OCS Study 

•

MMS 2005-002 

Effect of Temporal and Spatial 

Separation of Samples on Estimation 

of Impacts 

Final Technical Summary 

Final Study Report 

U.S. Department of the Interior 

Minerals Management Service

Pacific OCS Region 

OCS Study 

MMS 2005-002 

Effect of Temporal and Spatial 

Separation of Samples on Estimation 

of Impacts 

Final Technical Summary 

Final Study Report 

Authors 

Todd E. Minchinton 

Peter T. Raimondi 

Principal Investigator 

Peter T. Raimondi 

Prepared under MMS Cooperative 

Agreement No. 14-35-0001-30758 

by 

Coastal Marine Institute 

Marine Science Institute 

University of California 

Santa Barbara, CA 93106 

U.S. Department of the Interior 

Minerals Management Service Camarillo

Pacific OCS Region April 2005
Disclaimer 

This report has been reviewed by the Pacific Outer Continental Shelf Region, Minerals Management 

Service, U.S. Department of the Interior and approved for publication. The opinions, findings, 

conclusions, or recommendations in this report are those of the authors, and do not necessarily reflect 

the views and policies of the Minerals Management Service. Mention of trade names or commercial 

products does not constitute an endorsement or recommendation for use. This report has not been 

edited for conformity with Minerals Management Service editorial standards. 

Availability of Report

Extra copies of the report may be obtained from: 

U.S. Dept. of the Interior 

Minerals Management Service 

Pacific OCS Region 

770 Paseo Camarillo 

Camarillo, CA 93010 

Phone: 805-389-7621 

A PDF file of this report is available at: 

http://www.coastalresearchcenter.ucsb.edu/CMI/ 

Suggested Citation 

The suggested citation for this report is: 

Minchinton, Todd E., and Peter T. Raimondi. Effect of Temporal and Spatial Separation of Samples 

on Estimation of Impacts. MMS OCS Study 2005-002. Coastal Research Center, Marine Science 

Institute, University of California, Santa Barbara, California. MMS Cooperative Agreement Number 

14-35-0001-30758. 85 pages. 
Table of Contents 

FINAL TECHNICAL SUMMARY....................................................................................... 1 

FINAL STUDY REPORT...................................................................................................... 5 

Part I: INTRODUCTION, OBJECTIVES, AND APPROACH.................................. 5 

1.1 Introduction...................................................................................................... 5 

1.2 Objectives......................................................................................................... 5 

1.3 Approach.......................................................................................................... 6 

1.3.1 Background ..................................................................................... 6 

1.3.2 Study Sites....................................................................................... 6 

1.3.3 Target Species ................................................................................. 8 

1.3.4 Sampling Methods ........................................................................ 10 

 

Part II: SPATIAL AND TEMPORAL PATTERNS .................................................. 11

2.1 Introduction.................................................................................................... 11 

2.2 Methods.......................................................................................................... 11 

2.3 Results............................................................................................................ 12 

2.3.1 Anthopleura elegantissima............................................................ 12

2.3.2 Barnacles....................................................................................... 16 

2.3.3 Pollicipes polymerus..................................................................... 20

2.3.4 Mussels.......................................................................................... 21 

2.3.5 Silvetia compressa......................................................................... 25

2.3.6 Hesperophycus californicus.......................................................... 28

2.3.7 Mastocarpus papillatus................................................................. 29

2.3.8 Endocladia muricata..................................................................... 30

2.3.9 Mazzaella spp................................................................................ 33 

2.3.10 Phyllospadix scouleri/torreyi........................................................ 34

2.3.11 Pisaster ochraceus........................................................................ 37

2.3.12 Lottia gigantea .............................................................................. 41

2.3.13 Haliotis cracherodii ...................................................................... 43

2.4 Discussion ...................................................................................................... 49 

 

Part III: STATISTICAL POWER ANALYSIS.......................................................... 53

3.1 Introduction, Objectives, and Approach ........................................................ 53 

3.2 Methods.......................................................................................................... 54 

3.2.1 Before-After Design: Simulating Impacts and Calculating 

Power ............................................................................................ 54 

3.2.2 Before-After Design: Tests of Assumptions................................... 55 

 3.3 Results............................................................................................................ 55 

 3.3.1 Anthopleura elegantissima.............................................................. 59 

 3.3.2 Barnacles......................................................................................... 59 

 3.3.3 Pollicipes polymerus....................................................................... 59 

 3.3.4 Mussels............................................................................................ 59 

 3.3.5 Silvetia compressa........................................................................... 63 
 3.3.6 Hesperophycus californicus.............................................................63 

 3.3.7 Mastocarpus papillatus....................................................................63 

 3.3.8 Endocladia muricata........................................................................63 

 3.3.9 Mazzaella spp...................................................................................63 

 3.3.10 Phyllospadix scouleri/torreyi.........................................................63 

 3.3.11 Pisaster ochraceus.........................................................................66

 3.3.12 Lottia gigantea...............................................................................66

 3.3.13 Haliotis cracherodii.......................................................................68

 3.3.14 All species......................................................................................68 

 3.4 Discussion.....................................................................................................69 

ACKNOWLEDGMENTS ...............................................................................................73 

LITERATURE CITED ...................................................................................................74

 
List of Tables

Table I-1. Target species sampled at each of the 18 mainland sites .................................... 9 

Table II-1a, b, c. Results of ANCOVA analyses examining season and temporal patterns 

of abundance of target species ................................................................................. 15 

Table III-1a, b, c. Results of statistical tests of assumptions before power analysis ........ 56 

List of Figures 

Figure I-1. Rocky intertidal monitoring sites along the southern and central coast of 

California ................................................................................................................... 7 

Figure II-1a, b. Percent cover of Anthopleura at six sites................................................. 13 

Figure II-2a, b, c, d. Percent cover of barnacles at 15 sites .............................................. 17 

Figure II-3. Percent cover of Pollicipes at 3 sites.............................................................. 21 

Figure II-4a, b, c, d. Percent cover of mussels at 14 sites................................................. 22 

Figure II-5a, b. Percent cover of Silvetia at 7 sites........................................................... 26 

Figure II-6. Percent cover of Hesperophycus at 2 sites ..................................................... 29 

Figure II-7. Percent cover of Mastocarpus at 2 sites......................................................... 30 

Figure II-8a, b, c. Percent cover of Endocladia at 10 sites ............................................... 31 

Figure II-9. Percent cover of Mazzaella at 2 sites ............................................................. 34 

Figure II-10a, b, c. Percent cover of Phyllospadix at 9 sites............................................. 35 

Figure II-11a, b, c. Density of Pisaster at 10 sites ............................................................ 38 

Figure II-12a, b. Density of Lottia at 6 sites ..................................................................... 42 

Figure II-13a, b. Density of Haliotis at 7 sites .................................................................. 44 

Figure II-14a, b, c, d. Abundance of 13 target species for all sites................................... 46 

Figure III-1a, b, c. Percentage of comparisons detecting changes in abundance of target 

species ...................................................................................................................... 60

Figure III-2a, b, c. Power to detect changes in abundance of target species .................... 61 

Figure III-3a, b. Change in abundance of target species detectable with 80% power ...... 62 

 

 

Effect of Temporal and Spatial Separation of Samples 

1

 FINAL TECHNICAL SUMMARY 

STUDY TITLE: Effect of Temporal and Spatial Separation of Samples on Estimation of 

Impacts 

REPORT TITLE: Effect of Temporal and Spatial Separation of Samples on Estimation 

of Impacts 

CONTRACT NUMBER: 14-35-0001-30758 

SPONSORING OCS REGION: Pacific 

APPLICABLE PLANNING AREA(S): Southern and Central California 

FISCAL YEAR(S) OF PROJECT FUNDING: FY 99/00, FY 00/01 

COMPLETION DATE OF REPORT: April 2005

COSTS: FY 99/00 - $34,293; FY 00/01 - $34,293 

CUMULATIVE PROJECT COST: $68,568 

PROJECT MANAGER: Russell J. Schmitt

AFFILIATION: Coastal Research Center, Marine Science Institute, University of 

California, Santa Barbara, CA 93106 

PRINCIPAL INVESTIGATORS: 1Todd E. Minchinton, 2Peter T. Raimondi 

ADDRESSES: 1Department of Biological Sciences, University of Wollongong, 

Wollongong NSW, 2522 Australia; 2Department of Ecology and Evolutionary Biology, 

University of California, Santa Cruz, CA 95064

KEY WORDS: Abalone, Anemone, BACI design, Barnacle, Baseline data, Before-After 

design, California, El Niño, Environmental impacts, Intertidal, Limpet, Marine algae, 

Marine invertebrates, Monitoring, Mussel, Power analysis, Rockweed, Seasonal trend, Sea 

star, Surfgrass, Temporal Trend, Turfweed, Withering Syndrome

BACKGROUND: Since 1991 the Minerals Management Service has funded a monitoring 

program for Santa Barbara County, California, U.S.A, called the “Shoreline Inventory”. The 

inventory was to provide baseline data on rocky intertidal communities that could be used in 

the event of an oil spill to calculate losses due to the impact. The main logistical task of the 

inventory was to set-up and sample at regular intervals 9 permanent sampling sites in the 

rocky intertidal zone of Santa Barbara County. At each site species assemblages dominated 

by 13 species were targeted and sampled in permanent plots in spring and autumn of each 
Final Technical Summary – Minchinton and Raimondi 

2 

year. From 1991 to 1995 the Shoreline Inventory was expanded to 18 mainland sites across 4 

counties (San Luis Obispo County, Santa Barbara County, Ventura County, Los Angeles 

County) and 8 offshore sites on two islands (Catalina Island, Santa Cruz Island). This report 

summarizes the spatial and temporal trends in abundance for the 13 target species at the 18 

mainland sites and assesses using power analysis the ability of the Shoreline Inventory 

monitoring program to detect changes in the abundance of the target species. 

OBJECTIVES: The primary objective of this study was to use the database of the Shoreline 

Inventory to test quantitatively the critical assumption that estimation of consequences after 

an impact is possible for sites that were monitored before the impact. Specifically, we answer 

the following two questions: 

1. What are the spatial and temporal patterns of abundance for the 13 target species at 

the 18 mainland sites?

2. Based upon power analyses following simulated impacts to target species at each 

site, should there be any modifications to the sampling design of the monitoring 

program?

DESCRIPTION: The Shoreline Inventory includes 18 mainland sites extending from central 

to southern California. Five sites were established in autumn 1995 in San Luis Obispo 

County (Point Sierra Nevada, Piedras Blancas, Cayucos Point, Hazards, Shell Beach), 9 sites 

in spring 1992 in Santa Barbara County (Occulto, Purisima Point, Stairs, Boathouse, 

Government Point, Alegria, Arroyo Hondo, Coal Oil Point, Carpinteria), 2 sites in autumn 

1994 in Ventura County (Mussel Shoals, Old Stairs), and 2 sites in autumn 1994 in Los 

Angeles County (Paradise Cove, White’s Point). The Shoreline Inventory includes 13 key 

target species comprising both sessile and mobile macroinvertebrates and macroalgae that are 

generally the dominant occupiers of primary space at particular heights and exposures in the 

intertidal zone. Four target species (or species groups) are sessile or sedentary 

macroinvertebrates: (1) barnacles (the acorn barnacles Chthamalus fissus/dalli and Balanus 

glandula), (2) the gooseneck barnacle Pollicipes polymerus, (3) mussels (primarily Mytilus 

californianus), and (4) the sea anemones Anthopleura elegantissima and A. sola. Six target 

species are macroalgae: (5) the brown fucoid alga or rockweed Silvetia compressa (formerly 

Pelvetia compressa, Pelvetia fastigiata), (6) the brown fucoid alga or rockweed 

Hesperophycus californicus (formerly H. harveyanus), (7) the red alga Mastocarpus 

papillatus, (8) the red alga or turfweed Endocladia muricata, (9) the “rainbow” alga 

Mazzaella spp. (formerly Iridaea spp.), and (10) the marine angiosperm or surfgrass 

Phyllospadix torreyi/scouleri. Three target species are mobile invertebrates: (11) the ochre 

sea star Pisaster ochraceus, (12) the giant owl limpet Lottia gigantea, and (13) the black 

abalone Haliotis cracherodii. At each site permanent areas were established where target 

species were the dominant organisms, and then their abundances were sampled in spring and 

autumn of each year. 

To examine variation in the abundance of target species in space and time, summary statistics 

(mean and standard error of the mean) for all sites and sample periods were calculated and 

plotted graphically. Analyses of covariance (ANCOVA) procedures were used to assess 

seasonal and temporal patterns of abundance for each of the target species at each site. The 
Effect of Temporal and Spatial Separation of Samples 

3

ability of the monitoring program to estimate the effects of impacts was done using power 

analyses. Impacts to the abundance of target species were simulated and then the ability to 

detect these impacts was assessed under a before-after design. Based on these results, we then 

determined whether there should there be modifications to the sampling design of the 

Shoreline Inventory monitoring program. 

STUDY RESULTS AND SIGNIFICANT CONCLUSIONS: The Shoreline Inventory 

monitoring program has produced what is, in terms of its spatial and temporal extent, one of 

the most comprehensive sets of data ever collected for species living in rocky intertidal 

communities. Visual inspection of graphs and statistical analyses of the data reveal that the 

current sampling design and monitoring protocols have a great ability to show the natural 

limits of variation and to detect trends in the abundance of target species. Three types of 

temporal trends were clear: (1) short-term seasonal trends, (2) intermediate-term fluctuations 

on time scales ranging from one to several years, and (3) long-term changes for five or more 

years due to changes in abundance over the entire sampling period. First, short-term seasonal 

trends were apparent at a minority of sites for the acorn barnacles, the gooseneck barnacle 

Pollicipes and the sea star Pisaster, with abundances greater in autumn than in spring. The 

macroalgae exhibited more pronounced seasonal trends than the macroinvertebrates. At most 

sites, the cover of Silvetia, Mastocarpus and Phyllospadix was greater in autumn than in 

spring, whereas Endocladia was more abundant in spring than in autumn. A second striking 

and common trend in the data were intermediate-term fluctuations in abundance where there 

were relatively rapid declines sometimes followed by more gradual increases. These 

relatively rapid and common declines occurred from 1997 to 1998, which corresponds to the 

1997-98 El Niño event, which was the strongest of the century. Many target species 

experienced declines at some sites during the 1997/98 El Niño event, including barnacles, 

mussels, Endocladia, Silvetia, Phyllospadix, Anthopleura and Haliotis. The third obvious and 

consistent trend was the long-term declines in abundance over the entire monitoring period for 

some target species at several sites, including Haliotis, which experienced population crashes 

due to the fatal disease, withering syndrome. Detection of these temporal trends in abundance 

(seasonal, El Niño, disease) highlights the efficacy and utility of the current Shoreline 

Inventory monitoring program. 

For all target species that met assumptions of statistical tests (10 of 13 species; see section 

3.2.2), there was 80% power to detect 50% changes in abundance at α = 0.05. Indeed, for the 

majority of species and sites, changes in abundance of only 20% could be detected with 80% 

power at α = 0.05. Power to detect change varied among target species (but not among sites) 

and was greatest for mussels and least for Pisaster. Some species (Silvetia, Hesperophycus, 

Haliotis) and sites (e.g., Stairs) could not be assessed through power analysis because they 

failed to meet the assumptions of statistical tests. Alternative means should be considered to 

determine whether impacts could be detected for these species and sites should they occur. 

Indeed, additional more sophisticated analyses and designs should be explored to make 

greater use of the entire range of data. No changes should be made to the current monitoring 

program, but additional sites and target species, including means to monitor recruitment, 

should be added as resources permit. 
Final Technical Summary – Minchinton and Raimondi 

4 

STUDY PRODUCTS: 

Publications: 

Wilson, C. T., P. T. Raimondi, J. A. Altstatt, and T. E. Minchinton. Changes in intertidal 

community structure following mass mortality of the black abalone, Haliotis 

cracherodii, and implications for abalone recovery. Conservation Biology. In Review. 

Raimondi, P. T., C. M. Wilson, R. F. Ambrose, J. M. Engle and T. E. Minchinton. 2002. 

Continued declines of black abalone along the coast of California: are mass mortalities 

related to El Niño events? Marine Ecology Progress Series 242:143–152. 

Minchinton, T. E. and P. T. Raimondi. 2001. Long-term Monitoring of Rocky Intertidal 

Communities at the Channel Islands National Park: Summary of Spatial and Temporal 

Trends and Statistical Power Analysis. Report and presentation for the Scientific and 

Management Review of Monitoring Protocols for the Channel Islands National Park. 

29 pp. 

Research Presentations:

Minchinton, T. E. and P. T. Raimondi. 2001. Long-term Monitoring of Rocky Intertidal 

Communities at the Channel Islands National Park: Summary of Spatial and Temporal 

Trends and Statistical Power Analysis. Report and presentation for the Scientific and 

Management Review of Monitoring Protocols for the Channel Islands National Park. 

29 pp. 

Minchinton, T. E., P. T. Raimondi, D. V. Richards, G. E. Davis and K. D. Lafferty. 2001. 

Detecting Environmental Impacts: Monitoring Programs and Power Analysis. Poster 

presented at UC Toxic Substances Research & Training Program 14th Annual 

Research Symposium. Lake Tahoe, California. 

Minchinton, T. E., P. T. Raimondi, M. Wilson, R. F. Ambrose and J. M. Engle. 2000. 

Continued Declines of Black Abalone due to Withering Syndrome along the Coast of 

California. Poster presented at UC Toxic Substances Research & Training Program 

13th Annual Research Symposium. San Diego, California. April 2000. 

Becker, B. J. and MARINE members. 2001. MARINE (Multi-Agency Rocky Intertidal 

Network): Coordinated ecological monitoring on a regional scale. Displayed at various 

symposia. 
Effect of Temporal and Spatial Separation of Samples 

5

FINAL STUDY REPORT 

PART I: INTRODUCTION, OBJECTIVES, AND APPROACH 

1.1 Introduction 

Monitoring programs designed to detect natural and anthropogenic disturbances to populations 

and species assemblages in natural communities are becoming increasingly popular. The major 

assumption of all pre-impact monitoring studies is that the data collected will allow estimation of 

the consequences of an impact to ecological communities. One key assumption is that 

estimation of consequences after an impact will be possible for the sites that were monitored 

prior to the impact. The value of a coastline monitoring program depends on the assumption that 

estimations of impacts can be done accurately. Here, we report the results of data and statistical 

analyses that provide a sound basis for judging the utility of pre-impact monitoring programs to 

estimate accurately the consequences of impacts to ecological communities. Without testing the 

fundamental assumptions of such programs, any conclusion of ecological impact following 

perturbation will be challenged. Clearly, no local, State or Federal monitoring program will have 

served its purpose if the conclusions derived from the results cannot be vigorously defended. 

Since 1991 the Minerals Management Service has funded a monitoring program for Santa 

Barbara County, California, U.S.A, called the “Shoreline Inventory”. The inventory was to 

provide baseline data on rocky intertidal communities that could be used in the event of an oil 

spill to calculate losses due to the impact. The main logistical task of the inventory was to set-up 

and sample at regular intervals 9 permanent sampling sites in the rocky intertidal zone of 

southern and northern Santa Barbara County. At each site species assemblages dominated by 13 

species were targeted and sampled in permanent plots in spring and autumn of each year. From 

1991 to 1995 the Shoreline Inventory was expanded to 18 mainland sites across 4 counties (San 

Luis Obispo County, Santa Barbara County, Ventura County, Los Angeles County) and 8 

offshore sites on two islands (Catalina Island, Santa Cruz Island). An important feature of all 26 

sites is that they were selected, set-up and sampled using the same protocol. Therefore, the 

results from all sites are directly comparable. In its current form the Shoreline Inventory is one 

of the largest intertidal monitoring programs ever conducted. 

1.2 Objectives 

The primary objective of this study was to use the database of the Shoreline Inventory to test 

quantitatively the critical assumption that estimation of consequences after an impact is possible 

for sites that were monitored before the impact. 

Specifically, we answer the following two questions: 

1. What are the spatial and temporal patterns of abundance for the 13 target species at the 

18 mainland sites?
Final Study Report – Minchinton and Raimondi 

6 

2. Based upon power analyses following simulated impacts to target species at each site, 

should there be any modifications to the sampling design of the monitoring program?

1.3 Approach 

1.3.1 Background

The methods used in the Shoreline Inventory monitoring program, including a discussion of the 

pros and cons of different sampling approaches and protocols, can be found in Ambrose et al. 

(1995). Details of the monitoring program, which was developed based on those used by the 

National Park Service intertidal monitoring program (see Richards and Davis 1988), and rational 

for its design can be found in Engle et al. (1994). A detailed discussion of the selection of target 

species, sampling procedures and survey sites for the Shoreline Inventory is given in Engle et al. 

(1994) and Raimondi et al. (1999). The approach used for gathering data for the Shoreline 

Inventory is briefly explained below, but see the above references for more detail. 

1.3.2 Study Sites

Study sites were selected based upon key criteria. Sites had to be at locations with (1) species 

assemblages and ecological conditions representative of the southern and central California 

coastline, (2) relatively pristine habitats that were susceptible to human impacts, including oil 

spills, and (3) optimal conditions for long-term monitoring (accessible, abundant target species, 

large enough to establish permanent sampling areas). 

The Shoreline Inventory includes 8 island sites and 18 mainland sites (although new sites have 

been recently added). The 8 sites on Santa Cruz Island (Orizaba Cove, Prisoner’s Harbor, 

Scorpion Rock, Willows Anchorage, Trailer, and Fraser Cove) and 2 sites on Catalina Island 

(Bird Rock, Little Harbour) were established in autumn 1994. The 18 mainland sites extend 

from central to southern California and include 5 sites established in autumn 1995 in San Luis 

Obispo County (Point Sierra Nevada, Piedras Blancas, Cayucos Point, Hazards, Shell Beach), 9 

sites established in spring 1992 in Santa Barbara County (Occulto, Purisima Point, Stairs, 

Boathouse, Government Point, Alegria, Arroyo Hondo, Coal Oil Point, Carpinteria), 2 sites 

established in autumn 1994 in Ventura County (Mussel Shoals, Old Stairs), and 2 sites 

established in autumn 1994 in Los Angeles County (Paradise Cove, White’s Point). 

Only the 18 mainland sites are included in this study. The island sites were excluded from the 

study for two reasons. First, we wanted to focus on the mainland sites, which have received less 

attention than the island sites. Indeed, a similar study to this one includes all sites on Santa Cruz 

Island (see Minchinton and Raimondi 2001). In particular, we wanted to compare the response 

of target species between the 9 mainland sites north of Point Conception, an important 

biogeographic break, and the 9 sites south of Point Conception. Second, the mainland sites have 

been sampled most consistently and therefore present the most complete and comparable set of 

data, which is critical for data and statistical analysis of spatial and temporal patterns and power 

analysis. Location of the mainland study sites along the southern and central coastline of 

California is presented in Figure I-1. 
Effect of Temporal and Spatial Separation of Samples 

7

Figure I-1. Rocky intertidal monitoring sites along the southern and central coast of California.

Pt. Sal

Pt. Conception

Government Pt.

Boat House

Stairs

Purisima Pt.

N

S

W E

Santa 

Barbara

San Miguel Santa Rosa Santa

Cruz 

San Luis Obispo

35 N

34 N

120 W

Santa Barbara 

Channel

0 5 km

o

o

o

Cayucos Pt.

Piedras Blancas

Pt. Sierra Nevada

Hazards

Shell Beach

Alegria

Old Stairs

Ventura

LA

Coal Oil Pt.

Paradise Cove

Carpinteria

Arroyo Hondo

Mussel Shoals

White’s Pt.

Occulto

CALIFORNIA
Final Study Report – Minchinton and Raimondi 

8 

1.3.3 Target Species

Target species were selected based upon key criteria. Criteria used for species selection 

included: (1) ecological importance in structuring intertidal communities (abundant species, 

competitively dominant species, ecosystem engineers, major predators, etc.), (2) interesting local 

and regional distributions (e.g., species characteristic of particular intertidal heights or 

microhabitats or those at the limits of the biogeographic range), (3) well-studied with extensive 

literature available, and (4) human interest (vulnerable species, threatened species, harvested 

species, etc.). 

The Shoreline Inventory includes 13 key target species comprising both sessile and mobile 

macroinvertebrates and macroalgae that are generally the dominant occupiers of primary space at 

particular heights and exposures in the intertidal zone (see Raimondi et al. (1999) for a detailed 

discussion of the natural history of these target species). Four target species (or species groups) 

are sessile or sedentary macroinvertebrates: (1) barnacles (the acorn barnacles Chthamalus 

fissus/dalli and Balanus glandula), (2) the gooseneck barnacle Pollicipes polymerus, (3) mussels 

(primarily Mytilus californianus), and (4) the sea anemones Anthopleura elegantissima and A.

sola. Six target species are macroalgae: (5) the brown fucoid alga or rockweed Silvetia 

compressa (formerly Pelvetia compressa, Pelvetia fastigiata), (6) the brown fucoid alga or 

rockweed Hesperophycus californicus (formerly H. harveyanus), (7) the red alga Mastocarpus 

papillatus, (8) the red alga or turfweed Endocladia muricata, (9) the “rainbow” alga Mazzaella

spp. (formerly Iridaea spp.), and (10) the marine angiosperm or surfgrass Phyllospadix

torreyi/scouleri. Three target species are mobile invertebrates: (11) the ochre sea star Pisaster 

ochraceus, (12) the giant owl limpet Lottia gigantea, and (13) the black abalone Haliotis 

cracherodii. 

All 13 target species are included in this study, but not all target species are present at each site 

(generally about 4 to 10 target species at per site). The specific target species sampled at each of 

the 18 mainland study sites are listed in Table I-1. 
Effect of Temporal and Spa

9

Table I-1. Target species sampled at each of 18 mainland sites. 

County & Site Anthopleura Barnacles Pollicipes Mussels Silvetia Hesperophycus Mastocarpus Endocladia Mazzaella Phyllospadix San Luis Obispo 

Point Sierra Nevada x x x x x x x Piedras Blancas Cayucos Point x x x x x x Hazards x x x x x Shell Beach x x x x x 

Santa Barbara 

Occulto x x x Purisima Point Stairs x x x x x Boathouse x x x x x Government Point x x x x x x Alegria x x x x Arroyo Hondo x x x Coal Oil Point x x Carpinteria x x x x 

Ventura 

Mussel Shoals x x x x Old Stairs x x x x 

Los Angeles 

Paradise Cove x x x x White’s Point x x x 
Final Study Report – Minchinton and Raimondi 

10 

1.3.4 Sampling Methods

At each site permanent areas were established where target species were the dominant 

organisms, and then their abundances were sampled in spring and autumn of each year. For this 

study, this comprised 22 sample periods from spring 1992 to autumn 2002 (although the number 

of sample periods varied depending on when the site was established). The abundances of 

sessile and sedentary macroinvertebrates (barnacles, Pollicipes, Anthopleura, mussels) and 

macroalgae (Silvetia, Hesperophycus, Mastocarpus, Endocladia, Mazzaella) were quantified by 

assessing their percent covers from photographs of 5 replicate quadrats for each species at each 

site. The abundance of Phyllospadix was quantified in situ by determining its percent cover in 3 

replicate point-intercept line transects per site. The abundances of mobile species (Pisaster, 

Lottia, Haliotis) were quantified by counting them in situ in 3 replicate plots (5 for Lottia) and 

band transects (for Pisaster at some sites) for each species at each site. 

For logistical reasons, some sites and target species could not be sampled for some sample 

periods. When this happened frequently, the target species at that site was omitted because the 

data were not deemed sufficient for presentation and analysis. Of the 97 combinations of sites 

and species sampled, this occurred in only four instances: mussels and Pisaster at Carpinteria 

and Lottia at Old Stairs and Paradise Cove. This left 93 target species and site combinations for 

analysis (see Table I-1). On 16 occasions, an individual quadrat, transect or plot was not 

sampled for a particular target species at a particular site on a particular date. On 6 occasions, a 

replicate quadrat, transect or plot was damaged, probably by storms. For three of these 

replicates, a new area with the same abundance of the target species was located and 

subsequently sampled. For the others, the reduced number of replicates was subsequently 

sampled. No attempt was made to account for any of these missing data, but given that they 

occurred sporadically across species, sites and sample periods, and the entire set of data 

comprised thousands of data points, their absence had no important influence on data and 

statistical analyses. 
Effect of Temporal and Spatial Separation of Samples 

11

Part II: SPATIAL AND TEMPORAL PATTERNS 

2.1 Introduction 

The first objective was to determine the spatial and temporal patterns of abundance for the 13 

target species at the 18 mainland sites. Although this objective has previously been addressed 

for some of the sites and sample periods (see Ambrose et al. 1995, Raimondi et al. 1999), the 

current report analyzes data from a greater range of sites over a longer period. Indeed, this report 

updates these previous reports, providing the most current and comprehensive summary of the 

spatial and temporal patterns of abundance of target species for sites of the Shoreline Inventory. 

The purpose of summarizing these spatial and temporal trends is to examine changes in the 

structure and dynamics of the target species. Given the relatively long temporal series of data 

over a relatively large number of sites, it is possible to identify both short- (e.g., seasonal) and 

longer-term (e.g., El Niño events) changes in abundance over both local (e.g., sites separated by 

kilometers) and regional (e.g., sites separated by the biogeographic break at Point Conception) 

spatial scales. Because of the relatively large number of target species, it is possible to identify 

whether changes are species-specific or common across taxa. Investigating such spatial and 

temporal patterns of species abundance can help identify dramatic changes outside normal limits 

of variation and lead to considerable insights about the factors which might be responsible for 

such fluctuations (e.g., changes due to human impacts such as oil spills). 

2.2 Methods 

To examine variation in the abundance of target species in space and time, summary statistics 

(mean and standard error of the mean) for all sites and sample periods were calculated and 

plotted in two ways. First, summary statistics were calculated for each combination of site and 

sample period. Here replication comes from individual quadrats, plots or transects at a given site 

and sample period. Second, to examine broader patterns of abundance over time (i.e., for all 

sites combined), summary statistics were calculated for each sample period. Here replication 

comes from individual sites during a sample period. Because sites were established at different 

times, however, the level of replication varies with sample period. All sites were established by 

autumn 1995 and, therefore, except where data for a site were occasionally missing, the number 

of replicate sites remains constant after this date. 

Analyses of covariance (ANCOVA) procedures were used to assess seasonal and temporal 

patterns of abundance for each of the target species at each site using statistical software program 

SYSTAT. All available sample periods were used in analyses and data were not transformed. In 

this model, season was included as a categorical variable and time (i.e., sample period) was 

included as the covariate. This analysis allows determination of linear changes in abundance 

over time and of seasonal effects after removal of (linear) temporal trends. These linear analyses 

give an indication of whether abundances increased or decreased over time from the initiation of 

sampling until the most current sample. They do not, however, detect non-linear patterns in the 

data. For example, if the cover a target species decreased and then increased, which might 
Final Study Report – Minchinton and Raimondi 

12 

happen when there is an impact followed by a recovery, this might be detected as no trend. 

Given the large numbers of sites and extensive time series of data, such non-linear trends were 

apparent for species at some sites. It was beyond the scope of this report to use more 

sophisticated time series analyses to determine the underlying non-linear patterns in these data. 

Instead, these trends are described by visual inspection of the data, pointing out common patterns 

across species, space and time. 

Visual inspection of the graphs was done to determine whether temporal changes in abundance 

showed any spatial consistency among sites (i.e., geographic patterns such as latitudinal trends or 

differences around the biogeographic break at Point Conception). To facilitate these geographic 

comparisons, graphs and presentation of the data for sites are arranged from north to south. 

2.3 Results

2.3.1 Anthopleura

The anemone Anthopleura was sampled at Boathouse, Alegria, Coal Oil Point, and Carpinteria in 

Santa Barbara County and at Mussel Shoals and Old Stairs in Ventura County (Figure II-1a, b). 

On average for all sample dates, cover varied widely among sites from about 10 to 70%. There 

were no significant seasonal trends in the cover of Anthopleura (Figure II-1a, b; Table II-1a). 

Nevertheless, abundances at some sites (e.g., Boathouse, Carpinteria) were consistently greater 

in spring than in autumn, but this pattern was not obvious when all sites were combined (Figure 

II-14a). At all 6 sites the cover of Anthopleura decreased over time, and this trend was 

significant at Boathouse, Alegria and Coal Oil Point (Figure II-1a, b; Table II-1a), but was not 

apparent when all sites were combined (Figure II-14a). The most consistent and striking 

declines in cover occurred at some sites from 1997 to 1998 (e.g., Alegria, Carpinteria, Mussel 

Shoals, Old Stairs), but declines in cover were also apparent in the early and mid 1990s. At 

some sites cover increased after declines in 1997/98 (Figure II-1a, b). 
Effect of Temporal and Spatial Separation of Samples 

13

Figure II-1a: Mean (±se) percent cover of Anthopleura in spring (S) and autumn (A) from 1992 to 2002 at each of 

four sites. Blank spaces indicate that sampling was not done at that time. Note differences in scale on y-axis. 

0

10

20

30

40

50

0

20

40

60

80

100

0

10

20

30

40

50

0

10

20

30

40

50

Alegria

Boathouse

Cover of Anthopleura (%)

Coal Oil Point

Carpinteria

93 94 95 02

S A

92

S ASAS ASAS ASASASASAS A

96 99 00 01 97 98
Final Study Report – Minchinton and Raimondi 

14 

Figure II-1b: Mean (±se) percent cover of Anthopleura in spring (S) and autumn (A) from 1992 to 2002 at each of 

two sites. Blank spaces indicate that sampling was not done at that time. 

0

20

40

60

80

100

Cover of Anthopleura (%)

Mussel Shoals

Old Stairs

93 94 95 02

S A

92

S ASAS ASAS ASASASASAS A

96 99 00 01 97 98

0

20

40

60

80

100
Effect of Temporal and Spatial Separation of Samples 

15

Table II-1a. Results of ANCOVA analyses examining seasonal and temporal patterns of abundance of target 

species. (S) indicates greater abundances in spring. (A) indicates greater abundances in autumn. (+) indicates an 

increasing linear trend over time. (–) indicates a decreasing linear trend over time. Significant results are in bold. 

Species Site By Season Over Time 

Anthopleura Boathouse 0.171 0.003 (–) 

Anthopleura Alegria 0.864 0.005 (–) 

Anthopleura Coal Oil Point 0.393 0.009 (–) 

Anthopleura Carpinteria 0.069 0.383 

Anthopleura Mussel Shoals 0.935 0.154 

Anthopleura Old Stairs 0.311 0.208 

Barnacles Point Sierra Nevada 0.174 < 0.001 (–) 

Barnacles Cayucos Point 0.036 (A) < 0.001 (–) 

Barnacles Hazards 0.422 0.016 (–) 

Barnacles Shell Beach 0.604 0.681 

Barnacles Occulto 0.428 < 0.001 (–) 

Barnacles Stairs 0.410 < 0.001 (–) 

Barnacles Boathouse 0.866 0.940 

Barnacles Government Point 0.404 < 0.001 (–) 

Barnacles Alegria 0.014 (A) 0.097 

Barnacles Arroyo Hondo 0.124 0.005 (–) 

Barnacles Carpinteria 0.223 < 0.001 (–) 

Barnacles Mussel Shoals < 0.001 (A) 0.549 

Barnacles Old Stairs 0.030 (A) < 0.001 (–) 

Barnacles Paradise Cove 0.599 0.007 (–) 

Barnacles White’s Point 0.347 < 0.001 (–) 

Pollicipes Government Point 0.718 0.024 (–) 

Pollicipes Alegria 0.181 0.386 

Pollicipes Carpinteria 0.001 (A) < 0.001 (–) 

Mussels Point Sierra Nevada 0.537 0.155 

Mussels Cayucos Point 0.692 < 0.001 (–) 

Mussels Hazards 0.449 < 0.001 (–)

Mussels Shell Beach 0.916 < 0.001 (–)

Mussels Occulto 0.355 0.301 

Mussels Stairs 0.977 < 0.001 (–)

Mussels Boathouse 0.719 0.001 (–) 

Mussels Government Point 0.769 < 0.001 (–)

Mussels Alegria 0.128 < 0.001 (–)

Mussels Arroyo Hondo 0.194 < 0.001 (–)

Mussels Mussel Shoals 0.601 0.008 (–) 

Mussels Old Stairs 0.356 0.810 

Mussels Paradise Cove 0.375 0.038 (–) 

Mussels White’s Point 0.961 0.809 
Final Study Report – Minchinton and Raimondi 

16 

2.3.2 Barnacles

Barnacles (Chthamalus fissus/dalli and Balanus glandula) were sampled at all sites except 

Piedras Blancas, Purisima Point and Coal Oil Point (Figure II-2a-d). On average for all sample 

dates, cover varied considerably among sites from about 25 to 70%. At 4 of 15 sites (Cayucos 

Point, Alegria, Mussel Shoals, Old Stairs) there were significant seasonal differences in 

abundance, with cover consistently greater in autumn than in spring (Figure II-2a-d; Table II-1a). 

This was a minor seasonal trend with changes in cover by on average 5 to 10% and this trend 

was also apparent when all sites were combined (Figure II-14a). The cover of barnacles 

decreased significantly at 11 of 15 sites (Figure II-2a-d; Table II-1a) and this decrease was clear 

for all sites combined (Figure II-14a). At some sites (e.g., Stairs, Boathouse) cover declined 

dramatically from 80 to 10%, whereas at others the decline was less striking or not apparent at 

all. There were two times of synchronous decline among some sites, the periods from 1992 to 

1993 and 1997 to 1998, and this is reflected for all sites combined (Figure II-2a-d; Figure II14a). There were also dynamic trends in the data, with frequent and substantial decreases and 

increases in the cover of barnacles over time. At some sites declines were followed by increases 

in cover in subsequent years (e.g., Boathouse, Alegria, Arroyo Hondo, White’s Point), whereas 

at others cover did not increase after decline (e.g., Occulto, Stairs). 
Effect of Temporal and Spatial Separation of Samples 

17

Figure II-2a: Mean (±se) percent cover of barnacles in spring (S) and autumn (A) from 1992 to 2002 at each of 

four sites. Blank spaces indicate that sampling was not done at that time. Note differences in scale on y-axis. 

0

20

40

60

80

100

0

10

20

30

40

50

0

10

20

30

40

50

Cayucos Point

Point Sierra Nevada

Cover of Barnacles (%)

Hazards

Shell Beach

93 94 95 02

S A

92

S ASAS ASAS ASASASASAS A

96 99 00 01 97 98

0

20

40

60

80

100
Final Study Report – Minchinton and Raimondi 

18 

Figure II-2b: Mean (±se) percent cover of barnacles in spring (S) and autumn (A) from 1992 to 2002 at each of 

four sites. Blank spaces indicate that sampling was not done at that time. 

0

20

40

60

80

100 Stairs

Occulto

Cover of Barnacles (%)

Boathouse

Government Point

93 94 95 02

S A

92

S ASAS ASAS ASASASASAS A

96 99 00 01 97 98

0

20

40

60

80

100

0

20

40

60

80

100

0

20

40

60

80

100
Effect of Temporal and Spatial Separation of Samples 

19

Figure II-2c: Mean (±se) percent cover of barnacles in spring (S) and autumn (A) from 1992 to 2002 at each of 

four sites. Blank spaces indicate that sampling was not done at that time. 

0

20

40

60

80

100 Arroyo Hondo

Alegria

Cover of Barnacles (%)

Carpinteria

Mussel Shoals

93 94 95 02

S A

92

S ASAS ASAS ASASASASAS A

96 99 00 01 97 98

0

20

40

60

80

100

0

20

40

60

80

100

0

20

40

60

80

100
Final Study Report – Minchinton and Raimondi 

20 

Figure II-2d: Mean (±se) percent cover of barnacles in spring (S) and autumn (A) from 1992 to 2002 at each of 

four sites. Blank spaces indicate that sampling was not done at that time. 

0

20

40

60

80

100 Old Stairs

Cover of Barnacles (%)

Paradise Cove

White's Point

93 94 95 02

S A

92

S ASAS ASAS ASASASASAS A

96 99 00 01 97 98

0

20

40

60

80

100

0

20

40

60

80

100

2.3.3 Pollicipes polymerus

The gooseneck barnacle Pollicipes was only sampled at Government Point, Alegria, and 

Carpinteria in Santa Barbara County (Figure II-3). On average for all sample dates, cover varied 

among sites from about 15 to 40%. The cover of Pollicipes was significantly greater in autumn 

than in spring at Carpinteria, and this seasonal trend was apparent at Alegria but not at 

Government Point (Figure II-3; Table II-1a; Figure II-14a). The cover of Pollicipes declined 

gradually and significantly over time at Government Point and Carpinteria, but remained 

relatively consistent at Alegria (Figure II-1; Table II-1a). 
Effect of Temporal and Spatial Separation of Samples 

21

Figure II-3: Mean (±se) percent cover of Pollicipes in spring (S) and autumn (A) from 1992 to 2002 at each of 

three sites. Blank spaces indicate that sampling was not done at that time. Note differences in scale on y-axis. 

Government Point

Cover of Pollicipes (%)

Alegria

Carpinteria

93 94 95 02

S A

92

S ASAS ASAS ASASASASAS A

96 99 00 01 97 98

0

20

40

60

80

100

0

10

20

30

40

50

0

10

20

30

40

50

2.3.4 Mussels

Mussels (primarily Mytilus californianus) were sampled at all sites except Piedras Blancas, 

Purisima Point, Coal Oil Point and Carpinteria (sampled too infrequently for presentation) 

(Figure II-4a-d). On average for all sample dates, cover was consistently high and varied among 

sites from about 40 to 90%. There were no significant seasonal trends in mussel cover (Figure 

II-4a-d; Table II-1a). At 10 of 14 sites, mussel cover declined significantly over time (Figure 

II4a-d; Table II-1a; Figure II-14a). At most sites, the cover of mussels remained remarkably 

consistent from one sample period to the next, but at several sites cover was reduced by more 

than 50% (e.g., Shell Beach, Stairs, Arroyo Hondo, Old Stairs and also at Carpinteria, which is 

not presented). Declines were apparent at several sites from 1997 to 1998, and this was reflected 

when all sites were combined (Figure II-4a-d, Figure II-14a), but cover also decreased at other 

times or gradually over time. Similar to barnacles, decreases in cover were sometimes followed 

by increases, highlighting dynamic temporal patterns. 
Final Study Report – Minchinton and Raimondi 

22 

Figure II-4a: Mean (±se) percent cover of mussels in spring (S) and autumn (A) from 1992 to 2002 at each of four 

sites. Blank spaces indicate that sampling was not done at that time. 

0

20

40

60

80

100 Cayucos Point

Point Sierra Nevada

Cover of Mussels (%)

Hazards

Shell Beach

93 94 95 02

S A

92

S ASAS ASAS ASASASASAS A

96 99 00 01 97 98

0

20

40

60

80

100

0

20

40

60

80

100

0

20

40

60

80

100

 
Effect of Temporal and Spatial Separation of Samples 

23

Figure II-4b: Mean (±se) percent cover of mussels in spring (S) and autumn (A) from 1992 to 2002 at each of four 

sites. Blank spaces indicate that sampling was not done at that time. 

0

20

40

60

80

100 Stairs

Occulto

Cover of Mussels (%)

Boathouse

Government Point

93 94 95 02

S A

92

S ASAS ASAS ASASASASAS A

96 99 00 01 97 98

0

20

40

60

80

100

0

20

40

60

80

100

0

20

40

60

80

100
Final Study Report – Minchinton and Raimondi 

24 

Figure II-4c: Mean (±se) percent cover of mussels in spring (S) and autumn (A) from 1992 to 2002 at each of four 

sites. Blank spaces indicate that sampling was not done at that time. 

0

20

40

60

80

100 Arroyo Hondo

Alegria

Cover of Mussels (%)

Mussel Shoals

Old Stairs

93 94 95 02

S A

92

S ASAS ASAS ASASASASAS A

96 99 00 01 97 98

0

20

40

60

80

100

0

20

40

60

80

100

0

20

40

60

80

100
Effect of Temporal and Spatial Separation of Samples 

25

Figure II-4d: Mean (±se) percent cover of mussels in spring (S) and autumn (A) from 1992 to 2002 at each of two 

sites. Blank spaces indicate that sampling was not done at that time. Cover of Mussels (%)

Paradise Cove

White's Point

93 94 95 02

S A

92

S ASAS ASAS ASASASASAS A

96 99 00 01 97 98

0

20

40

60

80

100

0

20

40

60

80

100

2.3.5 Silvetia compressa

The brown fucoid alga or rockweed Silvetia compressa was sampled at Point Sierra Nevada, 

Cayucos Point, Hazards and Shell Beach in San Luis Obispo County and at Stairs, Boathouse 

and Government Point in Santa Barbara County (Figure II-5a, b). On average for all sample 

dates, cover was consistently high and varied among sites from 50 to 80%. There were strong 

seasonal trends in the cover of Silvetia, with 4 of 7 sites having greater cover in autumn than in 

spring by on average 20% (Figure II-5a, b; Table II-1b). Except at Boathouse, the cover of 

Silvetia declined significantly over time at the other 6 sites (Figure II-5a, b; Table II-1b). All 

sites were established with relatively great amounts of cover and, at most sites, cover appeared to 

decrease gradually over time, with reductions by as much as 50%. There was, however, a 

relatively minor but synchronous decline in cover at most sites from 1997 to 1998. These 

seasonal and temporal patterns were reflected for all sites combined (Figure II-14b). 
Final Study Report – Minchinton and Raimondi 

26 

Figure II-5a: Mean (±se) percent cover of Silvetia in spring (S) and autumn (A) from 1992 to 2002 at each of four 

sites. Blank spaces indicate that sampling was not done at that time. 

0

20

40

60

80

100 Cayucos Point

Point Sierra Nevada

Cover of Silvetia (%)

Hazards

Shell Beach

93 94 95 02

S A

92

S ASAS ASAS ASASASASAS A

96 99 00 01 97 98

0

20

40

60

80

100

0

20

40

60

80

100

0

20

40

60

80

100
Effect of Temporal and Spatial Separation of Samples 

27

Figure II-5b: Mean (±se) percent cover of Silvetia in spring (S) and autumn (A) from 1992 to 2002 at each of four 

sites. Blank spaces indicate that sampling was not done at that time. 

0

20

40

60

80

100 Stairs

Cover of Silvetia (%)

Boathouse

Government Point

93 94 95 02

S A

92

S ASAS ASAS ASASASASAS A

96 99 00 01 97 98

0

20

40

60

80

100

0

20

40

60

80

100
Final Study Report – Minchinton and Raimondi 

28 

Table II-1b. Results of ANCOVA analyses examining seasonal and temporal patterns of abundance of target 

species. (S) indicates greater abundances in spring. (A) indicates greater abundances in autumn. (+) indicates an 

increasing linear trend over time. (–) indicates a decreasing linear trend over time. Significant results are in bold. 

Species Site By Season Over Time 

Silvetia Point Sierra Nevada 0.638 < 0.001 (–) 

Silvetia Cayucos Point 0.004 (A) < 0.001 (–) 

Silvetia Hazards 0.022 (A) < 0.001 (–) 

Silvetia Shell Beach 0.675 < 0.001 (–)

Silvetia Stairs 0.010 (A) < 0.001 (–)

Silvetia Boathouse 0.051 0.827 

Silvetia Government Point 0.009 (A) 0.011 (–) 

Hesperophycus Point Sierra Nevada 0.898 < 0.001 (–) 

Hesperophycus Cayucos Point 0.408 0.002 (–) 

Mastocarpus Point Sierra Nevada < 0.001 (A) < 0.001 (–)

Mastocarpus Shell Beach 0.719 < 0.001 (–) 

Endocladia Cayucos Point 0.041 (S) < 0.001 (–) 

Endocladia Hazards < 0.001 (S) < 0.001 (–) 

Endocladia Shell Beach < 0.001 (S) < 0.001 (–) 

Endocladia Occulto < 0.001 (S) < 0.001 (–) 

Endocladia Stairs < 0.001 (S) 0.020 (–) 

Endocladia Boathouse 0.100 < 0.001 (–) 

Endocladia Government Point < 0.001 (S) 0.003 (–) 

Endocladia Old Stairs 0.645 < 0.001 (–) 

Endocladia Paradise Cove 0.141 < 0.001 (+) 

Endocladia White’s Point < 0.001 (S) < 0.001 (–) 

Mazzaella Point Sierra Nevada 0.221 0.622 

Mazzaella Hazards 0.157 0.148 

Phyllospadix Point Sierra Nevada 0.021 (A) 0.440 

Phyllospadix Cayucos Point 0.090 0.148 

Phyllospadix Stairs 0.020 (A) < 0.001 (–) 

Phyllospadix Government Point 0.002 (A) 0.118 

Phyllospadix Arroyo Hondo < 0.001 (A) 0.180 

Phyllospadix Coal Oil Point 0.135 < 0.001 (–) 

Phyllospadix Carpentaria < 0.001 (A) 0.003 (–) 

Phyllospadix Mussel Shoals 0.057 < 0.001 (–) 

Phyllospadix Paradise Cove 0.004 (A) 0.162 

2.3.6 Hesperophycus californicus

The fucoid alga or rockweed Hesperophycus californicus was only sampled Point Sierra Nevada 

and Cayucos Point in San Luis Obispo County (Figure II-6). On average for all sample dates, 

cover was about 25% at both sites. In contrast to Silvetia, there was no seasonal pattern in the 

abundance of Hesperophycus (Figure II-6, Table II-1b). Similar to Silvetia, the cover of 

Hesperophycus decreased significantly over time, with cover at both sites declining dramatically 

from about 90 to 20% (Figure II-6, Table II-1b). 
Effect of Temporal and Spatial Separation of Samples 

29

Figure II-6: Mean (±se) percent cover of Hesperophycus in spring (S) and autumn (A) from 1992 to 2002 at each 

of two sites. Blank spaces indicate that sampling was not done at that time. 

0

20

40

60

80

100

Cover of Hespe

rophycus (%)

Point Sierra Nevada

Cayucos Point

93 94 95 02

S A

92

S ASAS ASAS ASASASASAS A

96 99 00 01 97 98

0

20

40

60

80

100

2.3.7 Mastocarpus papillatus

The red alga Mastocarpus papillatus was only sampled at Point Sierra Nevada and Shell Beach 

in San Luis Obispo County (Figure II-7). On average for all sample periods, cover at both sites 

was about 20 to 30%. At Point Sierra Nevada there was a strong seasonal trend that was not 

apparent at Shell Beach, with cover of Mastocarpus in autumn greater than in spring by on 

average about 20% (Figure II-7; Table II-1b). At both sites, Mastocarpus decreased gradually 

and significantly over time, with no apparent pattern to the decline (Figure II-7; Table II-1b). 
Final Study Report – Minchinton and Raimondi 

30 

Figure II-7: Mean (±se) percent cover of Mastocarpus in spring (S) and autumn (A) from 1992 to 2002 at each of 

two sites. Blank spaces indicate that sampling was not done at that time. 

0

20

40

60

80

100

Cover of Ma

stoca

rpus (%)

Point Sierra Nevada

Shell Beach

93 94 95 02

S A

92

S ASAS ASAS ASASASASAS A

96 99 00 01 97 98

0

20

40

60

80

100

2.3.8 Endocladia muricata

The red alga or turfweed Endocladia muricata was sampled at Cayucos Point, Hazards and Shell 

Beach in San Luis Obispo County, Occulto, Stairs, Boathouse and Government Point in Santa 

Barbara County, Old Stairs in Ventura County and Paradise Cove and White’s Point in Los 

Angeles County (Figure II-8a-c). On average for all sampling dates, cover varied among sites 

from 20 to 50%. There were significant seasonal trends in the abundance of Endocladia at 7 of 

10 sites, with cover greater by about 10 to 25% in spring than in autumn (Figure II-8a-c; Table 

II-b). At 9 of 10 sites, Endocladia cover gradually and significantly decreased over time, 

whereas at Paradise Cove the cover of Endocladia increased significantly (Figure II-8a-c; Table 

II-b). At 8 of 10 sites there was a clear decline in abundance from 1997 to 1998, followed by a 

slight increase in cover at some sites (Figure II-8a-c). A synchronous pattern of decline among 

sites was also evident in 1992 and 1993 at sites in Santa Barbara County (Figure II-8a-c). The 

increased cover in autumn and temporal pattern of decline are evident when all sites are 

combined (Figure II-14b). 
Effect of Temporal and Spatial Separation of Samples 

31

Figure II-8a: Mean (±se) percent cover of Endocladia in spring (S) and autumn (A) from 1992 to 2002 at each of 

four sites. Blank spaces indicate that sampling was not done at that time. 

0

20

40

60

80

100 Hazards

Cayucos Point

Cover of Endocladia (%)

Shell Beach

Occulto

93 94 95 02

S A

92

S ASAS ASAS ASASASASAS A

96 99 00 01 97 98

0

20

40

60

80

100

0

20

40

60

80

100

0

20

40

60

80

100
Final Study Report – Minchinton and Raimondi 

32 

Figure II-8b: Mean (±se) percent cover of Endocladia in spring (S) and autumn (A) from 1992 to 2002 at each of 

four sites. Blank spaces indicate that sampling was not done at that time. 

0

20

40

60

80

100 Boathouse

Stairs

Cover of Endocladia (%)

Government Point

Old Stairs

93 94 95 02

S A

92

S ASAS ASAS ASASASASAS A

96 99 00 01 97 98

0

20

40

60

80

100

0

20

40

60

80

100

0

20

40

60

80

100
Effect of Temporal and Spatial Separation of Samples 

33

Figure II-8c: Mean (±se) percent cover of Endocladia in spring (S) and autumn (A) from 1992 to 2002 at each of 

two sites. Blank spaces indicate that sampling was not done at that time. Cover of Endocladia (%)

Paradise Cove

White's Point

93 94 95 02

S A

92

S ASAS ASAS ASASASASAS A

96 99 00 01 97 98

0

20

40

60

80

100

0

20

40

60

80

100

2.3.9 Mazzaella spp.

The alga Mazzaella spp. was only sampled at Point Sierra Nevada and Hazards in San Luis 

Obispo County (Figure II-9). On average for all sample periods, cover varied among sites from 

40 to 50%. There were no significant seasonal (although cover was on average greater in the 

autumn) or temporal trends for this suite of species (Figure II-9; Table II-1b; Figure II-14c). 

Nevertheless, the abundance of Mazzaella declined marginally at both sites from 1997 to 1998 

and then increased slightly thereafter (Figure II-9). 
Final Study Report – Minchinton and Raimondi 

34 

Figure II-9: Mean (±se) percent cover of Mazzella in spring (S) and autumn (A) from 1992 to 2002 at each of two 

sites. Blank spaces indicate that sampling was not done at that time. 

0

20

40

60

80

100

Cover of Mazzaella (%)

Point Sierra Nevada

Hazards

93 94 95 02

S A

92

S ASAS ASAS ASASASASAS A

96 99 00 01 97 98

0

20

40

60

80

100

2.3.10 Phyllospadix scouleri/torreyi

The surfgrass Phyllospadix scouleri/torreyi was sampled at Point Sierra Nevada and Cayucos 

Point in San Luis Obispo County, Stairs, Government Point, Coal Oil Point and Carpinteria in 

Santa Barbara County, Mussel Shoals in Ventura County and Paradise Cove in Los Angeles 

County (Figure II-10a-c). On average for all sample periods, cover was generally great and 

varied among sites from 50 to 90%. There were dramatic seasonal trends in the abundance of 

Phyllospadix, with 6 of 9 sites having on average about 5 to 20% and significantly greater cover 

in autumn than in the spring and the other sites, with the exception of Cayucos Point, showing a 

similar pattern (Figure II-10a-c; Table II-1b). At 4 of 9 sites there was a dramatic and significant 

decline in the cover of Phyllospadix (Figure II-10a-c,; Table II-1b). Reductions in cover 

occurred primarily from 1997 to 1998, and similar patterns were observed at two additional sites 

(Figure II-10a-c). At several sites (e.g., Carpinteria, Stairs), there were substantial increases in 

cover following these declines. These prominent seasonal and temporal trends remained clear 

when all sites were combined (Figure II-14c). 
Effect of Temporal and Spatial Separation of Samples 

35

Figure II-10a: Mean (±se) percent cover of Phyllospadix in spring (S) and autumn (A) from 1992 to 2002 at each 

of four sites. Blank spaces indicate that sampling was not done at that time. 

0

20

40

60

80

100 Cayucos Point

Point Sierra Nevada

Cover of Phyllospadix (%)

Stairs

Government Point

93 94 95 02

S A

92

S ASAS ASAS ASASASASAS A

96 99 00 01 97 98

0

20

40

60

80

100

0

20

40

60

80

100

0

20

40

60

80

100
Final Study Report – Minchinton and Raimondi 

36 

Figure II-10b: Mean (±se) percent cover of Phyllospadix in spring (S) and autumn (A) from 1992 to 2002 at each 

of four sites. Blank spaces indicate that sampling was not done at that time. 

0

20

40

60

80

100 Coal Oil Point

Arroyo Hondo

Cover of Phyllospadix (%)

Carpinteria

Mussel Shoals

93 94 95 02

S A

92

S ASAS ASAS ASASASASAS A

96 99 00 01 97 98

0

20

40

60

80

100

0

20

40

60

80

100

0

20

40

60

80

100
Effect of Temporal and Spatial Separation of Samples 

37

Figure II-10c: Mean (±se) percent cover of Phyllospadix in spring (S) and autumn (A) from 1992 to 2002 at each 

of two sites. Blank spaces indicate that sampling was not done at that time. Cover of Phyllospadix (%)

Paradise Cove

93 94 95 02

S A

92

S ASAS ASAS ASASASASAS A

96 99 00 01 97 98

0

20

40

60

80

100

2.3.11 Pisaster ochraceus

The ochre sea star Pisaster ochraceus was sampled at Point Sierra Nevada, Cayucos Point, 

Hazards and Shell Beach in San Luis Obispo County, at Stairs, Boathouse, Government Point 

and Arroyo Hondo in Santa Barbara County and at Mussel Shoals and Old Stair in Ventura 

County (Figure II-11b). On average for all sample periods, numbers varied among sites from 

less than 10 to about 30. In general, numbers were quite variable among plots and sites over 

time. At 2 northern sites in San Luis Obispo County, densities were significantly greater in 

autumn than in spring (Figure II-11a-c; Table II-1c). There were no other clear seasonal patterns 

at the other sites. Pisaster densities significantly increased at Point Sierra Nevada and Cayucos 

Point, significantly decreased at Mussel Shoals, and displayed a variety of temporal patterns at 

the other sites (Figure II-11a-c; Table II-1c). For all sites combined, there appears to be a slight 

decrease in the abundance of Pisaster from 1992 to 1993 and again after 1997, which is then 

followed by an increase (Figure II-14d) 
Final Study Report – Minchinton and Raimondi 

38 

Figure II-11a: Mean (±se) density of Pisaster in spring (S) and autumn (A) from 1992 to 2002 at each of four sites. 

Blank spaces indicate that sampling was not done at that time. Note differences in scale on y-axis. 

0

5

10

15

20

25 Cayucos Point

Point Sierra Nevada

Density of Pisaste

r (no. / plot)

Hazards

Shell Beach

93 94 95 02

S A

92

S ASAS ASAS ASASASASAS A

96 99 00 01 97 98

0

2

4

6

8

10

0

10

20

30

40

50

0

20

40

60

80

100
Effect of Temporal and Spatial Separation of Samples 

39

Figure II-11b: Mean (±se) density of Pisaster in spring (S) and autumn (A) from 1992 to 2002 at each of four sites. 

Blank spaces indicate that sampling was not done at that time. Note differences in scale on y-axis. 

Boathouse

Stairs

Density of Pisaste

r (no. / plot)

Government Point

Arroyo Hondo

93 94 95 02

S A

92

S ASAS ASAS ASASASASAS A

96 99 00 01 97 98

0

10

20

30

40

50

0

30

60

90

120

150

0

20

40

60

80

100

0

20

40

60

80

100
Final Study Report – Minchinton and Raimondi 

40 

Figure II-11c: Mean (±se) density of Pisaster in spring (S) and autumn (A) from 1992 to 2002 at each of two sites. 

Blank spaces indicate that sampling was not done at that time. Note differences in scale on y-axis. Density of Pisaste

r (no. / plot)

Mussel Shoals

Old Stairs

93 94 95 02

S A

92

S ASAS ASAS ASASASASAS A

96 99 00 01 97 98

0

5

10

15

20

25

0

10

20

30

40

50
Effect of Temporal and Spatial Separation of Samples 

41

Table II-1c. Results of ANCOVA analyses examining seasonal and temporal patterns of abundance of target 

species. (S) indicates greater abundances in spring. (A) indicates greater abundances in autumn. (+) indicates an 

increasing linear trend over time. (–) indicates a decreasing linear trend over time. Significant results are in bold. 

Species Site By Season Over Time 

Pisaster Point Sierra Nevada 0.014 (A) < 0.001 (+) 

Pisaster Cayucos Point < 0.001 (A) < 0.001 (+) 

Pisaster Hazards 0.752 0.904 

Pisaster Shell Beach 0.154 0.063 

Pisaster Stairs 0.124 0.399 

Pisaster Boathouse 0.380 0.703 

Pisaster Government Point 0.131 0.083 

Pisaster Arroyo Hondo 0.996 0.194 

Pisaster Mussel Shoals 0.427 0.009 (–) 

Pisaster Old Stairs 0.501 0.266 

Lottia Cayucos Point 0.885 0.010 (–) 

Lottia Hazards 0.480 0.335 

Lottia Stairs 0.404 < 0.001 (+) 

Lottia Boathouse 0.798 0.366 

Lottia Government Point 0.465 0.085 

Lottia Alegria 0.734 < 0.001 (+) 

Haliotis Point Sierra Nevada 0.533 0.006 (+) 

Haliotis Piedras Blancas 0.977 0.614 

Haliotis Cayucos Point 0.788 < 0.001 (–) 

Haliotis Purisima Point 0.822 < 0.001 (–) 

Haliotis Stairs 0.825 < 0.001 (–) 

Haliotis Boathouse 0.827 < 0.001 (–) 

Haliotis Government Point 0.483 < 0.001 (–) 

2.3.12 Lottia gigantea

The giant owl limpet Lottia gigantea was sampled at Cayucos Point and Hazards in San Luis 

Obispo County and Stairs, Boathouse, Government Point and Alegria in Santa Barbara County 

(Figure II-12b). On average for all sample periods, numbers varied among sites from 20 to 50. 

Densities were relatively consistent from one sample period to the next and there were no 

seasonal patterns of abundance (Figure II-12b; Table II-1c). The density of Lottia declined 

significantly at Cayucos Point, remained relatively constant at Hazards, Boathouse and 

Government Point and increased significantly at Stairs and Alegria (Figure II-12b; Table II-1c). 

Despite these patterns, there was a general decline at several sites from 1995 to 1997 and an 

overall increase in Lottia density from 1997 to 1998, which was particularly evident when all 

sites were combined (Figure II-14d). 
Final Study Report – Minchinton and Raimondi 

42 

Figure II-12a: Mean (±se) density of Lottia in spring (S) and autumn (A) from 1992 to 2002 at each of four sites. 

Blank spaces indicate that sampling was not done at that time. Note differences in scale on y-axis. 

0

10

20

30

40

50 Hazards

Cayucos Point

Density of Lottia (no. / plot)

Stairs

Boathouse

93 94 95 02

S A

92

S ASAS ASAS ASASASASAS A

96 99 00 01 97 98

0

20

40

60

80

100

0

20

40

60

80

100

0

10

20

30

40

50
Effect of Temporal and Spatial Separation of Samples 

43

Figure II-12b: Mean (±se) density of Lottia in spring (S) and autumn (A) from 1992 to 2002 at each of two sites. 

Blank spaces indicate that sampling was not done at that time. Note differences in scale on y-axis. Density of Lottia (no. / plot)

Government Point

Alegria

93 94 95 02

S A

92

S ASAS ASAS ASASASASAS A

96 99 00 01 97 98

0

20

40

60

80

100

0

10

20

30

40

50

2.3.13 Haliotis cracherodii

The black abalone Haliotis cracherodii was sampled at Point Sierra Nevada, Piedras Blancas, 

Cayucos Point and Purisima Point in San Luis Obispo County and at Stairs, Boathouse and 

Government Point in Santa Barbara County (Figure II-13a, b). On average for all sample 

periods, Haliotis numbers varied among sites from about 70 to less than 10 individuals per plot. 

There were no seasonal trends in abundance (Figure II-13a, b). There were dramatic and 

significant declines in the abundance of Haliotis at the 5 most southern sites, but not at the 2 

most northern sites, Piedras Blancas and Point Sierra Nevada, the latter site exhibiting a 

significant increase in Haliotis abundance (Figure II-13a, b; Table II-1c; Figure II-14d). The 

timing of declines varied with latitude, with declines happening sooner at the southern than the 

northern sites. 
Final Study Report – Minchinton and Raimondi 

44 

Figure II-13a: Mean (±se) density of Haliotis in spring (S) and autumn (A) from 1992 to 2002 at each of four sites. 

Blank spaces indicate that sampling was not done at that time. Note differences in scale on y-axis. 

0

20

40

60

80

100 Piedras Blancas

Point Sierra Nevada

Density of Haliotis (no. / plot)

Cayucos Point

Purisima Point

93 94 95 02

S A

92

S ASAS ASAS ASASASASAS A

96 99 00 01 97 98

0

2

4

6

8

10

0

20

40

60

80

100

0

20

40

60

80

100

0
Effect of Temporal and Spatial Separation of Samples 

45

Figure II-13b: Mean (±se) density of Haliotis in spring (S) and autumn (A) from 1992 to 2002 at each of three 

sites. Blank spaces indicate that sampling was not done at that time. Note differences in scale on y-axis. 

0

50

100

150

200

250 Stairs

Density of Haliotis (no. / plot)

Boathouse

Government Point

93 94 95 02

S A

92

S ASAS ASAS ASASASASAS A

96 99 00 01 97 98

0

10

20

30

40

50

0

50

100

150

200

250

0 0
Final Study Report – Minchinton and Raimondi 

46 

Figure II-14a: Mean (±se) percent cover of various species in spring (S) and autumn (A) from 1992 to 2002 for all 

sites (sites are replicates). 

0

20

40

60

80

100 Barnacles

Anthopleura

Cov

e

r (%)

Pollicipes

Mussels

93 94 95 02

S A

92

S ASAS ASAS ASASASASAS A

96 99 00 01 97 98

0

20

40

60

80

100

0

20

40

60

80

100

0

20

40

60

80

100
Effect of Temporal and Spatial Separation of Samples 

47

Figure II-14b: Mean (±se) percent cover of various species in spring (S) and autumn (A) from 1992 to 2002 for all 

sites (sites are replicates). Blank spaces indicate that sampling was not done at that time. 

0

20

40

60

80

100 Hesperophycus

Silvetia

Cover (%)

Mastocarpus

Endocladia

93 94 95 02

S A

92

S ASAS ASAS ASASASASAS A

96 99 00 01 97 98

0

20

40

60

80

100

0

20

40

60

80

100

0

20

40

60

80

100
Final Study Report – Minchinton and Raimondi 

48 

Figure II-14c: Mean (±se) percent cover of various species in spring (S) and autumn (A) from 1992 to 2002 for all 

sites (sites are replicates). Blank spaces indicate that sampling was not done at that time. 

0

20

40

60

80

100

Cover (%)

Mazzaella

Phyllospadix

93 94 95 02

S A

92

S ASAS ASAS ASASASASAS A

96 99 00 01 97 98

0

20

40

60

80

100

Figure II-14d: Mean (±se) density of various species in spring (S) and autumn (A) from 1992 to 2002 for all sites 

(sites are replicates). 

Pisaster

Density (no. / area)

Lottia

Haliotis

93 94 95 02

S A

92

S ASAS ASAS ASASASASAS A

96 99 00 01 97 98

0

40

80

120

160

200

0

10

20

30

40

50

0

10

20

30

40

50
Effect of Temporal and Spatial Separation of Samples 

49

2.4 Discussion 

The Shoreline Inventory monitoring program has produced what is, in terms of its spatial and 

temporal extent, one of the most comprehensive sets of data ever collected for species living in 

rocky intertidal communities. A primary objective of this long-term ecological monitoring 

program, and of this report, is to evaluate whether the amassed data has the ability to detect 

changes and show trends in the abundance of target species. To detect changes and show trends 

in the context of this monitoring program means that the current sampling design can provide an 

accurate picture of the natural variation in abundance of the target species. For the target species 

in this region, there are three known sources of natural of variation that can produce significant 

trends: (1) seasonal variation due to such factors as the growth of organisms, recruitment events, 

and winter storms, (2) variation due to episodic events such as years of great recruitment or mass 

mortalities, and (3) variation due to El Niño events (and longer-term phenomena at the scale of 

decades might also prove important). Only by establishing “normal” limits of variation can 

changes due to “abnormal conditions” be detected. Abnormal conditions might include oils 

spills (see Raimondi et al. 1999), disease such as Haliotis withering syndrome (see Raimondi et 

al. 2002), illegal harvesting, or longer-term changes due to unknown sources of variation such as 

rises in sea surface temperatures due perhaps to global warming. Finally, data for the monitoring 

program must allow the success of any management actions to be assessed. 

Visual inspection of graphs and statistical analyses of the data reveal that the current sampling 

design has a great ability to show the natural limits of variation and to detect trends in the 

abundance of target organisms. Three types of temporal trends were clear: (1) short-term 

seasonal trends, (2) intermediate-term fluctuations on time scales ranging from one to several 

years, and (3) long-term changes (particularly gradual decreases, but also increases) for five or 

more years due to changes in abundance over the entire sampling period for a particular species 

at a particular site. Interestingly, many of the spatial and temporal patterns of abundance and 

data and statistical analyses based on these trends were similar to those reported by Raimondi et 

al. (1999), even though the current data set included 4 more years of information (8 sample 

periods). Raimondi et al. (1999) have thoroughly discussed potential factors responsible for 

changes to the abundance of target species and community structure resulting from these 

changes, so these will not be repeated here (see also Ambrose et al. 1995). Only new and 

significant changes for sites and times not examined by Raimondi et al. (1999) are discussed. 

Many species exhibited short-term seasonal trends, but these were not consistent among sites or 

biogeographic regions north and south of Point Conception. Short-term seasonal trends were 

apparent at a minority of sites for the acorn barnacles, the gooseneck barnacle Pollicipes and 

Pisaster, with abundances greater in autumn than in spring. The macroalgae exhibited more 

pronounced seasonal trends than the macroinvertebrates. At most sites, the cover of Silvetia, 

Mastocarpus and Phyllospadix was greater in autumn than in spring, whereas the Endocladia 

was more abundant in spring than in autumn. Identical strong seasonal trends have been 

documented for Endocladia and Silvetia at several rocky intertidal sites on the Channel Islands 

(Minchinton and Raimondi 2001). The magnitude of these changes was relatively small with on 

average about 5 to 20% changes in abundance among seasons. Seasonal changes for species 

such as Pollicipes, Silvetia and Mastocarpus, where abundances are greater in autumn than 

winter, have been attributed to summer growth for Silvetia and winter storms that removed 
Final Study Report – Minchinton and Raimondi 

50 

barnacles and algae from the rock surface (Raimondi et al. 1999). Similarly, the cover of 

surfgrass Phyllospadix is reduced during winter by storms, which can move sand causing 

periodic burial and scouring of blades (Raimondi et al. 1999). 

A second striking and common trend in the data were intermediate-term fluctuations in 

abundance where there were relatively rapid declines sometimes followed by more gradual 

increases. These relatively rapid and common declines occurred from 1997 to 1998, which 

corresponds to the 1997-98 El Niño, which was the strongest of the century. Along the 

California coast, these events are associated with increased sea surface temperature, decreased 

nutrients, and increased frequency and intensity of storms, which can generate large amplitude 

swells, increased rainfall which leads to increased sediment load and reduce salinities in 

nearshore waters, and more floating debris (Raimondi et al. 1999). Raimondi et al. (1999) 

provided a detailed quantitative assessment of the effects of the 1997/98 El Niño event on rocky 

intertidal communities in southern and central California. They concluded that the abundances 

of barnacles, mussels, Endocladia, Silvetia, Phyllospadix, but not Lottia declined during the 

1997/98 El Niño event, and these conclusions are confirmed by the subsequent sampling 

presented in this report. In addition, similar but less spatially consistent declines occur at some 

sites for Anthopleura and Haliotis during this event. Indeed, a detailed analysis has shown that 

although declines of Haliotis due to withering syndrome over the past decade are not exclusively 

linked to times of El Niño, they may occur faster during times of El Niño events (see Raimondi 

et al. 2002). Interestingly, at several sites there appeared to be an increase in the density of 

Lottia (e.g., Cayucos Point, Stairs, Alegria) during this event. This increase might represent 

recruitment, but detailed analysis of the size-structure of the population is required to be certain 

and this should be done. 

Storm-related effects of the strong 1997-98 El Niño and other El Niño events during the 1980s 

and 1990s have also been shown to significantly reduce the abundance of these same target 

species inhabiting rocky shore communities along the coasts of the Channel Islands (Minchinton 

and Raimondi 2001). Minchinton and Raimondi (2001) found that the abundances of barnacles, 

mussels, rockweeds including primarily Silvetia, and Endocladia experienced declines at some 

sites during some of these periods of El Niño. Studies of rocky intertidal communities in 

California following the 1982-83 El Niño (Gunnill 1985, Murray and Horn 1989) also detected 

changes in species abundances, but these were not as pronounced as those reported by Raimondi 

et al. (1999) or Minchinton and Raimondi (2001). 

Interestingly, there was considerable variation in declines due to El Niño among species and sites 

(see also Raimondi et al. 1999, Minchinton and Raimondi 2001). At some sites, a particular 

target species declined during El Niño (e.g., barnacles), whereas at other sites its abundance 

remained constant. At some sites there were declines for some target species but not others. 

Finally, at some sites there were striking declines for all target species (e.g., Arroyo Hondo). 

Minchinton and Raimondi (2001), studying rocky shore communities in the Channel Islands, 

suggested that sites closer together and with the same aspect appeared to behave more similarly 

in their patterns of decline during El Niño, but they also pointed out a general lack of spatial 

consistency in the declines among sites. No common spatial trends, such as differences between 

sites north and south of Point Conception, in the declines during El Niño were observed here or 

by Raimondi et al. (1999), but this may require closer scrutiny linking environmental conditions 
Effect of Temporal and Spatial Separation of Samples 

51

such as wave amplitude and sea surface temperatures to declines at individual sites. These 

results again emphasize the importance of studying several target species at multiple sites. The 

ability of the monitoring program to detect changes due to El Niño is a significant result. 

Clearly, the sampling program is capable of detecting relatively small temporal changes in the 

abundance of target organisms. 

The third obvious and consistent trend was the long-term declines in abundance over the entire 

monitoring period for many target species at many sites. Of the 93 separate analyses for each 

species and site combination, abundances declined 56 times, increased on only 6 occasions, and 

for 31 analyses there was no detectable linear trend. The overwhelming number of decreases 

compared to increases may not be surprising because plots were initially chosen to contain 

substantial abundances of the target species and these plots were repeatedly sampled over time. 

For relatively long-lived species (relative to the current duration of the monitoring program), 

such as mussels and Silvetia, these declines may at least partially be an artifact of initial 

conditions as abundances are bounded at 100% cover and therefore there is a greater probability 

of decline than increase. For other shorter-lived organisms, such as barnacles and Endocladia

that would have likely turned over during the monitoring program, the long-term declines in 

abundance may be a “real” phenomenon. Indeed, with the exception of Haliotis, 5 of the 6 

increases were for the mobile species Lottia and Pisaster. Frequent declines might also be 

expected, however, because the largest El Niño event of the last century occurred in the middle 

of the sampling period and many target species appeared to be negatively affected by conditions 

during this period of anomalous environmental conditions and have not recovered. 

Nevertheless, particular species exhibited strong patterns of decline throughout the sampling 

period and these should be given particular consideration. Populations of the black abalone 

Haliotis were crashing during this time, a phenomenon also well documented at sites on the 

Channel Islands (see Raimondi et al. 2002 and references therein). The demise of the population 

appears to be due a fatal disease called withering syndrome caused by the infection of Haliotis

by a bacterium. The observed spatial pattern of decline, with the abundance of Haliotis 

decreasing from south to north is clearly detected by the sampling program and might suggest 

potential patterns of transmission and infection for the causative agent. As noted by Raimondi et 

al. (1999), there has been a consistent and continued decrease in mussel cover at the majority of 

sites, and the same could be said for several species of macroalgae. Raimondi et al. (1999) 

suggested that this might be due to removal of mussels by storms followed by a lack of 

survivorship of new recruits. Although cover remains high at some sites, these declines should 

be monitored for mussels as well as other species that appear to be decreasing for no apparent 

reason. For many long-lived species, the monitoring program is probably not long enough to 

make sound judgements about the factors responsible for declines, but certainly a lack of 

recruitment or mortality of recruits should be noted. Indeed, it is advised that the Shoreline 

Inventory be expanded to sample recruitment of target species where this is not being done 

already through size measurements. Impacts may indeed affect the larval and juvenile stages and 

not the adults of target species. 

Detection of these temporal trends in abundance (seasonal, El Niño, disease) highlights the 

efficacy and utility of the current Shoreline Inventory monitoring program. Expansion of the 

program to a broader range of sites so that larger-scale regional patterns of abundance of target 
Final Study Report – Minchinton and Raimondi 

52 

species (and species diversity) is advisable and would wisely recognizes the need for long-term 

ecological monitoring that is currently unsupported by mainstream funding agencies. 
Effects of Temporal and Spatial Separation of Samples 

53

Part III: STATISTICAL POWER ANALYSIS

3.1 Introduction, Objectives, and Approach 

One of the primary objectives of the long-term monitoring program is to compile a set of 

data that can be used to estimate the consequences of impacts (e.g., oil spill) to the target 

organisms living in the rocky intertidal community. It is often assumed that the preimpact program will collect data that have the ability to detect changes in abundance of 

the target organisms should an impact occur. Without assessment of this assumption, 

however, there is no way of knowing whether the monitoring program has been 

adequately designed to meet its expressed purpose. In this part of the report we test this 

critical assumption by using power analysis to determine the ability of the data collected 

from the long-term monitoring program to detect impacts to target organisms. The 

purpose of this analysis is to determine whether modifications to the sampling design of 

the monitoring program are necessary so that impacts may be adequately detected. 

The ability of the monitoring program to estimate the effects of impacts will depend on 

the statistical power to determine changes in the abundance of the target organisms. 

Statistical power is the ability of the sampling design to detect an impact when one has 

truly occurred. For example, if a monitoring program has statistical power of 80% at α = 

0.05 to detect a 50% change in abundance of one of the target organisms, this means that 

if an impact actually did occur that produced a 50% change in abundance, then this 

monitoring program has an 80% probability of correctly identifying that change. Within 

the scope of the monitoring program, there are several common sampling designs that can 

be used to detect impacts to target organisms (see Schmitt and Osenberg 1996). Two of 

the most common and widely used are the Before-After design and the Before-After / 

Control-Impact (or BACI) design. In a Before-After design, impacts are detected by 

statistically comparing differences in the abundance of organisms at a single site before 

and after the impact. In a BACI design, impacts are detected by statistically comparing 

differences in the abundance of organisms between control and impact sites before the 

impact to differences between these sites after the impact (Stewart-Oaten et al.1986). 

Here we provide, using only the before-after design, an assessment of the statistical 

power of tests to detect differences in the abundance of target species from data collected 

under the Shoreline Inventory. 

The questions we address are: (1) what is the statistical power to detect changes to the 

abundances of target species following an impact at one of the monitored sites, and (2) 

based on the results of power analyses, should there be modifications to the sampling 

design of the monitoring program. To do this, we first have to simulate future impacts to 

target species. Next, we determine the statistical power of tests to detect differences in 

abundance due to the impact. In particular, we ask two questions: (1) with what level of 

power can we detect a given level of change in abundance (10% to 50% change) at alpha 

levels of 0.05, 0.10, and 0.20, and (2) what level of change in abundance can we detect 

with 80% power at alpha levels of 0.05, 0.10 and 0.20. We also consider how the results 

vary among the target species and spatially by considering whether impacts are easier to 
Final Study Report – Minchinton and Raimondi 

54

detect for some species and sites. The answers to these questions will help guide 

modifications to the current sampling design of the monitoring program for each target 

species. Two particular modifications to the design (and thus sampling effort) to be 

considered are: (1) does the temporal sampling frequency (i.e., number of times sampled) 

provide adequate statistical power to detect impacts, and (2) does the spatial extent of the 

sampling program (i.e., number of sites sampled) provide adequate statistical power to 

detect impacts. 

3.2 Methods 

3.2.1 Before-After Design: Simulating Impacts and Calculating Power

In a before-after design, impacts are detected statistically by comparing mean abundances 

at a single site before and after an impact, typically with a t-test. Note that replication 

within a site and its associated variance is not used, as they are only relevant for ensuring 

that the mean abundance at a site is accurately represented. Because we are simulating 

impacts at these sites, all data fall in the pre-impact or before period. Therefore, to 

determine the ability of the monitoring program to detect differences in the abundance of 

target species in the event of a future impact, we simulated impacts, which were done as 

follows. First, the mean and standard deviation of the mean abundances at each sampling 

date in the before period were calculated (using untransformed data). This mean and 

standard deviation represented the mean and standard deviation for the before period. To 

enable comparisons of results of the power analyses among species and sites, only data 

from autumn 1995 to autumn 2002 were used. This was done because sites were 

established at different times from 1992 to 1995 and, therefore, some had been sampled 

more often than others. During this period of 7.5 years there was a continuous record of 

sampling over 14 or 15 sample periods for all species and site combinations. 

Impacts were simulated by decreasing the mean abundance calculated for the before 

period by increments of 10%, yielding values from 0 to 90% of the before period mean. 

This gave ten means for the after period representing impacts ranging from 10 to 100% of 

the mean in the before period. The standard deviation and sample size (n = 14 or 15) 

associated with these means was assumed to be the same as those for the mean in the 

before period. Using the differences between the means in the before and after period, 

the standard deviation, and the sample size, we calculated the power to detect significant 

impacts by using a two-sided, one sample t-test. The null hypothesis tested was that there 

is no impact. Power was calculated for each species at each site and for all species and 

sites combined with alpha levels of 0.05, 0.10, and 0.20 using the power analysis 

software program PASS. 

These analyses generated a power curve for each of the three levels of alpha, with the 

percent change in abundance on the x-axis (ranging from 0 to 100% of the mean 

abundance at the impacted site) and power on the y-axis. We extracted particular 

information from these curves, which are not shown, and present this summarized 

information. First, we determined how many of the before-after comparisons could 

detect 10, 20, 30, 40 and 50% changes (which was an appropriate range given the results) 
Effects of Temporal and Spatial Separation of Samples 

55

with 80% power. Second, for each before-after comparison we determined the mean 

level of power to detect 10, 20, 30, 40 and 50% changes in abundance. Third, for each 

before-after comparison we determined the mean level of change in abundance that could 

be detected with 80% power. 

3.2.2 Before-After Design: Tests of Assumptions

Before we can proceed with power analyses using t-tests, we have to determine whether 

several important assumptions are satisfied by the data in the before period. Two 

important assumptions are lack of trends and independence of random errors. Mean 

abundances at the site can trend positively or negatively over time. If this were to 

continue after the impact, then detection of an effect of the impact would be confounded. 

We tested for trends in mean abundance by linear regression of the mean abundances 

against sample period. Serial correlation (i.e., non-independence) of errors occurs when 

the differences in mean abundances vary as a function of time between the samples. 

Serial correlation of errors can lead to invalid estimates of the error variance and thus 

affect the outcome of statistical tests. We tested for serial correlation using DurbinWatson tests for mean abundances against sample period (α = 0.05). Inconclusive results 

were considered as failures. Significant trend or serial correlation (α = 0.05) excluded 

this before-after comparison from power analysis. 

Two aspects of the data compromise these tests and must be noted. First, for some 

species and site combinations, there is a missing sample period and this leads to irregular 

spacing among samples. How this affects the outcomes of these tests is not known. 

Omitting data so that samples were regularly spaced would result in a severe loss of data, 

so this was not done. Second, there are clearly large, long-lasting trends at some sites, 

likely due to El Niño events or other factors. The presence of these trends would likely 

result in the detection of more cases of non-independence than would otherwise be the 

case without these trends. Introducing a covariate to attempt to account for these trends 

was beyond the scope of this report (Stewart-Oaten et al. 1986). 

A final assumption that errors are normally distributed was not tested because the t-test is 

robust to all but the most extreme violations of this assumption (Stewart-Oaten et al. 

1992). For more detailed explanation of these assumptions, see Stewart-Oaten et al. 

(1986, 1992). 

3.3 Results 

For all species only 29% (27 of 93) of the before-after comparisons passed the tests for 

both assumptions (Table III-1). For 62% (52 of 93) of comparisons there were 

significant trends in the data, and the assumption of independence was violated for 77% 

(47 of 93) of comparisons (Table III-1). Consequently, for all species, power analyses 

could be done only for 27 before-after comparisons. 
Final Study Report – Minchinton and Raimondi 

56

Table III-1a. Results of statistical tests of assumptions before power analysis (see text for details). Data 

used were mean abundances at each site over time from autumn 1995 to autumn 2002. NS = not 

significant. * = significant. INCON = inconclusive results. Results in bold passed both tests of 

assumptions and the target species at that site was used in power analyses. 

Species Site Trend Independence 

Anthopleura Boathouse NS NS 

Anthopleura Alegria NS * 

Anthopleura Coal Oil Point NS INCON 

Anthopleura Carpinteria NS NS 

Anthopleura Mussel Shoals NS NS 

Anthopleura Old Stairs NS NS 

Barnacles Point Sierra Nevada * NS 

Barnacles Cayucos Point * * 

Barnacles Hazards * INCON 

Barnacles Shell Beach NS * 

Barnacles Occulto NS NS 

Barnacles Stairs * * 

Barnacles Boathouse * * 

Barnacles Government Point NS * 

Barnacles Alegria NS NS 

Barnacles Arroyo Hondo NS * 

Barnacles Carpinteria NS * 

Barnacles Mussel Shoals NS NS 

Barnacles Old Stairs NS NS 

Barnacles Paradise Cove NS NS 

Barnacles White’s Point NS NS 

Pollicipes Government Point NS NS 

Pollicipes Alegria NS NS 

Pollicipes Carpinteria * NS 

Mussels Point Sierra Nevada NS NS 

Mussels Cayucos Point * * 

Mussels Hazards * INCON 

Mussels Shell Beach * * 

Mussels Occulto NS NS 

Mussels Stairs * * 

Mussels Boathouse * * 

Mussels Government Point NS * 

Mussels Alegria NS * 

Mussels Arroyo Hondo NS * 

Mussels Mussel Shoals * * 

Mussels Old Stairs * NS 

Mussels Paradise Cove NS * 

Mussels White’s Point NS * 
Effects of Temporal and Spatial Separation of Samples 

57

Table III-1b. Results of statistical tests of assumptions before power analysis (see text for details). Data 

used were mean abundances at each site over time from autumn 1995 to autumn 2002. NS = not 

significant. * = significant. INCON = inconclusive results. Results in bold passed both tests of 

assumptions and the target species at that site was used in power analyses. 

Species Site Trend Independence 

Silvetia Point Sierra Nevada * NS 

Silvetia Cayucos Point * NS 

Silvetia Hazards * NS 

Silvetia Shell Beach * NS 

Silvetia Stairs * NS 

Silvetia Boathouse * NS 

Silvetia Government Point * * 

Hesperophycus Point Sierra Nevada * * 

Hesperophycus Cayucos Point NS * 

Mastocarpus Point Sierra Nevada NS NS 

Mastocarpus Shell Beach * NS 

Endocladia Cayucos Point * NS 

Endocladia Hazards * NS 

Endocladia Shell Beach * NS 

Endocladia Occulto * * 

Endocladia Stairs NS * 

Endocladia Boathouse * NS 

Endocladia Government Point NS NS 

Endocladia Old Stairs * NS 

Endocladia Paradise Cove NS NS 

Endocladia White’s Point * NS 

Mazzaella Point Sierra Nevada NS INCON 

Mazzaella Hazards NS NS 

Phyllospadix Point Sierra Nevada NS * 

Phyllospadix Cayucos Point NS NS 

Phyllospadix Stairs NS * 

Phyllospadix Government Point NS NS 

Phyllospadix Arroyo Hondo NS NS 

Phyllospadix Coal Oil Point NS * 

Phyllospadix Carpentaria NS * 

Phyllospadix Mussel Shoals * INCON 

Phyllospadix Paradise Cove NS * 
Final Study Report – Minchinton and Raimondi 

58

Table III-1c. Results of statistical tests of assumptions before power analysis (see text for details). Data 

used were mean abundances at each site over time from autumn 1995 to autumn 2002. NS = not 

significant. * = significant. INCON = inconclusive results. Results in bold passed both tests of 

assumptions and the target species at that site was used in power analyses. 

Species Site Trend Independence 

Pisaster Point Sierra Nevada * NS 

Pisaster Cayucos Point NS NS 

Pisaster Hazards NS NS 

Pisaster Shell Beach NS NS 

Pisaster Stairs NS INCON 

Pisaster Boathouse NS NS 

Pisaster Government Point NS * 

Pisaster Arroyo Hondo NS INCON 

Pisaster Mussel Shoals * INCON 

Pisaster Old Stairs NS * 

Lottia Cayucos Point * NS 

Lottia Hazards NS NS 

Lottia Stairs NS INCON 

Lottia Boathouse * INCON 

Lottia Government Point NS NS 

Lottia Alegria * INCON 

Haliotis Point Sierra Nevada * NS 

Haliotis Piedras Blancas NS * 

Haliotis Cayucos Point * * 

Haliotis Purisima Point * * 

Haliotis Stairs * * 

Haliotis Boathouse * * 

Haliotis Government Point * NS 

Some species passed the tests of assumptions more frequently than others. The sea 

anemone Anthopleura and the barnacle Pollicipes passed both assumptions for more than 

50% of comparisons. Barnacles, the red alga Mastorcarpus, the alga Mazzaella, 

surfgrass Phyllospadix and the sea star Pisaster successfully passed both assumptions for 

between 25 and 50% of comparisons. Mussels and the turfweed Endocladia passed both 

tests for less than 25% of comparisons. Data for comparisons with the brown fucoid 

algae or rockweeds Silvetia and Hesperophycus and the black abalone Haliotis never 

passed the tests for both assumptions. For the 5 species that had been monitored at 9 or 

more sites, both tests of assumptions were passed most frequently for barnacles (6 of 15 

comparisons) and Pisaster (4 of 10 comparisons) and least frequently for mussels (2 of 

14 comparisons). 

The 27 before-after comparisons that passed both tests were spread out roughly evenly 

across sites (Table III-1). Four sites were not involved in any of these comparisons: 

Piedras Blancas and Purisima Point, where only the black abalone Haliotis had been 

monitored, and Stairs and Coal Oil Point (Table III-1). For the 10 sites where at least 5 

target species had been monitored, both tests of assumptions were passed most frequently 
Effects of Temporal and Spatial Separation of Samples 

59

at Government Point (4 of 9 comparisons) and least frequently at Stairs (0 of 8 

comparisons.) 

3.3.1 Anthopleura

Under the before-after design, 67% (4 of 6) of comparisons passed the tests of both 

assumptions (Table III-1a). For all 4 sites a 20% change in the cover of Anthopleura was 

detectable with 80% power at all levels of alpha (Figure III-1a). Indeed, on average for 

all sites, the power to detect a 20% change was almost 100%, and 10% changes in 

abundance could be detected at 66, 75 and 83% power for alpha levels of 0.05, 0.10 and 

0.20 respectively (Figure III-2a). On average for all 4 sites, a 12% change (ranging from 

6 to 18%) in cover was detectable with 80% power at α = 0.05 (Figure III-3a). 

3.3.2 Barnacles

Under the before-after design, 40% (6 of 15) of comparisons passed the tests of both 

assumptions (Table III-1a). For all 6 sites a 40% change in the percent cover of barnacles 

was detectable with 80% power at α = 0.05 (Figure III-1a). At α = 0.10 and 0.20, at least 

a 30% change could be detected at this level of power for all comparisons. At one site, a 

10% change could be detected with 80% power at α = 0.05. On average for all 6 sites, 

analyses at α = 0.05 showed that 10, 20 and 30% changes in the cover of barnacles could 

be detected with 41, 75 and 94% power, respectively (Figure III-2a). On average for all 6 

sites, a 20% change (ranging from 7 to 30%) in barnacle cover is detectable with 80% at 

α = 0.05 and detectable change reduces to18 and 15% at α = 0.10 and 0.20, respectively 

(Figure III-3a). 

3.3.3 Pollicipes polymerus

Power analyses for Pollicipes could be done for 67% of sites because 2 of 3 comparisons 

passed the tests of both assumptions (Table III-1a). For both sites at least a 20% change 

in cover was detectable with 80% power at α = 0.05, and at one site a 10% change in 

cover is detectable (Figure III-1a). On average for both sites there was 100% and 78% 

power to detect 20 and 10% changes in cover at α = 0.05, respectively (Figure III-2a). 

These sites varied considerably, however, in their ability to detect small changes in cover, 

with power of 55% at one site and 100% at the other site (α = 0.05). On average for both 

sites, a 9% change (ranging from 4 to 14%) in cover was detectable with 80% power at α

= 0.05 (Figure III-3a). 

3.3.4 Mussels

Only 2 of 14 comparisons passed the tests for both assumptions and, consequently, power 

analyses for mussels could only be done for two sites (Table III-1a). For both sites, there 

was 100% power to detect a 10% change in the mussel cover at all levels of alpha (Figure 

III-1a, III-2a). Indeed, at both sites a 3% change in cover was detectable at 80% power at 

α = 0.05 (Figure III-3a). 
Final Study Report – Minchinton and Raimondi 

60

Figure III-1a: Percentage of before-after comparisons that could detect a 10, 20, 30, 40, or 50% change in 

cover with 80% power at α = 0.05, 0.10, and 0.20 for each of four target species. Numbers in parentheses 

indicate the number of replicate sites sampled for that species. 

Barnacles (n = 6)

Anthopleura (n = 4)

Percentage of Compa

risons

Pollicipes (n = 2)

Mussels (n = 2)

0.05 0.10 0.20

Alpha (α)

0

25

50

75

100

10% 30% 40% 20% 50% Change

0

25

50

75

100

0

25

50

75

100

0

25

50

75

100
Effects of Temporal and Spatial Separation of Samples 

61

Figure III-2a: Mean (±se) power to detect 10, 20, 30, 40, and 50% changes in cover at α = 0.05, 0.10, and 

0.20 for before-after comparisons for each of four target organisms. Numbers in parentheses indicate the 

number of replicate sites sampled for that organism. 

0

20

40

60

80

100 Barnacles (n = 6)

Anthopleura (n = 4)

Pow

e

r (%)

Pollicipes (n = 2)

Mussels (n = 2)

0.05 0.10 0.20

Alpha (α)

0

20

40

60

80

100

0

20

40

60

80

100

0

20

40

60

80

100

10% 30% 40% 20% 50% Change
Final Study Report – Minchinton and Raimondi 

62

Figure III-3a: Mean (±se) percent change in cover detectable with 80% power at α = 0.05, 0.10, and 0.20 

for before-after comparisons for each of eight target species. Numbers in parentheses indicate the number 

of replicate sites for each target organism. 

0

10

20

30

40

Anthopleura (n = 4) Barnacles (n = 6)

Change in Cover (%) Detectable with 80% Power

Pollicipes (n = 2) Mussels (n = 2)

0.05 0.10 0.20

Alpha (α)

0

10

20

30

40

0

10

20

30

40 Mastocarpus (n = 1) Endocladia (n = 2)

0

10

20

30

40 Mazzaella (n = 1) Phyllospadix (n = 3)

0.05 0.10 0.20
Effects of Temporal and Spatial Separation of Samples 

63

3.3.5 Silvetia compressa

Data for before-after comparisons with Silvetia never passed the tests of both 

assumptions (Table III-1b). Consequently, power analysis was not done for this species. 

3.3.6 Hesperophycus californicus

Data for before-after comparisons with Hesperophycus never passed the tests of both 

assumptions (Table III-1b). Consequently, power analysis was not done for this species. 

3.3.7 Mastocarpus papillatus

Power analysis for Mastocarpus was only done for 1 of 2 sites, which passed both tests of 

assumptions (Table III-1b). At this site, a 40% in percent cover was detectable with 82% 

power at α = 0.05 (Figure III-1b, III-2b). Power to detect 10, 20, and 30% changes in 

cover at α = 0.05 was 11, 30 and 58%, respectively (Figure III-2b). At 80% power and α

= 0.05, analysis could detect a 39% change in the cover of Mastocarpus (Figure III-3a). 

3.3.8 Endocladia muricata

Only 20% (2 of 10) of comparisons under the before-after design passed the tests of both 

assumptions and, therefore, power analyses were restricted to these two sites (Table III1b). The two sites differed dramatically in their ability to detect changes in the cover of 

Endocladia. At one site, there was 100% power to detect a 20% change in cover at α = 

0.05, whereas at the other power to detect a similar change was only 28% (Figure III-1b, 

III-2b). At 80% power and α = 0.05, one site could detect a 12% change in cover 

whereas the other could only detect a 42% change in cover (Figure III-3a). 

3.3.9 Mazzaella spp.

Only 1 of 2 sites passed tests of both assumptions (Table III-1b). At this site, there was 

86% power to detect a 10% change in cover at α = 0.05 (Figure III-1b, III-2b), and 9% 

cover was detectable with 80% power at α = 0.05 (Figure III-3b). 

3.3.10 Phyllospadix scouleri/torreyi

Under the before-after design, 33% (3 of 9) of comparisons passed both tests of 

assumptions and, therefore, power analyses were carried out at these three sites (Table 

III-1b). At 2 of 3 sites, at least a 10% change in cover of the surfgrass Phyllospadix

could be detected with 80% power at α = 0.05 (Figure III-1b). On average for all 3 sites, 

there was 82% power to detect a 10% change at α = 0.05, and almost 100% power to 

detect changes greater than 20% (Figure III-2b). Indeed, on average for all sites, a 9% 

change (ranging from 5 to 14%) in cover was detectable with 80% power at α = 0.05 

(Figure III-3b). 
Final Study Report – Minchinton and Raimondi 

64

Figure III-1b: Percentage of before-after comparisons that could detect a 10, 20, 30, 40, or 50% change in 

cover with 80% power at α = 0.05, 0.10, and 0.20 for each of four target species. Numbers in parentheses 

indicate the number of replicate sites sampled for that species. 

Endocladia (n = 2)

Mastocarpus (n = 1)

Percentage of Comparisons

Mazzaella (n = 1)

Phyllospadix (n = 3)

0.05 0.10 0.20

0

25

50

75

100

10% 30% 40% 20% 50% Change

0

25

50

75

100

0

25

50

75

100

0

25

50

75

100

000 0 0 00 0

0 0
Effects of Temporal and Spatial Separation of Samples 

65

Figure III-2b: Mean (±se) power to detect 10, 20, 30, 40, and 50% changes in cover at α = 0.05, 0.10, and 

0.20 for before-after comparisons for each of four target organisms. Numbers in parentheses indicate the 

number of replicate sites sampled for that organism. 

0

20

40

60

80

100 Endocladia (n = 2)

Mastocarpus (n = 1)

Power (%)

Mazzaella (n = 1)

Phyllospadix (n = 3)

0.05 0.10 0.20

Alpha (α)

0

20

40

60

80

100

0

20

40

60

80

100

0

20

40

60

80

100

10% 30% 40% 20% 50% Change
Final Study Report – Minchinton and Raimondi 

66

Figure III-3b: Mean (±se) percent change in abundance detectable with 80% power at α = 0.05, 0.10, and 

0.20 for each of two and all target species. Numbers in parentheses indicate the number of replicate sites or 

species sampled. Change in Cover (%) 

Detectable with 80% Power

0.05 0.10 0.20

Alpha (α)

0

10

20

30

40 Pisaster (n = 4) Lottia (n = 2)

0

10

20

30

40 All Species (n = 10)

0.05 0.10 0.20

Alpha (α)

3.3.11 Pisaster ochraceus

Data at 4 of 10 sites passed both tests of assumptions and were therefore used in power 

analyses (Table III-1c). At all sites, a 50% change in the density of Pisaster could be 

detected with 80% power at α = 0.05 (Figure III-1c). At two of these sites, a 40% change 

was detectable, but at none of the 4 sites was a 20% change detectable. On average for 

all sites, there was 87% power to detect a 40% change in abundance at α = 0.05 (Figure 

III-2c), but power dropped to 69, 41 and 15% to detect changes of 30, 20 and 10%, 

respectively. On average for all sites, a 35% change (ranging from 23 to 43%) in 

abundance was detectable with 80% power at α = 0.05 (Figure III-2c). 

3.3.12 Lottia gigantea

Data at 2 of 6 sites passed tests of both assumptions and were used in power analyses 

(Table III-1c). For both sites, at least a 20% change in the density of Lottia was 

detectable with 80% at α = 0.05, whereas neither site could detect a 10% change in 

abundance (Figure III-1c). Indeed, on average for both sites, power to detect a 20% 

change at α = 0.05 was almost 100%, whereas it was only 55% to detect a 10% change 

(Figure III-2c). On average for both sites, a 13% change (ranging from 13 to 14%) was 

detectable with 80% power at α = 0.05 (Figure III-3b). 
Effects of Temporal and Spatial Separation of Samples 

67

Figure III-1c: Percentage of before-after comparisons that could detect a 10, 20, 30, 40, or 50% change in 

cover with 80% power at α = 0.05, 0.10, and 0.20 for each of two and all target species. Numbers in 

parentheses indicate the number of replicate sites sampled for that species. 

Pisaster (n = 4)

Percentage of Compa

risons

Lottia (n = 2)

All Species (n = 10)

0.05 0.10 0.20

Alpha (α)

0

25

50

75

100

10% 30% 40% 20% 50% Change

0

25

50

75

100

0

25

50

75

100

000 0 0

0 0
Final Study Report – Minchinton and Raimondi 

68

Figure III-2c: Mean (±se) power to detect 10, 20, 30, 40, and 50% changes in abundance at α = 0.05, 

0.10, and 0.20 for before-after comparisons for each of two and all target species. Numbers in parentheses 

indicate the number of replicate sites or species sampled. 

0

20

40

60

80

100 Pisaster (n = 4)

Pow

e

r (%)

Lottia (n = 2)

0.05 0.10 0.20

Alpha (α)

0

20

40

60

80

100

10% 30% 40% 20% 50% Change

All Species (n = 10)

0

20

40

60

80

100

3.3.13 Haliotis cracherodii

Data for before-after comparisons with Haliotis never passed the tests of both 

assumptions (Table III-1c). Consequently, power analysis was not done for this species. 

3.3.14 All Species

For all species, there were 10 that passed both assumptions for at least one site and could 

be used for power analysis. Although combining across species in this manner may not 

be justified because there are differences among species in the power to detect changes in 

abundance, it provides an informative summary. For all species and sites, a 50% change 

in cover was detectable with 80% power at α = 0.05, and nearly a third (8 of 27) of all 

species and site combinations could detect a 10% change in abundance (Figure III-1c). 

Indeed, there is on average 80% power (ranging from 25 to 100%) to detect at least a 
Effects of Temporal and Spatial Separation of Samples 

69

20% change in abundance at α = 0.05 (Figure III-2c). On average for all species and site 

combinations, an 18% (ranging from 3 to 43%) change in abundance is detectable with 

80% power at α = 0.05 (Figure III-3b). 

3.4 Discussion 

The purpose of this power analysis was to determine whether modifications to the spatial 

and temporal aspects of the sampling design of the Shoreline Inventory monitoring 

program are necessary so that future impacts may be reliably detected. To do this, we 

determined the statistical power to detect changes to the abundances of target organisms 

following simulated impacts at each of the monitored sites using a before-after design. 

For all 27 species and site combinations, power analyses revealed that in all cases there 

was sufficient power to detect 50% changes in abundance with 80% power at α = 0.05 

(and power was slightly greater at higher levels of alpha). Detecting 50% changes in 

abundance with 80% power has been proposed as a reasonable goal for monitoring 

studies (Fairweather 1991). By these criteria, data from the current monitoring program 

are adequate and no major changes to its sampling design and monitoring protocols are 

necessary. Indeed, for 67% of comparisons, there was 80% power to detect 20% changes 

in abundance for all species and site combinations. The levels of acceptable change in 

abundance and power is, however, something to be determined be the managers of the 

Shoreline Inventory. Indeed, detection of a 50% reduction in abundance may be 

insufficient for some species, particularly if they are in decline. A lower level of 

detectable change, such as 20%, may be a better target for particularly vulnerable and 

valuable species. 

Only two other studies have done identical and extensive analyses under the same beforeafter sampling design used here. Ambrose (unpublished manuscript) analysed 7 target 

species across 8 sites in rocky intertidal communities in southern California, including 

some of the same sites and species as the current analysis, but over a shorter time period. 

He found that for 30 combinations of species and sites, there was at least 80% power to 

detect 50% changes in abundance at α = 0.05 for about 93% of the comparisons. For 

47% of comparisons, there was 80% power to detect 20% changes in abundance. 

Minchinton and Raimondi (2001) examined 6 target species across 22 sites on rocky 

shores of the Channel Islands in southern California. Their study included only 8 species 

and site combinations, but 100% and 88% of these comparisons could detect 50% and 

20% changes in abundance, respectively, with 80% power at α = 0.05. In this study of 27 

species and site combinations, 100% and 67% of comparisons could detect 50 and 20% 

changes in abundance, respectively, with 80% power at α = 0.05. In general, all three 

studies had substantial ability to detect changes from impacts should they occur. 

Differences in power among studies might reflect sample sizes, which ranged from 10 

sampling times in Ambrose (unpublished manuscript), 12 to 30 sampling dates in 

Minchinton and Raimondi (2001), and 14 to 15 sample periods in the current study. 

Two significant points made by Ambrose (unpublished manuscript) are important to reiterate here. First, although power analyses under the before-after sampling design in the 
Final Study Report – Minchinton and Raimondi 

70

above studies suggest that there is great power to detect change, it is important to point 

out that under this design, the majority of species and site combinations could not satisfy 

assumptions that would permit power analyses (see similar results in Ambrose 

unpublished manuscript, Minchinton and Raimondi 2001). Consequently, the majority of 

the data was excluded. Even under potentially more powerful designs, such as the BACI 

design, which was not conducted in this study, many of the assumptions prior to power 

analysis cannot be met (see Schroeter et al. 1993, Ambrose unpublished manuscript, 

Minchinton and Raimondi 2001). The extremely low pass rate of the tests of 

assumptions under both designs is probably partially due to the sampling of fixed plots. 

Sampling fixed plots may lead to greater serial correlation. The many broadscale, longterm trends in the data, which often varied among sites, however, also likely contributed 

to the high failure rate. This results in some species (Silvetia, Hesperophycus, Haliotis) 

and sites (e.g. Stairs) not being represented in power analyses. This may be a critical 

limitation of before-after and BACI analyses. Indeed, it is not surprising that long timeseries of data contain trends or non-independence which preclude these analyses. 

Moreover, some data satisfied assumptions and were included in power analyses despite 

demonstrating clear trends, particularly a decrease followed by an increase over time, but 

these were ignored. 

The presence of trends may not, however, be a critical limitation if the time-series can be 

adjusted for known trends (although this will be difficult for the short time series of the 

current set of data) or times of trends can be eliminated from the data set (although this 

would have to be clearly justified) (Stewart-Oaten et al. 1986). In the current set of data, 

it is clear that El Niño events, which sometimes result in a decrease in species abundance, 

introduces such complexities into the time series of data (Raimondi et al. 1999). Another 

clear example is the decline of Haliotis over time due to withering syndrome (see Altstatt 

et al. 1996, Raimondi et al. 2002). A final example is the consistent decrease in Silvetia

over time, which likely occurred because initial plots were chosen to have high cover and 

thus it is likely that this long-lived species will decline in abundance over time. More 

sophisticated analyses to detrend the data were beyond the scope of this study, but should 

be considered as a future task to ensure that all species (and perhaps sites) are 

represented. Given the scope of the set of data and the ability of the data that met 

assumptions to detect change, it is likely that more sophisticated analyses, including 

power analysis under a BACI design, will further demonstrate the adequacy of the current 

monitoring program to detect impacts. Importantly, regardless of assumptions and 

designs, all of the data should be used in determining whether an impact has occurred. 

Indeed, at this stage where few impacts (particularly large-scale impacts) have occurred, 

it is difficult to appreciate fully the actual, long-term importance of the Shoreline 

Inventory in space and time. Detection of seasonal variation, responses to El Niño (see 

Raimondi et al. 1999) and withering disease by abalone (see Raimondi et al. 2002) 

provides convincing evidence for the maintenance of the current spatial and temporal 

sampling frequencies. Indeed, the addition of more sites along the coast has been a 

valuable addition to the monitoring program (see Raimondi et al. 2002). 

Second, the initial selection of quadrats with relatively high and consistent abundances 

and then sampling of these fixed quadrats over time is likely responsible for the relatively 
Effects of Temporal and Spatial Separation of Samples 

71

low variability in the data over time and, consequently, the high level of power to detect 

change, particularly under a before-after design where data from only one site are used. 

This phenomenon is likely to be accentuated for longer-lived species such as mussels and 

Silvetia relative to shorter-lived species such as barnacles and Endocladia. Indeed, this 

prediction exactly matches what we found here: power to detect impacts was greater for 

mussels than for barnacles and Endocladia. 

The limitation of using these permanent quadrats is that conclusions, such as the power to 

detect an impact, are restricted to these areas and cannot be generalized in space within 

and among sites. Nevertheless, if one or multiple target species respond the same across 

multiple sites, then this should be a strong indication that the effect of an impact is real. 

Importantly, over time the initial starting conditions should become of lesser importance. 

Moreover, using fixed sites allows a permanent record of specific areas, which may have 

advantages in the long-term. Discussing the pros and cons of sampling fixed versus 

random plots is beyond the scope of this report, but maintaining numerous sites for each 

target species should allow for sound conclusions about spatial generality. 

A specific objective of this study was to determine whether the temporal sampling 

frequency (i.e., number of sample periods) provides adequate statistical power to detect 

impacts. Based on the ability of the sampling design to detect 50% (and often 20%) 

changes with 80% power, results clearly indicate that more frequent sampling is 

unnecessary. With the exception of Mastocarpus and Pisaster, there was 80% power to 

detect 20% changes in abundance for all other species for at least one site. Do these 

results suggest that less frequent sampling is necessary? At less frequent sampling 

intervals, there will be a greatly reduced ability to detect seasonal and other trends such 

as those due to El Niño. The cost of losing this information must be weighed against the 

current sampling effort, but it seems sensible for seasonal sampling to continue. 

Despite generally great power to detect impacts for all species, power varied among 

species. For species where at least 2 sites were sampled, power was greatest for detecting 

changes in the cover of mussels and least for Endocladia and Pisaster. Similarly, 

Ambrose (unpublished manuscript) and Minchinton and Raimondi (2001) also found 

relatively low power to detect changes in the cover of Endocladia relative to other 

species using before-after and BACI designs. Perhaps not surprisingly, Ambrose 

(unpublished manuscript), who analysed data from some of the same sites as in this 

study, also found that there was high power to detect changes in mussel cover. 

Minchinton and Raimondi (2001), studying sites on the Channel Islands, found the 

greatest power to detect changes in abundance was for the rockweeds including Silvetia

and Hesperophycus, but in this study, none of the data could satisfy the assumptions 

before analysis because these species declined significantly over time. 

The Shoreline Inventory monitoring program is one of the largest of its kind in terms of 

both spatial and temporal extent. The duration of the monitoring program meant that 

there was great power to detect changes in abundance, even though this also allowed only 

a small subset of the data to be analysed because of the high failure rate of tests of 

assumptions prior to analysis. Regardless, more sophisticated statistical techniques 
Final Study Report – Minchinton and Raimondi 

72

would have likely allowed more of the data to be analyzed. The extensive numbers of 

sites and sampling times and the use of multiple target species means that there will be 

great flexibility to do statistical analyses to detect future impacts. The greatest benefit of 

the data may be that they allow graphical interpretation and analysis of an extremely long 

time series of data for multiple species at multiple sites. Ultimately repeated observations 

of common trends in space and time may end up being the most powerful and appropriate 

method to determine correctly whether an impact has occurred or not.
Effects of Temporal and Spatial Separation of Samples 

73

ACKNOWLEDGEMENTS 

We thank Jessie Altstatt, the MMS MINT Team, and numerous assistants and volunteers 

for help in the field. We are grateful to many people for granting access to the study 

sites. These include Tim King and the Bixby Ranch Corporation and Brad Lendberg and 

the Cojo-Jalama Ranch (Government Point), the Vandenberg Air Force Base, especially 

Nancy Reed and the staff at the Recreation Center (Boathouse, Stairs, and Purisima 

Point), the United States Geological Survey (USGS) (Piedras Blancas), and the Hearst 

Corporation (Point Sierra Nevada). This work was funded by a Natural Sciences and 

Engineering Research Council (NSERC) of Canada Postdoctoral Fellowship to Todd E. 

Minchinton and awards to Peter T. Raimondi from the Mineral Management Service. 
Final Study Report – Minchinton and Raimondi 

74

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Raimondi. 1996. Recent declines of black abalone Haliotis cracherodii on the 

mainland coast of central California. Marine Ecology Progress Series 142:185-

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Ambrose, R. F., J. M. Engle, P. T. Raimondi, M. Wilson and J. Altstatt. 1995. Rocky 

Intertidal and Subtidal Resources: Santa Barbara County Mainland (Final Report). 

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92 pp. 

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during 1981-1983 including effects of severe storms and an El Niño. Journal of 

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Communities at the Channel Islands National Park: Summary of Spatial and 

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Interior. 127 pp. 

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Richards, D. V. and G. E. Davis. 1988. Rocky Intertidal Communities Monitoring 

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Effects of Temporal and Spatial Separation of Samples 

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The Department of the Interior Mission

As the Nation's principal conservation agency, the Department of the Interior has responsibility for most 

of our nationally owned public lands and natural resources. This includes fostering sound use of our 

land and water resources; protecting our fish, wildlife, and biological diversity; preserving the 

environmental and cultural values of our national parks and historical places; and providing for the 

enjoyment of life through outdoor recreation. The Department assesses our energy and mineral 

resources and works to ensure that their development is in the best interests of all our people by 

encouraging stewardship and citizen participation in their care. The Department also has a major 

responsibility for American Indian reservation communities and for people who live in island territories 

under U.S. administration. 

The Minerals Management Service Mission 

As a bureau of the Department of the Interior, the Minerals Management Service's (MMS) primary 

responsibilities are to manage the mineral resources located on the Nation's Outer Continental Shelf 

(OCS), collect revenue from the Federal OCS and onshore Federal and Indian lands, and distribute 

those revenues. 

Moreover, in working to meet its responsibilities, the Offshore Minerals Management Program

administers the OCS competitive leasing program and oversees the safe and environmentally sound 

exploration and production of our Nation's offshore natural gas, oil and other mineral resources. The 

MMS Royalty Management Program meets its responsibilities by ensuring the efficient, timely and 

accurate collection and disbursement of revenue from mineral leasing and production due to Indian 

tribes and allottees, States and the U.S. Treasury. 

The MMS strives to fulfill its responsibilities through the general guiding principles of: (1) being 

responsive to the public's concerns and interests by maintaining a dialogue with all potentially affected 

parties and (2) carrying out its programs with an emphasis on working to enhance the quality of life for 

all Americans by lending MMS assistance and expertise to economic development and environmental 

protection.