National Park Service 

U.S. Department of the Interior 

Natural Resource Program Center 

Vegetation Monitoring Protocol 

Great Lakes Inventory & Monitoring Network 

Natural Resource Report NPS/GLKN/NRR—2008/056 
ON THE COVER 

Black oak savanna at Miller Woods, Indiana Dunes National Lakeshore 

Photograph by: Sam Smyrk 
Vegetation Monitoring Protocol 

Great Lakes Inventory & Monitoring Network 

Natural Resource Report NPS/GLKN/NRR—2008/056 

Suzanne Sanders, Sarah E. Johnson, and Donald M. Waller 

National Park Service 

Great Lakes Network Office 

2800 Lake Shore Dr. East 

Ashland, Wisconsin 54806 

September 2008 

U.S. Department of the Interior 

National Park Service 

Natural Resource Program Center 

Fort Collins, Colorado 
ii 

The Natural Resource Publication series addresses natural resource topics that are of interest and 

applicability to a broad readership in the National Park Service and to others in the management 

of natural resources, including the scientific community, the public, and the NPS conservation 

and environmental constituencies. Manuscripts are peer-reviewed to ensure that the information 

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and is designed and published in a professional manner. 

Natural Resource Reports are the designated medium for disseminating high priority, current 

natural resource management information with managerial application. The series targets a 

general, diverse audience, and may contain NPS policy considerations or address sensitive issues 

of management applicability. Examples of the diverse array of reports published in this series 

include vital signs monitoring plans; monitoring protocols; "how to" resource management 

papers; proceedings of resource management workshops or conferences; annual reports of 

resource programs or divisions of the Natural Resource Program Center; resource action plans; 

fact sheets; and regularly-published newsletters. 

Views, statements, findings, conclusions, recommendations and data in this report are solely 

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Printed copies of reports in these series may be produced in a limited quantity and they are only 

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Internet or by sending a request to the address on the back cover. 

Please cite this publication as: 

Sanders, S., S. E. Johnson, and D. M. Waller. 2008. Vegetation monitoring protocol: Great Lakes 

Inventory & Monitoring Network. Natural Resource Report NPS/GLKN/NRR—2008/056. 

National Park Service, Fort Collins, Colorado. 

NPS D-080, September 2008 
iii 

Acknowledgements 

This protocol could not have been completed without the input of numerous individuals both 

within and outside of the Park Service. We are grateful to Chip Scott and Andrew Lister of the 

U.S. Forest Service’s Northern Monitoring Program. They have assisted with multiple aspects of 

plot selection. We are also grateful to Tony Olsen of the U.S. Environmental Protection Agency 

for assistance with algorithms for site selection. We wish to thank Mark Hart for his 

contributions on data handling in Section IV and to Sarah Pratt and Rebecca Key for their 

assistance with database development and PDA and GPS preparation. We thank several 

individuals associated with the University of Wisconsin-Madison, including Erika Mudrak, Dave 

Rogers, Rachel Collins, and Dan Olson, for contributing in various ways to this protocol. Finally, 

we would like to thank all who have spent time reviewing the various sections of this protocol. 

These people include Erik Beever, Mark Fulton, Joe O’Brien, Scott Weyenberg, Wendy Cass, 

Lee Frelich, and Christopher Woodall. The comments raised by these reviewers have, 

undoubtedly, raised the quality of the protocol. 

v 

Contents 

Page 

Acknowledgements........................................................................................................................iii 

Figures........................................................................................................................................... vii 

Tables ..........................................................................................................................................viii 

Background and Objectives ............................................................................................................ 1 

 Rationale for Monitoring Vegetation Communities ........................................................... 1 

 Network Parks and Ecosystems.......................................................................................... 1 

 Current Threats to Vegetation in NPS Units....................................................................... 3 

 Overview of Vegetation Monitoring at the Great Lakes Network Parks............................ 4 

 Measurable Objectives........................................................................................................ 6

Overall Sampling Design................................................................................................................ 9 

 Rationale for Selecting this Sampling Design over Others ................................................ 9 

 Sampling Frame, Plot Design, and Metrics ........................................................................ 9 

 Sampling Frequency, Replication, and Timing ................................................................ 16 

 Recommended Number of Sampling Sites and Frequency .............................................. 17 

 Sample Method Selection and Level of Change That Can be Detected........................... 18 

Field Methods ............................................................................................................................... 21 

 Field Season Preparations and Equipment Setup.............................................................. 21 

 Sequence of Events During Field Season ......................................................................... 21 

 Details of Taking Measurements ...................................................................................... 22 

 Post-Collection Processing of Samples ............................................................................ 22 

 End-of-Season Procedures................................................................................................ 22 

Data Handling, Analysis, and Reporting ...................................................................................... 25 

 Metadata Procedures......................................................................................................... 25

 Overview of Database Design .......................................................................................... 25 

 Data Entry, Verification and Editing ................................................................................ 26 

 Recommendations for Routine Data Summaries and Analyses to Detect Change........... 27 

 Recommended reporting schedule and format ................................................................. 27 

 Recommended Methods for Long-Term Trend Analysis................................................. 28 

 Data Archival Procedures ................................................................................................. 28 

Personnel Requirements and Training.......................................................................................... 29

 Roles and Responsibilities ................................................................................................ 29 

 Crew Qualifications .......................................................................................................... 30 

 Training............................................................................................................................. 31 
vi 

Operational Requirements ............................................................................................................ 33 

 Annual Workload and Field Schedule .............................................................................. 33 

 Facility and Equipment Needs.......................................................................................... 33 

 Startup Costs and Budget Considerations......................................................................... 34 

 Procedures for Revising the Protocol ............................................................................... 34 

Literature Cited ............................................................................................................................. 37 
vii 

Figures 

Page 

Figure 1. Nine National Park units of the Great Lakes Inventory & Monitoring Network............ 1 

Figure 2a. FIA Phase 2/3 plot layout ............................................................................................ 12 

Figure 2b. Standard FIA Phase 2/3 subplot diagram .................................................................... 13 

Figure 3. GLKN subplot, modified from the standard FIA Phase 2/3 subplot............................. 14 

Figure 4. Scale diagram of the hybrid plot .................................................................................. 15 
viii 

Tables 

Page 

Table 1. Sampling frame for each of the nine GLKN parks......................................................... 11 

Table 2. Area sampled for each metric under the FIA and hybrid designs .................................. 15 

Table 3. Park rotation schedule for the first two monitoring rotations......................................... 17 

Table 4. Sampling effort and minimum expected sample number at each park........................... 18 

Table 5. GLKN parks showing the number FIA Phase 2 and 3 plots........................................... 26 

Table 6. Estimated annual operating costs.................................................................................... 34 
ix 

Appendixes 

Page 

Appendix A: Standard operating procedures................................................................................ 41 

Appendix B: Field data forms..................................................................................................... 197

1 

Background and Objectives 

Rationale for Monitoring Vegetation Communities 

Terrestrial vegetation was ranked third among all 46 Vital Signs evaluated by the National Park 

Service (NPS) Great Lakes Network for incorporation into a long-term monitoring strategy. 

Terrestrial vegetation serves as an integrated measure of terrestrial ecosystem health by 

expressing information about climate, soils, and disturbance (Randerson et al. 2002), as well as 

browse (Côte et al. 2004), and exotic species invasion (Ojima et al. 1991). Further, terrestrial 

vegetation serves as a trophic base for other ecosystem components (Fortin et al. 2005). Because 

of this interwoven relationship between terrestrial vegetation and both biotic and abiotic 

components, we have developed a comprehensive protocol that incorporates the Network’s 

monitoring plans for terrestrial vegetation as well as those of related Vital Signs, including 

terrestrial pests and pathogens, problem species, succession, and soils. Because forests 

predominate the landscape in the Great Lakes Network national parks, this protocol will focus 

exclusively on forested vegetation. Protocols for non-forested terrestrial vegetation may be 

developed by the Network in the future, as time and funding allow (Route and Elias 2006). 

Network Parks and Ecosystems 

The nine park units of the Great Lakes Inventory & Monitoring Network (GLKN) extend from 

the boreal forests of northern Minnesota to the sand dunes of southern Lake Michigan (Figure 1) 

and represent the major freshwater ecosystems of the Upper Midwest. 

Figure 1. Nine National Park units of the Great Lakes Inventory & Monitoring Network. 

Apostle Islands National Lakeshore (APIS) is located near Bayfield in extreme northwestern 

Wisconsin. It consists of 21 islands, ranging in size from 1 to 4,000 hectares, and a 19-kilometer 

segment along the mainland shore of Lake Superior. The lakeshore is at the northwestern limits 

of the hemlock-white pine and northern hardwood forest and also contains elements of the 
2 

southern boreal forest. The lakeshore has a wide variety of sandscapes such as barrier spits, 

cuspate forelands, beaches, and dunes that provide unique habitats for plants and wildlife. In 

addition, 13,560 hectares of the park have been designated as wilderness. 

Grand Portage National Monument (GRPO) is located 56 kilometers northeast of Grand Marais, 

Minnesota in the extreme northeastern ‘arrowhead’ region of the state near the Canadian border. 

The monument protects 287 hectares of land including the historic trading post of the North 

West Company on Lake Superior, the site of Fort Charlotte on the Pigeon River, and a 13.7 

kilometer historic canoe portage trail that connects the two sites. The portage trail crosses several 

riparian areas and over the Grand Portage Highlands with an 240-meter rise in elevation. The 

area is covered by a near-boreal forest, with birch-aspen-spruce-fir communities dominating 

most sites. The Grand Portage trail corridor bisects the Grand Portage Band of Minnesota 

Chippewa Reservation. Surrounding land use greatly influences resource management at GRPO. 

Forestry practices on Reservation lands are carefully integrated with wildlife management 

objectives, and large areas are set aside to preserve the near wilderness character of the region. 

Indiana Dunes National Lakeshore (INDU) runs about 40 kilometers along southern Lake 

Michigan and includes 6,070 hectares. Biological diversity is one of the most significant features 

of the lakeshore and a primary reason for its establishment. This diversity is greater than most 

areas of similar size because Indiana Dunes is in several ecological transition zones, including 

where northern conifer forests meet the temperate hardwood forests and tallgrass prairies of the 

Midwest. Diverse habitat types include: beaches, bogs, prairies, black oak savannas, forests, 

wetlands, and marshes. The lakeshore is comprised of unconsolidated soils on which landforms 

range from open beach and active dunes to stabilized and extensively vegetated older dunes and 

moraines. Some dunes, like Mount Baldy, rise to heights of over 30 meters above the shoreline. 

Isle Royale National Park (ISRO) is a remote island archipelago in northwestern Lake Superior. 

It consists of one large island that is 45 miles long and nine miles wide surrounded by about 400 

small islands. The park protects 231,395 hectares of area extending up to 7.2 kilometers into 

Lake Superior from the outer islands. Of this total area, 53,426 hectares have been designated as 

wilderness. There are many inland lakes, ponds, and streams. The island is densely forested with 

northern boreal spruce-fir forest near the cool, moist shoreline of Lake Superior, and northern 

hardwoods in the warmer, drier interior. 

Mississippi National River and Recreation Area (MISS) includes 116 kilometers of the 

Mississippi River and six kilometers of the Minnesota River and encompasses about 21,800 

hectares of public and private land and water. Less than 20 hectares of land, all on islands within 

the river, are actually owned and under regulatory authority of the NPS. This section of river, 

some of which flows through metropolitan St. Paul and Minneapolis, is a major transportation 

corridor and yet a place for recreation and quiet in the midst of an urban environment. Numerous 

private, state, county, and other federal landowners make management of access, resource use, 

and development complex. The rivers themselves, and the riparian zones along the shorelines 

and islands, are the primary biotic communities of interest. 

Pictured Rocks National Lakeshore (PIRO) protects 28,893 hectares of land including 67 

kilometers of Lake Superior shoreline. The park is located along the south-central shore of Lake 
3 

Superior within a transition zone between the boreal and eastern deciduous forest biomes. 

Hardwood forests are prevalent, but conifers dominate some sites. Wetlands are common 

throughout the park. The cold, moist climate along the lakeshore greatly influences the biotic 

communities. The park is divided into two distinct ownership and management zones; the 

federally owned shoreline zone, and a non-federal buffer zone. Sustained yield timber harvesting 

and other consumptive uses are allowed in the buffer zone; however, these uses were intended by 

Congress to be subordinate to public recreation and the protection of “the usefulness and 

attractiveness of the lakeshore.” 

The St. Croix National Scenic Riverway (SACN) protects 37,529 hectares including 405 

kilometers of the St. Croix and Namekagon Rivers in eastern Minnesota and northwest 

Wisconsin. The park spans three major biomes: boreal forest, eastern deciduous forest, and oak 

and pine savanna. The rivers themselves, and the riparian zones along them, greatly influence the 

biotic communities. Wetlands are common throughout the park. The park is divided into two 

distinct management zones: the upper St. Croix and Namekagon Rivers, and the lower St. Croix 

River. Numerous private, state, county, and other federal landowners along the corridor make 

management of access and resource use complex. 

Sleeping Bear Dunes National Lakeshore (SLBE) protects 28,813 hectares of land and water 

along the northeastern shore of Lake Michigan. The lakeshore includes two large islands in Lake 

Michigan: North and South Manitou. There are 105 miles of Lake Michigan shoreline, 26 inland 

lakes, and four streams. The interior forested areas are dominated by American beech and several 

species of maple. 

Voyageurs National Park (VOYA) is located approximately 300 miles north of Minneapolis, 

Minnesota in the forested lake region along the Minnesota-Ontario border. Voyageurs comprises 

88,243 hectares, of which approximately 33,908 hectares (38%) are covered by lakes and ponds. 

Two large reservoirs, with hundreds of islands, form much of the lake area, but there are 26 

smaller lakes and hundreds of beaver ponds and drainage systems. The interior of the park is 

typical southern boreal spruce/fir forest, but deciduous trees dominate some areas. 

Current Threats to Vegetation in NPS Units 

The forests of the Great Lakes Network parks are threatened by a number of direct and indirect 

stressors. Direct stressors include several insect pests that are now present at the eastern Network 

parks and are spreading westward. These include the beech scale insect (Cryptococcus fagisuga, 

causal agent of beech bark disease) which was first discovered in Nova Scotia in 1911 (Houston 

1994) and is now present on beech trees in PIRO and in the counties immediately adjacent to 

SLBE and INDU. While there is some literature suggesting how the beech resource will respond 

to this threat, little is known about vegetation response following its decline, especially in 

network parks located at the northwestern edge of the beech range. Another insect pest, emerald 

ash borer was first reported near the Detroit, Michigan area in 2002 (Muirhead et al. 2006) and 

has been confirmed in the counties adjacent to both INDU and SLBE (Purdue University 

Extension, 2006). The anticipated degree of ash decline is unclear, nor is it known how the 

surrounding vegetation will respond following this decline. 
4 

Earthworms are another group of exotic pests adversely impacting the forests of the upper 

Midwest. Earthworms are believed to have been absent from areas directly affected by the 

Pleistocene glaciation, an expanse that includes all Network parks (Hendrix and Bohlen 2002). 

Although natural earthworm dispersion is relatively slow, on the order of 5 – 10 meters per year 

(Hale et al. 2005, Dymond et al. 1997, Marinissen and Van den Bosch 1992), the introduction of 

earthworms from recreational fishing greatly augments their spread into previously worm-free 

areas (Bohlen et al. 2004). Exotic European earthworms, which have been reported in multiple 

Great Lakes Network parks, rapidly change soil properties resulting in an increased thickness of 

the A soil horizon and a corresponding decrease in the organic horizon (Gundale 2005). 

Earthworm invasion impacts the plant community by removing the forest floor duff layer (Hale 

2004) and by reducing regeneration (Kostel-Hughes 1995). 

In addition to exotic species, changes in management and land use have led to uncharacteristic 

expansion of a few native taxa, such that they have become problem species. The most notable 

example of this is the irruption of the native white-tailed deer population. Browsing pressure by 

deer has reduced the abundance of certain native plant species (e.g., Canada yew (Taxus 

canadensis) (Allison 1990a, 1990b, 1992), eastern hemlock (Tsuga canadensis) (Mladenoff and 

Stearns 1993, Rooney et al. 2000), and many broad-leaved forest wildflower species to a fraction 

of their previous abundance (Rooney 2001, Russell 2001). Further, overbrowsing by deer is 

driving change at the community level by promoting the biotic homogenization of Great Lakes 

forests (Rooney et al. 2004). For these reasons, white-tailed deer is the major problem vertebrate 

threatening the health of Great Lakes forests. 

While direct stressors impart obvious identifiable change to forest structure, indirect stressors 

can be equally detrimental. This is especially true because the change occurs in a subtle manner 

over time and causal agents are often not easily identifiable so that reversing this trend is 

difficult. Regionally, shifts in overstory composition from pine (Pinus strobus and P. resinosa) 

to balsam fir (Abies balsamea) have been documented (Frelich and Reich 1995, MacLean and 

Gucciardo 2005) and are likely due, only in part, to altered fire regimes. Further, long-term 

decline of sugar maple (Acer saccharum) (Auclair 2005), white oak (Quercus alba) (Abrams 

2003), and hemlock (Kizlinski 2002) also pose problems to the health of Great Lakes Network 

Parks. In these instances, multiple causal agents are believed to contribute to the decline. 

Routine monitoring of forest health will provide an understanding of natural variability of 

vegetation in ‘benchmark’ areas where direct human disturbance (e.g., logging, development) no 

longer occurs. In addition, monitoring will provide an early warning of undesirable trends in 

vegetation, allow adaptive management of forest ecosystems, and allow for inferences about the 

effects of the above threats on both terrestrial vegetation and overall forest health 

Overview of Vegetation Monitoring at the Great Lakes Network Parks 

This protocol is intended to detail a comprehensive monitoring program with consistent methods 

across all nine Network parks. Prior terrestrial vegetation monitoring efforts have been 

undertaken at five of the Network parks, although the duration, extent, and scientific credibility 

have varied immensely among these efforts. 
5 

Permanent forest monitoring plots were established in 1997 on four islands at the Apostle Islands 

National Lakeshore (Meeker 2000). On Outer Island, plots were placed in four areas: 1) an old 

growth mature hemlock-hardwood stand, 2) an area adjacent to the old growth that was cut in the 

1960s, 3) an area cut in the 1940s but not burned, and 4) an area cut in the 1930s and burned. 

Other plots were established on the smaller islands with more boreal habitat. They include areas 

recently cut (York, cut in the early 1970s) and less disturbed forests on Devils and Raspberry 

Islands. Although these sites have been permanently marked, regular monitoring has not 

occurred since initial assessments in 1997-1998. In addition to Meeker’s work, Beals and Cottam 

established 76 vegetation survey plots on the islands from 1955 – 1958 (Beals and Cottam 1960). 

While these were not intended to be long-term monitoring points, 28 of these plots were revisited 

in 2005 to assess 50-year vegetation changes (Mudrak et al. in review). 

At Grand Portage National Monument, a system of permanent vegetation plots was established 

in 1986 to compare changes over time between interior forests and forests adjacent to clear cuts. 

Metrics for this study were remeasured in 1992, 1999, and 2004 and an analysis of these data 

was published in 2005 (MacLean and Gucciardo, 2005). On all plots, abundance and basal area 

of balsam fir (Abies balsamea (L.) P. Mill.) increased, while that of paper birch (Betula 

papyrifera Marsh.) and quaking aspen (Populus tremuloides Michx.) declined. The observed 

pattern is likely due in part, to succession, although a reduction of fire is also a primary cause. 

Unfortunately, only two plots were placed in each treatment and the majority of statistics were 

descriptive (e.g., means, percent change). 

Indiana Dunes National Lakeshore is one of the most botanically diverse national parks, located 

at the juncture of prairie, lake, and forest biomes. Approximately 30% of Indiana’s state listed 

rare plant species occur within the park boundaries (Gucciardo et al., 2004). Not surprisingly, 

much of the monitoring at the park has focused on rare plant species, rather than forest 

vegetation. 

Isle Royale National Park has the longest history of vegetation monitoring of any of the Network 

parks. In 1963, Peter Jordan of the University of Minnesota established plots to monitor the 

impact of moose on vegetation. These plots are arranged linearly into clusters that are oriented 

along a north – south (180 - 360 ̊) axis (for those clusters established prior to 1980) or oriented 

along a 160 - 340 ̊ axis (for those clusters established in 1980). Data collected include tree 

species, diameter at breast height (DBH), and percent cover for multiple forest layers. The Great 

Lakes I&M Network is currently working with Dr. Jordan toward publication of multiple 

manuscripts associated with this project. 

At Pictured Rocks National Lakeshore, 146 permanent plots were established over the five year 

period from 2000 – 2004. The plots were established using the plot layout of the Forest Service’s 

Forest Inventory and Analysis (FIA) program (Berchtold and Scott 2005), and metrics included 

all of those in the FIA Phase II program, as well as select indices of vegetation and down woody 

materials that are part of the Phase III program. Metrics on twenty of these plots were 

remeasured in 2005 and a report on short term change will be completed by 2008. 

The questions addressed by these studies vary, although none have focused on quantifying forest 

change in response to long-term environmental change. Further, these studies were limited in 
6 

scope with no attempt to integrate the results with other ecosystem attributes such as soils, 

insects, or mammals. Doing so will allow us to show associations between vegetation and 

associated attributes. Collectively, a network-wide monitoring system with clear, inferable 

objectives can answer questions about shifts in forest characteristics in response to increasing 

environmental pressures, as well as provide an assessment of overall forest health. 

Measurable Objectives 

The goals of this monitoring program are to detect forest change and to draw inferences about 

forest health, so that management recommendations can be provided to the parks. To accomplish 

this, the monitoring objectives are grouped into two tiers. The primary objective is to detect 

change directly in forest attributes. The specific questions that address this objective follow: 

1) What is the rate and direction of change of key species? We will measure species abundance 

(frequency) and size (basal area) and test for change between and among sample periods. This 

will allow us to determine whether metrics of individual species are increasing, in decline, or 

remaining constant. 

2) What is the rate and direction of change of plant community structure? We will examine the 

size distributions of all trees, as well as individual species to identify shifts in forest structure. 

This will help us understand both regeneration and succession. It will also allow us to infer 

potential drivers of change and, thus, potential threats to the biotic integrity of the Great Lakes 

Parks. 

3) What is the rate and direction of change of plant community composition? We will measure 

species richness and diversity and test for change in these variables. We will also test whether 

biotic homogenization (Olden and Rooney 2006) is occurring within given parks. 

The secondary monitoring objective is to test for change in Vital Signs and other indicators 

associated with vegetation. We will then attempt to relate these changes to long-term vegetation 

change. These indicators and specific questions are outlined below: 

Terrestrial Pests and Pathogens 

• Which targeted pests and pathogens are present on trees in terrestrial vegetation monitoring 

plots? We will search for selected pests and pathogens on trees that are included in the 

vegetation monitoring plots. 

• Within given habitat types, what are the pest and pathogen species collectively present? 

• To what extent are pests and pathogens damaging trees? We will calculate the percentage of 

trees are infested, and note the severity of damage on the infested part(s). 

For complete methods and analyses for monitoring terrestrial pests and pathogens, please see 

standard operating procedure (SOP) 8. 
7 

Problem Species 

• To what degree is ungulate browse evident on terrestrial vegetation? We will measure 

percent browse on key plant species within the vegetation plots and relate this to changes in 

plant species, communities, and community structure. We also will assess indirect impacts of 

ungulates using size metrics of herbaceous indicator species (see Ungulate Impact 

Monitoring SOP 9). 

For complete methods and analyses for monitoring ungulate impacts, please see SOP 9. 

Succession 

• Are Great Lakes Network forests exhibiting expected successional trajectories? We will use 

information on plant community structure change and place this into context with what is 

known about successional trajectories. Community change will then allow us to make 

inferences about the successional state of forests of Great Lakes Network national parks. 

For complete methods and analyses for monitoring successional change, please see SOP 10. 

Coarse woody materials 

• What is the volume (m3

/ha) in specific habitat types? 

• What is the magnitude and direction of change in volume in each habitat between two, and 

among many sampling events? For complete methods and analyses for monitoring down, 

woody, materials, please see SOP 11. 

9 

Overall Sampling Design 

Rationale for Selecting this Sampling Design over Others 

Sampling sites have been selected using a generalized random-tessellation stratified (GRTS) 

design (Stevens and Olsen 2004). The reasons for selecting the GRTS design were twofold. First, 

resulting sample points are spatially balanced, whereby there is a generally even dispersion of 

sampling sites over the area of interest. This eliminates potential autocorrelation problems that 

can arise when two or more sampling points are in close proximity (Stevens and Olsen 2000), 

while also ensuring that all areas within the sampling frame are represented. In addition, the 

GRTS design allows for sites to be added to or excluded from the original sampling plan, while 

still maintaining the spatial balance of the overall design. This is important because it is difficult 

to gauge exactly how many plots can be sampled within a given time period. The second asset of 

the GRTS method is that it is a probabilistic sampling design, whereby sampling points are 

randomly chosen from among those in a systematic grid, eliminating site selection bias (Stevens 

and Olsen 2000). One drawback with any random design is that it does not guarantee that plots 

will be located in areas of key management significance. This is addressed below. 

In addition to a GRTS based design, we also considered using an intensified version of the Forest 

Service Forest Inventory and Analysis (FIA) hexagon based sampling design. Forest Inventory 

and Analysis is a national program with plots placed in forested areas throughout the country. 

Plots were randomly located within 2,403 ha hexagonal shaped grid cells; this resulted in 

anywhere from one to 19 plots per park, depending on the size of the park. To increase the 

sampling effort per park for this protocol, we considered intensifying the effort by reducing the 

size of the grid cells. Two problems emerged, however, when we tried to apply the large-scale 

effort to the Network parks. First, the grid cell size and, hence, the number of plots, would be 

defined prior to sampling and based on the anticipated number of plots that can be sampled. In 

reality, technical problems can result that can significantly reduce the number of plots sampled in 

a given park. Alternatively, sampling in some regions can also go better than planned, such that 

there is time for sampling more plots than was planned. The structured nature of the FIA grid 

system does not allow for the addition or exclusion of plots while still maintaining the spatial 

balance. A second drawback with using the hexagonal grid system is that the fragmented nature 

and irregular shapes of the parks result in numerous grid cells that span park borders, such that 

these grid cells are only partially occupied by NPS land. There is general disagreement about 

methods to include or exclude plots that are located within these border cells (Andrew Lister, 

U.S. Forest Service, personal communication). 

Sampling Frame, Plot Design, and Metrics 

The sampling frame for this protocol typically includes all federally owned, forested lands within 

the Network parks. Here, sampling points will be selected from the entire spatial extent of 

forested areas of these parks. Two exceptions to this will be at Apostle Islands and Isle Royale. 

At the Apostle Islands, transportation to and between islands will be time prohibitive. In this 

case, we have selected a subset of seven islands plus the mainland that represent a gradient of the 

key ecological drivers of the park (deer browse, distance to mainland, exotic invasion, wind, 

temperature, human disturbance). At Isle Royale, sample sites will be placed on the main island, 

and not on the nearly 400 peripheral islands, due to transportation limitations. In addition to 

federally owned land, the sampling frame contains state and county owned land at the 
10 

Mississippi National River and Recreation Area. At this park, federally owned land represents 

only approximately 1% of the acreage, so we included all forested land there within the sampling 

frame. Please see Table 1 for specific details of individual park sampling frames. 

This protocol details our plans to monitor forested vegetation. We have adopted the guidelines 

similar to those of the FIA program to define what constitutes a “forest,” whereby there is a 10% 

minimum stocking requirement of tree species. The 10% requirement denotes that the site can 

either have 10% canopy cover at the time of sampling, or will likely return to that percentage in 

the future. As such, areas recovering from a blowdown will be included, as will previously 

managed grasslands now returning to the forested state. Tree species are defined a priori, and 

include, for example, some ‘shrubby’ species such as striped maple (Acer pensylvanicum L.) and 

paw paw (Asimina triloba [L.] Dunal), but not others such as speckled alder (Alnus incana [L.] 

Moench ssp. rugosa [Du Roi]). Based on these guidelines, habitats such as oak savannas and 

tamarack bogs will be included, while alder swamps will not. 

Sample sites will be selected using the GRTS method described above. While this approach uses 

spatial stratification to ensure that sample sites are equally distributed within the sampling frame, 

we will not stratify on any predefined landscape characteristic or qualifiable metric, such as 

vegetation type. The successional nature of vegetation, coupled with the fact that much of the 

forested areas in the Network are recovering from logging activity that had occurred prior to 

establishment of the parks, precludes the use of stratification. Stratifying on a dynamic variable, 

such as vegetation, will ultimately result in misclassification of sites and consequent analytical 

error (Elzinga et al. 2001). Further, a stratified sampling design will prevent inferences about 

those vegetation types not sampled (Debacker and Morrison 2005). One drawback that results 

from failing to stratify by vegetation type is that there is no guarantee of rare habitats ore areas of 

key management significance being included in the sampling scheme. To address this, we will 

designate up to 10 percent of the sampling sites at a given park as ‘index’ sites, chosen by the 

park natural resource manager. These sites are not part of the overall sampling design, but rather 

are intended to address park concerns related to specific management issues. For example, 

managers at Voyageurs National Park are interested in plots placed in the area burned by the 

Shoepack Lake fire in 2004. To avoid disrupting the spatial balance of the park sampling 

scheme, analysis of data from index sites will be independent of the analysis of data from the 

randomly chosen sites in most instances, although combined for some purposes as determined 

appropriate. The design of the index sites will vary depending on the park, the number of sites, 

and the management problem addressed. 
11 

Table 1. Sampling frame for each of the nine GLKN parks. Within these defined frames, sampling will also be reareas. 

Park Sampling Frame 

Apostle Islands NL Mainland along with Sand, York, Raspberry, Oak, Stockton, and Devils Islands. These areas 

 gradient in the severity of ecosystem drivers. 

Grand Portage NM Entire park Indiana Dunes NL Entire park Isle Royale NP Limited to mainland Mississippi River NRRA Entire park Pictures Rocks NL Entire park, including federally owned land within the inland buffer zone Saint Croix NSR All land within park boundaries owned by the National Park Service. This equals 61% of the 

 riverway. 

Sleeping Bear Dunes NL All federally owned land Voyageurs NP Entire park 
12 

Parks within the Great Lakes Network will use one of two monitoring plot designs. Although our 

preference is to use the same plot type at all Network parks, legacy vegetation data at certain 

parks, along with a familiarity and preference by park managers with one of the plot types, has 

ultimately resulted in two plot types chosen for monitoring. Although the plot designs will differ, 

data collection methods and metrics will be identical for the two types. This will validate 

analyses incorporating both plot types, provided appropriate statistical steps are first taken (see 

SOP 13, Comparisons and Tests with Multiple Plot Types). 

The first of the two plot designs chosen for the Great Lakes I&M Network’s long-term 

vegetation monitoring protocol is derived from that of the FIA program (hereafter referred to as 

the “FIA plot”); this will facilitate integration of analyses across a regional scale. We will be 

collecting most of the suite of FIA Phase 2 data (see below; U.S. Department of Agriculture 

2005) as well as data on selected Phase 3 indicators (pests and pathogens, coarse woody 

materials). In addition, we will augment the plots by adding additional sampling for some 

metrics (see Great Lakes I&M Network long-term vegetation monitoring plot, below). 

The standard FIA Phase 2/3 plot, without augmentation, is composed of four 7.32 m (24.0 ft) 

radius subplots (Figure 2a) in which the species, diameter at breast height (DBH), and height of 

all trees ≥ 12.7 cm (5 in) DBH are recorded. Within each subplot (Figure 2b) are transects at 30 ̊, 

150 ̊, and 270 ̊; along each of these transects, at 4.57 m from subplot center, a 1.0 m2

 quadrat is 

placed, from which understory data are recorded. Finally, sapling data (DBH, height, species, 

etc.) are collected in a 2.07 m (6.8 ft) radius microplot east of the subplot center. For a complete 

list of FIA Phase 2 and 3 metrics and indicators, please refer to the FIA National Core Field 

Guide (U.S. Department of Agriculture 2005) and the Forest Service Phase 3 indicator fact 

sheets available at http://fia.fs.fed.us/program-features/indicators/. 

Figure 2a. FIA Phase 2/3 plot layout. Each plot is composed of four 7.32 m subplots; three of 

these are spaced 36.58 m (120 ft) from a central subplot at azimuths of 120 ̊, 240 ̊, and 360 ̊. 
13 

Figure 2b. Standard FIA Phase 2/3 subplot diagram. For all trees ≥ 12.7 cm (5 in.) DBH that are 

located within each 7.32 m (24 ft.) radius subplot, the species, DBH, and height are recorded. For 

additional metrics recorded within the FIA Phase 2/3 subplot, please see the text. 

While it is important that the plot chosen by the Great Lakes I&M Network allow integration 

with the FIA program, the missions of the two programs differ. The plot layout of the FIA 

program was originally designed to answer questions about timber volume (McRoberts 2005); as 

such, data collection is skewed heavily toward overstory indices. In contrast, one of the primary 

goals of the Inventory & Monitoring Program is to detect forest change in response to 

environmental change. Because turnover occurs more frequently in the herbaceous and sapling 

layers, we will intensify data collection on indices in these layers where significant change can 

be detected in only a few years. 

The Great Lakes I&M Network long-term vegetation monitoring methods will include collecting 

data on all of the metrics outlined above, and also the following additions. Six 1 m2

 quadrats will 

be added to each subplot bringing the total to nine for each subplot (Figure 3) and 36 for each 

plot. These will be placed on transects radiating outward from the subplot center; to avoid spatial 

autocorrelation problems, quadrats on the same transect will be 5.0 meters apart (Johnson et al. 

2006). We will also supplement data collection at these quadrats by recording information on 

seedlings, in addition to herbs and shrubs. In addition to collection of quadrat data, we will also 

conduct walkthroughs of each 7.32 m radius subplots to note any additional species that are 

present that have not been recorded elsewhere. Not all quadrats fall within the subplot 

boundaries; hence, this will need to be taken into consideration when assessing quadrat-subplot 

correlations in species richness. Further, saplings will be measured on the entire subplot, rather 

than the smaller microplot, as is done in the FIA methods. This increased sampling effort at the 

herbaceous and sapling level is expected to facilitate detection of species (Johnson et al. 2006). 

Additionally, intensifying data collection of saplings will provide more detail about forest 

change. For a complete list of all indices of this protocol for which data are collected, please see 

the associated Standard Operating Procedures (SOPs), including SOP 6 – Field Methods and 
14 

Data Collection, SOP 8 – Pest and Pathogen Assessment, SOP 9 – Succession, and SOP 10 – 

Coarse Woody Materials. 

Figure 3. GLKN subplot, modified from the standard FIA Phase 2/3 subplot. The three transects 

on the GLKN subplot have been extended to include two extra quadrats, for a total of 36 

quadrats spaced 5-m apart. 

The second of the two plot designs chosen for the Great Lakes I&M Network’s long-term 

vegetation monitoring protocol was developed specifically for our monitoring effort and 

combines features from two established plots: the FIA plot described above, the Plant Ecology 

Lab (PEL) plot developed by John Curtis and used extensively in the upper Midwest throughout 

the 1950s and 60s. Consequently, this second plot design has been designated the “hybrid plot” 

(Johnson et al. 2006). 

The hybrid plot consists of three parallel 50 m east-west transects, spaced 50 meters apart 

(Figure 4). The species and DBH of all trees ≥ 2.5 cm DBH will be recorded in a 6 m wide belt 

around each transect (3 m on either side of center line). Because we are using 2.5 cm as the 

lower DBH limit, we are thus including “trees” (≥ 12.7 cm (5 in) DBH in FIA standards) and

“saplings” (≥ 2.5 cm (1 in) and < 12.7 cm DBH (5 in)) within this area. Shrubs will be measured 

in each of six 2.82 m radius shrub circles, centered at the endpoints of each transect. We record 

the species present and an estimate of percentage of cover for each of these species. The 

groundlayer will be assessed in 1 × 1 m quadrats spaced every 5 meters along each of the 

transects for a total of 30 quadrats in each plot. As in the FIA style plot, above, we will record all 

herbaceous species present, shrubs, and tree seedlings. 
15 

Figure 4. Scale diagram of the hybrid plot, showing three parallel belt transects and areas for 

data collection of metrics. 

Due to the different plot designs, the area sampled for each metric will differ between them. This 

is shown in Table 2, where the unaugmented FIA plot is also included, for illustration purposes. 

Table 2. Area sampled for each metric under the FIA and hybrid designs. 

Metric Plot design 

FIA plot (unaugmented) FIA plot (augmented) Hybrid 

Trees (DBH and abundance) 672 m2 672 m2 900 m2

Saplings 54 m2 672 m2 900 m2

Shrubs (percent cover) 0 m2

 54 m2 150 m2

Herbs (presence) 12 m2

 36 m2 30 m2

Coarse woody materials 88 m 88 m 150 m 

Although the layout of the two plots differ, the criteria for sampling trees, shrubs, saplings, and 

herbs is similar. Pooling data between multiple parks, or pooling park data with FIA data from 

the surrounding region, will be possible. 
16 

The methods of permanent marking of plots will vary between parks. In parks with a large 

percentage of designated or proposed wilderness (Apostle Islands NL, Isle Royale NP, 

Voyageurs NP), permanent marking will be minimal. In these cases, each subplot will be marked 

with a 30 cm piece of rebar, sunk into the ground at either the four subplot centers or the six 

transect ends. Where we are permitted, we will add yellow endcaps on the top of the rebar to 

facilitate relocations. At these parks, there may be no additional permanent marking, although 

the distance and angle to each permanent mark from each of three notable nearby trees (“witness 

trees”) will be recorded. At other parks, we will tag the witness trees using a nail and 

identification tag at the base of the tree. Additionally, extensive photographs may be taken at 

multiple distances and angles from the subplot center to aid in relocation. At other parks, 

permanent marking will be more thorough, but will still remain inconspicuous. Here, rebar (or 

other suitable marking material) will be sunk into the ground as in the wilderness parks, and 

possibly also at the ends of each FIA plot transect. Additionally, small metal pins (or similar 

material) may be placed at one corner of each 1 m2

 herbaceous quadrat to ensure exact 

relocation. Perfect relocation and placement of transects is key for two facets of this protocol. 

First, it is on these transects that woody materials will be assessed (see SOP 12). The erroneous 

inclusion or exclusion of woody materials can result in marked errors in the calculated fuel loads. 

Second, shifts in the location of the 1 m2 herbaceous quadrats result in sampling errors that 

incorrectly show species turnover is occurring (Lynch and Johnson 1974). Finally, metal pins 

may also be placed at the base of each witness tree. All plots will be relocated with the aid of a 

metal detector, GPS, and map, showing the permanently marked points. 

Sampling Frequency, Replication, and Timing 

Vegetation monitoring will be conducted on a six year rotation, with each park visited once 

every six years (Table 3). Initially, we had planned a five year rotation, with two smaller parks 

(Indiana Dunes NL and Grand Portage NM) sampled in the same year as a mid-size park. While 

this was accomplished during the first year of implementation, 2007, problems were 

encountered, relating to species phenology, training the field crew on vegetation at three sites, 

and a tight schedule prohibiting the ability to make up lost sampling due to inclement weather. 

Hence, sampling at these three parks will occur over two field seasons; this will allow additional 

plots to be established here an alleviate the problems noted above. 

During each visit to a given park, all plots within that park will be resampled. Cycles of forest 

surveys typically range between five and twenty years (Scott 1988). We feel that a six year 

rotation is a good balance between sampling too frequently, where trampling of the site can 

adversely impact the plant community, and sampling too intermittently, which reduces the ability 

to detect change over a given time period. Sampling at six year intervals will allow time for some 

change to occur, yet provide relatively frequent feedback to park natural resource managers. 

Additionally, annual monitoring would require sampling teams at all parks every year. Any 

statistical benefits accrued by monitoring a plot annually are outweighed by logistical and 

financial constraints. 
17 

Table 3. Park rotation schedule for the first two monitoring rotations. 

Park Year 

 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 

Indiana Dunes X X 

Grand Portage X X 

Saint Croix X X 

Voyageurs X X 

Sleeping Bear 

Dunes 

 X X 

Pictured Rocks X X 

Isle Royale X X 

Mississippi River X X 

Apostle Islands X X 

We concede that an ideal scenario would be to visit one sixth of the plots in each park each year. 

Thus, the plots at any given park would be sampled throughout the six year rotation, not only 

during one year. This would capture interannual variation and better assess the impact of cyclic 

insects and/or disease cycles. Unfortunately, this scenario is both logistically and financially not 

feasible. Seasonal field assistants (see Section V, below) are only expected to be familiar with 

the vegetation in the one or two parks in which they will be working in a given year, and training 

assistants on the vegetation of all Network parks is time prohibitive. Further, travel time between 

parks would result in the loss of valuable sampling time in the middle of the field season. 

Finally, in the current plan, the seasonals are duty-stationed at the park(s) in which they will be 

working. Under this arrangement, the Network does not pay travel costs, nor daily costs of meals 

and incidental expenses. Taking on these additional burdens would be cost prohibitive for the 

Network. 

Recommended Number of Sampling Sites and Frequency 

The division of sampling effort between the parks was determined by both top-down and bottomup approaches. For the top-down approach, the forested acreage in each park was used to 

calculate the percentage that this represents, of the total Network forested area (Table 4). These 

percentages were then used to assign the percentage of sampling effort (i.e., time) at each park. 

Since we are sampling on a six year rotation, a park with 17% of the overall sampling effort 

represents one full field season at that park, while a 8% effort represent half of a season, or 

roughly 6 – 7 weeks. We then estimated the minimum number of sites monitored by calculating 

both field crews completing one plot per field day and then slightly lowering this value to obtain 

a conservative estimate, accounting to weather and unforeseen setbacks. 
18 

Table 4. Sampling effort and minimum expected sample number at each park, calculated by a 

top-down approach. 

Park Forested 

Area 

(hectares) 

Percentage of total 

network forested area 

Percentage of 

sampling effort 

Minimum 

number of 

samples 

Apostle Islands 17,037 7.75 8 35 

Grand Portage 281 0.13 6 25 

Indiana Dunes 6,095 2.77 17 60 

Isle Royale 53,823 24.48 17 60 

Mississippi River 5,581 8.99 8 35 

Pictured Rocks 24,724 11.25 8 35 

Saint Croix River 26,828 12.20 11 40 

Sleeping Bear Dunes 25,981 11.82 8 35 

Voyageurs 45,325 20.61 17 60 

total 219,862 100 365 

The bottom-up approach used statistical power analyses to estimate the degree of change that can 

be detected. The target of the GLKN monitoring program is to be able to detect a 20% change in 

metrics over a given time period with 80% power and α = 0.1 (National Park Service 2006). This 

approach projects whether there is adequate sample size at all parks, but is especially relevant at 

the smaller parks where sample time might be limited. For these analyses, both plot types were 

compared and the results are outlined below. 

Sample Method Selection and Level of Change That Can be Detected 

In a direct comparison of the FIA and Hybrid plot designs, both methods detected a 20% change 

in species frequency and abundance for woody vegetation. Data from 20 sites showed that both 

plot designs had over 99% power to detect a 20% change in both tree basal area and tree density. 

Similarly, 80% power to detect change in shrub richness was attained with both plot types. 

However, higher power was attained with the Hybrid method (95.5 vs. 86.1), while surveying 

fewer sites (17 vs. 20) (Johnson et al. 2006). 

For herbaceous species, the Hybrid method was consistently better able to detect trends than the 

standard FIA method. For example, the Hybrid method had greater than 80% power to detect 

changes in fern frequency and forb species richness (87.2 and 89.9, respectively) while the FIA 

method had only 41.0 and 18.3%, for these two indicators, respectively (Johnson et al. 2006). 

Because of legacy data from FIA type plots at one Network park, and because of a familiarity 

with this plot by many park natural resource managers, we will use this plot style at selected 

parks, while opting for the hybrid plot at others. To address the low power observed in the 

standard FIA plots, we are modifying the plot layout of the FIA method. 

Based on the findings of Johnson et al. (2006), we will strive to sample at least 25 plots in each 

park. At two of these parks, Indiana Dunes NP and Grand Portage NM, the anticipated number 

of plots sampled will barely meet this threshold. In the event that at least 25 plots are not 

established and sampled at the end of the scheduled time in these parks, we will extend the visit 

to ensure that this minimum target is reached. For a detailed account of the plot comparisons and 
19 

power analyses, please refer to Supplemental Document 1: A comparison of sampling 

methodologies for long-term forest vegetation monitoring in the Great Lakes Network National 

Parks (Johnson et al. 2006).

21 

Field Methods 

Field Season Preparations and Equipment Setup 

Preparations for the field season are numerous, and must begin before the calendar year. Many of 

these preparations will necessarily overlap with the duties associated with concluding the 

previous season, such as report writing (see SOP #14, Procedures after the field season). Because 

of these demands, meticulous care must be taken to ensure all preseason procedures are followed 

in a timely manner. 

Early preseason preparations (November – February) are primarily of an administrative nature. 

Job announcements for seasonal employees must be submitted in November so that they can be 

posted in early January. Also during this time, a preliminary field schedule should be developed 

and submitted to the parks to determine whether the proposed sites and timeframes can be 

accommodated. In addition to submitting the preliminary field schedule, the project manager 

should also submit requests for park housing and permits (camping, collecting, research) and any 

other special requests (e.g., boat transportation). Once this schedule is finalized, the project 

budget for the season should be refined. Prior to the end of February, arrangements should be 

made for any needed training for the crew leader and crew members. This includes boat 

operation and first aid. Finally, during this time the project manager should ensure that the 

federally mandated background checks for employees have been initiated. 

Later preseason preparations (March – April) focus on equipment preparation and field 

readiness. The equipment list should be reviewed and all equipment should be inspected, with 

repairs made and new equipment ordered as needed. This includes checking all tapes for worn 

parts, assembling tents and looking for torn material, checking backpacks, and inspecting 

personal protective clothing. In addition to equipment, data collection procedures should be 

readied. Programming of personal data assistants (PDAs) will be completed, coordinates for the 

starting points for each site should be loaded into the field GPS, and park maps and notes for site 

locations should be organized. The plant list(s) for parks visited should be reviewed and printed 

and taxonomy changes addressed at this time. Finally, local experts in botany, entomology, and 

soils should be contacted and notified of the field crew’s work plans for the summer. This will 

open up a line of communication between the field crew and local experts as identification or 

other questions arise during the summer. 

Sequence of Events During Field Season 

Final preseason preparations involve training the field crew in preparation for sampling plots. 

Employment for the GS-7 field crew leader (see Personnel Requirements and Training, below) 

will typically begin two weeks prior to Memorial Day, while two GS-5 seasonal field assistants 

will start two weeks after the crew leader. Typically, the first two weeks following Memorial 

Day will focus exclusively on training. This includes undergoing mandatory federal training and 

first aid training. The crew will also participate in practice plots where the plot is established, and 

data are collected and entered into the database using hand-held PDAs. During this two week 

period, the crew will undergo taxonomic training as well as learn how to use the Trimble GPS 

units. The crew will also be briefed on use of park radios and work etiquette. This latter point 

will address how to interact with other park employees and how to answer questions from the 

public about the work of the vegetation monitoring program specifically, as well as the Inventory 
22 

and Monitoring Program in general. Finally, all field crew members will be required to pass a 

master proficiency test in plant identification before sampling can begin. 

 

The field crew (excluding the Program Manager) will always be duty stationed at the park where 

they are sampling, except when sampling at Grand Portage NM, where the community is very 

small and housing is limited. While at Grand Portage, they will be permanently based at St. 

Croix. Throughout the field season, the field crew and Program Manager will work on a four 10-

hour day schedule. This schedule will accommodate for travel time between Ashland and the 

parks by the Program Manager, allowing him/her to accomplish necessary administrative duties 

at the Network office. Although there is not a great deal of flexibility in the schedule, at most 

parks there are sufficient numbers of planned sampling sites that the loss of some due to poor 

weather or unforeseen circumstances will not compromise statistical power. At both Indiana 

Dunes NL and Grand Portage NM, we will ensure that at least 20 sites have been sampled. In the 

event that this is not initially accomplished, we will extend the duration of time at those sites 

until 20 have been sampled. 

 

Details of Taking Measurements 

We will be following many of the same measurement criteria and standards as the FIA program 

for both the FIA style plot and the hybrid plot. This includes details such as how to measure 

forking trees and how to treat leaning or down live trees. For more details on this, please see the 

Forest Service Phase 2 Field Guide (U.S. Department of Agriculture 2005) and Standard 

Operating Procedure 6: Field Methods and Data Collections. This will allow comparability 

between the two plot types used in the GLKN monitoring program. It will also allow 

comparability between data collected using the GLKN hybrid plot with that collected by the 

actual FIA program in the area surrounding the parks. 

We will primarily be relying on personal data assistants (PDAs) to enter field data at the time 

measurements are made. However, standard paper field forms printed on rite-in-the-rain® paper 

will be carried to the site and used if needed. These are included in Appendix A of this report. 

Post-Collection Processing of Samples 

The nature of this protocol is such that there will be minimal samples collected. One exception to 

this will be when unknown plant specimens are collected for identification. When this occurs, the 

seasonal crew will be responsible for getting this to the local botanical expert identified for that 

park (see SOP 1: Preseason preparations). For more detail on this, see SOP 7: Collection of 

Unknown Specimens for Identification. 

End-of-Season Procedures 

The procedures associated with completing the sampling season must be performed in earnest in 

the weeks immediately following the conclusion of sampling, and continued through the 

following April. Early post-field season procedures focus on both administrative tasks and data 

archival procedures. In August, performance reviews must be given to field crew members, and 

procedures associated with terminating their employment be completed. Throughout the month 

of September, data archival procedures should be carried out and park species lists and 

NPSpecies updated with new records. 

 
23 

Review of the protocol should be performed annually, in early October. Following protocol 

review, there is a three month period (mid-October – mid-January) where data analysis and 

report writing are the focus. Drafts of annual summary reports are due to the parks for review by 

January 15 of the year following monitoring (see SOP #15 – Reporting). Every six years, drafts 

of analysis and synthesis reports will also be due to the parks, on January 31st. The revised final 

versions of the annual summary reports analysis and synthesis reports are due March 1st and 

April 1st, respectively. An annual presentation will be made at the annual technical committee 

meeting, typically scheduled near the end of March. Finally, the month of April may be spent 

preparing the analysis and synthesis report for submission to a peer reviewed journal. Post field 

season procedures are spelled out in greater detail in SOP #16 – Post-season procedures. 

25 

Data Handling, Analysis, and Reporting 

Metadata Procedures 

Metadata allows potential data users to evaluate the quality and usefulness of the data 

based on an understanding of the complete process under which it was collected and 

maintained. Metadata development begins with project design and planning. The GLKN data 

manager has established a standard operating procedure for metadata generation and 

maintenance. In general, a metadata document will be initiated using the NPS Metadata Tools & 

Editor, Dataset Catalog, or ArcCatalog in the design and planning stage of a project. As the 

project progresses, this document will be augmented by the project manager to include relevant 

project details. Full development of metadata for project data will be completed after the field 

season is completed and the data set is certified by the project manager. Once metadata are 

complete, the Network data manager will save the document in one or more of four acceptable 

formats and parse the information into varying levels of information. Metadata are saved on the 

primary GLKN server along with data and copied to the I&M Program NR-GIS metadata server. 

 

The database design for GLKN data will include a means for recording the protocol version 

under which each piece of data is collected. The metadata document will, therefore, also contain 

information about protocol versions used to collect the data. 

 

For metadata associated with geospatial data, we will abide by Executive Order 12906, which 

mandates that every federal agency document all new geospatial data it collects or produces 

using the Federal Geographic Data Committee (FGDC) Content Standard for Digital Geospatial 

Metadata (CSDGM; www.fgdc.gov/metadata/contstan.html). All GIS data layers will be 

documented with applicable FGDC and NPS metadata standards. The Network will also generate 

FGDC-style metadata for non-spatial datasets that meet this standard, absent only the geospatialspecific elements. 

Data which are generated and/or managed outside the I&M Program, but used in analyses with 

GLKN data or distributed in any manner by the NPS, require the same level of documentation as 

GLKN-generated data. This includes data produced under contract with the NPS. For nonProgram data, the project manager will ask the originating entity for metadata. Any contract 

entered into by the GLKN with data producers will stipulate that FGDC-compliant metadata in 

HTML, ASCII, or XML format, or in a format specified by the project manager and approved 

the Network manager. The Network data manager will assist the project manager in metadata 

acquisition by providing tools, format protocols, and file transfer services. 

Overview of Database Design 

One goal of the general vegetation monitoring protocol is that GLKN be able to pool the protocol 

data with that of the Forest Service collected as part of the FIA program. The latter data are 

collected and made available by the Forest Service approximately one year following collection. 

While the Network is interested in comparing I&M program data with Forest Service FIA data 

collected within the parks (Table 5), we are primarily interested in comparisons with FIA data 

collected regionally, outside of the parks. These comparisons will allow information about the 

health of forests in Network parks to be placed into context with forests regionally, including 
26 

those on other federal land (e.g., national forests), state land (e.g., state parks and forests), and 

private land. 

Table 5. GLKN parks showing the number FIA Phase 2 plots (where tree data are collected) and 

Phase 3 plots (where down woody material data are collected) in each park. 

Park Phase 2/Phase 3 FIA plots 

Apostle Island NL 8/0 

Grand Portage NM 0/0 

Indiana Dunes NL 3/0 

Isle Royale NP 67/1 

Mississippi NRRA 2/0 

Pictured Rocks NL 28/1 

St. Croix NSR 20/1 

Sleeping Bear Dunes NL 26/2 

Voyageurs NP 33/2 

To facilitate analyses using FIA data, the GLKN database structure will be similar to that of the 

FIA program, and the variable names within the tables will be identical. The GLKN database 

will consist of nine tables (Survey, County, Plot, Subplot, Condition Class, Tree, Seedling, 

Boundary, and Subplot Condition Class) that will be linked by specific keys. For a complete 

description of the GLKN database, please refer to Supplemental Document 1: Great Lakes 

Network General Vegetation Database Structure and User Guide. 

 

The Great Lakes Network stores and integrates monitoring data from multiple protocols in a 

Microsoft SQL Server relational database management system (RDBMS) that is used to generate 

the analyses and reports required by GLKN, and drive an Internet Mapping System (IMS) used 

for exchange and visualization of GLKN’s monitoring data. 

Data Entry, Verification and Editing 

We will use paper data sheets to record data when in the field. The data will be collected by a 

team of two with one crew member recording data primarily on overstory trees, shrubs, and 

coarse woody materials. The second member of the field crew will primarily record data on 

herbaceous species. The datasheets will be photocopied at the earliest possible date, with one 

copy stored in the GLKN offices, and a second copy stored offsite. 

Once entered, all data will be printed and verified by comparing it with the original field 

recorded data sheet. Two people will perform this duty with one person reading the values from 

the original datasheet and the second person checking the values on the printout. 

During the initial year of protocol implementation (2007), we attempted to enter data directly 

into PDAs in the field. This could then be downloaded directly into the database, thus 

minimizing transcription errors. This turned out to be problematic for a number of reasons. In 

short, it is not possible to easily check the data before leaving a field site to make sure everything 

is present and that the data makes sense. We realized too late that there were a number of metrics 
27 

missing from various plot data. In addition, most of the sampling time is spent in a backcountry 

environment for up to four days in a row. Reliance on batteries and chargers is impractical. 

Recommendations for Routine Data Summaries and Analyses to Detect Change 

After each field season, all data are analyzed to produce annual summaries on the status of 

terrestrial vegetation in the park(s) sampled that year. These annual summaries provide the 

Network with current status information relevant to policy and management decisions. A variety 

of analytical approaches are utilized to assess the status of 1) environmental variables associated 

with terrestrial vegetation, 2) population (species-level) variables, 3) community structure 

variables, and 4) community composition variables at each park and across parks in the Great 

Lakes region. Because the procedures for this are detailed extensively in Standard Operating 

Procedure 8: Data Analysis, we are not presenting these here. 

Recommended reporting schedule and format 

Two types of reports will be produced for the General Vegetation Monitoring protocol. Annual 

summary reports will be produced yearly for the Vital Signs associated with the vegetation 

protocol that were monitored during the previous year. The primary audience for the annual 

summary reports will be parks. These summaries will be communications to document our 

efforts and convey the findings of the previous field season. At a minimum they will provide an 

introduction that describes why that Vital Sign is being monitored, an outline of the sampling 

strategy, including the number of sites sampled, parameters measured, and analyses performed, 

data summaries and a text explanation of the findings, and limited discussion section in which 

important results are interpreted. The project manager will take the lead in writing the report and 

will coordinate an internal review. Drafts of annual summary reports will be completed by 

January 15 for a two week review period by the parks. The final reports will be provided to parks 

on March 1 of the year following the monitoring. 

In addition to annual summary reports, detailed reports in which data are analyzed and 

synthesized will be produced on a periodic basis. For the general vegetation protocol, analysis 

and synthesis reports will be written every six years, after each completion of the sampling 

rotation. They will be written in the format of a scientific journal article (abstract, introduction, 

methods, results, discussion, literature cited) and will contain in-depth analyses as outlined in 

this protocol. Further, these comprehensive reports will place the observed results in both a 

regional and historical context by relating them to other published literature, discuss the 

significance of the results in terms of environmental change, and provide management 

recommendations based on the findings. 

The project manager (terrestrial ecologist) will take the lead in writing the analysis and synthesis 

reports, and will coordinate an internal review. The target audience of these reports will be the 

parks (primarily the natural resource managers), the Network, and both regional and Servicewide 

I&M. Outside of the park service, the target audience includes the four state departments of 

natural resources (Indiana, Michigan, Minnesota, and Wisconsin), the Northeastern Area Forest 

Health Protection unit and the North Central Research Station, both of the of the National Forest 

Service, and the broader scientific community. 
28 

Drafts of analysis and synthesis reports will be completed by January 31 of the year that follows 

completion of the monitoring cycle. The parks will have a 30 day period for comment and input. 

In addition, these drafts may also be sent to outside sources for further review. The extent of 

review will depend on the analytical complexity of the methods and the gravity of inference and 

recommendations. The final reports will be due on April 1 of the year following completion of 

the monitoring cycle. 

The first analysis and synthesis report will be written following a full rotation of monitoring. At 

this time, vegetation and the associated Vital Signs will have been monitored in each park for 

one season, allowing a comparison of vegetation characteristics across parks. In subsequent 

years, as parks are monitored repeatedly, more in-depth analyses will be conducted for individual 

parks as well as across parks. 

 

Recommended Methods for Long-Term Trend Analysis 

Each park will be visited and surveyed every six years. Once a given park has been sampled a 

second time, tests for changes in metrics can commence. It will be possible to test for trends with 

more sensitivity and to evaluate changes in trends once the third sampling event for any given 

park is complete. In addition to investigating changes in environmental variables and population 

and community vegetation variables, we also will be assessing the relationships among all of 

these variables. All long-term change and trend analyses are detailed extensively in Standard 

operating procedure 8: Data Analysis. Please refer to this for specific details. 

Data Archival Procedures 

The Access database file in which the vegetation monitoring data are stored will be backed up 

nightly and a copied backup tape is stored off-site each week. 
29 

Personnel Requirements and Training 

Roles and Responsibilities 

The Project Manager, or Terrestrial Ecologist for this protocol, is a liaison between the 

permanent Network staff, seasonal Network staff, and the park. The project manager is charged 

with organization and facilitation of all aspects of the long-term vegetation monitoring protocol. 

This includes the following: 

• hiring seasonal staff 

• developing an annual field schedule 

• acquiring the appropriate permits (NEPA, camping, collecting, etc.) 

• arranging training (first aid, CPR, FIA plot training, etc.) 

• refining the budget for the season 

• preparing, repairing, and purchasing equipment 

• preparing the database for the upcoming season (review species lists, add names of seasonals, 

etc.) 

• contacting local experts in botany, soils, and entomology to establish a working relationship 

with them prior to the start of the season 

• ushering crew members through administrative training (credit card use, etc.) and park 

specific plot work 

• overseeing seasonal staff during field season 

• identifying plants accurately and processing unknown plants for later identification 

• ensuring plot work meets the desired standards of quality 

• ensuring an adequate number of plots get measured each season and at each park 

• ensuring effective communication between park staff, the field crew, and other GLKN staff 

• reviewing and terminating the seasonal field crew members 

• reviewing and revising protocol 

• data analysis and report writing 

• presenting finding of previous season to the technical committee 

The GS-7 crew leader will be responsible for day to day completion of plot establishment and 

monitoring. The GS-7 will also be responsible for supervising the GS-4/5 seasonals when the 

project manager is not present. Specific duties include: 

• completion of mandatory training including first aid, boat training, and plot establishment 

• assisting with navigation to sampling points 

• identifying plants accurately and processing unknown plants for later identification 

• ensuring plot work meets the desired standards of quality 

• collection of plot data and entry into a PDA and/or tablet computer 

• transfer of data from the PDA to the tablet computer 

• entry of data from paper data sheets to tablet computer and error checking entered data 

• completion of timesheets for both the GS-7 and GS-4/5 seasonals 

• ensuring equipment is clean and functional prior to each trip 

• ensuring all needed equipment is collected and readied prior to each trip 
30 

• interact with park staff as needed to organize transportation and equipment needs. 

The GS-4/5 seasonals will be responsible for day to day completion of plot establishment and 

monitoring. Specific duties include: 

• completion of mandatory training including first aid, boat training, and plot establishment 

• assisting with navigation to sampling points 

• identifying plants accurately and processing unknown plants for later identification 

• ensuring plot work meets the desired standards of quality 

• collection of plot data and entry into a PDA and/or tablet computer 

• transfer of data from the PDA to the tablet computer 

• entry of data from paper data sheets to tablet computer and error checking entered data 

• ensuring equipment is clean and functional prior to each trip 

• ensuring all needed equipment is collected and readied prior to each trip 

Crew Qualifications 

The project manager must have an advanced degree in botany, plant science, or biological 

science with an emphasis in plants. He/she should also have demonstrated experience identifying 

the flora of the Great Lakes and surrounding region, acquired through a combination of work 

experience, education (graduate school research), and/or workshops and other training. The 

project manager must also have a minimum of one year experience leading a field crew. 

In addition to botanical and leadership skills, the project manager must be knowledgeable about 

multiple aspects of forest health. This includes identification of forest pests and pathogens, and 

the ability to identify (tree) diseases. The project manager should also be familiar with basic soil 

collection and classification techniques and be able to infer whether earthworms are present 

based on soil characteristics. Finally, the project manager must also have experience establishing 

permanent plots. 

The GS-7 level crew leader will oversee the other seasonal employees at the GS-4/5 level, when 

the project manager is not present. The GS-7 seasonal must have at least a four-year degree in 

botany, plant science, or biological science and have at least one year of vegetation based work 

experience equivalent to the GS-5 level. Alternatively, the GS-7 may have an advanced degree 

with an emphasis in vegetation. The GS-7 must also have at least one season (three months) of 

supervisory experience of field personnel and have demonstrated experience (work or education) 

identifying flora of the region(s) where sampling will occur in the year for which they are hired. 

Finally, they must demonstrate the ability to navigate with map, compass, and GPS. 

The GS-4/5 seasonal employees must have a four-year degree or at least 24 semester hours in 

course work in biology, botany, entomology, soil science, or forestry. Further, at least 9 semester 

hours must be directly related to the requirements of this position. This person must have 

demonstrated experience (work or education) identifying both overstory and understory 

vegetation of the region(s) where sampling will occur in the year for which they are hired. 
31 

All people on the crew (project manager and seasonals) must be physically fit and able to work 

in adverse environments. This includes working in extreme heat and cold, and in the presence of 

biting/stinging insects and poisonous plants. 

Training 

The project manager will be expected to attend a Forest Service sponsored FIA Phase 2 and 

Phase 3 training workshop if these are offered outside of the planned field sampling season. 

He/she will also be expected to attend a first-aid/CPR course. 

 

The seasonal field crew will arrive around Memorial Day. During this time, they will undergo 

training in administrative procedures, plot establishment, data collection, data entry, and first 

aid/CPR. 

 

Administrative training procedures will be performed over the first two days of employment. 

This will cover issues associated with computer use, credit card use, and time sheets. This 

training will be provided by the project manager. 

The bulk of the three week training time will be devoted to multiple aspects of vegetation 

monitoring. The seasonal employees will be instructed on how to establish the FIA or hybrid 

style plots, with emphasis given on the high degree of accuracy required with the distances, 

angles, and metrics measured. During this time, the crew will also be instructed on use of PDAs 

and the tablet computer for data collection and entry. The time devoted to vegetation monitoring 

training will be approximately two weeks. During this time, the crew members will also 

familiarize themselves with the vegetation of the park in which they are working. Plants that are 

encountered but cannot be readily identified during this time will be learned so that, when they 

are encountered in actual plots, they can be quickly identified. This training will be provided by 

the project manager. 

The crew will also participate in any park based first-aid and CPR training. This is usually 

provided after Memorial Day. Finally, the crew will be instructed on etiquette for interactions 

with the general public visiting the parks. The project manager will explain how to answer 

questions about their work, and the Network’s work. 

33 

Operational Requirements 

Annual Workload and Field Schedule 

Employment for the seasonal staff will begin three weeks prior to Memorial Day. This will allow 

time for administrative training and first aid training. Also during this period, the network 

botanist will be training the seasonal staff on plot establishment, data collection, and the local 

flora. In general, we will plan to begin actual plot sampling on the Tuesday immediately 

following Memorial Day. For many northern parks (Apostle Islands, Isle Royale, Pictured 

Rocks, Voyageurs) however, the phenology may be such that plots cannot be sampled until early 

to mid-June, depending on temperatures. Where this is the case, we will establish plots that are 

most easily accessible, and perhaps record data on the overstory species. We will then go back 

and collect data on the remaining metrics. At one park, Indiana Dunes, we will actually 

commence plot sampling the week prior to Memorial Day. This will be possible as this is 

southern most of the network parks. The earlier start data will allow an additional week of field 

sampling during the season. 

In general, sampling will run until the Labor Day weekend. The arrangement results in 14 weeks 

of plot sampling time (15 in the year in which Indiana Dunes is sampled) and 17 weeks total 

time. In some years, this may need to be modified, if plants begin to senesce early. If this is the 

case, plot sampling may be curtailed as early as mid-August. 

Facility and Equipment Needs 

We are attempting to conduct this protocol in as independent of a manner as possible. The 

budgets of many parks prevent the park from hiring a seasonal staff that could assist the 

vegetation monitoring crew with transportation and other logistics. Despite this, transportation 

remains a key issue in which we will need to rely somewhat on others. At Apostle Islands NL, 

we are limiting the monitoring there to the mainland and only seven of the 22 islands. 

Cooperation from park staff to shuttle the network staff between islands is essential. At Isle 

Royale NP, we have limited sampling sites to the main island, and to Passage Island. We will not 

be monitoring any of the approximately 400 islands that surround the mainland, due to the 

logistics of traveling to them. Unlike at the Apostle Islands, however, there is a commercial 

vessel that we will rely on to transport the field crew to sites around the island. At Saint Croix 

River, we will primarily access sites via canoe. As this is the case, we will rely on park staff to 

provide a shuttle back to the network vehicle at the completion of trips. This will occur 

approximately four - five times over the course of the sampling season. Finally, at Voyageurs 

NP, we may rely on the park airplane and pilot to fly the crew in to the most remote sites at some 

point during the sampling season. 

In addition to transportation, we will rely on the parks to, wherever possible, provide housing for 

the field crew. In many instances, this will only be for the seasonals during the four day layover 

between sampling events, as the crew will be in the backcountry during the sampling period. 

At all parks, regardless whether they are remote or not, we will depend on the parks for basic 

security and safety precautions. The field crew will be equipped with two radios (supplied by the 

Network) and will organize with the park staff on obtaining a call number prior to beginning 

sampling. At this point, the field crew will be briefed on proper radio etiquette of the park. 
34 

Startup Costs and Budget Considerations 

Startup costs for the general vegetation monitoring protocol are primarily for both IT supplies, 

general field supplies, and camping supplies. Information technology needs include two Trimble 

Geo XT handheld GPS units, radios, and cameras. All of these items have already been 

purchased by the network. General field gear includes transect tapes, chaining pins, and diameter 

tapes, among others. Camping supplies for project startup include four one-person tents, four 

backpacks for backcountry hiking, and four sleeping pads. Other camping supplies include 

stoves, filters, and first aid kits. All of this has already been purchased by the network. 

The annual operating costs for the general vegetation monitoring protocol are estimated to range 

between $54,000 and nearly $100,000 (Table 6) for the first six years. The majority of these 

expenses (73 – 82%) are for salary of the GS-5 and GS-7 field crew members. The marked 

increase in cost between 2008 and 2009 reflects the switch to a permanent crew leader. The 

salary for the terrestrial ecologist will be included in the annual network budget and is not 

presented here. 

In addition to salary, the annual operating costs include lodging (park campgrounds, shelters, 

backcountry campsites, and private campgrounds), transportation (car, boat), and meals and 

incidental expenses. All three of these vary between parks. 

Table 6. Estimated annual operating costs for conducting the general vegetation monitoring 

protocol. Estimates include lodging, transportation, meals and incidental expenses, and salary. 

Year Park (s) Annual Cost 

2007 INDU, GRPO, SACN $60,061 

2008 VOYA $54,186 

2009 SLBE, PIRO $100,644 

2010 ISRO $99,914 

2011 MISS, APIS $100,067 

Procedures for Revising the Protocol 

The long-term nature of the NPS monitoring program necessitates the need for flexibility to 

incorporate change. Refined field methods, advances in analysis techniques, and feedback from 

field crews and project managers can all contribute to improving the monitoring protocol. To 

systematically identify areas and procedures needing revision, we will review the protocol each 

year at the end of the field season (see SOP #16, Procedures after the field season). To minimize 

the changes to the protocol narrative, an attempt will be made to first revise SOPs without 

making changes in the protocol itself. However, if it is clear that changes will also be needed on 

the narrative, then it will also be revised. All changes will be reviewed by Network staff, and 

substantial changes (e.g., revised analysis techniques, significantly altered field methods) may 

also be sent to outside sources for input. The changes will be recorded in the revision history log 

of the appropriate SOP and/or narrative, copies of the revised material will be distributed to all 

relevant parties, and subsequently posted on the Network website. Further, the data manager will 

be notified of the change(s) so that the metadata of the project database will be updated. For a 

detailed description of protocol revision procedures, please see SOP #17, Revising the Protocol 

(Sanders and Elias 2006).
35 

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41

Appendix A: Standard Operating Procedures

43

Preparations and Equipment Setup Prior to Field Season 

Standard Operating Procedure #1 

Version 1.0 (7/1/08) 

Sarah Johnson 

University of Wisconsin – Madison 

Suzanne Sanders 

NPS – Great Lakes Network 

Revision History Log: 

Previous 

Version # 

Revision 

Date 

Author (with 

title and 

affiliation) 

Location in Document 

and Concise 

Description of Revision 

Reason for Change New 

Version # 

 

Add rows as needed for each change or set of changes tied to an updated version number 
44

1.0 Introduction 

Preparations for the field season are numerous, and must begin early in the calendar year. Many 

of these preparations will necessarily overlap with the duties associated with concluding the 

previous season, such as report writing. The purpose of this SOP is to enumerate preseason 

duties, so that none are skipped and important deadlines in this regard are not missed. This SOP 

specifically addresses 1) administration associated with hiring field staff, 2) field work 

schedules, 3) organization of field season equipment and supplies, and 4) data forms. 

1.1 November 

• Prepare position descriptions for seasonal employees and send these to the regional 

office. Get approval so these can be posted in early January. 

• Prepare position announcements and contact a human resources specialist for posting 

these. 

1.2 January 

• January 8 – 22: Have seasonal position descriptions posted on USAJobs. 

• January 2-31: Derive field sites and make plot maps for navigating to them. These maps 

should contain both topographic and aerial/satellite imagery. 

1.3 February 

• February 1 – 7: Develop preliminary field schedule. This should identify all potential 

field sites within the parks where sampling will occur, as well as identify the probable 

dates when they will be sampled. Development of a preliminary schedule should be 

coordinated with the entire Network so that sampling and staff demands can be spread 

out spatially and temporally between and within parks. 

• February 7 – 14: Submit preliminary field schedule to parks for approval. At this time, 

also submit housing and other requests (e.g., collecting and camping permits, boat 

transportation, research). 

• February 14 – 28: Arrange any needed preseason training that is conducted outside of the 

Network (e.g., boat operation, first aid). 

1.4 March 

• March 1 – 7: Review and refine budget for the upcoming season. The refined budget 

should incorporate non-park housing and transportation. 

• March 1 – 15: Review equipment list and get equipment organized and made ready. This 

allows time to make needed repairs (see bullet below) and to order replacement 

equipment. A list of field equipment needs for one four-person crew is listed in Table 1; 

if more than four people work on a crew, then equipment needs will change accordingly. 

• March 1 – 15: Check forester tapes for proper function and replace worn parts as needed. 

Check measurements on FIA ropes and hybrid shrub rope to ensure no stretch has 

occurred; make corrections as needed. 

• March 15 – 30: Review field notebooks and datasheets from previous surveys to identify 

any unique events that may be encountered within specific parks. 

• March 15 – 30: Review species lists from previous vegetation sampling efforts in a park 

and in surrounding areas and compare to reference manuals to identify species not 

recorded which have a probability of being recorded. Copies of theses combined species 
45

lists should be made and carried into the field as quick references in helping to identify 

unknown plants. 

Table 1. Field equipment list for FIA and Hybrid vegetation sampling 

Item Quantity 

Laptop computer for downloading data from PDAs into database 1 

List of plant species known or thought possible to exist in park 4 

Protocol narrative and SOPs 1 

Field guide detailing data collection procedures 4 

Site maps showing coordinates 1 set 

50m tape 6 

logger tapes (30 m auto retractable with nail at the end) 2 

1m quadrat frame 2 

Chaining pin with rope attached noting 2.82 m distance (shrub circle, hybrid method) 2 

Chaining pin with rope attached noting 2.07 m distance (microplot, FIA method) 2 

Chaining pin (or expanding pole) noting 7.32 m distance (subplot, FIA method) 2 

Calipers for measuring sapling diameters 2 

Compasses for navigation and plot layout (adjust/check declination) 4 

Clipboards and pencils for recording notes and data 2 

Rebar and other materials for permanent marking several 

Trimble GPS unit for navigating to site coordinates and entering plot data 2 

Garmin GPS unit as backup for the Trimble 2 

Digital camera for photographs of understory, canopy, unknown species, etc. 2 

Hammer for pounding in rebar 

Sonar distance measurers 2 

Cruiser vest for carrying equipment 4 

Hand lens for close-up viewing of vegetation 4 

Copies of datasheets on rite-in-the-rain paper Several 

Reference books for plant, insect, and disease identification (see Appendix 1 of this SOP 

for a bibliography of suggested books and articles) 

Several 

Field Guide SOP with details of taking measurements 2 

Park radio 2 

Insect repellent Several 

Insect proof shirts 4 

Sunscreen Several 

First Aid kit 2 

Plant press 2 

1.5 April 

• April 1 – 7: Coordinates for the FIA center-point and/or NPS Hybrid-method starting 

point for each site to be sampled must be loaded onto the Trimble GPS units. Also load 

regional topographic maps into the Trimbles. These GPS units also should be checked at 

this time to ensure accuracy, working order, and that the proper settings are loaded (see 

SOP 4, GPS Use, for details on settings). 
46

• April 1 – 15: Contact local experts in the botany, soils, and entomology of the park(s) in 

which field work will be conducted. The purpose of this is to establish a line of 

communication between the field crew and scientists who are knowledgeable about the 

local area. These experts can then be contacted during the field season if questions arise. 

Refer to Table 2 for suggested people. 

1.6 May 

• May: Field crew leader arrives in Ashland. The exact date will vary depending on when 

actual sampling will begin, which in turn, depends on which park will be sampled first 

that year. In some years, this may even occur in April. In general, however, the crew 

leader will arrive in Ashland two weeks prior to the field crew starting at their designated 

duty station. During their time in Ashland, the crew leader will assist the program 

manager with final preparations for the upcoming sampling season. This will familiarize 

the crew leader with procedures. The crew leader may also attend a boat training session 

on the St. Croix River during this time period. Finally, the program manager and crew 

leader will establish practice plots so that the crew leader will become familiar with 

techniques. 

• May (late): Field crew arrives at duty station. Again, this is approximate, depending on 

the park where sampling will commence in a give year. There will be a two to three week 

training period following their arrival but prior to sampling the first plot. During the first 

three days of this period, the crew will have two federally mandated training courses: 

credit card training required for all federal employees issued credit cards and federal 

information systems security awareness computer training. For both courses the crew 

members will be required to meet minimum proficiency scores. In addition, the crew 

members will be required to pass an American Red Cross approved CPR and First Aid 

training course. 

1.7 June 

• June (early): Final preparations for getting the field crew ready for establishing plots and 

collecting data will occur during this time. This will include training in establishing test 

plots and plant identification, training in use of park radios, and general park etiquette 

training. This last point will focus on interactions with other park employees, and the 

general public and will include how to answer questions about the work of the field crew 

specifically and the Inventory & Monitoring Program in general. Prior to initiating 

sampling, all field crew members will be required to pass a minimum proficiency test on 

plant identification. 
47

Table 2. List of local biologists for each Network park. In addition to those noted below, we will also rely on Joe 

5266, jobrien@fs.fed.us) for plant pathology and Steve Katovich (651-649-5264, steven_katovich@fs.fed.us) for 

Park Name Affiliation Area of Expertise Phone 

APIS Jim Meeker Northland College botany 715-682-1808 jmeeker@n Andy Goyke Northland College entomology 715-682-1550 agoyke@noGRPO Al Harris Northern Bioscience botany and entomology 807-344-7213 aharris@noINDU Julie Stumpf National Park Service botany 219-926-7561 x 323 julie_stump Dan Mason National Park Service botany 219-926-7561 daniel_masISRO Janet Marr Michigan Tech botany 906-337-5529 jkmarr@mtMISS Eric Epstein Wisconsin DNR botany 608-267-5038 eric.epsteinPIRO Janet Marr Michigan Tech botany 906-337-5529 jkmarr@mtSACN Eric Epstein Wisconsin DNR botany 608-267-5038 eric.epstein Emmet Judziewicz U.W. Stevens Point botany 715-346-4248 ejudziew@ Joe O’Brien U.S. Forest Service entomology 615-649-5266 jobrien@fs Todd Lanigan Wisconsin DNR (Eau Claire) forest pest specialist 715-839-1632 todd.lanigan Shane Weber Wisconsin DNR (Spooner) forest entomologist 715-635-4156 shane.webeSLBE Janet Marr Michigan Tech botany 906-337-5529 jkmarr@mtVOYA Al Harris Northern Bioscience botany and entomology 807-344-7213 aharris@no
48

SOP 1 Appendix 

Bibliographic List of Suggested Guides for Identifying Plants, Insects, and Diseases 

Allen, T. J., J. P. Brock, and J. Glassberg. 2005. Caterpillars in the Field and Garden: A 

Field Guide to the Butterfly Caterpillars of North America. Oxford University 

Press, New York, New York. 

Dean, C. C. 1940. Flora of Indiana. The Blackburn Press, Caldwell, New Jersey. 

Eggers, S. D., and D. M. Reed. 1997. Wetland Plants and Plant Communities of 

Minnesota and Wisconsin, 2nd edition. The U.S. Army Corps of Engineers, St. 

Paul, Minnesota. 

Fassett, N. C. 1951. Grasses of Wisconsin. The University of Wisconsin Press, Madison, 

Wisconsin. 

Gleason, H. A., and A. Cronquist. 1991. Manual of Vascular Plants of Northeastern 

United States and Adjacent Canada, 2nd edition. The New York Botanical 

Garden, New York, New York. 

Hazlett, B. 1991. The flora of Sleeping Bear Dunes National Lakeshore, Benzie and 

Leelanau Counties, Michigan. The Michigan Botanist 30(4):139-202. 

Johnson, W. T., and H. H. Lyon. 1991. Insects that Feed on Trees and Shrubs, 2nd 

edition. Cornell University Press, Ithaca, New York. 

Judziewicz, E. J., and R. G. Koch. 1993. Flora and vegetation of the Apostle Islands 

National Lakeshore and Madeline Island, Ashland and Bayfield Counties, 

Wisconsin. The Michigan Botanist 32(2):43-189. 

Mohlenbrock, R. H. 1999. The Illustrated Flora of Illinois. Ferns. Second Edition. 

Southern Illinois University Press, Carbondale. 

Read, R. H. 1975. Vascular plants of Pictured Rocks National Lakeshore, Alger County, 

Michigan. The Michigan Botanist 14(1):3-43. 

Sinclair, W. A., H. H. Lyon, and W. T. Johnson. 1987. Diseases of Trees and Shrubs. 

Cornell University Press, Ithaca, New York. 

Slavick, A. D., and R. A. Janke. 1987. The vascular flora of Isle Royale National Park. 

The Michigan Botanist 26(3):91-133. 

Soper, J. H. and M. L. Heimburger. 1982. Shrubs of Ontario. Ontario Public Museum, 

Toronto. 
49

Voss, E. G. 1972. Michigan Flora, Vols. 1 - 3. The Cranbrook Institute of Science, 

Bloomfield Hills, Michigan. 

Wells, James R., and P. W. Thompson. 1974. Vegetation and flora of Keweenaw County, 

Michigan. The Michigan Botanist (13(3):107-151. 

Wherry, E. T. 1961. The Fern Guide: Northeastern and Midland United States and 

Adjacent Canada. Dover Publications, Mineola, New York. 

51

Training Observers 

STANDARD OPERATING PROCEDURE #2 

Version 1.0 (7/1/08) 

Dan Olson and Sarah Johnson 

University of Wisconsin-Madison 

Suzanne Sanders 

NPS-Great Lakes Network 

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2.0 Introduction 

This Standard Operating Procedure explains the training procedures that all observers should 

follow to learn (1) how to identify wild herbs in various stages of growth, (2) how to accurately 

sample for trees and shrubs, and (3) how to follow standard sampling protocol and accurately 

establish sample plots. 

2.1 Herb sampling 

The most essential component for the collection of credible, high-quality herb data is welltrained and experienced observers. This cannot be overemphasized. The botanists responsible for 

collecting herb data will require considerable previous experience in the field identification of 

wild plants in all stages of growth. All crew members will benefit from a brief introduction to the 

common flora of the park, but in a short time it would be very difficult to train an inexperienced 

individual thoroughly enough to sample herbs accurately. 

Procedures: 

1. Before field season, the terrestrial ecologist and field technicians should familiarize 

themselves with the flora of the park by studying the list of species that are known to occur 

there and by consulting references for further information. This should also include looking 

at herbarium specimens of species known to be difficult to identify or distinguish from one 

another. This step is especially important if the ecologist or technicians have not already 

done work in the same region as the park. 

2. Once at the park, but prior to beginning sampling, the terrestrial ecologist and field 

technicians should spend time in the field to further familiarize themselves with the plant 

species present and the current growing conditions. 

3. The field technicians must be able to pass a minimum proficiency test for accurately 

identifying plants: all common species should be recognized in all stages of growth. The 

technicians should also be familiar with less common species that are known to occur in 

parks, and should be able to readily identify these by consulting reference materials. 

4. Train the technicians on the standard procedure for collecting and documenting unknown 

species for later identification, ensuring that populations of rare plants are not threatened by 

collecting and that specimens be well-preserved for future reference. Please refer to standard 

operating procedure #7: Collection of Unknown Plants for Identification. 

5. Train the technicians for specific techniques associated with herbaceous data collection. 

Ensure that they use their hand to move larger or apparent herbaceous vegetation that might 

be obscuring smaller or less conspicuous plants. Also ensure that the technicians are feeling 

certain plants for pubescence and other indentifying traits. 

6. If more than one person will be sampling herbs, have them sample several quadrats 

independently and compare the results. Resolve any differences in technique or taxonomy 

that arise. 
53

7. Refer to Appendix 1 of SOP # 1 for a list of reference materials. 

 

2.2 Tree and shrub sampling 

As with herb sampling, experience in field identification is critical to the accurate sampling of 

trees and shrubs. There are far fewer species of trees and shrubs than herbs, however, and often 

they are easier to identify. Thus, it is possible for a less experienced but motivated crew member 

to learn trees and shrubs well enough for accurate sampling with several days of training before 

the field season. Those with less experience should not hesitate to ask the terrestrial ecologist for 

assistance or confirmation should any uncertainty arise during the course of sampling. 

Procedures: 

1. Before the field season, field technicians should familiarize themselves with the tree and 

shrub species found in the park by studying the list of species that are known to occur there 

and by consulting references for further information. This step is especially important for 

technicians who have not already done field work in the same region as the park. 

2. Once at the park, but prior to beginning sampling, crew members should spend time in the 

field to further familiarize themselves with the tree and shrub species present and the current 

growing conditions. 

3. Field technicians must be able to pass a minimum proficiency test for accurately identifying 

trees and shrubs: all species should be recognized in all stages of growth. If any questions 

arise, the technicians should be able to resolve them by consulting reference materials. 

4. Train technicians in the proper use of forester tapes to accurately measure trees and breast 

height. Refer to SOP# 6: Field Methods and Data Collection for specific details of this. 

5. Train technicians in the accurate use of the FIA rope and Hybrid shrub rope for counting 

stems in shrub circles efficiently and accurately. 

 

2.3 General sampling methods and layout 

If more than one crew is sampling, all crews will work together to sample one or two sites at the 

start of the field season. This will ensure that the same methods are followed by all crews, and 

will allow any unforeseen problems or difficulties to be addressed and the protocol modified if 

necessary. 

Procedures: 

1. Train each technician on basic navigation, including GPS and compass use. Given 

coordinates, each member of the crew should be able to locate a point using the GPS. Each 

crew member also should be able to read a topographic map and be able to walk a straight 

line following a bearing on a compass. 

2. Following the sampling protocols detailed in later SOPs, sample a site with members from all 

crews present. The terrestrial ecologist should ensure that every crew member understands 
54

how the plot is located and established, and the purposes of each part of the sampling 

procedure. While under the observation of other technicians, each technician should 

participate in all of the sampling activities he/she will normally be responsible for. In 

addition, technicians should observe sampling activities for which they are not directly 

responsible, in case their help is needed in the future. 

3. During or after sampling, discuss the efficiency and accuracy of the methods used and make 

minor changes to techniques if necessary. Ensure that all technicians are thoroughly familiar 

with procedures and address any questions that arise. 

4. If there are multiple crews, select another site and let each crew sample it independently. 

Then compare results, and examine any differences that are apparent. 
55 

Safety 

STANDARD OPERATING PROCEDURE #3 

Version 1.0 (7/1/08) 

 

Sarah Johnson 

University of Wisconsin-Madison 

Suzanne Sanders 

NPS-Great Lakes Network 

Revision History Log: 

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56 

3.0 Introduction 

Safety of the field crew is the foremost concern during monitoring and travel to and from 

monitoring sites. The purpose of this SOP is to state the safety policies of the Great Lakes 

Network as they relate to the general vegetation monitoring protocol. 

3.1 First aid 

At a minimum, all members of the field crew must have basic first aid and CPR certification. 

The park where the field crew will be duty stationed will provide training for the certification 

during the two week time period after the field crew arrives, but before sampling begins. If it is 

not available during this time, technicians will attend a first aid and CPR course at the earliest 

possible date thereafter. 

3.2 Boat operation 

To ensure the safety of all people on the field crew, only those who have undergone boat 

operator training will be allowed to operate boats. If time and opportunity permit during the two 

week training period, crew members who have not undergone the training may be allowed to 

participate in it. 

3.3 General vehicle use 

Operation of National Park Service cars and trucks is limited to National Park Service employees 

only. Further, all members of the field crew must possess a valid driver’s license and sign the 

NPS motor vehicle operation waiver stating that they understand the rules of motor vehicle 

operation and alcoholic consumption. Vehicle operators are expected to abide by state and local 

laws; this includes all regulations within the park units. Specifically, seat belts must be worn at 

all times when the vehicle is being operated. Finally, employees should inform their supervisor 

if, at any point, they do not feel safe in the vehicle, either as a passenger or driver. 

 

3.4 Daily work schedule 

Long hours are a fact of field work and field crew members should expect that a fair percentage 

of work days will be greater than 10 hours. Although attempts will be made to limit the length of 

the work day to 10 hours, under no conditions will the length of the day exceed 11.5 hours. 

It is the responsibility of each crew member to be ready for work each day with his or her own 

adequate supply of food and water and in appropriate footwear for the terrain and weather. In the 

event of extreme temperature, wind, rain, or other storms, sampling for the day will be initiated 

or will be terminated if it has already begun. 

Two first aid kits will be supplied to the crew so that both of the two teams will have one. The 

crew members should familiarize themselves with them so that they know what can be treated in 

the field. 

A job hazard analysis for working in forested environments is included as an appendix of this 

SOP. 
57 

3.5 Alcoholic consumption and drug use 

Consumption of alcoholic beverages during the work day is strictly forbidden. Use of alcoholic 

beverages during off time is permitted. However, consumption occurring on Park Service 

property (e.g., park housing, back country campsites) must be within compliance of park rules 

(e.g., no glass containers). 

If a field crew member is deemed unfit for work due to excessive alcoholic consumption, he/she 

may be given the day off without pay, at the discretion of the ecologist or crew leader. 

Use of illegal drugs at any time on National Park property, whether work time or off hours, will 

not be permitted. Violation of this rule will result in disciplinary action and potentially, loss of 

employment. 

3.6 Pre-existing medical conditions 

Crew member with pre-existing medical conditions are encouraged, with discretion, to discuss 

these with their supervisor or other crew members. Knowing about conditions such as diabetes, 

allergies, and seizures can affect the actions taken by the crew leader or crew members in the 

event of an emergency. 

3.7 Injury reporting 

All injuries and illnesses must be reported using the national online safety information 

management system: www.smis.doi.gov. Once at this site, click on the “Accident Reporting” 

button. The type of claim filed will depend on the nature of the accident or injury. 
58 

Appendix A: Job Hazard Analysis Form 

REQUIRED EQUIPMENT & TOOLS 

FOR JOB 

REQUIRED SAFETY EQUIPMENT 

Working in Forested and Wetland 

Environments 

Raingear, park radio, leather boots, crew 

first aid kit, long pants and shirt, gloves. 

POTENTIAL HAZARDS SAFETY CONTROL FACTORS 

1) Slipping and falling 

2) Rain / Inclement weather 

3) snags and unstable trees 

4) Insects and Animals 

5) Allergies 

a) wear appropriate footwear 

b) slow down and watch for obstacles 

a) dress appropriately 

b) bring raingear 

c) wear sunscreen if appropriate 

d) drink plenty of water 

e) look for protection if weather becomes untenable 

a) communicate with fellow workers 

b) be wary of all snags and leaning trees 

c) do not lean up against any tree 

a) wear PPE including insect spray 

b) look before you step 

c) carry first aid kit 

d) make noise to warn animals of approach when 

hiking 

e) be informed of appropriate measures to take in 

case of a bear encounter 

a) inform co-workers maintain on crew allergy list 

carry and know how to use appropriate medications 

(epi-pen) if necessary 
59

Using the Global Positioning System 

STANDARD OPERATING PROCEDURE #4 

Version 1.0 (7/1/08) 

Mark Hart, Ulf Gafvert, David VanderMeulen, and Suzanne Sanders 

NPS – Great Lakes Network 

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60

4.0 Introduction 

This Standard Operating Procedure (SOP) provides guidance on some of the more common 

operations associated with global positioning system (GPS) units. This includes guidance for 

loading predetermined site locations, data dictionaries, and maps, as well as for post collection 

processing of samples. In addition, it also covers GSP standards to be met by GLKN. Although 

most GPS units are capable of multiple functions, only those operations relevant to the protocol 

established to monitor vegetation (Sanders et al. 2007) are discussed. GPS units currently (2007) 

used by the vegetation monitoring staff are the Trimble GeoXT and Garmin 76CSx. 

As of 2007, software pertinent to the use of these GPS units includes: 

• TerraSync (Trimble GeoXT) – single table forms software 

• GPS Pathfinder Office 4.00 (Trimble GeoXT) – desktop GPS processing software 

• ESRI ArcGIS 9.x (Trimble GeoXT or Garmin 76CSx) – desktop GIS software 

Due to the rapid development of commercial software and hardware capabilities, it is likely that 

other GPS units or software will be utilized in the future. Therefore, this SOP is meant to act as a 

working document that is updated periodically as new hardware and software become available. 

Although nomenclature may differ depending on what hard-software is utilized, this document 

should provide sufficient guidance on the general process of data collection using GPS tools until 

the SOP is revised. Vegetation monitoring staff will obtain unit-specific GPS training prior to 

deploying in the field. The training will include hands-on use, and will be designed to test all 

appropriate functions and operations prior to going out into the field. Additionally, this SOP is 

not intended to be exhaustive or simply a regurgitation of operating manuals, but a document that 

might be carried into the field or periodically reviewed by field technicians and project leaders. 

Although some of the information in this SOP is specific to the vegetation monitoring effort by 

GLKN, much of the text contained herein is applicable to other data collection efforts by GLKN 

staff. The objective of this document is to summarize GPS use guidelines applicable to the 

vegetation monitoring efforts at GLKN partner parks.

4.1 Role of GPS in Vegetation Monitoring 

GPS units are primarily used in two ways to support vegetation monitoring: 

Navigation: Field personnel will be supplied with both topographic and aerial imagery maps for 

navigation. In addition, permanent plot locations will be loaded into the GPS units prior 

commencement of field work. However, because the plot locations are in forested areas with 

little or no landmarks nearby, field crews will primarily rely on GPS units for navigation. 

Recording permanent plot locations: GPS units will be used to record the location of all six 

transect endpoints (hybrid plot) or all four subplot centers (FIA-style plot). 

4.2 Using Mapping-Grade GPS Units 

All mapping-grade GPS (sub-meter accuracy) users should become familiar with GLKN GPS 

collection procedures and relevant manufacturer’s user guides and operating manuals before 

GPS operation. For example, prior to using a Trimble GeoXT GPS unit, the following 

documents should be reviewed: 
61

• Appendix A of this SOP 

• GeoExplorer CE Series: Getting Started Guide 

• GPS Mapping for GIS with TerraSync and GeoExplorer CE Series or TerraSync Operation 

Guide v2.4x 

• Basic GPS Data Capture Using TerraSync: A Quick Start Guide 

Mapping-grade (sub-meter accuracy) GPS units provide the user with a variety of tools for field 

data collection. GLKN encourages the use of these units for most projects. These units can be 

used to acquire spatial data related to points, lines, and polygons along with associated, user 

defined, tabular attributes. Careful forethought and advanced planning are required to take 

advantage of these capabilities long before data collection begins. 

Data Dictionaries 

TerraSync software on mapping-grade GPS units is capable of using data dictionaries. Data 

dictionaries define the structure and rules to store attribute information about the feature being 

mapped and are customized for each project. GLKN data management personnel should be 

directly involved in the creation of data dictionaries. For the vegetation monitoring protocol, we 

will use data dictionaries to store the coordinates of transect endpoints (hybrid plot) or subplot 

centers (FIA style plots). 

GPS Settings 

Positional accuracy of GPS data can be affected by several factors that can be monitored and 

recorded with mapping-grade GPS units. Table 1 lists these factors, their definitions, and the 

standard settings for GLKN field work. All spatial data collected shall be analyzed for spatial 

accuracy and shall meet or exceed the National Map Accuracy Standards (Table 1 in Appendix 

A, and http://mapping.usgs.gov/standards). 
62

Table 1. GPS receiver settings, definitions, and standards for use at GLKN parks. 

Setting Name Definition GLKN Setting Standard 

Almanac File containing estimated position of 

satellites, time corrections, and 

atmospheric delay parameters 

Acquired automatically by GPS 

unit or from online sources within 

10 days prior to GPS field work 

Altitude reference Ellipsoid model Height above Ellipsoid (HAE) 

(preferred) or Mean Sea Level: if 

MSL is used, indicate Geoid Model 

Antenna height GPS antenna height above the 

ground 

Variable, usually 1.0 meters for 

handheld and 1.5 m for backpack 

Datum Geodetic model designed to fit a 

point on the earth’s surface to an 

ellipsoid 

NAD 83 (CONUS) [preferred] 

WGS 84 [GPS default, as fallback] 

Elevation mask The minimum angle above the 

horizon at which a GPS receiver 

will track a satellite 

15 degrees 

Feature types Geometry of spatial data GIS native formats; point, line and 

polygon are preferred 

Logging interval Time interval between the recording 

of individual GPS fixes 

Points: 1 second 

Minimum fixes for point 

positions 

Number of GPS fixes that are used 

to calculate a single position for a 

point feature 

50 fixes 

Mode 2 dimensional for horizontal 

positions and 3 dimension with an 

elevation position 

3-dimensional (4 satellite 

minimum) 

PDOP mask Positional Dilution of Precision, a 

GPS quality estimate based on 

satellite geometry 

6.0 or less 

Real-time settings GPS unit may be capable of 

performing differential correction of 

data during collection 

Select Integrated WAAS (unless 

using H-star, e.g., XH unit); setting 

will be ‘auto’ or ‘on’ 

Satellite vehicles Number of satellites used for 

position fixes 

4 minimum 

SNR mask Signal-to-Noise ratio is a measure 

of the satellite signal relative to 

background noise 

4.0 minimum, 6.0 or greater 

preferred 

Unit of measure Linear unit of measure Meter (metric) 

GPS signals are received in the WGS84 datum. Processing and transformation of the positional 

information to other datums can take place internally in the GPS unit or in software, either the 

GPS data processing software (see below) or in GIS software. GPS data that will receive no postprocessing differential correction, unlikely with a mapping grade receiver, can be collected in 

native WGS84 or NAD83 (CONUS), and ensuring that the datum used is recorded in the 

metadata. More likely with a mapping grade receiver, the GPS data will be differentially 

corrected after collection using data from one or more reference base stations. The majority of 

public base stations in the US are part of the National Geodetic Survey's Continuously Operating 

Reference Station (CORS) network; and output information in the NAD83 CORS 96 datum. 

Setting the GPS unit to record data in NAD83 CORS 96 datum will result in the most accurate 

spatial information when differentially corrected against a CORS base station. A very high 

precision mapping grade unit may use H-Star technology, which requires post collection 
63

differential correction; using the NAD83 CORS 96 datum and no real-time correction (WAAS) 

on the GPS unit, and correcting against a group of CORS base stations is necessary to realize the 

maximum accuracy from an H-Star receiver. 

Before beginning data collection, the GLKN data management staff or the GPS user should 

complete some mission planning tasks. Having printed copies of topographic maps with plot 

coordinates is essential to maximize field time and efficiency when navigating to sites. Many 

mapping-grade GPS units have the capability of storing and displaying background maps or GIS 

layers, which can be very helpful when navigating in the field. For example, topographic maps of 

the Saint Croix River, which also show landings and tributaries, can be used with GPS units. The 

GLKN data management staff can support preparation of these background layers and, if 

necessary, assist in loading them onto the GPS hardware. 

GPS units create files to store data during a field session using a prefix and date-time stamp as 

file names. For example: 

RMMDDHHx 

R – Unit Prefix 

MM – Month 

DD – Day 

HH – Hour 

X – a, b, c, etc., the order files are created within an hour 

If multiple GPS units are used for a project, a unique prefix (letter) should be assigned to each 

unit, which will ensure that downloaded files for each unit contain a unique identifier within the 

filename. For example, with three GPS units, the unique letters for the units could be N, G, and 

A. Those letters would serve as a prefix for the file n (e.g., N102715A, G102715A, and 

A102715A would indicate units N, G, and A, October 27, 15 hour, A first in hour). 

Each user should be familiar with the capabilities of the GPS hardware and field computers. If 

possible, vegetation monitoring staff should receive hands-on instruction from someone familiar 

with the equipment. At a minimum, the equipment user guides and operator’s manuals should be 

reviewed, and the operator should test the functions s/he intends to use. 

It is extremely important that each user become familiar with the battery power and memory 

capabilities of the GPS units. All units have limited battery and memory resources; these features 

should be thoroughly tested to gain an understanding of the power and memory limitations of the 

GPS units before being deployed in the field. It is possible to power or recharge these units from 

a DC power source, such as a vehicle power outlet. 

Data Collection 

Data collection should be performed using an approved data dictionary or database for the 

protocol, if applicable. Moreover, users should be mindful of the following concepts: Mappinggrade GPS units have additional features that aid in data collection. These include: 

• Offset Feature – Allows user to collect a feature when topography is such that getting next 

to or over the feature is impossible. For example, if the coordinates at a plot center or transect 
64

endpoint cannot be obtained, coordinates can be collected nearby and a distance and angle to 

the desired location recorded. The offset feature can then be used to determined the 

coordinates of the desired location. 

Data Processing: 

When data collection is complete for the day or round of monitoring, data are downloaded from 

the GPS unit to a computer. For Trimble GPS units, the proprietary software Pathfinder Office 

(or the GPS Analyst extension) is used to download, differentially correct, and then export the 

data to a GIS format. [Note: Trimble Pathfinder Office and GPS Analyst are relatively 

expensive. However, Trimble also offers a free data transfer utility to download data from the 

GPS units to a Windows-based PC]. Detailed step-by-step instructions are provided in Appendix 

B of this SOP. Differential correction is a post-processing procedure to improve upon raw GPS 

positions using base station data. Base stations consist of a GPS antenna and receiver positioned 

at a known location specifically to collect data from satellites. The distance between the base 

station(s) and the remote GPS receiver should be kept to a minimum. 

Differential correction should be conducted on all GPS data collected on a mapping grade 

receiver, even if data were collected using the real-time collection feature. Once the data are 

differentially corrected, they can be verified and edited. Unintentional features can be deleted 

and attributes can be reviewed. Please see Appendix B for specific step-by-step instructions on 

the downloading and differential correction processes. 

The last step in processing data is exporting the data set to GIS (such as ArcMap). Depending on 

the software used for this process, newly created files generated when exporting data are often 

assigned generic names. For example, if Pathfinder Office is used to export a file named 

‘VOYA2007.cor’ (.cor denotes that the file has already been differentially corrected) that only 

contains point features, the exported file will be named ‘point_ge.shp’. Great care should be 

taken to not overwrite this file when exporting other data, as the software will continue to use 

this generic naming convention the next time it is used. In addition, during the export process, 

the coordinate system to which the data will be exported to should be verified (Table 2). 

Managing the incoming GPS data can be a challenge, especially if there are multiple 

units per project. Common practices used by GLKN include: 

• Download all data to a computer or network drive that is regularly backed up. 

• Keep GPS data and GIS data separate through electronic file management. 

Directories and files names should not contain non-alpha-numeric characters 

and/or spaces (except underscores). 

• Keep GPS data in well-organized directories (see Hart and Gafvert (2005), GLKN 

Data Management Plan, for more details). 
65

Table 2. UTM zones for Great Lakes Network parks. 

Park UTM Zone Datum 

APIS 15 NAD 1983 (Conus) 

GRPO 16 NAD 1983 (Conus) 

INDU 16 NAD 1983 (Conus) 

ISRO 16 NAD 1983 (Conus) 

MISS 15 NAD 1983 (Conus) 

PIRO 16 NAD 1983 (Conus) 

SACN 15 NAD 1983 (Conus) 

SLBE 16 NAD 1983 (Conus) 

VOYA 15 NAD 1983 (Conus) 

Additional data attributes can be included in the data exports. Data attributes recommended by 

GLKN are listed in Table 3. 

Table 3. Recommended fields to be exported in addition to GPS features. 

All Features Point Features Line Features Area Features 

PDOP Height Length (2D) Area (2D) 

Correction status Position Length (3D) Perimeter (2D) 

Receiver type Perimeter (3D) 

Date recorded 

Data file name 

Total positions 

Data dictionary name 

 

At the end of a project, all data and background files should be removed from the GPS 

unit to free available memory. Data files should not be left on a unit if they have been 

properly downloaded and verified. In addition, some GPS units require their batteries to 

be re-charged periodically. Failure to do so can cause the GPS unit batteries to discharge 

completely, and may cause some files and software to be deleted. 

Additional information can be found at http://www.nps.gov/gis/gps/gps4gis/ , which 

describes the steps outlined here in greater detail. 

4.3 Using Recreational-Grade GPS Units 

Recreational-grade GPS units can be used to acquire location information when there are 

problems with the mapping grade GPS units working correctly. Recreational GPS units do not 

have data dictionaries for storing attribute information with the point location. However, using a 

recreational-grade unit to record transect endpoints and subplot centers at each site is a reliable 

alternative when mapping-grade units cannot be used. 

As with mapping-grade GPS units, personnel that employ recreational-grade GPS units should 

become familiar with GLKN GPS collection procedures and relevant manufacturer’s user guides 

and operating manuals before GPS operation. For example, prior to using a Garmin 76CSx 

(recreational-grade) GPS unit, the following documents should be reviewed: 
66

• Appendix A of this SOP 

• GPSMAP 76CSx Quick Start Guide 

• GPSMAP 76CSx Owner’s Manual and Reference Guide 

• Garmin MapSourceTM User’s Manual and Reference Guide 

Planning

If a recreational-grade GPS meets the criteria of the project, the unit chosen must have the 

capability of downloading collected data to a personal computer. Downloading data is usually 

accomplished with a parallel or USB cable connection. 

The monitoring sites must be loaded onto the GPS unit before departure to the field in the event 

that the recreational-grade GPS units will be used for navigation. Some recreational grade GPS 

units have the ability to store and display topographic maps, which can aid in navigation. These 

should also be loaded beforehand. 

Much of the data collected by GPS will eventually reside in a relational database. Each GPS 

feature collected should contain a unique identifier that relates the feature to an associated record 

in a database. For vegetation monitoring, the records associated with each GPS feature will be 

vegetation parameters. Since recreational GPS units have only one text field for input, careful 

consideration should be given to the use of this field and the design of unique identifiers. GLKN 

data management and GIS staff can assist in creating unique IDs on a project by project basis. 

Data Collection

Location data are captured by recreational-grade GPS units as waypoints. When taking a 

waypoint, enter the site ID or site designation in the text field provided. This ID should be 

consistent with the naming convention used in the data dictionaries. For example, transect 

endpoint 5 of plot 7045 will be labeled as “7045-5”; the center of subplot 2 at plot 4058 will be 

“4058-2”. 

Data Processing 

Data should be downloaded from GPS units once a day or after each field session. The DNR 

Garmin freeware product: (http://www.dnr.state.mn.us/mis/gis/tools/arcview/index.html ) can be 

used to download data from Garmin GPS units. Data should be downloaded both as a text file 

and a shapefile. Each file name should include the download date. Points should be checked for 

reasonable spatial accuracy and errors. Subsequent downloads should be error-checked in the 

same manner. When data collection is finished, all files should be compiled into one spatial file, 

and along with the raw downloads, should be saved to the appropriate location on Great Lakes 

Network servers. 

4.4 Metadata 

Regardless of the type of GPS unit used to collect data, all resulting GIS datasets need to have 

information documenting how the GPS data were collected. NPS requires that FGDC (Federal 

Geographic Data Committee, www.fgdc.gov/index.html ) compliant metadata be written for all 

geospatial layers created (Executive Order 12906). Until final FGDC metadata is written, the 

data collection and management process is incomplete. Tracking GPS projects depends on the 
67

complexity of the project, how many participants, length of project etc. Documentation can be a 

simple ‘readme’ text file, or a detailed daily log. 

The Great Lakes Network recommends formal metadata be written by the data collectors, as they 

are the ones familiar with the project and resulting data. However, Network data management 

and GIS staff are usually the ones documenting someone else’s work. Chapter 7 of the GLKN 

Data Management Plan (Hart and Gafvert 2005) includes a detailed discussion of metadata 

procedures. At a minimum, the following details should be documented to facilitate final FGDC 

metadata: 

• Name of project 

• Name(s) of data collectors 

• EHE/EPE or maximum PDOP (using 4 satellites) 

• Coordinate system (projection, datum, and zone) 

• Type (or types) of GPS units used 

• The range of field collection dates 

• Name of base station(s) used for differential correction 

• Name and version of software used for downloading 

• Any major editing performed on the raw data (e.g., moving of points) 

• All versions of data dictionaries used 

4.5 QA/QC 

Long-term monitoring is only useful if users have confidence in the data. Efforts to detect trends 

and patterns in ecosystem processes require high-quality, well-documented data that minimize 

error and bias. Data of inconsistent or poor quality can result in loss of sensitivity and lead to 

incorrect interpretations and conclusions. 

NPS Director’s Order #11B: Ensuring Quality of Information Disseminated by the National Park 

Service (www.nps.gov/policy/DOrders/11B-final.htm) specifies that information produced by 

the NPS must be of the highest quality and based on reliable data sources that are accurate, 

timely, and representative of the most current information available. Therefore, GLKN will 

establish and document procedures for quality assurance (QA) and quality control (QC) to 

identify and reduce the frequency and significance of errors at all stages in the data life cycle. 

Under these procedures, the progression from raw data to verified data to validated data implies 

increasing confidence in the quality of those data. Quality assurance and quality control 

procedures will document internal and external review processes and include guidance for 

addressing problems with data quality. 

Examples of general of QA/QC practices include: 

• Standardized field data collection forms 

• Use of field computers and automated data loggers 

• Proper calibration and maintenance of equipment 

• Training of field crew and data technicians 

• Database features such as built-in pick lists and range limits to reduce data entry errors 
68

• Automated error-checking routines 

Many of the standard operating procedures associated with the protocol for monitoring 

vegetation include a discussion of QA/QC as it relates to the protocol. Examples of QA/QC 

practices pertaining to use of GPS include: 

• Ensure that GPS-related software is periodically updated as it becomes available and has 

been tested. 

• For each plot, compare location positions for different visits, including the position recorded 

during establishment of the monitoring station. This will allow for an assessment of position 

accuracy over time. 

• Check to see if the accuracy of the GPS unit meets or exceeds the National Map Accuracy 

Standards shown in Table 1 of Appendix A. 

• Ensure that the appropriate coordinate system is used when collecting and exporting data. 

• Use mapping software (e.g., Pathfinder Office or ArcGIS 9.x) to view features (or 

waypoints) overlaid on a geo-referenced air photo or topographical map to check for 

accuracy. 

• If applicable, check the accuracy of the attribute(s) recorded on a GPS unit by using mapping 

software (see bullet above) and look-up tables or in spreadsheets generated after postprocessing is complete. 

A final report on data quality, including data collected by GPS, will be incorporated into the 

documentation for this project. Such documentation will include a listing of the specific methods 

used to assess data quality and an assessment of overall data quality prepared by the project 

manager. This is a necessary part of the data quality elements of the metadata file. 

4.6 Literature Cited 

Hart, M., and U. Gafvert. Editors, 2005. Data management plan: Great Lakes Inventory and 

Monitoring Network. National Park Service Great Lakes Inventory and Monitoring 

Network Report. GLKN/2005/20. 

Sanders, S., S. E. Johnson, and D. M. Waller. 2006. General vegetation monitoring protocol for 

the Great Lakes Network, Version 1.0. National Park Service, Great Lakes Network, 

Ashland, Wisconsin. 
69

Appendix A

This appendix discusses the role of GPS in GLKN data management and provides an explanation 

of the types of GPS units that are available. The text is taken directly from the GLKN GPS Field 

Collection Guide, which can be found in its entirety in Appendix K of the Great Lakes Inventory 

and Monitoring Data Management Plan (Hart and Gafvert 2005). 

Introduction 

Over the past decade new tools have been developed to help researchers collect and manipulate 

data while in the field. Global Positioning System (GPS) is one such tool. GPS is currently a 

constellation of 28 US Department of Defense satellites (as of 2006) orbiting 11,000 miles above 

the Earth, making a complete orbit approximately every 12 hours, and transmitting signals to 

Earth at precisely the same time. The position and time information transmitted by these 

satellites is used by a GPS receiver to triangulate a location coordinate on the earth using three or 

more satellites. 

Role of GPS in GLKN Data Management 

Data collected using GPS-enabled equipment represents all or part of the acquisition 

stage of an information resources lifecycle that includes several other stages (see Section 5.4 in 

GLKN Data Management Plan). The process and methodology used for acquisition, planning, 

data collecting, and post-processing incorporate several aspects of data management, including 

quality assurance, data storage and organization, and data stewardship. To promote data quality 

and simplify data management, the Great Lakes Network expects to use electronic data logging 

equipment for some data acquisition. However, parallel or complementary use of hand written 

data sheets and field notes will remain important for data collection activities. 

Types of GPS units 

At the most basic level GPS equipment can just consist of a GPS antenna and the associated 

signal processing circuitry. The antenna can be a standalone device, be incorporated in to a 

handheld unit, or be integrated into a larger electronic device, such as a personal data assistant 

(PDA), data logger, or portable computer. As technology evolves, the Great Lakes Network will 

continually try to use equipment which maximizes spatial accuracy; reduces hardware weight 

and user fatigue; and reduces database development, data manipulation, and transformation. 

There are three major types of GPS units that are based on the level of accuracy to which spatial 

data can be collected. Survey-grade GPS units are used for surveying tasks that require very high 

accuracy (1 cm or less). Mapping-grade units can map features from sub-meter to less than 5m 

accuracy, employing differential correction. Recreational-grade GPS units are sold primarily for 

outdoor sports and recreational activities. Accuracy using recreational GPS units ranges from 5 

to 30m. Most natural resource-related data collection requirements correspond to either the 

recreational-grade or mapping-grade. 

Figure 1 shows some of the major differences between these two types. Deciding which type of 

unit to use is an essential part of project planning, and depends on the end product needed. 

Mapping-grade GPS units are recommended for most GLKN field work; however, for some 

projects recreational-grade units can meet a project’s accuracy requirements and reduce the cost 
70

of field operations. The choice of GPS unit should be made by the project manager after 

consulting with the GLKN data management and GIS staff. 

Figure 1. Differences between different grades of GPS units. 

All resulting GIS data layers need to meet or exceed the National Map Accuracy Standards for a 

1:24,000 product (NPS GIS Data Standards, 2002 

http://www.nps.gov/gis/data_info/standards.html). Table 1 provides the allowable horizontal 

accuracy for some common scales. 
71

Table 1. Map scales and allowable error. 

Definitions 

Accuracy - The degree of conformance between the estimated or measured position, time, 

and/or velocity of a GPS receiver and its true time, position, and/or velocity as compared with a 

constant standard. 

Almanac -Data transmitted by a GPS satellite, which include orbit information on all the 

satellites, clock correction, and atmospheric delay parameters. The almanac is used to facilitate 

rapid satellite vehicle (SV) acquisition. The orbit information is a subset of the ephemeris data 

with reduced precision. 

Attribute – Tabular information about a specific feature. 

Base Station - GPS files collected continuously from community base stations, local base 

stations, or Continually Operating Reference Stations (CORS). Gathering base files will require 

an internet connection and software that dials into a server that houses the base station data 

collected at the same time of the rover. Data stored on these servers will not be available in realtime - hence this step is conducted after field collection. Trimble users would use the Differential 

Correction utility supplied in Pathfinder Office. 

Differential Correction - The merging of rover file data with base map data to correct position 

errors due to atmospheric interference. Autonomous data (rover) are collected in the field while 

base data are stored at the stationary base station. The two datasets are loaded into a postprocessing software package where corrections are applied. This process will reduce errors in the 

field collected data (the rover) by correlating and correcting for known errors recorded in the 

base file that has the same time tag. As distance between the rover and base file increase, there is 

degradation in post-processed accuracy. In general, a degradation of one part per million (1ppm) 

occurs as the distance between the base station and rover increases. For example, one millimeter 

of degradation occurs for every kilometer between base and rover. 

Datum (geodetic datum) – A mathematical model that is designed to fit a point on the earth’s 

surface to an ellipsoid. Commonly used datums are North American Datum (NAD) 1927, and 

NAD 1983, modeled to represent the North American continent. 

Scale Allowable Error 

1:40,000 33.8 meters (111 feet) 

1:31,680 16.1 meters (53 feet) 

1:24,000 12.2 meters (40 feet) 

1:20,000 10.1 meters (33 feet) 

1:12,000 6.1 meters (20 feet) 

1:9,600 4.9 meters (16 feet) 

1:4,800 2.4 meters (8 feet) 

1:2,400 1.2 meters (4 feet) 

1:1,200 0.6 meters (2 feet) 
72

Feature - A feature is the spatial location of a physical object, or some event or phenomenon. 

Features are often referred to as graphic data in a GIS. Examples include a tree (point), road 

(line), or land parcel (polygon). 

FGDC - The Federal Geographic Data Committee is a 19 member interagency committee 

composed of representatives from the Executive Office of the President, Cabinet-level and 

independent agencies who develop policies, standards, and procedures for organizations to 

cooperatively produce and share geographic data. (www.fgdc.gov/index.html) 

Global Positioning Systems (GPS) – a constellation of a minimum of twenty-four satellite 

vehicles orbiting the earth approximately every twelve hours at an approximate pacing of sixty 

degrees, between 11,000 – 12,000 miles above the surface of the Earth 

Lines – geographic term related to the scale that describe how a feature is drawn. Linesare linear 

measures of a feature (such as a line representing a trail) 

Mapping Grade – GPS receivers capable of attaining five meters of accuracy or better using 

differential correction. 

Metadata - Data about the data. Usually comes in the form of a text or html document with 

information on the dataset's quality, current projection, attributes, distribution and citation. In the 

National Park Service, this generally implies a file compliant to the FGDC Content Standard for 

Digital Geospatial Metadata. 

Multipath – error which occurs when a GPS signal sent from a satellite vehicle is bounced or 

redirected by an object, prior to reaching a GPS receiver. Multipath will cause the time it takes a 

GPS signal sent by a satellite vehicle to reach a GPS receiver to be inflated. This will cause 

inaccuracies in positions collected. 

Points – geographic term related to the scale that describe how a feature is drawn. Points are 

single dimensional features (such as a point representing a spring). 

Polygons - geographic term related to the scale that describe how a feature is drawn. Polygons 

have area associated with the feature (such as a circle representing a parking lot). 

Projection - A method of representing the earth's three-dimensional surface as a flat two 

dimensional surface. This normally involves a mathematical model that transforms the locations 

of features on the earth's surface to locations on a two-dimensional surface. 

Post Processing – utilizing base station data, GPS software, and data acquired by a GPS receiver 

in the field to gain an accurate fixed position. 

Triangulation - The process of determining the distance between points on the earth’s surface 

by dividing up a large area into a series of connected triangles, measuring a base line between 

two points, and then locating a third point by computing both the size of the angles made by lines 

from this point to each end of the base line and the lengths of these lines. 
73

Waypoint – a named 3 dimensional position on the earth’s surface, that is, having both a latitude 

and longitude. Waypoints are assigned to a fixed location in the field so it can be navigated to 

consistently and accurately through time.
74

Appendix B

This appendix discusses the step-by-step process for downloading and differentially correcting 

GPS data collected by the vegetation monitoring program. 
75

Overview 

This Standard Operating Procedure (SOP) is an addendum to the GLKN GPS Field Data 

Collection Guide, which covers more general GPS use. This document covers equipment and 

procedures specific to collecting location information with sub-meter accuracy. The collection of 

ground control information in development of ortho-photography from aerial imagery is used as 

an example, but the same principles will apply when other data collection requirements specify 

very high accuracy. 

This SOP is specific to the Trimble GeoXT unit; subsequent upgrades or new versions will 

require updating this document. Trimble XT Series GPS units are capable of acquiring sub-meter 

accuracy when used with an external antenna, (Zephyr antenna), and when the raw data collected 

in the field is properly post-processed using differential correction of H-Star data. 

The current GPS hardware for GLKN consists of a Trimble ProXT GPS Receiver, with a 

Bluetooth wireless connection to a PDA running Windows Mobile OS, and a Zephyr dual 

frequency antenna. 

Post-processing (differential correction) should be performed as soon as possible after data 

collection. The CORS base station data is usually available within a 2 hour lag time, though 

occasionally there are periods of time when data is not available, either due to malfunction of 

equipment, inadequate satellite coverage, or high ionospheric disturbance. These factors are not 

within the control of the field personnel collecting the data, but post-processing will uncover 

these problems. Any affected locations should be revisited to obtain high accuracy information. 

CORS stations maintain complete records for only 30 days, after which the data collection 

interval is reduced from 1, 5, or 30 second, depending on the station, to 30 minute intervals. This 

is done in order to save hard drive space. Differential correction files that are downloaded should 

be archived with the data, so that high temporal resolution base station files are available, in the 

event that post-processing needs to be redone on a dataset at a later time. 

Field Operations 

The steps below for field data collection are specific to the hardware configuration currently used 

by GLKN. The following settings will provide the best accuracy with the ProXT unit, and the 

Network will also provide a standard data dictionary (.ddf file), and Terrasync configuration file 

(.tcf file), in order to ensure those settings are programmed into the unit. 

In TerraSync Setup, select Logging Settings 

Verify: Log Velocity Data No 

 Measure and input precise antenna height 

 Allow Position Update Confirm 

 Confirm End Feature No 

 Filename Prefix J or S, depending on unit 

 Between Feature Logging Style Off 

In TerraSync Setup, select Coordinate System 
76

Verify:System UTM 

 Zone 16 North (for GRPO, INDU, ISRO, PIRO, SLBE) 

 15 North (for APIS, MISS, SACN, VOYA) 

 Datum NAD 1983 (Conus) CORS96 

 Altitude Reference Height Above Ellipsoid 

 Altitude and Coordinate Units Meters 

 Display USNG Off 

In TerraSync Setup, select Real-time Settings 

 Use Uncorrected GPS 

Post-processing the Data 

The Network is currently using Pathfinder Office (v.4.00) for completing differential correction 

on the field data files. Alternatively, ArcGIS GPS Analyst may be used, and is an extension to 

ArcGIS, available from Trimble. Both software packages effectively accomplish the same task 

using a nearly identical interface. 

Data files should be downloaded from the PDA shortly after returning from the field. This is 

done using the ‘Data Transfer’ option under Utilities within Pathfinder, and the files are 

downloaded as a bundled .ssf file. The PDA must be connected to the computer, and ActiveSync 

is used to establish the communication. In Pathfinder Office, select the file, and go through the 

differential correction utility. 

Figure 2. Differential correction wizard using Pathfinder Office. Note that the last line verifies 

that H-Star quality data was collected in the field. 

Select Automatic Standard Carrier and Code Processing radio button in the next dialog box, then 

proceed through verifying processing parameters when clicking ‘Next’ again. 
77

Selections in the next dialog box should be as indicated in the screen capture below: 

Figure 3. Select Use reference position from base providers 

Using reference position from base providers ensures that all data will be in WGS 84 reference 

frame (or datum). Trimble maintains a database in which all station locations are based on 

WGS84 (or ITRF). Selecting as above accesses that database rather than using the data directly 

from base files. Also click on ‘Confirm data and position before processing’ radio button. This 

will check for any discrepancies between the base provider file in the Trimble software with the 

actual base station location information. 

There are three relevant files in gathering information on each CORS station, and are accessible 

from the NGS website (http://www.ngs.noaa.gov/CORS/). These are the data sheet, log file and 

RINEX2 file. It is advisable to manually verify the location of the base station by downloading 

the data sheet from NGS. 

The following example is for Wisconsin Point 1 (WIS 1) 

RN1701 

*********************************************************************** 

 RN1701 DESIGNATION - ENTRY 

 RN1701 PID - RN1701 

 RN1701 STATE/COUNTY- WI/DOUGLAS 

 RN1701 USGS QUAD - SUPERIOR (1994) 

 RN1701 

 RN1701 *CURRENT SURVEY CONTROL 

 RN1701 ___________________________________________________________________ 

 RN1701* NAD 83(1997)- 46 42 16.09557(N) 092 01 01.14242(W) ADJUSTED 

 RN1701* NAVD 88 - 184.850 (meters) 606.46 (feet) ADJUSTED 

 RN1701 ___________________________________________________________________ 

This example indicates that the coordinate data provided is in NAD83, not WGS84. In this case, 

using location data directly from this CORS station will mix NAD83 data with the other WGS84 

station data. This would introduce significant error into the ‘corrected’ positions. Selecting 
78

Reference Position from Base Providers, (see Figure 3), will ensure that the correction from this 

station is done using WGS84. 

The log file provides additional information, for example, the Point of Contact agency 

information. In the case of Wisconsin Point 1, the U.S. Coast Guard is the agency, and this 

agency typically outputs correction data in NAD83 rather than WGS84. 

11. On-Site, Point of Contact Agency Information 

 Agency : U.S. Coast Guard 

 Preferred Abbreviation : (A10) 

 Mailing Address : (multiple lines) 

 Primary Contact 

 Contact Name : Supervisor, Shore Support (DGPS) C2CEN 

 Telephone (primary) : 757-686-4064 

 Telephone (secondary) : 

 Fax : 

 E-mail : 

 Secondary Contact 

 Contact Name : 

 Telephone (primary) : 

 Telephone (secondary) : 

 Fax : 

 E-mail : 

The RINEX2 file has information on observation frequency (1 sec, 5 sec, 30 sec), and whether 

single or dual frequency (L1 & L2) data is collected. Dual frequency data collection is required 

in order to post process HStar data. 

4-character ID : wis1 (# = 49871S001) 

Receiver type : ASHTECH Z-XII3 (# = 05486) (fw = RD00) 

Antenna type : ASH700829.3 SNOW (# = 11940) 

Time of start of window : 2007 Jan 1 00:00:00.000 

Time of end of window : 2007 Jan 1 23:59:30.151 

Time line window length : 23.99 hour(s), ticked every 3.0 hour(s) 

 antenna WGS 84 (xyz) : -154015.0664 -4379030.8537 4619413.5969 (m) 

 antenna WGS 84 (geo) : N 46 deg 42' 16.77" W 92 deg 00' 51.56" 

 antenna WGS 84 (geo) : 46.704659 deg 267.985678 deg 

 WGS 84 height : 140.5613 m 

|qc - header| position : 82 m 

Observation interval : 30.0000 seconds 

Total satellites w/ obs : 31 

NAVSTAR GPS SVs w/o OBS : 32 

NAVSTAR GPS SVs w/o NAV : 

Rx tracking capability : 12 SVs 

Poss. # of obs epochs : 2880 

Epochs w/ observations : 2880 

Possible obs > 0.0 deg: 32366 

Possible obs > 5.0 deg: 28409 

Complete obs > 5.0 deg: 24439 

 Deleted obs > 5.0 deg: 35 

Moving average MP1 : 0.580744 m 

Moving average MP2 : 0.614103 m 

Points in MP moving avg : 50 
79

No. of Rx clock offsets : 151 

Total Rx clock drift : +151.000000 ms 

Rate of Rx clock drift : +6.294 ms/hr 

Avg time between resets : 9.533 minute(s) 

Freq no. and timecode : 2 9857 ffffff 

Report gap > than : 10.00 minute(s) 

epochs w/ msec clk slip : 0 

other msec mp events : 1 (: 41) {expect ~= 1:50} 

IOD signifying a slip : >400.0 cm/minute 

IOD slips < 5.0 deg* : 0 

IOD slips > 5.0 deg : 15 

IOD or MP slips < 5.0*: 0 

IOD or MP slips > 5.0 : 16 

 * or unknown elevation 

 first epoch last epoch hrs dt #expt #have % mp1 mp2 

o/slps 

SUM 07 1 1 00:00 07 1 1 23:59 23.99 30 28409 24439 86 0.58 0.61 

1527 

Processing parameters are: 

Receiver tracking capability : 12 SVs 

Maximum ionospheric rate (L1) : 400.00 cm/min 

Report data gap greater than : 10.00 min 

Expected rms level of P1 multipath : 50.00 cm 

Expected rms level of P2 multipath : 65.00 cm 

Multipath slip sigma threshold : 4.00 cm 

% increase in MP rms for C/A | A/S : 100.00 % 

Points in MP moving averages : 50 

Minimum signal to noise for L1 : 0 

Minimum signal to noise for L2 : 0 

Elevation mask (cutoff) : 5.00 degrees 

Elevation comparison threshold : 25.00 degrees 

Orbit path spline fit sample time : 10 min 

SVs w/ code data for position try : 5 

Width of ASCII summary plot : 72 

Data indicators on summary plot : yes 

Do ionospheric observable : yes 

Do ionospheric derivative : yes 

Do high-pass ionosphere observable : no 

Do multipath observables : yes 

Do 1-ms receiver clock slips : yes 

Tolerance for 1-ms clock slips : 1.00e-02 ms 

Do receiver LLI slips : yes 

Do plot file(s) : no 

After viewing information on the stations, select the best group of available stations: 

The example shown below is for GRPO. All CORS (Continuously Operating Reference Stations) 

must be dual frequency carriers in order to process H-Star data. 
80

Figure 3. Selecting the available base stations 

Select stations based on the following criteria: 

Closest distance to GPS data collected 

Must be Dual Frequency Carrier if using H-Star data 

High ‘Integrity Index’ 

Data collection frequency preferably 1 second or 5 second rather than 30 second 

Once differential correction is complete, export the data using the Export utility in Pathfinder 

Office. Set the output coordinate system to UTM NAD83, zone 15 or 16, CORS 96 (see Table 1, 

below). If the user does not have Pathfinder office, there is a free data transfer utility from 

Trimble that will provide for bundling the raw export files from the PDA into an ssf file, which 

can then be sent to the GLKN Data Manager or GIS Specialist to complete the post-processing. 

(Go to: http://www.trimble.com/datatransfer.shtml) 
81

Figure 4. Export to shapefile utility in Pathfinder Office 

Additionally, check that all pertinent data attributes are selected for export within the shapefile. 

This is done from the Properties tab on the Export page. 
82

Figure 5. Ensure that all boxes above are checked when exporting 

corrected GPS files 

Park UTM Zone Datum 

APIS 15 NAD83 (CORS 96)

GRPO 16 NAD83 (CORS 96)

INDU 16 NAD83 (CORS 96)

ISRO 16 NAD83 (CORS 96)

MISS 15 NAD83 (CORS 96)

PIRO 16 NAD83 (CORS 96)

SACN 15 NAD83 (CORS 96)

SLBE 16 NAD83 (CORS 96)

VOYA 15 NAD83 (CORS 96)

 Table 1. UTM Zones for the nine GLKN parks 

Output from Pathfinder Office is in shapefile format with default name, but no projection 

information is created. The next step is to rename the file using standard naming conventions 

outlined in the GLKN Data Management Plan, open in ArcGIS, and use ArcToolbox to define 

the correct projection. Finally, archive the output files from Pathfinder Office. 
83

Plot Establishment 

STANDARD OPERATING PROCEDURE #5 

Version 1.0 (7/1/08) 

Suzanne Sanders 

NPS – Great Lakes Network 

Sarah Johnson 

University of Wisconsin - Madison 

Revision History Log: 

Previous 

Version # 

Revision 

Date 

Author (with 

title and 

affiliation) 

Location in Document 

and Concise 

Description of Revision 

Reason for Change New 

Version # 

 

Add rows as needed for each change or set of changes tied to an updated version number 
84

5.0 Introduction 

Potential sampling sites will be selected prior to fieldwork and will be limited to areas where at 

least 10% cover by tree species occurs (or can occur). These potential sites will be generated by a 

computer following a generalized random-tessellation stratified design (GRTS, Stevens and 

Olsen 2004). This design ensures that the points are both spatially balanced throughout the 

sampling frame while also randomly chosen. 

Prior to field work, all potential sites will be viewed using geographic information systems by 

overlaying the location on aerial photography or satellite imagery. This will eliminate many sites 

beforehand that are not in appropriate habitat (e.g., those potential sites on beaver ponds, parking 

lots, or other non-forested habitats). The remaining sites will be presented to the park’s natural 

resource manager for final comments. 

All potential sites that are not eliminated by the process described above will by visited for plot 

establishment and data collection. While at the site, the sampling crew will perform a final check 

with additional criteria as outlined below. 

5.1 Criteria for Accepting or Rejecting Sampling Sites in the Field 

Once a site is located in the field, it must be checked to ensure that the plot falls within 

acceptable sampling criteria. 

1) Within each transect or subplot, the ground must be at least 30% vegetated or potentially 

vegetated. For example, in many northern Network parks, there are large expanses of exposed 

bedrock. If any one transect or subplot is covered by >30% bedrock, the site must either be 

moved (see below) or abandoned as this cover type will never be vegetation. In contrast, areas 

that have recently been burned or experienced a blowdown will become vegetated over time. 

These areas will be monitored. In addition, the plot must not have an obstacle (stream, trail) that 

runs the length of a transect. If this is the case the plot must be moved to avoid this situation. A 

transect may cross, however, one of these obstacles. 

2) There must not be any obstacles that present a safety hazard to the crew. This includes, but is 

not limited to, steep cliffs within the transects or subplots and hornet/wasps nests directly where 

plot marking will be placed. 

3) The plot must be located within fully owned National Park Service lands. This latter point is 

especially relevant at Saint Croix NSR, where a large number of irregularly shaped, privately 

held inholdings are interlaced with the federally owned land on which monitoring will be 

conducted. 

If the three criteria outlined above are not met, a plot may be moved. Movement of a plot must 

only be to the closest location meeting the criteria outlined above (Figure 1). No plot will be 

moved greater than 100 meters from the original location. If the plot cannot be moved within 100 

m and meet the desired criteria, the plot will not be established and the site will not be 

monitored. 
85

 

Figure 1. The red lines in the image on the left show the irregular shape of parcels of federally 

owned land at Saint Croix NSR. The original plot coordinates are denoted by the red dot, with 

the yellow rectangle in the image on the left denoting the original plot location. This plot will be 

moved 49 m due west to fit it entirely into the federally owned parcel. 

5.2 Plot Establishment 

Sampling at Great Lakes Network parks will use one of two plot types: either the hybrid style 

plot or the FIA style plot (see protocol narrative). All plots at any given park, however, will be of 

the same type. Additionally, we will be using true north as the reference from which all bearings 

will be taken. At all parks and years where magnetic north differs from true north by 2° or more, 

we will account for declination. 

5.2.1. Hybrid Plot Establishment 

Hybrid plots (Johnson et al. 2006) consist of three parallel 50 m transects, spaced 50 meters apart 

(Figure 2). The GPS coordinates of the site will be the northwest corner of the plot, 

corresponding to point 1 on transect 1. 

Transect 1 will be laid out using a compass along with both a tape measure and electronic sonar 

distance measurers. Sonar measurers allow one to easily get a straight path through shrubs and 

brush. Pin flags will be placed along this path. A tape measure will then be pulled out along the 

path of the pin flags. This tape measure is needed to accurately place the location of the 
86

herbaceous quadrats (Figure 2, and SOP 6). Specific instructions on this process are presented 

below. 

Typically Person 1 will walk out a distance at approximately 90°, while Person 2 (at Point 1) 

then directs this person exactly to the correct angle (90°) using a compass. The distance that 

Person 1 walks will depend on the degree of brushyness. In more open plots, Person 1 may walk 

as far as about 25 meters, while in brushy plots, Person 1 may walk out less than 10 meters. A 

pin flag will be placed in the ground at the correct angle (90°) and the horizontal distance will be 

recorded. Person 2 will then pull the tape to the pin flag, ensuring that it is completely straight, 

and that it is as close to the ground as possible. Person 2 will then walk out again and this 

process is repeated until the entire 50 meters is laid out and Point 2 is established. Point 3 is 

established by following this same procedure from Point 1, with two exceptions. The angle 

followed for establishing Point 3 is 180°, instead of 90°. In addition, there will be no tape 

measure laid out. Pin flags will still be placed between Points 1 and 3 to aid in orientation when 

working in the plot. Following the establishment of Point 3, Transect 2 and Point 4 will be 

established in the same manner as Transect 1 and Point 2. Point 5 will be established in the same 

manner as Point 3. Finally, Transect 3 and Point 6 will be established in the same manner as 

Transect 1 and Point 2. It is important to note that all transect will be based on horizontal 

distances. GPS coordinates of all six points will be collected. 

At the completion of sampling, all pin flags will be pulled up. 
87

Figure 2. Diagram of the hybrid sampling plot.
88

5.2.2 FIA Plot Establishment 

The standard FIA Phase 2/3 plot is composed of four 7.32 m (24.0 ft) radius subplots (Figure 3) 

in which the species and diameter at breast height (DBH) of all trees are recorded. Within each 

subplot (Figure 4) are three transects at 30 ̊, and 150 ̊, and 270 ̊, along which quadrats are placed 

to record groundlayer data. The plot layout used by GLKN differs from the standard FIA phase 3 

plot in that groundlayer data will be collected at three quadrats along each transect, rather than 

one quadrat. In addition, GLKN will not be limiting sapling data measurements (trees ≥ 2.6 cm 

DBH and < 12.7 cm DBH) to the microplot as FIA does, because sapling data will be collected 

over the entire subplot. 

The GPS coordinates of the plot will be the center of subplot 1. The center of each additional 

subplot will be located by using a compass and either a tape measure or sonar distance 

measurers. To mark the three transects of each subplot, a compass and tape measure will be used. 

The tape will be laid down so that the herbaceous quadrats can be placed in the correct location 

and coarse woody debris data can be collected. The end of each transect will be demarcated with 

rebar sunk into the ground. 

Figure 3. overview diagram of FIA Phase 2/3 plot showing four subplots. 
89

Figure 4. Detail of an FIA style subplot. The Forest Service FIA program records groundlayer 

data only at the most internal 1×1 m quadrat along each transect; the Great Lakes I&M Network 

will record groundlayer data at all three 1×1 m quadrats along each transect. Additionally, the 

FIA program records sampling data only within the microplot; the Great Lakes I&M Network 

will not limit sapling data to this area, but will record it over the entire subplot. 

5.3 Permanently Marking Plots 

For hybrid plots, each of the six points at the end of the transects will be marked; where the FIA 

style plot is used, the center of each subplot will be marked. The degree of permanent marking 

will vary by park. Within parks with designated wilderness areas, marking will be minimal, 

while permanent marking at other parks will be slightly more visible. No plots will be marked in 

such a manner as to be evident from roads, trails, or other heavily used public use areas. 

In wilderness areas, a 30 cm (1 ft) piece of rebar will be sunk nearly flush with the ground at 

marking points. In the event that there is bedrock or some other obstruction preventing it being 

placed adequately into the ground, we will first attempt to hammer the rebar in at an angle so that 

the top of the piece is in the correct position. In the event that this is not possible, the rebar will 

be placed a certain distance away from the actual point center and we will record the distance 

and angle from the rebar to the actual target location. 

At some parks, a greater degree of permanent marking will be permitted, which will increase the 

ability to relocate transects and quadrats, and ensure consistency with regard to which trees are 

within transects. Here we will sink a 60 cm (2 ft) piece of rebar 30 cm into the ground so that 30 

cm remain above it. The top of this piece will be fitted with an yellow plastic cap over the rebar, 
90

to aid visibility. Since rebar can come out of the ground by frost heaving as well as other means, 

and because it can be difficult to relocate a point if there is only one piece of marking, we will 

identify three reference trees surrounding the rebar. The distance and angle of these trees to the 

rebar will be recorded. Specifically, the angle will be recorded by standing in front of the tree 

and facing the rebar, or by backsighting to the tree when straddling the rebar. At parks where it is 

permitted, we will also place unique identification tags at the base of these trees. 

At all permanent marking locations, the location will be recorded using a mapping grade (submeter accuracy) Trimble GPS. Initial attempts will be made to record this location with the 

Trimble unit set to a precision of delusion of position (PDOP) ≤ 6. However, if readings cannot 

be obtained, the PDOP will be adjusted upward as needed. 

5.4 Plot Reestablishment During Subsequent Site Visits 

Plots and points will be relocated using a combination of GPS, a metal detector, and reference 

stakes. Because sites will be visited only once every six years, permanent markers and the 

reference points will be checked during each visit and reestablished as needed. 

5.4.1 Reestablishing Points 

Where reference stakes are used, an attempt will be made to relocate the rebar designating the 

start point and three reference stakes. Once located, the distances and angles from the reference 

points to the start point will be checked. The distances should be within 5 cm and 2 ̊ from the 

values recorded during the original establishment of the plot. If this criteria is not met, it will be 

left to the discretion of the field crew leader to reposition the markers. For example, if the 

distance and angle from two markers to the start point has not changed, but the third one has 

changed, the crew leader will reposition the third marker based on the original placement values 

from the start point. In contrast, if the distances and angles from all three marker points are off, 

the crew leader may reposition the start point from the three reference points. 

Where reference stakes are not used, we will attempt to relocate the rebar using a metal detector 

and GPS. If it cannot be relocated, we will attempt to locate adjacent points, from which we will 

extrapolate the lost point. 

5.4.2 Recording Locational Data 

Following reestablishment of permanent points, the GPS coordinates will again be recorded 

during each site visit. Because of limitations on the locational accuracy of current GPS units, it is 

probable that these coordinates will differ from the coordinates recorded at the time of plot 

establishment. Nonetheless, it is hoped that both the continued improvement of GPS units and 

repeated visitation to sites will refine the actual site coordinates. 

5.5 Literature Cited 

Johnson, S. E., E. L. Mudrak, and D. M. Waller. 2006. A comparison of sampling methodologies 

for long-term forest vegetation monitoring in the Great Lakes Network National Parks, 

National Park Service, Great Lakes Inventory and Monitoring Network Report: 

GLKN/2006/03. 
91

Stevens, D. L., and A. R. Olsen. 2004. Spatially balanced sampling of natural resources. Journal 

of the American Statistical Association 99:262-278. 

93

Field Methods and Data Collection 

STANDARD OPERATING PROCEDURE #6 

Version 1.0 (7/1/08) 

Suzanne Sanders 

NPS-Great Lakes Network 

Revision History Log: 

Previous 

Version # 

Revision 

Date 

Author (with 

title and 

affiliation) 

Location in Document 

and Concise 

Description of Revision 

Reason for Change New 

Version # 

 

Add rows as needed for each change or set of changes tied to an updated version number 
94

6.0 Introduction 

This standard operating procedure defines the criteria for collection of data in the field. It is intended to 

ensure consistency between observers and sampling events. In addition, this SOP clarifies potentially 

ambiguous scenarios, eliminating uncertainty with measurements and data collection. It is intended to be 

a reference source, taken into the field and consulted as needed. 

6.1 Whole plot data 

• Plot identification – Record the predetermined plot identifier and date. 

• GPS points – When using the Hybrid plot, record the GPS locations of the six transect ends. When 

using the FIA plot, record the GPS locations of each of the four subplot centers. For both plot types, 

record locations to the hundredth of a meter (centimeter), using UTM coordinates with the North 

American Datum of 1983 (NAD83). 

• Kotar/NVCS forest type – Record the forest type either by using the Kotar Classification guidelines 

or the choices within the National Vegetation Classification System. This will be used in poststratification analyses. 

• Photopoints – For the Hybrid plot, photos should be taken at each of the six transect ends, facing 

directly into the plot. Therefore, at points 1, 3, and 5, the photos should be taken facing due east (90 ̊) 

while at points 2, 4, and 6, the photos should be taken facing due west (270 ̊). For the FIA plot, one 

photo will be taken at each subplot center. At subplot 1 center, the photo will be taken directly 

overhead. At the other subplot centers, the photo will be taken facing directly toward subplot 1. 

Therefore, at subplot 2, the photo will be taken at 180 ̊, at subplot 3, the photo will be taken at 300 ̊, 

and at subplot 4, 60 ̊. Record the photo identification number from the camera for each of the points. 

6.2 Tree data 

Trees ≥ 2.5 cm (1 in) in diameter are sampled within the belt transects (Hybrid plot) or subplots (FIA plot). 

‘Tally trees’ are defined as all live and standing dead trees in the transects the first time a plot is 

established, and all trees that grow into the transects thereafter. Details of measuring standing dead trees 

are presented below. Trees are alive if they have any living parts (leaves, buds, cambium) at or above 

the point of diameter measurement. Trees that have been temporarily defoliated are still alive. Once 

tallied, dead trees over 2.5 cm (1 inch) in diameter are tracked until they no longer qualify as standing 

dead. Working around dead trees is a safety hazard - crews should exercise extreme caution! 

Trees that are deemed unsafe to measure should be estimated. 

 
95

Standing dead: 

To qualify as a standing dead tally tree, dead trees must be at least 2.5 cm (1 in) in diameter, have a bole 

which has an unbroken height of at least 1.37 m (4.5 ft), and lean less than 45 degrees from vertical as 

measured from the base of the tree to 1.37 m. See Figures 1-3 for examples. 

Note: The angle on bent or curved trees is determined by drawing an imaginary straight line from the top 

of the tree to the base. 

Figure 1. This tree would be tallied because it is at least 1.37 m in unbroken actual length and at least 2.5 

cm at 1.37 m.

Figure 2. This tree would not be tallied because it does not have an unbroken length of 1.37 m. 

1.37 m 

0.5 m 
96

Figure 3. Other examples of dead trees. The tree on the left would be tallied since is at least 2.5 

cm at 1.37 m and has at least 1.37 m in unbroken actual length. The two trees on the right would 

not since they are leaning at an angle > 45 ̊ from vertical. 

• Species – Record the 6 digit code (Table 1) of each tree ≥ 2.5 cm diameter breast height (DBH, 1.37 

m) in each of the three 6 m wide belt transects (Hybrid plot) or each of the four 7.32 m radius subplots 

(FIA plot). 

• Diameter – Unless one of the following special situations is encountered, measure DBH at 1.37 m 

above the ground line on the uphill side of the tree. Round each measurement down to the nearest 

tenth centimeter. For example, a reading of 12.78 cm is recorded as 12.7 cm. In many situations, 

irregularities in the bole (e.g., forking trees, bulges) will impede the circumference measurement. 

Refer to the special instructions, below, for taking measurements in these instances. 

• Live/dead – Record whether the tree is living or dead. 

• Damage – If any damage is evident, record both the damage agent and the damage severity. 

Examine the bark as well as any branches, leaves/needles, and buds that are accessible and/or 

observable from the ground. Table 2 lists the damage agents that will be recorded as part of this 

protocol, and shows both general agents (boldface) and specific agents (normal font). Five specific 

damage agents, denoted with a “*” in Table 2, are designated as core agents in the FIA program; as 

such, for consistency, the presence of these agents will be assessed first. For detailed information on 

identification, life cycles, and ecology of these diseases, please refer to Appendix A: Pest and 

Pathogen Field Guide. 

To quantify the degree of damage, use the criteria shown in the “severity rating” column of Table 3, 

below. Consider the following information about locations affected by damage when collecting 

damage data: 

• Roots – Above ground up to 30 cm (12 in) on bole. 

• Bole – Main stem(s) starting at 30 cm (12 in), including forks up to a 10-cm (4 in) top. (A fork is at 

least 1/3 diameter of the main stem, and occurs at an angle <45° in relation to the main stem. 

• Branch – All other woody material. Primary branch(s) occur at an angle >45° in relation to bole. 

• Foliage – All leaves, buds and shoots. 

1.37 m 

1.37 m 

1.37 m 
97

Table 1. Identification codes and specific gravity values for tree species in Great Lakes Network parks. 

Specific gravity values are from Woodall and Williams 2005. 

Species 

code Species Specific gravity 

 Species 

code Species Specific gravity 

ABIBAL Abies balsamea 0.34 LARLAR Larix laricina 0.49 

ACESPX Acer spp. 0.49 LIRTUL Liriodendron tulipifera 0.40 

ACENEG Acer negundo 0.44 MACPOM Maclura pomifera 0.76 

ACENIG Acer nigrum 0.52 MALSPX Malus spp. 0.61 

ACEPEN Acer pennsylvanicum 0.44 MORSPX Morus spp. 0.59 

ACERUB Acer rubrum 0.49 MORALB Morus alba 0.59 

ACESA1 Acer saccharinum 0.44 MORRUB Morus rubra 0.59 

ACESA2 Acer saccharum 0.56 NYSSYL Nyssa sylvatica 0.46 

ACESPI Acer spicatum 0.44 OSTVIR Ostrya virginiana 0.63 

AESSPX Aesculus spp. 0.33 PICSPX Picea spp. 0.38 

AESFLA Aesculus flava 0.33 PICGLA Picea glauca 0.37 

AESGLA Aesculus glabra 0.33 PICMAR Picea mariana 0.37 

AILALT Ailanthus altissima 0.37 PINBAN Pinus banksiana 0.40 

AMESPX Amelanchier spp. 0.33 PINRES Pinus resinosa 0.41 

AMEARB Amelanchier arborea 0.33 PINSTR Pinus strobus 0.34 

AMEHUM Amelanchier humilis 0.33 PINSYL Pinus sylvestris 0.41 

AMEINT Amelanchier interior 0.33 PLAOCC Platanus occidentalis 0.46 

AMELAE Amelanchier laevis 0.33 POPSPX Populus spp. 0.37 

ASITRI Asimina triloba 0.47 POPBAL Populus balsamifera 0.31 

BETSPX Betula spp. 0.48 POPDEL Populus deltoides 0.37 

BETALL Betula alleghaniensis 0.55 POPGRA Populus grandidentata 0.36 

BETLEN Betula lenta 0.60 POPTRE Populus tremuloides 0.37 

BETNIG Betula nigra 0.56 PRUSPX Prunus spp. 0.47 

BETPAP Betula papyrifera 0.48 PRUPEN Prunus pensylvanica 0.36 

CARCAR Carpinus caroliniana 0.58 PRUSER Prunus serotina 0.47 

CARSPX Carya spp. 0.62 PRUVIR Prunus virginiana 0.36 

CARCOR Carya cordiformis 0.60 QUESPX Quercus sp. 0.58 

CARGLA Carya glabra 0.66 QUEALB Quercus alba 0.60 

CARLAC Carya lacinosa 0.62 QUEBIC Quercus bicolor 0.64 

CAROVA Carya ovata 0.64 QUEMAC Quercus macrocarpa 0.58 

CARTOM Carya tomentosa 0.64 QUERUB Quercus rubra 0.56 

CASDEN Castanea dentata 0.40 QUEVEL Quercus velutina 0.56 

CELOCC Celtis occidentalis 0.49 ROBPSE Robinia pseudoacacia 0.66 

CERCAN Cercis canadensis 0.58 SALSPX Salix sp. 0.36 

CORSPX Cornus spp. 0.64 SASALB Sassafras albidum 0.42 

CORFLA Cornus florida 0.64 SORAME Sorbus americana 0.42 

CRASPX Crataegus spp. 0.62 SORDEC Sorbus decora 0.42 

FAGGRA Fagus grandifolia 0.56 THUOCC Thuja occidentalis 0.29 

FRASPX Fraxinus spp. 0.54 TILAME Tilia americana 0.32 

FRAAME Fraxinus americana 0.55 TSUCAN Tsuga canadensis 0.38 

FRANIG Fraxinus nigra 0.45 ULMSPX Ulmus sp. 0.50 

FRAPEN Fraxinus pennsylvanica 0.53 ULMAME Ulmus americana 0.46 

FRAQUA Fraxinus quadrangulata 0.53 ULMPUM Ulmus pumila 0.46 

GLETRI Gleditsia triacanthos 0.60 ULMRUB Ulmus rubra 0.48 

ILEOPA Ilex opaca 0.50 UNKHAR unknown hardwood 0.51 

JUGSPX Juglans spp. 0.51 UNKSOF unknown softwood 0.38 

JUGCIN Juglans cinerea 0.36 UNKXXX unknown 0.46 

JUGNIG Juglans nigra 0.51 
98

1.37m 

Diameter point 

1.07m 

 Pith intersection 

Figure 5. Forked 

between 30cm –

Special DBH situations: 

1. Forked tree: In order to qualify as a fork, the stem in question must be at least 1/3 the diameter of the 

main stem and must branch out from the main stem at an angle of 45 degrees or less. Forks originate at 

the point on the bole where the piths intersect. Forked trees are handled differently depending on whether 

the fork originates below 30 cm, between 30 cm and 1.37 m, or above 1.37 m. 

Note: Fork does not have to be live to be considered as a Fork. 

• Trees forked below 30 cm. Trees forked in this region are treated 

as distinctly separate trees (Figure 4). Distances are measured 

individually to the center of each stem where it splits from the 

stump (Figures 4 - 6). DBH is measured for each stem at 1.37 m 

above the ground. When stems originate from pith intersections 

below 30 cm, it is possible for some stems to be within the 

limiting distance of the belt transect or subplot, and others to be 

beyond the limiting distance. If stems originating from forks that 

occur below 30 cm fork again between 30 cm and 1.37 m (Figure 

7-E), the rules in the next paragraph apply. 

• Trees forked between 30 cm and 1.37 m. Trees forked in 

this region are also counted as separate trees (Figures 5, 

7 D-F). The DBH of each fork is measured at a point 1.07 

cm (3.5 feet) above the pith intersection. When forks 

originate from pith intersections between 30 cm and 1.37 

m, the limiting distance is the same for all forks--they are 

either all on, or all off the plot--and is determined by the 

central stump. 

• Multiple forks are possible if they all originate from approximately the same point on the main 

stem. In such cases, measure DBH on all stems at 1.07 m above the common pith intersection 

(Figure 7-F). 

Once a stem is tallied as a fork that originated from a pith intersection between 30 cm and 1.37 

m, do not recognize any additional forks that may occur on that stem. Measure the diameter of 

such stems just below the base of stem separation as shown in Figure 7-E (i.e., do not move the 

point of diameter the entire 1.07 m above the first fork). 

• Trees forked at or above 1.37 m. Trees forked in this 

region count as one single tree (Figure 6). If a fork 

occurs at or immediately above 1.37 m measure 

diameter below the fork just beneath any swelling that 

would inflate DBH.

1.37m 1.37m 

Figure 4. 

Forked below 

1.37m

Pith 

intersection

Figure 6. One tree.

Diameter point
99

Figure 7. Summary of where to measure DBH, distance, and azimuth on forked trees. 

1.37m 

1.07m 

30cm 

1. Trees fork below 30cm 

2. Trees fork above 30cm 

1.37m 

30cm 

1.07m 1.07m 1.07m 
100

2. Stump Sprouts: Stump sprouts originate between ground level and 1.37 m on the boles of trees that 

have died or been cut. Stump sprouts are handled the same as forked trees, with the exception that 

stump sprouts are not required to be 1/3 the diameter of the dead bole. Stump sprouts originating below 

30 cm are measured at 1.37 m from ground line. Stump sprouts originating between 30 cm and 1.37 m 

are measured at 1.07 m above their point of occurrence. As with forks, rules for measuring distance 

depend on whether the sprouts originate above or below 30 cm. For multi-stemmed woodland species, 

treat all new sprouts as part of the same new tree. 

3. Tree with butt-swell or bottleneck: Measure these trees 46 cm 

above the end of the swell or bottleneck if the swell or bottleneck 

extends 91 cm or more above the ground (Figure 8). 

4. Tree with irregularities at DBH: On trees with swellings (Figure 9), bumps, depressions, and branches 

(Figure 10) at DBH, diameter will be measured immediately above the irregularity at the place it ceases to 

affect normal stem form. 

NC Note: If a normal diameter can not be obtained at or above 1.37 m, it is valid to measure the diameter 

below 1.37 cm. 

5. Tree on slope: Measure diameter at 1.37 m from the ground along 

the bole on the uphill side of the tree (Figure 11). 

1.37

Figure 10. Tree with branch. 

1.37m 

Diameter 

point 

Figure 9. Tree with swelling. 

Diameter 

point

1.37m

Figure 11. Tree on a slope. 

91cm or 

more 

Diameter point

46cm 

Figure 8. Bottleneck tree. 
101

6. Leaning tree: Measure diameter at 1.37 cm from the ground along the bole. The 1.37 m distance is 

measured along the underside face of the bole (Figure 12). 

7. Independent trees that grow together: If two or more independent stems have grown together at or 

above the point of DBH, continue to treat them as separate trees. Estimate the diameter of each. 

8. Missing wood or bark. Do not reconstruct the DBH of a tree that is 

missing wood or bark or at the point of measurement. Record the 

diameter, to the nearest 0.1 cm, of the wood and bark that is still attached 

to the tree (Figure 13). If a tree has a localized abnormality (gouge, 

depression, etc.) at the point of point of DBH, apply the procedure 

described for trees with irregularities at DBH (Figure 9 and 10). 

9. Live windthrown tree: Measure from the top of the root collar along 

the length to 1.37 m (Figure 14). 

10. Down live tree with tree-form branches growing vertical from main bole. When a down live tree, 

touching the ground, has vertical (less than 45 degrees from vertical) tree-like branches coming off the 

main bole, first determine whether or not the pith of the main bole (averaged along the first log of the tree) 

is above or below the duff layer. 

 

• If the pith of the main bole is above the duff layer, use the same forking rules specified for a forked 

tree, and take all measurements accordingly (Figure 15). 

 

• If the pith intersection of the main down bole and vertical tree-like branch occurs below 1.37 m from 

the stump along the main bole, treat that branch as a separate tree, and measure DBH 1.07 cm above 

the pith intersection for both the main bole and the tree-like branch. 

Figure 12. Leaning tree. 

DB

1.37

1.37m 

Root Collar 

Figure 14. Tree on the ground. 

Figure 13. Tree with part of stem 

missing. 

1.37m 
102

 

 

 

 

• If the intersection between the main down bole and the tree-like branch occurs beyond the 1.37 cm 

point from the stump along the main bole, treat that branch as part of the main down bole. 

• If the pith of main tree bole is below the duff layer, ignore the main bole, and treat each tree-like 

branch as a separate tree; take DBH from the ground, not necessarily from the top of the down bole 

(Figure 16). However, if the top of the main tree bole curves out of the ground towards a vertical angle, 

treat that portion of that top 

as an individual tree 

originating where the pith 

leaves the duff layer. 

 

Figure 16. Down tree below duff.

1.37 1.37

1.07m 

1.07m 

Figure 15. Down tree above duff.
103

12. Tree with curved bole (pistol butt tree): Measure along the bole on the uphill side (upper surface) of 

the tree (Figure 17). 

Figure 17. Tree with curved 

bole (pistol butt tree). 

1.37m 
104

Table 2. Damage agents to be scouted as part of the General Vegetation Monitoring Protocol. General agents are 

shown in boldface; specific agents in standard font. Asterisks indicate core damage agents as designated in the FIA 

program. 

11000 – Bark beetles

11010 – eastern larch beetle (Dendroctonus simplex) 

*11012 – red turpentine beetle (Dendroctonus 

valens) 

11029 – pine engraver (Ips pini) 

12000 – Defoliators

12031 – scarlet oak sawfly (Caliroa 

quercuscoccineae) 

12037 – large aspen tortrix (Choristoneura 

conflictana) 

12038 – spruce budworm (Choristoneura fumiferana) 

12039 – jackpine budworm (Choristoneura pinus)

12041 – larch casebearer (Coleophora laricella) 

12058 – yellownecked caterpillar (Datana ministra) 

12057 – walnut caterpillar (Datana integerrima) 

12058 – yellownecked caterpillar (Datana ministra) 

12061 – introduced pine sawfly (Diprion similis) 

12082 – fall webworm (Hyphantria cunea) 

12088 – aspen blotch miner (Lithocolletis 

tremuloidiella) 

12089 – gypsy moth (Lymantria dispar) 

12093 – eastern tent caterpillar (Malacosoma 

americanum) 

12096 – forest tent caterpillar (Malacosoma disstria) 

12136 – yellowheaded spruce sawfly (Pikonema 

alaskensis) 

12141 – elm leaf beetle (Pyrrhalta luteola) 

12153 – basswood thrips (Thrips calcaratus) 

14000 – Sucking insects

*14003 – balsam wooly adelgid (Adelges piceae) 

*14014 – hemlock wooly adelgid (Adelges tsugae) 

14015 – pine spittlebug (Aphrophora parabella) 

14016 – Saratoga spittlebug (Aphrophora 

saratogensis) 

15000 – Boring insects

15005 – two-lined chestnut borer (Agrilus bilineatus) 

15022 – Zimmerman pine moth (Dioryctria 

zimmermani) 

15082 – Asian long-horned beetle (Anoplophora 

glabripennis) 

15087 – emerald ash borer (Agrilus planipenis) 

15088 – hemlock borer (Melanophila fulvoguttata) 

15089 – Sirex woodwasp (Sirex noctilio) 

15090 – red oak borer (Enaphalodes rufulus) 

21000 – root/butt diseases

*21001 – Armillaria root disease (Armillaria spp.) 

*21010 – annosus root disease (Heterobasidion 

annosum) 

21028 – sudden oak death (Phytophthora ramorum) 

22000 – stem decays/cankers

22041 – beech bark disease (Neonectria faginata and

N. ditissima)

24000 – Decline complexes/dieback/wilts 

24022 – Dutch elm disease (Ophiostoma ulmi and O. 

novo-ulmi)

24004 – ash decline/yellows (multiple)

25000 – Foliage disease

25025 – anthracnose (Gnomonia spp.) 

25057 – siricoccus tip blight (Sirococcus conigenus) 

25058 – diplodia shoot blight (Diplodia pinea) 

26000 – Stem rust

26001 – white pine blister rust (Cronartium ribicola)

30000 – Fire 

41000 – Wild animals 

50000 – Abiotic damage 

50001 – air pollutants

50003 – drought

50004 – flooding/high water

50005 – frost

50008 – lightening

50009 – nutrient imbalances 

50011 – snow/ice 

50013 – wind/tornado 

70000 – Human activities

90000 – Unknown

90001 – broken top

90002 – dead top

90051 – canker/gall

90052 – open wound

90053 – resinosis 

90054 – broken 

90055 – damaged or discolored foliage 
105

Table 3. Severity ratings to describe tree damage. 

Agent Location Affected Severity Ratings 

Bark beetles 11000 Bole, Branches 

001 - Successful current attack 

002 - Previous year's successful attack 

003 - Top kill 

Defoliators 12000 Foliage Percent of foliage affected 

Sucking insects 14000 Bole, Branches, Foliage 

Percent of the circumference of a running 

3'-ft section of bole; percent of branches 

(crown volume) affected; percent of foliage 

affected. 

Wood borers 15000 Bole, Branches 

001 - Successful current attack 

002 - Previous year's successful attack 

003 - Top kill 

Root/butt diseases 21000 Roots 

001 – Live host tree within 30 ft of an 

infected tree 

002 – Live host tree with signs of root 

disease but no crown symptoms 

003 - Live host tree with signs of root 

disease also with crown symptoms 

Cankers (non-rust) 22000 Bole, Branches 

Percent of the circumference of a running 3-

ft section of bole; percent of branches 

(crown volume) affected. 

Decline complexes 24000 Any None 

Foliage diseases 25000 Foliage Percent of foliage affected 

Stem rusts 26000 Bole, Branches 

Percent of the circumference of a running 3-

ft section of bole; percent of branches 

(crown volume) affected. 

Fire 30000 Bole, Branches, Foliage 

Percent of the circumference of a running 3-

ft section of bole; percent of branches 

(crown volume) affected; percent of foliage 

affected. 

Wild animals 41000 Bole, Branches 

Percent of the circumference of a running 3-

ft section of bole; percent of branches 

(crown volume) affected. 

Abiotic 50000 Bole, Branches, Foliage 

Percent of the circumference of a running 3-

ft section of bole; percent of branches 

(crown volume) affected; percent of foliage 

affected. 

Human activities 70000 Bole, Branches, Foliage 

Percent of the circumference of a running 3-

ft section of bole; percent of branches 

(crown volume) affected; percent of foliage 

affected. 

Unknown/other 90000 Any None 
106

6.3 Coarse woody materials 

Data on coarse woody material will be collected along each of the three parallel 50 m horizontal distance 

transects (Hybrid method) or the three 7.32 m horizontal distance radii of each subplot (FIA method) 

using the line intercept method. Tally rules follow, below. For all pieces of coarse woody material tallied, 

the parameters recorded are: species, diameter at line intercept, small end diameter, large end diameter, 

length, and decay class. 

• Species – Record the 6 digit code (Table 1) of each piece of coarse woody material (≥ 0.91 m [3 ft] 

long and ≥ 7.6 cm [3 in] diameter) along the transect. 

• Diameter at line intercept – Record the diameter, in centimeters, at the point of intercept. This 

diameter should be taken perpendicular to the length of the log, regardless of the orientation of the 

piece to the transect line. Record to the tenths of centimeters, rounding down, rather than to the 

nearest tenth. 

• Small end diameter – Record the diameter, in centimeters, at the piece’s small end. This diameter 

will occur either at (1) the actual end of the piece, if the end has a diameter ≥ 7.6 cm, or (2) at the 

point where the piece tapers down to 7.6 cm in diameter. If the end is splintered or decomposing 

(sloughing off), measure the diameter at the point where it best represents the overall log volume. 

Record to the tenths of centimeters, rounding down, rather than to the nearest tenth. 

• Large end diameter – Record the diameter, in centimeters, at the piece’s large end. The large end 

will occur either at a broken end, a fracture, or at the root collar. If the end is splintered or 

decomposing (sloughing off), measure the diameter at the point where it best represents the overall 

log volume. Record to the tenths of centimeters, rounding down, rather than to the nearest tenth. 

• Length – Record the length of the piece that lies between the piece’s recorded diameter at small end 

and diameter at large end. For curved logs, measure along the curve. Record in meters and 

centimeters. For example, a log that is three meters and 54 centimeters would be recorded as 3.54. 

• Decay class – Record the decay class of the piece using the rules outlined in Table 4. Because 

decay conditions may vary along a piece, record the decay class that predominates. 
107

Table 1. Distinguishing characteristics of the five decay classes (From Woodall and Williams, 2005). 

Decay class Structural 

integrity 

Texture of 

rotten portions 

Color of wood Invading roots Branches and 

twigs 

1 sound, freshly 

fallen, intact 

logs 

intact, no rot; 

conks of stem 

decay absent 

original color absent if branches are 

present, fine 

twigs are still 

attached and 

have tight bark 

2 sound mostly intact; 

sapwood partly 

soft (starting to 

decay) but can’t 

be pulled apart 

by hand 

original color absent if branches are 

present, many 

fine twigs are 

gone and 

remaining fine 

twigs have 

peeling bark 

3 heartwood 

sound; piece 

supports its own 

weight 

hard, large 

pieces; sapwood 

can be pulled 

apart by hand or 

sapwood absent 

reddish brown 

or original color 

sapwood only branch stubs 

will not pull out 

4 heartwood 

rotten; piece 

does not support 

its own weight, 

but maintains its 

shape 

soft, small 

blocky pieces; a 

metal pin can be 

pushed into 

heartwood 

reddish or light 

brown 

throughout branch stubs 

pull out 

5 none, piece no 

longer maintains 

its shape, it 

spreads out on 

the ground 

soft; powdery 

when dry 

red-brown to 

dark brown 

throughout branch stubs 

and pitch 

pockets have 

usually rotted 

down 
108

Tally Rules for Coarse Woody Debris Sampling 

1. Tally only those trees whose central longitudinal axes intersect the transect line (Figure 18). 

Figure 18. Do not tally any CWD piece whose central longitudinal axis does not cross the transect line. 

2. Tally dead trees and stumps that are leaning ≥ 45 ̊ from vertical. Do not tally live trees or standing dead 

trees and stumps that are still upright and leaning < 45 ̊ from vertical. See section 6.2, above. 

3. The minimum length of any tally piece is 0.91 m (3 ft). 

4. The decay class of the piece determines whether or not the piece is tallied. 

For decay classes 1 to 4: tally a piece if it is ≥ 7.6 cm in diameter at the point of intersection with the 

transect. The piece must be ≥ 0.91 m (3 ft) in length and ≥ 7.6 cm (3 in) in diameter along that length. If 

the intersect diameter is close to 3.0 inches, measure the diameter to the nearest 0.1 cm to determine if 

the piece qualifies. 

For decay class 5: tally a piece if it is ≥ 12.7 cm (5.0 in) in diameter at the point of intersection with the 

transect. The piece must be ≥ 0.91 m (3 ft) in length and ≥ 12.7 cm (3 in) in diameter along that length. 

Only pieces that still have some shape and log form are tallied – humps of decomposed wood that are 

becoming part of the duff layer are not tallied. 

5. Tally pieces created by natural causes or by human activities such as cutting only if not systematically 

machine-piled. 

6. Tally a piece only if the point of intersection occurs above the ground. If one end of a piece is buried in 

the litter, duff, or mineral soil, the piece ends at the point where it is no longer visible. Measure the 

diameter and length at this point. 

7. If the central longitudinal axis of a piece is intersected more than once on a transect line or if it is 

intersected by two transect lines, tally the piece each time it is intersected (uncommon situation, see 

Figure 19). 
109

Figure 19. If the central longitudinal axis crosses a transect twice, then tally the piece twice. 

8. Tally a piece only once if the subplot center falls directly on the central longitudinal axis of the piece. 

Tally the piece on the 30 ̊ transect. 

9. If a piece is fractured across its diameter or length, and would pull apart at the fracture if pulled from 

either end or sides, treat it as two separate pieces. If judged that it would not pull apart, tally as ne piece. 

Tally only the piece intersected by the transect line. 

10. Do not tally a piece if it intersects the transect on the root side of the root collar. Do not tally roots. 

11. When the transect crosses a forked down tree bole or large branch connected to a down tree, tally 

each qualifying piece separately. To be tallied, each individual piece must meet the minimum diameter 

and length requirements. 

12. In the case of forked trees, consider the “main bole” to be the piece with the largest diameter at the 

fork. Variables for this fork, such as ‘decay class’ and ‘total length” should pertain to the entire main bole. 

For smaller forks or branches connected to the main bole (even if the main bole is not a tally piece), 

variables pertain only to that portion of the piece up to the point where it attaches to the main bole (see 

Figure 20). 
110

Figure 20. CWD tally rules for forked trees. 

6.4 Tree walkthrough 

Once trees in all of the transects have been tallied, perform a 20 minute time delimited walkthrough of the 

entire plot (50 meters × 100 meters) to determine the complete list of tree species that are present in 

each plot. It is possible that there are species that will be recorded here, but not in the transects. Record 

each species only once. When it is determined that all species have been identified, estimate the 

percentage of cover by canopy species (as likely seen from above) for the entire plot. Make sure to 

include the percentage of gap in the canopy. Since percentage of cover is estimated based on the aerial 

view only, the collective value for all species plus the gap should equal 100%. Similarly, since there are 

species being recorded that are only in the subcanopy, these species will have percent cover values of 

0% 

6.5 Shrubs and saplings 

Record all shrub species present in each of the 2.82 m radius shrub circles. For each species present, 

also record the percentage cover. Since each shrub circle is 25 m2

, a 1m2 area will be 4% cover. 

Record the species of qualifying tree seedlings in each 1 × 1 groundlayer quadrat. Qualifying tree 

seedlings must be at least 15 cm (for conifers) and 30 cm (for hardwoods) and exhibit signs of second 

year growth, such as bud scars. Seedlings must also be less than 2.5 cm diameter at breast height. In 

addition, record the count of each species present. 
111

6.6 Groundlayer 

Herbs, tree saplings, shrubs, and browse are all assessed in the thirty 1 × 1 groundlayer quadrats. For 

herbaceous plants, record each species that is rooted in each groundlayer quadrat. 

Record the species of qualifying tree saplings in each 1 × 1 groundlayer quadrat. Qualifying tree saplings 

must be at least 15 cm (for conifers) and 30 cm (for hardwoods) and exhibit signs of second year growth, 

such as bud scars. Seedlings must also be less than 2.5 cm diameter at breast height. In addition, record 

the count of each species present. 

Record the number of apparent individuals for each species of qualifying shrubs in each 1 × 1 

groundlayer quadrat. Qualifying shrubs must exhibit signs of second year growth, such as bud scars. 

6.7 All species walkthrough 

Following the completion of all thirty groundlayer quadrats, perform a 20 minute time delimited 

walkthrough of the entire plot (50 meters × 100 meters) to determine the complete list of herb species that 

are present in each plot. It is possible that there are species that will be recorded here, but not in the 

transects. Record each species only once. 

6.8 Browse 

Indirect browse: 

Indirect measures of browse by assessing indicator species height will be made in each groundlayer 

quadrat. For each park, three preidentified herbaceous species will be examined in each quadrat by 

counting the total number of each, noting how many of these are unbrowsed and non-reproductive, how 

many are unbrowsed and reproductive, and how many are browsed. The maximum height for each 

species will be recorded (to the tenth of a centimeter) and it will be noted whether this height is a leaf, 

reproductive structure, or stem that has been clipped. 

Direct browse: 

Direct measures of browse will be made be looking at the three closest shrubs or saplings from the 

northwest corner of each herbaceous quadrant, that are between 0.20 m and 1.8 m in height. It will be 

noted whether these have been browsed. If there are fewer than three shrubs or saplings within a 2 meter 

radius from the herbaceous quadrat, direct browse will be assessed on those that are present. It is 

possible that there will be no shrubs or saplings within a two meter radius; in these instances, no direct 

browse will be assessed for those quadrats. 

6.9 Acknowledgments 

We are indebted to the U.S. Forest Service, North Central Region both for providing text files of the FIA 

field guide and allowing us to incorporate it into the GLKN field guide. 

113

Procedures for Dealing with Unknown Species 

STANDARD OPERATING PROCEDURE #7 

Version 1.0 (7/1/08) 

Sarah Johnson 

University of Wisconsin – Madison 

Suzanne Sanders 

NPS – Great Lakes Network 

Revision History Log: 

Previous 

Version # 

Revision 

Date 

Author (with 

title and 

affiliation) 

Location in Document 

and Concise 

Description of Revision 

Reason for Change New 

Version # 

 

Add rows as needed for each change or set of changes tied to an updated version number 
 

114

7.0 Introduction 

Occasions will likely arise when the field crew is unable to identify species onsite. This standard 

operating procedure presents criteria for collecting and preserving specimens when these 

situations arise. 

Before collection of any plants or insects from the field, attempts should first be made to identify 

it onsite using reference books. When this is not possible, the guidelines below should be 

followed. 

7.1. Collection of herbaceous plants 

No plants are to be collected from within the units where data are collected (i.e., the belt 

transects, subplots, groundlayer quadrats, and shrub circles). To collect herbaceous plants, an 

attempt must first be made to locate individuals of the questioned species outside of the outer 

boundary of the plot. There must be at least five individuals (hereafter, this also refers to ramets 

if the unknown species is, or appears to be, clonal) present for one to be removed. If ten 

individuals are present, two may be collected. No more than two individuals of a given species 

should be collected at any given plot. If no individuals can be located outside of the outer plot 

boundary, one or two may be collected from within the boundary, provided they are not within 

the six meter wide belt transects. The same criteria for collecting outlined above applies. 

Prior to removal of the individual plant, we will photograph it attempting to show characteristics 

that will facilitate identification at a later time. This includes taking multiple close-up 

photographs of the flowers, fruits, and leaves, as well as whole plant pictures in which the plant 

size is placed in context with that of other plants. 

Caution should be exercised when selecting which plant(s) to collect. While it may be tempting 

to collect the largest plant(s) meeting the criteria above, this practice should be avoided. Larger 

plants may be contributing proportionately greater to species recruitment than smaller plants. 

Collection of plants should tend toward smaller individuals, provided there is sufficient material 

present for identification. 

An attempt should be made to remove the root as well as the aboveground portion of the plant. 

Soil should be knocked free from the roots to the extent possible without damaging them. The 

specimen should be placed in a plastic bag that is subsequently labeled with a permanent marker. 

The specimen should be pressed as soon as possible after collection. Until it is pressed, the 

specimen should be kept in as cool and dark of a place as possible, and not be allowed to be 

crushed. 

If there are no plants of the given species outside of the area where data are collected, or if there 

are less than five individuals present, the voucher specimens will not be removed from the area. 

In these instances, we will rely entirely on the photographs and notes collected at the site. 

7.2 Collection of woody plants 

In the event that woody plant species cannot be identified in the field after consulting reference 

manuals, portions of these plants may be cut and removed from the field for later identification. 

When collecting plants, an attempt must first be made to locate individuals of the same species 
 

115

outside of the outer boundary of the plot. To collect a stem containing reproductive structures, 

assure that at least five reproductive stems must be present in total. This can include one plant 

with five reproductive stems, five plants, each with one reproductive stem, or some combination 

of the two. If at least 10 reproductive stems are present, then two may be collected for later 

identification. If leaves are not present on the reproductive stem, or if the judgment is made that 

additional stems with leaves may aid identification, up to two stems containing leaves may be 

collected, provided there are at least 10 present near the plot. 

As detailed above in the herbaceous section, collection of smaller specimens, rather than larger 

ones, should be a priority. The specimen should be pressed within 12 hours after collection. If 

the specimen cannot be pressed immediately after collection, it should be placed in a plastic bag. 

If there are no individuals present outside of the outer plot area, an attempt should then be made 

to sample within the plot area but in trees rooted outside of the six meter wide transects. The 

same rules stated above, on the number of stems required for collection, apply here. No 

collecting of stem material should be made within the belt transects. 

If there are not woody individuals of the questionable species outside of the sampling units, we 

will take photographs to show as much detail of the species as possible. 

Reproductive material (i.e., viable seeds, non-viable seed husks, decaying acorns) may be 

collected at the discretion of the field crew, however caution must be exercised so that only a 

small portion is collected. 

7.3 Collection of insects and insect sign 

An attempt will be made to identify any species of insect (native or exotic) that is adversely 

affecting tree health. If the insect species (or the ensuing disease) cannot readily be made in the 

field, specimens may be collected for later identification. This can include individuals in either 

the adult or larval stages, or eggs. All samples will be collected in vials but will not be preserved. 

In addition to direct collection of insects, pieces of insect sign may also be collected. This can 

include leaves or stems fed on by the insects, as well as other identifying material (pieces of tent, 

silk, cocoons, chrysalides, etc.). 

Photographs will be taken to aid in identification and to show the extent of an infestation as 

needed. 

7.4 Collection of fungal and lichen material 

An attempt will be made to identify any species of fungus or lichen that is adversely affecting 

tree health. If the fungus (or the ensuing disease) cannot readily be made in the field, specimens 

may be collected in vials for later identification. Photographs may also be taken to detail the 

disease. 
 

116

7.5. Identification 

We will take voucher specimens, collected material, and photographs to subject matter experts 

for the park in which we are working. Selected experts have been identified a priori and are 

listed in Table 1, although consultations will not be limited to those listed in this table. 
117

Table 1. List of local biologists for each Network park. In addition to those noted below, we will also rely on Joe 5266, jobrien@fs.fed.us) for plant pathology and Steve Katovich (651-649-5264, steven_katovich@fs.fed.us) for Park Name Affiliation Area of Expertise Phone Email APIS Jim Meeker Northland College botany 715-682-1808 jmeeker@no Andy Goyke Northland College entomology 715-682-1550 agoyke@norGRPO Al Harris Northern Bioscience botany and 

entomology 

807-344-7213 aharris@nort

INDU Julie Stumpf National Park Service botany 219-926-7561 x 

323 

julie_stumpf@

 Dan Mason National Park Service botany 219-926-7561 daniel_masonISRO Janet Marr Michigan Tech botany 906-337-5529 jkmarr@mtuMISS Eric Epstein Wisconsin DNR botany 608-267-5038 eric.epstein@PIRO Janet Marr Michigan Tech botany 906-337-5529 jkmarr@mtuSACN Eric Epstein Wisconsin DNR botany 608-267-5038 eric.epstein@ Emmet 

Judziewicz 

U.W. Stevens Point botany 715-346-4248 ejudziew@uw

 Todd Lanigan Wisconsin DNR (Eau 

Claire) 

forest pest specialist 715-839-1632 todd.lanigan@

 Shane Weber Wisconsin DNR 

(Spooner) 

forest entomologist 715-635-4156 shane.weber@

SLBE Janet Marr Michigan Tech botany 906-337-5529 jkmarr@mtuVOYA Al Harris Northern Bioscience botany and 

entomology 

807-344-7213 aharris@nort

119

Pests and Pathogens 

STANDARD OPERATING PROCEDURE #8 

Version 1.0 (7/1/08) 

Suzanne Sanders 

NPS – Great Lakes Network

Revision History Log: 

Previous 

Version # 

Revision 

Date 

Author (with 

title and 

affiliation) 

Location in Document 

and Concise 

Description of Revision 

Reason for Change New 

Version # 

 

Add rows as needed for each change or set of changes tied to an updated version number 
 

120

8.0 Introduction 

Pests and pathogens can have tremendous impacts on trees, shrubs, and herbaceous plants 

(Johnson and Lyon 1991; Sinclair and Lyon 2005). It is important to document and 

quantify outbreaks and invasions of key taxa of insects, fungi, nematodes, and other pests 

so that associations may be drawn between pest presence and/or abundance and longterm vegetation dynamics. This standard operating procedure (SOP) outlines the process 

for doing this. 

This Terrestrial Pest and Pathogen indicator of forest health will be monitored within the 

context of the General Vegetation Monitoring Protocol (Sanders et al. 2006); therefore 

there is no separate monitoring protocol. Instructions for both field methods and analysis 

techniques are contained or referenced within this SOP. 

Because the Network will be monitoring terrestrial vegetation at each park only once 

every six years, pests and pathogens will also be documented at this interval. We concede 

that it is ideal to monitor annually, and to quantify pest and pathogen abundance directly. 

Because time demands and personnel costs are prohibitive, however, we will monitor for 

pests and pathogens while at terrestrial vegetation plots. Further, we will qualify and 

quantify pest and pathogen activity by the type and degree of damage to vegetation, 

rather than by assessing abundance of the disease agents directly. This is detailed below. 

The NPS-GLKN pest and pathogen sampling design and analyses are designed to answer 

the following specific questions: 

• Which targeted pests and pathogens are present in terrestrial vegetation monitoring 

plots? We will search for selected pests and pathogens (Box 1) on trees that are 

included in the vegetation monitoring plots. 

• Within given habitat types, what are the pest and pathogen species collectively 

present? 

• To what extent are pests and pathogens damaging trees? We will calculate the 

percentage of trees infested, and note the severity of damage on the infested part(s). 

The answers to these questions provide direct information about the health of forests in 

Great Lakes Network Parks. Further, this information can be combined with information 

on community structure, succession, and other indices forest growth and will allow us to 

draw inferences on overall forest health. 
 

121

8.1 Field Methods 

As part of the General Vegetation Monitoring Protocol, all trees for which DBH and 

species are recorded will be assessed for damage. This will occur by examining the bark 

as well as any branches, leaves/needles, and buds that are accessible and/or observable 

from the ground. Box 1 lists the damage agents that will be recorded as part of this 

protocol, and shows both general agents (boldface) and specific agents (normal font). 

Five specific damage agents, denoted with a “*” in Box 1, are designated as core agents 

in the FIA program; as such, for consistency, the presence of these agents will be 

assessed first. All damage agents will be recorded under the fields, 

DAMAGE_AGENT_1 (for the more severe damage if more than one core agent is 

present) and DAMAGE_AGENT_2 (for the agent causing the less severe damage if more 

than one is present). For detailed information on identification, life cycles, and ecology of 

these diseases, please refer to Appendix A: Pest and Pathogen Field Guide. 

To quantify the degree of damage, use the criteria shown in the “severity rating” column 

of Table 1, below. This will be recorded under the fields 

DAMAGE_SEVERITY_AGENT_1 – 2 (corresponding to the DAMAGE_AGENT, 

above). Consider the following information about locations affected by damage when 

collecting damage data: 

• Roots – Above ground up to 12 inches on bole. 

• Bole – Main stem(s) starting at 12 inches, including forks up to a 4-inch top. 

(A fork is at least 1/3 diameter of the main stem, and occurs at an angle <45° 

in relation to the main stem. 

• Branch – All other woody material. Primary branch(s) occur at an angle >45° 

in relation to bole. 

• Foliage – All leaves, buds and shoots. 
 

122

Box 1. Damage agents to be scouted as part of the General Vegetation Monitoring 

Protocol. General agents are shown in boldface; specific agents in standard font. 

Asterisks indicate core damage agents as designated in the FIA program. 

11000 – Bark beetles 

 11010 – eastern larch beetle (Dendroctonus simplex) 

 *11012 – red turpentine beetle (Dendroctonus valens) 

 11029 – pine engraver (Ips pini) 

 11047 – hickory bark beetle (Scolytus quadrispinosus) 

12000 – Defoliators 

 12031 – scarlet oak sawfly (Caliroa quercuscoccineae) 

 12037 – large aspen tortrix (Choristoneura conflictana) 

 12038 – spruce budworm (Choristoneura fumiferana) 

 12039 - jackpine budworm (Choristoneura pinus) 

 12041 – larch casebearer (Coleophora laricella) 

 12057 – walnut caterpillar (Datana integerrima) 

 12058 – yellownecked caterpillar (Datana ministra) 

 12061 – introduced pine sawfly (Diprion similis) 

 12082 – fall webworm (Hyphantria cunea) 

 12088 – aspen blotch miner (Lithocolletis tremuloidiella) 

 12089 – gypsy moth (Lymantria dispar) 

 12093 – eastern tent caterpillar (Malacosoma americanum) 

 12096 – forest tent caterpillar (Malacosoma disstria) 

 12136 – yellowheaded spruce sawfly (Pikonema alaskensis) 

 12141 – elm leaf beetle (Pyrrhalta luteola) 

 12153 – basswood thrips (Thrips calcaratus) 

14000 – Sucking insects 

 *14003 – balsam wooly adelgid (Adelges piceae) 

 *14014 – hemlock wooly adelgid (Adelges tsugae) 

 14015 – pine spittlebug (Aphrophora parabella) 

 14016 – Saratoga spittlebug (Aphrophora saratogensis) 

 

15000 – Boring insects 

 15005 – two-lined chestnut borer (Agrilus bilineatus) 

 15022 – Zimmerman pine moth (Dioryctria zimmermani) 

 15082 – Asian long-horned beetle (Anoplophora glabripennis) 

 15087 – emerald ash borer (Agrilus planipenis) 

 15088 – hemlock borer (Melanophila fulvoguttata) 

 15089 – Sirex woodwasp (Sirex noctilio) 

 15090 – red oak borer (Enaphalodes rufulus) 
 

123

Box 1, continued. 

21000 – root/butt diseases 

 *21001 – Armillaria root disease (Armillaria spp.) 

 *21010 – annosus root disease (Heterobasidion annosum) 

 21028 – sudden oak death (Phytophthora ramorum) 

22000 – stem decays/cankers 

 22041 – beech bark disease (Neonectria faginata and N. ditissima)

24000 – Decline complexes/dieback/wilts 

24022 – Dutch elm disease (Ophiostoma ulmi and O. novo-ulmi)

 24004 – ash decline/yellows (multiple) 

25000 – Foliage disease 

 25025 – anthracnose (Gnomonia spp.) 

 25057 – siricoccus tip blight (Sirococcus conigenus) 

 25058 – diplodia shoot blight (Diplodia pinea) 

26000 – Stem rust 

 26001 – white pine blister rust (Cronartium ribicola) 

30000 – Fire 

41000 – Wild animals 

50000 – Abiotic damage 

 50001 – air pollutants 

 50003 – drought 

 50004 – flooding/high water 

 50005 – frost 

 50008 – lightening 

 50009 – nutrient imbalances 

 50011 – snow/ice 

 50013 – wind/tornado 

70000 – Human activities 

90000 – Unknown 

 90001 – broken top 

 90002 – dead top 

 90051 – canker/gall 

 90052 – open wound 

 90053 – resinosis 

 90054 – broken 

 90055 – damaged or discolored foliage 
 

124

Table 1. Severity ratings to describe tree damage. 

Agent Location Affected Severity Ratings 

Bark beetles 11000 Bole, Branches 

001 - Successful current attack 

002 - Previous year's successful attack 

003 - Top kill 

Defoliators 12000 Foliage Percent of foliage affected 

Sucking insects 14000 Bole, Branches, Foliage 

Percent of the circumference of a running 

3'-ft section of bole; percent of branches 

(crown volume) affected; percent of foliage 

affected. 

Wood borers 15000 Bole, Branches 

001 - Successful current attack 

002 - Previous year's successful attack 

003 - Top kill 

Root/butt diseases 21000 Roots 

001 – Live host tree within 30 ft of an 

infected tree 

002 – Live host tree with signs of root 

disease but no crown symptoms 

003 - Live host tree with signs of root 

disease also with crown symptoms 

Cankers (non-rust) 22000 Bole, Branches 

Percent of the circumference of a running 3-

ft section of bole; percent of branches 

(crown volume) affected. 

Decline complexes 24000 Any None 

Foliage diseases 25000 Foliage Percent of foliage affected 

Stem rusts 26000 Bole, Branches 

Percent of the circumference of a running 3-

ft section of bole; percent of branches 

(crown volume) affected. 

Fire 30000 Bole, Branches, Foliage 

Percent of the circumference of a running 3-

ft section of bole; percent of branches 

(crown volume) affected; percent of foliage 

affected. 

Wild animals 41000 Bole, Branches 

Percent of the circumference of a running 3-

ft section of bole; percent of branches 

(crown volume) affected. 

Abiotic 50000 Bole, Branches, Foliage 

Percent of the circumference of a running 3-

ft section of bole; percent of branches 

(crown volume) affected; percent of foliage 

affected. 

Human activities 70000 Bole, Branches, Foliage 

Percent of the circumference of a running 3-

ft section of bole; percent of branches 

(crown volume) affected; percent of foliage 

affected. 

Unknown/other 90000 Any None 
 

125

8.2 Analyses 

Data will be summarized for each park and presented in tables and figures to show the current 

status after each year that a park is sampled. Hypothetical examples are shown below and include 

a summary table showing all pests and pathogens located in a given park for the season (Table 2) 

and a graph showing the percentage of trees infested by given species in multiple forest types 

(Figure 1). 

Table 2. All targeted species located at St. Croix NSR, including the number and percentage of 

plots (out of 28) on which the pest was located. (Hypothetical Table) 

Pest/Pathogen Number of Plots Present Percentage of Plots Present 

forest tent caterpillar 18 64 

jackpine budworm 15 54 

large aspen tortrix 13 13 

sirococcus shoot blight 12 43 

two-lined chestnut borer 6 21 

spruce budworm 9 32 

annosum root rot 5 18 

Figure 1. Mean percentage of defoliation on trees by three leaf defoliating insects in four habitat 

types at St. Croix NSR. (Hypothetical Figure) 

Analyses testing for either a change or trend may be performed after two or three visits to a park, 

respectively. These analyses, if performed, must be done so and interpreted with caution. Insect 

and disease outbreaks are frequently cyclic, often occurring in < 5 year intervals. Results 

indicating differences after five or 10 years will not necessarily be a consequence of linear 

change during that period. 

Rather than test for change in tree damage, for example, over a given time interval, we will likely 

use pest and pathogen damage as a covariate in other analyses. For example, we may be 
 

126

interested in determining whether pest/pathogen damage explains differences in species diversity 

between the two time periods. 

8.3 Acknowledgements 

We are grateful to Joe O’Brien, Steven Katovich, and Manfred Mielke of the U.S. Forest Service 

for providing reviews of earlier drafts of this SOP. We are also grateful to the U.S. Forest 

Service FIA program for providing us with information on the Damage Indicator and allowing us 

to incorporate portions of those guidelines into this SOP. 

8.4 Literature Cited 

Johnson, W. T., and H. H. Lyon. 1991. Insects that feed on trees and shrubs. Cornell University 

Press, Ithaca. 

Sanders, S., S. Johnson, and D. Waller. 2006. General vegetation monitoring protocol for the 

Great Lakes Network, Version 1.0. National Park Service, Great Lakes Network, 

Ashland, Wisconsin. 

Sinclair, W. A., and H. H. Lyon. 2005. Diseases of trees and shrubs. Cornell University Press, 

Ithaca. 

U.S. Department of Agriculture, Forest Service. 2005. Forest inventory and analysis national 

core field guide, volume 1: field data collection procedures for phase 2 plots, version 3.0. 

U.S. Department of Agriculture, Forest Service, Washington Office. Internal report. On 

file with: U.S. Department of Agriculture, Forest Service, Forest Inventory and Analysis, 

Rosslyn Plaza, 1620 North Kent Street, Arlington, Virginia. 
 

127 

Succession 

STANDARD OPERATING PROCEDURE #9 

Version 1.0 (7/1/08) 

Suzanne Sanders 

NPS – Great Lakes Network 

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Date 

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title and 

affiliation) 

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128 

9.0 Introduction 

The successional states present in Great Lakes Network national parks range from newly 

fallow fields (Sleeping Bear Dunes NL) to old growth (Apostle Islands and limited 

amounts at Voyageurs NP and Isle Royale NP). Most, however, are in various stages of 

primary or secondary succession, following historical logging events prior to park 

designations. 

 The successional trajectories followed by individual stands depend not only on site 

conditions such as soils and hydrography (Leak 1986), but also on past land use (Dale et 

al. 1990). Some historical practices, such as logging, can affect succession by 

accelerating the successional pathway (Abrams and Scott 1989) as well as by altering the 

trajectory (Bourgeois at al. 2004). Although more subtle, current and future events may 

also influence the course of succession. For example, longer fire intervals are allowing 

more shade tolerant species such as balsam fir (Abies balsamea) and paper birch (Betula 

papyrifera) to become important forest components (Frelich and Reich 1995), while early 

and mid-successional species such as red pine (Pinus resinosa) can become less common 

(Scheller, et al. 2005). Other current impacts on forest succession include competition 

from invasive tree species (Titus and Tsuyuzaki 2003), injury from insects and pathogens 

(Malmström and Raffa 2000), and differential species response to elevated CO2

(Catovsky and Bazzaz 1999). 

This standard operating procedure (SOP) is linked to the Great Lakes Network General 

Vegetation Monitoring Protocol (Sanders et al. 2007), which specifies a six year 

sampling rotation where each Network park visited once during this interval. This SOP 

provides guidance on analyzing and interpreting tree and sapling data from the General 

Vegetation Monitoring Protocol so that successional trajectories in Great Lakes Network 

national parks may be understood. Specifically, we hope to answer two key questions 

about succession in Network parks: 

• Is forest structure changing? We’ll measure basal area and density of all tree species 

≥ 2.5 cm (1 in) in monitoring plots and test for change between time periods. 

• Are the structural characteristics of Great Lakes Network forests moving toward 

those of stable, uneven-aged old-growth forests? We will compare the observed, 

surviving fraction in multiple size classes with the initial, baseline surviving fraction 

in these classes. 

9.1 Field Methods 

As part of the general vegetation monitoring protocol (Sanders et al. 2007), we will 

record the species, diameter at breast height (DBH), and height of all trees (DBH ≥ 12.7 

cm (5 in)) and saplings (DBH 2.5 cm (1 in) - <12.7 cm (5 in)) in either the four 7.32 m 

radius subplots (FIA method, Figure 1) or the three 6 × 50 m belt transects (hybrid 

method, Figure 2) that comprise each plot. We will also record DBH and species (when 

possible) of standing dead trees. 
 

129 

Figure 1. Diagram of FIA style monitoring plot showing four 7.32 m radius subplots. 

Figure 2. Hybrid plot, with 6 × 50 m belt transects demarcated by the dashed lines. 

9.2. Analysis Methods 

Summary analyses 
 

130 

Following the initial visit to each park, we will summarize the characteristics (density, 

basal area, down woody materials) at both the stand and individual species level (for key 

species only, Table 1). Summary graphs will be also presented, and will include density – 

diameter distribution graphs for different habitat types, showing the size distribution of 

multiple species during the previous season (Figure 3). 

From the diameter distributions recorded at the first park visit, we will calculate the 

baseline (initial) surviving fraction between DBH size classes. This is simply the ratio of 

stems in a larger size class to that in the next smaller class. Where initial size structure is 

being maintained, a constant ratio in the number of trees present in successive size 

classes will emerge (Meyer and Stevenson 1943) and the resulting graphs exhibit the 

characteristic reverse-J shape (Figure 4). This is typically the case in uneven-aged, and 

old growth forests where recruitment is balanced by mortality (Leak 1996). In younger 

forests, more common throughout GLKN parks, the diameter distribution shapes vary. 

Characteristic shapes can include a flattened bell shape (Ward and Smith 2000), 

indicating an even-aged forest from either massive disturbance or a previous timber 

harvest. Severe browse can be evidenced by marked by smaller surviving fractions in the 

lowest size classes. 

In addition to summary tables and graphs and diameter distribution graphs, additional 

summary information will be presented, as warranted, to display key traits about the 

initial forest structure. 
131

Table 1. Hypothetical table layout showing the site characteristics of all trees collectively and key individual spepine, red pine) at during year of plot establishment. Site Fuel loada Basal area Stand de tons acre-1 m2 ha-1 stems h

 All balsam fir white pine red pine all balsam fir w

2001 2002 2003 2004 ..... 2020 Mean a Includes coarse and fine woody materials; this is presented in English units to facilitate comparisons with other 
132

Figure 3. Hypothetical graph showing density-diameter relationships of four floodplain species 

in one season at SACN. 

Figure 4. Hypothetical graph showing the reverse-J distribution (red line) of sugar maple 

densities at Sleeping Bear Dunes NL. 
133

Trend analyses 

After the second and all sequential visits to a park, we will again present density-diameter 

distributions and calculate the surviving fraction between size classes and draw inferences about 

whether the site characteristics are moving toward those of stable, uneven-aged forests. We will 

also look at overall change in stand (plot) basal area and stem count between visits (or over 

time). 

In addition to calculating data on surviving fractions of all species collectively, we will also 

present graphs and tables showing changes in size and frequency of individual species. For 

example, for a given habitat, we may present density – diameter distribution graphs of a given 

species during multiple sampling years (Figure 5). This can be used to detect and/or highlight 

density changes in a particular size class. Depending on the shape of these graphs, they can be 

used to infer the drivers of this change. For example, in Figure 4, below, changes in the density 

of the two lowest size classes may indicate high sapling browse intensity. 

Figure 5. Hypothetical graphs showing the distribution of sugar maple size classes over four 

sampling periods. 

Finally, additional graphs, tables, and analyses will be presented, as warranted, to convey the 

successional states of Great Lakes Network forests. 

9.3. Literature Cited 

Abrams, M. D. and M. L. Scott. 1989. Disturbance-mediated accelerated succession in two 

Michigan forest types. Forest Science 35(1):42-49. 

Bourgeois, L., C. Messier, S. Brais. 2004. Mountain maple and balsam fir early response to 

partial and clear-cut harvesting under aspen stands of northern Quebec. 

Catovsky, S. and F. A Bazzaz. 1999. Elevated CO2 influences the responses of two birch species 

to soil moisture: implications for forest community structure. Global Change Biology 

5:205-518. 
134

Dale, V. H., L. K. Mann, R. J. Olson, D. W. Johnson, and K. C. Dearstone. 1990. The long-term 

influence of past land use on the Walker Branch forest. Landscape Ecology 4:211-224. 

Duchesne, L., R. Ouimet, J-D. Moore, and R. Paquin. 2005. Changes in structure and 

composition of maple-beech stands following sugar maple decline in Québec, Canada. 

Forest Ecology and Management 208: 223-236. 

Frelich, L. E., and P. B. Reich. 1995. Spatial patterns and succession in a Minnesota southernboreal forest. Ecological Monographs 65:325-346. 

Leak, W. H. 1986. Fifty years of compositional change in deciduous and coniferous forest types 

in New Hampshire. 1986. Canadian Journal of Forest Research 17:388-393. 

Leak, W. H. 1996. Long-term structural change in northern hardwoods. Forest Science 42:160-

165. 

Malmström, C. M., and K. F. Raffa. 2000. Biotic disturbance agents in the boreal forest: 

considerations for vegetation change models. Global Change Biology 6:35-48. 

Meyer, A. H. and Stevenson, D. D. 1943. The structure and growth of virgin beech-birch-maplehemlock forests in northern Pennsylvania. Journal of Agricultural Research 67:465-484. 

Sanders, S., S. E. Johnson, and D. M. Waller. 2007. General vegetation monitoring protocol for 

the Great Lakes Network, Version 1.0. National Park Service, Great Lakes Network, 

Ashland, Wisconsin. 

Scheller, R. M., D. J. Mladenoff, T. R. Crow, and T. A. Sickley. 2005. Fire absence on forest 

composition and landscape structure in the Boundary Waters Canoe Area, Northern 

Minnesota, USA. Ecosystems 8:396-411. 

Titus, J. H., and S. Tsuyuzaki. 2003. Influence of a non-native invasive tree on primary 

succession at Mt. Koma, Hokkaido, Japan. 

Ward, J. S., and D. M. Smith. 2000. Dynamics of Connecticut Hemlock Stands. Proceedings 

from the Symposium on Sustainable Management of Hemlock Ecosystems in Eastern 

North America. GTR-NE-267. 
135

Coarse Woody Materials 

STANDARD OPERATING PROCEDURE #10 

Version 1.0 (7/1/08) 

Suzanne Sanders, NPS-Great Lakes Network 

Revision History Log: 

Previous 

Version # 

Revision 

Date 

Author (with 

title and 

affiliation) 

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Add rows as needed for each change or set of changes tied to an updated version number 
136

10.0 Introduction 

This standard operating procedure (SOP) is intended to provide guidance for monitoring coarse 

woody materials in habitat types within the nine Great Lakes Network national parks.(CWM). 

This indicator of forest health will be monitored within the context of the General Vegetation 

Monitoring program (Sanders et al. 2008); therefore there is no stand-alone monitoring protocol. 

Field methods and analysis techniques are contained in this SOP, while specific details of field 

data collection are provided in SOP #6: Field methods and data collection. 

The NPS-GLKN woody materials sampling is designed to answer the following specific 

questions: 

 

• What is the CWM volume (m3

/ha) in specific habitat types? 

• What is the CWM biomass (kg/ha) in specific habitat types? 

• What is the density (pieces/ha) of CWM in specific habitat types? 

• What is the magnitude and direction of change in CWM volume, biomass, and density in 

each habitat between two or more sampling events? 

CWM sampling provides information about the health of forests in Great Lakes Network Parks. 

Further, this information can be combined with that of community structure, succession, and 

other indices of forest growth that allows us to develop holistic forest health inferences. 

10.1 Field Methods 

As part of the General Vegetation Monitoring Protocol, the Great Lakes Network will be using 

one of two monitoring plot designs, depending on the park where monitoring occurs (Sanders et 

al. 2007). At some parks, we will be using the “hybrid plot” design (Johnson et al. 2006). This 

plot design was developed to meet the needs of the Great Lakes Network by combining assets of 

other long-term monitoring sampling protocols used in the region. At other parks, we will be 

using a plot design similar to one used by the Forest Inventory and Analysis (FIA) program of 

the USDA Forest Service (USDA 2005). Regardless of the employed plot design, our 

measurement variable definitions will mirror that of the FIA program. For example, criteria for 

discerning between CWM decay classes will be identical between the two programs. This will 

enable comparisons of CWM data collected within the network parks with that from forested 

areas surrounding the parks. 

The hybrid plot is composed of three parallel 50 m horizontal distance transects, each oriented 

east-west (Figure 1). Data on CWM (≥ 0.91 m [3 ft] long and ≥ 7.6 cm [3 in] diameter) will be 

collected along each of these transects using the line intercept method (Brown 1974, de Vries 

1986; Woodall and Williams 2005). For most CWD pieces, the parameters recorded are transect 

intersection diameter, small end diameter, large end diameter, length, species, and decay class 

(Table 1). For pieces in decay class 5, the small- and large-end diameters are not recorded. In 

addition, the length must be ≥ 1.52 m (5 ft) and the diameter at the line intercept must be ≥ 13.0 

cm and the piece must be ≥ 13 high from the ground surface. 
137

Figure 1. The hybrid plot, showing the three parallel transects along which data on coarse woody 

materials will be collected. 
138

Table 1. Distinguishing characteristics of the five decay classes. From U.S. Department of 

Agriculture, 2006. 

Decay 

class 

Structural integrity Texture of rotten 

portions 

Color of 

wood 

Invading 

roots 

Branches and twigs 

1 sound, freshly fallen, 

intact logs 

intact, no rot; conks 

of stem decay 

absent 

original 

color 

absent if branches are 

present, fine twigs 

are still attached and 

have tight bark 

2 sound mostly intact; 

sapwood partly soft 

(starting to decay) 

but can’t be pulled 

apart by hand 

original 

color 

absent if branches are 

present, many fine 

twigs are gone and 

remaining fine twigs 

have peeling bark 

3 heartwood sound; piece 

supports its own weight 

hard, large pieces; 

sapwood can be 

pulled apart by 

hand or sapwood 

absent 

reddish 

brown or 

original 

color 

sapwood 

only 

branch stubs will 

not pull out 

4 heartwood rotten; piece 

does not support its own 

weight, but maintains its 

shape 

soft, small blocky 

pieces; a metal pin 

can be pushed into 

heartwood 

reddish or 

light brown 

throughout branch stubs pull 

out 

5 none, piece no longer 

maintains its shape, it 

spreads out on the ground 

soft; powdery when 

dry 

red-brown to 

dark brown 

throughout branch stubs and 

pitch pockets have 

usually rotted down 

The FIA plot (Figure 2) is composed of four 7.32 m (24 ft) horizontal distance radius subplots, 

with each subplot having three transects spanning the radius at 30 ̊, 150 ̊, and 270 ̊ (Figure 1). 

Coarse woody material is sampled along the entire length of all three transects in the same 

manner described for the hybrid plot, above. 

For both plot types, refer to SOP 6, Field methods and data collection, for specific tally rules on 

data collection. 
139

Figure 2. FIA style plot showing the three CWM transects within each of the four subplots. 

10.2 Estimation Methods 

Volume estimation for CWM based on a single transect of horizontal distance length L (m) is 

calculated by (Woodall and Williams 2005): 

 ∑==

n

i

i i y l

L

f y

1

( / ) 2

π (11.1) 

where y is the volume in m3

/ha, f = 10,000 m2/ha, yi is the volume of piece i (m3), and li is the 

length of piece i (m). 

The volume of individual pieces of CWM will be calculated following Smalian’s formula 

(Husch et al. 1972): 

 10,000

( / 8)( ) 2 2 D D l

V S L

m

+ = π (11.2) 

where DS is the small end diameter (cm) and DL is the large-end diameter (cm) and l is the length 

of the piece (m). 

Where the small- and large-end diameters are not recorded and the only diameter is at the line 

intercept location, the volume of the individual piece will be estimated using Huber’s formula 

(Husch et al. 1972): 

 10,000

( / 4)( )

2 D l V T

m

π = (11.3) 
140

where DT is the transect diameter (cm). Refer to Appendix A for an example calculation of 

CWM volume. 

While volume of CWM per unit area is important for many applications, estimates of CWM 

biomass are critical to carbon and fuel monitoring efforts. Because the biomass of any individual 

CWM piece is partially dependent on its species and stage of decay, CWM biomass estimates 

must incorporate is species and decay class specific density constants. Hence, biomass per unit 

area is calculated by: 

 kg/ha = (cubic meters per hectare) × 1000 kg/m3 × SpG × DCR (11.4) 

where SpG is the specific gravity for a given species (Table 2) and DCR is the decay reduction 

factor for a given decay class (Table 3, Woodall and Williams 2005). 

In addition to volume and biomass, the density of CWM pieces is also of interest to the Network. 

Estimation of the number of CWM pieces per hectare based on a single transect of length L is 

calculated by (Woodall and Williams 2005): 

 ∑=

=

n

i

i l

L

f y

1

(1/ ) 2

π (11.5) 

where y is the number of pieces per hectare, f = 10,000 m2

/ha, and li is the length of piece i (m). 

An example calculation of this is shown in Appendix A. 
141

Table 2. Identification codes and specific gravity values for tree species in Great Lakes Network 

parks. Specific gravity values are from Woodall and Williams 2005. 

Species 

code Species Specific gravity 

 Species 

code Species Specific gravity 

ABIBAL Abies balsamea 0.34 LARLAR Larix laricina 0.49 

ACESPX Acer spp. 0.49 LIRTUL Liriodendron tulipifera 0.40 

ACENEG Acer negundo 0.44 MACPOM Maclura pomifera 0.76 

ACENIG Acer nigrum 0.52 MALSPX Malus spp. 0.61 

ACEPEN Acer pennsylvanicum 0.44 MORSPX Morus spp. 0.59 

ACERUB Acer rubrum 0.49 MORALB Morus alba 0.59 

ACESA1 Acer saccharinum 0.44 MORRUB Morus rubra 0.59 

ACESA2 Acer saccharum 0.56 NYSSYL Nyssa sylvatica 0.46 

ACESPI Acer spicatum 0.44 OSTVIR Ostrya virginiana 0.63 

AESSPX Aesculus spp. 0.33 PICSPX Picea spp. 0.38 

AESFLA Aesculus flava 0.33 PICGLA Picea glauca 0.37 

AESGLA Aesculus glabra 0.33 PICMAR Picea mariana 0.37 

AILALT Ailanthus altissima 0.37 PINBAN Pinus banksiana 0.40 

AMESPX Amelanchier spp. 0.33 PINRES Pinus resinosa 0.41 

AMEARB Amelanchier arborea 0.33 PINSTR Pinus strobus 0.34 

AMEHUM Amelanchier humilis 0.33 PINSYL Pinus sylvestris 0.41 

AMEINT Amelanchier interior 0.33 PLAOCC Platanus occidentalis 0.46 

AMELAE Amelanchier laevis 0.33 POPSPX Populus spp. 0.37 

ASITRI Asimina triloba 0.47 POPBAL Populus balsamifera 0.31 

BETSPX Betula spp. 0.48 POPDEL Populus deltoides 0.37 

BETALL Betula alleghaniensis 0.55 POPGRA Populus grandidentata 0.36 

BETLEN Betula lenta 0.60 POPTRE Populus tremuloides 0.37 

BETNIG Betula nigra 0.56 PRUSPX Prunus spp. 0.47 

BETPAP Betula papyrifera 0.48 PRUPEN Prunus pensylvanica 0.36 

CARCAR Carpinus caroliniana 0.58 PRUSER Prunus serotina 0.47 

CARSPX Carya spp. 0.62 PRUVIR Prunus virginiana 0.36 

CARCOR Carya cordiformis 0.60 QUESPX Quercus sp. 0.58 

CARGLA Carya glabra 0.66 QUEALB Quercus alba 0.60 

CARLAC Carya lacinosa 0.62 QUEBIC Quercus bicolor 0.64 

CAROVA Carya ovata 0.64 QUEMAC Quercus macrocarpa 0.58 

CARTOM Carya tomentosa 0.64 QUERUB Quercus rubra 0.56 

CASDEN Castanea dentata 0.40 QUEVEL Quercus velutina 0.56 

CELOCC Celtis occidentalis 0.49 ROBPSE Robinia pseudoacacia 0.66 

CERCAN Cercis canadensis 0.58 SALSPX Salix sp. 0.36 

CORSPX Cornus spp. 0.64 SASALB Sassafras albidum 0.42 

CORFLA Cornus florida 0.64 SORAME Sorbus americana 0.42 

CRASPX Crataegus spp. 0.62 SORDEC Sorbus decora 0.42 

FAGGRA Fagus grandifolia 0.56 THUOCC Thuja occidentalis 0.29 

FRASPX Fraxinus spp. 0.54 TILAME Tilia americana 0.32 

FRAAME Fraxinus americana 0.55 TSUCAN Tsuga canadensis 0.38 

FRANIG Fraxinus nigra 0.45 ULMSPX Ulmus sp. 0.50 

FRAPEN Fraxinus pennsylvanica 0.53 ULMAME Ulmus americana 0.46 

FRAQUA Fraxinus quadrangulata 0.53 ULMPUM Ulmus pumila 0.46 

GLETRI Gleditsia triacanthos 0.60 ULMRUB Ulmus rubra 0.48 

ILEOPA Ilex opaca 0.50 UNKHAR unknown hardwood 0.51 

JUGSPX Juglans spp. 0.51 UNKSOF unknown softwood 0.38 

JUGCIN Juglans cinerea 0.36 UNKXXX unknown 0.46 

JUGNIG Juglans nigra 0.51 
142

Table 3. Decay reduction factors for calculating biomass (from Waddell, 2002). 

Species group Decay class Hardwoods Softwoods 

1 1.00 1.00 

2 0.84 0.78 

3 0.71 0.45 

4 and 5 0.45 0.42 

10.3 Data Summaries and Analyses 

Summarized data will be presented in figures and tables annually for each park preceding field 

sampling (e.g., biomass of coarse woody material in different habitats and regions, as well as 

proportion of plots in different biomass categories). 

To test for change between successive sampling events and among multiple sampling events, we 

will use repeated measures analysis of variance (ANOVA) (see SOP 14: Data summary and 

analysis). Prior to all analyses, assumptions (e.g., normality and sphericity) will be tested and 

modifications made if needed. Time and either habitat or region will be the main effects and 

CWM biomass will be the dependent variable. 

Results of the CWM inventory will be compared qualitatively with those describing plant 

community structure. This will allow inferences to be drawn relating fuel loads, community 

structure, successional states, and forest health. 

10.4 Acknowledgements 

The Great Lakes Network is grateful to Christopher Woodall of the U.S. Forest Service for 

providing reviews and comments on this SOP.
143

10.5 Literature Cited 

Brown, J. K. 1974. Handbook for inventorying downed woody material. General Technical 

Report INT-16. U.S. Department of Agriculture, Forest Service, Intermountain Forest 

and Range Experiment Station. 

De Vries, P.G. 1986. Sampling theory for forest inventory: a teach-yourself course. Chapter 13: 

Line-intersept sampling. Berlin, Germany: Springer-Verlang: 242-279. 

Husch, B., C.I. Miller, and T.W. Beers. 1972. Forest mensuration. New York, NY Ronald. 

Johnson, S.E., E.L. Mudrack, and D.M. Waller. 2006. A comparison of sampling methodologies 

for long-term forest vegetation monitoring in the Great Lakes Network National Parks. 

National Park Service, Great Lakes Inventory and Monitoring Network Report: 

GLKN/2006/03. 

U.S. Department of Agriculture, Forest Service. 2005. Forest inventory and analysis national 

core field guide, volume 1: field data collection procedures for phase 2 plots, version 3.0. 

U.S. Department of Agriculture, Forest Service, Washington Office. Internal report. On 

file with: U.S. Department of Agriculture, Forest Service, Forest Inventory and Analysis, 

Rosslyn Plaza, 1620 North Kent Street, Arlington, Virginia. 

U.S. Department of Agriculture, Forest Service. 200. Forest inventory and analysis national core 

field guide: field data collection procedures for phase 3 plots, North Central version 3.0. 

U.S. Department of Agriculture, Forest Service, Washington Office. 

Waddell, K.L. 2002. Sampling coarse woody debris for multiple attributes in extensive resource 

inventories. Ecological Indicators 1:139-153. 

Woodall, C., and M. S. Williams. 2005. Sampling protocol, estimation, and analysis procedures 

for the down woody materials indicator of the FIA program. Forest Service North Central 

Research Station General Technical Report NC-256. 
144

Appendix A: Example Calculations 

Below, we present sample calculations for estimating both CWM volume per hectare and pieces 

per hectare. The table below represents three pieces located along a 50 m transect for this 

example: 

Piece ID Species Length (m) Diameter (cm) Decay class 

1 Acer saccharum 6 8 4 

2 Quercus rubra 14 20 3 

3 Pinus strobus 8 12 1 

Coarse woody material volume per hectare: 

Volume estimation for coarse woody debris based on a single transect of length L is calculated 

by (Woodall and Williams 2005): 

 ∑=

=

n

i

i i y l

L

f y

1

( / ) 2

π (11.1) 

where y is the volume in m3

/ha, f = 10,000 m2/ha, yi is the volume of piece i (in meters), and li is 

the length of piece i (in meters). 

Where only transect diameter is measured at the line intercept location, the volume of the 

individual piece will be estimated using Huber’s formula (Husch et al. 1972): 

 10,000

( / 4)( )

2 D l

Vm

π = (11.3) 

where D2

 is the diameter (in cm) at the intercept point. 

Thus, volume for each of the three pieces is calculated as: 

Piece 1: Vm = (3.14/4) (8 cm)2 (6m) / 10,000 = (0.785)(64 cm2)(6m)/10,000 = 0.030144 m3

Piece 2: Vm = (3.14/4) (20 cm)2

 (14m) / 10,000 = (0.785)(400 cm2)(14m)/10,000 = 0.4396 m3

Piece 3: Vm = (3.14/4) (12 cm)2

 (8m) / 10,000 = (0.785)(144 cm2)(8m)/10,000 = 0.090432 m3

Volume for the transect is calculated as: 

∑=

= + +

n

i m

m

m

m

m

m

m

m ha

y

1

2 3 3 3

8

0.0904

14

0.4396

6

0.0301

2(50 )

(10,000 / )(3.14)

m m m m ha m m ha

m

m ha

y [0.0050 .0314 0.0113 ] 314 / 0.3301 103.71 /

100

31400 / 2 2 2 2 3

2

= × + + = × =
145

Coarse woody material biomass per hectare: 

Biomass estimation for coarse woody debris based on a single transect of length L is calculated 

by (Woodall and Williams 2005): 

 kg/ha = (cubic meters per hectare) × 1000 kg/m3 × SpG × DCR (11.4) 

where SpG is the specific gravity for a species and DCR is the decay reduction factor. 

Because SpG and DCR vary, this calculation should be used for each tree: 

Piece 1: kg m kg ha

m

m

m

m ha kg ha 1000 / 0.44 0.45 37.48 /

50

0.030144

2(50 )

(10,000 / )(3.14) / 3

2 2

= × × × × =

Piece 2: 

kg m kg ha

m

m

m

m ha kg ha 1000 / 0.56 0.71 1097.65 /

50

0.4396

2(50 )

(10,000 / )(3.14) / 3

2 2

= × × × × =

Piece 3: 

kg m kg ha

m

m

m

m ha kg ha 1000 / 0.34 1.00 193.02 /

50

0.0904

2(50 )

(10,000 / )(3.14) / 3

2 2

= × × × × =

then summed over all trees in the transect: 

total kg/ha = 37.48 kg/ha + 1097.65 kg/ha + 193.02 kg/ha = 1328.15 kg/ha 

Coarse woody material pieces per hectare: 

Estimation of the number of coarse woody debris pieces per hectare based on a single transect of 

length L is calculated by (Woodall and Williams 2005): 

 ∑=

=

n

i

i l

L

f y

1

(1/ ) 2

π (11.5) 

where y is the number of pieces per hectare, f = 10,000 m2

/ha, and li is the length of piece i (in 

meters). 

Thus, the number of pieces per hectare is calculated as: 

[(1/ 6 ) (1/14 ) (1/ 8 )] 2(50 )

(10,000 / ) 2

m m m

m

m ha

y = × + +

π
146

y = 314m / ha ×[(0.16 / m) + (0.071/ m) + (0.125/ m)]

y = 314m / ha × 0.356 / m = 111.78/ ha
147

Data Management 

STANDARD OPERATING PROCEDURE #11 

Joan Elias, Mark Hart, and Suzanne Sanders 

NPS-Great Lakes Network 

Revision History Log: 

Previous 

Version # 

Revision 

Date 

Author (with 

title and 

affiliation) 

Location in Document 

and Concise 

Description of Revision 

Reason for Change New 

Version # 

 

Add rows as needed for each change or set of changes tied to an updated version number 
148

11.0 Introduction 

Data collected under the vegetation monitoring protocol (Sanders et al. 2008) must be entered, 

quality-checked, documented, managed, and made available to others for a variety of purposes, 

such as management decision-making, research, and education. The Great Lakes Inventory & 

Monitoring Network (GLKN) adopted and amended a database from the U.S. Forest Service 

Forest Inventory and Analysis (FIA) program, providing the Network with a tailored venue for 

data storage and management, while also permitting compatibility with the FIA program. 

This standard operating procedure outlines data stewardship responsibilities and provides 

specific instructions and references for entering, quality checking, and managing vegetation 

.data.

11.1 Data Stewardship Roles and Responsibilities 

The purpose of data stewardship is to share the responsibility for managing data and information 

resources that are organized, useful, compliant, available, and safe. The demand for detailed, 

high quality data and information about vegetation requires a group of people working together 

to ensure that data are collected using appropriate methods, and that resulting datasets, reports, 

maps, and other derived products are well managed. 

The Great Lakes Network (GLKN) terrestrial ecologist serves as project manager for vegetation 

monitoring. The project manager will supervise data collection, provide project oversight, direct 

on-the-ground data collections, and provide cohesive links among data collection, synthesis, 

interpretation, and reporting. 

While the project manager must act as the steward for vegetation monitoring data for the 

Network, other project and GLKN personnel are also accountable for specific data management 

tasks. Table 1 lists stewardship responsibilities of personnel involved in the management of 

vegetation data. To ensure that all project data are managed properly, individuals must 

understand their responsibilities, communicate with one another, and assist one another as 

needed. 
149

Table 1. Data stewardship responsibilities of water quality monitoring personnel. 

Personnel Role Data management responsibilities related to vegetation monitoring 

Project Crew 

Member

Crew members collect and manage data with direction and guidance from the crew 

leader and/or project manager. Data collection includes obtaining GPS coordinates, 

and recording measurements and observations. Crew members are responsible for 

quality control by following data collection and recording instructions and by 

promptly verifying recorded data. Crew members may also perform data entry and 

verification. 

Project Crew 

Leader

The crew leader normally performs the same duties as the other crew members and 

ensures adherence to data collection and processing protocols, including data 

verification and documentation. The crew leader also works with the project 

manager on vegetation data management in the office. 

Project Manager 

(Network 

Terrestrial 

Ecologist)

The project manager is responsible for all project operations and results, and may 

also participate in field operations. The project manager ensures that data 

management activities are conducted according to established procedures and is 

responsible for data validation: approving the data content, quality, and 

documentation, as well as making decisions about data sensitivity and distribution. 

The project manager is responsible for evaluating project data at specified intervals, 

analyzing data for trends, and following reporting requirements. 

Network Data 

Manager

The network data manager ensures that vegetation monitoring data are organized, 

useful, compliant, available, and safe. The network data manager oversees activities 

related to training, user support, quality assurance, documentation, backups, 

archiving, and data maintenance and distribution. 

Network Data 

Specialist 

The network data specialist is the project manager’s primary contact for database 

assistance, including development of tables and queries and reporting needs. The 

network data specialist is also the initial contact for backups, archiving, and data 

maintenance and distribution. 

Network 

Quantitative 

Ecologist

The network quantitative ecologist assists the project manager with analyses, 

primarily for reporting on status and trends of the vegetation vital signs. 

11.2. Database Design 

The monitoring database is a complex MS Access database made of two components, a front-end 

database and the back-end database. The front-end contains a user interface portion along with 

various queries, forms, reports and visual basic code for underlying function of the database as a 

whole and use with the user interface. The user interface consists of various forms to help the 

user enter and validate data, along with the ability to query, through forms, the data for various 

QAQC and final reports. The back-end database consists of numerous lookup and data storage 

tables, which are linked to the front-end database. 

The Great Lakes Network will maintain one master copy of the vegetation at the Ashland office 

on a central server. This is the only copy that can be used to export data to other locations. 

Additional copies of the database will be used by GLKN personnel stationed at parks, but they 

can only be used as a conduit for data entry. For analysis, the data from the master copy at the 

Network, that has passed all QA/QC procedures, must be used.

11.3 Data Management Procedures 

11.3.1 Data Collection 

Data values are measured and obtained according the GLKN General Vegetation Monitoring 

Protocol and recorded on field forms (see SOP #6, Field Methods and Data Collection). Crew 
150

members are responsible for legible, accurate entries on field forms. As a first step to verify data, 

crew members will check and double-check the recorded values prior to leaving a plot; these 

checks will ensure both legibility and that data on all desired parameters has been recorded. 

Digital images of sample sites are acquired during site establishment (SOP #6) and at every visit 

thereafter. Crew members are responsible for proper settings and use of digital camera 

equipment and should refer to the user manual for details specific to the camera. 

GPS coordinates are stored as waypoints if using a recreational GPS unit, or as features if using a 

mapping-grade unit with a data dictionary. When possible, the GPS data will be differentially 

corrected to improve the accuracy of location coordinates. See SOP #3 for more information on 

using a GPS unit. 

11.3.2 Data Entry, Verification, and Documentation 

Prior to leaving a field site, sampling crews will review the field data collection forms and verify 

the completeness, accuracy, and legibility of each form. As soon as is possible following 

sampling, the crew leader will make a photocopy the each field form and ensure each copy is 

legible. Upon receiving the copies, the project manager will proofread the datasheets, making 

sure that they have been filled out completely. All data sheets should have been reviewed for 

completeness while in the field, however some deficiencies in data recording may not be 

identified until all data sheets have been reviewed as a group. Originals will be kept at the Great 

Lakes Network Office and photocopies will be stored offsite.

Project staff enters plot data into the vegetation monitoring database as soon as possible 

following each site visit. 

Data verification starts with the QA/QC steps that are detailed in SOP #18. As data are entered 

into the database, either by keyboard or using an import routine, a suite of QA/QC procedures 

exist that compare the entered data with expected formats and accepted data value ranges or 

domains. For many of the variables, the MS Access database will not accept out-of-bounds data, 

and correction is mandatory. Standard MSAccess queries have also been developed to parse out 

incorrectly entered data.

A user’s identification is assigned to each aspect of data handling, from collection through the 

final steps of QA/QC. On data sheets, the identity of the data collector(s) is recorded. Once 

entered, standard reports of data will be generated in MSAccess and printed out. Two people will 

perform a final QA/QC check by reading the original datasheets and checking these values on 

the reports. 

Executive Order 12906, mandates federal agencies to “...document all new geospatial data it 

collects or produces, either directly or indirectly...” using the Federal Geographic Data 

Committee Content Standard for Digital Geospatial Metadata. Vegetation monitoring meets the 

definition of geospatial data and, thus, GLKN is responsible for documenting all public datasets 

using metadata that meets that standard. Consult the GLKN Data Management plan (Hart and 

Gafvert 2005) for additional details about metadata procedures and requirements. 
151

11.3.3 Data Validation 

Prior to distributing the data for any type of use, the project manager validates the master 

vegetation monitoring database file according to procedures in the Quality Control and Quality 

Assurance SOP. Validation is performed at least once each year after data collection and entry 

are complete, usually by the end of each calendar year. If this annual validation does not meet 

scheduled reporting requirements of the Network, then the project manager may coordinate more 

frequent validation to meet needs, or reports can include a statement explaining that results are 

based on data that have not been validated. The statement should include an explanation of what 

significance this has for using preliminary data. 

11.3.4 Data Distribution 

GLKN has a number of different avenues for data distribution depending on the audience served 

and the degree of analysis and customization needed by the end-user (see GLKN’s data 

management plan, Hart and Gafvert 2005, for more details). One of the primary methods GLKN 

uses for data distribution is a website with an Internet Mapping Service (IMS) that allows users 

to explore and query monitoring data using spatial and nonspatial parameters. In addition to the 

IMS website, individual data requests can be accommodated by developing queries to produce 

reports of the desired information. 

11.3.4 Data Analysis and Reporting 

Project staff will follow the procedures for data analysis in SOP #14 and data reporting in SOP 

#15. Data summary statistics will typically include, but not be limited to: 

• Mean 

• Median 

• Standard deviation 

• Minimum 

• Maximum 

• Count 

• Percentiles (10, 25, 75, and 90th percentiles) 

• Standard error 

• Variance 

• Range 

• Mode 

• Sum 

• Kurtosis 

• Skew 
152

11.3.5 Data Folder and File Organization 

All data from the vegetation protocol should be stored, at the earliest possibility, on the GLKN 

central server. A diagram showing the folder structure is shown below (Figure 2). 

Figure 2. Great Lakes Network folder structure for information related 

to vegetation monitoring.

Files should be named in accordance with the file-naming standards in the GLKN Data 

Management plan (see section 6.4 of Hart and Gafvert 2005). Files have a ‘GLKN’ prefix, a 

descriptive element, and finish with a date element. For example, GLKN_ 

VegMon_20070605.doc contains vegetation field data from June 5, 2007. Do not use spaces in 

file names.

11.3.6 Data Archival and Distribution Procedures 

Data archiving serves two primary functions: it provides a source to retrieve a copy of any data 

set when the primary dataset is lost or destroyed, and it provides a data record that is an essential 

part of the QA/QC process. Original data will be archived at the Network office. Original data 

for printed forms are either the physical datasheets or exact and complete digital copies of the 

forms that capture all entries and notations. The unedited files are the original data for digital 

data. 

All digital data have a duplicate file created at the earliest opportunity. The data files on field 

computers and loggers must not be erased until the integrity of these data files are verified on the 

duplicate storage medium. The removal of original data files from a field computer or logger 

must be a balance of keeping memory available for new data collection and a need to keep data 

in their most original form for as long as possible. Field files should only be deleted when 

memory space is needed for new data collection. Complete details of the GLKN Server archiving 

procedure are found the Infrastructure chapter of GLKN’s Data Management Plan (Hart and 

Gafvert 2005); the general strategy for data archiving is also described in this plan and its 

appendices. 
153

11.4 Literature Cited 

Hart, M., and U. Gafvert. 2005. Data management plan: Great Lakes Inventory & Monitoring 

Network (draft). National Park Service Great Lakes Inventory and Monitoring Network 

GLKN/2005/20. 

Magdalene, S., D.R. Engstrom, and J. Elias. 2007. Large rivers water quality monitoring 

protocol, Version 1.0. National Park Service, Great Lakes Network, Ashland, Wisconsin. 

NPS (National Park Service). 1999. Natural Resource Challenge. Available from: 

<http://www.nature.nps.gov/challenge/index.cfm>. Accessed January 26, 2007.

Sanders, S., S. E. Johnson, and D. M. Waller. 2006. General vegetation monitoring protocol for 

the Great Lakes Network, Version 1.0. National Park Service, Great Lakes Network, 

Ashland, Wisconsin. 

155

Data Summary and Analysis 

STANDARD OPERATING PROCEDURE #12 

VERSION 1.0 (7/1/08) 

 

Sarah Johnson and Donald M. Waller 

University of Wisconsin – Madison 

Suzanne Sanders and Erik A. Beever 

Great Lakes Network

Revision History Log: 

Previous 

Version # 

Revision 

Date 

Author (with 

title and 

affiliation) 

Location in Document 

and Concise 

Description of Revision 

Reason for Change New 

Version # 

 

Add rows as needed for each change or set of changes tied to an updated version number 
156

12.0 Introduction 

This SOP provides information on analyses that will be used to prepare annual reports on static 

descriptions of terrestrial vegetation (14.1) and reports on changes in vegetation that will be 

produced every six years (14.2). This document also addresses the analyses that will be used to 

evaluate the relationships among population, community, and environmental variables (14.3). 

There are many advanced approaches to analyzing complex ecological data sets such as those 

produced by the Terrestrial Vegetation Monitoring Protocol and its associated Vital Signs. 

Priority should be given to the descriptive and statistical approaches described in sections 14.1-

14.3. Section 14.4 describes several possible extensions that may provide the Network an 

opportunity to identify more complex relationships and interactions among multiple response 

and predictor variables. Advances in complex analytical techniques will no doubt continue over 

time once data have been compiled over several sample periods. The Network should reassess 

their options at that time. 

This SOP specifically focuses on analyses that will be used to test the following primary and 

secondary objectives: 

Primary Objective Questions: 

1) What is the rate and direction of change of key species? 

2) What is the rate and direction of change of plant community structure? 

3) What is the rate and direction of change of plant community composition? 

Secondary Objective Questions:

1) Are there significant links among population and community variables in interpreting 

ecological change? 

2) Are there significant relationships among changes in population and community 

variables with changes in environmental indicators associated with vegetation? 

The General Vegetation Monitoring Protocol of the Great Lakes Network (GLKN) is designed to 

monitor many sites across several habitat types within each of nine parks. This approach 

immediately raises the issue of whether and to what degree data from particular sites, habitat 

types, and parks should be aggregated for analysis. Aggregating data clearly has the advantage 

of increasing sample sizes and thus statistical power for analyses of ecological change. It also 

has the effect of averaging local site variation and so better detecting and identifying the more 

geographically widespread and longer-term trends that are the focus of the GLKN monitoring 

program. That is, more important systematic regional trends will emerge and not be obscured by 

the sampling error that occurs within individual sites. If sites within a given habitat type are 

aggregated for analysis with similar sites at other parks sampled in different years, aggregated 

analyses also gain the advantage of averaging the year-to-year variation that is otherwise 

confounded with park-to-park variation by the sampling design. Thus, the advantage of 

aggregating sites within habitat types can apply both when aggregating data within parks and 

when aggregating data across parks. 

 

Although aggregating data within habitat types appears ecologically and statistically justified, 

aggregating data across habitat types could create problems both in terms of being able to detect 

ecological change and in terms of being able to correctly interpret the trends we detect. While 
157

such aggregation increases the sample size, it does so at the cost of inflating site-to-site 

(sampling) variance with the additional heterogeneity that exists among disparate habitat types. 

Introducing this among-habitat variance runs the real risk of not detecting significant trends that 

may be occurring within certain habitat types. This is because either such trends do not occur in 

other habitat types (swamping trends that only occur within one habitat type), or because trends 

may run in counter directions in different habitat types. This risk makes clear the importance of 

stratification, which will be done here via post-hoc analyses of plant community variation. 

Grouping sites into habitat types should be done either by overlaying sites onto GIS vegetation 

cover maps or by using ordination analysis of community composition. Relatively few habitat 

types should be identified to avoid the small sample sizes that result from excessive subdividing. 

12.1. Static Descriptions 

After each field season, all data are analyzed to produce annual summaries on the status of 

terrestrial vegetation in the park(s) sampled that year. These annual summaries provide the 

Network with current status information relevant to policy and management decisions. A variety 

of analytical approaches are utilized to assess the status of 1) environmental variables associated 

with terrestrial vegetation, 2) population (species-level) variables, 3) community structure 

variables, and 4) community composition variables at each park and across parks in the Great 

Lakes region. Individual plots are the unit of replication for all analyses. 

Environmental Variables 

Assessing the status of various environmental variables associated with terrestrial vegetation is 

an important first step prior to evaluating complex relationships among environmental and 

vegetation response metrics. Many of the environmental variables that will be assessed, 

including proposed evaluations, are listed in Table 1. Details on some of these environmental 

variables are provided in SOPs linked to the General Vegetation Protocol. Numerous other 

landscape-level variables not listed in Table 1 may be obtained from GIS or public databases that 

document climate trends, etc. 

Exploratory Data Analysis: All continuous data variables will be evaluated with descriptive 

statistics. These will include generation of means and standard errors, and bar charts that 

compare among sites, habitats or parks (Table 1). Categorical data, consisting of binary 

variables (e.g., presence/absence) and variables with greater than two categories, will be 

summarized in tables and bar charts. The tables will list all sites and will indicate presence or 

absence of all binary variables of interest. Summaries of these data will be presented in clustered 

bar graphs to make comparisons among sites at the habitat, park and/or regional scale. Similarly, 

categorical data with greater than two categories (e.g., logging history) will be presented in 

tables and in clustered bar graphs. 

Inferential Data Analysis: For some key variables (i.e., presence of forest pathogens, ungulate 

browse), we will determine if there are statistically significant differences among habitats or 

regions within a park to identify areas that may warrant more immediate management or more 

intensive monitoring. 

Chi-square goodness of fit tests will be used to assess significant differences in the presence of 

earthworms and ungulate browse (Table 1) among different habitats or regions within a park. 
158

We will test the null hypothesis of no difference among habitats or regions in the relative 

proportion of sites with pest presence. 

If the assumptions of homogenous variances and normality are met, Analysis of Variance 

(ANOVA) will be used to assess differences in continuous variables such as total fuel loads 

among groups (e.g., habitats or regions). Residual plots and differences in the magnitude of 

standard deviations between groups will be evaluated to determine if the variances are 

homogenous, and Q-Q plots will be evaluated to ascertain that there are no strong departures 

from normality. Non-parametric Mann-Whitney U or Kruskal-Wallis tests will be conducted 

if these assumptions are not met, even after attempting standard data transformations. In the case 

of ANOVA, significant main effects will be further evaluated with Tukey’s HSD post-hoc test to 

distinguish among groups (Sokal and Rohlf 1995, Von Ende 2001). 
159

Table 1. Planned static descriptions and analyses for tracking trends of various environmental variables that are 

vegetation metrics. The geographic area refers to the area that each of these variables was assessed within a give

woody debris is actually measured along linear transects instead of within a specific area. Inferential analyses sug

descriptions are intended to be used to assess differences among habitats or regions within parks. The landscape 

listed here are intended to be included in the ‘environmental matrix’ for ordination analyses as well as included ain more complex analyses described later in the SOP; they do not necessarily need to be evaluated individually in 

suggested for the other variables in this table. 

Environmental Variables Data Type Geographic Area Static Descriptions 

A

Soil Horizon Depths (m2) Continuous 0.28m2 bar charts, ANOVA 

t-

Presence of Forest 

Pathogens Categorical 673.99m2 tables or bar charts, X2 

Presence of Earthworms Categorical 0.28m2 tables or bar charts, X2 

Ungulate Browse Presence Categorical 53.85m2 tables or bar charts, X2 

Relative Percent Browsed 

Shrubs Continuous 673.99m2 bar charts, ANOVA 

t-

Coarse & Fine Woody 

Debris (m) Continuous 87.84m bar charts, ANOVA 

t-

Total Fuel Loads (m2) Continuous 673.99m2 bar charts, ANOVA 

t-

Logging History 

Categories Categorical ~1 ha site area NA 

Fire History Categories Categorical ~1 ha site area NA 

Forest Patch Size Continuous (from GIS) NA 

Distance to Roads Continuous (from GIS) NA 

Distance to Open Water Continuous (from GIS) NA 
 

160

Taxonomy 

Correct identification and documentation of plant species is an important aspect of monitoring. 

Unknown species should be identified as soon as possible and corrections updated in the 

database prior to continuing with any analyses. It is likely that the field crew botanist may 

improve his/her identification skills over the season, and it is imperative that they go back and 

make any needed changes to species that possibly were misidentified at sites sampled earlier in 

the season. Frequent quality control assessments will be made by the Network botanist, which 

will consist in field-checking crew member identifications and the quality of their entered data. 

Nonetheless, the large amount of data collected suggests that there will be a number of plants 

that cannot be identified, in particular because species flower/fruit at different times throughout 

the growing season. In many cases, identifying to genus (or even family) will be all that is 

possible. For all parks, the Integrated Taxonomic Information System (ITIS) will serve as the 

nomenclatural authority. 

Population Variables 

We will evaluate the status of key indicator species (Table 2) that we select from the tree, shrub 

and herb vegetation layers. Species lists will be constructed after each sampling period, and the 

Network Botanist and local resource managers will select key species based on their 

susceptibility to various factors such as deer browse, pathogens, reduced natural disturbance 

frequency, etc. Table 2 lists various species-specific metrics and the scale at which they will be 

evaluated within a given park after each sample period. Table 3 again lists these variables, but 

also includes the proposed methods for data evaluation. 

Exploratory Data Analysis: Descriptive statistics (e.g., means, standard deviations, skewness, 

and kertosis) should be computed and the data checked for approximate normality and 

homogenous variances. Bar charts will be used to compare the frequency of key indicator 

species at the site, habitat, and park levels. Species frequency is calculated as the number of 

sample units that a species is observed within at a given site. For example, the frequency of 

understory vascular plants will be calculated as the proportion of 1-m2

 quadrats that a species is 

present within 

.

Histograms will be generated to evaluate the distribution of total basal area (m2/ha) for key 

individual tree species within each habitat or park. These distributions will be based on the 

following diameter classes: 2.5-9.9, 10-19.9, 20.0-29.9, 30-39.9, 40-49.9, 50-59.9, >60 cm. 

Both relativized and unrelativized frequencies will be assessed with these distribution graphs. 

Inferential Data Analysis: In most cases, there likely will be no reason to conduct inferential 

statistical analyses on population variables for static assessment reports. However, exceptions 

that would make such static assessments of interest to resource managers may include the need 

to compare current abundances of a key species between or among areas in a park with different 

management or disturbance histories. In the case of tree density, we will use standard 2-sample 

t-tests or ANOVAs (greater than two regions) to test the null hypothesis that the abundance of a 

particular species is equivalent between or among specific regions. For evaluating differences in 

the frequency of key herb or shrub species between different regions, we will conduct nonparametric replicated G-tests for goodness of fit (Sokal and Rohlf 1995). We will test the null 
161

Table 2. List of key species and functional groups that will be evaluated in analyses of population-level change 

may be added or removed from this table over time, depending on unforeseen species loss or invasions and the o

power to track changes in these species within or across parks. 

Park 

Species APIS GRPO INDU ISRO MISS PIRO SACN S

Trees and Shrubs 

Abies balsamea (balsam fir) X Acer saccharum (sugar maple) X X X X Ailanthus altissima (tree-of-heaven) X X X Fagus grandifolia (beech) X X X X Fraxinus spp. (ash) X X X X X X X Pinus resinosa (red pine) X Pinus strobus (white pine) X Tsuga canadensis (eastern hemlock) X X X X Taxus canadensis (Canada yew) X X X X 

Herbs and Functional Herb Groups 

Arisaema triphyllum (jack-in-the-pulpit) X X X X X X X Actaea spp. (baneberry) X X X X X X X Carex spp. X X X X X X X Osmorhiza spp. (sweet root) X X X X X X X Broad-leaved lily & ‘lily-like’ herbs1 X X X X X X X Abiotically pollinated herbs X X X X X X X Biotically pollinated herbs X X X X X X X Abiotically dispersed herbs X X X X X X X Biotically dispersed herbs X X X X X X X Ferns and Fern-Allies X X X X X X X 

1 Clintonia borealis, Maianthemum canadense, Streptopus spp., Smilacina spp., Trillium spp., Polygonatum spp., a
 

162

hypothesis that the proportion of quadrats occupied by a given species remains constant between 

different park regions. G-tests are very useful for inferring differences in species abundance 

across a region, in that individual G values from particular sites that may often fall short of 

significance can be combined across sites to obtain more powerful tests of overall change 

(Wiegmann and Waller 2006). Such tests also allow one to calculate G-heterogeneity values, 

which provide a measure of the consistency of shifts across sites or regions. Because Type I 

errors may occur with so many G-tests, Holm’s (1979) correction will be used for the reported Pvalues. 

We also may wish to compare basal area distributions of key tree species between, for example, 

a burned and unburned region of a park. We will employ the non-parametric KolmogorovSmirnov two-sample test to test the null hypothesis of no difference between two empirical 

sample distributions (Sokal and Rohlf 1995). To conduct the test, we will calculate the relative 

expected frequencies of the key tree species within each size class by dividing the frequencies 

observed within each region by the sample size of that region. We then will calculate the 

absolute value of the difference (d) of each of these relative frequencies for each size class. The 

maximum unsigned difference (D) is considered the largest d value. To calculate a test statistic, 

D is multiplied by the product of the sample sizes for the two sample periods. This test statistic 

is then compared to a critical value to determine the probability of observing a difference of that 

magnitude (or greater) between the two distributions (Sokal and Rohlf 1995). In the case that the 

distributions are significantly different, the density-diameter distributions will be referred to in 

order to determine which size classes may be represented to a greater or lesser degree in the 

burned and unburned regions. 

Community Structure 

We will evaluate the distribution of standing vegetation structure in park forests. This will 

involve assessing the total size of trees and shrubs and the relative abundance of the herbaceous 

and groundlayer vegetation across sites (Tables 3 and 4). 

Exploratory Data Analysis: Total basal area of trees will be averaged over sites and displayed in 

bar graphs to compare standing structure within habitats or parks. These data on tree and sapling 

diameters will be combined, and the relative frequency of trees within diameter classes (2.5-9.9, 

10-19.9, 20.0-29.9, 30-39.9, 40-49.9, 50-59.9, >60 cm) will be graphed. Similar graphs will be 

constructed to evaluate the size distributions of standing dead trees among habitats and parks. 

We also will compare average height and relative frequency of shrubs (Table 4), irregardless of 

species, among different habitats or regions within a park. Similarly, we will evaluate the 

relative frequency of all herbaceous and woody groundlayer vegetation among different habitats 

or regions. We will again employ bar graphs for these descriptive comparisons. 
 

163

Table 3. Specific population-level (for key species) and community-level response metrics to be 

used in analyses. Note that relativizing many of these metrics for analyses (in addition to their 

raw form) will also offer important information. 

 Vegetation 

Layer Specific Metrics Geographic Area 

Population Variables 

Basal Area / ha 673.99m2

Density/ ha 673.99m2

Frequency 673.99m2

Alive:Dead Ratio 673.99m2

Trees and 

Saplings 

Basal Area-Density Distributions 673.99m2

 

Shrubs Frequency 53.85m2

 

Groundlayer Frequency 36m2

Community variables

Total Basal Area / ha 673.99m2

Total Density /ha 673.99m2

Species Richness 673.99m2

Trees and 

Saplings 

Mean Similarity to other Sites 673.99m2

 

Mean Total Height (cm) 53.85m2

Frequency 53.85m2

Species Richness 53.85m2

Non-Native: Native Ratio 53.85m2

Shrubs 

Mean Similarity 53.85m2

 

Species Richness 1m2, 36m2, 673.99m2

Non-Native: Native Ratio 36m2 , 673.99m2

Rel. Abund. of Functional Groups 36m2

Mean Similarity 36m2

Groundlayer

Forest Quality Index (FQI) 36m2
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Table 4. Planned static descriptions and analyses for tracking trends of various population and community varia

statistical tests that are listed under the ‘static descriptions’ column will not always need to be conducted (see tex

the Kolmogorov-Smirnov 2-sample test. 

Variables Static Descriptions (and possible analyses) Analyses for T

Population (single species) Variables Basal Area (trees) bar charts, t-test, ANOVA t-test, repeated mDensity (trees) bar charts, t-test, ANOVA t-test, repeated mDead:Alive Ratio (trees) bar charts, X2 t-test, repeated m

Density-Basal Area Distributions (trees) bar charts, K-S test K-S Relative Abundance (frequency of a species in any vegetation layer) bar charts, t-test, ANOVA, G-tests G-t

Community Structure Variables 

Total Basal Area / ha (trees) bar charts, t-test, ANOVA t-test, repeated mTree Total Density / ha (trees) bar charts, t-test, ANOVA t-test, repeated mMean Total Height (shrubs) bar charts, t-test, ANOVA t-test, repeated mRelative Frequency (all shrubs) bar charts, G-test G-

Total Density-Basal Area Distributions (trees) bar charts, K-S test K-S Total Density-Basal Area Distributions of  Dead trees bar charts, K-S test K-S Relative Frequency (all herbs) bar charts, G-test G-

Community Composition Variables 

Species Richness bar charts, ordination, t-test, ANOVA t-test, repeated measurMean Similarity bar charts, ordination t-test, repeated measurNon-Native: Native Ratio bar charts, ordination, X2 t-test, repeated measur

Relative Abundance (frequency) of Different  Functional Groups bar charts, ordination, t-test, ANOVA t-test, repeated measurFloristic Quality Index (FQI) bar charts, ordination, t-test, ANOVA t-test, repeated measur
 

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Inferential Data Analysis: As was mentioned above for population variables, we will not always 

need to conduct inferential statistical analyses for static assessments of community structure. 

These analyses only need be conducted if park managers have a priori interest in determining if 

there are differences in, for example, tree basal area among different habitats or regions with 

contrasting management or disturbance histories. We will compare total tree basal area or 

average shrub height between or among habitats or regions with 2-sample t-tests or ANOVAs,

and will use the Kolmogorov-Smirnov two-sample test to evaluate differences in total basal 

area distributions between two habitats or regions (see section C above). G-tests will be used to 

assess departures from an equivalent relative distribution of all herbaceous or woody 

groundcover between different habitats or regions. 

Community Composition 

We will use several descriptive approaches to evaluate the status of community composition in 

parks after each sample period (Tables 3 and 4). In addition to total species richness, we also are 

interested in the relative abundance of the various species making up this sum richness. Besides 

calculating the number of non-native species relative to native species, we also we will evaluate 

the relative abundance of species with different morphological or life history traits (‘functional 

traits’). Differential assemblages of species with different functional traits may elucidate 

patterns is composition that reflect specific disturbances. For example, Wiegmann and Waller 

(2006) documented species losses over a 50-year period in the Northwoods of Wisconsin. The 

declining species were typically those that rely on animals for pollination and/or dispersal and 

that are sensitive to herbivory by deer; whereas, abiotically pollinated or dispersed species, 

especially gramminoids, were noted as increasers. 

We also will determine how similar sites are to each other with respect to the identity and 

abundance of species at a site compared to all other sites, and will evaluate the quality of sites 

based on the identity of the species present at each site. 

Exploratory Data Analysis: Descriptive statistics, including means and standard errors, will be 

generated to evaluate species richness (number of species) within and among sites of a given 

habitat and/or park. These exploratory analyses will be conducted separately for each forest 

layer: trees, saplings, shrubs, seedlings, and herbs. Species richness of herbs will be assessed at 

the levels of 1-m2

, 36-m2 quadrats and within 673-m2 walkthrough areas at each site. Species 

richness does not take into account composition; hence, it is recommended that separate 

evaluations be conducted for native and non-native species richness. Native status will be 

determined by the National Park Service’s NPSpecies website: 

http://www.nature.nps.gov/biology/biologicalinventories/npspecies/. The ratio of native to nonnative species will be assessed to identify regions within each park that may be more highly 

invaded by exotic species. 

It is of particular importance to recognize that the number of species encountered in a given 

sample generally reflects the area sampled (the typical species-area curve) and/or the number of 

individuals encountered. This makes it necessary to use a standard area or number of individuals 

when comparing diversity patterns across habitats or parks, e.g., by constructing ‘rarefaction’ 

curves (Smith et al. 2000, Gotelli and Colwell 2001). Species-area and species-individual curves 
 

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can be created with the free program EstimateS (Colwell 2005). Examples of these graphs can 

be viewed in Johnson et al. (2006). 

To examine similarity in community composition among sites and to identify groups of sites of 

similar habitat, nonmetric multidimensional scaling (NMDS) ordination may be conducted in 

PC-ORD (McCune and Grace 2002). This will be achieved through the following main steps: 1) 

Primary matrices containing species frequency values within a given forest strata for all sites will 

be constructed. 2) Secondary matrices containing environmental variables associated with each 

site will also be created. These environmental variables can be quantitative or categorical, and 

can include: depth of various soil horizons, presence (or abundance) of pests and pathogens, 

deer browse frequency or intensity (see SOP 9), cover type as determined by dominant tree 

species, logging history categories, etc. Descriptive site metrics, such as native to non-native 

species ratio, relative site richness, etc., will also be included in this secondary matrix. 3) 

Ordination analyses will be conducted following procedures outlined in McCune and Grace 

(2002). We will use the Sørensen (Bray-Curtis, Beals 1984) distance measure for ordinations. 

Separate ordinations will be performed for trees, shrubs and herbs (the herb dataset will also 

include woody shrub and seedling species sampled within ‘herb’ plots). Sites falling close 

together in ordination space are more similar in composition than are plots lying further apart. 

Hierarchical cluster analysis will be used to define community types or ‘habitats’ among sites. 

Choice of linkage method and distance measure will be made following the guidelines in 

McCune and Grace (2002). 

Other ways to identify sites may also be used. For example, forest type grouping by the Kotar 

classification method (Kotar and Burger 1996, Kotar et al. 2002) classifies “site potential,” 

which is based on hydrology and nutrient availability, and assessed by the current understory 

species. 

Inferential Data Analysis: After identifying groups of sites with hierarchical cluster analysis, we 

will use Multi-response Permutation Procedure (MRPP) to test the null hypothesis of no 

difference between two or more groups. MRPP is a nonparametric procedure recommended for 

ecological community data, as it can be used without meeting the typical assumptions of 

multivariate normality and homogeneity of variances (McCune and Grace 2002). The same 

distance measure selected for ordination analyses, Sørensen, will be applied to these MRPP 

analyses. This analysis will be conducted with PC-ORD, which provides an estimate of the 

effect size and a p-value. The effect size is estimated with the ‘chance-corrected within-group 

agreement’ (A) statistic that provides an estimate of the difference in within-group homogeneity 

to that which would be expected at random. If A = 1, then sites within each group are identical; 

whereas, A = 0 if groups of sites are as different as would be expected by chance. Statistical 

significance among groups will depend largely on the effect size and sample size, and McCune 

and Grace (2002) recommend that care be taken in ascertaining that statistical significance 

translates into ecological significance. 

12.2. Detecting Change 

Each park will be visited and surveyed every six years. Once a given park has been sampled a 

second time, tests for changes in metrics can commence. It will be possible to test for trends 

with more sensitivity and to evaluate changes in trends once the third sampling event for any 
 

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given park is complete. In addition to investigating changes in environmental variables and 

population and community vegetation variables, we also will be assessing the relationships 

among all of these variables (section 14.3). Such tests, however, depend on an important initial 

step, taxonomy synchronization, described below in section B. 

Environmental Variables 

Exploratory Data Analysis: For each metric (Table 1), we will compute descriptive statistics, 

including means and standard errors, which we will then present in bar charts with bars clustered 

by sample year. Data from sites within specific habitats will be combined for these analyses, but 

we also will provide summaries across all sites at the park level. 

Inferential Data Analysis: We will take a similar approach that was described in the ‘Static 

Descriptions’ section, but we will extend our analyses so we can look at significant changes in 

variables over time. We will determine if there are significant increases or decreases in the 

presence of key variables (i.e., forest pathogens, earthworms, ungulate browse) over time across 

sites within specific habitats or regions within a park. We will use Chi-square goodness of fit

tests to test the null hypothesis of no difference between sample periods in the relative proportion 

of sites with pests or pathogens present among habitats or regions. 

 

Parametric paired t-tests can be used to assess changes between two sample periods for 

continuously distributed metrics (e.g., total fuel loads; Table 1) when the standard assumptions 

of homogenous variances and normality are met. Repeated measures ANOVA should be used 

to assess changes in metrics once more than two sample periods have been accumulated. 

Analyses will be conducted separately for each park or with park added as a factor to the 

ANOVA. The repeated measures ANOVA models will include time (years) as a within-subject 

factor and several possible between-subject variables, depending on the questions being asked. 

The within-subject factor refers to the response variable being measured repeatedly for all sites 

and the between-subject factors refer to the response variable being measured on sites within 

independent groups of sites exposed to different conditions. Examples of between-subject 

variables that we may use include: cover class, presence of pests or pathogens, soil type, and 

park when data are aggregated across parks to look for regional trends (see Table 1 for list of 

environmental variables). The factors will be included into the ANOVA model so as to correctly 

represent the nested structure. The standard assumptions of ANOVA will be checked, but we 

also will check the sphericity assumption specific to repeated measures ANOVA. Mauchly’s test 

for sphericity will be used to assess whether the variance-covariance matrix is such that the 

magnitude of differences between any two years of the within-subject variable (time) are equal 

(Von Ende 2001). Von Ende (2001) states that this assumption often is not met when the model 

includes time as a within-subject factor. If the sphericity assumption is not met, then a correction 

factor (i.e., Huynh-Feldt Epsilon) to adjust the degrees of freedom will be used, because Fstatistics for within-subject factors and their interactions with other variables become inflated 

with departures from sphericity (Von Ende 2001). 

 

Significant differences in time will be assessed with a priori within-subject contrasts and 

significant between-subject effects will be followed up with Tukey’s HSD post-hoc test to 

distinguish among groups (Sokal and Rohlf 1995, Von Ende 2001). Profile plots with the 

response variable on the y-axis, time (sample period) on the x-axis, and groups plotted with 
 

168

different symbols will aid in the interpretation of significant interaction terms. For significant 

interaction terms, contrasts will be constructed and used in univariate analyses (i.e., t-test or 

ANOVA) to assess significant differences between groups among sample periods. Contrasts will 

be constructed to assess significance between specific groups at specific time intervals to avoid 

inflating the Type I error rate with multiple unneeded analyses. Holm’s (1979) adjustment will 

be conducted on all p-values reported from these contrast assessments to further limit the 

probability of making Type I errors with multiple analyses. 

Taxonomic Synchronization 

As data will be collected by different botanists among parks and among years, potential 

taxonomy errors in identification could occur. In addition, updated taxonomy may result in 

different species names being used across sample periods. To address these issues, the taxonomy 

must first be synchronized among sample periods before conducting analyses. The following 

steps outline this process: 1) A list of all species found within each site for all sample periods 

will be generated. 2) Database queries will be run to produce tables that present the frequency of 

each species at each site by sample year. 3) This list will be reviewed by the Network Botanist 

to address any taxonomic discrepancies among years. For example, this exercise may indicate 

that the botanist identified Dryopteris intermedia at all sites one year, but that another botanist in 

another year only recorded D. carthusiana at these sites. In this case, these two species are 

known to be somewhat difficult to differentiate in the field, and voucher without numerous 

voucher specimens (which may not be allowed for some parks) one would not be able to discern 

which botanist was correct. 4) All decisions to lump species or to change species identifications 

will be documented in the metadata (see SOP 13). Changes should not be made to the original 

datasets, but instead be saved to a ‘synchronized’ dataset directly linked to the metadata. 5) This 

process will be repeated with the raw data each time new data become available.

Changes in Population Variables 

Typical plant community datasets are full of zeros or low numbers, reflecting the fact that most 

species are infrequent across plots and across sites (McCune and Grace 2002). We likely will 

have limited power to detect significant changes in the abundance of individual species within 

sites and habitat types (Johnson et al. 2006); although, data aggregated across the region may 

prove to be informative. We will thus be circumspect when making inferences about population 

changes. As more power can be attained with aggregated or composite indicators, we will place 

most of our emphasis on composite groupings of species based on functional traits or taxonomic 

relatedness (Johnson et al. 2006). The following proposed analyses will refer to ‘species’, 

however, this term should be replaced with ‘composite indicator’ in these cases where specieslevel analyses are not being conducted. The metrics and the proposed analyses that we will use 

to evaluate them are listed in Table 4. 

Exploratory Data Analysis: For groundlayer vegetation, we will generate species lists and 

compute the frequency of occurrence for key species for each sample period. We will calculate 

the change in these species’ frequencies between each successive sample period and present 

these results in histograms or line graphs. By plotting positive and negative values, the 

histograms will clearly depict increasing or decreasing frequencies of species. 

 
 

169

We will evaluate changes in average basal area and average density of key tree species by 

presenting these data, along with standard errors, in bar charts. Changing ratios of alive and 

dead individuals of these key species also will be monitored through time; we will present those 

data in stacked bar graphs. We will use clustered bar graphs for cases where we wish to compare 

the metrics over time between or among habitats or regions in a park. Because we also will have 

abundance and size information for trees, we will produce density-size distribution curves for 

select tree species. This will include species susceptible to browse, as well as those with known 

regeneration or disease problems. The data on tree and sapling diameters will be combined and 

the relative frequency of trees within specific diameter classes (2.5-9.9, 10-19.9, 20.0-29.9, 30-

39.9, 40-49.9, 50-59.9, and >60 cm) will be presented in line or bar graphs at the habitat or park 

levels. 

Inferential Data Analysis: Changes in species abundance over time will be evaluated using nonparametric replicated G-tests for goodness of fit (Sokal and Rohlf 1995) and repeated 

measures analysis of variance (ANOVA). These analyses will allow us to determine whether 

individual species’ (or composite groups’) abundances are significantly increasing, declining, or 

remaining constant. 

Replicated G-tests will be used to analyze the frequency data collected for herbs, shrubs and 

seedlings (Table 4). We will test the null hypothesis that the proportion of quadrats occupied by 

a given species remains constant over repeated sampling intervals. G-tests are very useful for 

inferring changes in species abundance across a region, in that individual G values from 

particular sites that may often fall short of significance can be combined across sites to obtain 

more powerful tests of overall change (Wiegmann and Waller 2006). Such tests also allow one 

to calculate G-heterogeneity values, which provide a measure of the consistency of shifts across 

sites or regions. Because Type I errors may occur with so many G-tests, Holm’s (1979) 

correction may be used when reported P-values are being considered. 

Repeated measures ANOVA will be used to analyze continuously distributed variables such as 

tree basal area and density (Table 4). The procedures for this analysis are described above in 

section A for detecting changes in environmental variables. We will determine if the densitydiameter distributions of key species are significantly changing with time by implementing the 

Kolmogorov-Smirnov two-sample test. This will provide a sensitive test of the null hypothesis 

that the density-diameter distributions for trees sampled one year is the same as the sample 

distribution observed in a second sample year. This test is described in more detail above in 

section 14.1 part C. 

Changes in Community Structure 

We will evaluate the distributions of the relative sizes of individuals within each stratum between 

sample periods to evaluate how community structure is changing. These comparisons of 

structural changes should provide insight into successional and regenerative processes in the 

parks. They can also be used to assess how vegetation is being affected by other drivers of 

change such as deer (see SOP 9). From our understanding of basic successional processes, we 

generally expect tree stem density to decrease and stand BA to increase over time. Tracking 

structural changes over time will also allow us to make connections with other ‘Vital Signs’, 

such as bird and mammal communities that depend on this structure for food and shelter. 
 

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Exploratory Data Analysis: Community structure will be assessed by examining the size 

distribution of all species combined across sites within specific habitats or regions of parks. 

Response variables to be analyzed include tree basal area, tree density and shrub height (Table 

4). Tables, graphs, or both that present means and measures of variability (e.g., standard errors) 

will be generated to provide a descriptive view of changes in size metrics through time. Total 

tree density-size distributions will be constructed for each sample period, and we will identify 

shifts in overall forest structure within specific habitats or regions. Diameter size classes (2.5-

9.9, 10-19.9, 20.0-29.9, 30-39.9, 40-49.9, 50-59.9, >60 cm) will be presented on the x-axis of 

these graphs. Similarly, we will evaluate changes in the size distributions of standing dead trees 

among habitats and parks. 

We will calculate the instantaneous rate of change (lnλ) in average basal area, average density, 

and average shrub height between two sample periods by using the equation 

 ln(X ̄ year 5) – ln(X ̄ year 1) = lnλ. 

We will use bar charts to evaluate the instantaneous rate of change in these metrics by graphing 

lnλ for each site within a specific habitat or region of a park. 

Inferential Data Analysis: Separate repeated measures ANOVAs (or paired t-tests if there are 

only two sample periods) will be conducted for each response variable to determine how these 

structural metrics are changing over time within each park. These response variables will be 

transformed if they do not meet the ANOVA assumptions of homoscedasticity and normality. 

Refer to section 14.2 part A for more details on this analysis. 

 

We will implement the Kolmogorov-Smirnov two-sample test to determine if there are 

differences in the density-diameter distributions of total tree structure, irregardless of species, 

between time periods. The procedures for this test are outlined above in section 14.1 part C. 

Changes in Community Composition 

The understory and overstory data will be evaluated (Table 3) to determine whether species 

richness has changed, whether the ratio of non-natives:natives has shifted, whether sites have 

become more or less similar in species composition and relative frequency, and whether site 

quality is changing, reflecting changes in the identity of species at a site. The magnitude and 

direction of change for each of these components of community assessment also will be 

assessed. 

Exploratory Data Analysis: Descriptive statistics and graphs will be generated to compare 

metrics associated with each forest layer through time prior to conducting more rigorous 

statistical analyses. These initial data summaries should include assessments on the following 

metrics: 1) mean quadrat richness (1-m2

 herb plots only), 2) mean site-level richness of the 

groundlayer, 3) ratio of non-natives:natives, 4) mean site-level shrub richness, 5) mean tree 

richness, 6) mean coefficient of covariation (FQI, see below), and 7) site similarity (see below). 
 

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We will determine if the biological integrity of sites has changed over time by tracking changes 

in the Floristic Quality Index (FQI). FQI is a highly sensitive quantitative tool that assesses the 

quality of a site, specifically the degree of degradation, by taking into consideration the identity 

of species at these sites (Swink and Wilhelm 1979, 1994). Use of a FQI is dependent upon 

coefficients of conservatism (C) that are assigned to all species in the flora by trained botanists. 

Values of C range from 0-10, with 10 referring to those species that are very sensitive to 

degradation; hence, they are never found outside of intact natural communities. Weedy species 

that show no devotion to specific habitats are represented by C values of 0 (Swink and Wilhelm 

1994). All non-native species will be scored a ‘0’ for these analyses. The original formula for 

computing FQI has been demonstrated to confound C with species richness (Rooney and Rogers 

2002), hence, it is suggested that average of C (C ̄ ) be used as the index of floristic quality 

(Wilhelm and Masters 1994, Francis et al. 2000, Rooney and Rogers 2002). Values of C have 

been assigned to floras within Illinois (Taft et al. 1997), Michigan (Herman et al. 1997) and 

Wisconsin (Bernthal 2003). Wisconsin values for C will be applied to species lists associated 

with sites sampled in lower Minnesota (Saint Croix National Scenic Riverway and Mississippi 

River and National Recreation Area); whereas, Ontario’s values of C (Oldham et al. 1995) will 

be applied to the species list produced at Voyageur’s National Park and Grand Portage National 

Monument. Because values are specific to habitats within each state, they may differ for species 

as one travels further away from that state. Hence, we will consult with Minnesota master 

botanists to assure that these values of C are suitable for species lists associated with Minnesota 

sites. We also will consult with MN botanists in the case some of the species on our list are not 

on Wisconsin or Ontario’s list of species with C values. Isle Royale National Park was taken 

into consideration when assigning C values to Michigan’s species (T. Reznicek, pers. 

communication), so we will use the Michigan database for that park, despite the fact that it likely 

is ecologically more similar to Minnesota and southern Ontario. 

 

We will compute the mean C (C ̄ ) for each site, with C values for each species within 1m2

groundlayer quadrats weighted by abundance (frequency at a site). We will calculate the 

instantaneous rate of change (lnλ) in C ̄ between two sample periods by using the equation 

 ln(C ̄ year 5) – ln(C ̄ year 1) = lnλ. 

We will compare the instantaneous rate of change in C ̄ across sites within a specific habitat by 

graphing lnλ for each site using bar charts with bars ascending or descending to depict positive 

or negative changes. 

Similarly, we will compute the instantaneous rate of change in numerous other vegetation 

metrics at the site level, such as groundlayer species richness, shrub species richness, frequency 

of woody vegetation in groundlayer quadrats, tree BA, tree density, etc. We will then generate 

matrix scatterplots to look for trends between the rates of change among these variables, 

including the rate of change in C ̄ . In addition, we will construct matrix scatterplots to look for 

relationships between these rates of change and static values (from most recent sample year) 

among sites for each of these metrics (e.g., C ̄ , groundlayer richness, average tree BA, etc.).

Tests of similarity among sites will be conducted using a collective index of species composition 

and relative frequency. To do this, the degree of similarity of each site to all other sites in a 
 

172

given park will be determined using the Sørensen (Bray-Curtis) distance (S) for each pair of sites 

using the formula: 

Sjk = 1 – [(Σ│xij - xik│) / Σ(xij + xik)]

where xij and xik are the number of quadrats (or microplots or subplots) in which species i is 

found at site j or k (McCune and Grace 2002, Rooney et al. 2004). Values of S close to zero 

indicate low similarity of composition (relative abundance) whereas values close to 1 indicate 

strong similarity between sites. The ecological analysis programs PRIMER (PRIMER-E Ltd.) or 

R can be used to quickly generate similarity matrices. 

 

Changes in similarity of community composition among sites sampled through time will be 

evaluated using NMDS ordination and vector change analyses. Separate ordinations will be 

conducted for trees, saplings, shrubs and herbs (the herb dataset will include woody shrub and 

seedling species sampled within ‘herb’ plots). For each ordination, a primary matrix containing 

species abundances for all sites stacked by year will be overlain by an environmental matrix that 

includes a column for year and other columns containing a variety of environmental variables 

such as soil horizon depth, presence/absence of pathogens, deer browse frequency, cover type, 

logging history, etc. The ordinations will be graphed and vectors used to connect sites sampled 

during different years. The length and direction of these vectors will be analyzed to determine 

the magnitude of change through time and to determine if sites are changing in a consistent 

manner (i.e., whether sites are converging, diverging, or shifting composition parallel to each 

other.) 

Inferential Data Analysis: Repeated-measures ANOVA (or paired t-tests if less than 3 sample 

periods) will be used to determine if mean quadrat richness (1 m2

 herb plots only), site-level 

richness, ratio of non-natives:natives, C ̄ , and site similarity values have significantly changed in 

each forest stratum through time. The same approach for repeated measures ANOVA will be 

used as outlined above in section 14.2 part A. The ratio of non-natives:natives will be calculated 

using the same approach as documented in section 14.1 part D. If it can be determined from 

these analyses that sites are becoming more similar with time, then it is concluded that biotic 

homogenization is occurring (Rooney et al. 2004, Olden and Rooney 2001). Reports of changes 

in similarity should not be made without considering the ecological context. Specifically, if 

biotic homogenization is increasing, then it should be noted whether this is based on species loss 

or gain. 

 

Exploring Relationships Among Variables 

Identifying links among variables will provide us with a better understanding of factors that 

likely are driving the changes that are observed in individual variables. We will use several 

analytical approaches to evaluate relationships within and among environmental, population, and 

community variables (Fig. 2). These analyses are intended to be cursory investigations into the 

very complex linkages and interactions among numerous response and predictor variables. More 

elaborate and powerful analyses are suggested in the ‘Possible Extensions’ section (14.4) of this 

document. In exploring these relationships (Fig. 2), we can generate numerous questions about 

direct and indirect linkages among variables. For instance, are changes in the understory 

population and composition variables associated with changes in the overstory? Are these 
 

173

changes in the understory related to changes in environmental variables? Are changes in the 

overstory related to changes in environmental variables? Are changes in the shrub layer 

associated with changes in the overstory and/or changes in environmental variables? Can 

changes in some environmental variables be attributed to changes in other environmental 

variables? We will conduct exploratory and inferential analyses to address each of these 

questions, along with other questions that likely will arise. 

These direct and indirect linkages among variables may be evaluated as static relationships, or 

may be a function of changes among variables (Fig. 2). We do not separate static descriptions 

and change detection in this section, as some of these variables can actually be combined in 

some analyses. For example, tree BA (e.g., current year’s data) and changes in tree BA may 

both be included as variables in analyses (e.g., as predictor variables in a multiple regression). 

Exploratory Data Analysis: Scatterplots are a descriptive tool that will allow us to identify linear 

and non-linear relationships, as well as no association, between variables that we assume a priori 

to be related. We will generate matrix scatterplots to evaluate the strength of trends among these 

quantitative environmental, population, and community variables (Table 4, Fig. 1) at the site 

level. These scatterplots will include static metrics (from the most recent sample year) and rates 

of change for all variables. Relationships between some variables may be confounded by habitat 

type or some other variable used to groups sites (e.g., presence or absence of deer). We will 

explore these relationships by plotting these different groups with markers of different shape or 

color. 

Profile plots will be used to explore trends and interactions between quantitative and categorical 

variables over time. Quantitative responses will be plotted on the Y-axis and time will be plotted 

on the X-axis. Sites grouped by different habitats or regions will be represented with separate 

lines on the plot. Intersecting lines will indicate that there is an interaction between this habitat 

grouping and time, with respect to the quantitative variable being graphed. 
 

174

Community Variables 

(Structure & Composition)

Population 

Variables

Environmental 

Variables

Community Variable Changes 

(Structure & Composition)

Population 

Variable Changes

Environmental 

Variable Changes

Static Descriptions Change Detection

Community Variables 

(Structure & Composition)

Population 

Variables

Environmental 

Variables

Community Variables 

(Structure & Composition)

Population 

Variables

Environmental 

Variables

Community Variables 

(Structure & Composition)

Population 

Variables

Environmental 

Variables

Community Variable Changes 

(Structure & Composition)

Population 

Variable Changes

Environmental 

Variable Changes

Community Variable Changes 

(Structure & Composition)

Population 

Variable Changes

Environmental 

Variable Changes

Community Variable Changes 

(Structure & Composition)

Population 

Variable Changes

Environmental 

Variable Changes

Static Descriptions Change Detection

Correlations 

Linear Regression 

Nonlinear Regression 

G-tests 

Transition Probabilities 

Path Analysis

• General Analyses:

Correlations 

Linear Regression 

Nonlinear Regression 

G-tests 

Path Analysis

• General Analyses:

Community Variables 

(Structure & Composition)

Population 

Variables

Environmental 

Variables

Community Variable Changes 

(Structure & Composition)

Population 

Variable Changes

Environmental 

Variable Changes

Static Descriptions Change Detection

Community Variables 

(Structure & Composition)

Population 

Variables

Environmental 

Variables

Community Variables 

(Structure & Composition)

Population 

Variables

Environmental 

Variables

Community Variables 

(Structure & Composition)

Population 

Variables

Environmental 

Variables

Community Variable Changes 

(Structure & Composition)

Population 

Variable Changes

Environmental 

Variable Changes

Community Variable Changes 

(Structure & Composition)

Population 

Variable Changes

Environmental 

Variable Changes

Community Variable Changes 

(Structure & Composition)

Population 

Variable Changes

Environmental 

Variable Changes

Static Descriptions Change Detection

Correlations 

Linear Regression 

Nonlinear Regression 

G-tests 

Transition Probabilities 

Path Analysis

• General Analyses:

Correlations 

Linear Regression 

Nonlinear Regression 

G-tests 

Transition Probabilities 

Path Analysis

• General Analyses:

Correlations 

Linear Regression 

Nonlinear Regression 

G-tests 

Path Analysis

• General Analyses:

Correlations 

Linear Regression 

Nonlinear Regression 

G-tests 

Path Analysis

• General Analyses:

Figure 1. Relationships among environmental and vegetation variables, and suggested analyses. 

These relationships will be evaluated to detect changes among variables, as well as to make some 

static descriptions. 

Inferential Data Analysis: For all variable pairs that indicate a possible linear relationship, we 

will conduct correlation analyses. Unlike regression analysis, correlation analysis does not 

distinguish between predictor and response variables, but instead tests whether or not two 

variables vary together (interdependence; Sokal and Rohlf 1995). Correlation analysis is 

especially useful when the direction of dependence between two variables is not known a priori. 

Pearson’s product-moment correlation coefficients will be computed if the data are 

bivariately normally distributed. In the case that these data do not meet that assumption, we will 

instead employ non-parametric Spearman’s rank correlations (Sokal and Rohlf 1995). 

Correlation coefficients are a measure of the degree of a linear relationship between two 

variables. These values range from -1 to 1, with -1 indicating a strong negative relationship, 1 

indicating a strong positive relationship, and 0 indicating no linear relationship between the two 

variables. Standard statistical packages also test the null hypothesis that the two variables are 

uncorrelated. We will implement Holm’s (1979) correction on all reported P-values. We will 

avoid drawing strict inferences regarding causation before fully exploring the ecological 

significance of these results. 

We will use multiple regression analysis to identify all predictor variables (quantitative or 

categorical) that significantly explain some of the variation in a continuous response variable 
 

175

(e.g., shrub height, C ̄ , etc.). Partial correlation coefficients will provide information about the 

degree that a single variable is correlated with the predictor variable when the effect of all other 

variables is held constant. We only will include response variables that appear from the 

scatterplots to be linearly associated with the predictor variables. 

Categorical predictor variables, such as deer presence, will be entered into the regression 

analysis as ‘dummy variables’ – deer presence will be scored as ‘1’ and absence scored as ‘0’. 

We will use Q-Q plots to check that the residuals are normally distributed, and we will construct 

residual plots to check that these data meet the assumption of homoscedastic error variances. If 

our data do not meet these assumptions, we will consider employing standard data 

transformations on the response variable (Y), the predictor variables (X) secondarily, and both 

response and predictor variables lastly. Sokal and Rohlf (1995) recommend considering 

logarithmic transformations on the predictor variable(s) in cases where proportional changes in 

the predictor variable result in a linear response by the response variable. This may apply to the 

relationship of tree BA and tree density – we generally expect that tree BA may increase with 

successive decreases in density. We will avoid complex transformations that may impede our 

ability to make meaningful interpretations of the results (Sokal and Rohlf 1995). Standard 

statistical packages should report values (‘tolerances’ or ‘variance inflation factors’) that indicate 

the degree of collinearity between predictor variables; we will remove variables from our model 

to avoid collinear relationships. In addition to reporting the adjusted R2

 values and overall Pvalue for the regression model, we also will report partial correlation coefficients, t-statistics and 

P-values for each predictor variable and interaction term in the model. 

We will use non-linear regression to assess statistical relationships between variables not fitting 

elliptical or circular patterns in the scatterplots (Sokal and Rohlf 1995). Standard multiple 

regression equations will be fitted with increasing powers of X so as to produce a line that better 

fits the non-linear data. We generally will not attempt polynomial equations with powers of X 

greater than the cubic term, as one degree of freedom is lost with each polynomial term that is 

added to the equation. We will proceed with the typical procedures for analyzing data with 

multiple regression analysis; however, we will not report significance for the coefficients of the 

powers of predictor variables (Sokal and Rohlf 1995). 

We will explore the effect of continuous variables on categorical (or a combination of categorical 

and quantitative) response variables with logistic regression analysis. In contrast with linear 

regression, where the response variable is continuous, logistic regression will allow us to 

determine predictor variables that significantly explain variation in a categorical response 

variable. These response variables are typically one of two possible values (e.g., present/absent 

or yes/no) and will be coded as 1 or 0. Thus, in logistic regression, the mean of the distribution 

will equal the proportion of 1s. For example, if evidence of earthworms (coded 1) is located at 

42 sites and no evidence of earthworms (coded 0) is found at 58 of 100 sites assessed, the mean 

of this distribution is 0.42. In logistic regression, the natural log of this probability will be 

determined (the logit) and maximum likelihood estimation will be used to estimate the 

probability of an event (earthworm presence, in this case) occurring. Besides probability, 

logistic regression can also be used to determine the variance in the dependent variable explained 

by the predictor variable and to order the importance of predictor variables if multiple ones are 

tested (Sokal and Rohlf 1995). 
 

176

Understanding a phenomenon in nature most often involves understanding the numerous factors 

and interactions leading to the event state. Multiple regression analysis offers the ability to deal 

with correlation among predictor variables to explore the effect that each of these variables has 

on the response variable when the effect of all other variables is held constant. Multiple 

regression, however, only considers one response variable, and is limited by not having the 

capability to test relationships between multiple predictor variables. Path analysis is an 

analytical approach that allows one to test potentially complicated patterns of causation among a 

set of several predictor variables (Mitchell 2001). The first step in path analysis is to construct a 

diagram that reflects hypothesized relationships among variables (for example see Fig. 2). These 

hypothesized causal relationships will be constructed prior to any exploratory analyses to avoid 

biasing the results. Quantitative and categorical variables can be included, however, categorical 

variables will be entered into these analyses as ‘dummy’ (0’s and 1’s) variables. It is 

recommended that there be 10-20 times the number of observations in the data set than there are 

number of factors included in the path analysis. Some of the major assumptions of path analyses 

include: 1) that causality be considered (logical order of variables), 2) that the effects be linear, 

and 3) that the effects be additive. The analysis also assumes that the residuals are normally 

distributed, so transformation of some variables may need to be considered (Mitchell 2001). 

Multiple regression equations will be analyzed for all response variables such as they are 

represented in the diagram. Standardized partial regression coefficients will be generated via 

each of these analyses. These constitute the path coefficients which can then be combined to 

determine the direct and indirect effects of predictor variables. Direct effects represent the 

proportion of variance in the response variable that can be attributed now to one specific 

predictor variable that affects the response variable directly. However, a given predictor variable 

may have indirect effects on a given response variable via the other variables (the products of the 

path coefficients connecting them). These direct and indirect effects can be summed to calculate 

the total effect of that predictor variable on a given response variable. This exercise allows us to 

determine if the direct and indirect effects differ in magnitude or in sign. These results will be 

presented visually in path diagrams with variably-sized arrows to indicate the magnitude of 

relationships between variables (with dashed lines depicting negative relationships (Mitchell 

2001). There are sometimes more than one path diagram that can be constructed to test 

hypothesized relationships among variables. Path analysis will be followed with structural 

equation modeling to evaluate the explanatory power of competing models (Mitchell 2001). 

Grace’s (2006) book also provides a helpful resource for structural equation modeling and path 

analysis. 
 

177

Forest 

Quality 

Index

Tree Basal 

Area

Shrub 

Frequency

Forest 

Patch Size

Relative % 

Browsed 

Shrubs

U

U

U

U

Forest 

Quality 

Index

Tree Basal 

Area

Shrub 

Frequency

Forest 

Patch Size

Relative % 

Browsed 

Shrubs

Forest 

Quality 

Index

Tree Basal 

Area

Shrub 

Frequency

Forest 

Patch Size

Relative % 

Browsed 

Shrubs

U

U

U

U

Figure 2. Example path diagram showing hypothesized relationships among variables considered to 

influence Forest Quality Index (C ̄ ) among sites. Residual variance, or “unknown” factors are represented 

as “U”. Direct causal relationships are presented with single-headed arrows on straight lines. Doubleended arrows on the curved line represents a correlation with unknown directionality. 

12.4. Possible Extensions 

As time, resources, and expertise allow, we will consider applying a number of advanced 

analyses to our data. Some of these possible analyses are discussed below; however, it should be 

kept in mind that this is not meant to be an exhaustive treatment of all possible analyses. As we 

become more familiar with working with these complex long-term data, we will surely develop a 

better sense of what analyses will best serve our needs. 

Once groups of sites have been identified, classification and regression tree analyses could 

then be used to determine if specific environmental variables (Table 1) predict site groupings (or 

communities). Regression tree analysis uses a recursive algorithm to identify environmental 

predictor variables associated within homogenous subsets of the overall dataset. This recursive 

partitioning approach identifies ecological relationships in the data that linear models may not 

capture. Other advantages of using regression trees include: 1) they are not affected by 

multicollinearity, 2) they easily deal with a mix of quantitative, ranked, and categorical variables, 

of which there can by more than two categories, 3) they are not affected by missing values, and 

4) they provide a useful visual representation to facilitate interpretation (Urban 2002). 

 

These data can be compiled for all sites within a park or across multiple parks (for those with 

available data) and regression tree analysis can be conducted to determine if understory 

structure can be attributed to specific environmental site characteristics. Such an evaluation will 

highlight sites or specific habitats within (and possibly among) parks that may currently be 

experiencing negative effects from pests, pathogens, or some other driver of change. 

Classification and regression tree analysis can be used to determine whether sites changing in 

a similar manner are associated with specific environmental predictor variables. To do this, 

homogeneous groups of sites based on similarity of vector changes must first be identified using 
 

178

a clustering technique such as the flexible-beta linkage method in PC-ORD (McCune and Grace 

2002). This clustering of sites should be based on the change matrix (McCune and Grace 2002, 

p 77). The standard Sørensen (Bray-Curtis) distance measure should not be used in this case, as 

it does not perform well when applied to vectors with negative exponents. Instead, the measure 

of Euclidean, Relativized Euclidian or Correlation distance should be selected (M. Fulton, pers. 

comm.). Environmental site characteristics (Table 1) can then be assessed to determine what 

influence they may have had on the organization of sites within clusters by using regression tree 

analyses available through a variety of commercial software such as S-PLUS (S-PLUS ®, 

Insightful, Inc.) or SPSS AnswerTree 3.1 (SPSS® Inc., Chicago, IL) as well as freeware 

(QUEST, CRUISE, GUIDE, and LOTUS) that can be downloaded from W. Loh’s website 

(http://www.stat.wisc.edu/~loh/). 

Rather than testing innumerable single-variable hypotheses about change in given variables, for 

several variables (e.g., absolute herbaceous cover, relative forb cover, frequency or cover of an 

individual forb species) we will utilize information-theoretic analyses to compare, in a 

strength-of-evidence framework, the relative plausibility of numerous competing models to 

determine which combination of variables best explains variability in those response variables. 

This approach ranks models of varying complexity not only on their fit to the data, but also with 

a criterion of parsimony (i.e., using the least complicated model possible to describe dynamics

succinctly). Information-theoretic approaches use a likelihood framework to rank models based 

upon how much information is lost when a given model is used to portray ‘truth.’ The true 

model is defined as one that represents reality without any error; i.e., a model that would contain 

all of the information about the system under investigation (Hobbs and Hilborn 2006). Akaike’s 

Information Criterion (Akaike 1973) thus estimates the expected value of information lost with 

the equation: AIC = -2 ln(L) + 2K . L is the likelihood associated with the maximum likelihood 

estimates of the model parameters, given the data set, and K denotes the number of parameters in 

the model (including all error terms). For this approach to be confirmatory rather than 

exploratory, the set of competing models must be specified before any analyses are undertaken 

(Burnham and Anderson 2002). In addition to computing AIC (or AICc for small sample sizes, 

QAIC for overdispersed data, or BIC for a Bayesian approach), we will also calculate ΔAIC, 

Akaike weight, and the evidence ratio for each model. Though these approaches are designed 

more to rank various models rather than identify importance of individual factors, we will 

calculate relative importance of each factor. If we are interested in using the models to predict 

future values of vegetation, model averaging will be performed. All analytical procedures will 

follow guidelines of Burnham and Anderson (2002). Beever et al. (2003, 2006) provide 

illustrations of uses of information-theoretic analyses to understand dynamics of natural 

resources in wilderness areas. See Table 5 for an example of a partially-filled-out table for a 

vegetation metric that might be especially important for the Great Lakes ecoregion.

Knowledge of past and current vegetation composition and environmental variables (Table 1) 

can be used to predict future vegetation composition in Network parks. Artificial neural 

networks (ANNs) can be useful for forecasting community composition, as these models make 

inferences based on generalized patterns in complex ecological data. ANNs take a nonmechanistic approach; hence, these are very pragmatic models for dealing with unforeseen and 

unidentified mechanisms responsible for current and changing vegetation composition (Tan and 

Smeins 1996). 
 

179

Table 5. Total herbaceous frequency, all understory species combined, at APIS during vegetation 

monitoring at 35 sites during 2007-2019. Presumably, 'Year' would account for interannual fluctuations 

in weather and other random factors. 'Deer' is the finest-scale estimate of density of white-tailed deer 

(Odocoileus virginiatus) at the site the year before the vegetation sampling. 'Cmty' denotes the vegetative 

community that is present at a site. A "+" sign means that all factors united by the symbol are put into the 

model; a "*" sign means that in addition to those two factors each being in the model, the interaction of 

the two factors is also present in the model. The null model has no factors (other than the error term), and 

gives evidence of how relatively meaningful the models are. All column definitions follow Burnham and 

Anderson (2002). 

model 

# model description # Parameters -2*LOG(l) AICc ΔAICc

Akaike 

weight 

Evidence 

ratio 

1 Year 2 35.4 54.7 0 0.45 1 

2 Deer density 

(="Deer") 2 42 56.8 2.1 0.15 2.89 

3 Veg. cmty type 

(="Cmty") 2 35.3 56.9 2.2 0.15 3.03 

4 Year + Cmty 3 37 58.6 3.9 0.06 6.98 

5 Year + Deer 3 41.7 58.8 4.1 0.06 7.62 

6 Year + Cmty + Deer 4 41.7 58.8 4.1 0.05 7.65 

7 Cmty + Deer 3 

8 Year + Tree Cover 3 

9 Year * Cmty 4 

10 Null 1 102.6 108.7 54 0 >106
 

180

12.5. Literature Cited 

Akaike, H. 1973. Information theory and an extension of the maximum likelihood principle. 

Pages 267-281 in B. N. Petrov and F. Csaki, editors. The second international symposium 

on information theory. Akad ́emiai Kiado, Budapest, Hungary. 

Beals, E. W. 1984. Bray-Curtis ordination: An effective strategy for analysis of multivariate 

ecological data. Advances in Ecological Research 14:1-56. 

Beever, E. A., P. F. Brussard, and J. Berger. 2003. Patterns of extirpation among isolated 

populations of pikas (Ochotona princeps) in the Great Basin. Journal of Mammalogy 

84:37-54. 

Beever, E. A., M. Huso, and D. A. Pyke. 2006. Multi-scale responses of soil stability and 

invasive plants to removal of non-native grazers from an arid conservation reserve 

Diversity and Distributions 12:258-268. 

Bernthal, T. W. 2003. Development of a floristic quality assessment methodology for Wisconsin. 

Wisconsin Department of Natural Resources, Bureau of Integrated Science Services. 

Madison, WI, USA: PUB-SS-986 2003. 

Burnham, K. P., and D. R. Anderson. 2002. Model selection and inference: a practical 

information-theoretic approach. 2nd edition. Springer-Verlag, New York, NY. 

Colwell, R. K. 2005. EstimateS: Statistical estimation of species richness and shared species 

from samples. Version 7.5. User's guide and application published at: 

http://purl.oclc.org/estimates. 

Francis, C. M., M. J. W. Austen, J. M. Bowles, and W. B. Draper. 2000. Assessing floristic 

quality in southern Ontario woodlands. Natural Areas Journal 20:66-77. 

Gotelli, N. J., and R. K. Colwell. 2001. Quantifying biodiversity: procedures and pitfalls in the 

measurement and comparison of species richness. Ecology Letters 4:379-391. 

Grace, J. B. 2006. Structural Equation Modeling and Natural Systems. Cambridge University 

Press, Cambridge, UK. 

Herman, K. D., L. A. Masters, M. R. Penskar, A. A. Reznicek, G. S. Wilhelm, and W. W. 

Brodozicz. 1997. Floristic quality assessment: Development and application in the state 

of Michigan (USA). Natural Areas Journal 17:265-279. 

Hobbs, N. T., and R. Hilborn. 2006. Alternatives to statistical hypothesis testing in ecology: A 

guide to self teaching. Ecological Applications 16:5-19. 

Holm, S. 1979. A simple sequentially repetitive multiple test procedure. Scandinavian Journal of 

Statistics 6:65-70. 
 

181

Johnson, S. E., E. L. Mudrak, and D. M. Waller. 2006. A comparison of sampling methodologies 

for long-term forest vegetation monitoring in the Great Lakes Network National Parks. 

National Park Service, Great Lakes Inventory and Monitoring Network, Ashland, WI. 

Great Lakes Network Technical Report: GLKN/2006/08. 140 pp. 

Kotar, J. and T. L. Burger. 1996. A guide to forest communities and habitat types of central and 

southern Wisconsin. Department of Forestry, University of Wisconsin-Madison. 

Kotar, J., J. Kovich, and T. L. Burger. 2002. A guide to forest communities and habitat types of 

northern Wisconsin. Second edition. Department of Forestry, University of WisconsinMadison. 

 

McCune, B., and J. B. Grace. 2002. Analysis of Ecological Communities. MjM Software Design, 

Gleneden Beach, Oregon. 

Mitchell, R. J. 2001. Path Analysis. Pages 217-234 in S. M. Scheiner and J. Gurevitch, editors. 

Design and Analysis of Ecological Experiments. Oxford University Press, Inc., Oxford. 

Olden, J. D., and T. P. Rooney. 2006. On defining and quantifying biotic homogenization. 

Global Ecology and Biogeography 15:113-120. 

Oldham, M. J., W. D. Bakowski, and D. L. Sutherland. 1995. Floristic quality assessment system 

for southern Ontario. Natural Heritage Information Centre, Ontario Ministry of Natural 

Resources. 

Rooney, T. P., and D. A. Rogers. 2002. The modified floristic quality index. Natural Areas 

Journal 22:340-344. 

Rooney, T. P., S. M. Wiegmann, D. A. Rogers, and D. M. Waller. 2004. Biotic impoverishment 

and homogenization in unfragmented forest understory communities. Conservation 

Biology 18:787-798. 

Smith, W., A. Solow, and C. Chu. 2000. An index of the contribution of spatial community 

structure to the species-accumulation curve. Ecology 81:3233-3236. 

Sokal, R. R., and F. J. Rohlf. 1995. Biometry. third edition. W. H. Freeman and Company, New 

York. 

Swink, F., and G. Wilhelm. 1979. Plants of the Chicago Region. 3rd edition. Morton Arboretum, 

Lisle IL. 

Swink, F., and G. Wilhelm. 1994. Plants of the Chicago Region. 4th edition. Indiana Academy of 

Science, Indianapolis. 
 

182

Taft, J. B., G. Wilhelm, D. Ladd, and L. A. Masters. 1997. Floristic Quality Assessment for 

vegetation in Illinois, a method for assessing vegetation integrity. Erigenia 15:1-24 and 

Appendix. 

Tan, S. S., and F. E. Smeins. 1996. Predicting grassland community changes with an artificial 

nerual network model. Ecological Modelling 84:91-97. 

Urban, D. L. 2002. Classification and regression trees. Pages 222-232 in B. McCune and J. B. 

Grace, editors. Analysis of Ecological Communities. MjM Software Design, Gleneden 

Beach, OR. 

Von Ende, C. N. 2001. Repeated-measures analysis: growth and other time dependent measures. 

Pages 134-157 in S. M. Scheiner and J. Gurevitch, editors. Design and Analysis of 

Ecological Experiments.Oxford University Press, Inc., Oxford. 

Wiegmann, S. M., and D. M. Waller. 2006. Biotic homogenization in forest understories: 

identity and traits of historical “winners” and “losers”. Biological Conservation 129:109-

123. 

Wilhelm, G., and L. A. Masters. 1994. Floristic changes after five growing seasons in burned and 

unburned woodland. Erigenia 13:141-150. 
 

183

Reporting 

STANDARD OPERATING PROCEDURE #13 

Version 1.0 (7/1/08) 

Suzanne Sanders and Joan Elias 

NPS – Great Lakes Network 

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13.0 Introduction 

A primary goal of the NPS Servicewide I&M Program is to ensure that the results and 

knowledge gleaned from monitoring are shared with all appropriate parties, especially the parks 

and their natural resource managers. Because the Network’s main focus is to assist parks with 

monitoring needs, we will strive to provide park managers with clear, meaningful products to 

convey our findings. 

While the Network primarily addresses concerns of the parks, its monitoring program has the 

potential to serve a much broader community. For example, monitoring projects can provide a 

starting point for external scientific research (especially to establish cause-effect relationships), 

and can provide insights for adaptive management on other public lands. The Network is also 

accountable to multiple organizations within the federal government, including the NPS I&M 

Program and the U.S. Congress. To ensure accountability and to meet the requests of all parties, 

we will provide the types of reports and communications detailed below.

13.1 Annual Summary Reports 

A summary report will be produced annually for the Vital Signs associated with the vegetation 

protocol that were monitored during the previous year. The primary audience for the annual 

summary reports will be parks. These summaries will be communications to document our 

efforts and convey the findings of the previous field season. At a minimum they will provide: 

• a brief introduction that describes why that Vital Sign is being monitored; 

• an outline of the sampling strategy, including the number of sites sampled, parameters 

measured, and analyses performed; 

• data summaries, including tables and figures to enhance visual presentation, as well as a 

text explanation of the findings; 

• any other relevant or significant findings; and 

• a limited discussion section in which important results are interpreted. 

The project manager (terrestrial ecologist (planned)) will take the lead in writing the report and 

will coordinate an internal review. Drafts of annual summary reports will be completed by 

January 15 for a two week review period by the parks. The final reports will be provided to parks 

on March 1 of the year following the monitoring. 

13.2 Analysis and Synthesis Reports 

Detailed reports in which data are analyzed and synthesized will be produced on a periodic basis, 

with the frequency depending on the given Vital Sign. For the general vegetation protocol, 

analysis and synthesis reports will be written every six years, after each completion of the 

sampling rotation. They will be written in the format of a scientific journal article (abstract, 

introduction, methods, results, discussion, literature cited) and will contain in depth analyses as 

outlined in the protocol (Sanders et al. 2008) and SOP #14 (Data summary and analysis). 

Further, these comprehensive reports will: 

• place the observed results in both a regional and historical context by relating them to 

other published literature; 

• discuss the significance of the results in terms of environmental change; and 
 

185

• provide management recommendations based on the findings. 

The project manager (terrestrial ecologist (planned)) will take the lead in writing the analysis and 

synthesis reports, and will coordinate an internal review. The target audience of these reports will 

be the parks (primarily the natural resource managers), the Network, and both regional and 

Servicewide I&M. Outside of the park service, the target audience includes the four state 

departments of natural resources (Indiana, Michigan, Minnesota, and Wisconsin); the 

Northeastern Area Forest Health Protection unit and the North Central Research Station, both of 

the of the National Forest Service; and the broader scientific community. 

Drafts of analysis and synthesis reports will be completed by January 31 of the year that follows 

completion of the monitoring cycle. The parks will have a 30 day period for comment and input. 

In addition, these drafts may also be sent to outside sources for further review. The extent of 

review will depend on the analytical complexity of the methods and the gravity of inference and 

recommendations. The final reports will be due on April 1 of the year following completion of 

the monitoring cycle. 

The first analysis and synthesis report will be written following a full rotation of monitoring. At 

this time, vegetation and the associated Vital Signs will have been monitored in each park for 

one season, allowing a comparison of vegetation characteristics across parks. In subsequent 

years, as parks are monitored repeatedly, more in-depth analyses will be conducted for individual 

parks as well as across parks.

13.3 Scientific Journal Articles 

Because the vegetation monitoring protocol has been designed with rigorous standards for 

sampling design and analysis, monitoring results are expected to be highly defensible and meet 

the standards of the peer-review process. The publication of monitoring results in scientific 

journals will allow the Network to reach the scientific community in a way that internal NPS 

reports cannot. Further, peer-reviewed publications can promote collaborative investigation by 

members of the scientific community, either independently or in cooperation with the Network. 

Ultimately, this process should foster a greater understanding of ecosystem components and 

processes. 

For these reasons, the Great Lakes I&M Network will strive to publish analysis and synthesis 

reports in peer-reviewed scientific journals. We will encourage the preparation of manuscripts by 

having reviewers of analysis and syntheses reports recommend whether publication is warranted 

and suggest appropriate journals. The quantitative ecologist and Network coordinator will track 

these recommendations and encourage and provide work time respectively.

13.4 Other Communications 

While reports are a definitive method of documenting the progress of each program, other means 

of communication can further disseminate information to a broader audience. To this end, we 

will provide the following additional types of communications: 

Briefings to park biologists: The project manager will present the findings from the water 

quality monitoring program to the biologists from the parks in which monitoring was conducted 
 

186

the previous year. These presentations, which will likely occur at the annual technical committee 

meeting in March, will provide a concise synopsis of monitoring results as well as management 

considerations. 

Conference presentations: When possible, the project manager will present monitoring results at 

regional and national scientific conferences. Such presentations will allow the Network to reach 

the broader scientific community, as well as land managers and conservation practitioners. 

Potential conferences include those sponsored by the Ecological Society of America, Society for 

Conservation Biology, The Wildlife Society, the Natural Areas Association, the NPS Water 

Professionals Meeting, and the George Wright Society. At a more local scale, the Western Great 

Lakes Research Conference, which is sponsored in part by the Network, is a valuable venue for 

information exchange.

13.5 Literature Cited 

Johnson, S.E., D.M. Waller, Suzanne Sanders, and Erik A. Beever. 2008. Standard operating 

procedure #14 Data Summary and Analysis. In General vegetation monitoring protocol 

for the Great Lakes Network, Version 1.0. National Park Service, Great Lakes Network, 

Ashland, Wisconsin. 

Sanders, S., and J. Elias. 2008. Standard operating procedure #17, Procedure for revising the 

protocol. In General vegetation monitoring protocol for the Great Lakes Network, 

Version 1.0. National Park Service, Great Lakes Network, Ashland, Wisconsin.

Sanders, S., S. E. Johnson, and D. M. Waller. 2008. General vegetation monitoring protocol for 

the Great Lakes Network, Version 1.0. National Park Service, Great Lakes Network, 

Ashland, Wisconsin. 
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Procedures after the Field Season 

STANDARD OPERATING PROCEDURE #14 

Version 1.0 (7/1/08) 

Suzanne Sanders 

NPS-Great Lakes Network 

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14.0 Introduction 

The procedures associated with completing the sampling season must be performed in 

earnest in the weeks immediately following the conclusion of sampling, and continued 

through the following April. The purpose of this SOP is to identify these procedures, 

thereby promoting better project management. Please note that the procedures noted here 

from January through April overlap with pre-season procedures, listed in SOP #1. 

14.1 August 

• Soil sample processing 

• September 1 – 7: Perform reviews of field crew members 

• September 1 – 14: Perform general administrative and personnel issues 

associating with terminating field crew members 

14.2 September 

• September 1 – 30: Perform data archival procedures 

• If any species previously unknown to occur in a park were observed, add these to 

the park species list, and to NPSpecies. 

14.3 October 

• October 1 – 14: Review protocol and make changes. 

• October 15 – 31: Begin data analysis and report writing 

14.4 November 

• November 1 – 30: Continue data analysis and report writing. 

14.5 December 

• December 1 – 31: Continue data analysis and report writing. 

14.6 January 

• January 1 – 15: Data analysis and report writing. 

• January 15: Present draft of annual summary report to all parks where monitoring 

occurred. They will then have two weeks to review it. 

• January 31: Present draft of analysis and synthesis report to all parks2

. They will 

then have 30 days to review it. 

14.7 February 

• February 1 – 28: Revise annual summary report based on comments by parks. 

14.8 March 

• March 1: Present final copy of annual summary report to parks where monitoring 

occurred. 

• March 15 – 31: Give presentation of results to technical committee at the annual 

meeting, usually scheduled at the end of March. 

 

2

 To be done every six years at the completion of each monitoring cycle. 
 

189

14.9 April 

• April 1: Present final copy of analysis and synthesis report to parks1. 

• April 1 – 30: At the discretion of the Terrestrial Ecologist and Network 

Coordinator, prepare analysis and synthesis report as a manuscript to a peer 

reviewed scientific journal1

. 

 

191

Procedures for Revising the Protocol 

STANDARD OPERATING PROCEDURE #15 

Version 1.0 (7/1/08) 

Suzanne Sanders and Joan Elias 

NPS – Great Lakes Network 

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15.0 Introduction 

Because of the long-term nature of the National Park Service’s monitoring program, the projects 

must necessarily accommodate change. Refined field methods, advances in analysis techniques, 

and feedback from field crews and project managers can all contribute to improving the 

monitoring protocol. The purpose of the current SOP is to define a systematic and routine 

process for incorporating these changes into the protocol. 

15.1 Steps for Revising the Protocol 

1. Attempt to incorporate the changes by first modifying only the SOP(s), without making 

changes to the protocol narrative. However, if it is clear that changes will also be needed on the 

narrative, then revise it as well. 

2. Make all revisions using the Track Changes feature of Microsoft Word. For minor changes, at 

least one other person must review the revision. If the change is more extensive, a discussion of 

the changes by Network staff is warranted before acceptance of the revision. For major changes, 

review from outside of the Network should be sought. Examples of major changes include 

modifications of the sampling design, significantly altered field methods, and revised analysis 

techniques. 

3. Record the changes in the revision history log of the SOP and/or in the narrative, as 

appropriate. Include the date of revision, full name(s) and affiliation(s) of author(s), description 

of and reasons for the changes, and section of SOP or narrative where changes were made. 

4. Rename the version of the SOP and/or narrative. For minor changes, only revise the version 

number after the decimal point (e.g., change V. 1.1 to V. 1.2). For major changes, revise the 

number before the decimal point (e.g., V. 2.3 to 3.0). Also change the version number of the SOP 

or protocol in the header or footer, as appropriate. 

5. Notify the data manager of the change(s) so that the metadata of the project database will be 

updated. 

6. Distribute the revised version to all appropriate parties, including the members of the field 

crew and appropriate GLKN staff. The revised version must also be posted on the Network’s 

website. 

7. Maintain a library of previous versions. Such historical information may be crucial for 

understanding, interpreting, and analyzing data. 
 

193

Quality Assurance/Quality Control 

STANDARD OPERATING PROCEDURE #16 

Version 1.0 (7/1/08) 

Suzanne Sanders 

NPS-Great Lakes Network 

Revision History Log: 

Previous 

Version # 

Revision 

Date 

Author (with 

title and 

affiliation) 

Location in Document 

and Concise 

Description of Revision 

Reason for Change New 

Version # 

 

Add rows as needed for each change or set of changes tied to an updated version number 
 

194

16.0 Introduction 

The success of the vegetation monitoring protocol is dependent upon the integrity of the data. 

This SOP details procedures for ensuring the data collected are of the highest possible quality, 

addressing standards to follow both while in the field, and afterward. 

16.1 Field Procedures 

Quality assurance/quality control procedures for all plots 

The standard data collection methods detailed in SOP 6: Field Methods and Data Collection 

should be followed, precisely. This will ensure accurate data collection and allow consistency 

between observers. In addition, each field team will be supplied with a checklist of criteria to 

address prior leaving a field site. This is designed to minimize human error, especially for 

parameters that cannot be easily obtained once a field team has depart a site. These include 

ensuring that: 

• The parking location for the boat/car is documented, and any relevant notes on plot access 

are included. 

• The Kotar or NVCS forest type is recorded. 

• All shrub circles have species cover percentages. 

• All transects have coarse woody debris recorded. 

• All groundlayer species listed have at least one box checked. 

• Any parameter where there was nothing located (e.g., no shrubs in a circle or no CWM on a 

transect) is marked as such. 

• Any tree species on the groundlayer sheet have abundance numbers associated with them. 

• The “dead” column is checked for all unknown dead trees. 

Quality assurance/quality control check plots 

At least 10% of plots in all years will be resampled by the program manager and a field assistant, 

with the resampling occurring within four weeks of the original sample date. The field assistant 

cannot be part of the team that conducted the original sampling. 

Data should meet the following guidelines for each resampled plot: 

• tree basal area for each transect should by ± 10% for each species 

• shrub cover for each shrub circle should be ± 5% for each species 

• the number and volume of coarse woody material pieces in each transect should be ± 5% for 

each species 

• the total number of species per plot should differ by no more than 3 

• the frequencies for individual groundlayer species within each transect should differ by no 

more than 10% 

To reduce the problems associated with trampling, no individual plot should be resampled more 

than once every 10 years. 
 

195

16.2 Data Processing 

Global positioning system data 

All global positioning system points should be differentially corrected within 30 days of 

collection, otherwise, the data collections interval of CORS stations is reduced from either 1, 5, 

or 30 second intervals, to 30 minute intervals. 

Vegetation data 

To locate and amend potential errors resulting directly from the manual entry process, all entered 

data should be checked with one person reading the original data sheets and a second person 

checking the Access report generated for each site/parameter. 

Standard queries should be run following the two person data checking process. These queries 

are intended to reveal mistakes resulting from data entry errors, or certain recording errors in the 

field. These queries include checks for the following: 

• multiple entries of an individual species within any transect in the groundlayer data 

• multiple entries of an individual species within any shrub circle 

• course woody material pieces entered that are less than 8 cm diameter at intercept 

• trees entered with DBH < 2.6 cm 

• tree species entered as seedlings in the groundlayer data without counts

 

197

Appendix B: Field Data Forms

 

199

Plot:____________ Date:____________ 

Kotar Type: ______________________________ 

NVCS Type: ______________________________ 

Photopoint picture numbers: 

Point Picture ID number 

1 

2 

3 

4 

5 

6 

Plot Access: 

________________________________________________________________________

________________________________________________________________________

________________________________________________________________________

________________________________________ 

Plot Description: 

________________________________________________________________________

________________________________________________________________________

________________________________________________________________________

________________________________________________________________________

________________________________ 

Check before leaving field site: 

 The Kotar and NVCS cover types have been recorded 

 The location of where the boat or car is parked (and any other access advise) is noted 

 All data sheets are present 

 All Unknown Dead Tree’s have “dead” checked 

 All shrub circles have species cover percentages 

 All transects have coarse woody debris 

 Any parameter where there was nothing (e.g., no shrubs in a circle or no CWM on a 

transect) is marked as such 

 All groundlayer species have boxes checked 

 Any tree species on the groundlayer sheet have numbers associated with them 
 

200

Reference Objects for Each Permanent Point 

Plot:____________ Name:_____________________________ Date:____________ 

For each of the 6 permanent points, record the distance and angle from 3 prominent trees 

to the point. 

Point Reference Tree 

Species DBH Tag # 

Distance to 

Point (m) 

Azimuth to 

Point (°) 

1 

1 

1 

2 

2 

2 

3 

3 

3 

4 

4 

4 

5 

5 

5 

6 

6 

6 
 

201

Tree Data Page ___ of ___ 

Plot:_____________ Name:________________________________ Date:_____________ 

Transect Species DBH Dead? Damage agent Severity 

 
 

202

Shrub and Sapling Datasheet Page ___ of ___ 

Plot:_____________ Name:________________________________ Date:_____________ 

Shrub Circle 1 

Species Percent Cover (Shrubs) Count (Saplings) 

 

Shrub Circle 2 

Species Percent Cover (Shrubs) Count (Saplings) 

 

Shrub Circle 3 

Species Percent Cover (Shrubs) Count (Saplings) 

 

Sapling = less than 2.6cm DBH and taller than 15cm for conifers or 30cm for hardwoods, with evidence of last 

year’s growth. 
 

203

Shrub and Sapling Datasheet Page ___ of ___ 

Shrub Circle 4 

Species Percent Cover (Shrubs) Count (Saplings) 

 

Shrub Circle 5 

Species Percent Cover (Shrubs) Count (Saplings) 

 

Shrub Circle 6 

Species Percent Cover (Shrubs) Count (Saplings) 

 

Sapling = less than 2.6cm DBH and taller than 15cm for conifers or 30cm for hardwoods, with evidence of last 

year’s growth. 
 

204

Coarse Woody Debris Data Page ___ of ___ 

Plot:_____________ Name:________________________________ Date:_____________ 

Transect Species 

Diameter at 

transect 

(cm) 

Diameter 

at small 

end (cm) 

Diameter at 

large end 

(cm) 

Length 

(X.X m) 

Decay Class 

 
 

205

Tree walkthrough data 

Plot:_____________ Name:______________________________ Date:_____________ 

Add one entry below for each unique tree species recorded in the tree transects. Then add any additional tree 

species located in a 20 minute timed walkthrough of the entire plot. During this time, also estimate the 

percentage of cover by canopy species (as likely seen from above) for the entire plot. Make sure to include the 

percentage of gap in the canopy. Since percentage of cover is estimated based on the aeriel view only, some 

species that do not reach the canopy will have values of 0%. For those species that do reach the canopy, the 

collective value (including the gap) should equal 100%. 

 Species Percentage of cover 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

21 

22 

Trees present only as saplings that were not recorded in shrub circles (add to herb walkthrough datasheet) 

____________________________________ 

____________________________________ 

____________________________________ 

____________________________________ 

____________________________________ 
 

206

Groundlayer Data Plot:_____________ Name:____________________ Date:_____________ Page __ of __ 

Check box if herb species present; enter count for tree species (>15 cm, conifer and > 30 cm hardwood). 

Transect Species 5 10 15 20 25 30 35 40 45 50 

 ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )

 ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )

 ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )

 ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )

 ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )

 ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )

 ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )

 ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )

 ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )

 ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )

 ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )

 ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )

 ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )

 ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )

 ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )

 ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )

 ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )

 ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )

 ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )

 ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )

 ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )

 ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )

 ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )

 ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )

 ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )

 ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )

 ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )

 ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )

 ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )

 ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )

 ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )

 ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )

 ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )

 ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )

 5 10 15 20 25 30 35 40 45 50 
 

207

Browse Data: Direct Effects 

Plot:_____________ Name:________________________________ Date:_____________ 

Transect 1 

Plot Species 1 Browsed? Species 2 Browsed? Species 3 Browsed? 

5 m 

10 m 

15 m 

20 m 

25 m 

30 m 

35 m 

40 m 

45 m 

50 m 

Transect 2 

Plot Species 1 Browsed? Species 2 Browsed? Species 3 Browsed? 

5 m 

10 m 

15 m 

20 m 

25 m 

30 m 

35 m 

40 m 

45 m 

50 m 

Transect 3 

Plot Species 1 Browsed? Species 2 Browsed? Species 3 Browsed? 

5 m 

10 m 

15 m 

20 m 

25 m 

30 m 

35 m 

40 m 

45 m 

50 m 

Comments: 

__________________________________________________________________________________________

__________________________________________________________________________________________

__________________________________________________________________________________________ 
 

208

Browse Data: Indirect Effects (Voyageurs NP) 

Plot:_____________ Name:______________________________ Date:_____________ 

Transect 1 

Quadrat 5 Quadrat 10 

Species max height 

(xx.x cm) 

l,rs,s # not repro/ 

unbrowsed 

# repro/ 

unbrowsed 

# 

browsed

 max height 

xx.x (cm) 

l,rs,s # not repro/ 

unbrowsed 

# repro/ 

unbrowsed # browsed 

C. borealis 

Streptopus sp. 

M. canadense 

Quadrat 15 Quadrat 20 

Species max height 

(xx.x cm) 

l,rs,s # not repro/ 

unbrowsed 

# repro/ 

unbrowsed 

# 

browsed 

 max height 

xx.x (cm) 

l,rs,s # not repro/ 

unbrowsed 

# repro/ 

unbrowsed # browsed 

C. borealis 

Streptopus sp. 

M. canadense 

Quadrat 25 Quadrat 30 

Species max height 

(xx.x cm) 

l,rs,s # not repro/ 

unbrowsed 

# repro/ 

unbrowsed 

# 

browsed 

 max height 

xx.x (cm) 

l,rs,s # not repro/ 

unbrowsed 

# repro/ 

unbrowsed # browsed 

C. borealis 

Streptopus sp. 

M. canadense 

Quadrat 35 Quadrat 40 

Species max height 

(xx.x cm) 

l,rs,s # not repro/ 

unbrowsed 

# repro/ 

unbrowsed 

# 

browsed 

 max height 

xx.x (cm) 

l,rs,s # not repro/ 

unbrowsed 

# repro/ 

unbrowsed # browsed 

C. borealis 

Streptopus sp. 

M. canadense 

Quadrat 45 Quadrat 50 

Species max height 

(xx.x cm) 

l,rs,s # not repro/ 

unbrowsed 

# repro/ 

unbrowsed # rowsed 

 max height 

xx.x (cm) 

l,rs,s # not repro/ 

unbrowsed 

# repro/ 

unbrowsed # browsed 

C. borealis 

Streptopus sp. 

M. canadense 

Transect 2 

Quadrat 5 Quadrat 10 

Species max height 

(xx.x cm) 

l,rs,s # not repro/ 

unbrowsed 

# repro/ 

unbrowsed 

# 

browsed 

 max height

xx.x (cm) 

l,rs,s # not repro/ 

unbrowsed 

# repro/ 

unbrowsed # browsed 

C. borealis 

Streptopus sp. 

M. canadense 

Quadrat 15 Quadrat 20 

Species max height 

(xx.x cm) 

l,rs,s # not repro/ 

unbrowsed 

# repro/ 

unbrowsed 

# 

browsed 

 max height 

xx.x (cm) 

l,rs,s # not repro/ 

unbrowsed 

# repro/ 

unbrowsed # browsed 

C. borealis 

Streptopus sp. 

M. canadense 

 
 

209

Quadrat 25 Quadrat 30 

Species max height 

(xx.x cm) 

l,rs,s # not repro/ 

unbrowsed 

# repro/ 

unbrowsed 

# 

browsed 

 max height 

xx.x (cm) 

l,rs,s # not repro/ 

unbrowsed 

# repro/ 

unbrowsed # browsed 

C. borealis 

Streptopus sp. 

M. canadense 

Quadrat 35 Quadrat 40 

Species max height 

(xx.x cm) 

l,rs,s # not repro/ 

unbrowsed 

# repro/ 

unbrowsed 

# 

browsed 

 max height 

xx.x (cm) 

l,rs,s # not repro/ 

unbrowsed 

# repro/ 

unbrowsed # browsed 

C. borealis 

Streptopus sp. 

M. canadense 

Quadrat 45 Quadrat 50 

Species max height 

(xx.x cm) 

l,rs,s # not repro/ 

unbrowsed 

# repro/ 

unbrowsed 

# 

browsed 

 max height 

xx.x (cm) 

l,rs,s # not repro/ 

unbrowsed 

# repro/ 

unbrowsed # browsed 

C. borealis 

Streptopus sp. 

M. canadense 

Transect 3 

Quadrat 5 Quadrat 10 

Species max 

height 

(xx.x cm) 

l,rs,s # not repro/ 

unbrowsed 

# repro/ 

unbrowsed 

# 

browsed

 max height 

xx.x (cm) 

l,rs,s # not repro/ 

unbrowsed 

# repro/ 

unbrowsed # browsed 

C. borealis 

Streptopus sp. 

M. canadense 

Quadrat 15 Quadrat 20 

Species max height 

(xx.x cm) 

l,rs,s # not repro/ 

unbrowsed 

# repro/ 

unbrowsed 

# 

browsed 

 max height 

xx.x (cm) 

l,rs,s # not repro/ 

unbrowsed 

# repro/ 

unbrowsed # browsed 

C. borealis 

Streptopus sp. 

M. canadense 

Quadrat 25 Quadrat 30 

Species max height 

(xx.x cm) 

l,rs,s # not repro/ 

unbrowsed 

# repro/ 

unbrowsed 

# 

browsed 

 max height 

xx.x (cm) 

l,rs,s # not repro/ 

unbrowsed 

# repro/ 

unbrowsed # browsed 

C. borealis 

Streptopus sp. 

M. canadense 

Quadrat 35 Quadrat 40 

Species max height 

(xx.x cm) 

l,rs,s # not repro/ 

unbrowsed 

# repro/ 

unbrowsed 

# 

browsed 

 max height 

xx.x (cm) 

l,rs,s # not repro/ 

unbrowsed 

# repro/ 

unbrowsed # browsed 

C. borealis 

Streptopus sp. 

M. canadense 

Quadrat 45 Quadrat 50 

Species max height 

(xx.x cm) 

l,rs,s # not repro/ 

unbrowsed 

# repro/ 

unbrowsed 

# 

browsed 

 max height 

xx.x (cm) 

l,rs,s # not repro/ 

unbrowsed 

# repro/ 

unbrowsed # browsed 

C. borealis 

Streptopus sp. 

M. canadense 
 

210

Unknown Plants 

Plot:____________ Name:_____________________________ Date:____________ 

Unknown ID 

# 

Photo ID #’s Plant Description 

 
 

211

All species walkthrough data 

Plot:_____________ Name:______________________________ Date:_____________ 

Add one entry below for each unique herbaceous species (or genera where appropriate) recorded 

in the groundlayer quadrats. Then add each additional herb species located in a 30 minute timed 

walkthrough of the entire plot. 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

21 

22 

23 

24 

25 

26 

27 

28 

29 

30 

31 

32 

33 

34 

35 

36 

 

The Department of the Interior protects and manages the nation’s natural resources and cultural heritage; provides scientific and 

other information about those resources; and honors its special responsibilities to American Indians, Alaska Natives, and 

affiliated Island Communities. 

NPS D-080, September 2008
National Park Service 

U.S. Department of the Interior 

Natural Resource Program Center 

1201 Oakridge Drive, Suite 150 

Fort Collins, CO 80525 

www.nature.nps.gov 

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