THRO)

89

Family Scientific Name Common Name(s) 

Piptatherum micranthum little-seed mountain-rice grass, littleseed ricegrass Poa arida Plains bluegrass Poa compressa Canada bluegrass, flat-stem blue grass Poa pratensis Kentucky bluegrass Schedonnardus paniculatus tumblegrass Schizachyrium scoparium little bluestem Sporobolus airoides alkali-sacaton Sporobolus cryptandrus sand dropseed Thinopyrum intermedium intermediate wheatgrass Vulpia octoflora eight-flower six-weeks grass, pullout grass, sixweeks 

fescue, sixweeks grass 

Polemoniaceae Collomia linearis narrow-leaf mountain-trumpet, slenderleaf collomia, tiny 

trumpet 

Phlox hoodii Hood's phlox, spiny phlox 

Polygalaceae Polygala alba white milkwort 

Polygala verticillata whorled milkwort 

Polygonaceae Eriogonum flavum alpine golden buckwheat, yellow eriogonum 

Eriogonum pauciflorum fewflower buckwheat, manybranch eriogonum Polygonum convolvulus black bindweed, black-bindweed, climbing buckwheat, 

climbing knotweed, cornbind, dullseed cornbind, pink smartweed, wild buckwheat 

Ranunculaceae Clematis ligusticifolia virgin'sbower, virgins bower, western white clematis 

Pulsatilla patens cutleaf anemone Thalictrum dasycarpum purple meadow-rue, purple meadowrue 

Rosaceae Agrimonia striata woodland grooveburr 

Dasiphora floribunda shrubby cinquefoil Geum triflorum old man's whiskers, prairie smoke Prunus pumila var. besseyi Great Lakes sand cherry, western sandcherry 

Prunus virginiana chokecherry, common chokecherry, Virginia 

chokecherry 

Rosa woodsii Wood's rose, woods rose, Woods' rose 

Rubiaceae Galium aparine bedstraw, catchweed bedstraw, cleavers, cleaverwort, 

goose grass, scarthgrass, sticky-willy, white hedge 

Galium boreale northern bedstraw 

Salicaceae Populus deltoides ssp. monilifera Plains cottonwood 

Santalaceae Comandra umbellate bastard toadflax Scrophulariaceae Orthocarpus luteus golden-tongue owl-clover, yellow owl's-clover, yellow 

owlclover 
Theodore Roosevelt National Park (THRO)

90

Family Scientific Name Common Name(s) 

Penstemon angustifolius broad-beard beardtongue, broadbeard beardtongue, 

narrowleaf penstemon 

Solanaceae Physalis virginiana lanceleaf groundcherry, Virginia groundcherry Urticaceae Parietaria pensylvanica Pennsylvania pellitory Violaceae Viola canadensis var. rugulosa Canadian white violet 

Viola nuttallii Nuttall violet, Nuttall's violet, yellow prairie violet Viola pedatifida crow-foot violet, prairie violet 

Vitaceae Parthenocissus vitacea thicket creeper, Virginia creeper, woodbine 
Wind Cave National Park (WICA)

91

Family Scientific Name Common Name(s) 

Aceraceae Acer negundo var. interius western boxelder Agavaceae Yucca glauca beargrass, Great Plains yucca, small soapweed, 

soapweed yucca, Spanish bayonet, yucca 

Amaranthaceae Amaranthus blitoides mat amaranth, prostrate pigweed 

Amaranthus retroflexus redroot pigweed, rough pigweed 

Anacardiaceae Rhus trilobata skunkbush sumac 

Toxicodendron rydbergii poison ivy, western poison ivy 

Apiaceae Lomatium foeniculaceum biscuitroot, carrot-leaf desert-parsley, desert biscuitroot 

Musineon tenuifolium slender wildparsley 

Apocynaceae Apocynum cannabinum prairie dogbane Asclepiadaceae Asclepias ovalifolia milkweed, oval-leaf milkweed 

Asclepias pumila plains milkweed Asclepias speciosa showy milkweed Asclepias viridiflora green antelopehorn milkweed, green comet milkweed, 

green milkweed 

Asteraceae Achillea millefolium var. occidentalis western yarrow 

Antennaria neglecta field pussytoes Antennaria parvifolia little-leaf pussytoes, Rocky Mountain pussytoes, small 

leaf pussytoes, smallleaf pussytoes 

Artemisia campestris wormwood sagewort Artemisia dracunculus false tarragon, green sagewort, silky wormwood, 

tarragon, wormwood 

Artemisia frigida fringed sagebrush, fringed sagewort, prairie sagewort Artemisia ludoviciana ssp. ludoviciana cudweed sagewort, gray sagewort, Louisiana sagewort, 

Louisiana wormwood, mugwort wormwood, prairie sage, white sagebrush 

Brickellia eupatorioides var. eupatorioides false boneset Cirsium arvense Californian thistle, Canada thistle, Canadian thistle, 

creeping thistle, field thistle 

Cirsium flodmanii Flodman thistle, Flodman's thistle Cirsium undulatum gray thistle, wavy-leaf thistle, wavyleaf thistle Cirsium vulgare bull thistle, common thistle, spear thistle Conyza canadensis Canada horseweed, Canadian horseweed, horseweed, 

horseweed fleabane, mares tail, marestail 

Dyssodia papposa dogbane dyssodia, dogweed, fetid dogweed, fetid 

marigold, prairie dogweed 
Wind Cave National Park (WICA)

92

Family Scientific Name Common Name(s) 

Echinacea angustifolia blacksamson, blacksamson echinacea Erigeron canus hoary fleabane Erigeron subtrinervis threeveined fleabane Eupatorium maculatum var. bruneri spotted joe-pye-weed 

Gutierrezia sarothrae broom snakeweed, broomweed, perennial snakeweed, 

stinkweed, turpentine weed, yellow top 

Helianthus annuus annual sunflower, common sunflower, sunflower, wild 

sunflower 

Helianthus maximiliani Maximilian sunflower Helianthus pauciflorus ssp. pauciflorus stiff sunflower Heterotheca villosa hairy false goldaster, hairy false goldenaster, hairy 

goldaster, hairy goldenaster 

Hymenopappus filifolius var. polycephalus manyhead hymenopappus Lactuca canadensis wild lettuce Lactuca serriola China lettuce, prickly lettuce, wild lettuce Liatris punctata dotted blazing star, dotted gayfeather Lygodesmia juncea rush skeletonplant, rush skeletonweed, skeletonplant, 

skeletonweed 

Machaeranthera pinnatifida var. pinnatifida lacy tansyaster 

Oligoneuron album prairie goldenrod Oligoneuron rigidum stiff goldenrod Oligoneuron rigidum var. rigidum stiff goldenrod Packera cana wooly groundsel Packera tridenticulata threetooth ragwort Ratibida columnifera prairie coneflower, redspike Mexican hat, upright prairie 

coneflower 

Rudbeckia hirta blackeyed Susan Senecio integerrimus var. integerrimus lambstongue groundsel, lambstongue ragwort 

Solidago canadensis Canada goldenrod, common goldenrod Solidago gigantea giant goldenrod Solidago missouriensis Missouri goldenrod, prairie goldenrod Solidago mollis ashy goldenrod, soft goldenrod, velvety goldenrod, 

woolly goldenrod 

Solidago nemoralis dyersweed goldenrod, gray goldenrod Solidago speciosa noble goldenrod, showy goldenrod Symphyotrichum ericoides white heath aster Symphyotrichum laeve smooth blue aster, geyer's aster Symphyotrichum lanceolatum var. hesperium white panicle aster Symphyotrichum oblongifolium aromatic aster 
Wind Cave National Park (WICA)

93

Family Scientific Name Common Name(s) 

Taraxacum officinale blowball, common dandelion, dandelion, faceclock Tetraneuris acaulis stemless hymenoxys, stemless four-nerve daisy Townsendia exscapa stemless Townsend daisy, stemless townsend-daisy, 

stemless townsendia 

Tragopogon dubius common salsify, goatsbeard, meadow goat's-beard, 

salsify, Western goat's beard, western salsify, wild oysterplant, yellow goat's beard, yellow salsify 

Boraginaceae Cryptantha celosioides buttecandle, minerscandle 

Cryptantha fendleri sanddune cryptantha Cynoglossum officinale common houndstongue, gypsy-flower, gypsyflower, 

hound's tongue, houndstongue 

Hackelia floribunda manyflower stickseed Lappula occidentalis var. occidentalis western stickseed 

Lithospermum incisum fringed gromwell, fringed puccoon, narrowleaf gromwell, 

narrowleaf pucoon, narrowleaf stoneseed 

Mertensia lanceolata lanceleaf bluebells, prairie bluebells Onosmodium molle false gromwell, smooth onosmodium, soft-hair 

marbleseed, softhair marbleseed 

Brassicaceae Alyssum desertorum desert alyssum, desert madwort 

Arabis glabra tower rockcress Arabis hirsuta var. pycnocarpa creamflower rockcress, hairy rockcress Camelina microcarpa false flax, littlepod falseflax, littleseed falseflax, small 

fruited falseflax, smallseed falseflax 

Descurainia pinnata green tansymustard, pinnate tansymustard, 

tansymustard, western tansymustard 

Draba reptans Carolina draba, Carolina whitlowgrass, creeping draba Erysimum capitatum plains wallflower, prairie rocket, sanddune wallflower, 

western wallflower 

Lepidium densiflorum common pepperweed, greenflower pepperweed, miner's 

pepperwort, miners pepperweed, peppergrass, prairie pepperweed 

Lesquerella ludoviciana foothill bladderpod, Louisiana bladderpod, silver 

bladderpod 

Sisymbrium altissimum Jim Hill mustard, tall tumblemustard, tumble mustard, 

tumblemustard, tumbleweed mustard 

Cactaceae Escobaria missouriensis var. missouriensis Missouri foxtail cactus 

Escobaria vivipara var. vivipara pink pincushioncactus, spinystar, spinystar cactus 
Wind Cave National Park (WICA)

94

Family Scientific Name Common Name(s) 

Opuntia fragilis brittle cactus, brittle pricklypear, fragile cactus, jumping 

cactus, little pricklypear 

Opuntia macrorhiza twistspine pricklypear, bigroot pricklypear Opuntia polyacantha Plains pricklypear 

Campanulaceae Campanula rotundifolia bluebell, bluebell bellflower, roundleaf harebell 

Triodanis perfoliata clasping bellwort, clasping Venus' looking-glass, 

common Venus' lookingglass, roundleaved triodanis, Venus lookingglass 

Capparaceae Polanisia dodecandra ssp. trachysperma western clammyweed 

Caprifoliaceae Symphoricarpos occidentalis western snowberry, wolfberry 

Viburnum lentago nanny-berry 

Caryophyllaceae Cerastium arvense field chickweed, field mouse-ear chickweed, starry 

chickweed 

Paronychia depressa spreading nailwort Silene antirrhina catchfly, sleepy campion, sleepy catchfly, sleepy silene Silene latifolia ssp. alba bladder campion, bladder-campion, evening lychnis, 

white campion, white cockle 

Chenopodiaceae Chenopodium pratericola desert goosefoot 

Salsola kali tumbleweed 

Commeliniaceae Tradescantia occidentalis prairie spiderwort, spiderwort, western spiderwort Convolvulaceae Evolvulus nuttallianus silver wild morning-glory Cupressaceae Juniperus communis common juniper, dwarf juniper 

Juniperus horizontalis creeping juniper Juniperus scopulorum Rocky Mountain juniper 

Cyperaceae Carex backii back sedge, Back's sedge 

Carex brevior brevior sedge, fescue sedge, shortbeak sedge Carex duriuscula needleleaf sedge, spike-rush sedge Carex filifolia threadleaf sedge Carex hystericina porcupine sedge Carex inops ssp. heliophila sun sedge Carex praegracilis slim sedge Carex sprengelii long-beak sedge, Sprengel sedge, Sprengel's sedge 

Equisetaceae Equisetum hyemale western scouringrush Euphorbiaceae Chamaesyce glyptosperma ribseed sandmat, ridgeseed spurge 

Chamaesyce serpyllifolia ssp. serpyllifolia thymleaf spurge 

Euphorbia brachycera horned spurge, Rocky Mountain spurge 
Wind Cave National Park (WICA)

95

Family Scientific Name Common Name(s) 

Euphorbia marginata smoke-on-the-prairie, snow-on-the-mountain, 

variegated spurge, whitemargined spurge 

Euphorbia spathulata roughpod spurge, warty spurge 

Fabaceae Amorpha canescens leadplant, leadplant amorpha 

Astragalus agrestis cock's-head, field milkvetch, purple milkvetch Astragalus crassicarpus ground-plum, groundplum milkvetch Astragalus gilviflorus var. gilviflorus Plains milkvetch Astragalus gracilis slender milkvetch Astragalus laxmannii Laxmann's milkvetch, prairie milkvetch Dalea candida var. oligophylla white prairieclover Dalea purpurea Purple prairieclover, violet dalea, violet prairie clover, 

violet prairie-clover 

Lathyrus polymorphus manystem peavine Medicago lupulina black medic, black medic clover, black medick, hop 

clover, hop medic, nonesuch, yellow trefoil 

Melilotus alba white sweetclover Melilotus officinalis yellow sweet-clover, yellow sweetclover Oxytropis campestris field locoweed Oxytropis lambertii Lambert crazyweed, Lambert locoweed, purple loco, 

purple locoweed, stemless loco, whitepoint locoweed 

Oxytropis sericea locoweed, silky crazyweed, silvery oxytrope, white 

crazyweed, white locoweed, white pointloco 

Pediomelum argophyllum silverleaf Indian breadroot, silverleaf scurfpea Pediomelum esculentum breadroot scurfpea, Indian breadroot, large Indian 

breadroot 

Psoralidium tenuiflorum slimflower scurfpea Thermopsis rhombifolia goldenpea, prairie thermopsis Vicia americana American deervetch, American vetch 

Grossulariaceae Ribes aureum var. villosum golden currant 

Ribes cereum wax currant Ribes oxyacanthoides Canadian gooseberry 

Hydrophyllaceae Ellisia nyctelea Aunt Lucy, ellisia, false babyblueeyes, waterpod 

Iridaceae Sisyrinchium montanum strict blue-eyed grass Lamiaceae Hedeoma drummondii Drummond's false pennyroyal, Drummond's pennyroyal 

Hedeoma hispida rough false pennyroyal Leonurus cardiaca common motherwort, motherwort Marrubium vulgare horehound, white horehound Mentha arvensis wild mint 
Wind Cave National Park (WICA)

96

Family Scientific Name Common Name(s) 

Monarda fistulosa var. menthifolia mintleaf beebalm, Oswego-tea, wild bergamot, 

wildbergamot beebalm, wildbergamot horsemint 

Salvia reflexa blue sage, lambsleaf sage, lanceleaf sage, Rocky 

Mountain sage, sage mint 

Liliaceae Allium cernuum nodding onion 

Allium textile prairie onion, textile onion, wild onion Asparagus officinalis asparagus, garden asparagus Calochortus nuttallii sego-lily Lilium philadelphicum wood lily Maianthemum racemosum ssp. racemosum false Solomons-seal, feathery false lily of the vally, 

feathery false Solomons-seal 

Maianthemum stellatum starry false lily of the valley Polygonatum biflorum Solomon's seal Zigadenus elegans mountain deathcamas Zigadenus venenosus var. gramineus meadow deathcamas 

Linaceae Leucocrinum montanum common starlily, star-lily 

Linum lewisii blue flax, Lewis blue flax, Lewis flax, prairie flax 

Linaceae Linum rigidum orange flax, stiff flax, stiffstem flax Malvaceae Sphaeralcea coccinea copper mallow, orange globemallow, red falsemallow, 

scarlet globemallow 

Nyctaginaceae Mirabilis linearis linearleaf four-o'clock, narrowleaf four-o'clock Onagraceae Calylophus serrulatus halfshrub calylophus, halfshrub sundrop, serrateleaf 

eveningprimrose, yellow sundrops 

Gaura coccinea scarlet beeblossom, scarlet gaura, Scarlet guara Oenothera coronopifolia crownleaf evening primrose, crownleaf eveningprimrose 

Oxalidaceae Oxalis stricta common yellow oxalis, sheep sorrel, sourgrass, toad 

sorrel, upright yellow woodsorrel, yellow woodsorrel 

Papaveraceae Argemone polyanthemos prickly poppy Pinaceae Pinus ponderosa ponderosa pine, rock pine, western yellow pine Plantaginaceae Plantago patagonica woolly Indianwheat, woolly plantain Poaceae Achnatherum hymenoides Indian ricegrass 

Andropogon gerardii big bluestem, bluejoint, turkeyfoot Aristida purpurea purple three-awn Bouteloua curtipendula sideoats grama 

Bouteloua gracilis blue grama 
Wind Cave National Park (WICA)

97

Family Scientific Name Common Name(s) 

Bouteloua hirsuta hairy grama Bromus japonicus Japanese brome, Japanese chess Bromus tectorum cheat grass, cheatgrass, downy brome, early chess, 

military grass, wild oats 

Buchloe dactyloides buffalograss Calamovilfa longifolia prairie sandreed Danthonia spicata poverty danthonia, poverty oatgrass, poverty wild oat 

grass 

Dichanthelium oligosanthes Heller's rosette grass, Scribner panic, Scribner's rosette 

grass, Scribners panicum 

Dichanthelium wilcoxianum fall rosette grass Elymus canadensis Canada wildrye Elymus elymoides ssp. brevifolius squirreltail Elymus trachycaulus slender wheatgrass Elymus virginicus Virginia wild rye Festuca ovina sheep fescue Hesperostipa comata ssp. comata needleandthread Hesperostipa spartea porcupinegrass Koeleria macrantha junegrass, prairie Junegrass Muhlenbergia cuspidata plains muhly Muhlenbergia racemosa green muhly, marsh muhly Nassella viridula green needlegrass Pascopyrum smithii pubescent wheatgrass, western wheatgrass Piptatherum micranthum little-seed mountain-rice grass, littleseed ricegrass Poa compressa Canada bluegrass, flat-stem blue grass Poa pratensis Kentucky bluegrass Schedonnardus paniculatus tumblegrass Schizachne purpurascens false melic grass Schizachyrium scoparium var. scoparium little bluestem 

Sporobolus cryptandrus sand dropseed Sporobolus heterolepis prairie dropseed Thinopyrum intermedium intermediate wheatgrass Vulpia octoflora var. octoflora eight-flower six-weeks grass, pullout grass, sixweeks 

fescue, sixweeks grass 

Polemoniaceae Collomia linearis narrowleaf mountaintrumpet, slenderleaf collomia, tiny 

trumpet 

Phlox alyssifolia alyssumleaf phlox, phlox Phlox andicola prairie phlox Phlox hoodii Hood's phlox, spiny phlox 
Wind Cave National Park (WICA)

98

Family Scientific Name Common Name(s) 

Polygalaceae Polygala alba white milkwort 

Eriogonum flavum alpine golden buckwheat, yellow eriogonum 

Eriogonum pauciflorum fewflower buckwheat, manybranch eriogonum Polygonum aviculare prostrate knotweed, yard knotweed Polygonum convolvulus black bindweed, climbing buckwheat, climbing 

knotweed, cornbind, dullseed cornbind, pink smartweed, wild buckwheat 

Rumex aquaticus western dock 

Primulaceae Dodecatheon pulchellum darkthroat shootingstar, Southern shootingstar 

Lysimachia ciliata fringed yellow-loosestrife 

Ranunculaceae Anemone cylindrica candle anemone, cottonweed 

Pulsatilla patens ssp. multifida Pasque flower Thalictrum dasycarpum purple meadow-rue, purple meadowrue 

Rosaceae Agrimonia striata woodland grooveburr 

Amelanchier alnifolia juneberry, pacific serviceberry, Saskatoon serviceberry, 

western serviceberry, western shadbush 

Cercocarpus montanus true mountain mahagony Potentilla concinna red cinquefoil Potentilla glandulosa sticky cinquefoil Potentilla hippiana horse cinquefoil, woolly cinquefoil 

Potentilla pensylvanica prairie cinquefoil Prunus americana American plum Prunus pumila var. besseyi Great Lakes sand cherry, western sandcherry 

Prunus virginiana var. melanocarpa chokecherry, common chokecherry, Virginia 

chokecherry 

Rosa woodsii Wood's rose, woods rose, Woods' rose Rubus idaeus ssp. strigosus red raspberry 

Rubiaceae Galium aparine bedstraw, catchweed bedstraw, cleavers, cleaverwort, 

goose grass, scarthgrass, sticky-willy, white hedge 

Galium boreale northern bedstraw 

Santalaceae Comandra umbellata ssp. pallida bastard toadflax 

Saxifragaceae Heuchera richardsonii alumroot, Richardson's alumroot Scrophulariaceae Besseya wyomingensis Wyoming besseya, Wyoming kittentail 

Castilleja sessiliflora downy paintedcup, Great Plains Indian paintbrush, 

Indianpaintbrush 

Penstemon albidus white penstemon Penstemon gracilis lilac penstemon, slender penstemon 
Wind Cave National Park (WICA)

99

Family Scientific Name Common Name(s) 

Verbascum thapsus big taper, common mullein, great mullein, mullein, velvet 

dock, velvet plant, woolly mullein 

Smilacaceae Smilax lasioneura Blue Ridge carrionflower Solanaceae Physalis heterophylla clammy groundcherry 

Physalis longifolia common groundcherry, long-leaf ground-cherry, longleaf 

groundcherry 

Physalis virginiana lanceleaf groundcherry, Virginia groundcherry Solanum triflorum cut-leaf nightshade 

Urticaceae Parietaria pensylvanica Pennsylvania pellitory 

Urtica dioica ssp. gracilis tall nettle, stinging nettle 

Verbenaceae Glandularia bipinnatifida var. bipinnatifida dakota verbena 

Verbena bracteata bigbract verbena, bracted vervain, carpet vervain, 

prostrate verbena, prostrate vervain 

Verbena hastata blue verbena, blue vervain, Simpler's-joy, swamp 

verbena 

Verbena stricta hoary verbena, hoary vervain, tall vervain, wooly 

verbena 

Violaceae Viola adunca blue violet, hook violet, hookedspur violet 

Viola nuttallii Nuttall violet, Nuttall's violet, yellow prairie violet Viola pedatifida crow-foot violet, prairie violet 

Vitaceae Parthenocissus vitacea thicket creeper, Virginia creeper, woodbine 

Vitis riparia river-bank grape