evolutionary ecology of host-parasite relationships: …
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University of New MexicoUNM Digital Repository
Biology ETDs Electronic Theses and Dissertations
Spring 4-13-2020
EVOLUTIONARY ECOLOGY OF HOST-PARASITE RELATIONSHIPS: ROLE OFHOST ECOLOGY, PHYLOGENY, ANDDEMOGRAPHICS IN SHAPING PARASITEEVOLUTIONErika Taylor GendronUniversity of New Mexico
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Recommended CitationGendron, Erika Taylor. "EVOLUTIONARY ECOLOGY OF HOST-PARASITE RELATIONSHIPS: ROLE OF HOST ECOLOGY,PHYLOGENY, AND DEMOGRAPHICS IN SHAPING PARASITE EVOLUTION." (2020). https://digitalrepository.unm.edu/biol_etds/263
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Erika Taylor Gendron Candidate
Department of Biology Department
This dissertation is approved, and it is acceptable in quality and form for publication: Approved by the Dissertation Committee: Dr. Sara V. Brant, Chairperson
Dr. Eric S. Loker
Dr. Thomas Turner
Dr. Christopher Witt
Dr. Charles Criscione
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EVOLUTIONARY ECOLOGY OF HOST-PARASITE RELATIONSHIPS:
ROLE OF HOST ECOLOGY, PHYLOGENY, AND DEMOGRAPHICS IN SHAPING PARASITE EVOLUTION
by
Erika Taylor Gendron (Ebbs)
B.S. Biology, California State University, Fresno, 2011
DISSERTATION
Submitted in Partial Fulfillment of the Requirements for the Degree of
Doctor of Philosophy
Biology
The University of New Mexico Albuquerque, New Mexico
May 2018
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ACKNOWLEDGMENTS
I would like to first acknowledge my primary supervisor Dr. Sara Brant who
has provided me with unlimited support and intellectual freedom over the past seven
years. Her guidance and enthusiasm for Parasitology has shaped my academic
development. I would also like to thank my co-supervisor Dr. Eric S. Loker for his
guidance and generosity over the years, and specifically for creating a productive
and supportive environment to study Parasitology. I would like to express my
gratitude to my dissertation committee, Dr. Chris Witt, Dr. Tom Turner and Dr.
Charles Criscione, for sharing their expertise and providing invaluable critiques of
this work.
I would like to thank my lab mates and members of the larger UNM
Parasitology group: Ms. Martina Laidemitt, Dr. Sarah Buddenborg, Mr. Jon Schultz,
Ms. Janeth Pena, Ms. Lijun Liu, Dr. Ramesh Devkota, Dr. Ben Hanelt, Ms. Rachel
Swanteson-Franz, Dr. Coen Adema, Dr. Si-Ming Zang, Dr. Bruce Hofkin, Dr. Lijing
Bu and Ms. Michelle Gordy. Thank you all for your feedback, encouragement and
most of all your friendship. Several undergraduate students have contributed to this
work, and my graduate experience as a whole, as such I would like to acknowledge;
Ms. D’Eldra Malone, Mr. Keith Keller, Ms. Emily Savris and Ms. Marissa Trujillo.
I would like to acknowledge the Museum of Southwestern Biology and its
Divisions for the many experiences and professional development opportunities it
has afforded me as a curatorial assistant. I would also like to acknowledge the
University of New Mexico’s Molecular Biology Facility, specifically Mr. George
Rosenberg for his patience and helpful conversations.
I would like to also thank my family for their constant support during my
pursuit of this degree. Specifically, my husband Daniel and son Jude whom helped
me maintain perspective during this process, my mother Sharon for her love and
encouragement and my father Michael for instilling curiosity about the natural world.
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EVOLUTIONARY ECOLOGY OF HOST-PARASITE RELATIONSHIPS:
ROLE OF HOST ECOLOGY, PHYLOGENY, AND DEMOGRAPHICS IN
SHAPING PARASITE EVOLUTION
by
Erika Taylor Gendron (Ebbs)
B.S. Biology, California State University, Fresno, 2011 PhD. Biology, University of New Mexico, Albuquerque 2018
ABSTRACT
Host-parasite systems exist across complex and ecologically heterogeneous
landscapes, and may occur across multiple taxonomically and ecologically disparate
host species. Under these conditions, mechanisms underlying microevolutionary
processes (i.e. gene flow, genetic drift) are not always clear, and may be mediated
by numerous co-occurring factors specific to individual hosts, host groups or life
stages. Host traits such as host immunology, demographics, phylogeny and ecology
are important factors, which may shape host-parasite relationships, and ultimately
evolutionary processes. Importantly, host traits acting at one life stage might impact
transmission at a different life stage. As such, the integrated nature of parasite life
cycles is an important consideration as host traits may act in concert to enhance or
constrain parasite evolution.
The research described herein used phylogeographic, phylogenomic, and
population genetic methods to further our understanding of how host traits impact the
evolutionary ecology of trematode systems, using avian schistosomes (Digenea:
Schistosomatidae) as a model. Much of this work specifically focused on the genus
Trichobilharzia and an important snail host Physa acuta. These studies in aggregate
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found that host habitat preference, migratory patterns and demographics were all
important factors in mediating parasite transmission. Further within Trichobilharzia
spp., which are parasites of ducks, two species which utilize P. acuta were found to
have substantially different population genetic patterns. Data from this study suggest
that duck habitat preference can increase (dabbling ducks) or dilute (diving ducks)
Trichobilharzia transmission. Further data suggest that certain combinations of hosts
may facilitate transmission into a broader geographic range. At the
macroevolutionary level this study finds support that host-switching has likely been
an important mechanism of schistosome diversification, and did not find evidence
suggesting a strong co-phylogenetic pattern among parasites and intermediate
hosts. In total this research provides a framework for understanding how host traits
may act to shape parasite transmission and subsequent evolution.
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TABLES OF CONTENTS
TABLE OF CONTENTS ....................................................................................... vi
LIST OF FIGURES............................................................................................. viii
LIST OF TABLES ............................................................................................... ix
INTRODUCTION ...................................................................................................1
References ................................................................................................7
CHAPTER I: SCHISTOSOMES WITH WINGS: HOW HOST PHYLOGENY
AND ECOLOGY SHAPE THE GLOBAL DISTRIBUTION OF
TRICHOBILHARZIA QUERQUEDULAE (SCHISTOSOMATIDAE) ................. 11
Abstract .................................................................................................... 11
Introduction............................................................................................... 12
Materials and Methods ............................................................................. 15
Results .................................................................................................... 18
Discussion ................................................................................................ 19
Acknowledgements .................................................................................. 27
References ............................................................................................... 28
Figures .................................................................................................... 35
Tables ..................................................................................................... 40
CHAPTER II: PHYLOGEOGRAPHY AND GENETICS OF THE
GLOBALLY INVASIVE SNAIL PHYSA ACUTA DRAPARUND 1805
(= HAITIA ACUTA, TAYLOR 2003), AND ITS POTENTIAL AS AN
INTERMEDIATE HOST TO LARVAL DIGENETIC TREMATODES ......................... 46
Abstract ....................................................................................................46
Introduction............................................................................................... 47
Materials and Methods ............................................................................. 51
Results ..................................................................................................... 58
Discussion ................................................................................................ 63
Conclusions .............................................................................................. 71
Acknowledgements .................................................................................. 72
References ............................................................................................... 72
Figures ..................................................................................................... 82
Tables ...................................................................................................... 85
CHAPTER III: COMPARATIVE MICROEVOLUTIONARY PATTERNS
OF CONGENERIC TREMATODE SPECIES REVEAL IMPORTANCE
OF HOST ECOLOGY IN PARASITE EVOLUTION .............................................. 96
Abstract ................................................................................................... 96
Introduction............................................................................................... 97
Materials and Methods ........................................................................... 101
Results .................................................................................................. 107
Discussion .............................................................................................. 115
Acknowlegments ................................................................................... 121
References ............................................................................................. 121
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Figures ................................................................................................... 127
Tables .................................................................................................... 132
CHAPTER IV: ILLUMINATING SCHISTOSOME DIVERSITY:
PHYLOGENOMICS AND DIVERSIFICATION OF SCHISTOSOMATIDAE
USING TARGETED SEQUENCE CAPTURE OF ULTRA-CONSERVED
ELEMENTS ............................................................................................................ 143
Abstract .................................................................................................. 143
Introduction............................................................................................. 144
Materials and Methods ........................................................................... 148
Results .................................................................................................. 151
Discussion .............................................................................................. 153
References ............................................................................................ 162
Figures ................................................................................................... 170
Tables ................................................................................................... 172
CONCLUSIONS ................................................................................................ 175
APPENDIX A – Additional Files for Chapter II .................................................. 181
APPENDIX B – Additional Files for Chapter III ................................................. 188
APPENDIX C – Additional Files for Chapter IV ................................................. 212
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LIST OF FIGURES CHAPTER I Figure 1 – cox1 phylogeny ................................................................................ 35 Figure 2 – ITS1 phylogeny ................................................................................ 36 Figure 3 – nd4 phylogeny ................................................................................... 37 Figure 4 – Blue wing group phylogeny .............................................................. 38 CHAPTER II Figure 1 - Map of sampled populations .............................................................. 82 Figure 2 – Relationships of recovered cox1 haplotypes ..................................... 83 Figure 3 – Bayesian skyline plots ....................................................................... 85 Figure 4 – Invasion timeline of Physa acuta ....................................................... 87 CHAPTER III Figure 1 - cox1 phylogeny ................................................................................ 127 Figure 2 – Minimum spanning network ............................................................ 129 Figure 3 – Bayesian skyline plots ..................................................................... 130 Figure 4 – Trichobilharzia summary tree .......................................................... 131 CHAPTER IV Figure 1 - ML analyses of concatenated UCE loci ............................................ 170 Figure 2 – Host-switching within Schistosomatidae ......................................... 171
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LIST OF TABLES
CHAPTER I Table 1 - Host association and locality origin of the specimens .......................... 40 Table 2 – Prevalence of T. querquedulae ........................................................... 44 Table 3 – Pairwise genetic distances .................................................................. 45 CHAPTER II Table 1 - Summary of range-wide population statistics ....................................... 88 Table 2 – Analysis of Molecular Variance ........................................................... 91 Table 3 – Pairwise genetic distances and ΦST values ......................................... 92 Table 4 – Reports of infection and trematode species richness ......................... 93 Table 5 - Matrix of correlation coefficients .......................................................... 95 CHAPTER III Table 1 - Summary of Trichobilharzia survey data ............................................ 132 Table 2 – Measures of host specificity .............................................................. 139 Table 3 – Analyses of molecular variance ........................................................ 141 Table 4 – Indices of genetic diversity ................................................................ 142 CHAPTER IV Table 1 - Summary of sequence capture data .................................................. 172 Table 2 – Summary of UCE alignments ........................................................... 174
1
INTRODUCTION
Theoretical Basis
Host-parasite (H-P) systems exist across complex and ecologically
heterogeneous landscapes and are shaped by many co-occurring factors
(host immunology, host physiology, evolutionary constraints and ecology).
The persistence of host-parasite relationships over time and space often
requires interaction among taxonomically and ecologically disparate host
species, each having differential impacts on parasite transmission and
evolution (Blouin et al., 1995; Criscione and Blouin, 2004; Steinauer et al.,
2007; Keeney et al., 2009; Archie and Ezenwa, 2011; Blasco-Costa and
Poulin, 2013) . As such, parasite ecology and evolution is inseparable from
the ecology, behavior and demographics of their host(s). In this way host
specific traits can act to enhance or constrain parasite population size
(Criscione and Blouin, 2005), geographic range (Blasco-Costa, 2012),
transmission (Archie and Ezenwa, 2011), and adaptive potential (Nuismer,
2009). Teasing apart the multilayered impacts of host-use on parasite
evolution requires a holistic understanding of evolutionary relationships of
both the parasites and associated host species, across multiple time-scales.
To contribute to the understanding of parasite evolutionary ecology,
specifically in relation to host specific traits this research integrated host-
parasite phylogeography, population genetics and phylogenomics.
Empirical data resulting from evolutionary studies of parasite populations
supports the idea that host specific traits (e.g. allogeneic/autogenic life stages,
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dispersal and range) mediate microevolutionary forces (gene flow, genetic
drift) and determine population structure within their associated parasites
(Blouin et al., 1995; Blouin et al., 1999; Criscione and Blouin, 2004; Blasco-
Costa, 2012; Blasco-Costa and Poulin, 2013). Yet beyond broad ecological
distinctions (i.e. allogeneic/autogenetic, migratory/non-migratory), within multi-
host systems little is known about what specific traits contribute to or
determine gene flow, genetic drift and effective population size (Ne) (Nadler,
1995; Criscione et al., 2005; Blasco-Costa and Poulin, 2013). These
parameters directly relate to, or can be predictive of, the evolution of drug
resistance (Blouin et al., 1995), local adaptation (Dybdahl and Lively, 1996;
McCoy et al., 2002; Nuismer, 2009), probability of host-switching (Hoberg and
Brooks, 2008) and ultimately speciation of parasite lineages (Huyse et al.,
2005).
Study System
This research focused on the ecological and evolutionary relationships
of digenetic trematodes and their hosts, primarily those within the family
Schistosomatidae (Weinland 1858). Schistosomatidae, is a medically and
biologically important group which occurs globally. Unlike most trematodes
schistosomes have a two-host life cycle (Cribb et al., 2003), which makes
them more tractable for the types of studies described herein. Additionally,
relative to many other trematode families, host-parasite associations within
Schistosomatidae are fairly well resolved and diverse (Snyder and Loker
2000; Brant and Loker, 2013), facilitating the study of how host-specific traits
influence parasite evolution. Avian infecting lineages (known as avian
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schistosomes) comprise the majority of schistosome diversity, and were the
primary focus of this work.
Avian schistosomes represent 10 of the 14 named genera within
Schistosomatidae, though recent molecular survey’s have confirmed that this
is an underestimate (Brant et al., 2006; Brant and Loker, 2013). Dioecious
adult schistosomes colonize the vascular system of their vertebrate definitive
host, either a mammal or a bird. There they mature and copulate, with the
female producing eggs that eventually leave the body with the host’s feces,
urine or nasal secretions, depending on the species. There is much variation
in reproductive strategy and sexual development within Schistosomatidae
(Morand and Müller-Graf, 2000; Loker and Brant, 2006) , specifically in
regards to the nature of the relationship between the male and female worms.
Adult body size, sexual dimorphism and life span also vary substantially
across the family (Loker and Brant, 2006).
Liberated eggs hatch and release a motile larval stage known as a
miracidium within an aquatic environment. Miracidia actively seek and
penetrate a gastropod intermediate host. Schistosome species are
remarkable with respect to the aggregate taxonomic range of intermediate
hosts they utilize, especially so for avian schistosomes where an estimated 15
families of snails are employed as intermediate hosts (Brant and Loker, 2013).
Any particular schistosome species however typically successfully exploits a
very limited range of gastropod host species. Prior studies (Brant et al., 2006)
and research herein suggest that intermediate host switching has likely been
an important mechanism of schistosome diversification at multiple time scales
(within and across genera). More interestingly however, is the fact that ‘host-
4
switches’ are not associated with deep nodes or long branches, indicating that
host-switching events were recent (Brant and Loker, 2013) and a lack of strict
co-phylogeny at deeper nodes. Schistosome species are considered to be
more specific (Horak et al. 2015) to their snail intermediate host, relative to
their definitive host, which is common among trematode families (Cribb et al.
2003). Jouet et al. (2010) found that European Trichobilharzia spp. are
specific to single species of snails. Some evidence of co-phylogeny exists
among Schistosoma spp. and snails within the family Planorbidae, suggesting
a possible history of co-speciation (Morgan et al. 2002). This pattern does not
appear to be supported within avian infecting genera like Trichobilharzia,
Dendritobilharzia or Bilharziella.
Within the snail intermediate host, asexual amplification occurs
producing a second motile larval stage known as cercaria. Cercariae actively
seek out their vertebrate definitive hosts, where they will penetrate the
epidermis and migrate through the hosts’ vasculature until they arrive in the
liver (generally), where they will undergo sexual maturation and sexual
reproduction. Cercariae are attracted to general vertebrate fatty acids, and will
often mistakenly attempt to penetrate unsuitable hosts (Horák et al., 2015).
The re-emerging zoonotic disease Human Cercarial Dermatitis (HCD), also
known as “Swimmer’s Itch” (Horák et al., 2015), occurs when non-human
schistosome cercariae accidentally penetrate human skin. HCD is of
increasing concern from a public health and economic perspective (Horák et
al., 2015). In relation to HCD and other snail-transmitted diseases,
understanding the biology and distribution of snail intermediate hosts has
broad significance.
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One important host to Trichobilharzia, specifically within North America,
is Physa acuta (Draparnaud, 1805). Physa acuta is endemic to North
America, but is now a globally invasive species, with populations established
on six of the seven continents (Bousset et al., 2014; Lydeard et al., 2016). In
general snails are adept invaders (Albrecht et al., 2009; 2008; Facon et al.,
2003; Hanson et al., 2013; Torchin et al., 2005) and in light of the fact that as
group they host over 10,000 species of trematodes (Cribb et al., 2003), their
biology is of great importance, specifically in regards to the potential for
invasive snail populations to sustain trematode life cycles. Surveys of invasive
snail populations suggest that there is a release of trematode burden following
host invasion, for at least a period, in concordance with the ‘Enemy-Release
hypothesis’ observed across host taxa (Keane, 2002; Torchin et al., 2003).
Snail-trematode surveys demonstrate that while trematode assemblages may
change infection within the hosts invasive range does occur (Blakeslee et al.,
2012; Torchin et al., 2005). When local parasites establish within invasive
hosts populations it is known as parasite spillback. It is unknown how, why or
when spillback would be expected to occur, or if there are general
determinants of spillback (Kelly et al., 2009). Free-living systems have shed
light on possible relevant factors (Hayes and Barry, 2007; Keane, 2002),
specifically in identifying the importance of demographic parameters such as
effective population size and background genetic diversity which are expected
to increase with time since invasion (Forsman, 2014; Rius and Darling, 2014).
Studies focusing on characterizing these parameters in combination with
parasite data are ideal to address the underlying mechanisms of host-parasite
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invasion dynamics and to identify factors relevant in modeling parasite
spillback.
Overall Contributions
How genetic variation is distributed across species, space and time
relates to persistance and diversification of parasites lineages. In total, all
chapters (1-4) included herein sought to describe patterns of genetic variation
within complex H-P systems. The H-P unit evolves at the population level, yet
studying parasite populations is challenging as populations are fragmented
across hosts, distinct life stages and ecological time and space (Prugnolle,
2005). Research presented herein provides a framework for understanding
how populations of parasites within migratory hosts are distributed over
ecological and evolutionary time.
With an emphasis on the biology schistosomes, much of this research
reported here focuses on characterizing microevolutionary patterns, primarily
genetic structure and diversity. These measures directly influence the
adaptability of the host-parasite system, and how the unit can respond to
biotic and abiotic changes such as increasing temperatures, chemotherapy
pressure or habitat loss. Data from this work suggest both host and parasite
demographic patterns may shape H-P interactions and influence
transmission.
This body of research investigated patterns of host-use and parasite
diversity (intra- and inter-specific) over ecological and evolutionary timescales
and made substantial contributions to our understanding of the evolutionary
ecology of parasitic multi-host helminths.
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CHAPTER I
Schistosomes with wings: how host phylogeny and ecology shape the
global distribution of Trichobilharzia querquedulae (Schistosomatidae)
AUTHORS: Erika T. Gendron (Ebbs), Eric S. Loker, Norm E. Davis, Veronica
Flores, Aylen Veleizan, Sara V. Brant
ABSTRACT
Migratory waterfowl play an important role in the maintenance and
spread of zoonotic diseases worldwide. An example is cercarial dermatitis,
caused when larval stages of schistosomes that normally develop in birds
penetrate human skin. Members of the genus Trichobilharzia
(Schistosomatidae), transmitted mainly by ducks, are considered to be major
etiological agents of cercarial dermatitis globally. To better understand the
diversity and distribution of Trichobilharzia spp., we surveyed ducks from
North America (United States and Canada), Argentina, South Africa and New
Zealand. To aid in species identification of the Trichobilharzia worms
recovered, regions of the Cox1, ND4 and ITS1 were sequenced.
Furthermore, we provide molecular phylogenetic evidence for the
cosmopolitan distribution and trans-hemispheric gene flow for one species,
Trichobilharzia querquedulae, previously thought to be restricted to North
America. These new samples from endemic non-migratory duck species
indicate that T. querquedulae transmission occurs within each of the regions
we sampled and that it is specific to the blue-winged + silver teal duck clade.
Prevalence within this host group is >95% across the known range of T.
querquedulae, indicating that transmission is common. Genetic divergence is
12
evenly distributed among continents, and no phylogenetic structure
associated with geography was observed. The results provide strong support
for the global distribution and transmission of T. querquedulae and represent,
to our knowledge, the first report of a cosmopolitan schistosome confirmed by
genetic data. These data are the first known to support trans-hemispheric
genetic exchange in a species responsible for causing cercarial dermatitis,
indicating that the epidemiology of this group of poorly known zoonotic
parasites is more complex than previously expected.
INTRODUCTION
Maintenance of complex life cycles over time and space is influenced
by a myriad of both evolutionary and ecological forces. Determinants of
host associations are essential in understanding the dynamics of any host-
parasite system and for efforts to model the emergence of zoonotic
parasites (Ostfeld and Keesing, 2000; Taylor et al., 2001; McKenzie, 2007).
For example, the degree to which a parasite has phylogenetic and/or
ecological constraints with its host range will largely determine the
predictability of transmission (Thompson, 2000; Hoberg and Brooks, 2008).
Within this context, we aimed to understand host associations and factors
shaping the diversity and distribution of a common group of trematodes in
waterfowl which are known for causing the globally re-emerging zoonotic
disease human cercarial dermatitis (HCD) (de Gentile et al., 1996; Kolářová
et al., 2010; Horak et al., 2015). This disease enters the human population
when avian schistosome (Schistosomatidae) cercariae penetrate human
skin, resulting in a severe, short-term allergic reaction (Horak et al., 2002;
13
Kourilova et al., 2004; Kolářová et al., 2013). Although the larvae are
unable to establish patent infections within a human host, there are intense
immune reactions that can be long lasting and painful (Kourilova et al.,
2004). While HCD is neither communicable nor fatal, it is still not without
economic and epidemiologic importance and is considered a substantial
public health problem (Horak and Kolarova, 2011; Soldánová et al., 2013;
Horak et al., 2015). Relatively little is known about the epidemiology of HCD
due to the diversity of schistosome species, the lack of data on the
preferred hosts and the timing of transmission, and the large geographical
scale over which outbreaks occur.
An increase in the incidence of HCD outbreaks (de Gentile et al., 1996;
Larsen et al., 2004) has been linked to altered or managed habitats that
support high densities of migratory and resident anseriform birds and
permissive snail host species (Larsen et al., 2004; Horak and Kolarova, 2011)
as well as to other forms of environmental degradation such as eutrophication
of water bodies (Valdovinos and Balboa, 2008; Soldánová et al., 2013). These
conditions have created an urgent need to better understand avian
schistosome species diversity, distribution, host use, associated host ecology
and life cycle maintenance, within a global framework. The avian schistosome
genus Trichobilharzia (Skrjabin and Zakharov, 1920) is most frequently
implicated in HCD outbreaks (Kolářová et al., 2010, 2013; Soldánová et al.,
2013; Horak et al., 2015) and is also the most speciose genus within the
family (Blair and Islam, 1983; Brant and Loker, 2009). Adults of
Trichobilharzia spp. primarily infect ducks and as larvae develop within
freshwater pulmonate snails in the families Physidae and Lymnaeidae (Blair
14
and Islam, 1983; Horak et al., 2002; Brant and Loker, 2009). Species diversity
and host use of Trichobilharzia spp. are best known in Europe and North
America (Aldhoun et al., 2009; Brant and Loker, 2009; Jouet et al., 2009,
2010; Rizevsky et al., 2011; Christiansen et al., 2014; Lawton et al., 2014).
But even in these well-studied areas, the presence and dynamics of particular
Trichobilharzia spp. in other bird hosts and in other countries that share those
bird hosts remain unknown. For example, are Trichobilharzia spp. shared
across continents or does each continent have its own species, or both?
Brant and Loker (2009) investigated host-parasite associations
among Trichobilharzia spp. within North America and found that one
species, Trichobilharzia querquedulae (McLeod, 1937), has a clear
association with a globally distributed clade of dabbling ducks (the ‘blue-
wing’ group) that includes the holarctically distributed northern shovelers
(Anas clypeata); the cinnamon (Anas cyanoptera) and blue-wing teal (Anas
discors) as well as several Southern Hemisphere endemic species
(Johnson and Sorenson, 1999). Species in the ‘blue-wing’ group share
specific ecological preferences for shallow freshwater bodies across both
their summer and winter grounds (Baldassarre et al., 1996; Kear, 2005).
Additionally, T. querquedulae infects Physa spp. snails, which are
ubiquitous in North America and one common species, Physa acuta
(Draparnaud, 1805), is globally invasive (Bousset et al., 2014). Until
recently, T. querquedulae had been reported from only the North American
‘blue-wing’ species (A. clypeata, A. cyanoptera and A. discors). Brant and
Loker (2009) sampled a phylogenetically diverse range of ducks (n = 299
individuals) within North America and found that T. querquedulae occurred
15
with a prevalence greater than 95%, exclusively within ‘blue-wing’ ducks.
Endemic, ecologically similar species of the ‘blue-wing’ group also occur in
the Southern Hemisphere (Kear, 2005), but heretofore, records for T.
querquedulae or other schistosome species are largely lacking for these
species. Is the association between T. querquedulae and members of the
‘blue-wing’ group consistent across hemispheres, and if so, what factors
contribute to the global maintenance of this association?
MATERIALS AND METHODS
Parasite collection
Ducks were collected from several locations in both the Northern and
Southern Hemispheres (Table 1); North America (United States and Canada),
South America (Argentina, 30° 5' 56.4'' S, 59° 29' 56.4'' W), Africa (South
Africa, 29° 12' 28.8'' S, 27° 11' 20.4'' E) and New Zealand (South Island, 44°
21' 3.6'' S, 170° 12' 32.4'' E). For the purposes of this paper, reasonable
sampling of hosts and localities from Europe is represented in GenBank. The
mesenteric and hepatic portal veins of ducks and geese were examined for
species of Trichobilharzia. Mesenteric veins were inspected visually for adult
worms, which were removed using Vanna’s micro scissors. Adult worms were
removed from the hepatic portal vein and liver by either crushing and washing
the liver in a series of decantation steps to isolate the worms or using a
syringe to push saline through the hepatic portal vein to collect any worms
that washed out. Schistosome samples were preserved in 95% ethanol for
genetic, and 80% ethanol for morphological analyses. Parasites were
deposited in the Museum of Southwestern Biology, Division of Parasites,
16
University of New Mexico, USA (Table 1). All work with vertebrate hosts was
conducted with the approval of the Institutional Animal Care and Use
Committee at the University of New Mexico (IACUC # 11-100553-MCC,
Animal Welfare Assurance # A4023-01).
Genetic analysis
DNA from the samples was extracted using a DNeasy Tissue Kit
(Qiagen, Valenicia, California, USA) following the manufacturer’s protocol
or by HotShot Lysis (Truett et al., 2000). We targeted two mitochondrial
gene regions and one nuclear region for PCR amplification (Takara Ex Taq
kit, Takara Biomedicals, Otsu, Japan), following protocols used by Brant
and Loker (2009). The mitochondrial cytochrome oxidase subunit I (Cox1)
gene (743 bp region, 5’ end) (primers in Brant and Loker, 2009) and the
NADH Dehydrogenase subunit 4 (ND4) gene (409 bp in the central portion
of the gene), using the ND4 primers TSND4_F4 5’ -
AGTCCTTATCCGGAGCGTTA-3’, TSND4_R4 5’-
AACCAGCAACACACAAAAACA-3’. The ND4 gene was targeted due to
high intraspecific variation relative to Cox1 and internal transcribed spacer
1 (ITS1) (Blouin et al., 1995; Webster et al., 2007), with a goal of
assessing intraspecific relationships across hemispheres and host species.
The nuclear gene region of ITS1 (733 bp, including the 3’ portion of 18S
rRNA and the 5’ portion of 5.8S) was amplified and sequenced using
primers from Dvorak et al. (2002). Sequencing reactions were performed
using the BigDye sequencing kit 3.1 (Applied Biosystems, Foster City,
California, USA). Sequences were edited using Sequencher 5.3 (Gene
17
Codes Corporation, Ann Arbor, MI, USA), aligned using ClustalW (Larkin et
al., 2007) and then modified manually. Sequences generated during this
study were submitted to GenBank (Table 1).
Reconstructing evolutionary relationships
Phylogenetic analyses included the samples collected herein as well
as the available T. querquedulae samples and appropriate outgroups for
Clade Q (sensu Brant and Loker, 2009; Table 1). Each gene was treated as
independent and the best models of nucleotide substitution was chosen
based on the Akaike information criterion (AIC; Akaike, 1974) using
JModelTest (Darriba et al., 2012) as follows: TN93+G for Cox1 (Tamura and
Nei, 1993), K2+G for ITS1 (Kimura, 1980), HKY + I for ND4 (Hasegawa,
Kishino and Yano, 1985). These specific models were incorporated into
Maximum Likelihood (ML) analyses.
Gene trees were analyzed independently using ML methods in the
program PAUP* 4.0 (Swofford, D. L. 2003. PAUP*. Phylogenetic Analysis
Using Parsimony (*and Other Methods). Version 4. Sinauer Associates,
Sunderland, Massachusetts, USA). Five hundred bootstrap replicates were
preformed within each gene tree analysis to statistically assess the resulting
topologies.
Bayesian Inference (BI) was performed using the program MrBayes
3.2.1 (Ronquist and Huelsenbeck, 2003), consisting of two replicated runs for
each locus with four Markov chain Monte Carlo (MCMC) chains, one cold and
three heated chains. Each analysis ran for 2,000,000 generations and was
sampled every 1000 generations. The analysis was terminated when the S.D.
18
of the split frequencies was or fell below 0.01, supporting convergence.
Likelihood parameters and convergence between runs were assessed using
the program Tracer v.1.5 (Rambaut et al., 2014 Tracer v1.6.
http://beast.bio.ed.ac.uk/Tracer). The first 25% of trees from each analysis
was discarded as burnin. Resulting phylogenetic trees were visualized and
manipulated using Fig Tree v. 1.4 (Rambaut and Drummond, 2009, FigTree
v1. 3.1. http://tree.bio.ed.ac.uk/software/figtree) and MEGA 6 (Tamura et al.,
2013).
Genetic distances were estimated, based on uncorrected p-distances
between all sequences for each locus, and then averaged within lineages
for comparison (Table 2), using the program MEGA 6 (Tamura et al., 2013).
RESULTS
Parasite infection and molecular identification
From our collections of endemic ducks in the three countries in the
Southern Hemisphere, T. querquedulae was found to be globally
distributed in ducks from the ‘blue-wing’ clade as well as in the sister clade
of silver teal ducks. In all three countries sampled, endemic ‘blue-wing’ and
allied species were infected with a prevalence of 95% or greater, similar to
what has been reported from the United States (Table 2).
Monophyly of T. querquedulae
Molecular phylogenetic analyses (ML, BI) show strong statistical
support for the monophyly of T. querquedulae populations, regardless of the
hemisphere sampled (Figs. 1 - 3). The resultant phylogenies show similar
19
gene trees among nuclear and mitochondrial genes, suggesting that
Southern Hemisphere samples belong to T. querquedulae and likely do not
have a hybrid origin.
This study provides sequence data for the mitochondrial ND4 gene
region of T. querquedulae, which had more genetic variation (Fig. 3, Table 3)
relative to Cox1 and ITS1. Gene tree analysis (ML, BI) shows a well-
supported monophyly of T. querquedulae and a lack of phylogenetic
resolution for internal nodes, concordant with the Cox1 and ITS1 gene trees
(Figs. 1, 2).
The low nodal support within the T. querquedulae clade, consistent with
what has been reported previously (Brant and Loker, 2009), is suggestive of
a lack of geographic structure and is consistent with gene flow across
hemispheres (Bouzid et al., 2008; Goulding and Cohen, 2014). Genetic
distances within and between continents and hemispheres support low
intraspecific divergence (Table 3). On average, for all genes analyzed,
genetic divergence within North America (approximately 2%) is comparable
with genetic divergence between Northern and Southern Hemisphere
populations (approximately 2.7%).
DISCUSSION
The survey results reported here provide the first known genetic
evidence for an avian schistosome, T. querquedulae, to have a global
distribution spanning three continents and New Zealand. Our sampling
indicates that the duck host range of T. querquedulae extends in the
Southern Hemisphere both to endemic members of the ‘blue-wing’ group
20
(Cape and New Zealand shovelers), and to members of a sister clade
containing the silver teal (see Johnson and Sorenson, 1999). Southern
Hemisphere worms were recovered from endemic non-migratory species
(Kear, 2005), thus we can infer that the infections were acquired locally. For
all loci analyzed, genetic divergence within and between Northern and
Southern Hemisphere individuals was low, suggesting their populations are
connected by trans-hemispheric (specifically, between Northern and
Southern hemispheres) migration of particular duck species. The
holarctically distributed northern shoveler (A. clypeata), for which annual
migrations between the Northern and Southern Hemispheres is common
(Peters et al., 2014), is likely the primary host species facilitating gene flow
across the range of T. querquedulae. It is also likely that populations of blue-
wing and cinnamon teal (A. cyanoptera and A. discors) that breed in North
America and winter in the Southern Hemisphere facilitate T. querquedulae
transmission, as they are seasonally sympatric with the endemic ‘blue-
winged’ ducks and their allies (silver teal) (Wilson et al., 2011). Estimated
genetic distances (Table 3) indicate that across its range T. querquedulae
populations are connected and genetically homogenized, although increased
population sampling is necessary to fully elucidate phylogeographic patterns.
More variable genetic markers, increased sampling and population genetic
analyses are required to determine how much gene flow occurs across the
hemispheres and the relative importance of different duck species in
maintaining the global transmission of T. querquedulae.
Through targeted collections of Southern Hemisphere endemic host
species, we find evidence to support the phylogenetic association between
21
T. querquedulae and the ‘blue-wing’ group uncovered by Brant and Loker
(2009) (host associations are summarized in Fig. 4). Our data suggest that
the duck host range of T. querquedulae includes, in addition to the ‘blue-
wing’ group, the sister clade that includes the silver teal. It is clear that the
definitive hosts of T. querquedulae are not a random sample of Anas duck
species and that definitive host phylogeny is important in life cycle
maintenance. Below we discuss several aspects of the host and parasites
biology which could maintain the observed phylogenetic association among
T. querquedulae and their specific duck hosts.
It is possible that these patterns are maintained as a consequence of
specialization that has occurred over an evolutionary time frame based on
host physiology or immune competence. It has been generally thought that
definitive host specificity is low within species of Trichobilharzia (Blair and
Islam, 1983; Horak et al., 2002), however with increased taxonomic sampling
of hosts and the inclusion of molecular data, patterns of duck host specificity
are becoming increasingly apparent in some Trichobilharzia spp. (Brant and
Loker, 2009, 2013a; Jouet et al., 2015). Similar phylogenetic affinities are
known to occur within other avian schistosome genera. For example,
Allobilharzia (Kolářová et al., 2006) in swans and Anserobilhariza (Brant et
al., 2013b) in geese, are just two examples where avian schistosome
transmission shows some phylogenetic constraints at the definitive host
level. The physiological, genetic and/or immunological barriers mediating
Trichobilharzia infections within duck hosts is currently unknown (Horak et
al., 2002) and, as suggested by the present results, in need of experimental
study. Experimentally, non-‘blue wing’ ducks such as mallards (McLeod and
22
Little, 1942) can support egg-laying infections of T. querquedulae. However,
natural infections of non-‘blue wing’ hosts appear to be rare, and our surveys
over the last 10 years have recovered a single infection of an immature
worm fragment within an American wigeon (Anas americana) in North
America (S.V. Brant, unpublished data).
It is likely that host ecological preferences, which are also influenced by
the hosts’ evolutionary history, play a key role in the persistence of the
association of T. querquedulae in one clade of ducks versus another. ‘Blue-
wing’ ducks are ecological specialists (Baldassarre et al., 1996), with a
preference for shallow marshy habitats. It can be hypothesized that shared
habitat preferences specifically relating to nesting site selection and natal
habitats can act as an encounter filter (Combes, 2001) among ‘blue-wing’
and allied species to restrain T. querquedulae host use. Nesting sites, where
the majority of Trichobilharzia transmission is likely occurring (Rau et al.,
1975; Brant and Loker, 2009), are non-randomly chosen (Clark and Shutler,
1999). Blums et al. (2002) showed that over a 23-year period female
northern shovelers (A. clypeata) exhibited greater than 88% nest site fidelity,
significantly more so than other sympatric duck species measured. Further,
northern shovelers and other ‘blue-wing’ species are known to select
fragmented small water bodies similar to wetland islands for nesting (Blums
et al., 2002), where the pair will live and breed. Males are known to guard
these ‘islands’ and exhibit territorial behavior (Poston, 1974), so much so
that availability of nesting sites limits population sizes (Vickery and Nudds,
1984). Generally, single to a few pairs occur at individual nesting sites, in
contrast to other dabbling and diving duck species (Vickery and Nudds,
23
1984), which may have more concentrated nesting sites and may be
sympatric with several different species of birds. Site and habitat fidelity
exhibited by the duck host likely results in stable transmission dynamics for
T. querquedulae. This also assumes that the intermediate host is present
and that at least peak cercarial shedding of T. querquedulae occurs during
this time as well, when the shovelers and teal are isolated for nesting and
raising of ducklings.
It could also be the case that the potential for temporal isolation is a
mechanism for the maintenance of this global host-parasite association.
Members of the ‘blue-wing’ group are the first duck species to migrate in
both the spring and the winter (Arzel et al., 2006), thus it would follow that
their ducklings may be the first to encounter schistosome cercariae in the
spring. This hypothesis may hinge on the importance of overwintering
infected snails in sustaining a majority of the transmission (Crews and Esch,
1986; Goater et al., 1989). This idea is plausible for a snail host of T.
querquedulae, Physa gyrina, as it is bivoltine, with overwintering snails
producing an early spring generation (Brown, 1979), and it is likely an
abundant permissive snail host within the duck host natal habitats (Pip,
1987). Data from this study support the hypothesis that migrating birds, in
particular northern shovelers, transfer infections among continents
suggesting that transmission may not be strictly confined to natal habitats.
However, habitat requirements by this particular clade of ducks would be the
same among species. Nonetheless, these stimulating ideas require empirical
studies documenting the dynamics of transmission at relevant spatial and
temporal scales.
24
It is clear that for T. querquedulae to persist in the Southern
Hemisphere, suitable snail intermediate hosts must be present. Although
the identity of the intermediate host(s) for T. querquedulae in the Southern
Hemisphere remains unknown, several possibilities exist, some of which
can help account for the broad geographic range and even definitive host
associations observed. In North America, Physa spp. are confirmed hosts
(McLeod and Little, 1942; Brant and Loker, 2009; Brant et al., 2011).
Physid diversity is highest in North America (Wethingtion, 2007), but
several species are native to, or have invaded, the Southern Hemisphere.
South America harbors several endemic physids, which could sustain T.
querquedulae transmission, for example, Physa marmorata (Guilding,
1828), which has now invaded parts of South Africa (Appleton, 2003).
Physa acuta (Draparnaud, 1805), is a well-documented global invader
that has established in much of the Southern Hemisphere (Bousset et al.,
2014) and may well serve as a host for T. querquedulae. Physid snails
commonly colonize the shallow freshwater habitats favored by ‘blue wing’
duck species.
Generally, Trichobilharzia spp. are specific to a single snail species or
a group of closely-related congeners (Kock, 2001; Rudolfova et al., 2005;
Brant and Loker, 2009; Jouet et al., 2009), and experimental and genetic
confirmation that a species of Trichobilharzia can infect multiple genera of
snails within a family has yet to be achieved. There is a report of an avian
schistosome Dendritobilhariza pulverulenta (Cheatum, 1941) found in both
Gyraulus (North America, New Zealand) and Anisus (Europe) snails but the
genetic identity of the worms from Anisus have not been confirmed as D.
25
pulverulenta, since there exists more than one species (Khalifa, 1976;
Vusse, 1980; Brant et al., 2011) and Dendritobilhariza loossi has been found
experimentally to use Anisus vortex in Europe (Akramova et al., 2011). Snail
surveys carried out in Europe (Rudolfova et al., 2005; Jouet et al., 2009b)
and Africa (Appleton and Brackenbury, 1998; Laamrani et al., 2005), have
failed to recover any Trichobilharzia spp. from native or invasive physids.
Two studies report schistosome cercariae from other physids in Europe
(Aplexa hypnorum, Gerard, 2004; Physa fontinalis, Rudolfova et al., 2005),
but neither schistosome belongs to the genus Trichobilharzia. It should be
noted, however, that even within North America the prevalence of T.
querquedulae in Physa spp. is very low and snail surveys for trematodes
frequently fail to recover T. querquedulae and other Trichobilharzia spp. (Loy
and Haas, 2001) from their presumptive snail hosts. However, survey
collections of physids at times of the year when these duck host have
ducklings are lacking, and that might be peak time for cercarial shedding.
It is even less common to find a genetically confirmed schistosome
conspecific infecting two different families of snails (Blair et al., 2001).
However one species, Trichobilharzia jequitibaensis (Leite et al., 1978), has
been reported to infect both Physidae and Lymnaeidae. Miracidia obtained
from naturally infected domestic Muscovy ducks (Cairina moschata
domestica) successfully infected both P. marmorata and Lymnaea columella
under experimental conditions. If such a situation were confirmed and shown
to be true for T. querquedulae, then its broad geographic range would be more
explicable since lymnaeid species diversity is much greater in the Eastern
Hemisphere than physid species diversity. It will be interesting to pursue and
26
verify these intriguing results with further experimentation, field collections
and sequence analyses. These details of both definitive and intermediate
host associations across a parasite’s range are necessary to understand
transmission dynamics as well as to model zoonotic cycles.
The example of T. querquedulae globally distributed within a rather
narrowly defined clade of ducks is instructive for those interested in the
epidemiology of HCD, and more broadly the transmission of waterfowl
zoonoses. The trans-hemispheric occurrence of T. querquedulae is certainly
aided both by the migratory behavior (trans-hemispheric migration of the
northern shoveler) and habitat preferences of definitive hosts as well as
habitat preferences of the snail hosts. Perhaps the shared host ecology of
duck hosts (habitat preference, nest site fidelity and timing of migration)
across both the Northern and Southern Hemispheres sustain transmission
and perpetuate the clear phylogenetic association between T. querquedulae
and the ‘blue-wing’ group. Further research is necessary to partition the
relative roles of host ecology and phylogeny in shaping the transmission of T.
querquedulae over evolutionary time. For example, experimental infections
evaluating permissiveness of non ‘blue-wing’ duck species and other snail
host species would be a logical next step providing data vital to model
transmission dynamics. The greater role duck host ecology plays, the more
dynamic one would expect host associations and transmission to be in
response to ecological change over evolutionary time, as opposed to a strict
specialist model. Human-mediated habitat alteration and climate change
have been implicated in affecting the transmission of other waterfowl
zoonoses (Reed et al., 2003; Fuller et al., 2012; Dijk et al., 2014) and are
27
likely to alter Trichobilharzia host associations over time as well. Together
with the fact that avian schistosomes of other genera infect different bird and
snail lineages, we must remember that HCD is caused by a number of
schistosome species, with markedly different patterns of host use, posing a
challenge for those interested in understanding the epidemiology of HCD.
Thus future studies on the specific ecological relationships among hosts and
worms, and on how permissive other species of ducks and snails are for T.
querquedulae will be an exciting path forward to both understanding parasite-
host biology as well as the ability to more specifically model zoonotic disease
emergence.
ACKNOWLEDGMENTS
Thank you to the following for invaluable help collecting birds—South
Africa: Museum of Southwestern Biology Bird Division: Mr. Andy Johnson,
Dr. Ernest Valdez, Dr. Robert W. Dickerman and Dr. Chris Witt; National
Museum Bloemfontein: Mr. Rick Nuttall and Mr. Dawie H. de Swardt. We
acknowledge technical support from the University of New Mexico Molecular
Biology Facility, which is supported by National Institutes of Health, USA
grant P30GM110907 from the Institute Development Award program of the
National Center for Research Resources, USA. This work was primarily
funded through a National Science Foundation, USA grant to S.V.B. (DEB
1021427) and a National Institutes of Health grant RO1 AI44913 to ESL. We
thank the following funding agencies; Agencia de Promoción Científica y
Técnica, Argentina PICT 1288-2011 and CONICET, Argentina PIP No.:
11220110100550 to VF.
28
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35
Figure 1. Maximum likelihood (ML) tree based on mitochondrial cytochrome oxidase
subunit I (cox1) sequence data. Branch support values less than 50% were not
shown. Values are reported as bootstrap support from ML analysis/ posterior
probabilities from Bayesian analysis. The shaded box includes all Trichobilharzia
querquedulae individuals analyzed, bolded taxa indicate samples collected from the
Southern Hemisphere. Accession numbers of other analyzed Trichobilharzia
samples can be found in Table 1.
36
Figure. 2. Maximum likelihood (ML) tree based on internal transcribed spacer region
1 (ITS1) sequence data. Branch support values less than 50% are not shown; values
are reported as bootstrap support from ML analysis/posterior probabilities from
Bayesian analysis. The shaded box includes all Trichobilharzia querquedulae
individuals analyzed, bolded taxa indicate samples collected from the Southern
Hemisphere. Accession numbers of other analyzed Trichobilharzia samples can be
found in Table 1.
37
Figure. 3. Maximum likelihood (ML) tree based on NADH Dehydrogenase subunit 4
(ND4) sequence data. Branch support values less than 50% were not shown; values
are reported as bootstrap support from ML analysis/posterior probabilities from
Bayesian analysis. The shaded box includes all Trichobilharzia querquedulae
individuals analyzed, bolded taxa indicate samples collected from the Southern
Hemisphere. Accession numbers of other analyzed Trichobilharzia samples
analyzed can be found in Table 1.
38
39
Figure. 4. Simplified Anas phylogeny reconstructed from Johnson and Sorensen
(1999). (A) Tree depicts phylogenetic relationships within Anas. Clades are
collapsed to include: pintails (A. acuta, A. georgica, A. bahamensis, A.
erythrorhyncha, A. capensis), green-winged teals (A. flavirostris, A. carolinensis, A.
crecca), Mallards (A. laysanensis, A. luzonica, A. platyrhynchos, A. poecilorhyncha,
A. zonorhyncha, A. diazi, A. rubripes, A. fulvigula, A. superciliosa, A. melleri, A.
undulata, A. sparsa), gray teals (A. gibberifrons, A. castanea, A. bernieri), Brown
teals (A. aucklandica, A. chlorotis), Wigeons (A. americana, A. sibilatrix, A. penelope,
A. strepera, A. falcata), silver teals (A. versicolor, A. puna, A. hottentota, A.
querquedula), blue-winged teals (A. smithii, A. rhynchotis, A. clypeata, A.
cyanoptera, A. discors, A. platalea), South American ducks (Speculanas specularis,
Amazonetta brasiliensis, Tachyeres pteneres, Lophonetta specularioides), outgroup
(Marmaronetta, Pteronetta, Cyanochen, Aythya, Asarcornis, Chenonetta, Callonetta,
Tadorna,Cairina, Aix, Sarkidiorni). (B) Tree depicts interspecific relationships within
the ‘blue-winged’ teal clade (shaded in A). Host taxa are labeled by infection status
with Trichobilharzia querquedulae ( experimental host (McLeod and Little, 1942);
☐, host not examined; , host examined/positive; , host examined/negative). All
survey data summarized was performed by the authors with the exception of A.
querquedulae (Kolarova et al., 1997).
40
Gene
Species Host Locality CO1 ND4 ITS MSB # Reference
W135 Trichobilharzia querquedulae
Anas clypeata LA FJ174497 KU057197 FJ174557 183 *
W137 T. querquedulae A. discors LA FJ174498 - FJ174558 19497 *
W142 T. querquedulae A. discors LA - KU057196 KP788761 19502 *
W148.1 T. querquedulae A. cyanoptera NM FJ174499 KU057194
FJ174559 18584 *
W148.2 T. querquedulae A. cyanoptera NM FJ174500 - - 18584 *
W153 T. querquedulae A. clypeata NM - KU057192 KP788763 18587 *
W155 T. querquedulae A. cyanoptera NM - - FJ174553 18589 *
W155.2 T. querquedulae A. cyanoptera NM - KU057200 - 18589 *
W155.3 T. querquedulae A. cyanoptera NM FJ174501 - KP788764 18589 *
W156 T. querquedulae A. discors NM FJ174502 KU057191 FJ174554 18590 *
W158 T. querquedulae A. clypeata NM FJ174503 - FJ174549 18592 *
W160 T. querquedulae A. discors NM - KU057201 KP788766 18594 *
W162 T. querquedulae A. clypeata NM FJ174504 KU057190 FJ174551 18602 *
W167 T. querquedulae A. discors NM - KU057202 - - *
41
W180 T. querquedulae A. cyanoptera CA FJ174505 - FJ174556 18573 *
W183 T. querquedulae A. clypeata CA FJ174506 KU057189 FJ174560 18575 *
W190 T. querquedulae A. discors CA FJ174507 - FJ174550 18582 *
W203 T. querquedulae A. clypeata AK FJ174508 - FJ174552 18636 *
W345 T. querquedulae A. clypeata MB FJ174509 KU057185 FJ174547 18626 *
W650 T. querquedulae A. smithii ZA - KU057205 KP788765 18990 This study
W664 T. querquedulae A. smithii ZA KU057180 - KP788762 19000 This study
W703 T. querquedulae A. rhynchotis NZ KU057181 KU057204 - 20792 This study
W704 T. querquedulae A. rhynchotis NZ KU057182 KU057203 - 20793 This study
Tshov T. querquedulae A. rhynchotis NZ KU057183 - KP788760 20794 This study
W833 T. querquedulae A. versicolor AR KU057184 KU057206 - - This study
E45 T. querquedulae A. discors FL FJ174510 - FJ174555 - *
E45.2 T. querquedulae A. discors FL - KU057199 - - *
E64 T. querquedulae A. discors FL
FJ174511 KU057198 - - *
SDS1006 T. querquedulae A. clypeata NE - - FJ174548 - *
W206.2 Trichobilharzia sp.
A A. americana AK - - KP788771 - *
W210 Trichobilharzia sp.
A A. americana AK - - KP788772 - *
W213 Trichobilharzia sp.
A A. americana AK FJ174527 - FJ174570 18636 *
42
W149 Trichobilharzia sp.
A A. americana NM - KU057193 - 18585 *
W205 Trichobilharzia sp.
B A. americana AK FJ174528 - KP788770 18638 *
W173 Trichobilharzia sp.
C
Lophodytes
cucullatus
PA FJ174529 - FJ174576 - *
W175 Trichobilharzia
physellae A.platyrhynchos PA - - KP788768 - *
W196 Trichobilharzia
physellae Aythya affinis NM - - KP788767 - *
W263 T. physellae Physa gyrina NM FJ174523 * FJ174562 178 *
RSFO1 Trichobilharzia
franki Radix auricularia FR - - AY795572 -
Ferte et al. 2005
RSFO3 T. franki R. auricularia FR HM131198 - AY795573 -
Ferte et al. 2005
HamRa6 T. franki R. auricularia GB - - KJ775868 - Lawton et al. 2014
HamRa7 T. franki R. auricularia GB - - KJ775869 - Lawton et al,
2014
CYA18 Trichobilharzia
regenti Cygnus olor FR HM439500 - HM439497 -
Jouet et al. 2010
DQ859919 Trichobilharzia
regenti Radix peregra - GeneID: 5333425 - -
Webster et al. 2007
43
Table 1: Host association and locality origin of the specimens used in this study. *Brant and Loker 2009. Locality abbreviations: LA = Louisiana USA, NM = New Mexico USA, CA = California USA, AK = Alaska USA, FL = Florida USA, NE = Nebraska USA, PA = Pennsylvania USA, FR = France, GB = England United Kingdom, MB = Manitoba Canada, ZA = Freestate South Africa, NZ = South Island New Zealand, AR = Corrientes Argentina. All samples collected for this study have been deposited in the Museum of Southwestern Biology (MSB) Division of Parasites at the University of New Mexico; parasite and hosts records (including GPS coordinates) can be accessed via the Arctos database
44
Species Location # Examined # Infected Reference
Anas clypeata North America 22 20 Brant and Loker 2009 Anas discors North America 20 20 Brant and Loker 2009 Anas discors North America 184 175 Garvon et al. 2011 Anas cyanoptera North America 12 12 Brant and Loker 2009 Anas smithii Anas hottentota
South Africa Kenya
2 2
2 0
This Study This Study
Anas rhynchotis New Zealand 3 3 This Study Anas versicolor Anas platalea
Argentina Argentina
3 1
3 0
This Study This Study
Table 2: Prevalence of T. querquedulae within members of the ‘blue-wing’ group and their allies.
45
Gene
Species CO1 ND4 ITS
T. querquedulae within NA
0.01 0.012 0.001
T. querquedulae within SH 0.012 0.017 0.004
T. querquedulae between NA and SH
0.012 0.015 0.003
T. querquedulae all sequences 0.011 0.012 0.002
T. querquedulae vs. T. franki 0.077 - 0.002
T. querquedulae vs. Trichobilharzia sp. A
0.082 0.11 0.002
T. querquedulae vs. T. physellae
0.082 0.13 0.002
Table 3. Genetic distances comparing ITS, CO1 and ND4 gene regions within and between global samples of Trichobilharzia querquedulae and between closely related Trichobilharzia spp. NA= North America, SH = Southern Hemisphere.
46
CHAPTER II
Phylogeography and genetics of the globally invasive snail Physa acuta Draparund 1805 (= Haitia acuta, Taylor 2003), and its potential as an
intermediate host to larval digenetic trematodes.
AUTHORS: Erika T. Gendron (Ebbs), Eric S. Loker and Sara V. Brant
ABSTRACT
Background: Physa acuta is a globally invasive freshwater snail native to
North America. Prior studies have led to conflicting views of how P. acuta
populations are connected and genetic diversity is partitioned globally. This
study aims to characterize phylogeographic and population genetic structure
within the native range of P. acuta, elucidate its invasion history and assess
global patterns of genetic diversity. Further, using meta-analytic methods, we
test the ‘Enemy-Release hypothesis’ within the P. acuta – digenetic trematode
system. The ‘Enemy-Release hypothesis’ refers to the loss of native parasites
following establishment of their host within an invasive range. Population
genetic data is combined with surveys of trematode infections to map range-
wide trematode species richness associated with P. acuta, and to identify
relevant host-population parameters important in modeling host-parasite
invasion.
Results: Phylogenetic analyses using mtDNA uncovered two major clades (A
& B). Clade A occurs globally while clade B was only recovered from the
Western USA. All invasive populations sampled group within Clade A, where
multiple independent source populations were identified from across North
America. Significant population genetic structure was found within the native
47
range of P. acuta, with some evidence for contemporary geographic barriers
between western and eastern populations. Mito-nuclear discordance was
found suggesting historical isolation with secondary contact between the two
mitochondrial clades. Trematode species richness was found to differ
significantly between native and invasive populations, in concordance with the
‘Enemy-Release hypothesis’. Further, our data suggests a positive
relationship between nucleotide diversity of invasive populations and
trematode prevalence and richness.
Conclusions: This study includes a wider geographic sampling of P. acuta
within its native range that provides insight into phylogeographic and
population genetic structure, range-wide genetic diversity and estimation of
the invasion history. Meta-analysis of P. acuta – trematode surveys globally is
consistent with the ‘Enemy-Release hypothesis’. Additionally, results from this
study suggest that host demographic parameters, namely genetic diversity,
may play an essential role in how parasite communities assemble within
invasive host populations. This knowledge can be used to begin to construct a
framework to model host-parasite invasion dynamics over time.
INTRODUCTION
Invasive species are an important consequence of global change (Sax et al.,
2005), therefore understanding their origin and invasion history (Estoup and
Guillemaud, 2010), as well as how genetic variation is partitioned across their range
(Facon et al., 2008), is vital. Population genetics provides a powerful tool by which
invasion processes can be revealed (Cristescu, 2015; Estoup and Guillemaud,
2010). Freshwater gastropods present an interesting case, as they are often
48
dispersed passively and distributed patchily (Jarne and Delay, 1991) in nature, but
human activities (i.e. aquaria trade, boats) may greatly alter dispersal, population
structure and consequently evolutionary dynamics (Facon et al., 2003; Jarne and
Delay, 1991).
One such invader is Physa acuta (Draparnaud, 1805)(= Haitia acuta, Taylor
2003), a globally invasive freshwater snail which is native to North America (Dillon et
al., 2002; Lydeard et al., 2016), now occurs on all continents except Antarctica
(Bousset et al., 2014; Wethington and Lydeard, 2007). Remarkable reproductive
plasticity has been reported within P. acuta, specifically in populations’ ability to alter
the number of generations per year (Clampitt, 1970; Monsutti and Perrin, 1999), a
characteristic that might contribute to the invasion success of P. acuta. Physa acuta
is known to displace native gastropods to become the dominant species over very
short periods of time (Appleton, 2003; Brackenbury and Appleton, 1993; Dobson,
2004). For example, doubling times as short as four weeks have been recorded from
European (invasive) P. acuta populations (Perrin, 1986).
The invasion history of P. acuta is muddled by two centuries of taxonomic
confusion ( summarized by Wethington and Lydeard, 2007), due in large part to
plasticity in shell phenotype and overestimation of nominal diversity. Consequently,
the timing of invasion and the connectivity of native and invasive populations remain
unclear. Interestingly, Physa acuta was first described from its invasive range in
France (Draparnaud, 1805) over two centuries ago and it was not until 12 years
later that it was described by Say (1817) from Pennsylvania, USA, under a different
name, Physa heterostropha. Nominal diversity of P. acuta-like snails reached at least
eight species (Burch, 1989; Taylor, 2003; Te, 1978) prior to the inclusion of
molecular genetic data (Pip and Franck, 2008; Wethington and Guralnick, 2004;
49
Wethington and Lydeard, 2007). Molecular genetic studies in addition to reproductive
isolation experiments (Dillon et al., 2002; Dillon and Wethington, 2004)
demonstrated the need to synonymize P. heterostropha (North America), P. virgata
(North America), P. integra (North America) and P. cubensis (Central and South
America) to a single species, Physa acuta (Draparnaud, 1805). Taylor (Taylor, 2003)
placed P. acuta and closely allied species into the genus Haitia, based primarily on
penial morphology, which has demonstrated phylogenetic utility (Taylor, 2003;
Wethington and Lydeard, 2007). However, current genetic studies support the use of
penial morphology to define species or species groups rather than higher level
taxonomy (Wethington and Lydeard, 2007), as such the use of Haitia has failed to
become widely adopted. Molecular phylogenetic analyses have also supported that
P. acuta occurs globally (Wethington and Lydeard, 2007).
The vast distribution, invasiveness and ubiquity of P. acuta have garnered the
interest of evolutionary biologists. Efforts have been made to elucidate the
population genetic structure of P. acuta at both regional (Bousset et al., 2004; Dillon
and Wethington, 2006; Escobar et al., 2007; Van Leeuwen et al., 2013) and global
(Bousset et al., 2014; 2004) scales. These studies reconstructed the genetic
structure of P. acuta primarily in its invasive range in Europe, with limited sampling
from native populations. Characterizing population structure within the native range
is foundational to resolving invasion history (Ghabooli et al., 2011). Limited sampling
from native populations limit possible hypotheses relative to a likely invasion history
scenario and identification of source populations. Further, those prior studies have
presented conflicting results of how genetic varation is partitioned globally, and
specifically whether genetic diversity is homogenous among native and invasive
populations (Bousset et al., 2014; 2004; Van Leeuwen et al., 2013). The primary
50
objective of this study was to address current gaps in our knowledge of the genetic
distribution of P. acuta by chacterizing range-wide population genetic and
phylogeographic patterns, and reconstruct its invasion history.
Apart from its remarkable distribution, P. acuta offers a unique lens to
understand host-parasite invasion dynamics, which are complex and largely
overlooked (Dunn, 2009; Kelly et al., 2009; Dunn and Hatcher, 2015). Physid snails
are important intermediate hosts to digenetic trematodes (Platyhelminthes, Digenea)
where they occur (Cichy et al., 2011; Gordy et al., 2016). More broadly, snails act as
first and second intermediate host to a large proportion of trematodes, over 10,000
species (Cribb et al., 2003). Despite the several papers discussing parasites in
invasive species, surprisingly little is known about how host population dynamics
might influence the invasion processes of their associated parasites (Kelly et al.,
2009; Dunn and Hatcher, 2015). Following invasion, host species are expected to
lose their associated trematode assemblages (Torchin et al., 2003) based on the
‘Enemy-Release hypothesis’ (Keane, 2002). This hypothesis posits that release of
regulation by natural enemies contributes to the rapid establishment of invasive
populations. It is also expected that invasive hosts may acquire a new indigenous
parasite assemblage within their invasive range. What shapes the assembly of the
new parasite community is unclear (Kelly et al., 2009). Here we measure P. acuta-
trematode species richness globally to test for release of digenetic trematodes within
invasive populations and secondarily test for correlations among trematode
infections and host population genetic parameters to identify factors that may be
relevant to host-parasite invasion, and discuss a framework to use these data to
being to model invasion dynamics.
51
MATERIALS AND METHODS
Specimen collection
Physa acuta populations were sampled opportunistically, between 1998–
2015, to document the biodiversity of their trematode parasites. The specimens used
in this study were collected from across North America (native range) as well as
several invasive localities (locality details in Additional Files 1 & 2) and represent P.
acuta over a 17-year period (Figure 1).
Snails were collected primarily from the waters edge using a mesh sieve or
plucked from vegetation. The snails were first screened for trematode infection by
leaving an individual snail in the well of a cell culture plate submerged in artificial
spring water over night. Snails were considered infected upon visual conformation of
mobile trematode larva (cercariae) via a dissection microscope. The cercariae and
their snail hosts, as well as the uninfected snails, were isolated and stored in 95%
ethanol and deposited into the Museum of Southwestern Biology (MSB), Division of
Parasites (Additional Files 1 & 2).
To improve geographic sampling and ensure we were working with a
monophyletic species, specimens from this study were combined in phylogenetic
analyses with known P. acuta samples published in the NCBI GenBank sequence
database (Additional File 1). Sequences were published between 2004 –2016, from
both native and invasive populations. 159 physid sequences were downloaded and
included in this study.
DNA extraction and PCR amplification
A small piece of tissue was taken from the head foot of individual snails. DNA
was extracted using the E.Z.N.A. Mollusc DNA kit (Omega Biotek) following the
manufacture’s protocol.
52
We sequenced two mitochondrial DNA loci (cox1 and 16S) as they are the
most abundant genetic marker available in the NCBI database and have been used
successfully to recover population genetic variation within P. acuta (Wethington and
Guralnick, 2004; Wethington and Lydeard, 2007). We also sequenced a nuclear
locus (ITS1) to assess mito-nuclear discordance of the recovered subclades.
We sequenced 509 bases of cox1 gene using the universal primers,
LCO11490: 5’-GGTCAACAAATCATAAAGATATTGG-3’ and LCO2198: 5’-
TAAACTTCAGGGTGACCAAAAAATCA-3’ (Folmer et al., 1994) and 469 bp of 16S
rDNA using Brh: 5’–CCGGTCTGAACTCAGATCACGT-3’ and Arl: 5’–
CGCCTGTTTAACAAAAACAT -3’ (Palumbi, 1991). Additionally we sequenced the
complete (495 bp) ITS1 internal transcribed spacer region (ITS-1S: 5’-
CCATGAACGAGGAATTCC CAG–3’, 5.8AS: 5’ –TTAGCAAACCGACCCTCAGAC –
3’) of a subset of the P. acuta individuals chosen to represent the recovered
mitochondrial clades. Sequencing reactions were performed using the BigDye
sequencing kit 3.1 (Applied Biosystems, Foster City, California, USA). Sequences
were edited using Sequencher 5.3 (Gene Codes Corporation, Ann Arbor, MI, USA),
and then aligned using ClustalW (Larkin et al., 2007) and further aligned manually if
necessary. GBlocks (Castresana, 2000) was used to identify and remove difficult to
align regions present within both the ITS1 and 16S alignments. Sequences
generated during this study were submitted to GenBank under the accession
numbers MF694400-MF694492, MG001330-MG001338 (Additional Files 1 & 2). All
alignments used in this study were deposited into TreeBase (www.treebase.org).
Phylogenetic analyses
Phylogenetic analyses of Physa species were performed for the purposes of
determining the in-group in all subsequent phylogeographic and population genetic
53
analyses. Aplexa elongata was used as an out-group (Wethington and Lydeard,
2007). Physa phylogenetic datasets were analyzed separately as cox1 (509 bp), 16S
(611 bp) and cox1+16S (1,120 bp). Relatively few ITS1 (525 bp) sequences exist
within the NCBI database (Additional Files 1 & 2), and were therefore unable to be
used in a concatenated analyses of Physa species. The T92+G model of nucleotide
substitution was used to model the cox1, 16S, ITS1 datasets, and HKY+G was used
to model the cox1+16S dataset. Models were chosen based on the Akaike
information criterion (AIC, Akaike, 1974) using JModelTest2 (Darriba et al., 2012).
Phylogenetic analysis of Physa species did not recover a supported sister
clade for P. acuta, and so, Physa spelunca was chosen as an out-group based on
Wethington and Lydeard (Wethington and Lydeard, 2007) and was used to root
subsequent in-group analyses. Samples accessioned in NCBI and/or were identified
as P. acuta via BLASTn search but had a genetic distance greater than 5% (distance
between P. acuta and P. spelunca) were excluded from in-group analyses. In-group
analyses included 151 individual snails and were performed to evaluate
phylogeographic patterns within P. acuta. Each locus was treated as an independent
dataset and analyzed separately. Optimal models of nucletotide substitiution were
found to be the same per gene region as used for phylogenetic analysis of Physa.
Maximum Likelihood (ML), Minimum Evolution (ME) and Bayesian Inference
(BI) were preformed on each phylogenetic dataset. ME and ML analyses were
performed in the program PAUP* 4.0 (Swofford, 2003). One thousand bootstrap
replicates were preformed within each gene tree analysis to statistically assess the
resulting topologies.
Bayesian Inference (BI) was performed using the program MrBayes v. 3.2.6
(Ronquist and Huelsenbeck, 2003), consisting of two replicated runs for each locus
54
with four Markov chain Monte Carlo (MCMC) chains, one cold and three heated
chains. Each analysis ran for 10,000,000 generations and was sampled every 1000
generations. The analysis was terminated when the standard deviation of the split
frequencies was or fell below 0.01, supporting convergence. Likelihood parameters
and convergence between runs were assessed using the program Tracer v.1.6
(Rambaut et al., 2014), based on EES values greater than 200. The first 2,500 trees
from each analysis were discarded as burnin. Resulting phylogenetic trees were
visualized and manipulated using Fig Tree v. 1.3 (Rambaut and A. Drummond, 2009)
and MEGA 6 (Tamura, 2011).
Recovered topologies were statistically compared to a null phylogenetic
hypothesis (HO) using an approximately unbiased (AU) test (Shimodaira, 2002),
where per site likelihood scores were bootstrapped (10,000 replications) using the
program CONSEL (Shimodaira and Hasegawa, 2001) to generate p-values. To
robustly test the ‘single source’ hypothesis we constrained all invasive individuals as
monophyletic, with an Eastern USA origin (HO1), using the cox1 dataset. Secondly, to
test for mito-nuclear discordance within P. acuta, we constrained the ITS1 topology
for complete concordance with recovered mitochondrial topologies (HO2).
Constrained trees were created in Mesquite (Maddison and Maddison, 2015). Per
site likelihood scores were calculated in PAUP* (Swofford, 2003) .
Genetic diversity and population structure
To characterize range-wide population genetic diversity and structure we
grouped native populations according to the freshwater ecological complex (FWEC,
Abell, 2000) from which they were collected. FWECs are based on delineations of
freshwater ecoregions determined by fish distributions (Maxwell et al., 1995).
55
FWEC’s are a subset of ecoregions and are designated based on major habitat type
and biological distinctiveness. We sampled 8 of the 10 North American FWECs
(Abell, 2000). We selected between 2-10 individual P. acuta from each population to
be used for genetic analysis.
Genetic diversity summary statistics of P. acuta populations were estimated
using DNAsp v. 5 (Librado and Rozas, 2009) based on the cox1 and 16S gene
regions; number of unique haplotypes (h), number of segregating (polymorphic) sites
(S), average number of nucleotide differences (K), haplotype diversity (Hd),
nucleotide diversity (π) and Watterson’s estimator (Θw, per site) were calculated. The
dataset of 151 individual P. acuta cox1 sequences consisted of 88 unique
haplotypes. A minimum spanning network was constructed to view connections
among haplotypes using the program PopART (Leigh and Bryant, 2015),
http://popart.otago.ac.nz/). Relationships among haplotypes (185 bases) were also
evaluated using BI, to determine ancestral relationships among haplotypes.
Parameters for BI were identical to those used in analyses described previously.
To estimate overall range-wide genetic structuring of P. acuta (cox1) a one-
way Analysis of Molecular Variance test (AMOVA (Excoffier et al., 1992)) was
performed in Arlequin v. 3.5 (Excoffier and Lischer, 2010). Populations were
partitioned by five geographical regions (North America, Eurasia, South America,
Africa, and Australasia): ΦCT estimates variation among the five regions sampled,
ΦSC estimates variation among localities within the regions and ΦST estimates
variation from all samples across all localities sampled. A second AMOVA was
preformed to assess population genetic structure within the native range of P. acuta
partitioned by sampling localities and the eight FWECs sampled.
56
To further evaluate population genetic structure within P. acuta, pairwise ΦST
(Reynolds et al., 1983) values were estimated from the cox1 dataset. Significance of
ΦST values (p < 0.05) was determined by permutation tests of 10,000 random
permutations, calculated in Arlequin v. 3.5 (Excoffier and Lischer, 2010).
Pairwise uncorrected p-distances were calculated for cox1, 16S and ITS1
datasets within and between FWEC, countries within the invasive range, within and
between the invasive and native ranges, and within and between recovered clades
identified through MSN and phylogenetic analysis using MEGA 6 (Tamura, 2011).
Demographic analysis
We estimated changes in the effective population size (Ne) over time by fitting
a Bayesian Skyline demographic model (BSP, Drummond et al., 2005) for each
FWEC and/or geographic region, recovered clades and the total cox1 dataset in
*BEAST v. 1.7.0 (Drummond et al., 2012). The HKY substitution model was used for
two simultaneous MCMC runs for between 3,000,000 and 80,000,000 iterations
sampling between every 3,000 and 10,000 steps. Analyses were run using three
mutation rates of 1e-4 (0.04%), 1e-6 (1%) and 4e-6 (4%) per million year, chosen to
reflect a range of possible rates of cox1 evolution based on other gastropod systems
(Reid et al., 1996; Crandall et al., 2008; Panova et al., 2011; Harris et al., 2013).
Convergence was checked (ESS of 200 or greater) and results visualized using
Tracer v.1.6 (Rambaut et al., 2014). BSP data from each analyzed dataset generated
in Tracer v. 1.6 was exported and visualized in Microsoft Excel for comparison.
Invasion history and meta-analysis of the trematodes of P. acuta
In conjunction with the molecular data generated by this study, a literature
search was carried out to compile reports of P. acuta outside of North America.
57
ISI Web of Knowledge was searched for 1) all reports of P. acuta outside of North
America and 2) surveys of trematode assemblages of P. acuta within both its native
and invasive range. Data complied was published from 1805 up to the end of 2016,
and recovered using combinations of keywords; Physa acuta, Physa, Physella,
Costella, Haitia, trematode, parasites, invasive gastropod, malacological survey,
trematode survey and cercarial survey. Studies cited by recovered articles were also
included.
In total, the final dataset included 196 published reports of Physa acuta
outside of North America and 7 trematode surveys from the native range of P. acuta
and 15 within its invasive range. Physa acuta-trematode surveys carried out by the
authors for the purposes of this study were also added to the meta-dataset. From
each P. acuta–trematode survey, observed species richness (So) and prevalence
(pr, percentage of patent infections) were calculated. So is defined as the number of
different trematode species occurring within a surveyed host population (Poulin,
1998). Because of the difficulties associated with identifying larval trematodes a
conservative approach to calculating So was taken, ‘species’ richness was
determined by number of trematode families recovered. Consequently, reported
measures of So from the native range are likely underestimates (Gordy et al., 2016;
Laidemitt et al., 2017). Only patent infections, where trematode cercariae were
being shed, were considered as infected. Evidence of metacercaria (trematode
cysts) within P. acuta were not included as the relationships between trematodes
and invertebrate second intermediate hosts are generally less specific (Evans and
Gordon, 1983; McCarthy and Kanev, 1990). Genetic results from this study were
used to assign population parameters as regional estimates for each study used
within the meta-analysis. For example, population genetic estimates from African
58
populations were assigned to all P. acuta–trematode surveys undertaken in Africa.
Australasian samples were excluded from these analyses due to small sample size.
Estimates of haplotype diversity (Hd) and nucleotide diversity (π), were assigned as
population parameters. Time since invasion (tsi) was estimated by assigning each
surveyed population into an ‘invasion phase’ category: phase 4: < 80 years since
invasion, phase 3: 80-180 years since invasion, phase 2: 180-280 years since
invasion and phase 1: 280 + (native range). ‘Invasion phases’ were determined
based on published reports of P. acuta invasions and genetic evidence provided by
this study. Correlation analyses of So, tsi and pr with Hd, π, and Ɵ were evaluated
using Pearson’s correlation coefficient. Probability (p) values were adjusted for
multiple comparisons using both Bonferroni and Benjamini-Hochberg corrections
(Benjamini and Hochberg, 1995).
RESULTS
Phylogenetic analyses
Cox1, 16S, 16S+cox1 and ITS1 gene tree analysis (Additional Files 3-6) of
Physa species demonstrates that samples included in this study form a clade with P.
acuta sequences published in the NCBI database (Additional Files 1&2), and
represent a single globally distributed species. All phylogenetic analyses failed to
resolve sister relationships to P. acuta, similar to the findings of Wethington and
Lydeard (Wethington and Lydeard, 2007).
In-group analyses demonstrate phylogenetic structuring (Figure 2, Additional
File 6) and revealed two clades, referred to hereafter as clade A and clade B.
Clades were moderately supported statistically for ME and ML analyses (cox1,
89/82; 16S, 89/88) though posterior probabilities from BI were strong (cox1, 1; 16S,
59
1). Clade A (n=123) is the largest, containing all invasive populations in addition to
populations recovered across the native range. While some individuals from clade A
and B were found sympatrically, clade B (n=26) was only recovered west of the
Rocky Mountains. Clades A and B were not recovered from the ITS1 phylogeny,
possibly suggesting mito-nuclear discordance (Additional File 5).
Phylogenetic analyses of the P. acuta in-group largely lacked resolution for
terminal nodes across all genes samples. We therefore employed approximately
unbiased topology tests to more robustly assess phylogenetic hypotheses. Using
this method we rejected the null hypotheses, of a single invasive origin (HO1, p= 2e-
7) and strict concordance of mitochondrial clades A and B within the ITS1 dataset
(HO2, p= 3e-5).
Genetic diversity and population structure
Indices of genetic diversity (cox1 and 16S) for each population sampled are
summarized in Table 1. In comparing native to invasive range, greater genetic
diversity (Hdnative = 0.97, Hdinvasive = 0.615; πnative = 0.0289, πinvasive = 0.0098) was
found within the native range. Haplotype diversity is consistently high among FWECs
sampled, often reaching 100% (Colorado, Coastal, Great Basin, Atlantic).
Within the cox1 and 16S datasets 88 and 43 unique haplotypes were
recovered, respectively. More genetic variability was recovered from the cox1
dataset and haplotype relationships were estimated by constructing a Minimum
Spanning Network (MSN; Figure 2A). The MSN corroborates the split of clades A
and B, however it also suggests that haplogroups may be further substructured.
FWEC’s do not appear to be a primary factor in shaping the genetic structure of P.
acuta. Clade A contains all of the invasive haplotypes. Many of the haplogroups
within clade A (I-V) are connected by haplotypes recovered from the Eastern United
60
States (Mississippi and Atlantic FWECs), indicating that these regions are likely a
source of invasions from the native range of P. acuta.
Clade B shows greater nucleotide diversity than clade A (even when invasive
haplotypes are removed, πCladeA= 0.019, πCladeB= 0.024). All haplotypes were
recovered from the Western United States (west of the Rocky Mountains). While
sampling is not sufficient to know if Clade B is truly geographically restricted, our
data suggests (1) clade B is a Western USA clade and (2) FWEC’s may play a
greater role in shaping western populations of P. acuta (Figure 2A, haplgroups VI
and VII).
Relationships among haplotypes were also examined using BI analysis
(Figure 2B). Haplotype relationships are identical to those observed from the MSN,
and the divergence of clade A and B is supported with 100% posterior probability.
Invasive haplotypes group with multiple source populations, from across the native
range. Together the MSN and BI analyses of haplotypes suggest that there have
been several distinct invasion events to the Eastern Hemisphere from multiple
sources across North America.
Overall population genetic structure of cox1 was evaluated by AMOVA (Table
2). Both range-wide and native-range AMOVA’s suggest a significant ΦCT (p<0.0001
and p<0.0001, respectively). In the native range 78.55% of genetic variation is
attributed to within population variation, indicating that overall FWEC’s, and the
biogeographic regions tested in general, are not important in structuring P. acuta
populations.
Pairwise uncorrected p-distances within and between populations of P. acuta
were calculated from the cox1 dataset (Table 3). Within the native range within
population genetic distance averages 0.0292 (~3%), relative to 0.0102 (~1%) within
61
the invasive range which are significantly lower (p=0.0006). Within population
genetic distances between the Western USA and Eastern USA (native range) vary
significantly (p=0.05), and average 0.0346 (~3.5%) and 0.022 (2.2%) respectively.
Overall western populations exhibit the highest within population genetic distances
while Africa and Asia demonstrate the lowest (x= 0.004, 0.4%).
Pairwise genetic distances and ΦST values were calculated (Table 3) and
assessed statistically (only significance values for ΦST are shown). The Mississippi
FWEC, the largest complex sampled, shows significant (p<0.05) genetic
differentiation from four of the six other FWECs (Rio Grande, Coastal, Colorado and
Atlantic) as well from Africa and Middle East. Similarly, the Atlantic FWEC shows
significant (p<0.05) genetic differentiation from four of the six other FWECs (Rio
Grande, Coastal, Colorado and Mississippi) as well from Europe, Australasia, Asia
and the Middle East.
Demographic and invasion history analyses
Bayesian Skyline Plots (Figure 3, Additional File 7) estimated changes in
effective population sizes over coalescent time. Results based on a 1% change per
million year rate of cox1 evolution (Figure 3) largely matched estimates using 0.04%
and 4% change per million year (Additional File 7). Regionally (Figure 3A) Ne is
greatest within North America and has been stable over the past 60,000 years.
Populations from the invasive range suggest much smaller effective sizes, congruent
with previous analyses of genetic diversity. Eurasian and African/Australasian
populations indicate a possible recent population expansion. When Clades A and B
are compared (Figure 3B), Clade A and the total P. acuta cox1 dataset suggests a
recent population decline which is likely an artifact of population subdivision. Heller
et al. (2013) showed subdivision can confound coalescent estimators of Ne and
62
result in an erroneous signal of population decline. There is no evidence (Figure 3C)
of population decline within the native range. There is evidence to suggest that
Clade B has recently undergone a demographic expansion (within the past 10,000
years), both clades A and B have comparatively similar contemporary Ne.
Invasion timeline and meta-analysis of the trematodes of P. acuta
Based on reports from the literature an invasion timeline is summarized in
Figure 4 (full bibliographic information can be found in Additional File 8). Findings
from this literature search informed subsequent time since invasion analysis.
Physa acuta – trematode surveys from both the native (n=7) and invasive
(n=15) range were compiled to estimate range-wide species richness (Table 4).
Average So within the native range (So = 3.25) was significantly greater (p=0.0016)
than within the invasive range (So = 0.3125). Western European (n=2) and Iranian
(n=1) populations of P. acuta were the only invasive populations surveyed where
patent infections were reported. Prevalence varied significantly (p=0.04) among
invasive (0.00319 %) and native ranges (0.2%). Variables were analyzed using
Pearson’s correlation coefficient (ρ), summarized in Table 5. All comparisons
between So, tsi, pr and the demographic variables (Hd, π, Ɵ) resulted in significant
unadjusted p-values, except for pr and Hd. To correct for multiple comparisons, p-
values were adjusted using BH and Bonferroni methods (Table 5). Following
adjustment, So, tsi and pr were found to significantly correlate with π and Ɵ (pBH=
0.003, pBH= 0.000, pBH= 0.033, respectively). π and Ɵ were found to have a
correlation coefficient of 1 (Table 5). There is a strong positive relationship between
tsi, π (ρ = 0.97) and So (ρ = 0.8) and a moderate correlation with pr (ρ = 0.57),
supporting the prediction that genetic diversity increases with time since invasion. In
total, these results suggest a significant relationship between demographic
63
parameters, namely background genetic variation, and the rate and diversity of
trematode infections.
DISCUSSION
Range wide population genetic patterns
Our results propose discordant population genetic patterns between the
native and invasive range of P. acuta. While haplotype diversity within the invasive
range is not low relative to other invasive snails, Melanoides tuberculata (Facon et
al., 2003), Haminoea japonica (Hanson et al., 2013) (within their invasive range), by
comparison there is an approximately 20% reduction in Hd relative to the native
range. Our analyses reveal that invasive populations have substantially less mtDNA
diversity relative to native populations (Table 1), a finding supported by numerous
studies of invasive gastropods (Facon et al., 2003; Hanson et al., 2013). Interestingly
however, this result is in contrast to recently published work on P. acuta. Bousset et
al. (Bousset et al., 2014) used 9 microsatellite loci to estimate range-wide patterns of
genetic diversity and reported no difference in neutral genetic variation between the
native and invasive ranges. These contradictory results are likely explained by the
fact that the Bousset et al.(2014) study was limited to five native populations, four of
the five were from the Eastern USA where many invasive haplotypes likely
originated. Consequently, Bousset et al.(2014) were unable to capture similar
amounts of genetic diversity revealed in this study. Discordance of mtDNA and
microsatellite data is not uncommon (Bayha et al., 2015) and both reveal aspects of
invasion history. Mitochondrial markers can often better detect subtle genetic
subdivision, due to reduced effective population size and lack of recombination
(Bayha et al., 2015; Larmuseau et al., 2010; Lukoschek et al., 2008). Consequently,
64
a genealogical approach utilizing mtDNA markers is frequently used to characterize
population genetic structure and identify source populations (Cristescu et al., 2001;
Facon et al., 2003; Hanson et al., 2013). Our findings based largely on mtDNA,
combined with those of Bousset et al. (2014) suggest P. acuta is overall a genetically
diverse species, relative to other invasive gastropods (Facon et al., 2003; Lounnas et
al., 2017; Städler et al., 2005). However, this study reports significant genetic
structuring within the native range and between some native and invasive
populations that has not been previously reported by other investigators. Further,
previously unknown mito-nuclear discordance (Figure 2, Additional Files 5 & 6)
suggests historical isolation followed by secondary contact has likely occurred
among native western and eastern populations.
Genetic structure within the native range and biogeographic considerations
The Eastern USA is hypothesized as the origin of all P. acuta (Bousset et al.,
2014; Dillon et al., 2002). Our data suggest that sampled genetic diversity is
comparable (marginally greater in Western USA) across the native range with
evidence of geographic structuring between the Eastern and Western USA
populations. Phylogenetic analyses suggest the existence of two clades (A & B)
within the native range; interestingly these clades are sympatric in the Western USA,
while clade B appears to be restricted to Western USA. Field surveys and
subsequent molecular analysis reveal that individuals belonging to clade A and/or B
can be collected side by side from the same water body in New Mexico, Colorado,
California and Nevada. Phylogenetic analysis (Additional Files 3-6) did not result in
sufficient resolution to reveal the geographic origin of P. acuta, however increased
sampling of western populations fails to support the Eastern origin hypothesis (Dillon
et al., 2002; Bousset et al., 2014; Lydeard et al., 2016). Several individuals (S94 &
65
S22) collected from California (Coastal FWEC) and one (S17) from New Mexico (Rio
Grande FWEC) were close matches genetically to P. acuta, but did not fall in the P.
acuta clade. Additionally the presumed sister species P. spelunca has only been
recovered from Wyoming (Turner and Clench, 1974) and did not have support as the
sister species to P. acuta from our analyses. Anecdotally, recent molecular genetic
studies of Physa (Rogers and Wethington, 2007; Gates et al., 2013; Moore et al.,
2015) may demonstrate a greater number of independent taxonomic units from
western waterways relative to the Eastern USA, suggesting that biogeographically
speaking, the west may have been important in physid diversification.
This study is the first to compare and assess discordance among nuclear and
mitochondrial markers within P. acuta. Discordance between ITS1 and mitochondrial
phylogenies could suggest incomplete lineage sorting, though more likely a recent
history of genetic exchange between once isolated populations. Clades A & B were
not recovered from the ITS1 gene tree (Additional File 5), and when the largely
unresolved ITS1 phylogeny was constrained to match the mitochondrial topology a
significantly (p=3e-5) worse phylogeny was recovered. Gene trees of the non-
recombining mitochondrial markers may suggest historical isolation between the
Western and Eastern USA. It may be hypothesized that the Rocky Mountains, or
associated waterways, have played a role in restricting gene-flow, which has been
shown relevant in other freshwater systems (Forister et al., 2004; Finn et al., 2006;
Hughes et al., 2009). It can also be hypothesized that anthropogenic dispersal within
North America or subsequent re-introduction from the invasive range into the
Western USA, has led to reticulation of the two clades (Turner, 2002), and
contemporary admixture within the Western USA. Another possible line of evidence
supporting this hypothesis is the recent discovery of two distinct mitochondrial
66
genomes, varying in size (Isolate A 14,490 vs Isolate B, 14,314 bp) and 37 indels,
collected from the same pond in New Mexico (Nolan et al., 2014). cox1, 16S and
ITS1 sequence from Nolan et al. (Nolan et al., 2014) was included in our
phylogenetic dataset and consistently parse out with clade A (Isolate A) and B
(Isolate B). At this time there has been no investigation into the biological relevance
or functional consequences of Isolate A versus B. Mitochondrial genome divergence
may in fact be a relic of once isolated populations conferring no functional
differences. The fact that invasive haplotypes have only been recovered from clade
A is an intriguing pattern warranting further investigations.
Invasion history of Physa acuta
Figure 4 summarizes records of P. acuta globally (see Additional File 8 for full
bibliographic information). These records, combined with population genetic
inference, lead to a hypothesized route of invasion. The first record of P. acuta
outside of North America was its initial description in France in 1805 (Draparnaud,
1805), over 200 years ago. It is hypothesized that these were the first founding
populations of P. acuta. Anderson (Anderson, 2003) was the first to posit that the P.
acuta discovered by Draparnaud (Draparnaud, 1805), in the Bordeaux region of
France, were introduced via the cotton trade from Mississippi USA during the 18th
century. Trade between France and the USA ceased during the Napoleonic wars
and began between USA and England, where records of P. acuta appear in the mid
1800’s (Forbes, 1853; Jeffreys, 1862). Western Europe (England and France) likely
maintained the first founding populations of P. acuta. However, in less than 100
years, populations were reported from Eastern Europe, Africa, Asia, South America,
the Middle East, the Caribbean, New Zealand and Australia. Prior studies have
suggested that the subsequent global invasion resulted from the spread of early
67
Western European colonizers (Anderson, 2003; Oscoz et al., 2010). If this was the
case we would expect 1) clustering of all invasive populations with Western
European haplotypes and 2) a stepping stone like model of haplotype relationships
(Reusch et al., 2010; Simon et al., 2011). This model was not supported by any of
the preformed population genetic analyses and further, an approximately unbiased
(AU, Shimodaira, 2002) topology test specifically rejected the monophyly of invasive
populations (p=2e-7).
Our data support that there have been multiple independent invasions into
Western Europe, likely from different sources (Figure 2). Data from this study
suggests that some European populations are closely allied with populations in Asia
and the Middle East and share the most common invasive haplotype, we infer that
this shared haplotype (Posada and Crandall, 2001) is related to the initial invasive
populations of P. acuta in Western Europe roughly 200 years ago, and has
subsequently spread east. It is more parsimonious to hypothesize that genetically
depauperate European populations expanded east than to assume that identical
haplotypes from a genetically diverse source (North America) founded three distinct
invasive regions.
Samples from Africa, South America and the Caribbean are related and are
likely the result of an independent invasion event, though it is currently not possible
to determine the directionality without more samples. Physa acuta invasions into
Africa have been more recent relative to European invasions. Extensive
malacological surveys across the African continent suggest the African invasion
occurred in the range of 1940-50’s, with populations reported from South Africa (De
Kock and Wolmarans, 1998; De Kock et al., 2002; Appleton, 2003), Namibia (Curtis,
1991), Morocco (Mohamed and Najat, 1998), Nigera (Fashuyi, 1990), and Kenya
68
(Brown, 1980). Records suggest a similar time frame for the South American and
Caribbean invasion (Paraense and Pointier, 2003; Collado, 2016). Surveys by
Albrecht et al. (2008) suggest that Lake Titicaca (Peru & Bolivia) was colonized 20
years ago.
Our data suggests haplotype connections between invasive populations and
individuals collected in the Western and Eastern USA and Canada. While our data
supports the initial invasions from the Southeastern USA to Western Europe, we also
find evidence of haplotype connections between the Coastal FWEC (California) and
the Middle East and Eastern Europe. Currently, based on our sampling it is unclear if
there are in fact Western source populations, or if this relationship is the result of the
re-introduction of invasive P. acuta from other continents into the Western USA. Our
data supports that all source populations have originated from Clade A, however, it
remains unclear if Western haplotypes of Clade A originated from the Eastern USA
or are the result of secondary contact from invasive populations, though these
scenarios are not mutually exclusive. Increased sampling within the western range is
necessary to fully elucidate the source of Clade A members recovered from the
Western USA. Founding events, within and from the native range, are likely the
result of the aquaria or aquatic plant trade as posited by multiple investigators
(Anderson, 2003; Appleton, 2003; Bousset et al., 2014; Hui Ng et al., 2015).
Host-Parasite invasion dynamics
Combining published reports of P. acuta occurrence with our genetic data we
found a significant relationship between genetic diversity (π and Ɵ) and trematode
prevalence (pr), and richness (SO). Nucleotide diversity (π) is a better measure of
background genetic variation than haplotype diversity, as it is more robust to the
stochastic effects of drift (Avise, 2000). Higher values of haplotype diversity relative
69
to nucleotide diversity were recovered from the invasive range suggesting rapid
expansion from founding populations (Avise, 2000).
We also found that time since invasion (tsi) significantly correlated with π, Ɵ
as well as pr and SO. Of all the invasive populations screened for trematodes and
included in this study there were only three reports of P. acuta shedding larval
trematodes (cercariae) within its invasive range (Table 4); France (Dreyfuss et al.,
2002), Spain (Toledo et al., 1998), Iran (Athari et al., 2006). Populations from
Western Europe (presumably the first invasive populations) and allied populations
from the Middle East have the greatest π of invasive populations sampled.
These data suggest that the rate and richness of trematode infection
increases positively with background genetic variation and time since invasion, and
leads to the prediction that over time as invasive P. acuta populations become more
genetically diverse their role in local trematode transmission will increase. Meta-
analysis was limited by population genetic sampling within the invasive range, which
will be necessary to more robustly model the relationship between genetic diversity
and trematode prevalence.
Overall results from meta-analyses support the ‘Enemy-Release’ hypothesis
(Keane, 2002; Torchin et al., 2003). There are numerous explanations for this within
the P. acuta – trematode system. Firstly, most founding populations likely consisted
of eggs or young snails, as common with the aquaria trade, and were therefore
uninfected. Secondly, there might be incompatibility among native parasites and
invasive host populations. An additional ecological explanation is that invasive P.
acuta populations often establish in highly modified habitats (i.e. drainage ditches,
man-made water-bodies (Anderson, 2003; Brackenbury and Appleton, 1993), which
may not be suitable to sustain a diversity of trematode lifecycles. As invasive
70
populations spread over time into a broader range of habitat types, one would expect
interaction with wildlife+trematode fauna to become more frequent with a greater
potential for native parasites to establish in P. acuta. For example, in countries such
as England and Germany, where the invasion history of P. acuta is well documented,
initial reports of P. acuta are from habitats such as botanical gardens that are known
to import aquatic vegetation, but less than a decade later, P. acuta is reported from
natural habitats (summarized by Vinarski, 2017).
Apart from directly hosting trematodes, invasive P. acuta populations might
play a role in diluting or enhancing transmission of indigenous parasites in several
ways. Physa acuta may act as a decoy host (Yousif et al., 1998; Chipev, 2009;
Munoz-Antoli et al., 2003) to indigenous trematode species, as it is an efficient
competitor to native snails often displacing them to become the dominant gastropod
within the community (Brackenbury and Appleton, 1993; Dobson, 2004). Dobson
(Dobson, 2004) showed that following its establishment in Mozambique, P. acuta
overtook Bulinus forskalii, a primary transmitter of human schistosomiasis
(Schistosoma hematobium), as the dominant snail. It has been shown experimentally
Schistosoma hematobium can infect P. acuta in Nigeria, though the parasite cannot
reach patency (Akinwale et al., 2011). There are also numerous reports of invasive
P. acuta acting as a reservoir for larval parasites, specifically as a second
intermediate host to trematodes. Similarly, non-host specific larvae of the invasive
nematode Angiostrongylus (=Parastrongylus) cantonensis were reported in Saudi
Arabia (Bin Dajem, 2009) and Egypt (Abo-Madyan et al., 2005) from P. acuta.
Host-parasite invasion dynamics are often underappreciated when
considering the impact of invasive species, especially when the associated parasites
are not causing notable pathology. Most commonly it seems hosts acquire a distinct
71
assemblage of indigenous parasites within their invasive range (Torchin et al., 2003)
over time, and the consequences to local transmission and biodiversity are not clear.
While the primary goal of this study was to characterize the population genetic
structure and invasion history of P. acuta, we aim to begin to use this system to
address the role of host demographics in the acquisition of local parasite
assemblages specifically, to identify potential predictors of parasite invasion success
for the purposes of modeling host-parasite invasion over time.
CONCLUSIONS
This study provides improved sampling of Physa acuta within its native range,
where mtDNA analyses reveal two phylogenetic clades (A & B) and previously
unknown population structure between the Eastern and Western USA. Increased
sampling of native populations allowed the estimation of the invasion history of P.
acuta. We report evidence for the occurrence of multiple independent invasion
events from different source populations across the USA and Eastern Canada. The
initial invasion into Western Europe occurred over 200 years ago, with more recent
invasions into the Southern Hemisphere (South America, Africa) occurring within the
last 20-80 years.
We also examined the relationship between tsi, pr, So and host-population
genetic parameters (Hd, π, Ɵ) to identify parameters of relevance for modeling host-
parasite invasion dynamics over time. We found a significant positive relationship
among π with trematode pr and So. These data suggest that, (1) host demographic
parameters might be integral in the assembly of parasite communities within invasive
host populations and (2) background genetic diversity may be an important
parameter to consider in future efforts to model host-parasite invasion dynamics.
72
Overall, our data supports predictions based on the ‘Enemy Release hypothesis’
(Keane, 2002; Torchin et al., 2003). Hosts, particularity snail intermediate hosts, are
substantially easier to sample and monitor over time and space relative to their
associated trematode assemblages and may prove a valuable and tractable tool to
model parasite invasion.
ACKNOWLEDGEMENTS
The authors would like to thank the following people for their help in the
collection and/or screening of the snails used in this study: Dr. Ramesh Devkota, Dr.
Ben Hanelt, Dr. Norm Davis, Ms. Martina Laidemitt, Mr. John Schlutz. The authors
would also like to thank Dr. Charles Criscione and three anonymous reviewers for
their helpful comments regarding this manuscript, and Dr. Thomas Turner for helpful
suggestions regarding data analysis. We acknowledge technical support from the
University of New Mexico Molecular Biology Facility, which is supported by National
Institutes of Health, USA grant P30GM110907 from the Institute Development Award
program of the National Center for Research Resources, USA. This work was
funded through a National Science Foundation, USA grant to SVB (DEB 1021427)
and a National Institutes of Health grant RO1 AI44913 to ESL.
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Figure 1. Map of sampled populations. Colors denote the Freshwater Ecological
Complexes from which P. acuta populations were collected.
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Figure 2. Relationships of recovered cox1 haplotypes. A. Minimum spanning
network of 88 P. acuta haplotypes. The network is shaded to depict the split between
clade A (pink) and B (green), within each clade haplogroups are denoted as I –VII.
B. BI Phylogeny of all 88 haplotypes. Nodes with a posterior probability of ≥ 95% are
denoted by a black circle. Haplotypes are colored by the FWEC they were collected
from.
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Figure 3. Bayesian Skyline Plots. Effective size over time plots estimated in
BEAST using a Bayesian Skyline prior using a mutation rate of 1% (1e-6) per million
years. Mean values are shown and confidence values were excluded for clarity. The
x-axis indicates time in ‘thousand years ago’ (kya). A. Range-wide estimates for
North America (dark grey, n=79), Central and South America (lime green, n=13),
Eurasia (pink, n=49) and Africa and Oceania (blue, n=7). North America dataset ran
for 50,000,000 generations, Eurasia 20,000,000 generations, Central/South America
and Africa/Australasia ran for 5,000,000 generations. B. Estimates of recovered
Clades, Clade A (aqua, n=116) and B (dark green, n=33) compared to total (light
grey, n=149). The total dataset and Clade A ran for 80,000,000 generations, Clade B
ran for 10,000,000 generations. C. Estimates by FWECs sampled: Rio Grande (red,
n=16), Coastal (blue, n=13), Colorado (purple, n=11), Atlantic (orange, n=9),
Mississippi (green, n=26), the Great Basin and St. Lawrence FWECs were excluded
because of low sample size. All FWEC datasets were run for 5,000,000 generations,
with the exception of Atlantic (3,000,000). Bayesian skyline plots were constructed in
TRACER, the data was exported and visualized using Excel.
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Figure 4. Invasion timeline of Physa acuta. 1. Absence of Physa acuta shells from
European fossil deposits older than 18th Century (Lozek, 1964). 2. Described by
Draparnaud (Draparnaud, 1805) near Bordeaux, France. Anderson (Anderson,
2003) posits likelihood of introduction from Mississippi USA via the cotton trade. 3.
Trade between USA and France ends during Napoleonic wars, trade with England
and USA begins. 4. First reports of P. acuta in England (Forbes, 1853; Jeffreys,
1862). 5. Reports of P. acuta all over United Kingdom by the latter half of 20th
Century (Ellis, 1926; Kerney, 1976; Stelfox, 1911; Welch, 1930) 6-12. Primary
citations for these reports are listed in Additional File 7.
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Region/Population FWEC N H Hd S K Π Θw
Native Range
USA
Gila River, AZ COL 3
Denver Area, CO COL 2
Fremont Co., CO COL 2
Chaffee Co., CO COL 1
Gafield Co., CO COL 1
Mesa Co., CO COL 1
Rio Blanco Co., CO COL 1
Yuma Co., CO COL 1
Total 12 11 0.98 54 17.16 0.0337 0.035
Woodward Park, CA COA 8
Los Banos WMA, CA COA 2
Coyote Creek, CA COA 3
Total 13 13 1 69 18.45 0.0363 0.044
New Harmony, IN MIS 2
Okoboji Lake, IA MIS 1
Reynolds County, MO MIS 1
Medicine Lake, MN MIS 1
Bench Creek Pond, MT MIS 10
Mormon Lake, NE MIS 12
Big Horn River, WY MIS 1
Stillwater, OK MIS 5
Total 24 21 0.989 86 13.93 0.02736 0.0453
Stillwater NWR, NV GRB 2
Total 2 2 1 20 20 0.0393 0.0393
Carson Nat. Fr., NM RIO 3
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Bosque Del Apache, NM RIO 9
Shady Lakes, NM RIO 2
Bitter Lake, NM RIO
Total 13 12 0.987 49 14.65 0.0278 0.029
Philadelphia, PA ATL 2 Charles Towne Landing SP,
SC ATL 7
Total 9 9 1 33 10.58 0.021 0.024
Canada
Point Peele NP, ON STL 1
Niagra River, ON STL 3
Total 4 3 0.833 14 8.167 0.016 0.015
NR Total 73 68 0.973 45.29 14.74 0.0289 0.033
Invasive Range
Cuba IR 3
Chile IR 10
Lake Titicaca, Peru IR 1
C&S AM
Total 14 6 0.604 13 2.879 0.0056 0.008
Kisumu, Kenya IR 4
AF Total 4 3 0.833 5 2.5 0.00493 0.005
France IR 4
Greece/Macadonia IR 23
Netherlands IR 1
EU Total 28 7 0.696 35 8.132 0.016 0.0177
New Zealand IR 1
Australasia IR 2
NZ/OZ 3 3 1 9 6 0.0118 0.012
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Total
South Korea IR 1
Thailand IR 1
Singapore IR 3
Maylasia IR 3
AS Total 7 2 0.286 6 1.714 0.0034 0.005
Iraq IR 2
Iran IR 12
MD Total 14 7 0.813 29 8.66 0.017 0.018
IR Total 70 28 0.615 16.16 4.98 0.0098 0.011
Global
Total 143 96 0.794 30.725 9.86 0.0193 0.023
Table 1. Summary of range-wide population statistics, cox1. Regional abbreviations: NR, native range, IR, invasive range, C&S AM, Central and South America, AF, Africa, EU, Europe, NZ/OZ, New Zealand and Australia, MD, Middle East, FWEC refers to Freshwater Ecological Complexes; COL = Colorado FWEC, COA = Coastal FWEC, MISS = Mississippi FWEC, GRB = Great Basin FWEC, RIO = Rio Grande FWEC, ATL = Atlantic FWEC and STL = St. Lawrence FWEC. Population statistic abbreviations: N, number of individuals sampled, H, number of haplotypes, Hd, haplotype diversity, S, segregating sites, K, average sequence divergence, π, nucleotide diversity, Θw, Wattersons estimator per site, Italicized numbers refer to population averages. (*) Indicates a statistically significant value (P<0.05).
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Source of Variation d.f. %
Variation Fixation indices
p value
Range Wide
Among Regions 6 3.19 ΦCT= 0.093 0.183
Among Populations within Regions 28 27.3 ΦSC = 0.282 < 0.001
Within Populations 110 69.52 ΦST= 0.305 < 0.001
Native Range
Among Regions 6 4.27 ΦCT= 0.043 0.228
Among Populations within Regions 14 19.18 ΦSC = 0.20 < 0.001
Within Populations 55 76.55 ΦST= 0.235 < 0.001
Table 2. Analysis of Molecular Variance
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1 2 3 4 5 6 7 8 9 10 11 12 13
1. Rio Grande 0.03 0.0673 0.0167 0.1571 0.2818 0.17422 0.0706 0.2668 0.2396 0.2926 0.377 0.2613 0.0288
2. Coastal 0.0366 0.0386 0.05412 0.08 0.1557 0.0269 0.2006 0.1841 0.1509 0.1573 0.2607 0.129 -0.0085
3. Colorado 0.0338 0.0398 0.0363 0.1694 0.3058 0.1857 0.0897 0.3418 0.24643 0.35611 0.4045 0.3038 0.0623
4. Mississippi 0.34 0.0364 0.0387 0.0288 0.0969 0.01138 0.022 0.0983 0.0745 0.00036 0.245 0.0662 -0.0125
5. Atlantic 0.0356 0.0364 0.0418 0.0283 0.0214 0.0773 0.2032 0.1869 0.17405 0.1216 0.0944 0.1189 0.0199
6. St. Lawrence 0.0295 0.0305 0.0348 0.0241 0.0211 0.0166 0.1199 0.0586 0.2199 0.2518 0.4667 -0.006 -0.0158
7. Great Basin 0.031 0.0328 0.0346 0.0327 0.0337 0.0281 0.0333 0.2469 0.2587 0.4986 0.4948 0.2333 -0.1357
8. Europe 0.0298 0.0325 0.0366 0.0251 0.023 0.0175 0.0288 0.0165 0.1726 0.0994 0.3923 0.0291 0.0932
9. Australasia 0.031 0.035 0.0373 0.025 0.022 0.0186 0.0311 0.0184 0.012 0.4158 0.6129 0.1178 -0.0355
10. Asia 0.0249 0.027 0.0318 0.017 0.0143 0.0116 0.0233 0.012 0.01 0.003 0.7128 0.0567 0.13
11. Africa 0.0343 0.036 0.0414 0.0277 0.016 0.02 0.0327 0.0221 0.02 0.01375 0.005 0.383 0.2029
12. Middle East 0.0317 0.0332 0.0378 0.0258 0.0228 0.0178 0.031 0.0181 0.019 0.0124 0.0228 0.0187 0.06245
13. S. & C. America 0.0311 0.0323 0.038 0.0243 0.0146 0.0173 0.0286 0.019 0.0174 0.0103 0.0056 0.0195 0.0058
Table 3. Pairwise genetic distances and ΦST values. Pairwise within and between FWEC/region genetic distances are shown in the lower diagonal. Within genetic distances are italicized. Pairwise ΦST values are shown in the upper diagonal, values with a p-value ≤0.05 are bolded.
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Country Citation # Individuals
Surveyed # Patent
Infections Prevalence So
Native Range
New Mexico (Bosque del Apache) This study 1,200 78 0.065 5
New Mexico (Eagles Nest) This study 65 5 0.07 2
Nebraska This study 120 16 0.13 4
Montana This study 165 3 0.018 2
Wyoming This study 26 8 0.31 1
Colorado This study 300 5 0.017 3
Mexico Barragán-Sáenz et al. 2009 496 109 0.22 5
Invasive Range
New Zeland Mitchell & Leung 2015 810 0 0 0
Benin Ibikounlé et al. 2009 345 0 0 0
Czech Republic Faltýnková 2005 57 0 0 0
Egypt Abo-Madyan et al. 2005 na 0 0 0
Egypt Bin Dajem 2009 704 0 0 0
Egypt Dahesh & Farid na 0 0 0
France Gerard et al. 2003 413 1 0.0024 1
Germany Faltýnková and Hass 2006 141 0 0 0
Iceland Skinnerson et al. 2008 737 0 0 0
Iran Athari et al. 2006 3,560 8 0.0022 1
Kenya Loker & Laidemitt, unpublished data 589 0 0 0
Morocco Laamrani et al. 2005 na 0 0 0
New Zeland This study 1,480 0 0 0
Spain Toledo et al. 1998 2,717 1 0.00037 1
Zambia Phiri et al. 2007 9 0 0 0
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Table 4. Reports of infection and trematode species richness of Physa acuta as first intermediate. na, data not given, SO, observed species richness calculated by the number of unique trematode families observed.
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SO tsi pr Hd π Ɵ
SO 0.8 0.77 0.54 0.76 0.77
tsi 0.0025 0.57 0.46 0.97 -0.98
pr 0.0039 0.032* 0.44 0.55 0.56
Hd 0.0614 0.06 0.096 0.5 0.5
π 0.003 0 0.033* 0.061 1
Ɵ 0.003 0 0.033* 0.061 0
Table 5. Matrix of correlation coefficients. Pearson’s correlation coefficients are shown in the upper diagonal and corresponding p-values, adjusted using Benjamini-Hochberg (BH) correction, are shown in the lower diagonal. p-values ≤0.05 are bolded. *Indicates p-values that significant under the BH correction, but not significant under the more conservative Bonferroni correction.
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CHAPTER III
Comparative microevolutionary patterns of congeneric trematode species reveal importance of host ecology in parasite evolution
AUTHORS: Erika T. Gendron (Ebbs), D’Eldra Malone, Eric S. Loker, Norm E. Davis, Sara V. Brant ABSTRACT
Identifying factors that shape microevolutionary forces in natural populations
is a fundamental goal of evolutionary biology. In multi-host parasite systems
identification of these factors can be confounded by disparate host ecologies and
evolutionary histories. One approach to teasing apart the relative influence of
ecology verses evolutionary history on observed microevolutionary patterns is to
compare congeners, which are assumed to have evolved under similar
immunological constraints. This research evaluates comparative population genetic
patterns across three congeneric Trichobilharzia species, sampled range-wide, to
determine ecological factors important in shaping parasite genetic diversity, genetic
effective size and gene flow. As adults Trichobilharzia spp. infect anatids (ducks,
geese and swans) and then use freshwater snails (Physidae and Lymnaeidae) as
intermediate hosts. Additionally Trichobilharzia survey data across 73 anatid species
is presented from which measures of host specificity were estimated, in an effort to
characterize anatid-Trichobilharzia host associations. We found higher levels of host
specificity than expected based on published experimental work, possibly mediated
by host ecology. Further comparative population genetic investigations revealed
substantial differences in the population structure and genetic diversity of the three
species of interest. In total, results from this study shed light on how specific host
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ecological traits impact parasite distribution, demography and microevolutionary
processes.
INTRODUCTION
Identifying factors that shape microevolutionary forces in natural populations is a
fundamental goal of evolutionary biology (Slatkin, 1985). In multi-host helminth
systems very little is known about what factors contribute to or determine gene flow,
genetic drift and effective size (Ne), (Nadler, 1995; Criscione et al., 2005). These
parameters relate directly to the evolution of drug resistance (Blouin et al., 1995),
local adaption (Dybdahl and Lively, 1996; Nuismer and Gandon, 2009), probability of
host-switching (Hoberg and Brooks, 2008) and ultimately speciation of parasite
lineages (Huyse et al., 2003). Considering the parasitic lifestyle is ubiquitous
(Weinstein and Kuris, 2016), this lack of knowledge represents a significant gap in
our understanding of microevolutionary processes within natural populations.
In parasite systems microevolutionary forces are mediated by one or more
host(s) and relate largely to the ecology of the individual host species (Nadler, 1995;
Criscione and Blouin, 2004; Criscione et al., 2005; Nieberding et al., 2006; Whiteman
et al., 2007; Barrett et al., 2008). From these studies and others it is acknowledged
broadly that host traits (i.e. life-history, phylogenetic history, host range, host vagility)
determine parasite genetic structure (Gorton et al., 2012; Blasco-Costa and Poulin,
2013).
Yet, it is unclear how parasite species partition themselves among hosts with
overlapping geographic and ecological ranges. Specifically, it is unknown to what
extent subtle ecological differences among closely related, sympatric and/or
parapatric, host species impact microevolutionary forces. This scenario facilitates
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encounter between the parasite species and a broad range of compatible hosts,
creating an arena of potential host-parasite interactions. Under these conditions
generalism for at least one life cycle stage might be expected, where the parasite
species utilizes the range of hosts available to it. Alternatively, strict specialization
may develop where a parasite species can only exploit a narrow range of hosts. A
third alternative, though not mutually exclusive, can be predicted, where parasite
species might develop ecological specificity to a host species or a subset of possible
host species, such that there is a reduction in realized host range due to predictable
host ecological traits or behaviors. Our definition of ecological specificity is similar to
that of ‘faux specialists’ proposed by Brooks and McLeenan (2002), though we are
specifically considering parasites whose range is narrowed by the ecology of one or
more host species. Like ‘faux specialists’, under a model of ecological specificity
parasite species may maximize the short-term gains of specialization without
compromising the long-term advantages of generalism (Hoberg and Brooks, 2008).
In terms of population structure, ecological specificity might act to reduce parasite
gene flow by narrowing infections to a subset of actual hosts among a broad range
of potential host species, leading to genetic differentiation and population genetic
patterns that co-vary with realized host range (Nadler, 1995; Poulin and Keeney,
2008; Archie and Ezenwa, 2011).
This research sought to ask how host ecological traits impact parasite
microevolution and to characterize host-parasite associations (generalists,
specialists, ecological specialists) when parasite species co-occur with a broad
ecological range of compatible hosts. To achieve this we took a comparative
population genetic approach among congeneric parasite species. Comparing
congeneric species is ideal as it is assumed that species evolved under similar
99
immunological and evolutionary constraints (Dawson et al., 2002; Benton 2009).
Consequently, microevolutionary differences observed are therefore likely to result
from ecological interrelationships.
This research focuses on species within the genus Trichobilharzia
(Schistosomatidae), specifically three North American taxa within the species
complex known as Clade Q (sensu Brant and Loker 2009). As adults, these worms
develop and reproduce sexually in a duck definitive host (Anatidae) and release
parasite eggs in their feces. A free-swimming larval stage (miracidium) hatches from
the egg to find the freshwater snail (Pulmonata) intermediate host where asexual
amplification occurs. Free-swimming larvae (cercariae) are released from the snail to
find a duck host, thus completing the life cycle. Trichobilharzia is a globally
distributed, species rich genus which infects a broad ecological and taxonomic range
of definitive anatid hosts (ducks, geese and swans) (Brant and Loker, 2009; 2013).
Observational (Blair and Islam, 1983; Brant and Loker, 2009; Jouet, 2009) and
experimental (McMullen and Beaver, 1945; Horak et al., 1999) data largely suggest
duck host use is not constrained by physiological and/or immunological filters,
suggesting a generalist association with anatid hosts. However, surveys in
combination with molecular systematic data (Brant and Loker, 2009; Ebbs et al.,
2016) suggest some Trichobilharzia-host associations appear more specialized than
expected based on host compatibility alone. Results from field collections show that
Trichobilharzia species often preferentially infect certain duck species or duck
ecological groups. Here we compare phylogeographic, population genetic and
demographic patterns of three North American congeners where with differing
patterns of duck host use; T. physellae (TP), T. querquedulae (TQ), and
Trichobilharzia sp. A (TA).
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We sampled TP, TQ and TA across the ranges of their hosts. TP occurs across
North America and is primarily associated with diving ducks, an ecological group of
ducks (e.g. Aythya, Bucelphala, Mergus) characterized by their preference of large
stable water bodies and diving feeding behavior (Baldassarre and Bolen, 1984). TQ
occurs globally infecting a genus of dabbling ducks, Spatula (Gonzalez et al., 2009)
specific to shallow water bodies. Based on current sampling, the host range of TA
(Brant and Loker, 2009) seems limited relative to TQ and TP, and thus far has been
recovered only from the American wigeon (Anas americana), which is described as a
generalist dabbling duck (Johnson and Greir, 1988).
Importantly, TP and TQ both utilize Physa spp. (Physidae) as their snail
intermediate host, which unites these parasites in space, time and in contact with a
similar sampling of duck host species. Physa spp. are abundant among different
habitats across North America, and parts of South America (Wethington and
Lydeard, 2007). One species, P. acuta is globally invasive occurring on 6 of the 7
continents (Bousset et al., 2014; Ebbs et al. In press). Physa spp. are also more
likely to be found in the preferred habitat of the duck host than not and thus
increases their probability of transmission. TA utilizes Stagnicola elodes
(Lymnaeidae), which is more restricted ecologically and geographically relative to
Physa, and achieves highest densities at higher latitudes (or higher elevations)
within North America. Generally, within digenetic trematodes there is a pattern of
greater specificity in regards to the first intermediate host (snail), which seems to
consistent within Trichobilharzia (Brant and Loker, 2009; Jouet, 2010). As such this
study will focus on adult worms and their associations among duck host species and
ecological groups.
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We surveyed Trichobilharzia spp. extensively within North America and some
Southern Hemisphere localities to assess duck host associations and evaluate
potential impacts on observed population genetic and demographic patterns.
Additionally, we discuss the microevolutionary consequences associated with
different levels of ecological specificity, specifically in relation to Trichobilharzia
persistence and diversification.
METHODS Host-Parasite Survey
Duck hosts were sampled globally (Table 1, Additional File 1). For the
purposes of this study the authors examined 346 avian, primarily anatid, hosts
(Table 1). Additionally, we compiled results from several published Trichobilharzia –
duck surveys to more robustly calculate host-range and make determinations of host
specificity (Table 1). Combined survey (this study + published) data totaled 1,358
examined birds, across 94 species (73 anatids) and 7 countries. From these data we
calculated several descriptive measures to capture duck host associations within
Trichobilharzia: prevalence, the proportion of infected hosts (Bush et al., 1997),
observed host range, the number of host species infected by a parasite species and
several specificity indices were calculated. As it can be assumed that parasites
capable of infecting a broad taxonomic range tend towards generalism, relative to
those infecting a single host genus the STD measures the taxonomic distinctness of
all hosts species used by a single parasite species (Poulin and Mouillot, 1999). As
rare hosts may disproportionately contribute a high STD (increased generalism) we
also used the weighted specificity index (STD*) (Poulin and Mouillot, 2005). STD* is
weighted by the parasites prevalence within each host. As members of
Trichobilharzia almost exclusively parasitize hosts within the family Anatidae, total
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taxonomic distinctiveness among hosts is limited and these measures fail to capture
the potential for ecological specificity, as such we developed a modified specificity
index (STD-modified). Based on the Poulin and Mouillot (2005) index, we distinguished
anatid hosts by ecological group rather than taxonomic group. Ecological groups
were assigned following determinations from The Sibley Guide to Birds (Sibley,
2000). We approximated the degree of ecological specificity, calculated as the
proportion of infections within each ecological group. Intensity and consequently
abundance are important parameters and are included in most attempts to quantify
specificity (Poulin and Mouillot 2005). Due to inherent difficulties of sampling
Trichobilharzia infrapopulations it is not feasible to obtain accurate measures of
intensity, as it is unlikely that entire infrapopulations are ever recovered and intact
worms are rare. As such, our measures are limited in this way.
To assess microevolutionary impacts of host specificity, correlation between
measures of specificity and species wide estimates of nucleotide diversity (π) and Θ
was tested by calculating Pearson’s correlation coefficients, as implemented in R
(http://www.R-project.org).
Hosts were either donated by hunters or obtained from the Museum of
Southwestern Biology, Bird Division (MSB) at the University of New Mexico
Albuquerque. All work with vertebrate hosts was conducted with the approval of the
Institutional Animal Care and Use Committee (IACUC) at the University of New
Mexico, USA (IACUC # 11- 100553-MCC, Animal Welfare Assurance # A4023-01).
Species of Trichobilharzia are parasites of the venous system and primarily reside in
the mesenteric and hepatic portal veins. Mesenteric veins were inspected for adult
worms with the aid of a dissecting microscope, and were removed using Vanna’s
microscissors. Adult worms were recovered by perfusing the hepatic portal vein with
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saline and then crushing the liver. Worms were then isolated in a series of
decantation steps. Trichobilharzia samples were preserved in 95% ethanol for
genetic and 80% ethanol for morphological assessment. In addition to the
schistosome species recovered from the anatid hosts, co-occurring parasites (liver,
kidney, heart and gastrointestinal tract) were also collected. All samples were
deposited as vouchers in the MSB, Division of Parasites (Additional File 1).
DNA Extraction, PCR Amplification and Sequencing
Trichobilharzia DNA was extracted using the QIAmp DNA Micro Kit (Qiagen,
Valenicia, California, USA), the DNeasy Blood and Tissue Kit (Qiagen, Valenicia,
California, USA) or by HotShot Lysis (Truett et al., 2000). We amplified two
mitochondrial and one nuclear gene region using primarily the Takara Ex Taq kit
(Takara Biomedicals, Otsu, Japan). For samples that were difficult to amplify, we
used either the GoTaq Flexi (Promega) or the Platinum Taq kit (Invitrogen) using 0.4
ul of Taq and 4ul of 2.5 mM MgCl2 per reaction. We sequenced a 743 bp (5’ end)
region of the cytochrome oxidase I gene (cox1), a 409 bp (5’ end) region of the
NADH 4 gene (nad4) and 733 bp of the internal transcribed spacer region 1 (ITS1,
including the 3’ end of 18S rRNA and the 5’ end of 5.8S). PCR protocols and primers
used were identical to those used by Brant and Loker (2009) (cox1, ITS1) and Ebbs
et al. (2016) (nad4).
Sequencing reactions were performed using the BigDye 3.1 sequencing kit
(Applied Biosystems, Foster City, California, USA). Sequences were edited using
Sequencher 5.3 (Gene Codes Corporation, Ann Arbor, MI, USA) and aligned using
ClustalW (Larkin et al., 2007) and adjusted manually as needed. Sequences
generated from this study are accessioned in the NCBI Gen Bank database.
Phylogenetic Analyses
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To evaluate intraspecific relationships and population structure phylogenetic
analyses of Clade Q (sensu Brant and Loker, 2009) were carried out. Phylogenetic
datasets were analyzed by gene (cox1, nad4, ITS1) and concatenated (cox1+ nad4
+ITS1). Mitochondrial genes (cox1 and nad4) are well suited for intraspecific studies
as there is no recombination and consequently have a higher sorting rate(Avise,
1987). Part of the nuclear rRNA complex, ITS1 was chosen to evaluate potential
mito-nuclear discordance. Appropriate models of nucleotide substitution were
selected using JModelTest2 (Darriba et al., 2012)based on the Akaike information
criterion (AIC (Akaike, 1974)).
Pairwise uncorrected p-distances were calculated in MEGA 6 (Tamura et al.,
2013)within and between the three species of Trichobilharzia to determine the in-
group for all subsequent intra-specific analyses. A cut off of ≤5% divergence for cox1
was chosen to delineate each in-group. In-group datasets included; TQ ncox1= 63,
nnad4=41, TP ncox1= 27, nnad4=14, TA ncox1= 12, nnad4=9.
We assessed haplotype relationships in our mitochondrial genes for the in-
group of TP, TQ and TA using minimum spanning network analysis in the program
POPart ((Leigh and Bryant, 2015) http://popart.otago.ac.nz/). Haplotypes were coded
by migratory flyway and host species. Migratory flyways are common way of
partitioning bird populations (Swanson et al., 1985) and their symbionts (Pearce et
al., 2009; Lam et al., 2012; Muñoz et al., 2013). Four North America flyways (Pacific,
Central, Mississippi and Atlantic) have been defined; each were sampled for adult
Trichobilharzia for this study. This study chose to focus primarily on adult worms, as
they represent the point of gene flow within the Trichobilharzia life cycle. However,
cercarial stages of TP and TA were included in some analyses to increase sample
size. Unique haplotypes differed by at least one base pair. All samples collected
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outside of North America were collected from the Southern Hemisphere and were
pooled together because of the small sample size.
Genetic diversity and population structure
To estimate the genetic diversity and population genetic structure of the TP,
TQ and TA infrapopulations, samples were grouped by the state or province the host
was collected from and then regionally by the migratory flyway from which the host
was collected. Indices of genetic diversity for each species and their respective
populations were estimated in DNAsp v. 5 (Librado and Rozas, 2009) for both cox1
and nad4; number of polymorphic sites (S), average number of nucleotide
differences (K) and nucleotide diversity (π). Uncorrected p-distances were calculated
between flyways, regions and host species in Arlequin 3.5 (Excoffier and Lischer,
2010)
One-way AMOVAs (Excoffier et al., 1992) were performed in Arelquin v. 3.5
(Excoffier and Lischer, 2010) to estimate overall population genetic structure (cox1).
A set of population genetic hypotheses was tested for each species. (1) Where
infrapopulations were grouped by host locality, and then regionally by flyway. For the
TQ dataset AMOVAs were run both with the inclusion and exclusion of Southern
Hemisphere populations. (2) We tested for the effect of host species on population
structure. (3) We tested for the effect of latitude by separating populations into high
vs. low latitude groups rather than the longitudinal groups designated by flyway. ΦST
estimates variation from all samples across all localities sampled, ΦCT estimates
variation among flyways, host species, or latitudinal group and ΦSC estimates
variation among localities within the flyways, host species, and latitudinal groups. We
also estimated pairwise ΦST (Reynolds et al., 1983) by locality, flyway and host
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species. Significance (P<0.05) was determined by permutation tests of 10,000
random permutations.
To test for isolation by distance patterns we constructed a geographic
distance matrix by converting geographic coordinates to Euclidean distances
between localities as implemented in Primer 7 (Clarke and Gorley, 2015). Correlation
between geographic distances and both uncorrected p-distances and pairwise ΦST
matrices were calculated using Spearman’s rank correlation also in Primer 7.
Demographic analyses
To assess contemporary and historical demographic patterns, Watterson’s
estimator (Θw) and Tajima’s D (D), were calculated (cox1) and assessed statistically
using coalescent simulations with a 95% confidence interval and 10,000
permutations (Table 1) in DNAsp (Librado and Rozas, 2009).
Changes in Ne over time were estimated by fitting a Bayesian Skyline
demographic model (BSP ( Drummond, et al., 2005)) for each of the three species of
interest based on cox1 *BEAST v. 1.7.0 (Drummond et al., 2012). The HKY
substitution model was used for two simultaneous MCMC runs for 10,000,000
iterations sampling every 1,000 steps. Mutation rates of 2% and 4% change per
million year were used to estimate mitochondrial genome evolution for schistosomes
(Schistosoma mansoni, (Morgan et al., 2005)). As substantial divergence within TA,
between the two flyways it was recovered from (Pacific and Central), was found
these populations were analyzed separately. Convergence was checked (ESS of
200 or greater) and results visualized using Tracer v.1.5 (Rambaut et al., 2014
Tracer v1.6. http://beast.bio.ed.ac.uk/Tracer). BSP data from each analyzed datasets
generated in Tracer v. 1.6 was exported and visualized in Microsoft Excel for
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comparison.
Genetic Diversity of Trichobilharzia
Genus wide genetic diversity was estimated for the purposes of assessing the
relationship between diversity (π, K and Θw) and measures of host specificity. The
cox1 sequences of other Trichobilharzia species published in the NCBI database
were used to estimate phylogenetic relationships (TN93+G+1) and patterns of
genetic diversity across the genus, using methods described previously.
Anserobilharzia brantae and Allobilharzia visceralis were selected as out groups
(Brant and Loker, 2013). Pairwise uncorrected p-distances were calculated within
and between each species in MEGA 6 (Tamura et al., 2013).
RESULTS
Trichobilharzia survey data
Schistosomes from a total of 1,358 birds were included in this study.
Specimens representing Trichobilharzia were only recovered from hosts within the
family Anatidae. Based on our examinations and those within the literature we found
464 hosts (out of 1,358, 34.2%) to be infected with at least one species of
Trichobilharzia. Co-infections were common, (Table 1), and were probably
underestimated as not all individuals were assessed genetically. Of the 73 anatid
species examined, 29 were found to be infected with Trichobilharzia (Table 1)
Using three different measures of host specificity, we found that TQ, TA, T.
stagnicolae, T. mergi, and T. anseri all showed evidence of duck host specificity (STD
values of 1). Using conventional indices based on taxonomic distinctiveness (Poulin
and Mouillot, 1999; 2005) TP and T. regenti had intermediate to high measures of
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STD, indicating generalism. When a modified indexing method was applied to capture
ecological distinctiveness, TP has a lower STD-modified value while T. regenti reached
the maximum value (STD-modified = 3, despite having a fewer number of observed host
species. It is important to note that STD and STD* were calculated under the
recommendations of (Poulin and Mouillot, 1999; 2005) and only incorporate two
taxonomic levels, consequently STD values can only vary between 1 and 2, where
two is maximum generalism. The STD-modified value vaired across four ecological
groups (divers, dabblers, sea ducks, geese/swans) and can vary between 1 and 4,
therefore the STD-modified of TP is moderate. Only STD was calculated for T. franki as
prevalence values were ambiguously reported due to co-infection with other
Trichobilharzia spp. and could not be included within these calculations. Further
there are known cryptic lineages within the T. franki complex (Jouet et al., 2010),
which could skew our measure of host specificity, for these reasons we did not make
any determinations regarding host associations within T. franki.
Approximating ecological specificity as the proportion of infections occurring
within a single duck ecological group, we determined 6 species to have ecological
specificity (TQ, TP, TA, T. stagnicolae, T. mergi, and T. anseri), as over 90% of their
infections were associated with one duck species or ecological group, while T.
regenti was found to be a generalist among anatids. It is likely that T. szidati also
falls within this category, based on species reports, however a lack of published
prevalence data (from duck hosts) did not allow its inclusion within these
calculations. We estimate that 93%, 100% and 100% of TP, TQ and TA,
respectively, infections occur within a single duck ecological group, and
consequently utilize a narrow selection of their potential host range (Anatidae). The
underlying mechanisms of this observed specificity are yet to be determined and will
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require experimental infections to confirm the physiological boundaries of
Trichobilharzia host use. Several undescribed Trichobilharzia lineages resulted from
our survey data but were excluded from our calculations of host range and ecological
specificity due to low sampling.
For the species of interest, our data supports that TP infects multiple genera
of diving ducks (Aythya, Bucephala, Mergus) and was recovered from 8 species
within 5 genera. The highest prevalence’s were recorded from Aythya (A. affinis,
27%, A. collaris, 26%, A. valisineria, 20%), which were determined to be core host
species, as 93% of all TP infections were found within these three host species
(Bush et al., 1997). The lowest prevalence values were found within Anas
platyrhynchos (12.5%), Clangula hyemalis (10%), Mergus merganser (10%),
Bucephala albeola (7.7%), and Anas strepera (2.8%), which were determined to be
satellite (Bush et al., 1997) host species. In concordance with previous studies
(Brant and Loker, 2009; Ebbs et al., 2016), TQ was found almost exclusively (98.3%)
from Spatula, or the ‘blue-wing’ duck group (Johnson and Sorenson, 1999), with a
single report (2.7%) from an American wigeon (Anas americana). Prevalence was
highest within Spatula discors (90%), but all Spatula species examined were infected
at a rate of ≥74%. Trichobilharzia sp. A was recovered exclusively (100%) from the
American wigeon (Anas americana), having the narrowest host range of the three
species, with a prevalence 27%. Anas americana was found to harbor the highest
Trichobilharzia species diversity of any of the host species examined, 6
Trichobilharzia species were recovered, compared to an average of 1.5
Trichobilharzia species per host species within North America (1.8 per host species
globally).
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Correlation of observed host range, indices of host specificity and species
wide estimates of genetic diversity (π) and Θ was evaluated by calculating Pearson’s
correlation coefficients. There was no correlation with observed host range and π (R
= -0.165) or Θ (R=0.147). All indices of host specificity were at least moderately
supported with π (R, STD= -0.571; R, STD*= -0.4995; R, STD-modified = -0.6077) and to a
lesser extent Θ (R, STD= -0.4375; R, STD*= -0.329; R, STD-modified = -0.4424).
Suggesting that ecological specialists were correlated with higher values of π and Θ.
Phylogeographic patterns among congeners
TRICHOBILHARZIA QUERQUEDULAE: Phylogenetic analysis of TQ
supports its global distribution (Ebbs et al. 2016, Figure 1) within Spatula.
Mitochondrial data suggests that several individuals (W345.3, W750.2 and W771,
~4.7% divergent) fall basal to the main TQ clade (Figure 1, Additional File 3).
Genetic distances were low enough to support the inclusion of these samples within
TQ in-group analyses, however this subclade may warrant further investigation as a
putative cryptic species, or divergent subpopulation. Interestingly, all individuals were
recovered from S. clypeata and primarily from northern latitudes. Apart from the
three samples in question, minimal phylogenetic structure was observed within TQ
with no pattern suggesting structure by geography or host use based on either ML or
BI analyses (Figure 1, Additional Files 3-5).
Minimum spanning networks (Figure 2) of cox1 revealed high amounts of
haplotype diversity within TQ, suggesting genetically diverse and well-connected
populations. Infrapopulations (within host) haplotype diversity was similar to
component population (across hosts) haplotype diversity, suggesting infrapopulation
recruitment occurs randomly over time and space (Nadler, 1995; Sire et al., 2001).
TQ haplotypes did not partition according to flyway, and Southern Hemisphere
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haplotypes did not group together and were not more or less divergent than Northern
Hemisphere haplotypes. No evidence of genetic structure in association with host
species by was found. An AMOVA testing for structure by latitude was found to be
significant, indicating possible population genetic differentiation between breeding
and wintering range.
TRICHOBILHARZIA PHYSELLAE: Two clades (TP1, TP2) were recovered
within TP based on cox1 analysis (Figure 1), taxa grouped within each subclade for
nad4 and cox1 + nad4 + ITS1 analyses (Additional Files 3-5), but subclades were
not supported statistically. Despite limited geographic sampling, representation
within the two subclades might suggest a distinction between eastern and western
populations of TP. Similar to TQ, minimum spanning networks of cox1 suggest
genetically diverse and well-connected populations (Figure 2). For TP two
haplogroups were recovered, equivalent to clades TP1 and TP2 in the mitochondrial
gene tree analyses. Haplotypes within TP1 were recovered from 3 of the 4 flyways.
TP2 was also recovered 3 of the 4 flyways, however 12/16 individuals were
recovered from the Central flyway. Networks were additionally coded by high versus
low latitude and host species, and host species, which provided no structure to
haplotype relationships. An AMOVA testing for structure by flyway was found to be
significant, indicating population genetic differentiation in association with migratory
flyway.
TRICHOBILHARZIA SP. A: Mitochondrial and concatenated datasets support
the monophyly of TA and an affinity to group with T. franki and Trichobilharzia sp. B
(Additional File 5). However, ITS1 analysis (Additional File 4) suggests that TA is
paraphyletic with TP, T. franki, and Trichobilharzia sp. B. This result could suggest
mito-nuclear discordance within the TA dataset that was not found within either TP or
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TQ. A single specimen (W249) grouped with support within Trichobilharzia sp. C,
rather than TP in cox1 (Figure 1). Geographical sampling was too limited to draw
phylogeographic inferences for a minimum spanning network of TA haplotypes.
No supported sister group relationships within Clade Q were found. BI of
mtDNA supported Trichobilharzia sp. B as sister to Trichobilharzia sp. A, and both
lineages infect American wigeon (Anas americana), though the snail host to
Trichobilharzia sp. B is currently unknown. This however was not found in the
analysis of ITS1 or for the concatenated dataset, cox1+nad4+ITS1. The cox1
analysis showed a South American Trichobilharzia lineage (W701) transmitted by a
physid snail (Pinto et al., 2014) as a well-supported sister clade to T. physellae,
however this relationship was not found in the ITS1 gene tree (Figure 1, Additional
File 4). No gene tree supported T. regenti or any other Trichobilharzia lineage as the
root of Clade Q (Figure 4).
Comparative population genetic structure
TRICHOBILHARZIA QUERQUEDULAE: Hierarchal AMOVA was used to test
multiple hypothesis of population genetic structure (Table 3). When structure by
flyway was tested, ΦSC (among populations within flyways) and ΦST (within
populations) were found to be marginally significant (p= 0.0528 ± 0.00619 and p=
0.0439 ± 0.00594, respectively) while ΦCT was found to be non-significant indicating
population structure is not mediated by migratory flyway. A second AMOVA was
performed excluding non-North American populations, to determine if Southern
Hemisphere populations were driving observed structure. Results from this analysis
(results not shown) were concordant with the total AMOVA. Instead of flyways,
populations were partitioned by latitudinal group (high vs. low) as a proxy for genetic
structure associated with breeding vs. wintering range. Analyses including (ΦCT=
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0.0441, p= 0.00196) and excluding (ΦCT= 0.0072, p=0.0332, results not shown)
Southern Hemisphere populations support latitude in structuring TQ populations.
Further significant population structure was found at all levels (Table 3) when latitude
was considered. No significant structure by host species was found. Pairwise ƟST
values among flyways are summarized in Additional File 9. When all populations
within a flyway were pooled no significant differentiation between flyway was found
within TQ. However, all North American flyways were found to be distinct from the
Southern Hemisphere populations. Pairwise ƟST between localities within flyways
and by host species were also measured (results not shown) which identified TQ
collected from Manitoba and New Zealand as the most distinct populations.
Interestingly, TQ populations collected from different host species (Spatula clypeata
vs. S. cyanoptera) from the same locality (New Mexico) were found to be
significantly distinct (p=≤0.01). A Spearman rank test was performed and correlation
between geographic distance and both pairwise ƟST and uncorrected p-distance was
found to be weak (ρ= -0.11 and ρ= 0.002, respectively) suggesting no relationship
between geographic distance and genetic diversity/structure.
TRICHOBILHARZIA PHYSELLAE: AMOVA by flyway indicated significant
differentiation among flyways was found (ΦCT= 0.55592, p= 0.02737) (Table 3),
supporting the population genetic hypothesis that flyways structure TP populations.
When populations were grouped according to latitudinal group no significant
population genetic structure was observed. When TP individuals were pooled by
host species they were collected from and then partitioned by host genera a
significant ΦST was recovered (p= 0.00293), however, neither the ΦSC nor ΦCT were
significant suggesting no real influence of host species. The TP dataset is more
limited in terms of sample size, yet significant differentiation was found between
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several flyways (Additional File 9). Pairwise ΦST between localities within flyways and
by host species were also measured (results not shown) which identified that TP
recovered from New Mexico were distinct from both Alaskan (ƟST= 0.2952, p<0.001)
and Michigan (ΦST= 0.24159, p<0.001) populations. The Mississippi and Atlantic
flyways harbored the highest within flyway divergence values, 1.2% and 0.95%
respectively in comparison to the Central (0.01%) and Pacific (0.048%). There was
no evidence that host species influenced population genetic structure.
Correlation between geographic distance and uncorrected p-distance was
moderate (ρ= 0.473) indicating a relationship between genetic diversity and
geography. When geographic distances were correlated with pairwise ΦST values no
relationship was found (ρ= -0.025).
TRICHOBILHARZIA SP. A: Since TA was only recovered from two flyways a
hierarchal AMOVA was not performed. Pairwise ΦST between the two flyways
sampled (Pacific and Central) was significant (ΦST=0.3283, p <0.001). High intra-
flyway divergence was recovered within the Central flyway (1.4%) relative to the
Pacific (0.36%) with an average p-distance of 1.38% between the two flyways. Due
to limited geographic sampling a Spearmen rank test was not performed.
Genetic diversity and demographic analyses
Haplotype diversity was similar across TP, TQ and TA, however nucleotide
diversity (π) and Ɵ were substantially different across the three species (Table 4).
Historical effective size was estimated by fitting a Bayesian Skyline model
(Drummond). Figure 3 shows estimates of Ne over time (kya) for TP, TQ, and TA
based on a 4% change/million years (Morgan, 2005), a lower rate of 2% was also
tested (Additional File 8). For TP and TQ estimates of Ne were almost double under
the 2% rate. Both TQ and TP indicate a recent population expansion, further
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supported by significant positive Tajima’s D values (Table 4). Over coalescent time
the effective size of TQ was approximately 3X greater than TP, suggesting this
species has a history of maintaining larger genetically diverse populations. Despite
having greater π than TQ, TA exhibited the smallest effective population size of the
three species with no evidence for notable historical demographic changes.
Genetic diversity within Trichobilharzia.
Using our data combined with published cox1 sequence for other
Trichobilharzia spp. in the NCBI database we estimated species wide genetic
diversity across Trichobilharzia (Table 4). Of the five additional species included,
none has comparable nucleotide diversity to the three species of interest, with the
exception of T. stagnicolae (π = 0.01326). Similarly, estimates of Ɵ were highest
among TQ, TP, TA and T. stagnicolae. Observed genetic diversity was highest
among species with high measures of ecological specificity. Species with the
broadest host range in terms of number of species and taxonomic distinctness
(Poulin and Mouillot, 2005) had the lowest estimates of π and Ɵ (Figure 4).
DISCUSSION
A primary goal of this study was to identify ecological factors (habitat preference,
migratory behavior, and host range) that are important to the microevolution of
Trichobilharzia, specifically under conditions of partially overlapping host ecology.
Remarkably little is known regarding the ecological determinates of Ne, π and Ɵ or
how genetic drift operates within complex, widespread parasite systems (Criscione
et al., 2005). These parameters have vital implications for disease ecology and
parasite control, as they determine how alleles (e.g. resistance alleles) move within
and between populations. Ɵ is often described as a measure for adaptive potential
(Criscione and Blouin, 2004; Gandon et al., 2008; Archie and Ezenwa, 2011) as
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such it may indicate a species genetic flexibility. In the case of Trichobilharzia spp. it
can be hypothesized that TQ has a greater adaptive potential than TP, and
theoretically populations may be more robust to environmental change (e.g. host
extinction, climate change).
Comparative population genetic analyses of TQ, TP and TA revealed
differences in population structure, genetic diversity and demographic histories.
Specifically, the results suggest that TQ is a common and genetically diverse
schistosome of Spatula spp. across four continents, with population structure likely
associated with duck migratory ranges, such that parasite population structure is
associated with latitude as a proxy for breeding and wintering grounds. No deeper
phylogeographic structure was found within TQ despite its global distribution.
Historical effective population sizes (Ne) of TQ were at least 3X greater than TP and
TA. Contemporary regional measures of π and Ɵ were high across the range of TQ,
including Southern Hemisphere populations, and largely matched species wide and
infra-population measures. In contrast, species wide TP maintains lower π and Ɵ.
Overall geography plays a more important role in the genetic structure of TP relative
to TQ. Phylogenetic analysis suggests the existence of two clades, possibly
separated by the Eastern and Western part of the range. Hierarchal AMOVA (Table
3) and pairwise ΦST values (Additional File 9) support geographic structuring,
potentially in association with migratory flyway, within TP, which would support an
east/west divide. It is important to remember, both TQ and TP rely on the same snail
intermediate hosts (Physa spp.), and are consequently united ecologically in time
and space, as such it is at the definitive host level where TQ and TP partition
themselves among different ecological host groups. We report a 63% difference in
the prevalence of TQ and TP across all hosts, and a 64% difference between the
117
single most common host of each species, suggesting a stark different in
transmission success. It can be hypothesized that as both species infect Physa
snails, the habitats that Spatula spp. (TQ) confer a greater probability of transmission
relative to Aythya spp. (TP). While experimental infections are necessary to rule out
inherent differences in host susceptibility, these results may imply that certain
combinations of hosts and ecologies facilitate the maintenance of high parasite
diversity and Ne.
This study sought to characterize microevolutionary patterns among three
congeneric trematode species, to evaluate how hosts with partially overlapping
ecologies might impact parasite population genetic structure and diversity.
Substantial differences in population genetic structure and demographic parameters
among TQ, TP and TA were found, and further patterns of genetic diversity and
ecological specificity varied across Trichobilharzia. In total this data found that
parasite microevolutionary patterns can vary with host-ecology among ecologically
specific and overlapping parasite species, specifically in terms of host habitat
preference and site fidelity. Here we discuss several potential reasons why and
associated consequences.
Infrapopulation diversity of TQ was equal to overall component population
diversity and the number of unique haplotypes within infrapopulations increased
linearly with the number of individuals sampled. This suggests that infrapopulation
recruitment (transmission) is random, likely occurring across time and space and a
well-mixed component population. Notably, the majority of worms sampled were
males due to the difficulty of recovering females from the duck host. The high level of
infrapopulation diversity observed suggests that duck hosts sampled maintain a
genetically diverse pool of male worms.
118
In contrast to TQ, infrapopulations of TP appear to be less divergent, possibly
suggesting that recruitment is biased regionally, perhaps associated with the overall
great population subdivision observed within TP relative to TQ. This result warrants
further investigation as it might indicate the potential for differential intraspecific
transmission dynamics and ultimately local adaption within TP. Infrapopulation
sampling of TA was insufficient to address population recruitment, however notably
multiple infrapopulations were sequenced from American wigeon (Anas americana)
which revealed Trichobilharzia species diversity unequaled by any of the other host
species sampled. Our data suggests that A. americana hosts at least 5
Trichobilharzia species, 4 of which are currently undescribed. Anas americana is
considered to be one of the most generalists species within Anas (Johnson and
Grier, 1988) in terms of its habitat preferences and thus it may be more likely to
encounter a variety of different snail species and their associated parasites. It can be
hypothesized that these generalist behaviors drive the high species diversity we
observe.
The hosts of TQ (Spatula clypeata, S. discors and S. cyanoptera in North
America) have a broad migratory range and are known to be locally opportunistic.
Habitat quality is the primary determinant of site selection for these species;
consequently ducks move locally to find optimal habitat, increasing their probability
of interacting with infected snails among different habitats (Johnson and Grier, 1988;
Osborn et al., 2017). Additionally, these ducks prefer shallow marsh habitat (Osborn
et al., 2017). These behaviors result in Spatula spp. spending a majority of their time
in Physa rich habitat, increasing the likelihood of encountering Trichobilharzia spp. In
contrast, North American Aythya spp. are highly philopatric, and occupy large stable
water bodies, where they are less likely to encounter snails. Habitat quality is not a
119
primary factor in site selection and consequently local movement of Aythya spp. is
limited (Johnson and Grier, 1988; Austin et al., 2016;). These behaviors in
combination decrease the likelihood of Aythya spp. to encounter locally available
Trichobilharzia. It can also be predicted that philopatric host species are more likely
to facilitate population genetic structure among their parasites, specifically in relation
to flyway. Which is a pattern we observed in TP.
Interestingly the American wigeon (A. americana), a dabbling duck, is one the
most generalist species within Anas (Johnson and Grier, 1988; Sibley, 2000). Anas
americana are known to spend the most time on land grazing relative to other Anas
spp., they can also be found in flowing river waters and brackish estuarine habitats.
Seasonally, A. americana individuals may occur in a diversity of habitat types, which
likely increases the probability of encounter to different species of Trichobilharzia
transmitting snails. We hypothesize the habitat generalism described for A.
americana drives the remarkably high Trichobilharzia lineage diversity recovered
within this species.
A secondary goal of this study was to attempt to quantify the nature of duck host
associations within Trichobilharzia (i.e. specialists= one host genera; generalists=
multiple host genera; ecological specialists= multiple host genera within an
ecological group; ecological generalists= multiple host genera that span multiple
ecological groups). The host range of a parasite (i.e. host specificity) is conceptually
determined by two filters the encounter and/or compatibility filter (Combes, 1991).
Prior studies suggest that Trichobilharzia are largely compatible with anatids
(waterfowl) and even some non-anatids. McMullen and Beaver (1945) demonstrated
experimentally TP could develop successfully in non-anatid birds (e.g canaries and
pigeons). Consensus regarding Trichobilharzia definitive host specificity would
120
suggest, under a traditional definition of host specificity, generalism. However, a
more systematic approach to experimentally defining duck host compatibility is
needed. In contrast, extensive field surveys and molecular systematic investigations
(Brant and Loker 2009, Ebbs et al. 2016, this study) have made clear that several
Trichobilharzia species (TQ, TP, TA and T. stagnicolae) are abundant in one or more
core duck species (or ecological group), and remarkably rare in other potential
(satellite) hosts species. Based the published experimental work discussed above it
might be predicted that immunological or evolutionary constrains (compatibility) are
unlikely, which would suggest that the encounter filter is narrowed by features
specific to hosts ecology (i.e ecological partitioning, temporal partitioning). Our data
suggests host specificity of Trichobilharzia spp. with either dabbling or diving ducks
is common. Divers and dabblers represent ecologically distinct groups which may be
co-distributed geographically but partition themselves ecologically by habitat
preferences and feeding behaviors (Baldassarre and Bolen, 1984), making them at
least seasonally allopatric (Austin et al., 2016). Juvenile and adult diving ducks
spend the majority of their time in deep water, however nesting sites occur near
shore putting breeding pairs and chicks in parapatry with dabbling ducks (Austin et
al., 2016).
In sum we investigated population genetic patterns of three congeneric
Trichobilharzia species which have a broad range of potential, sympatric or
parapatric, anatid hosts. We found evidence suggesting ecological specificity among
some Trichobilharzia species, and that specific host ecological traits may act within
conspecifics to reduce gene flow across potential host species resulting in population
genetic structure that co-varies with host traits. Species exhibiting ecological
specificity had on average higher measures of π and Ɵ relative to species infecting a
121
broader ecological range of potential hosts. Our data suggests that host ecological
traits mediate substantial differences in Trichobilharzia population genetic structure
and dynamics and relates to the adaptive potential of individual species.
ACKNOWLEDGMENTS
Thank you to the following for invaluable help collecting birds—Museum of
Southwestern Biology Bird Division, Mr. Andy Johnson, Dr. Ernest Valdez, Dr.
Robert W. Dickerman and Dr. Chris Witt; National Museum Bloemfontein: Mr. Rick
Nuttall and Mr. Dawid H. de Swardt.: Illinois Natural History Survey, Dr. Heath Hagy
and Mr. Josh Osborne; Dr. Vasyl Tkatch, Dr. Stephen Greimen; Dr. Sean Locke.
Individual duck hunters: Mr. Brad Bothner, Mr. Josh Butler, Mr. Tony Gilyard, Dr.
James Mckeehen and Mr. Bob Mckeehen, Mr. Matthew Snyder. Dr. Veronica Flores.
Individuals who assisted with host necropsies: Mr. Keith Keller and Ms. Emily Savris.
We acknowledge technical support from the University of New Mexico Molecular
Biology Facility, which is supported by National Institutes of Health, USA grant
P30GM110907 from the Institute Development Award program of the National
Center for Research Resources, USA. This work was primarily funded through a
National Science Foundation, USA grant to S.V.B. (DEB 1021427) and a National
Institutes of Health grant RO1 AI44913 to E.S.L.
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127
Pacific
Central
Mississippi
Atlantic S. Hemisphere
W137 W756.2 W752
W925 W157 W748
W162 W743 W767.4
W203 W137.2 W190 W183
W154.2 W750
W138.3 W160 W180 W159 SDS1006 W753 W777
W156 W153 W161
W345.2 W746 W756
W135 W142 W155.3 Tshov
W755.2 W754
W148 W767.3 W158
W2135 W664 W698 W166.2
VVT3023 W135.2 W833 W259 W747
W148.2 W154 W778
E45 W774 E64 W191 W152.4
W704 W181 W831 W749
W345.2 W750.2 W703
W345.3 Trichobilharzia sp. C
Trichobilharzia franki W701
W604 W194 W497 W234 W235 W510 W608 W193 W602
W249 W230 W211
W146 W147 W765 W765.5 W196 W765.6 W765.3 W256 W745 SAL92-09 W171 W236 W175 W765.4 W255 W263
Trichobilharzia sp. B W149.2 W511.2 W212
W182 W149
W192 VVT3043
W609 W601 W600 W611 W511 W607
Trichobilharzia regenti 0.02
1
1
1
.99
.99
.98
1
1
1
1
.96
1
.97
.96
1
.99
.99
.95
.99
I
II
128
Figure 1. cox1 phylogeny. BI inference of TQ, TP and TA, only posterior
probabilities ≥.95 are presented. Branches are colored by species; TQ= orange,
TP=green and TA=blue. Individual worms are labeled by the migratory flyway they
were collected from, denoted by a colored box. The two TP clades are denoted at I
and II.
129
Figure 2. Minimum spanning network of cox1
haplotypes. (A) Trichobilharzia querquedulae haplotypes, (B) T. physellae and (C)
Trichobilharzia sp. A. Haplotypes are colored by the migratory flyway the host was
collected from.
A B
C
130
Figure 3. Bayesian skyline plot of cox1 datasets. Estimates of Ne (y-axis) over
time (kya, x-axis). Lines are colored by species; TQ= orange, TP=green and
TA=blue.
0
50000
100000
150000
200000
250000
300000
0 500 1000 1500 2000 2500
Ne
Time (kya)
TQ TP TA
131
Figure 4. Trichobilharzia summary tree. (A) Bayesian phylogeny of cox1 gene
region, posterior probabilities ≥0.95 are denoted with an asterisk (*). DEG=Duck
ecological group, PD =Parasite distribution, SH=Snail host. Under snail host, blocks
with a white background indicate physid transmitted, while blocks with a black
background indicate lymnaeid transmitted. (B) Bar graph of species wide estimates
of nucleotide diversity (π) and (C) species wide estimates of Ɵ.
Trichobilharzia
physellae
W701
Trichobilharzia franki
Trichobilharzia sp. A
Trichobilharzia
querquedulae
Trichobilharzia sp. C
Trichobilharzia regenti
Trichobilharzia mergi
Trichobilharzia szidati
Trichobilharzia anseri
Trichobilharzia stagnicolae
Allobilharzia visceralis
Anserobilharzia brantae
DEG PD
NA
SA
EU
NA
GL
NA
EU
IC
EU
NA
EU
SH
0 0.005 0.01 0.015 0.02
0 0.01 0.02 0.03 0.04
A. B.
C.
**
**
*
*
* **
**
*
*
*
132
Avian Host No. examined
No. infected
% infected
Species of Trichobilharzia Locality Source
Anseriformes: Anatidae
Aix sposa 8 3 0.38 Trichobilharzia sp. C
FL, LA, NC, NM, PA Brant and Loker; This study
Alopochen aegyptiaca 9 0 0 ZA This study
Amazonetta brasiliensis 3 0 0 AR This study
Anas acuta 27 2 0.07 Trichobilharzia sp. E
AK, CA, LA, NV, NM, MB This study
Anas americana 37 10 0.27
Trichobilharzia sp. A,B,D,E & T. querquedulae
AK, CA, NC, NM, WA Brant and Loker; This study
Anas bahamensis 1 0 0 AR This study
Anas crecca (Europe) 15 9 0.6 T. szidati, T. franki FR Jouet et al. 2009
Anas crecca (Iceland) 2 0 0 IC Skirnisson and Kolarova 2008
Anas crecca (North America) 80 6 0.075 Trichobilharzia sp.
AK, CA, LA, MN, NE, NC, NM, MB Brant and Loker 2009; This study
Anas erythrorhyncha 5 2 0.4 Trichobilharzia sp. ZA This study
Anas flavirostris 10 0 0 AR This study Anas fulvigula 5 0 0 LA Brant and Loker 2009
Anas georgica 6 0 0 AR This study
Anas penelope 2 0 0 IC Skirnisson and Kolarova 2008
Anas platyrhynchos (Europe, 196 108 0.55 T. regenti FR Jouet et al 2009
133
nasal)
Anas platyrhynchos (Europe, visceral) 31 26 0.84
T. szidati, T. franki FR Jouet et al 2009
Anas platyrhynchos (Iceland, nasal) 15 11 0.733 T. regenti IC Skirnisson and Kolarova 2008
Anas platyrhynchos (Iceland, visceral) 15 10 0.667
Trichobilharzia sp. IC Skirnisson and Kolarova 2008
Anas platyrhynchos (North America) 24 3 0.125 T. physellae
AK, LA, MI, NM, PA Brant and Loker 2009; This Study
Anas rubripes 4 1 0.25 Trichobilharzia sp.
NC, NM, PA Brant and Loker 2009; This Study
Anas sibilatrix 5 1 0.2 Trichobilharzia sp. AR This study
Anas strepera 36 1 0.028 T. physellae
FL, LA, NC, NM, PA Brant and Loker 2009; This Study
Anas undulata 11 6 0.55 Trichobilharzia sp. ZA This study
Anser anser (nasal) 75 6 0.08 T. regenti FR, IC Jouet et al. 2015
Anser anser(visceral) 133 60 0.45 T. anseri FR, IC Jouet et al 2009; Skirnisson and Kolarova 2008; Jouet et al. 2015
Aythya affinis 72 19 0.26 T. physellae
AK, CA, FL, IL, LA, NC, NM, NY, ON, PA, WA Brant and Loker 2009; This study
Aythya americana 4 0 0 CA, LA, NM Brant and Loker 2009; This study
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Aythya collaris 8 2 0.25 T. physellae
CA, FL, LA, NC, NM Brant and Loker 2009; This study
Aythya ferina 18 2 0.11 T. regenti FR Jouet et al 2008; Jouet et al 2009
Aythya fuligula (nasal) 10 5 0.5 T. regenti Jouet et al 2008; Jouet et al 2009
Aythya fuligula (visceral) 1 1 1 T. franki FR Jouet et al 2009
Aythya malraia 11 0 0
AK, FL, PA, MB, IC
Brant and Loker; Skirnisson and Kolarova 2008; This study
Aythya novaeseelandia 20 18 0.9 Trichobilharzia sp. NZ Davis 2006
Aythya valisineria 5 1 0.2 T. physellae NM, NV Brant and Loker 2009
Branta canadensis 9 0 0
NC, NM, NV, MB Brant and Loker 2009
Branta hutchinsii 2 0 0 NM This study
Bucephala albeola 13 1 0.077 T. physellae
FL, NC, NM, NV, PA Brant and Loker; This study
Bucephala clangula 3 0 0 CA, NM Brant and Loker 2009
Bucephala islandica 1 0 0 IC Skirnisson and Kolarova 2008
Cairina moschata 2 0 0 PE This study
Callonetta leucophrys 4 0 0 AR This study
Chen caerulescens 11 0 0 LA, NM, MB Brant and Loker 2009; This study
Chen rossii 1 0 0 NM This study
Clangula hyemalis 10 1 0.1 T. physellae AK This study
Cygnus columbianus 18 0 0 NC, NV, NM Brant and Loker 2009; This study
Cygnus cygnus 30 0 0 IC Skirnisson and Kolarova 2008 Cygnus olor (visceral) 9 5 0.55 T. franki FR Jouet et al 2009
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Cygnus olor (nasal) 40 13 0.325 T. regenti FR Jouet et al 2009
Dendrocygna viduata 6 0 0 AR, ZA This study
Histrionicus histrionicus 3 0 0 AK Brant and Loker 2009
Lophodytes cucullatus 6 2 0.33 Trichobilharzia sp. C
LA, NC, PA Brant and Loker 2009; This study
Melanitta americana 1 0 0 AK This study
Melanitta fusca 5 0 0 AK ,MB Brant and Loker 2009
Melanitta perspicillata 3 0 0 AK Brant and Loker 2009
Mergus merganser (North America) 10 3 0.3
T. stagnicolae, T. physellae MI, NM Brant and Loker 2009; This Study
Mergus merganser (Europe) 1 1 1 T. regenti FR Jouet et al. 2009
Mergus serrator (Iceland) 20 19 0.95 T. mergi IC Skirnisson and Kolarova 2008
Mergus serrator (North America) 3 0 0 CA, NM This study
Netta erythrophthalmia 3 0 0 ZA This study
Oxyura jamaicensis 9 0 0
CA, FL, NC, NM, NV Brant and Loker 2009; This Study
Plectropterus gambensis 1 0 0 ZA This study
Somateria mollissima 1 0 0 MB This study
Somateria spectabilis 3 0 0 AK This study
Spatula clypeata (Europe, nasal) 5 2 0.4 T. regenti FR Jouet et al. 2009 Spatula clypeata (Europe,
visceral) 4 2 0.5 T. szidati FR Jouet et al. 2009
Spatula clypeata (North America) 53 39 0.74 T. querquedulae
AK, CA, FL, LA, NC, NE, NM, MB Brant and Loker; This study
Spatula cyanoptera 14 11 0.79 T. querquedulae
AR, CA, NM Brant and Loker 2009; This study
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Spatula discors 49 44 0.9 T. querquedulae
CA, FL, LA, MN, NM, PA Brant and Loker 2009; This study
Spatula platelea 1 0 0 AR This study
Spatula smithii 2 2 1 T. querquedulae ZA This study
Spatula versicolor 10 6 0.6 T. querquedulae AR This study
Tadorna cana 1 0 0 ZA This study
Thalassornis leuconotus 1 0 0 ZA This study
Non-Anatids Agelaius phoeniceus
(Passeriformes:Icteridae) 4 0 0 NM This study Alle alle (Charadriiformes:
Alcidae) 1 0 0 DE This study Anhinga anhinga (Suliformes:
Anhingidae) 10 0 0 FL, LA Brant and Loker 2009 Aramides ypecaha (Gruiformes:
Rallidae) 1 0 0 AR This study Aramus guarauna (Gruiformes:
Aramidae) 1 0 0 FL Brant and Loker 2009 Ardea herodias (Pelecaniformes:
Ardeidae) 2 0 0 NM This study Bostrychia hagedash
(Pelecaniformes: Threskiornithidae) 1 0 0 ZA This study Egretta intermedia
(Pelecaniformes: Ardeidae) 1 0 0 ZA Brant and Loker 2009 Egretta thula (Pelecaniformes:
Ardeidae) 1 0 0 LA Brant and Loker 2009 Egretta tricolor (Pelecaniformes:
Ardeidae) 1 0 0 LA Brant and Loker 2009
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Eudocimus albus (Pelecaniformes:
Threskiornithidae) 6 0 0 LA Brant and Loker 2009 Euplectes sp. (Passeriformes:
Ploceidae) 4 0 0 ZA This study Fulica americana (Gruiformes:
Rallidae) 8 0 0 NC, NM This study Gavia immer (Gaviiformes:
Gaviidae) 1 0 0 NM This study Larus californicus
(Charadriiformes: Laridae) 1 0 0 CA Brant and Loker 2009 Larus delawarensis
(Charadriiformes: Laridae) 15 0 0 CA, LA Brant and Loker 2009 Larus fuscus (Charadriiformes:
Laridae) 1 0 0 LA Brant and Loker 2009 Patagioenas leucocephala
(Columbiformes: Columbidae) 2 0 0 FL This study Pelecanus erythrorhynchos
(Pelecaniformes: Pelecanidae) 1 0 0 NM This study Phalacrocorax africanus
(Pelecaniformes: Phalacrocoracidae) 3 0 0 ZA This study
Phalacrocorax auritus (Pelecaniformes:
Phalacrocoracidae) 2 0 0 LA Brant and Loker 2009 Plegadis chihi (Pelecaniformes:
Threskiornithidae) 6 0 0 LA Brant and Loker 2009 Pluvialis dominica
(Charadriiformes: Charadriidae) 1 0 0 LA Brant and Loker 2009 Podiceps auritus
(Podicipediformes: Podicipedidae) 1 0 0 NM This study
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Podiceps nigricollis (Podicipediformes:
Podicipedidae) 1 0 0 NM This study Podilymbus podiceps
(Podicipediformes: Podicipedidae) 2 0 0 NM Brant and Loker 2009
Scopus umbretta (Pelecaniformes: Scopidae) 2 0 0 ZA This study
Sterna maxima (Charadriiformes: Laridae) 1 0 0 LA Brant and Loker 2009
Tachybaptus ruficollis (Podicipediformes:
Podicipedidae) 1 0 0 ZA This study Threskiornis aethiopicus
(Pelecaniformes: Threskiornithidae) 1 0 0 ZA This study
Uria aalge (Charadriiformes: Alcidae) 2 0 0 AK This study
Xanthocephalus xanthocephalus (Passeriformes:Icteridae) 1 0 0 NM This study
Table 1. Summary of Trichobilharzia survey data. Note that the majority of hosts were co-infected with other helminths and/or many with other Schistosomatid spp., these worms were accessioned with the associated Trichobilharzia records. Locality abbreviations are as follows: AK=Alaska, AR=Argentina, CA= California, DE=Delaware, FL= Florida, FR=France, IC=Iceland, LA=Lousiana, MB= Manitoba Canada, MI= Michigan, MN=Minnesota, NC= North Carolina, NE= Nebraska, NM = New Mexico, NV= Nevada, NZ= New Zealand, ON= Ontario Canada, PA=Pennsylvania, PE= Peru, PR= Puerto Rico, WA= Washington, ZA= South Africa.
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Species Host Species Prevalence STD STD* STD-
MOD
Obs. Host Range
Ecological Group % Eco Group
TQ Spatula discors 0.9 dabbling duck
Spatula cyanoptera 0.79 dabbling duck
Spatula clypeata 0.74 dabbling duck
Spatula versicolor 0.6 dabbling duck
Spatula smitthi 1 dabbling duck
Anas americana 0.03
Total 1.05 1.02 1 6 1 (dabbling ducks)
TP Aythya affinis 0.26 diving duck
Aythya collaris 0.25 diving duck
Aythya valisineria 0.2 diving duck
Bucephala albeola 0.077 diving duck
Mergus merganser 0.1 diving duck 0.93 (diving ducks)
Clangula hyemalis 0.1 sea duck
Anas strepera 0.028 dabbling duck
Anas platyrynchos 0.125 dabbling duck 0.07 (other ducks)
Total 1.86 1.82 1.9 8
TA Anas americana 0.27 ─ ─ ─ 1 dabbling duck 1 (dabbling ducks)
T. regenti Spatula clypeata 0.4 dabbling duck
Anas platyrynchos 0.64 dabbling duck 0.82 (dabbling ducks)
Aythya fuligula 0.5 diving duck
Aythya ferina 0.11 diving duck 0.05(diving ducks)
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Anser anser 0.08 swans/geese
Cygnus olor 0.325 swans/geese 0.13 (swans and geese)
Total 1.87 1.59 3 6
T. mergi Mergus serrator 0.95 ─ ─ ─ 1 diving duck 1 (diving ducks)
T. anseri Anser anser 0.45 ─ ─ ─ 1 swans/geese 1 (swans/geese)
T. stagnicolae Mergus merganser 0.2 ─ ─ ─ 1 diving duck 1 (diving ducks)
Table 2. Measures of host specificity.
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Source of Variation d.f. %
Variation Fixation indices
p value
T. querquedulae - Flyway
Among Flyways 4 -4.64 ΦCT= -0.0464 0.4858
Among Populations within Flyways
8 11.04 ΦSC =
0.10547 0.0528*
Within Populations 46 93.6 ΦST= 0.0639 0.0439*
T. querquedulae - Latitude
Among LG 2 4.41 ΦCT= 0.0441 0.00196
Among Populations within LG 10 4.42 ΦSC =
0.04621 0.0508*
Within Populations 46 91.17 ΦST=
0.08827 0.0616
T. querquedulae - Host spp.
Among HS 6 -0.47 ΦCT= -0.00472
0.4594
Among Populations within HS 13 8.34 ΦSC =
0.08303 0.0675
Within Populations 35 92.13 ΦST= 0.0787 0.0577
T. physellae – Flyway
Among Flyways 3 55.59 ΦCT=
0.55592 0.02737
Among Populations within Flyways
2 -42.73 ΦSC =
0.96217 0.8495
Within Populations 20 87.14 ΦST=
0.12864 0.1564
T. physellae - Latitude
Among LG 1 2.71 ΦCT=
0.02714 0.50733
Among Populations within LG 4 7.12 ΦSC =
0.07319 0.52884
Within Populations 20 90.17 ΦST=
0.09835 0.15934
T. physellae - Host spp.
Among HS 4 20.13 ΦCT=
0.20128 0.12219
Among Populations within HS 3 10.46 ΦSC
=0.13098 0.26588
Within Populations 17 69.41 ΦST= 0.3059 0.00293
Table 3. Analyses of molecular variance results. For both TQ and TP population structure was tested by flyway, latitude and host species. Significant p-values are denoted in bold, and (*) indicates values that were within one standard deviation of significance and were considered marginally significant.
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Taxa n Prevalence Pi K Theta Tajima's D
1 Trichobilharzia querquedulae 63 81% (90%) 0.017 11.9 0.04542 -2.4223**
2 Trichobilharzia physellae 27 18% (26%) 0.00696 4.868 0.01387 -2.25017**
3 Trichobilharzia sp. A 15 27% 0.01175 4.348 0.01342 -0.77396
4 Trichobilharzia franki 7 ─ 0.00503 3.524 0.0064 -
5 Trichobilharzia regenti 8 34% (64%) 0.00236 1.75 0.0026 -
6 Trichobilharzia anseri 6 45% 0.00251 1.867 0.00252 -
7 Trichobilharzia stagnicolae 8 20% 0.01326 10.93 0.01825 -
8 Trichobilharzia szidati 8 * 0.00731 5.429 0.00934 -
Table 4: Indices of genetic diversity across Trichobilharzia
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CHAPTER IV
Illuminating Schistosome Diversity: Phylogenomics and diversification of
Schistosomatidae using targeted sequence capture of ultra-conserved elements
AUTHORS: Erika T. Gendron (Ebbs), Lijing Bu, Eric S. Loker, Vasyl Tkach, Veronica Flores and Sara V. Brant
ABSTRACT
Schistosomatidae Stiles and Hassall 1898 is a medically significant family of
helminth parasites (Digenea: Trematoda), which infects mammals, birds (definitive
hosts) and aquatic snails (intermediate hosts). Currently there are 14 named genera,
of which interrelationships are largely unresolved. The lack of a resolved phylogeny
has encumbered our understanding of the schistosome evolution, specifically in
regard to patterns of host-use and the role of host-switching in diversification. This
research used targeted sequence capture of ultra-conserved elements (UCE) to
generate a phylogenomic dataset for estimation of schistosome interrelationships.
This dataset represents the largest phylogenetic effort within Schistosomatidae in
terms of genetic loci; we recovered an average of 500 and 1,500 UCE loci (of ~4,000
targeted) between two sequencing runs. Relative to published UCE datasets our
capture success was low, reasons for which are discussed. As such, UCE
alignments contained a substantial amount of missing data, which limited
downstream analyses. In light of this, we report increased resolution of two pivotal
genera Heterobilharzia and Schistosomatium, which supports a secondary host-
capture of mammals in North America. Support of other pivotal genera,
Macrobilharzia and Ornithobilharzia, varied among phylogenetic analyses. In total
application of sequence capture technology within Schistosomatidae increased our
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understanding of schistosome relationships. The implications of these results in
regard to host-use and schistosome diversification are discussed.
INTRODUCTION
Schistosomatidae Stiles and Hassall 1898 represent a biologically diverse
family of trematodes infecting birds or, mammals (definitive hosts) and aquatic snails
(intermediate hosts). Schistosomes are of great significance to human health as
three species within the genus Schistosoma Weinland, 1858 are the major etiological
agents of human schistosomiasis, one of the world’s most recalcitrant Neglected
Tropical Diseases still infecting over 250 million people globally (Tchuem Tchuenté
et al., 2017). Non-human schistosomes, particularly those associated with avian
definitive hosts, also cause Human Cercarial Dermatitis (swimmer’s itch) now
considered a re-remerging zoonotic pathogen (Horák et al., 2015). From the
perspective of parasite biology, schistosomes are remarkable in that they are the
only exclusively dioecious (Platt, 1997) family of digenetic trematodes. This derived
state has resulted in interesting patterns of dimorphic adult traits and reproductive
strategies across the family, including substantial variation in adult body size and
morphology (Morand and Müller-Graf, 2000). There is a remarkably broad range of
intermediate hosts employed by schistosomes, 15 families including members of
Caenogastropoda and Heterobranchia, with evidence for numerous intermediate
host-switching events (Brant and Loker, 2013).
Because of the medical importance, there has long been substantial interest
in understanding interrelationships, patterns of host-use and character evolution
within the Schistosomatidae (Weinland, 1858; Stiles and Hassall, 1898; Farley 1971;
Khali, 2002). However efforts were constrained by a lack of informative
145
morphological characters and adequate adult parasite material for description,
particularly among avian infecting lineages. The advent of molecular makers (Snyder
and Loker, 2000; Lockeyer et al., 2003; Brant et al., 2006) greatly aided efforts to
elucidate schistosome diversity, particularly within avian infecting genera which
proved to harbor cryptic diversity (i.e. Trichobilharzia, (Brant and Loker, 2009; Ebbs
et al., 2016), and supported the existence of 14 named genera and just over 100
named species (Brant and Loker, 2013). This diversity count is likely an
underestimate based on more recent taxon sampling of larval schistosomes (Jouet
et al., 2010; Flores et al., 2015; Brant et al., 2017; Pinto et al., 2017, this study).
Historically Schistosomatidae was divided into three subfamilies (McMullen and
Beaver 1945, Lu and Bai 1976, Khali 2002). However, molecular phylogenetic
methods combined with increased taxon sampling (Lockyer et al., 2003; Brant et al.,
2006) have not provided any support for the previous subfamily designations nor
reflected schistosome interrelationships. In fact schistosome interrelationships have
remained relatively unresolved, specifically at the deeper nodes (Snyder and Loker,
2000; Lockyer et al., 2003; Brant et al., 2006; Brant and Loker, 2013; Pinto et al.,
2017).
Genetic relationships within Schistosomatidae have thus far been based on
few loci, primarily markers within the ribosomal RNA complex (28S, 18S, ITS1 & 2)
and single mitochondrial gene, cox1. While these studies have made great strides in
advancing our knowledge of species diversity, they have failed to resolve deeper
nodes within the family (Snyder and Loker, 2000; Lockyer et al., 2003; Brant et al.,
2006; Brant and Loker, 2013; Pinto et al., 2017). Specifically, there is no resolution
regarding the phylogenetic placement of pivotal genera: Heterobilharzia,
Schistosomatium and Macrobilharzia. Both Heterobilharzia and Schistosomatium
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(herein referred to as HS clade) are monotypic mammalian infecting genera and
represent the only mammalian lineage found in North America. The major
mammalian clade (Schistosoma, Bivitellobilharzia, or the SB clade), which includes
those species that infect humans, is found in tropical and sub-tropical latitudes. Most
genetic studies have demonstrated an affinity between the HS clade with the derived
avian clade (herein referred to as the DAS clade; Trichobilharzia, Allobilharzia,
Anserobilharzia, Dendritobilharzia, Gigantobilharzia and Bilharziella) rather than the
SB mammalian clade. Such a relationship may not be surprising given that cercaria
within the DAS and HS have eyespots, whereas Schistosoma cercariae lack
eyespots (Khali, 2002). Only a single species of Macrobilharzia, M. macrobilharzia
infecting Anhinga anhinga in North America, is known from genetic data.
Macrobilharzia baeri (Fain 1955) has been reported from cormorants in Rwanda,
however no genetic data currently exists. Macrobilharzia has consistently failed to
group with any support with other schistosome lineages. Since schistosomes are
found in both mammalian and avian hosts, resolution of the phylogenetic position of
Macrobilharzia and the HS clade is necessary to know how many times
schistosomes have undergone definitive host-switches. Currently, it is hypothesized
that the HS clade is the result of a secondary host-capture within North America
(Snyder and Loker, 2000). Most current molecular phylogenies place the AO clade
as basal (though weakly supported) within Schistosomatidae (Brant et al., 2006;
Brant and Loker, 2013). Definitive resolution of basal genera is necessary to
understand ancestral definitive and intermediate host relationships among
schistosomes, and to develop hypotheses regarding diversification time.
This study sought to resolve phylogenetic relationships within
Schistosomatidae in an effort to understand patterns of host-use and host-switching,
147
in addition to character evolution. A resolved phylogeny is required to enable testing
of phylogenetic hypotheses regarding schistosome diversification, and can be
applied to address questions such as: have specific traits aided schistosome
diversification? How common is intermediate and definitive host-switching within
Schistosomatidae? Did the major schistosome clades radiate simultaneously?
Additionally, we expect a resolved phylogeny to provide direction for future collection
efforts of taxa that are under-sampled. For example, the intermediate host of
Bivitellobilharzia has remained a mystery despite extensive sampling (Devkota et al.,
2014a); definitive placement of the genus might suggest potential intermediate host
families for more targeted search efforts. Why some interrelationships have proved
particularly difficult to resolve despite increased taxon sampling is unclear. Brant et
al. (2006) suggests that early schistosomes underwent a rapid radiation from a
marine environment (AO clade) into freshwater snails and definitive hosts,
suggesting the main mammalian (SB clade) and avian (DAS clade) clades diversified
around the same time. Under this scenario substantial incomplete lineage sorting
(ILS) might be expected (Maddison and Knowles, 2006).
Recent developments in phylogenomic methods have proven effective in
resolving difficult groups (McCormack et al., 2013; Grégoir et al., 2015), particularly
where ILS was expected. One such method uses targeted sequence capture of ultra
conserved elements (UCEs) (Faircloth et al., 2012) to generate thousands of nuclear
loci for phylogenomic analysis. The use of UCEs as phylogenomic markers has been
successful at resolving historically problematic vertebrate groups (Esselstyn et al.,
2017; Faircloth et al., 2013; Prum et al., 2015) as well as an increasing number of
invertebrate groups (Blaimer et al., 2015; 2016a; Branstetter et al., 2016; Faircloth et
al., 2015). We applied similar methods within Schistosomatidae, representing the
148
first application of this technology within a parasitic helminth system, to resolve
schistosome interrelationships and address long standing questions of schistosome
evolution.
MATERIALS AND METHODS
Taxon sampling
Specimens used for this study are vouchered at the Museum of Southwestern
Biology, Parasite Division in Albuquerque New Mexico USA. Locality information,
host data and museum accession numbers are summarized in Additional File 1.
Schistosome samples were collected using standard parasitological procedures as
described in Brant and Loker (2009) and Ebbs et al. (2016). Specimens used for this
study were collected between 1999- 2017, and primarily preserved in 95% ethanol,
but some were also saved in RNAlater. For the sequence capture experiments, 13 of
the 14 named genera (and 3 as yet unnamed) were selected.
Sequence capture of ultra-conserved elements
19 samples were selected for targeted sequence capture of UCEs, specifically to
address relationships across Schistosomatidae and resolve deeper nodes that have
evaded phylogenetic placement in previous studies (i.e. Macrobilharzia, HS clade).
A custom bait set (18,550 baits, 120 nucleotides in length, 2X tiling density) was
designed using Schistosoma mansoni as a reference genome. Approximately 4,000
UCE loci were targeted. The bait set was manufactured by Arbor Biosciences
(www.arborbiosci.com).
Obtaining template DNA of sufficient quantity and quality proved to be a
significant challenge and limiting factor. Thus, several different extraction protocols
and modifications to sample preparation were used. Most samples were extracted
149
using silica-based columns from either the QIAamp DNA Micro Kit (Qiagen), which is
optimized for small amounts of tissue, or the DNeasy Blood and Tissue kit (Qiagen).
Additionally, organic extraction (E.Z.N.A. Mollusc DNA kit, Omega) and Hot Shot
Lysis (Truett et al., 2000) were also attempted. Different amounts of tissues and
worm body regions were also extracted to optimize extraction efficacy and reduce
host contamination. Samples were quantified using Qubit Fluorometric quantification
(Thermo Fisher) using the manufacturers buffers and following recommended
protocol. Samples selected for sequence capture ranged from 0.31- 2.36 ng of DNA,
generally lower then the 1 ng recommendation from MyBaits (MYbaits® Manual v.
3.02). Sample quality was assessed using a Bioanalyzer, however DNA quantity
was generally too low to accurately gauge fragment size. Across samples, fragment
size was not equal, with some samples showing significant amounts of degradation
and others relatively little. Measures were taken to avoid further degradation (i.e.
excluding vortexing, minimizing thawing/re-freezing of samples).
Library enrichment procedures for the MYcroarray MYBaits kit (MYbaits® Manual
v. 3.02) were followed, but with several modifications to the standard sequence
capture and library preparation protocols to accommodate low amounts of DNA and
variable fragment size. Our samples were divided into two distinct sequence capture
experiments and sequencing runs (R1 and R2), methods varied across R1 and R2
allowing for the comparison (Table 1). R1 used a standard sticky-end library
preparation coupled with standard amplification polymerase. R2 employed blunt end
library preparation chemistry and a uracil non-stalling amplification polymerase. This
step was done to reduce adapter dimers, which were abundant in R1 samples. For
all runs a size-selection step following library preparation was not preformed due to
such low DNA quantity. Between R1 and R2, hybridization temperatures were
150
modified (62C or 65C, respectively). Post-capture libraries were amplified for 12
cycles. Sequencing of paired-end, 100 bp read was conducted on a HiSeq 2000.
Processing and alignment of UCE data
Quality control of the raw reads included trimming adapter contamination and low
quality bases from reads, using the program Trimmomatic (Bolger et al., 2014) with
slide window size 4 bp and quality score 20, minimum read length 36 bp. Clean
reads were processed following the PHYLUCE software package (Faircloth, 2016).
The PHYLUCE workflow includes contig assembly using Trinity (Grabherr et al.,
2011), assembled contigs were then matched to the probe set using lastz (Harris,
2007). For species that have genome sequences, their genome sequences were
downloaded from NCBI ftp site. Where species had no draft genome assembled, but
SRA data available, we downloaded the Sequence Read Archive (SRA)
(https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3013647/) using the NCBI SRA-
toolkit (https://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?view=toolkit_doc). The SRA
data were converted to fastq format (fastq-dump in SRA toolkit) and trimmed
following same steps described above. Draft genome sequences were assembled
using MEGAHIT program (Li et al., 2015). UCE loci were mined from genome
sequences following the PHYLUCE protocol. An incomplete data matrix was
generated including mined UCE loci and those generated by this study. Samples
with low capture success, where fewer than 100 UCE loci were recovered
(n=4) were excluded from subsequent analyses. 13 alignments of variable matrix
completeness (~82%-36%) were created using MAFFT v. 2.2.7 (Liu et al. 2011) for
28 schistosome species and 5 out-group taxa. Only a single UCE locus was shared
among all samples, as such the final trimmed and aligned datasets all incorporated
various amounts of missing data. Missing data refers to the proportion of taxa not
151
represented for a given loci. For example, the “~27% missing” dataset allows
inclusion of loci where only 24 of the 33 taxa are represented.
Phylogenetic analyses
Shared UCE loci were mined from published genomes of 5 out-group taxa
within Digenea; Echinostoma caproni, Fascioloides gigantica, Opisthorchis viverrini,
Paragonimus westermani and Dicrocoelium dendriticum (Additional File 2). Gene
trees of the 113 best-represented UCE loci were analyzed in RaxML (Stamatakis,
2008) using ML and 500 thorough bootstrap replicates. Individual gene trees were
almost entirely unresolved at deeper nodes, and individual loci contained minimal
pairwise divergence. This, combined with the substantial amount of missing data
within our dataset, did not make our dataset ideal for robust species tree estimation
(Bayzid and Warnow, 2012; Hosner et al., 2016). We instead took a super matrix
approach and analyzed concatenated UCE loci (Faircloth, 2012; Faircloth et al.
2015, Warnow, 2015, Meiklejohn et al., 2016). ML analyses were conducted in
RaxML, using the GTRGAMMA substitution model (Faircloth, 2012; Faircloth et al.
2015). Nodal support was evaluated using thorough bootstrapping (500 replicates).
Supermatrices varied in data completeness from 82%-36% complete.
RESULTS
UCE enrichment and sequencing
Generally, we did not see a direct positive linear relationship between starting
tissue amount and recovered quantities of DNA. Multiplexed sequencing of enriched
libraries resulted in an average 6,939,218 (23,988–24,435,956) reads per sample,
with an average sequencing depth of 350X (58-1001X). An average of 44,060 (12-
442,043) contigs with a mean length of 325 bp were assembled. On average, we
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recovered ~500 and ~1,500 UCE loci in R1 and R2 respectively. Only a single locus
was shared among all taxa. This is a much smaller recovery rate relative to
published sequence capture datasets of vertebrates (Faircloth et al., 2013; 2015;
Esselstyn et al., 2017) and the few existing studies on invertebrates (Blaimer et al.,
2016a). The unevenness of capture success among samples is likely related to the
poor starting DNA quantity and quality (Blaimer et al., 2016b).
Phylogenetic analysis
Individual alignments (n=13) of concatenated UCE loci across a spectrum of
data completeness (~82% - 36.4%) were analyzed and their likelihood scores
calculated in RaxML. Log likelihood scores decreased linearly with the amount of
missing data within our alignments (Table 2). The best score was recovered from the
82% UCE alignment (-249,913.44), based on 113 UCE loci (41,615 bp). ML
estimation of this dataset showed the following results (Figure 1A): a) statistical
support for the main Schistosoma clades (Barker and Blair, 1996; Lawton et al.,
2011; Devkota et al., 2016); b) lack of statistical support for the position of
Macrobilharzia other than that it is a sister genus to Schistosomatid W688; c) lack of
support for Ornithobilharzia (AO clade) as basal; and d) statistical support for the HS
clade basal to the DAS clade and Bilharziella basal within the DAS clade. ~79% (258
UCE loci) and ~76% (470 UCE loci) complete alignments were congruent with the
82% alignment. The resultant phylogeny from the 73% UCE alignment (Figure 1B),
based on 756 UCE loci (265,282 bp) had a much lower likelihood score relative to
the 82% alignment (-1,487,900.85, Δln = 1,237,987.41) but did resolve those
relationships not supported in the above analyses. The 73% UCE alignment
recovered the following relationships: a) Schistosoma was supported as sister to the
DAS+HS+ Macrobilharzia clade; b) Macrobilharzia and Schistosomatid W688 were
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basal to a HS+DAS clade providing evidence for non- monophyly avian and
mammalian clades. In other words, the switch into mammals happened at least
twice; c) Bilhaziella polonica was basal to the DAS clade; d) Ornithobilharzia was
supported (AO clade, 100 bootstrap support) as a basal genus to remaining
Schistosomatidae. Alignments with ~70%-36% missing data were concordant with
the initial 82% trees in failing to resolve the placement of the AO clade and
Macrobilharzia, though the topologies were not all identical, however were not used
in subsequent treatments of the data increasing missing data may confound
phylogenetic accuracy (Wiens, 2006).
DISCUSSION
This is the first study to use UCE data and phylogenomic methods within
Schistosomatidae to address long-standing questions regarding the evolution and
interrelationships of any helminth group. This study reconstructed phylogenies
ranging from 113-2,179 UCE loci whereas prior phylogenetic investigations were
based on a maximum of four loci (Snyder and Loker, 2000; Lockyer et al., 2003;
Brant et al., 2006; Brant and Loker, 2013). UCE loci recovery and data
completeness was much reduced relative to vertebrate systems (McCormack et al.,
2013; Longo et al., 2017) and the few published invertebrate studies (Blaimer et al.,
2016a; Branstetter et al., 2016). Application of sequence capture methods (Faircloth
et al., 2012) proved challenging in a system limited because of diminutive size of the
organisms, availability of fresh material and few reference genomes. As such, the
resulting datasets contained extensive missing data, which limited downstream
phylogenetic analyses. However, despite these challenges the recovered UCE loci
were able to resolve some of the pivotal phylogenetic relationships within
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Schistosomatidae, and shed light on long standing questions regarding schistosome
evolution. All alignments (82%-36% complete) support the HS clade as sister to the
DAS clade, supporting the probability of a secondary host capture of mammals
(Snyder and Loker, 2000). Bilharziella was consistently supported as basal within the
DAS clade which included Dendritobilharzia, Gigantobilharzia, Avian Schistosome C
& D, Allobilharzia and Trichobilharzia. The Schistosoma clade was consistently
supported including three the main clades; S. mansoni, S. hematobium and S.
japonicum. The other Asian Schistosoma were not included within this study.
Between UCE datasets there was inconsistency in regards to the placement of
Ornithobilharzia (AO clade) and the Macrobilharzia clade. The 73% UCE alignment
alone supported the AO clade as basal within Schistosomatidae, a result that has
been commonly recovered using 28S and 18S+28S+cox1 data (Snyder, 2004; Brant
et al., 2006). Secondly, only the 73% UCE alignment supported the Macrobilharzia
clade (Macrobilharzia macrobilharzia + Schistosomatid W688).
Patterns of host-use
Marine spirochiids (Spirochiidae) are the sister group to Schistosomatidae
based on traditional genetic markers (Snyder, 2004; Brant et al., 2006), suggesting
ancestral proto-schistosomes evolved from lineages infecting ectothermic marine
turtles. Patterns of host-use are discussed relative to the 73%-UCE topology, as it
was the most resolved and has general support from previously published
phylogenies (Brant and Loker, 2013). The 73%-UCE ML analysis supports
Ornithobilharzia (AO clade) as a basal, suggesting that proto-schistosomes switched
into marine birds from marine turtles, likely diversifying within Chardiiformes (Figure
2), from which they are most commonly reported (Brant and Loker, 2013). However,
members of the AO clade can also be found in species of marine Anseriformes
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(Brant and Ebbs, unpublished data). It is likely that basal schistosome lifecycles
occurred entirely within marine environments, specifically within marine
caenogastropods. Including intermediate hosts within the families: Potamididae,
Batillariidae, Nassariidae, and Littorinidae. Brant and Loker (2013) point out that
marine habitats have been under sampled historically and may continue to be a
source of novel schistosome lineages. Our data would suggest that schistosomes
then colonized mammals diversifying into the SB clade (Figure 2). Contemporary
members of the SB clade are known to infect a range of mammalian groups:
Elephantidae, Rhinocerotidae, Bovidae, Rodentia, Hippopotamidae and Hominidae.
Intermediate host use of Bivitellobilharzia remains unknown, however proto-
Schistosoma is considered to have co-diversified with the amphibious
caenogastropod pomatiopsid snails (Davis, 1993; Snyder and Loker, 2000). Because
there are three species of Schistosoma that infect humans, the SB clade has been
well sampled relative to other schistosome clades. The SB clade represents a
substantial radiation within mammals (26 species) and relationships have been
largely resolved (Devkota et al., 2014a;2016; Lawton et al., 2011).
The 73%- UCE dataset places Macrobilharzia at the base of the DAS + HS
clade (Figure 1B). Suggesting avian host use is ancestral to the DAS+HS clade,
however it should be noted that the definitive host associations of Schistosomatid
W688 (Devkota et al., 2014b) have not been uncovered. This lineage is likely a sister
genus to Macrobilharzia, and cercaria have distinctive eyespots which is largely an
avian schistosome trait (except for the HS clade). Macrobilharzia baeri has been
described from the old world cormorants, however currently no genetic data exists.
The HS clade was supported as a basal clade to the DAS clade. Resolution of the
phylogenetic placement of Macrobilharzia and the North American mammalian
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schistosomes (HS clade) provides evidence that a secondary host-capture of
mammals occurred among more derived genera, as first postulated by Snyder and
Loker (2000). Interestingly Heterobilharzia americana Price 1929 is known to have a
very broad definitive host range as it has been recovered from dogs, horses,
raccoons, nutria and rabbits (Lee, 1962a; 1962b). Further, species level
investigations are required to verify if H. americana is a single species and not a
complex of cryptic species.
Bilharziella polonica, which broadly infects water birds (Anseriformes,
Gruiformes, Ciconiformes, Podicipeformes) was supported as the basal genus of the
DAS clade. Bilharziella utilizes planorbid snails suggesting the ancestral host of the
DAS clade. Planorbids are important hosts within the DAS clade as well utilized by:
Dendritobilharzia, Avian Schistosome C (Pinto et al., 2017), Avian Schistosome D
(this study) and Anserobilharzia. There have been at least 8 intermediate host-
switching events (Brant and Loker, 2013) within the DAS clade (discussed in the
following section), and a stronger association with Anseriformes (Dendritobilharzia,
Avian Schistosome C, Avian Schistosome D, Anserobilharzia, Allobilharzia and
Trichobilharzia). Species diversity within the DAS clade, particularly Trichobilharzia
(~35 species, Brant and Loker, 2013), is likely in large part due to colonization of
Anseriformes. Paradoxically, Anserobilharzia and Dendritobilharzia also utilize anatid
hosts, in addition to common intermediate hosts (Gyraulus spp. and related snails),
yet genera are species poor (based on genetic data), relative to Trichobilharzia.
Intermediate host switching
The breadth of taxonomic diversity of intermediate host use within
Schistosomatidae, particularly among members of the DAS clade, is remarkable and
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has been discussed as integral in schistosome diversification (Brant and Loker,
2013). Avian schistosomes alone are known to infect 15 families of snails (4 from
mammalian lineages), including representatives from Caenogasotropoda and
Heterobranchia. Intermediate host switching events are known to (potentially) result
in vicariance sufficient for speciation (Huyse et al., 2003; 2005). Interestingly among
schistosomes, most obviously within the DAS clade, intermediate host switching
events are marked by short inter-nodes and do not appear to be associated with
deep divergence events. This could indicate that 1) the breadth of intraspecific snail
use is not well understood, particularly within the DAS clade, 2) that schistosomes
diversified very recently and genetic differences among lineages (especially for
nuclear loci) have not had time to accumulate or 3) that schistosome lineages
maintain a certain flexibility in regards to intermediate host use that facilitates
intermediate host switching. Population level studies of schistosomes’ species
(Schistosoma japonicum; (Rudge et al., 2009; Da-Bing Lu et al., 2011),
Trichobilharzia spp. (Ebbs et al. 2016), and to a lesser extent Schistosoma mansoni
& S. hematobium (Sire et al., 1999; Theron et al., 2004; Crellen et al., 2016; Červená
et al., 2016) suggest that species maintain high genetic diversity and large effective
population sizes. For avian schistosomes this is likely the result of infecting highly
vagile hosts, which are likely to shape the demographics of individual schistosome
lineages. As a group, schistosomes were likely ancestrally parasites of migratory
marine birds (Charadriiformes: Laridae), as such they likely have a history of large
effective sizes and high genetic diversity (and thereby adaptive potential) (Blouin et
al., 1995; Blasco-Costa, 2012), which may have facilitated long-distances host-
switching (Huyse et al., 2005).
Schistosome Diversification
158
Recent efforts to describe schistosome biodiversity have demonstrated that
the DAS clade contains several unnamed genera (Brant and Loker, 2013, Brant et
al., 2017; Pinto et al., 2017). Notably many of the putative genera have been found
to have a broad geographic range and infect Anseriformes. For example, a study of
the putative new genus ‘Avian Schistosome C’ (Figure 1-3) was published (Pinto et
al., 2017) connecting populations collected from North America, South America and
Europe, in association with ducks and planorbid snails. Studies like these
demonstrate that there is still much to learn in terms of schistosome diversity.
However, as the majority of new lineages fall within the DAS clade it is unlikely that
increased taxon sampling will greatly alter phylogenetic inferences of Macrobilharzia
and the HS clade.
Studies describing novel schistosome lineages (Flores et al., 2017) also
demonstrate that intermediate host switching is ubiquitous within schistosomes,
specifically within the DAS clade, and has been an important factor in schistosome
diversification. Why some schistosome genera are species rich while others are
monotypic or species poor is an interesting question that remains unanswered. For
example, why has the HS clade failed to diversify in a similar manner, as
Schistosoma in the old world? Why did it not colonize humans? Heterobilharzia and
Schistosomatium, both monotypic genera, occur in common snail hosts
(Lymnaeidae) and broadly infect mammalian hosts. One potential explanation could
be that hominids and early members of the HS clade do not interact sufficiently in
time and/or space. Alternatively contemporary species diversity might be a poor
reflection of historical diversity, meaning that members of the HS clade might have
been reached maximum lineage diversity some time in the past. With these
considerations in mind, increased sampling at the population level of these genera
159
could inform on historical demographic patterns, species diversity and even
hybridization (Crellen et al., 2016). UCE loci have been shown to be effective at
resolving shallow time relationships, even when compared to ddRAD sequencing
methods (Leaché et al., 2015).
Without the availability of fossil data, for schistosomes and other helminth
parasites, no primary calibration points exist to estimate divergence times. As such,
Parasitologists often turn to host divergence data to derive secondary calibration
points (Jorge et al., 2018), under the assumption that parasites must have evolved
within the bounds of their hosts. This assumption might be sound where parasites
and hosts show evidence of co-phylogeny as the result of a long co-evolutionary
history, for example, among directly transmitted eco-parasites (Huyse and Volckaert,
2005; Whiteman et al., 2007; Hahn et al., 2015). However, as evident within
Schistosomatidae (Brant and Loker, 2013, this study) long distance host-switching at
the definitive and intermediate host level is common, eroding co-evolutionary
signature at the deeper nodes (Ellis et al., 2015). As such, the use of host-
divergence data should be used with great caution when applied to
Schistosomatidae. With this in mind it might be possible to provide some hard
bounds on particular nodes.
Marine spirorchiids (Spirochiidae) are the well supported sister clade to
Schistosomatidae (Snyder, 2004; Brant et al., 2006), and are known to infect marine
turtles. Based on 539 UCE loci it was estimated that living turtles (Shaffer et al.,
2017) originated during the mid-late Triassic. The snail family Nassariidae, one of the
common intermediate host families to Austrobilharzia (AO clade) is estimated to
have diverged roughly 120 mya (Galindo et al., 2016). Extant members which host
contemporary Austrobilharzia are thought to have diverged 30-40 mya.
160
Charadiiformes a primary definitive host group of the AO clade is thought to have
diverged between 23-16 mya (Baker et al., 2007). Correa et al. (2010) reconstructed
phylogenetic relationships among Lymnaeidae, and demonstrated a distinct split
among North American and Old World lymnaeids, and an accurate date for this split
could provide a hard bound on when the HS clade evolved in North America. This
does not assume that the HS clade co-diverged with North American lymnaeids, but
rather that they were unlikely to predate their hosts. Similarly the diversification of
modern ducks has been estimated to have occurred between (25-5 mya) (Sun et al.,
2017), which might be used as a hard bound on the divergence of Trichobilharzia.
Quite a bit is known, regarding the timing and origins of Schistosoma. Snyder
and Loker (2000), hypothesize Schistosoma (including S. japonicum) likely
originated in the mid-Miocene (16-11.6 mya), based largely on estimates by Davis
(1993) using fossil data to estimate the divergence of Pomatiopsidae snails, hosts to
the extant S. japonicum clade. The timing of Pomatiopsidae divergence was later
supported with genetic data which estimated divergence between 12-5 mya (Liu et
al., 2014). Using whole genome sequencing of several S. mansoni and S. rodhaini
individuals Crellen et al. (2016) estimated that these two sister species diverged
147.6-107.5 kya.
Phylogenomic considerations and future directions
Schistosomes posed several unique challenges to our phylogenomic efforts.
Firstly, across all genera, acquisition of adequate material presents inherent
difficulties. Adult worms are diminutive in size, reside in the hosts venous system
and are very fragile, often recovered as small fragments. Some species proved more
difficult than others in terms of acquiring an adequate sample of DNA and
subsequent capture success. For example Austrobilharzia and Anserobilharzia
161
yielded consistently poor DNA and had the lowest UCE capture rates, despite having
access to recently preserved tissue. In general adult worms performed the best for
recovered DNA, average number of reads, and UCE capture success. However,
among cercaria samples none of these measures improved on average by
increasing the number of individual cercaria extracted, suggesting this pattern is not
solely related to amount of starting tissue. Notably, whole adult worms often yielded
lower quantities of DNA relative to fragments of worms (Table 1).
Modifications of our sequence capture and library preparation protocols
greatly increased the recovery rate of UCE loci (a 3-fold increase). Future studies
preforming sequence capture on organisms with low quality or poor quality starting
tissues should incorporate these modifications. Additionally moving forward it would
be advisable to design a new bait kit to specifically target the most common 2,000
UCE loci recovered and increase bait tilling density to improve recovery of UCE loci.
While we were able to resolve some pivotal interrelationships within
Schistosomatidae, UCE loci proved less effective at resolving relationships than
expected based published phylogenies (Faircloth et al., 2015; Blaimer et al., 2016a).
There is no doubt that starting with DNA of low quantity and quality contributed to our
relatively poor sequence capture results, resulting in alignments that relied heavily
on missing data. However, this likely does not entirely explain the difficulties in
resolving schistosome relationships, as published studies with similarly low and
uneven recovery of UCE loci have resulted in well-resolved phylogenies (Blaimer,
2016b). Large amounts of sequence data are fairly robust to missing data
(Maddison, 1993; Wiens, 2006), and it has been demonstrated that incomplete taxon
sampling is more unfavorable to phylogenetic inference than missing data (Streicher
et al., 2016). As such, several known but unnamed genera were not included in this
162
dataset; their future inclusion may prove to increase resolution. As suggested
previously, the radiation resulting in contemporary schistosome diversity may be
particularity refractory to resolution for two possible reasons. Firstly, contemporary
genera might have radiated simultaneously and rapidly, resulting in high amounts of
incomplete lineage sorting (Maddison and Knowles, 2006), and among some genera
a hard polytomy (Maddison, 1989; Whitfield and Lockhart, 2007). Secondly,
colonization-extinction dynamics are common within parasites (Hoberg and Brooks,
2008), as such contemporary lineages might be more closely related to one or many
extinct lineages than to their most closely related extant taxa. A combination of these
factors might work in concert to make the resolution of Schistosomatidae, and
potentially other helminth taxa difficult, if not impossible to resolve.
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Figure 1: ML analyses of concatenated UCE loci. Phylogenies were estimated in
RaxML, GTRGAMMA+I. Black squares indicate a bootstrap support value of 100%.
(A) 82% complete (18% missing data) UCE dataset based on 110 loci (41,615 bp).
(B) 73% complete (27% missing data) UCE based on 756 loci (265,282 bp).
Dicrocoelium dendriticum
Opisthorchis viverrini
Echinostoma caproni
Fascioloides gigantica
Paragonimus westermani
Schistosoma rhodani
Schistosoma mansoni
Schistosoma mansoni
Schistosoma margebowi
Schistosoma mathetti
Schistosoma hematobium
Schistosoma curssoni
Schistosoma japonicum
Schistosoma japonicum
Schistosomatid W688
Macrobilharzia macrobilharzia
Ornithobilharzia canaliculata
Bilharziella polonica
Avian Schistosome D Dendritobilharzia pulverulenta Dendritobilharzia pulverulenta
Gigantobilharzia huronensis
Gigantobilharzia huronensis
Allobilharzia visceralis
Trichobilharzia regenti
Trichobilharzia querquedulae
Trichobilharzia physellae
Trichobilharzia sp. A
Trichobilharzia sp E.
Trichobilharzia stagnicolae
Trichobilharzia szidati
Schistosomatium douthitti
Heterobilharzia americana
98
98
0.05
Dicrocoelium dendriticum
Paragonimus westermani
Opisthorchis viverrini
Fascioloides gigantica
Echinostoma caproni
Ornithobilharzia canaliculata
Schistosoma mansoni
Schistosoma mansoni
Schistosoma rhodani
Schistosoma margebowi
Schistosoma mathetti
Schistosoma hematobium
Schistosoma curssoni
Schistosoma japonicum
Schistosoma japonicum
Schistosomatid W688
Macrobilharzia macrobilharzia
Bilharziella polonica
Avian Schistosome D Dendritobilharzia pulverulenta Dendritobilharzia pulverulenta
Gigantobilharzia huronensis
Gigantobilharzia huronensis
Allobilharzia visceralis
Trichobilharzia szidati
Trichobilharzia sp E.
Trichobilharzia stagnicolae
Trichobilharzia regenti
Trichobilharzia sp. A
Trichobilharzia querquedulae
Trichobilharzia physellae
Heterobilharzia americana
Schistosomatium douthitti
95
98
A. B.
Avian Schistosome C Avian Schistosome C
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Figure 2: Host-switching within Schistosomatidae. Important traits mapped onto
the the resolved 73% UCE phylogeny. Nodes are numbered as follows: 1= Switch
into avian hosts from an ectothermic reptilian ancestral host, 2= Switch from
Caenogastropoda into Heterobranchia, 3= Secondary host-capture of mammalian
hosts within North America, 4= Diversification within Anseriformes. Red triangles
indicate intra-host habitat shifts. Green triangles indicate switches between
Caenogastropoda into Heterobranchia. All branches have 100% bootstrap support
unless otherwise noted
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Run Sample ID Taxa Life
stage DNA [ug]
Raw Reads
No. of contigs
No. of bp UCEs
2 W352/W340 Anserobilharzia brantae C 0.562 668,282 1,158 330,978 48 2 W396/359 Austrobilharzia variglandis A 0.704 124,242 145 35,487 6
2 W930 Bilharziela polonica A 0.31 5,572,448 17,081 5,897,771 1677
2 W813 Dendritobilharzia pulverulenta A 1.32 7,352,118 26,978 8,405,837 1737
2 W926 Dendritobilharzia pulverulenta A 2.36
25,041,972 47,498 14,794,680 1631
2 W414/513 Gigantobilharzia huronensis C 0.701 14,030,53
2 242,326 72,500,699 1650
2 W678 Gigantobilharzia huronensis C 0.65 16,867,49
6 48,623 16,046,122 1582
2 W805 Heterobilharzia americana A 2.21 16,016,29
1 442,043 139,396,85
2 1784
2 W931 Macrobilharzia macrobilahrzia A 0.944 2,182,575 3,508 1,580,643 1590
2 W393 Ornithobilharzia canaliculata A 2.5 15,222,38
5 31,569 8,873,305 1441
2 W510/511 Trichobilharzia sp. A C 0.494 2,777,802 6,715 2,136,811 1964
1 W606 Trichobilharzia sp. E A - 9,124,167 22,342 16,933 592
2 W604 Trichobilharzia physellae A 0.224 1,133,317 2,683 952,777 1753
2 W929 Trichobilharzia querquedulae A 0.674 13,523,27
1 70,532 21,183,008 1576
2 W203 Trichobilharzia querquedulae A 0.408 68,006 75 19,185 2
1 W233 Trichobilharzia stagnicolae C 0.0531 10,207,44
0 18,675 13,662 658
1 W607 Avian Schistosome C C 0.212 4,784,485 34,656 53,303 1599
2 W342 Avian Schistosome D C 1.7 3,066,693 13,126 3,804,186 171
2 W216 Schistosomatid W216 C 0.63 23,988 12 3,050 0
2 W688 Schistosomatid W688 C 0.448 6,626,793 8,006 3,890,904 1428
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Table 1. Summary of sequence capture data. Methodologies between run 1 and run 2 are detailed within the methods section. Sample ID’s with a (/) indicates that samples were pooled in an attempt to increase DNA quantities. Schistosome life stage is indicated as A (adult) or C (cercaria).
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Alignment % missing
data No. Loci No. bp -logLN
1 18.19% 113 41615 -249,913.44
2 21.22% 258 95215 -552,102.19
3 24.25% 470 167917 -958,990.67
4 27.28% 756 265282 -1,487,900.85
5 30.31% 1,070 372577 -2,061,422.86
6 33.34% 1,364 474081 -2581373.332
7 39.40% 1,819 627118 -3350563.131
8 42.43% 1,970 627118 -3586512.511
9 48.90% 2,006 677378 -3809408.073
10 51.50% 2,082 736137 -3841912.875
11 54.55% 2,144 743745 -3866958.204
12 60.60% 2,159 753058 -3903325.447
13 63.60% 2,179 755774 -3912154.447
Table 2. Summary of UCE alignments.
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CONCLUSIONS
Host traits are important factors in shaping host-parasite interactions,
including, but not limited to; host immunology, physiology, demographics, range,
phylogeny and ecology. These factors are co-occurring, often across multiple
taxonomically and ecologically disparate host species, and can act to enhance or
constrain parasite population size, geographic range, transmission, and adaptive
potential. Additionally, host traits at one life stage may facilitate or reduce
transmission at another life stage, and are therefore integrative and inseparable in
shaping parasite evolution. This integrative nature is likely a characteristic of
trematode lifecycles and possibly multi-host parasites in general. This research
sought to further our understanding of how host traits impact the evolutionary
ecology of trematode systems, using schistosomes (Digenea: Schistosomatidae)
and associated hosts as a model. Research described here integrated
phylogeographic, phylogenomic, and population genetic methods, to investigate
host-parasite relationships across multiple timescales.
Much of this research focused on the schistosome genus Trichobilharzia.
Molecular phylogenetic methods were used to connect populations of Trichobilharzia
querquedulae from three continents (North America, South America, Africa) and New
Zealand. These data confirmed that T. querquedulae was a single globally
distributed species, with evidence for trans-hemispheric transmission. Further
extensive helminthological surveys of potential duck hosts revealed that T.
querquedulae maintained a specific association with Spatula (= Anas, ‘Blue-wing’
ducks) species. This relationship is likely maintained by a combination of duck host
phylogeny and ecology. There are several reasons why ecological traits specific to
Spatula species might facilitate the global transmission of T. querquedulae. Spatula
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spp. prefer shallow, marsh habitats where they feed at the surface. These habitats
are snail rich, increasing the likelihood of transmission between the snail
intermediate host (Physa spp.) and Spatula spp. Further one member of Spatula (S.
clypeata) is Holarctic in its distribution, and is known to migrate (at a lower
frequency) into the Southern hemisphere as part of its wintering range. Molecular
data confirms that endemic, non-migratory, Spatula spp. transmit T. querquedulae
within the Southern hemisphere, and may suggest that S. clypeata migration
facilities trans-hemispheric gene flow. The global transmission of T. querquedulae is
likely made possible by (though not confirmed) the global invasion of its snail host
Physa acuta. Data from this study suggest that certain definitive and intermediate
host combinations may act in concert to facilitate Trichobilharzia transmission,
supporting the integrative nature of schistosome lifecycles.
The observation of specificity between T. querquedulae and Spatula spp. was
unexpected as Trichobilharzia species were thought to non-specifically infect anatid
hosts (Anatidae: ducks, geese and swans). A closely related species T. physellae is
known to infect Passeriformesand Columbiformes under experimental conditions and
other Trichobilharzia species are reported from a broad range of anatid hosts. Using
the terminology of Combes (1999), these reports suggest that Trichobilharzia
maintains a wide compatibility filter. As such, the broad potential host range of T.
querquedulae indicates that the encounter filter is narrowed. Host specificity
observed between T. querquedulae and its definitive hosts led to more directed
comparative studies of North American Trichobilharzia species and their host
associations.
Surveys of Trichobilharzia and their duck hosts resulted in the finding that
some level of host specificity was common among Trichobilharzia species and their
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duck hosts. It was found that measures of host specificity including duck ecological
group often better captured duck host associations than considering host taxonomy
alone. One of the most interesting findings was that T. physellae, which was
considered a generalist using traditional measures of host specificity, was found to
be specific to ‘diving ducks’, when duck ecological group was considered. Diving
ducks represent an ecological group within Anatidae, characterized by feeding
behavior and habitat preferences. Trichobilharzia physellae was found to infect its
most common host (Aythya affinis) at a much lower rate (26%) relative to T.
querquedulae within its most common host (Spatula discors, 90%). This is likely the
result of decreased transmission success within the deeper water bodies (less
interaction with intermediate host) that diving ducks occupy.
This research also compared population genetic and phylogeographic
patterns among North American Trichobilharzia species found to be specific to either
dabbling or diving ducks; T. physellae (diving ducks), T. querquedulae (dabbling
ducks, Spatula spp.) and Trichobilharzia sp. A (dabbling ducks, Anas americana).
Importantly both T. physellae and T. querquedulae infect Physa spp. as their
intermediate host, which unites these species in ecological space and time.
Comparison of congeneric Trichobilharzia species revealed that different host
species structured parasite populations in different ways. Trichobilharzia physellae
populations appear to be structured in relation to the migratory flyways used by their
duck hosts, which is a common pattern found among symbionts of philopatric ducks.
Spatula spp. are less philopatric relative to Aythya as such migratory flyway does not
seem to impact the population structure of T. querquedulae. Instead we found an
association with latitude, which is likely a proxy for breeding versus wintering ground.
Genetic diversity among the three species was variable, the highest values of which
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were found in T. querquedulae and Trichobilharzia sp. A, possibly suggesting an
advantage to concentrating numbers within a narrow range of hosts (specialization).
The effective size of T. querquedulae was found to be over 3X greater than the other
two species. In sum this work demonstrated that Trichobilharzia-duck associations
are more specific than previously thought, and that individual duck hosts or host
groups can differentially impact Trichobilharzia microevolutionary patterns.
Along a similar vein phylogeographic, population genetic and demographic
patterns of an important intermediate host Physa acuta were estimated. Physa acuta
transmits at least three schistosome species (T. querquedulae, T. physellae and
Gigantobilharzia huronensis) as well as numerous other trematode species in North
America. This snail is of particular importance as it is now global in distribution, as
the result of a 200+ year invasion process. This research had two parts, 1)
characterize phylogeographic and population genetic patterns to reconstruct the
invasion history of P. acuta and 2) test the ‘Enemy-release hypothesis. Increased
sampling from the native range of P. acuta (North America), particularly from the
Western USA, provided greater insight into the phylogeography of P. acuta allowing
for identification of source populations. Using published parasitological surveys of P.
acuta in combination with those carried out for the purposes of this research we
found limited evidence of trematodes infecting invasive P. acuta (as first intermediate
host), supporting the ‘Enemy-release’ hypothesis. The few cases of trematode
infection that were found were in association with invasive populations in the oldest
and most genetically diverse regions of the invasive range. A significant positive
correlation, between time since invasion, genetic diversity, and being colonized by
local parasites was found. These results have significant implications for modeling
host-parasite invasion dynamics over time, and more broadly understanding how
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host traits might mediate parasite assembly. This data suggests that host
demographic parameters (nucleotide diversity, effective population size) are
important predictors of parasite assembly within invasive populations.
Apart for understanding host-parasite associations at the microevolutionary
level, this research also sought to characterize patterns at the macroevolutionary
level within Schistosomatidae. Evolutionary relationships within Schistosomatidae
have historically lacked resolution, impeding a clear appreciation for patterns of host
use. This research applied sequence capture technology to target ultra-conserved
elements (UCE loci) for phylogenomic analyses, to specifically resolve phylogenetic
placement of pivotal genera and identify host-switching events. Increased resolution
in regards to the North American mammalian schistosomes was found, which
supported that a secondary host capture occurred. Additionally, within the derived
avian clade where the majority of species diversity occurs, we find evidence for high
amounts of intermediate host switching. Suggesting that, 1) intermediate host
switching was an important mechanism of schistosome diversification and 2) an
overall lack of co-phylogenetic structure among schistosomes and their snail hosts.
Largely phylogenetic resolution was poorer than expected based on published
UCE phylogenies of vertebrate and some invertebrate taxa. There are likely several
reasons for this, which require further investigation. Firstly, sequence capture
success reported within this research is on average lower, and substantially more
uneven, relative to published studies. Decreased capture success was likely due to
poor DNA starting quantity and quality, which likely affected downstream
phylogenomic analyses. There is also the possibility, though not mutually exclusive,
that some nodes within Schistosomatidae may never be resolved. Previous authors
have suggested that Schistosomatidae underwent a rapid radiation, which might
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have resulted in a hard polytomy. Generally UCE loci recovered contained very few
informative sites, and suggested high amounts of incomplete lineage sorting.
This research investigated different host traits across different life stages and
time scales to determine their role shaping host-parasite relationships. We found
evidence for specificity within Trichobilharzia in regards to definitive host use, and
more specifically that duck habitat preferences may substantially impact
Trichobilharzia transmission and demographic patterns. Similarly host demographics
may play an integral role in parasite assembly under invasion conditions. In total
these data support that host traits are important mediators of host-parasite
relationships, and that host traits across life-stages are integrated often acting in
concert to enhance or constrain parasite transmission.
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APPENDIX A
CHAPTER II ADDITIONAL FILES
Additional File 1. cox1 phylogeny of Physa estimated using Bayesian
inference. Posterior probability values ≥.95 are shown. Taxon names are reflective
of the names assigned to sequences in the original NCBI records.
182
Additional Figure 2. Concatenated 16+cox1 phylogeny. Branch support values
≥.95/70 (PP/BS) are shown. Taxon names are reflective of the names assigned to
sequences in the original NCBI records.
183
Additional File 3. ITS1 phylogeny estimated using Bayesian inference pdf.
Physa acuta samples are colored according to the clade they were recovered from
based on mitochondrial gene tree analysis: pink = Clade A, green = Clade B. Physa
acuta taxa labeled as black did not group within P. acuta based on mitochondrial
gene trees, and were excluded from all in-group analyses (S17, S120 and all P.
zionis samples). ITS1 data only exists for HQ283259 and Pacuta_18S/ITS and were
therefore not included in mitochondrial gene tree analyses. Posterior probabilities are
illustrated as branch support values.
184
185
Additional Figure 4. 16S in-group analysis. Taxa are colored according to the
FWEC they were collected from. Phylogeny (A) was generated using ML methods
and phylogeny (B) was generated using BI. Branch support values ≥ 70 bootstrap
(A) and ≥ .95 posterior probabilities (B) are denoted by a black circle
186
Time (kya)
A.
B.
C.
1
10
100
1000
10000
100000
0 10 20 30 40 50 60
NAM 1%
NAM 0.04%
NAM 4%
EUR 1%
EUR 0.04%
EUR 4%
SAM 1%
SAM 0.04%
SAM 4%
AFOC 1%
AFOC 0.04%
AFOC 4%
1
10
100
1000
0 10 20 30 40 50 60 70
A 1%
A 0.04%
A 4 %
B 1%
B 0.04%
B 4%
TOT 1%
TOT 0.04%
Total 4%
1
10
100
1000
10000
0 5 10 15 20 25 30
ATL 1%
ATL 0.04%
ATL 4%
COA 1%
COA 0.04%
COA 4%
COL 1%
COL 0.04%
COL 4%
MISS 1%
MISS 0.04%
MISS 4%
RIO 1%
RIO 0.04%
RIO 4%
187
Additional File 5. Bayesian Skyline Plots. Effective size over time plots estimated
in BEAST using a Bayesian Skyline prior, mean values are shown and confidence
values were excluded for clarity. Three mutation rates (per million year): 1% (solid
line), 0.04% (dashed line) and 4%(circle line) were tested. (A) Range-wide estimates
for North America (NAM, dark grey, n=79), Central and South America (SAM, lime
green, n=13), Eurasia (EUR, pink, n=49) and Africa and Oceania (AFOC, blue, n=7).
North America dataset ran for 50,000,000 generations, Eurasia 20,000,000
generations, Central/South America and Africa/Oceania ran for 5,000,000
generations. B. Estimates by FWECs sampled: Rio Grande (RIO, red, n=16),
Coastal (COA, blue, n=13), Colorado (COL, purple, n=11), Atlantic (ATL, orange,
n=9), Mississippi (MISS, green, n=26), the Great Basin and St. Lawrence FWECs
were excluded because of low sample size. All FWEC datasets were run for
5,000,000 generations, with the exception of Atlantic (3,000,000). C. Estimates of
recovered Clades, Clade A (A, aqua, n=116) and B (B, dark green, n=33) compared
to total (TOT, light grey, n=149). The total dataset and Clade A ran for 80,000,000
generations, Clade B ran for 10,000,000 generations. Bayesian skyline plots were
constructed in TRACER, the data was exported and visualized using Excel.
188
APPENDIX B
CHAPTER III ADDITIONAL FILES
Additional File 1. Locality information for TQ, TP and TA. Migratory flyways (MWF). Voucher material housed at the Museum of Southwestern Biology, Parasite Division (MSB:Para).
189
Specimen ID Locality MFW Latitude: Longitude Date Host Species
MSB:Para: # Source
Trichobilharzia querquedulae
W135
Lousiana: Rockefeller Wildlife Refuge M
29.67304, -92.689902 11/15/2003 Anas clypeata 183
Brant and Loker 2009; Ebbs et al. 2016
W135.2
Lousiana: Rockefeller Wildlife Refuge M
29.67304, -92.689902 11/15/2003 Anas clypeata 183 This Study
W137
Lousiana: Rockefeller Wildlife Refuge M
29.67304, -92.689902 11/15/2003 Anas discors 19497
Brant and Loker 2009; This Study
W137.2
Lousiana: Rockefeller Wildlife Refuge M
29.67304, -92.689902 11/15/2003 Anas discors 19497 This Study
W138
Lousiana: Rockefeller Wildlife Refuge M
29.67304, -92.689902 11/15/2003 Anas clypeata 19498 This Study
W138.3
Lousiana: Rockefeller Wildlife Refuge M
29.67304, -92.689902 11/15/2003 Anas clypeata 19498 This Study
W140
Lousiana: Timberton Hunt Club M
30.1705, -90.868 11/13/2003 Anas discors 19500 This Study
W142
Lousiana: Timberton Hunt Club M
30.1705, -90.868 11/13/2003 Anas discors 19502
Ebbs et al. 2016; This Study
W148.1 New Mexico: San Marcial C
33.7131, -106.9579 3/25/2004
Anas cyanoptera 18584
Brant and Loker 2009; Ebbs et al. 2016
190
W148.2 New Mexico: San Marcial C
33.7131, -106.9579 3/25/2004
Anas cyanoptera 18584
Brant and Loker 2009; This Study
W152.1 New Mexico: San Marcial C
33.7131, -106.9579 4/6/2004 Anas clypeata 18587 This Study
W152.2 New Mexico: San Marcial C
33.7131, -106.9579 4/6/2004 Anas clypeata 18587 This Study
W152.4 New Mexico: San Marcial C
33.7131, -106.9579 4/6/2004 Anas clypeata 18587 This Study
W153 New Mexico: San Marcial C
33.7131, -106.9579 4/6/2004 Anas clypeata 18587
Ebbs et al. 2016; This Study
W154.1 New Mexico: San Marcial C
33.7131, -106.9579 4/6/2004
Anas cyanoptera 18588 This Study
W154.2 New Mexico: San Marcial C
33.7131, -106.9579 4/6/2004
Anas cyanoptera 18588 This Study
W155.1 New Mexico: San Marcial C
33.7131, -106.9579 4/6/2004
Anas cyanoptera 18589
Brant and Loker 2009; This Study
W155.2 New Mexico: San Marcial C
33.7131, -106.9579 4/6/2004
Anas cyanoptera 18589 Ebbs et al. 2016
W155.3 New Mexico: San Marcial C
33.7131, -106.9579 4/6/2004
Anas cyanoptera 18589
Brant and Loker 2009; Ebbs et al. 2016; This Study
W156 New Mexico: Bitter Lake C 33.45, -104.4 4/11/2004 Anas discors 18590
Brant and Loker 2009; Ebbs et al. 2016
W157 New Mexico: Bitter Lake C 33.45, -104.4 4/11/2004 Anas clypeata 18591 This Study
W158 New Mexico: Bitter Lake C 33.45, -104.4 4/12/2004 Anas clypeata 18592
Brant and Loker 2009
W159 New Mexico: Bitter Lake C 33.45, -104.4 4/12/2004 Anas discors 18593 This Study
191
W160 New Mexico: Bitter Lake C 33.45, -104.4 4/12/2004 Anas discors 18594
Ebbs et al. 2016; This Study
W161 New Mexico: Bitter Lake C 33.45, -104.4 4/12/2004
Anas cyanoptera 18605 This Study
W162 New Mexico: Bitter Lake C 33.45, -104.4 4/12/2004 Anas clypeata 18602
Brant and Loker 2009; Ebbs et al. 2016
W166.2 New Mexico: San Marcial C
33.7131, -106.9579 9/9/2004 Anas discors 181 This Study
W167 New Mexico: San Marcial C
33.7131, -106.9579 9/9/2004 Anas discors 26231
Ebbs et al. 2016; This Study
W180
California: Wister Unit Waterfowl Area P
33.284266, -115.57921 11/24/2004
Anas cyanoptera 18573
Brant and Loker 2009; This Study
W181 California: Imperial County P
33.284266, -115.579216 2004
Anas americana 18572 This Study
W183
California: Wister Unit Waterfowl Area P
33.284266, -115.57921 11/24/2004 Anas clypeata 18575
Brant and Loker 2009; Ebbs et al. 2016
W188
California: Wister Unit Waterfowl Area P
33.284266, -115.57921 11/24/2004
Anas cyanoptera 18580 This Study
W190
California: Wister Unit Waterfowl Area P
33.284266, -115.57921 11/24/2004 Anas discors 18582
Brant and Loker 2009
W191
California: Wister Unit Waterfowl Area P
33.284266, -115.57921 11/24/2004
Anas cyanoptera 18583 This Study
W203 Alaska: Yukon-Koyukuk Borough P
65.665, -149.098
5/29/2005
Anas clypeata 18636 Brant and Loker 2009
192
W204 Alaska: Yukon-Koyukuk Borough P
67.13369, -150.35594
5/30/2005
Anas clypeata 18637 This Study
W207 Alaska: Yukon-Koyukuk Borough P
67.492186, -149.86944
5/30/2005
Anas clypeata 18640 This Study
W2135 Alaska: North Slope Borough P
68.64636, -149.44891 6/1/2005 Anas clypeata 18647 This Study
W259 New Mexico: Bitter Lake C 33.45, -104.4 3/17/2006 Anas discors 19515 This Study
W345
Manitoba: Churchill, Goose Creek M
58.67583, -94.1677 8/3/2007 Anas clypeata 18626
Brant and Loker 2009; Ebbs et al. 2016
W345.2
Manitoba: Churchill, Goose Creek M
58.67583, -94.1677 8/3/2007 Anas clypeata 18626 This Study
W345.3
Manitoba: Churchill, Goose Creek M
58.67583, -94.1677 8/3/2007 Anas clypeata 18626 This Study
W650 South Africa: Free State Province SH
-29.208716, 27.18945 1/13/2012 Anas smithii 18990 Ebbs et al. 2016
W664 South Africa: Free State Province SH
-29.14433, 27.02565 1/21/2012 Anas smithii 19000 Ebbs et al. 2016
W698 North Dakota C - - Anas clypeata - This Study
W703
New Zealand, South Island, Lake Benmore SH
-44.35124, 170.2091 12/31/2012
Anas rhynchotis - Ebbs et al. 2016
W704
New Zealand, South Island, Lake Benmore SH
-44.35124, 170.2091 12/31/2012
Anas rhynchotis 20793 Ebbs et al. 2016
W743 New Mexico C - - Anas clypeata - This Study
W746 Florida: Hamilton County A
30.42307, -82.7936 1/12/2013 Anas clypeata - This Study
193
W747 Florida: Hamilton County A
30.42307, -82.7936 1/12/2013 Anas clypeata - This Study
W748 Florida: Hamilton County A
30.42307, -82.7936 1/12/2013 Anas clypeata - This Study
W749 Florida: Hamilton County A
30.42307, -82.7936 1/12/2013 Anas clypeata - This Study
W750 Florida: Hamilton County A
30.42307, -82.7936 1/12/2013 Anas discors - This Study
W750.2 Florida: Hamilton County A
30.42307, -82.7936 1/12/2013 Anas discors - This Study
W752 Florida: Hamilton County A
30.42307, -82.7936 1/12/2013 Anas clypeata 19534 This Study
W753 Florida: Hamilton County A
30.42307, -82.7936 1/12/2013 Anas clypeata - This Study
W754 Florida: Hamilton County A
30.42307, -82.7936 1/12/2013 Anas clypeata - This Study
W755.2 Florida: Hamilton County A
30.42307, -82.7936 1/12/2013 Anas discors - This Study
W756 Florida: Hamilton County A
30.42307, -82.7936 1/12/2013 Anas discors - This Study
W756.2 Florida: Hamilton County A
30.42307, -82.7936 1/12/2013 Anas discors - This Study
W757 Florida: Hamilton County A
30.42307, -82.7936 1/12/2013 Anas clypeata - This Study
W758 New Mexico: Eddy County C
32.4122, -103.938 2/2/2013 Anas clypeata - This Study
W762 Florida: Palm Beach County A
26.42068, -80.5853 2/21/2013 Anas discors 19283 This Study
W763 New Mexico: San Juan County C
36.44, -108.27213 3/24/2013 Anas clypeata - This Study
194
W766 Florida: Palm Beach County A
26.42068, -80.5853 2/21/2013 Anas discors 19284 This Study
W767 Florida: Palm Beach County A
26.42068, -80.5853 2/21/2013 Anas discors 19285 This Study
W767.2 Florida: Palm Beach County A
26.42068, -80.5853 2/21/2013 Anas discors - This Study
W767.3 Florida: Palm Beach County A
26.42068, -80.5853 2/21/2013 Anas discors - This Study
W767.4 Florida: Palm Beach County A
26.42068, -80.5853 2/21/2013 Anas discors - This Study
W771 Alaska: Fairbanks North Star Borough P
64.6655, -147.101388 10/8/2013 Anas clypeata 19474 This Study
W774
Minnesota: Polk County M
47.69405, -96.31216 9/21/2013 Anas discors 19475 This Study
W775
Minnesota: Polk County C
34.27864, -106.8527 1/9/2014 Anas discors 19576 This Study
W776
Minnesota: Polk County M
47.69405, -96.31216 9/21/2013 Anas discors 19481 This Study
W777
Minnesota: Polk County M
47.69405, -96.31216 9/21/2013 Anas discors 19482 This Study
W778
Minnesota: Polk County M
47.69405, -96.31216 9/21/2013 Anas discors 19483 This Study
W779
Minnesota: Polk County M
47.69405, -96.31216 9/21/2013 Anas discors 19484 This Study
W814
Minnesota: Polk County M
47.69405, -96.31216 9/21/2013 Anas discors 19617 This Study
W815
Minnesota: Polk County M
47.69405, -96.31216 9/21/2013 Anas discors 19625 This Study
W816
Minnesota: Polk County M
47.69405, -96.31216 9/21/2013 Anas discors 19626 This Study
195
W816.2
Minnesota: Polk County M
47.69405, -96.31216 9/21/2013 Anas discors - This Study
W831 Argentina: Corrientes SH
-30.0999, -59.49915 6/1/2013 Anas versicolor 23178 This Study
W833 Argentina: Corrientes SH
-30.0999, -59.49915 6/1/2013 Anas versicolor 23180
Ebbs et al. 2016; This Study
W845 New Zealand: South Island SH
-44.568863, 170.1872 5/24/2015
Anas rhynchotis 24887 This Study
W846 New Zealand: South Island SH
-44.568863, 170.1872 5/24/2015
Anas rhynchotis 24888 This Study
W857 North Carolina: Hyde County A 1/15/2016 Anas clypeata - This Study
W925 Puerto Rico A - - Anas discors - This Study
E64 Florida A - 10/1/2002 Anas discors 24777
Brant and Loker 2009; Ebbs et al. 2016
E45 Florida A - 10/1/2002 Anas discors 24778 Brant and Loker 2009
E45.2 Florida A - 10/1/2002 Anas discors - Ebbs et al. 2016
SDS1006 Nebraska C - 11/1/2004 Anas clypeata 24776
Brant and Loker 2009; This Study
TshovNZ New Zealand: South Island SH
Anas rhynchotis 20794 Ebbs et al. 2016
W413 Minnesota: Ramsey County M
45.0684361, -93.1257 6/17/2009 Physa gyrina 186 Brant et al. 2011
Trichobilharzia physellae
W146 New Mexico: Soccoro County C
33.7131, -106.9579 3/25/2004 Physa gyrina -
Brant and Loker 2009; This Study
W147 New Mexico: Soccoro County C
33.7131, -106.9579 3/25/2004 Physa gyrina -
Brant and Loker 2009; This Study
196
W150 New Mexico: Soccoro County C
33.7131, -106.9579 3/25/2004 Anas strepera 18586
Brant and Loker 2009; This Study
W171 Pennsylvania: Erie County A
41.170333, -80.086883 11/10/2004 Aythya affinis 18565
Brant and Loker 2009
W175 Pennsylvania: Crawford County A
41.59215, -80.4093666 11/10/2004
Anas platyrhynchos 18567
Ebbs et al. 2016; This Study
W176 Pennsylvania: Crawford County A
41.59215, -80.4093666 11/10/2004
Anas platyrhynchos 18568 This Study
W193 New Mexico: Chaves County C 33.45, -104.4 2005 Aythya affinis 18610
Brant and Loker 2009; This study
W194 New Mexico: Chaves County C 33.45, -104.4 2005 Aythya collaris 18611
Brant and Loker 2009; This study
W196 New Mexico: Chaves County C 33.45, -104.4 2005 Aythya affinis 18613
Ebbs et al. 2016; This Study
W211 Alaska: North Slope Borough P
68.64636, -149.44891 2005
Clangula hyemalis 18644 This Study
W212 Alaska: North Slope Borough P
68.98204, -148.83182 2005 Aythya affinis 18645
Brant and Loker 2009
W230I1I2 Mergus merganser eggs MI M
45.581, -84.697 2005
Mergus merganser -
Brant and Loker 2009; This study
W234 Michigan: Emmet County M
45.581, -84.697 2005 Physa gyrina 18656
Brant and Loker 2009
W235 Michigan: Emmet County M
45.581, -84.697 2005 Physa gyrina 18657
Brant and Loker 2009; This study
W236 Michigan: Emmet County M
45.581, -84.697 2005 Physa gyrina 18658
Brant and Loker 2009; This study
W249 Nevada: Churchill County P
39.9, -118.8166 2005
Aythya valisineria 18554
Brant and Loker 2009; This study
W255 New Mexico: Chaves County C 33.45, -104.4 2006
Bucephala albeola 19159
Brant and Loker 2009; This study
197
W256 New Mexico: Chaves County C 33.45, -104.4 2006 Aythya affinis 177
Brant and Loker 2009
W263 New Mexico: Sandoval County C
35.8485, -106.4907 2006 Physa gyrina 178
Brant and Loker 2009; This study
W408 New Mexico: Soccoro County C
33.79336, -106.8912 2008 Physa gyrina 18686 This Study
W497 New Mexico: Bernalillo County C
35.2166138, -106.59698 2009 Physa acuta 18700 This Study
W602 Alaska: Fairbanks P - 2011 Bucephala albeola 25267 This Study
W604 Alaska: Fairbanks P - 2011 Bucephala clangula 25269 This Study
W608 Alaska: Fairbanks P - 2011 Aythya collaris 25260 This Study
W745 Florida: Hamilton County A
30.42307, -82.7936 1/12/2013 Aythya collaris - This Study
W765 New Mexico: Soccoro County C
33.79336, -106.8912 2014 Aythya affinis - This Study
W765.3 New Mexico: Soccoro County C
33.79336, -106.8912 2014 Aythya affinis - This Study
W765.4 New Mexico: Soccoro County C
33.79336, -106.8912 2014 Aythya affinis - This Study
W765.5 New Mexico: Soccoro County C
33.79336, -106.8912 2014 Aythya affinis - This Study
W765.6 New Mexico: Soccoro County C
33.79336, -106.8912 2014 Aythya affinis - This Study
MGC804 Alberta C - - Physa gyrina - Gordy et al. 2016
SAL92-09 Canada: Alberta C - - Aythya americana - This Study
Trichobilharzia sp. A
198
W149.1 New Mexico: Socorro County C
33.7131, -106.9579 2004
Anas americana 18585
Brant and Loker 2009; This Study
W149.2 New Mexico: Socorro County C
33.7131, -106.9579 2004
Anas americana 18585
W182.1 California: Imperial County P
33.284266, -115.579216 2004
Anas americana 18574
Brant and Loker 2009
W182.2 California: Imperial County P
33.284266, -115.579216 2004
Anas americana -
W192 New Mexico: Sierra county C
32.9071, -107.3116 2004
Anas americana 18609
Brant and Loker 2009; This Study
W206 Alaska: Yukon-Koyukuk Borogh P
67.4093611, -150.104883 2005
Anas americana 18639 This Study
W206.2 Alaska: Yukon-Koyukuk Borogh P
67.4093611, -150.104883 2005
Anas americana 18639 Ebbs et al. 2016
W213 Alaska: North Slope Borough P
68.98204, -148.8318 2005
Anas americana 18646
Brant and Loker 2009
W213.2 Alaska: North Slope Borough P
68.98204, -148.8318 2005
Anas americana 18646 This Study
W510 New Mexico: Colfax county C
36.46363, -105.2733 2011
Stagnicola elodes 18705 This Study
W510.2 New Mexico: Colfax county C
36.46363, -105.2733 2011
Stagnicola elodes 18705 This Study
W511 New Mexico: Colfax county C
36.46363, -105.2733 2011
Stagnicola elodes 18706 This Study
W511_2 New Mexico: Colfax county C
36.46363, -105.2733 2011
Stagnicola elodes 18706 This Study
W512 New Mexico: Colfax county C
36.46363, -105.2733 2011
Stagnicola elodes 18707 This Study
W600 Alaska: Fairbanks P - 2011 Anas americana 25265 This Study
199
W601 Alaska: Fairbanks P - 2011 Anas americana 25266
Brant and Loker 2009; This Study
W607 Alaska: Fairbanks P - 2011 Anas americana 25258
Brant and Loker 2009; This Study
W609 Alaska: Fairbanks P - 2011 Anas americana 25259
Brant and Loker 2009; This Study
W611 Alaska: Fairbanks P - 2011 Anas americana 25262
Brant and Loker 2009; This Study
VVT3043 North Dakota C - - Anas americana -
Brant and Loker 2009; This Study
200
p-
distance
Taxa CO1 ND4 ITS1 Trichobilharzia querquedulae 0.01 0.014 0.002
vs. T. querquedulae like 0.047 0.035 -
vs. T. physellae 0.095 0.133 0.012
vs. Trichobilharzia sp. A 0.112 0.1 0.009
vs. T. franki 0.075 - 0.016
vs. Trichobilharzia sp. C 0.089 - 0.034
vs. W701 0.098 - 0.014
vs. T. regenti 0.125 0.129 0.337
vs. T. mergi 0.116 - -
vs. T. anseri 0.13 - -
vs. T. stagnicolae 0.121 - -
vs. T. szidati 0.106 - -
vs. Anserobilharzia brantae 0.141 - -
Trichobilharzia querquedulae - like - 0.036 -
Trichobilharzia phsyellae 0.009 0.034 0.006
vs. T. querquedulae like 0.083 0.136 -
vs. Trichobilharzia sp. A 0.107 0.136 0.006
vs. T. franki 0.081 - 0.014
vs. Trichobilharzia sp. C 0.095 - 0.027
vs. W701 0.066 - 0.016
vs. T. regenti 0.1 0.163 0.333
vs. T. mergi 0.131 - -
vs. T. stagnicolae 0.143 -
vs. T. anseri 0.126 - -
vs. T. szidati 0.124 - -
vs. Anserobilharzia brantae 0.14 - -
Trichobilharzia sp. A 0.014 0.007 0.003
vs. T. querquedulae like 0.1 0.098 -
vs. T. franki 0.096 - 0.011
vs. Trichobilharzia sp. C 0.093 - 0.029
vs. W701 0.097 - 0.008
vs. T. regenti 0.116 0.127 0.333
vs. T. mergi 0.124 - -
vs. T. anseri 0.135 - -
vs. T. stagnicolae 0.14 - -
vs. T. szidati 0.116 - -
vs. Anserobilharzia brantae 0.137 - -
Trichobilharzia sp. B 0.01 0.16 0.002
vs. Trichobilharzia sp. A 0.075 0.094 0.007
vs. Trichobilharzia sp. C 0.092 - 0.033
vs. T. physellae 0.103 0.159 0.01
vs. T. querquedulae 0.094 0.139 0.013
vs. T. fanki 0.081 - 0.008
Trichobilharzia franki 0.004 - 0.001
vs. Trichobilharzia sp. C 0.072 - 0.037
vs. W701 0.091 - 0.026
vs. T. regenti 0.093 - 0.34
vs. T. mergi 0.114 - -
vs. T. anseri 0.129 - -
vs. T. stagnicolae 0.135 - -
vs. T. szidati 0.121 - -
201
vs. Anserobilharzia brantae 0.145 - -
Trichobilharzia regenti 0.002 - 0.011
vs. T. mergi 0.111 - -
vs. T. anseri 0.116 - -
vs. T. stagnicolae 0.127 - -
vs. T. szidati 0.124 - -
vs. Anserobilharzia brantae 0.126 - -
Trichobilharzia mergi 0 - -
Trichobilharzia anseri 0.003 - -
Trichobilharzia stagnicolae 0.007 - -
Trichobilharzia szidati 0.008 - -
Anserobilhariza brantae 0.013 - -
vs. Allobilhariza visceralis 0.143 - -
Allobilharzia visceralis 0.01 - -
Additional File 2. Pairwise p-distances within and between Trichobilharzia species.
202
Additional File 3. nad4 Phylogeny. BI inference of TQ, TP and TA, only posterior
probabilities ≥.95 are presented. Branches are colored by species; TQ= orange,
TP=green and TA=blue. Individual worms are labeled by the migratory flyway they
were collected from, denoted by a colored box. Average pairwise p-distances
between TQ subclades are indicated.
203
Additional File 4. ITS1 Phylogeny. BI inference of TQ, TP and TA, only posterior
probabilities ≥.95 are presented. Branches are colored by species; TQ= orange,
TP=green and TA=blue.
204
W137 W752
W756.2 W162 W767.4 W743
W925 W748
W750.2 W203
W190 W183 SDS1006 W156 W159
W154.2 W750
W160 W180
W753 W777 W166.2
W664 W213 W831 W259
W833 W747
W778 E45 W774
W148.2 W154 W191 W152.4 W704
W153 W158
W746 W703
W749 W161 W181
W754 W756
W148 W767.3 W155.3
Tshov W755.2 W135 W142
Trichobilharzia sp. C
W701 W604
W194 W497 W234 W235
W193 W602
W249
W510 W608 W230
W211 W213
W146 W147 W236 W745
W196 W765.3
W765.6 W765 W765.5 W765.4 W256 W171 W175 W255 W263
W149 W212
W601 W609 W182
W192 W600 W511 W607
.97
.97
.99
0.02
Trichobilharzia franki
Trichobilharzia sp. B
Trichobilharzia regenti
Pacific
Central
Mississippi
Atlantic S. Hemisphere
205
Additional File 5. nad4 + cox1+ ITS1 phylogeny. BI inference of TQ, TP and TA,
only posterior probabilities ≥.95 are presented. Branches are colored by species;
TQ= orange, TP=green and TA=blue. Individual worms are labeled by the migratory
flyway they were collected from, denoted by a colored box.
206
Additional File 6. Minimum spanning network, showing relationships of TP
haplotypes by host genera.
207
Additional File 7. Minimum spanning network, showing relationships of TQ
haplotypes by host species.
208
Additional File 8. Bayesian skyline plot of cox1 datasets, all mutation
rates. Estimates of Ne (y-axis) over time (kya, x-axis), using a 2%(dashed line) and
4% (solid line) rate of cox1 change. TA populations were further subdivided by
flyway as significant among flyway divergence was suspected. Lines are colored by
species; TQ= orange, TP=green and TA=blue.
209
Trichobilharzia querquedulae
Pacific Central Mississippi Atlantic S. Hemisphere
AK CA NM NE ND MB MN LA FL PR AR ZA NZ
AK /
CA 0.03448 /
NM 0.10577 -0.02813 /
NE 1 -0.4 -0.34135 /
ND 0.71429 0.06728 -0.02875 0.5 /
MB 0.18519 0.39558** 0.38637* -0.04762 0.35 /
MN 0.2381 -0.00378 -0.02657 -0.23077 -0.09615 0.3543 /
LA -0.02703 -0.04893 0.00183 -0.31034 0.08042 0.39144 0.0293 /
FL -0.10238 -0.0388 -0.0066 -0.43789 -0.08456 0.29923** -0.04908 -0.02889 /
PR 1 0.5942 0.55854 1 0.85714 0.46341 0.64444 0.54217 0.3987 /
AR 0.5 0.0884 0.02967 0.2 0 0.34783 -0.03125 0.1289 -0.03954 0.73333 /
ZA 1 -0.47368 -0.56157 1 -1 -0.15789 -1 -0.40741 -0.5938 1 -1 /
NZ 0.37931 0.21437* 0.14928 0 0.04545 0.40517* 0.10526 0.17263* 0.10268* 0.64706 0.07692 -0.63636 /
Trichobilharzia physellae
Pacific Central Miss Atlantic
AK CA NM AB MI PA
AK /
CA -0.7193 /
NM 0.29524** 0.11111 /
AB -0.30667 1 -0.85507 /
MI 0.0582 -0.66667 0.24159** -0.33333 /
PA 0.12695 1 -0.25703 0 0.11111 /
Additional File 9. Pairwise ΦST values by flyway for TQ and TP. (*) indicates a p-value ≤ 0.05, (**) indicates a p-value ≤ 0.01.
210
Specimen ID Locality Host Species
MSB: Para: # NCBI Source
Trichobilharzia sp. B
W205 Alaska Anas americana 18638 ─ This study
W210 Alaska Anas americana 18643 ─ This study
Trichobilharzia stagnicolae
W164 New Mexico Stagnicola elodes 18659 FJ174461 Brant and Loker, 2009
W221 Michigan Mergus merganser 18649 ─ Brant and Loker 2009; This study
W222 Michigan Mergus merganser 18650 KP734304 Brant and Loker 2009
W223 Michigan Mergus merganser 18651 ─ Brant and Loker 2009; This study
W224 Michigan Stagnicola emarginata 18652 ─ Brant and Loker 2009; This study
W225 Michigan Stagnicola emarginata 19505 ─ Brant and Loker 2009; This study
W229 Michigan Stagnicola emarginata 179 FJ1744543 Brant and Loker 2009
W230 Michigan Mergus merganser 18652 ─ Brant and Loker 2009; This study
W231 Michigan Mergus merganser 18654 ─ Brant and Loker 2009; This study
W240 Michigan Stagnicola emarginata 18673 FJ174462 Brant and Loker, 2009
Trichobilharzia franki
BERS1 France Radix auricularia ─ HM131199 Jouet et al. 2010
EAN77 France Radix auricularia ─ HM131201 Jouet et al. 2010
BERS67 France Radix auricularia ─ HM131200 Jouet et al. 2010
STRS2 France Radix auricularia ─ HM131202 Jouet et al. 2010
FORS3 France Radix auricularia ─ HM131198 Jouet et al. 2010
FORS4 France Radix auricularia ─ HM131197 Jouet et al. 2010
Trichobilharzia regenti
ACI25 Iceland Anas platyrhynchos ─ HM439504 Jouet et al. 2010
BERS58 France Anas platyrhynchos ─ HM439502 Jouet et al. 2010
211
HAR1 France Mergus merganser ─ HM439501 Jouet et al. 2010
CYA18 France Cygnus olor ─ HM439500 Jouet et al. 2010
AC122 Iceland Anas platyrhynchos ─ HM439503 Jouet et al. 2010
EAN9 France Radix peregra ─ HM439499 Jouet et al. 2010
AY157190 Czech Republic Radix peregra ─ AY157190 Lockyer et al. 2003
Trichobilharzia szdati
Lsk2 Siberia Lymnaea stagnalis ─ JF838203 Korsunenko et al. 2012
Lsm20 Siberia Lymnaea stagnalis ─ JF838202 Korsunenko et al. 2012
Lsm7 Siberia Lymnaea stagnalis ─ JF838201 Korsunenko et al. 2012
Lsm4 Siberia Lymnaea stagnalis ─ JF838200 Korsunenko et al. 2012
Lsm3 Siberia Lymnaea stagnalis ─ JF838199 Korsunenko et al. 2012
Lsm2 Siberia Lymnaea stagnalis ─ JF838198 Korsunenko et al. 2012
Lsm1 Siberia Lymnaea stagnalis ─ JF838197 Korsunenko et al. 2012
AY157191 Czech Republic Lymnaea stagnalis ─ AY157191 Lockyer et al. 2003
Trichobilharzia anseri
ANS41 France Anser anser ─ KP901385 Jouet et al 2015
ANS40 France Anser anser ─ KP901384 Jouet et al 2015
ANS36 France Anser anser ─ KP901383 Jouet et al 2015
ANS32 France Anser anser ─ KP901382 Jouet et al 2015
FCI1 Iceland Radix balthica ─ KP901381 Jouet et al 2015
ICR9 Iceland Radix balthica ─ KP901380 Jouet et al 2015
Additional File 10. Trichobilharzia samples included in estimates of species wide diversity
212
APPENDIX C CHAPTER IV ADDITIONAL FILES
Taxa Sample ID Host Locality Lat:Long
MSB: PARA: #
Anserobilharzia brantae W340/352 Branta canadensis Manitoba, CAN 58.7419, -93.9747 176/18628
Austrobilharzia variglandis W697 Larus argentatus Quebec, CAN 48.674, -66.237 -
Bilharziella polonica W930 Anas platyrhynchos Ukraine - -
Dendritobilharzia pulverulenta W926 Anas crecca New Mexico, USA - -
Dendritobilharzia pulverulenta W813 Anas discors Minnesota, USA 47.69405, -96.31216 19616
Gigantobilharzia huronensis W414/513 Physa sp. New Mexico, USA 35.21166, -106.5969 18687/25488
Gigantobilharzia huronensis W678 Physa sp. New Mexico, USA 35.21166, -106.5969 -
Heterobilharzia americana W805 Procyon lotor Louisiana, USA 32.3258, -91.3855 19286
Macrobilharzia macrobilharzia W931 Anhinga anhinga Mississippi, USA - -
Ornithobilharzia canaliculata W393 Larus occidentalis California, USA - 18542
Trichobilharzia physellae W604 Bucephala clangula Alaska, USA - 25269
Trichobilharzia querquedulae W203 Anas clypeata Alaska, USA 65.665, -149.098 18636
Trichobilharzia querquedulae W929 Anas discors Puerto Rico, USA - -
Trichobilharzia stagnicolae W233 Stagnicola emarginata Michigan, USA 45.569,-84.71215 19509
Trichobilharzia sp. A W510/511 Stagnicola elodes New Mexico, USA 36.4636, -105.2733 18705
Trichobilharzia sp. E W606 Anas crecca Alaska, USA - 25257
Avian Schistosome C11 W607 Anas americana Alaska, USA - 25258
Avian Schistosome D W342 Gyraulus parvus Manitoba, CAN 58.7369, -93.7982 18619
Schistosomatidae W216 W216 Haminoea japonica California, USA 37.7678, -122.2776 18660
Schistosomatidae W688 W688 Indoplanorbis exustus Nepal 27.616575, 83.097119 18710
Additional File 1. Locality and accession information for schistosome specimens.
213
Sample ID Taxa UCE Loci
Schistosomatidae
SAMEA2201407 Allobilharzia visceralis 1254
PRJEB519 Schistosoma curassoni 2421
PRJNA273970 Schistosoma haematobium 2423
PRJEB2425 Schistosoma haematobium 2422
PRJNA354903 Schistosoma japonicum 2384
PRJNA286685 Schistosoma japonicum 2382
PRJEB13625 Schistosoma mansoni 2417
PRJNA302212 Schistosoma mansoni 2411
PRJEB523 Schistosoma mattheei 2422
PRJEB522 Schistosoma margrebowiei 2421
PRJEB526 Schistosoma rhodani 2424
SAMEA1920831 Schistosomatium douthitti 115
SAMEA2422295 Trichobilharzia regenti 2400
PRJEB4661 Trichobilharzia szidati 1219
Out-groups
PRJEB3954 Dicrocoelium dendriticum 58
PRJEB1207 Echinostoma caproni 296
PRJNA438721 Fasciola gigantica 260
PRJNA392273 Opisthorchis viverrini 214
PRJNA248332 Paragonimus westermani 246
Additional File 2. Published genomes mined for UCE loci.