National Park Service

U.S. Department of the Interior

Natural Resource Program Center

Alpine Vegetation Composition Structure and Soils

Monitoring Protocol

2010 version

Natural Resource Report NPS/ROMN/NRR—2010/277
ON THE COVER

Looking towards the 3rd GLORIA summit at Great Sand Dunes National Park and Preserve, 2008 

Photograph by: Phyllis Pineda Bovin, Great Sand Dunes National Park and Preserve
Alpine Vegetation Composition Structure and 

Soils Monitoring Protocol

2010 version

Natural Resource Report NPS/ROMN/NRR—2010/277

Isabel Ashton

E. William Schweiger

Jennifer Burke 

Donna Shorrock 

Davd Pillmore

Mike Britten

National Park Service

Rocky Mountain Inventory and Monitoring Network

1201 Oak Ridge Rd

Fort Collins, CO 80525

December 2010

U.S. Department of the Interior

National Park Service

Natural Resource Program Center

Fort Collins, Colorado
The National Park Service, Natural Resource Program Center publishes a range of reports that 

address natural resource topics of interest and applicability to a broad audience in the National 

Park Service and others in natural resource management, including scientists, conservation and 

environmental constituencies, and the public. 

The Natural Resource Report Series is used to disseminate 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.

All manuscripts in the series receive the appropriate level of peer review to ensure that the 

information is scientifically credible, technically accurate, appropriately written for the intended 

audience, and designed and published in a professional manner. This report received informal 

peer review by subject-matter experts who were not directly involved in the collection, analysis, 

or reporting of the data. Data in this report were collected and analyzed using methods based on 

established, peer-reviewed protocols and were analyzed and interpreted within the guidelines of the 

protocols.

Views, statements, findings, conclusions, recommendations, and data in this report do not 

necessarily reflect views and policies of the National Park Service, U.S. Department of the 

Interior. Mention of trade names or commercial products does not constitute endorsement or 

recommendation for use by the U.S. Government.

This report is available from the Rocky Mountain Inventory & Monitoring Network and the 

Natural Resource Publications Management website 

(http://www.nature.nps.gov/publications/NRPM). 

Please cite this publication as:

Ashton, I., E. William Schweiger, J. Burke, D. Shorrock, D. Pillmore, and M. Britten. 2010. 

Alpine vegetation composition structure and soils monitoring protocol: 2010 version. Natural 

Resource Report NPS/ROMN/NRR—2010/277. National Park Service, Fort Collins, Colorado.

NPS 960/106252, December 2010
Contents

Page

Figures............................................................................................................................................ vi

Tables............................................................................................................................................ vii

Appendices................................................................................................................................... viii

Executive Summary....................................................................................................................... ix

Acknowledgments.......................................................................................................................... xi

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

The National Park Service Inventory and Monitoring Program.............................................. 1

Alpine Vegetation and Rationale for Monitoring.................................................................... 2

History of Protocol Development............................................................................................ 4

Prior research and monitoring of alpine vegetation .......................................................... 4

Protocol development ......................................................................................................... 5

Measurable objectives ........................................................................................................ 5

Sampling Design............................................................................................................................. 7

Sentinel site design and criteria for sampling.......................................................................... 7

Timing of sampling ............................................................................................................. 8

Optional Alpine probability-based sample design and criteria for sampling.......................... 8

Field Methods ................................................................................................................................. 9

ROMN Standard Operating Procedures .................................................................................. 9

Managing a field season, permits, safety, and data requirements...................................... 9

GLORIA field manual ............................................................................................................. 9

Establishing and locating sites........................................................................................... 9

Plot layout........................................................................................................................... 9

Vegetation ......................................................................................................................... 10
Temperature...................................................................................................................... 11

Photographs...................................................................................................................... 11

Extended monitoring at GLORIA sentinel sites.................................................................... 11

Site context: measuring anthropogenic and natural disturbance..................................... 11

Soil condition .................................................................................................................... 12

Phenology ......................................................................................................................... 13

Treeline ............................................................................................................................. 13

N deposition ...................................................................................................................... 14

Field Methods for Optional alpine probability-based survey design .................................... 14

Establishing and locating sites......................................................................................... 15

Plot layout......................................................................................................................... 15

Vegetation ......................................................................................................................... 16

Soils and site context......................................................................................................... 16

Photographs...................................................................................................................... 17

Standard Operating Procedures (SOPs)................................................................................. 17

Data Management and Analysis ................................................................................................... 19

Overview of data management and procedures..................................................................... 19

Recommendations for routine data summaries and statistical analyses................................ 19

Status and trends and the GLORIA sentinel sites............................................................. 19

Design-based basic summaries......................................................................................... 22

Reference conditions......................................................................................................... 22

Reporting....................................................................................................................................... 25

Annual Reports...................................................................................................................... 25

Comprehensive synthesis and analysis.................................................................................. 26

Protocol publication and review ............................................................................................ 26
Administration and Operations for AlpineVCSS Monitoring ...................................................... 29

Introduction............................................................................................................................ 29

Annual work planning ........................................................................................................... 29

Budget.................................................................................................................................... 29

Compliance and permitting.................................................................................................... 29

Compliance ....................................................................................................................... 29

Permitting ......................................................................................................................... 30

Field monitoring .................................................................................................................... 30

Field crews........................................................................................................................ 31

Revising the Protocol.................................................................................................................... 33

Protocol evaluation ................................................................................................................ 33

Peer-review process............................................................................................................... 33

Change History ...................................................................................................................... 33

Literature Cited ............................................................................................................................. 35
vi

Figures

Page

Figure 1. Map of Rocky Mountain Inventory & Monitoring Network park units.......................... 2

Figure 2. Map of ecoregions in Glacier, Great Sand Dunes, and Rocky Mountain National 

Parks................................................................................................................................................ 4

Figure 3. GLORIA summit and plot layout at sentinel sites. ....................................................... 10

Figure 4. Plot layout for the AlpineVCSS survey design ............................................................. 16
vii

Tables

Page

Table 1. Location, elevation, date of establishment of GLORIA sentinel sites.............................. 7

Table 2. The phenophases that will be monitored at sentinel sites for five plant species ............ 13

Table 3. National Atmospheric Deposition Program stations relevant to GLAC, GRSA, and 

ROMO........................................................................................................................................... 14

Table 4. Standard Operating Procedures for AlpineVCSS protocol ............................................ 18

Table 5. AlpineVCSS protocol objectives and data collected relating to those objectives.. ........ 20

Table 6. Objectives of the optional alpine survey component of the AlpineVCSS protocol ...... 22

Table 7. Average annual costs for AlpineVCSS monitoring in GRSA and ROMO ................... 29
viii

Appendices

Page

Appendix 1. Bibliography and Investigator Annual Reports of Research Completed in 

ROMO, GRSA, and GLAC Pertaining to Alpine Vegetation ...................................................... 41

Appendix 2. Directions and Details of Site Access for GLORIA Work in GRSA and ROMO... 47

SOP AlpineVCSS Adapting GLORIA to National Parks............................................................. 49

SOP AlpineVCSS Site Disturbance: Describing Natural and Anthropogenic Disturbance to 

the Site and Surrounding Area...................................................................................................... 51

SOP AlpineVCSS Soils................................................................................................................ 57

SOP AlpineVCSS Phenology ....................................................................................................... 65

SOP AlpineVCSS Treeline........................................................................................................... 69

SOP AlpineVCSS N Deposition: Measuring Nitrogen Deposition Using Ion Exchange Resin 

Collectors; Optional...................................................................................................................... 71

SOP AlpineVCSS Data Analysis: Sentinel Sites Statistical Analysis.......................................... 75

SOP AlpineVCSS Reporting and Presentations........................................................................... 79

SOP AlpineVCSS GRTS design: Creating a Probability-Based Design for Alpine Surveys; 

Optional ........................................................................................................................................ 83

SOP AlpineVCSS Site Suitability: Determining Target Status of Survey Site in the Field; 

optional ......................................................................................................................................... 89

SOP AlpineVCSS Replacing Sites: Moving or Replacing Alpine Survey Sites; Optional.......... 93

SOP AlpineVCSS Plots: Plot Layout and Site Description for Alpine Survey Sites; Optional... 95

SOP AlpineVCSS Photographs: Photo Point Documentation for Alpine Survey Sites; 

Optional ........................................................................................................................................ 99

SOP AlpineVCSS Vegetation: Vegetation Sampling in Alpine Survey Sites; Optional ........... 103

SOP AlpineVCSS Willows: Willow Structure and Herbivore Impacts in Alpine Survey 

Sites; Optional ............................................................................................................................ 107

SOP AlpineVCSS Survey Data Analysis: Design Based Analyses; Optional ........................... 109
ix

Executive Summary

The Rocky Mountain Inventory & Monitoring Network (ROMN) includes a wide range of 

environments, ranging from mountain peaks to evergreen and deciduous forests to Western

grasslands. The six units of the Network include Glacier National Park, Grant-Kohrs Ranch 

National Historic Site, Little Bighorn Battlefield National Monument, Florissant Fossil Beds 

National Monument, Great Sand Dunes National Park and Preserve, and Rocky Mountain 

National Park. Here, we present a protocol to inventory and monitor alpine vegetation within the 

three mountainous units of the Network (Glacier National Park, Great Sand Dunes National Park 

and Preserve, and Rocky Mountain National Park).

The objectives of the Alpine Vegetation Composition, Structure, and Soil (AlpineVCSS)

Monitoring Protocol are to:

(1) Determine the status and trend in vegetation composition and structure of four sentinel alpine 

peak communities at a range of elevations. 

(2) Determine status and trend in cover of invasive and exotic plant species at four sentinel 

alpine peak communities at a range of elevations. 

(3) Determine the status and trend in soil temperature and snow cover period of four sentinel 

alpine peak communities at a range of elevations. 

(4) Determine the status and trend in soil condition based on a suite of physical and chemical 

properties that include: soil carbon and nitrogen content, pH, evidence of erosion, extent of 

bare (non-vegetated) soils at four sentinel alpine peak communities. 

(5) Determine the status and trend of herbivore disturbance based on the presence of feces, 

trampling, and browsing damage at four sentinel alpine peak communities at a range of 

elevations. 

A sixth “optional” objective is to determine status in vegetation structure, species composition, 

natural and anthropogenic disturbance and soil condition in alpine tundra within the parks. The 

ROMN would consider adding this objective should increased network funding allow us to 

characterize the status of vegetation and soils throughout alpine communities in the parks, not 

just at four select sentinel sites in each park.

To meet the AlpineVCSS objectives we have adapted methods from the Global Observation 

Research Initiative in Alpine Environments (GLORIA), an international monitoring network 

established in 2001 to assess and predict biodiversity changes in alpine communities in response 

to broad drivers such as climate. The methodology is extended by cooperators, such as the 

ROMN, to create a long-term monitoring network at the global scale. We use the GLORIA 

sampling design, which is a sentinel site approach, where vegetation is monitored on four 

summits in a region, or park, that vary in elevation from treeline to the limits of vascular plants. 

Species diversity, ground cover, browsing and trampling pressure, and soil temperatures are 

measured in an array of plots on the summit set in all cardinal directions. In addition to the 

components of the GLORIA protocol, the AlpineVCSS extends the monitoring to include 

measures of soil condition and chemistry, natural and human disturbance, phenology, and 

photographic documentation of treeline. The sentinel sites will be monitored annually or every 5 

years, as funding allows. We also develop optional methods for spatially-balanced probabilistic 

survey designs of alpine vegetation that can be implemented in the future. Ultimately, we aim for 
x

the ROMN AlpineVCSS protocol to provide a robust method for measuring status and trend in 

alpine vegetation and soils in the parks and to provide data that can be used in collaboration with 

other networks and programs to monitor climate change and its effects on alpine communities 

across the globe. 
xi

Acknowledgments

The authors (Rocky Mountain Network) are indebted to monitoring efforts that were developed 

prior to this program. In particular, the efforts of the Harald Pauli, Michael Gottfried, Daniella 

Hohenwallner, Karl Reiter, Riccardo Casale, and Georg Grabherr on the GLORIA Field ManualMulti-Summit Approach. Improvements to the protocol and adapting the protocol to fit NPS 

monitoring needs would have been impossible without help from Phyllis Pineda Bovin, Fred 

Bunch, Paul McLaughlin, Judy Visty, and John Spence. 

1

Background and Objectives

The National Park Service Inventory and Monitoring Program

The purpose of the National Park Service (NPS) Inventory & Monitoring (I&M) Program is to develop and 

provide scientifically credible information on the current status and long-term trends in the composition, 

structure, and function of park ecosystems. As part of the NPS’s effort to “improve park management 

through greater reliance on scientific knowledge,” a primary role of the I&M Program is to collect, 

organize, and make available natural resource data and to contribute to the NPS institutional knowledge by 

facilitating the transformation of data into information through analysis, synthesis, and modeling of specific 

key “vital signs.” The I&M Program defines the term “vital sign” as “a subset of physical, chemical, and 

biological elements and processes of park ecosystems that is selected to represent the overall health or 

condition of park resources, known or hypothesized effects of stressors, or elements that have important 

human values” (Fancy et al. 2009). 

The five goals of the I&M Program are to:

(1) Inventory the natural resources and park ecosystems under NPS stewardship to determine their nature 

and status. 

(2) Monitor park ecosystems to better understand their dynamic nature and condition and to provide 

reference points for comparisons with altered environments. 

(3) Establish natural resource inventory and monitoring as a standard practice throughout the NPS system 

that transcends traditional program, activity, and funding boundaries. 

(4) Integrate natural resource inventory and monitoring information into NPS planning, management, and 

decision making. 

(5) Share NPS accomplishments and information with other natural resource organizations and form 

partnerships for attaining common goals and objectives. 

These goals are accomplished through park-wide inventories and a long-term monitoring program. In 

establishing a service-wide natural resources inventory and monitoring program, the NPS created networks 

of parks that are linked by geography and shared natural resource characteristics. Working within networks 

improves the efficiency of inventory and monitoring because parks are able to share budgets, staffing, and 

other resources to plan and implement an integrated program.

The Rocky Mountain I&M Network (ROMN) is comprised of six NPS units: Glacier National Park 

(GLAC), Grant-Kohrs Ranch National Historic Site (GRKO), and Little Bighorn Battlefield National 

Monument (LIBI), Montana; and Florissant Fossil Beds National Monument (FLFO), Great Sand Dunes 

National Park and Preserve (GRSA), and Rocky Mountain National Park (ROMO), Colorado (Figure 1). 

The ROMN recognized 12 high priority vital signs for focused monitoring (Britten et al. 2007). The vital 

signs include: Wet and Dry Deposition, Weather and Climate, Water Chemistry, Surface Water Dynamics, 

Freshwater Communities, Invasive/Exotic Aquatic Biota, Groundwater Dynamics, Wetland Communities 

Invasive/Exotic Plants, Vegetation Composition, Structure, and Soils, Focal Species: Beaver, Elk, Grizzly 

Bear, and GRSA Endemic Insects; and Landscape Dynamics. 

The Alpine Vegetation Composition Structure and Soils Monitoring Protocol (AlpineVCSS) presented here 

directly addresses the monitoring of three vital signs: Invasive/Exotic Plants, Weather and Climate, and 
2

Vegetation Composition, Structure, and Soils. Three other high priority vital signs are indirectly linked to 

vegetation condition including: Landscape Dynamics, Wet and Dry Deposition, and Focal Species – Elk.

Figure 1. Map of Rocky Mountain Inventory & Monitoring Network park units

Alpine Vegetation and Rationale for Monitoring 

The alpine is defined as the vegetation communities that exist above tree line, which comprise 

approximately 3% of the global surface (Korner 2002). It is an environment characterized by extreme 

natural conditions including high winds, low temperatures, scouring and burial by snow and ice, low 

nutrient availability, high incident solar radiation, thin atmosphere, and a short growing season (Bowman 

2001). Alpine is present on all continents, but the elevational limit of trees and the transition to alpine tundra 

varies with latitude, such that alpine elevation increases with proximity to the equator (Gurevitch et al. 

2002). The vegetation community is small in stature and dominated by perennial herbaceous species with 

species composition varying strongly across environmental gradients of water availability (Walker et al. 

2001). While the tundra is typically dominated by grasses, sedges, and forbs, nonvascular plants and woody 

shrubs are also common. Compared to other grassland types, the alpine tundra shows high local diversity 

(Gough et al. 2000). Species that persist in the alpine have adapted to the extreme environment, but growth 

and reproduction are strongly limited by environmental conditions and nutrient availability. As a result, 

changes in weather and climate patterns, natural disturbance, nutrient budgets (due to atmospheric nitrogen 

(N) deposition), and human use may strongly influence the persistence of alpine communities. 
3

The alpine environment is represented in three of the ROMN parks: GLAC, ROMO and GRSA at 28%, 

20%, and 3% land cover, respectively (Figure 2). The ROMN selected alpine ecosystems as an important 

monitoring target in these parks for a number of reasons. First, the tundra is typified by spectacular 

wildflower displays that draw park visitors. Second, the alpine tundra supports numerous animals of 

management concern including bighorn sheep (Ovis canadensis), mountain goats (Oreamnos americanus), 

white-tailed ptarmagins (Lagopus leucura), and pikas (Ochotona princeps). Recent evidence suggests that 

climate changes have contributed to the extinction of pika populations (Grayson 2005), but how future 

changes will affect these and other alpine species remains unknown. The first step to understanding changes 

in fauna lies with understanding the patterns of biodiversity loss and gain in tundra vegetation. Third, much 

of the water resources in the West are derived from snowmelt, and the quality and quantity of this water is 

influenced by the ecosystem processes in the alpine tundra (Williams and Caine 2001). Fourth, because the 

alpine is particularly sensitive to climate change and atmospheric deposition and is globally distributed, it 

may provide an important indicator for change (Seastedt et al. 2004). Long-term records provide evidence 

for ongoing warming in high mountain environments (Price and Barry 1997). The extent of glacier loss, 

warming temperatures, and changes in snowpack have been well documented (e.g. Hall and Fagre 2003) but 

concurrent changes in vegetation composition and patterns are not well understood due to a paucity of longterm monitoring. Climate changes may affect biodiversity and species richness, by contributing to the 

extinction of some species while simultaneously increasing the elevation and latitudinal limitations of other 

species. A survey of summit sites in the Alps of Switzerland showed that vascular plants have been 

establishing at higher altitudes than recorded earlier (Grabherr et al. 1994). Thus, it is assumed that an 

upward and northward migration of plants, induced by recent climate warming, is an already ongoing 

process. In recent decades, the deposition of atmospheric N has increased in the Rocky Mountains due to the 

increasing growth of metropolitan areas and agriculture (Fenn et al. 2003). Alpine regions in the Rocky 

Mountains have a low capacity to sequester this excess N because of short growing seasons, shallow soils, 

and steep slopes that encourage rapid run-off (Fenn et al. 2003). There is evidence that N deposition has 

altered alpine lake chemistry and biota (Baron et al. 1994) and it can alter the structure of alpine 

communities (Bowman 2000). Finally, due to extreme conditions and remote access, the alpine tundra has 

had less direct exposure to invasive species, pests and pathogens, and fire than many ecosystems. It 

therefore becomes possible to examine changes in the status or trend of alpine tundra caused by changes in 

climate and atmospheric N deposition without the confounding affects of other stressors.
4

Figure 2. Map of ecoregions in Glacier, Great Sand Dunes, and Rocky Mountain National Parks. Boundaries of 

ecoregions were determined by elevation with best estimates based on ecological literature, maps, and park 

definitions. Alpine is defined as vegetation above 3500m in ROMO and GRSA and 1980m in GLAC. 

History of Protocol Development

Prior research and monitoring of alpine vegetation

Vegetation maps, experimental work, and surveys have been conducted in the alpine tundra of GRSA, 

GLAC, and ROMO (see Appendix 1 for a bibliography of published work and investigator annual reports 

relating to alpine vegetation in the three parks). Vegetation maps were recently completed for ROMO (Salas 

et al. 2005), GRSA (Salas et al. 2009), and GLAC (USGS-NPS 2007). The maps include detailed 

descriptions of vegetation alliances throughout the park, including an assortment of alpine communities. 

Alpine ecology has historically been of great interest in ROMO. Studies have examined community 

composition and effects of trampling (Willard et al. 2007) and changes in diversity at the alpine ecotone 

(Stohlgren et al. 2000). More recently, work has been done to describe patterns and effects of elk herbivory 

on willows (see Steltzer, Appendix 1) and the effects of N deposition on alpine communities (see Bowman, 

Appendix 1). Baron and colleagues have conducted ecological research and monitoring in the Loch Vale 

watershed in ROMO since 1982 (e.g., Baron et al. 2000). They have measured meteorological and 

hydrological parameters and N cycling in vegetation and soils to determine the watershed-scale response to 

atmospheric deposition and climate variability. The majority of the work, however, has focused on forested 

and subalpine areas and to date, there has not been long-term monitoring of alpine tundra in other areas. 

Compared with ROMO, there has been less emphasis on alpine vegetation research in GLAC and even less 

in GRSA. While the flora of GLAC has been well described and there has been work on the loss of rare 

alpine wetland plants (Lesica and Steele 1996), much of the work has focused on modeling efforts exploring 

the expansion of treeline (Klasner and Fagre 2002, Malanson et al. 2007). GRSA recently expanded to 

include alpine regions of the Sangre de Cristo Range. Previously, this was part of the San Isabel National 
5

Forest and historically there was extensive domestic sheep grazing in the area. While the vegetation has 

been described and mapped (Salas et al. 2009), there are few other references to vegetation work in the area. 

In summary, relatively few studies have explicitly monitored alpine vegetation in ROMO, GRSA, and 

GLAC. The ROMN AlpineVCSS protocol should help address this shortcoming by providing standardized

methods for long-term of monitoring of alpine vegetation in these three parks. 

Protocol development

The ROMN adapted an alpine monitoring protocol from the Global Observation Research Initiative in 

Alpine Environments (GLORIA), an international monitoring network established in 2001 to assess and 

predict biodiversity and temperature changes in alpine communities in response to broad drivers such as 

climate (Pauli et al. 2004). The goals of the GLORIA program include providing a global baseline for 

vegetation monitoring in alpine environments and assessing the risks of biodiversity loss and ecosystem 

instability from climate change. GLORIA collects monitoring data by using an array of plots to measure 

vegetation across sets of four neighboring peaks (Grabherr et al. 2000). The peaks vary in elevation from 

treeline to the limits of vascular plants; vegetation composition and structure, and soil temperatures are 

monitored within each plot. This methodology is extended by cooperators, such as the ROMN, to create a 

long-term monitoring network at the global scale. To enable ROMN parks to understand changes in alpine 

vegetation and climate in a global and regional context, we will follow the published GLORIA protocol 

(Pauli et al. 2004); from hereafter referred to as the GLORIA Field Manual. 

Following these methods, a GLORIA multi-summit site was established in 2003 in GLAC by Daniel Fagre 

of the United States Geological Service (USGS). The University of Colorado Mountain Research Station 

established a site outside of ROMO in 2007 that will complement a ROMO site within the park placed on 

the opposite side of the continental divide. Initiating GLORIA monitoring in ROMO and GRSA and 

continuing monitoring in GLAC will provide an invaluable transect of alpine monitoring from the Southern 

to Northern Rockies. In addition, our methods will be comparable to other NPS I&M Networks (including 

the North Coast and Cascades and Greater Yellowstone) that are considering GLORIA sites.

During the process of establishing sites in ROMO and GRSA, we found it necessary to make minor 

modifications to the published field methods, primarily to reduce the cost of dataloggers and use of 

permanent markers. These modifications are found in SOP AlpineVCSS Adapting GLORIA to National 

Parks. In addition to the minor modifications to the GLORIA field methods, we have expanded the protocol 

to include a suite of extended methods that will be implemented when funding is available. The extended 

methods were developed to allow us to gain a better understanding of local and regional changes in soil 

chemistry and ecosystem process driven by atmospheric N deposition, to better relate alpine work to the 

other ROMN vegetation monitoring efforts (e.g., grassland, shrubland, and woodlands and wetlands), and to 

increase our ability to determine the status of alpine tundra across the park. In brief, the extended methods 

include a modest array of soil parameters, descriptions of anthropogenic and natural disturbances at all sites, 

phenology, documentation on the extent of treeline, and an optional probability-based survey design. These 

methods are described below in Section 3-Section 4 and the corresponding AlpineVCSS SOPs. 

Measurable objectives

The goals for conducting ecological monitoring of alpine vegetation, within GLAC, GRSA, and ROMO are 

two-fold: to recognize and document long-term status and trends in vegetation structure and composition 

and to supply short-term feedback for managers to assess strategies to maintain and restore these 

communities. Status is defined as some statistic (e.g., a mean or a proportion) of a vital sign or measure over 

all monitoring sites in a park (from one to many) within a single or well bounded temporal window (Britten 

et al. 2007). Trend is a non-cyclic, directional change in a response measure that can be with or without 
6

pattern and, if it exists, always has a linear component (Urquhart et al. 1998). ROMN aims to determine the 

correlation between trends in vegetation and soil conditions and potential drivers of these changes, 

particularly climate and atmospheric N deposition. Specifically, for GLAC, GRSA, and ROMO the core 

objectives of the AlpineVCSS protocol are to: 

(1) Determine the status and trend in vegetation composition and structure of four sentinel alpine peak 

communities at a range of elevations. 

(2) Determine status and trend in cover of invasive exotic plant species at four sentinel alpine peak 

communities at a range of elevations. 

(3) Determine the status and trend in soil temperature and snow cover period of four sentinel alpine peak 

communities at a range of elevations. 

(4) Determine the status and trend in soil condition based on a suite of physical and chemical properties that 

include: surface stability, soil carbon (C) and nitrogen (N) content, pH, texture, evidence of erosion, extent 

of bare (non-vegetated) soils, and compaction at four sentinel alpine peak communities. 

(5) Determine the status and trend in grazing at four sentinel alpine peak communities at a range of 

elevations based on the presence of feces, trampling, and browsing damage. 

A sixth “optional” objective is to determine status in vegetation structure, species composition, natural and 

anthropogenic disturbance and soil condition in alpine tundra within the parks. The ROMN would consider 

adding this objective should increased network funding allow us to characterize the status of vegetation and 

soils throughout alpine communities in the parks, not just at four select sentinel sites in each park.

These objectives are the foundation upon which all assessments, interpretation, and additional measures are 

built. A secondary and future goal of the alpine monitoring program is to develop quantitative methods to 

assess ecosystem integrity and condition in the alpine tundra through the development of bioassessment 

models that will incorporate empirically-defined reference condition distributions and thresholds to identify 

both temporal and spatial departures from these distributions within a park. Reference conditions are 

distributions of key response variables that serve as criteria or thresholds by which we can compare all 

future data (Stoddard et al. 2006). For example, N critical loads have been established for alpine vegetation 

and it is estimated that deposition of greater than 4-10 kg N ha-1

yr-1 may cause changes at the species and 

community level (Bowman et al. 2006). If we then consider 4 kg N ha-1yr-1 a threshold for N deposition, we 

can describe the status of a given site by comparing measured N deposition to this value. Unfortunately, 

with the exception of N critical loads, few empirical and modeled reference conditions have been identified 

for the alpine. For instance, there is not a defined range of vascular plant diversity that can be considered as 

a reference for alpine communities. Developing robust reference condition distributions and thresholds for 

the alpine will take time and in the interim we will rely on survey data, other monitoring programs, and 

related research. Once reference conditions are established, we can determine the relative context of trends 

in vegetation and soil condition based on comparison of park estimates to referenced conditions. While still 

in the future, this remains a long-term goal because reference conditions and thresholds are key parts of a 

mature monitoring effort as they are the primary mechanisms for interpreting data and connecting to 

management objectives for a given park.
7

Sampling Design

Sentinel site design and criteria for sampling 

To meet the core objectives outlined above, we followed the sample design prescribed in the GLORIA Field 

Manual. The design consists of establishing four sentinel sites in the alpine on peaks representing an 

elevation gradient within a target region (e.g. the Sangre de Cristo Mountains, CO). The sites are established 

on the top of the peaks (summits) and should vary from just above treeline to the highest life zones of 

vegetation. Within one region, all four summits should share qualitatively similar geology, climate, 

disturbance, and land-use history (For more details see the GLORIA Field Manual, pages 9-15). Vegetation 

differences among the summits within a region should be driven primarily by elevation. The design consists 

of only four sentinel sites (summits) per region (in our case, only four sites within a park) but these are 

sampled intensively through time. Target regions and summits have been selected in GLAC, GRSA, and 

ROMO using the GLORIA criteria (Table 1).

The primary constraint with GLORIA monitoring is the inability to infer results easily from sentinel sites to 

other non-sampled locations. Since sites are chosen based on models or judgment, there is element of 

unknown site selection bias. It is therefore necessary to clearly specify that results apply to only the single 

sites (see Britten et al. 2007 for further discussion of the pros and cons of sentinel designs). However, there 

are numerous advantages to following the GLORIA sentinel sampling design for monitoring alpine tundra. 

Mainly, the reduced cost associated with accessing remote sites allows for visiting sites more often and 

monitoring a greater number of parameters within a site. We will therefore have increased power to detect 

trends in vegetation, temperature, exotics, soil condition, and herbivory at these sentinel sites. Moreover, by 

following an established protocol we join a global network where all cooperators are using similar methods, 

and thus we increase our ability to place changes within the parks in the context of regional and global 

patterns. 

Table 1. Location, elevation, date of establishment of GLORIA sentinel sites within GLAC, GRSA, and ROMO

GLORIA 

Summit 

code

Summit 

name

Latitude 

(decimal 

degrees)

Longitude 

(decimal 

degrees)

Elevation 

(m)

Vegetation zone

*estimated from photographs

Year 

established

Year of initial 

species 

composition

GRSA (GLORIA target region: GSD)

SIX Unnamed 37.861501 -105.492034 4000 alpine/ nival ecotone 2008 TBD

PAD Unnamed 37.87965 -105.478217 3870 upper alpine 2008 TBD

MUD Unnamed 37.887517 -105.47665 3700 lower alpine 2008 TBD

HUK

Unnamed

37.89615 -105.473733 3550

lower 

alpine/subalpine 

ecotone

2008 TBD

ROMO (GLORIA target region: RMN)

GLA Ida 40.380617 -105.770072 3862 upper alpine 2008 TBD

PIK Unnamed 40.392956 -105.790538 3715 lower/upper alpine 2008 TBD

VQS Unnamed 40.406275 -105.799238 3623 lower alpine 2008 TBD

JSM

Jackstraw

40.394304 -105.813083 3520

lower 

alpine/subalpine 

ecotone

2008 TBD

GLAC (GLORIA target region: GNP)

SWD Seward 48.870238 -113.679656 2717 upper alpine/ nival* 2004 2004

PKN Pitamakin 48.519130 -113.446469 2493 upper alpine* 2004 2004

BSN Bison 48.465154 -113.311013 2387 lower/upper alpine* 2003 2003

DGL Dancing 

Lady 48.424477 -113.312034 2245 lower/upper alpine* 2003 2003
8

Timing of sampling

The GLORIA Field Manual recommends visiting summits and recording vegetation composition every 5 

years at a time of peak phenology. To better understand annual variation and provide information to park 

management in the short-term, ROMN will visit VQS peak annually in ROMO for the first 5 years and 

thereafter every 5 years (SOP AlpineVCSS Adapting GLORIA to National Parks). Annual visits should 

prioritize sampling in the following order phenology, cover in 16 1-m

2 quadrat clusters, frequency in 100 

m2 plots, and summit area sections. Visiting sites annually will allow for determining the amount of 

variance among sites and years in key parameters, this in turn will provide data needed to perform power 

analyses. When these data are available, we will conduct power analyses to better estimate the intensity and 

length of sampling needed to detect a change in a resource. Specifically, we will estimate the length of time 

required to detect a change of 2%, a value defined from our other vegetation protocol (Manier et al. 2009)

and one that we feel may be on interest to land managers. 

Optional Alpine probability-based sample design and criteria for sampling

While our initial monitoring efforts will focus only on the sentinel sites, if and when time and budgets 

allow, we hope to increase our ability to make park-wide inferences by adding a survey design component 

to our monitoring approach. Probability-based surveys provide unbiased estimation of both status and, with 

repeated visits, trend across a resource (Larsen et al. 1995). When implemented successfully probabilitybased survey designs allow for unbiased inference from sampled sites to un-sampled elements of the 

resource of interest (Hansen et al. 1983). The goal of the optional probability-based design for the 

AlpineVCSS is to determine the status of alpine vegetation and soils in GRSA, GLAC, and ROMO in a 

given year. 

As with all ROMN survey designs (Britten et al. 2007), the alpine survey uses a Generalized Random 

Tessellation Stratified (GRTS) design (Stevens and Olsen 2003, 2004). The methods for the development of 

the survey design and site selection are described in SOP AlpineVCSS GRTS design. In brief, a probabilitybased survey design consists of implementing the following steps prior to field sampling: defining a 

resource or target population and any subpopulations of interest, creating a sample frame within the target 

population, selecting sites to visit within the sample frame, and determining when to sample. We define the 

target population as alpine vegetation in a park, the subpopulation as ecotypes, such as fellfield within 

tundra, and the sample frame as accessible alpine tundra of those ecotypes within a park. We will visit 40 

randomly located sites representing fellfield, dry meadow, and moist meadow tundra within a park and 

sample vegetation at peak phenology (June-August). In addition to describing each of these steps in more 

detail, we provide an example using alpine areas in GRSA in the SOP AlpineVCSS GRTS design. 
9

Field Methods

There are four components to conducting field work for the AlpineVCSS monitoring protocol: (1) following 

ROMN I&M protocols for managing a field season, safety, and NPS compliance; (2) completing the 

GLORIA protocol and field methods; (3) extended monitoring at GLORIA sentinel sites; and (4) field 

methods for the optional probability-based survey design. Below, we briefly outline the resources and 

methods for each of these components. 

All field sampling will be performed according to a set of SOPs. The purpose of detailed SOPs is to ensure 

that over time (and as personnel change) methods remain consistent to reduce bias and variance introduced 

by the observers (Oakley et al. 2003). The rationale for our approach and the field methods are summarized 

here, but methodological details are provided in each individual SOP. 

ROMN Standard Operating Procedures

Managing a field season, permits, safety, and data requirements

ROMN standard administrative and equipment operating procedures cover all methods to ensure safety, 

resource protection, and compliance of a field crew working in ROMO, GLAC, and GRSA. In particular, 

permits (SOP Admin Permitting) and adequate safety training (SOP Admin Safety Preparation and Training) 

are required prior to research activities. End of season procedures including maintenance and storage of 

equipment and data are found in SOP Admin End of Season. Guidance for hiring and training a field crew, 

finding housing and park logistics, communications, and use of vehicles can also be found (SOP Admin

Seasonal Hiring, SOP Admin Training, SOP Admin Park Logistics, SOP Equipment Communications, and

SOP Equipment Vehicles, respectively).

GLORIA field manual

Here, we briefly describe the field methods developed by the GLORIA network; detailed field methods are 

given in the GLORIA Field Manual. 

Establishing and locating sites

Four summits per park (sentinel sites) are indentified using the GLORIA criteria as described above in 

section 3.1. Sentinel site locations (Table 1) are then uploaded to Global Positioning System (GPS) units 

and field crews navigate to these locations by GPS (SOP Equipment Using a GPS unit) and traditional 

orienteering skills using large area topographic maps (e.g., Trails Illustrated), site descriptions and a 

compass. Detailed directions and travel times for accessing the sentinel sites in ROMO and GRSA can be 

found in Appendix 2. 

Plot layout 

Once at a summit, a series of 16 1 m2 plots are delineated and marked where the plots on each summit are 

clustered by aspect and 4 plots are in the N side of the summit, 4 are on the E side of the summit, etc (Figure 

3). Therefore the design consists of summits, aspects of summits, and plots nested within each of the 

aspects. There are a few considerations and modifications of the GLORIA design for NPS units (e.g, 

minimal marking and selecting sites with reduced impacts to park or public visitors) that are outlined in 

SOP AlpineVCSS Adapting GLORIA to National Parks. 
10

Figure 3. GLORIA summit and plot layout at sentinel sites (excerpted from the GLORIA Field Manual). Vegetation

Vegetation is measured in four ways in the GLORIA field methods. The following data are collected from 

all summits: (1) Species cover in summit regions, (2) species frequency and cover in 16-1 m2 plots per peak, 

(3) ground cover in the 1 m2 plots, and (4) grazing and trampling impact in the 1 m2 plots. Vegetation data 

are recorded to the species-level for vascular plants and non-vascular plants are classified as either 

bryophytes or lichens. Where it is possible, lichen and bryophytes will be identified to species.

The first vegetation response variable in the GLORIA Field Manual is the absolute cover of herbaceous 

vegetation canopy and ground cover (e.g., litter, bare soil). Cover is the vegetation covering the ground 

surface as viewed from above and will be recorded by species. Advantages of using cover to measure 

vegetation and substrate include: cover is a direct measure of vegetation biomass, cover equalizes the 

contribution of abundant, small stature species and species that are larger in size, but fewer in number, and 

cover estimations do not require an account of each individual, so assessments are quick (Elzinga et al. 

1998). Documenting the cover or extent of exposed surfaces (e.g., no vegetation cover) and characterizing 

the surface components also provides a means to assess the susceptibility of the surface to erosion. 

Species frequency counts are done within the 1 m2 plots where species presence is recorded in 100 10 x 10 

cm grid cells. The aim of the frequency counts is to record fine-scale changes in vegetation patterns. In each 

grid cell, the presence of scat and evidence of browsing and trampling is also recorded. This is done to 

estimate the impact of herbivores on the vegetation. 
11

Temperature

The GLORIA Field Manual recommends using buried dataloggers in the center of each 3 x 3 m quadrat 

cluster to measure soil temperature hourly at all peaks. Because composition and productivity of alpine 

vegetation may be driven by temperature and snow cover, it is critical to measure changes in temperature 

and snow cover over time. Soil temperature is measured rather than air temperature because buried loggers 

are less susceptible to damage and/or loss by animals and park visitors and avoid artificial extremes caused 

by high solar radiation. Soil temperature is used as a proxy for snow cover based on diurnal temperature 

range (temperatures remain near freezing and vary little with snow cover). We will download temperature 

loggers annually and replace them as needed to ensure a continuous temperature record. 

Photographs

In addition to soil temperature and vegetation cover, the GLORIA methods include extensive photo 

documentation. This includes photographs of the highest point of the summit, location of the dataloggers, 

and all 1 m2 plots. The methods are found in GLORIA Field Manual and the directions for use of digital 

cameras can be found in SOP Equipment Using a Digital Camera.

Extended monitoring at GLORIA sentinel sites

The GLORIA Field Manual describes methods for documenting changes in vegetation and temperature at 

the sentinel sites. Below, we describe the methods and rationale for extending this monitoring to include 

measuring anthropogenic and natural disturbance, soil condition, plant phenology, distance to treeline, and 

N deposition. 

Site context: measuring anthropogenic and natural disturbance

Observing and documenting land use and natural episodic events can provide insight into understanding 

status and trends in vegetation composition, structure, and soils. It is critical to document site-level 

disturbances especially when utilizing a small number of sentinel sites because in many cases, disturbance 

can act as a strong driver of change and cause direct alterations in community composition. For example, 

wildfire or logging may modify soils, alter the water balance, increase exposure sunlight, and introduce 

vectors for dispersal of new species (DeBano et al. 1998, Fleming and Baldwin 2008). Moreover, 

disturbances may be of particular interest to park staff because they may be able to reduce disturbances 

through changes in land management (e.g., reroute trails). In the alpine regions of GLAC, GRSA, and 

ROMO, current anthropogenic disturbances are often minimal and include trampling, heavily eroded hiking 

trails, and nearby roads. Historic disturbances such as logging and grazing may also be evident. 

At all sites, a field crew will document potential anthropogenic stressors in and around the area in a semiquantitative fashion. A site will be evaluated using a modified version of the 2008 Human Disturbance 

Index (HDI) developed by the Colorado Natural Heritage Program (Rocchio 2007) and the California Rapid 

Assessment Method for Wetlands (Collins et al. 2008). We use this HDI approach for evaluating site 

disturbance in all ROMN field protocols (e.g. streams, wetlands, grasslands) which allows for a greater 

understanding of park-level disturbance regimes. The index ranges from 0-100 % where the large values 

indicate increased disturbance. The site is surveyed and the HDI is calculated based on the severity or 

absence of disturbances within three general categories: buffer/landscape, hydrology, and physical 

disturbance. The buffer and landscape context includes measures of buffer width, adjacent land-use, degree 

of fragmentation within 1 km, and buffer condition. The HDI was modified for alpine vegetation to exclude 

many hydrological disturbances and metrics that are not relevant to upland vegetation; however, 

hydrological alterations relating to the presence of dikes, roads, and diversions are still recorded. Finally, 

physical disturbance includes metrics of soil disturbance from mechanical damage (e.g., 4WD vehicles) and 

onsite land use. The scores from each weighted category are combined to generate a HDI score following 

the equations presented in SOP AlpineVCSS Site Disturbance. 
12

In addition to human disturbances, natural disturbances may have profound effects on the status and trends 

in alpine vegetation and soils. In protected areas, such as National Parks, natural disturbances, rather than 

human disturbances, are more likely to be the primary drivers of vegetation change. For the AlpineVCSS 

protocol, we will measure natural disturbance at sites in a semi-quantitative fashion. Each site will be 

surveyed for the presence and degree of impact from beaver, native ungulates, rodents, pests and pathogens, 

frost heave, fire, and landslides. For instance, the presence of wallows, hoof prints, standing dead shrubs, 

and large amounts of scat may indicate an area used heavily by elk. As with the HDI, each disturbance will 

be scored and a weighted mean of the scores is used to calculate the final Natural Disturbance Index (NDI). 

The SOP AlpineVCSS Site Disturbance provides detailed instructions and equations for determining the 

NDI. 

Soil condition 

Soil at each site will be characterized to assess the potential for erosion and compaction of top soils, the 

storage, cycling, and abundance of nutrients and water available to plants, and the potential for acidification 

of the soil from the deposition of pollutants. We are particularly interested in determining the status and 

trend in soil condition because of its direct link to vegetation condition, nutrient cycling, and water quality. 

Changes in soil condition may be caused by physical disturbance or anthropogenic deposition of nutrients. 

In the alpine tundra of the Rocky Mountains, atmospheric N deposition may be an important driver of 

change in vegetation via its effects on soil chemistry and nutrient availability (Fenn et al. 2003). To better 

characterize and inventory soils park-wide, we follow similar methods for monitoring soil in lower elevation 

grasslands, shrublands, and woodlands (Manier et al. 2009).

The first step in characterizing soils is to determine the area of the site covered by vegetation and bare soil. 

Bare ground, litter, scree (loose, rock debris), rock (bedrock, boulders, and unmovable outcrops), lichens 

and bryophytes on soils are measured in plots in concert with vegetation sampling (GLORIA Field Manual). 

To complement these measures and to better understand soil condition, an initial soil characterization at 

each site will be done and includes measures of the following: soil texture, bulk density, nutrients and 

mineral concentrations (e.g., phosphorus, aluminum, calcium), pH, carbon (C) and N content, % organic 

matter, cation exchange capacity, and soil aggregate stability (SOP AlpineVCSS Soil). Following the initial 

characterization, full soils characterization will be completed every 5 years. If costs allow, soil pH and total 

C and N will also be measured annually for the first 5 years. With the exception of aggregate stability, all 

soil tests will be based on soil cores taken in the field and sent to a cooperating laboratory. 

The following is a more in-depth description of the soil characters we focus on and rationale for including 

them. Soil texture is a description of the percent sand, clay, and silt in a soil and is a critical component of 

soil monitoring (Elliott et al. 1999). Numerous soil properties are influenced by texture including: drainage, 

susceptibility to erosion, organic matter content, cation exchange capacity, and pH buffering capacity. For 

instance, soil with a high percentage of silt and clay particles has a greater erodibility than a sandy soil

under the same conditions. We expect that texture will not change as rapidly as vegetation or other soil 

properties and will be measured at only the initial site visit. Bulk density is a measure of soil mass per 

volume and is an indicator of soil strength and structure. Soil compaction leads to an increase in bulk 

density and can affect porosity of soils and plant growth. Measuring bulk density is also necessary to 

convert soil nutrient concentrations to a volumetric measure (Elliott et al. 1999). The ability of soils to retain 

positively charged ions is referred to as cation exchange capacity (CEC). Essential plant nutrients, such as 

calcium (Ca2+), magnesium (Mg2+), and potassium (K+) are cations and CEC describes the ability of 

negatively-charged clays and organic matter in the soil to hold on to these cations. Mineral deficiencies are 

more likely to occur and soil pH tends to decrease faster in soils with low CEC (Robertson et al. 1999). Soil 

organic matter is the fraction of the soil that consists of plant or animal tissue in various stages of 
13

decomposition. Soil organic matter enhances aggregate stability, CEC, the soil’s capacity to hold water, 

nutrient availability, and pH buffering capacity (Sollins et al. 1999). Soil pH measures the activity of 

hydrogen (H+) ions in soil solution. Many plant nutrients become less available to plants in acidic soils and 

atmospheric N deposition may lead to changes in soil pH (Robertson et al. 1999). Along with all the other 

soil parameters, we will estimate the concentration of plant mineral nutrients in the soil. Productivity is 

often linked to the availability and balance of plant nutrients. Total N and C content will also be measured 

and can be a useful indicator of ecosystem processes such as C sequestration, decomposition, and the 

potential for a site to leach nitrate (Sollins et al. 1999). 

Phenology

In addition to monitoring soil condition and site disturbance, we will also monitor plant phenology at all 

summits. The timing of reoccurring events in an organism’s life-cycle, or phenology, can be used as an 

indicator of climate change. In alpine systems, the growing season is short and the timing of leaf out and 

flowering is strongly influenced by the timing of snowmelt. To monitor phenology in our sentinel sites, we 

plan to record the life-stage for three indicator species at each site (SOP AlpineVCSS Phenology). ADD 

MORE ABOUT SPECIES CHOICE

We focus on vegetative and reproductive phenology including the following phenophases: emerging 

growth, unfolded leaves, withered leaves, open flowers, and ripe seeds (Table 2). Phenophases will be 

recorded on the population-level rather than for an individual plant, meaning all phenophases present for 

that species at a site will be recorded and the most common will be recorded as the dominant phase. 

Table 2. The phenophases that will be monitored at sentinel sites for five plant species 

Stage Definitions ( NPN 2009) 

Leaves: Emerging growth New growth of the plant is visible above the soil surface with the appearance of fresh green 

shoots that show no signs of aging. For each shoot, growth is considered "emerging" until the 

first leaf has unfolded.

Leaves: Unfolded In at least one location on the plant, an unfolded leaf is visible. A leaf is considered "unfolded" 

when it unrolls slightly from around the stem and begins to fall away at an angle.

Leaves: Withered Of the leaves that developed this season, virtually all (95-100%) are dried and dead.

Open flowers In at least one location on the plant, an open fresh flower is visible. Flowers are considered 

"open" when the reproductive parts are visible between open flower parts. Do not include dried 

flower parts that remain on the plant.

Ripe seeds In at least one location on the plant, a ripe seed is present. A ripe seed is hard when squeezed 

and is difficult to divide with a fingernail. Seeds may also be considered ripe when they fall into 

your hand when the plant is handled.

Treeline

Climate models predict that forests will expand upslope into alpine areas as temperatures continue to warm 

resulting in change at the forest-alpine ecotone that will have large effects on C storage, nutrient cycling, 

wildlife habitat, and hydrodynamics (Field et al. 2007). In the past 46 years in GLAC, repeated aerial 

photographs suggest that there has not been a shift in treeline, but where krummholz and forest patches 

existed, these have increased in density and area (Klasner and Fagre 2002) Such changes in land cover and 

extent of forest cover will be monitored remotely via the Landscape Dynamics Protocol (Burke et al. in 

preparation). However, describing changes on the ground is an important component in documenting local 

landscape change. To document changes in treeline, we developed a procedure to photograph and measure 

the distance from the highest point to the krummholz and treeline at each sentinel site (SOP AlpineVCSS

Treeline). The method outlines photographic documentation of treeline in all cardinal directions and an 

estimate of the distance from the highest point to the krummholz and treeline. 
14

N deposition

In high elevation systems with historically low nitrogen availability, the influx of N has caused changes in 

vegetation, soil, snow, and water chemistry (Fenn et al. 2003). The atmospheric deposition of nitrogen in the 

Rocky Mountains has increased in recent decades to greater than 6 kg ha-1yr-1 (NADP 2009). As a result, it 

is critical to have an understanding of the current N loadings in the alpine environment. Annual N loadings 

may provide a covariate in trend analyses (as a driver for change) and spatial variation in N loadings may 

add to our understanding of different vegetation patterns within and across parks. The National Atmospheric 

Deposition Program (NADP) measures wet deposition of N (ammonium and nitrate), calcium, sulfate, 

potassium, and chloride at sites throughout the Rocky Mountains (Table 3). In addition, in collaboration 

with ROMN the USGS Rocky Mountain Snow Chemistry Network samples snow at peak accumulation for 

nitrate, sulfate, and ammonium in GLAC, GRSA, and ROMO (Ingersoll et al. 2002, Ingersoll et al. 2009). 

While these data can provide a good understanding of current N loads in the region, there may be an interest 

in having a better understanding of site-level N deposition rates. This may be particularly relevant in areas 

of GRSA where NADP stations are distant from the park and/or at lower elevations. In SOP AlpineVCSS

Nitrogen Deposition, we outline an inexpensive method to measure annual wet deposition in resin columns 

adapted from a protocol of the United States Forest Service (Fenn and Poth 2004). In brief, a resin collector 

is constructed using a standard rainfall collector, ion exchange resin is added, 8 collectors per site are placed 

in the field for 1 year, and ions are extracted from the collector and analyzed for nitrate and ammonium. 

Since this method should only be implemented as needed, it is considered an optional SOP. 

Table 3. National Atmospheric Deposition Program stations relevant to GLAC, GRSA, and ROMO(from 

http://nadp.isws.illinois.edu/)

Park Unit Relevant station 

codes

Locations Elevation

(m)

Dates of operation

GLAC MT99 GLAC-St. Mary’s ranger station 1391 1/25/1983 - 11/28/1989

MT05 GLAC- Fire weather station 980 6/3/1980 - Present

GRSA CO00 Alamosa 2298 4/22/1980 - Present

CO91 Wolf creek pass 3292 5/26/1992 - Present

ROMO CO19 ROMO- Beaver Meadows 2490 5/29/1980 - Present

CO98 ROMO- Loch Vale 3159 8/16/1983 - Present

Field Methods for Optional alpine probability-based survey design 

The rationale and sampling design for a park-wide survey of alpine tundra is described above (Section 3.2) 

and in SOP AlpineVCSS GRTS design. Here, we briefly describe the field methods that will be conducted at 

each survey site. These methods are optional and will only be done if time and budgets allow for the 

intensive survey of alpine within a park. While we will measure species richness and composition, the field 

methods differ from the GLORIA Field Manual. We chose to change field methods for the survey because 

the GLORIA approach is labor intensive (e.g., 16-1 m2 plots per peak) and designed for sparsely vegetated 

peaks. Instead, we modified field methods from the ROMN Wetlands Ecological Integrity (WEI) protocol 

(Schweiger et al. in preparation) by including only those procedures relevant to upland vegetation (e.g., 

groundwater wells are not installed). The WEI methods were chosen because they are more efficient 

(multiple sites can be done per day), they have been implemented in alpine wet meadows in ROMO, are 

better adapted for vegetation that includes woody components (e.g., willows) than the GLORIA Field 

Manual, and most metrics are consistent with those measured in the GLORIA Field Manual (e.g., species 

cover in 1 m2 plots). 

The probability-based design field method involves five steps that are outlined below and described in detail 

in the corresponding SOPs. These include: establishing and locating sites, determining the target status of a 
15

site, replacing or moving sites, laying out plots within a site, photographing the site, and measuring 

vegetation and soils within the plots. 

Establishing and locating sites

The initial step in the AlpineVCSS survey is to determine where and when sampling will occur. Sites to 

sample in are determined by the sampling design (SOP AlpineVCSS GRTS design) and the exact timing by 

the relevant index period for the park. When sites are selected, site dossiers are created to include maps of 

each site and relevant metadata (directions, estimated travel time, etc.). Site locations are uploaded to GPS 

units. Field crews navigate to these locations by GPS (SOP Equipment Using a GPS unit) and traditional 

orienteering skills using large area topographic maps (e.g., Trails Illustrated), site descriptions and a 

compass. 

Once a field crew has navigated to a site, they must determine whether the site is appropriate for sampling 

and move or replace it if it is not suitable. There a few reasons for determining that a site is not appropriate 

including: safety, the presence of protected and sensitive resources (compliance) or the site is of the wrong 

target type. Safety and compliance issues are discussed in (SOP AlpineVCSS Site Suitability) and include 

issues like steep slopes and archeological artifacts. While the sample frame and GRTS sampling design 

were developed to include only areas of interest, they were based on GIS analyses and errors are inevitable 

based on incomplete ground truthing of data. For instance, the field crew may navigate to a site and find that 

rather than alpine fellfield it is sheer bedrock or an alpine wet meadow. In either case, the site is not in the 

appropriate target population and should be moved or replaced by the next site in the oversample list for the 

target type. To maintain the integrity of the survey design, strict rules regarding the moving of a survey site 

are followed. A site may only be shifted by a distance equal to the estimated GPS error (average of 4 m) 

plus the average positional accuracy in the base mapping methods (average of 9 m) used for the primary 

elements of the sample frame, plus (if needed) half of the longest axis of the field plot (7 m). This value is 

approximately 20 m. When sites are moved or replaced, it is critical that the fate of sites in the survey design 

be carefully recorded and tracked. The rules for moving and replacing sites are described in SOP 

AlpineVCSS Replacing Sites.

Plot layout 

After determining that the site location meets the requirements of sampling, the field crews will lay out 

vegetation sampling plots using meter tapes and flagging. The AlpineVCSS plot design consists of a series 

of nested plots. The largest plot, the macroplot, measures 10 x 10 m and contains one subplot (16 m2

) and 

four microplots (1 m2

) (Figure 4). Details regarding layout are provided in the SOP AlpineVCSS Plots. 

These sites will not have long-tem markers (e.g., rebar); instead, plots will be marked temporarily while 

sampling with survey pins and meter tapes. The plot design was chosen to include a large area appropriate 

for estimates of plant species diversity and willow recruitment and smaller plots where cover estimates and 

browsing are measured with increased accuracy.

Prior to sampling and before, after, or during the plot layout, it is critical that the site be adequately 

described to enable efficient location of sites in subsequent years and to provide a detailed overview of site 

environment (e.g., slope, aspect, etc.). A Sample Site Description Form should be used to clearly define 

(using UTM coordinates) and describe the location of the site (e.g., general topography, hydrology), and to 

describe the locations of the 4 corners of the macroplot and subplot (SOP AlpineVCSS Plots). 
16

Figure 4. Plot layout for the AlpineVCSS survey design

Vegetation

As in the sentinel GLORIA sites, canopy cover and diversity of plant species is estimated in plots of varied 

size. In the microplots (1 m2) and subplots (16 m2) canopy cover is measured. To better estimate site-level 

diversity, species presence is recorded in the 100 m2 macroplot. The detailed methods for measuring 

vegetation in the survey sites are described in SOP AlpineVCSS Vegetation. 

Unlike the sentinel sites that are located on dry summits, woody plants may be present in other alpine areas. 

Willows (Salix spp.) are one the most commonly found woody plant in the alpine. Positive feedbacks 

between the presence of shrubs, temperature, snowpack, and nutrient availability have accelerated the 

encroachment of woody shrubs in many tundra systems (Sturm et al. 2001, Sturm et al. 2005). However, as 

a preferred browse species for elk, willow-dominated communities have been reduced or become impaired 

due to over browsing by expanding elk populations in ROMO (Peinetti et al. 2002, Baker et al. 2005, Gage 

and Cooper 2005). Consequently, the protocol includes methods for determining the presence and 

regeneration of willows and extent of herbivore damage. These methods include counting seedlings and 

sapling and recording dieback, browsing damage, and height of willows in the subplots. For details, see SOP 

AlpineVCSS Willows.

Soils and site context

At each survey site, 3 soil cores will be taken to 20 cm at random locations adjacent to the microplots. The 

soils will be processed and analyzed for the same physical and chemical parameters as those from the 

sentinel sites (described in SOP AlpineVCSS Soils). Potential anthropogenic and natural stressors at each 

site are measured using the same methods as for the sentinel sites (SOP AlpineVCSS Site Disturbance). 
17

Photographs

The site will be photographed to aid in locating the site and to provide an invaluable tool for evaluating 

ecological change (Mast et al. 1997, Horsted and Grafe 2002). For example, processes such as tree or shrub 

invasion are readily apparent on photographs (Jakubos and Romme 1993). To aid in locating sites, the first 

set of photographs should be taken of a witness feature, such as a rock outcrop. The coordinates are 

recorded from a GPS and the distance and azimuth from the center of the plot to the location of witness 

features is determined by running a tape and using a compass. The second set of photographs involves 

collecting 8 photo points at fixed azimuths from the center of the macroplot (Figure 4). Finally, photographs 

of any noticeable disturbances, such as landslides, should be taken. A detailed discussion of photograph 

methods are presented in SOP AlpineVCSS Photographs and general instructions for the use of a digital 

camera can be found in SOP Equipment Using a Digital Camera. 

Standard Operating Procedures (SOPs)

A full list of AlpineVCSS SOPs is included below (Table 4). Details of methods discussed in this narrative 

are included in these separate SOPS. The detailed procedures for the sentinel sites including site selection, 

measuring plant composition, photographs, and installation and use of dataloggers are provided in the 

GLORIA Field Manual. 
18

Table 4. Standard Operating Procedures for AlpineVCSS protocol including published GLORIA Field Manual and 

associated methods, extended GLORIA methods, optional methods for a survey design, and shared ROMN SOPs. 

SOP title SOP description

GLORIA Field Manual The GLORIA Field Manual: Multi-Summit Approach (Pauli et al. 2004) 

SOPS for extended GLORIA 

SOP AlpineVCSS Adapting GLORIA to National Parks Considerations for adapting and establishing GLORIA in the NPS 

SOP AlpineVCSS Site Disturbance Site disturbance: Describing natural and anthropogenic disturbance to 

the site and surrounding area

SOP AlpineVCSS Soils Soil condition at GLORIA sentinel sites

SOP AlpineVCSS Phenology Phenology at GLORIA sentinel sites

SOP AlpineVCSS Treeline Detecting the current and future location of treeline at GLORIA sties. 

SOP AlpineVCSS N deposition Deploying ion exchange resin collectors to measure N deposition 

(optional)

SOP AlpineVCSS Data Analysis Guidelines for statistics and data summaries for GLORIA sentinel sites

SOPs for Survey Design (Optional)

SOP AlpineVCSS GRTS design Methods for developing a probability-based survey design of the alpine

SOP AlpineVCSS Site Suitability Determining target status 

SOP AlpineVCSS Replacing Sites Moving and replacing sites

SOP AlpineVCSS Plots Plot layout

SOP AlpineVCSS Photographs Photographic documentation

SOP AlpineVCSS Vegetation Vegetation composition in macro, sub, and microplot

SOP AlpineVCSS Willows Measuring willow regeneration and herbivore damage

SOP AlpineVCSS Survey Data Analysis Guidelines for statistics and data summaries for design-based 

analyses of optional survey sites

Shared ROMN SOPS

SOP Admin Permitting Permitting and compliance for monitoring in NPS units

SOP Admin Safety Preparation and Training Safety requirements and training (e.g., wilderness first aid)

SOP Admin Seasonal Hiring Hiring of field crew- seasonal or student employees

SOP Admin Training Protocol and ROMN training for field crew

SOP Admin Park Logistics Park logistics, housing, point of contacts

SOP Admin End of Season End of season, equipment storage, data entry

SOP Equipment Using a Digital Camera General guidelines for use of digital cameras

SOP Equipment Using a GPS unit General guidelines for GPS use

SOP Equipment Communications General guidelines for use of radios

SOP Equipment Vehicles General guidelines and regulations for use of vehicles

SOP DM Data Quality Control (QC) General data QA/QC procedures

SOP DM Data Deliverable Standards Data deliverable standards

SOP DM Archiving Data Procedures for archiving data
19

Data Management and Analysis

Overview of data management and procedures 

The ROMN is the owner and long-term steward for all AlpineVCSS data and the management of 

these data follow the ROMN Data and Information Management Plan (NPS 2006b). Data

management requirements for ROMN protocols include explicit procedures to enter, edit, 

document, store, and archive data (see SOP DM Data Quality Control, SOP DM Data 

Deliverable Standards and SOP DM Archiving Data). The GLORIA network maintains a 

database of soil temperatures, species composition, and photography for each target region in the 

network where the required format and details are found in the GLORIA Field Manual. Since the 

GLORIA network does not maintain a database that fits all the needs of the NPS, nor does it 

include any of the additional field methods (e.g., soil chemistry), we will maintain a database for 

alpine monitoring at the ROMN. We will extract data from this database annually, or as needed, 

to update the GLORIA network and maintain our connection to the international tundra 

monitoring community. 

We expect to complete the first draft of the AlpineVCSS database in 2011 and it will be built on 

the NRDT Phase III model (NPS 2007). We expect to revise the database structure with the 

inclusion of field data and refinement of the protocol over the next three years (2011-2014). As 

the database is developed and modified, the basic schematics and database structure will be 

included in future versions of this protocol. 

Recommendations for routine data summaries and statistical analyses 

To meet the objectives of the AlpineVCSS and to provide summary analyses for the scientific 

and management community, we expect to conduct a number of statistical analyses on all the 

data collected. While the details of the statistical methods may change, we outline below the raw 

data that will be collected at sentinel sites and the parameters that we will derive from the raw 

data (Table 5) and additional data that may be collected as part of the optional survey (Table 6). 

Below, we outline our general approach to statistical analyses but the approach and data are 

described in more detail in SOP AlpineVCSS Data Analysis and SOP AlpineVCSS Survey Data 

Analysis. 

Status and trends and the GLORIA sentinel sites

To examine status at GLORIA sentinel sites, we will use descriptive statistics and linear mixed 

effect models or nonparametric equivalents to examine the difference in parameters among 

parks, summits, and summit aspects. Models will contain fixed factors (park, summit, aspect) 

and random factors (plot nested in aspect). Multivariate methods, such as analysis of similarity 

and ordinations, will use percent cover of all species to examine the differences in community 

structure among summits and aspects. We will explore the relationship between physical factors, 

such as elevation and soil chemistry, and vegetation structure using linear regression and tests of 

correlation. Where appropriate, these physical factors may be used as covariates in linear models. 

To determine which factors or drivers affect a given parameter, we will use model selection 

procedures and determine the best fit models using Akaike’s Information Criteria (AIC) (Akaike 

1973, Burnham and Anderson 2002). We are particularly interested in understanding how levels 

of grazing, temperatures, precipitation, and N deposition may alter community structure. 
20

When two to three years of data are available, we may begin to examine and detect change at 

sentinel sites. To do this, we will use the same statistical methods described above but add year 

as a fixed factor. This will allow us to determine whether parameters, such as species richness, 

differ among years. After two to three years of data are available, we will conduct power 

analyses to determine the length of time and sampling effort required to detect a 2% change in 

vegetation. We will explore trends at GLORIA sentinel sites, when four or more years of data 

are available. To examine trends we will conduct repeated measures analyses where “plots” are 

the subject that is repeated over time. 

Where it is possible, the data collected at ROMO, GRSA, and GLAC will be compared to other 

GLORIA sites. We are particularly interested in comparing the patterns of vegetation and 

temperature changes within the Rocky Mountains. 

Table 5. AlpineVCSS protocol objectives and data collected relating to those objectives.Most raw data are 

collected in the field and the derived are calculated, but laboratory measures are indicated as lab. 

Objective Parameter GLORIA, 

extended 

GLORIA, 

or optional 

Raw/Derived Units or index

Status and trend in 

vegetation structure 

and species 

composition at four 

alpine peaks

Cover of scree, rock, vascular 

plants, lichens, bryophytes, 

bare ground, litter

GLORIA Raw %

Species cover (vascular plants) GLORIA Raw %

Species number GLORIA Derived species

Diversity Index GLORIA Derived shannon-weiner index, H'

Evenness, GLORIA Derived Peilou index, J

Mean Coefficient of 

Conservation

GLORIA Derived index C

Floristic Quality Index GLORIA Derived FQA index, based on C

Species frequency counts GLORIA Derived %

Relative frequency of species GLORIA Derived %

Species presence/absence GLORIA Raw categorical

Relative frequency of 

graminoids and forbs

GLORIA Derived %

Dominant phenophase extended Raw categorical

Date of first flower extended Derived Julian day

Status and trend in 

exotic invasive species 

at four alpine peaks

Number of exotic species 

present on summit

extended Derived species

Identity of exotic species 

present

extended Raw categorical

Percent cover of exotic species extended Raw %

Status and trend in 

grazing at four alpine 

peaks

Browsing presence/absence GLORIA Raw categorical

Scat presence/absence GLORIA Raw categorical

Trampling presence/absence GLORIA Raw categorical

Browsing frequency GLORIA Derived %

Scat frequency GLORIA Derived %

Trampling frequency GLORIA Derived %

Objective Parameter GLORIA, 

extended 

Raw/Derived Units or index
21

GLORIA, 

or optional 

Status and trend in soil 

temperature at four 

alpine peaks

Hourly temperature GLORIA Raw °C

Mean daily temperature GLORIA Derived °C

Minimum daily temperature GLORIA Derived °C

Maximum daily temperature GLORIA Derived °C

Mean annual temperature GLORIA Derived °C

Maximum annual temperature GLORIA Derived °C

Minimum annual temperature GLORIA Derived °C

Number of days above 100 °F 

in a given year 

GLORIA Derived days

First/last date of snow cover GLORIA Derived Julian day

Number of days of continuous 

snow cover

GLORIA Derived days

First/last date of hard freeze GLORIA Derived Julian day

Accumulated growing degree 

days

GLORIA Derived growing degree days

Diurnal temperature range GLORIA Derived °C

Status and trend in soil 

condition at four alpine 

peaks

Sand silt and clay extended Raw (lab) %

texture extended Derived (lab) NA

bulk density extended Raw (lab) g/m3

pH extended Raw (lab) pH

cation exchange capacity extended Raw (lab) cmolc/Kg soil

micro and macronutrients extended Raw (lab) %

organic matter extended Raw (lab) %

C:N extended Derived (lab) ratio 

surface flow evidence extended Raw ordinal

rills and gullies extended Raw ordinal

pedestals and terracettes extended Raw ordinal

Status and trend in site 

characteristics and 

disturbance at four 

alpine peaks

distance to road extended Derived m

human disturbance index extended Derived %

slope extended Raw degrees

elevation extended Raw m

nitrogen load (ammonium and 

nitrate)

extended Raw (lab) kg/ha

beaver disturbance extended Raw categorical

native ungulate disturbance extended Raw ordinal

frost heave disturbance extended Raw ordinal

rodent disturbance extended Raw ordinal

pest & pathogens disturbance extended Raw ordinal

landslide disturbance extended Raw ordinal

fire disturbance extended Raw ordinal

natural disturbance index extended Derived index; NDI
22

Design-based basic summaries

Where the optional alpine surveys are conducted to provide park-wide inference, we will use a 

design-based analysis which is a fundamentally different approach than the model-based 

approaches described above. In essence, parameters from different sites are determined and then 

weighted by the probability that that site was included in the sample design (inclusion 

probability) providing adjusted means and variance of the parameter that then represent the 

entire target population. From the GRTS AlpineVCSS survey designs we can generate more 

precise variance estimates using a proprietary technique developed by Stevens and Olsen (2003). 

Known as the local neighborhood variance, it is derived from smoothed or averaged contrasts 

among values in the local neighborhood of a sampled point. It provides estimates 20 to 60 

percent smaller (i.e., more precise) than similar traditional survey-design variance estimators 

(Horvitz and Thompson 1952). In summary, the data collected at each survey site are combined 

and used to represent the status of the alpine within a park. 

There is much overlap in the parameters that are collected at the optional survey sites and the 

sentinel sites (Table 5). The exceptions are as follows for the survey sites: species frequency will 

not be measured, temperatures will not be measured, and grazing/trampling measures will be 

replaced with those found in Table 6.

Table 6. Objectives of the optional alpine survey component of the AlpineVCSS protocol and related data

Objectives for Alpine Survey Parameter Raw/Derived units

Status of grazing in alpine tundra dieback Raw %

live stems browsed Raw %

dead stems Raw %

willow height class Raw ordinal

Willow cover raw m

Reference conditions

A final example of analysis we use involves comparisons of the status or trend in AlpineVCSS 

response with a defined threshold or criteria in a reference condition(s). Empirical or modeled 

reference conditions for most AlpineVCSS parameters are typically not known and will take 

time to develop. Several approaches for defining reference conditions and thresholds within 

reference distributions have been identified in the literature (e.g., Carpenter and Turner 2000, 

Williams and Tonnessen 2000, Stoddard et al. 2006) Methods the ROMN are exploring for 

AlpineVCSS response measures include: 1) measuring vital signs at sites that are minimally or 

least disturbed in or around a park and using these values as thresholds (Hughes et al. 1990); 2) 

using ambient, measured distributions of a vital sign as developed through survey designs in a 

park (with thresholds placed within these distributions arbitrarily at the 5th or 25th percentile) 

where dose-response relationships suggest break points or where maximum species richness 

occurs (Stoddard et al. 2006); 3) interpretations of historical conditions; and 4) best professional 

judgment (i.e., applying future condition concepts from park General Management Plans). The 

ROMN will develop reference conditions through cooperative research projects and 

adventitiously from outside research. Reference conditions and thresholds are key parts of a 
23

mature monitoring effort as they are the primary mechanisms for interpreting data and 

connecting to management objectives for a given park.

Once reasonable numeric thresholds or criteria exist for a given response, linear models can be 

used to compare measured parameters at the sentinel sites to these thresholds. For populationscale, design-based results, non-parametric tests that incorporate design structures are available. 

Specifically, the Wald statistic and two Chi-squared statistics suggested by Rao and Scott (1981)

can be used for testing differences (Kincaid et al. 2004). 

25

Reporting

The goal of the AlpineVCSS protocol is to produce scientifically sound, consistent, and 

comparable monitoring information that can be used to support park management and decision 

making. The NPS hopes to “improve park management through greater reliance on scientific 

knowledge” (NPS 2006a); and the production of reports and effective communication of 

scientific results serves as the final link in transforming data into information.

The network is preparing a detailed Communication Plan, which will identify audiences, best 

formats and media, and delivery systems for all ROMN results including AlpineVCSS 

information. Our specific internal audiences include 1) ROMN park managers, 2) ROMN park 

resource professionals and other park staff, 3) the ROMN Technical Committee, and the 

Intermountain Region, and NPS-wide I&M and Resource Stewardship Programs. Our external 

audiences include: 4) the academic community, 5) other government agencies, 6) non-profit/nongovernmental organizations and 7) the general public. 

ROMN reporting plans are outlined in Chapter 7 of the VS Monitoring Plan (Britten et al. 2007). 

The plan guides the general goals and concepts for reporting and communicating all ROMN data 

and information including monitoring results. We anticipate providing quality assured ROMN 

data and information including (non-sensitive) data, reports including annual reports, synthesis 

reports, and other products (such as project summaries) via the Internet. In general, the ROMN is 

committed to regular reporting of AlpineVCSS information to internal and external audiences to 

maximize the usefulness of our data. We will report annually on the protocol and include simple 

summaries of annual accomplishments (e.g., annual monitoring tasks or objectives and number 

and types of sample events by park), data summaries and discussion of noteworthy observations 

and/or issues. Comprehensive syntheses and reports will be produced every 6-10 years. 

Annual Reports

The routine preparation on a predictable and recurring basis of data summaries and basic 

interpretation can: 1) foster program support by establishing a client base, 2) motivate continued 

progress in program components, and 3) serve as the foundation for more comprehensive 

interpretive reports. Therefore, the ROMN will produce annual summaries of all monitoring 

activities and present these in the hierarchical web based format (they are also available in a 

summary form in a traditional report format). Annual reports provide a general accounting of 

yearly monitoring activities, issues and problems as they arise, and a status summary of 

measured indicators. All ROMN monitoring protocols are peer-reviewed and revised for 

improvements, and these will also be present in the hierarchical web delivery system.

We recommend that annual reports from the AlpineVCSS protocol include graphical and tabular 

summaries of statistically significant and biologically meaningful differences among summits 

and aspects, and the following at a minimum:

• Number and dates that sites were visited

• Bar graphs of species richness and floristic quality index (mean ± 1 standard error) by 

summit
26

• Bar graphs of percent cover of exotic invasive species, graminoids, and forbs by summit

• Scatterplot or line graph of daily mean, maximum, and minimum temperature at each 

summit.

• Table or text reporting number of days with soil temperatures above 100 °F, first and last 

days of continuous snow cover, and first and last date of hard freeze. 

Also on an annual basis, the ROMN will provide non-sensitive and non-proprietary monitoring 

data and information to the NPS Washington Support Office (WASO) data and information 

management and delivery systems. We will provide this information as bundled snapshot 

summaries for a particular field season, including one year’s worth of data will be distributed 

along with supporting protocols, reports, and any other documentation. These annual reports and 

the associated databases will be available to NPS, the Department of the Interior, and other land 

managers, Congress, and the Executive Branch.

Comprehensive synthesis and analysis

Detailed status and trend analyses and syntheses will be conducted at 6-10 year cycles 

(depending on the final realization of sampling). Comprehensive synthesis reports will also use 

the hierarchical web based format outlined in the ROMN Vital Signs Monitoring Plan (Britten et 

al. 2007). They will be available in a traditional report format made available in hardcopy and 

digitally from ROMN websites (i.e., via a portable document format). The comprehensive 

reports will include park- and network-level assessments. Park-level assessments will emphasize 

detecting and interpreting trends in individual vital sign measures, in interactions among 

drivers/stressors and responses measured at similar scales and across multiple scales and 

comparing data to reference conditions. For the AlpineVCSS protocol, we recommend that the 6-

10 year syntheses include complete analyses of trend and site status with particular attention to 

changes in species richness, presence of exotic species, loss of rare species, changes in dominant 

life forms, and changes in growing degree days and diurnal temperature range. Changes in 

vegetation structure over time or differences among sites should be examined in relation to two 

primary stressors – N deposition and climate. Climate effects can be inferred from soil 

temperature parameters and modeled data from PRISM data (2009). When measured, N 

deposition is best estimated from ion-exchange resin collectors or it can be inferred from nearby 

NADP sites or soil C:N and CEC. 

Where applicable, these comprehensive reports will be submitted for publication in peerreviewed scientific journals. Where evidence of resource degradation exists, mitigation measures 

will be recommended. Network-level assessments will compare status (e.g., number of species, 

areal extent of patch types per time unit) and trends of vital-sign measures among ROMN parks 

with qualitative summaries and quantitative (where possible) methods. Comparisons with 

regional NPS networks and other GLORIA sites also will be considered. 

Protocol publication and review

The primary component of the AlpineVCSS protocol is the sentinel site selection and field 

methods and these have been replicated from the GLORIA Field Manual (Pauli et al. 2004)

which has been published, implemented globally, and made available via the internet 

(http://www.gloria.ac.at/). The AlpineVCSS protocol, which also includes the GLORIA Field 
27

Manual, will be made available through the ROMN internet site and NPS Vital Signs Monitoring 

Protocol Database (http://science.nature.nps.gov/im/monitor/VitalSigns/BrowseProtocol.aspx).

Methodological details of the ROMN AlpineVCSS protocol will be re-evaluated after 1-2 

seasons of data collection and at the completion of a monitoring “cycle” (every 6-10 years). The 

purpose of the review is to evaluate the AlpineVCSS procedures and determine where 

procedures fall short of stated objectives. These reviews may include suggested modifications to 

field methods, data analysis, and reporting based on either scientific considerations or budgetary 

constraints. An official programmatic review of the AlpineVCSS vegetation approach to vital 

signs, including the objectives, designs, methods, and results as well as the ability of the network 

to sustain these protocols, will be conducted after at least two parks complete sample and 

resample events (~2-5 years). If changes to the protocol are made, these will be included as a 

new revision and made available in the manner described above. 

29

Administration and Operations for AlpineVCSS Monitoring

Introduction

The administration and operations required for the AlpineVCSS protocol fall under the auspices 

of the Annual Administrative Report and Work Plan (AARWP) process for the ROMN and all 

NPS I&M Networks. Each year, the ROMN Program Manager will assign an AlpineVCSS 

protocol “lead” responsible for all aspects of the annual work planning, field monitoring, data 

management and reporting cycle. This will be a ROMN Ecologist/Crew Leader (see Staffing 

Options in the ROMN Vital Signs Monitoring Plan, Britten et al. 2007).

Annual work planning

Annual work planning will include: determining annual sampling goals and objectives, 

budgeting, field season planning and preparation (permits, communication and planning with 

park staff, hiring field crew(s), arranging and conducting training, arranging housing, procuring 

equipment, arranging field crew transportation, etc.), and data management preparation. The 

Ecologist/Crew Leader will develop annual work plans and solutions in coordination with the 

Program Manager and Data Manager and ensure that AlpineVCSS plans integrate with the 

annual AARWP and ROMN Data and Information Management Plan.

We anticipate visiting GLORIA sentinel sites annually for the first five years at ROMO and 

GRSA (four sample events in each park) and every five years thereafter. GLORIA sites were 

established in GLAC in 2003-04 by the USGS and will be sampled every five years. The number 

of days required per sample event varies with access and weather, but we will plan for 2-3 days 

per event. 

Budget

An estimated annual budget for AlpineVCSS work will be developed by the ROMN 

Ecologist/Crew Leader and included in the annual AARWP. As a planning tool for AlpineVCSS 

monitoring and overall ROMN planning and budgeting, costs based on preliminary work in 

2008-2009 in ROMO and GRSA are presented in Table 7. GLORIA sites were established in 

GLAC in 2003-04 by the USGS and monitoring costs are covered through their programs; 

however, annual planning will include communication with USGS and GLAC staff to coordinate 

sampling GLORIA sites in the park.

The equipment cost to outfit one field crew is approximately $2000 with the primary expense 

being temperature dataloggers. The ROMN purchased AlpineVCSS field equipment for 2 crews 

in 2008. We anticipate replacement equipment costs to keep 2 AlpineVCSS crews operating to 

be $1000/yr.

Compliance and permitting

Compliance

Compliance is being handled on a park-by-park scale. The ROMN utilizes the Intermountain 

Region Environmental Screening Process for each protocol and park. 
30

Table 7. Average annual costs for AlpineVCSS monitoring in GRSA and ROMO 2008-2009. ROMN 

Ecologist/Crew Leader salary and travel is not accounted for here but in the general ROMN personnel 

and travel budget.

Item Cost

Crew members $1,500

Vehicle lease $2,500

Gas for vehicle $1,000

Travel for 2 crew members $2,500

Equipment $2,000 

Total $9,500

The general process is for the ROMN to designate an Interdisciplinary Team (IDT) of experts 

familiar with the park and protocol and potentially impacted park resources. The IDT usually 

consists of the Ecologist/Crew Leader, a park resource management specialist(s) with expertise 

in natural and cultural resources of the park, the park Compliance Officer (or designee), and 

other Subject Matter Experts (depending on specific resources that might be impacted (e.g., a 

wildlife biologist if there is a Threatened or Endangered animal potentially impacted). 

Considerations include National Historic Preservation Act, ethnographic resources, Endangered 

Species Act, Wilderness Act, Clean Water Act. 

The role of the IDT is to work through the environmental screening process to document 

potential impacts of ROMN monitoring activities, recommend mitigating measures and 

alternatives, and recommend the appropriate level of compliance to the park superintendent. To 

date ROMN IDTs have recommended categorical exclusions for ROMN monitoring protocols 

and superintendents have concurred. Compliance for the AlpineVCSS monitoring in ROMO and 

GRSA has been completed. Critical documents for this process include the protocols (this 

document), specifically the site layout and site marking, and the monitoring design (including the 

map of all proposed sampling locations with alternates and replacement locations). 

Permitting

ROMN policy is that NPS research permits will be acquired for each protocol and each park each 

field season. This is to ensure that ROMN monitoring activities are documented within the 

official NPS Research Permitting and Reporting System (RPRS web site). SOP Admin

Permitting details the steps and timing for obtaining a permit and reporting annual results for all 

monitoring activities.

Field monitoring

The Ecologist/Crew Leader will serve as the field monitoring Crew Leader during the field 

season. This includes supervising crews to accomplish annual sampling and other project goals. 

The Ecologist/Crew Leader will consider the safety of field crews as paramount and will never 

compromise safety to meet project goals. He/she will ensure that safety, training, 

communication, and pre and post season procedures are followed as laid out in SOP Admin

Safety Preparation and Training, SOP Admin End of Season, SOP Admin Logistics, and SOP 

Equipment Communications. 
31

The Ecologist/Crew Leader will also serve as the liaison with park staff and others (e.g., other 

cooperators) ensuring that they are aware of field monitoring plans and operations and that these 

are carried out in accordance with park policies and preferences.

Field crews

The ROMN monitoring program is evolving and the network continues to consider the costs: 

benefits of hiring options for field crews. Options include using volunteers, Student Conservation

Association (SCA) interns, other interns, students hired through a university cooperative 

agreement, employees hired under the Student Temporary Employment Program (STEP) or 

Student Career Employment Program (SCEP) authorities, employees hired by the ROMN under 

seasonal or term employment hiring authorities, and employees hired and managed by a ROMN 

park. 

In 2008-2009 years, the ROMN utilized volunteers, ROMN park employees, and ROMN staff to 

conduct AlpineVCSS monitoring. In addition, specialists were hired temporarily through a 

Professional Services Agreement to assist in the identification of non-vascular plants. The 

procedures for hiring and managing crew members under these options are documented in the 

network’s general field season SOP Admin Hiring.

Competent, well-qualified, and detail-oriented observers are essential for the collection of 

credible, high-quality vegetation data. Crew members must be skilled in accurately estimating 

species cover and identifying plant species. They should also have good organizational skills, 

and the ability to work methodically and consistently. Alpine areas are rugged, high elevation, 

and remote. Thus, crew members must also be physically fit, able to carry a 40 lb. pack and have 

tolerance for working outdoors in a range of conditions and be able to travel on foot to remote 

sites and camp in remote areas.

Training is essential for developing skilled field observers. The Ecologist/Crew Leader will be 

responsible for ensuring that field crews have adequate training for their work. This will include 

training in AlpineVCSS field and data management methods, safety, and communication (SOP 

Admin Training). For instance, all crew members should read and become familiar with the 

monitoring protocol and the flora of individual parks. Crew members will be trained in cover 

estimation at the start of the field season, and throughout the field season observers’ cover 

estimates will be compared to ensure consistency. Observer bias in the estimation of cover and 

the misidentification of species can affect the ability to detect trends in vegetation over time. 

33

Revising the Protocol

Protocol evaluation

Methodological details of the ROMN AlpineVCSS protocol are re-evaluated after 1-2 seasons of 

data collection. An official programmatic review of the AlpineVCSS vegetation approach to vital 

signs, including the objectives, designs, methods, and results as well as the ability of the network 

to sustain these protocols, will be conducted after at least two parks complete sample and 

resample events (~2-5 years). Subsequent reviews should be conducted at least every 10 years, 

and more frequently as desired by ROMN staff, WASO I&M, the ROMN Board of Directors and 

ROMN Technical Committee. Changes will be documented in the change history (below) and 

the newest version will be made available through the ROMN internet site and the NPS Vital 

Signs Monitoring Protocol Database 

(http://science.nature.nps.gov/im/monitor/VitalSigns/BrowseProtocol.aspx).

Peer-review process

The AlpineVCSS protocol is adapted from GLORIA Field Manual (Pauli et al. 2004; 

http://www.gloria.ac.at/?a=20 ). It includes additional design elements and measures to meet 

additional ROMN monitoring objectives. The ROMN protocol was developed and written with 

scientific and technical input and review by many partners including ROMN park resource 

managers, biologists and ecologists. The protocol benefitted from input and advice from other 

parks, networks and partners who have adapted GLORIA field methods (see 

Acknowledgements). The protocol benefitted from an informal editorial review by Tara Carolin, 

Gretchen Baker, and Regina Rochefort. Since the protocol is largely based on the published 

GLORIA Field Manual and we conducted an informal scientific and technical review of the 

additional measures and design elements, we have not sought additional peer review.

Change History

Previous 

version

Date Author Change description Reason

35

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41

Appendix 1. Bibliography and Investigator Annual Reports of 

Research Completed in ROMO, GRSA, and GLAC Pertaining to 

Alpine Vegetation

The following bibliography was compiled in May 2009 via searching Web of Science and NatureBib for 

articles that included the park names (Glacier, Great Sand Dunes, or Rocky Mountain), alpine or tundra, and 

plant or vegetation. The investigator annual reports were found in May 2009 via the NPS research and 

reporting system (https://science.nature.nps.gov/research/ac/ResearchIndex).

Bibliography 

Alftine, K. J., and G. P. Malanson. 2004. Directional positive feedback and pattern at an alpine tree line. 

Journal Of Vegetation Science 15:3-12.

Allen, T. R., and S. J. Walsh. 1993. Characterizing multi-temporal alpine snowmelt patterns for ecological 

inferences. Photogrammetric Engineering And Remote Sensing 59:1521-1529.

Allen, T. R., and S. J. Walsh. 1996. Spatial and compositional pattern of alpine treeline, Glacier National 

Park, Montana. Photogrammetric Engineering And Remote Sensing 62:1261-1268.

Baker, W. L., J. J. Honaker, and P. J. Weisberg. 1995. Using aerial-photography and GIS to map the foresttundra ecotone in Rocky-Mountain National-Park, Colorado, for global change research. 

Photogrammetric Engineering And Remote Sensing 61:313-320.

Baker, W. L., and P. J. Weisberg. 1995. Landscape analysis of the forest-tundra ecotone in Rocky-Mountain 

National-Park, Colorado. Professional Geographer 47:361-375.

Baker, W. L., and P. J. Weisberg. 1997. Using GIS to model tree population parameters in the Rocky 

Mountain National Park forest-tundra ecotone. Journal Of Biogeography 24:513-526.

Bamberg, S. A. 1961. Plant ecology of alpine tundra areas in Montana and adjacent Wyoming. Thesis. 

University of Colorado at Boulder. 

Baron, J., D. McKnight, and A. S. Denning. 1991. Sources of dissolved and particulate organic material in 

Loch Vale Watershed, Rocky-Mountain-National-Park, Colorado, USA. Biogeochemistry 15:89-110.

Baron, J. S., M. D. Hartman, L. E. Band, and R. B. Lammers. 2000. Sensitivity of a high-elevation Rocky 

Mountain watershed to altered climate and CO2. Water Resources Research 36:89-99.

Beaver, R. G. and P. Meinhardt. 1974. Effect of pentachlorophenol treated lumber on alpine ecology [1973 

progress report]. Pages 9-10 in C. J. Martinka.1973 Annual Research Summary, Glacier National Park. 

Glacier National Park, West Glacier, MT

Bekker, M. F. 2005. Positive feedback between tree establishment and patterns of subalpine forest 

advancement, Glacier National Park, Montana, USA. Arctic Antarctic and Alpine Research 37:97-107.

Bekker, M. F., and G. P. Malanson. 2008. Linear forest patterns in subalpine environments. Progress In 

Physical Geography 32:635-653.
42

Benedict, J. B., R. J. Benedict, C. M. Lee, and D. M. Staley. 2008. Spruce trees from a melting ice patch: 

evidence for Holocene climatic change in the Colorado Rocky Mountains, USA. Holocene 18:1067-

1076.

Bock, J. H., C. L. Jolls, and A. C. Lewis. 1995. The effects of grazing on alpine vegetation - A comparison 

of the Central Caucasus, Republic-Of-Georgia, with the Colorado Rocky-Mountains, USA. Arctic and 

Alpine Research 27:130-136.

Brooks, P. D., D. H. Campbell, K. A. Tonnessen, and K. Heuer. 1999. Natural variability in N export from 

headwater catchments: snow cover controls on ecosystem N retention. Hydrological Processes 13:2191-

2201.

Brown, D. G. 1994. Comparison of vegetation-topography relationships at the alpine treeline ecotone. 

Physical Geography 15:125-145.

Buechling, A., and W. L. Baker. 2004. A fire history from tree rings in a high-elevation forest of Rocky 

Mountain National Park. Canadian Journal of Forest Research-Revue Canadienne De Recherche 

Forestiere 34:1259-1273.

Butler, D. R., G. P. Malanson, and L. M. Resler. 2004. Turf-banked terrace treads and risers, turf exfoliation 

and possible relationships with advancing treeline. Catena 58:259-274.

Cairns, D. M. 1999. Multi-scale analysis of soil nutrients at alpine treeline in Glacier National Park, 

Montana. Physical Geography 20:256-271.

Cairns, D. M. 2001. A comparison of methods for predicting vegetation type. Plant Ecology 156:3-18.

Cairns, D. M., and J. D. Waldron. 2003. Sigmoid wave transitions at alpine treeline. Geografiska Annaler 

Series A-Physical Geography 85A:115-126.

Choate, C. M. 1963. Life above timberline. Naturalist. 14:26-32

Clow, D. W., and J. K. Sueker. 2000. Relations between basin characteristics and stream water chemistry in 

alpine/subalpine basins in Rocky Mountain National Park, Colorado. Water Resources Research 36:49-

61.

Cooper, D. J., J. Dickens, N. T. Hobbs, L. Christensen, and L. Landrum. 2006. Hydrologic, geomorphic and 

climatic processes controlling willow establishment in a montane ecosystem. Hydrological Processes 

20:1845-1864.

Das, B., R. D. Vinebrooke, A. Sanchez-Azofeifa, B. Rivard, and A. P. Wolfe. 2005. Inferring sedimentary 

chlorophyll concentrations with reflectance spectroscopy: a novel approach to reconstructing historical 

changes in the trophic status of mountain lakes. Canadian Journal of Fisheries And Aquatic Sciences 

62:1067-1078.

Debolt, A. and P. Lesica. 1986. Alpine plants of Glacier National Park. Pages 58-62 in J.Williams. Rocky 

Mountain Alpines. Timber Press, Portland, OR.

Fagre, D. B., D. L. Peterson, and A. E. Hessl. 2003. Taking the pulse of mountains: Ecosystem responses to 

climatic variability. Climatic Change 59:263-282.
43

Geddes, C. I. A., D. G. Brown, and D. B. Fagre. 2005. Topography and vegetation as predictors of snow 

water equivalent across the alpine treeline ecotone at Lee Ridge, Glacier National Park, Montana, USA. 

Arctic Antarctic and Alpine Research 37:197-205.

Gehring, J. L., and Y. B. Linhart. 1992. Population-structure and genetic differentiation in native and 

introduced populations of Deschampsia caespitosa (Poaceae) in the Colorado Alpine. American Journal 

of Botany 79:1337-1343.

Hadley, K.S. 1987. Vascular alpine plant distributions within the Central and Southern Rocky Mountains, 

U.S.A. Arctic and Alpine Research 19(3): 242-251.

Hall, M. H. P., and D. B. Fagre. 2003. Modeled climate-induced glacier change in Glacier National Park, 

1850-2100. Bioscience 53:131-140.

Hartley, E. 2000. Thirty-year monitoring of subalpine meadow vegetation following a 1967 trampling 

experiment at Logan Pass, Glacier National Park, Montana. Wilderness science in a time of change 

conference. Missoula, MT, Forest Service, Rocky Mountain Research Station, Ogden, UT May 23-27, 

1999:124-132 (http://www.fs.fed.us/rm/pubs/rmrs_p015_5/rmrs_p015_5_124_132.pdf).

Hartman, M. D., J. S. Baron, and D. S. Ojima. 2007. Application of a coupled ecosystem-chemical 

equilibrium model, DayCent-Chem, to stream and soil chemistry in a Rocky Mountain watershed. 

Ecological Modelling 200:493-510.

Hauer, F. R., J. S. Baron, D. H. Campbell, K. D. Fausch, S. W. Hostetler, G. H. Leavesley, P. R. Leavitt, D. 

M. McKnight, and J. A. Stanford. 1997. Assessment of climate change and freshwater ecosystems of the 

Rocky Mountains, USA and Canada. Hydrological Processes 11:903-924.

Hauer, F. R., J. A. Stanford, and M. S. Lorang. 2007. Pattern and process in Northern Rocky Mountain 

headwaters: Ecological linkages in the headwaters of the Crown of the Continent. Journal of the 

American Water Resources Association 43:104-117.

Helm, D. 1982. Multivariate analysis of alpine snow-patch vegetation cover near Milner Pass, Rocky 

Mountain National Park, Colorado, U.S.A. Arctic and Alpine Research 14(2): 87-95.

Hessl, A. E., and W. L. Baker. 1997a. Spruce-fir growth form changes in the forest-tundra ecotone of Rocky 

Mountain National Park, Colorado, USA. Ecography 20:356-367.

Hessl, A. E., and W. L. Baker. 1997b. Spruce and fir regeneration and climate in the forest-tundra ecotone 

of Rocky Mountain National Park, Colorado USA. Arctic and Alpine Research 29:173-183.

Holway, J.G. 1960. Phenological observations on the major alpine species in the vicinity of False Mummy 

Pass, Rocky Mountain National Park, Colorado. Thesis. Colorado State University, Fort Collins, CO

Klasner, F. L., and D. B. Fagre. 2002. A half century of change in alpine treeline patterns at Glacier 

National Park, Montana, USA. Arctic Antarctic and Alpine Research 34:49-56.

Lesica, P., and B. McCune. 2004. Decline of arctic-alpine plants at the southern margin of their range 

following a decade of climatic warming. Journal of Vegetation Science 15:679-690.
44

Lesica, P., and B. M. Steele. 1996. A method for monitoring long-term population trends: An example using 

rare arctic-alpine plants. Ecological Applications 6:879-887.

Linhart, Y. B., and J. L. Gehring. 2003. Genetic variability and its ecological implications in the clonal plant 

Carex scopulorum Holm in Colorado tundra. Arctic Antarctic and Alpine Research 35:429-433.

Malanson, G. P., and D. R. Butler. 1994. Tree tundra competitive hierarchies, soil fertility gradients, and 

Treeline Elevation In Glacier-National-Park, Montana. Physical Geography 15:166-180.

Marr, J. W. and B. E. Willard. 1970. Persisting vegetation in an alpine recreation area in the southern Rocky 

Mountains, Colorado. Biological Conservation. 2: 97-104

Mcneal, D.W. 1976. Annotated check list of the alpine vascular plants of Specimen Mountain, Rocky 

Mountain National Park, Colorado. Southwest-Naturalist. 20:423-435

Miller, O.K. 1980. High-elevation and common edible fungi of Glacier National Park. Pages 11-12 in K. L. 

Mcarthur.1979 Annual Research Summary, Glacier National Park. Glacier National Park, West Glacier, 

MT

Pemble, R. H. and S. A. Bamberg. 1968. New records of disjunct arctic-alpine plants in Montana. Rhodora: 

70: 103-112

Resler, L. M. 2006. Geomorphic controls of spatial pattern and process at alpine treeline. Professional 

Geographer 58:124-138.

Resler, L. M., D. R. Butler, and G. P. Malanson. 2005. Topographic shelter and conifer establishment and 

mortality in an alpine environment, Glacier National Park, Montana. Physical Geography 26:112-125.

Roush, W., J. S. Munroe, and D. B. Fagre. 2007. Development of a spatial analysis method using groundbased repeat photography to detect changes in the alpine treeline ecotone, Glacier National Park, 

Montana, USA. Arctic Antarctic And Alpine Research 39:297-308.

Spaulding, S. A., J.V. Ward and J. Baron. 1993. Winter phytoplankton dynamics in a subalpine lake, 

Colorado, USA. Arch Hydrobiology 129:179-198

Stevens, D. R. 1976. Problems of revegetation of alpine tundra. First conference on research in the National 

Parks. New Orleans, Louisiana, Rocky Mountain National Park, Estes Park, CO November 8-12, 1976

Stevens, D. R. 1976. Special needs and problems of high altitude revegetation in National Parks. High 

Altitude Revegetation Workshop Number Two. Ft. Collins, CO, Rocky Mountain National Park, Estes 

Park, CO April 5-6, 1976

Stevens, D. R. 1980. Impact of elk on alpine tundra in Rocky Mountain National Park. High Altitude 

Workshop 4. June 1980:228-241

Stohlgren, T. J., and R. R. Bachand. 1997. Lodgepole pine (Pinus contorta) ecotones in Rocky Mountain 

National Park, Colorado, USA. Ecology 78:632-641.

Stohlgren, T. J., A. J. Owen, and M. Lee. 2000. Monitoring shifts in plant diversity in response to climate 

change: a method for landscapes. Biodiversity and Conservation 9:65-86.
45

Sueker, J. K., D. W. Clow, J. N. Ryan, and R. D. Jarrett. 2001. Effect of basin physical characteristics on 

solute fluxes in nine alpine/subalpine basins, Colorado, USA. Hydrological Processes 15:2749-2769.

The Colorado Native Plant Society. 1989. Rare plants of Colorado. Rocky Mountain Nature Association, 

Colorado Native Plant Society.

Tomback, D. F., and L. M. Resler. 2007. Invasive pathogens at alpine treeline: Consequences for treeline 

dynamics. Physical Geography 28:397-418.

Walsh, S. J., D. R. Butler, G. P. Malanson, K. A. Crews-Meyer, J. P. Messina, and N. C. Xiao. 2003b. 

Mapping, modeling, and visualization of the influences of geomorphic processes on the alpine treeline 

ecotone, Glacier National Park, MT, USA. Geomorphology 53:129-145.

Weaver, T., D. Gustafson, and J. Lichthardt. 2001. Exotic plants in early and late seral vegetation of fifteen 

northern Rocky Mountain environments (HTs). Western North American Naturalist 61:417-427.

Weisberg, P. J., and W. L. Baker. 1995a. Spatial variation in tree regeneration in the forest-tundra ecotone, 

Rocky-Mountain National-Park, Colorado. Canadian Journal of Forest Research-Revue Canadienne De 

Recherche Forestiere 25:1326-1339.

Weisberg, P. J., and W. L. Baker. 1995b. Spatial variation in tree seedling and krummholz growth in the 

forest-tundra ecotone Of Rocky-Mountain National-Park, Colorado, USA. Arctic and Alpine Research 

27:116-129.

Werner, H. 1999. A plant survey of high alpine communities in Rocky Mountain National Park at the Trail 

Ridge Game Drive Site (5LR15).

Whitford, L. A. and Y. C. Kim. 1971. Algae from alpine areas in Rocky Mountain National Park, Colorado. 

American Midland Naturalist 85: 425-430

Willard, B. E. and J. W. Marr. 1971. Recovery of alpine tundra under protection after damage by human 

activities in the Rocky Mountains of Colorado. Biological Conservation 3:181-190

Willard, B.E. 1979. Plant sociology of alpine tundra, Trail Ridge, Rocky Mountain National Park, Colorado. 

Quarterly of the Colorado School of Mines 74: 1-119

Willard, B. E., D. J. Cooper, and B. C. Forbes. 2007. Natural regeneration of alpine tundra vegetation after 

human trampling: A 42-year data set from Rocky Mountain National Park, Colorado, USA. Arctic 

Antarctic and Alpine Research 39:177-183.
46

Recent Investigator Annual Reports from GLAC and ROMO Pertaining to Alpine 

Vegetation (none available for GRSA) May 2009

Glacier National Park 

Year Principal Investigator Research Topic

2007 Bowman Critical loads and N deposition in alpine vegetation.

2007 Sawyer Patterned ground in an alpine tundra environment. Studied rock circle 

patterns due to frost heave.

2004 Malanson Invasibility of tundra in Northern Rockies. Remote sensing and 

modeling of feedback and shifts in treeline.

2004 Hartley Long-term natural recovery of alpine vegetation from human 

trampling. Logan Pass. 40 year study of recovery.

2003 Fagre Invasibility in tundra. Model and field data on tree establishment in 

the tundra.

2002 Lesica Monitoring effects of global warming using rare plants in wet alpine 

tundra.

1994 Damm Inventory of alpine vegetation.

Rocky Mountain National Park

Year Principal Investigator Research Topic

2007 Bowman Critical loads and N deposition in alpine vegetation. 

2007 Benedict Paleoenvironmental significance of spruce trees melting from an ice 

patch above timberline in the Mummy Range. 

2007 Leukering Monitoring birds with a tundra site at Sundance Mountain.

2007 Steltzer Elk impacts on alpine willow communities.

2005 Hufford Phylogeography of Synthyris across the Rockies.

2005 Malanson Response of western mountain treeline to climatic variability and 

change.

2005 Wagner Life history and ecophysiology of some arctic-alpine annual plant 

species.

2004 Liptzin Effects of N decomposition and nutrient cycling at an ecotone. 

2001 Rhode Human trampling disturbance along alpine tundra social trails. 

2000 Weigant Vascular plant diversity at fellfields in RMNP. 

1998 Rochelle Pedogenic effects of krummholz migration

1997 Redente Soil N availability affects on alpine tundra recovery.

1994 Baker Potential response of forest tundra ecotone to global climate change.

1993 Willard Recovery of alpine from trampling

1992 Aitken Nival nests of pocket gophers. 

1992 Stohlgren Potential effects of global change on vegetation in the Colorado 

Rockies. 
47

Appendix 2. Directions and Details of Site Access for GLORIA Work 

in GRSA and ROMO

Notes on accessing GLORIA sites: GRSA 

Summit Code

Latitude 

(decimal 

degrees)

Longitude 

(decimal 

degrees)

Elevation 

(m)

Trailhead Approximate travel time 

SIX 37.861501 -105.492034 4000 Medano Lake 3 hours to campground; ~2 

hours to peak

PAD 37.87965 -105.478217 3870 Hudson Ditch 3 hours

MUD 37.887517 -105.47665 3700 Hudson Ditch 3 hours

HUK 37.89615 -105.473733 3550 Hudson Ditch 3 hours

Access to the summits requires ~3 hours of hiking and all field work should be completed prior to ~ 1 pm to 

avoid afternoon storms. We recommend the following access plans:

1. Summit 1: The highest peak, N of Mt. Herard, is most easily accessed via the Medano Lake Area. 

Access will require backpacking into the Medano Lake campsite. The trail is approximately 4 miles 

uphill. A 4WD high clearance vehicle is required for access to the trailhead. From the campsite, the 

peak can be accessed in about 2 hours following a trail to just past Medano Lake and then 

scampering up the steep SE face of the summit. 

2. Summits 2, 3, 4: The lower summits can be accessed from 3 areas: walking along a ridge from 

Music Pass, up and over two ridges from Medano Lake, or via the Hudson Ditch. All access routes 

require ~3 hours of hiking, while the hike from Hudson Ditch requires a greater increase in 

elevation, it provides the best and safest access because much of the hike is below treeline. The 

Hudson Ditch trailhead can be accessed from Medano Pass with a 4WD high clearance vehicle. 

There is a primitive campsite at the trailhead that could be used. From Hudson Ditch, access sites by 

bushwhacking and following game trails straight uphill, follow ridgeline to Muddy Hill. From 

Muddy Hill, head NW and up to the summits. All summits can be accessed easily from one another. 
48

Notes on accessing GLORIA sites: ROMO 

Summit 

Code

Latitude 

(decimal 

degrees)

Longitude 

(decimal 

degrees)

Elevation 

(m)

Trailhead Approximate travel 

time 

GLA 40.380617 -105.770072 3862 Milner Pass/ Ute Trail 2.5- 3 hours

PIK 40.392956 -105.790538 3715 Milner Pass/ Ute Trail 2 hours

VQS 40.406275 -105.799238 3623 Milner Pass/ Ute Trail 1.5 hours

JSM 40.394304 -105.813083 3520 Lake Irene 2 hours

All field work should be completed prior to ~ 2 pm to avoid afternoon storms. Access to all sites is possible 

on day hikes from the trailhead. However, campsites in the area of Jackstraw Mountain and Timber Lake 

may provide more efficient access. From the Milner Pass/Ute trail head, hike towards Mt. Ida and once in 

the alpine, leave the trail and navigate to the sites. VQS has a vision quest site (eg., a small stone structure) 

on the peak. Both VQS and PIK are not true peaks, but rather are ‘peaklets’- small bumps ~20 m above the 

existing tundra. To access JSM for day trips, park at Lake Irene and bushwhack to the peak. 
49

SOP AlpineVCSS Adapting GLORIA to National Parks

Rocky Mountain Inventory & Monitoring Network: 2010 version

Change History

Previous 

version

Date Author Change description Reason

Introduction

The National Park Service has played a large role in the protection and preservation of high elevation 

mountain ecosystems due to their natural splendor, unique assemblages of flora and fauna, and their critical 

function as a source of water for much of the United States. Despite protection, alpine systems are 

increasingly threatened by climate change and atmospheric pollution. The Global Observation Research 

Initiative in Alpine Environments (GLORIA) is an international effort to monitor alterations in biodiversity 

and vegetation patterns in the world's high mountain ecosystems caused by climate change. There is 

immense potential in fostering the partnership between GLORIA and NPS. However, there are challenges 

and unique opportunities when implementing research and monitoring in NPS units that may or may not be 

relevant to other areas. Below we outline tips on how our program (ROMN) implemented GLORIA 

effectively and modified the GLORIA Field Manual (Pauli et al. 2004) to meet NPS regulations.

Site Locations

The GLORIA methodology requires that summits that are selected should be within one climate region, of a 

similar geomorphology, bedrock material, and habitat situation. Moreover, the selected summits should be 

“pristine” or exhibit “low to moderate impact from land use.” Many appropriate sites within National Parks 

fall within the category of “no present land use and the influence of historic land use is negligible.” Still 

there are sites that are heavily visited by hikers and/or rock climbers or heavily used by wildlife. To pick the 

best summits:

• Avoid many visitors by choosing unnamed summits and summits without marked trails. 

• Consult park personnel and management to find the most popular areas with visitors and avoid these. 

• Consult park personnel and management to avoid known favorite spots of bighorn sheep, mountain 

goats, and elk. In addition to the potential damage to plots, repeated visitation may cause stress to the 

animal populations. 

• Where possible, avoid wilderness areas where access is very limited and markings are discouraged. 

• Locate plots on “peaklets” or small rises off the shoulder or ridge to a larger peak. 

• Find the best locations using topographic maps, hiking, and consulting with park personnel.

Long-term marking

The GLORIA methodology recommends short aluminum tubes to be used as plot markers on all corners of 

the 3 x 3 m quadrats (4 quadrats per summit; 16 markers per summit). The highest summit point should be 

marked with a small cross cut into solid rock or metal stakes. After discussion with park personnel, we 

found the following option best fit the needs to establish permanent plots while limiting visible markers and 

unmarked rebar in the tundra.

• Mark the highest summit point with a rebar and an aluminum rebar cap marked:

• “NPS ROMN I&M Veg (plot) 2008 DO NOT DISTURB”
50

• Mark only the lower left (looking towards the summit) corner of the 3 x 3m quadrat clusters with a 

labeled rebar (as above). This should lie 5 m in elevation below the summit (Figure 1). 

• Mark the remaining 3 corners of the 3 x 3m quadrat cluster with nails. The nails have labeled collars 

that lie flush with the soil surface marked “NPS ROMN I&M”

Figure 1. GLORIA plot layout

Dataloggers

The GLORIA protocol recommends using StowAway Tidbit temperature loggers buried in the center of all 

the 3 x 3 m quadrats (4 per peak; 16 per park). Due to considerations of short-term (cost) and long-term 

(battery replacement), we choose to use Hobo Onset UA-001-64 temperature alarm pendant 64K 

dataloggers. In 2008, the cost of the loggers for 2 parks (ROMO and GRSA), the base station, and software 

was $1593. Previous work at Niwot Ridge LTER suggests that these dataloggers typically last 1-2 years in 

inclement conditions. Batteries can be replaced to prolong the life of the logger. As of 2010, we have 

replaced loggers annually and our rate of failure has been 1 of 66 deployed loggers. 

Sampling

The GLORIA protocol recommends visiting summits and recording vegetation composition every 5 years at 

a time of peak phenology. To better understand annual variation in vegetation and provide information to 

park management in the short-term, we have chosen the most accessible peak in our network (RMN_VQS) 

to sample annually for the first 5 years and thereafter every 5 years. The annual visits are focused on 

vegetation sampling.

Literature Cited

Pauli, H., M. Gottfried, D. Hohenwallner, K. Reiter, R. Casale, and G. Grabherr, editors. 2004. The 

GLORIA field manual: multi-summit approach. European Communities, Belgium.
51

SOP AlpineVCSS Site Disturbance: Describing Natural and 

Anthropogenic Disturbance to the Site and Surrounding Area 

Rocky Mountain Inventory & Monitoring Network: 2010 version

Change History

Previous 

version

Date Author Change description Reason

Introduction

Observing and documenting land use and natural episodic events can provide insight into understanding 

status and trends in vegetation composition, structure, and soils. It is critical to document site-level 

disturbances because in many cases, disturbance can act as a strong driver of change and cause direct 

alterations in community composition. Moreover, disturbances may be of particular interest to park staff 

because they may be able to reduce disturbances through changes in land management (e.g., reroute trails). 

In the alpine regions of GLAC, GRSA, and ROMO, current anthropogenic disturbances are often minimal 

and include trampling, heavily eroded hiking trails, and nearby roads. Historic disturbances such as logging 

and grazing may also be evident. In addition to human disturbances, natural disturbances may have 

profound effects on the status and trends in alpine vegetation and soils. In protected areas, such as National 

Parks, natural disturbances, rather than human disturbances, are likely to be the primary drivers of change. 

This SOP provides detailed instructions outlining how to complete both natural and anthropogenic 

disturbance assessments. 

At all sites, a field crew will document potential anthropogenic stressors in and around the area in a semiquantitative fashion. A site will be evaluated using a modified version of the 2008 Human Disturbance 

Index (HDI) developed by the Colorado Natural Heritage Program (Rocchio 2007) and the California Rapid 

Assessment Method for Wetlands (Collins et al. 2008). The index ranges from 0-100 % where the large 

values indicate increased disturbance. As with the HDI, each disturbance will be scored and then the final 

calculated Natural Disturbance Index (NDI) is a weighted mean of the scores.

Steps

In order to get a more complete picture of each site and its surrounding landscape, crews should evaluate 

disturbance levels and sources as they are walking to each site and be aware of stressor types while doing 

field work. For this reason, under normal circumstances, crews should complete this form last as they are 

about to leave a site. Data collected to document anthropogenic stressors follow a modified version of the 

HDI that was developed by the Colorado Natural Heritage Program; crews will record natural disturbance in 

a separate section as these metrics are analyzed independently from the human disturbance measures.

Equipment

• HDI forms

• NDI forms

• pencil
52

Procedures

Human Disturbance

The Human Disturbance Index form (see below) is divided into three categorical sections: Alterations 

within buffers and Landscape Context, Hydrological Alterations, and Physical Disturbances. To use the 

form:

1. Score each metric in every category to best match the condition description. 

2. The Adjacent Land Use (Buffer) and Onsite Land Use (Physical Disturbance) metrics require 

additional steps to compute. These metrics are measured by documenting surrounding land use(s) in 

the site and within a 100m buffer, respectively. To calculate these scores, estimate the % area under 

each land use type (see Forms below). Do this separately for both Adjacent Land Use and Onsite 

Land Use metrics. Multiply these percentages by their corresponding coefficients. Add these 

products to reach a single total for each metric. Use the resulting subtotals to select the 

corresponding scores for the Adjacent and Onsite land use metrics on the main Human Disturbance 

Index form.

3. To calculate each category (e.g., Physical Disturbance) subtotal score, sum the two highest (worst) 

metric scores, divide by 20, and multiply by 100. 

4. Each category is assigned a weight. To calculate a final score for the site, multiply the category score 

by each respective weight, then sum the products to reach a final site score. The final score will fall 

between 0 and 100 with 0 being the most “pristine” and100 representing a highly disturbed site.

5. The calculations in Steps 2-4 can largely be done automatically in Excel or with a Visual Basic 

script. The Network recommends taking advantage of an automated process in order to minimize 

errors that might be more common if done manually in the field. 

Natural Disturbance

To complete the Natural Disturbance form:

1. Use the Key to assign a disturbance level to each of the seven metrics (plus “other”).

2. In the Comments field, indicate any qualifications to the assigned rating or any additional 

information that you want to convey about individual metrics

References

Rocchio, J. 2007. Floristic quality assessment indices for Colorado plant communities. Unpublished report 

prepared for the Colorado Department of Natural Resources and US EPA Region 8. Colorado Natural 

Heritage Program, Colorado State University, Fort Collins, Colorado.

Collins, J.N., E.D. Stein, M. Sutula, R. Clark, A.E. Fetscher, L. Grenier, C. Grosso, and A.Wiskind. 2008. 

California Rapid Assessment Method (CRAM) for Wetlands. Version 5.0.2. 151 pp.
53

Forms
54

\
55

57

SOP AlpineVCSS Soils

Rocky Mountain Inventory & Monitoring Network: 2010 version

Change History

Previous 

version

Date Author Change description Reason

1.0 Dec 2010 Ashton Reduce the number of soil tests Soil aggregate stability was difficult in 

the alpine because of wind and lack of 

soil. Reviewers suggested we focus on 

dynamic soil properties. 

Introduction

In addition to vegetation measures, soil at each site will be characterized to assess the potential for erosion 

and compaction of top soils, the storage, cycling, and abundance of nutrients and water available to plants, 

and the potential for acidification of the soil from the deposition of pollutants. This SOP describes the 

method for conducting surveys of soil condition and bulk density, and collecting soil cores in the field.

Procedures for the soil tests follow guidance by Robertson et al. (1999). Soil cores will be sent to a 

cooperating laboratory for the analysis of soil texture (0-20 cm), nutrients and mineral concentrations (e.g., 

P, Mg, Mn, Al , Ca), soil pH, carbon and nitrogen content, % organic matter, and cation exchange capacity. 

Soil Surface Condition

For each peak, a brief survey should be done to describe soil condition and potential for erosion in the area. 

There are three main classes to describe: surface flow, rills and gullies, and pedestals and terracettes and 

each of these can range from extreme degradation to none (Table 1). This survey should be done when first 

arriving at a site and the average conditions across the entire peak area are recorded (from the peak to 10 m 

below peak). The methods from Herrick et al. (2005) were developed for arid landscapes where wind, 

water, and overgrazing are active agents of soil erosion. In the alpine, rapid snow melt, hikers, and wildlife 

are the most likely agents of soil erosion patterns.

 
58

 

Figure 1. Photographs of soil surface conditions from Pellant et al (2005) based on arid landscapes. Top Left: 

Extreme surface flow indicative of high overland water flow. Top Right: An eroded gully with arrows indicating active 

erosion. Bottom Left: A plant pedestal caused by wind erosion, note the exposed roots. Bottom Right: A terracette 

caused by litter obstruction in the water flow pattern. 

Table 1. Classes for characterization of soil surface conditions (adapted from Herrick et al. 2005)

Degree of 

Degradation

Surface Flow Evidence Rills and Gullies Pedestals and Terracettes

Erosion caused by overland flow, i.e., 

as water moves across the soil surface. 

Evidenced by litter, soil or gravel 

redistribution along flow-paths, 

especially small debris piles. Windscoured areas (may create blowouts) 

are recognized by removal of finer 

particles of the topsoil, sometimes 

leaving gravel, rock, or exposed roots 

on the surface. Deposition areas are 

often associated with vegetation or 

topographic relief.

Rills are small eroded rivulets that are 

generally linear and do not necessarily 

follow microtopography. A gully is a 

channel that has been cut into the soil by 

moving water; they generally follow 

natural drainage patterns. Gullies and 

rills are a natural component of many 

landscapes, but excessive grazing, 

vehicles or road drainages may cause 

excessive formation or expansion

Pedestals are rocks or plants that 

appear elevated because of soil loss 

from wind or water erosion around them 

(also by frost heaving or soil/litter 

deposition). Terracettes are small 

benches of deposited soil behind 

obstacles. Both are important indicators 

of movement of soil by water and/or 

wind. Terracettes caused by livestock or 

wildlife movements, e.g., across 

hillsides, are not considered erosion, so 

they should not be considered here.

Extreme (XX) Extensive with active erosion and 

unstable soils (water and/ or wind 

caused); connections between flow 

paths lead to wide area affected

Rill formation is severe, well defined, and 

widespread. Gullies, when present, are 

actively down-cutting without vegetation 

to stabilize slopes and bed.

Many rocks and/ or plants pedestalled; 

exposed plant roots are common. 

Abundant across the area.

Moderate to 

Extreme (MX)

Cut/ eroded and depositional areas are 

common; occasionally connected.

Rill formation is moderate but active and 

well defined throughout most of the area.

Some rocks and plants with occasionally 

exposed roots; not ubiquitous across the 

area.

Moderate (MM) Erosion is minor with some evidence of 

erosion and deposition, but not 

widespread across large areas.

Some intermittent rill formation, not 

widespread, but in the most exposed 

areas only.

Some pedestals, but only in major flow 

paths and exposed slopes; occasional 

terracettes.

Slight to 

Moderate (SM)

Flow patterns are stable and short; 

minor erosion evidence only (few and 

infrequent).

No recent formation of rills. Gullies, if 

present, are stabilized by vegetation on 

slopes and in the bed. (Older features 

have rounded or muted edges.)

Some evidence of past pedestals in flow 

patterns, but recent pedestals and 

terracettes are rare.

None to Slight 

(NS)

Minimal evidence of current or past soil 

movement.

No current rill formation. Drainages are 

natural, stable channels.

Little to no evidence of pedestals or 

terracettes. (Uncommon or absent)
59

Soil bulk density & texture

Soil texture and soil bulk density describe the physical structure of the soil and influence many ecosystem 

processes, particularly water movement in soils (Elliot et al. 1999). Soil texture and density will be 

measured during the initial site visit and thereafter every 5-10 yr. Bulk density ranges between 0.6 and 1.8 g 

cm-3 and more typically ranges between 1.0 and 1.4 g cm-3

. Soil texture measures the percent composition 

of sand, silt, and clay (Elliot et al. 1999). Both measures can be used to better interpret other soil parameters. 

Furthermore, these parameters may provide covariates in analyses to better describe variation among sites. 

They are however, fairly stable properties of soils in the alpine and sampling can be done at the rate of once 

every ten years (or every other GLORIA sample event). 

Equipment

• Soil corer (marked at 20 cm)

• Whirlpaks or plastic bags

• Knife 

• Soil knife or trowel

• Sharpie or markers

Procedures

(1) Move 1 m downhill from the center of the lower edge of the quadrat array (e.g. 1 m below plot S21), 

this will be the location used for the core. There should be 1 core taken from each aspect (N, S, 

E,W).

(2) Remove surface vegetation and litter to expose mineral soil. 

(3) Carefully drive the soil core to 20 cm depth. While the corer remains in the soil, use trowel to 

excavate around it. Place the trowel under the corer to prevent soil from falling out the bottom and 

carefully remove the corer from the soil. If there is >15% gravel, cobbles, or stones and it is not 

possible to take a soil core follow the alternative method outlined below. 

(4) Check to make sure that the soil level inside the core is the same as outside. If it is noticeably lower, 

then compaction has occurred and one should consider discarding the core. If compaction is minor 

and limited to within the core, bulk density and derivative values will not be compromised; if, on the 

other hand, compaction has pushed soil below the core tip, then bulk density will be underestimated. 

If the sample is compromised, discard it, and move 1 m to the left (facing downhill) and take another 

core. 

(5) Using the knife, carefully cut the soil off at the bottom of the core. Discard. Place the entire soil 

corer into the whirlpak or plastic bag. Push the soil out of the core into the plastic bag. 

(6) Note the volume of the core. Bulk density is a measure of soil mass per volume. Therefore, it is 

essential to have an accurate volume of the corer. As long as the same soil corer is used, the volume 

should be constant and based on the diameter of the corer (2 cm) and depth of the core (20 cm). 

(7) Close and label the bag. Label with date, site, aspect, and volume. Keep the sample in the shade. 

(8) When possible, fill hole with litter and any loose dirt to reduce future erosion.

(9) The samples can be kept closed in plastic bags for a few days as long as they are kept cool. 

However, if you cannot ship samples to the laboratory within a week, the samples should be airdried. If this is the case, find a location inside (housing/laboratory/office), place samples on a table, 

open the top of sample bags, and let sit until dry (or until you ship them). If there is a fan available, 

running it above the samples will hasten this process. 

(10) When possible, pack the sample and others in a box. Mail to the following address requesting 

soil texture and bulk density analysis (current as of Jan 2009; check address at later dates):

Dr. James Self
60

Soil, water, and plant testing laboratory

Colorado State University

Room A319, Natural and Environmental Sciences Building

200 West Lake Street

Fort Collins, Colorado 80523-1120

Alternative method for bulk density in rocky soils; often will be applicable to alpine soils (USDA 1999):

Materials needed:

Field

• Plastic wrap

• 140 cc syringe

• Water

• Trowel/soil knife

• Sealable bags and marker pen

• 2-mm sieve

Lab

• Scale

• 30mL measuring scoop

• Paper cup or bowl

• Drying oven

(1) Dig a hole in a spot that is as level as possible. Dig a bowl shaped hole 3 in deep and approximately 5 in

diameter. Avoid compacting the soil while digging. Place all the soil and the gravel removed from the 

hole in a plastic bag. 

(2) Using the 2-mm sieve, sieve the soil in the plastic bag to separate the gravel. Collect the soil in a plastic 

sealable bag. Put the gravel aside to be used in Step 2. Seal and label the plastic bag. If the soil is too wet 

to sieve, don’t replace the rocks. Instead, dry and sieve later. The volume of gravel will need to be 

determined ad subtracted from the total volume of the soil sample taken in the field. 

(3) Line the hole with plastic wrap as shown in Figure 2. Leave some excess plastic wrap around the edge

of the hole. Place the sieved rocks and gravel carefully in the center of the hole on top of the plastic

wrap. Assure that the pile of rocks do not protrude above the level of the soil surface.

Figure 2. Hole with plastic wrap and stones used to determine soil bulk density. 

(4) Use the 140 cc syringe to keep track of how much water is needed to fill the lined hole. The level of the 

water should be even with the soil surface. Add water slowly. The amount of water represents the volume of 
61

soil removed. Record the total amount of water in cubic centimeters (1 cc = 1 cm3) on the soil datasheet 

(Table 2). Remove the rocks and place in a graduated cylinder 1/3 full of water. The change in the water 

level is the volume of rocks (1 mL = 1 cm3). 

In the lab:

(5) Weigh the soil sample in its bag. If the sample is too heavy for the scale, transfer about half of the 

sample to another plastic bag. The weights of the two sample bags will need to be added together. Enter the 

weight (sum of two bags, if applicable) on the soil datasheet. Weigh an empty plastic bag to account for the 

weight of the bag. Enter the weight (sum of two bags, if applicable) on the datasheet.

(6) Extract a subsample to determine water content and dry soil weight. Mix sample thoroughly in the bag 

by kneading it with your fingers. Take a 30mL (1/8-cup) level scoop subsample of loose soil (not packed 

down) from the plastic bag and place it in a paper cup (a glass or ceramic cup may be used).

(7) Weigh the soil subsample in its paper cup. Enter the weight on the datasheet. Weigh an empty paper cup 

to account for its weight.

(8) Place the paper cup containing the subsample in a microwave and dry for two or more four minute

cycles at full power. Open the microwave door for one minute between cycles to allow venting. Weigh 

the dry subsample in its paper cup and enter the weight on the datasheet. Alternatively, dry the soil in an 

oven at 105 ° C for 24 hours or overnight. To test that the sample is dry, weigh, dry the sample (for a 

couple more hours in the oven or minutes in the microwave), and reweigh. When there is no difference 

between the two weights, the sample is dry. 

CALCULATIONS 

Soil water content (g/g) = (weight of moist soil - weight of oven dry soil)/weight of oven dry soil

Soil bulk density (g/cm3) = oven dry weight of soil/volume of soil

Soil water-filled pore space (%) = volumetric water content x 100/soil porosity

Volumetric water content (g/cm3) = soil water content (g/g) x bulk density (g/cm3)

Soil porosity (%) = 1 – (soil bulk density /2.65)

Volume of Rocks (cm3) = Fill 1/3 of a graduated cylinder with water, and record the amount.

Add the rocks to the cylinder and record the change in the water level. The difference is the volume of rocks 

(1 mL = 1 cm3).

Volume of Soil (cm3) = Total soil volume - volume of rocks

Table 2. Data Sheet for soil bullk density via the excavation method for rocky soils

Sample 

site

(Target 

regionPeakAspect)

(n)

Volume 

of 

water 

(cm3)

(E) 

Weight 

of field 

moist 

soil + 

bag 

(grams)

(F)

Weight 

of bag 

(grams)

(G)

Weight 

of 

paper 

cup 

(grams)

(I)

Weight 

of 

paper 

cup + 

soil (g)

(K)

Dry 

weight 

of soil 

+ cup 

(g)

*(L) 

Dry 

weight 

of soil 

(g)

**(M) 

Soil 

H2O

content 

(g/g)

***Soil 

bulk 

density 

(g/cm3)

1

2

3

4

*Dry weight of subsample= (K-G) **Soil H2O content = (I-K)/L

***Soil bulk density= [(E-F)/(1 +M)]/(n) 

n=volume of soil in cm3 < 2mm
62

Soil pH, chemistry, and soil organic matter

Soil organic matter, carbon, pH and plant nutrient concentrations can provide a powerful index of 

productivity and ecosystem processes and are often determinants of vegetation structure (Sollins et al. 

1999). Global and local disturbances can alter soil chemistry which may result in altered vegetation 

composition. Soil cores will be taken during the initial site visit and thereafter every 5-10 yr and analyzed 

for a complete matrix of plant nutrients, % organic matter, cation exchange capacity, pH and total carbon 

and nitrogen. When funds are available, soil cores can be taken more frequently (annually) and a minimum 

analysis may include pH, total nitrogen and total carbon. 

Equipment

• Soil corer (marked at 20 cm)

• whirlpaks or plastic bags

• Soil knife or trowel

Procedures

(1) Select three random locations within a 3 m x 3 m plot array but not in any of the vegetation plots. Go to 

the first location.

(2) Remove surface vegetation and litter to expose mineral soil. 

(3) Carefully drive the soil corer to 20 cm depth. Remove corer and place soil from corer into whirlpak 

labeled with date, park, site, and transect. Use soil knife or trowel to assist in the removal, as needed. 

(4) When possible, fill hole with litter and any loose dirt to reduce future erosion.

(5) Move to the second and third random location and repeat steps 2-4, placing all three cores from one plot 

array into a single bag. This will result in 1 composite sample per aspect and four samples per summit. 

(6) Close whirlpak/bag and place in shade. 

(7) The samples can be kept closed in plastic bags for a few days as long as they are kept cool. However, if 

you cannot ship samples to the laboratory within a week, the samples should be air-dried. If this is the 

case, find a location inside (housing/laboratory/office), place samples on a table, open the top of sample 

bags, and let sit until dry (or until you ship them). If there is a fan available, running it above the 

samples will hasten this process. Alternatively, bring them back to the ROMN office and place in the 

drying oven. 

(8) When possible, pack the sample and others in a box. Mail to the following address requesting analysis 

for pH, electrical conductivity, cation exchange capacity, organic matter, nitrate-nitrogen, phosphorus, 

potassium, zinc, iron, manganese, copper, total nitrogen and carbon, calcium and aluminum. After the 

first visit or when money is limiting, this list can be reduced to pH, total carbon, and total nitrogen. 

Address is current as of Dec 2010, but please confirm address prior to mailing. 

Dr. James Self

Soil, water, and plant testing laboratory

Colorado State University

Room A319, Natural and Environmental Sciences Building

200 West Lake Street

Fort Collins, Colorado 80523-1120

Soil samples for optional survey design

For the optional alpine survey, repeat all tests above with the following replication:

• Soil surface condition: done at macroplot scale.

• Soil texture and bulk density: 2 cores from random locations outside the subplot but within the 

macroplot.
63

• Soil chemistry: 1 composite core taken from 3 random locations outside the subplot but within the 

macroplot.

References

Elliott, E.T., J.W. Heil, E.F. Kelly, H.C. Monger. 1999. Soil structural and other physical properties in G.P. 

Robertson (Ed). Standard Soil Methods for Long-Term Ecological Research. Oxford University Press. 

Cary, NC, USA. p 74-88

Herrick, J.E., J.W. Van Zee, K.M. Havstad, L.M Burkett and W.G. Whitford. 2005. Monitoring Manual for 

Grassland, Shrubland and Savanna Ecosystems. USDA-ARS Jornada Experimental Range, Las Cruces, 

New Mexico and University of Arizona Press, Tucson, Arizona. 2 vol., 236pp. available online at 

http://usda-ars.nmsu.edu/

Pellant, M., P. Shaver, D.A. Pyke, and J.E. Herrick. 2005. Interpreting indicators of rangeland health, 

version 4. Technical Reference 1734-6. U.S. Department of the Interior, Bureau of Land Management, 

National Science and Technology Center, Denver, CO. BLM/WO/ST-00/001+1734/REV05. 122 pp.

Robertson, G.P., P. Sollins, B.G. Ellis, K. Lajtha. 1999. Exchangeable ions, pH, and cation exchange 

capacity in G.P. Robertson (Ed). Standard Soil Methods for Long-Term Ecological Research. Oxford 

University Press. Cary, NC, USA. p 106-114

Sollins, P., C. Glassman, E.A. Paul, C. Swanston, K. Lajtha, J.W. Heil, E.T, Elliott. 1999. Soil carbon and 

nitrogen: pools and fractions in G.P. Robertson (Ed). Standard Soil Methods for Long-Term Ecological 

Research. Oxford University Press. Cary, NC, USA. p 89-105

USDA. 1999. Soil quality test kit guide. Soil Quality Institute. USDA 

http://soils.usda.gov/sqi/assessment/files/test_kit_complete.pdf

65

SOP AlpineVCSS Phenology

Rocky Mountain Inventory & Monitoring Network: 2010 version

Change History

Previous 

version

Date Author Change description Reason

Introduction

The timing of reoccurring events in a plant life-cycle, or phenology, can provide an indicator of global 

climate change. In alpine systems, the growing season is short and the timing of leaf out and flowering is 

strongly influenced by the timing of snowmelt. To monitor phenology in our sentinel sites, we plan to 

record the life-stage for three indicator species: Geum rossii, Silene acaulis ssp. subacaulescens, Eritrichium

nanum var. aretioides (Figure 1). The indicator species were chosen based on the ease of identification and 

widespread presence on the alpine peaks in ROMO and GRSA. 

Equipment

• Species list, key, and photographs

• Phenology datasheet

• Compass

• Meter tape

• Optional: 1 m2 frequency quadrat used to define the location of a plant

• Datasheet from the prior year with locations of individuals

• Camera

Procedures

1. For the initial visit (first year SOP is completed), find 3 individuals (or any defined unit, such as a 

clump) of each species on the peak (Table 1) for a total of 9 individual plants. Record location based 

on existing plot markers to allow for relocating the same plant in following years. The best choice is 

plants within a 1 m2 plot. Where possible, the 3 replicate plants should be spread across aspects. 

2. For each plant, identify the dominant phenophase (e.g., emerging leaves, open flowers) and take a 

photograph of the plant

3. This SOP should be done at least once per season but more records of at least 2 days apart are 

recommended. 

4. On subsequent visits, bring location data and print out photographs for individuals. Find individuals 

and start with step 2. 
66

Figure 1. Geum rossii, Silene acaulis, Eritrichium nanun 

References

National Phenology Network (2009). USA-NPN Plant Phenology Program. http://www.usanpn.org/

accessed 3/18/2009. Tuscon, AZ
67

Table 1. Sample phenology datasheet

Date:

Name:

Park:

Location(Target Location - Peak): 

Date Species No. Observer

Phenophase 

(Choose EL, UL, WL, OF, 

RS)

Location description (distance and direction to closest 

monument) Comments

Geum rossii 1

Geum rossii 2

Geum rossii 3

Silene acaulis ssp. 

subacaulescens 1

Silene acaulis ssp. 

subacaulescens 2

Silene acaulis ssp. 

subacaulescens 3

Eritrichium nanum var. aretioides 1

Eritrichium nanum var. aretioides 2

Eritrichium nanum var. aretioides 3

Definitions:

Emerging growth (EL): New growth of the plant is visible above the soil surface with the appearance of fresh green shoots that show no signs of aging. 

For each shoot, growth is considered "emerging" until the first leaf has unfolded.

Unfolded leaves (UL): In at least one location on the plant, an unfolded leaf is visible. A leaf is considered "unfolded" when it unrolls slightly from 

around the stem and begins to fall away at an angle.

All leaves withered (WL): Of the leaves that developed this season, virtually all (95-100%) are dried and dead.

Open flowers (OF): In at least one location on the plant, an open fresh flower is visible. Flowers are considered "open" when the reproductive parts are 

visible between open flower parts. Do not include dried flower parts that remain on the plant.

Ripe seeds (RS): In at least one location on the plant, a ripe seed is present. A ripe seed is hard when squeezed and is difficult to divide with a 

fingernail. Seeds may also be considered ripe when they fall into your hand when the plant is handled.

69

SOP AlpineVCSS Treeline 

Rocky Mountain Inventory & Monitoring Network: 2010 version

Change History

Previous 

version

Date Author Change description Reason

Introduction

Climate models predict that forests will expand upslope into alpine areas as temperatures continue to warm 

and changes at the forest-alpine ecotone will have large effects on C storage, nutrient cycling, wildlife 

habitat, and water dynamics (Field et al. 2007). Here, we describe a simple procedure to photograph and 

locate the krummholz and/or treeline at each sentinel site. Krummholz is a characteristic growth form of 

trees in the alpine, where trees are stunted, deformed, and resemble bushes due to great environmental 

stress, such as high wind and low temperatures. At lower elevations, where more favorable conditions exist, 

the same tree species that form into krummholz grow upright. Common krummholz species in the Rocky 

Mountains include Picea engelmannii, Pinus aristata, and Pinus albicaulis. The krummholz line is the 

upper-most limit in elevation that trees in krummholz form exist and treeline is the upper-most limit in 

elevation that upright trees exist. 

Steps

Equipment

• Meter tape

• Compass

• Range finder

• Camera

• Treeline Datasheet

• GPS unit

• For repeated visits, print outs of photographs and datasheets with location from past years

•

Procedures

Follow the procedures below to photograph the krummholz and/or treeline from each GLORIA peak in the 

N, S, E, and W directions. 

For the first time the SOP is implemented-

(1) Stand at the highest point on the peak (marked by rebar) and use a compass to determine N. Use the 

correct declination for the area.

(2) Determine photograph location: Looking N, determine if the tree line is visible. If it is not, walk directly 

N to where the tree line or krummolz line is visible. Use GPS to mark location and record waypoint and 

mark on GPS log. Measure the distance from the highest point to your location with a meter tape. 

(3) Take a photograph. Record photo number and time on photograph log. 

(4) Repeat steps 1-3 for the E, S, and W directions. 
70

For all subsequent times the SOP is implemented (every 5 years)-

(1) In office, find files and print out the 4 photographs per peak. Write on the photograph the location where 

it was taken (GPS waypoint, distance from HSP, etc). 

(2) Bring the photograph in the field and the location using landmarks, waypoints, and the photograph. 

(3) Take photograph and if there are changes, record a new waypoint for the location that the photograph 

was taken from. 

References

Field, C. B., L. D. Mortsch, M. Brklacich, D. L. Forbes, P. Kovacs, J. A. Patz, S. W. Running, and M. J. 

Scott. 2007. North America. Climate change 2007: Impacts, adaptation and vulnerability. Contribution 

of working group II to the fourth assessment report of the Intergovernmental Panel on Climate Change. 

Pages 617-652 in M. L. Parry, O. F. Canziani, J. P. Palutikof, P. J. van der Linden, and C. E. Hanson, 

editors. Climate Change 2007: Impacts, Adaptation and Vulnerability. Cambridge University Press, 

Cambridge, UK.
71

SOP AlpineVCSS N Deposition: Measuring Nitrogen Deposition 

Using Ion Exchange Resin Collectors; Optional 

Rocky Mountain Inventory & Monitoring Network: 2010 version

Change History

Previous 

version

Date Author Change description Reason

Introduction

The atmospheric deposition of nitrogen (N) in the Rocky Mountain tundra has increased in recent decades to 

greater than 6 kg/ha (NADP). Annual N loadings may provide a critical covariate in trend analyses (as a 

driver for change) and spatial variation in N loadings may add to our understanding of different vegetation 

patterns within and across parks. Here, we outline an inexpensive method to measure annual wet deposition 

in resin columns adapted from the United States Forest Service (Fenn and Poth 2004). This is an optional 

method and should be considered in areas where there are not nearby NADP stations or other data available 

to estimate N load. 

Steps

Equipment

To make and extract 36 IER columns (this is enough for 8 collectors and 1 blank at four GLORIA peaks in 

one park) you will need the following:

• 36 1.27 cm inner diameter x 35.6 cm polyvinyl chloride [PVC] tubes

• 36 7.1 cm inner diameter x 35.6 cm PVC tubes used to protect IER column 

• 36 PVC cap 1.27 diameter with 10 holes drilled into it to allow for drainage

• 36 7.1 cm diameter PVC cap

• Large bag of polyester floss

• 32 funnels with 20 cm diameter or 20 cm rain collector funnel 

• 10 m mesh screen cut into 32 30 x 30 cm squares to be placed in the funnel to keep out debris.

• 10 L Distilled water

• 36 PVC fittings 

• 60 mL of resin per collector (mixed anion/cation exchange resin), with a total ion exchange capacity 

of 33 cmolc, is sufficient to collect N equivalent to a field deposition 416 kg N ha1 of nitrogen (e.g., 

Amberlite MB 150)

• 100 ml graduated cylinder

• Laboratory style support clamp (that can fit PVC tubes)

• Latex gloves

• Scoop

• Scissors

• Cable ties

• Drill 

• 36 rebar or stakes 60 cm length
72

• Mallet to pound rebar or stakes

• 20 L 2 M KCl 

Procedures

Constructing a ion exchange resin (IER) column

1. Wear latex gloves

2. Drill 2 small holes at the top of the 1.27 cm PVC tube (big enough for the cable tie, ~ 3 mm 

diameter)

3. Drill 2 small holes in the rim of the funnel

4. Drill 2 holes into 7.1cm PVC

5. Drill ~10 holes into PVC cap to allow for drainage

6. Place 1.27 cm PVC into support clamp (IER column) with the holes at the top

7. Place a small amount of polyester floss into the bottom of the 1.27 cm PVC tube 

8. Cap tube with PVC cap with holes cut into it to allow for drainage 

9. Use scoop to measure 60 ml of resin in graduated cylinder

10. Pour resin into PVC tube on top of the polyester floss

11. Place a small amount of polyester floss onto the top of the resin

12. Label column on outside using a sharpie with a column number using a combination of deployment 

year and unique code (eg., 200901)

13. Cut mesh into ~30 x 30 cm square

14. Place mesh over top of funnel and use cable tie around the outside of the funnel to secure it in place

15. Place funnel into IER column

16. Thread cable tie through each hole in the funnel and attach to the IER column, then place IER 

column into 7.1 cm protector tube and thread cable tie through the outer tube, close (to secure the 

funnel and protector in place)

17. Write column code on the protector tube

18. Repeat to create 8 collectors per peak

19. Repeat procedures 1-17 but instead of a funnel, cap the protector tube with a PVC cap to create a 

IER field blank

Deploying IER columns in the field

At each GLORIA site, deploy 8 collectors with 2 pairs at each of the N, S, E, and W quadrant during the 

summer. 

1. In the N quadrant, move to approximately 2 m from the top of the 5 m plot array towards the high 

point of the peak

2. Drive a stake or rebar ~ 15 cm into the ground

3. Secure IER collector to rebar with PVC fittings and cable ties so that there is some room for 

drainage (~ 5 cm from ground)

4. Find rocks from outside of plot area and place them around IER collector to disguise it and add some 

shelter

5. Note collector location, date and time deployed, and column number

6. Move to approximately 2 m from the bottom of the 5 m plot array and repeat step 2-5

7. Move to the S, E, and W quadrant and repeat steps 1-6 

8. Place an IER field blank near the high point of the peak

9. Leave IER collectors in the field for up to 1 year 

10. When collecting IER columns, remove from the protector and funnel, and cap. Note date, time, 

location, and column number

11. Bring capped columns back to the laboratory
73

Laboratory Analyses

1. Place IER column in support clamp and place 500 ml beaker below column

2. Remove cap on top

3. Pour 100 mL DI water through column and into the beaker to rinse. Toss water in beaker

4. Pour 200 mL 2 M KCl through column into beaker

5. Repeat step 4 for a second extraction

6. Swirl extraction solution in beaker and pour into 4 pre labeled scintillation vials. 

7. Place 2 vials into freezer. These are backups and can be thrown out when laboratory analyses are 

completed and data has been QA/QCed.

8. Place 2 vials into refrigerator. 

9. If reusing the beaker: Rinse beaker with tap water than at least 3 times with DI water

10. Repeat steps 1-9 for all IER columns, including blanks. 

11. When all extractions are completed, send samples to a cooperating laboratory for analyses of nitrate 

and ammonium. A cooperative agreement or contract will be needed for analyses and it is important 

to use a laboratory with low detection limits. The University of Colorado, Kiowa Laboratory could 

perform these analyses in 2009 the approximate cost was $4 per sample. 

Data Processing

1. The laboratory should provide nitrate and ammonium values for each column and blanks as a value 

of mgN/L. 

2. For each peak, subtract the value in the blank from all 8 columns. 

3. Convert mgN/L to kgN/ha using the known volume of extract (0.4 L) and surface area of the PVC 

column (1.26 cm2). 1 ha = 100000000 cm2

X mg N/L * 0.4 L = Y mg N

Y mg N/ 1.26 cm2 * 100000000 cm2/ 1 ha * 1 kg/ 1000000 mg = Z mg N/ha

References

Fenn, M. E., and M. A. Poth. 2004. Monitoring nitrogen deposition in throughfall using ion exchange resin 

columns: a field test in the San Bernardino Mountains. Journal of Environmental Quality 33:2007-2014.

75

SOP AlpineVCSS Data Analysis: Sentinel Sites Statistical Analysis

Rocky Mountain Inventory & Monitoring Network: 2010 version

Change History

Previous 

version

Date Author Change description Reason

Introduction

To examine status at GLORIA sentinel sites, we will use descriptive statistics and linear mixed effect 

models or nonparametric equivalents to examine the difference in parameters among parks, summits, and 

summit aspects. Models will contain fixed factors (summit, aspect) and random factors (plot nested in 

aspect). Multivariate methods, such as analysis of similarity and ordinations, will use percent cover of all 

species to examine the differences in community structure among summits and aspects. We will explore the 

relationship between physical factors, such as elevation and soil chemistry, and vegetation structure using 

linear regression and tests of correlation. Where appropriate these physical factors may be used as covariates 

in linear models. To determine which factors or drivers affect a given parameter, we will use model 

selection procedures and determine the best fit models using Akaike’s Information Criteria (AIC) (Akaike 

1973, Burnham and Anderson 2002). We are particularly interested in understanding how levels of grazing, 

temperatures, and N deposition may alter community structure. When two to three years of data are 

available, we may begin to examine and detect change at sentinel sites. To do this, we will use the same 

statistical methods described above but add year as a fixed factor. This will allow us to determine whether 

parameters, such as species richness, differ among years. We will explore trends at GLORIA sentinel sites, 

when four or more years of data are available. To examine trends we will conduct repeated measures 

analyses where “plots” are the subject that is repeated over time. 

Steps

General Guidelines for Data QA/QC and parameters measured:

1. Prior to data analysis, all data should be entered and QA/QC should be performed within the 

guidelines of SOP DM Data Quality Control. Check notes and range of values for all parameters. 

Also check that you have data for the appropriate level (plot, aspect, or summit).

2. Derive variables according to equations listed in Table 1.

General Guidelines for Statistical Analyses:

Load R (2008) and the vegan (Oksanen et al. 2009) and nlme packages (Pinheiro et al. 2008). 

Univariate analyses:

• For status of sites and differences among sites, or aspects linear mixed models can be used. The 

summit and aspect are fixed factors, plots are considered random factors. A sample code to test for 

differences in the status of graminoid cover is:

model= lme (graminoid_cover~ summit*aspect, random=~1|plot)
76

Table 1. Parameters measured, units, QA/QC and derivations, unit of analyses, and suggested statistical tests

Parameter Raw/Derived units QA/QC unit of 

analyses Statistical tests

Cover of scree, rock, vascular 

plants, lichens, bryophytes, bare 

ground, litter

Raw % the sum of all 7 categories should add up to 100% plot and 

summit

univariate: linear mixed 

multivariate: anosim or 

permanova on Bray Curtis 

dissimilarity matrix 

Species cover (vascular plants) Raw % value for each vascular species present; not less than 0; includes 

layers so can be greater than 100

plot and 

summit

univariate: linear mixed 

multivariate: anosim or 

permanova on Bray Curtis 

dissimilarity matrix

Species number Derived species count number of vascular species present in summit area summit linear mixed model

Diversity Index Derived

shannonweiner index, 

H'

H'= Sum (relative abundance of each species/ ln(relative 

abundance); upper limit ~4 summit linear mixed model

Eveness Derived Peilou index, 

J J'=(diversity)/log(number of species); between 0 and 1 summit linear mixed model

Mean Coefficient of 

Conservation Derived index C between 1-10; the mean of the coefficient fo conservation assigned to each species present summit linear mixed model

Florisitic Quality Index Derived FQA FQA=C * square root(number of species) summit linear mixed model

Species frequency counts Derived % count the number of cells out of 100 where species is present; 

there should be a value for all species present plot NA

Relative frequency Derived % Divide species frequency by total frequency. Sum of all species 

should be 100%. plot linear mixed model

Species presence/absence Raw categorical presence of species in each grid cell of 1 m2 plot 100 cells 

w/in plots NA

Dominant phenophase Raw categorical leaves, flowers, seeds, by date summit descriptive statistics

Number of exotic species 

present on summit Derived species count number of species present in peak area summit linear mixed model

Identity of exotic species present Raw categorical species list summit NA

Percent cover of exotic species Raw % not less than 0; includes layers so can be greater than 100 summit linear mixed model

browsing presence/absence Raw categorical presence of browsing in each grid cell of 1 m2 plot 100 cells 

w/in plots NA

scat presence/absence Raw categorical presence of browsing in each grid cell of 1 m2 plot 100 cells 

w/in plots NA

trampling presence/absence Raw categorical presence of browsing in each grid cell of 1 m2 plot 100 cells 

w/in plots NA

browsing frequency Derived % Number of cells (0- 100) where browsing is present plot linear mixed model

scat frequency Derived % Number of cells (0- 100) where scat is present plot linear mixed model

trampling frequency Derived % Number of cells (0- 100) where trampling is present plot linear mixed model

hourly temperature Raw degC -40 deg > value < 50 aspect NA

mean daily temperature Derived degC mean of 24 hourly records from 1:00 to 24:00hrs aspect NA

minimum daily temperature Derived degC min of 24 hourly record aspect NA

maximum daily temperature Derived degC max of 24 hourly record aspect NA

mean annual temperature Derived degC mean of hourly record from Jan1-Dec31 aspect linear mixed model; covariate 

maximum annual temperature Derived degC max of hourly record from Jan1-Dec31 aspect linear mixed model; covariate 

minimum annual temperature Derived degC min of hourly record from Jan1-Dec31 aspect linear mixed model; covariate 
77

Parameter Raw/Derived units QA/QC unit of 

analyses Statistical tests

number of days above 100 degF 

in a given year Derived days count of days between Jan1-Dec31 where max daily temperature >100 degC aspect linear mixed model; covariate 

first/last date of snow cover Derived dates first/last dates of continuous snow cover. Snow cover is present 

when standard deviation in daily temperature=0 degrees aspect linear mixed model; covariate 

number of days of continuous 

snow cover Derived days Julian date of last snow cover Julian day -first snow cover aspect linear mixed model; covariate 

first/last date of hard freeze Derived dates first/last dates where minimum hourly temperature > -3 degC aspect linear mixed model; covariate 

accumulated growing degree 

days Derived growing degree days

use base of O degC; sum of (mean daily temperature-base) for 

days when mean>0 aspect linear mixed model; covariate 

diurnal temperature range Derived degC Difference between daily max and min temperatures aspect linear mixed model; covariate 

sand, silt and clay raw % aspect covariate 

texture derived NA classification based on % sand, silt, and clay aspect NA

bulk density raw g/m3 aspect NA

pH raw pH aspect linear mixed model

cation exchange capacity raw cmolc/Kg soil aspect linear mixed model

micro and macronutrients raw % aspect linear mixed model

organic matter raw % aspect linear mixed model

C:N derived ratio %C:%N aspect linear mixed model

surface soil aggregate stability raw ordinal

class 1-6 based on dip cycles; 1: 50% loss in 5 s, 2: 50% loss in 

30 s, 3: 50% loss in 5 min, 4: 10-25% of the soil remains after 5 

dipping cycles, 5: 25-75% remains, 6: 75%-100% remains

aspect

kruskal-wallis; spearman 

correlation; only compare within 

the same soil texture 

subsurface soil aggregate 

stability raw ordinal class 1-6 based on dip cycles; see above aspect

kruskal-wallis; spearman 

correlation; only compare within 

the same soil texture 

surface flow evidence raw ordinal 5 classes: extreme (XX), moderate to extreme (MX), 

moderate(MM), slight to moderate (SM), none to slight(NS) summit

rills and gullies raw ordinal 5 classes, see above summit

pedestals and terracettes raw ordinal 6 classes, see above summit

distance to road derived m derived from GIS summit

human disturbance index derived % derived from anthropogenic disturbances calculated; range from 0 

-100 (highly disturbed) summit

slope raw degrees summit

elevation raw m summit

nitrogen load (ammonium and 

nitrate) raw kg/ha aspect

beaver disturbance raw categorical

class 1-4 based on high to no disturbance; 1: high >50% of site 

has evidence, 2: medium >10% or old evidence, 3: low no sign on 

site but some within 1km; 4: no signs in site or within 1km 

summit

native ungulate disturbance raw ordinal class 1-4 based on high to no disturbance; see above summit

frost heave disturbance raw ordinal class 1-4 based on high to no disturbance; see above summit

rodent disturbance raw ordinal class 1-4 based on high to no disturbance; see above summit

pest & pathogens disturbance raw ordinal class 1-4 based on high to no disturbance; see above summit

landslide disturbance raw ordinal class 1-4 based on high to no disturbance; see above summit

fire disturbance raw ordinal class 1-4 based on high to no disturbance; see above summit

natural disturbance derived index; NDI derived from weighting natural disturbance categories; range from 

0-100 (highly disturbed) summit
78

• To examine change in a parameter over multiple years of data, but less than four, “year” 

can be added as a fixed factor. A sample code to test for changes in graminoid cover is:

model= lme (graminoid_cover~ summit*aspect*year, random=~1|plot)

• To examine trend in a parameter over more than four years use a repeated measures 

mixed model. A sample code to test for trend in graminoid cover is: 

model=lme (graminoid_cover~ summit*aspect, random=~year|plot, 

correlation=corAR1())

• To examine correlation among two variables (graminoid cover, soilN) use:

cor.test (graminoid_cover, soilN)

It is important to test that the data meet the assumptions of the statistical test you are performing 

prior to analysis and interpretation. It is critical to examine the residuals and determine if it is 

necessary to transform the variables and/or remove outliers. In some cases, nonparametric 

equivalents to mixed models (Kruskal-Wallis) can be performed. Often the unit of interest for the 

park will be at the site or peak level, rather than the aspect, in these cases the data can be 

averaged across aspect. 

Multivariate analyses:

The vegan package (2009) can be used for calculating diversity indices and for creating 

dissimilarity indices, ordinations, and permutational tests of similarity. Bray Curtis dissimilarity 

matrices are most appropriate for vegetation abundance data because it weights the most 

dominant species and ignores plots with similar absences or joint zeros. ANOSIM (analysis of 

similarity) is a multivariate equivalent of ANOVA and is conducted on the dissimilarity matrix. 

References

Akaike, H. 1973. Information theory and an extension of the maximum likelihood principle. 

Pages 267–281 in B. P. a. F. Czakil, editor. Proceedings of the second international 

symposium on information theory. Akademiai Kiado, Budapest.

Burnham, K. P., and D. R. Anderson. 2002. Model selection and multimodel inference: a 

practical information-theoretic approach. Springer-Verlag, New York.

Oksanen, J., R. Kindt, P. Legendre, B. O'Hara, G. L.Simpson, P.Solymos, M. Stevens and H. 

Wagner (2009).vegan: Community Ecology Package. R package version 1.15-2. http://cran.rproject.org/, http://vegan.r-forge.r-project.org/

Pinheiro,J., D. Bates, S. DebRoy, D. Sarkar and the R Core team (2008). nlme: Linear and 

Nonlinear Mixed Effects Models. R package version 3.1-90.

R Development Core Team (2008). R: A language and environment for statistical computing. R 

Foundation for Statistical Computing,Vienna, Austria. ISBN 3-900051-07-0, URL 

http://www.R-project.org.
79

SOP AlpineVCSS Reporting and Presentations

Rocky Mountain Inventory & Monitoring Network: 2010 version

Change History

Previous 

version

Date Author Change description Reason

Introduction

Reporting is an essential component of long-term monitoring because it documents what was 

done at a given time, informs park managers and the public about the I&M program and 

resources in their parks, and it creates a venue for field methods and analysis procedures to be 

peer-reviewed. There are a number of reporting products that will result from each monitoring 

effort. This SOP provides a few tips for many products, but focuses primarily on the annual 

report. 

Tips for Reporting & Presentations

Where possible, monitoring results should be presented in conjunction with an interpretation of 

how well or poorly these data meet management goals. There are numerous approaches to 

providing management context including the “Connect the Dots” framework, ecological report 

cards, reference conditions and thresholds, critical loads, and assessment points. Prior to 

publication or public presentations, it is crucial to have reference conditions vetted with park 

personnel. It is also important to clearly identify the appropriate level of inference for the 

monitoring data (e.g. specific sentinel sites vs. target population within a park). Below are listed 

a few tips for specific tips of reporting:

Presentations: Oral presentations should be accompanied by slides (most often Powerpoint).

Below are a number of guidelines for oral presentations:

• The current template for NPS presentations can be downloaded at:

http://www.graphics.nps.gov/templates.cfm

• All presentations should include an introduction and discussion of why the given 

resource is being monitored. The length of this discussion can vary based on audience 

(shorter for a well-versed technical committee) but it should never be omitted. 

• The objectives of the monitoring protocol should also be presented. 

• Where data is presented from published papers, these should be referenced on the slide

and sources should be referenced for photographs downloaded from the internet. 

• It is standard for NPS presentations to be made publically available, so it is important to 

edit slides and to include only non-sensitive information. 

• All statistical analyses should be presented with sample size, significance, and the test 

and when analyses are preliminary, this should be stated on the slide. 

• Prior to giving a presentation, check the slides on the computer that will be used for the 

talk with attention to figures, coloration, and fonts. 

• Estimate 1 minute per slide 
80

• For public audiences, prepare and review answers to controversial questions. For alpine 

vegetation monitoring these may include: importance of ecological monitoring, climate

change, fire management, elk/wildlife management, and invasive/exotic species 

management. 

Trend reports & peer-reviewed articles:

• Published reports and articles should be made available to the park where the work was 

done. 

• Acknowledgments should include the park technical committee member(s) and any other 

staff that helped with logistics. Field crew members and laboratories where soil/water 

analyses were done should also be acknowledged, 

• Authorship should be given to only those persons who had substantial input into at least 2 

parts of the process: method development, data collection, data analysis and writing. This 

excludes most, but not all, seasonal field technicians from authorship. 

• Where the report is not submitted to a peer-reviewed journal, it should be submitted to 

the Natural Resource Series for publication. 

I&M websites

• The ROMN network website should be updated annually during the spring of each year 

(April). 

• At a minimum, updates should include a statement about the work done that year (e.g. 

which park was monitored)

• Provide links to annual reports and any other documents pertaining to the protocol

• Where possible, list the planned work for the next summer field season. 

• Update with current photographs or results

Resource Briefs

• All Annual Reports and any other publications should be accompanied by a resource 

brief. The brief should be ~2 page summary that highlights results and links the results to 

management.

• Where applicable place a statement “Preliminary results, not peer-reviewed” in the brief

Annual Reporting

Table 1: Timeline for Annual Reporting 

Date Procedure

Mar 1 Send report to park and outside reviewers; send 

resource brief to parks

Mar 15 Receive reviews

Mar 31 Publish report and resource brief; submit IAR 

Annual Reports should be produced in the winter of each year to document the what, who, how, 

where, and when of the previous field season. There are a few purposes of this document: (1) 

fulfill the annual reporting requirements of the research permit, (2) inform park management 

about what was done in the park, (3) compile and interpret annual data and (4) provide insight 
81

into logistical details of the fieldwork for the next crew leaders. Two alpine annual reports from 

2009 can be used as templates for subsequent years (Ashton 2010a, Ashton 2010b). We 

recommend that annual reports from the AlpineVCSS protocol include graphical and tabular 

summaries of statistically significant and biologically meaningful differences among summits 

and aspects, and the following:

• Number and dates that sites were visited

• Bar graphs of species richness and floristic quality index (mean ± 1 standard error) by 

summit

• Bar graphs of percent cover of exotic invasive species, graminoids, and forbs by summit.

• Scatterplot or line graph of daily mean, maximum, and minimum temperature at each 

summit.

• Table or text reporting number of days with soil temperatures above 100 °F, first and last 

days of continuous snow cover, and first and last date of hard freeze. 

• An appendix should list all species recorded

• Another appendix should provide a journal of activities by day of field season. This can 

include basic field notes, sites visited, personnel involved, and other notes. 

Steps:

(1) Download the most recent template for a Natural Resource Data Series (NRDS) report at: 

http://www.nature.nps.gov/publications/nrpm/NRRNRTR.cfm

The annual reports are most appropriate for the data series because they provide only basic data 

summaries. 

(2) One report should be produced for each park where monitoring occurred. 

(3) The Title of the Report must include the park name and year of data collection.

(4) Conduct data analyses, make figures, tables, and write report. Often the introduction, 

objectives, and methods can be cut and paste from the previous year’s report. Include headings 

of Introduction, Methods, Results, and Discussion. 

(5) Send the draft to the ROMN staff for review

(6) Revise and send a second draft to at least 2 outside reviewers – one from the park of interest 

(often the Technical Committee representative) and one subject matter expert (often from 

another NPS network). 

(7) When reviews are received all comments should be transferred to a document and saved 

using the naming convention “Protocol_AnnualReport_Year_Reviews”. As the report is revised, 

document the response to reviewers within this document. Archive a copy of the reviews, the 

original draft, and the final version of the report. 

(8) Follow the instructions for submitting a report to the NRTR series. These can be found at: 

http://www.nature.nps.gov/publications/NRPM/procedure.cfm. Currently, this involves filling 

out a submission form, describing the review process, and emailing the manuscript to Fagan 

Johnson. 

For this: the network protocol lead and Mike Britten should be included as peer-review 

managers. 

(9) Revise the document as per directed by the publications office. Save the file as a .pdf and 

upload the final version to NRInfo. 

• Include in NRInfo the key words applicable to the monitoring protocol. For Alpine these 

include: alpine, monitoring, vegetation, climate change, soil temperature. 
82

• Link the record to the park where the data was collected and the Rocky Mountain 

Network

• Cut and paste the executive summary into NRInfo

(10) Update website with link to the publication on NRInfo and email network partners to 

announce the publication 

References

Ashton, I. (2010) Alpine vegetation composition, structure, and soils monitoring for Great Sand 

Dunes National Park and Preserve: 2009 Summary Report. Natural Resource Data Series 

NPS/ROMN/NRDS—2010/056. National Park Service, Fort Collins, Colorado.

Ashton, I. (2010) Alpine vegetation composition, structure, and soils monitoring for Rocky 

Mountain National Park: 2009 Summary Report. Natural Resource Data Series 

NPS/ROMN/NRDS—2010/057. National Park Service, Fort Collins, Colorado.
83

SOP AlpineVCSS GRTS design: Creating a Probability-Based 

Design for Alpine Surveys; Optional

Rocky Mountain Inventory & Monitoring Network: 2010 version

Change History

Previous 

version

Date Author Change description Reason

Introduction

While our initial monitoring efforts will focus only on the sentinel sites, if and when time and 

budgets allow, we hope to increase our ability to make park-wide inferences by adding a survey 

design component to our monitoring approach. Probability-based surveys provide unbiased 

estimation of both status and, with repeated visits, trend across a resource (Larsen et al. 1995) 

and when implemented successfully allow for unbiased inference from sampled sites to unsampled elements of the resource of interest (Hansen et al. 1983). The goal of the optional 

survey design for the AlpineVCSS is to determine the status of alpine vegetation and soils in 

GRSA, GLAC, and ROMO in a given year. Below, we describe each of the steps required to 

develop a GRTS survey design and provide an example using alpine areas in GRSA.

Steps

Defining the target population 

Our goal is to determine status in vegetation structure, species composition, natural and 

anthropogenic disturbance and soil condition in alpine habitats. For the AlpineVCSS we defined 

the target population by elevation (above 1980 m in GLAC and 3500 m in GRSA and ROMO) 

and community types, limited to non-aquatic vegetated communities above timberline. The 

elevations were best estimates based on ecological literature, maps, and park definitions. In the 

alpine regions of GRSA, GLAC, and ROMO there are numerous habitats that are not 

significantly vegetated by vascular plants, such as talus fields, cliff faces, and glaciers, and these 

are not included in our target population. We do not include wetlands, primarily alpine wet 

meadows, because they are included in the target population of the ROMN Wetland Ecological 

Integrity Protocol (Schweiger et al. in preparation). Within the target population, we are 

interested in a number of ecological types (subpopulations) of vegetation. The nomenclature of 

subtype varies by park because we use the classifications presented in the respective vegetation 

maps but all are based on the National Vegetation Classification System vegetation alliances. In 

GRSA these include three subpopulations: alpine fell-field alliances (fell-field tundra), alpine 

turf alliances (dry and moist meadow tundra), and alpine willow and spruce shrubland (shrub 

tundra). Alpine turf alliances are the most common, covering approximately 950 hectares or 58% 

of the resource. 

Defining the sample frame

Once the target population is defined, the sample frame was developed within GIS by 

intersecting the target population with known or expected areas of sensitive or protected sites and 
84

developed park infrastructure. After this, a detailed cost surface estimate was used to incorporate 

information about difficult access in backcountry settings. The ROMN cost-surface model 

computes relative travel times to sites based on factors such as the presence of trails, vegetation 

type, and slope angle (Frakes et al. 2007). In essence, this limits the target population to 

accessible areas where there are no buildings, roads, or sensitive resources. The cost surface 

model is continuous, but we used it to determine 8 discrete cost classes based on the hours 

required to access an area: 0-0.5, 0.5-1.0, 1.5-2.0, 2.0-2.5, 2.5-3.0, 3.0-3.5, and 3.5-4.0 hours. 

The target population was then classified by ecological type and the cost class for that polygon. 

In the case of GRSA this resulted in 28 unique categories, with alpine turf alliances of greater 

than 3.5 hours cost covering the largest area of the target population (15%). The resulting sample 

frame from GRSA is shown in Figure 1. 

Selecting sites 

After defining the target population and establishing a sample frame, survey sites are selected 

from within the sample frame. As with all ROMN survey designs (Britten et al. 2007), the alpine 

tundra survey uses a Generalized Random Tessellation Stratified (GRTS) design (Stevens and 

Olsen 2003, 2004). For the alpine surveys, we included 40 base sites and 120 oversample sites. 

The number of sites was chosen because it is operationally possible and we estimate that 

estimates of status require sample sizes in the range of 35–50 per reporting unit to generate a 

confidence interval around ±10% (Britten et al. 2007). If a base site is determined to be nontarget during fieldwork, for instance the site is a wetland, it will be replaced by the next available 

site with the same subpopulation type in the oversample list. Using a GRTS approach, the sites 

were allocated proportionally to each class within the sample frame with a slight bias towards 

sites that are easier to access. Later design-based data analyses will correct for these biases. The 

resulting design includes a map of site locations and for each site there is corresponding 

probability that it would be included in the design. The final survey design was chosen to reduce 

the sample variance of inclusion probabilities. The AlpineVCSS design was created in R using 

the ‘spsurvey’ package (Kincaid and Olsen 2008)(Kincaid and Olsen 2008). Copies of original 

files are preserved and maintained by ROMN including the statistical (R) code containing the 

original settings and functions, the sample frame shapefile(s) and summary data, and the design 

points. A sample R code for GRSA is provide in Table 1. 

Table 1. Sample R code for the GRTS survey design

library(spsurvey)

att <- read.dbf("GRSA_Alpine_SampleFrame") 

names(att) <- tolower(names(att))

head(att) # look at attributes that can be used to define unequal weight categories

####

DETAILS FOR DESIGN GRSA_uneqldsgn1, Sample size model: proportional allocation based on % of area in cost 

class biased by selecting areas that are easier to access 

####

GRSA_Alpine_uneqldsgn1_specs <-

list(none=list(panel=c(Base=40),

 seltype="Unequal",

 caty.n=c(

'Alpine Fell-Field Alliances-120min'=2,

'Alpine Fell-Field Alliances-150min'=3,

'Alpine Fell-Field Alliances-180min'=3,
85

'Alpine Fell-Field Alliances-210min'=2,

'Alpine Fell-Field Alliances-60min'=1,

'Alpine Fell-Field Alliances-90min'=2,

'Alpine Turf Alliances-0min'=1,

'Alpine Turf Alliances-120min'=5,

'Alpine Turf Alliances-150min'=4,

'Alpine Turf Alliances-180min'=3,

'Alpine Turf Alliances-210min'=3,

'Alpine Turf Alliances-30min'=3,

'Alpine Turf Alliances-60min'=2,

'Alpine Turf Alliances-90min'=4,

'Alpine Willow (Spruce) Shrubland Alliances-0min'=0,

'Alpine Willow (Spruce) Shrubland Alliances-120min'=0,

'Alpine Willow (Spruce) Shrubland Alliances-150min'=1,

'Alpine Willow (Spruce) Shrubland Alliances-180min'=0,

'Alpine Willow (Spruce) Shrubland Alliances-30min'=0,

'Alpine Willow (Spruce) Shrubland Alliances-60min'=1,

'Alpine Willow (Spruce) Shrubland Alliances-90min'=0),over=120))

set.seed(1234567)

GRSA_uneqldsgn1<- grts(

 design=GRSA_Alpine_uneqldsgn1_specs,

 DesignID='GRSAalpine',

 type.frame='area',

 src.frame='shapefile',

 in.shape='GRSA_Alpine_SampleFrame',

 att.frame=att,

 #stratum= 'vegcost',

 mdcaty= 'vegcost',

 #startlev=5,

 #maxlev=8,

 #maxtry=50,

 prjfilename='GRSA_Alpine_SampleFrame',

 out.shape='GRSA_Alpine_uneqldsgn1')

dsgnsum(GRSA_Alpine_uneqldsgn1) # summarizes design file

Timing of sampling (index period)

Index-period sampling focuses the time of sampling on the most ecologically relevant period(s) 

for a given response measure so the data collected will function as a useful barometer of a vital 

sign or of the condition of target populations within a given sampling interval (Landers et al. 

1998, Messer et al. 1991, Larsen et al. 1995). We define the index period for the AlpineVCSS 

protocol as the week(s) nearest to the peak of the growing season. Although it would be best to 

visit all sites on the same phenological date (e.g., 30 days into the growing season), this date 

changes each year and would be operationally impossible. Rather, we expect sampling to occur 

during the snow-free period in the alpine tundra (June-Aug). Annual crew schedules will be set 

and adjusted to accommodate the differential growing seasons at GRSA, ROMO, and GLAC and 

in the context of the targeted site list for that year, date of snow-melt and site elevation. 

Sample population

A sample population is the realization of a monitoring effort and may differ from the target 

population because of logistical and financial constraints and the realities of field work. We 

expect that within the AlpineVCSS protocol, the final sample population will be the vegetation 

and soil at survey sites within ecological site types that were successfully measured during the 
86

snow-free period in the alpine tundra. An example of a sample population might be, “accessible 

vegetation and soil without standing water during typical peak phenology in mid-July within 

alpine fellfield, alpine turf, and alpine shrublands at GRSA.” The sample population resulting 

from the survey design will be explicitly defined in all data analyses and interpretation. 

Figure 1. Sample frame and sites for survey designs in GRSA 
87

Literature Cited

Britten, M., E. W. Schweiger, B. Frakes, D. Manier, and D. Pillmore. 2007. Rocky Mountain 

Network vital signs monitoring plan. Natural Resource Report NPS/ROMN/ NRR-2007/010.

Frakes, B., C. Hurst, D. Pillmore, E. W. Schweiger, and C. Talbert. 2007. Travel time cost 

surface model. in. Rocky Mountain Inventory & Monitoring Network, Fort Collins.

Hansen, M. H., W. G. Madow, and B. J. Tepping. 1983. An evaluation of model-dependent and 

probability-sampling inferences in sample-surveys. Journal Of The American Statistical 

Association 78:776-793.

Kincaid, T., and T. Olsen. 2008. The spsurvey package. in R. D. C. Team, editor. R: A Language 

and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, 

Austria.

Landers, D. H., W. S. Overton, R. A. Linthurst, and D. F. Brakke. 1988. Eastern lake survey. 

Environmental Science & Technology 22:128-135.

Larsen, D. P., N. S. Urquhart, and D. L. Kugler. 1995. Regional-Scale Trend Monitoring Of 

Indicators Of Trophic Condition Of Lakes. Water Resources Bulletin 31:117-140.

Messer, J. J., R. A. Linthurst, and W. S. Overton. 1991. An EPA program for monitoring 

ecological status and trends. Environmental Monitoring and Assessment 17:67-78.

Schweiger, E. W., E. Gage, D. Shorrock, I. W. Ashton, J. Burke, D. Pillmore, and M. Britten. in 

preparation. Wetland ecological integrity. Rocky Mountain Inventory and Monitoring 

Network, National Park Service, Fort Collins, CO.

Stevens, D. L., and A. R. Olsen. 2003. Variance estimation for spatially balanced samples of 

environmental resources. Environmetrics 14:593-610.

Stevens, D. L., and A. R. Olsen. 2004. Spatially balanced sampling of natural resources. Journal 

Of The American Statistical Association 99:262-278.

89

SOP AlpineVCSS Site Suitability: Determining Target Status 

of Survey Site in the Field; optional

Rocky Mountain Inventory & Monitoring Network: 2010 version

Change History

Previous 

version

Date Author Change description Reason

Introduction

The location of our long-term monitoring sites is determined from a randomized design executed 

on the target sample frame as defined for each park. Because this selection is made in the office, 

prior to any field visitation, there may be circumstances that cause a design-selected location to 

be unsuitable for sampling. Upon arrival at a potential sampling site, field crews should use the 

following guidelines to determine suitability of the location for sampling.

Steps

Evaluate each site to determine that it meets accessibility and suitability requirements as follows:

Accessibility 

• No potential safety hazards (e.g., steep slopes >45o, crossing railroad tracks)

• No potential interference with sensitive resources (e.g., grave markers, artifacts, 

identified sensitive areas)

• No direct obstructions to plot locations and/ or associated sampling equipment at these 

locations (e.g., water body, river, road, railroad, sensitive resources)

Suitability

• Is the site representative? Do not sample if local features directly and significantly alter

site characteristics (e.g., monument and transect on a road or transect/ plots within or 

adjacent to a pen, work area, or other heavy management use area). 

• Is the site above treeline? Is the site within the target population—sites with a high cover 

of scree or bedrock and low cover of vascular plants (<25%) are not included. 

• Do not sample if the site is inundated by water frequently and continuously enough so 

that the vegetation community is becoming different from the surrounding upland 

vegetation communities (i.e., wetland species emerging or becoming dominant at the 

site).

• Do not sample if a paved road (in the interest of safety) or any heavily used dirt road or 

trail is within the 10 x 10 m macroplot. 

• When sites are deemed inaccessible and/ or unsuitable, refer to SOP VCSS Replacing

Sites rules to relocate the plot. 

• If these steps do not eliminate the interference with safety or the design, then reject the 

point as a sample site, record why the site was rejected on a Sample Site Description 

Form, and select the next replacement site on the list for the same ecological type you are 

replacing (i.e., from the list of “oversample sites”).

Resource protection overview 
90

National guidance for sensitivity restricts general public accessibility to geological and 

geophysical information and data concerning wells; the nature and specific location of (a) 

endangered, threatened, rare, or commercially valuable species, (b) minerals or paleontological 

objects, or (c) objects of cultural patrimony; the nature and location of any archaeological 

resource for which the excavation or removal requires a permit or other permission; or the 

specific location of any significant caves (FOIA, 2002).

Therefore, the protocol will:

• Contain measures for identifying and flagging sensitive information

• In cases where sensitive information is intentionally or unintentionally located in the 

field, provide a means of collecting data that does not jeopardize the sensitive resources. 

ROMN staff and cooperators will often be working in areas with sensitive cultural resources 

(these include, but are not limited to: bones, fossils, weapons, bullets, tools, human remains, etc.) 

and less frequently, sensitive natural resources (e.g., rare plant species or sensitive wildlife 

habitat). If avoiding these resources results in inability to properly sample the site (using above 

criteria), the site should be rejected and a new site selected (from the over-sample list for that 

ecological type). 

In cases where sensitive resources are near the edge of a site, or when simple movement of the 

plot will eliminate any potential effects on the resource, use the described method for relocating 

sites (see SOP AlpineVCSS Replacing Sites). 

Under no circumstances should you convey the location of sensitive objects to anyone besides 

official NPS staff. Under no circumstances should you manipulate, move, or otherwise alter 

sensitive cultural resources.

Natural

Crew members need to be aware of the locations and identification of rare and/or protected 

species because monitoring sites may intersect with a known population of species of concern. 

Additionally, crew members should be able to recognize rare species if they discover 

individual(s) at a site being sampled. In all cases, when details of the location of these species are 

known, that information should not be shared with the public. 

Wilderness management principles, including within de facto Wilderness, include important 

mandates guiding activities within large portions of GLAC and ROMO. Use of some 

mechanized equipment is restricted in these areas. ROMN monitoring protocols are designed to 

support compliance with Wilderness management regulations; in all cases, when working within 

Wilderness, motorized access is restricted and minimum necessary tools are mandatory. 

Cultural / Archeological

Sensitive cultural resources abound within ROMN, and I&M field staffs have a critical 

responsibility to protect these resources and avoid unnecessary impacts due to monitoring 

activities within the parks. Examples of such artifacts include peel-trees at GRSA, rocks on the 

sand-sheet at GRSA, historic ranching/ homesteading artifacts at GLAC, and ROMO, and other 

historic sites, including scientific sites at ROMO and GLAC.

Paleologic

Similarly, sensitive paleologic resources are widely distributed across ROMN parks, and I&M 

field staffs have a critical responsibility to protect these resources and avoid unnecessary impacts 

due to monitoring activities within the parks. 

Reporting: In cases where an important or very sensitive natural, cultural, or paleologic resource 

has been discovered, record the location with a waypoint in the GPS, take photographs, and take 
91

notes about the object(s) and location of the discovery. Report this information to the park unit 

staff.

References

Freedom of Information Act (FOIA). 1. 5 U.S.C. 552 (2000 & Supp. III 2003).

93

SOP AlpineVCSS Replacing Sites: Moving or Replacing 

Alpine Survey Sites; Optional

Rocky Mountain Inventory & Monitoring Network: 2010 version

Change History

Previous 

version

Date Author Change description Reason

Introduction

When a site is first visited, it must be evaluated for accessibility and suitability; criteria are 

provided in SOP AlpineVCSS Site Suitability. When there is an “obstruction,” use the following 

rules to relocate the plot. If these steps do not eliminate the interference with safety or the design, 

then reject the point as a sample site and record why the site was rejected, and select the next 

replacement site from the list of oversample sites. In order to maintain the designed sampling 

intensity in each season, and across the panel, when a site is rejected (or removed) it should be 

replaced with another site from the original sample design. 

Steps to Move Site

To maintain the integrity of the survey design, strict rules regarding the shifting of a survey site 

must be followed. A site may only be shifted by a distance equal to the estimated GPS error 

(average of 4m) plus the average positional accuracy in the base mapping methods (average of 

9m) used for the primary elements of the sample frame, plus (if needed) half of the longest axis 

of the field plot (7m). This value is approximately 20 meters.

(1) Move the center monument 10m along the 90° azimuth; if this does not eliminate the 

problem, move to 20m along the 90° azimuth line. 

(2) If moving in a 90° azimuth direction does not remove the interference, then move 10m from 

the original location along the 180° azimuth; if this does not eliminate the problem, move to 

20m along the 180° azimuth line.

(3) If moving in a 180° azimuth direction does not remove the interference, then move 10m from 

the original location along the 270° azimuth; if this does not eliminate the problem, move to 

20m along the 270° azimuth line.

(4) If moving in a 270° azimuth direction does not remove the interference, then move 10m from 

the original location along the 0° azimuth; if this does not eliminate the problem, move to 

20m along the 0° azimuth line.

(5) If none of these solutions work, then reject the point as a sample site and follow the steps 

outlined below. If one of the steps does work, record where the new center is located and use 

the GPS log to mark a waypoint at that location. 

Steps to Replace Site

(1) Complete a Site Description Form clearly noting site as “Rejected.”

(2) Locate the site list and map.

(3) Select the next site on the over-sample list and add it to the visit schedule for this panel. Be 

sure to select a site from the oversample list within the same subpopulation as the rejected 

site. 

95

SOP AlpineVCSS Plots: Plot Layout and Site Description for 

Alpine Survey Sites; Optional

Rocky Mountain Inventory & Monitoring Network: 2010 version

Change History

Previous 

version

Date Author Change description Reason

Introduction

The goal of the optional survey design is to determine the status of alpine vegetation and soils in 

GRSA, GLAC, and ROMO in a given year. This SOP describes the procedure for laying out 

plots in an alpine survey site. Prior to implementing the steps detailed here, the crew needs to 

determine whether the site center point is in the target vegetation type or needs to be replaced 

(SOP AlpineVCSS Site Suitability and SOP AlpineVCSS Replacing Sites). 

The overall approach involves delineating three plot types, laying out using a compass and 

several field tapes. The macroplot measures 10 m x 10 m (100 m2

) and contains a subplot (16 

m

2

) and four microplots (1 m2) (Figure 1). Basic layout involves setting up two field tapes 

orthogonally to one another and oriented along the cardinal directions. 

Figure 1. Diagram illustrating location and dimensions of nested plots laid out for alpine survey sites.
96

Steps

Equipment 

• Compass with declination properly set

• Field tapes (one 100 m tape and two 50 m in length)

• 1 m2 PVC plot frame

• Survey stake, pins or large diameter nails to temporarily fix tapes.

• GPS

• Survey flags

Procedures

Setting up a macroplot, microplot, and subplots 

1. Move to the center of the plot. Use GPS to mark this location and record location on GPS 

log. Place a survey stake in the ground at this location. 

2. Using compass, run the end of 100 m tape (tape #1) due north (0°) for exactly 14.14 m

from the center (Figure 2). Double check that tape is set along the correct azimuth. 

3. Temporarily pin end of tape using nail or survey stake; walk tape spool due south (180°) 

28.28 m and checking that tape is set along the correct azimuth. 

4. Using compass, run the end of 50 m tape (tape #2) due east (90°) for exactly 14.14 m. 

Check that tape is set along the correct azimuth. 

5. Temporarily pin end of tape using nail; walk tape spool due west (270°) a distance of 

28.28 m. Double check that tape is set along the correct azimuth. 

6. Connect corners of macroplot using remaining length of tapes #1 and #2.

7. Measure out 2.83 m from the center along each tape and temporarily mark with survey 

flags. These points correspond to the outer corners of the subplot and microplot corners.

8. Attach end of tape #3 to one corner and extend to connect all sides. 

9. Look at tapes to confirm that the dimensions are correct where the subplot is 4 m x 4 m 

and macroplot is 10 x 10 m.

Figure 2. Sequence of steps for plot layout

Site and Sampling Event Description 
97

After or during the plot set up, the crew should record details about the site location, date, time, 

weather, and topography on a site event datasheet. This will include the following fields: 

• Park 

• Site Code

• Date 

• Name of Recorder

• Name of Botantist

• Brief description of site 

• Travel directions and access time

• Name of the person that established the site 

• Location; UTMX, UTMY, UtmZone, and datum

• EstHError: Enter error from GPS unit (measure of horizontal accuracy of averaged 

points taken).

• Aspect: Enter aspect (azimuth) from center of site.

• Slope: Enter slope (percent) from center of site.

• GeneralEcoSite Type: Choose from fellfield, alpine turf, willow shrubland, or other.

• Notes: Enter any additional comments about this sampling location.

99

SOP AlpineVCSS Photographs: Photo Point Documentation for 

Alpine Survey Sites; Optional

Rocky Mountain Inventory & Monitoring Network: 2010 version

Change History

Previous 

version

Date Author Change description Reason

Introduction

This SOP describes the general procedure for taking and documenting digital photographs taken on site. 

Photographs have two primary purposes. First, they can be used to assist future crews in relocating plots 

particularly when they include notable landmarks such as rock outcrops or large trees. Second, photographs 

can also provide useful information regarding broad ecological processes such as tree or shrub invasion 

(Jakubos and Romme 1993).

The protocol involves collecting 8 photo points at fixed azimuths from the center of the plot module. 

Additional photographs may be taken at the discretion of crews. Examples include notable landscape 

features such as large trees or rock outcrops, which can be used as witness features. Photographs should also 

be taken of obvious anthropogenic or natural disturbances including any large patches of invasive species. 

Steps

Equipment list

• Digital camera 

• Small dry erase board and marker

• Scrap of cloth or tissue to use as eraser

• Compass, with declination properly set.

Procedures

For all photographs

• Before taking photos at a site, be sure to collect GPS waypoints of the center monument location; 

and record one close-up image (photo) of the GPS unit with date and time on the screen. This is 

done to enable GPS-photo linking. 

• Use a white-board placed within the foreground of the image to record park, site, and sample unit 

information within the image (see details below). Carefully align the board to minimize glare and 

reflection (e.g., angle slightly downward).

• Do not use the automatic date/time stamp feature on the camera. Date and time data are embedded in 

the exif data area by most digital cameras. If the image is being cataloged and documented, it has 

value – imprinting the image reduces the image quality and hence the image value. The exception 

may be for projects collecting massive numbers of photos – imprinted dates and time could facilitate 

data linking. 

• Set camera to the highest resolution and “uncompressed” (“tif” or “raw”) format; be sure the camera 

memory can still hold 30-40 images (allows working two sites between downloads). The resolution/ 

size trade-off can be manipulated if working in the backcountry and memory space is an issue. It is 

best to decrease the resolution for web use or thumbnails in the office using software, rather than in 
100

the field. Publication quality photos will be taken at a minimum of 4 megapixels. If the camera will 

allow, the resolution will be set at 1760 x 1168 or higher. The quality will be set for “super fine” or 

“high.”

• Unless specified otherwise, all photographs will be in landscape format (wider than tall).

• Ideally, no more than 1⁄4 (25%) of the photo should be sky, and no more than 1⁄4 (25%) of the photo 

should be obstructed (e.g., by nearby trees, rock outcrops, etc.); however, this may not be possible in 

all circumstances.

• Set the view (zoom) on the camera to the widest angle.

• Record the image number (i.e., camera code) along with the site and sample unit information in the 

photograph log. 

• Download photos after completion of each site, or as soon as possible when working away from the 

necessary equipment (e.g., in the backcountry). After file download is confirmed, delete the files 

from the camera.

• Any time the camera battery needs to be replaced, check to make sure the time and date are set 

correctly.

Site Overview Photographs

After the plots are laid out (SOP AlpineVCSS Plots) take photographs from the center towards any witness 

features, such as large rock outcrops or trees. 

When capturing the picture, hold the camera approximately 1.5 m above the ground surface (remember a 

maximum of 1⁄4 sky); record the site/date information on the white board, e.g., 

GRSA-00##

“Overview”

YYYY MM DD

A second person should hold the white-board in the near foreground, about 5 m away, and angled away 

from the sun.

Anthropogenic and Natural Disturbance Photographs

When applicable, take photographs of any specific stressors encountered during the initial reconnaissance of 

the site. This includes features seen within the plot and within 100 m of the plot. Include the white-board in 

any photos, as described above (substitute a note about the disturbance, e.g. elk bed, for “overview”).

Record photograph number and location in the photograph log.

Plot photographs

1. Prior to the start of data collection, but after tapes have already been laid out, take a sequence of 

photographs clockwise around the center of the plot module. 

2. Standing over the center, a photograph will be taken in 8 directions (45° angles) starting at 0° 

(Figure 1).

3. Stand at center of the plot and use a compass to determine N (0°).

4. Hold the camera approximately 1.5 m above the ground surface (remember a maximum of 1⁄4 sky).

5. Record the park, site, date, direction information on the white board.

6. A second person should hold the white-board in the near foreground, about 3 m away and angle the 

board away from the sun. 

7. Take a photo and only make duplicates if weather, light, exposure or similar conditions require a 

second attempt. Be sure to record the image number on the photograph log. 
101

.

Figure 1. Orientation of photo points taken at alpine survey sites.

Photo processing

1. Downloading the camera is similar to connecting any USB based peripheral. Connect the wires; turn 

the camera on; look for the camera using the Windows Explorer tool on the computer. 

2. Locate the photo files and copy all of them.

3. Create a destination folder (/AlpineVCSS200XX/Data/Photos/YYYYMMDD) where YYYMMDD 

represents the date of uploading.

4. Paste the files into the new folder. This folder and contents should be immediately set to read-only. 

5. Before removing the photographs from the camera, it is important to confirm that all photos have 

been copied and are uncorrupted. If there’s a problem, delete from the computer and re-copy. Once 

working files are confirmed, delete all photos from the camera.

References

Jakubos, B., and W. H. Romme. 1993. Invasion of subalpine meadows by lodgepole pine in Yellowstone 

National Park, Wyoming, U.S.A. Arctic and Alpine Research 25:382-390.

103

SOP AlpineVCSS Vegetation: Vegetation Sampling in Alpine Survey 

Sites; Optional

Rocky Mountain Inventory & Monitoring Network: 2010 version

Change History

Previous 

version

Date Author Change description Reason

Introduction

Monitoring plant species composition and cover is fundamental to understanding changes in alpine 

ecosystems. Cover measurements recognize the contribution of small species, are directly related to the 

biomass of most plant functional types, and “are easily visualized and intuitive” (Elzinga, Salzer and 

Willoughby 1998, p. 178). Moreover, cover measurements correlate with ecological processes (such as 

microbial composition and abundance, nutrient cycling and productivity). Finally, estimating cover remains 

one of the most common methods in vegetation studies allowing for comparisons between our work and

other national and local inventories. This SOP outlines the procedures to record canopy cover of all vascular 

species in microplots and subplots. Species presence is recorded in the larger 10 x 10 m macroplot and used 

for more robust estimates of site diversity. 

Steps

Equipment list

1. 1 m2 plot frame 

2. Clip board, pencil, and data forms

3. Compass (with declination set to appropriate value)

4. Local flora or identification guide(s)

5. 50 m field tapes

6. Plant press or notebook for unknown specimens 

7. Digital camera

8. Hand-held estimation tools

Procedures

Microplot cover

1. Place 1 m2 plot frame down in the microplot due N of the center (microplot #1). The frame should 

lie so that the survey flag is at the outer most corner of the plot (2.83 m from the center), and the 

meter tape from the center should run through the center of the plot leaving the opposite corner 1.83 

m from the center.

2. Examine the plot for the relative cover of vascular plants, solid rock, scree, lichens on soil (not 

covered by vascular plants), bryophytes on soil (not covered by vascular plants), bare ground, and 

litter. These categories correspond to the top cover surface types in the GLORIA Field Manual and 

should sum to 100%. 

3. Examine the plot and identify all vascular plant species. For each species, estimate the canopy cover 

class within the plot frame using the marks on the frame (10 x 10 cm sections = 1% of the area each) 

and hand-held estimation tools (circles and squares cut to 100cm2

) to assist you. 
104

Tools are used to assist in estimating cover because they are a known area; hold the tool over the plants in 

the plot to judge whether the cumulative cover of the species is half, one, or several (count) of these units. 

For small, well spaced species, create an index by approximating the number of individuals per 100cm2 

(1%) area; once you establish an index, always use the same index value for that species (i.e., at other plots 

and other sites). Divide the plot into half or quarters to allow for sub-estimates when the vegetation is dense, 

diverse or otherwise make the estimation complicated. We are estimating canopy cover; this means that you 

need to estimate the cover of an individual, or bunch, or indexed set of individuals, based on the 

approximate, irregular, but nearly regular, circumference of the leafy canopy cover. Do not include dead 

branches (e.g., of shrubs), but do include “dead centers” of bunchgrasses (e.g., Deschampsia spp.). Do not 

try to estimate “interlacing” of leaves with open space, but estimate the cover of the unit based on the 

perimeter. In most cases, the total cover within the plot will make up greater than 100% because it 

incorporates vertical structure. See Figure 1 for a sample of cover determination. 

Figure 1. Foliage polygons, indicated in black lines, for different types of plants (all shapes are intended to be leaves, not flowers). 

The 1m2 plot frame is indicated in gray, with red-gray dashes each being 10 cm in length. Cover of the species as represented 

here would be: A, 35%; B, 5%; C, 2%; D, 0.0%; and E, 7% (adapted from Symstad et al. 2006).

Use an alpha-numeric code for unknown species with a number and letter indicating growth form (e.g. S, G, 

F, T, V for Shrub, Graminoid, Forb, Tree, Vine) Number these unknowns continuously so that Unknown 

forb #1 is the same at every site in the park (if it occurs there). For example: Unk.G.1 = unknown graminoid 

#1; Unk.F.1=unknown forb #1.

For common, but unidentifiable species in the plots, assign and record an unknown code as above. Then fill 

in an “unknown species” form (Figure 2), and make detailed notes on species characteristics including stem 

and leaf morphology, perennial or annual (if known), etc. Survey the surrounding area for a well developed 

example – use the digital camera to take a close-up picture (or, series of photos if needed to show attributes, 

e.g., leaf and flower – be sure to record the photo identifier(s) in the dataform). Do not pick or collect any 

individuals, unless it is necessary for later identification, and we have permission of the park unit. Then, 

carefully collect a sample including the an entire individual down to enough root to show root tops (~2-4 

inches) and carefully place it in a prepared plant press. Instructions for pressing unknown plant specimens 

are found in Section 3.3. Specimens collected for identification must be destroyed once a determination has 

been made.

4. After recording cover data for species in microplots #1, work clockwise through microplots #2, #3, 

and #4 repeating steps 1-3.
105

Subplot cover

5. Repeat step 2-4 to collect surface types and vegetation cover in subplots (16 m2

) after completing 

cover in the microplots. The subplot includes all 4 microplots. 

Macroplot species presence

6. After completing vegetation cover estimates in the microplots (1 m2) and subplots (16 m2), examine 

the macroplot (100 m2). Document any species found in the macroplot that were not found in the 

smaller plots. Record only species presence, not cover. 

Guidelines for pressing unknown plant specimens

1. Pressing plants can be used to preserve specimens until identification is possible. Plants should be 

pressed as soon as possible after they have been collected. They are more easily arranged in the press 

if they are not wilted. Specimens should be carefully shaken to remove any dirt or debris that may be 

attached to roots, stems, leaves or flowers. Gently rinse muddy plants to remove mud and blot rinsed 

plants dry with paper or cloth toweling before pressing.

2. If a plant press is available, fill the plant press from the bottom first, working toward the top layers. 

A double piece of newsprint should be laid on the 1st blotting paper and cardboard layer with the 

fold of the paper at one edge of the plant press. The newsprint is then opened and the plant is 

carefully arranged on the bottom sheet. Place a field collection label alongside the plant. This label 

should contain important information about the specimen, such as date of collection, identification 

and description, location of collection site (GPS reading of latitude and longitude), digital camera 

image, etc. The top layer of newsprint is then folded over the top of the plant, and then it is covered 

with another blotter sheet, then another piece of cardboard. Note: if without a plant press, an 

alternative option would be to carefully place a specimen between the pages of a book or notebook. 

The press is filled, one layer at a time, then compressed using bolts or straps.

3. A good pressing job requires practice and patience. You must smooth and flatten leaves individually. 

Most leaves should be arranged with their top surface facing upward, but turn 1 or 2 leaves upside 

down to show their undersurfaces. If the plant is too brushy, trim away some of the foliage, but not 

so much that the general character and shape of the plant is betrayed. Roots are generally spread in 

the manner that they occur underground. If they are too thick, slice them lengthwise in order to thin 

them for pressing. Arrange flowers in various positions so that all their features can be shown. 

Seeds, cones, and fruits are generally air dried separately in a ventilated location out of direct light.

4. After filling with specimens, the press should be placed in a warm, ventilated location for several 

days to allow the plants to dry completely. This process can be enhanced in a warming oven or a 

chamber with a warming light bulb designed for that purpose. If plants are particularly moist, or 

many plants are being pressed at once, it is a good idea to open the press each day and replace the 

newsprint with dry sheets so that the specimens don’t mold. Check the plants each day and remove 

those that are completely dry. Thicker plants may take as long as two weeks to completely dry.
106

Figure 2. Sample of Unknown Species data form. 
107

SOP AlpineVCSS Willows: Willow Structure and Herbivore Impacts 

in Alpine Survey Sites; Optional

Rocky Mountain Inventory & Monitoring Network: 2010 version

Change History

Previous 

version

Date Author Change description Reason

Introduction

This SOP provides step-by-step instructions for assessing the potential effects of herbivory from elk on 

woody shrubs in the site, focusing on two metrics: the proportion of browsed shoots in selected plants and 

willow height. The objective of sampling is to provide a quick but quantitative approach to evaluating the 

effects of native herbivores on willows in the site. The SOP is meant to address the specific management 

issue of elk over abundance on the condition of willow communities, which in the ROMN network, is 

primarily a concern in Rocky Mountain National Park. 

Equipment

• Data form

• Pencils

• Clipboard

Procedures

Stand count data are collected along transects using the point-intercept method. Crews evaluate woody 

species height along two line intercept transects, one on the north east side of the plot and one on the south 

west side of the plot. All woody species are included in the stand counts. ‘Individuals’ are evaluated from 

south to north. For each ‘individual’ with any living or dead part crossing the tape (above ground), the 

species is recorded along with the starting and ending tape distance (in meters), the height class, and damage 

metrics.

For each woody species individual estimate the percent of stems (stems are defined as trunks or large 

branches originating from below ground) that are clearly dead. Do not quibble over marginal cases of death 

(“hmmm... I’m not sure if this whole stem is dead or not...” = not dead). Indications of a dead stem include 

dry and desiccated tissue, no living cambium under the bark, completely defoliated, broken apart, and full of 

fungus or other signs of rot, etc. The cause of stem death is not relevant. The proportion is taken over all 

unique (originating underground) stems - if there are approximately 200 stems (but do not count stems if 

more than a few dozen!) in a willow and only a few are dead the percent dead is very small! 

Methods for estimating percent crown dieback follow from the US Forest Service. Estimate the percentage 

of the live crown area, including the dieback area that has recent mortality of branches with fine twigs, 

which begins at the terminal portion of a branch and proceeds toward the trunk. Dieback should occur from 

the top of the crown down and from the outside in toward the main stem. Dieback is only considered when 

it occurs in the upper and outer portions of a tree. When whole branches are dead in the upper crown, 

without obvious signs of damage such as breaks or animal injury, assume that the branches died from the 

terminal portion of the branch. Dead branches in the lower portion of the live crown are assumed to 

have died from competition and shading and are not included. Dead branches in the lower live crown 
108

are not considered as part of crown dieback, unless there is continuous dieback from the upper and outer 

crown down to those branches. The crown base is defined as an imaginary horizontal line drawn across the 

trunk from the bottom of the lowest live foliage of the "obvious live crown" for trees and from the lowest 

live foliage of the lowest twig for saplings. The "obvious live crown" is described as the point on the tree 

where most live branches/twigs above that point are continuous and typical for a tree species (and/or tree 

size) on a particular site. The base of the live branch/twig bearing the lowest foliage may be above or below 

this line. Assume the perimeter of the crown is a two-dimensional outline from branch-tip to branch-tip, 

excluding snag branches and large holes or gaps in the crown.

For each woody species individual estimate the percent of apparently live stems originating from anywhere 

on the plant that have evidence of ungulate browse. Browse evidence includes stems stripped of all or most 

bark material, with a shredded appearance. Other indications are cleanly clipped stem tips, often with the 

clip at an angle. Evidence of browse is treated as a yes or no response – any evidence on a stem counts as a 

yes (the degree is not assessed). The proportion is taken over all living stems: if there are approximately 100 

stems (but do not count stems if more than a few dozen!) in a willow and 50 have at least some browse 

evidence the percent live browsed is 50%. 

Steps

1. Crew member navigates to the southern end of the NE plot edge;

2. A tape should already be extended along the plot edge. If not, place a tape with the 0 end on the 

plot corner;

3. The crew member begins walking north west along tape. At each instance where Salix, Alnus or

Populus species crosses the tape, the crew member records the species, the starting distance and 

ending distance (in meters) and the height class as follows (do not include low growing creeping 

forms of willows such as Salix arctica). If this same plant crosses the tape in multiple locations, 

a single height class is recorded. Use the first and final locations for a plant. Height is for the 

whole of the individual, not the limited portion of the planet crossing the tape. Start/stop 

distances will overlap if the plants along the tape do.

4. Estimate the percent dead, percent crown dieback and percent live browsed for each individual 

woody plant encountered on the line intercept. If there is an overwhelming number of individuals 

along a transect and there is a consistent level of percent dead/crown dieback/live browsed 

among stems it is permissible to assign general classes to bulk number of individuals. For 

example, record “20 height class 1 Salix planifolia with 10% browse, 15% die back and 5% 

dead. Percent of dead stems refers to the number of stems originating from below ground that 

were completely dead.

5. Repeat for the SW edge of the plot;

Table 1. Height class and corresponding height in meters for willows in alpine survey sites

Willow height 

class

Height (m)

1 <0.5

2 0.5- 1

3 1- 2

4 2- 3

5 > 3
109

SOP AlpineVCSS Survey Data Analysis: Design Based Analyses; 

Optional

Rocky Mountain Inventory & Monitoring Network: 2010 version

Change History

Previous 

version

Date Author Change description Reason

Introduction

Design-based inference is fundamentally different than model-based approaches because design-based 

inference does not depend on an assumed model of the relationship between samples and the true 

population. In a design-based approach, expectations are extended over all possible samples based on the 

weighted values (i.e., measures, averages, etc.) contributed from each sampled site; independence is insured 

by the design process. Calculation of means are modified from simple mean calculations by weighting 

(multiplier) each site mean using the inverse of the inclusion probability (from the original design) as the 

weight; thus, the park-wide average is extrapolated from individual samples based on the area represented 

by each site in the original design.

Steps

1. Assure that data you are using were previously quality controlled and that you have parameter values 

for the appropriate level of analysis (Table 1);

2. Verify that summation and averaging steps were conducted properly; always use site level means for 

analyses (unless a specific application requires otherwise);

3. Retrieve inclusion probabilities for each point from original design files;

4. Calculate site weights as 1 minus the inclusion probability (1 – Prob);

5. Calculate a weighted mean by summing the product of each site weight and the value of interest 

(e.g., cover) then divide by the product of the sum of the weights and the number of sites. This is 

represented by the equation:

WMi = Σ (wt)(Xbar i) / Ni (8)

Where: 

WMi = weighted Mean for Indicator ‘i’

wt = 1 – Prob. of Inclusion (unique for each site; normalized so sum of weights = 1); data from 

original design

Xbar i = site level mean estimate (e.g., percent cover, frequency, etc.);

Ni = the number of sites;
Table 1. Parameters, units, and QA/QC for optional AlpineVCSS survey design

Parameter Raw/Derived Units QA/QC Unit of analysis

Cover of scree, rock, vascular plants, 

lichens, bryophytes, bare ground, litter

Raw % the sum of all 7 categories should add up 

to 100%

microplot and 

subplot

Species cover (vascular plants) Raw % value for each vascular species present; 

not less than 0; includes layers so can be 

greater than 100

microplot and 

subplot

Species number Derived species count number of vascular species present 

in summit area

macroplot

Diversity Index Derived shannon-weiner index, H' H'= Sum (relative abundance of each 

species/ ln(relative abundance); upper 

limit ~4

macroplot

Eveness Derived Peilou index, J J'=(diversity)/log(number of species); 

between 0 and 1

macroplot

Mean Coefficient of Conservation Derived index C between 1-10; the mean of the 

coefficient of conservation assigned to 

each species present 

macroplot

Florisitic Quality Index Derived FQA FQA=C * square root(number of species) macroplot

dieback raw % value for each species present for no 

more than 25 individuals per species

site

live stems browsed raw % value for each species present for no 

more than 25 individuals per species

site

dead stems raw % value for each species present for no 

more than 25 individuals per species

site

willow height class raw ordinal class 1-5 based on height site

willow seedlings raw willows microplot

willow saplings raw willows subplot

mature willows raw willows value for each species present site

cover of mature willows raw % site
111

Number of exotic species present Derived species count number of species present in peak 

area

macroplot

Identity of exotic species present Raw categorical species list macroplot

Percent cover of exotic species Raw % not less than 0; includes layers so can be 

greater than 100

microplot and 

subplot

sand,silt and clay raw % macroplot

texture derived NA classification based on % sand, silt, and 

clay

macroplot

bulk density raw g/m3 macroplot

pH raw pH macroplot

cation exhange capacity raw cmolc/Kg soil macroplot

micro and macronutrients raw % macroplot

organic matter raw % macroplot

C:N derived ratio %C:%N macroplot

surface soil aggregate stability raw categorical class 1-6 based on dip cycles; see above macroplot

subsurface soil aggregate stability raw categorical class 1-6 based on dip cycles; see above macroplot

distance to road derived m derived from GIS site

human disturbance index derived % derived from anthropogenic disturbances 

calculated; range from 0 -100 (highly 

disturbed)

site

slope raw degrees site

elevation raw m site

beaver disturbance raw ordinal class 1-4 based on high to no 

disturbance; 1: high >50% of site has 

evidence, 2: medium >10% or old 

evidence, 3: low no sign on site but some 

within 1km; 4: no signs in site or within 

1km 

site

native ungulate disturbance raw ordinal class 1-4 based on high to no 

disturbance; see above

site
112

frost heave disturbance raw ordinal class 1-4 based on high to no 

disturbance; see above

site

rodent disturbance raw ordinal class 1-4 based on high to no 

disturbance; see above

site

pest & pathogens disturbance raw ordinal class 1-4 based on high to no 

disturbance; see above

site

landslide disturbance raw ordinal class 1-4 based on high to no 

disturbance; see above

site

fire disturbance raw ordinal class 1-4 based on high to no 

disturbance; see above

site

natural disturbance derived index; NDI derived from weighting natural 

disturbance categories; range from 0-100 

(highly disturbed) 

site
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Island Communities.

NPS 960/106252, December 2010
114

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