CALIFORNIA STATE UNIVERSITY, NORTHRIDGE
The Influence of the Structure of an Invasive Alga on the Distribution of Temperate Reef Fishes
A thesis submitted in partial fulfillment of the requirements
For the degree of Master of Science in Biology
By
Griffin Scott Srednick
May 2018
The thesis of Griffin Srednick is approved:
______Dr. Robert Carpenter Date
______Dr. Peter Edmunds Date
______Dr. Larry G. Allen Date
______Dr. Mark A. Steele, Chair Date
California State University, Northridge
ii Acknowledgements
I must begin by thanking my advisor, Dr. Mark Steele, who was instrumental in every stage of this work. Mark, the support and mentorship that you have provided throughout the past few years are unparalleled. Whether the discussion is on experimental design, goby wrangling, tomato harvesting, or jazz, I truly appreciate your guidance and couldn’t ask for a finer graduate experience. Thank you for giving me a chance, even after an interesting first introduction. I look forward to many more years of laughs.
Thank you to Dr. Peter Edmunds, whose advice on all aspects of this work was invaluable. Pete, thank you for investing your time and effort in me from the beginning. Your support has been fundamental in shaping my approach to science and leadership.
Thank you to Dr. Bob Carpenter for opening up my view to the world of algae. Your guidance on all things algae and dye-dipping lit a light bulb for me and also led to a lot of stained clothing. Also, thank you for bringing me along on all those repeated trips to Mosa’s. Hopefully we will go back for some duck burgers in the future.
To Dr. Larry Allen, thank you for all of your contributions but above all thank you for being you. Your positivity and fish-thusiasum (not a real word) foster a stellar environment for all of the students that surround you. We are all incredibly fortunate to have your support.
This research would not have been possible without the following funding sources: the
University of Southern California Wrigley Institute of Environmental Studies Fellowship, the
International Women’s Fishing Association, the Myers Oceanographic Trust, Lerner Gray
Memorial Fund, the PADI Foundation Scholarship, and CSU Northridge Graduate Student
Thesis Support.
iii There is no doubt that this project would have been impossible without support from the following people. First and foremost, there are two individuals that I am forever indebted to.
First, Sam Ginther, whose advice, mentorship, and friendship have been some of the highlights of my graduate career, and who convinced me to study this fascinating topic. Between time spent above and below the surface, I am immensely grateful for your wise words. To think, this all started while shivering between dives at Arrow Point, which leads me to Russell Dauksis. Russ, I cant put in words how grateful I am for all of your contributions: scientific, culinary, et al. I will never forget our time in the twin cities. Sam and Russ, I will eat oysters and take the shoreboat to
Foxtrot 4 with you guys anytime.
Steele lab, Mia Adreani, Babs Sanchez, Barb Weiser, Stacey Hilborn, Alexis Estrada,
George Jarvis, Erika Nava, Erin Jaco, Casey Benkwitt, Sam Ginther and Russell Dauksis, as well as honorary member Stephen Pang, each and every one of you is a gem. Thank you for all of your contributions as well as fostering such an incredible “positive” and “supportive” environment. I am truly honored to be part of this family and look forward to a lifetime of great friendship.
I would like to thank all of the USC Wrigley staff for their field support and hospitality.
A few folks in particular made all of this possible: Lauren Oudin-Czarnecki, Trevor Oudin, Juan
Aguilar, Jewely Aguilar, Kellie Spafford, Vivian Kim, Chad Burtram, Gordon Boivin and Phil
Lopez. I am immensely grateful to all of the Catalina family for a lifetime of experiences and wonderful friendships.
To the only person who knows this work better than me, Monique Torres, there is no way
I could have completed this without your support. Thank you. I promise, you will never hear me talk about a bear in the woods again.
iv To the puppy pack, James and Devin, I would have never gone down this path had we not shared countless exciting, empowering, and humbling experiences in the ocean together. Your friendship is one of my most cherished gifts. I look forward to many more years of going over the falls and eating tortas.
To my family, there are no words to explain how grateful and proud I am. But I will try my best to outline. Mom, you taught me the importance of a strong work ethic, good critical thought, and kindness. You also showed me how to believe in myself and how to grit my teeth and push through when things get tough. Dad, I blame you for all of this. You taught me how to swim, snorkel, and then dive. You showed me how to thrive in the underwater world and you taught me how to think and build creatively to solve my problems. To my uncle/cousin Marshall, thank you for showing me the power of the ocean. To my grandparents, your wisdom, stories, strength, and mentoring have and will continue to be the most important pieces of my development.
v Dedication
This is dedicated to Bill Jadiker.
vi
Table of Contents Signature Page ii Acknowledgements iii Dedication vi List of Tables viii List of Figures ix Abstract x
Chapter 1: Introduction 1
Chapter 2: The influence of the structure of an invasive alga on the distribution of temperate reef fishes
Introduction 5 Methods 9 Results 18 Discussion 25
Chapter 3: Effects of algal species versus height on the abundance and behavior of temperate rocky-reef fishes
Introduction 53 Methods 56 Results 62 Discussion 65
Literature Cited 82
vii List of Tables page 2.1: Models converting algal height to volume and surface area 37 2.2: Fishes observed during observational surveys 38 2.3: Algae observed during observational surveys 45 2.4: Results of DistLM predicting variation in the fish assemblage 46 from the algal assemblage. 2.5: Results of principal components analysis on the (a) algal assemb- 47 lage and (b) the physical substratum. 2.6: Results of multiple linear regression comparing models with algal 48 PCs, height, volume, and surface area, that predict variation in biotic fish attributes. 2.7: Results of multiple linear regression comparing models with algal 49 PCs, height, volume, and surface area, that predict variation in density of individual species of fish. 2.8: Results of DistLM predicting variation in the vertical distribution of 50 the fish assemblage from the algal assemblage. 3.1: Sample sizes for fish communities observed per thalli 78 3.2: Results of analyses of variance (ANOVA) testing the effects 80 (a) sites, (b) algal species, (c) algal height on the abundance of fishes in surveys of individual algal thalli.
viii List of Figures page 2.1: Santa Catalina Island with 8 study sites 35 2.2: Schematic representation of differences in the vertical distribution 36 of fishes between Macrocystis- and Sargassum-dominated reefs 2.3: Multidimensional scaling (MDS) plot showing similarities in the 39 fish assemblage among depth strata 2.4: Differences in the (a) species richness, (b) density, (c) vertical 40 distribution of the assemblage of fishes across time points 2.5: Differences in fish density (a) with C. punctipinnis, (b) without 41 C. punctipinnis among sites and time points 2.6: Differences in the density of Sargassum horneri and Macrocystis 42 among time points 2.7: Differences in fish density without C. punctipinnis and L. dalli 43 among time points and sites 2.8: Differences in the (a) density and (b) height of algae among time points 44 and sites 2.9: Relationships between summarized algal structure or algal predictor 51 variables and the (a) density, (b) species richness, (c) vertical distribution, and (d) average size of fishes 2.10: Relationships between summarized physical substratum variables and the 52 (a) density, (b) species richness, (c) vertical distribution, and (d) average size of fishes 3.1: Photograph of plots used in field experiments 71 3.2: Results of Experiment 1 showing differences in (a) fish abundance, 72 (b) species richness, and (c) behavior frequency among 3 algal treatments. 3.3: Results of Experiment 2 showing differences in (a) fish abundance, 73 (b) species richness, and (c) behavior frequency among 3 algal treatments 3.4: Results of Experiment 3 showing differences in (a) fish abundance, 74 (b) species richness, and (c) behavior frequency among 3 algal treatments 3.5: Differences in (a) fish abundance and (b) species richness among 75 species of algae in field observations 3.6: Differences in (a) fish abundance and (b) species richness among 76 sites in field surveys 3.7: Results of linear regression showing relationship between algal 77 height and fish abundance in field surveys 3.8: Results of linear regression showing relationship between algal 79 height and fish species richness in field surveys 3.9: Differences in the average size of fishes among (a) algal species 81 and (b) sites in field surveys
ix Abstract
The Influence of the Structure of an Invasive Alga on the Distribution of Temperate Reef Fishes
By
Griffin Scott Srednick
Master of Science in Biology
Algae are key foundation species within temperate subtidal reef systems as their productivity and structure can transform the physical composition of the reef landscape.
However, previous assessments of the effects of variation in the algal community on fishes have been mainly focused on changes in the density and identity of algae, and often ignore how the unique structure provided by each alga contributes to such patterns.
Recent warming of ocean temperatures has led to decreases in M. pyrifera and increases in the invasive alga Sargassum horneri along the southern California coast. While both species often form mono-specific stands, the physical structure (“architecture”) of S. horneri is different from that of M. pyrifera and other native algae with respect to height and shape. The goal of this study was to evaluate the effects of S. horneri on the temperate reef fish community at Santa
Catalina Island, California, with emphasis on how unique differences in the physical structure among algae influence the spatial distribution and behavior of fishes.
x Observational surveys were performed five times across a one-year period at seven sites, to examine distribution patterns of fishes and how they related to differences in the algal community at the scale of sites spread over several km. Additionally, I developed a method to predict volume and surface-area from measurements of height for a number of algae to explore how overall differences in algal structure contribute to variation in fish distribution. In combination, I used surveys of the algal and fish communities to assess the relative importance of algal identity versus size on distribution patterns. The spatial distribution and composition of the fish community varied across the five sampling periods and was partially explained by variation in the density and identity of algae. In particular, I found that S. horneri and M. pyrifera were important predictors of variation in the fish assemblage. However, variation in the fish community was better explained by algal size (i.e., height, volume, or surface area) than by algal density and identity.
To better understand the patterns observed at the scale of sites spread over several km, I performed a series of experiments and surveys of individual algae to test the relative importance of algal height versus identity on variation in abundance and behavior of fishes. In three separate experiments, I deployed isolated treatments of algae to examine differences in fish abundance and behavior among various species and size of algae. I found that fish abundance did not vary among algal species but rather among sizes of algae, even when compared across species. In addition, differences in the frequency of feeding and resting behavior were related to size of algae but did not vary among algal species. Field surveys of individual algae showed similar patterns: fish abundance and richness were positively related to algal size. However, differences in the fish community among algal species indicate that certain species (e.g., M. pyrifera) may be more important than others in providing habitat for fishes.
xi These results suggest that the habitat structure provided by algae is important for fishes, but patterns of fish distribution may be more related to size than species of algae. In addition, fishes do not appear to be negatively related to the structure provided by S. horneri, yet a reduction in the tallest form of algal structure on reefs, M. pyrifera, may have important impacts on the distribution and composition of the fish community. The findings of this study highlight the importance of considering how morphological differences among algae influence the fish community.
xii Chapter 1: Introduction
Structural habitat complexity strongly influences ecological communities. Vegetative structure (e.g., trees, algae, etc.) is particularly important. In some marine systems, variation in the density, composition, and type of algae influences the diversity, distribution, and interactions among marine fishes (Quast 1968; Ebeling et al. 1980; Foster & Schiel 1985; Holbrook et al.
1990). The diversity and abundance of temperate marine are often linked to the structure provided by fucoid or laminarialean algae (e.g., kelps). Changes in oceanographic and environmental conditions cause variation in the density, morphology, and composition of algal stands (Valesini et al. 2004; Pérez-Matus 2010). Such variation allows marine macroalgae to be important ecosystem engineers and foundation species by driving the distribution, dispersal, interactions, and stability of populations, and subsequently the community as a whole (Holbrook et al. 1990; Carr 1994; Levin & Hay 1996; Levin & Hay 2002; Sala & Graham 2002; Estes et al.
2004; Graham 2004; Steneck et al. 2002 Arkema et al. 2009).
Algae are structurally diverse, both within and among species (Brooks & Bell 2005;
Stewart et al. 2009). Differences in algal morphology and size can influence the distribution and interactions among members of reef communities (Crowder & Cooper 1982; Levin & Hay 2002;
Pérez-Matus & Shima 2010). Not all algal structure is provided by native species. Through human-mediated and natural vectors, non-native algae can invade and dominate habitats on temperate rocky reefs, modifying the visual complexity of the benthos. Invasive algae are of particular concern in southern California due to their potential impacts and past invasion history.
For example, Sargassum muticum was detected on the west coast of North American in the late
1970s, and has been known to form dense, invasive beds that can outcompete native algae at intertidal depths (Ambrose & Nelson 1982; Chapman et al. 2006; Miller et al. 2007; Monteiro et
1 al. 2009). However, it is not believed to be a dominant competitor within its southern California range.
Of the recent introductions of exotic marine algae on southern California rocky reefs, the arrival of Sargassum horneri has the most dramatic visual effects. Since its first documentation at Santa Catalina Island in 2006 (Miller et al. 2007; Miller & Engle 2009; Sprague et al. 2012),
Sargassum horneri has become increasingly common on subtidal rocky reefs throughout the southern California bight and into Baja California (Marks et al. 2015). Due to high dispersal potential, dense distribution, ability to self-fertilize, combined with increased availability of benthic substrata as a result of a decrease of Macrocystis pyrifera abundance due to warm water periods, S. horneri has become a dominant macroalga (Miller et al. 2007; Choi et al. 2008;
Marks et al. 2015). At Santa Catalina and Anacapa Islands, S. horneri has been recorded in dense monospecific stands (> 100 adult individuals per m2). These stands have an entirely different structure than those of the historically dominant macroalga on southern California rocky reefs,
Macrocystis pyrifera, as they do not extend as far into the water column from the benthos (< 2.5 m versus > 10 m, respectively).
Based on the importance of Macrocystis pyrifera as an ecosystem engineer and foundation species on temperate rocky reefs much uncertainty exists regarding the effects and likely duration of this shift in dominant macroalga. Thus, there has been an increase in research, mitigation, and public outreach efforts to reduce the spread and increase awareness regarding S. horneri in the public and private sectors. Yet, nearly 15 years after its introduction in California, there is still very little known regarding the effects of S. horneri on rocky reef communities. Of particular importance is its effect on economically important, recreational fishing resources.
Some recent work indicates that the effects on the fish assemblage of a shift from Macrocystis to
2 S. horneri as the dominant macroalga on shallow reefs may be somewhat weak, at least on a annual basis (Ginther 2017). However, there may be long-term negative effects on the recruitment of important recreational sport-fish that depend on the structure provided by
Macrocystis.
Not all accounts of S. horneri suggest that it has negative effects. Some preliminary results indicate that due to its ability to thrive during warm-water periods (such as those experienced in 2014 and 2015) when Macrocystis cannot persist, S. horneri may provide a source of structure and primary production that would otherwise be absent on rocky reefs
(Lindsay Marks in prep; Genoa Sullaway in prep). Additionally, while the structure of S. horneri is quite different than that of Macrocystis, it may provide a form of habitat that allows for the maintenance and stability of an alternative assemblage of organisms that depend of macroalgal structure.
The purpose of this thesis was to quantify the effects of S. horneri on the distribution and behavior of common rocky reefs fishes, with particular emphasis on how differences in algal morphology drive these effects. The studies performed serve to first, provide a snapshot of how the density, identity, size, and vertical distribution of fishes vary between Macrocystis and S. horneri dominated systems. Secondly, it provides a mechanistic description of why fishes shift in their distribution and behavior as a result of changes in algal habitat structure. Thirdly, I provide hypothetical expectations of how S. horneri may influence the interactions among fishes to better understand how variation in the algal community may shape fish assemblages in the future.
The studies described in this thesis were performed along the leeward coast of Santa
Catalina Island, which provided the ideal environment to understand the effects of S. horneri due to its high prevalence across reefs there, as well as the presence of persistent kelp forests at some
3 sites. Chapter 2 of this thesis reports observational, correlative, and laboratory techniques to summarize and assess the large-scale effects of a change in the dominant algal structure across rocky-reefs on the fish assemblage. It also describes a novel method to explore the relative importance of algal species versus general algal morphology in predicting variation in the distribution of fishes. In Chapter 3, I describe field observations and experimental manipulations of algae of different species and size to examine the relative importance of algal species versus size on the behavior and abundance of fishes. This chapter provides information on potential causes of the patterns observed in Chapter 2 and it offers a mechanistic and theoretical description of the effects of S. horneri on fishes.
4 Chapter 2: The influence of the thallus structure of an invasive alga on the distribution of
temperate reef fishes
Introduction Habitat structure is widely regarded as one of the key factors driving the diversity, distribution, and interactions among temperate marine fishes (Quast 1968; Ebeling et al. 1980;
Foster & Schiel 1985; Holbrook et al. 1990). Habitat structure can influence ecological processes by mediating or enhancing foraging (Crowder & Cooper 1982; Diehl 1992; Laegdsgaard &
Johnson 2001; Almany 2004; Forrester & Steele 2004; Catano et al. 2016), providing settlement habitat (Watanabe 1984; Raimondi 1990; Carr 1994; Morton & Anderson, 2013), and increasing niche diversity (Heck & Wetstone 1977). The abundance of many demersal and benthic organisms is positively correlated with habitat complexity (e.g., rugosity, substratum type, etc.) and it has been used to predict the spatial distribution and abundance of fishes in tropical and temperate environments (Angel & Ojeda 2001; Ryer et al. 2004; Demartini & Anderson 2007;
Komyakova et al. 2013).
Algal assemblages form physical structure in many nearshore environments that provides important shelter from predation (Holbrook et al. 1990; Anderson 1994; Gutow et al. 2012) and increased access to food (Jones et al. 1997; Steneck et al. 2002; Hovel et al. 2016). Giant kelp
(Macrocystis pyrifera), for instance, provides biogenic habitat for vertebrates and invertebrates, increasing the potential for interspecific interactions (Holbrook et al. 1990; Sala & Graham
2002; Graham 2004; Steneck et al. 2002). Variation in kelp abundance can have direct and indirect effects on the structure of the associated community (Estes et al. 2004; Arkema et al.
2009).
5 Algae are diverse structurally, both within and among species (Stewart et al. 2009; Pérez-
Matus & Shima 2010). Fish may respond to the size or shape of algae (Levin & Hay 1996), and certain algal morphologies may be more important for certain species or certain life history stages of a species (e.g., canopy shelter for Paralabrax clathratus or Brachyistius frenatus).
Additionally, habitat structure can influence body size. For example, where vegetation is dense, average body size of fishes may be smaller because it offers more refuge potential for smaller individuals and may be more challenging for larger individuals to navigate (Crowder & Cooper
1982; Robson et al. 2005). Fishes can be associated with algae within distinct vertical strata (i.e., depth), in order to access different types of shelter or food items (Moore & Hovel 2010).
Seasonal variation in the density, morphology, and composition of algal stands can drive differences in the composition of the fish assemblage (Pérez-Matus & Shima 2010), and the size of algae has been shown to be an important factor for fishes associating with them (Carr 1994;
Levin & Hay 1996; Wilson et al. 2014) or artificial algal units (Bell et al. 1987; Hovel et al.
2016). Thus, variation in the size, morphology, and density of algae can alter the distribution, body size, behavior and subsequent interactions among associated fishes, and in turn potentially can influence the stability of communities (Crowder & Cooper 1982; Holbrook et al. 1990;
Levin & Hay 1996; Arkema et al. 2009; Lewis & Anderson 2012).
Invasive species have the potential to profoundly affect local ecology, ecosystem stability, and environmental economics (Pimental et al. 2001). Since its first documentation at
Santa Catalina Island in 2006 (Miller et al. 2007; Miller & Engle 2009; Sprague et al. 2012), the invasive Asian alga, Sargassum horneri, has become more common on rocky reefs along the leeward coast of the island (Marks et al. 2015). Due to its strong competitive abilities with other local algae (i.e., self fertilization, high dispersal, fast growth) and senescence of M. pyrifera
6 during recent warm periods that increased availability of benthic substrata, S. horneri has become the dominant macroalga in many places (Miller et al. 2007; Choi et al. 2008; Marks et al. 2015). The structure provided by S. horneri differs greatly from that of the historically dominant macroalga on southern California rocky reefs, Macrocystis pyrifera. For example, it is much shorter (< 2.5 versus > 20 m), more finely branched, and has much smaller blades.
Additionally, S. horneri can be found forming dense monospecific stands (> 100 adult individuals per m2) (Marks et al. 2015).
Unlike perennial species (e.g., Macrocystis), S. horneri changes dramatically in density and size throughout the year (Yoshida et al. 1998). Recruitment occurs in the late spring and recruits develop into small fern-like structures (> 5 cm tall) through summer. Recruits often are extremely dense, creating monospecific, lawn-like stands across the reef landscape. Thalli grow rapidly during the fall into their immature stage (50-100 cm), characterized by the presence of pneumatocysts but absence of reproductive structures (Umezaki 1984). Vertical growth slows and reproductive structures form at maturity (75-100 cm) during winter. After release of fertilized eggs, S. horneri enters the senescent stage (100-250 cm). During this stage the algae begins to decompose and an epifaunal community is often present on the thalli and reproductive structures (Srednick, personal observations).
Despite ongoing research efforts, there are currently no published assessments on the local ecological effects of S. horneri. Unpublished information indicates that the responses of fishes to the dramatic change in the benthic landscape are subtle, and while fishes respond differently to variation in the algal assemblage and the introduction of S. horneri, the net effect may not be great (Ginther 2017). A field experiment, on the other hand, revealed that kelp bass,
Paralabrax clathratus, would recruit to Macrocystis readily but not to S. horneri, apparently due
7 to differences in size of the two macroalgae (Ginther 2017).
Researchers are currently evaluating how the shift in algal dominance from Macrocystis to S. horneri influences the algal community. However, the influence of this change in biogenic structure on the vertical distribution of fishes (e.g., throughout the water column), community assemblage and species interactions in California has not yet been evaluated. Fishes respond differently to changes in the algal community as fish differ in their habitat requirements
(Anderson 1994; Levin & Hay 1996; Hobson & Chess 2001; Pérez-Matus & Shima 2010). Thus, a reduction in Macrocystis abundance and an increase in a dense, low-lying invasive alga may not be detrimental for all species. For example, the habitat provided by S. horneri might affect the señorita (Oxyjulis californica) negatively, which uses tall vertical structure (e.g.,
Macrocystis), but it might have a positive effect on rock wrasse (Halichores semicinctus), which often feeds on algal epibionts (Hobson & Chess 2001). In this way, some reef fishes may benefit from increases in S. horneri.
Most studies exploring the influence of algal structure on temperate fishes and other organisms have measured algae using stipe counts, thallus density, blade counts, canopy cover, or height (Holbrook et al. 1990; Carr 1991; Anderson 1994). While these studies have been effective in measuring the attributes that may have important ecological effects, they do not take into consideration variation in algal structure across species, and thus do not measure some potentially important habitat attributes. Surface area (Harrod & Hall 1962; Dahl 1973;
Bartholomew 2002), volume (Sale et al. 1994; Warfe et al, 2008), and surface area/volume ratios
(Coull & Wells 1983; Bartholomew et al. 2000) have been used to assess the influence of habitat complexity on community structure (see reviews Kovalenko et al. 2012; Jana & Bairagi 2014).
Using these metrics can potentially be more informative than just algal height alone, as algae
8 vary in their morphology and growth pattern. Additionally, these metrics provide a means to look at variation in algal structure across species while taking into consideration the unique morphology of each species. However, no studies have used surface area or volume measurements to explore the effects of algal habitat structure across species on the distribution of fishes (but see Hacker & Steneck 1990 for effects on invertebrates).
My study aimed to understand how S. horneri influences the distribution and size of the rocky reef fish assemblage relative to the native algal assemblage. I also evaluated a novel method of predicting volume and surface area from height measurements at a large spatial scale, to predict variation in the fish assemblage. In particular, I addressed three questions: (1) Do decreases in macroalgae that extend into the water column and form canopy (e.g., M. pyrifera) and increases in benthic “matting” algae (e.g., S. horneri) cause kelp-associated fishes to shift towards benthic habitat? (2) How is the composition and size structure of the fish assemblage influenced by algal identity vs. algal size? (3) Is algal height, volume, or surface area more important in driving the distribution of fishes? Overall, I aimed to provide a mechanistic description of how S. horneri-induced changes in algal structure may impact rocky-reef fishes
(Figure 2).
Methods Study system
I studied rocky reef communities along a 14-km stretch of the leeward coast of the west end of Santa Catalina Island, 32 km off the coast of California, U.S.A. (33°26'N, 118°28’W). All study sites were selected based on their similarity in the following factors: rocky or cobble reef, availability of reef structure at 8 m or greater depth extending horizontally at least 250 m, and
9 not within an area where fishing was prohibited. The latter was important to avoid confounding effects that could be associated with comparing areas open versus closed to fishing (e.g., greater fish density, larger fish, etc.). Two of the study sites (Arrow Point and Indian Rock) were within the Lion Head to Arrow Point State Marine Conservation Area (SMCA), which prohibits harvest of invertebrates but allows fishing.
Habitat surveys
I surveyed flora and fish assemblages within three depth strata to quantify the vertical and horizontal distribution of fish and algae within distinct spatial zones, and evaluate how changes in the structure and density of algae may influence the identity, size, and abundance of fishes within rocky-reef habitats. I chose sites that exhibited similar types of substrata while varying in reef angle and relief, to explore how differences in rocky structure may contribute to differences in the algal and faunal community. Seven sites were chosen: Paradise Cove, Empire Landing,
Isthmus Reef, Indian Rock, Arrow Point, Beyond Arrow, and Parsons Landing (Figure 1). I surveyed these sites five times across a full year between July 2015 and July 2016, at approximately 3-month intervals to observe temporal changes in the algal community. All surveys were performed during non-crepuscular hours (between 0800 and 1400) and completed over a period of no greater than 10 days during each of the five periods.
I conducted underwater visual surveys along four permanent transects for which the benthic portion was at 8-10 m depth at each site. Permanent anchors (i.e., 1-m-long cable ties) were deployed to mark the beginning and end of each transect. Each transect was 30-m long, 2-m wide, and 2-m high, and was separated from the next by 20 m to ensure independence. To explore the vertical distribution of fish and flora, I surveyed the fish and algal assemblages in
10 three separate depth strata: the upper-water column (2-m depth), mid-water (> 2-m, ≤ 5-m depth), and benthic (8-10 m depth). Depth was determined with a dive computer (Suunto Zoop).
Surveys were performed from the surface downward to avoid bubbles from SCUBA divers affecting fishes in areas above the divers.
In all three depth strata, I identified, counted, and estimated the size (to the nearest cm standard length) of conspicuous fishes along a 30 x 2 x 2 m band transect. On the benthos, a second diver followed the first diver and recorded cryptic fish identity, number, and size within ten 0.5 x 0.5 m (0.25 m2) quadrats deployed on a stratified-random basis within ten 3-m-long sections along each transect. The first diver stayed 1 m above the bottom while surveying conspicuous fish to avoid disturbing the benthos and cryptic fishes.
Following cryptic fish surveys, the second diver recorded the density and height (to the nearest cm) of the algae Cystoseria neglecta, Sargassum palmeri, S. muticum, and S. horneri within the ten 0.25 m2 quadrats. Abundance of algal recruits (< 5 cm tall), as well as older stages of Dictyopteris undulata and Zonaria farlowii, were recorded as the percent of 25 marked cells
(area: 0.01 m2) within each quadrat that were dominated (> 50% of cell) by a species. I recorded the density, height, and number of blades or stipes of Eisenia arborea and M. pyrifera (> 50 cm tall), respectively, within a 2-m wide swath centered on the transect. In the mid and upper-water strata, I recorded the density and number of stipes of M. pyrifera within a one-meter swath on either side of the band transect. The presence of M. pyrifera individuals along transects at the mid or upper-water substrata was used as a proxy for height (e.g., M. pyrifera found at mid-water was 5 m tall).
Physical substratum type was recorded in each of the ten 0.25 m2 quadrats on each benthic transect at 16 uniformly spaced points within the quadrat. Substratum was placed in one
11 of six categories: sand, cobble (< 25 cm diameter), small boulder (26-50 cm diameter), medium boulder (51-75 cm diameter), large boulder (76-100 cm diameter), and bedrock (>101 cm diameter). Relief (i.e., the vertical change in benthic substratum height) was recorded every meter as the highest point of substratum off the seafloor within a 1-m2 area, centered on the transect. Substratum type was recorded during each sampling period, whereas relief was only recorded at the first sampling period (summer 2015), as it was not expected to change across periods on the permanent transects.
Quantifying algal structure
Stipe counts, thallus density, number of blades, canopy cover, and height of algae have all been used for assessing a single algal species, but morphological differences among species make this approach unsuitable for comparing among different species. I quantified the structure of a suite of morphologically diverse algal species in comparable ways in order to explore their relative influence on fish communities. I measured the height, surface area, and volume of
Cystoseria neglecta, M. pyrifera, Sargassum horneri, and S. palmeri, from which I aimed to predict surface area and volume from field measurements of height.
I collected individuals of different sizes from each species of macroalgae during June –
August 2015. I collected 15 thalli of Cystoseria neglecta, 21 thalli of M. pyrifera, 49 thalli of
Sargassum horneri, and 5 thalli of S. palmeri. These individuals represented every size and reproductive stage observed on surveys across all time points. All individuals were collected from depths of 6 – 9 m and placed in 150 µm-mesh bags to minimize the loss of algal tissue and epibionts. For each individual, I recorded the height (from base of thallus to apical tip), volume by displacement (rounded to nearest 20 mL) in a graduated cylinder (2000 mL) of water, and
12 surface area via dye-dipping method in the laboratory (as modified from Hoegh-Guldberg 1998 and Stewart & Carpenter 2003). When the volume of an individual was measured as less than 20 mL (the graduation interval for 2000 mL cylinder) a 100 mL graduated cylinder was used to estimate volume. The dye-dipping method consisted of dipping an alga in a solution of 5%
Triton X100 detergent and fresh water to prevent absorption of dye, then dipping it in 0.1%
Methylene-blue dye and fresh water for 5 s. The dyed alga then was placed in a salad spinner and spun for 10 rotations to remove excess dye. The alga then was placed into a container with 800 mL of filtered seawater and rinsed for 20 shakes (Hoegh-Guldberg 1998). I then took a 12-mL subsample of the rinsed fluid and measured its absorbance on a spectrophotometer at 520 nm
(Beckman DU 640). Surface area then was estimated by using a calibration line established using the same method on 8 pieces of waterproof paper cut to known area (r2 = 0.99). Dye-dipping was used to estimate surface area without confounding estimates with individual species variation in texture and porosity that might occur in image analysis.
Using algal height data from the transect surveys, I estimated volume and surface area along each transect with power equations (generated in SYSTAT version 13) for each of the following species: Macrocystis, S. horneri and S. palmeri (Table 1). For C. neglecta, only the height to volume relationship was examined due to logistical constraints. Algal volume and surface area were calculated as the values predicted from the power equations when applied to mean height and multiplied by density for each species on each transect, and then summed across all species on the transect. This method allowed me to separate effects of structural complexity
(measured as height, surface area, or volume) from species-specific effects of algae, and enabled me to evaluate how the distribution of fishes is related to certain algae species vs. the quantity of structure they provide.
13
Statistical Analyses
Multivariate analyses were used to understand the correlative relationships among multiple predictor and response variables. Fish and algal assemblage data were skewed and contained many zeros, violating the assumptions of parametric analyses. Thus I used permutational analyses of variance (PERMANOVA) to test for differences in the multivariate fish and algal assemblages, as well as abiotic habitat. All PERMANOVAs were tested on resemblance matrices based on Bray-Curtis dissimilarities, which measures the dissimilarity among objects (Clarke 1993). Resemblance matrices were summarized from multivariate abundance data within PRIMER, including a dummy variable (+1), allows samples that were blank or contained zeros to be included in the analyses (Clarke 1993). PERMANOVA analyses used 999 permutations for each analysis to calculate P-values.
A repeated-measures PERMANOVA tested for differences in the multivariate fish assemblage among the 7 sites (random), 5 time points (fixed), 3 water-column depth strata
(fixed), and replicate transects (nested within site by strata; random). Another repeated-measures
PERMANOVA tested for differences in the algal assemblage and abiotic habitat (substratum type and relief) among the 7 sites, 5 time points, and replicate transects. To better understand differences in the vertical distribution of fishes, I evaluated whether the proportion of individuals of each fish species in the water column differed among sites, time point, or transects using the same repeated-measures PERMANOVA model. To calculate proportion of fish in the water column, I considered both mid- and upper-water-column transects to represent the water column
(i.e., [mid-water + upper-water] / [benthic + mid-water + upper-water density]). Non-metric multidimensional scaling (nMDS) was used to visualize differences among sites (Clarke 1993) in
14 all multivariate responses.
I also used univariate repeated-measures analyses of variance (repeated-measures
ANOVA) to test for differences among sites, time points, and replicate transects on the univariate response variables total density, species richness, and proportion of total fish density in water column. I removed the fish Lythrypnus dalli from analyses involving total density due to its very high density and non-normal distribution; and I removed all cryptic fishes from analyses performed on the proportion of fish in the water column as the cryptic species observed were not expected to be in the water column (Stephens et al. 2006).
To explore how well a variety of algae and abiotic habitat variables measured at the benthic strata could predict variation in the multivariate fish assemblage, I used distance-based linear modeling (DistLM) in PRIMER. DistLM is an analog to multiple regression, but with a multivariate rather than univariate response. It is used to model the relationship between the variation in a multivariate dataset, such as a resemblance matrix, and one or more predictor variables (McArdle & Anderson 2001). Separate DistLMs were used to evaluate if the proportion of the fish assemblage in the water column or the entire fish assemblage (summed across depth strata) could be predicted by variation in the algal assemblage.
A DistLM also was used as an exploratory analysis to assess to what degree 13 variables representing algal-provided habitat predicted differences in the fish assemblage. Principal coordinates analysis (PCA) was used to summarize the many potential predictor variables into fewer principal components (PCs) that accounted for most of the variation in the predictor variables. Separate principal components (PCs) were derived from both the multivariate algal abundance data and the substratum data. Three principal coordinates (PCs) summarized ~45% of the variation in the algal assemblage (21.6%, 12.5%, and 11.3%, respectively).
15 I used multiple linear regression to compare how well variation in density of algal species versus different algal attributes predicted variation in the density, richness, and vertical distribution of fishes. Multiple linear regression (MLR) models were constructed with PCs that summarized the physical substratum plus one of the following metrics of algal density or quantity: algal PCs (i.e., algal species and density), height, volume, or surface area (n = 140 transects). The best model for each biotic response variable was selected by comparing AICc and r2 values.
To understand which species were driving the patterns in the overall fish assemblage, I performed analyses on individual fish species that were abundant and that are known to be influenced by variation in biotic and abiotic habitat, as noted in previous studies (Larson &
Demartini 1985; Anderson 1994; Carr 1994; Stephens et al. 2006). These species were Chromis punctipinnis, Halichores semicinctus, Hypsypops rubicundus, Lythrypnus dalli, Oxyjulis californica, and Paralabrax clathratus. Multiple linear regression (MLR) models tested how well the density of each species was predicted by physical substratum PCs plus one algal summary variable, either algal PCs (i.e., algal species and density), height, volume, or surface area. The best model for each species was selected by comparing AICc and r2 values. These analyses pooled time points and sites (i.e., time point and site were not included in models), thus assessing how well physical and biotic habitat features predicted spatio-temporal variation in the fish assemblage.
Fish size as related to algal attributes
Habitat structure could affect the size of fish that associate with it. The mean length (cm total length; TL) of all fish (species pooled) on each transect was used to evaluate the influence
16 of algal species versus size on fish size, as well test for differences in fish size among sites and time points. Lythrypnus dalli was removed from the data when calculating mean length because this species does not use macroalgae as habitat and it influenced size estimates strongly because of its small size and abundance (~13% of all fishes counted). Chromis punctipinnis made up about ~60% of all fishes on transects. To evaluate the impact of this numerical dominant on the outcome, I performed analyses with C. punctipinnis included and with it removed. Repeated- measures ANOVA was used to test for differences in fish size among sites, time points, and nested transects. Multiple linear regression models tested whether average fish size was predicted by algal PCs (i.e., algal species and density), height, volume, or surface area; or physical substratum PCs. The best model for each response variable was selected by comparing
AICc and r2 values.
Data transformation
Prior to performing all multivariate and univariate analyses, raw algae density data were converted to thallus/m2, to account for different measurement units (e.g., Macrocystis recorded at the transect scale versus S. horneri recorded at quadrat scale). For non-parametric analyses algal data were square-root transformed to satisfy assumptions of normality and homogeneity of variances (Sokal and Rohlf 1995). For principle components analyses algae density data were square-root transformed and were then standardized to z scores because not all data were measured on the same scale (e.g., density versus percent cover; see collection format in Table 3).
Reef relief data were log (x + 1) transformed prior to analyses to satisfy assumptions. Fish abundance was analyzed at the transect scale and was log (x + 1) transformed for non-parametric analyses, in order to reduce the contribution of highly abundant species (e.g., C. punctipinnis).
17 For univariate analyses, species richness as well as overall and individual fish density were log
(x + 1) transformed. Transects were used as the unit of replication (n = 420 total; 140 per depth stratum; and 140 for analyses in which density was estimated by averaging the 3 depth strata).
All algae and fish density data are presented as number/m2 and number/m3, respectively. All analyses were performed in PRIMER (v7.1) or SYSTAT (v13).
Results Thirty-five fish species from 18 families were observed on 420 transects. Of the 35 species recorded, 8 were found at all sites and 10 during all time points (Table 2). Another 25 species occurred at 3 or more sites and on 5 or more occasions.
Variation in the fish assemblage
The multivariate fish assemblage (density of each species) differed among sites and depth strata in a manner that was inconsistent among time points (RM PERMANOVA; site × strata × time point; pseudo-F48,252 = 1.70, p < 0.001). The fish assemblage did not vary significantly among transects within sites (transect; pseudo-F63,252 = 1.13, p = 0.16). The fish assemblage at the mid and upper-water depth strata were more similar than that of the benthic strata (Figure 3).
There were, on average, ~74% and ~36% fewer fish in the upper and mid-water strata, respectively, than at the benthos. Species richness of fishes was similar among time points (time point; F6,84 = 2.67, p = 0.14; Figure 4a) but differed among sites (site; F4,84 = 8.89, p < 0.001).
The pattern of differences among sites, however, was not consistent among time points (site x time point; F24,84 = 1.67, p = 0.046). Species richness did not differ among transects within sites
(transect [site]; F21,84 = 1.5, p = 0.10). Richness of fishes was greatest in the summer of 2015 and
18 gradually decreased by ~26% until the summer of 2016 where there was a 9.3% increase in richness.
The total density of all fishes was 1.7 times greater in summer, fall, and winter of 2015 than in spring and summer of 2016 (time point; F4,84 = 12.94, p < 0.001; Figure 4b) and was greatest at Paradise Cove and Empire Landing (site; F6,84 = 4.79, p = 0.003; Figure 4b). The pattern of differences among sites did not differ significantly among time point (site x time point;
F24,84 = 1.56, p = 0.07; Figure 5a). In particular, the overall density of fishes in fall of 2016 fell by 65% and 60% of the average at the Arrow Point and Beyond sites, respectively; whereas at the same time period, the density of fishes at Paradise Cove and Isthmus Reef peaked at 1.4 and
1.5 times the overall average, respectively. These differences were due, in part, to high abundances of C. punctipinnis, which made up of 66%, 56%, 79%, and 78% of the fish density at each site, respectively. However, when C. punctipinnis was removed from density estimates, the patterns across sites were not consistent among time points (site x time point; F24,84 = 1.79, p =
0.03; Figure 5b).
Variation in abiotic and biotic habitat attributes
Substratum composition along transects differed among time points (PERMANOVA: time point: pseudo-F6,21 = 22.52, p = 0.001), as well as within and among sites (transects(site): pseudo-F21,139 = 3.18, p = 0.001; site: pseudo-F6,21 = 6.15, p = 0.001). Mean substratum relief differed among sites (RM ANOVA: sites: F6,21 = 4.78, p = 0.003) and but not within sites (RM
ANOVA: transects(sites): F21,6 = 0.21, p = 0.997).
The density and identity of algae differed among and within sites (site: pseudo-F6,21 =
5.36, p < 0.001; transects(site): pseudo-F21,139 = 2.33, p < 0.001), as well as among time points
19 (time point: pseudo-F4,139 = 20.73, p < 0.001). The differences among sites, however, were inconsistent among time points (time point x site: pseudo-F21,139 = 2.60, p < 0.001: Figure 8a).
Additionally, there were differences in the mean height of algae among sites and time points
(RM ANOVA: time point x site: F24,84 = 7.58, p < 0.001), which most likely were caused by variation in the density of Macrocystis at two sites in particular, Beyond and Parsons Landing
(Table 3).
Predicting algal surface area and volume from height
Algal height was variable in how it predicted variation of volume and surface area for
Macrocystis, C. neglecta, S. horneri and S. palmeri (Table 1). S. horneri had the greatest surface area per unit height and Macrocystis had the lowest. However, the r2 values for some models indicated that structural attributes of some species were more predictable from height than others. In particular, the models predicting volume from height were most useful for Macrocystis
(r2 = 0.82) and S. horneri (r2 = 0.79), and least for C. neglecta (r2 = 0.21). The amount of variation in surface area explained by height was fairly similar between Macrocystis (r2 = 0.75),
S. horneri (r2 = 0.68), and S. palmeri (r2 = 0.70).
Predicting fish assemblage from habitat attributes
Algal attributes predicted ~21% of the variation in the fish assemblage (all depth strata combined) based on DistLM. Of the 13 variables, 7 algal variables contributed significantly to variation in the fish assemblage: densities of Macrocystis adults, Sargassum horneri adults and recruits, S. muticum adults and recruits, S. palmeri adults, and Dictypoteris (Table 4). Three algal
PCs accounted for 44% of the variation in the algal assemblage, and they represented variation in
20 (1) the tall kelp forest with short understory assemblage (e.g., Dictyopteris sp., Cystoseira sp., and adult Macrocystis, with reduced densities of S. palmeri and S. horneri); (2) more structurally complex but intermediate height algal assemblage (e.g., adult and juvenile S. horneri, adult S. palmeri, recruit S. muticum, and recruit Macrocystis); and (3) short kelp complex understory assemblage (e.g., adult Macrocystis, Z. farlowii, and adult and recruit S. palmeri with reduced S. horneri). PC 1 (β = 0.37, p < 0.001) was positively related to algal height while PCs 2 (β = 0.06, p = 0.5) and 3 (β = 0.06, p = 0.3) were negatively related, although not significantly.
Two PC’s summarized 70.8% of the variation in the physical substratum (47.5% and
23.3%, respectively) (Table 5b). Substratum PC 1 was correlated positively with variation in sand, bedrock, and relief, and negatively related to boulders and cobble, while PC 2 was correlated positively with cobble and negatively related to large boulders and sand.
The best multiple linear regression model for predicting total fish density, with C. punctipinnis included, contained the significant predictor variables algal height and substratum
PC 2 (which was positively related to cobble and negatively related to large boulders and sand)
2 (AICc = 21.62, r = 0.24, p < 0.001; Table 6; Figures 9 & 10). Total fish density was negatively related to algal height. This model, however, was just as effective as the model including algal
2 surface area and substratum PC 2 (AICc = 22.23, r = 0.24, p < 0.001, Figures 9 & 10). When C. punctipinnis was removed from fish density, a model including algal surface area (negatively related) and substratum PC 2 (negatively related) best explained variation in fish density (AICc =
2 128.52, r = 0.08, p = 0.009, Figures 9 & 10), but this model was only slightly better than the
2 model including algal height (AICc = 129.54, r = 0.07, p = 0.02), and neither explained much of the total variation in fish density. Fish species richness was best predicted by the model including
2 algal PCs (AICc = 257.61, r = 0.14, p = 0.001).
21
Predicting density of specific fish species from habitat attributes
Multiple linear regressions also were used to assess how densities of six common fish species were each related to algae: either algal species composition (as summarized by principal components) or algal structural attributes: height, surface area, or volume (Table 7). Densities of five of six species were significantly related to at least one of the structural measures of algae, whereas only three were significantly predicted by models including algal species composition.
Moreover, multiple linear regression models that included algal PCs were not the best predictor of density of any fish species.
Densities of all fish species were related negatively to height, volume, and surface area when included in the models, with the exception of O. californica, which was only negatively related to surface area. Models including algal height best predicted density (negatively) of L.
2 2 dalli (AICc = 351.32, r = 0.21, H. semicinctus (AICc = 123.92, r = 0.19), while models
2 including algal volume best predicted variation in P. clathratus (AICc = 122.15, r = 0.10), H.
2 2 rubicundus (AICc = 6.36, r = 0.19) and C. punctipinnis (AICc = 200.92, r = 0.18), although the
2 latter was virtually the same as the model including height (AICc = 200.93, r = 0.18). Models including surface area were not the best predictors for any species.
Densities of L. dalli (β = 0.30, p < 0.001), P. clathratus (β = 0.09, p = 0.004), H. rubicundus (β = 0.08, p < 0.001), and O. californica (β = 0.14, p = 0.02) were all related positively to Algal PC 2, on which density of S. palmeri and S. horneri loaded positively and recruit Macrocystis adult Macrocystis loaded negatively. Densities of L. dalli were significantly negatively related to algal PC 3 (β = - 0.17, p = 0.03), which was most strongly related
22 (positively) to abundance of S. palmeri and adult Macrocystis. P. clathratus was positively related to algal PC 3 (β = 0.07, p = 0.03).
Across all multiple linear regression models, substratum PC 1 significantly predicted variation in density of P. clathratus and H. semicinctus (Table 7). This PC was mostly strongly related to the abundance of bedrock. Substratum PC 2, which mostly represented the presence of cobble, significantly predicted variation in the density of C. punctipinnis and H. rubicundus in models also including surface area or volume of algae.
Vertical distribution of fishes
The proportion of the multivariate fish assemblage in the water column varied among time points and sites (PERMANOVA: time point: pseudo-F4,139 = 4.67, p = 0.002, sites: pseudo-
F6,84 = 2.98, p = 0.03; Figure 4c), but the patterns of differences among sites were not consistent among time points (PERMANOVA: time point x site; pseudo-F24,84 = 1.85, p = 0.02). Pooled over all species, the proportion of all fish in the water column at each site was also not consistent among time points (RM ANOVA; time point x site; F24,84 = 2.40, p = 0.002).
DistLM exploring how algae and physical substrata influenced the vertical distribution of the fish assemblage revealed that 14% of the variation in the proportion of fishes in the water column was explained by variation in the density and identity of algae. Of the 21 variables included in the model, variation in the density of M. pyrifera adults (~5 %), S. palmeri (~2 %) and Dictyopteris sp. (~3 %) significantly predicted variation in the vertical distribution of the fish assemblage (Table 8).
Multiple linear regression was used to understand how algal structural attributes and summarized physical substratum and algal variables (i.e., identity and density) contributed to
23 variation in the vertical distribution of fishes. The results showed that the proportion of fish in the water column may be greater in habitats that contain less cobble substratum and algae of greater height, volume, and surface area. Of the models compared, vertical distribution of fishes was best predicted by the model including algal surface area, to which the proportion of fish in
2 the water column was positively related (AICc = 306.3, r = 0.10; Table 6; Figure 9a). The
2 support for this model was similar to the models including volume (AICc = 307.3, r = 0.12;
2 Figure 9a) and height (AICc = 309.2, r = 0.12; Figure 9a). The model including algal PCs appeared to be the most parsimonious model (AICc = 305.65, r2 = 0.08), however it explained less variation. In all of these models, the substratum PCs most related to vertical distribution were PCs 1 (β = 0.03, p = 0.02) and 2 (β = -0.06, p = 0.004). Although the model including algal
PCs was significant (p = 0.02), it only explained roughly 1% of the variation.
Predictors of fish size
The average size of all fishes per transect differed among time points but not sites (RM
ANOVA; time point : F4,84 = 24.20, p < 0.001; site: F6,24 = 1.17, p = 0.36; time point x site: F24,84
= 1.40, p = 0.11; Figure 8). This likely was due to the prevalence of C. punctipinnis in certain time points. However, when C. punctipinnis was removed from size calculations, the differences in size among sites were inconsistent among time points (time point x site: F24,84 = 1.90, p =
0.016). Thus, I explored how differences in habitat attributes among sites may have influenced fish size. Multiple linear regression revealed that the overall size of fishes was best explained by
2 a model containing algal species composition as represented by algal PCs (AICc = 271.89, r =
0.13; Table 6; Figure 9d). In this model, fish size was negatively related to the algal PC 2, indicating that fish size was positively related to abundance of S. horneri and Dictyopteris but
24 less so to Macrocystis density. Although the model including surface area was more
2 parsimonious it explained less of the variation in fish size (AICc = 268.70, r = 0.08). When C. punctipinnis and L. dalli were removed from average length estimates, the model including algal
2 PCs was still the best predictor of fish length (AICc = 186.45, r = 0.13). In this model, however, fish size was positively related to the algal PC 1, which was positively related to Macrocystis, recruit S. horneri and density, but negatively related to S. palmeri density. Similarly, although the model including surface area was more parsimonious it was less explanatory (AICc = 180.70,
2 r = 0.07).
Discussion
The results of this study show that algal structure may be more important than algal identity in influencing the distribution patterns of temperate reef fish. In particular, algal surface area, volume, and height were consistently better predictors of fish distribution and richness than was algal species identity. Certain algal species may contribute more to differences in fish distribution than others, but likely as a result of differences in size. I found that the horizontal and vertical distribution of temperate reef fishes is related to S. horneri. In particular, there was positive relationship between fish density and S. horneri, and positive relationships between fish vertical distribution and algal size. This indicates that the overall density of fishes was greater in areas dominated by adult S. horneri, but a higher proportion of fishes were found high in the water column in the presence of taller algae.
Surface area, volume, and height of algae have been used as metrics to explore the effects of habitat structure on marine communities. As a two-dimensional estimate of size, surface area
25 provides a better estimate of the quantity of structure provided by macroalgae than does height because surface area differs among algal species of the same height. Thus, surface area may provide a better explanation for patterns in fish abundance caused by variation in the abundance of different algal species (Stoner 1979; Mattila et al. 2008). For example, benthic fishes (e.g.,
Halichoeres semicinctus) may be more influenced by the “bushiness” of algae, whereas water- column fishes (e.g., O. californica or C. punctipinnis) are more affected by variation in height, due to differences in behavior and habitat requirements (Hobson & Chess 2001). However, volume has been used to quantity structural complexity (Hacker & Steneck 1990; Anderson et al.
2005; Warfe et al. 2008) as well as to assess reef size (Steele 1996; Jordan et al. 2005), and may be useful in the context of algal structure. This result highlights the importance of considering other attributes than just algal height when assessing the influence of algal structure on fishes.
Algal volume was a useful predictor of variation in vertical fish distribution as well as the density of P. clathratus and H. rubicundus. This may be due, in part, to the unique relationships between height and surface area, and height and volume for each species. For example, the species that had the highest surface area per unit height was S. palmeri, while Macrocystis had the highest volume. Thus, algal height and volume were correlated positively with high densities of Macrocystis. This may have led to similar effects of height and volume on the density and vertical distribution of fishes and suggests the importance of certain structural attributes for fishes that may be overlooked by simple height estimates. Increased algal volume, for example, may provide important shelter area for fishes. These findings indicate that volume and surface area should be considered when attempting to assess the influence of algae on fishes.
A primary goal of this study was to explore the effects of an introduced alga on the distribution of fishes to provide insight regarding its potential effect on local ecology. My results
26 indicate that S. horneri may influence the distribution of fishes across the scales of sites and transects within sites. Specifically, the overall density of fishes was related significantly and positively to the principal component that S. horneri was tightly associated with, suggesting a positive effect of this invasive macroalga on fish density. The response of different fish species to variation in algal cover varied within the fish assemblage. Of the species explored, densities of
L. dalli, P. clathratus, H. rubicundus, and O. californica were positively related to algal PCs that were positively related to adult and recruit S. horneri; and H. rubicundus and C. punctipinnis were negatively related to adult Macrocystis. This finding suggests that some species may have benefitted from the introduction of an invasive species, whereas other species may have not.
In summer 2015, at the first survey period, fish density (without C. punctipinnis or L. dalli) was greatest at Arrow, Beyond, and Parsons. These sites also had the highest densities of
Macrocystis recorded at this time. Macrocystis began to decrease in density at all three sites until the summer of 2016 when there was a strong growth period (Figure 6). It is important to note that while the density of Macrocystis was comparable among sites between these periods, the morphology (i.e., stipe diameter and number of stipes per plant) was variable due to differences between mature versus recovering forests (O’Connor & Anderson 2010). A decrease in fish density coincided with the decrease in Macrocystis density at sites dominated by Macrocystis.
Fish density did return to levels observed the previous summer at some sites (Figure 7), however this pattern was not consistent among sites. Over time, there were differences in the recovery of fish communities at sites that in summer 2015 had dense Macrocystis stands (> 4.1 individuals per m2) versus sites with less dense stands (< 4.1 individuals per m2) (i.e., Empire Landing,
Indian Rock, Isthmus Reef, and Paradise Cove). At two Macrocystis dominated sites in particular
27 (Beyond and Arrow Point) fish density was actually lower that that at S. horneri dominated sites.
These findings indicate that the return of Macrocystis did not stimulate a recovery in fish density.
Past work has shown that fish communities return to rocky reefs following removal of
Macrocystis, and that fish density increases with Macrocystis density, irrespective of the morphological differences between mature versus recovering kelp forests (O’Connor &
Anderson 2010). Alternatively, based on the disproportional recovery of fish density at sites with dense Macrocystis versus S. horneri stands, the results presented here suggest that fishes do not return more quickly to sites dominated by Macrocystis than S. horneri. These patterns may be due to differences in the variability of fish communities between high and low structural complexity shifts. For example, when comparing cryptic reef fishes in kelp forest versus urchin barrens, Willis and Anderson (2003) detected greater variability in the fish assemblage at kelp- dominated sites than at barrens. These patterns are likely due the patchy distribution of select species that depend on kelp habitat (Jones 1984; Willis & Anderson 2003). However, there may be an optimal level of structural complexity at which a fish community stays fairly stable across spatial and temporal scales (Mittelbach 1981; Mattila et al. 2008; Jana & Bairagi 2014), and may have important implications for species interactions (Tanner 1975; Crowley 1978; Crowder &
Cooper 1982) and community stability (Nes & Scheffer 2007; Kovalenko et al. 2012).
Fish density was not related positively to complex physical substratum (i.e., relief and substratum type). However, while fish density was negatively related to structurally simple physical substratum, it was related positively to intermediate-sized algae, and was unrelated to the PCs 1 and 3 that described the presence of tall kelp and short kelp forest, respectively. This suggests that overall fish density was lower in areas where Macrocystis was present.
28 These results indicate that the composition of the algal assemblage influences the vertical distribution of fishes, as evidenced by a positive relationship with algal height. Previous studies have shown that the vertical distribution of fishes is linked to the presence of Macrocystis in the water column (Schmitt & Holbrook 1990; Anderson 1994; Carr 1994), which is typically the tallest species of algae on temperate reefs. Although I did encounter Macrocystis at a number of sites, the densities observed were not comparable to historic levels observed in other studies
(Carr 1991; Anderson 1994). I did not observe canopy-forming kelp at any of my sites over the course of the one-year sampling period. Specifically, although the tallest Macrocystis stand observed reached the surface, individuals were not tall enough to form distinct “canopy”. There was a positive relationship between the vertical distribution of fishes and algal height but not with the PC related to Macrocystis abundance. C. punctipinnis density decreased with algal height and was negatively related to the algal community defined by tall Macrocystis, indicating that C. punctipinnis are more abundant in habitats devoid of tall, canopy-forming algae. These results suggest that the size of macroalgae may be more important than species identity.
Fishes may modify behavior based on the presence of certain habitat structures. Many studies have characterized C. punctipinnis as a common kelp forest species that can be found either shoaling near reefs or in the water column in dense schools (Stephens et al. 1984;
Anderson et al. 1989; Demartini et al. 1989; Stephens et al. 2006). Although not recorded, I observed behavioral differences in C. punctipinnis between transects at the benthos versus those in the water column. In particular, C. punctipinnis in the water column were more homogenously distributed in a dense schooling configuration (50 + individuals), while those at benthic substrata were distributed in smaller groups (1 - 25 individuals). My results also revealed that C. punctipinnis density was related negatively to substrata of low structural complexity, positively
29 related to S. horneri and S. palmeri density, and unaffected by changes in Macrocystis density.
These findings suggest that C. punctipinnis modify their behavior based on the type of available habitat, and may be relatively unaffected by changes in algal composition (Figure 2). This response may be explained mechanistically: in a structurally simple habitat, prey fishes school with conspecifics to reduce risk of mortality by predation, whereas in a complex environment that provides more refuge they may break off into smaller schools to enhance foraging success
(Savino & Stein 1989; Lannin & Hovel 2011).
Density of P. clathratus, which was related positively to S. horneri dominated and low relief Macrocystis communities but not related to algal structural attributes, have been shown to modify their predation behavior based on their position in the water column (i.e., near bottom or mid-water) as well as the available structure (Hobson & Chess 2001). In particular, P. clathratus may attack from a resting position amid macroalgae, where its cryptic coloration may act as camouflage. However, in the absence of kelp, an individual is offered no concealment in the mid-water and thus it may shift its hunting to on or near the bottom. When encountered, I observed large (> 40 cm) P. clathratus utilizing the available algal habitat structure, although the purpose of this use was not assessed (but see Chapter 3). Consequently, P. clathratus may alter their distribution in response changes in algal structure and may also modify their behavior.
Thus, P. clathratus may be robust to changes in habitat structure.
Tall and dense S. horneri stands were most common in winter and spring of 2016. These consisted of thalli distributed relatively homogeneously and extending < 2 m into the water, resembling to a degree, a miniature kelp forest. There are no other species of local algae that grow to comparable density or height, and form monospecific stands at subtidal depths. During these periods, I recorded P. clathratus (< 40 cm TL) and other species navigating through the
30 understory beneath S. horneri canopy. Additionally, density of P. clathratus increased as S. horneri grew taller, presumably because the additional space below the canopy provided space for increased maneuverability, as well as improving my search capabilities. However, the fish assemblage above this canopy was rarely composed of any other species besides C. punctipinnis, which, when observed, were in relatively high abundance (Figure 2). I previously hypothesized that this environment would be too dense to allow effective navigation for most fish species; however, I detected no difference in species richness and only a slight increase in density of benthic stratum fishes in response to increases in S. horneri density during winter and spring
2016. This pattern suggests that the canopy provided by S. horneri at its peak height and density provides a usable habitat for fishes.
In this study, fish were smaller where tall Macrocystis were abundant. However, there were more large schools of small C. punctipinnis in areas dominated by low-lying macroalgae such as S. horneri. In particular, mean fish size was related positively to the surface area of algae and negatively related to the algal PC explaining high densities of S. horneri and high relief substratum when the abundant C. punctipinnis was included in the model. However, when C. punctipinnis were removed from the data, algal surface area was no longer an important predictor of body size, whereas smaller body size was associated with the algal PC describing higher Macrocystis and Z. farlowii density, and low-relief substratum, indicating that the size of fishes (without C. punctipinnis) was smaller in habitats that are of moderately tall algae but lower physical substratum relief. Past work as shown that body size affects how prey use habitat to circumvent predation (Werner & Hall 1974; Mittelbach 1981; Crowder & Cooper 1982;
Werner et al. 1983). In the context of this study, large clusters of small C. punctipinnis were
31 prevalent in structurally simple habitats likely to circumvent predators, whereas larger fish occupied structurally complex habitats where camouflage was readily available.
These findings indicate that smaller fish associate with larger macroalgae. Where macroalgae are large, there is less dense algal cover near benthic substrata (i.e., less understory algae). Thus fishes may occupy dense and tall algal communities to enhance survival or school in large numbers when such habitat is unavailable. For example, Werner et al. (1983a) showed that smaller prey fish disproportionately select foraging habitat that is vegetated (e.g., by understory algae) to avoid predation, even though resource availability is lower. Following Macrocystis removal and subsequent increase in benthic foliose algae, Carr (1989) found that habitat use by large fishes decreased while use by small fishes increased. A number of studies have detected a decrease in the dependence of fishes on algal structure as they increase in size, noting the use of complex structure as refuge for young settling fishes (Holbrook & Schmitt 1984; Choat &
Ayling 1987; Carr 1989; Holbrook et al. 1990). While smaller fish may be forced to occupy structurally complex habitats or form dense schools, larger fish have lower risk of predation and can inhabit a wider variety of habitats, with conspecifics of different sizes.
Although there were many statistically significant relationships between aspects of the fish assemblage and the algal assemblage, the majority of variation in the fish assemblage was not explained by the algal assemblage in this study. For example, the combined effects of algal surface area and substratum PCs only explained 24% of the variation in total fish density, leaving
76% of the variation unexplained. Some of the differences in the fish and algal assemblages among sampling periods may have been due to anomalously high sea surface temperatures that occurred as a result of El Niño Southern Oscillation (ENSO) and an oceanographic anomaly known as “The Blob” (Johnstone & Mantua 2014; Bond et al. 2015). In addition to normal
32 seasonal recruitment patterns, recruitment of certain fishes is associated with warmer sea surface temperatures (Ebeling & Laur 1985; Stephens et al. 1994), and such variation in recruitment may be responsible for some of the unexplained variation in the fish assemblage. For example, there was a 3-fold increase in 1–10 cm-long young-of-year (YOY) density from summer to fall 2015 sampling period. This spike was composed predominantly of C. punctipinnis, which were common at all sites at the benthos and in the water-column. This cohort appeared at all sites at during all subsequent sampling periods. Thus analyses were perfomred with and without C. punctipinnis to evaluate and remove the contribution of this large recruitment pulse. Nonetheless, it is difficult to disentangle the effects of temporal changes in the algal assemblage from other temporal changes that co-occur, such as seasonal recruitment.
These results show that S. horneri influences the vertical and lateral distribution of fishes, when compared to habitat containing the native algal assemblage. In particular, there was greater abundance of certain species (e.g., C. punctipinnis) in the water column and a greater overall abundance of fishes in areas of concentrated S. horneri stands. Overall fish density also decreased with algal height, indicative of a negative relationship with the tallest alga,
Macrocystis. Invasive species have the potential to influence the interactions among species and the stability of communities (Pimental et al. 2005; Chapman et al. 2006). Invasive algae, in particular, can outcompete native species and can subsequently monopolize habitat (Chapman et al. 2006). Notably, fishes appeared to make use of the unique structure provided by S. horneri stands, and shifted their behavior and distribution, presumably as a means of capitalizing on the resources available.
This study shows that fishes do not avoid areas dominated by S. horneri. On the contrary, fishes appear ready to use the habitat provided, but in a fairly different way than the habitat
33 provided by the native algal assemblage. However, the long-term effects of a change in algal structure on the population and community dynamics (e.g., fish recruitment, community and trophic web stability) may differ from what is observed over shorter periods, such as during my study, and detecting lag effects is important for understanding how systems change over time
(Dayton et al. 1998). Over the long term, sea-surface temperatures and the frequencies of storms and El Niño events are expected to increase (Easterling et al. 2000; Hansen et al. 2006; Meehl et al. 2007). Kelp forest communities are particularly vulnerable to these stressors (Harley et al.
2012, Wernberg et al. 2013; Filbee-Dexter et al. 2016; Reed et al. 2016), and reduction in kelp cover and increases in invasive species may have lasting effects on the community (Steneck et al. 2002; Graham 2004; Simonson et al. 2015).
This study provides insight into how kelp forest fishes may respond to changes in oceanographic conditions that change the biotic habitat structure provided by macroalgae. In particular, this study illustrates the importance of understanding the relative effects of species identity versus structural attributes of macroalgae on predicting fish assemblage structure; and it suggests that algal identity is less important than the quantity of structure it provides.
34 Figures
Figure 1. Study reefs along the leeward coast of Santa Catalina Island. Black stars refer to observational survey sites (Ch. 1). Experiments (Ch. 2) were conducted at Lion Head (white star) in summer 2016.
35
Figure 2. The hypothetical effects of a change in the dominant benthic macroalga on the vertical distribution of fishes. Reefs dominated by Macrocystis have higher richness and density of fishes throughout the water column. Reefs dominated by Sargassum horneri have fewer fish species high in the water column but have high density of schooling species (e.g., Chromis punctipinnis).
Behavior and habitat use of dominant predators (Paralabrax clathratus) and prey (C. punctipinnis) at the benthic level would differ between Macrocystis and S. horneri dominated reefs: camouflaged among Macrocystis blades above and schooling in small groups versus attack from resting position at bottom and schooling in large groups, respectively.
36
Table 1. Power equations relating algal volume and surface area to height. Height to surface area relationship was not determined for Cystoseira neglecta due to logistical constraints.
Alga n Height to Volume Height to Surface-area Model r2 Model r2 Macrocystis pyrifera 21 y = 5.69x0.87 0.82 y = 143.97x0.51 0.75 Sargassum horneri 49 y = 0.04x1.75 0.79 y = 13.88x1.02 0.68 Sargassum palmeri 5 y = 0.07x2.30 0.44 y = 3.90x1.77 0.70 Cystoseira neglecta 15 y = 487.83x-0.68 0.21 -- --
37 Table 2. Fishes observed on underwater visual transects between summer 2015 and summer
2016 listed from highest to lowest density, their habitat association (water column [WC], demersal, and benthic), observed size range (total length), and mean density per 1-m3 (±1 SD).
Habitat associations adapted from Stephens et al. 2006. Missing value for size range indicates species for which no size estimate was made.
Size range Scientific name Common name Habitat Mean density (cm) Lythrypnus dalli Bluebanded goby Benthic 1-5 12.521 ± 13.267 Chromis punctipinnis Blacksmith WC 3-29 1.222 ± 0.106 Hypsypops rubicundus Garibaldi Demersal 3-31 1.051 ± 0.031 Oxyjulis californica Senorita WC 2-21 0.141 ± 0.581 Halichores semicinctus Rock Wrasse Demersal 3-44 0.139 ± 0.144 Paralabrax clathratus Kelp bass WC 3-58 0.125 ± 0.082 Rhinogobiops nicholsii Blackeye goby Benthic 3-8 0.064 ± 0.032 Atherinops affinis Top smelt WC 12-25 0.053 ± 0.179 Scorpaena guttata Scorpionfish Benthic - 0.031 ± 0.108 Semicossyphus pulcher Sheephead Demersal 2-65 0.031 ± 0.036 Girella nigricans Opaleye WC 16-45 0.019 ± 0.063 Medialuna californensis Halfmoon WC 16-45 0.014 ± 0.029 Sarda chiliensis lineolata Pacific bonito WC 60 0.012 ± 0.141 Gymnothorax mordax Moray eel Benthic - 0.009 ± 0.058 Decapterus scombrinus Mexican scad WC 25 0.008 ± 0.066 Gibbonsia elegans Spotted kelpfish Benthic 10-12 0.006 ± 0.048 Lythrypnus zebra Zebra goby Benthic 4 0.006 ± 0.048 Heterostichus rostratus Giant kelpfish Benthic 4-49 0.003 ± 0.034 Sebastes serriceps Treefish Benthic 8-26 0.003 ± 0.034 Paraclinus integripinnis Reef finspot Benthic 8 0.003 ± 0.034 Paralabrax nebulifer Barred sand bass Demersal 20-45 0.003 ± 0.016 Seriola lalandi Yellowtail WC 35-65 0.002 ± 0.011 Anisotremus davidsonii Sargo WC 23-40 0.001 ± 0.008 Brachyistius frenatus Kelp perch WC 3-23 0.001 ± 0.004 Alloclinus holderi Island kelpfish Benthic 10-27 0.001 ± 0.001 Sebastes atrovirens Kelp rockfish Demersal 17-27 0.0002 ± 0.002 Rhacochilus toxotes Rubberlip surfperch Demersal 35-50 0.0002 ± 0.002 Heterdontus francisci Horn shark Benthic 40-60 0.0002 ± 0.002 Caulolatilus princeps Ocean whitefish Demersal 28 0.0002 ± 0.002 Sebastes auriculatus Brown rockfish Benthic 20-32 0.0002 ± 0.001 Embiotoca jacksoni Black surfperch Demersal 16-17 0.0001 ± 0.001 Rhacochilus vaca Pile perch Demersal 50 0.0001 ± 0.001 Apogon guadalupensis Guadalupe cardinal Benthic 12 0.0001 ± 0.001 Steriolepus gigas Giant sea bass WC 260 0.00005 ± 0.001 Balistes polylepis Finescale triggerfish Demersal 45 0.00005 ± 0.001
38
Figure 3. nMDS plot of the fish assemblage showing differences among depth strata, pooled across sites and time points (n = 420).
39
Figure 4. (a) Fish species richness, (b) density of fishes, and (c) proportion of fish (density) in the water-column. Error bars are ± 1SEM (n = 28 per time point).
40
Figure 5. (a) Total density of fishes and (b) density excluding C. punctipinnis at seven sites at five times. Error bars are ± 1SEM (n = 4 per site per time point).
41
Figure 6. Average densities of Sargassum horneri and Macrocystis pyrifera during different time periods. Error bars are ± 1SEM (n = 28 per site per time point).
42
Figure 7. Average total density of all fish except L. dalli and C. punctipinnis at seven sites during five time periods. Sites were Arrow Point (ARW), Beyond Arrow (BYD), Empire
Landing (EMP), Indian Rock (IND), Isthmus Reef (ISTH), Paradise Cove (PRDS), and Parsons
Landing (PRNS). Error bars are ± 1SEM (n = 4 per site per time point).
43
Figure 8. (a) Total density of algae (thalli) and (b) mean height of algae per transect at five sampling times and seven sites. Error bars are ± 1SEM (n = 4 per site per time point).
44
45
Table 4. Marginal test results of Distance-based Linear Modeling (DistLM) exploring the proportion of variation in the multivariate fish assemblage (pooled across depth strata) that was explained by variation in the density of algae. Variation explained shown as a proportion.
Significant contributors are shown in bold (p < 0.05). The full model r2 was 0.22.
Var. Variable Pseudo-F P explained Dictyopteris sp. 3.35 0.005 0.02 Z. farlowii 1.45 0.21 0.01 S. horneri recruit 5.79 0.001 0.04 S. horneri 3.00 0.006 0.02 S. palmeri recruit 2.13 0.05 0.02 S. palmeri 2.68 0.02 0.02 C. neglecta recruit 0.59 0.70 0.004 C. neglecta 2.02 0.08 0.01 S. muticum recruit 3.59 0.01 0.03 S. muticum 3.51 0.004 0.02 M. pyrifera recruit 0.58 0.703 0.00 M. pyrifera 9.35 0.001 0.06 E. arborea density 0.62 0.72 0.004
46
Table 5. Results of Principal Components Analysis (PCA) on the (a) algal assemblage and (b) physical substratum across all sites and time points. Values are correlations of individual variables with PC loadings. PCs shown explained 44.3% and 70.8% of the variation in algae and physical substratum, respectively.
(a) Algal Algal Algal PC 1 PC 2 PC 3 % variation 20.26 12.85 11.16 Dictyopteris sp. 0.42 0.26 0.33 Zonaria farlowii -0.21 0.09 0.45 S. horneri recruit 0.27 0.42 -0.08 Sargassum horneri 0.05 0.34 -0.51 S. palmeri recruit -0.26 0.20 0.05 S. palmeri -0.41 0.26 0.12 Cystoseira recruit 0.03 0.05 -0.52 Cystoseira 0.39 0.10 -0.15 S. muticum recruit -0.13 0.48 0.15 S. muticum -0.28 0.23 -0.15 Macrocystis 0.42 0.01 0.25 Macrocystis recruit 0.19 0.32 0.09 Eisenia 0.11 -0.36 -0.03
(b) Subs. Subs. PC 1 PC 2 % variation 47.5 23.3 Relief 0.07 -0.07 Bedrock 0.74 0.15 Large boulder -0.46 -0.53 Medium boulder -0.11 0.02 Small boulder -0.14 0.13 Cobble -0.44 0.63 Sand 0.10 -0.52
47
Table 6. Results of multiple linear regression testing how well fish density, species richness, vertical distribution, and size were predicted by physical substratum combined with either algal principle components ("Species"), algal height, volume, or surface area. Samples were pooled across sites and time points (n = 140). Fish density and size is shown without L. dalli (LYDA) and without both L. dalli and C. punctipinnis (CHPU). Proportion of fish density in the water column is shown as “water column fish”. Statistically significant p-values are in bold.
Algal Multiple Linear Regression Model Dependent variable Surface Species Height Volume area Fish density AIC 24.66 21.62 26.10 22.23 (no LYDA) r2 0.25 0.24 0.22 0.24 p < 0.001 < 0.001 < 0.001 < 0.001 Fish density AIC 133.53 129.54 133.08 128.52 (no LYDA, CHPU) r2 0.08 0.07 0.05 0.08 p 0.046 0.02 0.068 0.009 Fish richness AIC -257.61 264.70 259.80 268.90 r2 0.14 0.04 0.03 0.07 p 0.001 0.12 0.29 0.02 Water column AIC -305.65 309.70 307.30 306.30 fish r2 0.08 0.12 0.12 0.10 p 0.04 0.001 < 0.001 0.003 Mean fish size AIC -271.70 264.30 260.10 268.70 (cm; TL) r2 0.13 0.06 0.05 0.08 p 0.002 0.05 0.009 0.007 Mean fish size AIC -186.45 177.80 175.50 180.70 (no LYDA, CHPU) r2 0.13 0.05 0.05 0.07 (cm; TL) p 0.002 0.08 0.09 0.02
48
Table 7. Results of multiple linear regression testing how well densities of individual species were predicted by physical substratum combined with either algal principle components
("Species"), algal height, volume, or surface area. Samples were pooled across sites and time points (n = 140). Statistically significant p-values are in bold.
Summarized Algal Variables Fish species Surface PC 1 PC 2 PC 3 Height Volume area C. AIC 365.53 … … 200.93 200.92 202.73 punctipinnis r2 0.15 … … 0.18 0.18 0.17 β -0.11 0.06 -0.03 -0.10 -0.07 -0.08 p 0.02 0.16 0.55 < 0.001 < 0.001 < 0.001 L. dalli AIC 124.98 … … 351.32 353.49 359.35 r2 0.15 … … 0.21 0.18 0.16 β -0.16 0.31 -0.17 -0.16 -0.12 -0.12 p 0.05 < 0.001 0.03 < 0.001 < 0.001 < 0.001 P. AIC 204.01 … … 127.81 122.15 202.73 clathratus r2 0.19 … … 0.10 0.10 0.17 β -0.03 0.09 0.07 -0.03 -0.03 -0.01 p 0.33 < 0.001 0.03 < 0.001 < 0.001 < 0.001 H. AIC 17.38 … … 6.63 6.36 6.86 rubicundus r2 0.15 … … 0.18 0.19 0.18 β -0.05 0.08 -0.01 -0.09 -0.09 -0.09 p 0.04 < 0.001 0.54 < 0.001 < 0.001 < 0.001 H. AIC 119.93 … … 123.92 125.02 134.66 semicinctus r2 0.24 … … 0.19 0.18 0.13 β -0.19 0.04 -0.03 -0.17 -0.12 -0.12 p < 0.001 0.20 0.43 < 0.001 < 0.001 < 0.001 O. AIC 295.02 … … 297.48 297.06 294.19 californica r2 0.08 … … 0.03 0.03 0.06 β 0.09 0.14 0.01 0.03 0.01 0.01 p 0.14 0.02 0.82 0.19 0.27 0.05
49 Table 8. Results of Distance-based Linear Modeling (DistLM) exploring the amount of variation in the vertical distribution of the fish assemblage predicted by variation in the density of algae.
Variation explained shown as a proportion. Statistically significant predictors are in bold. The full model r2 was 0.14.
Var. Variable Pseudo-F P explained Dictyopteris sp. 3.67 0.008 0.03 Z. farlowii 2.20 0.08 0.02 S. horneri recruit 0.60 0.632 0.00 S. horneri 1.20 0.313 0.01 S. palmeri recruit 1.35 0.23 0.01 S. palmeri 2.87 0.03 0.02 C. neglecta recruit 0.81 0.51 0.006 C. neglecta 1.28 0.29 0.01 S. muticum recruit 0.99 0.42 0.01 S. muticum 1.05 0.346 0.01 M. pyrifera recruit 0.39 0.791 0.00 M. pyrifera 7.15 0.001 0.05 E. arborea density 1.14 0.36 0.008
50
Figure 9. Influence of algal height, volume, surface area, or algal PCs 1, 2, and 3 (i.e., algal predictor variables), on (a) fish density (without L. dalli), (b) fish species richness, (c) proportion of total fish density in the water column, and (d) the average size of fishes (without L. dalli).
Colored circles are statistically significant from zero while white circles are not. Standardized coefficients (±95% confidence intervals) from multiple regression models are shown.
51
Figure 10. Influence of physical substratum principle components from predictive models on (a) fish density (without L. dalli), (b) fish species richness, (c) proportion of total fish density in the water column, and (d) the average size of fishes (without L. dalli). PC 1 and PC 2 are represented as triangles and circles, respectively. Colored shapes are statistically significant from zero while white shapes are not. Substratum PC 1 was most positively correlated with variation in sand, bedrock, and relief, while PC 2 was positively correlated with cobble. Standardized coefficients
(±95% confidence intervals) from multiple regression models are shown.
52 Chapter 3: Separating effects of macroalgal height from species identity on the abundance and behavior of temperate rocky-reef fishes
Introduction The influence of biogenic structures on the composition, structure, and stability of ecological communities has been well-documented (Bell et al. 2012; Kovalenko et al. 2012). In temperate marine systems, stands of brown macroalgae provide important resources for many species (Dayton 1985; Foster & Schiel 1986). Temperate macroalgae are morphologically diverse, both within and among species, and can vary in their relative abundance and size across rocky reefs (Stewart et al. 2009; Pérez-Matus & Shima 2010). Temporal and spatial variation in the composition, density, and size of algal assemblages can have direct and indirect effects on the distribution and interactions of associated organisms (Estes et al. 2004; Arkema et al. 2009).
Abundance and diversity of fishes often are linked to variation in macroalgal assemblages. Previous experimental (Demartini & Roberts 1990; Holbrook et al. 1990;
Anderson 1991; Carr 1994; Levin & Hay 1996; Pérez-Matus et al. 2010) and descriptive studies
(Holbrook et al. 1990a, 1990b; Carr 1994; Willis & Anderson 2003; Pérez-Matus et al. 2007) have illustrated how the ecology of temperate fish assemblages often are shaped by variation in macroalgal attributes. Algal structure can influence predator-prey interactions by offering refuge to prey (Mitchell & Hunter 1970; Crowder & Cooper 1979, 1982; Nelson 1979; Heck &
Crowder 1991) or camouflage to predators (Hobson & Chess 2001) and can provide resource subsidies for many fishes (Steneck et al. 2002; Hovel et al. 2016). Fish behavior may be influenced by the size or shape of algae (Levin & Hay 1996), and certain algal morphologies may be more important for some species or life stages than others (e.g., canopy shelter for
Paralabrax clathratus or Brachyistius frenatus: Carr 1991; Anderson 1994; Pérez-Matus et al.
2010).
53 Variation in physical attributes (e.g., density, orientation, and height) of vegetative structure can influence predator behavior (Preisser et al. 2007; Farina et al. 2014), predator-prey interactions (Ware 1973; Rangeley & Kramer 1998), as well as predator diet and body size
(Cooper & Crowder 1979; Crowder & Cooper 1982). Previous studies have documented the effects of algal height on the distribution of reef fishes (Carr 1991; Anderson 1994), and some have noted that height may be more important predictors than macroalgal species identity (Levin
& Hay 1996). For example, Trebilco et al. (2015) found that 75% more fish biomass was present in areas with tall canopy-forming kelp than in areas with open canopy. Moreover, fishes respond to drastic changes in macroalgal assemblages (e.g., due to urchin barrens, storms, competition, climate change, removal, etc.) (Ebeling and Laur 1985; Holbrook et al. 1990; Dayton et al. 1998;
Steneck et al. 2002; Willis & Anderson 2003; Graham 2004; Cameron 2013).
How fishes use macroalgae depends not only on attributes of the algae, but also on the size of the fish. For example, following removal of Macrocystis and subsequent increase in benthic foliose algae, Carr (1989) found that habitat use by large fishes decreased while use by small fishes increased. A number of studies have detected a decrease in the density of fishes associated with algal structure as algae increase in size (Choat & Ayling 1987; Carr 1989;
Holbrook et al. 1990). This pattern likely is due to a decreased dependence on refugia provided by algal structure and an increase in foraging behaviors that maximize net energy intake (Werner and Hall 1979; Mittelbach 1981; Werner et al. 1983). Thus, variation in the size, morphology, and density of algae can alter the distribution, body size, behavior and subsequent interactions among individuals, and in turn can influence the structure of communities (Crowder & Cooper
1982; Holbrook et al. 1990; Heck & Crowder 1991; Levin & Hay 1996; Arkema et al. 2009;
Lewis & Anderson 2012).
54 Since its introduction in 2005, the Asian alga Sargassum horneri has become increasingly common on rocky reefs throughout the Southern California Bight and southward into Baja California, Mexico (Miller et al. 2007; Miller & Engle 2009; Sprague et al. 2012;
Marks et al. 2015). Recent warming of local ocean temperatures appears to have led to decreases in Macrocystis and increases in S. horneri density, with the increased prevalence of monospecific stands at certain locales (e.g., Santa Catalina and Anacapa Islands). Stands of S. horneri on reefs differ greatly from those of Macrocystis due differences in morphology and height (< 3 m vs. up to 45 m long; Abbott & Hollenberg 1976). However, it is often taller and denser than other macroalgae in southern California (Ginther 2016; Srednick & Steele in prep).
Its ability to dominate large stretches of rocky reef, and its greater height than other understory algae suggest it might have important effects on common kelp forest and rocky reef fishes.
Although researchers have begun assessing the local effects of S. horneri, there is uncertainty regarding the extent to which it is used by fishes and for what purpose (e.g., refuge or feeding). For example, the impact of this invasive alga on the invertebrate epifauna normally found on native algae and any subsequent effects on their fish predators are unknown. Recent research indicates that the effects of S. horneri on fishes are complex and subtle (Ginther 2016).
Thus, it is important to understand how variation in species composition and size of algae in combination may influence fishes, a topic that has been relatively unexplored (but see Levin &
Hay 1996 and Pérez-Matus & Shima 2010).
The present study explores the effects of algal species composition versus algae size on rocky reef fishes, with particular emphasis on the impact of the invasive alga S. horneri. I used experiments and field surveys to assess the effects of algal species identity versus height on the abundance, species composition, size, and behavior of fishes. Previous studies have
55 demonstrated that algal size within species is an important predictor of the fish community (Carr
1991, 1994; Levin & Hay 1996). However, no studies have tested the influence of algal size across species. I hypothesized that macroalgae of similar size, regardless of species, would exert the same effects on fish abundance, richness, behavior, and size. I expected that the abundance, richness, and size of fishes would be positively related to algal size. I collected behavioral data to provide a mechanistic explanation for the patterns observed.
Methods General Experimental Methods
Experiments were conducted at 10-m depth along a 150-m-long area on the west side of
Lion Head point at Santa Catalina Island, California (33°27’ N, 118°30’ W) between July 14th and August 7th 2016. I performed three experiments to evaluate how differences in the identity and size of algae influenced fish abundance and behavior, to evaluate the relative impacts of algal species versus height on fish distribution and behavior. I deployed earth anchors in open sand, 10-12 m from the reef-sand interface. Each of the 15 experimental plots consisted of three
0.5 m-long earth anchors placed in a triangle with sides measuring 0.5 m (total area = 0.125 m3), and connected by 0.5 m of 12-mm diameter nylon rope (Figure 1). For each of the three experiments, all algae were collected in 150-µm mesh bags within a 100-m-long area at 10-m depth at Arrow Point (33°28’ N, 118°32’ W). I selected algae haphazardly for each treatment and selected individuals of the same life-stage (e.g., senescent for S. horneri) and condition (e.g., degree of senescence). I then weighed (wet mass), assigned to a treatment, and transplanted these algae randomly across plots. Algae were connected directly to nylon rope with one or two cable ties. Surveys of experimental plots started 24 h after transplantation. All algae were removed
56 following each experiment and plots were left bare for 24 h before transplanting new algae for other trials.
Fish surveys were performed and presence of fishes were compared between the adjacent rocky reef and open sand habitat prior and after the installation of earth anchors to verify that fish observed on the experimental plots were attracted to them from the nearby natural reef and to ensure that there were no behavioral effects of earth anchors. All experimental plots were surveyed once daily between 0800 and 1100 over a four-day period. To avoid frightening away fishes, SCUBA surveys were performed by first slowly moving to within 7 m of a plot, then to within 5 m after 2 min, and then finally moving to the plot to search for small or cryptic fishes. I recorded the species and abundance of fishes within 1 m or clearly associating with algae.
Surveys took 2.5 min per plot.
I placed each fish observed into one of two behavioral categories: feeding (directly feeding on algae or algal epibionts) or resting (associating with algae for the purpose of refuge or predation). I chose these categories to summarize the most common behaviors seen on reefs as well as to assess differences in two important behavioral strategies (i.e., for feeding versus refuge) used by fishes in the presence of habitat structure (Crowder & Cooper 1982; Diehl 1992;
Steele 1999; Hobson & Chess 2001). Thus, for each plot at each time point, I recorded the fish abundance and number of fishes exhibiting each behavior (i.e., one behavior per fish). I was able to keep track of individual fishes and their behaviors due to the low densities of fish per plot (<
15 fish plot-1 across experiments; mean: 1.60 fish plot-1).
Three separate experiments were performed to evaluate how fish distribution and behavior was influenced by (1) algal identity, (2) algal size within species, and (3) algal identity vs. size between species.
57 Experiment 1 – Algal identity
This experiment compared the fish assemblage in the presence of the three common subtidal macroalgae: Cystoseira neglecta, Sargassum palmeri, and S. horneri, to evaluate the influence of algal identity on the abundance and behavior of fishes. Treatments consisted of three individuals of a species per triangular plot (n = 5 of each treatment) in order to reflect natural densities on rocky reefs. Average sizes per treatment were as follows: C. neglecta: height: 29.8 ±
6.02 cm, weight: 0.31 ± 0.06 kg; S. horneri: height: 46.8 ± 12.6 cm, weight: 0.18 ± 0.07 kg; and
S. palmeri: height: 31.0 ± 6.60 cm, weight: 0.32 ± 0.07 kg. Nearby reefs had similar densities of these size classes of these species during the experiment, with the exception of S. palmeri, which was denser on natural reefs at the same depth (C. neglecta: 1.00 ± 0.2 m-2, S. horneri: 0.60 ±
0.15 m-2, S. palmeri: 5.40 ± 0.72 m-2). By using the same density for all three species I was able to compare the effects among algae more readily.
Experiment 2 – Algal size
To better understand how algal size influences the abundance and behavior of fishes, I performed an experiment comparing the fish assemblage in the presence of a single species, S. horneri, of three different sizes: short (~0.5 m), medium (~1.5 m), and tall (~2 m) (n = 5 of each). Treatments consisted of one thallus per plot with the exception of the short treatment in which three individuals were placed in order to simulate natural densities on rocky reef as well as in an effort to standardize biomass. Average size per treatment was, short: height: 57.3 ± 6.5 cm, weight: 0.61 ± 0.16 kg; medium: height: 130.7 ± 11.9 cm, weight: 0.67 ± 0.43 kg; tall: height:
237.2 ± 12.3 cm, weight: 1.71 ± 0.51 kg. Individuals of the smaller size were relatively common
58 on the adjacent reef (0.6 ± 0.15 m-2) during the experiment, and, though less abundant, thalli of the size used in the medium and tall treatments could be found within 200 m of the study site.
Experiment 3 – Algal identity vs. size
This experiment evaluated how algal identity vs. size influenced the abundance and behavior of fishes. It used three treatments: (1) S. horneri and (2) M. pyrifera of the same height
(2 m), and (3) tall M. pyrifera (5 m) (n = 5 of each). Average size per treatment was 221.6 ± 34.8 cm height and 1.56 ± 0.24 kg weight for S. horneri; and 232.4 ± 73.8 cm height and 3.01 ± 1.34 kg weight, and 460.0 ± 97.6 cm height and 7.85 ± 0.89 kg weight for short and tall Macrocystis, respectively.
Surveys of Naturally Occurring Algae
In addition to the aforementioned field experiments, I performed surveys of fishes associating with naturally occurring algal thalli on nearby rocky reefs to evaluate if the patterns seen in the experiments were representative of those seen on natural reefs. Surveys of the fish assemblage were performed at five sites (Arrow Point, Big Geiger, Between Two Ferns,
Howland’s Landing, and Rippers Cove) on the leeward coast of Santa Catalina Island, from June
3rd – 30th, 2015. Sites were selected if they met the following criteria: rocky or cobble reef and reef extending to at least 10 m depth.
At each site, two divers on SCUBA performed roving surveys of fishes associated with isolated, single algal thalli. Surveys of each thalli were separated by ≥ 10 m to ensure the same fishes were not recounted. When it was not possible to survey completely isolated thalli, divers surveyed isolated benthic patches of an alga covering ≤ 1 m2. Roving diver surveys are time
59 effective and have been shown to provide a more comprehensive record of the fish assemblage in a given area when observing specific behaviors of fishes (Auster et al. 2011), though they are less useful for estimating density. The same two divers conducted all surveys to minimize potential observer bias. Surveys were performed on five species of algae: Cystoseira neglecta,
Eisenia arborea, Sargassum horneri, S. palmeri, and Macrocystis pyrifera. However, not every species was found at all five sites (Table 1). Surveys of individual thalli were pooled across sites for main analyses and sample sizes per alga were C. neglecta: n = 38, E. arborea: n = 11, M. pyrifera: n = 14, S. horneri: n = 55 S. palmeri: n = 10.
Roving surveys were conducted by searching for and haphazardly selecting algal thalli of any of the aforementioned species at depths between 5 - 8 m. From a position 5 m from the thallus, one diver would record the species, number, and body size (standard length, SL, estimated by trained eye to nearest cm) of fishes that were associated with the macroalga.
Surveys took 2 minutes per thallus, at which point a diver would search for the next thallus.
Fishes were considered to be associating with macroalgae if they were: (1) feeding on epibiota or the algae itself, (2) appeared to be using the structure as shelter (for ≥ 5 s), (3) following another individual associated with the algae, or (4) circling the algae for ≥ 5 s.
Statistical Analyses
All statistical analyses were performed using SYSTAT (Version 13). For the experiments, fish abundance data were averaged across days. One-way analyses of variance
(ANOVA) were used to test for differences among treatments in each of the following response variables: abundance, species richness, and body size of fishes for Experiments 1 and 2. A priori hypothesis testing was used for Experiment 3 to test whether there were differences in abundance
60 and species richness between the two shorter algal treatments, and if not, to test differences between treatments of short (short Macrocystis and S. horneri) and tall algae. Plots were used as replicates. All three response variables were √ (x + 0.5) transformed to satisfy assumptions of heterogeneity and normality. In Experiment 1, species richness and body size did not satisfy parametric assumptions even after transformation, and so non-parametric Mann-Whitney tests were used for these two variables.
Differences in fish behaviors among treatments were tested with log-linear analysis on untransformed frequency data (i.e., the number of individuals exhibiting each behavior). Results of these log-linear analyses should be viewed with some caution, however, due to low frequencies in some cells (Sokal & Rohlf 1994).
For the observational study on natural reefs, mixed-model ANOVAs were used to test for differences in the overall abundance, species richness, and total length of fishes among algal species (fixed) and sites (random). I used linear regression to evaluate the potential influence of algal height on abundance, species richness, and total length of fishes. Fish abundance, species richness, and fish length data were log (x + 1) transformed to satisfy assumptions of homogeneity of variances and normality (Sokal & Rohlf 1994). Individual algal thalli were used as the fundamental unit of replication. To assess responses of individual fish species to algae, additional one-way ANOVAs and linear regressions were performed on the abundance of each of seven species of fish (Table 1). For these analyses, sites at which a focal species was absent were dropped from analyses.
61 Results Experimental Studies
Experiment 1 – Algal identity
Algal identity of three similarly-sized macroalgae did not affect the fishes that associated with them. The abundance of fishes did not differ among treatments for the three macroalgal species Cystoseira neglecta, Sargassum horneri and S. palmeri (F 2,12 = 0.71 p = 0.51; Figure
2a). Fish species richness also did not differ among treatments (Mann Whitney-U test, U = 0.96, p = 0.62; Figure 2b). Furthermore, no differences were found in the frequency of behaviors among treatments (Log-Linear test, χ2 = 2.50, p = 0.29) (Figure 2c). Finally, there were no differences in the average length of fishes among algal species (Mann Whitney-U test, U = 5.6, p
= 0.06).
Experiment 2 – Algal size
Algal size affected the fishes associated with the three sizes of S. horneri. More fish were associated with larger thalli (ANOVA: F 2,12 = 4.26 p = 0.04) (Figure 3a). Medium and tall S. horneri also had more species associated with them than small thalli (F 2,12 = 4.10, p = 0.04)
(Figure 3b). However, no statistically significant differences were found in the frequency of behaviors among treatments (Log Linear test, χ2 = 1.41, p = 0.49; Figure 3c) even though resting was 1.75 times more frequent than feeding behavior, and was 6.5 times more prevalent in the medium and tall treatments than the short treatments. There were no differences in the average length of fishes among algal species (F 2,12 = 0.21, p = 0.80).
62 Experiment 3 – Algal identity vs. size
Algal identity did not affect fish abundance significantly, however height did. Fish abundance did not differ among treatments of S. horneri and short M. pyrifera (F 1,12 = 0.08, p =
0.78; Figure 4a). However, fish abundance was greater in the presence of tall M. pyrifera when
S. horneri and short M. pyrifera were combined into a single “short” treatment (F 1,12 = 6.53, p =
0.03). There were similar patterns in fish species richness to those seen with abundance: richness did not differ among treatments of S. horneri and short M. pyrifera (F 1,12 = 0.45, p = 0.51), and when combined into a single “short” treatment and compared with tall M. pyrifera there was greater richness in the tall treatment (F1,12 = 5.84, p = 0.03). There were no differences in the average length of fishes among algal species (F 2,12 = 0.49, p = 0.62).
Fish behaviors differed among the three algal treatments, as indicated by a significant interaction between treatment and behavior (Log Linear test, χ2 = 8.72, p = 0.01; Figure 4c). In particular, resting behavior was ~2.5 times more prevalent in the presence of tall M. pyrifera than in the shorter treatments. Resting was the most common behavior, being nearly 4 times as frequent as feeding behavior.
Individual Algal Surveys
A total of 674 fish of 13 different species were associated with 128 individual algal thalli at 5 study sites. Of these species, Lythrypnus dalli and Paralabrax clathratus were the most abundant: ~37% and ~23% of total, respectively. Total fish abundance (individuals of all species summed) differed among species of algae (F4,20 = 7.61, p = 0.001) with the greatest number of fish associated with M. pyrifera (10.43 ± 1.92 fish thallus-1) and fewest with S. palmeri (1.8 ±
0.70 fish thallus-1) (Figure 5). Abundance around the invasive S. horneri was intermediate (5.85
-1 ± 4.41 fish thallus ). Average length of fishes did not differ among algal species (F 4,10 = 0.83, p
63 = 0.53; Figure 10) or sites (F 4,10 = 0.40, p = 0.81).
Species richness of fishes also differed among algal species (F 4,123 = 6.21, p < 0.001), and was greatest in association with M. pyrifera (2.57 ± 0.36 species) and S. horneri (2.22 ± 0.14 species thallus-1) and lowest at S. palmeri (Figure 5). Fish abundance and species richness per alga also differed among sites (abundance: F4,3.56 = 3.76, p = 0.02; species richness: F4,16.59 =
3.11, p = 0.04; Figure 6), with abundance greatest at Arrow Point (7.93 ± 0.91 fish alga-1), and species richness greatest at Rippers Cove (2.57 ± 0.43 fish species alga-1). The patterns in abundance and species richness among algal species were consistent among sites (site x species, abundance: F 8,111 = 0.430, p = 0.90; species richness: F 8,111 = 3.11, p = 0.86).
Abundance of three common fishes, Paralabrax clathratus, Chromis punctipinnis and
Oxyjulis californica, differed among species of algae (Table 2). There was no difference, however, in the abundance of P. clathratus between S. horneri and Macrocystis (Tukey’s HSD: p = 0.997). O. californica was more numerous in the presence of Macrocystis and than at S. horneri (Tukey’s HSD: p = 0.02). C. punctipinnis abundance differed among algae (F 4,84 = 2.67, p = 0.04), but post-hoc tests did not reveal any significant pairwise differences in abundance among algae. The most abundant fish species, L. dalli, did not differ in abundance among algal species (F 4,123 = 1.08, p = 0.38).
Fish abundance increased with algal thallus height (r2 = 0.21, p < 0.001; Figure 7).
Species richness also increased with thallus height (r2 = 0.15, p < 0.001; Figure 8). P. clathratus responded more strongly to thallus height than other species, although abundance of C. punctipinnis, Halichoeres semicinctus, and Girella nigricans were also positively related to algal height (Table 2). The abundance of the most common fish species, L. dalli, however, was not related to algal height (r2 = 0.021, p = 0.10). There were no negative relationships between algal
64 height and fish abundance. Fish size also was not related to algal height (r2 = 0.001, p = 0.44).
Discussion
The results presented here indicate that the size of algae may be more important than species identity for understanding the distribution of temperate reef fishes. I found more fishes associated with taller algae and no differences in fish abundance or behavior among algae when tested experimentally. In addition, greater fish diversity was associated with the taller macroalgal treatment (tall Macrocystis) in the field experiment, as well as positively related to overall algal height in field surveys. While there were differences in the fish community among different species of algae in field surveys, I found that abundance and diversity were greatest and comparable (in regards to diversity) in the presence of M. pyrifera and S. horneri, the two tallest algae on these reefs.
This study also evaluated the impact of S. horneri on the composition and distribution of the fish community. I found that while fewer fish were associated with S. horneri than
Macrocystis on field surveys, when explored experimentally, there was no difference in fish abundance between similar sized thalli of the two species. Additionally, species richness was the same associated with S. horneri and Macrocystis of the same size but was greater in the tall
Macrocystis treatment. Thus, one might expect the fish community to differ between reefs dominated by tall Macrocystis versus shorter S. horneri, but this difference may simply be the result of differences in the size of the dominant macroalgae.
The patterns seen in the experiments differed from those observed in field surveys, specifically in that fish abundance differed more between Macrocystis and S. horneri in field surveys of natural reefs than on the experimental plots. These differences may have been due to differences in height or density between the two macroalgae on natural reefs. There were greater
65 densities of S. horneri than Macrocystis on surveyed reefs, which may have increased the likelihood of a homogenous distribution of fishes across S. horneri individuals, and caused larger aggregations of fishes at Macrocystis, which was less common. Additionally, Macrocystis was on average ~ 1.5 m taller than S. horneri on natural reefs (2.48 ± 1.33 m versus 1.10 ± 0.65 m, respectively). Whereas in the experiments, algae were deployed in equal densities and comparable biomass and/or height, which may have driven fishes to select the preferred form of algae.
These patterns could be explained simply by the increased habitat that taller algae provides. Alternatively, they may at least partially be attributable to the presence of algae higher in the water column. Carr (1991), in an experiment in which he placed both Macrocystis and S. palmeri in the water column and at the benthos, found that abundance of P. clathratus recruits was greater at algae in the water column than near the bottom. Experiments by Levin and Hay
(1996) showed a 7-fold higher abundance of fish associated with tall algae when compared to shorter algae. Many additional studies corroborate faunal use of vertical structure, highlighting the importance of structural complexity to prey survivorship (Heck & Crowder 1991; Anderson
1994; Christie et al. 2007). With regard to Macrocystis and S. horneri, recent work found that the vertical distribution and size of P. clathratus recruits is linked to the presence of tall algae
(Ginther 2016).
My experiments revealed that algal height but not species, affected the behavior of fishes associated with algae, however this was only observed when comparing Macrocystis and S. horneri of different heights. Frequency of resting behavior increased with size of algae but was less than (although not significant) the feeding behavior in smaller S. horneri treatments, likely
66 because smaller algae do not provide suitable refuge for predatory or prey fish (Carr 1994; Levin
& Hay 1996; but see effects on predators in Crowder & Cooper 1982).
The frequency of feeding versus resting behavior did not vary among C. neglecta, S. palmeri, and S. horneri. Although, the number of fishes feeding associated with S. horneri was greater than resting, a pattern that was opposite in the other two treatments. This suggests feeding may be the preferred use for this size class of S. horneri, while resting may be more prevalent in the presence of C. neglecta and S. palmeri.
Although I observed H. semicinctus and P. clathratus feeding in all experimental treatments, behaviors observed during experiments were largely species-specific with fishes categorized as resting were almost entirely P. clathratus (~ 96%) and fishes categorized as feeding were H. semicinctus (~ 82%). Consequently, the frequencies of these behaviors were correlated with the abundance of those species. Theoretically, a certain behavior, or the species associated with that behavior, may become more frequent across an entire reefscape characterized by a mono-specific stand of S. horneri at a specific height, a scenario that is common in fall and spring at Catalina Island (Marks et al. 2015).
The behavioral patterns observed in this study may offer a mechanistic explanation for the fish distribution patterns noted in Chapter 2. I observed fishes readily feeding on algae, targeting either epifauna or the algae itself. Generally, S. horneri appears to be avoided by most invertebrate herbivores as a result of high concentrations of phenolic compounds (Monteiro et al.
2009; Navarro 2009; Vogt 2010). Nonetheless, I observed both H. semicinctus and P. clathratus directly engulfing reproductive receptacles on senescent stage S. horneri. While some herbivores may avoid S. horneri throughout its recruit to mature life-stages, S. horneri may provide a source of food during senescent periods. Based on the high ambient density noted on nearby reefs
67 (Srednick, unpublished data), S. horneri may provide an important food source for select fish or invertebrates during periods of S. horneri senescence. However, it is not known whether the fishes observed were directly targeting the reproductive receptacles of algae or the associated epifauna. I did not directly quantify feeding in this study, but this behavior may explain some of the distribution patterns seen at a larger scale.
In field surveys of algal thalli, fish body size did not differ among species, but was positively related to thallus height. The body size of P. clathratus, the dominant piscivore, was related positively to algal height and individuals were most abundant in the presence of
Macrocystis and S. horneri, the tallest algae found on the study reefs. Carr (1991) showed that recruitment of P. clathratus is linked to tall algae, and other studies have suggested that adult P. clathratus may be attracted indirectly to this habitat for predation on conspecific recruits (Coyer
1979; Steele et al. 2002; Ginther 2016). Ginther (2016) found that P. clathratus do not recruit to
S. horneri, indicating that the association of older P. clathratus with S. horneri is established after recruitment.
The feeding strategy of predatory fishes can vary throughout their life span (Hobson &
Chess 2001). Smaller and younger predators (e.g., P. clathratus) often feed more frequently and from above on prey that are on or close to the benthos (e.g., crustaceans). However, as fish mature they become less dependent on vegetative structure for protection (Levin & Hay 1996).
Thus, adult dependence on habitat structure to enhance predation success may be indirect, and more directly related to the type and size of prey selected. Additionally, larger (i.e., older) fish may attack prey (usually fishes) from a camouflaged position with vegetation (Hobson et al.
1981). I observed more predatory P. clathratus exhibiting such behaviors associated with taller algae, a pattern that did not differ among algal species, notably between S. horneri and
68 Macrocystis. This suggests that P. clathratus utilizes the structure provided by tall algae despite the clear difference in the general structure of Macrocystis versus S. horneri.
The surveys on natural reefs and experiments conducted in this study revealed similar patterns, suggesting that the results of the experiments reflect natural patterns on reefs. Similar assessments of associations between fishes and algae show similar results (Levin et al. 2000;
Levin & Hay 2002; Pérez-Matus & Shima 2010), however it is difficult to predict how the behavior and abundance patterns found in this study exist on a larger scale (e.g., rocky reef scale or the scale addressed in Chapter 2). For example, the patterns observed at small (0.5 m2), isolated plots of algal treatments may not be representative of natural reefs, due to differences in the relative abundance of algae as well as variation in the composition and relief of physical substratum across a reef-scape. My experiments, however, did not explore any possible effects due to the relative density or size distribution of each alga, which can influence the distribution of fishes (Holbrook 1990; Carr 1991; Anderson 1994). Thus, larger-scale field experiments that control these factors across a continuous reef-scape are necessary to better understand how fishes interact with algal structure, and to determine how variation in algal structure may influence the density, behavior, and interactions among fishes.
I detected no relationships between fish size and algal size or species, and did not observe recruits of any fish species in experiments or field surveys. This may have been due to low movement of small fishes to the isolated experimental plots (e.g., due to high risk of predation) as well as a lack of localized recruitment. Young-of-year P. clathratus were abundant < 2 km from experimental plots during the same time period. Based on this information, I cannot ascertain how, if at all, the size or species of algae influences the size of fishes. More
69 comprehensive and large-scale studies are needed to better understand how variation in the size and composition of algal assemblages influence the size distribution of fishes.
In summary, the results of this study reveal that the size of algae may be more important than algal species in affecting fish distribution and behavior. Although this was not experimentally tested at a larger scale, other studies have alluded to a similar pattern (Carr 1991;
Anderson 1994; Levin & Hay 1996). My study, however, is the first to examine how differences in the size of S. horneri versus Macrocystis influence the abundance and behavior of reef fishes.
While the present results show that fishes readily use the habitat provided by S. horneri, the long-term effects of this non-native alga on fishes are unknown. Previous assessments show that while fishes respond differently to variation in the density of S. horneri, the net effect may not be large (Ginther 2016). Consequently, a change in the size (e.g., height) of biogenic structure may have greater implications for important temperate reef fishes than changes in the algal community.
This study provides the first assessment of the effects of invasive S. horneri on the distribution and behavior of rocky reef fishes. I found that fishes are less abundant in the presence of S. horneri versus Macrocystis, but this difference is more a result of algal size than of algal species. Thus, while there are notable differences in the physical structure and biology of
S. horneri versus native algae, there may not be clear negative effects on fishes. Due to its highly invasive and competitive biology, it is likely that S. horneri will persist at introduced sites and spread to additional locations (Marks et al. 2015). However, this study highlights the importance of considering algal morphology when assessing the impact of invasive algae on temperate reef fishes and suggests the use of algal size as a method of predicting relationships between fish and algae.
70
Figures
Figure 1. Experimental plot setup showing Sargassum horneri with three P. clathratus present (A). Each earth anchor was connected by 50 cm of nylon rope.
71
Figure 2. (a) Fish abundance, (b) fish species richness, and (c) frequency of fish behavior summed across species for three different algal species presented in standardized plots in Experiment 1. (Error bars represent ± 1 SE; n = 5 plots per treatment.)
72
Figure 3. (a) Fish abundance, (b) fish species richness, and (c) frequency of fish behavior summed across species on plots with short (0.5 m), medium (1.5 m), and tall (2.5 m) Sargassum horneri in Experiment 2. (Error bars represent ± 1 SE; n = 5 plots per treatment.)
73
Figure 4. (a) Fish abundance, (b) fish species richness, and (c) frequency of fish behavior summed across species on plots with Sargassum (2 m), short Macrocystis (2 m), and tall (5 m) Macrocystis in Experiment 3. (Error bars represent ± 1 SE; n = 5 plots per treatment.)
74
Figure 5. Fish abundance and species richness (no. thallus-1 ± SE) associated with thalli of five macroalgal species: Cystoseira neglecta (CYNE; n = 38), Eisenia arborea (EIAR; n = 11), Macrocystis pyrifera (MAPY; n = 14), Sargassum horneri (SAHO; n = 55), and S. palmeri (SAPA; n = 10).
75
Figure 6. Fish abundance and species richness (no. thallus-1 ± SE) at five sites: Arrow Point (n = 41), Geiger (n = 9), Two Ferns (n = 39), Howland’s Landing (n = 32), Rippers Cove (n = 7).
76
Figure 7. Relationship between fish abundance and algal height pooled across algal species. Each point represents an individual algal thallus (n = 128).
77 Table 1. Sample sizes for fish communities observed at individual algal thalli per site.
Arrow Howland's Rippers Species Big Geiger Two Ferns Overall Point Landing Cove Cystoseira neglecta 6 2 12 16 2 38 Eisenia arborea 3 1 7 0 0 11 Macrocystis pyrifera 10 0 4 0 0 14 Sargassum horneri 22 6 10 12 5 55 Sargassum palmeri 0 0 6 0 0 6
78
Figure 8. Relationship between fish species richness and algal height pooled across algal species. Each point represents a surveyed individual algal thallus (n = 128).
79
Table 2. Abundant fish species encountered on surveys of individual algal thalli and results of univariate ANOVAs testing for differences in abundance among sites and algal species; and linear regression testing the effects of algal height on abundance. Statistically significant values in bold.
80
Figure 9. Average size (cm TL) of fishes observed (a) on different algal species (thallus-1 ± SE): Cystoseira neglecta (CYNE; n = 38), Eisenia arborea (EIAR; n = 11), Macrocystis pyrifera (MAPY; n = 14), Sargassum horneri (SAHO; n = 55), and S. palmeri (SAPA; n = 10); and (b) at different sites: Arrow (n = 41), Geiger (n = 9), Two Ferns (n = 39), Howlands (n = 32), Rippers (n = 7).
81 Literature Cited Abbott, I.A. and G.J. Hollenberg. 1976. Marine Algae of California. Stanford, CA, USA: Stanford University Press. Almany, G. R. 2004. Does increasing habitat complexity reduce predation and competition in coral reef fish assemblages? Oikos 106: 275–284. Anderson, T. W., E. E. Demartini, and D. A. Roberts. 1989. The relationship between habitat structure, body size, and distribution of fishes at a temperature artificial reef. Bulletin of Marine Science 44: 681–697. Anderson, T. W. 1994. Role of macroalgal structure in the distribution and abundance of a temperate reef fish. Marine Ecology Progress Series 113: 279–290. Angel, a., and F. P. Ojeda. 2001. Structure and trophic organization of subtidal fish assemblages on the northern Chilean coast: The effect of habitat complexity. Marine Ecology Progress Series 217: 81–91. Arkema, K. K., D. C. Reed, and S. C. Schroeter. 2009. Direct and indirect effects of giant kelp determine benthic community structure and dynamics. Ecology 90: 3126–3137. Auster, P. J., D. Grenda, J. Godfrey, E. Heupel, S. Auscavitch, and J. Mangiafi. 2011. Behavioral Observations of Lilliputian Piscivores: Young-of-Year Sphyraena barracuda at Offshore Sub-Tropical Reefs (NW Atlantic Ocean). Southeastern Naturalist 10: 563–569. Auster, P. 2005. Behavior of piscivorous reef fishes varies with changes in landscape attributes and social context: integrating natural history observations in a conceptual model. Pages 115–127. Diving for science Proceedings of the American Academy of Underwater Sciences. Bartholomew, A. 2002. Total cover and cover quality: predicted and actual effects on a predator’s foraging success. Marine Ecology Progress Series 227: 1–9. Bartholomew, A. 2002. The importance of surface area versus space size relative to body size. Estuaries 25: 1045–1052. Bartholomew, A., R. J. Diaz, and G. Cicchetti. 2000. New dimensionless indices of structural habitat complexity: predicted and actual effects on a predator’s foraging success. Marine Ecology Progress Series 206: 45–58. Bell, J.D., M.Westoby and A.S. Steffe, 1987. Fish larvae settling in seagrass: do they discriminate between beds of different leaf density? Journal of Experimental Marine Biology and Ecology 111: 33–144. Bell, S., E. D. McCoy, and H. R. Mushinsky, eds. Habitat structure: the physical arrangement of objects in space. 2012. Springer Science & Business Media, Vol. 8. Bodkin, J. L. 1988. Effects of kelp forest removal on associated fish assemblages in central California. Journal of Experimental Marine Biology and Ecology 117: 227–238. Bond, N. A., M. F. Cronin, H. Freeland, and N. Mantua. 2015. Causes and impacts of the 2014 warm anomaly in the NE Pacific. Geophysical Research Letters 42: 3414–3420. Boström, C., E. L. Jackson, and C. A. Simenstad. 2006. Seagrass landscapes and their effects on associated fauna: A review. Estuarine, Coastal and Shelf Science 68: 383–403. Caley, M. J., and J. St. John. 1996. Refuge availability structures assemblages of tropical reef fishes. Journal of Animal Ecology 65: 414–428. Cameron, M. J. 2013. Relationships and interactions between temperate reef fish communities, physical habitat structure and marine protection. Masters Thesis Carr, M. H. M. H. 1991. Habitat selection and recruitment of an assemblage of temperate zone reef fishes. Journal of Experimental Marine Biology and Ecology 146: 113–137.
82 Carr, M. H. 1994. Effects of macroalgal dynamics on recruitment of a temperate reef fish. Ecology 75: 1320–1333. Carr, M. H. 1989. Effects of macroalgal assemblages on the recruitment of temperate zone reef fishes. Journal of Experimental Marine Biology and Ecology 126: 59-76 Catano, L. B., M. C. Rojas, R. J. Malossi, J. R. Peters, M. R. Heithaus, J. W. Fourqurean, and D. E. Burkepile. 2016. Reefscapes of fear: predation risk and reef heterogeneity interact to shape herbivore foraging behaviour. Journal of Animal Ecology 85:146–156 Chapman, D., M. Ranelletti, and S. Kaushik. 2006. Invasive marine algae: an ecological perspective. The Botanical Review 72: 153–178. Choat, J.H., Ayling, A.M., 1987. The relationship between habitat structure and fish faunas on New Zealand reefs. Journal of Experimental Marine Biology and Ecology 110: 257–284. Choi GH, Lee KH, Hyun IY, Kang PJ, Kim YS, Nam KW. 2008. Physiological differences in the growth of Sargassum horneri between the germling and adult stages. Journal of Applied Phycology 20: 729–735 Christie, H., N. M. Jørgensen, and K. M. Norderhaug. 2007. Bushy or smooth, high or low; importance of habitat architecture and vertical position for distribution of fauna on kelp. Journal of Sea Research 58: 198–208. Clarke, K. R. 1993. Non-parametric multivariate analyses of changes in community structure. Australian Journal of Ecology 18: 117–143. Cooper, W.E., Crowder, L.B., 1979. Patterns of predation in simple and complex environments. In: Clepper, H. (Ed.), Predator–Prey Systems in Fisheries Management. Sport Fishing Institute, Washington, DC, pp. 257–267. Coull, B.C., Wells, J.B.J., 1983. Refuges from fish predation: experiments with phytal meiofauna from the New Zealand rocky intertidal. Ecology 64: 1599–1609. Coyer, J. A. 1979. Ph.D. Thesis (University of California, Los Angeles). Crowder, L. B., and W. E. Cooper. 1982. Habitat structural complexity and the interaction between bluegills and their prey. Ecology 63: 1802–1813. Crowley M. 1978. Bering Sea. Oceans 11: 5–11 Dahl, a. L. 1973. Surface area in ecological analysis: Quantification of benthic coral-reef algae. Marine Biology 23: 239–249. Dayton, P. K., M. J. Tegner, P. B. Edwards, and K. L. Riser. 1998. Sliding baselines, ghosts, and reduced expectations in kelp forest communities. Ecological Applications 8: 309–322. Demartini, E. E., and D. A. Roberts. 1990. Effects of giant kelp (Macrocystis) on the density and abundance of fishes in a cobble-bottom kelp forest. Bulletin of Marine Science 46: 287– 300. DeMartini, E. E., and T. W. Anderson. 2007. Habitat associations and aggregation of recruit fishes on Hawaiian coral reefs. Bulletin of Marine Science 81: 139–152. DeMartini, E. E., D. A. Roberts, and T. W. Anderson. 1989. Contrasting patterns of fish density and abundance at an artificial rock reef and a cobble- bottom kelp forest. Bulletin of Marine Science 44: 881–892. Deza, A. A., and T. W. Anderson. 2010. Habitat fragmentation, patch size, and the recruitment and abundance of kelp forest fishes. Marine Ecology Progress Series 416: 229–240. Diehl, S. 1992. Fish predation and benthic community structure: the role of omnivory and habitat complexity. Ecology 73: 1646–1661. Easterling, D. R., G. A. Meehl, C. Parmesan, S. A. Changnon, T. R. Karl, and L. O. Mearns. 2000. Climate extremes: observations, modeling, and impacts. Science 289: 2068–2074.
83 Ebeling, A. W., R. J. Larson, W. S. Alevizon, and R. N. Bray. 1980. Annual variability of reef fish assemblages in kelp forests off Santa Barbara, California. Fisheries Bulletin 78: 361−367. Ebeling, A. W., and D. R. Laur. 1985. The influence of plant cover on surfperch abundance at an offshore temperate reef. Environmental Biology of Fishes 12: 169–179. Estes, J. a., E. Danner, D. F. Doak, B. Konar, A. M. Springer, P. D. Steinberg, M. T. Tinker, and Willi. 2004. Complex trophic interactions in kelp forest ecosystems. Bulletin of Marine Biology 74: 621–638. Farina, S., R. Arthur, J. F. Pagès, P. Prado, J. Romero, A. Vergés, G. Hyndes, K. L. Heck, S. Glenos, and T. Alcoverro. 2014. Differences in predator composition alter the direction of structure-mediated predation risk in macrophyte communities. Oikos 123: 1311–1322. Filbee-Dexter, K., C. J. Feehan, and R. E. Scheibling. 2016. Large-scale degradation of a kelp ecosystem in an ocean warming hotspot. Marine Ecology Progress Series 543: 141-152 Forrester, G. E., and M. A. Steele. 2004. Predators, prey refuges, and the spatial scaling of density-dependent prey mortality. Ecology 85: 1332–1342. Foster, M. S., and D. R. Schiel. 2010. Loss of predators and the collapse of southern California kelp forests: Alternatives, explanations and generalizations. Journal of Experimental Marine Biology and Ecology 393: 59–70. Foster, M., and D. Schiel. 1985. Ecology of giant kelp forests in California: a community profile. United States Fish and Wildlife Service Biological Report 85: 1–152. Fulton, C. J., and D. R. Bellwood. 2002. Patterns of foraging in labrid fishes. Marine Ecology Progress Series 226: 135–142. Gilinsky, E. 1984. The role of fish predation and spatial heterogeneity in determining benthic community structure. Ecology 65: 455–468. Graham, M. H., J. a Vásquez, A. H. Buschmann, M. Landing, M. Laboratories, M. L. Road, D. B. Marina, F. De Ciencias, and U. Católica. 2007. Global ecology of the giant kelp macrocystis : from ecotypes to ecosystems. Oceanography and Marine Biology 45: 39– 88. Graham, M. H. 2004. Effects of local deforestation on the diversity and structure of southern california giant kelp forest food webs. Ecosystems 7: 341–357. Granneman, J. E., and M. A. Steele. 2015. Effects of reef attributes on fish assemblage similarity between artificial and natural reefs. ICES Journal of Marine Science: Journal du Conseil 72: 2385–2397. Gutow, L., J. D. Long, O. Cerda, I. A. Hinojosa, E. Rothäusler, F. Tala, and M. Thiel. 2012. Herbivorous amphipods inhabit protective microhabitats within thalli of giant kelp Macrocystis pyrifera. Marine Biology 159: 141–149. Gutzwiller, K. J., and S. H. Anderson. 1992. Interception of moving organisms : influences of patch shape, size, and orientation on community structure. Landscape Ecology 6: 293– 303. Hacker, S. D., and R. S. Steneck. 1990. Habitat architecture and the abundance and body-size- dependent habitat selection of a phytal amphipod. Ecology 71: 2269–2285. Hamilton, J., and B. Konar. 2001. Implications of substrate complexity and kelp variability for south-central Alaskan nearshore fish communities. Fishery Bulletin 105: 189–196. Hansen, J., M. Sato, R. Ruedy, K. Lo, D. W. Lea, and M. Medina-Elizade. 2006. Global temperature change. Proceedings of the National Academy of Sciences of the United States of America 103: 14288–93.
84 Harley, C. D., K. M. Anderson, K. W. Demes, J. P. Jorve, R. L. Kordas, T. A. Coyle, and M. H. Graham. 2012. Effects of climate change on global seaweed communities. Journal of Phycology 48: 1064-1078 Harrod, J. J., and R. E. Hall. 1962. A Method for Determining the Surface Areas of Various Aquatic Plants. Hydrobiologia 20:173–178. Heck KL Jr, Wetstone GS. 1977. Habitat complexity and invertebrate species richness and abundance in tropical seagrass meadows. Journal of Biogeography 4: 135–142 Heck Jr., K.L., Crowder, L.B., 1991.Habitat structure and predator–prey interactions in vegetated aquatic systems. In: Bell, S.S., McCoy, E.D., Mushinsky, H.R. (Eds.), Habitat Structure: The Physical Arrangement of Objects in Space. Chapman and Hall, London, pp. 281– 299. Hobson ES, McFarland WN, Chess JR. 1981. Crepuscular and nocturnal activities of California nearshore fishes, with consideration of their scotopic visual pigments and the photic environment. US Fisheries Bulletin 79: 1–30 Hobson, E. S., and J. R. Chess. 2001. Influence of trophic relations on form and behavior among fishes and benthic invertebrates in some California marine communities. Environmental Biology of Fishes 60: 411–457. Hoegh-Guldberg, O. 1988. A method for determining the surface area of corals. Coral Reefs 7:113–116. Holbrook, S. J. S., M. H. M. Carr, R. J. Schmitt, and J. A. Coyer. 1990. Effect of giant kelp on local abundance of reef fishes: the importance of ontogenetic resource requirements. Bulletin of Marine Science 47: 104–114 Holbrook, S. J., R. J. Schmitt, and R. F. Ambrose. 1990. Biogenic habitat structure and characteristics of temperate reef fish assemblages. Australian Journal of Ecology 15: 489–503. Hovel, K. A., A. M. Warneke, S. P. Virtue-Hilborn, and A. E. Sanchez. 2016. Mesopredator foraging success in eelgrass (Zostera marina L.): Relative effects of epiphytes, shoot density, and prey abundance. Journal of Experimental Marine Biology and Ecology 474: 142–147. Jana, D., and N. Bairagi. 2014. Habitat complexity, dispersal and metapopulations: Macroscopic study of a predator-prey system. Ecological Complexity 17: 131–139. Johnson, D. 2006. Predation, habitat complexity, and variation in density-dependent mortality of temperate reef fishes. Ecology 87: 1179–1188. Johnstone, J. A., and N. J. Mantua. 2014. Atmospheric controls on northeast Pacific temperature variability and change, 1900–2012. Proceedings of the National Academy of Sciences of the United States of America 111: 14360–14365. Jones, C. G., J. H. Lawton, and M. Shachak. 1997. Positive and negative effects of organisms as physical ecosystem engineers. Ecology 78: 1946–1957. Jones, G. P. 1984. The influence of habitat and behavioral interactions on the local-distribution of the wrasse, pseudolabrus-celidotus. Environmental Biology of Fishes 10: 43–57. Jones, G. P. 1992. Interactions between herbivorous fishes and macro-algae on a temperate rocky reef. Journal of Experimental Marine Biology and Ecology 159: 217–235. Jordan, L. K. B., D. S. Gilliam, and R. E. Spieler. 2005. Reef fish assemblage structure affected by small-scale spacing and size variations of artificial patch reefs. Journal of Experimental Marine Biology and Ecology 326: 170–186.
85 Komyakova, V., P. L. Munday, and G. P. Jones. 2013. Relative importance of coral cover, habitat complexity and diversity in determining the structure of reef fish communities. PLoS ONE. 8: e831178 Konar, B., and J. A. Estes. 2003. The stability of boundary regions between kelp beds and deforested areas. Ecology 84: 174–185. Kovalenko, K. E., S. M. Thomaz, and D. Warfe M. 2012. Habitat complexity: approaches and future directions. Hydrobiologia 685: 1–17. Laegdsgaard, P., and C. Johnson. 2001. Why do juvenile fish utilise mangrove habitats? Journal of Experimental Marine Biology and Ecology 257: 229–253. Landeau L, Terborgh J. 1986. Oddity and the confusion effect in predation. Animal Behavior 34: 1372–1380. Lannin, R., and K. Hovel. 2011. Variable prey density modifies the effects of seagrass habitat structure on predator-prey interactions. Marine Ecology Progress Series 442: 59–70. Levin, P. S., and M. E. Hay. 1996. Responses of temperate reef fishes to alterations in algal structure and species composition. Marine Ecology Progress Series 134: 37–47. Levin, P. S., N. Tolimieri, M. Nicklin, and P. F. Sale. 2000. Integrating individual behavior and population ecology: the potential for habitat-dependent population regulation in a reef fish. Behavioral Ecology 11: 565–571. Levin, P. S., and M. E. Hay. 2002. Fish-seaweed association on temperate reefs: do small-scale experiments predict large-scale patterns? Marine Ecology Progress Series 232: 239–246. Lewis, L. S., and T. W. Anderson. 2012. Top-down control of epifauna by fishes enhances seagrass production. Ecology 93: 2746–57. Marks, L. M., P. Salinas-ruiz, D. C. Reed, S. J. Holbrook, C. S. Culver, J. M. Engle, D. J. Kushner, J. E. Caselle, J. Freiwald, J. P. Williams, R. Jayson, L. E. Aguilar-rosas, and N. J. Kaplanis. 2015. Range expansion of a non-native, invasive macroalga Sargassum horneri (Turner) C. Agardh, 1820 in the eastern Pacific. BioInvasions Records 4: 243– 248. Mattila, J., K. L. H. Jr, E. Millstein, E. Miller, C. Gustafsson, S. Williams, and D. Byron. 2008. Increased habitat structure does not always provide increased refuge from predation. Marine Ecology Progress Series 361: 15–20. McArdle, B. H., and M. J. Anderson. 2001. Fitting multivariate models to community data: A comment on distance-based redundancy analysis. Ecology 82: 290–297. Meager, J. J., T. a. Schlacher, and M. Green. 2011. Topographic complexity and landscape temperature patterns create a dynamic habitat structure on a rocky intertidal shore. Marine Ecology Progress Series 428: 1–12. Meehl, G.A., Stocker, T.F., Collins, W.D., Friedlingstein, P., Gaye, A.T., Gregory, J.M. et al. . 2007. Global climate projections. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (eds Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K.B., Tignor, M. & Miller, H.L.). Cambridge University Press, Cambridge, 747–845. Miller, K. a, and J. M. Engle. 2009. The natural history of Undaria pinnatifida and Sargassum filicinum at the California channel islands: non-native seaweeds with different invasion styles. Proceedings of the 7th California Islands Symposium: 131–140.
86 Mitchell, C. T., and J. R. Hunter. 1970. Fishes associated with drifting kelp, Macrocystis pyrifera, off the coast of southern California and northern Baja California. California Fish and Game 56: 288–297. Mittelbach, G. G. 1981. Foraging efficiency and body size: a study of optimal diet and habitat use by bluegills. Ecology 62: 1370–1386. Monteiro, C. A., A. H. Engelen, and R. O. P. Santos. 2009. Macro- and mesoherbivores prefer native seaweeds over the invasive brown seaweed Sargassum muticum: A potential regulating role on invasions. Marine Biology 156: 2505–2515. Moore, E. C., and K. A. Hovel. 2010. Relative influence of habitat complexity and proximity to patch edges on seagrass epifaunal communities. Oikos 119:1299–1311. Morton, D. N., and J. S. Shima. 2013. Habitat configuration and availability influences the settlement of temperate reef fishes (Tripterygiidae). Journal of Experimental Marine Biology and Ecology 449: 215–220. Morton, D. N., and T. W. Anderson. 2013. Spatial patterns of invertebrate settlement in giant kelp forests. Marine Ecology Progress Series 485: 75–89. Navarro CA (2009) Feeding rates of native herbivores on introduced and native seaweeds. M.S. Thesis, California State University, Fullerton, 59 pp Nelson, W. G., 1979. Experimental studies of selective predation on amphipods: consequences for amphipod distribution and abundance. Journal of Experimental Marine Biology and Ecology 38: 225-245. O’Connor, K. C., and T. W. Anderson. 2010. Consequences of habitat disturbance and recovery to recruitment and the abundance of kelp forest fishes. Journal of Experimental Marine Biology and Ecology 386: 1–10. Pérez-Matus Lara A. Ferry-Graham, Alfredo Cea, A., and and J. A. Vásquez. 2007. Community structure of temperate reef fishes in kelp-dominated subtidal habitats of northern Chile. Marine and Freshwater Research 58: 1069–1085. Pérez-Matus, A., and J. S. Shima. 2010. Disentangling the effects of macroalgae on the abundance of temperate reef fishes. Journal of Experimental Marine Biology and Ecology 388: 1–10. Pimental, D., McNair, S., Janecka, J.,Wrightman, J., Simmonds, C., O'Connell, C.,Wong, E., Russell, L., Zern, J., Aquino, T., Tsomondo, T., 2001. Economic and environmental threats of alien plant, animal, and microbe invasions. Agriculture, Ecosystems and Environment 84: 1–20. Polis, G. a., W. B. Anderson, and R. D. Holt. 1997. Toward an integration of landscape and food web ecology: The dynamics of spatially subsidized food webs. Annual Review of Ecology and Systematics 28: 289–316. Preisser, E. L., J. L. Orrock, and O. J. Schmitz. 2007. Predator hunting mode and habitat domain alter nonconsumptive effects in predator-prey interactions. Ecology 88: 2744–2751. Raimondi, P. T. 1990. Patterns, mechanisms, consequences of variability in settlement and recruitment of an intertidal barnacle. Ecological Monographs 60: 283–309. Rangeley, R. W., and D. L. Kramer. 1998. Density-dependent antipredator tactics and habitat selection in juvenile pollock. Ecology 79: 943–952. Reed, D., and M. Foster. 1984. The effects of canopy shadings on algal recruitment and growth in a giant kelp forest. Ecology 65(3): 937–948.
87 Reed, D. C., A. R. Rassweiler, R. J. Miller, H. M. Page, and S. J. Holbrook. 2016. The value of a broad temporal and spatial perspective in understanding dynamics of kelp forest ecosystems. Marine and Freshwater Research 67(1): 14–24 Robson BJ, Barmuta LA, Fairweather PG. 2005. Methodological and conceptual issues in the search for a relationship between animal body-size distributions and benthic habitat architecture Marine and Freshwater Research 56: 1–11 Ryer, C.H., Stoner, A.W., Titgen, R.H., 2004. Behavioral mechanisms underlying the refuge value of benthic habitat structure for two flatfishes with differing anti-predator strategies. Marine Ecology Progress Series. 268: 231–243 Sala, E., and M. H. Graham. 2002. Community-wide distribution of predator-prey interaction strength in kelp forests. Proceedings of the National Academy of Sciences of the United States of America 99: 3678–3683. Sale, P. F., J. a. Guy, and W. J. Steele. 1994. Ecological structure of assemblages of coral reef fishes on isolated patch reefs. Oecologia 98: 83–99. Savino, J. F., and R. a. Stein. 1989. Behavior of fish predators and their prey: habitat choice between open water and dense vegetation. Environmental Biology of Fishes 24: 287–293. Schmitt, F., D. Sluka, and M. Sullivan. 2002. Evaluating the use of roving diver and transect surveys to assess the coral reef fish assemblage off southeastern Hispaniola. Coral Reefs: 216–223. Schmitt, R. J., and S. W. Strand. 1982. Cooperative foraging by yellow-tail, Seriola lalandei (Carangidae), on two species of fish prey. Copeia 1982: 714–717. Schmitt, R. J., and S. J. Holbrook. 1985. Patch selection by juvenile black surfperch (Embiotocidae) under variable risk: interactive influence of food quality and structural complexity. Journal of Experimental Marine Biology and Ecology 85:269–285. Shima, J. S., C. W. Osenberg, and C. M. St Mary. 2008. Quantifying site quality in a heterogeneous landscape: recruitment of a reef fish. Ecology 89: 86–94. Simonson EJ, Metaxas A, Scheibling RE. 2015. Kelp in hot water: II. Effects of warming seawater temperature on kelp quality as a food source and settlement substrate. Marine Ecology Progress Series 537: 105−119 Sokal RR, Rohlf FJ (eds). 1995. Biometry, 3rd edn. Freeman, New York Sprague, J., K. Moore, J. Grunden, S. Ibarra, E. Mooney, G. Scheer, and D. Kushner. 2012. Channel Islands National Park Kelp Forest Monitoring Program Annual Report 2010. Natural Resource Data Series NPS/CHIS/N: 1–424. Steele, M. A. 1996. Effects of predators on reef fishes: Separating cage artifacts from effects of predation. Journal of Experimental Marine Biology and Ecology 198: 249–267. Steele, M. A. 1999. Effects of shelter and predators on reef fishes. Journal of Experimental Marine Biology and Ecology 233: 65–79. Steele, M. A., & Forrester, G. E. 2002. Variation in the relative importance of sublethal effects of predators and competitors on growth of a temperate reef fish. Marine Ecology Progress Series, 237, 233–245. Steneck, R. S., M. H. Graham, B. J. Bourque, D. Corbett, J. M. Erlandson, J. a. Estes, and M. J. Tegner. 2002. Kelp forest ecosystems: biodiversity, stability, resilience and future. Environmental Conservation 29:436–459. Stephens J. S. Jr., Morris PA, Zerba K, Love M. 1984. Factors affecting fish diversity on a temperate reef: the fish assemblage of Palos Verdes point 1974–1981. Environmental Biology of Fishes 11: 259–275
88 Stephens, J.S., Jr., Larson, R.J. & Pondella, D.J., Jr. 2006. Rocky reefs and kelp beds. In The Ecology of Marine Fishes: California and Adjacent Waters, L.G. Allen et al. (eds). Berkeley, California: University of California Press. Stewart, H., J. Fram, D. Reed, S. Williams, M. Brzezinski, S. MacIntyre, and B. Gaylord. 2009. Differences in growth, morphology and tissue carbon and nitrogen of Macrocystis pyrifera within and at the outer edge of a giant kelp forest in California, USA. Marine Ecology Progress Series 375: 101–112. Stoner A. 1979. Species-specific predation on amphipod crustacea by the pinfish Lagodon rhomboides: mediation by macrophyte standing crop. Marine Biology 55:201–207 Syms, C., and G. P. Jones. 2001. Soft corals exert no direct effects on coral reef fish assemblages. Oecologia 127: 560–571. Tanner, J. T. 1975. The stability and the intrinsic growth rates of prey and predator populations. Ecology 56: 855–867. Trebilco, R., N. K. Dulvey, H. Stewart, A. K. Salomon. 2015. The role of habitat complexity in shaping the size structure of a temperate reef fish community. Marine Ecology Progress Series 532: 197-211 Valesini, F. J., I. C. Potter, and K. R. Clarke. 2004. To what extent are the fish compositions at nearshore sites along a heterogeneous coast related to habitat type? Estuarine, Coastal and Shelf Science 60: 737–754. van Nes, E. H., and M. Scheffer. 2007. Slow recovery from perturbations as a generic indicator of a nearby catastrophic shift. The American Naturalist 169: 738–747. Vega Fernández, T., G. D’Anna, F. Badalamenti, and A. Pérez-Ruzafa. 2009. Effect of simulated macroalgae on the fish assemblage associated with a temperate reef system. Journal of Experimental Marine Biology and Ecology 376(1): 7-16 Vogt SC. 2010. Consumer food choices for native or non-native seaweeds from southern California waters. M.S. Thesis, California State University, Fullerton, 48 pp Ware, D. M. 1973. Risk of epibenthic prey to predation by rainbow trout (Salmo gairdneri). Journal of the Fisheries Research Board of Canada. 30:787-797. Warfe, A. D. M., L. A. Barmuta, and S. Wotherspoon. 2008. Quantifying habitat structure : surface convolution and living space for species in complex environments. Oikos 117: 1764–1773. Watanabe, J. 1984. The influence of recruitment, competition, and benthic predation on spatial distributions of three species of kelp forest gastropods (Trochidae: Tegula). Ecology 65: 920–936. Wernberg, T., D. A. Smale, F. Tuya, M. S. Thomsen, T. J. Langlois, T. de Bettignies, S. Bennett, and C. S. Rousseaux. 2012. An extreme climatic event alters marine ecosystem structure in a global biodiversity hotspot. Nature Climate Change 3: 78–82. Werner, E. E., and D. J. Hall. 1974. Optimal foraging and the size selection of prey by the bluegill sunfish (Lepoinis macrochirus). Ecology 55: 1042-1052. Werner, E. E., and D. J. Hall. 1979. Foraging efficiency and habitat switching in competing sunfishes. Ecology 60: 256- 264. Werner, E. E., J. F. Gilliam, D. J. Hall, and G. G. Mittelbach. 1983. An experimental test of the effects of predation risk on habitat use in fish. Ecology 64: 1540–1548. Willis, T., and M. Anderson. 2003. Structure of cryptic reef fish assemblages: relationships with habitat characteristics and predator density. Marine Ecology Progress Series 257:209– 221.
89 Willis, T., and M. Anderson. 2003. Structure of cryptic reef fish assemblages: relationships with habitat characteristics and predator density. Marine Ecology Progress Series 257: 209– 221. Wilson, S. K., C. J. Fulton, M. Depczynski, T. H. Holmes, M. M. Noble, B. Radford, and P. Tinkler. 2014. Seasonal changes in habitat structure underpin shifts in macroalgae- associated tropical fish communities. Marine Biology 161: 2597–2607. Yoshida, G., S. Arima, and T. Terawaki. 1998. Growth and maturation of the “autumn-fruiting type” of Sargassum horneri (Fucales, Phaeophyta) and comparisons with the “spring- fruiting type.” Phycological Research 46: 183–189. Zaret, T.M. (1980) Predation and freshwater communities. Yale University Press, New Haven.
90