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MOLLUSKS AS ECOLOGICAL INDICATORS: EXPLORING ENVIRONMENTAL AND ECOLOGICAL DRIVERS OF BIOLOGICAL AND MORPHOLOGICAL DIVERSITY
USING MOLLUSKS THROUGH SPACE AND TIME
By
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
© 2017 Sahale Casebolt
To my parents
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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.
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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
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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
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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
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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
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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
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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
By
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
11
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.
12
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.
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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
14
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
15
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.
16
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.
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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.
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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
19
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,
20
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
21
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
22
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
23
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).
24
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
25
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
26
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,
27
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
28
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
29
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
30
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
31
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
32
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).
33
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.
34
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).
35
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
36
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,
37
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.
38
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,
39
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
40
congeneric species some populations and some species display more inherent
morphologic variability.
41
Figure 2-1. Representative specimens of the seven Anadara taxa included in this analysis. Photos courtesy of author.
42
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).
43
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).
44
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.
45
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.
46
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.
47
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.
48
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,
49
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.
50
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
51
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. samples
Sampling 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
52
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
53
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
54
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.
55
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.
56
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.
57
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).
58
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
59
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
60
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).
61
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 specimens
Number of samples
Beta variance
Beta Shannon
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
62
(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).
63
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
64
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.
65
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).
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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
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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,
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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
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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).
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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.
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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 inter-
specific 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.
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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
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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
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variables in project design and sampling protocols may contribute to more effective use
of mollusk shells in marine and coastal research.
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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.
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A
B
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.
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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.
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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.
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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).
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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).
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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.
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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.
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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.
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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).
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Figure 3-11. Evenness by (A) substrate type (vegetated or non-vegetated) and (B)
windward/leeward. Each point is one sample.
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Figure 3-12. Rank abundance distribution plot of mollusk species in seagrass (green),
non-seagrass (red), windward (blue), and leeward (black) localities.
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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.
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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).
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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),
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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
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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
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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
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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
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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.
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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 (live)
0.8138922 0.8569617 0.8418819 0.7884773 0.5568156
median evenness (dead)
0.9007927 0.8696246 0.8665854 0.8643723 0.6926179
median diversity (live)
9.6977514 11.7197420 10.3239695 9.8199540 7.2145218
median diversity (dead)
12.5256358 11.8889616 11.1767625 11.3475669 7.3783498
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 =
96
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
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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,
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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
99
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.
100
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.
101
Figure 4-1. Locations of the five Florida Gulf Coast estuarine systems from which mollusk assemblage samples were obtained.
102
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.
103
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.
104
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.
105
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.
106
Figure 4-6. Evenness and standardized richness of sites (n > 45) color-coded by
estuary, for both dead (A) and live (B) assemblages.
107
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.
108
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.
109
Figure 4-9. Comparison of standardized richness of dead and live assemblages. A.
Live; B. Dead.
110
Figure 4-10. Evenness and diversity (richness) for samples pooled by live (open
circles), dead (closed circles), and estuary (color-codes).
111
Figure 4-11. Rarefaction curves of death assemblages of individual samples color-
coded by system/estuary.
112
Figure 4-12. Rarefaction curves of live assemblages of individual samples color-coded
by system/estuary.
113
Figure 4-13. Rarefaction curves for death assemblages in each of the five estuaries.
114
Figure 4-14. Rarefaction curves for live assemblages in each of the five estuaries.
115
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
116
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
117
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
119
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
120
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
121
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
122
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
124
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
125
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
126
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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.
