Mention of trade names or commercial products does not constitute endorsement or recommendation for their 

use by the United States government.

Citation:

NOAA National Centers for Coastal Ocean Science (NCCOS) 2005. A Biogeographic Assessment of the 

Channel Islands National Marine Sanctuary: A Review of Boundary Expansion Concepts for NOAA’s National 

Marine Sanctuary Program. Prepared by NCCOS’s Biogeography Team in cooperation with the National Marine Sanctuary Program. Silver Spring, MD. NOAA Technical Memorandum NOS NCCOS 21. 215 pp.
United States Department of National Oceanic and National Ocean Service

Commerce Atmospheric Administration

Carlos M. Gutierrez Conrad C. Lautenbacher, Jr. Charlie Challstrom

Secretary Administrator Assistant Administrator 

(Acting)

A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

A Review of Boundary Expansion Concepts for NOAA’s National Marine Sanctuary Program

Center for Coastal Monitoring and Assessment (CCMA)

NOAA/NOS/NCCOS

1305 East West Highway, SSMC-4, NSCI 1

Silver Spring, MD 20910

NOAA Technical Memorandum NOS NCCOS 21

November 2005

Editors: 

Randy Clark (NCCOS)

John Christensen (NCCOS)

Chris Caldow (NCCOS)

Jim Allen (SCCWRP)

Michael Murray (CINMS)

Sarah MacWilliams (CINMS)



A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

about this document

This biogeographic assessment represents the continuation of an ongoing partnership between the National 

Marine Sanctuary Program (NMSP) and the National Centers for Coastal Ocean Science (NCCOS). The purpose of this collaboration is to provide sanctuary managers with basic information on the distribution of marine 

flora and fauna relevant to the national marine sanctuaries they manage. This particular work, conducted in 

collaboration with the Channel Islands National Marine Sanctuary (CINMS) and members of the local research 

community, builds on a previous assessment developed for California’s other three national marine sanctuaries 

(NOAA, 2003). These efforts were undertaken specifically to support the management plan revision process 

mandated for each sanctuary. This process evaluates the degree that each sanctuary is meeting its goals and 

allows an opportunity for the public to determine if there are new directions or issues that they feel the sanctuary should address. One issue raised by the public during the CINMS management plan revision process was 

whether the sanctuary boundaries should be expanded. A significant portion of this document, therefore, is devoted toward providing a biogeographic assessment of the differing boundary concepts previously developed 

by CINMS in conjunction with the Sanctuary Advisory Council and general public. This was accomplished by a 

thorough analysis of the biogeographic datasets provided to the analytical team by the local research community. Additionally, the data gathered, analyses performed, and patterns of distribution observed should provide 

invaluable information to support science, education, and support other spatially-explicit management decisions.

The results of this assessment are available via both hard copy and CD-ROM. Also available on the CD-ROM 

are the data utilized to develop the Habitat Suitability Models along with the ArcGIS project files used to develop many of the figures within this report (e.g. species distribution, substrate and oceanographic maps). For 

more information on this effort please visit the NCCOS Biogeography Team webpage dedicated to this project 

at: http://ccma.nos.noaa.gov/ecosystems/sanctuaries/chanisl_nms.html or direct questions and comments to:

Mark Monaco

Biogeography Team Manager

National Oceanic & Atmospheric Administration

1305 East-West Hwy. (SSMC4, N/SCI-1)

Silver Spring, MD 20910

Phone: (301) 713-3028 x160

Email: mark.monaco@noaa.gov

Or

Chris Mobley

Channel Islands National Marine Sanctuary Manager

National Oceanic & Atmospheric Administration

113 Harbor Way, Suite 150

Santa Barbara, CA 93109

Phone: (805) 884-1465

Email: chris.mobley@noaa.gov
ii

A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Project Team

Satie Airamé-Partnership for Interdisciplinary Studies of Coastal Oceans

M. James Allen-Southern California Coastal Water Research Project Authority

Jay Barlow-Southwest Fisheries Science Center

Ken Buja-National Centers for Coastal Ocean Science

Chris Caldow-National Centers for Coastal Ocean Science

Harry Carter-Carter Biological Consulting

Jenn Caselle-Partnership for Interdisciplinary Studies of Coastal Oceans

John Christensen-National Centers for Coastal Ocean Science

Larry Claflin-National Centers for Coastal Ocean Science

Randy Clark-National Centers for Coastal Ocean Science

Michael Coyne-National Centers for Coastal Ocean Science

Kate Eschelbach-National Centers for Coastal Ocean Science

Sarah Fangman-Channel Islands National Marine Sanctuary

Glen Ford-R.G. Ford Consulting Company

Karin Forney-Southwest Fisheries Science Center

Steve Gaines-University of California, Santa Barbara

Tracy Gill-National Centers for Coastal Ocean Science

Brian Hatfield-United States Geological Survey

Jamie Higgins-National Centers for Coastal Ocean Science

Olaf Jensen-National Centers for Coastal Ocean Science

Julie Kellner-National Centers for Coastal Ocean Science

Mark Lowry-Southwest Fisheries Science Center

Sarah MacWilliams-Channel Islands National Marine Sanctuary

Chris Mobley-Channel Islands National Marine Sanctuary

Mark Monaco-National Centers for Coastal Ocean Science

Wendy Morrison-Georgia Tech University

Michael Murray-Channel Islands National Marine Sanctuary

Matt Pickett-NOAA Marine and Aviation Operations

Dan Pondella-Occidental College

Lynn Takata-California State Lands Commission

Mitchell Tartt-Office of National Marine Sanctuaries

Jenny Waddell-National Centers for Coastal Ocean Science

Ben Waltenberger-Channel Islands National Marine Sanctuary

Kim Woody-National Centers for Coastal Ocean Science
iii

A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

EXECUTIVE SUMMARY

The priority management goal of the National Marine Sanctuaries Program (NMSP) is to protect marine ecosystems and biodiversity. This goal requires an understanding of broad-scale ecological relationships and linkages 

between marine resources and physical oceanography to support an ecosystem management approach. The 

Channel Islands National Marine Sanctuary (CINMS) is currently reviewing its management plan and investigating boundary expansion. A management plan study area (henceforth, Study Area) was described that extends 

from the current boundary north to the mainland, and extends north to Point Sal and south to Point Dume. Six 

additional boundary concepts were developed that vary in area and include the majority of the Study Area. The 

NMSP and CINMS partnered with NOAA’s National Centers for Coastal Ocean Science Biogeography Team to 

conduct a biogeographic assessment to characterize marine resources and oceanographic patterns within and 

adjacent to the sanctuary. This assessment includes a suite of quantitative spatial and statistical analyses that 

characterize biological and oceanographic patterns in the marine region from Point Sal to the U.S.-Mexico border. These data were analyzed using an index which evaluates an ecological “cost-benefit” within the proposed 

boundary concepts and the Study Area. 

The sanctuary resides in a dynamic setting where two oceanographic regimes meet. Cold northern waters mix 

with warm southern waters around the Channel Islands creating an area of transition that strongly influences the 

regions oceanography. In turn, these processes drive the biological distributions within the region. This assessment analyzes bathymetry, benthic substrate, bathymetric life-zones, sea surface temperature, primary production, currents, submerged aquatic vegetation, and kelp in the context of broad-scale patterns and relative to the 

proposed boundary concepts and the Study Area. Boundary cost-benefit results for these parameters were variable due to their dynamic nature; however, when analyzed in composite the Study Area and Boundary Concept 

2 were considered the most favorable.

Biological data were collected from numerous resource agencies and university scientists for this assessment. 

Fish and invertebrate trawl data were used to characterize community structure. Habitat suitability models were 

developed for 15 species of macroinvertebrates and 11 species of fish that have significant ecological, commercial, or recreational importance in the region and general patterns of ichthyoplankton distribution are described. 

Six surveys of ship and plane at-sea surveys were used to model marine bird diversity from Point Arena to the 

U.S.-Mexico border. Additional surveys were utilized to estimate density and colony counts for nine bird species. 

Critical habitat for western snowy plover and the location of California least tern breeding pairs were also analyzed. At-sea surveys were also used to describe the distribution of 14 species of cetaceans and five species of 

pinnipeds. Boundary concept cost-benefit indices revealed that Boundary Concept 2 and the Study Area were 

most favorable for the majority of the species-specific analyses. Boundary Concept 3 was most favorable for bird 

diversity across the region. Inadequate spatial resolution for fish and invertebrate community data and incompatible sampling effort information for bird and mammal data precluded boundary cost-benefit analysis.

The final chapter integrates data and analyses from each of the preceding chapters utilizing two separate approaches. Cost-benefit indices were ranked for each biological group and for the oceanographic/physical parameters to provide a consistent and comprehensive evaluation of the boundary concepts. The Study Area and 

Boundary Concept 2 (see Chapter 1) ranked highest for the bird, fish, and mammal groups, as well as all the 

data in composite. The Study Area also ranked highest for macroinvertebrates. Second, select spatial data were 

integrated, based on data compatibility and spatial range, to identify areas of spatial coincidence which may reflect ecosystem “hotspots”. Habitat suitability models for fish and macroinvertebrates, along with bird and mammal sightings information were utilized to evaluate this spatial coincidence. Areas of highest spatial coincidence 

most closely resemble the spatial delineation for the Study Area and also include a broad area from the mainland 

south through San Clemente Island. 

Integration results highlight the Channel Islands and the area extending north to the mainland to Point Conception as an important ecosystem that supports a diverse array of biological communities. The boundary concepts 

that were favorably ranked incorporated large areas of the coastal mainland, due in part to the nearshore affinity 

exhibited by many of the analyzed species. Deep offshore environments away from the Channel Islands were 
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

correspondingly less favorable. Both the Study Area and Boundary Concept 2 are characterized by areas of 

increased upwelling, dynamic surface currents and eddies, and persistent thermal fronts. These concepts also 

include large areas of important habitats such as kelp, seagrasses, and wetlands along with a mixture of deep 

and shallow waters that many species depend on for all or part of their life cycles.

In compliance with the National Environmental Policy Act, the National Marine Sanctuary Program will incorporate this assessment with cultural and socio-economic analyses to prepare a Supplemental Environmental 

Impact Statement to fully analyze boundary change concepts.
A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

TABLE OF CONTENTS

CHAPTER 1 INTRODUCTION...............................................................................................................1

1.1 Project Background......................................................................................................1

1.2 Introduction to Biogeography..................................................................................5

1.3 Biogeography of the West Coast.............................................................................6

Marine Benthic Invertebrates .............................................................................................7

Marine Fishes.......................................................................................................................8

Seabirds and Shorebirds ....................................................................................................9

Marine Mammals..................................................................................................................10

1.4 The Four-Step Assessment Process.........................................................................11

Species Selection ................................................................................................................11

Data Collection and Synthesis ...........................................................................................11

Metric Development.............................................................................................................13

Analyses Review..................................................................................................................13

1.5 Assessment Outline.........................................................................................................14

Literature Cited......................................................................................................................15

CHAPTER 2 PHYSICAL AND OCEANOGRAPHIC SETTING .............................................................17

2.1 Physical Environment and Geology ........................................................................17

2.2 Climate and Meteorology ............................................................................................18

2.3 Physical Oceanography................................................................................................18

Offshore Ocean Currents....................................................................................................19

Waves....................................................................................................................................21

Long-Term Climate Perturbations......................................................................................21

2.4 Physiographic Complexity ...........................................................................................21

Data and Methods................................................................................................................21

Broad-scale Patterns...........................................................................................................22

Analysis of Boundary Concepts ........................................................................................23

Summary ..............................................................................................................................24

2.5 Benthic Substrate ...........................................................................................................26

Data and Methods................................................................................................................26

Broad-scale Patterns...........................................................................................................27

Analysis of Boundary Concepts ........................................................................................28

Summary ..............................................................................................................................30

2.6 Bathymetric Life-Zones..................................................................................................31

Data and Methods................................................................................................................31

Broad-scale Patterns...........................................................................................................31

Analysis of Boundary Concepts ........................................................................................32

Summary ..............................................................................................................................33

2.7 Sea Surface Temperature and Frontal Boundaries.........................................33

Data and Methods................................................................................................................33

Broad-scale Patterns...........................................................................................................34

Analysis of Boundary Concepts ........................................................................................35

Summary ..............................................................................................................................37

2.8 Surface Chlorophyll and Ocean Currents.........................................................38

Data and Methods................................................................................................................38

Broad-scale Patterns...........................................................................................................40

Analysis of Boundary Concepts ........................................................................................42

Summary ..............................................................................................................................45

2.9 Submerged Aquatic Vegetation: Eelgrass and Surfgrass ...........................47

 Data and Methods................................................................................................................47

Broad-scale Patterns...........................................................................................................48

Analysis of Boundary Concepts ........................................................................................50

Summary ..............................................................................................................................50
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

2.10 Kelp Distribution............................................................................................................50

Data and Methods................................................................................................................50

Broad-scale Patterns...........................................................................................................51

Analysis of Boundary Concepts ........................................................................................52

Summary ..............................................................................................................................52

Literature Cited......................................................................................................................52

CHAPTER 3 BIOGEOGRAPHY OF MARINE MACROINVERTEBRATES ..........................................57

3.1 Single Species Habitat Suitability Models (HSM).................................................57

Data and Methods................................................................................................................57

Rock crabs (Cancer spp.)...............................................................................................58

Black abalone (Haliotis cracherodii)...............................................................................60

Red abalone (Haliotis rufescens)...................................................................................61

White abalone (Haliotis sorenseni).................................................................................63

California market squid (Loligo opalescens) ..................................................................65

Sheep crab (Loxorhynchus grandis) ..............................................................................67

Spot shrimp (Pandalus platyceros) ................................................................................68

Ridgeback rock shrimp (Sicyonia ingentis) ....................................................................71

California spiny lobster (Panulirus interruptus)...............................................................72

California sea cucumber (Parastichopus californicus) ...................................................73

Warty sea cucumber (Parastichopus parvimensis)........................................................75

Red sea urchin (Strongylocentrotus franciscanus) ........................................................76

Purple sea urchin (Strongylocentrotus purpuratus)........................................................78

3.2 Macroinvertebrate Assemblage Structure .......................................................80

Data and Methods................................................................................................................80

Broad-Scale Patterns ..........................................................................................................81

Analysis of Boundary Concepts ........................................................................................82

Summary ..............................................................................................................................85

Literature Cited......................................................................................................................85

CHAPTER 4 BIOGEOGRAPHY OF MARINE FISHES .........................................................................89

4.1 Single Species Habitat Suitability Models (HSM).................................................89

Data and Methods................................................................................................................89

Thresher shark (Alopias vulpinus)..................................................................................90

Tope (Galeorhinus galeus).............................................................................................92

Leopard shark (Triakis semifasciata)..............................................................................95

Pacific angel shark (Squatina californica) ......................................................................97

Pacific sardine (Sardinops sagax)..................................................................................98

Northern anchovy (Engraulis mordax)............................................................................98

Bocaccio (Sebastes paucispinis)....................................................................................99

Cowcod (Sebastes levis)................................................................................................101

Lingcod (Ophiodon elongatus).......................................................................................104

Giant seabass (Stereolipis gigas)...................................................................................107

California sheephead (Semicossyphus pulcher)............................................................108

California halibut (Paralichthys californicus)...................................................................110

4.2 Fish Assemblage Structure ........................................................................................112

Data and Methods................................................................................................................112

Broad-scale Patterns...........................................................................................................115

Diversity ..................................................................................................................115

Assemblages ..........................................................................................................116

Analysis of Boundary Concepts ........................................................................................125

Summary ..............................................................................................................................125

4.3 Ichthyoplankton..............................................................................................................125

Data and Methods................................................................................................................125

Broad-scale Patterns...........................................................................................................128

California halibut (Paralichthys californicus)...................................................................128

Bocaccio (Sebastes paucispinis)....................................................................................128
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Pacific sardine (Sardinops sagax)..................................................................................129

Northern anchovy (Engraulis mordax)............................................................................129

Analysis of Boundary Concepts ........................................................................................130

Summary ..............................................................................................................................130

Literature Cited......................................................................................................................131

CHAPTER 5 BIOGEOGRAPHY OF MARINE BIRDS...........................................................................135

5.1 Marine Bird Single Species Analysis ........................................................................135

Data and Methods................................................................................................................135

Computer Database Analysis System (CDAS).......................................................135

Xantus’s Murrelet Telemetry Study .........................................................................138

Broad-scale Patterns and Analysis of Boundary Concepts ............................................138

Ashy storm-petrel (Oceanodroma homochroa)..............................................................138

California brown pelican (Pelecanus occidentalis californicus)......................................140

Double-crested cormorant (Phalacroorax auritus) .........................................................142

Brandt’s cormorant (Phalacrocorax penicillatus)............................................................142

Pelagic cormorant (Phalacrocorax pelagicus)................................................................145

Western snowy plover (Charadrius alexandrinus nivosus) ............................................147

Black oystercatcher (Haematopus bachmani)................................................................148

California least tern (Sterna antillarum browni)..............................................................149

Pigeon guillemot (Cepphus columba) ............................................................................149

Xantus’s murrelet (Synthliboramphus hypoleucus)........................................................150

Cassin’s auklet (Ptychoramphus aleuticus)....................................................................153

Other Marine Birds ..............................................................................................................156

Summary ..............................................................................................................................156

5.2 Marine Bird Diversity......................................................................................................157

Data and Methods................................................................................................................157

Broad-scale Patterns...........................................................................................................158

Analysis of Boundary Concepts ........................................................................................160

Summary ..............................................................................................................................162

Literature Cited......................................................................................................................163

CHAPTER 6 Biogeography of MARINE MAMMALS ...................................................................167

6.1 Cetacean Single Species Analysis.............................................................................167

Data and Methods................................................................................................................167

SWFSC Shipboard Suveys ............................................................................................168

SWFSC Aerial Surveys ..................................................................................................171

Computer Database Analysis System (CDAS) ..............................................................171

SWFSC Bottlenose Dolphin Aerial Survey.....................................................................171

Broad-scale Patterns and Analysis of Boundary Concepts ............................................172

Blue whale (Balaenoptera musculus).............................................................................172

Bottlenose dolphin (Tursiops truncatus).........................................................................173

Long-beaked and short-beaked common dolphins (Delphinus spp.).............................175

Gray whale (Eschrichtius robustus)................................................................................178

Humpback whale (Megaptera novaeangliae).................................................................178

Killer whale (Orcinus orca).............................................................................................179

Pacific white-sided dolphin (Lagenorhynchus obliquidens)............................................180

Risso’s dolphin (Grampus griseus) ................................................................................181

Additional Cetaceans ..........................................................................................................183

Dall’s porpoise (Phocoenoides dalli) ..............................................................................183

Fin whale (Balaenoptera physalus)................................................................................183

Harbor porpoise (Phocoena phocoena).........................................................................184

Minke whale (Balaenoptera acutorostrata) ....................................................................185

Northern righ-whale dolphin (Lissodelphis borealis) ......................................................185

Summary ..............................................................................................................................185

6.2 Pinnipeds and Southern Sea Otter ...........................................................................186

Pinnipedia (seals, sea lions, and fur seals).......................................................................186

Carnivora ..............................................................................................................................187

Data and Methods................................................................................................................187
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

California sea lion (Zalophus californianus californianus)..............................................187

Pacific harbor seal (Phoca vitulina richardsi) .................................................................188

Northern elephant seal (Mirounga angustirostris)..........................................................188

Northern fur seal (Callorhinus ursinus)...........................................................................188

Southern sea otter (Enhydra lutris nereis)......................................................................190

Broad-scale Patterns and Analysis of Boundary Concepts ............................................188

California sea lion...........................................................................................................188

Pacific harbor seal..........................................................................................................190

Northern elephant seal...................................................................................................191

Southern sea otter..........................................................................................................193

Summary ..............................................................................................................................194

Literature Cited......................................................................................................................196

CHAPTER 7 INTEGRATION OF ANALYSES .......................................................................................199

7.1 Numerical Integration...................................................................................................199

Data and Methods................................................................................................................199

Analysis of Boundary Concepts.........................................................................................199

7.2 Spatial Integration..........................................................................................................201

Data and Methods................................................................................................................201

Broad-scale Patterns and Analysis of Boundary Concepts.............................................202

Summary ..............................................................................................................................205

7.3 Threatened and Endangered Species......................................................................206

White abalone ................................................................................................................206

Western snowy plover....................................................................................................206

California brown pelican.................................................................................................206

California least tern ........................................................................................................207

Southern sea otter..........................................................................................................207

Blue whale......................................................................................................................207

Humpback whale............................................................................................................208

Summary ..............................................................................................................................208

Literature Cited......................................................................................................................209

Appendix A ..........................................................................................................................................211

Acknowledgements ......................................................................................................................215
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

List of Tables

Table 1.1.1. Total area, perimeter, and amount of mainland coastline included for the NAC, 

Study Area, and six boundary concepts................................................................................ 4

Table 2.4.1. Analysis of boundary concepts. ............................................................................................. 26

Table 2.5.1. Area (km2) and percentage of total area for habitat types within boundary concepts. ........... 28

Table 2.5.2. Analysis of habitat diversity within boundary concepts. ......................................................... 30

Table 2.6.1. Analysis of life-zone evenness within boundary concepts. .................................................... 33

Table 2.8.1. Analysis of chlorophyll within boundary concepts.................................................................. 45

Table 2.9.1. Analysis of SAV distribution within boundary concepts.......................................................... 50

Table 2.10.1. Analysis of kelp distribution within boundary concepts. ....................................................... 52

Table 3.1.1. Invertebrate species of interest for the CINMS biogeographic assessment. ......................... 57

Table 3.1.2. Analysis of rock crab habitat suitability within boundary concepts......................................... 60

Table 3.1.3. Analysis of black abalone habitat suitability within boundary concepts ................................. 62

Table 3.1.4. Analysis of red abalone habitat suitability within boundary concepts..................................... 63

Table 3.1.5. Analysis of white abalone habitat suitability within boundary concepts ................................. 65

Table 3.1.6. Analysis of California market squid habitat suitability within boundary concepts................... 67

Table 3.1.7. Analysis of sheep crab habitat suitability within boundary concepts...................................... 68

Table 3.1.8. Analysis of spot shrimp habitat suitability within boundary concepts ..................................... 70

Table 3.1.9. Analysis of ridgeback shrimp habitat suitability within boundary concepts ............................ 72

Table 3.1.10. Analysis of California spiny lobster habitat suitability within boundary concepts ................. 74

Table 3.1.11. Analysis of California sea cucumber habitat suitability within boundary concepts ............... 75

Table 3.1.12. Analysis of warty sea cucumber habitat suitability within boundary concepts ..................... 77

Table 3.1.13. Analysis of red sea urchin habitat suitability within boundary concepts............................... 78

Table 3.1.14. Analysis of purple sea urchin habitat suitability within boundary concepts .......................... 79

Table 3.2.1. Species assemblage results for SCCWRP survey data (1994, 1998) using the

Bray-Curtis dissimilarity metric with average means clustering ............................................ 82

Table 3.2.2. Mean frequency of occurrence for each SCCWRP site group............................................... 83

Table 4.1.1. Fish species of interest for the CINMS biogeographic assessment....................................... 89

Table 4.1.2. Analysis of adult thresher shark habitat suitability within boundary concepts........................ 92

Table 4.1.3. Analysis of juvenile thresher shark habitat suitability within boundary concepts ................... 92

Table 4.1.4. Analysis of adult tope habitat suitability within boundary concepts........................................ 94

Table 4.1.5. Analysis of juvenile tope habitat suitability within boundary concepts ................................... 95

Table 4.1.6. Analysis of adult leopard shark habitat suitability within boundary concepts ......................... 96

Table 4.1.7. Analysis of Pacific angel shark habitat suitability within boundary concepts ......................... 98

Table 4.1.8. Analysis of adult bocaccio habitat suitability within boundary concepts................................. 101

Table 4.1.9. Analysis of juvenile bocaccio habitat suitability within boundary concepts ............................ 101

Table 4.1.10. Analysis of adult cowcod habitat suitability within boundary concepts................................. 103

Table 4.1.11. Analysis of juvenile cowcod habitat suitability within boundary concepts ............................ 104

Table 4.1.12. Analysis of adult lingcod habitat suitability within boundary concepts ................................. 106

Table 4.1.13. Analysis of juvenile lingcod habitat suitability within boundary concepts............................. 106

Table 4.1.14. Analysis of giant seabass habitat suitability within boundary concepts ............................... 108

Table 4.1.15. Analysis of California sheephead habitat suitability within boundary concepts.................... 110

Table 4.1.16. Analysis of California halibut habitat suitability within boundary concepts........................... 112

Table 4.2.1. Summary of the datasets used to assess fish diversity and species assemblages............... 113

Table 4.2.2. Species assemblage results for the RecFIN CPFV data using the 

Bray-Curtis dissimilarity metric with average means clustering ............................................ 117

Table 4.2.3. Mean frequency of occurrence for each recreational site group............................................ 119

Table 4.2.4. Species assemblage results for the SCCWRP trawl data using the 

Bray-Curtis dissimilarity metric with average means clustering ............................................ 120

Table 4.2.5. Mean frequency of occurrence for SCCWRP site groups...................................................... 121

Table 4.2.6. Species assemblage results for the NMFS GSP data using 

Bray-Curtis dissimilarity metric with average means clustering ............................................ 122

Table 4.2.7. Mean frequency of occurrence for NMFS GSP site groups................................................... 123
A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Table 4.2.8. Species assemblage results for kelp visual census surveys using Bray-Curtis 

dissimilarity metric with average means clustering ............................................................... 123

Table 4.2.9. Mean frequency of occurrence for kelp visual census site groups ........................................ 124

Table 5.0.1. Status, habitat locations, and seasonal use of the requested marine birds in the 

Study Area............................................................................................................................. 136

Table 5.1.1. Data used for the analysis of marine bird distributions presented in this chapter.................. 137

Table 5.1.2. Ashy storm-petrel. At-sea sightings, effort, density, and Optimal-Area Index 

(OAI) for each boundary concept .......................................................................................... 140

Table 5.1.3. California brown pelican. At-sea sightings, effort, density, and Optimal-Area Index 

(OAI) for each boundary concept .......................................................................................... 142

Table 5.1.4. Double-crested cormorant. At-sea sightings, effort, density, and Optimal-Area Index

(OAI) for each boundary concept .......................................................................................... 144

Table 5.1.5. Brandt’s cormorant. At-sea sightings, effort, density, and Optimal-Area Index (OAI) 

for each boundary concept.................................................................................................... 145

Table 5.1.6. Pelagic cormorant. At-sea sightings, effort, density, and Optimal-Area Index (OAI) 

for each boundary concept.................................................................................................... 147

Table 5.1.7. Pelagic cormorant. Colony counts (total individuals) and Optimal-Area Index (OAI) 

for each boundary concept.................................................................................................... 147

Table 5.1.8. Western snowy plover. Critical habitat for the Pacific coast population of 

western snowy plover............................................................................................................ 148

Table 5.1.9. Black oystercatcher. Colony counts (total individuals) and Optimal-Area 

Index (OAI) for each boundary concept ................................................................................ 148

Table 5.1.10. California least tern. The maximum number of breeding pairs observed at each nesting 

site surveyed from 2001-2003 for each boundary concept (CDFG, DRAFT data).. ............. 149

Table 5.1.11. Pigeon guillemot. At-sea sightings, effort, density, and Optimal-Area 

Index (OAI) for each boundary concept ................................................................................ 150

Table 5.1.12. Pigeon guillemot. Colony counts (total individuals) and Optimal-Area Index (OAI) 

for each boundary concept.................................................................................................... 151

Table 5.1.13. Xantus’s murrelet. At-sea sightings, effort, density, and Optimal-Area Index (OAI) 

for each boundary concept.................................................................................................... 152

Table 5.1.14. Xantus’s murrelet. Telemetry relocations and Optimal-Area Index (OAI) for each 

boundary concept.................................................................................................................. 153

Table 5.1.15. Cassin’s auklet. At-sea sightings, effort, density, and Optimal-Area Index (OAI) 

for each boundary concept.................................................................................................... 156

Table 5.2.1. Summary of the six surveys that were used in the analysis of marine bird diversity...............157

Table 5.2.2. Analysis of bird diversity within boundary concepts ............................................................... 162

Table 6.1.1. Summary of marine mammal field surveys examined in this chapter.................................... 168

Table 6.1.2. Line transect parameters used to estimate the abundance of selected 

cetaceans within the six proposed boundary concepts, the current CINMS boundary, 

and the Study Area................................................................................................................ 168

Table 6.1.3. Blue whale. Sightings, estimated density and abundance, coefficient of variation (CV) 

and upper and lower 95% confidence limits for the abundance estimate, and the Optimal 

Area Index (OAI) for the six proposed boundary concepts, the No Action Concept (NAC), 

and the Study Area (SA) ....................................................................................................... 173

Table 6.1.4. Coastal bottlenose dolphin. Sightings, mean encounter rate, and estimated abundance

for four proposed boundary concepts and the Study Area.................................................... 175

Table 6.1.5. Long-beaked common dolphin. Sightings, estimated density and abundance, 

coefficient of variation (CV) and upper and lower 95% confidence limits for the 

abundance estimate (corrected for unidentified common dolphin sightings), and the 

Optimal Area Index (OAI) for the six proposed boundary concepts, the No Action 

Concept (NAC), and the Study Area (SA)............................................................................. 177
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Table 6.1.6. Short-beaked common dolphin. Sightings, estimated density and abundance, coefficient 

of variation (CV) and upper and lower 95% confidence limits for the abundance estimate

(corrected for unidentified common dolphin sightings), and the Optimal Area Index (OAI)

for the six proposed boundary concepts, the No Action Concept (NAC), and the Study

Area (SA). ............................................................................................................................. 177

Table 6.1.7. Humpback whale. Sightings, estimated density and abundance, coefficient of variation 

(CV) and upper and lower 95% confidence limits for the abundance estimate for the six 

proposed boundary concepts, the No Action Concept (NAC), and the Study Area (SA). ..... 179

Table 6.1.8. Risso’s dolphin. Sightings, estimated density and abundance, coefficient of variation 

(CV) and upper and lower 95% confidence limits for the abundance estimate, and the

Optimal Area Index (OAI) for the six proposed boundary concepts, the No Action

Concept (NAC), and the Study Area (SA)............................................................................. 182

Table 6.2.1. Summary of pinniped and sea otter surveys used in this chapter ......................................... 187

Table 6.2.2. Pacific Harbor seal. Total number of individuals, total area, and OAI for each boundary 

concept for SWFSC and CDFG 2002 surveys...................................................................... 192

Table 6.2.3. Southern Sea otter. Total number of individuals, mainland encounter rates, 

total area, and OAI for each boundary concept for Fall 2001 and Spring 2002 surveys....... 195

Table 7.1.1. Absolute OAI rankings for individual, groups, and the composite for all analyses................. 200

Table 7.1.2. Results of Kruskal-Wallis ranks sums test. ............................................................................ 201

Table 7.3.1. Analysis of white abalone habitat suitability within boundary concepts ................................. 207

Table 7.3.2. Critical habitat for the Pacific coast population of western snowy plover............................... 206

Table 7.3.3. California brown Pelican sightings and density OAI .............................................................. 206

Table 7.3.4. California least tern breeding pairs observed within boundary concepts, 2001-2003............ 207

Table 7.3.5. Spring and fall abundance estimates and OAI for southern sea otter.................................... 207

Table 7.3.6. Sightings, estimated abundance and density, and OAI for blue whales ................................ 207

Table 7.3.7. Sightings, estimated abundance and density for humpack whales ....................................... 208

Table 7.3.8. Ranked OAI for the federally listed threatened or endangered species ................................ 208

List of Figures

Figure 1.1.1. Map of the Channel Islands and specific coastal locations in the surrounding 

region of interest ................................................................................................................... 1

Figure 1.1.2a. Spatial delineations for the Study Area and boundary Concepts 1-4................................. 3

Figure 1.1.2b. Spatial delineation for boundary Concept 5....................................................................... 4

Figure 1.3.1. Pelagic life-zones off southern California............................................................................. 7

Figure 1.3.2. Benthic life-zones off southern California............................................................................. 8

Figure 1.3.3. Latitudinal range endpoints for 539 species of marine benthic invertebrates ...................... 9

Figure 1.3.4. Latitudinal range endpoints for 294 species of marine fishes .............................................. 10

Figure 1.3.5. Latitudinal range endpoints for 132 shore and seabird species........................................... 11

Figure 1.3.6. Latitudinal range endpoints for 49 marine mammal species................................................ 12

Figure 1.4.1. A hypothetical set of three boundary concepts and the ecological value 

of the area contained within them....................................................................................... 14

Figure 1.4.2. Trend in values of absolute and relative metrics and the OAI for the hypothetical 

example shown in Figure 1.1.9........................................................................................... 14

Figure 2.1.1. Bathymetric and geologic features for the region of interest................................................ 17

Figure 2.4.1. Map of physiographic complexity along the California coast (ranging from 32o to 39o

north latitude)...................................................................................................................... 24

Figure 2.4.2. Physiographic complexity within boundary concepts ........................................................... 25

Figure 2.4.3. Linear regression functions between concept area (km2) and mean physiographic 

complexity (left) and and the area (km2) of high physiographic complexity (right).............. 26

Figure 2.5.1. Benthic habitat types for the marine waters off California.................................................... 27

Figure 2.5.2. Location of hard substrate from NMFS and MMS datasets ................................................. 29

Figure 2.5.3. Benthic habitat types for the marine waters off southern California..................................... 29
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Figure 2.5.4. Regression of habitat diversity and total area for the current and proposed 

boundary concepts.............................................................................................................. 30

Figure 2.5.5. Evenness and richness estimates for habitat types within boundary concepts ................... 30

Figure 2.6.1. Distribution of benthic substrate types (hard or soft) off southern California ....................... 31

Figure 2.6.2. Distribution of bathymetric zones (m) off southern California .............................................. 32

Figure 2.6.3. Regression of habitat evenness and total area for the current and proposed 

boundary concepts.............................................................................................................. 33

Figure 2.7.1. Seasonal mean sea surface temperature (January, April, July, and October) 

averaged across years from 1993 to 2003 ......................................................................... 35

Figure 2.7.2. Sea surface temperature variance, calculated from mean monthly values for the 

period January 1993 to May 2003 ...................................................................................... 36

Figure 2.7.3. Left panel shows an example interpolated SST grid from GMT for January 1993............... 36

Figure 2.7.4. Persistence of SST fronts through time ............................................................................... 37

Figure 2.7.5. Areas with greatest SST front activity through time ............................................................. 37

Figure 2.7.6. Linear regression function between concept area (km2) and mean persistence 

of SST fronts ....................................................................................................................... 38

Figure 2.7.7. Linear regression function between total concept area (km2) and area of high 

SST frontal persistence....................................................................................................... 38

Figure 2.8.1. Average [ChlA] for grid cells in which data was present during November 

1997-December 1999 ......................................................................................................... 39

Figure 2.8.2. Interpolated mean chlorophyll concentration [ChlA] for the period between 

November 1997 and December 1999. Model extent ranges from 28o- 49o latitude............ 40

Figure 2.8.3. Interpolated mean chlorophyll concentration [ChlA] for the period between 

November 1997 and December 1999................................................................................. 41

Figure 2.8.4. Monthy mean [ChlA] for 1999. Estimates derived from data contained within the 

Study Area boundary........................................................................................................... 42

Figures 2.8.5a. Mean chlorophyll concentration [ChlA] for the months January to June 1999. 

Altimetry derived surface current velocity vectors for the same time periods 

are superimposed on the [ChlA] surface............................................................................. 43

Figures 2.8.5b. Mean chlorophyll concentration [ChlA] for the months of July to December 

1999 (left to right). Altimetry derived surface current velocity vectors for the same time 

periods are superimposed on the [ChlA] surface................................................................ 44

Figures 2.8.6. Interpolated mean chlorophyll concentration [ChlA] for the period between 

November 1997 and December 1999 with boundary concepts overlain ............................ 46

Figure 2.8.7. Linear regression function between concept area (km2) and mean [ChlA] .......................... 47

Figure 2.8.8. Linear regression function between the total concept area (km2) and area 

of high [ChlA] ...................................................................................................................... 47

Figure 2.9.1. Distribution of submerged aquatic vegetation within coastal California waters.................... 48

Figure 2.9.2. Distribution of submerged aquatic vegetation within the proposed boundary 

concepts in southern California........................................................................................... 49

Figure 2.9.3. Linear regression function between area of SAV distribution and total area of 

boundary concepts.............................................................................................................. 50

Figure 2.10.1. Kelp distribution based on aerial and multispectral surveys conducted by CDFG during 

1989, 1999, and 2002. Kelp polygons have been enlarged for better viewing ................... 51

Figure 2.10.2. Kelp distribution off southern California ............................................................................. 51

Figure 2.10.3. Regression of kelp area and total area for the current and proposed boundary 

concepts.............................................................................................................................. 52

Figure 3.1.1. CDFG commercial 10x10 nm fishery landings grids. Mean monthly landings 

(pounds) were reported within designated grids from 1996-2002....................................... 58

Figure 3.1.2. Rock crab (Cancer spp.) habitat suitability off central and southern California.................... 59

Figure 3.1.3. Rock crab (Cancer spp.) habitat suitability off southern California ...................................... 59

Figure 3.1.4. Regression of highly suitable habitat area for rock crabs and total area for the 

current and proposed boundary concepts .......................................................................... 60
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Figure 3.1.5. Black abalone habitat suitability off southern California....................................................... 61

Figure 3.1.6. Black abalone commercial landings data from CDFG CMASTR database, 

1990-1993, superimposed over predicted habitat suitability............................................... 61

Figure 3.1.7. Regression of highly suitable habitat area for black abalone and total area 

for the current and proposed boundary concepts ............................................................... 61

Figure 3.1.8. Red abalone habitat suitability off southern California ......................................................... 62

Figure 3.1.9. Red abalone commercial landings data from CDFG CMASTR database, 1990-1999, 

superimposed over predicted habitat suitability.................................................................. 63

Figure 3.1.10. Regression of highly suitable habitat area for red abalone and total area for the 

current and proposed boundary concepts .......................................................................... 63

Figure 3.1.11. White abalone habitat suitability off southern California..................................................... 64

Figure 3.1.12. White abalone commercial landings data from CDFG CMASTR database, 

1984-1999, superimposed over predicted habitat suitability............................................... 64

Figure 3.1.13. Regression of highly suitable habitat area for white abalone and total area for 

the current and proposed boundary concepts .................................................................... 65

Figure 3.1.14. California market squid habitat suitability off central and southern California.................... 66

Figure 3.1.15. Location of NMFS trawls (1999-2002) and squid mean log abundance 

superimposed over predicted habitat suitability.................................................................. 66

Figure 3.1.16. California market squid habitat suitability off southern California....................................... 66

Figure 3.1.17. Regression of highly suitable habitat area for California market squid and 

total area for the current and proposed boundary concepts ............................................... 67

Figure 3.1.18. Sheep crab habitat suitability off central and southern California ...................................... 67

Figure 3.1.19. Sheep crab commercial landings data from CDFG CMASTR database, 

1996-2000, superimposed over predicted habitat suitability............................................... 68

Figure 3.1.20. Sheep crab habitat suitability off southern California ......................................................... 68

Figure 3.1.21. Regression of highly suitable habitat area for sheep crab and total area 

for the current and proposed boundary concepts ............................................................... 68

Figure 3.1.22. Spot shrimp habitat suitability off central and southern California ..................................... 69

Figure 3.1.23. Spot shrimp commercial landings data from CDFG Commercial Trap Logs, 

1994-2001, superimposed over predicted habitat suitability............................................... 69

Figure 3.1.24. Spot shrimp commercial landings data from CDFG Commercial Trawl Logs, 

1994-2001, superimposed over predicted habitat suitability............................................... 70

Figure 3.1.25. Spot shrimp habitat suitability off southern California ........................................................ 70

Figure 3.1.26. Regression of highly suitable habitat area for spot shrimp and total 

area for the current and proposed boundary concepts....................................................... 70

Figure 3.1.27. Ridgeback shrimp habitat suitability off central and southern California............................ 71

Figure 3.1.28. Location of SCCWRP trawls (1994, 1998) and ridgeback shrimp mean 

abundance superimposed over predicted habitat suitability ............................................... 71

Figure 3.1.29. Ridgeback shrimp habitat suitability off southern California............................................... 71

Figure 3.1.30. Regression of highly suitable habitat area for ridgeback shrimp and total 

area for the current and proposed boundary concepts....................................................... 72

Figure 3.1.31. California spiny lobster habitat suitability off central and southern California .................... 73

Figure 3.1.32. California spiny lobster commercial landings data from CDFG Commercial Logs, 

1998-2002, superimposed over predicted habitat suitability............................................... 73

Figure 3.1.33. California spiny lobster habitat suitability off southern California ....................................... 73

Figure 3.1.34. Regression of highly suitable habitat area for California spiny lobster and total 

area for the current and proposed boundary concepts....................................................... 74

Figure 3.1.35. California sea cucumber habitat suitability off central and southern California.................. 74

Figure 3.1.36. Location of SCCWRP trawls (1994, 1998) and California sea cucumber mean

abundance superimposed over predicted habitat suitability ............................................... 74

Figure 3.1.37. California sea cucumber habitat suitability off southern California..................................... 75

Figure 3.1.38. Regression of highly suitable habitat area for California sea cucumber and 

total area for the current and proposed boundary concepts ............................................... 75

Figure 3.1.39. Warty sea cucumber habitat suitability off central and southern California........................ 76
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Figure 3.1.40. Warty sea cucumber habitat suitability off southern California........................................... 76

Figure 3.1.41. Regression of highly suitable habitat area for warty sea cucumber and total 

area for the current and proposed boundary concepts....................................................... 76

Figure 3.1.42. Red sea urchin habitat suitability off central and southern California ................................ 77

Figure 3.1.43. Red sea urchin habitat suitability off southern California ................................................... 77

Figure 3.1.44. Regression of highly suitable habitat area for red sea urchin and total area 

for the current and proposed boundary concepts ............................................................... 78

Figure 3.1.45. Purple sea urchin habitat suitability off central and southern California............................. 78

Figure 3.1.46. Purple sea urchin habitat suitability off southern California ............................................... 79

Figure 3.1.47. Regression of highly suitable habitat area for purple sea urchin and total 

area for the current and proposed boundary concepts....................................................... 79

Figure 3.2.1. Invertebrate diversity for individual SCCWRP trawls during 1994 and 1998 ....................... 81

Figure 3.2.2. Location of site groups for SCCWRP survey data (1994, 1998).......................................... 81

Figure 3.2.3. Overlay of invertebrate diversity and CINMS boundary concepts........................................ 84

Figure 4.1.1. Adult thresher shark habitat suitability off central and southern California........................... 91

Figure 4.1.2. Juvenile thresher shark habitat suitability off central and southern California ..................... 91

Figure 4.1.3. Adult thresher shark habitat suitability off southern California ............................................. 91

Figure 4.1.4. Regression of total habitat area for adult thresher shark and total area within the 

current and proposed boundary concepts .......................................................................... 91

Figure 4.1.5. Juvenile thresher shark habitat suitability off southern California ........................................ 92

Figure 4.1.6. Regression of total habitat area for juvenile thresher shark and total area 

within the current and proposed boundary concepts .......................................................... 92

Figure 4.1.7. Adult tope habitat suitability probability (HSP) off central and southern California .............. 93

Figure 4.1.8. Juvenile tope habitat suitability probability (HSP) off central and southern California ......... 93

Figure 4.1.9. Adult tope habitat suitability probability (HSP) off southern California ................................. 93

Figure 4.1.10. Regression of high probability habitat area for adult tope and total area 

within the current and proposed boundary concepts .......................................................... 94

Figure 4.1.11. Juvenile tope habitat suitability probability (HSP) off southern California .......................... 94

Figure 4.1.12. Regression of total habitat area for juvenile tope and total area 

within the current and proposed boundary concepts .......................................................... 94

Figure 4.1.13. Adult leopard shark habitat suitability probability (HSP) off central and southern 

California............................................................................................................................. 95

Figure 4.1.14. Adult leopard shark habitat suitability probability (HSP) off southern California ................ 96

Figure 4.1.15. Regression of high probability habitat area for adult leopard shark and total area within 

the current and proposed boundary concepts .................................................................... 96

Figure 4.1.16. Pacific angel shark habitat suitability off central and southern California .......................... 97

Figure 4.1.17. Pacific angel shark habitat suitability off southern California ............................................. 97

Figure 4.1.18. Regression of highly suitable habitat area for Pacific angel shark and total 

area within the current and proposed boundary concepts.................................................. 97

Figure 4.1.19. Adult bocaccio habitat suitability probability (HSP) off central and southern 

California............................................................................................................................. 99

Figure 4.1.20. Juvenile bocaccio habitat suitability probability (HSP) off central and southern 

California............................................................................................................................. 99

Figure 4.1.21. Adult bocaccio habitat suitability probability (HSP) off southern California ........................ 100

Figure 4.1.22. Regression of high probability habitat area for adult bocaccio and total area within the 

current and proposed boundary concepts .......................................................................... 100

Figure 4.1.23. Juvenile bocaccio habitat suitability probability (HSP) off southern California................... 100

Figure 4.1.24. Regression of high probability habitat area for juvenile bocaccio and total area within 

the current and proposed boundary concepts .................................................................... 100

Figure 4.1.25. Adult cowcod habitat suitability probability (HSP) off central and southern 

California............................................................................................................................. 102

Figure 4.1.26. Juvenile cowcod habitat suitability probability (HSP) off central and southern 

California............................................................................................................................. 102

Figure 4.1.27. Adult cowcod habitat suitability probability (HSP) off southern California .......................... 102
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Figure 4.1.28. Regression of high probability habitat area for adult cowcod and total area within 

the current and proposed boundary concepts .................................................................... 102

Figure 4.1.29. Juvenile cowcod habitat suitability probability (HSP) off central and southern 

California............................................................................................................................. 103

Figure 4.1.30. Regression of high probability habitat area for juvenile cowcod and total area within 

the current and proposed boundary concepts .................................................................... 103

Figure 4.1.31. Adult lingcod habitat suitability probability (HSP) off central and southern 

California............................................................................................................................. 105

Figure 4.1.32. Juvenile lingcod habitat suitability probability (HSP) off central and southern 

California............................................................................................................................. 105

Figure 4.1.33. Adult lingcod habitat suitability probability (HSP) in nearshore and offshore 

waters of southern California .............................................................................................. 105

Figure 4.1.34. Regression of high probability habitat area for adult lingcod and total area within the 

current and proposed boundary concepts .......................................................................... 105

Figure 4.1.35. Juvenile lingcod habitat suitability probability (HSP) in nearshore and offshore 

waters of southern California .............................................................................................. 106

Figure 4.1.36. Regression of high probability habitat area for juvenile lingcod and total area within the 

current and proposed boundary concepts .......................................................................... 106

Figure 4.1.37. Giant seabass habitat suitability off central and southern California.................................. 107

Figure 4.1.38. Giant seabass habitat suitability off southern California .................................................... 107

Figure 4.1.39. Regression of highly suitable habitat area for giant seabass and total area 

within the current and proposed boundary concepts .......................................................... 108

Figure 4.1.40. California sheephead habitat suitability off central and southern California....................... 109

Figure 4.1.41. California sheephead landings data from CDFG’s Commercial Passenger Fishing

Vessel (CPFV) database, 1998-2002, superimposed over predicted habitat suitability ..... 109

Figure 4.1.42. California sheephead habitat suitability off southern California ......................................... 109

Figure 4.1.43. Regression of highly suitable habitat area for California sheephead and total 

area within the current and proposed boundary concepts.................................................. 112

Figure 4.1.44. California halibut habitat suitability off central and southern California.............................. 111

Figure 4.1.45. Abundance of California halibut captured in SCCWRP trawls superimposed 

over predicted habitat suitability.......................................................................................... 111

Figure 4.1.46. California halibut landings data from CDFG’s Commercial Passenger Fishing

Vessel (CPFV) database, 1998-2000, superimposed over predicted habitat suitability ..... 111

Figure 4.1.47. California halibut habitat suitability off southern California................................................. 111

Figure 4.1.48. Regression of highly suitable habitat area for California halibut and total 

area within the current and proposed boundary concepts.................................................. 112

Figure 4.2.1. Fish diverisity calculated for individual RecFin hook and line trips (left) and 

mean diversity for trips within 5x5 minute grids (right)........................................................ 115

Figure 4.2.2. Fish diverisity calculated for individual SCCWRP trawls (left) and 

mean diversity of trawls within 5x5 minute grids (right) ...................................................... 116

Figure 4.2.3. Fish diverisity calculated for individual NMFS GSP trawls (left) and 

mean diversity of trawls within 5x5 minute grids (right) ...................................................... 116

Figure 4.2.4. Fish diverisity calculated for individual kelp visual census surveys (left) and 

mean diversity of surveys within 5x5 minute grids (right) ................................................... 117

Figure 4.2.5. Composite fish diversity (mean of standardized values across the four 

datasets (left) and effort within 5x5 minute grids (right)...................................................... 117

Figure 4.2.6. Location of site groups, RecFIN CPFV data ........................................................................ 118

Figure 4.2.7. Location of site groups for the RecFIN CPFV data within southern California 

(left) and around Anacapa Island........................................................................................ 119

Figure 4.2.8. Location of site groups, SCCWRP. ...................................................................................... 121

Figure 4.2.9. Location of site groups, NMFS GSP..................................................................................... 121

Figure 4.2.10. Location of site groups, kelp visual census surveys. ......................................................... 121

Figure 4.2.11. Composite fish diversity within boundary concepts............................................................ 126

Figure 4.3.1. Geographic extent of CalCOFI bongo net data.................................................................... 127
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Figure 4.3.2. CalCOFI bongo net effort data. ........................................................................................... 127

Figure 4.3.3. Estimated mean larval abundance for California halibut in CalCOFI bongo tows................ 128

Figure 4.3.4. Estimated mean larval abundance for bocaccio in CalCOFI bongo tows. ........................... 129

Figure 4.3.5. Estimated mean larval abundance for Pacific sardine in CalCOFI bongo tows................... 130

Figure 4.3.6. Estimated mean larval abundance for northern anchovy in CalCOFI bongo tows............... 130

Figure 5.1.1. Ashy storm-petrel. At-sea densities (individuals/km2) calculated for five minutes of 

latitude by five minutes of longitude grid cells from aerial and shipboard survey data 

collected from 1975-1997 and summarized in the Computer Database Analysis 

System v2.1 (MMS, 2001) .................................................................................................. 139

Figure 5.1.2. Ashy storm-petrel. Colony counts (corrected total individuals) 1989-1991.......................... 139

Figure 5.1.3. Brown pelican. At-sea densities (individuals/km2) calculated for five minute 

of latitude by five minutes of longitude grid cells from aerial and shipboard survey data 

collected from 1975-1997 and summarized in the Computer Database Analysis 

System v2.1 (MMS, 2001) .................................................................................................. 139

Figure 5.1.4. Brown pelican. Colony counts (corrected total individuals) 1989-1991................................ 139

Figure 5.1.5. Double-crested cormorant. At-sea densities (individuals/km2) calculated for 

five minutes of latitude by five minutes of longitude grid cells from aerial and shipboard 

survey data collected from 1975-1997 and summarized in the Computer Database 

Analysis System v2.1 (MMS, 2001).................................................................................... 143

Figure 5.1.6. Double-crested cormorant. Colony counts (corrected total individuals) 1989-1991............. 143

Figure 5.1.7. Brandt’s cormorant. At-sea densities (individuals/km2) calculated for five minute 

of latitude by five minutes of longitude grid cells from aerial and shipboard survey data 

collected from 1975-1997 and summarized in the Computer Database Analysis System 

v2.1 (MMS, 2001) ............................................................................................................... 144

Figure 5.1.8. Brandt’s cormorant. Colony counts (corrected total individuals) 1989-1991........................ 145

Figure 5.1.9. Pelagic cormorant. At-sea densities (individuals/km2) calculated for 

five minutes of latitude by five minutes of longitude grid cells from aerial and shipboard 

survey data collected from 1975-1997 and summarized in the Computer Database 

Analysis System v2............................................................................................................. 146

Figure 5.1.10. Pelagic cormorant. Colony counts (corrected total individuals) 1989-1991 ....................... 146

Figure 5.1.11. Western snowy plover. Designated critical habitat for the Pacific coast 

population of western snowy plover.................................................................................... 147

Figure 5.1.12. Black oystercatcher. Colony counts (corrected total individuals) 1989-1991..................... 148

Figure 5.1.13. California least tern. Maximum number of breeding pairs 2001-2003 .............................. 149

Figure 5.1.14. Pigeon guillemot. At-sea densities (individuals/km2) calculated for five minutes of 

latitude by five minutes of longitude grid cells from aerial and shipboard survey data

collected from 1975-1997 and summarized in the Computer Database Analysis

System v2.1. ....................................................................................................................... 150

Figure 5.1.15. Pigeon guillemot. Colony counts (corrected total individuals) 1989-1991 ......................... 151

Figure 5.1.16. Xantus’s murrelet. At-sea densities (individuals/km2) calculated for five minute 

of latitude by five minutes of longitude grid cells from aerial and shipboard survey data 

collected from 1975-1997 and summarized in the Computer Database Analysis

System v2.1 ........................................................................................................................ 152

Figure 5.1.17. Xantus’s murrelet. Colony counts (corrected total individuals) 1989-1991 ....................... 153

Figure 5.1.18. Xantus’s murrelet. Anacapa Island colony counts (estimated total individuals 

per shoreline segment) 1991-2002..................................................................................... 154

Figure 5.1.19. Xantus’s murrelet. San Clemente Island colony counts (estimated total 

individuals per shoreline segment) 1991-2002 ................................................................... 154

Figure 5.1.20. Xantus’s murrelet. San Miguel Island colony counts (estimated total individuals 

per shoreline segment) 1991-2002..................................................................................... 154

Figure 5.1.21. Xantus’s murrelet. Santa Barbara Island colony counts (estimated total 

individuals per shoreline segment) 1991-2002 ................................................................. 154

Figure 5.1.22. Xantus’s murrelet. Santa Catalina Island colony counts (estimated total individuals 

per shoreline segment) 1991-2002 ................................................................................... 154
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Figure 5.1.23. Xantus’s murrelet. Santa Cruz Island colony counts (estimated total individuals 

per shoreline segment) 1991-2002..................................................................................... 154

Figure 5.1.24. Xantus’s murrelet. Telemetry relocations during 1995-1997 and kernel density surface 

based on the relocation points............................................................................................ 155

Figure 5.1.25. Cassin’s auklet. At-sea densities (individuals/km2) calculated for five minutes of 

latitude by five minutes of longitude grid cells from aerial and shipboard survey data collected 

from 1975-1997 and summarized in the Computer Database Analysis System v2.1......... 155

Figure 5.1.26. Cassin’s auklet. Colony counts (corrected total individuals) 1989-1991 ............................ 156

Figure 5.2.1. The distribution of CDAS marine bird survey effort and sightings (left) and the total 

amount of effort within 5 minutes of latitude by 5 minutes of longitude grid cells (right) within 

the region from Point Arena to the U.S.-Mexico border ...................................................... 157

Figure 5.2.2. Estimated bird diversity from Point Arena, Calfiornia, to the U.S.-Mexico border................ 158

Figure 5.2.3. Overlay of estimated marine bird diversity and CINMS boundary concepts........................ 161

Figure 5.2.4. Regression of mean bird diversity and concept area........................................................... 162

Figure 5.2.5. Regression of the area of highest mean bird diversity and concept area ............................ 162

Figure 6.1.1. Survey tracks for the Southwest Fisheries Science Center (SWFSC) 

ship surveys 1991-2001...................................................................................................... 168

Figure 6.1.2. Survey tracks for the Southwest Fisheries Science Center (SWFSC) 

ship surveys 1991-2001 off central and southern California waters ................................... 169

Figure 6.1.3. Survey tracks for the Southwest Fisheries Science Center (SWFSC) ship surveys 

1991-2001 off southern California waters ........................................................................... 171

Figure 6.1.4. Survey effort (km of survey track) for the seven surveys of marine

mammals compiled in the Computer Database Analysis System (CDAS) v2.1.................. 172

Figure 6.1.5. Survey effort (kilometers of survey track) for the seven surveys of marine 

mammals compiled in the Computer Database Analysis System (CDAS) v2.1 off central 

and southern California waters ........................................................................................... 172

Figure 6.1.6. Blue whale. Sightings and group size (where available) from the Southwest 

Fisheries Science Center (SWFSC) ship surveys 1991-2001 and the seven surveys of 

marine mammals compiled in the Computer Database Analysis System (CDAS) v2.1...... 173

Figure 6.1.7. Bottlenose dolphin. Sightings and group size (where available) from the 

Southwest Fisheries Science Center (SWFSC) ship surveys 1991-2001 and the 

seven surveys of marine mammals compiled in the Computer Database Analysis 

System (CDAS) v2.1........................................................................................................... 174

Figure 6.1.8. Bottlenose dolphin (coastal population). Encounter rates (#/km) based on data 

from the Southwest Fisheries Science Center (SWFSC) aerial surveys 1990-2000.......... 174

Figure 6.1.9. Common dolphin (unidentified). Sightings and group size (where available) 

from the Southwest Fisheries Science Center (SWFSC) ship surveys 1991-2001 and 

the seven surveys of marine mammals compiled in the Computer Database Analysis 

System (CDAS) v2.1, 1975-1997........................................................................................ 175

Figure 6.1.10. Long-beaked common dolphin. Sightings and group size (where available) 

from the Southwest Fisheries Science Center (SWFSC) ship surveys 1991-2001 ............ 176

Figure 6.1.11. Short-beaked common dolphin. Sightings and group size (where available) 

from the Southwest Fisheries Science Center (SWFSC) ship surveys 1991-2001 ............ 176

Figure 6.1.12. Gray whale. Sightings and group size (where available) from the Southwest Fisheries 

Science Center (SWFSC) aerial surveys conducted near San Nicolas (1992-1993) 

& San Clemente (1998-2003) islands and the seven surveys of marine mammals 

compiled in the Computer Database Analysis System (CDAS) v2.1, 1975-1997............... 178

Figure 6.1.13. Humpback whale. Sightings and group size (where available) from the 

Southwest Fisheries Science Center (SWFSC) ship surveys 1991-2001 and the 

seven surveys of marine mammals compiled in the Computer Database Analysis 

System (CDAS) v2.1, 1975-1997........................................................................................ 179

Figure 6.1.14. Killer whale. Sightings and group size (where available) from the 

Southwest Fisheries Science Center (SWFSC) ship surveys 1991-2001 and the 

seven surveys of marine mammals compiled in the Computer Database Analysis 

System (CDAS) v2.1, 1975-1997........................................................................................ 181
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Figure 6.1.15. Pacific white-sided dolphin. Sightings and group size (where available) 

from the Southwest Fisheries Science Center (SWFSC) ship surveys 1991-2001 

and the seven surveys of marine mammals compiled in the Computer Database 

Analysis System (CDAS) v2.1, 1975-1997 ......................................................................... 181

Figure 6.1.16. Risso’s dolphin. Sightings and group size (where available) from the 

Southwest Fisheries Science Center (SWFSC) ship surveys 1991-2001 and the 

seven surveys of marine mammals compiled in the Computer Database Analysis 

System (CDAS) v2.1, 1975-1997........................................................................................ 182

Figure 6.1.17. Dall’s porpoise. Sightings and group size (where available) from the 

Southwest Fisheries Science Center (SWFSC) ship surveys 1991-2001 and the 

seven surveys of marine mammals compiled in the Computer Database Analysis 

System (CDAS) v2.1, 1975-1997........................................................................................ 183

Figure 6.1.18. Fin whale. Sightings and group size (where available) from the Southwest 

Fisheries Science Center (SWFSC) ship surveys 1991-2001 and the seven 

surveys of marine mammals compiled in the Computer Database Analysis System 

(CDAS) v2.1, 1975-1997..................................................................................................... 184

Figure 6.1.19. Harbor porpoise. Sightings and group size (where available) from the 

Southwest Fisheries Science Center (SWFSC) ship surveys 1991-2001 and the seven 

surveys of marine mammals compiled in the Computer Database Analysis System 

(CDAS) v2.1, 1975-1997..................................................................................................... 184

Figure 6.1.20. Minke whale. Sightings and group size (where available) from the 

Southwest Fisheries Science Center (SWFSC) ship surveys 1991-2001 and the 

seven surveys of marine mammals compiled in the Computer Database Analysis 

System (CDAS) v2.1, 1975-1997........................................................................................ 185

Figure 6.1.21. Northern right whale dolphin. Sightings and group size (where available) 

from the Southwest Fisheries Science Center (SWFSC) ship surveys 1991-2001 

and the seven surveys of marine mammals compiled in the Computer Database 

Analysis System (CDAS) v2.1, 1975-1997 ......................................................................... 186

Figure 6.2.1. California sea lion. Maximum total individuals (greatest number of individuals 

of all sex and age classes counted for each beach area surveyed out of the three most 

recent SWFSC surveys 2001-2003) for (left to right) Anacapa, San Clemente, San Miguel, 

San Nicolas, Santa Barbara, Santa Catalina, Santa Cruz, and Santa Rosa islands.......... 189

Figure 6.2.2. California sea lion. Maximum pups (greatest number of pups counted for each 

beach area surveyed out of the three most recent SWFSC surveys 2001-2003) for 

Anacapa, San Clemente, San Miguel, San Nicolas, Santa Barbara, and Gull islands....... 190

Figure 6.2.3. Harbor seal. Total number of individuals counted (all sex and age classes) 

at each surveyed site for the 2002 SWFSC census ........................................................... 191

Figure 6.2.4. Harbor seal. Total number of individuals counted (all sex and age classes) 

at each surveyed site for the 2002 CDFG census .............................................................. 191

Figure 6.2.5. Northern elephant seal. Maximum total individuals (greatest number of 

individuals of all sex and age classes counted for each beach area surveyed out of the 

three most recent SWFSC surveys 2001-2003) for San Clemente, San Miguel, 

San Nicolas, Santa Barbara, and Santa Rosa islands........................................................ 192

Figure 6.2.6. Northern elephant seal. Maximum pups (greatest number of pups counted for 

each beach area surveyed out of the three most recent SWFSC surveys 2001-2003) 

for San Clemente, San Miguel, San Nicolas, Santa Barbara, and Santa Rosa islands...... 193

Figure 6.2.7. Southern sea otter. Counts summarized by 20 km shoreline segment for Fall 2001 

and Spring 2002 (right) surveys conducted by CDFG, USGS-Biological Resources 

Division, and the Monterey Bay Aquarium.......................................................................... 194

Figure 6.2.8. Southern sea otter. Expansion of sea otter range in California from 1938 to 1998.............. 195

Figure 6.2.9. Southern sea otter. Three-year moving average of Spring sea otter survey counts since 

1984. Reprinted with permission from the USGS Western Ecological Research Center. .. 195

Figure 7.2.1. Broad-scale distribution of the overlap of fish and invertebrate highly suitable

habitat ................................................................................................................................. 202
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Figure 7.2.2. Overlap of fish and invertebrate highly suitable habitat off southern California ................... 203

Figure 7.2.3. Broad-scale distribution of bird and mammal co-occurrence............................................... 203

Figure 7.2.4. Bird and mammal co-occurrence off southern California ..................................................... 204

Figure 7.2.5. Broad-scale distribution of the composite spatial integration for bird, fish, invertebrate,

and mammal data ............................................................................................................... 204

Figure 7.2.6. Composite spatial integration of bird, fish, invertebrate, and mammal data off

southern California.............................................................................................................. 205
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Figure 1.1.1. Map of the Channel Islands and specific coastal locations in the surrounding region of interest. The red lines indicate the 

current boundaries for the Channel Islands National Marine Sanctuary and the pink line to the north is the southern boundary of the Monterey Bay National Marine Sanctuary.

CHAPTER 1 INTRODUCTION

Chris Caldow, Julie Kellner, M. James Allen, Satie Airamé, Steve Gaines

1.1 Project Background

The National Marine Sanctuary Program (NMSP) is currently updating and revising the management plans for 

each of its 13 sanctuaries. This process, which is open to the public, enables each site to revisit the reasons for 

sanctuary designation and assess whether they are meeting their goals, as well as to set new goals consistent 

with the mandates of the National Marine Sanctuaries Act. Issues raised by the public during this process are 

evaluated and a determination is made as to whether they will be incorporated into the updated plan. Many of 

these issues focus on topics such as the implementation of marine zoning or sanctuary boundary adjustments, 

both of which require information on the distribution of resources within and around the sanctuary. Recognizing 

this, NMSP and NOAA’s National Centers for Coastal Ocean Science (NCCOS) formalized an agreement to collaborate in the revision process by developing such information through a series of biogeographic assessments 

conducted in selected sanctuaries. The resulting products are then supplied to sanctuary managers and staff for 

use in the policy and decision making process. This collaborative effort began along the west coast of the U.S. 

with the Cordell Bank, Gulf of Farallones, and Monterey Bay national marine sanctuaries, and is herein centered 

on the Channel Islands National Marine Sanctuary (CINMS).

The current CINMS boundaries (Figure 1.1.1) were selected to provide adequate protection of local marine 

plants and animals given the nature of adjacent human uses and based on the limited information on the spatial distribution of threats, biota, and habitats that were available in 1980, the year the sanctuary was created. 

However, the CINMS management plan has not been updated since 1983 and new management issues have 

subsequently arisen, as has the availability of pertinent biological information. As a result, CINMS was one of the 

first sanctuaries to begin the management plan review process, which was initiated along with the formation of 

the Sanctuary Advisory Council in 1998. This was followed by a series of seven “public issue” scoping meetings 

along the coast of southern California and Washington D.C. in 1999.
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Three main factors have driven the sanctuary’s interest in considering a change to the CINMS boundaries: 1) 

an emerging understanding of how the sanctuary’s living resources are integrally connected to marine areas 

outside the CINMS boundary, 2) heightened awareness of human activities occurring outside the sanctuary that 

could pose threats to CINMS resources, and 3) high public interest in boundary expansion as expressed clearly 

during the 1999 public scoping meetings. These factors have been considered as the sanctuary’s management 

plan review process has evolved.

The issue of expanding the sanctuary’s boundary was first raised during public scoping meetings held in 1999, 

and has been an issue of continued interest to numerous constituents. A large number of scoping comments 

received suggested that sanctuary boundaries be expanded to incorporate more of the regional marine ecosystem and to allow CINMS to better address management issues associated with coastal watersheds, oil and 

gas development, water quality, and military activity. Other comments received were not in support of boundary 

expansion.

Following the scoping meetings, sanctuary staff worked closely with the Sanctuary Advisory Council and other 

constituents to better understand and assess issues underlying the possible need for expanding CINMS boundaries. In 2000, a literature review was commissioned to help understand the geographic range of ecological linkages among species and habitats found within the sanctuary (McGinnis, 2000). As a result of this assessment, 

the author recommended the area from Point Mugu northward to Point Sal (Figure 1.1.2a) as a connective unit. 

This area is referred to as the Study Area throughout this assessment. 

A range of initial boundary “concepts” then emerged from meetings and workshops held with the Sanctuary Advisory Council in 2000 and 2001. In assisting with the design of boundary concepts, Sanctuary Advisory Council 

members considered the known locations of key or unique habitats, oceanographic processes, marine species, 

marine and coastal human activities, potential threats to sanctuary resources, ease of boundary identification, 

and other factors. The resulting six boundary concepts ranged in scope from the existing CINMS boundary, the 

“No Action Concept” (NAC), to an expansion to the coastal mainland extending from Point Sal in the north to 

Point Mugu to the south (Figure 1.1.2a). 

The current CINMS boundary was designated under the authority of Title III of the Marine Protection, Research 

and Sanctuaries Act of 1972, now known as the National Marine Sanctuaries Act, and has remained unaltered 

since its establishment. The sanctuary is located in the Southern California Bight, 40 kilometers off the coast of 

Santa Barbara, California. It encompasses 3,745 km2 of seawater, and extends from the mean higher high water 

line to six nautical miles offshore around the northern Channel Islands (San Miguel, Santa Rosa, Santa Cruz and 

Anacapa) and Santa Barbara Island (Figure 1.1.1).

Boundary Concept 1 includes the entire Study Area recommended by McGinnis (2000), plus an additional portion over part of the Santa Lucia Bank. As the largest boundary concept, it encompasses the widest range and 

variety of habitats. Human uses encompassed include oil and gas exploration and development, commercial 

and recreational fishing, other types of recreation, harbors, watersheds and military use. This is the only concept 

that includes coastal areas adjacent to harbors. Concept 1a resembles Concept 1, except for the exclusion of 

offshore oil and gas leases and coastal ports and harbors (Figure 1.1.2a). 

Concept 2 incorporates much of the Study Area, and its area contains 62% of Concept 1. Unlike Concepts 1 

and 1a, the mainland coastal component of Concept 2 begins at Gaviota and extends slightly north of Point Sal 

thereby excluding the more urbanized areas of the mainland coast. Unlike the larger boundary concepts, the 

northward boundary of Concept 3 does not incorporate Point Arguello. It extends from the southern boundary of 

Vandenberg Air Force Base, south past Point Conception and east past Cojo Anchorage. The mainland coast 

component of Concept 3 extends to a small fraction of the mainland coastline including Point Conception, without 

overlapping state or federal oil and gas leases and without adjoining any urban coastal areas (Figure 1.1.2a). 

Concepts 4 (Figure 1.1.2a) and 5 (Figure 1.1.2b) include only offshore areas and do not include habitats associated with the mainland coast, such as mainland kelp beds, wetlands, and linkages to coastal watersheds. 

Concept 4 encompasses a larger area than the existing CINMS boundary, providing a contiguous connection 

between the northern Channel Islands and Santa Barbara Island. Concpet 5 is closest among the concepts to 

the existing sanctuary boundary, and essentially squares off the curved sanctuary boundary to aid in boundary 
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Figure 1.1.2a. Spatial delineations for the Study Area and boundary Concepts 1-4. 

122°W

122°W

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120°W

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118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

Point Conception

Point Arguello

Monterey Bay National Marine Sanctuary

Point Sal

Santa Catalina

San Clemente

San Nicolas

Santa Lucia Bank

Point Buchon

Point Mugu

Palos Verde Point

Santa Barbara

Anacapa Santa Cruz

Santa Rosa

San Miguel

Vandenberg AFB

Gaviota Cojo

Anchorage

Study Area

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33°N33°N

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36°N36°N

Point Conception

Point Arguello

Monterey Bay National Marine Sanctuary

Point Sal

Santa Catalina

San Clemente

San Nicolas

Santa Lucia Bank

Point Buchon

Point Mugu

Palos Verde Point

Santa Barbara

Anacapa Santa Cruz

Santa Rosa

San Miguel

Vandenberg AFB

Gaviota Cojo

Anchorage

Boundary Concept 1

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Point Conception

Point Arguello

Monterey Bay National Marine Sanctuary

Point Sal

Santa Catalina

San Clemente

San Nicolas

Santa Lucia Bank

Point Buchon

Point Mugu

Palos Verde Point

Santa Barbara

Anacapa

Santa Cruz

Santa Rosa

San Miguel

Vandenberg AFB

Gaviota Cojo

Anchorage

Boundary Concept 1a

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Point Conception

Point Arguello

Monterey Bay National Marine Sanctuary

Point Sal

Santa Catalina

San Clemente

San Nicolas

Santa Lucia Bank

Point Buchon

Point Mugu

Palos Verde Point

Santa Barbara

Anacapa Santa Cruz

Santa Rosa

San Miguel

Vandenberg AFB

Gaviota Cojo

Anchorage

Boundary Concept 2

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Point Conception

Point Arguello

Monterey Bay National Marine Sanctuary

Point Sal

Santa Catalina

San Clemente

San Nicolas

Santa Lucia Bank

Point Buchon

Point Mugu

Palos Verde Point

Santa Barbara

Anacapa Santa Cruz

Santa Rosa

San Miguel

Vandenberg AFB

Gaviota Cojo

Anchorage

Boundary Concept 3

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33°N33°N

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Point Conception

Point Arguello

Monterey Bay National Marine Sanctuary

Point Sal

Santa Catalina

San Clemente

San Nicolas

Santa Lucia Bank

Point Buchon

Point Mugu

Palos Verde Point

Santa Barbara

Anacapa Santa Cruz

Santa Rosa

San Miguel

Vandenberg AFB

Gaviota Cojo

Anchorage

Boundary Concept 4
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Area 

(km2)

Perimeter 

(km)

Mainland 

Coastline (km)

NAC 3745 653.34 0.00

5 4536 708.72 0.00

4 7981 831.00 0.00

3 9044 903.67 20.32

2 13736 1074.90 140.02

1 22613 1220.10 277.64

1a 22591 1239.23 277.64

SA 17093 1069.52 277.64

identification for enforcement, charting and navigation purposes. Total area and amount of mainland coastline 

are displayed in Table 1.1.1.

In 2001, the Sanctuary Advisory Council was unable to reach a consensus on which of the six boundary concepts to endorse, and the issue remained controversial with a variety of stakeholders. In 2002, following the 

Advisory Council discussions, the NMSP in consultation with the NOAA Administrator determined that there was 

a need to conduct additional data collection and analyses in order to make an appropriately informed decision 

on boundary expansion. In particular, it was determined that a detailed study of the Channel Islands regional 

biogeography was needed and would be conducted by NCCOS. Hence, the revised draft management plan and 

associated Draft Environmental Impact Statement (DEIS) do not contain an analysis of the boundary concepts 

discussed herein.

In 2003, NCCOS was asked by the NMSP to evaluate (from a biogeographic perspective) the six boundary concepts that had been previously developed by sanctuary staff and the Advisory Council, including the NAC. Identifying how these alternatives correspond to the distribution of critical biotic and habitat resources is a necessary 

component of assessing the qualities of one alternative over another. However, it is important to note that this 

biogeographic study is not a decision-making document for NOAA; rather, this study will help inform any future 

decision-making on sanctuary boundary change. 

Currently, the NMSP plans to incorporate and build on this biogeographic study, as well as previous work, to 

prepare a Supplemental Environmental Impact Statement (SEIS) that will present and fully analyze boundary 

change concepts (including the option of not changing the boundary). In compliance with the National Environmental Policy Act (NEPA), the potential environmental and socioeconomic impacts associated with any boundary change will be analyzed in the SEIS and made available for public review and comment. The process will 

be open and transparent to the public, involving significant discussion and input from the Advisory Council and 

other interested agencies and parties. After consideration and incorporation of comments received on the SEIS, 

a final agency determination on sanctuary boundary change will follow. Additional information about the public 

process conducted from 1999-2001 that led to the development of the seven boundary concepts analyzed in 

this report is currently available on the CINMS web site at the following locations: General background on the 

boundary change issue-http://channelislands.noaa.gov/manplan/boundaries.html; Sanctuary Advisory Council 

involvement with this issue-http://channelislands.noaa.gov/manplan/history.html; McGinnis (2000) report-http://

www.channelislands.noaa.gov/manplan/documents.html.

This biogeographic assessment was made possible by a wealth of studies, local assessments (e.g., marine 

reserves analyses), and advancements in remote sensing that have provided a variety of new spatial data that 

can be used to support selection of a boundary. This work complements and builds upon a similar effort recently 

122°W

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33°N33°N

34°N34°N

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36°N36°N

Point Conception

Point Arguello

Monterey Bay National Marine Sanctuary

Point Sal

Santa Catalina

San Clemente

San Nicolas

Santa Lucia Bank

Point Buchon

Point Mugu

Palos Verde Point

Santa Barbara

Anacapa Santa Cruz

Santa Rosa

San Miguel

Vandenberg AFB

Gaviota Cojo

Anchorage

Boundary Concept 5

Figure 1.1.2b. Spatial delineation for boundary Concept 5. 

Table 1.1.1. Total area, perimeter, and amount of 

mainland coastline included for the NAC, Study 

Area, and six boundary concepts. 
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

completed by NCCOS for three sanctuaries in central and northern California (Cordell Bank, Gulf of the Farallones, and Monterey Bay national marine sanctuaries; NCCOS, 2003). The biogeographic assessment for these 

three sanctuaries was conducted to identify important biological zones, time periods, and ecological linkages 

within an area that extends from Point Arena in the north to Point Sal in the south. The overlap in flora and fauna 

as well as the expertise of the research community allowed NCCOS to take advantage of contacts and data 

sources already developed through this earlier work. In addition, a supplemental report was developed for this 

prior assessment which describes the key ecosystems, species, and interactions occurring within that study 

region (Airamé et al., 2003). While the focus of the report was on central and northern California, it describes 

the surrounding regions as well. As a result, much of the current study draws on the information gathered and 

analyses conducted as part of that effort. With the addition of this report and biogeographic assessment, there is 

now integrated biogeographic information compiled for the California coastline from Pt. Arena south to the U.S. 

– Mexico border. This framework provides for future broad-scale analysis that goes well beyond the boundaries 

of individual sanctuaries and provides a strong foundation for managing sanctuaries not as isolated areas but as 

a network of interconnected habitats.

This assessment was conducted for the marine waters surrounding California’s Channel Islands and represents the culmination of a 24-month collaboration between NCCOS and CINMS. It was greatly assisted by the 

generous support of time and data provided by numerous researchers along the entire west coast. While the 

immediate focus of this assessment was to evaluate a series of boundary expansion concepts for the sanctuary, a biogeographic study such as this one should help to inform managers who need to make other spatially 

explicit management decisions for this region. Additionally, this assessment represents a summary of existing 

comprehensive, large-scale data sets. Missing Taxa or areas not covered may provide the driving force for future research necessary to fill these gaps. This assessment only considers biological, geological, and physical 

oceanographic data, and does not include other boundary analysis criteria (e.g. socioeconomics, management 

feasibility) that will be utilized by the NMSP management to make the ultimate decision in selecting a boundary 

alternative from the SEIS.

1.2 Introduction to Biogeography

Biogeography is the study of the geographic distribution of species. More specifically, it is the study of the relationship of species’ distribution patterns relative to geographical differences in the environment. It focuses 

on large-scale patterns in species distributions and classifies them into biogeographic regions, provinces, and 

life zones. Biogeographic regions are related to global climatic zones, with latitudinal differences in ranges of 

temperature, day length, and primary production. These are all important variables affecting distribution. Biogeographic provinces are biotically distinct geographic areas within a biogeographic region, and hence have similar 

ranges of day length and temperature but are distinct in other environmental characteristics. Life zones in the 

ocean generally represent major changes of environmental conditions (e.g., estuarine, coastal, open ocean) 

or bathymetric zones (with decreasing temperature and ambient light and increasing pressure occurring with 

increasing depth) (Hedgpeth, 1957; Allen and Smith, 1988; Allen, in press). Biogeographic provinces and life 

zones are adaptive, in that species living there must have specific adaptations to the environmental characteristic of the province or zone (e.g., to temperature range, seasonality of production, bathymetric pressure, light 

levels, etc.). Hence, the biota of these provinces and zones has developed over evolutionary time (Briggs, 1974; 

Allen, 1982a; Briggs, 1995). 

Throughout a biogeographic province, one might expect to find the same set of species occurring in a given 

habitat in a given life zone (Allen, 1982a; b). Similarly in an adjacent biogeographic province, one would find a 

somewhat different set of species in the same habitat in the same life zone (Allen, in press). While some species would be unique to each province, some broadly ranging species would occur in both provinces (Allen and 

Smith, 1988). Where two adjoining provinces (or life zones) meet, there is an ecotonal region where species 

common in each are found in lower abundance. With distance from the ecotone center these incidental species 

become less important and predictable to a community in a given habitat. 

It is important to understand the relative fidelity to and abundance of a species in a specific area. The distribution 

of biogeographic provinces has been relatively stable since the last ice age, although the location of boundaries between provinces varies somewhat with large-scale periodic and aperiodic climate changes (e.g., Pacific 

decadal oscillation, El Niño) (Allen et al., 2004). A species is typically more common and abundant within the 
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

main part of the biogeographic province(s) where it occurs. It occurs less frequently with greater variation of 

abundance near the end or outside of its typical biogeographic province (Andrewartha and Birch, 1954). 

Biogeographic assessments are important because they focus on the large-scale distribution of species rather 

than on local occurrences of species and hence provide the basis for predicting biota for a given habitat within 

a biogeographic province. Assessments of specific species involves mapping nursery grounds, spawning areas, 

feeding areas, migratory routes, and areas where they are fished (NOAA, 1990). This provides valuable information for determining essential habitat for protection. In addition to the information these assessments provide 

on a single species, they can provide information on the distribution of species diversity and richness of biota. 

Furthermore, these assessments help to identify which species form assemblages or communities, and how 

population and community measures, such as species diversity and richness, vary in the region. 

Biogeographic assessments are useful to coastal managers because they provide a basis for determining components of the biota that are typical of an area and are appropriate for management of species or habitats. Local 

assemblages are composed of species that are representatives of the biogeographic community and species 

that are incidental to the area (Allen, 1982a; b). In the former case, because of their persistence in the population, representative species can be more readily managed. In the latter case, incidental species are likely to vary 

greatly over time, either by chance or in response to climatic change, making management less likely to be successful in the long term. 

1.3 Biogeography of the West Coast

A number of biogeographic provinces and life zones occur along the California coast. There are two coastal biogeographic provinces: Oregonian and San Diegan. The Oregonian Province primarily extends from southeastern 

Alaska to Point Conception, and is part of the Eastern Boreal Pacific Region (Briggs, 1974; 1995). The Oregonian Province also extends southward beyond Point Conception along the outer islands of southern California, 

and in part reappears in upwelling areas off Baja California (Hubbs, 1949). The San Diegan Province (part of 

the warm-temperate California region, which also includes the Cortez Province of the Gulf of California) extends 

from Point Conception, California to Magdalena Bay, Baja California Sur (Briggs, 1974). However, in warm-regime years, some San Diegan species extend their ranges northward. Offshore are two provinces of the coldtemperate Oceanic Boreal Pacific Region. Offshore are two provinces of cold-temperate Ocean Boreal Pacific 

Region (McGowan, 1971). The Subarctic Province extends south along the California coast to Cape Mendocino, 

and the Transition Zone extends south from Cape Mendocino to Magdalena Bay. 

Several pelagic and benthic life zones occur in this region (Allen and Smith, 1988). Pelagic life zones consist of 

the Neritic Zone (water column over shelf to 200 m isobath) and three oceanic zones (over slope and basins): 

Epipelagic Zone (surface to 200 m); Mesopelagic Zone (200-1000 m); and Bathypelagic Zone (1000-4000 m; 

Figure 1.3.1). Benthic life zones (Allen, In press) include Intertidal, Inner Shelf (0-30 m), Middle Shelf (30-100 m), 

Outer Shelf (100-200 m), Mesobenthal (Upper) Slope (200-500 m), and Bathybenthal Slope (500-1000 m; Figure 

1.3.2). A separate Estuarine Zone consists of both water-column and benthic species (Hedgpeth, 1957).

Coastal biogeographic provinces differ in their distribution with depth, with the Oregonian Province extending 

further south with each successive benthic life zone (Allen, In press). In some cases, submergence occurs, with 

species occurring in shallow depth zones in central and northern California occurring in deeper life zones in 

southern California (Hubbs, 1949; Allen, In press).

The Channel Islands lie at the intersection between the warm-temperate San Diegan biota and the cold-temperate Oregonian biota. The California Current (which largely defines the California part of the Oregonian Province), 

flows south on the outer edge of the Southern California Bight (SCB) below Point Conception as the coast of 

southern California turns abruptly eastward. This current intersects the coast near Cape Colnett, Baja California 

(forming the southern end of the SCB). Part of the current flows north into the SCB, forming a large eddy, with 

warm water dominating the inner part of the SCB. This warm water zone comprises the southern California part 

of the San Diegan Province, whereas the outer islands of the SCB (San Miguel, Santa Rosa, and San Nicolas 

islands) largely have an Oregonian biota. Santa Cruz Island (eastern part), Anacapa Island, Santa Barbara 

Island, Santa Catalina Island, and San Clemente Island largely have a San Diegan biota (particularly the latter 

two islands). In a recent report by Airamé et al. (2003) describing the biogeography and ecological linkages of 
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

marine and estuarine ecosystems of central and northern California, a range endpoint analysis was conducted 

on eastern Pacific marine invertebrates, fishes, birds, and mammals to look for biogeographic breaks in their 

distributions along the west coast of North America. The transition zone between the Oregonian and San Diegan 

provinces, which is located within the region of interest, is emphasized as a result of these analyses. A short 

discussion of the results is presented below. 

Latitudes where the northern or southern extent of many species’ ranges end often corresponded to major 

oceanographic features. For example, at Point Conception (a known biogeographic boundary described above), 

the cool water of the California Current intersects with the relatively warm water of the California Countercurrent, 

which flows north along the coast of southern California. These areas were highlighted graphically in Figures 

1.3.3-1.3.6, where the longer bars equate to a greater number of species with range termini at the given latitude. The portion of the graphs enclosed by a black box highlight range endpoints within the region of interest. 

Analyzing latitudinal trends in this manner is a common technique applied to examine patterns of distribution, 

diversity, and structure in marine populations (Horn and Allen, 1978; Roy et al., 1994; Dawson, 2001). This type 

of information can, in turn, be used to identify distinct regions or transitional zones in the marine environment 

and allow managers a better understanding of their resources when making informed place-based management 

decisions.

Marine Benthic Invertebrates

The database used in this analysis (Figure 1.3.3) included 539 species of marine benthic invertebrates from the 

coast of California (G. Eckert, unpublished data). Information about each species was gathered from the primary 

literature and included the northern and southern range endpoints to the nearest 0.5° latitude. Results indicate 

significant transitions in fauna occurring at San Diego (32.5°N), the Channel Islands/Pt. Conception (33-34.5°N), 

Figure 1.3.1. Pelagic life-zones off southern California. 
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122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

0 50 100

Kilometers

Benthic Life Zones

Inner Shelf (0-30 m)

Middle Shelf (30-100 m)

Outer Shelf (100-200 m)

Mesobenthal Slope (200-500 m)

Bathybenthal Slope (>500 m)

Figure 1.3.2. Benthic life-zones off southern California. 

and Monterey Bay (36.5°N) with additional biogeographic breaks at Vancouver Island (49.5oN) and the Aleutian 

Islands (53°N). The large number of range endpoint peaks (both northern and southern) within the region of 

interest indicate that this is a transition zone where southern species are reaching their northern limits and are 

being replaced by northern species at their southern limits. Historical studies also support the findings shown 

here. Major barriers for eastern Pacific mollusks from Alaska to Baja California were found to occur at Vancouver 

Island (48-49°N), the northern Channel Islands (34.4°N), and Punta Eugenia (28.2°N) (Roy et al., 1994). Within 

California, Point Conception and Monterey Bay are recognized as biogeographical boundaries for ascidians, 

crabs, and shallow-water benthic mollusks (Hayden and Dolan, 1976; Valentine, 1966). Within southern California Newell (1948) found concurrent range endpoints at both San Clemente Island (33°N) and the northern 

Channel Islands (34°N) for marine mollusks. 

Marine Fishes

Northern and southern range endpoints of 294 Pacific coast fishes obtained from Eschmeyer et al., (1983) are 

shown to the nearest 0.5° latitude (Figure 1.3.4). The overwhelming majority of range endpoints occur along 

the central and southern California coasts. The four major biogeographic transitions starting in the south occur at San Diego (32.5°N), the Channel Islands/Pt. Conception (33.5-34°N), Monterey Bay (36.5°N) and finally 

San Francisco/Point Reyes (37.5°N). A few minor shifts in species composition occurred at Cape Mendocino 

(40.5°N), Vancouver Island (49.5°N), the Aleutian Islands (54°N), Kodiak Island (57.5°N) and Prince William 

Sound (60.5°N). As in the case of benthic marine invertebrates, the large number of range endpoint peaks (both 

northern and southern) within the study area indicate that this is a transition zone where southern species are 

reaching their northern limits and are being replaced by northern species at their southern limits. The two dominant orders within this sample of fishes, Perciformes (N=122) and Scorpaeniformes (N=78), exhibit different biogeographic patterns. Perciform fishes are generally distributed south of Point Reyes, which is the most distinct 
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biogeographic transition among members of this taxon. In contrast, Scorpaeniform fishes are distributed widely 

along the western coast of North America, from Baja California to the Bering Sea. A comprehensive analysis of 

the distribution of 500 species of marine fishes conducted by Horn and Allen (1978) also supports the results 

found here. The authors identify Point Conception as the most significant biogeographic boundary which could 

extend as far south as 30°S latitude. The authors note that Point Conception appears to be a more distinct 

boundary for southern species than northern species, which is consistent with results presented here.

Seabirds and Shorebirds

Distribution information used for this analysis was extracted from Peterson (1990), and included 132 shorebird 

and seabird species (Figue 1.3.5). Information on northern and southern range endpoints were summarized into 

2° latitudinal bins. The resulting histogram shows small breaks in central and southern California. Central California is the northern endpoint for the distribution of four species (black-vented shearwater, least bittern, black 

storm-petrel, and clapper rail) and the southern endpoint for five species (glaucous gull, fork-tailed storm-petrel, 

Barrow’s goldeneye, harlequin duck, and yellow-billed loon). While southern California is the northern endpoint 

for only three species (gull-billed tern, royal tern, and least storm-petrel), it is the southern endpoint for eight 

species (horned and red-necked grebes, mew gull, black scoter, common murre, pigeon guillemot, tufted puffin, and marbled murrelet). Overall, the distributions of most seabird and shorebird species found in the region 

of interest were wide ranging. Most of the northern range limits occurred in the Gulf of Alaska and Bering Sea, 

whereas most of the southern range limits occurred in Mexico. Although the coast of California does not present 

a significant biogeographic barrier for most seabirds and shorebirds, nearly ten percent of the species examined 

had a range terminus near southern California. 

Southern Range Endpoint (N=248)

North Range Endpoint (N=536)

Number of Species

35

45

55

60

50

40

100 50 0 50 100 30

Latitude

Figure 1.3.3. Latitudinal range endpoints for 539 species of marine benthic invertebrates. 
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Marine Mammals

49 marine mammal species were included in this range endpoint analysis. Information about each species was 

obtained from Burt and Grossenheider (1976) and included the northern and southern range endpoints in 2° latitudinal bins (Figure 1.3.6). The most significant boundary in California occurs near Point Conception. A few delphinid species, including the melon-headed whale, pygmy killer whale, false killer whale, short-finned pilot whale, 

and striped dolphin are found primarily south of this promontory, while others such as the northern right whale 

dolphin, Dall’s porpoise, harbor porpoise, Hubb’s beaked whale, and Stejneger’s beaked whale are found primarily north. This represents over twenty percent of the species examined in this study which is significant given 

that local oceanographic patterns and habitat features generally do not constrain the distributions of large marine 

mammals. The majority of marine mammals examined however, were widely distributed along the western coast 

of North America. Pinnipeds also exhibited wide distributions from Alaska to central or southern California and 

Baja California with no biogeographic breaks occurring in the region. Harbor seals are widespread in coastal 

habitats of the northern hemisphere. California sea lions are found from Vancouver Island to the southern tip of 

Baja California. Most of the population of Steller sea lions is in the Gulf of Alaska and the Bering Sea, but small 

populations are found along the coast as far south as central California. Northern elephant seals are distributed 

from the Aleutian Islands to Baja California. Although most of the worldwide population of northern fur seals is 

found on the Pribilof Islands, a small number of northern fur seals are found on Bogoslof Island in the southern 

Bering Sea, San Miguel Island off southern California, and the Farallon Islands off central California.

Southern Range Endpoint (N=135)

North Range Endpoint (N=293)

35

45

55

65

60

50

40

30

Number of Species

40 30 20 10 0 10 20 30

Latitude

Figure 1.3.4. Latitudinal range endpoints for 294 species of marine fishes. 
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40 30 20 10 0 10 20 30

24

28

32

36

40

44

48

52

56

60

64

Number of Species

Latitude

Southern Range Endpoint (N=65)

North Range Endpoint (N=89)

Figure 1.3.5. Latitudinal range endpoints for 132 shore and seabird species.

1.4 The Four-step Assessment Process

Species Selection

The initial step in the biogeographic assessment presented in the following chapters involved the identification 

of key species and the collection of relevant biological and physical data sets in the region of interest necessary 

to conduct spatial analyses. Recently, the state of California underwent a process to evaluate the region around 

the Channel Islands in order to determine which areas would be delineated as marine protected areas (MPA). 

One component of that process involved the identification of species whose distributions would be the biological 

focus of the decision. A working group was formed that developed a set of criteria to define species of interest 

around the Channel Islands. The list of species selected with these criteria includes: (1) species of economic 

and recreational importance, (2) keystone or dominant species, (3) candidate, proposed, or species listed under 

the Endangered Species Act, (4) species that have exhibited long-term or rapid declines in harvest and/or size 

frequencies, (5) habitat forming species, (6) indicator or sensitive species, and (7) important prey species. The 

list excludes species that are: (1) incidental, (2) at the edge of their ranges, or (3) highly migratory. The criteria 

by which the species were selected for the MPA effort were equally relevant for an analysis of the regional biogeography. However, the final species list for the biogeographic assessment was shortened, primarily for fish and 

invertebrates, due to the lack of sufficient spatial biological data.

Data Collection and Synthesis

Over 50 researchers along the west coast from federal and state agencies, non-governmental organizations, and 

academia were contacted in an effort to assemble all existing distributional data pertinent to the species selected 

above as well as their associated habitats. Once a data set was identified its utility was evaluated through exami-
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

24

28

32

36

40

44

48

52

56

60

64

Southern Range Endpoint (N=11)

North Range Endpoint (N=32)

10 8 6 4 2 0 2 4 6 8

Number of Species

Latitude

Figure 1.3.6. Latitudinal range endpoints for 49 marine mammal species.

nation of its spatial extent and quantity of information provided. As this study was dependent on pre-existing data 

rather than the collection of data specific to the questions asked, the type and quality of information collected 

was extremely variable from one data set to the next. Among the complexities of working with these inherently 

variable data were varying spatial and temporal coverages, as well as different methodologies employed in data 

collection. To the extent that differences precluded data sets from being combined and analyzed together, they 

were kept separate. Appendix B lists the data sets used in this assessment (as well as some that were identified, 

but not used) and the contact information for the data providers.

Broad-scale patterns were identified in the distribution of taxa based on species presence and absence, as well 

as abundance information where available. This step began by combining each unique data set into a common 

spatial framework within a Geographical Information System (GIS). An aggregate look across multiple species 

was conducted through examination of community metrics such as diversity and richness. In some cases, data 

were sufficient to perform clustering analyses to examine the co-occurrence of species at various locations. 

Patterns in the analyses conducted were then set in the context of the physical data layers (oceanography, 

bathymetry, and sediment). These layers were also utilized for modeling the potential distributions of specific 

invertebrates and fishes as existing data on individual species within those taxa was insufficient.

The next step in the process was the evaluation of the six boundary concepts with respect to resource distribution. 
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Metric Development

The choice of an appropriate metric for comparison of the different boundary concepts is a difficult one, and involves implicit value judgments. Since such judgments are policy decisions, and inherently beyond the scope of 

a biogeographic assessment, we have chosen to present three separate metrics along with a discussion of their 

biases and implied values. In each chapter, we present an absolute metric (count), a relative metric (density or 

mean), and the Optimal Area Index (OAI) for each boundary alternative. The absolute and relative metrics are 

provided because they are simple and intuitive. However, because these two metrics show biases for larger and 

smaller alternatives respectively, we have also chosen to present the OAI (explained below), which attempts to 

balance these two tendencies. None of these metrics is objectively better than another, and a thorough comparison of the boundary concepts will require consideration of all of them. 

A fundamental distinction can be made between metrics which are based on absolute quantity and those based 

on relative quantity. Examples of absolute metrics include the total number of blue whale observations recorded 

in boundary Concept 5, or the total area of above average bird density falling within the current CINMS boundaries. Examples of relative metrics include the number of blue whale observations/km2

 recorded in boundary 

Concept 5, or the average bird density within the current CINMS boundaries. Although the difference in wording 

is subtle, under many circumstances the results of absolute and relative metrics can be completely opposite. 

Consider a situation (illustrated in Figure 1.4.1) in which the area of greatest conservation value is concentrated 

in one location and that value declines with distance from this center. A set of hypothetical boundary concepts 

exist such that each boundary is centered on the location of highest conservation value, and each successively 

larger boundary encompasses the smaller. In this situation, absolute metrics will inherently favor the largest 

boundary. This is because, for absolute metrics, more is necessarily better (or at least no worse) when the 

smaller options are a subset of the larger ones. In our hypothetical example, relative metrics will inherently favor 

the smallest boundary. Since all boundaries are centered on the region of highest conservation value, expanding from the smallest can only add areas of relatively lower conservation value, thus reducing the magnitude of 

relative metrics such as means or densities. These relationships are illustrated in Figure 1.4.2.

For many of the species and community metrics discussed in this assessment, the hypothetical example above 

is an apt description of the situation. The current boundary of the CINMS was chosen in part because for many 

species it encompasses an area of optimal habitat. The smaller boundary concepts are also generally subsets 

of the larger concepts, with all options encompassing the current boundaries. To the extent that each species or 

community metric matches the hypothetical situation, absolute metrics will be biased toward the larger boundary 

concepts and relative metrics will favor the smaller.

Because of the inherent biases of absolute and relative metrics, we have included a third metric which attempts 

to provide a more balanced gauge of the relative merits of different boundary concepts. This third metric (the 

OAI) represents the relative increase in some measure of ecological value, divided by the relative increase in 

area compared to the current boundaries. The OAI is calculated using the formula:

 OAI=(B1-B0/B0)/(A1-A0/A0)

where B1 and B0 refer to the value of the ecological metric (e.g. sightings, diversity, richness, etc.) within the 

boundary concept and the current boundaries respectively, and A1 and A0 are the respective areas. In the OAI, 

the terms representing the difference in ecological value (numerator) and the difference in area (denominator) 

are both calculated relative to the current boundaries. This provides some balance against the previously discussed biases, but may not eliminate them entirely. 

Maps and spatial metrics of the boundary concepts evaluated using the above metrics are provided in Figure 

1.1.2. After evaluating the six boundary concepts provided by the sanctuary, the data was further examined to 

determine if areas of high biological significance within the study region were absent from those options but 

should be considered as candidate regions for incorporation.

Analyses Review

All analyses completed as part of the biogeographic assessment were reviewed. All data providers, together 

with others familiar with the data sets, and selected members of the CINMS Sanctuary Advisory Council, were 

consulted to obtain consensus on the analytical methodology utilized and to ensure accurate interpretation of 

the resulting patterns.
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

1.5 Assessment Outline

This assessment begins with a discussion of the physical setting (Ch. 2). The 

study area is described in this section 

in terms of the physical environment 

(geology, climate and meteorology, 

physical oceanography). Included in 

this chapter is a discussion of regional sea surface temperature patterns, 

chlorophyll, currents, and bathymetry 

as they are related to the boundary 

concepts. This places into context the 

four subsequent analytical chapters: 

marine invertebrates (Ch. 3), fishes 

(Ch. 4), birds (Ch. 5), and marine mammals (Ch. 6). Where data was sufficient, each of these chapters includes 

an analysis of community structure as 

well as a look at the individual species 

identified by the sanctuary as being of 

high importance. The marine mammal 

chapter is further refined with a section on pinnipeds and sea otters and 

another on cetaceans. Each chapter 

includes four major sections. The first 

section describes in detail the data 

and methodology used in the analysis 

of that particular taxa. The second includes an analysis of broad-scale patterns looking over the entire range for 

which data was available in the given 

data set. Following this, the focus is 

on the study area and the boundary 

concepts. Finally, a summary section 

discuses the resulting patterns uncovered in the analyses. Chapter 7, the 

integration, summarizes all the results 

and looks across all taxa for consistent 

patterns and contains an evaluation of 

how the different boundary concepts 

compare.

Figure 1.4.2. Trend in values of absolute and relative metrics and the OAI 

(rescaled for display) for the hypothetical example shown in Figure 1.4.1.

Figure 1.4.1. A hypothetical set of three boundary concepts (yellow lines), and the 

ecological value (red circles, with darker colors representing greater values) of the area 

contained within them.
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Literature Cited

Airamé, S., J.E. Dugan, K.D. Lafferty, H. Leslie, D.A. McArdle and R.R. Warner. 2003. Applying ecological criteria to marine 

reserve design: a case study from the California Channel Islands. Ecological Applications 13(1):S170-S184.

Allen, M.J. 1982a. Functional structure of soft-bottom fish communities of the southern California shelf. Ph.D. Dissertation. 

University of California, San Diego, California. 577 pp.

Allen, 1982b. Large-scale considerations in studies of resource partitioning. pp. 185-189. In: G. M. Cailliet and C. A. Simenstad (Eds.), Gutshop ‘81, Fish food habits studies. Washington Sea Grant, University of Washington, Seattle, WA.

Allen, M. J. In press. Continental shelf and upper slope. In: L. G. Allen, M. H. Horn, and D.J. Pondella, II (Eds.), Ecology of 

marine fishes: California and Baja California. University of California Press, Berkeley, CA.

Allen, M.J., and G.B. Smith. 1988. Atlas and zoogeography of common fishes in the Bering Sea and northeastern Pacific. 

NOAA Technical Report. NMFS 66. 151 pp.

Allen, M.J., R.W. Smith, E.T. Jarvis, V. Raco-Rands, B. Bernstein, and K. Herbinson. 2004. Temporal trends in southern 

California coastal fish populations relative to 30-year trends in oceanic conditions. pp. 264-285 In: S. B. Weisberg and D. 

Elmore (Eds.), Southern California Coastal Water Research Project Biennial Report 2003-2004, Southern California Coastal 

Water Research Project, Westminster, CA.

Andrewartha, H.G., and L.C. Birch. 1954. The distribution and abundance of animals. University of Chicago Press, Chicago, 

IL.

Briggs, J.C. 1974. Marine Zoogeography. McGraw-Hill Book Company. New York, NY. 475 pp.

Briggs, J.C. 1995. Global biogeography. Elsevier, New York, NY. 454 pp.

Burt, W.H., and R.P. Grossenheider. 1976. A field guide to the mammals. 3d ed. Houghton Mifflin Co, Boston, MA. 289 pp.

Dawson, M.N. 2001. Phylogeography in coastal marine animals: a solution from California? Journal of Biology 28:723-

736.

Eckert, G. Unpublished data. University of Alaska. 

Eschmeyer, W.N., E.S. Herald, and H. Hammann. 1983. A field guide to Pacific coast fishes of North America. Houghton 

Mifflin Company, Boston, MA. 336 pp.

Hayden, B.P., and R. Dolan. 1976. Coastal marine fauna and marine climates of the Americas. Journal of Biology 3:71-81.

Hedgpeth, J.W. 1957. Classification of marine environments. pp: 17-27. In: Hedgpeth, J.W. [Ed.] Treatise on marine ecology 

and paleoecology. Geological Society of America Memoranda 67(1).

Horn, M.H, and L.G. Allen. 1978. A distributional analysis of California coastal marine fishes. Journal of Biology 5:23-42.

Hubbs, C.L. 1949. Changes in the fish fauna of western North America correlated with changes in ocean temperature. Sears 

Foundation Journal of Marine Research 7(3):459-487.

McGinnis, M.V. 2000. A Recommended Study Area for the CINMS Management Planning Process: Ecological Linkages in 

the Marine Ecology from Point Sal to Point Mugu, including the Marine Sanctuary. Prepared for Channel Islands National 

Marine Sanctuary by the University of Claifornia, Santa Barbara. 50 pp. 

:

McGowan, J.A. 1971. Oceanic biogeography of the Pacific. pp: 3-74, In: B.M. Funnell and W. R. Riedel (Eds.), The micropaleontology of oceans. Cambridge University Press, Cambridge, England.

Newell, I.M. 1948. Marine molluscan provinces of western north America: a critique and a new analysis. pp: 155-166, In: 

Proceedings of the American Philosophical Society 92:155-166.

National Oceanic and Atmospheric Administration (NOAA). 1990. West Coast of North America strategic assessment: Data 

atlas, invertebrate and fish volume. Prepublication edition. Strategic Assessments Branch, National Ocean Service, NOAA. 

Rockville, MD. 112 pp.
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NOAA National Centers for Coastal Ocean Science (NCCOS). 2003. A biogeographic assessment off north/central California: To support the joint management plan review for Cordell Bank, Gulf of the Farallones, and Monterey Bay National 

Marine Sanctuaries: Phase 1 - Marine fishes, birds, and mammals. Silver Spring, MD. 145 pp.

Peterson, R.T. 1990. A field guide to western birds, 3rd Ed. Houghton Mifflin Company, New York, NY.

Roy, K.,D. Jablonski, and J. W. Valentine. 1994. Eastern Pacific molluscan provinces and latitudinal diversity gradient: No 

evidence for “Rapoport’s Rule.” pp: 8871-8874. In: Proceedings of the National Academy of Science 91.

Valentine, J.W. 1966. Numerical analysis of marine molluscan ranges on the extratropical northeaster Pacific shelf. Limnology and Oceanography 11: 198-211.
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CHAPTER 2 PHYSICAL AND OCEANOGRAPHIC SETTING

Julie Kellner, John Christensen, Randy Clark, Chris Caldow, Michael Coyne

2.1 Physical Environment and Geology

The following sections provide a brief overview of the physical and oceanographic environment for the region 

of interest. Much of the material is excerpted or summarized from the Draft Environmental Impact Statement for 

Channel Islands Marine Sanctuary (CINMS, 2000) and Ecological Linkages: Marine and Estuarine Ecosystems 

of Central and Northern California (Airamé et al., 2003a) reports. This section describes the physical, climatic 

and oceanographic setting near the Channel Islands and supplements the subsequent analytical chapters which 

provide spatially-articulated assessments of both dynamic and static habitats of the region.

Figure 2.1.1 shows the geologic and bathymetric features for the region of interest. The four northern Channel 

Islands (San Miguel, Santa Rosa, Santa Cruz and Anacapa) parallel the east-west trend of the coast, and vary 

from 20 to 40 km offshore. Santa Barbara Island lies about 64 km south of Point Mugu, California. These islands 

are all located within a unique oceanographic region known as the Continental Borderland (Norris and Webb, 

1990).

The Continental Borderland is located offshore of California between Point Conception and Punta Banda in Baja 

California (Mexico). Continued large-scale overriding of the North American Plate by the Pacific Plate in southern 

California caused movement along the San Andreas Fault System (Dailey et al., 1993). The Continental Borderland, with its wide shelf (up to 483 km seaward) and series of laterally shifted blocks, resulted from this movement 

(Dailey et al., 1993). Unlike most wide continental shelves that consist of gently sloping platforms interrupted by 

low banks and occasional canyons, the Continental Borderland is a region of basins and elevated ridges. The 

Channel Islands are the portions of the ridges that rise above sea level. The highest point in the Channel Islands 

is Picacho Diablo on Santa Cruz Island, with an elevation of 747 m. The seaward edge of the Continental Borderland is the Patton Escarpment, a true continental slope that descends 4,000 m to the deep ocean floor (Norris 

and Webb, 1990). Basin slopes account for 63% (49,753 km2) of the borderlands area (Norris and Webb, 1990). 

Basin floors represent 17% of the total area (13,260 km2), while the islands only comprise 1.1% of the total (880 

Figure 2.1.1. Bathymetric and geologic features of the region of interest.
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

km2). The basins nearest the mainland have the shallowest depths, flattest floors and thickest sediment fills and 

range in depth from 500 to 2,500 m. 

The Santa Barbara-Ventura Basin, oriented east-west in parallel with the coastline and the islands, lies between 

the islands and the mainland, and is approximately 500 m deep, 297 km long, up to 88 km wide, and approximately 13,000 km2

 in area. This basin is located within the Transverse Ranges Province, which also includes 

the Santa Ynez and Santa Monica Mountains, and the Channel Islands. The submerged portion of the Santa 

Barbara-Ventura Basin, an area of 5,179 km2 and approximately 97 km in length from Point Conception to Port 

Hueneme, is commonly referred to as the Santa Barbara Channel. The western entrance to the channel is approximately 97 km wide with a sill depth of about 450 m, whereas the Anacapa Passage, the eastern entrance to 

the channel, is more limited with a width of 19 km and a shallower sill depth of 200 m (Dever, 2004). The remaining basins in the Southern California Bight, such as the Santa Cruz Basin to the south of the northern Channel 

Islands and the Santa Monica-San Pedro Basin to the southeast, trend northwest-southeast. 

North of the Continental Borderland, the offshore Santa Maria Basin abuts the Santa Barbara-Ventura Basin at a 

location known as the “Amberjack High”. This basin extends north-northwest from Point Conception to Point Buchon and is approximately 160 km in length, 16 to 80 km wide, and approximately 7,769 km2

 in size. It is bounded 

on the east by the Hosgri and related fault zones, and on the west by Santa Lucia Bank.

There are at least 32 submarine canyons in the Continental Borderlands. Along the mainland coast are six prominent canyons that are thought to be related to the modern shoreline. Other coastal canyons appear to be related 

to the shoreline and lower sea levels during the Ice Age that ended approximately 12,000 years ago (Norris and 

Web, 1990). There are also canyons cut into offshore basins in the region (Dailey et al., 1993).

2.2 Climate and Meteorology

Santa Barbara County has a Mediterranean climate characterized by mild winters, when most rainfall occurs, 

and warm, dry summers. The regional climate is dominated by a strong and persistent high-pressure system that 

frequently lies off the Pacific coast (generally referred to as the Pacific High). The Pacific High shifts northward 

or southward in response to seasonal changes or the presence of cyclonic storms. In its usual position to the 

west of Santa Barbara County, the Pacific High produces an elevated temperature inversion. Coastal areas are 

characterized by early morning southeast winds, which generally shift to northwest later in the day. Transport of 

cool, humid marine air onshore by these northwest winds causes frequent fog and low clouds near the coast, 

particularly during night and morning hours in the late spring and early summer months.

The terrain around Point Conception, combined with the change in orientation of the coastline from north-south 

to east-west, can cause counterclockwise circulation (eddies) to form east of the point. These eddies fluctuate 

from time to time and place to place, leading to highly variable winds along the southern coastal strip. Point Conception also marks the change in the prevailing surface winds from northwesterly to southwesterly.

During the fall and winter months, the region is subject to Santa Ana winds, which are warm, dry, strong, and 

gusty winds that blow northeasterly from the inland desert basins through the mountain valleys and out to sea. 

Wind speeds associated with Santa Ana conditions are generally 24 to 32 km/h, although they can reach speeds 

in excess of 96 km/h.

2.3 Physical Oceanography

The oceanography in the Study Area is closely tied to the processes of the California Current System which 

forms the eastern portion of the clockwise North Pacific Subtropical Gyre and extends 3,000 km from the Straight 

of Juan de Fuca (Vancouver Island) to Baja California Sur. The California Current is predominantly a wind-driven 

system and encompasses three major currents: the equatorward California Current, the poleward California 

Undercurrent and the poleward Southern California Countercurrent (which occasionally combines with the Davidson Current north of Point Conception). 

In the Study Area, currents in the Santa Barbara Channel include patterns of warm, saline water from the Southern California Countercurrent and the colder water from the California Current. Upwelling often occurs where 

these water masses meet near the headlands of Point Arguello and Point Conception, as well as along much 
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of the California coast, depending on the season. Upwelling plumes expand southward from headlands and 

frequently enter the Santa Barbara Channel on the southern side of the western mouth (Atkinson et al., 1986). 

There can be a channelwide response to upwelling north of Point Conception (Auad et al., 1998). Oceanographic 

thermal fronts are abundant in the Santa Barbara Channel and form as a consequence of upwelling and current 

shear between the two primary currents (Harms and Winant, 1998).

Offshore Ocean Currents

Offshore circulation in the Study Area is dynamic and results from the interaction of large-scale ocean currents, 

local geography, and the unique basin and ridge topography of the ocean bottom in the Southern California 

Bight. The prevailing wind system of the North Pacific Ocean is the mid-latitude Westerlies, a belt of winds that 

blow from west to east between 30oN and 60oN. These westerly winds create the North Pacific Current that 

pushes water away from Asia towards the west coast of North America. As this trans-Pacific flow converges 

toward the North American coastline, it is deflected equatorward forming the eastern boundary of the California 

Current. This surface current is dominant year round, and appears as a slow, broad southeastern flow that transports cool, fresh, nutrient and oxygen-rich subarctic water equatorward. The California Current extends from the 

shelfbreak to an offshore distance of approximately 1,000 km, with strongest speeds at the surface and extending to at least 500 m in depth (Hickey, 1998), while the inshore section of the current is limited to the upper 200 

m over the continental slope (Hickey, 1979). North of Point Conception, the core lies about 100-200 km from the 

coast, with maximum equatorward velocities of 5-10 cm/s (Chelton, 1984). South of Point Conception, the core 

of the California Current flows further from the coast between 300-400 km offshore (Lynn and Simpson, 1987) 

with average speeds generally less than 25 cm/s (Reid and Schwatzlozse, 1962). Seasonal maxima in current 

speeds occurs in the summer to early fall.

South of Point Conception, a portion of the California Current turns shoreward into the Southern California Bight 

both north and south of the Channel Islands. Near San Diego, a larger branch of the California Current bends 

poleward into the SCB, where it is known as the Southern California Countercurrent. This nearshore countercurrent dominates the mean water circulation in the Southern California Bight during summer and winter (Hickey, 

1993) at poleward speeds of 10-20 cm/s (Oey, 1999). Huyer et al. (1989), Harms and Winant (1998) and Oey 

(1999) have suggested that this countercurrent is caused by equatorward weakening of the wind curl south of 

Point Conception.

The Southern California Countercurrent draws warmer water from the south and forces the water northwest 

through the southern Channel Islands and the Santa Barbara Channel (Dailey et al.,1993). Additionally, some of 

the countercurrent is deflected west into the California Current south of the northern Channel Islands, resulting in 

a seasonal counterclockwise gyre in the Southern California Bight called the Southern California Eddy (Lynn and 

Simpson, 1987, Hickey, 2000). In spring, when the countercurrent is at its minimum northward flow, equatorward 

surface flow prevails in the Southern California Bight (Hickey 1993). Hickey (1979) suggested that the Southern 

California Countercurrent may combine with the poleward Davidson Current north of Point Conception, the latter 

having peak flows during winter.

Underlying the California Current and the Southern California Countercurrent is a subsurface flow called the 

California Undercurrent, a narrow (10-40 km) poleward flow that extends the length of the coastline from Baja 

California to at least 50oN (Hickey, 1998). Originating in the eastern equatorial Pacific, the California Undercurrent can be characterized by a warm, saline, oxygen and nutrient-poor signature (Neander, 2001). Peak northward speeds of 30-50 cm/s usually occur in summer to early fall, being stronger at depths 100-300 m, and can 

be continuous over distances of more than 400 km along the continental slope (Collins et al., 1996; Pierce et al., 

2000) or can break into separating, mesoscale jets (Cornuelle et al., 2000; Barth et al., 2000).

Circulation in the Study Area is also influenced by coastal upwelling, a process regulated by prevailing winds 

and the orientation of the coastline. In the northern hemisphere, Ekman transport causes surface water to move 

~45 degrees to the right of the wind direction. Where surface water is pushed away from the coastline, deeper 

nutrient rich water rises to the surface creating an upwelling current. Along the north-south oriented coast of 

California, winds blowing from the north move surface water westward, away from the coastline, creating upwelling currents that bring colder water to the surface (San Francisco State University, 2000). North of Santa Cruz 

(>37oN), a strong seasonal contrast in winds results in favorable upwelling conditions in summer contrasted 

by downwelling during winter storms (Strub and James, 2000). From 35-37oN, modest storm activity results in 

monthly mean winds that remain upwelling-favorable year round (Strub and James, 2000). In contrast, upwell-
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

ing is rare along the mainland coast of the Santa Barbara Channel because the headlands at Point Conception 

shelter the east-west oriented channel from the strong northwesterly winds that generate upwelling (Love et al., 

1999). Point Conception is the southernmost major upwelling center on the west coast of the United States, and 

marks a transition zone between cool surface waters to the north and warm waters to the south (Love et al., 

1999). However, upwelled water from regions north of the Bight appears to enter the western end of the Santa 

Barbara Channel and move eastward along its southern boundary (Hickey, 2000).

The currents and upwelling effects, with their varying water temperatures, create at least three climatic/habitat 

zones in the Santa Barbara Channel and the surrounding region. Waters north of Point Conception and offshore 

and south of the Channel Islands are cool, and have biotic assemblages characteristic of northern and central 

California (Oregonian Biogeographic Province) (Airamé et al., 2003b). San Miguel Island is primarily influenced 

by the cool water of the California Current, and also lies in the Oregonian Biogeographic Province. The warm 

waters of the California Countercurrent dominate the Santa Barbara Channel and Santa Barbara and Anacapa 

Islands. These areas belong to the Californian Biogeographic Province (Airamé et al., 2003b). Eastern Santa 

Rosa Island and Santa Cruz Island occupy a transition zone between the cold and warm water provinces, and 

should be considered a third biogeographic region (Seapy and Littler, 1980; Airamé et al., 2003b)

Within the Santa Barbara Channel, a localized cyclonic gyre circulation pattern exists year-round (Hendershott 

and Winant, 1996; Lagerloef and Bernstein, 1988) with seasonal variations in intensity. In general, cool water enters the channel from the west and flows eastward along the Channel Islands, while warm water enters the channel from the east and flows westward along the coast. Harms and Winant (1998) identify six distinct variations; 

Upwelling, Relaxation, Cyclonic; Propagating Cyclones, Flood East, and Flood West. In the Upwelling pattern, 

there is a strong south and southeastward flow of cool water from Point Conception and along the north sides of 

the Channel Islands, and a weak warm water flow toward the northwest along the mainland. In the Relaxation

pattern, there is a strong northwestward flow of warm water into the channel from the east, and a weak inflow of 

cold water from the west. The Cyclonic pattern is an elongated, closed pattern created when the central eddy is 

strongest, and there is little flow into the channel from either the west or the east. In the Propagating Cyclones

pattern, small, tight circular flow cells form in the center of the Channel and drift toward the west. These four 

patterns form in spring, summer and fall, but the cyclonicity is strongest in summer and weakest in winter. In the 

winter, directional flow patterns form. The winter Flood East pattern consists of a strong eastward flow into the 

Channel along the coastline, and lesser eastward inflow along the Channel Islands. The winter Flood West pattern has a strong northwestward flow along the coast, and a weaker northwest flow along the islands.

Two opposing forces generate the cyclonic flow patterns: a poleward pressure gradient and an equatorward wind 

stress (Nishimoto and Washburn, 2002). In the warm waters of the Southern California Bight, sea level is higher 

than in the cold, upwelled waters north of Point Conception. This difference in sea level creates a poleward pressure gradient that draws water westward through the channel. Upwelling-favorable winds tend to drive strong 

eastward flow, opposing the westward pressure gradient. When the effects of wind equal that of the pressure 

gradient, the cyclonic flow patterns form. Imbalances in the two competing forces create the pattern variations 

described above.

Nishimoto and Washburn (2002) found that the eddy circulation in the Santa Barbara Channel extended to 

depths of at least 650 feet (200 m), or nearly half the total channel depth, and suggest that persistent cyclonic 

eddies play an important role in maintaining marine populations through climate changes. Cold water uplifted 

in the center of the eddy may provide an additional source of nutrients during a shift to a warm-water regime, 

increasing primary productivity and the amount of food available for fish. Nishimoto and Washburn (2002) found 

large aggregations of juvenile fishes concentrated in an eddy in the Santa Barbara Channel, and suggest that 

high food availability and feeding success contributed to faster growth and higher survivorship of these fishes. 

Nishimoto and Washburn (2002) also noted that the fishes were entrained in the eddy current in their larval 

stages and remained there until they passed the juvenile stage, when they grew strong enough to escape the 

circulating current. 

Hickey (2000) found that the sediments in ocean basins of the SCB are near anoxic to anoxic, and that the anoxic 

area is increasing. Expansion of the anoxic areas reduces the ability of the basin sediments to support marine 

life. The high ridges between the basins essentially prevent influx of oxygen-bearing water into the basins, which 

is important for maintaining oxygen levels. The events that bring oxygen to the basins are associated with processes in the upper water column. Strong upwelling and southeastward flow from the Santa Barbara Channel 
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

into the Santa Monica Basin appear to drive cold, denser water over the ridges into the basins, where it mixes 

with the ambient water confined within. Influxes of oxygen-bearing cold water occur only for a few days at a time, 

after intervals of several years (Hickey, 2000). The Santa Barbara Basin, which lies between the Channel Islands 

and the mainland, is relatively shallow (1,640 ft/500 m). An intense coastal upwelling event off Point Conception 

caused rapid renewal of the water in this basin (Hickey, 1993). Within the last 40 years, water in the Santa Barbara Channel has overturned several times (Hickey, 1993).

Waves

Waves in the Santa Barbara Channel are produced by seasonal swells crossing the open ocean, the sheltering effect of Point Conception and the Channel Islands, the variable wind fields that arise from the mountainous 

coastal and island topography, and the complex shallow water bathymetry within the channel (O’Reilly et al., 

2000). Deep water swells from winter storms typically enter the channel from the west or west-southwest, for the 

most part unbroken by the Channel Islands. West swells produce high waves along the south-facing coastline 

just south of Point Conception and at the eastern end of the channel south of Ventura. A massive fan of sediment 

deposited on the shelf by the Ventura and Santa Clara rivers concentrates much of the wave energy traveling 

eastward down the channel onto a narrow section of coastline near the mouth of the Santa Clara River channel 

(O’Reilly et al., 2000). When the deep water swell originates more from the west-southwest, this focusing zone 

shifts directly northward into the Ventura area. West swells can also produce large waves at Rincon Point west of 

Ventura, and at the south end of Santa Monica Bay near Redondo Beach. Wave heights increase along portions 

of the Channel Islands that border the south side of the channel (O’Reilly et al., 2000). 

In the summer, deep water swells originate in the south Pacific, and encounter the Channel Islands as they 

move north toward California. The islands shelter most of the channel and the south-facing coast from summer 

swells, significantly limiting wave heights. South swells from storms near New Zealand enter the western end of 

the channel, while those originating further east near South America are almost entirely obstructed. South swells 

travel past Anacapa Island and reach the coast near Ventura and Rincon Point. Rare swells originating from the 

southeast can reach the coast at Santa Barbara (O’Reilly et al., 2000).

Long-Term Climate Perturbations

Longer term climatic phenomena influencing the region include El Niño, Pacific Decadal Oscillation, and global 

warming. The recurring El Niño-Southern Oscillation pattern is one of the strongest in the ocean-atmosphere 

system. El Niño is defined by relaxation of the trade winds in the central and western Pacific, which can set off a 

chain reaction of oceanographic changes in the eastern Pacific Ocean. Off the coast of California, El Niño events 

are characterized by increases in ocean temperature and sea level, enhanced onshore and northward flow, and 

reduced coastal upwelling of deep, cold, nutrient-rich water. During this period, survivorship and reproductive 

success of planktivorous invertebrates and fishes decrease with plankton abundance. Marine mammals and 

seabirds, which depend on these organisms for food, suffer food shortages, leading to widespread starvation 

and decreased reproductive success.

Every 20-30 years, the surface waters of the central and north Pacific Ocean (20oN and poleward) shift several 

degrees from the mean temperature. Such shifts in mean surface water temperature, known as the Pacific 

Decadal Oscillation, have been detected 5 times during the past century, with the most recent shift in 1998. The 

Pacific Decadal Oscillation impacts production in the eastern Pacific Ocean and, consequently, affects organism 

abundance and distribution throughout the food chain. Ocean waters off the coast of California have warmed 

considerably over the last 40 years. It is not clear if this warming is a consequence of an interdecadal climate shift 

or global warming. In response to these three phenomena, some species have shifted their geographic ranges 

northward, altering the composition of local assemblages.

2.4 PHYSIOGRAPHIC COMPLEXITY

Data and Methods

The rationale behind examining physiographic complexity is to provide potential linkages to spatial patterns described in other chapters, and to provide a measure of context for subsequent discussions of observed regional 

biogeographic patterns. For example, offshore circulation patterns in the region result, in part, from the interaction of large-scale ocean currents, local geography, and the unique basin and ridge topography of the ocean bottom of the Southern California Bight (Airamé et al., 2003a). As discussed in chapter 2.2, these currents influence 

the distribution of living marine resources in the region. Furthermore, many taxa, particularly fishes, are known 
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to exhibit a strong affinity for areas of high structural complexity (Yoklavich et al., 2000, 2002; Hixon et al., 1991; 

Hixon and Tissot, 1992; Field et al., 2002; Starr, 1998; and Williams and Ralston, 2002). The analyses presented 

here are provided as a proxy for quantifying structure in the region (i.e., mesoscale rugosity), and should be 

interpreted with care, as they represent an estimate for only one neighborhood range (1 km). Similar analyses 

can be performed for an infinite set of ranges, each resulting in similar patterns with dimensions proportionate 

to the prescribed neighborhood.

The maps of physiographic complexity presented here were derived using a bathymetric grid produced by the 

California Department of Fish and Game (CDFG) in August 2000 (Figure 2.1.1-Chapter 2.1.1). This bathymetric 

grid was made from 75 tiled digital elevation models (DEM) that were mosaicked into a single grid, and resampled to a 200 m resolution. DEM’s were developed by the Teale Data Center under contract with CDFG, and 

have a geographic domain ranging from 31.9o to 42.5o north latitude. Physiographic complexity was calculated 

from these bathymetric data using a neighborhood statistical function in ArcView 3.2 (GIS), and represents the 

degree of variation in water depth (bathymetry) within a prescribed (and constant) area for the entire seascape. 

In this analysis, a standard deviation of water depth was calculated within a 1 km radius “moving-window”. The 

calculated standard deviation was then assigned to the centroid of that neighborhood. This analysis was performed by centering the moving window on each individual bathymetric grid cell in the source data, and resulted 

in an estimate of the standard deviation of bathymetry at a scale of 1 km for the entire region (Figure 2.4.1, 

mapped range is 32o to 39oN). This measure of complexity was chosen over calculating a standard slope value 

because it not only captures areas of high slope, but also highlights areas that typify the unique basin and ridge 

topography of the ocean bottom in the Southern California Bight.

To analyze patterns of physiographic complexity in relation to proposed boundary concepts, average variance 

was calculated inside each of the boundaries. The assumption of this analysis was that encompassing an area 

of higher average complexity is preferred, and that this complexity likely provides a more diverse complement of 

potential habitats (niches) for living marine resources. Average complexity within each concept was then used 

in calculating an Optimal Area Index (OAI). Since the average is a relative measure of physiographic complexity, we also provide an analysis of the absolute area of high complexity captured within each concept. In this 

analysis, results of the complexity map were classified into standard deviations, with areas in red representing 

locations where the complexity was equal to or greater than 2 standard deviations above the mean deviation 

(henceforth “high”; stippled area in Figure 2.4.1). The total area of high complexity contained within each concept 

was then estimated for use in the absolute OAI calculations.

Broad-scale Patterns

The spatially-articulated estimate of physiographic complexity resulted in a map that highlights areas of steep 

slopes, as well as regions of ridge and basin topography. Stippled areas in Figure 2.4.1 indicate where average 

complexity was classified as high. A continuous northwest-southeast trending area of high complexity can be 

seen running along the entire coastline. This area represents the continental slope, and is generally centered 

on the 2,000 m isobath (shown in green). The large reticulated area of physiographic complexity that is evident 

throughout the Southern California Bight generally consists of the ridge and basin topography first described in 

chapter 2.1. In fact, nearly all of the high complexity areas contained within the current Channel Islands sanctuary boundary can be attributed to these unique geologic features of the continental borderland rather than to 

continental slope per se.

Results indicate that the complement of sanctuaries along the California coastline (Gulf of the Farallones, Cordell 

Bank, Monterey Bay, and Channel Islands) capture large areas of high estimated complexity, with each sanctuary comprised of at least 20% high complexity area. In this analysis (ranging from 31.9o

 to 32.5o north latitude), 

the total area identified as high complexity was 95,255 km2. Roughly 8,251.1 km2 (8.7%) of this area is contained 

within the four California sanctuaries, with 1.4% of the total falling inside the current boundaries of the Channel 

Islands National Marine Sanctuary. A total of 36% of the area contained within CINMS boundaries was classified as having high complexity. This is the second largest proportion of any California sanctuary (Gulf of the 

Farallones-21.5%, Cordell Bank-30.5%, and Monterey Bay-42%). Most of the complex area contained within the 

Gulf of the Farallones, Cordell Bank, and Monterey Bay national marine sanctuaries is comprised of continental 

slope. In the Monterey Bay sanctuary, much of the physiographic variance is attributed to the Monterey, Soquel, 

Carmel Canyon complex.
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

The continental slope drops steeply from the edge of the continental shelf (~200 m) to depths of approximately 

3,000-4,000 m, where it reaches the abyssal plain. Waters of the continental slope are dark, cold, and under 

very high pressure. In general, the community structure of invertebrates and fishes along the continental slope 

vary markedly with depth (Airamé, et al., 2003a; NCCOS, 2003). This same trend is evident in the analysis of 

fish community structure presented in chapter 4.2, including those communities found in the ridge and basin 

structure of continental borderland in the Southern California Bight. Rockfishes (Sebastes spp.) and flatfishes 

(Pleuronectiformes) are some of the most common benthic fishes found inhabiting the region (Airamé, et al., 

2003a). Because many ecologically and commercially important fishes and invertebrates exhibit a strong affinity 

for physiographic complexity (Gabriel and Tyler, 1980; Matthews and Richards, 1991; Sullivan, 1995; Williams 

and Ralston, 2002; Love et al., 2002; Field et al., 2002; NCCOS, 2003), and their community structure is often 

classified by ecologists based on the underlying metric (bathymetry), we consider higher complexity to be a 

benefit in this analysis. Overall, it is likely that concepts characterized by a wide range of depths, coupled with a 

high degree of complexity, would exhibit the greatest potential to support a diverse faunal assemblage. As such, 

optimal area index (OAI) values presented in here are designed to highlight concepts with the highest relative 

complexity.

Analysis of Boundary Concepts

Results of this analysis highlight expansive and interconnected areas of physiographic complexity throughout the 

Southern California Bight – an expression of the ridge and basin topography that dominates the regional geology. 

Nearly all of the high complexity areas contained within the current Channel Islands NMS boundary (No Action 

Concept; NAC) can be attributed to these unique features. A total of 24% of the area contained within current 

CINMS boundaries was classified as having high physiographic complexity. The optimal area index (OAI) values 

calculated for each concept suggest that only concept 5 provides greater benefit (in terms of physiographic variance), as it is the only boundary that resulted in a mean complexity value greater than the NAC. 

As discussed in Chapter 1.4, two Optimal Area Index (OAI) values are presented here, the first of which is 

calculated using the mean complexity inside each of the concepts (henceforth “relative OAI”), and the second 

using the total area of high complexity captured inside each boundary (henceforth “absolute OAI”). Mean estimated complexity for the NAC was 21.2 with a coefficient of variation (CV) of 103.2%. Mean complexity and 

CV values for the remaining concepts, ranging from smallest in size to largest, are as follows: Concept 5-21.8, 

100.2%; Concept 4-19.3, 110.6%; Concept 3-18.6, 113.7%; Concept 2-16.7, 111.4%; Concept 1a-18.5, 115.6%; 

and Concept 1-18.5, 115.6% (Figure 2.4.2). Mean complexity for the Study Area boundary (defined in McGinnis, 2000) was estimated to be 16.5 with a CV of 114.3% (Figure 2.4.2). The relatively large CV values resulted 

from the highly variable nature of the estimate (standard deviation of bathymetry). For example, areas along the 

continental shelf show relatively little deviation in bathymetry within a 1 km2 neighborhood, while areas along the 

continental slope exhibit extreme differences in depth over short distances.

These results exhibit a weak, yet statistically significant (a=0.10) inverse relationship to concept size. This relationship is shown in Figure 2.4.3 as a linear regression function between concept area (km2

) and the mean 

complexity value calculated within the boundary (r2=0.43, P=0.08). On the other hand, the relationship between 

concept area and absolute area of high complexity was very predictable (and statistically significant), with larger 

concepts containing ever larger areas of high complexity. Figure 2.4.3 shows the linear regression function 

between the total concept area (km2) and the area of high complexity contained within the boundary (r2=0.98, 

P<0.0001). 

A more balanced metric to use in assessing the relative benefits of each concept as it relates to optimizing for 

high physiographic complexity is the OAI (Table 2.4.1, also see Chapter 1.4). While this metric decouples the 

relationships between concept area and the relative and/or absolute estimate to some extent, results of the OAI 

are still dependent upon the input data, absolute vs. relative measures. As such, we’ve provided OAI results for 

both mean and total area of high complexity. Results suggest that Concept 5, the minimum expansion concept, 

provides maximum benefit in terms of the mean complexity calculated for each concept (relative OAI) and in 

terms of the area of high complexity contained within each concept (absolute OAI). Because the mean OAI incorporated a negative value in the numerator for all boundary concepts except 5 (decreased complexity), the 

calculated value is necessarily negative. 
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Summary 

• Patterns of mean physiographic complexity highlight areas along the continental slope, as well as areas 

typical of ridge and basin topography. 

• Scientific literature supports the notion that many biological communities exhibit affinities for high complexity, 

and are often classified based on the underlying bathymetry estimate. 

• Of the boundary concepts under consideration, Concept 5 provides relatively large increases in relative and 

absolute physiographic complexity for its size. 

Figure 2.4.1. Map of physiographic complexity along the California coast (ranging from 32o to 39oN). Stippled areas indicate where 

complexity was >2 standard deviations above the mean. The green line demarcates to 2000 m isobath (a proxy for the continental 

slope).
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

33°N33°N

34°N34°N

35°N35°N

0 25 50 100

Kilometers

Physiographic Complexity:

Standard Deviation of Water Depth

(1000 meter neighborhood)

Original Data Sources (This is a derived product):

California Department of Fish & Game (CDFG)

Teale Data Center

Boundary Concept 5

High

Low

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

33°N33°N

34°N34°N

35°N35°N

0 25 50 100

Kilometers

Physiographic Complexity:

Standard Deviation of Water Depth

(1000 meter neighborhood)

Original Data Sources (This is a derived product):

California Department of Fish & Game (CDFG)

Teale Data Center

Boundary Concept 4

High

Low

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

33°N33°N

34°N34°N

35°N35°N

0 25 50 100

Kilometers

Physiographic Complexity:

Standard Deviation of Water Depth

(1000 meter neighborhood)

Original Data Sources (This is a derived product):

California Department of Fish & Game (CDFG)

Teale Data Center

Boundary Concept 3

High

Low

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

33°N33°N

34°N34°N

35°N35°N

0 25 50 100

Kilometers

Physiographic Complexity:

Standard Deviation of Water Depth

(1000 meter neighborhood)

Original Data Sources (This is a derived product):

California Department of Fish & Game (CDFG)

Teale Data Center

Boundary Concept 2

High

Low

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

33°N33°N

34°N34°N

35°N35°N

0 25 50 100

Kilometers

Physiographic Complexity:

Standard Deviation of Water Depth

(1000 meter neighborhood)

Original Data Sources (This is a derived product):

California Department of Fish & Game (CDFG)

Teale Data Center

Boundary Concept 1

High

Low

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

33°N33°N

34°N34°N

35°N35°N

0 25 50 100

Kilometers

Physiographic Complexity:

Standard Deviation of Water Depth

(1000 meter neighborhood)

Original Data Sources (This is a derived product):

California Department of Fish & Game (CDFG)

Teale Data Center

Study Area

High

Low

Figure 2.4.2. Physiographic complexity within boundary concepts. The No Action Concept (NAC) is shown as a red line, while the 

concepts are shown as a black line.
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

2.5 BENTHIC SUBSTRATE

Data and Methods 

In this chapter, geologic data for offshore California are analyzed to characterize the distribution of substrate 

types, and more specifically for the area around the Channel Islands National Marine Sanctuary. Analyses are 

based upon a comprehensive set of geologic data that were synthesized, classified, and mapped according to 

substrate and habitat type for the entire U.S. west coast. Benthic features from a variety of sources (side-scan 

sonar, bottom samples, seismic data, and multibeam bathymetry) were interpreted by geologic mapping experts 

and subsequently classified according to substrate and habitat type (Greene et al., 1999). This task was completed by the National Marine Fisheries Service (NMFS) as part of the development of an Environmental Impact 

Statement (EIS) that considers the designation of Essential Fish Habitat (EFH) for Pacific coast groundfish 

(NMFS, 2004). 

The substrate data ranges from Washington to the U.S.-Mexico border (32o-48.5o latitude) and from 50-200 km 

from the shoreline (excluding estuaries). Benthic substrate data for California encompasses nearly 165,000 km2

of the continental shelf and slope and are classified into approximately 33 different habitat types (Figure 2.5.1). 

The level of spatial resolution varies across the dataset based on the quantity and quality of the original data 

sources used to construct this substrate map. As such, fine scale inaccuracies may exist throughout the range 

of the data. 

In addition to assessing the distribution of substrate types throughout the southern California region, the map 

was used as an input for deterministic habitat suitability models (HSM) for a select group of fishes and inverteTable 2.4.1. Analysis of boundary concepts. Numbers in bold indicate an increase in the estimate when compared to the No Action 

Concept (NAC). Maximum calculated OAI numbers are shaded in gray. Delta (Δ) indicates a rate of change calculation, and is always 

expressed as a percent change from the NAC.

Figure 2.4.3. Linear regression functions between concept area (km2) and mean physiographic complexity (left), and the area (km2) of 

high physiographic complexity (right).

SA

NAC

5

4 3

2

1, 1a

1000

2000

3000

4000

5000

6000

7000

0 5000 10000 15000 20000 25000

Total Area (km2)

Area of High Complexity (km2

)

1, 1a

2

3

4

5

NAC

SA

14

16

18

20

22

0 5000 10000 15000 20000 25000

Total Area (km2)

Mean Complexity

Concept

Area 

(km2)

Mean 

Complexity

High 

Complexity 

Area (km2)

∆ Area 

(%)

∆ Mean 

Complexity 

(%)

∆ High 

Complexity 

Area (%)

Mean 

Complexity 

OAI (relative)

High 

Complexity Area 

OAI (absolute)

NAC 3745 21.20 1339 - - - - -

5 4536 21.79 1734 21.12 2.78 29.51 0.132 1.397

4 7981 19.25 2669 113.11 -9.20 99.31 -0.081 0.878

3 9044 18.61 2844 141.50 -12.22 112.37 -0.086 0.794

2 13736 16.72 3709 266.78 -21.13 177.00 -0.079 0.663

1a 22591 18.53 6777 503.23 -12.59 406.15 -0.025 0.807

1 22613 18.53 6785 503.82 -12.59 406.76 -0.025 0.807

SA 17093 16.48 4558 356.42 -22.26 240.39 -0.062 0.674
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

brates (Chapters 3.1 and 4.1). Linking the 33 classes of habitat types to species life history information from scientific literature was difficult; thus, a separate substrate attribute was used for habitat suitability modeling which 

defined the substrate as either hard or soft. Model results for invertebrates were reviewed in June 2004 by a 

panel of experts. They expressed concern that the map underestimated the amount of hard bottom, most notably 

at depths between 0-30 m along the mainland south of Point Conception and around the Channel Islands. Additional hard substrate data (MMS, 1987) were then provided by scientists from UCSB who converted non-digital 

substrate maps developed by the Minerals Management Service (MMS) into a format suitable for use within a 

GIS. These data extend from Morro Bay to the U.S.-Mexico border and were combined with the NMFS substrate 

data, resulting in a better estimation of hard substrate in southern California (Figure 2.5.2). Chapter 2.6 provides 

a more detailed analysis of these data. 

Broad-scale Patterns

Figure 2.5.3 displays the distribution of 33 habitat types off the coast of California encompassing a total area 

of 164,725 km2. Habitat types shaded in red reflect rocky outcroppings, ridges, reefs, and other hard bottom 

features. Areas shaded with orange and yellow generally display soft continental shelf substrate, while areas in 

green indicate soft substrate on the continental slope. The amount of area classified as hard bottom habitat accounts for approximately 10% of the total area, the majority of which is located south of Monterey Bay. The region 

Figure 2.5.1. Benthic habitat types for the marine waters off California (Greene et al., 1999).
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

north of Monterey is typically characterized by a broad continental shelf and slope comprised of soft substrate 

with scattered areas of hard bottom. In southern California, the area appears to be dominated more by sedimentary basins, ridges, and slope habitat. Large features are noticeable throughout the extent of the data: the Gorda 

Escarpment due west of Cape Mendocino, Cordell Bank west of Point Reyes, and the Davidson Seamount and 

Santa Lucia Bank southwest of Monterey Bay. In southern California, hard bottom areas are observed comprising features such as the Rodriguez Seamount, San Juan Seamount, Patton Escarpment, Cortes Bank, and Tanner Bank. Soft bottom features that are readily apparent are the basins of Monterey Canyon, Pioneer Canyon, 

and the Santa Barbara-Ventura Canyon in southern California. 

Analysis of Boundary Concepts 

Figure 2.5.3 displays benthic habitat types in southern California. Fifteen of the 33 habitat types found across California are found within the study area (Table 2.5.1). Percentages of hard substrates ranged from 5-8% of the total 

area within concept boundaries. Most hard substrate was located south of San Miguel and Santa Rosa Islands, 

around Anacapa Island and north of Santa Barbara Island. Concept 1a contained the most hard substrate (1,514 

km2), mostly attributed to the inclusion of the southern portion of Santa Lucia Bank. Soft substrates were mostly 

comprised of sedimentary shelf, slope, and basin habitat classifications (Table 2.5.1). 

Spatial heterogeneity of habitats was quantified using the Shannon Index of diversity, and was compared among 

boundary concepts. This approach has been recently applied to marine coral reef ecosystems (Jeffrey, 2005). The 

Shannon index (Shannon and Weaver, 1949) is one of the most commonly used diversity metrics in community 

ecology (Magurran, 1988). Typically, the index is used to characterize biological communities; however, the same 

principles apply when analyzing the habitat map. The index attempts to balance habitat richness (the number of 

unique habitat types) with habitat evenness (the amount of area among the habitat types). For a given number of 

habitat types, the Shannon index is highest when there are equal areas within each habitat type. The Shannon 

index (H’) was calculated using the formula:

Table 2.5.1. Area (km2) and percentage of total area for habitat types within boundary concepts.

Habitat Type NAC Concept 5 Concept 4 Concept 3 Concept 2 Concept 1a Concept 1 Study Area 

Area % Area % Area % Area % Area % Area % Area % Area %

Rocky Ridge (hard) 33.9 0.9 44.6 1.0 106.7 1.4 125.0 1.4 158.9 1.2 880.4 3.9 884.3 3.9 201.4 1.2

Rocky Shelf (hard) 135.8 3.6 137.6 3.0 163.5 2.1 187.9 2.1 236.6 1.7 264.9 1.2 263.2 1.2 265.0 1.6

Rocky Slope (hard) 120.6 3.2 157.8 3.5 265.1 3.3 284.5 3.3 325.2 2.4 366.5 1.6 365.4 1.6 366.7 2.1

Rocky Slope 

Canyon Wall (hard) 0 0 0.1 <0.01 0 0 0 0 0 0 0 0 0 0 0 0

Sedimentary Apron 

(soft) 0 0 0 0 0 0 0 0 0 0 1338.5 5.9 1333.8 5.9 0 0

Sedimentary Basin 

(soft) 379.8 10.2 640.6 14.2 2371.8 29.8 2772.8 30.7 3026.6 22.1 3812.2 16.9 3806.1 16.9 3817.1 22.4

Sedimentary Ridge 

(soft) 235.5 6.3 302.1 6.7 342.9 4.3 347.7 3.8 409.3 3.0 968.9 4.3 965.6 4.3 460.2 2.7

Sedimentary Shelf 

(soft) 1877.9 50.4 1934.0 42.8 2162.1 27.1 2360.3 26.1 3681.6 26.8 5026.6 22.3 5038.2 22.4 5049.2 29.6

Sedimentary Shelf 

Canyon Wall (soft) 5.7 0.2 5.7 0.1 5.7 0.1 5.7 0.1 5.7 <0.01 5.7 <0.01 5.7 <0.01 5.7 <0.01

Sedimentary Shelf 

Gully Floor (soft) 0.7 <0.01 0.7 <0.01 0.7 <0.01 0.7 <0.01 0.7 <0.01 0.7 <0.01 0.7 <0.01 0.7 <0.01

Sedimentary Slope 

(soft) 832.8 22.4 1167.7 25.8 2313.9 29.0 2596.0 28.8 5330.4 38.8 9222.6 41.0 9214.2 40.9 5309.3 36.9

Sedimentary Slope 

Canyon Floor (soft) 21.3 0.6 32.3 0.7 88.7 1.1 88.4 1.0 137.1 1.0 137.6 0.6 137.6 0.6 137.8 0.6

Sedimentary Slope 

Canyon Wall (soft) 81.6 2.2 99.0 2.2 98.8 1.2 110.9 1.2 111.0 0.8 186.9 0.8 186.9 0.8 157.6 0.9

Sedimentary Slope 

Gully Floor (soft) 0 0 0 0 0 0 0 0 21.2 0.2 18.6 0.1 21.2 0.1 21.2 0.1

Sedimentary Slope 

Landslide (soft) 0 0 0 0 46.3 0.6 148.8 1.6 277.3 2.0 284.5 1.3 284.3 1.3 284.5 1.7

∑ hard 290.2 7.8 340.2 7.5 537.2 6.7 597.3 6.6 720.8 5.3 1511.8 6.7 1512.8 6.7 833.0 4.9

∑ soft 3435 92.2 4182 92.5 7431 93.3 8433 93.4 13001 94.7 21003 93.3 20994 93.3 16243 95.1
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

where ni is the total area of the ith habitat 

type (S) in the sample, and N is the total 

area of the sample (Magurran, 1988). 

We supplement Shannon’s Index of diversity with a measurement of evenness 

calculated using Shannon’s equitability 

index:

E=H’/ ln (N)

Values of this index range from 0 to 1 and 

describe the dominance of habitat types; 

values close to 1 indicate that habitats 

are evenly distributed.

Figure 2.5.4 shows the calculated habitat 

diversity for each of the boundary concepts and the Study Area. Habitat diversity was lowest within the current boundary 

and increased with increasing concept 

area, with the exception of Concept 2, 

which had the third lowest diversity. Similarly, habitat richness (the total number of 

habitat types present) was lowest within 

the current boundary and increased with 

increasing concept area (Figure 2.5.5). 

Concept 2 and the study area exhibited 

low evenness values and displayed similar trends to diversity values. As expected, habitat richness increased as concept 

area increased, as well as diversity for 4 

of the 6 concepts. Diversity and evenness 

values for the Study Area and Concept 2 

were the lowest (Figure 2.5.5). While the 

area of new habitats gained for Concept 

2 and the study area were large, these 

gains were dominated by two habitat 

types. Thus habitat diversity and evenness values were depressed. 

Although habitat diversity was greatest 

within Concept 1a (Figure 2.5.6) and 

was not statistically significant (r2=0.44, 

p<0.17), analysis of diversity using the 

OAI (Table 2.5.2) indicate that Concept 5, 

the minimum expansion concept, provides 

the greatest benefit in terms of the proportional changes in area and diversity.

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

Hard Bottom

NMFS Data

MMS Data

0 25 50 100

Kilometers

Substrate

Figure 2.5.2. Location of hard substrate from NMFS and MMS datasets.

Figure 2.5.3. Benthic habitat types for the marine waters off southern California 

(Greene et al., 1999).

ln ni

N

ni

-Σ=1 N

S

H’ = [( ) ( )] ln ni

N

ni

-Σ=1 N

S

H’ = [( ) ( )]
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Summary 

• Data presented here provide the most current broad-scale representation of benthic habitats for the contiguous 

coastline of the western U.S.

• Patterns of habitat diversity and richness were positively correlated with the increasing area of boundary concepts.

• Of the six boundary concepts being considered, Concepts 1 and 1a provided the highest habitat diversity and 

richness, but Concept 5 ranked highest in the OAI.

0.56

0.58

0.6

0.62

0.64

NAC 5 4 3 2 1a 1 SA

Boundary Alternatives

Evenness

0

5

10

15

20

Richness

Evenness Richness

Figure 2.5.5. Evenness and richness estimates for habitat 

types within boundary concepts. Numbers on the x-axis represent concepts, NAC=No Action Concept, SA=Study Area.

SA

NAC

5

4

3

2

1, 1a

1.45

1.50

1.55

1.60

1.65

1.70

0 5000 10000 15000 20000 25000

Total Area (km2)

Diversity

Figure 2.5.4. Regression of habitat diversity and total area for 

the current and proposed boundary concepts. Numbers indicate concepts and NAC=No Action Concept, SA=Study Area.

Concept Area (km2)

Diversity 

(H ́)

∆ Area 

(%)

∆ Diversity 

(%)

OAI 

(absolute)

NAC 3475 1.49 - - -

5 4538 1.57 21.12 5.37 0.25

4 7981 1.60 113.11 7.38 0.07

3 9044 1.62 141.50 8.72 0.06

2 13736 1.55 266.78 4.03 0.02

1a 22591 1.69 503.23 13.42 0.03

1 22613 1.68 503.82 12.75 0.03

SA 17093 1.52 356.42 2.01 0.01

Table 2.5.2. Analysis of habitat diversity within boundary concepts. Numbers in bold 

indicate an increase in the estimate when compared to the No Action Concept (NAC). 

Maximum calculated OAI numbers are shaded in gray. Delta (Δ) indicates a rate of 

change calculation, and is always expressed as a percent change from the NAC.
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2.6 BATHYMETRIC LIFE-ZONES

Data and Methods

This section describes the geographic distribution of habitats of southern California classified by substrate type 

and depth. Within the region, bathymetry ranges from intertidal to over 4,000 m, and four zones have been recognized within this range which contain distinct biological communities (Airamè et al., 2003b). Within the top 30 

m, light penetrates surface waters, supporting a highly productive nearshore community. Shallow rocky reefs 

often support kelp forests that provide physical structure and an abundant source of food for subtidal organisms. 

Soft bottom habitats lack the physical structure and high production associated with kelp forests and rocky reefs 

and are commonly inhabited by many species of groundfish and invertebrates (Airamé et al., 2003a). Many species of rockfish and rock crabs inhabit rocky reefs at depths between 30-100 m, while several species of flatfish 

and molluscs occupy soft substrates. At depths of 100-200 m, rockfish such as bocaccio and cowcod, are common on rocky reefs, while several species of prawns inhabit soft substrates. Continental slope species such as 

sablefish, thornyheads, and dover sole inhabit hard and soft substrates at depths >200 m.

The previous section assessed the distribution of substrate types in the vicinity of the Channel Islands National 

Marine Sanctuary (CINMS). In this section, benthic substrate from the combined NMFS and MMS datasets is 

used in conjunction with bathymetry contours to provide a two-dimensional assessment of habitats within each 

boundary concept. Bathymetry is also categorized into four depth categories, according to the classifications 

described in Airamé et al., (2003b):

0-30 m Shoreline, photic zone

30-100 m Upper continental shelf

100-200 m Lower continental shelf

>200 m Continental slope

Habitat diversity is a measure of both richness (the total number of habitat categories present) and the evenness 

of their distribution. All eight of the combinations of bathymetry categories (0-30 m, 30-100m, 100-200m, >200m) 

and substrate groups (hard, soft) are present in the current CINMS boundaries (Airamé et al. 2003b), and thus 

in all of the boundary concepts. Therefore, richness is equivalent in all of the boundary concepts, and we can 

reduce our analysis to a discussion of the spatial evenness of the eight habitat combinations. Shannon’s equitability (EH) is a common metric used to describe evenness and can be calculated using the formula:

 where niis the area of habitat belonging 

to the i

th habitat type (S) in the boundary concept, and N is the total area of 

the boundary concept. Thus, ni/N is the 

proportion of habitat type i relative to 

the total area of that boundary concept 

(Magurran, 1988). Values for Shannon’s 

Equitability range from 0 to 1 with 1 being complete evenness.

Broad-scale Patterns

Rocky habitats are patchy both along 

the coastline as well as further offshore 

(Figure 2.6.1); however, much of the 

nearshore habitat around the Channel 

Islands is comprised of hard substrate. 

Depths greater than 200 m are a dominant feature of the broad-scale bathymetry (Figure 2.6.2). Habitats within the 

upper continental shelf (30-100 m) are 

Figure 2.6.1. Distribution of benthic substrate types (hard or soft) off southern California.

ln ni

N

ni

-Σ=1 N

S

ln(S)

[( ) ( )] ln ni

N

ni

-Σ=1 N

S

ln(S)

[( ) ( )]
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

the second most common, followed by lower continental shelf habitat (100-200 m). Shoreline habitats within the 

photic zone (0-30 m) are the least abundant.

Analysis of Boundary Concepts

All configurations considered here were dominated by soft substrate, which covered more than 85% of the total 

area within each concept (Figure 2.6.1). Likewise, depths greater than 200 m were predominant and increased 

as concept area increased (Figure 2.6.2). These continental slope habitats comprise 43% of the area within 

the current boundary, 67% of the Study Area, 51% of Concept 5, 68% of Concept 4, 69% of Concept 3, 70% of 

Concept 2, and 75% of Concepts 1 and 1a. Upper continental shelf followed in habitat dominance ranging from 

31% for the current boundary to 12 for Concepts 1 and 1a. Relative abundance also declined with increasing 

concept area.

Evenness of the eight habitat categories generally declined as area increased, and was greatest for the No Action Concept (Table 2.6.1). This trend is graphically represented in Figure 2.6.3 as a linear regression function 

between area (km2) and evenness (r2=0.66, p=0.01). This trend can primarily be attributed to the disproportionate gain in deeper habitat as more area is incorporated into a boundary concept. The current CINMS boundaries 

and the smaller concepts 5 and 4 are a more suitable choice based upon evenness alone.

Figure 2.6.2. Distribution of bathymetric zones (m) off southern California.

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

Bathymetry (m)

0-30

30-100

100-200

>200

0 25 50 100

Kilometers
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

A more balanced metric to use in assessing the relative representation of 

habitat types and depth is the Optimal 

Area Index (OAI) (Table 2.6.1). OAI 

takes into account the proportional 

change (%) in evenness moving from 

the NAC to each of the concepts under consideration. It also incorporates 

the proportional change (%) in area 

from the NAC. The negative value of 

the OAI for habitat evenness for all of 

the boundary concepts under consideration indicates that the current CINMS 

boundary is preferable. Compared to 

the other concepts, the NAC contains 

a more equitable distribution of the 8 

habitat categories.

Summary

• Upholding ecosystem biodiversity requires protection 

of a wide variety of representative and unique habitats. 

Habitat in Central and Southern California can be characterized by a range of substrate types and depths that 

provide structure for a variety of organisms, including kelp 

forest, soft bottom and rocky reef communities. 

• Similar to the broad-scale patterns, the current boundary of the CINMS encompasses a considerable amount 

of soft substrate. The bathymetric classes considered 

here (photic zone, upper and lower continental shelf, and 

continental slope) are all represented within the region 

and within the current CINMS boundaries where they are 

distributed with a relatively high degree of evenness.

• The current CINMS boundary provides a more equal representation of habitats (substrate type and depth) than 

the proposed boundary concepts.

2.7 SEA SURFACE TEMPERATURE AND FRONTAL BOUNDARIES

Pattens in sea surface temperature (SST) influence the distribution of marine species and habitats, particularly 

in areas of persistent SST fronts. The convergence of the northern and southern biogeographical provinces near 

the Santa Barbara Channel result in a dynamic biological and physical transition zone which influences the abundance and distribution of many organisms, including pelagic juvenile fishes, plankton and other microorganisms, 

benthic macroalgae, seagrasses, and invertebrates. Large-scale shifts in SST, and consequently frontal boundaries, affect the spatial and temporal distribution of these organisms. This chapter provides analysis of SST and 

frontal boundaries for the region of interest and provides physical context for subsequent biological analyses. 

Data and Methods

The sea surface temperature (SST) data presented here were obtained from the NOAA/NASA Pathfinder dataset. SST data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR); multi-purpose imaging instruments attached to the NOAA -7, -9, -11 and -14 polar orbiting satellites. They measure global 

cloud cover, sea surface temperature, and ice, snow and vegetation cover and characteristics. Daily, 8-day, and 

monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available 

on equal-angle grids of 4096 pixels/360 degrees (nominally referred to as the 9 km resolution), 2048 pixels/360 

degrees (nominally referred to as the 18 km resolution), and 720 pixels/360 degrees (nominally referred to as 

Figure 2.6.3. Regression of habitat evenness and total area for 

the current and proposed boundary concepts. Numbers indicate 

concepts and NAC=No Action Concept, SA=Study Area.

SA

NAC

5

4 3 2

1, 1a

0.50

0.60

0.70

0.80

0 5000 10000 15000 20000 25000

Total Area (km2)

Evenness

Concept Area (km2) Evenness

∆ Area 

(%)

∆ Evenness 

(%)

OAI 

(absolute)

NAC 3475 0.78 - - -

5 4538 0.73 21.12 -5.69 -0.27

4 7981 0.59 113.11 -23.87 -0.21

3 9044 0.58 141.50 -25.60 -0.18

2 13736 0.56 266.78 -27.80 -0.10

1a 22591 0.53 503.23 -31.27 -0.06

1 22613 0.53 503.82 -31.25 -0.06

SA 17093 0.59 356.42 -24.32 -0.07

Table 2.6.1. Analysis of life-zone evenness within boundary concepts. Numbers in 

bold indicate an increase in the estimate when compared to the No Action Concept 

(NAC). OAI estimates shaded in gray represent maximum observed benefit. Delta (Δ) 

indicates a rate of change calculation, and is always expressed as a percent change 

from the NAC.
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

the 54 km resolution or 0.5 degree resolution). Monthly averaged 9km data were used here to achieve the greatest possible spatial resolution and to minimize cloudiness (no data values) by averaging cloud-free data values 

across a month of data. The SST data encompassed 125 months from January 1993-May 2003. The AVHRR 

Oceans Pathfinder SST data were obtained through the online PO.DAAC Ocean ESIP Tool (POET) at the Physical Oceanography Distributed Active Archive Center (PO.DAAC), NASA Jet Propulsion Laboratory, Pasadena, 

CA. <http://podaac.jpl.nasa.gov/poet>.

To analyze spatial patterns in relation to proposed boundary concepts, sea surface temperature data for the 125 

months (January 1993-May 2003) were averaged across four months (January, April, July, October) representing typical seasonal temperature values, to provide an overall estimate of mean SST along the Pacific margin of 

the coterminous United States (ranging from 31°-46°N, Figure 2.7.1). Average SST was calculated for each of 

the months across all years for which data were available. In addition to sea surface temperature, we also present estimated SST variance over time and an analysis of frontal boundaries and their persistence through time 

as derived from SST data. Variance in SST over time was estimated for each cell by calculating the statistical 

variance of all data available in that cell from January 1993-May 2003 (Figure 2.7.2). Variance estimates ranged 

from 0 to 10.6. The resulting map was classified into four equal area quantiles. Two quantiles representing the 

areas of least variance were highlighted (0-25% and 25-50%).

Frontal boundaries were derived from SST data using a number of tools that make up the Generic Mapping 

Tool (GMT) software package developed and maintained by Paul Wessel (University of Hawaii) and Walter H. 

F. Smith (NOAA). GMT is available under the GNU public license and can be downloaded from the University 

of Hawaii, Honolulu, HI <http://gmt.soest.hawaii.edu>. To derive frontal boundaries, each monthly mean SST 

dataset was first converted to GMT’s grid format using the GMT tool nearneighbor (Figure 2.7.3). A slope function was run across each of these monthly SST coverages using GMT grdgradient to create new coverages in 

which the magnitude of the slope was calculated for each cell. In this case, the slope represents areas containing the steepest temperature gradient, or changes in SST, across some geographic extent space. The resulting 

coverage was then classified into 20 equal areas (5% quantiles) using GMT grdhisteq and the quantile with the 

greatest slope was extracted using GMT grdclip to represent the areas with the steepest temperature gradients 

(Figure 2.7.3). These areas of steep temperature gradient serve as a proxy for SST fronts. The resulting grids 

were added together using GMT grdmath to obtain a final grid containing cells with values representing the 

number of months during which an SST front occurred in each cell (Figure 2.7.4). This coverage of SST frontal 

persistence contained cell values ranging from zero to 65 months and was classified into standard deviations. 

The highest standard deviation (+3 above the mean, or 21-66 of the 125 available months) was considered to 

represent areas of persistent SST fronts.

Broad-scale Patterns

Broad-scale patterns referred to in this section include consideration of both spatial and temporal scales. It is 

understood that sea surface temperature patterns and their related features are ephemeral depending on the 

season, year, and the state of the El Niño-Southern Oscillation (ENSO). However, because sanctuary boundaries do not change seasonally, or with the ENSO, a decision was made to look at the overall spatial and temporal 

trends through both space and time to evaluate the mean expression of SST and its effects.

Sea surface temperature analysis displays the expected gradient of cold water in the north slowly warming towards the south, ranging from 0° to 23° Celsius. Of particular interest is that seasonal changes that bring warmer 

water to the north coincide with the onset of coastal upwelling along the northern California coast (Figure 2.7.1). 

The end result is that water temperatures along the northern California coast remain cool during the summer 

months and are at times cooler in the summer than during winter months. The expression of this system is most 

evident in the low variance in SST exhibited along the coast from the northern California border to Point Conception (Figure 2.7.2). This core of cool water provides a relatively stable environment that may contribute to 

apparent formation of northern and southern biogeographic provinces, and the formation of a transitional zone 

between them that is often ascribed to the Channel Islands area (McGinnis, 2000).

Analysis of potential SST frontal zones (Figures 2.7.4 and 2.7.5) suggest that the Channel Islands region exhibits a persistent concentration of steep temperature gradients. Persistent concentrations of high frontal density 

are not present all the time. These frontal systems are defined not as a single persistent front, but as a dynamic 

region characterized by a persistent high concentration of frontal features. In the analysis (ranging from 31°-47° 

degrees latitude), the total area defined as having a high concentration of persistent SST fronts was roughly 
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

70,000 km2, approximately 2% of the analysis area. Most the high area was confined to the coastline, with the 

largest area of frontal persistence observed in the area around the Channel Islands, most likely generated by 

the confluence of the cool south-bound California Current and warmer northbound Davidson Current. Frontal 

features extending along the northern California coast are generated by coastal upwelling events and the many 

eddies and gyres that spin out of the upwelling areas as they join the California Current.

Analysis of Boundary Concepts

The preceding discussion identified areas of high SST frontal persistence. Those areas closest to or within the 

current study area center around Sur Ridge, just south of Monterey Bay, and the Channel Islands. A total of 70% 

of the area contained within current CINMS boundaries was classified as having high SST frontal persistence. 

As such, it is important to note that the No Action Concept (NAC, current boundary) captures a large area of high 

frontal persistence. 

Figure 2.7.1. Seasonal mean sea surface temperature (January, April, July, and October) averaged across years from 1993 to 2003.
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Mean SST frontal persistence for the 

NAC was calculated to be 29.6 months. 

Mean frontal persistence for the remaining concepts, ranging from smallest in size to largest are as follows: 

Concept 5-27.9 months; Concept 4-

23.9 months; Concept 3-23.2 months; 

Concept 2-20.7 months; Concept 1a 

and 1-16 months. Mean frontal persistence for the study area boundary (defined in McGinnis, 2000) was estimated to be 19.4 months. Overall, mean 

SST frontal persistence decreases as 

area increases (Figure 2.7.6 and Table 

2.7.1). Because the area of high frontal persistence is proportionally concentrated around the Channel Islands 

themselves, as additional areas away 

from the islands are included, less area 

of high frontal persistence is captured.

Due to the dynamic nature of SST and 

frontal boundaries, OAI statistics were 

not generated for the boundary concepts. However, it is important to note 

Figure 2.7.2. Sea surface temperature variance, calculated from mean monthly 

values for the period January 1993 to May 2003. Cool tones represent low SST variance and warm tones represent high SST variance. Hatched area represents the 

lowest two quantiles.

Figure 2.7.3. Left panel shows an example interpolated SST grid from GMT for January 1993. Right panel shows a grid clip of the 

highest quantile from the January 1993 slope magnitude grid. Red areas indicate the top 5% of values and represent the steepest 

temperature gradients and are used to identify SST fronts.
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

that since 1993 the area around the 

northern Channel Islands has been 

centered among a large area of SST 

front activity (Figure 2.7.5). In general, 

Figure 2.7.7 suggests that there is a 

point of dimishing returns with regard to 

additional area protected versus inclusion of more frontal activity (illustrated 

by the fitted polynomial equation). Concept 2 provides a large increase in SST 

frontal area, over Concepts 3, 4, and 5, 

relative to the total area of each. Concepts 1 and 1a, on the other hand, offer only a small increase in SST frontal 

activity over Concept 2 relative to additional total area.

Summary

• A stable area of sea surface temperatures exists along the California coast, 

ranging from California’s northern border to Point Conception and extending 

offshore to 200 km.

• The Channel Islands National Marine 

Sanctuary encompasses a transition 

zone from cooler waters to the north 

and warmer waters to the south.

• A large portion of the current boundaries (~70%) include area identified as 

having persistent SST fronts. Concept 

2 provides the greatest increase in persistent SST front area before returns 

begin to diminish (area of high SST 

frontal activity versus total area).

Figure 2.7.4. Persistence of SST fronts through time. Colors indicate number of 

months during which a front was evident, with green representing fewer months (1 

std. dev. below the mean) to red (3 std. dev. above the mean).

Figure 2.7.5. Areas with greatest SST front activity through time.
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

SA

NAC 5

4

3

2

1, 1a

15

17

19

21

23

25

27

29

0 5000 10000 15000 20000 25000

Total Area (km2)

Mean Front Persistence (months)

2.8 Surface Chlorophyll and Ocean Currents

Data and Methods

Pelagic marine food webs, including that of the Southern California Bight and adjacent waters, are supported by 

phytoplankton production. Throughout the year phytoplankton serve as a food source for protozoans, zooplankton, bivalves, and larval fishes (e.g., anchovies and sardines), which in turn are the foundation of a complex food 

web that supports all coastal fauna. Analyses of chlorophyll-a and currents presented here are intended to provide context for discussion in subsequent chapters on invertebrate, fish, bird, and mammal biogeography, and 

to lay the foundation for discussing interconnectivities between oceanographic processes and biogeographic 

patterns in the region.

The surface chlorophyll data presented here were derived from remotely sensed global chlorophyll-a concentration estimates (mg/m3

) acquired using the Sea-viewing Wide Field-of-view Sensor (SeaWiFS). The purpose 

of SeaWiFS is to provide quantitative data to resolve the magnitude and variability of chlorophyll concentrations, and subsequently to estimate primary production by marine phytoplankton. Presented here are level-3 

processed data derived from the first 26 months of acquisition (November 1997-December 1999). This time 

period was selected because data for these months were readily available. Level 3 data consists of geophysical 

parameters binned to a 9x9 km (81 km2) global equal-area grid at daily, 8-day, monthly, and annual intervals. 

Level 3 geophysical parameters consist of five normalized water-leaving radiances (radiance data corrected for 

atmospheric light scattering and sun angles differing from nadir), and seven geophysical parameters derived 

from the radiance data. This level categorization was developed by NASA to indicate that the data have been 

post-processed to contain both geophysical parameters and geographic coordinates.

In addition, to gain a broader understanding of the observed large-scale patterns in surface chlorophyll, estimates 

of surface current vectors, showing both magnitude and direction are also presented. Current data were derived 

using ocean surface altimetry collected by the ERS-2 and TOPEX/POSEIDON satellites. Monthly averaged current vectors were developed for identical months and years as described for ocean color, and have a horizontal 

resolution of 0.25 degrees (~28 km). When superimposed on ocean color, these vectors exhibit clear spatial 

correlations with patterns in observed chlorophyll concentrations, and are used, in part, to set the oceanographic 

context for subsequent analyses of ocean color and other biological resource distributions in the region. All data 

and analyses presented in this section were derived from data made available through the Marine Conservation 

Biology Institute’s (MCBI) Baja California to Bering Sea Conservation Initiative (B2B). More information about 

MCBI and the Bering to Baja initiative can be found at <http://www.mcbi.org>.

To best achieve the study objectives of analyzing spatial patterns in relation to proposed boundary concepts, 

ocean color data for 26 months (November 1997-December 1999) were averaged to provide an estimate of mean 

chlorophyll-a concentration ([ChlA]) along the Pacific coast of the United States (ranging from 28o-49o latitude). 

Data from 2000 to present were available, however at the time of analysis these data were not post-processed 

and were not included. While ocean color is variable in all dimensions, a composite estimate (mean [ChlA]) was 

Figure 2.7.6. Linear regression function between concept area 

(km2) and mean persistence of SST fronts.

SA

NAC

5

4

3

2

1, 1a

2000

3000

4000

5000

6000

7000

0 5000 10000 15000 20000 25000

Total Area (km2)

High Front Persistence Area (km2

)

Figure 2.7.7. Linear regression function between total concept 

area (km2) and area of high SST frontal persistence.
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

analyzed as CINMS boundaries will not change in response to monthly variation in oceanographic condition. An 

average concentration was calculated only for grid cells in which data were present during all 26 months (Figure 

2.8.1). Excluding grid cells lacking full temporal coverage was done to minimize biases that may result from a 

disproportionate expression of seasonal [ChlA], thereby biasing the average value in that cell. Grid cells shaded 

in black indicate areas where one or more months were not available due to cloud cover, etc. Once calculated, 

mean values were then used to construct a model to estimate mean [ChlA] throughout the geographic extent using an interpolation technique (kriging) which resulted in a statistically smoothed raster surface (Figure 2.8.2). 

To accomplish this, the calculated mean [ChlA] was first assigned to a point at the center of the cell (i.e. the 

cell centroid). These point data were then tested for significant spatial autocorrelation using the Moran’s I and 

Geary’s C statistics (Kaluzny et al., 1998). Significant autocorrelation indicates that points that are nearer to one 

another tend to have more similar values than points that are far away (Legendre, 1993), and is prerequisite to 

accurate interpolation. Next, the spatial autocorrelation was described using a variogram, which summarizes the 

decrease in relatedness between pairs of points as the distance between them increases. Parameters of the 

resulting variogram were used in the kriging procedure, which provides a surface of predicted values, as well as 

a standard error map indicating regions of confidence in the accuracy of estimated mean surface chlorophyll. To 

avoid displaying estimates of modeled surface chlorophyll in areas where we have little confidence, the standard 

error map was used to clip (mask) the interpolated surface. The resulting map (Figure 2.8.3) displays interpolated mean [ChlA] for those regions where the standard error was in the lowest 30 percent. The modeled accuracy 

Figure 2.8.1. Average [ChlA] for grid cells in which data was present during November 1997-December 1999. Areas in black indicate 

grids cells where one or more months were not available. Warm tones indicate high mean [ChlA], while cool tones represent low mean 
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

was then assessed using standard cross-validation techniques. Regressing predicted values against observed 

values yielded a coefficient of determination (r2) of 0.97 (p<0.001).

Estimated patterns of ocean color should be interpreted with care, as they represent a composite of 26 months, 

some of which were considered by oceanographers to have taken place during a strong El-Niño period. As stated above, surface chlorophyll can be highly variable, and the average surface provided in this report is designed 

only to highlight areas of relatively persistent high [ChlA]. In addition to the mean [ChlA] map and analysis, mean 

monthly maps of surface chlorophyll and current vectors are also provided for 1999 (see broad-scale patterns 

below), but are not individually analyzed relative to the boundary concepts. 1999 was selected from the available 

years as it represents a relatively “normal” year in the El-Niño-Southern Oscillation (ENSO) cycle (Dandonneau 

et al., 2003). 

Broad-scale Patterns

The interpolated 26-month mean surface chlorophyll model resulted in estimated concentrations of near-surface 

chlorophyll that were higher (warm tones) nearshore than offshore throughout the analysis extent (Figure 2.8.3). 

Stippled areas on the map indicate areas where average [ChlA] was greater than 2 standard deviations above 

the mean (henceforth “high”). A conspicuous area of high [ChlA] can be seen centered on the nearshore waFigure 2.8.2. Interpolated mean chlorophyll concentration [ChlA] for the period between November 1997 and December 1999. Model 

extent ranges from 28o- 49oN. Warm tones indicate high mean [ChlA], while cool tones represent low mean [ChlA]. Areas clipped by 

the model standard error map appear black.
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

ters off Points Arguello and Conception, extending northward to Cambria, and eastward into the Santa Barbara 

Channel (SBC), settling into the Santa Barbara-Ventura Basin (refer to Figure 2.1.1). It is interesting to note that 

waters exhibiting relatively low [ChlA] can be found in the adjacent Santa Monica-San Pedro Basin which is 

deeper, and typically fed by warm, less nutrient-rich waters of the northward flowing California countercurrent. A 

second much larger area of high [ChlA] can be seen extending from just south of Monterey northward through 

the entire range of the analysis. In both cases, the seaward extension of elevated ocean color averages approximately 50 km, and then attenuates rapidly thereafter. 

Two smaller areas of relatively high [ChlA] also can be seen just offshore of Santa Monica and Newport Beach; 

however, these are likely expressions of nutrient enrichment resulting from allocthonous materials sent downstream of the Santa Anna River, and San Mateo, Malibu, and various other creeks. This may be further exacerbated by agricultural and urban runoff from the densely populated Los Angeles basin. As such, high [ChlA] is 

clearly not always a sign of a healthy marine environment. Nutrient enrichment (eutrophication) can lead to hypoxia which has the propensity to profoundly affect an ecosystem, and cause physiological stress to associated 

aquatic organisms. The Committee on Environment and Natural Resources (CENR) released a report in 2003 

which indicated that fertilizers and point source pollutants contribute the majority of nitrogen that is exported to 

marine and estuarine ecosystems in the southern California region (CENR, 2003). As such, it is not surprising to 

see this expressed in our analysis.

Figure 2.8.3. Interpolated mean chlorophyll concentration [ChlA] for the period between November 1997 and December 1999. Stippled areas on the map indicate where average [ChlA] was greater than 2 standard deviations above the mean. Warm tones indicate 

high mean [ChlA], while cool tones represent low mean [ChlA].
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Results also indicate that the complement of sanctuaries along the California coastline (Gulf of the Farallones, 

Cordell Bank, Monterey Bay, and Channel Islands) captures substantial areas of high estimated mean [ChlA]. In 

this analysis (ranging from 28o to 49o latitude), the total area identified as high [ChlA] was 97,584 km2. Roughly 

11,728 km2 (~11%) of this high area is contained within the four California Sanctuaries. Roughly 1% of the high 

area fell inside the boundaries of the Channel Islands National Marine Sanctuary (NAC). However, this represents 24% of the area contained within current CINMS boundaries. This is the smallest proportion of any California sanctuary (Gulf of the Farallones-85%, Monterey Bay-53%, and Cordell Bank-43%). 

Highest phytoplankton biomass in the region has generally been reported to occur in nearshore surface waters, 

with maxima most frequently occurring during the spring and summer upwelling season when nutrient content 

of surface waters is relatively high (Airamé et al., 2003a). Spatial and temporal trends in ocean color shown 

here corroborate these findings, with monthly mean [ChlA] maps for 1999 suggesting that peak phytoplankton 

concentrations occurred between March and June of that year (Figures 2.8.4 and 2.8.5a). Additionally, current 

vectors suggest that the spatial patterns in [ChlA] are largely controlled by geostrophic flow. This can be clearly 

seen during the summer months where current jets and filaments can be seen transporting phytoplankton offshore. Thus, many of the region’s biological resource distributions are influenced by these surface currents, and 

by other larger-scale ocean currents such as the southward flowing California Current and northward flowing 

southern California Countercurrent. This notion is not new, as many scientists have published their findings on 

the interrelationships between marine fauna and local oceanographic climate (Oedekoven et al., 2001; Ainley et 

al., 1994; Allen, 1994; Briggs et al., 1987; and Chelton et al., 1982). 

The current vectors provided here (Figures 2.8.5a,b) reveal the persistent cyclonic flow pattern discussed in 

section 2.3. These surface currents can be seen during each month south and west of Point Conception. This 

pattern of surface circulation meandered only slightly through the duration of 1999. It is interesting to note that 

during the spring and early summer, an anti-cyclonic flow pattern set up along coastal waters centered on Morro 

Bay. These counter-rotating flows were presumably the result of the convergence of the California Current and 

southern California Countercurrent. This dynamic system of eddies, gyres, jets, and filaments clearly impact the 

distribution of observable phytoplankton distribution. 

Analysis of Boundary Concepts

The preceding discussion identified a large region of high mean [ChlA] centered on Points Arguello and Conception, ranging from Cambria in the north southward along the shelf to Point Conception, where it then spreads 

eastward throughout the entire Santa Barbara-Ventura Basin. A total of 24% of the area contained within current 

CINMS boundaries was classified as having high [ChlA]. As such, it is important to note that the No Action Concept (NAC, current boundary) is reasonably well configured to capture areas of high [ChlA], and a review of the 

remaining concepts suggests that only boundary concepts 2 and 5 provide viable options in terms of optimizing 

expansion to capture areas of relatively high 

average primary productivity (Table 2.8.1). As 

discussed in Chapter 1.4, two Optimal Area Index (OAI) values are presented here. The first 

of which is calculated using the mean [ChlA] 

inside each of the concepts (henceforth “relative OAI”), and the second is calculated using 

the total area of high [ChlA] captured inside 

each boundary (henceforth “absolute OAI”).

Mean estimated [ChlA] for the NAC was calculated to be 1.22 with a coefficient of variation 

(CV) of 23.3%. Mean diversity and CV values 

for the remaining concepts, ranging from smallest in size to largest are as follows: Concept 

5-1.23, 24.3%; Concept 4-1.15, 29.0%; Concept 3-1.18, 30.4%; Concept 2-1.26, 29.3%; 

Concept 1a-1.22, 30.3%; and Concept 1-1.22, 

30.3% (Figure 2.8.6). Mean [ChlA] for the study 

Figure 2.8.4. Monthy mean [ChlA] for 1999. Estimates derived from data 

contained within the Study Area boundary. Smooth line indicates a third-order polynomial through the observed estimates.
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Figures 2.8.5a. Mean chlorophyll concentration [ChlA] for the months January to June 1999 (left to right). Altimetry derived surface 

current velocity vectors for the same time periods are superimposed on the [ChlA] surface. Large arrows indicate relatively strong flow 

(greater than mean), while small arrows indicate weak flow.
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Figures 2.8.5b. Mean chlorophyll concentration [ChlA] for the months of July to December 1999 (left to right). Altimetry derived surface 

current velocity vectors for the same time periods are superimposed on the [ChlA] surface. Large arrows indicate relatively strong flow 

(greater than mean), while small arrows indicate weak flow.
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

area boundary (defined in McGinnis, 2000) was estimated to be 1.29 with a CV of 30.3% (Figure 2.8.6). Of the 

concepts in question, only 2 and 5 exhibited a higher mean [ChlA] value than the NAC (Table 2.8.1). It is important to note that the Study Area also resulted in a high relative OAI, but is not currently under consideration 

as a concept. These results show no statistical relationship to concept size, unlike the predictable relationships 

discussed in chapter 1.4. This lack of trend is shown in Figure 2.8.7 as a linear regression function between concept area (km2

) and the mean [ChlA] value calculated within the boundary (r2=0.07, P=0.45). The relationship between concept area and absolute area of high [ChlA] is very predictable (and statistically significant), with larger 

concepts containing ever larger areas of high [ChlA]. Figure 2.8.8 shows the linear regression function between 

the total concept area (km2) and the area of high [ChlA] contained within the boundary (r2=0.99, P<0.0001). 

A more balanced metric to use in assessing the relative benefits of each concept as it relates to optimizing for 

high [ChlA] is the OAI (Table 2.8.1). While this metric decouples the predictable relationships between concept 

area and the relative and/or absolute estimate to some extent, results of the OAI are still dependent upon the 

input data – absolute vs. relative measures. As such, we’ve provided results of the OAI for both mean and area 

of high [ChlA]. Again, the OAI takes into account the proportional (%) change in [ChlA] as you step from the NAC 

to each of the concepts under consideration. It also incorporates the proportional change (%) in area from the 

NAC. Results suggest that Concept 5, the minimum expansion concept, provides maximum benefit in terms of 

the mean [ChlA] calculated for each concept (relative OAI), while concept 2 provides the highest absolute OAI 

value in terms of the area of high [ChlA] contained within each concept. Because the mean OAI incorporated a 

negative value in the numerator for concepts 1, 1a, 3, and 4 (decreased [ChlA]), the calculated value is necessarily negative. 

Summary 

• Patterns of mean [ChlA] show highest concentrations in nearshore waters during spring and summer, and in 

some cases, appear in locations known for upwelling. 

• Patterns of mean [ChlA] are clearly related surface current patterns.

• Of the boundary concepts under consideration, Concepts 2 and 5 provide relatively large increases in [ChlA] 

for their size in comparison to the NAC.

Concept Area (km2) Mean ChlA

High ChlA 

Area (km2) ∆ Area (%)

∆ Mean 

ChlA (%)

∆ High ChlA 

Area (%)

Mean 

ChlA OAI 

(relative)

High ChlA 

Area OAI 

(absolute)

NAC 3745 1.223 981 - - - - -

5 4536 1.233 1225 21.12 0.82 24.87 0.39 1.178

4 7981 1.146 2056 113.11 -6.30 109.58 -0.056 0.969

3 9044 1.176 2683 141.50 -3.84 173.50 -0.027 1.226

2 13736 1.263 4842 266.78 3.27 393.58 0.012 1.475

1a 22591 1.215 6437 503.23 -0.65 556.17 -0.001 1.105

1 22613 1.215 6414 503.82 -0.65 553.82 -0.001 1.099

SA 17093 1.291 6441 356.42 5.56 556.57 0.016 1.562

Table 2.8.1. Analysis of chlorophyll within boundary concepts. Numbers in bold indicate an increase in the estimate when compared to 

the No Action Concept (NAC). Maximum calculated OAI numbers are shaded in gray. Delta (D) indicates a rate of change calculation, 

and is always expressed as a percent change from the NAC.
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Figure 2.8.6. Interpolated mean chlorophyll concentration [ChlA] for the period between November 1997 and December 1999 with 

boundary concepts overlain. The No Action Concept (NAC) is shown as a red line, while the concepts are shown as a black line.

Boundary Concept 5 Boundary Concept 4

Boundary Concept 3 Boundary Concept 2

Boundary Concept 1 Study Area
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

2.9 SUBMERGED AQUATIC VEGETATION DISTRIBUTION: EELGRASS AND SURFGRASS

Data and Methods

Data presented here are a compilation of all currently available quality controlled submerged aquatic vegetation (SAV) GIS data sets for the west coast of the United States, ranging from 33o

 to 49o north latitude. The data 

were compiled from seventeen data sources. These sources were acquired over a large range of time periods, 

collected at several different spatial resolutions, and were collected using a variety methods, including: 1) aerial 

photography, 2) videography, 3) multispectral sensors, 4) sonar, and 5) standard field surveys. The temporal 

range of data used in this composite view of SAV is from 1987 through 2003. Data were originally developed 

for the Pacific States Marine Fisheries Commission in cooperation with the National Marine Fisheries Service 

(NMFS) Northwest Region and the Pacific Fishery Management Council to support the designation and conservation of Essential Fish Habitat (EFH) for Pacific Coast groundfishes. Data were consolidated and integrated in 

a GIS format to support spatially explicit habitat modeling and impact assessment on a coast-wide scale. 

This SAV dataset was developed by TerraLogic GIS, Inc., and was published in April 2004. Data developers 

urge caution when analyzing and interpreting the data, as they merely represent a regional (i.e., not persistent 

through time) view of SAV locations. It is also important to note that the distribution of SAV’s can be quite ephemeral. Areas without mapped SAV may contain seagrass; however, digital data were unavailable during this data 

compilation. To analyze distributions of SAV in relation to proposed concepts, the total area of SAV was estimated within each boundary. The area calculated represents combined areas of Eelgrass (Zostera marina) and 

surfgrass (Phyllospadix spp.).

Z. marina occurs from Alaska to Baja California along the Pacific coast of North America. Eelgrass beds are 

generally considered to be extremely productive habitats that support a rich assemblage of fish species, and 

provide refugia for the larval and juvenile stages (Valle et al., 1999; Leet et al., 2001). Eelgrass habitat also is an 

important resource for birds, as it has an associated rich benthic faunal community that provides forage species 

for waterfowl and other marine birds. In California’s bays and estuaries north of Monterey Bay, eelgrass also 

provides spawning habitat for Pacic herring (Clupea pallasii). Subsequently, birds such as scooters (Melanitta

spp.), bufehead (Bucephala albeola), and goldeneyes (Bucephala clangula), eat eggs deposited onto eelgrass 

by C. pallasii during the mid-winter spawn. In addition, birds such as surface-feeding ducks and the black brant 

(Branta nigricans) feed directly on eelgrass (Leet et al., 2001). Surfgrass (Phyllospadix spp.) is also considered 

to be a highly productive living habitat that provides shelter and resources for a variety of taxa (Stewart and Myers, 1980), including many fishes and invertebrates, such as the California spiny lobster (Panulirus interruptus) 

(Engle, 1979). 

Because SAV’s provide critical habitat for such a wide range of biological resources, the assumption of this 

analysis was that boundaries which encompass a larger area of SAV habitat are preferred, and that any addition 

of this habitat provides the potential for increased habitat and biological diversity. The total area of SAV habitat 

within each concept was then used in calculating an Optimal Area Index (OAI). Because estimates of SAV denFigure 2.8.8. Linear regression function between the total concept area (km2

) and area of high [ChlA].

SA

NAC 5

4

3

2

1, 1a

900

1700

2500

3300

4100

4900

5700

6500

0 5000 10000 15000 20000 25000

Total Area (km2)

[Chla] Area (km2

)

Figure 2.8.7. Linear regression function between concept area 

(km2) and mean [ChlA].

SA

NAC

5

4

3

2

1, 1a

1.10

1.15

1.20

1.25

1.30

0 5000 10000 15000 20000 25000

Total Area (km2)

Mean [Chla]
Chapter 2

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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

sity, standing stock, or biomass were unavailable (only the estimated area and distribution were available), only 

results of the absolute OAI are provided (see Section 1.4 for further discussion on OAI). 

Broad-scale Patterns

SAV distribution along the California coastline is patchy and discontinuous, with long stretches of the central 

coast – from San Francisco Bay south to Morro Bay – generally lacking significant areas of coverage (Figure 

2.9.1). It is important to note that the polygons that portray SAV distribution in this map have been greatly exaggerated so that they can be seen at this scale. Data indicate that Zostera can be found throughout the range of 

this analysis, while Phyllospadix is generally more abundant south of Point Arguello. Leet et al. (2001) reported 

that SAV’s are found to some degree in all of California’s larger bays and estuaries, including Humboldt Bay, 

Tomales Bay, San Francisco Bay, Monterey Bay, Morro Bay, and San Diego/Mission Bay. Furthermore, SAV’s 

are well established in several smaller open estuarine embayments along the coast. Maps presented here corroborate these reports.

In all, a total of 317 km2 of SAV was mapped along the entire Pacific coastline of the coterminous U.S. Maps indicate that the complement of National Marine Sanctuaries along the California coastline (Gulf of the Farallones, 

Cordell Bank, Monterey Bay, and Channel Islands) do not capture large areas of SAV. Of the four California 

sanctuaries, only the Gulf of the Farallones and Channel Islands capture measurable areas of SAV. Gulf of the 

Farallones NMS contains approximately 22 km2 (~7% of total mapped), while the CINMS contains approximately 

19 km2 (~6% of total mapped). Of these two sanctuaries, only the Channel Islands contained both Zostera marina and Phyllospadix spp.

125°W

125°W

124°W

124°W

123°W

123°W

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

117°W

117°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

37°N37°N

38°N38°N

39°N39°N

0 25 50 100 150

Kilometers

Submerged Aquatic Vegetation

Eelgrass

Surfgrass

Data Source:

NOAA National Marine Fisheries Service

Pacific States Marine Fisheries Commision

Pacific Fishery Management Council

Terra Logic GIS, Inc.

Eelgrass

Surfgrass

(Zostera marina)

(Phyllospadix spp.)

Figure 2.9.1. Distribution of submerged aquatic vegetation within coastal California waters.
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Figure 2.9.2. Distribution of submerged aquatic vegetation within the proposed boundary concepts in southern California.

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

Submerged Aquatic Vegetation

Eelgrass

Surfgrass

Data Source:

NOAA National Marine Fisheries Service

Pacific States Marine Fisheries Commision

Pacific Fishery Management Council

Terra Logic GIS, Inc.

Eelgrass

Surfgrass

(Zostera marina)

(Phyllospadix spp.)

0 15 30 60 90 120

Kilometers

Boundary Concept 2

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

Submerged Aquatic Vegetation

Eelgrass

Surfgrass

Data Source:

NOAA National Marine Fisheries Service

Pacific States Marine Fisheries Commision

Pacific Fishery Management Council

Terra Logic GIS, Inc.

Eelgrass

Surfgrass

(Zostera marina)

(Phyllospadix spp.)

0 15 30 60 90 120

Kilometers

Boundary Concept 3

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

Submerged Aquatic Vegetation

Eelgrass

Surfgrass

Data Source:

NOAA National Marine Fisheries Service

Pacific States Marine Fisheries Commision

Pacific Fishery Management Council

Terra Logic GIS, Inc.

Eelgrass

Surfgrass

(Zostera marina)

(Phyllospadix spp.)

0 15 30 60 90 120

Kilometers

Boundary Concept 5

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

Submerged Aquatic Vegetation

Eelgrass

Surfgrass

Data Source:

NOAA National Marine Fisheries Service

Pacific States Marine Fisheries Commision

Pacific Fishery Management Council

Terra Logic GIS, Inc.

Eelgrass

Surfgrass

(Zostera marina)

(Phyllospadix spp.)

0 15 30 60 90 120

Kilometers

Boundary Concept 4

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

Submerged Aquatic Vegetation

Eelgrass

Surfgrass

Data Source:

NOAA National Marine Fisheries Service

Pacific States Marine Fisheries Commision

Pacific Fishery Management Council

Terra Logic GIS, Inc.

Eelgrass

Surfgrass

(Zostera marina)

(Phyllospadix spp.)

0 15 30 60 90 120

Kilometers

Boundary Concept 1

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

Submerged Aquatic Vegetation

Eelgrass

Surfgrass

Data Source:

NOAA National Marine Fisheries Service

Pacific States Marine Fisheries Commision

Pacific Fishery Management Council

Terra Logic GIS, Inc.

Eelgrass

Surfgrass

(Zostera marina)

(Phyllospadix spp.)

0 15 30 60 90 120

Kilometers

Study Area
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Analysis of Boundary Concepts

While the total area of SAV within the 

current sanctuary boundary is modest 

(Table 2.9.1), it contributes approximately 46% of the total SAV beds contained within National Marine Sanctuaries along the California coast. Concepts 

1, 1a, and 2 would encompass an additional 0.41 km2

, as would the study 

area boundary. This amounts to a 2% 

increase in area of SAV for concepts 

1, 1a, and 2. Concept 3 would capture 

an additional 0.11 km2, or an increase 

of 0.5%. Figures 2.9.2 show the distribution of SAV beds relative to each 

boundary concept. Results exhibit a 

predictable and statistically significant 

positive relationship to concept size (at a=0.05). This relationship is shown in Figure 2.9.3 as a linear regression 

function between concept area (km2) and the area of SAV 

contained within each concept (r2=0.83, P=0.001). 

The optimal area index (OAI) values suggest that concepts 3, 2, 1, and 1a all would result in a net benefit in 

terms of SAV distributions, and that concept 2 would provide maximum benefit. This is due to the fact that while 

concepts 1 and 1a are substantially larger, they contain 

no further SAV beds than what is captured in concept 2.

Summary 

• SAV distribution along the coast of California is patchy 

and discontinuous.

• The Channel Islands NMS presently contributes 46% of 

all mapped SAV beds contained within all four California sanctuaries.

• Of the boundary concepts under consideration, concept 2 provides relatively large increases in SAV area for 

its size.

2.10 KELP DISTRIBUTION

Data and Methods 

Data presented here delineate kelp bed distribution (primarily Nereocystis leutkeana and Macrocystis spp.) 

along the coast of California, ranging from 32°-41° latitude. These data, developed by the California Department 

of Fish and Game (CDFG) Marine Region, are a subset of an entire west coast dataset that was used to support 

the National Marine Fisheries Service (NMFS) in the development of an environmental impact statement (EIS) 

that considers the designation of Essential Fish Habitat (EFH) for Pacific coast groundfish. Kelp data were digitized for use in a GIS from scanned aerial photos (1989 and 1999) and digital multispectral video data (2002). 

Assessments of accuracy for the abundance and distribution of kelp are uncertain due to the various sampling 

methods. Additionally, the strong association of kelp and the variability of oceanographic and climatic conditions 

may affect overall map accuracy. Therefore, kelp bed locations and extent may not reflect current or past conditions; however, the data are useful in identifying general patterns of kelp distribution and to highlight areas that 

have been known to support kelp growth. In this chapter, kelp data were analyzed to characterize its distribution 

along the coast of California and to compare spatial kelp coverage within the proposed boundary concepts.

Concept Area (km2)

SAV Area 

(km2)

∆ Area 

(%)

∆ SAV Area 

(%)

OAI 

(absolute)

NAC 3475 19.00 - - -

5 4538 19.00 21.12 0.00 0.000

4 7981 19.00 113.11 0.00 0.000

3 9044 19.11 141.50 0.58 0.004

2 13736 19.41 266.78 2.16 0.008

1a 22591 19.41 503.23 2.16 0.004

1 22613 19.41 503.82 2.16 0.004

SA 17093 19.41 356.42 2.16 0.006

Table 2.9.1. Analysis of SAV distribution within boundary concepts. Numbers in bold 

indicate an increase in the estimate when compared to the No Action Concept (NAC). 

OAI estimates shaded in gray represent maximum observed benefit. Delta (D) indicates a rate of change calculation, and is always expressed as a percent change from 

the NAC.

Figure 2.9.3. Linear regression function between area of SAV 

distribution and total area of boundary concepts.

SA

NAC 5 4

3

2

1, 1a

18.8

19.0

19.2

19.4

19.6

0 5000 10000 15000 20000 25000

Total Area (km2)

SAV Area (km2

)
Chapter 2

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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Kelp forests provide habitat that supports a vast trophic web. Species of 

polychaetes, amphipods, decapods, 

gastropods, and ophiuroids are common 

among kelp holdfasts, while sponges, 

tunicates, anemones, cup corals, and 

bryozoans are frequently found under 

kelp canopies. Kelp also provides refuge for many species of young-of-theyear and juvenile fishes, such as señorita (Oxyjulius californica) and surfperch 

(Brachyistius frenatus), which are common throughout the canopy. Several 

species of rockfish are abundant in kelp 

forests: blue rockfish (Sebastes mystinus), olive rockfish (S. serranoides), 

and black rockfish (S. melanops). Furthermore, kelp forests provide a large 

source of prey for piscivorous birds, such 

as gulls, terns, snowy egrets, great blue 

herons, and cormorants. At the higher 

end of the trophic chain, many mammals seek prey items among the kelp 

structure, including: sea otters, harbor 

seals, and California sea lions (Airamé 

et al., 2003a).

Giant kelp is of significant commercial value in central and southern California, where historically 100,000 tons 

are harvested annually, most of which comes from southern California (Tarpley, 1992). During the mid 1980’s 

kelp harvesting supported an industry worth $40 million. Few studies examine the potential ecological impacts 

of intensive and repeated harvesting of 

kelp. Of these studies performed, results 

indicate that harvesting does not have a 

significant effect to kelp abundance and 

distribution; however, more studies are 

needed to understand how harvesting 

affects invertebrate and fish populations 

(Airamé et al., 2003b).

Broad-Scale Patterns 

Dense forests of kelp grow in rocky nearshore (to 40 m) habitats along the entire 

California coast (Figure 2.10.1). The GIS 

kelp data is not segregated by species; 

however, giant kelp, Macrocystis pyr-

ifera, is the predominant species south of 

Santa Cruz, while bull kelp, Nereocystis 

leutkeana, is more common to the north 

(Airamé et al., 2003a). In central California, kelp beds are typically comprised of 

narrow bands that parallel the shoreline 

due to the steepness of the shore. Extensive areas of kelp were found along 

the shoreline from Cape Mendocino 

through the Gulf of the Farallones and 

Figure 2.10.1. Kelp distribution based on aerial and multispectral surveys conducted 

by CDFG during 1989, 1999, and 2002. Kelp polygons have been enlarged for better 

viewing.

126°W

126°W

125°W

125°W

124°W

124°W

123°W

123°W

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

117°W

117°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

37°N37°N

38°N38°N

39°N39°N

40°N40°N

0 25 50 100 150 200

Kilometers

Kelp Distribution

CDFG Marine Region

1989, 1999, 2002

Nereocystis & Macrocystis spp.

Figure 2.10.2. Kelp distribution off southern California.

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

117°W

117°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

0 25 50 100

Kilometers

Kelp Distribution

CDFG Marine Region

1989, 1999, 2002

Nereocystis & Macrocystis spp.
Chapter 2

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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Monterey Bay sanctuaries. In southern California extensive 

kelp beds can extend far offshore along rocky and well stabilized sandy bottoms which have less extreme relief than 

the region to the north (Miller and Geibel, 1973). Kelp was 

also broadly distributed in southern California, with large 

concentrations found from Point Conception to Point Mugu 

and considerable amounts were contained within the Channel Islands National Marine Sanctuary and southern islands 

(Figure 2.10.2).

Analysis of Boundary Concepts 

Figure 2.10.2 displays kelp distribution in southern California. Overall, kelp comprised 1% or less of the total area 

contained within each concept and abundance was significantly greater (r2

=0.90, p>0.0003) within the larger Concepts (1, 1a, 2 and the Study Area) than within Concepts 

3, 4, 5 and the NAC (Figure 2.10.3). The OAI was used to 

compare historic kelp coverage within each boundary concept (Table 2.10.1). Results indicated that the Study 

Area provided the most favorable gain of kelp habitat, however, this boundary is not considered as a concept. 

Therefore, Concepts 2 and 3 provide the most benefit in terms of kelp abundance and total area gained relative 

to the current CINMS boundary.

Summary 

• Kelp forests provide habitat that supports many species.

• Patterns of kelp distribution are highly variable and data presented here do not reflect current conditions.

• Results of the OAI suggest that Concept 2 exhibits the greatest benefit among boundary concepts in terms of 

kelp coverage.

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Figure 2.10.3. Regression of kelp area and total area for the 

current and proposed boundary concepts. Numbers indicate 

alternatives and NAC=No Action Concept, SA=Study Area.

SA

NAC 5 4

3

2

1, 1a

15

20

25

30

35

0 5000 10000 15000 20000 25000

Total Area (km2)

Kelp Area (km2

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Concept Area (km2)

High Kelp 

Area (km2)

∆ Area 

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∆ Kelp Area 

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1a 22591 33.0 503.23 73.68 0.15

1 22613 33.0 503.82 73.68 0.15

SA 17093 33.0 356.42 73.68 0.21

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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

CHAPTER 3 – BIOGEOGRAPHY OF MACROINVERTEBRATES

Randy Clark, Wendy Morrison, M. James Allen, Larry Claflin

Several hundred species of invertebrates inhabit the mainland shelf and slope of southern California. Many of 

these species are abundant in southern California and have biogeographic breaks near Point Conception and 

CINMS. Others are more transient and have population centers north or south of the region (Figure 1.1.5). Providing an ecological assessment of all invertebrate species within the region of interest is beyond the scope of 

this chapter. This chapter examines the potential areas of habitat suitability for important commercial, ecological, 

and recreational species as determined by the CINMS. Additionally, fisheries independent monitoring data were 

analyzed to explore macroinvertebrate community structure on the southern California continental shelf. 

3.1 Single Species Habitat Suitability Models (HSM)

Data and Methods

Habitat suitability modeling (HSM) is a tool for predicting the adequacy of habitat for a given species or assemblage of species. Models are constructed as a mathematical expression to provide an index of habitat quality as 

a function of one or more environmental variables. Model development can range from qualitative to quantitative, 

and is wholly dependent on the type of data being used to model the species in question (Brown et al., 2000; 

Clark et al., 2004). These mathematical expressions can then be mapped in a geographic information system 

(GIS) to portray areas of potential distribution for a given species. 

In this chapter, deterministic models of habitat suitability 

were developed based on published ranges of bathymetry, preference for benthic substrate types, and latitudinal 

gradients for 15 macroinvertebrate species (Table 3.1.1). 

Where information was adequate, species distributions 

were mapped using a qualitative measure of suitability: 

high, medium, and low. For example, the distribution of 

the black abalone (Haliotis cracherodii) was reported to 

occur primarily from the shallow intertidal to 10 m on 

hard substrate (Leet et al., 2001). Based on these data, 

high suitability was assigned to hard substrate between 

0-10 m, moderate suitability over hard substrates between 10-30 m, and all other habitats and depth zones 

were considered low suitability. Areas of high habitat 

suitability for each species were examined relative to 

the six boundary concepts using the Optimal Area Index 

(OAI). While there are many invertebrate species distributed throughout southern California (Chapter 1.3), the 

species listed in Table 3.1.1 were determined by project 

staff to have a significant commercial, ecological, and/or 

recreational importance within the southern California 

region. 

Benthic substrate suitability was based on preferences for hard or soft substrates found in scientific literature 

combined with expert opinion. Preferred bathymetric ranges were rounded to the nearest 10 m interval to integrate with the GIS bathymetry data. The GIS bathymetric layer was developed using various sources and 

mapped at 10 m increments for the entire west coast of the U.S. Data extend from the shoreline to approximately 

4,000 m. Refer to Chapter 2.10 for a more complete description of these data. For some species, information 

on latitudinal range was not available in the literature and expert opinion was used to provide information on 

latitudinal breaks. 

Typically, fisheries independent monitoring data are used to validate habitat suitability model results (Rubec 

et al., 1999; Clark et al., 2004); however, such data were unavailable for invertebrates at the extent and scale 

needed. Thus, commercial fisheries data (Commercial Master File, CMASTR) provided by California Department of Fish and Game (CDFG) Marine Region GIS Lab were mapped and superimposed over suitability maps 

Table 3.1.1. Invertebrate species of interest for the CINMS biogeographic assessment.

Common Name Scientific Name

rock crabs Cancer spp.

black abalone Haliotis cracherodii

red abalone Haliotis rufescens

white abalone Haliotis sorenseni

California market squid Loligo opalescens

sheep crab Loxorhynchus grandis

spot shrimp Pandalus platyceros

ridgeback rock shrimp Sicyonia ingentis

California spiny lobster Panulirus interruptus

California sea cucumber Parastichopus californicus

warty sea cucumber Parastichopus parvimensis

red sea urchin Strongylocentrotus franciscanus

purple sea urchin Strongylocentrotus purpuratus
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

for comparison. These provided monthly 

summaries of abundance or total weight 

of landings within 10x10 nautical mile grids 

(Figure 3.1.1). CMASTR data were used 

for validating models for black, red, and 

white abalone, purple and red sea urchins, 

and ridgeback prawn. CMASTR data are 

landings information (in pounds) recorded 

at processing docks. Commercial trawl 

and trap logs recorded by commercial 

fishermen were used to validate models 

for California spiny lobster, California and 

warty sea cucumbers, ridgeback and spot 

prawns, and rock crabs. Validation data 

were ranked by 33rd percentile and classified as high, medium, and low to be consistent with model results. Mapped model 

results were presented at a workshop during May 2004 and reviewed by a panel of 

invertebrate biologists from CDFG and the 

University of California, Santa Barbara.

Rock crabs (Cancer spp.)

Three species of rock crab (brown, red, and yellow) were modeled together because their distribution and habitat 

preferences are reported to be similar (Carroll and Winn, 1989; Leet et al., 2001). The brown rock crab occurs 

from Washington to central Baja California, whereas the red rock crab occurs from Alaska to central Baja California, and the yellow rock crab is found from Humboldt Bay to Magdalena Bay, Baja California. Rock crab abundance is highest from low intertidal levels to subtidal depths (1-60 m), and occur over both hard and soft substrates (Morris et al., 1980; Winn, 1985). Red and brown rock crabs are found to depths of 100 m (Schmidt, 1921; 

Winn, 1985) and the yellow rock crab’s depth range may extend to 140 m (Garth and Abbott, 1980; Winn, 1985). 

Although these species occur together throughout much of their range, brown rock crabs are more abundant in 

central California, red rock crabs dominate in northern California, and yellow rock crabs are most abundant in 

southern California (Carroll and Winn, 1989). Migration patterns are not described, though they are known to 

range randomly over several kilometers. Rock crabs are predators (feeding on a wide variety of invertebrates) 

and scavengers. Longevity is estimated to be 6 years or more (Leet et al., 2001).

Large-scale commercial harvest of rock crabs using traps began in 1950. Santa Barbara and the Channel Islands 

represent major fishery areas. A minor sport fishery, using hoop nets and star traps, exists. Rock crab landings 

through 1991 have steadily increased since the fishery opened, with some fluctuation. Other sources of mortality 

include predation by fishes, octopus, sea stars, and sea otters. Rock crab populations in the study area have not 

specifically been assessed; however, experimental trapping has shown that catches are lower in commercially 

exploited areas (Gotshall and Laurent, 1979; Morris et al., 1980; Leet et al., 2001).

Broad-scale Patterns

High suitability was determined to occur over hard and soft substrate types in waters between 0-60 m. Habitats 

between 60-90 m were classified as moderately suitable. Habitats at depths between 90-140 m were considered 

low suitability and habitats at depths greater than 140 m were considered outside the species range and unsuitable. High and moderately suitable habitats are abundant throughout California waters and, when combined, 

comprise a large portion of the continental shelf (Figure 3.1.2). Considerable amounts of highly suitable habitat 

were observed within Gulf of the Farallones, Monterey Bay, and Channel Islands National Marine Sanctuaries. 

Commercial data from CDFG CMASTR landings were limited and model validation was not conducted. 

Analysis of Boundary Concepts

Within the current sanctuary boundary approximately 655 km2 was considered highly suitable habitat for rock 

crabs (Figure 3.1.3). This area was comprised of nearshore waters around the northern Channel Islands to 70 

m. A similar ratio of area was considered to be moderately suitable, while the majority of the area within the current boundary was determined to be low suitability. No additional highly suitable habitat was observed within the 

125°W

125°W

124°W

124°W

123°W

123°W

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

117°W

117°W

116°W

116°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

37°N37°N

38°N38°N

39°N39°N

40°N40°N

0 25 50 100 150 200

Kilometers

Figure 3.1.1. CDFG commercial 10x10 nm fishery landings grids. Mean monthly 

landings (pounds) were reported within designated grids from 1996-2002.
Chapter 3

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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

larger boundaries of Concepts 4 and 5. A 10% relative increase of highly suitable habitat was seen within Concept 3 as the northern boundary extended partially to the mainland. Significant increases were observed within 

Concepts 1, 1a, 2 and the Study Area as the northern boundaries contained vast amounts of shallow nearshore 

waters along the mainland (Figure 3.1.4). Analysis of the absolute OAI calculations for the predicted distribution 

of highly suitable habitat for rock crabs indicate that the Study Area yielded the greatest increase of highly suitable habitat relative to the increase in area from the No Action Concept (NAC; Table 3.1.2); however, this boundary is not under consideration. Therefore, Concepts 1 and 1a ranked highest for the OAI statistic.

Summary

• Brown, red, and yellow rock crabs share similar habitat preferences; highly suitable habitat was defined to occur over hard and soft substrates between 1-60 m.

Figure 3.1.2. Rock crab (Cancer spp.) habitat suitability off central and 

southern California.

Figure 3.1.3. Rock crab (Cancer spp.) habitat suitability off southern California.

124°W

124°W

123°W

123°W

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

37°N37°N

38°N38°N

Habitat Suitability

Low

Moderate

High

Rock Crabs

0 25 50 100 150

Kilometers

Cancer spp.

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

Habitat Suitability

Low

Moderate

High

Rock Crabs

0 25 50 100

Kilometers

Cancer spp.
Chapter 3

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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

• Of the six boundary concepts being considered, Concepts 1 and 1a provide the optimal proportional change of 

suitable habitat/total area relative to the NAC.

Black abalone (Haliotis cracherodii)

Black abalone inhabit mid-low intertidal levels to depths of 6 m from Oregon to southern Baja California (Morris 

et al., 1980). They are readily identified by dark, bluish-black coloration, a smooth shell with 5 to 7 open respiratory holes, and relatively small size (5 to 20 cm as adults). Black abalone are relatively sedentary and typically 

found clustered in wet crevices, under boulders, or on the walls of surge channels along exposed shores. Black 

abalone are typically found within the intertidal to mid-intertidal zone (1-10 m) and a few may be found at depths 

of 20 m (Ault, 1985). H. cracherodii compete with sea urchins and other crevice-dwellers for space and food 

(Miller and Lawrence-Miller, 1993; Taylor and Littler, 1979). Where abundant, abalone may be stacked on top of 

each other, reaching densities of more than 100/m2 (Douros, 1987; Richards and Davis, 1993). Black abalone 

are slow-growing and long-lived, with recruitment being low and variable (Morris et al., 1980; Van Blaricom et al., 

1993). Growth rates depend on animal size, location, food availability, reproductive condition, and other factors. 

Absolute longevity has not been determined, but ages greater than 30 years appear likely based on tagging and 

other population studies (e.g., Van Blaricom et al., 1993).

Although once an important fishery resource throughout the study area, landings peaked in 1973 and declined 

thereafter (Leet et al., 2001). Sport and commercial black abalone fisheries have been closed since 1993. H. 

cracherodii populations in southern California suffered catastrophic declines since the mid-1980s that have 

resulted in a nearly complete disappearance of black abalone along mainland shores south of Point Conception (Miller and Lawrence-Miller, 1993), as well as at many of the Channel Islands (Lafferty and Kuris, 1993; 

Richards and Davis, 1993). Mortality was associated with withering syndrome (WS), in which the foot shrinks 

and weakened individuals lose their grip on rock surfaces (Antonio et al., 2000; Friedman et al., 1997; Gardner 

et al., 1995). WS has spread to populations north of Pt. Conception in recent years (Altstatt et al., 1996). Other 

sources of mortality include smothering by sand, dislodgment by storm waves, and predation by octopus, sea 

stars, fishes, and sea otters (Morris et al., 1980; Van Blaricom et al., 1993). Impacts from oil are little known, but 

North et al. (1965) reported black abalone mortality following a spill in Baja California. Because of low recruitment, slow growth, and already decimated reproductive populations, additional mortality from oil spills would be 

devastating, and recovery prospects long-term at best. It is important to note that results of the HSM described 

below do not capture these types of information, rather they identify habitats that are potentially suitable for particular species. 

Broad-scale Patterns

Highly suitable habitat for black abalone was determined to occur in nearshore waters ranging from 0-10 m on 

hard substrates and low on hard substrates between 10-20 m. All habitats at depths greater than 20 m were 

considered unsuitable. As such, highly suitable habitat for black abalone was limited to nearshore rocky habitats 

along the mainland and islands of California, especially around the Channel Islands (Figure 3.1.5). 

SA

NAC

5

4

3

2

1, 1a

600

1000

1400

1800

2200

0 5000 10000 15000 20000 25000

Total Area (km2)

Habitat Area (km2

)

Figure 3.1.4. Regression of highly suitable habitat area 

for rock crabs and total area for the current and proposed boundary concepts. Numbers indicate concepts 

and NAC=No Action Concept, SA=Study Area. 

Table 3.1.2. Analysis of rock crab habitat suitability within boundary concepts. 

Numbers in bold indicate an increase in the estimate when compared to the 

No Action Concept (NAC). OAI estimates shaded in gray represent maximum observed benefit. Delta (D) indicates a rate of change calculation, and 

is always expressed as a percent change from the NAC.

Concept

Area 

(km2)

High 

Suitability 

Area (km2)

∆ Area 

(%)

∆ High 

Suitability 

Area(%)

OAI 

(absolute)

NAC 3475 732 - - -

5 4538 739 21.12 0.96 0.05

4 7981 739 113.11 0.96 0.01

3 9044 812 141.50 10.93 0.08

2 13736 1363 266.78 86.20 0.32

1 22613 2150 503.82 193.72 0.38

1a 22591 2150 503.23 193.72 0.38

SA 17093 2150 356.42 193.72 0.54
Chapter 3

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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Commercial statistics for black abalone were 

available from CDFG CMASTR landings data 

during 1990-1993 (Figure 3.1.6). The majority of 

landings occurred in southern California, most 

notably around San Miguel, Santa Rosa, and San 

Nicolas islands. These data were not sufficient 

for statistical comparisons with the predicted 

HSM results however, six of the nine commercial 

grids that were ranked as high overlapped areas 

of high habitat suitability, and six of eight grids 

that were ranked as moderate overlapped high 

and moderately suitable habitats.

Analysis of Boundary Concepts

Highly suitable habitat for black abalone within 

the CINMS was spread throughout the islands 

comprising 66 km2 (Figure 3.1.5). An additional 

99 km2 of area was considered moderately suitable. No additional highly suitable habitat was 

gained within Concepts 4 and 5. Highly suitable 

habitat increased within the remaining concepts 

that have boundaries which include mainland 

shoreline (Figure 3.1.7). OAI results indicate that 

Concept 2 provides the greatest proportional 

change of suitable habitat/total area relative to 

the NAC (Table 3.1.3). 

Summary

• Black abalone have a narrow range of habitat 

distribution. Highly suitable habitat occurs over 

hard substrates at depths between 0-10 m.

• Sport and commercial fisheries for black abalone are currently closed.

• Of the six boundary Concepts being considered, Concept 2 provides the optimal proportional 

change of suitable habitat for black abalone/total 

area in relation to the NAC.

Red abalone (Haliotis rufescens)

Red abalone prefer to inhabit low intertidal and subtidal rocky 

substrates to depths of 30 m from Oregon to southern Baja California (Morris et al., 1980). In southern California, red abalone 

are most abundant from the intertidal zone to 30 m with a depth 

limit of 50 m (Leighton, 1968). This colder-water abalone is relatively sedentary on reef tops or in crevices. They feed on drift 

algae and, especially when young, on microscopic algal films. 

This species may live 20 years (Leet et al., 2001). Red abalone 

were once an important fishery in California, with landings peakFigure 3.1.5. Black abalone habitat suitability off southern California.

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

Habitat Suitability

Low

Moderate

High

Black Abalone

0 50 100

Kilometers

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

Catch rank

Low

Moderate

High

Black Abalone CMASTR

1990-1993

0 50 100

Kilometers

CDFG Marine Region GIS

Lab, Monterey

SA

NAC

5

4

3

2

1, 1a

50

100

150

0 5000 10000 15000 20000 25000

Total Area (km2)

Habitat Area (km2

)

Figure 3.1.7. Regression of highly suitable habitat area 

for black abalone and total area for the current and proposed boundary concepts. Numbers indicate concepts 

and NAC=No Action Concept, SA=Study Area.

Figure 3.1.6. Black abalone commercial landings data from CDFG 

CMASTR database, 1990-1993, superimposed over predicted habitat 

suitability.
Chapter 3

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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

ing in 1967 and steadily declining thereafter (Leet 

et al., 2001). They were common or abundant in 

the study area, especially along the northwestern islands, but now are uncommon except for 

areas at Santa Rosa and San Miguel Islands. 

The red abalone commercial and sport fishery 

is currently closed, except for sport take by free 

divers in northern California. Other sources of 

mortality include predation by crabs, octopus, 

sea stars, fishes, and sea otters.

Broad-scale Patterns

High suitability habitat for red abalone was determined to consist of hard substrates at depths 

between 0-30 m. Hard substrates between 30-

40 m were classified as moderate and low to 

50 m. While these high and moderately suitable 

habitats are intermittently located throughout the 

nearshore waters of California, it is difficult 

to display them from a state-wide scale. 

Thus, results for southern California are 

displayed in Figure 3.1.8. Warmer waters 

from the south reduce suitability at locations east of the midpoint of Santa Cruz 

island (approximately 119.8° W; Barsky, 

K. pers. comm.), where suitability was defined as moderate on hard substrates between 0-30 m and low at depths between 

30-50 m. In southern California, considerable amounts of highly suitable habitat are 

situated along the mainland west of Santa 

Barbara and around San Miguel, Santa 

Rosa, and Santa Cruz Islands. Moderate 

suitability in the nearshore mainland extends east of Point Mugu and around the 

southern Channel Islands. 

Commercial information for red abalone 

were available from CDFG’s CMASTR 

landings data from 1990-1999. Although 

landings occurred from Point Reyes 

through southern California, viewing the 

overlap with model results was difficult. 

Therefore, landings from southern California superimposed over model results are shown in Figure 3.1.9. Chi-square analysis indicated a significant correlation between CDFG landings data and predicted habitat suitability.

Analysis of Boundary Concepts

Highly suitable habitat for red abalone comprises 123 km2 within the current CINMS boundary (NAC). Although Concepts 4 and 5 increased in size, no additional highly suitable habitat was included. Within these boundaries, areas of 

high and moderate suitability were found primarily around San Miguel, Santa Rosa, the western half of Santa Cruz, 

and along the mainland west of Santa Barbara (Figure 3.1.8). Regression analysis revealed significant increases of 

highly suitable habitat within Concepts 1, 1a, 2, and the Study Area boundary compared to the smaller concepts (Figure 3.1.10). Although Concepts 1 and 1a contained the greatest amount of red abalone habitat, OAI results indicated 

that Concept 2 offers the most beneficial proportional change of suitable habitat/total area relative to the NAC (Table 

3.1.4). 

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

Habitat Suitability

Low

Moderate

High

Red Abalone

0 25 50 100

Kilometers

Figure 3.1.8. Red abalone habitat suitability off southern California.

Table 3.1.3. Analysis of black abalone habitat suitability within boundary concepts. Numbers in bold indicate an increase in the estimate when compared 

to the No Action Concept (NAC). OAI estimates shaded in gray represent 

maximum observed benefit. Delta (D) indicates a rate of change calculation, 

and is always expressed as a percent change from the NAC.

Concept

Area 

(km2)

High 

Suitability 

Area (km2)

∆ Area 

(%)

∆ High 

Suitability 

Area(%)

OAI 

(absolute)

NAC 3475 66 - - -

5 4538 66 21.12 0.00 0.00

4 7981 66 113.11 0.00 0.00

3 9044 74 141.50 12.12 0.09

2 13736 113 266.78 71.21 0.27

1 22613 137 503.82 107.58 0.21

1a 22591 137 503.23 107.58 0.21

SA 17093 137 356.42 107.58 0.30
Chapter 3

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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Summary

• Highest habitat suitability for red abalone 

occurs over hard substrate within 0-30 m.

• Red abalone distribution throughout southern California is broadly dispersed throughout the Channel Islands and along the mainland.

• The commercial fishery for red abalone 

closed in 1997.

• Of the six boundary concepts being considered, Concept 2 provides the optimal proportional change of suitable habitat for red abalone/total concept area relative to the NAC.

White abalone (Haliotis sorenseni)

Historically, adult and juvenile white abalone were most abundant at depths ranging from 20-60 m in warm waters from 

southern California to southern Baja California (Morris et al., 1980; Leet et al., 2001; 

Lafferty et al., 2004). Longevity has been 

estimated to be 25 years (Gotshall and 

Laurent, 1979; Davis et al., 1996). White 

abalone are sedentary, inhabiting open, 

exposed deep-water reefs with a kelp understory. Adults consume drifting and attached macroalgae. Juveniles are cryptic, 

hiding in crevices and beneath rocks where 

they feed on microalgal films (Davis et al., 

1996). 

The white abalone fishery developed late 

due to their preferred depth range (with the 

first reported commercial landings in 1968). 

Historically, abundance was highest along 

the southern and northeastern Channel Islands. Peak landings occurred in 1972 and 

decreased thereafter (Leet et al., 2001). Average density during periods of peak harvest 

in the 1970s was one abalone/m2. Density 

has dramatically decreased since then with 

recent surveys in the study area suggesting that density has decreased to 0.0001/m2

(Davis et al., 1998). Females must be within 

a few meters of a male during spawning 

for fertilization to occur. Present population 

densities in the study area apparently preclude successful spawning. Although some 

sections of the white abalone fishery have 

been closed since 1977 and the entire fishery has been closed since 1993, densities 

have continued to fall (Davis et al., 1998; 

Figure 3.1.9. Red abalone commercial landings data from CDFG CMASTR database, 1990-1999, superimposed over predicted habitat suitability.

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

Catch rank

Low

Moderate

High

Red Abalone CMASTR

1990-1999

0 25 50 100

Kilometers

CDFG Marine Region GIS

Lab, Monterey

SA

NAC 5 4

3

2 1, 1a

100

150

200

250

0 5000 10000 15000 20000 25000

Total Area (km2)

Habitat Area (km2

)

Figure 3.1.10. Regression of highly suitable habitat 

area for red abalone and total area for the current and 

proposed boundary concepts. Numbers indicate concepts and NAC=No Action Concept, SA=Study Area.

Table 3.1.4. Analysis of red abalone habitat suitability within boundary concepts. 

Numbers in bold indicate an increase in the estimate when compared to the 

No Action Concept (NAC). OAI estimates shaded in gray represent maximum 

observed benefit. Delta (D) indicates a rate of change calculation, and is always 

expressed as a percent change from the NAC.

Concept

Area 

(km2)

High 

Suitability 

Area (km2)

∆ Area 

(%)

∆ High 

Suitability 

Area(%)

OAI 

(absolute)

NAC 3475 123 - - -

5 4538 123 21.12 0.00 0.00

4 7981 123 113.11 0.00 0.00

3 9044 148 141.50 20.33 0.14

2 13736 230 266.78 86.99 0.33

1 22613 241 503.82 95.93 0.19

1a 22591 241 503.23 95.93 0.19

SA 17093 241 356.42 95.93 0.27
Chapter 3

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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Carlton et al., 1999). Subthreshold breeding density and continued predation (e.g., 

fish, octopus, and sea stars) suggest that 

recovery without significant human intervention is unlikely. White abalone are currently a candidate species for protection 

under the Environmental Species Act. 

Submersible surveys are being carried 

out to further evaluate population status 

and to explore possibilities for collection 

of specimens for a captive breeding program.

Broad-scale Patterns

Since white abalone prefer warm waters, 

their distribution is almost the transpose of 

that for red abalone. Highly suitable habitats occur east of Point Mugu (approximately 119.8°W) over hard substrates 

between 20-60 m. Hard substrates at 

depths less than 20 m and between 60-

70 m were considered moderate, while 

hard substrates between 70-80 were low 

suitability. Hard substrates at depths between 20-70 m west of Santa Barbara 

were classified as moderate and low at 

depths from intertidal to 20 m and 60-80 

m. The distribution of white abalone are 

limited to southern California, thus habitats above 35.5°N were considered to 

be low suitability. Accordingly, 565 km2 is 

considered highly suitable for white abalone in southern California (Figure 3.1.11). 

These habitats are generally found just off 

the mainland from Santa Barbara to the 

U.S.-Mexico border. Other areas of high 

suitability encompass the southern Channel Islands (Santa Catalina, San Clemente Islands, and San Nicolas), Anacapa 

Island, the eastern half of Santa Cruz Island, and portions of Tanner and Cortez 

Banks. 

CDFG’s CMASTR landings data from 1984-1999 were compared with HSM results to provide a measure of 

model validation (Figure 3.1.12). The data were insufficient for statistical analysis; however, visual observation 

indicates that catch patterns generally agree with model results. 

Analysis of Boundary Concepts

Approximately 52 km2 of habitat was considered highly suitable for white abalone within the current CINMS 

boundary (NAC). No additional highly suitable habitat was gained within Concepts 2, 3, 4 or 5. Highly suitable 

habitats located on the mainland east of Santa Barbara were gained within Concepts 1, 1a, and the Study Area 

(Figure 3.1.13). Most of the habitat classified as highly suitable were located further south and are not included 

in any of the boundary concepts (Figure 3.1.11). Despite this, OAI results indicate that Concepts 1 and 1a offer 

the best proportional change in highly suitable habitat area/total area gained relative to the NAC (Table 3.1.5). 

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

Habitat Suitability

Low

Moderate

High

White abalone

0 25 50 100

Kilometers

Figure 3.1.11. White abalone habitat suitability off southern California.

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

Catch rank

Low

Moderate

High

White abalone CMASTR

1984-1999

0 25 50 100

Kilometers

CDFG Marine Region GIS

Lab, Monterey

Figure 3.1.12. White abalone commercial landings data from CDFG CMASTR 

database, 1984-1999, superimposed over predicted habitat suitability.
Chapter 3

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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Summary

• White abalone is a candidate species under the Endangered 

Species Act.

• Suitable habitat for white abalone was determined to occur 

over hard substrate within 20-60 m. The majority of highly suitable habitat occurs south of the proposed boundary concepts.

• Of the six boundary concepts being considered, Concepts 1 

and 1a provide the optimal proportional change of suitable habitat for white abalone/total area in relation to the NAC.

California Market Squid (Loligo opalescens)

The California market squid occurs off southern Alaska to central Baja California. The market squid is pelagic, 

inhabiting coastal waters to 800 m (PFMC, 1998). Large numbers of squid gather to spawn in semi-protected 

bays, usually over a sand bottom with rocky outcroppings. Spawning often occurs from October through May in 

the study area among squid that are from 1 to 3 years of age; however, spawning may occur at other times in 

some years (Leet et al., 2001). Spawning may occur in deep waters (Roper et al., 1984). Eggs are deposited on 

the bottom in clusters, with juveniles emerging within approximately one month. Adults die after spawning. The 

diet of squid consists of small pelagic crustaceans, fishes, and benthic worms.

Market squid have been harvested in California since 1863. The California fishery shifted its emphasis to southern California in 1961, where it is currently centered. The fishery has been marked by large-scale fluctuations 

in landings, with no apparent overall trend. The present status of populations in the this region is unclear and is 

presently being evaluated by the California Department of Fish and Game. Squid are important prey for numerous fishes, birds, and marine mammals and their eggs are eaten by benthic echinoderms (Morris et al., 1980, 

Leet et al., 2001). The market squid is one of the principal diet items of Dall’s and Risso’s dolphins, pilot whales, 

sea lions, and elephant seals (Bonnell and Dailey, 1993). 

Broad-scale Patterns

Habitat suitability for market squid was considered to be high from 0-200 m, moderate between 201-500 m, 

and low at depths between 500-800 m. Since market squid are pelagic, suitability for substrate types was not 

considered. Thus, mapped suitability based on bathymetry results in extensive areas along the continental shelf 

of California and around the islands in the south (Figure 3.1.14). Smaller areas of moderate suitability extend 

seaward of high suitability habitats, followed by a broad area of low suitability in deeper (<800 m) waters. Based 

on the model, approximately 28,000 km2 of highly suitable habitat occurs off the coast of California. 

SA

NAC 5 4 3

2

1, 1a

50

55

60

0 5000 10000 15000 20000 25000

Total Area (km2)

Habitat Area (km2

)

Figure 3.1.13. Regression of highly suitable habitat 

area for white abalone and total area for the current 

and proposed boundary concepts. Numbers indicate 

concepts and NAC=No Action Concept, SA=Study 

Area.

Table 3.1.5. Analysis of white abalone habitat suitability within boundary concepts. Numbers in bold indicate an increase in the estimate when compared 

to the No Action Concept (NAC). OAI estimates shaded in gray represent 

maximum observed benefit. Delta (D) indicates a rate of change calculation, 

and is always expressed as a percent change from the NAC.

Concept

Area 

(km2)

High 

Suitability 

Area (km2)

∆ Area 

(%)

∆ High 

Suitability 

Area(%)

OAI 

(absolute)

NAC 3475 52 - - -

5 4538 52 21.12 0.00 0.00

4 7981 52 113.11 0.00 0.00

3 9044 52 141.50 0.00 0.00

2 13736 52 266.78 0.00 0.00

1 22613 60 503.82 15.38 0.03

1a 22591 60 503.23 15.38 0.03

SA 17093 60 356.42 15.38 0.04
Chapter 3

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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

National Marine Fisheries Service (NMFS) trawl 

samples from 1999-2002 (N=1,096) were superimposed over the predicted habitat suitability 

map to test model performance. Suitability values were extracted at the trawl sample location 

(Figure 3.1.15) and compared using correspondence analysis. A positive relationship was observed with NMFS trawl catches and HSM results 

(X2<0.0001, r2=0.17). High and moderate catches 

from the NMFS data were more correlated with 

high suitability, while low catches were correlated 

with moderate and low suitability. Some biases 

must be considered when interpreting these results. The data used for this comparison were 

taken from NMFS otter trawls, where sampling 

was not equivalent throughout the bathymetric 

range of the study area. Also, otter trawls may 

not effectively capture squid; purse seines are 

typically used in the squid commercial fishery 

(NMFS, 1998). 

Analysis of Boundary Concepts

Considerable amounts of highly suitable habitat 

were included within all boundary concepts and 

more than half of the total area within the current 

CINMS boundary (NAC) was considered highly 

suitable for market squid (Figure 3.1.17). Little 

additional habitat was gained within Concept 5 

while an increase of 20% was observed within 

Concept 4. Significant gains were observed in the 

larger concepts as their northern boundaries contained habitats near the mainland. Although Concepts 1, 1a, and the Study Area contained greater amounts of highly suitable habitat, comparison 

of OAI values indicates that Concept 2 provides 

the optimal proportional change of highly suitable 

habitat/total area gained relative to that of the 

NAC (Table 3.1.6). 

Summary

•California market squid is one of the most important commercial fisheries in California.

•Market squid are pelagic and highly suitable 

habitat occurs in waters between 0-200 m.

•Model performance tested well with NMFS trawl 

data.

•Of the six boundary concepts being considered, 

Concept 2 provides the optimal proportional 

change of highly suitable habitat for market squid/

total concept area relative to the NAC.

124°W

124°W

123°W

123°W

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

37°N37°N

38°N38°N

Log Abundance

0.30 - 0.47

0.48 - 1.14

1.15 - 3.78

California Market Squid

0 25 50 100 150 200

Kilometers

NMFS Trawl Survey

1999-2002

Figure 3.1.15. Location of NMFS trawls (1999-2002) and squid mean log 

abundance superimposed over predicted habitat suitability.

124°W

124°W

123°W

123°W

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

37°N37°N

38°N38°N

Habitat Suitability

Low

Moderate

High

California Market Squid

0 25 50 100 150 200

Kilometers

Figure 3.1.14. California market squid habitat suitability off central and 

southern California.

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

Habitat Suitability

Low

Moderate

High

California Market Squid

0 25 50 100

Kilometers

Figure 3.1.16. California market squid habitat suitability off southern 

California.
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Sheep crab (Loxorhynchus grandis)

The sheep crab is the largest member of the California spider crabs, with an adult carapace length 

ranging from 4-9 inches. They range from Cordell 

Bank to Baja California in sand or rocky substrate 

at depths ranging from 3-124 m. Male crabs winter in deep water, but both sexes migrate onshore 

in early spring to mate (Leet et al., 2001).

The Santa Barbara Channel and waters offshore of the northern Channel Islands represent 

major fishery areas for the sheep crab. Largescale commercial harvest of sheep crab whole 

body and claws began in 1984 and the fishery 

peaked in 1988, with retail values totaling $1.9 

million/year. Landings declined after 1990 when 

the use of gillnets was banned in shallow water, 

and again in 1994 when gillnets were universally 

phased out (Leet et al., 2001). 

Broad-scale Patterns

Sheep crab suitability was defined as high for both hard and soft substrate at depths from 10-60 m. Moderate 

suitability extended from intertidal waters to 10 m and between 60-100 m. Habitat suitability was low at depths 

between 100-130 m. Sheep crabs are most abundant south of Point Conception, thus habitats in waters less 

than 60 m, above 35°N were considered moderate suitability while remaining habitat was defined as low. Suitability was considered low for all habitats north of Point Reyes. As such, highly suitable habitat is abundant 

throughout southern California waters, extending along the mainland from Point Conception south to San Diego, 

and encompassing the Channel Islands (Figure 3.1.18). Moderately suitable habitat extends northward through 

the central California sanctuaries. 

Sheep crab landings data, recorded as mean pounds during 1996-2000, superimposed over HSM results are 

presented in Figure 3.1.19. During this time period, the commercial fishery was most active in southern California, most notably between the northern Channel Islands and Santa Catalina Island. Landings data were insufficient to compare statistically; however, upon further observation the landings data do not correlate well with 

HSM results. Landings data indicate that sheep crabs were harvested at depths deeper than that predicted as 

suitable. 

Analysis of Boundary Concepts

Approximately 20% of the total area (739 km2) within the current CINMS boundary was considered highly suitable 

SA

NAC

5

4

3

2

1, 1a

2000

3000

4000

5000

6000

0 5000 10000 15000 20000 25000

Total Area (km2)

Habitat Area (km2

)

Figure 3.1.17. Regression of highly suitable habitat area for California market squid and total area for 

the current and proposed boundary concepts. Numbers indicate concepts and NAC=No Action Concept, 

SA=Study Area. 

Table 3.1.6. Analysis of California market squid habitat suitability within 

boundary concepts. Numbers in bold indicate an increase in the estimate 

when compared to the No Action Concept (NAC). OAI estimates shaded 

in gray represent maximum observed benefit. Delta (D) indicates a rate 

of change calculation, and is always expressed as a percent change from 

the NAC.

Concept

Area 

(km2)

High 

Suitability 

Area (km2)

∆ Area 

(%)

∆ High 

Suitability 

Area(%)

OAI 

(absolute)

NAC 3475 2145 - - -

5 4538 2212 21.12 3.12 0.15

4 7981 2583 113.11 20.42 0.18

3 9044 2809 141.50 30.96 0.22

2 13736 4181 266.78 94.92 0.36

1 22613 5699 503.82 165.69 0.33

1a 22591 5681 503.23 164.85 0.33

SA 17093 5699 356.42 165.69 0.46

123°W

123°W

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

34°N34°N

35°N35°N

36°N36°N

37°N37°N

38°N38°N

Habitat Suitability

Low

Moderate

High

0 25 50 100 150

Kilometers

Sheep Crab

Figure 3.1.18. Sheep crab habitat suitability off central and southern 

California.
Chapter 3

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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

habitat for sheep crab (Figure 3.1.20). No highly 

suitable habitat was gained within the slightly 

larger Concepts 4 and 5. Concepts 1, 1a, 2, and 

the Study Area gained substantial amounts of 

highly suitable habitat with the inclusion of area 

near the mainland. Concepts 1 and 1a contained 

nearly three times the amount of highly suitable 

habitat than the NAC (Figure 3.1.21) and had the 

highest OAI value (Table 3.1.7). 

Summary

• Sheep crab are most abundant from Point Conception south to Baja California; highly suitable 

habitat was considered to occur over hard and 

soft substrates at depths between 10-60 m.

• The commercial fishery for sheep crab primarily 

occurs in southern California.

• Of the six boundary concepts being considered, 

Concepts 1 and 1a provided the best proportional 

gain of highly suitable habitat/total area relative 

to the NAC.

Spot shrimp (Pandalus platyceros)

Spot shrimp occur on rocky substrates in deep 

water (45–487 m) from Alaska to San Diego (Leet 

et al., 2001). Adults are generally found at the 

deeper end of this range, while juveniles are typically found in shallower waters (Sunada, 1984). 

The diet of spot shrimp consists of small crustaceans, plankton, molluscs, polychaetes, sponges, and carcasses (O’Clair and O’Clair, 1998). 

This species may live for more than 6 years.

A commercial fishery using trawling gear and 

traps began in southern California in 1974 (Leet 

et al., 1992). Spot shrimp populations have not 

been well studied. Landings have fluctuated 

Figure 3.1.20. Sheep crab habitat suitability off southern California.

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°

Hab Nitat Suitability

Low

Moderate

High

0 25 50 100

Kilometers

Sheep Crab

Figure 3.1.19. Sheep crab commercial landings data from CDFG CMASTR 

database, 1996-2000, superimposed over predicted habitat suitability.

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

Catch rank

Low

Moderate

High

0 25 50 100

Kilometers

Sheep Crab CMASTR

1996-2000

CDFG Marine Region GIS

Lab, Monterey

SA

NAC

5

4 3

2

1, 1a

700

1300

1900

2500

0 5000 10000 15000 20000 25000

Total Area (km2)

Habitat Area (km2

)

Figure 3.1.21. Regression of highly suitable habitat 

area for sheep crab and total area for the current and 

proposed boundary concepts. Numbers indicate concepts and NAC=No Action Concept, SA=Study Area.

Table 3.1.7. Analysis of sheep crab habitat suitability within boundary 

concepts. Numbers in bold indicate an increase in the estimate when 

compared to the No Action Concept (NAC). OAI estimates shaded in 

gray represent maximum observed benefit. Delta (D) indicates a rate of 

change calculation, and is always expressed as a percent change from 

the NAC.

Concept

Area 

(km2)

High 

Suitability 

Area (km2)

∆ Area 

(%)

∆ High 

Suitability 

Area(%)

OAI 

(absolute)

NAC 3475 739 - - -

5 4538 739 21.12 0.00 0.00

4 7981 739 113.11 0.00 0.00

3 9044 812 141.50 9.88 0.07

2 13736 1363 266.78 84.44 0.32

1 22613 2150 503.82 190.93 0.38

1a 22591 2150 503.23 190.93 0.38

SA 17093 2150 356.42 190.93 0.54
Chapter 3

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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

widely, with several good years followed by several poor years. Natural predators include octopus and fish. The 

northern portion of the Southern California Bight is one of the major population centers for this species (O’Clair 

and O’Clair, 1998; Leet et al., 2001).

Broad-scale Patterns

Highly suitable habitat for spot shrimp was determined to occur on hard and soft substrates within 

a wide bathymetric range (150-320 m). Moderate 

suitability extends from 40-150 m and 320-400 

m. Low suitability extended from 400-490 m. As 

such, most of California’s continental shelf is suitable habitat for spot shrimp (Figure 3.1.22), and is 

characterized by a narrow band of highly suitable 

habitat surrounded by extensive areas of moderate suitability. Considerable amounts of highly 

and moderately suitable habitat exist in southern 

California, most notably within the Santa Barbara 

Channel, the Santa Cruz Basin, and the offshore 

banks south of the Channel Islands. These areas correspond well with published areas of high 

abundance (Leet et al., 2001).

Model performance was tested with CDFG’s commercial trawl and trap logs from 1994-2001. CDFG 

commercial trap landings primarily occurred in 

southern California (Figure 3.1.23) around the 

Channel Islands and along the mainland. Landings in pounds were ranked similar to that for 

the HSM and compared using correspondence 

analysis. Chi-square analysis indicated a significant correlation (X2

=0.0072, r2=0.07). Commercial 

trawl landings (Figure 3.1.24) were more ubiquitous than trap landings, and chi-square results 

displayed a statistically significant correlation with 

model results (X2<0.0001, r2=0.17). 

Analysis of Boundary Concepts

Approximately 979 km2 of highly suitable habitat 

occurs within the current CINMS boundary (Figure 

3.1.25). Highly suitable habitat increased relatively by 12% within Concept 5, 50% within Concept 

4, and 70% within Concept 3. Significant gains of 

highly suitable habitat were observed within the 

large concepts which included areas in the northern portion of the Santa Barbara Channel and 

around Point Conception. Although Concepts 1 and 1a contained the largest amounts of highly suitable habitat 

(Figure 3.1.26), OAI results indicate that Concept 5 offered the optimal proportional change of suitable habitat 

gained/total area gained relative to the NAC (Table 3.1.8).

Summary

• Spot shrimp are typically a deep water species where highly suitable habitat occurs over hard and soft substrates at depths between 150-320 m .

• Comparisons of commercial data and habitat suitability were statistically significant.

• Of the six boundary concepts being considered, Concept 5 provided the optimal proportional change of highly 

suitable habitat for spot shrimp/total concept area relative to the NAC.

123°W

123°W

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

37°N37°N

38°N38°N

Spot Shrimp

Habitat Suitability

Low

Moderate

High

0 25 50 100 150

Kilometers

Figure 3.1.22. Spot shrimp habitat suitability off central and southern 

California.

Figure 3.1.23. Spot shrimp commercial landings data from CDFG Commercial Trap Logs, 1994-2001, superimposed over predicted habitat suitability.

123°W

123°W

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

37°N37°N

38°N38°N

Spot Shrimp-Commercial Trap

Logs, 1994-2001

Catch rank

Low

Moderate

High

0 25 50 100 150

Kilometers

CDFG Marine Region GIS

Lab, Monterey
Chapter 3

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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

Spot Shrimp

Habitat Suitability

Low

Moderate

High

0 25 50 100

Kilometers

Figure 3.1.25. Spot shrimp habitat suitability off southern California.

Figure 3.1.24. Spot shrimp commercial landings data from CDFG Commercial Trawl Logs, 1994-2001, superimposed over predicted habitat 

suitability.

123°W

123°W

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

37°N37°N

38°N38°N

Spot Shrimp-Commercial Trawl

Logs, 1994-2001

Catch rank

Low

Moderate

High

0 25 50 100 150

Kilometers

CDFG Marine Region GIS

Lab, Monterey

SA

NAC

5

4

3

2

1, 1a

900

1400

1900

2400

2900

0 5000 10000 15000 20000 25000

Total Area (km2)

Habitat Area (km2

)

Figure 3.1.26. Regression of highly suitable habitat 

area for spot shrimp and total area for the current and 

proposed boundary concepts. Numbers indicate concepts and NAC=No Action Concept, SA=Study Area.

Table 3.1.8. Analysis of spot shrimp habitat suitability within boundary concepts. Numbers in bold indicate an increase in the estimate when compared 

to the No Action Concept (NAC). OAI estimates shaded in gray represent 

maximum observed benefit. Delta (D) indicates a rate of change calculation, 

and is always expressed as a percent change from the NAC.

Concept

Area 

(km2)

High 

Suitability 

Area (km2)

∆ Area 

(%)

∆ High 

Suitability 

Area(%)

OAI 

(absolute)

NAC 3475 979 - - -

5 4538 1106 21.12 12.97 0.61

4 7981 1440 113.11 47.09 0.42

3 9044 1646 141.50 68.13 0.48

2 13736 2262 266.78 131.05 0.49

1 22613 2791 503.82 185.09 0.37

1a 22591 2780 503.23 183.96 0.37

SA 17093 2791 356.42 185.09 0.52
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Ridgeback rock shrimp (Sicyonia ingentis)

Ridgeback rock shrimp occur in subtidal depths 

(5-307 m) from Monterey Bay to central Mexico 

(Perez-Farfante, 1985). In the Southern California Bight, they are most abundant between 40-

160 m (Sunada, 1984). Preferred substrates are 

sand, shell, and mud (Sunada, 1984; Leet et al., 

2001). The diet is not well known, though it is 

suspected to be a detritus feeder as are related 

species. This species may live about 5 years.

A commercial fishery using trawling gear began 

in 1966. Landings decreased dramatically from 

1985 to 1991 (Leet et al., 2001). Surveys by the 

California Department of Fish and Game confirmed population declines since 1985. The study 

area includes one of the major population centers for this species (Leet et al., 2001).

Broad-scale Patterns

High habitat suitability was determined to occur 

over hard and soft substrates between 40-160 m. 

All substrates between 20-40 m and 160-250 m 

were considered moderate, while substrates at 

depths between 0-20 m and 250-310 m were defined as low suitability. Leet et al., (2001), state 

that ridgeback rock shrimp distribution begins 

near Monterey Bay and extends through Mexico 

and abundance is greater south of Point Conception. Therefore, suitability is higher below 35°N 

and extends southward. The distribution of high 

and moderately suitable habitat is similar to that 

of spot prawn by covering a wide region of the 

continental shelf and encompassing the Channel 

Islands (Figure 3.1.27). Highly suitable habitat is 

also found on the southern offshore banks. 

Model performance was tested with trawl data 

collected by Southern California Coastal Water Research Project (SCCWRP) during 1994 

and 1998. During these two years, 520 stations 

were sampled throughout southern California at 

depths to 200 m (Figure 3.1.28). Catches were 

ranked and compared to HSM results using chisquare analysis. Model results and catch rankings 

grouped well, although moderate catches were 

more closely correlated with high HSM rankings. 

Despite this, the comparison was statistically significant (X2

<0.0001, r2=0.20). 

Analysis of Boundary Concepts

Highly suitable habitat for ridgeback rock shrimp 

follows the contour of the continental shelf and 

encompasses a broad area around the Channel 

Islands (Figure 3.1.29). Approximately 30% of the 

area within the current CINMS boundary (1,138 

km2) was considered highly suitable habitat for 

Figure 3.1.27. Ridgeback rock shrimp habitat suitability off central and 

southern California.

Figure 3.1.28. Location of SCCWRP trawls (1994, 1998) and ridgeback 

rock shrimp mean abundance superimposed over predicted habitat suitability.

123°W

123°W

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

37°N37°N

38°N38°N

Habitat Suitability

1

2 - 5

6 - 10

0 25 50 100 150

Kilometers

Ridgeback Rock Shrimp

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

Abundance

( 1 - 14

( 15 - 79

( 80 - 2120

0 25 50 100

Kilometers

Ridgeback Rock Shrimp

SCCWRP Trawl Data

1994, 1998

Figure 3.1.29. Ridgeback rock shrimp habitat suitability off southern California.

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

Habitat Suitability

1

2 - 5

6 - 10

0 25 50 100

Kilometers

Ridgeback Rock Shrimp
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

ridgeback rock shrimp. Areas of suitable habitat increased with 

increasing concept size (Figure 3.1.30) but exhibited a relative 

decline in terms of the proportion of total area contained. Highly 

suitable habitat increased slightly within Concepts 4 and 5, while 

substantial gains were observed within the remaining concepts. 

The Study Area, which is not considered as a boundary concept, 

provided the highest OAI (Table 3.1.9) and Concept 2 produced 

the highest OAI value for the concepts under consideration. 

Summary

• Highly suitable habitat for ridgeback rock shrimp was considered to occur over hard and soft substrates at depths between 

40-160 m and below 35°N.

• The commercial fishery has declined in recent years but landings have been highest in southern California.

• Of the five boundary concepts being considered, Concept 2 provided the optimal proportional change of highly suitable habitat 

for ridgeback rock shrimp/total concept area 

relative to the NAC.

California spiny lobster (Panulirus interruptus)

California spiny lobster inhabit intertidal and 

subtidal hard substrates (to 80 m) from Monterey Bay to central Mexico, with most of the 

population occurring south of Point Conception (Morris et al., 1980; Leet et al., 2001). Juveniles (under 2 years) utilize shallow (5 m) 

vegetated reefs, especially surfgrass beds, 

as nursery habitats (Engle, 1979). Adults inhabit crevices in rocky areas, from which they emerge at night to forage on a wide variety of invertebrates, including worms, molluscs, and sea urchins. Spiny lobsters may live 30 

years or more (Leet et al., 2001).

Spiny lobsters have been commercially harvested using traps in California for over 100 years. Most of the fishery occurs in water less than 30 m deep, although the fishery has expanded to include deeper habitats. A sport 

fishery (hand capture) is popular among scuba divers in southern California. Other sources of mortality include 

predation by octopus and fishes. California spiny lobster populations have not been well studied; however, 

population levels appear to be fairly stable. Lobster populations are likely maintained by recruitment from Baja 

California facilitated by warm-water patterns over the past two decades. Landings declined from 1950 to 1975, 

then increased coincident with the establishment of escape ports for sub-legal sized lobsters in traps and development of the long-term warming trend (Leet et al., 2001).

Broad-scale Patterns

Highly suitable habitat for California spiny lobster was considered to occur over hard and soft substrates at depths 

between 0-30 m. Suitability was defined as moderate between 30-60 m, and low between 60-80 m. Leet et al., 

(2001), report that lobsters are rare north of Point Conception; therefore, suitability was considered moderate 

around Point Conception from approximately 34.5° N to 35°N and low above 35°N (Figure 3.1.31) to Monterey 

Bay. As such, highly suitable habitat is distributed primarily along the nearshore of the mainland and around the 

Channel Islands and offshore banks. 

Catch per unit effort (CPUE) data were available from CDFG’s commercial lobster fishery during 1998-2002 (Figure 3.1.32). These data were insufficient to compare statistically; however, visual observation shows that most 

landings grids occurred over habitats that were predicted to have high or moderate suitability; while, some grids 

SA

NAC 5

4

3

2

1, 1a

1200

1900

2600

3300

4000

0 5000 10000 15000 20000 25000

Total Area (km2)

Habitat Area (km2

)

Figure 3.1.30. Regression of highly suitable habitat area for ridgeback rock shrimp and total area for 

the current and proposed boundary concepts. Numbers indicate concepts and NAC=No Action Concept, 

SA=Study Area.

Table 3.1.9. Analysis of ridgeback rock shrimp habitat suitability within boundary concepts. Numbers in bold indicate an increase in the estimate when 

compared to the No Action Concept (NAC). OAI estimates shaded in gray 

represent maximum observed benefit. Delta (D) indicates a rate of change 

calculation, and is always expressed as a percent change from the NAC.

Concept

Area 

(km2)

High 

Suitability 

Area (km2)

∆ Area 

(%)

∆ High 

Suitability 

Area(%)

OAI 

(absolute)

NAC 3475 1423 - - -

5 4538 1516 21.12 6.54 0.31

4 7981 1701 113.11 19.54 0.17

3 9044 2124 141.50 49.26 0.35

2 13736 3024 266.78 112.51 0.42

1 22613 3617 503.82 145.18 0.31

1a 22591 3593 503.23 152.49 0.30

SA 17093 3617 356.42 145.18 0.43
Chapter 3

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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

with high CPUE occurred over habitats with low 

suitability. Examination of mean CPUE and maximum HSM value for each grid cell shows increasing CPUE with increasing habitat suitability. 

Analysis of Boundary Concepts

Approximately 9% (341 km2) of the current CINMS 

area (NAC) was considered highly suitable for 

spiny lobster (Figure 3.1.33). No additional gains 

of highly suitable habitat were observed within 

Concepts 4 and 5. A relative increase of 12% was 

observed within Concept 3, 55% within Concept 2, 

and 172% for the larger concepts (Figure 3.1.34). 

The Study Area provided the greatest proportional increase in suitable habitat/total area gained 

compared to the NAC; however, this boundary 

is not being considered as a concept. Of the six 

concepts under consideration, Concepts 1 and 

1a yielded the highest OAI value (Table 3.1.10).

Summary

• Highly suitable habitat for California spiny lobster occurs from Point Conception southward 

over hard and soft substrates at depths of 0-30 

m.

• CPUE data were not consistent with HSM results.

• Of the six boundary concepts being considered, 

Concepts 1 and 1a provide the optimal proportional gains in highly suitable habitat for California spiny lobster/total area relative to the NAC.

California sea cucumber (Parastichopus californicus)

California sea cucumbers inhabit low intertidal 

and subtidal waters to depths of 120 m from Alaska to central Baja California and occur mainly on 

soft-bottom habitats (Morris et al., 1980; Leet et 

al., 2001). Although relatively sedentary, they may 

move up to 4 m per day (Lambert, 1997). The 

diet of California sea cucumbers consist of detritus and small organisms, which they ingest with 

bottom sediments. This species may live up to12 

years. (Morris et al.,1980; Leet et al., 2001).

No sport fishery for this species exists. A commercial fishery for California sea cucumbers started 

in California in 1978 (Leet et al., 2001). In 1982, 

the center of the fishery shifted to southern California where they are harvested from the Santa 

Barbara channel by trawling. Other sources of 

mortality include predation by sea stars, fishes, 

and crabs.

Figure 3.1.31. California spiny lobster habitat suitablity off central and 

southern California.

Figure 3.1.32. California spiny lobster commercial landings data from 

CDFG Commercial Logs, 1998-2002, superimposed over predicted habitat suitability.

Figure 3.1.33. California spiny lobster habitat suitability off southern California.

124°W

124°W

123°W

123°W

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

37°N37°N

38°N38°N

Habitat Suitability

Low

Moderate

High

0 25 50 100 150 200

Kilometers

California Spiny Lobster

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

Mean CPUE

Low

Moderate

High

0 25 50 100

Kilometers

California Spiny Lobster

Trap Log Data, 1998-2002

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

Habitat Suitability

Low

Moderate

High

0 25 50 100

Kilometers

California Spiny Lobster
Chapter 3

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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Broad-scale Patterns

Highly suitable habitat was considered to occur 

over hard and soft habitats between 40-90 m; 

moderate suitability extends from the intertidal 

zone to 40 m and between 90-110 m. Low suitability was determined to occur between 110-120 

m. As such, highly suitable habitat is extensive 

comprising vast areas of the continental shelf 

throughout offshore California waters, including 

all national marine sanctuaries, and the southern 

islands (Figure 3.1.35). 

Trawl data from SCCWRP surveys (1994 and 

1998) were used to test model performance. 

Catches appeared to occur primarily close to the 

mainland of southern California (Figure 3.1.36) 

and around Santa Catalina Island. Chi-square results indicated a statistically significant relationship (X2

<0.0001, r2=0.09), with mean abundance 

increasing as habitat suitability increased. 

Analysis of Boundary Concepts

Highly suitable habitat comprises approximately 40% of the current CINMS boundary (Figure 

3.1.37). Slight gains were observed within Concepts 3, 4, and 5, while significant gains of highly 

suitable habitat were observed within Concepts 

1, 1a, 2, and the Study Area (Figure 3.1.38). Maximum benefit, in terms of the OAI, was observed 

for Concept 2, which yielded the best proportional 

change of highly suitable habitat/total area relative to the NAC (Table 3.1.11). The Study Area 

had the highest OAI value, but this concept is not 

under consideration.

SA

NAC 5 4

3

2

1, 1a

300

400

500

600

700

800

900

1000

0 5000 10000 15000 20000 25000

Total Area (km2)

Habitat Area (km2

)

Figure 3.1.34. Regression of highly suitable habitat 

area for California spiny lobster and total area for the 

current and proposed boundary alternatives. Numbers indicate concepts and NAC=No Action Concept, 

SA=Study Area.

Table 3.1.10. Analysis of California spiny lobster habitat suitability within 

boundary concepts. Numbers in bold indicate an increase in the estimate 

when compared to the No Action Concept (NAC). OAI estimates shaded 

in gray represent maximum observed benefit. Delta (D) indicates a rate of 

change calculation, and is always expressed as a percent change from 

the NAC.

Concept

Area 

(km2)

High 

Suitability 

Area (km2)

∆ Area 

(%)

∆ High 

Suitability 

Area(%)

OAI 

(absolute)

NAC 3475 341 - - -

5 4538 341 21.12 0.00 0.00

4 7981 341 113.11 0.00 0.00

3 9044 374 141.50 9.68 0.07

2 13736 516 266.78 51.32 0.19

1 22613 906 503.82 165.69 0.33

1a 22591 906 503.23 165.69 0.33

SA 17093 906 356.42 165.69 0.46

Figure 3.1.35. California sea cucumber habitat suitability off central and 

southern California.

Figure 3.1.36. Location of SCCWRP trawls (1994, 1998) and California 

sea cucumber mean abundance superimposed over predicted habitat 

suitability.

123°W

123°W

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

37°N37°N

38°N38°N

Habitat Suitability

Low

Moderate

High

0 25 50 100 150 200

Kilometers

California Sea Cucumber

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

Catch rank

Low

Moderate

High

0 25 50 100

Kilometers

California Sea Cucumber

SCCWRP Trawls 1994, 1998
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Summary

• Highly suitable habitat for California sea cucumbers occurs over all substrates between 40-90 m.

• A significant commercial fishery has recently developed in southern California.

• Of the five boundary concepts being considered, 

Concept 2 provided the best proportional change 

of highly suitable habitat for California sea cucumbers/total area relative to the NAC.

Warty sea cucumber (Parastichopus parvimensis)

Warty sea cucumbers habitat overlaps slightly 

with California sea cucumbers. Warty sea cucumbers occur predominantly in low intertidal waters 

to depths of 27 m from Monterey Bay to central 

Baja California and may range to 40 m (Morris et 

al.,1980; Leet et al., 2001). These warmer-water sea cucumbers are common on both soft substrates and rocky 

reefs. Warty sea cucumbers are common in the study area, though natural populations are poorly studied (Gotshall and Laurent, 1979; Morris et al., 1980). This slow-moving sea cucumber feeds on detritus and small organisms, which it ingests with bottom sediments. It may live about 12 years (Morris et al.1980; Leet et al., 2001).

No sport fishery for this species exists. A commercial fishery by hookah divers using rakes started in California in 

1978 (Leet et al., 1992). Other sources of mortality include predation by sea stars, fishes, crabs, sea otters, and 

bacterial diseases which may significantly reduce population sizes (Engle, 1994; Eckert et al., 2000).

Broad-scale Patterns

Highly suitable habitat for warty sea cucumbers was considered to occur over hard and soft substrates between 

the intertidal zone and 20 m. Moderately suitable habitats extended from 20-30 m, while habitats between 30 

-40 m were considered low suitability. Warty cucumbers are less abundant north of Point Conception (Leet et 

al., 2001), thus habitat suitability is moderate from the intertidal to 60 m north of 34.5°N to Monterey Bay (Figure 

3.1.39). Highly suitable habitat is located nearshore from Point Conception through southern California to San 

Diego. Also, highly suitable habitat encompasses most of the Channel Islands. 

No data were available for testing model performance.

SA

NAC

5

4

3

2

1, 1a

800

1150

1500

1850

2200

0 5000 10000 15000 20000 25000

Total Area (km2)

Habitat Area (km2

)

Figure 3.1.38. Regression of highly suitable habitat 

area for California sea cucumber and total area for 

the current and proposed boundary concepts. Numbers indicate concepts and NAC=No Action Concept, 

SA=Study Area.

Table 3.1.11. Analysis of California sea cucumber habitat suitability 

within boundary concepts. Numbers in bold indicate an increase in the 

estimate when compared to the No Action Concept (NAC). OAI estimates shaded in gray represent maximum observed benefit. Delta (D) 

indicates a rate of change calculation, and is always expressed as a 

percent change from the NAC.

Concept

Area 

(km2)

High 

Suitability 

Area (km2)

∆ Area 

(%)

∆ High 

Suitability 

Area (%)

OAI 

(absolute)

NAC 3475 854 - - -

5 4538 854 21.12 0.00 0.00

4 7981 854 113.11 0.00 0.00

3 9044 1095 141.50 28.22 0.20

2 13736 1651 266.78 93.33 0.35

1 22613 2117 503.82 147.89 0.29

1a 22591 2117 503.23 147.89 0.29

SA 17093 2117 356.42 147.89 0.41

Figure 3.1.37. California sea cucumber habitat suitability off southern 

California.

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

Habitat Suitability

Low

Moderate

High

0 25 50 100

Kilometers

California Sea Cucumber
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Analysis of Boundary Concepts

Less than 10% of the area (341 km2) within the 

current CINMS boundary was considered highly 

suitable for warty sea cucumbers (Figure 3.1.40). 

No additional gains of highly suitable habitat were 

contained within Concepts 4 and 5. Considerable 

gains of suitable habitat occurred within concepts 

that included area along the mainland (Figure 

3.1.41). The OAI was used to assess the relative abundance of highly suitable habitat within 

boundary concepts compared to the NAC (Table 

3.1.12). Although the Study Area ranked highest 

for the OAI, it is not a concept under consideration. Therefore, Concepts 1 and 1a are the most 

preferable for warty sea cucumber. 

Summary

• Suitable habitat for warty sea cucumbers consists of hard and soft substrates between 0-30 m 

south of 35°N.

• Of the six boundary concepts being considered, Concept 1a provided the best proportional 

change of highly suitable habitat for warty sea cucumbers/total concept area relative to the NAC.

Red sea urchin (Strongylocentrotus franciscanus)

Red urchins inhabit intertidal and subtidal rocky substrates to depths of 130 m from Alaska to central 

Baja California, but are most abundant from 10-30 

m (Bernard and Miller, 1973; Russo, 1979; Durhan 

et al., 1980; Barr and Barr, 1983). Red urchins are 

identified by their red, maroon, or black color and 

large size (Morris et al., 1980; Leet et al., 2001). 

When food is abundant, red urchins are relatively 

sedentary. However, when food is scarce, red urchin 

motility increases (to 1 m/day) (Harrold and Reed, 

1985). Red urchin spines are refuges for a variety of 

small invertebrates (including juvenile red urchins) and fishes (Tegner and Dayton, 1977). The diet of red urchins consists of a variety 

of red and brown algae, but the kelp Macrocystis is preferred. Red 

urchins compete with abalone for food and space. This species may 

live 20 years or more. (Morris et al.,1980).

A significant commercial fishery for red urchin began during the 

1970s in the study area (Leet et al., 1992). Commercial hookah divers harvest red urchins using rakes at depths of up to 33 m. Landings of red urchins increased from the beginning of the fishery until 

1989, after which the statewide fishery declined steadily through 

1996. Landings from the Channel Islands began to decline in the 

late 1970s. The relative abundance of red urchins in southern California has also declined (Carroll et al., 2000). Other sources of mortality include predation by sea stars, fishes, lobsters, and sea otters 

(Tegner and Dayton, 1981; Tegner and Levin, 1983; Rogers-Bennett, 1998; Leet et al., 2001).

SA

NAC 5 4

3

2

1, 1a

150

275

400

525

650

0 5000 10000 15000 20000 25000

Total Area (km2)

Habitat Area (km2

)

Figure 3.1.41. Regression of highly suitable habitat 

area for warty sea cucumber and total area for the current and proposed boundary concepts. Numbers indicate concepts and NAC=No Action Concept, SA=Study 

Area.

Figure 3.1.40. Warty sea cucumber habitat suitability off southern California.

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

Habitat Suitability

Low

Moderate

High

0 25 50 100

Kilometers

Warty Sea Cucumber

123°W

123°W

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

37°N37°N

38°N38°N

Habitat Suitability

Low

Moderate

High

0 25 50 100 150 200

Kilometers

Warty Sea Cucumber

Figure 3.1.39. Warty sea cucumber habitat suitability off central and 

southern California.
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Broad-scale Patterns

Highly suitable habitat for red sea urchins was considered to occur over hard and soft substrates between the depths of 10-30 m. Moderate suitability 

was assigned to depths from the intertidal zone to 10 

m and 30-90 m. Habitat suitability was considered 

low at depths between 90-130 m. As such, highly 

suitable habitat extends narrowly off the California 

mainland with broader areas located in southern 

California. Small bands of highly suitable habitat are 

also observed around the Channel Islands (Figure 

3.1.42). 

No commercial data were available for testing model 

performance. 

Analysis of Boundary Concepts

Approximately 7% (257 km2) of the total area within 

the current CINMS boundary is considered high 

suitability habitat for red sea urchins (Figure 3.1.43). 

These areas are located close to shore along the 

Channel Islands, with the greatest areas located on 

the northern shores of San Miguel and Santa Rosa 

Islands. No additional highly suitable habitat was 

gained with the increases of boundary size for Concepts 4 and 5. Suitable habitat increased by 33% 

within Concept 3, which included areas near Point 

Conception. Highly suitable habitat was significantly 

greater within Concepts 1, 1a, 2 and the Study Area 

(Figure 3.1.44), which can be attributed to increases 

in habitat near Point Conception and other nearshore 

areas along the mainland. Although the Study Area 

ranked highest for the OAI (Table 3.1.13), Concepts 

1 and 1a yielded the highest OAI ranking among the 

six concepts under consideration. 

Summary

• Highly suitable habitat for red sea urchins was determined to occur between 10-30 m on hard and 

soft substrates.

• The commercial fishery for red sea urchins occurs 

primarily in southern California. 

• Of the six boundary concepts being considered, 

Concepts 1 and 1a displayed the highest OAI ranking.

Concept

Area 

(km2)

High 

Suitability 

Area (km2)

∆ Area 

(%)

∆ High 

Suitability 

Area (%)

OAI 

(absolute)

NAC 3475 199 - - -

5 4538 199 21.12 0.00 0.00

4 7981 199 113.11 0.00 0.00

3 9044 228 141.50 14.57 0.10

2 13736 396 266.78 98.99 0.37

1 22613 625 503.82 214.07 0.42

1a 22591 625 503.23 214.07 0.43

SA 17093 625 356.42 214.07 0.60

Table 3.1.12. Analysis of warty sea cucumber habitat suitability within 

boundary concepts. Numbers in bold indicate an increase in the estimate when compared to the No Action Concept (NAC). OAI estimates 

shaded in gray represent maximum observed benefit. Delta (D) indicates a rate of change calculation, and is always expressed as a 

percent change from the NAC.

Figure 3.1.42. Red sea urchin habitat suitability off central and southern 

California.

Figure 3.1.43. Red sea urchin habitat suitability off southern California.

123°W

123°W

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

37°N37°N

38°N38°N

Habitat Suitability

Low

Moderate

High

0 25 50 100 150 200

Kilometers

Red Sea Urchin

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

Habitat Suitability

Low

Moderate

High

0 25 50 100

Kilometers

Red Sea Urchin
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Purple sea urchin (Strongylocentrotus purpuratus)

Purple urchins inhabit low intertidal and subtidal depths (to 160 m) from southern British Columbia (Canada) to 

central Baja California. They prefer intertidal and subtidal (to 30 m) rocky habitats with moderate to strong wave 

action, where they normally inhabit crevices or depressions (Kalvass, 1992). Purple urchins are identified by their 

purple color and relatively small size. The diet of purple urchins consists of a variety of red and brown algae, but 

the kelp Macrocystis is preferred. They are relatively sedentary when food is abundant, with motility increasing 

as food availability decreases (to 1 m/day) (Harrold and Reed, 1985). This species may live up to 30 years (Morris et al., 1980).

A minor fishery for purple urchins exists, but the small size and variable development of roe has precluded expansion of the fishery at this time. Other sources of mortality include predation by sea stars, fishes, lobsters, and 

sea otters (Tegner and Dayton, 1981; Tegner and Levin, 1983; Leet et al., 2001). Coincident with the decline 

of competing red urchins described above, purple urchin populations have increased markedly at many island 

sites, creating vast areas denuded of macroalgae (Harold and Reed, 1985; Ambrose et al., 1993; Engle, 1994; 

Richards et al., 1997; Carroll et al., 2000; Lafferty and Kushner, 2000).

Broad-scale Patterns

Highly suitable habitat for purple sea urchin overlaps that defined for red sea urchins (0-30 m) while moderate suitability extends to deeper waters (30-90 m). Suitability was considered low at depths between 90-160 m 

(Figure 3.1.45). As such, greater amounts of highly suitable habitat are available for purple sea urchins than for 

red urchins and the same pattern of distribution 

is observed with the exception of highly suitable 

habitat extending up to the intertidal zone for purple sea urchins.

No commercial data were available for testing 

model performance.

Analysis of Boundary Concepts

Approximately 341 km2 of suitable habitat was 

determined to occur within the current CINMS 

boundary (Figure 3.1.46). No additional gains of 

highly suitable habitat were observed within Concepts 4 and 5. Highly suitable habitat increased 

by 12% within Concept 3, while considerably 

larger gains were observed within the larger concepts (Figure 3.1.47). These gains were attributed to the inclusion of nearshore habitat along 

the mainland. Although the Study Area ranked 

SA

NAC 5 4

3

2

1, 1a

200

375

550

725

900

0 5000 10000 15000 20000 25000

Total Area (km2)

Habitat Area (km2

)

Figure 3.1.44. Regression of highly suitable habitat 

area for redsea urchin and total area for the current 

and proposed boundary concepts. Numbers indicate 

concepts and NAC=No Action Concept, SA=Study 

Area.

Table 3.1.13. Analysis of red sea urchin habitat suitability within boundary concepts. Numbers in bold indicate an increase in the estimate when 

compared to the No Action Concept (NAC). OAI estimates shaded in gray 

represent maximum observed benefit. Delta (D) indicates a rate of change 

calculation, and is always expressed as a percent change from the NAC.

Concept

Area 

(km2)

High 

Suitability 

Area (km2)

∆ Area 

(%)

∆ High 

Suitability 

Area (%)

OAI 

(absolute)

NAC 3475 257 - - -

5 4538 257 21.12 0.00 0.00

4 7981 257 113.11 0.00 0.00

3 9044 280 141.50 8.95 0.06

2 13736 472 266.78 83.66 0.31

1 22613 825 503.82 221.01 0.44

1a 22591 825 503.23 221.01 0.44

SA 17093 825 356.42 221.01 0.62

Figure 3.1.45. Purple sea urchin habitat suitability off central and southern California.

123°W

123°W

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

37°N37°N

38°N38°N

Habitat Suitability

Low

Moderate

High

0 25 50 100 150 200

Kilometers

Purple Sea Urchin
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

highest for the OAI, Concepts 1 and 1a provided 

the most beneficial proportional change of suitable habitat/total concept area gained relative to 

the NAC (Table 3.1.14). 

Summary

• Highly suitable habitat for purple urchins consists of hard and soft substrates within 0-30 m.

• Of the six boundary concepts being considered, 

Concepts 1 and 1a provided the optimal proportional change of suitable habitat/total concept 

area relative to the NAC.

Figure 3.1.46. Purple sea urchin habitat suitability off southern California.

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

Habitat Suitability

Low

Moderate

High

0 25 50 100

Kilometers

Purple Sea Urchin

SA

NAC 5 4

3

2

1, 1a

200

425

650

875

1100

0 5000 10000 15000 20000 25000

Total Area (km2)

Habitat Area (km2

)

Figure 3.1.47. Regression of highly suitable habitat 

area for purple sea urchin and total area for the current 

and proposed boundary concepts. Numbers indicate 

concepts and NAC=No Action Concept, SA=Study 

Area.

Table 3.1.14. Analysis of purple sea urchin habitat suitability within 

boundary concepts. Numbers in bold indicate an increase in the estimate when compared to the No Action Concept (NAC). OAI estimates 

shaded in gray represent maximum observed benefit. Delta (D) indicates a rate of change calculation, and is always expressed as a percent change from the NAC.

Concept

Area 

(km2)

High 

Suitability 

Area (km2)

∆ Area 

(%)

∆ High 

Suitability 

Area (%)

OAI 

(absolute)

NAC 3475 341 - - -

5 4538 341 21.12 0.00 0.00

4 7981 341 113.11 0.00 0.00

3 9044 374 141.50 9.68 0.07

2 13736 626 266.78 83.58 0.31

1 22613 1052 503.82 208.50 0.41

1a 22591 1052 503.23 208.50 0.41

SA 17093 1052 356.42 208.50 0.58
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

3.2 Macroinvertebrate Assemblage Structure

The primary objective of this chapter is to define biogeographic patterns of invertebrates and to provide an understanding of invertebrate multi-species community structure in a spatial context. Community metrics and multivariate statistics were used to analyze marine invertebrate species assemblages off southern California. Analyses 

were completed using data provided by the Southern California Coastal Water Research Project (SCCWRP) for 

41 species of demersal invertebrates. Five objectives were defined to:

• Calculate Shannon index of diversity for all invertebrates identified to species level in all trawls;

• Determine which species tend to co-occur (i.e., species assemblages); 

• Map locations that contained similar catches (i.e., site groups);

• Resolve where species assemblages were being caught by combining results from objectives 1 and 2; and, 

• Investigate boundary concepts using objectives listed above.

Data and Methods

Southern California Bight Regional Survey data obtained from SCCWRP consisted of 426 fisheries-independent 

trawl samples collected June to September in 1994 and 1998. Samples were collected with a 7.6 m headrope 

semiballoon otter trawl with 1.25 cm codend mesh towed for 5 minutes (in bays) to 10 minutes (on coast) along 

isobaths at each station, ranging in depth from from 2-215 m (Allen et al., 1998, 2002). In 1994, the survey 

targeted the mainland shelf at 10-200 m, whereas the 1998 survey added trawls near islands and within bay 

and harbor areas, sampling from 2-200 m (Allen et al., 1998, 2002). The data set contained information for 288 

invertebrate species in 426 trawls, but removal of rare species (see below) resulted in 41 species in 401 trawls 

analyzed for assemblage structure. 

Invertebrate diversity (H’) was calculated with the Shannon index of diversity (Shannon and Weaver, 1949): 

H’= -S[(ni/N) ln(ni/N)]

where ni is the number of individuals belonging to the ith species (s) in the sample, and N is the total number 

of individuals in the sample. Individual results are presented to show the distribution of effort and site diversity. 

Spatially summarized results also are provided to determine if larger spatial patterns were present that may have 

been masked by the high variability present in the individual trawl results. Using ArcGIS, 5 x 5 minute grids were 

created and mean diversity was calculated for each grid cell containing data. Results were sorted by diversity 

and divided into quintiles (i.e., each quintile contains 20% of the sites) for display. 

It is important to analyze not just the diversity of species present, but to also investigate which species are commonly found together. Clustering is “a technique for optimal grouping of entities according to the resemblance of 

their attributes as expressed by given criteria” (Boesch, 1977), or in short, a method that puts variables (sites, 

species, etc) into groups. Cluster analyses began with either a site by species, or species by site matrix of invertebrate abundances which resulted in species assemblages or site groups depending on which matrix was 

utilized. Invertebrates that were not present in at least 5% of the trawls were removed from this analysis. Rare 

species were removed because their occurrence is often due to chance, and can therefore negatively impact 

results (Boesch, 1977; Gauch, 1982). The 5% cutoff was implemented because it reduced the number of zeros 

present in the matrices, while keeping an adequate number of species for analysis. Abundance estimates were 

transformed because the raw data did not conform to assumptions of a normal distribution and homogeneity of 

variances. A fourth root transformation was utilized as it is invariant to scale changes (Field et al., 1982). Data 

were standardized by species abundances (i.e., the abundances for each species were adjusted such that the 

mean is zero and the standard deviation is one). This places all species on the same scale regardless of overall 

abundance, and ensures that abundant species did not overly influence the results. A series of exploratory analyses were performed on a variety of clustering methods to determine which consistently provided interpretable results without excessive chaining. When chaining occurs, entities fuse to a few nuclear groups one at a time rather 

than forming new groups, and make it impossible to divide the data into meaningful smaller groups (Boesch, 

1977). Two dissimilarity methods, Bray-Curtis and Jaccard (both paired with average means clustering) met 

these criteria. As such, the Bray-Curtis technique was chosen to allow for comparisons with previous analyses of 

the SCCWRP data (Allen et al., 1998, 2002). The Bray-Curtis dissimilarity coefficient (bjk) is calculated as:
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

where Xij is the ith attribute (column) measured on the jth object (row), and Xik is the ith attribute on the kth object 

(Romesburg, 1984). The Bray-Curtis dissimilarity metric often produces meaningful results with species abundance data, and is therefore one of the most widely used clustering methods in ecology (Boesch, 1977). Scree 

plots were used to determine where breaks in the similarity level occurred (McGarigal et al., 2000). Subsequently, group composition was analyzed to determine the best ecological groupings (i.e., if smaller or larger groups 

would provide a better ecological explanation; Boesch, 1977). 

To resolve where the invertebrate assemblages were being caught (i.e., interaction between species assemblages and site groups), the average frequency of occurrence for each species was calculated for each site group. 

This analysis is a modified nodal analysis (Boesch, 1977). By analyzing average frequencies for species by site 

groups, it was possible to determine which species assemblages were influential in forming the 

site groups. Spatial distribution was visualized by 

mapping the site groups in a GIS. A step-wise discriminant analysis was performed to determine if 

parameters such as depth, latitude, or effort were 

significantly different between site groups. 

Broad-scale Patterns

The SCCWRP trawls were concentrated in the 

Southern California Bight, and provided information on benthic species found in water less than 

215 m depth. Invertebrate diversity ranged from 

0-2.38 with a mean of 1.0 ± 0.6, and a median of 

0.95. Patches of high diversity exist northwest of 

San Miguel and Santa Cruz Islands, and southeast of Point Conception (Figure 3.2.1). Low diversity patches can be found on the southeast 

corner of Santa Catalina Island and within the 

Santa Barbara Channel near Carpinteria. There 

were no statistically significant relationships between diversity and any of the following: latitude 

(r2=0.0, P=0.36), longitude (r2=0.0, P=0.24), depth 

(r2=0.01, P=0.11), or effort (r2=0.0, P=0.47). 

Clustering resulted in 41 invertebrate species 

being combined into eight species assemblages 

(Table 3.2.1), and sites classified into eight distinct 

groups (Figure 3.2.2; Table 3.2.2). There was a 

clear relationship between species assemblages 

and depth. This relationship can be visualized in 

Table 3.2.2, where site groups have been ordered 

from shallow to deep. The tuberculate pear crab 

assemblage consists of shallow species, the California sand star assemblage mid-shelf species, 

and the fragile sea urchin assemblage deeper, 

offshore species. Three assemblages contain 

species that were not collected around the islands: tuberculate pear crab assemblage, shortspined sea star assemblage, and sandflat elbow 

Figure 3.2.1. Invertebrate diversity for individual SCCWRP trawls during 

1994 and 1998.

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

0 50 100

Kilometers

Invertebrate

Diversity

0.00 - 0.98

0.99 - 1.33

1.34 - 1.59

1.60 - 1.89

1.90 - 2.56

Figure 3.2.2. Location of site groups for SCCWRP survey data (1994, 

1998).

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

0 50 100

Kilometers

Invertebrate

Site Groups

1

2

4

5

8

9

10

11
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

crab. Site groups also form clear bands along a 

depth gradient off the coast (Figure 3.2.2). Site 

Groups 1 and 2 tend to be shallow and coastal, 

while Groups 4 and 8 are deeper and common 

around the islands. Groups 5, 9, 10, and 11 only 

contain a few sites each. The reasons these 

sites were isolated and grouped separately are 

not clear. Because the numbers of stations involved were so few, clustering could be a result 

of random conditions. Therefore, the clustering 

results for these groups are provided, but no 

discussion of these site groups was included. 

After using discriminate analysis (N=398) there 

were significant differences observed between 

site groups in all three parameters investigated: depth (r2

=0.79; F=213; P<0.0001), effort 

(r2=0.23; F=16; P<0.0001), and latitude (r2=0.03; 

F=2; P<0.06). Changes in areas targeted for 

trawling and large-scale weather/temperature 

patterns may have affected observed species 

assemblages. In 1994, the survey targeted the 

mainland shelf at 10-200 m, whereas the 1998 

survey added trawls near islands and within 

bay and harbor areas sampling from 2-200 m. 

In addition, 1998 was a strong El Niño year, with 

water temperatures much warmer than normal. 

The cluster results presented here, while based 

on both years (1994 and 1998), align closely 

with previous results from 1998 alone (Allen et 

al., 1998, 2002). 

The Channel Islands are divided into two main 

biogeographical provinces: the warm-temperate San Diegan and the cold-temperate Oregonian. San Miguel, Santa Rosa, and San Nicolas 

largely have Oregonian biota, while Santa Cruz 

(eastern part) Anacapa, Santa Barbara, Santa 

Catalina and San Clemente Islands contain 

San Diegan biota. Research on intertidal areas and kelp forests further separated the San Diegan province into 

two smaller groups (Murray et al., 1980; Murray and Littler, 1981; Littler et al., 1991; Pondella et al., 2005). All of 

these studies placed Santa Catalina and San Clemente into one group and Santa Barbara and Santa Cruz into 

a second group. It is important to note that most of these studies analyzed rocky intertidal flora and fauna, and 

are greatly influenced by local temperatures and conditions. The invertebrate site groups from this analysis do 

not segregate according to these provinces. However, because the SCCWRP trawls targeted benthic species 

up to 215 m, the species may be less influenced by surface currents and temperature and less likely to show the 

segregations detailed above. 

Analysis of Boundary Concepts

Areas of high invertebrate diversity were observed within the NAC and near the mainland which would be included 

within Concepts 3, 2, 1, 1a, and the Study Area. However, quantitative comparison of the boundary concepts was 

not possible due to the distribuiton of survey effort. Few samples were conducted in the deeper portions of the 

Southern California Bight and no surveys were conducted north of Point Conception. Given the available data 

highest mean invertebrate diversity was observed within Concept 2 (Figure 3.2.3). 

Table 3.2.1. Species assemblage results for SCCWRP survey data (1994, 

1998) using the Bray-Curtis dissimilarity metric with average means clustering. Assemblages are named for the most influential species in each 

group.

Group Common Name Scientific name

tuberculate pear crab 

assemblage

tuberculate pear crab

spiny sand star

blackspotted bay shrimp

navanax sea slug

yellowleg shrimp

Pyromaia tuberculata

Astropecten armatus

Crangon nigromaculata

Navanax inermis

Penaeus californiensis

shortspined sea star 

assemblage

shortspined sea star

spotwrist hermit

Pisaster brevispinus

Pagurus spilocarpus

sandflat elbow crab 

assemblage sandflat elbow crab Heterocrypta occidentalis

California sand star 

assemblage

California sand star

trailtip sea pen

California blade barnacle

mosaic sand star

fringed sand star

gray sand star

white sea urchin

red octopus

Pacific spiny brittlestar

brokenspine brittlestar

California sea cucumber

New Zealand paperbubble

California sea slug

ridgeback rock shrimp

slender sea pen

yellow sea twig

California market squid

eastern Pacific bobtail

Astropecten verrilli

Acanthoptilum spp.

Hamatoscalpellum californicum

Luidia armata

Luidia asthenosoma

Luidia foliolata

Lytechinus pictus

Octopus rubescens

Ophiothrix spiculata

Ophiura luetkenii

Parastichopus californicus

Philine auriformis

Pleurobranchaea californica

Sicyonia ingentis

Stylatula elongata

Thesea spp.

Loligo opalescens

Rossia pacifica

tower snail 

assemblage

tower snail

Alaska bay shrimp

thinbeak neck crab

rosy tritonia

Megasurcula carpenteriana

Crangon alaskensis

Podochela lobifrons

Tritonia diomedea

red sea star 

assemblage

red sea star

orange sand star

slenderclaw hermit crab

Mediaster aequalis

Astropecten ornatissimus

Paguristes turgidus

fragile sea urchin 

assemblage

fragile sea urchin

northern heart urchin

Pacific heart urchin

flagnose bay shrimp

moustache bay shrimp

California heart urchin

Allocentrotus fragilis

Brisaster latifrons

Brissopsis pacifica

Neocrangon resima

Neocrangon zacae

Spatangus californicus

sheep crab 

assemblage

sheep crab

armed box crab

Loxorhynchus grandis

Platymera gaudichaudii
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Table 3.2.2. Mean frequency of occurrence for each SCCWRP site group. N=trawl effort. Bold common names indicate principle species for each site group. Gray and blue shaded cells represent 

species presence in half or a quarter of the groups in that site, respectively.

Site Groups

2

N=87

1

N=73

5

N=4

11

N=4

4

N=182

10

N=3

9

N=7

8

N=41

tuberculate pear crab 57 34 0 0 7 0 0 0

spiny sand star 21 32 0 0 1 0 0 0

blackspotted bay shrimp 54 14 0 25 1 0 0 0

navanax sea slug 18 1 0 0 2 0 0 0

yellowleg shrimp 46 4 0 0 1 0 0 0

shortspined sea star 8 15 0 0 2 0 0 0

spotwrist hermit crab 6 10 0 0 5 0 14 0

sandflat elbow crab 3 29 0 0 6 0 0 0

California sand star 5 79 0 50 85 0 0 17

trailtip sea pen 1 1 0 0 39 0 14 22

California blade barnacle 0 7 0 0 25 0 0 10

California market squid 2 5 0 25 25 0 0 29

mosaic sand star 1 7 0 0 27 0 0 2

fringed sand star 0 3 0 0 12 0 0 0

gray sand star 1 10 0 0 46 0 14 61

white sea urchin 2 10 100 0 74 33 0 22

red octopus 0 3 25 100 29 0 0 17

Pacific spiny brittlestar 2 4 50 0 39 0 0 5

brokenspine brittlestar 0 0 0 0 20 0 14 0

California sea cucumber 5 3 0 0 63 33 0 41

New Zealand paperbubble 20 8 0 0 35 0 14 7

California sea slug 1 3 0 25 37 100 0 44

eastern Pacific bobtail 1 0 0 0 17 0 0 29

ridgeback rock shrimp 6 8 0 75 79 0 0 78

slender sea pen 3 7 0 0 20 0 0 5

yellow sea twig 0 0 0 0 28 0 43 5

tower snail 0 3 0 0 18 0 0 10

Alaska bay shrimp 1 0 0 0 12 33 0 0

thinbeak neck crab 0 1 0 0 9 33 0 5

rosy tritonia 0 1 0 0 10 0 0 7

red sea star 0 0 0 0 15 0 57 12

orange sand star 0 0 0 0 10 0 0 17

slenderclaw hermit crab 1 1 0 0 7 0 43 12

fragile sea urchin 0 0 0 0 14 0 29 95

northern heart urchin 0 0 0 0 4 0 0 59

Pacific heart urchin 0 0 0 0 1 0 0 44

flagnose bay shrimp 0 0 0 25 4 0 0 46

moustache bay shrimp 0 0 0 25 8 0 0 63

California heart urchin 0 0 0 0 9 0 0 34

sheep crab 10 1 0 0 4 0 0 2

armed box crab 0 1 0 0 9 0 0 2

Quantitative evaluation of species assemblages were not conducted but general trends can be described. Four site 

groups were located within the existing sanctuary boundary. Site groups 4 and 8 occurred most frequently within 

the current sanctuary boundary and were more numerous in the larger concepts that reached the mainland. The 

majority of the trawls were located east and south of the sanctuary and were not included in any of the concepts. 

Site groups 2, 10, and 11 were not present in the current sanctuary boundaries, however, they were included 

within the larger concepts. Site Group 2 occurred frequently along the mainland and was most prevalent south of 
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

0 50 100

Kilometers

Invertebrate

Diversity

0.00 - 0.98

0.99 - 1.33

1.34 - 1.59

1.60 - 1.89

1.90 - 2.56

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

0 50 100

Kilometers

Invertebrate

Diversity

0.00 - 0.98

0.99 - 1.33

1.34 - 1.59

1.60 - 1.89

1.90 - 2.56

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

0 50 100

Kilometers

Invertebrate

Diversity

0.00 - 0.98

0.99 - 1.33

1.34 - 1.59

1.60 - 1.89

1.90 - 2.56

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

0 50 100

Kilometers

Invertebrate

Diversity

0.00 - 0.98

0.99 - 1.33

1.34 - 1.59

1.60 - 1.89

1.90 - 2.56

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

0 50 100

Kilometers

Invertebrate

Diversity

0.00 - 0.98

0.99 - 1.33

1.34 - 1.59

1.60 - 1.89

1.90 - 2.56

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

0 50 100

Kilometers

Invertebrate

Diversity

0.00 - 0.98

0.99 - 1.33

1.34 - 1.59

1.60 - 1.89

1.90 - 2.56

Figure 3.2.3. Overlay of invertebrate diversity and CINMS boundary concepts. 

Boundary Concept 5 Boundary Concept 4

Boundary Concept 3 Boundary Concept 2

Boundary Concept 1 Study Area
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Palos Verdes Point. This group was defined by 87 shallow water trawls and these species may occur within the 

sanctuary, but shallow water trawls were limited within the sanctuary. It is recommended that additional surveys 

are needed to conduct an accurate spatial analysis for invertebrates. 

Summary

• Invertebrate diversity ranged from 0 to 2.38, with a mean of 1.0. No spatial patterns within the diversity results 

were identified. 

• Eight site groups and eight species assemblages were identified. There was a significant relationship between 

site groups and depth.

• In general, results indicate that invertebrate diversity and community structure may increase with the inclusion of 

coastal habitat that would be gained within the larger boundary concepts.

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CHAPTER 4 – BIOGEOGRAPHY OF MARINE FISHES

Randy Clark, Wendy Morrison, M. James Allen, John Christensen, Larry Claflin, Jen Casselle, Dan Pondella 

The Southern California Bight is a dynamic region of mixing water masses which leads to a wide diversity of 

organisms, including fishes. The southern range terminus of many high-latitude fishes, as well as the northern 

range terminus of equatorial species occurs around Point Conception [see Chapter 1.3] (Eschmeyer et al., 1983). 

While this chapter does not address all species that inhabit southern California waters, it does examine habitat 

suitability for 10 species of commercial, recreational, or ecological importance. Community structure analyses 

are also included, which examine data collected from groundfish trawls, recreational fishing surveys, and scuba 

surveys in kelp habitats. Finally, an assessment of larval fish abundance in southern California is described.

 

4.1 Single Species Habitat Suitability Models (HSM)

As described in Section 3.1, habitat suitability modeling (HSM) is a tool for predicting the adequacy of habitat for 

a given species or assemblage of species. Models are constructed as a mathematical expression to provide an 

index of habitat quality as a function of one or more environmental variables. Model development can range from 

qualitative to quantitative, and is wholly dependent on the type of data being used to model the species in question (Brown et al., 2000; Wright et al., 2000; Clark et al., 2004). These mathematical expressions can then be 

mapped in a geographic information system (GIS) to portray areas of potential distribution for a given species.

Currently, the National Marine Fisheries Service (NMFS) is developing an Environmental Impact Statement 

(EIS) to consider the designation of Essential Fish Habitat (EFH) for Pacific coast groundfish. Habitat suitability 

models expressed as habitat suitability probabilities (HSP), ranging from 0 (unsuitable) to 1 (most suitable), were 

developed by NMFS for federally managed groundfish based on a combination of fishery-independent trawl data 

and scientific literature (NMFS, 2004). Suitability values for species and life stages in the Groundfish Fisheries 

Management Plan (FMP) were based on three variables: benthic habitat, depth, and latitude. Model results are 

expressed as the probability that the composite of depth, substrate, and latitude at a given location is suitable 

for a given species. While many species of 

fish occur in southern California, the 12 species listed in Table 4.1.1 were determined 

to be representative of species that have 

significant commercial, ecological, and/or 

recreational importance within the southern California region. Five of these species 

(bocaccio, cowcod, lingcod, leopard shark, 

and tope [soupfin shark]) are federally managed so HSP values developed by NMFS 

for adults and juveniles were used in this 

chapter. Qualitative habitat suitability models (HSM) as described in Chapter 3.1, were 

developed for four species of non-federally 

managed fish and three federally managed 

pelagic/highly migratory species. 

Data and Methods

The framework GIS data (bathymetry, substrate type) were the same as those used by NMFS for groundfish 

HSP modeling (NMFS, 2004). Bathymetry was mapped at 10 m intervals and extended from the shoreline to 

4,000 m. Although there were 35 classifications of habitat type associated with the benthic substrate data (Chapter 2.9), information gathered from scientific literature was less resolved, and thus habitat preferences could only 

be confidently attributed to hard or soft substrate type (Chapter 2.10). The unaltered substrate map developed 

by NMFS was used to map their HSP model results while the substrate map combining NMFS and MMS data 

was used to map HSM results. 

NMFS HSP’s for federally managed groundfish were developed using trawl surveys (1977-2002) and a Habitat Use Database (HUD), which is a compendium of life history information collected from scientific literature. 

Table 4.1.1. Fish species of interest for the CINMS biogeographic assessment. 

Common and scientific names are from Nelson et al., 2004.

Common Name Scientific Name Management

thresher shark Alopias vulpinus Federal

tope (soupfin shark) Galeorhinus galeus Federal

leopard shark Triakis semifasciata Federal

Pacific angel shark Squatina californica State

Pacific sardine Sardinops sagax Federal

northern anchovy Engraulis mordax Federal

bocaccio Sebastes paucispinis Federal

cowcod Sebastes levis Federal

lingcod Ophiodon elongatus Federal

giant seabass Stereolepis gigas State

California sheephead Semicossyphus pulcher State

California halibut Paralichthys californica State
Chapter 4

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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Where information was adequate, NMFS trawl surveys were used to develop suitability values for bathymetry 

and latitude. Depth and latitude information from the HUD were used to develop suitability values for species that 

were poorly represented in NMFS trawl surveys and for developing suitability values for benthic habitat type and 

latitude for all species (NMFS, 2004). Adult bocaccio, cowcod, and lingcod suitability values for bathymetry and 

latitude were developed from NMFS survey data, while suitability values for adult leopard sharks and tope as 

well as all juvenile stages were developed from the HUD. HSP model results range from 0.01 (low habitat probability) to 0.999 (high habitat probability). Boundary analysis consited of taking a subset of the habitat information 

for the selected federally managed species and classifiying probability values by quintile (20%). The top quintile 

was determined to possess the highest habitat suitability and patterns were analyzed in the context of boundary 

concepts using the Optimal Area Index (OAI) described in Chapter 1.4. 

Suitability values for the remaining non-federally managed species (California halibut, California sheephead, giant seabass, and Pacific angel shark) were developed primarily from habitat associations described in scientific 

literature. Life history information from the Coastal Pelagic Species and Highly Migratory Species FMP’s were 

used to model northern anchovy, Pacific sardine, and thresher shark habitat suitability values. Habitat suitability 

for these species was modeled and mapped using a qualitative measure of suitability-high, medium, or low. For 

example, the distribution of California halibut (Paralichthys californicus) was reported to occur over soft substrates at depths between 0-200 m, with greater abundances occurring at 0-100 m. Based on this information, 

high suitability was assigned to soft substrates between 0-100 m, and moderate suitability on soft substrates 

between 100-200 m. Hard substrate between 0-200 m and all substrates >200 m were considered low suitability. 

OAI calculations compared the ratio of highly suitable habitat area and total area for all boundary concepts relative to that of the current Channel Islands sanctuary.

Typically, fisheries independent monitoring data are used to validate model results (Rubec et al.,1999; Clark et 

al., 2004). The models developed by NMFS for federally managed groundfish are currently being reviewed and, 

as a result, model performance was not conducted herein. Commercial Passenger Fishing Vessel data (CPFV) 

provided by California Department of Fish and Game (CDFG) were mapped and superimposed over suitability maps to assess model performance for California halibut and California sheephead. These data consist of 

abundance and location data collected by commercial party boats within CDFG’s fishing block system (a block 

represents 100 square miles). Additionally, trawl data collected by SCCWRP were used to assess California 

halibut model performance. 

Thresher shark (Alopias vulpinus)

The thresher shark is a large pelagic shark which is federally managed by the Highly Migratory Species Fisheries 

Management Plan (PFMC, 2003). Species managed by this FMP exhibit high variability in their distribution as 

they are most responsive to dynamic patterns of sea surface temperature, current patterns, and food availability. 

While information regarding thresher shark life history information is scarce, data are available from the drift gill 

net and the NMFS fishery observer program.

The thresher shark exhibits a circumglobal distribution, and is found from Goose Bay, British Columbia, to Baja 

California (Leet et al., 2001). Data from the management plan provide general descriptions of juvenile and adult 

distribution. Juveniles (<102 cm fork length) occur in oceanic waters off beaches and in shallow bays within the 

U.S.-Mexico EEZ border north to 37°N. They occur over bottom depths between 10-750 m, most commonly between 10-180 m. Adults (>66 cm fork length) also occupy oceanic waters off beaches and open coastal bays from 

the U.S.-Mexico Exclusive Economic Zone north to Cape Flattery, Washington. South of the Mendocino Escarpment, adult thresher sharks are most abundant in waters ranging in depth between 70-3,480 m (PFMC, 2003). 

The thresher shark is the leading commercial shark in California, with landings averaging 1.1 million pounds 

during 1977-1989. However, catches declined to 0.4 million pounds during 1990-1998 (Leet et al., 2001).

Broad-scale Patterns

Adult thresher shark habitat suitability was determined to be high at depths between 70-3,480 m, moderate at 

depths between 30-70 m and greater than 3,480 m, and low at depths less than 30 m. This relates to a vast 

area of high suitability along the west coast of the U.S. (Figure 4.1.1) with considerable amounts found within the 

central coast and Channel Islands sanctuaries.
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Juvenile habitat suitability was less extensive and shallower than that for adults; highly suitable habitat extends 

from the U.S.-Mexico border north to 37°N at depths between 10-180 m. Moderately suitable habitat was determined to occur in depths between 180-550 m, while depths >550 m were considered low suitability. Highly suitable habitat comprises a large portion of the continental shelf in southern California through the Monterey Bay 

NMS in central California (Figure 4.1.2). 

Analysis of Boundary Concepts

Approximately 75% of the total area of the current CINMS was considered highly suitable for adult thresher 

sharks (Figure 4.1.3). Highly suitable habitat comprised over 80% of the total area for all the boundary concepts. 

Although Concepts 1 and 1a contained the most highly suitable habitat area (Figure 4.1.4), OAI results indicate 

that Concept 4 was the most favorable of the six concepts under consideration (Table 4.1.2).

Approximately 50% of the total area within the current CINMS boundary was considered highly suitable for juvenile thresher sharks. These areas form a wide band encompassing the northern Channel Islands (Figure 4.1.5). 

Slight increases in the amount of highly suitable habitat were observed within Concepts 3, 4 and 5 and more 

significant gains were contained within the larger boundary concepts (Figure 4.1.6). OAI results comparing the 

relative gains of highly suitable habit/total concept area indicate that Concept 2 was optimal for juvenile thresher 

shark habitat (Table 4.1.3). 

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

0 25 50 100

Kilometers

Thresher shark

Adult

Habitat Suitability

Low

Moderate

High

Pacific Fishery Management Council

2003

Figure 4.1.3. Adult thresher shark habitat suitability off southern 

California.

SA

NAA

5 4 3

2

1, 1a

1000

1500

2000

2500

3000

0 5000 10000 15000 20000 25000

Total Area (km2)

Habitat Area (km2

)

Figure 4.1.4. Regression of total habitat area for adult 

thresher shark and total area within the current and 

proposed boundary concepts. Numbers indicate concepts and NAC=No Action Concept, SA=Study Area.

123°W

123°W

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

37°N37°N

38°N38°N

0 25 50 100 150 200

Kilometers

Thresher shark

Adult

Habitat Suitability

Low

Moderate

High

Pacific Fishery Management Council

2003

Figure 4.1.1. Adult thresher shark habitat suitability off central 

and southern California.

124°W

124°W

123°W

123°W

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

37°N37°N

38°N38°N

0 25 50 100 150 200

Kilometers

Thresher shark

Juvenile

Habitat Suitability

Low

Moderate

High

Pacific Fishery Management Council,

2003

Figure 4.1.2. Juvenile thresher shark habitat suitability off central 

and southern California.
Chapter 4

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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Summary

• The thresher shark has the highest commercial 

landings of all shark species in California.

• Highly suitable habitat for adult thresher sharks 

consists of waters with depths between 70-3,480 m. 

Highly suitable habitat for juveniles occurs in depths 

between 10-180 m.

• Concept 4 provides the most optimal gain of highly 

suitable habit for adult thresher sharks/total concept 

area gained relative to the NAC; Concept 2 was the 

most favorable for juveniles.

Tope (Galeorhinus galeus)

Tope distribution ranges from northern British Columbia to central Baja California and the Gulf of California 

and in temperate waters of the South Pacific, eastern 

North Atlantic, South Atlantic, and southwestern Indian Ocean (Ebert, 2003). Tope are a coastal-pelagic 

species, often associated with benthic habitats at 

depths between 2-471 m. Males and females segregate by sex. Males generally favor deeper waters, 

whereas females occur closer to shore (Compagno, 

1984; Leet et al., 2001). Males are also more abundant in the northern part of their range through northern California whereas females are more abundant 

in southern California (McCain, 2003). 

The tope has been one of the most economically important shark fisheries on the Pacific coast of the U.S. 

Historically, high demand for shark liver oil and fins 

for soup stock placed heavy pressure on the fishery, 

which collapsed during the 1940s. Currently, commercial and recreational fisheries target the shark 

Table 4.1.2. Analysis of adult thresher shark habitat suitability within 

boundary concepts. Numbers in bold indicate an increase in the estimate when compared to the No Action Concept (NAC). OAI estimates shaded in gray represent maximum observed benefit. Delta 

(D) indicates a rate of change calculation, and is always expressed 

as a percent change from the NAC.

Table 4.1.3. Analysis of juvenile thresher shark habitat suitability 

within boundary concepts. Numbers in bold indicate an increase 

in the estimate when compared to the No Action Concept (NAC). 

OAI estimates shaded in gray represent maximum observed benefit. Delta (D) indicates a rate of change calculation, and is always 

expressed as a percent change from the NAC.

Concept

Area 

(km2)

High 

Suitability 

Area (km2)

∆ Area 

(%)

∆ High 

Suitability 

Area(%)

OAI 

(absolute)

NAC 3475 2860 - - -

5 4538 3648 21.12 27.55 1.30

4 7981 7089 113.11 147.87 1.31

3 9044 8061 141.50 181.85 1.29

2 13736 12083 266.78 322.48 1.21

1 22613 18576 503.82 549.51 1.09

1a 22591 18598 503.23 550.28 1.09

SA 17093 14554 356.42 408.88 1.15

Concept

Area 

(km2)

High 

Suitability 

Area (km2)

∆ Area 

(%)

∆ High 

Suitability 

Area(%)

OAI 

(absolute)

NAC 3475 1930 - - -

5 4538 1971 21.12 2.12 0.10

4 7981 2250 113.11 16.58 0.15

3 9044 2446 141.50 26.74 0.19

2 13736 3692 266.78 91.30 0.34

1 22613 5060 503.82 162.18 0.32

1a 22591 5035 503.23 160.88 0.32

SA 17093 5060 356.42 162.18 0.46

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

0 25 50 100

Kilometers

Thresher shark

Juvenile

Habitat Suitability

Low

Moderate

High

Pacific Fishery Management Council,

2003

Figure 4.1.5. Juvenile thresher shark habitat suitability off southern California.

SA

NAC

5 4

3

2

1, 1a

1500

2500

3500

4500

5500

0 5000 10000 15000 20000 25000

Total Area (km2)

Habitat Area (km2

)

Figure 4.1.6. Regression of total habitat area for juvenile thresher shark and total area within the current and 

proposed boundary concepts. Numbers indicate concepts and NAC=No Action Concept, SA=Study Area.
Chapter 4

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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

for food and commercial landings have averaged about 

75,000 pounds annually since 1990. Little information 

exists for the recreational fishery (Leet et al., 2001). 

Broad-scale Patterns

NMFS model results indicate that suitable habitat for 

adult tope occurs over all substrate types at depths between 0-480 m. In southern California suitable habitat 

shifted to deeper water (60-480 m). Habitat suitability for adults was, on average, highest off the coast of 

Washington, Oregon and northern California, moderate in central California, and low throughout southern 

California. In northern California, highly suitable habitats exist offshore at depths between 140-370 m north 

of Cape Mendocino and decline to moderate suitability 

closer to shore (Figure 4.1.7). In central California, moderately suitable habitat occurs at depths of 180-330 m, 

which extends southward through Cordell Bank, Gulf of 

the Farallones, and northern Monterey Bay sanctuaries. Low suitability extends deeper to 480 m and shoreward. Habitat suitability was low in southern California at 

depths between 60-480 m.

Model results for juvenile tope exhibit similar distributions 

of habitat suitability in regards to bathymetry; however, 

highly suitable habitat extends further south (to Morro 

Bay) and high to moderate suitability habitats extend 

throughout southern California (Figure 4.1.8). Highest 

suitability values were observed at depths of 100-380 

m, and declined to moderate between 60-100 m. Low 

suitability extends from 0-60 m and 380-480 m. 

Analysis of Boundary Concepts

Highest HSP values for adult tope in southern California consisted of a long band of area situated parallel to 

the mainland and around the Channel Islands. These 

habitats comprised approximately 541 km2 of the NAC 

(Figure 4.1.9). Habitat area increased with increasing 

boundary concept area, with Concepts 1 and 1a containing the largest amount of high probability habitat 

(Figure 4.1.10). Mean probability was highest within the 

NAC and declined as boundary concept size increased 

(Table 4.1.4). As such, relative OAI results indicated that 

none of the concepts surpassed mean suitability values 

for the NAC; however, absolute OAI results, which are 

based on habitat area, suggest that Concept 5 was the 

most favorable (Table 4.1.4).

Nearly 90% of the total area of the NAC was considered 

suitable for juvenile tope; however, only 25% was considered high probability (Figure 4.1.11). While high probability habitat area increased within the larger concepts 

(Figure 4.1.12), relative to the NAC, mean probability 

declined (Table 4.1.5). Although each concept contained 

significant amounts of habitat, mean suitability for each 

concept did not exceed that of the NAC. Absolute OAI 

123°W

123°W

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

37°N37°N

38°N38°N

HSP

Low

Moderate

High

0 25 50 100 150 200

Kilometers

Tope-Adult

NMFS, 2004

Figure 4.1.7. Adult tope habitat suitability probability (HSP) off 

central and southern California.

123°W

123°W

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

37°N37°N

38°N38°N

HSP

Low

Moderate

High

0 25 50 100 150 200

Kilometers

Tope-Juvenile

NMFS, 2004

Figure 4.1.8. Juvenile tope habitat suitability probability (HSP) 

off central and southern California.

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

HSP

Low

Moderate

High

0 25 50 100

Kilometers

Tope-Adult

NMFS, 2004

Figure 4.1.9. Adult tope habitat suitability probability (HSP) off 

southern California.
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

results indicated that Concept 2 provides the optimal relative 

proportion of suitable habitat for juvenile tope (Table 4.1.5).

Summary

• Suitable habitat for adult and juvenile tope occurs over all substrates at depths ranging from 0-480 m. Suitability is low in California and increases with increasing latitude. 

• Mean habitat suitability decreases with increasing boundary 

concept size. Of the six boundary concepts being considered, 

Concept 5 provides the most favorable gain of adult tope habitat/total concept area relative to the NAC. Concept 2 was most 

favorable for juveniles.

Table 4.1.4. Analysis of adult tope habitat suitability within boundary concepts. Numbers in bold indicate an increase in the estimate when compared to the No Action Concept (NAC). OAI estimates shaded in gray represent 

maximum observed benefit. Delta (D) indicates a rate of change calculation, and is always expressed as a percent 

change from the NAC.

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

HSP

Low

Moderate

High

0 25 50 100

Kilometers

Tope-Juvenile

NMFS, 2004

Figure 4.1.11. Juvenile tope habitat suitability probability (HSP) 

off southern California.

SA

NAC

5

4

3

2

1, 1a

800

1300

1800

2300

2800

0 5000 10000 15000 20000 25000

Total Area (km2)

Habitat Area (km2

)

Figure 4.1.12. Regression of high probability habitat 

area for juvenile tope and total area within the current 

and proposed boundary concepts. Numbers indicate 

concepts and NAC=No Action Concept, SA=Study 

Area.

Concept Area (km2)

High 

Probability 

Area (km2)

Mean 

Probability

∆ Area 

(%)

∆ High 

Probability 

Area (%)

OAI 

(absolute)

OAI

(relative)

NAC 3475 541 0.086 - - - -

5 4538 642 0.083 21.12 2.12 0.88 -0.17

4 7981 750 0.068 113.11 16.58 0.34 -0.19

3 9044 945 0.071 141.50 26.74 0.53 -0.12

2 13736 1565 0.068 266.78 91.30 0.71 -0.08

1 22613 2015 0.052 503.82 162.18 0.54 -0.08

1a 22591 2008 0.051 503.23 160.88 0.54 -0.08

SA 17093 2015 0.067 356.42 162.18 0.76 -0.06

SA

NAC

5 4

3

2

1, 1a

500

925

1350

1775

2200

0 5000 10000 15000 20000 25000

Total Area (km2)

Habitat Area (km2

)

Figure 4.1.10. Regression of high probability habitat 

area for adult tope and total area within the current and 

proposed boundary concepts. Numbers indicate concepts and NAC=No Action Concept, SA=Study Area.
Chapter 4

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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Leopard shark (Triakis semifasciata)

Leopard sharks are found from southern Oregon to Baja California, Mexico, including the Gulf of California (Roedel and Ripley, 1950; Miller and Lea, 1972; Russo, 1975; Talent, 1976; Castro, 1983; Eschmeyer et al., 1983; 

Compagno, 1984; Lineaweaver and Backus, 1984; Adams, 1986; Smith and Abramson, 1990; Emmett et al., 

1991; Kusher et al., 1992; Love, 1996). The leopard shark is most abundant in California bays and estuaries 

and along the shoreline (Leet et al., 2001). Preferred habitats for adults include: sand and mud flats, sand and 

mud bottoms with scattered rocks near rocky reefs, and shallow kelp beds (Eschmeyer et al., 1983; Compagno, 

1984; Ferguson and Cailliet, 1990; Emmett et al., 1991; Love, 1996). Juvenile habitat is located in sand and mud 

habitats within coastal bays and estuaries.

The leopard shark is taken as both a food and game fish in California. Since 1991, commercial landings have 

averaged about 31,000 pounds per year, with the majority of landings occurring south of Point Piedras Blancas. 

Recreational landings are greater than the commercial catch with an estimated average of 45,000 sharks taken 

by anglers since 1993 (Leet et al., 2001). 

Broad-scale Patterns

Along the west coast of the U.S., suitable habitat for adult leopard sharks extends from the shoreline out to 

depths of 80 m. Suitability was low at depths greater than 40 m while higher values were found nearshore and 

in coastal bays and estuaries. Suitability of habitats associated with kelp beds located around offshore islands in 

central California were considered moderate, but decreased to low around the Channel Islands (Figure 4.1.13). 

Juvenile habitats predominantly occur in bays and estuaries and are not considered in these analyses.

Analysis of Boundary Concepts

In southern California, highest habitat probabilities were 

found in shallow habitats (<10 m) along the mainland 

from Pt. Conception through San Diego. Less suitable 

habitat extends out to 80 m (Figure 4.1.14). Less than 

1% of the total area of the NAC contained high probability habitat for adult leopard sharks and approximately 

30% was considered low or moderate. No additional 

habitat with high probability was gained within Concepts 

4 and 5. Modest gains were observed as boundaries expand northward and include habitats closer to the mainland. Concepts 1, 1a, and the Study Area contained the 

greatest amount of high probability habitat. (Table 4.1.6, 

Figure 4.1.15). 

Mean suitability was highest within the NAC and decreased with increasing boundary concept area. OAI 

124°W

124°W

123°W

123°W

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

37°N37°N

38°N38°N

HSP

Low

Moderate

High

0 25 50 100 150 200

Kilometers

Leopard shark-Adult

NMFS, 2004

Figure 4.1.13. Adult leopard shark habitat suitability probability 

(HSP) off central and southern California.

Table 4.1.5. Analysis of juvenile tope habitat suitability within boundary concepts. Numbers in bold indicate an 

increase in the estimate when compared to the No Action Concept (NAC). OAI estimates shaded in gray represent maximum observed benefit. Delta (D) indicates a rate of change calculation, and is always expressed as a 

percent change from the NAC.

Concept Area (km2)

High 

Probability 

Area (km2)

Mean 

Probability

∆ Area 

(%)

∆ High 

Probability 

Area (%)

OAI 

(absolute)

OAI

(relative)

NAC 3475 842 0.490 - - - -

5 4538 935 0.500 21.12 11.10 0.53 0.10

4 7981 1228 0.400 113.11 45.85 0.41 -0.16

3 9044 1456 0.360 141.50 72.88 0.52 -0.19

2 13736 2213 0.340 266.78 162.74 0.61 -0.11

1 22613 2715 0.270 503.82 222.38 0.44 -0.09

1a 22591 2700 0.270 503.23 220.54 0.44 -0.09

SA 17093 2714 0.350 356.42 222.24 0.62 -0.08
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

results indicated that the Study Area provides the optimal increase of leopard shark habitat/total area gained 

relative to the NAC; however, this boundary is not under consideration. Therefore, Concept 2 yielded the highest 

OAI when comparing total habitat area among the boundary concepts with the NAC (Table 4.1.6).

Summary

• The recreational fishery for leopard sharks is centered in southern California.

• Suitable habitat for adult leopard sharks occurs in waters less than 80 m over sand and mud habitats and areas 

containing kelp beds. High suitability occurs in coastal areas at depths less than 10 m.

• Mean habitat suitability decreases with increasing boundary concept size. Of the six boundary concepts being 

considered, Concept 2 provided the most favorable gain of adult leopard shark habitat/total area relative to the 

NAC.

Table 4.1.6. Analysis of adult leopard shark habitat suitability within boundary concepts. Numbers in bold indicate 

an increase in the estimate when compared to the No Action Concept (NAC). OAI estimates shaded in gray represent maximum observed benefit. Delta (D) indicates a rate of change calculation, and is always expressed as a 

percent change from the NAC.

Concept Area (km2)

High 

Probability 

Area (km2)

Mean 

Probability

∆ Area 

(%)

∆ High 

Probability 

Area (%)

OAI 

(absolute)

OAI

(relative)

NAC 3475 12 0.017 - - - -

5 4538 12 0.014 21.12 0.00 0.00 -0.84

4 7981 12 0.008 113.11 0.00 0.00 -0.47

3 9044 31 0.010 141.50 148.28 1.05 -0.29

2 13736 76 0.009 266.78 495.31 1.86 -0.18

1 22613 104 0.006 503.82 715.39 1.42 -0.13

1a 22591 104 0.006 503.23 715.39 1.42 -0.13

SA 17093 104 0.008 356.42 715.39 2.01 -0.15

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

HSP

Low

Moderate

High

0 25 50 100

Kilometers

Leopard shark-Adult

NMFS, 2004

Figure 4.1.14. Adult leopard shark habitat suitability probability 

(HSP) off southern California.

SA

NAC

5

4

3

2

1, 1a

10

20

30

40

50

60

70

80

90

100

110

120

0 5000 10000 15000 20000 25000

Total Area (km2)

Habitat Area (km2

)

Figure 4.1.15. Regression of high probability habitat 

area for adult leopard shark and total area within the 

current and proposed boundary alternatives. Numbers 

indicate alternatives and NAC=No Action Alternative, 

SA=Study Area.
Chapter 4

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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Pacific angel shark (Squatina californica)

Pacific angel sharks are reported to only occur in the 

eastern Pacific Ocean from southeastern Alaska to the 

Gulf of California. They are a benthic species usually 

found on flat sandy bottoms and in sand channels between reefs at depths ranging from 1-200 m (Leet et 

al., 2001). Pacific angel sharks became one of the more 

highly sought after commercial shark species in the 

Santa Barbara Channel during the 1980s. Angel sharks 

were targeted by gill net fishermen where landings exceeded one million pounds during 1985-1986. A minimum size limit and fishing area closures have contributed to a significant reduction in landings since 1986. The 

demand for angel shark products are now almost wholly 

provided by Mexican imports. No significant recreational 

fishery exists for angel sharks (Leet et al., 2001).

Broad-scale Patterns

The model developed for Pacific angel shark determined 

that highly suitable habitat occurs over soft substrates 

at depths >100 m, moderate suitability occurs over soft 

substrates between 100-150 m, and low suitability between 150-200 m. Along the coast of California, highly 

suitable habitat for Pacific angel sharks occurs on most 

of the continental shelf, comprising significant areas of 

the Gulf of the Farallones and Monterey Bay national 

marine sanctuaries and throughout southern California 

and the CINMS (Figure 4.1.16). 

No fisheries data were available for testing model performance. 

Analysis of Boundary Concepts

Approximately 32% of the total area within the current 

CINMS was considered highly suitable for Pacific angel 

sharks; 14% was moderately suitable (Figure 4.1.17). 

Small gains (>1%) of highly suitable habitat were observed within Concepts 4 and 5, and an increase of 200 

km2 occurred in Concept 3. Significant gains were also 

observed within the larger boundary concepts (Figure 

4.1.18). 

OAI values for the six concepts indicated that the Study 

Area provided the most favorable gain of highly suitable 

habitat for angel sharks per total area gained relative to 

the NAC (Table 4.1.7); however this boundary is not under consideration. Therefore, Concepts 1 and 1a were 

determined to be the most favorable boundary concepts 

with regard to angel shark habitat.

Summary

• Commercial and recreational fisheries for Pacific angel 

shark are insignificant in California waters.

• Highly suitable habitat was considered to occur over 

soft substrates at depths ranging from 0-100 m.

SA

NAC

5 4 3

2

1, 1a

1000

1500

2000

2500

3000

3500

0 5000 10000 15000 20000 25000

Total Area (km2)

Habitat Area (km2

)

Figure 4.1.18. Regression of total habitat area for Pacific angel shark and total area within the current and 

proposed boundary concepts. Numbers indicate concepts and NAC=No Action Concept, SA=Study Area.

123°W

123°W

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

37°N37°N

38°N38°N

Habitat Suitability

Low

Moderate

High

0 25 50 100 150 200

Kilometers

Pacific angel shark

Figure 4.1.16. Pacific angel shark habitat suitability off central 

and southern California.

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

Habitat Suitability

Low

Moderate

High

0 25 50 100

Kilometers

Pacific angel shark

Figure 4.1.17. Pacific angel shark habitat suitability off southern 

California.
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

• Of the six boundary concepts being considered, 

Concepts 1 and 1a provide the greatest proportional change of suitable habitat for Pacific angel 

shark/total area in relation to the NAC.

Pacific Sardine (Sardinops sagax) 

Pacific sardines are a small pelagic, schooling 

fish that inhabit coastal subtropical and temperate waters. They also occur in estuaries, but are 

most common in the nearshore/offshore environment. Pacific sardine are highly mobile and move 

seasonally along the coast. The overall population has three distinct stocks: northern (northern 

Baja California to Alaska), southern (off Baja California), and Gulf of California. The northern stock 

is federally managed by the Coastal Pelagic Species Fishery Management Plan (PFMC, 1998). 

Spawning grounds have been identified to occur in southern California and northern Baja California and an adult 

feeding ground has been determined to occur in central and northern California. Eggs and larvae occur nearly 

everywhere adults are found and are most abundant between 14°C and 15°C (see Chapter 4.4 for a more detailed discussion about larval distribution). Habitat suitability is difficult to determine due to their lack of affinity to 

substrate and bathymetry and their spatial and seasonal distribution is highly influenced by sea surface temperature (PFMC, 1998). As such, we present no maps of suitability.

Similar to the northern anchovy, Pacific sardines are important components of the trophic web of the California 

Current system. Eggs and larvae are consumed by invertebrate and vertebrate planktivores, and adults and 

juveniles are consumed by a variety of predators, including: fish (yellowtail, barracuda, tuna, mackerel, sharks), 

seabirds (pelicans, gulls, and cormorants), and marine mammals (sea lions, seals, porpoises, and whales).

Northern anchovy (Engraulis mordax)

Northern anchovy are a pelagic, schooling species that are typically found at the top of the water column. Northern anchovy are distributed from Queen Charlotte Island, British Columbia, to Magdalena Bay, Baja California. 

The overall population is divided into northern, central, and southern stocks. The southern stock is found entirely 

within Mexican waters, while the northern and central stocks are managed by the Coastal Pelagic Species Fishery Management Plan (PFMC, 1998). The central stock supports significant commercial fisheries in the U.S. and 

Mexico, and ranges from San Francisco, to Punta Baja, Baja California. The majority of this stock is located in 

the Southern California Bight. Since northern anchovy are pelagic, habitat suitability is difficult to determine because they display little or no preference to substrate and bathymetry. Instead, northern anchovy distribution is 

regulated more by sea surface temperature and currents which are highly variable. As such, we do not present 

maps of habitat suitability for this species.

The central stock is typically found in waters that range from 12°-21°C. There is high regional variation in age 

composition and size, with older and larger individuals further offshore and to the north. These patterns are 

accentuated during El Nino years (Methot, 1989). Juveniles are typically found near shore (at depths <90 m), 

which support at least 70% of the juvenile population (Methot, 1981; Smith, 1985). All life stages are found in 

the surface waters of the EZZ. See Chapter 4.4 for a further description of northern anchovy larval distribution.

Northern anchovy are an important component of the southern California trophic structure. Eggs and larvae 

are prey for a wide variety of invertebrate and vertebrate planktivores. Juveniles and adults are important food 

sources for a wide variety of predators, including: fish, birds (California brown pelican and least tern), and 

mammals. Furthermore, explicit links between brown pelican breeding success and anchovy abundance have 

been documented (PFMC, 1998).

Table 4.1.7. Analysis of Pacific angel shark habitat suitability within boundary concepts. Numbers in bold indicate an increase in the estimate when 

compared to the No Action Concept (NAC). OAI estimates shaded in gray 

represent maximum observed benefit. Delta (D) indicates a rate of change 

calculation, and is always expressed as a percent change from the NAC.

Concept

Area 

(km2)

High 

Suitability 

Area (km2)

∆ Area 

(%)

∆ High 

Suitability 

Area (%)

OAI 

(absolute)

NAC 3475 1194 - - -

5 4538 1201 21.12 0.59 0.06

4 7981 1201 113.11 0.59 0.01

3 9044 1310 141.50 9.72 0.16

2 13736 2157 266.78 80.65 0.69

1 22613 3237 503.82 171.11 0.77

1a 22591 3234 503.23 170.85 0.77

SA 17093 3237 356.42 171.11 1.09
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Bocaccio (Sebastes paucispinis)

Bocaccio range from central Baja California to Kodiak Island, Alaska (Miller and Lea, 1972; Hart, 1973) and are 

most abundant between Oregon and northern Baja California (Love et al., 2002). Bocaccio are considered a 

middle shelf species occurring at depths between 50-300 m (Allen and Smith, 1988). Adults are generally found 

in schools over rocky areas or as solitary individuals among rocky substrates (Yoklavich et al., 2000). Juvenile 

bocaccio typically occur in shallower waters than adults (Wilkins, 1980; Yoklavich et al., 2000) and settle over 

rocky substrates with algae cover or sandy areas with eelgrass (Love et al., 2002). Larvae are commonly found 

in the Southern California Bight and areas offshore of Monterey Bay (Leet et al., 2001; Love et al., 2002). Analysis of bocaccio larval data can be seen in Chapter 4.4. 

Historically, bocaccio was the dominant rockfish in California’s longline and bottom trawl fisheries. Prior to 1970, 

six million pounds were landed annually by California fisheries. Landings peaked in 1983 (15 million pounds) 

and have since declined steadily (0.5 million pounds in 1998). Recreational catches have shown similar declines 

(Leet et al., 2001).

Broad-scale Patterns

NMFS HSP model results indicate that suitable habitat for adult bocaccio occurs between 30-380 m with suitability being higher over hard substrates. Moderate suitability values were widely dispersed throughout California’s offshore hard bottom habitats, with considerable amounts located within the central California sanctuaries 

(Cordell Bank, Gulf of the Farallones, and Monterey Bay) and the Channel Islands NMS (Figure 4.1.19). Soft 

substrates at depths between 30-380 m were predicted as low suitability. 

Similarly, habitat suitability for juvenile bocaccio were highest over hard substrates but at shallower depths (0-

200 m). The majority of highly suitable habitat was located nearshore from northern California to Point Conception. Hard substrates south of Point Conception were lower than those to the north (Figure 4.1.20). Suitability 

values for soft substrates were low in California waters, while suitability was higher over shallow soft substrates 

in Oregon and Washington. 

Analysis of Boundary Concepts

Approximately 1,700 km2 of habitat was modeled as suitable for adult bocaccio within the current CINMS (NAC) 

boundary (Figure 4.1.21); however, only 225 km2 were classified as high probability habitat and were located in 

the southern portion of the sanctuary and around Anacapa Island. Additional habitat located south of Santa Rosa 

Island was included in Concepts 4 and 5, most of which was low probability. Modest gains of high probability 

habitat were observed in the larger concepts (Figure 4.1.22). Mean habitat probability was highest for the NAC 

(Table 4.1.8); however, on average, suitability was low throughout southern California. 

123°W

123°W

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

37°N37°N

38°N38°N

HSP

Low

Moderate

High

0 25 50 100 150 200

Kilometers

Bocaccio-Juvenile

NMFS, 2004

Figure 4.1.20. Juvenile bocaccio habitat suitability probability 

(HSP) off central and southern California.

123°W

123°W

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

37°N37°N

38°N38°N

HSP

Low

Moderate

High

0 25 50 100 150 200

Kilometers

Bocaccio-Adult

NMFS, 2004

Figure 4.1.19. Adult bocaccio habitat suitability probability (HSP) 

off central and southern California.
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Similar patterns of habitat distribution were observed for juvenile bocaccio; however, high probability habitats 

were significantly reduced (Figure 4.1.23). Only 28 km2 of area was classifed as high quality habitat. Concepts 

2, 1, 1a, and the Study Area contained greater amounts of high probability habitat (Figure 4.1.24) than the NAC; 

however, in comparison, mean probability was highest for the NAC (Table 4.1.9). These habitats were located 

south of Santa Rosa Island and around Anacapa Island. Additional areas of high probability habitat were located 

close to the mainland and around San Clemente and Santa Catalina Islands. 

OAI calculations for both adult and juvenile bocaccio indicate that Concept 2 provides the optimal proportional 

gain of total habitat/total area gained relative to that for the NAC. OAI results for juvenile bocaccio indicated that 

Concept 4 demonstrated the most optimal gain of habitat/total area relative to the NAC. Mean habitat probability 

was highest for the NAC for both adults and juveniles.

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

HSP

Low

Moderate

High

0 25 50 100

Kilometers

Bocaccio-Adult

NMFS, 2004

Figure 4.1.21. Adult bocaccio habitat suitability probability (HSP) 

off southern California.

SA

NAC

5

4

3

2 1, 1a

225

250

275

300

325

350

375

400

425

450

0 5000 10000 15000 20000 25000

Total Area (km2)

Habitat Area (km2

)

Figure 4.1.22. Regression of high probability habitat 

area for adult bocaccio and total area within the current 

and proposed boundary concepts. Numbers indicate 

concepts and NAC=No Action Concept, SA=Study 

Area.

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

HSP

Low

Moderate

High

0 25 50 100

Kilometers

Bocaccio-Adult

NMFS, 2004

Figure 4.1.23. Juvenile bocaccio habitat suitability probability 

(HSP) off southern California.

SA

NAC

5

4

3

2

1, 1a

20

40

60

80

100

0 5000 10000 15000 20000 25000

Total Area (km2)

Habitat Area (km2

)

Figure 4.1.24. Regression of high probability habitat 

area for juvenile bocaccio and total area within the current and proposed boundary concepts. Numbers indicate concepts and NAC=No Action Concept, SA=Study 

Area.
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Summary

• Commercial and recreational landings of bocaccio have declined over the past twenty years.

• The most suitable habitat for bocaccio in southern California occurs over hard substrates at depths ranging 

from 0-200 m, for juveniles and 30-380 m for adults.

• Of the six boundary concepts being considered, Concept 2 provides the most favorable gain of habitat for adult 

and juvenile bocaccio/total area relative to the NAC.

• Mean habitat probability was highest within the NAC.

Cowcod (Sebastes levis)

Cowcod range from Ranger Bank and Guadalupe Island, Baja California north to central Oregon. The majority 

of the preferred habitat for cowcod occurs in the Southern California Bight (Leet et al., 2001). Cowcod occur at 

depths from 21 to 366 m (Butler et al., 2003; Miller and Lea, 1972); adults generally favor depths of 180-275 m 

and juveniles are common at 20-100 m (Allen, 1982; Butler et al., 1999; Love et al., 2002; Butler et al., 2003). 

Adults are usually found over high-relief rocky habitats (Allen, 1982) and are generally solitary, but may aggregate (Love et al., 1990). Subadults have been found in association with ledges in submarine canyons and in 

crevices of isolated rock outcrops surrounded by mud (Yoklavich et al., 2000). Juveniles occur over sandy and 

clay bottoms (Love et al., 2002; Butler et al., 2003). Larval cowcod are almost exclusive to southern California 

in waters over the continental shelf adjacent to the northern Channel Islands at depths <2000 m (MacGregor, 

1986; Moser et al., 2000).

Table 4.1.8. Analysis of adult bocaccio habitat suitability within boundary concepts. Numbers in bold indicate an 

increase in the estimate when compared to the No Action Concept (NAC). OAI estimates shaded in gray represent maximum observed benefit. Delta (D) indicates a rate of change calculation, and is always expressed as a 

percent change from the NAC.

Concept Area (km2)

High 

Probability 

Area (km2)

Mean 

Probability

∆ Area 

(%)

∆ High 

Probability 

Area (%)

OAI 

(absolute)

OAI

(relative)

NAC 3475 225 0.030 - - - -

5 4538 234 0.028 21.12 4.10 0.19 -0.32

4 7981 389 0.023 113.11 72.52 0.64 -0.21

3 9044 405 0.021 141.50 79.84 0.56 -0.21

2 13736 428 0.017 266.78 89.85 0.34 -0.16

1 22613 440 0.012 503.82 95.44 0.19 -0.12

1a 22591 440 0.012 503.23 95.44 0.19 -0.12

SA 17093 440 0.016 356.42 95.44 0.27 -0.13

Table 4.1.9. Analysis of juvenile bocaccio habitat suitability within boundary concepts. Numbers in bold indicate 

an increase in the estimate when compared to the No Action Concept (NAC). OAI estimates shaded in gray represent maximum observed benefit. Delta (D) indicates a rate of change calculation, and is always expressed as 

a percent change from the NAC.

Concept Area (km2)

High 

Probability 

Area (km2)

Mean 

Probability

∆ Area 

(%)

∆ High 

Probability 

Area (%)

OAI 

(absolute)

OAI

(relative)

NAC 3475 28 0.096 - - - -

5 4538 30 0.081 21.12 4.10 0.24 -0.74

4 7981 30 0.048 113.11 72.52 0.04 -0.44

3 9044 43 0.048 141.50 79.84 0.36 -0.35

2 13736 70 0.051 266.78 89.85 0.55 -0.18

1 22613 89 0.043 503.82 95.44 0.42 -0.11

1a 22591 89 0.043 503.23 95.44 0.42 -0.11

SA 17093 89 0.057 356.42 95.44 0.59 -0.11
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Cowcod are important to the commercial and recreational fisheries. Combined, these fisheries amassed landings of 

213 tons in 1976, which had declined to 14 tons by 1999. Exploitation of fishing grounds near major ports has been 

high and productive fishing grounds in the Southern California Bight are found far offshore (Leet et al., 2001).

Broad-scale Patterns

Suitable habitats for adult cowcod occur over hard substrates between 70-360 m and are intermittently dispersed 

along the continental shelf between 45°-32.5°N latitude (Figure 4.1.25). Large concentrations of adult habitat are 

found near Monterey Bay and the Channel Islands. Suitable habitat for juveniles was predicted to occur over both 

hard and soft substrates at depths between 0-90 m. Suitable habitat for juveniles extends from Pt. Arena (40°N) 

through southern California and is highest over hard substrates along the mainland from the northern sanctuaries 

through Point Conception (Figure 4.1.26).

Analysis of Boundary Concepts

Approximately 1% (126 km2) of the total area within the NAC was considered high probability habitat for adult 

cowcod (Figure 4.1.27). Similar to bocaccio, adult cowcod habitats were primarily located south of the northern 

Channel Islands. Additional areas were located around San Clemente and Santa Catalina Islands and near 

Cortes Bank. The amount of high probability habitat increased significantly within Concepts 3 and 4; small gains 

123°W

123°W

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

117°W

117°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

37°N37°N

38°N38°N

HSP

Low

Moderate

High

0 25 50 100 150 200

Kilometers

Cowcod-Adult

NMFS, 2004

Figure 4.1.25. Adult cowcod habitat suitability probability (HSP) 

off central and southern California.

123°W

123°W

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

37°N37°N

38°N38°N

HSP

Low

Moderate

High

0 25 50 100 150 200

Kilometers

Cowcod-Juvenile

NMFS, 2004

Figure 4.1.26. Juvenile cowcod habitat suitability probability 

(HSP) off central and southern California.

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

HSP

Low

Moderate

High

0 25 50 100

Kilometers

Cowcod-Adult

NMFS, 2004

Figure 4.1.27. Adult cowcod habitat suitability probability (HSP) 

off southern California.

SA

NAC

5

4 3 2 1, 1a

120

160

200

240

280

0 5000 10000 15000 20000 25000

Total Area (km2)

Habitat Area (km2

)

Figure 4.1.28. Regression of high probability habitat 

area for adult cowcod and total area within the current 

and proposed boundary concepts. Numbers indicate 

concepts and NAC=No Action Concept, SA=Study 

Area.
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

were observed for concepts 1, 1a, 2, and the Study Area relative to Concepts 3 and 4 (Figure 4.1.28). As such, 

Concept 4 provided the most favorable gain of habitat for adult cowcod and total area gained relative to the NAC 

(Table 4.1.10).

High probability habitat for juvenile cowcod were limited within southern California. A considerable amount of 

area within the NAC was considered suitable; however, only 28 km2 was determined to be high probaility (Figure 

4.1.29). Other areas of high probability were observed in small patches adjacent to the mainland and around 

Santa Catalina Island. No additional gains were included within Concepts 4 and 5; a modest gain was observed 

within Concept 3 and considerable gains were noted within the larger concepts (4.1.30). Mean probability was 

greatest within the NAC (Table 4.1.11) and decreased with increasing concept size. Concept 2 yielded the highest OAI and was considered the optimal boundary concept for juvenile cowcod habitat. 

Summary

• Commercial and recreational landings of cowcod have declined steadily since 1976.

• Suitable habitat for adult cowcod was determined to occur over hard substrates between 70-360 m; juvenile 

habitat occurred over hard and soft substrates between 0-90 m.

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

HSP

Low

Moderate

High

0 25 50 100

Kilometers

Cowcod-Juvenile

NMFS, 2004

Figure 4.1.29. Juvenile cowcod habitat suitability probability 

(HSP) off southern California.

SA

NAC

5

4

3

2

1, 1a

20

40

60

80

100

0 5000 10000 15000 20000 25000

Total Area (km2)

Habitat Area (km2

)

Figure 4.1.30. Regression of high probability habitat area for juvenile cowcod and total area within the 

current and proposed boundary concepts. Numbers 

indicate concepts and NAC=No Action Alternative, 

SA=Study Area.

Table 4.1.10. Analysis of adult cowcod habitat suitability within boundary concepts. Numbers in bold indicate an 

increase in the estimate when compared to the No Action Concept (NAC). OAI estimates shaded in gray represent maximum observed benefit. Delta (D) indicates a rate of change calculation, and is always expressed as a 

percent change from the NAC.

Concept Area (km2)

High 

Probability 

Area (km2)

Mean 

Probability

∆ Area 

(%)

∆ High 

Probability 

Area (%)

OAI 

(absolute)

OAI

(relative)

NAC 3475 126 0.007 - - - -

5 4538 135 0.006 21.12 7.32 0.35 -0.68

4 7981 261 0.007 113.11 107.23 0.95 0.00

3 9044 268 0.006 141.50 112.33 0.79 -0.10

2 13736 268 0.004 266.78 112.86 0.42 -0.16

1 22613 273 0.002 503.82 116.38 0.23 -0.14

1a 22591 273 0.002 503.23 116.38 0.23 -0.14

SA 17093 273 0.002 356.42 116.38 0.33 -0.16
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

• Mean habitat probability decreases with increasing boundary concept size; however, Concept 4 provides the 

most favorable gain for adult cowcod habitat area relative to the NAC, while Concept 2 was the optimal for juvenile cowcod.

Lingcod (Ophiodon elongatus)

Lingcod range from Baja California to Kodiak Island in the Gulf of Alaska with a center of abundance located 

near British Columbia. Lingcod becomes less common toward the southern end of their range (Leet et al., 2001). 

Lingcod are generally found from the intertidal zone to depths of 475 m, but are most common on slopes of 

submerged banks with kelp and eelgrass beds, or habitats of ridges and boulders at depths <100 m (Giorgi and 

Congleton, 1984; Allen and Smith, 1988; Shaw and Hassler, 1989; NOAA, 1990; Emmett et al., 1991). Juveniles 

are usually found at shallower depths over sandy and rocky substrates, and are frequently found in estuaries 

(Hart, 1973; Fitch and Schulz, 1978; Shaw and Hassler, 1989; Emmett et al., 1991). Eggs and larvae occur 

in nearshore areas and small juveniles settle in estuaries and shallow waters along the coast (Emmett et al., 

1991). 

Lingcod support an important commercial and recreational fishery throughout their range. Lingcod are caught 

commercially by bottom trawls, handlines, set nets, and set lines and landings of this species averaged nearly 

three million pounds in California from 1972-1982. Landings oscillated between 1982-1989 and had declined to 

313,000 pounds by 1999. Recently there has been a shift away from the commercial fishery towards recreational 

catches. Approximately 890,000 pounds are landed annually by the recreational fishery in California. Landings 

from both the commercial and recreational fisheries occur predominantly in central and northern California (Leet 

et al., 2001).

Broad-scale Patterns

Hard substrates at depths between 0-430 m were suitable bottom types for adult lingcod. Shallower depths 

(<120 m) provided greater predicted habitat probabilities. In California waters, these habitats are found on the 

shallow portions of the continental shelf, offshore banks, and around the Farallon and Channel Islands (Figure 

4.1.31). Suitable habitats for juveniles were predicted to occur on all substrate types over the majority of the west 

coast continental shelf to depths of 180 m. Latitude strongly influenced juvenile lingcod habitat suitability; suitability was higher in northern latitudes. Suitability was low throughout the continental shelf of California (Figure 

4.1.32). 

Analysis of Boundary Concepts

Habitat probabilities were highest on hard substrates at depths <200 m and low at depths >200 m. The majority of suitable habitat for adult lingcod occurred in shallow waters among rocky bottoms or soft substrates with 

eelgrass and/or kelp beds along the mainland and around the northern Channel Islands (Figure 4.1.33). Approximately 73 km2

 of the Channel Islands Sanctuary was considered high probability habitat; areas of moderate 

probability occurred among the southern islands. No additional habitat was gained within Concept 5, while an 

additional 7 km2 was gained within Concept 4. The remaining concepts gained habitat along the mainland, nearly 

Table 4.1.11. Analysis of juvenile cowcod habitat suitability within boundary concepts. Numbers in bold indicate 

an increase in the estimate when compared to the No Action Concept (NAC). OAI estimates shaded in gray represent maximum observed benefit. Delta (D) indicates a rate of change calculation, and is always expressed as 

a percent change from the NAC.

Concept Area (km2)

High 

Probability 

Area (km2)

Mean 

Probability

∆ Area 

(%)

∆ High 

Probability 

Area (%)

OAI 

(absolute)

OAI

(relative)

NAC 3475 22 0.066 - - - -

5 4538 22 0.055 21.12 0.00 0.00 -0.79

4 7981 22 0.031 113.11 0.00 0.00 -0.47

3 9044 38 0.031 141.50 67.64 0.48 -0.37

2 13736 72 0.036 266.78 217.56 0.82 -0.17

1 22613 91 0.032 503.82 299.91 0.60 -0.10

1a 22591 91 0.032 503.23 299.91 0.60 -0.10

SA 17093 91 0.042 356.42 299.91 0.84 -0.10
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

tripling that of the NAC within Concepts 1, 1a, and the Study Area (Figure 4.1.34). Mean habitat suitability was 

low for all the concepts and none were higher than the NAC (Table 4.1.12). 

Overall, significantly more habitat was considered suitable for juvenile lingcod than adults. As such, almost half 

of the total area within the CINMS is considered suitable for juvenile lingcod (Figure 4.1.35) and 500 km2 were 

considered as high probability. Significant increases of high probability habitat were observed in the larger concepts (Figure 4.1.36, Table 4.1.13). Mean habitat probability was highest for the NAC and declined with increasing boundary concept size.

OAI results for mean habitat suitability indicate that none of the concepts contain higher quality habitat for adults 

and juveniles, on average, than the NAC. When comparing the total amount of high probability habitat available 

for adults and juveniles, Concept 2 provides the optimal increase of habitat/total area gained relative to the NAC 

(Tables 4.1.12 and 4.1.13).

Summary

• Fisheries for lingcod are prevalent in central and northern California, and catches have declined over the past 

30 years.

123°W

123°W

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

37°N37°N

38°N38°N

HSP

Low

Moderate

High

0 25 50 100 150 200

Kilometers

Lingcod-Adult

NMFS, 2004

Figure 4.1.31. Adult lingcod habitat suitability probability (HSP) 

off central and southern California.

123°W

123°W

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

37°N37°N

38°N38°N

HSP

Low

Moderate

High

0 25 50 100 150 200

Kilometers

Lingcod-Juvenile

NMFS, 2004

Figure 4.1.32. Juvenile lingcod habitat suitability probability 

(HSP) off central and southern California.

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

HSP

Low

Moderate

High

0 25 50 100

Kilometers

Lingcod-Adult

NMFS, 2004

Figure 4.1.33. Adult lingcod habitat suitability probability (HSP) 

off southern California.

SA

NAC 5 4

3

2

1, 1a

60

95

130

165

200

0 5000 10000 15000 20000 25000

Total Area (km2)

Habitat Area (km2

)

Figure 4.1.34. Regression of high probability habitat 

area for adult lingcod and total area within the current 

and proposed boundary concepts. Numbers indicate 

concepts and NAC=No Action Concept, SA=Study 

Area.
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

HSP

Low

Moderate

High

0 25 50 100

Kilometers

Lingcod-Juvenile

NMFS, 2004

Figure 4.1.35. Juvenile lingcod habitat suitability probability 

(HSP) off southern California.

SA

NAC

5

4

3

2

1, 1a

500

1000

1500

2000

0 5000 10000 15000 20000 25000

Total Area (km2)

Habitat Area (km2

)

Figure 4.1.36. Regression of high probability habitat 

area for juvenile lingcod and total area within the current and proposed boundary concepts. Numbers indicate concepts and NAC=No Action Concept, SA=Study 

Area.

Table 4.1.12. Analysis of adult lingcod habitat suitability within boundary concepts. Numbers in bold indicate an 

increase in the estimate when compared to the No Action Concept (NAC). OAI estimates shaded in gray represent maximum observed benefit. Delta (D) indicates a rate of change calculation, and is always expressed as a 

percent change from the NAC.

Table 4.1.13. Analysis of juvenile lingcod habitat suitability within boundary concepts. Numbers in bold indicate 

an increase in the estimate when compared to the No Action Concept (NAC). OAI estimates shaded in gray represent maximum observed benefit. Delta (D) indicates a rate of change calculation, and is always expressed as 

a percent change from the NAC.

Concept Area (km2)

High 

Probability 

Area (km2)

Mean 

Probability

∆ Area 

(%)

∆ High 

Probability 

Area (%)

OAI 

(absolute)

OAI

(relative)

NAC 3475 73 0.017 - - - -

5 4538 73 0.014 21.12 0.00 0.00 -0.84

4 7981 80 0.009 113.11 9.49 0.08 -0.42

3 9044 105 0.010 141.50 44.49 0.31 -0.29

2 13736 159 0.009 266.78 118.53 0.44 -0.18

1 22613 190 0.006 503.82 160.49 0.32 -0.13

1a 22591 190 0.006 503.23 160.49 0.32 -0.13

SA 17093 190 0.008 356.42 160.49 0.45 -0.15

Concept Area (km2)

High 

Probability 

Area (km2)

Mean 

Probability

∆ Area 

(%)

∆ High 

Probability 

Area (%)

OAI 

(absolute)

OAI

(relative)

NAC 3475 531 0.066 - - - -

5 4538 531 0.056 21.12 0.00 0.00 -0.72

4 7981 531 0.033 113.11 0.00 0.00 -0.44

3 9044 635 0.032 141.50 19.59 0.14 -0.36

2 13736 1362 0.035 266.78 156.28 0.59 -0.18

1 22613 1867 0.029 503.82 251.20 0.50 -0.11

1a 22591 1866 0.029 503.23 250.98 0.50 -0.11

SA 17093 1867 0.039 356.42 251.20 0.70 -0.11
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• Suitable habitat for adult lingcod occurs over hard and vegetated soft substrates at depths between 0-430 m; 

higher probability occurs at depths between 0-120 m. Juvenile lingcod habitat consists of both hard and soft 

substrates at depths <180 m. Habitat probability for both adults and juveniles is higher in the northwest and decreases towards the south. 

• Mean habitat probability decreases with increasing boundary concept size. Of the six boundary concepts being 

considered, Concept 2 provides the most favorable gain of adult and juvenile lingcod habitat/total concept area 

relative to the NAC.

Giant seabass (Stereolipis gigas)

The giant seabass ranges from Humboldt Bay, California to the tip of Baja California, Mexico, and occurs 

in the northern region of the Gulf of California. Within 

California it is rare north of Point Conception. Adults 

prefer nearshore rocky reefs, especially those with kelp 

beds, at depths ranging from 0-80 m. Adults may also 

be found foraging over sandy substrates. Juveniles are 

typically found among drifting kelp or over soft muddy 

or sandy bottoms (Leet et al., 2001).

Giant seabass grow slowly, mature at a relatively old 

age, and are thus susceptible to overfishing. Historically, commercial and recreational fisheries were most 

active in Mexico; however, since 1970 landings have 

declined significantly in both Mexican and California 

waters. This is due, in part, to a 1981 law that prohibits 

the take of giant seabass with the exception that commercial fishermen can keep two fish as incidental catch 

in gillnet and trammel net fisheries (Leet et al., 2001).

Broad-scale Patterns

Habitat suitability was modeled as high for rocky substrates and kelp bed habitats at depths ranging from 

0-50 m, moderate between 50-70 m, and low at depths 

between 70-80 m. Soft substrates at depths ranging 

from 0-70 m were considered moderate and low from 

70-80 m. Suitability was ranked low from 35°N through 

Monterey Bay. Highly suitable habitats for giant seabass are widely dispersed along the mainland from 

Morro Bay to San Diego, around the Channel Islands, 

and southern offshore banks (Figure 4.1.37). A much 

larger area of moderate suitability follows a similar spatial trend. 

No fisheries data were available to test model performance.

Analysis of Boundary Concepts

Approximately 5% of the total area of the current 

CINMS boundary was considered highly suitable, 

while 50% was considered moderately suitable (Figure 

4.1.38). The areas of high suitability are located close 

to shore among the northern islands, particulary in areas that have had kelp habitat. No high or moderately 

suitable habitats were included within Concepts 4 and 

5. High and moderately suitable habitats located along 

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

Habitat Suitability

Low

Moderate

High

0 25 50 100

Kilometers

Giant seabass

Figure 4.1.38. Giant seabass habitat suitability off southern 

California.

124°W

124°W

123°W

123°W

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

37°N37°N

38°N38°N

Habitat Suitability

Low

Moderate

High

0 25 50 100 150 200

Kilometers

Giant seabass

Figure 4.1.37. Giant seabass habitat suitability off central and 

southern California.
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the mainland were included within the larger concepts where Concepts 1, 1a, and the Study Area contained the 

highest amount of highly suitable habitat (Figure 4.1.39). The Study Area yielded the highest OAI value; however 

this boundary is not under consideration. As such, Concept 2 displayed the optimal increase of highly suitable 

habitat/total area gained relative to the NAC (Table 4.1.14).

Summary

• Giant seabass are protected by law prohibiting recreational catch and allowing 1-2 fish/day as incidental catch 

in commercial fisheries.

• Highly suitable habitat was determined to occur over rocky substrates and kelp beds at depths between 0-50 

m.

• Of the six boundary concepts being considered, Concept 2 provides the greatest proportional change of suitable habitat for giant seabass/total area in relation to the NAC.

California sheephead (Semicossyphus pulcher)

California sheephead range from Monterey Bay to the Gulf of California; however, they are not common north 

of Point Conception. Preferred habitat for sheephead occurs over rocky reefs and within kelp beds to depths 

of 90 m (Leet et al., 2001). Juveniles are most abundant between 3-30 m, while adults are most commonly 

distributed between 3-60 m (Love, 1996). Commercial landings of sheephead have recently increased due to 

the recent development of a live-fish fishery. During the 1990s, total landings of sheephead in California waters 

averaged approximately 91,000 kg/year. During 1994-1999, the live fish fishery accounted for 73-87% of the total 

sheephead landings (Leet et al., 2001); most of these are captured by hook-and-line (Love, 1996). Recreational 

landings of sheephead, as reported by party boats, have averaged approximately 25,000 fish per year during 

1990-1999. There is no evidence that the sheephead population is threatened by existing fishery practices (Leet 

et al., 2001).

Broad-scale Patterns

The model developed for California sheephead indicated that highly suitable habitat occurs over hard substrate 

and among kelp beds between 0-60 m. Moderate suitability was determined to occur over hard substrate and 

kelp beds between 60-70 m, while low suitability was assigned to all substrates at depths between 70-80 m. 

Low suitability was also assigned to all habitats north of 35.5°N. As such, the majority of highly suitable habitat is 

distributed nearshore along the California mainland from Point Conception to Palos Verdes Point (Figure 4.1.40). 

Highly suitable habitat is also located among the rocky habitats around the Channel Islands and some of the 

southern offshore banks. Distribution of moderately suitable habitat follows the same pattern. 

SA

NAC

5

4

3

2

1, 1a

180

235

290

345

400

0 5000 10000 15000 20000 25000

Total Area (km2)

Habitat Area (km2

)

Figure 4.1.39. Regression of high habitat suitability for 

giant seabass and total area within the current and proposed boundary concepts. Numbers indicate concepts 

and NAC=No Action Concept, SA=Study Area.

Table 4.1.14. Analysis of giant seabass habitat suitability within boundary concepts. Numbers in bold indicate an increase in the estimate when 

compared to the No Action Concept (NAC). OAI estimates shaded in 

gray represent maximum observed benefit. Delta (D) indicates a rate of 

change calculation, and is always expressed as a percent change from 

the NAC.

Concept

Area 

(km2)

High 

Suitability 

Area (km2)

∆ Area 

(%)

∆ High 

Suitability 

Area (%)

OAI 

(absolute)

NAC 3475 198 - - -

5 4538 198 21.12 0.00 0.00

4 7981 198 113.11 0.00 0.00

3 9044 229 141.50 15.66 0.12

2 13736 324 266.78 63.64 0.25

1 22613 375 503.82 89.39 0.19

1a 22591 375 503.23 89.39 0.19

SA 17093 375 356.42 89.39 0.27
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Commercial Passenger Fishing Vessel (CPFV) data provided by CDFG were used to assess model performance (Figure 4.1.41). A non-parametric chi-square test was used to compare ranked mean catch data by fishing block with the maximum habitat suitability value each block overlapped; results were statistically significant 

(X2<0.0001, r2=0.26). 

Analysis of Boundary Concepts

Only 5% (218 km2) of the area within the current boundary of CINMS was considered highly suitable habitat for 

sheephead; 68 km2 was considered moderately suitable (Figure 4.1.42). No additional high or moderately suitable habitat was gained as boundary sizes increased for Concepts 4 and 5. Small gains of highly suitable habitat 

were observed within Concept 3 with the inclusion of nearshore habitat around Point Conception. Significantly 

more of these habitats along the mainland were contained within Concepts 1, 1a, 2, and the Study Area (Figure 

4.1.43).

Although the Study Area and Concepts 1 and 1a contained the highest amount of highly suitable habitat, the 

amount of habitat/total area contained within Concept 2 relative to the NAC yielded the highest OAI for the six 

concepts under consideration (Table 4.1.15).

Summary

• The center for California sheephead distribution occurs in southern California.

• Highly suitable habitat was determined to occur over 

hard substrates and among kelp habitats at depths between 0-60 m.

• Sheephead landings from CPFV data exhibited statistically significant correlation with model results.

• Of the six boundary concepts being considered, 

Concept 2 provides the greatest proportional change 

of suitable habitat for halibut/total area relative to the 

NAC.

124°W

124°W

123°W

123°W

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

37°N37°N

38°N38°N

Habitat Suitability

Low

Moderate

High

0 25 50 100 150 200

Kilometers

California sheephead

Figure 4.1.40. California sheephead habitat suitability off central 

and southern California.

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

Mean Landings

Low

Moderate

High

0 25 50 100

Kilometers

California sheephead

CPFV 1998-2002

CDFG Marine Region GIS

Lab, Monterey

Figure 4.1.41. California sheephead landings data from CDFG’s 

Commercial Passenger Fishing Vessel (CPFV) database, 1998-

2002, superimposed over predicted habitat suitability

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

Habitat Suitability

Low

Moderate

High

0 25 50 100

Kilometers

California sheephead

Figure 4.1.42. California sheephead habitat suitability off 

southern California.
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California halibut (Paralichthys californicus)

California halibut are distributed from Quillayute River, Washington to southern Baja California, and are most common from Bodega Bay southward (Love, 1996). Adult and juvenile California halibut inhabit soft bottom habitats most 

commonly at depths less than 100 m, but some adults have been reported to 200 m. Juveniles are most commonly 

found in shallow embayments (Leet et al., 2001). California halibut are an important species in both the commercial 

and recreational fisheries of central and southern California. Since 1932, average annual commercial catch has been 

412,000 kg, with a recent peak in 1997 of 567,000 kg. Historically, halibut have been commercially harvested by three 

gear types: otter trawl, set gill and trammel nets, and hook-and-line. Set nets are the gear of choice, primarily in San 

Francisco Bay and southern California, due to trawl restrictions in state waters. The commercial fishery is centered from 

Bodega Bay southward into Mexico waters (Leet et al., 2001). Halibut are most commonly taken in recreational fisheries using hook-and-line and spear fishing. 

Broad-scale Patterns

Highly suitable habitat was determined to occur on soft substrate between 0-100 m, moderate suitability occurs on 

soft substrates between 100-150 m, and low suitability occurs at depths between 150-200 m (Figure 4.1.44). High and 

moderately suitable habitat comprises a large portion of the continental shelf including considerable area within Cordell 

Bank, Gulf of the Farallones, Monterey Bay, and Channel Islands national marine sanctuaries. 

Halibut captured in SCCWRP trawl samples (Figure 4.1.45) and Commercial Passenger Fishing Vessel (CPFV) data 

(Figure 4.1.46) were used to assess model performance. Halibut data from these sources were compared with model 

results using a non-parametric chi-square test. Chi-square results from SCCWRP trawls indicated a significant correSA

NAA 5

4

3

2

1, 1a

50

100

150

200

0 5000 10000 15000 20000 25000

Total Area (km2)

Habitat Area (km2

)

Figure 4.1.43. Regression of total habitat area for California sheephead and total area within the current and 

proposed boundary concepts. Numbers indicate concepts and NAC=No Action Concept, SA=Study Area.

Table 4.1.15. Analysis of California sheephead habitat suitability within boundary 

Concepts. Numbers in bold indicate an increase in the estimate when compared to 

the No Action Concept (NAC). OAI estimates shaded in gray represent maximum 

observed benefit. Delta (D) indicates a rate of change calculation, and is always expressed as a percent change from the NAC.

Concept Area (km2)

High 

Suitability 

Area (km2)

∆ Area 

(%)

∆ High 

Suitability 

Area (%)

OAI 

(absolute)

NAC 3475 218 - - -

5 4538 218 21.12 0.00 0.00

4 7981 218 113.11 0.00 0.00

3 9044 253 141.50 16.06 0.29

2 13736 353 266.78 61.93 0.60

1 22613 403 503.82 84.86 0.43

1a 22591 403 503.23 84.86 0.43

SA 17093 403 356.42 84.86 0.61
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

spondence between halibut catch and predicted habitat suitability (X2<0.0001, r2=0.11). Similarly, results using CPFV 

data showed a significant correlation (X2<0.0001, r2=0.17). 

Analysis of Boundary Concepts

Approximately 30% of the area within the current CINMS was predicted highly suitable habitat for California halibut. 

An additional 521 km2 was considered moderately suitable (Figure 4.1.47). Slight gains of highly suitable habitat were 

included as boundary size increased within Concepts 3, 4, and 5. Significant gains were included along the mainland 

within Concepts 1, 1a, 2, and the Study Area (Figure 4.1.48). The absolute OAI takes into account the proportional 

change (%) in highly suitable habitat and total area moving from the NAC to each of the concepts under consideration. 

While the Study Area includes the largest relative proportion of highly suitable habitat, this boundary is not under consideration as a boundary concept. OAI results for the remaining concepts (Table 4.1.16) indicate that Concepts 1 and 1a 

provide the greatest increase of highly suitable habitat relative to the increase in total boundary area from the NAC. 

Summary

• The commercial and recreational fisheries for California halibut extend over a wide range from Bodega Bay through 

southern California.

123°W

123°W

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

37°N37°N

38°N38°N

Habitat Suitability

Low

Moderate

High

0 25 50 100 150 200

Kilometers

California halibut

Figure 4.1.44. California halibut habitat suitability off central and 

southern California.

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

Catch rank

Low

Moderate

High

0 25 50 100

Kilometers

California halibut

SCCWRP Trawls

1994, 1998

Figure 4.1.45. Abundance of California halibut captured in SCCWRP trawls superimposed over predicted habitat suitability.

123°W

123°W

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

37°N37°N

38°N38°N

Mean Landings

Low

Moderate

High

0 25 50 100 150 200

Kilometers

California halibut

SCCWRP Trawls

1994, 1998

Figure 4.1.46. California halibut landings data from CDFG’s 

Commercial Passenger Fishing Vessel (CPFV) database, 1998-

2000, superimposed over predicted habitat suitability.

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

Habitat Suitability

Low

Moderate

High

0 25 50 100

Kilometers

California halibut

Figure 4.1.47. California halibut habitat suitability off southern 

California.
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• Highly suitable habitat for California halibut was determined to occur over soft substrates at depths <100 m.

• Halibut catch data from SCCWRP trawls and CPFV data exhibited statistically significant correlation with model 

results.

• Of the six boundary concepts being considered, Concepts 1 and 1a provide the greatest proportional change 

of suitable habitat for halibut/total area in relation to the NAC.

4.2 Fish Assemblage Structure

Community metrics and multivariate statistics were used to analyze marine fish species assemblages off southern California. Analyses were completed using four data sets (see Table 4.2.1) which contained fish abundance 

information for 364 species. Although none of the data sets were spatially and temporally comprehensive, results 

were combined to provide a region-wide assessment for fish community structure. Objectives for fish community 

analyses are as follows:

• Calculate Shannon index of diversity for each dataset;

• Determine which species co-occur (i.e., species assemblages); 

• Analyze trawl data to determine which locations contained similar catches/sightings and utilize a GIS to map 

the results (i.e., site groups);

• Resolve where species assemblages were being caught/identified by combining results from objectives 1 and 

2; and 

• Calculate OAI and assess boundary concepts with repsect to the analyses conducted for the objectives listed 

above.

Data and Methods

Recreational Fishery Information Network (RecFIN) Commercial Passenger Fishing Vessel (CPFV): RecFIN is 

a database that integrates state and federal marine recreational fishery sampling efforts. This dataset is a subset containing GPS coordinates for 680 CPFV trips during 1999 and 2001 at depths ranging from 0-2,200 m. 

Fishermen targeted species and visited between 1 and 22 locations during each trip. Each trip/location combination was considered a unique site and was used as a sample unit in analyses. RecFIN provided information on 

four hook and line fishing methods: free drift, stationary drift, anchor, and troll. The trolling trips were removed 

before analysis because they targeted pelagic species and therefore provide limited information about species 

assemblages or diversity. Shannon’s Diversity Index was calculated using 4,085 trip/location combinations which 

SA

NAC

5

4 3

2

1, 1a

1000

1500

2000

2500

3000

3500

0 5000 10000 15000 20000 25000

Total Area (km2)

Habitat Area (km2

)

Figure 4.1.48. Regression of high habitat suitability for 

California halibut and total area within the current and 

proposed boundary concepts. Numbers indicate concepts and NAC=No Action Concept, SA=Study Area.

Table 4.1.16. Analysis of California halibut habitat suitability within boundary concepts. Numbers in bold indicate an increase in the estimate when 

compared to the No Action Concept (NAC). OAI estimates shaded in gray 

represent maximum observed benefit. Delta (D) indicates a rate of change 

calculation, and is always expressed as a percent change from the NAC.

Concept

Area 

(km2)

High 

Suitability 

Area (km2)

∆ Area 

(%)

∆ High 

Suitability 

Area (%)

OAI 

(absolute)

NAC 3475 1194 - - -

5 4538 1201 21.12 0.59 0.06

4 7981 1201 113.11 0.59 0.01

3 9044 1310 141.50 9.72 0.16

2 13736 2157 266.78 80.65 0.69

1 22613 3237 503.82 171.11 0.77

1a 22591 3234 503.23 170.85 0.77

SA 17093 3237 356.42 171.11 1.09
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

captured 130 fish species. In order to evaluate species assemblages, species that were infrequently captured 

(less than 5% of the total trip/locations) were omitted. As a result, the dataset was reduced to 2,697 trip/locations 

which captured 18 fish species.

Southern California Coastal Water Research Project (SCCWRP): Southern California Bight Regional Survey 

data obtained from SCCWRP consisted of 426 fisheries-independent trawl samples collected between June and 

September in 1994 and 1998. Samples were collected with a 7.6 m headrope semiballoon otter trawl with 1.25 

cm codend mesh towed for 5 minutes (in bays) to 10 minutes (on coast) along isobaths at each station, and 

ranged in depth from 2-215 m (Allen et al. 1998, 2002). In 1994, the survey targeted the mainland shelf between 

10-200 m, whereas the 1998 survey added trawls near islands and within bay and harbor areas, sampling from 

2-200 m (Allen et al. 1998, 2002). Catch information for 150 species was used for Shannon Diversity calculations 

and (after omission of those species present in >5% of trawls) 48 species for assemblage analysis. For more 

information on sampling methodology, refer to Allen et al. (1998) and Allen et al. (2002).

National Marine Fisheries Service Groundfish Survey Program (NMFS GSP): Data from 477 fishery independent 

trawls ranging from 55-1,200 m in depth were collected June-November in 1977, 1989, 1992, 1995, and 1997-

2002. Gear included a nor’eastern trawl (127 mm stretched-mesh body; 89 mm stretched-mesh codend; and 32 

mm stretched-mesh codend liner) with a rubber bobbin roller which was trawled for 15-30 minutes on the bottom. 

Zimmerman’s (2003) analysis of benthic species biomass was used to cull out the trawls that did not fish the bottom. The final data set used for the diversity analysis contained 189 fish species. After removal of rare species, 

the dataset contained information on 59 fish species. See Shaw et al.(2000), Turk et al. (2001), Wilkins et al. 

(1998) and Zimmermann et al. (2001) for detailed information on trawl and survey methods. 

Kelp Visual Census: The Partnership for Interdisciplinary Studies of Coastal Oceans (PISCO) provided diver 

visual census data collected at 15 sites, while the Vantuna Research Group (VRG) provided data for 29 sites. 

Visual transect surveys, recording fish abundance and size, were completed by scuba divers along the bottom of 

the kelp forest. Each dataset used similar methods, yet transect distance differed, thus catch was standardized 

to a 2x60 m transect. Diversity analysis included 84 species of fish, while the assemblage analysis contained 45 

species.

Fish diversity (H’ Shannon index of diversity) (Shannon and Weaver, 1949) was calculated independently for 

each of the four datasets, which included abundance information for 364 species. The formula is expressed as: 

where ni is the number of individuals belonging to the ith species (s) in the sample, and N is the total number of 

individuals in the sample. Individual results for each survey method are presented to show the distribution of 

effort and site diversity. Gridded results were also provided to determine if larger spatial patterns were present 

that may have been masked by the high variability present between individual sites. Using ArcGIS, 5 x 5 minute 

grids were created and mean diversity was calculated for each grid cell containing data. The results were sorted 

by diversity and divided into quintiles (i.e. each quintile contains 20% of the sites). 

Table 4.2.1. Summary of the datasets used to assess fish diversity and species assemblages.

Dataset Gear

Geographic

Area

Habitat Type/

Depth Months Years

# Sampling 

Sites

# Species 

(diversity)

# Species 

(assemblage)

RecFIN Hookand-line California NA/

1-2200 m All 1999, 2001 4085 130 18

SCCWRP Trawl Southern 

California

soft substrate/

2-215 m

JuneAug 1994, 1998 425 150 48

NMFS GSP Trawl Southern

California

soft substrate/

55-1200 m

JuneNov

1977, 1989, 

1992, 1995, 

1997-2002

477 189 59

PISCO/

Vantuna

Scuba 

Diver 

visual 

survey

Southern 

California

Kelp bed/

NA

June, 

July 1999-2002 44 84 45

ln ni

N

ni

-Σ=1 N

S

H’ = [( ) ( )] ln ni

N

ni

-Σ=1 N

S

H’ = [( ) ( )]
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Diversity results form the four datasets were combined to provide an overall map of fish diversity. To standardize, gridded results from each dataset were classified by quintiles with 5 denoting the greatest diversity and 1 

the least diversity. Standardized diversity was then averaged where more than one diversity estimate was available for a cell. This technique removes differences that result from variable collection methods; however, it can 

minimize differences between habitats. For example, Allen (1985) analyzed multiple datasets and characterized 

fish habitats into three levels of diversity: high (kelp forests, deep rocky reefs and offshore soft bottom), medium (open coast sandy beaches, shallow rocky reefs, and harbor/nearshore soft bottom), and low (nearshore 

midwater, bay/estuary, and rocky intertidal). Thus selective habitat sampling may have been homogenized. To 

accurately compare areas, the same sampling method should be employed at all locations. However, lacking a 

comprehensive data set, standardization was considered a reasonable proxy.

It is important when analyzing fish community structure not just to analyze the diversity of species present, but 

also to investigate which species tend to co-occur. Clustering is a technique for optimal grouping of entities 

according to the resemblance of their attributes as expressed by given criteria (Boesch, 1977) or, in short, a 

method that puts variables (sites, species, etc) into groups. Cluster analyses was used to distinguish species 

assemblages and site groups. Data sets were initially filtered to remove incomplete or incorrect data (i.e., sites 

with coordinates that place them on land, stationed fishing trips that move greater than 0.01 degree latitude or 

longitude, etc). Fish that were not identified to species were removed (diversity analyses), as well as those present in less than 5% of the trawls (assemblage analyses). Rare species were removed from assemblage analyses because their occurrence is often due to chance and not biological response, and can therefore negatively 

impact results (Gauch, 1982; Boesch, 1977). The 5% cutoff was chosen because it reduced the number of zeros 

present, while keeping an adequate number of species for analysis. Because the raw abundance data did not 

conform to assumptions of a normal distribution and homogeneity of variances, fourth root transformations were 

utilized. This transformation was applied because it is invariant to scale changes (Field et al., 1982). Data were 

standardized by species abundance (i.e., abundance for each species was adjusted such that the mean is zero 

and the standard deviation is one). Exploratory analyses were conducted to investigate multiple resemblance 

metrics and clustering methods to determine which metric consistently provided interpretable results without 

excessive chaining. When chaining occurs, entities fuse to a few nuclear groups one at a time rather than forming new groups, and make it impossible to divide the data into meaningful smaller groups (Boesch, 1977). Two 

dissimilarity methods, Bray-Curtis and Jaccard (both paired with average means clustering), met these criteria. 

The Bray-Curtis dissimilarity coefficient (bjk) is calculated as:

where Xij is the ith attribute (column) measured on the jth object (row), and Xik is the ith attribute on the kth object 

(Romesburg, 1984). The Bray Curtis dissimilarity metric often produces meaningful results with species abundance data, and is therefore one of the most widely used cluster methods in ecology (Boesch, 1977). Scree plots 

were used to determine where breaks in the similarity level occurred (McGarigal et al., 2000). Subsequently, 

group composition was analyzed to determine the best ecological groupings (i.e. if smaller or larger groups 

would provide a better ecological explanation) (Boesch, 1977). Results from the Bray-Curtis dissimilarity metric 

were used to allow for comparisons with previously completed analyses of the SCCWRP data (Allen et al., 1998, 

2002). 

To determine interaction between species assemblages and site groups, the average frequency of occurrence 

for each species was calculated for each site group. This analysis is a modified nodal analysis (Boesch, 1977). 

By analyzing average frequencies for species in site groups it was possible to determine which species assemblages were influential in forming the site groups. Spatial distribution of the site groups was visualized by mapping the site groups in a GIS. A step-wise discriminant analysis was completed on each dataset to determine if 

parameters such as depth, latitude, or effort were significantly different between site groups.

bjk =

Xij-Xik

=1

Σ

n

-1

Σ

n

(Xij+Xik)

bjk =

Xij-Xik

=1

Σ

n

-1

Σ

n

(Xij+Xik)
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Broad-scale Patterns

Diversity

Kelp diver surveys exhibited the highest mean diversity (1.8 ± 0.5), followed by both SCCWRP and NMFS trawl 

datasets (1.4 ± 0.5 and 1.4 ± 0.4, respectively). The recreational data had the lowest diversity (0.6 ± 0.6). These 

differences likely reflect the variety of methods used as well as true differences in diversity among habitats. Due 

to this convolution, diversity results for each of the four datasets will be discussed independently. The composite 

diversity map displays many of the characteristics found in the individual datasets, and is therefore discussed 

last. 

The recreational hook and line data was the only dataset containing information for the entire coast of California 

(Figure 4.2.1); however, the distribution of effort was concentrated in central California (San Francisco to Monterey Bay) and southern California (south of Santa Barbara). Overall, there were statistically significant (P<0.05) 

correlations between diversity and effort, depth, latitude, and longitude. However, using individual linear regressions, these variables explained very little of the variance (r2

=0.01, 0.03, 0.03, and 0.06, respectively). The most 

striking pattern from the recreational data was the large number of trips with very low diversity that occurred in 

southern California offshore environments (Figure 4.2.1). These low diversity trips targeted pelagic species and 

recorded either no catch or only a few species, such as albacore or yellowtail, thus biasing diversity results. Unfortunately, all trips targeting pelagic species could not be removed without implementing an in-depth analysis 

of species captured. Instead, all trip/location combinations with species diversity of zero (44%) were removed 

before calculating mean diversity within the five minute grid cells because they can substantially lower mean 

diversity. In this analysis, sites with zero diversity do not represent marine areas that lack fish species, but rather 

they depict areas where the targeted fish was not collected. Other low diversity areas were found within and 

just offshore of San Francisco Bay. Cells with high mean diversity that were based on at least 3 data points are 

found: west of San Francisco around the Gulf of the Farallones and Cordell Bank; northwest of Monterey Bay, 

in approximately 300 m depth; and directly south of Monterey Bay. In addition, cells east of Anacapa Island and 

along the coast of southern California also exhibited consistent measurements of high diversity. 

High fish diversity observed in the SCCWRP trawls appear to be randomly dispersed throughout the study area. 

The Santa Barbara Channel has a large area of high diversity south of Carpinteria (Figure 4.2.2). Low diversity 

areas can be found west of San Miguel and Santa Cruz Islands, and south of Santa Catalina Island. There was 

a significant positive relationship between diversity and latitude, and a negative relationship between diversity 

and depth, but variance explained was minimal (r2=0.01 and 0.08, respectively). The relationship between catch 

and depth could be attributed to gear avoidance. Fish species may be able to respond visually and escape from 

trawls in shallow water where the light is brighter, but not in deep water. The majority of mean diversity calculations were based on less than 3 sampling points, thus confidence in the accuracy of mean diversity is uncertain 

due to limited samples. 

Figure 4.2.1. Fish diverisity calculated for individual RecFIN hook and line trips (left) and mean diversity for trips within 5x5 

minute grids (right).
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The majority (85%) of the NMFS GSP trawls were found north of Point Conception, allowing for only minor comparisons 

within the study area (Figure 4.2.3). Between Cape San Martin and Point Sal, a line of high diversity trawls were observed at approximately 200 m depth. High diversity trawls were infrequent south of Point Conception; 21 trawls (13%) 

were categorized into the top quintile. 

Fish diversity within kelp habitats was calculated using PISCO scuba transect data from 44 sampling stations (Figure 

4.2.4). Four of the nine sites representing the top 20% (quintile) were located on the coast north of Santa Barbara, 

two on San Nicolas, two on Santa Catalina, and one near San Clemente Island. Santa Cruz, Santa Catalina and San 

Clemente appear to have lower diversity sites south and east of the islands, while San Nicolas exhibited the opposite 

trend. Pondella et al. (2005) found lower overall diversity on warm water islands (Santa Catalina and San Clemente) 

when compared to colder islands to the north (Santa Cruz, San Nicolas and Santa Barbara). This overall pattern is 

supported by the results of this study. 

Patterns of mean standardized diversity for the composite of all datasets are displayed in Figure 4.2.5. There was only 

one cell that contained information from all four datasets, and 13 that contained information from three of the four. Seventy-five percent of the cells contained information from only one dataset. The composite dataset provides an overall 

diversity map for fishes in southern California; much of the coastal and marine habitats are covered by one of the four 

datasets. 

Figure 4.2.2. Fish diverisity for individual SCCWRP trawls (left) and mean diversity of trawls within 5x5 minute grids (right).

Figure 4.2.3. Fish diverisity for individual NMFS GSP trawls (left) and mean diversity of trawls within 5x5 minute grids (right).
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Assemblages 

Eighteen species of recreational fish from the 

RecFIN data were divided into six species assemblages (Table 4.2.2), and eighteen site 

groups (Figures 4.2.6-7). There was an attempt to 

reduce the number of site groups, but the degree 

of agreement between Bray-Curtis and Jaccard 

results decreased substantially. The strong agreement between methods highlights the importance 

of individual species in this data set. Each site 

group was based almost entirely on the presence 

or absence of one species (Table 4.2.3). Six of 

the eighteen (33%) site groups were only located 

south of Santa Barbara, while one site group was 

only located north of Monterey Bay. Fifteen (83%) 

site groups were found around Anacapa Island, 

highlighting the diversity of fishes found in this 

area.

A clear division was apparent between southern 

and northern species’ assemblages. Two of the 

Figure 4.2.5. Composite fish diversity (mean of standardized values across the four datasets) (left) and effort within 5x5 minute 

grids (right).

Table 4.2.2. Species assemblage results for the RecFIN CPFV data using 

the Bray-Curtis dissimilarity metric with average means clustering. Assemblages are named for the most influential species in each group. 

Group Common Name Scientific Name

vermilion rockfish

assemblage

vermilion rockfish

copper rockfish

starry rockfish

rosy rockfish

Sebastes miniatus

Sebastes caurinus

Sebastes constellatus

Sebastes rosaceus

greenspotted rockfish 

assemblage

greenspotted rockfish

bocaccio

Sebastes chlorostictus

Sebastes paucispinis

yellowtail rockfish

assemblage

yellowtail rockfish

lingcod

blue rockfish

Sebastes flavidus

Ophiodon elongatus

Sebastes mystinus

ocean whitefish

assemblage

ocean whitefish

California scorpionfish

honeycomb rockfish

Caulolatilus princeps

Scopaena guttata

Sebastes umbrosus

barred sandbass

assemblage

barred sandbass

kelp bass

California halibut

Pacific chub mackerel

Pacific barracuda

Paralabrax nebulifer

Paralabrax clathratus

Paralichthys californicus

Scomber japonicus

Sphyraena argentea

gopher rockfish

assemblage gopher rockfish Sebastes carnatus

Figure 4.2.4. Fish diversity for individual kelp visual census surveys (left) and mean diversity of surveys within 5x5 minute grids 

(right).
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Figure 4.2.6. Location of site groups, RecFIN CPFV data. 
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

species assemblages were comprised of predominantly Southern California Bight species: ocean whitefish and barred 

sand bass assemblages. The other assemblages contain species that are more common north of Point Conception. 

Step-wise discriminant analysis (N=3,002) revealed significant differences among site groups for: latitude (r2=0.59, 

F=234, P<0.0001), depth (r2=0.40, F=110, P<0.0001), fishing type (anchored vs. drift: r2=0.20, F=42, P<0.0001), and 

effort (r2=0.09, F=16, P<0.0001). These results suggest that species’ distributions are influenced by habitat variables 

such as depth and latitude, but could be based on either passive or active behaviors. The higher coefficients of determination associated with the habitat parameters compared to fishing effects suggests that habitat characteristics have 

a stronger influence on the site groups. 

SCCWRP trawls were conducted in 1994 and 1998, and were combined for this analysis. Trawls were concentrated in 

the southern California bight at depths to 215 m, and provided information on 62 fish species. These fish species were 

combined into 15 species assemblages (Table 4.2.4) and sites were divided into six distinctive groups (Figure 4.2.8; 

Table 4.2.5).

Figure 4.2.7. Location of site groups for the RecFIN CPFV data within southern California (left) and around Anacapa Island.

Table 4.2.3. Mean frequency of occurrence for each recreational site group. Shaded cells represent species present in over half of the 

groups in that site.

Species

Site Groups

2

N=179

5

N=94

1

N=145

16

N=168

6

N=99

18

N=135

15

N=64

14

N=89

17

N=111

12

N=154

11

N=226

13

N=160

7

N=299

4

N=316

9

N=237

10

N=193

3

N=165

8

N=183

vermilion rockfish 100 39 35 25 3 4 17 2 17 25 11 31 6 0 2 4 1 30

copper rockfish 7 100 20 26 6 3 6 2 2 8 3 11 0 0 0 2 0 5

starry rockfish 22 6 100 52 10 13 2 0 5 12 4 34 0 1 0 1 0 3

rosy rockfish 19 13 32 98 25 15 2 6 22 7 3 18 0 0 0 0 0 14

greenspotted rockfish 25 5 28 6 100 59 0 11 0 10 2 9 0 2 0 3 0 0

bocaccio 43 16 30 25 2 100 0 0 1 11 7 9 1 0 0 2 1 2

yellowtail rockfish 4 14 7 55 15 50 98 11 24 1 0 0 0 0 0 1 0 7

lingcod 25 39 28 36 3 33 0 100 39 5 15 9 4 0 2 4 2 39

blue rockfish 2 19 10 31 0 2 48 0 100 3 1 1 0 0 0 1 0 41

ocean whitefish 4 1 10 4 1 0 0 0 1 100 33 46 4 17 1 5 1 3

California scorpionfish 2 4 32 2 3 0 0 1 0 8 100 31 37 16 21 18 7 0

honeycomb rockfish 0 1 9 1 0 0 0 0 1 7 20 99 2 1 0 11 1 0

barred sandbass 1 0 0 0 0 0 0 0 0 5 5 8 100 21 40 2 0 0

kelp bass 0 0 0 0 0 1 0 7 0 5 4 4 19 100 8 1 1 0

California halibut 0 0 0 0 0 0 0 0 0 2 0 1 7 4 100 0 5 2

Pacific chub mackerel 6 0 4 1 2 0 0 0 0 19 3 10 33 28 20 74 5 1

Pacific barracuda 1 0 0 0 0 0 0 2 0 1 4 1 26 27 1 0 100 0

gopher rockfish 1 16 1 5 0 0 31 0 4 5 11 4 1 5 0 1 1 100
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Discriminate analysis (N=413) revealed significant differences between site groups in all four 

parameters investigated: depth (r2=0.88, F=597, 

P<0.0001), effort (r2=0.37, F=48, P<0.0001), 

year (r2=0.10, F=9, P<0.0001), and latitude 

(r2=0.07, F=6, P<0.0001). Also, a clear relationship between species assemblages and depth 

was observed. 

This relationship can be visualized in Table 

4.2.5, where site groups have been ordered 

from shallow to deep. Both the northern anchovy 

and the California halibut assemblages consist 

of shallow species, but the northern anchovy 

assemblage includes schooling species often 

present inside bays, and the California halibut 

assemblage contains species more characteristic of the inner shelf and the outer limits of the 

bays. Similarly, two assemblages contain midshelf species (the hornyhead turbot and longfin 

sanddab), but the latter has species with a more 

southern distribution. The longspine combfish 

and stripetail rockfish assemblages consist of a 

combination of middle and outer-shelf species, 

while the rex sole, greenstriped rockfish and 

spotted cusk-eel assemblages contain deeper 

water species. Effort varied between offshore 

and bay habitats, which could explain the significant relationship between site groups and effort. Changes in areas targeted for trawling and 

large-scale weather/temperature patterns may 

have affected observed species assemblages. 

In 1994, nearshore ecosystems were targeted, 

while trawl effort focused on islands and bay areas during 1998. In addition, 1998 was a strong 

El Niño year, with water temperatures much 

warmer than normal. The cluster results presented here, while based on both years (1994 

and 1998) align closely with previous results 

from 1998 alone (Allen et al., 1998, 2002).

Seven species assemblages (Table 4.2.6) and 

four site groups (Figure 4.2.9) were identified 

from the NMFS benthic trawls. Most of the 

trawls were located north of Point Conception, 

so limited information was available for the southern bight area. Frequency of occurrence for fish species in each 

site group provides information on the interaction between species assemblages and site groups (Table 4.2.7).

Only two of the parameters investigated with the discriminant analysis (N=466) were found to be significant: depth 

(r2=0.87, F=1035, P<0.0001) and effort (r2=0.09, F=16, P<0.0001). Latitude did not have a significant effect (r2<0.01, 

F=1.4, P=0.24), and longitude was excluded from the analysis because it was confounded with latitude (r2=0.83). 

Corrections were made for effort, but effort still accounted for 9% of the variance. Depth explained 87% of the 

variation, which can be visualized in Figure 4.2.9; the four groups partition themselves into obvious depth contours. 

Again, the break-out of species from this data-set fell into known species-depth associations. The Pacific sanddab, 

Table 4.2.4. Species assemblage results for the SSCWRD trawl data using 

the Bray-Curtis dissimilarity metric with average means clustering. Assemblages are named for the most influential species in each group. 

Group Common Name Species Name

white croaker 

assemblage

white croaker

northern anchovy

Pacific sardine

queenfish

Genyonemus lineatus

Engraulis mordax

Sardinops sagax

Seriphus politus

California halibut 

assemblage

California halibut

barred sandbass

spotted turbot

diamond turbot

thornback

Paralichthys californicus

Paralabrax nebulifer

Pleuronichthys ritteri

Plueronichthys guttulata

Patyrhinoidis trisenata

specklefin midshipman specklefin midshipman Porichthys myriaster

white seaperch 

assemblage

white seaperch

shiner perch

Phanerodon furcatus

Cymatogaster aggregata

hornyhead turbot a

ssemblage

hornyhead turbot

speckled sanddab

California lizardfish

California scorpionfish

California tonguefish

fantail sole

Pleuronichthys verticalis

Citharichthys stigmaeus

Synodus lucioceps

Scorpaena guttata

Symphurus atricauda

Xystreurys liolepis

longfin sanddab 

assemblage

longfin sanddab

bigmouth sole

yellowchin sculpin

Citharichthys xanthostigma

Hippoglossina stomata

Icelinus quadriseriatus

Pacific argentine

 assemblage

Pacific argentine

bay goby

Aregentina sialis

Lepidogobius lepidus

longspine combfish 

assemblage

longspine combfish

English sole

pink seaperch

Zaniolepis latipinnis

Parophrys vetulus

Zalembius rosaceus

Pacific sanddab 

assemblage

Pacific sanddab

stripetail rockfish

slender sole

Dover sole

plainfin midshipman

shortspine combfish

Citharichthys sordidus

Sebastes saxicola

Lyopsetta exilis

Microstomus pacificus

Porichthys notatus

Zaniolepis frenata

California skate Californa skate Raja inornata

pygmy poacher 

assemblage

pygmy poacher

roughback sculpin

Odontopyxis trispinosa

Chitonotus pugetensis

rex sole assemblage

rex sole

blackbelly eelpout

Pacific hake

splitnose rockfish

blacktip poacher

Glyptocephalus zachirus

Lycodes pacifica

Merluccius productus

Sebastes diploproa

Xeneretmus latifrons

greenstriped rockfish 

assemblage

greenstriped rockfish

pink rockfish

greenblotched rockfish

Sebastes elongatus

Sebastes eos

Sebastes rosenblatti

spotted cusk-eel 

assemblage

spotted cusk-eel

spotfin sculpin

greenspotted rockfish

halfbanded rockfish

Chilara taylori

Icelinus tenuis

Sebastes chlorostictus

Sebastes semicinctus

Gulf sanddab Gulf sanddab Citharichthys fragilis
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Figure 4.2.8. Location of site groups, SCCWRP.

Table 4.2.5. Mean frequency of occurrence for SCCWRP site 

groups. Shaded cells represent species present in more than 

50% of the groups in that site. 

Site Groups

10

N=73

1

N=33

3

N=64

5

N=63

11

N=75

12

N=106

northern anchovy 42 3 2 3 1 5

white croaker 90 24 9 11 11 1

Pacific sardine 22 0 0 3 0 2

queenfish 75 3 5 5 0 2

California halibut 63 73 56 41 11 1

barred sand bass 33 94 16 8 1 0

spotted turbot 30 39 30 2 0 0

diamond turbot 10 45 2 0 0 0

thornback 15 3 13 0 0 0

specklefin midshipman 32 9 8 22 23 0

white seaperch 44 0 9 0 3 0

shiner perch 33 6 3 3 0 0

hornyhead turbot 19 0 63 75 64 15

speckled turbot 19 6 100 40 25 1

California lizardfish 47 15 73 92 77 18

California scorpionfish 3 3 13 52 27 10

California tonguefish 49 12 39 86 81 7

fantail sole 21 12 42 37 17 1

longfin sanddab 3 0 36 97 80 13

bigmouth sole 0 0 17 92 84 35

yellowchin sculpin 0 0 13 60 91 17

Pacific argentine 0 0 0 0 25 26

bay goby 5 0 3 6 63 9

longspine combfish 0 0 0 10 77 39

English sole 5 0 30 19 55 47

pink seaperch 0 0 2 21 81 45

stripetail rockfish 0 0 0 2 57 70

Pacific sanddab 0 0 14 37 87 93

slender sole 0 0 0 0 12 75

Dover sole 0 0 0 2 52 95

plainfin midshipman 1 0 5 6 75 74

shortspine combfish 0 0 2 0 7 79

California skate 5 0 11 29 28 7

pygmy poacher 0 0 0 3 25 4

roughback sculpin 0 0 9 16 23 6

rex sole 0 0 0 0 0 35

blackbelly eelpout 0 0 0 0 5 33

Pacific hake 0 0 0 0 0 27

splitnose rockfish 0 0 0 0 1 22

blacktip poacher 0 0 0 0 0 36

greenstriped rockfish 0 0 0 0 5 25

pink rockfish 0 0 0 0 8 16

greenblotched rockfish 0 0 0 0 9 29

spotted cusk-eel 0 0 0 2 7 34

spotfin sculpin 0 0 0 0 5 21

greenspotted rockfish 0 0 0 0 5 18

halfbanded rockfish 0 0 0 0 3 28

gulf sanddab 0 0 0 0 17 19

Figure 4.2.10. Location of site groups, kelp visual census 

surveys.

Figure 4.2.9. Location of site groups, NMFS GSP.
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

bocaccio, Dover sole, and longspine thornyhead assemblages contain mid-shelf, outershelf, mesopelagic, and deep bathypelagic 

species respectively. 

Diver surveys were only available at 44 

sites, which were clustered into ten site 

groups. Discriminant analysis found significant differences among site groups in latitude (N=39, r2

=0.67, F=11, P<0.0001) and 

depth (N=39, r2=0.43, F=4, P=0.005). Eight 

species assemblages were identified, and 

are arranged from northwest to southeast 

in Table 4.2.8. There was an obvious partitioning of the site groups between islands 

(Figure 4.2.10; Table 4.2.9). The assemblages segregate into three large groups: 

cold water sites around Point Conception 

(Groups 7-10), intermediate sites on Santa 

Cruz (western side) and San Nicolas Islands 

(Groups 5-6), and warm water sites on San 

Clemente, Santa Catalina, Santa Barbara, 

Anacapa, and Santa Cruz (eastern side) Islands (Groups 1-4). Within the warm water 

sites, Group 1 was the most wide-spread 

with sites on all of the islands. Group 2 is 

only located on Santa Catalina and San 

Clemente, which were characterized by 

warm water species. San Clemente Island 

shows a definite partitioning between the 

northwest and southeastern sections of the 

island. Within the intermediate sites, there is 

a definite separation of the sites found on 

San Nicolas Island. These sites have a lower occurrence of the northern fish species 

(i.e. lingcod assemblage) than the other intermediate sites in Group 6. The cold water 

site groups are located around Point Conception. Groups 9 and 10 contain only one 

site each, at Alegria and Cojo. Alegria was 

distinct from all other site groups, and only 

contains six fish species. The kelp species 

assemblages also divide into meaningful 

groups. The kelp bass assemblage is composed of many fish species common to kelp 

habitats. These species were more common 

at intermediate and southern site groups 

than northern site groups. There were two 

species assemblages with a northern distribution: lingcod and walleye surfperch.

Fish species diversity varied with each dataset due to the methods used and habitats 

sampled. The kelp visual surveys yielded 

Table 4.2.6. Species assemblage results for the NMFS GSP data using BrayCurtis dissimilarity metric with average means clustering. Assemblages are 

named for the most influential species in each group. 

Group Common Name Species Name

greenspotted rockfish 

assemblage

greenspotted rockfish

greenstriped rockfis

widow rockfish

Sebastes chlorostictus

Sebastes elongatus

Sebastes entomelas

blackbelly eelpout 

assemblage

blackbelly eelpout

spotted cusk-eel

Lycodes pacificus

Chilara taylori

Pacific sanddab 

assemblage

Pacific sanddab

petrale sole

white croaker

lingcod

English sole

Pacific pompano

curlfin sole

plainfin midshipman

halfbanded rockfish

pink seaperch

longspine combfish

Citharichthys sordidus

Eopsetta jordani

Genyonemus lineatus

Ophiodon elongatus

Parophrys vetulus

Peprilus simillimus

Pleuronichthys decurrens

Porichthys notatus

Sebastes semicinctus

Zalembius roaceus

Zaniolepis latipinnis

bocaccio 

assemblage

bocaccio

Pacific argentine

slender sole

chilipepper rockfish

shortbelly rockfish

stripetail rockfish

Pacific electric ray

northern anchovy

spiny dogfish

Sebastes paucispinis

Argentina sialis

Lyopsetta exilis

Sebastes goodei

Sebastes jordani

Sebastes saxicola

Torpedo californica

Engraulis mordax

Squalus acanthias

Dover sole 

assemblage

Dover sole

sablefish

sandpaper skate

rex sole

spotted ratfish

bigfin eelpout

black eelpout

Pacific hake

filetalk cat shark

longnose skate

aurora rockfish

splitnose rockfish

blackgill rockfish

bank rockfish

brown cat shark

blacktail snailfish

California grenadier

shortspine thornyhead

darkblotched rockfish

Microstomus pacificus

Anoplopoma fimbria

Bathyraja interrupta

Glyptocephalus zachirus

Hydrolagus colliei

Lycodes cortezianus

Lycodes diapterus

Merluccius productus

Parmaturus xaniurus

Raja rhina

Sebastes aurora

Sebastes diploproa

Sebastes melanostomus

Sebastes rufus

Apristurus brunneus

Careproctus melanurus

Nezumia stelgidolepis

Sebastolobus alascanus

Sebastes crameri

longspine thornyhead 

assemblage

longspine thornyhead

giant grenadier

California slickhead

Pacific flatnose

black skate

twoline eelpout

Pacific viperfish

Pacific grenadier

deepsea sole

snakehead eelpout

black hagfish

longnose cat shark

Sebastolobus altivelis

Albatrossia pectoralis

Alepocephalus tenebrosus

Antimora microlepis

Bathyraja trachura

Bothrocara brunneum

Chauliodus macouni

Coryphaenoides acrolepis

Embassichthys bathybius

Embryx crotalinus

Eptatretus deani

Apristurus kampae

threadfin slickhead threadfin slickhead Talismania bifurcata
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Site Groups

5

N=89

3

N=109

2

N=159

1

N=109

greensplotched rockfish 7 21 1 0

greenspotted rockfish 13 17 1 0

widow rockfish 13 12 1 0

blackbelly eelpout 15 19 4 0

spotted cusk-eel 11 23 2 0

Pacific sanddab 96 27 0 0

petrale sole 75 32 7 0

white croaker 63 1 0 0

lingcod 63 30 2 0

English sole 82 38 1 0

Pacific pompano 52 3 0 0

curlfin sole 47 3 0 0

plainfin midshipman 91 19 1 0

halfbanded rockfish 56 6 1 0

pink seaperch 84 4 0 0

longspine combfish 34 3 1 0

bocaccio 38 54 3 0

Pacific argentine 29 27 2 2

slender sole 36 83 25 0

chilipepper rockfish 63 76 9 0

shortbelly rockfish 53 58 4 0

stripetail rockfish 52 94 9 0

Pacific electric ray 37 38 8 2

northern anchovy 29 4 1 0

spiny dogfish 75 55 35 1

Dover sole 61 95 100 79

sablefish 38 78 97 92

sandpaper skate 0 31 49 5

rex sole 62 92 91 3

spotted ratfish 61 74 58 2

bigfin eelpout 13 52 72 6

black eelpout 1 12 58 13

Pacific hake 47 94 99 34

filetail cat shark 3 10 60 25

longnose skate 15 40 57 20

aurora rockfish 0 4 78 8

splitnose rockfish 6 86 74 1

blackgill rockfish 0 3 52 1

bank rockfish 6 16 25 0

brown cat shark 6 12 42 73

blacktail snailfish 0 17 22 50

California grenadier 0 0 12 15

shortspine thornyhead 3 30 87 99

darkblotched rockfish 2 15 11 0

longspine thornyhead 0 0 31 100

giant grenadier 0 0 1 69

California slickhead 0 1 0 90

Pacific flatnose 0 0 0 70

black skate 0 0 1 37

twoline eelpout 0 0 1 68

Pacific viperfish 1 0 4 21

Pacific grenadier 0 0 2 55

deepsea sole 0 0 2 68

snakehead eelpout 0 0 0 32

black hagfish 0 0 3 24

longnose cat shark 0 0 13 5

threadfin slickhead 0 1 0 49

Table 4.2.7. Mean frequency of occurrence for 

NMFS GSP site groups. Shaded cells represent 

species present in more than %50 of the groups 

in that site.

Group Common Name Species Name

lingcod 

assemblage

lingcod

tubesnout

spotfin surfperch

kelp greenling

rainbow seaperch

cabezon

brown rockfish

gopher rockfish

copper rockfish

black-and-yellow rockfish

yellowtail rockfish

blue rockfish

grass rockfish

olive rockfish

Ophiodon elongatus

Aulo rhynchus flavidus

Hyperprosopon anale

Hexagrammos decagrammus

Hypsurus caryi

Scopaenichthys marmoratus

Sebastes auriculatus

Sebastes carnatus

Sebastes caurinus

Sebastes chrysomelas

Sebastes flavidus

Sebastes mystinus

Sebastes rastrelliger

Sebastes serraniodes

walleye surfperch 

assemblage

walleye surfperch

white seaperch

jacksmelt

Hyperprosopon argenteum

Phanerodon furcatus

Atherinopsis californiensis

bocaccio 

assemblage

bocaccio

striped seaperch 

Sebastes paucispinis

Embiotoca lateralis

shiner surfperch shiner surfperch Cymatogaster aggregata

sargo 

assemblage

sargo

barred sandbass

horn shark

Anisotremus davidsonii

Paralabrax nebulifer

Heterodontus francisci

spotted kelpfish spotted kelpfish Gibbonsia elegans

jack mackerel 

assemblage

jack mackerel Trachurus symmetricus

Pacific barracuda Sphyraena argentea

kelp bass 

assemblage

kelp bass

island kelpfish

kelp perch

ocean whitefish

blacksmith

pile perch

black perch

opaleye

giant kelpfish

garibaldi

rock wrasse

halfmoon

bat ray

senorita

rubberlip seaperch

California sheephead

treefish

kelp rockfish

Paralabrax clathratus

Alloclinus holderi

Brachyistius frenatus

Caulolatilus princeps

Chromis punctipinnis

Rhacochilus vacca

Embiotoca jacksoni

Girella nigricans

Heterostichus rostratus

Hypsypops rubicundus

Halichoeres semicinctus

Medialuna californiensis

Myliobatis californica

Oxyjulis californica

Rhacochilus toxotes

Semicossyphus pulcher

Sebastes serriceps

Sebastes atrovirens

Table 4.2.8. Species assemblage results for kelp visual census surveys using 

Bray-Curtis dissimilarity metric with average means clustering. Assemblages are 

named for the most influential species in each group. 
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the highest diversity, followed by the benthic trawls, and then the recreational hook and line. The differences in 

methods could account for much of the variability. 

Gridded mean diversity did not reveal obvious spatial patterns. In all datasets, there do not appear to be any trends 

between the islands and the coast, with all areas showing a mixture of high diversity and low diversity sites. The 

highest coefficient of determination was observed for the kelp dataset, where the sites located the farthest north 

and west contained slightly higher diversity than those located the farthest south and east. Depth was significantly 

different in the recreational and SCCWRP datasets, but in both cases, explained less than 10% of the variance in 

diversity. 

SIte Groups

7

N=3

8

N=2

9

N=1

10

N=1

6

N=4

5

N=5

3

N=5

4

N=1

1

N=16

2

N=5

lingcod 100 50 100 0 50 40 0 0 0 0

olive rockfish 33 50 100 0 100 60 20 0 13 20

black-and-yellow rockfish 100 50 0 0 50 40 0 0 6 0

blue rockfish 100 50 0 0 100 20 0 0 13 0

brown rockfish 67 50 0 0 50 40 0 0 0 0

cabezon 100 100 0 0 50 40 20 0 13 0

copper rockfish 67 100 0 0 75 0 0 0 0 0

gopher rockfish 67 0 0 0 50 20 0 0 0 0

grass rockfish 67 100 0 0 0 0 0 0 6 0

kelp greenling 100 50 100 0 0 0 0 0 0 0

rainbow seaperch 100 100 100 0 75 100 0 0 25 40

spotfin surfperch 100 100 0 0 0 0 0 0 0 0

tubesnout 100 0 0 0 0 40 0 0 0 0

yellowtail rockfish 100 50 0 0 25 0 20 0 0 0

walleye surfperch 33 100 0 100 25 0 20 0 19 20

jacksmelt 0 100 0 0 0 0 0 0 6 60

white seaperch 100 100 100 100 50 0 40 100 13 20

bocaccio 33 0 100 100 25 100 20 0 6 0

striped seaperch 100 0 100 0 75 100 0 0 19 0

shiner surfperch 67 0 0 0 0 0 0 0 13 20

sargo 0 0 0 0 0 0 40 0 44 20

barred sandbass 0 0 0 0 25 0 20 0 25 20

horn shark 0 0 0 0 25 20 60 0 0 20

spotted kelpfish 0 0 0 0 0 0 40 100 25 0

jack mackerel 0 0 0 0 0 0 60 0 6 60

Pacific barracuda 0 0 0 0 0 20 20 0 6 40

kelp bass 100 100 100 100 100 100 100 100 100 100

bat ray 0 0 0 0 50 40 80 0 38 80

black perch 100 100 100 0 100 100 100 100 100 60

blacksmith 0 0 0 0 100 100 100 0 100 100

California sheephead 33 0 0 0 100 100 100 100 100 100

garibaldi 0 0 0 0 75 80 100 100 100 100

giant kelpfish 67 100 100 0 50 0 0 0 88 100

halfmoon 0 0 0 0 75 100 100 0 100 100

island kelpfish 0 0 0 0 0 20 100 0 56 20

kelp rockfish 100 50 100 0 100 100 40 0 63 0

kelp perch 67 100 100 100 75 80 60 100 100 100

ocean whitefish 0 0 0 0 75 40 100 0 38 100

opaleye 0 0 0 0 100 100 100 100 100 100

pile perch 100 100 100 0 100 100 100 0 75 40

rock wrasse 0 100 0 0 100 60 100 100 100 100

rubberlip seaperch 67 100 0 0 100 80 60 0 56 0

senorita 100 100 100 100 100 100 100 100 100 100

treefish 0 0 0 0 50 100 60 0 56 20

Table 4.2.9. Mean frequency of occurrence for kelp visual census site groups. Shaded cells represent 

species present in more than 50% of the groups in that site.
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There were similarities in the assemblage results from the four datasets. Depth was significantly different among 

site groups within all four datasets, and latitude was significant in the recreational, SCCWRP, and kelp datasets. 

These results agree with previous investigations (Horn and Allen, 1978; Gabriel and Tyler, 1980; Allen and Smith, 

1988; Matthews and Richards, 1991; Sullivan, 1995; Allen et al. 1998, 2002; Williams and Ralston, 2002). Latitude 

and/or longitude were significantly different between site groups in all datasets, but explained little of the variance 

(r2=0.01-0.22).

The Channel Islands are divided into two main biogeographical provinces: the warm-temperate San Diegan and 

the cold-temperate Oregonian. San Miguel, Santa Rosa, and San Nicolas Islands typically contain Oregonian biota, 

while Santa Cruz (eastern part) Anacapa, Santa Barbara, Santa Catalina and San Clemente Islands contain San 

Diegan biota. Research on intertidal areas and kelp forests further separated the San Diegan province into two 

smaller groups (Murray et al., 1980; Murray and Littler, 1981; Littler et al., 1991; Pondella et al., 2005). All of these 

studies placed Santa Catalina and San Clemente into one group and Santa Barbara and Santa Cruz into a second 

group. 

Results from the kelp survey data presented here separate into the San Diegan and Oregonian provinces. The 

southern site groups (Groups 1-4) are San Diegan, while the intermediate and northern site groups (5-10) are Oregonian. These results show limited support for further division of the San Diegan province. Group 2 is only found on 

Santa Catalina and San Clemente Islands, but Group 1 is found on all of the islands in the San Diegan province.

There are six recreational groups that can be considered southern as they are located south of Point Conception 

and contain warm-water species: Groups 3, 4, 7, 11, 12, and 13. All seven site groups present on San Miguel and/or 

Santa Rosa Islands were also present on other islands, demonstrating that this region could be considered a transition zone between provinces. Overall, the recreational patterns are not easily discerned. 

The NMFS and SCCWRP trawls did not differentiate site groups among islands. Because both of these data sets 

targeted benthic species, they may be less influenced by surface currents and temperature and less likely to show 

the segregations detailed above. 

Analysis of Boundary Concepts

Evaluating the potential impacts of fish diversity and species assemblages in relation to boundary concepts was not 

attempted due to the disparate nature of the fish datasets (unequal effort, different sampling methods). However, 

results from these analyses provide some understanding of highly diverse fish habitats and species assemblages. 

Kelp forests have been well documented as supporting diverse communities of invertebrates and fish and our 

analyses are in agreement. Additionally, numerous soft bottom benthic habitats exhibited high diversity over the 

continental shelf and slope (Figure 4.2.11). Several ecologically important species assemblages were identified to 

occur within the region of interest, e.g. kelp species, shelf soft bottom species, and slope soft bottom species. In 

contrast, we were unable to provide comprehensive fish data captured over hard substrates, although this habitat 

type is critical for west coast rockfish species. In order to fully quantify diveristy and assemblage patterns, additional 

spatial and temporal data is recommended. 

Summary

• Fish diversity was highest for the kelp dataset (1.8±0.5), followed by both SCCWRP and NMFS trawl datasets 

(1.4±0.5 and 1.4±0.4, respectively). The recreational data had the lowest diversity (0.6±0.6). 

• Depth was significantly different between site groups in all four data sets, while latitude and effort were significantly different in three of the four data sets.

4.3 Ichthyoplankton

Data and Methods

The maps of regional ichthyoplankton (planktonic fish larvae) distribution presented here were derived from 

data provided by the California Cooperative Oceanic Fisheries Investigations (CalCOFI) consortium. CalCOFI 

represents a unique partnership of the California Department of Fish and Game (CDFG), the NOAA Fisher-
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122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

0 50 100

Kilometers

Invertebrate

Diversity

Fish Diversity

Low

Moderate

High

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120°W

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0 50 100

Kilometers

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Diversity

Fish Diversity

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Moderate

High

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0 50 100

Kilometers

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Diversity

Fish Diversity

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Moderate

High

122°W

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0 50 100

Kilometers

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Diversity

Fish Diversity

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Moderate

High

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33°N33°N

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35°N35°N

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0 50 100

Kilometers

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Diversity

Fish Diversity

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Moderate

High

122°W

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121°W

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33°N33°N

34°N34°N

35°N35°N

36°N36°N

0 50 100

Kilometers

Invertebrate

Diversity

Fish Diversity

Low

Moderate

High

Figure 4.2.11. Composite fish diversity within boundary concepts. 

Boundary Concept 5 Boundary Concept 4

Boundary Concept 3 Boundary Concept 2

Boundary Concept 1 Study Area
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

ies Service (NMFS) and the Scripps 

Institution of Oceanography (University of California, San Diego). The 

CalCOFI partnership was formed in 

1949 to study the ecological aspects 

of the collapse of the Pacific sardine (Sardinops sagax) populations 

off California; however, recently its 

focus has shifted to a more general study of the marine environment 

off the coast of California and to the 

management of its living resources. 

More information about CalCOFI and 

its member institutions can be found 

at: <http://www.calcofi.org>.

Since 1949, CalCOFI has organized 

cruises to measure the physical and 

chemical properties of the California 

Current System and to census living 

resource populations. Currently, 2-3 

week cruises are conducted on a quarterly basis. Data presented here range 

from 20.2°-47.4° north latitude (~Baja 

California to Washington State), and 

from 107°-139° west longitude (Figure 4.3.1). Data were collected during 

a total of 46 years, ranging from 1951 

through 2002 (missing years: 1970, 

1971, 1973, 1976, 1979, and 1982). 

Over this time period, a total of 35,495 

unique bongo net tows were performed 

on 276 cruises, resulting in the collection of 318 unique ichthyoplankton 

taxa. Figure 4.3.2 shows a summary 

distribution of collection effort between 

1951 and 2002, and depicts the total 

number of bongo net tows taken. This 

map is not intended to be a proxy for 

a quantitative estimate of the cumulative number of kilometer-hours towed 

in a given region; rather it is designed 

to highlight areas of long-term scientific focus. The geographic extent and 

spatial framework (hexagons) shown 

in Figure 4.3.2 are used later in this 

chapter in analyzing abundance and 

distribution patterns. Specifics on the 

CalCOFI bongo net towing protocols 

can be found at <http://www.calcofi.

org/newhome/cruises/equip/nets.htm>.

Because these data span many years representative of both warm and cool water events in the El Niño-Southern Oscillation (ENSO) cycle, and 2-3 periods in the longer term ocean climatology of the Pacific Decadal Oscillation (PDO), 

a spatially-articulated analysis of total ichthyoplankton abundance trends is not presented. Many scientists have been 

studying the effects of ocean climate on larval distribution, and the results have necessarily varied based on the time 

period(s) and spatial domain of focus. Most agree that signals related to ocean climate can be seen in the abundance 

Figure 4.3.1. Geographic extent of CalCOFI bongo net data.

Figure 4.3.2. CalCOFI bongo net effort data.
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data (Loeb et al., 1983; Moser et al. 1993; Sakuma and Ralston, 1995); however, these effects are confounded by interaction, and are thus difficult to isolate and quantify. In analyzing the CalCOFI data, we found that because of the high 

degree of spatial and temporal variability, grouping all years and all species obscures distribution patterns. Although 

a species-level analysis of ichthyoplankton is beyond the scope of this report, we’ve chosen to focus our analyses on 

four individual species representative of both groundfishes (California halibut and bocaccio) and pelagic fishes (Pacific 

sardine and northern anchovy).

Distribution and abundance patterns for these four species were mapped by sampling all bongo tows into a hexagonal spatial framework. The total standardized number of individuals per tow was log-transformed (log10[Nstd+1]) and 

assigned to the hexagon in which it fell. Hexagons are 25 km wide (east-west) by 37 km long (north-south). Values in 

each hexagon represent the average log-transformed abundance of all tows in the hexagon, and were classified into 

20th percentiles (quintiles). White hexagons indicate areas where bongo tows were performed, but resulted in no catch 

for that species. Because of the natural variability in larval abundance, estimated patterns of larval abundance must be 

interpreted with care, as they represent a composite of 46 years of sampling. 

Broad-scale Patterns

California halibut (Paralichthys californicus)

Adult halibut are reported to prefer soft bottom habitats in coastal waters of 100 m or less, with greatest abundances 

occurring in waters shallower than 30 m. Spawning also generally occurs in shallow coastal waters, accordingly peak 

egg and larval densities are found in waters less than 100 m, and within 6.5 km from shore (Leet et al., 2001). Analysis 

of P. californicus larval data presented here corroborate these reports, with a clear nearshore trend in peak abundance 

(Figure 4.3.3). Incidences of halibut larvae in bongo nets beyond 6.5 km from shore are few, and where present, 

generally fall in the lowest 40% of estimated abundance values. All areas 

classified in the top 20% of halibut larval abundance are found in hexagons 

adjacent to the shore.

Even though sampling effort north 

of Morro Bay is reduced compared 

to effort in the Southern California 

Bight, an analysis of variance suggested that mean larval abundance is 

significantly lower (P<0.003, F=8.92, 

DF=376) north of Pt. Conception 

(mean=0.005) than south of the promontory (mean=0.02). In general, the 

entire coastline along the Southern 

California Bight, including nearshore 

areas around San Miguel, Anacapa, 

and San Clemente Islands, is characterized by high halibut larval density. 

As such, the Santa Barbara Channel 

and the current sanctuary boundary capture large areas of high larval 

abundance for this species. Furthermore, any concept that captures areas nearshore to the mainland will 

likely include areas of high larval 

abundance (e.g., 1 and 1a). Monterey Bay, Gulf of the Farallones, and Cordell Bank national marine sanctuaries, while 

sampled less frequently, exhibited few cases of high halibut larval density.

Bocaccio (Sebastes paucispinis)

Bocaccio are live-bearing (viviparous) fish. Off central and northern California, brood release occurs from late 

winter through spring, with peaks in February. In southern California, the brood release is more protracted, 

ranging from October through July, peaking in January (Leet et al., 2001, Love et al., 2002). Female bocaccio 

Figure 4.3.3. Estimated mean larval abundance for California halibut in CalCOFI bongo 

tows. Data are classified into quintiles (map range: 32° to 42°N).
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in southern California have been reported to produce multiple broods in 

a season. This is more uncommon 

in the north (Leet et al., 2001). Larvae have been observed as far as 

480 km from shore. Larval bocaccio 

grow rapidly, and young of the year 

juveniles recruit to rocky and/or eelgrass habitats in nearshore waters 

(Leet et al., 2001, Love et al., 2002). 

The analysis of larval bocaccio distribution presented here agrees with 

the reported literature. Two distinct 

areas of relatively high larval density 

can be seen – the first and largest 

area is centered just south of Santa 

Rosa Island in the Southern California Bight (Figure 4.3.4). As such, 

the current CINMS boundary (or No 

Action Concept, NAC) is well configured to capture a large area of high 

average bocaccio larval abundance. 

This is an expression of the high larval abundances that, on average, 

appear in the late winter months. A 

more diffuse area of high bocaccio 

larval abundance can be seen to the north of Monterey Bay, and farther offshore. These areas resulted from 

larval catches in early spring. Overall, an analysis of variance suggested that mean bocaccio larval abundances 

are no different (P<0.29, F=1.13, DF=376) north of Pt. Conception (mean=0.038) than south of the promontory 

(mean=0.049).

Pacific sardine (Sardinops sagax)

The Pacific sardine population is believed to be comprised of three subpopulations: 1) the Gulf of California, 2) 

a southern population off Baja California, and 3) a northern subpopulation. Most of the sardines occurring in the 

study area are part of the northern subpopulation which ranges from Baja California to Alaska (Leet et al., 2001). 

It is thought that the northern subpopulation’s center of density is located off central and southern California. 

Spawning occurs year-round between Pt. Conception in the north to Magdalena Bay (Baja, California) in the 

south, with peaks from early spring through summer. Most of the spawning activity is believed to occur approximately 250 km from shore, with reports out to 560 km. Sardine spawning activity is influenced significantly by 

water temperature, and centers of spawning can vary widely based on ENSO driven ocean climate (Leet et al., 

2001).

The analyses of larval Pacific sardines presented here suggest that distributions are highly variable, with maximum observed abundances ranging from coastal waters of the Southern California Bight out to 450 km southwest of Point Conception, and 300 km west of Monterey Bay in central California (Figure 4.3.5). An analysis of 

variance indicated that mean larval abundance is significantly lower (P<0.003, F=21.27, DF=376) north of Pt. 

Conception (mean=0.013) than south of the promontory (mean=0.063). This is likely due to the preponderance 

of optimum spawning temperatures south of the point, coupled with mesocale circulation patterns that dominate 

in the region during these months and entrain larval fishes (Figure 2.4.6, Chapter 2). Figure 2.5.1 (Chapter 2) 

indicates that mean spring and summer sea surface temperatures in areas of peak larval abundance range from 

14°C (57.2°F) to 17°C (62.6°F), bounding the optimum spawning temperature which is reported by Leet et al. 

(2001) as ranging between 15°-16°C (59°-61°F).

Northern anchovy (Engraulis mordax)

Northern anchovy are widely distributed along the Pacific coast of North America, with a central subpopulation 

that ranges from San Francisco in the north to Punta Baja in the south. The center of this population is reported 

Figure 4.3.4. Estimated mean larval abundance for bocaccio in CalCOFI bongo tows. 

Data are classified into quintiles (map range: 32° to 42°N).
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

to occupy the Southern California 

Bight. Anchovy peak spawning occurs in late winter through spring 

(Leet et al., 2001). The analyses of 

larval northern anchovy presented 

here corroborate the results presented by Leet et al. (2001), with an expansive area of maximum observed 

abundance throughout the entire 

Southern California Bight (Figure 

4.3.6). Results of an analysis of variance (P<0.001, F=101.84, DF=376) 

suggest that mean abundances of 

larval anchovy are an order of magnitude higher south of Point Conception (mean=1.0) than to the north 

(mean=0.18). As such, the current 

CINMS boundary captures large areas of high larval abundance for this 

species.

Analysis of Boundary Concepts

Due to the high degree of spatial and 

temporal variation in larval distributions as a whole, coupled with highly 

variable sampling efforts through 

the long history of the CalCOFI program, we have chosen not to sample 

the described broad-scale patterns 

into each of the Sanctuary boundary 

concepts in an effort to develop an 

Optimal Area Index (OAI). Data for 

each of the four species presented 

here suggest that the Southern California Bight, including the CINMS 

and all boundaries under consideration, capture areas important to larval fishes of the region. The data analyzed here are insufficient in spatial 

resolution (Figure 4.3.2) to develop 

a robust and confident estimate of 

potential “manageable” differences 

among boundary concepts.

Summary

• Larval abundance and distribution along the Pacific coast of North 

America can be highly variable, both 

spatially and temporally.

• Of the four species presented here, 

three exhibited higher larval abundance south of Point Conception 

than to the north (northern anchovy, 

Pacific sardine, and bocaccio).

• California halibut did not exhibit differences in mean larval abundance north vs. south of Point Conception.

Figure 4.3.5. Estimated mean larval abundance for Pacific sardine in CalCOFI bongo 

tows. Data are classified into quintiles (map range: 32° to 42°N).

Figure 4.3.5. Estimated mean larval abundance for northern anchovy in CalCOFI bongo 

tows. Data are classified into quintiles (map range: 32° to 42°N).
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• In general, the Southern California Bight, including the current and proposed CINMS boundary concepts, capture areas important to larval fishes of the region.

• Concepts which include more nearshore habitat are likely to include more halibut larvae.

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CHAPTER 5 – BIOGEOGRAPHY OF MARINE BIRDS

Olaf Jensen, Harry Carter, Glenn Ford, Julie Kellner, John Christensen

Over 195 species of marine birds use open water, shore, or island habitats in the Southern California Bight south 

of Point Conception (Baird, 1993). As many as 10 additional species use the coast along Vandenberg Air Force 

Base. The region of interest is located along the Pacific Flyway, a major migratory route for birds, and acts as 

a stopover during both north (April through May) and south (September through December) migrations. The 

months of June and July are peak months for transient shorebirds (Lehman, 1994). The Channel Islands and 

the Southern California mainland coast provide breeding and nesting sites for many species of seabirds and 

shorebirds, including several listed as threatened or endangered. The status and distribution of the 11 requested 

bird species within the study area are summarized in Table 5.0.1. This chapter is divided into a single species 

analysis of the 11 requested species (Chapter 5.1), and a community analysis of all bird species sampled in atsea surveys (Chapter 5.2).

5.1 Marine Bird Single Species Analysis

Data and Methods

Data sources for this section are summarized in Table 5.1.1 and discussed in greater detail below. Three types 

of georeferenced survey data for marine birds (the term marine birds is used to encompass both seabirds and 

shorebirds) were available for this report: shipboard and aerial at-sea surveys, breeding colony surveys, and 

Xantus’s murrelet telemetry data. Although no data were available in any of these surveys for snowy plover 

(Charadrius alexandrinus) or western snowy plover (Charadrius alexandrinus nivosus), the critical habitat delineations published in the Federal Register (USFWS, 1999) allow for some spatial analysis of the distribution 

of important snowy plover habitat. Scientific names and spellings of common names follow the Patuxent Bird 

Identification Infocenter (Gough et al., 1998). For display purposes, all maps were classified by quintile (five 

intervals, each of which contains approximately 20% of the data), except where fewer than five discrete values 

occurred. For discussion in this chapter, northern Channel Islands are: San Miguel, Santa Rosa, Santa Cruz, 

Anacapa, and Santa Barbara Islands.

A recent comprehensive survey of marine birds in the Southern California Bight was conducted in 1999-2002; 

however georeferenced data from these surveys were not available at the time of publication. From May 1999 to 

January 2002, aerial at-sea surveys were conducted by Humboldt State University (HSU) and the U.S. Geological Survey (USGS) for seabirds and marine mammals from Point Piedras Blancas to the U.S.-Mexico border, 

including the entire Southern California Bight (McChesney et al. 2000, 2001; Capitolo et al. 2003). Surveys were 

funded mainly by USGS, with additional support from the Minerals Management Service (MMS), U.S. Navy 

(USN) and California Department of Fish and Game (Office of Spill Prevention and Response; CDFG-OSPR). 

At-sea and shoreline roosting surveys were conducted: May and September 1999; January, May, and September 2000; January, May, and September 2001; and January 2002. These efforts used the same methods and 

essentially covered the same areas of the continental shelf examined by surveys during 1975-1977 below Point 

Conception and by surveys during 1980-1983 north of Point Conception (Briggs et al. 1987). Higher at-sea survey effort and greater coverage of coastline areas (on shore and at sea) occurred for each survey in 1999-2002, 

but they were conducted less frequently than in earlier surveys. Databases from 1999-2002 HSU/USGS surveys 

were not available for this report but will be available in the near future. 

Computer Database Analysis System (CDAS)

At-Sea Surveys

Five at-sea surveys from the period 1975-1997 were compiled in CDAS v2.1 (MMS, 2001) and used to derive 

density maps for eight of the requested bird species:

Ashy storm-petrel (Oceanodroma homochroa)

California brown pelican (Pelecanus occidentalis californicus)

Double-crested cormorant (Phalacrocorax auritus)

Brandt’s cormorant (Phalacrocorax penicillatus)

Pelagic cormorant (Phalacrocorax pelagicus)
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary Common Name Scientific Name Status / Season Federal / State Status Habitat Type Habitat Location within Study Area Breeding (location) Shorebirds Western snowy plover Charadrius alexandrinus nivosus Fairly common/winter Fairly common to Uncommon /summer Federally listed as Threatened Beaches, sandy and rocky shores, Jetties Vandenberg Air Force Base, Holl- ster Ranch, Ormond Beach, Point Mugu Lagoon, Coal Oil Point Vandenberg Air Force Base, Hollster Ranch, Ormond Beach, Point Mugu La- goon, Coal Oil Point, Channel Islands Black oystercatcher Haematopus bachmani Uncommon/year-round Beaches, sandy and rocky shores, jetties Vandenberg Air Force Vandenberg Air Force Base, Channel Islands

Seabirds

Ashy stormpetrel Oceanodroma homochroa Common/spring, winter, fall Casual/winter California Department of Fish and Game species of special concern Open ocean Channel Islands San Miguel, Santa Cruz, Anacapa, Santa Barbara, San Clemente IslandsCalifornia brown pelican

Pelecanus occidentalis californicus Common/year-round Federally listed as Endangered Inshore ocean waters, bays and harbors Most areas Anacapa, Santa Barbara Islands

Double-crested cormorant

Phalacrocoraxauritus

Common/winterUncommon/summer

California’s Department of Fish and Game species of special concern

Open ocean, lakes, reservoirs, large streams, creeks and rivers

Santa Ynez River, Hollster Ranch, Devereux Slough, UCSB Lagoon, Goleta Slough, Ormond Beach

Anacapa, San Miguel, Santa Barbara, San Clemente Islands, San Diego Bay

Brandt’s cormorant

Phalacrocoraxpenicillatus

Uncommon to adundant/winter

Fresh and/or salt water marshes, estuaries, mudflats, tidal lagoons, and brackish water, lakes, reservoirs, large streams, creeks and rivers, inshore ocean waters, bays and harbors

Vandenberg Air Force Base, Hollster Ranch, Channel Islands

Vandenberg Air Force Base,San Miguel, Santa Rosa, Santa Cruz, Santa Barbara, Anacapa, San Nicolas, San Clemente Islands

Pelagic cormorant

Phalacrocorax pelagicus

Fairly common tocommon/winterRare to fairly common/summer

Open ocean, beaches, sand and rocky shores, jetties

Vandenberg Air Force Base, Hollster Ranch, Channel Islands Vandenberg Air Force Base, San Miguel, Santa Rosa, Santa Cruz, Anacapa, Santa Barbara Islands

California least tern

Stena antillarum browni

Rare to Common/yearround Federally and State- listed as Endangered

Beaches, sandy and rocky shores, jetties, inshore ocean waters, bays and harbors

Vandenberg Air Force Base, Devereux Slough, UCSB Lagoon, Carpinteria Marsh, Ormond Beach, Point Mugu Lagoon

Vandenberg Air Force Base, Devereux Slough, Ormond Beach, Point Mugu Lagoon

Pigeon guillemot

Cepphus columba Common/summer

Beaches, sandy and rocky shores, jetties, inshore ocean waters, bays and harbors

Vandenberg Air Force Base, Hollster Ranch, Channel Islands Vandenberg Air Force Base, San Miguel, Santa Rosa, Santa Cruz, Anacapa, Santa Barbara Islands

Xantus’smurrelet 

Synthliiboramphus hypoleucus

Uncommon/spring, summerRare/fall, winter

Open ocean Channel Islands

San Miguel, Santa Cruz, Anacapa, Santa Barbara, San Clemente, Santa Catalina Islands

Cassin’s auklet Ptychoramphus aleuticus Fairly common/year-round Open ocean Channel Islands San Miguel, Santa Cruz, Anacapa, Santa Barbara Islands

Figure 5.0.1. Status, habitat locations, and seasonal use of the requested marine birds in the region of interest (Source: 

Working Draft Environmental Impact Statement for Channel Islands National Marine Sanctuary: Affected Environment Section, 2000).
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Pigeon guillemot (Cepphus columba)

Xantus’s murrelet (Synthliboramphus hypoleucus)

Cassin’s auklet (Ptychoramphus aleuticus)

A sixth survey, the Southern California Bight ship survey, which is used in the calculation of bird diversity (Chapter 5.2) was not used in the calculation of individual species densities because of the difficulties of reconstructing 

survey effort for this survey. This was not problematic for the calculation of diversity for the reasons discussed in 

that chapter, but it precludes the calculation of density. 

Bird densities were calculated for five minutes of latitude by five minutes of longitude cells. Densities were calculated as the number of individuals sighted divided by the area searched for each species in each five-minute 

cell. Areas searched were calculated as the total length of survey track in each cell multiplied by the effective 

strip width. Cells in which fewer than 5 km of survey track occurred were dropped from this analysis. For calculation of total individuals and density within the boundary concept, all grid cells which intersect the boundaries 

of a concept are included in calculations for that concept. This method is required because effort in the CDAS 

data set is summarized by cells. Consequently, any analysis involving effort is limited in spatial resolution by this 

five-minute cell size. 

Breeding Colony Surveys

Surveys of marine bird breeding colonies were conducted in 1989-1991 by researchers from Humboldt State 

University (HSU) and the U.S. Fish and Wildlife Service (USFWS), and were funded by the Minerals Management Service (MMS) (Carter et al., 1992, 1995a). The survey counted birds, nests, and potential nesting sites 

for most breeding species. Additionally, the size of the breeding population at each site was estimated based 

on adjustments to the counts mentioned above or by capture-recapture methods. Details of the population estimates are given in Carter et al. (1992). All analysis of the breeding colony survey in this report is based on these 

site-specific population estimates. Breeding colony survey data were available for:

Ashy storm-petrel (Oceanodroma homochroa)

California brown pelican (Pelecanus occidentalis californicus)

Double-crested cormorant (Phalacrocorax auritus)

Brandt’s cormorant (Phalacrocorax penicillatus)

Pelagic cormorant (Phalacrocorax pelagicus)

Table 5.1.1. Data used for the analysis of marine bird distributions presented in this chapter.

Survey Source Dates Platform Months

CDAS- Minerals Management Service Aerial 

Surveys R.G. Ford 1980-1983 airplane (low altitude) Year-round

CDAS-California Department of Fish and 

Game, Office of Spill Prevention and Response R.G. Ford 1994-1997 airplane (low altitude) Year-round

CDA3-Southern California Bight Low Aerial 

Survey R.G. Ford 1975-1978 airplane (low altitude) Year-round

CDAS-Seabird Ecology Study R.G. Ford 1985 ship and airplane

March and 

May

CDA3-Southern California Bight, Minerals 

Management Service Survey R.G. Ford 1995-1997 airplane (low altitude) Year-round

Breeding Colony Surveys-Humbolt State University and U.S. Fish and Wildlife Service H.R. Carter 1989-1991 various April-August

Xantus’s Murrelet Breeding Colony SurveysHumbolt State University H.R. Carter 1991-2002 various April-May

Xantus’s Murrelet Telemetry Study-U.S. Geological Survey and Humbolt State University H.R. Carter 1995-1997 Airplane and fixed 

station radio receivers April-June

Snowy plover critical habitat-U.S. Fish and 

Wildlife Service

Federal 

Register 1999

DRAFT California least tern breeding surveyCalifornia Department of Fish and GameLyann 

Comrack 2001-2003
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Black oystercatcher (Haematopus bachmani)

California least tern (Sterna antillarum browni)

Pigeon guillemot (Cepphus columba)

Xantus’s murrelet (Synthliboramphus hypoleucus)

Cassin’s auklet (Ptychoramphus aleuticus)

More recent surveys of California least tern nesting areas were provided by Lyann Comrack (CDFG, draft data). 

The maximum number of least tern breeding pairs observed at each site was complied from these CDFG surveys conducted in 2001, 2002, and 2003. Additionally, recent colony survey data for Xantus’s murrelet were provided by H. Carter (HSU, unpubl. data). These data were compiled from surveys conducted from 1991-2002 and 

are summarized in Burkett et al., (2003). Because these data are available as summaries for individual shoreline 

segments rather than point counts they are presented for each island individually. 

Xantus’s Murrelet Telemetry Study

Data presented in this section represent the results of a radio telemetry survey of Xantus’s murrelet in the Southern California Bight conducted by the U.S. Geological Survey (USGS) and HSU from 1995 to 1997 (Whitworth 

et al., 2000; Carter et al., 2000). Murrelets were captured at sea near Santa Barbara Island and 153 individuals 

were fitted with radio transmitters. Marked individuals were relocated mainly by aerial telemetry surveys conducted between April and June of 1995, 1996, and 1997. A receiving station on North Peak, Santa Barbara Island 

was also used to relocate marked individuals within a few km of the island. A raster density map of re-locations 

was created for visualization purposes using the kernel density function of ArcView Spatial Analyst (ESRI, 2003) 

with a radius of 15 km and a cell size of 2 km. Analysis of the boundary concepts was conducted on the re-location points not the density map. 

Broad-scale Patterns and Analysis of Boundary Concepts

Ashy storm-petrel (Oceanodroma homochroa)

Ashy storm-petrel is a small procellariiform (Family Hydrobatidae) whose year round range extends from Cape 

Mendocino, California to northern Baja California, Mexico. CINMS is at the southern end of their core breeding 

and feeding distribution. This species nests mainly in cavities in rock piles and cliffs on offshore California islands 

with only one known nesting site outside of California on Islas Los Coronados, Mexico. Small numbers nest on 

small rocks near the coast at Point Reyes, south of Monterey, and possibly on the mainland at Vandenberg AFB 

(McChesney et al., 2000; Whitworth et al., 2002; Brown et al., 2003). Due to their widely dispersed and offshore 

foraging habits, ashy storm-petrels are likely to be underestimated by at-sea surveys, and their cryptic nests 

make it difficult to census colonies. Breeding colony surveys from 1989-1991 estimated a worldwide breeding 

population of 7,207 birds with 43% of the breeding population found in the northern Channel Islands (Carter et 

al., 1992). Although this estimate is greater than previous estimates, differences in survey sites and methods 

preclude any inference of a trend. Due to pollutant impacts and other factors, numbers may be declining in the 

northern Channel Islands (H. Carter, unpubl. data). Numbers have declined at the Farallon Islands due to high 

predation (Sydeman et al., 1998). Ashy storm-petrel is listed as a California “Species of Special Concern,” but is 

not federally endangered or threatened. 

At-sea sightings indicate that areas of high ashy storm-petrel density occur along the continental slope off Central California, with notable concentrations between Point Arena and Monterey Bay (including the deep waters 

of the Monterey Canyon), and in the Southern California Bight. Sightings also occur on the shelf between Point 

Buchon and Point Conception.

Analysis of the observed patterns of ashy storm-petrel sightings (Figure 5.1.1) and density relative to the proposed boundary concepts indicates that Concept 3 provides the greatest increase in both sightings (absolute 

metric) and mean density (relative metric) for its relative size compared to the current boundary (NAC; Table 

5.1.2). Most of the additional sightings within the study area but outside of the current sanctuary boundaries occur in the Santa Barbara Channel. It should be noted that most of the increase in sightings and density observed 

in Concept 3 relative to the next smallest concept (Concept 4) is due to inclusion of one additional grid cell in 

which 55 ashy storm-petrels were observed.

In 1989-1991, breeding colonies of ashy storm-petrel (Figure 5.1.2) were found on San Miguel, Santa Cruz, and 

Santa Barbara Islands. More recent surveys (H. Carter, unpubl. data) have found greater numbers on Santa 
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Figure 5.1.1. Ashy storm-petrel. At-sea densities (individuals/km2) calculated for five 

minutes of latitude by five minutes of longitude grid cells from aerial and shipboard 

survey data collected from 1975-1997 and summarized in the Computer Database 

Analysis System v2.1 (MMS, 2001).

Figure 5.1.2. Ashy storm-petrel. Colony counts (corrected total individuals) 1989-1991 

(Source: Carter et al., 1992). 
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Cruz Island and colonies on Anacapa and San Clemente Island. Most colonies are within the current sanctuary boundaries as well as the boundaries of all of the concepts, and consequently no Optimal Area Index (OAI) 

analysis was done for the ashy storm-petrel colony data.

California brown pelican (Pelecanus occidentalis californicus)

California brown pelicans (Family Pelecanidae) range from southern British Columbia to northern South America 

with the California subspecies breeding from California to the Pacific coast of southern Mexico. The CINMS is at 

the northern edge of this subspecies breeding range and feeding distribution. They nest in a variety of habitats on 

offshore islands. The 1991 breeding colony survey resulted in an estimate of 11,916 breeding birds in California 

with all of the breeding population of California occurring in the northern Channel Islands (Carter et al., 1992). 

This survey indicated a continued increase in abundance of pelicans since the low of 76 nests in 1977, reflecting partial recovery from pollutant impacts. Since 1991, pelicans have oscillated in numbers between years (F. 

Gress, pers. comm.). However, much larger numbers of pelicans occur in late summer and fall in the Santa 

Barbara Channel and the Southern California Bight when many disperse northward from large colonies in the 

Gulf of California (Jaques et al., 1996; Capitolo et al., 2003). The California brown pelican is a state and federally 

listed endangered species.

At-sea sightings indicate that areas of high brown pelican density can be found on the north side of Santa Cruz 

Island, and along the coast from Point Sal to Point Mugu. Much of the eastern half of the Santa Barbara Channel 

also exhibits high brown pelican density.

Analysis of the observed patterns of brown pelican sightings (Figure 5.1.3) and density relative to the proposed 

boundary concepts indicates that Concepts 1 and 1a provide the greatest increase in sightings (absolute metric) for its relative size compared to the NAC (Table 5.1.3). The density-based OAI, however, favors Concept 5, 

the smallest concept other than the NAC. The smaller concepts (3-5) fail to capture the region of high pelican 

density along the mainland coast and in the eastern Santa Barbara Channel, while the largest concepts (1 and 

1a) include large areas of low pelican density along the shelf. Concept 2 exhibits both of these flaws in relation 

to brown pelican distributions.

Only two breeding colonies of brown pelican (Figure 5.1.4) were recorded in California in the 1989-1991 breeding colony survey. The larger colony (10,680 individuals) is found on West Anacapa Island, and a smaller colony 

(1,236 individuals) is found on Santa Barbara Island. 

No analysis of boundary concepts was conducted for brown pelican breeding colonies since none of the concepts encompass any colonies not contained within the NAC.

Table 5.1.2. Ashy storm-petrel. At-sea sightings, effort, density, and Optimal Area Index (OAI) for each boundary concept. Values in 

bold indicate an increase in the estimate when compared to the NAC. Shaded OAI estimates reflect the concept with the greatest 

positive value.

Concept

Total

Individuals

Total Effort

(Area 

swept km2)

Density

(Individuals 

per km2)

 Area 

(km2)

∆ Individuals

(%)

∆ Density

(%)

∆ Area

(%)

OAI

Individuals

(absolute

metric)

OAI

Density

(relative

metric)

NAC 2 448.2 0.004 3745 - - - - -

5 3 492.3 0.006 4538 50 36.56 21.12 2.37 1.73

4 38 748.3 0.051 7981 1800 1038.02 113.11 15.91 9.18

3 95 984.6 0.096 9044 4650 2062.25 141.50 32.86 14.57

2 106 1589.1 0.067 13736 5200 1394.85 266.78 19.49 5.23

1a 128 2460.3 0.052 22613 6300 1065.91 503.82 12.50 2.12

1 128 2460.3 0.052 22591 6300 1065.91 503.23 12.52 2.12

SA 107 2115.2 0.051 17093 5250 1033.64 356.42 14.73 2.90
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Figure 5.1.4. California brown pelican. Colony counts (corrected total individuals) 

1989-1991 (Source: Carter et al., 1992).

Figure 5.1.3. California brown pelican. At-sea densities (individuals/km2) calculated 

for five minutes of latitude by five minutes of longitude grid cells from aerial and shipboard survey data collected from 1975-1997 and summarized in the Computer Database Analysis System v2.1 (MMS, 2001).
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Double-crested cormorant (Phalacrocorax auritus)

Double-crested cormorant (Family Phalacrocoracidae) is found in marine, estuarine, and freshwater habitats 

throughout most of North America. CINMS is within the core breeding range of the P. a. albociliatus subspecies. 

In California, they nest in a variety of habitats including flat, sloping, and cliff areas of the mainland coast and offshore rocks and islands, as well as trees and artificial habitats. The 1989-1991 breeding colony survey resulted 

in an estimate of 10,037 breeding birds in marine and estuarine habitats of California with 25% of the breeding 

population of California occurring in the northern Channel Islands (Carter et al., 1992). The survey showed an 

increase in abundance of double-crested cormorant in the northern Channel Islands since earlier (1975-1980) 

surveys (Carter et al., 1995a, b). This increase reflected some recovery from pollutant impacts, but recent surveys have shown declines (Capitolo et al., 2004). The northern Channel Islands are one of the few areas in North 

America where double-crested cormorants are declining (Carter et al., 1995b). The double-crested cormorant is 

listed as a California “Species of Special Concern,” but is not federally endangered or threatened. 

At-sea sightings showed the highest densities of double-crested cormorant in San Francisco Bay, well to the 

north of the CINMS study area. However, an additional region of relatively high densities exists in nearshore 

waters along both sides of the Santa Barbara Channel. The highest densities in Southern California were found 

in nearshore waters from Point Conception to Point Hueneme.

Analysis of the observed patterns of double-crested cormorant sightings (Figure 5.1.5) and density relative to 

the proposed boundary concepts indicates that Concepts 1 and 1a provide the greatest increase in sightings 

(absolute metric) for its relative size compared to the NAC (Table 5.1.4). The density-based OAI favors Concept 

3, which includes a region of high density around Point Conception. As with brown pelican, the smaller concepts 

(4 and 5) fail to capture the region of high double-crested cormorant density along the mainland coast, while the 

largest concepts (1 and 1a) include large areas of low density along the shelf. Again, Concept 2 exhibits both of 

these flaws.

Breeding colonies of double-crested cormorant (Figure 5.1.6) exist on San Miguel, West Anacapa, and Santa 

Barbara Islands, with the two largest colonies (720 and 768 individuals) in Southern California found on West 

Anacapa and Santa Barbara Islands respectively. A small colony was recently found at San Clemente Island (H. 

Carter, unpubl. data). Mainland colonies exist at Morro Bay and San Diego Bay.

No analysis of boundary concepts was conducted for double-crested cormorant breeding colonies since none of 

the concepts encompass any colonies not contained within the NAC.

Brandt’s cormorant (Phalacrocorax penicillatus)

Brandt’s cormorant (Family Phalocrocaracidae) is the most abundant cormorant in California, ranging along the 

Pacific coast from southern Vancouver Island, Canada to southern Baja California, Mexico. CINMS is within the 

core of their breeding range and feeding distribution. Nesting habitat for this species is variable and includes flat 

or sloping surfaces of offshore islands as well as mainland cliffs. Surveys of all known large (>100) breeding colTable 5.1.3. California brown pelican. At-sea sightings, effort, density, and Optimal Area Index (OAI) for each boundary concept. 

Values in bold indicate an increase in the estimate when compared to the NAC. Shaded OAI estimates reflect the concept with the 

greatest positive value.

Concept

Total

Individuals

Total Effort

(Area 

swept km2)

Density

(Individuals 

per km2)

 Area 

(km2)

∆ Individuals

(%)

∆ Density

(%)

∆ Area

(%)

OAI

Individuals

(absolute

metric)

OAI

Density

(relative

metric)

NAC 374 448.2 0.834 3745 - - - - -

5 452 492.3 0.918 4538 20.88 10.03 21.12 0.99 0.47

4 540 748.3 0.722 7981 44.39 -13.52 113.11 0.39 -0.12

3 913 984.6 0.927 9044 144.12 11.12 141.50 1.02 0.08

2 1355 1589.1 0.853 13736 262.30 2.19 266.78 0.98 0.01

1a 2546 2460.3 1.035 22591 580.75 24.01 503.23 1.15 0.05

1 2546 2460.3 1.035 22613 580.75 24.01 503.82 1.15 0.05

SA 2546 2115.2 1.204 17093 580.75 44.25 356.42 1.63 0.12
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Figure 5.1.5. Double-crested cormorant. At-sea densities (individuals/km2) calculated 

for five minutes of latitude by five minutes of longitude grid cells from aerial and shipboard survey data collected from 1975-1997 and summarized in the Computer Database Analysis System v2.1 (MMS, 2001).

Figure 5.1.6. Double-crested cormorant. Colony counts (corrected total individuals) 

1989-1991 (Source: Carter et al., 1992).
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onies in 1989-1991 resulted in an estimate of 83,394 breeding birds in California with 29% of this population occurring 

in the northern Channel Islands (Carter et al., 1992). This survey indicated a notable increase in abundance of Brandt’s 

cormorants in the northern Channel Islands since surveys in 1975-1980. However, more recent surveys have shown a 

decline since 1991 (Capitolo et al., 2004). Brandt’s cormorant is not a federal endangered or threatened species. 

At-sea survey data suggest that Brandt’s cormorant is widely distributed in nearshore waters of central and southern 

California with notable concentrations in the Gulf of the Farallones, near Point Sur, Morro Bay, Point Conception, and 

the northern Channel Islands (with the exception of Santa Barbara Island). 

Analysis of the observed patterns of Brandt’s cormorant sightings (Figure 5.1.7) and density relative to the proposed 

boundary concepts indicates that Concept 3 provides the greatest increase in sightings (absolute metric) for its relative 

size compared to the NAC (Table 5.1.5). 

Density (relative metric), however, was 

highest for Brandt’s cormorant within 

the NAC and consequently the densitybased OAI is negative for all of the concepts. Much of the increase in density 

for Concept 3 relative to the smaller concepts (4, 5, and the NAC) is due to the 

inclusion of two additional grid cells near 

Point Conception.

Breeding colonies of Brandt’s cormorant 

(Figure 5.1.8) exist on seven Channel 

Islands (all except Santa Catalina), with 

major colonies (top quintile, 1,548-5,606 

individuals) on San Miguel, Santa Rosa, 

Santa Cruz, and San Nicolas Islands. 

Mainland colonies in Southern California 

exist at Point Buchon, Morro Bay, Vandenberg AFB, Santa Barbara, La Jolla, 

and along the coast between Point Piedras Blancas and Cape San Martin.

No analysis of boundary concepts was 

conducted for Brandt’s cormorant breeding colonies since none of the concepts 

encompass any colonies not contained 

within the NAC.

Table 5.1.4. Double-crested cormorant. At-sea sightings, effort, density, and Optimal Area Index (OAI) for each boundary concept. Values 

in bold indicate an increase in the estimate when compared to the NAC. Shaded OAI estimates reflect the concept with the greatest positive value.

Concept

Total

Individuals

Total Effort

(Area 

swept km2)

Density

(Individuals 

per km2)

 Area

(km2)

∆ Individuals

(%)

∆ Density

(%)

∆ Area

(%)

OAI

Individuals

(absolute

metric)

OAI

Density

(relative

metric)

NAC 3 448.2 0.007 3745 - - - - -

5 3 492.3 0.006 4536 0.00 -8.96 21.12 0.00 -0.42

4 3 748.3 0.004 7981 0.00 -40.10 113.11 0.00 -0.35

3 11 984.6 0.011 9044 266.67 66.91 141.50 1.88 0.47

2 18 1589.1 0.011 13736 500.00 69.23 266.78? 1.87 0.26

1a 40 2460.3 0.016 22591 1233.33 142.90 503.23 2.45 0.28

1 40 2460.3 0.016 22613 1233.33 142.90 503.82 2.45 0.28

SA 40 2115.2 0.019 17093 1233.33 182.53 356.42 3.46 0.51

Figure 5.1.7. Brandt’s cormorant. At-sea densities (individuals/km2) calculated for five 

minutes of latitude by five minutes of longitude grid cells from aerial and shipboard 

survey data collected from 1975-1997 and summarized in the Computer Database 

Analysis System v2.1 (MMS, 2001).
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Pelagic cormorant (Phalacrocorax pelagicus)

The pelagic cormorant (Family Phalacrocoracidae) ranges along the north Pacific coast with the southern end 

of their breeding range in North America occurring at Santa Barbara Island, with occasional nesting of a few 

individuals at Islas Los Coronados, Mexico. Nesting habitat includes cliffs on the coast and on islands. The 1989-

1991 breeding colony survey resulted in an estimate of 14,345 breeding birds in California with 19% of the breeding population of California occurring in the northern Channel Islands (Carter et al., 1992). The survey showed 

a small decrease in abundance of pelagic cormorant since earlier (1975-1980) surveys (Carter et al., 1995b). 

Somewhat greater abundance was found in southern California. While this is likely due to greater survey effort, 

numbers at Anacapa Island have increased (F. Gress, pers. comm.). Pelagic cormorant is not a endangered or 

threatened species. 

At-sea sightings indicate that pelagic cormorant are widely distributed in the nearshore waters of central and 

southern California, with high densities observed near many of the major points, including; Point Buchon, Point 

Sal, Point Arguello, and Point Conception. Additional high density areas exist near San Miguel, Santa Rosa, and 

Santa Cruz Islands.

Figure 5.1.8. Brandt’s cormorant. Colony counts (corrected total individuals) 1989-

1991 (Source: Carter et al., 1992).

Table 5.1.5. Brandt’s cormorant. At-sea sightings, effort, density, and Optimal Area Index (OAI) for each boundary concept. Values 

in bold indicate an increase in the estimate when compared to the NAC. Shaded OAI estimates reflect the concept with the greatest 

positive value.

Concept

Total

Individuals

Total Effort

(Area 

swept km2)

Density

(Individuals 

per km2)

 Area

(km2)

∆ Individuals

(%)

∆ Density

(%)

∆ Area

(%)

OAI

Individuals

(absolute

metric)

OAI

Density

(relative

metric)

NAC 377 448.2 0.841 3745 - - - - -

5 377 492.3 0.766 4538 0.00 -8.96 21.12 0.00 -0.42

4 378 748.3 0.505 7981 0.27 -39.95 113.11 0.00 -0.35

3 477 984.6 0.484 9044 26.53 -42.40 141.50 0.19 -0.30

2 526 1589.1 0.331 13736 39.52 -80.85 266.78 0.15 -0.23

1a 589 2460.3 0.239 22591 56.23 -71.54 503.23 0.11 -0.14

1 589 2460.3 0.239 22613 58.23 -71.54 503.82 0.11 -0.14

SA 589 2115.2 0.278 17093 56.23 -66.89 356.42 0.16 -0.19
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Analysis of the observed patterns of 

pelagic cormorant sightings (Figure 

5.1.9) and density relative to the proposed boundary concepts indicates 

that Concept 3 provides the greatest 

increase in sightings (absolute metric) for its relative size compared to 

the NAC (Table 5.1.6). Density (relative metric), however, was highest for 

pelagic cormorant within the NAC and 

consequently the density-based OAI 

is negative for all of the concepts. 

Much of the increase in sightings for 

Concept 3 relative to the smaller concepts (4, 5, and the NAC) is due to the 

inclusion of one additional high density grid cell near Point Conception. 

Concepts 1, 1a, and 2 encompass additional high density grid cells off Point 

Sal and Point Arguello.

Breeding colonies of pelagic cormorant (Figure 5.1.10) exist on San 

Miguel, Santa Rosa, Santa Cruz, Anacapa (West, Middle, and East), and 

Santa Barbara Islands, with major colonies (top quintile, 99-444 individuals) 

found on San Miguel, Santa Rosa, and 

Santa Cruz Islands. Southern California mainland colonies exist from Point 

Arguello north to Cape San Martin.

Analysis of the distribution of pelagic 

cormorant breeding colonies within 

the proposed boundary concepts suggests that Concept 2 provides the 

greatest increase in breeding individuals for its relative size compared to 

the NAC (Table 5.1.7). The increase 

in breeding individuals for Concept 2 

relative to the smaller concepts (3, 4, 

5, and the NAC) is due to the inclusion 

of three additional colonies on Points 

Arguello, Purisima, and Sal. Concepts 

1 and 1a also encompass these colonies and one additional colony of 4-5 

pairs, but their larger areas result in 

lower values for the OAI.

Figure 5.1.9. Pelagic cormorant. At-sea densities (individuals/km2) calculated for five 

minutes of latitude by five minutes of longitude grid cells from aerial and shipboard 

survey data collected from 1975-1997 and summarized in the Computer Database 

Analysis System v2.1 (MMS, 2001).

Figure 5.1.10. Pelagic cormorant. Colony counts (corrected total individuals) 1989-

1991 (Source: Carter et al., 1992).
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Western snowy plover

(Charadrius alexandrinus nivosus)

Western snowy plover is a shorebird 

(Family Charadriidae) which nests in 

sand, gravel, and salt pans on coastal 

beaches from southern Washington 

to southern Baja California, Mexico. 

CINMS is in the southern half of their 

core breeding range and feeding distribution. No information was available 

on population status or distribution 

within the study area; however, some 

individuals are known to nest on Santa Rosa, San Miguel, and San Nicolas 

Islands (P. Martin and G. Smith, pers. 

comm.).

Along the U.S. Pacific coast of Washington, Oregon, and California, 290 km 

of coastline have been designated as 

critical habitat for the western snowy 

Table 5.1.6. Pelagic cormorant. At-sea sightings, effort, density, and Optimal Area Index (OAI) for each boundary concept. Values 

in bold indicate an increase in the estimate when compared to the NAC. Shaded OAI estimates reflect the concept with the greatest 

positive value.

Concept

Total

Individuals

Total Effort

(Area 

swept km2)

Density

(Individuals 

per km2)

 Area

(km2)

∆ Individuals

(%)

∆ Density

(%)

∆ Area

(%)

OAI

Individuals

(absolute

metric)

OAI

Density

(relative

metric)

NAC 20 448.2 0.045 3745 - - - - -

5 20 492.3 0.041 4536 0 -8.96 21.12 0.00 -0.42

4 20 748.3 0.027 7981 0 -40.10 113.11 0.00 -0.35

3 33 984.6 0.034 9044 65 -24.88 141.50 0.46 -0.18

2 41 1589.1 0.026 13736 105 -42.18 266.78 0.39 -0.16

1a 57 2460.3 0.023 22591 185 -48.08 503.23 0.37 -0.10

1 57 2460.3 0.023 22613 185 -48.08 503.82 0.37 -0.10

SA 57 2115.2 0.027 17093 185 -39.61 356.42 0.52 -0.11

Table 5.1.7. Pelagic cormorant. Colony counts (total individuals) and Optimal Area Index (OAI) for each boundary concept. Values in bold indicate an 

increase in the estimate when compared to the NAC. Shaded OAI estimates 

reflect the concept with the greatest positive value.

Concept

Total

Individuals

 Area

(km2)

∆ Individuals

(%)

∆ Area

(%)

OAI

Individuals

(absolute

metric)

NAC 2687 3745 - - -

5 2687 4536 0.00 21.12 0.000

4 2687 7981 0.00 113.11 0.000

3 2687 9044 0.00 141.50 0.000

2 2790 13736 3.83 266.78 0.014

1a 2799 22591 4.17 503.23 0.008

1 2799 22613 4.17 503.82 0.008

SA 2799 17093 4.17 356.42 0.012

Figure 5.1.11. Western snowy plover. Designated critical habitat for the Pacific coast 

population of western snowy plover (Source: USFWS, 1999). Designated critical habitat is mapped for locales in the vicinity of CINMS; this map is not inclusive of all designated snowy plover critical habitat.
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

plover (USFWS, 1999). The current CINMS sanctuary boundary (NAC) and Concepts 4 and 5 do not include any designated 

critical habitat (Figure 5.1.11; Table 5.1.8). Concept 3 includes 

2.3 km of critical habitat shoreline, less than 1% of the total designated habitat. Concept 2 encompasses 27.4 km (9.4%). The 

largest concepts (1 and1a) and the Study Area include 70 km 

(24%) of the designated western snowy plover critical habitat. 

Because there was no critical habitat designated within the NAC, 

it is not possible to calculate the OAI for western snowy plover 

critical habitat.

Black Oystercatcher (Haematopus bachmani)

Black oystercatcher is a large shorebird (Family Haemotopidae), 

ranging along the Pacific coast from the Aleutian Islands to central Baja California, Mexico. CINMS 

is at the southern end of their core 

breeding and feeding distribution. The 

black oystercatcher nests along rocky 

shorelines in mixed aggregations with 

other marine birds, and is known to hybridize with American oystercatcher in 

the Channel Islands. The 1989-1991 

breeding colony surveys resulted in 

an estimate of 888 breeding birds in 

California, with 30% of the breeding 

population occurring in the northern 

Channel Islands (Carter et al., 1992). 

The cryptic, solitary nests make this 

a rough estimate, and population 

trends could not be determined from 

this survey. However, recent population declines are suspected in parts of 

Alaska. The black oystercatcher is not 

a endangered or threatened species. 

No at-sea sightings were available for 

this shorebird. 

Breeding colonies of black oystercatcher (Figure 5.1.12) exist on all 

eight of the Channel Islands with major 

colonies (top quintile, 8-27 individuals) 

occurring on San Miguel, Santa Cruz, 

Anacapa, Santa Barbara, and San Nicolas Islands. Numerous smaller colonies 

(less than 8 individuals each) exist on 

Santa Rosa Island. 

Analysis of the distribution of black oystercatcher breeding colonies within the 

proposed boundary concepts suggests 

that Concept 2 provides the greatest increase in breeding individuals for its relative size compared to the NAC (Table 

5.1.9). The increase in breeding individuals for Concept 2 relative to the smaller 

concepts (3, 4, 5, and the NAC) is due to 

Table 5.1.8. Western snowy plover. Critical habitat for 

the Pacific coast population of western snowy plover.

Concept

 Area

(km2)

Critical Habitat Length of 

Shoreline (km)

NAC 3745 0.00

5 4536 0.00

4 7981 0.00

3 9044 2.29

2 13736 27.35

1a 22591 69.85

1 22613 69.85

SA 17093 69.85

Table 5.1.9. Black oystercatcher. Colony counts (total individuals) and Optimal Area Index (OAI) for each boundary concept. Values in bold indicate an 

increase in the estimate when compared to the NAC. Shaded OAI estimates 

reflect the concept with the greatest positive value.

Concept

Total

Individuals

 Area

(km2)

∆ Individuals

(%)

∆ Area

(%)

OAI

Individuals

(absolute

metric)

NAC 267 3745 - - -

5 267 4536 0.00 21.12 0.000

4 267 7981 0.00 113.11 0.000

3 269 9044 0.75 141.50 0.005

2 278 13736 4.12 266.78 0.015

1a 278 22591 4.12 503.23 0.008

1 278 22613 4.12 503.82 0.008

SA 278 17093 4.12 356.42 0.012

Figure 5.1.12. Black oystercatcher. Colony counts (corrected total individuals) 1989-

1991 (Source: Carter et al., 1992).
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the inclusion of three additional colonies at Point Arguello. Concepts 1 and 

1a also encompass these colonies, but 

their larger areas result in lower values 

for the OAI.

California least tern

(Sterna antillarum browni)

Five subspecies of least tern (Family 

Sternidae) are found in North America, with the California least tern nesting on beaches and sand dunes from 

the San Francisco Bay area south to 

Baja California, Mexico (Carter et al., 

1992). CINMS is in the southern half 

of their core breeding distribution. A 

comprehensive survey of least terns 

in 1999 (Keane, 2001) estimated 

a California breeding population of 

3,451 to 3,674 pairs at 36 colonies. 

The ten largest colonies (Mission Bay 

Mariner’s Point, Santa Margarita River 

North Beach, NAB Ocean, NAS Alameda, Huntington Beach, Delta Beach 

North, L.A. Harbor Pier, NAWS Point 

Mugu, Batiquitos Lagoon and Tiuana 

River) represented 76.6% of the statewide breeding pairs. Numbers are declining in several areas in California 

(R. Jurek, pers. comm.). California least tern is listed as both a California 

and Federal endangered species. 

There were no recorded breeding colonies of California least tern within 

the NAC or Concepts 3-5 between 2001-2003 (Figure 5.1.13; CDFG, draft 

data). Concept 2 encompasses one colony at Vandenberg AFB with a 

maximum of 79 breeding pairs (Table 5.1.10). Concepts 1 and 1a and the 

study area encompass an additional 4 colonies with a maximum of 676 

breeding pairs. Because no colonies exist within the NAC, it is not possible to calculate the OAI for least tern colonies.

Pigeon guillemot (Cepphus columba)

Pigeon guillemot range throughout the North Pacific coasts with the southern 

end of their breeding range and feeding distribution in North America occurring 

at Santa Barbara Island. They nest mainly in rock crevices on mainland and 

island cliffs. The 1989-1991 breeding colony survey resulted in an estimate of 

15,470 breeding birds in California, with 21% of the breeding population occurring in the northern Channel Islands (Carter et al., 1992). The survey showed 

little change in the abundance of pigeon guillemot overall in California since earlier (1975-1980) surveys (Carter et 

al., 1995a). However, greater abundance was observed at colonies on San Miguel, Santa Cruz, Anacapa, and Santa 

Barbara Islands, likely due to greater survey effort and different survey techniques. Population trends are not known for 

Southern California. Pigeon guillemot is not an endangered or threatened species. 

At-sea sightings indicate that within Central and Southern California, areas of high pigeon guillemot density are found in 

nearshore waters near Monterey Bay (off Santa Cruz and Carmel), near Point Reyes and the Farallon Islands, off Point 

Arguello, and, most notably, around the northern Channel Islands (San Miguel, Santa Rosa, and Santa Cruz Islands).

Analysis of the observed patterns of pigeon guillemot sightings (Figure 5.1.14) and density relative to the proposed boundary concepts indicate that Concept 2 provides the greatest increase in sightings (absolute metric) 

for its relative size compared to the NAC (Table 5.1.11). Density (relative metric), however, was highest for 

Figure 5.1.13. California least tern. Maximum number of breeding pairs 2001-2003 

(Source: CDFG, draft data).

Table 5.1.10. California least tern. Maximum number of breeding pairs observed at 

each nesting site surveyed from 2001-2003 

for each boundary concept (CDFG, draft 

data). 

Concept

 Maximum

Breeding Pairs

Area

(km2)

NAC 0 3745

5 0 4536

4 0 7981

3 0 9044

2 79 13736

1a 676 22591

1 676 22613

SA 676 17093
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pigeon guillemot within the NAC and 

consequently the density-based OAI is 

negative for all of the concepts. Much 

of the increase in density for Concept 

2 relative to the smaller concepts (3, 4, 

5, and the NAC) is due to the inclusion 

of two or three additional high density 

grid cells near Point Arguello.

Breeding colonies of pigeon guillemot 

(Figure 5.1.15) exist on San Miguel, 

Santa Rosa, Santa Cruz, Anacapa, 

and Santa Barbara Islands, with major colonies (top quintile, 130-953 individuals) found on San Miguel, Santa 

Rosa, and Santa Cruz Islands. Southern California mainland colonies exist 

from Point Conception north to Lopez 

Point.

Analysis of the distribution of pigeon 

guillemot breeding colonies within the 

proposed boundary concepts suggests 

that Concept 2 provides the greatest 

increase in breeding individuals for its 

relative size compared to the NAC (Table 5.1.12). The increase in breeding 

individuals for Concept 2 relative to the 

smaller concepts (3, 4, 5, and the NAC) is due to the inclusion of 11 additional colonies on and around Points 

Conception, Arguello, Purisima, and Sal. Concepts 1 and 1a also encompass these colonies and one additional 

colony of 5 individuals, but their larger areas result in lower values for the OAI.

Xantus’s murrelet (Synthliboramphus hypoleucus)

Xantus’s murrelet is a small alcid (Family Alcidae) that nests only in southern California and northwestern Baja 

California, Mexico. CINMS is at the northern edge of Xantus’s murrelet breeding range, but not their feeding distribution. They nest in crevices and under bushes on offshore islands and rocks. The 1989-1991 breeding colony 

survey resulted in an estimate of 1719-1727 breeding birds in California. All of this breeding population occurs 

in the northern Channel Islands, with the vast majority nesting at Santa Barbara Island, and smaller numbers at 

San Miguel and Santa Cruz Islands (Carter et al., 1992). This survey estimate was substantially lower than that 

Figure 5.1.14. Pigeon guillemot. At-sea densities (individuals/km2) calculated for five 

minutes of latitude by five minutes of longitude grid cells from aerial and shipboard 

survey data collected from 1975-1997 and summarized in the Computer Database 

Analysis System v2.1 (MMS, 2001).

Table 5.1.11. Pigeon guillemot. At-sea sightings, effort, density, and Optimal Area Index (OAI) for each boundary concept. Values 

in bold indicate an increase in the estimate when compared to the NAC. Shaded OAI estimates reflect the concept with the greatest 

positive value.

Concept

Total

Individuals

Total Effort

(Area 

swept km2)

Density

(Individuals 

per km2)

 Area

(km2)

∆ Individuals

(%)

∆ Density

(%)

∆ Area

(%)

OAI

Individuals

(absolute

metric)

OAI

Density

(relative

metric)

NAC 61 448.2 0.136 3745 - - - - -

5 61 492.3 0.124 4536 0.00 -8.96 21.12 0.00 -0.42

4 61 748.3 0.082 7981 0.00 -40.10 113.11 0.00 -0.35

3 67 984.6 0.068 9044 9.84 -50.00 141.50 0.07 -0.35

2 76 1589.1 0.048 13736 24.59 -64.86 266.78 0.09 -0.24

1a 79 2460.3 0.032 22591 29.51 -76.41 503.23 0.06 -0.15

1 79 2460.3 0.032 22613 29.51 -76.41 503.82 0.06 -0.15

SA 79 2115.2 0.037 17093 29.51 -72.56 356.42 0.08 -0.20
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Figure 5.1.15. Pigeon guillemot. Colony counts (corrected total individuals) 1989-1991 

(Source: Carter et al., 1992).

Table 5.1.12. Pigeon guillemot. Colony counts (total individuals) and Optimal Area Index (OAI) for each boundary concept. Values in bold indicate an 

increase in the estimate when compared to the NAC. Shaded OAI estimates 

reflect the concept with the greatest positive value.

Concept

Total

Individuals

 Area

(km2)

∆ Individuals

(%)

∆ Area

(%)

OAI

Individuals

(absolute

metric)

NAC 3218 3745 - - -

5 3218 4536 0.00 21.12 0.000

4 3218 7981 0.00 113.11 0.000

3 3247 9044 0.90 141.50 0.006

2 4777 13736 48.45 266.78 0.182

1a 4782 22591 48.60 503.23 0.097

1 4782 22613 48.60 503.82 0.096

SA 4782 17093 48.60 356.42 0.136

from earlier (1975-1977) surveys due to an apparent decline at Santa Barbara Island, but differences in survey 

technique make it difficult to infer trends. More recent surveys (H. Carter, unpubl. data) have found new nesting 

areas at Anacapa, Santa Cruz and Santa Catalina Islands. More recent monitoring indicates that this species 

has continued to decline at Santa Barbara Island (Whitworth et al., 2003; Burkett et al., 2003; P. Martin, pers. 

comm.). Xantus’s murrelet was recently recommended for listing as “Threatened” by the California Department 

of Fish and Game (California Regulatory Notice Register, March 5, 2004). 

The at-sea pattern of Xantus’s murrelet, as observed in the CDAS dataset, differs from that observed in the telemetry study. Both sources of information should be considered in order to fully understand the biogeography of 

this species in Southern California.

Although scattered observations of Xantus’s murrelets occur in shelf waters off Central California in the fall and in 

the Santa Barbara Channel throughout the year, two cores of their distribution appear to occur in Southern California: (a) near the northern Channel Islands, and (b) near San Diego. The northern core reflects birds foraging 
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from colonies in the northern Channel 

Islands, while the southern core reflects 

foraging from the large colony at Islas 

Los Coronados.

Analysis of the observed patterns of Xantus’s murrelet sightings (Figure 5.1.16) 

and density relative to the proposed 

boundary concepts indicates that Concept 3 provides the greatest increase in 

sightings (absolute metric) for its relative size compared to the NAC (Table 

5.1.13). The density-based OAI favors 

Concept 4, which includes some of the 

sightings in the Santa Barbara Channel. 

None of the concepts encompass the 

apparent core of the distribution in the 

southern Channel Islands.

Breeding colonies of Xantus’s murrelet (Figure 5.1.17) observed during 

the 1989-1991 survey on San Miguel 

(Prince Island), Santa Cruz, and Santa 

Barbara Islands, with the largest colony 

(1402 individuals) on Santa Barbara Island. More recent surveys of Xantus’s 

murrelet (H. Carter, unpubl. data) also 

found colonies on Anacapa (East, Middle, and West, Figure 5.1.18), San Clemente (Figure 5.1.19), San Miguel (Mainland) (Figure 5.1.20), Santa Barbara 

(Figure 5.1.21), Santa Catalina (Figure 5.1.22), and Santa Cruz (Figure 5.1.23) Islands, with the largest colonies (two 

shoreline segments had 150-400 pairs each) found on Santa Barbara Island. No colonies were found on Santa Rosa 

or San Nicolas Islands.

No analysis of boundary concepts was conducted for Xantus’s murrelet breeding colonies since none of the concepts 

encompass colonies not already contained within the NAC. 

Relocation of radiomarked Xantus’s murrelets from Santa Barbara Island occurs throughout the western Southern 

California Bight, with scattered observations occurring as far north as Monterey Bay (Whitworth et al., 2000; Figure 

5.1.24). During the breeding season (April to June) the density of relocations were highest around the northern Channel 

Islands from west of San Miguel Island to Point Mugu, around Santa Barbara Island, and to the south of San Nicolas 

Figure 5.1.16. Xantus’s murrelet. At-sea densities (individuals/km2) calculated for five 

minute of latitude by five minute of longitude grid cells from aerial and shipboard survey 

data collected from 1975-1997 and summarized in the Computer Database Analysis 

System v2.1 (MMS, 2001).

Table 5.1.13. Xantus’s murrelet. At-sea sightings, effort, density, and Optimal Area Index (OAI) for each boundary concept. Values 

in bold indicate an increase in the estimate when compared to the NAC. Shaded OAI estimates reflect the concept with the greatest 

positive value.

Concept

Total

Individuals

Total Effort

(Area 

swept km2)

Density

(Individuals 

per km2)

 Area

(km2)

∆ Individuals

(%)

∆ Density

(%)

∆ Area

(%)

OAI

Individuals

(absolute

metric)

OAI

Density

(relative

metric)

NAC 1 448.2 0.0022 3745 - - - - -

5 1 492.3 0.0020 4536 0 -8.96 21.12 0.00 -0.42

4 5 748.3 0.0067 7981 400 199.48 113.11 3.54 1.76

3 7 984.6 0.0071 9044 600 218.65 141.50 4.24 1.55

2 8 1589.1 0.0050 13736 700 125.64 266.78 2.62 0.47

1a 8 2460.3 0.0033 22591 700 45.74 503.23 1.39 0.09

1 8 2460.3 0.0033 22613 700 45.74 503.82 1.39 0.09

SA 8 2115.2 0.0038 17093 700 69.52 356.42 1.96 0.20
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Island. Modest densities of relocations 

was found west of the Santa Barbara 

Channel, between San Miguel Island 

and Point Sal, but these observations 

were from birds dispersing northward to 

central California in late summer (Whitworth et al., 2000).

Analysis of observed patterns of radiomarked Xantus’s murrelet relocations 

relative to the proposed boundary concepts indicates that Concept 5 provides 

the greatest increase in sightings (absolute metric) for its relative size compared 

to the NAC (Table 5.1.14). As expected, 

however, more sightings are included in 

the larger concepts. Each successively 

larger concept appears to encompass 

new clusters of relocations, with the exception of Concept 1, which does not 

encompass any more relocations than 

Concept 1a. 

Cassin’s auklet

(Ptychoramphus aleuticus)

Cassin’s auklet is a small alcid (Family 

Alcidae) that ranges from the Aleutian 

Islands to central western Baja California, Mexico. CINMS is south of their 

core breeding distribution. They nest 

in burrows and crevices on offshore islands. The 1989-1991 breeding colony 

survey resulted in an estimate of 56,572 

breeding birds in California, with 22% of 

this population occurring in the northern 

Channel Islands (Carter et al., 1992). 

Although the survey showed an overall 

decline in abundance of Cassin’s auklet since 1975-1980 surveys (Carter et 

al., 1995a), numbers of breeding birds 

at Santa Cruz Island were found to be 

greater than in the 1975-1977 surveys, 

but a trend could not be determined. 

Continued decline since 1991 is suspected (H. Carter, unpubl. data). Cassin’s auklet is not an endangered or threatened species.

At-sea sightings indicate that Cassin’s auklet densities appear to be greater to the north of the CINMS study area with 

the highest densities occurring north of Point Año Nuevo near the large Farallon Islands colony. Nevertheless, there are 

many sightings of Cassin’s auklet within the study area and within the current CINMS boundaries. Areas of relatively 

high density exist within the western Santa Barbara Channel especially to the north of San Miguel and Santa Rosa 

Islands, adjacent to the large Prince Island colony. 

Analysis of the observed patterns of Cassin’s auklet sightings and density (Figure 5.1.25) relative to the proposed 

boundary concepts indicates that Concept 2 provides the greatest increase in both sightings (absolute metric) and 

density (relative metric) for its relative size compared to the NAC (Table 5.1.15). Concepts 1 and 1a also displayed high 

Figure 5.1.17. Xantus’s murrelet. Colony counts (corrected total individuals) 1989-

1991 (Source: Carter et al., 1992).

Table 5.1.14. Xantus’s murrelet. Telemetry relocations and Optimal Area Index 

(OAI) for each boundary concept. Values in bold indicate an increase in the estimate when compared to the NAC. Shaded OAI estimates reflect the concept with 

the greatest positive value.

Concept

Total

Relocations

 Area

(km2)

∆ Relocations

(%)

∆ Area

(%)

OAI

Relocations

(absolute

metric)

NAC 127 3745 - - -

5 223 4536 75.59 21.12 3.579

4 308 7981 142.52 113.11 1.260

3 313 9044 146.46 141.50 1.035

2 371 13736 192.13 266.78 0.720

1a 412 22591 224.41 503.23 0.448

1 412 22613 224.41 503.82 0.445

SA 411 17093 223.62 356.42 0.627
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Figure 5.1.18. Xantus’s murrelet. Anacapa Island colony counts 

(estimated total individuals per shoreline segment) 1991-2002 

(Source: H. Carter, unpublished data).

Figure 5.1.19. Xantus’s murrelet. San Clemente Island colony 

counts (estimated total individuals per shoreline segment) 1991-

2002 (Source: H. Carter, unpublished data).

Figure 5.1.20. Xantus’s murrelet. San Miguel Island colony 

counts (estimated total individuals per shoreline segment) 1991-

2002 (Source: H. Carter, unpublished data).

Figure 5.1.21. Xantus’s murrelet. Santa Barbara Island colony 

counts (estimated total individuals per shoreline segment) 1991-

2002 (Source: H. Carter, unpublished data).

Figure 5.1.22. Xantus’s murrelet. Santa Catalina Island colony 

counts (estimated total individuals per shoreline segment) 1991-

2002 (Source: H. Carter, unpublished data).

Figure 5.1.23. Xantus’s murrelet. Santa Cruz Island colony 

counts (estimated total individuals per shoreline segment) 1991-

2002 (Source: H. Carter, unpublished data).
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

values for both OAI’s, while the smaller concepts (3-5) showed only modest increases in sightings and lower densities 

compared to the NAC. Much of the improvement in sightings and densities observed for the larger concepts (1, 1a, and 

2) is due to the inclusion of an area of high density off Point Sal.

Breeding colonies of Cassin’s auklet (Figure 5.1.26) exist on San Miguel, Santa Cruz, and Santa Barbara Islands, with 

the two largest colonies (547 and 8922 individuals) in Southern California found on San Miguel Island (Prince Island 

and Castle Rock).

Figure 5.1.24. Xantus’s murrelet. Telemetry relocations during 

1995–1997 and kernel density surface based on the relocation 

points (cell size=2 km, radius=15 km). Black shading indicates 

no data available. (Source: USGS ands HSU; Whitworth et al., 

2000; Carter et al. 2000). 

Figure 5.1.25. Cassin’s auklet. At-sea densities (individuals/km2) calculated for five 

minutes of latitude by five minutes of longitude grid cells from aerial and shipboard 

survey data collected from 1975-1997 and summarized in the Computer Database 

Analysis System v2.1 (MMS, 2001).
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No analysis of boundary concepts was 

conducted for Cassin’s auklet breeding 

colonies since none of the concepts 

encompass any colonies not contained 

within the NAC. 

Other Marine Birds

In addition to the requested species, 

several other species of marine birds 

occur in the CINMS or possible concept areas in relatively large numbers 

at certain times of year or in certain 

years. Additional seabirds and shorebirds found in the study area may 

include the Pacific loon, western 

grebe, sooty shearwater, pink-footed 

shearwater, black-vented shearwater, 

Brant, surf scoter, red-necked phalarope, red phalarope, Heermann’s gull, 

Bonaparte’s gull, California gull, ringbilled gull, common murre, rhinoceros 

auklet, and the tufted puffin. While no 

quantitative analysis was conducted 

for these species, it is important to 

recognize that they may be impacted 

by changes to the boundaries of the 

CINMS and further investigation into 

these species may be warranted.

Summary

• Of the five boundary concepts being considered in addition to the NAC, the concept with the highest OAI value 

varies considerably depending on the particular species being considered.

• For many species, the NAC and the smaller Concepts 4 and 5 showed lower OAI values than the larger Concepts, 1, 2, and 3.

Figure 5.1.26. Cassin’s auklet. Colony counts (corrected total individuals) 1989-1991 

(Source: Carter et al., 1992).

Table 5.1.15. Cassin’s auklet. At-sea sightings, effort, density, and Optimal Area Index (OAI) for each boundary concept. Values 

in bold indicate an increase in the estimate when compared to the NAC. Shaded OAI estimates reflect the concept with the greatest 

positive value.

Concept

Total

Individuals

Total Effort

(Area 

swept km2)

Density

(Individuals 

per km2)

 Area

(km2)

∆ Individuals

(%)

∆ Density

(%)

∆ Area

(%)

OAI

Individuals

(absolute

metric)

OAI

Density

(relative

metric)

NAC 149 448.2 0.332 3745 - - - - -

5 156 492.3 0.317 4536 4.70 -4.68 21.12 0.22 -0.22

4 229 748.3 0.306 7981 53.69 -7.95 113.11 0.47 -0.07

3 288 984.6 0.293 9044 93.29 -12.01 141.50 0.66 -0.08

2 1115 1589.1 0.702 13736 648.32 111.06 266.78 2.43 0.42

1a 1819 2460.3 0.739 22591 1120.81 122.40 503.23 2.23 0.24

1 1819 2460.3 0.739 22613 1120.81 122.40 503.82 2.22 0.24

SA 1481 2115.2 0.700 17093 893.96 110.61 356.42 2.51 0.31
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Chapter 5.2 Marine Bird Diversity

Data and Methods

The marine bird diversity data presented in this section are derived from six at-sea surveys (including both marine and aerial platforms) of marine birds from the period 1975-1997. The results of these surveys are compiled 

in the Computer Database Analysis System (CDAS) v2.1 (MMS, 2001). The surveys used in this analysis are 

summarized in Table 5.2.1. Although CDAS contains survey data along the west coast of Washington, Oregon, 

and California, this analysis focused on those sightings south of Point Arena. The location of bird sightings and 

the distribution of survey effort are shown in Figure 5.2.1. A total of 95 bird species were observed in the combined surveys. Although some shorebirds are included in the list, these at-sea surveys were not designed to 

sample shorebirds or nesting colonies.

The Shannon index of diversity (Shannon and Weaver, 1949) was chosen for this analysis because it is one of 

the most commonly used diversity metrics in community ecology and has relatively small statistical bias when 

sample sizes are large (as is the case with this source data) (Magurran, 1988). The Shannon index attempts to 

balance species richness (i.e., the total number of unique species) with species evenness (i.e. the distribution 

of individuals among the species). For a given number of individuals and species, the Shannon index is highest 

when there is an equal number of individuals of each species. 

Table 5.2.1. Summary of the six surveys that were used in the analysis of marine bird diversity. The information in this table reflects 

the data used in this analysis, which in some cases may be a temporal and geographic subset of the entire survey.

Survey Dates Platform Months Total sightings Total individuals

Minerals Management Service Aerial 

Surveys 1980-1983 airplane (low altitude) Year-round 28525 91298

California Department of Fish and 

Game, Office of Spill Prevention and 

Response 1994-1997 airplane (low altitude) Year-round 7751 71151

Southern California Bight Low Aerial 

Survey 1975-1978 airplane (low altitude) Year-round 4250 17741

Seabird Ecology Study 1985 ship and airplane

March and 

May

2212 8641

Southern California Bight Ship Survey 1975-1978 ship Year-round 17693 58719

Southern California Bight, Minerals 

Management Service Survey 1995-1997 airplane (low altitude) Year-round 9780 46199

Figure 5.2.1. The distribution of CDAS marine bird survey effort and sightings (left) and the total amount of effort within 5 minutes of 

latitude by 5 minutes of longitude grid cells (right) within the region from Point Arena to the U.S.-Mexico border.
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Since the CDAS data includes summaries for 5 minutes of latitude by five minutes of longitude grid cells, we 

calculated total observed diversity for each 5 minute cell. The Shannon index (H’) was calculated using the formula:

where ni is the number of individuals belonging to the ith species (S) in the sample (five minute grid), and N is the 

total number of individuals in the sample (Magurran, 1988).

To aid in the analysis and visual interpretation of the diversity map, the estimated diversity was then interpolated using kriging to provide a statistically smoothed one km raster surface. To accomplish this, the calculated 

diversity for each five minute cell was first assigned to a point at the center of the cell (i.e. the cell centroid). 

These point data were subsequently tested for significant spatial autocorrelation using the Moran’s I and Geary’s 

C statistics. A finding of significant autocorrelation indicates that points that are nearer to one another tend to 

have more similar values of diversity than points that are far away (Legendre, 1993), and is a prerequisite to accurate interpolation. Next, the spatial autocorrelation was described using a variogram, which summarizes the 

decrease in relatedness between pairs of points as the distance between them increases. The parameters of 

the variogram were used in a geostatistical interpolation technique known as kriging, which provides a surface 

of predicted values as well as a standard error surface indicating the regions in which we have higher or lower 

confidence in the accuracy of estimated diversity. To avoid displaying estimates of diversity in areas where we 

have little confidence in the prediction, the standard error map was used to clip the diversity surface. The resulting map (Figure 5.2.2) displays interpolated bird diversity for those regions where the standard error was in the 

lowest 25 percent. 

The estimated patterns of bird diversity should be interpreted with care, as they represent a compilation of six 

surveys with different methods occurring over a period of nearly 25 years. The distribution and abundance of 

some species are known to have changed since 1975 (the earliest data used in this analysis). A drawback 

common to nearly all diversity metrics 

is the strong positive and non-linear 

(He et al., 1994) correlation between 

diversity and sampling effort. As sampling effort increases in a given region, 

the calculated diversity within that region increases as well. Consequently, 

when sampling effort varies over a 

given area (as it does within the project study area) some of the observed 

patterns in diversity may be related to 

patterns in the distribution of sampling 

effort. For this reason, we have included a map of sampling effort (Figure 

5.2.1) to be considered alongside the 

map of diversity (Figure 5.2.2).

Broad-scale Patterns

The marine bird diversity model resulted in several meso-scale patches (tens 

to hundreds of kilometers in size) from 

Point Arena in the north to the U.S.-

Mexico border in the south. Regions of 

high estimated diversity (warm tones) 

appear along the entire stretch, with a 

large patch extending from the shelf 

waters north of Cordell Bank National 

Figure 5.2.2. Estimated bird diversity from Point Arena, Calfiornia to the U.S.-Mexico 

border. Stippled areas delineate zones representing the 75th percentile of the estimate. 

ln ni

N

ni

-Σ=1 N

S

H’ = [( ) ( )] ln ni

N

ni

-Σ=1 N

S

H’ = [( ) ( )]
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Marine Sanctuary through the Gulf of the Farallones and Monterey Bay national marine sanctuaries along the 

shelf break terminating in the region of Monterey Bay and Point Sur (Figure 5.2.2). A second conspicuous area of 

high estimated diversity appears approximately 140 km west of Monterey Bay in the open waters over the Guide 

Seamount. Farther to the south another much smaller patch of high diversity appears in the vicinity of the Santa 

Lucia Bank. This small patch appears to be a seaward extension of the most prominent extent of high diversity, 

which ranges along the shelf from Morro Bay in the north down to Point Conception. This significant feature then 

spreads throughout the entire Southern California Bight, with concentrations around the Channel Islands, the 

Santa Barbara Channel, and southern shelf areas. 

In general, model results indicate that the current arrangement of sanctuaries along the California coastline captures substantial areas of high estimated diversity. In this analysis (ranging from 39° to 32°N latitude), the total 

area represented by the top 25% of the diversity estimate (Figure 5.2.2, stippled area) was 33,881 km2. Roughly 

5,770 km2 (17%) of this overall area is contained within the four California sanctuaries, with 6% falling inside the 

boundaries of the Channel Islands National Marine Sanctuary. A total of 61% of the area contained within current CINMS boundaries was classified as having high marine bird diversity. This is the largest proportion of any 

California sanctuary. 

More than 195 species of birds occupy coastal and/or offshore aquatic habitats in the SCB (McGinnis, 2000). 

Although many of these species are widely distributed along the west coast, the area of upwelling off Point Arguello/Conception has long been discussed as a key attraction for many of the region’s seabird species (Briggs 

et al., 1987). The convergence of two distinct water masses, coupled with elevated productivity associated with 

upwelling, attracts birds typical of both cool temperate and warm subtropical waters, and contributes to the diversity of the bird community (Baird, 1993). 

Linkages between oceanographic character, marine biological productivity, and bird populations have been a 

topic of considerable study (Briggs et al., 1987; Ainley and Boekelheide, 1990; Ainley et al., 1995; Roemmich and 

McGowan, 1995; Sydeman et al., 1997; Schoenherr et al., 1999). Upwelling in the SCB has been correlated to 

relatively high concentrations of krill and secondary consumers off the northern Channel Islands. In turn, these 

pelagic invertebrates and forage fishes attract seabirds to the open ocean over the continental shelf around the 

Channel Islands. Sooty shearwaters (Puffinis griseus), which are among the most numerous seabirds in the region, forage on fish, squid, and euphausiids (Chu, 1984). Shortbelly rockfish, anchovy, and sardine are among 

the primary foods of common murres (Uria aalge), Brandt’s cormorants (Phalacrocorax penicillatus), and rhinoceros auklet chicks (Cerorhinca monocerata). Murres and some other seabirds feed principally on euphausiids in 

the spring, before juvenile fish and anchovies are available (Ainley and Boekelheide, 1990; Ainley et al., 1995). 

California brown pelicans (Pelecanus occidentalis californicus) feed primarily on northern anchovy, Pacific sardine, and Pacific mackerel. Cassin’s auklets (Ptychoramphus aleuticus) depend on euphausiids and mysids as 

their primary food supply (Sydeman et al., 1997). Rhinoceros auklets frequent waters of the continental slope, 

where they feed on euphausiids, oceanic squid, and fishes, including lanternfishes and Pacific saury. Adult 

rhinoceros auklets are also known to consume sablefish and juvenile lingcod found in deep waters far offshore 

(Airamé et al., 2003). The diet of the ashy storm-petrel is poorly known, but includes euphausids.

While these trophic linkages do not explain all of the diversity model results, they do corroborate many of the 

emerging patterns. Each of the high diversity areas identified in the results section occurs near well known upwelling centers (Breaker and Gilliland, 1981; Kelly, 1985; Breaker and Mooers, 1986; Huyer and Kosro, 1987; 

Tracy, 1990; Brink and Cowles, 1991; Schwing et al., 1991; Breaker and Broenkow, 1994; Rosenfeld et al., 

1994), including the area near Point Arena, the area near Point Año Nuevo, the nearshore waters directly adjacent to Point Sur, and, as described above, the area of upwelling near Point Arguello/Conception.

Another likely contributing factor in the expression of patterns of bird diversity is proximity to nesting sites. The 

Farallon Islands are the highest density area for nesting seabirds along the California coast and offshore islands/

rocks (Airamé et al., 2003). Over 300,000 adult birds nest on the islands in May, which represents the height of 

the breeding season, although two species (common murre and Cassin’s auklet) comprise over half of all birds.

Twelve species of marine birds, including common murre, Cassin’s and rhinoceros auklets, pigeon guillemot, 

tufted puffin, western gull, cormorants (double-crested, Brandt’s, and pelagic), ashy and Leach’s storm-petrels, 

and black oystercatcher, breed on the Farallon Islands (Ainley and Boekelheide, 1990; Schoenherr et al., 1999). 

This concentration of individuals and species likely influences the broad band of relatively high diversity south 

and seaward of the Farallones. Most of the remainder of California breeding seabird populations nest on the 
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northern Channel Islands, including: Leach’s, ashy and black storm-petrels; California brown pelican; doublecrested, Brandt’s, and pelagic cormorants; western gull; pigeon guillemot; Xantus’s murrelet; Cassin’s and rhinoceros auklets; and tufted puffin. Over 200,000 adult birds nest on the islands in April-May, which represents 

the height of the breeding season. Although not condensed into such a small area as the Farallon Islands, the 

Channel Islands populations of breeding seabirds are of similar magnitude and diversity as the Farallon Islands. 

Additionally, the Channel Islands contain the entire U.S. populations of black storm-petrel, California brown pelican, and Xantus’s murrelets, plus over 33% of the world populations of ashy storm-petrels and Xantus’s murrelets. While the Farallon Islands also hosts over 33% of the world population of ashy storm-petrels, its populations 

of other species do not reach significant portions of world populations. Thus, in several ways, Channel Islands 

populations are more important than the Farralon Islands populations on a world scale. Breeding populations in 

the Channel Islands clearly influence diversity estimates in this area.

Analysis of Boundary Concepts

The preceding discussion identified a large region of high bird diversity centered on the Channel Islands, ranging along the shelf from Morro Bay in the north down to Point Conception, where it then spreads throughout the 

entire Southern California Bight. This region corresponds roughly with the general region of range termini between 36° and 26°N and the southern part of the California Current Upwelling System. A total of 61% of the area 

contained within current CINMS boundaries was classified as having high (top 25%) marine bird diversity – the 

largest proportion of any California Sanctuary. As such, it is important to note that the current sanctuary boundary captures areas of high marine bird diversity; however, a review of the remaining concepts clearly suggests 

that an expansion could provide further conservation benefit in terms of preserving areas of high bird diversity. In 

this section we will use the current boundary (NAC) as a reference point against which the remaining concepts 

and analyses will be compared. 

Mean estimated diversity for the NAC was calculated to be 1.49 with a coefficient of variation (CV) of 8.8%. Mean 

diversity and CV values for the remaining concepts, ranging from smallest in size to largest, are as follows: Concept 5-1.49, 8.7%; Concept 4-1.52, 9.9%; Concept 3-1.53, 9.8%; Concept 2-1.50, 10%; Concept 1a-1.37, 20.3%; 

and Concept 1-1.38, 20.4%. Mean diversity for the Study Area boundary is estimated to be 1.49 with a CV of 

9.9% (Figure 5.2.3, also see Table 5.2.2.) As discussed in Chapter 1.4 which describes absolute versus relative 

metrics, results shown here are generally predictable, with a trend of larger areas exhibiting lower mean diversity 

values than smaller ones. This trend is graphically represented in Figure 5.2.4 as a linear regression function 

between area (km2) and mean diversity (r2=0.60, P=0.02). It should be noted, however, that the trend shown in 

this figure is largely driven by concepts 1 and 1a, and that while the trend is predictable, concepts 2, 3, and 4 

are higher than expected. This indicates that the boundary configuration for these concepts disproportionately 

captures areas of high bird diversity, and that any of these concepts would be a suitable choice for expansion. 

Clearly, concepts 1 and 1a would be a less suitable choice based on mean diversity alone. The relationship 

between the absolute areas of high diversity (Figure 5.2.2, stippled area) is even more predictable than mean 

diversity, with larger concepts containing ever larger areas of high diversity (Table 5.2.2). Figure 5.2.5 shows 

the linear regression function between the total area (km2) and the area of high diversity contained within each 

concept (r2=0.91, P < 0.01).

A more balanced metric to use in assessing the relative conservation value for bird diversity is the Optimal Area 

Index (OAI) (Table 5.2.2). While this metric decouples the predictable relationships between concept area and 

conservation value to some extent, results of the OAI are still dependent upon the input data – absolute vs. relative measures. As such, we’ve provided results of the OAI for both mean and absolute bird diversity. Again, the 

OAI takes into account the proportional (%) change in diversity as you step from the NAC to each of the concepts 

under consideration. It also incorporates the proportional change (%) in area from the NAC. 

In both cases, the OAI indicate that concepts 3 and 4 provide the largest conservation value per area gained 

(Table 5.2.2). Because the mean OAI incorporated a negative value in the numerator for concepts 1 and 1a (decreased mean diversity), the calculated value is necessarily negative. Likewise, because the absolute count of 

high diversity area always increases with each concept, the OAI values are positive.
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Figure 5.2.3. Overlay of estimated marine bird diversity and CINMS boundary concepts. The Study Area (upper left) is not a 

concept currently under consideration, but is analyzed to provide a point of comparison to the McGinnis report. Concept 1 is 

shown with the “cutout” and is to be used as a representative map for both Concepts 1 and 1a.

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

Modeled Avian Diversity

0 15 30 60 90 120

Kilometers

Study Area

High

Low

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

Modeled Avian Diversity

0 15 30 60 90 120

Kilometers

Boundary Concept 1

High

Low

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

Modeled Avian Diversity

0 15 30 60 90 120

Kilometers

Boundary Concept 2

High

Low

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

Modeled Avian Diversity

0 15 30 60 90 120

Kilometers

Boundary Concept 3

High

Low

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

Modeled Avian Diversity

0 15 30 60 90 120

Kilometers

Boundary Concept 4

High

Low

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

Modeled Avian Diversity

0 15 30 60 90 120

Kilometers

Boundary Concept 5

High

Low
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Table 5.2.2. Analysis of bird diversity within boundary concepts. Numbers in bold indicate an increase in the estimate when 

compared to the No Action Concept (NAC). Values in bold indicate an increase in the estimate when compared to the NAC. 

Shaded OAI estimates reflect the concept with the greatest positive value. 

Concept

Area

(km2)

Mean Bird

Diversity

High

Diversity

Area (km2)

∆ Area

(%)

∆ Mean

Diversity

(%)

∆ High

Diversity

Area (%)

Mean Bird 

Diversity OAI 

(relative)

High 

Diversity 

Area OAI 

(absolute)

NAC 3745 1.485 2284 - - - - -

5 4536 1.487 2812 21 0.13 23.12 0.00638 1.094

4 7981 1.523 5507 113 2.56 141.11 0.02262 1.248

3 9044 1.53 6421 141 3.03 181.13 0.02141 1.28

2 13736 1.502 8791 267 1.14 284.89 0.00428 1.068

1a 22591 1.372 10391 503 -7.61 354.95 -0.01512 0.705

1 22613 1.375 10401 504 -7.41 355.39 -0.0147 0.705

SA 17093 1.489 9914 356 0.27 334.06 0.00076 0.937

SA

NAC 5

4 3

2

1, 1a

1.35

1.40

1.45

1.50

1.55

0 5000 10000 15000 20000 25000

Total Area (km2)

Mean Bird Diversity

SA

NAC

5

4

3

2

1, 1a

2000

4000

6000

8000

10000

12000

0 5000 10000 15000 20000 25000

Total Area (km2)

Mean Bird Diversity

Figure 5.2.4. Regression of mean bird diversity 

and concept area. Numbers indicate concepts, and 

NAC=No Action Concept; SA=Study Area.

Figure 5.2.5. Regression of the area of highest mean 

bird diversity and concept area. Numbers indicate concepts, and NAC=No Action Concept; SA=Study Area. 

Summary

• Patterns of marine bird diversity appear to reflect the distribution of known upwelling regions and areas of high 

productivity, as well as breeding areas.

• The current boundaries of the CINMS encompass a region of high bird diversity and a significant concentration 

of seabird breeding areas.

• Of the six boundary concepts being considered, Concepts 3 and 4 provide relatively large increases in mean 

bird diversity relative to the NAC; only a few small new breeding areas would be added in any concept being 

considered. 
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Chapter 6 BIOGEOGRAPHY OF MARINE MAMMALS

Olaf Jensen, Karin Forney, Jay Barlow, Brian Hatfield, Mark Lowry

The waters around Point Conception and the northern Channel Islands are influenced by cool and warm water 

masses and strong nearshore upwelling which makes this area highly productive. Because of this productive 

environment, this region contains a rich fauna of marine mammals. The area around Point Conception is a significant biogeographic mixing zone for many species of marine mammals (Chapter 1.3). 

6.1 Cetacean Single Species Analysis

This chapter presents a description of the distribution patterns of nine cetacean species off central and southern 

California, as well as an analysis of how these distribution patterns relate to the proposed CINMS boundary concepts. The nine cetacean species chosen by CINMS staff for individual analysis represent those species which 

are common in the region of interest and include:

Blue whale (Balaenoptera musculus) 

Bottlenose dolphin (Tursiops truncatus)

Short-beaked common dolphin (Delphinus delphis)

Long-beaked common dolphin (Delphinus capensis) 

Gray whale (Eschrichtius robustus) 

Humpback whale (Megaptera novaeangliae) 

Killer whale (Orcinus orca)

Pacific white-sided dolphin (Lagenorhynchus obliquidens)

Risso’s dolphin (Grampus griseus) 

In addition to these requested species, five additional cetaceans are briefly discussed at the end of this chapter. 

Spatial patterns for these species, though not selected for detailed analysis, are described within the sanctuary 

and the six proposed boundary concepts and include:

Dall’s porpoise (Phocoenoides dalli)

Fin whale (Balaenoptera physalus)

Harbor porpoise (Phocoena phocoena)

Minke whale (Balaenoptera acutorostrata)

Northern right-whale dolphin (Lissodelphis borealis)

Spatial patterns of many cetaceans are not static. Cyclic movements exist on many time scales from seasonal 

migrations to distributional shifts related to interannual scale climate events, such as the El Niño/La Niña Southern Oscillation (ENSO). An analysis of seasonal distribution patterns is beyond the scope of this project, and in 

some cases, is not feasible given the available data. The analysis presented here describes general patterns, 

in many cases averaged over different seasons and years. Where seasonal movements are known, they are 

mentioned in the text. More detailed information about seasonal distribution patterns of cetaceans in California 

waters can be found in Forney and Barlow (1998). In addition to seasonal movements there are also trends in the 

abundance and spatial patterns of some cetaceans. Where known, these trends are also mentioned in the text. 

Because of the patchy distribution of many marine organisms in time and space, the general patterns described 

here may not be a good predictor of abundance at a specific location and time. This is particularly true for species such as blue and humpback whales which track aggregations of seasonal prey (e.g., krill or small schooling 

fish), but is also likely to be the case for many other cetaceans that aggregate. For such species, the average 

abundances estimated in this report will most often be higher than the actual abundance at a particular time, but 

during high use periods, abundance may greatly surpass the average. 

Data and Methods

Several types of geo-referenced survey data for cetaceans were used in this report, including: shipboard surveys 

from the NMFS Southwest Fisheries Science Center (SWFSC), shipboard and aerial surveys compiled for the 

Minerals Management Service (MMS) in the Computer Database Analysis System (CDAS), and an aerial survey 

of bottlenose dolphin (SWFSC). These surveys are summarized in Table 6.1.1 and discussed below. Only the 

SWFSC ship survey and the aerial bottlenose dolphin survey were used to develop densities or encounter rates 
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and abundance estimates for this report. This was only possible for certain species (listed below) for which there 

were sufficient numbers of sightings within the different boundary concepts. When properly corrected for survey 

effort, these sightings can be used to estimate density; however, without such corrections the sightings can only be plotted to confirm the 

presence of a given species at a given time and location. Such plots 

were developed for all of the requested species. Because of uneven 

distribution of effort, the absence of sightings does not necessarily 

indicate the absence of a species in a given area. Although only the 

requested species and five additional cetaceans commonly found in 

the Study Area were plotted, surveys generally recorded all marine 

mammals sighted. An analysis of the different boundary concepts was 

conducted for all species for which quantitative abundance estimates 

could be calculated. This analysis includes the Optimal Area Index 

(OAI), a metric for comparing boundary concepts that is discussed in 

greater detail in Chapter 1.4.

SWFSC Shipboard Surveys

Line-transect surveys of marine mammals were conducted by the 

SWFSC from late July through early November in 1991, 1993, and 

1996, and from late July through early December in 2001. The surveys were conducted off California in all years and additionally off 

Washington and Oregon in 1996 and 2001. Survey tracks are shown 

Survey Dates Platform Months

Marine Mammal 

Sightings

Marine Mammal 

Individuals

Southwest Fisheries 

Science Center Ship 

Surveys

1991, 1993, 

1996, 2001 ship July-December 2963 87402

Southwest Fisheries 

Science Center Aerial 

Southern California 

Bight Surveys

1992-1993

1998-2003 airplane Year-round 37 (Gray whale) -

Southwest Fisheries 

Science Center Coastal 

Bottlenose Dolphin 

Surveys

1990-2000 airplane

FebruaryDecember 

(most effort in 

summer)

311 3190

Minerals Management 

Service Aerial SurveysCDAS1980-1983 airplane

(high altitude) Year-round 221 777988

Minerals Management 

Service Aerial SurveysCDAS1980-1983 airplane

(low altitude) Year-round 4089 40528

California Dept. of Fish 

and Game, Office of 

Spill Prevention and 

Response-CDAS

1994-1997 airplane

(low altitude) Year-round 351 1027

Southern California 

Bight High Aerial 

Survey-CDAS

1975-1978 airplane

(high altitude) Year-round 695 68557

Southern California 

Bight Low Aerial 

Survey-CDAS

1975-1978 airplane

(low altitude) Year-round 1319 15067

Southern California 

Bight Ship SurveyCDAS1975-1978 ship Year-round 3209 112136

Minerals Management 

Service Survey-CDAS 1995-1997 airplane(low altitude) Year-round 898 3437

Table 6.1.1 Summary of marine mammal field surveys examined in this chapter.

Figure 6.1.1. Survey tracks for the Southwest 

Fisheries Science Center (SWFSC) ship surveys 

1991-2001.
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in Figures 6.1.1 and 6.1.2. Details of the survey methods are 

described fully by Barlow et al. (2001). Briefly, surveys were 

conducted aboard the R/V David Starr Jordan and the R/V 

McArthur. Three observers, two with 25x binoculars and one 

with the unaided eye, recorded marine mammal sightings, 

including species, group size, and perpendicular distance. 

Results of these surveys have been used by other researchers to estimate the abundance of cetaceans along the U.S. 

West Coast using line-transect methods (Barlow, 2003). 

Here, SWFSC shipboard survey data are used to calculate 

density estimates within the current CINMS boundary, No 

Action Concept (NAC), Study Area (McGinnis, 2000), and 

the six proposed boundary concepts for the following cetaceans:

Blue whale (Balaenoptera musculus) 

Short-beaked common dolphin (Delphinus delphis)

Long-beaked common dolphin (Delphinus capensis) 

Unidentified common dolphin (Delphinus spp.)

Humpback whale (Megaptera novaeangliae) 

Risso’s dolphin (Grampus griseus) 

Density and abundance of the above cetaceans were calculated using line-transect methods (Buckland et al., 

1993), and ABUND4 (J. Barlow, pers.comm.), a Fortran program, was used to estimate abundance from this 

line-transect data using the equation:

where A equals the area of the region of interest, n equals the number of sightings, S is the mean group size, 

f(0) is the sighting probability density at zero perpendicular distance, L is the track length, and g(0) is the probability of seeing a group directly on the trackline. The parameters A, n, S, and L are intuitive and easily calculated 

from the data. The line transect parameters f(0) and g(0) require some explanation: The first parameter, f(0), is 

a component of the detection function which describes the decrease in sighting probability as the perpendicular 

distance from the transect line increases. The value of f(0) is the inverse of the Effective Strip Width (ESW), 

which determines the effective area searched, taking into account the detection function. The other major linetransect parameter, the detection probability on the transect line or g(0), is used to correct for animals that may 

have been missed on the transect line, either because they were diving and not available to be seen (availability 

bias) or because they were available to be seen but missed by the observer team (perception bias). Availability 

bias cannot be estimated empirically from the survey data and is generally determined from information on dive 

times and surface intervals of marine mammals. Availability bias during shipboard surveys is expected to be 

greatest for species that dive for prolonged periods, such as beaked whales and sperm whales, and low for most 

dolphins and other large whales. Perception bias, in contrast, is generally highest for small groups of inconspicuous cetaceans, and lowest for large whales and large, active dolphin schools. Values of g(0) used in this analysis 

were obtained from Barlow (2003) and include both sources of bias when possible. 

Values of f(0) were determined empirically by fitting hazard rate detection functions to the sighting distances. 

Stratification and pooling of the data were used to obtain the simplest and best fitting models. Both group size and 

geographic strata were tested, and Akaike’s Information Criterion (AIC) was used to select between competing 

models. To examine the value of geographic stratification, detection functions were fit to the entire pooled data 

sets (all four years) and a geographical subset of the data in Southern California (from 32.3° to 36°N and east 

of 122°W), which corresponds more closely to the region of the CINMS boundary concepts. AIC was compared 

between the pooled data and the sum of the two strata (southern California and all other west coast locations), 

and the geographic stratification that minimized AIC was selected. For all species except common dolphins and 

blue whale, the pooled data for the entire data set was selected. 

126°W

126°W

124°W

124°W

122°W

122°W

120°W

120°W

118°W

118°W

32°N32°N

34°N34°N

36°N36°N

38°N38°N

0 50 100 200

Kilometers

SWFSC Ship Surveys

July-December

1991, 1993, 1996, 2001

1991

1993

1996

2001

Figure 6.1.2. Survey tracks for the Southwest Fisheries Science Center (SWFSC) ship surveys 1991-2001 off 

central and southern California waters.

N=A*n*S*f(0)/2*L*g(0)
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The two common dolphin species and unidentified common dolphins were pooled for estimation of the detection function in order to increase the number of sightings and improve the precision of the f(0) estimate for this 

category. Estimates of f(0) were lower for the southern California subset than for the entire dataset. Lower f(0) 

indicates a greater effective strip width (ESW) and suggests that for some reason (e.g., calmer conditions or 

behavioral differences) common dolphins were more visible within southern California than for the West Coast 

as a whole. Because of this difference, and a slight improvement in AIC (830.23 for the pooled data compared to 

828.97 for the geographically stratified data), geographically specific f(0)’s were used for common dolphins. 

Although Barlow (2003) used three group size strata (1-20, 20-60, and >60 individuals) when estimating f(0) 

coastwide for common dolphin, only two group size strata were used in this study because the f(0) values for 

groups of 20-60 and >60 were similar (0.464 and 0.451 respectively) in the southern California stratum, and pooling provided more robust sample sizes for variance estimation. 

Density and abundance were estimated separately for the two common dolphin species and unidentified common 

dolphins, using the pooled estimates of f(0) and g(0). The unidentified common dolphin densities and abundances were then pro-rated to the two species according to the proportional abundances of the two species occurring 

in each concept. Unidentified common dolphins represented 3-10% of the estimated abundance of long-beak 

common dolphins and 4-7% of short-beak common dolphins within the boundary concepts. Confidence intervals 

for the combined (identified+unidentified) estimates of common dolphin abundance were approximated based 

on the coefficient of variation for each of the identified common dolphin abundance estimates. This was judged to 

be reasonable because the unidentified common dolphins represented only a small proportion of the combined 

abundance estimate and were expected to contribute little to the variance of the overall abundance estimate. 

For blue whales, geographic stratification resulted in a lower f(0) and improved model fit, while group size stratification (1-2 and >2) did not. 

Too few Risso’s dolphin and humpback whale sightings were available for accurate f(0) estimation using only 

data from the southern California stratum; therefore coastwide values of f(0) were taken from Barlow (2003) for 

these species. A summary of input parameters for all species is provided in Table 6.1.2.

Because density and abundance are estimated independently for the individual boundary concepts, 

it is possible that a larger concept that completely 

contains a smaller one could have a lower estimated abundance. This counterintuitive result can occur when few or no sightings are recorded in the 

portion of the larger concept that does not overlap 

the smaller concept. Such a contradiction reflects 

the uncertainty associated with abundance estimation at the scales in this study. Such examples 

underscore the importance of considering the confidence interval of each estimate, not just the point 

estimates, when comparing boundary concepts.

For the remaining requested cetaceans, too few 

sightings were recorded within the region to accurately estimate density. Sightings from the SWFSC 

shipboard surveys are shown for the above species 

and for:

Bottlenose dolphin (Tursiops truncatus): Offshore 

Stock

Gray whale (Eschrichtius robustus) 

Killer whale (Orcinus orca)

Pacific white-sided dolphin (Lagenorhynchus obliquidens) 

Overall 1-20 >20

Common dolphin

f(0) 0.963 0.518

CV f(0) 0.289 0.212

g(0) 0.77 1

CV g(0) 0.14 0

Blue whale

f(0) 0.349

CV f(0) 0.13

g(0) 0.9

CV g(0) 0.07

Humpback whale

f(0) 0.346

CV f(0) 0.15

g(0) 0.9

CV g(0) 0.07

Risso’s dolphin

f(0) 0.73 0.459

CV f(0) 0.16 0.2

g(0) 0.74 1

CV g(0) 0.39 0

Table 6.1.2. Line transect parameters used to estimate the abundance of selected cetaceans within the six boundary concepts, the 

current CINMS boundary, and the Study Area. Numbers in bold are 

from Barlow, 2003.
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SWFSC Aerial Surveys

Additional sightings from SWFSC aerial surveys are displayed for the gray whale, because they are winter/spring 

migrants through California waters, and no sightings were 

made during the summer/fall shipboard surveys. Unlike the 

ship surveys, these aerial surveys were restricted to small 

areas within the Southern California Bight (Southern California Bight) (Figure 6.1.3) and were conducted year-round, 

approximately every 1-2 months. Surveys in the area surrounding San Nicolas Island were conducted in 1992-1993, 

and a second set of surveys around San Clemente Island 

were conducted in 1998-2003. Details of the survey methods are found in Carretta et al. (1995 and 2000). Because of 

the geographically focused nature of these surveys, the distribution of sightings viewed at a broader scale (i.e. the entire 

Southern California Bight or southern California) largely reflects the distribution of survey effort. Nevertheless, this survey provides useful recent information about the location of 

gray whale sightings within surveyed areas of the Southern 

California Bight. As with all geographically focused surveys, 

the absence of sightings does not necessarily indicate the 

absence of a species in a given area. 

Computer Database Analysis System (CDAS)

Seven at-sea surveys from the period 1975-1997, compiled by R.G. Ford Consulting Co., in CDAS v2.1 (MMS, 

2001) were used to display sightings and effort for the following cetaceans:

Blue whale (Balaenoptera musculus)

Bottlenose dolphin (Tursiops truncatus): Coastal and Offshore Stocks

Common dolphin (Delphinus spp.) (not identified to species)

Gray whale (Eschrichtius robustus)

Humpback whale (Megaptera novaeangliae)

Killer whale (Orcinus orca)

Pacific white-sided dolphin (Lagenorhynchus obliquidens)

Risso’s dolphin (Grampus griseus)

This list represents all of the requested cetacean species. Effort is represented by the total length of survey track 

in each five minute of latitude by five minute of longitude grid cell (Figures 6.1.4 and 6.1.5). Although effort for the 

bird surveys in the CDAS data set was represented by area swept, this conversion was not used for displaying 

cetacean survey effort because different effective strip widths apply for different cetaceans and different surveys. 

The CDAS data, though comprehensive and thorough, were recorded over a period of more than 20 years, during which time the distributions of some species are known to have varied considerably (Forney and Barlow, 

1998). Additionally, the quality of geographical position data and effort information varied during the study period. 

Consequently, no quantitative analysis was conducted on the CDAS data for this report. 

SWFSC Bottlenose Dolphin Aerial Survey

Aerial surveys of California coastal bottlenose dolphins were conducted by SWFSC in May 1990; April, June, 

August, October, and December 1991; February, April, and July 1992; May-August 1993; July 1994; May 1999; 

and June 2000. The surveys covered the mainland coast from Point Montara to the U.S.-Mexico border and the 

Channel Islands. Coastal bottlenose dolphins are associated with nearshore habitat spending 99% of their time 

within about 500m of shore (Hanson and Defran, 1993). Aerial surveys were conducted at an altitude of 213 m 

within 300 to 500 m of shore by three observers: inshore (facing shoreward), offshore (facing seaward), and belly 

(facing down). Further details of the survey methods are reported by Carretta et al., (1998). 

Encounter rates for bottlenose dolphin were calculated for 20 km shoreline segments by dividing the total number of sightings by the total length of survey track. Portions of survey track in which the sea state was rougher 

than Beaufort 4 were eliminated as were those portions for which the glare on the inshore observer window (from 

122 121.5 121 120.5 120 119.5 119 118.5 118 117.5 117 116.5 116

W Longitude

30

30.5

31

31.5

32

32.5

33

33.5

34

34.5

35

N Latitude

Pacific Ocean

San Diego

Figure 6.1.3. Survey tracks for the Southwest Fisheries Science Center (SWFSC) ship surveys 1991-2001 off 

southern California waters.
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

which the majority of sightings were made) and the belly window obscured more than 75% of the viewing area. 

The shoreline segments adjacent to restricted air space near Point Mugu, Camp Pendleton, and Los Angeles International Airport were treated in the same manner as other segments; however, encounter rates in these areas 

may not accurately reflect bottlenose dolphin abundance because the survey aircraft was frequently required to 

change course or altitude for safety reasons. Shoreline segments with less than 5 km of effort were eliminated 

from the analysis. Although all of the Channel Islands were surveyed at least once, only two on-effort bottlenose 

dolphin sightings were recorded off Santa Catalina Island. Encounter rates were therefore estimated only for the 

mainland, and no OAI analysis was conducted for this species. Minimum abundance estimates were calculated 

for each of the concepts that include parts of the mainland coast. These estimates were calculated by multiplying the average encounter rate within each concept by the total length of mainland coast. Because this method 

does not account for individuals that were present but not sighted (Carretta et al., 1998), it represents a minimum 

estimate. Because sightability was not considered, it was not possible to calculate the uncertainty associated 

with the abundance estimates.

Broad-scale Patterns and Analysis of Boundary Concepts

Blue whale (Balaenoptera musculus)

Although stock structure of blue whales in the North Pacific has been hypothesized to include one (Donovan, 

1991) to five (Reeves et al., 1998) sub-stocks, the most recent U.S. stock assessments for this species (Carretta et al., 2002) includes an Eastern North Pacific stock in addition to the Hawaiian stock. The Eastern North 

Pacific stock, which feeds in California waters during the summer and fall and migrates to waters off Mexico and 

Central America during the winter (Calambokidis et al., 1990), is believed to be separate from the Gulf of Alaska 

population (Rice, 1992). The most recent abundance estimate for this stock, based on a weighted average of 

the estimates from the 1991-1996 SWFSC ship surveys (Barlow, 1997) and a 1993 mark-recapture survey 

(Calambokidis and Steiger, 1994) was 1,940 individuals (Carretta et al., 2002). Blue whale is a federally listed 

endangered species.

Sightings of blue whales from the SWFSC ship surveys and the CDAS surveys (Figure 6.1.6) occurred throughout the shelf, slope, and offshore waters of southern California. A notable cluster of sightings was found to the 

west of San Miguel Island in shelf waters. Because of the uneven distribution of survey effort, the pattern of sightings should be used only as confirmation that blue whales do exist in a given area; the absence of sightings for 

this widely ranging species may reflect insufficient survey effort rather than real absence from the area. 

Estimates of the summer and fall abundance of blue whales within the NAC, the six boundary concepts, and 

the Study Area were derived from the 1991-2001 SWFSC ship surveys described above and are summarized 

in Table 6.1.3. Because of the relatively small number of on-effort sightings (4-14) and the uncertainty in the 

Figure 6.1.4. Survey effort (km of survey track) for the seven surveys of marine mammals compiled in the Computer Database 

Analysis System (CDAS) v2.1. 

Figure 6.1.5. Survey effort (km of survey track) for the seven surveys of marine mammals compiled in the Computer Database 

Analysis System (CDAS) v2.1 off central and southern California 

waters.
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

line transect input parameters, confidence 

intervals for the abundance estimates are 

wide and overlap substantially among different concepts. The wide intervals show 

that abundance cannot be estimated 

precisely, and the overlap indicates that 

the differences in estimated abundance 

among concepts are not likely to be statistically significant. Nevertheless, large differences in estimated blue whale density 

and abundance exist among the concepts. 

Blue whale sightings were numerous within the NAC and exhibited higher estimated 

density than any of the concepts or the 

Study Area. Sharp increases in estimated blue whale abundance relative to that 

of the NAC are apparent in Concepts 1, 

1a, and 2. The OAI shows that, although 

none of the concepts provide higher density than the NAC, Concepts 1 and 1a provide the greatest relative increase in blue 

whale abundance for the smallest relative 

increase in area. It is important to remember, however, that blue whales aggregate 

in areas where their prey (krill) are concentrated. Any boundary concept might, 

therefore, contain a larger number of blue 

whales during times when krill densities 

are high.

Bottlenose dolphin (Tursiops truncatus)

Two populations of bottlenose dolphin are found in California waters: 1) an offshore population found at distances greater than 1 km from the mainland shore in the Southern California Bight and extending to the offshore 

limits (300 nmi) of the SWFSC ship surveys throughout much of California waters; and 2) a coastal population 

that is found primarily within 500 m of the mainland shore from San Francisco south into Baja California, Mexico 

(Carretta et al., 2002). The abundance of the offshore population in U.S. west coast waters during the 1991-1996 

SWFSC ship surveys was estimated at 956 individuals (Barlow, 1997). The most recent average estimate for the 

#*

#*

#*

#*

#*

#*

#*

#* #*

#*

#*

#*

#*

#*

#*

#* #*

#*

#*

#*

#*

#*

#* #*#*

#*

#*

#*#*

#*#*#* #*

#*

#*#*#*#* #*#* #*

#*

#* 124°W

124°W

122°W

122°W

120°W

120°W

118°W

118°W

34°N34°N

36°N36°N

38°N38°N

0 50 100 200

Kilometers

SWFSC

Average Group Size

1.0

1.1 - 1.9

2.0

2.1 - 3.0

3.1 - 5.0

CDAS and SWFSC

Marine Mammal Surveys

Blue Whale

CDAS

Group Size

#* 1

#* 2

#* 3

#* 4 - 5

#* 6 - 8

Figure 6.1.6. Blue whale. Sightings and group size (where available) from the 

Southwest Fisheries Science Center (SWFSC) ship surveys 1991-2001 and the 

seven surveys of marine mammals compiled in the Computer Database Analysis 

System (CDAS) v2.1, 1975-1997.

Concept Area (km2

)

SightingsEstimatedDensityEstimatedAbundanceCVLower95% CIUpper95% CI∆ Area (%)∆ Density (%)∆ Abundance(%)Density OAI(relative)AbundanceOAI (absolute)

NAC 3745 4 0.00807 30 0.93 6 141 - - - - -

5 4536 4 0.00712 32 0.78 8 124 21.12 -11.77 6.67 -0.557 0.316

4 7981 4 0.004 32 0.73 9 115 113.11 -50.43 6.67 -0.446 0.059

3 9044 4 0.00358 32 0.72 9 114 141.50 -55.64 6.67 -0.393 0.047

2 13736 7 0.006 82 1.34 11 598 266.78 -25.65 173.33 -0.096 0.65

1a 22591 14 0.00587 133 0.4 63 283 503.23 .27.26 343.33 -0.054 0.68

1 22613 14 0.00587 133 0.4 63 283 503.82 -27.26 343.33 -0.054 0.681

SA 17093 8 0.0053 91 0.44 40 208 356.42 -33.95 203.33 -0.095 0.57

Table 6.1.3. Blue whale. Sightings, estimated density and abundance, coefficient of variation (CV), and upper and lower 

95% confidence limits for the abundance estimate, and the Optimal Area Index (OAI) for the six boundary concepts, the No 

Action Concept (NAC), and the Study Area (SA). Abundance and density values in bold reflect increases from the NAC and 

shaded OAI values represent maximum observed benefit.
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

coastal population, based on 1999-2000 

tandem aerial surveys by the SWFSC (a 

subset of the data was used to map encounter rates in this report), is 206 individuals (Carretta et al., 2002). A multi-year 

average estimate of the number of individuals in the Study Area was presented 

by Carretta et al. (2002), because some 

individuals in this population spend part of 

the time off Mexico and farther north along 

the central California coast. Although the 

abundance of the coastal population in 

California overall appears to be stable 

(Dudzick, 1999), there is movement 

along the coast, some of which appears 

to be related to seasonal and interannual 

changes in water temperature (Hansen 

and Defran, 1990; Wells et al., 1990). Bottlenose dolphins are not federally listed as 

a threatened or endangered species. 

Sightings of bottlenose dolphin from the 

SWFSC ship surveys and the CDAS surveys (Figure 6.1.7) occur mostly in shelf and 

nearshore waters of the Southern California 

Bight. Both populations, coastal and offshore, are apparent in the sightings. A string 

of sightings likely to be from the coastal population occurs along the coast from west of 

Santa Barbara to Ventura and another between Dana Point and San Diego. Sightings 

that can be attributed to the offshore population occur throughout the Southern California Bight with a cluster of sightings from the 

SWFSC surveys found in the Santa Cruz 

Basin. Because of the uneven distribution of 

survey effort, the pattern of sightings should 

be used only as confirmation that bottlenose 

dolphin do exist in a given area; the absence 

of sightings may reflect insufficient survey 

effort rather than real absence from the area. 

No abundance estimates were calculated for 

the offshore stock of bottlenose dolphin. 

Encounter rates of coastal bottlenose dolphin derived from the 1990-2000 SWFSC 

aerial surveys designed specifically for this 

population (Figure 6.1.8) vary along the 

central and southern California coast, with 

the highest encounter rates observed to the 

south of Santa Barbara. Notable hotspots 

(encounter rates in the highest quintile or 0.311-0.864 individuals/km) for this species occur from Carpinteria to Ventura, 

Point Dume to Santa Monica, San Pedro Bay to Newport Beach, and near Oceanside to La Jolla. Many of these areas 

of high encounter rates contain long sandy beaches and/or river mouths.

Estimates for the mean encounter rates and abundance of the coastal stock of bottlenose dolphin within Concepts 1-

3 and the Study Area were derived from the 1990-2000 SWFSC aerial coastal bottlenose dolphin surveys described 

Figure 6.1.7. Bottlenose dolphin. Sightings and group size (where available) from 

the Southwest Fisheries Science Center (SWFSC) ship surveys 1991-2001 and 

the seven surveys of marine mammals compiled in the Computer Database Analysis System (CDAS) v2.1, 1975-1997.

Figure 6.1.8. Bottlenose dolphin (coastal population). Encounter rates (#/km) 

based on Southwest Fisheries Science Center (SWFSC) aerial surveys, 1990-

2000.
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

above and are summarized in Table 

6.1.4. Mean encounter rates and estimated abundance were greatest in 

Concepts 1 and 1a and the Study 

Area. Substantial increases in both the 

mean encounter rate and the estimated 

abundance were observed for each increase in shoreline length. Abundance 

of coastal bottlenose dolphin in those 

concepts which do not include portions 

of mainland shore, is assumed to be 

zero since coastal bottlenose dolphin 

are not known to occur away from the 

mainland shore. 

Long-beaked and short-beaked 

common dolphins (Delphinus spp.)

Two distinct species of common dolphin, the long-beaked (Delphinus capensis) and the short-beaked (Delphinus 

delphis) common dolphin, have been recognized in the eastern North Pacific based on genetic and morphological 

differences (Heyning and Perrin, 1994; Rosel et al., 1994). Within California coastal waters, the distribution of the 

two species overlaps. Long-beaked common dolphins are found in nearshore (<50 nmi of the coast) waters from 

Baja California, Mexico to central California. Short-beaked common dolphins have a broader distribution along 

the west coast of North America, extending from approximately the California/Oregon border south into equatorial 

waters (Carretta et al., 2002). Short-beaked common dolphins may also be found farther from the coast, with many 

sightings in the SWFSC ship surveys occurring near the offshore limit (300 nmi) of the survey. Although common 

dolphins are frequently spotted during aerial surveys, the two species cannot be reliably distinguished from the air 

(Forney et al., 1995). The most recent abundance estimate for the California stock of long-beaked common dolphin based on data from the 1991-1996 SWFSC ship surveys (Barlow, 1997) is 32,239 individuals (Carretta et al., 

2002). Estimated short-beaked common dolphin abundance throughout its U.S. West Coast range, based on the 

same data, is 373,573 individuals. Although these abundance estimates are for different geographic regions (stock 

assessments are for individual stocks which may have different geographic boundaries), analysis of the same data 

restricted to California shows that shortbeaked common dolphin are the most 

abundant cetacean in California waters. 

The distributions of both species appear 

to vary seasonally and interannually with 

highest densities of long-beaks in California waters occurring during warm-water 

events (Heyning and Perrin, 1994). Neither 

species of common dolphin is considered a 

threatened or endangered species.

Sightings of common dolphins are divided 

into those that were not identified to species from the SWFSC ship surveys and the 

CDAS surveys (Figure 6.1.9) and those 

from the SWFSC ship surveys that could 

be identified as either long-beaked (Figure 

6.1.10) or short-beaked (Figure 6.1.11). 

Common dolphins not identified to species 

were frequently sighted in the Southern 

California Bight in the CDAS surveys and 

were twice sighted in Monterey Bay. The 

SWFSC surveys include several sightings 

in shelf waters between Point Conception 

and Point Piedras Blancas as well as many 

Concept

 Area

(km2)

Mainland

Shoreline

(km)

 Individuals

Sighted

Mean Encounter

Rate (#/km

Estimated

Abundance

NAC 3745 0 - - -

5 4536 0 - - -

4 7981 0 - - -

3 9044 20.32 5 0.04 1

2 13736 140.02 199 0.11 15

1a 22591 277.64 1112 0.23 63

1 22613 277.64 1112 0.23 63

SA 17093 277.64 1112 0.23 63

Table 6.1.4. Coastal bottlenose dolphin. Sightings, mean encounter rate, and estimated 

abundance for four boundary concepts and the Study Area (SA). No analysis was done 

for the NAC or Concepts 4-5 since encounter rates were calculated for the mainland 

coast.

Figure 6.1.9. Common dolphin (Delphinus spp). Sightings and group size (where 

available) from the Southwest Fisheries Science Center (SWFSC) ship surveys 

1991-2001 and the seven surveys of marine mammals compiled in the Computer 

Database Analysis System (CDAS) v2.1, 1975-1997. 
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

throughout the Southern California Bight. 

Sightings of long-beaked common dolphins occurred predominantly in inshore 

shelf waters from Point Piedras Blancas 

south to Newport Beach. There were several sightings in the Santa Barbara Channel and near Anacapa Island. Sightings of 

short-beaked common dolphins were much 

more numerous and occurred throughout 

central and southern California shelf and 

offshore waters, although offshore sightings predominate north of Monterey Bay. 

Because of the coarse distribution of survey effort, the pattern of sightings should 

be used only as confirmation that common 

dolphins do exist in a broad geographic 

area. The absence of sightings within 

smaller geographic areas may reflect the 

distribution of survey effort rather than real 

absence from the area. 

Estimates of the summer and fall abundance of long-beaked and short-beaked 

common dolphins within the NAC, the six 

boundary concepts, and the Study Area, 

were derived from the 1991-2002 SWFSC 

ship surveys described above and are 

summarized in Tables 6.1.5 (long-beaks) 

and 6.1.6 (short-beaks). These results represent the combined estimates of speciesspecific abundance and, because many 

common dolphins could not be identified to 

species, an area-specific proportion of the 

estimated unidentified common dolphin 

abundance. Confidence intervals for the 

combined (identified + pro-rated unidentified) abundance estimates were approximated based on the coefficient of variation 

of the abundance estimates for identified 

sightings only. Because of the relatively 

small number of on-effort sightings (3-7 for 

long-beaks and 4-19 for short-beaks) and 

the uncertainty in the line transect input 

parameters, confidence intervals for the 

abundance estimates are wide and overlap 

substantially among different concepts. The 

overlapping confidence intervals indicate 

that the differences in estimated abundance 

among concepts are not statistically significant. 

Estimated long-beaked common dolphin density is highest in Concept 2 and estimated abundance is highest in Concepts 1, 1a, and the Study Area. Notable increases in estimated abundance relative to that of the NAC are apparent 

with each increase in concept size with the exception of Concept 5, which shows a 7% increase relative to the NAC. 

The OAI shows that, of the proposed boundary concepts, Concept 2 provides the greatest relative increase in both 

density and abundance for the smallest relative increase in area. 

Figure 6.1.10. Long-beaked common dolphin. Sightings and average group size 

(where available) from the Southwest Fisheries Science Center (SWFSC) ship 

surveys 1991-2001.

Figure 6.1.11. Short-beaked common dolphin. Sightings and average group size 

(where available) from the Southwest Fisheries Science Center (SWFSC) ship 

surveys 1991-2001.
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

Estimated short-beaked common dolphin density is highest in Concept 4 and estimated abundance is highest in Concepts 1, 1a, and the Study Area. Estimated abundance for this species seems to fall into three relatively distinct groupings: the NAC and Concept 5, with approximately 2,500 individuals; Concepts 2-4, with around 10,000 individuals; 

and Concepts 1, 1a, and the Study Area, with around 20,000 individuals. The OAI shows that Concept 4 provides the 

greatest relative increase in both density and abundance for the smallest relative increase in area. 

Abundance estimates of the two common dolphin species present two apparent contradictions. While there are more 

sightings of short-beaked common dolphin, and this species is the most abundant cetacean in California waters, estimated abundance is higher for long-beaked common dolphin within several of the boundary concepts. The higher 

estimated abundance for long-beaked common dolphin is in part due to the larger average group size of this species 

(e.g., in Concept 1, the average group size for long-beaked common dolphin was 480 individuals while the average 

group size for short-beaked common dolphin was 140 individuals). Although short-beaked common dolphins are more 

abundant throughout California, this partly reflects their broader distribution into offshore waters. Within certain areas, 

including some of the boundary concepts, long-beaked common dolphins are more abundant because they have a 

more nearshore distribution. Concept Area (km2

)

Sightings(Short-beaked)Sightings(Delphinus spp.)Corrected DensityCorrectedAbundanceCVLower 95% CIUpper 95% CI∆ Area (%)∆ Density (%)∆ Abundance (%)Density OAI(relative)AbundanceOAI(absolute)

NAC 3745 4 0 0.62 2330 - - - - - - - -

5 4536 4 0 0.55 2489 - - - 21.12 -11.83 6.82 -0.560 0.323

4 7981 8 2 1.17 9356 1 1830 47838 113.11 88.39 301.55 0.781 2.666

3 9044 8 2 1.05 9503 1 1859 48591 141.50 68.86 307.86 0.487 2.176

2 13736 9 2 0.78 10756 0.95 2234 51799 266.78 25.85 361.65 0.097 1.356

1a 22591 19 3 0.92 20713 0.57 7324 58579 503.23 47.35 788.97 0.094 1.568

1 22613 19 3 0.92 20733 0.57 7331 58636 503.82 47.35 789.85 0.094 1.568

SA 17093 16 2 1.13 19321 0.6 6517 57286 356.42 81.66 729.25 0.229 2.046 Concept Area (km2

)

Sightings(Long-beaked)Sightings(Delphinus spp.)Corrected DensityCorrectedAbundanceCVLower 95% CIUpper 95% CI∆ Area (%)∆ Density (%)∆ Abundance (%)Density OAI(relative)AbundanceOAI(absolute)

NAC 3745 3 0 1.41 5262 - - - - - - - -

5 4536 3 0 1.24 5620 - - - 21.12 -11.83 6.80 -0.560 0.322

4 7981 4 2 0.75 5967 1.06 1089 32693 113.11 -46.79 13.40 -0.414 0.118

3 9044 4 2 0.67 6061 1.01 1172 31355 141.50 -52.31 15.18 -0.370 0.107

2 13736 6 2 1.72 23649 0.74 6476 86362 266.78 22.52 349.42 0.084 1.310

1a 22591 7 3 1.16 26115 0.69 7686 88730 503.23 -17.73 396.29 -0.035 0.787

1 22613 7 3 1.16 26141 0.69 7694 88816 503.82 -17.73 396.78 -0.035 0.788

SA 17093 7 2 1.59 27138 0.66 8351 88191 356.42 12.98 415.73 0.036 1.166

Table 6.1.5. Long-beaked common dolphin. Sightings, estimated density and abundance, coefficient of variation (CV) and upper and 

lower 95% confidence limits for the abundance estimate; and the Optimal Area Index (OAI) for the six boundary concepts, the No 

Action Concept (NAC), and the Study Area (SA). Abundance and density values in bold reflect increases from the NAC and shaded 

OAI values represent maximum observed benefit.

Table 6.1.6. Short-beaked common dolphin. Sightings, estimated density and abundance, coefficient of variation (CV) and upper 

and lower 95% confidence limits for the abundance estimate; and the Optimal Area Index (OAI) for the six boundary concepts, the No 

Action Concept (NAC), and the Study Area (SA). Abundance and density values in bold reflect increases from the NAC and shaded 

OAI values represent maximum observed benefit.
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Gray whale (Eschrichtius robustus)

Gray whales are currently found only in 

the North Pacific with two separate stocks 

recognized (Angliss and Lodge, 2002). The 

Western North Pacific stock is distributed 

throughout eastern Asia (Rice, 1981; Rice 

et al., 1984) while the Eastern North Pacific stock occurs from its summer feeding 

habitat in the northern Bering and Chukchi 

Seas (Rice and Wolman, 1971; Berzin, 

1984; Nerini, 1984) to its winter calving 

habitat along the west coast of Baja California, Mexico (Rice et al,. 1984). The fall 

(southbound) migration begins in November-December (Rugh et al., 2001) and the 

spring (northbound) migration occurs from 

mid-February through May (Rice et al., 

1981, 1984; Poole, 1984). The most recent 

estimate of the size of the Eastern North 

Pacific gray whale stock based on systematic counts of migrating (southbound) 

whales by shore-based observers at Granite Canyon, CA in 1997-98 is 26,635 individuals (Angliss and Lodge, 2002). There 

is evidence of a generally positive trend in 

gray whale abundance since 1992-1993. 

The gray whale was removed from the endangered species list in 1994.

Sightings of gray whales from the SWFSC aerial surveys and the CDAS surveys (Figure 6.1.12) reflect the broad 

nearshore distribution of this species during its migration through California waters. Gray whale sightings occur 

in nearshore waters throughout the Southern California Bight, including the Santa Barbara Channel, and near 

the Channel Islands. The cluster of sightings around San Clemente Island is probably more reflective of survey 

effort than a particular preference for this location, although San Clemente Island lies along one of the gray 

whales’ migratory routes through the Southern California Bight. Because of the uneven distribution of survey effort, the pattern of sightings should be used only as confirmation that gray whales do exist in a broad area; the 

absence of sightings may reflect insufficient survey effort rather than real absence from the area. Quantitative 

comparison of the different boundary concepts was not possible due to the lack of sightings during the SWFSC 

ship surveys, which do not take place during the gray whale migration season. 

Humpback whale (Megaptera novaeangliae) 

Evidence from survey data and genetic analyses supports the division of humpback whales into three populations within U.S. Pacific waters (Carretta et al., 2002), one of which migrates from coastal Central America and 

Mexico to the west coast of the U.S. and into British Columbia during the summer and fall (Steiger et al., 1991; 

Calambokidis et al., 1993). This population, referred to as the Eastern North Pacific stock, passes through the 

Study Area during its summer and fall migration. The most recent abundance estimate for this stock based on a 

1998-2000 mark-recapture survey (Calambokidis et al., 2001) was 856 individuals and a modest upward trend 

in abundance since 1990 is apparent (Carretta et al., 2002). Humpback whale is a federally listed endangered 

species.

Sightings of humpback whales from the SWFSC ship surveys and the CDAS surveys (Figure 6.1.13) occur 

most frequently in shelf waters to the north of Point Conception. Scattered sightings also occur in the Southern 

California Bight (including several in the Santa Barbara Channel) and in offshore waters. Because of the uneven 

distribution of survey effort, the pattern of sightings should be used only as confirmation that humpback whales 

do exist in a given area; the absence of sightings may reflect insufficient survey effort rather than real absence 

from the area. 

Figure 6.1.12. Gray whale. Sightings and group size (where available) from the 

Southwest Fisheries Science Center (SWFSC) aerial surveys conducted near 

San Nicolas (1992-1993) and San Clemente (1998-2003) islands and the seven 

surveys of marine mammals compiled in the Computer Database Analysis System (CDAS) v2.1, 1975-1997.
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Estimates of the summer and fall abundance of humpback whales within the 

NAC, the six boundary concepts, and the 

Study Area were derived from the 1991-

2001 SWFSC ship surveys described 

above and are summarized in Table 6.1.7. 

Because some of the sightings recorded 

as “Unidentified Large Whale” (including 

one that fell in Concepts 1 and 1a) were 

likely to be humpback whales (Carretta et 

al., 2002), abundance estimates in Concepts 1 and 1a may be negatively biased. 

Very small numbers of on-effort sightings 

(0-4) make the density and abundance 

estimates for this species extremely uncertain. This uncertainty is reflected in the 

wide and overlapping confidence intervals. The wide intervals show that abundance can not be estimated precisely, and 

the overlap indicates that the differences 

in estimated abundance among concepts 

are not likely to be statistically significant. 

No on-effort sightings were recorded within the NAC and only 1 on-effort sighting 

was recorded in Concepts 3-5, resulting in 

abundance estimates of approximately 10 

individuals for these three concepts. Four on-effort sightings occurred in Concepts 1, 1a, 2, and the Study Area 

resulting in abundance estimates of approximately 50 individuals for these four areas. Because no on-effort 

sightings were recorded in the NAC, it was not possible to calculate the OAI for humpback whales. It is important 

to remember, however, that humpback whales aggregate in areas where their prey (krill and small schooling fish) 

are concentrated. Any boundary concept could, therefore, contain a larger number of humpback whales during 

times when prey densities are high. 

Killer whale (Orcinus orca)

Relatively little is known about the killer whales found in California waters compared to the well-studied populations of Alaska and the Pacific Northwest. Nevertheless, four separate types of killer whales have been identified 

and regularly sighted in California. These groups differ in their behavior, genetics, distribution, coloration and preferred prey (Ford and Fisher, 1982; Baird and Stacey, 1988; Baird et al., 1992; Hoelzel et al. 1998). Three of the 

four types found in California waters (the so-called ‘resident’, ‘transient’, and ‘offshore’ types) were first identified 

and characterized in the eastern North Pacific. The fourth (the “LA pod”) has only been recorded off southern and 

central California and off Baja California, Mexico. The killer whale is not federally listed as threatened or endanFigure 6.1.13. Humpback whale. Sightings and group size (where available) from 

the Southwest Fisheries Science Center (SWFSC) ship surveys 1991-2001 and 

the seven surveys of marine mammals compiled in the Computer Database Analysis System (CDAS) v2.1, 1975-1997.

Concept

 Area

(km2) Sightings

Estimated

Density

Estimated

Abundance CV

Lower 95%

CI

Upper 95%

CI

NAC 3745 0 0 0 - - -

5 4536 1 0.00234 11 1 2 56

4 7981 1 0.00131 10 1 2 51

3 9044 1 0.00118 11 1 2 56

2 13736 4 0.00375 52 2.33 4 754

1a 22591 4 0.0023 51 0.91 11 234

1 22613 4 0.00226 51 0.91 11 234

SA 17093 4 0.0031 53 0.82 13 216

Table 6.1.7. Humpback whale. Sightings, estimated density and abundance, coefficient of variation (CV) and upper 

and lower 95% confidence limits for the abundance estimate for the six boundary concepts, the No Action Concept 

(NAC), and the Study Area (SA). SIghtings and density values in bold reflect increases from the NAC.
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gered; however, the Eastern North Pacific Southern Resident stock of killer whales, found primarily in the Pacific 

Northwest but occasionally seen off California, was listed as “depleted” under the Marine Mammal Protection 

Act in May 2003 and was designated as “endangered” by the State of Washington in April 2004. Furthermore, 

in November 2001, this stock of killer whale was listed as endangered by Canada’s Committee on the Status of 

Endangered Wildlife in Canada (COSEWIC).

Resident-type killer whales have primarily been sighted from the Aleutian Islands south to Puget Sound, although there have been sightings of members of two resident pods as far south as Monterey Bay (N. Black, 

pers. comm.; Carretta et al., 2002). No sightings of this type have been recorded in southern California. The most 

recent estimate of the size of the resident killer whale population in southern British Columbia, Canada through 

central California based on direct counts of identified individuals is 82 individuals (Carretta et al., 2002). 

Transient-type whales are unpredictable in their seasonal movements and travel throughout an extensive range 

with some individuals recorded in both central California and Southeast Alaska (Goley and Straley, 1994). Transients are the most frequently spotted type of killer whale off of central California (Black et al., 1997). They specialize on hunting marine mammals including seals and sea lions as well as large whales (such as gray whales) 

and their calves during seasonal whale migrations. The most recent estimate of the size of the Eastern North 

Pacific Transient stock of killer whales is a minimum of 346 individuals (Angliss and Lodge, 2002), of which 105 

individuals have been identified in California (Black et al., 1997). 

Offshore-type killer whales, first identified as a separate group off western Vancouver Island, Canada in the 

1980’s, are less well studied than residents and transients. The first offshore-type individuals in California were 

identified from photos taken in 1993 off of Point Conception, however, they may have been present in this area 

since the mid-1980s (Black et al., 1997). More recently, this type has been documented off Los Angeles and in 

Monterey Bay (Black et al., 1997). The offshore-type travels in larger groups, is more vocal than transient-types, 

and has not been observed feeding on marine mammals. The most recent estimate of the size of the offshoretype killer whale population in Washington, Oregon, and California based on the 1991-1996 SWFSC ship surveys is 285 individuals (Carretta et al., 2002). This is considered a conservative estimate. 

The “LA Pod,” named for the location where they were commonly observed during the 1980s, appears to be a 

distinct type that occurs primarily off Baja California, Mexico, but occasionally found off southern or central California. Members of this group were first photographed in 1982 and have been spotted from about San Francisco 

south to the Sea of Cortez, Mexico. They have never been observed feeding on marine mammals (Black et al., 

1997).

Few sightings of killer whales were recorded in the SWFSC ship surveys and the CDAS surveys (Figure 6.1.14). 

Scattered sightings occur along the shelf and slope (with a few offshore sightings) north of Point Conception. 

Only two sightings exist in the Southern California Bight, one near Santa Barbara and one off San Diego. Because of the uneven distribution of survey effort, the pattern of sightings should be used only as confirmation that 

killer whales do exist in the broad area surveyed; the absence of sightings may reflect insufficient survey effort 

rather than real absence from the area. 

Because so little distributional information or survey sightings exist for killer whales in the Study Area, it is difficult 

to evaluate the potential impacts of different boundary concepts. Concepts that have the potential to protect killer 

whales’ prey species, including marine mammals such as gray whales and pinnipeds, as well as a variety of fish 

and cephalopod species, may provide indirect benefits to killer whales as well. 

Pacific white-sided dolphin (Lagenorhynchus obliquidens)

Pacific white-sided dolphins are found throughout the temperate waters of the North Pacific, with most sightings 

in California waters occurring over the shelf and slope. Two forms of this species occur off California: a northern 

form ranging from the Southern California Bight north to Alaska, and a southern form found from Baja California, Mexico north to approximately 36°N (Carretta et al., 2002). Although both forms are found in the Southern 

California Bight, genetic (Lux et al., 1997) and morphological (Walker et al., 1986; Chivers et al., 1993) differences indicate little mixing. They are treated as one stock for management purposes, because the two forms are 

indistinguishable in the field. Seasonal and interannual movements along the U.S. West Coast have been documented, with greater numbers of Pacific white-sided dolphins found in California waters during cool-water peri-
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ods, such as the winter months (Green et 

al., 1992; Forney, 1994). Within California, the abundance of Pacific white-sided 

dolphins can vary seasonally by an order 

of magnitude, and they are considerably 

more common in the Southern California 

Bight during winter (Forney and Barlow, 

1998). The most recent stock assessment 

(Carretta et al., 2002) estimates a population size of 25,825 individuals along the 

U.S. west coast, based on the 1991-1996 

SWFSC ship surveys. Pacific white-sided 

dolphin is not listed as a threatened or endangered species.

Pacific white-sided dolphins were frequently sighted during the CDAS surveys and 

occasionally recorded during the SWFSC 

ship surveys (Figure 6.1.15). Many sightings occurred along the shelf and slope 

(with a few offshore sightings) throughout 

central and southern California. Sightings 

were also scattered throughout the Southern California Bight. Relatively few sightings of this species were recorded during 

the SWFSC ship surveys because of the 

previously mentioned seasonal changes 

in abundance. Because of the uneven 

distribution of survey effort, the pattern of 

sightings should be used only as confirmation that Pacific white-sided dolphins 

do exist in a given area; the absence of 

sightings may reflect insufficient survey 

effort rather than real absence from the 

area. Quantitative comparison of the different boundary concepts was not possible for Pacific white-sided dolphins due to 

the lack of adequate numbers of sightings 

in the summer/fall SWFSC ship surveys.

Risso’s dolphin (Grampus griseus) 

Within U.S. Pacific waters, Risso’s dolphin 

are divided into two stocks, a Hawaiian 

stock, and a California/Oregon/Washington stock. Green et al. (1992) suggest that 

Risso’s dolphin in California move northward into Oregon and Washington in late 

spring and summer. The southern end of 

this stock’s range appears to occur somewhere along the coast of Baja California, 

Mexico, with a large gap between this 

stock and Risso’s dolphins found in equatorial waters. Although Risso’s dolphin are generally found in slope and offshore waters in Washington, Oregon, 

and northern California, they are also found in large numbers in shelf waters off southern and central California 

(Carretta et al., 2002). The most recent abundance estimate for the California/Oregon/Washington stock, based 

on data from the 1991-1996 SWFSC ship surveys (Barlow, 1997), is 16,483 individuals (Carretta et al., 2002). 

The distribution of Risso’s dolphin is highly variable, however, and seasonal and interannual shifts are common 

Figure 6.1.14. Killer whale. Sightings and group size (where available) from the 

Southwest Fisheries Science Center (SWFSC) ship surveys 1991-2001 and the 

seven surveys of marine mammals compiled in the Computer Database Analysis 

System (CDAS) v2.1, 1975-1997.

Figure 6.1.15. Pacific white-sided dolphin. Sightings and group size (where available) from the Southwest Fisheries Science Center (SWFSC) ship surveys 1991-

2001 and the seven surveys of marine mammals compiled in the Computer Database Analysis System (CDAS) v2.1, 1975-1997.
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(Forney and Barlow, 1998). Risso’s dolphin 

is not considered a threatened or endangered species.

Risso’s dolphins were frequently sighted 

during the SWFSC ship surveys and the 

CDAS surveys (Figure 6.1.16). Many sightings occur along the shelf and slope (with a 

few offshore sightings) throughout central 

and southern California. Sightings are also 

scattered throughout the Southern California Bight with clusters of sightings at both 

the western and eastern ends of the Santa 

Barbara Channel, but relatively few in the 

Santa Barbara Channel itself. Because of 

the uneven distribution of survey effort, the 

pattern of sightings should be used only as 

confirmation that Risso’s dolphins do exist in 

a given area. The absence of sightings may 

reflect insufficient survey effort rather real 

absence from the area. 

Estimates of the summer and fall abundance 

of Risso’s dolphin within the NAC, the five 

boundary concepts, and the Study Area 

were derived from the 1991-2001 SWFSC 

ship surveys described above and are summarized in Table 6.1.8. Because of the relatively small number of on-effort 

sightings (4-21) and the uncertainty in the line transect input parameters, confidence intervals for the abundance estimates are wide and overlap substantially among different concepts. The wide intervals show that abundance cannot 

be estimated precisely, and the overlap indicates that the differences in estimated abundance among concepts are not 

likely to be statistically significant. Estimated Risso’s dolphin density is highest in Concept 3, and estimated abundance 

is highest in Concept 1 and the Study Area. Notable increases in estimated abundance relative to that of the NAC are 

apparent with each increase in concept size, with the exception of Concept 5, which shows only a 7% increase. Estimated abundance in Concept 4, for example, is more than twice as great as in the NAC, and the next largest boundary, 

Concept 3, has an estimated abundance approximately three times higher than the NAC. The OAI shows that, of the 

proposed boundary concepts, Concept 3 provides the greatest relative increase in both density and abundance for the 

smallest relative increase in area. Overall, the OAI is highest for the Study Area. Concept Area (km2

)

SightingsEstmatedDensityEstimatedAbundanceCVLower 95% CIUpper 95% CI∆ Area (%)∆ Density (%)∆ Abundance (%)Density OAI(relative)AbundanceOAI(absolute)

NAC 3745 4 0.12831 481 0.54 178 1296 - - - - -

5 4536 4 0.11313 513 0.54 190 1383 21.12 -11.83 6.65 -0.56 0.315

4 7981 10 0.12535 1000 0.46 424 2360 113.11 -2.31 107.90 -0.02 0.954

3 9044 12 0.16215 1466 0.46 621 3460 141.50 26.37 207.78 0.186 1.447

2 13736 13 0.13464 1849 0.44 811 4217 266.78 4.93 284.41 0.018 1.066

1a 22591 21 0.12975 2931 0.45 1263 6801 503.23 1.12 509.39 0.002 1.012

1 22613 21 0.12975 2934 0.45 1265 6808 503.82 1.12 509.98 0.002 1.012

SA 17093 21 0.1788 3056 0.42 1387 6734 356.42 39.33 535.34 0.11 1.502

Table 6.1.8. Risso’s dolphin. Sightings, estimated density and abundance, coefficient of variation (CV) and upper and lower 

95% confidence limits for the abundance estimate; and the Optimal Area Index (OAI) for the six boundary concepts, the 

No Action Concept (NAC), and the Study Area (SA). Abundance and density values in bold reflect increases from the NAC 

and shaded OAI values represent maximum observed benefit.

Figure 6.1.16. Risso’s dolphin. Sightings and group size (where available) from 

the Southwest Fisheries Science Center (SWFSC) ship surveys 1991-2001 and 

the seven surveys of marine mammals compiled in the Computer Database Analysis System (CDAS) v2.1, 1975-1997.
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Additional Cetaceans

In addition to the requested species, several other species of cetaceans are known to occur within the region, including 

Dall’s porpoise, fin whale, harbor porpoise, minke whale, and northern right-whale dolphins. Although no quantitative 

analysis was conducted for these species, it is important to recognize that they may be impacted by changes to the 

boundaries of the CINMS and further investigation into these species may be warranted. While a complete analysis 

of the biogeography of these additional species is beyond the scope of this project, distribution maps and a brief discussion for each of these species is included here. Several species of beaked whales have also been recorded in the 

Study Area; however, little is known about 

the distribution of these poorly studied cetaceans.

Dall’s porpoise (Phocoenoides dalli)

Dall’s porpoise are found throughout the 

temperate shelf, slope, and offshore waters 

of the U.S. West Coast where they exhibit 

seasonal and interannual movements that 

appear to be related to changes in oceanographic conditions (Forney et al., 1995). 

They are most abundant off southern California in the winter. The California/Oregon/

Washington stock size was estimated in the 

most recent stock assessment report (Carretta et al., 2002) at 116,016 individuals 

based on the 1991-1996 SWFSC ship surveys (Barlow 1997), with an estimated 1,500 

additional individuals in Washington inland 

waters (Calambokidis et al., 1997). Dall’s 

porpoise is not a federally listed endangered 

or threatened species.

Dall’s porpoise were commonly sighted in 

shelf waters throughout central and southern California during the SWFSC ship surveys and the CDAS surveys (Figure 6.1.17). Many sightings were recorded in the Santa Barbara Channel, off Point 

Conception, and just south of Santa Cruz and Anacapa Islands. Because of the uneven distribution of survey effort, 

the pattern of sightings should be used only as confirmation that Dall’s porpoise do exist in a given area; the absence 

of sightings may reflect insufficient survey effort rather than real absence from the area. Larger concepts are likely to 

encompass greater numbers of this widely distributed cetacean. 

Fin whale (Balaenoptera physalus)

Although three fin whale stocks are recognized in U.S. North Pacific waters, little is known about the population structure of this species. Year round aggregations of fin whales have been recorded in central and southern California with 

lower abundance in California waters during the winter and spring (Dohl et al., 1983; Forney et al., 1995). The California/Oregon/Washington stock size was estimated in the most recent stock assessment report (Carretta et al., 2002) at 

1,851 individuals based on the 1993 and1996 SWFSC ship surveys (Barlow and Taylor, 2001); however, this is thought 

to be a slight underestimate because not all fin whales could be identified to species in the field. Fin whale is a federally 

listed endangered species.

Fin whales have been sighted in shelf, slope, and offshore waters throughout central and southern California during 

the SWFSC ship surveys and the CDAS surveys (Figure 6.1.18). Within the Southern California Bight only one sighting 

was recorded in the Santa Barbara Channel and scattered sightings occurred to the south of the CINMS. Because of 

the uneven distribution of survey effort, the pattern of sightings should be used only as confirmation that fin whale do 

exist in a given area; the absence of sightings may reflect insufficient survey effort rather than real absence from the 

area. Concepts 1, 1a, and 2 encompass a cluster of sightings on the shelf waters to the northwest of the CINMS, 

Figure 6.1.17. Dall’s porpoise. Sightings and group size (where available) from 

the Southwest Fisheries Science Center (SWFSC) ship surveys 1991-2001 and 

the seven surveys of marine mammals compiled in the Computer Database Analysis System (CDAS) v2.1, 1975-1997.
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with Concepts 1 and 1a encompassing 

several additional sightings in slope and 

offshore waters.

Harbor porpoise (Phocoena phocoena)

Along the west coast of the U.S., harbor 

porpoise are found in coastal waters from 

Alaska south to Point Conception. Harbor 

porpoise on the tend to form geographically and genetically distinct sub-populations 

with little mixing or movement among them 

(Chivers et al., 2002). A Morro Bay stock 

of harbor porpoise is one of four stocks 

identified in California waters by the most 

recent stock assessment report (Carretta 

et al., 2002). The Morro Bay stock ranges 

from about Point Sur to Point Conception, 

although the northern boundary which divides the Morro Bay stock from the Monterey Bay stock is uncertain because of 

a lack of genetic samples in this region. 

The most recent estimate of the size of the 

Morro Bay stock based on a 1997-1999 

aerial survey is 932 individuals (Carretta et 

al., 2002). Harbor porpoise is not federally 

listed as threatened or endangered. 

Harbor porpoise were commonly sighted 

in nearshore and shelf waters north of 

Point Conception during the SWFSC ship 

surveys and the CDAS surveys (Figure 

6.1.19). Because of the uneven distribution of survey effort, the pattern of sightings should be used only as confirmation 

that harbor porpoise do exist in a given 

area; the absence of sightings may reflect 

insufficient survey effort rather than real 

absence from the area. Concepts 1, 1a, 

and 2 (and a small portion of Concept 3) 

as well as the Study Area, extend north 

of Point Conception and may include an 

unknown number of individuals from the 

Morro Bay stock. 

Minke whale (Balaenoptera acutorostrata)

Two minke whale stocks are recognized 

in U.S. North Pacific waters, an Alaskan 

stock that is believed to be migratory, and 

a California/Oregon/Washington stock. In 

California, minke whales are present yearround (Dohl et al., 1983; Forney et al., 

1995; Barlow, 1997) and some individuals are thought to establish home ranges 

(Dorsey et al., 1990). The California/Oregon/Washington stock size was estimated in the most recent stock assessment report (Carretta et al., 2002) at 631 individuals based on the 1991-1996 SWFSC ship surveys (Barlow 

1997). Minke whale is not federally listed as threatened or endangered.

Figure 6.1.19. Harbor porpoise. Sightings and group size (where available) from 

the Southwest Fisheries Science Center (SWFSC) ship surveys 1991-2001 and 

the seven surveys of marine mammals compiled in the Computer Database Analysis System (CDAS) v2.1, 1975-1997.

Figure 6.1.18. Fin whale. Sightings and group size (where available) from the 

Southwest Fisheries Science Center (SWFSC) ship surveys 1991-2001 and the 

seven surveys of marine mammals compiled in the Computer Database Analysis 

System (CDAS) v2.1, 1975-1997.
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Although scattered sightings of minke 

whales have been recorded in shelf, 

slope, and offshore waters off central California during the SWFSC ship surveys 

and the CDAS surveys (Figure 6.1.20), 

the bulk of sightings from the CDAS surveys occurred in the Southern California 

Bight, with a cluster of sightings around 

the northern Channel Islands. Because 

of the uneven distribution of survey effort, 

the pattern of sightings should be used 

only as confirmation that minke whales 

do exist in a given area; the absence of 

sightings may reflect insufficient survey 

effort rather than real absence from the 

area. The cluster of sightings around the 

northern Channel Islands is encompassed 

by all of the concepts including the NAC. 

Concepts that extend to the south and 

connect the northern Channel Islands to 

Santa Barbara Island appear likely to encompass more minke whales.

Northern right-whale dolphin

(Lissodelphis borealis)

Along the west coast of the U.S., northern right-whale dolphins are found primarily in temperate shelf and slope 

waters. Abundance of this species in California waters is greatest during cold-water months (Forney et al., 1995), 

and they are thought to range south to Baja California, Mexico during cold periods. Northern right-whale dolphins 

in U.S. west coast waters are considered a single California/Oregon/Washington stock due to insufficient genetic 

evidence of subpopulations (Dizon et al., 1994). The size of this stock was estimated in the most recent stock 

assessment report (Carretta et al., 2002) at 13,705 individuals based on the 1991-1996 SWFSC ship surveys 

(Barlow 1997). Northern right whale dolphin is not considered threatened or endangered.

Northern right-whale dolphins were frequently sighted in shelf and slope waters throughout central and southern 

California during the SWFSC ship surveys and the CDAS surveys (Figure 6.1.21). Although sightings are recorded throughout the Southern California Bight, relatively few were recorded in the Santa Barbara Channel. Because of the uneven distribution of survey effort, the pattern of sightings should be used only as confirmation that 

northern right-whale dolphins do exist in a given area; the absence of sightings may reflect insufficient survey 

effort rather than real absence from the area. Concepts 1 and 1a, and, to a lesser extent Concept 2, encompass 

a cluster of sightings along the shelf and slope which are not encompassed by any of the smaller concepts. 

Summary

• Marine mammal distributions within the region exhibit pronounced geographical heterogeneity with the consequence that the impact of extending the CINMS boundaries will vary depending on the specific areas added to 

the sanctuary. In general, 

• Concepts that include the mainland will increase coastal bottlenose dolphin and both species of common 

dolphin sightings. 

• Concepts that extend offshore will include a greater number of blue, fin, and humpback whales.

• Concepts that include waters between the northern Channel Islands and Santa Barbara Island added sightings for northern right-whale dolphins, offshore bottlenose dolphins, and Risso’s dolphin.

• Concepts that extend north of Point Conception would add harbor porpoise and increase the abundance 

Figure 6.1.20. Minke whale. Sightings and group size (where available) from the 

Southwest Fisheries Science Center (SWFSC) ship surveys 1991-2001 and the 

seven surveys of marine mammals compiled in the Computer Database Analysis 

System (CDAS) v2.1, 1975-1997.
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

of temperate cetacean species, such as 

Pacific white-sided dolphin and Dall’s 

porpoise.

• Of those species for which abundance 

could be estimated (blue whale, bottlenose 

dolphin, long-beaked and short-beaked 

common dolphin, humpback whale, and 

Risso’s dolphin), Concepts 1 and 1a provide the greatest estimated abundance 

within their boundaries, though density is 

often higher in the smaller concepts. This 

could be a sampling artifact, or indicate 

that the smaller areas contain a greater 

proportion of appropriate habitat for these 

species.

• Although their populations could not be 

estimated quantitatively, killer whales and 

gray whales are known to use the waters 

around and within the CINMS as feeding 

and migratory habitat, respectively.

• The abundance estimates presented here 

are intended as approximate guidelines 

only, because the surveys on which they 

are based were designed for other purposes and, therefore, do not provide data at the most appropriate temporal and spatial scales for an examination of boundary concepts. Furthermore, while the abundances presented 

here may be considered estimates of the average number of animals that may be found, in fact, it is likely that 

considerably larger aggregations may occur at times, particularly for feeding blue and humpback whales. This is 

true as well for dolphins that occur in groups of hundreds or thousands, or are known to exhibit large seasonal 

and interannual changes in distribution.

6.2 Pinnipeds and Southern Sea Otter

(Portions of this section are reprinted with permission from McGinnis (2000)).

Pinnipedia (seals, sea lions and fur seals)

Historically, six species of pinnipeds have occurred in the region of interest. These include four members of the 

family Otaridae and two representatives of the family Phocidae. In addition, a single sighting of one more phocid species has been reported in southern California. Two of the six species are listed as threatened under the 

Endangered Species Act (ESA).

Of the four otarid seals, the California sea lion (Zalophus californianus c.) is unquestionably the most abundant 

(Barlow et al., 1997). The Steller sea lion (Eumetopias jubatus) had two rookeries on San Miguel Island, but 

these rookeries have not been occupied since the 1982-1983 El Niño. The Steller sea lion is listed as threatened 

under the ESA. The northern fur seal (Callorhinus ursinus) has two rookeries on San Miguel Island. The Guadalupe fur seal (Arctocephalus townsendi) has been reported on San Nicolas and San Miguel Islands in very small 

numbers, usually from one to three individuals. A few strandings have occurred along the mainland coast (Hanni 

et al. 1997; Santa Barbara Marine Mammal Center, unpublished records). The Guadalupe fur seal is listed as 

threatened under the ESA.

Of the two species of phocid seals, the northern elephant seal (Mirounga angustirostris) is by far the most common, with rookeries at San Miguel, Santa Rosa, San Nicolas, and Santa Barbara Islands (Barlow et al., 1997). 

The Pacific harbor seal (Phoca vitulina richardsi) is common throughout the region, with numerous haulout and 

rookery sites throughout the Channel Islands and along the mainland coast (Barlow et al., 1997). The ribbon seal 

(Histriophoca fasciata), an Arctic species, is rare in California (Woodhouse, 2000).

Figure 6.1.21. Northern right-whale dolphin. Sightings and group size (where 

available) from the Southwest Fisheries Science Center (SWFSC) ship surveys 

1991-2001 and the seven surveys of marine mammals compiled in the Computer 

Database Analysis System (CDAS) v2.1, 1975-1997. 
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Carnivora

The southern sea otter (Enhydra lutris nereis), a member of the mustelid family (which includes weasels), is the 

only marine representative of the order Carnivora. The southern sea otter is listed as threatened under the ESA. 

It has undergone drastic population changes, from an estimated pre-exploitation population of 16,000 individuals 

in California (Laidre et al., 2001), to near extinction in the early 1900s, to a current California population of around 

2,300 individuals. The current population in the Channel Islands is considered an experimental population and 

investigations are currently underway to determine whether the translocation project at San Nicolas Island has 

been successful (USFWS, 2003).

Data and Methods

Surveys used in this chapter are summarized in Table 6.2.1. Counts of the four consistently sighted pinnipeds 

are presented for rookery and haulout sites in the Channel Islands and the Southern California mainland. At-sea 

distributions of pinnipeds and sea otter are difficult to estimate because they are not reliably sampled by the 

available at-sea visual surveys which mainly target either birds or cetaceans. At-sea distributions of pinnipeds 

have been estimated in previous biogeographic assessments, but at a scale (10 minutes of latitude by 10 minutes of longitude) that limits their usefulness for the current assessment of CINMS boundary concepts. The lack 

of at-sea distribution data means that conclusions in this section of the report reflect the distribution of haulout 

and rookery areas only. The waters nearest the high use rookery and haulout areas are clearly important, but 

pinnipeds also forage far from these sites. Sea otters are more closely associated with nearshore habitats, but 

are known to migrate considerable distances (Wendell et al., 1984).

California sea lion (Zalophus californianus californianus)

Sea lion data presented in this section are derived from aerial photo surveys conducted by the Southwest Fisheries Science Center (SWFSC) from 2001-2003. The surveys are conducted in July to coincide with the end of 

the pupping season and include all major rookeries and haulout sites. These survey results form the basis of 

SWFSC’s stock assessment of California sea lion (Carretta et al., 2002). Counts at each location are total number of individuals (all sex and age categories) and total pups. Sightings should be considered minimum estimates 

of overall numbers since an unknown fraction of the population is at sea at any given time, and some pups may 

have already left the rookery. Data are georeferenced by beach codes which correspond to shoreline segments 

of varying length.

Pacific harbor seal (Phoca vitulina richardsi)

Harbor seal data presented in this section are comprised of a SWFSC aerial photo survey and an aerial photo 

and ground survey conducted by the California Department of Fish and Game (CDFG). Despite the fact that the 

two surveys occurred during the same time period, late May through mid-June 2002, counts for the Channel 

Islands differed by more than a factor of 2 between the two surveys, with 1,735 harbor seals counted at 61 sites 

Survey Dates Platform Months Location

Total Unique

Survey Sites

Total

Individuals

NOAA, Southwest Fisheries 

Science Center,

California Sea Lion Survey

2001-2003 Aerial Photo July Channel Islands

(8 islands) 127 230788

NOAA, Southwest Fisheries 

Science Center, Harbor Seal 

Survey

2002 Aerial Photo May-June

California Coast

(south of Pt. Sal

and Channel 

Islands

160 5271

California Dept. of Fish and 

Game, Harbor Seal Survey 2002 Aerial Photo/Grount May-July

Californa Coast

and Channel 

Islands

NA 18784

NOAA, Southwest Fisheries 

Science Center, Northern 

Elephant Seal Survey

1998, 

2000-2001 Aerial Photo JanuaryFebruary

Channel Islands

(5 islands) 121 116548

Sea Otter Survey

(Multi-agency 2001-2002 Aerial Photo/Ground

November 

and May

California Coast

(Pt. Montara to

Santa Barbara)

Fall-1150

Spring-1061

Fall-2012

Spring-2139

Table 6.2.1. Summary of pinniped and sea otter surveys used in this chapter.
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in the CDFG survey and 3,878 counted at 144 sites in the SWFSC survey. Sweetnam and Read (2002) attribute 

the differences to the time of day and tidal state. Results from both surveys are presented to give an idea of 

intra-annual variability and population estimate uncertainty. Both surveys are georeferenced by a single latitude 

and longitude point for each site.

Northern elephant seal (Mirounga angustirostris)

Elephant seal survey data are extracted from an aerial photo survey (with the exception of San Clemente Island 

which was surveyed on the ground) of rookeries conducted by SWFSC in January and February 2001. Data are 

presented as the total number of individuals (all sex and age categories) and total number of pups (including live 

and dead pups). Stock assessments for elephant seals derive estimated population size by multiplying total pups 

counted at rookeries by the ratio of total individuals to pups because all age classes are not ashore at the same 

time (Carretta et al., 2002). Data are georeferenced by beach codes as for California sea lion (see above).

Northern fur seal (Callorhinus ursinus)

No georeferenced northern fur seal rookery data were available for this report. Two separate stocks of northern 

fur seal are recognized in U.S. waters: an Eastern Pacific stock and a San Miguel Island stock (Carretta et al., 

2002). The San Miguel Island stock was established in the late 1950s or early 1960s (DeLong, 1982) and has 

generally increased since the first live pup counts in 1972. El Niño events are associated with both adult female 

and pup mortality and have had dramatic impacts on the population in 1982-1983 and 1997-1998. The most recent assessment of the San Miguel Island stock (2002 survey) estimates the population size at 7,784 individuals, 

and suggests continued rebuilding of the stock since the 1997-1998 El Niño event. Beginning in 1996, fur seals 

re-established a small breeding population on the South Farallon Islands, with fewer than 10 pups produced 

each year from 1997-2001 (Pyle et al. 2001).

Southern sea otter (Enhydra lutris nereis)

Sea otter data presented in this section are gathered from land-based surveys conducted by CDFG, USGSBiological Resources Division, and the Monterey Bay Aquarium in November 2001 and May 2002. Sightings 

are georeferenced by 500 m shoreline segment, and were summarized by 20 km shoreline segment for clearer 

display.

Broad-scale Patterns and Analysis of Boundary Concepts 

California sea lion

In order to account for interannual differences in survey effort (i.e., not all beach areas were surveyed in all years) 

and beach use, summary maps depict maximum recent usage calculated as the maximum number of individuals 

or pups counted at a beach area in the three most recent surveys. Although California sea lions do frequently 

haul out on man made objects such as barges and piers on the mainland, there are no known natural haulout 

areas or rookeries on the mainland (M. Lowry, pers. comm.). 

Major haulout areas (top two quintiles, 620-12,760 individuals at maximum recent use) for California sea lions 

exist on East Anacapa, San Clemente, San Miguel, San Nicolas, and Santa Barbara Islands (Figure 6.2.1). Additional less populated haulout areas (less than 578 individuals at maximum recent use) exist on Santa Catalina 

and Santa Rosa Islands, and on Gull Island near Santa Cruz Island. 

Major rookery areas (top two quintiles, 451-6,668 pups at maximum recent usage) exist on San Clemente, San 

Miguel, San Nicolas, and Santa Barbara Islands (Figure 6.2.2). Additional less-populated rookery areas (less 

than 373 pups at maximum recent usage) exist on East Anacapa and on Gull Island near Santa Cruz Island. 

No sea lion pups were counted on Santa Catalina or Santa Rosa Islands. There are no differences among the 

boundary concepts in terms of the number of sea lion haulout or rookery sites encompassed. However, this data 

cannot account for differences which may exist in the at-sea abundance of California sea lions within the different concepts.
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Figure 6.2.1. California sea lion. Maximum total individuals (greatest number of individuals of all sex and 

age classes counted for each beach area surveyed out of the three most recent SWFSC surveys 2001-

2003) for (left to right) Anacapa, San Clemente, San Miguel, San Nicolas, Santa Barbara, Santa Catalina, 

Santa Cruz, and Santa Rosa Islands.
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

119°25'0"W

119°25'0"W

34°0'N34°0'N

0 0.5 1 1.5 2

Kilometers

SWFSC Pinniped Survey California

}

Haulout Area

1 - 39

40 - 249

250 - 450

451 - 775

776 - 6668

California Sea Lion 2001-2003

(Maximum Total Individuals)

Figure 6.2.2. California sea lion. Maximum pups (greatest number of pups counted for each beach area 

surveyed out of the three most recent SWFSC surveys 2001-2003) for (left to right) Anacapa, San Clemente, San Miguel, San Nicolas, Santa Barbara, and Gull Islands.

Pacific harbor seal

Counts of Pacific harbor seals at haulout areas are shown for the SWFSC survey (Figure 6.2.3) and the CDFG 

survey (Figure 6.2.4). Overlap in coverage between flights in the CDFG survey resulted in some haulout areas 

being surveyed more than once. Therefore separate symbols are used to depict the different flights.

Major haulout areas (more than 20 individuals) for harbor seal exist on all of the Channel Islands other than 

Santa Barbara Island, which includes only one (CDFG survey) or two (SWFSC survey) haulout areas, each with 

fewer than 10 individuals. Other major haulout areas exist along the mainland coast near the Channel Islands, 

including notable sites at Point Conception, Point Arguello and Point Sal.

Many of the most populated harbor seal haulout areas are located on the northern Channel Islands within the 

current boundaries of the CINMS. A total of 56%, according to the SWFSC survey, or 33%, according to the 

CDFG survey of the harbor seal population south of Pt. Sal surveyed in 2002 is within the current CINMS boundaries. While Concepts 4 and 5 may offer greater potential protection for harbor seals at sea, these options do 

not include any additional haulout areas that are not currently within the sanctuary. Concept 3 encompasses the 
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haulout area at Pt. Conception; Concept 2 

further incorporates the haulout areas at 

Pts. Arguello, Purisima, and Sal, and one 

site just west of Santa Barbara; and Concept 1 includes all of these haulout areas, 

plus one additional site near Point Hueneme. The CDFG data shows an additional 

site located between Santa Barbara and 

Ventura that is included only in Concept 1. 

The OAI calculations (Table 6.2.2) convey 

that Concepts 2 (based on the SWFSC 

data) or 3 (based on CDFG data) offer the 

greatest relative increase in harbor seal 

abundance for the smallest relative increase in area.

Northern elephant seal

Northern elephant seal summary maps are 

presented in the same manner as those 

described for California sea lion, in that 

they depict maximum recent usage calculated as the maximum number of individuals or pups counted at a beach area out of 

the three most recent surveys. The haulout 

and rookery areas for northern elephant 

seal represent all known consistently used 

locations in southern California (M. Lowry, 

pers.comm.). The results of these surveys 

conducted since the early to mid 1980s 

(depending on the island) show that San 

Miguel Island is consistently the largest elephant seal rookery in the Southern California Bight (Lowry, 2002). 

Major haulout areas (top two quintiles: 211-

5,223 individuals at maximum recent use) 

for Northern elephant seal exist on San 

Miguel, San Nicolas, and Santa Rosa Islands (Figure 6.2.5). Additional less populated haulout areas (less than 211 individuals at maximum recent use) exist on Santa 

Barbara and San Clemente Islands. 

Major rookery areas (top two quintiles: 

148-2794 pups at maximum recent use) 

exist on San Miguel, San Nicolas, and 

Santa Rosa Islands (Figure 6.2.6). Additional less-populated rookery areas (less 

than 148 pups at maximum recent use) exist on Santa Barbara and San Clemente Islands.

There are no differences among the boundary concepts in terms of the number of elephant seal haulout or 

rookery sites encompassed. However, this data cannot account for differences which may exist in the at-sea 

abundance of northern elephant seal within the different concepts.

Figure 6.2.3. Pacific harbor seal. Total number of individuals counted (all sex and 

age classes) at each surveyed site for the 2002 SWFSC census.

Figure 6.2.4. Pacific harbor seal. Total number of individuals counted (all sex and 

age classes) at each surveyed site for the 2002 CDFG census.
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Figure 6.2.5. Northern elephant seal. Maximum total individuals (greatest number of individuals of all sex 

and age classes counted for each beach area surveyed out of the three most recent SWFSC surveys 

2001-2003) for (left to right) San Clemente, San Miguel, San Nicolas, Santa Barbara, and Santa Rosa 

Islands.

Concept

 Area

(km2)

Total

Individuals

SWFSC

Total

Individuals

CDFG

∆ Individuals

SWFSC

(%)

∆ Individuals

CDFG

(%)

∆ Area

(%)

OAI

SWFSC

OAI

CDFG

NAC 3745 2943 1361 - - - -

5 4536 2943 1361 0 0 21.12 0 0

4 7981 2943 1361 0 0 113.11 0 0

3 9044 3431 1965 16.58 44.38 141.50 0.12 0.31

2 13736 3894 2263 32.31 66.27 266.78 0.12 0.25

1a 22591 4181 2897 42.07 112.86 503.23 0.08 0.22

1 22613 4181 2897 42.07 112.86 503.82 0.08 0.22

SA 17093 4181 2897 42.07 112.86 356.42 0.12 0.32

Table 6.2.2. Pacific harbor seal. Total number of individuals, total area, and Optimal Area Index (OAI) for each boundary concept 

for SWFSC and CDFG 2002 surveys. Abundance values in bold reflect increases from the NAC and shaded OAI values represent maximum observed benefit.
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Figure 6.2.6. Northern elephant seal. Maximum pups (greatest number of pups counted for each beach 

area surveyed out of the three most recent SWFSC surveys 2001-2003) for (from left to right) San Clemente, San Miguel, San Nicolas, Santa Barbara, and Santa Rosa Islands.

Southern sea otter

Southern sea otter summary maps (Figure 6.2.7) show fall 2001 and spring 2002 distributions of sea otters along 

the California mainland. Georeferenced survey data within the Channel Islands were not available; however, the 

U.S. Fish and Wildlife Service does survey the San Nicolas Island experimental population approximately every 

two months (Carretta et al., 2002). The most recent recovery plan (USFWS, 2003) for this species estimates a 

population size of 27 individuals for the San Nicolas Island colony and states that more than 70 births have been 

recorded at this colony between 1987 and 2002. Sea otters have also been sighted irregularly near Point Bennett 

on San Miguel Island, with 14 individuals from this area captured and relocated between 1990 and 1993. More 

recently, 4 individuals were recorded in this area in a 1999 aerial survey, and no sightings were recorded during a 

September 2001 ground survey (USFWS, 2003). Historical expansion of the Southern sea otter’s range (Figure 

6.2.8) suggests that the mainland coast near the CINMS may provide increasingly important habitat for sea otter 

as the population grows; however, a three-year moving average (Figure 6.2.9) of the census results suggests 

that the population is not currently growing (USFWS, 2003).

Substantial differences exist among the six boundary concepts in terms of their potential impacts on sea otters. 

The current boundaries and Concepts 4 and 5 include only the small population at San Miguel Island. Reli-
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able estimates of the size of this population are not available. For comparisons of 

boundary concepts, the higher 1999 aerial 

survey estimate of 4 individuals was used. 

To evaluate the sensitivity of the boundary 

concept analysis to this estimate, a high estimate of 25 individuals for the San Miguel 

Island population was also tested. While 

the absolute values of the OAI depend on 

the estimated population size within the 

current boundaries, the relative value of 

the OAI among concepts is not affected, 

therefore results (Table 6.2.3.) are shown 

only for calculations based on the estimate 

of 4 individuals within current boundaries.

Concepts 1, 1a, 2, and 3 all include portions of the mainland coast. To the extent 

that sea otter protection is a goal of the 

CINMS, Concepts 1, 1a, and 2 offer the 

greatest benefit. All of these options incorporate portions of the coast between 

Point Conception and Point Sal, which is 

in the primary range of the Southern sea 

otter. At this point in time, Concepts 1 and 

1a appear to encompass little additional 

sea otter habitat than that encompassed 

by Concept 2. If the Southern sea otter’s 

range continues to expand, however, the 

additional section of mainland coast to the 

south of Santa Barbara that is included in 

Concepts 1 and 1a may become important habitat. Although the coast south of 

Point Conception was designated as an 

otter management zone in 1986 (USFWS, 

2003), and otters found in this zone were 

originally translocated out of the zone, this 

practice has been discontinued. Based on 

the most recent available surveys of sea 

otters, Concept 2 provides the greatest 

benefit in terms of sea otters encompassed 

by the boundaries for the smallest relative 

change in area. The OAI results support 

this conclusion for both the spring and fall 

survey data.

Summary

• The current boundaries of the CINMS encompass important haulout and rookery areas for California sea lion, 

harbor seal, northern elephant seal, and northern fur seal.

• San Miguel Island is used by all of these pinnipeds and has some of the most heavily used haulout and rookery 

areas in southern California for California sea lion, northern elephant seal, and northern fur seal.

• Although reliable estimates of at-sea distributions of pinnipeds at a scale useful for evaluating boundary concepts are not available, much of the waters surrounding the CINMS is likely to be important transit and foraging 

habitat for pinnipeds.

Figure 6.2.7. Southern sea otter. Counts summarized by 20 km shoreline segment for Fall 2001 (top) and Spring 2002 (bottom) surveys conducted by CDFG, 

USGS-Biological Resources Division, and the Monterey Bay Aquarium.
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• Of the six boundary concepts being considered, Concepts 2 and 3 provide relatively large increases in harbor 

seal abundance within their boundaries relative to area.

• Expansion of the CINMS to include sections of mainland coast (Concepts 1, 1a, 2, and 3) would substantially 

increase the amount of occupied sea otter habitat within sanctuary boundaries. Of the six boundary concepts 

being considered, Concept 2 provides the greatest relative increase in sea otter abundance per area added.

Figure 6.2.8. Southern sea otter. Expansion of sea otter range in California from 

1938 to 1998. Reprinted with permission 

from The Otter Project Inc.

Figure 6.2.9. Southern sea otter. Three-year moving average of spring sea otter survey counts since 1984. Reprinted with permission from the USGS Western Ecological Research Center.

Concept Area (km2

)

Individuals(spring)SpringEncounterRate (#/km)Individuals(fall)FallEncounterRate (#/km)∆ Area (%)∆ Individuals(spring)∆ Individuals(fall)OAI Spring(absolute)OAI Fall(absolute)

NAC 3745 4 NA 4 NA - - - - -

5 4536 4 NA 4 NA 21.12 0 0 0 0

4 7981 4 NA 4 NA 113.11 0 0 0 0

3 9044 11 1.6 6 0.4 141.50 175 50 1.24 0.35

2 13736 89 0.62 41 0.46 266.78 2125 925 7.97 3.47

1a 22591 92 0.55 58 0.425 503.23 2200 1350 4.37 2.68

1 22613 92 0.55 58 0.425 503.82 2200 1350 4.37 2.68

SA 17093 92 0.55 58 0.425 356.42 2200 1350 6.17 3.79

Table 6.2.3. Southern sea otter. Total number of individuals, mainland encounter rates, total area, and OAI for each 

boundary concept for Fall 2001 and Spring 2002 survey. Numbers in bold indicate an increase from the NAC and shaded 

OAI values represent maximum observed benefit.
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porpoises, (Phocoena phocoena), indicated by molecular genetic analyses. Journal of Cetacean Research and Management 

4(2):111-122.

Dawson, M.N. 2001. Phylogeography in coastal marine animals: a solution from California? Journal of Biogeography 28:723-736.

DeLong, R.L. 1982. Population biology of northern fur seals at San Miguel Island, California. Ph.D. Thesis, University of California, 

Berkeley, California. 185 pp.

Dizon, A., C. LeDuc, and R. LeDuc. 1994. Intraspecific structure of the northern right-whale dolphin (Lissodelphis borealis): The 

power of an analysis of molecular variation for differentiating genetic stocks. CalCOFI Rep.35:61-67.

Dohl, T.P., R.C. Guess, M.L. Duman, and R.C. Helm. 1983. Cetaceans of central and northern California, 1980-83: Status, abundance, and distribution. Final Report to the Minerals Management Service, Contract No. 14-12-0001-29090. 284 pp.

Donovan, G.P. 1991. A review of IWC stock boundaries. Report of the International Whaling Commission, Special Issue 13:39-

68.

Dorsey, E.M., S.J. Stern, A.R. Hoelzel, and J. Jacobsen. 1990. Minke whale (Balaenoptera acutorostrata) from the west coast of 

North America: individual recognition and small-scale site fidelity. Report of the International Whaling Commission, Special Issue 

12:357-368.

Dudzik, K.J. 1999. Population dynamics of the Pacific coast bottlenose dolphin (Tursiops truncatus). M.S. Thesis, San Diego State 

University, San Diego, California 92182. 63 pp.

Ford, J.K.B. and H.D. Fisher. 1982. Killer whale (Orcinus orca) dialects as an indicator of stocks in British Columbia. Report of the 

International Whaling Commission 32:671-679.

Forney, K.A. 1994. Recent information on the status of odontocetes in Californian waters. U.S. Department of Comm, NOAA Tech. 

Memo. NMFS-SWFSC-202. 87 pp.

Forney, K.A., J. Barlow, and J.V. Carretta. 1995. The abundance of cetaceans in California waters. Part II: Aerial surveys in winter 

and spring of 1991 and 1992. Fisheries Bulletin 93:15-26.

Forney, K.A. and J. Barlow. 1998. Seasonal patterns in the abundance and distribution of California cetaceans, 1991-92. Marine 

Mammal Science 14(3):460-489.

Goley, P.D. and J.M. Straley. 1994. Attack on gray whales (Eschrichtius robustus) in Monterey Bay, California, by killer whales (Orcinus orca) previously identified in Glacier Bay, Alaska. Canadian Journal of Zoology 72:1528-1530.

Green, G., J.J. Brueggeman, R.A. Grotefendt, C.E. Bowlby, M.L. Bonnell, and K.C. Balcomb, III. 1992. Cetacean distribution and 

abundance off Oregon and Washington. Chapter 1. In: Oregon and Washington Marine Mammal and Seabird Surveys. OCS 

Study 91-0093. Final Report prepared for Pacific OCS Region, Minerals Management Service, U.S. Department of the Interior, Los 

Angeles, California.

Hanni, K.D., D.J. Long, R.E. Jones, P. Pyle, and L.E. Morgan. 1997. Sightings and strandings of Guadalupe fur seals in central and 

northern California, 1988-1995. Journal of Mammalogy 78:684-690.

Hansen, L.J. and R.H. Defran. 1990. A comparison of photo-identification studies of California coastal bottlenose dolphins. Report 

of the International Whaling Commission Special Issue 12:101-104.

Hanson, M.T. and R.H. Defran. 1993. The behavior and feeding ecology of the Pacific coast bottlenose dolphin, Tursiops truncatus. 

Aquatic Mammals 19:127-142.

Heyning, J.E. and W.F. Perrin. 1994. Evidence for two species of common dolphins (Genus Delphinus) from the eastern North 

Pacific. Contr. Nat. Hist. Mus. L.A. County, No. 442.

Hoelzel, A.R., M.E. Dahlheim, and S.J. Stern. 1998. Low genetic variation among killer whales (Orcinus orca) in the Eastern North 

Pacific, and genetic differentiation between foraging specialists. Journal of Heredity 89:121-128.

Horn, M.H., and L.G. Allen. 1978. A distributional analysis of California coastal marine fishes. Journal of Biogeography 5:23-42.

Laidre, K., R. Jameson, D. DeMaster, 2001. An estimation of carrying capacity for sea otters along the California Coast. Marine 

Mammal Science 17(2):294-309.
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Lowry, M. Personal communication. National Marine Fisheries Service, Southwest Fisheries Science Center.

Lowry, M.S. 2002. Counts of Northern Elephant seals at rookeries in the southern California Bight 1981-2001. U.S. Department of 

Commerce, NOAA Technical Memorandum, NMFS-SWFSC-345. 64 pp.

Lux, C.A., A.S. Costa, and A.E. Dizon. 1997. Mitochondrial DNA population structure of the Pacific white-sided dolphin. Report of 

the International Whaling Commission 47:645-652.

McGinnis, M.V. 2000. A Recommended Study site for the CINMS Management Planning Process: Ecological Linkages in the 

Marine Ecology from Point Sal to Point Mugu, including the Marine Sanctuary. A Report to the Channel Islands National Marine 

Sanctuary, NOAA. 50 pp.

MMS (Minerals Management Service). 2001. Marine Mammal and Seabird Computer Database Analysis System Washington, 

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Co.), Portland, Oregon for the Minerals Management Sevice, Pacific OCS Region, Order No. 1435-01-97-PO-4206.

Nerini, M. 1984. A review of gray whale feeding ecology. pp: 423-450, In M. L. Jones, S. L. Swartz, and S. Leatherwood (Eds.), The 

Gray Whale, Eschrichtius robustus. Academic Press, Inc., Orlando. xxiv + 600 pp.

Poole, M.M. 1984a. Migration corridors of gray whales along the central California coast, 1980-1982. Pp. 389-407. In: M. L. Jones, 

S. L. Swartz, and S. Leatherwood (Eds.), The Gray Whale, Eschrichtius robustus. Academic Press, Inc., Orlando. xxiv + 600 pp.

Pyle, P., D.L. Long, and J. Schonewald. 2001. Historical and recent colonization of the South Farallon Islands, California, by Northern fur seals (Callorhinus ursinus). Journal of Marine Mammal Science 17(2): 397-402.

Reeves, R.R., P.J. Clapham, R.L. Brownell, Jr., and G K. Silber. 1998. Recovery plan for the blue whale (Balaenoptera musculus). 

Office of Protected Resources, NMFS, NOAA, Silver Spring, MD. 30 pp.

Rice, D.W. 1981. Status of the eastern Pacific (California) stock of the gray whale. pp: 181-187. In: Food and Agriculture Organization. 1981. Mammals in the Seas. vol. III. General Papers and Large Cetaceans. Food and Agriculture Organization, Rome, Italy.

Rice, D.W. 1992. The blue whales of the southeastern North Pacific Ocean. pp. 1-3 In: Alaska Fisheries Science Center, Quarterly 

Report (Oct.-Dec.).

Rice, D.W., and A.A. Wolman. 1971. The Life History and Ecology of the Gray Whale, Eschrichtius robustus. The American Society 

of Mammalogists Special Publication 3. 142 pp.

Rice, D.W., A.A. Wolman, D.E. Withrow, and L.A. Fleischer. 1981. Gray whales on the winter grounds in Baja California. Report of 

the International Whaling Commission 31:477-493.

Rice, D.W., A.A. Wolman, and H.W. Braham. 1984. The gray whale, Eschrichtius robustus. Marine Fisheries Review 46(4):7-14.

Rosel, P.E., A.E. Dizon, and J.E. Heyning. 1994. Population genetic analysis of two forms of the common dolphin (genus Delphinus) utilizing mitochondrial DNA control region sequences. Marine Biology 119:159-167.

Roy, K., D. Jablonski, and J.W. Valentine. 1994. Eastern Pacific molluscan provinces and latitudinal diversity gradient: No evidence 

for “Rapoport’s rule”. pp: 8871-8874. In: Proceedings of the National Academy of Science 91.

Rugh, D.J., K.E. W. Shelden, and A. Schulman-Janiger. 2001. Timing of the southbound migration of gray whales. Jouirnal of Cetacean Research and Management. 3(1):31-39.

Santa Barbara Marine Mammal Center. Unpublished records. Santa Barbara, CA.

Steiger, G.H., J. Calambokidis, R. Sears, K.C. Balcomb, and J.C. Cubbage. 1991. Movement of humpback whales between California and Costa Rica. Journal of Marine Mammal Science 7:306-310.

Sweetnam, D. and B. Read. 2002. Preliminary results from the 2002 harbor seal aerial surveys conducted by the California Department of Fish and Game. (PSRG-6).

U.S. Fish and Wildlife Service (USFWS). 2003. Final Revised Recovery Plan for the Southern Sea Otter (Enhydra lutris nereis). 

Portland, Oregon. xi + 165 pp.

Walker, W.A., S. Leatherwood, K.R. Goodrich, W.F. Perrin, and R.K. Stroud. 1986. Geographical variation and biology of the Pacific 

white-sided dolphin, Lagenorhynchus obliquidens, in the north-eastern Pacific. In Bryden, M. M. and R. Harrison (Eds.), Research 

on Dolphins, p. 441-465. Clarendon Press, Oxford.

Wells, R.S., L.J. Hansen, A.B. Baldridge, T.P. Dohl, D.L. Kelly, and R.H Defran. 1990. pp: 421-431. In: S. Leatherwood and R. R. 

Reeves (Eds.), The Bottlenose Dolphin. Academic Press, Inc., San Diego.

Wendell, F.E., J.A. Ames, and R.A. Hardy. 1984. Pup dependency period and length of reproductive cycle: estimates from observations of tagged sea otters, Enhydrea lutris, in California. California Fish and Game 70:89-100.
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CHAPTER 7 INTEGRATION

Randy Clark, Chris Caldow, John Christensen, Julie Kellner

Previous chapters examined taxon-specific patterns of abundance, distribution, and community structure, as well 

as, physical/oceanographic data to evaluate boundary expansion concepts being considered by CINMS. For a 

review of the boundary concepts see Appendix A. The intent of this chapter is to provide, where possible, a synoptic overview of these patterns. The analyses conducted within this chapter will examine the degree to which 

the cumulative set of prior analyses favors one concept over another. They will also depict the extent to which 

areas deemed important for each taxonomic group (invertebrates, fishes, birds, mammals) co-occur across the 

study area and which boundary concepts capture these important regions. This visual depiction of these important regions can also be utilized to suggest further concepts that warrant future consideration. Finally, the last 

section highlights species within this assessment listed as threatened or endangered under the Endangered 

Species Act and how their distributions relate to the boundary concepts.

7.1 numerical integration

Data and Methods

The OAI (Optimal Area Index), defined in Chapter 1.4, provides a standardized set of information to assess the 

physical and biological environment in the context of sanctuary boundary expansion. Absolute OAI results for 

48 analyses were used to rank boundary concepts in terms of ecological benefit and area gained. Only absolute 

OAI values were included in this integration due to their commonality across datasets. Ranks were calculated 

for individual analyses within 5 analytical groups: physical setting (e.g. sea surface temperature, kelp distribution), invertebrates, fishes, birds, and mammals, and for the composite of all analyses (Table 7.1.1). The final 

composite rank for each concept was determined by calculating the mean rank for all analyses. A standard Kruskal-Wallis rank sums test (Sokal and Rohlf, 1969) was used to determine whether mean ranks were statistically 

different across boundary concepts for each analytical grouping, as well as for the composite of these ranks 

(Table 7.1.2).

Analysis of Boundary Concepts

The Study Area boundary received the highest ranking for 56% of the individual analyses, and highest for all the 

taxonomic and physical setting groups, and the composite analyses. Concept 2 ranked highest for 17% of the 

individual analyses and ranked second, behind the Study Area, for all groups, except invertebrates. The Study 

Area was overwhelmingly favored for all but one of the invertebrate analyses, while more variability was observed within the other groups. Statistically significant differences were found for each of the groupings with the 

exception of the physical setting group. The variables in this group are a combination of dynamic and static parameters and tended to rank on the opposite ends of the expansion spectrum. For example, ocean color ranked 

higher in larger concepts, while substrate type and physiographic complexity ranked high in smaller concepts. 

When compared as a group, no difference in mean rank was observed; however, the remaining group analyses 

suggest that the difference in ranks is real. No significant difference was observed between the Study Area and 

Concept 2 for fish, birds, and mammal group rankings, but they were significantly different than the other concepts. The Study Area was statistically different than all other concepts for invertebrates and for all the data in 

composite (Table 7.1.2). 

High rankings were predominant for concepts whose northern boundary included portions of the nearshore 

habitats associated with the mainland. Concepts that included portions of the mainland benefitted from the gain 

of complex nearshore habitats (kelp, seagrasses, rocky areas) and areas of high primary productivity that are 

typically associated with high species richness and diversity. Rankings increased for these concepts as their total 

nearshore area increased; however, this pattern was not linear. Concepts 1, 1a, and the Study Area contained 

the same amount of mainland coastal area, but Concepts 1 and 1a ranked behind the Study Area and Concept 

2, respectively. OAI rankings tended to be lower for Concepts 1 and 1a due to large areas of deep water habitat 

which were less significant for the composite of species analyzed. These areas extend offshore west of Point 

Conception, including a portion of the Santa Lucia Bank (Figure 1.1.2). This contributed negatively to the ratio of 

analytical results and the increase in boundary size relative to the current CINMS boundary. 

It has been well documented that the convergence of warm and cold water masses and their associated biota 

create a unique and diverse ecosystem around Point Conception and the CINMS. Patterns of OAI results support this and provide a good example of the physical and biological linkages within this region.
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Table 7.1.1. Absolute OAI rankings for individual groups and the composite for all analyses.

Concept

5 4 3 2 1A 1 SA

Physical Setting Physiographic Complexity 1 2 5 7 3.5 3.5 6

Benthic Substrate 1 2 3 6 4.5 4.5 7

Bathymetric Life-Zones 7 6 5 2.5 2.5 4 1

Ocean Color/ChlA 4 7 3 2 6 5 1

Seagrasses 6.5 6.5 4 1 4 4 2

Kelp 6.5 6.5 3 2 4.5 4.5 1

Mean 4.33 5.00 3.83 3.42 4.17 4.25 3.00

Rank 6 7 3 2 4 5 1

Marine Invertebrates Brown rock crab 6.5 6.5 5 4 2.5 2.5 1

Red rock crab 6.5 6.5 5 4 2.5 2.5 1

Yellow rock crab 6.5 6.5 5 4 2.5 2.5 1

Black abalone 6.5 6.5 5 2 3.5 3.5 1

Red abalone 6.5 6.5 5 1 3.5 3.5 1

White abalone 5 5 5 5 2.5 2.5 1

California market squid 7 6 5 2 3.5 3.5 1

Sheep crab 6.5 6.5 5 4 2.5 2.5 1

Spot shrimp 1 5 4 3 6.5 6.5 2

Ridgeback rock shrimp 7 6 3 2 4.5 4.5 1

California spiny lobster 6.5 6.5 5 4 2.5 2.5 1

California sea cucumber 5 7 6 2 3.5 3.5 1

Warty sea cucumber 6.5 6.5 5 4 2.5 2.5 1

Red sea urchin 6.5 6.5 5 4 2.5 2.5 1

Purple sea urchin 6.5 6.5 5 4 2.5 2.5 1

Mean 6.00 6.27 4.87 3.27 3.17 3.17 1.13

Rank 6 7 5 4 2.5 2.5 1

Marine Fishes Pacific angel shark 6 7 5 4 2.5 2.5 1

Thresher shark (adult/juvenile) 2.5 2.5 2.5 2.5 7 6 5

Leopard shark (adult) 6.5 6.5 5 2 3.5 3.5 1

Tope (adult/juvenile) 1 7 4 3 5.5 5.5 2

Cowcod (adult/juvenile) 5 1 2 3 5 5 4

Bocaccio (adult/juvenile) 5 1 2 3 6.5 6.5 4

Lingcod (adult/juvenile) 7 6 5 2 3.5 3.5 1

Giant seabass 6.5 6.5 5 2 3.5 3.5 1

California sheephead 6.5 6.5 5 2 3.5 3.5 1

California halibut 6 7 5 4 2.5 2.5 1

Mean 5.20 5.10 4.05 2.75 4.30 4.20 2.10

Rank 7 6 5 2 4 3 1

Marine Birds Ashy storm-petrel 7 3 1 2 5 6 4

Black oystercatcher 6.5 6.5 5 1 3.5 3.5 2

Brandt’s cormorant 6.5 6.5 1 3 4.5 4.5 2

Brown Pelican 5 7 4 6 2.5 2.5 1

Cassin’s auklet 7 6 5 2 3 4 1

Double-crested cormorant 6.5 6.5 4 5 2.5 2.5 1

California least tern 6 6 6 3.5 1.5 1.5 3.5

Pelagic cormorant 6.5 6.5 5 1 3.5 3.5 2

Pigeon guillemot 7 6 5 1 3.5 3.5 2

Xantus’s Murrelet 1 2 3 4 6.5 6.5 5

Comminity/Bird diversity 3 2 1 4 6.5 6.5 5

Mean 5.64 5.27 3.64 2.95 3.86 4.05 2.59

Rank 7 6 3 2 4 5 1
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7.2 Spatial Integration

Data and Methods

In addition to the numerical integration of OAI values, spatial data for fish, invertebrates, marine birds, and mammals were overlaid to identify areas of potential ecological importance across taxa. This allows a visual examination of the spatial coincidence of these species which may reflect ecosystem “hotspots”. 

Integration of datasets is a difficult task due 

to varying sampling techniques and issues 

of scale. Often, there is a disparity between 

spatial and temporal scales of biological data 

(McGowan et al., 1998) where inadequate 

spatial coverage may create unwanted bias 

within specific areas. As such, only datasets 

with similar spatial ranges and sampling techniques were considered suitable for this integration and certain datasets that were analyzed in previous chapters were not included. 

Based on these criteria, the datasets utilized 

for this analysis were the fish and invertebrate 

habitat suitability models (HSM) and bird and 

mammal sightings data (MMS, 2001). For a 

more complete description of these datasets 

refer to Chapter 3 (invertebrates), Chapter 4 

(fish), Chapter 5 (birds), and Chapter 6 (mammals). 

Selecting an appropriate scale to display the integrated data was crucial in order to depict trends at the smallest 

scale possible without making the assessment so general that the entire region becomes homogeneous. Various scales of data exist within the datasets used in this assessment: HSM data were based on bathymetric and 

benthic substrate data ranging from tens to hundreds of meters and bird and mammal sightings data ranged 

from tens to hundreds of kilometers. In order to optimize the search for pattern and to maximize the number of 

samples in a grid cell for the bird and mammal data, a cell size of ~950 km2 was chosen. HSM data were then 

sampled into the same sized grid cells used for bird and mammal sightings, which were the limiting factor between the two types of data (Figure 7.2.1). HSM models and bird and mammal sightings data were integrated 

separately since they were the most comparable. Then, to provide a synoptic overview, all four data sets were 

combined in the final composite integration.

The areas of highest habitat suitability for all 25 fish and invertebrate species listed in Table 7.1.1 were superimposed and linked to the larger grid cells. The number of species with highly suitable habitat within each grid cell 

was then categorized by quintile (20%). 

Concept

5 4 3 2 1A 1 SA

Marine Mammals Blue whale 5 6 7 3 2 1 4

Long-beaked common dolphin 5 6 7 1 4 3 2

Short-beaked common dolphin 7 1 2 6 4.5 4.5 3

Risso’s dolphin 7 6 2 3 4.5 4.5 1

Pacific harbor seal 6.5 6.5 5 1 4 3 2

Southern sea otter 6.5 6.5 5 1 3.5 3.5 2

Mean 6.17 5.33 4.67 2.50 3.83 3.17 2.33

Rank 7 6 5 2 4 3 1

Composite Composite Mean 5.56 5.52 4.26 3.01 3.77 3.72 2.05

Composite Rank 7 6 5 2 4 3 1

Table 7.1.1. (cont).

Group X2 Probability

Highest Concept

Ranking

Physical Setting 4.27 0.64 Study Area

Invertebrates 80.52 <0.0001 Study Area

Fish 21.1 0.0018

Study Area/

Concept 2

Birds 27.57 0.0001

Study Area/

Concept 2

Mammals 25.88 0.0002

Study Area/

Concept 2

Composite 140.19 <0.0001 Study Area

Table 7.1.2. Results of Kruskal-Wallis ranks sums test. X2 values with 

probabilities less than 0.05 were considered statiscally significant.
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Bird and mammal at-sea sightings 

data (MMS, 2001) were examined 

to determine spatial co-occurrence. 

Sightings data for eight of the ten 

bird species listed in Table 7.1.1 (excluding black oystercatcher and California least tern) and four of the six 

mammal species (excluded Pacific 

harbor seal and Southern sea otter). At-sea sightings data were also 

available for bottlenose dolphins, 

gray whales, humpback whales, Pacific white-sided dolphins, Dall’s porpoise, and minke whales, but lacked 

sufficient data for OAI analysis in 

Chapter 6. Presence/absence data 

for these species were analyzed to 

determine areas of co-occurrence 

within each grid cell. Abundance 

and estimated density data were not 

used due to difficulties in standardizing effort and sampling techniques. 

The number of species within each 

grid cell were then categorized by 

quintile (20%).

Finally, a composite integration was 

conducted to provide a comprehensive spatial analysis for all the datasets mentioned above. Grid cell values for 

fish and inveretebrate habitat suitability overlap and bird and mammal co-occurrence were summed and categorized by quintile (20%). Overall, 43 species were examined in this analysis. 

Broad-scale Patterns and Analysis of Boundary Concepts

Areas of highest overlap for fish and invertebrate habitat suitability occur predominantly off southern California, 

from Morro Bay to San Diego (Figure 7.2.1). Overlap is lower north of Morro Bay as many of the species analyzed have southerly ranges, rarely extending north of Point Conception. Patterns of overlap were correlated with 

depth, where low values were observed in deep slope waters and increasing values over the continental shelf. 

Overlap was highest in waters near the mainland and islands off southern California. 

Within southern California (Figure 7.2.2), areas of highest habitat suitability overlap occurred along the mainland 

from Morro Bay to San Diego, around the Channel Islands, and a large area over Cortes Bank. This pattern 

coincides with complex nearshore benthic habitat types, including kelp, submerged seagrass beds, rocky reefs 

or hardbottom, and soft substrate. This area of overlap also coincides with areas of high primary productivity 

(see Chapter 2.8) associated with nearshore upwelling near Point Conception and the Channel Islands (Dever, 

2004). 

While the patterns of overlap are broader than the original maps of suitability based on the smaller scale bathymetry and substrate maps, the analysis highlights the nearshore environment of southern California and the 

ecological linkages within the Santa Barbara Channel described by McGinnis (2000). Oceanographic processes 

in the region foster the transport of materials, such as nutrients and fish and invertebrate larvae, between the 

marine (islands) and coastal habitats and are primary food sources that support biological communities. 

Broad-scale patterns of bird and mammal at-sea sightings data (Figure 7.2.3) identify three regions of high cooccurrence (upper 20%, 13-17 species): Point Reyes, Monterey Bay, and a large area encompassing the northern Channel Islands and extending northwest to Point Conception. Smaller areas of high co-occurrence were 

observed southeast of Santa Barbara Island and Cortes Bank. The regions in central California have been previously highlighted as areas supporting high bird biomass, density, and diversity (NCCOS, 2003). The highlighted 

areas around Point Reyes, Monterey Bay, and much of southern California have been identified as hotspots for 

bird diversity in this assessment (Chapter 5.2). 

124°W

124°W

123°W

123°W

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

37°N37°N

38°N38°N

0 20 40 80 120 160

Kilometers

Fish and Invertebrate HSM

Habitat Suitability Overlap

Species Overlap

1 - 3

4 - 5

6 - 7

8 - 9

10 - 18

Figure 7.2.1. Broad-scale distriibution of the overlap of fish and invertebrate highly suitable habitat. 
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Within southern California, high cooccurrence was observed throughout most of the Santa Barbara Channel, including the area from Point 

Conception through the northern 

Channel Islands and smaller areas 

south of Santa Barbara Island and 

Cortes Bank. Many species of birds 

and mammals are widely distributed 

along the west coast and patterns 

of abundance are highly correlated 

with areas of high primary productivity and plankton density (Airamé 

et al., 2003; Croll et al., 2005). 

Known areas of pronounced upwelling, such as Point Conception, are 

generally linked with areas of high 

physiographic complexity and dynamic currents and eddies which 

tend to concentrate phytoplankton, 

zooplankton, and secondary consumers. (Baltz and Morejohn, 1977; 

Ainley and Sanger, 1979; Briggs et 

al., 1984; Briggs and Chu, 1987; 

Chu, 1984; Ainley et al., 1996; Forney and Barlow, 1998; NCCOS, 

2003). The expression of the patterns observed in Figure 7.2.4 are 

not static and can be highly variable 

and difficult to predict (Mann and 

Lazier, 1996); however, the patterns of distribution are found near 

well known areas of high primary 

production (Huyer and Kosro, 1987; 

Brink and Cowles; 1991; Rosenfeld 

et al., 1994).

Based on these data, it is apparent 

that many bird and mammal species utilize the area between the 

northern Channel Islands and Point 

Conception. These results may be 

biased due to the fact that most of 

the bird species chosen have nesting grounds within the region. 

The areas around Cordell Bank, 

the Farallon Escarpment, Monterey Bay, and Point Conception 

are well known for their productivity and diverse biological communities (NCCOS, 2003). Areas of high 

physiographic complexity (canyons, 

ridges, banks, and shelf breaks) and 

distinctive oceanographic features 

associated with seasonal upwelling 

Figure 7.2.3. Broad-scale distribution of bird and mammal co-occurrence. Source: 

CDAS, MMS (2000).

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

0 25 50 100 150

Kilometers

Fish and Invertebrate HSM

Habitat Suitability Overlap

Number of species

1 - 3

4 - 5

6 - 7

8 - 9

10 - 18

Figure 7.2.2. Overlap of fish and invertebrate highly suitable habitat off southern California.
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(plumes, fronts, sea surface temperature variation, currents, and eddies) affect biological distributions 

patterns at many trophic levels. 

Broad-scale patterns for the composite of bird, fish, invertebrate, and 

mammal data highlight these areas 

of productivity. Species overlap 

was highest in many shelf regions 

with these characteristics and low in 

deeper habitats. 

Within southern California, areas of 

high species overlap extend along 

the mainland from Morro Bay to 

Santa Monica Bay. A wide area encompasses the area from Point Conception to Point Dume, and south 

through the northern Channel Islands, including all of the Santa Barbara Channel. Another large area 

extends from Palos Verdes Point to 

San Clemente Island. Smaller areas 

of significance are observed around 

Santa Barbara Island, San Nicolas 

Island, and over Cortes Bank. 

The area consisting of Point Conception and the CINMS is unique 

in that it is located in a transition 

zone between two biogeographical provinces: the warm Californian 

Province and the cooler Oregonian 

Province. Characteristics of this 

zone include a mix of shallow and 

deep water habitats, increased intensity of upwelling, dynamic eddies and surface currents, and persistent thermal fronts as a result of 

the meeting of the major currents 

(Southern California Countercurrent 

and California Current) from these 

provinces (Harms and Winant, 

1998; McGinnis, 2000). This combined with important coastal habitats 

such as kelp, seagrass beds, and 

wetlands promote diverse assemblages of marine birds, fish, invertebrates, and mammals (Cross and 

Allen, 1993; U.S. Air Force, 1997; 

Schroeder, 1999; McGinnis, 2000; 

NCCOS, 2003). Although all species 

of marine birds, fish, invertebrates, 

and mammals were not represented 

in the spatial analyses, it is obvious that this transition zone is highlighted as an important ecological area for the 

species included in these analyses. 

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

0 50 100

Kilometers

Spatial Integration - Marine bird and

mammal species co-occurrence

Species co-occurrence

1 - 4

5 - 7

8 - 10

11 - 12

13 - 17

Data Source: Computer

Database Analysis System,

Minerals Management Service,

2001.

Figure 7.2.4. Bird and mammal co-occurrence off southern California. Source: CDAS, 

MMS (2000).

124°W

124°W

123°W

123°W

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

37°N37°N

38°N38°N

0 50 100 200

Kilometers

Composite Spatial Integration

Fish and Invertebrate HSM

Bird and Mammal Presence/Absence

Species Overlap

1 - 4

5 - 8

9 - 12

13 - 19

20 - 34

Figure 7.2.5. Broad-scale distribution of the composite spatial integration for bird, fish, 

invertebrate, and mammal data.
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121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

0 25 50 100 150

Kilometers

Composite Spatial Integration

Fish and Invertebrate HSM

Bird and Mammal Presence/Absence

Bird Diversity

Species Overlap

1 - 4

5 - 8

9 - 12

13 - 19

20 - 34

Figure 7.2.6. Composite spatial integration of bird, fish, invertebrate, and mammal data off southern California.

Summary

* The Study Area and Concept 2 ranked first and second, respectively, for most biological groups and physical 

processes and for the composite of all OAI analyses.

* The overlap of fish and invertebrate habitat suitability appears to be correlated with nearshore environments, 

predominantly kelp, seagrass beds, and rocky substrates.

* Marine bird and mammal co-occurrence appears to be associated with known centers of upwelling and primary 

production.

* Ecological hotspots occur in continental shelf and nearshore waters from Point Conception through the northern Channel Islands, where spatial patterns of bird, fish, invertebrate, and mammal habitat overlap. 
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A Biogeographic Assessment of the Channel Islands National Marine Sanctuary

7.3 Threatened and endangered species

Seven of the species considered in this bioassessment are listed as threatened or endangered under the Endangered Species Act of 1973: 

White abalone Haliotis sorenseni Endangered

Western snowy plover Charadrius alexandrinus nivosus Threatened

California brown pelican Pelecanus occidentalis californicus Endangered

California least tern Sterna antillarum browni Endangered

Southern sea otter Enhydra lutris nereis Threatened

Blue whale Balaenoptera musculus Endangered

Humpback whale Megaptera novaeangliae Endangered

Biogeographic patterns of these species, with respect to the boundary concepts, are recapped for each of these 

species.

White abalone

Approximately 77 km2 of habitat were considered 

suitable for white abalone within the current CINMS 

sanctuary boundary (NAC). This estimate remained 

the same for the smaller Concepts 2, 3, 4 and 5 (Table 7.3.1). The addition of nearshore waters along 

the mainland for the Study Area and Concepts 1 

and 1a provide additional suitable habitat. The OAI 

analysis indicates that these larger concepts (SA, 

1, and 1a) offer the greatest proportional change in 

habitat area/total area gained relative to the NAC.

Western snowy plover

290 km of coastline along the U.S. Pacific coast (Washington, Oregon and California) have been designated as critical habitat for the western snowy plover (USFWS 1999). The current CINMS sanctuary boundary (NAC) and Concepts 4 and 

5 do not include any designated critical habitat (Table 7.3.2). Concept 3 includes 

2.3 km of critical habitat shoreline, less than 1% of the total designated habitat. 

Concept 2 encompasses 27.4 km (9.4%). The larger Concepts 1, 1a and the 

Study Area include 24% (70 km) of the designated western snowy plover critical 

habitat. Because there was no critical habitat designated within the NAC, it is not 

possible to calculate the OAI for western snowy plover critical habitat.

California brown pelican

Analysis of the observed patterns of pelican sightings and density relative to the 

proposed boundary concepts indicates that the Study Area and Concepts 1 and 

1a provide the greatest increase in sightings (absolute metric) for its relative size 

compared to the NAC (Table 7.3.3). The density-based 

OAI, however, favors Concept 5, the smallest concept. 

The smaller concepts (3-5) fail to capture the region of 

high pelican density along the mainland coast and in 

the eastern Santa Barbara Channel, while the largest 

concepts (1 and 1a) include large areas of low pelican 

density along the shelf. 

No analysis of boundary concepts was conducted for 

brown pelican breeding colonies since none of the concepts encompass any colonies not contained in the 

NAC.

Concept

High Suitability

Area (km2)

D Area 

(%)

D High Suitability

Area (%)

OAI

(absolute)

NAC 77 - - -

5 77 21.12 0 0

4 77 113.11 0 0

3 77 141.50 0 0

2 77 266.78 0 0

1a 93 503.23 20.78 0.04

1 93 503.82 20.78 0.04

SA 93 356.42 20.78 0.06

Table 7.3.1. Analysis of white abalone habitat suitability within boundary 

concepts. 

Concept

Critical Habitat:

Length of

Shoreline (km)

NAC -

5 0

4 0

3 2.29

2 27.35

1a 69.85

1 69.85

SA 69.85

Table 7.3.2. Critical habitat for the 

Pacific coast population of western 

snowy plover.

Concept

Total

Individuals

Density

(Individuals/km2)

OAI

(absolute)

OAI

(relative)

NAC 374 0.834 - -

5 452 0.918 0.99 0.47

4 540 0.722 0.39 -0.12

3 913 0.927 1.02 0.08

2 1355 0.853 0.98 0.01

1a 2546 1.035 1.15 0.05

1 2546 1.035 1.15 0.05

SA 2546 1.204 1.63 0.12

Table 7.3.3. California brown pelican sightings and density OAI.
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California least tern

There were no recorded breeding colonies of least tern within the NAC or Concepts 

3-5 during 2001–2003 (Table 7.3.4). Concept 2 encompasses 1 colony at Vandenberg 

AFB for a maximum of 79 breeding pairs. Concepts 1 and 1a and the Study Area 

encompass an additional 4 colonies for a maximum of 676 breeding pairs. Because 

no colonies exist within the NAC, it is not possible to calculate the OAI for least tern 

colonies.

Southern sea otter

Substantial differences exist among the six boundary concepts in terms of their potential impacts on sea otters. The current boundary and Concepts 4 and 5 include only 

the small population at San Miguel Island. Reliable estimates of the size of this population are not available. Concepts 1, 1a, 2, and 3 all include portions of the mainland 

coast (Table 7.3.5). To the extent that sea otter protection is a goal of the CINMS, Concepts 1, 1a, and 2 offer the greatest benefit. All of these options incorporate portions 

of the coast between Point Conception and Point Sal, which is in the primary range of 

the Southern sea otter. At this point in time, Concepts 

1 and 1a appear to encompass little additional sea 

otter habitat than encompassed by Concept 2. If the 

southern sea otter’s range continues to expand, however, the additional section of mainland coast to the 

south of Santa Barbara that is included in Concepts 1 

and 1a may become important habitat. Based on the 

most recent available surveys of sea otters, Concept 2 

provides the greatest benefit in terms of sea otters encompassed by the boundaries for the smallest relative 

change in area. The OAI results support this conclusion for both the spring and fall survey data.

Blue whale

Because of the relatively small number of on-effort 

sightings (4-14) and the uncertainty in the line transect 

input parameters, confidence intervals for the abundance estimates are wide and overlap substantially among the 

concepts (Table 7.3.6). The NAC does seem to be well placed to capture regions of high blue whale density within the 

Southern California Bight as it exhibits higher estimated density than any of the other concepts. Sharp increases in estimated blue whale abundance relative to that of the NAC are apparent in Concepts 1, 1a, and 2. The OAI shows that, 

although none of the concepts provide higher density than the NAC, Concepts 1 and 1a provide the greatest relative 

increase in blue whale abundance for the relative increase in area.

Concept

Maximum

Breeding Pairs

NAC 0

5 0

4 0

3 0

2 79

1a 676

1 676

SA 676

Table 7.3.4. California least tern 

breeding pairs observed within 

boundary concepts, 2001-2003. 

Source: CDFG, draft data.

Concept

Spring/Fall

Total Individuals

Spring/Fall

Average Mainland

Encounter Rate

(#/km)

OAI 

Spring

OAI 

Fall

NAC 8 - - -

5 8 NA 0 0

4 8 NA 0 0

3 17 1.00 0 0

2 130 0.54 0 0

1a 140 0.47 20.78 0.04

1 140 0.47 20.78 0.04

SA 140 0.47 20.78 0.06

Table 7.3.5. Spring and fall abundance estimates and OAI for southern sea otter. 

Concept Sightings

Density

(individuals/km2)

Estimated

Abundance

OAI

(absolute)

OAI

(relative)

NAC 4 0.00807 30 - -

5 4 0.00712 32 0.316 -0.557

4 4 0.00400 32 0.059 -0.446

3 4 0.00358 32 0.047 -0.393

2 7 0.00600 82 0.650 -0.096

1a 14 0.00587 133 0.680 -0.054

1 14 0.00587 133 0.681 -0.054

SA 8 0.00530 91 0.570 -0.095

Table 7.3.6. Sightings, estimated abundance and density, and OAI for blue whales.
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Humpback whale

Because some of the sightings recorded as “Unidentified Large Whale” 

(including one that fell in Concepts 1 and 1a) were likely to be humpback 

whales (Carretta et al. 2002), abundance estimates in Concepts 1 and 1a 

may be negatively biased. Very small numbers of sightings (0-4) make the 

density and abundance estimates for this species extremely uncertain. No 

sightings were recorded within the NAC and only 1 sighting was recorded 

in Concepts 3-5 resulting in abundance estimates of approximately 10 

individuals for these three concepts (Table 7.3.7). Four sightings occurred 

in Concepts 1, 1a, 2 and the study area, resulting in abundance estimates 

of approximately 50 individuals for these four areas. Because no sightings 

were recorded in the NAC, it was not possible to calculate the OAI for 

humpback whales.

Summary 

Table 7.3.8 provides a summary of the boundary concept metrics for each of the Federally-listed species. Numerical values for each concept are ranked in ascending order (highest values receive the lowest rank); ranks for tied 

values are averaged. For species that do not have an OAI calculation for each concept (i.e. when the ecological 

metric was 0 inside the current sanctuary boundary), rankings were based upon a modified OAI, calculated as: 

OAImodified = B1-B0/A1-A0

where B1-B0 equals the difference of the metric (diversity, habitat area, etc) and A1-A0 equals the difference of 

total area between a given boundary concept and the NAC. This modified metric omits the relative weighting of 

the NAC to the difference in ecological value and the difference in area; however, it is suitable for ranking the 

concepts as it maintains the relative relationships between the gains in ecological value per gain in total area. As 

discussed in the Introduction, absolute metrics inherently favor the largest concept because each successively 

larger boundary encompasses the smaller concept. Accordingly, the Study Area and the larger concepts 1,1a 

and 2 are generally more favorable than the smaller concepts. This is generally upheld for metrics that are based 

on either absolute or OAI values.

Concept Sightings Density

Estimated

Abundance

NAC 0 0 0

5 1 0.00234 11

4 1 0.00131 10

3 1 0.00118 11

2 4 0.00375 52

1a 4 0.00230 51

1 4 0.00226 51

SA 4 0.00310 13

Table 7.3.7. Sightings, estimated abundance 

and density for humpack whales.

Concept 1 1a 2 3 4 5 SA

Area (km2) 22613 22591 13736 9044 7981 4536 17093

White abalone

High Suitability area 2.5 2.5 5.5 5.5 5.5 5.5 1

Western snowy plover

critical habitat (km) 2.5 2.5 4 5 6.5 6.5 1

California brown pelican

Total Individuals 2.5 2.5 6 4 7 5 1

California least tern

Breeding Pairs 2.5 2.5 4 6 6 6 1

Southern sea otter

Mean Individuals across

Spring and Fall

3.5 3.5 1 5 6.5 6.5 2

Blue whale

sightings 1.5 1.5 3 7 6 5 4

Humpback whale

Estimated Abundance 3.5 3.5 2 6 5 1 7

Overall Ranking 2.5 2.5 4 6 7 5 1

Table 7.3.8. Ranked OAI for the 86 federally listed threatened or endangered species.
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Appendix A

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

Point Conception

Point Arguello

Monterey Bay National Marine Sanctuary

Point Sal

Santa Catalina

San Clemente

San Nicolas

Santa Lucia Bank

Point Buchon

Point Mugu

Palos Verde Point

Santa Barbara

Anacapa Santa Cruz

Santa Rosa

San Miguel

Vandenberg AFB

Gaviota Cojo

Anchorage

Study Area

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

Point Conception

Point Arguello

Monterey Bay National Marine Sanctuary

Point Sal

Santa Catalina

San Clemente

San Nicolas

Santa Lucia Bank

Point Buchon

Point Mugu

Palos Verde Point

Santa Barbara

Anacapa Santa Cruz

Santa Rosa

San Miguel

Vandenberg AFB

Gaviota Cojo

Anchorage

Boundary Concept 1

Appendix A. Spatial extent for the proposed boundary concepts.
Appendix A

Channel Islands National Marine Sanctuary

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212

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

Point Conception

Point Arguello

Monterey Bay National Marine Sanctuary

Point Sal

Santa Catalina

San Clemente

San Nicolas

Santa Lucia Bank

Point Buchon

Point Mugu

Palos Verde Point

Santa Barbara

Anacapa

Santa Cruz

Santa Rosa

San Miguel

Vandenberg AFB

Gaviota Cojo

Anchorage

Boundary Concept 1a

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

Point Conception

Point Arguello

Monterey Bay National Marine Sanctuary

Point Sal

Santa Catalina

San Clemente

San Nicolas

Santa Lucia Bank

Point Buchon

Point Mugu

Palos Verde Point

Santa Barbara

Anacapa Santa Cruz

Santa Rosa

San Miguel

Vandenberg AFB

Gaviota Cojo

Anchorage

Boundary Concept 2
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Channel Islands National Marine Sanctuary

Appendix A

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

Point Conception

Point Arguello

Monterey Bay National Marine Sanctuary

Point Sal

Santa Catalina

San Clemente

San Nicolas

Santa Lucia Bank

Point Buchon

Point Mugu

Palos Verde Point

Santa Barbara

Anacapa Santa Cruz

Santa Rosa

San Miguel

Vandenberg AFB

Gaviota Cojo

Anchorage

Boundary Concept 3

122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

Point Conception

Point Arguello

Monterey Bay National Marine Sanctuary

Point Sal

Santa Catalina

San Clemente

San Nicolas

Santa Lucia Bank

Point Buchon

Point Mugu

Palos Verde Point

Santa Barbara

Anacapa Santa Cruz

Santa Rosa

San Miguel

Vandenberg AFB

Gaviota Cojo

Anchorage

Boundary Concept 4
Appendix A

Channel Islands National Marine Sanctuary

page

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122°W

122°W

121°W

121°W

120°W

120°W

119°W

119°W

118°W

118°W

33°N33°N

34°N34°N

35°N35°N

36°N36°N

Point Conception

Point Arguello

Monterey Bay National Marine Sanctuary

Point Sal

Santa Catalina

San Clemente

San Nicolas

Santa Lucia Bank

Point Buchon

Point Mugu

Palos Verde Point

Santa Barbara

Anacapa Santa Cruz

Santa Rosa

San Miguel

Vandenberg AFB

Gaviota Cojo

Anchorage

Boundary Concept 5
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Channel Islands National Marine Sanctuary

Acknowledgements

This body of work would not have been accomplished without the cooperation, time, data, and effort from 

numerous individuals. We would like to thank everyone who participated in this significant undertaking. 

The following is a list of those who have helped make this work possible:

Allison Bailey

Tom Barnes

Kristine Barsky

Carol Blanchette

Andy Brooks

John Calambokidis

Rich Charter

Elizabeth Clarke

Steve Copps

Steve Crook

Robert Delong

Jenny Dugan

Mary Elaine Dunaway

Jack Engle

Jim Estes

Mary Gleason

Gary Greene

Pete Haaker

Sally Holbrook

Beth Horness

George Hunt

Shanta Keeling

Aimee Keller

Mike Kenner

Brian Kinlan

David Kushner

Bob Leeworthy

Milton Love

Terry Maas

Bruce McCain

Gerry McChesney

Will McClintock

Mike McCrary

Michelle McCutchan

Matt Merrifield

Sally Mizrock

Steven Murray

Dave Ono

Christie Pattengill-Semmens

Wayne Perryman

Fred Piltz

Steve Ralston

Bob Read

Paul Reilly

Dan Richards

John Richards

Ed Roberts

Jana Robertson

Kaustov Roy

Greg Sanders

Donna Schroeder

Steve Schroeter

Paul Scott

Natalie Senyck

Mike Shane

Steve Shimek

Alex Stone

Rick Stumpf

Dale Sweetnam

John Takekawa

Ian Taniguchi

Teresa Turk

John Ugoretz

Wade Van Buskirk

Bob Warner

Libe Washburn

Michelle Wilson

Deb Wilson-Vandenberg

Karen Worcester

Nancy Wright

Mary Yoklavich

Mark Zimmermann
United States Department of Commerce

Carlos M. Gutierrez 

Secretary

National Oceanic and Atmospheric Administration 

Vice Admiral Conrad C. Lautenbacher, Jr. USN (Ret.)

Under Secretary of Commerce for Oceans and Atmospheres

National Ocean Service

Charlie Challstrom

Assistant Adminstrator (Actg.)