Development of Monitoring Protocols to Detect Change in 

Rocky Intertidal Communities of Glacier Bay National 

Park and Preserve

Open-File Report 2010–1283

Prepared in cooperation with the National Park Service

U.S. Department of the Interior

U.S. Geological Survey

Development of Monitoring Protocols to Detect 

Change in Rocky Intertidal Communities of Glacier 

Bay National Park and Preserve 

By Gail V. Irvine

Prepared in cooperation with the National Park Service

Open-File Report 2010-1283

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

U.S. Geological Survey, Reston, Virginia: 2010

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Suggested citation:

Irvine, G.V., 2010, Development of monitoring protocols to detect change in rocky intertidal communities of 

Glacier Bay National Park and Preserve: U.S. Geological Survey Open-File Report 2010-1283, 140p.

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iii

Contents

Abstract ......................................................................................................................................................... 1

Introduction.................................................................................................................................................... 2

Methods......................................................................................................................................................... 3

Field Surveys and Sampling ...................................................................................................................... 4

Aerial Surveys and Description of Habitat Types ................................................................................... 4

Coarse-Grained (CG) Sampling ............................................................................................................. 5

Fine-Grained (FG) Sampling .................................................................................................................. 6

Analytical Methods..................................................................................................................................... 7

Estimating Abundance ........................................................................................................................... 7

Trend Analyses ...................................................................................................................................... 8

Power Analyses...................................................................................................................................... 8

Results......................................................................................................................................................... 11

Abundance of Taxa.................................................................................................................................. 11

Trend Analyses ........................................................................................................................................ 11

Power Analyses ....................................................................................................................................... 11

Fine-Grained Sampling ........................................................................................................................ 11

Coarse-Grained Sampling, Alpha = 0.05.............................................................................................. 12

Coarse-Grained Sampling, Varying Alpha............................................................................................ 13

Comparison of Coarse-Grained and Fine-Grained Sampling ............................................................... 13

TerraStat Power Reanalyses................................................................................................................ 14

Discussion ................................................................................................................................................... 14

Study Design............................................................................................................................................ 15

Comparison with Other Intertidal Monitoring Designs .............................................................................. 17

Power, Trend Detection, and Impact Analysis ......................................................................................... 18

Variation in the Ability to Detect Change among Taxa............................................................................. 19

Design Issues for Glacier Bay Intertidal Monitoring ................................................................................. 20

Why Monitor the Intertidal? ...................................................................................................................... 23

Conclusions ................................................................................................................................................. 25

Acknowledgments ....................................................................................................................................... 26

References Cited......................................................................................................................................... 27

Appendix A. Methods for the 1997 Sampling, including Aerial Survey Standard Operating Procedures ..... 61

Appendix B. TerraStat Report...................................................................................................................... 75

Appendix C. Additional Power Analyses...................................................................................................... 91

Appendix D. Standard Operating Procedures for Coarse-Grained Sampling ............................................ 119

Appendix E. Glacier Bay Intertidal Monitoring Access Database............................................................... 131
iv

Figures

Figure 1. Location of Glacier Bay National Park and Preserve in southeast Alaska ................................... 30

Figure 2. Locations and segment numbers of the coarse-grained and fine-grained sites in Glacier Bay 

National Park and Preserve......................................................................................................................... 31

Figure 3. Layout of the various sampling methods used during the fine-grained surveys........................... 32

Figure 4. Quadrat used for point-contact sampling at the fine-grained sampling level................................ 33

Figure 5. Comparison of the power of three point-intercept sampling methods conducted as part 

of the fine-grained sampling protocol in 1997 .............................................................................................. 34

Figure 6. Power to detect a 10 percent decrease in the predominant sessile species as related to the 

number of transects sampled ...................................................................................................................... 35

Figure 7. Power to detect population trends based on 4 years of data (1997–2001) in A. barnacles; B. 

Mytilus; and C. Fucus .................................................................................................................................. 36

Figure 8. Power to detect population trends based on 3 years of data (1998–2001) in A. barnacles; B. 

Mytilus; and C. Fucus .................................................................................................................................. 37

Figure 9. Power to detect decreases in Mytilus populations as related to the number of points 

sampled per meter of each transect ............................................................................................................ 38

Figure 10. Power to detect decreases in Fucus populations as related to the number of points 

sampled per meter of each transect ............................................................................................................ 39

Figure 11. Power to detect decreases in barnacle populations as related to the number of points 

sampled per meter of each transect ............................................................................................................ 40

Figure 12. Power to detect a decrease in Mytilus populations as a function of the number of

transects sampled at each site .................................................................................................................... 41

Figure 13. Power to detect a decrease in Mytilus populations in relation to the number of sites ................ 42

Figure 14. Change in the power to detect a decrease in Mytilus populations as a result of 

varying the number of points sampled per meter with respect to 25, 20, and 15 sites................................. 43

Figure 15. Power to detect trends of differing magnitudes at three levels of alpha (α = 0.05, 0.10, 0.20) .. 44

Figure 16. Comparison in the power of fine-grained and coarse-grained vertical transect 

sampling to detect a 10 percent decrease in Mytilus, Fucus, and barnacles ............................................... 45

Figure 17. Comparison of the power of coarse-grained and fine-grained sampling methods 

to detect trends in the predominant sessile species .................................................................................... 46

Figure 18. Statistical power comparison for Mytilus with 25 sites............................................................ 47

Figure 19. Statistical power comparison for Mytilus with 15 sites. .............................................................. 48

Figure 20. Statistical power comparison for Fucus ..................................................................................... 49

Figure 21. Statistical power comparison for barnacles................................................................................ 50

Figure 22. Mean percent cover of the predominant sessile species at Berg Bay........................................ 51
v

Tables

Table 1. Parameters used in the power analyses..................................................................................... 52

Table 2. The number of hits (counts) of a species or taxon during each year of sampling 

along the vertical transects. ......................................................................................................................... 53

Table 3. Trend results by site for percent cover of Mytilus. ...................................................................... 56

Table 4. Trend results by site for percent cover of Fucus......................................................................... 57

Table 5. Trend results by site for percent cover of barnacles................................................................... 58

Table 6. Conversion of short-term trends, expressed as percent change per year, into long-term trends.59

Conversion Factors

Inch/Pound to SI

Multiply By To obtain

Length

foot (ft) 0.3048 meter (m)

Area

acre 4,047 square meter (m2)

acre 0.4047 hectare (ha)

acre 0.4047 square hectometer (hm2) 

acre 0.004047 square kilometer (km2)

square foot (ft2) 929.0 square centimeter (cm2)

square foot (ft2) 0.09290 square meter (m2)

SI to Inch/Pound

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

millimeter (mm) 0.03937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

kilometer (km) 0.5400 mile, nautical (nmi) 

meter (m) 1.094 yard (yd) 

Area

hectare (ha) 2.471 acre

square meter (m2) 0.0002471 acre 

square meter (m2) 10.76 square foot (ft2) 
vi

List of Acronyms

3D Three dimensional

BACI Before-After-Control-Impact (design)

BACIP Before-After-Control-Impact-Paired (design)

CG Coarse-grained sampling

FG Fine-grained sampling

GRTS Generalized random-tesselation stratified (survey design)

MARINe Multi-Agency Rocky Intertidal Network

MHHW Mean higher high water

MLLW Mean lower low water

NHP National Historical Park

NP National Park

NP&P National Park and Preserve

NPS National Park Service

PISCO Partnership for Interdisciplinary Study of Coastal Oceans

USGS U.S. Geological Survey

WEST, Inc. Western EcoSystems Technology, Inc.

List of Symbols

α – alpha Type I error level (probability of rejecting the null hypothesis when it is true), acceptable

 error level

 β – beta 1- β = power of a statistical test (the probability of rejecting the null hypothesis when it is

 false and should be rejected)

° degrees

< less than

≤ less than or equal to

> greater than 

≥ greater than or equal to 
1

Development of Monitoring Protocols to Detect 

Change in Rocky Intertidal Communities of Glacier 

Bay National Park and Preserve 

By Gail V. Irvine

Abstract

Glacier Bay National Park and Preserve in southeastern Alaska includes extensive 

coastlines representing a major proportion of all coastlines held by the National Park Service. 

The marine plants and invertebrates that occupy intertidal shores form highly productive 

communities that are ecologically important to a number of vertebrate and invertebrate 

consumers and that are vulnerable to human disturbances. To better understand these 

communities and their sensitivity, it is important to obtain information on species abundances 

over space and time. During field studies from 1997 to 2001, I investigated probability-based 

rocky intertidal monitoring designs that allow inference of results to similar habitat within the 

bay and that reduce bias. Aerial surveys of a subset of intertidal habitat indicated that the original 

target habitat of bedrock-dominated sites with slope less than or equal to 30 degrees was rare.

This finding illustrated the value of probability-based surveys and led to a shift in the target 

habitat type to more mixed rocky habitat with steeper slopes. Subsequently, I investigated 

different sampling methods and strategies for their relative power to detect changes in the 

abundances of the predominant sessile intertidal taxa: barnacles -Balanomorpha, the mussel

Mytilus trossulus and the rockweed Fucus distichus subsp. evanescens. I found that lowerintensity sampling of 25 randomly selected sites (= coarse-grained sampling) provided a greater 

ability to detect changes in the abundances of these taxa than did more intensive sampling of 6 

sites (= fine-grained sampling). Because of its greater power, the coarse-grained sampling 

scheme was adopted in subsequent years. This report provides detailed analyses of the 4 years of 

data and evaluates the relative effect of different sampling attributes and management-set 

parameters on the ability of the sampling to detect changes in the abundances of these taxa. The 

intent was to provide managers with information to guide design choices for intertidal 

monitoring. I found that the coarse-grained surveys, as conducted from 1998 to 2001, had power 

ranging from 0.68 to 1.0 to detect 10 percent annual changes in the abundances of these 

predominant sessile species. The information gained through intertidal monitoring would be 

useful in assessing changes due to climate (including ocean acidification), invasive species,

trampling effects, and oil spills.
2

Introduction

For Glacier Bay and many other Alaska national parks, the National Park Service’s 

mission “to protect unimpaired” the lands and resources within its jurisdiction is complicated by 

two main factors. First is the lack of knowledge of the status of park resources, their dynamics 

through time, and the relationships among species and processes. This lack of knowledge makes 

it difficult to separate natural variation from anthropogenic effects, thus hampering the ability of 

managers to know when and if they can intervene to effect change. The second complicating 

factor is large spatial scale. The marine coastline of Glacier Bay National Park and Preserve 

(NP&P; fig. 1) spans about 1,720 km (Irvine and others, 1994) and for Glacier Bay proper—the 

focus of this study—1,109 km (Irvine, 1998). The Glacier Bay coastline comprises about 40

percent of the estimated 4,210 km coastline held by all national parks in Alaska. Because the 

Alaskan national parks contain almost 70 percent of the marine coastline of all US national parks 

(Irvine and others, 1994), Glacier Bay has a disproportionately large share of the marine 

coastlines managed by the NPS in both Alaska and the USA.

The 1989 Exxon Valdez oil spill in south-central Alaska contaminated more than 2,000 

km of coastal habitat and associated biota, including areas of Kenai Fjords and Katmai NP&Ps.

This spill illustrated the vulnerability of these coastal environments to anthropogenic effects. 

Following that event, the Alaska region of the NPS created a Coastal Programs Division for the 

purpose of developing inventory and monitoring programs for the extensive national park 

coastlines in Alaska. In 1996, I received funding to develop monitoring protocols and studies that

address landscape-scale variation in the coastal biological communities of national parks. Based 

on jurisdictional, budget and logistical considerations, I was asked to concentrate effort at 

Glacier Bay NP&P.

Monitoring involves determination of the abundance or status of species through time,

accomplished by repeated measurements or counts. The ability to detect change or trends 

depends on the design of the monitoring, sample size, level of certainty selected, and the natural 

variability in spatial and temporal patterns of species abundances. As the variability in 

abundance or any other parameter increases, the ability to detect change in that parameter 

decreases, given equal sampling effort. Therefore, it is more difficult to detect trends in species 

that are rare or whose abundance is highly variable. Sample variation may be reduced and the 

power to detect change increased by stratifying the populations and communities along known 

gradients of variation, or by increasing sampling effort. Increased sampling costs more, while 

stratified sampling reduces the ability to make inferences beyond the strata sampled, unless all 

populations are accounted for in the stratification process. 

One of the challenges faced by resource managers in designing and implementing a 

monitoring program is how to balance the need to sample across broad physical scales for greater 

spatial inference versus the need to sample intensively enough to detect temporal changes in 

biological communities. Intensive sampling is often conducted over smaller scales, and inference 

may be limited to selected habitat types, sites, plots, and species.
3

The major objective of this study was to develop a probability-based approach to 

monitoring intertidal assemblages inhabiting protected rocky substrates, so that the results of the 

sampling could be extended to similar habitat within Glacier Bay proper. Initially, aerial surveys 

were conducted to classify coastal segments and determine the locations of these rocky 

substrates. Based on findings from the aerial surveys, the target habitat type was defined as:

coastal areas comprised of bedrock (> 1 percent) and/or ≥ 76 percent cobble/boulder substrate, 

with slopes ≤ 60°. Extensive sampling was conducted at 25 randomly selected sites under the 

coarse-grained sampling regime. More intensive sampling of 6 sites from the lower half of the 

bay (a subset of the 25 sites), constituted the fine-grained sampling. These fine-grained sites 

were all cobble/boulder-dominated sites.

Sampling primarily targeted all macro sessile species and large mobile invertebrates. 

Analyses to test the power of the sampling to detect changes in abundance of species

concentrated on the three most abundant sessile taxa in the sampled habitat type: barnacles, the 

mussel Mytilus trossulus, and the rockweed Fucus distichus subsp. evanescens. Continued 

sampling of all sessile species will allow detection of changes in the distribution and abundance 

of sessile species spatially and temporally within Glacier Bay proper. 

The results of the sampling conducted from 1997 to 2001 have inference to the selected 

habitat type within Glacier Bay proper for the coarse-grained sampling, and to a narrower band 

of cobble/boulder habitat within Glacier Bay for the fine-grained sampling.

In this final report, I summarize the study, review key findings presented in earlier 

reports, and describe final results leading to a recommended protocol for detecting change in the 

abundance of key sessile species of rocky intertidal communities in Glacier Bay.

Methods

Planning and fieldwork for this study were conducted from 1996 to 2001. In 1996, I held 

meetings with Glacier Bay staff to discuss goals for the project, potential stressors affecting the 

coastal areas, and approaches to the study. We decided to focus the study in Glacier Bay proper

because the park staff rated protected rocky intertidal habitat as their top choice for monitoring, 

and they were most concerned about potential effects from oil spills and the immigration of sea 

otters (Enhydra lutris), both of which they thought were more likely to affect communities 

within the bay. Fieldwork was carried out in 1997, 1998, 1999, and 2001.

This study used a probability-based, multilevel design that included: 

a. Aerial surveys of a systematically selected subset of coastal segments to characterize 

their habitat attributes, assess the frequency of different habitat types, and provide a pool 

of sites of known habitat type for further sampling, 

b. Coarse-grained (CG) or lower intensity sampling of a large number of randomly selected 

sites of a selected habitat type, and 

c. Fine-grained (FG) or more intensive sampling of a few sites of cobble/boulder habitat, 

selected as a subset of the CG sampling sites; the FG sampling also tested the efficacy of 

additional sampling methods. 
4

I examined the ability of the CG and FG sampling plans to detect trends in the 

predominant sessile species through power analyses. Power can be defined as the ability to detect 

a change when one is occurring. Statistically, power is the probability of rejecting the null 

hypothesis when it is false and should be rejected (Zar, 1984). As part of the power analyses, 

sampling attributes, such as the number of sites, transects, and intensity of point sampling, were 

varied to examine their effect on the power of particular sampling plans to detect change. 

Additionally, I examined the interplay between sampling attributes and management-set 

parameters (that is; the level of annual change to be detected and error levels) on power (table 1). 

The methods used in the three levels of surveys and sampling listed above are described 

in detail in Irvine (1998) and in appendix A. They are reviewed below, followed by description 

of the analytical methods.

Field Surveys and Sampling

Aerial Surveys and Description of Habitat Types

The goal of the aerial surveys was to characterize intertidal habitat types and their 

frequencies in Glacier Bay. The categorical abundances of substrate and spatially dominant 

sessile biota, as well as slope, were described for each segment surveyed.

The first step in defining a set of sites to survey was the division of a digitized coastline 

of Glacier Bay NP&P (Geiselman and others, 1997) into 200-m length segments. The 1,109 km 

coast of Glacier Bay proper yielded 5,545 segments. I estimated that 250 segments could be 

aerially surveyed during one tide series. Beginning with a random start in Glacier Bay proper, 

each 23rd segment was selected to be surveyed. Then, from a fixed-wing plane, 241 of the 250 

segments were classified categorically for habitat type, slope, and biota (Appendix A: Aerial 

Survey Standard Operating Procedure). Nine of the segments were found to be inappropriate and 

were not surveyed; details on the exclusions are in appendix A. Summary data from the aerial 

surveys were reported in Irvine (1998). These described segments then formed a pool of sites 

with known habitat types that were available for subsequent stages of the intertidal sampling.

Prior to the aerial surveys, the target habitat type had been defined as predominantly (≥

76 percent cover) bedrock substrate with slopes ≤ 30o. The results of the aerial surveys led to a 

change in the targeted habitat type for monitoring, because only one of the 241 segments 

surveyed had these characteristics. Even increasing the acceptable slope to ≤ 60o added only two

more segments to the pool. Thus, in consultation with NPS and Center staff, I decided to define

the selected habitat type as segments dominated by cobbles and boulders (≥ 76 percent cover)

and/or having a bedrock component (≥ 1 percent); slope was defined as ≤ 60o. This created a 

pool of 111 segments, which formed the sampling frame of the target habitat type.
5

Coarse-Grained (CG) Sampling

From the pool of 111 segments with the target habitat type, 30 sites were randomly 

selected for CG sampling (fig. 2). Five sites were eliminated after the initial draw because they 

were either too steep, were not true beaches (for example, no land exposed at high tide), or were 

not accessible because the site was in a wildlife protection area (appendix A).

CG sampling was conducted in 1997, 1998, 1999, and 2001. Each 200-m-long site was 

located in the field using global positioning system coordinates determined from the original 

segmentation of the digitized coastline. The area sampled was the area between mean higher

high water (MHHW), defined by biological characteristics, and the 0-m tide level (mean lower

low water, MLLW), set with information from the tide program Tides & Currents for Windows, 

version 2.5a (Nautical Software, Inc., 1997). A horizontal segment line delimiting the upper 

bound of the site was laid along the MHHW contour. Six vertical-line transects running parallel 

to the elevational gradient and perpendicular to the shoreline were laid from MHHW to the 0-m 

tide level. The start of the first vertical transect was determined randomly within the first 33 m of 

the horizontal segment line; the succeeding transect lines were laid out systematically, at 33-m 

intervals with respect to the first. If a vertical transect fell on a section of the segment that was 

unsampleable due to steep slope or freshwater input, then that transect and the remaining 

transects were shifted the horizontal distance of the unsampleable area to the right, facing shore. 

The locations of the transects were randomized again each year.

Two types of sampling, each targeting different types of species, were conducted along 

the vertical transects. Sessile species were targeted by 3-dimensional (3D), point-intercept 

sampling along the vertical transects. All species (sessile and mobile), including multiple layers 

of the same species under the point were recorded, from top to bottom, until the first substrate 

was found. The substrate also was recorded. Large mobile invertebrate species (starfish, sea 

urchins, and large chitons) were targeted in band surveys that extended 1 m to each side of the 

vertical transect line. Species identifications were made to the lowest taxonomic level possible in 

the field during all surveys (appendix A).

In 1997, the CG sampling used a base sampling intensity of 1 point/m along each vertical 

transect. Although my original plan was to sample at 5 points/m, I reset the sampling intensity to 

1 point/m based on the time it took to do the 3D sampling in an initial field test and the objective 

of sampling one site per day. As field crews became more experienced and most sites were found 

to have shorter transects than those in the field test, I increased the sampling intensity to 2 

points/m (x.0-m and x.6-m marks). The x.6-m mark was selected as I anticipated that pointintercept sampling at the FG sites would be conducted at 5 points/m, each 20 cm, and that future 

sampling of CG sites likely would be conducted at 20-cm intervals. At a few sites, sampling was 

conducted at > 2 points/m. Thus, in 1997, 17 of the 25 sites were sampled at ≥ 2 points/m and 8 

sites (sites 62, 63, 69, 216, 217, 218, 223, and 224) were sampled at 1 point/m. Beginning in 

1998, I increased the sampling intensity to 5 points/m at all 25 sites to increase the probability of 

detecting less-common species and to increase the precision of the estimates. 
6

Fine-Grained (FG) Sampling

Fine-grained sampling was conducted in 1997 at 6 of the 25 CG sites (fig. 2; appendix 

A). I narrowed the focus of the FG site selection in two ways. First, I narrowed the geographical 

focus to allow easier access to the sites by skiff from park headquarters at Bartlett Cove (located 

slightly to the east of site 59; fig. 2), because the more intensive FG sampling necessitated 

multiple days of effort at each site. Second, I decided, in consultation with Center and Park staff,

to concentrate efforts on predominantly cobble/boulder sites; only those sites with ≥ 76 percent

cobble/boulder substrate were considered for inclusion.

Six sites were selected from the CG suite of sites that fit these habitat and geographic 

parameters. Below the juncture of the two arms in Glacier Bay there were eight CG sites that had

been defined as predominately cobble/boulder habitat during the aerial surveys. One of those, 

site 88, the most distant from Bartlett Cove in this band, was discarded because the CG surveys 

had revealed that little cobble/boulder was present. The most southerly geographic site also was 

discarded. This resulted in the selection of six sites that ranged from just south of the two arms to 

slightly south of Bartlett Cove (fig. 2). 

In 1997, these sites were sampled using both the FG and CG methodologies. The FG data 

from these sites have inference to the range of cobble/boulder habitat within the band or region 

that the sites occupy, as these sites also were part of the CG set of sites, which had been 

randomly selected from the pool of the selected habitat type. Thus, the FG sites are an 

approximately random sample of predominantly cobble/boulder habitat within the exact band 

they occupy.

The FG sampling consisted of multiple types of sampling (fig. 3), which are detailed in 

appendix A. I briefly describe four of the sampling types here: (1) vertical transect by point

intercept; (2) horizontal transect by point intercept, (3) quadrat, by point intercept and counts; 

and (4) band survey by counts. All point-intercept methods targeted sessile species, but counted 

all species, sessile and mobile, under points. The first sampling type was point-intercept 

sampling along 10 vertical transects sampled at 5 points/m. The 3D point-intercept sampling 

methods were the same as those used in the CG sampling. The vertical transects were laid out in 

a similar manner as those in the CG sampling, except that the sampling of 10 transects led to a 

smaller (20-m) systematic distance between transects. 

In the second sampling type, 30 horizontal transects were sampled per site. Each 

measured 10 m in length and they were arrayed three per vertical transect, with the location and 

elevation determined by random-systematic methods. The length of a vertical transect was 

divided into thirds and a random number identified the location within one of the zones where 

the horizontal transect origin would be laid. The other two horizontal transects were set at 

systematic intervals from the first within the two other vertical zones of the beach. Horizontal 

transects were sampled by point-intercept sampling (targeting sessile species) that used the same 

intensity (5 points/m) and sampling methodology as employed for vertical transects. 
7

In the third sampling method, a quadrat was sampled at each juncture of the vertical and 

horizontal transects, with 3 arrayed per vertical transect. This led to a total of 30 quadrats 

sampled per site. Quadrats were 1/9 m2 in area and contained a grid of 36 intersections at which 

points were sampled (fig. 4). Quadrat point-intercept sampling targeting sessile species used the 

same 3D methodology as used for vertical and horizontal transects. Additional sampling within 

quadrats included counts of small mobile invertebrates and subsampling counts of littorine snails 

and barnacle spat/recruits (fig. 4). 

The fourth FG sampling method, band surveys, targeted large mobile invertebrates by 

sampling 1-m-wide bands on each side of the vertical and horizontal transect lines (fig. 3). 

Methods used were the same as described earlier in the CG sampling. 

Analytical Methods

The analytical methods used to estimate the abundance of species/taxa, analyze trends for 

the predominant taxa, and estimate the power of different sampling schemes to detect changes in 

abundance for the these major taxa are described below.

Estimating Abundance 

A general bay-wide measure of the abundance of species can be given by the total 

number of hits (counts) of a species across all sites sampled by the CG sampling. Within-year 

and 3-year (1998–2001) comparisons of the abundance of different species can be made because

the total number of points sampled in any one year and for all years (the total) would be the same 

for all species. 

Percent cover was the metric of abundance for sessile species at the transect and site

levels. Percent cover can be calculated for all species sampled by point-intercept sampling.

However, for the power analyses, which comprise the majority of the analyses presented, 

specific percent cover measures for each transect at each site were calculated for the predominant 

sessile species or species groups (also referred to as taxa):

1. The mussel, Mytilus trossulus,

2. The brown alga or rockweed, Fucus distichus subsp. evanescens (note:

formerly known as Fucus gardneri), and

3. All barnacles (Balanus glandula, Semibalanus balanoides, Semibalanus 

cariosus, Chthamalus dalli, Balanomorpha, and barnacle spat/recruits). Note:

all species were recorded to the lowest taxonomic level possible in the field, 

but for the purposes of analysis, all barnacle taxa and categories (for example, 

spat/recruits) were combined.

For each site, the percent cover was calculated by dividing the number of hits of the 

selected species on each transect, by the number of points counted along the transect. Because 

the counts were done in 3D and multiple layers of a species could be encountered at each point, 

the percent cover had the potential to exceed 100 percent. 
8

Due to differences in the intensity of point sampling between 1997 and all subsequent 

years, data analyses were performed on two different CG datasets: a 4-year (1997–2001) dataset 

and a 3-year (1998–2001) dataset. In 1997, vertical transects at all sites were sampled 

consistently at a minimum of 1 point/m, whereas in 1998–2001, sampling was conducted at 5 

points/m. To maximize the temporal comparison of data from the 1997 to 2001 period, data from 

1998 to 2001 were reduced such that only 1 point/m data were included in the 4-year (1997–

2001) dataset. When the 1998–2001 datasets were reduced, the single point selected to be 

included was always that at the meter mark (for example, 1.0 m, 2.0 m, etc.), which matched the 

sampling conducted in 1997. The 3-year (1998–2001) dataset, which used the full intensity (5 

points/m) data, was used in a number of analyses, including those where the effect of variation in 

the intensity of point sampling on power was being examined.

Trend Analyses

Trend analyses for the predominant sessile taxa (barnacles, Mytilus and Fucus) were 

conducted by TerraStat Consulting Group (hereafter referred to as TerraStat; see appendix B for 

contact information). The 1998–2001 data for each species from each of the 25 sites were tested 

for exponential trends by fitting a linear regression to log-transformed abundance (percent cover)

data. Site and regional trends were evaluated; regional is defined as Glacier Bay proper. 

Power Analyses

MONITOR software (James P. Gibbs, 1995) was used to estimate the power (1-β) of CG

and FG sampling, as well as the influence of a number of parameters on power. For multiple-site, 

multiple-year analyses, MONITOR uses a route regression approach to test for trends. For 

greater detail, see appendix B or Gibbs and Ramirez de Arellano (2007). I used a version of 

MONITOR with a corrected exponential model (modified MONITOR, sensu Hatch, 2003) in my 

analyses of the 1997–2001 data.

After I completed the analyses using the corrected exponential version of MONITOR, a 

general concern arose regarding assumptions used by the MONITOR program. To address this 

concern, I contracted with TerraStat to review and reanalyze a small, but key subset of CG 1998–

2001 data. These included power analyses for the three major sessile taxa at 25 and 15 sites.

Described below are the methods I used to analyze the CG data, the FG data, and 

comparisons between the CG and FG data. These are followed by the methods used by TerraStat.
9

In my use of MONITOR for multiyear CG data analyses, each site represented a ‘plot’ as 

defined in MONITOR guidance documents and all plots were weighted equally. I selected the

exponential trend option, a constant coefficient of variation, and specified use of two-tailed ttests. The significance level (alpha, or α) was set at 0.05, except for those specific cases where 

alpha was varied to examine its effect on power. My runs of the program consisted of 500 

iterations. The biological data used for the analyses were a mean (“plot count”) and variance 

(“plot variance”) measure for a species at a site for the time period under analysis, either the 3-

year (1998, 1999, 2001) or 4-year (1997, 1998, 1999, 2001) periods. As described earlier, 3-year 

data were sampled at 5 points/m intensity, and 4-year data were reduced, for consistency, to 1 

point/m intensity. Details on why I split the data into different time periods were presented above

and related to the reduced intensity of point sampling in 1997. The percent cover for each taxon

was determined as described, prior to calculating the individual site means and variances needed 

for computing power in MONITOR. 

Statistical analyses to calculate the “plot count” and “plot variance” required to run 

MONITOR were conducted using the software program StatView for Windows (version 4.57; 

Abacus Concepts, Inc., 1996). The percent cover along each transect for each of the predominant 

taxa at each site was imported from Microsoft© Excel into StatView. The mean percent cover for 

a taxon at each site was an average of the percent cover calculated for the six transects over all 3

or 4 years, depending on the dataset used. This mean for a taxon at each site was the plot count 

used in MONITOR. 

In my analyses, plot variance, the second variable required by MONITOR, was the 

residual mean-square-error of a linear regression on untransformed data (percent cover of a taxon 

for each transect at a site over time). 

I examined the relative importance of different parameters on the power to detect changes 

in the abundance of the three major sessile taxa (table 1). The largest number of analyses used 

the CG data with α = 0.05. MONITOR results are presented graphically.

For some analyses, fitting a trend line to the calculated points enabled extrapolation of 

data to determine how increased sampling of the number of points per meter, number of 

transects, number of sites, etc. would increase the power of the monitoring protocol. In general, 

these extrapolations were more useful when calculated power was low. Data reductions also 

were made. For example, the 1998–2001 CG data allowed the effect of the number of sites, 

number of transects within a site, and intensity of point sampling to be assessed through 

reductions of the data, whereas the 1997–2001 CG data allowed the number of sites and transects 

to be reduced. Site and transect data reductions were made using the mean and standard error of 

the regression calculated from either the 1 point/m data (1997–2001) or the 5 points/m data 

(1998–2001). When the number of sites was reduced, I used one random subset of sites to 

estimate power. 

Most CG analyses were conducted on similar parameter sets for all three predominant 

sessile taxa. However, in a few cases only Mytilus data were used, because prior analyses 

indicated that power results for Mytilus were intermediate between those for barnacles (higher) 

and Fucus (lower) (Irvine, 1998). Thus, the results for Mytilus should indicate general trends in 

how different parameters affect the estimation of statistical power. I chose to use only Mytilus

data to examine effects of more complex combinations of variables (for example, number of 

sites, level of population change, and number of points sampled per meter). 
10

The MONITOR program also was used to analyze the 1997 FG sampling data. Although 

the program was designed primarily for looking at the variance of counts made through time

(Thomas and Krebs, 1997; Hatch, 2003), when only 1 year of data is available for computing

power analyses, then replicate sampling within a site acts as a proxy for temporal sampling (that 

is, each transect represents a count). Then the mean and variance used in the MONITOR 

program are computed using the within-site, within-year results of sampling. In my analyses of 

the 1997 FG sampling data, subsets of the samples were selected (systematically and/or 

randomly); means and variances for those data subsets were computed and then used in 

MONITOR. Parameters examined for the FG MONITOR runs were similar to those used in the 

3- or 4-year CG sampling analyses (table 1). Many runs based on the 1997 data are reported in 

Irvine (1998). 

Additionally, I compared the power of CG and FG sampling to detect changes in the 

abundance of species, based on vertical transect data, because this method was common to both 

sampling regimes. As the FG sampling was conducted only in 1997, I wanted to include the 1997 

CG data in the comparisons. Therefore, FG data were compared to CG 1997 data or CG 1997–

2001 data, all taken at 1 point/m sampling intensity. Analyses were conducted for the three main 

sessile taxa across various sampling schemes that involved data from either the 6 FG/CG sites or 

the full set of 25 CG sites. 

Effort was not standardized between the FG and CG sampling regimes. The closest 

comparison, with respect to effort, would be that for the FG and CG 1997 sampling conducted at 

the same six sites. 

TerraStat performed power analyses on a subset of the 1998–2001 CG data using a more 

recent version of MONITOR (version 10.0; Gibbs and Ramirez de Arellano, 2007), as well as 

some different parameters (appendix B). The intent of the reanalyses was to determine if changes 

to the MONITOR program or changes to parameters used in the analyses caused large 

differences in estimated statistical power.

TerraStat compared three different analyses: (1) use of TerraStat parameters (see below) 

in MONITOR version 10.0; (2) use of my original parameters in MONITOR version 10.0; and 

(3) my original results, which had used my parameters in the corrected exponential version of 

MONITOR. Additionally, TerraStat examined the effect of the number of sites, by contrasting 

the power obtained by sampling 25 sites versus 15 sites. 

The parameters used by TerraStat in its power analyses included: (a) plot means—the 

initial year’s plot means; (b) plot variance—the standard error of the residuals from an 

exponential-regression fit to untransformed densities; (c) site reductions—power averaged over 

10 random subsets of the 25 sites; and (d) number of iterations—1,000 (appendix B). 
11

Results

Obtaining 4 years of data has allowed: (a) assessment of the abundance of different taxa, 

(b) analysis of trends in the abundance of the predominant sessile taxa at a site and regionally, 

and (c) power analyses. Collectively, these analyses support the goals of detecting long-term 

trends in the abundance of taxa and determining the most appropriate methods for doing so. 

Abundance of Taxa

Fucus, barnacles and Mytilus were much more abundant than the 78 other taxa 

encountered in the CG point-intercept sampling along vertical transects (table 2). In the sampling 

conducted in 1998–2001, of the 61,736 species hits (counts), Fucus was counted 19,773 times

(32 percent), Mytilus 17,903 times (29 percent), and all barnacles 16,047 times (26 percent). 

These three taxa accounted for 82 percent of all species hits. The next most abundant grouping of 

taxa, all green algae, comprised 3,603 hits (5.8 percent). This disparity in relative abundance led 

to the focus on the three most abundant sessile taxa for subsequent analyses. 

Trend Analyses

There was considerable variability in the abundance of the predominant sessile taxa, both 

spatially and temporally (1998–2001), which affected the ability to detect site and regional trends 

(tables 3–5; appendix B). No significant regional trends were determined for Mytilus or Fucus. 

Only barnacles had a significant regional trend (mean trend = +22.5 percent; 2-tailed t-test, pvalue = 0.0095; appendix B). For barnacles, all significant trends at individual sites were positive 

(seven sites), whereas significant trends at sites were more mixed in sign for Mytilus (three

positive, five negative) and Fucus (four positive, one negative) (tables 3–5). The largest range in 

magnitude of trends among sites occurred for Fucus (–70 percent per year to +220 percent per 

year, appendix B). Further results and details of trend analyses are presented in appendix B. 

Power Analyses

I present my power analyses results for: FG sampling, CG sampling, and comparisons of 

the CG and FG sampling. All these analyses were conducted with α = 0.05, except for those 

specified CG analyses where alpha level was varied. These are followed by results of the 

TerraStat reanalyses, which include comparisons to some of my results.

Fine-Grained Sampling

The FG data were used to evaluate the power of different point-intercept sampling 

methods (vertical transects, horizontal transects, and quadrats) to detect change in the abundance 

of sessile species. Across species, vertical transect sampling provided the greatest power to 

detect change, generally followed by horizontal transect sampling, then quadrat sampling (fig. 5).
12

The power to detect change varied consistently among the three main sessile taxa, with 

the greatest power for barnacles, somewhat less power for Mytilus, and least for Fucus (fig. 5). 

The only species group for which the FG sampling of six sites provided sufficient power (> 0.8) 

was barnacles; for this group, the vertical transect sampling produced the highest power (> 0.9) 

(fig. 5C). The number of vertical transects needed to detect 10 percent decreases in Mytilus and 

Fucus at 0.8 power, when only six sites are sampled, was estimated to be 20 and 35 transects,

respectively (fig. 6); these numbers are 2–3.5 times higher than the 10 vertical transects sampled 

under the FG regimen.

Additional analyses of the 1997 FG data that examined the combined influence on power 

of varying the number of vertical transects and number of points per meter sampled, indicated 

that increasing both the number of transects and the number of points sampled per meter

generally increases power. Only for Fucus, where power was low and variable, were some of the 

extrapolated relationships not as consistent as for Mytilus and barnacles (appendix C, figs. C1–

C7).

Coarse-Grained Sampling, Alpha = 0.05

Analyses of the 3- and 4-year CG datasets allowed comparison of the effect of the 

number of years versus the intensity of point sampling on power. The power of the CG sampling 

of 25 sites, conducted over 4 years (1997–2001) at 1 point/m intensity (fig. 7), was very similar 

to that found for analyses of the 3-year (1998–2001) CG data taken at 5 points/m intensity (fig. 

8). This indicates that increased temporal sampling can compensate for decreased intensity of 

point sampling.

I found that attempting to increase power by increasing the number of points sampled per 

meter is not very efficient. The ability to detect 10 percent decreases changes little as CG 

sampling is increased from 1 to 5 points/m, and remains high for all three taxa (figs. 9–11). In 

most of the 5 percent and 3 percent annual change graphs (figs. 9–11), the power lines are fairly 

flat or gently sloping, except for Mytilus. At any given sampling intensity, the power is greater 

for barnacles and Mytilus, and lesser for Fucus. If the minimum goal is to have a power of at 

least 0.8 to detect a 10 percent annual change in abundance, then a sampling intensity of 1 

point/m in the CG sampling is sufficient for barnacles and Mytilus. For barnacles and Mytilus,

there is very high power (> 0.98) to detect 10 percent annual changes, even at a sampling 

intensity of 1 point/m.

Increasing the number of transects increases power, as was demonstrated by analyses of 

the CG (1998–2001) data for Mytilus (fig. 12). Only one transect/site is needed to achieve a 

power of 0.8 to detect a 10 percent decrease in Mytilus. At two transects/site the power to detect 

a 10 percent annual change is > 0.9 (fig. 12A). With six transects/site, the power to detect a 5

percent annual change is 0.8 (fig. 12B).

Increasing the number of sites sampled increases the power to detect changes in Mytilus 

abundance, as illustrated by analysis of CG 1997–2001 data (fig. 13). Sampling 25 sites provides 

a 1.0 probability of detecting a decrease of 10 percent per year. In fact, sampling 10 or more sites

per year produced a power of ≥ 0.9 to detect 10 percent decreases in Mytilus (fig. 13A). A 5

percent decrease at 0.8 power can be detected by the CG sampling of 25 sites (fig. 13B). 
13

When both the number of sites and the number of points per meter sampled are increased, 

the power to detect trends in Mytilus abundance generally increases (fig. 14). Analyses of the 

1998–2001 CG data indicated that a > 90 percent probability (0.9 power) to detect a 10 percent

decrease in Mytilus was readily accomplished by sampling ≥ 15 sites at 1 or more points per 

meter (fig. 14A). Detecting a 5 percent decrease with 0.9 power would necessitate sampling more 

points per meter: for 25 sites, about 6 points/m; for 20 sites, about 8 points/m (fig. 14B).

Detecting a 5 percent decrease at a power of 0.8 can be accomplished by sampling 25 sites at 

about 3 points/m, 20 sites at about 6 points/m, or 15 sites at about 9 points/m.

Coarse-Grained Sampling, Varying Alpha

When the accepted error level (alpha) is increased, the power to detect changes in the 

abundance of the predominant taxa increases (fig. 15). Analysis of the 1997–2001 CG data

indicated that power was high (≥ 0.9) to detect 10 percent changes in the abundance of Mytilus

and barnacles at a range of alphas (0.05–0.20). The power to detect changes in abundance of 

Fucus generally was lower than for the other taxa. A 0.8 probability of detecting a 10 percent

change in Fucus generally required α ≥ 0.10, except for a negative 10 percent change where α =

0.05 sufficed (fig. 15). Detecting 5 percent decreases in all three taxa at a power of 0.8 or greater

requires α ≥ 0.20 (fig. 15). 

Additional analyses of the effects on power of varying alpha in combination with other 

parameters support the increase in power when alpha is increased along with increases in number 

of transects; lesser effects on power occur when alpha and the number of points sampled per 

meter are increased (appendix C, figs. C8–C26). 

Comparison of Coarse-Grained and Fine-Grained Sampling

The power of the CG and FG sampling plans varies for the different species, but the CG 

sampling of 25 sites is more powerful than the FG sampling of 6 sites (fig. 16). There was less 

difference between these two sampling schemes for barnacles, which was the only taxon that was 

adequately sampled (power > 0.8) by the FG sampling. For mussels and Fucus, however, the CG 

sampling at 25 sites was distinctly more powerful (fig. 16). 

Comparisons of various sampling plans using only 1997 data indicate that CG sampling 

of 25 sites has more power than the FG sampling of 6 sites, which in turn has more power than 

the CG sampling of 6 sites. These results for 1997 data support the positive influence on power 

of increasing the number of sites or the intensity of sampling. In particular, note that the 1-year 

CG sampling of 25 sites has more power than the 1-year FG sampling of 6 sites (fig. 16). 

The most equivalent comparison of FG and CG effort and power is comparison of the 

1997 data for the 6-site analyses (fig. 16). These analyses are for data from the same set of sites. 

Effort is somewhat greater for the FG sampling (10 transects/site sampled at 5 points/m) versus

the CG sampling (6 transects/site sampled at 1 point/m), and the difference in power reflects that 

increased effort. There is no equivalent comparison of effort and power for the 25-site CG 

sampling versus the 6-site FG sampling; effort is much greater for the 25-site CG sampling.
14

The multispecies results, condensed in figure 17, compare the relative power of the CG 

(1997–2001) sampling and the FG (1997) sampling to detect changes in abundance. For all taxa, 

the multiyear CG sampling of 25 sites has more power to detect change. These results reflect the 

positive effects on power of increasing the number of sites sampled or temporal sampling. 

TerraStat Power Reanalyses 

The TerraStat results for barnacles and Mytilus were quite similar to those I obtained in

previous analyses (figs. 18, 19, 21), including analyses for Mytilus that examined the effect of 

number of sites on power (figs. 18 and 19). However, TerraStat results for Fucus indicated a 

decrease of 5–10 percent in power to detect changes in its abundance (fig. 20 and appendix B). 

This decrease in power for Fucus appeared to be due primarily to the differences in starting 

parameters used rather than to changes to the MONITOR program, because the starting mean for 

Fucus in the new analysis was an average of 4 percent lower than the values I used (appendix B). 

Note that the results of my original analyses shown in figure 20 are aligned with reanalyses that 

used my original parameters in the newer MONITOR 10.0. This also indicates that it is a change 

in the parameters used for the TerraStat Fucus analyses, rather than the changes in the 

MONITOR program that caused the differences in power results for Fucus. The similarity in 

power results for barnacles and mussels when both MONITOR 10.0 and its earlier version are 

used, and when both TerraStat and my parameters are used, provide support for being able to 

directly interpret prior analytical results for those species groups. The TerraStat findings of 

somewhat reduced power to detect change in Fucus abundance should be taken into account 

when interpreting my earlier analyses. Implications of this finding for long-term monitoring 

design are examined in section, “Discussion.”

TerraStat analyses that manipulated the number of sites found that decreasing the number 

of sites sampled from 25 to 15 decreases the power to detect trends for the three predominant 

sessile taxa (figs. 18–21). For Mytilus and barnacles, this change is minimal at the 10 percent

trend detection level, but becomes more important for detection of smaller annual trends (figs. 

18, 19, 21). However, for Fucus, the TerraStat analyses indicated lesser ability to detect trends:

even with 25 sites sampled, the ability to detect 10 percent trends was below the 0.8 power level 

specified as the minimum desired level, although the ability to detect negative 10 percent

decreases almost reached the 0.8 level (fig. 20). When only 15 sites are sampled, the power to 

detect 10 percent annual trends for Fucus significantly decreased (fig. 20).

Discussion

During the Exxon Valdez oil spill in 1989, extensive intertidal habitats and their resident 

biological communities were affected directly by oil. In the injury assessment following the spill, 

great emphasis was placed on statistically sound sampling designs that allowed results to be 

more broadly generalized, and on the need to sample intensively enough to detect effects. As a 

result, probability-based designs were used in some studies (for example, McDonald and others, 

1995; Peterson and others, 2001). This setting, coupled with the recognition that the NPS lacked

baseline data for the biological resources on its coasts, stimulated the interest of the NPS in 

developing probability-based designs for long-term monitoring of intertidal communities along 

national-park coasts. 
15

Study Design

One objective of monitoring is to detect change in populations or communities through 

time. However, the ability to detect change is related to the extent of the variation that exists, and 

the design and intensity of sampling. Managers of expansive national parks, such as Glacier Bay 

NP&P, are faced with what seem to be daunting challenges imposed by the tremendous physical 

scale and remoteness of the environments that they manage.

An approach to reconciling these different issues is through use of probability-based

designs. Such designs allow the results of surveys to be extrapolated to the universe of sites from 

which the sampled sites were selected (the sampling frame). Deviations from the design cause 

some reduction in the extent or scope of inference.

A central purpose of this project was to provide managers with an analysis of several 

probability-based monitoring designs conducted at different scales of inference and effort. The 

three levels of survey and sampling included aerial surveys, CG sampling of many sites, and FG

sampling of a few sites. An adaptive-sampling approach was used, where results of the first stage 

of surveys informed the decision making that led to the next sampling stage, etc.

The first stage, the aerial surveys, characterized the frequency of different intertidal 

substrate types within Glacier Bay. Of the 250 segments identified, 9 could not be surveyed, thus 

slightly reducing the extent of inference to the universe of coastal segments delineated within 

Glacier Bay proper. The systematically selected set of characterized segments also is available 

for other studies, thus facilitating further establishment of probability-based studies in Glacier 

Bay. This pool of sites has since been used by researchers examining the effects of sea otters on 

intertidal clams (Bodkin and others, 2007).

The results of the aerial surveys led to a revision in the characteristics of habitat types

selected for the monitoring program. The finding that bedrock-dominated habitat with slope ≤

60° was rare was unanticipated, even by resident NPS staff. The abundance of cobble/boulder 

habitat, and its location in the more protected waters of Glacier Bay where it is likely to be 

stable, led to its inclusion along with bedrock as the habitats of focus (Irvine, 1998). The

unexpected results of the aerial surveys illustrate one of the values of probability-based surveys

for avoiding bias.

At the CG sampling level, 5 of the 30 randomly selected sites could not be sampled, for 

reasons given in section, “Methods.” The elimination of these five sites somewhat reduces the 

extent of inference of the sampled CG sites to what was defined as target habitat within Glacier 

Bay. Except for the site eliminated because it was in a wildlife protection area, these eliminations 

may provide an estimate of the error of the segmentation process and the aerial surveys in 

correctly describing intertidal habitat within Glacier Bay.

The error rates can be estimated for both the aerial surveys and the selection of CG sites.

During the aerial surveys, 9 (or 3.6 percent) of the 250 segments selected were flown but not 

surveyed. The reasons for not surveying a segment included: (a) segments actually were stream 

banks and not intertidal habitat (n=4), which likely is an error arising from the digitization of the 

coastline; (b) segments were snow covered at the time of the survey (n=3), therefore could not be 

surveyed; and (c) segments apparently had changed since the charts used for the digitized 

coastline were created. Segments in this latter case occurred in areas of rapid sedimentation and,

despite their coordinate matches, did not resemble the ArcInfo segment maps. The snow-covered 

segments also might have had a slightly higher elevation, because the tides had not removed the 

snow.
16

During the selection of CG sites, 5 of 30 randomly selected segments were eliminated 

because they were in a wildlife protection area, too steep, or not a true beach (had no land 

exposed at high tide). After subtracting the one site that was in a wildlife protection area, these

four eliminated segments constituted 13.33 percent of those selected. These represented errors in 

the classification process, but because all were surveyed at low tide, it would have been difficult 

during the surveys to determine those that were not true beaches. 

The CG and FG sampling plans were designed to examine the ability to effectively 

sample the variation expressed in intertidal communities across different scales. The scales 

reflected geographic variation and within-site (local) variation. These variations were addressed, 

respectively, by increasing the number of sites sampled and their geographic scope, and by 

increasing the intensity of sampling. Geographic inference differed for the CG and FG sampling

plans, due to the selection of FG sites from a narrower band of Glacier Bay (approximately the 

lower half of the bay). The decision to sample cobble/boulder-dominated sites during the FG 

sampling reduced the inference to that habitat within the narrower geographic band. Although I 

found some decrease in power to detect change in Mytilus geographically, based on analyses of 

1-year data (Irvine, 1998), I have not conducted similar analyses for other intertidal species nor 

on multiyear data. Further analyses might indicate if a geographical bias exists. 

A major question posed in this study was whether sampling many sites less intensively 

(CG sampling of 25 sites) had higher power to detect trends than did more intensive sampling of 

fewer sites (FG sampling of 6 sites). I found that the CG sampling had greater power to detect 

changes in the abundance of the three predominant sessile taxa.

For within-site sampling that targeted sessile taxa, vertical transects were selected as a 

sampling technique for several reasons. First, they sample across the elevational zonation of 

species, which is one of the major sources of variation in intertidal communities. Second, if 

climate change occurs, then the vertical distribution of species could be affected. For example, 

zones could shift vertically without the relative abundance of a species changing across a beach. 

If fixed quadrats or horizontal transects initially are established within zones, then changes noted 

in the abundance of a species or an assemblage might reflect changes for that position, but might 

not reflect changes occurring across the beach. Vertical transects also provide a good approach 

for sampling cobble/boulder habitat, as the draped transect lines sample topographically diverse 

substrates better than quadrats, due to the complication of variation in surface area sampled 

within quadrats. In addition to these rationales for using vertical transects, I found that sampling 

of vertical transects has greater power to detect changes in abundance of the major sessile taxa 

compared to the other point-intercept methods tested (quadrats and horizontal transects; fig. 5). 

Further support for the usefulness of vertical transects comes from a study on bedrock substrates 

in southern California that found that sampling of randomly placed vertical transects more 

precisely estimated the percent cover of species than did fixed plots (Miller and Ambrose, 2000).

The effort and cost associated with the CG and FG sampling regimes is not equivalent, as 

could be expected from the large difference in the number of sites sampled. This is discussed in 

section, “Design Issues for Glacier Bay Intertidal Monitoring.”
17

Comparison with Other Intertidal Monitoring Designs

For this study, a probability-based monitoring design was planned to test whether the 

variation that exists in rocky-intertidal assemblages in Glacier Bay could be sampled sufficiently 

to allow trend detection of the predominant sessile species, while providing inference to the 

universe of the selected habitat type within the bay. The goal was to provide managers with the 

ability to broadly generalize changes in abundance. Most sampling of rocky intertidal 

environments has not followed a probability-based monitoring approach, but has relied on 

selected sites (Channel Islands National Park (NP) — Richards and Davis, 1988; Olympic NP; 

Kinnetic Laboratories, Inc., 1992; Partnership for Interdisciplinary Study of Coastal Oceans 

[PISCO]; and Multi-Agency Rocky Intertidal Network [MARINe] — Ambrose and others, 1995; 

Raimondi and others, 1999) and often fixed plots or transects.

The population approach taken by Channel Islands NP staff led to target species being 

followed in fixed plots at selected sites (Richard and Davis, 1988). The intent was that the results 

would provide trend information for those species, as well as for early detection of abnormal 

conditions. The MARINe program primarily has sites throughout California, but in connection 

with PISCO has stretched geographically to include some sites in Mexico, the Pacific Northwest

(including British Columbia) and Alaska. Sites are selected and intentionally spaced, and the 

core protocol targets specific algal and invertebrate assemblages. However, the use of vertical 

transects and some randomization in their placement allows sampling to be extrapolated to the 

site. Biodiversity sampling at the site provides data with great taxonomic definition. Band 

surveys and quadrat sampling also are conducted at sites. The creation of a broader network of 

sites allows the detection of regional and latitudinal biogeographical changes that may result 

from broader-scale effects due to oceanographic or climate change. Changes to sites from oil 

spills or other effectors can then be understood within a broader context (P.T. Raimondi, 

University of California, Santa Cruz, pers. comm., 2008). 

Selecting sites, quadrats or transect locations without a random component to the choice 

limits the ability to make broader inference from the results. When sites, or other sampling units,

are selected to be similar, then it is likely that variation would be reduced. If so, then the power 

to detect change should be increased. The growth in development of sampling designs for impact 

and monitoring studies is discussed in Schmitt and Osenberg (1996) and Murray and others

(2006).

Two additional probability-based designs for intertidal monitoring are in progress in 

Alaska. One is an offshoot of the Glacier Bay design, which I adapted for Sitka National 

Historical Park in southeast Alaska. Sitka has a short coastline (approximately 1 km), so the 

whole beach is defined as the sampling frame. Sessile species are being sampled by vertical 

transects set systematically with a random start; large mobile invertebrates currently are sampled 

by band surveys along the vertical transects; and small mobile invertebrates are sampled in 

quadrats placed systematically along the transects. Power analyses were conducted to test 

whether the design can detect desired levels of change in target species (Irvine and Madison, 

2008).
18

Since the Glacier Bay and Sitka intertidal monitoring projects were initiated, another 

probability-based approach has been developed, the generalized random-tessellation stratified 

(GRTS) survey design (McDonald, 2004; Stevens and Olsen, 2004). This approach creates a 

spatially balanced sample and has flexibility for adding sampling units while maintaining the 

spatial balance. Thus, it improves upon both systematic and simple random designs. The only 

intertidal monitoring project I am aware of that is using this approach is that of the Southwest 

Alaska Network of the National Park Service (Bodkin and others, 2008). This project is currently 

in the design and testing phase.

Power, Trend Detection, and Impact Analysis

Power is the ability to detect a trend if one is, in fact, occurring (or as stated earlier, the 

probability of rejecting the null hypothesis when it is false and should be rejected). A priori 

power analyses are used during the monitoring design phase to estimate how effective the 

monitoring plan is likely to be. Because these analyses often used limited temporal data, they 

should not be viewed as being predictive, just the best estimate of how various designs will 

perform. 

The levels that are set for power and other aspects of the monitoring design vary amongst 

programs. Channel Islands NP has set, as their goal for monitoring, a power of 0.8 to detect a 40

percent change in species abundance through time, with an alpha of 0.05. It is not clear over 

what time frame this 40 percent change is computed. The North American Amphibian 

Monitoring Program (http://www.pwrc.usgs.gov/naamp/) previously stated on their website that 

monitoring programs should be able to detect population trends with a power of 0.9, but an alpha 

of 0.20. Their rationale for setting a relatively high alpha value is that it is more important, from 

a conservation standpoint, to detect declines than to be correct about whether they are occurring

(Sam Droege, U.S. Geological Survey, written comm., 2009). The signaling of a decline could 

initiate further sampling or research to clarify the trend or investigate causes. For threatened or 

declining species, however, failing to detect a trend could be detrimental to the species and to 

management, therefore setting a smaller error rate (for example, α = 0.01) may be an important 

aspect of the monitoring design (Gibbs and Ramirez de Arellano, 2007). 

Setting the levels of change to be detected is a management decision, but it should be 

made with cognizance of how short-term changes compound over time. Even small annual 

changes of 5 percent quickly accelerate over time to appreciable levels of change (table 6). 

Various types of power analyses can be performed. I have focused on comparing the 

ability of different sampling strategies to detect trends in the predominant sessile species through 

use of the MONITOR program. Trend detection is the goal of most monitoring programs so the 

power to detect trends in counts or abundance is assessed frequently. Another type of power 

analysis has focused on the ability to assess impacts, and uses power analysis to compare one site 

before and after impact (Before-After sampling design) or comparisons of two or more sites 

(Before-After Control Impact [BACI], or Before-After-Control-Impact Paired [BACIP] designs)

(for example, Schroeter and others, 1993; Osenberg and others, 1996; Minchinton and Raimondi, 

2001). Additional permutations of this impact type of sampling design have been suggested (for 

example, Stewart-Oaten and others, 1986; Underwood, 1994). These types of analyses could be 

performed on Glacier Bay data if there were suspected or known effects to one or a group of 

sites. Having a number of sites which could act as controls for an affected site, with both 

categories followed for multiple sampling intervals both before and after an event (an 

asymmetrical BACI design, Underwood, 1994) might provide the best option for assessing the 

degree of impact to that one site. Thus, the data gathered through time from the network of sites 

provides a backdrop against which impacts can be assessed.
19

Variation in the Ability to Detect Change among Taxa

Power to detect change varied consistently among the three predominant sessile taxa.

Highest power to detect change occurred for barnacles, followed by lower power for the mussel 

Mytilus trossulus, then the rockweed Fucus distichus subsp. evanescens. Power will be related 

inversely to variability and/or abundance. 

Power analyses of Channel Islands (California) rocky intertidal data also indicate

differences among taxa, with greatest power to detect changes for rockweeds, lower power for 

the California mussel Mytilus californianus, then barnacles, and lowest power for Endocladia

muricata, a red alga (Minchinton and Raimondi, 2001). Because the species comprising these 

taxonomic groups, as well as their abundances and ecology, differ from those in Glacier Bay, it 

is not surprising that the power relationships among taxa are not the same. For example, the 

rockweeds are comprised of entirely different taxa: Silvetia compressa (formerly Pelvetia 

fastigiata) and Hesperophycus californicus (formerly H. harveyanus) (see MARINe website:

http://www.marine.gov/Research/CoreSurveys/SeaweedOfCalifornia.html). The Glacier Bay 

sites also are protected, while the Channel Island sites are exposed rocky (bedrock) habitat. The 

study design also influences the power results; because many of the plots at Channel Islands 

were selected to be within zones dominated by these target taxa (Richards and Davis, 1988), the 

abundances of the target taxa generally are fairly high. The geographical variation in power for 

similar taxa (for example, rockweeds) indicates that we should use caution when borrowing 

information from other areas to guide power analyses.

There is some indication from Glacier Bay data that greater variation in the abundance of 

a species (only Mytilus data for 1997 were examined) led to decreased power. The trend in 

power was related to geographical distribution of sites, with decreased power to detect change in 

Mytilus for sites lower in the bay (Irvine, 1998). This decreased power may be related to the 

increased species diversity found in the lower part of the bay (Sharman, 1990). 

The geographical trend in the power to detect change in Mytilus abundance calls to the 

fore another issue, which is that the abundances of the major space-holding species in the 

intertidal are not independent. Competition for space occurs, and can be mediated by predation 

and disturbance (for example, Connell, 1961; Paine, 1966; Dayton, 1971; Sousa, 1979). Thus, 

variation in one species can affect variation in other species. If additional species become more 

common in Glacier Bay as glaciers recede or as climate warms, then the relative abundances, and 

hence power to detect change in barnacles, Mytilus, and Fucus may change. Because this 

sampling program is not targeting specific species, but is assessing the biotic composition of a 

site, differences in the occurrence and relative abundance of species through time will be 

assessed. The species that are dominant now may not be dominant at some time in the future. 

Sampling at the CG level may make it difficult to detect species with low abundances, however, 

and could be combined periodically with more intensive sampling, site surveys, or timed surveys 

to increase detection of less-common species. 
20

Design Issues for Glacier Bay Intertidal Monitoring

The power and design of a monitoring program will be influenced by both managementset parameters (for example, the desired power, level of change to be detected, and alpha level), 

and sampling parameters (for example, number of sites, transects, quadrats, etc.). The biological 

data provide the basic information needed for assessing how to sample to achieve the 

management-set parameters. Another factor greatly affecting the ultimate sampling design is 

cost. I consider first how different designs (for example, CG and FG) and parameters affect 

power, then discuss effort and cost considerations.

CG sampling provides more power to detect change than the FG sampling, which is due 

primarily to the increase in the number of sites sampled under the CG regime. Increasing the 

number of sites, number of transects, or intensity of points sampled per meter all increase the 

power to detect trends. The effect of increasing sites and transects is pronounced, generally more 

so than increasing the intensity of point sampling. It may be harder to observe the effect of 

increasing point sampling, as very low-intensity point sampling has high power to detect change 

for barnacles and Mytilus. However, the power curves associated with increasing point sampling

for barnacles and Fucus have fairly flat or gentle slopes, indicating that increasing the number of 

points sampled per meter has little influence on power. 

Manipulations of alpha (error level) had relatively straightforward effects on power, 

which were attenuated when power reached very high levels (for example, 1.0; fig. 15). 

Increasing alpha increased the ability to detect trends, but, in general, the effect seems to be 

somewhat less than the effect of increasing the number of sites (fig. 15 versus figs. 18–21). 

How do the CG and FG sampling differ in effort and cost? I stated in the “Results”

section that the most equivalent comparison of effort and power between the CG and FG 

sampling is rendered by comparing the power analyses of the same six sites sampled by each 

method in 1997 (fig. 16). The increased intensity of the FG sampling led to somewhat increased 

power to detect change for all three of the predominant sessile taxa, with the greatest increase in 

power for barnacles. If we examine the effect of increasing the number of sites on power by 

comparing the analyses of CG data (1997, 6-site data versus 1997, 25-site data; fig. 16), we see a 

solid increase in power for all three sessile taxa. Multiyear sampling of the 25 CG sites leads to 

further increases in power (fig. 16). 

The major difference in cost or effort between the CG and FG sampling is exacted by the 

logistical cost associated with sampling more sites. Originally I had planned on sampling two

CG sites per tide, but the distances between sites meant that only some site pairs could be 

sampled consistently on one tide (probably five pairs; although the exact number depends on the 

number of people sampling and their expertise). If we compare the FG and CG vertical 

transect/band sampling in terms of people-days, the FG sampling costs 18 people-days (3 people 

× 1 site/day × 6 sites), and the CG sampling costs 40–60 people-days (minimum 2 people × 0.8-1 

site/day × 25 sites = 40–50 people-days). Having three people doing the CG sampling increases 

the likelihood of being able to sample two sites per day, but the overall number of people-days 

would be 60. Thus, the CG sampling is approximately 2–3 times as expensive, in people-days, as 

the FG sampling, but samples 4 times as many sites. In the upper reaches of Glacier Bay, it is 

very helpful to have the support of a larger vessel than a skiff, but that further increases the costs. 
21

I considered setting initial conditions high: power of 0.9 to detect a 10 percent annual 

change in the abundance of the predominant taxa, with an alpha of 0.05 (Irvine, 1998). However, 

the reanalysis by TerraStat supports lowering the power level to 0.8, or perhaps even lower for 

Fucus. A power level of 0.8 would encompass annual changes of 5–10 percent in Mytilus and 

barnacles sampled at 25 sites (figs. 18 and 21), 10 percent annual changes in Mytilus and 

barnacles sampled at 15 sites (figs. 19 and 21), and would come close to being able to detect 10

percent decreases in Fucus sampled at 25 sites (fig. 20). However, the power to detect 10 percent

increases in Fucus is less, approximately 0.68 (fig. 20). This decrease in power from previous 

analyses is attributed primarily to the reduced plot mean estimate used for Fucus in the new 

analyses (average decrease of 4 percent in starting means; appendix B). The new analyses used 

the starting year mean values at each site as the plot or site mean input to MONITOR, whereas,

my previous analyses used an average value for a taxon over all years. Increased temporal 

sampling is likely to increase the power to detect trends in Fucus. This is illustrated by the 

finding of very similar power for the 4-year (1997–2001, 1 point/m) and 3-year (1998–2001, 5 

points/m) datasets (figs. 7 and 8, respectively); in this case increased temporal sampling 

compensated for lower sampling intensity.

In addition to increasing temporal sampling, there are other options for increasing the 

power to detect change in Fucus. One would be increasing the number of sites, which provides 

the greatest increase in power (fig. 20). Increasing the number of transects sampled/site or the 

error level (alpha) also enhance power, but to a lesser degree. Increasing the number of points 

sampled/m has a yet smaller effect on power. Because the CG sampling, as conducted from1998 

through 2001, provides high power to detect 10 percent annual changes in Mytilus and barnacles, 

no adjustments are needed to the CG sampling plan to effectively sample those taxa. 

An alternative method for increasing power would be to make the vertical transects at the 

sites fixed in location, thus reducing the spatial variability over time. The data thus far have been 

from new (re-randomized) transect locations at a site each year. Because the power to detect 

trends for the major sessile species is relatively high, the challenges of marking and relocating

precise transect locations may not warrant the effort. If quadrat sampling for small mobile 

invertebrates is coupled with vertical transect sampling in the future, then it probably would be 

worthwhile to fix the location of transects and quadrats to reduce the spatial component of

variation in abundance due to re-randomization of sampling locations within a site each year. 

The abundance of small mobile invertebrates is likely more sensitive to changes in substrate 

composition.

If we assume that increased data collection over time would increase the power to detect 

change for Fucus, then the recommended sampling protocol for these sessile intertidal 

assemblages is to conduct the CG sampling scheme annually at the 5 points/m level. The 

suggested protocol is detailed in appendix D. Because the newer power analyses indicate

somewhat reduced ability to detect change in Fucus (fig. 20), I do not recommend reducing the 

number of sites sampled. Power analyses of the FG data provide an example of the repercussions 

on sampling intensity that result when only a small number of sites are sampled. In order to 

achieve the same 0.8 level of power to detect 10 percent decreases in the main sessile taxa, the 

number of transects per site would need to be increased to approximately 35 for Fucus and 20 for 

Mytilus when only 6 sites are sampled; current sampling is sufficient for barnacles (fig. 6). 
22

Although an equivalent level of power might be achieved by sampling fewer sites more 

intensely, there is additional value to having a larger number and greater spread of sites 

throughout the bay than was the case for the FG sampling. Note, however, that a random 

selection of a few sites from the entire bay might have good geographic spread and broad 

inference, but would not necessarily increase the probability of detecting different events. The 

logistic costs of visiting a few, quite dispersed sites also could be very high or prohibitive.

Sampling a larger number of sites is likely to increase the capture of particular types of 

effects, events, and changes, as well as increasing the likelihood that these sites better reflect the 

overall condition of the bay. If effects do occur that are likely to affect a small subset of the sites

(for example, ice scour or trampling effects), having a larger number of sites increases the ability 

to compare affected versus unaffected sites. For example, site 69 (Berg Bay, fig. 1) was 

noticeably different from other sites in 1997 in being dominated by barnacles. I hypothesized 

that the site had been disturbed (probably by ice) prior to the spring set of barnacles in 1997. 

Later I learned from crab researchers that much of Berg Bay froze in the winter of 1996–97 

(Tom Shirley, Texas A&M University, and Jim Taggart, U.S. Geological Survey, oral commun.). 

The pattern of species abundances since then (fig. 22) is giving us a trajectory of the rate and 

pattern of secondary succession following a major site disturbance. Additionally, the slow 

recovery of Fucus, as compared to barnacles and mussels, indicates that it might be an important 

indicator of disturbance or change to intertidal communities.

Results detailed in this report indicate that generally high power exists to detect trends in 

the major space-dominating sessile taxa. As more temporal data are acquired, it is likely that the 

power to detect changes in trends of these sessile taxa will increase (for example, CG data, 25 

sites, 1997 versus 1997–2001 data, fig. 16). An increase in power with additional data and 

increased temporal spread of data indicates that, in the future, the park could evaluate whether to 

sample these 25 sites every other year (or less frequently), or perhaps combine sampling the 25

sites every other year with sampling a subset in the intervening years for some indication of 

annual changes or major events. 

This report has concentrated on analyses of sampling to detect trends in the major sessile 

taxa. For more complete sampling of intertidal assemblages, protocols are needed for sampling 

small mobile invertebrates (such as predatory snails, limpets, and littorine snails), and more 

effective sampling of large mobile invertebrates. Some data exist for both of these groups that 

could assist in proposing more effective sampling. Small mobile invertebrates were sampled 

within quadrats during the FG sampling. Large mobile invertebrates were sampled via band 

surveys along the vertical transects in both CG and FG sampling, but were uncommon. Sampling 

should be modified to concentrate sampling for this latter group (notably starfish) on the lower 

areas of the intertidal (Irvine and Madison, 2008). 

Small and large mobile-invertebrate species can have dramatic effects on the abundances 

of the major sessile species under natural conditions (e.g. Irvine, pers. obs.), and following oil 

spills (Cubit and Connor, 1993; Highsmith, and others, 1996; Irvine, 2000). Therefore,

development of protocols to effectively sample these groups would greatly strengthen an 

intertidal monitoring program and increase understanding of changes occurring to these 

communities. 
23

The sampling protocol as designed thus far has the power, if implemented and carefully 

executed, to enable the park to assess changes occurring to the major sessile species in these 

intertidal communities throughout Glacier Bay over the long term. To achieve and further this 

goal, I provide the following comments based on this study:

Implementation of the CG monitoring protocol for sessile species, as conducted from 

1998–2001 (that is, sampling of 25 sites, 6 vertical transects/site, 5 points/m) will allow the park 

to detect 10 percent annual changes in the abundance of barnacles and Mytilus. Annual sampling 

will increase the ability to detect events, but sampling could be conducted at greater time 

intervals (for example, biennially or triennially).

The somewhat reduced ability to detect trends for Fucus argues against reducing the 

number of sites; however, increasing the time series of data is likely to increase the power to 

detect trends for Fucus. 

If cost is an issue, the number of sites might be reduced, but this would reduce power to 

detect change in abundance of Fucus. If the number of sites sampled is reduced, the number of 

transects, points sampled per meter, etc. would need to be increased to maintain or increase 

power. 

There are advantages to maximizing the number of sites sampled, while maintaining a 

random selection of sites. A GRTS design would allow greater flexibility for altering the number 

of sites while maintaining spatial balance of the sites. However, shifting to a GRTS design would 

result in a loss of applicability of many of the results of this study and the ability to extend the 

timeline of data from sites already established. 

To more completely sample the macrobiota of the rocky intertidal communities within 

Glacier Bay, development of effective monitoring protocols for small mobile invertebrates and 

large mobile invertebrates should be considered.

Why Monitor the Intertidal?

The data obtained indicate that we can monitor the major sessile intertidal taxa. Why 

should we? Intertidal communities are very productive and diverse assemblages of species with 

extensive ties to terrestrial and marine species. The complexity and strength of the links has been 

illustrated by oil spills and a wealth of observation and investigation. The particular sessile 

species focused on here are all spatial dominants over broad geographical extents and are 

significant ecologically. Mussels and rockweed provide structure to the intertidal that can 

increase the species diversity of the communities. Additionally, mussels are an extremely 

important prey for many species including other invertebrates, fishes, birds, and mammals

(including humans). Immigrating sea otters are expected to have effects on both intertidal and 

shallow subtidal species, including mussels, as their numbers continue to increase. Benthicfeeding waterfowl (for example, scoters [Melanitta spp.], goldeneyes [Bucephala spp.], and 

harlequin ducks [Histrionicus histrionicus) currently may be having a more extensive effect than 

sea otters. Surveys of marine predators in Glacier Bay have estimated that these benthic-feeding 

waterfowl have a combined population size greater than 30,000 individuals, with seasonal shifts 

in the abundance of different groups (Drew and others, 2008). Barnacles, another important prey

for a number of species, also are included in the diets of black bears (Ursus americanus) in 

Glacier Bay and brown bears (U. arctos) in Katmai NP&P. These intertidal communities, located 

at the juncture of the atmosphere, ocean and land are vulnerable to effects from all three 

directions.
24

Monitoring of these rocky intertidal habitats falls under the mantle of long-term 

ecological monitoring. Although the direct relationship of the findings to management actions 

may not always be clear (Oakley and Boudreau, 2000), with time and development of 

understanding of natural patterns in species abundances and distributions, a greater 

understanding of the Glacier Bay ecosystem will result. 

The findings from long-term monitoring may contribute substantially to the ability to 

assess effects to the intertidal from issues or stressors of concern to the park. Some of these are: 

oil spills, immigration of sea otters, shifts in species abundances and distributions due to local or 

global climate change, and invasive species. One potential invasive species that could have large 

effects on the intertidal communities of Glacier Bay is the green crab, Carcinus maenas. The 

green crab is spreading north up the west coast of North America, and by 1999 had reached 

British Columbia (U.S. Environmental Protection Agency, 2009).

Linking intertidal monitoring at Glacier Bay with monitoring planned for Sitka National 

Historical Park (NHP) would allow more robust assessment of biogeographical changes in 

species abundances and distributions caused by climate change or the advance of invasive 

species. Expanding the linkage of Sitka NHP and Glacier Bay NP&P studies to monitoring 

programs being set up in Prince William Sound and the NPS Southwest Area Network parks

would expand our ability to understand community dynamics across the Gulf of Alaska, and 

detect effects from broader-scale environmental drivers, including climate change.

One of the major threats of climate change is acidification of the oceans resulting from

increased atmospheric CO2 (for example, Orr and others, 2005). Sessile invertebrates with 

calcareous shells (for example, mussels and barnacles) or whose larval forms have such shells 

will be particularly vulnerable to changes in ocean acidity. Thus, consistent sampling of ocean 

water as well as intertidal sessile species may indicate important changes in the marine 

environment that are not detected so readily in pelagic systems. The visibility and accessibility of 

marine intertidal communities, as well as their complexity, are some of the reasons why these 

communities have been studied intensively. This knowledge base increases the potential for 

understanding changes that may occur, even though it does not eliminate the need for focused 

research. 

Thus, having and implementing an effective monitoring protocol for intertidal 

communities in Glacier Bay may aid detection and interpretation of major changes that are likely 

to occur. This study provides the first step in that direction by providing an effective protocol for 

assessing changes occurring to the major sessile taxa in the rocky intertidal communities. 
25

Conclusions

The probability-based survey and sampling approaches used in this study have provided a 

broad characterization of intertidal habitats within Glacier Bay, as well as statistical comparison 

of different sampling approaches that varied in their inference, intensity, and techniques. The 

three levels of surveys included: aerial surveys, CG sampling, and FG sampling. At each level,

there were some reductions in inference based on: segments that were classified inappropriately 

(aerial surveys), segments that could not be sampled (CG sampling), or geographical and habitat 

restrictions (FG sampling). 

Rocky habitat, including cobble/boulder and bedrock substrates, was the focus of on-site 

surveys. Analyses presented focus on the three predominant sessile taxa: barnacles; the mussel 

Mytilus trossulus; and the rockweed Fucus distichus subsp. evanescens. The CG sampling, 

which involved sampling 25 randomly selected sites at relatively low intensity, provided a higher

degree of power to detect changes in the predominant sessile taxa than did the more intensive FG 

sampling of 6 sites. The FG sites, a subset of the CG sites, were cobble/boulder dominated and 

were located in the lower half of the bay. 

I provide data illustrating the effect on power of different sampling methods (quadrats, 

vertical transects, and horizontal transects), sampling parameters (number of sites, number of 

transects, intensity of point sampling per meter), and management-set parameters (the level of 

change to be detected, certainty [alpha]).

Point-intercept sampling of vertical transects had greater power to detect change than did 

similar sampling of horizontal transects and quadrats. The number of sites sampled had the 

largest effect on power, followed by the number of transects sampled and alpha levels. The 

number of points sampled per meter had the least effect on power. There were consistent 

differences among species in the ability of the sampling to detect changes in their abundances.

There was greater power to detect changes in abundance of barnacles, somewhat less power for 

the mussel Mytilus, and least power for the rockweed Fucus.

The CG sampling regime carried out from 1998 through 2001 would provide the park a 

means to assess changes to these intertidal taxa and communities on a broad spatial scale. Under 

this approach, sampling would occur annually (or at greater time intervals) at the 25 established 

sites, using 6 vertical transects per site, sampled at 5 points/m. This sampling provides a power 

of 0.8 or greater, with an alpha of 0.05, to detect annual changes of 10 percent for barnacles and

Mytilus. The ability to determine change for Fucus is somewhat less, particularly for increases in 

abundance; however, acquiring more data over time should increase the ability to distinguish 

trends. Further effort is needed to develop effective protocols for small and large mobile 

invertebrates. 

The data from this project provide managers with substantial information that will be 

useful in designing and implementing a long-term intertidal monitoring program with sufficient 

power to detect trends for important sessile taxa inhabiting rocky shores within Glacier Bay. 

Data from this study and future monitoring also may be used to assess impacts to these biological 

communities from such causes as climate change, invasive species, oil spills, trampling, or the 

immigration of sea otters.
26

Acknowledgments

This project has been inspired and supported by the input of various statisticians,

including Lyman McDonald (WEST, Inc.), Mark Udevitz (USGS), and Alice Shelly (TerraStat). 

Numerous people have made significant contributions including: Jim Bodkin, Kim Kloecker, 

Tammy Gage, Megan Ferguson, Karen Vandersall, George Esslinger, Dan Monson, Jennifer 

Mondragon, Jeffrey Mondragon, Amy Delorenzo, Todd Stoltey, Erica Madison, Jim de la

Bruere, Dave Douglas (USGS); Mandy Lindeberg (National Oceanic and Atmospheric 

Administration); Jennifer Williams (Sitka NHP); and Lewis Sharman, Bill Eichenlaub, Mary 

Beth Moss, and Larry Basch (Glacier Bay NP&P). In addition to NPS staff input, I appreciate the 

financial support for the fourth year of sampling and logistical support. Special thanks to Bill 

Eichenlaub, Jennifer Mondragon, and Jeffrey Mondragon for creation of the Microsoft© Access 

database. Additional thanks to reviewers of different drafts of this report (Karen Oakley, Eric 

Knudsen, Heather Coletti, Erik Beever, Lyman McDonald, and Tony DeGange). Initial funding 

was from USGS National Resources Preservation Program; additional support was provided by 

the National Park Service (Glacier Bay NP&P), and the U.S. Geological Survey. 
27

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29

Paine, R.T., 1966, Food web complexity and species diversity: The American Naturalist, v. 100,

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70D756FBBFAE88257416005A73DE?OpenDocument .

Zar, J.H., 1984, Biostatistical analysis: Prentice-Hall, Inc., NJ, 718 p.
30

Figure 1. Location of Glacier Bay National Park and Preserve (red box) in southeast Alaska (black box in 

inset). 
31

Figure 2. Locations and segment numbers of the coarse-grained and fine-grained sites in Glacier Bay 

National Park and Preserve. Fine-grained sites are a subset of the coarse-grained sites.
32

Figure 3. Layout of the various sampling methods used during the fine-grained surveys.
33

Figure 4. Quadrat used for point-contact sampling at the fine-grained sampling level. Littorine snails and 

barnacle spat/recruits were subsampled in the indicated areas.
34

Mytilus - 10% Decrease

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Vertical Transects Horizontal Transects Quadrats

Method of Sampling

Power

A

Fucus - 10% Decrease

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Vertical Transects Horizontal Transects Quadrats

Method of Sampling

Power

B

Barnacles - 10% Decrease

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Vertical Transects Horizontal Transects Quadrats

Method of Sampling

Power

C

Figure 5. Comparison of the power of three point-intercept sampling methods conducted as part of the finegrained sampling protocol in 1997. At six sites, 10 vertical transects, 30 horizontal transects, and 30 

quadrats were sampled. A. Power to detect a 10 percent decrease in Mytilus. B. Power to detect a 10

percent decrease in Fucus. C. Power to detect a 10 percent decrease in barnacles. α = 0.05. 
35

10% Decrease

y = 0.0487x + 0.4552

R2

 = 0.9408

y = 0.0355x + 0.0939

R2

 = 0.9753

y = 0.0202x + 0.0713

R2

 = 0.9954

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 5 10 15 20 25 30 35 40

Number of transects

Power

Barnacles

Mytilus

Fucus

Figure 6. Power to detect a 10 percent decrease in the predominant sessile species as related to the 

number of transects sampled. A linear trend is projected based on data collected from 1997 fine-grained 

sampling of six sites. Each transect was sampled at a frequency of 5 points/meter. α = 0.05.
36

Barnacles

Population Trend Power

-10% 1

-5% 0.904

-3% 0.458

-2% 0.28

-1% 0.092

0% 0.068

1% 0.094

2% 0.212

3% 0.446

5% 0.862

10% 1

Mytilus

Population Trend Power

-10% 1

-5% 0.83

-3% 0.424

-2% 0.23

-1% 0.072

0% 0.04

1% 0.09

2% 0.222

3% 0.42

5% 0.768

10% 1

Fucus

Population Trend Power

-10% 0.974

-5% 0.49

-3% 0.228

-2% 0.124

-1% 0.072

0% 0.052

1% 0.052

2% 0.132

3% 0.188

5% 0.422

10% 0.944

0 0.2 0.4 0.6 0.8 1

-10%

-3%

-1%

1%

3%

10%

Population Trend

Power

B

0 0.2 0.4 0.6 0.8 1

-10%

-3%

-1%

1%

3%

10%

Population Trend

Power

C

0 0.2 0.4 0.6 0.8 1

-10%

-3%

-1%

1%

3%

10%

A

Figure 7. Power to detect population trends based on 4 years of data (1997–2001) in A. barnacles; B. 

Mytilus; and C. Fucus. Data are based on sampling of 25 sites, 6 transects, 1 point/meter. α = 0.05. 
37

Barnacles

Population Trend Power

-10% 0.998

-5% 0.74

-3% 0.398

-2% 0.158

-1% 0.088

-0% 0.056

1% 0.088

2% 0.148

3% 0.308

5% 0.74

10% 0.996

Mytilus

Population Trend Power

-10% 1

-5% 0.838

-3% 0.456

-2% 0.17

-1% 0.082

-0% 0.066

1% 0.092

2% 0.216

3% 0.43

5% 0.776

10% 1

Fucus

Population Trend

-10% 0.912

-5% 0.366

-3% 0.162

-2% 0.092

-1% 0.06

-0% 0.034

1% 0.062

2% 0.08

3% 0.158

5% 0.37

10% 0.86

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

-10%

-5%

-3%

-2%

-1%

-0%

1%

2%

3%

5%

10%

Population Trend

Power

B

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

-10%

-5%

-3%

-2%

-1%

-0%

1%

2%

3%

5%

10%

Population Trend

Power

C

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

-10%

-5%

-3%

-2%

-1%

-0%

1%

2%

3%

5%

10%

Population Trend

Power

A

Figure 8. Power to detect population trends based on 3 years of data (1998–2001) in A. barnacles; B. 

Mytilus; and C. Fucus. Sampling was conducted at 25 sites, 6 transects, 5 points/meter. α = 0.05.
38

Mytilus

(Trend = 5% Decrease)

y = 0.0616x + 0.5576

R2

 = 0.8274

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 1 2 3 4 5 6 7 8 9 10

Points/meter

Power

B

Mytilus

(Trend = 3% Decrease)

y = 0.0486x + 0.2202

R2

 = 0.9767

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Points/meter

Power

C

Mytilus

(Trend = 10% Decrease)

y = 0.0018x + 0.9922

R2

 = 0.75

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 1 2 3 4 5

Points/meter

Power

A

Figure 9. Power to detect decreases in Mytilus populations as related to the number of points sampled per 

meter of each transect. Data are based on 3 years of sampling (1998–2001) at 25 sites, 6 transects at each 

site. α = 0.05. A. 10 percent decrease; B. 5 percent decrease; C. 3 percent decrease. Points on graphs 

indicate data; lines are linear projections of the trends suggested by these data.
39

Fucus

10% Decrease

y = 0.001x + 0.8874

R2

 = 0.0049

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 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

Points/meter

Power

A

Fucus

5% Decrease

y = 0.008x + 0.3536

R2

 = 0.1912

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 2 4 6 8 1012141618202224262830323436384042444648505254565860

Points/meter

Power

B

Fucus

3% Decrease

1 2 3 4 5

y = -0.0014x + 0.1718

R2 = 0.0189

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 2 4 6 8 10 12 14 16 18

Points/meter

Power

C

Figure 10. Power to detect decreases in Fucus populations as related to the number of points sampled

per meter of each transect. Data are based on 3 years of sampling (1998–2001) at 25 sites, 6 transects at 

each site. α = 0.05. A. 10 percent decrease; B. 5 percent decrease; C. 3 percent decrease. Points on 

graphs indicate data; lines are linear projections of the trends suggested by these data.
40

Barnacles

 5% Decrease

y = 0.0176x + 0.6844

R2

 = 0.4879

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 1 2 3 4 5 6 7 8 9 10 11 12

Points/meter

Power

B

Barnacles

3% Decrease

y = 0.025x + 0.2514

R2

 = 0.8067

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 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

Points/meter

Power

C

Barnacles

10% Decrease y = -0.0004x + 1.0008

R2

 = 0.5

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 1 2 3 4 5

Points/meter

Power

A

Figure 11. Power to detect decreases in barnacle populations as related to the number of points sampled 

per meter of each transect. Data are based on 3 years of sampling (1998–2001) at 25 sites, 6 transects at 

each site. α = 0.05. A. 10 percent decrease; B. 5 percent decrease; C. 3 percent decrease. Points on 

graphs indicate data; lines are linear projections of the trends suggested by these data.
41

Mytilus 

10% Decrease

y = 0.0315x + 0.8443

R2

 = 0.6813

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 1 2 3 4 5

Number of Transects

Power

A

Mytilus 

5% Decrease

y = 0.0958x + 0.2436

R2

 = 0.9963

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 1 2 3 4 5 6 7 8

Number of Transects

Power

B

Mytilus 

3% Decrease

y = 0.0505x + 0.1032

R2

 = 0.9684

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 2 4 6 8 10 12 14 16 18

Number of Transects

Power

C

Figure 12. Power to detect a decrease in Mytilus populations as a function of the number of transects 

sampled at each site. Data are based on 3 years of sampling (1998–2001), 25 sites sampled, transects 

sampled at a frequency of 5 points/meter. A. 10 percent decrease; B. 5 percent decrease; C. 3 percent

decrease, α = 0.05. Points on graphs indicate data; lines are linear projections of the trends suggested by 

these data.
42

Mytilus

10% Decrease

y = 0.0167x + 0.6552

R2

 = 0.6903

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 2 4 6 8 10 12 14 16 18 20 22 24 26

Number of Sites

Power

A

Mytilus

5% Decrease

y = 0.0316x + 0.0842

R2

 = 0.9743

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 5 10 15 20 25 30

Number of Sites

Power

B

Figure 13. Power to detect a decrease in Mytilus populations in relation to the number of sites. A. 10

percent decrease; B. 5 percent decrease. Data are based on 4 years (1997–2001) sampling of 6 transects 

sampled at 1 point/meter at each site. α = 0.05. Points on graphs indicate data; lines are linear regressions 

of trends suggested by these data.
43

Mytilus 

10% Decrease

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 1 2 3 4 5

Points/meter

Power

25 Sites

20 Sites

15 Sites

A

Mytilus 

5% Decrease

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 1 2 3 4 5 6 7 8 9 10 11 12 13

Points/meter

Power

25 Sites

20 Sites

15 Sites

B

Figure 14. Change in the power to detect a decrease in Mytilus populations as a result of varying the 

number of points sampled per meter with respect to 25, 20, and 15 sites. A. 10 percent decrease; B. 5

percent decrease. Data are based on 3 years (1998–2001) of sampling 6 transects at each site. α = 0.05. 

Points on graphs indicate data; lines are linear regressions of trends suggested by these data.
44

Barnacles

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

-15% -10% -5% 0% 5% 10% 15%

Trend

Power

alpha=0.05

alpha=0.10

alpha=0.20

B

Mytilus

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

-15% -10% -5% 0% 5% 10% 15%

Trend

Power

alpha=0.05

alpha=0.10

alpha=0.20

A

Fucus

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

-15% -10% -5% 0% 5% 10% 15%

Trend

Power

alpha=0.05

alpha=0.10

alpha=0.20

C

Figure 15. Power to detect trends of differing magnitudes at three levels of alpha (α = 0.05, 0.10, 0.20). 

Data are based on 4 years (1997–2001) of coarse-grained sampling: 25 sites, 6 transects/site, 1 

point/meter. A. Mytilus; B. barnacles; C. Fucus.
45

10% Decrease

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

FG (1997) 

6 Sites

CG (1997) 

6 Sites

CG (1997-01) 

6 Sites

CG (1997) 

25 Sites

CG (1997-01) 

25 Sites

Method of Sampling

Power

Mytilus

Fucus

Barnacles

Figure 16. Comparison in the power of fine-grained and coarse-grained vertical transect sampling to detect 

a 10 percent decrease in Mytilus, Fucus, and barnacles. Fine-grained sampling (1997 only) included 10 

transects at each of 6 sites, each transect sampled at 5 points/meter. Coarse-grained data (1997–2001) 

used in these analyses are derived from sampling 6 transects/ site at 1 point/meter. Analyses of both 

coarse-grained sampling at the 6 fine-grained sites and all 25 sites are included as a comparison. 
46

Coarse Grained

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

-15% -10% -5% 0% 5% 10% 15%

Trend

Power

Fucus

Mytilus

Barnacles

A

Fine Grained

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

-15% -10% -5% 0% 5% 10% 15%

Trend

Power

Fucus

Mytilus

Barnacles

B

Figure 17. Comparison of the power of coarse-grained and fine-grained sampling methods to detect trends 

in the predominant sessile species. α = 0.05 A. Coarse-grained (1997–2001): 25 sites, 6 transects/site, 1 

point/meter. B. Fine grained (1997): 6 sites, 10 transects/site, 5 points/meter. 
47

0

0.2

0.4

0.6

0.8

1

-0.10 -0.08 -0.06 -0.04 -0.02 0.00 0.02 0.04 0.06 0.08 0.10

Trend Magnitude

Statistical Power

Original M10 TS Parameters M10 Orig Parameters

Figure 18. Statistical power comparison for Mytilus with 25 sites. Results from the original analyses are 

compared to those from two sets obtained with MONITOR, version 10: (1) using TerraStat parameters and 

(2) using the original parameters. See appendix B for more details.
48

0

0.2

0.4

0.6

0.8

1

-0.10 -0.08 -0.06 -0.04 -0.02 0.00 0.02 0.04 0.06 0.08 0.10

Trend Magnitude

Statistical Power

Original M10 TS Parameters M10 Orig Parameters

Figure 19. Statistical power comparison for Mytilus with 15 sites. See caption in figure 18 for more detail.
49

Figure 20. Statistical power comparison for Fucus. See caption figure 18 for greater detail. In addition to 

the three runs examining power for 25 sites, also there is a run of MONITOR 10 using data from 15 sites. 
50

Figure 21. Statistical power comparison for barnacles. See captions for figures 18 and 20.
51

0

5

10

15

20

25

30

35

40

45

50

1997 1998 1999 2000

Year

Percent Cover

Mytilus

Fucus

Barnacles

Figure 22. Mean percent cover of the predominant sessile species at Berg Bay. Data were collected in 

1997–2000, from the sampling of six transects at 1 point/meter. Error bars represent ±1 standard error.
52

Table 1. Parameters used in the power analyses. 

Parameter Categories Parameter Options

Species

Sessile dominants Mytilus, Fucus, all barnacles

Sampling Attributes

Sampling design Coarse-grained (25 sites; multiyear sampling; 

6 vertical transects/site; 1–5 points/m) 

Fine-grained (6 sites; 1997 only; multiple methods, 

including 10 vertical transects/site, 5 points/m)

Sampling method (fine-grained) Vertical transects (10/site)

Horizontal transects (30/site)

Quadrats (30/site)

Number of sites Coarse grained (25-site pool)

Fine grained (6-site pool)

Number of transects 6 max. for Coarse-grained sampling

10 max. for Fine-grained sampling

Number of points per meter 1–5 points/m for coarse- and fine-grained

sampling

Management Parameters

Alpha level 0.05, 0.10, 0.20

Level of annual change to detect

10 percent, 5 percent, 

3 percent
53

Table 2. The number of hits (counts) of a species or taxon during each year of sampling along the vertical 

transects. 

[Data are from the 1998–2001 coarse-grained sampling of 25 sites, with 6 vertical transects/site sampled at 5 

points/m intensity. The total number of hits can be greater than the total number of points sampled because layers of 

biota are recorded during the point-intercept sampling]

Taxonomic Groupings/Species 1998 1999 2001 Grand Total

PLANTS

Bacillariophyta (Diatoms)

Diatom 68 159 25 252

Chlorophyta (Green algae)

Acrosiphonia spp. 103 70 152 325

Enteromorpha intestinalis 73 44 38 155

Prasiola meridionalis 10 1 11

Small filamentous green alga 40 112 149 301

Ulothrix spp. 14 14

Ulvales 573 894 1,341 2,808

Phaeophyta (Brown algae)

Agarum clathratum 1 1

Alaria fistulosa 1 1

Alaria marginata 45 121 117 283

Alaria spp. 195 5 200

Cymathere triplicata 1 1

Desmarestia spp. 2 2 4

Fine filamentous brown algae 116 16 22 154

Fucus distichus subspecies evanescens 6,140 7,222 6,411 19,773

Laminaria saccharina 1 1

Leathesia difformis 2 2

Petalonia fascia 4 6 12 22

Scytosiphon/Melanosiphon spp. 7 8 2 17

Small foliose brown alga 6 6

Soranthera ulvoidea 9 20 15 44

Thick brown algal crust 4 4

Rhodophyta (Red algae)

Cryptosyphonia woodii 17 22 39

Encrusting coralline algae 8 1 3 12

Endocladia muricata 8 8

Gigartinaceae 123 129 252

Gloiopeltis furcata 1 1

Halosaccion americanum 38 28 61 127

Mastocarpus papillatus 17 82 202 301

Neorhodamela/Odonthalia spp. 148 193 317 658

Palmaria callophylloides 135 201 406 742

Palmaria spp. 223 337 231 791

Polysiphonia/Pterosiphonia spp. 249 144 50 443

Porphyra spp. 72 56 50 178

Red algal crust- fleshy 3 7 2 12
54

Small filamentous red alga174 12

Small foliose red alga11

Angiospermae (Flowering plants)

Elymus arenarius (beachgrass)76 13

Lichen

Verrucaria spp. 34 34 90 158

ANIMALS

Porifera (Sponges)

Halichondria spp.11

Haliclona permollis11

Cnidaria/ Anthozoa : Sea anemones

Epiactis spp.11

Urticina crassicornis314

Nemertea (Nemertean worms)

Amphiporus spp.2316

Emplectonema gracile167

Paranemertes peregrina213

Annelida / Polychaeta (Polychaete worms)

Tube worm 171 18

Mollusca / Polyplacophora (Chitons)

Katharina tunicata33

Tonicella spp.11

Mollusca / Gastropoda : Limpets

Acmaea mitra11

Lottia pelta235

Lottidae <8mm 28 81 40 149

Tectura fenestrata22

Tectura persona 70 16 79 165

Tectura scutum 38 124 54

Mollusca / Gastropoda: Snails

Buccinum sp.11

Lacuna spp. 19 19

Littorina scutulata 14 14

Littorina sitkana 733 656 463 1,852

Littorina spp. 121 121

Margarites sp.11

Nucella canaliculata22

Nucella egg case22

Nucella lima 27 46 27 100

Nucella spp. 241 25

Searlesia dira11

Snail, unidentified44

Mollusca / Gastropoda - Other

Archidoris/Anisodoris spp.11

Nudibranch, unidentified 11

Siphonaria thersites44

Mollusca / Bivalvia

Hiatella arctica812 11
55

Mytilus trossulus 6,244 4,519 4,140 14,903

Arthropoda / Crustacea: Barnacles

Balanus glandula/Semibalanus balanoides 3,274 2,281 4,546 10,101

Balanomorpha 1,407 1 1,475 2,883

Barmacle spat/recruits <2mm 1,276 29 1,420 2,725

Chthamalus dalli 1 1

Semibalanus cariosus 71 132 134 337

Arthropoda / Crustacea: Isopods

Isopoda 2 2 4

Arthropoda / Crustacea : Decapods (Crabs)

Hemigrapsus spp. 1 1

Paguridae (hermit crabs) 6 3 9

Echinodermata / Asteroidea (Sea stars)

Evasterias troschelli 4 4

Leptasterias epichlora 3 2 5

Echinodermata / Echinoidea (Sea urchins)

Strongylocentrotus droebachiensis 24 2 26

Echinodermata / Holothuroidea (Sea cucumbers)

Sea cucumber, unidentified 19 19

Urochordata / Ascidiacea (Tunicates)

Tunicate, solitary 11 11

Total Number of Hits 21,958 17,703 22,075 61,736

Total Number of Points Sampled 23,614 20,830 20,516 64,960
56

Table 3. Trend results by site for percent cover of Mytilus.

[A linear regression was fit to log-transformed density data for each site to test for exponential trends. The trends are 

represented by the slopes. Significant negative (-) and positive (+) trends are indicated in the right-most column.

Greater detail is in appendix B]

Site 

No. Site Name Slope p-value Trend

36 Bear Track 0.126 0.472

69 Berg Bay 0.288 0.023 +

63 Between Pt Carolus & Ripple Cove -0.385 0.109

113 Blue Mouse Cove 0.076 0.273

217 Drake Island - Lower -0.107 0.111

216 Drake Island - Mid -0.434 0.001 –

218 Drake Island - Upper -0.200 0.089

89 Geikie Inlet - Lower 0.036 0.747

88 Geikie Inlet - Upper 0.154 0.150

59 Lester Island -0.218 0.209

215 Little Sturgess Island -0.059 0.803

143 Mt Abdallah Outwash -0.112 0.242

12 Muir Inlet - Lower 0.405 0.002 +

2 Muir Inlet - Upper 0.813 0.000 +

62 Pt Carolus -0.167 0.100

151 Rendu Inlet 0.102 0.763

200 Russel Island -0.471 0.010 –

108 Scidmore Bay -0.024 0.886

220 Shag Cove -0.135 0.212

214 Sturgess Island -0.224 0.177

142 Tarr Inlet -0.014 0.867

164 Tidal Inlet - Lower -0.071 0.576

163 Tidal Inlet - Upper -0.389 0.004 –

223 Willoughby Island - East -0.282 0.029 –

224 Willoughby Island - West -0.692 0.001 –
57

Table 4. Trend results by site for percent cover of Fucus. 

[A linear regression was fit to log-transformed density data for each site to test for exponential trends. The trends are 

represented by the slopes. Significant negative (-) and positive (+) trends are indicated in the right-most column. 

Greater detail is in appendix B]

Site 

No. Site Slope p-value Trend

36 Bear Track -0.611 0.055

69 Berg Bay 1.17 0.000 +

63 Between Pt Carolus & Ripple Cove 0.641 0.111

113 Blue Mouse Cove 0.068 0.908

217 Drake Island - Lower -1.01 0.002 –

216 Drake Island - Mid 0.411 0.047 +

218 Drake Island - Upper 0.157 0.339

89 Geikie Inlet - Lower 0.818 0.001 +

88 Geikie Inlet - Upper 0.555 0.015 +

59 Lester Island 0.444 0.211

215 Little Sturgess Island 0.410 0.407

143 Mt Abdallah Outwash -0.120 0.564

12 Muir Inlet - Lower 0.152 0.652

2 Muir Inlet - Upper 0.253 0.451

62 Pt Carolus 0.317 0.097

151 Rendu Inlet 0.310 0.471

200 Russel Island -1.212 0.006 -

108 Scidmore Bay 0.103 0.743

220 Shag Cove 0.107 0.660

214 Sturgess Island 0.217 0.510

142 Tarr Inlet -0.352 0.267

164 Tidal Inlet - Lower 0.421 0.055

163 Tidal Inlet - Upper -0.653 0.088

223 Willoughby Island - East -0.149 0.655

224 Willoughby Island - West 0.665 0.064
58

Table 5. Trend results by site for percent cover of barnacles. 

[A linear regression was fit to log-transformed density data for each site to test for exponential trends. The trends are 

represented by the slopes. Significant negative (-) and positive (+) trends are indicated in the right-most column. 

Greater detail is in appendix B]

Site 

No. Site Slope p-value Trend

36 Bear Track -0.011 0.974

69 Berg Bay 0.071 0.771

63 Between Pt Carolus & Ripple Cove 0.116 0.713

113 Blue Mouse Cove 0.449 0.019 +

217 Drake Island - Lower 0.002 0.989

216 Drake Island - Mid 0.097 0.557

218 Drake Island - Upper 0.093 0.676

89 Geikie Inlet - Lower -0.014 0.887

88 Geikie Inlet - Upper 0.279 0.333

59 Lester Island 0.570 0.057

215 Little Sturgess Island 0.437 0.026 +

143 Mt Abdallah Outwash 0.373 0.004 +

12 Muir Inlet - Lower -0.169 0.076

2 Muir Inlet - Upper 0.494 0.018 +

62 Pt Carolus -0.039 0.899

151 Rendu Inlet 0.462 0.092

200 Russel Island 0.426 0.013 +

108 Scidmore Bay 0.468 0.083

220 Shag Cove 0.044 0.668

214 Sturgess Island 0.007 0.979

142 Tarr Inlet 0.363 0.259

164 Tidal Inlet - Lower 0.008 0.962

163 Tidal Inlet - Upper 0.049 0.795

223 Willoughby Island - East 0.470 0.006 +

224 Willoughby Island - West 0.350 0.022 +
59

Table 6. Conversion of short-term trends, expressed as percent change per year, into long-term trends.

[The latter is the total percent change expected over periods ranging from 5 to 20 years, if the annual trend occurs 

during each of the 5, 10, 15, or 20 years. In effect, this is like compounding interest, and is calculated easily, for 

example, -3 percent over 15 yrs = (0.97)15 = 0.6333, or 100 percent - 36.67 percent]

Trend per year

Short-term versus long-term trends

-5 percent -3 percent -2 percent

Trend over 5 years -22.62 percent -14.13 percent -9.61 percent

Trend over 10 years -40.13 percent -26.26 percent -18.29 percent

Trend over 15 years -53.67 percent -36.67 percent -26.14 percent

Trend over 20 years -64.15 percent -45.62 percent -33.24 percent
60

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61

APPENDIX A – Methods for the 1997 Sampling, including 

Aerial Survey Standard Operating Procedures

Methods for the 1997 Sampling, including Aerial Survey Standard 

Operating Procedures

The Aerial Survey Standard Operating Procedures (SOP) is included here for its detail and 

because it was not included in Irvine (1998). Following that is a section of Methods from Irvine

(1998), which concentrates on the coarse-grained (CG) and fine-grained (FG) sampling and just 

briefly refers to the aerial surveys. In the Methods section, note that the table and figure numbers 

refer to ones in the current report, unless indicated as being found only in the 1998 report. 
62

September 30, 1997

Standard Operating Procedures

for

AERIAL SURVEYS

for

Development of Coastal Monitoring Protocols and Process-Based Studies to Address LandscapeScale Variation in Coastal Communities of Glacier Bay National Park and Preserve (GLBA), 

Katmai National Park and Preserve (KATM), and Wrangell-St. Elias National Park and Preserve 

(WRST)

Purpose

The purpose of monitoring programs is to detect change in populations or communities through 

time. If populations and communities are stable, then only an inventory of the populations or 

communities would be required. An inventory over large areas requires sampling to make 

inferences regarding populations over the area of interest. Glacier Bay proper encompasses over 

one thousand kilometers of coastline. As this is far too large to be thoroughly sampled, aerial 

surveys were carried out in order to provide a preliminary assessment of substrate and biota 

compositions and thus aid in selection of the sites to be sampled as part of the coarse and finegrained sampling levels of the project. The aerial surveys provided estimates of the relative 

abundance of different substrate types (bedrock, cobble/boulder, pebble/gravel, sand/silt/mud, 

coarse sand) within segments as well as biota (Fucus, mussels, barnacles). These standard 

operating procedures were used in 1997 when performing aerial surveys for the coastal 

monitoring protocol development study. 

Site Selection

Within Glacier Bay National Park and Preserve the coastline of Glacier Bay proper (the inner 

part of Glacier Bay NP&P, from Pt. Carolus and Pt. Gustavus northward) was divided into 200 

m segments using ArcInfo software and a digitized coastline (CD-ROM; Geiselman, J. et al. 

1997). This resulted in the identification of 5,545 segments representing a coastline of 1,109 km. 

Based on our assessment that we could aerially survey 250 segments during one minus tide 

cycle, a random number was used to identify a starting segment, and every twenty-third segment 

was then systematically chosen, resulting in 250 segments. Ultimately, 241 segments were 

surveyed by fixed-wing airplane. Segments 173, 174, and 175 were not surveyed because they 

were part of a stream bank rather than intertidal areas. Segment 192 also was not an intertidal site 

and was not surveyed. Segments 185 and 186 were not surveyed because they were on a large 

mud delta criss-crossed by streams and still snow-covered. The areas matching the coordinates 

for segments 17 and 205 did not even vaguely resemble the segment maps from ArcInfo, thus 

they were not surveyed. This area of the Park has been undergoing rapid sedimentation and is 

now quite different than when the last charts were created. Segment 190 was not sampled 

because it was still covered in ice and snow. See figure A-1 for a map of the study area and 

segments.
63

Methods

1. A map of each segment was printed on a data sheet (fig. A-2).

2. A Bellanca Scout on floats equipped with a Trimble 2000 GPS was used to fly the surveys. 

This is a two seat plane where the observer is seated directly behind the pilot and thus can 

view out of both sides of the plane. The coordinates for the segments were loaded from 

ArcInfo onto data cards for use with the plane=s GPS. 

3. Locating the segment. Previous to the sampling we had established a technique to determine 

the 200 m segment from the air. We found that at an altitude of 150 feet, flying 300m 

diameter circles, the distance from the point directly beneath the plane to the point beneath 

the center of the wingtip was 200m ∀ 15%. The pilot used the Trimble to navigate to one 

endpoint of the segment. He would then adjust the altitude and bank into a turn and identify 

markers on the beach (e.g. streams, bedrock outcroppings, large boulders) to use as segment 

boundaries. Frequently, the location of the segment could be verified by details on the 

segment maps (fig. A-2). 

4. Flying the segment. The aerial surveys were flown 3-8 May 1997, during minus tides. If 

daylight permitted, surveys were flown 2 hours before and after the low tide using the Juneau 

tide tables adjusted for Bartlett Cove. Because of the low altitudes and techniques employed 

in the surveys, we stopped flying if winds were >10–15 knots. Recommendations for future 

surveys would be to use the tide tables for the nearest tide station (Bartlett Cove, Willoughby 

Island, Composite Island, Muir Inlet) and sample as long as the tide is lower than 0 MLLW 

(mean lower low water). 

Usually, the pilot flew in tight circles (diameter − 300m) at an altitude of 150–200 feet 

centered over the beach. In steep, constricted areas (e.g. Johns Hopkins Inlet) he would fly 

figure eights over the water, rather than circles. He would fly as many circles or figure eights 

as needed to gather the required data. Early in the surveying process, six or seven passes 

were needed, but this dropped to four or five by the end of the surveying period. Depending 

on light conditions, an “offshore” circle was flown at the minimum altitude in addition to the 

“above beach” circles to better view the barnacle line.

Previous to the sampling, we had familiarized ourselves with the appearances of the 

various substrates and biota from the air. Nevertheless, each morning early in the week of 

sampling and whenever we felt it necessary, we ground-truthed the data we had collected. 

5. Data collection.

Slope: Upon approaching the segment, the slope category was estimated using the water 

surface as the horizontal, and recorded on the data sheet. Categories for slope were: 0-

30Ε, 31-60Ε, 61-90Ε. (Note: Slope data are missing for Segment 6)

Segment area: Once the length of the 200m segment had been identified, the high tide line 

was mentally placed by noting the edge of the Verrucaria (black lichen) zone, the snow 

line, the tree or grass line, or whatever else could be used to assess the line. This now 

created a large rectangle 200m long, from the water to the high tide line.
64

a. Substrate: The substrate types were determined during the approach and initial fly-over. 

First, substrates that were not present were marked as 0% on the data sheet. Next, 

substrates that were very minimal were marked 1-25%. Finally, the segment rectangle 

was mentally broken into chunks (either vertical bands, horizontal stripes or a 

combination of both) and the substrates that were present were assigned percent 

coverages. Percent coverages of substrates should not exceed 100%. Substrate categories 

were based on the sediment surface mapping protocol developed by the Glacier Bay 

Coastal Mapping team. The substrate categories used in the aerial surveys were: 

bedrock, identified as a continuous rock surface; cobble/boulder, identified as larger than 

tennis ball size rocks; pebble/gravel, identified by having particles smaller than tennis 

ball size yet still recognizable as separate particles; sand/silt/mud, identified by not 

having recognizable individual particles, often appearing as one continuous surface; and 

coarse sand. Usually, silt/mud could be identified separately from sand because it became 

deeply channeled, had many colorful algae growing on it, and seeped out thus clouding 

the water. (Note: substrate percent coverages are missing for Segment 197, though 

substrates present are listed).

b. Biota: During approach and substrate determination, a preliminary scan for Fucus, 

mussels, and barnacles was made. After substrate determinations were finished, any 

species that was not present was marked as 0%. Next, species that were very minimal 

were marked 1–25%. Finally, the segment rectangle was mentally broken into chunks 

(either vertical bands, horizontal stripes or a combination of both) and the species that 

were present were assigned percent coverages. Total percent coverages of biota on a 

segment ranged from <100% to >100%. Fucus, growing in dense beds down to small 

clumps, was readily identifiable from the air. If mussels were present as a continuous 

band they were easy to identify. If mussels were patchy or covered with barnacles or 

barnacle spat, they were more difficult to identify from the air. The surest method for 

recognizing mussels was to scan the sides of any boulders or larger cobbles. Mussels, 

even if covered by barnacle spat, contrasted with the surface of the larger rocks. On 

segments comprised of pebble/gravel, mussels were extremely difficult to recognize and 

were possibly underestimated. The barnacle zone was recognizable by its distinctive 

greyish-chalky color. The pilot usually had to experiment with altitude and distance 

offshore to bring this color separation into view. If barnacles were patchy or the segment 

was pebble/gravel, barnacles were more difficult to recognize and may have been 

underestimated. Several other biotic groups were occasionally visible and recognizable, 

although not widespread throughout the park: encrusting red algae, green algae 

(individual species could not be differentiated from the air), subtidal brown algae (e.g. 

Alaria), subtidal urchin barrens, and sea stars. These were noted on the data sheets but no

estimates of percent coverage were given. 

Geiselman, J., Dunlap, J., Hooge, P., and D. Albert. (Eds.). 1997. Glacier Bay Ecosystem GIS 

CD-ROM set. U.S. Geological Survey and Interrain Pacific. Anchorage and Juneau, Alaska.

References

Authors? 1997. Sediment surface mapping protocol for Glacier Bay Coastal Mapping, Coastal 

Monitoring, and Benthic Surveys. (sedprot.w51)
65

Figure A-1. Map of the study area showing Glacier Bay proper. Each red dot represents one of the 200m 

segments sampled during the 1997 aerial survey. The eight “X” marks show sites that were not sampled. 
66

Figure A-2. An example of a completed data sheet/segment map from the aerial surveys. The thickened 

line delineates the 200m segment. The numbers 276 and 278 are the endpoints corresponding to the GPS 

coordinates given in the upper left corner. 
67

Methods (from Irvine, 1998)

Objective 2: Sampling Design

Methods used in the three sampling designs [a. broad-scale inferential, b. nested 

inferential (=coarse-grained), and c. nested, intensive (=fine-grained)] are detailed in the 

preceding Aerial Survey SOP and below. The first level (a) used aerial surveys, while (b) and (c) 

used on-site sampling of differing degrees of intensity. Figure numbers in this section refer to 

figures within the main body of the report.

A. Broad-scale, inferential (= Aerial surveys). See Aerial Survey SOP (preceding this section)

B. Nested inferential (selected habitat, coarse-grained).

Under the nested, coarse-grained category, a smaller and more selected set of sites was 

sampled at a greater level of detail than the broad aerial categorization of the coast accomplished 

under (A) above. The first decision was to select a habitat type for examination. Stratification by 

habitat type should reduce the level of variability of species abundances. As mentioned above, 

we initially had been asked by the park to study protected rocky (bedrock) habitat. However, 

only 3 of the 241 segments surveyed by air were comprised of 76% or greater bedrock, with a 

slope <60 degrees. At that point, we had the option to: (1) conduct more aerial surveys to 

increase the number of segments of the targeted habitat type, (2) relax the habitat parameters to 

include both bedrock and a more predominant habitat type, or (3) change the habitat choice 

altogether. We decided on the basis of further discussions with Center staff (including Mark 

Udevitz) and the Park staff, to relax the habitat parameters and include unconsolidated substrates 

(cobble/boulder) as a target of this study. All those segments with bedrock (≥1%) and/or 76–

100% cobble/boulder were included.

This change in targeted habitats resulted in a larger pool (111) of the original 241 

segments being considered for further study. Thirty segments were randomly selected for 

sampling. However, several selected segments had to be deleted because they were too steep, 

had no land exposed at high tide, or were in wildlife protection areas where no access was 

allowed. Twenty-five sites (fig. 2) were ultimately sampled (Protocol for Coarse-Grained 

Sampling).

Coarse-grained surveys were undertaken at this selected subset of protected bedrock and 

cobble/boulder sites. During surveys we: (1) verified the substrate composition of the segment 

and its associated assemblage of species, (2) laid out 6 line transects running parallel to the 

elevational gradient (vertical transects) from mean high high water (MHHW) to the 0 foot tide 

level ( mean low low water, MLLW), (3) sampled a systematic set of points along the transects 

(1 point/m in 1997; 5 pts/m in later years) to indicate zonation as well as the relative abundance 

of species along the transect tape, (4) sampled band transects along the transect line (1 m to each 

side) for larger organisms (starfish, sea urchins, and large chitons), (5) conducted a CTD 

(conductivity, temperature depth) cast off of each site, and (6) photographed the site (still shots).

The sampling area of the segment was demarcated at its upper side by a horizontal 

segment line laid at the lower edge of the Verrucaria (black lichen) zone, defined as 

approximately 20% coverage of Verrucaria, usually close to the juncture of the barnacle and 

Verrucaria zones. On some beaches, the beach wrack and/or beach grass were used as indicators 
68

of the highest tide levels. The horizontal segment line was laid parallel with the shoreline. Six 

line transects were laid out parallel to the elevational gradient (“vertical“) and perpendicular to 

the shoreline, running from the horizontal segment line down to mean MLLW. The 0' tide level 

was determined by using the Tides and Currents computer program (Nautical Software, Inc.) for 

the sampling date and nearest tide station location (Bartlett Cove, Willoughby Island, Composite 

Island, or Muir Inlet). The start of the first vertical transect line was determined randomly within 

the first 33 m of the horizontal segment line; the succeeding transect lines were laid out 

systematically with respect to the first. One point was sampled at each meter interval of the 

vertical transects (0, 1, 2 m, etc.): Note: at some sites sampling was increased in density, and in 

later years, sampling was conducted at 5 points/m. Point sampling was 3-dimensional; all species 

underlying the point were recorded, from top to bottom, and substrate underlying the point was 

also recorded.

We expected to sample two sites per low tide, six days of low tides per low tide cycle, for 

two low tide cycles, giving 24 total expected sites sampled, however, the sites were spaced 

sufficiently far apart that usually only one site could be sampled on a tide. Data obtained were 

the percent coverage of sampled species, densities of species counted in the swath surveys, and 

measures of zonation as reflected in the sampling of the vertical transects.

Initial sampling in May at a site (#63) with very long transects (up to 140m) caused a reexamination of the intensity of sampling that was possible at this level of the survey. Although 

we had planned originally to sample every 20 cm, we had to scale the sampling to one point per 

meter in order to be able to complete the sampling of 6 transects by 3 people in one tide. So the 

one point sampled per meter of transect length became the standard for all coarse-grained level 

sampling. When we moved up bay and found the transects were considerably shorter, we 

increased the density of sampling (in order to increase the biological information on a site), but 

analyses using 1997 data, except where noted, compare all coarse-grained sites at the standard 

level of sampling (one point/meter).

C. Intensive sampling (selected habitat, fine-grained)

The shift in habitats focused on in Level B (the coarse-grained surveys) had 

consequences for the selection of sites for Level C (the fine-grained, intensive sampling). The 

lack of bedrock-dominated segments with slopes less than 60 degrees in the aerial surveys 

prompted relaxation of the habitat parameters, which resulted in a mixed habitat type becoming 

the focus in Level B. The 25 Level B sites contained bedrock and/or cobble/boulder substrates, 

with the mix ranging from 76% or greater bedrock with 1–25% cobble/boulder to 76% or greater 

cobble/boulder with no bedrock. These sites had been drawn from the pool of all sites containing 

bedrock, plus all 76% or greater cobble/boulder sites; these latter did not have to have a bedrock 

component.

The methods of selecting and sampling the sites affect the ability of the park to generalize 

from the selected sites to other similar sites. We planned to sample six sites at this level. Two 

factors weighed in narrowing the focus of sites and habitats selected for fine-grained sampling. 

First, in light of the variability in the habitat mix of the coarse-grained sites, we decided, in 

consultation with Center and Park staff to concentrate efforts on predominately cobble/boulder 

sites. In part, the park advocated for consideration of this habitat type (or subset of Level B 

habitat mix) because of its greater abundance within Glacier Bay proper than low-angle bedrock 

sites. Only those sites with 76% or greater cobble/boulder were considered for inclusion. Having 
69

a bedrock component did not eliminate a site. For example, Site 69 was included, and it 

contained both 76% or greater cobble/boulder and 1–25% bedrock. Second, we decided to 

narrow the geographical scope of the sites sampled in this third level, to sites within a region 

readily accessible from Bartlett Cove by skiff, in order to allow for frequent access. Thus the 

region from just south of the two arms to approximately the Bartlett Cove level was chosen. Two 

methods of maintaining inference to the broader pool of sites were apparent. One, to choose a 

subset of the sites from level B that fit the parameters described above, or two, to draw a new 

pool of sites from Level A that fit the parameters. We decided to choose a subset of those from 

level B, in part because we then had the advantage of comparing the two types of sampling at the 

same sites. In total, below the juncture of the two arms in Glacier Bay proper, were eight sites 

that had been defined as predominately cobble/boulder by the aerial surveys. One of those in 

Geike was discarded because the level B surveys had revealed that little cobble/boulder was 

present. The most southerly geographic outlier was also dropped. This resulted in the selection of 

six sites that ranged from just south of the two arms to slightly south (but across bay) of Bartlett 

Cove (fig. 2). The data from these sites is applicable to the range of cobble/boulder habitat within 

the band or region that the sites occupy, as these sites were originally randomly selected from a 

pool of the selected habitat type (Level B).

Within-site sampling

All of these sites had been sampled under the coarse-grained regime. The sampling at the 

fine-grained level included sampling that was similar to the coarse-grained level, although more 

intensive, plus additional methods of sampling. Locating the site and delineating the horizontal 

segment tape were accomplished in the same manner as under the coarse-grained procedures.

The within-site sampling included: 

1. vertical transects, 

2. horizontal transects, 

3. band sampling of both vertical and horizontal transects,

4. quadrat sampling, both point-contact and counts of small mobile invertebrates

5. mussel sampling, and 

6. fixed quadrats (at one site),

7. photographs of the various sampling units, and 

8. some additional marking of the site. 

The layout of the sampling is depicted in figure 3.

1. Vertical transects: 

Line transects (vertical transects) were laid out as under the coarse-grained protocol, except 10 

instead of 6 were laid out. These transects were positioned parallel to the elevational gradient of 

the segment, running from the horizontal segment line down to MLLW. The start of the first 

vertical transect along the horizontal segment tape was determined by choosing a random 

number between 0 and 20, and the remaining transects were placed systematically relative to the 

first. Transect lines were draped so as to generally conform to the substrate, rather than being 

stretched taut. The intention was that all exposed surfaces had an equal probability of being 

sampled.
70

Point sampling of Vertical (and Horizontal) Transects: Systematically selected points along the 

vertical transects were sampled to provide estimates of the relative percent cover of biota along 

the transects, as well as the relative percent cover of underlying substrates. This sampling of 

points also provides information on the zonation of species. At fine-grained sites, five points 

were sampled per meter (each 20 cm).

Points were sampled 3-dimensionally, noting the species and substrate under each point. The 

right edge of the tape and the distance hash marks along the tape were used as cross hairs to 

mark the point. We used knitting needles to facilitate the 3D sampling of the naturally 

heterogeneous topography for all types of point sampling on the fine-grained level (vertical and 

horizontal transects, and point-contact quadrats). The knitting needle was used to follow the 

point perpendicularly from the tape to the substrate. All species underlying the point were 

recorded, in order from the top down, including multiple “hits“ or layers of the same species. 

The substrate, which also could be multi-layered, was recorded. Organisms were identified to the 

lowest taxonomic level possible in field sampling. Some taxa are grouped because they could not 

be discriminated in the field readily. Substrates were classified according to a modified 

Wentworth-scale (appendix D1.2, this report).

2. Horizontal transects: 

(a). Determining the elevations at which horizontal transects and quadrats are set. As set out in 

the initial protocol, the total tidal range of a site was going to be determined by assessing the 

angle of the slope, and multiplying the sine of that angle by the length of the transect (the 

hypotenuse). We planned to define three elevational zones by dividing the tidal range into thirds. 

A random number was then going to be used as a multiplier to determine the point within each 

vertical zone to be sampled, so that each zone was sampled at the same proportionate elevation. 

The same random number (0.71) was applied to all six sites sampled this round in order to 

facilitate comparisons between sites. This number set the sampling locations fairly high within 

each zone.

After determining the tidal heights for sampling along each transect, the actual locations would 

have been determined through use of the tide programs. Although we initially set up our 

calculations at Site 63 in this manner, we could not follow the procedure because the high tides 

were not going to be high enough for us to locate, via the tide tables, the actual locations of the 

upper horizontal transects. Thus, we modified the procedure in the field, and decided that we had 

to assume an equal slope and use the distance of the transect line as a proxy of the elevational 

gradient. So, in the field, the transect length was divided into thirds: a low, middle, and high 

portion, and a random number (0.71) was multiplied by the length of each third to determine the 

location for placement of the horizontal transects. For example, if the transect length was 45 m, 

each 1/3 segment equals 15 m, and the sampling location within each third is the random number 

(0.71) times 15 m = 10.65 m from the lower end of that third of the transect. This same method 

was used for all sampling of fine-grained sites in 1997.
71

(b). Laying and sampling horizontal transects. Horizontal transects were 10 m in length, with the 

origin at the vertical tape, and the horizontal tape laid to the right as one faces shore. They were 

run parallel with the water’s edge, unless they were constrained in other ways. For example at 

site 69, where the bedrock cliff jutted out and made sampling a line parallel with the water 

impossible, then the transect line was run parallel with the segment line. Point sampling was 

conducted in the same manner and at the same intensity as for the vertical transects. The 

sampling under each point was 3D, and was recorded as for the vertical transects.

3. Band sampling: 

Band surveys were conducted along both vertical and horizontal transects as was done in the 

coarse-grained sampling. A band one meter to each side (total width of 2 meters) of the transect 

tape was surveyed for the presence and abundance of specific species, primarily mobile species 

or larger, rarer species unlikely to be sampled well by point sampling (band species were 

identified on the species identification list). The number of each band species observed per meter 

was recorded in the appropriate place on the data sheet. If no band species were observed, that 

was noted. During the band surveys, substrate was not disturbed, therefore species that more 

commonly are found in the crevices between cobbles/boulders or under them were probably 

under-represented (e.g., the six-armed starfish, Leptasterias hexactis).

At some sites, a dense Fucus zone made it difficult to observe band species. In these cases, the 

bands were scanned from 0 MLLW to the bottom of the Fucus zone (this was noted on the data 

sheet as well as the distances along the transect line that were searched). In most cases, the Fucus

zone was scanned, but Fucus was not moved if it was dense. In 1997, dense Fucus occurred at 

only a few sites, primarily in the lower bay area.

4. Quadrat sampling (33.3 cm × 33.3 cm quads): 

Quadrats used were 1/9 m2

, with an inner diameter of 33.3 cm per side. A grid of lines was set 

across the quadrat frames (see fig. 4), and the 36 intersections of the grid created the sampling 

points. Quadrats were laid out in a systematic manner at the interstices of the vertical and 

horizontal transects, with the lower edge of the quadrat set along the upper side of the horizontal 

transect, and the left edge to the right of and along the vertical transect. The quadrat frame was 

allowed to lie naturally relative to the slope and heterogeneity of the substrate. Occasionally, a 

quadrat was unstable and had to be supported in order to be assessed. Once the quadrat frame 

was set, points were sampled perpendicular to the plane of the quadrat. Points were assessed on 

substrates with slopes up to 90 degrees; substrates where the angle was >90 degrees 

(underhangs) were ignored. Sampling of points was done in a 3-dimensional manner as for the 

vertical and horizontal transects. The 3-dimensional assessment of points included counting 

multiple layers of biota (of the same or different species), and multiple substrates if they were 

discernible.

Prior to sampling, photos were taken of both the regular quadrat and the adjacent mussel 

counting quadrat (see below), as the sampling could disturb the position of biota within the 

quadrat. Usually, a single photo of both the quadrat and adjacent mussel count quadrat was 

taken. However, if the quadrats lay at different angles separate photos were taken because one 

photo couldn’t be taken normal (perpendicular) to both.
72

After assessing the point contacts, counts were made of mobile species inside the quadrat, 

including: chitons, limpets, Nucella spp. (predatory gastropods), nemerteans, sea urchins, and 

starfish. Counts of mobile species were done to the lowest species distinctions. Limpets <8 mm 

were lumped into Lottiidae, due to difficulties of accurately identifying small limpets (Nora 

Foster, pers. comm.). In addition to counts of mobile species, the quadrat was scanned for the 

presence of other sessile species not counted under the points (including species like Mytilus, 

whose abundance was highly variable within quadrats). These species were listed as present. 

Mytilus was not uniformly scanned for presence in 1997.

A small subquadrat (10 × 10 cm) set within the upper left of the 33.3 cm × 33.3 cm quadrat was 

used to assess the density of littorine snails. In 1997, Littorina scutulata was noted by one 

observer to be present only at Site 59; L. sitkana having been the predominant species observed. 

Other observers either saw only L. sitkana, or did not discriminate species.

Barnacle spat. The density of barnacle spat was assessed in the upper left quadrant (5 x 5 cm) of 

the subquadrat (10 × 10 cm) in which littorines were counted. For 1997, barnacle spat for the 

quadrat sampling within the fine-grained sampling, which took place late July to August, was 

defined as barnacles from 0–2 mm diameter. In the coarse-grained sampling, which took place in 

June to mid-July, barnacle spat was considered 0–1 mm.

5. Mussel quadrat sampling: 

There were two types of mussel quadrat sampling: (a) sampling within the mussel zone along 

each vertical transect (with collection of mussels and Fucus), and (b) non-disruptive sampling set 

systematically along the vertical transects. In both types of mussel sampling, a square 500 cm2

quadrat was used.

(a). Sampling within the mussel zone followed the layout and sampling procedure of the Exxon 

Valdez oil spill Nearshore Vertebrate Predator (NVP) Project mussel sampling, with one minor 

placement deviation noted below. Briefly, the length and location of the mussel zone was defined 

along each vertical transect, and a random number from 0–1.0 was used as a multiplier to 

establish the location of the lower horizontal edge of the quadrat within the mussel zone. The 

random number was multiplied by the length of the mussel zone, then the number was added to 

the upper edge of the mussel zone to determine the quadrat location relative to the distance down 

the vertical transect. In our sampling, the 500 cm2 quadrat was displaced one quadrat width to the 

left of the vertical transect line, so as to not interfere with the second type of mussel quadrat 

sampling, in which the quadrat was placed adjacent to the vertical transect line (at three locations 

along each line). One quadrat was sampled per vertical transect line under the first type of 

mussel sampling. In these quadrats, all mussels and Fucus were collected from the quadrat, 

placed into a labeled plastic bag, and frozen upon return to the ship, or kept cooled until returned 

from the field. While removing the mussels and Fucus, if shape of the substrate allowed, then all 

surfaces were scraped of biota in order to be able to use these clearings to follow recruitment and 

succession. Back in the lab, mussels were measured, and the length and reproductive status of 

Fucus plants were recorded.
73

(b). Systematic sampling along the vertical transect lines involved non-destructive sampling of 

500 cm2 quadrats laid out systematically at three points along each vertical transect line. The 

quadrats were laid out at the intersections of the vertical and horizontal transects, on the left hand 

side of the vertical transect line, as one faces shoreward. The lower right hand corner of the 500 

cm

2 quadrat is opposed, across the transect line, from the lower left hand corner of the 33.3 cm ×

33.3 cm quadrat, and both have their lower edge at the elevational level defined by the horizontal 

transect (fig. 3). As mentioned above in the discussion of the 33.3 cm × 33.3 cm quadrat 

sampling, photos were taken of both quadrats before they were assessed. Three quadrats were 

placed along each vertical transect line, for a total number of quadrats sampled across the site of 

30. Within these quadrats, mussels were counted, but not collected and substrates were not 

disturbed. Because mussels were sometimes very dense, the quadrat frame was subdivided into 

three sections with line to facilitate counting, and tally counters were used. Any mussel even 

partially within the quadrat frame was counted, irrespective of the amount of its shell in the 

quadrat. All mussels that could be observed without disturbing substrates within the quadrat were 

counted, including ones in crevices or beneath underhangs, and the smallest identifiable mussels.

6. Fixed quadrats: 

In 1997, time constraints and design challenges led to fixed quadrats being set up and sampled 

only at Site 69. Ten fixed quadrats (33.3 cm × 33.3 cm) were laid out and marked on boulders of 

sufficient size. At this site, the boulders were selected arbitrarily and were spread over the length 

f the site. These quadrats were sampled in the same manner as the other main quadrat sampling 

on the beach: 36 points assessed, counts of small mobile invertebrates, subsample counts of 

littorines and barnacle spat, and scans for other sessile species. Photographs of quadrats are also 

taken.
74

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75

APPENDIX B – TerraStat Report
76

Review of

“Development of Coastal Monitoring Protocols and Process-Based Studies to Address LandscapeScale Variation in Coastal Communities of Glacier Bay National Park and Preserve: Phase II: 

Development and testing of monitoring protocols for selected intertidal habitats and assemblages 

(Draft Final Report; 2002)”

By Alice Shelly

TerraStat Consulting

5002 Lodge View Lane

Austin, TX 78731

alice@blarg.net

May 14, 2007

This report summarizes my review of the power analysis detailed in your 2002 draft final coastal monitoring 

report for Glacier Bay. Overall, it seems that the power analysis was adequate with reasonable results. 

Although there were some estimation methods that were not ideal, I have now verified (using the updated 

program MONITOR 10.0) that these differences did not significantly affect the power estimation for mussels 

(Mytilus trossulus, MY) and barnacles (BARN). The statistical power for rockweed (Fucus gardneri, FU) 

may have been overestimated in the coastal monitoring report by 5–10%. There remains some uncertainty 

in the results due to the assumption of lognormal errors, which is not ideal for MY and BARN. The only way 

to address this uncertainty would be with an independent simulation program which would allow for 

simulations from empirical error distributions or a more suitable fitted distribution.

With multiple sites, program MONITOR estimates statistical power for a trend monitoring program 

assuming an analysis using route regression. Route regression consists of a linear or exponential 

regression relating years to the estimated percent cover for each site. The slope of the regression line for 

each site is the estimated trend for that site, and the mean of these slopes is tested for differences from 

zero using a t-test. To estimate power, the user inputs site means and variances. MONITOR simulates 

normal or log-normal residuals around projected linear or exponential trend lines as specified by the user. 

Variability among site trends is an optional feature. In version 10.0, the variance can also be partitioned into 

variance among years and variance within years (although this feature is not yet functioning properly per 

James Gibbs 4/3/07).
77

For the Glacier Bay power analysis, the questions of interest are:

1) Given the variability observed over the sampled 3–4 years, what magnitudes of trend were we 

likely to detect for various species?

2) Would varying the number of sites, the number of transects, or the number of samples per transect 

result in large differences to statistical power?

3) Is statistical power more sensitive to changes in sampling design (e.g., number of sites, # of 

transects, # samples/transect, etc.) or to changes in parameters (e.g., different levels of alpha)? 

4) Would varying the sampling type (point sampling of quadrats, horizontal transects or vertical 

transects) result in large differences to statistical power? If there were differences in power among 

the sampling types, is that difference related to the number of points sampled in each? 

The analysis run for Glacier Bay (coarse-grained sites) was a post-hoc analysis, in that the power 

estimates were based on detecting trends within the time period already sampled. With 3–4 years of data, 

linear regression was run on untransformed percent cover estimates for each transect. The variance of the 

residuals from the linear regression was input to MONITOR as the site variance for each site. The average 

cover over all transects over all sampled years was input as the site mean for each site. The effects of 

different sampling designs were analyzed by estimating different variance estimates corresponding to each 

sampling design.

For exponential trend, a log-normal distribution for within-year sampling variability at each site was 

assumed. This assumption was not tested to see if it was a reasonable one for the Glacier Bay cover data. 

Issues with MONITOR (as used)

The new version of MONITOR allows the sampling variance to be partitioned between within-year and 

between-year components. The authors do not expect this to be a critical difference to the power results, 

but it would be interesting to recalculate power using this feature. The variance components could be 

estimated from existing data.

MONITOR 10.0 also allows the user to specify if the mean and the variance are related over time. This is a 

very common scenario, and changes to this component may affect the power estimates more than any of 

the other issues mentioned here. 

Although this does not affect the power analysis, I am not sure why there is not an analysis of the observed 

trends in the report. Since the researchers expect an exponential trend, and this is a post-hoc power 

analysis, the analysis itself should have been conducted first. In the process of selecting the best analysis, 

the distribution of the errors would be tested and the shapes of the trends could be discussed. This would 

also alert the analyst as to whether there are any errors in the data set.

Issues with Data Analysis and Input Parameters

The plot or site means input into program MONITOR should have been the starting mean values in Year 0, 

rather than the average across all years. If the research question were to detect trends in an ongoing 

monitoring program, the mean of all current data might be appropriate, but in the case of a post-hoc test 

such as this, the mean of the first year of sampling should be used. This is not likely to have a large effect 

on the power results.
78

The sampling variance should be estimated by the empirical variance of the residuals from an exponential 

regression fit, rather than a linear fit. This would match the assumptions of program MONITOR. The results 

are likely to be similar, depending on the magnitude and shape of any existing trends. 

There are some other minor things that I may have done differently, but as stated in the start of this

document, I think the overall methods were reasonable. 

To answer some of the issues raised above, I evaluated data for MY, FU, and BARN collected in 1998–

2001 on six vertical transects with five points per meter. Results reported below include an analysis of 

existing trends, data distributions, revised parameter estimates, and limited statistical power results from 

MONITOR 10.0 for comparison purposes. The intention is to determine if the changes to program 

MONITOR and/or changes to parameters suggested above cause large differences in estimated statistical 

power. 

Limited Re-analysis

Testing Assumptions

The metric being tested for trends at this site is percent cover, estimated by counts divided by the number 

of points sampled (multiplied by 100). The estimates can exceed 100% due to layered species. 

MY

I subtracted out the site/year means and tested the combined residuals for normality using Shapiro-Wilks 

goodness-of-fit test with alpha =0.05. The tails of the distribution were too thin to fit a normal distribution (pvalue = 0.0000), but there was no skew. When the sites were tested separately, all but three had 

approximately normal residuals (sites 63 and 151 have a slight skew, and site 220 appears bimodal). This 

could indicate that variance differs across sites or that these three sites were causing the lack of normality 

overall. When the residuals were standardized for variance, the distribution was skewed with a shortened 

range (p-value = 0.0000).

I also log-transformed the cover (after adding the constant 1 to allow for zeros) and tested the combined 

residuals for normality with the same result (no improvement in fit for log-normal assumption). All but three 

individual sites (site 36 and 215 had a few “outlier” transects with low cover; site 220 appears bimodal) 

were approximately normal after log-transformation.

The above indicates that neither normal nor log-normal simulations are ideal for estimating statistical power 

for these data. An empirical distribution or a box-cox transformation would provide a better fit. However, 

MONITOR 10.0 is the best available software for estimating the power. 

The mean (site/year) and variance are related, as shown in figure B1, so the constant CV option in 

MONITOR 10.0 should be selected.

FU

The distribution of the combined residuals again had thin tails and did not fit a normal distribution (p-value = 

0.0000). When the sites were tested separately, nine sites failed the normality test – mainly for skewness. 

The log-transformed combined residuals are approximately normally distributed (Shapiro-Wilks p-value = 

0.19). The lognormal distribution is the appropriate choice for FU. As with MY, the mean and variance are 

related (see fig. B2).
79

BARN

The distribution of the combined residuals again had thin tails and did not fit a normal distribution (p-value = 

0.0001). When the sites were tested separately, four sites failed the normality test – mainly for skewness 

(sites 63, 108, 200, and 218). The log-transformation did not approximate normality (p-value = 0.0001), with 

four different individual sites also failing (sites 69, 163, 214, and 216). As with the other species, the mean 

and variance are related (see fig. B3).

Testing for Exponential Trends

A linear regression was fit to log-transformed density data for each site for each species to test for 

exponential trends.

MY

The trends (slopes) for MY ranged from a decrease of 50% per year (Willoughby Island - West) to an 

increase of 125% per year (Muir Inlet – Upper), and the slopes were approximately normally distributed. 

The average trend was a decrease of 8%, which was not significantly different from zero using a two-tailed 

t-test (p-value = 0.21). Trends for individual sites are displayed in table B1. There are significant positive 

and negative trends observed at some individual sites, but no significant regional trend in MY over the 

years 1998–2001. Note that the individual regression significance results are only valid if the 

measurements from year to year are independent. Since the transects are randomly selected in each year, 

these should be approximately independent.

FU

The trends for FU ranged from a decrease of 70% per year (Russel Island) to an increase of 220% per year 

(Berg Bay), and the slopes were approximately normally distributed. The average trend was an increase of 

13%, which was not significantly different from zero using a two-tailed t-test (p-value = 0.27). Trends for 

individual sites are displayed in table B2. There are significant positive and negative trends observed at 

some individual sites, but no significant regional trend in FU over the years 1998–2001. 

BARN

The trends for BARN ranged from a decrease of 16% per year (Muir Inlet - Lower) to an increase of 77% 

per year (Lester Island). The distribution of slopes failed the test for normality at alpha = 0.05 (p-value = 

0.0095). However, the t-test is fairly robust to non-normality, and the result for the nonparametric Wilcoxon 

test was similar. The average trend was an increase of 22.5%, which was significantly different from zero 

using a two-tailed t-test (p-value = 0.00005). Trends for individual sites are displayed in table B3. There are 

significant positive trends observed at some individual sites, and a significant increasing regional trend in 

BARN over the years 1998–2001.

MONITOR Parameter Estimates

The plot means for MONITOR are the starting means for the trends. These should be the 1998 plot means, 

as displayed in tables B4-6. The plot variances, shown in the tables as standard deviations, were estimated 

by the residual variance from exponential regressions fit to untransformed densities. These plot variances 

include all sources of variability: spatial variability, sampling error, and random variability due to year effects 

(e.g., weather). The final version of MONITOR 10.0 will have capability to account for random variability 

due to year as a separate parameter, but in the existing beta version (as of 5/7/07) this capability is not 

correctly working (personal communication, James Gibbs). However, the parameters may be of some 

interest in understanding the sources of variability for the different species. The variance components for 
80

between versus within years (displayed in tables B4-6 as standard deviations) were estimated using 

restricted maximum likelihood estimates for random components of variance model. 

For MY, the variation among years (after adjusting for trend) is effectively zero for all but six sites. At least 

for the existing three years of data, the majority of sites have very little variance due to random year effects 

(relative to within year variance). In this case, partitioning the variance is unlikely to have a strong effect on 

statistical power. The two plots in figure B4 show an example of two sites with different variance 

characteristics. MY cover at Shag Cove, in the top plot, has very little variability among years after the trend 

is removed. At Upper Drake Island, however, there is variability both among and within years.

For FU, the results are similar to those for MY, except that there are nine sites with some annual variability. 

Barnacles have mixed results across sites, with some sites exhibiting more variability among years than 

within years. Thus, power for BARN would be the most likely to change if the components of variance are 

split.

MONITOR 10.0 Analysis

The statistical power was estimated using MONITOR 10.0 dated 3/14/07 at this web address:

http://www.esf.edu/efb/gibbs/MONITOR/.

Power was estimated with the original parameters used by USGS, as well as with the new parameters 

estimated and discussed above. The plot means were originally the mean of all samples across three 

years, whereas I used the means of the first year’s samples only. The plot standard deviations were 

originally the standard error of the residuals from a linear regression on untransformed data. I used the 

standard error of the residuals from an exponential regression. For reduced numbers of sites, the original 

analysis used one random subset of sites to estimate the power. I averaged the statistical power over 10 

random subsets of the 25 sites. I also used 1000 instead of 500 simulations in MONITOR, to create more 

stable results.

The plots in figures B5 and B6 show the power estimates for MY with 25 and 15 sites with 6 transects, each 

having five points per meter. The yellow triangles show results from the original report (partial series). The 

brown circles show the current runs on MONITOR 10.0 using the original parameters, but with 1000 

replications. The blue squares show the current runs on MONITOR 10.0 with the TerraStat parameters. 

The results show that the original power estimates for MY using earlier parameter estimates and previous 

versions of MONITOR were almost identical to current estimates.

The plot in figure B7 shows that the change in parameterization had the effect of reducing the statistical 

power for FU by 5-10%. This is likely due to the reduced plot mean estimates – the new parameters 

represent an average decrease of 4% in the starting mean. In contrast, Mytilus and Barnacle plot means 

had an average increase in starting mean (although there was high variability across sites.) Results for FU 

using MONITOR 10 are similar to previous results when the original parameters are used. The power for 15 

sites was not estimated in the previous report.

The plot in figure B8 shows that the original power results for BARN are equivalent to the estimates with 

MONITOR 10, and with revised parameters.
81

Site/Year Mean Percent Cover

Site/Year Variance

0 20 40 60 80

0 200 400 600 800 1000 1200 1400

Figure B1. Variance of MY cover within a year at each site plotted against the average cover.

Site/Year Mean Percent Cover

Site/Year Variance

0 50 100 150

0 2000 4000 6000

Figure B2. Variance of FU cover within a year at each site plotted against the average cover.
82

Site/Year Average Percent Cover

Site/Year Variance

0 20 40 60

0 100 200 300 400

Figure B3. Variance of BARN cover within a year at each site plotted against the average cover. -20 0 20 40 Average Percent Cover Residuals

0 1 2

Year

Shag Cove

-20 0 20 40

Average Percent Cover Residuals

0 1 2

Year

Drake Island, Upper

FigureB4. Comparison of residual variance sources within and between years for MY percent cover for two 

selected sites.
83

0

0.2

0.4

0.6

0.8

1

-0.10 -0.08 -0.06 -0.04 -0.02 0.00 0.02 0.04 0.06 0.08 0.10

Trend Magnitude

Statistical Power

Original M10 TS Parameters M10 Orig Parameters

Figure B5. Statistical power comparison for MY with 25 sites.

0

0.2

0.4

0.6

0.8

1

-0.10 -0.08 -0.06 -0.04 -0.02 0.00 0.02 0.04 0.06 0.08 0.10

Trend Magnitude

Statistical Power

Original M10 TS Parameters M10 Orig Parameters

Figure B6. Statistical power comparison for MY with 15 sites.
84

0

0.2

0.4

0.6

0.8

1

-10% -5% 0% 5% 10%

Original M10 TS Parameters M10 Orig Parameters M10 15 sites

Figure B7. Statistical power comparison for FU.

0

0.2

0.4

0.6

0.8

1

-0.1 -0.05 0 0.05 0.1

Original M10 TS Parameters M10 Orig Parameters M10 - 15 Sites

Figure B8. Statistical power comparison for BARN.
85

Table B1. Trend results by site for percent cover of MY.

Site 

Number Site Name Slope p-value Trend

36 Bear Track 0.126 0.472

69 Berg Bay 0.288 0.023 +

63 Between Pt Carolus & Ripple Cove -0.385 0.109

113 Blue Mouse Cove 0.076 0.273

217 Drake Island - Lower -0.107 0.111

216 Drake Island - Mid -0.434 0.001 -

218 Drake Island - Upper -0.200 0.089

89 Geikie Inlet - Lower 0.036 0.747

88 Geikie Inlet - Upper 0.154 0.150

59 Lester Island -0.218 0.209

215 Little Sturgess Island -0.059 0.803

143 Mt Abdallah Outwash -0.112 0.242

12 Muir Inlet - Lower 0.405 0.002 +

2 Muir Inlet - Upper 0.813 0.000 +

62 Pt Carolus -0.167 0.100

151 Rendu Inlet 0.102 0.763

200 Russel Island -0.471 0.010 -

108 Scidmore Bay -0.024 0.886

220 Shag Cove -0.135 0.212

214 Sturgess Island -0.224 0.177

142 Tarr Inlet -0.014 0.867

164 Tidal Inlet - Lower -0.071 0.576

163 Tidal Inlet - Upper -0.389 0.004 -

223 Willoughby Island - East -0.282 0.029 -

224 Willoughby Island - West -0.692 0.001 -
86

Table B2. Trend results by site for percent cover of FU.

Site 

Number Site Slope p-value Trend

36 Bear Track -0.611 0.055

69 Berg Bay 1.17 0.000 +

63 Between Pt Carolus & Ripple Cove 0.641 0.111

113 Blue Mouse Cove 0.068 0.908

217 Drake Island - Lower -1.01 0.002 -

216 Drake Island - Mid 0.411 0.047 +

218 Drake Island - Upper 0.157 0.339

89 Geikie Inlet - Lower 0.818 0.001 +

88 Geikie Inlet - Upper 0.555 0.015 +

59 Lester Island 0.444 0.211

215 Little Sturgess Island 0.410 0.407

143 Mt Abdallah Outwash -0.120 0.564

12 Muir Inlet - Lower 0.152 0.652

2 Muir Inlet - Upper 0.253 0.451

62 Pt Carolus 0.317 0.097

151 Rendu Inlet 0.310 0.471

200 Russel Island -1.212 0.006 -

108 Scidmore Bay 0.103 0.743

220 Shag Cove 0.107 0.660

214 Sturgess Island 0.217 0.510

142 Tarr Inlet -0.352 0.267

164 Tidal Inlet - Lower 0.421 0.055

163 Tidal Inlet - Upper -0.653 0.088

223 Willoughby Island - East -0.149 0.655

224 Willoughby Island - West 0.665 0.064
87

Table B3. Trend results by site for percent cover of BARN.

Site 

Number Site Slope p-value Trend

36 Bear Track -0.011 0.974

69 Berg Bay 0.071 0.771

63 Between Pt Carolus & Ripple Cove 0.116 0.713

113 Blue Mouse Cove 0.449 0.019 +

217 Drake Island - Lower 0.002 0.989

216 Drake Island - Mid 0.097 0.557

218 Drake Island - Upper 0.093 0.676

89 Geikie Inlet - Lower -0.014 0.887

88 Geikie Inlet - Upper 0.279 0.333

59 Lester Island 0.570 0.057

215 Little Sturgess Island 0.437 0.026 +

143 Mt Abdallah Outwash 0.373 0.004 +

12 Muir Inlet - Lower -0.169 0.076

2 Muir Inlet - Upper 0.494 0.018 +

62 Pt Carolus -0.039 0.899

151 Rendu Inlet 0.462 0.092

200 Russel Island 0.426 0.013 +

108 Scidmore Bay 0.468 0.083

220 Shag Cove 0.044 0.668

214 Sturgess Island 0.007 0.979

142 Tarr Inlet 0.363 0.259

164 Tidal Inlet - Lower 0.008 0.962

163 Tidal Inlet - Upper 0.049 0.795

223 Willoughby Island - East 0.470 0.006 +

224 Willoughby Island - West 0.350 0.022 +
88

Table B4. Plot means and standard deviations for MY percent cover.

Site 

Number Site

Plot Means USGS 

Original 

Mean 

Estimate

Plot 

Standard 

Deviation

USGS 

Original 

SD 

Estimate

Standard 

Deviation 

Among 

Years

Standard 

Deviation 

Within 

1998 1999 2001 Years

2 Muir Inlet – Upper 2.48 4.59 14.2 6.8 2.99 3.26 0.00 2.99

12 Muir Inlet – Lower 19.7 14.7 42.8 27.4 8.42 8.45 4.04 7.70

36 Bear Track 11.8 7.6 13.5 11.0 5.15 5.33 2.14 4.83

59 Lester Island 1.39 1.34 0.47 1.07 1.26 1.28 0.00 1.26

62 Pt Carolus 1.13 1.12 0.520 0.9 0.679 0.686 0.00 0.679

63 Between Pt Carolus & Ripple Cove 8.57 5.41 3.19 5.73 5.30 5.50 0.00 5.30

69 Berg Bay 25.6 33.5 42.0 33.7 11.8 12.2 0.00 11.8

88 Geikie Inlet - Upper 6.61 8.18 9.27 8.02 3.27 3.38 0.00 3.27

89 Geikie Inlet - Lower 28.2 27.7 31.1 29.0 11.4 11.7 0.00 11.4

108 Scidmore Bay 28.7 28.6 25.7 27.7 15.9 16.4 0.00 15.9

113 Blue Mouse Cove 22.1 21.9 25.9 23.3 5.67 5.80 0.00 5.67

142 Tarr Inlet 26.1 18.8 25.1 23.3 6.81 7.03 2.90 6.36

143 Mt Abdallah Outwash 24.5 23.5 19.5 22.5 6.69 6.85 0.00 6.69

151 Rendu Inlet 8.61 12.9 8.8 10.1 9.90 10.2 0.00 9.90

163 Tidal Inlet - Upper 26.4 19.5 13.1 19.7 7.02 7.34 0.00 7.02

164 Tidal Inlet - Lower 59.7 59.8 55.8 58.4 20.5 21.1 0.00 20.5

200 Russel Island 56.1 45.4 25.8 42.2 17.9 18.2 0.00 17.9

214 Sturgess Island 22.6 27.8 14.7 21.7 10.5 10.5 3.62 10.1

215 Little Sturgess Island 33.4 29.0 23.1 28.5 16.6 17.1 0.00 16.6

216 Drake Island - Mid 72.2 45.9 27.2 48.4 21.6 22.8 0.00 21.6

217 Drake Island - Lower 52.2 34.3 42.2 42.9 9.7 10.6 6.48 8.08

218 Drake Island - Upper 76.8 38.3 49.3 54.8 20.2 22.5 10.9 18.0

220 Shag Cove 54.0 44.9 40.3 46.4 16.8 17.4 0.00 16.8

223 Willoughby Island - East 40.8 36.4 25.5 34.2 11.0 11.1 0.00 11.0

224 Willoughby Island - West 85.5 44.3 24.2 51.3 24.0 26.7 0.00 24.0
89

Table B5. Plot means and standard deviations for FU percent cover.

Site 

Number Site

Plot Means USGS 

Original 

Mean 

Estimate

Plot 

Standard 

Deviation

USGS 

Original 

SD 

Estimate

Standard 

Deviation 

Among 

Years

Standard 

Deviation 

Within 

1998 1999 2001 Years

2 Muir Inlet - Upper 25.6 49.1 33.5 36.1 34.2 35.4 0.00 34.2

12 Muir Inlet - Lower 3.37 19.2 4.85 9.14 9.34 9.62 8.36 6.15

36 Bear Track 12.6 1.22 1.13 4.97 9.41 10.4 0.00 9.41

59 Lester Island 14.9 22.5 26.9 21.4 16.5 17.1 0.00 16.5

62 Pt Carolus 31.7 53.7 50.7 45.4 17.6 18.5 3.15 17.4

63 Between Pt Carolus & Ripple Cove 4.08 19.9 22.9 15.6 27.3 28.2 0.00 27.3

69 Berg Bay 1.67 4.21 23.9 9.94 8.14 8.75 0.00 8.14

88 Geikie Inlet - Upper 0 0.282 4.34 1.54 3.66 3.82 0.00 3.7

89 Geikie Inlet - Lower 9.37 26.8 46.8 27.7 18.6 19.1 0.00 18.6

108 Scidmore Bay 19.6 36.1 37.6 31.1 22.4 23.2 0.00 22.4

113 Blue Mouse Cove 45.3 40.8 26.3 37.5 46.1 47.4 0.00 46.1

142 Tarr Inlet 3.80 13.0 1.00 5.94 8.76 8.81 5.38 7.5

143 Mt Abdallah Outwash 45.2 91.7 40.0 59.0 37.8 38.3 25.4 31.2

151 Rendu Inlet 7.30 11.2 13.8 10.8 13.8 14.2 0.00 13.8

163 Tidal Inlet - Upper 8.84 11.2 0.581 6.86 9.64 9.54 1.75 9.53

164 Tidal Inlet - Lower 43.4 74.7 72.3 63.5 37.4 38.9 0.00 37.4

200 Russel Island 63.3 37.1 11.2 37.2 23.6 24.4 0.00 23.6

214 Sturgess Island 71.2 22.1 68.4 53.9 42.0 43.2 22.9 37.3

215 Little Sturgess Island 17.8 52.0 31.0 33.6 36.7 38.1 6.58 36.3

216 Drake Island - Mid 42.8 42.8 89.5 58.4 35.2 36.0 0.00 35.2

217 Drake Island - Lower 95.8 64.1 25.1 61.6 25.5 25.7 0.00 25.5

218 Drake Island - Upper 31.9 27.3 45.0 34.7 18.2 18.6 0.00 18.2

220 Shag Cove 65.9 158 69.1 97.6 69.8 71.6 46.4 57.9

223 Willoughby Island - East 16.8 6.70 15.1 12.8 12.1 12.5 2.02 12.0

224 Willoughby Island - West 6.74 20.9 30.6 19.4 29.4 30.3 0.00 29.4
90

Table B6. Plot means and standard deviations for BARN percent cover.

Site 

Number Site

Plot Means USGS 

Original 

Mean 

Estimate

Plot 

Standard 

Deviation

USGS 

Original 

SD 

Estimate

Standard 

Deviation 

Among 

Years

Standard 

Deviation 

Within 

1998 1999 2001 Years

2 Muir Inlet - Upper 3.79 4.99 8.79 5.86 2.81 2.88 0.0 2.8

12 Muir Inlet - Lower 52.7 37.7 40.4 43.6 11.0 11.8 1.8 10.9

36 Bear Track 24.3 2.25 24.3 16.9 12.0 12.2 12.5 5.9

59 Lester Island 7.77 3.32 24.9 12.0 6.73 7.44 4.0 5.8

62 Pt Carolus 25.1 3.94 24.3 17.8 12.0 12.2 11.6 6.9

63 Between Pt Carolus & Ripple Cove 16.5 5.11 19.4 13.7 12.0 12.3 5.8 11.0

69 Berg Bay 41.6 13.1 47.4 34.0 17.5 17.5 17.6 9.3

88 Geikie Inlet - Upper 10.6 4.30 15.4 10.1 7.17 7.27 4.0 6.3

89 Geikie Inlet - Lower 37.2 29.8 35.8 34.3 11.4 11.8 0.0 11.4

108 Scidmore Bay 18.5 10.5 39.2 22.7 16.6 17.0 5.8 15.8

113 Blue Mouse Cove 30.3 19.9 68.6 39.6 17.1 17.3 12.3 13.6

142 Tarr Inlet 25.7 4.84 51.8 27.4 14.1 15.0 16.2 3.9

143 Mt Abdallah Outwash 20.9 18.6 43.8 27.8 8.49 8.30 5.7 7.0

151 Rendu Inlet 17.3 7.74 37.2 20.7 11.4 11.6 8.5 8.8

163 Tidal Inlet - Upper 29.5 12.2 32.9 24.9 14.3 14.5 9.9 11.7

164 Tidal Inlet - Lower 46.0 24.7 47.6 39.4 17.4 17.7 11.3 14.5

200 Russel Island 30.3 24.2 70.5 42.7 15.3 15.3 10.2 12.8

214 Sturgess Island 40.6 10.5 39.9 30.3 16.6 16.9 16.8 8.7

215 Little Sturgess Island 20.7 11.2 49.5 27.1 11.5 12.1 10.2 7.7

216 Drake Island - Mid 35.9 15.2 43.6 31.6 14.2 14.2 13.6 8.5

217 Drake Island - Lower 47.7 20.6 48.9 39.1 16.4 16.7 15.4 10.1

218 Drake Island - Upper 37.4 10.7 46.2 31.4 17.5 17.5 17.2 9.9

220 Shag Cove 34.2 23.5 38.4 32.0 10.5 10.6 6.4 9.0

223 Willoughby Island - East 23.3 19.5 51.9 31.6 12.4 12.7 6.8 11.0

224 Willoughby Island – West 27.6 23.6 49.3 33.5 12.4 12.4 5.7 11.4
91

APPENDIX C – Additional Power Analyses 

         

y = 0.0355x + 0.0939

R2 = 0.9753

y = 0.0335x + 0.0781

R2 = 0.9866

y = 0.0285x + 0.0653

R2 = 0.9961

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 2 4 6 8 10 12

Power

Number of transects

Mytilus - 10% Decrease 5 points/meter

3 points/meter

1 point/meter

A

y = 0.0355x + 0.0937

R2 = 0.9991

y = 0.0335x + 0.0783

R2 = 0.9995

y = 0.0284x + 0.0677

R2 = 0.9999

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 5 10 15 20 25 30

Power

Number of transects

Mytilus - 10% Decrease

5 points/meter

3 points/meter

1 point/meter

B

Figure C1. Change in the power to detect a 10% decrease in Mytilus as a result of varying the number of 

transects sampled per site with respect to 1, 3, and 5 points sampled per meter. Sampling was conducted 

in 1997 at 6 sites. α = 0.05. A. Points graphed using data. B. Linear trend of power based on data. 
92

y = 0.022x + 0.3387

R2 = 0.9356

y = 0.022x + 0.1773

R2 = 0.9356

y = 0.007x + 0.1557

R2 = 0.5951

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0123456

Power

Points/meter

Mytilus - 10% Decrease 10 transects

5 transects

3 transects

A

y = 0.022x + 0.3387

R2 = 1

y = 0.022x + 0.1773

R2 = 1

y = 0.007x + 0.1557

R2 = 0.9996

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 10 20 30 40 50 60 70

Power

Points/meter

Mytilus - 10% Decrease 10 transects

5 transects

3 transects

B

Figure C2. Change in the power to detect a 10% decrease in Mytilus as a result of varying the number of 

points sampled per meter with respect to 3, 5, and 10 transects per site. Sampling was conducted in 1997 

at 6 sites. α = 0.05. A. Points graphed using data. B. Linear trend of power based on data. 
93

y = 0.0202x + 0.0713

R2 = 0.9954

y = 0.0118x + 0.1143

R2 = 0.9456

y = 0.0202x + 0.0406

R2 = 0.979 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 2 4 6 8 10 12

Power

Number of transects

Fucus - 10% Decrease 5 points/meter

3 points/meter

1 point/meter

A

y = 0.0202x + 0.0713

R2 = 0.9954

y = 0.0118x + 0.1143

R2 = 0.9456

y = 0.0202x + 0.0406

R2 = 0.979

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 10 20 30 40 50 60

Power

Number of transects

Fucus - 10% Decrease

5 points/meter

3 points/meter

1 point/meter

B

Figure C3. Change in the power to detect a 10% decrease in Fucus as a result of varying the number of 

transects sampled per site with respect to 1, 3, and 5 points sampled per meter. Sampling was done in 

1997 at 6 sites. α = 0.05. A. Points graphed using data. B. Linear trend of power based on data. 
94

y = 0.0065x + 0.2318

R2 = 0.4894

y = 0.012x + 0.1207

R2 = 0.9643

y = 0.0045x + 0.1185

R2 = 0.1378

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0123456

Power

Points/meter

Fucus - 10% Decrease

10 transects

5 transects

3 transects

A

y = 0.0065x + 0.2318

R2 = 0.4894

y = 0.012x + 0.1207

R2 = 0.9643

y = 0.0045x + 0.1185

R2 = 0.1378

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 10 20 30 40 50 60 70 80

Power

Points/meter

Fucus - 10% Decrease 10 transects

5 transects

3 transects

B

Figure C4. Change in the power to detect a 10% decrease in Fucus as a result of varying the number of 

points sampled per meter with respect to 3, 5, and 10 transects per site. Sampling was done in 1997 at 6 

sites. α = 0.05. A. Points graphed using data. B. Linear trend of power based on data. 
95

y = 0.0487x + 0.4552

R2 = 0.9408 y = 0.0592x + 0.2144

R2 = 0.9866

y = 0.0572x + 0.1446

R2 = 0.9794

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 2 4 6 8 10 12

Power

Number of transects

Barnacles - 10% Decrease 5 points/meter

3 points/meter

1 point/meter

A

y = 0.0487x + 0.4552

R2 = 0.9408

y = 0.0592x + 0.2144

R2 = 0.9866

y = 0.0572x + 0.1446

R2 = 0.9794

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 2 4 6 8 10 12 14 16

Power

Number of transects

Barnacles - 10% Decrease

5 points/meter

3 points/meter

1 point/meter

B

Figure C5. Change in the power to detect a 10% decrease in barnacles as a result of varying the number 

of transects sampled per site with respect to 1, 3, and 5 points sampled per meter. Sampling was done in 

1997 at 6 sites. α = 0.05. A. Points graphed using data. B. Linear trend of power based on data. 
96

y = 0.0565x + 0.6398

R2 = 0.9925

y = 0.079x + 0.3357

R2 = 0.9652

y = 0.0675x + 0.2088

R2 = 0.9401

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0123456

Power

Points/meter

Barnacles - 10% Decrease 10 transects

5 transects

3 transects

A

y = 0.0565x + 0.6398

R2 = 0.9925

y = 0.079x + 0.3357

R2 = 0.9652

y = 0.0675x + 0.2088

R2 = 0.9401

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 2 4 6 8 10 12

Power

Points/meter

Barnacles - 10% Decrease 10 transects

5 transects

3 transects

B

Figure C6. Change in the power to detect a 10% decrease in barnacles as a result of varying the number 

of points sampled per meter with respect to 3, 5, and 10 transects per site. Sampling was done in 1997 at 6 

sites. α = 0.05. A. Points graphed using data. B. Linear trend of power based on data. 
97

0

0.2

0.4

0.6

0.8

1

-0.15 -0.05 0.05 0.15

Power

Trend

Mytilus 10 transects

5 transects

3 transects

A

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

-0.15 -0.05 0.05 0.15

Power

Trend

Fucus 10 transects

5 transects

3 transects

B

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Power

Trend

Barnacles 10 transects

5 transects

3 transects

C

Figure C7. Power to detect trends of differing magnitudes at 3, 5, and 10 transects sampled per site. Data 

are based on the 1997 fine-grained sampling of 6 sites. Each transect is sampled at 5 points/meter. α = 

0.05. A. Mytilus; B. Fucus; C. Barnacles. 

   
98

Barnacles

Population Trend Power

-10% 1

-5% 0.82

-3% 0.446

-2% 0.242

-1% 0.134

0% 0.078

1% 0.142

2% 0.258

3% 0.404

5% 0.786

10% 0.998

Mytilus

Population Trend Power

-10% 0.996

-5% 0.75

-3% 0.382

-2% 0.178

-1% 0.106

0% 0.1

1% 0.114

2% 0.222

3% 0.366

5% 0.694

10% 0.996

Fucus

Population Trend Power

-10% 0.93

-5% 0.398

-3% 0.244

-2% 0.142

-1% 0.11

0% 0.076

1% 0.114

2% 0.154

3% 0.228

5% 0.426

10% 0.868

0 0.2 0.4 0.6 0.8 1

-10%

-3%

-1%

1%

3%

10%

0 0.2 0.4 0.6 0.8 1

-10%

-3%

-1%

1%

3%

10%

0 0.2 0.4 0.6 0.8 1

-10%

-3%

-1%

1%

3%

10%

Figure C8. Power to detect population trends based on 3 years of data (1998-2001) in A. barnacles; B. 

Mytilus; and C. Fucus. Sampling was done on the level of 25 sites, 6 transects, 1 point/meter. α = 0.10. 
99

Barnacles

Population Trend Power

-10% 0.998

-5% 0.796

-3% 0.45

-2% 0.278

-1% 0.176

0% 0.1

1% 0.144

2% 0.276

3% 0.424

5% 0.798

10% 0.994

Mytilus

Population Trend Power

-10% 1

-5% 0.808

-3% 0.468

-2% 0.258

-1% 0.154

0% 0.124

1% 0.144

2% 0.228

3% 0.416

5% 0.75

10% 0.996

Fucus

Population Trend Power

-10% 0.95

-5% 0.46

-3% 0.266

-2% 0.212

-1% 0.12

0% 0.098

1% 0.114

2% 0.182

3% 0.234

5% 0.492

10% 0.88

0 0.2 0.4 0.6 0.8 1

-10%

-3%

-1%

1%

3%

10%

0 0.2 0.4 0.6 0.8 1

-10%

-3%

-1%

1%

3%

10%

0 0.2 0.4 0.6 0.8 1

-10%

-3%

-1%

1%

3%

10%

Figure C9. Power to detect population trends based on 3 years of data (1998-2001) in A. barnacles; B. 

Mytilus; and C. Fucus. Sampling was done on the level of 25 sites, 6 transects, 2 points/meter. α = 0.10. 

   
100

Barnacles

Population Trend Power

-10% 1

-5% 0.84

-3% 0.452

-2% 0.294

-1% 0.132

-0% 0.122

1% 0.15

2% 0.28

3% 0.45

5% 0.8

10% 1

Mytilus

Population Trend Power

-10% 0.998

-5% 0.888

-3% 0.526

-2% 0.29

-1% 0.154

-0% 0.094

1% 0.162

2% 0.286

3% 0.474

5% 0.83

10% 1

Fucus

Population Trend Power

-10% 0.946

-5% 0.488

-3% 0.25

-2% 0.148

-1% 0.12

-0% 0.084

1% 0.126

2% 0.176

3% 0.208

5% 0.47

10% 0.926

0 0.2 0.4 0.6 0.8 1

-10%

-3%

-1%

1%

3%

10%

0 0.2 0.4 0.6 0.8 1

-10%

-3%

-1%

1%

3%

10%

0 0.2 0.4 0.6 0.8 1

-10%

-3%

-1%

1%

3%

10%

Figure C10. Power to detect population trends based on 3 years of data (1998-2001) in A. barnacles; B. 

Mytilus; and C. Fucus. Sampling was done on the level of 25 sites, 6 transects, 3 points/meter. α = 0.10. 

   
101

Barnacles

Population Trend Power

-10% 1

-5% 0.828

-3% 0.498

-2% 0.288

-1% 0.154

-0% 0.136

1% 0.152

2% 0.238

3% 0.414

5% 0.816

10% 0.998

Mytilus

Population Trend Power

-10% 1

-5% 0.894

-3% 0.532

-2% 0.304

-1% 0.152

-0% 0.094

1% 0.188

2% 0.302

3% 0.506

5% 0.894

10% 1

Fucus

Population Trend Power

-10% 0.948

-5% 0.47

-3% 0.246

-2% 0.184

-1% 0.1

-0% 0.082

1% 0.136

2% 0.17

3% 0.292

5% 0.48

10% 0.898

0 0.2 0.4 0.6 0.8 1

-10%

-3%

-1%

1%

3%

10%

0 0.2 0.4 0.6 0.8 1

-10%

-3%

-1%

1%

3%

10%

0 0.2 0.4 0.6 0.8 1

-10%

-3%

-1%

1%

3%

10%

Figure C11. Power to detect population trends based on 3 years of data (1998-2001) in A. barnacles; B. 

Mytilus; and C. Fucus. Sampling was done on the level of 25 sites, 6 transects, 4 points/meter. α= 0.10. 

   
102

Barnacles

Population Trend Power

-10% 1

-5% 0.868

-3% 0.492

-2% 0.29

-1% 0.15

-0% 0.116

1% 0.14

2% 0.256

3% 0.466

5% 0.808

10% 1

Mytilus

Population Trend Power

-10% 1

-5% 0.914

-3% 0.564

-2% 0.306

-1% 0.166

-0% 0.088

1% 0.134

2% 0.316

3% 0.528

5% 0.834

10% 1

Fucus

Population Trend Power

-10% 0.946

-5% 0.508

-3% 0.274

-2% 0.142

-1% 0.14

-0% 0.084

1% 0.102

2% 0.138

3% 0.268

5% 0.474

10% 0.922

0 0.2 0.4 0.6 0.8 1

-10%

-3%

-1%

1%

3%

10%

0 0.2 0.4 0.6 0.8 1

-10%

-3%

-1%

1%

3%

10%

0 0.2 0.4 0.6 0.8 1

-10%

-3%

-1%

1%

3%

10%

Figure C12. Power to detect population trends based on 3 years of data (1998-2001) in A. barnacles; B. 

Mytilus; and C. Fucus. Sampling was done on the level of 25 sites, 6 transects, 5 points/meter. α = 0.10. 
103

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

012345

Power

Points/meter

Mytilus

alpha=0.10

(Trend = 10% Decrease)

A

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

01234567

Power

Points/meter

Mytilus

alpha=0.10

(Trend = 5% Decrease)

B

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Power

Points/meter

Mytilus

alpha=0.10

(Trend = 3% Decrease)

C

Figure C13. Power to detect a decline in Mytilus populations as a function of the number of points sampled 

per meter of each transect. Data are based on 3 years of sampling (1998-2001) at 25 sites, 6 transects at 

each site. α = 0.10. A. 10% decrease; B. 5% decrease; C. 3% decrease. Points on graphs indicate data; 

lines are linear projections of the trends suggested by these data. 
104

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Power

Points/meter

Fucus

alpha=0.10

(Trend = 5% Decrease)

B

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Power

Points/meter

Fucus

alpha=0.10

(Trend = 3% Decrease)

C

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

01234567

Power

Points/meter

Fucus

alpha=0.10

(Trend = 10% Decrease)

A

Figure C14. Power to detect a decline in Fucus populations as a function of the number of points sampled 

per meter of each transect. Data are based on 3 years of sampling (1998-2001) at 25 sites, 6 transects at 

each site. α = 0.10. A. 10% decrease; B. 5% decrease; C. 3% decrease. Points on graphs indicate data; 

lines are linear projections of the trends suggested by these data. 
105

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

012345

Power

Points/meter

Barnacles

alpha=0.10

(Trend = 10% Decrease)

A

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

012345

Power

Points/meter

Barnacles

alpha=0.10

(Trend = 5% Decrease)

B

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Power

Points/meter

Barnacles

alpha=0.10

(Trend = 3% Decrease)

C

Figure C15. Power to detect a decline in barnacle populations as a function of the number of points 

sampled per meter of each transect. Data are based on 3 years of sampling (1998-2001) at 25 sites, 6 

transects at each site. α = 0.10. A. 10% decrease; B. 5% decrease; C. 3% decrease. Points on graphs 

indicate data; lines are linear projections of the trends suggested by these data. 
106

y = 0.0182x + 0.9137

R2 = 0.54

y = 0.0945x + 0.3864

R2 = 0.9216

y = 0.0626x + 0.1675

R2 = 0.9906

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

01234567

Power

Transects

Mytilus (alpha = 0.10) 10% Decrease

5% Decrease

3% Decrease

A

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 2 4 6 8 10 12 14

Power

Transects

Mytilus (alpha = 0.10)

10% Decrease

5% Decrease

3% Decrease

B

Figure C16. Power to detect declines in Mytilus populations (10%, 5%, and 3%) as a function of the 

number of transects sampled at each site. Transects were sampled at a frequency of 1 point/meter. Data 

are based on the analysis of 25 sites sampled 4 times (1997-2001). α = 0.10. A. Points graphed using data. 

B. Linear trend of power based on data. 
107

Barnacles

Population Trend Power

-10% 1

-5% 0.9

-3% 0.6

-2% 0.416

-1% 0.274

-0% 0.214

1% 0.238

2% 0.444

3% 0.584

5% 0.884

10% 1

Mytilus

Population Trend Power

-10% 1

-5% 0.838

-3% 0.524

-2% 0.334

-1% 0.248

-0% 0.194

1% 0.24

2% 0.368

3% 0.496

5% 0.816

10% 0.998

Fucus

Population Trend Power

-10% 0.982

-5% 0.632

-3% 0.382

-2% 0.264

-1% 0.206

-0% 0.21

1% 0.244

2% 0.26

3% 0.34

5% 0.548

10% 0.928

0 0.2 0.4 0.6 0.8 1

-10%

-3%

-1%

1%

3%

10%

0 0.2 0.4 0.6 0.8 1

-10%

-3%

-1%

1%

3%

10%

0 0.2 0.4 0.6 0.8 1

-10%

-3%

-1%

1%

3%

10%

Figure C17. Power Power to detect population trends based on 3 years of data (1998-2001) in 

A. barnacles; B. Mytilus; and C. Fucus. Sampling was done on the level of 25 sites, 6 transects, 

1 point/meter. α = 0.20. 
108

Barnacles

Population Trend Power

-10% 1

-5% 0.906

-3% 0.604

-2% 0.376

-1% 0.262

0% 0.178

1% 0.254

2% 0.408

3% 0.574

5% 0.864

10% 1

Mytilus

Population Trend Power

-10% 1

-5% 0.904

-3% 0.578

-2% 0.376

-1% 0.272

0% 0.214

1% 0.254

2% 0.358

3% 0.552

5% 0.876

10% 1

Fucus

Population Trend Power

-10% 0.976

-5% 0.688

-3% 0.384

-2% 0.294

-1% 0.2

0% 0.22

1% 0.208

2% 0.304

3% 0.386

5% 0.572

10% 0.958

0 0.2 0.4 0.6 0.8 1

-10%

-3%

-1%

1%

3%

10%

0 0.2 0.4 0.6 0.8 1

-10%

-3%

-1%

1%

3%

10%

0 0.2 0.4 0.6 0.8 1

-10%

-3%

-1%

1%

3%

10%

Figure C18. Power to detect population trends based on 3 years of data (1998-1999) in A. barnacles; B. 

Mytilus; and C. Fucus. Sampling was done on the level of 25 sites, 6 transects, 2 points/meter. α = 0.20. 
109

Barnacles

Population Trend Power

-10% 1

-5% 0.928

-3% 0.606

-2% 0.422

-1% 0.248

-0% 0.174

1% 0.25

2% 0.426

3% 0.598

5% 0.92

10% 0.998

Mytilus

Population Trend Power

-10% 1

-5% 0.926

-3% 0.65

-2% 0.45

-1% 0.3

-0% 0.194

1% 0.268

2% 0.504

3% 0.62

5% 0.926

10% 1

Fucus

Population Trend Power

-10% 0.98

-5% 0.67

-3% 0.382

-2% 0.304

-1% 0.23

-0% 0.216

1% 0.218

2% 0.292

3% 0.39

5% 0.576

10% 0.98

0 0.2 0.4 0.6 0.8 1

-10%

-3%

-1%

1%

3%

10%

0 0.2 0.4 0.6 0.8 1

-10%

-3%

-1%

1%

3%

10%

0 0.2 0.4 0.6 0.8 1

-10%

-3%

-1%

1%

3%

10%

Figure C19. Power to detect population trends based on 3 years of data (1998-2001) in A. barnacles; B. 

Mytilus; and C. Fucus. Sampling was done on the level of 25 sites, 6 transects, 3 points/meter. α = 0.20. 
110

Barnacles

Population Trend Power

-10% 1

-5% 0.934

-3% 0.6

-2% 0.406

-1% 0.268

0% 0.212

1% 0.244

2% 0.402

3% 0.602

5% 0.886

10% 1

Mytilus

Population Trend Power

-10% 1

-5% 0.93

-3% 0.654

-2% 0.456

-1% 0.3

0% 0.158

1% 0.282

2% 0.444

3% 0.63

5% 0.912

10% 1

Fucus

Population Trend Power

-10% 0.974

-5% 0.652

-3% 0.43

-2% 0.242

-1% 0.202

0% 0.222

1% 0.22

2% 0.264

3% 0.422

5% 0.654

10% 0.98

0 0.2 0.4 0.6 0.8 1

-10%

-3%

-1%

1%

3%

10%

0 0.2 0.4 0.6 0.8 1

-10%

-3%

-1%

1%

3%

10%

0 0.2 0.4 0.6 0.8 1

-10%

-3%

-1%

1%

3%

10%

Figure C20. Power to detect population trends based on 3 years of data (1998-2001) in A. barnacles; B. 

Mytilus; and C. Fucus. Sampling was done on the level of 25 sites, 6 transects, 4 points/meter. α = 0.20. 
111

Barnacles

Population Trend Power

-10% 1

-5% 0.93

-3% 0.668

-2% 0.384

-1% 0.252

-0% 0.22

1% 0.3

2% 0.436

3% 0.624

5% 0.892

10% 1

Mytilus

Population Trend Power

-10% 1

-5% 0.928

-3% 0.666

-2% 0.484

-1% 0.298

-0% 0.196

1% 0.32

2% 0.456

3% 0.69

5% 0.92

10% 1

Fucus

Population Trend Power

-10% 0.984

-5% 0.654

-3% 0.366

-2% 0.3

-1% 0.262

-0% 0.206

1% 0.212

2% 0.236

3% 0.368

5% 0.62

10% 0.956

0 0.2 0.4 0.6 0.8 1

-10%

-3%

-1%

1%

3%

10%

0 0.2 0.4 0.6 0.8 1

-10%

-3%

-1%

1%

3%

10%

0 0.2 0.4 0.6 0.8 1

-10%

-3%

-1%

1%

3%

10%

Figure C21. Power to detect population trends based on 3 years of data (1998-2001) in A. barnacles; B. 

Mytilus; and C. Fucus. Sampling was done on the level of 25 sites, 6 transects, 5 points/meter. α = 0.20. 
112

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 1 2 3 4 5 6 7 8 9 10

Power

Points/meter

Mytilus

alpha=0.20

(Trend = 10% Decrease)

A

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

012345678

Power

Points/meter

Mytilus

alpha=0.20

(Trend = 5% Decrease)

B

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 1 2 3 4 5 6 7 8 9 10

Power

Points/meter

Mytilus

alpha=0.20

(Trend = 3% Decrease)

C

Figure C22. Power to detect a decline in Mytilus populations as a function of the number of points sampled 

per meter of each transect. Data are based on 3 years of sampling (1998-2001) at 25 sites, 6 transects at 

each site. α = 0.20. A. 10% decrease; B. 5% decrease; C. 3% decrease. Points on graphs indicate data; 

lines are linear projections of the trends suggested by these data. 
113

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 1 2 3 4 5 6 7 8 9 10

Power

Points/meter

Fucus

alpha=0.20

(Trend = 10% Decrease)

A

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Power

Points/meter

Fucus

alpha=0.20

(Trend = 5% Decrease)

B

0

0.2

0.4

0.6

0.8

1

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Power

Points/meter

Fucus

alpha=0.20

(Trend = 3% Decrease)

C

Figure C23. Power to detect a decline in Fucus populations as a function of the number of points sampled 

per meter of each transect. Data are based on 3 years of sampling (1998-2001) at 25 sites, 6 transects at 

each site. α = 0.20. A. 10% decrease; B. 5% decrease; C. 3% decrease. Points on graphs indicate data; 

lines are linear projections of the trends suggested by these data. 
114

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

012345

Power

Points/meter

Barnacles

alpha=0.20

(Trend = 10% Decrease)

A

0

0.2

0.4

0.6

0.8

1

0 1 2 3 4 5 6 7 8 9 10 11 12

Power

Points/meter

Barnacles

alpha=0.20

(Trend = 5% Decrease)

B

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 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

Power

Points/meter

Barnacles

alpha=0.20

(Trend = 3% Decrease)

C

Figure C24. Power to detect a decline in barnacle populations as a function of the number of points 

sampled per meter of each transect. Data are based on 3 years of sampling (1998-2001) at 25 sites, 6 

transects at each site. α = 0.20. A. 10% decrease; B. 5% decrease; C. 3% decrease. Points on graphs 

indicate data; lines are linear projections of the trends suggested by these data. 
115

y = 0.0079x + 0.9625

R2 = 0.5066

y = 0.0575x + 0.6041

R2 = 0.8696

y = 0.0631x + 0.3385

R2 = 0.9755

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

01234567

Power

Transects

Mytilus (alpha = 0.20) 10% Decrease

5% Decrease

3% Decrease

A

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 2 4 6 8 10

Power

Transects

Mytilus (alpha = 0.20) 10% Decrease

5% Decrease

3% Decrease

B

Figure C25. Power to detect declines in Mytilus populations (10%, 5%, and 3%) as a function of the 

number of transects sampled at each site. Transects were sampled at a frequency of 1 point/meter. Data 

are based on the analysis of 25 sites sampled over 4 years (1997-2001). α = 0.20. A. Points graphed using 

data. B. Linear trend of power based on data. 

   
116

y = 1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45

Power

Alpha

Mytilus - 10% Decrease A

y = 1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45

Power

Alpha

Barnacles- 10% Decrease B

y = 0.0119ln(x) + 1.0151

R2 = 0.7949

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45

Power

Alpha

Fucus - 10% Decrease

C

Figure C26. The relationship between alpha and the power to detect a 10% decline in the populations of 

the predominant species A. Mytilus, B. barnacles, C. Fucus. All data are based on 4 years of sampling 

(1997-2001) at the coarse-grained level: 25 sites, 6 transects, 1 point/meter. 
117

y = 0.0659ln(x) + 1.047

R2 = 0.9346

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6

Power

Alpha

Mytilus - 5% Decrease A

y = 0.0434ln(x) + 1.0373

R2 = 0.947

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.1 0.2 0.3 0.4 0.5

Power

Alpha

B Barnacles- 5% Decrease

y = 0.1834ln(x) + 1.0377

R2 = 0.9935

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Power

Alpha

C Fucus - 5% Decrease

Figure C27. The relationship between alpha and the power to detect a 5% decline in the populations of the 

predominant species. A. Mytilus, B. barnacles, C. Fucus. All data are based on 4 years of sampling (1997-

2001) at the coarse-grained level: 25 sites, 6 transects, 1 point/meter. 

 
118

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119

APPENDIX D – Standard Operating Procedures for 

Coarse-Grained Sampling

Standard Operating Procedures

for

Coarse-Grained Intertidal Monitoring Protocol

Sampling at this coarse-grained level has as its purpose the broad-scale sampling of a number of sites 

that have been selected so as to be representative of a particular habitat type.

Purpose

The selection of which particular segments to survey under the coarse-grained sampling level involved 

several steps: (1) the characterization of habitat types of an inferential set of segments within Glacier 

Bay proper (see aerial survey SOP), (2) selection of the habitat type for examination, and (3) selection of 

specific segments of the chosen habitat type.

Site Selection

1. Characterization of habitat types with Glacier Bay proper

Within Glacier Bay National Park and Preserve the coastline of Glacier Bay proper (the inner part of 

Glacier Bay NP&P, from Pt. Carolus and Pt. Gustavus northward) was divided into 200 m segments 

using ArcInfo software and a digitized coastline (Geiselman, J. et. al., 1997). This resulted in the 

identification of 5,545 segments representing a coastline of 1,109 km. Based on our assessment that we 

could aerially survey 250 segments, a random number was used to identify a particular segment, and 250 

segments were then systematically chosen relative to that initial randomly chosen start. Two hundred 

and forty-two of these segments were surveyed by fixed-wing airplane (see SOP for aerial surveys). The 

aerial surveys provided estimates of the relative abundance of different substrate types (bedrock, 

cobble/boulder, pebble/gravel, sand/silt/mud, coarse sand) within segments as well as biota (Fucus, 

mussels, barnacles). The survey results provided a characterization of the habitat types within Glacier 

Bay proper, and a pool of segments of different habitat types that had inference to the surveyed region. 

2. Selection of the habitat type and the specific segments for examination

We initially had been asked by the park to study protected rocky (bedrock) habitat. However, only 3 of 

the 241 (one of the 242 segments surveyed lacked slope data) segments surveyed by air were comprised 

of 76% or greater bedrock, with a slope of <60 degrees. We decided, on the basis of further discussions 

with Center staff and the Park staff, to relax the habitat parameters and include unconsolidated substrates 

(cobble/boulder) as well as bedrock sites as targets of this study. Thus the combined habitat type was 

defined as any segments having from 1–100% bedrock with slopes <60 degrees and/or 0% bedrock but 

76–100% cobble/boulder and also a slope <60 degrees. This resulted in a pool of more than 100 

potential coarse-grained sampling sites. Thirty sites were randomly chosen from this pool. Since the 

substrate percent coverages and slopes were estimates from the aerial surveys, an additional thirty sites 

were randomly chosen as extras in case closer inspection led to the elimination of many from the initial 

thirty. Elimination criteria included: inaccessibility because the site was too steep, or was a designated 

wildlife protection area; the presence of too much mud; lack of emergence during high tides (the site 
120

was not a true beach). Twenty-five of the original thirty selected segments were ultimately sampled 

(fig. 1).

Within-Site Sampling:

After (1) locating a site with GPS and photos, the within-site sampling involves: (2) establishment of the 

horizontal segment line at the approximate MHHW line, (3) establishing vertical transects, (4) point 

sampling of vertical transects, (5) sampling of bands that included each vertical transect, (6) photos, and 

(7 ) transect slope determinations.

1. Site Location

Navigate to the 200m beach segment using the PLGR (Y-code data) GPS unit, aided by use of maps and 

photographs. The GPS coordinates indicate the location of the site’s upper left corner (fig. D1).

One permanent marker, a 2-foot length of rebar, was placed on the left-hand side of the segment, above 

the site. The marker was placed perpendicular to the 200 m segment tape, upbeach enough to be well 

above the highest high tide and often into the terrestrial vegetation. A stainless steel 1 1/4" washer with 

“USGS/BRD, 1997 ###” stamped into it (### is the segment number) was attached to the rebar by a 

cable tie. Orange flagging tape was tied around the rebar to make it visible in the photos and easier to 

relocate. The rebar stake was z-sparred (Splash Zone, a marine epoxy) in place if the substrate was 

suitable. Notes on the placement, including distance and compass bearings from the top left of the 200m 

segment (the beginning of the horizontal segment line), were recorded in the field notes for the site.

At some sites, additional rebar were added above the horizontal segment line at 100m and 200m. Also, 

for at least some of the six fine-grained sites sampled in 1997 (sites 36, 59, 63, 69, 89, and 217), there 

were rebar markers placed above each of the vertical transect lines. Under the fine-grained sampling 

there were 10 vertical transects sampled at each of these sites, each 20 meters apart, but with the first 

transect location determined randomly within the first 20 meters (e.g., systematic placement with a 

random start).

2. Establish horizontal segment line

The horizontal segment line is established at the approximate MHHW (mean higher high water) line by 

laying out 200m of measuring tape, starting at the left upper edge of the segment and running across the 

beach. The segment line follows the lower Verrucaria (black lichen) zone (defined as approximately 

20% coverage of Verrucaria), at the juncture of the Verrucaria and barnacle zones. Lay the tape parallel 

to the shoreline. If Verrucaria is not visible or is not continuous, estimate the highest tide line using cues 

of beach wrack and/or the beach grass line or above the upper barnacle zone. Follow the contours of the 

beach/shoreline. If you come to narrow but deep crevices or indentations, do not run the segment line up 

into the space, rather cut across it maintaining a line parallel with the shoreline. In some cases littorine 

snails will be found above the horizontal segment line. Note the distance from the site-marking rebar 

(above the left edge of the segment) to the horizontal segment line (MHHW). If 100 m and 200 m rebar 

markers are present, also note the distances from those to the horizontal segment line.

3. Establish Vertical Transects

Six line transects (“vertical” transects) are positioned parallel to the elevational gradient of the segment, 

running from the horizontal segment line down to MLLW (mean lower low water, the 0-m [=0-ft] tide 

level). The start of the first vertical transect is randomly chosen while the remaining transects are placed 

systematically every 33m relative to the location of the first. Prior to sampling, establish a list of 25 
121

random numbers between 0 and 33 to be used to establish the location of the first vertical transect for 

each of the 25 coarse-grained sites. The random number indicates the distance along the horizontal 

segment tape to the start of the first transect. For example, if the random number for a site is 12, the first 

vertical transect is located 12m from the left edge of the segment. Succeeding transects are set every 33

m (at 45, 78, 111, 144, and 177 m from the left end of the 200 m segment tape, when the random 

number is 12). If a vertical transect falls on a section of the segment that is unsampleable due to steep 

slope or fresh water input, then determine the horizontal distance of the unsampleable area and shift the 

remaining transects that distance to the right (fig. D2). Vertical transects are run perpendicular to the 

shoreline. On concave beaches, positioning the transects perpendicular to the shoreline keeps them from 

overlapping (fig. D3). Natural variation in the complexity of the topography necessitates some decisionmaking in the layout of the transect lines. For example, when large rocky outcrops too high for sampling 

intervened, transect lines were interrupted (Site 108), with the rocky outcrop declared unsampleable, and 

the transect resumed on the shore-side of the rocky outcrop. The standard should be transect lines laid 

perpendicular to the shoreline. Transect lines are draped so as to generally conform to the substrate as 

they are laid, rather than being stretched taut. The intention is that all exposed surfaces have an equal 

probability of being sampled. The lowest extent of the transect, the 0-m tide level, is determined by 

using the Tides and Currents computer program (Nautical Software, Inc.) for the sampling date and 

nearest tide station location (Bartlett Cove, Willoughby Island, Composite Island, or Muir Inlet). At the 

time of the 0-m tide (MLLW), other activities are stopped temporarily and all transect tapes are run to 

the water line, with the tape looped to indicate the 0-m location. 

4. Point sampling of Vertical Transects

Selected points along the vertical transects are sampled to provide estimates of the relative percent 

coverage of biota along the transects, and across the site, as well as the relative percent coverage of 

underlying substrates. The sampling of points along these transects also gives a record of zonation of 

species. The density of sampling is five points sampled per meter, sampled each 20 cm (e.g., 0, 0.2, 

0.4m, etc.). Tapes should be laid with the zero meter mark of the tape at the horizontal segment tape.

Points are sampled by noting the species and substrate at each point, using the right edge of the 

tape and the distance hash marks along the tape as cross hairs to mark the point. Use a pencil, 

knitting needle or other pointer to follow the point perpendicularly from the tape to the substrate. 

Sampling is 3-dimensional. Record all species underlying the point, including multiple “hits” or 

layers of the same species, then the substrate. Dense Fucus and mussels can be difficult to assess 

accurately in three-dimensional sampling (sampling probably tends to under-estimate the 

layering). Organisms are identified to the lowest taxonomic level possible in field sampling. 

Some taxa are grouped because they cannot be discriminated in the field readily (appendix 

D1.1). Use the abbreviations from the species list when recording. Substrates are classified 

according to a modified Wentworth-scale (appendix D1.2). The format for recording data is as 

follows: species/substrate (e.g. FU/BR = Fucus over Bedrock). If there is more than one species 

when following the point down to the substrate (e.g. littorine [Littorina sitkana] on a piece of 

Fucus which covers a mussel) then use the following format: (species, species, 

species.....)/substrate (e.g. (LSI,FU,MY)/BR). Multiple sequential layers or hits of the same 

species can be indicated by superscripts, e.g., FU3

. If there is silt on another substrate, record the 

underlying substrate. An example of a completed data sheet is included in appendix D2.
122

5. Band Sampling

The purpose of sampling a wider swath or band around each vertical transect, is to sample the 

density of larger or more mobile invertebrates that are less likely to be well-sampled by the 

points. After the points have been sampled on a vertical transect, band swaths are done. Scan the 

swath 1m to each side of the transect for selected large or mobile species (starfish, sea urchins, or 

large chitons). Count the number of each band species observed per meter and record the number 

and location in the appropriate place on the data sheet. If no band species are observed, be sure to 

note that none were seen.

6. Transect Slope Descriptions

Take the slope of each vertical transect line, using a hand-held clinometer (e.g., Suunto 

clinometer). If there appears to be more than one distinct slope (e.g., the slope changes 

significantly along the transect line), take readings for each section, but also get an overall slope 

for the transect line by taking a measurement from the bottom to the top or vice versa. Also take 

the compass bearing of each transect line. Note if the compass bearing is from the bottom to the 

top or vice versa. Also note if magnetic or true north was used (thus far we have only used 

magnetic north). 

7. Photos

The purpose of taking photographs is to record locations of transect lines, and to obtain a visual 

record of the geomorphology and biology, both along transect lines and across the site from 

several spots. Both slide and print film originally were used; these will be supplanted by digital 

photography. Prints provide easily used visual records and aid relocating the site marker and 

transect locations. Slide film was used for longer term records of transect shots and general site 

photos and for use in talks. Photos are taken from the bottom of each transect looking towards 

the top, from the top of each transect looking down, from the top left area looking towards the 

top right, and top right towards top left, from the bottom left looking towards the bottom right, 

bottom right to left, and especially from approximately midbeach between transects 3 and 4 

looking right and left, from the top left towards the permanent marker or from the bottom left 

towards the permanent marker, and of anything that is of particular interest. Photos of the 

vertical transects from the top looking down were often taken, but not routinely. All photos are 

recorded in the field notes or on transect data sheets and later entered into a photolog.

8. Data entry and the Access database

These topics are discussed in appendix E.

Reference

Geiselman, J., Dunlap, J., Hooge, P., and D. Albert. (Eds). 1997. Glacier Bay Ecosystem GIS 

CD-ROM set. U.S. Geological Survey and Interrain Pacific. Anchorage and Juneau, Alaska.
123

Figure D1. Directional terms used in the coastal monitoring protocol development study.
124

Figure D2. A rough depiction of the arrangement of vertical transects and how to deal with unsampleable 

areas.
125

Figure D3. A schematic of vertical transects laid out at a concave beach. Note that the transect lines are 

laid perpendicular to the shoreline.
126

Appendix D1

D1.1. List of species, substrates, and abbreviations used in the 1997 coastal monitoring protocol 

development project. Species noted during band swath searches are marked with an asterisk. 

D1.2. The modified Wentworth scale used to classify substrates. This is taken from the sediment surface 

mapping protocol for Glacier Bay coastal mapping, coastal monitoring, and benthic surveys SOP, 

sedprot.w51.
127

D1.1. Species and substrate abbreviations used in the 1997 coarse-grained sampling.

* = BAND SPECIES

MUSSELS RED ALGAE

Modiolus modiolus MO articulated coralline algae AR

Musculus spp. MUS Cryptosyphonia woodii CRY

Mytilus trossulus MY Encrusting coralline algae EC

Endocladia muricata ED

CLAMS Gigartinaceae GG

Hiatella arctica HI Gloiopeltis furcata GL

Halosaccion glandiforme HA

BARNACLES Mastocarpus papillatus MA

Balanomorpha BA Neorhodamela/Odonthalia spp. NEO

Balanus glandula/Semibalanus balanoides BG Palmaria callophylloides PC

Chthamalus dalli CD Palmaria spp. PA

Semibalanus cariosus SC Polysiphonia/Pterosiphonia spp. PS

Barnacle spat/cyprids <2mm BS Porphyra spp. PR

Red algal crust- fleshy RE

LIMPETS Small filamentous red FIR

Acmaea mitra ACM Small foliose red FOR

Diadora spp. DI

Lottia digitalis LD BROWN ALGAE

Lottia pelta LP Agarum clathratum AG

Lottidae <8mm or questionable LO Alaria fistulosa AF

Tectura fenestrata TF Alaria marginata (or ALM) AL

Tectura persona TP Coilodesme spp. CL

Tectura scutum TES Costaria costata COS

Cymathere triplicata CY

SNAILS Desmarestia spp. DE

Buccinum spp. BU Fine filamentous brown FIB

Fusitriton oregonensis FUS Fucus gardneri FU

Lacuna spp. LA Laminaria bongardiana LB

Littorina scutulata LSC Laminaria saccharina LS

Littorina sitkana LSI Leathesia difformis LEA

Littorina spp. LIT Petalonia fascia PET

Margarites spp. MAR Scytosiphon/Melanosiphon spp. SM

Neptunea spp. NEP Small foliose brown FOB

Nucella canaliculata NC Soranthera ulvoidea SOR

Nucella emarginata NUE Thick brown crust TBC

Nucella lamellosa NLA

Nucella lima NLI GREEN ALGAE

Onchidella borealis ON Acrosiphonia spp. AC

Searlesia dira SE Enteromorpha intestinalis EN

Siphonaria thersites SI Prasiola meridionalis PM

Small filamentous green FIG

CHITONS* Ulothrix spp. UO

Cryptochiton stelleri CR Ulvales UV

Katharina tunicata KA

Mopalia spp. MOP SEAGRASS

Tonicella spp. TO Phyllospadix serrulatus PHY

Zostera marina ZO
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* = BAND SPECIES

ANEMONES LICHENS

Anthopleura artemesia ANA Verrucaria spp. VE

Anthopleura spp. AN

Anthopleura xanthogrammica AX SPONGES SG

Epiactis spp. EP Halichondria spp. HCH

Urticina crassicornis UR Haliclona spp. HCL

URCHINS* BRYOZOA BRY

Strongylocentrotus droebachiensis SD

Stronglyocentrotus franciscanus SF HYDROZOA

Hydroids HY

SEA CUCUMBERS

Cucumaria miniata CU TUNICATES

Eupentacta quinquesemita EUP Compound TC

Parastichopus spp. PAR Solitary TS

SEA STARS* CRUSTACEANS CRU

Asteroidea - unknown AS

Crossaster papposus CP AMPHIPODA AM

Dermasterias imbricata DER

Evasterias troschelli EV ISOPODA IS

Henricia spp. HE Idotea spp. ID

Leptasteria hexactis LE

Orthasterias koehleri OR HERMIT CRABS HC

Pisaster ochraceus PI

Pycnopodia helianthoides PY SMALL CRABS

Solaster spp. SO Hemigrapsus spp. HG

BRITTLE STARS SUBSTRATES

Ophiuroid OP Bedrock BR

Boulder BO

NUDIBRANCHS NU Cobble CO

Archidoris/Anisodoris spp. AA Pebble PE

Gravel GR

NEMERTEANS Sand SA

Amphiporus spp. - fleshy or white NA Mud MU

Emplectonema (or NG) EM Shell SH

Paranemertes (or NP or NB) PAN

POLYCHAETES

Nereid NE

Pectinaria granulata PEC

Phyllodocidae - scale worm PH

Serpulids SER

Spirobids SP
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D1.2. Substrate categories used in the 1997 coarse-grained sampling.

Substrate Wentworth Size Description

bedrock continuous rock surface NA

boulder >256 mm diameter head size or larger

cobble 64-256 mm diameter billiard ball size to head size

pebble 4-64 mm diameter pea size to billiard ball size

gravel 2-4 mm diameter BB size to pea size

sand 1/16-2 mm diameter just gritty in fingers to pin head size

mud/silt NA floury coating, not gritty, similar to clay

shell NA whole shell or identifiable fragments
130

Appendix D2

D2.1. An example of a completed data sheet from a vertical transect sampled during the 1999 coarse-grained 

sampling.
131

APPENDIX E – Glacier Bay Intertidal Monitoring Access 

Database

ACCESS GENERAL INFORMATION

Getting Started with Access

Access is a relational database. This means that you can store information in multiple tables and 

relate the tables to each other. In other words, if your database had one table with names and 

addresses and another table with names and phone numbers, you could type in the phone number 

and Access would give you address of that person (or vice versa). Thus, Access could also tell you 

at which sites certain species were found or all the sites that contain a certain percentage of a 

particular substrate.

Database Objects

There are six types of objects in Access: Tables, Queries, Forms, Reports, Macros and Modules 

(fig. E1). In the figure below, each of these objects will resemble the tab of a hanging file or a 

manila folder. Objects consist of the data and the tools you will need in Access. In order to work 

with one of these objects, click once on the “tab” and a list of all existing files for the database will 

appear.

Figure E1. Illustration of the six types of objects found in Access: Tables, Queries, Forms, Reports, Macros 

and Modules.
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Tables

Raw data are stored in tables. Any changes made within a table will directly affect the data stored 

in that table. Because it is possible to permanently change the entire data set when working in table 

view, it is strongly recommended that you do not work directly in a table unless absolutely 

necessary. A better alternative is to work with forms and queries.

Forms

Forms show raw data from tables organized in a more “presentable” or customized format. 

Generally this means that the data presented in Forms mimics the layout of the datasheets used in 

the field, facilitating the data entry process. 

The data shown in Forms are still raw data and any changes to the data made in Forms will be 

permanently saved. Although the integrity of the data can still be compromised when using Forms, 

it is preferable to perform data entry or changes to the data in Forms view. The possibilities of 

altering large portions of data are greatly reduced.

Queries

Queries are questions that you ask the database and they are used to extract information (e.g. At 

what sites can Nucella be found?). Queries are very flexible and by designing exactly what you 

need you can search, sort and retrieve specific data. Access has many types of queries, but 

generally the most commonly used queries are: Select, Update, Append and Delete Queries.

Select Queries

A select query, as you might guess, selects information from one or more tables based on criteria 

that you specify. Once the query has run, the specific information that you asked for is displayed in 

a table. 

Update, Append, and Delete Queries 

All of these queries are action queries. Action queries allow you to change the data in existing 

tables and you can change many records with a single operation. Be very careful when using 

action queries!! The best method when using an action query is to design your query as a select 

query, run the query and look at the records that you have selected. If your query selected the 

records that you want to act upon, then change the query to an action query. To change the type of 

query, click on the Query pull down menu and select the type of query you need (fig. E2). Next you 

can use the Query Design Pane to specify what you need updated, deleted or appended.
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Figure E2. Showing the procedure to change the type of query.

Saving Queries and Naming Queries

Since queries are frequently used, it is easy to end up with too many of them saved into the 

database, therefore it is important to only save the queries that will be regularly used. If you create 

a query that will be used only once do not save it. Attempt to give the queries that you save a very 

specific or informative name ...so that it is easy to tell what they do when you look at them later

(e.g., fig. E3).

Figure E3. Some of the queries in the intertidal monitoring database. Note that the names begin with “qry.”
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DATA ENTRY PROCEDURES

Upon opening the Intertidal Monitoring database you see the screen displayed in figure E4. Select 

the “Edit Data” button to begin data entry.

Figure E4. Screen displayed upon opening Intertidal Monitoring database.

The screen should now appear as shown in figure E5. To add observation photos, click on “The 

Add Observation Photos” button. If you wish to begin data entry for a new site, click the “Enter 

Data for New Site and Year” button. If you have already begun data entry for a site or if you wish 

to edit data for a previously entered site, click the “Edit Data for an Existing Site and Year” button. 

Figure E5. The form that routes you to various ways to edit and enter data.
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a) Edit Data for a New Site and Year

After you click the button (shown in fig. E5) Access will prompt you for the SiteID. There is a 

pull-down menu (fig. E6) or you can type in the 3-digit site number. Next Access will ask you for 

the Observation Date (fig. E6), simply type the date the transects were run.

Figure E6. As you enter data for a new site and year, Access will ask for the SiteID and then the Observation 

Date.

Next the data entry form will appear (fig. E7). The form automatically opens to transect 1. Enter 

the transect length, the horizontal distance, and the slope. 
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Figure E7. The data entry form.

Then you begin entering data. Each point must have a substrate. Type in any species that were 

found on that point. You can type in the species code or use the pull-down menu to select a species 

(Note: Access will not let you type in a species that is not in the pull-down menu. This will 

hopefully avoid data entry errors from typos). If there were multiple species (or multiple hits of the 

same species) each hit needs to be entered. For example, figure E8 shows the data entry form for: 

FU3

, LSI, FU, BG / BO on meter mark 2.6. 

Once you have entered the species and substrate for a point, click the “Add Point” button. A new 

point will be added, the meter mark will automatically be 0.2 cm more and the point will have the 

same slope and the same substrate as the previous point. 

It is often easier to do data entry without using the mouse and these shortcuts may be helpful:

– If the cursor is in Species Code: “Ctrl” and “Tab” (at the same time) will take you to 

Substrate

– If the cursor is in Substrate: “Enter” once takes you to Add Point, “Enter” again, adds a

new point and puts your cursor in Substrate of the new point. 
137

You can move between points or transects by using the arrows shown in figure E8.

To add another transect at the same site, click the “New Transect” button. Access will 

automatically add 1 to the transect number. Begin data entry.

Figure E8. Data entry form after data for meter mark 2.6 (FU3, LSI, FU, BG / BO) have been entered.

b) Edit Data for an Existing Site and Year

Use this option if you have already entered some data for a site (for example one transect, or part of 

a transect) and you would like to continue entering data for that site or you would like to edit the 

data that has already been entered.
138

At the Edit Data prompt (fig. E5) click on the button to edit data for an existing site and year. A 

form will appear (fig. E9) where you can use a pull-down menu to choose the observation data and 

site number that you wish to edit.

Figure E9. The prompt to edit data for a site and observation date that already exist in the database.

When you click “OK” the data entry form will appear (fig. E8) and you can make the changes that 

need to be made. Remember you can move between transects and points using the arrows shown in 

figure E8.
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EXPORTING DATA

NOTE: It is very easy to export any table from Access, however, since Access is a relational 

database all the information you may want to export may be spread out in several tables. In order to 

export information from multiple tables, you need to write or use a select query. The select query 

will create a custom table with all the information that you need so that it can be exported.

To Export a table:

Go to the Intertidal Database window (fig. E1). If you cannot see this window, use the Window 

pull-down menu at the top of the screen and choose “Intertidal Monitoring: Database.” In the 

Intertidal Database window, click on the Tables tab. List of all the tables in the database will 

appear. To export a table, click on it to highlight it. Next, use the File pull down-menu at the top of 

the screen and choose Save As/Export.

To Export from a Query:

Go to the Intertidal Database window (fig. E1). If you cannot see this window, use the Window 

pull-down menu at the top of the screen and choose “Intertidal Monitoring: Database.” In the 

Intertidal Database window, click on the Queries tab. If the query you want already exists, simply 

double click on the query, wait for Access to run the query and create a table. Then from the File 

pull-down menu choose Save As/Export to export the table. You could also write a new query and 

export the table that you create.

For example to export all of the data for the entire database: Go to the Intertidal Database window 

(fig. E1). If you cannot see this window, use the Window pull-down menu at the top of the screen 

and choose “Intertidal Monitoring: Database.” In the Intertidal Database window, click on the 

Queries tab. Double click on the “qryAllCriticalFields” query. It will take Access a moment to run. 

A table will show all the data that will be created. Use the File pull down menu and chose Save 

As/Export. 
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For more information concerning the research in this report, contact the 

Director, Alaska Science Center

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4210 University Dr.

Anchorage, Alaska 99508-4650

http://alaska.usgs.gov
Irvine, G.V.—Development of Monitoring Protocols to Detect Change in Rocky Intertidal Communities of Glacier Bay National Park and Preserve—OFR 2010–1283