RELATIONSHIPS BETWEEN EELGRASS (ZOSTERA MARINA) HABITAT

CHARACTERISTICS AND JUVENILE DUNGENESS CRAB

(CANCER MAGISTER) AND OTHER INVERTEBRATES IN

SOUTHERN HUMBOLDT BAY, CALIFORNIA, USA

by

Kathleen Janice Williamson

A Thesis

Presented to

The Faculty of Humboldt State University

In Partial Fulfillment

Of the Requirement for the Degree

Master of Art

In Biology

May, 2006

RELATIONSHIPS BETWEEN EELGRASS (ZOSTERA MARINA) HABITAT

CHARACTERISTICS AND JUVENILE DUNGENESS CRAB

(CANCER MAGISTER) AND OTHER INVERTEBRATES IN

SOUTHERN HUMBOLDT BAY, CALIFORNIA, USA

by

Kathleen Janice Williamson

We certify that we have read this study and that it conforms to acceptable standards of scholarly presentation and is fully acceptable, in scope and quality, as a thesis for the degree of Masters of Arts.

Approval by the Master’s Thesis Committee

Frank J. Shaughnessy, Major Advisor Date

David G. Hankin, Committee Member Date

Jeffery M. Black, Committee Member Date

Sean F. Craig, Committee Member Date

Susan C. Schlosser, Committee Member Date

Michael R. Mesler, Graduate Coordinator Date

Donna E. Schafer, Dean Research and Graduate Studies Date

ABSTRACT

Relationships between eelgrass (Zostera marina) habitat characteristics and juvenile Dungeness crab (Cancer magister) and other invertebrates in southern Humboldt Bay, California, USA

Kathleen Janice Williamson

The relationship between eelgrass bed characteristics and animal use is important

in better understanding what the optimal habitat features are for specific taxa. The focus

of this study was to look at structural and geographical aspects of eelgrass habitat in

southern Humboldt Bay, California to determine if presence or abundance and size of

common eelgrass invertebrates can be predicted. Animals were selected to represent

epibenthic, epifaunal and infaunal groups that are commonly found within the eelgrass

habitat. Each month, from May to August 2004, fourteen sites were randomly sampled

from the eelgrass beds of southern Humboldt Bay (40º44’ to 40º41’ N, 124º13’ to

124º15’ W). The epibenthic decapod crustaceans, Cancer magister, Crangon spp. and hippolytid shrimp were sampled by fishing paired traps at each site for ~ 48 hours.

Epifaunal gastropods, Phyllaplysia taylori, were gathered from eelgrass samples and infaunal Macoma nasuta clams were removed from mud cores collected at each site.

Above and belowground eelgrass samples were collected from each site in order to describe the vegetation structure in which the animals were captured. Independent variables measured from eelgrass samples included shoot density, shoot biomass, mean shoot length, variation in shoot length, root/rhizome biomass and epiphyte load. Site- specific variables obtained for each site included distance to the nearest channel, distance

iii

to the entrance channel and elevation. Multiple regression was used to analyze

relationships between animal population numbers and sizes to vegetation structure and

location. Logistic regression was used to analyze presence/absence of Crangon.

Vegetation and habitat variables differed in the degree to which they were able to predict number and size of C. magister, Crangon spp. and P. taylori, and temporal trends were noted for all of these animals. Dungeness crab were more numerous in areas characterized by high shoot density, homogeneous shoot lengths and proximity to a channel. Mean Dungeness crab size increased over time. Larger crabs were associated with lower shoot density than smaller crabs. Shoot density and distance to the nearest channel were significant predictors of Crangon presence/absence. The addition of sculpin presence/absence to the analysis was significant, and indicated that Crangon were rarely present when sculpin were present. A negative relationship between Crangon size and epiphyte load was found. Phyllaplysia taylori were more numerous in eelgrass beds containing greater shoot biomass. Smaller P. taylori were more prevalent in eelgrass characterized by greater shoot biomass and homogeneous shoot lengths than larger P. taylori. Vegetation structure and location were not found to be important predictors of

Macoma nasuta and hippolytid shrimp abundance or size in southern Humboldt Bay.

Results of this study indicate that eelgrass habitat structure is important in structuring the size and distribution of some animals in southern Humboldt Bay but not others.

iv

ACKNOWLEDGEMENTS

I would like to begin by thanking my family; Mom, Dad, and sister, Karen for

believing in me. Your support and encouragement helped me more than you can image.

I wish to acknowledge and thank the members of my committee; Drs. Frank

Shaughnessy, David Hankin, Jeffrey Black, and Sean Craig, and Susan Schlosser for their

comments, contributions, and assistance with completing this thesis, especially my

master advisor Frank Shaughnessy for helping make a better scientist out of me.

So many friends helped me and for that I am truly grateful and thank you all.

Dave Weskamp, thanks so much for the boat! I often thought I might sink and even came

close a few times but would never have completed my field work without it. Greg

Bender, Deanna Phelps, Erin Schraeder, Chase Williams, Erin Quist and Cate Roscoe

were a great help with field sampling. I really appreciated your willingness to get up

before the roosters and slog through muck with me. Patrick McDaniel, Mike Gough and

Chaeli Judd my GIS gurus, thank you so much…especially Chaeli for the beautiful maps!

Thanks to Jeff Robinson at the Humboldt Bay Harbor, Recreation, and

Conservation District for providing me with a variety of information; Troy Nicolini at the

National Weather Service for weather data and Dr. Bill Bigg, who was a great help with

some of the tougher aspects of my analyses. I am very appreciative.

Special thanks to Greg Bender who readily helped me with anything I needed and

often at the last minute. Your abilities as boat repairman, field helper, cook, counselor,

photographer and friend helped see me through the rough times. It means so much to me

that you were always there. You truly helped make this possible.

v

TABLE OF CONTENTS

ABSTRACT...... iii

ACKNOWLEDGEMENTS...... v

TABLE OF CONTENTS...... vi

LIST OF TABLES...... viii

LIST OF FIGURES ...... ix

LIST OF APPENDICES...... x

INTRODUCTION ...... 1

METHODS ...... 7

Site Description and Selection...... 7

Animal Response Variables...... 10

Predictors of Animal Abundance and Size...... 11

Statistical Analyses...... 13

RESULTS ...... 16

Cancer magister...... 16

Caridean Shrimp ...... 19

Crangon spp...... 19

Hippolytidae...... 23

Phyllaplysia taylori...... 23

Macoma nasuta...... 27

DISCUSSION...... 32

Cancer magister...... 32

Caridean Shrimp ...... 34

vi

TABLE OF CONTENTS, continued

Crangon spp...... 34

Hippolytidae...... 36

Phyllaplysia taylori...... 37

Macoma nasuta...... 38

Summary...... 40

LITERATURE CITED ...... 42

APPENDIX...... 47

vii

LIST OF TABLES

Table Page

1. Summary of dependent and independent variables analyzed using multiple or logistic regression. Significant predictors used in each best fitting multiple linear regression model are noted, indicating a positive (+) or negative (-) relationship. Significant predictors used in the best fitting logistic regression model are indicated by x. If applicable, months excluded from an analysis, number of outliers removed, and transformations applied to the dependent variable are indicated ...... 15

2. Results of multiple linear regression analyses for best fitting models testing for effects of vegetation structure, time and bed location on mean number of Dungeness crab (Cancer magister) trapped May – July, 2004, and mean size trapped May – August 2004, in southern Humboldt Bay, CA...... 18

3. Results of logistic regression analysis for best fitting model testing vegetation structure, time, bed location and presence/absence of sculpin on separating areas with Crangon spp. from those without, May – August 2004 in southern Humboldt Bay, CA ...... 20

4. Results of multiple linear regression analysis for best fitting model testing for effects of vegetation structure, time and bed location on mean size of bay shrimp (Crangon spp.) trapped May – August 2004 in southern Humboldt Bay, CA...... 21

5. Results of multiple linear regression analyses for best fitting models testing for effects of vegetation structure, time and bed location on mean number and size of broken-back shrimp (Hippolytidae) trapped May – August 2004 in southern Humboldt Bay, CA ...... 24

6. Results of multiple linear regression analyses for best fitting models testing for effects of vegetation structure, time and bed location on density and mean size of Phyllaplysia taylori collected May – June 2004 in southern Humboldt Bay, CA...... 26

7. Results of multiple linear regression analyses for best fitting models, testing for effects of vegetation structure, time and bed location on mean density and shell length of small (< 21 mm), and large (≥ 21 mm) bent-nosed clam (Macoma nasuta) collected May – August 2004 from sites in southern Humboldt Bay, CA ...... 29

viii

LIST OF FIGURES

Figure Page

1. Map of Humboldt Bay, California indicating southern Humboldt Bay study area (40º44’ to 40º41’ N, 124º13’ to 124º15’ W). Map courtesy of C. Judd, 2006...... 8

2. Map of southern Humboldt Bay, California, indicating eelgrass distribution (green) and major channels. Random sites were sampled monthly from May to August 2004. Map courtesy of C. Judd, 2006 ...... 9

3. Mean number and carapace width (mm) of Dungeness crab (Cancer magister) trapped May – August 2004 from sites in southern Humboldt Bay, CA. Error bars are 95% confidence intervals...... 17

4. Mean number and total body length (mm) of bay shrimp (Crangon spp.) trapped May – August 2004 from sites in southern Humboldt Bay, CA. Error bars are 95% confidence intervals...... 22

5. Mean number and total body length (mm) of broken-back shrimp (Hippolytidae) trapped May – August 2004 from sites in southern Humboldt Bay, CA. Error bars are 95% confidence intervals...... 25

6. Mean density and body length (mm) of Phyllaplysia taylori collected May – August 2004 from sites in southern Humboldt Bay, CA. Error bars are 95% confidence intervals ...... 28

7. Mean density and shell length (mm) of bent-nosed clam (Macoma nasuta) collected May – August 2004 from sites in southern Humboldt Bay, CA. Error bars are 95% confidence intervals...... 31

ix

LIST OF APPENDICES

Appendix Page

A. Universal Transverse Mercator (UTM) coordinates and geographic data associated with sites sampled May – August 2004 in southern Humboldt Bay, CA. Map datum is zone 10, NAD-27 ...... 47

B. Summary of eelgrass, epiphyte, and sculpin data collected May – August 2004 from sites in southern Humboldt Bay, CA. Aboveground eelgrass collected from 0.25 m2 quadrats; belowground eelgrass data expanded to reflect 0.25 m2, 15 cm depth; epiphyte weight per leaf area sampled (cm2) and sculpin collected from traps fished ~ 48 hours ...... 49

C. Summary of Cancer magister, Crangon spp. and hippolytid shrimp captured in traps fished ~ 48 hours, Phyllaplysia taylori collected from 0.25 m2 quadrats, and Macoma nasuta collected from mud cores, May – August 2004 from sites in southern Humboldt Bay, CA...... 53

x

INTRODUCTION

Seagrass communities perform a vital role in estuarine ecosystems. They are

highly productive and structurally complex environments that provide significant refuge from predation and serve as nursery grounds for the young of many species (Hemminga and Duarte 2000; Williams and Heck 2001). Greater numbers of animal species are sheltered in seagrass communities than any other soft-bottom marine habitat (Williams and Heck 2001). Estimates of average net primary production place seagrass meadows among the most productive ecosystems on the planet (Hemminga and Duarte 2000;

Zieman and Wetzel 1980). Some seagrass biomass is used directly as a food source by vertebrate grazers such as manatees, dugongs, green turtles and various species of fish and waterfowl (Valentine and Heck 1999). Migratory black brant (Branta bernicla nigricans) utilize eelgrass (Zostera marina L) as their primary food source during their non-breeding season (Moore et al. 2004). Most seagrass biomass, however, enters food webs through a detrital pathway. This is due to large quantities of structural carbohydrates, high C/N ratios, and the presence of phenolic compounds that contribute to the low nutritional value of seagrass (Williams and Heck 2001).

In addition to the above and belowground biomass of seagrass, algal epiphytes

heavily colonize blades and shoots, contributing substantially to the total primary

production of seagrass beds (Williams and Heck 2001; Zieman and Wetzel 1980).

Epiphyte productivity can be considerable, often equaling or exceeding the aboveground

productivity of the seagrass itself (Hemminga and Duarte 2000; Williams and Heck 2001;

Zieman and Wetzel 1980). Typical estimates of epiphyte productivity are between

1 2 20 - 60% of the aboveground seagrass productivity (Hemminga and Duarte 2000).

Although lower in biomass, epiphytes are higher in nutritional quality for consumers and can replace their standing stock within a few days (Williams and Heck 2001).

A defining characteristic of seagrass habitats is structural complexity. In the eelgrass, Zostera marina, denser stands equate to greater above and belowground biomass. Differing leaf lengths, branching reproductive shoots and algal epiphytes add to the complex surroundings. Greater protection may benefit prey species by increasing hiding places and decreasing the ability of predators to detect and efficiently capture prey

(Hemminga and Duarte 2000; Williams and Heck 2001). Species of fish and shellfish, many of which are commercially or recreationally important, seek shelter and forage within the complex eelgrass beds (Barnhart et al. 1992; Orth et al. 1984; Phillips 1984;

Sogard and Able 1991). Other species settle in eelgrass beds during their larval stages and spend their entire existence within the protected meadows. Aboveground, hydrodynamic currents are modified by macrophyte structure, creating lower water flow near the bottom and enhanced deposition of suspended matter such as invertebrate larvae, fine sediments, plankton and detritus (Fonseca et al. 1993; Hemminga and Duarte 2000;

Williams and Heck 2001). The macrophytic leaves also provide a platform from which epiphytic algae and diatoms grow. Belowground an extensive root-rhizome matrix stabilizes the sediment while adding protected living spaces for infaunal invertebrates

(Webster et al. 1998). Consequently, species richness and faunal abundance is generally greater in vegetated seagrass habitats than in nearby unvegetated habitats (Boström and

Bonsdorff 1997, 2000; Mattila et al. 1998; Sogard and Able 1991; Webster et al. 1998).

3 Many species of decapod crustaceans utilize the protected seagrass meadows as a nursery ground (Hemminga and Duarte 2000; Perkins-Visser et al. 1996;). Settlement often occurs in discrete pulses following a pelagic larval phase (Hemminga and Duarte

2000). In Grays Harbor, Washington, large numbers of actively swimming Dungeness crab (Cancer magister) megalopae enter coastal and estuarine waters in the late spring to early summer months and metamorphose into benthic juveniles (Eggleston and

Armstrong 1995). Juveniles prefer structurally complex habitats to bare mud (Eggleston and Armstrong 1995; Fernandez et al. 1993; McMillan et al. 1995). Protected eelgrass beds remain their home until they reach a critical size of approximately 30 mm (carapace width, CW). At that time, protection is afforded by their size and they move out into subtidal habitats (McMillan et al. 1995).

Eelgrass (Zostera marina L) is one of many species of seagrasses found in bays and estuaries around the globe (Phillips 1984). Occupying low intertidal and shallow subtidal zones, eelgrass forms monospecific stands that are widely distributed throughout the temperate regions of the North Pacific and North Atlantic (Kozloff 1983; Phillips

1984). Belonging to the family Zosteraceae, Z. marina is a monocot flowering plant and not a true grass. Eelgrass morphology is characterized by long leafy shoots that extend vertically in the water. The shoots are attached to a system of horizontal rhizomes that are mostly buried in substrate ranging from dense sand to very soft mud (Kozloff 1983).

Shoots usually consist of 3 to 5 ribbon-like leaves that grow from the shoot in an alternating manner (Keller and Harris 1966).

4 In Humboldt Bay, California, USA, meadows of Zostera marina form an ecologically important environment comprising approximately 20% of the total bay area

(Shapiro and Assoc. 1980). This highly productive habitat is susceptible to natural and anthropogenic stresses. Natural threats include storms, sedimentation, habitat loss due to invasive species, potential overgrazing by waterfowl such as brant or Canada geese, and disease (Moore et al. 2004; Phillips 1984; Rasmussen 1977). Eelgrass wasting disease, caused by the protist Labyrinthula zosterae, results in loss of leaves and eventual death of the plant (Muehlstein 1992). Wasting disease is capable of killing huge expanses of eelgrass, as it did in the north Atlantic during the 1930’s (Rasmussen 1977).

Anthropogenic pressures include depleted water quality, silt accumulation from over logged watersheds, nutrient loading, dredging, heavy recreational use, and aquaculture

(Phillips 1984). As coastal use and development continues in commercial, residential, and recreational industries in California, it seems unavoidable that coastal habitats will continue to experience adverse stress. In 2000, over 75% of California’s population lived in coastal counties (Kildow and Colgan 2005). While the population of Humboldt

County is sparse in comparison to coastal counties in southern parts of the state, the local population increased nearly 8% between 1990 and 2004 (US Census). Managers are aware that existing problems must be addressed and act proactively to limit problems that other coastal counties have experienced resulting from population growth (HBHRCD

2005).

The relationship between the abundance and size of animals in seagrass communities and the structure of the seagrass habitat itself depends on the natural history

5 of the animal and the degree to which the seagrass vegetation affects recruitment patterns, vulnerability to predation and disturbance, and their access to food. The relationship between eelgrass habitat structure and the invertebrates using this habitat in Humboldt

Bay is not well understood. Over 500 invertebrate species inhabit Humboldt Bay

(HBHRCD 2005) and in this study I will describe habitat relationships for a subset of taxa representing epibenthic, epifaunal, and infaunal species that are ecologically or economically important. These include: Dungeness crab (Cancer magister), bay shrimp

(Crangon spp.), broken-back shrimp (Family Hippolytidae), Taylor’s sea hare

(Phyllaplysia taylori), and bent-nosed clam (Macoma nasuta). My hypotheses about the relationship between the abundance and size of each of the animals and attributes of the eelgrass habitat vary depending on the natural history of the species and its potential vulnerability to predation.

Dungeness crab, Cancer magister Dana, is a highly motile, epibenthic marine crustacean. Because adults are rarely found in Humboldt Bay but juveniles are abundant

(Emmett et al. 1991), Humboldt Bay may be an important nursery area. I predict that juvenile crabs will be more numerous in habitats exhibiting greater shoot density and that less crabs will be captured over time. I further predict that crab size will be negatively correlated with shoot density and that the mean size (CW) of crabs will increase over time, peaking at approximately 30 to 35 mm.

Smaller crustaceans such as shrimp are equally important inhabitants of eelgrass communities, playing a key ecological role in transferring energy from primary producers to higher trophic levels (Hemminga and Duarte 2000). Because of the refuge afforded by

6 increased habitat structure, I predict that greater numbers of shrimp will be captured in denser eelgrass habitat.

Phyllaplysia taylori Dall, is an epifaunal opisthobranch grazer that depends exclusively on eelgrass blades for habitat (Barnhart et al. 1992). P. taylori reside on the eelgrass leaves, grazing unselectively from the plethora of epiphytic algae, invertebrates and fungi that cover the leaves like a film (Beeman 1968). Previous research suggests the existence of a mutualistic relationship between P. taylori and Zostera marina, with food and habitat provided by the eelgrass (Bridges 1975; Keiser 2004). The grazing activity has a beneficial affect on Z. marina by regulating epiphyte biomass, resulting in enhanced eelgrass growth (Hootsman and Vermaat 1985; Nelson 1997; van Montfrans et al. 1982). Based on the hypothesis of a mutualistic relationship, I predict that P. taylori density will be correlated with greater eelgrass biomass and lesser epiphyte abundance.

The bent-nosed clam, Macoma nasuta Conrad, is a burrowing infaunal bivalve found in Humboldt Bay and most intertidal mud flat habitats between Kodiak Alaska and the tip of Baja California, Mexico (Coan 1971; Ricketts et al. 1985). Because M. nasuta is a broadcast spawner and the macrophytic eelgrass structure slows water currents and facilitates larval deposition (Fonseca et al. 1983), I predict that clams will be more prevalent in eelgrass characterized by greater eelgrass biomass.

The objective of this thesis is to determine if any correlative relationships exist between eelgrass habitat attributes and select invertebrates in southern Humboldt Bay. It is hoped that this study will be useful to future experiments of causal relationships between the abundance and size of invertebrates and eelgrass habitat characteristics.

METHODS

Site Description and Selection

Humboldt Bay is comprised of three subbays and is well circulated by mixed

semi-diurnal tides. The watershed that influences Humboldt Bay is small (~578 km2), with no major rivers in the region draining into the bay (Barnhart et al. 1992). Weather conditions from March through August lead to increased evaporation and decreased runoff. Resultant physical changes to the water in the bay include water temperatures that increase with distance up the main channels and bay-wide salinity increases (>33.6 ppt; Barnhart et al. 1992). This study was conducted in the eelgrass beds of southern

Humboldt Bay, California (40º44’ to 40º41’ N, 124º13’ to 124º15’ W) from May to

August 2004 (Figure 1). The southern part of Humboldt Bay is characterized by extensive tidal flats and vast beds of eelgrass that cover 788 ha (Judd 2006), or approximately 38% of the total area of southern Humboldt Bay (Shapiro and Assoc.

1980).

Sample sites were selected using ARC-GIS software (Figure 2). Randomized northing and easting coordinates were generated in Excel and overlaid with an aerial mosaic photograph of Humboldt Bay taken in 2000. LIDAR elevation maps were intended for use in selecting sites with elevations between ± 0.3 m MLLW, however elevations were later found to range between approximately -0.6 and +0.3 m MLLW.

Coordinates were excluded if they were not within eelgrass habitat or were located within

30 meters of another site previously selected for sampling during that particular month

(Appendix A). The beds east of Hookton Channel were excluded in order to maintain

7 8

Study Area

Figure 1. Map of Humboldt Bay, California indicating southern Humboldt Bay study area (40º44’ to 40º41’ N, 124º13’ to 124º15’ W). Map courtesy of C. Judd, 2006.

9

Figure 2. Map of southern Humboldt Bay, California, indicating eelgrass distribution (green) and major channels. Random sites were sampled monthly from May to August 2004. Map courtesy of C. Judd, 2006.

10 adequate distance from a separate eelgrass research project occurring concurrently within southern Humboldt Bay. High quality aerial maps of southern Humboldt Bay indicating sampling locations and associated landmarks were used in the field for locating sample sites. Monthly sampling events began in early May 2004 and continued for four months, ending in early August 2004. The study period was selected to coincide with juvenile

Dungeness crab usage. Monthly sampling was accomplished over five to seven consecutive days, with initial monthly sampling dates separated by 28 or 29 days.

Fourteen different randomly selected sites were sampled each month.

Animal Response Variables

Post-settlement juvenile crab and shrimp were sampled using paired minnow traps placed side by side and approximately 0.5 m apart. The traps were identical, with the exception of the entrance size, which was used to target different crab sizes. Small entrance traps targeted shrimp and early instar crabs that are more vulnerable to predation. Large entrance traps allowed capture of older instar crabs that may have been excluded from the smaller sized trap entrances. The traps were constructed of 4.8 mm plastic mesh (Item MT1, Aquatic Eco-Systems, Inc.), 43.2 x 22.9 cm with a 22 or 63.5 mm entrance hole. The traps were deployed for two tidal cycles (~ 48 hours). Upon retrieval of the traps, crabs were counted and carapace width (CW) measurements were taken to the nearest mm. Shrimp were identified to the lowest possible taxon, counted, and total lengths (TL) were measured to the nearest mm. Crab and shrimp were released in the bay. While not targeted, a number of fish were also captured in the traps. They were enumerated, identified to the lowest possible taxon, and released.

11 Sampling of the herbivorous opisthobranch Phyllaplysia taylori was conducted in the laboratory by examining eelgrass samples that were collected at each site. Following the placement of traps, two 0.25 m2 quadrats were haphazardly tossed approximately two

meters from the traps and one meter apart. All shoots within each quadrat were cut at the

mud-water interface, placed in labeled Ziploc bags and either refrigerated or frozen until

subsequent laboratory processing could occur. All shoots and blades from the bagged

eelgrass samples were examined. P. taylori were enumerated and expanded to reflect #

m-2. The first 30 animals removed from each sample were measured to the nearest mm.

Macoma nasuta, an infaunal bivalve, was sampled using a cylindrical core (13 cm

diameter by 15 cm deep; 1,991 cm3). Following removal of the two aboveground

eelgrass samples at each site, mud cores were taken from within each quadrat area. Each

core was placed in a labeled Ziploc bag and processed later the same day. The mud was

sieved through a 4.8 mm screen. Eelgrass roots and rhizomes were set aside for further

processing. M. nasuta were enumerated and expanded to reflect an m2 surface area (#

0.15 m-3). Length was measured to the nearest mm.

Predictors of Animal Abundance and Size

In order to describe the structure of the vegetation from which animals were

trapped or collected, eelgrass samples gathered from each site were processed further.

Eelgrass shoot density (# m-2) and shoot length (cm) were determined by counting and

measuring each shoot from the base of the shoot to the tip of the longest leaf. A sub- sample of five shoots were selected from each sample by random number and set aside for epiphyte analysis. The remaining shoots were dried in a plant drier at 38ºC for a

12 minimum of one week to determine aboveground shoot biomass (g dry wt m-2). Dried

samples were weighed to the nearest 0.001 gram. The leaves set aside for epiphyte

sampling were processed for biomass determination following the completion of epiphyte

processing. Their weights were added to each quadrat total.

Because P. taylori is an epiphytic grazer, epiphyte load (mg dry wt cm-2) was estimated from the eelgrass samples. Epiphytes were removed from three of the five selected shoots by scraping both sides of the leaves onto a pre-weighed piece of aluminum foil. Epiphyte scrapings were then dried in a plant drier at 38ºC for a minimum of two days. Final dried epiphyte weights were calculated by subtracting the

pre-measured foil weight from the final epiphyte and foil weight.

Leaf surfaces were digitally photographed with a 3 megapixel Canon Camedia or

Fuji Finepix camera. The leaf images from leaves containing epiphytes were contrasted in black and white using Image J (National Institutes of Health) spatial analysis software.

If necessary, the images were manually corrected in Adobe Photoshop to ensure that all leaf areas were correctly identified. Image J was then used to calculate the leaf surface areas (cm2). Length and width were measured from images of all leaves sampled for epiphytes. Because leaf surface areas were not calculated for leaves lacking epiphytes, simple linear regression was used to determine a coefficient for estimating the surface area of these leaves based on their length and width measurements (Estimated surface area = 1.00758 (length cm * width cm) – 0.10323; R2 = 0.9856).

Belowground eelgrass biomass consisted of roots and rhizomes collected from the

mud cores (13 cm diameter by 15 cm deep; g dry wt 1,991 cm-3). The roots and rhizomes

13 were cleaned and dried in a plant drier at 38ºC for a minimum of one week. Biomass was weighed to the nearest 0.001 gram then expanded to reflect g dry wt m-2.

Measurements on site-specific variables were collected to describe physical features associated with each location sampled. Site elevations (m) were estimated based on the most accurate dataset available; 100-m grid elevations collected by Jeff Moore using inverse distance weighting interpolation (Moore 2002). Elevations were assigned to each sampling site based on closest proximity between the elevation coordinates and

sampling location coordinates. Distance to the nearest channel (m) was collected in the field by stretching a tape measure from the sampling site out to the nearest edge of the eelgrass bed. Distances were recorded to the nearest 0.3 m. Distance from the entrance of southern Humboldt Bay to each sample site was calculated in the lab using ARC-GIS software.

Statistical Analyses

The relationship between animal densities and sizes to eelgrass vegetation

structure and other habitat attributes were analyzed using multiple regression or logistic

regression (Number Crunching Statistical Software, 2004). For multiple regression, the

assumption of normally distributed residuals was assessed visually with normal

probability plots (NPP). Independent variables were selected for inclusion in each best

fitting model after comparing all possible regression models derived from the entire set of

independent variables. Dependent and independent variables are listed in Table 1. The

R2 values from the best 1-term and 2-term models were compared. If the addition of a

term increased the model R2 significantly, the 1-term model was discarded and the 2-term

14 model was compared to the best model containing 3-terms. This process was repeated with increasing model terms until the two R2 values stabilized. The best model

containing fewer terms was selected. When necessary, outliers were removed and

transformations were applied to the dependent variable to normalize the residuals (Afifi

et al. 2004). Outliers were identified by examining r-student vs. hat diagonal plots.

Another issue in multiple regression, multicollinearity of descriptive variables, was assessed by checking variance inflation factors (VIF) and eigenvalues for proposed

models (Mendenhall and Sincich 1996). If a VIF was greater than 10 or an eigenvalue

condition number was greater than 100, the variable selection process was repeated

without the suspected collinear variable.

Because so few crangonid shrimp were captured over the course of the study, the

assumption of normally distributed residuals, which is necessary for multiple regression,

was violated. In order to analyze these data, logistic regression, which does not assume a

multivariate normal distribution (Afifi et al. 2004), was used to identify variables

separating sites with shrimp from those without. The animal abundance variable was

converted into a binary response variable indicating presence (1) or absence (0) of

Crangon. Analysis of deviance was used on the saturated (full) model to reduce the

number of predictor variables to those most statistically significant. After significant

variables were identified, deviance analyses were conducted on all possible combinations

of those selected variables. The final model was chosen by comparing from the various

models, the percentage of data correctly classified by the model, and chi-squared results,

which test the significance of each predictor individually and the model as a whole.

Table 1. Summary of dependent and independent variables analyzed using multiple or logistic regression. Significant predictors used in each best fitting multiple linear regression model are noted, indicating a positive (+) or negative (-) relationship. Significant predictors used in the best fitting logistic regression model are indicated by x. If applicable, months excluded from an analysis, number of outliers removed, and transformations applied to the dependent variable are indicated.

Distance Distance Variation Sculpin to the to the Shoot Shoot Shoot Epiphyte Root Excluded # Time Elevation in shoot present/ Transform2 nearest entrance length density biomass load biomass months1 Outliers length absent channel channel

Cancer magister # + - - + A log

Cancer magister size + - Crangon spp. x x x presence/absence Crangon spp. size + - 1

Hippolytidae # - 1 cbrt

Hippolytidae size - - 2 cube

Phyllaplysia taylori # - + Jy/A log Phyllaplysia taylori + + - Jy/A log size Macoma nasuta # - sqrt Macoma nasuta size - + - 2 log (L < 21 mm) Macoma nasuta size - - (L ≥ 21 mm) 1. Jy = July, A = August 2. log = log10 (x+1), cbrt = x1/3, cube = x3, sqrt = x1/2

15

RESULTS

Cancer magister

The abundance of Dungeness crabs was related to eelgrass habitat characteristics using multiple regression. This analysis covered the first three sampling periods (May to

July 2004) because only one crab was captured during the fourth sampling period. The one crab captured in August measured 35 mm, within the 30 to 35 mm maximum size range I predicted to catch prior to the crabs leaving the eelgrass habitat (Figure 3). A log10 (x+1) transformation was applied to the data to meet the assumption of normally distributed residuals. Shoot density, variation in shoot length, distance to the nearest

channel and time were significant predictors of crab abundance and were included in the

model (Table 2). C. magister were collected in eelgrass ranging from 212 to 1,016 shoots m-2 and distances ranging from 4 to 75 meters from a channel. Crabs were more numerous in habitat characterized by the following; greater shoot density, less variable shoot length, and closer proximity to the channel. There was a slight positive relationship between the number of crabs collected and time. The model was significant and accounted for over 37% of the variation in the data (Table 2).

A total of 41 crabs were measured and multiple regression was used to determine variables that best predicted size. Shoot density and time were significant predictors and were included in the model (Table 2). Small juvenile crabs were more prevalent in habitat characterized by greater shoot density than large juvenile crabs. The mean size of crabs increased over the course of the study from 12.2 mm to 35.0 mm (Figure 3). This model accounted for over 57% of the variation in the data (Table 2).

16 17

10 40 Number Collected Size 8 30

# 6 20

4

Cancer magister 10

2 Carapace Width (mm)

0 0

May June July August

Figure 3. Mean number and carapace width (mm) of Dungeness crab (Cancer magister) trapped May – August 2004 from sites in southern Humboldt Bay, CA. Error bars are 95% confidence intervals.

18 Table 2. Results of multiple linear regression analyses for best fitting models testing for effects of vegetation structure, time and bed location on mean number of Dungeness crab (Cancer magister) trapped May – July, 2004, and mean size trapped May – August 2004, in southern Humboldt Bay, CA.

β t p

Abundance1 Intercept 0.0047

Shoot density 0.0026 2.992 0.0049

Time 0.1445 2.458 0.0188

Distance to the nearest channel -0.0010 -2.301 0.0271

Variation in shoot length -0.0074 -2.033 0.0493

Size2 Intercept 13.2464

Time 5.9644 5.659 <0.0001

Shoot density -0.0115 -3.085 0.0038

1. Statistics for the full model. R2 = 0.3727, Adj. R2 = 0.3049, F = 5.497, P = 0.0014

2. Statistics for the full model. R2 = 0.5781, Adj. R2 = 0.5559, F = 26.034, P < .0001

19 Caridean Shrimp

Crangon spp.

Logistic regression was used to identify variables most significantly separating

areas with Crangon shrimp from those without. Distance to the nearest channel, presence

of sculpins and shoot density were identified as the most important predictors of shrimp

presence. The model correctly classified over 78% of the sites (90% without Crangon,

47% with Crangon; Table 3). Crangonids were found throughout a moderate range of

shoot densities (154 – 610 shoots m-2) and at distances between 5 and 66 meters from a

channel, although most were within 30 meters. Crangon were rarely captured with

sculpin. The relationship between channel distance and shoot density was negative. For

example, the model predicts crangonids can be found within 45 meters of a channel in

eelgrass characterized by density less than 200 shoots m-2. As distance to the channel

decreases, the shoot density may increase, so that at 15 meters from the channel, density

may double to 400 shoots m-2 and Crangon would still be present.

A total of 23 Crangon spp. were measured and multiple regression analysis was

used to determine variables that best predicted size. One outlier was removed from the

analysis. Epiphyte load and time were significant predictors and were included in the

model (Table 4). The relationship between Crangon length and epiphyte load was

negative. Smaller Crangon were more prevalent in eelgrass beds characterized by greater

epiphytes than larger Crangon. The mean size of crangonids increased over the course of the study from 53.4 mm to 61.5 mm (Figure 4). The model was significant, accounting for 50% of the variation in the data.

20 Table 3. Results of logistic regression analysis for best fitting model testing vegetation structure, time, bed location and presence/absence of sculpin on separating areas with Crangon spp. from those without, May – August 2004 in southern Humboldt Bay, CA.

Term Omitted χ2 p

All1 15.843 0.0012

Distance to the nearest channel 8.107 0.0044

Sculpin (presence/absence) 6.042 0.0140

Shoot density 4.982 0.0256

% Correctly classified: (0) 90.2%, (1) 46.7%, (Total) 78.6%

1. Tests the significance of the model

21

Table 4. Results of multiple linear regression analysis for best fitting model testing for effects of vegetation structure, time and bed location on mean size of bay shrimp (Crangon spp.) trapped May – August 2004 in southern Humboldt Bay, CA.

β t p

Intercept 49.8391

Time 5.4104 4.305 0.0004

Epiphyte weight -182.5332 -2.170 0.0429

Statistics for the full model. R2 = 0.5093, Adj. R2 = 0.4576, F = 9.860, P = 0.0012

22

10 80 Number Collected Size 8 70

6 60 spp. #

4 50 Crangon Crangon Total LengthTotal (mm) 2 40

0 0

May June July August

Figure 4. Mean number and total body length (mm) of bay shrimp (Crangon spp.) trapped May – August 2004 from sites in southern Humboldt Bay, CA. Error bars are 95% confidence intervals.

23 Hippolytidae

Multiple regression analysis was used to determine if eelgrass habitat variables

were related to the number of hippolypid shrimp captured. One outlier was removed and

data were cube root (x1/3) transformed to meet the assumption of normally distributed

residuals. Eelgrass habitat characteristics were not significant predictors of hippolytid

shrimp. There was a statistically significant decrease in the number of shrimp captured

over the course of the study (Table 5). The model predicting shrimp abundance over time

accounted for nearly 33% of the variation in the data (Table 5).

A total of 356 hippolytid shrimp were measured and multiple regression was used

to determine variables that best predicted size. Two outliers were removed and the data

was cube (x3) transformed to meet the assumption of normally distributed residuals.

Distance to the entrance channel and time were the only significant predictors of shrimp size and were included in the model (Table 5). Smaller hippolytid shrimp were more prevalent in areas farther from the entrance channel than were larger shrimp. The mean size of hippolytid shrimp decreased over the course of the study from 36.9 mm in May to

32.9 mm in August (Figure 5). The model was weak, however it was significant, accounting for 11% of the variation in the data (Table 5)

Phyllaplysia taylori

Phyllaplysia taylori densities from May and June were analyzed using multiple regression. A log10 (x+1) data transformation was applied to the data to meet the

assumption of normally distributed residuals. Shoot biomass and time were significant

predictors of P. taylori density and were included in the model (Table 6). There was a

24 Table 5. Results of multiple linear regression analyses for best fitting models testing for effects of vegetation structure, time and bed location on mean number and size of broken-back shrimp (Hippolytidae) trapped May – August 2004 in southern Humboldt Bay, CA.

β t p

Abundance1 Intercept 2.5281

Time -0.5019 -5.106 <0.0001

Size2 Intercept 60,282.0870

Distance to the entrance channel -2.0395 -4.293 <0.0001

Time -3,792.5500 -4.843 <0.0001

1. Statistics for the full model. R2 = 0.3256, Adj. R2 = 0.3131, F = 26.070, P < .0001

2. Statistics for the full model. R2 = 0.1115, Adj. R2 = 0.1065, F = 22.029, P < .0001

25

40 40

30 30

20 20 Hippolytidae #

10 10 LengthTotal (mm)

0 Number Collected 0 Size

May June July August

Figure 5. Mean number and total body length (mm) of broken-back shrimp (Hippolytidae) trapped May – August 2004 from sites in southern Humboldt Bay, CA. Error bars are 95% confidence intervals.

26 Table 6. Results of multiple linear regression analyses for best fitting models testing for effects of vegetation structure, time and bed location on density and mean size of Phyllaplysia taylori collected May – June 2004 in southern Humboldt Bay, CA.

β t p

Density1 Intercept 2.2141

Time -0.5585 -5.182 <0.0001

Shoot biomass 0.0021 2.595 0.0156

Size2 Intercept 1.1156

Shoot biomass -0.0013 -19.438 <0.0001

Variation in shoot length 0.0057 10.977 <0.0001

Time 0.1219 16.749 <0.0001

1. Statistics for the full model. R2 = 0.5508, Adj. R2 = 0.5149, F = 15.330, P < .0001

2. Statistics for the full model. R2 = 0.4477, Adj. R2 = 0.4464, F = 334.283, P < .0001

27 positive relationship between P. taylori density and shoot biomass and density was greatest in May (Table 6). The model accounted for over 55% of the variation in the data and was significant (Table 6).

A total of 1,306 P. taylori were measured during the study, 1,241 of which were collected during May and June. A log10 (x+1) transformation was applied to the data to meet the assumption of normally distributed residuals. Shoot biomass, variation in shoot length and time were significant predictors of P. taylori size and were included in the model (Table 6). The relationship between animal size and shoot length variation was positive, while the relationship between size and shoot biomass was negative. This model suggests that smaller P. taylori were more prevalent in habitats characterized by greater shoot biomass. Larger animals were more prevalent in areas exhibiting more variation in shoot length. The mean size of P. taylori increased from 15.6 mm in May to

21.6 mm in June (Figure 6). Although the July and August data were not included in this analysis, the mean size further increased to 33.0 mm in July before dropping to 11.9 mm in August (Figure 6). This model accounted for 44% of the variation in the data and was significant (Table 6).

Macoma nasuta

Macoma nasuta density was analyzed using multiple regression. A square root

(x1/2) transformation was applied to the data to meet the assumption of normally

distributed residuals. Distance to the entrance channel was the only significant variable

predicting clam density (Table 7). M. nasuta density was greater in closer proximity to

28

600 40

480 -2 30

360 Density m 20

240 Length (mm) Length 10 120 Phyllaplysia taylori

Density 0 0 Size

May June July August

Figure 6. Mean density and body length (mm) of Phyllaplysia taylori collected May – August 2004 from sites in southern Humboldt Bay, CA. Error bars are 95% confidence intervals.

29 Table 7. Results of multiple linear regression analyses for best fitting models, testing for effects of vegetation structure, time and bed location on mean density and shell length of small (< 21 mm), and large (≥ 21 mm) bent-nosed clam (Macoma nasuta) collected May – August 2004 from sites in southern Humboldt Bay, CA.

β t p

Density1 Intercept 2.6839

Distance to the entrance channel -0.0001 -3.059 0.0035

Sm. Size2 Intercept 1.1890

Variation in shoot length 0.0052 3.187 0.0019

Shoot length -0.0025 -2.673 0.0088

Shoot density -0.0001 -2.105 0.0377

Lg. Size3 Intercept 39.4125

Root/rhizome biomass4 -0.0233 -2.727 0.0073

Time -0.9274 -2.042 0.0432

1. Statistics for the full model. R2 = 0.1477, Adj. R2 = 0.1319, F = 9.355, P = .0035

2. Statistics for the full model. R2 = 0.1232, Adj. R2 = 0.0972, F = 4.731, P = .0039

3. Statistics for the full model. R2 = 0.0977, Adj. R2 = 0.0834, F = 6.825, P = .0015

4. Root biomass expanded to reflect m2 area

30 the entrance channel. The model was significant and accounted for less than 15% of the variation in the data (Table 7).

A total of 236 M. nasuta clams were measured. Graphical analysis indicated a bi- modal size distribution, therefore lengths were divided into 2 size classes (small < 21 mm, large ≥ 21 mm) for separate multiple regression analysis. For the small length data, two outliers were removed and a log10 (x+1) transformation was applied to the data to normalize the residuals. Shoot length, shoot density, and variation in shoot length were significant predictors and were included in the model (Table 7). Multicollinearity was present, as was indicated by a VIF > 8, however the test results did not exceed the predetermined VIF exclusion value of 10. Negative relationships existed between clam length and the variables shoot density and shoot length. A positive relationship was found between clam length and variation in shoot length. Smaller M. nasuta clams were more prevalent in areas with fewer shoots, shorter shoots, and greater variation in shoot lengths. The model accounted for only 12% of the variation in the data but was significant (Table 7).

Root/rhizome biomass and time were the only significant predictors of size for large-sized Macoma nasuta. Larger clams were more prevalent in eelgrass characterized by less root/rhizome biomass (Table 7). Clam size decreased over the course of the study

(Figure 7). The model accounted for less than 10% of the variation in the data but was significant (Table 7).

31

750 30

600 2 20 450 Density m Density

300 10 (mm) Length

Macoma nasuta Macoma 150

Density Size 0 0

May June July August

Figure 7. Mean density and shell length (mm) of bent-nosed clam (Macoma nasuta) collected May – August 2004 from sites in southern Humboldt Bay, CA. Error bars are 95% confidence intervals.

DISCUSSION

Determining the specific aspects of structural complexity and geographic features that are important in shaping the assemblage of animals inhabiting eelgrass communities is both ecologically interesting and fundamentally valuable to resource managers. The intent of this study was to target ecologically important animals occupying different trophic positions to elucidate what, if any, physical factors of their habitat are important in predicting their population numbers and sizes. The life histories of these animals are varied; some use eelgrass habitat during a temporary portion of their lives, as in a nursing or spawning ground, many spend their entire lives within the vegetation, and others ephemerally occupy eelgrass beds. For this reason, factors predicting the abundance or size of a particular animal will depend upon the season and life cycle stage of the animal.

Cancer magister

I hypothesized that juvenile crabs would be positively associated with structurally complex habitat characterized by increased shoot density. Furthermore, I predicted that the number of Dungeness crabs captured would decrease over the course of the study.

Consistent with my hypothesis, there was a strong positive relationship between the number of crabs collected and shoot density. This finding is supported by previous studies that have shown that post-settlement juvenile crabs are denser in structurally complex habitat (e.g. Z. marina) than bare sand or mud (Eggleston and Armstrong 1995,

Fernandez et al. 1993).

32 33 Dungeness crabs were captured from eelgrass beds during each month sampled, however inconsistent with my hypothesis, the model did not indicate a negative temporal relationship with number of crabs captured. Weather conditions coupled with the trapping technique may explain the small number of captures at the beginning of the study. A storm system passing through the region in early May brought increased wind and waves to the estuary that did not occur during other sampling periods. Although the traps were staked to the mud flats, there may have been more movement of the traps during the storm activity, hindering the ability of small crabs from entering the traps. It is also possible that megalopae settlement occurred late in 2004. Actively swimming megalopae larvae appear in the offshore waters of California from early March to mid

April (Emmett et al. 1991). Tidal currents aid in their transport to shallow water estuarine areas where they settle out and rapidly metamorphose into benthic crabs

(Emmett et al. 1991).

Consistent with my hypothesis, crab size was negatively correlated with shoot density. Very small juvenile crabs benefit from the increased protection from predation that is offered by structurally complex habitats (Fernandez et al. 1993). Reilly (1983) found that few juvenile Dungeness crabs larger than 30 mm were recovered from the stomachs of their predators, indicating that predation is likely more intense for small, 0+ aged crabs.

The mean size of C. magister increased over time as predicted, from 12.2 mm in

May to 35 mm in August. Although only one crab was captured in August, it corroborated my hypothesis that post-settlement juvenile crabs in southern Humboldt Bay

34 are dependent upon their habitat as early instars but that after reaching a size of approximately 30 – 35 mm, vegetation structure is not an important factor in defining suitable habitat (McMillan et al. 1995).

Caridean Shrimp

Shrimp are not economically significant in Humboldt Bay, however they provide an ecologically important link in the estuarine food web as a favored component in the diet of marine mammals, fish and larger crustaceans, including commercially important rockfish species (Sebastes spp.) and Dungeness crab (Butler 1980; Hieb 1999; Krygier and Horton 1975). Members belonging to two caridean shrimp families were collected during this study. The bay shrimp, Crangon franciscorum and C. nigricauda (Family:

Crandonidae), are primarily carnivorous and exhibit a tolerance for wide ranges in temperature and salinity (Hieb 1999, Wahle 1985). Broken-back shrimp, Heptacarpus spp. and Hippolyte spp. (Family Hippolytidae.) belong to the largest family of shrimp in the north Pacific Ocean (Bulter 1980, Jensen 1995).

Crangon spp.

Few Crangon were captured during this study, however consistent with my

hypothesis, shoot density was an important attribute separating areas with crangonids

from those without. Distance to the nearest channel was identified as the most important

habitat variable. Crangonids were found throughout a moderate range of shoot densities

(154 – 610 shoots m-2) and at distances between 5 and 50 meters from the channel,

although most were within 30 meters of the channel. These variables were better at

describing areas where the shrimp were absent rather than where they were present.

35 Examination of the trap bycatch suggests a possible explanation for the low number of captures and greater ability of predicting Crangon absence than presence. The trapping techniques utilized for collecting decapod crustaceans resulted in the bycatch of various fish and invertebrates. Most notable were three-spine stickleback (Gasterosteus aculeatus) and Pacific staghorn sculpin (Leptocottus armatus). Staghorn sculpin are opportunistic, generalist predators, that feed on nereid polychaetes, fish, bivalves and crustaceans (Armstrong et al. 1995). Their diets change with relative prey abundance and accessibility. Commonly found in estuaries, they are abundant as both adults and juveniles in Humboldt Bay (Emmett et al. 1991). Decapod crustaceans are an important component in the diet of both adult and juvenile L. armatus and they are known to prey on Cancer magister and Crangon spp. (Armstrong et al. 1995; Emmett et al. 1991; Hieb

1999; Reilly 1983; Tasto 1975). Armstrong et al. (1995) found Crangon spp. and Cancer magister composed approximately 22% of the overall summer diet of older juvenile and adult staghorn sculpin (> 70 mm).

Adding the presence/absence of staghorn sculpin to the current analysis clarified an important aspect of the Crangon distribution: Crangon were rarely present when sculpin were present. This correlation suggests that Crangon may have been absent because they were preyed upon by the sculpin. Interestingly, the presence of sculpin was not important in predicting the abundance of either Dungeness crab or hippolytid shrimp.

It should be noted that sculpin were not dissected to determine stomach contents however they are visual predators and the traps did not separate the decapods from the fish. If sculpin were responsible for the small number of crangonids captured, an artifact of the

36 trap environment, conspicuous characteristic or behavior of Crangon, or combination of these elements may have contributed to the predation on Crangon and not the other decapod crustaceans

Vegetation structure and time were important in describing some variation in the size of crangonids. The mean size of Crangon increased over the course of the study and smaller shrimp were correlated with greater epiphyte load than larger shrimp.

Interestingly, the size range of the captured crangonids was indicative of adults but the study occurred during the time of year when juveniles should have been present (Butler

1980; Emmett et al. 1991; Hieb 1999; Jensen 1995). This supports the assertion that sculpin may have been responsible for the small number of crangonids captured.

Hippolytidae

Eelgrass habitat structure was not an important factor predicting hippolytid

shrimp abundance or size. Hippolytid shrimp may exemplify a group of generalists whose presence and size were poorly predicted because eelgrass bed structure and location may not be important attributes dictating their distribution. It is also possible that habitat variables may be important predictors of size and distribution for particular species in this family but only a family level analysis was possible since species identification within this family is extremely difficult. This point is easily illustrated by comparing the size ranges and habitat characteristics of the two genera belonging to the hippolytid family that are likely to be found in southern Humboldt Bay. Hippolyte spp. are described as small in size (< 40 mm) and members of the genus usually live on vegetation such as eelgrass. Heptacarpus spp. however, are small to moderate in size

37 (< 60 mm), and while some species are found in and around eelgrass beds, they can occur in other habitats such as upon soft bottoms or among macroalgae (Butler 1980; Hieb

1999; Jensen 1995). Understanding habitat characteristics of hippolytid shrimp is therefore probably not possible until better taxonomic resolution is achieved.

Phyllaplysia taylori

Consistent with my predictions, a significant positive relationship between P. taylori density and shoot biomass was found. I did not however find the significant negative relationship I predicted between animal density and epiphytes. This result was surprising because grazing by invertebrates is commonly known to regulate epiphyte biomass, (Hootsman and Vermaat 1985; Nelson 1997; van Montfrans et al. 1982), P. taylori is an epiphytic grazer (Beeman 1968), and P. taylori abundance has been found to be negatively correlated with both shoot biomass and epiphyte abundance (Keiser 2004).

One possible explanation for my finding is that as eelgrass biomass increases, light availability to the eelgrass decreases as the plant begins to shade itself, resulting in decreased epiphyte growth.

Hypotheses regarding P. taylori size were not made prior to this study however significant findings were discovered. Smaller P. taylori were more prevalent in eelgrass meadows characterized by greater biomass and homogeneity of shoot lengths than larger

P. taylori. This may be explained by a simple mathematical relationship. Greater biomass equates to greater leaf surface area available for both habitat and nutrition

(Keiser 2004). As was mentioned previously, P. taylori density was greater in areas with higher shoot biomass. If smaller animals require less habitat and food resources than

38 larger animals, more small animals could successfully occupy the same area of eelgrass beds as fewer large animals.

Macoma nasuta

The clam Macoma nasuta is a numerically dominant animal in eelgrass habitats

(Kozloff 1983). In addition, it is a very hardy organism and can inhabit softer, siltier mud and survive in stagnant water that would kill other species (Ricketts et al. 1985).

During this study, M. nasuta was the most prevalent animal collected, found in nearly

95% of sites sampled in southern Humboldt Bay. Eelgrass habitat attributes were not found to be important predictors of density or size, however, weak yet significant relationships were found.

One possible explanation for lack of relationships between M. nasuta and eelgrass habitat relates to the plasticity of their feeding mode. Bivalves employ two primary feeding mechanisms, deposit feeding and suspension (filter) feeding (Dame 1996).

Deposit feeders ingest food particles that have been deposited on or within the substrate, sucking sediment and water through their incurrent siphon like a vacuum hose (Dame

1996; Hemminga and Duarte 2000). Because this method relies on settlement of food particles, deposit-feeding bivalves benefit from lower velocity waters in which deposition can occur. Suspension feeders remove food particles from the water by pumping and filtering water through their gills. Research conducted on the suspension-feeding clam

Mercenaria mercenaria indicated a positive relationship between growth and flow speed

(Grizzle et al. 1992). Based solely on hydrodynamics, one might predict decreased growth of suspension-feeding clams within a seagrass bed due to the baffling effect of the

39 macrophyte structure. Complicating matters is that feeding studies on M. nasuta have alternately labeled this species as a suspension feeder (Reid and Reid 1969) and a deposit feeder (Hylleberg and Gallucci 1975). In eelgrass beds in the Baltic Sea, Macoma balthica employs a selective feeding strategy, switching from suspension feeding to deposit feeding when organic particles are available on the sediment surface (Hemminga and Duarte 2000). Hylleberg and Gallucci (1975) imply that since M. nasuta is found in habitats ranging from sand to mud, different feeding strategies may exist, and it is possible that this species is capable of selective feeding methods.

Larger M. nasuta were negatively correlated with higher root/rhizome biomass.

Brenchley (1982) found that the mobility of M. nasuta decreased 12-fold in dense root- rhizome mats of Zostera marina, and larger clams were more restricted than smaller clams. The addition of small crustacean or polychaete tube mats resulted in an even further decline in the mobility of large clams. In contrast, smaller M. nasuta were more numerous in structurally complex eelgrass characterized by increased shoot density, and longer, homogeneous shoot lengths. Because M. nasuta is a widely dispersed broadcast spawner, decreased water currents caused by the eelgrass canopy could facilitate deposition of planktonic larvae, contributing to greater settlement success (Fonseca et al.

1983).

Macoma nasuta is an example of an animal whose presence and size were poorly predicted by vegetation structure and habitat variables. Previous studies investigating the effects of seagrass cover on clams have resulted in various outcomes, indicating that complex interactions exist among predation disturbance, sediment stability and food

40 supply, and these effects vary in relative importance based on clam size, site differences, and hydrographic regime (Coen and Heck 1991; Irlandi 1996; Peterson and Quammen

1982; Peterson et al. 1984). The plasticity of M. nasuta feeding strategies and wide dispersal would allow M. nasuta to occupy various locations within the bay (Hylleberg and Gallucci 1975; Rae 1979; Reid and Reid 1969). Certain characteristics of the eelgrass habitat could be important in determining settlement patterns, however post settlement mortality may obscure the ability to define any important relationships. Since

M. nasuta is euryhaline, eurythermal and tolerant of anoxic conditions (Ricketts et al.

1985), the likely source of mortality would be predation from Crangon, fish, and crabs, all of which occur in the eelgrass beds of southern Humboldt Bay (Peterson and

Quammen 1982; Tasto 1975; Wahle 1985).

Summary

This study showed that eelgrass bed structure and location in southern Humboldt

Bay were important in predicting the abundance and size of some of the animals in this study. Cancer magister, Crangon spp. and Phyllaplysia taylori were all correlated with certain habitat attributes, whereas Macoma nasuta and hippolytid shrimp relationships with habitat structure were poor. Dungeness crab abundance was greater in areas closer to the nearest channel characterized by increased shoot density. Dungeness crab size increased over time, and larger individuals were associated with lower shoot density than smaller crabs. Shoot density and distance to the nearest channel were significant predictors of Crangon presence/absence. When the presence of sculpin was added to the analysis, it indicated that Crangon were almost never present when sculpin were present.

41 Phyllaplysia taylori were more numerous in eelgrass containing greater shoot biomass.

Smaller individuals were more prevalent in eelgrass characterized by greater shoot biomass and homogeneous shoot lengths than larger conspecifics. Vegetation structure and location were not found to be important predictors of Macoma nasuta or hippolytid shrimp in southern Humboldt Bay. The sampling design utilized in this study may not have been sensitive to species specific patterns of hippolytid shrimp, or other factors not considered in this study may be important in structuring their size and distribution. As is the case for similar studies in other estuaries around the world (Orth et al. 1984), results of this study indicate that habitat structure and location are important in structuring the size and distribution of some animals but not others.

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APPENDIX

Appendix A. Universal Transverse Mercator (UTM) coordinates and geographic data associated with sites sampled May – August 2004 in southern Humboldt Bay, CA. Map datum is zone 10, NAD-27.

Distance to Distance to Elevation Month Easting Northing nearest channel entrance channel (m) (m) (m) May 396076 4508293 -0.11 87.9 2,451.3 394860 4507226 -0.03 33.6 3,863.4 395407 4509631 0.03 12.9 1,629.0 395719 4507705 -0.57 64.5 3,102.9 395920 4509797 -0.17 63.0 1,159.2 396654 4509404 0.20 30.0 1,289.7 394823 4508228 -0.02 5.1 3,041.7 395560 4509373 -0.52 15.9 1,706.4 394003 4506970 0.12 25.2 4,518.0 395326 4506373 0.20 28.2 4,470.6 396013 4506485 0.31 18.3 4,210.8 394443 4508051 0.00 21.9 3,409.8 394781 4506366 0.20 30.3 4,662.0 394688 4506863 0.22 21.9 4,258.5 June 396018 4510437 -0.34 30.0 686.4 394772 4507816 0.13 8.7 3,403.8 395684 4508242 -0.39 17.4 2,615.7 395539 4509250 -0.54 13.8 1,814.1 394102 4507781 0.19 79.5 3,829.8 396518 4509651 0.23 51.0 1,055.7 393979 4507074 0.21 27.9 4,449.0 396466 4509021 -0.21 25.5 1,677.6 394519 4507331 0.21 13.2 3,939.9 394452 4507166 0.17 52.5 4,112.7 394653 4508025 0.15 28.5 3,301.8 394816 4507369 -0.02 14.7 3,759.0 394782 4506286 0.28 51.9 4,734.0 395948 4506492 0.20 47.7 4,214.4

47 48 Appendix A, continued

Distance to Distance to Elevation Month Easting Northing nearest channel entrance channel (m) (m) (m) July 395417 4509204 -0.31 34.5 1,923.6 396847 4509398 0.15 4.5 1,309.2 395845 4509737 0.07 66.0 1,251.6 394273 4508260 0.21 58.8 3,368.1 395738 4506956 -0.57 4.8 3,808.8 396054 4509640 -0.13 62.1 1,213.2 396226 4508647 -0.32 6.6 2,079.0 395517 4510079 0.02 47.7 1,285.2 395264 4506504 0.21 107.7 4,365.6 394717 4507581 0.12 26.7 3,628.2 395180 4507310 -0.04 7.8 3,653.1 396378 4506594 0.07 69.0 4,065.3 396557 4509082 0.20 57.0 1,609.5 394423 4507182 0.17 42.3 4,114.5 August 396434 4508688 0.03 90.0 2,006.4 395539 4507625 -0.24 4.8 3,234.3 395389 4509523 -0.31 24.0 1,713.0 395758 4509703 0.11 6.0 1,332.9 394313 4508193 -0.05 21.3 3,388.8 394096 4508009 0.18 26.7 3,667.5 394248 4507782 0.20 80.1 3,735.9 396510 4509344 0.09 19.8 1,357.2 396784 4509718 0.31 77.4 988.8 396353 4506979 -0.05 40.5 3,688.8 395848 4510021 0.15 74.7 1,049.7 394381 4507346 0.19 187.5 4,002.0 394129 4506845 0.20 29.1 4,549.5 394888 4506677 0.20 18.6 4,339.5

Appendix B. Summary of eelgrass, epiphyte, and sculpin data collected May – August 2004 from sites in southern Humboldt Bay, CA. Aboveground eelgrass collected from 0.25 m2 quadrats; belowground eelgrass data expanded to reflect 0.25 m2, 15 cm depth; epiphyte weight per leaf area sampled (cm2) and sculpin collected from traps fished ~ 48 hours.

Mean Standard Sculpin Shoot density Shoot biomass Root biomass Epiphyte weight Month shoot length deviation of trapped (#) (g) (g) (mg) (cm) shoot length (#)

May 79.29 36.67 119.0 62.90 34.62 0.011 0 52.53 22.89 128.5 44.17 18.34 0.006 0 51.03 19.24 76.5 21.35 28.16 0.003 0 80.70 24.09 60.5 39.18 35.16 0.031 0 69.69 23.84 81.5 41.10 27.56 0.004 0 40.66 17.71 93.5 19.41 12.85 0.080 0 68.75 27.48 152.5 63.54 32.56 0.005 0 86.92 32.03 99.5 65.57 33.76 0.003 0 57.51 26.06 79.5 30.15 32.63 0.006 0 43.80 14.85 38.5 11.98 7.99 0.016 0 42.07 18.96 78.0 17.40 7.62 0.007 11 48.44 18.63 113.5 29.57 39.46 0.003 0 78.04 33.11 126.0 45.78 33.95 0.002 0 51.33 19.70 140.5 31.85 32.01 0.008 0 49

Appendix B, continued

Mean Standard Sculpin Shoot density Shoot biomass Root biomass Epiphyte weight Month shoot length deviation of trapped (#) (g) (g) (mg) (cm) shoot length (#)

June 82.52 37.27 74.0 36.14 20.2 0.032 0 67.91 36.63 142.0 42.44 22.3 0.013 1 115.32 56.67 120.0 62.54 40.4 0.009 0 76.44 40.58 131.0 47.90 53.6 0.019 0 75.80 37.81 110.0 40.62 31.3 0.000 0 64.41 30.31 191.0 61.02 31.7 0.002 1 56.93 26.42 111.5 29.84 30.5 0.013 1 72.25 40.64 195.0 65.57 50.7 0.004 0 70.24 36.46 176.5 54.05 44.7 0.016 1 59.01 34.83 254.5 59.68 54.2 0.008 0 34.70 14.58 106.0 15.98 16.1 0.000 0 43.73 19.23 152.5 23.81 20.6 0.004 0 44.55 26.95 105.5 18.37 25.2 0.000 0 50.40 21.99 58.5 26.30 40.2 0.002 3

50

Appendix B, continued

Mean Standard Sculpin Shoot density Shoot biomass Root biomass Epiphyte weight Month shoot length deviation of trapped (#) (g) (g) (mg) (cm) shoot length (#)

July 149.44 54.80 62.5 36.56 43.1 0.009 1 68.97 28.14 99.0 27.12 16.5 0.007 1 112.05 60.90 93.5 45.60 37.9 0.012 0 70.59 38.35 126.0 33.06 35.3 0.014 2 92.55 45.33 97.5 36.73 31.8 0.041 0 121.37 68.28 126.0 62.87 63.7 0.124 1 124.12 63.47 98.0 51.95 56.9 0.038 0 118.60 52.50 66.5 40.09 39.3 0.015 1 99.18 42.94 82.5 47.58 49.9 0.036 1 75.43 40.47 119.5 42.88 46.3 0.037 1 71.61 35.99 120.5 31.17 34.6 0.035 0 78.04 36.67 92.0 30.38 22.7 0.054 0 89.76 43.96 128.0 45.71 33.2 0.101 2 82.55 39.85 130.0 43.20 31.9 0.059 0

51

Appendix B, continued

Mean Standard Sculpin Shoot density Shoot biomass Root biomass Epiphyte weight Month shoot length deviation of trapped (#) (g) (g) (mg) (cm) shoot length (#)

August 90.56 53.78 84.5 21.07 37.5 0.002 0 84.40 46.62 58.0 16.73 38.1 0.047 0 96.63 52.74 38.0 11.54 50.8 0.044 0 51.74 21.48 74.5 15.35 24.5 0.117 2 74.98 40.19 48.0 13.06 27.7 0.133 2 69.75 38.39 75.0 22.73 32.4 0.012 1 91.84 40.27 50.5 13.33 45.0 0.032 1 93.68 51.88 95.0 31.98 57.3 0.034 0 60.52 34.59 69.5 23.45 14.0 0.073 0 58.08 26.46 72.5 15.09 35.0 0.029 0 113.32 46.17 53.5 21.52 17.5 0.018 0 50.83 27.80 141.0 22.83 30.8 0.013 0 61.17 28.41 54.0 10.60 8.1 0.032 2 30.29 12.90 112.5 10.35 18.5 0.037 0 52

Appendix C. Summary of Cancer magister, Crangon spp. and hippolytid shrimp captured in traps fished ~ 48 hours, Phyllaplysia taylori collected from 0.25 m2 quadrats, and Macoma nasuta collected from mud cores, May – August 2004 from sites in southern Humboldt Bay, CA.

Cancer magister Crangon spp. Hippolytidae Phyllaplysia taylori Macoma nasuta

Size Size Size Size Size # # # # # range range range range range Collected Collected Collected Collected Collected (mm) (mm) (mm) (mm) (mm) May 0 0 34 23 - 41 170 8 - 20 6 17.3 - 43.5 0 0 22 10 - 45 243 6 - 17 1 12.3 0 1 59 6 32 - 40 91 11 - 24 0 0 0 7 33 - 39 105 9 - 21 0 0 0 74 35 - 42 155 9 - 30 6 13.8 - 38.1 0 0 5 36 - 45 58 10 - 26 8 11.5 - 41.7 0 1 52 31 32 - 42 89 8 - 22 6 30.5 - 37.6 0 0 27 32 - 42 200 9 - 18 3 11.2 - 39.7 2 17 – 18 3 52 - 55 13 32 - 39 57 13 - 31 2 16.9 - 40.7 0 3 52 - 55 5 34 - 41 33 13 - 25 3 16.4 - 42.5 0 0 4 33 - 38 92 12 - 31 1 12.2 0 1 52 3 34 - 40 56 7 - 23 2 20.5 - 35,8 2 7.5 – 11 0 11 33 - 45 92 9 - 27 5 10.2 - 41.2 1 7.3 0 0 49 9 - 25 7 11.0 - 28.2 53

Appendix C, continued

Cancer magister Crangon spp. Hippolytidae Phyllaplysia taylori Macoma nasuta

Size Size Size Size Size # # # # # range range range range range Collected Collected Collected Collected Collected (mm) (mm) (mm) (mm) (mm) June 3 23 - 24 5 47 - 82 24 18 – 41 9 33 - 5 2 6 8.3 - 36.0 3 16.7 – 20.8 0 0 4 22 - 26 8 12.0 - 41.0 1 23.0 1 52 0 64 12 - 29 5 31.5 - 39.3 1 14.5 0 3 35 – 37 56 16 - 30 2 25.8 - 36.0 0 0 7 23 – 39 55 14 - 28 1 18.6 4 10.0 – 20.3 0 16 33 – 41 56 7 - 23 5 12.5 - 34.5 0 0 1 31 23 21 - 31 3 12.8 - 40.3 2 15.5 – 16.0 0 1 23 13 - 28 8 5.0 - 45.8 1 23.0 0 2 31 - 39 42 15 - 27 1 40.0 2 10.3 – 22.3 0 10 33 - 39 43 12 - 24 1 39.0 3 17.5 – 22.1 1 67 4 29 - 40 40 14 - 32 2 10.5 - 11.8 4 20.3 – 25.0 0 1 29 31 18 - 30 4 13.0 - 41.5 1 15.5 0 1 35 30 16 - 32 10 11.5 - 41.5 0 1 61 0 5 20 - 30 2 11.0 - 11.7

54

Appendix C, continued

Cancer magister Crangon spp. Hippolytidae Phyllaplysia taylori Macoma nasuta

Size Size Size Size Size # # # # # range range range range range Collected Collected Collected Collected Collected (mm) (mm) (mm) (mm) (mm) July 0 0 0 7 26 - 31 5 10.0 - 30.8 3 29.8 – 32.5 0 1 27 0 9 13.5 - 40.5 0 1 66 2 24 – 35 0 3 23.8 - 35.3 0 0 1 30 12 25 - 35 5 12.0 - 36.8 0 1 67 2 36 – 37 7 27 - 47 5 11.8 - 34.0 0 0 0 1 32 9 11.0 - 41.5 0 1 56 4 30 – 40 0 11 13.8 - 40.5 1 30.0 1 63 0 9 28 - 50 4 13.8 - 38.5 0 0 1 41 15 27 - 39 2 10.3 - 17.0 1 26.0 0 3 29 – 34 0 8 12.8 - 38.0 4 14.3 – 26.0 0 0 0 3 15.3 - 32.0 0 0 5 35 – 38 0 4 10.0 - 34.3 0 0 2 20 0 6 10.8 - 44.8 1 20.5 0 4 28 - 40 0 1 37.8

55

Appendix C, continued

Cancer magister Crangon spp. Hippolytidae Phyllaplysia taylori Macoma nasuta

Size Size Size Size Size # # # # # range range range range range Collected Collected Collected Collected Collected (mm) (mm) (mm) (mm) (mm) August 0 0 1 29 0 11 13.3 - 39.0 0 1 62 1 32 0 2 11.0 - 21.0 0 0 4 36 – 43 0 2 33.5 - 34.3 0 0 0 0 4 14.5 - 36.8 0 0 0 0 1 15 0 0 0 2 31 - 32 4 14.3 - 19.3 0 0 0 2 6 - 7 5 14.5 - 42.8 0 0 1 33 0 8 10.0 - 36.8 0 0 0 0 6 13.8 - 34.3 0 0 3 29 – 32 10 6 - 16 1 9.8 1 35.0 0 1 30 0 5 11.0 - 40.3 0 0 2 31 – 33 0 0 20.8 - 39.8 0 0 1 22 0 4 32.8 0 1 61 0 0 1 13.3 - 39.0

56