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MOLLUSKS AS ECOLOGICAL INDICATORS: EXPLORING ENVIRONMENTAL AND ECOLOGICAL DRIVERS OF BIOLOGICAL AND MORPHOLOGICAL DIVERSITY USING MOLLUSKS THROUGH SPACE AND TIME

SAHALE CASEBOLT

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

2017

To my parents

ACKNOWLEDGMENTS

I thank my advisor, Dr. Michał Kowalewski, as well as members of my dissertation committee (Dr. Mark Brenner, Dr. Doug Jones, Dr. Ellen Martin, Dr. Gustav

Paulay, and Dr. Tom Frazer), for support and assistance with completion of this dissertation. I also thank the numerous graduate students, post docs, and museum staff members whose comments, feedback, and suggestions were helpful throughout my time as a graduate student. These people include, but are not limited to: Jackie Wittmer,

Carrie Tyler, Troy Dexter, Austin Hendy, Adiel Klompmaker, Alexis Rojas, Katherine

Cummings, Katherine Estes, Roger Portell, John Slapcinsky, Felipe Opazo, Savanna

Barry, Shamindri Tennakoon, Kris Kusnerik, and Laura Cotton.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 7

LIST OF FIGURES ...... 8

ABSTRACT ...... 10

CHAPTER

1 INTRODUCTION TO DISSERTATION ...... 12

2 SPECIES-SPECIFIC DIFFERENCES IN MORPHOLOGICAL VARIABILITY AND DISPARITY WITHIN THE GENUS ANADARA (BIVALVIA) ...... 17

Abstract for Chapter 2 ...... 17 Introduction for Chapter 2 ...... 18 Materials and Methods for Chapter 2 ...... 21 Specimen Selection and Imaging ...... 21 Museum Lots: Sampling Units of Individual Populations ...... 23 Shell Shape Measurement ...... 24 Measuring Morphological Variability ...... 26 Results for Chapter 2 ...... 27 Morphospecies Ordination ...... 27 Morphological Variation and Disparity: Intra-population Morphological Variation ...... 29 Morphological Variation and Disparity: Intraspecific Morphological Variation ... 30 Morphological Variation and Disparity: Intraspecific Disparity ...... 31 Morphological Variation and Disparity: Interspecific Morphological Variation ... 32 Confounding Factors: Allometry and Sampling Coverage ...... 33 Discussion for Chapter 2 ...... 34 Landmark-Based Discrimination of Morphospecies ...... 34 Morphological Variability...... 35 Intraspecific and Interspecific Variation and Disparity ...... 38 The Potential Role of Geography in Morphological Variation ...... 38 Summary for Chapter 2...... 39

3 MOLLUSK SHELLS ARCHIVE SPATIAL STRUCTURING WITHIN BENTHIC COMMUNITIES AROUND SUBTROPICAL ISLANDS ...... 47

Abstract for Chapter 3 ...... 47 Introduction for Chapter 3 ...... 48 Materials and Methods for Chapter 3 ...... 50 Study Area ...... 50

Sampling Methods ...... 50 Analytical Methods ...... 53 Results for Chapter 3 ...... 55 Taxonomic Composition of the Samples ...... 55 Taxon Abundance and Occurrence Patterns ...... 56 Tests of Statistical Significance and Beta Diversity ...... 60 Multivariate Ordination ...... 61 Species Dominance and Evenness Patterns ...... 63 Beta Diversity ...... 63 Pairwise Comparisons and Spatial Structuring ...... 64 Discussion for Chapter 3 ...... 65 Taxonomic Composition of Molluscan Assemblages ...... 65 Small and Large-scale Spatial Structuring...... 65 The influence of Energy Level on Molluscan Assemblages...... 66 The Influence of Seagrass Habitat on Mollusk Assemblages ...... 69 Evenness and Species Dominance Patterns ...... 72 Summary for Chapter 3...... 73

4 SPATIAL BIODIVERSITY PATTERNS IN SEAGRASS-ASSOCIATED MOLLUSK COMMUNITIES ALONG FLORIDA’S GULF COAST ...... 87

Abstract for Chapter 4 ...... 87 Introduction for Chapter 4 ...... 88 Materials and Methods for Chapter 4 ...... 90 Site Selection and Collection Methods ...... 90 Sample Processing ...... 91 Analytical Methods ...... 92 Results for Chapter 4 ...... 93 Sample Taxonomic Composition and Rank Abundance ...... 93 Richness and Evenness ...... 94 Statistical Significance ...... 95 Rarefaction Curves ...... 96 Discussion for Chapter 4 ...... 96 Summary for Chapter 4...... 99

5 CONCLUDING REMARKS ...... 115

APPENDIX

A LIST OF SAN SALVADOR MOLLUSK TAXA AND TOTAL OCCURRENCES ..... 118

B LIST OF SEAGRASS-ASSOCIATED FLORIDA GULF COAST MOLLUSK TAXA ...... 123

LIST OF REFERENCES ...... 126

BIOGRAPHICAL SKETCH ...... 142

LIST OF TABLES

Table page

2-1 Multivariate tests for pairwise differences between species centroids (δ2)...... 28

2-2 Morphological variation (δ1) within species...... 31

2-3 Intraspecific and interspecific disparity (δ2) measured as mean pairwise...... 32

3-1 List of transect localities, sample numbers, and corresponding environmental .. 51

3-2 Taxa found only on the windward side of the island. Columns show the total .... 58

3-3 Taxa found only on the leeward side of island. Columns show the total shell .... 59

3-4 Beta diversity as measured in all data, and within groups of samples...... 61

4-1 Median evenness and diversity values for each of the five estuaries...... 95

LIST OF FIGURES

Figure page

2-1 Representative specimens of the seven Anadara taxa included ...... 41

2-2 A regional map of localities of the 359 Anadara specimens analyzed here ...... 42

2-3 Landmark locations ...... 43

2-4 Principal components ordination of all specimens based on geometric ...... 44

2-5 Morphological variation (δ1) within museum lots ...... 45

2-6 Disparity [δ2] of lots...... 46

3-1 Map of San Salvador Island, Bahamas ...... 75

3-2 Sediment sample collection along transects in seagrass...... 76

3-3 The 25 most abundant mollusk species in the entire dataset ...... 77

3-4 The 25 most common mollusk species in the entire dataset ...... 78

3-5 The probability (p) of finding a leeward or windward only species ...... 79

3-6 Nonmetric multidimensional scaling (NMDS) plot ...... 80

3-7 Non-metric multidimensional scaling (NMDS) plots with convex hulls ...... 81

3-8 Standardized species richness and species diversity ...... 82

3-9 Beta diversity of nine groups of samples ...... 83

3-10 Each point shows two measurements of similarity ...... 84

3-11 Evenness ...... 85

3-12 Rank abundance distribution plot of mollusk species in seagrass ...... 86

4-1 Locations of the five Florida Gulf Coast estuarine systems ...... 101

4-2 A rank abundance distribution curve (Whittaker plot) for the pooled death ...... 102

4-3 A rank abundance distribution curve (Whittaker plot) for the pooled live ...... 103

4-4 Evenness (Hurlbert’s PIE) and standardized richness of samples (n > 30) ...... 104

4-5 Evenness and standardized richness of samples (n > 30) color-coded by ...... 105

4-6 Evenness and standardized richness of sites (n > 45) color-coded by estuary 106

4-7 Box plots of sample-level comparison of standardized diversity and evenness 107

4-8 Box plots of site-level (within locality) comparison of standardized diversity .... 108

4-9 Comparison of standardized richness of dead and live assemblages...... 109

4-10 Evenness and diversity (richness) for samples ...... 110

4-11 Rarefaction curves of death assemblages of individual samples color-coded .. 111

4-12 Rarefaction curves of live assemblages of individual samples color-coded. .... 112

4-13 Rarefaction curves for death assemblages in each of the five estuaries...... 113

4-14 Rarefaction curves for live assemblages in each of the five estuaries...... 114

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

MOLLUSKS AS ECOLOGICAL INDICATORS: EXPLORING ENVIRONMENTAL AND ECOLOGICAL DRIVERS OF BIOLOGICAL AND MORPHOLOGICAL DIVERSITY USING MOLLUSKS THROUGH SPACE AND TIME

Sahale Casebolt

August 2017

Chair: Michał Kowalewski Major: Geology

Mollusks and their associated death assemblages (accumulations of dead shell material) can be used to evaluate and document ecological, geological, and evolutionary patterns and processes. The three research projects in this dissertation explore different ways that mollusks and their death assemblages can be used as ecological indicators and as a way to address fundamental questions about morphological variation. The first project (Chapter 2) uses museum specimens to examine morphological patterns in Caribbean and Central American members of the bivalve genus Anadara (Arcidae), and finds that populations and species of this bivalve genus may differ inherently in terms of within-group morphological variability and among-group disparity. The second project (Chapter 3) uses assemblages of mollusk shells to assess the spatial organization of mollusk communities on San Salvador

Island, Bahamas, and finds that benthic mollusk communities are characterized by a predictable spatial organization controlled primarily by physical (storm energy) and, secondarily, biological (seagrass vegetation) processes. The third project (Chapter 4) explores patterns of mollusk taxonomic diversity within seagrass ecosystems along the

Gulf coast of peninsular Florida, highlighting the utility of live/dead studies and nested spatial hierarchies as a way to assess different aspects of regional ecosystem biodiversity. Together, the three projects that comprise this dissertation examine environmental and ecological drivers of mollusk diversity across multiple spatial and temporal scales in an attempt to contribute to the larger body of knowledge on mollusks, and in particular, to understand their potential utility in ecological assessment and conservation paleobiology.

CHAPTER 1 INTRODUCTION TO DISSERTATION

The research in this dissertation covers a broad range of paleontological and ecological topics, but is unified by the theme of utilizing information provided by mollusk assemblages. Mollusks can be used to evaluate and document both large-scale and small-scale ecological, geological, and evolutionary patterns. This information exists in multiple forms, including mollusk taxonomic diversity, mollusk species abundances, and mollusk morphological characteristics.

Mollusks are a diverse phylum with marine, freshwater, and terrestrial species.

They have a long evolutionary history and a relatively complete fossil record due to the high preservation potential of their shells (Boardman et al., 1987). The number of described extant species is estimated to be somewhere around 100,000, and the number of described fossil species is estimated to be around 35,000 (Ruppert et al.,

2009), but their true taxonomic diversity remains unknown. Recent research suggests that the actual number of species may be much larger than previously realized as a consequence of numerous cryptic and undescribed taxa (Bouchet et al., 2002).

Mollusks have successfully adapted to a variety of environments, and within these environments they perform many important ecological functions (Gutierrez et al.,

2003). For example, mussels and oysters filter large amounts of water and provide physical structure that acts as refugia and breeding areas for many other species (Coen et al., 2007). Mollusks also fill roles as an important food source for other organisms, as herbivorous grazers, and as active predators in marine environments. Because of these ecological roles, mollusks can be important indicators of various aspects of the ecosystems in which they reside.

Examples of the use of mollusks as environmental indicators are pervasive in the scientific literature, spanning a wide range of environments in all regions of the world, as well as across multiple scientific subdisciplines (e.g. environmental science, ecology, paleontology, geology). For example, mollusks have been used to: 1) provide information about water contamination in Florida (Cantillo et al., 1997), 2) diagnose environmental changes caused by nutrient pollution (Ferguson, 2008), 3) indicate ecosystem conditions in places like the Amazon (Aller, 1995), and 4) provide information relevant to habitat conservation in terrestrial environments (Douglas, 2011;

Shimek, 1930).

In addition to their utility for modern ecological research, mollusk shells and their associated death assemblages (accumulations of dead shell material) can provide valuable and otherwise unknowable information about past ecosystems (Kidwell, 2002,

2007, 2013b; Rousseau et al., 1993) and evolutionary trends (Crampton and Maxwell,

2000b; Geary et al., 2010; Geary et al., 2002; Roopnarine, 1995). Death assemblages have been demonstrated to be informative about modifications in shallow marine systems (Kidwell, 2009) and fossil/subfossil shell remains can be a valuable tool in the emerging field of conservation paleobiology (Dietl and Flessa, 2011; Dietl et al., 2015).

This interdisciplinary field uses multiple lines of evidence from past conditions to inform modern conservation (Miller, 2011; Rick and Lockwood, 2013; Willis and Birks, 2006), for which mollusk shells are among the most useful and powerful lines of evidence

(Kowalewski et al., 2009).

The three research projects in this dissertation explore different ways that mollusks and their death assemblages can be used as ecological indicators to address

fundamental questions about morphological evolution. The projects span spatial scales from local, such as within the confines of the relatively small San Salvador Island in the

Bahamian archipelago (Chapter 3), to the focused regional scale of Florida’s Gulf Coast systems (Chapter 4), to the more broadly regional scale of the Caribbean and western

Pacific fossil and modern Anadara bivalves (Chapter 2). In addition to the spatial component of the research, these projects also have a paleontological (i.e. time) component. Although many of the specimens utilized in this dissertation were modern, a large number were either subfossils (i.e. shell assemblages collected from modern marine environments, which nevertheless usually contain shells that are several hundreds or thousands of years old), or true fossils specimens, as with those collected in the Miocene-age lithological formations of the Panama Canal (i.e. many of the specimens in Chapter 2).

The research project entitled “Species-specific differences in morphological variability and disparity within the genus Anadara (Bivalvia)” (Chapter 2), uses monospecific lots from museum collections as proxies for populations to examine morphological patterns in seven species of the bivalve genus Anadara (Arcidae). These species include the Central American fossil taxon Anadara dariensis, and modern species from both the Western Atlantic and the Eastern Pacific. Morphological variation and disparity vary greatly across and within these taxa, and most of this variability is manifested at the intra-population level (i.e. within individual museum lots). These differences in variability cannot be explained by time-averaging, allometry, or differential sampling coverage. Results from this research suggest that morphological disparity may be a composite, multi-scale product of extrinsic and intrinsic factors and that populations

and species may inherently differ in terms of within-group morphological variability and among-group disparity.

The research project entitled “Mollusk shells archive spatial structuring within benthic communities around subtropical islands” (Chapter 3), uses surficial assemblages of mollusk shells to assess spatial organization of local benthic ecosystems. Bulk sediment samples, collected along transects around San Salvador

Island in the Bahamas, were analyzed to evaluate the distribution and ecological characteristics of mollusk-dominated benthic communities. The bulk samples yielded a total of 20,608 specimens, which represented a minimum of 181 mollusk species.

Indirect multivariate ordinations (NMDS) separated samples by locality, even in the case of transects that were sampled in different parts of the same bay, indicating that shell assemblages faithfully archive local differences in mollusk communities. At the island- wide scale, a clear faunal separation is observed between the windward and leeward sides of the island, suggesting that water energy represents an overriding regional driver controlling local mollusk community composition. Within each of these energy regimes, the faunal composition of mollusk assemblages is controlled primarily by the presence or absence of seagrass vegetation. This research highlights the use of benthic mollusk communities to characterize a predictable spatial organization controlled primarily by physical (wind energy) and, secondarily, biological (seagrass vegetation) processes. This research suggests that this type of non-invasive sampling of dead mollusks is a viable strategy for examining processes that drive spatial structuring of marine communities.

The research project entitled “Spatial biodiversity patterns in seagrass- associated mollusk communities along Florida’s Gulf Coast” (Chapter 4), looks at alpha diversity in mollusk communities and assemblages of the seagrass ecosystems in the

Gulf Coast of Florida. This project highlights the unique mollusk biodiversity of Florida’s

Gulf Coast estuarine systems and the importance of accounting for differences in individual estuarine systems and multiple levels of spatial scale to capture seagrass ecosystem biodiversity.

Together, these projects aspire to contribute to the body of research on mollusk ecology, diversity, and evolution. There is increased urgency, given the unprecedented pace of environmental change in recent years, for research that can help us understand all aspects of Earth’s marine ecosystems, and the ways in which they have been in the past, and how they may change in the future.

CHAPTER 2 SPECIES-SPECIFIC DIFFERENCES IN MORPHOLOGICAL VARIABILITY AND DISPARITY WITHIN THE GENUS ANADARA (BIVALVIA)

Abstract for Chapter 2

The morphological variation that distinguishes biological species, and underlies most concepts of biological diversity, can be quantified in many fossil and modern taxa using geometric morphometrics, including landmark-based measures of within-group morphological variability and among-group disparity. Here, using monospecific lots from museum collections (i.e., proxies for populations), we examined intrapopulation, intraspecific, and interspecific morphological patterns of 359 valves within and between seven congeneric species of the bivalve genus Anadara (Arcidae). These species include the Central American fossil taxon Anadara dariensis, and modern species from both the Western Atlantic and the Eastern Pacific. The fossil species Anadara dariensis has an intermediate morphology relative to the six modern Anadara taxa included in this analysis and displays strongest morphological overlap with Pacific species of Anadara.

Morphological variation and disparity vary greatly across and within these taxa, and most of this variability is manifested at the intra-population level, i.e., within individual museum lots. These differences in variability cannot be explained by time-averaging, allometry, or differential sampling coverage. The results indicate that morphological disparity may be a composite, multi-scale product of extrinsic and intrinsic factors.

Regardless of causation, this analysis indicates that, even within congeneric taxa, populations and species may differ inherently in terms of within-group morphological variability and among-group disparity.

Introduction for Chapter 2

Bivalve shells are highly variable in morphology across orders and families

(Gosling, 2003; Ruppert et al., 2009), which is a testament to the diversity of their life styles (Stanley, 1970a) and the length and complexity of their evolutionary histories

(Cheetham et al., 1987). Although this morphological variation is reduced within genera and species, these finer taxonomic units can nevertheless yield empirical data that augment our understanding of ecological and evolutionary processes (e.g. (Kelley,

1989; Marko and Jackson, 2001; Sousa et al., 2007).

Variation in bivalve morphology, observed within and across congeneric species, reflects a combined effect of individual growth trajectories (Alunno-Bruscia et al., 2001;

Crampton and Maxwell, 2000a), shifts or fluctuations in the average shape within populations over time (Geary et al., 2010; Stanley and Yang, 1987), and differing amounts of genetic or ecophenotypic variation naturally present in bivalve populations and species (Krapivka et al., 2007; Laudien et al., 2003; Sousa et al., 2007). The resulting variation in morphology, or lack thereof, within and among bivalve species and populations may be a consequence of scale-dependent environmental, biogeographic, and evolutionary processes that underlie important but often poorly understood biodiversity patterns.

In this study, we use a subsample of species from the genus Anadara as a model system to examine morphological variability within and across congeneric species. The genus Anadara (Arcidae) is widespread in the fossil record of Central America (Todd,

2001) and is often among the most abundant taxa in particular stratigraphic units

(Jackson et al., 1999). Like fossil Anadara taxa, the extant species of Anadara are also relatively abundant, and some are economically important (e.g. locally harvested for

food). Huber (Huber, 2010) reported as many as 14 extant Eastern Pacific species and seven extant Caribbean species of Anadara belonging to four subgenera. Despite their ubiquity, importance, and abundance, the phylogenetic relationships among these species remains highly uncertain (Todd, 2001). To provide a morphometric perspective on Anadara species, we assess morphological variability within and across monospecific populations (approximated by museum lots), which are expected to be meaningful phylogenetic units.

Although the genus Anadara purportedly has 69 extant species within seven subgenera (Huber, 2010), and is globally distributed, we restricted our analysis to seven

Central American species that exhibit an elongated shell shape. Following Huber

(2010), this subset contains species from multiple subgenera within the genus (five species from the subgenus Anadara (Anadara), one species from the subgenus

Anadara (Diluvarca), and one species from the subgenus Anadara (Scapharca)). We focused our analysis exclusively on elongated forms because the inclusion of the rounder forms would have broadened the morphological range of the analysis, thereby obscuring minor differences within and among the elongate forms. Including only elongate forms ensures that we are consistent with our research objective of assessing variability in a subset of congeneric species, maximally constrained in terms of morphology. Furthermore, our taxon selection aims to restrict data to a specific region, so morphologically appropriate species occurring outside of central America are excluded from our analysis (e.g., Anadara crebricostata and Anadara jousseaumei, which occur in Australia and Malaysia, respectively). Finally, the selected species set reflects the limitations of the availability of museum specimens. For example,

specimens of Anadara formosa, Anadara biangulata and Anadara tuberculosa are elongated forms that occur in Central America, but were not available in sufficient quantities within the timeframe of this project. The resulting seven species that we use for this analysis, although not an all-inclusive monophyletic clade, nevertheless retains its utility as a regional morphological group. Although there are published molecular analyses of subsets of Arcidae bivalves (Marko, 2002), the taxonomic and phylogenetic status of the family, including Anadara and its subgenera, remains largely unresolved.

Any attempt to restrict our species selection to a perfectly monophyletic group would be somewhat circumspect, even if we comprehensively examined morphologic variation within and among all known species of a single subgenus (e.g. Anadara (Anadara)).

This is a common and familiar problem for paleontologists, for whom gaps in the phylogeny are the norm. Our patchy taxon selection is not an insurmountable hurdle, nor does it invalidate the analysis, because we do not claim to represent a comprehensive analysis of morphological variation within and among Anadara. Instead, we aim to measure this variation within a morphologically constrained subset of this clade to explore morphological variations at the finest scales of populations, species, and congeneric taxa.

Biologists, and in particular paleontologists, have long been interested in documenting patterns of morphological variability. Morphological variability can be measured numerically using multivariate methods (Briggs et al., 1992). Many of these investigations were conducted across large temporal and spatial scales, where disparity

(which can be thought of as the between-group difference in morphology) is measured and compared across multiple clades or higher taxa. For example, multiple studies have

investigated the dynamics of morphological disparity during major geological transitions, including mass extinctions, climate shifts, or other significant global shifts in Earth’s history (Briggs et al., 1992; Foote, 1993, 1994; Hughes et al., 2013; Kolbe et al., 2011;

Lupia, 1999; Shen et al., 2008). However, investigations of morphological variability within smaller taxonomic units such as genera, species, and populations can also be informative (Balatanas et al., 2002; Collar et al., 2005; Mahler et al., 2010) by providing insights into a variety of evolutionary and morphological topics. Here we aim to measure and compare fine-scale morphological variability and explore how morphological variability changes within and across a subset of morphologically similar species within the genus Anadara.

Materials and Methods for Chapter 2

Specimen Selection and Imaging

For this analysis we compared a total of 359 left valves of seven different

Anadara species from the invertebrate zoology and paleontology collections at the

Florida Museum of Natural History (FLMNH), the Smithsonian Institution (USNM), and the Los Angeles County Museum (LACM). The seven species (Figure 2-1) are: Anadara

(Rasia) dariensis (203 specimens in 29 lots), Anadara (Anadara) notabilis (Röding,

1798) (32 specimens in 4 lots), Anadara baughmani Hertlein, 1951 [=Anadara

(Diluvarca) secernenda (Lamy, 1907)] (10 specimens in 3 lots), Anadara (Scapharca) transversa (Say, 1822) (33 specimens in 4 lots), Anadara floridana (Conrad, 1869)

[=Anadara (Anadara) secticostata (Reeve, 1844)] (22 specimens in 3 lots), Anadara

(Anadara) concinna (Sowerby I, 1833) (50 specimens in 11 lots), and Anadara

(Anadara) emarginata (Sowerby I, 1833) (9 specimens in 8 lots). These species occur in different subregions of Central America (Figure 2-2). Anadara dariensis is a fossil

known from throughout the southern Caribbean, but is most notable for occurring in the richly fossiliferous Gatun Formation (Jackson et al., 1999; Hendy, 2013). Two of the extant species occur in the Eastern Pacific, and the remaining four species occur in the

Western Atlantic.

All specimens used in this study were obtained from existing museum collections with the permission of collection managers or curators. Images were acquired and no specimens were destroyed or permanently removed from the collections. No collecting or other research permits are required for acquisition of images from museum specimens.

The seven selected species share similar shell morphology (elongate- subquadrate) and are thus part of a morphologically distinct subset of all Anadara species, a genus that also includes non-elongate morphospecies (e.g., Anadara bifrons,

Anadara aequatorialis, Anadara cepoides). The selected species also have a long, straight hinge line. As a testament to their similarity, several of the specimens were misidentified in the museum collections and subsequently reassigned to the correct species for this project. The morphological similarities among the seven Anadara species included in this analysis do not necessarily suggest that the species designations are suspect, but rather that the relative similarities and differences in morphology may be informative for inferring shared evolutionary history, convergence, or environmental influences on shell shape.

The seven species differ in their radial rib number (information not captured in our landmark scheme described below), umbo prominence, hinge length relative to valve length, and in the curvature of the anterior and posterior ends of the valves. These

differences still overlap to varying degrees among the seven species. Curvature of the anterior and posterior ends of the valves, and the umbo prominence, as well as other subtle shape differences within and between the seven species, is captured in the landmark scheme because our landmarks are placed on the umbo, hinge line, and around the anterior and posterior muscle scars. The left valve in these species is slightly larger and overlaps the right valve. Only the left valves were used in this analysis.

Specimens were photographed with the margin of the left valve parallel to a horizontal plane to standardize potential distortion caused by the curvature of the shell interior. All left valves from a lot were included in this analysis unless they were broken or damaged in a way that precluded landmark placement.

Museum Lots: Sampling Units of Individual Populations

Museum specimens are organized into museum lots, which are monospecific sets of specimens collected from a single locality. Lots represent the finest-scale groups available in this analysis and vary considerably in both the number of specimens they contain (from one to 29 left valves) and the size range of left valves. The lot is a powerful grouping variable because, in the absence of genetic and environmental information associated with individual specimens, knowing that specimens are from the same or different lots gives us insight into population-level morphological information.

Lots collected from modern ecosystems are likely to represent monospecific populations under the assumption that specimens from specific collecting sites that were identified as single morphospecies truly represent a single interbreeding population. This assumption may be violated if morphologically cryptic species are present (Lee &

Foighil, 2004; Vrijenhoek et al., 1994) or if morphospecies were misidentified/mislabeled

(see below).

In the case of fossil specimens, lots are also likely to represent populations that were related genetically and lived in comparable environmental settings. Nevertheless, substantial time-averaging is expected for mollusk death assemblages in marine settings (Behrensmeyer et al., 2000; Flessa et al., 1993; Kidwell, 2013a; Kidwell and

Bosence, 1991; Kowalewski, 1996, 2009). The fossil lots in our analysis potentially include specimens from different centuries or millennia, whereas those from modern ecosystems might include specimens spanning less than a decade to multiple centuries.

We therefore expect somewhat higher genotypic and ecophenotypic variability within fossil lots compared to modern lots.

Shell Shape Measurement

We utilized 2D geometric morphometrics to analyze shape variation among valves of the seven Anadara species. This method uses a set of Cartesian coordinates placed at certain points, or landmarks, on each specimen. These landmarks are hypothesized to be biologically homologous from one valve to another.

Geometric morphometrics has been used successfully to differentiate between similarly shaped species (Alibert et al., 2001; Douglas et al., 2001; Mitteroecker et al.,

2005), and has proven to be a particularly useful for paleontologists, who need to work around the absence of genetic material (Carvajal-Rodríguez et al., 2006; De

Meulemeester et al., 2012; Leyva-Valencia et al., 2012; Young et al., 2010).

Specifically, many studies have measured the directionality and magnitude of morphological variation, and changes among and within molluscan taxa, using non- landmark as well as landmark-based morphometric methods (Geary, 1990; Geary et al.,

2010; Geary et al., 2002; Kolbe et al., 2011; Rufino et al., 2006; Serb et al., 2011).

Previous studies have determined that landmark-based morphometric analyses of

mollusk shells are informative across multiple observational scales, ranging from studies that characterize and distinguish closely related bivalve species (Marko and

Jackson, 2001) to those that use mollusk morphology to draw broad biogeographic and geologic conclusions (Aberhan, 2001).

The scheme employed here includes ten landmarks located on the umbo, hinge teeth, and around the anterior and posterior adductor muscle scars (Figure 2-3). This landmark choice is similar to those employed in other studies using geometric morphometrics in bivalves (Bush et al., 2002; Cano-Otalvaro et al., 2012; Morais et al.,

2013) and represents a practical compromise between selection of truly homologous points (e.g. landmarks 1, 2, and 10 in our landmark scheme) and finding a sufficient number of landmarks to perform a meaningful analysis. Because bivalve shells have limited options for placing homologous Type I landmarks, it is often necessary to include some landmarks that may be considered Type II or Type III landmarks, e.g. those placed at the anterior and posterior endpoints of a valve.

Because our landmarks are placed primarily on anatomical areas with a high degree of functional value (e.g. adductor muscles are involved with burrowing in infaunal bivalves), they have the potential to capture functional morphological information. For example, changes in the shape of adductor muscles might indicate differences in the lifestyle or habitat requirements of a bivalve (Morton, 1981; Stanley,

1970b, 1972).

Here, generalized least-square Procrustes (GLS) was used to remove differences among specimens caused by landmark translation, rotation, and scaling, leaving only shape information. This method is considered to be among the most

appropriate superimposition techniques (Zeldith et al. 2004; Webster and Sheets,

2010). A Principal Components Analysis based on the Procrustes coordinates was used to visualize shape differences among specimens in a reduced multivariate space.

Because the number of variables produced by GLS exceeds the actual number of degrees of freedom, the statistical evaluation of differences in mean shapes between groups was evaluated using resampling techniques (Webster and Sheets, 2010).

Measuring Morphological Variability

Because the specimens used in this project are monospecific museum lots, three levels of comparison are possible: (1) within lots (within populations), (2) across lots within species (intraspecific), and (3) across lots and species (interspecific). Two measures of morphological variability can be distinguished. The first, often referred to as ‘morphological variation’ (Zelditch et al. 2004), evaluates the shape variability among specimens with a given group (population, species, set of species, etc.). Here, morphological variation is estimated by the ‘Procrustes Variance’ (package: geomorph)

(Adams and Otarola-Castillo, 2013), which can be estimated from Procrustes coordinates or PCA scores, as the sum of the diagonal elements of the group covariance matrix divided by the number of observations in the group, i.e., the sum of variances. This measure of morphological variation, referred to here as δ1, can be applied hierarchically to individual lots, individual species, or the entire dataset. Note that a given group requires a minimum of 2 specimens (preferably more) to estimate δ1.

The between-group difference in morphology, often referred to as disparity, is another measure of morphological variability that assesses differences in mean shape between sets of specimens. Various strategies can be employed to evaluate disparity, depending on the sample structure and phylogenetic scale of data at hand. Here,

pairwise distances between mean shapes of groups were used as a measure of disparity (δ2). This metric can be applied hierarchically to measure disparity among monospecific lots and among species (pooled sets of conspecific lots).

The exploratory analyses were complemented by multivariate statistical tests.

Parametric statistical methods are inappropriate here because sample sizes are highly variable across lots and lot groups, and also because degrees of freedom are unfairly inflated when implementing Procrustes superimposition (Webster and Sheets 2010).

Consequently, the data were evaluated using permutational statistical tests available in the packages vegan (Oksanen et al., 2015b) and geomorph (Adams and Otarola-

Castillo, 2013) and ad hoc designed randomization models. The latter were aimed at assessing statistical differences in morphological variation (δ1). Specimens were randomized across groups (e.g., lots within species) and δ1 estimates were recomputed

10000 times for each randomized group to assess the distribution of δ1 expected under the null model of homogenous morphological variation across all groups. Repeated simulations indicate that all estimates are reasonably stable at 10000 iterations.

Landmark acquisition, morphometric procedures, statistical analyses, and plotting procedures were conducted using R, version 3.2.0 (R Development Core Team,

2015).

Results for Chapter 2

Morphospecies Ordination

In the ordination plane defined by the first two principal components, which together account for 40% of the total variance, the specimens of Anadara (n = 359) form a continuous cloud of points, with the seven analyzed species defining variably overlapping specimen groups (Figure 2-4). This pattern persists when data are

examined in higher dimensions (Figure 2-4). The first four principal components cumulatively account for 61% of total variance.

A visual assessment of the ordination space indicates that the species differ in both their morphologies and their morphological variation. The fossil specimens of A. dariensis ordinate centrally, indicating an intermediate morphology comparable to average morphology of all modern species combined (Figure 2-4). Although there is considerable morphological overlap on the ordination plots, three out of four Western

Atlantic species (A. notabilis, A. baughmani, and A. transversa) do not overlap with one another and ordinate peripherally relative to all other species. The fourth Western

Atlantic species (A. floridana) is located centrally and overlaps marginally with two of the other three Western Atlantic species. The Pacific species (A. concinna, A. emarginata) show nearly complete overlap with the A. dariensis cluster. The species vary significantly in mean shape (F=32.8 p = 0.001; Randomized Goodall’s Test), and most species are significantly different from one another in pairwise tests for differences in mean shape (Permutational MANOVA, Table 2-1).

Table 2-1. Multivariate tests for pairwise differences between species centroids (δ2). Permutational MANOVA (Anderson, 2001b) performed separately for each pair of species. Each analysis is based on 10000 iterations. The reported p- values represent 21 comparison-wise tests, but most of the comparisons remain significant even when the stringent Bonferroni correction is applied.

Pair # First species Second species p-value n1 n2 1 (A) A. dariensis (B) A. notabilis 0.0001** 203 32 2 (A) A. dariensis (C) A. baughmani 0.0001** 203 10 3 (A) A. dariensis (D) A. transversa 0.0001** 203 33 4 (A) A. dariensis (E) A. floridana 0.0001** 203 22 5 (A) A. dariensis (F) A. concinna 0.0001** 203 50 6 (A) A. dariensis (G) A. emarginata 0.0009** 203 9 7 (B) A. notabilis (C) A. baughmani 0.0001** 32 10 8 (B) A. notabilis (D) A. transversa 0.0001** 32 33 9 (B) A. notabilis (E) A. floridana 0.0001** 32 22

Table 2-1. Continued

Pair # First species Second species p-value n1 n2 10 (B) A. notabilis (F) A. concinna 0.0001** 32 50 11 (B) A. notabilis (G) A. emarginata 0.0001** 32 9 12 (C) A. baughmani (D) A. transversa 0.0001** 10 33 13 (C) A. baughmani (E) A. floridana 0.0001** 10 22 14 (C) A. baughmani (F) A. concinna 0.0001** 10 50 15 (C) A. baughmani (H) A. emarginata 0.0162* 10 9 16 (D) A. transversa (E) A. floridana 0.0001** 33 22 17 (D) A. transversa (F) A. concinna 0.0001** 33 50 18 (D) A. transversa (G) A. emarginata 0.3106 33 9 19 (E) A. floridana (F) A. concinna 0.0001** 22 50 20 (E) A. floridana (G) A. emarginata 0.0485* 22 9 21 (F) A. concinna (G) A. emarginata 0.0048* 50 9 * Significant at α = 0.05 without Bonferroni correction. **Significant at α = 0.05 with Bonferroni correction.

Morphological Variation and Disparity: Intra-population Morphological Variation

At the level of individual museum lots, the finest indivisible sampling units assumed to represent monospecific populations, morphological variation (δ1) spans almost one order of magnitude across individual lots, including multiple lots that display either significantly elevated or significantly depressed levels of the within-group shape variability (Figure 2-5). Moreover, morphological variation observed within some individual lots is comparable to, or even exceeds, the morphological variation observed in some species, when measured across all lots within that species. For example, one individual lot of A. transversa (lot 37, n=11, δ1=0.011) is ~5 times more variable than all specimens of A. notabilis pooled across 3 lots (n=31, δ1=0.002). The δ1 estimates remain virtually identical when potential effects of allometry are minimized using

Centroid Size.

Although there is overlap in lot variability among species (e.g. the least variable

A. floridana lot is less variable than the most variable A. dariensis lot), the taxonomic

structure of lot variation (some species have more variable lots than other species) is statistically significant (χ=36.8; p < 0.0001, Kruskal-Wallis test). Results of the test remain unchanged when data are adjusted for allometric variability (χ=36.6; p < 0.0001).

The highest δ1 values occur in lots of A. transvera, and the lowest values occur in certain lots of A. dariensis. In general, the lots of the western Atlantic species (A. notabilis, A. transversa, and A. floridana) display relatively higher δ1 values compared to the lots of the eastern Pacific and Panama species (baughmani, dariensis, and concinna), which display relatively lower δ1 values (Figure 2-5). The distribution of δ1 values is right skewed, with most lots displaying relatively low morphological variation

(Figure 2-5). As above, none of the results change notably when potential effects of allometry are minimized.

Morphological Variation and Disparity: Intraspecific Morphological Variation

The morphological variation within species (i.e., lots pooled within species) parallels within-population patterns, with the highest δ1 values observed for A. transversa and the lowest δ1 values observed for A. dariensis (Table 2-2). For five of seven species, the observed intraspecific morphological variation (δ1) is significantly elevated compared to null expectations estimated by species-level randomization

(Table 2-2, “Pooled lots”). For one species (A. dariensis), δ1 is depressed significantly and for one species (A. concinna), δ1 does not differ significantly from randomization results. The average morphological variation of a lot within a given species generally approximates the variation observed for specimens pooled across all lots within a given species (compare columns 1 vs. 3 and 2 vs. 4 in Table 2-2). That is, on average, the magnitude of morphological variation within single populations/lots is comparable to the

overall intraspecific variability across lots. The results are consistent whether analyses are conducted with or without allometric adjustments (Table 2-2).

Table 2-2. Morphological variation (δ1) within species. (A) Means of lots, (B) Allometry- adjusted means of lots, (C) Pooled lots, (D) Allometry-adjusted pooled lots. Means of lots are arithmetic means of Procrustes variances calculated separately for each lot. Pooled lots are single estimates of Procrustes variances calculated for all lots pooled within each species. Allometry- adjusted estimates based on regression residuals on Centroid Size. Significance values estimated for pooled lots only, based on 1000 randomized datasets with specimens randomly assigned to species.

Species A B C D Anadara baughmani 0.006747 0.006234 0.006718 (+) 0.006243 Anadara concinna 0.003740 0.003548 0.003726 (n) 0.003559 Anadara dariensis 0.002239 0.002166 0.002300 (-) 0.002238 Anadara emarginata 0.005694 0.005657 0.005590 (+) 0.005763 Anadara floridana 0.005190 0.004729 0.005503 (+) 0.005191 Anadara notabilis 0.006073 0.005199 0.007430 (+) 0.005734 Anadara transversa 0.009081 0.008918 0.008985 (+) 0.008809 Symbols: (+) significantly elevated morphological variation, (-) significantly depressed morphological variation, (n) not significant.

Morphological Variation and Disparity: Intraspecific Disparity

The intraspecific disparity [δ2], measured as pairwise distances between mean shapes, estimates disparity among intraspecific lots/populations. In the studied Anadara species, the pairwise distances between intraspecific lots are significantly lower than distances between interspecific lots (Table 2-3, Figure 2-6; χ=53.4, p << 0.001; Kruskal-

Wallis Test). The same conclusions can be reached after applying allometry adjustment

(Table 2-3, Figure 2-6; χ=60.9, p << 0.001). The intraspecific disparity of lots varies significantly across species (χ=52.1, p << 0.001 without adjusting for allometry and

χ=43.9, p << 0.001 for allometry-adjusted estimates). The between-lot disparity (δ2)

(Table 2-3) and average within-lot morphological variation (δ1) (Table 2-2) appear closely linked: species with elevated δ2 tend to be dominated by lots with high δ1. The only exception is Anadara baughmani, a species represented by three lots and 10

specimens only. If this species is excluded, the δ1 and δ2 show monotonic relation (i.e.,

Spearman rank correlation = 1, p = 0.003).

Table 2-3. Intraspecific and interspecific disparity (δ2) measured as mean pairwise distance between mean shapes of lots. (A) Mean pairwise distance between mean shapes of lots; (B) Allometry-adjusted mean pairwise distance. Mean pairwise distances were computed for each species separately, for all intraspecific comparisons (within species) and for all interspecific comparisons (among species). All computations were repeated for allometry- adjusted distances.

Species A B Anadara baughmani 0.0004 0.0003 Anadara concinna 0.0011 0.0009 Anadara dariensis 0.0004 0.0005 Anadara emarginata 0.0017 0.0013 Anadara floridana 0.0013 0.0021 Anadara notabilis 0.0024 0.0010 Anadara transversa 0.0031 0.0031

Among-species 0.0033 0.0031 Within-species 0.0006 0.0006

Morphological Variation and Disparity: Interspecific Morphological Variation

By combining all the specimens in the analysis, we can measure the level of morphological variation across all Anadara species included in this analysis. This genus-level morphological variability [δ1] is a cumulative product of within-lot, within- species, and between-species variation that can be further amplified by difference in mean shapes (disparity) across lots and species [δ1]. The δ1 for the total dataset is

0.0040 (or 0.0037 for allometry-adjusted data), which is lower than δ1 observed for five out of 7 species (Table 2-1). This is not surprising given that (1) the data are dominated by specimens of Anadara dariensis, which are morphologically the least variable of all species, and (2) morphospaces of species overlap, thus minimizing the impact of interspecific disparity δ2 on interspecific morphological variability (δ1).

Confounding Factors: Allometry and Sampling Coverage

The different levels of variation (intraspecific, interspecific, etc.) can be affected by confounding factors related to allometry and sampling coverage. Allometry is unlikely to have played an important role in this analysis, given that all allometry-adjusted analyses yielded outcomes consistent with those carried out for unadjusted data. Also, none of the 20 landmark coordinates used here displayed a strong correlation with the centroid size: the highest observed correlation coefficient r is only 0.37 (Landmark 8, x coordinate, which is the ventral point of the posterior muscle scar), indicating that centroid size accounts for only 13.7% of variance in that landmark (r2 = 0.137). Similarly, centroid size is weekly correlated with PC1 and PC2 scores, accounting for 16.9% and

0.5% of variance in PC1 and PC2 scores, respectively.

The effect of differences in body size within or across lots can be evaluated more directly by testing for correlation between centroid size and measures of morphological variation. For δ1, the correlation between within-lot disparity and mean centroid size is low and statistically insignificant (r = 0.128, p = 0.401). Similarly, the variance in centroid size within lots appears to have little effect on δ1 (r = 0.177, p = 0.245). In the case of δ2 values, the pairwise distance between two lots shows weak positive correlation with the absolute difference in mean centroid size of those lots (r = 0.23, p << 0.0001). This weak correlation is statistically significant, reflecting a huge number of pairwise comparisons (n = 2345), and is also suspect because comparisons are dependent, potentially inflating the power of this test. In any case, the r2 of 0.052 suggests that only

~5% of variance in the observed δ2 can be accounted for by differences in centroid size.

Quantitative measures of morphological variation are potentially influenced by sampling effects such as differences in lot size, i.e., the number of specimens in the lot.

In our analysis, however, there is no significant correlation between lot sample size and within-lot estimates of δ1. The spearman rank correlation is very low and insignificant for both unadjusted (r = -0.008, p = 0.96, n = 46) and allometry-adjusted (r = -0.04, p =

0.81, n = 46) data. These results indicate that differences in morphological variation observed across lots cannot be attributed to differences in sample size.

Discussion for Chapter 2

Landmark-Based Discrimination of Morphospecies

The landmark-based ordination fails to delineate predefined species as distinct morphogroups. Formally defined species overlap with one another, in some cases appreciably. Also, species do not form distinct morphological groups with similar levels of morphological variability. Although this does not invalidate the current morphospecies of Anadara, as their definition involves multiple criteria that cannot be captured exhaustively using landmark approaches, the results suggest that morphospecies included in this analysis cannot be distinguished reliably based on the set of landmarks used here. Partial species differentiation, however, is possible: mean shapes differ significantly between many pairs of species (Table 2-1) and certain pairs of species are non-overlapping (e.g., A. notabilis and A. transversa). Because we have deliberately selected a subset of Anadara species that are morphologically most similar to each other the outcome of this analysis is not unexpected. Neither the failure to discriminate distinct morphogroups, nor the clear separation of certain pairs of species, can be attributed to confounding effects of allometry or sampling coverage. Similar outcomes, where morpometrics methods failed to fully differentiate morphospecies, were reported previously for other taxa (Kowalewski et al., 1997; Serb et al., 2011).

Morphological Variability

The observed differences in morphological variability (δ1) across lots/populations of Anadara are substantial (Figure 2-5) and taxonomically non-random: populations of some species display consistently elevated (or suppressed) morphological variation compared to populations of other species. Moreover, most of the morphological variability in this analysis is accounted for by this within-lot (intra-population) variability, so the variability measured at the lot level is largely, but not entirely, responsible for the variability across lots, both within and across species.

The simplest explanation for intraspecific and interspecific differences in morphological variability would be allometry: lots that represent single ontogenetic cohorts may be expected to be less variable in shape than lots that include specimens that vary in size and ontogenetic development. As documented above, however, our results cannot be explained by allometry or differential sampling of different size classes.

Another simple explanation for differing variability within populations is that the differences in morphological variability are caused by time-averaging. In modern samples, a lot contains shells that belong to generally contemporaneous specimens relative to fossil samples that experienced comparable environmental conditions. In fossil samples, the lot may contain shells that accumulated over a long stretch of time, during which the environmental conditions and genotype of the source populations may have shifted or fluctuated, potentially driving higher morphological variation. If, however, time-averaging were the cause of greater within-population variation among our specimens, the fossil assemblages of Anadara dariensis should be more variable than the lots from the modern species. Yet the results indicate that intra-population variability

of the fossil Anadara dariensis is actually suppressed when compared to intra- population variability observed for the modern species. This outcome is consistent with previous studies, which showed that morphological variability is not elevated by time- averaging. For example, the landmark-based analyses of disparity in populations of the bivalve Mercenaria (Bush et al., 2002) demonstrated that disparity was comparable for biological populations and fossil populations time-averaged over millennial or longer time scales. Similar results were also reported for the Holocene brachiopod Terebratalia transversa (Krause, 2004). Results reported here are consistent with previous studies in demonstrating that population-level morphological variation is not inflated by time- averaging.

After ruling out allometry, sampling coverage effects, or time-averaging as viable drivers of the observed differences of within-lot variability, we postulate three potential biological explanations. First, the population-level variability may be purely phenotypic and reflect differences in habitat patchiness across localities. Thus, in a highly patchy habitat, the population may be more variable in morphology than in a homogenous habitat. Bivalves are known for their ecophenotypic plasticity, including water depth

(Claxton et al., 1998; Fuiman et al., 1999), wave exposure (Akester and Martel, 2000), and substrate type (Newell and Hidu, 1982). These variables could potentially be relevant factors for explaining some of the variation among the Anadara specimens in this study.

Second, the differences may be genotypic, with different species displaying different levels of population-level variability. The fact that within-lot variability varies significantly across species is consistent with this hypothesis. This is an intriguing result,

suggesting that disparity may be largely a genotypic, population-level effect, even in the case of organisms that are very similar morphologically and likely to display substantial phenotypic responses to environmental factors. The genotypic effects may either reflect intrinsic differences among species or difference in size of the local gene pool, i.e., differences in genetic connectivity across local populations.

A third explanation is that populations with elevated morphological variability contain cryptic species. This is a viable explanation, given the rapidly growing evidence that cryptic species are widespread among marine invertebrates, including bivalves

(Lee and Foighil, 2004; Vrijenhoek et al., 1994) and gastropods (Collin, 2000).

Of the three hypotheses (ecophenotypic, genotypic, and cryptic), the ecophenotypic hypothesis seems least likely. There is no reason to expect that congeneric species, characterized by congruent ecologies and inhabiting a similar range of habitats, would be exposed to different levels of habitat heterogeneity and affected differentially by environmental factors. The fact that morphological variability in populations/lots of Anadara is strongly linked to species identity points to non- ecophenotypic morphospace drivers. However, distinguishing between genotypic causes, related to connectivity within meta-populations or species-specific gene pools, and the presence of cryptic species, is challenging. In the absence of molecular data, not yet available at this time for present-day species and inaccessible for fossil species, we conclude that the observed pattern is likely to be genetically driven, caused by either inherent differences between congeneric species or due to the presence of cryptic species.

Intraspecific and Interspecific Variation and Disparity

The morphological variation (δ1) and disparity (δ2) differ significantly between the seven Anadara species, both at the intra-specific level of lot-to-lot comparisons and in terms of species-level morphological variability. These patterns mirror the intra- population variation across species. The species with elevated levels of the intra- population morphological variability (δ1) also display elevated levels of intraspecific and interspecific variability (δ1) and disparity (δ2), again suggesting that species-specific genetic differences or the presence of cryptic species are the probable explanations for observed intra-specific and inter-specific differences in morphological variation and disparity.

The Potential Role of Geography in Morphological Variation

The morphological variability and disparity (δ1 and δ2) appear to vary predictably across the sampled regions, with higher δ1 and δ2 values observed for the western

Atlantic species compared to the Panama and Pacific species. Multiple causal explanations can be postulated for this pattern. One is that the fossil Panama species and modern eastern Pacific species are genetically distinct from the western Atlantic species. Whether the eastern Pacific species or western Atlantic species are more closely related to the fossil species is impossible to test using genetic data because

Anadara dariensis is extinct. Strong morphological overlap and similar levels of morphological variation and disparity among the Pacific species and the fossil Panama species, however, is potentially consistent with a relatively closer phylogenetic relationship between these species relative to the Atlantic species.

Finally, a more heterogeneous geographic setting of the eastern Atlantic region may have contributed to the higher disparity within and across Atlantic species. First,

the multiple biogeographic barriers may have limited genetic connectivity between intraspecific populations species thus elevating intraspecific disparity. Moreover, because of biogeographic barriers, the eastern Atlantic has been partitioned into multiple smaller basins with different oceanographic characteristics, which may have contributed to elevated interspecific disparity across the eastern Atlantic species.

Summary for Chapter 2

We utilized a geometric morphometric analysis to explore morphological variation and disparity within and between lots (proxies for biological populations) across seven fossil and extant species of Anadara. These species cannot be fully differentiated using the landmark-based approaches. Although the included Anadara species appear to be morphologically similar, and presumably represent similar life styles, morphological variation and disparity vary greatly across these taxa, with most variability occurring at the population (lot) level.

These species-specific differences are most likely driven by genotypic rather than phenotypic differences. These differences may be a consequence of the presence of cryptic species or intrinsic differences in genetic variability/connectivity across the studied species. Geographic fragmentation of the eastern Atlantic by multiple biogeographic barriers, however, may have further contributed to elevated intraspecific and interspecific disparity in the eastern Atlantic species. In contrast, time-averaging, allometry, and sampling coverage cannot explain the differences in disparity and morphological variability. Whereas the unambiguous interpretation of causative drivers

(cryptic species, genetic connectivity, interspecific genetic differences, biogeographic differences) cannot be archieved here, the results demonstrate that even within

congeneric species some populations and some species display more inherent morphologic variability.

Figure 2-1. Representative specimens of the seven Anadara taxa included in this analysis. Photos courtesy of author.

Figure 2-2. A regional map of localities of the 359 Anadara specimens analyzed here: (A) Anadara dariensis (203 specimens); (B) Anadara notabilis (32 specimens); (C) Anadara baughmani (10 specimens); (D) Anadara transversa (33 specimens); (E) Anadara floridana (22 specimens); (F) Anadara concinna (50 specimens); (G) Anadara emarginata (9 specimens).

Figure 2-3. Landmark locations: 1 = Umbo; 2 = Middle of the anterior tooth; 3 = Upper right (dorsal anterior) corner of anterior adductor muscle scar; 4 = Upper left (posterior dorsal) corner of anterior adductor muscle scar; 5 = Bottom (ventral) point of the anterior adductor muscle scar; 6 = Bottom (ventral) point of the posterior adductor muscle scar; 7 = Anterior point of the posterior muscle scar; 8 = Upper (ventral) point of the posterior muscle scar; 9 = Posterior point of the posterior muscle scar; 10 = Posterior tooth (left-most tooth).

Figure 2-4. Principal components ordination of all specimens based on geometric morphometrics landmark data. Specimens are labeled by species and individual species are indicated by convex hulls. A. Ordination plot of PC1 and PC2. B. Ordination plot of PC3 and PC4.

Figure 2-5. Morphological variation (δ1) within museum lots. Numbers represent δ1 estimates of individual lots and colors indicate regions: green=extant western Atlantic species, red=extant eastern Pacific species, black=fossil (Panama and eastern Pacific) species. The gray lines summarize the outcome of a randomization (1000 iterations) with specimens randomly assigned to lots (i.e., a null model of expected variability in morphological variation for lots with invariant morphological variation). The thick gray line represents the means of all simulations, thin gray lines represent 95% confidence bands, and dashed gray lines represent 99% confidence bands. Lots outside the confidence bands display statistically significantly departure from morphological variation expected for a homogeneous system in which all lots are characterized by the same morphological variation.

Figure 2-6. Disparity [δ2] of lots. Pairwise distances between lots based on Procrustes (x axis) and allometry-adjusted Procrustes (y axis). Gray circles denote interspecific comparisons and black circles represent intraspecific comparisons, respectively. Because both variables are strongly right-skewed, the distance values were transformed using the 4th power root.

CHAPTER 3 MOLLUSK SHELLS ARCHIVE SPATIAL STRUCTURING WITHIN BENTHIC COMMUNITIES AROUND SUBTROPICAL ISLANDS

Abstract for Chapter 3

Surficial assemblages of mollusk shells may provide minimally invasive, quantitative data that are potentially adequate for assessing spatial organization of local benthic ecosystems. Here, 61 bulk samples collected along 12 transects were analyzed to evaluate distribution and ecological characteristics of mollusk-dominated benthic communities around San Salvador Island, Bahamas. The bulk samples yielded a total of

20,608 specimens, which represented a minimum of 181 mollusk species. Indirect multivariate ordinations (NMDS) separated samples by locality, even in the case of transects sampled in different parts of the same bay, indicating that shell assemblages faithfully archive local differences in mollusk communities. At the regional scale, a clear faunal separation is observed between windward and leeward sides of the island, suggesting that water energy represents an overriding regional driver that controls local community composition. Within each energy regime, the faunal composition of mollusk assemblages is primarily controlled by seagrass vegetation. Results indicate that San

Salvador Island benthic communities are characterized by a predictable spatial organization controlled primarily by physical (wind energy) and secondarily, biological

(seagrass vegetation) processes. That these patterns can be discerned so clearly by sampling shell assemblages suggests that non-invasive sampling of dead mollusks is a viable strategy for examining processes that drive spatial structuring of marine communities.

Introduction for Chapter 3

Mollusks are widespread and abundant in many marine environments (Gutierrez et al., 2003), and represent the dominant component of the fossil record of animals

(Alroy, 2010). Because of the resilience of their hard external shells, and the relatively high preservation potential of the environments in which they commonly live, mollusk communities often leave behind a robust record of their existence in the form of a death assemblage (Johnson, 1965). Compositional discrepancies among mollusk death assemblages from different locations and/or time periods are frequently attributed to factors that broadly influence ecosystem biodiversity, such as anthropogenic disturbance or climate change (Kidwell, 2007; Warwick and Turk, 2002). Given their ubiquity and accessibility, the role of mollusk death assemblages in marine community assessment has long been established (Seilacher, 1985; Zenetos, 1996). Increasingly, however, the broader scientific community is recognizing a wider variety of applications for which mollusk death assemblages can serve as an important assessment tool (Dietl et al., 2016; Dietl and Flessa, 2011; Kidwell, 2013b; Leshno et al., 2016).

Mollusks are extremely useful as environmental indicators, for both paleoecologists (Boucot, 1981) and marine ecologists (Antoine, 2001; Limondin-

Lozouet and Antoine, 2001). Differences among mollusk communities, and the death assemblages they leave behind, are excellent proxies for a variety of environmental variables at varying temporal and spatial scales. If we can learn more about how sensitive, reactive, and reflective the community structure and the species composition of mollusk death assemblages are to various environmental factors (e.g. sub-habitat type, storm frequency, etc.), the information archived in death assemblages will become even more helpful for detecting environmental change (Holland et al., 2001; Weber,

2001), and for observing how marine communities shift over long periods of time in response to certain environmental factors. For example, numerous studies have used mollusks to address the presence and magnitude of anthropogenic disturbance

(Ferguson, 2008; Kidwell, 2007; Kowalewski et al., 2000; Mannino and Thomas, 2002;

Sandweiss et al., 1996), non-anthropogenic environmental disturbance (Miller et al.,

1992; Poirier et al., 2009), ancient environmental gradients (Lafferty et al., 1994), and climate change (Dyke et al., 1996; Rousseau et al., 1993).

To fully utilize the information obtained from mollusk assemblages in marine ecosystems, we must account for the spatial scale of these systems (Boström et al.,

2006). Spatial scales of relevance to investigators using death assemblages as an assessment tool can range from the wide, global-scale (Renema et al., 2008), to the narrow, habitat-scale (Miller et al., 1992; Poirier et al., 2010). In addition, there is a need for increased understanding of how various factors, such as storm frequency and habitat heterogeneity, may affect mollusk assemblages at different spatial scales.

Unfortunately, the role of individual environmental factors in shaping mollusk communities are still not well understood at the relatively small (e.g. habitat) spatial scale, which is the scale at which many marine ecological studies are conducted. The objective of this project is to explore spatial structuring in marine mollusk assemblages and its physical and biological drivers by looking at whether mollusk assemblages display differences between: 1) environments that experience different storm energy levels (windward vs. leeward sides of an island), and 2) between seagrass and non- seagrass habitats.

Materials and Methods for Chapter 3

Study Area

San Salvador is a relatively small subtropical island, measuring approximately 21 km (13 mi) from north to south, and 8 km (5 mi) from east to west. The island is located on the easternmost edge of the Bahamian archipelago in the Atlantic Ocean

(coordinates: 24°06′N 74°29′W). Marine mollusks are abundant and diverse in the sediments on the shallow carbonate shelf around the island. San Salvador is an appropriate locality for addressing questions about subtropical carbonate marine ecosystems because of its relatively low level of anthropogenic disturbance and the easy accessibility of its marine habitats (Gerace et al., 1998). Like most Bahamian islands, San Salvador is host to a variety of marine habitat types, including patchreefs, lagoons, open sand, and both sheltered and unsheltered seagrass beds.

Sampling Methods

We sampled transects at 12 localities around San Salvador Island (Figure 3-1,

Table 3-1) during two collecting trips in November 2013 and May 2014. Four of the localities are characterized as windward and eight of the localities are characterized as leeward, based on the storm and wind intensity at the sites. Most of the windward localities are on the eastern side of the island, with the exception of Pigeon Creek, which is on the southeastern side of the island, but is classified here as leeward because it is in a protected bay with lower wind and storm energy than the other eastern localities. Five transects are in locations with varying densities of seagrass vegetation

(Figure 3-2A), and seven transects are in locations with sandy unvegetated substrate

(Figure 3-2B). Transects run perpendicular to the shoreline, and samples were collected every 10 or 30 meters using a measuring tape. Sample number ranges from 4 to 6

samples per transect. All samples were collected at shallow, subtidal water depths, averaging 11 feet (3.4 m) and ranging from 4 feet (1.2 m) at the shallowest to 21 feet

(6.4 m) at the deepest, not adjusted for tidal fluctuations. Because water depth was relatively constant, it was not a meaningful variable, either among or within transects.

Table 3-1. List of transect localities, sample numbers, and corresponding environmental (leeward/windward) and biological (seagrass/unvegetated) characteristics.

Transect Locality Windward/Leeward Sand/Seagrass No. Sampling samples distance 1 Graham’s Harbor West Leeward Seagrass 5 30m 2 Graham’s Harbor East Leeward Seagrass 5 30m 3 Rice Bay Windward Seagrass 5 10m 4 East Beach Windward Sand 5 10m 5 Dim Bay Windward Sand 6 10m 6 Pigeon Creek Leeward Seagrass 6 10m 7 French Bay Windward Seagrass 5 10m 8 Grotto Beach Leeward Sand 5 10m 9 Columbus Landing Leeward Sand 5 30m 10 Telephone Point Leeward Sand 4 10m 11 Bonefish Bay Leeward Sand 5 10m 12 Sand Dollar Beach Leeward Sand 5 10m Total: 61

Each sample consisted of approximately two quarts of unconsolidated surficial

(sediment depth < 10 cm) marine sediment, collected using SCUBA (Figure 3-2).

Samples were wet-sieved into a small size fraction (1-3 mm) and a large size fraction (>

3 mm). To standardize the sample size, we identified with a microscope the first 300 mollusk shells picked from the smaller fraction, and all of the mollusks in the same proportion by weight of the larger size fraction. For example, if we picked through 50%

(measured by weight) of a sample’s small size fraction sediment to acquire 300 specimens of mollusks, we then picked all mollusks from 50% (measured by weight) of

the larger size fraction (> 3 mm) sediment. Mollusks picked from sediment in the smaller size fractions were abundant and diverse compared to mollusks picked from sediment in the larger size fractions. The appropriate sample size for measuring diversity in mollusks, or other similarly distributed organisms, depends on the characteristics of the sample, but 300 individuals usually gives an accurate and robust measurement of diversity (Fatela and Taborda, 2002; Patterson and Fishbein, 1989). Broken shells of bivalve mollusk taxa were counted only if they included the umbo. Broken gastropods, scaphopods, and chitons were counted only if the shell fragment consisted or more than

50% of the complete shell.

Mollusk species were identified to the finest possible taxonomic level using mollusk taxonomic compendia (Huber et al., 2015; Mikkelsen and Bieler, 2007; Redfern,

2013) and taxonomic expertise at the Florida Museum of Natural History. In some cases, closely related species within a genus could not be distinguished reliably from one another because of weathering, shell size (juveniles without distinguishing adult characters), and/or unresolved taxonomy. The most significant case of this is the taxon

Cerithium sp., which is the most abundant taxon in our dataset. This taxon contains the two species Cerithium eburneum and Cerithium litteratum. We combined these closely related species into one genus-level taxon because of the difficulty of reliably distinguishing the two species as juveniles (the majority of specimens), and due to their naturally high morphological variability.

Although both live and dead individuals were counted, only a small number of shells were from live individuals. Live specimens were not abundant enough within the samples to conduct statistically meaningful live/dead comparisons. Our use of the death

assemblage to characterize mollusk communities is validated by research showing that death assemblages generally resemble live communities (Kidwell, 2001), including spatial structuring of regional ecosystems (Tyler and Kowalewski, 2017).

Analytical Methods

Taxon occurrence data were adjusted to account for individual organisms with multiple shell parts (i.e., bivalves, chitons) by dividing the occurrence data of relevant taxa by the number of parts per individual. Because of this correction, our dataset does not inflate the number of individuals, which would inappropriately imply an increase in the power of statistical comparisons.

For multivariate ordination and statistical tests, as well as additional plots figured in the results section, we used several diversity indices (standardized species richness,

Berger-Parker index) as needed to assess different aspects of diversity (e.g. richness, evenness) among and within mollusk assemblage samples. Bray-Curtis dissimilarity

(Bray and Curtis, 1957) is an index commonly used by ecologists to quantify differences between samples based on species abundance data. This index varies from 0 to 1, with

0 being an exact match in species composition and abundance of two samples, and 1 being no overlap is species composition and abundance of two samples. The Berger-

Parker index (Berger and Parker, 1970) is a measure of species dominance, as quantified by the proportional abundance of the single most abundant species. We use this index to explore dominance patterns in the mollusk assemblage samples.

We employed a permutational multivariate analysis of variance, also known as

PERMANOVA (Anderson, 2001a), to test the null hypothesis that samples do not differ in mollusk faunal composition (Bray-Curtis dissimilarities). PERMANOVA is similar to

MANOVA but is more appropriate for our dataset because the permutation procedure

used to obtain p-values does not require an assumption of multivariate normality and variance-covariance homogeneity of the data.

There are a multiple ways to measure and conceptualize beta diversity

(Anderson et al., 2011). We measured beta diversity using Shannon beta and Beta variance following Tyler and Kowalewski (Tyler and Kowalewski, 2017). Shannon beta incorporates relative abundance information and is appropriate for exploring beta diversity on a hierarchy of spatial scales, and Beta variance is a measure of within- habitat heterogeneity in community composition (Anderson et al., 2011; Jost, 2007;

Tyler and Kowalewski, 2017).

Non-metric multidimensional scaling (NMDS) is a multivariate ordination that aims to collapse the species composition information in each sample from multiple dimensions (species) into just two or three dimensions, shown as bivariate plots. The spatial positions of samples relative to one another on these plots can be interpreted based on various environmental and spatial variables. NMDS is an indirect gradient analysis, in which external environmental data are used for interpretation, rather than used directly in the analysis. Non-metric multidimensional scaling is a distance-based ordination method that maximizes rank order correlation and is non-parametric (it does not assume a normal distribution of the data) (Clarke, 1993). We used the Bray-Curtis similarity coefficient (Bray and Curtis, 1957), which is preferred for ecological community analyses because it is not affected by changes in species that are not present in two communities, or additions of communities, so the similarity attributed to the absences of species does not inappropriately affect the calculation of sample similarity.

The NMDS analysis includes samples with 100 or more specimens (n ≥ 100).

Some of the samples have low mollusk count yields (n < 100), and therefore not all samples appear in the NMDS plot. Rare taxa (e.g. taxa with only one specimen) were included in the analysis, following research on similar types of data (Reich, 2014). We used both custom written R script and various R packages for multivariate ordinations, diversity analyses, statistical analyses, and figure generation (Oksanen et al., 2015a; R

Development Core Team, 2015).

Results for Chapter 3

Taxonomic Composition of the Samples

A total of 20,608 shells were identified from the sediment samples, representing

181 mollusk taxa (Appendix 1). These included 52 bivalve taxa (29% of the taxa, 48% of the specimens), 118 gastropod taxa (65% of the taxa, 51% of the specimens), 4 scaphopod taxa (2% of taxa, less than 1% of the specimens) and 7 chiton taxa (4% of the taxa, less than 1% of the specimens). The three most abundant taxa in the entire dataset are the gastropod Cerithium sp. (n = 3556, 17% of all specimens), the small mytilid bivalve Crenella divaricata (n = 2346, 11% of all specimens) and the venerid bivalve Transenella sp. (n = 2179, 11% of all specimens). Other common species included the gastropod Eulithidium thalassicola (n = 1821), the bivalve Ctenocardia guppyi (n = 1044), and the gastropod Finella adamsi (n = 946). The raw (not standardized) sample species richness ranged from 8 taxa in a sample from East

Beach, to 56 taxa, also a sample from East Beach, with an average samples species richness of 35.11 taxa.

Taxon Abundance and Occurrence Patterns

Taxon abundance patterns differ depending on the environmental energy

(windward/leeward) and biological (seagrass/unvegetated) characteristics of the transect locality (Figure 3-3). The most common species in the leeward-side samples was Crenella divariacata, but Cerithium sp. and Transenella sp. were nearly as abundant, with each of these taxa representing between 10% and 15% of the total specimens at leeward sites. In contrast, the most common species in the windward samples was Cerithium sp. (30% of the specimens), with all other species being much less common (each < 10% of the samples). Similarly, the most common species in the unvegetated samples was Cerithium sp. with around 20% of the specimens, whereas the most common species in the vegetated samples was Crenella divaricata, followed closely by Cerithium sp.

Overall, the leeward samples and the vegetated samples display more evenness in species abundances compared to windward and unvegetated sites, which are both dominated by Cerithium sp. shells. The contrast in evenness is most apparent between leeward and windward samples.

Combinations of these grouping variables (four combinations possible) show distinctly different patterns of species abundances (Figure 3-4) from the species abundances of the basic (uncombined) grouping variables (Figure 3-3). Cerithium sp. shells dominate the veg+wind, unveg+wind, and unveg+lee to varying extents, the highest relative abundance of this taxon being in the unveg+wind samples, where they make up over 35% of the specimens. The only sample combination that did not have

Cerithium sp. as the most abundant taxon are those from veg+lee samples, in which

Crenella divaricata is the most abundant species, representing 25% of the specimens.

Evenness patterns in these combined sets of samples are similar to the evenness patterns of the uncombined sets. The unvegetated and windward samples have lower eveness than the other samples, primarily because of the high abundance of the Cerithium sp. taxon. Several of the taxa that are less abundant in the overall dataset

(i.e., ranked lower in overall abundance) display slightly elevated abundances in the combined sets relative to other taxa. For example, in the unveg+wind, nearly 10% of the specimens are Zebina browniana and nearly 5% are Patelloida pustulata. Similarly, the veg+wind has relatively high abundances of Ervilia concentrica (3rd most abundant taxon in these samples, at just under 10%) and Lucina pensylvanica (4th most abundant taxon in these samples).

Species occurrences in relation to the primary environmental variable (leeward and windward) in the dataset can further inform us about how and to what extent these samples differ from each other and from the entire combined dataset. A large number of the species in our dataset were found exclusively in samples collected from either the windward side or the leeward side of the island (Table 3-2 and Table 3-3). Among the

33 taxa that are found only on the windward side of the island (Table 3-2), the most abundant is the chiton Choneplax lata (n = 12), and the second most abundant is the venerid bivalve Anomalocardia puella (n = 11). The majority (29 of the 33 taxa) of the windward-only species (Table 2) occurrences were limited to just one or two shells, and their occurrence in exclusively windward samples is attributable to chance (Figure 3-5).

Table 3-2. Taxa found only on the windward side of the island. Columns show the total shell count (n), proportion of total (Prop), and number of samples in which the taxon occurs (Occs).

TNC Species n Prop Occs TNC055 Choneplax lata 12 0.00250 6 TNC047 Anomalocardia puella 11 0.00230 3 TNC014 Dimyella starcki 6 0.00125 5 TNC165 Synaptocochelea picta 4 0.00083 4 TNC016 Isognomon radiatus 2 0.00042 1 TNC017 Kellia sp. 2 0.00042 2 TNC018 Lasaeid sp. 2 0.00042 1 TNC030 Crenella sp. 2 0.00042 2 TNC068 Bittiolum varium 2 0.00042 1 TNC094 Rimula aequisculpta 2 0.00042 2 TNC111 Agathotoma sp. 2 0.00042 2 TNC142 Cymatium nicobaricum 2 0.00042 2 TNC160 Isotriphora peetersae 2 0.00042 2 TNC166 Truncatella clathrus 2 0.00042 2 TNC009 Chama sarda 1 0.00021 1 TNC019 Orobitella floridana 1 0.00021 1 TNC034 Berthella stellata 1 0.00021 1 TNC061 Chelidonura sp. 1 0.00021 1 TNC062 Aplysia parvula 1 0.00021 1 TNC064 Engina turbinella 1 0.00021 1 TNC080 Vexillum moniliferum 1 0.00021 1 TNC085 Opalia pumilio 1 0.00021 1 TNC087 Leucozonia ocellata 1 0.00021 1 TNC093 Montfortia emarginata 1 0.00021 1 TNC098 Morum oniscus 1 0.00021 1 TNC101 Berthelina sp. 1 0.00021 1 TNC131 Crassiclava apicata 1 0.00021 1 TNC145 Rissoella sp. 1 0.00021 1 TNC168 Parviturbo weberi 1 0.00021 1 TNC177 Cylindrobulla beauii 1 0.00021 1

There are a higher number of taxa (n = 54) found exclusively in samples from the leeward side of the island (Table 3-3) compared to the number found in windward samples. The most abundant of these is the Glycimerid bivalve Tucetona pectinata (n =

268), the second most abundant is the bivalve Strigilla mirabilis (n = 115), and the third

most abundant is the bivalve Carditopsis smithii (n = 36). In contrast to the counts of the windward-only taxa, the counts of some of these leeward-only taxa were relatively high.

The probability of these samples occurring exclusively in the leeward samples by chance is low (Figure 3-5). The windward-only species (black) are much less likely to occur in the leeward sites than the leeward-only species (grey) are to occur in the windward sites (Figure 3-5).

Table 3-3. Taxa found only on the leeward side of island. Columns show the total shell count (n), proportion of total (Prop), and number of samples in which the taxon occurs (Occs).

TNC Species n Prop Occs TNC015 Tucetona pectinata 268 0.01728 29 TNC042 Strigilla mirabilis 115 0.00741 13 TNC012 Carditopsis smithii 36 0.00232 11 TNC117 Dentimargo redferni 21 0.00135 11 TNC046 Phlyctiderma semiaspera 9 0.00058 4 TNC066 Caecum lineicinctum 6 0.00039 3 TNC173 Vermicularia spirata 6 0.00039 4 TNC103 Arene venustula 5 0.00032 2 TNC114 Ithycythara sp. 5 0.00032 3 TNC003 Arca zebra 4 0.00026 3 TNC039 Solemya occidentalis 4 0.00026 2 TNC050 Petricola lapicida 4 0.00026 3 TNC167 Astralium phoebium 4 0.00026 3 TNC007 Laevicardium mortoni 3 0.00019 2 TNC020 Planktomya henseni 3 0.00019 3 TNC078 Mitromica foveata 3 0.00019 2 TNC089 Fissurella barbadensis 3 0.00019 2 TNC123 Phyllonotus pomum 3 0.00019 2 TNC144 Retusa sulcata 3 0.00019 1 TNC152 Terebra alba 3 0.00019 3 TNC161 Latitriphora albida 3 0.00019 2 TNC174 Megalomphalus sp. 3 0.00019 3 TNC036 Cumingia antillarum 2 0.00013 1 TNC060 Japonactaeon punctostriatus 2 0.00013 1 TNC067 Calliostoma jujubinum 2 0.00013 2 TNC104 Alaba incerta 2 0.00013 2 TNC122 Murexiella macgintyi 2 0.00013 2

Table 3-3. Continued

TNC Species n Prop Occs TNC137 Oscilla somersi 2 0.00013 1 TNC143 Daphanella sp. 2 0.00013 1 TNC153 Terebra sp. 2 0.00013 2 TNC005 Basterotia elliptica 1 0.00006 1 TNC028 Botula fusca 1 0.00006 1 TNC032 Gregariella coralliophaga 1 0.00006 1 TNC038 Semele bellastriata 1 0.00006 1 TNC056 Ischnochiton erythronotus 1 0.00006 1 TNC058 Stenoplax boogii 1 0.00006 1 TNC059 Ischnochiton sp. 1 0.00006 1 TNC063 Heliacus cylindricus 1 0.00006 1 TNC084 Cycloscala echinaticosta 1 0.00006 1 TNC092 Lucapina sowerbii 1 0.00006 1 TNC095 Rimula frenulata 1 0.00006 1 TNC107 Echinolittorina mespillum 1 0.00006 1 TNC115 Pyrogocythara cinctella 1 0.00006 1 TNC133 Monilispira mayaguanaensis 1 0.00006 1 TNC135 Chrysallida sp. 1 0.00006 1 TNC136 Eulimastoma didymium 1 0.00006 1 TNC141 Cymatium labiosum 1 0.00006 1 TNC151 Strictispira sp. 1 0.00006 1 TNC154 Circulus orbignyi 1 0.00006 1 TNC157 Teinostoma sp. 1 0.00006 1 TNC158 Teinostoma umbilicatum 1 0.00006 1 TNC175 Vermetid sp. 1 0.00006 1 TNC176 Ascobulla ulla 1 0.00006 1 TNC179 Graptacme calamus 1 0.00006 1

Tests of Statistical Significance and Beta Diversity

The PERMANOVA test indicated that leeward and windward localities differ significantly in faunal composition (F = 10.1057, p < .001, R2 = 0.15089), that seagrass and non-seagrass localities differ significantly in faunal composition (F = 6.2451, p <

.001, R2 = 0.09324), and that sample localities differ significantly in faunal composition

(F = 2.6240, p < .05, R2 = 0.03918).

Measurements of beta diversity (Table 3-4), reveal that Shannon beta diversity is higher on the leeward side of the island (Shannon beta = 0.5276550) compared to the windward side of the island (Shannon beta = 0.4676875), and higher in unvegetated localities (Shannon beta = 0.5843836) compared to vegetated localities (Shannon beta

= 0.5268329).

Table 3-4. Beta diversity as measured in all data, and within groups of samples.

Name of samples Number of Number Beta Beta specimens of variance Shannon samples 1 All 2852.6203 52 0.1188 0.6417467 2 Leeward 2126.0008 39 0.1006 0.5276550 3 Windward 726.6195 13 0.1100 0.4676875 4 Unvegetated 1597.3819 29 0.1172 0.5843836 5 Vegetated 1255.2384 23 0.0957 0.5268329 6 Vegetated+windward 494.9444 9 0.1019 0.3739211 7 Vegetated+leeward 760.2940 14 0.0594 0.3129812 8 Unvegetated+windward 231.6752 4 0.0977 0.3083752

Multivariate Ordination

Nonmetric multidimensional scaling (NMDS) displays grouping patterns among the samples (Figure 3-6A-C and Figure 3-7). The stress level for our NMDS analysis is

0.191 (for k = two dimensions), indicating that the ordination may provide acceptable representation of the multidimensional relationship between the samples, as stress values < 0.2 are usually considered interpretable.

The NMDS plot (Figure 3-6A-C) displays a clear delineation between leeward and windward sample sites (Figure 3-6A), with the strongest differentiation occurring on the first NMDS axis. This clear differentiation corroborates the observed differences in species composition between high-energy windward sites and low-energy leeward sites

(Figure 3-3 and Figure 3-4, Table 3-2 and Table 3-3), as well as the statistical significance (PERMANOVA results) of the leeward and windward differentiation.

The difference between vegetated and unvegetated sites within the two energy regimes (windward and leeward) is also apparent in the NMDS plot (Figure 3-6B and 3-

6C). On the leeward side of the island, the vegetated samples cluster together in the upper left of the plot relative to the unvegetated samples (Figure 3-6B). Similarly, on the windward side of the island, the vegetated sites also separate from the unvegetated sites (Figure 3-6C), although this pattern is more diffuse than for the leeward side.

In addition to grouping based on those environmental and biological variables, the samples from within each transect (labeled numerically from 1 to 12) also generally group together relative to samples from other transects. For example, samples from

French Bay (Figure 3-6, #7), Grotto Beach (Figure 3-6, #8) and Sand Dollar Beach

(Figure 3-6, #12) each groups together in distinct locality clusters, with some points nearly overlapping. At other localities, such as Rice Bay (Figure 3-6, #3), Telephone

Point (Figure 3-6, #10), and Bonefish Bay (Figure 3-6, #11), the sample group together more loosely, with each clustering into a diffuse group that overlaps somewhat with samples from other localities. Even these more loosely grouped localities, however, cluster within a specific region of the plot. This non-random clustering of samples indicates that there are within-locality similarities in mollusk species diversity and abundance, and that spatial structuring occurs even at the among-transect scale.

The overall pattern from the NMDS plot is one of samples structuring primarily by energy level (Figure 3-6C), secondarily structuring by vegetation type within each energy regime (Figures 3-6B-C), and finally, clustering by locality (numerical codes).

Plotting taxa in the multivariate space of the NMDS plot illustrates which taxa characterize different sample groups (Figure 3-7). These results corroborate the patterns displayed in earlier plots and figures. For example, in the multivariate space occupied by leeward samples on the right side of this NMDS plot, two of the most abundant leeward-exclusive taxa (see Table 3-3) are the bivalves Tucetona pectinata and Strigilla miarabilis (Figure 3-7, #27 and #25). Taxa that characterize the multivariate space occupied by seagrass samples within the green convex hull, include Acteocina sp., Atys shapri, and Nassarius sp., whereas taxa that characterize the multivariate space occupied by unvegetated sites outside the green convex hull include Finella adamsi, Gemma gemma, and Acanthochitona pygmaea.

Species Dominance and Evenness Patterns

Leeward samples display higher evenness than windward samples (Figure 3-8).

Leeward samples (grey points) appear in the upper left of the plot, indicating higher evenness. No windward samples (black points) appear in that region of the plot, indicating that these samples lack higher evenness. There is more variation in evenness among leeward samples (grey points appear across the whole plot), compared to windward samples, but this could be a consequence of the fact that there are more leeward samples than windward samples. Both the leeward and windward sample groups display a comparable range of values of species dominance as measured by the

Parker-Berger Index (Figure 3-8).

Beta Diversity

Beta diversity as measured in groups of samples can indicate where turnover is highest (Figure 3-9). Among groups of samples with low beta diversity, the species composition is relatively homogenous, whereas among groups of samples with high

beta diversity, the species composition exhibits high levels of patchiness, or turnover. In our samples of mollusk assemblages, beta diversity is highest when measured within the entire dataset (Figure 3-9, #1) compared to any subset of samples within this dataset (Figure 3-9, #2-9). Of the subsets of data, the highest beta diversity is measured in unvegetated samples (Figure 3-9, #4). Vegetated samples display somewhat lower beta diversity (Figure 3-9, #5), although still elevated compared to most other subsets of samples. Leeward samples (Figure 3-9, #2) display elevated beta diversity relative to windward samples (Figure 3-9, #3). Sample subsets of two variables

(Figure 3-9, #6-9), have lower beta diversity values, which is expected due to the smaller number of samples and more constrained variables within these sample groups.

Overall, beta diversity is lowest within groups of vegetated leeward samples (Figure 3-9,

#7), and relatively high (compared to other subsets with two grouping variables) in unvegetated leeward samples (Figure 3-9, #9).

Pairwise Comparisons and Spatial Structuring

Pairwise comparisons of samples in terms of differences in faunal composition

(Figure 3-10), indicate that as the comparison scale of sample pairs increases from within-transect to between-transect to within-region to between-region, the similarity of the mollusk assemblages in the samples decreases. Within a single transect, the samples are relatively similar, and transects differ from one another even when the other variables (windward/leeward and unvegetaed/vegetated) remain constant. The biggest change in similarity at these five different hierarchical levels of sample pairings occurs after the within-transect level. Changes (decreases in similarity) at subsequent levels (after this first within-transect level) are less substantial in magnitude.

A rank abundance distribution plot of mollusk species in seagrass, non-seagrass, windward, and leeward localities, shows that they are relatively similar in rank abundance patterns (Figure 3-12). Evenness by substrate type (vegetated or non- vegetated) and windward/leeward is also relatively similar between these two groups of samples (Figure 3-11).

Discussion for Chapter 3

Taxonomic Composition of Molluscan Assemblages

The taxonomic composition and richness of our samples is comparable to that found by other researchers on San Salvador Island (Reich, 2014), as well as at other comparable seagrass localities (Urra et al., 2013). The taxonomic diversity is higher than that found previously in comparable benthic environments (Brook, 1978), possibly because of our inclusion of small shells that contain the majority of the specimens and increase taxonomic diversity of samples by inclusion of species found only in small size fractions.

Small and Large-scale Spatial Structuring

Mollusk assemblages in our samples vary in species composition by transect

(samples within a transect group together with respect to similarity relative to samples from different transects), indicating that there is small-scale spatial structuring in the samples. This difference between samples at a scale of tens of meters is indicated by the scatter of within-transect points in the NMDS plots. Although there is some overlap of samples from different localities, the multivariate ordination shows that the species occurrences and abundances are clearly not homogenized completely at the scale of

San Salvador Island (a scale of several km).

With beta diversity being higher in the entire dataset than in subsets of samples, we infer that mollusk assemblages display spatial structuring within the confined coastal region of San Salvador Island, and that sediment containing mollusk death assemblages does not evenly mix evenly at this scale, but rather exhibits species turnover among samples, preserved as patchiness.

The relationship between spatial scale and similarity indicates spatial structuring

(Figure 3-10). This is somewhat surprising at the relatively small scale of San Salvador

Island. This island-scale mollusk assemblage variation is attributable to storm frequency/intensity primarily, and seagrass presence secondarily, as drivers of differences in mollusk assemblages among localities.

The influence of Energy Level on Molluscan Assemblages

Because of its relatively exposed location on the easternmost side of the

Bahamian Archipelago, on the edge of the Bahamian carbonate platform, San Salvador

Island experiences hurricanes frequently. Although these storms impact the entire island, they generally hit the windward, or southeastern, side of the island with greater force than the more sheltered leeward, or northwestern, side of the island. This difference in storm frequency/intensity over time on the eastern and western sides of

San Salvador island has left a strong enough record that its signal can be recovered from cores taken from the island’s lake beds (Park, 2012; Park et al., 2009), and also from differences in isotopic values of the island’s land snail shells (Baldini et al., 2007).

These studies reveal that the northwestern (leeward) side of San Salvador Island has a history of storm intensity and/or frequency similar to the Gulf of Mexico and other more sheltered regions of the Caribbean, whereas the southeastern (windward) side of San

Salvador has a history of relatively elevated storm intensity and/or frequency. These

records reveal that storm energy can measurably vary between the sides of even a relatively small island such as San Salvador Island. Energy level can manifest in multiple, related ways: it can be the occurrence of infrequent intense storms (e.g. hurricanes) that differentially impact the shorelines of a small island, or it can be the occurrence of relatively higher average wave energy on the windward side of the island.

The effects of storm frequency on living marine invertebrate communities has been documented in multiple taxonomic groups at multiple locations, including holothuroids in Guam (Kerr et al., 1993), fossil corals in Japan (Hongo and Kayanne,

2009), copepods in Hawaii (Hassett and Boehlert, 1999), and foraminifera on Grand

Cayman Island (Li and Jones, 1997). These studies generally show that differences in storm frequency result in differences in the taxonomic composition of leeward and windward sites, because of one or more factors involving sediment disturbance, suspension, or stability levels, or wave energy that is too high or low for some taxa.

Consistent with the findings of those studies that document differential windward/leeward marine invertebrate communities/assemblages, the mollusk assemblages in our samples are also different on the leeward vs. the windward sides of

San Salvador Island. This could be a consequence of either ecological or physical factors, or a combination of the two. For example, storms and wave energy can influence marine benthos and the death assemblages they leave behind, through (1) the ecological consequences of increased sand deposition and/or particle suspension, and

(2) the physical consequences of shell transport and sediment mixing.

Sand deposition and particle suspension (1) has well-known effects on many marine organisms, such as reef-building corals that need clear turbid water to thrive,

and lesser-known effects on other marine organisms (Fabricius et al., 2007; Gibson and

Atkinson, 2003). Sand deposition has also been shown to have some influence on the spatial characteristics of marine benthic communities, possibly increasing spatial variation, although the overall effects of sedimentation in this regard remain unclear

(Díaz-Tapia et al., 2013). Even though high levels of sand deposition and particle suspension are unlikely to affect most mollusk taxa directly, many of which are infaunal, the broader ecological consequences could play a role in the marine communities that these mollusks rely on, thus impacting them indirectly.

Sediment transport and mixing (2) is a second potential factor that may contribute to differences between the leeward and windward mollusk assemblages in our samples. Sediment transport is relevant here because the mollusk shells that comprise our sampled mollusk death assemblages are a fundamental component of

San Salvador Island’s unconsolidated marine carbonate sediment.

Although storm mixing is widely assumed to homogenize marine sediments on an annual basis (Li and Jones, 1997), some studies refute this idea. For example, although transport is common in the fossil record of shell beds (Zuschin et al., 2005), multiple studies have shown that sediment mixing and transport do not erase the environmental gradients preserved in marine death assemblages, and that fine-scale spatial resolution is preserved through a storm event (Barbour, 2002; Miller, 1997; Miller et al., 1992). Heterogeneity is the norm in natural environments, and even seemingly homogenous environments can display heterogeneity (Hewitt et al., 2005). Small-scale habitat heterogeneity likely contributes to biodiversity in marine soft sediment habitats

(Hewitt et al., 2005). Among our samples we found that there is a moderate amount of

within-transect grouping throughout the island, including on the windward side, suggesting that storms do not mix sediments completely, and that the spatial signal from purportedly heterogeneous communities may persist in the death assemblage.

Moreover, we observed that some of the windward samples seem to actually be more distinctly grouped by transect than the leeward samples, possibly indicating even higher heterogeneity across windward localities/transects compared to leeward localities. This, however, could also be a consequence of higher levels of meter-scale within-transect mixing.

These results support previous findings that localized patchiness persists despite storm events, and that storms do not entirely eliminate patchiness and environmental zonation patterns in shell beds (Barbour, 2002; Miller, 1997; Miller et al., 1992).

Studying this patchiness in shell beds, to better understand how mollusk death assemblages are spatially preserved, may have important implications for measuring and mapping biodiversity, and for increasing the effectiveness of marine conservation efforts (Hewitt et al., 2005).

The Influence of Seagrass Habitat on Mollusk Assemblages

We observed that among the sites sampled in this project, mollusk assemblages from seagrass and non-seagrass localities were different. These results were expected, given previous findings that seagrass beds on San Salvador Island and elsewhere have distinct mollusk communities compared to non-seagrass beds (Reich, 2014). The presence of seagrass appears to be an important factor in grouping the sites in our study by mollusk assemblage similarity, second only to storm frequency/intensity

(leeward vs. windward).

Seagrass habitats are important components of many regional marine ecosystems (Blandon and Ermgassen, 2014; Costanza et al., 1998; Vassallo et al.,

2013). They are productive and support high levels of biodiversity (Duarte, 2002; Duarte and Chiscano, 1999). Marine invertebrates play an integral role within seagrass habitats, and the density and diversity of benthic fauna in seagrass beds is generally higher than in non-seagrass sediments (Connolly, 1997; Edgar et al., 1994; Orth et al.,

1984; Stoner, 1980). Mollusks are no exception to this finding. They are diverse in seagrass beds, and can play important roles in benthic processes (Creed and Kinupp,

2011). For example, suspension-feeding bivalves and seagrass have a mutually beneficial relationship (Peterson and Heck Jr, 2001). The environmental heterogeneity and biological productivity of seagrass habitats are likely to be among the main reasons for this increased diversity, however researchers still know surprisingly little about how exactly seagrass-associated fauna utilize seagrass habitats. Given the apparent importance of seagrass habitats to marine benthos, it is perhaps not surprising that faunal transitions from seagrass to sand sites can be quite sudden, with narrow transitional zones (Barnes and Hamylton, 2013).

As with the difference between leeward and windward mollusk assemblages, the difference between the seagrass vs. non-seagrass mollusk assemblages could be a consequence of either ecological or physical factors, or a combination of the two. More explicitly, the presence of seagrass could influence marine benthos and the death assemblages they leave behind through (1) the ecological functions that seagrass beds provide to living mollusks, and (2) the physical effects that seagrass has on shell death assemblage by transport and stabilization.

The first explanation (1) for why the seagrass and non-seagrass mollusk assemblages differ has to do with the ecological effects of seagrass habitats on the mollusk community. Seagrass beds provide a structurally complex habitat with hiding areas, places where organisms can anchor (e.g., seagrass leaf blades and roots), and a larger surface area for organisms to utilize. These ecological roles may affect both interspecific and intra-specific interactions, potentially contributing to the underlying cause of the observed differences in mollusk assemblages between seagrass and non-seagrass samples.

A second explanation (2) for why the seagrass and sand sites differ is that seagrass beds might play a role in stabilizing unconsolidated marine sediments, effectively inhibiting sediment movement (Fonseca, 1989; Fonseca and Fisher, 1986;

Scoffin, 1970). The particular distribution of marine fauna observed within, among, and between seagrass and non-seagrass environments, such as our sampling localities on

San Salvador Island, might be a consequence, in part, of the stabilizing property of seagrass (Fonseca and Fisher, 1986).

The ways that hydrodynamic factors influence the distribution of seagrass fauna, however, are still not well understood (Fonseca and Fisher, 1986), and some researchers suggest that sediment instability is not important in regulating mollusk species density and diversity in seagrass lagoons (Young and Young, 1982). Similarly,

(Brook, 1978) found that a high standing crop of seagrass might not be the main factor influencing macrofaunal abundance and that the taxonomic composition varies widely in seagrass communities.

Understanding seagrass-associated mollusk communities, and the ways they differ from non-seagrass mollusk communities, is important because mollusk assemblages have the potential to be a powerful assessment tool for both modern and fossil seagrass ecosystems. For example, although the sedimentological record might not distinguish between seagrass and sand, the mollusk assemblages preserved in this sediment probably do, and may even retain information about fine-scale seagrass patchiness and environmental gradients (Creed and Kinupp, 2011; Ferguson and Miller,

2007; Miller, 1988). Specifically, mollusks have been utilized as environmental indicators of broader community characteristics in seagrass ecosystems because of their ecological variability and the relatively large proportion of seagrass ecosystem resources they use (Creed and Kinupp, 2011).

The relative importance of the ecological and/or physical factors that account for the observed differences between seagrass- and non-seagrass-associated mollusks on

San Salvador Island remain inconclusive. Our results, however, indicate that the influence of seagrass, although secondary to the influence of storms, is significant.

Evenness and Species Dominance Patterns

The leeward samples displayed higher evenness (Figure 3-8), and this could be a consequence of several factors. One possibility is that the cerith shells that dominate many of the groups of samples (see Figures 3-3 and 3-4), and especially the windward samples, are responsible for the lower evenness observed in the windward transects.

The high relative abundance of these cerith shells could be caused by the erosion- resistance of their shells, a higher or lower tendency for post-mortem transport, tolerance of the taxon to disturbed habitats, or faster recolonization of disturbed areas relative to other mollusks. Ceriths might do well, or at least not as poorly as other

mollusk species, in disturbed areas with high wave and storm energy. Higher evenness in the leeward samples could reflect better preservation of taxa with more delicate shells, which were potentially washed out if shell assemblage at the windward localities.

Summary for Chapter 3

We assessed mollusk shell assemblages in samples from 12 transect localities around San Salvador Island with the aim to better understand spatial structuring and the roles of environmental and biological variables on shell assemblage composition at within-island spatial scales.

Our results suggest that mollusk death assemblages exhibit a high level of local spatial structuring. The assemblages differ between one side of the island and the other, from one habitat type to another, and from one transect to another. Specifically, the mollusk assemblages differ in species composition on leeward and windward sides of the island, and within the leeward side, ther differ between seagrass and unconsolidated sand localities. These results contradict the purported homogenizing effects that higher energy and unconsolidated sediment are presumed to have on the distribution of mollusk shells.

Researchers who use mollusk assemblages as proxies for environmental variability, for measuring regional diversity, or for other purposes, need to account for the potential significance of not only regional variables like climate and productivity, but also of more local variables like site geography and the spatial structuring of samples.

Some of these local geographic and spatial variables, like leeward vs. windward, may potentially override more commonly considered environmental variables like habitat type, e.g., the presence of seagrass. Considering the effects of these local-scale spatial

variables in project design and sampling protocols may contribute to more effective use of mollusk shells in marine and coastal research.

Figure 3-1. Map of San Salvador Island, Bahamas. The 12 transect localities are labeled clockwise from top with names and numerical codes. The inset shows the location of San Salvador Island within the Caribbean.

Figure 3-2. Sediment sample collection along transects in seagrass (A) and unvegetated (B) localities of San Salvador Island using SCUBA. Photos courtesy of author.

Figure 3-3. The 25 most abundant mollusk species in the entire dataset, and their percentages in samples grouped by leeward, windward, vegetated, and unvegetated localities.

Figure 3-4. The 25 most common mollusk species in the entire dataset, and their percentages in samples grouped by vegetated+windward, vegetated+leeward, unvegetated+windward, and unvegetated+leeward localities.

Figure 3-5. The probability (p) of finding a leeward or windward only species in the type of sample it was not found in, based on their sample size (n).

Figure 3-6. Nonmetric multidimensional scaling (NMDS) plot (k = 2, stress = 0.191). All samples colored by leeward and windward (A). Subset of leeward samples colored by unvegetated and vegetated (B). Subset of windward samples colored by unvegetated and vegetated (C).

Figure 3-7. Non-metric multidimensional scaling (NMDS) plots with convex hulls surrounding areas that encompass vegetated (green), leeward (grey), and windward (black) samples. For clarity, sample points are not shown. The top 29 most abundant species are shown, numbered alphabetically, where they occur in the multivariate space of the plot.

Figure 3-8. Standardized species richness and species diversity (measured as Parker- Berger Index) plotted for each sample (Leeward samples = grey; Windward samples = black). Each sample’s location on the plot is an indication of relative evenness. Samples in the bottom right of the plot have high richness, but lower overall diversity (Parker Berger Index), indicating lower evenness, compared with samples in the upper left of the plot, which have low richness, but higher diversity (Parker Berger Index), indicating higher evenness.

Figure 3-9. Beta diversity of nine groups of samples (within eight subsets of samples (2-9), and within the entire group of samples (1)) as Beta variance and Beta Shannon. See Table 3-4.

Figure 3-10. Each point shows two measurements of similarity (Bray-Curtis and Spearman rank correlation) for one pairwise comparison. Pairs are either within the same transect (black stars), within the same region (within leeward or within windward) (black dots), or between different regions (leeward vs. windward) (yellow dots). Similarity is generally highest (higher values on both axes) within transect, and lowest (lower values on both axes) between regions, indicating overall spatial structuring of the mollusk assemblage data. Plot inset shows only the bray Curtis similarity of the pairwise comparisons to highlight the shifting levels of similarity between the five hierarchical levels within the dataset (region = windward/leeward and habitat = vegetated/unvegetated).

Figure 3-11. Evenness by (A) substrate type (vegetated or non-vegetated) and (B) windward/leeward. Each point is one sample.

Figure 3-12. Rank abundance distribution plot of mollusk species in seagrass (green), non-seagrass (red), windward (blue), and leeward (black) localities.

CHAPTER 4 SPATIAL BIODIVERSITY PATTERNS IN SEAGRASS-ASSOCIATED MOLLUSK COMMUNITIES ALONG FLORIDA’S GULF COAST

Abstract for Chapter 4

We sampled live and dead mollusk assemblages from five estuarine systems along the central Gulf Coast of Florida to assess mollusk biodiversity and explore the role of spatial scale in benthic community spatial structuring. The systems (from north to south) are Waccassasa, Crystal, Homosassa, Chassahowitzka, and Weeki Watchee river estuaries. All of these estuarine systems have seagrass beds, but they are subject to different amounts of nutrient input and anthropogenic disturbance. Samples were collected using a Venturri suction dredge and both live and dead mollusks from each sample were counted and identified. Overall, the death assemblage totaled 122,344 specimens and 129 species, whereas the live assemblage totaled 8034 specimens and

80 species. There is substantial variation in alpha diversity (site-level species richness), with Chassahowitzka and Crystal river estuaries having the highest median levels of alpha diversity and Weeki Wachee having the lowest median levels of alpha diversity.

Within these estuary systems, however, there is substantial site-to-site variability in alpha diversity, discernable both at the site and replicate sample level. Similarly, beta diversity varies notably across and within estuaries. The regional gamma diversity exceeds estuary level gamma diversity. The results indicate that even within a relatively homogenous habitat type such as seagrass dominated soft-sediment bottom, there is substantial variability in diversity levels, including both local and regional biodiversity hotspots. Spatial mapping of biodiversity provides useful guidelines for the assessment of ecosystem services and developing restoration and conservation efforts.

Introduction for Chapter 4

Seagrasses play a vital role in the ecology of the world’s oceans (Blandon and

Ermgassen, 2014; Costanza et al., 1998; Vassallo et al., 2013). They are very productive habitats and often support elevated levels of biodiversity relative to many other marine habitat types (Duarte, 2002; Duarte and Chiscano, 1999). Moreover, seagrass habitats provide multiple ecosystem services by enhancing the production of fish and other animal populations (Blandon and Zu Ermgassen, 2014; Cullen-Unsworth and Unsworth, 2013), sequestering carbon (Fourqurean et al., 2012), stabilizing sediment and protecting shorelines from erosion (De Boer, 2007), and a primary productivity that is estimated to be around 1% of the net primary productivity of the global ocean (Duarte and Chiscano, 1999).

Increasingly, seagrass habitats in many regions throughout the world are in decline (Orth et al., 2006; Walker and McComb, 1992; Waycott et al., 2009). As seagrass habitats deteriorate, the organisms that rely on these habitats are also threatened (Hughes et al., 2009). Additional seagrass monitoring efforts are needed to assess the rate of seagrass habitat loss (Duarte, 2002).

Florida has long been host to expanses of seagrass habitat, as documented by fossil seagrass beds preserved as carbon imprints in west-central parts of the state

(Lumbert et al., 1984). The Big Bend area along Florida’s Gulf Coast is currently the northern limit for American tropical seagrasses (Iverson and Bittaker, 1986) and represents one of the most extensive seagrass meadow systems in North America

(Mattson and Bortone, 1999). Today, seagrass beds cover 3000 km2 in the Big Bend region of Florida, most of which is comprised of two species, Thalassia testudinum

(turtle grass) and Syringodium filiforme (manatee grass) (Iverson and Bittaker, 1986).

Factors that control the size of seagrass beds within their natural range include the inhibiting influence of increased water turbidity nearshore, the inhibiting influence of low salinity around river mouths, and the inhibiting influence of limited light in deeper waters

(Iverson and Bittaker, 1986). Of these, light is generally considered the primary factor affecting seagrass production (Dennison, 1987). Data collected over long time periods indicate that seagrass beds near the mouths of some rivers along Florida’s Gulf Coast are being lost or reduced possibly because of increased sediment loading and other factors that negatively impact the light environment (Hale et al., 2004).

Marine invertebrates play a key role in the ecology of seagrass ecosystems, and the density and diversity of benthic fauna in seagrass beds is generally higher than in soft bottom sediments when seagrasses are absent (Connolly, 1997; Edgar et al., 1994;

Orth et al., 1984; Stoner, 1980). Above ground plant biomass is correlated with invertebrate species abundance and richness, likely because of the added protection and surface area that dense seagrass foliage provides (Heck Jr and Wetstone, 1977).

The environmental heterogeneity and biological productivity of seagrass habitats are likely to be among the main reasons for the high degree of faunal diversity characteristics of seagrass beds. Nevertheless, researchers still know surprisingly little about the ecology of many associated fauna.

Mollusks are an important component of the invertebrate fauna associated with seagrass beds. They are both numerous and ecologically important within seagrass beds (Creed and Kinupp, 2011), especially from a food web perspective (Peterson and

Heck Jr, 2001). Furthermore, mollusks associated with seagrass can be useful for revealing modifications to shallow marine ecosystems (Kidwell, 2009);(Feser, 2015),

and there is growing evidence that shell remains of dead individuals provide a reliable proxy for assessing the historical state of ecosystems on both local and regional scales

(Kidwell, 2013a, 2015; M Kowalewski et al., 2009; Schone and Surge, 2005; Tyler and

Kowalewski, 2017). Mollusk shell beds have ages that can range back of hundreds to thousands of years (Flessa, 1993; Flessa et al., 1993; Flessa and Kowalewski, 1994;

Murray-Wallace and Belperio, 1994; Powell and Davies, 1990).

Materials and Methods for Chapter 4

Site Selection and Collection Methods

We sampled mollusks at 15 sites (approximately 60 samples) representing five estuarine systems along the central Gulf coast of Florida over multiple field seasons

(Summer/Fall, 2014 and Summer 2016). The systems investigated were (from north to south) Waccassassa, Crystal, Homosassa, Chassahowitzka, and Weeki Wachee river estuaries (Figure 4-1). All of these estuarine systems are characterized by extensive seagrass cover, but they have different amounts of nutrient input and anthropogenic disturbance. These five localities were included in previous studies (Barry, 2016;

Cummings, 2016; Frazer et al., 1998; Frazer et al., 2002; Jacoby et al., 2009), and consequently have detailed seasonal records of measured water quality, nutrient characteristics, and seagrass diversity and abundance. Specific sampling stations within each estuarine system correspond to water quality stations that were established nearly two decades ago (Frazer et al., 1998).

Live and dead mollusk assemblages were sampled by removing controlled volumes of plant material and surficial sediment from seagrass beds. Sediment was collected by SCUBA using a Venturri suction dredge (5.1cm PVC pipe with 1.4cm reducer nozzle connected to a 757 L min-1 pump) fitted with a 700 µm mesh collection

bag. The suction dredge was timed for two minutes of suction time within a circular sampling area of 0.29 m2 (a plastic bin with an inside diameter of 61 cm, in which the bottom had been removed to allow it to be pushed into the sediment) following the sampling methods of Barry (2016). This method controls for the volume of seafloor sampled, and was demonstrated previously to be effective for sampling seagrass invertebrates (Terlizzi and Russo, 1997). After vacuum collection, sediment was wet- sieved with 1mm mesh size and placed in plastic bags and frozen until laboratory processing of live and dead mollusks. Sampled sediment depth ranged from the sediment-water interface (0 cm) to approximately 10 cm deep, depending on the hardness of the substrate at the site and the amount of soft sediment. Importantly, this method collects mollusks from multiple micro-habits, some of which can be challenging to collect in other ways, including mollusks that graze above the sediment on seagrass blades, mollusks that encrust on hard substrate (e.g. oysters), and mollusks that burrow into soft sediment. This method also captures the smaller mollusks that are easily overlooked and often undersampled using other approaches. At each site within each of the five aforementioned estuaries, we collected four replicate samples. Replicates were roughly 5m apart and located within four distinct quadrants (NE, SE, SW, NW) relative to the boat’s anchor position.

Sample Processing

All samples, as indicated above, were wet sieved in the field to remove sediment and other particles < 1 mm. Samples were subsequently sorted in the lab into four size fractions: 1-2, 2-4, 4-8, and > 8mm. Both live and dead mollusks were picked, counted, and identified from these four size factions. For the death assemblage, we picked all shells that were at least 80% complete. Fragmented and broken shells that were

estimated to have less than 80% of their original material were excluded. The species abundance dataset was adjusted to account for occurrences of species with multiple hard parts per individual (e.g., bivalves with two valves vs. gastropods with one shell per individual).

When the total sediment volume for either the 2-4mm or the 1-2mm size fractions exceeded 100ml, only live mollusks from a 100ml subsample of sediment and dead mollusks from a 25ml subsample of sediment were picked and identified. This was necessary to effectively manage processing times. The reduced volumes, nevertheless, yielded abundant and diverse mollusk assemblages. For the two larger size fractions (4-

8 and > 8mm), the live and dead mollusks were identified from the entire volume of the original sediment sample.

We used multiple sources for mollusk species identification, including taxonomic compendia (Mikkelsen and Bieler, 2007; Redfern, 2013; Tunnell, 2010), reference collections from previous sampling efforts (Barry, 2016; Cummings, 2016), and malacology experts at the Florida Museum of Natural History. Species names were updated using the most recent nomenclature from the World Registry of Marine Species

(Costello et al., 2013).

Analytical Methods

Taxon occurrence data were adjusted to account for individual organisms with multiple shell parts (i.e. bivalves, chitons) by dividing the occurrence data for those taxa by the number of parts per individual. This correction in our dataset does not inflate the number of individuals, which would inappropriately increase the power of statistical comparisons. We test for statistically significant differences in diversity and eveness among groups with a one-way ANOVA (Kruskal-Wallis rank sum test). Our null

hypothesis is that the five estuaries have the same diversity and evenness. We used rarefaction/sample standardization methods to derive standardized estimates of species richness and assess mollusk diversity in the estuarine systems. Rarefaction is a type of subsampling that enables one to compare samples of different sizes. A steep slope indicates that true species richness is not sampled adequately and also reflects high evenness of sampled communities, whereas a flattening off of the curve indicates that most common species have been captured by samples and may also point to higher dominance of one or a few species in the sampled communities. To measure beta diversity, we used multiple recently developed metrics, including Shannon Beta, Beta

Variance, and Beta dispersions (Anderson et al., 2011). We used both custom written R script and various R packages for statistical analyses and figure generation (Oksanen et al., 2015a; R Development Core Team, 2015).

Results for Chapter 4

Sample Taxonomic Composition and Rank Abundance

A total of 130,378 shells were identified from the sediment samples, representing

130 mollusk taxa. The death assemblage has far more specimens (n=122,344) and species (S=129) compared to the live assemblage (n= 8,034, S = 80). Rank abundance plots are used to show patterns of relative species abundance, species dominance, and evenness. These are a component of biodiversity, and enable us to compare the taxa within the live and death assemblages visually. The rank abundance distribution plots reveal that the most abundant death assemblage species is the small bivalve

Parastartre triquetra (Figure 4-2). The second and third most abundant death assemblage species are Transennella spp. and Cerithium muscarum (Figure 4-3). The rank abundance plot for the death assemblage (Figure 4-2) reveal a relatively shallow

gradient compared to the life assemblage curve (Figure 4-3), which indicates that these death assemblages are relatively more even in species composition when compared to the live assemblages.

The most common species in the live assemblage Transennella spp., which is the second most common taxon in the death assemblages, and is much more common than the second most abundant species in the live assemblages (Brachidontes exustus), which indicates somewhat lower evenness in the live assemblage than in the death assemblage. The second and third most common species in the live assemblage

(Brachidontes exustus and Astyris lunata, respectively) are not common in the death assemblage.

Richness and Evenness

Plots of evenness vs. standardized richness (Figures 4-4 to 4-10) indicate substantial variation in diversity within each estuary system, both in standardized richness (alpha diversity) and evenness. In most samples, measures of evenness and diversity are positively related; i.e. lower species diversity equates to lower evenness, when just a few species occur in very high abundances and additional species are rare.

Sorting among systems (within-system grouping) was evident, but there is still a high degree of overlap. For both live and dead, there is substantial variation in local diversity of seagrass-associated mollusks within each estuarine system. The estuarine system with the most variation in evenness and diversity is Weeki Wachee, which has both high and low diversity samples in the live and death assemblages. Following Weeki Wachee,

Crystal River also exhibited substantial variation in the sample evenness and diversity, but only in the death assemblages. In the live assemblage, the evenness and richness are all relatively high.

Overall, evenness and diversity tended to be slightly less for in live assemblages from all sites (Figure 4-10, Table 4-1). Samples collected from Weeki Wachee (both live and dead) are lower in both evenness and richness/diversity compared to the other systems, and are particularly low in richness (Figure 4-7 and 4-8). The greatest disparity between diversity/evenness of live and dead assemblages was observed to occur in

Chassahowitzka. The estuary with the least disparity between diversity/evenness in live and dead samples is Crystal River. Homosassa and Waccasassa show comparable levels of diversity/evenness in both their pooled live and pooled dead samples. Live assemblages were represented by much smaller sample sizes, and it is thus expected that they would exhibit greater variability than death assemblages, which were represented by more sample material.

Table 4-1. Median evenness and diversity values for each of the five estuaries.

Measurement CHA CRY HOM WAC WEE median evenness 0.8138922 0.8569617 0.8418819 0.7884773 0.5568156 (live) median evenness 0.9007927 0.8696246 0.8665854 0.8643723 0.6926179 (dead) median diversity 9.6977514 11.7197420 10.3239695 9.8199540 7.2145218 (live) median diversity 12.5256358 11.8889616 11.1767625 11.3475669 7.3783498 (dead)

Statistical Significance

A one-way ANOVA on rank data (Kruskal-Wallis rank sum test) was carried out to test the null hypotheses that diversity and evenness did not differ among the sites.

There is significant variation in median standardized diversity (species richness) across estuaries for both live assemlage data (Kruskal-Wallis chi-squared = 12.434, df = 4, p =

0.0144) and dead assemblage data (Kruskal-Wallis chi-squared = 10.955, df = 4, p =

0.02707). There are also significant or near significant differences in median evenness across estuaries for live assemblages (Kruskal-Wallis chi-squared = 8.9158, df = 4, p =

0.06324) and death assemblages (Kruskal-Wallis chi-squared = 9.5064, df = 4, p =

0.04962).

Rarefaction Curves

Rarefaction curves for both the live and dead assemblages (Figures 4-11 to 4-

14) show relatively flat (horizontal) lines for Weeki Wachee samples and a majority of the other estuary samples, with the exception of Wacasassa, which are more steeply sloped (vertical). This steeper slope indicates that there may be higher richness in this system than what we were able to capture in our sampling efforts. Some of the Crystal

River samples are also steeply sloped, indicating that they may also be undersampled for richness. Based on these rarefaction curves, Homosassa appears to be the most undersampled of the live assemblages. Overall, the rarefation curves suggest that sample level data likely under represents richness, but that the pooled data more adequately capture richness.

Discussion for Chapter 4

The five estuarine systems that we sampled along the central Gulf Coast of

Florida (Waccassassa, Crystal River, Homosassa, Chassahowitzka, and Weeki

Wachee) are relatively shallow (< 2m) and support extensive seagrass beds (Choice et al., 2014). Seagrass beds function as primary producers, stabilize unconsolidated marine sediment, provide food and shelter to support marine life, and serve as substrate for numerous algae and small animals (Dawes et al., 1985), all of which are relevant to

and may influence the composition/diversity of the mollusk communities that inhabit them.

The alpha diversity and taxonomic composition of mollusks in the assemblages from the five estuarine systems that we sampled generally match the taxonomic findings of other projects from these and similar localities (Barry, 2016; Howard, 1987). For example, the high abundances of Transennella spp. and Cerithium muscarum in the samples is not surprising given the ubiquity of these species in the sampling localities and throughout the region.

Statistically significant differences in live and dead mollusk assemblages between the five localities are consistent with the hypothesis that these estuarine systems are unique in the details of their biodiversity, and that taxonomic diversity scales with space; i.e., regional diversity is higher than system-scale diversity.

Differences in diversity between localities could be consequence of several potentially related physical and biological factors, including the amount and type of submerged aquatic vegetation, salinity, light (related to water clarity), degree of anthropogenic disturbance, and spring outflow volume and/or consistency, and freshwater inflow (Barry et al., 2017). The higher levels of mollusk diversity measured for Chassahowitzka and

Crystal River could be caused by their variable but generally abundant submerged aquatic vegetation (seagrass and macroalgae) (Barry, 2016; Barry et al., 2017). Mollusk and other invertebrate communities play an integral role within vegetated marine habitats, and the density and diversity of these invertebrates is higher in seagrass habitats compared to adjacent non-vegetated habitats (Connolly, 1997; Edgar et al.,

1994; Orth et al., 1984; Stoner, 1980). Aquatic vegetation in Chassahowitzka,

Homosassa, and Crystal River is controlled by substrate, light, and salinity (Barry, 2016;

Choice et al., 2014), so these factors may indirectly influence mollusk communities through their impact on vegetation. If vegetation in these systems is impacted by changes in freshwater delivery as a consequence of a reduction in spring discharge

(Bush and Johnston, 1988; Yobbi and Knochenmus, 1990), or groundwater nitrate enrichment (Ham and Hatzell, 1996), there could be effects on mollusk communities as well.

The lower species richness and evenness in some of the Weeki Watchee samples could be caused by low productivity in the system and/or increased anthropogenic disturbance. It could also reflect lower mineral content in Weeki Wachee water (Yobbi and Knochenmus, 1990), or low bottom salinities (i.e., more mixed than

Crystal River) (Yobbi and Knochenmus, 1990). Weeki Wachee also exhibits the greatest range of evenness and diversity values among the five estuarine systems. One possible explanation is that this system has greater habitat heterogeneity than the other systems. Another potential explanation is that seasonal recruitment events, or synchronous die offs of certain taxa, create extremely high abundances of certain species and relatively low abundances of others. An additional explanation, related to that above, is that this system could have been subjected to storm events that influenced the sampled death assemblage. During a storm event, the death assemblage can be transported into storm beds, which were sorted such that shells of one size and/or taxon washed into an area that we sampled. This explanation, however, is not supported by previous findings that indicate storms do not erase patterns of localized distributions within shell beds (Miller et al., 1992). Among the five river systems, the

lower evenness and diversity values for live compared to dead assemblages (Figure 4-

10) are expected given that live communities are generally less even and diverse than their associated death assemblages (Kidwell, 2002). This is a consequence of the time- averaged nature of shell accumulations, which are more likely to accrue species from mollusk communities over time, in contrast to the relatively narrow time slice represented by the live assemblage.

Summary for Chapter 4

Florida’s seagrass beds are an important part of the regional marine ecosystem, but like many coastal habitats worldwide, they are subject to an increasing array or environmental stressors. Shells beds (mollusk death assemblages) and live mollusk assemblages can yield information about recent changes in seagrass beds that may provide valuable insight into both past and present local and regional biodiversity.

We sampled alpha diversity of live and dead mollusk assemblages from five estuarine systems along the central Gulf coast of Florida (Waccasassa, Crystal River,

Homossasa, Chassahowitzka, and Weeki Wachee) to assess alpha-level mollusk biodiversity and explore the role of spatial scale in benthic community spatial structuring. Differences in live and dead mollusk assemblages between the localities are statistically significant. Both evenness and alpha diversity are slightly depressed in live assemblages from the sampled localities compared to the dead assemblages.

Chassahowitzka and Crystal River have the highest median levels of alpha diversity, whereas Weeki Wachee has the lowest median levels of diversity. This is apparent in both in live and dead assemblages, although live displays more variability. Weeki

Wachee, however, also has the greatest range of values for evenness and diversity across the study dimension.

This project enables us to explore spatial structuring of seagrass-associated alpha-level biodiversity within mollusk communities on the Gulf coast of peninsular

Florida. Increasing our understanding of the biodiversity dynamics within this system may yield important insights into how to best protect and utilize this valuable resource for the future.

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Figure 4-1. Locations of the five Florida Gulf Coast estuarine systems from which mollusk assemblage samples were obtained.

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Figure 4-2. A rank abundance distribution curve (Whittaker plot) for the pooled death assemblage, with the top ten species color-labeled in the legend.

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Figure 4-3. A rank abundance distribution curve (Whittaker plot) for the pooled live assemblage, with the top ten species color-labeled in the legend.

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Figure 4-4. Evenness (Hurlbert’s PIE) and standardized richness of samples (n > 30), color-coded by estuary for both dead (A) and live (B) assemblages.

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Figure 4-5. Evenness and standardized richness of samples (n > 30) color-coded by estuary and site number, both dead (A) and live (B) assemblages.

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Figure 4-6. Evenness and standardized richness of sites (n > 45) color-coded by estuary, for both dead (A) and live (B) assemblages.

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Figure 4-7. Box plots of sample-level comparison of standardized diversity and evenness across the five estuary systems for both live (B and D) and dead (A and C) assemblages.

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Figure 4-8. Box plots of site-level (within locality) comparison of standardized diversity and evenness across the five estuary systems, for both live (B and D) and dead (A and C) assemblages.

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Figure 4-9. Comparison of standardized richness of dead and live assemblages. A. Live; B. Dead.

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Figure 4-10. Evenness and diversity (richness) for samples pooled by live (open circles), dead (closed circles), and estuary (color-codes).

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Figure 4-11. Rarefaction curves of death assemblages of individual samples color- coded by system/estuary.

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Figure 4-12. Rarefaction curves of live assemblages of individual samples color-coded by system/estuary.

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Figure 4-13. Rarefaction curves for death assemblages in each of the five estuaries.

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Figure 4-14. Rarefaction curves for live assemblages in each of the five estuaries.

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CHAPTER 5 CONCLUDING REMARKS

Mollusk shells have the potential to yield information about the morphology, evolution, ecology, and biodiversity of the animals and communities from which they originate. In addition, they can inform researchers about non-biological topics, such as those relating to earth’s past and present climate (Wu et al., 2002), environmental toxins

(Rittschof and McClellan-Green, 2005), and sedimentary geology (e.g. taphonomy, stratigraphy, etc.)(Scarponi and Kowalewski, 2007). Much of this information can act as a proxy for larger systems, and can therefore be applied broadly to address a variety of questions. This broader applicability means that the use of mollusks as ecological indicators is potentially a very powerful tool for ecologist, paleontologist, geologists, and biologists.

One development resulting from the increasing recognition of fossils (especially

“subfossils” that represent the most recent millennia) as indicators of long-term ecosystem changes has been the emergence of the field of conservation paleobiology.

Conservation paleobiology is a relatively young sub discipline that addresses issues related to biodiversity preservation by synthesizing traditional questions in conservation biology with the paleontological record. Examples of studies that would be considered conservation paleobiology are varied, and include taxa ranging from microfossils (Slate and Jan Stevenson, 2000) to vertebrates (Rick and Lockwood, 2013). Often, the goal is to establish a baseline, or a natural range of variability (to take into account the normal variability of natural systems) for reference to a modern ecosystem (Dietl and Flessa,

2011). For example, establishing the extent of human induced invasive species on

Galapagos Islands (van Leeuwen et al., 2008), or establishing a historical baseline for

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the Colorado River Delta (Kowalewski et al., 2000). Mollusks play an important role in conservation paleobiology due to their high preservation potential and biological diversity, and three case studies of their use as ecological indicators to explore environmental and ecological drivers of diversity are presented in this dissertation.

The three research projects in this dissertation cover different aspects of mollusk evolution, ecology and biodiversity. The first research project addresses morphological variability and disparity within the genus Anadara (Bivalvia), and suggests that morphological disparity may be a composite, multi-scale product of extrinsic and intrinsic factors and that populations and species may differ inherently in their morphological disparity. The results demonstrate that even within congeneric species, some populations and some species are inherently more variable morphologically. This type of study is potentially significant in contributing to efforts to understand the full extent of biological diversity as it can be measured in fossil organisms, and also because the understanding of within-species variability is an underappreciated aspect of measuring and monitoring past and present biodiversity.

The second research project uses mollusk assemblage samples from San

Salvador Island in the Bahamas to characterize a predictable spatial organization, controlled primarily by physical (wind energy) and, secondarily, biological (seagrass vegetation) processes. The finding that local geographic and spatial variables (e.g. leeward vs. windward) may potentially override more commonly considered environmental variables like habitat type (e.g. the presence of seagrass) is a potentially useful finding because considering the effects of these local-scale spatial variables in

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project design and sampling protocols may contribute to more effective use of mollusk shells in marine and coastal research.

The third research project looks at alpha diversity within five estuarine seagrass ecosystems along the Gulf Coast of Florida, and reveals the importance of unique physical and biological characteristics of estuaries and the role of spatial scale in capturing seagrass-associated mollusk community biodiversity. Alpha diversity varies notably within and between estuary systems, indicating that biodiversity is not homogenous either locally or regionally. Thus, even within a single habitat type

(seagrass meadows) there is substantial heterogeneity in local and regional biodiversity.

Death assemblages, which represent a long term (time-averaged) record of seagrass habitats, track the alpha diversity of living communities, indicating that regional biodiversity patterns and local biodiversity hotspots have been remarkably stable over centennial to millennial time scales. The mapping of local and regional biodiversity hotspots, using both live and dead mollusks, potentially provides a valuable spatial and historical perspective that can inform restoration and conservation management of these valuable seagrass beds and other similar habitats.

The rapidly changing environmental conditions that are a reality of the modern world call for scientists to develop novel ecosystem and species assessment tools, and to strengthen and perfect the existing ones (Louys, 2012). By examining environmental and ecological drivers of mollusk diversity across spatial scales and through time, the three research projects summarized in this dissertation attempt to contribute meaningfully to the larger body of knowledge on mollusks, and in particular, to their utility in ecological assessment and conservation paleobiology.

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APPENDIX A LIST OF SAN SALVADOR MOLLUSK TAXA AND TOTAL OCCURRENCES

List of taxa and their total occurrences (n) in the entire dataset for Chapter 3.

Class Family Genus Species n Bivalvia Arcidae Acar domingensis 117 Bivalvia Arcidae Arca imbricata 138 Bivalvia Arcidae Arca zebra 4 Bivalvia Arcidae Barbatia cancellaria 184 Bivalvia Basterotiidae Basterotia elliptica 1 Bivalvia Cardiidae Ctenocardia guppyi 1044 Bivalvia Cardiidae Laevicardium mortoni 3 Bivalvia Cardiidae Papyridea semisulcata 2 Bivalvia Chamidae Chama sarda 1 Bivalvia Chamidae Chama macerophylla 19 Bivalvia Condylocardiidae Carditopsis bernardi 9 Bivalvia Condylocardiidae Carditopsis smithii 40 Bivalvia Crassatellidae Crassinella lunulata 16 Bivalvia Dimyidae Dimyella starcki 6 Bivalvia Glycymerididae Tucetona pectinata 268 Bivalvia Isognomonidae Isognomon radiatus 2 Bivalvia Lasaeidae Kellia sp. 2 Bivalvia Lasaeidae Lasaeid sp. 2 Bivalvia Lasaeidae Orobitella floridana 1 Bivalvia Lasaeidae Planktomya henseni 3 Bivalvia Lasaeidae Semierycina sp. 41 Bivalvia Limidae Ctenoides mitis 12 Bivalvia Limidae Limatula hendersoni 74 Bivalvia Lucinidae Ctena orbiculata 46 Bivalvia Lucinidae Divalinga quadrisulcata 224 Bivalvia Lucinidae Lucina pensylvanica 377 Bivalvia Lucinidae Parvilucina costata 431 Bivalvia Mytilidae Botula fusca 1 Bivalvia Mytilidae Brachidontes exustus 82 Bivalvia Mytilidae Crenella sp. 2 Bivalvia Mytilidae Crenella divaricata 2346 Bivalvia Mytilidae Gregariella coralliophaga 1 Bivalvia Nuculidae Nucula calcicola 10 Bivalvia Pleurobranchidae Berthella stellata 1 Bivalvia Pteriidae Pteria colymbus 14 Bivalvia Semelidae Cumingia antillarum 2 Bivalvia Semelidae Ervilia concentrica 799 Bivalvia Semelidae Semele bellastriata 2

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Bivalvia Solemyidae Solemya occidentalis 4 Bivalvia Tellinidae Scissula candeana 54 Bivalvia Tellinidae Scissula similis 490 Bivalvia Tellinidae Strigilla mirabilis 115 Bivalvia Tellinidae Tellinella listeri 5 Bivalvia Thraciidae Asthenothaerus hemphilli 5 Bivalvia Ungulinidae Diplodonta sp. 6 Bivalvia Ungulinidae Phlyctiderma semiaspera 9 Bivalvia Veneridae Anomalocardia puella 11 Bivalvia Veneridae Chione elevata 319 Bivalvia Veneridae Gemma gemma 236 Bivalvia Veneridae Petricola lapicida 4 Bivalvia Veneridae Timoclea pygmaea 47 Bivalvia Veneridae Transennella sp. 2179 Chiton Acanthochitonidae Acanthochitona pygmaea 158 Chiton Acanthochitonidae Acanthochitona floridanus 1 Chiton Acanthochitonidae Choneplax lata 12 Chiton Ischnochitonidae Ischnochiton erythronotus 1 Chiton Ischnochitonidae Stenoplax bahamensis 17 Chiton Ischnochitonidae Stenoplax boogii 1 Chiton Ischnochitonidae Ischnochiton sp. 1 Gastropoda Acteonidae Japonactaeon punctostriatus 2 Gastropoda Aglajidae Chelidonura sp. 1 Gastropoda Aplysiidae Aplysia parvula 1 Gastropoda Architectonicidae Heliacus cylindricus 1 Gastropoda Buccinidae Engina turbinella 1 Gastropoda Bullidae Bulla occidentalis 105 Gastropoda Caecidae Caecum lineicinctum 6 Gastropoda Calliostomatidae Calliostoma jujubinum 2 Gastropoda Cerithiidae Bittiolum varium 2 Gastropoda Cerithiidae Cerithium sp. 3556 Gastropoda Cerithiopsidae Cerithiopsis academicorum 11 Gastropoda Cerithiopsidae Seila sp. 5 Gastropoda Colloniidae Emiliotia rubrostriatus 5 Gastropoda Columbellidae Columbella mercatoria 14 Gastropoda Columbellidae Steironepion minus 7 Gastropoda Columbellidae Suturoglypta sp. 36 Gastropoda Columbellidae Zafrona sp. 73 Gastropoda Conidae Conus sp. 23 Gastropoda Costellariidae Mitromica foveata 3 Gastropoda Costellariidae Vexillum exiguum 15 Gastropoda Costellariidae Vexillum moniliferum 1 Gastropoda Cylichnidae Acteocina sp. 707 Gastropoda Cystiscidae Gibberula sp. 84

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Gastropoda Cystiscidae Granulina sp. 54 Gastropoda Epitoniidae Cycloscala echinaticosta 1 Gastropoda Epitoniidae Opalia pumilio 1 Gastropoda Eulimidae Melanella eburnea 213 Gastropoda Fasciolariidae Leucozonia ocellata 1 Gastropoda Fissurellidae Diodora listeri 19 Gastropoda Fissurellidae Fissurella barbadensis 3 Gastropoda Fissurellidae Hemimarginula dentigera 31 Gastropoda Fissurellidae Hemimarginula pileum 5 Gastropoda Fissurellidae Lucapina sowerbii 1 Gastropoda Fissurellidae Montfortia emarginata 1 Gastropoda Fissurellidae Rimula aequisculpta 2 Gastropoda Fissurellidae Rimula frenulata 1 Gastropoda Haminoeidae Atys sharpi 142 Gastropoda Haminoeidae Haminoea elegans 16 Gastropoda Harpidae Morum oniscus 1 Gastropoda Hipponicidae Cheilea striata 5 Gastropoda Hipponicidae Hipponix antiquatus 41 Gastropoda Juliidae Berthelina sp. 1 Gastropoda Liotiidae Arene cruentata 30 Gastropoda Liotiidae Arene venustula 5 Gastropoda Litiopidae Alaba incerta 2 Gastropoda Litiopidae Litiopa melanostoma 77 Gastropoda Littorinidae Echinolittorina meleagris 3 Gastropoda Littorinidae Echinolittorina mespillum 1 Gastropoda Littorinidae Echinolittorina tuberculata 15 Gastropoda Lottiidae Lottia leucopleura 19 Gastropoda Lottiidae Patelloida pustulata 248 Gastropoda Mangeliidae Agathotoma sp. 2 Gastropoda Mangeliidae Brachycythara alba 12 Gastropoda Mangeliidae Cryoturris sp. 3 Gastropoda Mangeliidae Ithycythara sp. 5 Gastropoda Mangeliidae Pyrogocythara cinctella 1 Gastropoda Mangeliidae Tenaturris inepta 3 Gastropoda Marginellidae Dentimargo redferni 21 Gastropoda Marginellidae Hyalina sp. 2 Gastropoda Marginellidae Volvarina sp. 13 Gastropoda Modulidae Modulus modulus 86 Gastropoda Muricidae Dermomurex pauperculus 2 Gastropoda Muricidae Murexiella macgintyi 2 Gastropoda Muricidae Phyllonotus pomum 3 Gastropoda Nassariidae Nassarius sp. 124 Gastropoda Naticidae Natica livida 59 Gastropoda Neritidae Smaragdia viridis 165

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Gastropoda Olivellidae Olivella nivea 51 Gastropoda Phasianellidae Eulithidium bellum 203 Gastropoda Phasianellidae Eulithidium thalassicola 1821 Gastropoda Phenacolepadidae Plesiothyreus rushii 3 Gastropoda Pseudomelatomidae Crassiclava apicata 1 Gastropoda Pseudomelatomidae Dallspira bandata 3 Gastropoda Pseudomelatomidae Monilispira mayaguanaensis 1 Gastropoda Pseudomelatomidae Pilsbryspira leucocyma 10 Gastropoda Pyramidellidae Chrysallida sp. 1 Gastropoda Pyramidellidae Eulimastoma didymum 1 Gastropoda Pyramidellidae Oscilla somersi 2 Gastropoda Pyramidellidae Pyramidella dolabrata 2 Gastropoda Pyramidellidae Sayella laevigata 84 Gastropoda Pyramidellidae Turbonilla sp. 10 Gastropoda Ranellidae Cymatium labiosum 1 Gastropoda Ranellidae Cymatium nicobaricum 2 Gastropoda Raphitomidae Daphanella sp. 2 Gastropoda Retusidae Retusa sulcata 3 Gastropoda Rissoellidae Rissoella sp. 1 Gastropoda Rissoidae Rissoina redferni 61 Gastropoda Rissoidae Schwartziella yoguii 83 Gastropoda Rissoidae Simulamerelina sp. 39 Gastropoda Rissoidae Zebina browniana 586 Gastropoda Scaliolidae Finella adamsi 946 Gastropoda Strictispiridae Strictispira sp. 1 Gastropoda Terebridae Terebra alba 3 Gastropoda Terebridae Terebra sp. 2 Gastropoda Tornidae Circulus orbignyi 1 Gastropoda Tornidae Cochliolepis parasitica 50 Gastropoda Tornidae Teinostoma semistriatum 90 Gastropoda Tornidae Teinostoma sp. 1 Gastropoda Tornidae Teinostoma umbilicatum 1 Gastropoda Triphoridae Iniforis gudeliae 8 Gastropoda Triphoridae Isotriphora peetersae 2 Gastropoda Triphoridae Latitriphora albida 3 Gastropoda Triphoridae Marshallora sp. 17 Gastropoda Triviidae Niveria quadripunctata 2 Gastropoda Trochidae Pseudostomatella erythrocoma 14 Gastropoda Trochidae Synaptocochelea picta 4 Gastropoda Truncatellidae Truncatella clathrus 2 Gastropoda Turbinidae Astralium phoebium 4 Gastropoda Turbinidae Parviturbo weberi 1 Gastropoda Turbinidae Tegula fasciata 7 Gastropoda Turbinidae Tegula gruneri 80

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Gastropoda Turbinidae Turbo castanea 124 Gastropoda Turritellidae Torcula sp. 4 Gastropoda Turritellidae Vermicularia spirata 6 Gastropoda Vanikoridae Megalomphalus sp. 3 Gastropoda Vermetidae Vermetid sp. 1 Gastropoda Volvatellidae Ascobulla ulla 1 Gastropoda Volvatellidae Cylindrobulla beauii 1 Scaphopoda Dentaliidae Antalis sp. 97 Scaphopoda Dentaliidae Graptacme calamus 1 Scaphopoda Dentaliidae Graptacme semistriata 23 Scaphopoda Gadilidae Polyschides sp. 32 Taxa = 181 n = 20608

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APPENDIX B LIST OF SEAGRASS-ASSOCIATED FLORIDA GULF COAST MOLLUSK TAXA

List of seagrass-associated Florida Gulf Coast mollusk taxa in Chapter 4.

Abra eaqualis Acteocina candei Agathotoma sp. Anadara transversa Tampaella tampaensis Tellina texana Ameritella versicolor Anodontia alba Anomalocardia cuneimeris Anomia simplex Arcopsis adamsi Arcuatula papyria Argopecten irradians concentricus Ascobulla ulla Astyris lunata Bittiolum varium Boonea impressa Brachidontes exustus Brachycythara biconica Bulla occidentalis Buscotypus plagosus Busycon sinistrum/contrarium Caecum imbricatum Caecum pulchellum Cardites floridanus Caryocorbula chittyana Cerithideopsis costata Cerithiopsis sp. Cerithium atratum Cerithium muscarum Cerodrillia spp. Chione elevata Codakia orbicularis Columbella rusticoides Conasprella spp. Costoanachis semiplicata Crassostrea virginica Crepidula plana complex Crepidula spp. Cumingia lamellosa

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Cumingia tellinoides Cyclostremiscus suppressus Dentalium laqueatum Dentimargo eburneolus Diodora cayenensis Ensis minor Epitonium albidum Epitonium sp. Eulithidium thalassicola Eupleura sulcidentata Fasciolaria tulipa Cinctura hunteria Gouldia cerina Fulguropsis spirata Granulina hadria Haminoea elegans Haminoea sp. A Ischadium recurvum Japonactaeon punctostriatus Kurtziella c.f. limonitella Laevicardium mortoni Limaria pellucida Longchaeus candidus Longchaeus suturalis Lucinisca nassula Lyonsia floridana Macoma brevifrons Macoma constricta Marginella sp. Meioceras nitidum Melanella sp. Melongea corona Merisca aequistriata Mitromica foveata Modiolus americanus Modiolus squamosus Modulus modulus Mulinia lateralis Muricidae sp. Musculus lateralis Mysella planulata Mytilidae sp. Mytilopsis leucophaeata Nassarius albus/consensus Nassarius vibex

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Neritina virginea Neverita duplicata Nucula proxima Nuculana acuta Nuculana concentrica Oliva sayana Olivella spp. Ostrea equestris Parastarte triquetra Parvanachis ostreicola Phosinella cancellata complex Pilsbryspira leucocyma Prunum apicinum Prunum succineum Psammotreta intastriata Pteria colymbus Pyrgocythara filosa Pyrgocythara hemphilli Pyrgocythara plicosa Pyrgospira ostrearum Pyrgospira tampaensis Radiolucina amianta Rubellatoma diomedea Rubellatoma rubella Sayella laevigata Schwartziella catesbyana Solen viridis Stewartia floridana Suturoglypta iontha Tagelus plebeius Tereba protexta Teinostoma cocolitoris Timoclea grus Trachycardium egmontianum Transennella spp. Turbo castanea Turbonilla sp. Turridae sp. Urosalpinx cinerea Urosalpinx perrugata Urosalpinx tampaensis Volvarina sp. Vexillum exiguum Vitreolina conica Vitrinella floridana

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BIOGRAPHICAL SKETCH

Sahale Casebolt was born in Tacoma, Washington, USA. She received a bachelor’s degree in biology from Oberlin College and a master’s degree in earth and environmental sciences from the University of Iowa. In 2017 she received her Ph.D. in geology from the University of Florida, where she worked with advisor Dr. Michal

Kowalewski in the invertebrate paleontology division of the Florida Museum of Natural

History. Her dissertation research involves multiple topics related to mollusk evolution and ecology, reflecting a longstanding interest in biology, ecology, paleontology, and environmental conservation.

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