EVOLUTION of PHENOTYPIC PLASTICITY: INSIGHTS from ECHINOID LARVAE by BENJAMIN G. MINER a DISSERTATION PRESENTED to the GRADUATE

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EVOLUTION OF PHENOTYPIC PLASTICITY: INSIGHTS FROM ECHINOID LARVAE

BENJAMIN G. MINER

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA 2003

Benjamin G. Miner

This dissertation is dedicated to my parents, who always supported my dreams.

ACKNOWLEDGMENTS

This dissertation could not have been completed and would not have been as enjoyable without the help and support of many people. I would like to first acknowledge my late advisor Larry R. McEdward for his encouragement and support. I will miss him. I also thank my current advisors, Craig Osenberg and Colette St Mary, for adopting me and challenging me to think more critically. Marta Wayne, David Julian,

Gustav Paulay, Dan Brazeau, and Kaoru Kitajima helped me to think more broadly. In addition, I thank Diana Padilla, Steven Morgan, Greg Wray, Richard Strathmann, John

Lawrence, and Richard Emlet for all their time and help.

My fellow graduate students provided an excellent learning environment. I would especially like to thank James Vonesh for almost always being louder than me, and for his contagious excitement for science. I would also like to thank my co-conspirators (Nat

Seavy, Mike McCoy, Suhel Quader, Becca Hale, Kavita Isvaran, Eric Sanford, Bruno

Pernet, Jenny Hoffman, Jason Hodin, and Marney Pratt) who helped distract me with many side projects. My 13 labmates exposed me to a variety of disciplines and organisms through their various research interests. I benefited greatly from our lab discussions. Members of the Friday Harbor Laboratories Losers Club, Jon Allen

(president), Scot Santagata (ex-president), Eric Edsinger-Gonzalez, Tansy Clay, and

Melissa Wilson, made my time at FHL much more enjoyable.

I thank the directors of the Friday Harbor Laboratories, Dennis Willows; and the

Bodega Marine Laboratories, Ernie Chang, for providing me with space. I thank the staff

iv at both institutions for their assistance. Grants in aid of research from the Society of

Comparative and Integrated Biology, and Sigma XI; and the Alan Kohn, Robert Fernald,

McLaughlin, and Grinter fellowships funded my dissertation research.

Lastly, I thank Melissa Wilson and my family for their support, encouragement, and love.

TABLE OF CONTENTS Page

ACKNOWLEDGMENTS...... iv

LIST OF TABLES ...... viii

LIST OF FIGURES...... ix

ABSTRACT...... x

1 INTRODUCTION: A BRIEF REVIEW OF PHENOTYPIC PLASTICITY...... 1

Introduction...... 1 What is Phenotypic Plasticity?...... 2 Types of Phenotypic Plasticity...... 4 Adaptive vs. Nonadaptive Phenotypic Plasticity ...... 7 Testing for Adaptive Phenotypic Plasticity ...... 11 The Evolutionary and Ecological Consequences of Plasticity...... 14

2 DIFFERENT CUES FOR DIFFERENT FOLKS: THE TIMING AND CUES OF INDUCIBLE OFFENSES IN ECHINOID LARVAE...... 16

Introduction...... 16 Materials and Methods...... 17 Results...... 23 Discussion...... 26 Pre-Feeding Response...... 26 Different Species Use Different Cues...... 27

3 EVOLUTION OF PHENOTYPIC PLASTICITY IN SEA URCHIN LARVAE: A TRADE-OFF BETWEEN ARM LENGTH AND STOMACH SIZE...... 30

Introduction...... 30 Materials and Methods...... 32 Results...... 36 Discussion...... 38

vi 4 EFFECTS OF FINE GRAIN ENVIRONMENTAL VARIABILITY ON MORPHOLOGICAL PLASTICITY ...... 43

Introduction...... 43 Materials and Methods...... 46 Results...... 50 Discussion...... 52

5 EFFECTS OF ENVIRONMENTAL VARIATION ON THE EVOLUTION OF PHENOTYPIC PLASTICITY...... 56

Introduction...... 56 The Model...... 58 Reaction Norms ...... 58 Environmental Variation...... 59 Within-Individual Variation ...... 60 Among-Individual Variation...... 61 Benefits ...... 62 Costs...... 63 Calculating Fitness...... 64 Results...... 65 Discussion...... 67

6 SUMMARY AND CONCLUSIONS ...... 72

Adaptive Plasticity in Plutei...... 72 Environmental Variation and Phenotypic Plasticity ...... 75 Future Directions...... 76 Adaptive Larval Feeding-Structure Plasticity...... 76 Environmental Variation and the Evolution of Plasticity...... 76 Other Directions...... 77

APPENDIX

A FITNESS CALCULATIONS FOR THE SLOPE OF PLASTICITY ...... 78

B FITNESS CALCULATIONS FOR THE RANGE OF PLASTICITY...... 79

LIST OF REFERENCES ...... 80

BIOGRAPHICAL SKETCH ...... 93

vii

LIST OF TABLES

Table page

2-1. Results of a nested ANCOVA ...... 23

3-1. Results of a nested 2-factor ANCOVA...... 37

6-1. Review of studies demonstrating feeding-structure plasticity in echinoids...... 74

viii

LIST OF FIGURES

Figure page

1-1. Graph of a reaction norm...... 2

2-1. Photomicrograph of a pluteus (S. purpuratus)...... 21

2-2. Response of postoral arm length in sea urchin larvae (S. purpuratus) to putative cues...... 24

2-3. Response of postoral arm length in sanddollar larvae (D. excentricus) to putative cues...... 25

3-1. Photomicrograph of a pluteus (S. purpuratus)...... 35

3-2. Response of arm length and stomach size in Experiment 1...... 38

3-3. Response of arm length and stomach size in Experiment 2...... 39

3-4. The relationship between the change in stomach size and arm length...... 41

4-1. Experimental design...... 47

4-2. Dorsal view of a pluteus showing the three morphological measurements made.. .. 48

4-3. Relationships between total arm length and time, midline body length and time, and adjusted total arm length and time...... 51

5-1. Two types of evolutionary change in plasticity...... 59

5-2. Four types of environmental variability...... 61

5-3. Evolutionary differences between the slope and range of plasticity...... 65

5-4. Effect of just the benefit function...... 66

5-5. Effect of the cost function...... 68

5-6. Example of how changes in the range of plasticity can be confounded with changes in the slope of plasticity...... 70

Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

EVOLUTION OF PHENOTYPIC PLASTICITY: INSIGHTS FROM ECHINOID LARVAE

Benjamin G. Miner

December 2003

Cochair: Colette M. St. Mary Cochair: Craig W. Osenberg Major Department: Zoology

Within the last 30 years, biologists have become increasingly interested in the ability of the environment to influence the phenotype of an organism (i.e., phenotypic plasticity). An example is the larvae of sea urchins and sand dollars (plutei). Plutei respond to different concentrations of food by producing long feeding structures when food is scarce and short feeding structures when food is abundant. Although the match between the inducing agent (food) and the induced phenotype (feeding structure) suggests that plasticity is adaptive, few experiments test this hypothesis. In Chapters 2 and 3, I investigate several of the requirements for plasticity to be considered adaptive in plutei.

In Chapter 2, I demonstrate that larvae of two echinoid species respond morphologically to food concentrations before ingestion of food, but that the cues that elicit the response are different for each species. The use of different cues by the two species raises the question of whether feeding structure plasticity has evolved

x independently in larvae of sea urchins and sand dollars. In Chapter 3, I demonstrate that there is a trade-off between the development of long arms and large stomachs. The benefits of long arms have long been known, and this trade-off provides an explanation for why feeding-structure plasticity might have evolved— when food is scarce there is an advantage to increasing allocation to long arms because capturing food is limiting growth; whereas when food is abundant there is a advantage to increasing allocation to the stomach because assimilating food is limiting growth.

In Chapter 4, I document the response of plutei to the amount of variation in food concentrations experienced by an individual. This previously unrecognized morphological response to the amount of environmental variation suggests that plasticity might evolve under conditions previously thought to favor a fixed phenotype. In

Chapter 5, I present the results of a mathematical model, with which I explore how changes in the amount of environmental variation might affect the evolution of plasticity.

Results suggest that the shape of a reaction norm is more sensitive than the slope to changes in the amount of long-term environmental variation.

xi CHAPTER 1 INTRODUCTION: A BRIEF REVIEW OF PHENOTYPIC PLASTICITY

Introduction

During the last century, major advances were made in our understanding of evolution (Fisher 1930, Wright 1969, Maynard Smith 1972, Mayr 1972, Gould 1977,

Giddings 1989). Much the work during the “modern synthesis” was based on the idea that genes specified fixed phenotypes; and phenotypic variation or polymorphisms within populations or species were attributed to genetic differences among individuals with different phenotypes. However, research has demonstrated that phenotypes are not fixed, and can be influenced by the environment in which an organism occurs (Clausen et al.

1940, Bradshaw 1965, Schlichting 1986, Harvell 1990, 1994, Karban & Baldwin 1997,

Schlichting & Pigliucci 1998, Tollrian & Harvell 1999, Pigliucci 2001, West-Eberhard

2003). For example, genetically identical individuals (i.e., clone-mates) of certain species of the cladoceran Daphnia produce large helmets when reared with predators and small helmets when reared without (Dodson 1974, Havel 1985). This phenomenon of the environment specifying some aspect of an individual’s phenotype is called phenotypic plasticity.

Several questions arose from the realization that phenotypic plasticity is common in nature. First, can phenotypic plasticity evolve, and if so how? Second, how is natural selection affected by phenotypic plasticity, given that phenotypic variation does not necessarily reflect genetic variation? Lastly, how does phenotypic plasticity affect

1 2 ecological processes? In this chapter, I review the literature on phenotypic plasticity and explore these questions (some more thoroughly than others).

What is Phenotypic Plasticity?

Phenotypic plasticity is defined as the ability of the environment to influence the phenotype of an individual (Bradshaw 1965). The term phenotypic plasticity does not imply that the response is adaptive. This definition of plasticity is illustrated graphically with reaction norms (Schlichting & Pigliucci 1998). A reaction norm is the relationship between the phenotype and the environment (Woltereck 1909, Schmalhausen 1949).

Phenotypic plasticity occurs when the value of the phenotype is different in different environmental conditions (i.e., slope ≠ 0) (Fig. 1-1). It is worth noting that reaction norms also represent non-plasticity when the reaction norm is linear with a slope of 0

(Fig. 1-1).

So what is not considered phenotypic plasticity? There are two types of phenotypic variation that are not examples of plasticity. The first is phenotypic variation among Phenotype

Environment

Figure 1-1. Graph of a reaction norm. Solid lines represent 3 examples of plasticity. Dotted line represents a fixed phenotype that is not plastic.

3 individuals that results from the underlying genetic differences in those individuals. The second is phenotypic variation that results from developmental errors, referred to as development noise by Waddington (1957). The differences resulting from developmental noise are considered random and not necessarily the result of the environment (Bradshaw

1965).

Although the above definition of phenotypic plasticity is accepted by most biologists and seems straightforward, there is often debate about what is and is not plasticity. Confusion about plasticity can arise from the term phenotype (defined by

Johannsen in 1911). Because a phenotype is often made up of many characters and is the result of developmental and physiological processes, the term phenotype can represent many things. Thus, it is important to clearly specify the phenotype in question.

Environmental tolerance, canalization, and homeostasis often lead to confusion about plasticity when the phenotype is ambiguous. Environmental tolerance is often equated with phenotypic plasticity (Gabriel & Lynch 1992, Gabriel 1999). However, environmental tolerance does not represent any aspect of an organism’s phenotype, but instead represents the ability of an organism to perform in a range of environmental conditions. Yet, the underlying mechanisms that result in environmental tolerance may represent plasticity (but not necessarily). Tolerance represents plasticity when an organism’s morphology, behavior, or physiology is altered in different environments. An example of this is when different amounts of a heat shock protein (hsp) are expressed, or different hsps are expressed under different temperatures (Lindquist 1986, Buckley et al.

2001). However, tolerance does not represent plasticity when morphology, behavior, and physiology are fixed but perform well in a wide range of environments. As with

4 tolerance, canalization (Waddington 1942) and homeostasis are confused with plasticity.

However, both actually describe situations where a trait is invariant in different environmental conditions. Again, confusion arises because the mechanisms that cause canalization or homeostasis can be plastic. Thus, clearly defining the phenotype is important to avoid confusion.

Types of Phenotypic Plasticity

Often a more specific term than phenotypic plasticity is used to identify a response to different or changing environments. These more specific terms are helpful because the term phenotypic plasticity is so broad. There are three common ways in which phenotypic plasticity is more specifically defined: 1) the type character involved in the response is more narrowly defined (e.g., life history, behavior, morphology, physiology, etc.); 2) the putative function of the response is specified (e.g., inducible defense or inducible offense); and 3) the developmental mechanisms of the response (e.g., a developmental switch, or phenotypic modulation). The drawbacks of these subcategories are that similarities among them are overlooked and an integration of different disciplines might be limited. The benefits are that more information is provided by a more specific term, and differences among subcategories or similarities within a subcategory are more easily addressed.

Much of the work on phenotypic plasticity, which began in the 1960s and 70s, has focused on inducible defenses (Gilbert 1966, Cook & Johnson 1967, Dodson 1974).

Inducible defenses are defined as any type of defense that is induced by an environmental cue (Harvell 1990). A few examples are noxious chemicals produced by plants when attacked by herbivores (Karban & Myers 1989), changes in a prey’s calcium carbonate structure when predators are present (Harvell 1984, Lively 1986a, Appleton & Palmer

1988), and changes in swimming behavior of plankton when predators are present (De

Meester et al. 1999).

In contrast to inducible defenses, inducible offenses have received less attention

(Karban & Agrawal 2002) (though the plant literature might be an exception). Inducible offenses are defined as any type of environmentally induced response that increases the acquisition of resources. An example in plants is heterophylly. Some species of plants produce different-shaped leaves depending on whether leaves are in or out of water

(Cook & Johnson 1967, Winn 1999, reviewed by Wells & Pigliucci 2000). It is assumed that the different leaf shapes are better suited for acquiring light or nutrients in the respective environments, though this is not always the case (Winn 1999). An example in animals is the morphological response to food in the marine gastropod Lacuna (Padilla

1998). Juveniles produce different-shaped radular teeth depending on the type of food they encounter.

Although responses are typically labeled as defenses or offenses, they are not mutually exclusive. It is possible that a particular induced phenotype can improve both defenses and resource acquisition. For example, sea urchin and sand dollar larvae produce larger feeding structures by increasing the length of calcium carbonate skeletal rods in response to low food concentrations (Boidron-Mètairon 1988). Larger feeding structures allow larvae to capture more food (Hart and Strathmann 1994). It is also possible, though not tested, that longer skeletal rods decrease mortality by predators— long spines in other types of larvae probably deter predators (Morgan 1992, 1995). Thus, a larger feeding structure might improve both resource acquisition and defenses.

Alternative phenotypes can also have different functions, where one phenotype improves

6 the acquisition of resources and the other improves defenses. For example, tadpoles respond to predators by increasing the size of their tail relative to the body (a defense;

Van Buskirk and Relyea 1998), but this increase in defense decreases competitive ability

(Relyea 2002, Relyea & Hoverman 2003). Thus, one morphology is considered the defensive morphology and the other the competitive morphology.

Terms that describe the underlying mechanisms of a response are less common in the literature (probably because in most cases the mechanism is unknown). In her review of developmental plasticity, Smith-Gill (1983) identified two mechanisms that would result in plasticity. The first, termed developmental conversion, describes a situation where the environment trips a developmental switch, which results in a phenotypic change. The products of developmental conversion are discrete phenotypes—in other words, a reaction norm described by a step function. The second, termed phenotypic modulation, describes a situation where the environment alters rates of reactions. The product of phenotypic modulation is a continuous response to the environment. The major difficulty with these terms is that they were defined by the mechanisms presumed to produce the shape of a response (e.g., a linear response to the environment). However there was and still is no evidence that suggests that continuous reaction norms or reaction norms described with a step function are controlled by the proposed mechanisms. In addition, the mechanisms of phenotypic plasticity are often complex and involve many physiological and developmental processes (Pigluicci 1996).

Finally, two additional terms discussed by Stearns (1989) more specifically describe phenotypic plasticity. The first is polyphenism and is similar to the term developmental conversion, but does not imply anything about the developmental

7 mechanisms that underlie the response. Polyphenisms are defined when two or more discrete phenotypes are produced in response to environmental conditions. An example is the African butterfly Bicyclus anynana, which produces two distinct phenotypes depending on the environment. In the dry season, buttlerflies are a drab brown and inactive; but during the wet season they are more brightly colored, more active, and have larger eyespots (Brakefield & Larsen 1984). The second term is phenotypic flexibility, and describes the situation where an individual can change or reverse its phenotype throughout its life or during a life-history stage. For example, some species of birds completely absorb their digestive track during long migrations, but then grow new digestive tracks upon reaching their destination (Biebach 1998, Piersma 1998).

Adaptive vs. Nonadaptive Phenotypic Plasticity

There are four requirements for selection to favor or maintain plasticity (Bradshaw

1965, Harvell & Tollrian 1999): 1) the environment must be variable, 2) there must be some cue that provides information about the current or future state of the environment,

3) the plastic response must be heritable, and 4) alternative phenotypes must have greater fitness in different environmental conditions.

Plasticity can evolve in environments that vary spatially, temporally, or both

(Levins 1963, 1968, Via & Lande 1985, Lively 1986b, 1999, de Jong 1990, 1995, Moran

1992, Gavrilets & Scheiner 1993, Via et al. 1995, Tufto 2000, Sultan & Spencer 2002).

The spatial-temporal scales of environmental variation in combination with the spatial range and lifetime of an organism translate into evolutionarily relevant types of variation typically called fine- and coarse-grain variation (Levins 1968), or within-individual and among-individual environmental variation, respectively. Fine-grained variation results when an individual experiences different environmental conditions within its lifetime.

For example, a sessile individual can experience fine-grained variation if the environment changes temporally within an individual’s lifetime, or when a non-sessile individual moves between patches that differ in some environmental condition. In contrast, coarse- grained variation results when related individuals experience different environmental conditions, but an individual does not experience environmental variation. Course- grained variation can also result from spatial or temporal variation in environmental conditions. Examples include a mother depositing offspring into ponds with different conditions, or successive generations experiencing different conditions in a pond.

The time scale of environmental variation is important for the evolution of plasticity. When the time-scale in which the environment fluctuates is long relative to the generation-time of an organism, then selection is expected to favor a fixed phenotype that is specialized for the current environment (van Tienderen 1991, Berrigan & Koella 1994, van Tienderen & Koelewijn 1994, de Jong 1995, Sibly 1995, Scheiner 1998, but see

Sultan & Spencer 2002 for a counter example). Selection is also expected to favor a fixed phenotype when environmental conditions fluctuate relatively quickly compared to the time required by an individual to change its phenotype, so that an organism cannot produce the appropriate phenotype before the environment changes (Padilla & Adolph

1996). This problem of lag time is most relevant for inducible morphologies that typically require days to weeks to change, compared to behavioral responses which are often more rapid.

Although important for the evolution of plasticity, the grain of the environment is rarely explicitly discussed in empirical studies, yet it can affect the interpretations of plasticity experiments. Almost all plasticity studies rear individuals in different constant

9 environments, representing the case of coarse-grain variation (e.g., Boidron-Mètairon

1988, Smith and Palmer 1994, Reimer and Tedengren 1996, McCollum and Leimberger

1997, Padilla 1998, Lorenzon et al. 1999, Mittelbach et al. 1999). However, if plasticity has evolved in response to fine-grained variation then it is unclear how to interpret these experimental results. Furthermore, the lack of attention to environmental grain has also limited our understanding of the type of grain for which plasticity is adapted (Winn

1996).

Information about the current or future state of the environment must be available to an organism, if selection is to favor plasticity. Models initially suggested that very reliable cues were required. Thus the evolution of plasticity in many organisms was thought to be limited by the lack of such reliable cues (Lively 1986b, Harvell 1990,

Moran 1992). However, more recent models suggest that plasticity will still evolve even when cues are less reliable, as long as some information is gained from cues (Getty 1996,

Lively 1999, Tufto 2000, Sultan & Spencer 2002). The degree of reliability required is in part dependent on the net benefits of plasticity. In cases where there is a large net benefit from plasticity, less reliable cues are required for selection to favor plasticity; whereas when the net benefit is small, more reliable cues are required (Getty 1996). One aspect of cue reliability that is rarely addressed is how reliability changes through time. The element of time is important because in many cases of plasticity, a response needs to occur prior to the environmental change. As the time needed for phenotypic change increases, so does the need for cues that provide reliable information about future environments (anticipatory plasticity, Schlichting & Pigliucci 1995). The reliability of a cue is therefore best represented as a function of time. When cues are not reliable and

10 selection opposes plasticity, but the environment still varies, alternative strategies are expected to evolve—like adaptive coin flipping (Kaplan & Cooper 1984, Clauss &

Venable 2000).

Although plasticity is considered to be a response to the environment, there must be a genetic component in order for natural selection to act on plasticity. Many studies have demonstrated genetic variation for plasticity, and that plasticity can be altered by artificial selection (reviewed by Scheiner 2002). Although natural selection almost certainly affects plasticity, the target of selection is still unclear. Two opposing views are common in the literature (Via 1993a,b, Schlichting & Pigliucci 1993, Scheiner 1993, de Jong

1995, reviewed by Via et al. 1995). The first suggested by Falconer (1952) is that selection acts upon the correlation structure among different characters. The second, suggested by Schmalhausen (1949) suggests that selection acts upon the reaction norm.

Although these two opposing views represent a rift among plasticity biologists, in most cases different conclusions about the evolution of plasticity do not depend on the different views (Via et al. 1995), and over the last 10 years most studies have focused on reaction norms (reviewed in Pigliucci 2001 Ch. 10).

Finally, there must be a trade-off in fitness between the alternative phenotypes produced in response to different environments—i.e., the most fit phenotype in one environment cannot be the most fit phenotype in all environments. Otherwise, selection will favor the phenotype that performs best in all environments (Levins 1963, 1968, Via

& Lande 1985, Lively 1986b, 1999, de Jong 1990, 1995, Moran 1992, Gavrilets &

Scheiner 1993, Via et al. 1995, Tufto 2000, Sultan & Spencer 2002). A common type of trade-off is the allocation of energy to different traits. In situations where a larger

11 energetically expensive structure increases fitness in an environment, producing a smaller structure when the condition is absent saves energy. Other types of trade-offs have also been documented. Agrawal and Karban (1999) reviewed several alternative trade-offs that have been reported in the plant-insect literature (several of which also apply to other systems). An obvious example is when alternative morphologies each perform better in their respective environments, but cannot be produced simultaneously. This is probably the case in several species of marine gastropods. Juvenile snails of the genus Lacuna produce different-shaped radular teeth when reared on different types of food (Padilla

1998). Sharp teeth are produced in the presence of kelp, and appear well designed to excavate kelp tissue. Blunt teeth are produced in the presence of diatoms, and seem well designed to scrape the algal film from rock surfaces. However, a sharp tooth cannot also be blunt. Since the amount of material required for either type of tooth structure is very similar, allocation costs seem unlikely.

Testing for Adaptive Phenotypic Plasticity

Determining whether phenotypic plasticity is adaptive is difficult. In many cases, there is a match between the inducing agent and the responding phenotype (e.g., individuals produce different feeding structures in response to different food concentrations). This match suggests that plasticity is adaptive, but is far from conclusive. Probably the most common approach for testing whether plasticity is adaptive is to experimentally test for a fitness trade-off between alternative phenotypes

(e.g., Harvell 1986, Lively 1986c, Nunney & Cheung 1997, Van Buskirk & Relyea

1998). Individuals are typically reared in two environmental conditions until the alternative phenotypes are induced. Each of the alternative phenotypes is then placed in both environmental conditions, so that all 4 phenotype-environment combinations are

12 represented (e.g., phenotype 1 in environment 2). The performance (some estimate of fitness) is then compared between the two alternative phenotypes in a given environmental condition. Plasticity is considered adaptive when a phenotype induced by an environmental condition performs better in that condition than an alternative phenotype induced by another environmental condition.

Unfortunately, there are several problems with interpreting the results from this experimental design. Researchers interpret the differences between the treatments to be caused by differences in performance by the different phenotypes. However, these results might be caused by a cost due to switching environments. Recall that individuals are initially induced in one of the two environmental conditions. Then, some individuals are placed in their native environment, whereas others are switched to the other environmental condition. If there is a cost to switching, which seems reasonable if individuals are physiologically stressed or individuals begin to adjust their phenotype to the new environmental condition, then the differences among treatments are confounded and it is not possible to determine whether a fitness trade-off exists. Individuals that are switched from one environment to the other, and may pay a cost of switching, are also the individuals with the phenotype that is mismatched during the performance part of the experiment and should perform less well. Although studies that address this issue are rare, Stockhoff (1992) demonstrated that there can be a cost to diet switching.

Interpretation problems also arise when differences between treatments suggest that plasticity is maladaptive—i.e., a phenotype induced in a particular environmental condition performs worse in that condition than an alternative phenotype that is induced by another environmental condition. The interpretation problem arises from differences

13 between the initial environments used to induce each phenotype (Woods & Harrison

2002). If the initial environmental conditions affect individuals differently, then it is not possible to determine whether differences between treatments arise from differences in performance of the phenotypes or from differences in the initial environment. Currently, it is unclear whether the effects of switching environments or of initial environments are relatively large compared to the performance effects of the phenotypes. Thus, it is difficult to interpret previous studies of adaptive plasticity.

Several alternative approaches to testing for adaptive plasticity have been suggested recently. Schmitt et al. (1999) proposed that genetic manipulations and genetic engineering might provide insights into whether plasticity is adaptive, although no studies have yet adopted this approach. Phylogenetic comparative methods (Harvey & Pagel

1991, Martins & Hansen 1997, Martins 2000) are another approach that can provide evidence for adaptive plasticity (Pigliucci et al. 1999, Pollard et al. 2001, Van Buskirk

2002a). This approach will be most informative when there are clear hypotheses about the expected relationships between plasticity and the correlate of interest. In addition, meta-analysis (Osenberg et al. 1999) can provide another important tool for studying adaptive plasticity. With meta-analysis, the response to environmental conditions can be standardized among studies. By combining comparative methods with meta-analysis, many more studies can be incorporated and thus provide more information about whether plasticity is adaptive.

Given the difficulties with testing for adaptive plasticity, many different lines of evidence are required before plasticity can be considered adaptive. Potential sources of evidence include experiments on the cues that elicit a response, the trade-offs among the

14 alternative phenotypes, the function or benefit of alternative phenotypes, the costs of plasticity, genetic/developmental mechanisms of the response, and natural history.

Evolutionary and Ecological Consequences of Plasticity

Theoretical and empirical evidence suggests that plasticity evolves and is a target of selection (discussed above). So, how does plasticity alter evolutionary processes? There is debate over whether plasticity actually accelerates or suppresses evolutionary change

(West-Eberhard 1989, Whitlock 1996, Ancel 2000, Agrawal 2001, Price et al. 2003).

Two verbal models state that plasticity will accelerate evolution because plasticity allows individuals to persist in novel environments, during which time natural selection will rapidly change the population (Agrawal 2001, Price et al. 2003). Through this process, plasticity could facilitate evolutionary change and might lead to higher speciation rates.

However, other models demonstrate that plasticity can slow evolutionary change when the environment is course-grained. One model (Whitlock 1996) suggests that a population that is specialized for a narrow range of environments is more likely to fix beneficial alleleles compared to a plastic population that inhabits a wider range of environments. As a result, the non-plastic population can evolve more rapidly to change, and will accumulate fewer deleterious mutations. A second model (Ancel 2000) argues that plasticity can both accelerate and suppress evolutionary change, depending on the net benefit of plasticity relative to non-plasticity.

Plasticity also has important consequences for ecological processes. The most studied example is trait-mediated indirect interactions (Werner & Anholt 1996, Pecor &

Werner 1997, 2001, Anholt & Werner 1999, Raimondi et al. 2000, Langerhans & DeWitt

2002, Relyea & Yurewicz 2002, Trussell et al. 2002). Behavioral and morphological responses of one species to other members of the community affect food web dynamics.

For example, frog tadpoles reduce their amount of foraging for food (periphyton) in the presence of predators. Thus, predators indirectly influence the amount of periphyton because tadpoles consume less when predators are present (Pecor & Werner 2001). It is also likely that plasticity might explain in part non-additive effects in survivorship

(reviewed by Sih et al. 1998)—the independent effects of predators in isolation are different from the effects of predators in combination. Non-additive effects on survivorship are currently explained by interactions between predators (e.g., interference, or facilitation). However, non-additive effects might result if a prey’s response is not the same when only one type of predator is present compared to when two or more types of predators are present.

CHAPTER 2 DIFFERENT CUES FOR DIFFERENT FOLKS: THE TIMING AND CUES OF INDUCIBLE OFFENSES IN ECHINOID LARVAE

Introduction

Many organisms are challenged with surviving in environments that vary both spatially and temporally. Some organisms have met this challenge by producing different phenotypes that are specific to different environmental conditions (phenotypic plasticity)(Bradshaw 1965, Karban & Baldwin 1997, Schlichting & Pigliucci 1998,

Tollrian & Harvell 1999, Pigliucci 2001, West-Eberhard 2003). To evolve phenotypic plasticity, an organism must be able to gain information about environmental conditions via cues (Harvell 1990). In aquatic species with inducible defenses, individuals typically respond to either chemicals emitted from predators or contact with a predator (e.g.,

Gilbert 1966, Krueger & Dodson 1981, Harvell 1986, Lively 1986a, Appleton and

Palmer 1988, Brönmark and Pettersson 1994, McCollum & Leimberger 1997, Leonard et al. 1999, Langerhans & DeWitt 2002). However, little is known about the cues that elicit another common type of phenotypic change (inducible offenses) in aquatic species.

Inducible offenses are defined as plasticity that improves the acquisition of resources

(Karban & Agrawal 2002).

Studies have documented inducible offenses in larvae of asteroids (bipinnaria;

George 1994, 1999), ophiuroids (ophioplutei; Strathmann unpublished data, Miner unpublished data), echinoids (echinoplutei; Boidron-Mètairon 1988, Hart and Scheibling

1988), and gastropods (veligers; Strathmann et al. 1993, Estrella Klinzing and Pechenik

16 17

2000). These larvae all exhibit a similar response to food conditions. Larvae produce a long band of cilia when food is scarce, and a short band of cilia when food is abundant— ciliary bands are used by larvae to swim and capture particles (Strathmann 1971,

Strathmann 1987, Gallager 1988, Hart 1991). Only one author has investigated the cues that elicit a change in the size of larval feeding structures, and concluded that plutei use dissolved organic compounds, like amino acids and glucose, to detect food conditions

(Shilling 1995). However, it is difficult to interpret the results because beakers were not replicated (i.e., only 1 beaker was used for each treatment), and therefore treatment and beaker effects cannot be disentangled. In addition, larval size was not measured, and therefore it is not possible to determine whether differences in arm length were due to differences in overall growth or plasticity in shape.

In this study, I investigated three general types of cues plutei might use to detect food conditions: 1) chemicals associated with algae, 2) physical contact with particles of similar size to algae, and 3) the presence of actual algal cells. I also tested whether plutei can morphologically respond prior to the ability to feed because larvae of echinoderms and gastropods that morphologically respond to food concentrations develop from eggs fertilized in the water column and are exposed to food conditions before larvae can capture particles.

Materials and Methods

To determine the cues that elicit a phenotypic response in plutei, I studied two species of echinoid, Strongylocentrotus purpuratus, a sea urchin, and Dendraster excentricus, a sanddollar. Initial experiments on each species were performed at different locations, and because I obtained different results for each species, I re-ran the

18 experiments with a similar experimental design at the same location to verify the initial results.

The initial experiment on S. purpuratus was performed at the Bodega Marine

Laboratory (BML), University of California, Davis during the spring of 1999. Divers collected adult urchins subtidally off the coast of Santa Barbara, California in February

1999. I transported these urchins to BML where they were held in tanks with running seawater and were fed the kelp Macrocystis pyrifera. The initial experiment on larvae of

D. excentricus was performed at the Friday Harbor laboratories (FHL), University of

Washington during the summer of 1999. Adult sanddollars were collected from Crescent

Beach, Orcas Island, Washington during spring and summer 1999 and held in flow through sea tables with sand. Follow-up experiments on both species were performed at

FHL later in the summer of 1999. Adult sanddollars were used from the same collections as the initial experiment. Adult sea urchins were obtained from submerged containers attached to the FHL dock. These urchins were collected at least 4 months before my study, maintained in these containers, and fed the kelp Nereocystis luetkeana.

In all experiments, I induced adults to spawn by injecting 0.5 M KCl into their body cavities (Strathmann 1987). For each experiment, eggs of 1 female were fertilized with sperm from 1 male (fertilization was > 90% in all experimental runs), after which I rinsed the embryos with Millipore®-filtered (0.45 µm) seawater (FSW). The embryos were held in glass beakers with 2 L of FSW for 1 day until they hatched. Hatched blastulae were placed in plastic beakers with 1 L of fresh FSW at densities of

≈ 2 larvae ml-1. Beakers were randomly assigned a location on the seawater table and a

19 treatment. Throughout development, larvae were maintained at ambient ocean temperatures (≈ 11-13oC) by placing beakers on a wet table with running seawater.

In the initial experiments, three of the four treatments exposed larvae to different putative cues: algal contact, chemical contact, and physical contact. The fourth treatment, no cue, was the control. For the algal-contact treatment, larvae were given the unicellular alga Dunaliella tertiolecta (6 cells/µL). For the chemical-contact treatment, larvae were given algal supernatant. Algal cells were centrifuged into a pellet, the algal culture medium was removed, and cells were re-suspended in FSW with the same volume of decanted algal culture medium. After 1 hour the algal cells were removed via centrifugation, and the supernatant was added to the appropriate beakers. For the physical-contact treatment, larvae were given plastic beads similar in diameter

(polystyrene beads, 4.5 µm in diameter, from Duke Scientific Corp.) to the algal cells

(6 beads/µL). For the control treatment, nothing but FSW and larvae were added to the beakers. An additional algal-contact treatment was added to the initial experiment on

D. excentricus. For this treatment, larvae were given another species of unicellular alga,

Rhodomonas lens (6 cells/µL), to determine whether plutei responded differently to the two algal species. In the second set of experiments, I removed the physical contact treatment because there was no evidence from the initial experiments that larvae used physical contact as a cue, and exposed larvae to only three treatments: algal contact

(D. tertiolecta), chemical contact, and control.

Although there are a number of ways in which treatments might differ, the following differences provide easily interpretable conclusions about the cues that elicit a plastic response. If larvae from only the algal-contact treatment have significantly

20 shorter arms than larvae from the control treatment, and there is no difference among the control and other treatments, then algal contact is implicated as the cue. If the average postoral arm length in both the chemical- and algal-contact treatments are similar and less than the control, and all other treatments are similar to the control, then chemical cues are implicated. Lastly, if postoral arm length in both the physical- and algal-contact treatments are similar and less than the control, and all other treatments are similar to the control, then physical contact is implicated as the cue.

To determine whether plutei can respond prior to the ability to feed, I exposed larvae to the experimental treatments for 3 days. Because the experiment began with day-old hatched blastulae, larvae were 4 days old at the end of the experiment. For both

S. purpuratus and D. excentricus, larvae are first able to ingest particles when they are approximately 5 days old at the temperatures of these experiments (Strathmann 1987,

B.G. Miner unpubl. data). Since, a few larvae had particles in their stomachs at the end of the initial experiment, I ran the second experiment for a shorter duration (1/2 day less).

At the end of the second experiment, I did not observe any food in the stomach of any larva.

For all experiments, I used a nested experimental design with replicate larvae in each of the replicate beakers, where beakers were nested in treatment. Except in the initial experiment with D. excentricus, I replicated each treatment with 4 beakers. In the initial experiment on D. excentricus, I used 3 beakers per treatment. All beakers were stirred with paddles similar to that described by Strathmann (1987) to maintain algal cells and beads in suspension, and treat all beakers the same. After 2 days (i.e., larvae were

3 days old), I changed the water in each beaker by siphoning 90% of the water through

Nytex mesh (40x60 µm), which allowed algal cells and beads to be removed but not the larvae. I refilled each beaker with FSW and added the appropriate putative cue again.

Although I did not quantify larval survivorship, I observed < 10 dead larvae or disembodied skeletons in a beaker (approximately 200 larvae were placed in each beaker) during the water change and at the end of the experiment, suggesting that larval survivorship was very high.

At the end of each experiment, approximately 20 larvae were haphazardly removed from each beaker and preserved with 1% formalin. Within 12 hours, I measured the length of the skeletal rods in the postoral arm and body (left side) of each larva

(dorsal view; Fig. 2-1). Fifteen larvae per beaker were measured in the initial experiment on S. purpuratus. However, in light of the relatively small amount of variance among

Post-oral arm length

Bodyrod length

Figure 2-1. Photomicrograph of a pluteus (S. purpuratus) illustrating that postoral arm length was measured from the tip of the postoral arm skeleton to where it intersects with the transverse bodyrod and that bodyrod length was measured from its tip to where it intersects the transverse bodyrod.

22 measurements within a beaker, I only measured 6 larvae per beaker in the 3 other experiments. The length of the postoral arm provides a good estimate of the length of the ciliary band, and the length of the bodyrod provides a good estimate of larval size for the early developmental stage I investigated (4-armed larvae) (Strathmann et al. 1992,

McEdward and Herrera 1999). Measurements were made with a compound microscope and an ocular micrometer (20x).

To analyze the data of an experiment, I log-transformed all data and used a nested

ANCOVA with ln(bodyrod length) as the covariate to compare ln(postoral arm length) among treatments and beakers. A nested model was used because larvae from a beaker were not independent. I used an ANCOVA to correct for larval size because plutei can take up dissolved organic matter (Manahan et al. 1983, Shilling & Bosch 1994), which could affect the growth of larvae in different treatments. With this analysis, I can determine whether differences among treatments are a result of arm length plasticity or larval growth. I first tested for homogeneity of slopes among all beakers of an experiment, and in all 4 experiments slopes were not significantly different (p-values for the test of homogeneity of slopes: S. purpuratus initial 0.11, second 0.66, and

D. excentricus initial 0.91, second 0.97). Therefore, the treatment-covariate interaction term was removed from the final nested ANCOVA model, which had the following terms: treatment, beaker(treatment), ln(bodyrod length), and a constant. To test for differences in all pair-wise comparisons, I used the post hoc Tukey test with the beaker(treatment) mean square and degree of freedom for the error term.

Results

In all experiments, postoral arm length varied significantly among treatments

(Table 2-1, Figs. 2-1 and 2-2). With the exception of the second experiment on

S. purpuratus there was no significant effect of bodyrod length (i.e., larval size)

(Table 2-1). Thus, differences in arm length are the result of plasticity and not differences in overall growth. However, I retained the covariate in the nested ANCOVA model, so that any estimates of treatment effects are adjusted for differences in size (even though such effects appear to be weak and were not detectable).

The differences among treatments indicate that cues induced a plastic response in postoral arm length prior to the ability of larvae to consume particles. In all experiments, larvae from at least one treatment produced shorter postoral arms than larvae from the

Table 2-1. Results of nested ANCOVA testing for the effects of treatment (i.e., type of cue present), beaker (a random effect nested in treatment), and ln(bodyrod length) on ln(postoral arm length) for each of the 4 experiments on larvae of the sea urchin (S. purpuratus) or the sanddollar (D. excentricus). Effect df F-ratio p-value S. purpuratus Initial Experiment treatment 3, 12 40.71 < 0.0001 beaker(treatment) 12, 223 1.07 0.38 ln(bodyrod) 1, 223 0.28 0.60 Second Experiment treatment 2, 9 10.49 0.004 beaker(treatment) 9, 59 1.16 0.34 ln(bodyrod) 1, 59 4.27 0.043 D. excentricus Initial Experiment treatment 4, 10 9.33 0.002 beaker(treatment) 10, 74 0.76 0.67 ln(bodyrod) 1, 74 0.77 0.38 Second Experiment treatment 2, 9 8.814 0.008 beaker(treatment) 9, 59 3.38 0.002 ln(bodyrod) 1, 59 0.36 0.55

24 control (Figs. 2-2 and 2-3)—larvae from the algal-contact treatment had smaller arms than controls in all experiments (Figs 2-2 and 2-3).

Although both species responded prior to their ability to feed, they responded to different cues. In both experiments on S. purpuratus, larvae from the algal treatment

a 130 A 125

120

115 b 110

105 Post-oral arm length (microns) length arm Post-oral 100 Control Physical Chemical Algal

100 a B 95

90 b 85

75 Post-oral arm length (microns) length arm Post-oral 70 Control Chemical Algal

Figure 2-2. Response of postoral arm length in sea urchin larvae (S. purpuratus) to putative cues in the (A) initial and (B) second experiments. The histogram shows the mean responses (± 1 SE) of size-corrected postoral arm length among treatments. Different lowercase letters indicate treatments that were significantly different from other treatments.

25 produced postoral arms that were 12% (initial expt.) and 16% (second expt.) shorter than larvae from the control (Fig. 2-2). However, postoral arm lengths in both the chemical- and physical-contact treatments were similar to the control (Fig. 2-2). These two experiments suggest that larvae of S. purpuratus use algal contact to detect food early in development.

205

a A 200

b 195

190 Post-oral arm length (microns) length arm Post-oral 185 Control Physical Chemical Algal-1 Algal-2

140 a B

135

b 130

125 Post-oral arm length (microns) length arm Post-oral 120 Control Chemical Algal

Figure 2-3. Response of postoral arm length in sanddollar larvae (D. excentricus) to putative cues in the (A) initial and (B) second experiments. The histogram shows the mean responses (± 1 SE) of size-corrected postoral arm length among treatments. Different lowercase letters indicate treatments that were significantly different from other treatments.

In contrast, in both experiments on Dendraster excentricus, larvae from both the algal- and chemical-contact treatments responded similarly and had postoral arms that were ~ 6% shorter than larvae from the control treatment, and only the physical-contact treatment was similar to the control (Fig. 2-3). These two experiments indicate that larvae of D. excentricus use chemical cues, and possibly algal contact to detect food early in development.

Discussion

Pre-Feeding Response

The differences among treatments for both species demonstrate that larvae can detect food conditions and alter their morphology prior to their ability to consume food.

This pre-feeding response provides insights into the general mechanism of feeding structure plasticity. Since plutei are not using the amount of energy assimilated or algae ingested as cues, at least for the initial response, larvae probably have epidermal receptors to collect information about food conditions. The ciliary band is assumed to have receptors and an associated nervous system that can detect algae because to feed larvae reverse a small local section of the ciliary band when algal cells pass close by

(Strathmann 1971, Burke 1978, 1983, Hart 1991). In both S. purpuratus and D. excentricus, cholinergic and serotonergic cells are present and associated with the ciliary band early in larval development before larvae can feed (Burke 1978, 1983, Bisgrove &

Burke 1986). Additionally, electrical pulses, interpreted as action potentials, have been recorded when ciliary reversals are induced (Mackie et al. 1969). Larvae also more readily ingest inert particles soaked in seawater with algae than particles soaked in seawater without algae (Rassoulzadegan & Stathmann 1984), however this might result from particles being selected at the mouth and not at the ciliary band. The correlation

27 between the observed early morphological response and the development of the larval nervous system, which is probably involved in feeding, suggests that the larval nervous system is likely involved in feeding structure plasticity.

An ultimate explanation for the pre-feeding response is that it evolved because of a selective pressure to reduce the time between the onset of feeding and the induction of the appropriate feeding morphology. Plutei of both species investigated develop from relatively small eggs (S. purpuratus ≈ 80 µm diameter, D. excentricus ≈ 120 µm diameter) and require exogenous food less than a day after larvae gain the ability to feed when yolk reserves are exhausted (McEdward 1984, 1986a,b). In other words, larval growth rates are dependent on exogenous food as soon as larvae can feed. Thus, selection probably favors larvae that can produce the optimal feeding morphology when feeding begins. However, plutei require at least a day to produce different morphologies

(McEdward & Herrera 1999). Therefore, responding to food conditions prior to the ability to feed is required for larvae to produce the appropriate morphology when feeding first begins.

Different Species Use Different Cues

An unexpected result of the experiments was that larvae of S. purpuratus and

D. excentricus morphologically responded to different cues—larvae of S. purpuratus responded to the presence of algal cells only, whereas larvae of D. excentricus responded to a chemical cue as well. Because these two species diverged from a common ancestor during the Triassic over 200 million years ago (Smith 1984), this result raises the question of whether plasticity is homologous among echinoids or has evolved more than once within the class. Currently, all eight species investigated for feeding structure plasticity with small eggs (<150 µm diameter) have larvae that produce smaller arms

28 when food is relatively abundant (Boidron-Mètairon 1988, Hart & Schiebling 1988,

Strathmann et al. 1992, Fenaux et al. 1994, Hart & Strathmann 1994, McWeeney 1995,

Bertram & Strathmann 1998, McEdward & Herrera 1999, Reitzel 2002). However, for species with large eggs and feeding larvae, plasticity is substantially reduced, and possibly lost in several species (Eckert 1995, Reitzel 2002, B.G. Miner unpubl. data).

Additionally, although no species with non-feeding larvae have been investigated, it is probably safe to assume that they are also not plastic. These studies suggest that selection favors plasticity in larvae of small-egg species and not large-egg species, which seems reasonable given that larvae that develop from small eggs are much more dependent on exogenous food than larvae that develop from large eggs.

Evidence in support of the hypothesis that plasticity evolved multiple times within echinoids requires that species with plasticity evolve from different non-plastic ancestors.

One way to test this hypothesis is with a phylogenetic analysis of plasticity among echinoids. Unfortunately, only a few species have been tested for feeding structure plasticity, and given the limited taxonomic sampling any results would not be very informative. Alternatively, we could use egg size as a proxy for plasticity and investigate the number of times species with small eggs evolved from species with large eggs. Most larval ecologists believe that it is very unlikely that feeding larvae evolved from non- feeding larvae within echinoids because the likelihood of evolving all the same morphological and physiological machinery necessary to feed as other feeding larvae within the class is exceedingly small (Strathmann 1987). However, small-egg species with feeding larvae could have evolved from large-egg species with feeding larvae, and under certain conditions this is predicted by theory (Vance 1973, McEdward 1997,

Levitan 2000, McEdward & Miner 2003). Unfortunately, there are no phylogenetic analyses of egg sizes among echinoids with feeding larvae, only between feeding and non-feeding larvae. Thus, it is difficult to say whether the observed differences in cues might represent homology or homoplasy in feeding structure plasticity within echinoids.

Despite the evolutionary origin of plasticity within the two species investigated, the differences in cues emphasize the need for a better mechanistic understanding of why species respond differently under similar condition. Among plasticity studies, comparative studies often focus on whether reaction norms, the relationship between the phenotype and the environment, differ among populations or species. Although these studies are important, studies that focus on proximate explanations for differences in reaction norms are also needed.

CHAPTER 3 EVOLUTION OF PHENOTYPIC PLASTICITY IN SEA URCHIN LARVAE: A TRADE-OFF BETWEEN ARM LENGTH AND STOMACH SIZE

Introduction

Most species of benthic marine invertebrates have complex life histories (Levin &

Bridges 1995, McEdward & Miner 2001), with embryos developing into planktonic larvae before they settle, metamorphose, and take up benthic life (Thorson 1950).

Among species with a planktonic stage, many have feeding larvae that must acquire food from the water column to metamorphose because they develop from small, energy-poor eggs (Emlet et al. 1987). Larval echinoderms and molluscs use one or more bands of cilia to feed and swim (Strathmann 1971, Strathmann & Leise 1979, Strathmann 1987), and the size of these ciliary structures is plastic. (Boidron-Mètairon 1988, Hart &

Scheibling 1988, Strathmann et al. 1993, George 1994, 1999, Estrella Klinzing &

Pechenik 2000). Larvae reared with low concentrations of food produce longer ciliary bands than larvae reared with high food concentrations. Changes in the length of the ciliary band occur through changes in the structures that support the band: larval arm length in echinoderms (Boidron-Mètairon 1988, Hart & Scheibling 1988, George 1994,

1999), and size of the velar lobes in gastropods (Strathmann et al. 1993, Estrella Klinzing

& Pechenik 2000). This match between the inducing agent and the nature of the phenotypic response (i.e., food concentration induces changes in feeding structures) suggests that the response is adaptive.

30 31

To better understand the advantages of plasticity in echinoid larvae (i.e., plutei), researchers have investigated the possible benefits of long- and short-armed plutei. Hart and Strathmann (1994) demonstrated that larvae with longer arms capture more food than larvae with shorter arms. However, the question then arises, why not produce long arms in all environments? One possible explanation is that there is a trade-off between arm length and other developmental processes. For example, Strathmann et al. (1992) demonstrated that juvenile development begins at an earlier developmental stage in short- armed plutei than in long-armed plutei. Because planktonic mortality is likely high

(Rumrill 1990, Lamare & Barker 1999), investing in juvenile structures might decrease the duration a larva spends in the plankton and thus, the chance of dying. If energy is limited, then an energetic trade-off may exist between feeding structure size and juvenile structures, such that longer arms reduce development time.

Other trade-offs might also exist, but have not been investigated. For example, differences in food concentration also induce changes in stomach size (Strathmann et al.

1992, Fenaux et al. 1994). Plutei produce large stomachs (and short arms) when food is abundant, and small stomachs (and long arms) when food is scarce (Strathmann et al.

1992, Fenaux et al. 1994). If larger stomachs require more energy to produce, then there may be a trade-off between allocation to feeding structures (arm length) and the internal organ that processes the acquired food (stomach size). However, it is not clear whether food simply distends the stomach, or whether larger stomachs are caused by changes in morphogenesis. Fortunately, this can be examined experimentally because many echinoderm larvae undergo an initial period of development prior to feeding on external food but during which their development can be affected by food concentration (see

Chapter 2). By examining the response of arm length and stomach size during these early

(pre-feeding) periods, the trade-off can be examined more directly.

To test whether there is a trade-off between arm length and stomach size, I reared plutei of two species of Northeastern Pacific sea urchins, Strongylocentrotus purpuratus and S. franciscanus in different food concentrations and quantified larval arm length and stomach size. To answer whether a large stomach is caused by food distending the stomach, or changes in morphogenesis, I quantified the stomach size of pre-feeding larvae (i.e., larvae that had not ingested food particles) in different food concentrations.

If pre-feeding larvae respond to food concentrations, then large stomachs likely result from changes in morphogenesis and require more energy than smaller stomachs. I also tested whether the magnitude of response in arm length was correlated with the magnitude of response in stomach size. If a trade-off exists then the responses should be negatively correlated, so that a large response in arm length is associated with a large but opposite response in stomach size.

Materials and Methods

I conducted experiments at the Bodega Marine Laboratory (BML), University of

California, Davis in the spring of 1999. A month prior to experiments, divers with

SCUBA collected adult urchins Strongylocentrotus purpuratus and S. franciscanus off the coast of Santa Barbara, California. Sea urchins were transported in coolers with seawater to BML, where they were held in tanks with running seawater and given the kelp Macrocystis pyrifera.

I induced urchins of both species to spawn by injecting them with 1 ml of 0.5 M

KCl (Strathmann 1987). Eggs and sperm were collected in Millipore®-filtered (0.45 µm) seawater (FSW). For both species, eggs from 3 females were fertilized with sperm of

1 male (fertilization > 95%). The embryos were rinsed with FSW, and placed in 2 L glass beakers (3 beakers per species) with 2 L of FSW. Beakers were held on a wet-table with running seawater for 1 day until blastulae hatched. Hatched blastulae of a species from the 3 initial beakers were mixed together, and then blastulae were placed in 1 L plastic beakers at densities of ~ 2 larvae/ml. Beakers were randomly assigned a treatment and location on the wet-table. The running seawater in the wet-table maintained the water temperature in beakers at 12 ± 1ºC during the experiments.

I conducted two fully crossed 2-factoral experiments. In the first experiment, larvae of both species were exposed to two concentrations of food: 0.2 cells/µL, and

6 cells/µL. I will refer to this experiment as experiment 1. In the second experiment, larvae of both species were exposed to two other food concentrations: 2 cells/µL, and

12 cells/µL. I will refer to this experiment as experiment 2. Larvae were given the unicellular alga Dunaliella tertiolecta from algal cultures in log-phase growth. To remove the algal medium before adding algae to the experimental beakers, I centrifuged algae into a pellet, decanted the medium, and re-suspended them in FSW. Each treatment was replicated with 4 beakers. All beakers were stirred with a paddle system to maintain a homogenous mixture of larvae and food (Strathmann 1987). I changed the water in all the beakers after 2 days by siphoning 90% of the water in a beaker through Nytex mesh

(60 µm). This allowed me to remove the water and food, but retain the larvae in the beakers. I then refilled each beaker with fresh FSW, and added algae at the appropriate concentration. I observed very few dead larvae during the water change or at the end of the experiment, suggesting that larval mortality was low.

Larvae were exposed to experimental treatments for 4 days, and since the experiment began with 1-day old larvae, they were 5-days old at the end of the experiment. The end of the experiment coincided with the onset of feeding in both species at 12 ºC (Strathmann 1987). I investigated pre-feeding larvae to determine whether stomach size responded prior to larvae ingesting algal cells.

At the end of the experiments, I haphazardly chose and preserved in 1% formalin

6 (experiment 1) and 10 larvae (experiment 2) from each beaker. Within 12 hours, I measured with a compound microscope and an ocular micrometer the length of the postoral arm skeletal rod, length of the body skeletal rod, and length of the stomach. The postoral arm estimates the length of the ciliary band, and the bodyrod estimates the overall size of the larvae (McEdward & Herrera 1999). The postoral arm and bodyrod were measured from their tips to where they intersect with the transverse bodyrod, and stomach diameter was measured as the length along the midline of the body (Fig. 3-1). I also observed whether larvae had food in their stomachs. I observed at most 2 algal cells in the stomach of a larva, but most larvae had empty stomachs. In addition, I have observed in other experiments with post-feeding larvae that food is retained in the stomach after preservation in 1% formalin.

I analyzed the log-transformed data for each experiment with a nested 2-factor

ANCOVA model with ln(bodyrod length) as the covariate to compare ln(postoral arm length) and ln(stomach diameter) among treatments of an experiment. I nested larvae within beakers, because larvae from a beaker were not independent. I estimated the effect of the covariate ln(bodyrod length) because I wanted to verify that differences among treatments were the result of changes in the shape of larvae, indicating feeding

35 structure plasticity, and not an increase in larval size due to maternal allocation or larval growth. Even though pre-feeding plutei did not consume algal particles, they can take up dissolved organic matter (Mananah et al. 1983, Shilling & Bosch 1994). Because of the nested design, I first verified that the slopes of the relationships were similar among all beakers of an experiment.

In both experiments the slopes were not significantly different among beakers for either response variable (F15,64 = 0.01, p = 0.92 and F15,128 = 1.15, p = 0.32 for postoral arm length in experiments 1 and 2 respectively, and F15,64 = 1.39, p = 0.18 and

F15,128 = 1.10, p = 0.37 for stomach size). I therefore removed the covariate-treatment interaction term from the nested ANCOVA model to test for the effects of species, food concentration, ln(bodyrod length), beaker(food concentration), and food

Post-oral arm length

Bodyrod length Stomach Length

Figure 3-1. Photomicrograph of a pluteus (S. purpuratus) illustrating that postoral arm length was measured from the tip of the postoral arm skeleton to where it intersects with the transverse bodyrod, bodyrod length was measured from its tip to where it intersects the transverse bodyrod, and stomach length was measured along the midline of the larval body.

36 concentration*species. When the interaction between food concentration and species was significant, I used an independent contrast test to determine whether the difference between the two food concentrations was significant for each species.

I also tested with linear regression through the origin whether there was a negative relationship between the response in stomach size and the response in arm length. The response was expressed as the difference between the low and high food treatment means for arm length, or stomach size for each species-experiment combination.

Results

In both experiments there were significant interactions between species and food concentration (Table 3-1), which suggests that the two species responded differently to food concentrations. Despite these interactions, larvae of both species tended to produce smaller stomachs and longer postoral arms in the treatments with less food

(Figs. 3-2&3-3), indicating that prior to ingesting particles stomach size and arm length respond to food concentration. Because the effect of ln(bodyrod length) (i.e., larval size) in both experiments was not significant (Table 3-1), these responses to food concentration were a result of changes in shape indicating feeding structure plasticity.

In experiment 1, larvae of both species reared with less food produced smaller stomachs (S. purpuratus ~ 17 µm and 14% smaller; S. franciscanus ~ 18 µm and 4%;

Table 3-1, Fig. 3-2). Furthermore, larvae produced longer postoral arms when reared with less food (S. purpuratus ~ 17 µm and 14%: F1, 12 = 77.81, p < 0.001, and

S. franciscanus ~ 13 µm and 5%: F1, 12 = 9.48, p = 0.01). In experiment 2, only larvae of

S. purpuratus produced significantly smaller stomachs when reared with less food

(S. purpuratus: ~ 12 µm and 17% smaller, F1, 12 = 25.50, p < 0.001; and S. franciscanus:

~ 4 µm and 4% smaller, F1, 12 = 1.44, p = 0.25). A similar result was observed for arm

37 length. Only larvae of S. purpuratus produced significantly longer arms when reared with less food (S. purpuratus: ~ 10 µm and 8% larger, F1, 12 = 14.18, p = 0.003; and

S. franciscanus: ~ 5 µm and 2% larger, F1, 12 = 0.67, p = 0.43).

The magnitude of response provides evidence for a trade-off between arm length and stomach size. The greater the difference in arm length between the low and high food treatments, the greater the opposite difference in stomach size (Figs. 3-2 and 3-3).

Furthermore, there was a significant negative relationship between the change in stomach size and the change in arm length (F1,3 = 105.26, p = 0.002; Fig. 3-4).

Table 3-1. Results of nested 2-factor ANCOVA testing for the effects of treatment (species and food concentration), interaction between food concentration and treatment, beaker (a random effect nested in treatment), and ln(bodyrod length) on ln(postoral arm length) or ln(stomach length) for experiments 1 and 2 on larvae of S. purpuratus and S. franciscanus. Effect df F-ratio p-values ln(postoral arm length) Experiment 1 species 1, 12 524.32 < 0.001 [food] 1, 12 70.55 < 0.001 species*[food] 1, 12 16.48 0.002 beaker(treatments) 12, 79 0.87 0.58 ln(bodyrod length) 1, 79 0.03 0.86 Experiment 2 species 1, 12 773.86 < 0.001 [food] 1, 12 10.60 0.007 species*[food] 1, 12 4.60 0.05 beaker(treatments) 12, 143 1.14 0.33 ln(bodyrod length) 1, 143 0.70 0.41 ln(stomach length) Experiment 1 species 1, 12 17.57 0.001 [food] 1, 12 96.89 < 0.001 species*[food] 1, 12 1.16 0.32 beaker(treatments) 12, 79 1.32 0.22 ln(bodyrod length) 1, 79 0.14 0.71 Experiment 2 species 1, 12 65.13 < 0.001 [food] 1, 12 20.13 < 0.001 species*[food] 1, 12 7.64 0.02 beaker(treatments) 12, 143 1.32 0.21 ln(bodyrod length) 1, 143 0.15 0.70

Discussion

A requirement for the evolution of plasticity is a fitness trade-off between alternative phenotypes, so that a single phenotype is not the most fit in all environments

(Bradshaw 1965, Harvell 1990). Currently, the adaptive explanation for feeding structure plasticity in plutei is based on the argument that there is an energetic trade-off between arm length and juvenile development. Longer arms allow plutei to capture more food when food is scarce (Hart & Strathmann 1994), but are energetically expensive. Shorter

145 85 A Arm Length Stomach Size

135 * 75 m) µ

m) 125 65 µ

115 * 55 0.2 6 290 105

Arm Length (µm) Length Arm Length ( B Stomach Length Length(µm)( 280 * 95

270 85 * 260 75 0.2 6 Food concentration (cells / µl)

Figure 3-2. Response of arm length and stomach size in Experiment 1 for both species: A) S. purpuratus and B) S. franciscanus. The left x-axis and dotted gray line indicate the response in arm length, and the right x-axis and solid black line indicate the response in stomach size. An “*” specifies that arm length was significantly different between food concentrations for a species. Error bars represent ± 1 standard error.

39 arms require less energy, and this energy savings is used to accelerate the development of the juvenile, shorten development time, and thus decrease planktonic mortality

(Strathmann et al. 1992). However, it is unclear whether the accelerated development of juvenile structures is a result of an energetic trade-off between arms and juvenile structures, or just the result of short-armed larvae being fed more food than long-armed larvae.

145 80 Arm Length A Stomach Size 135 * 70

125 60 m) µ

m) * µ

115 50 212 300 115 B Arm Length Arm( Length 290 105 Stomach Length ( Length Stomach

280 95

270 85 212 Food concentration (cells / µl)

Figure 3-3. Response of arm length and stomach size in Experiment 2 for both species: A) S. purpuratus and B) S. franciscanus. The left x-axis and dotted gray line indicate the response in arm length, and the right x-axis and solid black line indicate the response in stomach size. An “*” specifies that stomach length was significantly different between food concentrations for a species. Error bars represent ± 1 standard error

In this study, I investigated another possible energy trade-off between arm length and stomach size, which might help to explain the evolution of larval feeding structure plasticity. The pre-feeding stomach size response to food concentrations documented in this study (Figs. 3-2&3-3) demonstrates that larger stomachs are at least in part caused by differences in morphogenesis. Because arm length and stomach size responded in opposite directions, and because both arms and stomachs are probably costly to manufacture and maintain, it is likely that there is a trade-off between arm length and stomach size (Fig. 3-4). Furthermore, the magnitude of responses in the two structures was similar. For example, larvae of S. franciscanus exhibited very little change in arm length and little change in stomach size in experiment 2 (Fig. 3-3B). Yet, in experiment

1, when a large difference in arm length was induced, the induced change in stomach size was also large. These similar changes in magnitude but in opposite directions, are consistent with a trade-off.

This evidence for an energetic trade-off between arm length and stomach size raises the possibility that larval structure feeding plasticity has evolved in plutei due to fitness trade-offs between long arms and large stomachs in different environments. Energy is either used to produce longer arms or larger stomachs, but because energy is limited larvae cannot do both. When food is scarce, the amount of exogenous food acquired is limited by how much food a larva can capture. However, when food is abundant, the amount of energy acquired is limited by the amount of food assimilated. Thus, long arms provide a fitness advantage when food is scarce and large stomachs provide a fitness advantage when food is abundant. Energetic trade-offs between the size of the digestive tract and other traits appear to also explain plasticity in the size of digestive tract for

41 birds, reptiles, and fish (Ruohonen & Grove 1996, Biebach 1998, Piersma 1998,

Holmberg et al. 2002).

The trade-off between arm length and stomach size might also be important for the evolution of plasticity in other taxa. Larvae of some seastars, brittlestars, and marine snails also change the length of their ciliary band in a similar manner to plutei

(Strathmann et al. 1993, George 1994, 1999, Estrella Klinzing & Pechenik 2000, B.G.

Miner unpubl. data). In seastars, larvae produce larger stomachs and shorter ciliary bands when they are given more food (George 1994, 1999), although these experiments did not isolate developmental changes in stomach size from food distending stomachs.

Unfortunately, there are no data on whether stomachs of snail or brittlestar larvae respond. Evidence for a trade-off between arm length and stomach size in gastropod larvae would be especially interesting given that larval feeding structure plasticity almost certainly evolved independently in echinoderms and gastropods.

0 m

µ -5

-10 y = -1.137x -15 r2 = 0.8214

-20 stomach size stomach ∆ -25 0 5 10 15 20 ∆ arm length µm

Figure 3-4. The relationship between the change in stomach size and arm length. The change in stomach size and arm length was calculated as the difference between the mean response in low and high food treatments for each species- experiment combination.

In conclusion, the pre-feeding response in stomach size indicates that exogenous food induces different stomach sizes not by food stretching the stomach, but through changes in morphogenesis. Furthermore, the magnitude of response was similar but opposite in arm length and stomach size. These results suggest that there is an energetic trade-off between arm length and stomach size, and raise the possibility that larval feeding structure plasticity in plutei evolved due to fitness trade-offs between long arms and larger stomachs in different environments.

CHAPTER 4 EFFECTS OF FINE GRAIN ENVIRONMENTAL VARIABILITY ON MORPHOLOGICAL PLASTICITY

Introduction

The ability of an organism to alter its morphology in different environments is common in plants and animals (Schlichting 1986, Harvell 1994, Kingsolver & Huey

1998, Schlichting & Pigliucci 1998, Tollrian & Harvell 1999) and influences the ecology and evolution of many systems (West-Eberhard 1989, Schlichting & Pigliucci 1998,

Tollrian & Harvell 1999, Agrawal 2001). For example, in a simple food web a predator can influence the morphology or behavior of a prey, which in turn can alter the strength of the interaction between that prey and other species (Peacor & Werner 1997, Raimondi et al. 2000, Trussell et al. 2002). These trait-mediated indirect effects can have a greater impact on community dynamics than the direct effects of consumption (Peacor & Werner

2001). Currently, most of our ecological and evolutionary understanding of morphological plasticity comes from experiments where investigators induced morphologies in constant environments with different means (e.g., high vs. low food, or predator vs. no predator). This approach implicitly assumes that an individual experiences a constant environment, but that related individuals can experience different environmental conditions (i.e., that the environment is coarse grained; Levins 1968). In reality however, environments will vary at a variety of scales, and in some environments an individual will experience a substantial amount of environmental variability (e.g., Van

Buskirk 2002b). Therefore, it is surprising that in over 20 years of empirical research on

43 44 morphological plasticity, we found only 2 studies that explicitly addressed whether differences in environmental variability per se can induce alternative morphologies (Ali

& Wooton 1999, Ashmore & Janzen 2003)—especially considering that behavioral ecologists began studying risk sensitivity (behavioral responses to variability) more than two decades ago (Caraco 1980, Real 1980). If many organisms alter their morphology in response to the amount of environmental variability experienced by an individual, then our current understanding of the evolution and ecology of morphological plasticity may be incomplete. For example, because the mean and variance of an environment are often positively correlated, it is possible that many observed plastic responses are adaptations to different amounts of environmental variability, not different mean environments.

In this study, we address whether sea urchin larvae (plutei) change their morphology in response to the amount of environmental variability in food concentration.

Plutei feed and swim with a band of cilia that wraps around a larva’s body and arms

(Strathmann 1971, Hart 1991) and changes length depending on the concentration of food the larva experiences (Boidron-Mètairon 1988, Hart & Scheibling 1988). When plutei are reared in low concentrations of food they increase the length of the ciliary band by increasing the length of their arms, whereas in high concentrations of food plutei produce short arms. The functional consequences of both long and short arms with fixed levels of food (high or low) have been investigated (Strathmann et al. 1992, Hart & Strathmann

1994). However, food (phytoplankton) availability in the ocean is spatially variable both horizontally and vertically (Lalli & Parsons 1993). Since plutei migrate vertically semi- diurnally (Pennington & Emlet 1986), an individual larva certainly encounters different concentrations of food both within and among days.

To determine whether plutei respond to fine-grained environmental variability, we reared plutei with different fluctuating densities of food so that treatments had the same mean food concentration, but differed in the variability in food. Although two previous studies investigated whether individuals could respond morphologically to amount of environmental variation (Ali & Wooton 1999, Ashmore & Janzen 2003), their experimental design allow for two interpretations of the their results. In both studies, individuals assigned to the variable treatments started in an environment that exceeded the mean condition, and were then switched to a condition below the mean (e.g., mean = 20°C, treatment 1 = 22 then 18°C, and treatment 2 = 24 then 16°C). This allows for two interpretations of observed treatment effects: 1) individuals responded to different amounts of variability (the interpretation that supports the working hypothesis), or

2) individuals responded to different levels of an environmental condition at a specific age or stage (an alternative explanation)—e.g., the food concentration at day 3 determines morphology at day 10. To separate these effects, an additional treatment is required that reverses the timing of the environmental fluctuations—e.g., if a variable treatment alternates between 24 & 16°C another treatment is needed that alternates between 16 and 24°C. We will refer to this treatment as the reciprocal treatment, and included it in our experiment.

We were also interested in how well plutei morphologically tracked our imposed food fluctuations. Padilla and Adolph (1996) demonstrated with a mathematical model that plastic responses to environmental variability are maladaptive if there is a large time lag between when the environment changes and the morphological response, so that the

46 induced morphology is inappropriate for the current environment. Thus, the time scale at which an individual can respond morphologically influences the evolution of plasticity.

Materials and Methods

Adult sea urchins, Lytechinus variegatus (Lamarck), were collected from the Gulf of Mexico off the Steinhatchee River, FL (29o 40.54’N, 83o 28.16’ W) and held in aquaria for two months at the University of Florida. Adults were fed Dry Tenera Blue algae (Tenera Fish Food) 2 to 3 times per week. One male and one female were spawned

16 on October 2000 with standard techniques (Strathmann 1987). Average egg size was

98.7 µm (± 2.91 SD, n = 25). Eggs were fertilized and held in 200 ml of Millipore®- filtered seawater (0.45 µm) (FSW) for 24 hours until hatching. Approximately five hundred hatched blastulae were added to each of twelve 2 L beakers with 1.6 L of FSW and fed.

We used a nested experimental design with replicate larvae nested in each of three replicate beakers per treatment. The four treatments all had a mean food concentration of

4 cells/µL but had different amounts of variability: low, medium, and high (Fig. 4-1).

Under the highest level of variation, we included a reciprocal treatment (0-8, Fig. 4-1) to test for stage or age-specific effects. We chose to use these concentrations of food because studies have demonstrated that plutei of L. variegatus are food limited when concentrations are less than 8 cells/µL (Herrera 1998). Larvae were exposed to treatments for 16 days, and at an advanced stage (8-armed) at the completion of the experiment.

Larval beakers were maintained in an incubator at 28°C and stirred with paddles

(Strathmann 1987) to maintain a relatively homogeneous mixture of food and larvae.

Larvae were fed a cultured unicellular alga (Dunaliella tertiolecta). Algal culture

47 densities were measured with a hemocytometer and the appropriate number of cells for the beakers were centrifuged and re-suspended in FSW to remove any toxins that may have accumulated in the algal medium. We cleaned beakers every two days by siphoning water through Nytex mesh (60x40 µm) that allowed the water and food to pass through but not larvae (i.e., reverse filtration). Eighty-five percent of the water in a beaker was reverse filtered, and 1.0 L of FSW was added and reverse filtered again. Beakers were then refilled with FSW to 1.6 L, and new food added. Although algal densities declined between water changes, the mean food concentration was probably very similar among all the treatments for the following reasons. After the experiment ended, we quantified the change in algal density via algal reproduction and death after 2 days in beakers similar to our experimental beakers, but lacking larvae, at 2 initial algal concentrations

(2 cells/µL and 8 cells/µL). The mean algal density decreased approximately proportional to the initial density (2 cells/µL decreased by 0.34 ± 0.12 cells/µL, and

8 cells/µL decreased by 0.6 ± 0.25 SD cells/µL). We did not quantify the effects of larval

Treatment = Low (4-4) 0 cells ul-1

-1 Treatment = Med (6-2) 2 cells ul

4 cells ul-1 Treatment = High (8-0)

6 cells ul-1 Treatment = High (0-8) 8 cells ul-1

0 4 8 12 16 Days

Figure 4-1. Graphical representation of the experimental design. Each block indicates the food concentration in beakers at the beginning of that 2-day interval.

48 grazing, but based on published measurements of maximum clearance rates (Hart &

Strathmann 1994) larval grazing could have decreased the concentration of food in a beaker by at most half. However, the decreases in food by larval grazing, as with algal demographics, should be proportional to algal density, and therefore the means among all treatments should still be approximately equal.

Every 2 days for 16 days, eight larvae per beaker were preserved in 1% formalin.

We later measured the length of the skeletal rods that support the postoral (PO) and posterodorsal (PD) arms (left side of larvae), and the length of the midline body

(Fig. 4-2). Because the ciliary band runs the length of the larval arms, total arm length

(PO + PD) is a good estimate of larval feeding structure length (McEdward & Herrera

1999). Midline body length provides an estimate of larval size (Strathmann et al. 1992,

McEdward & Herrera 1999). Measurements were taken on a compound microscope with

C B

Figure 4-2. Dorsal view of a pluteus showing the three morphological measurements made: A) postoral arm (PO) length, B) posterodorsal arm (PD) length, and C) midline body length.

49 differential interference contrast optics (10x) in three dimensions (McEdward 1984,

1985). This allowed accurate measurements when the arms or midline body were not parallel to the focal plane.

For all statistical tests, we used the natural log of the raw data from Days 4, 8, 12, and 16 (i.e., the days at which all treatments had received a full cycle of high and low food and thus had the same mean concentration of food). To compare total arm length and midline body length among treatments, we analyzed the beaker means with a profile analysis—a combination of repeated-measures ANOVA and MANOVA tests (von Ende

2001). We used beaker means because larvae from a beaker were not independent.

Differences in total arm length however, could result from a change in the shape of larvae, representing arm length plasticity, or due to a change in overall size, representing larval growth. Therefore, we also performed a profile analysis on the adjusted means of the beakers to correct for differences in larval size among treatments. To obtain the adjusted means, we estimated the common slope for all beakers at a given date for the relationship of total arm length vs. midline body length. The common slope was found by minimizing the sum of the squared deviations using Equation 4-1.

=+β + ln(arm _ lengthijk ) A ij (ln( body _ length ijk )) e ijk , (4-1) where i is the treatment, j is the beaker, k is the larva, Aij is the intercept for the ith treatment and jth beaker, β is the common slope, and eijk is the error. We also tested for the homogeneity of slopes at each date. For all dates except day 12, we found no significant difference in slopes among the beakers. We therefore calculated the adjusted mean for total arm length for each beaker at the grand mean midline body length for each date (excluding day 12) and analyzed these data with a profile analysis. To determine

50 whether there were stage- or age-specific effects, we tested whether the high variation treatments (8-0 and 0-8) were statistically different with an independent-contrasts test.

Results

Larvae reared with the low variation in food (4-4) produced significantly longer arms than larvae reared with medium (6-2) and high variation (8-0 & 0-8) diets

(F3,8 = 7.04, p = 0.012; Fig. 4-3A). Larvae from the low variation treatment also had longer bodies than larvae in the medium and high treatments (F3,8 = 11.66, p = 0.003;

Fig. 4-3B). The interaction between time and treatment was not significant for total arm length (Wilks’ Lambda F9,14 = 1.87, p = 0.138; Fig. 4-3A) nor midline body length

(Wilks’ Lambda F9,14 = 1.79, p = 0.155; Fig. 4-3B). Although larger larvae had longer arms, the difference in total arm length was not driven by larval size alone. When we corrected for size among the treatments, larvae reared in the low variation treatment still had significantly longer arms than larvae from the medium and high treatments

(F3,8 = 4.33, p = 0.043; Fig. 4-3C). At day 16, larvae from the low variation treatment

(4-4) had an average total arm length that was 35% larger than the average of larvae from the high variation 8-0 treatment. The interaction between time and treatment was not significant (Wilks’ Lambda F6,14 = 1.66, p = 0.203).

There was no indication that stage- or age specific effects explained the observed differences among treatments. First, the high variation treatments were not statistically different from one another (Wilks’ Lambda F3,6 = 0.48, p = 0.708). Second, the relative differences in total arm length among treatments (4-4 > 6-2 ≈ 8-0 ≈ 0-8; Fig. 4-3A) are

1600 Low4 (4-4) Med6.2 (6-2) 1200 High8 (8-0) High0.8 (0-8)

m) 800 µ (

400 A Total Arm Length Length Arm Total 0 0481216 600

m) 400 µ

200 Length ( Length Midline Body Body Midline B

0 0481216 1600

m) 1200 µ

800

Length ( Length 400 C Adjusted Total Arm 0 0 4 8 12 16 Time (days)

Figure 4-3. Relationship between A) total arm length and time, B) midline body length and time, and C) adjusted total arm length and time for each treatment. Error bars indicate ± 1 standard error. Given the 2-day feeding pattern, larvae had been given the same mean amount of food on days 4, 8, 12, and 16.

52 inconsistent with a stage- or age-specific effect—stage- or age-specific effects are problematic when there is an ordered response with initial food concentration (e.g., 0-8 ≥

4-4 ≥ 6-2 ≥ 8-0, or 0-8 ≤ 4-4 ≤ 6-2 ≤ 8-0).

Despite the observed effects of variability, larvae were not able to track the 2-day fluctuations in food. In other words, larvae that experienced reduced food during a 2-day interval did not have a greater change in total arm length compared to larvae that received more food (Fig. 4-3A). Furthermore, the high variation treatments (8-0 and 0-8) did not change their relative positions to one another every two days, after their food regimes were reversed (Fig. 4-3A).

Discussion

Our experiment indicates that the size of plutei feeding structures can change in response to different amounts of environmental variability. Larvae in the medium and high variation treatments produced shorter arms than larvae in the low variation treatment. Although larger larvae had longer arms, larval size did not fully account for the differences in arm length among treatments, indicating that our results are best explained by morphological plasticity (Fig. 4-3C). Furthermore, the similarity between the high variation treatments (8-0 and 0-8) and the relative differences among treatments supports the hypothesis that environmental variability caused the observed differences in arm length, and contradicts the alternative hypothesis that differences resulted from stage- or age-specific effects. Although previous investigators have tested for the effects of environmental variability (Ali & Wooton 1999, Ashmore & Janzen 2003), they did not include a reciprocal treatment, and therefore it is unclear whether environmental variability, or stage- and age-specific effects caused the observed differences among

53 treatments. Thus, our study is the first to demonstrate that organisms can morphologically respond to environmental variability per se.

Currently, theoretical studies predict that plasticity should evolve either when the environment is constant within an individual’s lifetime but differs among related individuals (i.e., the environment is coarse grained), or when an individual experiences a variable environment (i.e., the environment is fine grained) and the organism can respond rapidly enough to track the current conditions (Via & Lande 1985, Gomulkiewicz &

Kirkpartrick 1992, Schlichting & Pigliucci 1995, Pigliucci 2001). In contrast, plasticity is thought to be maladaptive (i.e., selection favors fixed phenotypes) when the environment is fine grained and fluctuates faster than the organism’s response time

(Padilla & Adolph 1996)—thus, the organism cannot produce the “correct” morphology before the environment changes. These theoretical predictions about the evolution of plasticity however, follow from the assumption that plasticity is only an adaptation to different mean conditions in an environment. Given our results, the possibility arises that plasticity can evolve in response to variability per se, and is adaptive even under the conditions currently thought not to favor the evolution of plasticity (i.e., the environment is fine grained and there are large lag times). For example, the environment is fine grained and an individual cannot track changes in the mean condition. However, different morphologies might each perform best in an environment with a different amount of variability, and an individual that can detect the amount of environmental variability and respond adaptively should have greater fitness than a non-plastic individual. Thus, plasticity in response to the amount of environmental variability should evolve. New theoretical models are needed to determine whether a plastic response to

54 environmental variability affects our current understanding of the evolution of phenotypic plasticity; and if so, how.

The response to variability in the environment also prompts the question; what change or changes in the environment are individuals responding to? In our simple design, the minimum and maximum food concentrations, and the variance and frequency of food fluctuations differed among treatments. Because plutei produced shorter arms when variability increased, plutei could have responded to the variance, frequency, or maximum concentration in food. However, because the mean concentration of food was similar in all treatments, it is unlikely that plutei responded to a change in the mean. This is interesting in light of the fact that investigators studying plasticity focus on differences in the mean condition of some environmental factor. As with our experiment, experimental manipulations in plasticity studies also affect the environment in multiple ways. For example, when the mean food concentration is experimentally increased, the maximum and variance in food concentration likely increase too. It is therefore possible that in previous experiments on plasticity in plutei (Boidron-Mètairon 1988, Hart &

Scheibling 1988, Strathmann et al. 1992, Fenaux et al. 1994, Hart & Strathmann 1994) and other taxa, phenotypic differences among treatments were caused by differences in the variance or maximum of food concentration and not the mean. Furthermore, it is possible that a plastic response is an integrated response to two or more factors (e.g., the mean and the variance). Existing studies do little to shed light on the particular environmental factors that elicit plasticity.

If the goal is to document plasticity, then distinguishing among these different possibilities is not important. However, if the goal is to understand or predict the

55 evolutionary and ecological consequences of plasticity then a better understanding of the type of environmental change (e.g., the mean, variance, minimum, or maximum) that elicits a plastic response is important. Of course, the natural environment will not usually vary in the same way as in our experimental treatments. This presents a problem because we would like to estimate the magnitude of a plastic response with an experimentally derived function, f(x), that describes the relationship between the morphology and the environment (x). It is therefore important to understand what “x” is, and whether two or more parameters (e.g., x1, the mean, and x2, the variance) are required to predict the magnitude of response, f(x1…xi). Although our experiment cannot provide an answer to this question, our results do highlight that functions based only on environmental means may be inadequate. We hope that our results spur investigators to more carefully consider the effects of fine grain environmental variability when studying morphological plasticity.

CHAPTER 5 EFFECTS OF ENVIRONMENTAL VARIATION ON THE EVOLUTION OF PHENOTYPIC PLASTICITY

Introduction

The ability of an individual to modify its morphology in response to different environmental conditions (i.e., phenotypic plasticity) has important consequences for many disciplines of biology (Falconer 1952, Bradshaw 1965, Schlichting 1986, Tollrian

& Harvell 1999, Agrawal 2001, Pigliucci 2001, Price et al. 2003, West-Eberhard 2003).

One of the primary tools for studying plasticity, both empirically and theoretically, is the reaction norm (Schlichting & Pigliucci 1998)—the relationship between the phenotype and the environment (Woltereck 1909, Schmalhausen 1949). Reaction norms provide an intuitive graphical view of how an organism's phenotype changes in different environments, and identify plasticity when a phenotype differs among environments (i.e., as a reaction norm with a slope ≠ 0; see Schlitching & Pigliucci 1998).

Changes in the slope of reaction norms represent one important way in which plasticity can evolve (Pigliucci 2001). Many empirical and theoretical studies have investigated how organisms respond or evolve in two environmental conditions (e.g.,

Harvell 1984, Via & Lande 1985, Lively 1986a,b, de Jong 1990, van Tienderen 1991,

Brönmark & Miner 1992, Ottenheim et al. 1998, Leonard et al. 1999, Agrawal 2000).

Thus, reaction norms are often represented as linear relationships. Changes in the shape of reaction norms are also considered important for the evolution of plasticity

56 57

(Schlichting and Pigliucci 1998), but have received less attention (Stearns & Koella 1986,

Gomulkiewicz & Kirkpatrick 1992, Gavrilets & Scheiner 1993). One particular type of shape change, which I will refer to as the range of plasticity, is the range of environments in which an individual can produce different phenotypes. Specifically, the range of plasticity can be defined as the range of environments in which the slope of the reaction norm is non-zero. Because of energetic, biomechanical, or physiological constraints, all organisms can only respond to a finite range of environments, and thus the range of plasticity probably characterizes one aspect of shape for all reaction norms. Even in situations where a developmental switch has been documented (e.g., sex determination in reptiles Shine 1999, or the presence/absences of horns in dung beetles Moczek et al.

2002), the range of plasticity concept is valid, and represents the narrow range of environments where the switch occurs.

Given that the slope and range of plasticity represent important characters of the reaction norm that can potentially change evolutionarily, I was interested in understanding how selective pressures might influence the evolution of each. One factor that likely has a strong evolutionary influence on the reaction norm is the amount of environmental variation. Since a requirement for the evolution of plasticity is that the environment varies at some appropriate scale (Bradshaw 1965), changes in the long-term patterns of environmental variation probably exert a selective pressure on reaction norms.

For example, Van Buskirk (2002) tested with a phylogenetic comparative analysis whether the amount of environmental variation was correlated with the amount of plasticity. He investigated 16 species of anurans, and quantified the morphology of larvae reared with and without predators. He also surveyed different habitats to estimate

58 the amount of variation in predator abundance that larvae of each species experienced.

As predicted, he found that tadpoles that were more plastic experienced greater amounts of environmental variation, suggesting that changes in long-term patterns of environmental variation select for plasticity.

To better understand how changes in long-term patterns of environmental variation affect the evolution of plasticity, I developed an optimality model. With this model, I explored how selection might drive changes in the slope and range of plasticity when the amount of environmental variation changes. The model identified which slope or range of plasticity maximized fitness under a given amount of environmental variation.

The Model

I compared the relative fitness of genotypes with different reaction norms to determine which slope or range was optimal in environments with different amounts of variation. To calculate fitness (w), I defined benefit (b) and cost (c) functions, as well as how the environment (e) varied. With these equations, fitness was calculated as the difference between the benefits and costs averaged across all environment values encountered by a genotype.

Reaction Norms

I investigated reaction norms with either different slopes (s) or different ranges (r) of plasticity. I specified reaction norms with different slopes of plasticity with the equation p()ees= i , where p is the phenotype produced when the environment has a value of e (Fig. 5-1A). I specified reaction norms with different ranges of plasticity with the equation p()eer= min | , | (Fig. 5-1B). In other words, reaction norms with different ranges of plasticity had a slope of 1 and intercept of 0 for environmental values between

Phenotype ∆ Slope

Phenotype ∆ Range

Environment

Figure 5-1. Two types of evolutionary change in plasticity: A) a slope change, and B) a range of response change. The solid line indicates the initial reaction norm of a population, and the dotted line represents an evolutionary change in the population.

0 and r, and a slope of 0 and an intercept of r when the environmental value was greater than r.

Environmental Variation

I specified two types of environmental variation that are prevalent in the literature: the environmental variation experienced by an individual, which I will term “within- individual variation”, and the variation experienced by different individuals with the same genotype, “among-individual variation”—these are equivalent to Levins (1968) fine- and coarse-grained environments, respectively. In the case of among-individual variation, I assumed that the variation arises from spatial (instead of temporal) differences in a landscape. Throughout, I will refer to an environment as the collection of

60 environmental values that a genotype experiences, thus within an environment there can be many environmental values, which depend on the amount of environmental variation.

For both within- and among-individual variation, I investigated two scenarios:

1) the mean and variation in the environment are positively correlated; and 2) the mean is fixed at 0 and only the amount of environmental variation changes. Regardless of how the environment varies, I assumed for simplicity that an individual detected the value of the environment without error and responded instantaneously to it. Violations of this assumption will affect the results when the genotypes systematically respond differently, or when the rate of response is slow relative to the frequency of environmental fluctuations. For the latter case, a genotype with a fixed phenotype will be favored more often (Padilla & Adolph 1996).

Within-Individual Variation

I described the values of the environment over time with Equation 5-1 for the scenario where the mean and variance in the environment are correlated, and

Equation 5-2 for the scenario where the mean is fixed but the variance changes.

aa et()=−i cos(2) t + , (5-1) 22

a et()= i sin(2) t , (5-2) 2 where a is the amplitude of variation, and t is time. The two functions (sin and cos) were only used so that at time 0 the environmental condition was also 0. For both models, increases in a increased the amplitude, and therefore the amount of variation in the environment (Figs. 5-2A&B). Although changes in the frequency of variation also are important for the evolution of plasticity (Padilla & Adolph 1996), changes in the

61 frequency do not affect the results of this model because I assumed that an individual instantaneously responded to environmental changes.

Among-Individual Variation

I modeled the situation in which an individual experienced a constant environment, but different individuals experienced different environments. Changes in the amount of environmental variation were modeled as changes in the frequency of individuals that experience each environment. I described this relationship as a normal distribution with a mean of µ and standard deviation of σ. To represent the situation where the mean and the variance are correlated, I fixed the lower limit of the distribution at 0. Thus as the mean

2 1 a=2 a=2 AB

a=1 1 0 a=1 Environment

0 p 2 p -1 p 2 p Time

2 2 µ=1 C D σ =0.25 1 1 µ=2 σ =0.5

Probability Density Probability 0 2 4 0 -1 0 1 2 Environment

Figure 5-2. Four types of environmental variability: A) within-individual variation where the mean and standard deviation are positively correlated, B) within- individual variation where the mean is fixed at zero, C) among-individual variation where the mean and standard deviation are positively correlated, and D) among-individual variation where the mean is fixed at zero.

62 environment increased so did the variance and the upper limit (Fig. 5-2C). For the situation where only the variance changed, I fixed the mean at 0 and allowed the standard deviation to change (Fig. 5-2D). By using a normal distribution for both situations, as opposed to a gamma or lognormal distribution for the situation where the mean and variance are correlated, distributions were symmetrical about the mean, which facilitated comparisons.

Benefits

For convenience, I defined the benefit function so that phenotypes received the greatest benefit only when the phenotype and environment were equal. For example, if the environmental value equaled 0.7, the phenotype receiving the maximal benefit was also 0.7. If the environmental value changed to 0.2, then a phenotype with a value of 0.2 received the maximal benefit. An individual received a benefit less than the maximum when the phenotype and environment were not equal, and this decrease in benefit was proportional to the mismatch. Given the assumption that an individual can instantaneously respond, the benefit (b) received by an individual with a particular reaction norm is calculated as

be()=− 1 | e − pe ()|, (5-3) where e is the environmental value (also equal to the phenotype that receives that greatest benefit), and p(e) is the reaction norm that describes which phenotype is produced in an environment with a value of e. Thus, if an individual produced a phenotype of 0.8 in the environment with a value of 1, then in that environment it performed 20% worse than an individual that produced the phenotype with the maximal benefit (i.e., phenotype = 1).

Likewise, in the environment with a value of 0.5, an individual with the phenotype of 0.4 performs 10% worse than the phenotype of 0.5.

By substituting the reaction norms specified above (see Reaction norms),

Equation 5-3 was rewritten as

=− −i bes () 1 | e es |, (5-4) to compare different slopes, and as

=+− ber () min|1,(1 r ) e |, (5-5) to compare different ranges.

Costs

I incorporated two costs into this model. The first corresponds to the cost of producing a particular phenotype, typically referred to as a production cost (cp) (DeWitt et al. 1998), which I assumed was proportional to the size of the phenotype produced.

The second cost corresponds to the cost of maintaining the ability to respond (cm), which

I assumed was proportional to the largest possible phenotype that an individual could produce, and was paid regardless of whether the largest phenotype was produced. I incorporated this second cost because organisms that have the potential to respond to a wider range of environmental conditions must maintain some capacity to change (e.g., increased energy reserves). The cost function is therefore

=+ii ce() cmL p c p pe (), (5-6) where c is the total cost, cm is the cost of maintaining the ability to respond, pL is largest possible phenotype for that reaction norm, cp is the cost of responding, and p(e) is the phenotype in environment e.

Calculating Fitness

I calculated the absolute fitness for a genotype (i.e., a given reaction norm) as the average difference between the costs and benefits incurred in each environment (Eq. 5-7).

=−() () wbexcexdxij, ∫ ()(), (5-7) where x represents time for the within-individual environmental variation case and space for the among-individual case. The appropriate equations for benefits, costs, and environmental variation were substituted into Equation 5-7 to investigate the four different combinations of environmental variation (e.g., within-individual variation where the mean and variance are correlated)(Appendices A&B). Fitnesses for genotypes with slopes (s) and ranges (r) between 0 and 1.6 were calculated for environments with amounts of variation (either a, µ, or σ) between 0 and 1.6.

To facilitate comparisons, I calculated relative fitnesses by rescaling the absolute fitness of each genotype to the genotype with the maximum fitness in that environment:

w = ij, wRij, , (5-8) max(wj ) where wR,i,j is the relative fitness of genotype j in environment i, max(wj) is the fitness of the genotype with the greatest fitness in environment i, and wi,j is the absolute fitness of genotype j in environment i. The reaction norm with the largest absolute fitness for an environment with a given amount of variation had a relative fitness equal to 1.

To understand how the different costs of plasticity affect how the slope and range of plasticity evolve, I independently varied the two costs of plasticity.

Results

The results of the different models revealed that the slope and range of plasticity should be affected differently by selection imposed by environmental variation (Fig. 5-3).

Only two optimal slopes were ever found: 1) a slope equal to 0, which represents a fixed non-plastic phenotype, and 2) a slope equal to 1, in which the individual perfectly matched its environment. For the range of plasticity the results were quite different.

There were many optimal ranges of plasticity. These differences between the slope and range of plasticity are highlighted by comparing the relative fitnesses of reaction norms in environments with increasing variation (Fig. 5-3). The type of environmental variation did not qualitatively affect the results for the slope, or the range of plasticity (Fig. 5-3).

Within Individual Among Individuals µ&σ correlated µ fixed at 0 µ&σ correlated µ fixed at 0 1.6

1.0 Slope

0.0 0.00.8 1.6 0.00.8 1.6 0.00.8 1.6 0.00.8 1.6 1.6

1.0 Range

0.0 0.00.8 1.6 0.00.8 1.6 0.00.8 1.6 0.00.8 1.6 aa µ σ Amount of Environmental Variation

Figure 5-3. Evolutionary differences between the slope and range of plasticity. The relative fitnesses for the relationship between the slope or range of plasticity and the amount of environmental variation. White indicates a relative fitness of 1 and darker shades represent lower relative fitness. The dotted line indicates the slope or range of the genotype with the greatest fitness. The cost of both cm and cp is 0.2.

The benefit function alone produced a selective regime that affected the slope and range differently. When both cm and cp were 0, the most fit genotype had a reaction norm with a slope of 1 and the decreased benefits for genotypes with slopes other than 1 resulted in stabilizing selection, regardless of the amount of environmental variation

(Fig. 5-4). However, selection was directional and not stabilizing for the range of plasticity when costs were absent. Although ranges of plasticity less than the greatest environmental value experienced by a genotype had lower fitness (i.e., r < max(e)), there was a maximal fitness plateau and the relative fitness for all ranges greater than the maximum value of the environment were equal to 1.

1.6

0.8

0.0

1.6

0.8 Range Slope

0.0 0.10.8 1.6 a

Figure 5-4. Effect of just the benefit function. The relative fitness of the relationship between the slope or range of plasticity and the amount of environmental variation (a) when both cm and cp equal 0. White indicates a relative fitness of 1 and darker shades represent lower relative fitness. Because the results were similar for the different types of environmental variation, I have only presented the situation of within-individual variation where the mean and standard deviation is correlated.

The cost of maintaining the ability to respond (cm) and production costs (cp) had qualitatively different effects. As the cost of maintaining the ability to respond (cm) increased so did the amount of environmental variation at which there was a shift from an optimal genotype with a slope of 0 to an optimal genotype with a slope of 1 (Fig. 5-5,

st 1 column). Increasing the production cost (cp) only affected the strength of selection, and not the genotype with the optimal slope (Fig. 5-5, 2nd column). In contrast, increases in the costs had different effects on the range of plasticity than on the slope. When the cost of maintaining the ability to respond (cm) was small, selection was stabilizing and favored only the genotype with a range that equaled the greatest value of the environment experienced by a genotype. Thus, there was a positive linear relationship between the optimal range and the amount of environmental variation. As costs continued to increase this relationship between the optimal range and environmental variation became shallower until the cost was sufficiently large and selection favored a range of 0 (i.e., no plasticity), regardless of the amount of environmental variation (Fig. 5-5, 3rd column).

The production cost however, had no effect on the range of plasticity (Fig. 5-5,

4th column). Furthermore, the effects of each cost were independent and did not depend on the value of the other.

Discussion

Evolution of the slope of plasticity has been studied in previous models (Via &

Lande 1985, Lively 1986b, de Jong 1990, van Tienderen 1991). The results from the model presented here are qualitatively similar. That is, the genotype with a slope of plasticity that was identical to the benefit function (in the case of my model this was a slope of 1) had the highest fitness when costs were relatively low. However, when costs became sufficiently large, a non-plastic genotype was favored. No intermediate slopes

68 were ever optimal. The costs only affected the whether or not plasticity evolved. The model presented here also suggests that the amount of environmental variation affects whether a plastic genotype will evolve. As the amount of environmental variation increased, the costs sufficient to select against plastic genotypes also increase.

The amount of environmental variation had a much different affect on the range of plasticity than on the slope. When costs were relatively small the genotype with a range of plasticity equal to the greatest environmental value experienced by that genotype was

1.6 1.6 cm=0.1 cm=0 cm=0.1 cm=0 cp=0 cp=0.1 cp=0 cp=0.1 1.0 1.0

0.0 0.0 1.6 c =0.4 1.6 m cm=0 cm=0.4 cm=0 c =0 p cp=0.4 cp=0 cp=0.4 1.0 1.0

0.0 0.0 Range of plasticityRange Slope of plasticity 1.6 1.6 cm=0.7 cm=0 cm=0.7 cm=0 cp=0 cp=0.7 cp=0 cp=0.7

1.0 1.0

0.0 0.0 0.00.8 1.6 0.00.8 1.6 0.00.8 1.6 0.00.8 1.6 Amount of environmental variation (a)

Figure 5-5. Effect of the cost function. The relative fitness of the relationship between the slope or range of plasticity and the amount of environmental variation for different value combinations of cm and cp. White indicates a relative fitness of 1 and darker shades represent lower relative fitness. The dotted line indicates the slope or range of the genotype with the greatest fitness (the absence of a line indicates that more than 1 genotype had a relative fitness of 1, and the white area indicates the optimal fitness plateau). Because the results were similar for the different types of environmental variation, I have only presented the situation of within-individual variation where the mean and standard deviation is correlated.

69 optimal. As the costs increased, the range of the optimal genotype decreased, until the costs were sufficiently large and a genotype with a fixed phenotype was optimal. These results suggest that changes in the amount of long-term environmental variation are important for the evolution of the range of plasticity, and thus the shape of the reaction norm. In the few models that investigated the shape of reaction norms (Stearns & Koella

1986, Gomulkiewicz & Kirkpatrick 1991, Gavrilets & Scheiner 1993), the shape was affected by the benefit function or by genetic constraints. Thus, the underlying benefit function, genetic constraints, and the amount of environmental variation all appear to influence the evolution of reaction norm shape.

The costs of plasticity are known to have a strong influence on the evolution of plasticity (DeWitt et al. 1998, Pigliucci 2001). The production costs in particular are often sighted as important determinants for whether plasticity evolves (Tollrian &

Harvell 1998, Pigliucci 2001). However, the results presented here demonstrate that the production costs may have a stronger influence on the strength of selection for a particular slope than on whether plasticity evolves. In contrast, the cost of maintaining the ability to respond did influence whether plasticity evolved or not (effects on the slope of plasticity), and the optimal range of plasticity. This cost is especially interesting because it allowed selection to alter part of the reaction norm that was greater than any environmental value experienced by a genotype.

Interestingly, the predictions from this model are inconsistent with the results found in an empirical study (Van Buskirk 2002a). Van Buskirk investigated tadpoles of 16 species with a comparative analysis and demonstrated that there was a positive correlation among plasticity and the amount of environmental variation. Plasticity was

70 measured as the phenotypic difference between tadpoles reared with and without predatory dragonfly larvae. Thus, the slope of plasticity was quantified. However, my results suggest that the slope of plasticity should not correlate with the amount of environmental variation.

One possible explanation for the incongruence between the model and Van

Buskirk's results is that estimates of changes in the slope of the reaction norm actually represent changes in the range of plasticity. When phenotypes are only measured in two environments the slope and range of plasticity can be confounded (Fig. 5-6). Thus, Van

Buskirk’s measurement of “plasticity” might represent changes in the range of plasticity.

Indeed, the model predicts that increased environmental variation should lead to

Most variable A

Least variable Phenotype

B Phenotype

Environment

Figure 5-6. Example of how changes in the range of plasticity can be confounded with changes in the slope of plasticity. A) Represents the true reaction norms of 4 hypothetical populations, and B) represents our interpretation if measurements are only made at the arrows.

71 increased range of plasticity, a prediction that is consistent with the data. More investigations that quantify the shape of the reaction norm, by considering more than two environments, are needed to determine whether presumed differences among the slopes of reaction norms found in the common two-environment experiment actually represent differences in the range of plasticity (vs. true differences in slopes).

To summarize, I have investigated one aspect of reaction norm shape, which I called the range of plasticity, and found that it responded fundamentally differently to changes in long-term patterns of environmental variation than the slope of a reaction norm. In addition, the discrepancy between predictions of the models presented in this paper and an empirical study highlights the growing need for empirical studies that investigate more than two environments.

CHAPTER 6 SUMMARY AND CONCLUSIONS

Adaptive Plasticity in Plutei

There are several requirements for natural selection to favor or maintain phenotypic plasticity. First, the environment must vary. Second, there must be reliable cues so that an organism can detect the current or future state of the environment. Third, plasticity must be heritable. Lastly, there must be a fitness trade-off between the alternative phenotypes so that one is not the most fit in all environments. However, to actually document that plasticity is favored or maintained by natural selection is difficult, and multiple lines of evidence are important for determining whether plasticity is adaptive.

Here, I review the evidence that supports the hypothesis that plasticity is adaptive in plutei.

The response in feeding structure to food concentrations suggests that feeding structure plasticity is adaptive in plutei. Furthermore, the response is in the opposite direction to the expected effect of food. That is, larvae produce a longer (not shorter) feeding structure when food is scarce than when food is abundant. However, this is hardly conclusive evidence that the response to food is adaptive.

The functional consequences of long arms and short arms provide further evidence for the adaptive nature of plasticity in plutei. Hart and Strathmann (1994) demonstrated that larvae with longer arms captured more food than larvae with shorter arms, suggesting that long arms are advantageous when food concentrations are low. The advantage of short arms when food is abundant is less clear. The current hypothesis is that short arms

72 73 require less energy and this “saved” energy is allocated to the development of juvenile structures (Strathmann et al. 1992, Hart & Strathmann 1994). The accelerated development of juvenile structures reduces the larval period and hence decreases mortality. The difficulty with testing this hypothesis is that the effects of short arms and increased food cannot be decoupled. The experiments in Chapter 2 were an attempt to

“trick” plutei (with plastic beads) into producing short arms, and thus decouple food and morphology. In Chapter 3, I provide evidence for an alternative advantage of short arms.

My experiments suggest that there is a trade-off between longer arms and larger stomachs. It is therefore possible that long arms allow larvae to capture more food, and large stomachs allow larvae to process more food. Yet, because of energy constraints larvae cannot produce both long arms and large stomachs. Thus, selection has favored larvae that produce long arms when food is scarce (allowing them to capture more food), and short arms/large stomachs when food is abundant (allowing them to process more food).

In Chapter 2, I demonstrate that plutei use cues (chemicals emitted by algae or actual contact with algae), which are similar to the cues used by other species for which there is evidence that plasticity is adaptive. This widespread use of chemical cues and physical contact among species with presumably adaptive plasticity suggests that these types of cues are reliable, and are adequate for the evolution of plasticity.

The last line of evidence is that feeding structure plasticity occurs in larvae of many other species, both within echinoderms (Boidron-Mètairon 1988, Hart & Scheibling

1998, Strathmann et al. 1992, George 1994, 1999, Fenaux et al. 1994, Bertram &

Strathmann 1998, Herrera 1998, Reitzel 2002, B.G. unplub. data), and molluscs

(Strathmann et al. 1993, Estrella Klinzing & Pechenik 2000). This phylogenetic distribution of plasticity represents one or both of the following situations: 1) plasticity has been maintained within these groups for much of the Phanerozoic, or 2) plasticity is convergent and has evolved independently more than once. In addition, larvae of species that are less dependent on exogenous food (i.e., develop from relatively large eggs) are less responsive to food concentrations than more food-dependent species (Table 6-1).

This observation is consistent with the adaptive hypothesis; as food becomes less important for plutei the advantages of larval feeding structure plasticity decrease and selection favors a fixed phenotype.

Taken together, these lines of evidence support the idea that plasticity is adaptive in plutei. In addition, evidence presented here for plutei also has implications for whether

Table 6-1. Review of studies demonstrating feeding-structure plasticity in echinoids. Early response was in larvae with < 8 arms. Species Egg size Early Response Reference response Strongylocentrotus 80 µm Yes Yes Ch. 2&3 purpuratus Paracentrotus 90 µm Yes Yes Strathmann et al. 1992, lividus Fenaux et al. 1994 Melitta tenius 100 µm Yes Yes Reitzel 2002 Lytechinus 110 µm Yes Yes Boidron-Mètairon 1988, variegatus McEdward & Herrera 1999, Ch. 4 Strongylocentrotus 115 µm Yes Yes Ch. 3 franciscanus Dendraster 125 µm Yes Yes Boidron-Mètairon 1988, excentricus Hart 1994, Ch. 2 Clypeaster 150 µm Yes Yes Reitzel 2002 subdepressus Strongylocentrotus 160 µm Yes Yes Bertram and Strathmann droechachiensis 1998 Encope michelini 175 µm No No Reitzel 2002 Leodia 190 µm No No Reitzel 2002 sexiesperforata Clypeaster rosaceus 260 µm No Yes B. Miner unplub. data

75 plasticity is adaptive in larvae of other groups (Strathmann et al. 1993, George 1994,

1999, Estrella Klinzing & Pechenik 2000).

Environmental Variation and Phenotypic Plasticity

Plastic responses to environmental variation experienced by an individual have received little attention, and most studies rear individuals in different but constant environments. However, within-individual environmental variation (i.e., fine-grained variation) is probably common in nature. For example, plutei probably experience both short- and long-term variation in food concentrations, because plutei make daily vertical migrations in the water column and food fluctuates both temporally and spatially over very small scales (e.g., convergence zones and vertical mixing by currents and winds) and large scales (e.g., upwelling and relaxation periods and seasonal changes).

The question therefore arises, can plutei respond to the amount of environmental variation experienced by an individual? Results from my experiment suggest that plutei do respond to the amount of environmental variation. This response to the amount of within-individual environmental variation provides a previously unrecognized situation for when plasticity can evolve. That is, when the environment fluctuates too quickly for an individual to respond to the changes in the mean, individuals might evolve the ability to respond to the variance instead of evolving a fixed phenotype. Similar requirements are necessary for a response to variation as for a response to the mean condition. In addition, because changes in the mean condition and variation in that condition might be correlated in nature, it is possible that a response to different mean conditions might actually represent a response to different amounts of variation in that condition. This highlights the need to further study responses to within-individual environmental variation (Winn 1996).

In contrast to empirical studies, theoretical studies have considered the effects of both within- and among-individual environmental variation. Though none have addressed how the amount of environmental variation affects the evolution of plasticity.

I developed a simple model to answer this question. With this model, I investigated how natural selection might affect a reaction norm when long-term patterns in the amount of environmental variation change. The model demonstrated that the shape of the reaction norm was more sensitive to changes in the amount of environmental variation than the slope of the reaction norm. This model highlights the need to separate changes in the slope of a reaction norm from changes in the shape of a reaction norm, and provides new insights into how the evolution of plasticity is affected by the amount of environmental variation. Both Chapters 4 and 5 provided new insights in the evolution of plasticity.

Future Directions

I briefly discuss several future directions that are logical extensions of my dissertation research, as well as other topics that I wish to pursue.

Adaptive Larval Feeding-Structure Plasticity

• Investigate genetic variation for feeding structure plasticity in plutei.

• A formal phylogenetic analysis of larval feeding structure plasticity. This was an alternative dissertation chapter that I decided not to include.

Environmental Variation and the Evolution of Plasticity

• Study the effects of frequency and variance in isolation to determine how they influence the response to environmental variation.

• Study whether species have different ranges of plasticity.

• Further develop my model to investigate how natural selection might affect other non-linear reaction norms.

Other Directions

• Developmental mechanisms that underlie larval feeding plasticity in plutei.

• A study investigating the contribution of plasticity to non-additive effects of multiple predators. This will be the focus of my postdoctoral work.

APPENDIX A FITNESS CALCULATIONS FOR THE SLOPE OF PLASTICITY

Within individual environmental variation Mean and variance correlated

aaaa aa was( , )=−−∫ 1iiiii cos(2 t ) +−− s cos(2 t ) + − c s − c s − cos(2 t ) + dt 2222mp 22

Mean fixed

aa a was( , )=−∫ 1ii sin(2 t ) − s sin(2 t ) − c iii s − c s sin(2 t ) dt 22mp 2

Within genotype environment variation Mean and variance correlated

µµσ=−−−−iiii ws(,)∫ d (Cmp ,,)1 e() esecscsede

Mean fixed

σσ=−−−−iiii ws(,)∫ d (0,, e )1() esecscsedemp

The equations used to calculate fitness of different slopes of plasticity. For the two situations where the mean is fixed at zero, the absolute value of environment is required so that negative values do not cancel positive one. For the within genotype variation, the function d(x,y,z) equals the probability density function with a normal distribution.

78 APPENDIX B FITNESS CALCULATIONS FOR THE RANGE OF PLASTICITY

Within individual environmental variation Mean and variance correlated

aa aa war( , )=+−−+−−−+∫ min 1,() 1 riiii cos(2 t ) c s c s cos(2 t ) dt 22mp 22

Mean fixed

aa war( , )=+−−−∫ min 1,() 1 riiii sin(2 t ) c s c s sin(2 t ) dt 22mp

Within genotype environment variation Mean and variance correlated

µµσµ=+−−−() iii wr(,)∫ d (, (),)min1,1 e() recscsedemp

Mean fixed

σσ=+−−−() iii wr(,)∫ d (0,, e )min1,1() recscsedemp

The equations used to calculate fitness of different ranges of plasticity. For the two situations where the mean is fixed at zero, the absolute value of environment is required so that negative values do not cancel positive one. For the within genotype variation, the function d(x,y,z) equals the probability density function with a normal distribution.

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BIOGRAPHICAL SKETCH

Benjamin G. Miner was born on April 27, 1972 in Rome, New York. After

2 months he moved to southern California where he grew up. During his childhood and adolescent years, he spent much of his time at the beach surfing and bodyboarding, and amazingly graduated from El Toro High School in 1990. He attended Irvine Valley

Community College for 2 ½ years, and then transferred to the University of California,

Santa Cruz, where in 1996 he earned a Bachelor of Arts degree in Marine Science.

After graduation, Ben was hired as a summer intern at the California Academy of

Sciences in San Francisco with Rich Mooi. Ben continued to pursue work in academics and was hired by Steve Gaines as a research technician in the fall of 1996, where he worked until the fall of 1997.

In the winter of 1998, Ben started graduate school at the University of Florida with

Larry McEdward. After several failed dissertation ideas, he settled on the topic of phenotypic plasticity in echinoid larvae. In addition to the work done at the University of

Florida, he conducted experiments at the Bodega Marine Laboratories (CA), the Friday

Harbor Laboratories (WA), and the Keys Marine Laboratories (FL). In the summer of

2001, Ben’s advisor, Larry McEdward, passed away unexpectedly. Ben transferred to

Colette St. Mary’s and Craig Osenberg’s labs where he completed his dissertation in the fall of 2003.

After graduation, Ben will move back to California where he will start a 3-year postdoctoral position with Steven Morgan at the Bodega Marine Laboratories, University

93 94 of California, Davis. The project will focus on how phenotypic plasticity influences the dynamics between prey (intertidal mussels) and multiple predators (seastars, whelks, and crabs).