National Park Service 

U.S. Department of the Interior 

Natural Resource Program Center 

Protocol for Ground-based Monitoring of Vegetation in 

the Southwest Alaska Network 

Natural Resource Report NPS/SWAN/NRR—2010/205 
ON THE COVER 

Mid-elevation low shrub (Betula glandulosa) tundra, Lake Clark National Park & Preserve. 

Photograph by: Amy Miller 
Protocol for Ground-based Monitoring of Vegetation 

in the Southwest Alaska Network 

Natural Resource Report NPS/SWAN/NRR—2010/205 

Amy E. Miller, William L. Thompson, Dorothy C. Mortenson, and Claudette Moore 

National Park Service 

Southwest Alaska Network 

240 West 5th Avenue 

Anchorage, Alaska 99501 

June 2010 

U.S. Department of the Interior 

National Park Service 

Natural Resource Program Center 

Fort Collins, Colorado
ii 

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This report is available from the Southwest Alaska Inventory & Monitoring Network website 

(http://science.nature.nps.gov/im/units/swan/index.cfm?theme=reports_pub) and the Natural 

Resource Publications Management website (http://www.nature.nps.gov/publications/NRPM). 

Please cite this publication as: 

Miller, A. E., W. L. Thompson, D. C. Mortenson, and C. Moore. 2010. Protocol for groundbased monitoring of vegetation in the Southwest Alaska Network. Natural Resource Report 

NPS/SWAN/NRR—2010/205. National Park Service, Fort Collins, Colorado. 

NPS 95/100370, June 2010
iii 

Contents 

Figures............................................................................................................................................. v 

Tables............................................................................................................................................. vi 

Appendices.................................................................................................................................... vii 

Acknowledgements......................................................................................................................viii 

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

1.1 Introduction........................................................................................................................... 1 

1.2 Rationale for Monitoring Vegetation in SWAN parks ......................................................... 2 

1.2.1. Management Concerns..................................................................................................... 2 

1.2.2. Rationale for Ground-based Monitoring.......................................................................... 3 

1.3 Measurable Objectives.......................................................................................................... 4 

2 Sampling Design.......................................................................................................................... 5 

2.1 Rationale for Selecting this Sampling Design over Others .................................................. 5 

2.1.1. Accessibility ...................................................................................................................... 7 

2.1.2. Estimated Sample Size Required to Detect Change across all Vegetation Classes ......... 8 

2.1.3. Constraining the Sample to Selected Vegetation Classes................................................. 9 

2.2 Site Selection ...................................................................................................................... 13 

2.3 Recommended Sample Size and Sampling Frequency....................................................... 13 

2.4 Targeted Sampling in Sensitive Communities.................................................................... 18 

2.5 Change Detection................................................................................................................ 19 

3 Methods...................................................................................................................................... 19 

3.1 Preparation for field season ................................................................................................ 20 

3.1.1. Recruiting........................................................................................................................ 20 

3.1.2. Logisitics, Data Uploads, and Preparation of Working Database................................. 21 

3.1.3. Training........................................................................................................................... 21 

3.2 Field methods...................................................................................................................... 21 

3.2.1. Plot Layout...................................................................................................................... 21 

3.2.2. Plot Descriptive Data ..................................................................................................... 22 

3.2.3. Plot Photos...................................................................................................................... 22 

3.2.4. Species Composition ....................................................................................................... 22 

3.2.5. Tree Measurements......................................................................................................... 22 

3.2.6. Coarse Woody Debris, Litter and Duff........................................................................... 24 

3.3 End-of-season procedures................................................................................................... 25 

3.3.1. Processing of Collections ............................................................................................... 25 

3.3.2. Equipment Inventory....................................................................................................... 25 

4 Data Processing, Analysis and Reporting.................................................................................. 25 

4.1 Project Information Management Overview ...................................................................... 25 

4.2 Database Design and Data Management ............................................................................ 25 

4.2.1. Data Backups.................................................................................................................. 27 

4.2.2. Data Verification ............................................................................................................ 27 

4.2.3. Geospatial Data.............................................................................................................. 27 

4.2.4. Metadata ......................................................................................................................... 27 

4.3 Data Reduction and Analysis.............................................................................................. 27 

4.3.1. Reporting and Product Development.............................................................................. 28 

4.3.2. Records Management...................................................................................................... 28 
iv 

5 Operational Requirements ......................................................................................................... 28 

5.1 Personnel Requirements and Qualifications ....................................................................... 28 

5.2 Annual Workload and Field Schedule ................................................................................ 28 

5.3 Facility and Equipment Needs............................................................................................ 28 

5.4 Startup Costs and Budget Considerations........................................................................... 29 

6 Literature Cited .......................................................................................................................... 30 
v 

Figures 

Figure 1. Parks of the Southwest Alaska Network ........................................................................ 1 

Figure 2. Proposed plot locations in LACL................................................................................. 15 

Figure 3. Proposed plot locations in KATM................................................................................ 16 

Figure 4. Proposed plot locations in KEFJ .................................................................................. 17 

Figure 5. SWAN plot layout ........................................................................................................ 23 

Figure 6. Plot monument stamped with the plot id number......................................................... 24 

Figure 7. Layout of nested quadrats............................................................................................. 24 

Figure 8. Summary of CAKN database structure as model for SWAN. ..................................... 26 
vi 

Tables 

Table 1. Hierarchy of vegetation monitoring projects in the SWAN............................................ 3 

Table 2. Cover changes, cover types of interest, and separability of cover types......................... 4 

Table 3. Characteristic vegetation classes targeted for monitoring in the SWAN....................... 10 

Table 4. Percent cover of landcover classes in the three largest parks of the SWAN .........Error! 

Bookmark not defined.

Table 5. Interannual variability and rates of change in species attributes reported for high 

elevation and high latitude ecosystems......................................................................................... 14 

Table 6. Panel design for monitoring vegetation in the SWAN.................................................. 18 

Table 7. Simulation results: ability to detect different levels of total decline for a true average 

total decline of 50% over 31 years................................................................................................ 18 

Table 8. Annual work cycle for ground-based vegetation monitoring ....................................... 20 

Table 9. Staffing needs for SWAN ground-based vegetation monitoring .................................. 29 
vii 

Appendices 

Appendix A: Standard Operating Procedures............................................................................. 37

Appendix B: Field Data Forms................................................................................................. 126

Appendix C: Using S-DRAW to Generate GRTS Samples..................................................... 150

Appendix D: Simulation results for evaluating minimum number of points and grids of points 

required to detect change in two landcover classes, Lake Clark National Park & Preserve ...... 172

Appendix E: Simulation results for evaluating minimum sample size and sample frequency 

required for detecting a specified total change in vegetation plots within SWAN parks ........... 179

Appendix F: SAS Code for Summary Statistics....................................................................... 196

Appendix G: Using BugsXLA and WinBUGS to Estimate Trends from Vegetation Plot Data

..................................................................................................................................................... 210

Appendix H: Guidelines for Voucher Collections ................................................................... 231

Appendix I: Plant Association Descriptions............................................................................. 234

Appendix J: Proposed Database Structure.............................................................242 
viii 

Acknowledgements 

We thank Andrea Woodward, U.S. Geological Survey, and Carl Roland, National Park Service, 

for developing and sharing the sampling designs that are the foundation for this protocol: 

Woodward, A.E., Hutten, K.M., Boetsch, J.R., Acker, S.A., Rochefort, R.M., Bivin, M.M., and 

L.L. Kurth. 2009. Forest vegetation monitoring protocol for national parks in the North Coast 

and Cascades Network: U.S. Geological Survey Techniques and Methods 2-A8, 228 pp. 

(http://pubs.usgs.gov/tm/tm2a8/) 

Roland C, Oakley K, Debevec EM, Loomis P. 2004. Monitoring vegetation structure and 

composition at multiple spatial scales in the Central Alaska Network. U.S. Dept. Interior, 

National Park Service, Fairbanks, AK. (http://science.nature.nps.gov/im/monitor/protocols/

CAKN_Vegetation.zip) 

Additional guidance came from Amy Symstad, U.S. Geological Survey; Page Spencer, National 

Park Service; Teresa Hollingsworth, U.S. Forest Service; Bill Bowman, University of Colorado; 

Torre Jorgenson, ABR, Inc.; and Matthew Carlson, Alaska Natural Heritage Program. All field 

work in the pilot phases of this project was performed by the authors. 
1

1 Background and Objectives 

1.1 Introduction 

This protocol outlines methods for ground-based monitoring of vegetation in the Southwest 

Alaska Network (SWAN) following a sampling design developed by the North Coast and 

Cascades Network (NCCN; Woodward et al. 2009). Vital signs addressed by this protocol are 

vegetation composition and structure and sensitive plant communities. Standard Operating 

Procedures (Appendix A) and field data forms (Appendix B) outline sampling procedures.

Vegetation is integral to ecosystem function, energy transfer, and element cycling, and serves as 

a sensitive indicator of environmental conditions. Coastal Aleutian, low Arctic, interior-boreal, 

and Pacific coastal floras converge in southwest Alaska, resulting in a floristically rich area that 

supports approximately 60% of the state’s vascular taxa (University of Alaska Fairbanks 

Museum Herbarium, ARCTOS, 2004). In the Southwest Alaska Network (SWAN; Fig. 1), the 

structure and composition of vegetation are expected to respond to increasing variability in 

climate and the occurrence of pathogens and pests; increasing occurrence of exotic species; and 

increasing pollutant loads. 

Figure 1. Parks of the Southwest Alaska Network (from Bennett et al. 2006). Park area is expressed in millions of 

hectares (M ha): Aniakchak National Monument & Preserve (ANIA; 0.2 M ha); Lake Clark National Park & Preserve 

(LACL; 1.6 M ha); Katmai National Park & Preserve (KATM; 1.7 M ha) and Alagnak Wild River (ALAG; 0.01 M ha); 

and Kenai Fjords National Park (KEFJ; 0.3 M ha). Ground-based monitoring is planned for the three largest parks 

(LACL, KATM, KEFJ), which total more than 3.6 million hectares. 
2

Shifts in the structure and composition of sensitive plant communities, in particular, are expected 

to serve as early indicators of ecosystem change, as these communities often occur at the edge of 

their environmental tolerance (Spicer and Chapman 1990, Lesica and McCune 2004, Epstein et 

al. 2004). 

1.2 Rationale for Monitoring Vegetation in SWAN parks 

 

1.2.1. Management Concerns 

Enabling legislation for the SWAN parks (ANILCA 1980; §101(b) and §201(7)) establishes as 

broad purposes the need ... to preserve in their natural state extensive unaltered arctic tundra, 

boreal forest, alpine meadows, and coastal rain forest ecosystems. These ecosystems provide 

forage and habitat for Dall sheep, caribou, and brown bears; subsistence resources to humans; 

and scenic qualities that enrich visitor experience. All are protected under ANILCA (§201(1), 

§201(7), and §202(2)). 

These focal ecosystems that are valuable to wildlife and humans – low arctic tundra, boreal 

forest, alpine, and coastal forest – are expected to be sensitive to a number of drivers, including 

climate (e.g., temperature; Spicer and Chapman 1990, Epstein et al. 2004), pollutant loads, and 

physical disturbance (e.g., Auerbach et al. 1997). Increased drought stress (Barber et al. 2000) 

and insect disturbance (Logan et al. 2003); increasing fuel loads and changes in fire frequency 

and severity (Flannigan and Van Wagner 1991; Stocks et al. 1998); and increased occurrence of 

exotic species (Dukes and Mooney 1999) are potential management concerns associated with 

regional warming. In the SWAN, regional-scale spruce bark beetle (Dendroctonus rufipennis) 

and localized noctuid moth (Sunira verberata) outbreaks have altered stand structure in recent 

years, and the resultant dieback will likely result in short- or long-term changes in species 

composition. Fuel loads are expected to increase with increasing forest mortality, likewise 

increasing fire risk in some areas. 

Increasing temperatures may also result in changes in species composition across ecosystem 

types. Warming experiments in the arctic have shown an increase in shrub cover (Walker et al. 

2006), consistent with the rise of alder and willow in the paleoecological record (Hu et al. 2002, 

Brubaker et al. 2001) and the broad-scale expansion of shrubs in recent decades (Suarez et al. 

1999, Sturm et al. 2001, Silapaswan et al. 2001, Klein et al. 2005). Experimental warming has 

also resulted in a loss of lichen cover (Cornelissen et al. 2001), concomitant with an increase in 

herbaceous species and shrubs (Klein et al. 1987). Lichens are a major component of the winter 

diet for caribou and an important source of nutrients during the calving season (Klein 1982, 

Barten et al. 2001), and thus reductions in lichen cover associated with other ecosystem changes 

could have measureable effects on caribou populations over the long-term. 

Additional effects of warming may include broader shifts in species’ ranges, including the 

southward migration of Sitka spruce (Capps 1937), the regional expansion of alder in southwest 

Alaska (Heusser 1983, Nelson 2004), and the displacement of obligate alpine species to higher 

elevations by more competitive mid-elevation species (Lesica and McCune 2004). Paradoxically, 

increased warming could also lead to greater frost damage in some species, which in turn could 

affect ecosystem productivity at decadal scales or longer (Gu et al. 2008; Bokhorst et al. 2008). 
3

Comprehensive reviews of the potential effects of climate, pollutants, fire and other disturbances 

on vegetation are provided by Roland et al. (2004) and Woodward et al. (2009). 

1.2.2. Rationale for Ground-based Monitoring 

Vegetation composition and structure can be described at a range of spatial and temporal scales 

that meet different monitoring objectives (Table 1). The wall-to-wall coverage and long time 

scales that characterize remotely-sensed imagery make it an appropriate tool for park-wide 

inferences of vegetation change. Remote sensing data are valuable in detecting landscape-scale 

changes in dominant vegetation types, such as large-scale shrub expansion (Sturm et al. 2001, 

Silapaswan et al. 2001) and changes in productivity (Myneni et al. 1997, Verbyla 2008 ) and 

surface hydrology (Klein et al. 2005, Riordan et al. 2006). In the SWAN, Landsat TM/ETM+ 

(Kennedy et al. 2006, 2009), MODIS (Reed et al. 2009), orthorectified aerial photos, and highresolution IKONOS data (e.g., Jorgenson 2007) are being used to document park-wide changes 

in the distribution and abundance of major cover types (e.g., ice/snow, rock, coniferous forest, 

tall shrubs, surface water); landscape-level disturbances or transitions (e.g., forest mortality, 

channel migration, pond drying or infilling); changes in altitudinal and latitudinal tree- and 

shrub-line; and interannual variation in productivity and growing season length. 

Table 1. Hierarchy of vegetation monitoring projects in the SWAN. 

Protocol Spatial Extent & Scale Parks Frequency Monitoring Objectives 

Remote 

sensing 

 Wall-to-wall coverage 

 Coarse to moderate 

scale 

 Platforms: Landsat, 

IKONOS, air photos 

 ALAG 

 ANIA 

 KATM 

 KEFJ 

 LACL 

Decadal to 

Multidecadal 

 Describe distribution of dominant 

vegetation 

 Document landscape-level disturbance 

 Surface hydrology and glacier extent 

 Encroachment of woody vegetation 

 Phenology 

Ground-based 

monitoring 

 Accessible areas 

 Probabilistic design 

 Fine scale 

 KATM 

 KEFJ 

 LACL 

Decadal  Describe representative vegetation 

classes 

 Document changes in structure, 

composition of canopy and understory 

 Measure environmental variables 

Ground-based 

monitoring 

 Targeted environments 

 Fine scale 

 KATM 

 KEFJ 

 LACL 

Decadal  Describe sensitive vegetation types 

 Document changes in structure, 

composition of canopy and understory 

Remote sensing techniques provide a powerful tool for detecting change on the landscape but 

have several limitations, particularly at finer spatial scales. First, subtle spectral changes, many 

of which are of interest to the SWAN (e.g., low shrub encroachment into tundra) are difficult to 

detect and must be assigned a low level of confidence (Kennedy et al. 2006, 2009; Table 2). 

Second, the change of interest must be of a large enough geographic extent to cover several 

pixels (e.g., ≥0.25 ha for Landsat TM/ETM+; ≥25 m2 for IKONOS), whereas many changes of 

interest in the SWAN are finer-scale and/or discontinuous in extent (e.g., changes in lichen 

cover). Because changes detected by remote sensing must be relatively extensive, longer time 

frames (e.g., decades to centuries) are also required to detect change. Lastly, frequent cloud 

cover and a short growing season limit the number of usable images available to the SWAN. 
4

Ground-based vegetation monitoring is intended to target changes in vegetation structure, species 

composition, and selected environmental variables that are too subtle spectrally or occur at too 

fine a scale to monitor through remote sensing techniques alone. Estimates of species richness 

and diversity, for example, or species turnover, can be acquired only through ground-based 

measurements at the community scale. Effects of overstory change (e.g., shrub encroachment; 

treeline migration) on understory composition (e.g., lichen cover) are likewise detected only 

through ground-based measurements. 

Table 2. Cover changes, cover types of interest, and separability of cover types (Kennedy et al. 2006).

Cover change Beginning type Ending type Spectral separability 

(good, marginal, poor) 

Conifer trees Soil, herbaceous Good 

Soil Good 

Avalanche/landslide 

Broadleaf trees/shrubs 

Herbaceous Marginal 

Dead trees at approximately 

> 75% mortality* 

Good 

Dead trees at 25%-75% 

mortality* 

Marginal 

Conifer trees 

Dead trees at < 25% 

mortality* 

Poor 

Defoliation at unknown 

(high) rate, captured in year 

of mortality 

Good 

Insect/disease-related 

mortality/defoliation 

Alder shrubs/tree 

Defoliation at unknown 

(high) rate, captured in years 

after mortality 

Likely poor 

All vegetation Volcanic ash Likely good 

Volcanic ash Herbaceous vegetation Marginal/poor 

Volcanic eruptions/ash 

deposits 

Volcanic ash Alder/other shrub Good 

Change in snow/ice Glacier Exposed rock Good 

cover or extent 

Glacier Alder/other shrub Good 

Encroachment of 

vegetation into glacial 

forefront 

Exposed rock/soil Alder/other shrub Good 

Exposed rock/soil Alder/other 

shrub/herbaceous 

Marginal/good (SE aspects) 

Poor (NW aspects) 

Encroachment of 

shrubs into subalpine 

or alpine Herbaceous Alder/other shrub Marginal (SE aspects) 

Poor (NW aspects) 

Overtopping of lichen Lichen Low shrubs Marginal/poor 

by shrub Lichen Alder/tall shrub Marginal/good 

Infilling of ponds with 

vegetation 

Water Herbaceous vegetation Good 

Water Soil/rock Good 

Vegetation Good 

Water Good 

Changes in river 

course 

Vegetation 

Soil/rock Good 

Change in tidal wetland Vegetation Soil/mud Good 

conditions Vegetation (flooded) Vegetation (dry) Poor 

*Mortality percentages are based on ocular estimates of areal cover, and are approximate. 

1.3 Measurable Objectives 

Ground-based monitoring objectives for the SWAN are to document trends in the structure, 

composition, and demography of selected vegetation classes. We are interested in the direct 
5

response of vegetation to environmental change and thus focus on late-successional communities 

that have not experienced recent stand-level disturbance, and in changes that are not readily 

documented through other means (e.g., remote sensing techniques). 

The specific measurable objective is to estimate long-term trends in community composition and 

structure (e.g., percent cover of dominant species; percent cover by growth form and height 

class; frequency of selected dominant, subdominant, and indicator species) of late-successional 

communities from a random sample of three elevation bands (0-450 m; >450-900 m; >900 m) in 

the three largest parks (KATM, KEFJ, and LACL).

Structure and composition:

For vascular and common non-vascular species in selected vegetation classes and sensitive 

communities: 

1. Document changes in species occurrence. 

2. Document changes in species frequency. 

3. Document changes in species cover. 

4. Document changes in species height. 

5. Document changes in species composition (richness and diversity indices). 

Additionally, for trees ≥12.0 cm DBH (cf. Roland et al. 2004): 

6. Document changes in indicators of forest health; e.g., 

a. Number of live and dead trees 

b. Number of insect-damaged trees 

c. Number and decay class of snags 

d. Estimated volume of large woody debris 

e. Mean canopy height 

Processes: 

1. Document changes in woody species establishment. 

2. Document changes in seedling and sapling densities, and stand biomass, if applicable. 

3. Document changes in the rate and cause of tree mortality, if applicable. 

4. Document changes in active layer depth, if applicable. 

5. Document changes in the growth rate of trees at selected sites. 

2 Sampling Design 

2.1 Rationale for Selecting this Sampling Design over Others 

The SWAN uses a sampling frame developed by the NCCN (Woodward et al. 2009) that draws a 

Generalized Random-Tesselation Stratified (GRTS, Stevens 1997; Stevens and Olsen 2004) 

sample from fixed elevation bands. The design uses a two-stage sampling scheme, wherein the 

stage 1 GRTS sample is evaluated in a GIS for safety and accessibility, and the stage 2 sample 
6

that results is evaluated by field reconnaissance for plant communities of interest. The goal of 

this design is to define the sampled population (vegetation) based on accessibility and fixed 

features of the landscape; to choose a GRTS sample from all accessible plots within these fixed 

features; and to then establish permanent plots in a selected number of vegetation classes in each 

sampled population. Assumptions that underlie the NCCN sampling frame are as follows 

(Woodward et al. 2009): 

 Monitoring will focus on changes in community structure and composition resulting from 

long-term environmental change, rather than succession following stand-level 

disturbance. Mid- to late-successional communities will be targeted for monitoring. 

 Monitoring must distinguish between interannual variability (e.g., in herbaceous cover) 

and long-term trends in species composition. 

 Monitoring will be based on an inferential statistical design, with sampled populations 

derived from static characteristics (e.g., elevation) and adequate replication of defined 

community types to detect changes in the abundance of individual species and growth 

forms. 

Additionally, logistical and cost considerations require the following: 

 Accessibility of plots is critical for crew safety and for timely collection of data. 

 Sample size must be limited, yet replication must be adequate to detect change. 

We use three 450 m-wide elevation bands (0-450 m; >450-900 m; >900 m) that are essentially 

treated as separate sampling frames within each park. The GRTS sample that is selected is 

confined to an access layer (GIS) that takes into account topography and barriers to travel, and 

that includes criteria for safe access to sites (Mortenson and Miller 2008; Woodward et al. 2009). 

Within the GRTS sample, we document vegetation at all sites but intensively sample specific 

vegetation communities within each elevation band (cf. Woodward et al. 2009). A GRTS 

sample has the advantage of spatial dispersion, but without a repeating interval. Unlike a 

systematic sample, a GRTS sample has an excellent variance estimator, enabling the investigator 

to exclude sites when they are inaccessible or are not within the population of interest. Likewise, 

for plots that experience a major disturbance, we can substitute new plots from the sample frame 

(GRTS sample) to replace those that are no longer within the population of interest. We deviated 

from the NCCN sampling design in the following ways: 1) we used 450 m elevation bands 

rather than 300 m bands; 2) we placed limits on the time that we would spend accessing a site 

from our base camp (2 hours travel, one-way); and 3) we spent the first several weeks of the field 

season conducting ground and overflight reconnaissance of sites, such that we could more 

efficiently plan our subsequent travel and could target sites clustered at pre-established access 

points. In this way, we were able to establish some GRTS sites out of sequence (and thus 

minimize travel costs) because we knew up front, from the reconnaissance, which sites we would 

be able to accept as part of the GRTS sample. We generally sample 2-6 plots from a given 

access point (base camp). In addition, we 4) modified the NCCN plot layout and sampling 

methods to reduce the time spent at a site (we complete a plot in 4-8 hours; Section 3.2.1.), and 

5) adopted a form developed by the Northern Great Plains Network (NGPN) to document the site 

selection process (A. Symstad, personal communication; Section 2.2.; Appendix 7). We opted to 

sample fewer plots intensively (i.e., one plot per day rather than several), because we felt that the 
7

travel time (on foot) required to ensure that plots are well-dispersed should not exceed the time 

spent collecting data. 

An alternative sampling frame using a 2-stage systematic grid design has been developed by the 

CAKN (Roland et al. 2004). The advantages of the 2-stage grid design are that it retains 

information about spatial relationships at a range of scales and therefore enables park-wide 

inferences regarding vegetation change. It also samples across all communities and, as such, is 

expected to capture unanticipated change (Roland et al. 2004). The 2-stage grid approach 

assumes that communities occur at a high enough frequency on the landscape to be adequately 

sampled. Clustering of sample points into mini-grids concentrates the landscape-scale sampling 

efforts within confined study areas, with the intent of reducing sampling cost. The U.S. Forest 

Service Forest Inventory and Analysis (FIA) program also uses a grid design, but FIA methods 

do not meet SWAN monitoring objectives for understory composition (Woodward et al. 2009; 

Sanders et al. 2008), nor are data available for parks other than KEFJ (n = 4). 

We chose an access-based GRTS design over the systematic grid design, as model simulations 

based on the LACL landcover dataset (Section 2.1.2.) indicated that the advantages of a twostage grid approach (park-wide inference) would be outweighed by the high costs associated 

with sampling. In other words, the large sample sizes required for change detection in any one 

vegetation class make park-wide inferences of vegetation change from ground-based sampling 

logistically and fiscally unrealistic in the SWAN. Instead, we are using remote sensing 

techniques to infer landscape-level changes, where appropriate (Section 1.2.2). 

2.1.1. Accessibility 

Ground-based monitoring in the SWAN is challenging because the constituent parks are large, 

remote, and difficult to access except via fixed-wing aircraft, helicopter, or boat. Access can be 

delayed at any time by weather, and travel time on the ground is frequently compromised by 

topography (e.g., deep ravines and river channels), dense vegetation, extensive wetlands, and 

wildlife (e.g., bears). To account for these difficulties, we developed access layers for the parks 

using path-distance analysis tools in ArcGIS (LACL and KATM; Mortenson and Miller 2008) 

and/or manual interpretation (KEFJ), following Woodward et al. (2009). We assigned a 3-5 km 

buffer to entry points into the backcountry (e.g., lakes, landing strips) that was then modified by 

calculating the cost of travel, in terms of time, to monitoring plots. Travel time was estimated 

using existing landcover, topographic, and hydrographic layers. Barriers to travel included 

private inholdings, glaciers, slopes >50o, and major river crossings (Mortenson and Miller 2008). 

The access layers developed for vegetation sampling represent 7% of vegetated land area in 

LACL, 24% in KEFJ, and 31% in KATM. Given the trade-off between sampling across the 

entire landscape (high cost, low sustainability) and sampling a subset of the landscape that is 

readily accessible (low cost, high sustainability), we placed a priority on having a sustainable 

sample population over the long term. For broad-scale, ground-based vegetation monitoring 

(Sections 2.1-2.3), our inference will be limited to accessible and higher priority areas (cf. 

Woodward et al. 2009). Targeted sampling will address sensitive communities and areas of 

management concern that occur infrequently on the landscape and/or are relatively inaccessible 

(Section 2.4). 
8

2.1.2. Estimated Sample Size Required to Detect Change across all Vegetation Classes 

We conducted simulations to investigate the minimum level of change that could be detected 

when the relative proportion of two landcover classes were changed across fixed time periods. 

The simulations tested the efficacy of sampling single points versus clusters (mini-grids) of 

points in a randomized design and estimated sample sizes required to detect minimum levels of 

change for a known level of change (true change) across all landcover classes. We used 

landcover class codes assigned to each 30 m × 30 m cell as surrogates for ground vegetation plot 

data at Time 1. We randomly selected 50% of these cells and changed their landcover class 

designations for Time 2. Thus, within our simulation context, the change was being applied at 

the landcover class level. Although this was not a perfect solution, it did allow us to use an 

empirically based spatial distribution of landcover classes (vegetation) rather than assuming one 

(e.g., uniform [random], negative binomial, etc.), the latter of which can produce increasingly 

misleading simulation results the farther away the assumed spatial distribution is from reality. 

Prior to running the simulations, we generated an MS Access database for the GIS access layers 

that included UTM coordinates attributed with elevation and landcover class in each park. The 

database was imported into SAS (SAS Institute, Inc., 2004, 2006) to assign elevation classes, and 

then exported as .txt files to S-DRAW (http://www.west-inc.com/computer.php) to select GRTS 

samples (Appendix C). Elevation classes consisted of 450 m elevation bands (0-450 m, >450-

900 m, >900 m) in each park. In LACL, the >900 m elevation band was divided into N and S 

sections (dividing line = Turquoise Lake, 60.78 oN (NAD83)), with a pixel size of 25 used for 

the northern section, and pixel size of 50 for the southern section. A pixel size of .01 was used 

for the 0-450 m and >450-900 m elevation bands. Differences in pixel size were required to 

generate a well-distributed sample across the latitudinal gradient, and to minimize clustering 

during sample selection (sequential selection of points). The cell sizes were 30 m × 30 m in the 

landcover data set. During our preliminary analyses, we discovered that the fragmentary nature 

of our underlying vegetation data created problems when selecting GRTS samples. Therefore, 

we used a range of pixel sizes in S-Draw and visually evaluated the spatial balance of the sample 

generated from each one. This was essentially a trial-and-error approach. We then used the 

pixel size that produced the best result for each vegetation class/elevational band combination. 

We further divided the 0-450 m elevation band into interior and coastal sites (dividing line = 

Hickerson Lake, -152.83 oW) by manually selecting coastal sites in a GIS. We did this rather 

than generate a separate set of sample points because previous attempts to split the 0-450 m 

elevation band had resulted in poor sample distributions and clustering during point selection. In 

KATM, a pixel size of 1 was used for all elevation bands, and separate GRTS populations were 

generated for spruce and non-spruce landcover classes, for the reasons cited above for LACL 

(low frequency of suitable sites; maintenance of spatial dispersion; minimize clustering). 

We ran simulations to investigate the minimum level of change that could be detected across all 

landcover classes when either 20% or 50% of a specific class was changed across two time 

periods (Appendix D). Up to 50 samples of single points, 2×2 grids of points, and 3×3 grids of 

points were chosen in each simulation run. Points were selected under a GRTS design from 

specific landcover classes occurring at different frequencies (0.75% - 43%) within each elevation 

band in LACL. Using the most frequently occurring landcover class, and assuming a true change 

of 50%, simulation results indicated that at least 12 single points (plots) would have to be 

selected to have at least an 80% chance of detecting a >0 change with 90% confidence (W. 
9

Thompson, unpublished data; Appendix D). Under these same conditions, at least 33 points 

would have to be selected to meet the >0 criterion when 20% of the landcover class had been 

changed. As the minimum detectable change was increased from >0, the required number of 

replicates also increased (e.g., 43 replicates to detect a minimum change of 10% when the true 

change was 50%). When landcover classes occurred at frequencies ≤16% of the elevation band, 

change was essentially undetectable with an 80% level of confidence, even with a sample size of 

50. Minimum sample sizes were as much as 70% lower for 2×2 and 3×3 grids of points, but the 

use of grids required that four (2×2) to nine (3×3) times as many points be sampled per sampling 

unit (i.e., a grid of points versus single points as replicates). As a result, the total number of plots 

sampled in a grid array would be ≥25% greater than for a random array of single points. 

The simulations indicated that by using a GRTS sampling approach across all vegetation classes, 

as approximated by landcover class, we would have sufficient replication to detect a trend in 

plot-level data only if a vegetation class occurred at a high frequency (e.g., ≥43%) on the 

landscape. Furthermore, we would need at least 43 plots to detect a 10% change in that class 

with 90% confidence when the true change was 50%. Given a field season of 8-10 weeks with a 

minimum of two days of travel per sampling trip, we assumed that approximately 32-40 plots 

could be sampled over the course of a year. Thus, it would take two years to accrue a sufficient 

number of plots across all vegetation classes to detect change over time in one vegetation class in 

a single elevation band in a single park. At the same time, we would be sampling a number of 

plots (i.e., in rarer vegetation classes) that would tell us nothing about the change(s) in question, 

but that would be contributing noise to the trend analysis. A second set of simulations (Section 

2.3; Appendix E) examined minimum levels of change in species cover that could be detected 

with a GRTS sample in a single landcover class. 

2.1.3. Constraining the Sample to Selected Vegetation Classes 

Limited resources and a need for adequate replication required that we focus ground-based 

monitoring in relatively broad vegetation classes representative of a range of climate and 

elevation zones in south central and southwest Alaska (Table 3; Table 4). We have adopted the 

Woodward et al. (2009) two-stage sampling approach that enables us to focus our sampling on 

specific vegetation classes without stratifying on vegetation (Section 2.2). Selection criteria for 

these vegetation classes follow: 

Classes of interest are relatively common in, and representative of, a given elevation band 

Classes are late-seral and/or relatively stable, and thus change is not primarily successional 

Classes are expected to be sensitive to changes in environmental drivers (e.g., climate) 

Classes are of known value ecologically or culturally, and are of interest to management 
10

Table 3. Characteristic vegetation classes (ecosystems) targeted for monitoring in the SWAN. 

Elevation Band LACL KATM KEFJ 

Low (0-450 m) Interior closed spruce and 

spruce woodland 

(Picea glauca) 

Interior closed spruce and 

spruce woodland 

(Picea glauca) 

Coastal spruce 

(Picea sitchensis) 

Mid (>450-900 m) Interior spruce woodland 

(Picea glauca) 

Interior spruce woodland 

(Picea glauca) 

- 

Mid (>450-900 m) Low & dwarf shrub tundra 

(Betula glandulosa) 

Low & dwarf shrub tundra 

(Betula glandulosa) 

- 

High (> 900 m) Dwarf shrub tundra – alpine Dwarf shrub tundra – alpine Dwarf shrub tundra – 

alpine 

Monitoring focuses on questions that could not otherwise be addressed by remote sensing 

techniques (e.g., low shrub encroachment into tundra; loss of lichen cover; Table 2). Vegetation 

classes selected for the current phase of monitoring occur along a gradient from relatively warm, 

wet coastal environments to colder, drier boreal and subarctic environments and high alpine 

environments limited by temperature and growing season length. Mean annual temperature 

ranges from -8 °C to 4 °C across the gradient. Mean annual precipitation ranges from 400–600 

mm in the western interior to 3000–4000 mm at higher elevations and along the eastern 

coastlines (Davey et al. 2007). 

We did not select alder and willow classes, in spite of their abundance, because these classes are 

generally representative of early-successional environments, and are spectrally distinct and can 

be readily identified in even moderate-resolution remotely sensed data. In addition, the shrub 

classes afford poor visibility on the ground, which becomes a serious safety issue in bear 

country. Mixed hardwood-conifer and deciduous forest types were not considered at this time 

for similar reasons (e.g., early to mid-successional; spectral characteristics; safety). 

On the Alaska Peninsula, the selected associations include low elevation interior spruce forest (0-

450 m); mid-elevation white spruce woodland (450-900 m); mid-elevation low and dwarf shrub 

communities (450-900 m), and alpine dwarf shrub-fellfield communities (>900 m) (Viereck et al. 

1992). Treeline elevation ranges from approximately 450 m to 550 m across the north-south 

gradient. In western Prince William Sound, coastal rainforest dominates at low elevations, while 

dwarf shrub tundra communities occupy exposed alpine ridges (DeVelice et al. 1999). 

Descriptions of the vegetation classes and anticipated changes follow: 

1. Coastal Sitka spruce forest – Old-growth Sitka spruce (Picea sitchensis) communities 

between 0-450 m in KEFJ. These forests provide habitat for the marbled murrelet, black 

bears, and a myriad of other wildlife species, and are mentioned prominently in the enabling 

legislation for the ANILCA parks. Mountain hemlock (Tsuga mertensiana) may be a 

secondary species in the overstory. Understory species in these communities include 

blueberry (Vaccinium ovalifolium), devil’s club (Oplopanax horridus), and salmonberry 

(Rubus spectabilis). In KEFJ, closed conifer forest comprises 18% of the vegetated area and 

3% of total land area. A smaller percentage of that is old-growth. Although these 

community types are expected to remain stable through time, changes in understory 

composition associated with overstory disturbance, or with changing climatic conditions, 
11

could be observed over the long term. Funds permitting, monitoring of old-growth Sitka 

spruce and/or Lutz spruce (Picea × lutzii) could extend to LACL in the future, where coastal 

spruce forest comprises approximately 1% of land area. 

Table 4. Percent cover of landcover classes in the three largest parks of the SWAN. †Landcover classes have been 

aggregated, where appropriate, to account for differences in landcover interpretation among parks. Percentages are 

calculated as a proportion of total vegetated land area in the park and rounded to the nearest 1%. ‡Dwarf shrub includes 

prostrate shrub class (LACL), all dwarf shrub classes (LACL; KATM), and the alpine herbaceous class (KEFJ). 

†Landcover Class LACL KATM KEFJ 

Coastal spruce <1 - 18 

Interior closed spruce <1 <1 - 

Interior open spruce 5 3 - 

Interior spruce woodland 4 2 - 

Mixed conifer-hardwood forest 6 2 <1 

Deciduous forest 2 7 <1 

Alder 23 28 29 

Willow 2 11 1 

Other shrub 4 3 6 

‡Dwarf shrub tundra 21 23 15 

Herbaceous meadow 2 8 3 

Wet meadow 4 4 - 

Lichen 2 <1 - 

Gravel & sparsely vegetated 25 7 26 

2. Interior low elevation white spruce forest – Open and closed white spruce (Picea glauca) 

forest communities between 0-450 m in LACL and KATM. Canopy cover ranges from 25-

60% (open canopy) to 60%-100% (closed canopy). Deciduous trees comprise <25% of the 

total canopy cover. Sites are generally well-drained. Understory species may include Kenai 

birch (Betula papyrifera var. kenaica), prickly rose (Rosa acicularis), Labrador tea (Ledum

spp.), and lignonberry (Vaccinium vitis-idaea), with alder (Alnus sinuata), Bebb willow 

(Salix bebbiana), and field horsetail (Equisetum arvense) in wetter sites. These communities 

are considered stable and some may be very old (Viereck et al. 1992). Low elevation spruce 

forest comprises approximately 3% of land area in LACL and KATM. Monitoring will focus 

on changes in stand structure and understory species composition, e.g., in response to spruce 

bark beetle or other insect activity, and increased warming and drought stress. Changes in 

fuel loads, seedling and sapling densities, and understory shrub cover will also be 

documented. 

3. Interior mid-elevation white spruce woodland – White spruce (Picea glauca) woodland 

communities between 450-900 m in LACL and KATM. Canopy cover ranges from 10-25%. 

Understory species include dwarf birch (Betula glandulosa), Labrador tea (Ledum spp.), 

lingonberry (Vaccinium vitis-ideae), and reindeer lichen (Cladina spp.). These communities 

are considered late-seral and are thought to be limited by a combination of temperature and 

wind exposure (Viereck et al. 1992). In parts of LACL and KATM, they overlie 

discontinuous permafrost. Rising treeline, permafrost degradation, shrub expansion, frost 

damage, and/or loss of lichen cover are among the anticipated changes in these communities. 
12

Mid-elevation white spruce woodland comprises approximately 5% of land area in LACL 

and <1% in KATM. Funds permitting, monitoring could be extended to low elevation (0-

450 m) spruce woodland sites in the future. 

4. Interior mid-elevation low and dwarf shrub communities – Open low scrub and dwarf scrub 

(shrub) communities between 450-900 m in LACL and KATM. Dwarf birch (Betula 

glandulosa; Betula nana), Labrador tea (Ledum spp.), lingonberry (Vaccinium vitis-ideae), 

lowbush blueberry (V. uliginosum), crowberry (Empetrum nigrum), bearberry 

(Arctostaphylos alpina), willows (e.g., Salix arctica, S. polaris, S. reticulata), sedges (e.g., 

Carex bigelowii ssp. lugens) and reindeer lichen (Cladina spp.) are common species in these 

classes. These communities are considered late-seral, but may transition into shrub-tussock 

or shrub birch-ericaceous shrub bogs with increasing moisture, or into dwarf shrub-fellfield 

communities if moisture decreases and/or wind exposure increases (Viereck et al. 1992). In 

parts of LACL and KATM, they overlie discontinuous permafrost. Caribou, bears, ground 

squirrels and many species of birds use these habitats. Shrub expansion, spruce 

establishment, frost damage, and/or loss of lichen cover are among the possible changes that 

could occur in these communities. In LACL, they comprise approximately 40% and 12% of 

land area in the 450-900 m and >900 m elevation bands, respectively. In KATM, they 

comprise approximately 60% and 38% of land cover. 

 

5. Alpine dwarf and prostrate shrub communities – Dwarf and prostrate shrub tundra 

communities at elevations >900 m in KEFJ, LACL, and KATM. Dominant species on drier 

sites include mountain avens (Dryas spp.), lingonberry (Vaccinium vitis-idaea), least willow 

(Salix rotundifolia), and alpine azalea (Loiseleuria procumbens). Wetter sites are dominated 

by partridgefoot (Luetkea pectinata), mountain heather (Harrimanella stelleriana), arctic 

willow (Salix arctica) and/or Aleutian mountain heath (Phyllodoce aleutica). Where soils 

are well developed, graminoids (e.g., Carex microchaeta, Hierochloe alpina, Luzula arcuata) 

and forbs provide forage for Dall sheep, mountain goats, and marmots. These plant 

communities are considered stable (Viereck et al. 1992), although alpine areas are expected 

to be sensitive to climate change (Giorgi et al. 1997, Mote el al. 2003). Dwarf birch (Betula

spp.) or alder (Alnus spp.) may invade as sites become more favorable for seedling 

establishment (Viereck et al. 1992). Declines in species diversity as a result of local 

extinctions and shifts in dominance (Walker et al. 2006, Lesica and McCune 2004); 

increased height and density of shrubs (Walker et al. 2006); decreased lichen cover (Wahren 

et al. 2005, Cornelissen et al. 2001); and changes in phenology (Welker et al. 1997) are 

among the potential changes that could occur in high elevation communities. Dwarf and 

prostrate shrub tundra communities (>900 m) comprise approximately 62% of accessible 

land area in LACL and 38% in KATM. In KEFJ, dwarf shrub and sparse vegetation 

communities comprise approximately 9% of ice-free land area. 

To determine vegetation class in the field, we use definitions from Viereck et al. (1992) as 

follows: coniferous woodland >75% of total tree cover in coniferous species; canopy cover 10%-

25%; open coniferous forest >75% coniferous species; canopy cover 25%-60%; low shrub/dwarf 

shrub ≥25% shrub cover (low shrub: 20 cm - 1.5 m; dwarf shrub vegetation: <20 cm). 

Community types (Viereck Level V classes; Viereck et al. 1992) identified for monitoring are 

listed in Appendix I. 
13

2.2 Site Selection 

The Stage 1 GRTS sample generated within the access layer (Section 2.1.1.) is manually 

evaluated in a GIS to estimate travel distance from proposed base camps, identify stream 

crossings and other potential barriers not captured by the path-distance analysis, examine 

potential vegetation using air photos or other high resolution imagery, and eliminate points 

separated by less than 200 m (cf. Woodward et al. 2009). The points that are retained become the 

population for the Stage 2 sample that is evaluated in the field (Woodward et al. 2009). During 

the field reconnaissance, we use site selection criteria developed by Symstad et al. (2009) and 

apply the classification keys developed by Viereck et al. (1992) and DeVelice et al. (1999) to 

determine whether a site falls within the class of interest (SOP 2). In all cases, we exclude areas 

on slopes >25o to minimize ground disturbance. We thus record attributes for all points visited, 

as outlined by Woodward et al. (2009), but retain only a subset of sites falling in the vegetation 

categories of interest for long-term monitoring. 

2.3 Recommended Sample Size and Sampling Frequency 

We used a second set of simulations to evaluate minimum sample size and sample frequency 

required to detect a specified level of change in a specific landcover type. These simulations 

investigated the minimum level of total change in plant cover that could be detected with 95% 

confidence under a rotating panel design for populations subjected to different levels of true total 

change (30%, 40%, and 50%) over a 31-year period (Appendix E). We varied the number of 

plots that would be measured in a given vegetation class, elevation band, and year (n = 6, 8, 12, 

and 24 plots/year), and ran the simulations at two sampling frequencies (5 and 10 years) to 

estimate coefficients of variation (CVs) required to capture a minimum detectable total change 

(MDTC). 

We used estimates of interannual variability in 4-6 plots/vegetation class sampled in LACL 

(Miller et al. 2009; Table 5) and rates of change reported in the literature (Table 5) to select an 

MDTC of 25% as a reasonable level of total change that could be detected given a true total 

change of 50% over 31 years. Specifying a MDTC as the benchmark comparison in simulations 

ensures both an ecologically interpretable quantity and a conservative estimate of sampling effort 

(i.e., larger samples collected more often). Our goal was to detect a positive or negative 1.6% 

annual trend (50% MDTC over 31 years) with 95% confidence for the major species that define 

the vegetation community. 

To detect a 1.6% annual trend at a sampling frequency of five years (Table 6), we will sample 8 

plots per vegetation class × elevation band × park combination (total = 24-32 plots per year). 

Under this design, a MDTC of 25% for species cover should be detected when CVs for plot 

variables are 30% or less (Table 7; Appendix E). A subset of plots targeted for repeat sampling 

will be visited for two consecutive years every four years to provide an estimate of interannual 

variability (Table 6). Overstory attributes in Panel 6 will be measured in only the first of the two 

consecutive years. A strong argument can be made for sampling less frequently (e.g., every 10 

years), but doing so would require larger sample sizes (Appendix 4) and preclude any analysis of 

trends for at least three decades.
14

Table 5. Interannual variability and rates of change in species attributes reported for high elevation and high latitude 

ecosystems. Range of values for interannual variability includes the upper end of the range only; all categories 

included attributes that showed 0% interannual variability. High values for interannual variability in shrub cover in 

subarctic tundra were attributed to frost damage that occurred between the two sampling years; variation in shrub 

cover is generally lower. No estimates of interannual variability were available for frequency data. 

Ecosystem Attribute Range 

 -Interannual variabilitySubarctic tundra1 Shrubs 

Cover by growth form 

Cover by species 

Species occurrence 

Graminoids 

Cover by growth form 

Cover by species 

Species occurrence 

20-45% 

5-45% 

0-10% 

70% 

60% 

50% 

 

Alpine2 Graminoids 

Cover by species 

Community composition 

15-40% 

10% 

 

 -Long-term changeAlpine2 Graminoids 

Cover by species ~5% per decade 

 

Alpine3 Shrubs 

Cover by growth form 1.9-6.6% per decade 

 

Arctic tundra4 Shrubs 

Cover by growth form 1.2% per decade 

 

Boreal forest5 Graminoids and forbs 

Cover by species ~75-125% per decade†

 

1

A. Miller, Unpublished data 2007-2008 

2

Bowman et al. 2006. Ecol. Appl. 16:1183-1193 

3

Cannone et al. 2007. Front. Ecol. Environ. 5:360-364 

4

Chapin et al. 2005. Science 310:657-660 

5

Boucher and Mead. 2006. Forest Ecol. Manag. 227:233-246. †Rates cited are for a 13-year period following forest disturbance. 

Proposed monitoring sites by elevation band and vegetation class are shown for LACL (Fig. 2), 

KATM (Fig. 3), and KEFJ (Fig. 4). For each park, twelve to twenty points are shown for each stratum. 

These points represent the first set of sites to be evaluated (lowest in the GRTS selection order), from 

which eight plots per stratum will be established. The actual number of GRTS points evaluated during 

site selection may be greater (or less) than 20.
15

Figure 2. Proposed plot locations in LACL. A GRTS sample was drawn from an access layer defined by slope 

and terrain features (purple). The sample population was stratified by elevation. Twenty plots per elevation 

band are shown for low and mid-elevation spruce woodland, mid-elevation dwarf shrub, and alpine vegetation 

types. Eight plots per stratum will be sampled. Coastal Sitka spruce/Lutz spruce sites (not shown) may be 

added at a later date. 
16

Figure 3. Proposed plot locations in KATM. Access layer is shown in purple, as in Fig. 2. Twenty plots per elevation 

band are shown for low and mid-elevation spruce woodland, mid-elevation dwarf shrub, and alpine vegetation types. 

Ten plots are shown for mid-elevation spruce woodland. Eight plots per stratum will be sampled. No coastal sites 

are planned at this time.
17

Figure 4. Proposed plot locations in KEFJ. Access layer is shown in purple, as in Fig. 2. Twelve plots per elevation 

band are shown for low elevation coastal forest and alpine vegetation types. Eight plots per stratum will be sampled. 
18

Table 6. Panel design for monitoring vegetation in the SWAN, in which a panel is a group of plots that is always sampled during 

the same year (McDonald 2003). Each X in panels 1-5 represents 24 plots or 3 park × elevation band × vegetation class 

combinations (n = 8 plots per elevation band x vegetation class combination). In Panel 6, which is sampled for 2 consecutive years 

beginning every 5th year, each X represents 4-6 plots. We expect to sample 1-2 panels per field season, for a total of 24-30 plots 

per year in a given park. 

 Year

Panel 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 

1 X X X X X 

2 X X X X 

3 X X X X 

4 X X X X 

5 X X X X 

6 X X X X X X X X 

Table 7. Simulation results showing the probability of detecting different levels of total decline for a true average total decline of

50% over 31 years with 95% confidence (n = 8 plots/year; sampling frequency 5 years; Appendix 4). Values in bold font indicate at 

least an 80% chance of detecting the stated minimum detectable total change. The <0% change category represents the 

traditional definition of statistical power, i.e., the chance of detecting a change regardless of its magnitude, given that a change 

occurs. 

 Estimated percent (%) chance of detecting the listed or smaller 

total change given a true average total change of -50%a

CV ( pˆ ) 

(%) <0%b ≥-5% ≥-10% ≥-15% ≥-20% ≥-25% ≥-30% ≥-35% ≥-40% ≥-45% ≥-50% 

5 100 100 100 100 100 100 100 100 100 99 7 

10 100 100 100 100 100 100 100 100 98 70 6 

15 100 100 100 100 100 100 100 99 83 44 7 

20 100 100 100 100 >99 99 96 88 63 31 7 

25 >99 >99 >99 99 98 95 87 72 46 22 6 

30 99 99 98 95 92 84 73 58 36 19 7 

35 96 94 92 88 82 73 60 47 29 16 6 

40 91 88 84 79 72 60 49 38 24 14 6 

45 86 82 76 70 64 53 44 34 22 14 6 

50 81 76 70 64 57 47 38 29 19 12 6 

2.4 Targeted Sampling in Sensitive Communities 

Rare habitats of management concern are addressed under sensitive communities. Field methods 

are the same as those used in the GRTS design (SOP 3-11), but sampling is targeted in 

community types of interest. Site selection is accomplished using air photos, high resolution 

satellite imagery, and associated tools in a GIS environment, and site number and locations are 

finalized during overflights and/or field visits. Plot locations are randomized within the area(s) 

of interest, with a revisit interval of 10 years. Data from the sensitive communities sites are 

analyzed independently of the GRTS sites assigned within the access layer, and inferences 

regarding community change in targeted areas are thus limited to the targeted area(s) of interest. 

As of 2009, long-term monitoring sites have been established on nunataks (n = 11), in spruce 

bark beetle-affected forest stands (n = 8), and in salt marsh complexes (n = 3). In salt marshes, a 

number of communities are sampled across an environmental gradient, as outlined in a separate 

protocol (Jorgenson et al. 2009). The cost of sampling sensitive communities has ranged from 

<$10,000 to approximately $35,000 during the years spanning 2005-2008. Sampling bouts have 
19

been limited to 1-2 weeks and have been scheduled between GRTS sampling trips, or have been 

scheduled independently with a separate crew of specialists. 

 

2.5 Change Detection

Long-term monitoring will provide an estimate of ecosystem response to environmental change. 

Summary statistics for species-level nested frequency data and point-intercept cover data, and for 

ocular estimates of cover by growth form, will be calculated by plot for each sampling date (SOP 

12; Appendix F). Methods for change detection (Appendix G) rely on a Bayesian approach. 

Analyses will address the following questions: 

1) Does species composition (richness, diversity) within and among plots show a directional 

change across sampling dates? Do species cover and frequency show a directional change 

through time? 

Changes in species richness (species counts), diversity, and cover within sampling frames may 

be estimated between two points in time using repeated measures analysis or Bonferronicorrected paired t-tests. Changes in frequency data can be measured using nonparametric (e.g., 

chi-square) tests (SOP 12). 

2) Does species composition converge or diverge among plots through time? 

Changes in species composition may be evaluated across sampling frames using nonmetric 

multidimensional scaling (NMS) or other ordination techniques (SOP 12). Vectors linking 

measurements at different points in time can then be used to display the direction of change for 

each plot. Differences in the direction of vectors among plots or regions can be evaluated by 

standardizing plot vectors to a common origin, and/or by using a multiresponse permutation 

procedure (MRPP) with Euclidean distance to test whether the direction of change in species 

composition varies among regions (cf. Boucher and Mead 2006). Differences in mean vector 

length (magnitude of change) can be evaluated among regions using analysis of variance 

(ANOVA) and a-posteriori multiple comparisons. The limitation to this approach is that it 

requires a large number of samples across time. 

A null model approach (cf. Schaefer et al. 2005) has been used to separate significant temporal 

change from the underlying variability at a site where multiple years of data are available. The 

model is based on the empirical relationship between species abundance and the coefficient of 

variation (CV) of abundance over time. Community change over time is assessed by comparing 

expected community structure (from randomly generated communities) to target samples. The 

benefit of the approach is that the expected communities can be analyzed with any metric of 

community similarity, and that the approach requires only a few samples to detect change. 

Regressions of similarity indices against time can provide evidence of a directional change 

(convergence or divergence) in species composition among plots (e.g., Inouye and Tilman 1995). 

3 Methods 

Details regarding field methods, data processing, and data delivery are included in the SOPs. An 

outline of the annual work cycle (Table 8) and description of work components are outlined 
20

below. Unless otherwise noted, field methods are identical for broad-scale monitoring of 

vegetation and composition and targeted sampling of sensitive communities. 

3.1 Preparation for field season 

3.1.1. Recruiting 

The Project Lead will be responsible for recruiting and hiring. Crew members should have 

botanical experience, preferably with the flora of Alaska, extensive backcountry experience, and 

be very physically fit. Recruiting should begin in December-January so that hiring and 

background checks can be completed by the beginning of the field season. 

Table 8. Annual work cycle for ground-based vegetation monitoring. Tasks are outlined by project stage. 

Responsibility for task completion, and timeline for execution and/or completion of tasks, is shown below. 

Project Stage Task Description Responsibility Timing 

Announce seasonal biotech positions Project Lead by Jan 15 

Scheduling and logistics, including ordering equipment and 

supplies 

Crew Leader by Apr 15 

Submit permit applications and mimimum tool justifications, if 

applicable 

Crew Leader by Apr 15 

Hire seasonal field crew Project Lead by Apr 15 

Prepare project workspace Data Manager/Crew Leader by May 15 

Distribute field calendars, including flight requests, to park staff Crew Leader by June 1 

Load data dictionary and plot coordinates into GPS units (SOP 1) Crew Leader by June 1 

Prepare and print field maps (SOP 1) Crew Leader by June 1 

Implement working database copy Data Manager by June 1 

Print hard copies of data forms Crew Leader by June 1 

Database/GPS training as needed for field crew Data Manager/GIS Specialist by June 1 

Bear safety training; aviation training for field crew Law Enforcement by June 15 

Preparation 

(Section 3A) 

Field sampling protocols; calibrate cover estimations among crew 

members 

Project Lead/Crew Leader by June 30 

Field data collection (SOPs 2-10) Field Technicians June-Aug 

Review data forms Field Technicians daily 

Review Trimble Rover files Crew Leader daily 

Review data forms Crew Leader weekly 

Process voucher specimens (SOP 11) Field Technicians daily 

Data Collection 

(Section 3B) 

Crew de-briefings - operations Crew Leader monthly 

Download, differentially correct, export & review GPS data (SOP 

11) 

Crew Leader every 7-10 d 

Download/process photos (SOP 11) Field Technicians every 7-10 d 

Data entry/data review Field Techs/Crew Leader every 7-10 d 

Data review – QA/QC & validation using DB tools Data Manager/Crew Leader by Sept 30 

Upload lat/longs to database Data Manager/Crew Leader by Sept 30 

Complete in-house specimen determinations Field Techs/Crew Leader by Oct 15 

Send specimens to UAF for final determinations Crew Leader by Oct 15 

Data Entry & 

Processing 

(Sections 3C) 

Deliver mounted specimens to NPS curators Crew Leader by Nov 15 

Metadata Identify sensitive information contained in the data set Data Manager by Oct 31 

(Section 3C) Update project metadata records Crew Leader/Data Manager by Oct 31 

QA/QC field data & deliver to Data Manager Project Lead by Nov 30 

Upload into master database, SWAN Digital Library 1 Data Manager by Jan 15 

Data Verification & 

Delivery 

(Section 3C) Update GIS data sets and associated metadata records; store in 

SWAN Digital Library 

Data Manager/GIS Specialist by Mar 15 

Data Analysis 

(Section 3D) 

Summary statistics (all years); change detection (after 3rd

iteration) derived from final data files (SOP 12) 

Project Lead /Biometrician by Mar 15 

Export automated summary reports from database Data Manager by Mar 15 

Generate maps for annual report Project Lead /Data Manager by Mar 15 

Reporting 

(Section 3D) 

Complete annual report following NRPM guidelines Project Lead/Biometrician by Apr 15 

Draft annual report to Network Coordinator for review Project Lead by Apr 30 

Upload completed report to SWAN Digital Library 1 Data Manager by May 30 

Create NatureBib 3 record, post to NPS clearinghouse Data Manager by June 15 

Product Delivery 

(Section 3D) 

Update NPSpecies 4

 records Data Manager by Dec 15 
21

Submit data sets to NPS Data Store 2 Data Manager after 2 yrs 

Store finished products in SWAN Digital Library 1 Records Data Manager upon receipt 

Management 

(Section 3D) 

Review, save and/or delete interim project files following upload 

of final report to SWAN Digital Library 5 

Project Lead by May 30 

Inventory equipment and supplies Crew Leader by Sep 15 

De-brief field crew Project Lead/Crew Leader by Sep 15 

Season Close-out 

(Section 3D) 

Document needed changes to field sampling protocols, analysis 

& reporting procedures, or database 

Project Lead/Crew Leader/Data 

Manager 

by Apr 15 

1

 The SWAN Digital Library is a hierarchical digital filing system stored on the SWAN file servers (Mortenson 2005). Network users 

have read-only access to files, except where sensitivity of data may preclude general access. 2

 NPS Data Store is a clearinghouse for natural resource data and metadata (http://science.nature.nps.gov/nrdata). Only nonsensitive information is posted. Refer to the protocol section on sensitive information for details. 3

 NatureBib is the NPS bibliographic database (http://www.nature.nps.gov/nrbib/index.htm). The application has the capability of 

storing and providing public access to image data (e.g., PDF files) associated with each record. 

4

 NPSpecies is the NPS database and application for maintaining park-specific species lists and observation data 

(http://science.nature.nps.gov/im/apps/npspp/index.htm). 5

 NPS Director’s Order 19 provides a schedule indicating the amount of time that the various kinds of records should be retained. 

Available at: http://data2.itc.nps.gov/npspolicy/DOrders.cfm 

3.1.2. Logisitics, Data Uploads, and Preparation of Working Database 

The Crew Leader is responsible for all aspects of field preparation (SOP 1), except where noted. 

Beginning in late February or March, the Crew Leader will inventory and test field equipment; 

order any needed equipment or supplies; arrange for housing and computer access for the field 

crews; load plot coordinates and data dictionaries into the GPS; and apply for a research permit 

(http://science.nature.nps.gov/research). Field emergency plans will be distributed to dispatchers 

prior to each round of field work. Pending completion of the monitoring database, the Data 

Manager will upload a working copy of the database for use by technicians in the field. 

3.1.3. Training 

The Project Lead and Crew Leader will train seasonal field crews each year during the first half 

of June. Prior to field work, crew members must complete Basic Aviation Training (B-3) and 

bear safety training, and at least one crew member must be shotgun certified. Data collection 

will occur from late June through early September in 5- to 10-day tours and will include one 

office day approximately every 10 work days for data management and equipment maintenance. 

Crews will be stationed in one park for the duration of the field season. The Project Lead will 

work with the crew on an as-needed basis for 3-6 weeks during the summer, and will be 

available at the beginning and end of each tour to answer questions and address concerns. 

3.2 Field methods 

3.2.1. Plot Layout 

Plot layout follows a simplification of the design used by the NCCN (Woodward et al. 2009); 

i.e., a 50 m × 50 m (0.25 ha) monitoring plot encloses an intensively sampled inner plot of 30 m 

× 30 m (0.09 ha) (Fig. 5; SOP 2). The inner plot is approximately one-tenth the area of FIA and 

NCCN plots (Woodward et al. 2009), and is roughly 4.5-fold greater in size than plots used in 

the CAKN (Roland et al. 2004). During the pilot testing phase, we qualitatively evaluated 

different plot dimensions. We found that a 30 m × 30 m inner plot was large enough to capture 

variation in forested stands, but small enough to fall within relatively homogeneous areas of 

vegetation, and thus seemed appropriate to sample a range of vegetation types. The 30 m × 30 m 

area also coincides with the pixel size for Landsat imagery, which we are using in a separate 

project to document landscape-level change. 
22

The SW corner of the 30 m × 30 m inner plot is permanently marked with an aluminum stake 

and stamped monument cap (cf. Roland et al. 2004; Fig. 6), and the five remaining 30-m transect 

endpoints are marked with labeled wooden stakes. Galvanized steel spikes (6′′) pounded to 

below the soil surface at the NW, NE, and SE corners aid in relocation. The rationale for using 

square plots is outlined in Woodward et al. (2009). 

The 30 m × 30 m intensive inner plot is divided into fifteen 4-m2

 nested quadrats located at 7-m 

intervals along the 30-m transects (Figs. 5 and 7). Plot boundaries are oriented along cardinal 

directions. Travel along transects is limited to ≥ 1 m from the outer edge of transect lines (i.e., 

west side of Transects 1 and 2, east side of Transect 3) to minimize trampling. 

3.2.2. Plot Descriptive Data 

Physical attributes recorded at each plot include slope angle and azimuth, elevation, topographic 

position, disturbance regime, slope shape, drainage characteristics and evidence of frost action, 

fire, and human or animal disturbance (SOP 3). Biotic plot variables that are recorded include 

vegetation classification, landcover classification, and dominant species. 

3.2.3. Plot Photos 

At each plot, we photograph (1) each 30-m transect from the 0-m and 30-m endpoints, (2) the 0-

m baseline (SW to SE plot corners of the 30 m × 30 m inner plot), and (3) each 0.25-m2

, 1- m2, 

and 4- m2

 nested quadrat (SOP 4). In addition, we opportunistically take photos that show the 

location of the plot in the larger landscape. 

3.2.4. Species Composition 

Vascular plant abundance and vegetation structure in the permanent plots are quantified using 

point-intercept transects recorded at 50-cm intervals along the 30-m transects, within five 

vertical strata (≤ 20 cm; >20-50 cm; >50-100 cm; >100-400 cm; >400 cm; SOP 5; modified from 

Roland et al. 2004), resulting in 180 point counts per plot. Bryophytes (mosses, liverworts), 

lichens, and litter are recorded as separate classes. Unknown species, previously undocumented 

species, and/or species of conservation concern are collected following protocols established by 

the University of Alaska Museum of the North (Appendix H). All vascular and non-vascular 

species are recorded using USDA codes (http://plants.usda.gov/), where applicable. 

Presence/absence of vascular species is recorded in nested frequency plots (4-m2

, 1-m2, and 0.25-

m2

; SOP 6; modified from Roland et al. 2004). Due to time constraints, occurrence of nonvascular species is recorded only in the 1-m2

, and 0.25-m2 quadrats. 

Cover by growth form is recorded in 4-m2

 quadrats using ocular estimates of cover in 1% 

increments (SOP 6). We minimize observer effects by using 2-m quadrat frames gridded into 

1% sections as recommended by Sykes et al. (1983); using pairs of observers; and estimating 

actual cover rather than using cover classes. Estimates to 1% are most precise when cover is 

very high (95-100 %) or very low (e.g., 0-5%), but we do not expect estimates to be precise at 

1% in any part of the range. 

3.2.5. Tree Measurements

The structure of the tree layer is quantified through seedling counts in 4-m2 quadrats (SOP 6); 

sapling counts in the 30 m × 30 m inner plot (SOP 7); measurement of all trees ≥ 12 cm dbh in 
23

the 30 m × 30 m plot (SOP 7); and coring of the four largest trees in the outer 10-m buffer of the 

plot (SOP 8; all methods modified from Roland et al. 2004). Tree canopy cover is estimated at 

the plot center and four outer corners using a densiometer (SOP 7). These data allow us to 

characterize stand structure as stem densities per size class and basal area per species, and also 

provide information on forest condition, including insect activity, pathogens, physical damage 

and related factors. Monitoring objectives for forested stands are outlined in Section 1.3. 

Figure 5. SWAN plot layout (not to scale). The intensively sampled inner plot (30 m × 30 m) consists of three parallel 

transects oriented along cardinal directions. Nested quadrats (black) are spaced at 7 m intervals, from 0 m to 28 m, 

along each transect. Species frequency is measured in nested 4-m2

, 1-m2, and 0.25-m2 plots. Species cover is 

measured by point-intercept at 50 cm intervals along transects. Tree seedling counts are conducted within 4-m2

quadrats. Mature trees are cored within a 10-m wide buffer surrounding the plot, and trees are mapped across the 

entire 50 m × 50 m (0.25 ha) plot. 
24

Figure 6. Plot monument stamped with the plot id number marks the SW corner of the inner 30 m × 30 m plot. 

Figure 7. Layout of nested quadrats. Five nested quadrats are located at 7-m intervals along each 30-m transect, for 

a total of 15 quadrats per plot. Quadrats at the 0-m end of the transect are oriented in the SW corner on Transects 1 

and 2, and in the SE corner on Transect 3. 

3.2.6. Coarse Woody Debris, Litter and Duff 

Cover of coarse woody debris and litter are quantified along point-intercept transects and in the 

4m2

 quadrats. The depth of duff and litter layers are estimated as part of the soil description 

(SOP 9). The size and decay class of logs (≥7.5 cm diameter) and fine fuels (<7.5 cm diameter) 
25

are measured at forested sites using a modification of FIA methods (SOP 10; modified from 

Woodward et al. 2009) and the line-intersect method (Van Wagner 1968; Brown 1974). 

 

3.3 End-of-season procedures 

3.3.1. Processing of Collections 

Taxonomic determinations and preparation of voucher specimens (SOP 11) should occur prior to 

data upload into the master database. 

3.3.2. Equipment Inventory 

Field season close-out should include an inventory of equipment and filing of all field notes and 

data forms (SOP 11). Notes should document any deviations from established protocols, 

highlight observations of interest, and provide suggestions for improving the training or field 

season procedures for the future. 

4 Data Processing, Analysis and Reporting 

4.1 Project Information Management Overview 

Project information management includes the following steps, as outlined in the SWAN Data 

Management Plan (Mortenson 2006): 

 Preparation – Training, logistics planning, print forms and maps 

 Data acquisition – Field trips to acquire data 

 Data entry & processing – Data entry and uploads into the working copy of the database, 

GPS data processing, etc. 

 Quality review – Data are reviewed for quality and logical consistency 

 Metadata – Documentation of the year’s data collection and results of the quality review 

 Data certification – Data are certified as complete for the period of record 

 Data delivery – Certified data and metadata are delivered for archival and uploaded to the 

master project database 

 Data analysis – Data are summarized and analyzed (SOP 12; Appendix F, G) 

 Product development – Reports, maps, and other products are developed 

 Product delivery – Deliver reports and other products for posting and archival 

 Posting & distribution – Distribute products as planned and/or post to NPS 

clearinghouses 

 Archival & records management – Review analog and digital files for retention 

 Season close-out – Review and document needed improvements to project procedures or 

infrastructure, complete administrative reports, develop work plans for the coming season 

4.2 Database Design and Data Management 

The proposed database structure for SWAN represents a modification of the MS Access database 

developed for the CAKN (Roland et al. 2004; Fig. 8; Appendix J). Automatic entry of 

identification field values through the use of nested sub-forms will allow for quality control and 

the automation of several important database functions. Digital images recorded at each sample 

site will be entered and may be viewed from within the database. Database development (SQL) 

is expected to begin in the winter of 2009-2010.
26

Figure 8. Summary of CAKN database structure (Roland et al. 2004) as a model for proposed SWAN database. The 

SWAN database will build on this structure but use site locations, rather than mini-grids, as a primary key. The 

master database is expected to run on SQL server. 

The database will be divided into two components – one for entering, editing and error-checking 

data for the current season (i.e., the working database copy), and another that contains the 

complete set of certified data for the monitoring project (i.e., the master project database). A 

functional comparison of these two components is provided in Woodward et al (2009). 

Each of these components will be based on an identical underlying data structure (tables, fields 

and relationships). The working database will be implemented in Microsoft Access to permit 

greater flexibility when implementing on computers with limited or unreliable network access. 

The master database will likely be implemented in Microsoft SQL Server to take advantage of 

the backup and transaction logging capabilities of this enterprise database software. 

The working database application will have separate screens for data entry, data review, and 

quality validation tools. The master database application will contain the analysis and 

summarization tools, including pre-formatted report output and exports to other software (e.g., 

for analysis and graphics production). 

During the field season, each crew will be provided with their own copy of a working database 

into which they enter, process, and quality-check data between sampling trips (i.e., field data 

collection is on paper forms, as of 2009). Once data for the field season have been reviewed for 

QA/QC, they will be uploaded into the master database, which is then used to inform all 

reporting and analysis. This upload process is performed by the Data Manager, using a series of 

pre-built append queries. 
27

4.2.1. Data Backups 

Data must be backed up every day that new data are entered. The backup files should be 

compressed to save drive space, and may be deleted after the data have passed a quality review 

and been uploaded into the master database.

4.2.2. Data Verification 

At regular intervals and at the end of the field season the Crew Leader and/or Data Manager 

should inspect the data being entered to check for completeness and catch avoidable errors. The 

Data Manager may also periodically run the Quality Assurance Tools that are built into the frontend working database application to check for logical inconsistencies and data outliers. 

The working database application facilitates this process by showing the results of pre-built 

queries that check for data integrity, data outliers and missing values, and illogical values. The 

user may then fix these problems and document the fixes. Not all errors and inconsistencies can 

be fixed, in which case a description of the resulting errors and why edits were not made is then 

documented and included in the metadata. 

4.2.3. Geospatial Data 

The Project Lead and GIS Specialist will work together to review the surveyed coordinates and 

other geospatial data for accuracy. The purpose of this joint review is to make sure that 

geospatial data are complete and reasonably accurate, and also to determine which coordinates 

will be used for subsequent mapping and field work. 

4.2.4. Metadata 

The Data Manager and GIS Specialist will facilitate Federal Geographic Data Committee 

(FDGC)-compliant metadata development by creating and parsing metadata records from the 

information provided by the Project Lead. 

4.3 Data Reduction and Analysis 

For most measurements, the plot is used as the unit of replication within elevation band, and data 

reduction will consist of pooling or averaging values across transects or quadrats (Appendix F). 

Tree canopy cover, tree density and mortality are presented as a plot-wide percentages based on 

count data. Plot means will be averaged across sites to derive a grand mean and variance for 

each vegetation × elevation combination. 

To quantify interannual variability in plot attributes, we will measure one set of plots in two 

consecutive years beginning every fifth year (Panel 6; Table 6) and then rest them for three years 

(cf. Woodward et al. 2009; Symstad et al. 2009). Bayesian hierarchical models will be used to 

estimate trends (SOP 12; Appendix G). Due to the inherent variability in ecological systems, 

obtaining a precise estimator of trend often requires many repeat observations. A minimum of 

10-15 years of sampling was recommended to detect a trend in water quality data collected by 

the U.S. Environmental Protection Agency (Urquart et al. 1998), and at least 20 years to describe 

a trend in most variables associated with vegetation change in the northern Great Plains (Gitzen 

et al. 2009). Simulations run on pilot vegetation data collected in the SWAN suggest that 

interannual variability may be lower in subarctic ecosystems, but that detection of long-term 

trends will likely require 20-30 years of data, depending upon the magnitude of change. 
28

4.3.1. Reporting and Product Development 

The Project Lead will submit an Investigator Annual Report online, and produce a detailed annual 

summary report and a cumulative five-year summary report after each complete rotation of the panel 

design. Contents of the annual and five-year reports are summarized in Woodward et al. (2009). An 

example of an annual report for a SWAN data set is provided by Miller et al. (2009). Guidance on 

formatting can be found on the Natural Resource Publications Management website 

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

4.3.2. Records Management 

All project files should be reviewed, organized, and/or purged by the Project Lead, in 

consultation with the Data Manager, on an annual basis following guidelines stipulated in NPS 

Director’s Order 19. Project responsibilities and annual timelines are summarized in Table 8. 

5 Operational Requirements 

5.1 Personnel Requirements and Qualifications 

Personnel needs are outlined in Table 10. The staffing scenario assumes that a field crew of one 

GS-7/9 biological technician or botanist (term – crew leader) and two GS-5 biological 

technicians (seasonal – crew members/field technicians) can complete three to four panels 

(elevation band × vegetation type × park) per field season. The staffing scenario does not 

account for the additional personnel required for short-term side projects (e.g., revisit of sensitive 

communities), as support for those projects will be garnered through collaboration with park 

resource specialists and ongoing assistance agreements. The Project Lead (PI) is expected to 

spend approximately 0.1-0.2 FTE in the field, with the remaining time allocated to data analysis, 

reporting, recruiting and outreach. 

The Crew Leader must have strong taxonomic skills and extensive backcountry experience. 

Familiarity with Alaska/Yukon, Rocky Mountain, and/or Pacific Northwest floras is desirable, as 

is field experience in Alaska. Previous experience supervising field crews in vegetation surveys 

is also desired. The Crew Leader and Field Technicians must be physically fit and prepared to 

spend extended periods of time in the backcountry. 

5.2 Annual Workload and Field Schedule 

Responsibilities for the annual work flow are outlined in Table 8. Field crews are recruited in 

January-February and hired by mid-March. Seasonal training occurs during the first week of 

June, and field work occurs from approximately mid-June through late August. Species 

determinations, data entry, QA/QC and data analysis should be completed by January, and an 

annual report generated by the project lead by late April. Preparation of five-year reports is 

expected to take additional time. 

5.3 Facility and Equipment Needs 

Each panel will be completed in a single park in a single year (Table 6), and field work will 

rotate among the three largest parks (KATM, LACL, KEFJ) over consecutive field seasons. 

Housing for crews (3-4 persons/crew), access to government computers, and transportation to 

and from field sites will be required during the field season (June-August). Field crews will be 

stationed at the park in which the majority of field work will occur. Seasonal employees will be 
29

responsible for rent in NPS housing. SWAN will provide round-trip transportation from 

Anchorage to the duty station at the start and end of the field season, and meals and equipment in 

the field (bear fence, bear canisters, tents, stoves, water filters, etc.). Crew members will need to 

provide their own personal gear and clothing. 

5.4 Startup Costs and Budget Considerations 

Major costs associated with this project are for field crew salaries and transportation to/from the 

field. Equipment costs are expected to be minimal. Estimated flight hours per season are 

expected to range from 60-80 hours, including reconnaissance flights, in the early phases of 

monitoring, to 35-40 hours once sites are established. As of 2009, hourly rates for OAS charters 

ranged from $420-$640/hr, plus fuel, resulting in an annual operating budget for flights alone of 

approximately $25,000-$30,000. Use of DOI aircraft will reduce these costs, as will collocation 

of sites. In 2009, salary for a full-time GS-7 biological technician ranged from $41,000-$53,500. 

Salary for a seasonal GS-5 biological technician ranged from $12,000-$15,000 for the three 

month period, not including overtime. Thus, the minimum budget for one year (salary for three 

biological technicians + flights), not including PI salary or travel, is estimated to be 

approximately $90,000-$108,000 (FY2009). If necessary, costs could be reduced in a couple of 

ways: the sampling could be scaled back either by 1) reducing the number of field personnel, 

and thus likely reducing the number of sites sampled in a given year, or 2) increasing the 

sampling interval (e.g., from five to ten years). 

Table 9. Staffing needs for SWAN ground-based vegetation monitoring. Staffing scenario assumes that the Project 

Lead (PI) is a full-time NPS employee with 1 field crew (GS-7 term biological technician or botanist + 2 GS-5 

biological technicians). 

Staff Grade PP1-6 PP10-12 PP13-14 PP15-19 PP20 PP21-23 PP24-27 FTEs 

Project 

Lead 

(Ecologist) 

GS-12 Data 

analysis 

Reporting 

Outreach 

Recruiting 

 Training 

Data 

collection 

Data 

collection 

(PP15) 

 Data review 

(PP21) 

Data 

analysis 

Reporting 

Outreach 

Recruiting 

0.6 

Crew 

Leader 

GS-7 Field 

prep 

Training 

Data 

collection 

Data 

collection 

Close-out 

Data review 

Voucher prep 

Taxonomic ids 

 0.5-0.6 

Seasonal 1 GS-5 Training 

Data 

collection 

Data 

collection 

Close-out 

Data review 

 0.3 

Seasonal 2 GS-5 Training 

Data 

collection 

Data 

collection 

Close-out 

Data review 

 0.3 

Total 1.7-1.8 
30

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37

Appendix A: Standard Operating Procedures 

SOP 1: Pre-field procedures 

SOP 2: Plot establishment 

SOP 3: Plot attributes 

SOP 4: Photos 

SOP 5: Cover estimates 

SOP 6: Frequency estimates 

SOP 7: Tree measurements 

SOP 8: Tree cores 

SOP 9: Soils 

SOP 10: Coarse woody material 

SOP 11: Sample processing and post-field procedures 

SOP 12: Downloading and processing HOBO data 

SOP 13: Data analysis and reporting 
38

Vegetation Monitoring Protocol for the

Southwest Alaska Network (SWAN) 

Standard Operating Procedure (SOP) # 1 

Pre-field procedures – Field preparation 

Version 1.0 (December 2008) 

Revision History Log: 

Version No. Revision 

Date 

Author Changes Made Reason for Change 

 

Procedures: 

I. Plan activities 

1. Prepare packets of data sheets and maps for each sampling session. 

a. Photocopy data sheets onto 2-sided Rite-in-the-Rain® paper and collate into sets for each 

10-day sampling period. 

b. Print maps that show GRTS point locations in potential sampling areas. Record magnetic 

declinations for each area (http://www.ngdc.noaa.gov/geomagmodels/Declination.jsp); 

all compasses will need to be set to this (these) declinations in order to record bearings as 

true north in the field. 

2. If you are preparing for a repeat visit to a site, laminate a complete set of the plot photos. 

Include, at minimum, photos of the transect lines, 4-m2

 quadrats, plot monument location, 

and temperature sensor location, if applicable. 

3. Download a PDOP almanac for the Trimble GPS unit. Position Dilution of Precision 

(PDOP) must be less than 6 to achieve desired precision. To download the PDOP almanac 

and map it using Pathfinder Office, do the following: 

a. Open your internet browser and on the address line type: ftp://ftp.trimble.com/pub/eph/ - 

press enter. Double click on the following file: current.ssf – check the date to make sure 

it is current. 

b. Save this to your computer in the following location: C:/ Program Files/ Common 

Files/Trimble/Almanacs. 
39

c. Open Pathfinder office by double clicking on the icon on the desktop – the Select Project

window will appear. Select or create a project corresponding to the sampling area. 

d. On the file bar, click on Utilities and choose Quick Plan from the drop-down menu. 

e. Select Date by clicking on the calendar. Click on Prev Month or Next Month if the wrong 

month is displayed. Press ok to close the dialog box. 

f. Edit Point defines the latitude and longitude of the location where observations are taken. 

Click on keyboard and enter the general latitude and longitude (degrees and minutes) of 

your field site. Do not fill in the fields for height or seconds. Close Edit Point by 

pressing ok.

g. The Status window will open, summarizing the information. Confirm and close the 

window. 

h. You will now be in the Plan: session # window. Click on Graphs and choose Number of 

SV’s and PDOP to get a graphical image of the number of visible satellites and the PDOP 

for the specified day and time. If your PDOP is above 6, that’s too high and the GPS unit 

will reject readings. This also occurs when there are less than 4 satellites available. 

Therefore, you must schedule your field work for a date and time when PDOP is less than 

6 and there are 4 or more satellites available. 

i. Make PDOP graphs for the first half of the field season. Export these to Acrobat 

Distiller and print them. Laminate these for field use and place into the packets of data 

sheet described above. You will repeat this procedure to make PDOP graphs for the rest 

of the field sites midway through the season. 

II. Inventory and prepare field supplies 

1. Repair or replace damaged field equipment. Order field supplies according to the list below. 

a. Repair tents and apply additional seam sealer, if applicable. 

b. Test the camping stoves to make sure they are in working order. Purchase white gas. 

Three quarts can usually cover a crew of three for a 10-day sampling trip. 

c. Order monument caps and stakes: 

Surv-Kap® inc. 

P.O. Box 27367 

Tucson, AZ 85726 U.S.A. 

www.surv-kap.com 

Tel: 1.800.445.5320 Fax: 1.520.792.2030 

Item number: TFS-31⁄4-18 

Description: 18′′× 3⁄4′′ dia. triple-fluted (TF) aluminum stake with 31⁄4′′ dome cap 
40

2. Update the field set of dichotomous botanical keys with any current or newly-acquired keys. 

Additionally, inspect the filed keys for condition and repair or replace as necessary. 

3. Prepare a plant press with adequate foam blotters and cardboard pieces. Be sure that there is 

sufficient newspaper to press up to 100 vascular plant specimens per sampling trip (one 

specimen per page). 

4. Print, fold and number sufficient nonvascular plant specimen envelopes for each trip – 

enough for up to 400 specimen collections per sampling trip. Pre-label with NPS accession 

and catalog numbers. 

5. Inspect and clean the 5-gallon water jugs and the bear-resistant food containers (BRFC) – 

distribute enough food containers among crew; usually 3 of the smaller size BRFC’s are 

sufficient for one person for one 10 day sampling trip. 

6. Charge all batteries that will be used in the field, including batteries for the Trimble, the 

digital camera and the satellite phone.

III. Equipment 

Per technician: 

 2 Fine-point Sharpie permanent markers 

 3 mechanical pencils w/ lead refills

 1 metal clipboard 

 1 hand lens 

 1 compass 

 1 clinometer 

Figure 1.1. When ordering the 

monument caps, have Surv-Kap 

pre-stamp the outside row of the 

monument cap with the park 

code(s) of interest: 

e.g., LACL-_ _ _ _-_ _-_ _ _ 

Year, elevation band, and GRTS 

number will be stamped onto 

caps in the field, once it is known 

that a plot will be sampled. 
41

 1 can bear spray + holster 

 Rite-in-the-Rain field notebooks for the season – up to four per crew member per season 

Per crew: 

 Trimble GPS with charged battery and appropriate files loaded, with pelican case 

 Garmin GPS – w/ generated (1st sample) or actual point locations (subsequent samples) 

 Transect staff with densitometer 

 Laser rangefinder 

 16′′ fine-gauge knitting needles or pins for low stature point-intercept work 

 Aluminum monument caps & fluted stakes (1 cap + stake per plot) 

 16′′ wooden stakes (5 per plot) 

 6′′ galvanized nails (3 per plot) 

 Stamp kit for monument caps 

 Hammer 

 Rubber mallet 

 3 × 30-m tapes 

 2 × 50-m tapes 

 2 × Digital cameras + memory cards, with soft case 

 Waterproof plastic cases for GPS and camera 

 2 × Dry-erase board + pens 

 Flagging tape 

 Extra AA and D-cell batteries 

 Retractable metal tape measure (e.g., 2 m) 

 Trowel 

 Hori-horis 

 Spherical densiometer 

 20 each quart and gallon size Ziploc freezer bags 

 Quadrat frames – 0.25-m2; 1 and 4-m2

 Soil depth probe 

 Collection container (Tupperware® and sealable plastic bags) 

 Plant presses w/ blotters, ventilators & papers for 200 specimens (per sampling trip) 

 3 × DBH tapes (cm) 

 3 × Increment borers 10′′ and 14′′ with extra spoon extractors and WD-40 

 Plastic straws 

 Lighters 

 Electricians tape 

 Plastic ‘quiver’ for storing cores and empty straws 

 Pre-numbered aluminum tags + galvanized nails 

 Stapler 

 Fuels gauge 

Laminated sheets: 

 Maps showing GRTS point locations, as generated from ArcGIS 

 USDA-NRCS species codes for SWAN vascular and non-vascular plants 
42

 Viereck codes 

 Canopy classes 

 Crown length classes 

 Pathology codes 

 Physical damage codes 

 Decay classes 

 Soils codes 

Data sheets, maps and field emergency plans:

 15 sets of datasheets per 10-day sampling session 

 USGS quadrangle maps (1:63,000) 

 Emergency field plan(s) with maps (for Dispatch) 

Communal camping gear: 

 Bear fence (poles, line, grounding rod and charger; D-cell batteries) 

 Grass clippers for fenceline 

 Hand-held radio plus batteries (2 extra sets) 

 Satellite phone with extra battery and after-hour emergency phone numbers 

 Waterproof hard case (pelican case) for satellite phone 

 Stove and fuel bottle(s) 

 White gas 

 Cook set(s) 

 Water filter(s) 

 Lighters + boxed matches 

 Large bear-proof can(s) 

 Backpacker-sized bear-proof cans 

 Trash compactor bags 

 Duct tape 

 Cook tent (optional) 

 First-aid kit 

 5-gallon water jug 

 Tents 

Office Equipment: 

 Laptop loaded with all required software and files (e.g., ArcGIS; Pathfinder Office; DNR 

Garmin; GPS PhotoLink; copy of database; image files; SOPs and Appendices, including 

class codes and species codes) 

 Keyboard and mouse for laptop 

 Dissecting microscope and kit 

 Card reader for downloading photos 

 A/C adaptor for charging sat phone batteries 

 External hard drive for file backups 
43

Vegetation Monitoring Protocol for the 

Southwest Alaska Network (SWAN) 

Standard Operating Procedure (SOP) # 2 

Plot Selection & Establishment 

Version 1.0 (December 2008) 

Revision History Log: 

Version No. Revision Date Author Changes Made Reason for Change 

 

Procedures: 

I. Plot selection 

1. Office Evaluation - ArcGIS is used to view points generated from the GRTS sample 

(hereafter referred to as ‘GRTS points’). The access layer developed for each park is 

intended to eliminate barriers to travel, but all points should be reviewed in a GIS prior to 

field reconnaissance. 

2. Field Evaluation – A site is rejected for the following reasons (Symstad et al. 2009): 

a) Site is dangerous or prohibitively difficult to access – e.g., travel is through extensive 

areas of poor visibility (e.g., >200 m of dense vegetation with ≤10 m visibility) and/or 

high brown bear densities; access requires dangerous plane or boat landing, difficult 

stream crossing(s), or travel through steep (≥50° slope) terrain 

b) Travel time to site is excessive - one-way travel time from nearest access point >2 h 

c) Site is dangerous or prohibitively difficult to work on – e.g., sites that occur in areas of 

poor visibility or high bear densities; loose rock above or underfoot; hazard trees 

d) Site occurs on private property – e.g., sites that occur within private inholdings or 

within 100 m of private lodges or cabins. The GIS access layer should have eliminated 

most, if not all, of these areas. 

d) Site could be damaged by establishment of a monitoring plot – e.g., plot 

establishment would result in long-term (>1 year) damage to vegetation and/or substrate. 

Sites with slope >25° are excluded. If species of conservation concern are present at the 

site, extreme care must be taken to protect the population and surrounding substrate. 
44

e) Site is located within 100 m of a GRTS point higher in the selection order – i.e., for 

GRTS points that occur in close proximity to one another, use the point highest in the 

selection order and drop subsequent points if they occur in the same community type. 

A permanent plot is established if the site (50 m × 50 m) meets the following criteria in the field: 

a) Plant community is a targeted type for the selected elevation band (Appendix I) 

b) Vegetation is homogeneous and representative of the surrounding area (e.g., ≥100 m 

radius) 

c) Slope angle does not exceed 25 degrees

d) Non-targeted inclusions (e.g., streams, outcrops, etc.) comprise ≤ 10% of the plot

e) Mean tree age ≥80 years for low elevation spruce forest communities (Woodward et 

al., 2009); mean tree age ≥50 years for mid-elevation spruce woodland communities (A. 

Miller, unpublished data, Katmai National Park & Preserve, 2007). 

A reconnaissance data sheet is completed in the field only for sites (GRTS points) that are not 

selected for monitoring. Temporary rejection criteria include conditions that are present at the 

time of reconnaissance, but that could change through time (e.g., land ownership, access 

constraints, temporary hazards; Symstad et al. 2009). Because of the high cost associated with 

travel, every attempt should be made to minimize the number of sites rejected. An offset of up to 

100 m is acceptable if an adjacent 50 m × 50 m area would meet criteria for acceptance. 

II. Upload GPS Points from ArcGIS 

1. In the office, load GRTS points into a Garmin GPS (e.g., Garmin Map76S), as follows: 

a) Connect the GPS via a comport and open ArcMap 

b) In ArcMap, highlight the layer to be transferred in the Table of Contents 

c) If multiple .shp files will be loaded from ArcMap with the same identifier (e.g., FID 

assigned by ArcMap), create a unique id column in the attribute table. Highlight the new 

field (e.g., ‘GRTS’)  calculate values  type in a park code or unique prefix plus the 

FID (e.g., “LACL-02-“&[FID])  ok. This will generate a unique identifier for each 

GRTS point. 

2. Transfer points from ArcMap to DNR Garmin: 

a) Open DNR Garmin 

b) Set the projection in DNR Garmin (File  Set projection) prior to transferring the file 

from ArcMap. Projection and datum must be the same in the Garmin as they are in 

ArcMap (Projection: Alaska Albers Equal Area Conic; Datum: NAD83). 

c) Load the points onto the Garmin GPS unit: 

i) File  Load from  Arc  Layer; or, alternatively, 

ii) File  File type  .shp  browse to .shp file 

iii) Select the identifier that will be assigned to each point (e.g., ‘GRTS’). 

3. Transfer points from DNR Garmin to the Garmin Map76S select Waypoint  Upload 
45

See also: http://inpakroms03web/RGR/helpdesk/cheat/GPSDataStepsIntoArcGIS_AK2006.doc

(accessed 5/14/08). 

III. Navigate to site 

1. In the field, select the GRTS point that you will travel to in the Garmin waypoints folder, 

select the “Go To” tab and press enter. Your point should appear on the screen with a map 

and a distance to the point. Select a navigation mode (i.e., compass screen, map screen, etc.). 

2. Navigate to within 1-2 m of the GRTS point using USGS quad sheets and the GPS. Refresh 

the ‘Go To’ often as you approach the point. 

IV. Plot establishment 

1. Using criteria in Section I, above, determine whether the point will be retained as a long-term 

monitoring plot. 

2. For sites that meet plot criteria, install a permanent marker consisting of a 3⁄4 ′′ diameter, 18′′

aluminum stake and 3 1⁄4 ′′ diameter monument cap (Surv-Kap, Inc.) within 1-2 m of the 

GRTS point. Keep all personnel and gear at least 20 m south or west of this point to avoid 

ground disturbance within the plot. The monument cap should be stamped with the park 

code, year, and five digit plot code assigned by the GRTS sample (e.g., LACL-2008-02-042) 

prior to attaching it to the stake. Pound both cap and stake to within 5 cm of the ground 

surface using a rubber mallet. 

3. Establish three, parallel 30-m transects on a bearing of 0 oN (true north) as shown in Fig. 2.1. 

The plot monument marking the SW corner of the plot will serve as the 0-m endpoint for 

Transect 1. All other transect endpoints will be marked with 16′′ wooden stakes labeled with 

permanent ink (T1-30 m, T2-0 m, T2-30 m, T3-0 m, T3-30 m) and 6′′ galvanized spikes or 

short sections of rebar installed below the ground surface. Keep the tapes taut at the ground 

surface and ensure that the transect measures exactly 30 m in length (i.e., account for length 

of tape used to secure the 0-m end of transect). Walk to the outside of transect (tape) lines to 

avoid trampling in the plot. Tapes stretched along the northern (30-m) and southern (0-m) 

ends of the plot should be used to verify that the transects are parallel and located at a 

distance of 14 m and 30 m east of T1, respectively (Fig. 2.1). 
46

4. Record the location of the transect endpoints using a Trimble GeoXT or comparable GPS 

device with sub-meter accuracy. Set the antenna height to 10 cm and hold the GPS unit 

completely still while logging points. Use the highest precision possible; e.g., a maximum 

PDOP of 6.0 and at least 4 satellites. Collect at least 20 waypoints at each transect endpoint. 

If you must reduce the precision of the GPS to attain more satellites, increase the number of 

data points that you log for each point. Record the rover file id, the lat/long (NAD83; 

Figure 2.1. Layout of SWAN vegetation 

monitoring plot with monitoring transects 

oriented 0° true north. Plot dimensions 

are 30 m x 30 m, with Transects 1 and 3 

forming the west and east boundaries of 

the plot, respectively. The SW corner of 

the plot (T1-0m) is marked with a labeled 

monument cap. All other transect 

endpoints are marked with labeled 

wooden stakes. Trees are cored within 

the outer 10 m band that forms the 50 m x 

50 m plot. 

Five 4-m2

 nested quadrats are located at 

7-m intervals along each transect, as 

shown boxed in red. Nested quadrats are 

used to record species occurrence 

(frequency) and cover by growth form 

(SOP 6). Cover by species is recorded at 

0.5 m intervals along each transect using 

point-intercept measurements (SOP 5). 

When establishing plots and/or conducting 

plot measurements, observers should 

position themselves to the west of 

Transects 1 and 2, and to the east of 

Transect 3 to minimize plot disturbance. 
47

decimal degrees) and the elevation (m) at the monument stake on the summary data sheet 

(SOP 3). A data dictionary developed for point collection requires the following input: 

 Plot SW (monument cap) 

 Transect endpoint (endpoints for Transects 1-3) 

5. For forested plots, a 10 m buffer around the 30 m × 30 m monitoring plot will be marked 

with pin flags (outer dimensions 50 m × 50 m; Fig. 2.1). The lat/long coordinates of the 

outer corners of the 50 m × 50 m plot do not need to be recorded. 

V. Equipment 

 3 1⁄4′′ monument cap (with park code and 5-digit plot id) 

 18′′ aluminum monument stake 

 extra stake and cap (sometimes the caps do not fit the stakes) 

 5 wooden stakes 

 8 pin flags 

 Trimble GeoXT or comparable GPS unit 

 hard rubber mallet 

 compass (1 per technician) set to appropriate declination 

 5 fiberglass reel tapes, each a minimum of 30 meters length 

 Permanent ink marker (e.g., Sharpie) 

 Stamp kit 

 Small hammer 

 Flagging 

 Electrical tape 

VI. Literature cited 

Symstad, A.J., R. Gitzen, D. Licht, C.L. Wienk, and A. Thorstenson. 2009. Plant community 

composition and structure monitoring protocol for the Northern Great Plains I&M 

Network – Draft. U.S. Dept. of Interior, National Park Service, Rapid City, South 

Dakota. 

Woodward, A.E., Hutten, K.M., Boetsch, J.R., Acker, S.A., Rochefort, R.M., Bivin, M.M., and 

L.L. Kurth. 2009. Forest vegetation monitoring protocol for national parks in the North 

Coast and Cascades Network: U.S. Geological Survey Techniques and Methods 2-A8, 

228 pp. 
48

Vegetation Monitoring Protocol for the 

Southwest Alaska Network (SWAN) 

Standard Operating Procedure (SOP) # 3 

Plot Attributes – Physical & Biotic Characteristics 

Version 1.0 (December 2008) 

Revision History Log: 

Version No. Revision 

Date 

Author Changes Made Reason for Change 

 

This Standard Operating Procedure provides instructions for recording physical and biotic 

characteristics of the 50 m × 50 m permanent monitoring plots. 

Procedures: 

 

I. Plot data sheet 

If any of the fields on this sheet are equal to zero, enter ‘none.’ If any of the fields are unknown 

to the observer, enter ‘unknown’ (‘unk’). Do not leave fields blank. 

Date: The calendar date on which field sampling is performed 

Park: Four-letter park code (LACL, KATM, or KEFJ) 

Plot ID: Elevation band (01, 02, or 03) plus GRTS id; e.g., ‘01-016 ‘denotes GRTS point no. 

16 (firing order = 16) for elevation band no. 1 (0-450 m) 

Crew: Names of all crew members involved in sampling the plot 

Time: The time plot work starts, using military time (00:00 – 24:00 hrs) 

GPS: Make and model (e.g., Trimble GeoXT) 

Elevation: Elevation in meters from the Trimble GPS at the monument stake (T1-0m) 

Plot Slope (o): Use a clinometer to measure the slope angle of the plot. Hold the clinometer 

parallel to the ground and trained on an object above the ground at eye height. Check that the 

value you measure for slope is the same facing both uphill and downhill. The reading on the 

clinometer is the percent slope (right scale) or slope angle (left scale). In the upslope direction, 

the reading will be (+), while in a downslope direction it will read (-). Often, an upslope and 

downslope measurement will be averaged to determine average slope steepness, but the 
49

direction of the reading (+ or -) is not included; on a uniform slope, if + and - reading were 

averaged, the average would always be zero. 

Plot aspect (o): Record plot aspect using a compass with magnetic declination set for the area 

in which you are working (http://www.ngdc.noaa.gov/geomagmodels/Declination.jsp). Record 

the declination used. All bearings will be recorded using true north. Hold the compass flat, 

point it directly perpendicular to the slope contour, directed downslope, and measure the true 

azimuth of the slope. 

Endpoint coordinates: Record the latitude and longitude for each transect endpoint (T1-0m, 

T1-30m, T2-0m, T2-30m, T3-0m, T3-30m) using a Trimble GPS (e.g., Trimble GeoXT), or 

comparable, with sub-meter accuracy. Record the Rover file name for each set of plot 

coordinates, as lat/longs will be differentially corrected at a later date.

Dominant species: Briefly survey the plot and list the three most abundant taxa (by cover) in 

each of the following categories using USDA-NRCS codes (http://plants.usda.gov/) for 

vascular and nonvascular plants. 

Viereck Class IV - V: Record the Viereck code (Viereck et al., 1992) to at least Level IV that 

best describes the plot. Vegetation within the plot should be homogeneous and should contain 

no more than one Level IV class. 

Parent material: Record the parent material, such as granitic bedrock, mixed alluvium, etc. 

Evidence of humans: Record any signs of humans or human activity visible from the plot. 

Drainage: Choose the category (listed below) that most closely describes the drainage 

characteristics of the soil at a site. Steep slopes are well drained due to water running off the 

inclined surface, whereas the drainage characteristics of gently sloping sites and flat areas are 

mostly a function of the permeability of the substrate. Permeability ranges from very welldrained on coarse gravels to extremely impeded drainage on permafrost and/or soils with high 

clay content. 

• Rapid – runoff/ hill-slope 

• Rapid – coarse, permeable substrate 

• Impeded – low permeability 

• Saturated soil (water-table at or near soil surface) 

• Scattered standing water 

• Aquatic 

Slope position: Select the most appropriate category on the data sheet (listed below) that 

provides the location of the plot relative to the surrounding topography: 

• Ridgetop 

• Saddle 

• Upper 1/3 slope 
50

• Middle 1/3 slope 

• Lower 1/3 slope 

• Valley bottom - specify whether on colluvium, stream terrace, or active floodplain 

Slope type: concave, convex, planar, undulating 

Microrelief type: Select the type of micro-relief evident in plot from the following pick-list, 

which is also listed on the datasheet; reference the NRCS Glossary of Landform and Geologic 

Terms (NSSH Part 629; http://soils.usda.gov/technical/handbook/contents/part629.html) for a 

description of the specific landform. 

• Bedrock outcrops 

• Mass-movement - erosional surface; e.g., gully, slump scar, debris flow 

• Mass-movement - depositional surface; e.g., rock slide, talus pile 

• Turf hummock; e.g., tussock formations (Eriophorum or Carex) 

• Earth hummock; e.g., micro-relief due to physical processes, such as solifluction 

lobes and frost heaves 

• Moss hummock; e.g., micro-relief due to moss accumulation 

• Depressions; e.g., nivation hollows, thermokarst. 

• Animal burrows 

• Planar surface - level, non-hummocky terrain 

Evidence of fire: List any evidence that a fire has occurred in the plot area, including 

charcoal, burnt trees, healed or fresh fire scars, down wood with scorch marks or charcoal, or 

other evidence of fire in the vicinity. 

Evidence of wind: Record any direct physical evidence of wind on the vegetation or substrate; 

e.g., krumholz, dune formations, etc. 

Wind exposure: Assign one of the following categories based on the apparent exposure of the 

plot to prevailing wind: 

• Exposed = the plot has no shelter from prevailing winds; e.g., an open ridge top or an 

open flat area that is distant from windbreaks 

• Moderate = the plot has some protection from wind in the form of topographic relief, 

but is generally open and exposed to prevailing wind currents, and protection is from 

only one direction. 

• Protected = the plot is substantially protected from prevailing wind in two directions; 

e.g., the plot is situated in the lee of ridge top. 

• Sheltered = the plot is sheltered for prevailing winds from three directions. Reserve 

this category for spots that likely receive very little strong wind because of their 

position on the landscape. 

Notes: Record any additional information that is pertinent to the plot in the ‘Notes’ sections on 

pages 1 and 24 of the data sheets, including the following: 

• Structure: Record any observations about the structure or composition of the plot. 

• Successional status: Record the successional status of the plot and surrounding area. 
51

• Disturbance: Record any evidence of disturbance to the vegetation, including animal 

activity, and/or signs of stress in the vegetation (e.g., heart rot, broken tops, red 

needles, etc.). 

• Phenology: Record all observations regarding phenology, residual snowpack, 

standing water (e.g., from snowmelt), and/or frost damage. 

II. Field notebook 

1. Every technician will keep a field notebook. The lead technician is responsible for ensuring 

that a complete set of plot observations are compiled and transferred to the data sheets at the 

end of every day. Ancillary information should be included in the notebooks, if applicable: 

• Wildlife: Include any sightings of animals, with a complete description of what the 

animal was doing, how long it was in view, where it was sighted, etc. 

• Weather conditions 

• Travel time to/from site

• Notes regarding route to site, hazards, etc. 

2. The lead technician will keep detailed notes regarding issues that arise during plot selection, 

establishment, and sampling. 

III. Equipment 

• USDA-NRCS codes for vascular and nonvascular species (http://plants.usda.gov) 

• Complete key to Viereck et al. (1992) 

• Trimble XT GPS unit or comparable unit 

• Clinometer 

• Compass 

• Plot overview data sheet 

IV. Literature cited 

Viereck, L.A., Dyrness, C.T., Batten, A.R., and K.J. Wenzlick. 1992. The Alaska vegetation 

classification. U.S. Forest Service, Pacific Northwest Region, General Technical Report 

PNW-GTR-286, 278 pp. 

Woodward, A.E., Hutten, K.M., Boetsch, J.R., Acker, S.A., Rochefort, R.M., Bivin, M.M., and 

L.L. Kurth. 2009. Forest vegetation monitoring protocol for national parks in the North 

Coast and Cascades Network: U.S. Geological Survey Techniques and Methods 2-A8, 

228 pp. 
52

Vegetation Monitoring Procedure for the 

Southwest Alaska Network (SWAN) 

Standard Operating Procedure (SOP) # 4 

Digital Photography 

Version 1.0 (December 2008) 

Revision History Log: 

Version No. Revision 

Date 

Author Changes Made Reason for Change 

 

Repeat photos are extremely valuable for documenting changes in vegetation and landscape 

attributes through time. This SOP describes general methods for photo-documentation of 

permanent plots. 

Procedures: 

1. Use a medium resolution format for photos. The typical file size of photos ranges between 

400 KB – 1 MB. Rarely will you need to store images in the highest resolution format for 

routine photo-documentation. 

2. Use a dry-erase board to index all photos. This step is essential to verify plot photos. The 

board should include the following information: park, plot ID, date, and transect number. 

Additional information will include direction (east, west, north and south), plot frame size, or 

other pertinent information required for that particular photo. Always photograph the dryerase board before the subject. 

3. Table 4.1 outlines the photos required for each plot. Additional detail is provided in SOP #6 

(nested frequency). 

Equipment: 

 Camera with waterproof case; extra batteries; and memory cards ≥ 1 GB 

 Dry-erase board and dry-erase pens 

 Silica desiccant to store with the camera during wet weather 

 Backup camera with all accessories 
53

Table 4.1. Photo requirements at each plot. 

Image Total # of images Comments 

Plot overview Varies Should include key landscape features that will help to 

relocate the plot in the future. 

Monument cap at T1-0m At least 1 From the S-SW looking toward the monument. Photograph 

from several meters away to capture surrounding features 

that would help to relocate the monument. 

Southern baseline 1 From T1-0m looking east (90°) 

Transect lines 2 per transect Photograph each transect from the T-0m and T-30m 

endpoints. Set the photo frame so that the transect fills 

about 80% of the frame with a 20% skyline. 

Nested quadrats (SOP 6) 15 per transect Fill the photo frame with the plot frame starting with the 

0.25m2

 plot. Take additional photos using the same 

technique for the 1m2

 and 4m2 plots. You will have to 

adjust your position slightly to account for the increased 

size of the plot frame. 

Ecotones Varies Photograph any ecotones that occur within the plot or very 

close to the plot and document any noteworthy 

physiographic features in relation to the plot 
54

Vegetation Monitoring Procedure for the 

Southwest Alaska Network (SWAN) 

Standard Operating Procedure (SOP) # 5 

Species Cover 

Version 1.0 (December 2008) 

Revision History Log: 

Version No. Revision 

Date 

Author Changes Made Reason for Change 

 

This Standard Operating Procedure provides instructions for using point-intercept measurements 

to estimate vegetative cover and structure in the permanent 30 m × 30 m plot. Horizontal and 

vertical distribution of plant cover are recorded at defined intervals along the three 30-m 

transects. 

Procedures 

I. General guidelines

1. Cover measurements are recorded along the three 30-m transects. One person calls out ‘hits’ 

along the point-intercept transect (reader), while a second person records the observations 

(recorder). 

2. To the extent possible, the reader and recorder should stay outside of the 30 m × 30 m 

intensive plot to avoid trampling vegetation inside the plot (i.e., reader and recorder should 

stand to the west of Transects 1 and 2 and to the east of Transect 3). Measurements should 

be made to the inside of the plot (i.e., east of the tape on Transects 1 and 2, and west of the 

tape on Transect 3). 

3. Photograph each transect prior to 

measurement. Record the date, 

plot ID, transect no. and distance 

along transect (0 m or 30 m) on the 

dry-erase board (Fig. 5.1.a). 

Second, photograph the transect 

line from the recorded endpoint, 

sighting down the entire transect, if 

possible, and including the skyline, 

as shown (six photos, total; Fig. 

5.1.b). 

Figure 5.1. Photograph the dry-erase board with the 

complete plot information (a), then photograph the transect 

line (b). 

a b 
55

II. Point-intercept measurements 

1. Transect tapes should be firmly anchored, straight and taut, with meter increments facing up. 

2. Starting at the south (0-m) end of a transect, the reader drops a pin perpendicular to the 

ground at 0.5 m intervals along the tape, starting at 0.5 m and ending at 29.5 m. In taller 

vegetation (≥50 cm), use a sampling rod (Figure 5.3). In low-stature vegetation, a rigid pin 

will suffice. The reader calls out species that are “hit” within each height class at each point 

(Fig. 5.2). USDA-NRCS four-character plant codes are used to record vascular species 

(http://plants.usda.gov/). Write out the complete Latin name for any species for which the 

USDA code is unknown. Height classes are as follows: (1) <50 cm, (2) 50 – 100 cm, (3) 1 – 

4 m, and (4) >4 m. Cover codes (Table 5.1) are used to record hits for non-vascular plants 

and substrate classes. 

Table 5.1. Cover codes for non-vascular plants and substrate classes. 

Non-vascular 

feature 

Code Notes 

Moss M Enter it as a class, not by species 

Lichens LI Enter it as a class, not by species 

Standing dead SD Consists of non-living, woody plant parts held above the ground surface, 

including dead branches on all, or portions of, shrubs or trees. 

Downed wood DW Downed wood (on ground surface) with diameter > 1 cm. 

Litter LT Detached leaves, branches, etc., on the ground surface, and standing dead 

material on graminoids. Includes woody fragments on the ground surface < 1 

cm diameter and animal scat. 

Cryptogamic crust CR Soil crust comprised of cyanobacteria, lichens, mosses and fungi. Use if soil is 

stabilized (not bare ground) but free-living mosses or lichens are not 

distinguishable. 

Bare ground BG Bare mineral soil (sand, silt clay matrix). The bare ground category is reserved 

for open soil that does not support a protective layer of plant growth. Do not 

record bare ground for soils that occur under moss or lichens. 

Gravel G The code is “G”; this consists of ground surface covered with rock fragments 

sized from 2mm to 64 mm (6.4 cm ~ 2.5 inches) 

Rock RK Consists of a ground surface covered with rock fragments sized larger than 64 

mm (6.4 cm ~ 2.5 inches). 

Standing water SW Consists of standing water occupying the surface of the ground 

Running water RW Consists of running water occupying the surface of the ground 

from Elzinga et al. 1998

Figure 5.2. Point-intercept method of measuring cover. 
56

3. For frost-damaged plants, record (FD) following the species code; e.g., if Ledum palustre 

ssp. decumbens is encountered with frost damage (red needles) in the current year, record it 

as LEPAD (FD). Frost damage hits must be recorded separately from non-damaged hits. 

4. For standing dead material (e.g., standing dead trees or shrubs, in which the entire plant is 

dead), record (SDD) following the species code, as in (3), above. For standing dead branches 

attached to a living plant, record (SDL) following the species code. The SDL designation 

should be limited to branches that are terminally dead, but not to dead portions of a branch if 

the apical meristem is live. 

5. For each unknown species encountered, collect a specimen from outside of the 30 m × 30 m 

plot, following guidelines in Appendix H. Assign a tracking identifier (e.g., catalog and 

accession no.) to each specimen, and provide a detailed description of the collection on the 

datasheet. 

6. Instructions for use of the sampling rod are outlined in Roland et al. (2004) and summarized 

as follows: 

a. Place the base of the sampling rod on the ground adjacent to the tape. Level the rod 

using the bubble level on the pin bracket, and ensure that the pin will be aligned at the 

desired location on the transect tape. The base of the sampling rod must be at the same 

level as the surface where the pin will touch the ground. Extend the rod to 2 m to 

measure overstory vegetation. 

b. Record hits as follows: For the overstory vegetation, record only vegetation that 

actually is within the point target on the densitometer viewer. For the understory 

vegetation, record data only when the point of the pin intersects the vegetation. Enter 

only one hit for any given species per height class, even if you intercept it multiple times. 

At the ground level, it is possible to record multiple cover classes (e.g., litter, moss, and 

lichen) if they overlay one another. In addition, record the type of underlying, non-living 

substrate (e.g., litter, gravel, bedrock, soil). 

Figure 5.3. Sampling rod used to measure 

species cover by the point-intercept 

method in vegetation ≥50 cm tall. A level 

and densitometer attached to the rod are 

used to ensure that the rod is held 

vertically, and that overstory vegetation 

intercepted in the densitometer is recorded 

as a ‘hit.’ Species cover is recorded within 

each height class (0-50 cm; >50-100 cm; 

>1-4 m; >4 m). 

Photo from Roland et al. (2004). 
57

III. Equipment 

 Sampling rod and extra pins 

 3 × 30 m fiberglass reel tapes 

 2 m metal tape 

 Percent cover data sheets (3 copies) 

 USDA codes for vascular and nonvascular species (Appendices 10-11). 

 Ziploc collection bags 

 Sharpie to label bags 

 Camera 

 Dry-erase board & dry-erase pens 

As of 2009, sampling rods were constructed by Jon Holmgren: 

Jon’s Machine 

350 Goldstream Rd. 

Fairbanks, Alaska 99712 

(907) 457-5000 

IV. Literature cited 

Roland, C., Oakley, K., Debevec, E.M., and P. Loomis. 2004. Monitoring vegetation structure 

and composition at multiple spatial scales in the Central Alaska Network (Version 1.0). 

National Park Service, Inventory & Monitoring Program, Alaska Region, Anchorage, 

Alaska, 50 pp. 
58

Vegetation Monitoring Protocol for the 

Southwest Alaska Network (SWAN) 

Standard Operating Procedure (SOP) # 6 

Frequency 

Version 1.0 (December 2008) 

Revision History Log: 

Version No. Revision 

Date 

Author Changes Made Reason for Change 

 

This SOP provides instructions for measuring species occurrence (frequency) and cover by 

growth form. Measurements are made in nested quadrats arrayed along the three 30-m transects. 

Procedures: 

I. General Guidelines 

1. Five nested quadrats (0.25 m2, 1 m2 and 4 m2; Fig. 6.1) are sampled along each of the 

three 30-m transects (15 quadrats total). The quadrats are established on the east side of 

Transects 1 and 2, and the west side of Transect 3, as shown in SOP 2, Fig. 2.1. 

Figure 6.1. Nested quadrats (0.25-m2, 1-m2 and 

4-m2

) are placed systematically at 7-m intervals 

along each 30-m transect. The 0.25-m2

 and 1-m2

quadrats are located in the southwest corner of 

the 4-m2

 quadrat on Transects 1 and 2, as shown. 

On Transect 3, the 0.25-m2

 and 1-m2 quadrats are 

located in the southeast corner of the 4-m2

quadrat, as measurements are taken to the west 

of the transect (SOP 2). 

Presence/absence of species is recorded within 

each quadrat frame, starting with the 0.25-m2

frame. Cover by growth form is recorded in the 4-

m2 quadrat using ocular estimates. USDA-NRCS 

species codes are used to record vascular and 

non-vascular species. 

4 m2

1 m2

0.25 m2
59

Figure 6.2. Nested quadrat photos. The dry-erase board with complete 

plot information is photographed first (a); followed by the 0.25-m2

 quadrat 

(b); 1-m2

 quadrat (c); and 4-m2 quadrat (d). 

a b 

c d 

2. As with cover measurements (SOP 5), the reader and recorder should stand to the west of 

Transects 1 and 2 and to the east of Transect 3 while recording frequency. While the 

reader may need to step around the 4-m2

 quadrat to document all species, one should 

never step inside of the quadrat. 

3. Quadrats must be placed exactly at 7-m intervals (i.e., the southern edge of the nested 

quadrats should be located at 0, 7, 14, 21, and 28 m) along each transect to ensure 

accurate relocation on subsequent sampling dates. Quadrat frames should be aligned at 

their SW (Transects 1 and 2) or SE (Transect 3) corner and must lie squarely against the 

transect tape and parallel to the ground surface. Any deviation from the normal placement 

of the quadrats must be recorded in the plot notes. 

4. Photograph the nested 

quadrat once it has been 

established. As in SOP 5, 

record the date, plot ID, 

transect and quadrat 

number on the dry-erase 

board (Fig. 6.2.a), and 

photograph it prior to 

photographing the 

quadrat frames. Each 

quadrat frame should be 

photographed from a 

height of ca. 1.3 m over 

the top of the quadrat(s), 

with the intent of filling 

the photo frame (Fig. 

6.2.b-c). The photo of the 

4-m2

 frame should be 

taken from the corner in 

which the frames are 

nested (SW or SE; Fig. 6.2.d). 

II. Frequency measurements 

1. Record the occurrence of all vascular and non-vascular species that occur within each 

quadrat frame, starting with the 0.25-m2

 quadrat and moving out to the 1-m2 and 4-m2

quadrats. A species’ presence is indicated by its frequency code, as follows: 0.25 m2

 = 

1; 1 m2 = 2; 4 m2 = 3. For example, all taxa that occur in the 0.25-m2 frame will be 

assigned a frequency of 1. Once all species have been identified in the 0.25-m2

 frame, 

the reader will move on to the 1-m2

 quadrat frame and assign any new species 

encountered a frequency value of 2 (Fig. 6.3). USDA-NRCS four-character plant codes 

are used to record vascular and non-vascular species (http://plants.usda.gov/). The 

complete Latin name should be recorded for any taxa for which the USDA code is 
60

unknown or unavailable. Vascular species are recorded in all quadrats; nonvascular 

species are recorded in the 0.25-m2

 and 1-m2 quadrats only. 

2. Trees and other single-stemmed plants must be completely rooted within the greater 4-m2

quadrat to be considered present. Shrubs and other multiple-stemmed plants must be at 

least partially rooted within the 4-m2

 quadrat. Overhanging leaves or branches of a plant 

not rooted in the quadrat will not be considered an occurrence in the nested frequency 

measurements. 

3. For each unknown species encountered, collect a specimen from outside of the 30 m × 30 

m plot, following guidelines in Appendix H. Assign a tracking identifier (e.g., catalog 

and accession no.) to each specimen, and provide a detailed description of the collection 

on the datasheet. 

III. Percent cover by growth form 

1. Estimate the percent cover of species by growth form using ocular estimates in the 4-m2

quadrat frame. Use growth forms (habits) assigned by USDA-NRCS (http://plants.usda.gov/) 

to determine species’ groupings. For example, Salix alaxensis and Ledum palustre ssp.

decumbens are designated as shrubs by NRCS, even though Salix may grow as tall as 

neighboring trees and Ledum may exhibit nearly prostrate growth. For these estimates, 

quantify anything that is within the 3-dimensional boundary of the quadrat (imagine the z

dimension to extend beyond the highest strata in the plot). Overhanging leaves or branches 

are counted in the cover estimates, even if the plant itself is not rooted in the plot. Note that 

in this sense, cover estimates differ from the frequency measurements (Section II.2). As 

such, total cover of vegetation in the 4-m2

 quadrat may exceed 100 percent, due to 

overlapping and overhanging foliage. The area represented by each 20 cm × 20 cm square in 

Figure 6.3. Example of a 

datasheet showing frequency 

values for species recorded 

along a transect in Lake Clark 

National Park and Preserve. 

USDA-NRCS codes are used to 

designate vascular and nonvascular species. For each 

nested quadrat along the 30-m 

transect, a frequency value is 

recorded if the species is 

present in either the 0.25-m2

quadrat (frequency = 1), the 1- 

m2

 quadrat (frequency = 2) or 

the 4-m2

 quadrat (frequency = 

3). A species’ presence in the 

smallest quadrat is always 

recorded, even if it occurs in all 

three quadrats. If the species is 

absent in all three quadrats, the 

field is left blank. 
61

the 4-m2

 quadrat frame equals 1 percent of the total plot. Crew members should practice, and 

calibrate, their cover estimates using the quadrat frame and Fig. 6.4. 

Percent graminoids: Estimate the total percent cover of all grasses and sedges in the 4-m2 quadrat. 

Percent dwarf shrub: Estimate the total cover of all dwarf shrub species. 

Percent shrub: Estimate the total cover of all shrub species. 

Percent trees: Estimate the total cover of trees, including overhanging branches. 

Number of seedlings: Record number of tree seedlings (any tree species less than 1.37 m tall) in the quadrat. 

2. Record percent cover of the ground layer (substrate types) in the 4-m2 quadrat frame, as in 

(1), above. Cover estimates for the ground layer must total 100 percent. 

Percent cryptogamic crust: Estimate the total percent cover of cryptobiotic crust in the 4-m2

 quadrat. Free-living 

lichens and mosses should be quantified separately. 

Percent moss: Estimate the total cover of all moss, including moss occurring under other growth forms. 

Percent lichen: Estimate the total cover of all lichens, as for moss. 

Percent bedrock: Estimate the total cover of all continuous exposed bedrock 

Percent cobble: Estimate the total cover of all rock fragments larger than 6.4 cm (~2.5 inches) diameter. 

Percent gravel: Estimate the total cover of all rock fragments between 2 mm-64 mm diameter. 

Percent bare soil: Estimate the total cover of all bare soil and rock fragments less than 2 mm diameter. 

Percent litter: Estimate the total cover of litter in the quadrat. Litter consists of all downed and dead plant 

herbaceous material, plus all woody material less than 1 cm diameter, lying on the soil surface. 

Percent standing dead: Estimate the total cover of all non-living plant parts held above the ground surface, 

including dead standing grasses, shrubs, trees, and dead limbs on live trees. 

Percent wood: Estimate the total cover of dead, downed wood greater than 1 cm diameter. 

IV. Equipment 

 0.25 m2, 1 m2 and 4 m2 quadrat frames 

 Frequency data sheets 

 Camera & memory cards 

 Dry-erase board or diving board 

 Erasable markers or pencils 

 Collection bags 

 Sharpies 

 USDA species codes for vascular and non-vascular species 

 Hand lens 

 Taxonomic key(s) 
62

 Figure 6.4. Reference scatter plots for cover estimation (from Terry and Chilingar 1955). 

. 
63

Vegetation Monitoring Protocol for the 

Southwest Alaska Network (SWAN) 

Standard Operating Procedure (SOP) # 7 

Tree measurements 

Version 1.0 (December 2008) 

Revision History Log: 

Version No. Revision 

Date 

Author Changes Made Reason for Change 

 

This SOP provides instructions for measuring trees and saplings in the 30 m × 30 m plot with the 

objective of estimating tree density, basal area, and size-class distribution for tree species. 

Methods are adapted from Roland et al. (2004), Woodward et al. (2009), and USFS Forest 

Inventory and Analysis field procedures for Alaska (FIA 2003). 

Procedures 

I. Definitions 

DBH (diameter at breast height) = 1.37 m above the ground 

Seedling = any tree species < 1.37 m tall 

Sapling = any tree species ≥ 1.37 m tall and DBH < 12.0 cm 

Tree = any tree species ≥ 12.0 cm DBH (Table 7.1) 

Condition Class = ‘live’ or ‘dead’ individual 

 Table 7.1. Tree species 

Species Authority USDA Code TSN 

Picea glauca (Moench) Voss PIGL 183295 

Picea mariana (P. Mill) B.S.P. PIMA 183302 

Picea sitchensis (Bong.) Carr. PISI 183309 

Picea × lutzii Little PILU 194777 

Tsuga mertensiana (Bong.) Carr TSME 183402 

Betula papyrifera Marsh. BEPA 19489 

1

Betula papyrifera var. kenaica (W.H. Evans) A. Henry BEPAK 19493 

Populus tremuloides Michx. POTR5 195773 

2

Populus balsamifera ssp. balsamifera L. POBAB2 22454 

Populus balsamifera ssp. trichocarpa (Torr. & Gray ex Hook.) Brayshaw POBAT 22455 

1

Syn: Betula kenaica W.H. Evans (USDA Code: BEKE2; TSN: 195224; accepted local name in NPSpecies) 

2

Syn: Populus balsamifera L. (USDA Code: POBA2; TSN: 22453; also accepted by NPSpecies) 
64

II. Tree and sapling measurements 

2. Following the completion of cover and frequency measurements, map all trees ≥12 cm 

DBH in the 30 m × 30 m plot. Viereck et al. (1992) define Level III forest classes as 

follows: needleleaf woodland (10-24% canopy cover), open forest (25-60% cover), and 

closed forest (60-100% cover). In forested sites with canopy cover >25%, map the largest 

trees first. In each half of the plot, locate the trees by referencing their position relative to 

the closest transect line, and record the approximate N/S distance along the transect to the 

nearest 0.1 m. Use a laser rangefinder or tape to record the E/W distance from the N/S 

point on the transect (Fig. 7.1). Map distances to the center of the tree bole, at ground 

level. Minimize trampling in the plot by working in one half of the plot at a time, starting 

at one end (e.g., south) and working toward the other end. To the extent possible, avoid 

walking within 2 m of transect lines, where quadrats are sampled. 

2. Record the following data by tree for all trees in the plot: 

• Species: USDA species code (Table 7.1) 

• 1⁄2 plot: west (W; between T1-T2) or east (E; between T2-T3) half of plot (Fig. 7.1) 

• Transect: transect along which the N/S distance (Y-coordinate) is measured (T1, T2, or T3; Fig. 7.1) 

• Dist N/S: distance in meters along the transect, from 0 m, that forms the Y-coordinate (Fig. 7.1) 

• Dist E/W: distance in meters, perpendicular to the transect, measured from the transect to the center 

of the tree bole; i.e., the X-coordinate (Fig. 7.1) 

• X-Y Coordinates: calculated from the 1⁄2 plot designation and N/S & E/W measurements (see 

example above) 

• DBH: Diameter at breast height (1.37 m) measured in cm using a DBH tape (Fig. 7.2). Trees must be 

≥ 12 cm DBH to qualify for mapping. See notes regarding DBH measurements below (3). 

• Height: Estimate tree height to the nearest 0.1 m using a laser rangefinder or clinometer (Figs. 7.3-

7.5) 

• Cond (L/D): Record if the tree is live (L) or dead (D) 

• Crown class: Record crown class code (Table 7.2; Fig. 7.6) 

Figure 7.1. Mapping trees off of the X-Y 

grid formed by transect lines. Trees must 

be rooted ≥50% within the plot to be 

mapped. Map variables: 1⁄2 plot is 

recorded as the eastern (E) or western (W) 

half of the plot. Transect is recorded as the 

nearest transect (T1, T2, or T3) along which 

the north-south distance is measured. 

Distance N/S is recorded as the distance 

from 0 m (southern endpoint) in meters 

along the transect tape. Distance E/W is 

recorded as the distance in meters between 

the transect line and the tree bole. 

In the example shown: 

1⁄2 plot = E 

Transect = T3 

Distance N/S = 10 m 

Distance E/W = 5 m 

(X-Y coordinates: X=25 m; Y=10 m) 
65

• Crown length: Record the percent of tree bole occupied by live crown (Table 7.3) 

• Pathogens: Record pathogen code (Table 7.4), if applicable. Record ‘NONE’ if no damage is 

evident. 

3. Diameter at breast height (DBH): For trees forked at or below breast height (≤1.37 m), 

both forks will be measured and assigned the same tree number with a letter suffix. For 

example, if a tree has two forks at 1.37 m, label the tree ‘12A’ and ‘12B’ on the data sheet, 

and record the diameters for each separate fork. However, if a fork has a departure angle of 

greater than 45° from the bole, the fork is classified as a limb, and only the largest, upright 

leader should be measured. 

Diameter tapes are calibrated so that when the tape is wrapped around the circumference of a 

tree, the tape is actually showing the diameter of the tree [C (circumference) =  (diameter); 

Fig. 7.2]. The other side of the d-tape is the lineal tape. A common error is to read the lineal 

side of the tape instead of the d-tape side. Be sure to check your reading of the tape to make 

sure the number you have called out for diameter actually makes sense. 

Figure 7.2. Linear (top) and diameter (bottom) sides of a diameter tape. 

http://www.cfr.washington.edu/Classes.ESC.221/Classes.Esc.221.BAK/skills/VegTerrain%20Field%20Procedures.do

c.htm

4. Tree height: Use a laser rangefinder, following manufacturer’s directions, to estimate tree 

height to the nearest 0.1 m. 

5. Sapling density and biomass: Tally the number of saplings in the eastern and western half 

of the plot by species and condition code (live/dead), as above. Record ‘0’ if there are no 

saplings in a particular species × condition category. In stands with canopy cover ≤25%, 

record DBH and height for all saplings. In stands with canopy cover >25%, record sapling 

DBH and height for saplings ≥4 cm DBH and tally all saplings of lesser DBH. 

6. Assign crown class and condition codes to trees in plot: Descriptions adapted from the U.S. 

Forest Service Forest Inventory & Analysis (FIA; 2003) program follow. 

 
66

Table 7.2. Crown class codes 

Crown 

class 

code 

Crown class name Crown class description 

1 Open Grown Trees with crowns which have received full light from above and all sides 

throughout their lifespan, particularly during early development. Their forms or 

crown shapes have not been influenced by other trees. 

2 Dominant Trees with crowns extending above the general level of the crown canopy and 

receiving full light from above and partly to the sides; taller than the average 

trees in the stand, with crowns well developed but possibly crowded on the 

sides. 

3 Codominant Trees with crowns at the general level of the crown canopy and receiving full 

light from above but comparatively little from the sides; usually with mediumsized crowns and somewhat crowded on the sides. 

4 Overtopped Trees with crowns entirely below the general level of the crown canopy, 

receiving no direct light from above or from the sides. Includes small trees and 

regeneration under forest canopy. 

5 Dead tree Tree is dead, either standing dead or dead and down. 

Figure 7.6. Crown classes for closed forest; crown class codes from Table 7.2. 

Environment Canada Ecological Monitoring and Assessment Network 

(http://www.eman-rese.ca Cited 30 December 2008) 

 Table 7.3. Crown length codes 

 

Crown 

length code 

Crown length percent 

1 1 - 25 

2 26 - 50 

3 51 - 75 

4 76 - 100 

3 3 

3 

2

3

4

3
67

Table 7.4. Pathogen codes (adapted from Roland 2004) 

 

III. Canopy Cover 

1. Use a spherical densiometer to take a reading in each of the four cardinal directions 

(N/S/E/W) at the four corners of the plot (T1-0 m; T1-30 m; T3-0 m; T3-30 m) and at plot 

center (T2-15 m). Hold the instrument level and far enough away from your body so that 

your head is just outside of the mirrored grid (approximately 30-45 cm away from your 

body). Record the number of dots NOT occupied by canopy (Fig. 7.7). Multiply the total 

count by 1.04 to obtain the percent of overhead area not occupied by canopy. Subtract 100 

from this value to estimate the canopy cover in percent. Average the four readings (N/S/E/W) 

to provide an estimate of canopy cover from that point. Record calculated percent cover (not 

the point counts) on the data sheet.

http://www.clallam.net/streamkeepers/assets/applets/CanopyCl.pdf 

 

Pathogen code Pathogen/damage description 

BAPE Bark peeling 

BRRU Broom rust 

COFU Conk fungi 

DELE Dead leader 

DYIN Dying 

OOPI Oozing pitch 

SPTR Split trunk 

WOUN Wound 

SPBE Spruce bark beetle 

SPEN Spruce engraver 

LEMI Leaf miner 

Figure 7.7. Example of a densitometer reading 

used to estimate canopy cover. In this case, 48 of 

96 grid points are not occupied by canopy. 

Thus, 48 × 1.04 = 49.92 and 

% canopy cover = 100 - 49.92 = 50.08, or 50% 
68

IV. Basal Area and Biomass Calculations 

Calculate basal area for trees within the 30 m × 30 m (0.09 ha) inner plot as follows: 

Basal area (cm) = π × (DBH/2)2 

Calculate aboveground live and dead biomass (trees and saplings) of white spruce using 

allometric equations derived for Picea glauca (Harding and Grigal 1985): 

Dry mass (kg) = 0.069 × (DBH (cm))2.324 × (height (m))0.210

 

V. Equipment 

 Tree and sapling data sheets (1 per plot) 

 Laser range finder with field instructions 

 Clinometer 

 30 m reel tape 

 2 Diameter tapes (metric) 

 Compass 

 Spherical densitometer 

 Increment borers 

VI. Literature cited 

Forest Inventory and Analysis (FIA). 2003. Field procedures for the coastal Alaska inventory. 

USDA Forest Service, Pacific Northwest Research Station, Forestry Sciences Lab, 

Region 10, Alaska. Anchorage, Alaska, 182 pp. 

Harding, R.B., and D.F. Grigal. 1985. Individual tree biomass estimation equations for 

plantation-grown white spruce in northern Minnesota. Can. J. For. Res. 15:738-739. 

Roland, C., Oakley, K., Debevec, E.M., and P. Loomis. 2004. Monitoring vegetation structure 

and composition at multiple spatial scales in the Central Alaska Network (Version 1.0). 

National Park Service, Inventory & Monitoring Program, Alaska Region, Anchorage, 

Alaska, 50 pp. 

Viereck, L.A., Dyrness, C.T., Batten, A.R., and K.J. Wenzlick. 1992. The Alaska vegetation 

classification. U.S. Forest Service, Pacific Northwest Region, General Technical Report 

PNW-GTR-286, 278 pp. 

Woodward, A.E., Hutten, K.M., Boetsch, J.R., Acker, S.A., Rochefort, R.M., Bivin, M.M., and 

L.L. Kurth. 2009. Forest vegetation monitoring protocol for national parks in the North 

Coast and Cascades Network: U.S. Geological Survey Techniques and Methods 2-A8, 

228 pp. 
69

Vegetation Monitoring Protocol for the 

Southwest Alaska Network (SWAN) 

Standard Operating Procedure (SOP) # 8 

Tree cores 

Version 1.0 (December 2008) 

Revision History Log: 

Version No. Revision 

Date 

Author Changes Made Reason for Change 

 

This SOP provides instructions for collecting tree cores and fuels data with the objective of 

obtaining an estimate of forest stand age and CWD fuel loads. Methods are adapted from 

Woodward et al. (2009), USFS Forest Inventory and Analysis field procedures for Alaska (FIA 

2003), and the point intersect methods of Van Wagner (1968) and Brown (1974). Trees will be 

cored only once, at the time of plot establishment. Fuels measurements will be repeated at every 

subsequent site visit. 

Procedures:

I. Tree cores 

1. Core only white (Picea glauca = PIGL), black (P. mariana = PIMA), Sitka (P. sitchensis 

= PISI) or Lutz (Picea × lutzii = PILU) spruce trees. 

2. Collect cores only from trees ≥12 cm DBH that fall between the 30 m × 30 m inner plot 

and 50 m × 50 m outer plot boundary (i.e., from the 10-m wide buffer surrounding the 30 

m × 30 m inner plot). Core the largest diameter tree near each plot corner. 

3. Record the distance (m) and azimuth of each cored tree relative to the nearest plot corner. 

4. Tag the tree with a pre-numbered aluminum tag. Record the tag number on the datasheet. 

5. Core the tree at a height of approximately 30 cm above ground level, and above any butt 

swell or other abnormality in the tree bole. For trees in sloping terrain, core from the side 

of the tree. For leaning trees, core on the uphill side. It may be necessary to remove 

some lower branches to get at the base of the tree. 

6. If possible, the core should include the pith and the outer bark. If a core is substantially 

off-pith, collect a second core that comes closer to intersecting the center of the tree. 

Place the core in 1-2 protective straws and label the straw with the plot id, date, tag 

number, DBH (cm), and height above root crown (ARC), in cm. Secure both ends of 

the straw with electrical tape or a lighter. 

7. For each tree cored, record the following: 
70

• Tag #: Record the number on the pre-labeled aluminum tag used to mark the cored tree 

• Species: USDA species code (PIGL, PIMA, PISI or PILU) 

• Cond (L/D): Record if the tree is live (L) or dead (D) 

• Transect endpoint: Record closest transect endpoint (e.g., T1-30), from which a distance and 

azimuth to the tree are measured 

• Dist: distance in meters from transect endpoint to cored tree 

• Azimuth: azimuth from transect endpoint to cored tree (declination set to true north) 

• DBH: Diameter at breast height (1.37 m) measured in cm using a DBH tape. 

• Core height: Height above root crown (ARC) at which tree was cored, usually 30-40 cm 

• Tree Height: Estimate tree height to the nearest 0.1 m using the laser rangefinder 

• Notes: Include pathogen/damage codes (SOP 7), if applicable. If the largest tree(s) have rotten pith 

and do not yield complete cores, make a note of that and core one or more smaller trees. 

II. Ladder Fuels 

1. For each tree cored, record the following regarding ladder fuels: 

• Tag #: As above, in Section I 

• Crown radius: Estimate crown radius from the center of the bole to the mean branch length (m) 

• Height to dead ladder fuel: Estimate the height from the ground to the lowest dead branch (m) 

• Height to live ladder fuel: Estimate the height from the ground to the lowest dead branch (m) 

• Height to main crown: Estimate the height from the ground to the main crown of the tree (m) 

III. Equipment 

 Tree Data sheets (1 per plot) 

 10” and 14” increment borer and extra spoons (extractors) 

 WD-40 and rags for cleaning borers 

 Plastic straws (4 per plot plus extras) and extra fine-tipped sharpies 

 Electrical tape and cigarette lighters 

 Plastic ‘quiver’ for storing cores and empty straws 

 Loggers tape of at least 16 m length 

 Diameter (DBH) tapes (cm) 

 Laser range finder 

IV. Literature cited 

Forest Inventory and Analysis (FIA). 2003. Field procedures for the coastal Alaska inventory. 

USDA Forest Service, Pacific Northwest Research Station, Forestry Sciences Lab, 

Region 10, Alaska. Anchorage, Alaska, 182 pp. 

Woodward, A.E., Hutten, K.M., Boetsch, J.R., Acker, S.A., Rochefort, R.M., Bivin, M.M., and 

L.L. Kurth. 2009. Forest vegetation monitoring protocol for national parks in the North 

Coast and Cascades Network: U.S. Geological Survey Techniques and Methods 2-A8, 

228 pp. 
71

Vegetation Monitoring Protocol for the 

Southwest Alaska Network (SWAN) 

Standard Operating Procedure (SOP) # 9 

Soil sampling 

Version 1.0 (December 2008) 

Revision History Log: 

Version No. Revision 

Date 

Author Changes Made Reason for Change 

 

This SOP provides instructions for field soil descriptions adjacent to the 30 m × 30 m permanent 

plots. The objective for measuring soil parameters is to obtain data relating soil attributes to 

plant growth and ecosystem function for each permanent plot – data are ancillary and will not be 

included in trend estimates. Soils will be collected only once, at the time of plot establishment. 

Procedures 

I. Excavate and photograph soil profile 

1. A soil sample will be collected only in areas that have received clearance during the 

permitting process. No soils will be collected near known archeological sites.

2. Soils descriptions will be completed adjacent to, and outside of, one of the cardinal corners 

of the 30 m × 30 m plot. Use a trowel to expose a 30 cm soil profile (measured from the base 

of the vegetation), taking care not to disturb the soil structure (Fig. 9.1). 

3. Photograph the soil profile with a measuring tape in place for scale (0 cm at base of 

vegetation). Refill the exposure and replace vegetative mat to minimize site disturbance 

when descriptions are completed. 

II. Soil description 

1. Draw and label a sketch of the soil profile consistent with field descriptions (e.g. Figure 9.1) 

2. Record the maximum depth of observation in the soil profile, in cm 

3. Record depth (from surface) of each of the following layers in the soil profile: 

A. Ground surface cover – The ground surface is covered to some extent at least part of the year by 

vegetation, rock fragments, and/or litter (Figure 9.1). Fresh litter is defined as detached, undecomposed plant 

material lacking weathered mineral particles. 
72

 Table 9.1. Fresh litter codes

code Litter description 

CL Coniferous litter 

DL Deciduous litter (incl. shrub litter) 

GH Graminoid/Herbaceous litter 

LT Litter (undifferentiated) 

WD Woody debris 

Table 9.2. Living mat codes

code Living mat description 

DS Dwarf shrub 

MO Moss 

LI Lichen 

ML Moss and lichen 

GR Graminoid (rhizomatous/turf) 

 Table 9.3. Rock fragments codes 

code Rock fragments description 

G Gravel (2 mm <  < 76 mm) 

R Rock (  76 mm) 

B. Organic layer – An organic layer is typically present below living vegetation (Figure 9.1). It is 

dominated by the presence of organic material in varying stages of decomposition. Often the color differs 

from that of fresh litter, and fungal mycelium may be present. Some original plant material will be 

recognizable, but will usually be modified by decay processes. The mineral fraction of the organic layer is 

only a small percentage of the weight of the material. One or more organic horizons may be present. If 

significant changes in color, nature of organic material, or degree of decomposition are present, describe each 

organic horizon separately. 

 

 Table 9.4. Organic layer codes 

code Organic layer description 

AL Deciduous Leaves (incl. shrub) 

CN Coniferous Needles 

GH Graminoid/Herbaceous 

LI Lichen 

ML Moss & Lichen 

MO Moss 

OT Other (describe) 

UN Unknown 

W Wood 

C. Mineral soil - exists below the organic horizon (when present) and is at least 80 percent by weight 

mineral grains. There are almost no recognizable decomposing plant parts, but roots and woody debris may 

be present (Figure 9.1). One or more mineral soil horizons may be present. If significant changes in color or 

texture are present, describe each mineral soil horizon separately. 

III. Soil sample 

Collect approximately 100 g dry weight equivalent (approximately 100 cc) of mineral soil 

from the top 10-20 cm of the soil profile. Remove living plant material and undecomposed 

organic matter (duff) from the soil plug and transport out of the field in a labeled quartsized Ziploc freezer bag. Soils should be sieved (2 mm) and air dried within 48 h after 

leaving the field. Soil analyses will include texture, SOM content, total C and N, and pH. 
73

Figure 9.1. Soil profile showing ground surface cover, organic layer, and mineral soil. Layer names are shown on the 

right. Three mineral soil horizons are shown in this illustration; however, in the interest of time and relevance, only 

the layers in the uppermost 30 cm of the profile would be described in accordance with this SOP. 

IV. Depth to hard surface

1. Identify and record depth to hard surface adjacent to all four 30 m × 30 m plot corners. 

Probe to the hard surface using a 3-mm to 5-mm diameter, 1-m metal rod inserted several 

times at each plot corner. 

i. Maximum depth – Record maximum depth (cm) to hard surface 

ii. Hard surface code – Record composition of hard surface (Table 9.10). Rock fragments are 

identified by sharp, high-pitched vibrations/sound and depths are typically variable. Frozen ground 

is identified by a dull, low-pitched vibration/sound. Ice crystals may be present at tip of rod when 

extracted. Depths to frozen ground are typically more regular than depth to rock fragments. 

Table 9.10. Hard surface codes: 

code Composition of hard surface 

R Rock Fragment 

B Bedrock

F Frozen Ground 

V. Temperature sensor installation 

1. A temperature sensor will be installed at a subset of readily accessible, permanent plots to 

monitor soil temperature (based on rank in the GRTS selection). 

2. The sensor should be programmed to collect hourly temperature observations following 

the manufacturer’s instructions. 

3. Deploy sensor approximately 1 m due west of the plot monument at a depth of 10 cm 

(measured from the base of vegetation). Tie a piece of flagging to the sensor to aid in 
74

relocation; flagging should be long enough to reach to the soil surface. The sensor should 

make contact with the soil at 10 cm depth (no air pockets), and the intact vegetation and 

soil plug should be replaced over the top of the sensor and pressed firmly into place. 

4. Record the sensor make, model and serial number. As of 2009, HOBO Pro v1 loggers 

(http://www.onsetcomp.com/) were being used (temperature range: -20 to 70 °C; precision: 

± 0.2 °C), and were installed with the long axis parallel to the soil surface (horizontally). 

Record the date and time the temperature sensor was deployed. Record the sensor 

location using a GPS with sub-meter precision (e.g., Trimble GeoXT), and describe its 

exact location (e.g., 1 m from plot monument at 275°). 

5. If the sensor is downloaded or removed during a revisit, record the date and time of data 

retrieval or removal. Sensors will be downloaded every 1-2 years. Data management 

and data processing guidelines for continuous (logger) data are outlined in SOP 12. 

VI. Equipment 

 Measuring tape and small ruler 

 Soils data sheet and data tables 

 Soil probe (1 m, 0.5 cm diameter) 

 Trowel or hori-hori 

 Temperature sensor (pre-programmed; optional) 

 Digital camera 

 Sample bags (e.g., quart Ziplocs) 
75

Vegetation Monitoring Protocol for the 

Southwest Alaska Network (SWAN) 

Standard Operating Procedure (SOP) # 10 

Measurement of Coarse Woody Material 

Version 1.0 (December 2008) 

Revision History Log: 

Version No. Revision 

Date 

Author Changes Made Reason for Change 

 

This SOP outlines procedures for estimating coarse woody material (CWM) along 30 m 

transects. Methods are adapted from Woodward et al. (2009) and USFS Forest Inventory and 

Analysis field procedures for Alaska (FIA 2003). Estimation of CWD loads follows equations 

outlined in Van Wagner (1968) and Brown (1974). 

Procedures – General: 

Down woody components measured in this study will be limited to coarse woody debris (CWD), 

fine woody debris (FWD), duff and litter, and will be measured at a selected subset of forested 

sites. CWM is sampled using the line intersect sampling method (also called planar intersect 

method). In this method, transects are established, and individual pieces of CWD or FWD are 

tallied if the central axis of the piece is intersected by the transect. 

I. Definitions 

CWD 

In this inventory, CWD includes downed, dead tree and shrub boles, large limbs, and other 

woody pieces that are severed from their original source of growth and lying on the ground. 

CWD also includes dead trees (either self-supported by roots, severed from roots, or 

uprooted) leaning > 45 degrees from vertical. For multi-stemmed trees, only tally stems 

that are dead, detached, and on the ground, or dead and leaning > 45 o

 from vertical. 

Table 10.1. Coarse woody debris (CWD) requirements for tallying a piece that intersects the transect. 

CWD Type Lean Diameter length 

Decay Class 1-4 > 45 o from vert > 7.5 cm > 1 m long 

Decay Class 5 > 45 o from vert > 12.5 cm and > 12.5 cm high > 1 m long 

Dead tree > 45 o from vert > 7.5 cm > 1 m long 
76

CWD does not include: 

 Woody pieces < 7.5 cm in diameter at the point of intersection with the transect. 

 Dead trees leaning 0 to 45 degrees from vertical. 

 Dead shrubs, self supported by their roots. 

 Trees showing any sign of life. 

 Stumps that are rooted in the ground (i.e. not uprooted). Thus, using this method, 

stumps < dbh are not recorded in the tree tally or the DWM survey. 

 Dead foliage, bark or other non-woody pieces that are not an integral part of a bole or 

limb (bark attached to a portion of a piece is an integral part). 

 Roots or main bole below the root collar 

FWD 

In this inventory, FWD includes downed, dead branches, twigs, and small tree or shrub 

boles that are not attached to a living or standing dead source. FWD can be connected to a 

larger branch, as long as this branch is on the ground and not connected to a standing dead 

or live tree. Only the woody branches, twigs and fragments that intersect a transect are 

counted. FWD can be connected to a down, dead tree bole or down, dead shrub. FWD can 

be twigs from shrubs and vines. FWD must be no higher than 6 feet above the ground to be 

counted. Further subdivisions of FWD are outlined in Table 10.2. 

Table 10.2. Fine woody debris (FWD) size class, and transect length and measurement location 

Category of FWD Size Class Diameter range Transect length 

(slope distance) 

Transect location 

(slope distance) 

Small FWD 1 0.3 cm to 0.6 cm 2 m 5 m to 7 m 

Medium FWD 2 >0.6 cm to 2.5 cm 2 m 5 m to 7 m 

Large FWD 3 >2.5 cm to 7.5 cm 3 m 5 m to 8 m 

FWD does not include: 

 Woody pieces ≥ 7.5 cm diameter at the point of intersection with the transect. 

 Dead branches connected to a live tree or shrub, or to a standing dead tree or shrub. 

 Dead foliage (i.e. pine or fir needles, or leaf petioles). 

 Bark fragments or other non-woody pieces that are not an integral part of a branch, 

twig, or small bole. 

 Small pieces of decomposed wood (i.e. chunks of cubical rot). 

II. Locating and establishing line transects 

Transects (30 m) are established as outlined in SOP 2. LWD tallies are conducted after pointintercept and nested frequency plots have been sampled. Formulas used to estimate biomass 

from the data contain an adjustment for slope. It is helpful to have a size gauge available until 

your eye is ‘trained’ to recognize the 3 size classes. Examples include a plastic or cardboard card 

with 3 notches cut for each size class, or a set of 3 dowels representing each size class. 
77

III. Sampling methods for coarse woody debris 

1. Coarse woody debris (CWD) is sampled if the central longitudinal axis intersects the 

transect. 

 intersection

Figure 10.1. CWD tallied when central longitudinal axis intersects the transect. 

2. Tally dead trees IF they are leaning > 45 degrees from vertical. Do not tally live trees or 

standing dead trees and stumps that are still upright and leaning < 45 degrees from vertical. 

Most CWD will be lying on the ground. 

3. The minimum length of any tally piece is 1 meter. When CWD pieces are close to 1 meter, 

measure the length to the nearest 1 cm to determine if it is ≥ 1 meter. 

4. Decay class (Table 10.3) of the piece determines whether or not the piece is tallied. 

For decay classes 1 to 4: Tally pieces ≥ 7.5 cm 

in diameter at the point of intersection with the 

transect. The piece must be ≥ 1 meter in length 

and ≥ 7.5 cm or more in diameter along that 

length (Fig. 10.2). 

For decay class 5: Tally pieces ≥ 12.5 cm in 

diameter at the point of intersection and ≥ 12.5 

cm high from the ground. The piece must be ≥

1 meter in length and ≥ 12.5 cm or more in 

diameter along that length. The reason for 

treating decay class 5 pieces differently is that 

they are difficult to identify, especially when 

heavily decomposed. Only pieces that still 

have some shape and log form are tallied; 

humps of decomposed wood that are becoming 

part of the duff layer are not tallied. Do not record diameter at the small end and diameter at the 

large end for decay class 5.

5. Tally pieces created by natural causes or human activities (such as cutting) if it has not 

been piled. Pieces that are part of a windrow or log jumble at the bottom of a steep-sided 

ravine in which individual pieces are impractical to tally separately, shall be estimated 

and noted as an estimate. 

6. Tally a piece only if the point of intersection occurs above the ground. If one end of a 

piece is buried in the soil, the piece ends at the point where it is no longer visible. 

Measure the diameter and length at this point. 

Central 

longitudinal

axis

Diameter is > 7.5 cm for at least 1 m. 

Tally if intersected by the transect in this segment. 7.4 cm 

 diam 

Diameter is < 7.4 cm.

Do not tally (as CWD) if intersected 

by the transect in this segment. 

Figure 10.2. Tallying CWD for Decay Classes 1-4 
78

7. If the central longitudinal axis of a piece is intersected by two transect lines, tally the 

piece each time it is intersected (uncommon situation; Fig. 10.3). 

Tally piece twice 

 Points of intersection 

Tally piece twice

Figure 10.3. CWD tally rules: intersections. 

8. If a piece is fractured across its diameter or length, and would pull apart at the fracture if 

pulled from either end or sides, treat it as two separate pieces. If judged that it would not 

pull apart, tally as one piece. Tally only the piece intersected by the transect line. 

9. Do not tally a piece if it intersects a transect on the root side of the root collar. Do not 

tally roots. 

10. When a transect crosses a forked down tree bole or large branch connected to a down tree 

(Fig. 10.4), tally each qualifying piece separately. To be tallied, each individual piece 

must meet the minimum diameter and length requirements. 

11. In the case of forked trees, consider the “main bole” to be the piece with the largest 

diameter at the fork. Characteristics for this fork such as length and decay class should 

pertain to the entire main bole. For smaller forks, or branches connected to a main bole 

(even if the main bole is not a tally piece) characteristics pertain only to that portion of 

the piece up to the point where it attaches to the main bole (Figure 10.4). 

IV. Sampling methods for fine woody debris 

Fine Woody Debris (FWD) is only sampled when intersected by a transect. Pieces are counted 

and recorded by size class (Table 10.2)

Notes: 

 Only count a piece of FWD if it intersects a transect. 

 Only sample FWD that intersects a transect from the ground to a height of 2 meters. 

 Do not count conifer needles or non-woody parts of a tree or shrub. 

 Do not count rotted pieces of a larger log. 

 Individual diameters are not recorded for FWD. 

Accurate counts of FWD can be conducted efficiently up to about 50 pieces for small and 

medium size classes, and up to 20 pieces for the large size class. After that, crews can begin 

estimating counts in a systematic fashion. Transects that fall on very dense FWD where 

counting is nearly impossible, can be sub-sampled and calculated. For example, an accurate 

count can be conducted on a 0.5 meter-section of the transect and then multiplied by 4 to 

provide an estimate for the 2 meter transect, as long as the crew feels that the remaining 
79

transect has a similar density of FWD pieces. If a transect intersects an unusually large pile of 

material, crews should make their best estimate and describe the situation in the comment 

section. 

Figure 10.4. CWD tally rules for forked trees. 

If rocks, logs, or other obstructions are present along a transect, include any FWD that is 

present on top of these obstructions in the respective FWD counts. If the obstructions are so 

large (huge boulder) that the top surface cannot be seen, assume the count is zero in this area, 

and continue counting if there is transect line beyond the boulder. 

V. Sampling methods for irregularly-shaped or broken down woody material 

If a transect intersects the log at the decayed or splintered end (i.e. the portion where it is not 

considered part of the log because it is falling apart), record the diameter at this location as the 

intersect diameter, but record the large end and small end diameter according to established 

rules (i.e. at the points where they best represent the log volume). If the splintered end appears 

to be two separate pieces (i.e. a major split located just at the end) treat it as one log and take a 

Large branch tallied 

as one piece

SMALL-END 

DIAMETER = 7 cm

<— Main bole 

(not tallied)

Larger diameter fork

considered the 

LARGE-END main bole

DIAMETER 

at fork = 25 cm

Each fork is tallied 

as a separate piece

CWD Total Length

LARGE-END 

DIAMETER = 45 cm Transect line

LARGE-END 

DIAMETER 

= 37 cm 
80

diameter around the end (take two measurements if it is odd shaped; Fig. 10.4). Length is 

measured between the large and small end diameters. 

Transect Diameter: 

Record the diameter at the point where a transect intersects the longitudinal center of the piece. 

The diameter is recorded to the nearest centimeter. If the diameter is close to 7.5 cm, measure 

the diameter to the nearest 2-3 mm to determine if the piece is actually ≥ 7.5 cm and a valid 

tally piece. 

Small Diameter: 

Record the diameter at the small end of the piece. The diameter is recorded to the nearest 

centimeter. The diameter at the small end occurs either at 1) the actual end of the piece, if the 

end has a diameter ≥ 7.5 cm, or 2) at the point where the piece tapers down to 7.5 cm in 

diameter. If the end is splintered or decomposing (sloughing off), measure the diameter at the 

point where it best represents the overall log volume (Fig. 10.5). 

Large Diameter: 

Record the diameter at the large end of the piece. The diameter is recorded to the nearest 

centimeter. The large end will occur either at a broken or sawn end, at a fracture, or at the root 

collar. If the end is splintered or decomposing (sloughing off), measure the diameter at the 

point where it best represents the overall log volume (Fig. 10.5). 

DIAMETER MEASUREMENTS

Small end diameter tapers 

to 7.5 cm before end of log

Small end diameter measured at 

the point before the log splinters or 

crumbles

Large end diameter measured at 

the point that best represents the 

overall log volume (before the 

wood crumbles and falls apart 

due to decay)

____________________________________________________________________________________________ 

Figure 10.5. Measuring the length of a splintered log

15 cm diameter

7.5 cm diameter 45 cm diameter
81

Total Length: 

Record the total length of the piece that lies between the piece’s recorded small and large end diameters

(Fig. 10.5). For Decay Class 5, small and large end diameters are not recorded for a log, therefore the 

length is measured between the two physical ends of the log. For curved logs, measure along the curve. 

The minimum log length is 1 meter before it is a valid tally log. CWD total length is recorded to the 

nearest centimeter. 

Decay Class: 

Record a 1-digit code indicating the decay class of the piece (Table 10.3). Code the decay class which 

predominates along the recorded CWD TOTAL LENGTH of the piece (Table 10.3). 

Table 10.3. Log Decay Class Descriptions (developed for Douglas-fir). 

Class Bark Structural 

Integrity 

Texture of Rotten 

Portions 

Color of 

Wood 

Invading Roots & 

Vegetation 

Branches and 

Twigs 

1 Intact Sound, freshly 

fallen, intact logs 

Intact, no rot; conks 

of stem decay 

absent 

Original color Roots absent; 

no vegetation 

If branches are 

present, fine 

twigs are still 

attached and 

have tight bark 

2 Intact Sound Mostly intact; 

sapwood partly soft 

(starting to decay) 

but can’t be pulled 

apart by hand 

Original color Roots absent; 

no surviving 

vegetation 

If branches are 

present, many 

fine twigs are 

gone and 

remaining fine 

twigs have 

peeling bark 

3 Sloughing Heartwood sound; 

piece supports its 

own weight 

Hard, large pieces; 

sapwood can be 

pulled apart by 

hand or sapwood 

absent 

Reddishbrown or 

original color 

Roots in sapwood 

only; conifer 

seedlings 

Branch stubs 

will not pull out 

4 Detached or 

absent 

Heartwood rotten; 

piece does not 

support its own 

weight, but 

maintains its shape 

Soft, small blocky 

pieces; a metal pin 

can be pushed into 

heartwood 

Reddish or 

light brown 

Roots through-out; 

TSHE < 15 cm 

dbh, small shrubs, 

moss 

Branch stubs 

pull out 

5 Detached or 

absent 

None, piece no 

longer maintains its 

shape, it spreads 

out on ground 

Soft; powdery when 

dry 

Red-brown to 

dark brown 

Roots through-out; 

TSHE to 200 cm 

dbh, shrubs, 

some large, moss 

Branch stubs 

and pitch 

pockets have 

usually rotted 

down 

Note: CWD decay class 5 pieces can be difficult to identify because they often blend into the duff and 

litter layers. They must still resemble a log, therefore, the first tally rule is that they must be > 12.5 cm in 

diameter, > 12.5 cm from the surface of the ground, and at least 1 meter long. Decomposed logs that are 

slightly elevated ‘humps’ on the ground are not tallied. 

The chart above was developed primarily for Douglas-fir in the Pacific Northwest. At the present time, 

there are no other charts available to use to describe decay classes for other species or locations. 

Concentrate on the structural integrity and texture when estimating a decay class for CWD logs. Decay 

classes for white spruce will differ from those above. 

If a log is case hardened (hard, intact outer sapwood shell) but the heartwood is rotten, code this log as a 

CWD decay class 2 with a hollow piece. CWD decay class 1 should be reserved for ‘freshly fallen’ logs 

that are completely intact (i.e. recent windfalls, or harvest). 
82

Hollow pieces 

Record ‘Y’ (yes) if the piece is hollow, or ‘N’ (no) if the piece is not hollow (Fig. 10.6). A piece is 

considered hollow if a cavity extends at least 0.6 meters along the central longitudinal axis of the piece, 

and the diameter of the entrance to the cavity is at least 1⁄4 of the diameter of the piece where the entrance 

occurs. The entrance occurs at the point where wood is present completely around the circumference of 

the cavity. The length of the cavity begins at that point. 

________________________________________________________________________ 

 The entrance is the first point on the 

 piece with wood all around the cavity 

The diameter at the entrance 

of the cavity must be > 1⁄4 of the 

total diameter of the log at that 

point 

Length of cavity must be > 0.6 meters before it 

is classified as a hollow log. 

________________________________________________________________________ 

Figure 10.6. Hollow log with cavity. 

Fire charred: 

Record the approximate % of the log’s surface that has been charred by fire. Only examine the visible 

surface of the log. 

FWD – Fine Woody Debris: 

Tally the number of pieces within each size class along each transect. Write the total count per transect in 

the space beside the tally. If there is no tally on a transect, enter zero’s for the count. Count only what can 

be seen from a “bird’s-eye view”; do not dig under any litter or debris to count pieces not normally seen. 

If a transect intersects an unusually large pile of material, crews should make their best estimate and 

describe the situation in the comment section. 

Load calculations: 

Coars woody debris (CWD) loads are quantified using the line-intersect method (Van Wagner 1968, 

Brown 1974), and assuming a specific gravity of 0.34 g/cm3

 for white spruce (Taylor et al. 1982): 

Volume of wood per unit area (m3

/ha) = V = [π

2

 × Σ (diameter (m))2]/[8 × (transect length (m))]

Weight per unit area (kg/ha) = V × specific gravity (kg/m3

) 

IV. Literature cited 

Brown, J.K. 1974. Handbook for inventorying downed woody material. USDA Forest Service Gen. Tech. 

Rep. No. INT-16. 
83

Forest Inventory and Analysis (FIA). 2003. Field procedures for the coastal Alaska inventory. USDA 

Forest Service, Pacific Northwest Research Station, Forestry Sciences Lab, Region 10, Alaska. 

Anchorage, Alaska, 182 pp. 

Taylor, F.W., Wang, E.I.C., Yanchuk, A., and M.M. Micko. 1982. Specific gravity and tracheid length 

variation of white spruce in Alberta. Canadian Journal of Forest Research 12:561-566. 

Van Wagner, C.E. 1968. The line-intersect method in forest fuel sampling. Forest Science 14:20-26. 

Woodward, A.E., Hutten, K.M., Boetsch, J.R., Acker, S.A., Rochefort, R.M., Bivin, M.M., and L.L. Kurth. 

2009. Forest vegetation monitoring protocol for national parks in the North Coast and Cascades 

Network: U.S. Geological Survey Techniques and Methods 2-A8, 228 pp. 
84

Vegetation Monitoring Protocol for the 

Southwest Alaska Network (SWAN) 

Standard Operating Procedure (SOP) # 11 

Post-field procedures – Sample processing and equipment inventory 

Version 1.0 (December 2008) 

Revision History Log: 

Version No. Revision 

Date 

Author Changes Made Reason for Change 

 

This SOP outlines procedures for proper processing and storage of collections, and for data entry and backups. 

Procedures - General: 

I. Processing of soil samples 

1. Sieve and air dry soils upon return from field. 

 

2. Store dried soils in ziploc labeled with the park, date, plot id, and plot location (lat/long). 

3. At the end of the field season, mail the soil samples to the UAF soils lab in Palmer, AK, for processing. 

As of 2009, our contact at the Experiment Station is 

Laurie Wilson

University of Alaska 

Agricultural and Forestry Experiment Station 

533 East Fireweed 

Palmer, AK 99645 

Email: pnlaw@uaa.alaska.edu

Tel: (907) 746-9482 

II. Processing of vascular and nonvascular plant collections 

1. Transfer vascular plant specimens into a fresh plant press if they are wet. Otherwise, specimens may 

remain in the press that was used in the field. Ensure that specimens inside the press dry quickly by 

setting the press outside on a warm, breezy day, or inside, in front of a fan. This facilitates drying, 

prevents mildewing, and retains the natural colors of the specimens. Nonvascular specimen envelopes 

constructed of acid-free cotton archive-quality paper should also be placed in a warm, well-ventilated 

area to facilitate quick drying and prevent mold or mildew from forming. Liverwort specimens must be 

refrigerated to prevent degradation of oil bodies and other sensitive structures. Specimen envelopes 

that have been damaged should be replaced to ensure the data recorded on them are retained. 
85

2. Dry specimens that can be removed from the plant press will be organized by genus into folders (for 

vascular plant specimens) and into specimen boxes (for nonvasculars). Some liverwort taxa will need 

to be refrigerated rather than stored in specimen boxes. Field collection sheets and packets will be 

inspected to ensure that required information, including collector, collection date, collection number, 

and site are on the specimen. 

3. Once the field season is complete, the specimens will be examined in the herbarium and identified 

according to the most recent accepted taxonomic keys for the project. Often, multiple keys will be 

helpful in determining the identity of a particular specimen. The first cut at specimen identification is 

performed by one or more lead technicians on the project. 

4. A NPS-ANCS accession and catalog number will be assigned to each collection. All collections will be 

documented in a file organized by park and field season, to be submitted with the collections to the NPS 

curators (Table 10.1). 

Table 11.1. Required fields for vascular and non-vascular collections 

FIELD DESCRIPTION 

Family Family that specimen belongs to, following USDA-NRCS PLANTS 

Trinomial Taxonomic name following USDA-NRCS nomenclature, unless otherwise 

specified 

Associated species For non-vascular specimens, incidental taxa that may be present in the voucher 

collection. For vascular specimens, list other species present in the community. 

USGS quadrangle USGS quadrangle name 

Locality description Brief description of collection site area 

Microhabitat description Brief description of microhabitat in which the collected specimen occurs 

Plant description For non-vascular specimens, noteworthy properties of the collected specimen 

(e.g., chemical test results for certain lichen taxa) 

locality name official name of collection site area 

Plot id Plot id 

Latitude Degrees decimal minutes, NAD 83 

Longitude Degrees decimal minutes, NAD 83 

Elevation Meters 

Slope Degrees 

Aspect Degrees (true north) 

Collector Person who collected the specimen 

Collection number Unique alpha-numeric collection identifier assigned by collector 

Determined by Name of person responsible for official determination (e.g., from ALA) 

Determination date Date of official determination 

Verified by Name of person responsible for re-verifying the official determination, if applicable 

NPS accession number For NPS-ANCS+ database/NPS archives 

NPS catalog number For NPS-ANCS+ database/NPS archives 

5. Preliminary NPS identifications will be verified by botanists at the University of Alaska Museum 

Herbarium (ALA), or by other specialists (e.g., an authority for a particular group), as recommended by 

ALA. The SWAN will maintain an ongoing CESU Task Agreement with ALA for taxonomic 

determinations. Once determinations by ALA are complete, all specimens will be returned to SWAN 

and archived at NPS facilities, unless ALA requests a permanent loan. As of 2009, our contact at ALA 

is 

Carolyn Parker 

University of Alaska Museum of the North - Herbarium 

907 Yukon Dr. 

PO Box 756960 

Fairbanks, AK 99775 

Email: fnclp1@uaf.edu 

Tel: (907) 474-7109 
86

6. After final determinations are received from ALA, the original field data sheets will be annotated prior 

to data entry. Herbarium labels will be prepared in the following format: 

7. The project manager will maintain and update a master database with the accepted taxonomy for the 

project. This database will be archived each year in order to facilitate any changes in species 

nomenclature that may need to occur in the future. Nomenclature for this project will follow that of the 

Flora of North America and the USDA-NRCS PLANTS database. 

8. Specimens will be mounted onto archival herbarium paper for vascular plants, and into acid-free 

archival specimen packets for nonvascular plants, and transferred to herbarium cabinets at the NPS 

Regional Office and/or in the affiliated park for storage. Specimen data is entered into ANCS+, the 

NPS cataloging database. 

III. Processing Tree Cores 

1. Transfer tree cores from straws to core mounts as outlined by the Grissino-Mayer lab 

(http://web.utk.edu/~grissino/mounts.htm; accessed 5/19/2010). Core mounts can be purchased (see URL 

for sources) or constructed from 9 mm × 9 mm hardwood strips. 

2. Request NPS-ANCS accession and catalog numbers for the tree core collection. Label all mounted cores 

with accession and catalog numbers, and submit to the NPS curators along with a digital (.xls) file 

documenting collection information (site location, date of collection, tree species, tree age, etc.). 

IV. Digital Images 

1. Download, GPS PhotoLink, and archive digital images. Instructions for using GPS PhotoLink are 

provided at http://science.nature.nps.gov/im/units/mwr/documents/GeoTagPhotos_QStart_Handout.pdf 

(accessed 5/19/2010). Document photo contents in an interim .xls file (Table 10.2) and transfer data to 

ThumbsPlus. 
87

Table 11.2. Required fields for interim photo log 

FIELD DESCRIPTION 

Date Date stamp on photo 

Photo id Frame no. assigned by camera software 

Plot id If applicable 

Latitude For plot photos, degrees decimal minutes of plot SW corner; NAD 83 

Longitude For plot photos, degrees decimal minutes of plot SW corner; NAD 83 

Description Description of photo contents (e.g., Transect/Quadrat No.), including direction of camera view, if 

applicable 

Photographer Photographer 

Camera Make/model of camera 

Notes Relevant notes, if applicable 

2. Organize images in folders by plot. Panoramic photos should be saved to a separate subfolder within 

each image folder. Save a backup of all files to an external drive. 

3. Verify that two sets of the images have been saved and then delete all images from the camera’s 

memory card(s). Organize photo equipment for the next excursion to the field. 

V. GPS data 

1. Download the GPS data from the Trimble after each trip. To do this, complete the following: 

a. Place the GeoXT on the receiver cradle, which should be connected to the PC. Open Pathfinder 

Office and the specific folder created for the plots that were sampled. 

b. Click on Utilities and then Data transfer.... 

c. Click on the Receive tab. Click Add and select Data File. Select the files for the site and click 

Open. Click Add for a second time, and select Almanac. Click OK. 

d. Click on Transfer All. When the transfer is complete, check to make sure the files transferred 

successfully to the designated folders. 

2. Once the data is downloaded, post-process it. In order for post-processing to occur, the data files must 

have been closed on the Trimble for at least one hour. Do not delay post-processing until the end of the 

field season. To post-process, follow these steps: 

a. Click on Utilities and then Differential Correction.

b. Click Browse and select all of the raw (.ssf) files that you just downloaded. 

c. Click on Internet Search, then New, and then select to copy a list from the internet. Press Ok. 

d. Select a base provider, usually the one closest to the field sites that is functional. Under 

Provider Properties, click on Base Station. Select “use reference position from base file”. 

Click OK. 

e. Click Ok to perform the internet search. View Reference Position and click OK. You will be at 

the Differential Correction main screen. Click on Settings. Under Output, select “Corrected 

Only”. Under Code Processing, Select “With Velocity Filtering” and “Correct Velocity 

Records”. Close the box. 
88

f. Click OK to start the differential correction of the site data. The goal is to have 100% of the 

data points differentially corrected. If errors occur, try to use a different base provider. View 

the corrected data in Pathfinder Office. It will be located in the folder specified for the plot(s) 

recently sampled. The file extension for differentially corrected points is .cor. 

3. Back-up a copy of the raw and the corrected files to an external drive. 

4. Leave the Trimble on the cradle until it is fully charged. DO NOT delete data from the Trimble until 

the end of the field season, when all data have been downloaded, differentially corrected, backed up, 

and viewed and corrected, if necessary, in a GIS. 

VI. Data sheets 

1. Upon return from the field, sort the data sheets by plot, indicate ‘DATA NOT COLLECTED’ on any 

blank sheets, photocopy one complete set, and scan the originals. Originals and photocopies will be 

filed with the SWAN vegetation program, in Anchorage. Photocopy and scan each field notebook for 

reference during data entry. Scanned notes will be filed with the scanned data sheets in each plot 

folder. 

Additional Procedures – End of Field Season: 

I. Inventory field gear 

1. After the field season is completed, conduct an inventory of all gear. Complete the Equipment 

Inventory checklist (Attachment 1). Inspect each item for any repairs or replacement parts that are 

needed. Record on the data sheet in the appropriate column. Specify the brand and model of each 

backpack and tent on the datasheet to facilitate repair. 

2. Provide a list of equipment needs to the project manager so that items can be repaired or replaced 

before the start of the next field season. 

II. Organize and store field equipment 

1. After the field season is completed, clean all field gear. Wash and thoroughly dry all sleeping bags, 

and store them loosely in the large storage bags. Clean out all backpacks and tents and hang them out 

to dry thoroughly before storing them. 

2. Remove any remnant gas from the camp stoves before placing them into storage. 

3. Wash all bear-proof cans and dry thoroughly before storing. Thoroughly dry all water containers. 

4. Remove all dried plant specimens from the presses. Loosely secure the straps so the press will not be 

compressed while in storage. 

5. Remove all files from the Trimble and Garmin GPS units, and all images from the camera memory 

cards AFTER the data have been saved on at least 2 media. All sensitive and electronic equipment will 

be stored in Anchorage between field seasons. 

Attachment 1. Equipment Inventory checklist – end of season inventory 
89

Date: ____________ Initials: ____________

Equipment Quantity Repairs or Replacement needed? Be specific 

Metal clipboards 

Hand lens 

Compass 

Clinometer 

Laser rangefinder 

Densiometer 

Bear spray 

Rite-in-the-Rain field notebooks 

Trimble GPS 

Garmin GPS 

Flagging tape 

Monument caps + fluted stakes 

Stamp kit for monument caps 

16′′ wooden stakes 

6′′ galvanized nails 

Hammer 

Rubber mallet 

30-m fiberglass reel tapes 

50-m fiberglass reel tapes 

Digital camera + memory cards 

Waterproof plastic case 

Memory cards (1 GB or greater) 

Dry-erase board + pens 

Waterproof plastic case 

Soft case for camera 

Case for memory cards + accessories 

16′′ fine-gauge knitting needles or pins 

Transect staff with extra pins 

Metal tape measure (e.g., 2 m) 

Trowel 

Densiometer 

Laser rangefinder 

0.25m2 quadrat frames 

PVC sections - 1m2 and 4m2 plot frames 

1-m soil probe 

Collection containers 

Hori-hori 

Plant presses 

DBH tapes (cm) 

18′′ increment borer 

10′′ increment borer 

Extra spoon extractors 

WD-40 

Plastic straws 

Lighters 

Electricians tape; duct tape 

Storage container for cores 

Pre-numbered aluminum tags + nails 
90

Date: ____________ Initials: ____________

Equipment Quantity Repairs or Replacement needed? Be specific 

Handheld radio + batteries 

Satellite phone 

Pelican case for satellite phone 

Bear fence (poles, line, charger) 

Grass clippers 

Camp stoves – Coleman 4-burner 

Camp stoves – backpacking stove 

White gas 

Cook set(s) 

Water filter/pump(s) 

Lighters 

Large bear-proof cans 

Backpacker-sized bear-proof cans 

Cook tent (mega-mid) 

First-aid kit(s) 

5-gallon water jugs 

Large capacity backpack(s) 

Daypack(s) 

2-man tent with fly 

3-man tent with fly 

USGS quad sheets (1:63,000) 

Extra data sheets 

Extra batteries (AA, D-cell) 

 
91

Vegetation Monitoring Protocol for the 

Southwest Alaska Network (SWAN) 

Standard Operating Procedure (SOP) # 12 

Post-field procedures – Data processing for HOBO downloads 

Version 1.0 (October 2009) 

Revision History Log: 

Version No. Revision 

Date 

Author Changes Made Reason for Change 

 

This SOP outlines procedures for HOBO data downloads, data entry, analysis, and backups. 

Authors: Jeff Shearer and Claudette Moore, SWAN freshwater ecologists 

Date: December 31, 2009 

Procedures - General: 

This standard operating procedure describes the process for using proprietary software to download and 

export data collected with automated data loggers for archiving and analysis purposes. Automated data 

loggers record continuous observations of soil temperature (hourly) at selected vegetation monitoring plots. 

Disadvantages to using automated data loggers include errors in data or complete failure to record data due 

to improper programming and/or severe environmental conditions (e.g., extreme cold). In addition, the user 

must be familiar with the operation of all software needed to program the instruments and download stored 

data. This SOP provides step by step instructions to guide users through the process of downloading and 

archiving raw data following acquisition in the field. 

As of December 2009, Onset HOBO® Water Temp Pro v2 data loggers and HOBOware Pro® v2.3.7 

software are being used to record soil temperature in SWAN vegetation monitoring plots. Because future 

software upgrades may not be compatible with older file versions, all raw proprietary software data files 

will be archived in .csv format prior to analysis. 
92

Data File Naming 

Data processing generates multiple files of differing formats and extensions containing the same data, and 

proper data management will be critical to file organization during downloading, editing and archival 

phases. For example, soil temperature data from an Onset HOBO® Water Temp Pro v2 sensors are saved 

in raw data form as a *.hobo file, as a *.csv file for archival, and as a revised *.csv file after quality 

assurance checks on the data. Saving data files with the proper file naming convention is a critical step in 

the data management process. Table 12.1 provides the file naming conventions for all files generated from 

automated data loggers used in monitoring soil temperature. All file names should, at a minimum, contain 

the 9-character plot ID (park-year-elevation-plot), the date recorded or downloaded, and the data format 

version (e.g., raw, export, or QA/QC). 

Table 12.1. File naming conventions for aquatic monitoring data collected with automated data loggers. 

Parameter Required Software File Naming Convention †File Name Example 

Soil temperature Onset BoxCar Pro “park code_year_elevation_plotID+ LACL200802006 

_temp_20090613raw 

Onset HOBOWare Pro #_3-digit depth+ ‘cm’ _‘temp’_ 

 YYYYMMDD+format” 

†All file names must contain a 9-character plot ID code, temperature designator (‘temp’), download date, and data 

format (raw, export, or QA/QC) 

Soil Temperature Data 

The objective of monitoring soil temperature is to document soil freeze-thaw dates associated with seasonal 

transitions, to estimate the timing of snowpack development and snowmelt, and to provide a basis for 

comparison among sites of different elevation and soil type. Information collected through this 

methodology is a time-series dataset containing date, time, and soil temperature (°C) data. Instructions 

below provide a step by step process for programming temperature loggers prior to deployment, recovering 

data files from temperature loggers and exporting data for archival. 

Programming Temperature Loggers Prior to Deployment 

Temperature data loggers can be programmed in the office or in the field immediately prior to deployment. 

Sensor specifications are presented in Table 12.2.

Table 12.2 Specifications for HOBO Water Temp Pro v2 data loggers. 

Logger Type Part 

Number 

Operational 

Range 

Accuracy Waterproof 

Depth 

Optic Reader Communication / 

Coupler part # 

HOBO Water 

Temp Pro v2 

H22-..1 -20° to 70° C 0.2° C over 0° to 

50° C 

120 m Optic USB Base Station or 

Waterproof Shuttle Coupler2-C 

The HOBO Water Temp Pro v2 requires an optical reader to program and download data. Onset products 

are not compatible with USB technology. There are two types of optical readers that can be used with the 

Pro v2: 1) the Optic USB Base Station, which requires a computer connection, and 2) the Waterproof 

Shuttle, which can be used in the field to download the HOBO remotely without a computer connection 

(Figure 12.1), or can be connected to the computer with a portable cable for programming and 

downloading. HOBOware Pro software is used to communicate with HOBO water temp Pro v2 and TidBit 

v2 devices, as outlined in steps 1-8 below. 
93

 Figure 12.1. Optical readers for Onset HOBO Water Temp Pro v2 and TidBit v2. 

Programming steps: 

1. Open HOBOware Pro® for Water Temp Pro v2s. 

2. Connect the optical reader (Figure 12.1) to the computer using the USB or serial port connections. 

If using a serial port, ensure that the correct Com Port is active. If you have administrative rights to 

your computer, you can troubleshoot problems if the correct port is not selected. If you don’t have 

administrative rights, you will need to seek administrative support to determine which com port is 

in use. 

3. Optical readers for the older versions of HOBO devices (Pro v1 and TidBit) require that the face of 

the logger be held flush to the face of the optical reader. It is not necessary to remove the Pro v1 

logger from the protective rubber boot. The TidBit does not use a protective boot. 

4. The newer HOBO Water Temp Pro v2 loggers fit into a coupler, and must be aligned by matching 

the arrows on the Pro v2 logger and the coupler. This requires that the Pro v2 loggers be removed 

from protective boot to fit into the coupler. Match the arrows on the logger and the coupler to 

ensure proper connection with the optical reader. Press the lever on the coupler to make the 

connection. A green light will appear if the contact is correct. If no green light appears (on the USB 

Base Station) or the red Fail light appears (on the Waterproof Shuttle) twist the logger in the coupler 

slightly and try again. 

5. Using HOBOware Pro, select Device / Launch. 

 
94

6. After the device is launched, a dialogue box will come up with the logger model and serial number. 

Click OK to select the logger. This will bring up the Launch dialogue box. 

7. The dialogue box will show the battery status and the serial number. Type in the desired file name 

(Table 12.1). Set the logging interval to 1 hr. Be sure to check this, as new loggers are set to a 

default of 1 min. Select the launch option based on when the logger will be deployed. Generally, a 

delayed start date and a start time of 00:00 am is programmed. 

8. Click Launch when all the programming information is complete. Remove the logger from the 

coupler. Continue programming loggers until all loggers are programmed. Close the program when 

finished. Use a permanent marker to mark the depths on newly deployed loggers so that they can be 

placed at the correct depths. Store the loggers in the rubber boot with cap. The loggers can also be 

deployed with just a cap that fits over the face to protect it from bio-fouling. These caps are smaller 

than the ones that come with the protective boot. 

Downloading Data from Temperature Loggers 

The temperature loggers will most often be downloaded in the field and redeployed. The Water Temp Pro 

v2 loggers can be downloaded with a Waterproof Shuttle, which is a stand-alone optical reader and can 

download data and reset the logging schedule remotely, or with an Optic USB Base Station, which requires 

a computer connection for communication and power. The Waterproof Shuttle can also be connected to the 

computer and will function like the USB Base Station reader. The steps below present the two methods 

used to download data from the temperature loggers. 

If the loggers are to be downloaded and redeployed in the field, create a folder in My Documents to store 

the raw data files. Name the parent folder with the 9-character site ID. Name the child folder with the 

download data (e.g., \lclar01\20100612). 

Be sure to have extra temperature loggers packed with the field gear in case a logger is missing or 

communication can not be established. Also, include the correct couplers for the Water Temp Prov2 

(Coupler2-C with blue label). 
95

Optic USB Base Station Reader 

1. Connect the Optic USB Base Station reader to the computer via an open USB port and open 

HOBOware Pro. 

2. If the logger is housed in a protective boot, remove it and clean any residue from the face of the 

logger. Place it into the coupler – be sure to align the arrows on the logger and the coupler. Press the 

lever and look for the green light to single a connection. 

3. Select Device / Readout. The logger will be identified by its serial number. Click OK to select the 

logger. Click no when prompted to stop logging. This will allow the logging to continue on the 

schedule previously programmed. 

Note: If using BoxCarPro software to download a Pro v1, select Logger / HOBO Water Temp Pro / 

Readout. If downloading a TidBit v1 select Logger / Readout. 

4. If data are downloaded in the field, save the *.hobo (HOBOware Pro) or *.dtf (Boxcar Pro) files to 

the directory created on the field laptop as noted above (\lclar01\20100612). The file name will 

default to the description entered when the logger was programmed (see above). Add the download 

date and the data type to the end of the file name 

using the YYYMMDD format (e.g., 

LACL_lclar01_005m_20091204raw). Make sure 

that there is a depth value included in the plot title 

and that it corresponds to the correct depth on the 

temperature array. 

5. The Plot Setup window will pop up next. Plot the 

temperature in Celsius and include the battery 

voltage as this can help track potential errors in the 

data. Uncheck the boxes related to the Internal 

Logger Events. Click Plot in the lower right. 

6. The data will be plotted and the tabular data will 

appear in the upper left part of the screen. Close the 

file by clicking the X in the tab with the file name. 
96

7. Continue to download the temperature loggers from the array. If communication can not be 

established, replace the logger with a new logger. Program the new logger following the steps listed 

above. Note the serial numbers of the old and new loggers and the depth in a field notebook. 

8. Try to establish communication with the logger when back in the office. If communication can not 

be established, send the logger(s) back to Onset for data retrieval. 

9. If data are downloaded in the office, save the hobo files to the following directory: 

J:\SWAN\Vital_Signs\Veg_Landcover\Data\Soil_Temperature 
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10. Save the file using the following naming convention: plot id + date [YYMMDD] of download + 

‘raw’ (e.g., LACL_2007_02_006_090910_raw) 

Optic Waterproof Shuttle Remote Download 

When using a USB waterproof shuttle to download files, the temperature logger will continue to log hourly 

data. Logging can not be stop unless the shuttle is connected to a computer and communicating through 

HOBOware Pro. If the logger is operating properly, there is no need to stop and restart the logging 

program. 

Check the battery life of the waterproof shuttle before going into the field if you plan to download the 

HOBO temperature loggers remotely. The shuttle is powered by two AA batteries and will not establish 

communication with a logger if the battery power is low. Directions for changing the batteries are located 

on the shuttle. 

1. Open HOBOware Pro. Connect the waterproof shuttle to the computer using the portable cable. 

Select Device \ Manage Shuttle. The battery level is reported in this window. If the batteries need 

changing, you will get a warning message. 

2. After the batteries are changed, the shuttle will need to be launched to set the proper data and time. 

Reconnect the shuttle to the computer and select Device \ Manage Shuttle. The battery level should 

now be reported as good. Click Launch Shuttle. 

3. Be sure to pack the correct couplers for the Water Temp Prov2 (Coupler2-C with blue label) and 

Tidbit v2 (Coupler2-D with orange label). 

4. Place the logger into the coupler, align the arrows and press the level. A yellow light will blink 

during the data transfer process. When the download is complete, the green OK light will be 

displayed until the level is pressed to turn it off. 
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5. If the red Fail light appears, twist the logger slightly and try again. If communication can not be 

established, replace the logger with a new logger. Program the new logger with the correct site ID. 

Record the serial numbers of the old and new loggers and the soil depth in the field notebook. 

6. Upon returning to base camp or the office, attach the waterproof shuttle to the computer with the 

remote cable. Open HOBOware Pro and select Device \ Manage Shuttle. When the shuttle is 

connected, the files that were downloaded in the field will be displayed in the window. The status 

will read “not offloaded” and the boxes will be checked 

7. Click the Offload Checked button in the lower right. Do not check the delete contents after 

offloaded box in case there are problems. There is another option to delete the files once the data 

have been viewed. 

8. The offloaded files will appear in a new screen and you will now have an option to save these files. 

The directory will default to the last folder in which data were saved, and a new default folder will 

be created (e.g.,\ShuttleReadout12_07_09_08_43_12_AM_AKST). The folder name indicates that 

the files were loaded from a shuttle with the current date and time relative to the time zone and 

standard or day light saving time. 
99

9. If you are operating in the field, browse to the temporary soil temperature folder on the laptop and 

save the files to that folder until the files can be moved to the J: drive. Note: if the default path is 

changed in the above step, the selected folder will not have the default shuttle folder name shown 

above. Click the Save Checked in the lower right. 

10. The file name cannot be edited during this process and will have to be edited in Windows Explorer 

after the download process is completed. 

11. Close the shuttle window and open the files that were downloaded to check for valid data. Select 

File \ Open DataFile and navigate to the folder where the data files were saved. Once the data files 

have been reviewed, close the files and return to the shuttle manager. 

12. If data in the files are missing or otherwise irregular, download the files from the shuttle again. 

Select Device \ Shuttle Manager. When the window comes up the status will show up as 

“offloaded”. Check the box(es) of the files to be downloaded a second time. A new default shuttle 

folder with the data and current time will be created. If the data still appear irregular, make a note 

in the field notebook to replace that logger and send it in to Onset for data retrieval. 
100

13. To delete the files on the shuttle, select Device \ Shuttle Manager. Check the boxes by each 

offloaded file and then select Delete Checked in the lower right. Select ‘yes’ to delete the files from 

the shuttle. Once the files have been deleted, the files previously listed will be cleared. 

14. Open Windows Explorer and navigate to the folder in which the files were saved. Rename the files 

using the following naming convention: plot id + date [YYMMDD] of download + ‘raw’ (e.g., 

LACL_2007_02_006_090910_raw) 

Data Export for Archival

The purpose of this section is to outline the steps needed to convert proprietary software files containing 

raw data into a universal format for long-term data archival. These steps will ensure that datasets are 

available for future use, even as software expires or software upgrades prevent opening old data files. All 

raw proprietary software data should be exported and saved in .csv format. If .csv format is not an export 

option for a particular software, export the raw data in .txt format and then convert the .txt file to .csv 

format in MS Excel. 

Steps if using BoxCar® Pro 

1. Open raw data file and select File / Export / Custom. 
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2. In Export Custom box, select Preferences and set Date / Time Separator and Data Separator to 

“Comma.” Select OK, then select the Export button. 

3. Save exported file as a .txt file in the following directory: 

J:\SWAN\Vital_Signs\Veg_Landcover\Data\Soil_Temperature. File naming convention is the 

same as for the raw data file. 

Steps if using HOBOware® Pro 

1. If the software has been newly installed or has been recently upgraded, open HOBOware Pro and 

verify that your export settings are set like those shown below. Select File / Preference. Select 

General from the left side, then expand Export Settings and set the following: 

Column separator – comma 

Check the following boxes: 

-separate data and time into two columns; 

-include logger serial number. 

Date format – M D Y 

Date separator – slash 

Time format – 24 hr 
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Leave the remaining defaults as set. Click OK at the bottom right and close the Export Settings, 

close the Preferences window. 

2. Open raw data file and select File / Export Points as Excel Text/ Export to a single file. Select 

Export to a single file, then click the Export button. 

3. Save exported file as a .csv file in the following directory: 

J:\SWAN\Vital_Signs\Veg_Landcover\Data\Soil_Temperature. File naming convention is the 

same as for the raw data file. 
103

4. Continue to export the files following the above steps until all HOBO files have been exported to 

*.csv files. Close the program. 

Using AQUARIUS Time Series to Process Time Series Data

Introduction to Aquarius Time-Series Software 

AQUARIUS is a software platform designed to simplify the management and analysis of time-series data. 

The software uses a visual environment - the Aquarius WhiteboardTM - a simplified workflow management 

system to process and analyze large time-series datasets. Data are processed by dragging a series of 

toolboxes onto the whiteboard workspace, which are then connected (wired) to each other in a variety of 

ways to provide a powerful yet easy to use work environment. Although examples shown are for 

freshwater monitoring data, continuous soil temperature data can be processed using the same methods. 

Toolboxes are categorized by topic in the toolbox pane. Toolboxes are equipped with input ports located 

on the left side of the toolbox and output ports located on the right side of the tool box. The user should be 

familiar with the toolboxes listed in Figure 12.2 prior to processing SWAN time series data. Additional 

information on use of the software can be found in the user manual or training videos, which can be 

accessed online at http://www.aquaticinformatics.com/main/ or through the help menu. 

Figure 12.2 Primary toolboxes used to process SWAN time series water quality data. 

Toolbox 

Category Frequently Used Toolboxes Toolbox Category Frequently Used Toolboxes 

Data Input 

 

Math and 

Statistics 
104

Date 

Preprocessing 

Visualization 

and 

Reporting 

Flagging and 

Grading Data Output 

Correction 

 

Another feature of the software is the way in which data processing is tracked. The software tracks all 

changes to the raw dataset, creates a corrected data file and stores this information in a comma delimited 

*.txt file that can be exported for metadata tracking purposes. This feature standardizes the way in which 

changes to processed data are tracked. Meta data files provide important documentation on these datasets 

and are required as part of the SWAN data management protocol. 

This section of the SOP describes how to use AQUARIUS Time-Series to process SWAN soil temperature 

data collected from temperature loggers. The steps for each data type are presented separately. Prior to 

processing data files in AQUARIUS, we will first discuss preparation of the raw data file in comma 

separated value (.csv) format. 

Preparing the Comma Separated Value (.csv) Files 

The .csv files that were exported in the above procedures may need to be modified prior to importing into 

AQUARIUS. The water quality data (HOBO and YSI) files can be imported without any modifications to 

the .csv file. Because of the metadata header information associated with the level logger files, it is easier 

to modify the .csv files prior to importing them into AQUARIUS. 

Processing Temperature Data 

SWAN currently has twelve soil temperature loggers (HOBO Pro V2) deployed in Lake Clark (n = 2) and 

Katmai (n = 10). Additional deployments in both parks are expected in the coming years. Each 

temperature logger will have one file per year. The steps below will show how to process data for the 

current year (new and existing loggers) and how to append data into an existing soil temperature data set. 

To simplify the file management, create a working AQUARIUS folder to manage the Aquarius files (e.g., 

aquarius_LACL_2009) and copy all .csv files for the AQUARIUS session into the folder. Once the final 

products have been completed, the files can be moved to a permanent location and the temporary folder can 

be deleted. 

Importing Comma Separated Value (.csv) files 

This process will load temperature data collected from loggers deployed year-round. 

1. Open AQUARIUS and drag Import from File Toolboxes into the whiteboard. 
105

2. Double click the first toolbox to begin loading data. Click load and navigate to the working 

Aquarius folder. Set file type to .csv to remove other files from view. 

3. Select the first file to import (e.g., naknl01_005m_20090923). In the first step of the import 

process, the default is set to Time Series. The HOBO data files are all time series data. Click next. 
106

4. In step 2, the user will set the Start import feature. This feature sets the pointer on the first row of 

data to import. In the Start import box, click the up arrow to include the header columns. Click 

next. 

5. In step 3, the user will determine which columns to import and set the format for the date and time. 

Be sure to process all the columns before pressing the next button. The first column is the date. 

Click the Format Date / Time radio button and type in mm/dd/yyyy. Select the time column and 

Click the Format Date / Time radio button and type in HH:MM:SS PM. If the time is formatted to 

24 hr, enter HH:MM:SS. AQUARIUS is case-sensitive for these commands. The date is in lower 

case and the time is in upper case. 
107

6. Select the temperature column. Notice that the Data radio button is select. Fill in the description 

(temperature) and units (C). Select the battery column and click the Do not Import radio button. 

Notice that the format and description sections become grayed out. Select any additional columns 

that may be part of the .csv file (coupler detached, etc.) and click the Do not Import radio button. 

Notice that the columns selected for import have headers, while those that weren’t selected say 

Skip. Click next. 

7. Step four is the final step and allows the user to save the configuration file. This is a small script 

that can be used to import additional .csv files with the exact same layout. If the .csv files differ at 

all, an error message will be displayed when additional file are imported. Click the save 

configuration box. 

8. The .cfg file will be saved in the current working folder. Select browse and rename the 

configuration file as the name of the plot _ temperature_year (yyyy) _ import (e.g., 

LACL_2008_02_006_temperature_2009_import). Click save to close the save configuration 
108

window. The final screen of the import wizard will show the path of the saved configuration file. 

Click finish to complete the import process. 

 

9. When the file has been loaded a new window will appear. The left side displays the name of the file 

that was imported and the right side provides information about the file. Scroll through the right 

side and view the number of records that were imported. Each file should include several thousand 

records. If the file did not import correctly – checking this value is the first clue. 

10. Continue importing the temperature files in sequence by depth, if applicable (e.g., if more than one 

logger was used to record temperature at different soil depths). The configuration file can now be 

used to import the remaining file. Select browse and navigate to the folder, select the .cfg file and 

click save. 

11. Using the example given for lake temperature arrays, click the load button to import the 10 m depth 

next. The file will load automatically and appear in the left screen. Check the total samples as 

above. Select load and import the 20 m depth. There are now three files loaded into the Import File 

toolbox. They should be displayed in the left box. 
109

12. Click done and notice that the green box now has three active output ports on the right side. Each 

port corresponds to a depth data set. Select any arrow and right click to perform a quick view. The 

data will be displayed graphically. The tabular data appears to the left. Click the X to close the 

quick view image. Do not minimize the view. 

Note 1: When screen views are minimize in AQUARIUS, the program becomes inactive and it can appear 

that it is frozen. Check for minimized screens in the left corner if the whiteboard appears inactive. 

Note 2: To help with toolbox management in complex whiteboards it is helpful to rename the toolboxes as 

data are imported and functions are carried out. These toolboxes can be copied and pasted into other 

whiteboards for additional analysis. 

13. Right click on the toolbox with the loaded data and select the Properties page from the pick list. 

The properties box will appear. Type in the plot ID + the month and year of the offload date 

(mm/yy) (e.g., LACL-2007-02-006 09/09). 
110

14. Repeat steps 1-13 to load in the remaining temperature array files for the remaining depths, if 

applicable. Load three to four files into each toolbox. If you are loading two files for a given year, 

the white board will have six Import from File toolboxes. 

15. Save this whiteboard file using the following naming convention plot ID + year of download + AQ 

(e.g., LACL_2007-02-006_2009_AQ) 
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Note: If recording temperature data for multiple soil depths at a site, another helpful tip is to keep the 

depths in order in the toolboxes (i.e., load the depths sequentially from the surface to the bottom). If the 

incorrect depth is loaded into a tooxbox or the depths are out of order, files can be deleted or reordered to 

maintain consistency. 

16. Double click on the toolbox to bring up the import file window. Select the file that is out of 

sequence. To delete the file right click and select delete. To reorder the files, select the file and 

move it to the proper location to reorder the depths. 

Signal Joining 

The next step is to join data signals for depths with more than one file per year (i.e. for arrays where 

multiple data downloads occurred within one year). If there is only one depth file for the year, proceed to 

the Data Corrections section. 

1. Select the Data Preprocessing tab and drag a Signal Joining toolbox into the whiteboard. The 

default toolbox has two input data ports available indicating that two files can be joined. If there are 

more files that need to be joined, right click the toolbox and change the value of the Input Ports 

Time Series to equal the number of datasets to be joined. 

2. Using the example of the lake temperature arrays, wire the two 5 m datasets to the input port on the 

Signal Joining toolbox. Be sure that the data files are wire in the proper time sequence. The earliest 

data should always be wired to the top input port and additional datasets added sequentially. 

Rename the toolbox (right click and select properties pane) with the toolbox action + 5-digit 

waterbody code + ID + year + depth (e.g., join lclar01 2009 5m). 

3. Double click on the Signal joining toolbox. Each dataset will appear in a different color. Update 

the description of the dataset in the Signal Join Description field with the water body code and site 

ID + depth + 2009 (e.g., lclar01_005m_2009). Click the Save Data to Output Port and Exit icon. 
112

4. Once the join function has been run, the output port will become active. Right click on the output 

port. The entire dataset will be one color indicating that the datasets are now joined in one file. 

Notice that there is still a gap in the time series data. This will be addressed with the Correction 

toolbox. 

5. Join files for all depths following steps 1-3. Each depth will require its own Join toolbox. Rename 

the toolboxes and save the AQUARIUS file. 
113

Data Correction 

Now that the datasets have been joined, the data will need to be corrected. Corrections include smoothing 

data and correcting gaps created during periods when the temperature array is serviced and pulled out of the 

water. Generally, data gaps will be corrected using a cubic spline correction. This fills the data gap using a 

cubic interpolation by assessing data before and after the gap. Each correction is recorded in the Correction 

History Manager. The corrections can be undone if necessary. If there is only one file for the year and the 

temperature array was not pulled up to service, there will be no corrections applied to the dataset. Proceed 

to the Calculating Daily Summaries section. 

1. Open the Correction Tab from the Toolbox pane and drag a Correction toolbox into the whiteboard 

for each depth. Wire the Signal Join toolbox to the Correction toolbox. 

2. Double click the Correction toolbox and review the data. The correction window features two 

areas. The Correction Control pane to the left shows the correction features that a user can apply to 

the dataset. The Correction History Manager records all changes to the raw data and can be 

exported as a metadata .txt file. 
114

3. Known data corrections will include periods where the temperature array was downloaded creating 

gaps and erroneous temperatures in the dataset. Start the correction process by correcting the most 

obvious spikes in the data. Using the vertical zoom icon, zoom into the spike data values. 
115

4. This area of the dataset needs to be smoothed to remove air temperature values and connected to 

remove the data gap. Use the Mark Region icon to select the data to be corrected and connected. 

5. Use the delete region icon to remove the selected data points. A window will pop up showing the 

information that is related to the delete data task. The comment field will have Delete region 

already entered. Type in an additional comment stating that the erroneous data is associated with 

the data download. Keep these statements brief. 

Note: In the new version (2.6) of the software there will be an Add to List feature that allows the user to 

add the comment to a pick list. Add the comment to the pick list to keep comments standard and reduce 

typing. This comment will be available for future deletions related to data download periods. 

6. Notice that there is now information in the Correction History Manager. 
116

7. If the data points need to be restored, click undo in the Correction History Manager. 

8. Now that the erroneous data points have been removed, apply a cubic spline correction to the gap. 

Use the Mark Region Icon to include the entire gap. The data points to the left and right of the gap 

must be included in the mark region selection. In the Correction Control Pane, select the Gap 

Correction Radio Button and Cubic Spline from the Interp. Method pull down menu. Then click the 

Interp. Selected button. This will apply the correction to this data gap only. 

9. Another pop up window will appear showing the data gap to be corrected and the type of gap 

correction. The comment field will show that a cubic spline was applied to this data gap. Add a 

comment stating that this correction fills a gap created when during the temperature array 

download. 

10. The added comment can be added to a pick list to keep comments standard and reduce typing. If 

this is the first run through, select the Add to List button and click OK. This comment will be 

available for future cubic spline corrections. 
117

11. The data gap will be corrected and a new line of information will be added to the Correction History 

Manager. The new data points should blend into the graphic display of the dataset. 

12. Select the Reset Axis icon to show the full dataset. The large data spike that was obvious is now 

removed. Select the Save Data to Output Port and Exit icon. 
118

13. When the toolbox is double clicked, a pop up window appears asking if you want to load the 

corrected data. Selecting yes will display the corrected data; selecting no will display the raw data. 

When the output port is selected, both the raw and corrected files will be displayed. 

14. Right click on the Correction toolbox to bring up the properties pane. Rename the correction 

toolbox with the toolbox action + water body code and site ID + depth + 2009 (e.g., correct lclar01 

005m 2009). 

15. Generally, there are two data files for each depth for each year. If the temperature array has been 

serviced during the summer, there may be additional time periods to clean up. Consult the field 

notebook to identify other time periods in which erroneous data may have been recorded. 

16. Complete corrections for all depths following steps 1-15. Save the whiteboard. 

Threshold Flagging – under development 

Calculating Daily Summaries 

The data sets have now been joined and corrected for all depths. The next step is to summarize the hourly 

data values into daily mean values for each depth. 

1. Select the Math and Statistics Tab from the Toolbox pane. Drag a Descriptive Statistics toolbox 

into the whiteboard for each depth and wire them to the Corrections toolboxes. 

2. Double click the Descriptive Statistics toolbox. The Input Time Series pane allows the user to set 

the parameters that will be used to summarize the data. Select the Corrected Data radio button. 

This will apply the analysis to the corrected data. Allow the summary to be performed on the entire 

dataset. Select the daily radio button. 
119

3. In the Descriptive Statistics pane, check both boxes for the Mean and Standard Deviation values. 

The right check computes the value and the left check will allow the values to be saved to an output 

port. Click the Compute button. 

4. A set of daily values will be generated and displayed in the Descriptive Statistics toolbox. Select 

the Save Data to Output Port and Exit icon. Rename the Descriptive Statistics toolbox with the 

toolbox action + water body code and site ID + depth + 2009 (e.g., daily lclar01 005m 2009). 

5. Complete steps 1-4 for each depth. Save the whiteboard. 

Data Export – under development

When the daily means and standard deviations have been completed for each depth, the files are ready to be 

exported. Two files will be exported to the .csv format. One file will include the full range of daily depths 

and the other file will include the 5 m daily average and standard deviation. 

1. Select the Data Output tab in the Toolbox pane and drag an Export to File toolbox into the 

whiteboard. Right click the toolbox to open the Properties Pane. Change the number of input ports 

to the number of files that are to be exported. If the depths range from 5 m to 100 m, eleven ports 

will be needed. 
120

2. Double click the Export File toolbox. All the files connected to an input port will be listed in the 

Input ports pane. Check the corrected box from the list of options between Input ports pane and the 

Table columns pane. Highlight each file starting with the 5 m depth and click add. The file will 

display in the Table columns pane. 

3. When all the files have been added, click export to file. Browse to the working folder and save the 

file using the following naming convention: 4-letter park code + year of plot establishment (YYYY) 

+ 2-digit elevation band + site ID + temperature + daily + year (YYYY) (e.g., 

LACL_2007_02_006_temperature_daily_2009). 
121

4. Drag another Export to File toolbox into the whiteboard. Open the Properties pane and change the 

input port to 2. For multiple depth measurements, wire the next set of daily depth mean and 

standard deviation output ports to the Export to File input ports. 

5. Double click the toolbox. Select corrected and add the two files to the Table Columns pane. Click 

Export to file and save the file using the following naming convention: 4-letter park code + 5-digit 

water body code + site ID + temperature_depth + daily + year (YYYY) (e.g., 

LACL_2007_02_006_temperature_50cm_daily_2009). Save and close the white board. Close 

Aquarius. 
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Processing Surface Temperature Data 

There will be only one file for the surface temperature data with a date range of 4 to 5 months. No signal 

joining or correction will be necessary. 

1. Open a new whiteboard and drag in an Import from File toolbox. Load the data using the 

configuration file (cfg) used to load the temperature data. 

2. Right click on the output port for a quick view of the data to verify there are no erroneous data 

points. 

3. Drag in a Descriptive Statistics toolbox, wire it to the Import from File toolbox and run the daily 

min, max, mean and SD summaries. Save the results to the output port. 

4. Drag in an Export to File, right click the toolbox, open the properties pane and change the number 

of input ports from 1 to 4. 

5. Wire the toolboxes and export the daily summaries following the steps 2 and 3 in the previous 

section. 
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Vegetation Monitoring Protocol for the 

Southwest Alaska Network (SWAN) 

Standard Operating Procedure (SOP) # 13 

Data Analysis and Reporting 

Version 1.0 (December 2008) 

Revision History Log: 

Version No. Revision 

Date 

Author Changes Made Reason for Change 

 

This SOP outlines a general approach for summarizing data and analyzing for long-term trends in species 

composition. Consultation with a statistician is advised. 

I. Summary statistics 

1. Analyze by elevation band and vegetation class within and across parks. 

2. Develop summary statistics for species composition within strata and year (Appendix F). Variables 

may include, but are not limited to, cover by growth form; cover by species (for abundant species only); 

species occurrence (all species); seedling and sapling density; and size-class distribution. 

3. Report summary statistics for a given stratum for all years sampled. Quantify interannual variation 

within and among plots (e.g., Fig. 13.1; Appendix F). 

Figure 13.1. Interannual 

variability in species cover 

associated with frost damage in 

2007 (Miller et al. 2009; Appendix 

5). Mean cover (± 1 S.E.) was 

estimated by point-intercept in 

late June of both years. Percent 

damage shown for each species 

refers to the proportion of the 

population that sustained frost 

damage. 

Interannual variability in plot 

attributes can likewise be 

displayed across sites and years, 

as shown in Woodward et al. 

(2009).
124

II. Change or trend detection – Bayesian hierarchical models 

Bayesian hierarchical models (random effects models) will be used to analyze for long-term trends in plot 

variables within each park × elevation band × vegetation class combination (Appendix G). We will use an 

MS Excel add-in (BugsXLA) and freeware program WinBUGS to fit these models from within an MS 

Excel environment. 

III. Graphical analysis – ordination 

Nonparametric ordination (e.g., nonmetric multidimensional scaling [NMS]) and cluster analysis can be 

used to locate plots in multivariate space and are useful in data exploration. Species values are 

standardized to give uncommon and common species equal weight, and rare species are removed. 

Ordination plots are tested for dissimiliarity between sampling dates: vectors between two points in time 

provide a descriptive, graphical depiction of change (Fig. 13.2). 

Tests for convergence or divergence among groups of sites use a simple rank correlation between date and 

between-group dissimilarity. Tests for progressive change in species composition (dissimilarity in species 

composition across sampling dates) can use the correlation or rank correlation between time and 

dissimilarity with a baseline condition (Philippi et al. 1998). Nonparametric test statistics include 

Spearman’s rank correlation, Kendall’s tau, and the Mantel test of association between two distance 

matrices (Philippi et al. 1998). COMDYN (http://www.mbr-pwrc.usgs.gov/software.html#a) or similar 

software can be used to estimate species richness and turnover (colonization/extinction) rates in targeted 

vegetation types, and provides a more quantitative estimation of change. Change can be quantified by 

examining summary statistics for variables of interest (Table 13.1). 

Figure 13.2. Nonmetric 

multidimensional scaling 

diagrams of vegetation plots 

sampled in (a) four ecoregions 

and (b) seven forest types in 

1987 and 2000 (Boucher and 

Mead 2006). The direction and 

magnitude of change in species 

composition for each plot is 

indicated by a directional vector. 

Vectors in the inset show the 

direction and strength of the 

correlation of species and 

environmental variables with the 

ordination axes. 
125

 

Table 13.1. Cover (mean ± SE) for species included in the above ordination (Boucher and Mead 2006). Number of plots is indicated in 

parentheses for each species. Mean cover of fireweed and bluejoint increased by 95% and 163%, respectively, between 1987 and 2000 in the 

Southern Kenai Lowland. Mean cover of hemlock trees and saplings approximately doubled over the sampling interval. 

IV. Reporting 

Annual reports will summarize the previous year’s work and will be organized as follows: 

1. Introduction: Brief description of monitoring objectives and sampling design 

2. Methods: Reference protocol and describe any changes that were implemented 

3. Results: Site descriptions, data summaries; major findings 

4. Discussion: Interpretation of results; ecological significance 

5. Literature cited 

Five-year reports will use the same format, but will address longer-term trends in community composition 

and structure through the analysis of multi-year data sets. All reports will follow NPS Natural Resource 

Publication guidelines, using the pre-formatted MS Word template available on the NRPM website 

(http://www.nature.nps.gov/publications/NRPM/). See Miller et al. (2009) for an example of an annual 

technical report (NRTR format). 

Literature cited 

Boucher, T.V., and B.R. Mead. 2006. Vegetation change and forest regeneration on the Kenai Peninsula, 

Alaska following a spruce beetle outbreak, 1987-2000. Forest Ecology and Management 227:233-

246. 

Miller, A.E., W.L. Thompson, and C. Moore. 2009. Vegetation composition and structure: baseline 

monitoring in the Southwest Alaska Network, 2008 annual summary report. Natural Resource 

Technical Report NPS/SWAN/NRTR-2009/213. (http://science.nature.nps.gov/im/units/swan/ 

index.cfm?theme=reports_pub). Accessed 18 September 2009. 

Phillipi, T.E., P.M. Dixon, and B.E. Taylor. 1998. Detecting trends in species composition. Ecological 

Applications 8:300-308.
126

Appendix B: Field Data Forms 
127

Site evaluation checklist – SWAN Vegetation Monitoring – Version 1.0 (SOP #2) 

Date: __________________________ Park:________________ Plot ID:______________________________ 

Recorders: _________________________________________ Travel time to site:______________________ 

Description of best route to site:________________________________________________________________ 

_____________________________________________________________________________________________

____________________________________________________________________________________________ 

Description of access constraints (1):___________________________________________________________ 

___________________________________________________________________________________________ 

___________________________________________________________________________________________ 

Permanent Rejection Criteria

Yes No Description 

  1. Site is dangerous or prohibitively difficult to access 

  2. Travel time to site from nearest access point is >2 h 

  3. Site is dangerous or prohibitively difficult to work on 

  4. Site occurs on private property 

  5. Site could be damaged by establishment of a monitoring plot 

  6. Site is located within 100 m of a previously selected GRTS point 

Temporary Rejection Criteria

Yes No Description 

  7. Not a targeted vegetation association 

  8. Slope angle ≥25 degrees 

  9. Not representative of surrounding vegetation (inclusion) 

  10. No suitable plot location within 100 m 

Photos

View Photo id Description – dominant species 

North 

South 

East 

West 

Ground cover (close-up) 

Ground cover (close-up) 

Veg. structure (close-up) 

Veg. structure (close-up) 

Viereck Level IV-V Class: _______________________________________________________________________ 

Notes:_______________________________________________________________________________________

_____________________________________________________________________________________________

_____________________________________________________________________________________________

_____________________________________________________________________________________________

_____________________________________________________________________________________________

_____________________________________________________________________________________________
128

Plot data sheet – SWAN Vegetation Monitoring – Version 1.0 (SOP #3) 

Date: Park: Plot ID: Crew: 

Time: GPS: Elevation (m): Plot slope (o): PloPlot year (establ.): Declination (o): General location – notes:All lat/long = NAD 83 Elevation recorded from GPS at SW corner (Transect 1 = 0 m) of plot; slope & aspect are for plot mean Transect 1 = 0 m Transect 1 = 30 m Transect 2 = 0 m Transect 2 = 30 m Transect 3 = 0 m Lat: Lat: Lat: Lat: Lat: Long: Long: Long: Long: Long: Dominant species Tree: Shrub/dwarf shrub: Graminoid: Herbaceous: Viereck Class IV Parent Material (if known): Bedrock or residuum Alluvium Till Colluvium Loess Lacustrine sediment Other (describe): Evidence of humans: building; airstrip; campsite; social trail; developed trail; garbage; people nearby; equipment; artifacts; other 

Drainage: Rapid – runoff / hillslope Rapid - coarse, permeable substrate Impeded – low permeability Saturated soil (H2O table) 

Scattered standing H2O 

Aquatic Slope position: ridgetop, saddle, upper 1/3, middle 1/3, lower 1/3 (toe), valley bottomNotes: 

Slope type: concaMicro-relief type: 

Bedrock outcrops Mass movement (eroMass movement (depTurf hummocks Earth hummocks Moss hummocks Depressions Animal burrows Planar surface USDA-NRCS landf

Evidence of fire: Evidence of wind: Wind exposure cla
129

Soils/surface characteristics data sheet – SWAN - Vegetation Monitoring Program - Version 1

Date: _______________________________ Park:__________________ Plot ID:__________________________________Recorder: _________________________________ Reader:__________________________________ Describe soil profile at least 2 m outside of SW corner of plot. Temperature logger, if installed, should be located approximately 1 m west of plot monument.  Soil profile: Observation depth 

Descriptor Top (cm) Bottom (cm) Decomposition pH Texture Moisture Root 

Litter layer --- Living mat - - - Rock fragments - - - Organic mat - - Mineral 

Soil Temperature Sensor: 

Make & model Serial no. Depth (cm) Time installed Latitude 

Location description (distance/azimuth from SW corner/plot monument: 

Depth to hard surface (active layer or rock):

Plot 

corner 

Transect 

location Depth (cm) D

SW T1-0 

NW T1-30 

SE T3-0 

NE T3-30 

Notes:

_______________________________________________

__________________________________________________________________________________________________________
130

Point-intercept data sheet – SWAN Vegetation Monitoring – Version 1.0 (SOP #5) 

30 m transect (south - north) – page 1 

Date: __________________________ Park:________________ Plot ID:________________________ 

Transect No: ____________ Recorder: _____________________Reader:____________________________ 

Record species code, cover type code or collection # for each hit. (FD) = frost damage; (SD) = standing dead tree/shrub

Point >4m 1-4 m 50 – 100 cm <50 cm 

0.5 m 

1.0 m 

1.5 m 

2.0 m 

2.5 m 

3.0 m 

3.5 m 

4.0 m 

4.5 m 

5.0 m 

5.5 m 

6.0 m 

6.5 m 

7.0 m 

7.5 m 

8.0 m 

8.5 m 

9.0 m 

9.5 m 

10.0 m 

10.5 m 

11.0 m 

11.5 m 

12.0 m 

12.5 m 

13.0 m 

13.5 m 

14.0 m 

14.5 m 

15.0 m 

Cover type codes: moss (M); lichen (LI); litter (LT); gravel (G); rock (RK); bare soil (BG); down wood (DW); standing dead 

(dead tree/shrub) (SD); dead branches on live tree/shrub (SDL); current-year frost damage (FD); scat (SC); standing water (W). 
131

Point-intercept data sheet – SWAN Vegetation Monitoring – Version 1.0 (SOP #5)

30 m transect (south - north) – page 2 

Date: _______________________________Plot ID:________________ Transect No: ____________ 

Recorder: _________________________________ Reader:__________________________________ 

Record species code, cover type code or collection # for each hit. (FD) = frost damage; (SD) = standing dead tree/shrub

Point >4m 1-4 m 50 – 100 cm <50 cm 

15.5 m 

16.0 m 

16.5 m 

17.0 m 

17.5 m 

18.0 m 

18.5 m 

19.0 m 

19.5 m 

20.0 m 

20.5 m 

21.0 m 

21.5 m 

22.0 m 

22.5 m 

23.0 m 

23.5 m 

24.0 m 

24.5 m 

25.0 m 

25.5 m 

26.0 m 

26.5 m 

27.0 m 

27.5 m 

28.0 m 

28.5 m 

29.0 m 

29.5 m 

Cover type codes: moss (M); lichen (LI); litter (LT); gravel (G); rock (RK); bare soil (BG); down wood (DW); standing dead 

(dead tree/shrub) (SD); dead branches on live tree/shrub (SDL); current-year frost damage (FD); scat (SC); standing water (W). 
132

Quadrat data sheet – SWAN Vegetation Monitoring – Version 1.0 (SOP #6) – page 1

 

Date: __________________________ Park:________________ Plot ID:________________________ 

Transect No: ____________ Recorder: _____________________Reader:____________________________ 

Vascular species: Record frequency by species using USDA species codes or collection number (if unknown). 

Species Code or 

Collection No. 

Quad 1 

0m 

Quad 2 

7 m

Quad 3 

14 m 

Quad 4 

21 m 

Quad 5 

28 m 

 

 

 

 

 

 

 

 

 

 

 

 

 

Frequency: 1 = 0.25 m2

; 2 = 1 m2; 3 = 4 m2; blank = absent 

Growth form: T = tree; S = shrub; DS = dwarf shrub; F = forb; FN = fern; G = graminoid; L = lycophyte; H = horsetail; NV= nonvascular 

See (http://plants.usda.gov/) for vascular species list and associated growth form data. NA = not applicable. 
133

Quadrat data sheet – SWAN Vegetation Monitoring – Version 1.0 (SOP #6) – page 1 

Date: __________________________ Park:________________ Plot ID:________________________ 

Transect No: ____________ Recorder: _____________________Reader:____________________________ 

Non-vascular species: Record frequency by species using USDA species codes or collection number (if unknown). 

Species Code or 

Collection No. 

Quad 1 

0m 

Quad 2 

7 m

Quad 3 

14 m 

Quad 4 

21 m 

Quad 5 

28 m 

 

 

 

 

 

 

 

 

 

 

 

 

 

Frequency: 1 = 0.25 m2

; 2 = 1 m2; 3 = 4 m2; blank = absent 

Growth form: T = tree; S = shrub; DS = dwarf shrub; F = forb; FN = fern; G = graminoid; L = lycophyte; H = horsetail; NV= nonvascular 

See (http://plants.usda.gov/) for vascular species list and associated growth form data. NA = not applicable. 
134

Point-intercept data sheet – SWAN Vegetation Monitoring – Version 1.0 (SOP #5) 

30 m transect (south - north) – page 1 

Date: __________________________ Park:________________ Plot ID:________________________ 

Transect No: ____________ Recorder: _____________________Reader:____________________________ 

Record species code, cover type code or collection # for each hit. (FD) = frost damage; (SD) = standing dead tree/shrub

Point >4m 1-4 m 50 – 100 cm <50 cm 

0.5 m 

1.0 m 

1.5 m 

2.0 m 

2.5 m 

3.0 m 

3.5 m 

4.0 m 

4.5 m 

5.0 m 

5.5 m 

6.0 m 

6.5 m 

7.0 m 

7.5 m 

8.0 m 

8.5 m 

9.0 m 

9.5 m 

10.0 m 

10.5 m 

11.0 m 

11.5 m 

12.0 m 

12.5 m 

13.0 m 

13.5 m 

14.0 m 

14.5 m 

15.0 m 

Cover type codes: moss (M); lichen (LI); litter (LT); gravel (G); rock (RK); bare soil (BG); down wood (DW); standing dead 

(dead tree/shrub) (SD); dead branches on live tree/shrub (SDL); current-year frost damage (FD); scat (SC); standing water (W). 
135

Point-intercept data sheet – SWAN Vegetation Monitoring – Version 1.0 (SOP #5)

30 m transect (south - north) – page 2 

Date: _______________________________Plot ID:________________ Transect No: ____________ 

Recorder: _________________________________ Reader:__________________________________ 

Record species code, cover type code or collection # for each hit. (FD) = frost damage; (SD) = standing dead tree/shrub

Point >4m 1-4 m 50 – 100 cm <50 cm 

15.5 m 

16.0 m 

16.5 m 

17.0 m 

17.5 m 

18.0 m 

18.5 m 

19.0 m 

19.5 m 

20.0 m 

20.5 m 

21.0 m 

21.5 m 

22.0 m 

22.5 m 

23.0 m 

23.5 m 

24.0 m 

24.5 m 

25.0 m 

25.5 m 

26.0 m 

26.5 m 

27.0 m 

27.5 m 

28.0 m 

28.5 m 

29.0 m 

29.5 m 

Cover type codes: moss (M); lichen (LI); litter (LT); gravel (G); rock (RK); bare soil (BG); down wood (DW); standing dead 

(dead tree/shrub) (SD); dead branches on live tree/shrub (SDL); current-year frost damage (FD); scat (SC); standing water (W). 
136

Quadrat data sheet – SWAN Vegetation Monitoring – Version 1.0 (SOP #6) – page 1 

Date: __________________________ Park:________________ Plot ID:________________________ 

Transect No: ____________ Recorder: _____________________Reader:____________________________ 

Vascular species: Record frequency by species using USDA species codes or collection number (if unknown). 

Species Code or 

Collection No. 

Quad 1 

0m 

Quad 2 

7 m

Quad 3 

14 m 

Quad 4 

21 m 

Quad 5 

28 m 

 

 

 

 

 

 

 

 

 

 

 

 

 

Frequency: 1 = 0.25 m2

; 2 = 1 m2; 3 = 4 m2; blank = absent 

Growth form: T = tree; S = shrub; DS = dwarf shrub; F = forb; FN = fern; G = graminoid; L = lycophyte; H = horsetail; NV= nonvascular 

See (http://plants.usda.gov/) for vascular species list and associated growth form data. NA = not applicable. 
137

Quadrat data sheet – SWAN Vegetation Monitoring – Version 1.0 (SOP #6) – page 1 

Date: __________________________ Park:________________ Plot ID:________________________ 

Transect No: ____________ Recorder: _____________________Reader:____________________________ 

Non-vascular species: Record frequency by species using USDA species codes or collection number (if unknown). 

Species Code or 

Collection No. 

Quad 1 

0m 

Quad 2 

7 m

Quad 3 

14 m 

Quad 4 

21 m 

Quad 5 

28 m 

 

 

 

 

 

 

 

 

 

 

 

 

 

Frequency: 1 = 0.25 m2

; 2 = 1 m2; 3 = 4 m2; blank = absent 

Growth form: T = tree; S = shrub; DS = dwarf shrub; F = forb; FN = fern; G = graminoid; L = lycophyte; H = horsetail; NV= nonvascular 

See (http://plants.usda.gov/) for vascular species list and associated growth form data. NA = not applicable. 
138

Point-intercept data sheet – SWAN Vegetation Monitoring – Version 1.0 (SOP #5) 

30 m transect (south - north) – page 1 

Date: __________________________ Park:________________ Plot ID:________________________ 

Transect No: ____________ Recorder: _____________________Reader:____________________________ 

Record species code, cover type code or collection # for each hit. (FD) = frost damage; (SD) = standing dead tree/shrub

Point >4m 1-4 m 50 – 100 cm <50 cm 

0.5 m 

1.0 m 

1.5 m 

2.0 m 

2.5 m 

3.0 m 

3.5 m 

4.0 m 

4.5 m 

5.0 m 

5.5 m 

6.0 m 

6.5 m 

7.0 m 

7.5 m 

8.0 m 

8.5 m 

9.0 m 

9.5 m 

10.0 m 

10.5 m 

11.0 m 

11.5 m 

12.0 m 

12.5 m 

13.0 m 

13.5 m 

14.0 m 

14.5 m 

15.0 m 

Cover type codes: moss (M); lichen (LI); litter (LT); gravel (G); rock (RK); bare soil (BG); down wood (DW); standing dead 

(dead tree/shrub) (SD); dead branches on live tree/shrub (SDL); current-year frost damage (FD); scat (SC); standing water (W). 
139

Point-intercept data sheet – SWAN Vegetation Monitoring – Version 1.0 (SOP #5)

30 m transect (south - north) – page 2 

Date: _______________________________Plot ID:________________ Transect No: ____________ 

Recorder: _________________________________ Reader:__________________________________ 

Record species code, cover type code or collection # for each hit. (FD) = frost damage; (SD) = standing dead tree/shrub

Point >4m 1-4 m 50 – 100 cm <50 cm 

15.5 m 

16.0 m 

16.5 m 

17.0 m 

17.5 m 

18.0 m 

18.5 m 

19.0 m 

19.5 m 

20.0 m 

20.5 m 

21.0 m 

21.5 m 

22.0 m 

22.5 m 

23.0 m 

23.5 m 

24.0 m 

24.5 m 

25.0 m 

25.5 m 

26.0 m 

26.5 m 

27.0 m 

27.5 m 

28.0 m 

28.5 m 

29.0 m 

29.5 m 

Cover type codes: moss (M); lichen (LI); litter (LT); gravel (G); rock (RK); bare soil (BG); down wood (DW); standing dead 

(dead tree/shrub) (SD); dead branches on live tree/shrub (SDL); current-year frost damage (FD); scat (SC); standing water (W). 
140

Quadrat data sheet – SWAN Vegetation Monitoring – Version 1.0 (SOP #6) – page 1 

Date: __________________________ Park:________________ Plot ID:________________________ 

Transect No: ____________ Recorder: _____________________Reader:____________________________ 

Vascular species: Record frequency by species using USDA species codes or collection number (if unknown). 

Species Code or 

Collection No. 

Quad 1 

0m 

Quad 2 

7 m

Quad 3 

14 m 

Quad 4 

21 m 

Quad 5 

28 m 

 

 

 

 

 

 

 

 

 

 

 

 

 

Frequency: 1 = 0.25 m2

; 2 = 1 m2; 3 = 4 m2; blank = absent 

Growth form: T = tree; S = shrub; DS = dwarf shrub; F = forb; FN = fern; G = graminoid; L = lycophyte; H = horsetail; NV= nonvascular 

See (http://plants.usda.gov/) for vascular species list and associated growth form data. NA = not applicable. 
141

Quadrat data sheet – SWAN Vegetation Monitoring – Version 1.0 (SOP #6) – page 1 

Date: __________________________ Park:________________ Plot ID:________________________ 

Transect No: ____________ Recorder: _____________________Reader:____________________________ 

Non-vascular species: Record frequency by species using USDA species codes or collection number (if unknown). 

Species Code or 

Collection No. 

Quad 1 

0m 

Quad 2 

7 m

Quad 3 

14 m 

Quad 4 

21 m 

Quad 5 

28 m 

 

 

 

 

 

 

 

 

 

 

 

 

 

Frequency: 1 = 0.25 m2

; 2 = 1 m2; 3 = 4 m2; blank = absent 

Growth form: T = tree; S = shrub; DS = dwarf shrub; F = forb; FN = fern; G = graminoid; L = lycophyte; H = horsetail; NV= nonvascular 

See Appendix F for vascular species list and associated growth form data. NA = not applicable.
142

Quadrat data sheet – SWAN Vegetation Monitoring – Version 1.0 (SOP #6) 

Date: _______________________________ Park:__________________________ Plot ID:___________________________Recorder: _________________________________ Reader:__________________________________ 

Record percent cover and tree seedling counts in 4m2 quad for each living and non-living parameter: 

Transect 1 

BedrockBoulder Cobble Gravel Bare Soil Crypto Crust Litter Dead Stand Down wood Lichen Moss Graminoid Forb Dwarf shrub 

Q1 – 0 mQ2 – 7 m

Q3 – 14 mQ4 – 21 mQ5 – 28 m

Transect 2 

BedrockBoulder Cobble Gravel Bare Soil Crypto Crust Litter Dead Stand Down wood Lichen Moss Graminoid Forb Dwarf shrub 

Q1 – 0 mQ2 – 7 m

Q3 – 14 mQ4 – 21 mQ5 – 28 m

Transect 3 

BedrockBoulder Cobble Gravel Bare Soil Crypto Crust Litter Dead Stand Down wood Lichen Moss Graminoid Forb Dwarf shrub 

Q1 – 0 mQ2 – 7 m

Q3 – 14 mQ4 – 21 mQ5 – 28 m

Note tree species (seedlings), where applicable: Transect 1:_________________________________________________________________________________________________Transect 2:_________________________________________________________________________________________________Transect 3:_________________________________________________________________________________________________Additional notes: 

____________________________________________________________________________________________________________________________________________________________________________________________________________

_____________________________________________________________________________________________________
 

143

Tree and sapling data sheet – SWAN Vegetation Monitoring Program – Version 1.0 

(SOP #7) – page 1 

Date: __________________________Park:__________________Plot ID:____________________________ 

Recorder: _________________________________ Reader:__________________________________ 

TREES (>12.0 cm DBH) 

Tree# Species Cond 

(L/D) 

1⁄2 Plot 

(E/W) 

Transect No. & 

Dist N/S (m) 

Dist E/W 

(m) 

X (m) Y (m) DBH 

(cm) 

Height 

(m) 

Crown 

class 

Crown 

length 

Comments 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

21 

22 

23 

24 

25 

26 

27 

28 

29 

30 

31 

32 

33 

34 

35 

36 

37 

38 

Record the transect number and N/S distance from the transect tape closet to the tree. Record the distance and direction (E/W) from the N/S point on the transect 

tape. Calculate X and Y values (m) for tree location in plot. 

Crown classes: open grown (1); dominant (2); co-dominant (3); intermediate (4); overtopped (5); dead standing tree (6). 
 

144

Tree and sapling data sheet – SWAN Vegetation Monitoring Program – Version 1.0 

(SOP #7) – page 2 

Date: __________________________Park:__________________Plot ID:_____________________________ 

Recorder: _________________________________ Reader:__________________________________ 

TREES (>12.0 cm DBH) 

Tree# Species Cond 

(L/D) 

1⁄2 Plot 

(E/W) 

Transect No. & 

Dist N/S (m) 

Dist E/W 

(m) 

X (m) Y (m) DBH 

(cm) 

Height 

(m) 

Crown 

class 

Crown 

length 

Comments 

39 

40 

41 

42 

43 

44 

45 

46 

47 

48 

49 

50 

51 

52 

53 

54 

55 

56 

57 

58 

59 

60 

61 

62 

63 

64 

65 

66 

67 

68 

69 

70 

Record transect number and N/S distance from transect tape closet to the tree. Record distance and direction (E/W) from the N/S point on the transect tape. 

Calculate X and Y values (m) for tree location in plot. Use densiometer in 4 cardinal directions to estimate mean canopy cover; calculate %. 

Crown classes: open grown (1); dominant (2); co-dominant (3); intermediate (4); overtopped (5); dead standing tree (6). 

Canopy cover: North South East West Average cover 

Plot center 

T1-0 

T1-30 

T3-0 

T3-30 
 

145

Tree and sapling data sheet – SWAN Vegetation Monitoring Program – Version 1.0 

(SOP #7) – page 3 

Date: __________________________Park:__________________Plot ID:_____________________________ 

Recorder: _________________________________ Reader:__________________________________ 

SAPLINGS (>0 cm DBH, <12.0 cm DBH) 

1⁄2 Plot 

(E/W) 

Species Cond 

(L/D) 

DBH 

(cm) 

Crown 

class 

 1⁄2 Plot 

(E/W) 

Species Cond 

(L/D) 

DBH 

(cm) 

Crown 

class 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Estimate DBH to nearest 0.1 cm using calipers. Estimate mean crown class and mean height for all saplings in 1⁄2 plot. 

Crown classes: open grown (1); dominant (2); co-dominant (3); intermediate (4); overtopped (5); dead standing tree (6). 
 

146

Tree core & fuels data sheet – SWAN Vegetation Monitoring – Version 1.0 

(SOPs #8 & 10) 

Date: ________________________ Park:_________________ Plot ID:_________________________________ 

Reader:________________________________________Recorder: ____________________________________ 

Tree cores: Core trees within a 10 m-wide buffer outside of 30 m × 30 m plot. Core 2 largest trees per 1⁄2 plot. 

Tag 

# 

Species Cond 

(L/D) 

DBH 

(cm) 

Tree 

height 

(m) 

Core height 

(cm ARC) 

Location description 

 

Location description: record the transect number and N/S + E/W distance from the transect tape closet to the tree; alternatively, record 

distance and azimuth from closest corner stake. 

Ladder fuels: Reference cored trees above. Measure radius and branch heights to nearest 0.1 m 

Tag 

# 

Crown 

radius (m) 

Ht to dead 

ladder fuel 

(m) 

Ht to live 

ladder fuel 

(m) 

Ht to main 

crown (m) 

Notes 

 

Notes:________________________________________________________________________________________________

_____________________________________________________________________________________________________

_____________________________________________________________________________________________________

_____________________________________________________________________________________________________

_____________________________________________________________________________________________________

_____________________________________________________________________________________________________

_____________________________________________________________________________________________________

_____________________________________________________________________________________________________

_____________________________________________________________________________________________________

_____________________________________________________________________________________________________

_____________________________________________________________________________________________________

_____________________________________________________________________________________________________

_____________________________________________________________________________________________________

_____________________________________________________________________________________________________

_____________________________________________________________________________________________________

_____________________________________________________________________________________________________

_____________________________________________________________________________________________________ 
 

147

Down woody material: Record coarse woody debris (CWD – 1000 h) by size and decay class along 30 m transects. Tally fine 

woody debris (FWD – 1 h; 10 h; 100 h) intersected by each transect. Minimum length of CWD = 1.0 m 

1000 h fuels (>3” or 7.62 cm) 100 h 10 h 1 h 

Transect Species Decay 

class 

Transect 

diameter 

(cm) 

Length 

(m) 

Max/min 

diameter 

(cm) 

No. pieces No. pieces No. pieces 

 

 

 

FMH fuels definitions: 1000 hr (>3” or 7.62 cm); 100 hr (1”-3” or 2.54-7.62 cm); 10 hr (0.25”-1” or 0.64-2.54 cm); 1 hr (<0.25” or <0.64 

cm). 
 

148

Quadrat data sheet – SWAN Vegetation Monitoring – Version 1.0 (SOP #6) – page 2 

Date: __________________________ Park:________________ Plot ID:________________________ 

Transect No: ____________ Recorder: _____________________Reader:____________________________ 

Vascular or Nonvascular (circle one): use USDA species code or collection code (if unknown).

Species Code or 

Collection No. 

Quad 1 

0m 

Quad 2 

7 m

Quad 3 

14 m 

Quad 4 

21 m 

Quad 5 

28 m 

 

 

 

 

 

 

 

 

 

 

 

 

 
 

149

Quadrat data sheet – SWAN Vegetation Monitoring – Version 1.0 (SOP #6) – page 2 

Date: __________________________ Park:________________ Plot ID:________________________ 

Transect No: ____________ Recorder: _____________________Reader:____________________________ 

Vascular or Nonvascular (circle one): use USDA species code or collection code (if unknown).

Species Code or 

Collection No. 

Quad 1 

0m 

Quad 2 

7 m

Quad 3 

14 m 

Quad 4 

21 m 

Quad 5 

28 m 

 

 

 

 

 

 

 

 

 

 

 

 

 
 

150

Appendix C: Using S-DRAW to Generate GRTS Samples 

Author: Bill Thompson, SWAN Quantitative Ecologist 

Date: 24 April 2009 

Purpose: This document describes the step-by-step approach for using S-DRAW 1.0 

(http://www.west-inc.com/programs/S-Draw1b.zip) and SAS 9.2 Windows XP Professional x64 

edition (www.sas.com) to select generalized random tessellation stratified (GRTS) samples. 

Examples provided are for generating GRTS samples for freshwater monitoring, but the methods 

are identical to those used to generate GRTS samples from an access layer for vegetation 

monitoring. SAS is a commercial software package used to manipulate the raw data file of all 

grid points into input and file formats that can be read by S-DRAW, and to manipulate the files 

outputted by S-DRAW into .dbf files that can be easily read by ArcGIS 9.2. S-DRAW is a 

freeware program used for choosing GRTS samples; see the S-DRAW 1.0 User’s Guide 

(SDRAW_users_guide.pdf) in http://www.west-inc.com/programs/S-Draw1b.zip for details. 

Step 1: Generate a file or files containing UTM x-y coordinates for a grid of points overlaid on 

the lake of interest. The spacing of the points will be dictated by the size of the lake and by the 

perceived minimum distance required to minimize dependency of measurements among water 

quality samples. For instance, grid points on Lake Clark are spaced 1 km apart, whereas they are 

spaced 0.25 km apart on Lake Kontrashibuna. If a lake is divided into different subareas, a file of 

grid point locations should be created for each subarea. 

SAS v. 9.2 Windows XP Professional x64 edition will import various file formats, such as DBF, 

CSV, TXT, WKn, JMP, SPSS, Stata, Paradox, MS Excel 4, 5 or 95, etc. The only file formats 

that are not supported by the x64 edition Import/Export wizards are the ones that use the MS Jet 

data provider (MS Excel 97, MS Access 97 and higher; see 

http://support.sas.com/kb/17/075.html for details). These file formats still can be imported into 

and exported from the 64-bit version of SAS, but they require a separate procedure (see Step 4). 

Step 2: Create a folder structure on the C-drive or other relevant drive that will house the various 

data files used or generated from this process, such as the following. 
 

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The key folders above are 

DBF_results: .dbf file(s) with the coordinates of the GRTS sample points 

Raw_data: file(s) of grid points created in Step 1. 

SAS_data: SAS data files (.sas7bdat) 

Txt_input: text file in S-DRAW input format 

Txt_results: text file(s) outputted by S-DRAW that contain(s) the GRTS selected points. 

The user can name these folders whatever he/she wishes, but should create them for each lake 

sampled (e.g., Brooks Lake, Lake Kontrashibuna) and, if desired, by the year the GRTS sample 

is chosen. 

Step 3: Create a SAS library to house the SAS files generated in the next steps. 

3a: Double-click on the SAS icon to start the program. Click on the Add New Library

button in the top tool bar and the following pop-up window appears. 
 

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3b: Enter a name for the SAS library ("grtslake" in this example), click on the box to the left of 

Enable at startup, click on the Browse... button, navigate to the place on the C-drive or relevant 

drive where the SAS_data subfolder is located, select it, and click OK. SAS library names are 

limited to a maximum of 8 characters. 
 

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Step 4: Import the file(s) created in Step 1 into SAS to convert it/them to the input format and 

file format required by S-DRAW. 

(Note: Later versions of MS Excel and MS Access can be saved in an earlier file format, e.g., an 

Excel 97 file can be saved as an Excel 95 file. The SAS Import Wizard then can be used to 

import the MS Excel 95 file as described in Step 4a1 and the user can proceed to Step 4b.) 

4a1: For file formats except MS Excel 97 and later or MS Access 97 and later, use the Import 

Wizard in SAS to import the file(s) created in Step 1, convert it/them to SAS file format(s), and 

assign it/them to a SAS library. 

4a1-i: Double-click on the SAS icon to start the program if it is not already running. 

Select Import Data... from the File drop-down menu. 
 

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4a1-ii: Select the relevant file format from the drop-down list in the middle of the pop-up 

window and click on the Next> button. In this example, we will be importing .dbf files. 

4a1-iii: The next pop-up window asks you to browse to the drive location containing the file you 

would like to import. Click on the Browse... button, navigate to the file's location on the drive, 

select the file, and click on the Next> button. 
 

155

4a1-iv: The next pop-up window allows the user to name the imported file and select a SAS 

library in which to store this file in SAS file format. Select the SAS library created in Step 3 by 

clicking on the down arrow button next to the Library: box and clicking on the library name. 

(Note: the list of libraries to choose from will vary depending on how many have been 

previously created.) 
 

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4a1-v: Click in the box under Member:, enter a name for the imported file (e.g., 

"Kontrash_lower" in the example below), and click on the Finish> button. 

4a1-vi: Repeat the previous steps in 4a1. for importing additional files. 

4a2: For file formats MS Excel 97 and later or MS Access 97 and later, use SAS PC files server 

to import them. 
 

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Perform the following steps to install the PC files server. 

4a2-i: Download the PC files server program from the following location to your choice of 

directory on your PC/laptop that is going to run this application. 

ftp://ftp.sas.com/techsup/download/blind/zqjpcfileserver.zip

4a2-ii: Unzip the zqjpcfileserver.zip file on your pc, it will unzip to the 

pcfilesrv__9210__prt__xx__sp0__1 subdirectory where you stored the zip file. 

4a2-iii: In the unzipped directory pcfilesrv__9210__prt__xx__sp0__1, double 

click on the setup.exe. This will start the install. 

4a2-iv: The setup.exe will install the PC files server in the C:\Program 

Files\SAS\PCFilesServer directory. 

4a2-v: Start the PC files server in one of two ways. 

 a. Select START=>Programs=>SAS=>PC files server 

 b. Go to the C:\Program Files\SAS\PCFilesServer\9.2 directory and double click on 

 the PCFILESERVER.EXE program. 

4a2-vi: After starting up the .exe, the PC files server window will pop up. This window contains 

the server name (e.g., ‘inplacl37102’ for the laptop above) and Port Number (i.e., 8621) that will 

be needed in the SAS code in Step 7. DO NOT CLOSE the PC files server window during the 

import/export procedure – it has to be running for the code in Step 4a2-vii to work. 
 

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4a2-vii: To use the PC files server to import an MS Excel file, copy the following SAS code 

into the SAS Editor window, modify as needed, and click on the Run button in the top 

toolbar: 

 proc import dbms=excelcs 

 out=yoursasdata 

 datafile="yourdirectory\yourfile.xls" replace; 

 port=8621; 

 server="yourpcname"; 

 sheet="yoursheet"; 

 run; 

 quit; 

 The “out=” statement specifies the SAS library name (if relevant) and SAS data file 

 name. The “datafile=” statement indicates the name of the MS Excel file that is being 

 imported and its location on the drive. The “sheet=” statement specifies the name of 

 the particular sheet that is to be imported from the MS Excel spreadsheet. 

 Here is example SAS code, where the sheet "freq_toSAS" in the MS Excel file 

 "LACL_GRTS_2007_2008_CoverFreq_SAS_080923.xls" is converted to a SAS data 

 file "freqdata" and saved to the SAS library "laclfreq": 

 proc import dbms=excelcs 

 out=laclfreq.freqdata 

 datafile="C:\Bill\LACL_GRTS_2007_2008_CoverFreq_SAS_080923.xls" replace; 

 server="inplacl37102"; 

 port=8621; 

 sheet="freq_toSAS"; 

 run; 

 quit; 
 

159

 (Note: The names above should be in double quotes; SAS will return an error in the log 

window if they're placed within single quotes. Also, if the server name is longer than 31 

characters, you will receive a “Failed to connect to the Server” error message in the SAS 

log window. If this occurs, you will need to replace the server name with your 

computer’s IP address. You can find the IP address by typing in “cmd” (without quotes) 

in the START=>RUN window and clicking OK. This will bring up a DOS window. 

Type in “ipconfig” (without quotes) after the DOS prompt and hit return. The IP address 

should be listed in a DOS window similar to the one below. Close down the DOS 

window by clicking on the “x” in the upper right corner.) 

 

 Use the following SAS code to import an MS Access file. 

 proc import dbms=accesscs 

 out=yoursasdata 

 datatable=youraccesstable; 

 database="yourdirectory\yourfile.mdb"; 

 server="yourpcname"; 

 port=8621; 

 run; 

 quit;

4b: Convert the SAS file(s) created in Step 4a1 or 4a2 to generate input formats that can be read 

by S-DRAW and save them to the SAS library created in Step 3. This example uses the 

"UTM_x-coordinate UTM_y-coordinate Sample_Point_ID" style input format in S-DRAW but 

other input formats are available depending on how the sample is chosen; see the S-DRAW 

User’s Guide (SDRAW_users_guide.pdf) in http://www.west-inc.com/programs/S-Draw1b.zip

for details. 

4b1: Click on the SAS Explorer icon on the top tool bar to view the file(s) created in Step 

4a1 or 4a2 to ensure data are correct and to check the column variable names, which will be used 

in SAS code below. 

4b2: Copy and paste the following lines of SAS code into the SAS Editor window, modify as 

needed, and click on the Run button in the top toolbar. The only modifications will be to 

the top lines of code beginning with "%let"; see the instructions after the asterisk on each line. 

(Note: text between an asterisk and a semi-colon is treated as a comment by SAS and hence will 

be ignored.) 
 

160

%let inpfile=grtslake.kontrash_lower; * to the right of the equal sign, 

 specify the name of SAS library 

 (before the period) and the name of 

 SAS file containing grid points 

 (after the period); 

%let id=uniqueid; * to the right of the equal sign, 

 specify the variable name containing 

 the unique id of each grid point. 

 If there is not a variable for this, 

 specify "_N_" (without quotes); 

%let utm_x=xcoord_utm; * to the right of the equal sign, 

 specify the variable name containing 

 the UTM x-coordinate of each grid 

 point; 

%let utm_y=ycoord_utm; * to the right of the equal sign, 

 specify the variable name containing 

 the UTM y-coordinate of each grid 

 point; 

%let outfile=grtslake.kontr_low_SDRAW; * to the right of the equal sign, 

 specify the name of SAS library 

 (before the period) and the name of 

 SAS file of grid points in the input 

 format of S-DRAW (after the period); 

***** DO NOT CHANGE ANYTHING BEYOND THIS POINT *** 

***** UNLESS YOU KNOW WHAT YOU ARE DOING. ***; 

data &outfile; 

 set &inpfile; 

 id=&id; 

 keep &utm_x &utm_y id; 

run; 

The above code should be rerun for each file of grid points, with file names modified 

accordingly. 

Step 5: Export the SAS file(s) created in Step 4 as space-delimited text files so they can be read 

into S-DRAW. 

 

5a: Select Export Data... from the File drop-down menu. 
 

161

5b: Use the drop-down arrow buttons next to the Library: box to select the SAS library (Step 3) 

and next to the Member: box to select the SAS file produced in Step 4. Click on the Next>

button. 

5c: Select Delimited File (*.*) from the drop-down menu. Click on the Next> button. 
 

162

5d: Click on the Browse... button, navigate to the Txt_input folder (Step 2) on the drive, type in 

the name of the text file (kontrash_lower.txt in this example) in the File Name: box, and click on 

the Save button. 

5e: Click on the Finish button to export the file. 
 

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5f: The .txt file produced above will need to have the header information (variable names) 

removed from the file before it can be read into S-DRAW. Use Windows Explorer or other 

means to navigate to the Txt_input folder on the drive. Use Notepad or other word processor to 

open the .txt file, highlight the variable names in the header, delete them, and save the file in its 

original .txt format. 
 

164

Step 6: Use S-DRAW to select GRTS samples of grid points from each .txt file created in Step 

5. 

6a: Place the S-Draw.exe file in the Txt_input folder on the relevant drive. 

6b: Double-click on S-Draw.exe to initiate the program. The following window pops up. 

6c: Click on 2-dimensional, Coordinates, and ID’s. Type in the correct values for Sample size

(=15 in this example), Pixel size (=1 as default) and Random Seed (choose a number between - 2 

billion and + 2 billion or use the default -1). Use the default settings for Randomize and Output 

sample in reverse hierarchical order. The user can enter the total grid points in the input file into 

Population Size or leave this box blank and allow the program to enter this value. A Random 

Seed value of -1 draws a random number from the computer's clock. A GRTS sample can be 

replicated by using the same random number seed. 
 

165

6d: Click on the Browse button next to the Input frame box, navigate to the .txt file created in 

Step 5 (e.g., kontrash_lower.txt), single click on the file, and click on the Open button. 

6e: Click on the Browse button next to the Output file box, navigate to the folder where you 

would like to save the output file (e.g., Txt_results from Step 2), type in the name of the output 

file (e.g., kontrash_lower_pixel1.txt), and click on the Save button. 

6f: Click on the OK button to run the program and generate an output file containing the GRTS 

sample of grid points. 
 

166

S-DRAW creates an output .txt file with the following information (in part). 

The UTM coordinates are rounded to 2 decimal places in the output file. Also, selecting the best 

pixel size to produce a spatially balanced sample is not straightforward. The default value of 1 

will usually suffice, but it is recommended to reselect GRTS samples at other pixel sizes (e.g., 

0.01, 10, and 25) for each input file to provide alternatives to review in case a pixel size of 1 is 

insufficient. Hence, the pixel size was included in the name of the output file in this example 

(i.e., kontrash_lower_pixel1.txt). Output file names produced from additional GRTS samples 

from the same input file would include the pixel size used (e.g., kontrash_lower_pixel01.txt for a 

pixel size of 0.01). 

6g: Repeat the previous steps for each input file and pixel size. 
 

167

Step 7: Use SAS to import the S-DRAW output files produced in Step 6, remove the header 

information from these files, and export them as .dbf files that can be read into ArcGIS 9.2. 

7a: Copy and paste the following lines of SAS code into the SAS Editor window, modify as 

needed, and click on the Run button in the top toolbar. The only modifications will be to 

the top lines of code beginning with "%let" and to the directory path/file name inserted in double 

quotes after the infile statement farther down the program. See the instructions after the asterisk 

on each line. This SAS code should be modified accordingly and rerun for each S-DRAW output 

file produced in Step 6. 

%let outpfile=grtslake.kontr_low_pix1; * to the right of the equal sign, 

 specify the name of the SAS library 

 (before the period) and the name of 

 of the SAS-formatted file 

 containing the GRTS sample of grid 

 points (after the period); 

%let inpfile=grtslake.kontr_low_SDRAW; * to the right of the equal sign, 

 specify the name of SAS library 

 (before the period) and the name of 

 SAS file of grid points in the input 

 format of S-DRAW (after the period) 

 that was created in Step 4b; 

%let utm_x=xcoord_utm; * to the right of the equal sign, 

 specify the variable name containing 

 the UTM x-coordinate of each grid 

 point; 

%let utm_y=ycoord_utm; * to the right of the equal sign, 

 specify the variable name containing 

 the UTM y-coordinate of each grid 

 point; 

options noxwait noxsync; * do not modify this code; 

data a; * do not modify this code; 

 %let _EFIERR_=0; * do not modify this code; 

**** After the infile statement and within the double quotes, insert the 

  directory path and relevant S-DRAW output text file name produced in 

** ** Step 6. Do not modify the "delimiter" statement or any code following 

  it.; 

infile 

"C:\Bill\Aquatic\GRTS_2009\Lake_Kontrashibuna\Txt_results\kontrash_lower_pixe

l1.txt" 

delimiter = ' ' MISSOVER DSD lrecl=32767 firstobs=21 ; 

** ******************************************************* 

  DO NOT CHANGE ANYTHING BEYOND THIS POINT UNLESS ** ** 

  YOU KNOW WHAT YOU ARE DOING. ** ** 

*********************************************************; 

informat VAR1 $8. ; informat S_DrawB $13. ; informat XCoord 14.2 ; 

informat YCoord 14.2 ; informat VAR5 $55. ; informat _3_55_41 $1. ; 

informat Inclprob 9.8 ; informat ID 10. ; informat VAR9 $1. ; 
 

168

informat VAR10 $1. ; informat VAR11 $14. ; informat VAR12 $1. ; 

informat VAR13 $1. ; informat VAR14 $12. ; informat VAR15 $12. ; 

informat VAR16 $4. ; informat VAR17 $1. ; informat VAR18 $13. ; 

informat VAR19 $5. ; informat VAR20 $12. ; informat VAR21 $4. ; 

informat VAR22 $14. ; informat VAR23 $13. ; informat VAR24 $9. ; 

informat VAR25 $15. ; informat VAR26 $6. ; informat VAR27 $6. ; 

format VAR1 $8. ; format S_DrawB $13. ; format XCoord 14.2 ; 

format YCoord 14.2 ; format VAR5 $55. ; format _3_55_41 $1. ; 

format Inclprob 9.8 ; format ID 10. ; format VAR9 $1. ; format VAR10 $1. ; 

format VAR11 $14. ; format VAR12 $1. ; format VAR13 $1. ; format VAR14 $12. ; 

format VAR15 $12. ; format VAR16 $4. ; format VAR17 $1. ; format VAR18 $13. ; 

format VAR19 $5. ; format VAR20 $12. ; format VAR21 $4. ;format VAR22 $14. ; 

format VAR23 $13. ; format VAR24 $9. ; format VAR25 $15. ; format VAR26 $6. ; 

format VAR27 $6. ; 

input VAR1 $ S_DrawB $ XCoord YCoord VAR5 $ _3_55_41 $ Inclprob ID VAR9 

$ VAR10 $ VAR11 $ VAR12 $ VAR13 $ VAR14 $ VAR15 $ VAR16 $ VAR17 $ VAR18 

$ VAR19 $ VAR20 $ VAR21 $ VAR22 $ VAR23 $ VAR24 $ VAR25 $ VAR26 $ VAR27 $ ; 

if _ERROR_ then call symputx('_EFIERR_',1); 

&utm_x=xcoord; 

&utm_y=ycoord; 

keep &utm_x &utm_y ID; 

run; 

data b; 

 set a; 

 pt_order=_N_; 

run; 

data b1; 

 set b; 

 id_drop=1; 

 keep pt_order id id_drop; 

 proc sort; 

 by id; 

run; 

data &outpfile; 

 merge &inpfile b1; 

 by id; 

 if id_drop=1; 

 drop id_drop id; 

 proc sort; 

 by pt_order; 

run; 

Step 7b: Use the Export Wizard in SAS to convert the SAS-formatted output files containing 

GRTS-chosen points that were generated in Step 7a to .dbf formats and place them in the 

DBF_results folder (Step 2). 

7b-i: Select Export Data... from the File drop-down menu. 
 

169

7b-ii: Use the drop-down arrow buttons next to the Library: box to select the SAS library (Step 

3) and next to the Member: box to select the SAS file produced in Step 7a. Click on the Next>

button. 
 

170

7b-iii: Select the file format (.dbf) for the export file from the drop-down list and click on the 

Next> button. 

7b-iv: Click on the Browse... button, navigate to the DBF_results folder (Step 2) on the drive, 

type in the name of the text file (kontrash_lower_pixel1.dbf in this example) in the File Name:

box, and click on the Save button. 

7b-v: Click on the Finish button to export the file. The resulting .dbf file will contain the UTM 

x-coordinate, UTM y-coordinate and sample order of GRTS points (pt_order). 
 

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172

Appendix D: Simulation results for evaluating minimum 

number of points and grids of points required to detect 

change in two landcover classes, Lake Clark National Park & 

Preserve 

William L. Thompson, Quantitative Ecologist, Southwest Alaska Network 

Introduction 

We conducted simulations to investigate the minimum level of change that could be detected 

when either 20% or 50% of points (plots) associated with two landcover classes were changed 

across 2 time periods within various elevational zones from the current landcover map for Lake 

Clark National Park and Preserve (LACL). The remaining points associated with other landcover 

classes remained unchanged during these 2 time periods. Up to 50 samples of single points, 2 × 2 

grids of points, and 3 × 3 grids of points were chosen in each simulation run under a generalized 

random-tessellation stratified (GRTS; Stevens and Olsen 2004) design from 2 landcover classes 

occurring at different observed frequencies (0.6% - 61%) within 3 elevation bands (Table D.1). 

Band 3 was further split into northern and southern portions of LACL. We also used simulations 

to evaluate effects of 30 m, 60 m, 120 m, and 240 m spacing among points (subplots) within 2 × 

2 and 3 × 3 grids. All points in the reduced park-wide grid (sampled population) were spaced 30 

m apart. 

Methods 

The following describes the general steps for performing the simulations: 

 

1. Generate an ACCESS database containing UTM and Albers coordinates, elevation, and 

landcover class codes for each point deemed accessible in LACL from the existing 

landcover map. 

2. Import the ACCESS database into program SAS (SAS Institute Inc. 2004, 2006) and 

assign stratum values (Table D.1) to each accessible point. Remove points with >10o 

slopes to ensure sampled locations will be on relatively flat areas and remove points with 

unwanted landcover class codes (i.e., water, marine mudflats, and unknown 

classification). Use SAS PROC SURVEYSELECT to randomly select 20% and 50% of 

points from each of 2 landcover classes (dwarf shrub tundra and prostrate shrub tundra) 

that spanned a range of percent frequencies (0.60% to 61%) in accessible areas across the 

3 elevational zones. Assign a new landcover class code to the randomly selected point 

within a given landcover class and place this value in the relevant “20% change” and 

“50% change” columns while retaining the original landcover class values for all other 

points. Place the results in a separate file for each landcover class. 

3. Use SAS to export the data into .txt files in formats that can be read by freeware program 

S-DRAW (http://www.west-inc.com/computer.php). Each file should be specific to each 

elevational zone category. 
 

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4. Use program S-DRAW to select 1000 GRTS samples of 50 single points in accessible 

areas from each elevational zone category. Import the 1000 sample results files for each 

elevational zone category into SAS. 

5. Use SAS to generate 2 × 2 and 3 × 3 grids of points (and their coordinates) of various 

spacing (30 m, 60 m, 120 m, and 240 m) for each GRTS-selected single point in the 

results files. Match-merge the grid points by coordinates with the change files produced 

from Step 2 and assign appropriate attributes to the newly generated grid point records. 

Note that there may be less than 50 samples of 2 × 2 or 3 × 3 grids per simulation run 

because of the configuration and fragmentary nature of the access areas, e.g., a grid 

cannot be generated if the GRTS-selected single point is too close to the access boundary, 

the elevational zone boundary, the slope boundary (i.e., points on steep slopes were 

removed), or an adjacent GRTS-selected grid. 

6. Use the simple random sample variance estimator in SAS PROC SURVEYMEANS to 

estimate the proportion of single points that changed landcover class codes from time 1 to 

time 2, and the associated 90% confidence interval, for each GRTS sample of size 2, 3, 4, 

..., 50 within each simulation run. Use the cluster sample variance estimator in SAS 

PROC SURVEYMEANS to estimate the proportion of 2 × 2 and 3 × 3 grid of points that 

changed landcover class codes from time 1 to time 2, and the associated 90% confidence 

interval, for each GRTS sample of size 2, 3, 4, ..., 50 (or maximum sample size) within 

each simulation run. These two variance estimators are conservative approximations to 

the GRTS variance estimator, which is currently not available in SAS. The cluster sample 

estimator accounts for the spatial dependency of points within sample grids. 

 

7. Compute the percentage of times that the lower 90% confidence limit of the estimated 

proportion is greater than and/or equal to some specified minimum detectable change 

(MDC) level for GRTS samples of size 2, 3, 4, ..., 50 (or maximum sample size) across 

all simulations runs for single points (i.e., no./1000) and for grids of points various 

spacing and configurations (i.e., no./observed maximum with > 250 runs). This 

percentage represents the probability of detecting an MDC of the specified size based on 

a given GRTS sample size for each level of true change (Figure A2.1) 

An important issue to keep in mind for Step 7 is the difference between the true change and the 

MDC. As soon as one takes a sample, there is uncertainty associated with the estimate so the 

change that can be detected (i.e., MDC) will be always less than the true change. Statistical 

power analysis is based on the null hypothesis of no change, which in this case is the same as 

assessing whether or not the lower 90% confidence limit is above 0. Note that statistical power 

increases with increasing effect size (i.e., true change, such as 20% or 50%) because the 

confidence interval, and hence the lower confidence limit, is shifted away from 0. Nonetheless, 

statistical power analysis only indicates if there was a change at a given level of confidence, 

unless you assume the cited true change value (model) is correct, i.e., a statistically significant 

trend equals the true trend value used in power calculations. Thus, it does not provide the 

magnitude of the change, nor does it allow an interpretation of the biological importance of the 

change level. For instance, it would be unsettling if the analysis only concluded evidence of a 

change (>0%) when the true change was 50%. Therefore, in this simulation exercise, we 

compared the lower 90% confidence limit to a range of MDCs to provide information on the 

magnitude of change that could be detected (i.e., >0%, ≥2.5%, ≥5%, ≥10%, ≥15%, and ≥0.20%) 

for a true change of either 20% or 50% applied to points with a specific landcover class. For 
 

174

comparative purposes, an MDC >0% was included in results to show what a traditional "power 

analysis" would conclude. 

Figure D.1. An illustration of how the probability of detecting a change is calculated for different levels of true 

change. In this example, 90% confidence intervals (CIs; horizontal lines) of the change estimate are generated by 

three simulation runs from samples of points where 20% (true change; black arrow) of all points were changed from 

time 1 to time 2. The lower limit of the top 90% CI is greater than 0 so a change was detected, but the lower limits of 

the other 2 CIs were at 0 so they did not detect a change. Consequently, in this simple example, there is a 33% (1/3) 

chance of detecting a >0% change when the true change is 20%. The lower limit of the top CIs also exceeds 2.5%, 

so there is a 33% (1/3) chance of detecting at least a 2.5% change when the true change is 20%. None of the lower 

90% confidence limits is at or above 5% so there is no chance of detecting a change with 90% confidence at this 

level and higher. 

Results 

Due to severe logistical limitations of sampling ground plots in SWAN parks, our simulation 

results should be interpreted based on the maximum number of points (plots) that could be 

measured in LACL during a given season. That is, at most 25 single points could be surveyed in 

LACL per year, whereas at most six 2 × 2 grids and three 3 × 3 grids could be sampled. Based on 

these criteria, we would have an approximately 80% chance to detect a change (>0%) with 90% 

confidence when there was actually a 50% change in points for landcover classes occupying 

12%, 24%, 46% and 61% of a given elevational zone category (Table D.2). This level of change 

would be equivalent to a true change of 6% (i.e., 50% × 12% = 6%), 12%, 23%, and 30.5%, 

respectively, if extrapolated to all points and landcover classes in a given elevational zone if one 

assumes the changes applied to points of specific landcover classes exactly mimic the spatial 

patterns of change if applied across all points and landcover classes, which is very unlikely. The 

only instance when a change (>0%) could be detected for a true change of 20% in a landcover 

class code was when this landcover class occupied 61% of the points in an elevational zone 

category. This same scenario was also the only instance when an MDC was within an order of 

magnitude of the true change applied within a landcover class code (i.e., ≥5% MDC for a true 

50% change; Table D.2). 
 

175

Simulations based on 60 m spacing among points within both 2 × 2 (Table D.3) and 3 × 3 grids 

(Table D.4) produced the best results across all point spacing options so we will focus on their 

results. The only change scenario for sampling 2 × 2 grids that met the minimum criteria for 

probability of change detection (0.8 or 80%) and for sample size was when 50% of points were 

changed for a landcover class that occupied 61% of an elevation zone category. Simulations 

indicated that one could at best achieve an MDC (5%) that was within an order of magnitude of 

the true change (Table D.3). The results for 3 × 3 grids were even more sobering; one could at 

best expect to detect a change (>0%) when in fact a true change of 50% had occurred (Table 

D.4). Thus, GRTS samples of single points had a greater ability to detect change than either 2 × 

2 or 3 × 3 grids requiring similar levels of sampling effort. 

Literature Cited 

SAS Institute Inc. 2004. SAS/STAT® 9.1 user’s guide. SAS Institute, Inc., Cary, North 

Carolina, USA. 

SAS Institute Inc. 2006. Base SAS® 9.1.3 procedures guide, Volumes 1, 2, 3, and 4. Second 

edition. SAS Institute Inc., Cary, North Carolina. 

Stevens, D. L., Jr., and A. R. Olsen. 2004. Spatially balanced sampling of natural resources. 

Journal of the American Statistical Association 99:262-278. 

Table D.1. Number of total points and points of selected landcover classes (LC) in accessible areas with slopes 

≤10o

 in 4 elevational zone categories in LACL that were used in change simulations. LC code 15 is dwarf shrub 

tundra and LC code 16 is prostrate shrub tundra. 

Elevational 

zone identifier 

Elevation Total pts. in zone LC code 

No. pts. of LC code 

(freq. of occurrence) 

1 ≤457 m (1500 ft) 219355 15 14330 (7%) 

 16 1268 (0.60%) 

2 >457 m and 

≤914 m (3000 ft) 

191551 15 45336 (24%) 

 16 14366 (8%) 

3 (N) >914 m - N of 

Turquoise Lake 

26454 15 2448 (9%) 

 16 12180 (46%) 

3 (S) >914 m - S of 

Turquoise Lake 

1506 15 182 (12%) 

 16 923 (61%) 
 

176

Table D.2. Percentage of 1000 simulation runs in which the lower 90% confidence limit (CL) of the estimated 

change rate from 50 GRTS samples (n) of single points was equal to and/or above a specified change (= 

minimum detectable change) in a specific landcover class (LC; Table 1) occurring in various frequencies of 

occurrence for true changes of 20% and 50% during 2 time periods in accessible areas with slopes ≤10o

 in 4 

elevational zone categories of LACL. 

 

Percentage of samples in 1000 simulation runs in which the 

lower 90% CL was equal to and/or above the specified change 

ratea

LC % 

frequency 

occurrence 

Elev. 

zone 

cat. 

True change 

(LC only) 

 

>0%b ≤2.5% ≤5% ≤10% ≤15% ≤20% 

0.60 1 20% 0.2% 0% 0% 0% 0% 0% 

 50% 1% 0% 0% 0% 0% 0% 

8 2 20% 20% 0% 0% 0% 0% 0% 

 50% 59% 15% 0% 0.% 0% 0% 

12 3(S) 20% 64% 17% 3% 0% 0% 0% 

 50% 78% 

(n=24)

78% 

(n=49) 

52% 12% 1% 0% 

24 2 20% 76% 27% 5% 0% 0% 0% 

 50% 78% 

(n=21)

79% 

(n=31) 

66% 26% 5% 2% 

46 3(N) 20% 80% 

(n=35) 

60% 22% 1% 0% 0% 

 50% 80% 

(n=13)

79% 

(n=19)

79% 

(n=25)

74% 31% 7% 

61 3(S) 20% 80% 

(n=23)

80% 

(n=42) 

59% 18% 1% 0% 

 50% 79% 

(n=8)

79% 

(n=12)

80% 

(n=16)

81% 

(n=20)

80% 

(n=30) 

71% 

a Sample sizes shown in parentheses indicate the observed number of GRTS samples (n) needed to have 

approximately an 80% chance or more of detecting the specified rate of change with 90% confidence given the true 

change within each elevational zone category. Values in bold font meet the minimum requirements for probability of 

change detection and for maximum number of points (plots) that could be realistically sampled during a single 

season. 

b

 The >0% change category represents the traditional definition of statistical power, i.e., the probability of detecting a 

change regardless of its magnitude. 
 

177

Table D.3. Percentage of simulation runs in which the lower 90% confidence limit (CL) of the estimated change rate for 

different numbers of GRTS samples (n) of 2 × 2 grids, with points spaced 60 m apart, was equal to and/or above a specified 

change (= minimum detectable change) in a specific landcover class (LC; Table 1) occurring in various frequencies for true 

changes of 20% and 50% during 2 time periods within accessible areas of ≤10o slope in 4 elevational zone categories of 

LACL. The maximum number of 2 × 2 grids that could be selected was less than 50 under each sampling scenario due to the 

asymmetrical configuration and fragmentary nature of accessible areas (elevational zone 1: n=41; elevational zone 2: n=41; 

elevational zone 3[N]: n=46; and elevational zone 3[S]: n=24). This also produced a variable number of simulation runs 

(range = 223 - 2158; nearly all ≥1000) across sample sizes, with lower numbers at the higher sample sizes. The percentages 

shown below were calculated based on the actual number of simulation runs for each sample size. 

 

Percentage of GRTS samples in simulation runs in which the lower 

90% CL was equal to and/or above the specified change ratea

LC % 

frequency 

occurrence 

Elev. zone 

cat. 

True change 

(LC only) 

 

>0%b ≥2.5% ≥5% ≥10% ≥15% ≥20% 

0.60 1 20% 1% 0% 0% 0% 0% 0% 

 50% 7% 0% 0% 0% 0% 0% 

8 2 20% 62% 1% 0% 0% 0% 0% 

 50% 80% 

(n=30) 

15% 3% 0% 0% 0% 

12 3(S) 20% 81% 

(n=20) 

26% 4% 0% 0% 0% 

 50% 79% 

(n=10) 

81% 

(n=20) 

51% 7% 1% 0% 

24 2 20% 79% 

(n=15) 

57% 7% 0% 0% 0% 

 50% 82% 

(n=8) 

83% 

(n=14) 

80% 

(n=23) 

40% 2% 1% 

46 3(N) 20% 82% 

(n=11) 

79% 

(n=28) 

52% 1% 0% 0% 

 50% 91% 

(n=7) 

81% 

(n=8) 

81% 

(n=11) 

80% 

(n=27) 

54% 7% 

61 3(S) 20% 85% 

(n=7) 

82% 

(n=12) 

80% 

(n=21) 

32% 7% 0% 

 50% 83% 

(n=4)

86% 

(n=5)

83% 

(n=5)

80% 

(n=7) 

84% 

(n=11) 

81% 

(n=18) a Numbers shown in parentheses indicate the observed number of GRTS samples (n) of 2 × 2 grids of points needed 

for about an 80% percent chance or more of detecting the specified rate of change with 90% confidence within a 

given elevational zone category. Values in bold font meet the minimum requirements for probability of change 

detection and for maximum number of grids that could be realistically sampled during a single season. b

 The >0% change category represents the traditional definition of statistical power, i.e., the probability of detecting a 

change regardless of its magnitude. 
 

178

Table D.4. Percentage of simulation runs in which the lower 90% confidence limit (CL) of the estimated change rate for 

different numbers of GRTS samples (n) of 3 × 3 grids, with points spaced 60 m apart, was equal to and/or above a 

specified change (= minimum detectable change) in a specific landcover class (LC; Table 1) occurring in various 

frequencies for true changes of 20% and 50% during 2 time periods within accessible areas of ≤10o slope in 4 

elevational zone categories of LACL. The maximum number of 3 × 3 grids that could be selected was less than 50 

under each sampling scenario due to the asymmetrical configuration and fragmentary nature of accessible areas 

(elevational zone 1: n=32; elevational zone 2: n=34; elevational zone 3[N]: n=41; and elevational zone 3[S]: n=12). 

This also produced a variable number of simulation runs (range = 269 - 2232; nearly all ≥1000) across sample sizes, 

with lower numbers at the higher sample sizes. The percentages shown below were calculated based on the actual 

number of simulation runs for each sample size. 

 

Percentage of GRTS samples in simulation runs in which the lower 

90% CL was equal to and/or above the specified change ratea

LC % 

frequency 

occurrence 

Elev. 

zone 

cat. 

True change 

(LC only) 

 

>0%b ≥2.5% ≥5% ≥10% ≥15% ≥20% 

0.60 1 20% 2% 0% 0% 0% 0% 0% 

 50% 3% 0% 0% 0% 0% 0% 

8 2 20% 81% 

(n=31) 

0% 0% 0% 0% 0% 

 50% 80% 

(n=20) 

10% 0% 0% 0% 0% 

12 3(S) 20% 84% 

(n=11) 

28% 5% 0% 0% 0% 

 50% 83% 

(n=7) 

79% 

(n=12) 

60% 10% 0% 0% 

24 2 20% 80% 

(n=8) 

79% 

(n=29) 

15% 0% 0% 0% 

 50% 85% 

(n=6) 

80% 

(n=8) 

79% 

(n=13) 

52% 3% 0% 

46 3(N) 20% 87% 

(n=7) 

80% 

(n=16) 

64% 0% 0% 0% 

 50% 86% 

(n=5) 

82% 

(n=6) 

80% 

(n=8) 

79% 

(n=19) 

52% 5% 

61 3(S) 20% 80% 

(n=4) 

87% 

(n=6) 

83% 

(n=8) 

51% 6% 0% 

 50% 85% 

(n=3)

93% 

(n=4) 

89% 

(n=4) 

85% 

(n=5) 

81% 

(n=6) 

81% 

(n=10) a Numbers shown in parentheses indicate the observed number of GRTS samples (n) of 3 × 3 grids of points needed 

for about an 80% percent chance or more of detecting the specified rate of change with 90% confidence within a 

given elevational zone category. Values in bold font meet the minimum requirements for probability of change 

detection and for maximum number of grids that could be realistically sampled during a single season. b

 The >0% change category represents the traditional definition of statistical power, i.e., the probability of detecting a 

change regardless of its magnitude. 
 

179

Appendix E: Simulation results for evaluating minimum 

sample size and sample frequency required for detecting a 

specified total change in vegetation plots within SWAN parks 

William L. Thompson, Quantitative Ecologist, Southwest Alaska Network 

Introduction 

We conducted simulations to investigate the minimum level of total change that could be 

detected with 95% confidence by various sample sizes of plots (n = 6, 8, 12, and 24) measured in 

a given year at two different sampling frequencies (5 and 10 years) under a rotating panel design 

for populations subjected to different levels of true total change (30%, 40%, and 50%) during 31-

year periods. A panel refers to the number of plots that is always sampled during the same year 

(McDonald 2003). Preliminary fieldwork in SWAN parks indicated that 24 plots would be the 

maximum number that could be measured with certainty in any given year, whereas 6 plots were 

conjectured to be the minimum necessary for reasonably precise trend estimators. We included 

two other factors of 24 (i.e.., 8 and 12 plots) as additional sample size alternatives to evaluate via 

simulation. Preliminary simulation results were similar for cover proportions between 5%-50%, 

so we used cover proportions of 5%, 25% and 45% and averaged their results for each run. The 

simulation approach detailed below is adapted from Kissling et al. (2007), with results presented 

in the subsequent section. 

Methods 

The following describes the general steps for performing the simulations. 

 

1. Randomly generate 1000, 31-year time series for all combinations of 24 plots with true 

cover proportions (p) of 5%, 25%, and 45% across a range of coefficients of variation 

( p][CV ; 5%-50% by 5% increments) under a specified finite rate of population growth 

( ). 

 a. Randomly generate  estimates (i.e., 

ˆ) in the time series for each plot from a 

truncated Normal distribution whose mean is the specified growth rate raised to the 

power of the relevant year i (i.e., i  ) and whose standard error ( ] [SE  ) is based on the 

average for three populations of Astrocaryum mexicanum (   0162.0][SE ; AlvarezBuylla and Slatkin 1994). Although this species is not native to Alaska, we used it in the 

simulations because it was the only published value for a SE of lambda that we could find 

in the literature. The lower and upper limits of this Normal distribution are set to the 

possible extremes for  (e.g., 0.1 and 1.5). The range of  values used in the 

simulations correspond to the desired levels of true total change for each plot over 31 

years, which are   978.0 (50% change),   9835.0 (40% change), and   9885.0

(30% change). 

 b. Randomly generate a different initial estimated cover proportion ( 0 pˆ ; time 0) for each 

plot from a truncated Normal distribution whose mean is the specified value (5%, 25%, 

or 45%) and whose standard error ( p]SE[ ) corresponds to the specified cover proportion 
 

180

and coefficient of variation (5%-50% by 5% increments), where  (CV)SE( ppp ). 

The truncated Normal distribution has a lower bound of 0 and an upper bound of 1. 

Calculate the estimated proportions ( pi ˆ ) of each plot for years i =1 to 31 in each 

simulated time series based on i

i pp 

ˆ ˆˆ  0 (Eberhardt 1987), where the carets (^) refer to 

estimators of each parameter. 

 c. This step incorporates temporal (process) variation through the randomly generated 

ˆs 

for each time step and sampling variation by randomly generating 0 pˆ s for each time step 

and plot-specific time series of observations. 

2. Estimate the trend in estimated cover proportions for each simulated time series and 

compute the percentage of times in 1000 simulation runs that the upper limit of the onesided 95% confidence interval of each trend estimate is either equal to and/or below the 

annual rates of change (decline) associated with levels of total change of interest for a 31-

year period (Fig. A6.1). 

 a. Take the natural log of the response (cover proportion) variables to account for the 

multiplicative nature of how the 

ˆs were generated. 

 b. Use AICc model selection criterion (Burnham and Anderson 2002) to choose the bestfitting covariance structure for the repeated measures, log-linear regression model based 

on a single randomly simulated times series of 24 plots per year for 31 years (generated 

as described in Step 1). 

 c. Use the covariance structure and model chosen in Step 2.b. to estimate trend for each 

simulated time series under the different rotating panel designs (i.e., 6, 8, 12 or 24 plots 

measured in a given year every 5 or 10 years; Fig. A6.2), coefficients of variation (5%-

50%), and true total change (30%, 40% or 50%) during a 31-year period. 

 d. Summarize the number of times out of 1000 that the upper one-sided 95% confidence 

interval of each trend estimate is either equal to and/or below the annual rates of change 

(decline) associated with levels of total change of interest for a 31-year period for each 

combination of sample size, sample frequency and coefficient of variation. (Note: the 

upper confidence limit of a 95% confidence 

 

3. Use results from Step 2 to generate graphs displaying the percent chance of detecting a 

user-specified minimum total change (minimum detectable total change [MDTC]) with 

95% confidence for given the level of true total change (30%, 40% or 50%) over 31 years 

for different sample size-sample frequency combinations across the range of coefficients 

of variation (0%-50% by 1% increments). 

 a. Use a statistical software program (e.g., SAS; SAS 2008) or freeware program (e.g., R; 

http://www.r-project.org/) to fit simulation results from Step 2 (5%-50% ] [CV p by 5% 

increments) with a logistic regression model to estimate predicted probabilities of 

detecting the user-specified MDTC across the range of coefficients of variation (0%-

50%) by 1% increments. 
 

181

-0.03 -0.02 -0.01 0 0.01 0.02

Figure E.1. An illustration of how we used simulations to calculate chance of detecting a given total change for 

different levels of true change. In this example, one-side 95% confidence intervals (horizontal lines with terminal 

arrows) of the annual change estimate (- [1 - λ]) were generated by three simulation runs within which a -50% true 

total change (  978.0 or annual change estimate = -0.022) was applied to three simulated, 31-year time series of 

observations. The upper limit of the bottom 95% confidence interval is greater than 0 so no change was detected, 

but the upper limits of the other 2 confidence intervals were below 0 so they did detect a change. Consequently, in 

this simple example, there is a 67% (2/3) chance of detecting a <0% total change, with 95% confidence, when the 

true change was -50%. The upper limit of the top two confidence intervals also were less than or equal to -0.007 

(equivalent to a 20% decline over 31 years), so there was a 67% (2/3) chance of detecting up to a -20% total change 

when the true total change was -50%. 

b. The response variable in the logistic regression model in Step 3a. is the number of times 

out of 1000 that the upper one-sided 95% confidence interval of each trend estimate is 

either equal to and/or below the annual rates of change (decline) associated with levels of 

total change of interest for a 31-year period, whereas the predictor variable is the 

associated p)(CV (5%-50% by 5% increments). 

Results and Discussion 

We used a -25% total change as our desired MDTC for a CV≤30% based on interannual 

variability in 4-6 plots/vegetation class sampled in Lake Clark National Park and Preserve 

(Miller et al. 2009) and rates of change reported in the literature (Chapin et al. 2005, Boucher 

and Mead 2006, Bowman et al. 2006, Canone et al. 2007). Further, we felt this MDTC would be 

reflective of an even greater, and likely ecologically important, true change. 

- (1 - λ) 
 

182

Year 

Panel 

design 1 2 3 4 5 6 7 8 9

1

0

1

1

1

2

1

3

1

4

1

5

1

6

1

7

1

8

1

9

2

0

2

1

2

2

2

3

2

4 

2

5 

2

6 

2

7 

2

8 

2

9

3

0

3

1

5 panels X X X X X X X

sampled X X X X X X 

every 5 X X X X X X 

years X X X X X X 

 X X X X X X 

5 panels X X X X

sampled X X X 

every 10 X X X 

years X X X 

 X X X 

10 panels X X X X

sampled X X X 

every 10 X X X 

years X X X 

 X X X 

 X X X 

 X X X 

 X X X 

 X X X 

 X X X 

Figure E.2. Simulations were based on the top row of each panel design with 1 year added for another sample point. 

A complete rotating design with either 7 temporal sample points (5-year interval) or 4 temporal sample points (10-

year interval) per panel would require a 35-year series for sampling 5 panels and a 40-year series for sampling 10 

panels. Simulation results among rows within each panel design were similar. 

Both sampling and temporal variability were associated with each estimate of total change, so 

our goal was to use simulation results to determine the sampling effort (no more than 24 plots 

per year) and frequency required to achieve an MDTC that was as close to the true total change 

as logistically feasible for a CV≤30%. There were no sample size and sampling frequency 

combinations under a true change of -30% that would allow us to feasibly meet our objectives 

under our samping constraints. Conversely, simulation results for a true total change of -50% 

(Table E.3) met our sampling objectives but this true change was not close enough to our 

MDTC. However, under a true total change of -40%, we would be able to sample 8 plots per 

vegetation class × elevation band × park combination (total = 24 plots per year) every five years 

that would provide us with at least an 80% chance to detect at least a -20% total change over 31 

years with 95% confidence (Table E.2). Our simulation results indicated this same level of 

sampling effort/frequency had a similar chance of detecting at least a -25% total change, with the 

same level of confidence, given a true -50% change (Table E.3). 

Simulation models are approximations; we never know what the true change or trend is in reality 

so we cannot assume that a “statistically significant” change (e.g., <0%) observed from actual 

data corresponds exactly to the true change value used in simulations. Specifying an MDTC as 

the benchmark comparison in simulations ensures a conservative estimate of sampling 

effort/frequency (i.e., larger samples, collected more often). For instance, if we used <0% as the 

benchmark comparison, we would only need to sample half as many plots per vegetation class × 

elevation band × park combination (Table E.2). Our simulations also did not include the 4-6 

vegetation plots that would be co-located with weather stations so that results would be even 

more conservative. Given the large uncertainty inherent in forecasting future sampling 

conditions, it is better to oversample than to undersample. 
 

183

Literature Cited 

Alvarez-Buylla, E. R., and M. Slatkin. 1994. Finding confidence limits on population growth 

rates: three real examples revised. Ecology 75:255-260. 

Boucher, T. V., and B. R. Mead. 2006. Vegetation change and forest regeneration on the Kenai 

Peninsula, Alaska following a spruce beetle outbreak, 1987-2000. Forest Ecology and 

Management 227:233-246. 

Bowman, W. D., J. R. Gartner, K. Holland, and M. Wiedermann. 2006. Nitrogen critical loads 

for alpine vegetation and terrestrial ecosystem response: are we there yet? Ecological 

Applications 16:1183-1193. 

Burnham, K. P., and D. R. Anderson. 2002. Model selection and multimodel inference: a 

practical information-theoretic approach. Springer-Verlag, New York, NY. 

Cannone, N., S. Sgorbati, and M. Guglielmin. 2007. Unexpected impacts of climate change on 

alpine vegetation. Frontiers in Ecology and the Environment 5:360-364. 

Chapin, F.S. III, et al. 2005. Role of land surface changes in Arctic summer warming. Science 

310:657-660. 

Eberhardt, L. L. 1987. Population projections from simple models. Journal of Applied Ecology 

24:103-118. 

Kissling, M. L., M. Reid, P. M. Lukacs, S. M. Gende, and S. B. Lewis. 2007. Understanding 

abundance patterns of a declining seabird: implications for monitoring. Ecological 

Applications 17:2164-2174. 

McDonald, T. L. 2003. Review of environmental monitoring methods: survey designs. 

Environmental Monitoring and Assessment 85:277-292. 

Miller, A.E., W.L. Thompson, and C. Moore. 2009. Vegetation composition and structure: 

baseline monitoring in the Southwest Alaska Network, 2008 annual summary report. 

Natural Resource Technical Report NPS/SWAN/NRTR—2009/213. National Park 

Service, Fort Collins, Colorado. 

SAS Institute, Inc. 2008. SAS/STAT® 9.2 user’s guide. SAS Institute Inc., Cary, NC. 
184

Table E.1. Ability to detect different levels of total decline for a true average total decline of 30% over 31 years with 95

confidence for a range of sample sizes (plots/yr), coefficients of variation for vegetation cover proportions (CV[ pˆ ]), and 

intervals. Five, 25 and 45 percent cover were used as estimated cover proportions in simulations and their results were afor each combination to obtain the percent chance to detect a change. Years 1, 6, 11, 16, 21, 26, and 31 were sampled fointerval and years 1, 11, 21, and 31 for a 10-year interval. 

Estimated percent (%) chance of detecting the listed or more positiv

total change given a true average total change of -30%a

Plots/yr 

Sample 

interval (yr) CV( pˆ ) (%) <0%b ≤-5% ≤-10% ≤-15% ≤-20% ≤-25% 

6 5 5 100 100 100 100 100 87  10 100 100 >99 99 90 41 

 15 >99 >99 97 83 60 23 

 20 97 93 80 61 40 15 

 25 86 76 61 45 28 12 

 30 72 61 46 32 21 10  35 58 48 36 26 18 10  40 47 39 30 22 15 9  45 38 31 24 18 14 8  50 36 29 22 17 13 8  10 5 100 100 100 100 >99 75 

 10 100 100 >99 96 78 32 

 15 99 97 89 72 49 19 

 20 90 82 67 49 32 13 

 25 76 65 51 37 22 11  30 61 51 38 26 18 10  35 48 40 31 23 16 9  40 41 34 26 20 14 9  45 33 28 22 17 14 9  50 31 26 21 16 12 8 8 5 5 100 100 100 100 100 92 

 10 100 100 100 >99 94 50 

 15 100 >99 98 90 70 30 
185

Table E.1.---continued. 

Estimated percent (%) chance of detecting the listed or more positiv

total change given a true average total change of -30%a

Plots/yr 

Sample 

interval (yr) CV( pˆ ) (%) <0%b ≤-5% ≤-10% ≤-15% ≤-20% ≤-25% 

8 5 20 99 96 88 71 49 21  25 92 85 70 53 36 16 

 30 80 70 55 41 27 13 

 35 66 57 45 32 23 12  40 56 46 36 26 18 11  45 46 38 29 21 16 9  50 43 35 28 22 16 10  10 5 100 100 100 100 100 83 

 10 100 100 >99 98 86 40 

 15 >99 98 94 80 59 25 

 20 95 89 77 59 41 18 

 25 83 74 61 45 30 15 

 30 68 59 46 34 24 12  35 57 48 38 29 20 12  40 48 41 31 24 18 10  45 40 35 27 21 17 11  50 38 33 27 20 16 11 12 5 5 100 100 100 100 100 97 

 10 100 100 100 100 98 64 

 15 100 100 >99 97 83 39  20 >99 99 95 82 62 29 

 25 97 92 82 66 46 22 

 30 90 82 68 52 35 17 

 35 77 67 54 41 29 16  40 67 58 47 34 25 13  45 57 49 38 29 20 12  50 53 45 36 28 20 12 
186

Table E.1.---continued 

Estimated percent (%) chance of detecting the listed or more positiv

total change given a true average total change of -30%a

Plots/yr 

Sample 

interval (yr) CV( pˆ ) (%) <0%b ≤-5% ≤-10% ≤-15% ≤-20% ≤-25% 

12 10 5 100 100 100 100 100 92  10 100 100 100 >99 94 54 

 15 >99 >99 99 90 72 33 

 20 99 96 87 72 52 23 

 25 90 83 72 57 41 20 

 30 79 72 58 43 30 16  35 67 58 47 35 26 15  40 59 51 42 31 22 13  45 49 42 34 26 20 13  50 46 40 33 26 21 13 24 5 5 100 100 100 100 100 >99 

 10 100 100 100 100 100 82 

 15 100 100 100 >99 96 57 

 20 100 100 >99 96 80 43  25 >99 99 95 83 65 32 

 30 98 94 85 69 52 27 

 35 93 86 74 58 42 23 

 40 84 76 63 51 38 21 

 45 79 69 57 44 32 18  50 71 62 51 41 30 18  10 5 100 100 100 100 100 99 

 10 100 100 100 100 99 71 

 15 100 100 100 99 89 48  20 >99 99 97 88 70 38 

 25 98 95 87 73 56 30 

 30 91 85 74 59 45 26 

 35 83 75 63 50 38 23 

 40 74 66 55 45 34 21 
187

Table E.1.---continued 

Estimated percent (%) chance of detecting the listed or more positiv

total change given a true average total change of -30%a

Plots/yr 

Sample 

interval (yr) CV( pˆ ) (%) <0%b ≤-5% ≤-10% ≤-15% ≤-20% ≤-25% 

24 10 45 67 60 50 39 31 19  50 61 54 46 37 29 20 a Average percent of 1000 simulation runs in which the upper one-sided 95% confidence limit of the annual change estim

extrapolated to estimated total change) was equal to or greater than each listed total change. Values in bold font indicate 

detecting the stated minimum detectable total change. 

b The <0% change category represents the traditional definition of statistical power, i.e., the chance of detecting a change 

regardless of its magnitude, given that it is present. 

Figure E.3. Probabilitrecording a minimum dchange of 20% over a 3period with 95% confidthe true change is 30%curves are for replicate 24) sampled over 5- anintervals. Coefficient o(CV) in the plot variabcover, frequency) is shX-axis. A CV of up to tolerated when a high nreplicates (n = 24 plotssampling interval (5 yr
188

Table E.2. Ability to detect different levels of total decline for a true average total decline of 40% over 31 years with 95

confidence for a range of sample sizes (plots/yr), coefficients of variation for vegetation cover proportions (CV[ pˆ ]), and 

intervals. Five, 25 and 45 percent cover were used as estimated cover proportions in simulations and their results were afor each combination to obtain the percent chance to detect a change. Years 1, 6, 11, 16, 21, 26, and 31 were sampled fointerval and years 1, 11, 21, and 31 for a 10-year interval. 

Estimated percent (%) chance of detecting the listed or more positive 

total change given a true average total change of -40%a

Plots/yr 

Sample 

interval (yr) CV( pˆ ) (%) <0%b ≤-5% ≤-10% ≤-15% ≤-20% ≤-25% ≤-30% ≤-35% 

6 5 5 100 100 100 100 100 100 100 96  10 100 100 100 100 100 >99 95 56 

 15 100 100 100 >99 99 92 68 31 

 20 100 >99 99 96 90 72 47 21 

 25 98 96 93 86 75 56 34 16 

 30 92 87 79 68 57 40 24 12 

 35 82 75 64 54 44 31 20 11 

 40 70 64 54 44 36 25 18 11  45 61 54 46 38 31 22 16 10  50 55 48 40 34 28 21 14 10  10 5 100 100 100 100 100 100 >99 89 

 10 100 100 100 100 100 99 85 45 

 15 100 >99 >99 98 96 82 55 25 

 20 99 98 95 90 80 60 37 18 

 25 94 90 84 75 64 44 27 15 

 30 84 78 68 58 47 32 20 11 

 35 71 65 56 47 39 28 18 10  40 61 54 46 39 32 23 15 10  45 53 47 40 33 27 20 14 10  50 46 41 36 30 25 19 14 10 8 5 5 100 100 100 100 100 100 100 99 

 10 100 100 100 100 100 >99 97 66 

 15 100 100 100 100 >99 96 78 39 
189

Table E.2.---continued. 

Estimated percent (%) chance of detecting the listed or more positive 

total change given a true average total change of -40%a

Plots/yr 

Sample 

interval (yr) CV( pˆ ) (%) <0%b ≤-5% ≤-10% ≤-15% ≤-20% ≤-25% ≤-30% ≤-35% 

8 5 20 100 >99 >99 98 95 81 55 28  25 99 98 95 90 83 64 41 21 

 30 96 92 86 77 66 48 30 16 

 35 87 82 72 63 52 38 26 14 

 40 77 72 63 53 44 32 22 13  45 70 63 55 45 38 26 18 12  50 63 56 48 40 33 24 16 11 8 10 5 100 100 100 100 100 100 100 94 

 10 100 100 100 100 100 >99 91 54 

 15 100 100 >99 >99 98 88 65 32 

 20 >99 99 98 94 87 70 46 25 

 25 97 94 89 82 71 54 34 19 

 30 89 85 77 67 56 40 27 15 

 35 78 72 64 56 47 35 24 14  40 69 63 55 47 39 29 20 13  45 60 55 47 40 34 25 18 12  50 54 48 41 35 30 23 17 12 12 5 5 100 100 100 100 100 100 100 >99 

 10 100 100 100 100 100 100 99 79 

 15 100 100 100 100 100 99 88 50  20 100 100 100 >99 99 90 69 36 

 25 >99 >99 99 97 92 76 53 29 

 30 99 98 94 87 77 58 39 21 

 35 94 90 84 75 64 47 32 18 

 40 87 82 75 65 56 41 27 16 

 45 81 75 66 57 47 35 24 15 

 50 75 68 59 51 43 32 23 14 
190

Table E.2.---continued. 

Estimated percent (%) chance of detecting the listed or more positive 

total change given a true average total change of -40%a

Plots/yr 

Sample 

interval (yr) CV( pˆ ) (%) <0%b ≤-5% ≤-10% ≤-15% ≤-20% ≤-25% ≤-30% ≤-35% 

12 10 5 100 100 100 100 100 100 100 98  10 100 100 100 100 100 100 98 67 

 15 100 100 100 >99 99 95 78 42 

 20 >99 >99 >99 98 94 81 58 31  25 99 98 95 90 83 66 45 26 

 30 94 91 85 77 67 50 34 19 

 35 87 82 74 66 56 42 30 19 

 40 78 73 66 58 48 37 25 16  45 71 65 57 49 42 31 23 15  50 65 60 52 45 37 29 21 14 24 5 5 100 100 100 100 100 100 100 100 

 10 100 100 100 100 100 100 >99 94 

 15 100 100 100 100 100 100 98 70 

 20 100 100 100 100 >99 99 87 53  25 100 100 100 >99 99 92 72 42 

 30 >99 >99 99 97 92 79 57 33 

 35 99 98 96 91 83 67 47 28  40 97 94 89 82 73 58 40 25 

 45 94 90 84 76 67 52 35 22 

 50 89 85 79 69 60 46 32 20 

 10 5 100 100 100 100 100 100 100 >99 

 10 100 100 100 100 100 100 >99 85  15 100 100 100 100 100 99 92 60 

 20 100 100 >99 >99 99 94 76 46 

 25 100 >99 99 98 94 82 62 37  30 99 98 95 91 84 69 49 29 

 35 96 93 88 82 74 59 42 27 

 40 90 86 79 71 63 50 37 25 
191

Table E.2.---continued. 

Estimated percent (%) chance of detecting the listed or more positive 

total change given a true average total change of -40%a

Plots/yr 

Sample 

interval (yr) CV( pˆ ) (%) <0%b ≤-5% ≤-10% ≤-15% ≤-20% ≤-25% ≤-30% ≤-35% 

24 10 45 85 80 74 66 58 45 34 22  50 80 75 69 61 53 42 30 20 a Average percent of 1000 simulation runs in which the upper one-sided 95% confidence limit of the annual change estim

extrapolated to estimated total change) was equal to or greater than each listed total change. Values in bold font indicate 

detecting the stated minimum detectable total change. 

b The <0% change category represents the traditional definition of statistical power, i.e., the chance of detecting a change 

regardless of its magnitude, given that it is present. 

Figure E.4. Probarecording a minimdetectable change 20% over a 31-yea95% confidence wchange is 40%. Recurves are for repli= 6-24) sampled ovyear intervals. Covariation (CV) in tvariable (e.g., coveis shown on the X-Greater CVs are tothe MDC<<true ch
192

Table E.3. Ability to detect different levels of total decline for a true average total decline of 50% over 31 years with 95

confidence for a range of sample sizes (plots/yr), coefficients of variation for vegetation cover proportions (CV[ pˆ ]), and 

intervals. Five, 25 and 45 percent cover were used as estimated cover proportions in simulations and their results were aeach combination to obtain the percent chance to detect a change. Years 1, 6, 11, 16, 21, 26, and 31 were sampled for a 5interval and years 1, 11, 21, and 31 for a 10-year interval. 

Estimated percent (%) chance of detecting the listed or more positive 

total change given a true average total change of -50%a

Plots/yr 

Sample 

interval 

(yr) 

CV( pˆ ) 

(%) <0%b ≤-5% ≤-10% ≤-15% ≤-20% ≤-25% ≤-30% ≤-35% ≤-40% ≤-4

6 5 5 100 100 100 100 100 100 100 100 100 9 10 100 100 100 100 100 100 100 100 96 6

 15 100 100 100 100 100 100 >99 97 76 3

 20 100 100 100 >99 >99 98 94 83 53 2 25 >99 >99 99 98 97 92 80 63 37 1

 30 98 97 95 92 87 77 66 50 29 1

 35 94 92 88 83 75 65 52 38 23 1

 40 87 83 77 70 63 53 42 31 19 1

 45 79 75 68 62 55 46 37 27 17 1 50 73 68 62 55 49 40 32 24 16 1 10 5 100 100 100 100 100 100 100 100 >99 9

 10 100 100 100 100 100 100 100 >99 89 4

 15 100 100 100 100 100 >99 98 90 63 3

 20 100 100 >99 99 98 94 86 70 43 2

 25 99 99 97 95 90 81 68 52 31 1

 30 95 92 89 84 78 67 56 42 25 1

 35 87 84 78 72 65 54 44 34 21 1

 40 78 73 67 61 55 46 37 27 17 1 45 70 65 59 53 47 39 31 24 16 1 50 63 59 53 48 42 35 29 22 16 18 5 5 100 100 100 100 100 100 100 100 100 9

 10 100 100 100 100 100 100 100 100 98 7
193

Table E.3.---continued. 

Estimated percent (%) chance of detecting the listed or more positive 

total change given a true average total change of -50%a

Plots/yr 

Sample 

interval 

(yr) 

CV( pˆ ) 

(%) <0%b ≤-5% ≤-10% ≤-15% ≤-20% ≤-25% ≤-30% ≤-35% ≤-40% ≤-4

8 5 15 100 100 100 100 100 100 100 99 83 4 20 100 100 100 100 >99 99 96 88 63 3

 25 >99 >99 >99 99 98 95 87 72 46 2

 30 99 99 98 95 92 84 73 58 36 1

 35 96 94 92 88 82 73 60 47 29 1

 40 91 88 84 79 72 60 49 38 24 1

 45 86 82 76 70 64 53 44 34 22 1

 50 81 76 70 64 57 47 38 29 19 1

 10 5 100 100 100 100 100 100 100 100 100 9

 10 100 100 100 100 100 100 100 >99 94 5

 15 100 100 100 100 100 >99 99 94 72 3

 20 100 100 >99 >99 99 97 91 78 52 2

 25 >99 99 99 97 94 88 76 60 39 2

 30 97 95 92 89 84 75 63 49 32 1

 35 91 88 84 80 74 63 51 41 27 1

 40 84 81 75 68 62 52 44 33 22 1

 45 77 73 67 61 54 45 37 30 20 1 50 71 67 61 56 50 42 34 27 18 112 5 5 100 100 100 100 100 100 100 100 100 >

 10 100 100 100 100 100 100 100 100 >99 8 15 100 100 100 100 100 100 100 >99 92 5

 20 100 100 100 100 100 >99 99 95 75 4

 25 >99 >99 >99 >99 >99 98 94 84 56 3 30 >99 >99 99 98 96 91 83 69 45 2

 35 98 98 96 94 91 83 72 58 38 2

 40 96 95 92 87 82 72 60 47 31 1

 45 93 90 86 80 75 64 53 42 28 1
194

Table E.3.---continued. 

Estimated percent (%) chance of detecting the listed or more positive 

total change given a true average total change of -50%a

Plots/yr 

Sample 

interval 

(yr) 

CV( pˆ ) 

(%) <0%b ≤-5% ≤-10% ≤-15% ≤-20% ≤-25% ≤-30% ≤-35% ≤-40% ≤-4

12 5 50 90 86 81 75 69 58 47 37 24 1 10 5 100 100 100 100 100 100 100 100 100 9

 10 100 100 100 100 100 100 100 100 98 7

 15 100 100 100 100 100 100 100 98 83 4 20 100 100 100 100 >99 99 96 87 64 3

 25 >99 >99 >99 99 98 94 86 72 47 2

 30 99 98 96 94 91 83 74 60 40 2

 35 95 94 91 87 82 73 63 50 33 2

 40 91 88 84 79 72 63 53 42 28 1

 45 86 82 76 70 65 56 46 37 25 1

 50 80 77 71 66 59 50 42 34 24 1

24 5 5 100 100 100 100 100 100 100 100 100 1

 10 100 100 100 100 100 100 100 100 100 9 15 100 100 100 100 100 100 100 100 99 7

 20 100 100 100 100 100 100 100 >99 90 5 25 100 100 100 >99 >99 >99 99 95 76 4

 30 100 100 100 >99 >99 99 95 85 62 3 35 >99 >99 >99 99 98 94 88 75 53 3

 40 >99 99 99 98 95 89 78 65 44 2

 45 99 98 96 94 90 82 71 58 39 2 50 98 96 94 90 86 77 66 53 36 2

 10 5 100 100 100 100 100 100 100 100 100 1

 10 100 100 100 100 100 100 100 100 100 8 15 100 100 100 100 100 100 100 100 84 6

 20 100 100 100 100 100 100 >99 97 81 5 25 >99 >99 >99 >99 >99 99 96 87 65 3

 30 >99 >99 99 99 98 94 87 75 54 3
195

Table E.3.---continued. 

Estimated percent (%) chance of detecting the listed or more positive 

total change given a true average total change of -50%a

Plots/yr 

Sample 

interval 

(yr) 

CV( pˆ ) 

(%) <0%b ≤-5% ≤-10% ≤-15% ≤-20% ≤-25% ≤-30% ≤-35% ≤-40% ≤-4

24 10 35 99 99 98 96 93 87 78 66 47 3 40 98 97 95 91 87 78 69 57 40 2

 45 95 93 90 85 81 72 62 51 36 2 50 92 90 86 81 76 67 57 47 34 2

a Average percent of 1000 simulation runs in which the upper one-sided 95% confidence limit of the annual change estim

extrapolated to estimated total change) was equal to or greater than each listed total change. Values in bold font indicate 

detecting the stated minimum detectable total change. 

b The <0% change category represents the traditional definition of statistical power, i.e., the chance of detecting a change 

 of its magnitude, given that it is present. 

Figure E.5. Probability orecording a minimum detchange (MDC) of 20% ovyear period with 95% conwhen the true change is 5Response curves are for rplots (n = 6-24) sampled 10-year intervals. Coeffivariation (CV) in the plot (e.g., cover, frequency) is the X-axis. High CVs are and change is generally dwhen the MDC<<true ch
 

196

Appendix F: SAS Code for Summary Statistics 

William L. Thompson, SWAN Quantitative Ecologist 

1 Species Abundance: Nested Frequency Data 

*************** Freq_SummaryStats_20100505.sas ******************* 

* * 

* This program summarizes plant species count data for 5 * 

* nested quadrats (0.25m^2 < 1m^2 < 4m^2) along 3 transects at * 

* GRTS-selected vegetation monitoring sites. The program code * 

* below that is followed by a comment field in UPPERCASE text * 

* has to be modified by the user. Changes to macrovariables * 

* (preceded by %let) should ONLY occur on the RIGHTHAND side of * 

* the equal sign. This is where the user specifies the name of * 

* that variable in the imported raw data file. * 

* * 

* Written by Bill Thompson * 

* NPS SWAN Quantitative Ecologist * 

* 7 February 2008 * 

* * 

* Last Modified 5 May 2010 * 

* * 

******************************************************************; 

%let inpfil=grts.freq1; * SPECIFY THE SAS LIBRARY AND FILE 

NAME CONTAINING THE RAW SPECIES FREQUENCY DATA;

%let outfil1=grts.freq_within_results; * SPECIFY THE SAS LIBRARY AND FILE 

FOR THE RESULTS FILE CONTAINING SUMMARIZED QUADRAT 

 FREQUENCY DATA AND SITE-LEVEL 

SPECIES OCCURRENCE DATA WITHIN SITES;

%let outfil2=grts.freq_across_results; * SPECIFY THE SAS LIBRARY AND FILE 

FOR THE RESULTS FILE CONTAINING SUMMARIZED QUADRAT 

 FREQUENCY DATA AND SITE-LEVEL 

SPECIES OCCURRENCE DATA ACROSS SITES;

%let site_id=site_id; * SPECIFY VARIABLE NAME CONTAINING 

SITE IDENTIFIER;

%let date=date; * SPECIFY VARIABLE NAME CONTAINING 

DATE;

%let elev_bnd=elev_bnd; * SPECIFY VARIABLE NAME CONTAINING 

ELEVATION BAND;

%let veg_class=veg_clas; * SPECIFY VARIABLE NAME CONTAINING 

VEGETATION COVER CLASS;

%let year=year; * SPECIFY VARIABLE NAME CONTAINING 

YEAR;

%let transect=transect; * SPECIFY VARIABLE NAME CONTAINING 

TRANSECT IDENTIFIER;

%let quad_no=quad_no; * SPECIFY VARIABLE NAME CONTAINING 

QUADRAT IDENTIFIER;

%let species=species; * SPECIFY VARIABLE NAME CONTAINING 

THE PLANT SPECIES;

******************************************************* 

****** Please do not change anything beyond this ****** 

****** point unless you know what you are doing. ****** 

*******************************************************;

****** This section of code summarizes the "nested frequency data," i.e., 

******* 
 

197

****** the number of occurrences of each species within 3 nested quadrats, 

******* 

****** where freq1 = 0.25m^2, freq2 = 1m^2, and freq3=4m^2 within sites 

******* 

****** and across sites by elevation band, veg class and species. 

*******;

ods listing close; * suppress unwanted printing;

run; 

data a; 

 set &inpfil; 

 drop &date &year; 

 proc sort; 

 by &elev_bnd &veg_class &site_id &transect &quad_no; * Sort data to the 

finest temporal (date) and spatial (quadrat) scales;

run; 

proc transpose data=a out=b; * Transpose the data 

to convert data to numeric format and for summarization;

 by &elev_bnd &veg_class &site_id &transect &quad_no; 

run; 

data b; 

 set b; * Proc Transpose 

uses "col1" as the default variable name for the column of 

 transposed data;

if col1=1 then cat1=1; * Create a variable 

indicating a "hit" for a given species in the 0.25m^2 quadrat;

if col1=2 then cat2=1; * Create a variable 

indicating a "hit" for a given in the 1m^2 quadrat;

if col1=3 then cat3=1; * Create a variable 

indicating a "hit" for a given species in the 4m^2 quadrat;

if cat1=. then cat1=0; 

if cat2=. then cat2=0; 

if cat3=. then cat3=0; 

if cat1=1 then cat3=1; * Account for 

0.25m^2 quadrat being nested within 4m^2 quadrat;

if cat2=1 then cat3=1; * Account for 1m^2 

quadrat being nested within 4m^2 quadrat;

if cat1=1 then cat2=1; * Account for 

0.25m^2 quadrat being nested within 1m^2 quadrat;

rename _name_=&species; 

drop col1 _label_; 

proc sort; 

 by &elev_bnd &veg_class &site_id &species; 

run; 

proc surveymeans data=b mean stderr cv; * within-site 

estimates for 0.25m^2 quadrats;

 cluster &site_id; 

 cluster &transect; 

 domain &elev_bnd*&veg_class*&species*&site_id; 

 var cat1; 

 ods output Domain=freq_025m2_within; 

run; 

proc surveymeans data=b mean stderr cv; * within-site 

estimates for 1m^2 quadrats;

 cluster &site_id; 

 cluster &transect; 

 domain &elev_bnd*&veg_class*&species*&site_id; 

 var cat2; 

 ods output Domain=freq_1m2_within; 

run; 
 

198

proc surveymeans data=b mean stderr cv; * within-site 

estimates for 4m^2 quadrats;

 cluster &site_id; 

 cluster &transect; 

 domain &elev_bnd*&veg_class*&species*&site_id; 

 var cat3; 

 ods output Domain=freq_4m2_within; 

run; 

data freq1; 

 set freq_025m2_within (drop=varname); 

 rename mean=mean_025m2; 

 rename stderr=stderr_025m2; 

 rename cv=cv_025m2; 

 proc sort; 

 by &elev_bnd &veg_class &site_id &species; 

run; 

data freq2; 

 set freq_1m2_within (drop=varname); 

 rename mean=mean_1m2; 

 rename stderr=stderr_1m2; 

 rename cv=cv_1m2; 

 proc sort; 

 by &elev_bnd &veg_class &site_id &species; 

run; 

data freq3; 

 set freq_4m2_within (drop=varname); 

 rename mean=mean_4m2; 

 rename stderr=stderr_4m2; 

 rename cv=cv_4m2; 

 proc sort; 

 by &elev_bnd &veg_class &site_id &species; 

run; 

data &outfil1; * final results file 

for within-site estimates;

 merge freq1 freq2 freq3; 

 by &elev_bnd &veg_class &site_id &species; 

drop domainlabel; 

run; 

proc surveymeans data=b mean stderr cv; * across-site 

estimates for 0.25m^2 quadrats;

 cluster &site_id; 

 cluster &transect; 

 domain &elev_bnd*&veg_class*&species; 

 var cat1; 

 ods output Domain=freq_025m2_across; 

run; 

proc surveymeans data=b mean stderr cv; * across-site 

estimates for 1m^2 quadrats;

 cluster &site_id; 

 cluster &transect; 

 domain &elev_bnd*&veg_class*&species; 

 var cat2; 

 ods output Domain=freq_1m2_across; 

run; 
 

199

proc surveymeans data=b mean stderr cv; * across-site 

estimates for 4m^2 quadrats;

 cluster &site_id; 

 cluster &transect; 

 domain &elev_bnd*&veg_class*&species; 

 var cat3; 

 ods output Domain=freq_4m2_across; 

run; 

data freq1a; 

 set freq_025m2_across (drop=varname); 

 rename mean=mean_025m2; 

 rename stderr=stderr_025m2; 

 rename cv=cv_025m2; 

 proc sort; 

 by &elev_bnd &veg_class &species; 

run; 

data freq2a; 

 set freq_1m2_across (drop=varname); 

 rename mean=mean_1m2; 

 rename stderr=stderr_1m2; 

 rename cv=cv_1m2; 

 proc sort; 

 by &elev_bnd &veg_class &species; 

run; 

data freq3a; 

 set freq_4m2_across (drop=varname); 

 rename mean=mean_4m2; 

 rename stderr=stderr_4m2; 

 rename cv=cv_4m2; 

 proc sort; 

 by &elev_bnd &veg_class &species; 

run; 

data &outfil2; * final results 

file for within-site estimates;

 merge freq1a freq2a freq3a; 

 by &elev_bnd &veg_class &species; 

drop domainlabel; 

run; 

ods listing; 

run; 
 

200

2 Percent Cover by Species: Point-intercept Data 

************ PtIntcpt_SummaryStats_SAS_20091203.sas ************ 

* * 

* This program has two parts. The first part summarizes plant * 

* species count data for 59 0.5m points along 3, 30m transects * 

* at GRTS-selected vegetation monitoring sites. This part of * 

* the program is labeled _Count Data Summary Stats_. Results * 

* are generated only for those species encountered at least * 

* once. The program code below that is followed by a comment * 

* field in UPPERCASE text has to be modified by the user. * 

* Changes to macrovariables (preceded by %let) should ONLY * 

* occur on the RIGHTHAND side of the equal sign. This is * 

* where the user specifies the name of that variable in the * 

* imported raw data file. Also, there is a line of code near * 

* the bottom of this part of the program (followed by a * 

* comment in UPPERCASE text) that can be commented out with an * 

* asterisk if the user wishes to include plant species that * 

* were not encountered at a given site. * 

* * 

* The second part of this program estimates proportions, * 

* approximate std errors and approximate CVs from plant * 

* species count data for the transects described above. This * 

* part of the program is labeled _Propns, SEs and CVs_. These * 

* estimates are approximations because SAS currently does not * 

* have the GRTS variance estimator. Results are generated * 

* only for those species specified by the user. There are two * 

* lines of code in the middle of the second part of this * 

* program (preceded by comments in UPPERCASE text) that may be * 

* modified by the user to specify the plant species of * 

* interest in height class=1 and height class=1-4. * 

* * 

* Written by Bill Thompson * 

* NPS SWAN Quantitative Ecologist * 

* 7 February 2008 * 

* * 

* Last Modified 3 December 2009 * 

* * 

****************************************************************; 

%let inpfil=grts.pt_intcpt1; * SPECIFY THE SAS LIBRARY AND 

FILE NAME CONTAINING 

 THE RAW POINT INTERCEPT 

COUNTS OF PLANT SPECIES;

%let outfil1=grts.pt_intcpt1_results; * SPECIFY THE SAS LIBRARY AND 

FILE FOR THE RESULTS 

 FILE CONTAINING SUMMARIZED 

POINT INTERCEPT DATA 

 BY SITE, DATE, HEIGHT 

CLASS, AND SPECIES;

%let outfil2=grts.pt_intcpt1_insite1_results; * SPECIFY THE SAS LIBRARY AND 

FILE FOR THE RESULTS FILE 

 CONTAINING ESTIMATED 

PROPORTIONS, STD ERRORS, and CVS 

 WITHIN SITES FOR HT 

CLASS=1;

%let outfil3=grts.pt_intcpt1_insite14_results; * SPECIFY THE SAS LIBRARY AND 

FILE FOR THE RESULTS FILE 

 CONTAINING ESTIMATED 

PROPORTIONS, STD ERRORS, and CVS 
 

201

 WITHIN SITES FOR HT 

CLASSES=1-4;

%let outfil4=grts.pt_intcpt1_acrsite1_results; * SPECIFY THE SAS LIBRARY AND 

FILE FOR THE RESULTS FILE 

 CONTAINING ESTIMATED 

PROPORTIONS, STD ERRORS, and CVS 

 ACROSS SITES FOR HT 

CLASS=1;

%let outfil5=grts.pt_intcpt1_acrsite14_results; * SPECIFY THE SAS LIBRARY AND 

FILE FOR THE RESULTS FILE 

 CONTAINING ESTIMATED 

PROPORTIONS, STD ERRORS, and CVS 

 ACROSS SITES FOR HT 

CLASSES=1-4;

%let site_id=site_id; * SPECIFY VARIABLE NAME CONTAINING 

SITE IDENTIFIER;

%let elev_bnd=elev_bnd; 

%let veg_class=veg_class; 

%let transect=transect; * SPECIFY VARIABLE NAME CONTAINING 

TRANSECT IDENTIFIER;

%let point_m=point_m; * SPECIFY VARIABLE NAME CONTAINING 

TRANSECT POINT IDENTIFIER;

%let ht_class=ht_class; * SPECIFY VARIABLE NAME CONTAINING 

HEIGHT CLASS CATEGORY;

%let year=year; 

%let date=date; 

%let species=species; 

%let elev_bnd=elev_bnd; 

%let veg_class=veg_class; * SPECIFY VARIABLE NAME 

CONTAINING DATE IDENTIFIER;

%let transect=transect; * SPECIFY VARIABLE NAME CONTAINING 

TRANSECT IDENTIFIER;

%let point_m=point_m; * SPECIFY VARIABLE NAME CONTAINING 

TRANSECT POINT IDENTIFIER;

%let ht_class=ht_class; * SPECIFY VARIABLE NAME CONTAINING 

HEIGHT CLASS CATEGORY;

%let htnum1=1; * SPECIFY VALUE OF HEIGHT CLASS 

FOR SUBSETTED SPECIES FOR HEIGHT CLASS=1;

%let htnum4=4; * SPECIFY VALUE OF HEIGHT CLASS 

FOR SUBSETTED SPECIES FOR HEIGHT CLASS=1-4;

******************************************************* 

****** Please do not change anything beyond this ****** 

****** point unless you know what you are doing. ****** 

*******************************************************;

ods listing close; * Suppress 

unwanted printed output from PROC SURVEYMEANS;

run; 

******************************************************* 

***************Count Data Summary Stats *************** 

*******************************************************;

data a1; 

 set &inpfil; 

 drop &date &year; 

 proc sort; 
 

202

 by &site_id &elev_bnd &veg_class &ht_class &transect &point_m; * Sort 

data to the finest temporal (date) and spatial (point) scales;

run; 

proc transpose data=a1 out=b1; * Transpose the data to 

convert them to numeric format and for summarization;

 by &site_id &elev_bnd &veg_class &ht_class &transect &point_m; 

run; 

data b1; 

 set b1; 

proc sort; 

 by &site_id &elev_bnd &veg_class &ht_class _name_; 

run; 

proc means sum noprint data=b1; * Sum across all counts 

(1 or 0) to generate total number of hits across all 0.5m points;

 var col1; 

by &site_id &elev_bnd &veg_class &ht_class _name_; 

output out=c1 sum=pt_int_hits; 

run; 

**** create output file of results for first part of program;

Data &outfil1; 

 set c1; 

 rename _name_= species; 

 if pt_int_hits ne 0; * THIS LINE CAN BE 

COMMENTED OUT BY PLACING AN ASTERISK AT THE BEGINNING OF THIS LINE 

 IF YOU WANT TO INCLUDE 

SPECIES THAT WERE NOT ENCOUNTERED AT A SITE;

 pct_cover=(pt_int_hits/_freq_)*100; * Divide the number of 

hits by the total number of 0.5m points;

 pct_cover=round(pct_cover,0.01); 

 drop _type_ _freq_; 

run; 

************************************************************ 

****************** Propns, SEs and CVs ********************* 

************************************************************;

data a; 

 set &inpfil; 

 proc sort; 

 by &site_id &elev_bnd &veg_class &ht_class &transect &point_m; * Sort 

data to the finest temporal (date) and spatial (point) scales;

run; 

proc transpose data=a out=b name=&species; * Transpose the 

data to convert them to numeric format and for summarization;

 by &site_id &elev_bnd &veg_class &ht_class &transect &point_m; 

run; 

***** ht class=1 species;

data c; 

 set b; 

if &ht_class=&htnum1; 

************************************************** 

****** SPECIFY THE PLANT SPECIES FOR HEIGHT ****** 
 

203

****** CLASS = 1 THAT WILL BE USED IN THIS ****** 

****** ANALYSIS. ****** 

**************************************************;

if &species= 'CACA4' or &species='EMNI' or &species='LEPAD' or 

&species='VAUL' or &species='VAVI' or &species='LI'

 or &species='BEGL' or &species='BEGL_SD'or &species='ARAL2' or 

&species='ARFR2' or &species='ARARA2' or &species='PIGL' or &species='CALU2'

or &species='SAGL' or &species='SAPU15'

or &species='ALVIS' or &species='CAMI4' or &species='CAUN2' or 

&species='COSU4' or &species='DILA' or &species='DROC' or &species='FEAL' or 

&species='HIAL3' or &species='TRSP2' 

 or &species='SABA3' or &species='SABE2' or &species='SPST3' or 

&species='SAAR27' or &species='LOPR' or &species='RHCA5' or &species='SARE2'; 

************************************************** 

**************************************************, 

********** variance estimates within sites, ht class=1 species;

proc sort data=c; 

 by &site_id ; 

run; 

proc surveymeans data=c mean stderr cv; 

cluster &transect; 

var col1; 

domain &species*&ht_class; 

by &site_id; 

ods output Domain=ht1_within; 

run; 

********* variance estimates across sites, ht class=1 species;

proc sort data=c; 

by &elev_bnd &veg_class; 

run; 

proc surveymeans data=c mean stderr cv; 

cluster &site_id; 

domain &species*&ht_class; 

var col1; 

by &elev_bnd &veg_class; * estimates by sampled (non-domain) components;

ods output Domain=ht1_across; 

run; 

***** ht class=1-4 species;

data d; 

 set b; 

 if &ht_class<=&htnum4; 

************************************************** 

****** SPECIFY THE PLANT SPECIES FOR HEIGHT ****** 

****** CLASS = 1-3 THAT WILL BE USED IN ****** 

****** THIS ANALYSIS. ****** 

**************************************************;

if &species= 'BEGL' or &species='BEGL_SD' or &species='PIGL'; 

************************************************** 
 

204

**************************************************;

********** variance estimates within sites, ht class=1-4 species;

proc sort data=d; 

 by &site_id &elev_bnd &veg_class; 

run; 

proc surveymeans data=d mean stderr cv; 

cluster &transect; 

domain &species*&ht_class; 

var col1; 

by &site_id &elev_bnd &veg_class; * estimates by sampled (non-domain) 

components;

ods output Domain=ht14_within; 

run; 

********* variance estimates across sites, ht class=1-4 species;

proc sort data=d; 

 by &elev_bnd &veg_class; 

run; 

proc surveymeans data=d mean stderr cv; 

cluster &site_id; 

domain &species*&ht_class; 

var col1; 

by &elev_bnd &veg_class; * estimates by sampled (non-domain) 

components;

ods output Domain=ht14_across; 

run; 

******** create output files of results for second part of program;

data &outfil2; 

 set ht1_within; 

 drop varname domainlabel; 

 if cv=. then cv=0; 

 proc sort; 

 by &site_id &ht_class &species; 

run; 

data &outfil3; 

 set ht14_within; 

 drop varname domainlabel; 

 if cv=. then cv=0; 

 proc sort; 

 by &site_id &ht_class &species; 

run; 

 

data &outfil4; 

 set ht1_across; 

 drop varname domainlabel; 

 if cv=. then cv=0; 

 if stderr=. then stderr=0; 

 proc sort; 
 

205

 by &ht_class &species; 

run; 

data &outfil5; 

 set ht14_across; 

 drop varname domainlabel; 

 if cv=. then cv=0; 

 if stderr=. then stderr=0; 

 proc sort; 

 by &ht_class &species; 

run; 

ods listing; * re-engage printed output;

run; 
 

206

3 Percent Cover by Growth Form: Ocular Estimates 

************* lacl_grts_cover_4m2_var_090209.sas ************** 

* * 

* This program estimates proportions, approximate standard * 

* errors, and approximate CVs of growth forms within 5, 4m^2 * 

* quadrats along 3, 30m transects at GRTS-selected sites. * 

* The program code below that is followed by a comment field * 

* in UPPERCASE text has to be modified by the user. * 

* * 

* Written by Bill Thompson * 

* NPS SWAN Quantitative Ecologist * 

* 22 February 2008 * 

* * 

* Last Modified 9 February 2009 * 

* * 

***************************************************************; 

%let inpfil=grts.cover_4m2; * SPECIFY THE SAS LIBRARY 

AND FILE NAME CONTAINING THE RAW POINT INTERCEPT 

 COUNTS OF PLANT 

SPECIES;

%let outfil_wi=grts.cover_4m2_within_site_results; * SPECIFY THE SAS LIBRARY 

AND FILE FOR THE RESULTS FILE CONTAINING 

 ESTIMATED 

PROPORTIONS, STD ERRORS, AND CVS WITHIN SITES BY YEAR;

%let outfil_acr=grts.cover_4m2_across_site_results; * SPECIFY THE SAS LIBRARY 

AND FILE FOR THE RESULTS FILE CONTAINING 

 ESTIMATED 

PROPORTIONS, STD ERRORS, AND CVS ACROSS SITES BY YEAR AND GROWTH 

 FORM;

%let site_id=site_id; * SPECIFY VARIABLE NAME CONTAINING 

SITE IDENTIFIER;

%let date=date; * SPECIFY VARIABLE NAME CONTAINING 

DATE IDENTIFIER;

%let year=year; * SPECIFY VARIABLE NAME CONTAINING 

YEAR IDENTIFIER;

%let vegclass=veg_class; * SPECIFY VARIABLE NAME CONTAINING 

VEGETATION CLASS;

%let elev=elev_bnd; 

%let transect_no=transect; * SPECIFY VARIABLE NAME CONTAINING 

TRANSECT IDENTIFIER;

%let quad_no=quad_no; * SPECIFY VARIABLE NAME CONTAINING 

QUADRAT IDENTIFIER;

%let growth_form=growth_form; * SPECIFY VARIABLE NAME CONTAINING 

GROWTH FORM;

%let growth_form_label='growth_form'; * SPECIFY LABEL FOR GROWTH FORM;

******************************************************* 

****** Please do not change anything beyond this ****** 

****** point unless you know what you are doing. ****** 

*******************************************************;

ods listing close; * Suppress unwanted 

printed output from PROC SURVEYMEANS;

run; 

data a; 

 set &inpfil; 
 

207

 *if &date =1;

 

 proc sort; 

 by &year &vegclass &elev &site_id &date &transect_no &quad_no; 

* Sort data to the finest temporal (date) and spatial (quadrat) 

 scales;

run; 

proc transpose data=a out=b name=&growth_form; * Transpose the data 

to convert them to numeric format and for summarization;

 by &year &vegclass &elev &site_id &date &transect_no &quad_no; 

run; 

data b; 

 set b; 

 label &growth_form=&growth_form_label; 

proc sort data=b; 

 by &year; 

 run; 

proc surveymeans data=b mean stderr cv; * within-site 

estimates;

cluster &site_id; 

cluster &transect_no; 

domain &elev*&vegclass*&growth_form*&site_id; 

var col1; 

by &year; 

ods output Domain=cov_4m2_within; 

run; 

proc surveymeans data=b mean stderr cv; * across-site 

estimates;

cluster &site_id; 

domain &elev*&vegclass*&growth_form; 

var col1; 

by &year; 

ods output Domain=cov_4m2_across; 

run; 

data &outfil_wi; * final results file 

for within-site estimates;

 set cov_4m2_within; 

 drop varname; 

 proc sort; 

 by &year &elev &vegclass &site_id &growth_form; 

run; 

data &outfil_acr; * final results file 

for across-site estimates;

 set cov_4m2_across; 

 drop varname; 

 proc sort; 

 by &year &elev &vegclass &growth_form; 

run; 

ods listing; * re-engage output 

window printing;

run; 
 

208

4 Transposing Cover and Frequency Data to List Format 

************* SAS_transpose_cover_freq_data_20100317.sas ************** 

* * 

* This program transposes the cover frequency data collected in veg * 

* quadrats and recorded in an MS Excel file to list format for data * 

* analyses (e.g., PC-ORD). This program assumes the original Excel * 

* file has been imported into SAS, converted to a SAS file format, * 

* and saved to a SAS library. The example code below assumes this * 

* converted Excel file is in the SAS Temporary (i.e., Work) directory * 

* but a user-defined library can be used instead. The 64-bit version * 

* of SAS will only import an Excel 95 or earlier file via the Import * 

* Wizard - see the instructions in the following file for how to * 

* import an Excel 97 or later file into SAS-64. * 

* * 

* SAS_64bit_Import_Export_EXCEL_ACCESS_files_instructions_090126.doc * 

* * 

* The user should enter the variable (column) names from the * 

* converted SAS data file into the the righthand side of the equal * 

* signs in the specified section below. Theses variables will be the * 

* ones appearing in list format in the fianl results file. Be sure * 

* the variables names to the right of the equal sign are entered * 

* EXACTLY as they appear in the imported SAS data file (formerly, the * 

* original Excel file). In the next section after that, the user * 

* should list all other variables contained in the imported SAS data * 

* so they can be removed from the final, list-formatted results file * 

* generated by this program. These appear as macro variables in the * 

* DROP statement in the first data step. The results file produced * 

* by this program (as defined in the macro variable "outfil") must * 

* be exported from SAS into Excel format - the simplest way to do * 

* this is via the SAS Export Wizard (see instruction file cited above * 

* for other options). * 

* * 

* Program Written By: * 

* Bill Thompson, SWAN Quant. Ecologist * 

* 16 March 2010 * 

* * 

* Last Modified: 17 March 2010 * 

***********************************************************************;

%let inpfil=work.nonvasc; * SPECIFY THE SAS LIBRARY AND FILE NAME 

CONTAINING THE RAW SPECIES FREQUENCY DATA;

%let outfil=work.nonvasc_freq; * SPECIFY THE SAS LIBRARY AND FILE FOR 

THE RESULTS FILE CONTAINING THE LIST 

 * FORMAT OF THE QUADRAT FREQUENCY 

DATA;

*** ENTER VARIABLE NAMES IN SAS DATA FILE HERE ***;

%let site_id=site_id; * SPECIFY VARIABLE NAME CONTAINING SITE 

IDENTIFIER;

%let transect_no=transect; * SPECIFY VARIABLE NAME CONTAINING 

TRANSECT IDENTIFIER;

%let quad_no=QUAD_NO; * SPECIFY VARIABLE NAME CONTAINING 

QUADRAT IDENTIFIER;

%let species=SPECIES; * SPECIFY VARIABLE NAME CONTAINING 

SPECIES;

%let quad_loc=QUAD_LOC; * SPECIFY VARIABLE NAME CONTAINING 

QUADRAT LOCATION 

 (I.E., 0.25 M^2, 1 M^2, or 4 M^2);
 

209

*** SPECIFY ALL OTHER VARABLES THAT ARE NOT TO BE *** 

*** INCLUDED IN THE FINAL RESULTS FILE (EXCLUDE *** 

*** SPECIES VARIABLES FROM THIS LIST). ***;

%let vegclass=veg_class; 

%let year=year; 

%let elev=elev_band; 

%let park=park; 

%let date=date; 

data a; 

 set &inpfil; 

 drop &vegclass &year &elev &park &date; * SPECIFY VARIABLES THAT WILL 

NOT BE INCLUDED IN THE FINAL RESULTS FILE;

 *************************************************** 

 * DO NOT CHANGE ANYTHING BEYOND THIS POINT UNLESS * 

 * YOU KNOW WHAT YOU ARE DOING. * 

 ***************************************************;

 proc sort; 

 by &site_id &transect_no &quad_no; * Sort data to the finest 

temporal (date) and spatial (quadrat) scales;

run; 

proc transpose data=a out=b; * Transpose the data to convert 

data to numeric format and for summarization;

 by &site_id &transect_no &quad_no; 

run; 

data c; 

 set b; 

 drop _label_; 

 if col1 ne .; 

 rename col1=&quad_loc; 

 &species=_name_; 

 drop _name_; 

 run; 

data &outfil; 

 retain &site_id &species &transect_no &quad_no &quad_loc; 

set c; 

run; 
 

210

Appendix G: Using BugsXLA and WinBUGS to Estimate 

Trends from Vegetation Plot Data 

William L. Thompson, Quantitative Ecologist, Southwest Alaska Network 

1 Introduction 

This appendix describes how to use an MS Excel add-in, BugsXLA (Woodward 2005), and 

freeware program WinBUGS (Lunn et al. 2000) to fit Bayesian hierarchical models (Royle and 

Dorazio 2008) for estimating trends from vegetation data collected on ground plots. BugsXLA 

serves as the front-end interface for WinBUGS so that all data analyses described in this 

appendix can be performed from within MS Excel. Interested readers should consult the 

reference guide for BugsXLA (http://www.axrf86.dsl.pipex.com/) for more details regarding its 

use. Lawson (2008) used BugsXLA to estimate variance components within a staggered nested 

design. 

2 Software Requirements and Installation 

This analysis requires a 32-bit or a 64-bit version of MS Windows 98 or later, MS Notepad, MS 

Excel 2000 or later, WinBUGS version 1.4.x or later (Lunn et al. 2000), and BugsXLA version 

4.0 or later. MS Excel and MS Notepad are standard components of the MS Office suite and MS 

Windows, respectively. Installation instructions for WinBUGS and BugsXLA are described on 

their websites, http://www.mrc-bsu.cam.ac.uk/bugs/winbugs/contents.shtml and 

http://www.axrf86.dsl.pipex.com/. Users of Windows Vista and/or a 64-bit Windows operating 

system should note the following recommendations from the WinBUGS webpage: 

 There appears to be a problem with installing WinBUGS and/or various patches in 

Windows Vista. Vista doesn't seem to like anyone overwriting files in the "C:\Program 

Files" directory (regardless of permissions). Hence we recommend that WinBUGS be 

installed elsewhere, e.g. "C:\". 

 If all else fails (for example with a 64-bit machine), you can download a zipped version 

of the whole file structure (http://www.mrc-bsu.cam.ac.uk/bugs/winbugs/winbugs14.zip) 

and unzip it into Program Files or wherever you want it. WinBUGS makes no changes to 

the Registry. 

WinBUGS should be installed first, along with its latest patch, before BugsXLA is installed as an 

add-in to Excel. Users of Windows Vista and/or a 64-bit Windows operating system will likely 

have to change the default file locations of the WinBUGS program specified within the 

BugsXLA add-in to the file locations created as per the above recommendations. This can be 

done either during installation or after via the BugsXLA Options button on the Excel 

toolbar. The Options for BugsXLA pop-up window shows the default file locations for a 32-bit 

Windows operating system other than Vista. 
 

211

The same pop-up window below shows modified WinBUGS file locations under a 64-bit 

operating system. 

Be sure to click on before exiting this window. This same window can be used to 

modify default settings of the categories listed in the tabs across the top. 
 

212

3 Example Analysis in BugsXLA ─ Estimating a Trend in 

Vegetation Cover 

This section provides a step-by-step description for fitting a Bayesian hierarchical model in 

BugsXLA and WinBUGS to estimate a trend in vegetation cover data. This example is based on 

simulated vegetation cover data that mimic those that will be collected as part of SWAN's 

ground vegetation monitoring program. Thus, we simulated cover proportions for 5 quadrats 

within 3 transects within 8 random plots sampled once every 5 years over 31 years (n=7 time 

samples per plot). An annual decline was applied to each time series of cover percentages for 

each plot that produced an overall decline of 35% over 31 years (  985.0 or trend of -0.115; 

e.g., see Appendix E). These data are for a single species, but multispecies data can be easily 

accommodated as described below. 

3.1 Data Entry Format 

Estimated cover proportions (e.g., est_p) and unique identifiers for year, plot, transect and 

quadrat should be entered into Excel in column format such as the following truncated example 

(i.e., data are in 840 rows containing all combinations of year [7], plot [8], transect [3] and 

quadrat [5]). Note the user can name these variables as desired. 

The unique identifiers for plot, transect and quadrat are numerical in the above example, but can 

be text or alphanumeric as well. More columns can be added for other factors of interest, such as 

species, if desired. Count data, such as are collected via point-intercept sampling, would include 

columns with the count (number of hits) of each plant species (e.g., a variable called y) in 59 

points sampled within each transect (e.g., a variable called n). 
 

213

3.2 Data and Model Specifications 

3.2.1 Reading Data into BugsXLA 

After data have been entered into Excel, click on the Bayesian Hierarchical Generalized Linear 

Model (HGLM) icon in the Excel toolbar to generate the following pop-up window. 

Click inside the Data Range box and highlight the columns and rows that contain the data. Keep 

the default settings of "Data is ... in columns" and "Names in first row." 
 

214

Note that all columns of data read into BugsXLA must have the same number of rows. 

3.2.2 Defining Variable Types

The next step is to define the variables as variates (continuous variables) or factors (discrete or 

categorical variables). The default setting lists all variables as variates. Plot, transect and quadrat

are all factors so they will have to be redefined as such. Click on the Set Variable Types button to 

bring up the Create Factors pop-up window. 

Click on plot, transect and quadrat, in turn, and use the button to move each one into the 

FACTORS panel. 
 

215

Next, the levels within each factor have to be defined in their desired order. The default is to list 

them as they appear in the data, e.g., 1-8 for plot, 1-3 for transect, and 1-5 for quadrat. Even 

though these are the desired number and order that will be used in this analysis, the factor levels 

have to be confirmed for each one via the button. Click on the plot variable to 

highlight it and then click on to bring up the Edit Factor Levels window. 

Click on OK to set the factor levels for plot. Repeat this procedure for the other factor level 

variables and then click on OK in the Create Factors window. Again, the Edit Factor Levels

procedure has to be performed even if the default levels will be used as specified; otherwise, the 

following error window will be generated when attempting to run a model. 
 

216

These changes are now set in the Excel worksheet so this process will not have to be repeated for 

future analyses performed in this file. 

3.2.3 Choosing a Model

A response variable in the form of an estimated proportion is typically modeled using a binomial 

error distribution and a logit link. However, BugsXLA will only accept proportions in the form 

of counts/total (e.g, y/n mentioned in the Data Entry Format section), which works for pointintercept data but not for single estimates of percent cover collected in quadrats under SWAN's 

vegetation protocol. Therefore, we recommend starting with a lognormal distribution and an 

identity link because the lower bound is zero like the estimated proportions. This model should 

provide a reasonable fit as long as the observed proportions do not vary a lot over the 0-1 

interval, even though the lognormal's upper bound is infinity. The adequacy of the fit of any 

model must be checked (see Assessing Model Fit) before accepting the output as reasonable. If 

this or other model options offered in BugsXLA are concluded to be inadequate, then the data 

will have to be modeled directly in WinBUGS, which has a steeper learning curve but offers 

extensive modeling options, or via some other program such as freeware program R (R Core 

Development Team 2009) that can run WinBUGS from within it (e.g., R2WinBUGS library; 

Sturtz et al. 2005). Begin with a distribution whose limits best match those of the response 

variable; truncating the lower and/or upper limits of a distribution may be a possibility as well. 

The lognormal and other error distributions can be selected from the drop-down menu under 

Distribution as shown below. The identity link is the only option with the lognormal. 

3.2.4 Specifying the Response Variable 

The name of the response variable (e.g., est_p) should be entered into the box next to Response. 
 

217

If these were data collected under point-intercept sampling, the response variable would be 

entered in the y/n format, which is the column of total counts of a given species per transect (e.g., 

y) divided by the column of total points sampled in a transect (e.g., n) (note that the user can 

name these variables whatever he or she wishes). 

3.2.5 Specifying and Selecting a Covariance Structure 

Data on vegetation composition and structure will be collected repeatedly from the same plots 

over time, referred to as repeated measures data, so temporal dependency among samples must 

be properly accounted for in the model through inclusion of a covariance structure. BugsXLA 

offers two or three options for specifying the covariance structure in WinBUGS (depending on 

the error distribution): 1) autoregressive 1 (AR1; exponential correlation model); 2) AR1 with 

measurement error (not available for the Poisson distribution); and 3) compound symmetry 

(uniform correlation model). 

AR1 structures are implemented by clicking in the box to the left of Longitudinal Model in the 

Hierarchical Generalized Linear Model window and entering the a model statement in the form 

<model type>{<Unit>/[<Time>]} in a larger box that appears to the right of Longitudinal Model. 

Note that this format only allows specification of the topmost factor level or Unit, which is plot

in our example. The Time option does not have to be included when observations are equally 

spaced and in sequence (see the BugXLA reference guide and help files for more details). 

A compound symmetry structure is incorporated through the Random option under Factors in 

the Hierarchical Generalized Linear Model window. A factor or nested factor (see next section) 

is entered into the box to the right of Random. 
 

218

An important step in modeling repeated measures data is choosing a reasonable covariance 

structure. One approach is to run the model of interest with different covariance structures, 

compute the DIC model selection criterion (Spiegelhalter et al. 2002) for each run, and select the 

covariance structure that produces the lowest DIC value (see Specifying Prior Distributions 

Model Checking Criteria). In our example, we used a compound structure because of how the 

example data were simulated, but generally the user should use the DIC criterion to select the 

best-fitting covariance structure. 

3.2.6 Specifying Factors 

Factor variables are entered into the relevant option box under Factors in the Hierarchical 

Generalized Linear Model window. A nested structure is specified using the "/" character 

between two factors or among three or more factors, where the higher level factor is listed first. 

In our example, quadrats are nested within transects and transects are nested within plots, so we 

denote this as "plot/transect/quadrat". Other operators are accepted by BugsXLA, such as the 

product operator "*" to denote interaction; see the BugsXLA reference guide or help files for a 

complete list and examples. 

3.2.7 Specifying Covariates 

Covariates are entered into the relevant option box under Covariates in the Hierarchical 

Generalized Linear Model window. In our example, year is an independent covariate whose 

estimated coefficient provides the trend estimate so it is entered into the box to the right of 

Independent. If we were interested in trend estimates by some factor level, such as by vegetation 

height category, we would use the product operator (*) to create an interaction between year and 

the factor variable containing vegetation height category. 
 

219

3.3 Markov Chain Monte Carlo (MCMC) and Output Options 

The Markov Chain Monte Carlo (MCMC) procedure requires an adequate number of "burn-in" 

samples to be discarded to achieve convergence so that reasonable parameter estimates can be 

obtained from the posterior distribution. What is "adequate" depends on the nature of the data 

and the complexity of the specified model. Based on the literature and on his experience 

modeling various data sets, Woodward (2005) offered suggestions on number of burn-in 

samples, number of samples from the posterior distribution, thinning, and number of chains for 

simple, regular and complex models. These default values are offered in BugsXLA. 

In our example for modeling repeated measures data, we conservatively set the burn-in to 

50,000; BugsXLA allows a maximum burn-in of 100,000 to be specified. We also set the number 

samples to 20,000, the thinning interval to 50, and the chains to 2, which are consistent with 

default values for a complex model. Note that the thinning interval retrieves every Kth sample 

from those taken from the posterior distribution and is used to reduce effects of high 

autocorrelation when present. It is much better to err on the side of being overly conservative in 

setting these values; the cost of being too conservative will only be longer computing time, 

whereas being too liberal could yield unreasonable model results. As discussed in the next 

section, we recommend running at least two chains to help assess convergence. 

Click on to bring up the WinBUGS MCMC & HGLM Options window. 
 

220

Use the up or down arrows to set each of the MCMC Options. The Auto Quit option is 

automatically set to No when two or more chains are specified; this option keeps the WinBUGS 

program window open after the model has been run so that various model assessment tools can 

be accessed (see Assessing Model Fit). Make R script and Create BugFolio create output text 

files containing R and WinBUGS code and related material, respectively, in case the user would 

like to manually edit the code and run it directly in R or in WinBUGS. BugsXLA also will 

export data in a format that can be read directly into WinBUGS. 

Click on , which returns you to the Hierarchical Generalized Linear Model window. 

3.4 Specifying Prior Distributions and Model Checking Criteria

An advantage of Bayesian modeling is the ability to incorporate prior information in the form of 

prior distributions. When prior information is not available, noninformative or diffuse priors 

often are used. BugsXLA provides default settings for all parameters that approximate diffuse 

priors based on those recommended in the literature (Woodward 2005). See the BugsXLA 

reference guide and help files for an extensive discussion of specifying prior distributions. We 

will use the default priors in BugsXLA for our example. 

Click on OK in the Hierarchical Generalized Linear Model window to bring up the Prior 

Distributions window. 
 

221

Click on to display the Model Checking Options window. 

Click on DIC and Checking Functions to save the DIC value, Bayesian p-values and residual 

plots to the output sheets after the model has been run in WinBUGS. Click OK to return to the 

Prior Distributions window. 

3.5 Initiating WinBUGS from BugsXLA 

Once the model and outputs have been specified, click on to initiate the WinBUGS 

program. Three windows are spawned from this action - the WinBUGS program, the WinBUGS 

License Agreement, and the WinBUGS Log file (Notepad). The Log file shows the progress of 

the model run. The progress is also displayed in the left of the gray bottom margin of the 

WinBUGS program window (i.e., "model is updating"). 
 

222

When the model run has completed, the message in the bottom left margin changes to something 

like "updates took 6521 s", where "s" refers to the number of seconds. The log file will have 

show some of the estimated coefficients, and will end with the text "display(window)" and a 

blinking cursor. DO NOT CLOSE the log window or the WinBUGS program window at this 

point. WinBUGS will need to be active to access some of its model assessment tools (see next 

section). 

3.6 Model Outputs

3.6.1 Assessing Model Fit 

Assessing the fit of the model is an essential step in modeling process, regardless of the model 

form or software program in use. Model convergence is an important component of any model, 

but is especially relevant to hierarchical generalized linear models, whether they are fitted based 

on Frequentist (likelihood) or Bayesian (likelihood plus prior) approaches. There is no single 

criterion that indicates goodness of fit in Bayesian hierarchical generalized linear models, so we 

will discuss several tools that should be used in conjunction with one another to assess 

convergence and model fit. 
 

223

Sample Monitor Tool: Click on Inference=>Samples... in the WinBUGS program menu bar 

to bring up the Sample Monitor Tool window. 

The node box contains a list of random variables fitted in the model. These can be used to assess 

model fit. The default * will generate results for all variables listed in the drop-down menu. We 

will focus on "V.Coeff" for this example, which is the WinBUGS name for the year variable in 

our simulated data. Click on the down arrow of the drop-down menu and select "V.Coeff". 

We will be using the , , and tools to assess different aspects of 

model fit. 
 

224

Click on to display a plot of both chains of values from the MCMC samples. 

The chains are shown in two colors and should be mixed together rather than one chain separated 

from the other (see p. 55 in the WinBUGS reference manual [http://www.mrcbsu.cam.ac.uk/bugs/winbugs/manual14.pdf] for an example of poorly mixed chains). The above 

plot indicates that a lack of mixing did not occur, but this diagnostic alone cannot be used to 

confirm proper mixing occurred. 

Click on to display a plot of the modified Gelman-Rubin convergence statistic (Gelman 

and Rubin 1992, Brooks and Gelman 1998). 

The lines should stabilize around the 1.0 value to indicate convergence. There will be some 

initial bounce in the blue line as the algorithm gets started. The above plot indicates 

convergence. 

Click on to generate a plot showing the degree of autocorrelation in the chains for the 

parameter of interest. 

The desired condition is to have the mass mostly or entirely on 0 lag, as above. This is where the 

thinning interval plays an important role. 
 

225

Click on to view a plot displaying the estimated posterior distribution. 

This distribution should be unimodal (i.e., one obvious peak) and not severely skewed. The data 

for our example were simulated from a truncated Normal distribution, so it is not surprising that 

the posterior distribution also approximates a Normal. 

After reviewing these various plots and confirming reasonable convergence and model fit, 

proceed to the BugsXLA window by closing the WinBUGS program window, which generates 

the following pop-up window. 

Click Yes and the Import Results (Options) window will appear. 
 

226

Click on the box next to All in the left-hand panel. Either keep the default names of the Excel 

sheets that will be generated (write to sheet...) or rename them as desired. Click and, 

once the import process is complete, Excel spreadsheets containing model output and model 

diagnostics will be added to the existing Excel file. The default brings up the model output sheet 

("Sheet1 out" under the default name). 

The top eight rows display the model parameters and their estimated coefficients, measures of 

precision and 95% credible intervals. The last column shows the WinBUGS name for these 

parameters, which match those listed in the drop-down menu of the Sample Monitor Tool 

discussed previously. The rest of the information in this sheet refers to model specifications and 
 

227

run time (note that WinBUGS can take hours to run [14.5 in this case] when fitting large data 

sets and complex models). 

Bayesian P-values and Residual Plots: Click on the "Modl Chks(1)" tab at the bottom of the 

Excel file to bring up the sheet containing model diagnostics requested under Checking 

Functions in the Model Checking Options window. 

Calculating Bayesian or posterior p-values involves generating a replicate data set and counting 

the number of times the replicate is more extreme than the observation. The closer these values 

are to 0.5, the better the replicated data match the observed data. Values closer to 0 or 1 indicate 

larger differences and may indicate a poorly fitting model when there are many such values 

(Ntzoufras 2009). Bayesian p-values are listed under the column labeled "PrExt"; values 

highlighted in red may indicate suspect values. Plots of these values that are less than 0.1 are 

shown in the second of four rows of plots to the right of the tabular data. 
 

228

The other three rows contain standard residual plots and are interpreted as such. Although there 

are some values close to 0 in the second row of plots, overall the plots look okay and seem to 

indicate a reasonable, albeit not close, fit. 

3.6.2 Interpreting Model Output 

The estimates of interest are the median, 2.5% (lower credible limit) and 97.5% (upper credible 

limit) of the year parameter, which provide the estimate of trend and its 95% Bayesian credible 

interval. These estimates were obtained from the posterior distribution, which in this case was 

very nearly symmetrical, so the mean and the median are the same. However, posterior 

distributions are often skewed so the mean and median will not be the same. the median is the 

estimate that should be used. 
 

229

The modeling results indicate an average decline of 1.13% per year over 31 years, with a 95% 

credible interval of -0.0128 to -0.00979. These results are similar to those obtained by using 

likelihood-based PROC GLIMMIX (SAS Institute, Inc. 2008) to fit a generalized linear mixed 

model with a binomial error distribution and a logit link (trend estimate = -0.116, 95% 

confidence interval: -0.01308 to -0.01012). The interpretation of the credible interval is that there 

is a 95% chance that the true trend (i.e., -0.115) occurs within -0.01280 and -0.00979 because 

parameters are considered random variables under the Bayesian paradigm. This differs from the 

proper interpretation of a Frequentist-based confidence interval, which would indicate that if the 

same data were sampled over many times in the same way, on average 95% of the confidence 

intervals would contain the true trend. The other parameter estimates displayed in the output 

sheet include intercept, variance components (plot, transect within plot, and quadrat within 

transect within plot), overall variance, and residual precision (CV of random residual 

component). 

4 Literature Cited 

Brooks, S., and A. Gelman. 1998. General methods for monitoring convergence of iterative 

simulations. Journal of Computational and Graphical Statistics 7:434-455. 

Gelman, A., and D. B. Rubin. 1992. Inference from iterative simulation using multiple 

sequences. Statistical Science 7:457-511. 

Lawson, J. 2008. Bayesian interval estimates of variance components used in quality 

improvement studies. Quality Engineering 20:334-345. 

Lunn, D. J., A. Thomas, N. Best, and D. Spiegelhalter. 2000. WinBUGS─A Bayesian modelling 

framework: concepts, structure, and extensibility. Statistics and Computing 10:325-337. 

User's guide and application available from (http://www.mrcbsu.cam.ac.uk/bugs/welcome.shtml). Accessed 16 September 2009. 

Ntzoufras, I. 2009. Bayesian Modeling Using WinBUGS. Wiley, Hoboken, New Jersey. 

R Development Core Team. 2009. R: A Language and Environment for Statistical Computing. R 

Foundation for Statistical Computing, Vienna, Austria. Program available from 

(http://www.r-project.org/). Accessed 16 September 2009. 

Royle, J. A., and R. M. Dorazio. 2008. Hierarchical Modeling and Inference in Ecology: The 

Analysis of Data from Populations, Metapopulations and Communities. Elsevier-Academic 

Press, New York, New York. 

SAS Institute, Inc. 2008. SAS/STAT 9.2 User's Guide. SAS Institute, Inc., Cary, North Carolina. 
 

230

Spiegelhalter, D. J., N. G. Best, B. P. Carlin, and A. van der Linde. 2002. Bayesian measures of 

model complexity and fit (with discussion). Journal of the Royal Statistical Society, B

64:583-639. 

Sturtz, S., U. Ligges, and A. Gelman. 2005. R2WinBUGS: A package for running WinBUGS 

from R. Journal of Statistical Software 12:1-16. 

Woodward, P. 2005. BugsXLA: Bayes for the common man. Journal of Statistical Software 

14(5):1-18. User's guide and application available from (http://www.axrf86.dsl.pipex.com/). 

Accessed 16 September 2009. 
 

231

Appendix H: Guidelines for Voucher Collections 

David F. Murray and Carolyn Parker, University of Alaska Museum - Herbarium 

Introduction 

The vascular plant flora is not well known over large areas of Alaska. Each new collection gives 

us more information about the species of plants present, their various forms, and their overall 

distribution. This information is of primary interest to plant taxonomists and is essential 

background for plant geographers and ecologists. It has also become increasingly obvious that an 

inventory of plant diversity is a foundation for efforts to conserve natural resources, and plant 

specimens are the physical documentation of this diversity. 

Plant collections, suitably preserved and mounted, provide a permanent physical record of our 

natural history heritage. The following remarks and guidelines are intended to aid the student, 

amateur botanist, and professional from fields other than taxonomy to prepare plant collections. 

Certain minimum standards must be observed so that the specimens can be identified and also be 

of lasting scientific value. 

Methods 

How to Collect 

The Plant Conservation Roundtable, an informal group of botanists and conservationists in 

Washington, D. C., has prepared Conservation Guidelines (Natural Areas Journal, 1986, vol. 6, 

no. 3, pgs. 31, 32), which provide ethical standards for plant collecting. Several of their points 

need mentioning here. To paraphrase: we must obtain permission and permits as required by law 

and the standards of decent behavior; collect with discretion lest we encourage others who might 

collect indiscriminately; avoid placing any population of plants at risk of extirpation--if there is 

any doubt, do not collect. Therefore, avoid collecting rare plants (see Candidate Threatened and 

Endangered Plants of Alaska by Murray and Lipkin, available from the U.S. Fish and Wildlife 

Service, Anchorage, and the University of Alaska Museum, Fairbanks). 

Look for plants with well-developed flowers and/or fruits. The taxonomy of vascular plants is 

based on complete and mature specimens, therefore accurate identifications are more likely with 

good material at hand. The exceptions are trees and shrubs, which generally can be determined 

from mature leaf and stem structures alone. Unless you want to show some unusual form of 

variation, take plants that are representative of the population you are sampling. While the plants 

are fresh and tissues are still flexible, gently tap the root mass against your foot or the ground to 

remove as much soil as you can. Whenever possible, entire plants should be collected; that rule 

is, of course, limited to those plants small enough to be placed within a single sheet of folded 

newspaper. Although we emphasize that flowers and fruits are generally essential, do not fail to 

get the basal leaves and roots as well. Usually a sturdy trowel will suffice to dig up plants. An 

army surplus trenching tool or an old ice-axe is excellent too. Remember to replace your divots. 

A pocket knife is adequate for collecting portions of trees, shrubs, and large herbs, but pruning 

shears make the job much easier. Shrubs and trees can be collected by taking the tips of branches 

or sections of stems with mature leaves (and flowers or fruits,whenever possible). Be sure to note 

the total height of the plant. Very large herbaceous plants, such as tall umbellifers, are often a 

problem. Cut away and discard most of the stem, but retain at least a representative leaf with 
 

232

petiole and short section of attached stem, the infloresence, and the stem base with a portion of 

the root. Collect enough material to fill a folded sheet of newspaper. 

A plant press is unwieldy, to say the least, and it is best left in the vehicle, at home, or at the base 

camp. Protect the specimens in large, plastic freezer bags. If you plan to carry the fresh 

specimens with you all day, "zip-locks" are good containers, since you can blow air into them 

before zipping them shut; the air will act as a cushion and offer some protection against damage 

while in a backpack. Plastic tupperware-like boxes are the high-tech solution. 

Documentation 

The scientific value of specimens depends on the specimen and on the data you take when 

making the collection. It is important to adopt a system to insure that the data for each specimen 

are recorded faithfully and consistently. Remember that these notes must be intelligible to 

another worker who may be processing your plants later on. If you are collecting just a few 

plants, you may write all the field notes directly on the newspaper in which the plant is pressed. 

Preferably, every collector should keep a pocket-size field notebook in which you record each 

collection. This is critical; don't trust your memory! 

If this is your first collection, simply start with #1 and proceed consecutively so that each 

collection has a unique number. Assign a new collection number to each collection from a single 

locality on any one day. If the same species is collected on the same day, but at a different 

locality, assign it a different number. If the same species is collected again at the same location 

as before, but on a different day, give it a different number also. Do not mix different species of 

plants on a sheet. 

 

Essential data include where (general and specific locality, general and specific habitat, 

elevation), when (day, month, year), and by whom collected (your name). Additional 

information may include exposure (N, S, E, W), slope angle (flat, gentle, steep) soil texture 

(gravel, sand, loam) and moisture (wet, moist, dry), flower color (some blossoms fade with 

drying, some colors intensify), odor, relative abundance (abundant, common, infrequent, rare), 

and conspicuous use by animals. A page of your field notebook may look like this: 

 Kodiak Island 

 21 Jul 1994 

 Monaska Bay - sea cliffs behind beach - elev. 1-10 m.s.m. 

 

 245. Campanula sp. 

 common in s-facing rock crevices, flowers blue 

 246. Draba hyperborea

 rare in dry rock crevices 

 247. unknown shrub 

 1.5 m. tall, growing in seepage at base of cliff in wet, rocky soil 

 

 Monaska Bay - gravel strand beaches - elev. 1-2 m.s.m. 

 248. Senecio pseudo-arnica

 common, growing with Leymus, upper gravel beach zone 

` 249. unknown crucifer 

 rare, purple flowers, scattered plants in sand, lower beach zone 
 

233

When pressing the specimens, the collection number is placed on the sheet of newspaper 

containing the specimens, which relates those specimens back to the data in your notebook. 

Ideally, this field notebook becomes part of the permanently curated collection. 

Specimen Preservation 

Place the fresh specimens in a sheet of folded newspaper. When folded, the size of the paper 

should conform to the size of the plant press, blotters, and cardboard ventilators. The dried 

specimens will be mounted on a sheet of herbarium paper a bit smaller than the folded 

newspaper, thus each paper of specimens equals a minimum of one finished herbarium sheet. 

A little extra time taken while pressing plants will make a critical difference in the quality of the 

herbarium specimens. Lay out the plant(s) in a "natural pose". Bend or fold the stems into V or N 

shapes as necessary to fit the plants within the folded sheet of newspaper. Be certain that both 

leaf surfaces are exposed and flower/fruit parts are clearly visible, that is not covered by stems or 

leaves. When placing several small plants in a sheet of newspaper, arrange them so they do not 

overlap. In essence, attempt to display all diagnostic features so they will show after the plant 

has been dried and firmly glued to a herbarium sheet. Cut open thick, moist stems, rootstocks, 

dense cushions, etc., so the specimens will be flatter and dry more quickly. A deep longitudinal 

cut will allow you to expose the inner portions of both halves of thick roots and stems with the 

uncut portion serving as a hinge. Plants such as water lily, skunk cabbage, moss campion, and 

broomrape require this sort of special handling. 

Place each sheet of specimens between two botanical blotters and these in turn between two 

cardboard ventilators. Repeat this arrangement until all the specimens have been processed. Tie 

the press tightly with straps or rope. Place the press in the sun where it will be exposed to 

breezes or over a source of gentle heat. 

Dry the plants as quickly as possible. Dried correctly, the specimens retain much of their original 

color. The best practice is to check your press every day to determine progress and remove dry 

plants. Be alert for signs of mold or darkening plant tissues; the appearance of either means the 

plants are drying too slowly and a heat source is called for. Be very careful, however, with 

artificial heat. Do not "cook" or scorch the plants, as they will become discolored and very 

brittle. 

You can increase the rate of drying by exchanging wet blotters for dry ones (the first change 

after 24 hours), using extra cardboards, and putting the thicker and more succulent specimens 

toward the outside of the press. Even these efforts are challenged by the lush herbaceous 

vegetation in wetter regions of Alaska, unless you have a source of warm, dry air--a heated 

indoor space. 

If you have only a few specimens, they can be kept in the press until you are ready to process 

them. More commonly you will need the press space for more plants, so remove the dry plants, 

leaving them in their newspapers. When you have accumulated a stack three or four inches thick, 

wrap the specimens in newspaper and tie them snugly between cardboards. The bundles of dry 

specimens should be placed in sturdy boxes. Dried, bundled, and boxed specimens can be mailed 

directly to the Curator of the Herbarium, 907 Yukon Drive, University of Alaska Museum, 

Fairbanks AK 99775-6960. 
 

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Appendix I: Plant Association Descriptions 

Selected plant communities (Viereck Level V) of Kenai Fjords National Park, Lake 

Clark National Park and Preserve, and Katmai National Park and Preserve 

List includes plant communities (Viereck Level V; Viereck et al. 1992) that fall within broad 

vegetation classes targeted for sampling in SWAN. Interior and alpine communities are 

referenced in Viereck et al. (1992) and described elsewhere (citations follow). Sitka spruce and 

coastal alpine communities are also referenced in DeVelice et al. (1999). Due to the lack of data 

from south central and southwest Alaska, not all communities encountered may have been 

previously described. Viereck Level IV and V classes sampled by the Central Alaska Network 

(CAKN) are marked with an asterisk (*). 

1 Coastal Sitka spruce forest (0-450 m) 

I.A.1.a. Sitka spruce closed forest (60-100% cover) 

Picea sitchensis/Echinopanax horridum (DeVelice et al. 1999) 

Picea sitchensis/Rubus spectabilis-Echinopanax horridum (DeVelice et al. 1999)

Picea sitchensis/Vaccinium ovalifolium (DeVelice et al. 1999)

Picea sitchensis/Vaccinium ovalifolium-Echinopanax horridum (DeVelice et al. 1999)

Picea sitchensis/Vaccinium ovalifolium/Dryopteris dilatata (DeVelice et al. 1999)

Picea sitchensis/Oplopanax horridus-Rubus spectabilis/Cornus canadensis (Alaback 1980b; 

Martin et al. 1985; Neiland 1971a; Stephens et al. 1969) 

Picea sitchensis/Oplopanax horridus/Circaea alpina (Pawuk and Kissinger 1989) 

2 Interior low and mid-elevation white spruce (0-900 m) 

I.A.2.e. White spruce open forest (25-60% cover) 

*Picea glauca-P. mariana/Ledum groelandicum-Vaccinium vitis-idaea/Pleurozium schreberi 

(Viereck 1989) 

*Picea glauca/Betula glandulosa/Hylocomium splendens (Hettinger and Janz 1974; Viereck 

1970b, 1975, 1979; Williamson and Peyton 1962) 

*Picea glauca/Betula nana-Vaccinium uliginosum/feathermosses (Craighead et al. 1988) 

Picea glauca/Betula glandulosa/Cladonia spp. (Racine and Anderson 1979; Viereck 1979) 

Picea glauca/Salix spp./Ledum decumbens/Vaccinium vitis-ideae (Yarie 1983) 

I.A.3.c. White spruce woodland (10-25% cover) 

*Picea glauca/Betula glandulosa/feathermosses-Cladonia spp. (Hettinger and Janz 1974; Racine 

1975; Viereck 1975, 1979; Williamson and Peyton 1962) 

*Picea glauca/Vaccinium spp.-Empetrum nigrum (Craighead et al. 1988) 

*Picea glauca/Vaccinium uliginosum-Carex bigelowii (Craighead et al. 1988) 

*Picea glauca/Ledum groenlandicum-Vaccinium vitis-ideae/feathermosses (Dyrness et al. 1988) 

Picea glauca/Cladonia spp. (Racine 1976) 

I.A.3.e. White spruce-black spruce woodland (10-25% cover) 

*Picea mariana-P. glauca/Betula glandulosa/feathermosses (Viereck 1979) 

Picea glauca-P. mariana/lichens (Foote 1983) 

Picea mariana-P. glauca/Rubus chamaemorus-Ledum decumbens-Vaccinium spp. (Craighead et 

al. 1988) 
 

235

3 Interior mid-elevation low and dwarf shrub communities (0-900 m) 

II.C.1.a. Shrub birch – closed low scrub 

*Betula nana (Craighead et al. 1988, Racine and Anderson 1979, Hopkins and Sigafoos 1951) 

II.C.2.c. Mesic shrub birch-ericaceous shrub – open low scrub 

*Betula glandulosa/Vaccinium uliginosum-Empetrum nigrum-Ledum decumbens/lichens 

(Anderson 1974; Batten 1977; Hanson 1953; Hettinger and Janz 1974; Hultén 1966; Jorgenson 

1984; Kessel et al. 1960; Pegau 1968; Steigers et al. 1983; Webber et al. 1978; Young and 

Racine 1978) 

*Betula glandulosa/Festuca altaica-Vaccinium spp./feathermosses-lichen (Hanson 1951; 

Hettinger and Janz 1974; Pegau 1972; Viereck 1963) 

*Betula glandulosa-Vaccinium spp.-Carex bigelowii (Churchill 1955; Hanson 1950) 

*Betula glandulosa-Salix spp./Carex bigelowii-Ledum decumbens/feathermosses-lichens 

(Hanson 1951; Scott 1972) 

*Betula nana-Rubus chamaemorus-Ledum decumbens-Vaccinium spp. (Craighead et al. 1988) 

4 Alpine dwarf and prostrate shrub communities (>900 m) 

II.D.1.a. Dryas tundra – dwarf scrub 

*Dryas octapetala (Craighead et al. 1988; Drew and Shanks 1965; Hanson 1953; Hettinger and 

Janz 1974; Johnson et al. 1966; Nodler et al. 1978; Pegau 1968; Viereck 1963) 

*Dryas octapetala-Salix arctica-Oxytropis nigricans (Bos 1967) 

Dryas octapetala-Arctostaphylos alpina (Jorgenson 1984, Webber et al. 1978, Young 1974b) 

Dryas octapetala-Carex microchaeta (Webber et al. 1978) 

Dryas integrifolia (Hettinger and Janz 1974; Komarkova and Webber 1978; Webber and Walker 

1975) 

Dryas integrifolia-Vaccinium spp. (Drew and Shanks 1965; Jorgenson 1984) 

Dryas octapetala-Vaccinium uliginosum-Salix reticulata (Anderson 1974) 

Dryas octapetala/Hierochloe alpina (DeVelice et al. 1999) 

II.D.1.c. Dryas-lichen tundra – dwarf scrub 

*Dryas octopetala-lichens (Anderson 1974; Brock and Burke 1980; Childs 1969; George et al. 

1977; Hanson 1951; Spetzman 1959) 

*Dryas octapetala-lichens-Oxytropis nigrescens-Salix phlebophylla-Carex microchaeta

(Johnson et al. 1966) 

Dryas octopetala-Cetraria spp.-Cladonia spp. (Pegau 1968; Viereck 1962) 

Dryas octopetala-Empetrum nigrum-Salix arctic-Cetraria spp.-Cladonia spp. (Young and 

Racine 1978) 

Dryas integrifolia-lichens (Drew and Shanks 1965; Hanson 1951; Komarkova and Webber 1978; 

Webber and Walker 1975) 

II.D.2.a. Bearberry tundra – dwarf scrub 

Arctostaphylos alpina-Vaccinium vitis-idaea (Hanson 1953) 

Arctostaphylos alpina-Rhododendron camtschaticum (Pegau 1968) 

Arctostaphylos alpina-Vaccinium spp.-Empetrum nigrum-Cassiope tetragona-lichens (Jorgenson 

1984) 

II.D.2.b. Vaccinium tundra – dwarf scrub 

Vaccinium vitis-idaea-Dryas octapetala-Empetrum nigrum-Festuca altaica (Scott 1974) 
 

236

Vaccinium vitis-idaea-Salix phlebophylla-Arctostaphylos alpina (Anderson 1974) 

Vaccinium vitis-idaea-Empetrum nigrum-Cladina spp. (Racine and Anderson 1979) 

Loiseleuria procumbens-Vaccinium uliginosum-Salix arctica-Ledum decumbens (Griggs 1936) 

Ledum decumbens-Vaccinium vitis-idaea-Cetraria spp. (Hanson 1951) 

Vaccinium uliginosum-V. vitis-idaea (Hettinger and Janz 1974) 

Vaccinium uliginosum-Empetrum nigrum-Ledum decumbens-Cladonia spp. (Steigers et al. 1983) 

Vaccinium uliginosum-lichens (Craighead et al. 1988) 

II.D.2.c. Crowberry tundra – dwarf scrub 

Empetrum nigrum (DeVelice et al. 1999) 

Empetrum nigrum-Arctostaphylos alpina (DeVelice et al. 1999, Fries 1977, Bos 1967)

Empetrum nigrum-Vaccinium uliginosum (DeVelice et al. 1999, Hultén 1962) 

Empetrum nigrum-Vaccinium spp. (Friedman 1982, Racine and Young 1978, Griggs 1936) 

Empetrum nigrum-Carex bigelowii-Arctostaphylos alpina (Bos 1967) 

Empetrum nigrum-Salix arctica-Cetraria spp. (Young and Racine 1978) 

II.D.3.a. Willow tundra – dwarf scrub 

Salix rotundifolia (Komarkova and Webber 1978, White et al. 1975, Klein 1959) 

Salix arctica-S. rotundifolia-Empetrum nigrum (Shacklette et al. 1969) 

Salix arctica-Empetrum nigrum (DeVelice et al. 1999)

Salix rotundifolia/Carex microchaeta (DeVelice et al. 1999) 

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p. M.S. thesis. 

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241

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242 

Appendix J: Proposed database design 

The proposed database design for the SWAN vegetation monitoring program is based upon a MS 

Access database developed for the Denali Long Term Ecological Monitoring (LTEM) Program 

and modified for the Central Alaska Network (CAKN) (Figs. J.1, J.2; Roland et al. 2004). SOPs 

for data entry and QA/QC will be developed pending development of a structurally similar SQL 

database for the SWAN. 

Three primary types of tables comprise the database: 

1. Reference tables, which contain attribute data on individual records such as species and 

are denoted with the prefix “ref_”. 

2. Data tables, into which the actual field data are entered (e.g., cover transects, species 

composition, tree measurements, etc.) and are denoted by the prefix “tbl_”. 

3. Cross-reference tables, which are the products of combinations of data tables and 

reference tables, which are denoted by the prefix “xref_” in the database structure. 

The database design allows for entry of numerous iterations of observations from a single point 

in space (the permanent plot). While the CAKN sampling design is founded on a two-stage grid, 

the SWAN sampling design utilizes a single set of randomized (GRTS) points weighted by 

elevation and landcover class. As such, CAKN tables referencing the mini-grid or points within 

a grid will be reduced to a single plot identifier in the SWAN version. 

I. Data model 

The relational database (Fig. J.1) for this program contains of a variety of tables, queries and 

forms to handle data entry, validation, export and analysis. The sample event table, 

tbl_sample_event, serves as a focal point in the data model tying 14 separate data tables to 

appropriate plot identifiers in tbl_sample_point. The field sample_event_num serves as the 

primary key for tbl_sample_event and as the foreign key in each of the 14 data tables. Values for 

sample_event_num consist of a concatenation of the four-letter park code, four-digit year, twodigit elevation code, and three-digit GRTS code. For example, "LACL_2008_02_016" represents 

GRTS point 016 in elevation band 2 (450-900 m) sampled in Lake Clark National Park and 

Preserve (LACL) in 2008. A sample event is defined as a single trip to a single plot in the sample 

design. Thus this field ties together the spatial and temporal aspects of sampling for the 

program. 
 

243 

Figure J.1. Simplified database relationship diagram showing primary relationships in the 

CAKN_vegetation.MDB database. 

II. Description of Tables and Fields in the CAKN Vegetation Monitoring Database 

The following descriptions of the tables in the database include the linkages between tables (Fig. 

J.2). To reduce redundancy of the text, all linkages between reference tables (ref_) and data 

tables (tbl_) are only listed under the reference table heading. 

Table Name: ref_azimuth

This is a reference table that contains values for different categories of azimuth for describing the 

aspect of the vegetation plot – such as “south”, “southwest” etc. This info is recorded on the plot 

data sheet. The code for slope and azimuth is entered in the Plot Physical Data entry screen. 

This table is linked to the table tbl_plot_physical_data. 

Field Name Field Type Field Size Field Desc

azimuth_code Text 50 one or two letter code for azimuth/compass direction

azimuth_desc Text 255 describes azimuth/compass direction

 

Table Name: ref_cross_section

This is a reference table that contains values for different categories of slope cross-section for 

describing the shape of the slope of the vegetation plot – such as “convex” or “concave” etc. The 

code for slope cross-section is entered in the Plot Physical Data entry screen. This table is 

linked to the table tbl_plot_physical_data. 

Field Name Field Type Field Size Field Desc

cross_section_code Text 50 abbreviated description of cross section

cross_section_desc Text 255 description of microtopography in cross section
 

244 

Figure J.2. Diagram of database design developed for CAKN, including tables and relationships among 

variables. 

 

Table Name: ref_crown_class 

This is a reference table that contains values for the canopy position (crown class) of trees 

observed in the vegetation plots. This information is recorded in the field on the Tree and 

Sapling data sheet, and the Increment Coring field data sheet. The data are entered into the Tree 

and Sampling entry screen and Tree Increment Coring entry screen. This table is linked to the 

tables tbl_plot_tree_data; tbl_plot_sapling_data; and tbl_increment_core_field.

Field Name Field Type Field Size Field Desc

crown_class_code Text 50 number assigned to specific crown class
 

245 

crown_class_name Text 50 name of specific crown class

crown_class_desc Text 255 description defining specific crown classes

Table Name: ref_crown_length 

This is a reference table that contains values for the length of the live canopy (crown length) of 

trees observed in the vegetation plots. This information is recorded in the field on the Tree and 

Sapling data sheet. The data are entered into the Tree and Sapling entry screen. This table is 

linked to the table tbl_plot_tree_data through the field crown_length_code. 

Field Name Field Type Field Size Field Desc

crown_length_code Text 50 numerical code associated with different percent intervals

crown_length_percent Text 50 percent of total possible living crown observed

 

Table Name: ref_digital_photo_image_type 

This is a reference table that contains the image type, either single shot or panoramic, for each 

photo. In the field, these data are recorded in the photo log book. The data are entered in the 

Plot Photo Data screen. This table is linked to the table tbl_digital_photo through the field 

digital_photo_image_type_code. 

Field Name Field Type Field Size Field Desc

digital_photo_image_type Text 50 describes type of photo

digital_photo_image_type Text 50 describes type of photo - panorama or single image

Table Name: ref_digital_photo_quality 

This is a reference table that contains the image quality for each photo. Image quality is decided 

when the photo is saved onto a hard drive from the digital compact flash card. The data are 

entered in the Plot Photo Data screen. 

Field Name Field Type Field Size Field Desc

digital_photo_quality_cod Text 50 assessment of photo quality, same as description

digital_photo_quality_des Text 255 assessment of photo quality

 

Table Name: ref_digital_photo_subject 

This is a reference table that contains the subject for each photo. Image quality is decided when 

the photo is saved onto a hard drive from the digital compact flash card. The data are entered in 

the Plot Photo Data screen. 

Field Name Field Type Field Size Field Desc

digital_photo_subject_cod Text 50 abbreviated description of photo subject

digital_photo_subject_des Text 255 detailed description of photo subject

 

Table Name: ref_drainage 

This is a reference table that describes the type of drainage of the plot. These categories are then 

used to group the dataset by class for trend analysis. These data are linked to the 

tbl_plot_physical_data table by the field drainage_code. 

Field Name Field Type Field Size Field Desc

drainage_code Text 50 abbreviated description of drainage/permeability

drainage_desc Text 255 description of relative drainage/permeability at site
 

246 

Table Name: ref_frost_action 

This is a reference table that describes the severity of frost action present at each plot. The 

values in this table are the different levels of frost-action. This table is related to the 

tbl_plot_physical_data by the field frost_action_code. 

Field Name Field Type Field Size Field Desc

frost_action_code Text 50 one word code for type of frost action

frost_action_desc Text 255 describes type of frost action observed at site

 

Table Name: ref_geomorphic_disturbance 

This reference table describes the type of disturbance regimes that may potentially occur at each 

plot, such as “deglaciation” or “wind scour”. This table is related to the tbl_plot_physical_data 

by the field geomorphic_disturbance_code. 

Field Name Field Type Field Size Field Desc

geomorphic_disturbance_d Text 255 desc. of type of geomorphic disturbance observed at site

geomorphic_disturbance_c Text 50 abbreviated description of type of disturbance

 

Table Name: ref_gps_type 

This reference table contains values for the model of GPS used to record data in the field. 

Currently the standard unit used is the Trimble Geo 3, and in the field it is listed on the Point 

Data sheet. These data are entered under the Enter GPS Info for Sample Point screen on the 

main data entry menu. The table is linked to the table tbl_sample_point by the field gps_type. 

Field Name Field Type Field Size Field Desc

gps_type Text 50 abbreviated code for type and make of gps unit

gps_desc Text 255 describes type and make of gps unit used in field

 

Table Name: ref_horizontal_distance 

This reference table lists the points at which the vegetation is sampled along the cover transects. 

Data are recorded at each of these points (spaced every 50 cm along the tape) on the Cover Transect 

data sheet in the field. In the database, these data are entered under the Transect Cover Data screen. 

This reference table is linked to the table tbl_cover_transect_data by the field distance_code. 

Field Name Field Type Field Size Field Desc

distance_code Text 50 distance along a transect written as number only

distance_point Long Integer 4 distance along a transect written in centimeters

distance_desc Text 255 left blank

Table Name: ref_landcover_class 

This reference table contains values for the different classes contained in the existing SWAN 

landcover maps (KEFJ, KATM, LACL). The codes in this table allow both a field determination 

of land cover class and a GIS determination based on the actual map to be recorded in the tbl 

_plot_biotic_data. The ref_landcover_class table is linked to the tbl _plot_biotic_data through 

the fields landcover_code and GIS_landcover_code. 

Field Name Field Type Field Size Field Desc

landcover_class2 Text 50 describes the second landcover class, if present
 

247 

sorter Long Integer 4 number associated with specific landcover class; same as 

 landcover code

landcover_class1 Text 50 describes the landcover class of the plot

landcover_code Text 50 number associated with specific landcover class

landcover_name Text 50 describes landcover class category

 

Table Name: ref_litter 

This is a reference table that contains values describing the type of litter that can be present at a 

site. These data are part of the soils protocol at each cardinal point, and are recorded on the Soils 

Data sheet. During data entry, these data are entered in the Soil Field Observations screen 

under the field titled Litter_Layer_Kind. The Soil Field Observations screen is located under 

the Soils tab on the main menu. The ref_litter table is linked to the tbl_soil_field_observation

by the field litter_code. 

Field Name Field Type Field Size Field Desc

litter_code Text 50 same as litter_description

litter_desc Text 255 describes source of litter

 

Table Name: ref_living_mat 

This is a reference table that contains values describing the type of living (vegetative) mat 

existent over the soil, which can be present at a site. For example, “dwarf scrub” or 

“herbaceous”. These data are part of the soils protocol at each cardinal point, and are recorded 

on the Soils Data sheet. During data entry, these data are entered in the Soil Field Observations

screen under the field titled Living_Mat_Kind. The Soil Field Observations screen is located 

under the Soils tab on the main menu. The ref_living_mat table is linked to the 

tbl_soil_field_observation by the field living_mat_code. 

Field Name Field Type Field Size Field Desc

living_mat_code Text 50 abbreviation of description

living_mat_desc Text 255 describes general composition of living mat

 

Table Name: ref_microrelief 

This reference table contains values of the types of micro-relief present in the plot, for example, 

bedrock outcrops or moss hummocks. The table ref_microrelief is linked to the 

tbl_plot_physical_data and the tbl_soil_field_observation by the field microrelief_code. 

Field Name Field Type Field Size Field Desc

microrelief_code Long Integer 4 unique number for each different category of microrelief 

microrelief_desc Text 255 describes type of microrelief observed

 

Table Name: ref_organic_mat 

This is a reference table that contains values describing the type of organic mat existent over the 

soil, which can be present at a site. For example, “coniferous leaves” or “fine roots”. These data 

are part of the soils protocol at each cardinal point, and are recorded on the Soils Data sheet. 

During data entry, these data are entered in the Soil Field Observations screen under the field 

titled Organic_Mat_Kind. The Soil Field Observations screen is located under the Soils tab on 

the main menu. The ref_living_mat table is linked to the tbl_soil_field_observation by the field 

organic_mat_code. 
 

248 

Field Name Field Type Field Size Field Desc

organic_mat_code Text 50 two letter code for material composing organic mat

organic_mat_desc Text 255 describes composition of organic mat

 

Table Name: ref_parent_material 

This is a reference table that contains values for broad categories of parent material , such as 

“alluvium” or “loess”. This table is linked to the table tbl_plot_physical_data by the field 

parent_material_code. 

Field Name Field Type Field Size Field Desc

parent_material_code Text 50 same as description; describes rock source

parent_material_desc Text 255 describes source of rock at site

 

Table Name: ref_pathogen 

This reference table contains the types of pathogen infection of trees that may be present in the 

plots. In the field, these data are entered on both the Tree and Sapling Data sheet (for the trees in 

the plot) and on the Tree Coring sheet. In the database, these data are recorded the field 

Pathogen on the Plot Tree Data screen under the Plot Tree, Sapling tab in the main data screen. 

They are also recorded in the field Pathology on the Tree Cores data screen. The ref_pathogen

table is linked to the tbl_plot_tree_data table by the field pathogen_code. 

Field Name Field Type Field Size Field Desc

pathogen_code Text 50 same as pathogen_desc

pathogen_desc Text 255 describes wounds and pathogens observed in tree

 

Table Name: ref_personnel 

This reference table contains a list of all the individuals who have worked on this project, their 

initials and the capacity in which they served in the program. In the data entry process, this 

information is added under the Enter personnel data screen in the field Personnel. The 

ref_personnel table is linked to the xref_sample_personnel table by the field personnel_code. 

Field Name Field Type Field Size Field Desc

personnel_last_name Text 50 last name of crew member

personnel_desc Text 255 official position/capacity of crew member

personnel_first_name Text 50 first name of crew member

personnel_code Text 50 initials of crew member

Table Name: ref_quad 

This reference table contains values describing the codes corresponding to the quadrats and plot 

quadrants, and the order they are sampled in. This table is linked to the 

tbl_plot_species_composition table by the field quad_code. 

 

Field Name Field Type Field Size Field Desc

quad_desc Text 255 written description of quadrat or quadrant

quad_code Text 50 alphanumeric code for quadrat or quadrant

 

Table Name: ref_quadrant 

This reference table lists the four quadrants of the plots and their corresponding one letter codes. 
 

249 

This table is linked to the following tables by the field quadrant_code: tbl_increment_core_field, 

tbl_plot_sapling_data, tbl_plot_species_collection and tbl_plot_tree_data. 

 

Field Name Field Type Field Size Field Desc

quadrant_code Text 50 one letter code for quadrant – A,B, C, or D.

quadrant_desc Text 255 written description of quadrant

 

Table Name: ref_quadrat

This is a reference table that lists the names and codes for the 4 m2 quadrats. It is linked to the 

following tables by the field quadrat_code: tbl_plot_species_collection, tbl_quadrat_biotic_data

and tbl_ quadrat_physical_data. 

Field Name Field Type Field Size Field Desc

quadrat_desc Text 255 written description of quadrat

quadrat_code Text 50 alphanumeric code for quadrat

Table Name: ref_restrictive_feature 

This is a reference table that contains values describing restrictive features that prevent the 

technician from coring a complete sample of soil at a point. At the plot, this data is recorded on 

the Soils Data sheet. In the database, these data are recorded in the field Res_feature on the Soil 

Field Observations screen under the Soils tab on the main screen. This table is linked to the 

table tbl_soil_field_observation by the field restrictive_feature. 

Field Name Field Type Field Size Field Desc

restrictive_feature_desc Text 255 describes type of restrictive feature of soil depth 

restrictive_feature_code Text 50 same as description

Table Name: ref_slope_position

This is a reference table containing the values describing the location of the plot relative to its 

position on a slope, e.g. “upper 1/3 slope” or “toe slope”. This table is linked to the 

tbl_soil_field_observation table and the tbl_plot_physical_data table by the field 

slope_position_code. 

Field Name Field Type Field Size Field Desc

slope_position_code Text 50 same as description

slope_position_desc Text 255 describes relative position of plot on slope 

Table Name: ref_slope_shape 

This reference table contains values describing the water reception index (from -3 to +3) for the site 

relative to the surrounding topography. This table is linked to the tbl_plot_physical_data table by 

the field slope_shape_code. 

Field Name Field Type Field Size Field Desc

slope_shape_sort Integer 2 used to sort the entries 

slope_shape_code Text 50 unique number associated with degree of concavity or 

 convexity

slope_shape_desc Text 255 describes degree of convexity or concavity of slope

 

Table Name: ref_slope_type 
 

250 

This reference table contains the values “simple” and “convex”, which are used to describe the 

overall type of slope the plot is located on. 

Field Name Field Type Field Size Field Desc

slope_type_code Text 50 same as description

slope_type_desc Text 255 describes slope as "simple" or "convex"

 

Table Name: ref_soil_point 

This is a reference table that contains the 4 cardinal directions at which soils are sampled for the 

plot. These data are entered in the field Soil_Point on the Soils entry screen. This table is linked 

to the tbl_soil_field_observation table by the field soil_point_code. 

Field Name Field Type Field Size Field Desc

soil_point_code Text 50 one letter code standing for cardinal direction

soil_point_desc Text 255 describes cardinal direction of soil point

 

Table Name: ref_surface_cover 

This reference table contains values for cover of the surface of the soil at the sampling point, for 

example, “bare ground” or “living moss”. In the field, they are recorded on the Soils Data sheet. 

In the database they are entered in the field Surface_Cover_Kind on the Soils entry screen. 

Field Name Field Type Field Size Field Desc

surface_cover_code Text 50 one or two letter code for each type category

surface_cover_desc Text 255 describes what type of material is covering land surface

 

Table Name: ref_surface_moisture 

This table contains values for surface moisture of the surface of the soil at the sampling point. In 

the field, they are recorded on the Soils Data sheet. In the database they are entered in the field 

Surface_Moisture on the Soils entry screen. 

Field Name Field Type Field Size Field Desc

surface_moisture_code Text 50 describes relative moisture of land surface; one word

surface_moisture_desc Text 255 describes relative moisture of land surface

Table Name: ref_taxon

This reference table is a foundation of this database. This table contains a record for each plant 

taxon (both vascular and non-vascular) that has been observed in the sample plots for this 

program. This table contains a variety of attributes of each plant taxon including details about 

the growth form, life history, conservation status among other attributes. These attribute fields 

are used to combine species (form pools) or to summarize different attributes of the vegetation. 

This is a critical table for analysis of the monitoring data. This reference table is linked to all of 

the data tables in the database that contain species information (through the species_code field) 

including tbl_transect_cover_data, tbl_plotspecies_composition_data, 

tbl_tree_and_sapling_data etc. 

Field Name Field Type Field Size Field Desc

growth_form2 Text 50 a list modifier of growth form classes

vasc_aknhp_g_rank Text 50 AKNHP global rank

species_code Text 50 CAKN code for taxon of interest - 

inf_name Text 50 infraspeciofic name for taxon
 

251 

inf_rank Text 50 infraspecific_rank of taxon

growth_form1 Text 50 primary growth form of taxon

growth_form2_code Text 50 code for modifier of growth form classes

scientific_name Text 200 scientific_name is of ref_taxon

range_category1 Text 50 distribution category of taxon, CP=circumpolar, 

 AB=amphiberingian, etc...

vascular_code Text 50 this field identifies whether the taxon is a vascular or a 

 nonvascular plant

composite_species_code1 Text 50 code for the group in which this species_code is lumped

composite_species_code2 Text 50 additional code for the group in which this species_code 

 is lumped 

composite_constituent_spe Text 255 list of species codes that make up the composite species 

 code

composite_constituent_spe Text 255 additional list of species codes that make up the 

 composite species code

growth_form1_code Text 50 code for primary growth form of taxon

endem_code Text 50 code for type of endemic - Ak, Beringia , etc

usda_code Text 50 USDA Plants database acronym for taxon

diff_plants_DB Text 50 Is USDA accepted name different than Denali accepted 

 synonym - yes or no

transect_cover_element Text 50 transect cover element class for taxon - vascular plant, 

 lichen, bryophyte, 

infra_author Text 50 authors for infraspecific name (where applicable)

usda_name Text 200 Plants database synonym for taxon - full scientic name 

 with authorities

vasc_aknhp_s_rank Text 50 AKNHP state rank

endem Text 50 is the taxon an endemic one? 1= yes, 0 = no

endem_type Text 50 what endemic category is taxon; endemic_type is

family_name Text 50 family taxon belongs to

species_author Text 50 authors for species name

genus_name Text 50 genus_name of taxon

species_name Text 50 species_name of taxon

duration Text 50 is taxon perennial (=PE) annual (=AN) biennial (=BI) or 

 facultatively all of 

Table Name: ref_terrain 

This is a reference table that describes the terrain. It is linked to the tbl_plot_physical_data table 

by the terrain_code field. 

Field Name Field Type Field Size Field Desc

terrain_code Text 50 one word description of terrain, same as terrain_desc

terrain_desc Text 255 describes terrain (foothills, mountainous, etc)

 

Table Name: ref_transect 

This table contains the values for the three transects, 16, 6a and 6b. In the database these are 

entered in the transect field on the main data entry screen. Ref_transect is linked to 

tbl_sample_transect by the transect_code field. 

 

Field Name Field Type Field Size Field Desc

transect_code Text 50 unique code for each of the 3 transect types

transect_desc Text 255 describes which transect is being sampled

Table Name: ref_tree_condition 

This reference table contains values for tree condition, either “live” or “dead”. These data are 

recorded on the Tree and Sapling datasheet for both trees and saplings in the field. In the 

database, they are entered into the field Tree Condition on the Plot Tree Data screen under the 

Plot Tree, Sapling tab on the main menu. This table is linked to the tbl_plot_tree_data and the 
 

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tbl_plot_sapling_data tables by the tree_condition_code field. 

Field Name Field Type Field Size Field Desc

tree_condition_desc Text 255 identifies whether tree is alive or dead

tree_condition_code Text 50 code is simply either "alive" or "dead"

 

Table Name: ref_vertical_strata 

This reference table contains the point categories at which the vertical strata of vegetation are 

measured during the point transect sampling procedure. In the database, these data are entered 

in the Transect Cover Data screen in the field Vertical Strata. This table is linked to the 

tbl_cover_transect_data table by the vertical_strata_code. 

Field Name Field Type Field Size Field Desc

vertical_strata_code Text 50 identifies strata interval between lower and upper 

 boundaries. 

strata_lower_bound Long Integer 4 identifies the value of the lower boundary of each strata;

strata_upper_bound Long Integer 4 identifies the value of the upper boundary of each strata; 

 

Table Name: ref_viereck_class 

This reference table contains information concerning all of the Viereck vegetation classes that 

have been observed in the vegetation monitoring program. In this way, it is similar to the 

ref_taxon table that contains information on species attributes. In the field, the Viereck class 

data are recorded on the Point Data sheet. In the database, they are entered in the Viereck field 

on the Plot Biotic Viereck Classes screen, which is located through the Plot Biotic Data screen. 

This table is linked to the xref_plot_biotic_viereck table by the viereck_code field. 

Field Name Field Type Field Size Field Desc

level_ii_name Text 50 Viereck vegetation class level_ii_name 

viereck_iv_code Text 50 Viereck vegetation class level _iv_code 

viereck_desc Text 255 Name of viereck class

level_v_name Text 200 Viereck vegetation class level_v_name 

level_iv_name Text 100 Viereck vegetation class level_iv_name 

level_iii_name Text 50 Viereck vegetation class level_iii_name 

viereck_v_code Text 50 Viereck vegetation class viereck_v_code 

viereck_iii_code Text 50 Viereck vegetation class viereck_iii_code 

viereck_ii_code Text 50 Viereck vegetation class viereck_ii_code 

viereck_i_code Text 50 Viereck vegetation class _i_code 

viereck_code Text 50 Viereck vegetation class

level_i_name Text 50 level_i_name 

Table Name: ref_wind_exposure_class 

This reference table contains values for the relative exposure of the site to wind. In the field, 

these data are recorded on the Point Data sheet. In the database, they are recorded on the Plot 

Physical Data screen in the Wind expose field. This table is linked to the tbl_plot_physical_data

table by the field ID. 

Field Name Field Type Field Size Field Desc

Description Text 50 describes relative exposure to wind

ID Long Integer 4 unique number associated with each code

wind_exposure_code Text 50 description of wind exposure abbreviated to one word

 

Table Name: tbl_composite_soil_field 
 

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This data table stores data about the soil samples taken in the field. This table is linked to the 

table tbl_soil_field_observation through the field composite_soil_field_num, and through the 

sample event field to tbl_sample_event. These data are recorded in the field on the Soils Data 

sheet. 

Field Name Field Type Field Size Field Desc

subsample_count Long Integer 4 number of samples taken from plot and combined into 

 one 

 comments Memo 0 any additional observations recorded in the field or on 

 data entry

coarse_fragment_desc Memo 0 description of the course fragment of the soil

field_pH Double 8 pH of sample determined in the field using field pH 

 meter

less_than_2mm_samp_wt Double 8 wt. of soil fraction of size class < 2mm,

greater_than_2mm_samp_wt Double 8 wt. of soil fraction of size class > 2mm, 

 is_soil_point_south Boolean 1 was a soil sample taken at the south point? (yes or no)

is_soil_point_east Boolean 1 was a soil sample taken at the east point? (yes or no)

is_soil_point_north Boolean 1 was a soil sample taken at the north point? (yes or no)

composite_soil_field_num Text 50 Unique ID describing which samples have been 

 combined. is_soil_point_west Boolean 1 was a soil sample taken at the west point? (yes or no)

sample_event_num Text 50 plot identifier (park, year, elevation, GRTS point) 

Table Name: tbl_composite_soil_lab 

This data table stores information about soil samples collected for the vegetation monitoring 

program derived from laboratory analyses of these samples. This table is linked to the tables 

tbl_soil_field_observation, and tbl_composite_soil_field through the sample event field to 

tbl_sample_event. These data are not recorded onto specific data sheets, but are recorded onto a 

spreadsheet by lab personnel. Each line on this spreadsheet corresponds to an individual soil 

sample, taken in the field (and field attributes are recorded on the soils data sheet). 

Field Name Field Type Field Size Field Desc

comments Memo 0

sand_percent Double 8 percent of sand in sample

silt_percent Double 8 percent of silt in sample

clay_percent Double 8 percent of clay in sample

carbon_autoanalyzer Double 8 Percent carbon of sample – by ignition loss

nitrogen_autoanalyzer Double 8 Percent nitrogen of sample – by ignition loss

total_p Double 8 Total phosphorus of sample

available_p Double 8 Dissolved phosphorus not bound within the chemical 

 cation_exchange_capacity Double 8 capacity of soil sample for cation exchange

mass_loss_on_ignition_pe Double 8 percent loss of mass on ignition

sample_event_num Text 50 plot identifier (park, year, elevation, GRTS point) 

munsell_soil_color Text 50 description of soil color based on Munsell color chart

greater_than_2mm_samp_wt Double 8 weight (g) of particles of sample that are > 2mm diameter

less_than_2mm_samp_wt Double 8 weight (g) of particles in sample that are <2 mm diameter

less_than_2mm_samp_wet Double 8 field wet weight of fraction of sample <2 mm diameter

less_than_2mm_samp_dry Double 8 lab dried weight of fraction of sample >2 mm diameter

lab_pH Double 8 ph of sample, measured in the lab

Table Name: tbl_cover_transect_data 

This data table stores all of the observations acquired on the cover transects (recorded in the field 

on the cover transect data sheet). Each record in this table consists of a single “hit” observed 

along the vegetation cover transects. A record joins four attributes – 1) a specific sample event; 

2) a specific location along a specific transect; 3) a specific vertical stratum; and 4) a specific 

taxon (or other cover element such as moss, lichen, down wood, etc...). This table is linked to 
 

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the tbl_sample_event table through the field sample_event_num. 

Field Name Field Type Field Size Field Desc

cover_transect_id Long Integer 4 this field is a unique number for each record in the 

 database

distance_point Long Integer 4 the distance along the transect, written only as a number

comments Memo 0 any comments on data entry at this point, or notes from 

 the field

slope_angle Long Integer 4 slope_angle is slope of tape

strata_lower_bound Long Integer 4 the lower number of a given interval

vertical_strata_code Text 50 the interval along the vertical strata in centimeters,

distance_code Text 50 the distance along the transect in centimeters

species_code Text 50 6 letter code linked to "ref_taxon" for transect element 

 encountered

sample_transect_num Text 50 identifies plot, and which transect was sampled 

sample_event_num Text 50 plot identifier (park, year, elevation, GRTS point)

entryDateSort_num Date 8 field to sort records based on system date and time

Table Name: tbl_digital_photo 

This data table stores all of the data attached to each photo taken for the project. This table is 

linked to the table tbl_sample_event by the sample_event_num field and tbl_sample_transect by 

the field sample_transect_num. It is linked to the tbl_image_location table by the field 

digital_photo_location_id. It is linked to the following tables by the field digital_photo_id: 

tbl_plot_species_collection_data, tbl_quadrat_physical_data, and tbl_plot_physical_data. 

Field Name Field Type Field Size Field Desc

digital_photo_image_type Text 50 code describing type of photo taken

digital_photo_subpath Text 50 subpath to location where the file is stored

digital_photo_location_id Long Integer 4 randomly generated number

digital_photo_desc Memo 0 more elaborate description of photo subject, from photo 

 log

digital_photo_drive Text 50 computer drive that photo is located on

digital_photo_file Text 50 the photo's file name on the computer, written as 

 "number_img.jpg"

digital_photo_quality_cod Text 50 assessment of relative quality of image, linked to 

 ref_photo_quality_code

digital_photo_num Text 50 unique number generated by camera

digital_photo_subject_cod Text 50 written description of photo subject, linked to 

 ref_digital_photo_subject

is_digital_image_present Boolean 1 is digital photo present? (yes/no)

sample_transect_num Text 50 unique number identifying the transect the image is from 

sample_event_num Text 50 plot identifier (park, year, elevation, GRTS point)

digital_photo_id Long Integer 4 randomly generated unique number

digital_photo_path Text 50 computer pathway to locate photo

digital_photo_category_co Text 50 code describing general category of photo subject, linked 

Table Name: tbl_image_location 

This data table stores information about the location of stored digital images that are viewed 

within the image window on the Plot_photos tab located on the Main Data Entry screen. The 

path of each image is noted in this table, which is linked to the tbl_digital_photo by the field 

digital_photo_location_id. 

Field Name Field Type Field Size Field Desc

image_location_id Long Integer 4 randomly generated unique number

sample_event_num Text 50 plot identifier (park, year, elevation, GRTS point)

imgloc_drive Text 50 computer drive where images are located

imgloc_path Text 255 computer pathway to find where images are located
 

255 

imgloc_desc Text 255 description of subject of images

 

Table Name: tbl_image_software 

This table contains the values for the types of software used for this program, and the location of 

each program on the computer’s hard drive. 

Field Name Field Type Field Size Field Desc

image_software_id Long Integer 4

imgsoft_extension Text 50 type of extension at end of file name, indicating type of 

 image

imgsoft_name Text 50 type of software used to organize/manipulate images

imgsoft_locationfile Text 255 computer pathway to locate where images are filed

 

Table Name: tbl_increment_core_field 

This data table contains information on the spruce trees that are cored for this program, and on 

the cores that are taken from these trees. These data are recorded onto the Increment Core data 

sheet in the field, and entered into the Increment Core Field Observations screen in the 

database. This table is linked to the tables tbl_sample_event and tbl_increment_core_lab by the 

field sample_event_num. 

Field Name Field Type Field Size Field Desc

sample_event_num Text 50 plot identifier (park, year, elevation, GRTS point)

quadrant_code Text 255 1 letter code for which Quadrant is associated with core

core_modifier Text 1 modifier informing what number a core is when there is 

species_code Text 50 6 letter species code from ref_taxon 

tree_DBH Double 8 diameter of tree at breast height, measured in centimeters

tree_core_diameter Double 8 diameter of tree at core height, measured in centimeters

distance_from_plot_center Double 8 distance from plot center to cored tree, in meters

field_estimated_tree_height Double 8 height of tree if it is measured in the field

core_height Double 8 distance from the ground at which core was taken (cm) 

azimuth Double 8 specific azimuth of the cored tree on its slope

pathogen_code Text 255 shortened code for pathogens observed in cored tree

calculated_tree_height Double 8 tree height - result of formula calculating tree height 

 based on angles and distance

distance_for_height Double 8 distance from tree in meters at which angles were 

 observed

angle_base_degrees Double 8 angle to the bottom of the cored tree, measured in degrees 

 with a clinometer

angle_top_degrees Double 8 angle to the top of the cored tree, measured in degrees 

 with a clinometer

crown_class_code Text 50 code describing relative position to other trees, linked to 

 ref_crown_class

azimuth_true_from_plot_ce Double 8 corrected azimuth from plot center to tree, calculated 

 post-field

azimuth_magnetic_from_pl Double 8 magnetic azimuth from plot center to cored tree

slope Double 8 slope in immediate vicinity of cored tree, measured in 

 degrees

 

Table Name: tbl_increment_core_lab 

This data table contains information on the lab results for the cores from spruce trees that are 

taken for this program. These data are entered into the Increment Core Lab Observations

screen in the database. This table is linked to the tables tbl_sample_event and 

tbl_increment_core_field by the field sample_event_num. 

Field Name Field Type Field Size Field Desc

species_code Text 50 6- letter code for species
 

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core_total_growth Double 8 # of millimeters shown in core

decade_id Text 50 ID number for decade for decade growth (value shown in 

 previous field)

core_mean_annual_growth Double 8 millimeters of growth in the last decade before core

core_mean_annual_growth Double 8 total growth/number of years

annual_growth_penultimat Double 8 # millimeters growth in year two years previous to core 

 being taken

annual_growth_previous_ Double 8 # millimeters growth in year previous to core being taken

annual_growth_current_ye Double 8 # millimeters growth in year core taken

increment_core_lab_num Text 50 identifier number of the core assigned in the lab

last_growth_year Date 8 #millimeters growth in current year

core_modifier Text 1 gives core a unique identifier for when multiple cores are 

 from same tree.

quadrant_code Text 50 code of the quadrant the tree was cored in, A, B, C or D

sample_event_num Text 50 Concatenation of site number and sampling iteration

core_year_count Long Integer 4 number of annual growth rings

core_insects_tree_pith_cod Text 50 code for any insect infestations present in the core

 

Table Name: tbl_plot_biotic_data 

This table contains all the general data describing the biotic aspects of each plot. This table is linked 

to the tbl_sample_event table by the field sample_event_num, and to the xref_plot_biotic_viereck

table by the plot_biotic_id field. 

Field Name Field Type Field Size Field Desc

nearest_tree_species_code Text 50 6 letter species code from ref_taxon for tree nearest to 

 plot center

nearest_tree_distance Double 8 distance of nearest tree from plot center, in meters

animal_sign_code Text 50 code for sign of animals in plot

comments Memo 0 anything noteworthy about site recorded in the field, or 

 comments on data

nearest_shrub_species Text 50 6 letter species code from ref_taxon for shrub nearest to 

 plot center

gis_landcover_code Text 50 linked to ref_landcover_class, DENA landcover 

 description tallest_shrub_height Double 8 height of tallest shrub, in meters

Human_evidence Text 50 evidence of human activity in the vicinity of the plot

tallest_tree_height Double 8 height of tallest tree in plot, in meters

field_landcover_code Text 50 linked to ref_landcover_class, DENA landcover 

 description nearest_shrub_distance Long Integer 4 distance of nearest shrub from center, in meters

viereck_class_count Long Integer 4 total number of Viereck classes in plot

sample_event_num Text 50 plot identifier (park, year, elevation, GRTS point)

tallest_tree_species_code Text 50 6 letter species code from ref_taxon for tallest tree in plot

plot_biotic_id Long Integer 4 unique identifier, randomly generated number

tallest_shrub_species_code Text 50 6 letter species code from ref_taxon for tallest shrub in 

 plot

 

Table Name: tbl_plot_physical_data 

This table contains all the general data describing the physical aspects of each plot. This table is 

linked to the tbl_sample_event table and to the tbl_digital_photo table by the field 

sample_event_num. 

Field Name Field Type Field Size Field Desc

comments Memo 0 any additional noteworthy information recorded at site

vert_slope_shape_code Text 50 code for vertical shape of ground in plot

max_microrelief_in_plot Long Integer 4 maximum microrelief observed in plot, in decimeters

microrelief_code Long Integer 4 description of dominant microrelief in plot, linked to 

 ref_microrelief

altimeter_elevation Long Integer 4 elevation of plot read from altimeter

off_180 Integer 2 absolute value in degrees of true aspect minus 180, which 
 

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 yield degrees 

wind_exposure_class Long Integer 4 description of relative exposure to wind at plot, linked to 

digital_photo_id Long Integer 4 database assigned ID for digital photo

gps_elevation Long Integer 4 elevation of plot in meters calculated by gps unit in field

horz_slope_shape_code Text 50 horizontal shape of ground in plot

slope_position_code Text 50 choice of 8 codes from ref_ slope_position 

area_average_azimuth_mag Double 8 prevailing aspect of land surface within 100 meters of 

 plot

evidence_of_wind Text 50 written description of type of wind evidence observed, if 

 any

evidence_of_fire Text 50 written description of type of fire evidence observed, if 

 any

slope_length_for _plot Long Integer 4 slope length, from ridge to foot, on which plot is located .

slope_type Text 50 enter "simple" or "complex" from field data sheet

landform_descriptor Text 50 description of landform of the plot area

area_average_azimuth_corre Long Integer 4 prevailing aspect corrected to match current declination

azimuth_magnetic Double 8 magnetic aspect of the plot's slope

plot_physical_id Long Integer 4 randomly generated unique number

elevation Double 8 elevation of plot, derived from gps, in meters

sample_event_num Text 50 plot identifier (park, year, elevation, GRTS point)

azimuth_code Text 50 links to ref_azimuth, letter code describing plot aspect

azimuth_corrected Double 8 magnetic aspect for plot corrected to match current 

 declination

area_average_slope Double 8 average slope angle in within 100 meters of the plot, in 

 degrees

horizontal_microrelief Text 50 amount of horizontal microrelief in the plot

lithology Text 50 type of rock at plot, if known

geomorphic_disturbance_c Text 50 disturbance regimes assessed at site 

frost_action_code Text 50 code describing frost action observed at plot

drainage_code Text 50 describes relative impediment of water at plot

vertical_microrelief Text 50 vertical microrelief in plot

parent_material_code Text 50 source and composition of rock 

slope Double 8 the slope angle of the plot measured with a clinometer

 

Table Name: tbl_plot_sapling_data

This table contains the data for tree saplings, which are recorded in the field on the Tree and 

Sapling data sheet. In the database, these data are entered in the Plot Sapling Data screen under 

the Plot Tree, Sapling tab on the Main Data Entry screen. This table is linked to the 

tbl_sample_event table by the field sample_event_num. 

Field Name Field Type Field Size Field Desc

comments Memo 0 any additional noteworthy information recorded at plot

tree_condition_code Text 50 describes whether tree is alive or dead 

DBH Text 50 diameter of sapling at breast height, in centimeters

species_code Text 50 6 letter species code linked to ref_taxon

quadrant_code Text 50 1 letter code describing quadrant, linked to ref_quadrant

sample_event_num Text 50 plot identifier (park, year, elevation, GRTS point)

crown_class_code Text 50 numerical code describing relative dominance 

sample_point_sapling_id Long Integer 4 randomly generated unique number

Table Name: tbl_plot_species_collection_data 

This table contains data for each plant collection made for the project. In the field these data are 

entered in the field notebook belonging to the lead technician for either vascular or non-vascular 

plants. This table is linked to the tbl_sample_event table by the sample_event_num field, to the 

tbl_digital_photo table by the field digital_photo_id, to the tbl_sample_transect table by the 

sample_transect_num field. 

Field Name Field Type Field Size Field Desc

quadrant_code Text 50 linked to ref_quadrant_code 
 

258 

sample_transect_num Text 50 number identifying transect species was collected from

comments Memo 0 comments made in field notes or during data entry

digital_photo_id Long Integer 4 unique value assigned by the database

catalog_num Text 50 ANCS+ NPS catalog number

have_voucher Boolean 1 is voucher present? yes/no

determined_species_code Text 50 code from ref_taxon positively identifying voucher

field_id_species_code Text 50 6 letter code from ref_taxon describing tentative field ID

plant_collection_id Long Integer 4 randomly generated unique number

 quadrat_code Text 50 linked to ref_quadrat code, identifies which quadrat 

 species was collected 

 sample_event_num Text 50 plot identifier (park, year, elevation, GRTS point) collection_number Text 50 collector's initials and unique collection number

Table Name: tbl_plot_species_composition

This table contains records for all species documented in the plots. In the field, these data are 

recorded on the Point Transect and Quadrat Species Composition data sheets. In the database, 

these data are entered into the screens located under the Transect Cover Data and Quadrat 

Data Sheet tabs in the Main Data Entry screen. This table is linked to the tbl_sample_event

table by the sample_event_num field. 

 

Field Name Field Type Field Size Field Desc

sample_event_num Text 50 plot identifier (park, year, elevation, GRTS point)

comments Memo 0 any observation recorded in field, often regarding 

 phenology

species_nonvasc_percent_c Double 8 the percent cover of the species in question within the 4m 

 quadrat

quad_code Text 50 alpha numeric code for quadrat, linked to ref_quad

species_composition_id Long Integer 4 randomly generated unique number

species_code Text 50 six letter code for taxon observed, linked to ref_taxon

Table Name: tbl_plot_tree_data

This data table contains all field observations of trees made within the 200 m2 permanent plots 

that are of diameter greater than 12 cm at breast height (137 cm). These observations are 

recorded on the Tree and Sapling data sheet, and are entered into the Plot Tree Data screen 

under the Plot Tree, Sapling tab on the Main Data Entry screen. It is linked to the table 

tbl_sample_event by the sample_event_num field. 

Field Name Field Type Field Size Field Desc

tree_condition_code Text 50 description of whether tree is alive or dead

quadrant_code Text 50 one letter code linked to ref_quadrant describing which 

 quadrant trees are from

comments Memo 0 any additional observations written on field data sheet or 

 regarding data 

pathogen_code Text 50 describes any pathogens or wounds 

crown_length_code Text 50 code describing percent of potential crown which is living

crown_class_code Text 50 description of relative height/dominance of tree in plot

sample_event_num Text 50 plot identifier (park, year, elevation, GRTS point) azimuth_to_tree_magnetic Double 8 azimuth from plot center to tree, measured in field with 

 no declination

distance_to_tree Double 8 distance in meters from plot center to tree

species_code Text 50 6 letter code linked to ref_taxon describing tree species

azimuth_to_tree_corrected Double 8 azimuth from plot center to tree, corrected in office to fit 

 current declination

sample_point_tree_id Long Integer 4 sample_point_tree_id identifies tbl_plot_tree_data

dbh Text 50 diameter of tree at breast height, in centimeters

Table Name: tbl_quadrat_biotic_data 
 

259 

This table contains the percent cover data of the biotic categories for each quadrat in the plots. 

In the field these data are recorded on the Quadrat Data sheet, and in the database are entered 

into the Quadrat Biotic Parameters screen under the Quadrat Data tab. This table is linked to 

the table tbl_sample_event by the sample_event_num field. 

Field Name Field Type Field Size Field Desc

forb_percent Double 8 percent cover of all forbs observed in quadrat

dwarf_shrub_percent Double 8 percent cover of all dwarf shrubs observed in quadrat

shrub_percent Double 8 percent cover of shrubs observed in quadrat

tree_percent Double 8 percent cover of all trees observed in quadrat

sample_event_num Text 50 plot identifier (park, year, elevation, GRTS point)

quadrat_biotic_id Long Integer 4 randomly generated unique number

graminoid_percent Double 8 percent cover of all graminoids observed in quadrat

quadrat_code Text 50 alphanumeric code linked to ref_quadrat

lichen_percent Double 8 percent cover of all lichen observed in quadrat

viereck_class_quadrat Text 50 describes Viereck class specifically of quadrat

comments Memo 0 any observations written on field data sheet or regarding 

 data entry

moss_percent Double 8 percent cover of all moss observed in quadrat

Table Name: tbl_quadrat_physical_data

This table contains the percent cover data of the physical categories for each quadrat in the plots. 

In the field these data are recorded on the Quadrat Data sheet, and in the database are entered 

into the Quadrat Physical Parameters screen under the Quadrat Data tab. This table is linked 

to the table tbl_sample_event by the sample_event_num field and to the table tbl_digital_photo

by the field digital_photo_id. 

Field Name Field Type Field Size Field Desc

off_180_degrees_true_aspe Long Integer 4 absolute value of aspect difference from 180 degrees (off 

 of due south)

soil_depth1 Double 8 soil depth in centimeters from NE corner of quadrat

soil_depth2 Double 8 soil depth in centimeters from SW corner of quadrat

digital_photo_id Long Integer 4 the database assigned unique value for the digital photo

woody_debris_percent Double 8 percent cover of all woody debris observed in quadrat

standing dead Double 8 percent cover of all standing dead observed in quadrat

azimuth_corrected Double 8 magnetic aspect corrected in office to match current 

 declination

comments Memo 0 any additional observations recorded on field data sheet 

 or regarding data 

litter_percent Double 8 percent cover of all litter observed in quadrat

bare_percent Double 8 percent cover of all bare ground observed in quadrat

gravel_percent Double 8 percent cover of all gravel observed in quadrat

cross_section_code Text 50 one word code describing microtopography 

azimuth_magnetic Double 8 aspect of quadrat measured in field with no declination

slope Double 8 percent slope of quadrat measured with a clinometer

quadrat_code Text 50 alphanumeric code linked to ref_quadrat sample_event_num Text 50 plot identifier (park, year, elevation, GRTS point)

 quadrat_physical_id Long Integer 4 randomly generated unique number

rock_percent Double 8 percent cover of all rock observed in quadrat

 

Table Name: tbl_sample_event

This data table is a primary foundation table within this database. It links the place where sampling 

occurred with a specific sampling instance. Each time measurements are made according to the 

protocols of this program at one of the sampling locations, we call it a “sample event” and it will 

have a unique identifier that consists of the place and date of sampling, or the entry in the 

sample_event_num field. This table is linked to all of the data tables within the database through this 
 

260 

field. It is linked to the table tbl_sample_point through the sample_point_num field. 

Field Name Field Type Field Size Field Desc

phenology Text 255 desc. of overall phenology at plot at the time of sample 

 event

sample_event_num Text 50 plot identifier (park, year, elevation, GRTS point) sample_date Date 8 date of sample event

event_type Text 50 code describing type and iteration of sample 

current_mag_declination Double 8 mag declination unique to each sampling event

crew initials Text 50 initials of everyone present for sampling event

conditions Text 255 the weather at time of sample event

Point_sequence_within_day Long Integer 4 number describing chronological order of sample event 

 within the day

comments Memo 0 comments on data entry or describing plot

julian_date Long Integer 4 Julian date of sample event

time_start_measurements Date 8 time that sampling of plot began

time_stop_measurements Date 8 time that sampling of plot ceased

current_mag_dec_direction Text 50 E or W

 

Table Name: tbl_sample_point 

This table contains data pertaining to the physical location of each plot monument (plot SW 

corner). Most of these data are written on the Point Data sheet in the field and are imported into 

the database from the .dbf file downloaded from the Trimble GPS unit. The data for generated 

longitude and latitude of the center point are generated by Arcview. This table is linked to the 

tbl_sample_event table by the field sample_event_num, and to the tbl_sample_point_species

table by the sample_point_num field. 

Field Name Field Type Field Size Field Desc

max_HDOP Long Integer 4 maximum hdop recorded on field gps unit

actual_latitude Double 8 latitude of sample point measured in the field with a gps 

 unit

gps_date Date 8 date recorded from gps unit at point

point_type Text 50 was the point gps'd at the center point or elsewhere?

comments Memo 0 comments from the field or on data entry

usgs_quad Text 50 name of usgs quad map in which point is located

gps_error_term Text 50 for error distance on GPS point for non-Trimble units

max_PDOP Long Integer 4 maximum pdop recorded on field gps unit

actual_longitude Double 8 longitude of sample point measured in the field with a gps

corr_type Text 50 was gps data differentially corrected?

generated_longitude Double 8 arcview generated longitude of sample point

generated_latitude Double 8 arcview generated latitude of sample point

sample_event_num Text 50 plot identifier (park, year, elevation, GRTS point) 

gps_type Text 50 type of gps unit used, linked to ref_gps_type

measurement_status Text 50 was point sampled - if not, why not!

gps_time Date 8 time recorded from gps unit at point

gps_elevation_meters Long Integer 4 elevation in meters read from gps unit at point

gps_vertical_precision Long Integer 4 vertical precision of gps information

gps_horizontal_precision Long Integer 4 horizontal precision of gps information

gps_std_dev Long Integer 4 standard deviation of gps information

Table Name: tbl_sample_point_species 

This data table contains the complete species data set for each plot. This table is linked to the 

tbl_sample_point table by the field sample_point_num. In the database, these data are entered 

into the screens located under the Transect Cover Data and Quadrat Data Sheet tabs in the 

Main Data Entry screen. 
 

261 

Field Name Field Type Field Size Field Desc

sample_event_num Text 50 plot identifier (park, year, elevation, GRTS point)

species_code Text 50 six letter code linked to ref_taxon

Table Name: tbl_sample_transect 

This table contains data relating a particular transect to a particular sampling event. These data 

are entered into the Sample_Transect fields on the Main Data Entry screen in the database. 

This table is linked to the following tables by the field sample_transect_num: 

tbl_cover_transect_data, tbl_digital_photo, tbl_plot_species_collection, and 

xref_sample_personnel. 

Field Name Field Type Field Size Field Desc

comments Memo 0 comments from the field or on data entry

sample_transect_num Text 50 concatenation of sample event number and transect code

sample_event_num Text 50 plot identifier (park, year, elevation, GRTS point) transect_code Text 50 identifies which transect is being sampled

sample_point_count Long Integer 4 the number of sample points for each transect

vertical_strata_count Long Integer 4 the number of vertical strata

 

Table Name: tbl_soil_field_observation 

This data table contains soils observation data from the field. These data are entered on the Soils 

data sheet and in the database are entered into the Soil Field Observations screen. This table is 

linked to the tbl_sample_event table and to the tbl_composite_soil_field table through the field 

sample_event_num. 

Field Name Field Type Field Size Field Desc

is_sample_integrated Boolean 1 was sample integrated with other samples from point? 

 yes/no

litter_code Text 255 linked to ref_litter, identifies type(s) of litter at sample 

 point

restrictive_feature_code Text 255 identifies whether rock or permafrost is restrictive

horizontal_microrelief Double 8 numerical value in centimeters of maximum horizontal 

 microrelief at point

vertical_microrelief Double 8 numerical value in centimeters of maximum vertical 

 microrelief at point

microrelief_code Long Integer 4 linked to ref_microrelief, describes type of microrelief at 

 sample point

slope Double 8 slope in degrees at soil sample point

azimuth_magnetic Double 8 magnetic aspect of slope at soil sample point

organic_mat_depth Double 8 depth in centimeters of organic mat

organic_mat_code Text 255 identifies composition of organic mat

living_mat_depth Double 8 depth in centimeters of living mat

current_weather Text 255 weather at time of sample event

litter_depth Double 8 depth in centimeters of litter

anomalies Text 255 notes on any anomalies observed at point

soil_temp Double 8 soil temperature in Celsius at sample point

air_temp Double 8 air temperature in Celsius at sample point

sample_time Date 8 time that sample was taken

composite_soil_field_num Text 50 concatenation of sample event num and all soil point 

 codes

why_no_sample Text 255 if no sample was taken, explain why.

is_sample_taken Boolean 1 was sample taken? yes/no

soil_point_code Text 255 one letter code identifying cardinal direction of soil pt

sample_event_num Text 50 plot identifier (park, year, elevation, GRTS point)

soil_field_obs_num Text 50 concatenation of sample event number and soil point code

living_mat_code Text 255 identifies growth form(s) of living mat at point

comments Memo 0 comments from the field or on data entry

surface_moisture_class Text 50 describes surface moisture at point
 

262 

surface_cover Text 50 describes type of material on surface of plot

slope_position_code Text 255 describes relative position of sample point 

restrictive_feature_depth Double 8 depth in centimeters of restrictive feature

Table Name: xref_plot_biotic_viereck 

This cross- reference table relates the plot Viereck classification to the tbl_plot_biotic_data table 

by the field plot_biotic_id. This table functions as a cross-reference table because there can be 

multiple Viereck classes within a single plot. 

Field Name Field Type Field Size Field Desc

viereck_polygon Text 50 viereck_polygon is of xref_plot_biotic_viereck

plot_biotic_viereck_id Long Integer 4 plot_biotic_viereck_id identifies xref_plot_biotic_viereck

plot_biotic_id Long Integer 4 plot_biotic_id identifies xref_plot_biotic_viereck

viereck_code Text 50 viereck_code partly identifies xref_plot_biotic_viereck

viereck_inplot_code Text 50 partly identifies xref_plot_biotic_viereck

viereck_percent_of_plot Double 8 viereck_percent_of_plot is of xref_plot_biotic_viereck

viereck_extent Text 50 viereck_extent is of xref_plot_biotic_viereck

Table Name: xref_quadrat_biotic_species 

This cross-reference table contains data quantifying the count and species of seedlings in each 

quadrat. It functions as a cross-reference table because there can be more than one species of 

seedlings in a quadrat. This table links to the tbl_quadrat_biotic_data table by the field 

quadrat_biotic_id. 

Field Name Field Type Field Size Field Desc

seedling_condition_class Text 50 seedlings counted are live or dead?

seedling_species_count Long Integer 4 of xref_quadrat_biotic_species

seedling_species_code Text 50 partly identifies xref_quadrat_biotic_species

quadrat_biotic_specied_id Long Integer 4 identifies xref_quadrat_biotic_species

quadrat_biotic_id Long Integer 4 quadrat_biotic_id identifies xref_quadrat_biotic_species

 

Table Name: xref_sample_personnel 

This cross reference table contains data about the personnel working on the project. It allows the 

database to recognize that a technician can perform multiple duties, for instance, a technician can 

be both a reader and a recorder for data on the point transects. This table is linked to the 

tbl_sample_event table and the tbl_sample_transect table by the code sample_event_num. 

Field Name Field Type Field Size Field Desc

data_id Long Integer 4 data_id is of xref_sample_personnel

sample_personnel_id Long Integer 4 sample_transect_id identifies xref_sample_personnel

sample_event_num Text 50 sample_event_num identifies xref_sample_personnel

personnel_code Text 50 personnel_code is of xref_sample_personnel

sample_transect_num Text 50 sample_transect_num is of xref_sample_personnel

observer_type Text 50 observer_type is of xref_sample_personnel

data_table_code Text 50 data_table is of xref_sample_personnel

III. Literature cited 

Roland, C., Oakley, K., Debevec, E.M., and P. Loomis. 2004. Monitoring vegetation structure 

and composition at multiple spatial scales in the Central Alaska Network (Version 1.0). 

National Park Service, Inventory & Monitoring Program, Alaska Region, Anchorage, 

Alaska, 50 pp. 
 
 

The Department of the Interior protects and manages the nation’s natural resources and cultural heritage; provides scientific and 

other information about those resources; and honors its special responsibilities to American Indians, Alaska Natives, and affiliated 

Island Communities. 

NPS 95/100370, June 2010 
 

National Park Service 

U.S. Department of the Interior 

Natural Resource Program Center

1201 Oakridge Drive, Suite 150 

Fort Collins, CO 80525 

www.nature.nps.gov 

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