Topographic Complexity and Benthic Community Variability Within a Kelp

Topographic Complexity and Benthic Community Variability

Within a Kelp Forest in Monterey Bay, CA

A thesis submitted to

the faculty of Moss Landing Marine Laboratories and

California State University, Monterey Bay

In Partial Fulfillment

of the Requirements for the Degree

Master of Science in Marine Science

By:

Eric J. Sandoval

December 2005 APPROVED FOR MOSS LANDING MARINE LABORATORIES

Dr. H. Gary Greene, Professor Moss Landing Marine Laboratories

Dr. Michael Foster, Professor Moss Landing Marine Laboratories

Dr. Rikk Kvitek, Professor California State University, Monterey Bay

APPROVED FOR CALIFORNIA STATE UNIVERSITY MONTEREY BAY

Eric J. Sandoval

Geological features are enduring and recurrent and therefore, can act as indicators of habitat and community types across a range of scales. By understanding species and habitat associations, predictions of species composition can be made about a benthic community based on available habitat. In a kelp forest, topographic complexity can affect an organism, by modifying flow, altering food availability, altering light availability, and provide refuges and barriers that fragment the habitat. There are many qualitative ways to evaluate topographic complexity. Rugosity is a quantitative measure and is defined as the ratio of surface area to planar area. Using habitat maps developed in GIS from multibeam bathymetry data, regions of varying rugosity were mapped in southern Monterey Bay, CA. Associated benthic communities were examined to elucidate spatial patterns and similarities in community composition. In addition, transect rugosity, significant wave height (Hs), depth, the number of edges, the number of walls and the number of crevices were used to compare environmental spatial patterns with biological spatial patterns. Results from non-metric multidimensional scaling

(MDS) plots and Analysis of Similarity (ANOSIM) indicated no differences among high, medium or low rugosity classes, but did indicate significant sample site differences.

Results from a Biological-Environmental (BIO-ENV) analysis procedure suggest that Hs, transect rugosity and depth correlate best with community composition variation, but only explain up to 0.35 of the variation. It is apparent that benthic kelp forest communities are difficult to predict with remote sensing techniques due to the multidimensional aspect of numerous species and environmental variables. Spatial autocorrelation of species within the communities makes it difficult to design parametric

i tests. In fact, the spatial autocorrelation is not confined to one scale. Spatial legacy and small scale species interactions may be much more influential at predicting community composition and spatial structure. These results suggest that variables that were previously thought to be important in predicting benthic community composition and spatial structure may in fact work in combination with other unexamined variables. By examining species/habitat associations at multiple scales and showing strong species to habitat correlation, a more accurate and detailed assessment of benthic communities can be made and allow researchers to refine spatial predictions of these communities.

ACKNOWLEDGEMENTS

The work on this thesis could not have been completed without the help and support of a number of people. I’d like to thank Dr. William O’Reilly and staff at the Coastal Data

Information Program (CDIP) for graciously running the wave model and providing the oceanographic data for this work. Vince Christensen, Matt Levey, Pat Iampietro, Laurie

McConnico and Rob Leaf were all instrumental in helping out with the diving, data collection and species identification. I’d also need to thank Joe Bizzarro, Wade Smith, and Matt Levey, for many helpful comments, Matt Forrest for his assistance with bryozoan identification and

Michael Langford and Christopher Jones for their logistical support.

I sincerely thank my thesis committee members, Gary Greene, Mike Foster and Rikk Kvitek for years of guidance, support and valuable discussions. I’d especially like to thank Rikk

Kvitek for allowing me the opportunity to work in the Seafloor Mapping Lab at CSUMB. My work was partially funded through the Dr. Earl and Ethyl Meyers Oceanographic and Marine

Biology Trust and Packard Foundations. The hydrographic survey was conducted aboard the

R/V MacGinnitee and funded by State of California Department of Fish and Game Grant

#FG8293MR awarded to Rikk Kvitek.

I’d also like to thank students from the Phycology, Geology and Ichthyology Labs at Moss

Landing Marine Laboratories for their helpful comments. Lastly I’d like to thank my family and friends for their continuous support and encouragement. I’m especially grateful to my understanding wife Cheryl who endured this long process. My life would not be complete without her loving and unselfish contributions.

iii

TABLE OF CONTENTS

ABSTRACT i ACKNOWLEDGEMENTS iii TABLE OF CONTENTS iv LIST OF TABLES AND FIGURES v INTRODUCTION 1 OBJECTIVES 6 SITE DESCRIPTION AND LOCAL GEOLOGY 7 ECOLOGICAL SETTING 7 GEOLOGY SITE DESCRIPTION 8 METHODS 12 HABITAT MAPPING 12 GIS ANALYSIS 13 HABITAT VARIABLES AND WAVE MODEL 15 BIOLOGICAL SURVEY 16 STATISTICAL ANALYSIS 17 RESULTS 21 SURVEY AND HABITAT VARIABLES 21 WAVE MODEL 22 COMMUNITY ANALYSIS 22 DISCUSSION 26 CONCLUSION 40 LITERATURE CITED 42 APPENDIX I. 84 APPENDIX II. 104 APPENDIX III. 108

LIST OF TABLES AND FIGURES

TABLE 1. List of values for Environmental and Habitat Variables 51 TABLE 2. List of Species Surveyed in Southern Monterey Bay 53 TABLE 3. List of Sampled Species with significant Global Spatial Autocorrelation Test results 55 TABLE 4. 2-Way Crossed ANOSIM for Community Analysis 57 TABLE 5. Between Sites Pair-wise Comparison Results 58 TABLE 6. BIOENV Results 59 TABLE 7. 2-Way Crossed ANOSIM for Community Analysis of Significant Spatially Autocorrelated Species 60 TABLE 8. BIOENV Results for Significantly Autocorrelated Species Pooled Groupings 61 TABLE 9. Spatial Scale of Significance for the Moran’s Local Test of Spatial Autocorrelation at the p=0.05 level 62

FIGURE 1. Bathymetric map of the study site 63 FIGURE 2. Map of California and San Andreas Fault 64 FIGURE 3. Lithology map of study site 65 FIGURE 4. Geologic structural map of study site 66 FIGURE 5. Triangulation Integrated Network (TIN) surface model of selected (middle) cell 67 FIGURE 6. Rugosity values within rock habitat in the 10-20 meter depth zone 68 FIGURE 7. Two-dimensional diagram of an Edge, measurement criteria 69 FIGURE 8. Map of sampling sites 70 FIGURE 9. Photo of a medium rugosity site with diver and meter stick as scale reference. 71 FIGURE 10. Theoretical diagram of Moran’s I correlogram 72 FIGURE 11. A theoretical MDS plot if samples cluster according to rugosity class and a two stage wave exposure gradient. 73 FIGURE 12. Graph of average number of walls, crevices and edges for each site 74 FIGURE 13. Graph of average number of walls, crevices and edges for each rugosity class 75 FIGURE 14. Significant Wave Height for Sites 1 thru 6, December 1, 2001 thru January 31, 2002 76 FIGURE 15. PCA Ordination Plot of Environmental Variables 78 FIGURE 16. MDS Ordination Plot of Algae Percent Cover Estimates 79

LIST OF TABLES AND FIGURES (cont)

FIGURE 17. MDS Ordination Plot of Algae Swath Abundance Estimates 80 FIGURE 18. MDS Ordination Plot of Invertebrate Percent Cover Estimates 81 FIGURE 19. MDS Ordination Plot of Invertebrate Swath Abundance Estimates 82 FIGURE 20. Theoretical diagram of habitat mapping work flow integrating patterns of environmental factors and patterns of biological interactions. 83

INTRODUCTION

One goal of benthic habitat characterization and mapping is to develop predictions of the distribution of species from the physical parameters that define where those species live and which can be remotely sensed (Kvitek et al., 1999). Allee et al., (2000) defines habitat as: “an identifiable and distinct association of physical characteristics and associated biological assemblage occupied by an organism or community of organisms.”

The importance of shallow water, nearshore, habitat is well reported, especially at large scales (eg. rocky shores, sandy shores, mud flats etc, reviewed in Ricketts et al., 1985).

Habitat plays an important role in structuring marine benthic communities, but its contribution has not been well studied at intermediate scales (10-100 meters) because of the difficulty of separating habitat effects from other environmental influences (McCoy and Bell, 1991; Smith and Witman, 1999). In a kelp forest, topographic complexity can affect an organism, by modifying flow, altering food availability, altering light availability, and affecting settlement via scour, burial and flow interactions with gravity

(Foster, 1975a; Weaver, 1977; Denny, 1988; Vogel, 1994; Goldberg and Foster, 2002).

Topographic complexity may also provide shelter from physical stress, inhibit foraging predators, and modify the availability of resources and their rates of acquisition (Emson and Faller-Fritsch, 1976; Genin et al., 1986; Safriel and Ben-Eliahu, 1991), while providing refuges and barriers that fragment the habitat, resulting in more heterogeneous assemblages (Sebens, 1991; Raimondi, pers com 2005).

Non-uniform spatial distribution of resources and abiotic conditions may strongly affect patterns of species diversity and distribution in marine environments (Sebens, 1991).

Because all species within an ecosystem can not be sampled, understanding how habitat influences patterns of benthic communities can help ecologists evaluate large areas of the marine environment quickly and effectively.

The importance of scale cannot be overlooked when describing or examining habitat.

Greene et al. (1999) proposed a habitat characterization scheme where definitions would be based on four scales: Mega (100’s kilometers to 10’s kilometers), Meso (1 kilometer to 10 meters), Macro (1 to 10 meters) and Micro (< meter). This classification scheme was developed for habitat in depths of 30- 6000 meters. However, the rationale of using scale and geomorphic features for classification can be applied to shallower habitat, such as rocky substratum and its associated heterogenous communities. It is also important to understand that the scale of response may vary from species to species (Fortin and

Dale, 2005). Because habitat studies can examine communities that contain multiple species, analyses need to account for the multidimensional aspects associated with a large number of species and environmental variables.

Many factors can account for patterns of spatial heterogeneity observed in sessile marine communities on rocky substrata. Predation, physical or biological disturbances and larval recruitment have been documented to influence patterns of species diversity over homogeneous habitat, hundreds of meters in area (Paine, 1966; Connell, 1961; Dayton,

1971; Paine and Levin, 1981; Dethier, 1984; Sousa, 1985; Gaines and Roughgarden,

1985; Wethey, 1985; Underwood and Fairweather, 1989; Witman, 1992). Spatial competition (Sebens, 1986) can also influence diversity at small scales (meters) but is unlikely to account for patterns observed at larger scales (10-100’s of meters).

Macrocystis spp. is an exception and is known to affect algal growth and settlement

through shading (Reed and Foster, 1984). In continuous rock habitat, rugosity may

influence these patterns of spatial heterogeneity (Foster, 1975b; Raimondi, pers com

2005).

Rugosity is one measure of topographic complexity and is defined as the ratio of surface

area to planar area of a select 2-dimentional space. Many species of invertebrates depend

upon highly rugose substratum for shelter (Keough and Downes, 1982; Walters, 1992).

As a result, local assemblages can be influenced by the rugosity of the associated

substratum (Sebens, 1991; Lapointe and Bourget, 1999). Past studies generally indicate

horizontal rock platforms have high algal cover, while adjacent rock walls have higher sessile invertebrate cover (Weinberg, 1978; Witman and Cooper, 1983; Logan et al.,

1984; Sebens, 1986). Based on the evidence from experimental manipulations, these differences in algal and invertebrate cover are influenced by a combination of light, grazing, settlement and rugosity (Sebens, 1986; Baynes, 1999; Goldberg and Foster,

2002). Other studies have also revealed that rugosity can influence benthic community structure by shading, enhancement of larval settlement, and providing refuge from grazers (Foster, 1975b; Russ, 1980; Keough and Downes, 1982; Baynes, 1999). These studies examined micro habitat/species relationships, but little to no work has been done to compare macro-scale (tens of meters) differences.

The understanding of how communities are organized and influenced by topographic complexity has been based on studies conducted in patches of habitat measuring <10‘s of square meters is area (Kareiva and Andersen, 1988; Jackson, 1991). It is uncertain how

3 the processes that maintain diversity can be extrapolated to larger scales to explain patterns of diversity (Riklefs, 1987; Schulter and Ricklefs, 1993; Wiens et al., 1993).

Research conducted within patch types (< 1 meter) has focused on smaller scale structuring processes, such as competition and predation. Consequently, patterns of species composition that are maintained by between-patch processes or large scale elements have not been well studied.

Previous subtidal studies that involve habitat classification have used SCUBA diving,

ROV video, drop camera or other observational techniques to classify and assess habitat

(Genin et al., 1986; Leonard & Clark, 1993; Hiscock, 1997; Williams and Leach, 1999).

Defining habitat over a large area in this manner is not only difficult but time consuming and expensive. Because many benthic habitats can be defined by their geology, geophysical techniques (side scan sonar, multibeam bathymetry and seismic reflection profiling) are critical in determining habitat structure and these techniques are now being used to investigate benthic habitats (Auster et al., 1995; Van De Beuque et al., 1999;

Greene et al., 1999; Iampietro et al., 2005). Understanding and defining the local geologic setting facilitates the recognition and delineation of habitat boundaries. In the

Monterey Bay region, geologic structure is expressed as joints and fractures in rock, deformation, folding or faulting of lithologic units and the geometric outline of lithologic contacts (Greene, 1977). The use of modern geophysical techniques and hydrographic surveys allow researchers to examine the marine environment remotely and with precision (Kvitek et al., 1999).

Remote sensing and Geographic Information Systems (GIS) now allow researchers to

examine and expeditiously map large areas of the marine environment, that until recently

were too costly or time consuming to examine (Allen, 1992; Booth et.al,. 1996; Blondel

and Murton, 1997; Rukavina, 1997; Kvitek et. al, 1999). Large bathymetric data sets can be processed and visually displayed for mapping depth, slope, aspect, rugosity and substratum. Currently, most of the understanding of community structure is based on small scale (meters) studies (Foster, 1975b; Sebens, 1986; Jackson, 1991; Lapointe and

Bourget, 1999). However, GIS and remote sensing tools can facilitate the examination and explanation of habitat and benthic communities over a landscape (kilometers) by allowing researchers to quickly view and identify habitat patterns. Most studies that have used hydrographic techniques, have examined data at 5-meter resolution or greater

(Kvitek et al., 1999). Since most invertebrate and algal species respond to factors at scales less than 5-meters, a finer resolution should be examined.

To make accurate predictions about invertebrate and algae distribution, hydrographic data should be collected and analyzed at 1-meter resolution over the entire landscape

(kilometers). I hypothesized that patterns of species composition would correlate with the heterogeneous patterns of rugosity. Benthic invertebrate and algal communities within a kelp forest should vary in regions of low, medium and high rugosity due to

variability in shading, turbulence, and refugia. Descriptions of discrete habitats based on

high resolution multibeam sun-shaded imagery, GIS rugosity analysis, wave exposure,

and small scale habitat variables were used to examine the relationship between habitat

characteristics and the benthic community structure. These relationships were examined

5 by stratifying rugosity within rocky habitat sample sites and comparing percent cover and relative abundance of the benthic invertebrate and algal species.

OBJECTIVES

The primary objectives of this study were to: 1) map and define the geomorphology and lithology in the nearshore environment of the northern margin of the Monterey Peninsula

2) use the resultant map to define rugosity, depth and habitat types based on the habitat classification scheme proposed by Greene et al., 1999 at 1-meter scale 3) measure percent cover and relative abundance of the benthic invertebrate and algal species in 10-20 meters depth and test for community differences among habitat types and 4) examine spatial autocorrelation, the scale of significance for autocorrelation and relationships among small scale habitat variables and community structure. The results provide important information to address the larger issue of benthic community variability within heterogeneous rock habitat, evaluate habitat mapping processes, and examine spatial structure of benthic kelp forest communities.

SITE DESCRIPTION AND LOCAL GEOLOGY

ECOLOGICAL SETTING

Kelp forests dominate the southern Monterey Bay nearshore environment. These forests are dominated by surface canopy forming, large brown algae, Macrocystis pyrifera and

Nereocystis luetkeana. Both of these species can grow in depths of up to 40 meters

(Figure 1), provide a vertically-structured habitat through the water column, and affect

the algal and invertebrate communities below them due to shading, flow disruption, and

nutrient production (Foster and Schiel, 1985). The benthic communities within these

kelp forests are dominated by rocky substrata intermixed with sand channels. This type

of environment is suitable for various sessile invertebrate and algal species (Dayton,

1985). Comprehensive surveys by Pearse and Lowry (1974) and Edwards (2001),

catalog numerous species, but do not rigorously examine their distribution within the

rocky substrata.

Patch stability and catastrophic storms are important factors which regulate kelp forest

communities (Dayton and Tegner, 1984), but are not static factors such as lithology

types. Southern Monterey Bay is susceptible to westerly and northerly storm swell and

parts of the benthic community must withstand seasonal sediment burial and scour. The

wave exposure gradient that exists at this site has structured the algal community and the

seasonal removal of turf algae and kelp can increase the abundance of opportunistic

early-successional species by creating new space for settlement and increasing bottom

irradence (Foster, pers com., 2002). This study site is exposed to El Nino’s warm ocean

temperatures, low nutrients and violent storms (reviewed in Wooster and Fluharty, 1985) 7 which can structure the spatial distribution and composition of benthic algal and invertebrate communities in the area (Harrold, et. al., 1988).

GEOLOGY SITE DESCRIPTION

The southern Monterey Bay area is located within the active tectonic setting of the San

Andreas fault system, the boundary between the Pacific and North American plates. This area also lies within the Monterey Bay Fault Zone, a major component of the San

Andreas fault system (Figure 2). Previous geologic studies done in the offshore area

(Greene, 1977; McCulloch and Greene, 1989; Greene, 1990; Storlazzi and Field, 2000) demonstrate the importance of uplift and fracturing of the basement rock in the region, which produce the most prominent habitat type in the study area. In addition, the southeasterly sediment transport in the area continuously modifies benthic habitat. For example, areas of sediment accumulation and scour are distinct ephemeral benthic habitat. The onshore geomorphology consists of rocky shores frequently interrupted by sand channels, and coarse grain sand beaches (Storlazzi and Field, 2000). The subtidal habitat includes fractured granite, large and small boulders, cobble and channels filled with coarse to fine grained sand (pers. obs.) Sand channels cut through rocky banks composed of cobble and boulders, producing an area of diverse habitats.

Geology of the northern part of the Monterey Peninsula is divided into five lithologic units (Figure 3). Cretaceous granitic rocks are found in the nearshore marine environment and are mainly granodiorite and quartz diorite that have been mapped to a water depth of 40 meters (Clark et al., 1997; Wagner et al., 2002). Onshore, granodiorite is also present. Pleistocene old dune deposits and marine terrace deposits that contain gravel, sand, silt and clay fraction sediment are located on wave cut surfaces (Clark et al., 8

1997; Wagner et al., 2002), which are present throughout the cities of Monterey and

Pacific Grove. The Monterey Formation, has numerous exposures near the city of

Monterey and is comprised of siltstone to soft claystone, hard, siliceous mudstone,

porcelanite and soft, impure diatomite (Clark et al., 1997; Rosenberg, 2001). Most of the

geologic contacts in the nearshore are inferred (Greene, 1977; Clark et al., 1997; Wagner et al., 2002) because until recently, shallow water geophysical and geological data were not available.

Active tectonics in the southern Monterey Bay region has produced many different faults that have deformed the bedrock. Southern Monterey Bay lies within the Monterey Bay fault zone, which is comprised of several faults including the Chupines, Hatton-Canyon,

Navy, and Sylvan Thrust faults (Figure 4). Several discontinuous, steeply dipping,

northwest-striking faults comprise the Chupines fault zone. These faults separate

granodiorite to the southwest from the Monterey Formation to the northeast (Herold,

1935; Greene, et al., 1973; Greene, 1977). McCulloch and Greene (1989) show an

offshore extension of the Chupines fault along the sea floor in the nearshore area of

southern Monterey Bay. Greene et al. (2002) and Wagner et al. (2002) show this fault to

extend across the southern Monterey shelf and across the Monterey Canyon to become

one of the longest faults in the region.

The Hatton Canyon fault zone is a group of northwest-striking, near-vertical reverse

faults that juxtapose sedimentary rock of the Monterey formation against Pleistocene

terrace deposits. The fault has rotated terrace deposits and offsets beds of the Monterey

formation against fluvial terrace and landslide deposits (Rosenberg, 2001). Colluvium

(dated at 2,080±40 yr B.P.) by Rosenberg and Clark (1994) overlays the fault and thins abruptly on the upthrown side of the fault suggesting Holocene movement. This is also an indication that local deformation of the basement and bedrock is continuing today

(Rosenberg and Clark, 1994).

The Navy fault is a northwest-striking, steep, southwest-dipping fault extending from

Carmel Valley northwest to Monterey Bay. Local shearing, structural discordances, offset Pleistocene fluvial terrace deposits and the discontinuity of en echelon westerly- trending fold axes delineate the Navy fault. Structural discordances in the Monterey

Formation and truncated fold axes suggest that the Navy fault continues northwestward to join an offshore fault (Rosenberg and Clark, 1994). Linear drainages, aligned benches, saddles, the presence of northwest-trending thrust faults and en echelon fold axes are consistent with a right-lateral strike-slip motion (Rosenberg, 2001). However, seismologic evidence suggests a combination of reverse and right-lateral motion

(Rosenberg and Clark, 1994). The difference in elevation of granitic basement rock is only 200 feet and suggests that much of the displacement on the Navy fault is strike-slip

(Rosenberg and Clark, 1994).

The Sylvan Thrust fault consists of thrust faults that offset Pleistocene marine terrace deposits and older rocks. A 2-mile-long portion of the thrust fault offsets the Sylvan coastal terrace against the Monterey Formation. The Sylvan thrust joins the Navy fault onshore where it juxtaposes Pleistocene deposits against the Monterey Formation. Right-

10 lateral motion along the southwest-dipping fault, suggest that the thrusting is related to strike-slip motion on this northwest-trending fault (Rosenberg and Clark, 1994).

All of these faults influence the structure of the study area and are exhibited through local deformation, folding and fractures of the bedrock. At small scales, the fracturing of bedrock provides heterogeneous habitat through an increase of topographic complexity.

Additionally, a majority of faults mapped in southern Monterey Bay have an average orientation of N50W (Greene, 1970) that corresponds with fractures of many rocks within the study area (pers. obs.).

METHODS

HABITAT MAPPING

The geology and sedimentary processes of the Monterey Bay area have been previously

described (Greene, 1977 and 1990; Clark et. al., 1997; Storlazzi and Field, 2000; Greene et. al., 2002). Mapped faults and geophysical data were used to aid interpretation, and lithologic contacts were identified using multibeam, sunshaded imagery and ground- truthed by SCUBA surveys. Multibeam bathymetry data and maps from Greene (1977,

1990) were used to classify lithology, map depth contours and analyze rugosity within the study site.

Bathymetric data were collected using a Reson 8101 multibeam echosounder. The

Reson 8101 transducers consist of bow-pole-mounted linear array with separate units for

transmitting and receiving. The 150o swath width is made up of 101 1.5o beams, which

have a maximum depth penetration of 300 meters. Coastal Oceanographics Hypack™

software was used for survey design and execution. All raw data were logged using a

Triton-Elics International (TEI) Isis™ data acquisition system. Sound velocity profiles

were collected using an AML SV+™ sound velocity profiler. Differential Global

Positional System (DGPS) position data were generated by a Trimble 4700™ GPS with

differential corrections provided by a Trimble ProBeacon™ receiver and a TSS HDMS™

motion sensor provided heave, pitch, heading, and roll data.

Bathymetric data were post-processed using CARIS HIPS™ (Interactive Visualization

Systems Inc., New Brunswick, Canada) hydrographic data cleaning system software.

Soundings were adjusted to MLLW using predicted tide charts for the local region and erroneous soundings were removed in CARIS HIPS™. No automated filters were used during editing. Soundings were exported from CARIS HIPS™ as x,y,z ASCII text (shoal biased) with 1m spacing and Fledermause™ (Universal Systems Ltd., New Brunswick,

Canada) was used to generate 5-meter isobath contours (Figure 1) and a grid of 1-meter cell size. ASCII text data were processed and converted to grid format using TNT

Mips™ (MicroImages, Inc. Lincoln, NB) and Arcview™ (ESRI, Inc. Redlands, CA).

Bathymetry grid data were converted to grayscale, sunshaded imagery with 45 degree sun

elevation and 315 azimuth. Using coastline data (georeferenced vectors) as a reference

layer, geologic contacts were scanned, georeferenced, and attributed as a GIS layer.

Spatial accuracy is estimated at 1.5 to 2 meters based upon field measurements and GPS

groundtruthing.

GIS ANALYSIS

The surface area and planar area were calculated ssing gridded bathymetric data. A

Triangular Integrated Network (TIN) was created by connecting lines to center points of

each grid cell. For each cell in the grid, surface areas were calculated from triangular

areas clipped from eight triangles. Each triangle was created from the center points of a

central cell and two adjacent cells. The area of the triangle represented the surface area

of the space bounded by the three points and the triangle area was clipped so that it only

represented the portion that overlaid the central cell (Jenness, 2001). The surface area of

the central cell was calculated by summing the clipped areas of the surrounding eight

13 triangles (Figure 5). The surface ratio of the cell was calculated by dividing the surface area by the cell size and this ratio was defined in this study as rugosity.

Rugosity cell values were classified using the natural breaks or Jenks optimization method (Jenks, 1967). This is also known as the goodness of variance fit (GVF) and it breaks a data population into categories by minimizing the squared deviations of the class means. Using this statistical method, each cell was divided into low, medium and high

“rugosity” classifications. The method creates categories that correspond to a flat measure (rugosity of 1.0) and creates a classification scheme that can be replicated or applied to sites outside the Monterey Bay region. A rugosity value of 1 is the lowest and flattest measure, while theoretically the highest value can be infinite. The rugosity map, depth contour map, sun-shaded imagery and geologic/subtrata map were combined to produce a habitat map of the study site as outlined in Greene et al. (1999). Rocky habitat was divided into the three topographic complexity or rugosity classes (Figure 6). This categorization ensured that all areas of varing rugosity would have an equal chance of being sampled. This method is more advantageous than random sampling when spatial autocorrelation is evident in the environmental dataset (rugosity). Transects were then randomly located within each of these rugosity classes using the DNR Random Sample

Generator GIS tool developed by the State of Minnesota, Department of Natural

Resources (www.dnr.state.mn.us/mis/gis/index.html). A single rugosity value was calculated for each transect to use as a habitat variable in multivariate ordination plots and comparison with biologic similarity matrices. Average transect rugosity was calculated from the TIN surface model for all 54 transects.

HABITAT VARIABLES AND WAVE MODEL

Several abiotic factors, in addition to the biological information were recorded during the biological survey: the number of crevices (10 cm to 50 cm wide), edges 90-120 degrees with at least 30 cm edge depth (Figure 7), and vertical surfaces (walls) greater than 0.5 meter in height along the transect tape. These were used to describe sub-meter topographic complexity. Since the bathymetric survey could not resolve topographic complexity at a scale less than 1 meter, this additional information was obtained in situ to evaluate trends and compare species distribution with trends of crevices, edges and vertical surfaces.

To evaluate wave exposure, significant wave heights were calculated using the Coastal

Data Information Program (CDIP) revised nearshore wave model (O’Reilly and Guza,

1991; O’Reilly and Guza, 1993). This model uses directional wave height and period data logged from the NOAA 46042 buoy in Monterey Bay. Because shallow banks and coastal submarine canyons partially shelter the coastline from deep ocean surface waves, the wave climate on the Monterey Peninsula is complex due to reflection, refraction, diffraction and dissipation. Changes in incident wave frequency, direction, bathymetry errors, and the presence of currents or bottom dissipation are not accounted for within the model (O’Reilly and Guza, 1993). A value was calculated for each transect position, for each hour of each day through December 1, 2001 to January 31, 2002. These months were chosen because they historically exhibit the largest significant wave heights throughout the year (O’Reily, per coms).

These data were then compiled to produce maximum Hs, average daily maximum Hs, average Hs, average daily minimum Hs and minimum Hs for each transect. Due to the sample transects close proximity to shore, wave refraction and reflection affected the model efficiency and reliability. The CDIP model was modified to account for this variable (O’Reily, pers com, 2004). Also, due to the spatial distance of transect separation within a site, Hs resolution was not detailed enough to account for the spatial scale. Therefore, the model interpolated identical values for transects within a site due to these spatial scale limitations. The model is being rigorously tested and verified and is considered the best available model for nearshore wave spectrum predictions (H.L.

Tolman, pers com, 2004).

BIOLOGICAL SURVEY

For the purpose of this study, southern Monterey Bay is divided into two areas: (A) Lucas

Point to Lovers Point, and (B) Cabrillo Point to MacAbee Beach (Figure 8). These areas were chosen as separate entities because a large area of sand habitat exists between

Lovers Point and Cabrillo Point (minimal kelp habitat). In each area (A and B), three sample sites that contained low, medium and high rugosity substratum classes were randomly chosen from 16 potential sites. Three transects per rugosity class were surveyed for a total of nine transects per site or fifty-four total transects.

Benthic communities were evaluated by using SCUBA to determining the relative abundance of individual organisms and estimating percent cover with line-point intercept transects (Figure 9). Similar to the point-contact bar method, a line with knots regularly tied along its length is used to record species found directly under each knot on the line

(Kimura and Foster, 1984; Liddell & Ohlhorst, 1987; Trunnell & Nelson, 1989; Heine,

1999). In theory, random sampling must be used to obtain an unbiased estimate of the

population. However, in practice the statistics resulting from systematic sampling may

be no worse than those of random sampling, unless there is some periodicity in the

distribution pattern of the sampled organism that coincides with the sampling interval

(Southwood, 1978; Ripley, 1981). Percent cover is calculated by dividing the number of

times a species is contacted by the number of knots surveyed. Because of algal layering,

total cover estimates can exceed 100 percent. This method is advantageous because it

accounts for the three-dimensional structure of substrate and sessile organisms (Dahl

1973). Based on habitat patch size and sampling cost-benefit analysis, 20 points along a

ten meter long transect tape were used to estimate percent cover. Estimates of abundance

for macro invertebrates (> 20cm) and macro algae were obtained from sampling within a

2 meter by 10 meter swath (20 m2 area).

STATISTICAL ANALYSIS

Percent cover, as well as swath abundance estimates were separated into algae and

invertebrate categories, resulting in a total of four categories for analysis. If a species

was found in less than four transects it was excluded from analysis to reduce

dimensionality and improve interpretation of ordination plots. As part of the data

screening process, spatial autocorrelation of individual species estimates were tested

using a global Moran’s Index calculated in ArcView 9.0® (ESRI, 2004). Individual

species that showed significant spatial autocorrelation were pooled together into two groups: Percent Cover estimates and Abundance estimates. Differences among rugosity classes were also tested using these community groupings.

Individual species that had a global spatial autocorrelation p value of less than 0.10 were selected for further spatial analysis. A weighted Moran’s omnidirectional correlogram was used to evaluate the spatial scale at which a species exhibited significant autocorrelation. This method accounts for the reduced number of samples that occur when smaller scales are analyzed. These correlograms were calculated using CrimeStat®

(Levine, 2004) with a 30 bin and 200 simulation settings. The plot indicates how concentrated or distributed spatial autocorrelation is for the variant. A Monte Carlo simulation is run to estimate approximate confidence intervals or random I-value envelope (Levine, 2004). Ninety-five percent confidence envelopes were generated and

I-values that plotted outside of these envelopes were significantly autocorrelated.

Generally, as distance increases, the envelope shrinks (Figure 10).

To test for differences among rugosity classes (nested within sites), percent cover and swath abundance community data were evaluated using the analysis of similarity

(ANOSIM) and non-metric multi-dimensional scaling (MDS) techniques in the Plymouth

Routines in Multivariate Ecological Research (PRIMER®) software packages (Clarke &

Warwick, 2001) Non-parametric methods were used due to significant spatial autocorrelation and covariance among species. ANOSIM is analogous to analysis of variance (ANOVA) but compares similarity matrices rather than raw counts or percent cover. Similar to the ANOVA F-value, ANOSIM generates an R-statistic which is then compared to Monte-Carlo simulations to generate a P-value. If R=1, all replicates within a factor group are more similar to each other than any replicates from another group. If

R=0, similarities between and within groups are the same on average. R values reflect

variance and sample size. A total of 999 permutations were run for each analysis. MDS

is a constrained ordination technique and was chosen because of the spatial

autocorrelation, non normality and unequal variances of the data. Also, this technique is

able to incorporate all species sampled, regardless of abundance or occurrence.

All data analyzed with PRIMER® techniques were root-root transformed to reduce the contribution of very abundant species so that these species will not mask the contribution of rare species in the multivariate analysis. Species similarity matrices were constructed using the Bray-Curtis similarity coefficient and ordination of the samples was performed using MDS, which graphically depicts relative relationships between samples. The species similarity diagram is based on rank order of dissimilarity distances. Samples similar in community composition will plot close together and those that are less similar plot further apart. The distances between samples in separate MDS diagrams are relative to each other, but the orientations of the MDS diagrams are not. The extent in which the display of ranked samples agrees in MDS with the original similarity matrix rank order is evaluated with a “stress” value. If the display of samples in the MDS diagram and the matrix rank order are in perfect agreement, stress will equal 0. A stress value less than

0.1 depicts an ordination that displays the relationships well. A stress value between 0.1 and 0.2 should be interpreted with caution (Clarke 1993) and values of 0.2 to 0.3 indicate multiple dimensions (variables) are driving the spatial structure. The stress of an MDS diagram was plotted in 2-D, and was compared to 3-D ordinations to examine the accuracy of these patterns. Generally, 3-D ordinations have a lower stress value. For this study, the sampling design is optimized to evaluate community similarities within rugosity classes relative to the two regions of the study site. Theoretically, if differences

19 occur along the stratified sampling design, samples will cluster accordingly in the MDS plots (Figure 11).

The Bray Curtis similarity matrices that were generated from the biological data were also compared with environmental and habitat variable similarity matrices using

Biological-Environmental (BIOENV) routine of the PRIMER® application. These variables were first evaluated using Principal Component Analysis (PCA) to determine spatial association and reduce dimensionality. Because different units of measure were incorporated (significant wave height, depth, rugosity index, number of edges, number of crevices, etc), data were log X+1 transformed. Similarity matrices were generated using a Euclidean distance similarity matrix of the transformed data.

RESULTS

SURVEY AND HABITAT VARIABLES

A total of 75 hydrographic survey lines (125 Kilometers) were completed. The entire

coverage area exceeded 6.21 km2. Approximately 0.508 km2 of this area was identified as rocky habitat. Rocky habitat was further stratified to include only habitat within a 10-

20 meter depth range, thereby reducing the study site area to only 0.484 km2.

Through numerous ground-truthing SCUBA dives and on-shore geology observations, all

rocky habitat was classified as granodiorite porphory. Based on the rugosity

classification analysis (see below), the study site comprises 2.2% high complexity, 16.4%

medium complexity, and 81.4% low complexity. Each class equals 0.01 km2, 0.079 km2, and 0.395 km2, respectively. Rugosity values calculated for the entire study area, range

from 1.000 to 3.180. From the Jenks Optimization method, rugosity was classified into 3

classes: low (1.000 to 1.096), medium (1.097 to 1.345), and high (1.346 to 3.180). These

treatments (classes) were evaluated to compare differences in percent cover and relative

abundance of benthic species. The number of walls, crevices, edges and average depth

were recorded for each transect (Table 1). The number of crevices, walls and edges was

consistent throughout all sites, except site six. The data indicates that this region may

have a higher average number of crevices (Figure 12). The average number of walls,

crevices, and edges appeared consistent among high, medium and low rugosity classes,

although high variance is evident (Figure 13).

WAVE MODEL

The wave model estimated average significant wave heights (Hs). A total of 8796 Hs data points were interpolated to the nearest centimeter for 62 days between December 1,

2001 and January 31, 2002 (Figure 14). Some data drops occurred due to buoy technical malfunctions. The Principle Component Analysis (PCA) plot suggests that the number of crevices, edges, walls and average depth are associated with each other, while average transect rugosity has little association with these factors (Figure 15). As expected, all of the significant wave height factors are closely associated with each other. Average wave height had the strongest vector magnitude and was chosen to represent all of the significant wave height variables to reduce statistical dimensionality for the remaining analysis.

COMMUNITY ANALYSIS

A total of 13 algal species and 64 invertebrate species were observed in the percent cover and swath abundance estimates (Table 2). More species were identified, but were not analyzed because they were found in less than four transects (Appendix I). Of these species, one alga and thirteen invertebrate species exhibited significant spatial autocorrelation (Table 3, Appendix II), while two algae and seven invertebrate species exhibited a 90-95% likelihood of spatial autocorrelation. Only Macrocystis pyrifera exhibited spatial autocorrelation in both the percent cover and swath abundance estimates.

The 2-way crossed ANOSIM results suggest that algae swath, algae cover, invertebrate

swath and invertebrate cover categories (P=0.058, P=0.143, P=0.366 and P=0.186,

respectively) did not have significant community differences, among high, medium and

low rugosity classes (Table 4). However, all four categories do exhibit significant

community differences among sampling sites (P< 0.001). Pairwise comparisons of

sample sites for Algae Cover estimates show that site one has strong differences with

sites three, four and five and site four has strong differences with site five (Table 5). For

Algae Abundance estimates site one had differences with three, four and five, site two

had differences with four and five, site three had differences with four and site four

differed with five. For Invertebrate Abundance estimates all site pairs had strong differences except sites one and two, although this pair did have a high R value of 0.741.

For Invertebrate Cover estimates: site one had differences with three, four, five, and six, site three had differences with four, five and six, site four had differences with five and six and site five differed with six

MDS ordination plots were generated for Algae Percent Cover, Algae Swath Abundance,

Invertebrate Percent Cover and Invertebrate Swath Abundance estimates with 2-D stress values of 0.21, 0.16, 0.27, and 0.25, respectively (Figures 16 thru 19). No pattern was detected in the 2-D or 3-D ordination plots. The 3-D ordination plots had lower stress values for Algae Percent Cover, Algae Swath Abundance, Invertebrate Percent Cover and

Invertebrate Swath Abundance estimates of 0.14, 0.09, 0.18, and 0.19, respectively. The

BIO-ENV results showed low correlation between communities and environmental and

habitat variables (Table 6). Algae Percent Cover estimates had the highest correlation

with one group of two (rugosity and average Hs) variables at 0.202 and another group of

three (rugosity, average Hs and number of walls) variables, also at 0.202. Algae abundance estimates had the highest correlation (0.204) with the group of average depth, and average Hs. Invertebrate percent cover estimates had the highest correlation (0.350) with the group of average depth, average Hs, number of walls, number of crevices and number of edges. Invertebrate abundance estimates had the highest correlation with one

group of three (average depth, average Hs and number of edges) variables at 0.172 and

with average Hs, also at 0.172.

As stated previously, 23 species showed some level of spatial autocorrelation and were

thereby pooled into two groups. These groups were also analyzed for patterns and the

results of the ANOSIM suggest that both pooled groups were significantly different

among sites (p<0.001), but were not among rugosity classes (Table 7). BIO-ENV results

showed correlation of 0.332 between Percent Cover estimates and a group of three

variables: rugosity, average depth, average Hs. A correlation of 0.212 was found

between Abundance estimates and a group of three variables: average depth, average Hs

and number of edges. All other environmental and habitat variables had lower

correlations to Percent Cover and Abundance pooled groups than the aforementioned

values (Table 8).

Significant spatial autocorrelation was detected at varying spatial scales (Table 9). Due

to sampling design and bin numbers, the lowest resolution for determining significant

spatial scale was 95 meters. Asterina miniata, Balanophyllia elegans, Cucumaria

miniata, Mitra idea and Boltenia villosa were spatially autocorrelated at a scale of 120 to

200 meters. Loxorhynchus cripatus, Corynactis californica, Mimulus foliatus, Corallina

sp, Hippodiplosia insculpta, Hymenamphiaster cyanocrypta were spatially autocorrelated

at a scale of 210 to 300 meters. Orthasterias koehleri, Macrocystis pyrifera, Balanus nubilus, Ceratostoma foliatum, and Anthopleura sola were spatially autocorrelated at a scale of 350-400 meters. Halocynthia aurantia, Pisaster giganteus, Phragmatopoma californica, Costazia costazi, Parastichopus parvimensis were spatially autocorrelated at a scale of 480-720 meters. One species, Pisaster ochraceus had a significant spatial scale of less than 95 meters.

DISCUSSION

Generally, descriptions of habitats derived from multi-beam sonar surveys are based on topographic heterogeneity, sediment characteristics and some prior knowledge of the regional geology (Greene et. al., 1999). Acoustic techniques have problems with rapid changes in depth, seafloor reflectivity changes, shadows and the ability to resolve 3- dimensional characteristics (overhangs and tunnels). Despite these limitations, remote sensing techniques that utilize acoustic technology allow researchers to rapidly assess the marine benthic habitat. For this study sun-shaded relief imagery was used to identify rocky habitat and high resolution bathymetry was used to create detailed maps and implement a stratified sampling design. The targeted habitat for this study was only 7.8% of the surveyed area, so the use of GIS and remote sensing provided an efficient method for determining survey sites. By stratifying the sampling into three rugosity classes, the entire rugosity range could be sampled. Eighty-one percent of the rocky habitat was classified as low rugosity, so by using the traditional standard random sampling technique, many of the high and medium rugosity transects might not have been sampled due to the sparse occurrence. This type of sampling design problem has been recognized in other research (Syms, 1995) when investigators do not examine the full range of topographic complexity within a study site. If a study incorporates only a portion of the habitat or topographic complexity, different or conflicting results can occur. The biological response (i.e., abundance) can increase with complexity, decrease with complexity or remain unchanged. This study is one example of how habitat mapping can enhance sampling design decisions and identify the habitat of interest.

The transect rugosity values, number of walls, crevices and edges provided additional

components from which to examine relationships between habitat variables and benthic

community composition changes. In addition to these habitat variables, average Hs was

used as a proxy for wave exposure. Wave exposure is known to structure benthic kelp

forest communities (Graham, 1997; Dayton, 1985; Foster & Schiel, 1985; Edwards,

2001; Harrold, et. al., 1988; Graham, et. al., 1997). There are two types of spatial

structuring scale which were analyzed for this study: meso and macro scale. Average Hs

was used to examine meso scale trends, while habitat variables were used to examine

macro scale effects. The environmental variables of this study do not explain the

variance of the benthic communities (see more below). One confounding problem is the

multiple scales at which the environmental and habitat variables were collected. Because

the wave model could not resolve within site differences, much of the explanatory

variance at the local level may have been lost. Until the remote sensing tools that are

used for data collection (i.e., wave buoy and modeling algorithm) and analysis are

improved, the examination of this parameter may be cost prohibitive.

COMMUNITY ANALYSIS

The weighted Moran’s omnidirectional correlogram used to evaluate the spatial scale of

significance for autocorrelated species showed that species with a global P-value of 0.10

had a significant (< 0.05) P-value at a lower scale (lag). The lag for individual species ranged widely and suggests that the communities associated with these species may function at multiple scales. However, analyzing benthic communities in kelp forests at one scale may be an oversimplification. The correlograms indicates that significant

27 spatial clustering occurs at multiple scales for individuals in the community. This may be an effect of multiple structuring environmental factors, not one or two (i.e. rugosity and significant wave height). Pisaster ochraceus showed significant spatial autocorrelation at less than 95 meters. P. ochraceus is a common intertidal species, but is uncommon in subtidal habitat (pers obs). It was only found in transects at site six, which would explain the scale of significance. The scale of significance for Macrocystis pyrifera for percent cover and abundance estimates was 380 and 360 meters, respectively. This was the only species which exhibited autocorrelation in both the percent cover and abundance estimates. These results suggest that the spatial autocorrelation of Macrocystis pyrifera is rather stable regardless of sample unit. All other species showed spatial autocorrelation at scales between 120 and 720 meters, with no clear pattern.

Interestingly, previous work by Donnellen (2004) using a similar correlogram

(semivariagram) approach, found that kelp canopies from Monterey to Point Sur exhibited a patch size of 1.6 km. His work used techniques involving aerial photo analysis and spanned a period of six years. Percent cover and abundance estimates for

Macrocystis spp. in this study showed a significant spatial scale of 380 and 360 meters, respectively. Although lower spatial scales of significance were anticipated based on the different sampling methods (number of plants and percent cover vs. canopy area) the extreme differences in scale are surprising. Two reasons for these large differences are 1) this study analyzed spatial scale of one year while the previous study examined six years of data and 2) Donnellen’s (2004) research focused on kelp canopies whereas this study focused on the benthic communities. Sample units for both studies were optimized accordingly.

Results from the blocked design and analysis suggest a meso scale structuring component, but not necessarily a trend. ANOSIM results indicated that the rugosity classes nested within site groups did not have significant differences, but sample sites showed significant differences in community structure. R-values measure differences in community blocks, but are not effective at evaluating dimensionality of the communities.

The R-values are computed from the underlying, full dimensional similarity matrices (6,

12, 17, 23) and are not dependent on the MDS 2-dimensional stress values. The two- dimensional MDS ordination plots did not adequately capture all of the dimensionality of community variability. Although the three dimensional plots did provide lower stress values, the stress values never approached reasonable levels, except for Algae Abundance estimates (0.09). This may indicate that macroalgae populations of Macrocystis pyrifera,

Dictyoneurum californica, Dictyoneuropsis reticulata, Cystoseira osmundacea,

Laminaria setchellii, and Chondrocanthus corymbifera have ordination patterns that should not be reduced below three dimensions. This doesn’t mean that three environmental variables are structuring the community, only that separation and visualization of this structure may need to be done in 3 dimensions. Ultimately, sample variance could not be grouped or classified within two-dimensional space, indicating higher dimensional variability.

The R statistic values for the rugosity classes were negative. Chapman and Underwood

(1999) found that in practice, ecological data may have negative R statistic values that indicate a community is species poor and individuals have a heavily, clustered spatial distribution. As a result, within group variability may be extreme. In addition, Clarke

and Warwick (2001) noted that calculation of negative R values also requires a major

stratifying factor to be encompassed within each group and its effect ignored in the

analysis. Therefore, as further evidence to support the MDS and Moran’s I analysis, the

R-values suggest that the classes that were chosen for rugosity are confounded by

stronger factor(s) that are driving the overlying spatial structure.

Since the ANOSIM, MDS and Moran’s I analysis results suggest that rugosity classes

were not an appropriate way to explain community composition similarities or spatial

structure, the BIOENV results were used to explore the contribution of habitat variables

and wave exposure (significant wave height) to the underlying spatial structure of the

benthic community. Algae percent cover estimates and the community derived from

these estimates showed spatial structuring correlation with a combination of significant

wave height and transect rugosity, but at a very low level. The combination of transect

rugosity and the number of walls observed was also influential, but also at a low value.

Algae percent cover estimates were dominated by Calliarthron cheilosporioides,

Chondrocanthus corymbifera, Macrocystis pyrifera, Rhodymenia spp, and uncrusting coralines. Previous studies (Weinberg, 1978; Witman and Cooper, 1983; Logan et al.,

1984; Sebens, 1986; Goldberg and Foster, 2002) have found that algae preferentially recruit to horizontal surfaces when compared with vertical surfaces. Also, irradiance is limited by the extent of kelp canopy cover that in turn is controlled by Hs (Graham, et. al., 1997). Irradiance as a function of Hs, has significant effects on sporophyte density.

These indirect effects of Hs and the number of walls are probably the reason why both factors have such a low correlation in the BIOENV analysis.

Depth and average Hs had the highest correlation with the spatial structure of the Algae

Abundance estimates. Even though depth was confined in this study to 10-20 meters, this range still has some influence when combined with Hs. As stated earlier, Hs is used as a proxy for wave exposure and there are known effects of wave exposure on kelp distribution (Foster, 1982; Dayton et. al., 1984; Dayton, 1985; Harrold, et. al., 1988; and

Graham, 1997). The results from this study may be a result of geography as opposed to depth. Transects in greater depths were in close proximity to sand habitat. Almost all of the species examined in this study are all found on rocky habitat and are susceptible to sand scour. Pachycerianthus fimbriatus and Diopatra ornata are exceptions. Landscape metrics such as distance to habitat edge were not examined because the sampling design was not optimized for this question. Distance to rocky habitat edge may be an important structuring metric if sand scour proves to be a strong component influencing benthic community composition and the spatial structure of these communities.

The spatial structure of Invertebrate Percent Cover estimates is best described by the combination of four environmental variables: depth, average Hs, the number of walls and the number of crevices. This suggests that invertebrate percent cover is responding to environmental variables at a macro scale (number of wall, depth and crevices) and meso scale (Hs). Sponges, Paracyathus sternsi, Hippodiplosia insculpta, Diaperoecia californica, Corynactis californica, Balanus crenatus, Balonophylia elegans, Astrangia lajollaensis, Asterina miniata, and Abietinaria spp. were the most abundant species in invertebrate percent cover estimates. Patterns of spatial distribution for Paracyathus sternsi, Corynactis californica, Balonophylia elegans,and Astrangia lajollaensis ,are mediated by larval-adult interactions and between species competition (Chadwick, 1991).

Results of this study suggest depth and Hs do not explain much of the spatial distribution of these cup coral species and appear to lend more support to Chadwick’s (1991) conclusions that these species are mediated by small scale species interactions. The number of walls would also appear to support Chadwick’s (1991) conclusion, since her research also concluded that vertical distribution was influence by the same small scale species interactions.

Invertebrate Abundance is best correlated with the combination of three variables (depth, average Hs and number of edges) or Hs alone. Asterina miniata, Henricia leviuscula,

Loxorhynchus crispatus, Mimulus foliatus, Mitra idea, Orthasterias koehleri, Pisaster giganteus, Pycnopodia helianthoides, Scyra acutifrons, Strongylocentrotus purpuratus, and Urticina lofotensis are all mobile organisms which are more susceptible to dislodgement than sessile organisms (Denny, 1988). Hs may play a role on the distribution of these organisms. Additional studies utilizing experimental manipulations could help clarify the relationship between Hs and mobile and sessile invertebrate communities.

All four community categories had one variable in common. Average Hs in combination with other habitat variables, showed some correlation with the spatial pattern of these communities. Depth appears to be the second most important environmental variable that correlates with community variability. These two variables are commonly known influence marine benthic communities. What is interesting in this study is how little the environmental variables explain community similarity/disimilarity. No correlation value was higher than 0.350, which suggests that alternative variables are influencing the

spatial structure of these communities. The overall spatial patterns may be influenced or

confounded by autocorrelated species. Based on the data from this study, benthic

community response (spatial structure) to wave exposure may not be exhibited as a trend,

but rather a patchy response. Because kelp forests reduce flow by as much as fifty

percent (Jackson and Winant 1983; Jackson 1984; and Eckman et. al., 1989), the spatial

structuring influence of wave exposure may not be high within study sites (meso- scale of

1-2 km). This interpretation of a patchy response to a global environmental trend may

contribute to the discussion of conflicts published in previous studies by Foster (1982) and Harrold et al. (1988). Harrold et al. (1988) suggests that Foster’s south facing

“protected” site is susceptible to southern storms and that is why the communities at that site exhibit “exposed” characteristics as defined by Harrold et al. (1988). The data from this study suggests wave exposure (average Hs) is not a West-East trend at this site. Site

3 of this study would be classified as “more exposed” than site 4-6 according to Harrold et al. (1988), but average Hs indicates it may actually be more protected due to refraction.

Previous studies (Edwards, 2001; Foster and Van Blaricom, 2001) analyzed marine communities at much larger scale (greater than 2 kilometers) with qualitatively designated wave exposure levels (i.e. “exposed” and “protected”). Sites chosen to represent extremes in wave exposure can exhibit differences during analysis, but may not help predict community composition at sites that are not on the extreme ends of a wave exposure gradient. To appropriately test trend vs. patchy structure of wave exposure community response, an additional set of sites would be necessary in a “protected” wave exposure environment. Monterey harbor could have been a potential region to establish sample sites, but the area lacked sufficient rocky habitat at the appropriate depth with sufficient rugosity. Another option is to deploy a low cost datalogging instrument array

33 that would allow direct Hs measurement for each transect or sample and improve the resolution of wave exposure data (Moore, 1999).

To examine whether autocorrelated species were responding differently to environmental and habitat variables than the entire community, these species were pooled together for analysis. Even with pooled communities of spatial autocorrelated species, habitat variables explained little of the benthic community variability. BIOENV results for the pooled communities were similar to the full community BIOENV analysis with low correlation values of 0.332 and 0.212 for percent cover and abundance estimates, respectively. This lends further support that additional variables are structuring the benthic communities.

One assumption of habitat studies is that spatial structure and/or autocorrelation reflect spatial patterns of current environmental conditions that control community processes.

Local variations or gradients of environmental variables are assumed to cause the spatial autocorrelation and the existing spatial patterns of benthic communities can be modeled using existing conditions of environmental variables. Unfortunately, other processes can influence the community and may cause uniform, trends or patchy structure.

To complicate matters further, past events are reflected in the spatial legacy. These can be considered patterns of disturbance, prior colonization or dispersal events, or large scale grazing events (i.e., urchin barrens). Ecologists studying habitat-species relationships should consider spatial legacy when trying to make spatial predictions about marine benthic communities. Spatial legacy is difficult, if not impossible to measure, but

biological or community patterns can be measured. Analyzing the relationship of

multiple environmental patterns and biological interaction patterns (i.e. recruitment and

competition), ecologists can model the spatial patterns and define habitat from these

models. These definitions will then lead to improved mapping capabilities and hopefully

increase our level of understanding and ability to predict benthic community structure

(Figure 20).

“Identifying pattern leads to an understanding of the system that gave rise to it. Most ecological questions are aimed at a better understanding of the complexity of nature and how it works, by testing hypothesis about ecological processes and their interactions”

(Fortin and Dale, 2005). Through observation, pattern detection, experimentation, and modeling, ecologists working on habitat studies can build upon their predictive abilities.

This study used habitat mapping as a means to make predictions about marine benthic community structure. Ultimately, the mapping provides a means to discriminate potential habitat vs. non-habitat, such as rocky reef and sand. The variables that were used to define the conditions within the potential habitat (rocky reef) were assumed to account for 100% of the spatial variability. The variables selected explained only 20-30% of the spatial variability and community similarities.

The motivation for performing spatial analysis is to detect pattern, which is the first step

to understanding the complexity of natural systems. However, that is only the beginning

when studying pattern/process interactions. Ecologists should try to understand the

process that generates the pattern, which may be the sum of many processes, and thereby

increases the complexity of the system that is driving the pattern. This understanding is

35 critical because imposing arbitrary and inappropriate scales in the sampling design, can distort identified spatial patterns and thereby lead to incorrect generalizations or interpretations of ecological processes.

The link between pattern and process is not always clear and in this case, the process that was assumed to quantify a pattern may have been inappropriately chosen. As researchers develop and modify their habitat classification schemes, ecological information and patterns should be considered so these schemes reflect the real world. Most habitat classification schemes are generalized for one spatial scale and are focused on a particular species or genus, particularly Sebastes. In the case of this study, I used a deepwater classification scheme that was originally designed to classify rockfish habitat

(Greene et.al, 1999). It may be appropriate to modify or create a specialized scheme for benthic invertebrate and algal communities.

In general, observations of a community start with an initial spatial pattern or spatial legacy. This spatial legacy is influenced and modified by some ecological process or several processes, which results in a new spatial pattern. Habitat mapping needs to incorporate these processes to precisely predict where or when a species will be observed.

The general problem with our current methodology for habitat mapping is defining habitat before a strong link is established between biological communities and the habitat variables we choose. If a simplified approach is the only option, then there should be an understanding that predictions will be general at best or conflicting at worst.

Understanding these issues is important if we want to study, monitor or protect these benthic communities.

Many state and federal agencies have the responsibility of monitoring and protecting important habitats such as marine reserves and spawning grounds. The Marine Life

Management Act (MLMA) has a goal to collect baseline data in an effort to understand changes and anthropogenic effects over time (California Dept of Fish and Game, 2001).

Because it is unreasonable to sample all biologic populations along our coastlines, a more efficient method must be implemented to collect baseline data. By understanding species/habitat associations, researchers can make predictions about a benthic community based on available habitat. A multi-disciplinarian approach using geology, remote sensing and a biological survey allows researchers to examine benthic community structure at multiple scales and can contribute to the development of more comprehensive and precise methods for the prediction of community composition and distribution. To adequately describe and locate areas of diverse marine ecosystems, an assessment of nearshore marine communities must be implemented. Because our coastlines are so vast, this assessment should use a methodology that is fast and cost effective.

This study outlines a method, that is cost effective and allows for rapid assessment. If an assessment methodology can show a strong link between benthic communities and a chosen habitat, then it may be possible to make accurate predictions about the ecology or community structure of benthic organisms based solely on habitat characteristics.

Previous works (Arrhenius, 1921; Williams, 1943; Lack, 1969) have made predictions about dominant species or functional groups but only at a large scale (kilometers). The species in this study show autocorrelation at scales of <95 meters to 2.6 kilometers.

Based on the ANOSIM and MDS results, rugosity at the scale examined, does not

37 strongly predict benthic community spatial structure or community composition similarities. BIOENV results suggest that small scale habitat variables and Hs do not have strong correlations with benthic community structure or composition.

The results from this study do not imply cause and effect relationships but suggest relationships between community composition, spatial structure, wave exposure and topographically related forces are interacting at multiple scales. It may be difficult to determine what factors directly cause the spatial patterns of the benthic communities for several reasons. Researchers may inappropriately define the scale of study, as evidenced in this study by the fact that few of the correlograms examined had the same spatial scale of significance. Another reason is the need of researchers to simplify complex datasets for interpretative purposes. This simplification can mask or eliminate detectable patterns from the analysis. To evaluate the benthic community as a whole, one sample size was chosen. This is a cost/benefit decision, not an ecological decision. These decisions may cause researchers to disregard an undetected pattern at a different spatial scale due to inappropriate sample size. Regardless of the number of sample sizes chosen, there will always be interpretation of the initial biological response variable (i.e. number of species per given area) and the explanatory environmental variables (i.e. interpolated wave exposure, averaged rugosity, or averaged depth). These interpretations and decisions may create artifacts before any analysis is conducted. To effectively manage and interpret data from community composition analyses, ecologists will always need to categorize and interpolate, but it may be more beneficial to do this at multiple scales and sample units. The practicality of this suggestion may be questioned, but to truly model benthic communities and make accurate predictions, these methods may be necessary.

With remote sensing methods, ecologists must question more than just the statistical significance of results. The use of non-parametric ordination methods as a substitute for classified habitat data may allow us to better relate patterns of these benthic communities with correlated processes. In this manner, I had hoped to minimize subjective interpretation of the benthic landscape and habitat-species distribution. Although I was not completely successful at choosing environmental variables that could explain more than 35% of the variability, the habitat mapping methods do provide a means to optimize sampling design and spatial analysis.

CONCLUSION

This study used existing habitat mapping methods to identify benthic kelp forest habitat and examine the benthic communities that were associated with these habitats. It showed some general trends that support findings of previous work, but also added new insight with the methods used to assess spatial variability and benthic community structure. The development of spatial statistics, GIS and remote sensing has created tremendous opportunity for researchers to quantify and explain patterns in marine ecosystems, but these new tools must be used with caution.

Based on the results of this study, it’s apparent that benthic kelp forest communities are difficult to predict with remote sensing techniques due to the multidimensional aspect of numerous species and environmental variables. Spatial autocorrelation of species within the communities makes it difficult to design parametric tests. In fact, the spatial autocorrelation is not confined to one scale. Spatial legacy and small scale species interactions may be much more influential at predicting community composition and structure. The difficult question is: how do researchers map these variables when pursuing habitat studies?

The hypothesis that rugosity is a prime factor in predicting benthic community composition and spatial structure was not supported by these results and may work in combination with other unexamined variables. I suggest future studies should evaluate topographic complexity and benthic community patterns at multiple scales, preferably at scales less than 1-meter over the entire landscape of a study site. By examining

40 habitat/species associations at a smaller scale, and showing strong habitat to species correlation, a more accurate and detailed assessment of benthic communities can be made. This would be an important step for habitat studies because it would allow marine reserve managers to make better decisions for Marine Protected Area site design, location and monitoring.

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TABLE 1. Values for Environmental and Habitat Variables.

AVG Average Max Avg Daily Max Min Avg Daily Min Transect Rugosity DEPTH Hs Hs Hs Hs Hs Walls Crevice Edges 101 1.0340 39 1.0110 3.3700 1.4158 0.20 0.7313 0 2 7 102 1.1766 38.5 1.0110 3.3700 1.4158 0.20 0.7313 3 4 10 103 1.1427 34 1.0110 3.3700 1.4158 0.20 0.7313 2 6 5 104 1.9114 35 1.0110 3.3700 1.4158 0.20 0.7313 1 2 4 105 1.5560 32 1.0110 3.3700 1.4158 0.20 0.7313 4 7 9 106 1.0391 42.5 1.0110 3.3700 1.4158 0.20 0.7313 4 1 7 107 1.1612 47 1.0110 3.3700 1.4158 0.20 0.7313 6 7 8 108 1.0366 44.5 1.0110 3.3700 1.4158 0.20 0.7313 1 2 3 109 1.5593 40.5 1.0110 3.3700 1.4158 0.20 0.7313 1 11 0 201 1.2843 55 0.9743 3.4700 1.3869 0.21 0.6862 2 3 3 202 1.9610 41 0.9743 3.4700 1.3869 0.21 0.6862 0 4 4 203 1.2214 44.5 0.9743 3.4700 1.3869 0.21 0.6862 4 3 8 204 1.0254 54.5 0.9743 3.4700 1.3869 0.21 0.6862 2 8 7 205 1.7665 45 0.9743 3.4700 1.3869 0.21 0.6862 4 14 4 206 1.4075 49 0.9743 3.4700 1.3869 0.21 0.6862 2 11 13 207 1.1345 49.5 0.9743 3.4700 1.3869 0.21 0.6862 3 6 5 208 1.0190 49.5 0.9743 3.4700 1.3869 0.21 0.6862 0 0 11 209 1.0123 49.5 0.9743 3.4700 1.3869 0.21 0.6862 3 10 13 301 1.4406 36 0.3960 2.1700 0.6669 0.05 0.2268 3 1 2 302 1.0617 38.5 0.3960 2.1700 0.6669 0.05 0.2268 1 3 9 303 1.2062 38 0.3960 2.1700 0.6669 0.05 0.2268 2 3 8 304 1.4570 38 0.3960 2.1700 0.6669 0.05 0.2268 3 3 6 305 1.0149 42.5 0.3960 2.1700 0.6669 0.05 0.2268 0 5 13 306 1.3813 41.5 0.3960 2.1700 0.6669 0.05 0.2268 6 6 7 307 1.1771 43 0.3960 2.1700 0.6669 0.05 0.2268 1 3 7

AVG Average Max Avg Daily Max Min Avg Daily Min Transect Rugosity DEPTH Hs Hs Hs Hs Hs Walls Crevice Edges 308 1.0168 45 0.3960 2.1700 0.6669 0.05 0.2268 1 6 2 309 1.1535 43 0.3960 2.1700 0.6669 0.05 0.2268 2 10 7 401 1.2213 36 0.8241 2.3900 1.1739 0.19 0.6435 2 5 7 402 1.0360 24.5 0.8241 2.3900 1.1739 0.19 0.6435 3 10 12 403 2.0558 38.5 0.8241 2.3900 1.1739 0.19 0.6435 0 3 6 404 1.4822 31 0.8241 2.3900 1.1739 0.19 0.6435 3 2 7 406 1.0333 37 0.8241 2.3900 1.1739 0.19 0.6435 3 7 7 407 1.2105 38 0.8241 2.3900 1.1739 0.19 0.6435 3 1 3 408 1.0988 30.5 0.8241 2.3900 1.1739 0.19 0.6435 2 4 4 409 1.0271 39.5 0.8241 2.3900 1.1739 0.19 0.6435 1 9 11 410 1.7006 42.5 0.8241 2.3900 1.1739 0.19 0.6435 0 6 0 501 1.4553 41 0.2034 0.5200 0.2797 0.05 0.2197 1 5 2 502 1.3315 34.5 0.2034 0.5200 0.2797 0.05 0.2197 4 4 8 504 1.0401 37.5 0.2034 0.5200 0.2797 0.05 0.2197 0 1 1 505 1.0438 51.5 0.2034 0.5200 0.2797 0.05 0.2197 1 4 14 506 1.0653 43 0.2034 0.5200 0.2797 0.05 0.2197 2 9 4 507 1.4446 40 0.2034 0.5200 0.2797 0.05 0.2197 3 7 7 508 1.3893 41 0.2034 0.5200 0.2797 0.05 0.2197 4 10 8 509 1.3303 51.5 0.2034 0.5200 0.2797 0.05 0.2197 1 8 10 510 1.3439 44 0.2034 0.5200 0.2797 0.05 0.2197 3 9 10 601 1.2642 40 0.2497 0.9600 0.3915 0.03 0.2102 1 12 3 602 1.0580 37 0.2497 0.9600 0.3915 0.03 0.2102 3 8 3 603 1.1734 39.5 0.2497 0.9600 0.3915 0.03 0.2102 2 11 9 604 1.2083 44 0.2497 0.9600 0.3915 0.03 0.2102 2 4 2 605 1.3166 44 0.2497 0.9600 0.3915 0.03 0.2102 3 1 3 606 1.0947 51 0.2497 0.9600 0.3915 0.03 0.2102 2 15 22 607 1.0342 52 0.2497 0.9600 0.3915 0.03 0.2102 2 10 12 608 1.9226 42.5 0.2497 0.9600 0.3915 0.03 0.2102 4 10 0 609 1.5463 36 0.2497 0.9600 0.3915 0.03 0.2102 2 4 5

TABLE 2A. List of Subtidal Algae Species Surveyed in Southern Monterey Bay.

Calliarthron cheilosporioides Laminaria setchellii Callophyllis violacea Macrocystis pyrifera Chondrocanthus corymbifera Rhodymenia californica Corallina spp. Rhodymenia spp. Cystoseira osmundacea Unknown encrusting corraline Dictyoneuropsis reticulata Unknown red crust Dictyoneurum californicum

TABLE 2B. List of Subtidal Invertebrate Species Surveyed in Southern Monterey Bay

Abientinaria spp. Loxorhyncus crispatus Anthopleura sola Megathura crenulata Asterina miniata Metridium farcimen Astrangia lajollaensis Mimulus foliatus Balanophylia elegans Mitra idea Balanus aquila Ophiothrix spiculata Balanus crenatus Orthasterias koehleri Balanus nubilus Pachycereanthus fimbriatus Boltenia villosa Pandalus gurneyi Bugula californica Paracyathus sternsii Ceratostoma foliatum Parastichopus californicus Corynactis californica Parastichopus parvimensis Costazia costazi Phragmatopoma californica Craniella arb Phyllactis sp. Crassedoma giganteum Pisaster giganteus Cryptochiton stelleri Pisaster ochraceus Cucumaria miniata Pugettia producta Cystodytes lobatus Pycnopodia helianthoides Dermasterias imbricata Scyra acutifrons Diaperoecia californica Serpurorbis squamigerus Didemnum carnulentum Strongylocentrotus franciscanus Diopatra ornata Strongylocentrotus pupuratus Dodecacacia fewkesi Styela montereyensis Eunerdmania claviformis Stylaster californica Halliotis rufescens Tethya aurantia Halocynthia aurantia Unknown bryozoan Henricia leviuscula Unknown Sea Lemon

Hermisenda californica Unknown sponge Hippodiplosia insculpta Unknown tunicate Hymenamphiastra cyanocrypta Urticina crassicornis Kelletia kelletii Urticina lofotensis Loxorhynchus grandis Urticina piscivor

TABLE 3. Sampled species with significant global spatial autocorrelation test results. ** = There is 5-10% likelihood that this clustered pattern is the result of random chance. * = The expected value for the 54 transects sampled is -0.0188679245283018

Sig Sampling Category Species Moran's Index * Variance Z Score Level Comment Algae % Cover Corallina sp. 0.367395225 0.014340262 3.225557049 < 0.10 ** Algae % Cover Macrocystis pyrifera 0.202897094 0.012451188 1.987410836 < 0.05 clustered Algae Swath Counts Macrocystis pyrifera 0.129600402 0.007002614 1.774204699 < 0.10 ** Invertebrate % Cover Balanophylia elegans 0.633231389 0.015472607 5.242422192 < 0.01 clustered Invertebrate % Cover Corynactis californica 0.334861438 0.016160740 2.782534029 < 0.01 clustered Invertebrate % Cover Costazia costazi 0.191285235 0.014668983 1.735145621 < 0.10 ** Invertebrate % Cover Hippodiplosia insculpta 0.305118142 0.015222469 2.625933833 < 0.01 clustered Hymenamphiaster Invertebrate % Cover cyanocrypta 0.267618766 0.015684251 2.287560316 < 0.05 clustered Phragmatopoma Invertebrate % Cover californica 0.206834677 0.010218341 2.232782205 < 0.05 clustered Invertebrate Swath Counts Asterina miniata 0.394922061 0.016585107 3.213074802 < 0.01 clustered Invertebrate Swath Counts Anthopleura sola 0.225719249 0.015173269 1.985610653 < 0.05 clustered Invertebrate Swath Counts Balanus nubilus 0.201248916 0.015138970 1.788978441 < 0.10 ** Invertebrate Swath Counts Boltenia villosa 0.431194579 0.015768533 3.584075848 < 0.01 clustered Invertebrate Swath Counts Ceratostoma foliatum 0.192393695 0.014432698 1.758518086 < 0.10 ** Invertebrate Swath Counts Cucumaria miniata 0.827652919 0.015670605 6.762305389 < 0.01 clustered Invertebrate Swath Counts Halocynthia aurantia 0.173529151 0.012835799 1.698192860 < 0.10 ** Invertebrate Swath Counts Loxorhynchus cripatus 0.261633070 0.011285798 2.640391166 < 0.01 clustered

Sig Sampling Category Species Moran's Index * Variance Z Score Level Comment Invertebrate Swath Counts Mimulus foliatus 0.183032357 0.014099938 1.700310845 < 0.10 ** Invertebrate Swath Counts Mitra idea 0.463271054 0.015359156 3.890349046 < 0.01 clustered Invertebrate Swath Counts Orthasterias koehleri 0.242391731 0.015730812 2.083036024 < 0.05 clustered Parastichopus Invertebrate Swath Counts parvimensis 0.209975966 0.015106379 1.861911952 < 0.10 ** Invertebrate Swath Counts Pisaster giganteus 0.194352336 0.016389406 1.665508534 < 0.10 ** Invertebrate Swath Counts Pisaster ochraceus 0.684464994 0.002588311 13.824603801 < 0.01 clustered

TABLE 4. 2-Way Crossed ANOSIM for Community Analysis. The R-statistic: If R=1, all replicates within a factor group are more similar to each other than any replicates from another group. If R=0, similarities between and within groups are the same on average. R values reflect variance and sample size. 999 permutations were run for each analysis. ns= not significant and ** = P Value <0.001

Sample Type Factor R-Statistic P-Value Algae Swath Site 0.528 ** Rugosity 0.123 0.058 Algae Cover Site 0.701 ** Rugosity 0.08 0.143 Invertebrate Swath Site 0.817 ** Rugosity 0.021 0.366 Invertebrate Cover Site 0.848 ** Rugosity 0.078 0.186

TABLE 5. Between sites pair-wise comparisons results The R-statistic for pair-wise comparisons is on a scale of 0 to 1: R greater than 0.75 = pairs are well separated, R greater than 0.50=pairs overlap but are different, R between 0.50 and 0.25= pairs’ separation should be interpreted with caution and R less than 0.25= pairs are barely separable. R values in bold type indicate pair-wise comparisons that exhibit strong differences.

Algae Algae Invertebrate Invertebrate Cover Abundance Abundance Cover Sites R Statistic R Statistic R Statistic R Statistic 1,2 0.519 0.704 0.741 0.667 1,3 1 0.889 1 1 1,4 0.963 1 1 0.963 1,5 0.963 1 1 1 1,6 0.741 0.593 1 1 2,3 0.407 0.667 1 0.704 2,4 0.481 0.963 1 0.519 2,5 0.667 0.815 1 0.63 2,6 0.037 0.407 1 0.556 3,4 0.704 0.926 1 1 3,5 0.519 0.667 1 0.852 3,6 0.37 0.333 1 0.926 4,5 0.759 1 1 1 4,6 0.444 0.481 1 1 5,6 0.519 0.593 1 1

TABLE 6. BIO-ENV results. Habitat variable descriptions: 1=transect rugosity, 2=average depth, 3=average Hs, 4=number of walls, 5=number of crevices, 6=number of edges.

Number of Variables Sampling Method Variables Correlation codes Algae Percent Cover 2 0.202 1,3 Community 3 0.181 3,4,6 3 0.182 3,5,6 3 0.197 1,3,6 3 0.201 1,3,5 3 0.202 1,3,4 4 0.185 1,3-5 4 0.192 1,3,4,6 4 0.201 1,3,5,6 5 0.183 1,3-6 Algae Swath Abundance 1 0.142 2 Community 2 0.204 2,3 3 0.155 2-4 3 0.169 2,3,6 3 0.174 1-3 3 0.188 2,3,5 4 0.143 1-3,6 4 0.143 2,3,5,6 4 0.153 2-5 4 0.162 1-3,5 Invertebrate Percent Cover 2 0.293 3,4 Community 3 0.297 2,4,5 3 0.299 3-5 3 0.346 2-4 4 0.311 2-4,6 4 0.328 1-4 4 0.350 2-5 5 0.298 1-4,6 5 0.322 2-6 5 0.335 1-5 Invertebrate Swath Abundance 1 0.172 3 Community 2 0.159 3,6 3 0.149 1-3 3 0.157 3,5,6 3 0.172 2,3,6 4 0.148 1-3,5 4 0.154 1-3,6 4 0.167 2,3,5,6 5 0.149 2-6 5 0.152 1-3,5,6 59

TABLE 7. Two-way crossed ANOSIM for community analysis of significant spatially autocorrelated species. The R-statistic: If R=1, all replicates within a factor group are more similar to each other than any replicates from another group. If R=0, similarities between and within groups are the same on average. R values reflect variance and sample size. 999 permutations were run for each analysis. ** = P Value <0.001

Sample Type Factor R-Statistic P-Value Percent Cover Site 0.816 ** Rugosity 0.087 0.149 Swath Estimates Site 0.923 ** Rugosity 0.011 0.430

TABLE 8. BIO-ENV results for significantly autocorrelated species pooled groupings habitat variable descriptions: 1=transect rugosity, 2=average depth, 3=average Hs, 4=number of walls, 5=number of crevices, 6=number of edges.

Number of Sampling Method Variables Correlation Variables Percent Cover Estimates 1 0.274 3 Best results 1 0.171 2 2 0.317 2,3 2 0.287 1,3 2 0.189 1,2 2 0.151 3,4 3 0.332 1-3 3 0.177 2-4 3 0.155 1,3,4 4 0.181 1-4

Abundance Estimates 1 0.172 2 Best results 2 0.211 2,3 2 0.157 2,6 3 0.212 2,3,6 3 0.180 2,3,5 3 0.159 2-4 3 0.153 1-3 4 0.171 2,3,5,6 4 0.170 2-4,6 4 0.163 1-3,6

TABLE 9. Spatial scale of significance for the Moran’s Local test of spatial autocorrelation at the p=0.05 level

Scale at 0.05 Species Significance (m)

Pisaster ochraceus <95 Balanophylia elegans 120 Cucumaria miniata 130 Mitra idea 180 Boltenia villosa 200 Asterina miniata 200 Loxorhynchus crispatus 210 Corynactis californica 220 Mimulus foliatus 220 Corallina sp. 230 Hippodiplosia insculpta 260 Hymenamphiaster cyanocrypta 300 Orthasterias koehleri 350 Macrocystis pyrifera (swath) 360 Balanus nubilus 370 Macrocystis pyrifera (cover) 380 Ceratostoma foliatum 380 Anthopleura sola 400 Halocynthia aurantia 480 Pisaster giganteus 500 Phragmatopoma californica 510 Costazia costazi 540 Parastichopus parvimensis 720

595400 596300 597200 598100 599000 599900 055600 055600 4 4 . 054900 054900 4 4 054200 054200 4 4

Study Areas 5-meter contours

Sunshaded imagery 053500 053500 4 4 Bathymetric Contours

30-m 25-m 55-m 50-m 45-m Monterey 20-m 40-m 35-m 052800 052800 4 4 15-m

10-m

0 225 450 900 1,350 1,800 0 125 250 500 750 1,000 Meters Coordinate System: UTM, WGS 84, z10N Meters 052100 052100 4 4 595400 596300 597200 598100 599000 599900

FIGURE 1. Bathymetric and sunshaded imagery map of the study site.

FIGURE 2. Map of California and faults. The image is a sun shaded, color enhance image of the USGS digital elevation model.

FIGURE 3. Lithology Map of Study Site. Southern Monterey Bay, CA. From Wagner et.al., 2002 65

FIGURE 4. Geologic structural map of study site. Southern Monterey Bay, CA. From Wagner et.al., 2002

FIGURE 5. Triangulation Integrated Network (TIN) surface model of selected (middle) cell. The TIN of the middle cell is created by clipping a TIN (of the middle and eight surrounding cells). A TIN for each cell can be created using this method (From Jenness, 2001).

FIGURE 6. Rugosity values within rock habitat in the 10-20 meter depth zone. Dark red pixels = high rugosity values of 1.346-3.180. Red pixels = medium rugosity values of 1.097-1.345. Pink pixels = low rugosity values of 1.000-1.097. A perfectly flat pixel would have a rugosity value of 1, while the highest value could be an infinite value.

FIGURE 7. Two-dimensional, side-view diagram of an edge, measurement criteria. This figure depicts the criteria that were evaluated to determine whether an edge was counted or excluded from the habitat variable survey.

S1 S2 A

Pacific Grove

S4 S5 B

S6 Monterey

FIGURE 8. Map of sampling sites Areas: (A) Lucas Point to Lovers’ Point, (B) Cabrillo Point to MacAbee Beach. Sample sites S1 thru S6 are sites where low, medium and high rugosity substratum were sampled

FIGURE 9. Photo of a medium rugosity site with diver and meter stick as scale reference. This photo depicts a site with a rugosity rating of 1.25. Maximum vertical relief is approximately 1.6 m. Diver is using a 1- meter bar as a guide to survey the 20m2 swath.

95% C.I.

I value 0

95% C.I.

-1 Distance

FIGURE 10. Theoretical diagram of Moran’s I correlogram. X axis is the distance, Y-axis is the I value. Dashed lines represent the confidence interval for Monte Carlo simulations, the blue line represents theoretical exponential data curve common in ecological studies and the red line represents a theoretical curve of patchy ecological data.

FIGURE 11. A theoretical MDS plot if samples cluster according to rugosity class and a two stage wave exposure gradient. Position of groups are arbitrary and for illustrative purposes. Dashed line indicates potential separation due to wave exposure gradient. In this illustrative example, site differences are weak or irrelevant in defining spatial structure of the benthic community.

Average Number of Walls, Crevices and Edges per Site

8 e t i per S

er Walls b

m 6 Crevice u Edges e N verag

A 4

0 123456 Sites

FIGURE 12. Average number of walls, crevices and edges for each site. Standard error bars are included

10 er

b 8 m

u Walls Crevice e N Edges erag 6 Av

0 Low Med High Rugosity Class

FIGURE 13. Average number of walls, crevices and edges for each rugosity class. Standard error bars are included

Site 1

4.00

3.50

3.00 s)

2.50 ght (H i e H e 2.00 ant Wav

ic 1.50 f Signi 1.00

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3.00 s)

2.50 ght (H i e H e 2.00 nt Wav a 1.50 fic Signi 1.00

0.50

0.00 Site 3

4.00

3.50 )

s 3.00 (H

t h

ig 2.50 e ve H

a 2.00 t W

an 1.50 ic if n g i 1.00 S

0.50

0.00

1 1 2 2 0 01 200 /20 /20 200 200 /1/ 16 31 15/ 30/ 12 1/ 1/ 12/ 12/

FIGURE 14a. Significant Wave Height for Sites 1, 2 and 3, December 1, 2001 thru January 31, 2002.

Site 4

4.00

3.50

3.00 s)

2.50 ght (H i e H e 2.00 nt Wav a 1.50 fic Signi 1.00

0.50

0.00 Site 5

4.00

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3.00 s)

2.50 ght (H Hei e 2.00 ant Wav

ic 1.50 f gni i S 1.00

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0.00 Site 6

4.00

3.50 )

s 3.00 H ht ( 2.50

ve Heig 2.00

atn Wa 1.50 c ifi n g i

S 1.00

0.50

0.00

1 1 02 00 2001 200 20 /2002 6/2 1/ /1 /3 12/1/ 1/15/ 1/30 12 12 FIGURE 14b. Significant Wave Height for Sites 4, 5 and 6, December 1, 2001 thru January 31, 2002.

FIGURE 15. PCA Ordination Plot of Environmental Variables Small scale complexity categories of Edges, Wall and Crevices associate with average depth. All wave height categories closely associate with each other and rugosity associates with none of the other categories.

Stress: 0.21

High

Med

Low

FIGURE 16. MDS Ordination Plot of Algae Percent Cover Estimates. The 2-D plot has a stress value of 0.21, while the 3-D plot has a value of 0.14. Symbols of high, medium and low rugosity categories are shown for each sample.

Stress: 0.16

High

Med

Low

FIGURE 17. MDS Ordination Plot of Algae Swath Abundance Estimates. The 2-D plot has a stress value of 0.16, while the 3-D plot has a value of 0.09. Symbols of high, medium and low rugosity categories are shown for each sample.

Stress: 0.27

High

Med

Low

FIGURE 18. MDS Ordination Plot of Invertebrate Percent Cover Estimates. The 2-D plot has a stress value of 0.27, while the 3-D plot has a value of 0.18. Symbols of high, medium and low rugosity categories are shown for each sample.

Stress: 0.25

High

Med

Low

FIGURE 19. MDS Ordination Plot of Invertebrate Swath Abundance Estimates. The 2-D plot has a stress value of 0.25, while the 3-D plot has a value of 0.19. Symbols of high, medium and low rugosity categories are shown for each sample.

Environmental Pattern

Environmental Pattern model Define Mapping Biological Habitat Interaction Pattern Pattern

Interaction Pattern

FIGURE 20. Theoretical diagram of habitat mapping work flow integrating patterns of environmental factors and patterns of biological interactions.

APPENDIX I. Algae Percent Cover * Indicates species was omitted from analysis due to rare occurance *Botryocladia Calliarthron *Calliarthron *Callophyllis *Callophyllis Callophyllis Chondrocanthus Corallina Sample pseudodichotoma cheilosporioides tuberculosum firma spp violacea corymbifera sp 101 0 0 0 0 0 0 0.05 0 102 0 0 0 0 0 0 0.2 0 103 0 0.15 0 0 0 0 0.15 0 104 0 0.15 0 0 0 0 0.15 0 105 0 0.15 0 0 0 0 0.15 0 106 0 0.1 0.05 0 0 0 0.1 0 107 0 0.05 0 0 0 0 0 0 108 0 0.05 0.05 0 0 0 0.25 0 109 0 0 0 0 0 0 0 0 201 0 0 0 0 0 0 0 0.05 202 0 0 0 0 0 0 0.05 0 203 0 0 0 0 0 0 0.05 0.05 204 0 0 0 0 0 0 0.05 0.1 205 0 0.05 0 0 0 0.1 0.4 0.1 206 0 0 0 0 0 0 0 0.1 207 0 0 0 0 0 0 0 0 208 0 0 0 0 0 0 0 0.1 209 0 0.05 0 0 0 0 0 0 301 0 0 0 0 0 0 0 0 302 0 0 0.05 0.05 0 0 0 0 303 0 0.05 0 0.1 0 0 0.05 0 304 0 0 0 0 0 0 0.15 0 305 0 0 0 0 0 0 0 0 306 0 0 0 0 0 0 0 0 307 0 0.05 0 0 0 0 0 0 308 0 0 0 0 0 0.05 0 0 309 0 0 0 0 0 0 0 0 401 0 0 0 0 0 0 0 0 402 0 0 0 0 0 0 0.35 0 403 0 0 0 0 0 0 0 0 404 0 0.15 0 0 0 0 0.05 0 406 0 0 0 0 0 0 0.05 0 407 0 0 0 0 0 0 0.1 0 408 0 0 0 0 0 0 0.3 0 409 0 0 0 0 0 0 0 0 410 0.05 0 0 0 0 0.05 0.05 0 501 0 0 0 0 0 0.05 0 0 502 0 0 0 0 0 0 0 0 504 0 0 0 0 0 0 0 0 505 0 0 0 0 0 0 0 0 506 0 0 0 0 0 0 0 0 507 0 0 0 0 0 0 0 0 508 0 0.1 0 0 0 0.1 0 0 509 0 0 0 0 0 0 0 0 510 0 0 0 0 0 0 0 0 601 0 0 0 0 0 0 0 0 602 0 0.05 0 0 0.25 0 0 0 603 0 0 0 0 0 0 0 0 604 0 0 0 0 0 0 0.2 0 605 0 0 0 0 0 0 0.15 0 606 0 0 0 0 0 0 0 0 607 0 0 0 0 0 0.05 0 0 608 0 0 0 0 0 0 0.15 0 609 0 0 0 0 0 0 0.05 0

84 APPENDIX I. Algae Percent Cover * Indicates species was omitted from analysis due to rare occurance *Cystoseira Dictyoneuropsis Dictyoneurum *Egregia *Eurystomella *Fauchea Macrocystis *Prionitis *Rhodoglossum Sample osmundacea reticulata californicum menziessi bilabiata spp pyrifera lanceolata roseum 101 00 0000000 102 0 0 0 0 0 0 0.1 0 0 103 0 0 0.05 0 0 0 0 0 0 104 0 0 0.05 0 0 0 0 0 0 105 00 0000000 106 0 0 0 0 0 0 0 0.05 0.05 107 00 0000000 108 0 0.05 0 0 0 0 0 0 0 109 00 0000000 201 00 0000000 202 00 0000000 203 0 0.05 0 0 0 0 0 0 0 204 0 0.1 0.15 0 0 0 0 0 0 205 00 0000000 206 0 0.1 0.1 0 0 0 0.1 0 0 207 00 0000000 208 0 0 0 0 0 0 0.1 0 0 209 0 0 0 0 0.05 0 0.05 0 0 301 00 0000000 302 0 0 0 0.1 0 0 0.05 0 0 303 0.05 0.05 0 0 0 0 0 0 0 304 0 0.05 0 0 0 0 0.05 0 0 305 00 0000000 306 0 0 0 0 0 0 0.05 0 0 307 0 0 0 0 0 0 0.15 0 0 308 0 0 0 0 0 0 0.05 0 0 309 0 0.05 0 0 0 0 0.15 0.05 0 401 00 0000000 402 0 0 0 0.05 0 0 0.15 0 0 403 0 0 0 0.05 0 0 0 0 0 404 0 0 0 0 0 0 0.5 0 0 406 00 0000000 407 0 0 0 0 0 0 0.15 0 0 408 0 0 0 0 0 0 0.15 0 0 409 0 0 0 0 0 0 0.15 0 0 410 0 0 0 0 0 0 0.05 0 0 501 00 0000000 502 0 0 0 0 0 0 0.1 0 0 504 0 0.05 0 0 0 0 0 0 0 505 0 0 0 0 0 0 0.1 0.05 0 506 00 0000000 507 00 0000000 508 0 0 0 0 0 0 0.1 0 0 509 0 0 0.15 0 0 0 0 0 0 510 0 0 0 0 0 0 0.15 0 0 601 0 0 0 0 0 0 0.05 0 0 602 0 0 0 0 0 0.05 0 0 0 603 0 0.05 0 0 0 0 0.1 0 0 604 0 0.1 0 0 0 0 0.15 0 0 605 0 0 0 0 0 0 0.15 0 0 606 0 0 0.05 0 0 0 0 0 0 607 00 0000000 608 0 0 0.05 0 0 0 0.05 0 0 609 0 0 0.15 0 0 0 0.05 0 0

85 APPENDIX I. Algae Percent Cover * Indicates species was omitted from analysis due to rare occurance Rhodymenia Rhodymenia Unknown Unknown *unknown small Sample californica spp Shell Debris encrusting coralline Red crust red alga 101 0.05 0 0.15 0.1 0.15 0 102 0.5 0 0 0.05 0 0 103 0.15 0 0 0.05 0 0 104 0.15 0 0 0.05 0 0 105 0.4 0 0 0.05 0 0 106 0.25 0 0 0 0 0 107 0.5 0 0 0.2 0 0 108 0.25 0.3 0 0.05 0 0 109 0 0.1 0 0.1 0 0 201 0.1 0 0.05 0 0.05 0 202 0.25 0.05 0 0 0 0 203 0.05 0.15 0 0 0.05 0 204 0.15 0.05 0.1 0 0 0 205 0.2 0.05 0 0 0.05 0 206 0.1 0.15 0 0 0 0 207 0.1 0.25 0 0 0 0 208 0 0 0.15 0 0.15 0 209 0 0 0.05 0 0 0 301 0.15 0 0 0.15 0 0 302 0 0 0 0.2 0.15 0 303 0.1 0.1 0 0.3 0 0 304 0 0.1 0.1 0.05 0 0 305 0 0 0 0.25 0 0 306 0.1 0.2 0 0.2 0 0 307 0 0 0 0.1 0.05 0 308 0 0 0 0.15 0.1 0 309 0 0 0 0.35 0.15 0 401 0.45 0 0 0 0 0 402 0.3 0 0 0.05 0.05 0 403 0 0.2 0.1 0.05 0 0 404 0 0.25 0.05 0.15 0 0 406 0.3 0.1 0.05 0 0 0 407 0.25 0.1 0 0 0 0 408 0.15 0 0.05 0 0 0 409 0 0 0 0.1 0 0 410 0 0.45 0 0.15 0 0 501 0 0.05 0 0.05 0 0 502 000 0 0 0 504 0 0 0.05 0.05 0 0.2 505 0 0.05 0 0.05 0 0.1 506 0 0 0 0.1 0 0 507 0 0 0 0.15 0 0 508 0 0 0 0.05 0 0 509 0.1 0.05 0.05 0.1 0 0 510 0 0 0 0.1 0 0 601 0.05 0 0 0.1 0 0 602 0 0.05 0.1 0.05 0 0 603 0 0 0.05 0 0 0 604 0.15 0 0 0 0 0 605 0.3 0 0.05 0 0 0 606 0.05 0 0.2 0.05 0 0 607 0.3 0 0 0 0 0 608 0.15 0 0 0.05 0.05 0 609 0.05 0.2 0 0 0 0

86 Algae Swath Abundance * Indicates species was omitted from analysis due to rare occurance

*Botryocladia Chondracanthus Cystoseira *Desmarestia Dictyoneuropsis Dictyoneurum *Egregia *Fauchea Sample pseudodichotoma corymbifera osmundacea sp. reticulata californicum menziessi spp. 101 0 1 11 0 2 0 0 0 102 0 4 2 0 28 0 0 0 103 0 3 10 0 25 1 0 0 104 0 3 0 0 0 1 0 0 105 0 3 0 0 3 0 0 0 106 0 2 6 0 6 0 0 0 107 0 0 0 0 17 0 0 0 108 0 5 0 3 16 0 0 0 109 0 0 0 0 0 3 0 0 201 0 0 0 0 4 0 0 0 202 0 1 0 0 1 0 0 0 203 0 1 0 0 2 0 0 0 204 0 1 0 0 21 3 0 0 205 0 8 0 0 0 1 0 0 206 0 0 0 0 2 10 0 0 207 0 0 0 0 0 1 0 0 208 0 0 0 0 0 1 0 0 209 0 0 2 0 0 4 0 0 301 0 0 0 0 0 0 0 0 302 0 0 5 0 0 2 1 0 303 0 1 9 0 1 5 0 0 304 0 3 0 0 1 1 0 0 305 0 0 0 0 0 2 0 0 306 0 0 0 0 0 0 0 0 307 0 0 0 0 0 4 0 0 308 0 0 0 0 0 4 0 0 309 0 0 7 0 1 15 0 0 401 0 0 0 0 0 0 0 0 402 0 7 0 0 0 0 1 0 403 0 0 1 0 0 0 1 0 404 0 1 0 0 0 0 0 0 406 0 1 0 0 0 0 0 0 407 0 2 0 0 0 0 0 0 408 0 6 0 0 0 0 0 0 409 0 0 0 0 0 0 0 0 410 1 1 0 0 0 0 0 0 501 0 0 0 0 0 0 0 0 502 0 0 0 0 0 0 0 0 504 0 0 8 0 1 10 0 0 505 0 0 0 0 0 4 0 0 506 0 0 0 0 0 0 0 0 507 0 0 0 0 0 0 0 0 508 0 0 0 0 0 1 0 0 509 0 0 0 0 34 1 0 0 510 0 0 0 0 0 1 0 0 601 0 0 0 0 0 0 0 0 602 0 0 0 0 0 0 0 1 603 0 0 0 0 2 0 0 0 604 0 4 0 0 3 0 0 0 605 0 3 0 0 5 0 0 0 606 0 0 0 0 0 9 0 0 607 0 0 0 0 0 2 0 0 608 0 3 0 0 0 2 0 0 609 0 1 0 0 0 2 0 0

87 Algae Swath Abundance * Indicates species was omitted from analysis due to rare occurance

Laminaria Macrocystis Sample setchellii pyrifera 101 01 102 02 103 01 104 01 105 01 106 01 107 00 108 00 109 01 201 00 202 00 203 00 204 00 205 00 206 02 207 10 208 02 209 11 301 05 302 04 303 01 304 01 305 06 306 01 307 010 308 02 309 07 401 115 402 013 403 02 404 050 406 00 407 01 408 013 409 02 410 24 501 01 502 02 504 02 505 04 506 02 507 02 508 06 509 01 510 06 601 12 602 23 603 04 604 03 605 02 606 00 607 00 608 31 609 02

88 Invertebrate Percent Cover * Indicates species was omitted from analysis due to rare occurance

Abietinaria Asterina Astrangia Balanophyllia *Balanus Balanus Bare Bare *Boltenia *Bowerbankia Bugula Sample sp. miniata lajollaensis elegans aquila crenatus Rock Sand villosa gracilis californica 101 0 0.05 0 0.05 0 0.1 0 0.2 0 0 0 102 0.1 0.05 0 0.05 0 0.05 0 0 0 0 0 103 0 0.1 0.05 0 0 0.15 0 0 0 0 0 104 0 0.1 0.05 0 0 0.15 0 0 0 0 0 105 0 0.15 0 0 0 0.2 0 0 0 0 0 106 0.05 0.05 0 0.15 0 0.05 0 0.15 0 0 0 107 0.05 0.05 0.1 0.2 0 0.25 0 0 0 0 0.05 108 0.25 0.05 0.15 0.05 0 0 0 0.05 0 0 0.05 109 0.05 0.05 0 0.1 0 0.1 0 0 0 0 0 201 0.25 0 0.05 0 0 0 0.05 0.1 0 0 0 202 0 0 0 0.05 0 0.55 0 0 0 0 0 203 0.05 0.05 0.05 0 0 0.15 0 0 0 0 0 204 0.05 0 0.05 0 0 0 0 0 0 0 0 205 0 0.05 0.1 0.2 0 0.25 0 0 0 0 0.05 206 0.1 0 0 0 0 0.35 0 0.1 0 0.05 0.05 207 0.05 0.1 0.05 0.15 0.05 0.15 0 0 0 0 0.05 208 0 0.25 0 0 0 0.25 0 0.15 0 0 0 209 0.15 0.05 0.05 0.05 0 0.3 0 0.05 0.05 0 0 301 0 0 0.05 0.1 0 0.45 0 0 0 0 0 302 0.25 0.05 0 0.05 0 0.15 0 0.3 0 0 0 303 0.05 0 0 0.15 0 0.15 0 0 0 0 0 304 0.15 0.1 0.2 0.1 0 0.2 0 0 0 0 0 305 0.45 0 0 0.1 0 0.15 0 0.1 0 0 0 306 0.25 0.1 0 0.15 0 0.55 0 0 0 0 0 307 0.1 0.05 0 0.1 0 0.35 0 0 0 0 0 308 0.1 0.05 0.05 0.05 0 0.3 0.05 0.15 0 0 0 309 0.05 0.1 0 0.1 0 0.05 0 0.1 0 0 0 401 0 0 0 0 0.05 0.5 0.05 0 0 0 0.05 402 0 0.05 0.1 0 0 0.15 0 0 0 0.05 0 403 0.05 0.05 0 0 0 0 0 0.05 0 0 0 404 0 0.15 0 0 0 0.15 0 0 0 0 0 406 0 0.1 0 0.25 0 0.15 0.05 0 0 0 0 407 0.2 0 0 0 0 0.25 0 0.05 0 0 0 408 0 0.1 0 0 0 0.1 0 0 0 0 0 409 0.05 0 0 0.05 0 0.3 0 0 0 0 0.05 410 0 0.1 0 0 0 0.2 0 0 0 0 0 501 0.05 0 0 0.2 0 0.4 0.05 0 0 0 0 502 0 0.15 0.25 0.2 0 0.3 0 0 0 0 0.1 504 0.35 0.05 0 0.15 0 0.05 0 0 0 0 0 505 0.6 0.05 0.15 0 0 0 0 0 0 0 0.1 506 0.1 0 0.15 0.1 0 0.15 0 0 0 0 0.1 507 0.05 0 0.1 0.05 0 0.25 0 0 0 0 0.05 508 0.05 0.05 0.1 0.1 0 0.15 0 0 0 0 0.2 509 0.1 0.05 0.1 0.1 0.05 0.1 0 0.05 0 0 0.05 510 0.35 0 0.25 0.1 0 0.15 0 0 0 0 0 601 0.05 0.05 0 0.1 0 0.05 0.05 0.05 0 0 0 602 0 0.1 0 0.35 0 0.2 0.05 0 0 0 0.1 603 0.15 0.05 0 0.05 0 0.1 0 0.05 0 0 0.05 604 0 0.1 0 0.45 0 0.05 0 0 0 0 0 605 0 0.05 0 0.2 0 0.05 0 0 0 0 0.05 606 0.4 0 0.15 0 0 0.05 0 0 0 0 0.05 607 0.3 0.1 0.2 0.1 0 0.05 0 0 0 0 0 608 0.05 0 0.15 0.2 0 0.15 0.1 0 0 0 0.1 609 0 0.05 0.15 0.2 0 0.1 0 0 0 0 0

89 Invertebrate Percent Cover * Indicates species was omitted from analysis due to rare occurance

*Calliostoma Celleporella *Clavelina Corynactis Costazia *Crassedoma *Dermasterias Diaperoecia Didemnum Sample ligatum hyalina huntsmani californica costazi giganteum imbricata californica carnulentum 101 0 0 0 0 0.05 0 0 0 0 102 0 0.05 0 0 0.05 0 0 0 0 103 0 0.1 0 0.1 0 0 0 0.1 0 104 0 0.1 0 0.1 0 0 0 0.1 0 105 0 0.1 0 0.25 0 0 0 0.05 0.05 106 0 0.05 0 0 0 0 0 0.1 0 107 0 0.1 0.05 0.1 0.05 0.05 0 0.15 0 108 0 0 0 0.05 0 0 0 0 0 109 0 0.05 0 0.05 0 0 0 0.15 0 201 0 0 0 0.1 0 0 0 0.1 0 202 0 0 0 0.25 0 0 0 0.05 0 203 0 0 0 0.35 0 0 0 0.05 0 204 0 0 0 0 0 0 0 0 0.05 205 0 0 0 0.1 0 0 0 0.1 0.05 206 0 0.1 0 0.05 0 0 0.05 0 0 207 0 0.05 0.05 0.2 0 0 0 0.3 0 208 0000000 00 209 0 0 0 0 0 0 0 0.05 0 301 0 0.1 0 0.15 0.05 0 0 0.05 0.05 302 0000000 00 303 0 0 0 0.1 0.05 0 0 0.05 0 304 0 0.05 0 0 0.15 0 0 0.05 0.05 305 0000000 00 306 0 0.05 0 0.1 0.1 0 0 0 0 307 0 0 0 0 0.1 0 0 0.1 0.05 308 0 0 0 0.05 0 0 0 0 0 309 0 0 0 0.1 0.05 0 0 0.05 0.05 401 0 0.35 0 0.15 0 0 0 0.15 0 402 0.05 0.15 0 0.1 0 0 0 0.1 0.1 403 0 0 0 0 0 0 0 0.05 0 404 0 0.05 0 0.15 0 0 0 0.1 0.05 406 0 0.05 0 0.1 0 0 0 0.05 0 407 0 0.2 0 0 0 0 0.05 0 0 408 0 0.1 0 0.15 0.05 0 0 0.15 0 409 0 0 0 0.25 0.05 0 0 0.25 0 410 0 0.05 0 0 0 0 0 0 0.05 501 0 0 0 0 0.05 0 0 0.15 0 502 0 0 0 0.05 0 0 0 0.05 0 504 0 0 0 0 0 0 0 0.05 0 505 0 0 0 0 0 0 0 0.05 0 506 0 0 0 0 0.05 0 0 0.05 0 507 0 0 0 0 0 0 0 0.2 0.05 508 0 0 0 0 0 0 0 0.05 0 509 0 0 0 0.05 0 0 0 0.1 0 510 0000000 00 601 0 0 0 0.15 0 0 0 0.05 0 602 0 0 0 0.15 0 0 0 0 0 603 0 0 0 0.15 0 0 0 0.1 0 604 0 0 0 0.2 0 0 0 0.15 0 605 0 0 0 0.2 0 0 0 0.2 0 606 0 0 0 0 0 0 0 0.05 0 607 0 0 0 0 0 0 0 0.1 0 608 0 0 0 0 0 0 0 0.1 0 609 0 0 0 0.1 0 0 0 0.2 0

90 Invertebrate Percent Cover * Indicates species was omitted from analysis due to rare occurance

Diopatra Dodecaceria *Eudistylia *Eunerdmania *Eurystomella Hippodiplosia Hymenamphiaster *Lebbeus Sample ornata fewkesi polymorpha claviformis bilabiata insculpta cyanocrypta grandimanus 101 0 0 0 0 0 0.1 0 0 102 0 0 0 0 0 0.4 0 0 103 0 0 0 0 0 0.3 0 0 104 0 0 0 0 0 0.3 0 0 105 0 0 0 0 0 0.2 0 0 106 0 0 0 0 0 0.1 0 0 107 0 0 0 0 0 0.1 0 0 108 0 0 0 0 0 0.35 0 0 109 0 0.1 0 0 0 0.1 0 0 201 0 0.05 0 0 0 0.05 0 0 202 0 0 0 0 0 0.05 0 0 203 0 0 0.05 0 0 0 0.05 0 204 00 0 0 0 0 0 0 205 00 0 0 0 0 0 0 206 0 0 0 0 0 0.05 0 0 207 0 0 0 0 0 0.05 0.05 0 208 00 0 0 0 0 0 0 209 0 0 0 0 0.05 0 0 0 301 00 0 0 0 0 0 0 302 00 0 0 0 0 0 0 303 0 0.05 0 0 0 0 0 0 304 0 0.1 0 0 0 0 0.05 0 305 0.05 0 0 0 0 0 0.1 0 306 0.05 0.05 0 0 0 0.05 0 0 307 0.1 0.05 0 0 0 0 0.05 0 308 0 0 0 0 0 0 0.05 0 309 0 0.15 0 0 0 0 0 0 401 00 0 0 0 0 0 0 402 0 0 0 0 0 0.1 0 0 403 0 0 0 0 0 0.05 0 0 404 0 0 0 0 0 0.1 0 0 406 0 0 0 0 0 0 0.1 0 407 0 0 0 0 0 0.15 0.05 0 408 0 0 0 0 0 0.05 0 0 409 0 0.1 0 0 0 0 0.05 0 410 0 0.2 0 0.15 0 0.3 0 0.05 501 0 0.1 0 0 0 0 0 0 502 0 0.1 0 0 0 0 0 0 504 00 0 0 0 0 0 0 505 0 0.05 0 0.05 0 0 0 0 506 0 0 0 0 0 0 0.05 0 507 0 0.05 0 0 0 0 0 0 508 0.1 0 0 0 0 0.05 0.1 0 509 0 0.1 0 0 0 0 0.05 0 510 00 0 0 0 0 0 0 601 0 0 0 0 0 0 0.15 0 602 00 0 0 0 0 0 0 603 0 0 0 0 0 0 0.05 0 604 0 0 0 0 0 0 0.05 0 605 00 0 0 0 0 0 0 606 00 0 0 0 0 0 0 607 0 0 0 0 0 0 0.05 0 608 0 0.05 0 0 0 0 0.05 0 609 0 0 0 0 0 0 0.1 0

91 Invertebrate Percent Cover * Indicates species was omitted from analysis due to rare occurance

*Leucetta Loxorhynchus *Loxorhynchus Membranipora *Orthasterias *Pachycerianthus Paracyathus Sample losangelensis crispatus grandis spp koehleri fimbriatus sternsi 101 00 0 00 0 0 102 00 0 00 0 0 103 00 0 00 0 0 104 00 0 00 0 0 105 00 0 00 0 0 106 0.05 0 0 0 0.05 0 0.05 107 0 0 0 0 0 0 0.05 108 0 0 0 0 0 0 0.05 109 0 0 0 0.05 0 0 0.05 201 0 0 0 0 0 0 0.05 202 0 0 0 0.05 0 0 0 203 00 0 00 0 0 204 0 0 0 0 0 0.05 0 205 0 0 0 0.05 0 0 0.05 206 00 0 00 0 0 207 00 0 00 0 0 208 0 0 0 0 0 0 0.05 209 0 0 0 0 0 0 0.05 301 0 0.05 0 0 0 0 0.15 302 00 0 00 0 0 303 0 0 0 0 0 0 0.05 304 00 0 00 0 0 305 00 0 00 0 0 306 0 0 0 0 0 0 0.05 307 00 0 00 0 0 308 00 0 00 0 0 309 0 0 0 0 0 0 0.05 401 0 0 0.05 0.05 0 0 0 402 0 0 0 0.25 0 0 0 403 0 0 0 0.15 0 0 0 404 0 0 0 0.1 0 0 0 406 00 0 00 0 0 407 0 0 0 0.05 0 0 0 408 0 0 0 0.05 0 0 0 409 0 0.05 0 0 0 0 0 410 0 0.2 0 0 0 0 0 501 0 0 0 0 0 0 0.05 502 0 0 0 0 0 0 0.05 504 00 0 00 0 0 505 0 0 0 0 0 0 0.05 506 0 0 0 0 0.05 0 0.1 507 0 0 0 0 0 0 0.05 508 0 0 0 0 0 0 0.05 509 0 0 0 0 0 0 0.05 510 0 0.05 0 0 0 0 0 601 0 0 0 0 0 0 0.05 602 00 0 00 0 0 603 0 0 0 0.05 0 0 0.1 604 00 0 00 0 0 605 0 0 0 0 0 0 0.05 606 0 0 0 0 0.05 0 0.1 607 0 0 0 0 0 0 0.05 608 0 0 0 0.05 0 0 0 609 0 0 0 0 0 0 0.05

92 Invertebrate Percent Cover * Indicates species was omitted from analysis due to rare occurance

*Phidolopora Phragmatopoma *Pisaster Pisaster *Pisaster *Plumularia *Pugettia *Pugettia Pycnopodia Sample labiata californica brevispinus giganteus ochraceus sp producta richii helianthoides 101 000000000 102 000000000 103 000000000 104 000000000 105 000000000 106 000000000 107 0 0 0 0.05 0 0 0 0 0 108 000000000 109 0.05 0 0 0 0 0 0 0 0 201 000000000 202 000000000 203 0 0 0 0.05 0 0 0 0 0 204 000000000 205 000000000 206 000000000 207 000000000 208 0 0 0 0 0 0 0 0.05 0 209 000000000 301 0 0 0 0.05 0 0 0 0 0 302 000000000 303 000000000 304 000000000 305 000000000 306 000000000 307 0 0 0 0.1 0 0 0 0 0 308 000000000 309 0 0 0 0 0 0 0 0 0.05 401 0 0 0 0.05 0 0 0 0 0 402 000000000 403 0 0.1 0 0 0 0 0 0 0 404 0 0 0 0.05 0 0 0.05 0 0 406 0 0 0 0 0 0 0 0 0.05 407 0 0.15 0 0 0 0 0 0 0 408 0 0.05 0 0 0 0 0 0 0 409 0 0 0 0 0 0 0 0 0.05 410 000000000 501 0.1 0 0 0 0 0 0 0 0 502 0 0 0 0 0 0 0 0 0.05 504 0 0.05 0 0.05 0 0 0 0 0 505 000000000 506 0 0 0 0.15 0 0 0 0 0.05 507 000000000 508 000000000 509 000000000 510 000000000 601 000000000 602 000000000 603 000000000 604 0 0 0.05 0 0 0 0 0 0 605 000000000 606 000000000 607 000000000 608 0 0 0 0 0 0.05 0 0 0 609 0 0 0 0 0.05 0 0 0 0

93 Invertebrate Percent Cover * Indicates species was omitted from analysis due to rare occurance

*Scyra Serpulorbis Shell *Strongylocentrotus *Stylela Stylaster *Tethya Unknown *Unknown Sample acutifrons squamigerus Debris purpuratus montereyensis californica aurantia Bryozoan Hydroid 101 0 0 0.15 0 0 0 0 0.05 0 102 0.05 0 0 0 0 0 0 0 0 103 0 0 0 0 0.05 0 0 0 0 104 0 0 0 0 0 0.05 0 0 0 105 0 0 0 0 0 0.05 0 0.05 0 106 0 0.05 0 0 0 0 0 0.05 0 107 0 0.05 0 0 0 0 0 0.1 0 108 0 0 0 0 0 0 0 0.2 0 109 0 0.1 0 0 0 0 0.05 0.35 0 201 0 0 0.05 0 0 0.05 0 0 0 202 0 0 0 0 0 0.05 0 0 0 203 0.05 0 0 0 0 0.05 0 0 0 204 0 0 0.1 0 0 0 0 0 0 205 0000 0 0000 206 0000 0 0000 207 0000 0 0000 208 0 0 0.15 0 0 0 0 0.05 0 209 0 0.05 0.05 0 0 0 0.05 0 0 301 0000 0 0000 302 0 0 0 0 0 0 0 0.15 0 303 0 0 0 0 0 0 0.05 0 0 304 0 0 0.1 0 0 0 0 0.2 0 305 0000 0 0000 306 0 0 0 0 0 0 0 0.05 0 307 0000 0 0000 308 0000 0 0000 309 0000 0 0000 401 0 0 0 0 0 0 0 0.1 0 402 0000 0 0000 403 0 0 0.1 0 0 0 0 0 0 404 0 0 0.05 0 0 0 0 0 0.05 406 0 0 0.05 0 0 0 0 0 0 407 0000 0 0000 408 0 0 0.05 0 0 0 0 0 0 409 0 0 0 0 0 0.05 0 0 0 410 0000 0 0000 501 0 0 0 0 0 0.05 0 0 0 502 0 0.05 0 0 0 0.15 0 0 0 504 0 0 0.05 0 0 0 0 0 0 505 0000 0 0000 506 0000 0 0000 507 0 0 0 0 0 0.15 0 0.05 0 508 0000 0 0000 509 0 0 0.05 0 0 0 0 0 0 510 0000 0 0000 601 0 0.05 0 0.05 0 0 0 0 0 602 0 0 0.1 0 0 0 0 0 0 603 0 0 0.05 0 0 0 0 0 0 604 0000 0 0000 605 0 0 0.05 0 0 0 0 0 0 606 0 0 0.2 0 0 0 0 0 0 607 0000 0 0000 608 0 0.05 0 0 0.05 0 0 0 0.05 609 0000 0 0000

94 Invertebrate Percent Cover * Indicates species was omitted from analysis due to rare occurance

Unknown Unknown *Urticina *Urticina *Urticina Sample Sponge Tunicate crassicornis lofotensis piscivora 101 0.05 0 0 0 0 102 0.05 0 0 0 0 103 0.15 0 0 0 0 104 0.15 0 0 0 0 105 0.15 0 0 0 0 106 00000 107 00000 108 00000 109 00000 201 0.1 0 0 0.05 0 202 0.1 0 0 0 0 203 0.4 0 0 0 0 204 0.3 0 0 0 0 205 00000 206 0 0 0 0 0.05 207 00000 208 00000 209 00000 301 00000 302 00000 303 0 0.05 0 0 0 304 0 0.05 0 0 0 305 0.05 0 0 0 0 306 00000 307 0.05 0 0 0 0 308 00000 309 00000 401 0.15 0 0 0 0 402 0.05 0 0 0 0 403 0.5 0 0 0 0 404 0.05 0 0 0 0 406 0.25 0 0 0 0 407 0.25 0 0.05 0 0 408 0.1 0 0 0 0 409 0.05 0.05 0 0 0 410 00000 501 00000 502 00000 504 00000 505 0.05 0 0 0 0 506 00000 507 00000 508 00000 509 00000 510 00000 601 00000 602 00000 603 00000 604 00000 605 00000 606 00000 607 0 0 0 0 0.05 608 0 0.1 0 0 0 609 0.15 0 0 0 0

95 Invertebrate Swath Abundance * Indicates species was omitted from analysis due to rare occurance *Anisodoris Anthopleura *Aplysia *Archidoris Asterina Balanus Balanus Boltenia *Cancer *Cancer Sample nobilis sola californica odhneri miniata aquila nubilus villosa antennarius magister 101 0 2 0 0 64 0 0 0 0 0 102 0 2 0 0 57 0 0 9 0 0 103 0 3 0 0 58 0 0 0 0 0 104 0 0 0 0 21 0 0 6 0 0 105 0 1 0 0 53 0 0 0 0 0 106 0 2 0 0 91 0 0 3 0 0 107 0 0 0 0 53 0 0 9 0 0 108 0 0 1 0 72 0 1 7 0 0 109 0 0 0 0 54 0 1 10 0 0 201 0 0 0 0 46 0 0 0 0 0 202 0 0 0 0 43 0 1 0 0 0 203 0 0 0 0 35 0 0 0 0 0 204 0 0 0 0 35 0 0 0 0 0 205 0 0 0 0 41 0 0 1 0 0 206 3 0 0 4 15 0 0 3 0 0 207 0 0 0 0 40 1 0 1 1 0 208 0 0 0 0 81 0 0 2 0 0 209 0 0 0 0 47 0 0 6 0 0 301 0 0 0 0 95 0 0 8 0 0 302 0 1 0 0 59 0 0 18 0 0 303 0 4 0 0 58 0 0 2 0 0 304 0 1 0 0 89 0 3 2 0 0 305 0 5 0 0 46 0 2 17 0 0 306 0 0 0 0 81 0 1 7 0 0 307 0 4 0 0 70 0 0 8 0 0 308 0 4 0 0 89 0 1 12 0 0 309 0 1 0 0 49 0 0 10 0 0 401 0 0 0 0 56 1 0 0 0 0 402 0 1 0 0 49 0 0 0 0 0 403 0 0 0 0 75 0 0 2 0 0 404 0 0 0 0 18 0 0 0 0 0 406 0 1 0 0 92 0 0 2 1 0 407 0 0 0 0 62 0 0 3 0 0 408 0 1 0 0 32 0 0 0 0 0 409 0 0 0 0 86 0 3 0 0 0 410 0 0 0 0 57 0 1 1 0 0 501 0 1 0 0 44 0 5 6 0 0 502 0 0 0 0 58 1 0 2 0 0 504 0 0 0 0 85 0 5 1 0 0 505 0 0 0 0 37 0 3 2 0 0 506 0 0 0 0 55 0 3 3 0 0 507 0 0 0 0 43 0 5 1 0 0 508 0 0 0 0 73 0 0 4 0 0 509 0 1 0 0 102 1 0 10 0 0 510 0 0 0 0 82 3 0 7 0 0 601 0 0 0 0 85 0 1 0 0 0 602 0 0 0 0 77 0 0 0 0 0 603 0 0 0 0 88 0 0 0 0 0 604 0 0 0 0 87 0 0 0 0 0 605 0 0 0 0 97 0 0 0 0 0 606 0 0 0 0 89 0 0 0 0 0 607 0 0 3 0 101 0 0 0 0 1 608 0 0 0 0 52 0 0 0 0 0 609 0 0 0 0 62 0 0 0 0 0

96 Invertebrate Swath Abundance * Indicates species was omitted from analysis due to rare occurance *Cancer Ceratostoma *Clavelina Craniella Crassedoma Cryptochiton *Cryptolithodes Cucumaria *Cucumaria Sample productus foliatum huntsmani arb giganteum stelleri sitchensis miniata piperata 101 00 000 3 0 0 0 102 10 003 1 0 0 1 103 00 000 0 0 0 0 104 00 000 0 0 2 0 105 02 000 1 0 0 0 106 00 000 0 0 0 0 107 00 101 1 0 1 0 108 00 000 0 0 0 0 109 00 001 0 0 0 0 201 00 000 0 0 3 0 202 00 000 0 0 0 0 203 01 000 0 0 0 0 204 00 000 0 0 0 0 205 00 000 0 0 1 0 206 00 000 0 0 0 0 207 01 101 0 0 3 0 208 02 000 0 0 1 0 209 01 001 0 0 0 0 301 00 000 0 0 0 0 302 01 000 0 0 0 0 303 00 000 2 0 0 0 304 01 001 0 0 0 0 305 00 000 0 0 0 0 306 01 020 0 0 0 0 307 01 001 1 0 0 0 308 04 011 0 0 0 0 309 01 000 0 0 0 0 401 00 000 0 0 0 0 402 00 000 0 0 0 0 403 00 030 0 0 0 0 404 00 000 0 0 0 0 406 00 000 0 0 0 0 407 00 020 0 0 0 0 408 00 000 1 0 0 0 409 00 006 1 0 2 0 410 00 000 0 0 0 0 501 02 001 0 1 0 0 502 00 000 0 0 0 0 504 01 000 0 0 0 0 505 00 002 0 0 1 0 506 00 001 0 0 1 0 507 03 000 0 0 0 0 508 01 001 1 1 1 0 509 00 002 1 0 2 0 510 00 002 0 0 0 0 601 00 000 0 0 0 0 602 00 000 0 0 0 0 603 00 000 0 0 2 0 604 00 000 0 0 3 0 605 00 000 0 0 4 0 606 00 000 0 0 0 0 607 00 000 0 0 3 0 608 00 000 1 0 0 0 609 00 000 1 0 0 0

97 Invertebrate Swath Abundance * Indicates species was omitted from analysis due to rare occurance Cystodytes Dermasterias *Diaulula *Dirona Eunerdmania Haliotis Halocynthia Henricia Hermisenda Sample lobatus imbricata sandiegensis albolineata claviformis rufescens aurantia leviuscula crassicornis 101 00 00 00000 102 00 00 00020 103 40 00 00040 104 00 00 00030 105 10 00 00011 106 00 01 00010 107 50 00 00070 108 40 00 00020 109 12 0 0 0 0 0 0 6 0 201 00 00 00010 202 20 00 00000 203 08 00 00010 204 40 00 00020 205 30 00 00010 206 61 00 00010 207 10 00 00060 208 00 00 00000 209 00 00 00030 301 00 00 00001 302 00 30 00200 303 80 00 00010 304 10 00 00026 305 00 01 01200 306 00 00 01101 307 10 00 00000 308 10 00 11100 309 90 20 11003 401 00 00 00000 402 00 00 00000 403 00 00 00000 404 00 00 01000 406 00 00 00000 407 01 00 00000 408 00 00 00000 409 60 00 01000 410 01 00 10000 501 40 00 00000 502 01 00 00110 504 00 00 00000 505 00 00 10000 506 01 00 00000 507 00 00 00000 508 00 00 02000 509 00 00 00000 510 10 00 00000 601 00 00 00000 602 02 00 00000 603 00 00 00020 604 01 00 00000 605 00 00 00020 606 02 00 00000 607 00 00 00010 608 00 00 00040 609 00 00 00020

98 Invertebrate Swath Abundance * Indicates species was omitted from analysis due to rare occurance Kelletia *Leptosynapta Loxorhynchus Loxorhynchus *Mediaster Megathura Metridium Mimulus Mitra Sample kelletii albicans crispatus grandis aequalis crenulata farcimen foliatus idea 101 11 2 0 00000 102 00 2 0 02000 103 00 0 0 00000 104 00 2 0 00000 105 00 3 0 00001 106 00 3 0 00011 107 01 2 0 00000 108 00 2 0 00000 109 10 4 0 00000 201 00 0 0 10000 202 00 0 0 00000 203 00 0 0 00000 204 00 0 0 00000 205 00 5 1 00000 206 00 0 1 01000 207 00 3 1201000 208 00 1 1 10000 209 00 1 2 00000 301 00 3 0 00013 302 00 1 0 00002 303 00 5 0 01031 304 00 3 0 00052 305 00 2 0 00016 306 00 0 0 00027 307 00 0 0 00005 308 00 0 0 01002 309 40 5 1 00012 401 00 0 1 00000 402 00 0 0 01000 403 00 1 0 00000 404 00 1 0 00000 406 00 3 0 00000 407 00 2 1 00000 408 00 0 1 00000 409 00 3 0 00070 410 00 8 0 00011 501 40 130 00043 502 00 7 0 00014 504 00 300 00012 505 00 0 0 00011 506 00 180 00023 507 00 1 0 00210 508 00 5 0 00032 509 20 0 0 001612 510 10 3 0 00334 601 10 0 0 00000 602 00 0 0 00000 603 00 0 0 00000 604 00 0 0 10000 605 00 0 0 00200 606 00 0 0 00000 607 00 0 0 00000 608 00 0 0 00000 609 00 0 0 00100

99 Invertebrate Swath Abundance * Indicates species was omitted from analysis due to rare occurance *Ophiopteris Ophiothrix Orthasterias Pachycerianthus Pandalus Parastichopus Parastichopus Phyllactis Sample papillosa spiculata koehleri fimbriatus gurneyi californicus parvimensis sp. 101 000 0 01 00 102 001 0 00 00 103 000 0 00 00 104 000 0 00 00 105 000 0 00 00 106 001 3 00 20 107 000 0 00 00 108 001 0 00 00 109 000 0 02 00 201 001 0 01 00 202 001 0 00 00 203 000 0 00 00 204 000 3 00 10 205 000 0 00 00 206 000 0 00 00 207 000 0 00 00 208 000 8 00 00 209 000 1 00 00 301 000 0 30 00 302 000 1 70 32 303 000 0 00 30 304 002 0 00 30 305 001 3 70 30 306 402 0 00 03 307 001 1 40 00 308 101 4 80 11 309 100 1 80 01 401 000 0 00 00 402 001 0 00 00 403 000 0 00 00 404 000 0 00 00 406 010 0 00 00 407 001 0 00 10 408 000 0 00 20 409 080 0 60 00 410 010 0 00 00 501 000 0 10 00 502 003 0 10 02 504 004 0 00 10 505 0100 0 0 0 0 0 506 002 0 30 00 507 001 0 00 00 508 0 0 3 3 20 0 0 0 509 062 2 20 00 510 000 10190 00 601 000 0 02 00 602 000 0 00 00 603 000 5 01 00 604 000 1 00 00 605 000 6 00 00 606 003 5 01 00 607 002 0 01 00 608 000 0 00 00 609 000 0 00 00

100 Invertebrate Swath Abundance * Indicates species was omitted from analysis due to rare occurance *Pisaster Pisaster Pisaster Pugettia *Pugettia Pycnopodia Scyra Strongylocentrotus Sample brevispinus giganteus ochraceus producta richii helianthoides acutifrons franciscanus 101 03010 0 0 0 102 0120 00 1 5 0 103 0270 01 3 3 0 104 0160 00 0 0 0 105 0300 00 1 0 6 106 03000 0 0 3 107 0270 00 0 2 0 108 08000 1 2 1 109 09000 0 6 0 201 08000 2 1 0 202 0400 00 0 1 0 203 0300 00 0 1 1 204 08000 2 0 0 205 04000 0 0 0 206 0230 00 1 0 6 207 0380 00 0 0 0 208 01001 2 0 0 209 05000 0 0 0 301 0201 00 2 181 302 0140 00 0 2 0 303 0100 00 1 6 0 304 0120 00 0 6 0 305 0200 00 1 3 2 306 0180 10 0 4 0 307 0281 00 0 3 0 308 06000 2 2 0 309 0190 00 1 112 401 0280 00 1 0 0 402 0200 00 0 0 0 403 09010 1 0 0 404 0210 30 3 0 2 406 03001 1 0 0 407 09000 0 0 0 408 0280 00 0 0 0 409 0110 00 1 400 410 00000 0 6 0 501 04000 2 8 0 502 0200 10 2 111 504 0140 10 1 6 0 505 05000 0 7 0 506 09000 3 6 0 507 0200 00 1 5 21 508 0210 00 0 123 509 0110 10 2 300 510 0120 10 0 6 0 601 0100 00 0 0 0 602 7100 00 0 0 0 603 38000 0 0 0 604 03800 1 0 1 605 10800 0 0 0 606 02000 0 0 1 607 02500 0 0 0 608 0160 00 0 0 0 609 05100 0 0 0

101 Invertebrate Swath Abundance * Indicates species was omitted from analysis due to rare occurance Strongylocentrotus Styela Stylaster Tethya Unknown *Unknown *Urticina Urticina Urticina Sample purpuratus montereyensis californica aurantia Sea Lemon Sponge coriacea crassicornis lofotensis 101 010000003 102 021000001 103 14 42 0 1 0 0 0 0 0 104 061020000 105 411710001 106 050120000 107 880400002 108 340000001 109 4 14 0 12 0 0 0 0 0 201 001100007 202 001200006 203 115000000 204 030000003 205 220000061 206 120400007 207 000320007 208 000420000 209 000200001 301 520510002 302 020101000 303 050410002 304 030130000 305 110420100 306 020610003 307 060520000 308 0001410001 309 020910005 401 000100002 402 030300011 403 010000010 404 10 0 0 0 0 0 0 0 1 406 030100000 407 000100010 408 19 13 0 0 1 0 0 0 0 409 831010004 410 220000000 501 111100000 502 021310102 504 020000000 505 010210000 506 000300001 507 211000002 508 710100001 509 400020001 510 110200000 601 13 0 0 2 0 0 0 0 0 602 000100000 603 000500000 604 200100000 605 000100000 606 000400000 607 300100003 608 010000000 609 010000000

102 Invertebrate Swath Abundance * Indicates species was omitted from analysis due to rare occurance Urticina Sample piscivora 101 0 102 0 103 0 104 0 105 0 106 0 107 0 108 1 109 0 201 0 202 0 203 3 204 6 205 0 206 1 207 0 208 2 209 6 301 0 302 0 303 0 304 0 305 0 306 2 307 2 308 2 309 6 401 0 402 0 403 3 404 0 406 0 407 2 408 0 409 0 410 0 501 0 502 0 504 0 505 0 506 0 507 0 508 3 509 0 510 2 601 0 602 0 603 0 604 0 605 0 606 0 607 1 608 0 609 0

103

APPENDIX II.

List of Sampled Species and Global Spatial Autocorrelation Test results. 1 =While somewhat clustered, the pattern may be due to random chance.2 = Spatial pattern is neither clustered nor dispersed. 3 = There is a 5-10% likelihood that this clustered pattern is the result of random chance. 4 = While somewhat dispersed, the pattern may be due to random chance. * = The expected value for the 54 transects sampled is -0.0188679245283018

Sampling Category Species Moran's Index * Variance Z Score Sig Level Comment Algae % Cover Calliarthron cheilosporioidies 0.129624494 0.015340840 1.198891034 > 0.10 1 Algae % Cover Callophyllis violacea -0.010810954 0.013490736 0.069367169 > 0.10 2 Algae % Cover Chondrocanthus corymbifera 0.171542497 0.015415516 1.533598496 > 0.10 1 Algae % Cover Corallina sp. 0.367395225 0.014340262 3.225557049 < 0.10 3 Algae % Cover Dictyoneuropsis reticulata -0.168031278 0.015133996 -1.212509996 > 0.10 4 Algae % Cover Dictyoneurum californicum -0.061905379 0.014099378 -0.362448746 > 0.10 2 Algae % Cover Macrocystis pyrifera 0.202897094 0.012451188 1.987410836 < 0.05 clustered Algae % Cover Rhodymenia californica 0.157847792 0.016093114 1.393012899 > 0.10 1 Algae % Cover Rhodymenia spp 0.020884710 0.014876868 0.325919363 > 0.10 1 Algae Swath Counts Chondrocanthus corymbifera 0.171542497 0.015415516 1.533598496 > 0.10 1 Algae Swath Counts Cystoseira osmundacea 0.072135843 0.014901101 0.745504358 > 0.10 2 Algae Swath Counts Dictyoneuropsis reticulata 0.050517655 0.014291147 0.580411046 > 0.10 2 Algae Swath Counts Dictyoneurum californicum 0.063498825 0.014093556 0.693811720 > 0.10 2 Algae Swath Counts Laminaria setchelli 0.061117398 0.013173704 0.696877343 > 0.10 2 Algae Swath Counts Macrocystis pyrifera 0.129600402 0.007002614 1.774204699 < 0.10 3 Invertebrate % Cover Abietinaria sp. 0.082573181 0.015440303 0.816368159 > 0.10 2 Invertebrate % Cover Asterina miniata -0.048620535 0.015587585 -0.238306374 > 0.10 2 Invertebrate % Cover Astrangia lajollaensis 0.096000632 0.016161878 0.903556355 > 0.10 2 Invertebrate % Cover Balanophylia elegans 0.633231389 0.015472607 5.242422192 < 0.01 clustered

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Sampling Category Species Moran's Index * Variance Z Score Sig Level Comment Invertebrate % Cover Balanus crenatus 0.160465409 0.016105134 1.413119360 > 0.10 1 Invertebrate % Cover Bugula californica 0.103529523 0.014769369 1.007143601 > 0.10 1 Invertebrate % Cover Corynactis californica 0.334861438 0.016160740 2.782534029 < 0.01 clustered Invertebrate % Cover Costazia costazi 0.191285235 0.014668983 1.735145621 < 0.10 3 Invertebrate % Cover Diopatra ornata 0.048442514 0.011985455 0.614830152 > 0.10 2 Invertebrate % Cover Diaperoecia californica 0.175263233 0.016031213 1.533246771 > 0.10 1 Invertebrate % Cover Didemnum carnulentum 0.051857612 0.015424154 0.569476192 > 0.10 2 Invertebrate % Cover Dodecaceria fewkesi 0.061513197 0.015191237 0.652165000 > 0.10 2 Invertebrate % Cover Hippodiplosia insculpta 0.305118142 0.015222469 2.625933833 < 0.01 clustered Invertebrate % Cover Hymenamphiaster cyanocrypta 0.267618766 0.015684251 2.287560316 < 0.05 clustered Invertebrate % Cover Loxorhynchus crispatus 0.019173949 0.005368445 0.519203547 > 0.10 2 Invertebrate % Cover Paracyathus sternsi -0.062159548 0.015863617 -0.343718381 > 0.10 2 Invertebrate % Cover Phragmatopoma californica 0.206834677 0.010218341 2.232782205 < 0.05 clustered Invertebrate % Cover Pisaster giganteus -0.003765550 0.012727369 0.133867734 > 0.10 2 Invertebrate % Cover Pycnopodia helianthoides 0.043509777 0.014428826 0.519294671 > 0.10 2 Invertebrate % Cover Serpulorbis squamigerius 0.034426809 0.013955857 0.451134503 > 0.10 2 Invertebrate % Cover Stylaster californica 0.075849241 0.013004151 0.830591900 > 0.10 2 Invertebrate % Cover Unknown bryozoan 0.119961242 0.012730080 1.230453353 > 0.10 1 Invertebrate % Cover Unknown sponge 0.158180538 0.014502432 1.470184149 > 0.10 1 Invertebrate % Cover Unnown tunicate -0.026628475 0.011111324 -0.073622344 > 0.10 2 Invertebrate Swath Counts Asterina miniata 0.394922061 0.016585107 3.213074802 < 0.01 clustered Invertebrate Swath Counts Anthopleura sola 0.225719249 0.015173269 1.985610653 < 0.05 clustered Invertebrate Swath Counts Balanus aquila 0.046356442 0.008999882 0.687529706 > 0.10 2 Invertebrate Swath Counts Balanus nubilus 0.201248916 0.015138970 1.788978441 < 0.10 3 Invertebrate Swath Counts Boltenia villosa 0.431194579 0.015768533 3.584075848 < 0.01 clustered Invertebrate Swath Counts Ceratostoma foliatum 0.192393695 0.014432698 1.758518086 < 0.10 3 Invertebrate Swath Counts Craniella arb 0.125576350 0.011771870 1.331305113 > 0.10 1

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Sampling Category Species Moran's Index * Variance Z Score Sig Level Comment Invertebrate Swath Counts Crassedoma giganteum 0.043222344 0.011656865 0.575085816 > 0.10 2 Invertebrate Swath Counts Cryptochiton stelleri -0.002959798 0.014030021 0.134304286 > 0.10 2 Invertebrate Swath Counts Cucumaria miniata 0.827652919 0.015670605 6.762305389 < 0.01 clustered Invertebrate Swath Counts Cystodytes lobatus 0.093551424 0.014798238 0.924136369 > 0.10 2 Invertebrate Swath Counts Dermasterias imbricata -0.079357912 0.005731084 -0.799033706 > 0.10 2 Invertebrate Swath Counts Eunerdmania claviformis 0.016996425 0.013574525 0.307822693 > 0.10 2 Invertebrate Swath Counts Haliotis rufescens 0.101354521 0.013955857 1.017670782 > 0.10 1 Invertebrate Swath Counts Halocynthia aurantia 0.173529151 0.012835799 1.698192860 < 0.10 3 Invertebrate Swath Counts Henricia leviuscula 0.154938884 0.015196851 1.409905439 > 0.10 1 Invertebrate Swath Counts Hermisenda calfiornica 0.076689117 0.007418085 1.109473114 > 0.10 1 Invertebrate Swath Counts Kelletia kelletii 0.011934137 0.012030712 0.280823930 > 0.10 2 Invertebrate Swath Counts Loxorhynchus cripatus 0.261633070 0.011285798 2.640391166 < 0.01 clustered Invertebrate Swath Counts Loxorhynchus grandis 0.046090245 0.002739025 1.241182809 > 0.10 1 Invertebrate Swath Counts Megathura crenulata 0.006022935 0.013291243 0.215902208 > 0.10 2 Invertebrate Swath Counts Metridium farcimen 0.037167238 0.002709096 1.076585262 > 0.10 1 Invertebrate Swath Counts Mimulus foliatus 0.183032357 0.014099938 1.700310845 < 0.10 3 Invertebrate Swath Counts Mitra idea 0.463271054 0.015359156 3.890349046 < 0.01 clustered Invertebrate Swath Counts Ophiothrix spiculata 0.031750749 0.011350538 0.475119159 > 0.10 2 Invertebrate Swath Counts Orthasterias koechleri 0.242391731 0.015730812 2.083036024 < 0.05 clustered Invertebrate Swath Counts Pachycerianthus fimbriatus -0.040974887 0.014533904 -0.183374074 > 0.10 2 Invertebrate Swath Counts Pandalus gurneyi 0.114986317 0.012966781 1.175482187 > 0.10 1 Invertebrate Swath Counts Parastichopus californicus 0.031044371 0.014006801 0.421733460 > 0.10 2 Invertebrate Swath Counts Parastichopus parvimensis 0.209975966 0.015106379 1.861911952 < 0.10 3 Invertebrate Swath Counts Phyllactis sp 0.158761268 0.012316316 1.600567270 > 0.10 1 Invertebrate Swath Counts Pisaster giganteus 0.194352336 0.016389406 1.665508534 < 0.10 3 Invertebrate Swath Counts Pisaster ochraceus 0.684464994 0.002588311 13.824603801 < 0.01 clustered Invertebrate Swath Counts Pugettia producta 0.060369772 0.011595909 0.735833297 > 0.10 2

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Sampling Category Species Moran's Index * Variance Z Score Sig Level Comment Invertebrate Swath Counts Pycnopodia helianthoides 0.087206357 0.016325482 0.830189220 > 0.10 2 Invertebrate Swath Counts Scyra acutifrons 0.151794246 0.012579316 1.521628890 > 0.10 2 Invertebrate Swath Counts Strongylocentrotus franciscanus -0.013690759 0.006063066 0.066488410 > 0.10 2 Invertebrate Swath Counts Strongylocentrotus purpuratus -0.074624297 0.014484275 -0.463282751 > 0.10 2 Invertebrate Swath Counts Styela montereyensis 0.099714866 0.007067558 1.410545069 > 0.10 1 Invertebrate Swath Counts Stylaster californica 0.038880258 0.007780592 0.654684405 > 0.10 2 Invertebrate Swath Counts Tethya aurantia 0.179387116 0.014797662 1.629775116 > 0.10 1 Invertebrate Swath Counts Unknown sea lemon 0.224211071 0.016080970 1.916864050 > 0.10 3 Invertebrate Swath Counts Urticina crassicornis -0.013324799 0.009281115 0.057537982 > 0.10 2 Invertebrate Swath Counts Urticina lofotensis 0.222944603 0.015588193 1.936782746 > 0.10 3 Invertebrate Swath Counts Urticina piscivora 0.129032845 0.014866990 1.212994698 > 0.10 1

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APPENDIX III.

Anthopleura sola Correlogram

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s 0.2 e u l a v

x Anthopleura sola

de 0 (-) 0.025 C.I. n 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 2600 2700 2800 2900 3000 3100 I (+) 0.025 C.I. n's ra o

M -0.2

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-0.8 Scale (meters)

A weighted Moran’s omnidirectional correlogram for Anthopleura sola. Dashed lines depict the upper and lower limits of the 0.05 confidence interval. The solid line is the Moran’s Index values for Anthopleura sola. The intercept of the solid and dashed lines is the scale of significance for spatial autocorrelation.

108

Asterina miniata Correlogram

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x v Asterina miniata e 0 (-) 0.025 C.I. 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 2600 2700 2800 2900 3000 3100 (+) 0.025 C.I. n's Ind a

Mor -0.2

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-0.8 Scale (meters)

A weighted Moran’s omnidirectional correlogram for Asterina miniata. Dashed lines depict the upper and lower limits of the 0.05 confidence interval. The solid line is the Moran’s Index values for Asterina miniata. The intercept of the solid and dashed lines is the scale of significance for spatial autocorrelation.

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Balanophylia elegans Correlogram

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x v Balanophylia elegans e 0 (-) 0.025 C.I. 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 2600 2700 2800 2900 3000 3100 (+) 0.025 C.I. n's Ind a -0.2 Mor

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-1 Scale (meters)

A weighted Moran’s omnidirectional correlogram for Balanophylia elegans. Dashed lines depict the upper and lower limits of the 0.05 confidence interval. The solid line is the Moran’s Index values for Balanophylia elegans. The intercept of the solid and dashed lines is the scale of significance for spatial autocorrelation.

110

Balanus nubilus Correlogram

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x v Balanus nubilus e 0 (-) 0.025 C.I. 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 2600 2700 2800 2900 3000 3100 (+) 0.025 C.I. n's Ind a -0.2 Mor

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A weighted Moran’s omnidirectional correlogram for Balanus nubilus. Dashed lines depict the upper and lower limits of the 0.05 confidence interval. The solid line is the Moran’s Index values for Balanus nubilus. The intercept of the solid and dashed lines is the scale of significance for spatial autocorrelation.

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Boltenia villosa Correlogram

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l 0

a 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 2600 2700 2800 2900 3000 3100

x v Boltenia villosa e (-) 0.025 C.I. (+) 0.025 C.I. n's Ind a -0.5 Mor

-1

-1.5 Scale (meters)

A weighted Moran’s omnidirectional correlogram for Boltenia villosa. Dashed lines depict the upper and lower limits of the 0.05 confidence interval. The solid line is the Moran’s Index values for Boltenia villosa. The intercept of the solid and dashed lines is the scale of significance for spatial autocorrelation.

112

Ceratostoma foliatum Correlogram

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e 0.4 u l a

x v Ceratostoma foliatum e 0.2 (-) 0.025 C.I. (+) 0.025 C.I. n's Ind a

Mor 0 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 2600 2700 2800 2900 3000 3100

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-0.6 Scale (meters)

A weighted Moran’s omnidirectional correlogram for Ceratostoma foliatum. Dashed lines depict the upper and lower limits of the 0.05 confidence interval. The solid line is the Moran’s Index values for Ceratostoma foliatum. The intercept of the solid and dashed lines is the scale of significance for spatial autocorrelation.

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Corralina sp. Correlogram

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0.6 e u l a 0.4 x v Corralina sp. e (+)0.025 C.I. (-)0.025 C.I.

n's Ind 0.2 a Mor 0 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 2600 2700 2800 2900 3000 3100

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-0.6 Scale (meters)

A weighted Moran’s omnidirectional correlogram for Corralina sp.. Dashed lines depict the upper and lower limits of the 0.05 confidence interval. The solid line is the Moran’s Index values for Corralina sp.. The intercept of the solid and dashed lines is the scale of significance for spatial autocorrelation.

114

Corynactis californica Correlogram

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x v Corynactis californica e 0 (-) 0.025 C.I. 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 2600 2700 2800 2900 3000 3100 (+) 0.025 C.I. n's Ind a -0.2 Mor

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-0.8

-1 Scale (meters)

A weighted Moran’s omnidirectional correlogram for Corynactis californica. Dashed lines depict the upper and lower limits of the 0.05 confidence interval. The solid line is the Moran’s Index values for Corynactis californica i. The intercept of the solid and dashed lines is the scale of significance for spatial autocorrelation.

115

Costazia costazi Correlogram

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x v Costazia costazi e 0.2 (-) 0.025 C.I. (+) 0.025 C.I. n's Ind a 0

Mor 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 2600 2700 2800 2900 3000 3100

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-0.8 Scale (meters)

A weighted Moran’s omnidirectional correlogram for Costazia costazi. Dashed lines depict the upper and lower limits of the 0.05 confidence interval. The solid line is the Moran’s Index values for Costazia costazi. The intercept of the solid and dashed lines is the scale of significance for spatial autocorrelation.

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Cucumaria miniata Correlogram

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l 0.5 a

x v Cucumaria miniata e (-) 0.025 C.I. (+) 0.025 C.I. n's Ind a 0

Mor 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 2600 2700 2800 2900 3000 3100

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-1 Scale (meters)

A weighted Moran’s omnidirectional correlogram for Cucumaria miniata. Dashed lines depict the upper and lower limits of the 0.05 confidence interval. The solid line is the Moran’s Index values for Cucumaria miniata. The intercept of the solid and dashed lines is the scale of significance for spatial autocorrelation.

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Halocynthia aurantia Correlogram

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x v Halocynthia aurantia e 0 (-) 0.025 C.I. 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 2600 2700 2800 2900 3000 3100 (+) 0.025 C.I. n's Ind a

Mor -0.2

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-0.8 Scale (meters)

A weighted Moran’s omnidirectional correlogram for Halocynthia aurantia. Dashed lines depict the upper and lower limits of the 0.05 confidence interval. The solid line is the Moran’s Index values for Halocynthia aurantia. The intercept of the solid and dashed lines is the scale of significance for spatial autocorrelation.

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Hippodiplosia insculpta Correlogram

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x v Hippodiplosia insculpta e 0 (-)0.025 C.I. 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 2600 2700 2800 2900 3000 3100 (+)0.025 C.I. n's Ind a -0.2 Mor

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-0.8

-1 Scale (meters)

A weighted Moran’s omnidirectional correlogram for Hippodiplosia insculpta. Dashed lines depict the upper and lower limits of the 0.05 confidence interval. The solid line is the Moran’s Index values for Hippodiplosia insculpta. The intercept of the solid and dashed lines is the scale of significance for spatial autocorrelation.

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Hymenamphiaster cyanocrypta Correlogram

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x v Hymenamphiaster cyanocrypta e 0 (-) 0.025 C.I. 0 100 200 300 400 500 600 700 800 900 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250 260 270 280 290 300 310 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 (+) 0.025 C.I. n's Ind a -0.2 Mor

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-1 Scale (meters)

A weighted Moran’s omnidirectional correlogram for Hymenamphiaster cyanocrypta. Dashed lines depict the upper and lower limits of the 0.05 confidence interval. The solid line is the Moran’s Index values for Hymenamphiaster cyanocrypta. The intercept of the solid and dashed lines is the scale of significance for spatial autocorrelation.

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Loxorhynchus crispatus Correlogram

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0 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 2600 2700 2800 2900 3000 3100 e u l a

x v Loxorhynchus crispatus e -0.2 (-) 0.025 C.I. (+) 0.025 C.I. n's Ind a Mor -0.4

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-0.8 Scale (meters)

A weighted Moran’s omnidirectional correlogram for Loxorhynchus crispatus. Dashed lines depict the upper and lower limits of the 0.05 confidence interval. The solid line is the Moran’s Index values for Loxorhynchus crispatus. The intercept of the solid and dashed lines is the scale of significance for spatial autocorrelation.

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Macrocystis pyrifera (cover) Correlogram

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x v Macrocystis pyrifera e 0 (-) 0.025 C.I. 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 2600 2700 2800 2900 3000 3100 (+) 0.025 C.I. n's Ind a

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A weighted Moran’s omnidirectional correlogram for Macrocystis pyrifera percent cover. Dashed lines depict the upper and lower limits of the 0.05 confidence interval. The solid line is the Moran’s Index values for Macrocystis pyrifera. The intercept of the solid and dashed lines is the scale of significance for spatial autocorrelation.

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Macrocystis pyrifera (swath) Correlogram

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a 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 2600 2700 2800 2900 3000 3100

x v Macrocystis pyrifera e -0.1 (-) 0.025 C.I. (+) 0.025 C.I. n's Ind a -0.2 Mor

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-0.6 Scale (meters)

A weighted Moran’s omnidirectional correlogram for Macrocystis pyrifera swath abundance. Dashed lines depict the upper and lower limits of the 0.05 confidence interval. The solid line is the Moran’s Index values for Macrocystis pyrifera. The intercept of the solid and dashed lines is the scale of significance for spatial autocorrelation.

123

Mimulus foliatus Correlogram

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e 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 2600 2700 2800 2900 3000 3100 u l a

x v -0.1 Mimulus foliatus e (-) 0.025 C.I. -0.2 (+) 0.025 C.I. n's Ind a

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A weighted Moran’s omnidirectional correlogram for Mimulus foliatus. Dashed lines depict the upper and lower limits of the 0.05 confidence interval. The solid line is the Moran’s Index values for Mimulus foliatus. The intercept of the solid and dashed lines is the scale of significance for spatial autocorrelation.

124

Mitra idea Correlogram

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x v Mitra idea e (-) 0.025 C.I. (+) 0.025 C.I. n's Ind a 0

Mor 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 2600 2700 2800 2900 3000 3100

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-1 Scale (meters)

A weighted Moran’s omnidirectional correlogram for Mitra idea. Dashed lines depict the upper and lower limits of the 0.05 confidence interval. The solid line is the Moran’s Index values for Mitra idea. The intercept of the solid and dashed lines is the scale of significance for spatial autocorrelation.

125

Orthasterias koehleri Correlogram

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x v Orthasterias koehleri e 0.2 (-) 0.025 C.I. (+) 0.025 C.I. n's Ind a 0

Mor 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 2600 2700 2800 2900 3000 3100

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A weighted Moran’s omnidirectional correlogram for Orthasterias koehleri. Dashed lines depict the upper and lower limits of the 0.05 confidence interval. The solid line is the Moran’s Index values for Orthasterias koehleri. The intercept of the solid and dashed lines is the scale of significance for spatial autocorrelation.

126

Parastichopus parvimensis Correlogram

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x v Parastichopus parvimensis e 0.2 (-) 0.025 C.I. (+) 0.025 C.I. n's Ind a

Mor 0 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 2600 2700 2800 2900 3000 3100

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-0.6 Scale (meters)

A weighted Moran’s omnidirectional correlogram for Parastichopus parvimensis. Dashed lines depict the upper and lower limits of the 0.05 confidence interval. The solid line is the Moran’s Index values for Parastichopus parvimensis. The intercept of the solid and dashed lines is the scale of significance for spatial autocorrelation.

127

Phragmatopoma californica Correlogram

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0.6 e u l a 0.4 x v Phragmatopoma californica e (-) 0.025 C.I. (+) 0.025 C.I.

n's Ind 0.2 a Mor 0 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 2600 2700 2800 2900 3000 3100

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A weighted Moran’s omnidirectional correlogram for Phragmatopoma californica. Dashed lines depict the upper and lower limits of the 0.05 confidence interval. The solid line is the Moran’s Index values for Phragmatopoma californica. The intercept of the solid and dashed lines is the scale of significance for spatial autocorrelation.

128

Pisaster giganteus Correlogram

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x v Pisaster giganteus e 0 (-) 0.025 C.I. 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 2600 2700 2800 2900 3000 3100 (+) 0.025 C.I. n's Ind a

Mor -0.2

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-0.8 Scale (meters)

A weighted Moran’s omnidirectional correlogram for Pisaster giganteus. Dashed lines depict the upper and lower limits of the 0.05 confidence interval. The solid line is the Moran’s Index values for Pisaster giganteus. The intercept of the solid and dashed lines is the scale of significance for spatial autocorrelation.

129

Pisaster ochraceus Correlogram

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e 1.5 u l a

x v Pisaster ochraceus e 1 (-)0.025 C.I. (+)0.025 C.I. n's Ind a

Mor 0.5

0 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 2600 2700 2800 2900 3000 3100

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-1 Scale (meters)

A weighted Moran’s omnidirectional correlogram for Pisaster ochraceus. Dashed lines depict the upper and lower limits of the 0.05 confidence interval. The solid line is the Moran’s Index values for Pisaster ochraceus and does not intercept the confidence “envelope.” This suggests that the spatial autocorrelation of Pisaster ochraceus is at a spatial scale smaller than the scale of the study and analysis.

130