Population Structure, Phylogeography, and Conservation
of Two North American Arctic (Oeneis spp.) Butterflies
by
Angela Elizabeth Gradish
A Thesis
presented to
The University of Guelph
In partial fulfillment of requirements
for the degree of
Doctor of Philosophy
in
Environmental Sciences
Guelph, Ontario, Canada
© Angela Elizabeth Gradish, 2014
ABSTRACT
Population Structure, Phylogeography, and Conservation
of Two North American Arctic (Oeneis spp.) Butterflies
Angela Elizabeth Gradish
University of Guelph, 2014
Advisor:
Dr. G. W. Otis
The Macoun’s arctic butterfly [MA, Oeneis macounii (W. H. Edwards)] is
distributed across northern North America. In contrast, the White Mountain arctic
butterfly [WMA, Oeneis melissa semidea (Say)] is endemic to the alpine zone of
the White Mountains, New Hampshire, USA, and is of conservation concern.
Macoun’s arctic and WMA adults occupy fragmented habitats, and the extent of
genetic exchange among their allopatric populations has never been assessed.
Additionally, the MA and WMA are biennial, yet emerge every year over all or
parts of their range as two sympatric, allochronic cohorts, likely reproductively
isolated by their asynchronous adult emergence. Using mtDNA and AFLPs
markers, I elucidated the spatial and temporal genetic population structures of
the MA and the WMA to infer their demographic histories; conservation
management requirements; and the extent of reproductive isolation among
allopatric populations and between sympatric, allochronic cohorts. For the WMA,
I also conducted a mark-release-recapture study to determine its adult
distribution, dispersal patterns, and mating behaviour.
The MA exhibited significant spatial genetic structuring, and dispersal and
gene flow likely is limited among most allopatric populations. Patterns in mtDNA
diversity and divergence suggested that the MA may have re-colonized from a
single eastern refugium in association with jack pine. I found little evidence for
genetic differentiation between sympatric, allochronic MA cohorts.
My field observations revealed that the WMA likely does not exhibit a true
lek mating system. I observed WMA adult movements among some meadows,
and, correspondingly, there was no genetic differentiation among samples
collected from each meadow. Therefore, the WMA can be managed spatially as
a single population. Although the WMA allochronic cohorts were differentiated on
the basis of AFLPs, further analyses are required to confirm that they are
reproductively isolated. Presently, conservation management of the WMA should
focus on increasing its population size.
Finally, I reviewed existing studies of sympatric, allochronic divergence
generated by asynchronous, biennial life cycles in insects. I discuss empirical
knowledge gaps about biennial insect demography and life history, the answers
to which will be crucial for concluding if allochrony acts as a significant
reproductive isolating mechanism in sympatry for biennial insects.
ACKNOWLEDGEMENTS
I am indebted to many people for helping to make this project a success. First I
would like to thank my advisor, Dr. Gard Otis, and the rest of my advisory committee,
Drs. Nusha Keyghobadi, Felix Sperling, and Steve Marshall. Gard, thank you for your
support and guidance, and especially for your unwavering belief in my ability to see this
through. Nusha, thank you for always making time for me and for being a wonderful and
patient teacher. Felix and Steve, thank you for your invaluable input and advice.
Funding for my project was graciously provided by a Natural Sciences and
Engineering Research Council of Canada Industrial Post-Graduate Scholarship
(NSERC-IPS) in partnership with the Cambridge Butterfly Conservatory (CBC). Thank
you to Adrienne Brewster, my supervisor at the CBC, for your enthusiasm and support
of my research.
Thank you to the following professors for lending me equipment that enabled me
to conduct my molecular work in Guelph: Drs. Cynthia Scott-Dupree, Robert Hanner,
Marc Habash, Rebecca Hallett, and Jonathan Schmidt.
My WMA study would not have been possible without the assistance and
cooperation of many people. First and foremost, Thanushi Eagalle and Amy Reinert, my
field assistants, thank you for braving the alpine zone with me and providing
companionship during those long, vertical climbs. Thank you to the White Mountain
National Forest Service and New Hampshire Fish and Game Department for granting
me permission to conduct my field study, Howie Wemyss for allowing me and my Golf
unlimited access to the Mount Washington Auto Road, and Chris Costello of the Bartlett
iv
Experimental Forest for setting me up with a temporary home. Kent McFarland, thank
you for sharing your WMA and alpine zone wisdom and for introducing me to the early
morning world of bird research. Finally, I am grateful to the following people for
collecting specimens and/or sharing their knowledge of O. melissa: Yohann Racine,
Norbert Kondla, Crispin Guppy, and Dave Beresford.
To my fellow colleagues and friends Lauren, Kruti, Braden, Justin, Jon, Maryam,
Michael, Andrew M., Amanda, Connor, Lisa, Nathan, Christie, Shannon, and Randy,
thank you for your friendship and for always being there to celebrate and commiserate
with me. An extra big thank you to Lindsay for so patiently teaching me the ins and outs
of AFLPs.
Mom, thank you for your continued love, encouragement, and enthusiasm, and
for fostering a love for learning and education in me. Oma, thank you for the smarts and
always reminding me to be happy above all else. To the rest of the Gradishes: I’m done
school now. For real this time. Thank you for your love and support through the long
haul.
Finally, Andrew, when I said ‘to whatever comes next’ at the end of my last thesis
acknowledgements, I didn’t imagine that would be dual PhDs. Yet here we are. So once
again: much love and thanks; I absolutely couldn’t have done it without you.
v
TABLE OF CONTENTS
ACKNOWLEDGEMENTS ...............................................................................................iv
TABLE OF CONTENTS ..................................................................................................vi
LIST OF ACRONYMS AND ABBREVIATIONS ...............................................................ix
LIST OF TABLES ............................................................................................................xi
LIST OF FIGURES ........................................................................................................ xiii
Chapter 1: GENERAL INTRODUCTION ........................................................................ 1
1.1 Population Genetic Structure ................................................................................. 1
1.1.1 Spatial Genetic Structure: Allopatric Isolation .................................................. 2
1.1.2 Temporal Genetic Structure: Allochronic Isolation in Periodical Taxa .............. 4
1.1.3 Population Genetic Structure: Conservation Implications ................................ 6
1.2 Butterflies in Population Genetics Research and the Genus Oeneis ..................... 7
1.3 Study Species, Molecular Marker Choice, and Thesis Structure ........................... 9
Chapter 2: POPULATION GENETIC STRUCTURE AND PHYLOGEOGRAPHY OF
THE MACOUN’S ARCTIC BUTTERFLY (OENEIS MACOUNII) .................................. 17
2.0 Abstract ................................................................................................................ 17
2.1 Introduction .......................................................................................................... 18
2.2 Materials and Methods ......................................................................................... 22
2.2.1 Sample Collection and DNA Extraction ......................................................... 22
2.2.2 mtDNA Amplification ...................................................................................... 27
2.2.3 AFLP Analysis ............................................................................................... 27
2.2.4 Data Analyses ................................................................................................ 29
2.3 Results ................................................................................................................. 32
2.3.1 mtDNA ........................................................................................................... 32
2.3.2 AFLPs ............................................................................................................ 45
2.4 Discussion............................................................................................................ 51
2.4.1 Population Structure and Contemporary Barriers to Gene Flow .................... 51
2.4.2 Phylogeographic History ................................................................................ 55
2.4.3 Conservation Implications .............................................................................. 62
vi
Chapter 3: NOTES ON THE DEMOGRAPHY, LIFE HISTORY, AND BEHAVIOUR OF
THE WHITE MOUNTAIN ARCTIC BUTTERFLY (OENEIS MELISSA SEMIDEA) ...... 67
3.1 Abstract ................................................................................................................ 67
3.2 Introduction .......................................................................................................... 68
3.3 Field Methods ...................................................................................................... 69
3.4 Results and Discussion ........................................................................................ 71
3.4.1 Adult Life History and Demographics ............................................................. 71
3.4.2 General Adult Behaviour and Mating System ................................................ 75
Chapter 4: POPULATION STRUCTURE AND CONSERVATION GENETICS OF THE
WHITE MOUNTAIN ARCTIC BUTTERFLY (OENEIS MELISSA SEMIDEA) ............... 81
4.1 Abstract ................................................................................................................ 81
4.1 Introduction .......................................................................................................... 82
4.2 Materials and Methods ......................................................................................... 87
4.2.1 Sample Collection .......................................................................................... 87
4.2.2 DNA Extraction .............................................................................................. 87
4.2.3 mtDNA Amplification ...................................................................................... 89
4.2.4 AFLP Analysis ............................................................................................... 89
4.2.5 Data Analysis ................................................................................................. 91
4.3 Results ................................................................................................................. 94
4.3.1 mtDNA ........................................................................................................... 94
4.3.2 AFLPs ............................................................................................................ 96
4.4 Discussion............................................................................................................ 99
4.4.1 Tissue Type and DNA Amplification .............................................................. 99
4.4.2 Genetic Diversity .......................................................................................... 101
4.4.3 Population Genetic Structure ....................................................................... 102
4.4.4 Conservation Implications ............................................................................ 106
Chapter 5: THE POTENTIAL FOR SYMPATRIC, ALLOCHRONIC DIVERGENCE IN
BIENNIAL INSECTS: A REVIEW ............................................................................... 109
5.1 Evidence for reproductive isolation between sympatric, allochronic cohorts ...... 112
5.2 The formation of sympatric, allochronic cohorts ................................................. 120
5.3 Why do rare cohorts remain rare? ..................................................................... 122
5.4 Conclusions and areas for further research ....................................................... 125
vii
Chapter 6: GENERAL DISCUSSION AND CONCLUSIONS ..................................... 128
6.1 Population structure and phylogeography of the Macoun’s arctic butterfly ........ 128
6.1.1 Summary of results ...................................................................................... 128
6.1.2 Future research ........................................................................................... 130
6.2 Population structure and conservation of the White Mountain arctic butterfly .... 132
6.2.1 Summary of results ...................................................................................... 132
6.2.2 Conservation implications and management recommendations .................. 134
6.3 Sympatric, allochronic divergence in biennial insects ........................................ 138
6.4 Conclusions ....................................................................................................... 140
LITERATURE CITED .................................................................................................. 142
Appendix 1: LABORATORY PROTOCOL USED TO AMPLIFY THE
MITOCHONDRIAL GENE CYTOCHROME C OXIDASE I (COI) FOR OENEIS
MACOUNII AND OENEIS MELISSA SEMIDEA ......................................................... 165
Appendix 2: LABORATORY PROTOCOL USED TO GENERATE AMPLIFIED
FRAGMENT LENGTH POLYMORPHISMS (AFLPs) FOR OENEIS MACOUNII AND
OENEIS MELISSA SEMIDEA .................................................................................... 167
Appendix 3: METADATA FOR CYTOCHROME C OXIDASE I (COI) SEQUENCES
GENERATED FROM OENEIS MACOUNII AND OENEIS MELISSA SEMIDEA
SAMPLES ANALYZED IN THIS STUDY. ................................................................... 173
viii
LIST OF ACRONYMS AND ABBREVIATIONS
AFLP
amplified fragment length polymorphism
AMOVA
analysis of molecular variance
AT
Appalachian Trail
BL
Bigelow Lawn
BSA
bovine serum antibody
bp
base pair(s)
COI
cytochrome c oxidase subunit I
CP
Cow Pasture
Fst
fixation index; degree of inbreeding within subpopulations relative to the
total population
GT
Gulf Tanks
Hd
haplotype diversity
He
expected heterozygosity
IBD
isolation by distance
LGM
last glacial maximum
MA
Macoun’s arctic butterfly
ML
Monticello Lawn
MRR
mark-release-recapture
mtDNA
mitochondrial DNA
Ne
effective population size
PCoA
principal coordinates analysis
PPL
proportion of polymorphic loci
ix
rfu
relative fluorescent units
R-L
restriction-ligation
WMA
White Mountain arctic butterfly
π
nucleotide diversity
x
LIST OF TABLES
Table 2.1. Collection locality information, the number of individuals successfully
amplified for AFLPs (n AFLP) and mtDNA (n mtDNA), and the COI haplotypes found at
each location for Oeneis macounii samples analyzed in this study. .............................. 24
Table 2.2. Summary statistics for 39 Oeneis macounii populations based on mtDNA and
AFLP markers. .............................................................................................................. 33
Table 2.3. Results of analyses of molecular variance (AMOVA) of mtDNA and AFLP
data for Oeneis macounii. ............................................................................................. 34
Table 2.4. Pairwise mtDNA (top right) and AFLP (bottom left) Fst estimates for 40
Oeneis macounii populations. Bolded values are not significant at α = 0.01. ................ 35
Table 2.5. Summary of the AFLP phenotype scoring and mismatch error analysis
results for the five selective primer combinations used to amplify Oeneis macounii
samples. ........................................................................................................................ 46
Table 3.1. Summary of Oeneis melissa semidea adult capture data. ........................... 72
Table 4.1. Location of each alpine meadow where samples were collected, the number
of individuals successfully amplified for mtDNA (n mtDNA) and AFLPs (n AFLPs), and
the cytochrome c oxidase I (COI) haplotypes found within each meadow subpopulation
for the Oeneis melissa semidea samples analyzed in this study. .................................. 92
Table 4.2. Within subpopulation and within sampling year genetic diversity estimates for
Oeneis melissa semidea. Sampling year estimates are reported as mean values. ....... 95
Table 4.3. Results of analyses of molecular variance (AMOVA) of mtDNA and AFLP
data for seven subpopulations of Oeneis melissa semidea........................................... 95
Table 4.4 Summary of the AFLP phenotype scoring and results of mismatch error
analysis for the six selective primer combinations used to amplify Oeneis melissa
semidea samples. ......................................................................................................... 96
Table 4.5. Levels of genetic diversity in natural populations of lepidopteran species
measured using AFLPs. .............................................................................................. 102
Table A1.1. Primer sequences used to amplify the COI gene for Oeneis macounii and
Oeneis melissa semidea. ............................................................................................ 165
Table A1.2. Protocol for PCR amplification of the COI gene for Oeneis macounii and
Oeneis melissa semidea. ............................................................................................ 166
Table A2.1. Oligonucleotides used for the AFLP analysis of Oeneis macounii and
Oeneis melissa semidea. ............................................................................................ 169
xi
Table A2.2. Protocol for annealing EcoRI and MseI adaptors for use in restrictionligation reactions. ........................................................................................................ 170
Table A2.3. Protocol for restriction-ligation (R-L) reactions. ........................................ 171
Table A2.4.Protocol for pre-selective PCR amplification. ............................................ 172
Table A2.5. Protocol for selective PCR amplification. ................................................. 172
Table A3.1. Barcode of Life Data System (BOLD) process identification numbers,
collection location information, and haplotype identification numbers for Oeneis
macounii and Oeneis melissa semidea cytochrome c oxidase I (COI) sequences
generated in this study (NA = missing data). ............................................................... 173
xii
LIST OF FIGURES
Figure 1.1. An adult Macoun’s arctic butterfly (Oeneis macounii), marked during a field
study. Photo: Laura Burns. ............................................................................................ 11
Figure 1.2. An adult White Mountain arctic butterfly (Oeneis melissa semidea). Photo:
Kent McFarland. ............................................................................................................ 11
Figure 2.1. Sampling locations (n = 62) of Oeneis macounii adults analyzed in this
study. Blue circles represent populations where adults emerge in even-numbered years,
white circles represent populations where adults emerge in odd-numbered years, and
grey circles represent populations where adults emerge in both even and odd years.
. ..................................................................................................................................... 23
Figure 2.2. Relationship between Euclidean geographic (Ln [1 + geographic distance
(km)]) and a) mtDNA genetic distance between populations [Fst/(1-Fst)] (r2 = 0.132,
P<0.0001), b) mtDNA genetic distance between individuals (r2 = 0.0384, P<0.0001) for
Oeneis macounii. Each point corresponds to a pair of populations (a) or individuals (b).
...................................................................................................................................... 42
Figure 2.3. Principle coordinates analyses of mtDNA data for Oeneis macounii based on
a pairwise Fst matrix (a) and an individual x individual genetic distance matrix (b). ....... 43
Figure 2.4. Haplotype network for COI sequences for Oeneis macounii. Circle sizes are
proportional to the total number of individuals with that haplotype. Colours represent the
geographic region and year class (even- or odd-numbered years where the haplotype
was found, while the size of each coloured section indicates the frequency of that
haplotype within the respective geographic region relative to its total frequency. Bars
represent missing/inferred haplotypes........................................................................... 44
Figure 2.5. Relationship between Euclidean geographic (Ln [1 + geographic distance
(km)]) and a) AFLP genetic distance between populations [Fst/(1-Fst)] (r2 = 0.0729,
P<0.0001), and b) AFLP genetic distance between pairs of individuals (r2 = 0.00907,
P<0.0001) for Oeneis macounii. Each point corresponds to a pair of populations (a) or
individuals (b). ............................................................................................................... 48
Figure 2.6. Principle coordinates analyses of AFLP data for Oeneis macounii based on
a pairwise Fst matrix (a) and an individual x individual genetic distance matrix (b). ....... 49
Figure 2.7. Estimated population structure for Oeneis macounii AFLPs inferred by
STRUCTURE under an admixture model of K = 2. Each bar represents an individual,
and the bar colours indicate the proportion of its genotype that belongs to each of K = 2
clusters. For display purposes, individuals were grouped by population and arranged
from west to east. The demarcation between all even and odd-year populations and all
even-year populations to the west and east of mid-Manitoba, respectively, is indicated
below the graph. ............................................................................................................ 50
xiii
Figure 3.1 Distribution of adult Oeneis melissa semidea on the alpine zone of Mts.
Washington and Jefferson, New Hampshire, USA determined by mark-releaserecapture (MRR). Black lines indicate the areas surveyed for adults [Bigelow Lawn (BL),
Gulf Tanks (GT), Cow Pasture (CP), and Monticello Lawn (ML)]. White and blue points
indicate that the capture or sighting was made in 2011 and 2012, respectively. Triangles
indicate locations where individual females were captured, circles where individual
males were captured, and squares where an adult was sighted, but not captured. ...... 70
Figure 4.1. Approximate locations of the four sedge meadows on the alpine zone of the
White Mountains, New Hampshire, from which samples of Oeneis melissa semidea
were collected for this study: Monticello Lawn (ML) on Mount Jefferson; and Cow
Pasture (CP), Gulf Tanks (GT), and Bigelow Lawn (BL) on Mount Washington. .......... 88
Figure 4.2. Principle coordinates analysis of AFLP data for Oeneis melissa semidea
based on an individual x individual genetic distance matrix. ......................................... 98
Figure 4.3. Estimated population structure for Oeneis melissa semidea AFLPs inferred
by STRUCTURE under an admixture model of K = 2. Each bar represents an individual,
and the bar colours indicate the proportion of its genotype that belongs to each of K = 2
clusters. Individuals are grouped based on the meadow [Bigelow Lawn (BL), Cow
Pasture (CP), Gulf Tanks (GT), and Monticello Lawn (ML)] and year [2011 (11) or 2012
(12)] from which they were sampled. Vertical black lines delimit the groups of individuals
belonging to each meadow subpopulation. ................................................................... 98
Figure 4.4 Relationship between Euclidean geographic distance (Ln [1 + geographic
distance (km)]) and the genetic differentiation between individuals (r2 = -0.036) based
on AFLPs for Oeneis melissa semidea. Each point corresponds to a pair of individuals.
...................................................................................................................................... 99
xiv
Chapter 1
GENERAL INTRODUCTION
1.1 Population Genetic Structure
Understanding the phenomena of population and species divergence is of
fundamental interest and importance to evolutionary biology. In general, divergence is
preceded by a reduction or cessation of gene flow between populations, and therefore a
central focus of evolutionary research is identifying the barriers to genetic exchange that
initiate and maintain divergence (Coyne and Orr 2004). Few natural populations can be
considered truly panmictic; instead, most exhibit some degree of genetic structure, or
heterogeneity in haplotype or allele frequencies, across a single population or series of
subpopulations (Holsinger and Weir 2009, Freeland et al. 2011). Patterns in genetic
structuring are influenced by the interplay of gene flow among individuals or
subpopulations and the relative strength of localized drift and selection (Foll and
Gaggiotti 2006, Nève 2009, Freeland et al. 2011). Gene flow in turn may be limited by
environmental factors and/or innate biological characteristics of the species (Collinge
2000, Keyghobadi et al. 2005, Darvill et al. 2010, Graham and Burg 2012).
Characterizing a species’ genetic structure thus can provide a wealth of insight into the
abiotic and biotic factors underlying patterns of genetic diversity and driving population
divergence and speciation (Avise 2000, Hey and Machado 2003, Foll and Gaggiotti
2006, Raeymaekers et al. 2012).
1
1.1.1 Spatial Genetic Structure: Allopatric Isolation
Allopatric isolation is widely accepted to be a principle cause of population and
species divergence (Coyne and Orr 2004), and thus studying genetic variation in a
spatial context is key to understanding the processes and patterns of evolution (Avise
2000, Foll and Gaggiotti 2006, Knowles and Carstens 2007, Raeymaekers et al. 2012).
Most organisms consist of multiple geographically discrete populations that display
some degree of genetic structuring, arising from both historical and contemporary
patterns of gene flow (Freeland et al. 2011). For organisms inhabiting previously
glaciated areas, modern day patterns in the genetic differentiation among populations
can be partially attributed to glacial and post-glacial demographic and vicariant events,
and thus can be analyzed to make inferences about their biogeographical history.
During the last glacial maximum (LGM), the ranges of such organisms became
restricted to ice-free refugia, sometimes fragmented among several, and following the
retreat of the glaciers, populations expanded into previously-glaciated areas (Pielou
1991, Hewitt 1996). The specific nature of these historical scenarios (e.g., number and
location of refugia used; mode and pace of colonization; areas of secondary contact) is
associated with characteristic patterns of extant spatial genetic variation (Nichols and
Hewitt 1994, Hewitt 1996, Ibrahim et al. 1996, Avise 2000, Hewitt 2000). For instance,
organisms that colonized from multiple refugia typically display strong genetic
structuring with unique haplotypes or alleles confined to distinct geographic regions
(Pugarin-R and Burg 2012). In contrast, a general lack of genetic structuring with
geographically-widespread, shared haplotypes or alleles and high diversity suggests a
rapid colonization from a single refugium (de Jong et al. 2011, Pugarin-R and Burg
2
2012). To a large extent, patterns are species-specific, reflecting the unique life history
and behavioural traits and demographic histories of the individual species (Soltis et al.
2006, Vila et al. 2011, Pugarin-R and Burg 2012). However, comparison of multiple
species from the same geographic area often reveals somewhat congruent historical
patterns (Hewitt 1996, Brunsfield et al. 2001, Soltis et al. 2006). Therefore,
phylogeographic analyses of spatial genetic structuring can be used to make inferences
about the biogeographical history of specific organisms and the broader historical
patterns for a given geographic region (Avise 2000, Dincă et al. 2011).
The spatial genetic structuring of a species also reflects the present-day degree
of reproductive isolation among populations: in general, a reduction in gene flow leads
to increased population differentiation and structuring. Levels of gene flow largely
depend on the extent of dispersal among subpopulations, which varies greatly both
intra- and interspecifically (Kuras et al. 2003, Bowler and Benton 2005). At the most
basic level, species display differences in their innate tendency or ability to disperse,
ranging from very sedentary (low gene flow and strong population structuring) to highly
mobile (high gene flow and weak population structuring) (Peterson and Denno 1998,
Avise 2009, Habel and Schmitt 2009). However, many abiotic and biotic factors that
also vary within and between species affect these basic traits (Bowler and Benton 2005,
Avise 2009). For most species, geographic distance limits dispersal and gene flow to
some extent, a phenomenon known as isolation by distance (IBD) (Nève 2009,
Freeland et al. 2011). In general, IBD patterns are strongest for moderately mobile
species, while highly sedentary or mobile species, because they display differentiation
among all or few populations, respectively, are predicted to display weak IBD
3
relationships (Peterson and Denno 1998). Furthermore, both natural and anthropogenic
landscape features commonly alter dispersal and gene flow. While mountain ranges
and large bodies of water obviously restrict dispersal for many terrestrial species,
depending on the species, even seemingly insignificant features, such as roads (e.g.,
Baur and Baur 1990, Marsh et al. 2005, Shepard et al. 2008) and small streams (e.g.,
Marsh et al. 2007), may act as major impediments to movement. Finally, habitat
characteristics, such as fragmentation, persistence, and patch quality and size, can
induce significant, even sometimes adaptive, changes in dispersal and hence patterns
of genetic exchange (VanDyck and Matthysen 1999, Bowler and Benton 2005,
Keyghobadi 2007). Therefore spatial patterns in genetic variation of a species also can
provide insight into its life history, behaviour, and habitat use, and how these traits are
predicted to evolve in response to environmental change.
1.1.2 Temporal Genetic Structure: Allochronic Isolation in Periodical Taxa
More controversial is the idea that populations may become reproductively
isolated and structured in sympatry (Mayer 1947, Bush 1969, Coyne and Orr 2004,
Bolnick and Fitzpatrick 2007, Fitzpatrick et al. 2008). Most sympatric divergence theory
and research has focused on population division and assortative mating due to
disruptive sexual selection, or niche or host shifts (Coyne and Orr 2004). However,
gene flow also may be curtailed in sympatry when an environmental or developmental
event splits a population into allochronic subpopulations (i.e., those reproducing at
different times) in the absence of a host or habitat shift (Alexander and Bigelow 1960,
Coyne and Orr 2004, Hendry and Day 2005). Allochronic divergence primarily has been
4
investigated in the context of populations of animals or plants breeding in different
seasons (Alexander and Bigelow 1960, Abbot and Withgott 2004, Maes et al. 2006,
Friesen et al. 2007, Santos et al. 2007, Santos et al. 2010), times within a season
(Ording et al. 2010, Bell 2012, Yamamoto and Sota 2012), or even times within the
same day (Knowlton et al. 1997, Clifton and Clifton 1999, Miyatake et al. 2002, Danley
et al. 2007). However, often overlooked is the potential for allochronic isolation and
divergence arising from asynchronous periodical life cycles, resulting in populations
breeding in different years. Periodical animals have life cycles of a duration of k years,
where k > 1, with adults appearing synchronously every kth year (Heliövaara et al.
1994). Most periodical species consist of sympatric or parapatric, allochronic
populations, typically referred to as cohorts or broods, over part of their range,
presumably reproductively isolated by their asynchronous adult emergence (Scott 1986,
Heliövaara et al. 1994). To date, the most compelling evidence for allochronic
divergence initiated or maintained by asynchronous, periodical life cycles comes from
periodical cicadas (Magicicada spp.) and pink salmon [Oncorhynchus gorbuscha
(Walbaum)]. In the case of cicadas, each species with a 13-year life cycle appears to
have been isolated, and subsequently speciated, independently from its respective 17year sister species by the elimination of a 4-year nymphal diapause (Marshall and
Cooley 2000, Cooley et al. 2001). Pink salmon are biennial, requiring 2 years to
develop, and spawn in rivers in north-western North America and Asia (Aspinwall 1974).
Most streams harbor sympatric even- and odd-year cohorts. Although morphologically
identical, asynchronous cohorts within streams are more genetically diverged than
synchronous cohorts from different streams (Aspinwall 1974, Beacham et al. 1988,
5
Brykov et al. 1996), and outbreeding depression has been observed in F2 hybrids of
alternate-year cohorts (Gharrett et al. 1999). These observations suggest that gene flow
between sympatric salmon cohorts is restricted by allochrony. Thus, although sympatric,
allochronic isolation most certainly is not a widespread phenomenon (Coyne and Orr
2004), it may be significant to the evolution of periodical taxa (Scott 1986, Heliövaara et
al. 1994). Despite this, few studies have examined the temporal genetic structuring of
periodical taxa.
1.1.3 Population Genetic Structuring: Conservation Implications
Historically, the extinction risk of threatened populations and species was
assumed to largely be related to environmental and demographic changes; however, it
is now understood that population genetics and genetic structuring play a substantial
role in their short and long term destiny (Spielman et al. 2004, Frankham 2010). By
definition, endangered or threatened taxa exhibit small, and usually declining,
population sizes. Compared to large populations, small, isolated populations typically
experience increased genetic drift and inbreeding, resulting in a loss of genetic diversity
and individual fitness (Frankham 1996, Nève 2009, Frankham 2010). Consequently,
individual survival and reproduction, and, in turn, the population’s ability to evolve in
response to environmental change in the long-term, are reduced, ultimately translating
into a heightened extinction risk (Spielman et al. 2004, Frankham et al. 2010).
Depending on the resulting underlying structure, population fragmentation, the
separation of a population into discontinuous subpopulations, may exacerbate these
6
genetic processes, further accelerating population decline. If sufficient gene flow is
maintained, a fragmented population will retain the genetic characteristics of an unfragmented population of the same total size; however, a lack of gene flow among
fragments results in increased genetic structuring and reduced local effective population
sizes (Keyghobadi 2007, Frankham et al. 2010). Within isolated fragments, drift and
inbreeding will accelerate, leading to the erosion of diversity within and the eventual
extinction of local subpopulations, and, in turn, an overall reduction in genetic diversity
(Frankham et al. 2010). Therefore, a fragmented, structured population often is at an
increased risk of extinction compared to a continuous, unstructured population of the
same overall size (Frankham et al. 2010). As such, knowledge of the population genetic
structure and diversity of threatened species is crucial for assessing its extinction risk
and establishing plans for effective conservation management (Desalle and Amato
2004).
1.2 Butterflies in Population Genetics Research and the Genus Oeneis
Due to their staggering taxonomic, behavioural, and ecological diversity, insects
are ideal model organisms for evolutionary- and conservation-based population genetic
studies. Insects are by far the most abundant and diverse group of animals, comprising
over half of the world’s known biodiversity both in terms of biomass and species
numbers (Foottit and Adler 2009, New 2009). They play an instrumental role in the
functioning of virtually all terrestrial and fresh water ecosystems, and have helped to
vastly advance our knowledge of the natural world (Scudder 2009). These traits are
particularly true of butterflies. Owing to their conspicuous and charismatic nature, our
7
ecological, biological, and taxonomic knowledge of butterflies rivals that of any other
group of insects (New 1991). Butterflies exist in all terrestrial habitat types, where they
display great inter- and intraspecific variation in habitat specificity, life history traits,
dispersal behaviour, and population structure (New 1991). Because they are especially
sensitive to environmental change and can be monitored relatively easily, they also
make excellent indicators for general ecosystem functioning and environmental change
(New 1991, Thomas 2005). Many butterfly species are in decline from habitat loss and
climate change, and hence, they are increasingly the targets of conservation efforts.
Their public appeal means that such butterflies serve as ambassadors for the
preservation of their associated habitats and the less captivating organisms they may
contain (Ehrlich 2003). All of these traits make butterflies ideal model organisms for the
fields of ecology and evolution, conservation biology, and animal behaviour (Ehrlich
2003, Harper et al. 2003). Genetic analyses of butterflies continue to provide invaluable
insight into population and species evolution, and the factors contributing to population
extinction and recovery (e.g., Baguette et al. 2000, Harper et al. 2003, Rubinoff and
Sperling 2004, Gompert et al. 2006, Keyghobadi et al. 2006, Brattström et al. 2010,
Bromilow and Sperling 2011, Habel et al. 2011, Stephens et al. 2011).
My research focused on butterflies from the genus Oeneis, commonly known as
the ‘arctics’. The genus has a Holarctic distribution, with 12 species occurring in North
America. In western North America, arctic butterflies occur from the White Mountains in
Arizona and the Sierras of California north to arctic tundra of Alaska. Their range
continues east across Canada to Nova Scotia. In the east, the southernmost
populations are found in northern New York, the White Mountains of New Hampshire,
8
and Maine (Howe 1975, Brock and Kaufman 2003). Across their range, arctic butterflies
typically are limited to specific, restricted areas in northern or montane regions
characterized by short growing seasons (Dunlop 1962, Masters and Sorensen 1969,
Troubridge et al. 1982, Clayton and Petr 1992). As most species are univoltine with a
brief flight period and reside in remote locations, often with unpredictable and harsh
weather, arctic butterflies generally are understudied (Masters et al. 1967, Burns 2013).
Thus, population genetic characterization of these butterflies would be particularly
useful for providing novel insight into their life histories, behaviour, demographics, and
evolutionary histories. Of particular interest is that while most arctic butterflies have
confirmed biennial lifecycles (i.e., requiring 2 years for development, with adults
emerging every other year) (Masters 1974, Douwes 1980, Scott 1986, Brock and
Kaufman 2003), many populations produce adults annually (Sperling 1993, Brock and
Kaufman 2003, G. W. Otis, unpub. data). These populations likely consist of two
sympatric, biennial cohorts, one emerging in even-numbered years, the other in oddnumbered years, reproductively-isolated by their asynchronous emergence schedules.
This asynchronous emergence presents the opportunity for sympatric, allochronic
divergence via asynchronous periodical life cycles, a phenomenon that has yet to be
directly investigated within this genus.
1.3 Study Species, Molecular Marker Choice, and Thesis Structure
The Macoun’s arctic butterfly (MA) [Oeneis macounii (W. H. Edwards)] (Figure
1.1) and the White Mountain arctic butterfly (WMA) [Oeneis melissa semidea (Say)]
(Figure 1.2) served as my specific arctic butterfly models. My overall goal was to
9
characterize their spatial and temporal population structures to elucidate their
demographic histories, conservation status, and/or dispersal behaviour; and to test for
significant genetic variation between sympatric, allochronic cohorts. To achieve this, I
primarily used genetic data generated from mitochondrial DNA (mtDNA) and amplified
fragment length polymorphism (AFLP) markers. Due to its low recombination,
uniparental inheritance, and relatively high mutation rate, mtDNA has been widely used
for phylogeographic and population genetic studies of animals (Avise 2009, Nève 2009,
Freeland et al. 2011). Specifically, the mtDNA gene cytochrome c oxidase subunit I
(COI) is frequently applied in such studies of insects at the intra and interspecific levels
(e.g., Caterino et al. 2000, Sperling 2003, Rubinoff and Sperling 2004, Lohman et al.
2008, Borer et al. 2010, Wu et al. 2010a, de Jong et al. 2011, Schoville et al. 2011, Vila
et al. 2011, Bertheau et al. 2013). However, despite the utility of mtDNA, genetic
analyses ideally should employ multiple markers (Roe and Sperling 2007, Sperling and
Roe 2009, Dupuis et al. 2012): different markers display different inheritance patterns,
genealogies, levels of diversity, and mutation rates, and thus can illuminate different
biological and historical features of species (Hoffman et al. 2009, Freeland et al. 2011,
Qu et al. 2012).
10
Figure 1.1. An adult Macoun’s arctic butterfly (Oeneis macounii), marked during a field
study. Photo: Laura Burns.
Figure 1.2. An adult White Mountain arctic butterfly (Oeneis melissa semidea). Photo:
Kent McFarland.
11
The AFLP technique (Vos et al. 1995) allows for the rapid and inexpensive
generation of a large number of informative markers without requiring prior genomic
knowledge of the study organism (Mueller and LaReesa Wolfenbarger 1999, Bensch
and Akesson 2005). Like other DNA fingerprinting techniques (RAPDs, ISSRs), AFLPs
allow for a genome-wide assessment of variation; however, AFLPs generally are
comparatively more informative, reproducible, and robust (Meudt and Clarke 2007).
Because of their high resolution and variability, AFLP markers are particularly useful for
elucidating intraspecific genetic structuring and diversity (Mueller and LaReesa
Wolfenbarger 1999, Bensch and Akesson 2005, Meudt and Clarke 2007), and are
increasingly being successfully applied to such studies of lepidopteran species (e.g.,
Takami et al. 2004, Gompert et al. 2006, Timm et al. 2006, Kronforst et al. 2007,
Kronforst and Gilbert 2008, Krumm et al. 2008, Schroeder and Degen 2008, Timm et al.
2008, Brattström et al. 2010, Collier et al. 2010, Franklin et al. 2010, Wu et al. 2010a,
Crawford et al. 2011, Leidner and Haddad 2011, Tao et al. 2012, Yamamoto and Sota
2012).
Chapter 2 of my thesis [Population genetic structure and phylogeography of the
Macoun’s arctic butterfly (Oeneis macounii)] focuses on the MA (Figure 1.1). The MA is
distributed across most of Canada and adjacent Minnesota and Michigan, USA (Howe
1975, Layberry et al. 2001), where it is generally associated with mature jack pine
(Pinus banksiana Lamb.) or lodgepole pine (Pinus contorta Dougl. ex. Loud.) stands
established on sand or loam (Masters et al. 1967, Masters 1972, Layberry et al. 2001).
Its habitat is fragmented from natural and anthropogenic factors, and thus despite its
broad distribution, the MA exists as numerous allopatric populations separated by a
12
wide range of geographic distances. Recent field observations indicate that the MA
displays limited dispersal (Burns 2013), suggesting that even geographically-proximate
populations may be reproductively isolated. Given its current range and habitat
associations, the MA’s present day spatial genetic structure also was undoubtedly
influenced by the glacial cycles of the late Pleistocene. My first objective was to
determine the extent and pattern of genetic differentiation among allopatric populations
of the MA from over its entire range to infer contemporary and historical factors that
have shaped its spatial structure. Additionally, the MA is confirmed biennial, and,
compared to most arctic butterflies, has a well-documented adult emergence pattern
and schedule (Masters 1972, 1974, Sperling 1993, Burns 2013; G. W. Otis unpub.
data).Therefore, it serves as an ideal model to examine the role of allochronic isolation
in the evolution of this genus and biennial insects in general. The MA generally consists
of two main allopatric and allochronic cohorts: a western cohort emerging primarily in
odd-numbered years that extends from eastern British Columbia to western Manitoba,
and an eastern cohort emerging exclusively in even-years occurring from eastern
Manitoba to western Quebec (Masters 1974, Layberry et al. 2001). However, smaller
even-year populations also exist in the foothills of the Rocky Mountains east of Calgary,
and in the interior of British Columbia the more common year of emergence is in evennumbered years. Still other populations in the foothills of the Rockies emerge annually,
and my second objective was to determine if these sympatric, allochronic cohorts are
genetically differentiated.
Chapters 3 and 4 detail my conservation-focused studies of the WMA (Figure
1.2). The WMA is rare and extremely localized, occurring only within an 80 ha area of
13
the alpine zone of the White Mountains, New Hampshire (McFarland 2003). Because of
its rarity and restricted range, the WMA has been ranked globally as G5T2 (overall, O.
melissa is secure and widespread, but subspecies semidea is critically imperiled) and
by New Hampshire as S2 (imperiled because rarity or other factors demonstrably make
it very vulnerable to extinction) (McFarland 2003, New Hampshire Fish and Game
Department 2006). Because of the alpine zone’s extreme and unpredictable weather
and logistical difficulties in accessing areas where adults occur, monitoring and study of
the WMA has proven challenging (Anthony 1970, McFarland 2003). Thus, very little has
been quantified regarding its behaviour, habitat use, and population structure, and yet
this information will be of utmost importance for its preservation. Of particular concern,
the WMA may exhibit significant population fragmentation and isolation (Anthony 1970,
McFarland 2003) that may elevate its extinction risk and have management
implications. WMA adults are reportedly localized within four disjunct alpine meadows
dominated by their host plant, Bigelow’s sedge (Carex bigelowii Torr. ex. Schwein), with
very few adults sighted in intervening areas among meadows (McFarland 2003). This
suggests that adult dispersal is low, and thus that the WMA population may be
structured into multiple, reproductively isolated allopatric subpopulations. To date, there
has only been one attempt to confirm and quantify this suspected lack of genetic
exchange: Anthony (1970) initiated a mark-release-recapture (MRR) study of WMA
adults, but, due to undisclosed logistical difficulties, ultimately abandoned his attempt.
However, he did note significant differences in wing morphology between some
meadows, consistent with a lack of adult dispersal and gene flow (Anthony 1970).
14
Within all alpine meadows, the WMA emerges annually. However, given that all
other O. melissa subspecies are biennial (Layberry et al. 2001, Brock and Kaufman
2003), and that the alpine zone climate is not conducive to annual development, the
WMA likely requires 2 years for development. Thus the WMA may be further structured
and isolated into two sympatric, allochronic cohorts. Interestingly, this possible temporal
structuring of the WMA has never been investigated. Elucidating the WMA’s spatial and
temporal population structure and dispersal patterns will be of use to accurately assess
its risk of extinction and design of appropriate conservation management strategies.
In Chapter 3 [Notes on the demography, life history, and behaviour of the White
Mountain arctic butterfly (Oeneis melissa semidea)], I summarize my attempt at a MRR
study of the WMA. For butterflies, the use of MRR methods is useful for providing
insight into census population size, distribution, and dispersal patterns (Keyghobadi et
al. 2003). Furthermore, employing such direct observational techniques in conjunction
with genetic analyses greatly strengthens conclusions about dispersal and population
isolation (Vandewoestijne and Baguette 2004a, Leidner and Haddad 2011). I also
attempted more thorough behavioural observations of adults to further illuminate the
WMA’s habitat use and mating system. These data will be important for detecting WMA
population changes related to habitat loss or modification (McFarland 2003) and for the
development of captive rearing techniques, respectively.
In Chapter 4 [Population structure and conservation genetics of the White
Mountain arctic butterfly (Oeneis melissa semidea)], I present the results of my genetic
assessment of the WMA population. To date, genetic analysis has played an
indispensible role in the management of threatened species (Petit et al. 1996, Desalle
15
and Amato 2004, Fallon 2006, Scribner et al. 2006, Charman et al. 2010, Frankham
2010), and is now extensively used in the study of endangered butterfly populations
(e.g., Baguette et al. 2000, Harper et al. 2003, Rubinoff and Sperling 2004,
Vandewoestijne and Baguette 2004b, Gompert et al. 2006, Sigaard et al. 2008,
Crawford et al. 2011, Habel et al. 2011, Stephens et al. 2011, Proshek et al. 2013).
Genetic data are particularly useful for taxa like the WMA for which direct observation
and study is difficult. Using mtDNA and AFLPs, I determine levels of genetic diversity,
and the degree of genetic differentiation among suspected meadow subpopulations and
between putative allochronic cohorts. I compare these results with my field observations
of adult distribution and dispersal, and discuss the implications of my findings for the
conservation management of the WMA.
In Chapter 5 I present a review and discussion of the potential for sympatric,
allochronic divergence of asynchronous, biennial insect populations. Although this
mechanism for divergence has been recognized previously (Scott 1986, Heliövaara et
al. 1994), very few studies have directly examined the differentiation between biennial,
allochronic insect cohorts (Douwes and Stille 1988, Heliövaara et al. 1988, Väisänen
and Heliövaara 1990, Kankare et al. 2002). I review the existing empirical studies on
allochronic isolation in biennial insects, including the results of my work with the MA and
WMA, and discuss existing knowledge gaps about biennial insect development,
population demographics, and evolutionary histories that preclude a definitive
conclusion about the general importance of this isolating mechanism.
Finally, in Chapter 6 I provide a summary of the key findings from my research
and discuss areas for further study.
16
Chapter 2
POPULATION GENETIC STRUCTURE AND PHYLOGEOGRAPHY OF THE
MACOUN’S ARCTIC BUTTERFLY (OENEIS MACOUNII)
2.0 Abstract
The Macoun’s arctic butterfly [Oeneis macounii (W. H. Edwards)] occurs across
Canada and parts of the northern US in association with jack (Pinus banksiana Lamb.)
and lodgepole (Pinus contorta Dougl. ex. Loud.) pine. Its current distribution is highly
fragmented, but the extent of reproductive isolation among allopatric MA populations is
unknown. Although the MA also is biennial, adults emerge every year in some
populations around the Rocky Mountains. These populations are assumed to consist of
two alternate-year cohorts, providing the opportunity for sympatric divergence via
allochronic isolation. I used mtDNA (COI) and AFLP markers to elucidate the MA’s
range-wide spatial and temporal population structure and phylogeographic history. Both
markers revealed high diversity and a low, but significant, degree of spatial structure.
These characteristics likely are remnant of a historically larger, more continuous
population that has since become fragmented, and the MA now exhibits low dispersal
and gene flow among allopatric populations, even over fine geographic distances.
Although AFLPs indicated weak divergence between one pair of sympatric, allochronic
cohorts, overall there was little evidence for differentiation resulting from allochronic
isolation for MA populations. A general lack of phylogeographic structure and low
divergence among mtDNA haplotypes suggests a recent expansion from a single
refugium, and based on this and the phylogeographic histories of jack and lodgepole
17
pine, a number of post-glacial colonization scenarios for the MA are discussed. Finally,
the implications of these data on the conservation of some MA populations at risk of
extirpation are addressed. My research sheds new light on the life history of the MA and
provides a useful framework for further analyses of allopatric and allochronic divergence
among MA populations.
2.1 Introduction
Extant patterns in the genetic variation of organisms are a consequence of both
historical events and contemporary patterns of gene flow. At the last glacial maximum
(LGM) in North America (15 000 – 20 000 years ago), Canada and the northern US
were covered by the Laurentide and Cordilleran ice sheets (Pielou 1991). Many
organisms survived the LGM in ice-free refugia, sometimes in multiple, isolated areas,
and following the retreat of the ice sheets at the end of the Pleistocene, expanded their
ranges into previously glaciated areas (Pielou 1991, Hewitt 1996). The genetic
characteristics and geographic distributions of such species retain the signature of
these past events, and can be used to infer their demographic and evolutionary
histories, and to provide general clues to the location of glacial refugia, post-glacial
colonization routes, and vicariance events for a given geographic region (Nichols and
Hewitt 1994, Hewitt 1996, Avise 2000, Habel et al. 2010b). For instance, a pattern of
strong geographic genetic structuring, with high divergence among populations and
unique haplotypes/alleles confined to specific geographic areas, suggests isolation in
multiple refugia, the location of which often correspond to extant areas of high genetic
diversity (Burg et al. 2006, Pugarin-R and Burg 2012). In contrast, a lack of geographic
18
structuring and low divergence among populations typically corresponds to a rapid postglacial expansion from a single refugium (Pugarin-R and Burg 2012).
In the present day, gene flow among populations is a fundamental, on-going
determinant of genetic differentiation: in its absence, populations generally will diverge
via genetic drift and/or local adaptation (Slatkin 1987). Levels of gene flow may be
influenced not only by geographic factors (e.g., habitat fragmentation, topographical
features), but also biological characteristics of the organism itself, including dispersal
behaviour (Habel and Schmitt 2009, Yamamoto and Sota 2012), phenology (Cooley et
al. 2003, Santos et al. 2010, Yamamoto and Sota 2012), and host plant specificity
(Peterson and Denno 1998). Thus, characterizing an organism’s genetic structure can
provide insight not only into its demographic history, but also the life history and
behavioural traits that continue to foster its evolution (Hewitt 2001, Knowles and
Carstens 2007, Schmitt et al. 2010).
My study focused on the Macoun’s arctic butterfly [MA; Oeneis macounii (W. H.
Edwards)], a boreal forest species broadly distributed across much of Canada and parts
of the northern United States (Layberry et al. 2001, Brock and Kaufman 2003). The MA
typically occurs in close association with mature jack pine (Pinus banksiana Lamb.) or
lodgepole pine (Pinus contorta Dougl. ex. Loud.) stands (Masters 1972, Layberry et al.
2001) containing rough-leaved rice grass (Oryzopsis asperifolia Michx.), the purported
host plant of MA larvae. Currently, both pine species are fragmented, both naturally and
by human activities, and consequently, although widespread, the MA exists as
numerous allopatric populations separated by a variety of geographic distances. The
extent of reproductive isolation among such fragmented populations ultimately depends
19
on dispersal behaviour (i.e., ability or propensity to disperse among habitat patches and
the distance over which dispersal will occur) (Darvill et al. 2010). Field studies of the MA
are scarce, but recent observations suggest that adults display limited dispersal activity
(Burns 2013), like some other butterfly habitat specialists (e.g., Hill et al. 1996,
Schtickzelle et al. 2006). Thus the MA may exhibit significant spatial population
structuring, although the geographic scale over which gene flow might be restricted is
unclear. A lack of dispersal also may have conservation implications for some MA
populations, particularly those already suspected to be at risk of extirpation.
Superimposed on the MA’s fragmented distribution is an asynchronous adult
emergence schedule. The MA is biennial, requiring 2 years to complete development,
with adults emerging in even- or odd-numbered years in different areas (Masters 1974;
Figure 1). Most literature states that the MA consists of two main, asynchronous
cohorts: an even-year eastern cohort occurring from eastern Manitoba to eastern
Ontario, and a western cohort emerging mainly in odd years extending from western
Manitoba to central British Columbia (Masters 1974, Layberry et al. 2001). However, the
true pattern is more complex. Even-year MA populations also exist in the foothills of the
Rocky Mountains in Alberta (Bird et al. 1995) and the interior of British Columbia
(Guppy and Shepard 2001), and still other populations in these areas emerge in both
years (Bird et al. 1995, Guppy and Shepard 2001). Such seemingly annual populations
in otherwise biennial animals presumably consist of two asynchronous (i.e., one
emerging in even-numbered years, the other in odd-numbered years), biennial cohorts
(Heliövaara et al. 1994, Brykov et al. 1996, Kankare et al. 2002). While it is feasible that
extreme climatic conditions could routinely cause some individuals to emerge off year,
20
creating the opportunity for gene flow between cohorts, field observations and collection
records of the MA (G. W. Otis unpub. data) and observations of some other periodical
insects (Douwes 1980, Kankare et al. 2002, Abbot and Withgott 2004) suggest that this
rarely occurs. Thus, these alternate-year cohorts may be reproductively-isolated,
creating the opportunity for sympatric, allochronic divergence. Although studies indicate
that allochrony acts as an important isolating mechanism for some periodical animals
(Brykov et al. 1996, Simon et al. 2000, Cooley et al. 2001), to date the differentiation
between sympatric, allochronic cohorts has been assessed only for two biennial insect
species (Heliövaara et al. 1988, Väisänen and Heliövaara 1990, Kankare et al. 2002).
Here I characterize the temporal and range-wide spatial genetic structure of the
MA using both mitochondrial DNA (mtDNA) and amplified fragment length
polymorphisms (AFLPs). While mtDNA is widely used for historical phylogeographic
inferences (Avise 2004), AFLPs are useful for examining more contemporary population
genetic processes at a finer scale (Bensch and Akesson 2005). Study of the MA in both
a contemporary and historical context promises to provide novel insight into its life
history and the abiotic and biotic factors that have shaped its present day spatial and
temporal geographic and genetic distributions. Furthermore, phylogeographic analyses
of widespread organisms such as the MA can help reveal broader historical patterns for
a given geographic area and/or type of organism (Brunsfield et al. 2001, Hewitt 2001,
Soltis et al. 2006, Jiménez-Mejías et al. 2012). To date, few phylogeographic
assessments have focussed on widespread North American insects, particularly
butterflies (Brunsfield et al. 2001, Soltis et al. 2006). Finally, three MA populations within
national and provincial parks (Algonquin Provincial Park, ON; Riding Mountain National
21
Park, MB; and Isle Royale National Park, MI) are currently of conservation interest
(Burns 2013; G. W. Otis pers. comm.), and assessing the diversity and distinctiveness
of these populations would aid in their conservation management. My specific
objectives were to: (1) assess the current degree of genetic differentiation among
allopatric MA populations over fine and coarse geographic scales; (2) determine if
allochronic isolation promotes the divergence of sympatric, alternate-year MA
populations; and (3) provide preliminary insight into the MA’s demographic history and
potential post-glacial colonization scenarios in light of its current genetic structure and
the phylogeographic histories of jack and lodgepole pine.
2.2 Materials and Methods
2.2.1 Sample Collection and DNA Extraction
From 2000 – 2012, between 1 and 20 adult MA were collected from sites across
the majority of its range (Figure 2.1; Table 2.1). Specimens were immediately placed in
glassine envelopes and held alive in a cooler at 4-8°C until, for most specimens, two
legs were removed from each individual within 24 h and placed in 95% ethanol.
However, approximately 20 individuals were left intact and stored in envelopes until
extraction. Whole specimens and legs in ethanol were stored at -20°C until use. In total,
350 individuals were sampled from 62 localities (Table 2.1).
Genomic DNA was extracted from a single whole leg per individual using the
QIAgen DNeasy® Blood and Tissue Kit following the manufacturer’s protocol. DNA was
eluted in 75 μl of Buffer AE and stored at -20°C prior to amplification.
22
Figure 2.1. Sampling locations (n = 62) of Oeneis macounii adults analyzed in this
study. Blue circles represent populations where adults emerge in even-numbered years,
white circles represent populations where adults emerge in odd-numbered years, and
grey circles represent populations where adults emerge in both even and odd years.
23
Table 2.1. Collection locality information, the number of individuals successfully amplified for AFLPs (n AFLP) and mtDNA
(n mtDNA), and the COI haplotypes found at each location for Oeneis macounii samples analyzed in this study.
Code
1
Location
Latitude
Longitude
Year(s)
n AFLP
n mtDNA
COI Haplotype(s)
HMH_E
100 Mile House, BC
51.64
-121.27
2010
10
8
ma4, ma67, ma70, ma77, ma78, ma85
-
100 Mile House, BC
51.14
-121.27
2011
3
3
ma4, ma71, ma76
AG_E
Agassiz Provincial Forest, MB
50.03
-96.25
2008
10
2
ma28, ma51
-
Algonquin Provincial Park, ON
45.96
-78.05
2008
3
3
ma4, ma38, ma39
-
Belair, MB
50.62
-96.53
2008
3
3
ma4, ma8, ma64
BI_E
Beltrami Island State Forest, MN
48.71
-95.44
2008
10
10
ma28, ma45, ma46, ma47, ma51, ma52
-
Bluenose Mountain, BC
50.19
-119.07
2008
1
1
ma4
BS_O
Bragg Creek (22 km S), AB
50.74
-114.6
2009
6
5
ma4, ma84, ma96
-
Bragg Creek, Moose Mountain, AB
50.94
-114.84
2002
1
1
ma68
BSK_O
Bragg Creek, ski hill, AB
50.98
-114.58
9
9
ma4, ma94, ma97, ma98
BSK_E
Bragg Creek, ski hill, AB
50.98
-114.58
4
3
ma4
-
Bragg Creek, Sperling farm, AB
50.92
-114.53
2009
2006, 2010,
2012
2000
3
3
ma4
BSP_O
Bragg Creek, Sperling farm, AB
50.92
-114.53
2009, 2011
6
5
ma4, ma67
-
Buck Mountain, AB
53.05
-114.73
2009
1
0
-
CA_O
Canwood, SK
53.33
-106.59
2009
9
8
ma4, ma6, ma8, ma10, ma19, ma20, ma21
CH1_O
Chetwynd, BC
55.62
-121.31
2009
9
7
CH2_O
Chetwynd, BC
55.63
-121.86
2011
10
9
-
Cochrane (48km NW), AB
51.46
115.04
2009
0
1
ma4, ma67, ma86, ma87, ma88
ma4, ma23, ma24, ma69, ma73, ma74,
ma75
ma4
CR_O
Creighton, SK
54.87
-102.26
2009, 2011
3
4
ma1, ma4, ma13
DE_O
Devon, AB
53.4
-113.76
2009
7
6
ma4, ma10
EF_E
Ear Falls, ON
50.62
-93.2
2010
5
5
ma28, ma31, ma32, ma33, ma34
-
Enterprise (20 km S), NWT
60.34
116.46
2011
0
1
ma44
FA_O
Fort Assiniboine (15 km NE), AB
54.47
-114.55
2011
9
8
ma4, ma20, ma89, ma90, ma91
-
Fort Vermilion, AB
58.42
-116.15
2011
0
1
ma92
-
Grand Rapids (44 km N), MB
53.56
-99.34
2011
1
1
ma54
GP_O
Grande Prairie, AB
55.09
-118.81
2009
5
3
ma4, ma100
HA_O
Harlan, SK
53.62
-109.88
2009
7
6
ma4, ma10, ma11, ma12, ma22
24
Code
1
Location
Latitude
Longitude
Year(s)
n AFLP
n mtDNA
COI Haplotype(s)
HR_O
Hay River (96 km E), NWT
60.53
-114.4
2011
8
7
ma4, ma42, ma43
HI_O
Hinton, AB
53.41
-117.79
2009
5
5
ma4, ma93, ma99
-
Hinton (15 km SW), AB
53.41
-117.79
2010
2
2
ma4, ma93
HO_O
Hondo, AB
55.04
-114.05
2009
6
5
HB_E
Huckleberry Butte, BC
51.55
-121.11
2010
15
14
-
Huckleberry Butte, BC
51.55
-121.11
2011
1
1
ma4, ma10, ma101
ma4, ma70, ma71, ma78, ma79, ma80,
ma81, ma82, ma83, ma84
ma70
IH_O
Irwin Hill, AB
51.28
-114.68
2009
9
5
ma4, ma94, ma95
IH_E
Irwin Hill, AB
51.29
-114.69
2010, 2012
4
4
ma4
IR_E
Isle Royale National Park, MI
48.16
-88.5
2012
10
4
ma28, ma53
-
Jesmond Mountain, BC
51.31
-121.92
2005
2
2
ma4, ma67
KA_E
Kananaskis, BC
51.04
-115
2010
9
9
ma4, ma65, ma66
-
LaRonge (120 km S), SK
54.17
-105.96
2011
2
2
ma6, ma7
-
LaRonge (34 km S), SK
54.8
-105.3
2011
1
1
ma4
LL_O
Loon Lake, SK
54.03
-109.33
2009
5
4
ma4, ma9, ma10
MA_O
Macdowall, SK
52.93
-106.05
2011
10
10
ma1, ma2, ma3, ma4, ma5, ma8
MAS_O
Mafeking (10 km S), MB
52.6
-101.09
2009
4
2
ma4
-
Mafeking (23 km N), MB
52.8
-101.1
2009
1
0
-
-
McCullough Lake, BC
49.78
119.18
2011
0
1
ma72
MS_E
McKenzie Station, ON
48.55
-88.94
2008
5
5
ma25, ma28, ma39, ma40, ma41
-
Moose Mountain Road, AB
50.88
-114.75
2009
1
1
ma4
-
Narrow Hills, SK
53.93
-104.64
2009
3
3
ma1, ma14
-
Nazko, BC
52.74
-123.61
2010
2
2
ma4, ma70
-
Nazko, BC
53.01
-123.61
2011
1
1
ma4
-
Pink Mountain, BC
57.12
-122.68
2004
1
1
ma23
PA_O
Prince Albert (15 km NE), SK
53.28
-105.51
2009
7
7
ma4, ma10, ma15, ma16, ma17, ma18
RD_O
Redwater Dunes, AB
53.9
-112.98
2009
10
9
ma4, ma89, ma102
RM_O
Riding Mountain National Park, MB
50.68
-99.89
2009
9
8
ma4, ma28, ma55
-
Route 39, MB
54.58
-100.63
2011
3
3
ma4, ma7
R647_E
Route 647, ON
49.89
-93.45
2010
9
9
ma4, ma25, ma26, ma27, ma28, ma29
25
Code
1
-
Location
Sandilands Provincial Forest
(Lonesand Trail), MB
Sandilands Provincial Forest (near
dump), MB
Sandilands Provincial Forest (site
PL89B), MB
Sibbald Lake, AB
51.05
-114.29
2010
2
2
ma4
SLC_E
St. Louis County, MN
47.27
-91.85
2008
10
10
ma27, ma28, ma29, ma48, ma49, ma50
-
St. Maarten Junction (28 km N), MB
51.95
-98.82
2011
1
0
-
-
St. Maarten Junction (4 km S), MB
51.67
-98.74
2011
1
1
ma28
STC_E
Stanley (cemetery), ON
48.38
-89.56
2008, 2010
6
6
ma4, ma25, ma28, ma36
STL_E
Stanley (Legion), ON
48.39
-89.6
2008, 2010
7
7
ma27, ma28, ma30, ma35, ma36, ma37
WE_O
Westray (25 km SE), MB
53.4
-101.29
2009
9
8
ma1, ma4, ma44, ma56, ma57, ma58
SAL_E
SAD_E
SAP_E
Latitude
Longitude
Year(s)
n AFLP
n mtDNA
49.21
-96.32
2008
12
11
ma4, ma28, ma60, ma61, ma62, ma63
49.27
-96.1
2008
5
5
ma4, ma26, ma27, ma28, ma59
49.39
-96.19
2008
5
5
ma4, ma27, ma28
1
COI Haplotype(s)
Population codes were assigned to locations from which four or more individuals were successfully amplified. Letters before the underscore
represent an abbreviation of the collection locality; the letter following the underscore indicates whether samples were collected from that location in
an even – (‘E’) or odd – (‘O’) numbered year.
26
2.2.2 mtDNA Amplification
Almost the entire mitochondrial gene cytochrome c oxidase subunit I (COI) was
amplified in two overlapping segments of approximately 750 bp each using the primer
pairs Lyn/K525 and Jerry/Pat2 (Bromilow and Sperling 2011; Table A1.1, Appendix 1).
A detailed COI amplification protocol is provided in Appendix 1. Amplified fragments
were cleaned of residual primers and excess dNTPs using a USB® PCR Product PreSequencing Kit (Affymetrix, Santa Clara, CA) following the manufacturer’s protocol. All
PCR products were sequenced in both forward and reverse directions using the same
primers as for initial amplification and BigDye® terminator cycle sequencing on a 3730S
Genetic Analyzer (Applied Biosystems) at the Genomics Facility, Advanced Analysis
Centre, University of Guelph.
Sequences were manually edited and aligned in Sequencher v4.9 and Mega 5,
respectively.
2.2.3 AFLP Analysis
AFLP profiles were generated for 349 individuals using a protocol modified from
Clarke and Meudt (2005) and Applied Biosystem’s AFLP Plant Mapping Kit (Applied
Biosystems, Foster City, CA). The detailed AFLP protocol is provided in Appendix 2.
Twenty-two selective primer pairs were screened for high quality, reproducibility, and
polymorphism. Based on these criteria, five selective primer combinations were used to
generate AFLP profiles: EcoRI-AAC/MseI-CTC, EcoRI-AAC/MseI-CTG, EcoRIACT/MseI-CTC, EcoRI-ACT/MseI-CAT, and EcoRI-ACA/MseI-CTT (Vos et al. 1995).
27
Negative controls were included in each step of the protocol to ensure that no
contamination had occurred.
Prior to fragment analysis, selective amplification products were diluted 25x with
water. AFLP fragments were then separated and sized (LIZ3730 size standard, Applied
Biosystems) on a 3730S Genetic Analyzer (Applied Biosystems) at the Genomics
Facility, Advanced Analysis Centre, University of Guelph.
AFLP fragment sizes and peak heights were determined using GeneMapper ®
v4.0 (Applied Biosystems) following a semi-automated approach (Crawford et al. 2011).
GeneMapper was set to assign bins (loci) between 100 and 500 bp at peaks of at least
100 rfu. The resulting bins were then examined manually to ensure proper placement.
Bins containing shoulder peaks or peaks that also fell into adjacent bins were removed,
and when necessary, bins were adjusted to centre over peak distributions. Each AFLP
profile was then examined, and any individuals whose DNA failed to amplify were reamplified or removed from further analysis.
AFLP peak heights from GeneMapper were normalized and scored in
AFLPScore (Whitlock et al. 2008). AFLPScore uses a mismatch error rate (i.e., the
percentage of differences in phenotype among replicate samples) to objectively identify
optimal thresholds for phenotype-scoring that minimize genotyping error. A locusselection threshold is applied, and loci with mean peak heights below this threshold are
removed from the analysis. A phenotype-calling threshold is then applied to the retained
loci to score peaks as present or absent. The mismatch error rate was estimated for
each primer pair separately by replicating the entire AFLP protocol starting from the
restriction-ligation step (i.e., two aliquots from the same DNA extraction were replicated)
28
for 42 individuals. Various combinations of locus- and phenotype-calling thresholds
were tested to obtain a mean mismatch error of <5%, and all loci containing singleton
peaks (i.e., peaks present in only one individual) were removed from analysis.
2.2.4 Data Analyses
Preliminary analyses on the AFLP data revealed differentiation between
sampling locations (hereafter referred to as populations) separated by as little as 3 km.
Therefore, sampled individuals were pooled as a single population only if they were
collected within 3 km of each other. Likewise, allochronic samples were considered
sympatric if collected within 3 km of each other. These criteria resulted in many
populations with small sample sizes (n < 10). Thus, population-based analyses were
conducted on the better-sampled areas, while individual-based analyses included all
samples. Populations of four or more individuals (n=38 for AFLP; n=35 for mtDNA) were
included in population-based analyses, as this criterion allowed the retention of two
pairs of sympatric, allochronic cohorts in the AFLP data set [Irwin Hill, AB even (IH_E)
and odd (IH_O) and Bragg Creek, AB even (BSK_E) and odd (BSK_O)].
Summary Statistics
Intra-population genetic diversity indices based on AFLPs were calculated using
AFLP-SURV v1.0 (Vekemans 2002). Allele frequencies were estimated for each
population using the Bayesian method with non-uniform prior distribution and assuming
Hardy-Weinberg genotypic proportions (Zhivotovsky 1999), and were then used to
29
calculate the proportion of polymorphic loci at the 5% level and unbiased estimates of
expected heterozygosity following the method of Lynch and Milligan (1994).
For the mtDNA data, haplotype diversity (Hd) and nucleotide diversity (π) were
calculated for each population in Arlequin v3.5.1.3 (Excoffier and Lischer 2010). Two
tests, Tajima’s D and Fu’s Fs (Fu 1997), also were implemented in Arlequin to assess
departures from the assumptions of selective neutrality and stable population size. To
estimate the relationships among haplotypes, a minimum spanning network was
created using Arlequin and visualized in HapStar v0.7 (Teacher and Griffiths 2011). The
haplotype network was then drawn manually.
Population-Based Analyses
To assess the degree of genetic differentiation between populations, pairwise F st
estimates were calculated in AFLP-SURV (AFLPs) and Arlequin (mtDNA), with
statistical significance based on 100 000 (AFLPs) or 10 100 (mtDNA) random
permutations. Population subdivision was further assessed for both markers with an
analysis of molecular variance (AMOVA) in Arlequin based on 10 000 permutations.
GenAlEx v6.0 (Peakall and Smouse 2006) was then used to identify patterns in the
genetic differentiation among populations. Each test was conducted separately on the
AFLP and mtDNA data. First, a principal coordinates analysis (PCoA) was performed on
the pairwise Fst matrices generated in AFLP-SURV and Arlequin. Second, a Mantel test
(Mantel 1967) was used to test for a correlation between genetic (pairwise Fst) and
Euclidean geographic (km) distance, or isolation by distance (IBD) relationship, among
30
the populations. Both distance measures were linearly transformed, and the test
significance was based on 9999 permutations.
Individual-Based Analyses
To estimate the number of genetically homogenous populations in the entire
AFLP data set, a Bayesian clustering analysis was performed using STRUCTURE
v2.3.4 (Pritchard et al. 2000). Using Markov Chain Monte Carlo simulations,
STRUCTURE estimates the posterior probability that there are K populations in the data
set and assigns individuals to each of K populations. Permutations were performed
under the admixture model with correlated allele frequencies. A burn-in of 100 000 and
run length of 400 000 was used to test K = 1 - 20, with 15 runs per value of K. Structure
Harvester v0.6.93 (Earl and vonHoldt 2012) was used to infer the most likely K following
the method of Evanno et. al.(2005). Results from the STRUCTURE runs for the most
likely K were combined using the program CLUMPP (Jakobsson and Rosenberg 2007)
and subsequently visualized as a bar graph using the program DISTRUCT (Rosenburg
2004).
PCoA and Mantel tests also were performed using all individuals from the AFLP
and mtDNA data sets as described for the population-based analyses; however,
individual-by-individual genetic distance matrices [i.e., the total number of differences
(peak presence v. absence for AFLPs; nucleotide differences for mtDNA) between
AFLP profiles or mtDNA sequences of each pair of individuals], calculated for each
marker type in GenAlEx, were used in place of the pairwise Fst matrices.
31
2.3 Results
2.3.1 mtDNA
Genetic Diversity
A 1400 bp fragment of COI containing 119 polymorphic sites was generated for
298 individuals (Appendix 3, Table A3.1). In total, 102 haplotypes were identified, 79 of
which were private (i.e., unique to a single population) (Table 2.1). One population, Irwin
Hill even (IH_E), was homogenous for haplotype ma4 (Table 2.2). Otherwise, intrapopulation haplotype diversity ranged from 0.3333 [Devon, AB (DE_O)] to 1 [Sandilands
Provincial Forest, MB (SAD_E), McKenzie Station, ON (MS_E), and Ear Falls, ON
(EF_E)], but was moderate to high for the majority of populations (Table 2.2). In
contrast, nucleotide diversity was generally low, ranging from 0.000238 [Devon, AB
(DE_O)] - 0.040867 [Isle Royale National Park, MI (IR_E)].
Population Structure
The mtDNA AMOVA indicated significant genetic structuring among the tested
populations (global Fst = 0.1749; P<0.0001), with 17.5% of the total variation due to
variation among populations (Table 2.3). Pairwise Fst’s generally were larger than for
AFLPs, but fewer comparisons were significant (Table 2.4). Hay River, NWT (HR_O),
Harlan, SK (HA_O), and Isle Royale National Park, MI (IR_E) were significantly and
comparatively highly differentiated from almost all other populations (Table 2.4). There
was no difference between Irwin Hill, AB even (IH_E) and odd (IH_O), the only
sympatric, allochronic comparison in the mtDNA data set (Table 2.4).
32
Table 2.2. Summary statistics for 39 Oeneis macounii populations based on mtDNA and
AFLP markers.
mtDNA
Population
HMH_E
AFLPs
Hd (SE)
π (SE)
D*
Fs*
PPL (%)
He (SE)
0.89 (0.111)
0.0013 (0.000950)
-0.992
-2.88
57.5
0.17 (0.0108)
AG_E
-
-
-
-
61.4
0.20 (0.0115)
BI_E
0.87 (0.0850)
0.0013 (0.000914)
-0.626
-2.11
63.7
0.21 (0.0114)
-
-
-
-
53.7
0.22 (0.0115)
BS_O
0.70 (0.218)
0.00086 (0.000759)
-1.05
-0.186
55.2
0.19 (0.0109)
BSK_O
0.58 (0.183)
0.00064 (0.000552)
-1.61
-1.28
63.3
0.22 (0.0115)
BSP_O
0.60 (0.175)
0.00043 (0.000469)
1.22
0.626
55.6
0.21 (0.0118)
CA_O
0.96 (0.0772)
0.00133 (0.000964)
-0.920
-4.80
63.3
0.21 (0.0112)
CH1_O
0.86 (0.137)
0.0019 (0.00132)
-0.963
-0.943
63.3
0.21 (0.0112)
CH2_O
0.92 (0.0920)
0.0015 (0.00107)
-1.59
-3.53
66.0
0.21 (0.0113)
CR_O
0.83 (0.222)
0.00071 (0.000708)
-0.710
-0.887
-
-
DE_O
0.33 (0.215)
0.00024 (0.000313)
-0.933
-0.00275
56.0
0.20 (0.0113)
EF_E
1.0 (0.127)
0.0024 (0.00174)
-0.807
-2.00
55.6
0.21 (0.0114)
FA_O
0.86 (0.108)
0.0013 (0.000964)
-0.920
-1.32
62.5
0.19 (0.0108)
GP_O
-
-
-
-
56.4
0.22 (0.0120)
HA_O
0.93 (0.122)
0.0018 (0.00130)
-0.206
-1.62
49.4
0.18 (0.0116)
HB_E
0.93 (0.0508)
0.0013 (0.000890)
-1.65
-7.19
53.7
0.19 (0.0112)
HI_O
0.80 (0.164)
0.00071 (0.000665)
0.243
-0.475
56.0
0.20 (0.0112)
HO_O
0.70 (0.218)
0.00086 (0.000759)
-1.05
-0.186
60.2
0.22 (0.0113)
HR_O
0.67 (0.160)
0.0022 (0.00151)
1.44
2.31
60.6
0.24 (0.0123)
IH_E
0 (0)
0 (0)
0
n/a
54.8
0.21 (0.0113)
IH_O
0.70 (0.218)
0.00057 (0.000569)
-0.973
-0.829
59.5
0.18 (0.0110)
IR_E
0.5 (0.265)
0.041 (0.0226)
-0.754
1.71
59.1
0.18 (0.0112)
KA_E
0.42 (0.191)
0.00032 (0.000351)
-1.36
-1.08
65.3
0.21 (0.0109)
LL_O
0.83 (0.222)
0.00083 (0.000791)
0.592
-0.658
55.2
0.23 (0.0122)
MA_O
0.78 (0.137)
0.0014 (0.000958)
-0.964
-1.95
64.5
0.22 (0.0118)
-
-
-
-
56.4
0.23 (0.0117)
MS_E
1 (0.127)
0.0021 (0.00157)
-0.747
-2.24
58.7
0.23 (0.0115)
PA_O
0.95 (0.0955)
0.0027 (0.00178)
-0.819
-1.70
55.2
0.19 (0.0113)
RD_O
0.42 (0.191)
0.00064 (0.000552)
-1.61
0.134
61.4
0.21 (0.0116)
RM_O
0.82 (0.101)
0.0045 (0.00274)
0.414
2.84
57.1
0.20 (0.0116)
R647_E
0.89 (0.0910)
0.0012 (0.000850)
-0.526
-2.77
66.0
0.22 (0.0107)
BSK_E
MAS_O
33
Table 2.2 cont.
mtDNA
AFLPS
Hd (SE)
π (SE)
D*
Fs*
PPL (%)
He (SE)
SAD_E
1.0 (0.127)
0.0016 (0.00121)
-0.562
-2.86
57.9
0.23 (0.0118)
SAL_E
0.85 (0.0852)
0.0012 (0.000855)
-1.22
-2.04
68.3
0.21 (0.0110)
SAP_E
0.80 (0.164)
0.00071 (0.000665)
0.243
-0.475
56.4
0.22 (0.0115)
STC_E
0.80 (0.172)
0.0010 (0.000846)
0.600
-1.07
57.9
0.23 (0.0123)
STL_E
0.95 (0.0955)
0.0016 (0.00116)
-1.04
-2.91
59.1
0.24 (0.0123)
-3.29
65.6
0.21 (0.0113)
-2.36
64.1
0.21 (0.0113)
Population
SLC_E
0.78 (0.137)
0.00086 (0.000674)
-1.80
WE_O
0.93 (0.0844)
0.0016 (0.00111)
-0.856
mtDNA: haplotype diversity (Hd), nucleotide diversity (π), Tajima’s D (D), Fu’s Fs (Fs); AFLP: proportion
of polymorphic loci (PPL), expected heterozygosity (H e); “-“ indicates missing data
* bolded values of Tajima’s D and Fu’s Fs are significant at α=0.05 and 0.02, respectively
Table 2.3. Results of analyses of molecular variance (AMOVA) of mtDNA and AFLP
data for Oeneis macounii.
Marker
Source of Variation
d.f.
Sum of
Squares
Variance
Components
Percentage of
Variation
mtDNA
Among Populations
34
70.81
0.17725
17.49
Within Populations
212
177.2
0.83595
82.51
Total
246
248.0
1.01320
Among Populations
37
1437
2.2518
9.51
Within Populations
257
5504
21.217
90.49
Total
294
6941
23.469
AFLPs
34
Table 2.4. Pairwise mtDNA (top right) and AFLP (bottom left) Fst estimates for 40
Oeneis macounii populations. Bolded values are not significant at α = 0.01.
HMH_E
HMH_E
AG_E
BI_E
BS_O
BSK_E
BSK_O
BSP_O
-
0.18641
0.10585*
-
0.14314
0.10659
-
-
-
-
-
0.09405
-
0.1243
0.14127
-
0.01487
0.1
AG_E
0.051
BI_E
0.0941
0
BS_O
0.0686
0.0321
0.0622
BSK_E
0.1441
0.0808
0.0949
0.0344
BSK_O
0.0888
0.047
0.0499
0.0312
0
BSP_O
0.0932
0.0307
0.0527
0.0029
0
0
BSP_E
-
-
-
-
-
-
-
-
0.09798
CA_O
0.0754
0.0212
0.0232
0.0614
0.0366
0.0296
-
CH1_O
0.0364
0.0258
0.0453
0.012
0.0101
0
-
CH2_O
0.0438
0.0204
0.0388
0.0293
0.061
0.0298
-
CR_O
-
-
-
-
-
-
-
DE_O
0.0819
0.0626
0.0625
0.0251
0.0254
0
-
EF_E
0.0774
0
0
0.05
0.0931
0.0514
-
FA_O
0.0391
0.0117
0.0333
0
0.071
0.0277
-
GP_O
0.075
0.0386
0.0511
0.0311
0.0201
0
-
HA_O
0.2386
0.1749
0.1387
0.1987
0.1836
0.1597
-
HR_O
0.0916
0.0277
0.0247
0.0582
0.0736
0.0413
-
HB_E
0
0.0218
0.055
0.0562
0.0985
0.0516
-
HI_O
0.0329
0.0389
0.0599
0.0052
0.0892
0.0256
-
HO_O
0.1184
0.0363
0.0325
0.0381
0
0.0036
-
IH_E
0.089
0.0713
0.0768
0.0386
0.0571
0.0354
-
IH_O
0.0531
0.0514
0.0737
0
0.0597
0.0249
-
IR_E
0.1661
0.0781
0.0679
0.1343
0.1473
0.1368
-
KA_E
0.0729
0.0438
0.0492
0.0038
0.0099
0
-
LL_O
0.1809
0.1207
0.0962
0.1538
0.088
0.0595
-
MA_O
0.0629
0.0262
0.0322
0.0522
0.0498
0.0383
-
MAS_O
0.0543
0
0
0.0257
0.0554
0.0118
-
MS_E
0.0672
0
0.0175
0.0332
0.0901
0.0433
-
PA_O
0.054
0.0257
0.0362
0.0414
0.0798
0.0553
-
R647_E
0.1384
0.0219
0.0108
0.1006
0.0673
0.0557
-
RD_O
0.0566
0.0185
0.0276
0.021
0.0328
0.013
-
RM_O
0.1103
0.0305
0.0204
0.0263
0.0554
0.0457
-
SAD_E
0.0962
0
0
0.0532
0.0526
0.0313
-
SAL_E
0.0949
0
0
0.0415
0.0725
0.0357
-
SAP_E
0.0596
0
0.0113
0.0508
0.1005
0.0537
-
SLC_E
0.0909
0
0
0.0666
0.0888
0.0538
-
STC_E
0.1092
0.0193
0.0032
0.0783
0.0743
0.0526
-
STL_E
0.1838
0.0803
0.057
0.1331
0.0998
0.088
-
WE_O
0.0798
0.0207
0.0277
0.0317
0.0497
0.0225
-
35
Table 2.4 cont.
BSP_E
CA_O
CH1_O
CH2_O
CR_O
DE_O
EF_E
HMH_E
-
0.10823
0.08691
0.10668
0.09107
0.13074
0.18085
AG_E
-
-
-
-
-
-
-
BI_E
-
0.12979
0.08552
0.1044
0.07781
0.10368
0.04426
0.02679
0.02308
0
0
0.02185
0.11538
BS_O
-
BSK_E
-
-
-
-
-
-
-
BSK_O
-
0.05538
0.06226
0.01
0
0
0.19606
BSP_O
-
0.07895
0
0.03085
0.12621
0.18847
0.10714
-
-
-
-
-
0.04573
0.00775
0
0.15233
0.0019
0
0.03886
0
0
0
0.11735
0.05085
0.08891
BSP_E
-
CA_O
0.04414
0.0431
CH1_O
0.0058
0.0004
CH2_O
0.0199
0.0206
0
CR_O
-
-
-
-
DE_O
0.0035
0.0519
0
0.0519
-
EF_E
0.0619
0.0172
0.0192
0.0341
-
0.0553
FA_O
0.0171
0.0261
0
0.0038
-
0.0309
0.0125
GP_O
0
0.0243
0.0091
0.0175
-
0
0.0316
HA_O
0.1782
0.14
0.154
0.1196
-
0.1624
0.1361
HR_O
0.0476
0.0297
0.0327
0.0253
-
0.0569
0.009
HB_E
0.044
0.0315
0.0125
0.0227
-
0.0589
0.0517
0.0288
0.17378
HI_O
0.0211
0.041
0
0.0113
-
0.0314
HO_O
0.0167
0.0201
0.0186
0.0435
-
0.0378
0.04
IH_E
0.0141
0.0847
0.0264
0.0257
-
0.0387
0.0547
IH_O
0.0143
0.0479
0.0026
0.0106
-
0.0197
0.05
IR_E
0.15
0.0772
0.1163
0.1094
-
0.1364
0.0779
KA_E
0.019
0.0151
0
0.0044
-
0.012
0.0471
LL_O
0.1086
0.0652
0.0718
0.0834
-
0.0885
0.1082
MA_O
0.0456
0.0024
0.0166
0.0104
-
0.0513
0.0225
MAS_O
0.0314
0
0
0.0032
-
0.012
0
MS_E
0.0612
0.0248
0.0297
0.0145
-
0.0661
0.0085
PA_O
0.0351
0.0154
0.016
0.0051
-
0.0746
0.0135
R647_E
0.071
0.0137
0.0593
0.0701
-
0.0803
0.0282
RD_O
0.018
0
0
0
-
0.0278
0.0096
RM_O
0.0456
0.027
0.0245
0.0425
-
0.0597
0.0292
SAD_E
0.0319
0.0054
0.0175
0.0359
-
0.0485
0
SAL_E
0.0423
0.0197
0.0285
0.0372
-
0.0565
0
SAP_E
0.0607
0.0167
0.033
0.028
-
0.0617
0
SLC_E
0.063
0.019
0.0395
0.0348
-
0.0627
0
STC_E
0.0724
0.0283
0.038
0.0257
-
0.0748
0.0027
STL_E
0.1099
0.0638
0.0909
0.0816
-
0.0999
0.0534
WE_O
0.0339
0.0119
0.0103
0.0273
-
0.0398
0.0213
36
Table 2.4 cont.
FA_O
GP_O
HA_O
HR_O
HB_E
HI_O
HO_O
0.10823
-
0.19263
0.3354
0
0.15564
0.10585
AG_E
-
-
-
-
-
-
-
BI_E
0.12979
-
0.23295
0.34494
0.16785
0.14189
0.09405
BS_O
0.02679
-
0.16205
0.28413
0.0536
0.08333
0
BSK_E
-
-
-
-
-
-
-
BSK_O
0.05538
-
0.22795
0.35889
0.07696
0.10215
0.01487
BSP_O
0.07895
-
0.21709
0.32427
0.10658
0.2
0.1
BSP_E
-
-
-
-
-
-
-
CA_O
0
-
0.01308
0.30665
0.11495
0.08108
0
CH1_O
0.01396
-
0.11859
0.24291
0.11542
0
0.02308
CH2_O
0.04573
-
0.15975
0.28775
0.08635
0
0
CR_O
0.00775
-
0.14351
0.2643
0.03035
0.09091
0
DE_O
0.02941
-
0.19375
0.34055
0.0606
0.15141
0
EF_E
0.12153
-
0.19701
0.24958
0.2076
0.09836
0.11538
-
0.09963
0.24567
0.11495
0.08108
0.02679
-
-
-
-
-
0.32694
0.22991
0.20298
0.13721
0.35234
0.31119
0.28413
0.11439
0.06869
HMH_E
FA_O
GP_O
0.0206
HA_O
0.1813
0.1583
HR_O
0.0337
0.0276
0.1185
HB_E
0.0228
0.0512
0.1956
0.0526
HI_O
0
0.008
0.1882
0.0537
0.0404
HO_O
0.0405
0
0.1559
0.0254
0.0596
0.0566
IH_E
0.0319
0.0636
0.1727
0.0552
0.0555
0.0457
IH_O
0.0023
0.013
0.1739
0.0632
0.0297
0
0.036
IR_E
0.106
0.134
0.1979
0.0841
0.1308
0.1318
0.1072
KA_E
0.0171
0.0162
0.1297
0.0252
0.0495
0.0149
0.0334
LL_O
0.1331
0.079
0.0813
0.0912
0.1264
0.1329
0.0655
0.08333
0.0592
MA_O
0.0269
0.0167
0.1221
0.0251
0.0403
0.0298
0.0323
MAS_O
0.0065
0.0266
0.0858
0.0165
0.0163
0.0226
0.0016
MS_E
0.0131
0.0412
0.1468
0.0166
0.0426
0.0337
0.0657
PA_O
0.008
0.0437
0.1385
0.0271
0.0432
0.025
0.0467
R647_E
0.0796
0.0606
0.1578
0.0443
0.076
0.1095
0.0357
RD_O
0
0.0115
0.1344
0.0251
0.0278
0.0243
0
RM_O
0.0285
0.0535
0.1471
0.0234
0.0671
0.0594
0.0204
SAD_E
0.024
0.0424
0.1457
0.0015
0.0511
0.0632
0.0235
SAL_E
0.0201
0.0433
0.1603
0.0187
0.0465
0.0653
0.0288
SAP_E
0.019
0.0437
0.1723
0.0237
0.0442
0.0464
0.0554
SLC_E
0.0412
0.0547
0.1627
0.0224
0.0523
0.0784
0.0397
STC_E
0.0589
0.07
0.0695
0.0174
0.059
0.0815
0.0446
STL_E
0.1146
0.0874
0.0775
0.0566
0.1304
0.1399
0.0668
WE_O
0.0126
0.039
0.1535
0.0187
0.0391
0.0443
0.0142
37
Table 2.4 cont.
IH_E
IH_O
IR_E
KA_E
LL_O
MA_O
MAS_O
0.06529
0.10632
0.40868
0.17116
0.17131
0.17879
-
AG_E
-
-
-
-
-
-
-
HMH_E
BI_E
0.03829
0.0892
0.36126
0.14181
0.16108
0.19324
-
BS_O
0
0
0.43154
0.04965
0.12262
0.10859
-
BSK_E
-
-
-
-
-
-
-
BSK_O
0
0
0.45455
0
0.16289
0.11338
-
BSP_O
0.19463
0.125
0.53315
0.17803
0.25127
0.15371
-
BSP_E
-
-
-
-
-
-
-
CA_O
0
0.01878
0.3743
0.06907
0
0.08046
-
CH1_O
0
0.01869
0.29527
0.08696
0.07379
0.04315
-
CH2_O
0
0
0.32992
0.02083
0.03226
0.06464
-
CR_O
0
0.00641
0.44444
0.06596
0.13333
0
-
DE_O
0
0.01573
0.57775
0
0.05703
0.10301
-
EF_E
0.09454
0.125
0.2678
0.23628
0.14157
0.19792
-
FA_O
0
0.01878
0.3743
0.06907
0.10357
0.09152
-
GP_O
-
-
-
-
-
-
-
HA_O
0.15084
0.17265
0.36371
0.27041
0.14254
0.16883
-
HR_O
0.27953
0.29577
0.42295
0.39515
0.29691
0.34615
-
HB_E
0
0.05738
0.36945
0.0955
0.13769
0.15476
-
HI_O
0.11111
0.1
0.47853
0.16757
0.20213
0.11667
-
HO_O
0
0
0.43154
0.04965
0
0.0894
-
0
0.48113
0
0.22222
0.05857
-
0.57143
0.02951
0.15617
0.10526
-
0.59591
0.46667
0.40109
-
0.25659
0.16188
-
0.12686
-
IH_E
IH_O
0.0251
IR_E
0.1736
0.1603
KA_E
0.0249
0.0051
0.1126
LL_O
0.1371
0.1298
0.1445
0.0766
MA_O
0.0792
0.0518
0.058
0.0266
0.0673
MAS_O
0.0115
0.0129
0.0844
0.0018
0.0557
0.0107
MS_E
0.0698
0.0481
0.0684
0.0264
0.1067
0.0351
0.0031
PA_O
0.0726
0.0412
0.084
0.0237
0.1069
0
0
R647_E
0.1139
0.1069
0.0784
0.0614
0.0785
0.0612
0.03
RD_O
0.0393
0.0234
0.0999
0.0105
0.0664
0.0122
0
RM_O
0.0721
0.0552
0.1001
0.0231
0.1085
0.0505
0.002
SAD_E
0.0682
0.0765
0.0647
0.0425
0.0842
0.0182
0
-
SAL_E
0.0697
0.0573
0.079
0.0418
0.0954
0.0265
0
SAP_E
0.0677
0.0623
0.0726
0.0445
0.1253
0.031
0.0077
SLC_E
0.0868
0.0812
0.0673
0.0529
0.1082
0.0401
0
STC_E
0.0569
0.0823
0.0623
0.0404
0.0608
0.0282
0
STL_E
0.1269
0.1329
0.1214
0.0913
0.0426
0.0776
0.0386
WE_O
0.0599
0.0258
0.0957
0.0105
0.0829
0.0255
0
38
Table 2.4 cont.
MS_E
PA_O
R647_E
RD_O
RM_O
SAD_E
SAL_E
0.2
0.12745
0.22408
0.12746
0.36975
0.21286
0.28724
AG_E
-
-
-
-
-
-
-
BI_E
0.02015
0.15675
0.03388
0.1243
0.10276
0.02114
0.07773
BS_O
0.125
0.05342
0.16238
0.01487
0.37355
0.15
0.24525
BSK_E
-
-
-
-
-
-
-
BSK_O
0.2014
0.10087
0.19643
0
0.39522
0.21499
0.27356
BSP_O
0.18182
0.08586
0.17378
0.09798
0.46394
0.22222
0.29241
BSP_E
-
-
-
-
-
-
-
CA_O
0.15541
0.0276
0.17998
0.0169
0.32143
0.16283
0.25611
CH1_O
0.07721
0.01449
0.08696
0.03619
0.21728
0.07777
0.17774
CH2_O
0.13082
0.04494
0.15
0.01786
0.27482
0.13138
0.22846
CR_O
0.1018
0
0.15217
0.00907
0.39241
0.13616
0.23634
DE_O
0.18705
0.0612
0.19274
0
0.46692
0.22341
0.27842
EF_E
0
0.11378
0
0.17906
0.04063
0
0.02498
HMH_E
FA_O
0.15541
0.063
0.17998
0
0.32143
0.16283
0.25611
GP_O
-
-
-
-
-
-
-
HA_O
0.20544
0.04306
0.27164
0.18182
0.3935
0.22534
0.32928
HR_O
0.2957
0.24963
0.36997
0.32946
0.45455
0.27362
0.41348
HB_E
0.19814
0.1457
0.21167
0.09063
0.32422
0.20505
0.27112
HI_O
0.16667
0.04803
0.21089
0.10215
0.42693
0.2
0.28425
HO_O
0.125
0.03454
0.16238
0
0.37355
0.15
0.24525
IH_E
0.11111
0.00444
0.1354
0
0.02931
0.1587
0.22983
IH_O
0.13636
0.0522
0.16581
0
0.40492
0.16667
0.25071
IR_E
0.40109
0.26583
0.39204
0.4978
0.54161
0.34028
0.38274
KA_E
0.24605
0.13535
0.22917
0
0.47418
0.27191
0.31333
LL_O
0.15761
0.03448
0.22741
0.16289
0.44424
0.19922
0.29485
MA_O
0.21166
0
0.23649
0.11945
0.35609
0.21875
0.30207
-
-
-
-
-
-
-
0.11558
0
0.2014
0.03485
0
0.01541
0.18538
0.09484
0.27403
0.13345
0.24992
0.19643
0.02934
0
0.01443
0.39522
0.21499
0.28152
0.01922
0
MAS_O
MS_E
PA_O
0.0215
R647_E
0.0368
0.0773
RD_O
0.0206
0.0136
0.0487
RM_O
0.0413
0.0323
0.0435
0.0227
SAD_E
0.0018
0.0246
0
0.005
0.01
0
SAL_E
0.012
0.0367
0.0144
0.0207
0.0098
0
SAP_E
0
0.0268
0.032
0.01
0.0508
0.0001
0.0134
SLC_E
0.0035
0.0402
0.0119
0.0305
0.0296
0
0
STC_E
0
0.0508
0.0288
0.0411
0.0361
0
0.0126
STL_E
0.0504
0.1179
0.0446
0.0659
0.096
0.052
0.0667
WE_O
0.0107
0.0133
0.0376
0.0035
0.0086
0.0123
0.0154
39
Table 2.4 cont.
SAP_E
SLC_E
STC_E
STL_E
WE_O
0.15564
0.40396
0.22033
0.15084
0.15038
-
-
-
-
-
BI_E
0
0.1573
0.10064
0
0.14954
BS_O
0.08333
0.4
0.16258
0.05547
0.05207
BSK_E
-
-
-
-
-
BSK_O
0.10215
0.43103
0.20354
0.10072
0.06859
BSP_O
0.2
0.43686
0.24483
0.10541
0.09801
BSP_E
-
-
-
-
-
CA_O
0.08108
0.37174
0.16271
0.09607
0.09524
CH1_O
0.01408
0.25591
0.10584
0.04462
0.05289
CH2_O
0.03872
0.33494
0.12598
0.07223
0.02663
CR_O
0.09091
0.4053
0.15932
0.03448
0
DE_O
0.15141
0.46341
0.22857
0.07752
0.06558
EF_E
0
0.04984
0.08008
0
0.14298
HMH_E
AG_E
FA_O
0.08108
0.37174
0.16271
0.09607
0.09524
GP_O
-
-
-
-
-
HA_O
0.20298
0.42671
0.25
0.18547
0.18399
HR_O
0.31119
0.48263
0.34334
0.2963
0.30353
HB_E
0.11439
0.37954
0.19431
0.14225
0.11961
HI_O
0.16667
0.43854
0.22137
0.10256
0.07473
HO_O
0.08333
0.4
0.16258
0.02711
0.05207
IH_E
0.11111
0.43838
0.17757
0.00444
0
IH_O
0.1
0.41957
0.17851
0.05379
0.04762
IR_E
0.42953
0.42684
0.30348
0.51875
0.31183
KA_E
0.16757
0.48509
0.26192
0.12372
0.10935
LL_O
0.20213
0.44718
0.23642
0.11269
0.08434
MA_O
0.15335
0.40171
0.22034
0.16113
0.00793
MAS_O
-
-
-
-
-
MS_E
0
0.0625
0
0
0.15966
PA_O
0.08509
0.32851
0.14736
0.11111
0.02474
R647_E
0
0.0436
0.07353
0
0.19309
RD_O
0.10215
0.43103
0.20354
0.10072
0.09197
RM_O
0.07834
0.01582
0.24051
0.05317
0.14286
SAD_E
0
0.02196
0.05959
0
0.16681
SAL_E
0.00413
0.01087
0.1579
0.03238
0.26398
0.10194
0.06764
0
0.09827
0.26882
0.09305
0.36781
0
0.17106
SAP_E
SLC_E
0
STC_E
0.0214
0.0112
STL_E
0.0621
0.0449
0.0076
WE_O
0.0214
0.0354
0.0362
40
0.11304
0.0934
The mtDNA population-based (r2 = 0.132; P<0.0001; Figure 2.2a) and individualbased (r2 = 0.0384; P<0.0001; Figure 2.2b) Mantel tests revealed a significant, positive
correlation between genetic differentiation and geographic distance.
Both PCoA analyses indicated two genetic clusters roughly corresponding to the
year-of-emergence divide that occurs in mid-Manitoba. For the population-based PCoA,
one cluster contained all western, even- and odd-year populations with the exception of
Riding Mountain National Park, MB (RM_O), while the other contained all even-year,
eastern populations and Riding Mountain National Park, MB (RM_O) (Figure 2.3a). With
the exception of some individuals from Chetwynd, BC (CH1_O), Riding Mountain
National Park, MB (RM_O), and St. Maarten Junction, MB, one cluster (cluster 1, Figure
2.3b) from the individual-based PCoA consisted almost entirely of individuals from
eastern, even-year populations. The second cluster contained the remaining eastern,
even-year and western odd- and even- year individuals (cluster 2, Figure 2.3b).
Phylogeographic History
The mtDNA haplotype network exhibited a star-like pattern, with a central,
common haplotype differing from a large number of low frequency haplotypes by one or
two nucleotide changes (Figure 2.4; note that populations were grouped into larger
geographic regions for ease of display in the haplotype network). This pattern suggests
weak geographic structuring and overall low levels of population divergence,
characteristic of a recent, rapid population expansion.
41
a
1.6
1.4
Fst/(1-Fst)
1.2
1.0
0.8
0.6
0.4
0.2
0.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
Linearized Individual x
Individual Genetic Distance
Ln [1 + Geographic Distance (km)]
b
3.0
2.5
2.0
1.5
1.0
0.5
0.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
Ln [1 + Geographic Distance (km)]
Figure 2.2. Relationship between Euclidean geographic (Ln [1 + geographic distance
(km)]) and a) mtDNA genetic distance between populations [Fst/(1-Fst)] (r2 = 0.132,
P<0.0001), b) mtDNA genetic distance between individuals (r2 = 0.0384, P<0.0001) for
Oeneis macounii. Each point corresponds to a pair of populations (a) or individuals (b).
42
a
Coordinate Axis 2 (54.78%)
KA_E
IH_E
HB_E BSP_O
BSK_O HMH_E
RD_O
DE_O
CR_O
BS_O
IH_O
CA_O
HO_O
MA_O
FA_O
LL_O
BE_E
CH2_O
R647_E
SAL_E
RM_O
STL_E
CH1_O
HI_O
SAP_E
SAD_E
STC_E
WE_O
EF_E
SLC_E
MS_E
PA_O
HA_O
HR_O
IR_E
Coordinate Axis 1 (39.31%)
b
Coordinate Axis 2 (22.91%)
cluster 2
cluster 1
Coordinate Axis 1 (15.74%)
Figure 2.3. Principle coordinates analyses of mtDNA data for Oeneis macounii based on
a pairwise Fst matrix (a) and an individual x individual genetic distance matrix (b).
43
Figure 2.4. Haplotype network for COI sequences for Oeneis macounii. Circle sizes are proportional to the total number of
individuals with that haplotype. Colours represent the geographic region and year class (even- or odd-numbered years)
where the haplotype was found, while the size of each coloured section indicates the frequency of that haplotype within
the respective geographic region relative to its total frequency. Bars represent missing/inferred haplotypes
44
Most populations shared a common, widespread haplotype (ma4). The remaining
shared haplotypes were generally confined to distinct geographic regions [e.g., ma28,
with the exception of Riding Mountain National Park (RM_O), was confined to eastern,
even-year populations; ma70 occurred only in even- and odd-year British Columbia
populations; ma10 occurred only in some populations in Saskatchewan and Alberta;
Figure 2.4].
For the majority of populations, both neutrality and demographic expansion tests
were non-significant, supporting a model of stable population size under neutral
evolution (Table 2.2). However, Tajima’s D and Fu’s Fs were negative and significant
for one and eight populations, respectively, indicating an excess of rare alleles
compared to expectations under neutrality and demographic expansion for these
groups. For three populations [Hay River (HR_O), Riding Mountain National Park
(RM_O), and Isle Royale National Park (IR_E)], Fu’s Fs was positive, indicating a
deficiency of alleles, possibly associated with a recent population bottleneck or
balancing selection (Table 2.2).
2.3.2 AFLPs
Out of 349 individuals, AFLP profiles were successfully generated for 339.
Preselective or selective amplification failed for the remaining 10 individuals. Following
error-rate analyses of the five selective primer pairs, a total of 259 loci were retained
with a mean mismatch error of 3% (Table 2.5).
.
45
Table 2.5. Summary of the AFLP phenotype scoring and mismatch error analysis
results for the five selective primer combinations used to amplify Oeneis macounii
samples.
Scoring Threshold (rfu)
Selective Primer
Combination
EcoRI-ACT +
MseI-CAT
EcoRI-ACT +
MseI-CTC
EcoRI-AAC +
MseI-CTC
EcoRI-AAC +
MseI-CTG
EcoRI-ACA +
MseI-CTT
Locus
Phenotype
Mismatch
Error Rate (%)
Initial Number
of Loci
Number of
Loci Retained
1500
600
4.49
129
53
900
1100
2.88
81
71
1500
400
2.54
105
44
1600
300
3.73
106
44
1300
400
2.79
96
47
Mean: 3.21
Total: 517
Total: 259
Genetic Diversity
Measures of genetic diversity from the AFLP data set were relatively high and
similar among all populations: expected heterozygosity and the proportion of
polymorphic loci ranged from 0.17 [100 Mile House, BC (HMH_E)] – 0.24 [Hay River,
NWT (HR_O) and Stanley, ON (STL_E)] and 49.4 [Hay River, NWT (HA_O)] – 68.3%
[Sandilands Provincial Forest, MB (SAL_E)], respectively (Table 2.2).
Population Structure
As for mtDNA, the AFLP AMOVA indicated significant genetic structuring among
the tested populations (global Fst = 0.0951; P<0.0001), but with proportionally less of the
total variation (9.5%) due to genetic variation among populations (Table 2.3). Likewise,
the AFLP-SURV global Fst test for population genetic differentiation was significant
(global Fst = 0.0509; P<0.0001). Pairwise Fst estimates (Table 2.4) were generally low,
46
ranging from 0-0.2386, but the majority of comparisons were significant, even at fine
geographic scales [e.g., Stanley Legion, ON (STL_E) and Stanley Cemetery, ON
(STC_O) were only 3 km apart, Table 2.4]. Isle Royale National Park, MI (IR_E), and
Harlan, SK (HA_O), were comparatively highly differentiated from almost all other
populations, as was true for the mtDNA analyses (Table 2.4). There was no consistent
pattern concerning allochronic isolation: while differentiation between even- and oddyear samples from Irwin Hill, AB (IH_E and IH_O) was significant, there was no
differentiation between allochronic samples from Bragg Creek, AB (BSK_E and BSK_O)
(Table 2.4).
Both the population-based (r2 = 0.0729; P<0.0001; Figure 2.5a) and individualbased (r2 = 0.0907; P<0.0001; Figure 2.5b) Mantel tests indicated a significant
relationship between genetic and geographic distance.
The AFLP population-based PCoA failed to identify any patterns in the genetic
differentiation between populations (Figure 2.6a). In contrast, when all individuals were
included, the PCoA revealed two main genetic clusters (Figure 2.6b). However, there
was no spatial or temporal pattern to the assignment of individuals to each cluster, and
only 15.88% of the total variation in the data set was explained by the first two
coordinates.
Similarly, the STRUCTURE analysis indicated the most probable number of
populations to be two, but with no consistent spatial or temporal pattern to the
assignment of individuals to each genetic cluster (Figure 2.7).
47
a
0.4
Fst/(1-Fst)
0.3
0.3
0.2
0.2
0.1
0.1
0.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
Linearized Individual x
Individual Genetic Distance
Ln [1+Geographic Distance (km)]
b
16.0
14.0
12.0
10.0
8.0
6.0
4.0
2.0
0.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
Ln [1+Geographic Distance (km)]
Figure 2.5. Relationship between Euclidean geographic (Ln [1 + geographic distance
(km)]) and a) AFLP genetic distance between populations [Fst/(1-Fst)] (r2 = 0.0729,
P<0.0001), and b) AFLP genetic distance between pairs of individuals (r2 = 0.00907,
P<0.0001) for Oeneis macounii. Each point corresponds to a pair of populations (a) or
individuals (b).
48
a
Coordinate Axis 2 (48.29%)
LL_O
HA_O
BSK_E
DE_O
BSK_O
GP_O
KA_E
BSP_O
IH_O
HO_O
STL_E
IH_E
CH1_O RD_O
MAS_O
STC_E
MA_O
CA_O
BS_O
CH2_O
HR_O
HI_O
R647_E
WE_O RM_O
SAD_E
SAL_E
FA_O
HB_E
BI_E
PA_O MS_E
EF_E
IR_E
SLC_E
HMH_E
AG_E
SAP_E
Coordinate Axis 1 (28.54%)
Coordinate Axis 2 (9.82%)
b
Coordinate Axis 1 (6.06%)
Figure 2.6. Principle coordinates analyses of AFLP data for Oeneis macounii based on
a pairwise Fst matrix (a) and an individual x individual genetic distance matrix (b).
49
Figure 2.7. Estimated population structure for Oeneis macounii AFLPs inferred by STRUCTURE under an admixture
model of K = 2. Vertical black bars separate sampling locations, while each coloured bar represents an individual. The bar
colours (blue and orange) indicate the proportion of its genotype that belongs to each of K = 2 clusters. For display
purposes, individuals were grouped by population and arranged from west to east. The demarcation between all even and
odd-year populations and all even-year populations to the west and east of mid-Manitoba, respectively, is indicated below
the graph.
50
2.4 Discussion
2.4.1 Population Structure and Contemporary Barriers to Gene Flow
Allopatric Isolation
Results from both mtDNA and AFLPs were generally congruent, and
indicated a significant degree of genetic structuring for the MA. However, despite
the MA’s widespread, fragmented distribution, both markers revealed relatively
weak population differentiation and relatively high levels of genetic diversity.
These patterns may be indicative of on-going gene flow, which acts to
homogenize populations and maintain overall genetic diversity (Slatkin 1985).
For organisms like the MA with widespread distributions, gene flow among
distant populations would be achieved via an innately high dispersal propensity,
vector transport, or a stepping stone dispersal pattern (Frankham 2010, CasselLundhagen et al. 2013). Alternatively, population homogeneity and high diversity
may be remnant characteristics of a formally larger, panmictic population that has
recently fragmented (Cassel-Lundhagen et al. 2013). That is, the genetic
consequences of population fragmentation take time to manifest, and therefore,
recently isolated populations may appear genetically similar due to incomplete
lineage sorting despite a lack of gene flow among them (Templeton et al. 1995,
Keyghobadi 2007, Qu et al. 2012).
My results, combined with direct observations of the MA’s dispersal
behaviour, suggest that recent population isolation, rather than on-going gene
flow, is responsible for the generally low levels of differentiation among many
51
allopatric populations observed in my study. Overall, both genetic markers
revealed a pattern of isolation by distance, indicating that in the absence of
specific physical barriers, geographic distance generally limits genetic exchange
for the MA. Furthermore, AFLPs revealed significant differentiation among most
allopatric populations, suggesting that gene flow in the MA may be significantly
restricted over very fine spatial scales. These results are consistent with other
insect habitat specialists that display a limited ability or propensity to disperse
among fragmented habitat patches, and for which even seemingly insignificant
natural or anthropogenic features can act as major barriers to genetic exchange
(Thomas 2005, Collier et al. 2010, Hemme et al. 2010, Crawford et al. 2011,
Leidner and Haddad 2011). Indeed, recent mark-recapture studies of the MA
populations at Riding Mountain National Park (RM_O) and Sandilands Provincial
Forest (SAL_E, SAD_E, SAP_E) in Manitoba revealed limited adult dispersal
activity and distance (Burns 2013). For the Sandilands population, an unpaved
road transecting the forest seemed to provide a substantial, although incomplete,
barrier to adult movement (Burns 2013). Thus, it is unlikely that MA adults
regularly disperse long distances. Instead, the MA’s current geographic
distribution and spatial structuring likely largely reflect the recent fragmentation of
a historically more contiguous population (de Jong et al. 2011, CasselLundhagen et al. 2013) following natural and anthropogenic habitat loss. Many of
the resulting allopatric MA populations likely are now isolated and in the early
stages of divergence.
52
Allochronic Isolation
Although allopatric isolation has promoted the genetic divergence of MA
populations, I did not find corresponding, consistent evidence for restricted gene
flow due to allochronic isolation. Pairwise Fsts based on AFLPs indicated weak,
but significant, differentiation for only one sympatric, alternate-year cohort
comparison [Irwin Hill, AB even (IH_E) and odd (IH_O)], while clustering
analyses did not reveal any patterns in genetic differentiation associated with
emergence year-class (Figures 2.3, 2.6, 2.7). Sampling error may have
contributed to these results: where sympatric, allochronic cohorts of biennial
insects exist, one cohort generally is rare, or associated with a comparatively
lower adult emergence (Heliövaara and Väisänen 1984, Sperling 1993, Kankare
et al. 2002), and I generally lacked samples of the rarer cohort for the MA
allochronic populations.
To date, the divergence between sympatric, allochronic cohorts has only
been directly assessed for two other biennial insects with contrasting results:
While both genetic (Heliövaara et al. 1988) and morphological (Väisänen and
Heliövaara 1990) differentiation was reported between allochronic cohorts of the
pine bark bug, Kankare et al. (2002) did not observe genetic divergence between
sympatric, allochronic cohorts of the biennial moth Xestia tecta. As Kankare et al.
(2002) noted for X. tecta, two alternative hypotheses may explain the observed
lack of differentiation within the MA due to allochronic isolation arising from
asynchronous biennial emergence. First, allochronic isolation may prevent
genetic exchange between sympatric, alternate year cohorts of biennial insects,
53
but some of the cohorts studied thus far have not been isolated for a sufficiently
long period of time for genetic differences to accumulate (Kankare et al. 2002).
The areas occupied by sympatric, allochronic MA cohorts around the Rocky
Mountains were colonized relatively recently. If the alternate year populations
(i.e., the even year populations east of the Rockies) were derived from the
predominant population in the region (e.g., the odd-year population east of the
Rockies), then they formed even more recently (Heliövaara et al. 1994).
Furthermore, because the cohorts occupy identical environments, and thus are
subjected to nearly identical selective pressures, divergence between them likely
proceeds slowly primarily via drift (Coyne and Orr 2004).
Second, on-going gene flow between sympatric, allochronic cohorts may
be preventing their divergence. That is, the allochronic isolation between cohorts
might be ‘leaky’, with some individuals from one cohort emerging off-year, via a
life cycle deceleration (3 years) or acceleration (1 year), into the alternate year
cohort. Although field and museum specimen surveys suggest that off-year
emergences are rare for the MA (G. W. Otis, unpub. data), a lack of rearing and
developmental studies make it difficult to ascertain conclusively how common offyear emergences generally are. For the most intensively-studied periodical
insect, they appear common: individuals and entire broods of periodical cicadas
have repeatedly been observed to emerge in alternate years (Williams and
Simon 1995). Furthermore, developmental studies indicate that insect
populations commonly display individual variation in the length and number of
diapauses, and therefore in the timing of adult emergence (Tauber and Tauber
54
1981, Danks 1992). Nevertheless, even if off-year emergences are rare,
population genetic theory predicts that as few as a single migrant per generation
may be required to prevent divergence between populations (Wright 1931).
Although I did not observe divergence between the allochronic cohorts at
Bragg Creek, AB (BSK_E and BSK_O), consistent with a hypothesis of on-going
gene flow between cohorts, the allochronic cohorts at Irwin Hill, AB (IH_E and
IH_O) were significantly differentiated on the basis of AFLPs. However, while the
Bragg Creek, AB samples from both year classes were collected from the exact
same location, alternate-year Irwin Hill, AB samples were collected 1.5 km apart.
Thus, given the MA’s low dispersal behaviour and fine-scale spatial genetic
structuring, I cannot rule out that allopatric, as opposed to allochronic, divergence
was the mechanism responsible for the observed differentiation between the
Irwin Hill cohorts.
2.4.2 Phylogeographic History
Similar to some other widespread lepidopterans inhabiting previously
glaciated areas (Joyce and Pullin 2001, Lohman et al. 2008, de Jong et al. 2011,
Cassel-Lundhagen et al. 2013), my mtDNA results (the star-like haplotype
network with a large number of haplotypes separated from a central haplotype by
very few mutational steps; a general pattern of high haplotype diversity combined
with low nucleotide diversity; and overall weak population divergence) all suggest
a recent, rapid population expansion for the MA (Slatkin and Hudson 1991, Wu et
al. 2010b, Schoville et al. 2011, Graham and Burg 2012, Pugarin-R and Burg
55
2012). Three primary models have been proposed for how organisms expanded
into previously glaciated areas, each resulting in a distinctive genetic pattern
(Ibrahim et al. 1996). My data exhibits characteristics of both the phalanx and
pioneer expansion models. The phalanx model describes a gradual wave of
individuals moving via short distance dispersal, and is associated with the
retention of more diversity and less divergence among populations (Ibrahim et al.
1996). In contrast, the pioneer model also involves some long distance dispersal,
which results in areas of higher genetic differentiation due to founder effects. The
generally low population divergence and high diversity observed in my study,
combined with its apparent low dispersal behaviour, could suggest a phalanx
model of post-glacial expansion for the MA. However, two populations [Hay
River, NWT (HR_O) and Isle Royale National Park, MI (IR_E,)], both occurring in
remote locations and/or at the periphery of the MA’s range, showed evidence of
a recent bottleneck (positive Fs values) and also were comparatively highly
differentiated. As Cassel-Lundhagen et al. (2013) note, it is difficult to imagine
how the MA could have colonized such an expansive area so rapidly without
engaging in higher dispersal activity and at least some long distance movements.
For many animals, the costs and benefits associated with dispersal will depend
on local demographic (e.g., sex ratios, population density, inbreeding levels) and
environmental (e.g., habitat quality, food availability) conditions, conditions that
will vary over time and space (Slatkin 1985, Lenormand 2002, Bowler and
Benton 2005, Clobert et al. 2009). In response, many animals have evolved a
plastic, situation-dependant dispersal strategy (Slatkin 1985, Bowler and Benton
56
2005, Berthouly-Salazar et al. 2012). For example, some insects show an
increased tendency to disperse with increasing population density (Otronen and
Hanski 1983, Doak 2000, Bowler and Benton 2005). The MA also may exhibit a
more fluid dispersal strategy, and while generally it displays limited dispersal,
conditions during its colonization (e.g., higher population densities, more
continuous habitat) may have been conducive to more frequent and longer
distance movements (Nichols and Hewitt 1994).
The geographic distribution of genetic diversity and patterns of divergence
among haplotypes of specific species can provide clues to the number and
location of glacial refugia they used. For instance, a pattern of geographicallywidespread, high frequency haplotypes in combination with generally low
divergence among populations suggests a colonization from a single refugium,
while geographically-localized, relatively deeply diverged haplotypes and high
differentiation among populations typically reflects isolation within multiple refugia
(Avise 2009, Bertheau et al. 2013). The location(s) of refugial populations
generally corresponds to extant areas of high genetic diversity (Hewitt 1996).
Patterns in the divergence among and the geographic distribution of the
MA COI haplotypes display characteristics expected under both single and
multiple refugia scenarios: haplotype ma4 was present in almost every MA
population and haplotype divergence generally was low (Figure 2.4), which could
suggest that the MA occupied a single refugium. However, other haplotypes were
geographically-localized (Figure 2.4), suggestive of a colonization from multiple
refugia. Furthermore, although my study revealed a range of intra-population
57
haplotype diversity levels, haplotype diversity was moderate to high for most MA
populations. This lack of a consistent pattern in the diversity and divergence
among populations and in the geographic location of haplotypes makes it difficult
to estimate the potential number and location of refugia the MA might have
occupied using my data alone. However, the phylogeographic histories of jack
and lodgepole pine have been described (Godbout et al. 2005, Godbout et al.
2008). Because there is a general correlation between the phylogeographic
history of animals and their associated vegetation (Hewitt 1996), it is feasible that
the MA tracked the glacial and post-glacial movements of one or both pine
species and/or their associated boreal vegetation. While lodgepole pine
colonized the northwest from multiple areas on the west coast and south of the
Rocky Mountains (Godbout et al. 2008), jack pine colonized from three disjunct,
eastern refugia: two eastern coastal populations, and a population west of the
Appalachian Mountains and south of the Great Lakes, herein referred to as the
southern population (Godbout et al. 2005). Based on the phylogeographic
histories of both pine species and my genetic data, a number of colonization
scenarios are possible for the MA.
The presence of haplotype ma4, ancestral to all other observed
haplotypes and encompassing the entire range of the MA, combined with the
generally low divergence among populations, could suggest a recent, common
ancestry from a single refugium for all populations. Similarly, haplotype ma29,
confined to populations in Ontario and Minnesota (Table 2.1), were derived from
haplotype ma67, found only in British Columbia and Alberta (Table 2.1, Figure
58
2.4), further implying that extant eastern and western MA populations may have
been derived from a single ancestral population.
The east-west adult emergence pattern and mtDNA divide along midManitoba suggests that Lake Agassiz may have acted as an early barrier to the
MA’s colonization [the lack of a corresponding pattern based on AFLPs likely
reflects incomplete lineage sorting: mtDNA displays 1/4 of the effective
population size (Ne) of nuclear markers, and thus is expected to sort faster than
the mostly nuclear AFLPs (Avise 2009)]. Thus perhaps the most likely hypothesis
assuming a colonization from a single refugium is an eastern post-glacial origin in
association with the southern jack pine refugial population under the following
scenario: from this refugium, the MA could have tracked jack pine north, and then
to the west and east of Lake Agassiz, creating an eastern and western
population largely isolated by the lake. Assuming the refugial MA population
consisted of a single year class (i.e., even or odd), one population subsequently
was pushed onto an alternate-year schedule, likely following a major climatic
event. As Lakes Agassiz and Ojibway receded, the MA, along with jack pine,
filled in its distribution across Manitoba and Ontario. Here, the MA genetic divide
between east and west that was initiated by the lakes would have been
preserved. Upon secondary contact, the western and eastern populations would
have remained isolated by their alternate-year emergence schedules. However, it
is unclear if secondary contact did occur: if it had, we might expect an extant
zone of parapatry with populations emerging in both years through Manitoba.
Thus, they may remain isolated in allopatry. Consistent with this colonization
59
scenario is that the MA populations in Ontario, Minnesota, southern Manitoba,
and Saskatchewan, areas that would have been colonized early had the MA
followed this route, were among the most genetically diverse in my study.
However, one population does not exactly fit the west-east pattern of
divergence along central Manitoba: despite occurring in western Manitoba and
emerging in odd-years, the Riding Mountain National Park (RM_O) population
contained haplotype ma28, which was otherwise only present in eastern, evenyear populations (Table 2.1). Nevertheless, the colonization scenario I propose
above is still possible. Its current widespread eastern distribution despite a
current lack of gene flow among allopatric MA populations and low divergence
from the ancestral haplotype ma4 suggests that ma28 evolved relatively early,
and may have existed in the MA refugial population. Thus the Riding Mountain
population may have simply retained ma28 from the refugium. Alternatively, but
less likely given the MA’s low dispersal, ma28 in Riding Mountain may be the
result of recent (i.e., since colonization from its refugium and significant draining
of Lake Agassiz) introgression from nearby eastern, even-year population(s).
An east coast colonization origin with jack pine also could account for the
high diversity observed in some of the more eastern MA populations. However,
by the time the MA would have reached western Ontario, Lake Agassiz would
have drained significantly (Pielou 1991) making the existence of the genetic and
emergence divide in Manitoba more difficult to explain. Additionally, if the MA had
followed this route, we might expect extant populations in Quebec or along the
east coast. However, with the exception of two records from southwestern
60
Quebec, the MA is not known east of Ontario; it is impossible to determine if it
became extirpated from these areas following colonization from the coast or if it
simply never existed there.
A final alternative for the MA (assuming a single refugium) is a western
refugium associated with lodgepole pine, and a subsequent west to east
colonization. Although some populations in BC also displayed high diversity, this
scenario seems less likely. The Rocky Mountains likely now pose a barrier to MA
movement, as I observed haplotypes unique to both sides of the mountains. Yet
there was no corresponding genetic clustering associated with the mountains,
and thus, these areas likely have been colonized and subsequently isolated more
recently than areas in the east.
Alternatively, the COI clustering corresponding to the emergence divide
along mid-Manitoba, combined with the unique haplotypes largely confined to
populations to the east and west of this area [e.g., ma28, with the exception of
Riding Mountain National Park (RM_O), was confined to eastern, even-year
populations; ma70 occurred only in even- and odd-year British Columbia
populations; ma10 occurred only in some populations in Saskatchewan and
Alberta; Figure 2.4] could suggest multiple eastern and western refugia for the
MA. Furthermore, there was a weak tendency for populations in the west (BC)
and east (ON, MI, MB, and eastern SK) to display the highest genetic diversity,
while central populations (western SK, AB, NWT) were less diverse (Table 2.2).
These patterns indicate that the eastern, even-year MA populations may have
been derived from one eastern jack pine refugial population containing haplotype
61
ma28, while the western even and odd-year populations were derived from one
or more separate western lodgepole pine refugia containing haplotypes 70, 67,
and/or 10.
However, isolation among several refugia typically results in more deeply
diverged (i.e., separated by a larger number of nucleotide changes) haplotypes
than that observed for the MA (Avise 2009, Bertheau et al. 2013). Furthermore,
as I previously discussed, the widespread presence of an ancestral haplotype,
such as ma4 for the MA, is typically considered evidence of isolation within a
single refugium. However, the MA displays a comparatively long life cycle, and
thus has experienced fewer generations, and hence less evolutionary time, since
the Wisconsin glacial period began. Given this, it’s still possible that the MA was
isolated within multiple refugia, and that its generally low haplotype divergence
reflects its long life cycle duration, while haplotype ma4 may have been retained
from a single ancestral MA population that predated the Wisconsin glaciation.
2.4.3 Conservation Implications
Although the MA generally is understudied, a number of populations
occurring within national or provincial parks have been more closely monitored,
and three such populations [Algonquin Provincial Park, ON, Riding Mountain
National Park, MB (RM_O), and Isle Royale National Park, MI (IR_E)] are of
conservation interest. The Algonquin and Riding Mountain populations are small,
and, based on annual butterfly counts (Algonquin: G. W. Otis pers. comm.) and
collection records (Riding Mountain: Burns 2013), both populations appear to be
62
in decline. Compared to large populations, small, isolated populations will
experience an accelerated loss of genetic diversity via inbreeding and genetic
drift, resulting in a loss of fitness and an increased risk of extinction (Frankham
1996, Spielman et al. 2004, Frankham et al. 2010). Although my analyses did not
reveal particularly low genetic diversity for the Algonquin and Riding Mountain
(Tables 2.1 and 2.2) populations, their on-going isolation and declining size
means that they remain at risk of diversity loss and eventual extirpation. The
Riding Mountain and Algonquin populations generally were not significantly
genetically distinct from other MA populations, and both were genetically most
similar to eastern, even-year populations (Table 2.4; Figures 2.3, 2.4, and 2.6).
Similarly, recent field studies indicated that the Riding Mountain population did
not exhibit appreciable differences in life history traits compared to the MA
population in Sandilands Provincial Forest, MB (SAD_E, SAL_E, and SAP_E),
from which it is certainly reproductively isolated at present (Burns 2013). Thus,
transplanting adults from nearby even-year MA populations to supplement the
Riding Mountain and Algonquin populations could be a viable option for their
conservation management.
Of particular interest is the apparent genetic distinctiveness of the MA
population on Isle Royale (IR_E), a highly isolated island located in Lake
Superior that harbours a number of threatened populations. I found that the Isle
Royale MA population was significantly and comparatively highly differentiated
from all other populations, but least differentiated from surrounding eastern
mainland populations. These patterns are consistent with other island insect
63
populations that early in their post-glacial expansion colonized an island via few
individuals, and subsequently became isolated from the surrounding mainland
(Estoup et al. 1996, de Jong et al. 2011). Because of founder effects, such island
populations often display reduced genetic diversity in addition to high
distinctiveness (Estoup et al. 1996, Frankham 1997). For instance, genetic data
suggest that the Gray Wolf (Canis lupus lycaon) potentially colonized Isle Royale
via a single female, and the resulting population, now suffering a severe loss of
genetic diversity, is considered endangered (Wayne et al. 1991). Although Fu’s
Fs was positive for the MA Isle Royale population, indicating a potential recent
bottleneck, it displayed levels of genetic diversity comparable to all other tested
populations. Similar results have been reported for the Isle Royale deer mouse
(Peromyscus maniculatus) population despite a lack of immigration from
surrounding mainland populations (Vucetich et al. 2001). The loss of genetic
diversity following a bottleneck depends on both the initial size of the founding
population and subsequently, how well the founding population establishes: if the
new population increases in size quickly, it likely will not suffer an appreciable
loss of diversity (Futuyma 2009). Vucetich et al. (2001) postulated that the high
diversity displayed by the Isle Royale deer mouse population reflected a
comparatively large population size, attained rapidly following colonization
because of reduced interspecific competition (Vucetich et al. 2001). Recent field
surveys suggest that Isle Royale also presently supports a ‘robust’ MA population
(Otis 2012), which may suggest that, similar to the deer mouse, the MA
population that founded Isle Royale grew rapidly, maintaining a level of diversity
64
comparable to its source population(s). Alternatively, balancing selection, also
implied by a positive Fu’s Fs, may be maintaining diversity in the Isle Royale
population. Further genetic analyses, possibly with additional nuclear markers,
and additional demographic monitoring will be required to further describe the
Isle Royale genetic characteristics. Nevertheless, although its seemingly large
size and levels of genetic diversity suggest that the Isle Royale MA population is
not immediately threatened, its continued isolation, genetic distinctiveness, and
confinement within a national park will continue to make it of conservation
interest.
In conclusion, my study provides preliminary insight into the genetic
structuring and phylogeography of the MA. The observed patterns in genetic
diversity and differentiation largely reflect a recent, shared demographic history
for all MA populations. Although my study, combined with the phylogeographic
histories of jack and lodgepole pine, generate some interesting hypotheses for
the glacial and post-glacial scenarios possible for the MA, additional genetic and
modeling analyses (Elith and Leathwick 2009, Cassel-Lundhagen et al. 2013),
combined with an increased sampling effort of some populations, will help to
further elucidate its history. Currently, dispersal and thus gene flow likely are
limited among many allopatric MA populations, but it remains unclear if
allochronic isolation promotes sympatric population divergence in biennial insects
like the MA.
My study is only the second to investigate the genetic differentiation
between sympatric, allochronic cohorts of a biennial insect, and thus further
65
genetic study of the MA and other biennial species certainly is warranted.
However, genetic data are most valuable when complimented with demographic
and biological knowledge of the study organism. Thus, the lack of developmental
and demographic studies, which could illuminate the mechanisms behind off-year
emergences and the frequency at which they occur, remains an impediment to
understanding the significance of allochronic isolation in the divergence of the
MA and other biennial insects.
66
Chapter 3
NOTES ON THE DEMOGRAPHICS, LIFE HISTORY, AND BEHAVIOUR OF
THE WHITE MOUNTAIN ARCTIC BUTTERFLY (OENEIS MELISSA SEMIDEA)
3.1 Abstract
The White Mountain arctic butterfly [WMA; Oeneis melissa semidea (Say)]
is endemic to the alpine zone of the Presidential Range of the White Mountains,
New Hampshire. Although it has been listed as imperiled, many biological
characteristics of the WMA important for its conservation assessment and
management are unknown. I conducted field studies in 2011 and 2012 to further
characterize the WMA’s demographics, life history, and behaviour. In both years,
adults emerged in mid-June, and occurred on Mts. Washington and Jefferson in
association with Bigelow’s sedge (Carex bigelowii Torr. ex Schwein). On both
mountains, adult abundance was generally very low, suggesting that the
population has declined considerably since its first description. I observed adults
dispersing among some of the meadows on Mt. Washington, but I was unable to
confirm if they moved between Mts. Washington and Jefferson. Adults generally
congregated on rocky ledges and out-croppings, where males employed both
perching and patrolling mate-locating strategies. In addition to elevation, it
appears that adults use other cues when choosing areas to congregate. Finally,
although some other Oeneis species engage in male territoriality characteristic of
a lek mating system, my observations suggest that WMA males are not truly
territorial.
67
3.2 Introduction
The White Mountain arctic [WMA; Oeneis melissa semidea (Say)] is
endemic to the alpine zone of the Presidential Range of the White Mountains,
New Hampshire, USA. Within this area, populations occur on Mts. Washington
and Jefferson, where adults are localized in alpine meadows dominated by
Bigelow’s sedge (Carex bigelowii Torr. ex Schwein) (Anthony 1970, McFarland
2003). Because of its rarity and severely restricted range, the WMA has been
listed as “imperiled” at both state and global levels. As an alpine organism, the
WMA is particularly threatened with habitat loss via climate change (Parmesan
2006, Konvička et al. 2010).
To date, most of our biological and demographic knowledge of the WMA
comes from the initial descriptions of Scudder (1891; 1901). Since then, only
Anthony (1970) and McFarland (2003) have attempted to systematically monitor
or study the WMA, and Anthony (1970) deemed his own study to be
inconclusive. Consequently, many aspects of the WMA’s biology and behavior
remain unconfirmed or unknown. For instance, an estimate of present-day
population size is lacking. Furthermore, although the WMA population is
purportedly spatially structured into isolated fragments (Anthony 1970;
McFarland 2003), adult dispersal capacity and patterns have never been
determined. Finally, WMA males appear to aggregate in leks where they await
the arrival of females (McFarland 2003), but its mating system has never been
definitively characterized. Current, detailed knowledge of such demographic and
68
behavioral characteristics will be critical for the continuing conservation
assessment and management of the WMA.
Over two field seasons, I attempted to further quantify the WMA’s behavior
and demography in the context of its conservation. However, consistent with the
experiences of Anthony (1970) and McFarland (2003), making systematic and
quantifiable observations of the WMA proved challenging: access to adults
required long hikes, and the steep and rocky terrain made following or capturing
adults very difficult. Mount Washington also routinely experiences harsh and
unpredictable weather, and hence a limited number of days was suitable for adult
butterfly activity. Nevertheless, I made some novel observations significant to the
WMA’s conservation that I summarize here.
3.3 Field Methods
I conducted field studies from 22 June – 14 July, 2011, and 22 June – 15
July, 2012. Each year, the area that I surveyed included the alpine meadows
described by Anthony (1970) (Cow Pasture, Bigelow Lawn, and Gulf Tanks on
Mt. Washington; and Monticello Lawn on Mt. Jefferson), but also encompassed
most of the intervening and adjacent areas containing Bigelow’s sedge (Figure
3.1).
69
Figure 3.1. Distribution of adult Oeneis melissa semidea on the alpine zone of
Mts. Washington and Jefferson, New Hampshire, USA determined by markrelease-recapture (MRR). Black lines indicate the areas surveyed for adults
[Bigelow Lawn (BL), Gulf Tanks (GT), Cow Pasture (CP), and Monticello Lawn
(ML)]. White and blue points indicate that the capture or sighting was made in
2011 and 2012, respectively. Triangles indicate locations where individual
females were captured, circles where individual males were captured, and
squares where an adult was sighted but not captured.
70
I surveyed each meadow at least every other day, or as weather
permitted. In total, Cow Pasture, Bigelow Lawn, Gulf Tanks, and Monticello Lawn
was surveyed 9, 5, 5, and 1 times, respectively, in 2011; and 9, 4, 7, and 2 times,
respectively, in 2012. Because the weather conditions on Mt. Washington are
variable and subject to rapid change, the number of days I was able to survey
and the amount of time spent surveying in each meadow on any given day was
highly variable. As such, my surveys were unavoidably biased, and thus I was
unable to accurately estimate several adult population characteristics (e.g.,
overall size and density, density by meadow, sex ratio).
During surveys, I employed mark-release-recapture in an attempt to
assess adult distribution, movements, longevity, and population size. To uniquely
mark individuals, I applied small dots to the ventral wing surface of one side of
the body with water-based, colored (red, green, blue, or yellow) paint markers
(Sharpie® poster-paint) using a position-based numbered coding system
(Southwood 1980).
Occasionally, I searched for eggs, larvae, and pupae at the bases of
sedge plants or under small, moveable rocks.
3.4 Results and Discussion
3.4.1 Adult Life History and Demographics
Adults were present for the duration of each study period. Based on the
degree of wing wear of the first individuals captured, adults likely first emerged
71
on ~22 June in 2011, and may have emerged as early as 15 June in 2012. By
the end of each study period, approximately 85% of adults demonstrated some
degree of wing wear, indicating that they were near the end of their flight period.
I estimate the flight periods were approximately 30 and 36 days in duration in
2011 and 2012, respectively.
Adults were located in almost every sedge-containing area of the Mt.
Washington alpine zone. Similarly, adults was located in association with
Bigelow’s sedge on Mt. Jefferson, but was concentrated southeast of the summit
(Figure 3.1). On both mountains, adult density generally decreased with
decreasing elevation, and the beginning of the krummholz (i.e., tree line) marked
the limit of adult distribution. I did not locate any eggs, larvae, or pupae.
In total, 187 and 182 adults were marked in 2011 and 2012, respectively
(Table 3.1). Each year, the ratio of males to females caught was approximately
2:1 (Table 3.1). Very few recaptures was made (8 each year; Table 3.1),
precluding an accurate estimate of population size.
Table 3.1. Summary of Oeneis melissa semidea adult capture data.
Recapture
Distance (m)
Days to
Recapture
Year
Captured
Males
Females
Recaptures
mean
min
max
mean
min
max
2011
187
126
61
8
305.7
28.1
787.0
5
1
8
2012
182
110
69
8
66.3
14.8
172.0
2
1
7
72
However, the WMA population certainly has declined dramatically since its
earliest descriptions. At the turn of the last century, Scudder (1901) encountered
a large and robust WMA population, claiming that “During the entire month of
July the butterflies swarm over the rocks and sedgy plateaus of the upper
summits…” and that “…hundreds, perhaps thousands, are annually captured by
enthusiastic collectors…”. In stark contrast, I discovered that WMA adults were
low in numbers and localized. As McFarland (2003) reported, I could survey large
areas and encounter only one or two adults until reaching an area of
congregation. These congregations typically only contained 10 - 15 adults.
Although the WMA emerges annually, it likely is biennial (i.e., requires 2
years for development) (McFarland 2003), as is the case for all other Oeneis
species (Scott 1986, Layberry et al. 2001). Most biennial insects emerge every
year over parts of their range (Heliövaara and Väisänen 1984, Scott 1986,
Heliövaara et al. 1994, Kankare et al. 2002), and these seemingly annual
emergences are assumed to represent two sympatric, allochronic cohorts (i.e.,
one emerging in odd-numbered years and the other emerging in even-numbered
years) (Scott 1986, Heliövaara et al. 1994, Kankare et al. 2002). The WMA also
is presumably structured into two allochronic cohorts. Where sympatric,
allochronic cohorts of biennial insects exist, one cohort usually is consistently
less common (Masters 1974, Mikkola 1976, Heliövaara and Väisänen 1984,
Scott 1986, Heliövaara et al. 1988, Sperling 1993, Kankare et al. 2002). Despite
our almost identical capture rates each year, I observed fewer adults overall in
2012. Because of my initial field experience in 2011, I became more proficient at
73
capturing adults (i.e., I knew where to locate them and how to most effectively
net them), and therefore, I believe that I caught a higher proportion of the adult
population in 2012. Therefore, the WMA even-year cohort may be smaller;
however, additional monitoring will be required to confirm this.
The average time between capture and recapture of an adult was 5 and 2
days in 2011 and 2012, respectively, with a maximum of 8 days (Table 3.1). The
average distance between capture and recapture of an adult was 306 and 66 m
in 2011 and 2012, respectively (Table 3.1). In 2011, a male originally captured at
Gulf Tanks was recaptured 787 m away at Bigelow Lawn. The distance that this
male covered indicates that adults are capable of dispersing among all meadows
on Mt. Washington. Although I did not directly observe dispersal between Cow
Pasture and the other two meadows, I routinely encountered adults in areas
among all three meadows. Thus the populations in the meadows of Mt.
Washington are likely not isolated from each other as suggested by Anthony
(1970). However, it remains unclear if adults actively disperse between Mt.
Washington and Mt. Jefferson. These mountains are separated by the Great Gulf
ravine (approximately 2.5 km wide when measured between Cow Pasture and
Monticello Lawn), which adults may be unwilling or unable to cross. Yet, adults
may occasionally be carried between these two areas by the wind (Anthony
1970). I did not observe adult movement between these mountains, but this
could reflect in part the low numbers of adults that I marked on Mt. Jefferson (8
and 12 in 2011 and 2012, respectively).
74
Until recently, the WMA has been reported only from Mt. Washington and
Mt. Jefferson. However, McFarland (2003) observed one adult on Mt. Monroe in
2002 and noted the presence of suitable habitat. On 11 July 2012, a hiker
familiar with the WMA encountered an adult on the southern slope of Mt. Monroe
(E. Elinski, pers. comm.). I was unable to survey this area, and thus it remains
unclear whether adults consistently occur on Mt. Monroe.
3.4.2 General Adult Behaviour and Mating System
Adults were most active on sunny days with winds below 30 km/h.
However, even under cloudy skies with winds up to 60 km/h, some adults would
fly if disturbed. Adults were wary and had strong and rapid flight: I occasionally
observed them flying into 40 – 50 km/h winds to avoid capture. They frequently
dove or crawled deep into rock piles if repeatedly disturbed or if I attempted to
net them from directly above while they basked. Following capture and marking, I
gently placed adults on a rock, where they typically basked briefly before flying
away. White Mountain Arctic adults rarely nectar, but have been observed
feeding on Moss Campion (Silene acaulis), Mountain Sandwort (Arenaria
groenlandica), and various Vaccinium spp. (Scudder 1901, McFarland 2003). In
2011, I observed one female nectaring from Mountain Cranberry (Vaccinium
vitis-idaea).
Adults generally congregated on rocky ridges or small rocky outcroppings.
These were typically characterized by a relatively flat area of sedge on the uphill
side of a rocky ledge that bordered a rocky slope. The drop in elevation below
75
the ledge was usually steep in the case of ridges, but rather slight in the case of
small outcroppings. As is the case for other Oeneis species (Guppy 1962,
Troubridge et al. 1982), this use of raised landscape features by the WMA has
been interpreted as hilltopping (McFarland 2003), a mate-encounter system in
which males congregate at high points in the landscape where they await the
arrival of females (Shields 1967, Baughman and Murphy 1988). However, the
ledges occupied by WMAs were often not the most elevated in relation to the
surrounding area. For instance, the north slope of the Mt. Washington summit
contains numerous ridges along a drop in elevation of approximately 120 m.
Males and females routinely occurred on all of these ridges and occasionally on
the rocky slopes between them. Moreover, adults frequently were found on small
ridges or outcroppings at the bases of large slopes, despite the presence of
seemingly identical habitat upslope. In Colorado, Oeneis chryxus (Doubleday)
displays similar behaviour, congregating on slopes of varying elevation (Daily et
al. 1991). The authors hypothesized that where males choose to congregate in a
given season is dictated by female distribution and movement, and that to
intercept females, males align themselves with bare areas that females was likely
to move towards. Rather than simply congregating at high points in the
landscape, it appears the WMA also uses additional visual cues when choosing
areas in which to congregate.
Congregated WMA males appeared to use a combination of perching and
patrolling as mate-locating strategies, as described by Scott (1974). They
frequently perched on rocks and alternated between lateral basking and
76
spontaneous (i.e., initiated without obvious stimulus or disturbance), presumably
patrolling, flights. Males also engaged in spiral flights with passing conspecifics
and other flying insects. Other butterfly species (Suzuki 1976, Lederhouse 1982,
Alcock 1983), including some Oeneis species (Dunlop 1962, Guppy 1962,
Masters et al. 1967, Daily et al. 1991, Clayton and Petr 1992), engage in a
similar suite of behaviors, and these behaviors have been interpreted by some
authors as male territoriality associated with a lek mating system (Dunlop 1962,
Guppy 1962, Masters et al. 1967, Lederhouse 1982, Alcock 1983, Knapton 1985,
Clayton and Petr 1992, McFarland 2003). Yet the behaviour of the WMA differed
both from some other Oeneis species and the definitional criteria for lekking
(Bradbury 1981, Baker 1983). First, while territories of true lekking species
remain fixed for several days in succession or longer (Baker 1983), the sites
occupied by individual WMA males was not temporally stable. As an example,
during an extended period of favorable weather between 9 and 13 July, 2012, I
was able to conduct daily surveys of a ridge in Cow Pasture where adults
consistently occurred. Each day, I observed 10 – 15 adults, 85 – 95% of which I
was able to capture and mark. Despite our high capture rate, I only made two
recaptures on subsequent days, even on the fifth visit. Furthermore,
approximately 90% of the adults captured each day showed at least some wing
wear, indicating that although these adults had clearly emerged at least a few
days prior to capture, I had not previously encountered them at that site. Thus,
either the adults was resident on the ridge continuously but a large proportion of
them was inactive on any given day, or most adults moved away from the ridge.
77
In contrast, Oeneis chryxus males consistently occupy the same sites for many
days in succession, which is typical for lek-forming species (Dunlop 1962,
Knapton 1985).
Second, the area occupied and/or patrolled by individual males often
overlapped with other males without stimulating aggression between them. Males
often perched within 2 m of each other, but also occasionally perched and
basked directly beside each other on the same rock. Patrolling flights usually was
of short distances (< 2m from point of initiation), but some individuals would fly
and resume perching at a new site up to 15 m away. In either case, the area
patrolled by a given male routinely contained multiple perching males. True
butterfly territories typically contain only the resident male (Dunlop 1962,
Lederhouse 1982, Knapton 1985), and ‘intruders’ are promptly driven away by
the resident male (Davies 1978, Lederhouse 1982).
Third, following any type of flight, WMA males frequently did not return to
the same rock or site from which they departed. Conversely, other purportedly
territorial butterfly species (Lederhouse 1982, Wickman and Wiklund 1983),
including O. chryxus (Knapton 1985, Daily et al. 1991), consistently return to their
original perch immediately following patrolling or spiral flights.
Fourth, although spiral flights may be interpreted as a form of territorial
defense (Lederhouse 1982, Alcock 1983), others view such flights as
investigative, being used facilitate mate recognition (Scott 1974, Suzuki 1976,
Daily et al. 1991, Clayton and Petr 1992). This latter explanation appears
78
applicable to the WMA, as spiral flights often occurred between males and
females, and, in three cases, ended immediately in copulation.
Fifth, male territories at leks are by definition devoid of oviposition and
feeding sites (Bradbury 1981), yet areas of WMA male congregation contained
both. The rocks on which males perched was almost invariably surrounded by
Bigelow’s sedge, the purported ovipositional site for females and larval host plant
(Scudder1891, 1901). Many flowering plants also occurred in these areas,
including Mountain Cranberry and Mountain Sandwort, on which WMA adults
occasionally feed.
Thus, although at first glance the WMA appeared to display behaviors
typical of hilltopping and lekking species, our more in-depth observations indicate
otherwise. As has been suggested for other perching butterflies displaying similar
behavior (Scott 1974, 1986, Suzuki 1976), it appears that the WMA is not truly
territorial. Further study will be required to determine the specific abiotic or biotic
cues for WMA adult congregation, and to further characterize its mating behavior.
In conclusion, while quantifiable study of the WMA was unsuccessful, I did gain
some novel insight into its life history and behavior. First, I was able to obtain
information on the WMA’s adult distribution and dispersal behavior. Although the
localized meadow populations likely are not isolated from each other, the
population as a whole appears to be in decline. Second, I was able to better
characterize its male mate-locating behavior, which involves male aggregation on
rocky ledges but none of the other traits of leks. This system seems to be
different from that of other species of the genus Oeneis. Not only does this study
79
contribute to our knowledge of Oeneis butterflies in general, but this information
also should aid the WMA’s conservation assessment and recovery efforts.
80
Chapter 4
POPULATION STRUCTURE AND CONSERVATION GENETICS OF THE
WHITE MOUNTAIN ARCTIC BUTTERFLY (OENEIS MELISSA SEMIDEA)
4.1 Abstract
The White Mountain arctic butterfly [WMA; Oeneis melissa semidea (Say)]
is endemic to the alpine zone of Mts. Washington and Jefferson, New
Hampshire, USA, and because of its small and declining population size, it is
listed as imperiled. White Mountain arctic adults occur only within four alpine
meadows, and it has been suggested that dispersal, and hence gene flow, may
be restricted among meadows. Furthermore, although the WMA likely is biennial
(i.e., requires 2 years for development) like all other species of Oeneis, adults
emerge annually. Thus the WMA population may be further structured into two
even and odd-year allochronic cohorts, reproductively isolated by their
asynchronous adult emergence. I assessed the spatial (among meadows) and
temporal (between even and odd year cohorts) genetic structure and diversity of
the WMA using mtDNA and AFLP markers generated from non-lethally sampled
wing and leg tissue. I found no evidence for restricted gene flow among
meadows. AFLPs indicated weak differentiation between alternate year cohorts;
however, it remains unclear whether this resulted from allochronic reproductive
isolation or genetic drift. Despite the WMA’s small population size and isolation,
levels of AFLP genetic diversity were generally high. Management efforts should
focus on increasing the WMA’s current population size.
81
4.1 Introduction
Taxa displaying highly restricted distributions and small population sizes
are of clear conservation concern (Kuras et al. 2003, Spielman et al. 2004, Habel
et al. 2010a). Compared to large populations, small populations will experience
an accelerated loss of genetic diversity via genetic drift and inbreeding,
eventually translating into reduced individual fitness and an inability to adapt to
novel environmental stressors (Frankham 1996, Harper et al. 2003, Habel et al.
2009, Frankham et al. 2010). In the absence of dispersal and gene flow,
population fragmentation exacerbates and accelerates these processes, and
leads to genetic differentiation and structuring among local subpopulations
(Keyghobadi 2007, Frankham et al. 2010, Agnarsson et al. 2012). Even without a
reduction in overall population size, the effective population size (Ne) within each
population fragment will be reduced, leading to increased drift and inbreeding
within, and an increased risk of extirpation of, local subpopulations (Saccheri et
al. 1998, Keyghobadi 2007, Sigaard et al. 2008, Frankham et al. 2010). Taxa that
experience these demographic and genetic processes ultimately may face an
enhanced likelihood of extinction (Saccheri et al. 1998, Spielman et al. 2004,
Keyghobadi 2007, Frankham et al. 2010). Thus knowledge of a threatened
population’s genetic characteristics and structuring is critical for initial
conservation assessments, and subsequently for successful long-term population
recovery and maintenance.
The White Mountain arctic butterfly [WMA; Oeneis melissa semidea (Say)]
is considered an alpine relict, a remnant of a historically widespread taxon that
82
became disjunct and isolated, presumably following upward latitudinal and
altitudinal range shifts induced by post-glacial warming during the late
Pleistocene (Opler and Krizek 1984, McFarland 2003, Schmitt et al. 2010). Its
documented distribution is limited to approximately 80 ha of the alpine zone of
the Presidential Range of the White Mountains, New Hampshire (McFarland
2003, New Hampshire Fish and Game Department 2006). Alpine organisms such
as the WMA are particularly threatened by climatic warming as they likely already
exist at the altitudinal limit of their physiological and ecological requirements
and/or those of their larval host plants; that is, with continued climatic warming,
they will effectively be pushed off the mountain top (Thomas 2005, Habel et al.
2010c, Konvička et al. 2010, Todisco et al. 2010). Recent surveys suggested that
the current WMA population is extremely small (i.e., maximum observed density
of 15 adults per 1000 m walked) (McFarland 2003), and that it has declined
substantially since its earliest description (Scudder 1901). Based on these
factors, the WMA has been ranked as imperilled at state and global levels
(McFarland 2003, New Hampshire Fish and Game Department 2006).
In addition to its small size, the WMA population purportedly exhibits
significant fragmentation and subpopulation isolation that while potentially
accelerating its decline, has yet to be confirmed or quantified. WMA adults occur
in close association with their sole larval host plant, Bigelow’s sedge (Carex
bigelowii Torr. ex Schwein), the distribution of which is naturally fragmented and
concentrated within alpine meadows. WMA adults are reportedly localized in four
of these sedge meadows, three on Mt. Washington and one on the adjacent Mt.
83
Jefferson (Anthony 1970, McFarland 2003; Figure 4.1). Some butterflies show a
low ability or propensity to disperse among such fragmented habitat patches (e.g.
(Brussard et al. 1974, Britten et al. 1995, Vandewoestijne and Baguette 2004b,
Keyghobadi et al. 2006, Sigaard et al. 2008, Leidner and Haddad 2010, Crawford
et al. 2011), and historically, few WMA adults have been observed in areas of the
alpine zone between meadows (McFarland 2003). This suggests that dispersal,
and hence, gene flow, may be limited among meadows for the WMA (Anthony
1970, McFarland 2003). To date, there has only been one attempt to quantify the
degree of isolation among the suspected WMA meadow subpopulations
(Anthony 1970). Although (Anthony 1970) ultimately deemed his study
inconclusive, he did report significant adult morphological differentiation among
some meadows.
In addition to the potential structuring among meadows, the WMA
population may be temporally structured. Many species in the genus Oeneis
emerge as adults in alternate years, a phenomenon known as biennialism (i.e.,
they require 2 years for development). However, most biennial insect species
have annual emergences over portions of their range (Heliövaara and Väisänen
1984, Scott 1986, Heliövaara et al. 1994, Kankare et al. 2002, Brock and
Kaufman 2003). Such populations are assumed to consist of two sympatric
cohorts emerging in alternate years (i.e. one in even-numbered years, the other
in odd-numbered years) (Scott 1986, Heliövaara et al. 1994). Oeneis melissa fits
that pattern: it is stated to be biennial but with adults flying every year in some
areas (Layberry et al. 2001, Brock and Kaufman 2003). Because the climate of
84
the alpine zone of Mt. Washington likely is not conducive to annual development,
the WMA presumably also is biennial and consists of two sympatric, alternateyear cohorts. If that is true, these two cohorts likely are reproductively isolated by
their asynchronous adult emergence schedules: Although gene flow would occur
if some individuals experience a life cycle acceleration or deceleration and
emerge off-year, this generally is considered a rare occurrence in periodical
insects (Douwes and Stille 1988, Heliövaara et al. 1994, Kankare et al. 2002).
Structuring of the WMA population into two reproductively isolated allochronic
cohorts would have two important consequences that have been previously
overlooked. First, relative to two cohorts with considerable gene flow between
them, each cohort would experience elevated drift, loss of diversity, and hence
be at an increased risk of extirpation. This would translate into a more immediate
risk of extinction for the WMA than presently appreciated. Second, isolation
between the cohorts caused by their asynchronous, biennial life cycles would
provide the opportunity for sympatric divergence, an interesting evolutionary
phenomenon for which little evidence exists (Douwes and Stille 1988, Heliövaara
et al. 1988, Väisänen and Heliövaara 1990, Heliövaara et al. 1994, Kankare et al.
2002).
The small population size and restricted distribution of the WMA, in
combination with a fragmented population with potentially low genetic exchange,
makes it especially susceptible to extinction via natural stochastic events and
population genetic processes. Yet despite its apparent vulnerability, very little is
known about the WMA’s demography and population structure, precluding the
85
design and implementation of an appropriate conservation management plan.
Monitoring and empirical study of the WMA has proven exceedingly challenging
as access to some areas where adults occur is difficult and the Mt. Washington
alpine zone routinely experiences harsh and unpredictable weather (McFarland
2003, Slack and Bell 2006). As such, genetic data would be particularly useful for
studying and characterizing the WMA population and enabling conservation
plans to be developed. Not only are genetics now recognized to play a major role
in the fate of threatened populations (Spielman et al. 2004, Frankham et al.
2010), but molecular data also can be used to infer behavioural and demographic
characteristics that may not otherwise be easily discernible or attainable through
traditional monitoring (Charman et al. 2010, Darvill et al. 2010, Crawford et al.
2011). Thus far, genetic analysis has played an indispensible role in the study of
numerous endangered butterfly populations (e.g., Harper et al. 2003,
Vandewoestijne and Baguette 2004b, Gompert et al. 2006, Sigaard et al. 2008,
Habel et al. 2010c, Crawford et al. 2011, Kodandaramaiah et al. 2012).
The objective of my study was to assess the genetic population structure
and levels of genetic diversity of the WMA population using mtDNA and AFLP
markers. In particular, I aimed to determine the extent of reproductive isolation
among the suspected allopatric and allochronic WMA subpopulations. These
data will further illuminate the extinction risk faced by the WMA, and can aid the
implementation of a conservation management plan.
86
4.2 Materials and Methods
4.2.1 Sample Collection
WMA adults are confirmed to occur within four alpine sedge meadows:
Cow Pasture (CP), Bigelow Lawn (BL), and Gulf Tanks (GT) on Mt. Washington;
and Monticello Lawn (ML) on Mt. Jefferson (Anthony 1970, McFarland 2003;
Figure 4.1). From late June – mid July, 2011 and 2012, these areas were
surveyed for adult WMA as part of a mark-release-recapture (MRR) study
(Chapter 3). During MRR, tissue was non-lethally sampled from 145 adults (15 to
20 per meadow per year, for a total of 80 in 2011 and 65 in 2012) in accordance
with permission granted by the US Forest Service and New Hampshire Fish and
Game Department. In 2011, a small (approximately 5 mm2) piece of hind-wing
was removed from adults using tweezers. These wing samples subsequently
proved difficult to extract and/or amplify DNA from (see below); therefore, in
2012, a single mid-leg from each adult was sampled instead. All tissue was
immediately placed in 95% ethanol and stored at -20°C until use.
4.2.2 DNA Extraction
Genomic DNA was extracted from whole wing pieces or legs using the
QIAgen DNeasy® Blood and Tissue Kit following the manufacturer’s protocol with
the following modification: DNA was eluted in 50 μl of Buffer AE. DNA extracts
were stored at -20C.
87
Figure 4.1. Approximate locations of the four sedge meadows on the alpine zone
of the White Mountains, New Hampshire, from which samples of Oeneis melissa
semidea were collected for this study: Monticello Lawn (ML) on Mount Jefferson;
and Cow Pasture (CP), Gulf Tanks (GT), and Bigelow Lawn (BL) on Mount
Washington.
88
4.2.3 mtDNA Amplification
Approximately 1500 bp of the mitochondrial gene cytochrome c oxidase
subunit I (COI) was amplified by polymerase chain reaction (PCR) in two
overlapping segments of approximately 750 bp each using the primer pairs
Lyn/EvaG and Jerry/Pat2 (Table A1.1; Appendix 1). Refer to Appendix 1 for
master mix recipe and thermalcycling conditions. Amplified fragments were
cleaned of residual primers and excess dNTPs using the PCR Product PreSequencing Kit from USB: 1 μl each of Exonuclease I (10 units/μl) and rShrimp
Alkaline Phosphatase (2 units/μl), 3 μl of water, and 5 μl of PCR product were
combined and incubated for 15 min at 37°C and 15 min at 80°C in a thermal
cycler. All PCR products were sequenced in both forward and reverse directions
using the same primers as for initial amplification and BigDye® terminator cycle
sequencing on a 3730S Genetic Analyzer (Applied Biosystems) at the Genomics
Facility, Advanced Analysis Centre, University of Guelph. Sequences were
manually edited and aligned in Sequencher v4.9 and Mega v5.05, respectively.
4.2.4 AFLP Analysis
AFLP profiles were generated using a protocol modified from Clarke and
Meudt (2005) and Applied Biosystems’ AFLP Plant Mapping Kit (Applied
Biosystems, Foster City, CA) (refer to Appendix 2 for the detailed AFLP protocol).
Twenty-five selective primer pairs were screened for high quality, reproducibility,
and high levels of polymorphism. Based on these criteria, six selective primer
89
combinations were used to generate AFLP profiles: EcoRI-AAC + MseI-CAC,
EcoRI-AAC + MseI-CTC, EcoRI-AAG + MseI-CAG, EcoRI-ACA + MseI-CAA,
EcoRI-ACC + MseI-CTA, EcoRI-ACG + MseI-CAA (Vos et al. 1995). Negative
controls were included in each step of the protocol to ensure that no
contamination had occurred.
Prior to fragment analysis, selective amplification products were diluted
25x with water. AFLP fragments were then separated and sized (LIZ3730 size
standard, Applied Biosystems) on a 3730S Genetic Analyzer (Applied
Biosystems) at the Genomics Facility, Advanced Analysis Centre, University of
Guelph.
AFLP fragment sizes and peak heights were determined using
GeneMapper® v4.0 (Applied Biosystems) following a semi-automated approach
(Crawford et al. 2011). GeneMapper was set to assign bins (loci) between 50 and
500 bp at peaks of at least 50 rfu. The resulting bins were then examined
manually to ensure proper placement. Bins containing shoulder peaks or peaks
that also fell into adjacent bins were removed, and when necessary, bins were
adjusted to centre over peak distributions. Each AFLP profile was then
examined, and any individuals that failed to amplify were re-amplified or removed
from further analysis.
AFLP peak height data from GeneMapper were normalized and scored in
AFLPScore (Whitlock et al. 2008). AFLPScore uses a mismatch error rate (i.e.,
the percentage of differences in phenotype among replicate samples) to
objectively identify optimal thresholds for phenotype-scoring that minimize
90
genotyping error: A locus-selection threshold is applied, and loci with mean peak
heights below this threshold are removed from analysis. A phenotype-calling
threshold is then applied to the retained loci to score peaks as present or absent.
For the WMA AFLP data set, the mismatch error rate was estimated for each
primer pair separately by replicating the entire AFLP protocol starting from the
restriction-ligation step (i.e., two aliquots from the same DNA extraction were
replicated) for 10-15 individuals. Various combinations of locus- and phenotypecalling thresholds were tested to obtain a mean mismatch error of <5%, and all
loci containing singleton peaks (i.e., peaks present in only one individual) were
removed from analysis.
4.2.5 Data Analysis
For population-based analyses, each meadow and collection year were
treated as separate subpopulations (Table 4.1). As all samples from ML11 failed
to amplify (see below), the total number of subpopulations analyzed was 7.
Genetic Diversity
Intra-population genetic diversity indices based on AFLPs were calculated
for each subpopulation and each sampling year using AFLP-SURV v1.0
(Vekemans 2002).
91
Table 4.1. Location of each alpine meadow where samples were collected, the
number of individuals successfully amplified for mtDNA (n mtDNA) and AFLPs (n
AFLPs), and the cytochrome c oxidase I (COI) haplotypes found within each
meadow subpopulation for the Oeneis melissa semidea samples analyzed in this
study.
Alpine Meadow
Cow Pasture
Gulf Tanks
Bigelow Lawn
Monticello Lawn
Year
2011
2012
2011
2012
2011
2012
2012
Code
CP11
CP12
GT11
GT12
BL11
BL12
ML12
Latitude
44.278
44.278
44.273
44.273
44.263
44.263
44.300
Longitude
-71.292
-71.292
-71.307
-71.307
-71.307
-71.307
-71.314
n mtDNA
11
9
12
8
14
9
10
n AFLPs
8
14
3
14
6
15
12
COI
Haplotype(s)
wma1
wma1
wma1, wma4
wma1
wma1
wma1, wma2
wma1, wma3
Allele frequencies were estimated for each subpopulation using the
Bayesian method with non-uniform prior distribution and assuming HardyWeinberg genotypic proportions (Zhivotovsky 1999), and were then used to
calculate the proportion of polymorphic loci at the 5% level and unbiased
estimates of expected heterozygosity following the method of Lynch and Milligan
(1994).
For the mtDNA data, haplotype diversity (Hd) and nucleotide diversity (π)
were calculated for each subpopulation and all samples within each year
combined in Arlequin v3.5.1.3 (Excoffier and Lischer 2010).
Population Structure
The partitioning of genetic variation among subpopulations was assessed
for both markers with an analysis of molecular variance (AMOVA) in Arlequin
based on 10 100 permutations. Pairwise Fst estimates were then calculated for
AFLPs in AFLP-SURV, with statistical significance based on 100 000 random
92
permutations. Initially, Fst estimates were calculated with all subpopulations
included, which revealed significant differentiation between sampling years.
Thus, three separate comparisons were performed instead. First, the degree of
differentiation among meadow subpopulations within each sampling year was
determined. Neither of these comparisons was significant; therefore, to assess
differentiation between allochronic cohorts, all samples from 2011 were
combined and compared to all samples from 2012.
GenAlEx v6.0 (Peakall and Smouse 2006) was then used to identify
patterns in the genetic differentiation among individuals based on AFLPs. An
individual x individual genetic distance matrix [i.e., the total number of differences
(peak presence/absence) between the AFLP profiles of each pair of individuals]
was generated in GenAlEx, and then used to perform a principal coordinates
analysis (PCoA) to visualize patterns in the genetic differentiation between
individuals. A Mantel test (Mantel 1967) also was performed to test for a
correlation between genetic and Euclidean geographic distance (km), or isolation
by distance (IBD) relationship, among individuals. Both distance measures were
linearly transformed, and the test significance was based on 9999 permutations.
To estimate the number of genetically-homogenous populations in the
entire AFLP data set, a Bayesian clustering analysis was performed using
STRUCTURE v2.3.4 (Pritchard et al. 2000). Using Markov Chain Monte Carlo
simulations, STRUCTURE estimates the posterior probability that there are K
populations in the data set and assigns individuals to each of K populations.
Permutations were performed under the admixture model with correlated allele
93
frequencies. A burn-in of 100 000 and run length of 400 000 were used to test K
= 2 – 8, with 20 runs per value of K. Structure Harvester v0.6.93 (Earl and
vonHoldt 2012) was used to infer the most likely K following the method of
Evanno et. al.(2005). Results from the STRUCTURE runs for the most likely K
were combined using the program CLUMPP (Jakobsson and Rosenberg 2007)
and subsequently visualized as a bar graph using the program DISTRUCT
(Rosenburg 2004).
4.3 Results
4.3.1 mtDNA
A 1386 bp fragment of COI was successfully generated for 73 individuals
(Table 4.1; Appendix 3, Table A3.1). Most samples that failed to amplify were
extracted from wing tissue collected in 2011. Overall, COI diversity was low.
Three polymorphic sites and four haplotypes differing from each other by a single
base were identified. One haplotype, h1, was common to 70 individuals, while
the remaining three each occurred in single individuals from BL12, ML12, and
GT11. Intra-subpopulation haplotype diversity and nucleotide diversity ranged
from 0 – 0.22 and 0 – 0.00016, respectively (Table 4.2). The mtDNA AMOVA did
not reveal significant differentiation among subpopulations (Fst = 0.00057; P =
0.4569; Table 4.3).
94
Table 4.2. Within subpopulation and within sampling year genetic diversity
estimates for Oeneis melissa semidea. Sampling year estimates are reported as
mean values.
mtDNA
AFLPs
Hd (SE)
π (SE)
PPL
He (SE)
BL11
0
0
100
0.460 (0.007750)
CP11
0.18 (0.144)
0.00013 (0.000206)
100
0.433 (0.008620)
Subpopulation
GT11
0.17 (0.134)
0.00012 (0.000195)
100
0.349 (0.01175)
2011 All
0.12 (0.0926)
0.000084 (0.000134)
100
0.414 (0.02812)
BL12
0.22 (0.166)
0.00016 (0.000236)
99.3
0.259 (0.01335)
CP12
0
0
98.6
0.264 (0.01345)
GT12
0
0
100
0.315 (0.01220)
ML12
0.20 (0.154)
0.00014 ( 0.000220)
100
0.315 (0.01237)
2012 All
0.11 (0.0801)
0.000076 (0.000114)
99.5
0.288 (0.01284)
mtDNA: haplotype diversity (Hd), nucleotide diversity (π); AFLP: proportion of polymorphic loci
(PPL), expected heterozygosity (He)
Table 4.3. Results of analyses of molecular variance (AMOVA) of mtDNA and
AFLP data for seven subpopulations of Oeneis melissa semidea.
d.f.
Sum of
Squares
Variance
Components
Among Populations
6
0.331
0.00003
0.06
Within Populations
66
3.615
0.05477
99.94
Total
72
3.945
0.05480
Among Populations
6
94.915
0.61346
5.97
Within Populations
65
628.571
9.67033
94.03
Total
71
723.486
10.28379
Marker
Source of Variation
mtDNA
AFLPs
95
Percentage of
Variation
4.3.2 AFLPs
Out of 145 individuals, AFLP profiles were successfully generated for only
72 (Table 4.1). As for COI, of the samples for which preselective or selective
amplification failed, the majority (64) were extracted from wing tissue collected in
2011, including all samples collected from ML. A total of 146 loci were retained
following error-rate analysis, with a mean mismatch error of 4.5% (Table 4.4).
Table 4.4 Summary of the AFLP phenotype scoring and results of mismatch error
analysis for the six selective primer combinations used to amplify Oeneis melissa
semidea samples.
Scoring Threshold (rfu)
Selective Primer
Combination
EcoRI-AAC +
MseI-CAC
EcoRI-AAC +
MseI-CTC
EcoRI-AAG +
MseI-CAG
EcoRI-ACA +
MseI-CAA
EcoRI-ACC +
MseI-CTA
EcoRI-ACG +
MseI-CAA
Locus
Phenotype
Mismatch Error
Rate (%)
Initial Number
of Loci
Number of
Loci Retained
2700
50
4.17
71
18
2900
50
4.49
66
13
2900
50
6.04
89
28
2400
200
4.44
84
24
2300
200
4.00
100
30
2700
50
3.99
97
33
Mean: 4.52
Total: 507
Total: 146
96
In contrast to mtDNA, levels of genetic diversity based on AFLPs were
consistently high across all subpopulations, with the proportion of polymorphic
loci and expected heterozygosity ranging from 0.986 – 1.0 and 0.259 – 0.460,
respectively (Table 4.2).
The AFLP-based AMOVA (global Fst = 0.0597, P < 0.0001, Table 4.3)
indicated significant genetic structuring among subpopulations. When meadow
subpopulations were compared, there was no significant differentiation among
them within either sampling year (2011: Fst = 0.0573, P = 0.0529; 2012: Fst =
0.0045, P = 0.1015). However, all samples from 2011 were significantly
differentiated from all samples collected in 2012 (global Fst = 0.1014, P < 0.0001,
pairwise Fst = 0.1056).
Both the PCoA (Figure 4.2) and STRUCTURE (Figure 4.3) analysis based
on AFLP individual genetic distance identified two genetic clusters. For the
PCoA, there was no pattern associated with meadow or sampling year in the
assignment of individuals to each group (Figure 4.2). However, STRUCTURE
indicated that BL11 and CP11 formed a separate genetic cluster from all other
meadow subpopulations (Figure 4.3).
Finally, the AFLP individual-based Mantel test did not indicate a significant
relationship between genetic and geographic distance (r2 = 0.0013; P = 0.29;
Figure 4.4).
97
Coordinate Axis 2 (13.1 %)
Coordinate Axis 1 (8.5 %)
Figure 4.2. Principle coordinates analysis of AFLP data for Oeneis melissa
semidea based on an individual x individual genetic distance matrix.
Figure 4.3. Estimated population structure for Oeneis melissa semidea AFLPs
inferred by STRUCTURE under an admixture model of K = 2. Each bar
represents an individual, and the bar colours (blue and yellow) indicate the
proportion of its genotype that belongs to each of K = 2 clusters. Individuals are
grouped based on the meadow [Bigelow Lawn (BL), Cow Pasture (CP), Gulf
Tanks (GT), and Monticello Lawn (ML)] and year [2011 (11) or 2012 (12)] from
which they were sampled. Vertical black lines delimit the groups of individuals
belonging to each meadow subpopulation.
98
Linearized Individual x
Individual Genetic Distance
12
10
8
6
4
2
0
0.0
0.5
1.0
1.5
2.0
Ln [1+Geographic Distance (km)]
Figure 4.4 Relationship between Euclidean geographic distance (Ln [1 +
geographic distance (km)]) and the genetic differentiation between individuals (r2
= -0.036) based on AFLPs for Oeneis melissa semidea. Each point corresponds
to a pair of individuals.
4.4 Discussion
4.4.1 Tissue Type and DNA Amplification
To obtain DNA for conservation genetic studies of threatened insects,
non-lethal tissue sampling is now routinely employed (Charman et al. 2010,
Monroe et al. 2010). For lepidopterans, both leg (Williams et al. 2003, Vila et al.
2009, Marschalek et al. 2013) and wing (Rose et al. 1994, Lushai et al. 2000,
Keyghobadi et al. 2005, Keyghobadi et al. 2006, Keyghobadi et al. 2009, Vila et
al. 2009, Hamm et al. 2010, Crawford et al. 2011, Keyghobadi et al. 2013) tissue
have served as excellent sources of high-quality, amplifiable DNA. Specifically,
COI sequences and AFLPs have been successfully generated from wing tissue
samples of multiple lepidopteran species [COI: Spanish moon moth (Graellsia
isabelae; Vila et al. 2009); Painted Lady (Vanessa cardui; Hamm et al. 2010),
99
and Eyed Brown (Satyrodes eurydice; Hamm et al. 2010); AFLPs: Mormon
metalmark butterfly (Apodemia mormo; Keyghobadi et al. 2009, Crawford et al.
2011); Behr’s hairstreak butterfly (Satyrium behrii; Keyghobadi et al. 2009)]. In
my samples, however, while WMA leg tissue generally amplified well, I was
unable to obtain COI sequences or AFLP profiles for the majority of individuals
from which wing tissue was sampled. Both WMA tissue types were subjected to
identical storage conditions, and thus sample degradation likely did not contribute
to the observed difference in amplification. Although less DNA generally was
obtained from wing samples, altering the concentration of wing tissue extracts
(i.e., concentrating them, or increasing or decreasing the volume added to PCR
reactions) did not improve amplification (data not shown).
Poor DNA amplification also can result from the presence of PCR
inhibitors, a number of which occur naturally in some arthropod tissue types. For
instance, Boncristiani et al. (2011) reported that honey bee compound eye tissue
inhibited RT-PCR amplification of deformed wing virus. Compounds found in the
scales of butterfly wings also can interfere with PCR (Hamm et al. 2010). In
particular, melanins, the ubiquitous pigments responsible for the black, grey, and
brown wing scale colourations in butterflies (Koch and Kaufmann 1995, Shawkey
et al. 2009), are known to inhibit PCR by interfering with DNA polymerase activity
(Eckhart et al. 2000) and binding to template strands (Opel et al. 2010). The
WMA’s hindwings overall are dark black and brown, heavily scaled, and display a
thick fringe of dark hair. Thus, in contrast to the leg samples, the WMA wing
100
sample DNA extracts may have contained low amounts of template DNA and an
abundance of PCR inhibitors, likely melanin, that prevented their amplification.
4.4.2 Genetic Diversity
Estimates of genetic diversity in the WMA based on mtDNA were low. In
contrast, the WMA generally displayed high AFLP diversity relative to other
Lepidoptera (Table 4.5). Population genetic theory predicts that small, isolated
populations will experience a loss of genetic diversity (Frankham et al. 2010),
and correspondingly, many such natural populations (Frankham 1996, Spielman
et al. 2004), including those of various endangered lepidopteran species (Habel
et al. 2009, Crawford et al. 2011, Dieker et al. 2013), exhibit low diversity. The
rate at which a declining population loses diversity depends both on its initial size
and the organism’s generation length: larger populations of longer generation
durations will lose diversity more slowly (Frankham et al. 2010). Although
presently small, descriptions from the turn of last century indicate that the WMA
population formally was large and robust (Scudder 1901), and hence likely
genetically diverse. Furthermore, as the WMA presumably is biennial, its
generation length is 2 years rather than 1 year or fewer for most insect species.
Thus despite its long-standing isolation and current small size, the WMA’s
relatively high AFLP diversity may simply be remnant of its historically larger
population size combined with its longer generation duration. The corresponding
lack of mtDNA diversity reflects that mtDNA displays approximately 1/4 the
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effective population size of the mostly nuclear AFLPs, and hence is expected to
lose diversity faster.
Table 4.5. Levels of genetic diversity in natural populations of lepidopteran
species measured using AFLPs.
Species
PPL (%)
a
He
b
Pieris rapae
10.1 - 24.0
0.0442 - 0.0638
Pieris melete
Study
Takami et al. 2004
6.4 - 16.5
0.0306 - 0.0556
Lycaides melissa melissa
90.9
NR
c
Gompert et al. 2006
Lycaides melissa samuelis
86.7
NR
Gompert et al. 2006
Cydia pomonella
35.2 - 98.9
0.0600 - 0.180
Timm et al. 2006
Ostrinia nubilalis
72.0 – 94.0
0.237 - 0.376
Krumm et al. 2008
Tortrix viridana
62.7 - 77.9
0.129 - 0.160
Schroeder and Degen 2008
Grapholita molesta
58.2 - 99.4
0.100 - 0.186
Timm et al. 2008
84.9
NR
Brattström et al. 2010
13.8 - 41.9
0.0661 - 0.139
Collier et al. 2010
82.8
0.165 - 0.303
Franklin et al. 2010
Atrytonopsis new species 1
68.5 - 100
0.274 - 0.416
Leidner and Haddad 2010
Holcocerus hippophaecolus
51.2 - 77.6
0.150 - 0.204
Tao et al. 2012
Vanessa atalanta
Theclinesthes albocincta
Trichoplusia ni
Takami et al. 2004
49.4 - 68.3
0.170 – 0.240
Gradish unpub. data; Chapter 2
Oeneis melissa semidea
98.6 - 100
0.259 - 0.460
current study
a
b
c
PPL = proportion of loci polymorphic at 5%; He = expected heterozygosity; NR = data not reported
Oeneis macounii
4.4.3 Population Genetic Structure
Although mtDNA structuring was not apparent, population-based (Fst)
analyses of AFLPs revealed overall weak, but significant, genetic structuring for
the WMA. However, there was no evidence of AFLP differentiation among
samples collected from the different meadows within each of the two sampling
years, and I did not observe an isolation by distance relationship. These
observations could reflect either historic (i.e., the meadow subpopulations are
102
presently reproductively isolated but were not in the recent past) or on-going
gene flow among the meadows. Given my MRR observations (Chapter 3), which
strongly suggest that WMA adults routinely move among all meadows on Mt.
Washington, it is likely that genetic exchange currently occurs among all
meadows. However, it is interesting that the WMA samples from ML on Mt.
Jefferson were not differentiated from samples collected on Mt. Washington. The
mountains are separated by the Great Gulf Ravine and lower elevation alpine
areas devoid of sedge, and such intervening areas of unsuitable habitat may
impede dispersal of specialist butterflies among fragmented habitat patches,
even at fine spatial scales (e.g., Harper et al. 2003, Keyghobadi et al. 2006,
Leidner and Haddad 2010, Crawford et al. 2011, Bergerot et al. 2012, Dieker et
al. 2013). Specifically, some alpine and montane butterflies display differentiation
among populations occupying different mountains within the same chain (Schmitt
et al. 2005, Sekar and Karanth 2013), even at relatively fine geographic scales.
For instance, populations of Erebia epiphron on mountains separated by only 4
km were significantly differentiated on the basis of allozymes (Schmitt et al.
2005). Furthermore, I did not observe adult movements between ML and any
other meadow during my MRR study, and, to my knowledge, there have been no
WMA adult sightings along the hiking trails connecting Mts. Washington and
Jefferson. Thus, although my genetic results suggest that gene flow routinely
occurs between these areas, it remains unclear whether this results from active
adult dispersal. Alternatively, as (Anthony 1970) suggested, WMA adults may
routinely be carried between the mountains by the wind. As the prevailing winds
103
are from the northwest, wind-mediated adult movements would primarily be
unidirectional, from Mt. Jefferson to Mt. Washington. In this case, the ML
meadow subpopulation, already smaller than the Mt. Washington population,
may be experiencing a more rapid decline.
Although I did not observe genetic structuring among meadows, my AFLP
Fst analyses did reveal low, but significant, differentiation between allochronic
cohorts of the WMA. This differentiation could indicate that the WMA allochronic
cohorts are reproductively isolated. In that scenario, gene flow between
sympatric, allochronic cohorts of biennial insects would be prevented if all adults
within each cohort consistently emerge every second year. However, it is
possible that some individuals routinely could experience a life cycle acceleration
or deceleration and emerge off-year into the alternate cohort. Although field
observations suggest that off-year emergences are rare for some periodical
insects (Aspinwall 1974, Kankare et al. 2002), including O. macounii (G.W. Otis
unpub. data), for others they appear to be relatively common (Lloyd and White
1976, Douwes 1980, Danks 1992, Williams and Simon 1995). Furthermore,
developmental studies indicate that many insects display individual variation in
development and life cycle length (Tauber and Tauber 1981, Danks 1992).
To date, there have been very few empirical assessments of divergence
between sympatric, allochronic cohorts of biennial insects, and results from
existing studies are mixed. For instance, no differentiation associated with
allochronic isolation has been observed for Erebia embla (Thunberg) (Douwes
and Stille 1988) and Xestia tecta (Hüber) (Kankare et al. 2002), and some
104
sympatric cohorts of the Macoun’s arctic butterfly (Oeneis macounii; Chapter 2).
Yet differentiation has been observed between allochronic cohorts of the pine
bark bug (Aradus cinnamomeus Panzer; (Heliövaara et al. 1988, Väisänen and
Heliövaara 1990); Oeneis ivallada, a member of the O. chryxus butterfly complex
(Nice and Shapiro 2001); and other cohorts of the Macoun’s arctic butterfly
(Chapter 2). This discrepancy among studies could simply reflect variation in the
time of isolation between different cohorts; that is, some cohorts may have been
isolated more recently, and thus have not yet diverged appreciably (Kankare et
al. 2002). However, for most studies for which differentiation has been reported,
factors potentially contributing to observed differentiation other than allochronic
isolation (e.g., allopatric isolation, small sample sizes) are difficult to rule out.
Furthermore, all studies employing multiple marker types, including this one,
have observed differentiation for only one marker (Nice and Shapiro 2001,
Kankare et al. 2002, Chapter 2). Therefore, although theoretically possible, there
remains a lack of consistent, compelling empirical evidence for reproductive
isolation between sympatric, allochronic cohorts of biennial insects.
Given this, and the WMA’s small population size, I cannot rule out the
possibility that genetic exchange does occur between the WMA allochronic
cohorts via off-year emergences, and that the differentiation observed between
them is simply the result of genetic drift. Drift causes random fluctuations in
population allele frequencies from generation to generation, and in small
populations these fluctuations are more pronounced (Futuyma 2009), potentially
resulting in significant differences in allele frequencies between generations.
105
Unfortunately, I was unable to estimate effective population size (Ne) for the
WMA, as I only had access to samples from a single year for each cohort.
Nevertheless, field surveys strongly suggest that each cohort is small (McFarland
2003; Chapter 3). Therefore, drift could have resulted in significant variations in
allele frequencies between the cohorts, making them appear differentiated
despite gene flow between them. Additional sampling from each cohort is
required to obtain estimates of Ne and thus the probability that drift, as opposed
to reproductive isolation, is dictating the observed differentiation between the
allochronic WMA cohorts.
4.4.4 Conservation Implications
Compared to other endangered butterflies, the WMA is not currently
particularly threatened by reduced genetic diversity. However, its continued
isolation and declining population size will inevitably result in the loss of diversity.
Therefore, management efforts should focus on improving its current population
size and maintaining connectivity among meadows.
My results indicate that genetic exchange occurs among the four alpine
meadows on Mts. Washington and Jefferson, and therefore, the WMA can be
managed spatially as a single genetic population. Although adults most likely
actively and regularly disperse among all Mt. Washington meadows, further
monitoring is recommended to determine the level of and direction of movements
between the mountains. The Mt. Jefferson population currently is comparatively
small, and may be at a more immediate risk of decline and extirpation if adult
106
movement primarily or solely occurs in the direction of Mt. Washington via the
prevailing winds. If dispersal to ML is indeed low, transplantation of adults from
Mt. Washington to supplement Mt. Jefferson may be warranted.
It remains unclear if gene flow occurs between the WMA allochronic
cohorts. Additional sampling and genetic analyses are recommended to
conclusively determine the extent of reproductive isolation between the cohorts.
If they do prove to be isolated, the WMA’s overall Ne will be comparatively
reduced, and its extinction risk may thus be higher than presently appreciated.
Furthermore, based on the results of this study, the cohorts should be managed
as independent units until such time that additional evidence conclusively
demonstrates gene flow between the even- and odd-numbered year cohorts to
preserve the unique process of sympatric, allochronic divergence. For instance, if
captive rearing and release is implemented as a management strategy to
increase the WMA’s population size (New Hampshire Fish and Game
Department 2006), breeding stocks should be established from each cohort
separately to avoid artificially creating gene flow between the cohorts.
Finally, the future vitality of the WMA population ultimately will depend on the
maintenance of its alpine habitat, specifically its host plant, Bigelow’s sedge.
Monitoring for changes to the abundance and distribution of Bigelow’s sedge
thus should be implemented. Furthermore, although the White Mountains alpine
zone, as part of the White Mountains National Forest and Mount Washington
State Park, already is designated as protected land, the Mount Washington
summit is a major tourist attraction and experiences a high volume of foot traffic.
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Human-mediated disturbance of alpine zone vegetation, which could negatively
impact both sedge plants and WMA eggs and larvae, should continue to be
monitored and mitigated more effectively than at present.
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Chapter 5
THE POTENTIAL FOR SYMPATRIC, ALLOCHRONIC DIVERGENCE
IN BIENNIAL INSECTS: A REVIEW
While geographic isolation has long been accepted as a principle cause of
population and species divergence, the concept of sympatric speciation, or
divergence occurring without spatial barriers to gene flow, remains controversial
(Futuyma and Mayer 1980, Coyne and Orr 2004, Bolnick and Fitzpatrick 2007).
Sympatric speciation research historically has primarily focused on population
division and reproductive isolation following the exploitation of novel niches or
hosts, where the ensuing differential selective pressures often result in
populations temporally isolated by asynchronous developmental or mating times,
creating a further barrier to gene flow (Turelli et al. 2001, Coyne and Orr 2004,
Bolnick and Fitzpatrick 2007). However, in the case of allochronic speciation,
temporal isolation also may act as a principle cause of sympatric divergence if
initiated by a phenological change in the absence of a host or habitat shift
(Alexander and Bigelow 1960, Santos et al. 2007, Santos et al. 2011, Yamamoto
and Sota 2012). In this scenario, a climatic, developmental, or mutational event
creates otherwise ecologically-identical sympatric subpopulations
instantaneously reproductively isolated in time, a phenomenon most often
observed in insects (Tauber and Tauber 1989, Abbot and Withgott 2004). For
instance, phenological shifts in annual insects may create sympatric populations
mating in different seasons within a year, different times within a season, or at
different times of the day (Miyatake et al. 2002, Abbot and Withgott 2004, Danley
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et al. 2007, Santos et al. 2007, Santos et al. 2010, Santos et al. 2011, Bell 2012,
Yamamoto and Sota 2012). In many cases, temporal isolation has led to genetic
differentiation between such allochronic populations (Abbot and Withgott 2004,
Santos et al. 2007, Santos et al. 2010, Santos et al. 2011, Yamamoto and Sota
2012). Alternatively, allochronic isolation can arise in periodical insects: those
exhibiting life cycle durations of k years (where k > 1), with adults emerging and
reproducing synchronously every kth year (Bulmer 1977, Heliövaara et al. 1994).
In this case, a temporary shift in life cycle duration in part of a population creates
subpopulations reproductively isolated by asynchronous adult emergence.
Periodical cicadas provide the most famous example of this phenomenon:
extensive evidence suggests that allochronic isolation generated by permanent 4
year life cycle accelerations coupled with allopatric isolation has played a major
role in multiple, independent speciation events between 17 and 13 year cicadas
(Cooley et al. 2001, Cooley et al. 2003).
This review focuses on allochronic isolation in insects associated with
biennialism, the most common form of insect periodicity. While not generally
widespread, biennialism is moderately common in insects inhabiting higher
latitudes and altitudes (Pickford 1953, Gabbutt 1959, Masters 1974, Douwes
1980, Scott 1986, Danks 1992, Heliövaara et al. 1994). The adults of biennial
insects, requiring 2 years for development, emerge every other year. However,
the geographic emergence pattern of biennial insects typically is variable and
complex, with adults appearing in even- or odd- numbered years in different
locations. Most species consist of two or more main, geographically widespread
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allochronic and allopatric or parapatric populations, typically referred to as
cohorts. For example, the ranges of several biennial insects show an east/west
emergence division, with the vast majority of adults in the west emerging in one
year class (i.e., even- or odd-numbered years), and adults in the east emerging
in the opposite year class (Masters 1974, Heliövaara and Väisänen 1984,
Kankare et al. 2002). However, of particular interest is that within these main
allopatric cohorts, there are often smaller regions where adults occur every year
(Douwes 1980, Knapton 1985, Scott 1986, Daily et al. 1991, Clayton and Petr
1992, Sperling 1993, Kankare et al. 2002, Brock and Kaufman 2003). These
areas of annual emergence in otherwise biennial insects are assumed to consist
of two sympatric, allochronic cohorts. Usually, one year class is associated with a
comparatively smaller adult emergence, in which case the cohorts are often
respectively referred to as ‘common’ and ‘rare’. Because such cohorts are
presumably reproductively isolated by their asynchronous adult emergence
schedules (Douwes 1980, Scott 1986, Kankare et al. 2002), some authors have
asserted that they should, in effect, be considered incipient cases of sympatric
speciation initiated and maintained by allochrony (Heliövaara et al. 1988,
Heliövaara et al. 1994).
Coyne and Orr (2004) listed several criteria that should be met to make a
convincing case of sympatric divergence or speciation: in addition to currently
being sympatric, the populations or species must (1) show considerable
reproductive isolation and (2) a historical allopatric phase between them must be
deemed very unlikely. Thus, whether allochronic cohorts of biennial insects can
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be considered plausible cases of incipient sympatric speciation depends on if
they in fact are, and remain, isolated, and how they initially are formed.
Allochrony in biennial insects remains an understudied phenomenon, and thus,
despite the recognized potential for sympatric divergence between
asynchronous, biennial cohorts, we lack data on these factors.
Here, I review allochrony generated by asynchronous, biennial life cycles
as a potential mechanism for sympatric divergence in insects, and outline the
empirical knowledge gaps that prevent us from concluding that allochronic insect
cohorts indeed represent viable cases of incipient sympatric speciation. First, I
summarize the existing studies of divergence between sympatric, allochronic
biennial insect cohorts and discuss possible interpretations of the results.
Second, I outline the potential mechanisms by which allochronic cohorts may
come to exist in sympatry. Third, I discuss possible reasons why sympatric,
allochronic cohorts often are considerably unequal in size, and how this may
impact the stability of the reproductive isolation between them.
5.1 Evidence for reproductive isolation between sympatric, allochronic
cohorts
Population divergence and speciation require a strong reduction in gene
flow, and asynchronous adult emergence, if obligate and unvarying, prevents
genetic exchange between sympatric, allochronic cohorts of biennial insects.
However, it is possible that adults emerging off-year (i.e., adults emerging in an
alternate year class via a life cycle deceleration or acceleration) could create
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gene flow between allochronic cohorts, preventing their divergence (White 1978,
Coyne and Orr 2004). If allochronic cohorts are indeed isolated, they should
show evidence of genetic and/or morphological divergence. To date, few studies
have directly examined the differentiation between sympatric, allochronic cohorts
of biennial insects.
One of the first and perhaps most comprehensive studies of allochronic
isolation in a biennial insect focused on the pine bark bug (Aradus cinnamomeus
Panzer) in Finland. Heliövaara and Väisänen (1984, 1987) initially conducted
extensive range-wide sampling of the pine bark bug and documented the
geographic variation in both its life cycle and year class of adult emergence.
Their efforts revealed that while the pine bark bug’s development required 3
years in northern Finland, it was biennial in the south. The biennial portion of its
range could be further divided into two parapatric, allochronic majority cohorts
(western odd-numbered years and eastern even-numbered years), referred to
herein as the “common cohorts”. A very narrow (3.5 km) zone of overlap was
discovered between the two common cohorts, where pine bark bug adults
occurred annually in approximately equal densities (Heliövaara and Väisänen
1984). Within each common cohort, a smaller, allochronic minority cohort was
observed (Heliövaara and Väisänen 1984), herein referred to as the “rare
cohorts”. Individuals from each rare cohort were comparatively scarce, with fewer
than one rare cohort individual observed for every 1000 individuals from the
common cohort on average (Heliövaara et al. 1988). Samples collected from 30
localities were then used to assess the genetic (Heliövaara et al. 1988) and
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morphological (Väisänen and Heliövaara 1990) differentiation among the cohorts.
Allozyme analyses indicated that the allochronic common cohorts, and each
common cohort and its respective allopatric, synchronic rare cohort, were
genetically indistinguishable (Heliövaara et al. 1988). However, weak, but
significant, differentiation was observed between each rare cohort and its
respective sympatric, allochronic common cohort. Subsequent analyses of 27
body size measurements indicated significant morphological differences between
each common cohort and between each rare cohort and its respective sympatric,
allochronic cohort (Väisänen and Heliövaara 1990). However, similar to the
genetic analyses, no significant morphological differentiation was observed
between each common cohort and its respective allopatric, synchronic rare
cohort. Because the sympatric, allochronic pine bark bug cohorts were
genetically and morphologically differentiated, the authors concluded that they
were completely reproductively isolated by their asynchronous adult emergence
schedules (Heliövaara et al. 1988, Väisänen and Heliövaara 1990).
A potential confounding factor in the pine bark bug studies is whether
some of the allochronic samples were in fact sympatric. While the western
allochronic cohorts appear to have been sampled from the same location, the
collection locations for the eastern odd-year rare and eastern even-year common
samples were separated by at least 50 km (Heliövaara et al. 1988). Yet in both
cases, the authors considered the allochronic cohort sampling locations
sympatric to each other. However, depending on the pine bark bug’s dispersal
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behaviour, allopatric isolation may have contributed to the divergence, at least
between the eastern allochronic common and rare cohorts.
In contrast to the pine bark bug studies, subsequent investigations, all
focused on lepidopteran taxa, generally have revealed weak or no evidence for
divergence between allochronic cohorts of biennial insects. For instance,
Douwes and Stille (1988) employed allozymes to assess the degree of allopatric
and allochronic isolation among populations of Erebia embla (Thornburg), which
displays a biennial life cycle in the southern portion of its Fennoscandian range.
Four allopatric populations were compared, three of which emerged in even
years and the other in odd years. Although these populations were not sympatric,
I include the study here as one of the very few to directly acknowledge the
potential for allochronic divergence in biennial insects. Although the even-year
populations differed significantly from each other, there was no significant
differentiation between the odd year population and all even year populations
combined, indicating that differentiation among E. embla populations stemmed
from spatial rather than temporal isolation (Douwes and Stille 1988). Similar
results were reported for the biennial moth Xestia tecta (Hüber) (Kankare et al.
2002). In Finland, X. tecta adults generally emerge in odd years in the east and
even years in the west. However, in some regions, a comparatively rare
sympatric, allochronic cohort also exists. Adults were sampled from three
cohorts, the main western-even and eastern-odd cohorts (both allochronic and
allopatric) and a rare eastern-even cohort (allochronic and sympatric to the
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eastern-odd samples), and compared using allozymes. No significant
differentiation was observed between any of the cohorts (Kankare et al. 2002).
All other studies involving biennialism and allochrony have focused on
Oeneis butterflies in North America. For instance, Nice and Shapiro (2001)
examined phylogenetic relationships among members of the O. chryxus complex
(O. c. stanislaus, O. c. chryxus, and O. ivallada) from the Sierra Nevada.
Although their purpose was not to assess the differentiation between allochronic
cohorts, they did include sympatric, even and odd year samples of O. ivallada in
their analyses. While these samples were not differentiated based on mtDNA,
phylogenetic analyses based on allozymes grouped the even-year samples
separately from the sympatric odd-year samples (Nice and Shapiro 2001).
More recently, I studied allochronic divergence in the Macoun’s arctic
butterfly [Oeneis macounii (W. H. Edwards)] (Chapter 2). The Macoun’s arctic is
widespread across Canada and parts of the northern US, and emerges
predominantly in odd-numbered years in the west and even-numbered years in
the east. However, emergence occurs in both years in some populations around
the foothills of the Rocky Mountains in Alberta and British Columbia, where
generally fewer adults are observed in even-years and odd-years, respectively.
Using samples collected from its entire range, the genetic structuring of the
Macoun’s arctic was assessed based on mtDNA and AFLPs, including several
comparisons of sympatric, allochronic cohorts. Although significant spatial
structuring was observed, there was no evidence for divergence related to
allochronic isolation for either marker type, with one exception: AFLPs revealed
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weak, but significant, differentiation between one pair of sympatric, allochronic
cohorts.
I also investigated allochronic isolation in the White Mountain arctic
butterfly [Oeneis melissa semidea (Say)] (Chapter 4). The White Mountain arctic
is endemic to the White Mountains in New Hampshire, where its entire range is
restricted to approximately 80 ha of the alpine zone. Here, adults emerge
annually in seemingly equal densities. Because all other Oeneis species are
biennial and the alpine zone weather is not conducive to annual development,
the White Mountain arctic population likely consists of two completely sympatric,
allochronic cohorts. I compared samples collected over 2 years using mtDNA
and AFLP markers. While mtDNA divergence was not observed, AFLPs
indicated low differentiation between the allochronic cohorts. However, because
of the White Mountain arctic’s extremely small population size, this differentiation
could have arisen via genetic drift despite gene flow between the cohorts.
Therefore, I could not definitively conclude that the allochronic cohorts were
reproductively isolated.
Taken together, these studies provide modest evidence for the existence
of reproductive isolation between sympatric, allochronic cohorts of biennial
insects. More commonly, a lack of differentiation has been reported. One
possible interpretation of these studies is that sympatric, allochronic cohorts are
reproductively isolated, but some cohorts have not been isolated long enough for
genetic differences to accrue. That is, the genetic similarity between some
cohorts may simply reflect incomplete lineage sorting. Most biennial insects
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inhabit previously-glaciated areas that have been colonized relatively recently,
and their sympatric, allochronic cohorts may have formed since colonization via
life cycle changes of some individuals. Because this process could have
occurred at any point since colonization, there is undoubtedly intraspecific
variability in the time of formation of, and hence the time since isolation between,
different allochronic cohorts. Furthermore, because such cohorts occupy
essentially identical environments and thus experience identical selective
pressures, their divergence would proceed slowly, primarily via drift (Coyne and
Orr 2004). Thus perhaps those cohorts for which a lack of differentiation was
observed were simply isolated more recently and are still in the early stages of
divergence.
Alternatively, the evidence to date could suggest that sympatric,
allochronic cohorts of biennial insects are not consistently reproductively isolated.
Gene flow between the cohorts could occur if some individuals experience a life
cycle acceleration or deceleration via the elimination or addition, respectively, of
a diapause, and emerge off-year into the alternate year cohort. Although
diapause often is considered obligate, for many insect species its induction and
maintenance is partially influenced by environmental conditions experienced
throughout development, which can vary at the individual level (Tauber and
Tauber 1981, Danks 1992). Thus some insect populations display individual
variation in the length, timing, and number of diapauses, and hence timing of
adult emergence (Tauber and Tauber 1981, Philippi and Seger 1989, Danks
1992). For instance, intrapopulation variation in diapause and emergence time
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has been reported for some annual insects, where, during adult emergence,
small numbers of individuals may remain in diapause until the following year
(Tauber and Tauber 1981, Philippi and Seger 1989). Perhaps unsurprisingly,
variation in individual emergence times is more common in species inhabiting
unpredictable environments such as desert and arctic habitats, the very type of
environment that many biennial insects inhabit (Danks 1992).
Our current knowledge of the frequency of off-year emergences of most
biennial insects is primarily limited to a handful of species for which field
observations have been reported. For populations of a single year class (i.e.,
those emerging primarily or exclusively in even- or odd- numbered years) of
these species, off-year emergences have rarely, or, in some cases, never, been
observed (Aspinwall 1974, Douwes and Stille 1988, Kankare et al. 2002).
However, it is often unclear how thorough, both in terms of geographic coverage
(i.e., how many populations were observed?) and intensity (i.e., how often did
observations occur and over what time period?) these field efforts were. Indeed,
off-year emergences have been repeatedly documented for the intensivelystudied periodical cicacas: four-year developmental accelerations from 17 to 13
years are occasionally observed (Lloyd and White 1976), and additionally small
numbers of individuals frequently emerge one year before or after their
respective brood (Williams and Simon 1995). For many insects, such emergence
plasticity was not appreciated until it was discovered during rearing studies
(Tauber and Tauber 1981, Philippi and Seger 1989). Given these factors and the
apparent potential for emergence variability in insects generally, off-year
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emergences of biennial insects may be a regular phenomenon for a small
number of individuals.
Finally, these studies share experimental design features that may have
affected estimates of allochronic differentiation. First, most studies employed
allozymes, a marker for which many species display limited variation (Parker et
al. 1998). This lack of variation can limit the number of allozyme loci available for
analyses and, in turn, their information content. Parker et al. (1998) suggested
that 10-20 polymorphic allozyme loci are necessary to provide adequate
statistical confidence; the studies reviewed here employed 4-10 loci. Second,
most of the studies for which sample size was reported suffered from low sample
sizes of at least one cohort for at least one type of analysis (Heliövaara et al.
1988, Väisänen and Heliövaara 1990, Nice and Shapiro 2001, Chapter 2 and 4).
Sample size can affect estimates of population structure, and smaller sample
sizes may artificially inflate the degree of variability in allele frequencies among
populations (Sinclair and Hobbs 2009, Hale et al. 2012). Thus, sampling error
cannot be ruled out for at least some sympatric, allochronic cohort comparisons.
5.2 The formation of sympatric, allochronic cohorts
For divergence or speciation to be considered sympatric, reproductive
isolation must have been initiated in sympatry; that is, a former allopatric phase
between sympatric populations must be ruled out (Coyne and Orr 2004). For
biennial insects, allochronic cohorts could arise in sympatry via a temporary life
cycle acceleration or deceleration of a portion of a population as discussed
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above. Alternatively, sympatric cohorts may be established following the range
expansion and secondary contact of two formally allopatric and allochronic
cohorts. In this case, allochrony would be acting secondarily to reinforce isolation
and divergence initiated in allopatry. For example, most streams harboring
biennial pink salmon contain both even- and odd-year cohorts. Initial studies
revealed that allochronic cohorts within streams were more genetically diverged
than synchronic cohorts from different streams (Aspinwall 1974, Beacham et al.
1988, Brykov et al. 1996). Moreover, outbreeding depression has been observed
in F2 hybrids of allochronic broods (Gharrett et al. 1999). Thus, the pink salmon
cohorts were touted as a likely case of incipient sympatric speciation driven by
allochronic isolation. However, subsequent analyses based on mtDNA have
indicated that the cohorts had originated in separate streams approximately 1
million years previously, and thus, allochrony was in fact maintaining divergence
initiated in allopatry (Brykov et al. 1996, Polyakova et al. 1996).
Determining whether sympatric, allochronic cohorts arose in allopatry or
sympatry requires knowledge of the evolutionary relationships among biennial,
allochronic cohorts, which is currently lacking for most insect species. However,
there is some evidence to suggest that at least some cohorts may have
experienced a former allopatric phase. For example, while each rare cohort of
the pine bark bug was morphologically and genetically differentiated from its
respective sympatric, allochronic common cohort, it was not differentiated from
its respective allopatric, synchronic common cohort (Heliövaara et al. 1988,
Väisänen and Heliövaara 1990). This suggests that each rare cohort was derived
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from its respective allopatric, synchronic common cohort. It is plausible that the
common cohorts historically were allopatric, coming into contact following a
range expansion, and the rare cohorts may be remnants of a previously more
extensive range overlap between them.
Nevertheless, because temporary off-year emergences do occur in
periodical insects, it is possible that some allochronic cohorts have formed in
sympatry. However, this scenario is evolutionarily interesting for two opposing
reasons: although temporary life cycle shifts can create allochronic isolation and
the opportunity for sympatric divergence, the same process could then feasibly
create gene flow between the resulting cohorts and curtail that divergence
(Williams and Simon 1995).
5.3 Why do rare cohorts remain rare?
Where sympatric, allochronic cohorts of a biennial insect exist, very often
one cohort is comparatively scarce (Masters 1974, Mikkola 1976, Heliövaara and
Väisänen 1984, Scott 1986, Heliövaara et al. 1988, Sperling 1993, Kankare et al.
2002). Sometimes the difference is stark, with only one or two individuals of the
rare cohort observed in some years. Interestingly, regardless of species or
population, the size of these rare cohorts has never been observed to increase
over time (Mikkola 1976, Douwes 1980, Sperling 1993, Kankare et al. 2002).
Two hypotheses could explain this phenomenon, both of which have important
implications for the importance of allochrony as a reproductive isolating
mechanism for biennial insects.
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First, it has been proposed that the same mechanisms that lead to the
evolution of periodicity – predator-prey/parasitoid-host interactions and intraspecific competition – may prevent one cohort from attaining a larger size
(Mikkola 1976, Douwes 1980, Heliövaara and Väisänen 1984). In brief, the
predator-prey/parasitoid-host interaction hypothesis suggests that periodicity
evolves as an adaptation against predation/parasitization. In the case of biennial
insects, the prey/hosts of specialized predators/parasitoids (i.e., those
specializing on a specific life stage) primarily are present only every other year;
thus the predator/parasitoid can reproduce when its host is present, but the next
year, in the absence of its biennial host, becomes scarce again (Heliövaara et al.
1994). While this cycle keeps the predator/parasitoid population in check, it also
prevents the establishment of host individuals that emerge off-year when
predator/parasitoid populations are highest. Alternatively, the intraspecific
competition hypothesis proposes that resource competition between different age
classes (i.e., different nymphal/larval instars or immatures and adults) of
sympatric, allochronic cohorts prevents one year class from establishing
(Heliövaara and Väisänen 1984, Heliövaara et al. 1994). Depending on the
species, evidence for both processes exists (Heliövaara et al. 1994, Kankare et
al. 2002). However, it has not been considered that, regardless of the specific
mechanism(s) that prevent their establishment, the rare cohorts’ consistently
small size may impact its long term persistence, and in turn, the maintenance of
allochronic divergence. It is well established that small populations are at an
increased risk of extinction. First, they generally are more vulnerable to
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stochastic environmental events (Frankham et al. 2010). Second, small
populations will experience an accelerated loss of genetic diversity via inbreeding
and drift, leading to a reduction in fitness (Frankham 1996, Spielman et al. 2004).
These processes, combined with the purported external processes that
continuously prohibit their growth, may very well eventually drive many rare
cohorts to extirpation. In fact, this has apparently occurred for at least one
population of a biennial insect. Historically, the Macoun’s arctic butterfly occurred
in Riding Mountain National Park, Manitoba in both even and odd-numbered
years, with comparatively few adults present in even years. However, despite
intensive searches in recent years, adults have not been observed in an evennumbered year since 1968, and thus the even-year cohort is now considered
extirpated (Burns 2013). Therefore, even if they are presently reproductively
isolated, it may be short-sighted to assume that asynchronous, biennial cohorts
will survive long enough for divergence to occur.
Second, it is possible that rare cohorts are not actually independent
populations, but instead represent regular off-year emergences of individuals of a
single, mostly biennial population (i.e., the common cohort) in numbers too low to
establish a viable population (Heliövaara et al. 1988). Some insects display
considerable intraspecific variation in diapause and life-cycle length at the
population level, mainly due to geographic variation in environmental conditions
(Tauber and Tauber 1981, Danks 1992). This is true of several biennial insects
that transition to an annual or triennial life cycle over parts of their range
(Heliövaara and Väisänen 1984, Danks 1992). On this basis, perhaps for a given
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biennial species, some populations are more genetically and/or environmentally
prone to individual off-year emergences, and thus display them on a regular
basis. This could explain both why rare cohorts rarely establish, and why a lack
of differentiation has been observed between most sympatric, allochronic
cohorts.
To date, Heliövaara et al. (1988) have been the only authors to broach this
possibility, hypothesizing that the rare pine bark bug cohorts could simply be
individuals of the allochronic common cohorts regularly emerging off-year.
However, because the rare cohort individuals were differentiated from the
sympatric, allochronic common cohorts, they concluded that the rare cohorts
were indeed independent populations (Heliövaara et al. 1988). Yet, the overall
lack of differentiation observed in other studies, combined with the general
potential for off-year emergences in insects, suggests that the possibility that rare
cohorts simply represent regular off-year emergences of common cohorts cannot
be definitively ruled out for all biennial insects.
5.4 Conclusions and areas for further research
Historically, sympatric speciation has been considered to be a very rare
phenomenon (Futuyma and Mayer 1980, Coyne and Orr 2004). However,
asynchronous, periodical life cycles, particularly those of biennial insects, may
represent a potentially underappreciated and intriguing possibility for sympatric
divergence. In theory, allochronic biennial cohorts formed in sympatry via life
cycle changes are reproductively isolated by their asynchronous adult
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emergence schedules. Although limited field observations and empirical studies
provide some preliminary evidence for reproductive isolation between sympatric,
allochronic cohorts of biennial insects, it generally remains an understudied and
unproven phenomenon. Thus, our knowledge of the life histories and historical
biogeography of biennial insects currently is insufficient to conclude if sympatric,
allochronic cohorts represent viable cases of incipient sympatric speciation.
All studies to date of allochronic isolation in biennial insects have directly
or indirectly assessed the degree of differentiation between sympatric,
allochronic cohorts, often based on allozymes. The results of these studies are
mixed and inconclusive. Additional studies applying more variable DNA-based
markers may provide better resolution. However, perhaps more important will be
directly assessing the potential for gene flow between cohorts by determining the
frequency of off-year adult emergences. Although off-year emergences generally
are considered rare based on limited field observations, there are few empirical
data to support this claim. Quantifying the frequency of off-year emergences will
require intensive, repeated, and purposeful monitoring of biennial populations in
the ‘wrong’ year, and to my knowledge, there has been no such undertaking.
Moreover, insects display inter-population variability in life cycle length (Tauber
and Tauber 1981, Danks 1992), and even if adults rarely emerge off-year in
alternate year populations, it remains possible that regional climatic conditions or
genetic characteristics make populations of sympatric, allochronic cohorts more
prone to off-year emergences. Therefore, in conjunction with more intensive field
monitoring, rearing and developmental studies should be conducted to fully
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elucidate the factors that promote off-year emergences and hence influence their
frequency.
In addition to assessing reproductive isolation between cohorts, further
phylogeographic analyses can be conducted to determine the likelihood that
sympatric, allochronic cohorts have indeed formed in sympatry. Finally,
demographic and genetic studies should be performed on rare cohorts to
establish their potential long-term viability. All of these data will be necessary to
conclude if allochrony initiates and maintains reproductive isolation between
sympatric cohorts of biennial insects.
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Chapter 6
GENERAL DISCUSSION AND CONCLUSIONS
The primary goal of my research was to elucidate the spatial and temporal
population structure of the Macoun’s arctic butterfly (MA; Oeneis macounii) and
the White Mountain arctic butterfly (WMA; Oeneis melissa semidea). For the MA,
I assessed range-wide spatial genetic patterns to infer its demographic history,
dispersal behaviour and patterns of gene flow, and to provide preliminary insight
into the genetic characteristics of several populations of conservation concern.
For the WMA, I combined genetic and mark-release-recapture techniques to
address long-standing questions about its spatial population structure, dispersal,
and male territoriality in the context of its conservation management. For both
butterfly taxa, I was particularly interested in examining whether allochronic
isolation has resulted in divergence between sympatric, alternate-year cohorts.
Here I summarize the major findings of my research and discuss their
implications.
6.1 Population structure and phylogeography of the Macoun’s arctic
butterfly
6.1.1 Summary of results
My analyses indicated that the MA exhibited significant spatial genetic
structuring. I observed a pattern of isolation by distance and most allopatric
populations were significantly differentiated, even over geographic distances as
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small as 3 km. These observations suggest that the MA exhibits low dispersal,
corresponding with recent field observations that adults are quite sedentary. Yet
population genetic differentiation overall was weak, while genetic diversity was
high. As the MA currently exhibits low dispersal, these genetic characteristics
and its expansive distribution likely are remnant of a historically more continuous
habitat and population that has recently become more fragmented.
I did not find conclusive evidence for reproductive isolation between
sympatric, allochronic cohorts of the MA: of the two pairs of sympatric cohorts
that I analyzed, I only observed significant differentiation between one pair on the
basis of AFLPs. Alternate year samples of that population were collected
approximately 2 km apart, so, given the MA’s apparent low dispersal, allopatric
isolation may have contributed to this differentiation. Alternatively, both pairs of
cohorts may have been reproductively isolated, but one has been isolated longer,
and thus more genetic differences have accrued. Finally, for all allochronic
comparisons, I lacked samples for at least one cohort; therefore, sampling error
also may have affected estimates of differentiation.
I also analyzed patterns in mtDNA diversity and differentiation for the MA
in a historical context to elucidate its glacial and post-glacial demographic history.
The presence of widespread, shared haplotypes, high haplotype diversity, and
overall low mtDNA differentiation suggested that the MA likely expanded its
range recently from a single glacial refugium. Assuming that it tracked the glacial
and post-glacial movements of jack (Pinus banksiana Lamb.) and/or lodgepole
(Pinus contorta Dougl. ex. Loud.) pine habitats, a number of historical scenarios
129
are possible for the MA. However, given the two mtDNA clusters I observed, one
east and one west of mid-Manitoba, the MA most likely re-colonized in
association with jack pine from its southeastern refugium, moving north and then
west and east around Lake Agassiz. This scenario also accounts for the
corresponding adult emergence divide (odd-year vs even year) along the same
area.
6.1.2 Future research
As Burns (2013) noted, the MA is generally understudied. Burns (2013)
provided the only other empirical, quantitative assessment of the MA,
characterizing the adult life history, dispersal, distribution, and sexual segregation
of two populations in Manitoba. Prior to this, the last publication on the MA was
by Masters (1974), who detailed his personal observations on the MA’s habitat
use, distribution, and emergence patterns. Thus, many aspects of the MA’s
biology, ecology, and demographics remain unknown.
Although my research has provided important, preliminary insights into the
MA’s genetic population structure, additional analyses are warranted. For my
study, samples were obtained from virtually all known MA populations from
across its entire range. This degree of sampling is exceptional, given its wide
distribution and relatively short flight period. However, this sampling regime also
resulted in a limited amount of time to sample from individual locations, and
hence, small sample sizes from many locations. This was especially true for
sympatric, allochronic MA cohorts, for which I generally lacked samples of the
130
rarer cohort. Small sample sizes can affect estimates of population differentiation
(Sinclair and Hobbs 2009, Hale et al. 2012), and thus analyses employing
additional samples may provide further insight into the MA’s population
structuring and demographic history.
Taken together, my genetic analyses and recent field observations by
Burns (2013) indicate that the MA exhibits low dispersal. However, other than
simply geographic distance, it remains unknown what specific landscape features
or behavioural traits may limit movements and gene flow for the MA. Determining
these factors will require detailed characterization of its habitat use, dispersal
behaviour, and genetic structure over restricted geographic areas. This
information will be useful for predicting population changes in response to
environmental change, which will be particularly important for the conservation
management of threatened MA populations.
Currently, the populations in Algonquin Provincial Park, ON; Riding
Mountain National Park, MB; and Isle Royale National Park, MI, are the only MA
populations identified as possibly threatened. The Algonquin population is very
small and localized, and on-going monitoring has revealed that it’s in decline (G.
W. Otis pers. comm.). Similarly, the Riding Mountain population is small,
localized, and believed to be at risk; moreover, the even year population has
apparently been extirpated within the last 50 years (Burns 2013). In comparison,
the Isle Royale population is highly isolated and genetically differentiated from
mainland populations. Park staff have thought it to be declining in size and
potentially at risk of extirpation, but recent field work suggests it is robust and not
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presently at risk (Otis 2012). When examined more broadly, my analyses
suggest that a large proportion of allopatric MA populations are reproductively
isolated, which places smaller MA populations at more immediate risk of
extirpation via accelerated rates of inbreeding and loss of genetic diversity
(Frankham 1996, Frankham et al. 2010). Furthermore, the continued loss of jack
and lodgepole pine stands from anthropogenic activities (e.g., logging, oil
extraction, urban development) will exacerbate fragmentation and isolation of MA
populations. Therefore, conservation assessments of additional MA populations
may be warranted.
6.2 Population structure and conservation of the White Mountain arctic
butterfly
6.2.1 Summary of Results
Because of a lack of empirical data on its biology, demographics, and
behaviour, specific conservation management tactics for the WMA have yet to be
developed or implemented despite its threatened status. Population
fragmentation and isolation were suspected for the WMA because adults were
reportedly localized within four alpine meadows [Cow Pasture (CP), Bigelow
Lawn (BL), Gulf Tanks (GT), and Monticello Lawn (ML)], or relatively flat, lowerelevation areas of the alpine zone dominated by its larval host plant Bigelow’s
sedge (Carex bigelowii Torr. ex. Schwein), with few adults observed in
intervening areas (Anthony 1970, McFarland 2003). However, my field
observations (Chapter 3) and genetic analyses (Chapter 4) indicated that the
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WMA does not exhibit spatial population structuring. While I generally observed
WMA adults in higher densities within the four alpine meadows, I also frequently
observed WMA adults on the higher elevation sedge-lined ridges and slopes
between meadows on Mt. Washington. Thus, my study revealed that the WMA
displays a more continuous distribution on Mt. Washington than previously
described (Chapter 3; Figure 3.1). This discrepancy between my observations
and earlier reports likely reflects that my study area was larger and my surveys
more intensive.
Using MRR, I also obtained the first direct observations of WMA adult
dispersal, revealing movements between BL and GT and the capacity to disperse
at least 800 m. Concordant with my field observations, my genetic analyses did
not indicate differentiation among BL, GT, and CP, and thus dispersal and
genetic exchange likely occurs across the WMA’s entire distribution on Mt.
Washington. Although WMA adults have never been reported from the areas
between Mts. Washington and Jefferson, and I did not observe dispersal
between these areas, the ML subpopulation also was not differentiated from
those in the meadows on Mt. Washington. Although this indicates that genetic
exchange occurs between the mountains, it remains unclear if this results from
active dispersal or adults being transported by the wind.
I also investigated whether the WMA was structured into two sympatric,
allochronic cohorts, a possibility that, to my knowledge, has never been
considered. My AFLP analyses indicated that samples from alternate years were
significantly differentiated; however, I was unable to conclusively determine if this
133
resulted from a lack of gene flow between the cohorts. Alternatively, it is possible
that some gene flow does occur between the cohorts due to accelerated or
decelerated larval development, but because of their small population size,
genetic drift is causing large fluctuations in allele frequencies between years
(Chapter 4).
6.2.2 Conservation implications and management recommendations
My genetic and MRR results indicate that the WMA can be managed
spatially as a single genetic population. Presently human-assisted transfer of
adults among meadows on Mt. Washington is not necessary. However, as the
WMA subpopulation on Mt. Jefferson appears to be smaller, it may be at a more
imminent risk of decline and extirpation, especially if adults are not regularly
emigrating from Mt. Washington to ML. To ensure the persistence of the WMA
within ML, transfer of adults from Mt. Washington or augmentation with reared
immatures or adults (see below) may be warranted.
Ideally, regular surveys should be undertaken to monitor for changes to
the WMA’s population size and distribution. However, as previously reported
(Anthony 1970, McFarland 2003), I found observation and study of the WMA to
be difficult. First, the Mt. Washington alpine zone weather is notoriously poor and
unpredictable (McFarland 2003, Slack and Bell 2006), resulting in very few days
(3-8 days within the flight period of adults) suitable for adult WMA activity.
Second, adults are very cryptic when resting on rocks and they rarely fly unless
disturbed, making them difficult to detect during traditional monitoring along
134
transects (e.g., Thomas 1983, Pollard and Yates 1993, Royer et al. 1998). Third,
their inconspicuousness and strong flight, in combination with the alpine zone’s
rugged terrain and strong winds, made capturing WMA adults difficult, which
limits the applicability of MRR monitoring techniques. Therefore, designing and
implementing a standard and effective monitoring scheme for the WMA may not
be practical. As such, periodic genetic analyses, which could be used to assess
changes to population size indirectly via estimates of Ne and genetic diversity,
and to detect changes in subpopulation connectivity, would be particularly useful
for monitoring the WMA.
Although I was unable to obtain estimates of census or effective
population sizes, repeated surveys strongly suggest that the WMA population
currently is small and has declined appreciably in the past 100 years (Scudder
1901, McFarland 2003). Thus, a conservation management program for the
WMA should include efforts to maintain, and ideally increase, its population size.
For some insects, this has been directly achieved through the release of captivebred immatures or adults (New 2009), and it has been suggested that a rearing
technique be developed for the WMA (New Hampshire Fish and Game
Department 2006). However, because of its biennial development, captive
breeding promises to be challenging and will require additional insight into the
mating behaviour and developmental requirements of the WMA.
The vitality and growth of the WMA population will depend heavily on the
continued presence and abundance of its host plant. Thus, monitoring for
changes to the distribution and abundance of Bigelow’s sedge also should be
135
implemented. The biggest threat to Bigelow’s sedge likely is climate change and
its effects on the alpine zone of the White Mountains. Additionally, Mt.
Washington is routinely visited by a large number of hikers and Auto Road
tourists, and off-path foot traffic on the alpine zone may destroy sedge plants and
the WMA eggs and larvae inhabiting them. Although the alpine zone is protected
and signs are posted to discourage people from walking off-path, during my field
studies I often witnessed tourists parking at CP and walking onto the alpine zone
directly over areas in which WMA adults were common. Such activity should be
further mitigated with improved signage and increased patrolling by state
authorities.
The WMA has repeatedly been confirmed on Mts. Washington and
Jefferson; however, its distribution within the Presidential Range may be larger
than currently assumed: in 2001, two adults were observed on Mt. Adams, and
more recently, adults have been reported several times on Mt. Monroe
(McFarland 2003, E. Elinski, pers. comm). Furthermore, several sedge meadows
exist on Mt. Madison, but it is unknown if the WMA occurs there (McFarland
2003). The extent of the WMA’s distribution should be confirmed by surveying
these mountains more thoroughly. Even if these areas do not currently harbour
the WMA, they may serve as suitable sites for introduction of females or
immatures from Mt. Washington or of captive-reared individuals as part of an
attempt to increase population size.
Given the large number of people who hike Mt. Washington and
surrounding peaks, it would be advantageous to implement a citizen-scientist
136
program to supplement WMA monitoring. The Appalachian Trail (AT), the most
popular trail used to hike the Presidential Range alpine zone, runs across Mt.
Monroe, BL and GT on Mt. Washington, ML on Mt. Jefferson, and Mt. Adams,
and thus many hikers regularly pass through areas where the WMA is confirmed
or suspected to occur. Educational materials (e.g., posters, pamphlets) with
instructions on how to identify WMA adults and how to report sightings could be
provided at the Mt. Washington summit building and the Appalachian Mountain
Club® huts along the AT. Citizen reporting would provide insight into changes to
adult abundance, distribution, and flight period, and help determine if the WMA
occurs on Mts. Monroe and Adams.
The WMA’s temporal population structure should be examined further. If
the WMA is structured into two, isolated biennial cohorts, they should be
managed as independent units to ensure the preservation of allochronically
derived genetic differences that reflect a unique evolutionary process. For
instance, if captive rearing and release is implemented as a management
strategy, a breeding stock should be established from each cohort separately to
avoid creating gene flow artificially between the cohorts. Genetic analyses using
more samples and rearing studies to confirm consistent biennial development
would conclusively establish the existence and extent of reproductive isolation
between WMA allochronic cohorts.
Finally, the management recommendations that I have suggested for the
WMA may be applied to other threatened Oeneis relict populations [e.g., the
Katahdin arctic (Oeneis polixenes katahdin), the Melissa arctic (Oeneis melissa
137
melissa) population in the Gaspé, QB, Canada, etc.]. The Katahdin arctic in
particular displays a very similar life history to the WMA: it occurs only within
alpine sedge meadows on Mt. Katahdin in Maine, USA, and is stated to be
biennial yet emerges every year (Maine Department of Inland Fisheries and
Wildlife 2009). Furthermore, field study of the Katahdin arctic is hampered by
poor weather and inaccessibility, and thus although it is listed as endangered,
little is known about its life history, demography, or management requirements
(Maine Department of Inland Fisheries and Wildlife 2009). Therefore, like the
WMA, genetic characterization of the Katahdin arctic population, including an
assessment of differentiation between allochronic cohorts, may be particularly
useful for its conservation management and monitoring.
6.3 Sympatric, allochronic divergence in biennial insects
In Chapter 5, I reviewed the potential for sympatric, allochronic divergence
of insect populations resulting from asynchronous, biennial lifecycles. Sites in
which adults emerge annually are common in otherwise biennial taxa; these
probably represent two sympatric allochronic cohorts reproductively isolated by
their alternate-year emergence schedules. Although gene flow between the
cohorts could be created if some adults emerge ‘off-year’ via a life cycle
acceleration or deceleration, this generally is considered rare but remains
unquantified. However, of the few studies that have directly examined allochronic
divergence in biennial insects, some have reported significant differentiation
between sympatric, allochronic cohorts, while others have not. Likewise, my
138
genetic analyses on the MA and WMA produced evidence for reproductive
isolation between sympatric, allochronic cohorts that was inconsistent between
locations (Chapters 2 and 4). Two hypotheses may explain why only some
allochronic cohorts are differentiated (Kankare et al. 2002). First, sympatric,
allochronic cohorts truly may be reproductively isolated, but some cohorts have
not been isolated sufficiently long for genetic differences to accrue. Second, gene
flow generated by off-year emergences of adults prevents cohorts from diverging,
and alternative factors (e.g., experimental sampling error, genetic drift, allopatric
isolation) have created or contributed to the divergence observed between them.
Determining if sympatric, allochronic cohorts of biennial insects are indeed
reproductively isolated will require additional life history, demographic, and
genetic data. For biennial insect populations that emerge annually, it is
impossible to directly observe off-year emergences, and thus to gauge their
potential frequency, populations that emerge every other year must be
monitored. To date, unquantified field reports (Douwes and Stille 1988, Kankare
et al. 2002, G. W. Otis pers. comm.) and museum records (G. W. Otis pers.
comm.) from such populations suggest that adults of some species rarely
emerge off-year. However, it is unclear how intensive field observation has been
in terms of number of surveys or populations monitored for various species
believed to be biennial. Quantifying the frequency of off-year emergences will
require intensive, repeated, and purposeful monitoring of biennial populations in
the ‘wrong’ year, and, with the exception of some MA populations (G. W. Otis
pers. comm.), to my knowledge, there have been very few undertakings of this
139
nature. However, some insect taxa display inter-population variability in life cycle
duration (Tauber and Tauber 1981, Danks 1992), and thus even if adults rarely
emerge off-year in these populations, it remains possible that regional climatic
conditions or genetic characteristics make populations of sympatric, allochronic
cohorts more prone to off-year emergences. Rearing studies therefore would be
particularly useful for determining the factors that may induce variation in life
cycle duration of biennial insects.
In conjunction with field and rearing studies, additional genetic analyses
would be useful to indirectly infer the extent of isolation between sympatric,
allochronic cohorts and the neutral and/or selective processes driving their
divergence. Such analyses should employ multiple, highly variable markers and
a large number of samples collected over multiple years. However, obtaining an
adequate number of samples from the rarer cohort for many allochronic cohorts
promises to be challenging. For some rare cohorts, very few individuals may be
observed in a given year (e.g., Heliövaara et al. 1988, Sperling 1993), and
therefore sampling may be required over many years. The WMA could serve as
an excellent model system in this respect, as it emerges every year in roughly
equal densities and in higher numbers than other rare cohorts. Therefore a larger
number of samples could be obtained more quickly.
6.4 Conclusions
My project was one of the few to focus on the MA and WMA, butterflies
that have proven difficult to study in the field because of their cryptic appearance
140
and sedentary behaviour (Anthony 1970, McFarland 2003, Burns 2013). By
characterizing their population structures using primarily genetic data, I was able
to make inferences about their dispersal, demographic histories, and
conservation management requirements. My field study also generated novel
insights into the WMA’s distribution, dispersal, and male territorial behaviour. Not
only is this information important for understanding the evolutionary history and
biology of the MA and WMA, but also for predicting future population trends in
response to climate and environmental change, and for designing effective
conservation management plans. Furthermore, my findings lay the necessary
groundwork for additional study of the MA and WMA, as well as other Oeneis
species. I also am the first to directly examine the potential for allochronic
divergence, a process that remains understudied, in this genus. Although I found
some evidence for reproductive isolation between allochronic cohorts of the MA
and WMA cohorts, in neither case was it conclusive. Further investigations of
asynchronous, biennial insect cohorts may reveal an underappreciated avenue
for sympatric divergence, a process that while theoretically possible, has proven
difficult to demonstrate in nature.
141
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164
Appendix 1
LABORATORY PROTOCOL USED TO AMPLIFY
THE MITOCHONRIAL GENE CYTOCHROME C OXIDASE I (COI)
FOR OENEIS MACOUNII AND OENEIS MELISSA SEMIDEA
Table A1.1. Primer sequences used to amplify the cytochrome c oxidase I (COI) gene for Oeneis macounii and Oeneis
melissa semidea.
Primer Name
Sequence (5'-3')
Size (bp)
Species Amplified
Reference
CACTTTCTTGGGAAATAATATGTGA
25
O. melissa semidea
designed for this study
ACAAATCATAAGGATATTGGAAC
23
O. macounii/O. melissa semidea
Bromilow and Sperling 2011
ACTGTAAATATATGATGATGAGCTCA
26
O. macounii/O. melissa semidea
Bromilow and Sperling 2011
Jerry
CAACATTTATTTTGATTTTTTGG
23
O. macounii/O. melissa semidea
Bromilow and Sperling 2011
Pat2
TCCATTACATATAATCTGCCATATTAG
28
O. macounii/O. melissa semidea
Bromilow and Sperling 2011
EvaG
Lyn
K525
165
Table A1.2. Protocol for PCR amplification of the cytochrome c oxidase I (COI) gene for
Oeneis macounii and Oeneis melissa semidea.
Reaction Component
water
PCR buffer
MgCl
dNTPs
Taq
forward primer
reverse primer
DNA template
Total
Concentration
10X
25 mM
10 mM
5 U/μL
10 mM
10 mM
10-100 ng
Volume (μl)
11.4
2
3
0.5
0.1
1
1
1
20
PCR thermalcycling conditions for COI amplification: Oeneis macounii: one cycle of
94°C for 4 min; 30 cycles of 94°C for 30 s, 45°C for 30 s, and 72°C for 30 s; and a final
hold of 72°C for 8 min; Oeneis melissa semidea: one cycle of 94°C for 4 min; 35 cycles
of 94°C for 30 s, 45°C for 30 s, and 72°C for 30 s; and a final hold of 72°C for 8 min.
166
Appendix 2
LABORATORY PROTOCOL USED TO GENERATE AMPLIFIED FRAGMENT
LENGTH POLYMORPHISMS (AFLPs) FOR OENEIS MACOUNII
AND OENEIS MELISSA SEMIDEA
AFLP profiles were generated for Oeneis macounii and Oeneis melissa semidea
using a protocol modified from Clarke and Meudt (2005) and Applied Biosystem’s AFLP
Plant Mapping Kit (Applied Biosystems, Foster City, CA). Genomic DNA (50 – 100 ng)
was digested with MseI and EcoRI restriction enzymes and ligated to MseI and EcoRI
adaptors (Table A2.1) simultaneously in a total reaction volume of 11 μl (see Tables
A2.2 and A2.3 for detailed restriction-ligation protocol). Restriction-ligation (R-L)
fragments were then diluted with 89 μl of TE0.1 buffer and stored at -20°C. Diluted R-L
fragments were amplified in a pre-selective PCR using the primers Eco-A and Mse-C
(Table A2.1) in a total reaction volume of 10 μl (8 μl master mix + 2 μl diluted R-L
template; see Table A2.4 for detailed pre-selective PCR protocol). The pre-selective
primers are complementary to the adaptors with one additional nucleotide (A for EcoRI
and C for MseI) at the 3’ end. Only restriction fragments with bases complementary to
these additional nucleotides (i.e., T and G) next to the restriction sites will be amplified,
reducing the number of fragments to approximately 1/16 of the original amount (Meudt
and Clarke 2007). Pre-selective products were diluted with 12.5 μl of TE0.1 buffer and
stored at -20°C. Diluted pre-selective products were then amplified by PCR using
selective primers (Eco-AXX and Mse-CXX; Table A2.1) in a total reaction volume of 10
μl (8.5 μl master mix + 1.5 μl diluted pre-selective PCR product; see Table A2.5 for
detailed selective amplification protocol). The selective primers contain two additional
nucleotides at the 3’ end, which further reduces the number of fragments to
167
approximately 1/256 of the initial amount. Five (EcoRI-AAC/MseI-CTC, EcoRIAAC/MseI-CTG, EcoRI-ACT/MseI-CTC, EcoRI-ACT/MseI-CAT, and EcoRI-ACA/MseICTT) and six (EcoRI-AAC + MseI-CAC, EcoRI-AAC + MseI-CTC, EcoRI-AAG + MseICAG, EcoRI-ACA + MseI-CAA, EcoRI-ACC + MseI-CTA, EcoRI-ACG + MseI-CAA)
selective primer pairs were used for the selective amplification of Oeneis macounii and
Oeneis melissa semidea, respectively (Table A2.1). Prior to fragment analysis, selective
PCR products were diluted 25x with water. Negative controls were included in each step
of the protocol to ensure that no contamination had occurred.
168
Table A2.1. Oligonucleotides used for the AFLP analysis of Oeneis macounii and
Oeneis melissa semidea.
Protocol Step
Restriction-Ligation
Oligonucelotide
*EcoRI Adaptor 1
EcoRI Adaptor 2
Sequence (5'-3')
CTCGTAGACTGCGTACC
AATTGGTACGCAGTCTAC
Size (bp)
17
18
MseI Adaptor 1
MseI Adaptor 2
GACGATGAGTCCTGAG
TACTCAGGACTCAT
16
14
Eco+A
Mse+C
GACTGCGTACCAATTCA
GATGAGTCCTGAGTAAC
17
17
Eco+AXX
GACTGCGTACCAATTCAAC
GACTGCGTACCAATTCAAG
GACTGCGTACCAATTCACA
GACTGCGTACCAATTCACC
GACTGCGTACCAATTCACG
GACTGCGTACCAATTCACT
19
19
19
19
19
19
Mse+CXX
GATGAGTCCTGAGTAACAA
GATGAGTCCTGAGTAACAC
GATGAGTCCTGAGTAACAG
GATGAGTCCTGAGTAACTC
GATGAGTCCTGAGTAACAT
GATGAGTCCTGAGTAACTA
GATGAGTCCTGAGTAACTC
GATGAGTCCTGAGTAACTT
19
19
19
19
19
19
19
19
Pre-Selective PCR
Selective PCR
†
*EcoRI and MseI adaptors must be annealed separately before use in restriction-ligation reactions (Table
A2.2).
†
All Eco-AXX primers were fluorescently-labelled with FAM dye.
169
Table A2.2. Protocol for annealing EcoRI and MseI adaptors for use in restrictionligation reactions.
Annealing Reaction
EcoRI
Reaction Component
EcoRI Adaptor 1
EcoRI Adaptor 2
water
*T10E1
Total
Concentration
1000 μM
1000 μM
Volume (μl)
1
1
108
90
200
MseI Adaptor 1
MseI Adaptor 2
water
*T10E1
Total
1000 μM
1000 μM
10
10
90
90
200
MseI
*T10E1 = 10 mM Tris, 1 mM EDTA
Each master mix was prepared separately, placed in a thermalcycler for 8 min at 94°C
followed by 10 min at 22°C, and centrifuged at 1400 g for 10 s.
170
Table A2.3. Protocol for restriction-ligation (R-L) reactions.
Reaction Component
water
T4 DNA ligase buffer with ATP
NaCl
BSA
MseI Adaptor*
EcoRI Adaptor*
MseI (1 U)
EcoRI (5 U)
T4 DNA ligase
genomic DNA (μl)
Total
Concentration Volume (μl)
0.55
10x
1.1
0.5 M
1.1
1 mg/ml
0.55
100 U
1
500 U
1
1U
0.1
5U
0.05
100 Weiss U
0.05
40-300 ng
5.5
11
*MseI and EcoRI adaptors must be annealed as described in Table A2.2 before
use in R-L reaction.
R-L reactions were held at 24°C for 16 h in a thermalcycler, diluted with 89 μl TE buffer,
and stored at -20°C.
171
Table A2.4.Protocol for pre-selective PCR amplification.
Reaction Component
water
betaine
PCR buffer
MgCl
dNTPs
Mse-C primer
Eco-A primer
*Platinum® Taq DNA polymerase
diluted R-L product
Total
*produced by Life Technologies
Concentration Volume (μl)
1.5
3M
3.5
10x
1
25 mM
0.65
10 mM
0.25
10 μM
0.5
10 μM
0.5
5 U/μl
0.1
2
10
TM
Preselective PCR thermalcycling conditions: one cycle of 72°C for 2 min; 25 cycles of
94°C for 20s, 56°C for 30 s, and 72°C for 2 min; and a final hold of 60°C for 30 min.
Table A2.5. Protocol for selective PCR amplification.
Reaction Component
water
PCR buffer
MgCl
dNTPs
*Platinum® Taq DNA polymerase
Eco+AXX primer
Mse+CXX primer
diluted pre-selective PCR product
Total
*produced by Life Technologies
Concentration Volume (μl)
4.4
10x
1
25 mM
1.75
10 mM
0.25
5 U/μl
0.1
10 μM
0.5
10 μM
0.5
1.5
10
TM
Selective PCR thermalcycling conditions: one cycle of 94°C for 2 min; 94°C for 20 s,
66°C for 30s decreasing by 1°C/cycle, and 72°C for 2 min for 10 cycles; 94°C for 20 s,
56°C for 20 s, and 72°C for 2 min for 22 cycles; and a final hold of 60°C for 30 min.
172
Appendix 3
METADATA FOR CYTOCHROME C OXIDASE I (COI) SEQUENCES GENERATED FROM OENEIS MACOUNII AND
OENEIS MELISSA SEMIDEA SAMPLES ANALYZED IN THIS STUDY
Table A3.1. Barcode of Life Data System (BOLD) process identification numbers, collection location information, and
haplotype identification numbers for Oeneis macounii and Oeneis melissa semidea cytochrome c oxidase I (COI)
sequences generated in this study (NA = missing data).
BOLD
Process ID
Species
Collection
Date
State/Province
Region
Lat
Long
Haplotype
ID
OEN001-14
Oeneis macounii
18/06/11
British Columbia
100 Mile House
51.14
-121.27
ma4
OEN002-14
Oeneis macounii
18/06/11
British Columbia
100 Mile House
51.14
-121.27
ma71
OEN003-14
Oeneis macounii
18/06/11
British Columbia
100 Mile House
51.14
-121.27
ma76
OEN004-14
Oeneis macounii
18/06/10
British Columbia
100 Mile House
51.64
-121.27
ma77
OEN005-14
Oeneis macounii
18/06/10
British Columbia
100 Mile House
51.64
-121.27
ma70
OEN006-14
Oeneis macounii
18/06/10
British Columbia
100 Mile House
51.64
-121.27
ma70
OEN007-14
Oeneis macounii
18/06/10
British Columbia
100 Mile House
51.64
-121.27
ma78
OEN008-14
Oeneis macounii
18/06/10
British Columbia
100 Mile House
51.64
-121.27
ma70
OEN009-14
Oeneis macounii
18/06/10
British Columbia
100 Mile House
51.64
-121.27
ma67
OEN010-14
Oeneis macounii
18/06/10
British Columbia
100 Mile House
51.64
-121.27
ma4
OEN011-14
Oeneis macounii
18/06/10
British Columbia
100 Mile House
51.64
-121.27
ma85
OEN012-14
Oeneis macounii
26/06/08
Manitoba
Agassiz Provincial Forest
50.03
-96.25
ma51
OEN013-14
Oeneis macounii
26/06/08
Manitoba
Agassiz Provincial Forest
50.03
-96.25
ma28
OEN014-14
Oeneis macounii
07/06/08
Ontario
Algonquin Provincial Park
45.96
-78.05
ma38
OEN015-14
Oeneis macounii
29/06/08
Ontario
Algonquin Provincial Park
45.96
-78.05
ma39
OEN016-14
Oeneis macounii
29/06/08
Ontario
Algonquin Provincial Park
45.96
-78.05
ma4
OEN017-14
Oeneis macounii
22/06/08
Manitoba
Belair
50.62
-96.53
ma8
OEN018-14
Oeneis macounii
22/06/08
Manitoba
Belair
50.62
-96.53
ma4
OEN019-14
Oeneis macounii
22/06/08
Manitoba
Belair
50.62
-96.53
ma64
OEN020-14
Oeneis macounii
23/06/08
British Columbia
Bluenose Mountain
50.19
-119.07
ma4
OEN021-14
Oeneis macounii
09/07/11
Alberta
Bragg Creek
55.48
-114.53
ma4
OEN022-14
Oeneis macounii
04/07/09
Alberta
Bragg Creek
55.48
-114.53
ma4
OEN023-14
Oeneis macounii
04/07/09
Alberta
Bragg Creek
55.48
-114.53
ma4
173
BOLD
Process ID
Species
Collection
Date
State/Province
Region
Lat
Long
Haplotype
ID
OEN024-14
Oeneis macounii
04/07/09
Alberta
Bragg Creek
55.48
-114.53
ma67
OEN025-14
Oeneis macounii
04/07/09
Alberta
Bragg Creek
55.48
-114.53
ma67
OEN026-14
Oeneis macounii
08/07/00
Alberta
Bragg Creek
55.48
-114.53
ma4
OEN027-14
Oeneis macounii
08/07/00
Alberta
Bragg Creek
55.48
-114.53
ma4
OEN028-14
Oeneis macounii
08/07/00
Alberta
Bragg Creek
55.48
-114.53
ma4
OEN029-14
Oeneis macounii
21/06/12
Alberta
Bragg Creek
50.98
-114.58
ma4
OEN030-14
Oeneis macounii
26/06/10
Alberta
Bragg Creek
50.98
-114.58
ma4
OEN031-14
Oeneis macounii
26/06/10
Alberta
Bragg Creek
50.98
-114.58
ma4
OEN032-14
Oeneis macounii
25/06/09
Alberta
Bragg Creek
50.74
-114.6
ma4
OEN033-14
Oeneis macounii
25/06/09
Alberta
Bragg Creek
50.74
-114.6
ma96
OEN034-14
Oeneis macounii
25/06/09
Alberta
Bragg Creek
50.74
-114.6
ma4
OEN035-14
Oeneis macounii
25/06/09
Alberta
Bragg Creek
50.74
-114.6
ma84
OEN036-14
Oeneis macounii
25/06/09
Alberta
Bragg Creek
50.74
-114.6
ma4
OEN037-14
Oeneis macounii
25/06/09
Alberta
Bragg Creek
50.98
-114.58
ma4
OEN038-14
Oeneis macounii
25/06/09
Alberta
Bragg Creek
50.98
-114.58
ma94
OEN039-14
Oeneis macounii
25/06/09
Alberta
Bragg Creek
50.98
-114.58
ma4
OEN040-14
Oeneis macounii
25/06/09
Alberta
Bragg Creek
50.98
-114.58
ma97
OEN041-14
Oeneis macounii
25/06/09
Alberta
Bragg Creek
50.98
-114.58
ma4
OEN042-14
Oeneis macounii
25/06/09
Alberta
Bragg Creek
50.98
-114.58
ma98
OEN043-14
Oeneis macounii
25/06/09
Alberta
Bragg Creek
50.98
-114.58
ma4
OEN044-14
Oeneis macounii
25/06/09
Alberta
Bragg Creek
50.98
-114.58
ma4
OEN045-14
Oeneis macounii
25/06/09
Alberta
Bragg Creek
50.98
-114.58
ma4
OEN046-14
Oeneis macounii
27/06/09
Saskatchewan
Canwood
53.33
-106.59
ma19
OEN047-14
Oeneis macounii
27/06/09
Saskatchewan
Canwood
53.33
-106.59
ma8
OEN048-14
Oeneis macounii
27/06/09
Saskatchewan
Canwood
53.33
-106.59
ma4
OEN049-14
Oeneis macounii
27/06/09
Saskatchewan
Canwood
53.33
-106.59
ma20
OEN050-14
Oeneis macounii
27/06/09
Saskatchewan
Canwood
53.33
-106.59
ma10
OEN051-14
Oeneis macounii
27/06/09
Saskatchewan
Canwood
53.33
-106.59
ma6
OEN052-14
Oeneis macounii
27/06/09
Saskatchewan
Canwood
53.33
-106.59
ma10
OEN053-14
Oeneis macounii
27/06/09
Saskatchewan
Canwood
53.33
-106.59
ma21
OEN054-14
Oeneis macounii
22/06/11
British Columbia
Chetwynd
55.63
-121.86
ma23
OEN055-14
Oeneis macounii
22/06/11
British Columbia
Chetwynd
55.63
-121.86
ma24
OEN056-14
Oeneis macounii
22/06/11
British Columbia
Chetwynd
55.63
-121.86
ma4
OEN057-14
Oeneis macounii
22/06/11
British Columbia
Chetwynd
55.63
-121.86
ma4
OEN058-14
Oeneis macounii
22/06/11
British Columbia
Chetwynd
55.63
-121.86
ma69
174
BOLD
Process ID
Species
Collection
Date
State/Province
Region
Lat
Long
Haplotype
ID
OEN059-14
Oeneis macounii
22/06/11
British Columbia
Chetwynd
55.63
-121.86
ma73
OEN060-14
Oeneis macounii
22/06/11
British Columbia
Chetwynd
55.63
-121.86
ma74
OEN061-14
Oeneis macounii
22/06/11
British Columbia
Chetwynd
55.63
-121.86
ma4
OEN062-14
Oeneis macounii
22/06/11
British Columbia
Chetwynd
55.63
-121.86
ma75
OEN063-14
Oeneis macounii
22/06/09
British Columbia
Chetwynd
55.62
-121.31
ma86
OEN064-14
Oeneis macounii
22/06/09
British Columbia
Chetwynd
55.62
-121.31
ma87
OEN065-14
Oeneis macounii
22/06/09
British Columbia
Chetwynd
55.62
-121.31
ma4
OEN066-14
Oeneis macounii
22/06/09
British Columbia
Chetwynd
55.62
-121.31
ma88
OEN067-14
Oeneis macounii
22/06/09
British Columbia
Chetwynd
55.62
-121.31
ma4
OEN068-14
Oeneis macounii
22/06/09
British Columbia
Chetwynd
55.62
-121.31
ma4
OEN069-14
Oeneis macounii
22/06/09
British Columbia
Chetwynd
55.62
-121.31
ma67
OEN070-14
Oeneis macounii
26/06/09
Alberta
Cochrane
51.46
-115.04
ma4
OEN071-14
Oeneis macounii
01/07/11
Saskatchewan
Creighton
54.87
-102.26
ma4
OEN072-14
Oeneis macounii
01/07/11
Saskatchewan
Creighton
54.87
-102.26
ma4
OEN073-14
Oeneis macounii
01/07/09
Saskatchewan
Creighton
54.87
-102.26
ma1
OEN074-14
Oeneis macounii
01/07/09
Saskatchewan
Creighton
54.87
-102.26
ma13
OEN075-14
Oeneis macounii
18/06/09
Alberta
Devon
53.4
-113.76
ma10
OEN076-14
Oeneis macounii
18/06/09
Alberta
Devon
53.4
-113.76
ma4
OEN077-14
Oeneis macounii
18/06/09
Alberta
Devon
53.4
-113.76
ma4
OEN078-14
Oeneis macounii
18/06/09
Alberta
Devon
53.4
-113.76
ma4
OEN079-14
Oeneis macounii
18/06/09
Alberta
Devon
53.4
-113.76
ma4
OEN080-14
Oeneis macounii
18/06/09
Alberta
Devon
53.4
-113.76
ma4
OEN081-14
Oeneis macounii
16/06/10
Ontario
Ear Falls
50.62
-93.2
ma31
OEN082-14
Oeneis macounii
16/06/10
Ontario
Ear Falls
50.62
-93.2
ma32
OEN083-14
Oeneis macounii
16/06/10
Ontario
Ear Falls
50.62
-93.2
ma28
OEN084-14
Oeneis macounii
16/06/10
Ontario
Ear Falls
50.62
-93.2
ma33
OEN085-14
Oeneis macounii
16/06/10
Ontario
Ear Falls
50.62
-93.2
ma34
OEN086-14
Oeneis macounii
24/06/11
Northwest Territories
Enterprise
60.34
-116.46
ma44
OEN087-14
Oeneis macounii
26/06/11
Alberta
Fort Assiniboine
54.47
-114.55
ma89
OEN088-14
Oeneis macounii
26/06/11
Alberta
Fort Assiniboine
54.47
-114.55
ma20
OEN089-14
Oeneis macounii
26/06/11
Alberta
Fort Assiniboine
54.47
-114.55
ma90
OEN090-14
Oeneis macounii
26/06/11
Alberta
Fort Assiniboine
54.47
-114.55
ma89
OEN091-14
Oeneis macounii
26/06/11
Alberta
Fort Assiniboine
54.47
-114.55
ma4
OEN092-14
Oeneis macounii
26/06/11
Alberta
Fort Assiniboine
54.47
-114.55
ma4
OEN093-14
Oeneis macounii
26/06/11
Alberta
Fort Assiniboine
54.47
-114.55
ma4
175
BOLD
Process ID
Species
Collection
Date
State/Province
Region
Lat
Long
Haplotype
ID
OEN094-14
Oeneis macounii
26/06/11
Alberta
Fort Assiniboine
54.47
-114.55
ma91
OEN095-14
Oeneis macounii
25/06/11
Alberta
Fort Vermilion
58.42
-116.15
ma92
OEN096-14
Oeneis macounii
23/06/09
Alberta
Grand Prairie
55.09
-118.81
ma100
OEN097-14
Oeneis macounii
23/06/09
Alberta
Grand Prairie
55.09
-118.81
ma4
OEN098-14
Oeneis macounii
23/06/09
Alberta
Grand Prairie
55.09
-118.81
ma4
OEN099-14
Oeneis macounii
02/07/11
Manitoba
Grand Rapids
53.56
-99.34
ma54
OEN100-14
Oeneis macounii
19/06/09
Saskatchewan
Harlan
53.62
-109.88
ma11
OEN101-14
Oeneis macounii
19/06/09
Saskatchewan
Harlan
53.62
-109.88
ma4
OEN102-14
Oeneis macounii
19/06/09
Saskatchewan
Harlan
53.62
-109.88
ma12
OEN103-14
Oeneis macounii
19/06/09
Saskatchewan
Harlan
53.62
-109.88
ma10
OEN104-14
Oeneis macounii
19/06/09
Saskatchewan
Harlan
53.62
-109.88
ma4
OEN105-14
Oeneis macounii
19/06/09
Saskatchewan
Harlan
53.62
-109.88
ma22
OEN106-14
Oeneis macounii
24/06/11
Northwest Territories
Hay River
60.53
-114.4
ma42
OEN107-14
Oeneis macounii
24/06/11
Northwest Territories
Hay River
60.53
-114.4
ma4
OEN108-14
Oeneis macounii
24/06/11
Northwest Territories
Hay River
60.53
-114.4
ma42
OEN109-14
Oeneis macounii
24/06/11
Northwest Territories
Hay River
60.53
-114.4
ma42
OEN110-14
Oeneis macounii
24/06/11
Northwest Territories
Hay River
60.53
-114.4
ma43
OEN111-14
Oeneis macounii
24/06/11
Northwest Territories
Hay River
60.53
-114.4
ma42
OEN112-14
Oeneis macounii
24/06/11
Northwest Territories
Hay River
60.53
-114.4
ma43
OEN113-14
Oeneis macounii
24/06/09
Alberta
Hinton
53.41
-117.79
ma4
OEN114-14
Oeneis macounii
24/06/09
Alberta
Hinton
53.41
-117.79
ma93
OEN115-14
Oeneis macounii
24/06/09
Alberta
Hinton
53.41
-117.79
ma4
OEN116-14
Oeneis macounii
24/06/09
Alberta
Hinton
53.41
-117.79
ma99
OEN117-14
Oeneis macounii
24/06/09
Alberta
Hinton
53.41
-117.79
ma93
OEN118-14
Oeneis macounii
24/06/09
Alberta
Hinton
53.41
-117.79
ma93
OEN119-14
Oeneis macounii
24/06/09
Alberta
Hinton
53.41
-117.79
ma4
OEN120-14
Oeneis macounii
21/06/09
Alberta
Hondo
55.04
-114.05
ma4
OEN121-14
Oeneis macounii
21/06/09
Alberta
Hondo
55.04
-114.05
ma101
OEN122-14
Oeneis macounii
21/06/09
Alberta
Hondo
55.04
-114.05
ma4
OEN123-14
Oeneis macounii
21/06/09
Alberta
Hondo
55.04
-114.05
ma4
OEN124-14
Oeneis macounii
21/06/09
Alberta
Hondo
55.04
-114.05
ma10
OEN125-14
Oeneis macounii
20/06/11
British Columbia
Huckleberry Butte
51.55
-121.11
ma70
OEN126-14
Oeneis macounii
17/06/10
British Columbia
Huckleberry Butte
51.55
-121.11
ma79
OEN127-14
Oeneis macounii
27/06/10
British Columbia
Huckleberry Butte
51.55
-121.11
ma80
OEN128-14
Oeneis macounii
27/06/10
British Columbia
Huckleberry Butte
51.55
-121.11
ma81
176
BOLD
Process ID
Species
Collection
Date
State/Province
Region
Lat
Long
Haplotype
ID
OEN129-14
Oeneis macounii
27/06/10
British Columbia
Huckleberry Butte
51.55
-121.11
ma70
OEN130-14
Oeneis macounii
27/06/10
British Columbia
Huckleberry Butte
51.55
-121.11
ma4
OEN131-14
Oeneis macounii
27/06/10
British Columbia
Huckleberry Butte
51.55
-121.11
ma82
OEN132-14
Oeneis macounii
27/06/10
British Columbia
Huckleberry Butte
51.55
-121.11
ma70
OEN133-14
Oeneis macounii
27/06/10
British Columbia
Huckleberry Butte
51.55
-121.11
ma83
OEN134-14
Oeneis macounii
27/06/10
British Columbia
Huckleberry Butte
51.55
-121.11
ma4
OEN135-14
Oeneis macounii
27/06/10
British Columbia
Huckleberry Butte
51.55
-121.11
ma4
OEN136-14
Oeneis macounii
27/06/10
British Columbia
Huckleberry Butte
51.55
-121.11
ma71
OEN137-14
Oeneis macounii
27/06/10
British Columbia
Huckleberry Butte
51.55
-121.11
ma84
OEN138-14
Oeneis macounii
17/06/10
British Columbia
Huckleberry Butte
51.55
-121.11
ma70
OEN139-14
Oeneis macounii
17/06/10
British Columbia
Huckleberry Butte
51.55
-121.11
ma78
OEN140-14
Oeneis macounii
29/06/09
Alberta
Irwin Hill
51.29
-114.69
ma4
OEN141-14
Oeneis macounii
29/06/09
Alberta
Irwin Hill
51.29
-114.69
ma4
OEN142-14
Oeneis macounii
29/06/09
Alberta
Irwin Hill
51.29
-114.69
ma4
OEN143-14
Oeneis macounii
29/06/09
Alberta
Irwin Hill
51.29
-114.69
ma4
OEN144-14
Oeneis macounii
29/06/09
Alberta
Irwin Hill
51.28
-114.68
ma4
OEN145-14
Oeneis macounii
29/06/09
Alberta
Irwin Hill
51.28
-114.68
ma4
OEN146-14
Oeneis macounii
29/06/09
Alberta
Irwin Hill
51.28
-114.68
ma4
OEN147-14
Oeneis macounii
29/06/09
Alberta
Irwin Hill
51.28
-114.68
ma94
OEN148-14
Oeneis macounii
29/06/09
Alberta
Irwin Hill
51.28
-114.68
ma95
OEN149-14
Oeneis macounii
26/07/05
British Columbia
Jesmond Mountain
51.31
-121.92
ma67
OEN150-14
Oeneis macounii
26/07/05
British Columbia
Jesmond Mountain
51.31
-121.92
ma4
OEN151-14
Oeneis macounii
01/07/10
Alberta
Kananaskis
51.04
-115
ma4
OEN152-14
Oeneis macounii
01/07/10
Alberta
Kananaskis
51.04
-115
ma4
OEN153-14
Oeneis macounii
01/07/10
Alberta
Kananaskis
51.04
-115
ma4
OEN154-14
Oeneis macounii
01/07/10
Alberta
Kananaskis
51.04
-115
ma65
OEN155-14
Oeneis macounii
01/07/10
Alberta
Kananaskis
51.04
-115
ma4
OEN156-14
Oeneis macounii
01/07/10
Alberta
Kananaskis
51.04
-115
ma4
OEN157-14
Oeneis macounii
01/07/10
Alberta
Kananaskis
51.04
-115
ma4
OEN158-14
Oeneis macounii
01/07/10
Alberta
Kananaskis
51.04
-115
ma66
OEN159-14
Oeneis macounii
01/07/10
Alberta
Kananaskis
51.04
-115
ma4
OEN160-14
Oeneis macounii
30/06/11
Saskatchewan
LaRonge
54.8
-105.3
ma6
OEN161-14
Oeneis macounii
30/06/11
Saskatchewan
LaRonge
54.8
-105.3
ma7
OEN162-14
Oeneis macounii
01/07/11
Saskatchewan
LaRonge
54.17
-105.96
ma4
OEN163-14
Oeneis macounii
20/06/09
Saskatchewan
Loon Lake
54.03
-109.33
ma9
177
BOLD
Process ID
Species
Collection
Date
State/Province
Region
Lat
Long
Haplotype
ID
OEN164-14
Oeneis macounii
20/06/09
Saskatchewan
Loon Lake
54.03
-109.33
ma10
OEN165-14
Oeneis macounii
20/06/09
Saskatchewan
Loon Lake
54.03
-109.33
ma10
OEN166-14
Oeneis macounii
20/06/09
Saskatchewan
Loon Lake
54.03
-109.33
ma4
OEN167-14
Oeneis macounii
30/06/11
Saskatchewan
Macdowall
52.93
-106.05
ma1
OEN168-14
Oeneis macounii
30/06/11
Saskatchewan
Macdowall
52.93
-106.05
ma1
OEN169-14
Oeneis macounii
30/06/11
Saskatchewan
Macdowall
52.93
-106.05
ma1
OEN170-14
Oeneis macounii
30/06/11
Saskatchewan
Macdowall
52.93
-106.05
ma2
OEN171-14
Oeneis macounii
30/06/11
Saskatchewan
Macdowall
52.93
-106.05
ma3
OEN172-14
Oeneis macounii
30/06/11
Saskatchewan
Macdowall
52.93
-106.05
ma1
OEN173-14
Oeneis macounii
30/06/11
Saskatchewan
Macdowall
52.93
-106.05
ma1
OEN174-14
Oeneis macounii
30/06/11
Saskatchewan
Macdowall
52.93
-106.05
ma5
OEN175-14
Oeneis macounii
30/06/11
Saskatchewan
Macdowall
52.93
-106.05
ma8
OEN176-14
Oeneis macounii
30/06/11
Saskatchewan
Macdowall
52.93
-106.05
ma4
OEN177-14
Oeneis macounii
30/06/09
Manitoba
Mafeking
52.6
-101.09
ma4
OEN178-14
Oeneis macounii
30/06/09
Manitoba
Mafeking
52.8
-101.1
ma4
OEN179-14
Oeneis macounii
30/07/11
British Columbia
McCullough Lake
49.78
-119.18
ma72
OEN180-14
Oeneis macounii
27/06/08
Ontario
McKenzie Station
48.55
-88.94
ma25
OEN181-14
Oeneis macounii
27/06/08
Ontario
McKenzie Station
48.55
-88.94
ma28
OEN182-14
Oeneis macounii
27/06/08
Ontario
McKenzie Station
48.55
-88.94
ma40
OEN183-14
Oeneis macounii
27/06/08
Ontario
McKenzie Station
48.55
-88.94
ma41
OEN184-14
Oeneis macounii
27/06/08
Ontario
McKenzie Station
48.55
-88.94
ma39
OEN185-14
Oeneis macounii
07/07/06
Alberta
Moose Mountain Road
50.88
-114.75
ma4
OEN186-14
Oeneis macounii
06/07/02
Alberta
Moose Mountain
53.49
-114.75
ma68
OEN187-14
Oeneis macounii
28/06/09
Saskatchewan
Narrow Hills
53.93
-104.64
ma1
OEN188-14
Oeneis macounii
28/06/09
Saskatchewan
Narrow Hills
53.93
-104.64
ma14
OEN189-14
Oeneis macounii
28/06/09
Saskatchewan
Narrow Hills
53.93
-104.64
ma14
OEN190-14
Oeneis macounii
21/06/11
British Columbia
Nazko
53.01
-123.61
ma4
OEN191-14
Oeneis macounii
22/06/10
British Columbia
Nazko
52.74
-123.61
ma70
OEN192-14
Oeneis macounii
22/06/10
British Columbia
Nazko
52.74
-123.61
ma4
OEN193-14
Oeneis macounii
25/06/04
British Columbia
Pink Mountain
57.12
-122.68
ma23
OEN194-14
Oeneis macounii
28/06/09
Saskatchewan
Prince Albert
53.28
-105.51
ma15
OEN195-14
Oeneis macounii
28/06/09
Saskatchewan
Prince Albert
53.28
-105.51
ma4
OEN196-14
Oeneis macounii
28/06/09
Saskatchewan
Prince Albert
53.28
-105.51
ma10
OEN197-14
Oeneis macounii
28/06/09
Saskatchewan
Prince Albert
53.28
-105.51
ma16
OEN198-14
Oeneis macounii
28/06/09
Saskatchewan
Prince Albert
53.28
-105.51
ma17
178
BOLD
Process ID
Species
Collection
Date
State/Province
Region
Lat
Long
Haplotype
ID
OEN199-14
Oeneis macounii
28/06/09
Saskatchewan
Prince Albert
53.28
-105.51
ma17
OEN200-14
Oeneis macounii
28/06/09
Saskatchewan
Prince Albert
53.28
-105.51
ma18
OEN201-14
Oeneis macounii
19/06/09
Alberta
Redwater Dunes
53.9
-112.98
ma4
OEN202-14
Oeneis macounii
19/06/09
Alberta
Redwater Dunes
53.9
-112.98
ma4
OEN203-14
Oeneis macounii
19/06/09
Alberta
Redwater Dunes
53.9
-112.98
ma4
OEN204-14
Oeneis macounii
19/06/09
Alberta
Redwater Dunes
53.9
-112.98
ma4
OEN205-14
Oeneis macounii
19/06/09
Alberta
Redwater Dunes
53.9
-112.98
ma4
OEN206-14
Oeneis macounii
19/06/09
Alberta
Redwater Dunes
53.9
-112.98
ma102
OEN207-14
Oeneis macounii
19/06/09
Alberta
Redwater Dunes
53.9
-112.98
ma4
OEN208-14
Oeneis macounii
19/06/09
Alberta
Redwater Dunes
53.9
-112.98
ma89
OEN209-14
Oeneis macounii
19/06/09
Alberta
Redwater Dunes
53.9
-112.98
ma4
OEN210-14
Oeneis macounii
29/06/09
Manitoba
Riding Mountain National Park
50.68
-99.89
ma28
OEN211-14
Oeneis macounii
29/06/09
Manitoba
Riding Mountain National Park
50.68
-99.89
ma55
OEN212-14
Oeneis macounii
29/06/09
Manitoba
Riding Mountain National Park
50.68
-99.89
ma28
OEN213-14
Oeneis macounii
29/06/09
Manitoba
Riding Mountain National Park
50.68
-99.89
ma28
OEN214-14
Oeneis macounii
29/06/09
Manitoba
Riding Mountain National Park
50.68
-99.89
ma4
OEN215-14
Oeneis macounii
29/06/09
Manitoba
Riding Mountain National Park
50.68
-99.89
ma55
OEN216-14
Oeneis macounii
29/06/09
Manitoba
Riding Mountain National Park
50.68
-99.89
ma28
OEN217-14
Oeneis macounii
29/06/09
Manitoba
Riding Mountain National Park
50.68
-99.89
ma4
OEN218-14
Oeneis macounii
02/07/11
Manitoba
Route 39
54.58
-100.63
ma7
OEN219-14
Oeneis macounii
02/07/11
Manitoba
Route 39
54.58
-100.63
ma4
OEN220-14
Oeneis macounii
02/07/11
Manitoba
Route 39
54.58
-100.63
ma4
OEN221-14
Oeneis macounii
15/06/10
Ontario
Route 647
49.89
-93.45
ma25
OEN222-14
Oeneis macounii
15/06/10
Ontario
Route 647
49.89
-93.45
ma26
OEN223-14
Oeneis macounii
15/06/10
Ontario
Route 647
49.89
-93.45
ma27
OEN224-14
Oeneis macounii
15/06/10
Ontario
Route 647
49.89
-93.45
ma28
OEN225-14
Oeneis macounii
15/06/10
Ontario
Route 647
49.89
-93.45
ma28
OEN226-14
Oeneis macounii
15/06/10
Ontario
Route 647
49.89
-93.45
ma29
OEN227-14
Oeneis macounii
15/06/10
Ontario
Route 647
49.89
-93.45
ma4
OEN228-14
Oeneis macounii
15/06/10
Ontario
Route 647
49.89
-93.45
ma4
OEN229-14
Oeneis macounii
15/06/10
Ontario
Route 647
49.89
-93.45
ma4
OEN230-14
Oeneis macounii
23/06/08
Manitoba
Sandilands Provincial Forest
49.39
-96.19
ma28
OEN231-14
Oeneis macounii
23/06/08
Manitoba
Sandilands Provincial Forest
49.39
-96.19
ma4
OEN232-14
Oeneis macounii
23/06/08
Manitoba
Sandilands Provincial Forest
49.39
-96.19
ma28
OEN233-14
Oeneis macounii
23/06/08
Manitoba
Sandilands Provincial Forest
49.39
-96.19
ma27
179
BOLD
Process ID
Species
Collection
Date
State/Province
Region
Lat
Long
Haplotype
ID
OEN234-14
Oeneis macounii
23/06/08
Manitoba
Sandilands Provincial Forest
49.39
-96.19
ma4
OEN235-14
Oeneis macounii
23/06/08
Manitoba
Sandilands Provincial Forest
49.27
-96.1
ma28
OEN236-14
Oeneis macounii
23/06/08
Manitoba
Sandilands Provincial Forest
49.27
-96.1
ma59
OEN237-14
Oeneis macounii
23/06/08
Manitoba
Sandilands Provincial Forest
49.27
-96.1
ma4
OEN238-14
Oeneis macounii
23/06/08
Manitoba
Sandilands Provincial Forest
49.27
-96.1
ma27
OEN239-14
Oeneis macounii
23/06/08
Manitoba
Sandilands Provincial Forest
49.27
-96.1
ma26
OEN240-14
Oeneis macounii
23/06/08
Manitoba
Sandilands Provincial Forest
49.21
-96.32
ma60
OEN241-14
Oeneis macounii
23/06/08
Manitoba
Sandilands Provincial Forest
49.21
-96.32
ma28
OEN242-14
Oeneis macounii
23/06/08
Manitoba
Sandilands Provincial Forest
49.21
-96.32
ma61
OEN243-14
Oeneis macounii
23/06/08
Manitoba
Sandilands Provincial Forest
49.21
-96.32
ma28
OEN244-14
Oeneis macounii
23/06/08
Manitoba
Sandilands Provincial Forest
49.21
-96.32
ma28
OEN245-14
Oeneis macounii
23/06/08
Manitoba
Sandilands Provincial Forest
49.21
-96.32
ma62
OEN246-14
Oeneis macounii
23/06/08
Manitoba
Sandilands Provincial Forest
49.21
-96.32
ma60
OEN247-14
Oeneis macounii
23/06/08
Manitoba
Sandilands Provincial Forest
49.21
-96.32
ma4
OEN248-14
Oeneis macounii
23/06/08
Manitoba
Sandilands Provincial Forest
49.21
-96.32
ma4
OEN249-14
Oeneis macounii
23/06/08
Manitoba
Sandilands Provincial Forest
49.21
-96.32
ma28
OEN250-14
Oeneis macounii
23/06/08
Manitoba
Sandilands Provincial Forest
49.21
-96.32
ma63
OEN251-14
Oeneis macounii
28/06/10
Alberta
Sibbald Lake
51.05
-114.29
ma4
OEN252-14
Oeneis macounii
28/06/10
Alberta
Sibbald Lake
51.05
-114.29
ma4
OEN253-14
Oeneis macounii
02/07/11
Manitoba
St. Maarten Junction
51.67
-98.74
ma28
OEN254-14
Oeneis macounii
27/06/08
Ontario
Stanley
48.38
-89.56
ma36
OEN255-14
Oeneis macounii
17/06/10
Ontario
Stanley
48.38
-89.56
ma4
OEN256-14
Oeneis macounii
27/06/08
Ontario
Stanley
48.38
-89.56
ma36
OEN257-14
Oeneis macounii
27/06/08
Ontario
Stanley
48.38
-89.56
ma25
OEN258-14
Oeneis macounii
27/06/08
Ontario
Stanley
48.38
-89.56
ma28
OEN259-14
Oeneis macounii
27/06/08
Ontario
Stanley
48.38
-89.56
ma36
OEN260-14
Oeneis macounii
30/06/09
Manitoba
Westray
53.4
-101.29
ma1
OEN261-14
Oeneis macounii
30/06/09
Manitoba
Westray
53.4
-101.29
ma56
OEN262-14
Oeneis macounii
30/06/09
Manitoba
Westray
53.4
-101.29
ma57
OEN263-14
Oeneis macounii
30/06/09
Manitoba
Westray
53.4
-101.29
ma4
OEN264-14
Oeneis macounii
30/06/09
Manitoba
Westray
53.4
-101.29
ma57
OEN265-14
Oeneis macounii
30/06/09
Manitoba
Westray
53.4
-101.29
ma44
OEN266-14
Oeneis macounii
30/06/09
Manitoba
Westray
53.4
-101.29
ma4
OEN267-14
Oeneis macounii
30/06/09
Manitoba
Westray
53.4
-101.29
ma58
OEN268-14
Oeneis macounii
17/06/10
Ontario
Stanley
48.39
-89.6
ma28
180
BOLD
Process ID
Species
Collection
Date
State/Province
Region
Lat
Long
Haplotype
ID
OEN269-14
Oeneis macounii
17/06/10
Ontario
Stanley
48.39
-89.6
ma28
OEN270-14
Oeneis macounii
17/06/10
Ontario
Stanley
48.39
-89.6
ma30
OEN271-14
Oeneis macounii
27/06/08
Ontario
Stanley
48.39
-89.6
ma35
OEN272-14
Oeneis macounii
27/06/08
Ontario
Stanley
48.39
-89.6
ma36
OEN273-14
Oeneis macounii
27/06/08
Ontario
Stanley
48.39
-89.6
ma27
OEN274-14
Oeneis macounii
27/06/08
Ontario
Stanley
48.39
-89.6
ma37
OEN275-14
Oeneis macounii
24/06/08
Minnesota
Beltrami Island State Forest
48.71
-95.44
ma45
OEN276-14
Oeneis macounii
24/06/08
Minnesota
Beltrami Island State Forest
48.71
-95.44
ma46
OEN277-14
Oeneis macounii
24/06/08
Minnesota
Beltrami Island State Forest
48.71
-95.44
ma47
OEN278-14
Oeneis macounii
24/06/08
Minnesota
Beltrami Island State Forest
48.71
-95.44
ma28
OEN279-14
Oeneis macounii
24/06/08
Minnesota
Beltrami Island State Forest
48.71
-95.44
ma47
OEN280-14
Oeneis macounii
24/06/08
Minnesota
Beltrami Island State Forest
48.71
-95.44
ma47
OEN281-14
Oeneis macounii
24/06/08
Minnesota
Beltrami Island State Forest
48.71
-95.44
ma28
OEN282-14
Oeneis macounii
24/06/08
Minnesota
Beltrami Island State Forest
48.71
-95.44
ma51
OEN283-14
Oeneis macounii
24/06/08
Minnesota
Beltrami Island State Forest
48.71
-95.44
ma28
OEN284-14
Oeneis macounii
24/06/08
Minnesota
Beltrami Island State Forest
48.71
-95.44
ma52
OEN285-14
Oeneis macounii
07/06/12
Michigan
Isle Royale National Park
48.16
-88.5
ma53
OEN286-14
Oeneis macounii
07/06/12
Michigan
Isle Royale National Park
48.16
-88.5
ma28
OEN287-14
Oeneis macounii
07/06/12
Michigan
Isle Royale National Park
48.16
-88.5
ma53
OEN288-14
Oeneis macounii
07/06/12
Michigan
Isle Royale National Park
48.16
-88.5
ma53
OEN289-14
Oeneis macounii
26/06/08
Minnesota
St. Louis County
47.27
-91.85
ma28
OEN290-14
Oeneis macounii
26/06/08
Minnesota
St. Louis County
47.27
-91.85
ma27
OEN291-14
Oeneis macounii
26/06/08
Minnesota
St. Louis County
47.27
-91.85
ma28
OEN292-14
Oeneis macounii
26/06/08
Minnesota
St. Louis County
47.27
-91.85
ma28
OEN293-14
Oeneis macounii
26/06/08
Minnesota
St. Louis County
47.27
-91.85
ma28
OEN294-14
Oeneis macounii
26/06/08
Minnesota
St. Louis County
47.27
-91.85
ma28
OEN295-14
Oeneis macounii
26/06/08
Minnesota
St. Louis County
47.27
-91.85
ma29
OEN296-14
Oeneis macounii
26/06/08
Minnesota
St. Louis County
47.27
-91.85
ma48
OEN297-14
Oeneis macounii
26/06/08
Minnesota
St. Louis County
47.27
-91.85
ma49
OEN298-14
Oeneis macounii
26/06/08
Minnesota
St. Louis County
47.27
-91.85
ma50
OEN299-14
Oeneis melissa semidea
02/07/11
New Hampshire
Mount Washington
44.264
-71.306
wma1
OEN300-14
Oeneis melissa semidea
02/07/11
New Hampshire
Mount Washington
44.263
-71.308
wma1
OEN301-14
Oeneis melissa semidea
02/07/11
New Hampshire
Mount Washington
44.263
-71.308
wma1
OEN302-14
Oeneis melissa semidea
04/07/11
New Hampshire
Mount Washington
44.265
-71.306
wma1
OEN303-14
Oeneis melissa semidea
04/07/11
New Hampshire
Mount Washington
44.265
-71.305
wma1
181
BOLD
Process ID
Species
Collection
Date
State/Province
Region
Lat
Long
Haplotype
ID
OEN304-14
Oeneis melissa semidea
05/07/11
New Hampshire
Mount Washington
44.261
-71.308
wma1
OEN305-14
Oeneis melissa semidea
05/07/11
New Hampshire
Mount Washington
44.247
-71.306
wma1
OEN306-14
Oeneis melissa semidea
01/07/11
New Hampshire
Mount Washington
NA
NA
wma1
OEN307-14
Oeneis melissa semidea
02/07/11
New Hampshire
Mount Washington
44.785
-71.407
wma1
OEN308-14
Oeneis melissa semidea
08/07/11
New Hampshire
Mount Washington
44.265
-71.306
wma1
OEN309-14
Oeneis melissa semidea
01/07/11
New Hampshire
Mount Washington
NA
NA
wma1
OEN310-14
Oeneis melissa semidea
01/07/11
New Hampshire
Mount Washington
NA
NA
wma1
OEN311-14
Oeneis melissa semidea
01/07/11
New Hampshire
Mount Washington
NA
NA
wma1
OEN312-14
Oeneis melissa semidea
01/07/11
New Hampshire
Mount Washington
NA
NA
wma1
OEN313-14
Oeneis melissa semidea
22/06/11
New Hampshire
Mount Washington
44.28
-71.29
wma1
OEN314-14
Oeneis melissa semidea
22/06/11
New Hampshire
Mount Washington
44.28
-71.29
wma1
OEN315-14
Oeneis melissa semidea
27/06/11
New Hampshire
Mount Washington
44.279
-71.29
wma1
OEN316-14
Oeneis melissa semidea
27/06/11
New Hampshire
Mount Washington
44.279
-71.29
wma1
OEN317-14
Oeneis melissa semidea
27/06/11
New Hampshire
Mount Washington
44.279
-71.29
wma1
OEN318-14
Oeneis melissa semidea
27/06/11
New Hampshire
Mount Washington
44.278
-71.294
wma1
OEN319-14
Oeneis melissa semidea
27/06/11
New Hampshire
Mount Washington
44.279
-71.295
wma1
OEN320-14
Oeneis melissa semidea
02/07/11
New Hampshire
Mount Washington
44.277
-71.293
wma1
OEN321-14
Oeneis melissa semidea
02/07/11
New Hampshire
Mount Washington
44.278
-71.294
wma1
OEN322-14
Oeneis melissa semidea
06/07/11
New Hampshire
Mount Washington
44.279
-71.295
wma1
OEN323-14
Oeneis melissa semidea
08/07/11
New Hampshire
Mount Washington
44.279
-71.298
wma1
OEN324-14
Oeneis melissa semidea
30/06/12
New Hampshire
Mount Washington
44.263
-71.31
wma2
OEN325-14
Oeneis melissa semidea
30/06/12
New Hampshire
Mount Washington
44.262
-71.31
wma1
OEN326-14
Oeneis melissa semidea
30/06/12
New Hampshire
Mount Washington
44.262
-71.31
wma1
OEN327-14
Oeneis melissa semidea
30/06/12
New Hampshire
Mount Washington
44.263
-71.31
wma1
OEN328-14
Oeneis melissa semidea
30/06/12
New Hampshire
Mount Washington
44.264
-71.305
wma1
OEN329-14
Oeneis melissa semidea
09/07/12
New Hampshire
Mount Washington
44.262
-71.308
wma1
OEN330-14
Oeneis melissa semidea
10/07/12
New Hampshire
Mount Washington
44.264
-71.307
wma1
OEN331-14
Oeneis melissa semidea
10/07/12
New Hampshire
Mount Washington
44.263
-71.308
wma1
OEN332-14
Oeneis melissa semidea
10/07/12
New Hampshire
Mount Washington
44.262
-71.308
wma1
OEN333-14
Oeneis melissa semidea
22/06/12
New Hampshire
Mount Washington
44.28
-71.292
wma1
OEN334-14
Oeneis melissa semidea
22/06/12
New Hampshire
Mount Washington
44.28
-71.29
wma1
OEN335-14
Oeneis melissa semidea
22/06/12
New Hampshire
Mount Washington
44.28
-71.286
wma1
OEN336-14
Oeneis melissa semidea
22/06/12
New Hampshire
Mount Washington
44.279
-71.291
wma1
OEN337-14
Oeneis melissa semidea
22/06/12
New Hampshire
Mount Washington
44.278
-71.291
wma1
OEN338-14
Oeneis melissa semidea
22/06/12
New Hampshire
Mount Washington
44.279
-71.295
wma1
182
BOLD
Process ID
Species
Collection
Date
State/Province
Region
Lat
Long
Haplotype
ID
OEN339-14
Oeneis melissa semidea
29/06/12
New Hampshire
Mount Washington
44.28
-71.299
wma1
OEN340-14
Oeneis melissa semidea
29/06/12
New Hampshire
Mount Washington
44.279
-71.299
wma1
OEN341-14
Oeneis melissa semidea
30/06/12
New Hampshire
Mount Washington
44.278
-71.291
wma1
OEN342-14
Oeneis melissa semidea
23/06/12
New Hampshire
Mount Washington
NA
NA
wma1
OEN343-14
Oeneis melissa semidea
23/06/12
New Hampshire
Mount Washington
NA
NA
wma1
OEN344-14
Oeneis melissa semidea
23/06/12
New Hampshire
Mount Washington
44.271
-71.306
wma1
OEN345-14
Oeneis melissa semidea
23/06/12
New Hampshire
Mount Washington
44.271
-71.306
wma1
OEN346-14
Oeneis melissa semidea
23/06/12
New Hampshire
Mount Washington
44.27
-71.307
wma1
OEN347-14
Oeneis melissa semidea
23/06/12
New Hampshire
Mount Washington
44.272
-71.306
wma1
OEN348-14
Oeneis melissa semidea
23/06/12
New Hampshire
Mount Washington
44.272
-71.306
wma1
OEN349-14
Oeneis melissa semidea
23/06/12
New Hampshire
Mount Washington
44.272
-71.306
wma1
OEN350-14
Oeneis melissa semidea
24/06/12
New Hampshire
Mount Jefferson
44.302
-71.317
wma1
OEN351-14
Oeneis melissa semidea
24/06/12
New Hampshire
Mount Jefferson
44.301
-71.314
wma1
OEN352-14
Oeneis melissa semidea
24/06/12
New Hampshire
Mount Jefferson
44.299
-71.315
wma1
OEN353-14
Oeneis melissa semidea
06/07/12
New Hampshire
Mount Jefferson
44.298
-71.317
wma1
OEN354-14
Oeneis melissa semidea
06/07/12
New Hampshire
Mount Jefferson
44.301
-71.314
wma3
OEN355-14
Oeneis melissa semidea
06/07/12
New Hampshire
Mount Jefferson
44.3
-71.314
wma1
OEN356-14
Oeneis melissa semidea
06/07/12
New Hampshire
Mount Jefferson
44.301
-71.315
wma1
OEN357-14
Oeneis melissa semidea
06/07/12
New Hampshire
Mount Jefferson
44.301
-71.315
wma1
OEN358-14
Oeneis melissa semidea
06/07/12
New Hampshire
Mount Jefferson
44.3
-71.314
wma1
OEN359-14
Oeneis melissa semidea
06/07/12
New Hampshire
Mount Jefferson
44.3
-71.314
wma1
OEN360-14
Oeneis melissa semidea
27/06/11
New Hampshire
Mount Washington
44.27
-71.307
wma1
OEN361-14
Oeneis melissa semidea
27/06/11
New Hampshire
Mount Washington
44.27
-71.307
wma1
OEN362-14
Oeneis melissa semidea
27/06/11
New Hampshire
Mount Washington
44.27
-71.308
wma1
OEN363-14
Oeneis melissa semidea
27/06/11
New Hampshire
Mount Washington
44.271
-71.308
wma1
OEN364-14
Oeneis melissa semidea
27/06/11
New Hampshire
Mount Washington
NA
NA
wma1
OEN365-14
Oeneis melissa semidea
02/07/11
New Hampshire
Mount Washington
44.271
-71.308
wma4
OEN366-14
Oeneis melissa semidea
02/07/11
New Hampshire
Mount Washington
44.271
-71.308
wma1
OEN367-14
Oeneis melissa semidea
05/07/11
New Hampshire
Mount Washington
44.27
-71.307
wma1
OEN368-14
Oeneis melissa semidea
05/07/11
New Hampshire
Mount Washington
44.271
-71.306
wma1
OEN369-14
Oeneis melissa semidea
01/07/11
New Hampshire
Mount Washington
44.272
-71.308
wma1
OEN370-14
Oeneis melissa semidea
05/07/11
New Hampshire
Mount Washington
44.273
-71.306
wma1
OEN371-14
Oeneis melissa semidea
01/07/11
New Hampshire
Mount Washington
NA
NA
wma1
183