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POPULATION GENETIC DIFFERENTIATION, MATING SYSTEM, AND EFFECTIVE POPULATION SIZE OF THE TULIPTREE (LIRIODENDRON TULIPIFERA L.) IN THE
MID-ATLANTIC UNITED STATES
A Dissertation submitted to the Faculty of the
Graduate School of Arts and Sciences of Georgetown University
in partial fulfillment of the requirements for the degree of
Doctor of Philosophy in Biology
By
Ricardo Gutierrez Ozuna, M.Sc.
Washington DC December 19, 2017
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Copyright 2017 by Ricardo Gutierrez Ozuna All Rights Reserved.
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POPULATION GENETIC DIFFERENTIATION, MATING SYSTEM, AND EFFECTIVE POPULATION SIZE OF THE TULIPTREE (LIRIODENDRON TULIPIFERA L.) IN THE
MID-ATLANTIC UNITED STATES
Ricardo Gutierrez Ozuna, M.Sc.
Thesis Advisor: Matthew B. Hamilton, Ph.D.
ABSTRACT
Continuous natural habitats are increasingly transformed into anthropogenic
landscapes—mosaics of natural habitats fragments surrounded by heterogeneous mixtures of
different land use. Anthropogenic habitat transformation is particularly evident in the clearing of
forests. Forests that have reached a great age with little or no anthropogenic disturbance (old-
growth) are increasingly rare. At the same time, urbanization—one of the most readily apparent
human-mediated effects on the landscape—continues to expand, and as a result, an increasing
percentage of forests are becoming part of urban landscapes and ecosystems. The goal of this
thesis is to investigate population genetic effects of anthropogenic landscapes on long-lived
forest trees at different time scales: immediate, through the estimation of the mating system;
short-time, through the estimation of effective population size using linkage disequilibrium based
methods; and long-time, through the estimation of population genetic differentiation. To
accomplish this goal, I researched the tuliptree (Liriodendron tulipifera L.), a tree in the family
Magnoliaceae native to Eastern North America that has a wide geographic distribution in the
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Southeast and mid-Atlantic United States and occurs in diverse habitats, including both old-
growth remnants and forest patches embedded in urban areas where it reproduces and recruits
naturally.
Chapter 1 describes the development and validation of novel L. tulipifera genomic
microsatellite (SSR) genetic marker loci using high-throughput sequencing and bioinformatics
methods. These new SSR markers facilitated empirical study of population genetic patterns in L.
tulipifera. Chapter 2 compares the population genetic differentiation and the mating system
among old-growth forests and urban forest patches. L. tulipifera population genetic
differentiation in the mid-Atlantic U.S. was characterized by a pattern of isolation by distance
(IBD), suggesting that urbanization has not had a major impact on regional genetic
differentiation patterns which have evolved over hundreds of generations in the past. Despite
marked environmental differences between old-growth and urban forest, all populations shared a
fully outcrossed mating system without any variation in self-fertilization that can accompany loss
of pollinators. Chapter 3 compares the strength of random genetic drift estimated from the
effective number of breeders (Nb) from seedlings and from mixed-age reproductively mature
trees (Ne {mixed ages}). These two sampling methods showed similar and small effective population
sizes, suggesting genetic drift is a major determinant of polymorphism within populations and of
genetic differentiation among populations.
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To my mom.
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ACKNOWLEDGMENTS
I am immensely grateful to my advisor, Dr. Matthew Hamilton, for his constant advice
and guidance. I would also like to extend my gratitude to my thesis committee members: Dr.
Gina Wimp, Dr. Martha Weiss, and Dr. John Braverman, whose comments were deep and
insightful, and who worked patiently to ensure I completed the process. I owe a debt of gratitude
to Jack McGuire and Ana Carolina da Silva Antunes for their assistance in lab work.
I would like to express gratitude towards the Smithsonian Environmental Research
Center in Edgewater, Maryland; James Madison’s Montpelier in Orange, Virginia; Western
Shore Conservancy in Prince George County, Maryland; and Saddler's Woods Conservation
Association in Haddon Township, New Jersey for allowing me sampling specimens. I am greatly
indebted to the Georgetown Environment Initiative and the Center for the Environment at
Georgetown University for funding my research. Many thanks go to the Consejo Nacional de
Ciencia y Tecnología (CONACYT) in Mexico for granting me a doctoral fellowship.
I am grateful from the bottom of my heart to the friends I made during this journey for
their encouragement to keep going. My deep appreciation goes especially to Megan Wallen, Xin
Huang, Ewa Krzyszczyk, and Pasha Tabatabai. Last but not least, I would like to thank my
family, in particular my mom, for her endless support and love.
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TABLE OF CONTENTS
INTRODUCTION .......................................................................................................................... 1 !
CHAPTER 1. Identification and characterization of microsatellite loci in the tuliptree,
Liriodendron tulipifera (Magnoliaceae) ....................................................................................... 10
Introduction .............................................................................................................................. 10
Methods and Results ................................................................................................................ 11
Microsatellite development .............................................................................................. 11
Microsatellite data analysis .............................................................................................. 13
Conclusion ............................................................................................................................... 14
!
CHAPTER 2. Population genetic differentiation and mating system of a long-lived tree in old-
growth forests and urban forest patches ....................................................................................... 15
Introduction .............................................................................................................................. 15
Materials and Methods ............................................................................................................. 22
Study species .................................................................................................................... 22
Field collections ............................................................................................................... 24
Genetic analysis ............................................................................................................... 24
Statistical analysis ............................................................................................................ 25
Genetic polymorphism ......................................................................................... 25
Population genetic differentiation ........................................................................ 25
Pattern of genetic differentiation ......................................................................... 27
Mating system ...................................................................................................... 27
Results ...................................................................................................................................... 28
Genetic polymorphism ..................................................................................................... 28
Population genetic differentiation .................................................................................... 28
Pattern of genetic differentiation ..................................................................................... 29
Mating system .................................................................................................................. 29
Discussion ................................................................................................................................ 30
Genetic polymorphism ..................................................................................................... 30
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Population genetic differentiation .................................................................................... 31
Pattern of genetic differentiation ..................................................................................... 33
Mating system .................................................................................................................. 34
CHAPTER 3. Small effective population sizes in a common, widespread, long-lived tree ........ 37
Introduction .............................................................................................................................. 37
Materials and Methods ............................................................................................................. 43
Study species .................................................................................................................... 43
Field collections ............................................................................................................... 44
Genetic analysis ............................................................................................................... 45
Effective number of breeders and effective population size ............................................ 45
Life history simulations ................................................................................................... 47
Results ...................................................................................................................................... 48
Discussion ................................................................................................................................ 49
!APPENDIX A: Tables ................................................................................................................. 56
!APPENDIX B: Figures ................................................................................................................ 72
!REFERENCES ............................................................................................................................ 75
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LIST OF TABLES
Table 1.1. Characteristics of 23 polymorphic genomic microsatellite loci isolated from
Liriodendron tulipifera ................................................................................................................ 56
Table 1.2. Locality and voucher information for the Liriodendron tulipifera samples used for the
development of polymorphic microsatellite markers .................................................................. 58
Table 1.3. Genetic properties by individual and pooled sampled locations of 23 polymorphic
microsatellite markers developed in Liriodendron tulipifera ...................................................... 59
Table 2.1. Site information for the Liriodendron tulipifera samples used .................................. 60
Table 2.2. Microsatellite marker loci used to genotype Liriodendron tulipifera seedlings . ........ 61
Table 2.3. Summary measures of genetic polymorphism for each sampling sampling site ........ 62
Table 2.4. Pairwise standardized genetic differentiation (F’ST) and Euclidean geographic
distances in kilometers for all sampling site pairs ....................................................................... 63
Table 2.5. Analysis of molecular variance (AMOVA) for 384 seedlings from 8 sampling sites in
2 forest types ................................................................................................................................ 64
Table 2.6. Estimates of L. tulipifera mating system parameters for each sampling site obtained
using the maximum-expectation method implemented in MLTR and a Bayesian method
implemented in BORICE ............................................................................................................. 65
Table 3.1. Sampling locations and total number of samples collected ........................................ 66
Table 3.2. Microsatellite marker loci used to genotype Liriodendron tulipifera seedlings and
adult individuals ........................................................................................................................... 67
Table 3.3. Effective of breeders (!") estimated from linkage disequilibrium in single age cohorts
of Liriodendron tulipifera ............................................................................................................ 68
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Table 3.4. Effective population size estimates from linkage disequilibrium in single age (!") and
mixed-age cohorts samples (!# $%&#'()*# ) of Liriodendron tulipifera ..................................... 69
Table 3.5. Correction factor point estimates, and two confidence intervals for correction factor
point estimates used ..................................................................................................................... 70
Table 3.6. Effective population size, Ne; effective number of breeders, Nb; Nb/Ne ratio; mean
number of offspring per parent per time period, +; variance in reproductive success among adults
in one time period, Vk; generation length (mean age of parents of a newborn cohort), G;
estimated from eight life-history scenarios .................................................................................. 71
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LIST OF FIGURES
Figure 2.1. Relationship between pairwise F’ST / (1 − F’ST) and natural logarithm of geographic
distances (in km) for nine L. tulipifera sampling sites ................................................................. 72
Figure 3.1. Comparison of effective population size estimates from linkage disequilibrium in
single age (,-) and mixed-age cohorts samples (,. /01.2(34. ) of Liriodendron tulipifera ..... 73!
Figure 3.2. Median r^2 permute for r^2_{c} ............................................................................... 74
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INTRODUCTION
Across the globe, natural ecosystems are being converted to human-dominated
landscapes at increasing rates, with potentially significant evolutionary consequences.
Anthropogenic habitat change is particularly evident in the clearing of forests. In the continental
United States, 95 percent of forests have been logged since the European settlement began
(Brown et al. 1998). Forests that have reached a great age with little or no anthropogenic
disturbance (old-growth) are increasingly rare. In the United States less than one percent of
eastern forests are considered as old-growth, existing only as small scattered remnants (Davies
1996). At the same time, urbanization continues to expand, and as a result, an increasing
percentage of forests are becoming part of urban landscapes and ecosystems.
Urbanization is a dramatic anthropogenic environmental change; alterations in
temperature (urban heat island effect), increased inputs of inorganic nutrients, and increased area
of impervious surfaces are among its consequences. Urban areas frequently represent convergent
environments, in which urban ecosystems in different geographic areas are more similar to one
another than they are to their surrounding natural environments (reviewed in McKinney 2006;
Pickett et al. 2011). Because of this environmental convergence, urban areas can serve as
valuable unplanned, somewhat replicated experiments that offer unique opportunities to study
the direction, rate, and repeatability of evolutionary change (Donihue and Lambert 2015). Recent
studies have shown that urbanization affects both nonadaptive and adaptive evolution (reviewed
in Johnson and Munshi-South 2017). For example, urbanization causes loss of genetic
polymorphism within and increases genetic differentiation among populations, which has been
interpreted as a consequence of stronger genetic drift in urban areas. However, few direct
measurements of genetic effective population size have been made in the context of urbanization.
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Further, by transforming natural areas such as forests to impervious surfaces like roads,
buildings, and parking structures, urbanization also creates barriers to dispersal for some
organisms. Consequently, gene flow is often reduced among urban populations, thereby
contributing to genetic differentiation among populations (Johnson and Munshi-South 2017).
Studies of evolution in urban environments have focused predominately on relatively short-lived
animal species with low dispersal capabilities such as salamanders, lizards, and mice.
Considerably less is known about how urbanization affects the evolution of plants in general and
long-lived trees in particular.
This dissertation was designed to evaluate population genetic impacts of urbanization on
a long-lived tree. Although this project was designed and initiated in 2012, a more recent paper
by Johnson et al. (2015) summarized five consensus predictions for possible effects of
urbanization on plants. These predictions included a range of evolutionary processes that can be
impacted by urbanization. These predictions are as follows: (1) the amount of genetic
polymorphism will be different between urban and non-urban populations; (2) natural selection
will differ between urban and non-urban populations due to environmental changes associated
with urbanization; (3) genetic drift will be stronger in urban areas relative to rural areas because
of smaller population sizes; (4) genetic differentiation between urban and non-urban populations
should be proportional to the size of urban areas; and (5) insect-pollinated plants will have higher
rates of self-pollination in urban areas due to reductions in the abundance and diversity of
pollinators. An extensive literature search found no publications or studies testing all of these
predictions with a long-lived forest tree.
I tested versions of these five predictions in the tuliptree (Liriodendron tulipifera L.). To
test these hypotheses, I compared urban and old-growth populations in the highly developed
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mid-Atlantic region of the United States. L. tulipifera is an excellent species for these studies
because within its wide geographic distribution in the eastern United States, this species occurs
in diverse habitats, including both old-growth remnant forests and forest patches embedded in
urban areas where it reproduces and recruits naturally. The tuliptree is a pioneer species as well
as a long-lived large canopy tree that may survive for as long as 300 years (Beck 1990). Further,
it has a short-lived seed bank (Clark and Boyce 1964), so seeds and seedlings will experience
recent environmental conditions. Following a chapter describing the identification and
characterization of microsatellite (SSR) marker loci in L. tulipifera, tests of Johnson et al.'s
(2015) predictions 1, 4, 2, 5, and 3 will be described (in that order) below.
To provide means to test these predictions, Chapter 1 describes the development and
validation of L. tulipifera genomic SSR markers. These novel loci avoided the potential
limitations of previously reported SSR markers. Although there are numerous SSR markers
available for L. tulipifera, most of them have been developed from expressed sequence tags
(EST) derived from transcribed regions (Xu et al. 2006, Yang et al. 2012, Xu et al. 2010, Zhang
et al. 2015). EST-SSRs are located in or near functional genes, and as a consequence, are more
likely to be affected by natural selection (Ellis and Burke 2007). In addition, genomic
(noncoding, nontranscribed) microsatellite loci have been reported for Liriodendron chinense
and cross-amplified in L. tulipifera (Yao et al. 2008). However, cross-species amplification of
SSR loci might result in an ascertainment bias, which means that allele length is longest in the
species from which the markers were developed. This leads to low levels of variation of
homologous SSR loci in closely related species (Ellegren et al. 1995). The likely non-neutral
evolution of EST-SSRs, and the potential ascertainment bias introduced by cross-species
amplification might affect population genetic analyses involving estimations of intraspecific
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genetic polymorphism, gene flow, and genetic distances (both within and among populations).
Therefore, in order to identify and characterize SSR marker loci that do not suffer from
ascertainment bias and show high levels of polymorphism, we used high-throughput sequencing
and bioinformatics methods. We fully describe the development of these marker loci in
Gutiérrez-Ozuna and Hamilton (2017). During this process, we also identified thousands of SSR
loci and potentially amplifiable loci (PALs, with primer sequences), which we have made
available as Supplementary Information in Gutiérrez-Ozuna and Hamilton (2017). Further, raw
reads from L. tulipifera whole-genome sequencing were made publicly available at the NCBI's
Short Read Archive (SRA) under project accession number PRJNA331147, BioSample:
SAMN05417503, and may serve as a resource for the community.
Chapter 2 compares the population genetic differentiation and the mating system in L.
tulipifera among four urban forest patches and four old-growth remnant forests sampled in the
mid-Atlantic U.S. To test prediction 1, I obtained measures of genetic polymorphism in each
sampling site, including the average number of different alleles per locus (ka), the average
number of private alleles per locus (kp), and the multilocus average observed heterozygosity
(HO). I observed no differences in the levels of genetic polymorphism between urban and old-
growth sites, which does not support prediction 1. In order to test predictions 2 and 4, I used the
fixation index (FST) to quantify population genetic differentiation. Genetic differentiation was
greater among old-growth forests than among urban forest patches, which initially could be
interpreted as support for prediction 4 that there is genetic divergence between urban and non-
urban populations. However, this is likely to be the result of closer geographic proximity of
urban forests patches, rather than an effect of urbanization causing genetic divergence between
urban and old-growth populations. Furthermore, prediction 2 should lead to a process of isolation
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by adaptation (IBA), whereby local adaptation reduces gene flow among populations because of
reduced establishment success of migrants with lower fitness from populations with different
environmental conditions. IBA will lead to a pattern of isolation by environment (IBE).
However, I did not find support for prediction 2 because population genetic differentiation was
characterized by a pattern of isolation by distance (IBD) instead of IBE. This result suggests that
urbanization per se has not had a major impact on regional genetic differentiation patterns of L.
tulipifera, which is better explained by neutral processes of gene flow declining with geographic
distance.
In plants, the mating system characterizes and quantifies the distribution of mating unions
in a population. Mating system is a component of gene flow, influencing gene exchanges within
and among populations. While outcrossing facilitates population integrity, selfing acts as a
barrier to gene flow. I estimated the mating system of each population to test prediction 5 that
insect-pollinated plants will evolve higher rates of self-pollination in urban areas. Contrary to
this prediction, I found that all populations shared a fully outcrossed mating system without any
variation in rates of self-fertilization. This suggests that pollination is not altered in urban areas,
despite marked environmental differences between old-growth and urban forests, consistent with
studies showing that changes associated with urbanization are not detrimental to pollinators (e.g.,
Carper et al. 2014). L. tulipifera (as many other species in Magnoliaceae) is outcrossed by many
different pollinators, including flies, beetles, honeybees, and bumblebees, which suggests that
mating system does not have strong dependency on pollinators.
One possible interpretation to the lack of differences between urban and old-growth
forests described in Chapter 2 is that the genetic measures used here have poor power to detect
urbanization effects. In Chapter 2, I tested for population genetic effects of urbanization at two
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different temporal scales. Because gene flow tends to homogenize allele frequencies while
genetic drift tends to cause divergence due to fixation or loss of alleles within demes, the balance
between these two evolutionary forces is a major determinant of population genetic
differentiation. Any inference about processes that we make based on FST estimates, such as the
effective number of migrants (Nem), assumes an equilibrium between these two opposite
evolutionary forces, a process that can take a very long time to be reached. Therefore, it is not
surprising that we did not observe genetic divergence between urban and old-growth
populations, because urbanization is a relatively recent process compared to the genetic origin of
the populations. It is likely that the differentiation patterns observed here evolved many
generations in the past. This deep time evolutionary history may be one of the reasons why many
studies of forest tree populations have not found significant consequences of habitat
fragmentation and disturbance due to human activities such as logging, a result that has been
called the “paradox of forest fragmentation genetics” (Kramer et al. 2008). In contrast, the other
genetic measure used here, the mating system, can reach its equilibrium in a single reproductive
event, which for L. tulipifera is one year. Disturbances causing deviations from random mating
will lead to departure from Hardy-Weinberg expected genotype frequencies (HWE) in the
offspring from that reproductive event. Nevertheless, the system will return to HWE in a single
event of random mating (one reproductive season). In the case of the mating system, I used a tool
that has the potential to detect immediate effects to urbanization; however, I still found no
differences. This finding suggests that there are indeed no differences between urban and old-
growth populations, rather than the mating system being insensitive to recent effects.
Chapter 3 tests prediction 3 that genetic drift is stronger in urban areas, using the
effective number of breeders (,-), a measure of the strength of genetic drift in a single age
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cohort for species with overlapping generations. I compared ,- between urban and old-growth
populations which was estimated using a method based on two-locus disequilibrium. I observed
that ,- estimates did not differ between urban and old-growth sites and were uniformly small,
even in old-growth populations that could be considered as reservoirs of genetic polymorphism.
Genetic drift was strong in both forest types, which is inconsistent with prediction 3. Because ,-
quantifies genetic drift over short timescales relative to ecological processes, it is unlikely that
the lack of an urbanization effect on genetic drift was an artifact of the genetic measure used.
With pairs of loci experiencing free recombination, two-locus disequilibrium is expected to
decay to nearly zero in few generations. Similar to the logic used for the mating system in
Chapter 2, this means that ,- as employed in this study has the potential to detect recent
differences in the strength of genetic drift, if they exist. In addition, Chapter 3 compares effective
population size estimates from two common sampling approaches, newborn seedlings (,-) and
mixed-age reproductively mature trees (,. /01.2(34. ). Consistent with modeling results that
showed Nb and Ne are expected to be very similar in this long-lived iteroparous species,
,. /01.2(34. estimates were also small and did not differ from ,- estimates. The small
effective population sizes observed in this study suggest that genetic drift is a major determinant
of polymorphism within populations and of genetic differentiation among populations, regardless
of forest type.
In summary, I have tested for urbanization effects on population genetic patterns for a
long-lived organism with overlapping generations, and high passive dispersal potential; most
other recent tests for population genetic impacts of urbanization have focused on short-lived
organisms with low dispersal capabilities. I have evaluated versions of all of Johnson et al.'s
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(2015) predictions, and found no support for predictions 1, 3, 4, and 5. In addition, I found little
support for prediction 2. The similar levels of genetic polymorphism observed across all
populations do not support prediction 1 that the amount of genetic polymorphism will be
different between urban and old-growth populations. Greater genetic differentiation among old-
growth forests than among urban forest patches could initially be interpreted as support for
prediction 4. However, this is likely to be the result of closer geographic proximity of urban
forests patches. Finding IBD instead of IBE is inconsistent with prediction 2 of adaptation to
urban environments. A fully outcrossing mating system shared among all populations does not
support prediction 5 that insect-pollinated plants will have higher rates of self-pollination in
urban areas. Uniformly small ,- estimates among populations suggest strong genetic drift in
both forest types, which is inconsistent prediction 3 that genetic drift is stronger in urban areas.
In addition, the experimental approach used in this thesis was novel because it examined
potential evolutionary effects of urbanization at three different temporal scales: (i) immediate,
using mating system estimates; (ii) relatively short-time, using ,- estimates, an approach that
has been rarely used in the context of urbanization; and (iii) long-time, estimating population
genetic differentiation. In particular, mating system and ,- as employed in this study have the
potential to detect effects of urbanization in the very recent past if they exist. This allowed me to
rule out the alternative explanation that the lack of any urbanization effects was an artifact of the
time to register changes of the genetic measures used. The lack of differences in mating system
and ,- instead suggest that there are indeed no effects of urbanization on some key evolutionary
processes that shape genetic polymorphism within and among populations of this long-lived tree.
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Despite the increasing number of studies documenting effects of urbanization on patterns
of genetic polymorphism within and among populations, it is currently not clear whether urban
evolution is a universal phenomenon. This thesis highlights a series of population genetic
phenomena that are going to be manifest differently in long-lived and short-lived organisms. For
example, we already know that short-lived and self-compatible plants tend to have greater
genetic differentiation at smaller scales than long-lived and outcrossing plants such as trees, and
therefore the former are likely expected to exhibit stronger adaptation to local conditions. Based
on differences in life history traits, population genetic theory can help to refine predictions on
how urbanization can affect evolutionary processes. The Johnson et al. (2015) summary could
potentially be updated with the data and information generated in this thesis about what we
might expect for long-lived and short-lived organisms.
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CHAPTER 1
Identification and characterization of microsatellite loci in the tuliptree, Liriodendron
tulipifera (Magnoliaceae)1
Introduction
Liriodendron tulipifera L., commonly known as tuliptree, tulip poplar, or yellow poplar, is
a pioneer tree in the family Magnoliaceae native to Eastern North America. It has a wide
geographic distribution in the Southeast and mid-Atlantic United States and occurs in diverse
habitats. In order to facilitate population genetic analyses of effective population size and
population structure, we developed genomic microsatellite (SSR) markers without the potential
limitations of previously reported SSRs. Liriodendron tulipifera SSRs have been developed from
expressed sequence tags (EST; Xu et al. 2006, Xu et al. 2010, Yang et al. 2012, Zhang et al.
2015) located in or near functional genes, and as a consequence, they are more likely to be
affected by natural selection (Ellis and Burke 2007). Liriodendron chinense (Hemsl.) Sarg.
genomic (noncoding, nontranscribed) microsatellite loci have been cross-amplified in L.
tulipifera (Yao et al. 2008). Cross-species amplification of microsatellite loci might result in
ascertainment bias, where polymorphism is reduced when loci are employed in related species
(Ellegren et al. 1995). Preliminary tests of loci from Yao et al. (2008) carried out with 10 L.
tulipifera individuals showed low polymorphism (results not shown). Non-neutral evolution or
ascertainment bias can potentially impact the estimation of population genetic parameters.
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!1!This chapter has been published as: Gutiérrez-Ozuna, R., and Hamilton, M.B. (2017). Identification and characterization of microsatellite loci in the tuliptree, Liriodendron tulipifera (Magnoliaceae). Applications in Plant Sciences, 5(8), 1700032.
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Therefore, we identified and characterized polymorphic genomic microsatellite loci in L.
tulipifera using Illumina next-generation sequencing and a bioinformatics pipeline.
Methods and Results
Microsatellite development
Total DNA from leaves of one L. tulipifera individual collected on the main campus of
Georgetown University in Washington, DC, USA was extracted using the DNeasy Plant Mini
Kit (QIAGEN, Valencia, California, USA). A genomic DNA library for Illumina paired-end
sequencing was prepared from 4 µg of DNA following the PCR-free library prep kit from
Illumina (Illumina, San Diego, California, USA). DNA was sheared to 550 bp and sequenced as
150 bp paired-end reads on an Illumina HiSeq 2500 at Virginia Bioinformatics Institute at
Virginia Tech. We used PAL_FINDER_v0.02.04 (Castoe et al., 2012) to extract reads
containing perfect microsatellites (uninterrupted and identical repeats). The reads were imported
to PAL_FINDER and analyzed in two different ways: 1) as Illumina paired-end reads filtered to
include ≥12 tri-, ≥10 tetra-, ≥8 penta-, and ≥6 hexa-nucleotide repeats, and 2) as FASTQ
sequence files converted to FASTA format, treated as 454 single-end reads, and filtered to
include ≥15 di-, ≥10 tri-, ≥8 tetra-, ≥6 penta-, and ≥4 hexa-nucleotide repeats. One potential
advantage of using both methods is the development of loci with a broader range of amplified
fragment sizes. In both cases, we identified microsatellite loci with flanking sequences suitable
for PCR primer design or potentially amplifiable loci (PALs). Raw reads were deposited in
NCBI's Short Read Archive (SRA, Project: PRJNA331147, BioSample: SAMN05417503).
Summaries of reads containing microsatellite repeats and PALs (with primer sequences) detected
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using both methods are available as Supplementary Information together with the published
paper.
We selected a set of 77 PALs to empirically assess amplification using three individuals.
We amplified each locus in 25µL PCR reactions [1X OneTaq Standard reaction buffer, 160µM
dNTPs, 0.2µM forward primer, 0.2µM reverse primer, 1.25 units OneTaq DNA polymerase
(New England BioLabs, Ipswich, Massachusetts, USA), 1µL template DNA (concentration was
not determined), and ddH20 to 25µL]. Thermocycling conditions were 94°C (30s), followed by
30 cycles of denaturation at 94°C (30 s), annealing (30s, Table 1.1), and extension at 68ºC (30s),
and a final extension of 68°C (5 min). Fifty-one primer pairs yielded products of the expected
size without non-specific amplification and were then tested for polymorphism in seven
individuals, by visualizing PCR products on 3% agarose gels. Of these 51 loci, 23 were
polymorphic and used to genotype 12 to 20 individuals collected from three old-growth locations
in the native range of L. tulipifera (Table 1.2). Because L. chinense, the other single species in
Liriodendron, has a restricted geographic distribution in China and Vietnam, we were not able to
test for cross-species amplification. Since cross-amplification of genomic SSRs has limited
success in plants (Merritt et al. 2015) and success declines as genetic divergence increases
(Barbará et al. 2007), we did not test for cross-amplification in other Magnoliaceae.
For fragment analyses, PCR products were fluorescently labeled either using primers
tailed with a 5’ M13(-21) sequence following Schuelke (2000) or using primers with a 5’
fluorophore and amplified in multiplex (Table 1.1). In the tailed primer labeling method, two
PCR reactions were carried out using the same reverse primer. The first PCR used an M13(-21)-
tailed locus-specific forward primer, while the second used a universal fluorescently labeled
M13(-21) as a forward primer. The products of the first PCR were purified using StrataPrep PCR
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Purification Kit (Agilent Technologies, Santa Clara, California, USA), and then used as the
template for the second PCR. Fluorescent products were electrophoresed on an ABI PRISM
3100 Genetic Analyzer, and amplicon sizes were estimated with either orange or red DNA size
standard (MCLAB, San Francisco, California, USA) and GeneMapper software 3.7 (Applied
Biosystems, Foster City, California, USA) using the local southern sizing algorithm.
Microsatellite data analysis
Genotypes appeared diploid, displaying at most two alleles per locus per individual. Data
were analyzed by sampled location and as a pooled population (Table 1.3). For each locus,
number of alleles (k), observed heterozygosity (HO), expected heterozygosity under random
mating (HE), and polymorphism information content (PIC = 1 − 7089
0:; −
27087=
89=:0>;
9(;0:; ; Botstein et al. 1980) for the pooled population, were estimated using Cervus
3.0.3 (Kalinowski et al. 2007). We used Genepop 4.2 (Rousset 2008) to test deviation from
Hardy-Weinberg expected heterozygote frequency (HWE) using default values for Markov chain
parameters, and to estimate the fixation index (F = (@A − @B) @A; Hamilton 2009) for the
pooled population.
Population genetic parameters are listed for each sampled location showing loci exhibited
2-12 alleles, with almost all alleles common to each location (Table 1.3). Lack of population
differentiation (DEF = 0.077 estimated using Genepop) justified pooling genotypes from the three
locations. In the pooled population, observed and expected heterozygosities ranged from 0.233 to
0.865, and 0.272 to 0.876, respectively. Six loci showed significant deviations from HWE (Table
1.3) with deficits of heterozygotes which could have numerous causes. One hypothesis is non-
random mating, which we tested using INEST (Chybicki and Burczyk 2009) with the individual
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inbreeding model (IIM) run for 50,000 burn-in and 500,000 total cycles. The estimated average
coancestry coefficient over all loci (f) was 0.041, with a 95% highest posterior density interval of
[0.000, 0.085], indicative of an outcrossing species. Another hypothesis for the deficit of
heterozygosity is the presence of null alleles. Frequencies of null alleles estimated with INEST
using IIM are listed in Table 1.3. The six loci with significant deficits of heterozygotes also
showed evidence of null allele frequencies greater than zero.
When comparing models including null alleles (n), mating among relatives (f), and
genotyping failures (b) using INEST, the full model (nfb) was found to best fit the data by the
lowest deviance information criterion value (DIC = 5905.837), showing that all three parameters
contributed to observed genotype frequencies. The nb model, without mating among relatives,
exhibited the closest DIC value (5916.991), but the difference was greater than 10, indicating
stronger support for the nfb model (Igor J. Chybicki, pers. comm.).
Conclusion
These 23 microsatellite markers do not suffer from ascertainment bias and show high
levels of polymorphism. Taken together with thousands of PALs, this study provides resources
for population genetic studies of L. tulipifera.
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CHAPTER 2
Population genetic differentiation and mating system of a long-lived tree in old-growth
forests and urban forest patches.
Introduction
Continuous natural habitats are increasingly transformed into anthropogenic
landscapes—mosaics of natural habitats fragments surrounded by heterogeneous mixtures of
different land use. Anthropogenic habitat transformation is particularly evident in the clearing of
forests. The estimated cumulative loss of forests across the world over a period of 5,000 years is
1.8 billion hectares—an average net loss of 360,000 hectares per year (Williams 2002). In the
continental United States, 95 percent of forests have been logged since the European settlement
began (Brown et al. 1998). Since 1700, a quarter of the world’s forests have been converted to
agriculture (Boakes et al. 2010). Forests that have reached a great age with little or no
anthropogenic disturbance (old-growth) are increasingly rare. Less than one percent of Europe’s
old-growth forests remains (Brown et al. 1998). Similarly, in the United States less than one
percent of eastern forests are considered as old-growth, existing only as small scattered remnants
often found in areas that either were protected or escaped harvesting for example due to their
inaccessibility (Davis 1996). At the same time, urbanization continues to expand, and as a result,
an increasing percentage of forests are becoming part of urban landscapes and ecosystems.
Urbanization is a dramatic anthropogenic environmental change—alterations in
temperature (urban heat island effect), increased inputs of inorganic nutrients, and more
impervious surface cover are only some of its consequences. Recent studies have shown that
urbanization affects both nonadaptive and adaptive evolutionary processes (reviewed in Johnson
and Munshi-South 2017). For example, it has been suggested that industrial pollution can elevate
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mutation rates, e.g., in the herring gull, Larus argentatus (Yauk et al. 1996, 2000). Crested
anoles, Anolis cristatellus, evolve longer limbs and more toepad lamellae (expanded subdigital
scales used for clinging) in urban environments, which could increase locomotory performance
on artificial surfaces (Winchell et al. 2016), representing evidence for adaptive phenotypic
evolution in urban areas. Studies on urban evolution have focused predominately on relatively
short-lived animal species. Considerably less is known about how urbanization affects the
evolution of plants in general and long-lived trees in particular.
Johnson et al. (2015) summarized five consensus predictions for possible effects of
urbanization on plants. These interrelated predictions included a range of evolutionary processes
that can be impacted by urbanization. One example of these evolutionary processes is stronger
genetic drift in cities as a result of reduced population sizes caused by urban encroachment or
strong bottlenecks during colonization of urban areas. Johnson et al.’s (2015) prediction 1
suggests that genetic polymorphism in urban areas will be decreased as a consequence of these
increased rates of genetic drift, and the results from the very few studies conducted on short-
lived plants are consistent with this hypothesis. For example, the yellow toadflax, Linaria
vulgaris, an herbaceous plant shows lower genetic polymorphism in urban populations than in
non-urban ones (Bartlewicz et al. 2015). Considerably less is known about whether populations
of long-lived trees exhibit lower genetic polymorphism in urban than in non-urban areas.
Additionally, old-growth forests are assumed to be potential reservoirs of genetic polymorphism
for plants, yet there is little empirical evidence to support or contradict this view. However, if
this assumption is correct, the levels of genetic polymorphism can be particularly different when
comparing old-growth forests with urban forest patches.
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Reduced rates of gene flow among urban areas is a common outcome of urbanization
because the transformation of natural areas to impervious surfaces creates barriers to dispersal
for some organisms (Johnson and Munshi-South 2017). This may in turn contribute to the
genetic differentiation among urban areas caused by genetic drift. Johnson et al.’s (2015)
prediction 4 suggests that there is genetic divergence between urban and non-urban plant
populations due to decreased gene flow from non-urban to urban areas, in addition to increased
genetic drift in urban areas. The degree of genetic divergence among populations is often
quantified by estimators of FST (Wright 1942), which can be related to the combined processes of
gene flow and genetic drift using different models. For example, under an infinite island model,
Wright (1931) showed that for diploid nuclear loci FST ≈ 1/(1+4Nem) for relatively low rates of
gene flow (m). Island models are not spatially explicit and assume equal rates of gene flow
among demes. As a result, all demes are equally differentiated. However, because levels of gene
flow in nature tend to decline as a function of geographic distance, genetic differentiation among
populations is expected to increase with geographic distance, a pattern known as isolation by
distance or IBD (Wright 1943). IBD is widely used as a neutral null hypothesis in population
genetic differentiation studies and can be tested by comparing levels of genetic differentiation
and geographic distances between pairs of subpopulations.
Dispersal and gene flow may be asymmetric among populations, and a number of neutral
models explicitly incorporating this variability have been proposed. For example, the isolation by
resistance (IBR) model (McRae 2006) includes effects of irregular habitat assuming that wider or
multiple dispersal pathways allow greater gene flow than a single narrow pathway. Using IBR,
McRae and Beier (2007) showed that landscape features previously considered as barriers do not
block gene flow but rather change gene flow’s rate. In addition, asymmetric gene flow can be
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due to other processes such as monopolization, where initial colonizers have a numerical
advantage that reduces establishment success of later migrants, which in turn reduces gene flow
among subpopulations over time (De Meester et al. 2012). Monopolization may result in a
pattern of isolation by colonization (IBC). Under IBC, genetic structure reflects the temporal
colonization history (founder effects), therefore there is not necessarily a relationship between
genetic differentiation and geographical distances or environmental differences (Orsini et al.
2013). It is also possible that natural selection affects gene flow rate, for instance, through the
process of isolation by adaptation (IBA). In this process, divergent local adaptation reduces gene
flow among subpopulations because of reduced establishment success of lower fitness migrants
from subpopulations with different environmental conditions, thereby increasing subpopulation
divergence via genetic drift. IBA leads to a pattern of isolation by environment (IBE, Nosil et al.
2009).
Gene flow in plants is achieved by pollen and seed dispersal. However, gene flow by
pollen exceeds gene flow by seed (Hamilton and Miller 2002). Gene flow by pollen is suggested
to be more important in the maintenance of genetic polymorphism than gene flow by seed
(Ellstrand, 2014). Therefore, in animal-pollinated species, the pollinators and their movement
through the landscapes can have a profound influence on genetic differentiation as well as on
mating system. Studies on genetic differentiation and gene flow in populations of animal-
pollinated plants in human-modified landscapes have largely focused on mixed agricultural-
forest systems (Aguilar et al. 2008; Eckert et al. 2010). In these fragmented landscapes, pollen
dispersal in insect-pollinated trees can extend several kilometers, for example, in Swietenia
humilis (White et al. 2002) and Dinizia excelsa (Dick et al. 2003, reviewed in Dick et al. 2008).
Although there has been almost no research on gene flow among insect-pollinated trees in urban
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landscapes, the very few studies comparing urban populations of trees have found similar
findings to those from populations in mixed agricultural–forest landscapes. For example, Noreen
et al. (2016) showed that insect-mediated gene flow persisted among individuals of the tropical
tree species Koompassia malaccensis in an urban landscape, even across >2.5 km of impervious
surface. These findings suggest that urban matrices may not be a barrier to gene flow for insect-
pollinated trees. If gene flow happens equally along similar environments and across different
environments within dispersal limits, this could lead to a pattern of IBD.
In contrast, gene flow can be strongest among similar environments (e.g., among urban
sites), resulting in IBE. This pattern may arise by several mechanisms including response to
natural selection in different environments, and environmentally mediated phenotypic plasticity.
Johnson et al.’s (2015) prediction 2 suggests that the ecological and environmental differences
between urban and nonurban areas will cause divergent selection in these two environments.
Empirical support for this hypothesis has been found in short-lived plants such as the
hawksbeard, Crepis sancta, where urbanization affects selection on seed dispersal-related traits
(Cheptou et al. 2008). In another example, Yakub and Tiffin (2017) using a common garden
experiment, showed that plants of Virginia pepperweed, Lepidium virginicum, grown from seeds
from urban areas bolted sooner, had fewer leaves, grew larger, had an extended time between
bolting and flowering, and produced more seeds than plants grown from seeds from rural areas.
These traits have been suggested to allow plants to thrive in urban environments. Furthermore,
urbanization leads to convergent environments, in which urban ecosystems in different
geographic areas are more similar to one another than urban areas are to their surrounding
natural environments (reviewed in McKinney 2006; Pickett et al. 2011). Based on this, parallel
evolution of urban populations in response to natural selection in urban areas has also been
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predicted. Studies that have found support for these adaptive predictions have been also
conducted on short-lived plant species. For example, the white clover, Trifolium repens,
consistently evolved decreased antiherbivore chemical defenses across four cities (Thompson et
al. 2016). As mentioned before, an IBE pattern can also be caused by environmentally mediated
phenotypic plasticity, as opposed to genetically based adaptive mechanisms. For example, within
urban areas, earlier (and in a few cases later) flowering time compared to rural areas has been
reported for plants, including several tree species (e.g., Hepper 2003, Jeong et al. 2011, Mimet et
al. 2009, Neil and Wu 2006, Primack et al. 2004, Roetzer et al. 2000), a phenomenon that has
been attributed to be a consequence of higher temperatures due to the urban heat island effect.
Shifts in flowering phenology may increase gene flow by pollen transfer among urban
synchronized populations, as well as decrease gene flow among populations that differ in
flowering phenology. This gene flow can lead to less genetic differentiation among urban sites
compared to differentiation among old-growth forests (which may exhibit more diverse
flowering phenology), and eventually can result in a pattern of IBE.
Gene flow tends to homogenize allele frequencies while genetic drift tends to cause
divergence due to fixation or loss of alleles within demes. Any inference about processes that we
make based on FST estimates, such as the effective number of migrants (Nem), assumes an
equilibrium between these two opposite evolutionary forces, which can take a very long time (in
generations) to be reached. Current genetic differentiation patterns can be the result of historical
processes. Examples of historical differentiation patterns are the genetic signatures of Pleistocene
glaciations in a large number of taxa (e.g., Shönswetter et al. 2002, Fraser et al. 2009, Harris and
Taylor 2009, Lanier et al. 2015), including many tree species in North America, such as
Liriodendron tulipifera (Sewell et al., 1996, Fetter 2011). Because the impacts of urbanization
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on gene flow and genetic drift are relatively recent, they will not necessarily be readily
observable in population genetic differentiation. In contrast, the mating system, a component of
gene flow, might potentially be affected immediately after environmental changes associated
with urbanization take place. Mating system can reach its equilibrium (Hardy-Weinberg
expected genotype frequencies or HWE) in a single reproductive event, which in trees frequently
takes one year (numerous tree species reproduce annually). Disturbances causing deviations from
random mating will lead to departure from HWE in the offspring from that reproductive event.
However, the system will return to HWE if the population mates again at random. In plants, the
mating system can be described by parameters such as outcrossing and selfing rates. Since plants
are sessile, the mating system can vary in response to several factors that affect outcross
pollination, such as the abundance, foraging behavior, and visitation rates of pollinators. Johnson
et al.’s (2015) prediction 5 suggests that insect-pollinated plants in urban areas will have higher
rates of self- pollination due to reductions in the abundance and diversity of pollinators. Similar
to the case of studies of gene flow in trees, our knowledge on the impacts of anthropogenic
activities on mating system of trees comes largely from studies in mixed agricultural–forest
landscapes, many of which have shown that habitat fragmentation caused by deforestation
decreases the outcrossing rate of several species (Aguilar et al. 2008, Eckert et al. 2010, Ferreira
et al. 2013). The few studies estimating the mating system of tree populations in urban areas
have documented increased selfing rates in smaller urban forest patches. For example, Wang et
al. (2010) and Noreen et al. (2016) working with Chinese pine, Pinus tabulaeformis, and
Koompassia malaccensis, respectively. Yet, these studies have restricted their comparisons to
tree populations in urban landscapes. There has been almost no research on comparing mating
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system between urban forest patches and natural areas such as old-growth forests, which is one
of the foci of this study.
In this study, I investigated population genetic effects of urbanization on forest tree
populations using the long-lived tuliptree (Liriodendron tulipifera). Twelve polymorphic nuclear
microsatellite marker loci were used to genotype single age cohort seedlings collected at nine
geographic locations in the mid-Atlantic U.S. I estimated genetic polymorphism, genetic
differentiation, and mating system to test versions of four of the five predictions proposed by
Johnson et al. (2015) on the effects of urbanization on plant evolution: Prediction 1, the amount
of genetic polymorphism will be different between urban and non-urban populations. Prediction
2, natural selection will differ between urban and non-urban populations due to environmental
changes associated with urbanization. Prediction 4, there is genetic differentiation between urban
and non-urban populations. Prediction 5, insect-pollinated plants will have higher rates of self-
pollination in urban areas due to reductions in the abundance and diversity of pollinators.
Materials and Methods
Study species
In this study, I utilized the tuliptree (Liriodendron tulipifera, L.), a deciduous, broadleaf
tree in the family Magnoliaceae native to Eastern North America that is commercially valued for
wood and as a honey tree (Beck 1990, Griffith 1991). It is both a pioneer species as well as a
long-lived large canopy tree (life span is usually about 200 to 250 years, and some individuals
may live up to 300 years, Beck 1990), reaching 100-150 feet (30.5!45.7 m) in height and 2-5
feet (0.6!1.5 m) DBH at maturity. L. tulipifera is self-compatible, with a mixed-mating system
(Brotschol et al. 1986, Griffith 1991, Houston and Joehlin 1989, Sewell 1992), pollinated (in
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decreasing order of abundance) by flies (Muscidae), beetles (Coleoptera), honeybees (Apis spp.),
and bumblebees (Bombus spp.; Beck and Della-Bianca 1981), and has wind-dispersed seeds that
remain viable for up to four years under natural conditions in the forest litter (Clark and Boyce
1964). Therefore, seedlings are representative of recent ecological and evolutionary processes.
L. tulipifera is widespread and a component of numerous native eastern forests from
southern New England, west through southern Ontario and Michigan, south to Louisiana and
central Florida (Beck and Della-Bianca 1981), including the heavily urbanized mid-Atlantic
region. Although several native species do not do well in cities and no longer occur there, within
its range, L. tulipifera is found in both old-growth remnants and forest patches embedded in
urban areas where it reproduces and recruits naturally, which makes it an excellent species model
for this project. In addition, because the seedlings stage is the most sensitive phase of the life
cycle to environmental changes (Harper 1977, Silvertown and Charlesworth 2001, Leck et al.
2008), seedlings are expected to be particularly sensitive to anthropogenic habitat disturbance
such as urbanization.
Most old-growth forests in North America are not considered virgin forests (i.e. free from
disturbance of modern humans) due to extensive encroachment, hunting, agriculture, and timber
harvesting. In the case of eastern temperate forests, there are several definitions of old-growth,
with no clear thresholds regarding disturbance frequency or age (White and Lloyd 1994, Leverett
1996). The old-growth sites analyzed in this study were chosen from the study of McGarvey et
al. (2015), who used the operational definition of old-growth as forests with a stand age of ≥ 150
years, following Cogbill (1996). Furthermore, the metropolitan areas investigated here were
originally mostly covered by forests in which L. tulipifera was commonly found (Ward 1881,
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Adams et al. 2003). The urban forest patches in this study are remnants of the original native
forests which were encroached by urban development.
Field collections
I collected one single age cohort of seedlings (age zero) in urban forest patches in large
metropolitan areas and in old-growth forest remnants in the mid-Atlantic U.S. during the
summers of 2015 and 2016. In addition, a suburban forest close to the north geographic range
limit of this species was sampled (Southbury, Connecticut; Table 2.1). To ensure that seedlings
were one single age cohort and had germinated in the year they were collected, sampled
seedlings had both cotyledons still attached and the first set of true leaves. At each sampling site,
42-48 seedlings were collected haphazardly in an area of approximately 2,000 m2. The total
sample size was 426 seedlings from 9 geographic locations, four old-growth, four urban, and one
suburban (Table 2.1).
Genetic analysis
Twelve microsatellite markers previously developed specifically for L. tulipifera
(Gutierrez-Ozuna and Hamilton 2017) were used to genotype all sampled individuals. Three
multiplex microsatellite sets (Table 2.2) were amplified in 6.75µL multiplex PCR reactions with
the Type-it Microsatellite PCR Kit (QIAGEN, Valencia, California, USA). Each reaction
contained 3.125µL Type-it Multiplex PCR Master Mix, 1.25 µL 10x primer mix (1-20µM of
each primer, see Table 2.2 for primer-specific concentrations), 1.375 µL ddH2O, and 1 µL of
DNA template (concentration was not determined). Thermocycling conditions were 5 min at
95ºC, followed by 32 cycles of 30 s at 95ºC, 90 s at 57ºC, and 60 s at 72ºC, and finally 30 min at
60ºC to promote 3’ untemplated A nucleotide addition. Fluorescent PCR products were
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electrophoresed on an ABI PRISM 3100 Genetic Analyzer, using either orange or red DNA size
standard (MCLAB, San Francisco, California, USA). Alleles were binned and scored with the
microsatellite plugin version 1.4.4 implemented in Geneious version 9.1.3
(https://www.geneious.com, Kearse et al. 2012) using the local southern sizing algorithm and
were double-checked manually. To create expected allele size bins, I estimated expected
fragment sizes from the microsatellite sequences reported in Gutierrez-Ozuna and Hamilton
(2017) with ±1 base pair estimation error.
Statistical analysis
Genetic polymorphism. To test prediction 1 that the amount of genetic polymorphism is
different between urban and non-urban populations, for each sampling site, I estimated the
following measures of genetic polymorphism averaged over the 12 loci using GenAlEx version
6.5 (Peakall and Smouse 2006, 2012): number of alleles (ka); effective number of alleles (ke =
1/( 708)H
0:; ), where pi is the frequency of the ith allele and k is the number of alleles; number of
private alleles per locus (kp); observed heterozygosity (HO = observed frequency of
heterozygotes / n), where n is the number of genotyped individuals; and expected heterozygosity
(HE = 1 − 708H
0:; ). In addition, to gain insight into the mating system, the fixation index (F =
(HE - HO) / HE) was estimated for each sampling site.
Population genetic differentiation. Because FST depends on the distribution of allele
frequencies (Charlesworth 1998, Hedrick 1999, Jakobsson et al. 2013), it may not range from 0.0
to 1.0 with more than two alleles and can be very small even when no alleles are shared between
two sites. Therefore, to test prediction 4 that urban and old-growth populations have diverged, I
employed a “standardized” measure of FST, called F’ST (Hedrick 2005). F’ST is calculated by
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dividing observed FST for a given locus by the maximum theoretical FST based on the
heterozygosity at that locus. Unlike FST, F’ST will be 1.0 at complete differentiation, even with
high genetic polymorphism within sites, and will be zero when there is no differentiation. F’ST
was estimated using GenoDive (Meirmans and Van Tienderen, 2005). To evaluate if the degree
of genetic differentiation among sites was due to differences among habitats (old-growth vs.
urban), I assessed the partition of total genetic polymorphism between forest types (old-growth
vs urban), among and within sampling sites, and within individuals by carrying out a hierarchical
analysis of molecular variance (AMOVA) on Euclidean pairwise distances among individuals
implemented in GenAlEx. The expected distribution to test for statistical significance of variance
components using permutation tests was based on 999 random permutations. In addition, the
following five hierarchical fixation indices (F-statistics) were estimated using GenAlEx: 1) the
proportion of genetic variation accounted for by allele frequency differences between forest
types, i.e., old-growth vs urban (FRT = I38 [I38 + I-8 + IL8]); 2) the proportion of genetic
variation accounted for by allele frequency differences among sampling sites within a forest type
(FSR = I-8 [ I-8 + IL8]); 3) the proportion of genetic variation accounted for by allele frequency
differences among sampling sites (FST = [I38 + I-8] [I38 + I-8 + IL8]), where I38 is the variance
component due to differences between forest types, I-8is the variance component due to
differences among sampling sites, and IL8 is the variance component due to differences among
alleles within sampling sites; 4) deviation from Hardy-Weinberg expected genotype frequencies
(HWE) in a sampling site (FIS = [HS – HO] / HS); and 5) deviation from HWE in the total
population (FIT = [HT – HO] / HT ), where HT is the expected heterozygosity in the total
population, HS is the mean expected heterozygosity in sampling sites, and HO is the mean
observed heterozygosity in sampling sites.
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Pattern of genetic differentiation. Prediction 2 suggests that plant populations will
experience divergent selection between urban and old-growth forests, which can eventually
result in a pattern of IBE. To test the neutral null hypothesis of genetic differentiation among
populations, IBD, the correlation between a matrix of linearized genetic distances F’ST / (1 −
F’ST) (Rousset 1997) and natural logarithm (ln) of geographic distances in kilometers (km) was
examined. I tested the significance of the observed relationship by using a Mantel test (Mantel
1967) with a total of 10 000 random permutations used to generate an empirical null distribution
for the data set as implemented in GenoDive.
Mating system. In order to test prediction 5 that insect-pollinated plants will have higher
rates of self-pollination in urban areas, I estimated the mating system at the sampling site level
using two methods. The first, according to the mixed mating model for many unlinked loci
described by Ritland and Jain (1981), using MLTR program from Ritland (2002). The mating
system parameters estimated were multilocus outcrossing rate (tm), single-locus outcrossing rate
(ts), mating among relatives (commonly referred to as biparental inbreeding, tm − ts) and
multilocus paternity correlation (rp(m)) or proportion of full sibs among outcrossed seedlings.
These parameters were estimated using the maximum-expectation algorithm, and allowing for
the presence of null alleles. Standard errors of the estimates were approximated as the standard
deviation of 1000 bootstrap replicates. Furthermore, neighborhood size, i.e., the number of
pollen donors, was estimated as 1/rp(m) (Ritland, 1989). In the second method, Bayesian estimates
of population outcrossing rates (t-max) and fixation indices (F) were estimated with BORICE
(Koelling et al. 2012) based on a chain of 100 000 steps with a burn-in of the first 10000 steps.
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Koelling et al. (2012) showed that with simulated data, BORICE provides accurate estimates of
outcrossing rate when family sizes are small and maternal genotypes are unknown, as is the case
here.
Results
Genetic polymorphism
The levels of genetic polymorphism were similar between urban and old-growth forests
(Table 2.3). The average number of alleles per locus ranged from 6.08 to 6.33 in urban forest
patches and from 6.17 to 6.83 in old-growth forests. The average effective number of alleles
varied from 3.59 to 4.07 in urban forests and from 3.39 to 4.47 in old-growth forests. Private
alleles were infrequent, being absent in two urban populations (average frequency per locus
ranged from 0 to 0.25). All sampling sites showed high values of heterozygosity. The average HE
values varied from 0.67 to 0.70 in urban forests and from 0.66 to 0.72 in old-growth forests. All
corresponding average HO values were slightly lower, ranging from 0.56 to 0.60 in urban forests
and 0.58 to 0.60 in old-growth forests. The fixation index varied from 0.13 to 0.20 in urban
forests and from 0.10 to 0.17 in old-growth forests.
Population genetic differentiation
Because observed heterozygosities were very high, maximum DEF was much less than
one, e.g., global maximum DEF was 0.285 when considering the four urban and four old-growth
sampling sites, and 0.301 when Southbury was included. Global D′EF for the eight sites (four
urban and four old-growth) was 0.237, and 0.273 when Southbury was included. Pairwise D′EF
ranged from 0.087 to 0.436 (Table 2.4). When sampling sites were grouped by forest type, D′EF
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was higher when considering the four old-growth sites (0.242) compared to the four urban sites
(0.155). The average geographic distance among old-growth forests was 200.5 km and among
urban forest patches was 26.4 km.
Pattern of genetic differentiation
Mantel's test showed a significant correlation between genetic distance and geographical
distance when eight sampling sites (the four urban and four old-growth) were considered (r =
0.648, p < 0.001), and this correlation increased when Southbury was included (r = 0.759, p =
0.003; Figure 2.1). Analysis of molecular variance (AMOVA) based on D′EF values indicated
that most of the genetic polymorphism (73%) occurred within individuals, also known as
heterozygosity. The variation among individuals contributed 19% and the variation among
sampling sites contributed 5% to the observed genetic polymorphism. Differences between urban
and old-growth sites explained 2% of the total genetic polymorphism (Table 2.5). Results were
largely the same when Southbury was included in the analysis.
Mating system
The fixation index (F) estimated with GenAlEx and BORICE were low in all sampling
sites, ranging from 0.13 to 0.20, and from 0.06 to 0.18, respectively. FIS was low, being 0.15
when estimated using GenAlEx and 0.10 when estimated using BORICE. Estimates of the
outcrossing rate were high in all sampling sites. Based on the results from MLTR, the multilocus
outcrossing rate (tm) and single-locus outcrossing rate (ts) ranged from 0.800 to 1.000 and from
0.804 to 1.000, respectively (Table 2.6). The difference between the multilocus and the single-
locus outcrossing rates (tm−ts) ranged from -0.004 to 0.008. It was positive in most sampling
sites, suggesting that some mating among relatives occurred. Based on the multilocus paternity
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correlation, the estimated number of pollen donors varied from 21.74 to 125.00. Using BORICE,
the posterior distributions for outcrossing rate (t-max) were high, ranging from 0.69 to 0.91
(Table 2.6).
Discussion
Genetic polymorphism
The urban forest patches investigated here are remnants of former native forests that were
once more extensive, but were encroached by urban development. Prediction 1 suggests that loss
of natural habitat is expected to reduce population sizes, which will result in lower genetic
polymorphism within urban populations when compared to non-urban populations. However,
contrary to this prediction, urban forests and old-growth forests exhibited similar levels of
genetic polymorphism. These results suggest that the ecological and environmental differences
between old-growth and urban forests did not result, at least in the time since urbanization, in
loss of genetic diversity in this long-lived tree in urban areas as observed in short-lived species
such as the yellow toadflax, Linaria vulgaris (Bartlewicz et al. 2015). Additionally, this lack of
differences in genetic polymorphism between urban and old-growth forests does not support the
assumption and limited empirical evidence that old-growth forests are reservoirs of genetic
polymorphism. Our results are in contrast to some studies of herbaceous plants that reported a
positive correlation of genetic polymorphism and forest age. For example, the broad buckler-
fern, Dryopteris dilatata (Reisch et al. 2007), and the white trillium, Trillium grandiflorum
(Vellend 2004). In contrast, similar to our results, some of the few studies on trees have found
that old-growth forests do not harbor higher genetic polymorphism. For example, Mosseler et al.
(2003) did not find differences in some measures of genetic polymorphism such as the mean
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number of alleles per locus and the mean observed heterozygosity in red spruce, Picea rubens,
between old-growth and young forest stands. Another example is Zhu and Lou (2013), who
compared Oriental thuja, Platycladus orientalis, old-growth (> 300 yrs.) populations and young
(< 100 yrs.) populations in both planted and natural forests. They did not find differences in
genetic polymorphism including mean observed heterozygosity and allelic richness among old-
growth populations (both natural and planted) and young natural populations. In another
example, Chhatre and Rajora (2014) found that old-growth and second-growth natural
populations of the eastern white pine, Pinus strobus, have similar levels of genetic
polymorphism measured, for example, as mean number of alleles per locus, allelic richness, and
mean observed heterozygosity.
Prediction 1 is based on one of the most commonly reported patterns of urban evolution
in short-lived organisms, decreased genetic polymorphism within urban populations, which has
been attributed to increased genetic drift. Thus, the lack of differences in genetic polymorphism
between urban and old-growth forests observed in this study may be due to similar rates of
genetic drift in these two environments. A version of this hypothesis was tested in Chapter 3.
Population genetic differentiation
Genetic differentiation was greater among old-growth forests than among urban forests,
which could be interpreted as support for prediction 4 that urban and non-urban populations have
diverged genetically. However, this could also be interpreted as a result of greater geographic
distances among old-growth forests than among urban forest patches (see discussion below about
the pattern of genetic differentiation). In addition, AMOVA indicated that most of the genetic
polymorphism in L. tulipifera occurred both within individuals and within sampling sites, while
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the differences among sites explained only a small portion of the total genetic polymorphism.
This result is similar to that found by Kovach (2009), who assessed L. tulipifera genetic
polymorphism in populations from two physiographic regions in southeastern U.S. (mountains
vs. coastal plain) using AFLPs, and showed that, while about 89% of the total polymorphism is
maintained within populations, differences among populations (which spanned an area similar to
the sampling area here) explained about 6%. These results are consistent with the general
observed pattern that tree species maintain more polymorphism within rather than among
populations, even when samples are taken from across a species' geographic range (e.g., Hamrick
et al. 1992, Hamrick and Godt 1996, Loveless and Hamrick 1984). In this study, the lowest
percentage of genetic polymorphism (2%) was explained by the differences between forest types
(urban vs. old-growth). This result was also similar to the study by Kovach (2009) wherein
differences between mountains and coastal plain regions explained about 5% of the total
polymorphism (although this percentage was not statistically different than zero).
Knowledge of the levels and patterns of genetic polymorphism are critical to directing
conservation efforts. For example, most of the genetic polymorphism may occur within
populations (little differentiation) or among populations (higher differentiation). Each of these
situations requires different strategies for the conservation of genetic polymorphism. In this
study, genetic differentiation among populations was low. Thus, the conservation of a particular
forest remnant may be less important, because its genetic polymorphism will also be maintained
elsewhere. On the contrary, if there had been high genetic differentiation among populations, it
would have been required that every one of the differentiated populations be protected in order to
conserve genetic polymorphism. This study shows the value of urban forest patches for the
conservation of genetic polymorphism.
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Pattern of genetic differentiation
The genetic differentiation among L. tulipifera populations in the mid-Atlantic U.S. was
characterized by a pattern of isolation by distance (IBD). This hypothesis was supported by a
significant Mantel test between a matrix of linearized D′EF and a matrix of geographical distance.
This finding does not support prediction 2 that plant populations will experience divergent
selection between urban and nonurban environments, because this type of selection could
eventually result in a pattern of isolation by environment (IBE) instead of IBD. Finding IBD in
this study is not completely surprising because even insect-pollinated trees whose pollen
dispersal can extend several kilometers in fragmented landscapes have limited dispersal. IBD is
ubiquitous in nature. A significant correlation between genetic and geographic distance has been
found in most of the studies in which IBD has been tested (Meirmans 2012). In contrast, IBE has
been observed in fewer species. For example, Lee and Mitchell-Olds (2011) showed that water
availability explains the discrete pattern of genetic divergence (east-west) that the mustard
Boechera stricta has in its native range. However, Lee and Mitchell-Olds (2011) did not
completely rule out IBD, which played a fundamental role in the continuous pattern of
divergence between north and south genetic groups of this mustard species.
Low gene flow and strong selection against genotypes adapted to other habitats are
among the factors predicted to promote local adaptation (Kawecki and Ebert 2004). Consistent
with this, most empirical evidence of local adaptation comes from herbaceous plants with low
levels of gene flow and under strong selection. In contrast to gene flow in herbaceous plants,
gene flow is strong and extensive in trees, as suggested for instance by the low levels of genetic
differentiation among tree populations. For example, when measured as GST (a multi allelic
analogue of FST among a finite number of demes; Nei 1973), population genetic differentiation
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was on average for woody long-lived perennial species 0.084 and for annuals 0.355 (Hamrick et
al. 1992). Because of its homogenizing influence, strong and extensive gene flow in trees can
oppose not only the divergence caused by genetic drift but also can counteract all but the
strongest local natural selection, preventing local adaptation. Therefore, adaptation to urban
environments and parallel evolution of urban populations in response to natural selection in
urban areas are likely not realistic for many tree species.
Mating system
Previous studies have shown that L. tulipifera mating system can vary among populations
and geographic regions with different environments. For example, using allozymes, Brotschol et
al. (1986) reported large outcrossing rate differences between mountain and coastal plain
populations in North Carolina (t = 0.86 vs. t = 0.55, respectively). In contrast, in this study,
populations in all sites were found to be almost completely or completely outcrossing. As the
multilocus outcrossing rates were equal or very close to unity, the selfing rate can be considered
very low to zero in each sampling site, regardless of urban forest type, which does not support
prediction 5 that insect-pollinated plants will have higher rates of self- pollination in urban areas.
In flowering plants, a major factor favoring the evolution of outcrossing is inbreeding depression
(Lloyd 1979, Lande and Schemske 1985, Schemske and Lande 1985). Self-compatible trees,
including species with mixed mating systems often display high inbreeding depression (Duminil
et al. 2009, Ishida, 2006; Robertson et al. 2011, Rodger and Johnson 2013). Although one
possible explanation for the fully or almost fully outcrossing mating system observed in all the
populations is embryo abortion due to early inbreeding depression, Sewell (1992) showed
through controlled pollinations that L. tulipifera is capable of seed germination from self-
fertilized ovules. Sewell (1992) suggested that in L. tulipifera, inbreeding depression caused by a
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pre-fertilization or post-pollen tube germination mechanism is unlikely because self and outcross
pollen are equally competitive at the early stages of pollen tube germination. Thus, if L.
tulipifera exhibits inbreeding depression, it must take place after the germination of the pollen
tube. As inbreeding depression can occur after fertilization, sampling late stages in the life cycle
may decrease the ability to detect mating system differences. For example, Kanashiro (1990)
observed increases in heterozygosity in L. tulipifera from age class 0 (newly germinated
seedlings) to age class 1 (saplings of 3-5 years old), and from age class 1 to age class 2 (saplings
of 7-10 years old), which he interpreted as due to the elimination of homozygous genotypes
because of lower fitness. In this study, to maximize the capture of germinated seeds from self-
fertilization, I sampled seedlings of age class zero. This allowed me the estimation of mating
system early in the life cycle before inbreeding depression could be fully manifest. Therefore, it
is unlikely that the lack of differences among populations and between forest types is explained
(at least completely) by inbreeding depression early in the fertilization process.
All populations sharing a fully outcrossed mating system might suggest that there is no
pollinator limitation in any of the sampling sites analyzed that could lead to reduction of
outcross-fertilized ovules. This assumption might be supported by studies showing that several
types of human disturbance, including urbanization, are not detrimental to pollinators. For
example, in a meta-analysis, Winfree et al. (2009) found that although both abundance and
species richness of unmanaged bees were significantly reduced by different anthropogenic
disturbance types (including logging, agriculture, and fires), the effects were not strong. These
effects were statistically significant only in study systems where extremely little natural habitat
remains. In addition, Winfree et al. (2009) found that the abundance of managed honey bees was
not associated with degree of anthropogenic disturbance. Furthermore, gene flow can even be
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enhanced if urban areas support more abundant and diverse pollinator communities than old-
growth forests. For example, Carper et al. (2014) observed higher bee abundance and species
richness in suburban forests in a metropolitan area when compared to natural forests. These
authors found that at the local scale, bee abundance and species richness were positively related
to the abundance and richness of flowering species within forests, while at the landscape scale
they were positively related to the proportion of developed open areas (e.g., yards and roadsides).
Additionally, our data suggest that some mating among relatives occurred in each of these sites.
This hypothesis is supported by most sites showing a positive difference between the multilocus
and the single-locus outcrossing rates (tm−ts).
The time to equilibrium between gene flow and genetic drift that produces population
genetic differentiation is long. Urbanization is a relatively recent process compared to the genetic
origin of the populations. Therefore, it is not surprising that we did not observed genetic
divergence between urban and old-growth populations. This is one potential explanation why
many studies of forest trees have not found significant population genetic consequences of
fragmentation (e.g., reduced genetic polymorphism, and increased genetic structure) due to
recent human activities such as logging, which has been referred to as the “paradox of forest
fragmentation genetics” (Kramer et al. 2008). In contrast, due to its dynamic nature and short
time scale, mating system can be influenced by recent and rapid environmental changes brought
on by urbanization. The estimation of the mating system allowed me to rule out the alternative
explanation that the lack of any urbanization effects was an artifact of the time to register
changes of the genetic measures used. The lack of differences in mating system instead suggests
that there are indeed no effects of urbanization on some key ecological and evolutionary
processes that shape genetic polymorphism within and among populations of this long-lived tree.
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CHAPTER 3
Small effective population sizes in a common, widespread, long-lived tree
Introduction
The genetic effective population size (Ne), a concept introduced by Wright (1931), is the
population genetic parameter that determines the rate of genetic drift in an idealized Wright–
Fisher population with discrete generations and sampling restricted only to the gamete pool
(Fisher 1930; Wright 1931). Ne is required to predict levels and dynamics of genetic
polymorphism because allele frequency changes in finite populations are dictated by the balance
between genetic drift and the other evolutionary forces. For example, the balance between the
input of new mutations and genetic drift (4Neµ for diploid nuclear loci) determines the expected
levels of genetic polymorphism. The balance between genetic drift and gene flow (4Nem for
diploid nuclear loci in a finite island model) determines the expected levels of allele frequency
divergence among subpopulations or demes (genetic differentiation). Also, the effectiveness of
natural selection to maintain advantageous alleles or purge deleterious ones is determined by the
product of Ne and the intensity of selection (Nes, where s represents the selection coefficient,
Charlesworth 2009, Hamilton 2009). Because of its role in these fundamental evolutionary
processes, estimates of Ne are a powerful predictive tool in conservation genetics to maintain
both genetic polymorphism and adaptive potential of populations under anthropogenic
environmental change (Frankham 1995, Schwartz et al. 2007, Palstra and Ruzzante 2008, Hare
et al. 2011).
Worldwide, urban centers are increasing in area and in human population size (Grimm et
al. 2008). Urbanization results in severe environmental change, including alterations in
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temperature (urban heat island effect), more impervious surface cover, and elevated pollution.
Furthermore, the similar environments associated with urbanization are predicted to have
evolutionary consequences such as convergent adaptation and isolation by environment (IBE).
Recent studies have implicated urbanization as a cause of stronger genetic drift in short-lived
animals such as mice and salamanders. This assumption comes from the most commonly
reported patterns of urban evolution, decreased genetic polymorphism within and increased
genetic differentiation among urban populations (reviewed in Johnson and Munshi-South 2017).
Similarly, in plants, stronger genetic drift in urban areas is one of the consensus predictions
about how urbanization is expected to affect evolution in urban environments (see Johnson et al.
2015). However, few direct measurements of genetic effective population size (Ne) have been
made in the context of urbanization (but see Unfried et al. 2012).
There are several genetic methods to estimate (Ne), which can be divided into temporal
and single-sample methods. Temporal methods use variance in allele frequency between samples
from the same population collected from at least two points in time. Single-sample methods can
be based on neighborhood size (Wright 1943), heterozygote excess when Ne is ≤ 30 (Pudovkin et
al. 1996, Balloux 2004, Zhdanova and Pudovkin 2008), coancestry (Nomura 2008), and linkage
disequilibrium (hereafter LD; Sved 1971; Hill 1977, 1981; Waples 2006, 2008; Sved 1971; Sved
et al. 2013). The logic behind Ne estimates using LD is that correlation between alleles at pairs of
loci are not expected at unlinked loci in an infinitely large population. However, in a finite
population allelic correlations arise just by chance. The smaller Ne, the larger the expected LD
(Hill 1977, 1981). Methods to estimate Ne based on LD have advantages over other methods. For
example, with microsatellite data, Ne estimates based on LD tend to have higher precision than
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estimates based on the temporal method, if the latter is based on samples taken few generations
apart (Waples and Do 2010).
Methods to estimate Ne based on LD assume a Wright-Fisher population model with
discrete generations and semelparity. However, actual biological populations depart from this
model, for instance, because of iteroparity (termed polycarpy for plants) and overlapping
generations, as is the case of long-lived plant species. In age-structured populations, each cohort
of individuals experiences genetic drift in different ways and at different times during the life
cycle. For example, through: i) age-specific mortality, because each age class has a different
expected probability of surviving into the next age class via random survival (lx), and ii)
expected fecundity at age x (bx), because the parents are individuals of different age classes
contributing unequally to the pool of gametes (Felsenstein 1971; Jorde and Ryman 1995). In
populations with overlapping generations, analysis of individuals belonging to a single age
cohort provides estimates of the effective size in a given age cohort, known as the effective
number of breeders or Nb (Waples 1989). Nb expresses the strength of genetic drift in one
reproductive event. Both Ne and Nb are influenced by the same factors, however, they measure
drift over different temporal scales. Nb mainly reflects local demographic and evolutionary
processes that occur on a seasonal or year to year basis, in contrast to the generational basis of
Ne. Understanding and estimating Nb is important for several reasons. Estimates of Nb may be
more suitable to elucidate evolutionary and ecological processes occurring at short timescales
than estimates of generational Ne (see Waples and Antao 2014). An example is in understanding
the impacts of urbanization in trees where Nb measures contemporary genetic drift.
Estimates of Nb are also important because they may be used to predict generational Ne
when the relationship between these two parameters is known. For example, in populations with
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discrete generations and complete reproductive synchrony, Nb and Ne are equal (Hare et al.
2011). In contrast, Nb and Ne can be very different under other types of life history (Waples
1990a, 1990b; Waples et al. 2013). In organisms with age-structure, most of our knowledge on
the relationship between Nb and Ne has come from work on semelparous fish species, often
economically important Salmoniformes (e.g., Waples 1990a, 1990b, 2002, 2005). In contrast, so
far distinction between Nb and Ne has rarely been made in plants, where Nb is often estimated as
generational Ne and as neighborhood size (e.g., Schaal 1980). Further, while young-of-the-year
sampling is commonly carried out in fish and other vertebrates for Nb estimates, such samples are
rarely obtainable for tree species. Instead, what is commonly done is to sample mixed-age
individuals, principally reproductively mature adult trees. Waples and Do (2010) hypothesized
that LD in mixed-age individuals should approximately estimate Ne if individuals are sampled at
random from a number of consecutive age cohorts roughly equal to the generation length. Some
studies such as Robinson and Moyer (2013), and Waples et al. (2014) have found support for this
hypothesis by simulating age-structured genetic data for several iteroparous species and applying
a method based on LD to estimate Ne. These studies also found that differences between Nb and
generational Ne values can be explained by differences in simple life history traits. However,
these studies were focused on relatively short-lived species. For example, in Waples et al.
(2014), the bottlenose dolphin (Tursiops truncatus) had the highest adult lifespan (AL, maximum
number of years during which an individual can reproduce), which was 27 years. Less is known
about the relationship between Nb and Ne in long-lived, iteroparous organisms such as numerous
tree species where maximum age is much longer (Condit et al. 1995; Chambers et al. 1998) and,
as a consequence, the range of age cohorts present among the breeders is usually larger, and
mixed-age samples are likely taken from non-consecutive age cohorts.
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Nb/Ne ratio in iteroparous species, including some plants, is highly correlated with two
simple life-history traits frequently influenced by growth rate, age at reproductive maturity (",
first age with bx > 0) and adult lifespan (AL, maximum age ! " + 1; Waples et al. 2013). Studies
have shown that urbanization has effects on growth rates of trees that could potentially alter "
and AL, and thereby impact Nb/Ne. For example, Searle et al. (2012) showed increased growth in
Quercus rubra L. urban-grown seedlings relative to those grown at rural sites. In a major
analysis of urban and rural trees around the world, Pretzsch et al. (2017) showed that urban trees
in the boreal and subtropical zones grow faster than their rural counterparts. In contrast, urban
trees in temperate zones grew significantly less than rural ones. Increased growth in cities is
presumably due to the urban heat island effect (higher temperatures), that may stimulate
photosynthetic activity and extend the growing season. Higher CO2 concentrations, larger
atmospheric N-deposition, and lower ozone concentration in urban areas compared to the
surrounding landscapes might also foster growth rates. Accelerated growth may mean earlier
ages at maturity, more rapid ageing, and shortened lifespan, which in turn can have effects on Nb,
Ne, and their relationship.
Although estimates of Ne and Nb based on LD have recently expanded, especially in
vertebrates, they remain rare in plants. The few examples include American eelgrass, Vallisneria
americana (Hydrocharitaceae; Lloyd et al. 2012); rice, Oryza sativa (Poaceae; Morais Júnior et
al. 2017); and palms, e.g., Chamaedorea alternans (Arecaceae; Peñaloza-Ramírez et al. 2016).
Even fewer empirical studies have focused in long-lived plants, including cycads, e.g., several
species of Zamia (Zamiaceae; Meerow et al. 2012), and Cycas (Cycadaceae; Feng et al. 2016,
2017); and trees, e.g., Euptelea pleiospermum (Eupteleaceae; Wei and Jiang 2011), Fagus
crenata (Fagaceae; Inanaga et al. 2016), and Platanus racemosa (Platanaceae; Johnson et al.
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2016) but none of them to directly compare the strength of genetic drift in urban and non-urban
habitats.
This study estimated the genetic effective population size in both urban and old-growth
populations of the long-lived tuliptree, Liriodendron tulipifera, in the mid-Atlantic U.S. Twelve
polymorphic nuclear microsatellite marker loci were used to genotype single age cohort
seedlings collected at nine geographic locations, as well as reproductively mature mixed-age
trees sampled at three of the same sites. The observed average LD between pairs of loci was used
to estimate ,- for seedlings and the effective population size for the mixed-age adult trees
(,. /01.2(34. ). To test the consensus prediction that genetic drift will be stronger in urban areas
(Johnson et al.’s [2015] prediction 3), I compared ,- between urban and old-growth populations.
To evaluate sampling approaches, ,- and ,. /01.2(34. were compared in the context of
demographic models that predict the relationship between Nb and generational Ne for long-lived
iteroparous species such as L. tulipifera. This latter comparison is critical to the interpretation of
the effective population size estimates and is especially relevant to studies of tree species where
seedling sampling is often difficult or impossible. It was observed that ,- estimates did not
differ between urban and old-growth sites and were uniformly small even in old-growth
populations that could be considered as reservoirs of genetic polymorphism. In addition,
,. /01.2(34. estimates were also small and did not differ from ,- estimates, consistent with
modeling results that showed Nb and Ne are expected to be very similar in this long-lived
iteroparous species.
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Materials and Methods
Study species
The tuliptree (Liriodendron tulipifera, L.) is a diploid (Liang et al. 2011), deciduous,
broadleaf tree in the family Magnoliaceae native to Eastern North America that is commercially
valued for wood and as a honey tree (Beck 1990). It is both a pioneer species as well as a long-
lived large canopy tree whose lifespan is usually about 200 to 250 years (some individuals may
live up to 300 years; Beck 1990, Busing 1995), with age at first reproduction (seed production)
reported as 15 to 20 years (Olson 1969; Renshaw and Doolittle 1958; Lusk et al 2007). L.
tulipifera reaches 100-150 feet (30.5!45.7 m) in height and 2-5 feet (0.6!1.5 m) DBH at
maturity. It is a prolific seeder, with a common seedfall of 741,000 to 1,482,000/ha (300,000 to
600,000/acre; Beck 1990). Seeds are wind-dispersed and remain viable for up to four years under
natural conditions in the forest litter (Clark and Boyce 1964), indicating that L. tulipifera does
not form a persistent soil seed bank. L. tulipifera is widespread and a component of numerous
native eastern forests from southern New England, west through southern Ontario and Michigan,
south to Louisiana and central Florida (Beck and Della-Bianca 1981), including the heavily
urbanized mid-Atlantic region. Although several native species do not do well in cities and no
longer occur there, within its range, L. tulipifera is found in both old-growth remnants and forest
patches embedded in urban areas where it reproduces and recruits naturally, which makes it an
excellent species model for this project.
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Field collections
Sampling was carried out during the summers of 2015 and 2016. To estimate effective
number of breeders (,-), one single age cohort of seedlings (age zero) were collected from nine
geographic locations (four urban forest patches, four old-growth forest remnants, and one
suburban forest) in the mid-Atlantic U.S. (Table 3.1). The metropolitan areas investigated here
were originally mostly covered by forests in which L. tulipifera was commonly found (Ward
1881; Maryland Geological Survey 1929; USDA-NRCS 1998; Adams et al. 2003). The urban
forest patches in this study are remnants of the original native forests which were encroached by
urban development. Most old-growth forests in North America are not considered virgin forests
(i.e. free from disturbance of modern humans) due to extensive encroachment, hunting,
agriculture, and timber harvesting. In the case of eastern temperate forests, there are several
definitions of old-growth, with no clear thresholds regarding disturbance frequency or age
(White and Lloyd 1994, Leverett 1996). The old-growth sites analyzed in this study were chosen
from the study of McGarvey et al. (2015), who used the operational definition of old-growth as
forests with a stand age of ≥ 150 years, following Cogbill (1996). The suburban site sampled in
this study was located close to the north geographic range limit of this species (Southbury,
Connecticut). At each sampling site, 42-48 seedlings were collected haphazardly in an area of
approximately 2,000 m2. To ensure that seedlings were one single age cohort and had germinated
in the year they were collected, sampled seedlings had both cotyledons still attached and the first
set of true leaves. To obtain estimates from mixed-age samples (,. /01.2(34. ), 12-20 adult
trees were haphazardly sampled from three of the nine locations where seedlings were collected
(Table 3.1). The total sample size was 426 seedlings, and 52 adult trees.
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Genetic analysis
Twelve microsatellite markers previously developed specifically for L. tulipifera
(Gutiérrez-Ozuna and Hamilton 2017) were used to genotype all sampled individuals. Three
multiplex microsatellite sets (Table 3.2) were amplified in 6.75µL multiplex PCR reactions with
the Type-it Microsatellite PCR Kit (QIAGEN, Valencia, California, USA). Each reaction
contained 3.125µL Type-it Multiplex PCR Master Mix, 1.25 µL 10x primer mix (1-20µM of
each primer), 1.375 µL ddH2O, and 1 µL of DNA template (concentration was not determined).
Thermocycling conditions were 5 min at 95ºC, followed by 32 cycles of 30 s at 95ºC, 90 s at
57ºC, and 60 s at 72ºC, and finally 30 min at 60ºC to promote 3’ untemplated A nucleotide
addition. Fluorescent PCR products were electrophoresed on an ABI PRISM 3100 Genetic
Analyzer, using either orange or red DNA size standard (MCLAB, San Francisco, California,
USA). Alleles were binned and scored with the microsatellite plugin version 1.4.4 implemented
in Geneious version 9.1.3 (https://www.geneious.com, Kearse et al. 2012) using the local
southern sizing algorithm and were double-checked manually to create expected allele size bins,
I estimated expected fragment sizes from the microsatellite sequences reported in Gutiérrez-
Ozuna and Hamilton (2017) with ±1 base pair estimation error.
Effective number of breeders and effective population size
The microsatellite genotype data were used to estimate ,- and ,. empirically per
sampling location with the LD-based method. ,- estimates were obtained using exclusively
seedlings of age class zero. In addition, because it has been shown that generational Ne is best
estimated using random samples of reproductively mature adults (Robinson and Moyer 2013),
,. estimates were obtained using exclusively reproductively mature mixed-age trees
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(,. /01.2(34. ). Although commonly referred to as methods based on LD, they rely on the
correlation of alleles at independent (not physically linked) loci. Effective population size is
estimated from observed average disequilibrium between several pairs of loci in multiple
individuals sampled from one population. With pairs of loci experiencing free recombination,
two-locus disequilibrium is expected to decay to nearly zero in few generations. This observed
disequilibrium, measured by O8, is caused not only by genetic drift but also by other factors
including physical linkage of loci, mutation, population structure, natural selection, finite sample
size, within-locus disequilibrium (e.g., excess of homozygosity) and genotyping artifacts such as
null alleles. Therefore, to estimate effective population size reflecting the strength of genetic drift
alone, it is required estimates of O8 that avoid or that have been corrected for the disequilibrium
contributed by the factors mentioned above (O8 LPQQ.LR.2 ).
Ne and Nb can be estimated from O8 LPQQ.LR.2 ) using the general relationship:
,.STOS,- = S;
V QW(LPQQ.LR0P9SX3LRPQ Equation 1
The software SpEED-Ne (Hamilton et al. 2018) was used to estimate r2 and the
correction factor in Equation 1, permuting among loci but not within loci (maintaining allelic
pairs). I used three confidence intervals: 1) 95% confidence interval for the correction factor
determined by permutation, 2) jackknife over individuals, and 3) jackknife over individuals with
an inverse hyperbolic tangent transformation (see Jones et al. 2016). The distinction between the
latter two has to do with jackknife performance when there is a large number of non-independent
comparisons. Because these are all pairwise comparisons, the number of non-independent
comparisons grows rapidly and as the sample sizes gets bigger. Jones et al. (2016) argued that
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this post hoc transformation corrects for non-independent comparisons and improves the
coverage performance of the estimated confidence interval.
Life history simulations
To examine how life history traits affect Nb, Ne, and their relationship (Nb/Ne) in a long-
lived, iteroparous organism, I conducted computer simulations under a specific model
implemented in the computer program AgeNe (Waples et al. 2011). This program uses a
discrete-time, age-structured, and deterministic model. It assumes a closed population of constant
census size, where individuals of age x produce an average of bx offspring (age-specific
fecundity) and then survive to age x + 1 with probability sx (age-specific survival). Both bx and sx
are independent of events in previous time periods. The fraction of individuals in a cohort
surviving to age x is lx. AgeNe tracks only individuals that survive to their first birthday, so
fecundities are scaled to result in a stable population that produces a fixed number (N1) of
individuals per cohort that survive to age 1. Given a specified life history table and a value for
N1, AgeNe estimates the total population size, the adult population size, and numbers in each age
class (Nx), as well as Ne and Nb. Because the required detailed demographic data to use AgeNe is
not available for L. tulipifera, the survival and fecundity of another long-lived tree species, the
hoop pine, Araucaria cunninghami, were used. This table was originally reported as stage-based
data by Enright and Watson (1991), and given in Appendix S2 of Waples et al. (2013) as an age-
based life table, which generates a Type III survivorship curve. Trees have Type III survivorship
curves (Harcombe 1987), where mortality is a decreasing function of age (Pinder et al. 1978).
Based on the hoop pine life history table, eight life-history scenarios (A-H) were
designed differing either in age-specific survival probabilities (A, B), age at reproductive
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maturity (C, D), or maximum age (E-H). In scenario A, survival probability was the lowest in
age 1 (s1 = 0.05) and increased from age 2 onwards. To examine how increased mortality in early
years affects Nb, Ne, and their relationship (Nb/Ne) in a long-lived tree, age 2 survival probability
was decreased from 0.26 to 0.05 in scenario B. In this scenario, survival probabilities were the
lowest in both ages 1 and 2 (s1 = 0.05 and s2 = 0.05), increasing from age 3 onwards. Waples et
al. (2013) showed that two frequently related life history traits, age at reproductive maturity (",
first age with fecundity greater than zero) and adult lifespan (AL, maximum age ! " + 1), are
highly correlated with Nb/Ne in iteroparous, relatively short-lived species. To examine how "
affects Nb, Ne, and Nb/Ne in long-lived trees, this life history trait was delayed from 6 years to 16
and 31 years in scenarios C and D respectively, keeping age-specific survival probabilities
unchanged from original. Further, because AL is related to maximum age in addition to ", the
effects of maximum age were examined, shortening it from 400 years in scenario E to 300 years
in scenario F, 200 years in scenario G, and 100 years in scenario H.
Results
Effective number of breeders (,-) for each of the nine sampling sites (four urban, four
old-growth, and one suburban) are presented in Table 3.3. There was a twofold variation in the
median ,- from 10.73 to 112.94. However, ,- confidence intervals indicated no differences
between these estimates across sampling sites. In addition, there were no differences in median
,- between urban and old-growth sites. Furthermore, at the three sites in which the two ways of
sampling were performed (single age cohort of seedlings, and mixed-age samples of adult trees),
,- and ,. /01.2(34. estimates were not different from each other (Table 3.4). The median
,. /01.2(34. varied from 45.77 to 64.61. The overlapping of the confidence intervals of ,- and
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49
,. /01.2(34. suggested no differences between these estimators (Table 3.4 and Figure 3.1).
Table 3.5 shows correction factor point estimates, and two confidence intervals for these
correction factor estimates: 1) jackknife over individuals (Figure 2), and 2) jackknife over
individuals with an inverse hyperbolic tangent transformation, which were very consistent.
The results of the simulations of populations using life-history data are presented in Table
3.6. Ne decreased from 15.9 in Scenario A (95% mortality the first year) to 3.1 in Scenario B
(95% mortality in both the first and second years). Similarly, Nb decreased from 16.7 in Scenario
A to 3.3 in Scenario B. The ratio Nb/Ne was close to one in both scenarios A and B. When age at
first reproduction was delayed, Ne decreased from 14.6 in scenario C (16 years) to 12.9 in
Scenario D (31 years). A similar pattern was observed in Nb, which decreased from 14.3 in
Scenario C to 11.3 in Scenario D. In both scenarios C and D, Nb/Ne ratio was close to one. When
maximum age was shortened, Ne increased steadily, ranging from 12.9 in Scenario E (maximum
age of 400 years) to 31.2 in Scenario H (maximum age of 100 years). Likewise, Nb increased
from Scenario E to Scenario H. However, the increments were not as pronounced as those
observed in Ne, ranging from 11.3 to 14.3. The ratio Nb/Ne was close to one in scenarios E and F,
and decreased to 0.76 in scenario G and 0.46 in scenario H.
Discussion
The most commonly reported patterns of urban evolution, decreased genetic
polymorphism within and increased genetic differentiation among urban populations, have been
attributed to increased genetic drift (Johnson and Munshi-South 2017). However, few direct
measurements of the strength of genetic drift in urban and non-urban habitats via estimates of
genetic effective population size have been made. In this study, ,- estimates did not differ
!!
50
between urban forest patches and old-growth forests. Additionally, ,- estimates were uniformly
small among populations, even in old-growth populations that could be considered as reservoirs
of genetic polymorphism. Therefore, contrary to the prediction that the environmental changes
associated with urbanization cause stronger genetic drift, this evolutionary force was strong in
both forest types.
The absence of population genetic consequences of anthropogenic disturbance on forest
tree species has also been documented in other partially deforested systems such as mixed
agricultural-forest landscapes. For example, habitat fragmentation is expected to lead to similar
population genetic patterns to those predicted for urban evolution, decreased genetic
polymorphism within and increased genetic differentiation among plant populations due to
increased genetic drift, elevated mating among relatives, and reduced gene flow (Young et al.,
1996). However, empirical studies have shown that not all habitat loss and fragmentation events
result in a decrease of genetic diversity in forest tree populations (reviewed in Young et al. 1996;
Lowe et al. 2005, 2015). The scarcity of significant consequences of habitat fragmentation and
disturbance in forest tree populations due to human activities such as logging, has been referred
to as the “paradox of forest fragmentation genetics” (Kramer et al. 2008).
One of the mechanisms that have been proposed to explain this paradox is the long-lived
nature of trees, because most forest fragmentation is recent and current populations of long-lived
trees may simply pre-date it (Kramer et al. 2008; Lowe et al. 2015). Urbanization is a relatively
recent phenomenon as well. However, this study avoids the paradox by analyzing seedlings
instead of adult trees. In plants, the seedling stage is the most sensitive phase of the life cycle to
environmental changes (Harper 1977, Silvertown and Charlesworth 2001, Leck et al. 2008).
Therefore, seedlings are expected to be particularly sensitive to anthropogenic habitat
!!
51
disturbance such as urbanization. Furthermore, L. tulipifera has a short-lived seed bank, and as a
consequence, seedlings are representative of recent ecological and evolutionary processes. By
focusing on seedlings, I had the potential to detect recent processes acting on genetic drift. In
addition, ,- quantifies genetic drift over short timescales relative to ecological processes.
Therefore, the lack of a difference in ,- is best explained by lack of an urbanization effect,
rather than as a consequence of temporal averaging of drift over many generations.
Extensive gene flow is a mechanism that potentially explains the observed absence of
genetic consequences of urbanization in seedlings. Noreen et al. (2016) have shown that insect-
mediated gene flow persisted among individuals of the tropical tree species Koompassia
malaccensis in an urban landscape, even across >2.5 km of impervious surface. Several of
Noreen et al. (2016) results are similar to findings from insect-pollinated trees in mixed
agricultural–forest landscapes in which pollen dispersal can extend several kilometers in
fragmented landscapes, e.g., in Swietenia humilis (White et al. 2002) and Dinizia excelsa (Dick
et al. 2003). Extensive pollen movement could swamp out the localized effects of urbanization,
if they indeed exist.
It is noteworthy that L. tulipifera, a common species with large fecundities exhibited
estimates of both ,- and ,. /01.2(34. on order of ten or one hundred. Small effective
population sizes have also been observed in other species. For example, in marine fish, Ne has
been shown to be three to six orders of magnitude less than population census size (N), or even
smaller (e.g. Hauser et al. 2002, Turner et al. 2002). Because many of these species have very
large census sizes and high fecundities, small Ne estimates have been treated with skepticism and
are potentially explained by downward biased caused by aspects of sampling methods or
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52
estimation methodology. For example, Waples (2006) suggested that estimates of Ne based on O8
for unlinked loci are strongly biased downwards and he proposed a regression fit estimator based
on simulated data. Small ,.. estimates can also be the result of not taken into account processes
and factors that could increase the total amount of observed disequilibrium (in addition to the
disequilibrium caused by genetic drift). For example, admixture can occur among genetically
differentiated populations and increase the degree of LD when individuals from these
subpopulations with differentiated allele frequencies end up mixing in the sample, which can in
turn downwardly bias ,.. Another potential factor to explain small ,. estimates is the presence
of null alleles. For example, Hamilton et al. (2018) using simulated data showed that the widely
employed Waples (2005) Ne estimator has very large variance when null alleles are present,
which could yield biased ,. estimates. Lastly, another factor that could produce small Ne
estimates is age-structure. For example, in age-structured populations, young-of-the-year
samples produce Nb estimates. If age structure is not taken into consideration, these estimates
could be interpreted as generational Ne estimates instead of Nb estimates, and these two
parameters can be very different under certain types of life history. For example, Nb can be
smaller than Ne, as in the case of several Salmoniformes. Waples (1990a, 1990b) showed that in
an overlapping generation model, with semelparous and asynchronous reproduction (salmon
model), Ne is approximately equal to g*Nb, where g is the generation length (average age of
parents). In contrast, Nb can be greater than Ne in iteroparous organisms. For example, by
analyzing 63 iteroparous species of animals and plants, Waples et al. (2013) found that Nb > Ne
in over half the species.
!!
53
The effective population sizes observed in this study are truly small because I accounted
for each of the above-mentioned factors that can produce small estimates. For example, given
that there is evidence of null alleles in some of the loci used in this study (Gutiérrez-Ozuna and
Hamilton 2017), I did not use the widely employed Waples (2006) Ne estimator implemented in
the program LDNe (Waples and Do 2008), which has been shown to be subject to troubles with
null alleles (see Hamilton et al. 2018). Instead, the permutation approach implemented in the
software SpEED-Ne was used to estimate within-locus disequilibrium caused by the combination
of finite sampling and null alleles. Further, although admixture LD caused by occurrence of more
than one subpopulation can downwardly bias ,., Waples and England (2011) used computer
simulations to show that unless the migration rate is very low (which leads to highly
differentiated subpopulations), there is not much of an impact on estimates of ,.. In this
particular case, there is low population genetic differentiation among L. tulipifera sampling sites.
For example, Gutiérrez-Ozuna and Hamilton (manuscript in preparation) estimated L. tulipifera
population genetic differentiation in the mid-Atlantic U.S., finding that global D′EF was 0.273,
which suggests that admixture is not a major factor contributing to the total amount of observed
disequilibrium in this study. Finally, the distinction between Nb and Ne was drawn. To estimate
Nb, an extra effort to sample single age cohorts was made. To achieve this, I collected seedlings
germinated the same year of collection. In addition, exclusively mixed-age samples of adult trees
were used to estimate Ne (,. /01.2(34. ).
It is helpful to understand the conditions under which Nb might be much less than
generational Ne in order to know if a given empirical estimate represents ,. or ,-. As mentioned
above, in some iteroparous organisms such as fish and other vertebrates, Nb and Ne can be very
different. Contrastingly, this study predicts based on simulations that Nb and Ne values are
!!
54
similar in long-lived trees. The simulations manipulating life history traits generated Nb/Ne ratios
close to one across most scenarios examined. Nb and Ne only diverged increasingly as maximum
age was reduced to one half or to one quart of its original value, which increased Ne values and
yielded Nb/Ne ratios of 0.76 and 0.46, respectively. This was because if age at maturity is kept
unchanged, shortening maximum age decreases adult lifespan (maximum age ! " + 1), and this
latter life history trait has been also shown to be highly correlated with Nb/Ne (Waples et al.
2013). The lack of disparity between Ne and Nb observed in most scenarios suggests that the
strength of genetic drift in long-lived trees is expected to be similar per reproductive event and
per generation.
The relationship between Nb and Ne has rarely been examined empirically in very long-
lived species with overlapping generations and iteroparity such as trees. Here, Nb, by definition,
was estimated analyzing seedlings belonging to a single age cohort. This way of sampling is not
practical in many tree species because identifying seedlings based on phenotypic characteristics
can be challenging, especially if they are a small size or highly affected by herbivory. Seedling
identification is particularly difficult in some environments such as tropical forests due to the
high number of species. The most common sampling approach in trees is sampling mixed-age
reproductively mature individuals. However, consistent with our modeling results, empirical
,. /01.2(34. estimates did not differ from ,- estimates, suggesting that the two sampling
approaches give similar information about the strength of genetic drift in long-lived trees.
Understanding the relationship between Nb and Ne is important, for example, to predict
adaptation to urban environments because most of the population genetic theory on adaptation
and genetic drift-natural selection balance is based on the concept of generational Ne. For
!!
55
example, natural selection will dictate allele frequencies if 4Nes >> 1, the selection coefficient (s)
is large and Ne is not very small. In contrast, if 4Nes << 1, either s is extremely small or genetic
drift is very strong because Ne is very small (as is the case of this study). In either of the two
latter scenarios, genetic drift will dictate the allele frequencies (Kimura 1983). In this study,
empirical estimates of ,. /01.2(34. were small and did not differ from ,- estimates in the sites
where the two ways of sampling were performed, consistent with modeling results that showed
that Nb and Ne are expected to be very similar in long-lived trees. In addition, ,- estimates were
small across all populations, which might suggest that generational Ne is also small in each site.
Therefore, it might be predicted that only genes with major effects and under very strong natural
selection will experience adaptation not only in urban but also in old-growth populations.
Most of our knowledge on population genetic effects of urbanization come from short-
lived organisms such as mice, salamanders, and herbaceous plants. This study adds to this
literature by extending the age distribution. In addition, this study is novel because it examines
urbanization effects in tree populations quantifying directly the strength of genetic drift through
measurements of genetic effective population size (Ne), rather than other approaches that have
been used. Uniformly small estimates of both ,- and ,. /01.2(34. across all populations
documented here suggest that genetic drift is going to be a substantial force in terms of
predicting levels of genetic polymorphism through time and also generating population genetic
differentiation in L. tulipifera.
!!
56
APPENDIX A: Tables
Table 1.1. Characteristics of 23 polymorphic genomic microsatellite loci isolated from
Liriodendron tulipifera.
Locus Primer sequences (5’—3’) Repeat
motif
Observed
amplicon
size (bp)
Ta
(ºC)
Fluorescent
labeling
method
Identifiera
Lt006 F: GTGGTAACGCATGAGATGGC AAG12 416-437 58 M13-labeled KX869968
R: TGAGCTTTCCATCAGGTGAGC
Lt011 F: GCATGGACATGGTGTAACCC AAT12 223-250 58 6-FAM1 KX869967
R: TTCCATGTGTGCCCTACTGC
Lt014 F: CTCACATGAACAACAAGGAAACC AAT16 108-126 58 M13-labeled HWI-
1KL163:137:H7DJKADXX:1:2116:14
211:16050 1:N:0:CTTGTA R: CTGGGATCTGCACTGATTGG
Lt023 F: CGTGGCCAGCATCTTGTAGC AAC16 113-140 58 M13-labeled HWI-
1KL163:137:H7DJKADXX:1:2214:45
79:5389 1:N:0:CTTGTC R: CAAGAACAACGAGGCAAACAGC
Lt025 F: AGTTGGGAATTGGGACAAGG ATC13 105-120 57 M13-labeled HWI-
1KL163:137:H7DJKADXX:1:2114:75
09:71461 1:N:0:CTTGTA R: TTCAGTGCTCCAGTTTTCACG
Lt032 F: GCTCCTACAAACATCAAAAGC AAG13 95-122 50 M13-labeled HWI-
1KL163:137:H7DJKADXX:1:2109:25
16:54908 1:N:0:CTTGTA R: CAAAACCCATTTCGTGTTCC
Lt035 F: CACAGAGCTTGGTGCTTTACG AAT16 89-119 56 NED1 HWI-
1KL163:137:H7DJKADXX:1:1115:14
496:36717 1:N:0:CTTGTA R: AAGTCCATGTTCCACTCATTCG
Lt036 F: TTGAAGTTTGAATCCCCATCC ATC13 107-143 50 VIC3 HWI-
1KL163:137:H7DJKADXX:1:1109:19
109:85000 1:N:0:CTTGTA R: TGATTGGGCCATGTTAATCG
Lt043 F: TCATCTTCCTTTGGTTTGCC AAG12 119-149 50 6-FAM3 HWI-
1KL163:137:H7DJKADXX:1:2103:58
25:78158 1:N:0:CTTGTA R: TGGGGATTTGACAGAGAACG
Lt052 F: TGGTCCCGAGATTGTTCACC AAC14 94-115 57 M13-labeled HWI-
1KL163:137:H7DJKADXX:1:2106:81
32:35499 1:N:0:CTTGTA R: TCTTACCCACCACAACCATCG
Lt054 F: CCTCGTAGTGTTGATCGTTGC AAT16 88-121 57 NED2 HWI-
1KL163:137:H7DJKADXX:1:1214:10
945:60086 1:N:0:CTTGTA R: TCAACCTTCCACCAGTGTCC
Lt059 F: CTGCCCCTTCAAATTCTTGG AGG12 93-111 55 NED3 HWI-
1KL163:137:H7DJKADXX:1:2102:16
887:46302 1:N:0:CTTGTA R: AATTGCGTGAAGCTCAGACC
Lt060 F: CCTACTCTTCCGGAGTTTCG AAT16 126-150 57 M13-labeled HWI-
1KL163:137:H7DJKADXX:1:1215:81
50:67169 1:N:0:CTTGTA R: GGGATGGGGTGGAATATAAGC
Lt061 F: TTGGCGGATGATTGAGAGC AAG14 122-146 55 PET3 HWI-
1KL163:137:H7DJKADXX:1:2205:15
636:87476 1:N:0:CTTGTA R: TTAATGCCGTGGGTTCTGC
Lt064 F: AAGGATGACTTTCACTGAGG AAT13 120-156 53 6-FAM2 HWI-
1KL163:137:H7DJKADXX:1:1203:17 R: CATTGGGACTTTATTTCTCTCC
!!
57
908:71099 1:N:0:CTTGTA
Lt066 F: TCTGGCCCTTGATACTGTGG ATC12 134-152 57 M13-labeled HWI-
1KL163:137:H7DJKADXX:1:1109:17
69:97275 1:N:0:CTTGTA R: CCCACTTGGGTGTTTCAGG
Lt068 F: AAACTCCCTAACAAGGGTCTCC AAT16 89-131 55 VIC2 HWI-
1KL163:137:H7DJKADXX:1:2211:40
01:84575 1:N:0:CTTGTA R: ACCACAACACAGAAACAATGGG
Lt070 F: TTCTCCGCCATCGTCTTACC ATC12 112-133 57 6-FAM1 HWI-
1KL163:137:H7DJKADXX:1:2211:16
362:43312 1:N:0:CTTGTA R: CTAATGAACGGTCGGGATGG
Lt075 F: CATCGCCATTGTTTTCTCTGC AAT13 119-134 55 M13-labeled HWI-
1KL163:137:H7DJKADXX:1:1201:45
54:96377 1:N:0:CTTGTA R: TAACTGCCTGCGTATCATCC
Lt077 F: TATCCAAACGGCCCTTAACC AAT14 84-105 55 HEX1 HWI-
1KL163:137:H7DJKADXX:1:1214:22
79:95306 1:N:0:CTTGTA R: GATCACAAAACTCCCACATGC
Lt079 F: GGGAGACTCGGCTTTAATCGCC ATC12 76-94 61 PET2 HWI-
1KL163:137:H7DJKADXX:1:2215:19
866:60074 1:N:0:CTTGTA R: GAGTGGAGTAGCGGGACAGG
Lt080 F: GGCCTGAATTCCTTTGTTCCC AAT12 130-151 57 M13-labeled HWI-
1KL163:137:H7DJKADXX:1:1107:42
32:34637 1:N:0:CTTGTA R: CCCTCAATGTACACGCTTGC
Lt081 F: ATGGATTCCGGCAAGTCTCC AAT17 96-123 57 M13-labeled HWI-
1KL163:137:H7DJKADXX:1:2115:20
835:65097 1:N:0:CTTGTA R: TGAGGAAGAGAACAAACAGGGG
Note: Ta, annealing temperature.
In column Fluorescent labeling method 1, 2 or 3 indicate PCR multiplex sets. a Numbers are either GenBank accession numbers or Illumina sequence identifiers associated
with NCBI's Short Read Archive Project: PRJNA331147, BioSample: SAMN05417503.
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58
Table 1.2. Locality and voucher information for the Liriodendron tulipifera samples used for the
development of polymorphic microsatellite markers.
Location County, State Latitude Longitude Voucher no.a
No. of
individuals
sampled
Frog Canyon,
Smithsonian
Environmental Research
Center
Anne Arundel,
Maryland
38.884284
-76.552695
MARY1021991
20
James Madison’s
Landmark Forest
Orange,
Virginia
38.226667 -78.179444 MARY1021990 20
Saddler’s Woods Camden, New
Jersey
39.900722 -75.057750 MARY1021989 12
a Voucher samples for each location were deposited in the Norton-Brown Herbarium of the
University of Maryland, College Park, Maryland, USA (MARY).
!!
Table 1.3. Genetic properties by individual and pooled sampled locations of 23 polymorphic microsatellite markers developed in
Liriodendron tulipifera.
Locus Montpeliera Frog Canyona Saddler’s Woodsa Pooled locations
N k HE HO N k HE HO N k HE HO N k HE HO PIC F NullIIM Lt006 10 6 0.789 0.800 10 2 0.100 0.100 10 3 0.689 0.700 30 6 0.637 0.533 0.591 0.166 0.074 Lt011 20 6 0.726 0.600 19 7 0.834 0.789 12 5 0.779 0.833 51 9 0.789 0.725 0.749 0.081 0.039 Lt014 10 6 0.768 0.600 10 5 0.800 0.800 10 6 0.816 0.700 30 7 0.821 0.700 0.782 0.149 0.078 Lt023 10 5 0.505 0.500 10 5 0.616 0.600 10 6 0.779 0.800 30 6 0.767 0.633 0.715 0.177 0.075 Lt025 10 4 0.658 0.900 10 2 0.268 0.300 10 3 0.426 0.300 30 4 0.462 0.500 0.420 -0.085 0.056 Lt032 10 5 0.700 0.700 10 5 0.711 0.900 10 5 0.779 0.700 30 8 0.809 0.767 0.773 0.053 0.036 Lt035 19 7 0.750 0.263*** 12 6 0.815 0.167*** 11 4 0.571 0.273* 42 10 0.826 0.238*** 0.793 0.714 0.402† Lt036 20 8 0.856 0.850 20 8 0.856 0.750 12 6 0.812 0.917 52 9 0.850 0.827 0.822 0.027 0.017 Lt043 20 6 0.805 0.750 20 5 0.703 0.300*** 12 7 0.862 0.667** 52 7 0.807 0.558*** 0.771 0.311 0.098† Lt052 9 4 0.699 0.667 10 6 0.684 0.700 10 5 0.805 0.900 29 7 0.782 0.759 0.744 0.030 0.036 Lt054 19 7 0.764 0.579 20 7 0.782 0.700 10 6 0.826 0.100*** 49 11 0.806 0.531*** 0.773 0.344 0.171† Lt059 20 5 0.583 0.650 20 4 0.674 0.500* 12 5 0.717 0.917 52 6 0.694 0.654 0.646 0.059 0.030 Lt060 10 5 0.774 0.600 10 5 0.695 0.500 10 7 0.789 0.700 30 9 0.775 0.600 0.733 0.228 0.101 Lt061 20 4 0.458 0.450 20 3 0.555 0.050*** 12 3 0.301 0.333 52 6 0.492 0.269*** 0.432 0.455 0.130† Lt064 20 5 0.750 0.600 17 7 0.693 0.176*** 12 5 0.725 0.417 49 10 0.825 0.408*** 0.791 0.508 0.238† Lt066 10 4 0.695 0.600 10 4 0.574 0.600 10 2 0.521 0.500 30 4 0.598 0.567 0.519 0.053 0.064 Lt068 20 12 0.837 0.900 20 10 0.873 0.850 12 10 0.783 0.833 52 13 0.876 0.865 0.855 0.013 0.014 Lt070 20 3 0.578 0.450 19 5 0.629 0.632 12 3 0.663 0.667 51 5 0.632 0.569 0.557 0.102 0.059 Lt075 10 2 0.395 0.500 10 2 0.268 0.100 10 2 0.100 0.100 30 4 0.272 0.233 0.254 0.145 0.168 Lt077 20 4 0.742 0.700 19 6 0.725 0.842 12 6 0.641 0.750 51 7 0.765 0.765 0.717 0.000 0.033 Lt079 20 4 0.391 0.400 20 3 0.188 0.200 12 3 0.420 0.417 52 4 0.320 0.327 0.297 -0.021 0.025
Lt080 10 3 0.542 0.200* 10 5 0.774 0.200** 10 4 0.616 0.300* 30 6 0.654 0.233*** 0.587 0.647 0.438† Lt081 10 6 0.811 0.600 10 5 0.774 0.700 10 6 0.763 0.800 30 7 0.809 0.700 0.766 0.137 0.065
Note: N, number of individuals genotyped; k, number of alleles; HE, expected heterozygosity under random mating; HO, observed heterozygosity; *p<0.05, **p<0.01, ***p<0.001, for hypothesis test of Hardy-Weinberg expected genotype frequencies; PIC, polymorphism information content; F, fixation index; NullIIM, estimate of null allele frequency given the individual inbreeding model (IIM); † 95% highest posterior density interval does not include zero. a See Table 1.2 for locality and voucher information.
59
!!
60
Table 2.1. Site information for the Liriodendron tulipifera samples used.
Sampling site Forest
type County, State Latitude Longitude n
Belt Woods
Old-
growth
Prince George, Maryland 38.9036915 -76.7598648 48
Frog Canyon Anne Arundel, Maryland 38.8842837 -76.5526953 48
Montpelier Orange, Virginia 38.216053 -78.16418 48
Saddler's Woods Camden, New Jersey 39.9007314 -75.0577526 48
British Embassy
Urban
Washington, D.C. 38.921773 -77.063099 48
Johns Hopkins Baltimore, Maryland 39.3287183 -76.6241754 48
Rock Creek Washington, D.C. 38.952632 -77.040443 48
Georgetown Washington, D.C. 38.908537 -77.077736 48
Southbury Suburban Southbury, Connecticut 41.499565 -73.179114 42
Note: n, number of genotyped individuals.
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61
Table 2.2. Microsatellite marker loci used to genotype Liriodendron tulipifera seedlings.
Locus Multiplex set 5’ fluorophore Primer concentration (each
primer, µM)
Lt011 1 6-FAM 20
Lt035 1 NED 20
Lt036 3 VIC 2
Lt043 3 6-FAM 5
Lt054 2 NED 2
Lt059 3 NED 2
Lt061 3 PET 2
Lt064 2 6-FAM 10
Lt068 2 VIC 2
Lt070 1 6-FAM 1
Lt077 1 HEX 1
Lt079 2 PET 2
!!
Table 2.3. Summary measures of genetic polymorphism for each sampling sampling site (with standard errors in parentheses),
including forest type, number of genotyped individuals (n), average number of different alleles per locus (ka); average number of
effective alleles per locus (ke), average number of private alleles per locus (kp); multilocus average observed heterozygosity (HO),
multilocus average expected heterozygosity (HE), and the fixation index (F).
Site Forest type n ka kea kp HO
b HEc Fd
Belt Woods
Old-growth
48 6.75 (0.79) 4.21 (0.58) 0.08 (0.08) 0.60 (0.06) 0.72 (0.03) 0.17 (0.08)
Frog Canyon 48 6.83 (0.66) 4.47 (0.58) 0.08 (0.08) 0.58 (0.06) 0.71 (0.05) 0.15 (0.07)
Montpelier 48 6.17 (0.73) 3.39 (0.38) 0.08 (0.08) 0.59 (0.05) 0.66 (0.04) 0.10 (0.04)
Saddler's Woods 48 6.75 (0.60) 3.65 (0.35) 0.25 (0.13) 0.58 (0.06) 0.68 (0.05) 0.13 (0.07)
British Embassy
Urban
48 6.17 (0.56) 4.07 (0.52) 0.00 (0.00) 0.56 (0.07) 0.68 (0.05) 0.20 (0.07)
Johns Hopkins 48 6.33 (0.64) 3.91 (0.53) 0.17 (0.11) 0.59 (0.07) 0.67 (0.06) 0.15 (0.06)
Rock Creek 48 6.33 (0.67) 3.68 (0.38) 0.17 (0.11) 0.59 (0.06) 0.70 (0.03) 0.16 (0.07)
Georgetown 48 6.08 (0.63) 3.59 (0.38) 0.00 (0.00) 0.60 (0.03) 0.69 (0.02) 0.13 (0.04)
Southbury Suburban 42 4.83 (0.47) 2.42 (0.26) 0.00 (0.00) 0.45 (0.06) 0.53 (0.05) 0.13 (0.06)
Note: a ke = 1/( $%&)(
%)* b HO = observed frequency of heterozygotes / n c HE = 1 − $%&(
%)* d F = (HE - HO) / HE where pi is the frequency of the ith allele.
62
!!
Table 2.4. Pairwise standardized genetic differentiation (F’ST, below diagonal) and Euclidean geographic distances in kilometers
(above diagonal) for all sampling site pairs.
Site Belt
Woods
Frog
Canyon Montpelier
Saddler's
Woods
British
Embassy
Johns
Hopkins
Rock
Creek Georgetown Southbury
Belt Woods — 18.1 144.1 183.5 26.3 48.7 24.9 27.5 419.2
Frog Canyon 0.150 — 158.6 171.1 44.4 49.8 42.9 45.5 408.2
Montpelier 0.213 0.221 — 327.1 123.8 182.0 127.5 121.9 560.5
Saddler's
Woods 0.329 0.235 0.264 — 203.8 148.5 200.3 205.6 238.1
British
Embassy 0.250 0.192 0.254 0.241 — 59.0 4.0 1.9 436.9
Johns
Hopkins 0.274 0.259 0.320 0.341 0.087 — 55.1 60.9 378.5
Rock Creek 0.235 0.205 0.266 0.297 0.106 0.152 — 5.9 433.1
Georgetown 0.133 0.238 0.251 0.345 0.193 0.230 0.118 — 438.8
Southbury 0.387 0.419 0.376 0.419 0.436 0.423 0.382 0.424 —
63
!!
64
Table 2.5. Analysis of molecular variance (AMOVA) for 384 seedlings from 8 sampling sites in
2 forest types.
Source df SS MS Est. Var. % Fixation
indices p-value
Between forest
types
1
63.954
63.954
0.090
2%
FRT = 0.20
0.001
Among sampling
sites
6 175.526 29.254 0.251 5% FST = 0.74 0.001
Among individuals 376 1951.469 5.190 0.902 19% FIS = 0.210 0.001
Within individuals 384 1300.000 3.385 3.385 73% FIT = 0.269 0.001
Total 767 3490.949
4.629 100%
Note: df, degrees of freedom; SS, sum of squares; MS, mean squares; Est. Var., estimated of
variance; %, percentage of total variation; p-value based on 999 permutations. FRT refers to
between forest type to total, FST refers to sampling sites to total, FIS refers to among individuals
within sampling site, FIT refers to within individuals to total. FSR (sampling sites within forest
type) was 0.055 (p-value =0.001).
!!
Table 2.6. Estimates of L. tulipifera mating system parameters for each sampling site obtained using the maximum-expectation
method implemented in MLTR and a Bayesian method implemented in BORICE. Estimates from MLTR include multilocus
outcrossing rate (tm), singlelocus outcrossing rate (ts), multilocus correlation of paternity (rp(m)), and number of pollen donors (1/rp(m);
with standard deviations in parentheses). Estimates from BORICE include outcrossing rate (t-max) and the fixation index (F; with
credible intervals, 2.5 and 97.5 percentiles, in parentheses).
Sampling site Population
type
MLTR BORICE
tm ts tm-ts rp(m) 1/ rp(m) t-max F
Belt Woods
Old-growth
0.800 (0.113) 0.804 (0.092) -0.004 (0.035) 0.000 (0.000) (—) 0.86 (0.73-0.95) 0.07 (0.02-0.14)
Frog Canyon 0.993 (0.022) 0.986 (0.003) 0.007 (0.020) 0.008 (0.002) 125.00 0.85 (0.71-0.95) 0.08 (0.02-0.15)
Montpelier 0.910 (0.009) 0.905 (0.003) 0.005 (0.007) 0.046 (0.007) 21.74 0.85 (0.71-0.95) 0.08 (0.02-0.15)
Saddler's Woods 0.999 (0.000) 0.997 (0.001) 0.002 (0.000) 0.029 (0.005) 34.48 0.81 (0.63-0.95) 0.09 (0.02-0.19)
Georgetown
Urban
0.967 (0.006) 0.959 (0.002) 0.008 (0.005) 0.018 (0.001) 55.56 0.81 (0.67-0.92) 0.10 (0.04-0.17)
British Embassy — (—) — (—) — (—) — (—) (—) 0.80 (0.61-0.89) 0.13 (0.06-0.22)
Johns Hopkins 0.942 (0.008) 0.937 (0.001) 0.005 (0.007) 0.025 (0.002) 40.00 0.91 (0.75-0.97) 0.06 (0.01-0.12)
Rock Creek — (—) — (—) — (—) — (—) (—) 0.84 (0.69-0.94) 0.08 (0.02-0.17)
Southbury Suburban 1.000 (0.000) 1.000 (0.003) 0.000 (0.000) 0.023 (0.007) 43.48 0.69 (0.51-0.83) 0.18 (0.08-0.29)
Note: Dashes indicate that parameters were not estimated due to software limitations.
65
!!
66
Table 3.1. Sampling locations and total number of samples collected.
Sampling site Population type Sample size
Seedlings
1) Montpelier, VA
Old-growth
48
2) Belt Woods, MD 48
3) Frog Canyon (SERC), MD 48
4) Saddler’s Woods, NJ 48
5) Georgetown, DC
Urban
48
6) British Embassy, DC 48
7) Rock Creek Park, DC 48
8) Johns Hopkins, Baltimore, MD 48
9) Southbury, CT Suburban 42
Adult trees
1) Montpelier, VA
Old-growth
20
2) Frog Canyon (SERC), MD 20
3) Saddler’s Woods, NJ 12
!!
67
Table 3.2. Microsatellite marker loci used to genotype Liriodendron tulipifera seedlings and
adult individuals.
Locus Multiplex set 5’ fluorophore Primer concentration (each
primer, µM)
Lt011 1 6-FAM 20
Lt035 1 NED 20
Lt036 3 VIC 2
Lt043 3 6-FAM 5
Lt054 2 NED 2
Lt059 3 NED 2
Lt061 3 PET 2
Lt064 2 6-FAM 10
Lt068 2 VIC 2
Lt070 1 6-FAM 1
Lt077 1 HEX 1
Lt079 2 PET 2
!!
68
Table 3.3. Effective of breeders (!") estimated from linkage disequilibrium in single age cohorts
of Liriodendron tulipifera. Results of delete-one jackknifing over individuals. (!" =$ %& '()*+'',*-.+/$01*-+' ). Correction factor is median r^2 permute for r^2_{c}. Estimates are
allele frequency weighted (AFW).
Sampling site Population type Sampling
approach Median !"
!" confidence
interval
Belt Woods
Old-growth
Seedlings 48.63 39.98 - 60.86
Montpelier Seedlings 47.8 39.18 - 62.01
Frog Canyon Seedlings 112.94 72.20 - 168.52
Saddler's Woods Seedlings 27.3 23.51 - 30.59
Johns Hopkins
Urban
Seedlings 39.51 32.74 - 54.95
Rock Creek Seedlings 91.9 65.57 - 140.99
British Embassy Seedlings 25.94 22.59 - 28.54
Georgetown 2015 Seedlings 16.53 14.88 - 20.58
Southbury Suburban Seedlings 10.73 9.81 - 11.68
!!
69
Table 3.4. Effective population size estimates from linkage disequilibrium in single age (!") and
mixed-age cohorts samples (!, 2.3,4)15, ) of Liriodendron tulipifera. Results of delete-one
jackknifing over individuals. (!, = $ %& '()*+'',*-.+/$01*-+' ). Correction factor is median r^2
permute for r^2_{c}. Estimates are allele frequency weighted (AFW).
Sampling site Sampling approach Effective population
size estimate
Effective population size
confidence interval
Montpelier Seedlings 47.8* 39.18 - 62.01
Adults 64.61 29.46 - 88.64
Frog Canyon Seedlings 112.94* 72.20 - 168.52
Adults 61.74 24.21 - 126.30
Saddler's Woods Seedlings 27.3* 23.51 - 30.59
Adults 45.77 14.90 - 40.59
* Estimates correspond to effective number of breeders (!").
!!
Table 3.5. Correction factor point estimates (r^2 correction factor is median r^2 permute for r^2_{c}), and two confidence intervals
for correction factor point estimates used in this study: 1) jackknife over individuals, and 3) jackknife over individuals with Jones et
al. (2016) inverse hyperbolic tangent transformation. Estimates are allele frequency weighted (AFW).
Sampling site Sampling
approach
r^2_{comp} AFW
point estimate
Delete-one jackknifing over
individuals percentile confidence
interval
Delete-one jackknife over
individuals with Jones et al. (2016)
transformation normal distribution
confidence interval
Belt Woods Seedlings 0.037449 0.036072 - 0.038932 0.036274 - 0.038624
British Embassy Seedlings 0.04531 0.044140 - 0.047217 0.043996 - 0.046624
Georgetown 2015 Seedlings 0.049662 0.045691 - 0.051899 0.047351 - 0.051973
Johns Hopkins Seedlings 0.037974 0.035602 - 0.039717 0.036504 - 0.039444
Southbury Seedlings 0.064968 0.062455 - 0.067904 0.061736 - 0.068199
Rock Creek Seedlings 0.034749 0.033486 - 0.036205 0.033458 - 0.036040
Montpelier Seedlings 0.034519 0.032922 - 0.036054 0.033037 - 0.036001
Adults 0.068742 0.067344 - 0.074898 0.064586 - 0.072895
Frog Canyon Seedlings 0.034466 0.033493 - 0.036132 0.033390 - 0.035543
Adults 0.082675 0.079915 - 0.091042 0.077787 - 0.087559
Saddler's Woods Seedlings 0.040997 0.039687 - 0.042969 0.039511 - 0.042483
Adults 0.110112 0.111041 - 0.125195 0.101268 - 0.118938
70
!!
Table 3.6. Effective population size, Ne; effective number of breeders, Nb; Nb/Ne ratio; mean number of offspring per parent per time
period, !; variance in reproductive success among adults in one time period, Vk; generation length (mean age of parents of a newborn
cohort), G; estimated from eight life-history scenarios.
Parameters Scenario A Scenario B Scenario C Scenario D Scenario E Scenario F Scenario G Scenario H
Ne 15.9 3.1 14.6 12.9 16.2 17.0 19.2 31.2
Nb 16.7 3.3 14.3 11.3 17.1 16.5 14.5 14.3
Nb/Ne 1.05 1.06 0.98 0.88 1.06 0.97 0.76 0.46
G 169.124 169.24 181.332 197.407 169.124 157.648 128.903 76.178
!"##$"% 1.604 1.838 1.598 1.598 1.598 1.599 1.603 1.615
!&'()*'# 2.000 2.00 2.00 2.00 2.00 2.000 2.000 2.000
+!"##$"% 190.552 1124.394 223.054 281.623 186.071 192.928 219.366 223.968
+!&'()*'# 42552.676 218416.00 49544.113 61188.523 41718.258 36990.645 26884.301 9760.308
71
!!
72
APPENDIX B: Figures
Figure 2.1. Relationship between pairwise F’ST / (1 − F’ST) and natural logarithm of geographic
distances (in km) for nine L. tulipifera sampling sites (Mantel r = 0.759, p = 0.003). The dashed
line represents a linear regression line fit to the data.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0 1 2 3 4 5 6 7
F'ST
/ (1 − F'
ST)
ln (geographic distance)
!!
73
Figure 3.1. Comparison of effective population size estimates from linkage disequilibrium in
single age (!") and mixed-age cohorts samples (!# $%&#'()*# ) of Liriodendron tulipifera.
Results of delete-one jackknifing over individuals. (!#+,-!" = - /0 12(3411#35%46-7)3541 ). r^2
correction factor is median r^2 permute for r^2_{c}. Estimates are allele frequency weighted
(AFW). Error bars represent confidence intervals from jackknifing over individuals.
0
20
40
60
80
100
120
140
160
180
Montpelier Frog Canyon Saddler's Woods
Effe
ctiv
e pop
ulat
ion
size
est
imat
es
Nb Ne
!!
74
! Figure 3.2. Median r^2 permute for r^2_{c}. Estimates are allele frequency weighted (AFW).
Error bars represent confidence intervals from jackknifing over individuals.
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
Bel
t Woo
ds
Brit
ish
Emba
ssy
Geo
rget
own
2015
John
s H
opki
ns
Sout
hbur
y
Roc
k C
reek
Mon
tpel
ier
Frog
Can
yon
Sadd
ler's
Woo
ds
r^2_
{com
p} A
FW
!!
75
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