Population Structure and Gene Flow in Two Rare, Isolated Quercus Species: Q. Hinckleyi and Q. Pacifica by JANET RIZNER BACKS B

Population structure and gene flow in two rare, isolated Quercus species:

Q. hinckleyi and Q. pacifica

JANET RIZNER BACKS

B.A., Loretto Heights College M.A., University of Wisconsin, Madison M.S., Northeastern Illinois University M.S., University of Illinois at Urbana-Champaign

THESIS

Submitted as partial fulfillment of the requirements for the degree of Doctor of Philosophy in Biological Sciences in the Graduate College of the University of Illinois at Chicago, 2014

Chicago, Illinois

Defense Committee:

Mary V. Ashley, Chair and Advisor Roberta Mason-Gamer Emily Minor Andrew Hipp, Morton Arboretum Andrea Kramer, Chicago Botanic Garden

ACKNOWLEDGMENTS

Special thanks to my advisor, Dr. Mary Ashley, for affording me the opportunity to pursue graduate research in oaks, and especially Quercus hinckleyi, and giving me the space and freedom to learn new disciplines and skills. The Ashley Lab has been a source of friendship, support, encouragement, and sharing of the ups and downs of research. Without all of the people in the Lab, especially Saji Abraham, David Zaya, and

Eun Sun Kim, this work would not have been possible. Thank you to my Committee for reading and listening to my proposals and finally my results. I appreciate your time and help. Funding for this research was through the University of Illinois at Chicago Hadley

Grant.

My research of Quercus hinckleyi was aided by the help of many people in Texas.

Special thanks to Martin Terry and Molly Klein of Sul Ross University in Alpine, TX, volunteers from Texas Master Naturalists, and Troy Rinehart, Rio Grande Mining Co., for their assistance in locating and collecting specimens for this study. Collection permits were granted by Texas Parks and Wildlife Department and the U.S. Fish and

Wildlife Service. The National Park Service at Guadalupe Mountains National Park permitted taking samples of the putative hybridizers, Q. pungens and Q. vaseyana; collecting was done by Jeremy Markuson with the NPS. Likewise, my research of

Quercus pacifica could not have been completed without the help of many people in

ACKNOWLEDGMENTS (continued)

California. Collection of samples from the three islands was made possible through

Scott Sillett, Smithsonian Migratory Bird Center. Collectors were: Sarah Ratay, Catalina

Island Conservancy; Jongmin Yoon & Michelle Desrosiers, Colorado State University; and Mario Pesendorfer, University of Nebraska. Mainland samples of Q. berberidifolia and Q. dumosa were collected with the help of Martha Witter, Tarja Sagar, Tony Valois,

Crystal Anderson, and Keith Lombardo, all of the National Park Service. Photographs were taken by William Backs of Evanston, Illinois.

Finally, thank you to my friends and family members, some of whom are no longer with me in life, but always will be with me in spirit. These people have made me who I am.

For your support and encouragement, special thanks to my husband Bill Backs and to

E.D.D. for her words of wisdom without which I would not have ‘done what I knew I wanted to do.’

JRB

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Table of Contents

Chapter Page

1. A STORY OF SURVIVAL OF A RARE, ISOLATED SPECIES: A TOUGH, LITTLE WEST TEXAS OAK, QUERCUS HINCKLEYI C.H. MULLER ...... 1

1.1 Introduction ...... 1

1.2 Materials and Methods ...... 4

1.2.1 Study species ...... 4

1.2.2 Study sites and sample collection ...... 6

1.2.3 DNA extraction and microsatellite analysis ...... 7

1.2.4 Data analysis ...... 8

1.3 Results ...... 10

1.4 Discussion ...... 16

1.4.1 Quercus hinckleyi as a rare isolated species ...... 16

1.4.2 Conservation management ...... 19

1.5 References ...... 22

2. HYBRIDIZATION AS A CONSERVATION CONCERN FOR THE THREATENED SPECIES, QUERCUS HINCKLEYI C.H. MULLER (FAGACEAE) ...... 27

2.1. Introduction ...... 27

2.2 Materials and Methods ...... 30

Table of Contents (continued)

2.2.1 Study Species ...... 30

2.2.2 Study Sites and Sample Collection ...... 32

2.2.3 Microsatellite Genotyping ...... 32

2.2.4 Genetic data analysis ...... 33

2.3 Results ...... 34

2.4 Discussion ...... 39

2.5 References ...... 43

3. GENETIC CLUSTERING AND GENE FLOW IN AN ENDEMIC CHANNEL ISLAND OAK: QUERCUS PACIFICA K. NIXON & C.H. MULLER (FAGACEAE) ...... 47

3.1 Introduction ...... 47

3.2 Materials and Methods ...... 51

3.2.1 Study Species ...... 51

3.2.2 Genetic Analysis ...... 52

3.2.3 Data analysis ...... 56

3.3 Results ...... 58

3.4 Discussion ...... 66

3.5 References ...... 71

Table of Contents (continued)

4. UNRAVELLING THE TAXONOMIC PUZZLE OF THREE CALIFORNIA SHRUB OAK SPECIES: QUERCUS DUMOSA NUTT., Q. BERBERIDIFOLIA LIEBM. AND Q. PACIFICA NIXON & C.H. MULLER ...... 80

4.1 Introduction ...... 80

4.2 Materials and Methods ...... 83

4.2.1 Genetic Analysis ...... 83

4.2.2. Data analysis ...... 85

4.3. Results ...... 86

4.4 Discussion ...... 92

4.5 References ...... 94

5. VITA ...... 99

LIST OF TABLES

TABLE I: Q. HINCKLEYI RAMETS AND MULTILOCUS GENOTYPES...... 10

TABLE II: Q. HINCKLEYI DESCRIPTIVE STATISTICS...... 11

TABLE III: DESCRIPTIVE STATISTICS FOR Q. HINCKLEYI, Q. PUNGENS, AND Q. VASEYANA...... 35

TABLE IV: Q. PUNGENS AND Q. VASEYANA: PROPORTION OF MEMBERSHIP OF EACH PRE-

DEFINED POPULATION IN EACH OF THE 2 INFERRED CLUSTERS...... 36

TABLE V: INDIVIDUALS IDENTIFIED MORPHOLOGICALLY AS Q. HINCKLEYI OR Q. PUNGENS

WITH INFERRED LEVELS OF INTROGRESSION...... 37

TABLE VI: QUERCUS PACIFICA SAMPLE (ID) LOCATIONS...... 54

TABLE VII: DESCRIPTIVE STATISTICS FOR QUERCUS PACIFICA BY ISLAND ON WHICH IT IS

FOUND...... 59

TABLE VIII: PAIRWISE DIFFERENTIATION INDEX USING GST AND DJOST FOR Q. PACIFICA ON

SANTA ROSA, SANTA CRUZ AND SANTA CATALINA...... 60

TABLE IX: Q. PACIFICA INFERRED CLUSTER MEMBERSHIP BASED ON SPATIAL CLUSTERING

ANALYSIS...... 65

TABLE X: Q. BERBERIDIFOLIA, Q. DUMOSA, AND Q. PACIFICA DESCRIPTIVE STATISTICS...... 87

TABLE XI: Q. BERBERIDIFOLIA, Q. DUMOSA AND Q. PACIFICA INTROGRESSION...... 91

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LIST OF FIGURES

FIGURE 1-1: Q. HINCKLEYI LEAVES. AVERAGE LENGTH = 1 CM...... 5

FIGURE 1-2: Q. HINCKLEYI COLLECTION SITES...... 7

FIGURE 1-3: Q. HINCKLEYI INFERRED ADMIXTURE PROPORTIONS IN EACH GENETIC CLUSTER

FOR INDIVIDUAL PLANTS (COLUMNS) ...... 13

FIGURE 1-4: Q. HINCKLEYI PRINCIPAL COORDINATES ANALYSIS OF VARIANCE...... 14

FIGURE 1-5: SPATIAL ISOLATION OF Q. HINCKLEYI CLUMPS OF A SINGLE CLONE AT SITE S1. . 15

FIGURE 1-6: Q. HINCKLEYI CLONAL GROWTH PATTERNS AT SITE S1...... 16

FIGURE 2-1: Q. HINCKLEYI COLLECTED AT SHAFTER AND BIG BEND RANCH STATE PARK. Q.

PUNGENS AND Q. VASEYANA COLLECTED AT GUADALUPE MTS. N.P. ADDITIONAL Q.

PUNGENS COLLECTED AT BIG BEND RANCH STATE PARK. CURRENT RANGES OF Q.

PUNGENS, Q. VASEYANA, AND Q. HINCKLEYI SHOWN BELOW...... 29

FIGURE 2-2: PRINCIPAL COORDINATES ANALYSIS OF Q. HINCKLEYI, Q. PUNGENS, AND Q.

VASEYANA...... 38

FIGURE 3-1: Q. PACIFICA INFERRED ADMIXTURE PROPORTIONS IN EACH GENETIC CLUSTER

FOR INDIVIDUAL PLANTS (COLUMNS)...... 62

FIGURE 3-2: SCATTERPLOT OF INDIVIDUALS ON THE TWO PRINCIPAL COMPONENTS...... 63

FIGURE 3-3: Q. PACIFICA PRINCIPAL COORDINATES ANALYSIS OF VARIANCE...... 64

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LIST OF FIGURES (CONTINUED)

FIGURE 3-4: FIVE INFERRED SPATIAL CLUSTERS BASED ON GENELAND ANALYSIS: RESULTS

PLOTTED AT LOCATIONS FROM WHICH SAMPLES WERE COLLECTED...... 66

FIGURE 4-1: RANGE MAPS OF Q. BERBERIDIFOLIA, Q. DUMOSA SENSU STRICTO, AND Q.

PACIFICA...... 82

FIGURE 4-2: Q. BERBERIDIFOLIA, Q. DUMOSA, AND Q. PACIFICA COLLECTION SITES...... 84

FIGURE 4-3: QUERCUS DUMOSA AND Q. BERBERIDIFOLIA PRINCIPAL COORDINATES ANALYSIS

OF VARIANCE ...... 89

FIGURE 4-4: Q. BERBERIDIFOLIA, Q. DUMOSA, AND Q. PACIFICA INFERRED ADMIXTURE

PROPORTIONS IN EACH GENETIC CLUSTER FOR INDIVIDUAL PLANTS (COLUMNS) ...... 90

LIST OF ABBREVIATIONS

BBRSP Big Bend Ranch State Park

DAPC Discriminant Analysis of Principal Components

DNA deoxyribonucleic acid

GPS Global Positioning System

GUMO Guadalupe Mountains National Park

HWE Hardy-Weinberg equilibrium

IUCN International Union for Conservation of Nature and Natural Resources

PCoA Principal Coordinates Analysis

PCR polymerase chain reaction

UMG unique molecular genotype

SUMMARY

My doctoral dissertation research uses DNA microsatellite analysis to determine

population structure, genetic variability, and gene flow within and among areas in which

each of two rare, isolated scrub oak species are found. Quercus hinckleyi C.H. Muller is

a threatened species whose U.S. range is essentially limited to one county in West

Texas. Quercus pacifica K. Nixon & C.H. Muller is an endemic species found on three of the eight Channel Islands off the coast of California. While one is isolated by land and the other by sea, both of these species exist on what are in effect islands: Q. hinckleyi has been identified at a handful of sites separated by Chihuahuan Desert terrain; Q. pacifica is found on three offshore islands, Santa Rosa, Santa Cruz and Santa Catalina, separated by open waters of the Pacific Ocean. Their present ranges were affected by climatic changes approximately 10,000 years ago and they are both located in areas shaped by human disturbance. Possible hybridization has been suggested for each species.

In the first chapter, I examine the rare Quercus hinckleyi C.H. Muller. Low levels of genetic variability, inbreeding, and limited gene flow are three possible threats to small, isolated plant populations as exemplified by this species. Quercus hinckleyi has survived over the past 10,000 years in a region in which the climate has become increasingly xeric. While more prevalent after the last ice age, its U.S. range is now limited to a few locations in one county in West Texas. This chapter looks at the

SUMMARY (CONTINUED) genetic diversity of the relict metapopulation and resultant conservation implications. I used microsatellites to genotype a total of 204 ramets collected from three locations in

Presidio County, Texas, that represent the known occurrences of Q. hinckleyi.

Analyses of eight loci were used to determine levels of genetic variability, population structure and clonal growth. Genetic diversity for the sampled plants was high: for the total metapopulation, the mean number of alleles (Na) was 17.875; the mean observed heterozygosity (Ho) was 0.807 and the mean expected heterozygosity (He) was 0.853.

There was no evidence of inbreeding as measured by the fixation index, FIS. Population structure analyses showed two distinct subpopulations with significant differentiation, as shown by GST (0.033, Bonferroni corrected p = 0.001) and DJOST (0.451, Bonferroni corrected p = 0.001), unique alleles and genetic clustering. High clonality was discovered at the two smallest sites, with only seven unique genotypes among 58 ramets sampled. One clone was approximately 30 m across. Sexual reproduction appears to be present at the other sites, as indicated by less extensive cloning. Overall,

I found that Q. hinckleyi is not genetically depauperate despite its rarity, although unique genets are reduced because of cloning. Asexual reproduction may in fact have allowed the small relict populations to survive extreme environmental change as their range has dwindled. The level of genetic diversity and differentiation among the remaining Q. hinckleyi sites warrants protection and preservation of them all.

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SUMMARY (CONTINUED)

In the second chapter, I characterize hybridization between Q. hinckleyi and two putative hybridizing species, Q. pungens Liebm. and Q. vaseyana Buckley.

Hybridization among oaks is well-documented and is of special concern in conservation efforts directed toward threatened or endangered Quercus species. The two potential hybridizers in this study were sampled at Guadalupe Mountains National Park (GUMO), approximately 320 km from Q. hinckleyi. Quercus pungens and two possible hybrids located in near proximity to the relict populations of Q. hinckleyi were also sampled.

Genetic variability was high in all three species, with mean number of alleles per locus ranging from 12.625 to 17.875, mean observed heterozygosity from 0.734 to 0.807, and mean expected heterozygosity from 0.851 to 0.869. Quercus hinckleyi is genetically differentiated from the putative hybridizers and has two distinct genetic clusters within its metapopulation. The two hybridizer species from GUMO, where they are sympatric, are not differentiated. The population of Q. pungens found near Q. hinckleyi is genetically distinct from the GUMO samples and has 5 of 8 genets with greater than

90% Q. hinckleyi introgression. Only two of the 14 Q. hinckleyi in close proximity to this population had Q. pungens introgression. Bayesian clustering analysis showed five percent of the samples identified as Q. hinckleyi in the field were hybrids and one putative hybrid was confirmed genetically. Overall, while there is some hybridization within the Q. hinckleyi population, there is no evidence of genetic swamping. This may

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SUMMARY (CONTINUED)

be explained by the spatial isolation of the Q. hinckleyi remnants relative to other oak

species and by its common asexual (cloning) method of reproduction.

In the third chapter, I looked for evidence of founder events or limited gene flow in

Quercus pacifica K. Nixon & C.H. Muller, an endemic oak found on three of the eight

California Channel Islands, Santa Rosa, Santa Cruz, and Santa Catalina. I examined

levels of genetic variability and genetic differentiation within and between islands among

a total of 133 leaf samples genotyped at eight DNA microsatellite loci. There was no

evidence of cloning or inbreeding in Q. pacifica. Levels of genetic variability were high

and comparable to mainland oaks, with the mean number of alleles per locus per island

ranging from 15.3 to 16.8, mean Ho ranging from 0.795 to 0.828, and mean He ranging

from 0.832 to 0.862. Genetic differentiation measured by GST and DJOST was small but significant. Principal Coordinates Analysis (PCoA) showed broadly overlapping distributions of samples from all three islands. Bayesian structural analysis indicated three genetic clusters with each individual composed of an admixture of the three.

Principal component/discriminant analysis also showed three genetically related groups with individuals within them scattered across the different islands. While Bayesian spatial clustering analysis inferred barriers to gene flow between the islands, barrier permeability was also indicated. Individuals on both Santa Rosa and Santa Cruz shared genetic clusters with individuals on Santa Catalina and with each other. The

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SUMMARY (CONTINUED) high levels of genetic variation and low levels of genetic differentiation found in this study suggests that Q. pacifica originated from a common, genetically diverse ancestral form and that gene flow among islands has maintained genetic continuity over great distances. While pollen movement in oaks has previously been shown to occur at long distances, these results suggest gene flow occurs over more than 100 km of open ocean, the distance between the northern islands and Santa Catalina. It is also possible that multiple colonization events between the islands have occurred, perhaps through seed dispersal by birds and humans. The results of this research suggest that the model of insular genetic isolation of species is not universal and that natural dynamics can result in a different picture.

Finally, chapter four looks at the genetic support for a recent reclassification of three

California shrub oaks. Understanding the taxonomic structure and species boundaries among California oaks has proven challenging because of introgression and hybridization. The ‘Quercus dumosa shrub oak complex’ has been revised based on morphology, habitat, and associated plant communities, but no genetic evaluation had been completed prior to this research. I used DNA microsatellite analysis with eight loci to examine three of the taxa previously identified within the complex: Q. berberidifolia

Liebm., Q. dumosa Nutt. sensu stricto, and Q. pacifica Nixon & C.H. Muller. Based on the revised taxonomy, Q. berberidifolia is a common, widespread species, Q. dumosa

SUMMARY (CONTINUED) sensu stricto is rare with a limited range, and Q. pacifica is an endemic found on only three of the Channel Islands. A total of 200 individuals were genotyped: 43 Q. berberidifolia and 24 Q. dumosa collected on mainland California and 133 Q. pacifica collected from the three Channel Islands that comprise its range. Allelic diversity was comparable to that found for other California oaks. All loci were polymorphic; mean expected heterozygosities ranged from 0.802 for Q. dumosa to 0.878 for Q. berberidifolia, and there was no evidence for inbreeding. Each species had private alleles not found in the other species. There was significant genetic differentiation among the species, with the greatest between the two mainland species, Q. dumosa and Q. berberidifolia (GST=0.029, DJOST = 0.341). Bayesian analysis identified three genetic clusters which corresponded to species delineations, with 187 individuals

(93.5%) having >80% ancestry in the inferred cluster of their species. However, some gene flow, both from mainland to island and from island to mainland, appears to occur.

In short, this study found that these three taxa, once considered a single species, show significant levels of genetic differentiation supporting the reclassification despite evidence of gene flow among them.

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1. A STORY OF SURVIVAL OF A RARE, ISOLATED SPECIES: A TOUGH, LITTLE

WEST TEXAS OAK, QUERCUS HINCKLEYI C.H. MULLER

1.1 Introduction

Quercus hinckleyi (Hinckley’s oak) is a classic example of an extremely rare species

with small populations in a fragmented landscape. Its U.S. range is now essentially limited to Presidio County1 in West Texas, although it was more prevalent across the

Chihuahuan Desert region in the late Wisconsin/early Holocene period (approximately

18-9 ka) when the climate was more mesic (Nixon et al., 1997). Studies of packrat

midden macrofossils, coupled with carbon dating, allow reconstruction of the paleo-plant assemblage communities in the Chihuahuan Desert from the Middle Wisconsin to the

Late Holocene (approximately 40 ka to the present) (Van Devender & Spaulding, 1979).

There is evidence of a pinyon-juniper-oak woodland complex in the area for at least

30,000 years, including macro-fossils of Q. hinckleyi. Although the North American ice

sheets were far removed, glacial fluctuations drove weather patterns within its range

and as climate changed the pinyon disappeared about 11 ka ago, while the juniper

1 The only known specimen from outside of Presidio County was collected on the eastern side of The Solitario in Big Bend Ranch State Park which is actually in Brewster County (University of Texas Herbarium Plant Resources Center, 2012).

continued until about 9 ka. Quercus hinckleyi survives to this day, although its numbers

have precipitously declined and it is now a threatened endemic (Van Devender, 1990).

Cornelius H. Muller, who originally identified the species, described the scarcity and clonal habits of Q. hinckleyi (Muller, 1951). At that time only one population was located and no seedlings or young plants were found. This observation, along with the morphological homogeneity of the plants, led Muller to conclude that he had found a single clone of great age. In addition to stands NW of Solitario Peak, close to the location of the type specimen, two sites have been identified near Shafter (Powell,

1998) and a limited amount of sexual reproduction has been reported. Immediate threats to the relict populations in Texas are the low number of stands with few individuals, wildlife and insect predation, possible hybridization, poor regeneration from seed (Kennedy & Poole, 1992) and climate change.

Small, isolated plant populations and those reproducing clonally exhibit loss of genetic diversity and, in some cases, reduced fitness. A review of plant population studies

published between 1987 and 2005 (Leimu et al., 2006) found significantly positive

correlations between population size, fitness levels, and genetic diversity, suggesting

negative consequences for small, fragmented populations. Based on these results, the

relict populations of Q. hinckleyi may suffer loss of genetic variation, inbreeding, and

reduced fitness across the metapopulation. In addition, the fragmented nature of the

range of the remaining populations may limit gene flow. These factors, along with

abiotic restrictions of reduced habitat and environmental stochasticity, could then

contribute to extirpation of the isolated populations or even species extinction.

The inevitable genetic decline of small plant populations has, however, been called into

question. A review by Young et al. (1996) concludes that small populations are not

necessarily lacking in genetic diversity and alleles lost may be those that were rare

when the population was isolated; these populations may in fact be repositories for

maintaining genetic diversity. A more complex dynamic may better characterize small,

isolated populations, especially for plant species that can reproduce both sexually and

clonally, experience gene flow by seed and pollen dispersal, are long-lived and have

long generation times (Kramer et al., 2008). Clonal reproduction itself may be viewed

as an adaptive response to habitat fragmentation and environmental disturbances that

maximizes lifespans of individual genotypes (Jeník, 1994, May et al., 2009), reduces

genetic drift (Hamrick et al., 1992), and limits introgression and swamping by other

species (Muller, 1951). While in an obligate sexual species, small populations with few

genotypes may experience reproductive failure and eventual extinction (Weekley et al.,

2002), plants which can reproduce asexually appear to better withstand extinction

events even in small, fragmented populations (Honnay & Bossuyt, 2005). This

research, the first comprehensive genetic study of Q. hinckleyi, characterizes the

neutral genetic diversity of Q. hinckleyi, the clonal and genetic structure of its remaining populations, and the subsequent implications for its conservation management. DNA microsatellite analysis was used to examine genetic variability, reproductive modes,

inbreeding, and population structure. Microsatellites generally result in a much greater level of resolution for landscape and population genetic analysis than other markers and have been successfully applied to a number of oak species (Aldrich et al., 2002, Ashley et al., 2010, Dow & Ashley, 1998, Isagi & Suhandono, 1997, Steinkellner et al., 1997,

Dow et al., 1995, Kampfer et al., 1998). They also allow accurate, non-invasive identification of unique genotypes among the ramets in a clonal stand (Brzyski, 2010,

Ortego et al., 2010, Wilk et al., 2009, Parks & Werth, 1993), important for determining true population sizes (Tepedino, 2012) and selecting plants for conservation initiatives.

1.2 Materials and Methods

1.2.1 Study species

Quercus hinckleyi (Fagaceae) is listed as a threatened species under the U.S

Endangered Species Act and by the state of Texas and is identified as ‘critically threatened’ on the IUCN Red List. The species grows in an arid sub-tropical environment on dry limestone and sandstone slopes at approximately 1000-1400 m elevation. It is recognizable by its shrub-like thicket growth pattern (maximum height

0.75 m) and grey-green thickened holly-like leaves with pink petioles (Fig. 1-1).

Figure 1-1: Q. hinckleyi leaves. Average length = 1 cm.

Photo by W. Backs

It reproduces both clonally and sexually (Poole et al., 2007). Based on Muller (1951), growth ring counts on individual aerial stems showed them to be relatively short-lived, from 7 to 9 years, although the age of clones could not be determined. Through excavation he found that rhizomes were 4 to 15 cm long. Clumps, in which stems grew from old rhizomes, were themselves small (1 to 3 dm in diameter). With time, rhizome connections between clumps are broken, giving the above-ground appearance of separate individuals (Muller, 1951). Staminate catkins are generally 3-5 mm long with few flowers; flowering is in the spring; acorns which are single and produced annually are ovoid and approximately 8-12 mm wide by 10-20 mm long (Nixon et al., 1997,

Muller, 1970).

1.2.2 Study sites and sample collection

Leaf samples used in this study were collected from all shrub clumps at two small sites

(S1 and S2) approximately 3 km apart and a third site (B1) approximately 60 km from them. Site B1 includes a stand (identified for this study as site B1a) that is in the vicinity of the original type location described by Muller in 1950 (Fig. 1-2). Leaves were taken around the perimeter of each clump. A total of 204 ramets were sampled overall and

GPS coordinates taken to identify their locations. Samples were stored at room temperature with Drierite desiccant (W.A. Hammond Drierite Co., Ltd.) until DNA extraction.

Figure 1-2: Q. hinckleyi collection sites.

1.2.3 DNA extraction and microsatellite analysis

Genomic DNA was extracted from 20-30 mg of dry sample homogenized to a fine powder using a DNeasy Plant Kit (QIAGEN). DNA concentrations were measured using a NanoDrop spectrophotometer (Thermo Scientific). Eight microsatellite loci and primers developed and utilized successfully with other oaks in the white oak group

(Quercus subgenus Quercus) (Isagi & Suhandono, 1997, Craft & Ashley, 2007, Ashley

et al., 2010, Dow et al., 1995) were used in this study. Primers QpZAG1/5, QpZAG15,

QpZAG110, and QpZAG9 were originally developed for Q. petraea (Steinkellner et al.,

1997); QpZAG15 and QpZAG11 for Q. robur (Kampfer et al., 1998); and MSQ4 and

MSQ13 for Q. macrocarpa (Dow et al., 1995), all in subgenus Quercus. M69–2M1 was developed for Q. myrsinifolia in subgenus Cyclobalanopsis (Isagi and Suhandono

1997). PCR amplification followed the protocol as previously described (Abraham et al.,

2011) using labeled forward primers as presented in Schuelke (2000). PCR products

(0.9 – 1.5 µL) were genotyped on an ABI 3730 DNA Analyzer using GeneScan ™ - 500

LIZ500® Size Standard (Applied Biosystems). All genotypes were scored using Applied

Biosystems GeneMapper, version 3.7.

1.2.4 Data analysis

To test for clones, the R-package ALLELEMATCH (Galpern et al., 2012) was used to

identify unique multilocus genotypes (UMGs) and provide a match probability, Psib, to verify unique individuals. Psib is the probability that a sample is a sibling, rather than a

replicate, of a UMG. Based on these results, the individual ramet samples were

collapsed into UMGs, which were then used in subsequent analyses.

Descriptive statistics, such as allele frequency, observed and expected heterozygosity,

fixation index, and number of private alleles, were calculated using GenAlEx 6.501

(Peakall & Smouse, 2012, Peakall & Smouse, 2006). Genetic variation among

populations was measured by number of alleles (Na) and observed (Ho) and expected

heterozygosity (He). Levels of inbreeding were ascertained through the fixation index,

FIS. Private alleles (alleles unique to a given population), which can be indicative of reduced gene flow and drift, were identified.

To examine population structure, three analyses were used: STRUCTURE 2.3.4, PCoA

(GenAlEx 6.501), and the R-package DEMEtics. STRUCTURE (Pritchard et al., 2000,

Falush et al., 2003, Falush et al., 2007, Hubisz et al., 2009) infers population structure of multilocus polymorphic genotypes using Bayesian analysis. The following

STRUCTURE protocol was used: 50K burnin with 100K MCMC for the Admixture

Model using LOCPRIOR with sampling locations as prior for a potential K of 1 to 7 at 30 reps each. Best K was determined by calculating l(K) and delta K (Evanno et al., 2005) using STRUCTURE HARVESTER (Earl & vonHoldt, 2012). PCoA, Principal

Coordinates Analysis, provides a visual representation of patterns resulting from a multivariate analysis of multiple loci and multiple samples. Individuals are presented two-dimensionally based on genetic variance with genetically similar individuals clustering in the diagram. DEMEtics (Gerlach et al., 2010) calculates the differentiation indices GST (Nei, 1973) and DJOST (Jost, 2008). Values can range from 0 to 1, with higher values indicating greater genetic differentiation between pairs of populations. P- values are obtained using bootstrap resampling. To reduce the possibility of Type I errors caused by multiple pairwise comparisons of a single data set, Bonferroni correction was made to the p-values. While GST has been widely used to measure genetic differentiation, DJOST has been shown to be a better indicator for polymorphic loci with more than two alleles (Jost, 2009, Gerlach et al., 2010). DEMEtics checks for

Hardy-Weinberg Equilibrium (HWE) in the input populations and loci and uses alleles if

all populations are in HWE for a given locus, or genotypes if not (Goudet et al., 1996).

1.3 Results

Out of the 204 ramets sampled, 123 unique genotypes were identified with Psib < 0.02

for each (Table I). Ramet data was collapsed and all further analysis was done using the 123 unique genotypes.

Table I: Q. hinckleyi ramets and multilocus genotypes.

Site Ramets Sampled Unique Genotypes* S1 42 4 S2 16 3 B1 129 102 B1a 17 14 Total 204 123 * psib < 0.02

Based on the results of the population structure analysis described below, Sites S1, S2,

and B1a were grouped as Population 1 and site B1 was identified as Population 2.

Sample size, number of alleles, observed and expected heterozygosity, and fixation

index are shown by locus and population in Table II. All loci used were polymorphic

with alleles per locus ranging from nine to 18. Although number of alleles varies with

population size, as would be expected, genetic diversity was high at all sampling sites.

For the total metapopulation, mean number of alleles (Na) was 17.875, mean observed

heterozygosity (Ho) was 0.807, and mean expected heterozygosity (He) was 0.853. The mean fixation index (FIS) of 0.036 showed no evidence for inbreeding. While some

individual loci violated Hardy-Weinberg expectations, there were no consistent patterns

across loci or populations. There were 27 alleles found in Population 1 that were not

present in Population 2, and 50 in Population 2 not found in Population 1. Among the

three sites that make up Population 1, S1 and S2 together had 39 alleles not found in

B1a, while B1a had 34 not found in S1/ S2.

Table II: Q. hinckleyi descriptive statistics.

Population 1 (Sites S1, S2, B1a) Population 2 (Site B1)

Locus N Na Ho He FIS N Na Ho He FIS Q1/5 20 10 0.900 0.799 -0.127 101 15 0.901 0.892 -0.010 Q110 18 11 0.833 0.861 0.032 101 14 0.752 0.875 0.140 Q11 21 9 0.667 0.734 0.091 102 10 0.588 0.600 0.019 Q9 19 10 0.684 0.842 0.188 93 16 0.871 0.886 0.017 QM69 20 11 0.850 0.864 0.016 101 11 0.881 0.843 -0.045 MSQ4 19 15 0.789 0.907 0.130 101 17 0.673 0.868 0.224 Q15 21 12 0.762 0.793 0.039 101 15 0.881 0.878 -0.003 MSQ13 19 15 0.895 0.880 -0.017 102 18 0.922 0.818 -0.126 N = number of samples, Na = number of alleles, Ho = observed heterozygosity, He = expected heterozygosity, FIS = fixation index. (GenAlEx 6.501).

STRUCTURE results were input into STRUCTURE HARVESTER for computation of mean LnP(K), and ∆K, which indicate the most appropriate K value (number of genetic clusters given the multilocus genotype data) (Evanno et al., 2005). The mean of estimated Ln probability increased sharply from K=1 to K=2, but then less sharply from

K=3 to K=6. The value of ∆K peaked at K = 2. These results point toward two distinct genetic clusters. Output from STRUCTURE HARVESTER was run through CLUMPP

(Jakobsson & Rosenberg, 2007) which clusters individuals into genotypes and in turn those results were fed into DISTRUCT (Rosenberg, 2004) to produce a visual

representation of the clusters (Fig. 1-3). The two small sampling sites, S1 and S2, and the site near where the species was originally identified, B1a, grouped into one genetically distinct cluster (Population 1). The larger sampling site, B1, was genetically a single cluster (Population 2). There was limited introgression between the two populations.

Figure 1-3: Q. hinckleyi inferred admixture proportions in each genetic cluster for individual plants (columns).

Admixture Model using LOCPRIOR (STRUCTURE 2.3.4), best K= 2 (per Evanno method).

This structure was supported by the PCoA analysis which shows differentiation between

Population 1 (S1, S2, B1a) and Population 2 (B1) with some genetic overlap as well as

high genotypic diversity within the Population 1 sites (Fig. 1-4). Significant differentiation between the two populations was also found with GST at 0.033 (Bonferroni corrected p =

0.001) and DJOST at 0.451 (Bonferroni corrected p = 0.001).

Figure 1-4: Q. hinckleyi Principal Coordinates Analysis of variance.

Quercus hinckleyi

S1 S2

Coord. 2 Coord. B1a B1

Coord. 1

Principal coordinate 1 and 2 account for 7.18% and 5.73% of the variation, respectively (GenAlEx 6.501).

Clonal structure was characterized at one of the sampling sites, S1, where ramets were

collected from every clump of Q. hinckleyi present. Figure 1-5 shows two ramet clusters

from this site and is representative of the appearance of the growth pattern and the

spatial isolation of the clusters. Figure 1-6 presents this site in more detail, showing

waypoint (GPS coordinate) identifiers for 13 locations within the site. Microsatellite

analysis confirmed that the ramets sampled at this site represented only four unique

genotypes, shown by circled areas. There were open areas between all individual clumps, with a maximum distance of approximately 30 m between ramets of the same clone.

Figure 1-5: Spatial isolation of Q. hinckleyi clumps of a single clone at site S1.

Photo by W. Backs

Figure 1-6: Q. hinckleyi clonal growth patterns at site S1.

Circles indicate unique genotypes. The clonal circle with dashed lines contains ramets separated by approximately 30 m (arrowed line).

1.4 Discussion

1.4.1 Quercus hinckleyi as a rare isolated species

The first objective of this research was to examine the genetic variability of the relict

populations of Q. hinckleyi. Study findings contradict the conventional view that small,

isolated populations will have low levels of allelic diversity, limited heterozygosity, and

inbreeding. Instead, the observed patterns of genetic diversity reveal a genetically

viable population and no evidence of inbreeding. While the existence of clones limits

the number of unique individuals, allelic diversity and levels of heterozygosity across the

metapopulation (Population 1 and 2) are comparable to non-threatened oak species

(Craft et al., 2002, Abraham et al., 2011, Dutech et al., 2005). In sum, although Q.

hinckleyi is limited in numbers, substantial genetic diversity still exists.

The remnant populations of Q. hinckleyi exhibit strong population differentiation. As

mentioned above, in more mesic times the range of Q. hinckleyi was much more

extensive than it is today. The persistence of two differentiated populations, each

maintaining genetic diversity, may best be understood in the context of a once more

widespread species. Remnants of the historic population, now isolated, are persisting

in limited numbers. These are not outliers or pioneers on the fringe exhibiting founder

effects or genetic bottlenecks, but survivors which remained as the plant assemblages

around them disappeared in a changing climate. As such, each represents a unique

and variable collection of alleles.

Findings at the two small sites, S1 and S2, may help explain how asexual (clonal)

reproduction protects allelic diversity. Though asexual reproduction exists across the

metapopulation, these sites are predominately clonal. Ramets separated by as much

as 30 m with no apparent connections are in fact clones sharing identical multi-locus

genotypes. Although there are only seven individual genets here, they show relatively

high levels of genetic variation and no evidence of inbreeding. Studies of packrat

midden macrofossils found near Shafter and reaching to the Big Bend area (Powell,

1998, Van Devender, 1990) confirm that this relict population was within a much more

extensive range of Q. hinckleyi. This suggests that while the species was disappearing

around it, this disjunct group of clones was able to survive. A similar type of relict survival has been reported for a California scrub oak clone believed to be over 13,000

years old (May et al., 2009). While some feel that clonal reproduction with a lack of

sexual reproduction merely prolongs the time to extinction (Silvertown, 2008), the

identification of this clonal group suggests that clones can be a repository of genetic

diversity contributing to species survival. In this case, it appears that asexual reproduction has served to protect against loss of alleles and inbreeding.

It remains unclear, however, why there is so little sexual reproduction at these sites.

Possible reasons include human-induced habitat degradation, poor regeneration from

seed coupled with limited number of genets, and wildlife and insect predation. Both of

the sites are on private land and may have experienced plant and seed loss to

introduced herbivores in the past (USFWS, 2009). In addition, oaks are predominantly

outcrossing, and there may be genetic barriers to seed production because of the

limited number of genotypes available for crossing. As clones get larger, flowers are

surrounded by more pollen from individuals of the same clone and may result in less

viable seed set (Handel, 1985). Finally, there were few signs of acorns as well as

evidence of herbivory when samples were collected, suggesting plant and seed

predation is a factor limiting recruitment of new genets.

The sites in Big Bend Ranch State Park (B1 and B1a) are more genetically diverse with

a greater number of unique individuals and higher genetic variability. There are two

distinct subpopulations, each containing unique alleles. Although a few clones were

identified within these two subpopulations, asexual reproduction was limited. Sexual reproduction, with resulting acorns, appears to be present. This is a positive factor for continued diversity because genetic mixing adds new genotypes to the population. In addition, while clones are literally rooted to their locations, acorns are movable and are the only natural means of dispersing Q. hinckleyi to new areas. Whether accelerated

climate changes and unforeseen human activities will eventually lead to its extirpation in

its restricted United States range remains to be seen, but the fact that genetic diversity

and sexual reproduction persist in the remaining populations argues for continued

conservation efforts for this species.

1.4.2 Conservation management

The second objective of the study was to examine the viability of remaining Q. hinckleyi populations and the implications for conservation management strategies.

Documenting the genetic diversity of species at risk is one of the conservation criteria defined by the IUCN and others (McNeely et al., 1990). By providing a detailed characterization of the existing Q. hinckleyi metapopulation in West Texas, this study fulfills two priority one tasks in the Hinckley Oak Recovery Plan: #3212 to assess genetic viability and needs, and #3231 to determine types of reproduction and contribution to population (Kennedy & Poole, 1992, USFWS, 2009). The findings that show high levels of genetic variability and clonal as well as sexual reproduction, and that provide identification of unique genetic individuals can now be used to refine the recovery plan.

This leads to two specific conservation questions. First, are current levels of protection sufficient to preserve the genetic diversity that this study has identified? The answer, in short, is that there are some gaps. Conservation of Q. hinckleyi should begin with

sustaining the known relict populations in order to preserve as much genetic variation

as possible. While the significant differentiation among the sites argues for the

preservation of them all, levels of protection vary. At this time, Q. hinckleyi within Big

Bend Ranch State Park is protected because it is listed as threatened by the State of

Texas, but concerns about climatic and anthropogenic effects remain. For example, a recent well-planned and researched conservation effort, the reintroduction of desert bighorn sheep (Ovis canadensis) (Brewer & Hernandez, 2011), may create a conservation conflict. While the sheep are an historic species, Q. hinckleyi is no longer a dominant shrub and the juxtaposition of threatened oak species and introduced herbivore could have unintended negative consequences for Q. hinckleyi. The sites near Shafter are on private land where Q. hinckleyi does not have protected status.

While the rarity of Q. hinckleyi is appreciated by residents near these locations

(personal observations), there is nothing that would legally prevent ecological

degradation or collection of plant material from these sites. Finally, since the habitat in

which Q. hinckleyi is found in Texas is comparable to the Chihuahuan Desert of Mexico,

Q. hinckleyi may be found there as well, and in fact specimens of possible Q. hinckleyi

hybrids have been collected (Nixon et al., 1997). The distribution of this rare species

thus illustrates the need to coordinate conservation efforts across international borders.

Second, how can the results of this study inform potential reintroduction efforts?

Although the limited number of genets is a conservation concern, the overall genetic variability found by this study supports continued conservation efforts for Q. hinckleyi.

The importance of the ability to map clones and identify unique molecular genotypes

cannot be overstated. This enables accurate population counts and selection of

appropriate plants for conservation initiatives. Because of the limited numbers of Q.

hinckleyi, conservation management strategies in addition to site protection may be

necessary. These may include supplementing the numbers of unique genotypes by

hand pollination and translocation of genets among remaining populations or to suitable

ex situ locations. Long-term conservation of oaks provides its own challenges. Acorns

do not remain viable in seed banks, and are inappropriate candidates for planting out to produce fresh acorns because of the long maturation time of oaks. Advances in micropropagation and cryopreservation may offer ex situ solutions (Kramer & Pence,

2012). Quercus hinckleyi conservation efforts will be challenging, but the inclusion of the genetic data provided by this study facilitates more informed management strategies and will benefit future planning.

1.5 References

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Brzyski, J. R. 2010. Isolation and characterization of microsatellite markers in the rare clonal plant, Spiraea virginiana (Rosaceae). American Journal of Botany 97: e20- e22.

Craft, K. J. & Ashley, M. V. 2007. Landscape genetic structure of bur oak (Quercus macrocarpa) savannas in Illinois. Forest Ecology and Management 239: 13-20.

Craft, K. J., Ashley, M. V. & Koenig, W. D. 2002. Limited hybridization between Quercus lobata and Quercus douglasii (Fagaceae) in a mixed stand in central coastal California. American Journal of Botany 89: 1792-1798.

Dow, B. & Ashley, M. 1998. High levels of gene flow in bur oak revealed by paternity analysis using microsatellites. Journal of Heredity 89: 62-70.

Dow, B., Ashley, M. & Howe, H. 1995. Characterization of highly variable (GA/CT) n microsatellites in the bur oak, Quercus macrocarpa. Theoretical and Applied Genetics 91: 137-141.

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Galpern, P., Manseau, M., Hettinga, P., Smith, K. & Wilson, P. 2012. ALLELEMATCH: an R package for identifying unique multilocus genotypes where genotyping error and missing data may be present. Molecular Ecology Resources 12: 771-778.

Gerlach, G., Jueterbock, A., Kraemer, P., Deppermann, J. & Harmand, P. 2010. Calculations of population differentiation based on GST and D: forget GST but not all of statistics! Molecular Ecology 19: 3845-3852.

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Hamrick, J. L., Godt, M. J. W. & Sherman-Broyles, S. L. 1992. Factors influencing levels of genetic diversity in woody plant species. New Forests 6: 95-124.

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Jeník, J. 1994. Clonal growth in woody plants: A review. Folia Geobotanica 29: 291- 306.

Jost, L. 2008. GST and its relatives do not measure differentiation. Molecular Ecology 17: 4015-4026.

Jost, L. 2009. D vs. GST: response to Heller and Siegismund (2009) and Ryman and Leimar (2009). Molecular Ecology 18: 2088-2091.

Kampfer, S., Lexer, C., Glössl, J. & Steinkellner, H. 1998. Characterization of (GA)n microsatellite loci from Quercus robur. Hereditas 129: 183-186.

Kennedy, K. & Poole, J. 1992. HlNCKLEY OAK (Quercus hinckeyi) RECOVERY PLAN. U.S. Fish and Wildlife Service Region 2, Albuquerque, New Mexico.

Kramer, A. T., Ison, J. L., Ashley, M. V. & Howe, H. F. 2008. The paradox of forest fragmentation genetics. Conservation Biology 22: 878-885.

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Leimu, R., Mutikainen, P. I. A., Koricheva, J. & Fischer, M. 2006. How general are positive relationships between plant population size, fitness and genetic variation? Journal of Ecology 94: 942-952.

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2. HYBRIDIZATION AS A CONSERVATION CONCERN FOR THE THREATENED

SPECIES, QUERCUS HINCKLEYI C.H. MULLER (FAGACEAE)

2.1. Introduction

Oaks as a genus have muddled and perhaps spurred efforts to delineate a theory of

species going back to Darwin (Darwin, 2009). In the twentieth century, they were

acknowledged as the ‘stimulus’ for the idea of the ecological species concept (Van

Valen, 1976). The biological species concept, which focuses on reproductive isolation

as the defining attribute of a unique species, has been confounded by their apparent

ease of hybridization (Coyne & Orr, 2004, Mayr, 1996, Burger, 1975). Oak’s frequent

introgression continues to be a challenge when developing a taxonomic hierarchy of

Quercus species (Nixon, 2002).

Hybridization has been described as both a threat and as a benefit. On the one hand it

can be a risk to species identity (López-Pujol et al., 2012, Haig & Allendorf, 2006, Vila et al., 2000) because regional genetic diversity is diminished (Abbott, 1992). From this perspective, introduced non-native species can genetically overwhelm native plants and even non-viable hybrids can affect regional species by usurping reproductive material

(and energy) that might be used in the sexual reproduction of the native plant (Zaya,

2013). In the case of oaks, the natural tendency to hybridize does not necessarily require introduction of non-natives; proximity to other species in the same Quercus

subgenus is enough (Craft et al., 2002, Petit et al., 2004, Abraham et al., 2011,

Burgarella et al., 2009). Alternatively it may be a means of introducing novel genetic material that can lead to new, stable, well-adapted hybrid types (Arnold, 2004). In this

view, hybridization is a means of conserving the genetic variability of species that are

not viable in the long-term because of changing environmental conditions (Briggs, 1997,

Willis & McElwain, 2002, Anderson & Stebbins Jr, 1954), and a case can be made for

protecting zones of hybridization because of the novel adaptations found there

(Thompson et al., 2010). Both of these viewpoints play a part in conservation

strategies.

This study assesses potential hybridization between Q. hinckleyi, listed as a threatened

species (Department of the Interior, 1988), and other oaks. Although a number of oaks

in the white oak subgenus (Quercus) have ranges that coincide with Q. hinckleyi

(Powell, 1998), this study focuses on two that have been identified as possible

hybridizers: Q. pungens (Kennedy & Poole, 1992, Nixon, 1997) and Q. vaseyana (Terry

& Scoppa, 2010). The U.S. range of Q. hinckleyi is essentially limited to Presidio

County in West Texas. Quercus pungens and Q. vaseyana have wider ranges that

overlap with Q. hinckleyi (Powell, 1998) (Fig. 2-1). Macrofossils show that Q. hinckleyi

and Q. pungens were both part of a once more extensive pinyon-juniper-oak woodland

complex. As the climate became more xeric over the last 10,000 years, however, the

plant assemblage gradually changed, ranges shifted, and Q. hinckleyi became the rare

species it is today (Van Devender, 1990).

Figure 2-1: Q. hinckleyi collected at Shafter and Big Bend Ranch State Park. Q. pungens and Q. vaseyana collected at Guadalupe Mts. N.P. Additional Q. pungens collected at Big Bend Ranch State Park. Current ranges of Q. pungens, Q. vaseyana, and Q. hinckleyi shown below.

Guadalupe Mts. N.P.

TEXAS

Shafter

Big Bend Ranch State Park MEXICO

154 km

Q. pungens Q. vaseyana Q. hinckleyi

The initial Hinckley Oak Recovery Plan listed hybridization as a potential threat

(Kennedy & Poole, 1992), and the follow up review continues to list assessment of

hybridization as an important recovery action (USFWS, 2009). This is the first

application of genetic analysis focusing on hybridization of Q. hinckleyi. Microsatellite genotyping in oaks has been effective in resolving different species known to hybridize

(Muir et al., 2000), determining levels of interspecies hybridization (Craft et al., 2002), investigating pollination patterns (Abraham et al., 2011), studying gene flow (Burgarella et al., 2009, Valbuena-Carabana et al., 2005), and looking into plant invasion mechanisms (Petit et al., 2004).

This research has three goals related to Q. hinckleyi and the two putative hybridizing species, Q. pungens and Q. vaseyana: first, to examine levels of introgression from the putative hybridizing species within the relict Q. hinckleyi populations; second, to determine the level of genetic differentiation between the two putative hybridizers; and third, to examine the genetic identity of morphologically identified hybrids between Q. hinckleyi and Q. pungens or Q. vaseyana at its relict sites.

2.2 Materials and Methods

2.2.1 Study Species

The focal species in this study, Q. hinckleyi C.H. Muller (common name: Hinckley Oak),

is listed under the U.S Endangered Species Act and by the state of Texas as a

threatened species and is categorized as ‘critically threatened’ on the IUCN Red List. In

the United States it is found in two genetically distinct populations (Chapter 1) in

Presidio County, Texas (Powell, 1998) on predominately limestone substrates in

Chihuahuan Desert habitat at elevations of approximately 1000–1400 m. Since this

region extends into Mexico, Q. hinckleyi might be found there as well (Nixon, 1997),

although its presence and status is not known. Its small (1–1.5 cm diameter), grey-

green thickened spiny leaves distinguish it from other species. In its native environment

it grows as a shrub-like thicket with a maximum height of approximately 0.75 m.

(Kennedy & Poole, 1992, Poole et al., 2007, Muller, 1951). While some acorn

reproduction has been reported, Q. hinckleyi reproduces readily through underground

rhizomes, forming clonal patches and clusters (Muller, 1970). The Hinckley Oak

Recovery Plan issued by the U.S. Fish and Wildlife Service identifies a number of

threats to the relict U.S. populations, including low population numbers and few

individuals, wildlife and insect predation, possible hybridization with Q. pungens Liebm.

(see below) and poor regeneration from seed (Kennedy & Poole, 1992).

Quercus pungens Liebm. (common name: Sandpaper Oak, Scrub Oak) and Q.

vaseyana Buckl. (common name: Vasey Shin Oak; previously var. of Q. pungens) are

both common species across the Trans-Pecos region (although Q. pungens is more

widespread) and are found on limestone substrate on desert slopes; they form low

shrubs and sometimes small trees (Q. pungens: 2–3 m; Q. vaseyana: 7 m). Both are

found in the same region as Q. hinckleyi as well as in the Guadalupe Mountains.

Leaves of Q. pungens are stiff, with coarsely toothed margins; the upper surface is

lustrous while lower surfaces are densely pubescent. The common name reflects the

feel of the leaves. Quercus vaseyana leaves are oblong, either entire or with 3–5

toothed or lobed margins; upper surfaces are lustrous and lower surfaces pubescent, but also somewhat lustrous green (Powell, 1998, Miller & Lamb, 2006, Nixon, 1997).

2.2.2 Study Sites and Sample Collection

In 2009 and 2012, 204 leaf samples from Q. hinckleyi ramets were collected from the

remnant populations in Presidio County near Shafter and from the vicinity of the

Solitario in Big Bend Ranch State Park (BBRSP). Two possible hybrids in close

proximity to Q. hinckleyi at the Shafter sites were also sampled in 2009. In order to

include putative hybridizers with no possibility of introgression by Q. hinckleyi, 20 Q.

pungens and 15 Q. vaseyana individuals were sampled in Guadalupe Mountains

National Park (GUMO) in 2010 approximately 320 km from the Q. hinckleyi populations

(Fig. 2-1). Leaves from nine additional Q. pungens ramets were collected in 2012 from

a small group in BBRSP near a stand of Q. hinckleyi, several of which were clustered

along the drip line of the largest Q. pungens.

2.2.3 Microsatellite Genotyping

DNeasy Plant MiniKit (Qiagen) was used to extract DNA from approximately 20 mg of

dry leaf material. DNA concentrations and quality were verified on NanoDrop

spectrophotometer (Thermo Scientific). Genotyping was completed using eight primer

pairs previously used with oaks in the Quercus (White Oak) subgroup of which the three

species in this study are members: QpZAG1/5, QpZAG15, and QpZAG110

(Steinkellner et al., 1997), QpZAG15 and QpZAG11 (Kampfer et al., 1998), MSQ4 and

MSQ13 (Dow et al., 1995), and M69–2M1 (Isagi and Suhandono 1997). A description

of the PCR amplification protocol has been published elsewhere (Abraham et al., 2011).

PCR products (0.9 – 1.5 µL) were genotyped on the ABI 3730 DNA Analyzer using

LIZ500 ladder (Applied Biosystems). Applied Biosystems GeneMapper, version 3.7

was used for genotype scoring. After genotyping, individuals were tested for cloning

using ALLELEMATCH (Galpern et al., 2012) and clones were collapsed into unique

genotypes that were used in the following analyses.

2.2.4 Genetic data analysis

Allele frequency, observed and expected heterozygosity, and fixation index were

determined using GenAlEx 6.501 (Peakall & Smouse, 2012, Peakall & Smouse, 2006).

Three methods were used to examine levels of hybridization and species differentiation:

STRUCTURE 2.3.4 (Pritchard, Stephens et al. 2000; Falush, Stephens et al. 2003;

Falush, Stephens et al. 2007; Hubisz, Falush et al. 2009), Principal Coordinates

Analysis (PCoA) using GenAlEx 6.501, and the R-package DEMEtics (Gerlach et al.,

2010). STRUCTURE performs Bayesian clustering analysis to infer genetic populations

based on multi-locus genotypes and computes the proportion of the inferred clusters in

each individual. PCoA employs multivariate analysis across multiple loci and samples

and presents a visual representation of genetic structural patterns. DEMEtics calculates

differentiation indexes GST and DJOST using bootstrap resampling and provides

Bonferroni corrected p-values.

To analyze genetic differentiation between the Q. pungens and Q. vaseyana individuals,

the Admixture Model of STRUCTURE with LOCPRIOR (using sampling locations as

prior) for K 1 to 7, with 100,000 MCMC and 50,000 burnin for 10 reps each was used.

Best K was determined by calculating l(K) and delta K (Evanno et al., 2005) using

STRUCTURE HARVESTER (Earl & vonHoldt, 2012). The STRUCTURE procedure was then rerun using Best K with MCMC of 250,000 and burnin of 50,000 to get the proportions of membership of the sampling locations in the inferred clusters.

For purposes of hybrid analysis, STRUCTURE parameters were set to use a mixed- ancestry model with no prior population information (Thompson et al., 2010) and set

K=3, corresponding to the three species in the study. Initial burnin was 50,000 iterations followed by an MCMC of 250,000 iterations. ANCESTDIST options were activated to capture 95% posterior probability intervals (Blair & Hufbauer, 2009).

Individuals were identified as hybrid if the q-value (the posterior probability of an individual belonging to a single genetic cluster) was < 0.85 (Abraham et al., 2011).

2.3 Results

Out of 204 ramets, Q. hinckleyi had 123 unique genotypes. All Q. pungens and Q. vaseyana individuals sampled in GUMO were genetically unique. Of the nine Q. pungens collected in BBSRP eight were unique genets.

All loci were polymorphic and highly variable for each of the three species (Table III).

Mean number of alleles, mean observed heterozygosity, and mean expected heterozygosity respectively for Q. hinckleyi were 17.875, 0.807, and 0.853, for Q. pungens 12.625, 0.734, and 0.851, and for Q. vaseyana 13.250, 0.789, and 0.869. The

mean FIS over all populations was 0.096, indicating no significant deviation from Hardy-

Weinberg Equilibrium. Each of the species had alleles not observed in the other

species; mean number of private alleles across loci was 7.125 for Q. hinckleyi, 1.500 for

Q. pungens and 1.875 for Q. vaseyana.

Table III: Descriptive statistics for Q. hinckleyi, Q. pungens, and Q. vaseyana.

Q. hinckleyi Q. pungens Q. vaseyana Locus N Na Ho He FIS N Na Ho He FIS N Na Ho He FIS Q1/5 121 16 0.901 0.897 -0.005 28 11 0.643 0.853 0.246 16 11 0.750 0.824 0.090 Q110 119 17 0.765 0.898 0.148 27 11 0.593 0.817 0.275 16 11 0.813 0.871 0.067 Q11 123 13 0.602 0.636 0.054 28 13 0.821 0.882 0.069 16 17 0.875 0.920 0.049 Q9 112 18 0.839 0.904 0.072 27 18 0.926 0.922 -0.004 15 16 0.933 0.893 -0.045 QM69 121 16 0.876 0.871 -0.005 28 8 0.500 0.765 0.347 16 9 0.688 0.832 0.174 MSQ4 120 21 0.692 0.894 0.226 28 12 0.643 0.839 0.233 16 12 0.500 0.834 0.400 Q15 122 17 0.861 0.870 0.011 28 12 0.750 0.820 0.085 16 16 0.938 0.904 -0.037 121 25 0.917 0.854 -0.074 28 16 1.000 0.911 -0.098 16 14 0.813 0.869 0.065 MSQ13 N = number of samples, Na = number of alleles, Ho = observed heterozygosity, He = expected heterozygosity, FIS=fixation index. (GenAlEx 6.501)

STRUCTURE results for the Q. pungens and Q. vaseyana analysis indicated they

cluster into two genetic groups. Quercus pungens and Q. vaseyana from GUMO cluster

into one group (hereafter referred to as the ‘Q. pungens/Q. vaseyana’ cluster), while the

Q. pungens genotypes from BBRSP are genetically distinct from them (Table IV).

Table IV: Q. pungens and Q. vaseyana: Proportion of membership of each pre-defined population in each of the 2 inferred clusters.

Given Population Inferred Cluster Number of 1 2 Individuals Q. pungens (GUMO) 0.004 0.996 20 Q. pungens (BBRSP) 0.710 0.290 8 Q. vaseyana (GUMO) 0.042 0.958 15

(STRUCTURE 2.3.4)

Previous research (Chapter 1) found that Q. hinckleyi is differentiated into two well- defined subpopulations. One includes individuals from the Shafter sites and the stand in

BBRSP closest to the Q. pungens collected there (B1a) and the other is comprised of the remaining individuals sampled in BBRSP (B1). Results of the STRUCTURE hybrid analysis showed six individuals identified in the field as Q. hinckleyi have < 85% Q.

hinckleyi inferred ancestry and are likely hybrids. Six of the eight individuals identified

as Q. pungens in BBRSP have < 85% of the inferred ancestry found in the ‘Q.

pungens/Q. vaseyana’ cluster with the remaining percentage in the cluster of the Q.

hinckleyi population geographically closest to it (Table V).

Table V: Individuals identified morphologically as Q. hinckleyi or Q. pungens with inferred levels of introgression.

Sample ID Q. hinckleyi Q. pungens/Q. vaseyana Q. hinckleyi Population1 QUHI065 (S2) 83% 17% QUHI190 (B1a) 11% 89% QUHI191 (B1a) 77% 23% Population2 QUHI093 (B1) 28% 72% QUHI153 (B1) 72% 28% QUHI162 (B1) 10% 90% Q. pungens BBRSP QUPU021 21% 79% QUPU023 99% 1% QUPU024 99% 1% QUPU026 99% 1% QUPU027 98% 2% QUPU029 99% 1% (Criteria for hybrid: posterior probability of the individual belonging to the genetic cluster associated with its species is < 0.85)

PCoA results confirm Q. hinckleyi is genetically differentiated from Q. pungens and Q.

vaseyana and that, while Q. pungens and Q. vaseyana from GUMO are not genetically

distinct, Q. pungens collected in BBRSP is differentiated from them (Fig. 2-2).

Figure 2-2: Principal Coordinates Analysis of Q. hinckleyi, Q. pungens, and Q. vaseyana.

Principal Coordinates (PCoA)

Coord.2

Coord. 1 Q.hinckleyi (Pop 1 Shafter) Q.hinckleyi (Pop 1 BBRSP ) Q.hinckleyi (Pop 2) Q.vaseyana (GUMO) Q.pungens (GUMO) Q.pungens (BBRSP) Potential Hybrid

Circles = Q. hinckleyi, squares = Q. pungens, triangles = Q. vaseyana, diamonds = potential hybrids. Principal coordinate 1 and 2 account for 6.79% and 5.44% of the variation, respectively (GenAlEx 6.501).

The limited differentiation between Q. pungens and Q. vaseyana from GUMO was

confirmed with GST at 0.007 (Bonferroni corrected p = .003) and DJOST at 0.104

(Bonferroni corrected p = 0.039). Genetic distance between Q. hinckleyi and the

putative hybridizers is greater. For Q. hinckleyi and Q. pungens, GST is 0.039

(Bonferroni corrected p = 0.003) and DJOST is 0.457 (Bonferroni corrected p = 0.003); for

Q. hinckleyi and Q. vaseyana GST is 0.029 (Bonferroni corrected p = 0.003) and DJOST is

0.415 (Bonferroni corrected p = 0.003).

Of the two individuals that were suspected of being hybrids, the one that exhibits

features of both Q. hinckleyi and Q. vaseyana (Terry & Scoppa, 2010), was verified

genetically to be a hybrid, with 50% Q. hinckleyi and 50% Q. pungens/Q. vaseyana

inferred ancestry. The other individual, though in close proximity to Q. hinckleyi, was

genetically 99% in the ‘Q. pungens/Q. vaseyana’ genetic cluster.

2.4 Discussion

This research examines three questions related to Q. hinckleyi and the putative

hybridizing species, Q. pungens and Q. vaseyana. First, is there evidence for

introgression from these other oak species within the relict Q. hinckleyi populations?

Low levels of hybridization were found between Q. hinckleyi and the Q. pungens/Q.

vaseyana GUMO cluster with no evidence of genetic swamping. Overall, approximately

95% of the samples identified as Q. hinckleyi in the field have predominantly Q.

hinckleyi inferred ancestry. The relative isolation of the few remaining Q. hinckleyi

plants, along with the species’ propensity to form clones, may contribute to the low levels of genetic introgression. While these findings bode well for continued uniqueness of the Q. hinckleyi individuals, the fact remains that they are in a threatened position due to small numbers in their native U.S. range, potential natural and human threats, and a rapidly changing climate.

Second, is there genetic differentiation between the two potential hybridizers, Q. pungens and Q. vaseyana? Quercus pungens and Q. vaseyana from GUMO cluster together genetically in agreement with reports that they form hybrid swarms in areas where they are sympatric (Nixon, 1997). Further research, based on samples from allopatric populations, is needed to clarify the genetic differentiation between them. The

Q. pungens stand from BBRSP is genetically distinct from the GUMO cluster, which may be explained by the high proportion of Q. hinckleyi introgression in these individuals.

Lastly, is there genetic confirmation for classification of morphologically identified hybrids between Q. hinckleyi and Q. pungens or Q. vaseyana at its relict sites? The two

putative hybrids that were examined, both collected near Q. hinckleyi at the Shafter

sites, were resolved. One sample fell into the Q. pungens/Q. vaseyana genetic cluster

and did not show Q. hinckleyi introgression. The other, which exhibits physical

characteristics of both Q. hinckleyi and Q. vaseyana, was verified to be a hybrid. It is

genetically evenly split between Q. hinckleyi and Q. pungens/Q. vaseyana and therefore

may be an F1 hybrid, although more loci would be needed to confirm this.

Overall, the study confirmed that hybrid plants do not necessarily exhibit external features of the introgressed species. Plants that are identifiable as one species or the other may in fact be admixtures of the two. While this was not the case for the vast majority of Q. hinckleyi, the plants identified morphologically as Q. pungens in BBRSP

are highly admixed at the neutral loci examined in this study. While these plants do not

exhibit Q. hinckleyi characteristics, over 90% of the inferred ancestry in five of the eight

that were genotyped is within the cluster of Q. hinckleyi. This finding concurs with other

Quercus hybridization studies which found individuals identified as morphologically

‘pure’ but with high levels of genetic introgression by other species (Ortego & Bonal,

2010, Lee & Choi, 2014). Several important conclusions can be drawn from these

findings: genetic analysis is a crucial component for final determination of levels of

introgression (Burgarella et al., 2009); and hybrids, even morphologically unidentifiable

hybrids, may be repositories of genetic material of threatened species.

One of the conservation tasks in the ‘Hinckley Oak (Quercus hinckleyi) Recovery Plan’

is to examine hybridization as a potential threat. There is no evidence of genetic

swamping from other oaks, lending support for continuation of the protected status of

the remaining Q. hinckleyi as a unique species. Sustaining the remaining populations

and surrounding habitat, both in protected areas and on private land, will be crucial to its

continued survival.

Finally, a stand of Q. pungens is acting as a repository of Q. hinckleyi genetic material.

How should these individuals be treated? The broad conservation question is whether

hybrids should be protected at a time of heightened threats to biodiversity. Some feel the answer is no if they may potentially overwhelm an endangered species. Others recognize that in peripheral zones the hybrid swarm may represent an area of increased biodiversity that should be preserved (Thompson et al., 2010). In an attempt to justify

its acceptability, some ask if hybridization is the result of a ‘natural’ process, a part of

evolution, or the result of human disturbance (Allendorf et al., 2001). Even in cases in

which it is accepted, there is difficulty defining a clear-cut strategy for delimiting a

suitable level. Official policy regarding hybrids as defined by the Endangered Species

Act has been debated since its enactment in 1973. It has gone from no mention of

hybrids in the initial Act, to a proposed new terminology change in 1996 shying away

from the actual word ‘hybrid’ and referring to an ‘intercross’ policy which provided

flexibility in the way that hybrids are treated. As of this writing, the policy was never

adopted or formally withdrawn (Haig & Allendorf, 2006). As discussed above, hybrids

do not necessarily exhibit recognizable parental characteristics, and they can act as

storehouses for genetic variation found in threatened species. While it is true that

endangered species can be genetically swamped by congener species, it is not always

the case, and rather than focusing on hybridization, conservation management may be

better served by protecting threatened habitat (Kothera et al., 2007) which may include

hybrids. To preserve the Q. hinckleyi genetic variability that may be stored in the

neighboring oak species, protection of the cryptic Q. pungens should be included as

part of Q. hinckleyi’s conservation strategy.

2.5 References

Abbott, R. J. 1992. Plant invasions, interspecific hybridization and the evolution of new plant taxa. Trends in Ecology & Evolution 7: 401-405.

Allendorf, F. W., Leary, R. F., Spruell, P. & Wenburg, J. K. 2001. The problems with hybrids: setting conservation guidelines. Trends in Ecology & Evolution 16: 613- 622.

Anderson, E. & Stebbins Jr, G. 1954. Hybridization as an evolutionary stimulus. Evolution: 378-388.

Arnold, M. L. 2004. Transfer and origin of adaptations through natural hybridization: were Anderson and Stebbins right? The Plant Cell Online 16: 562-570.

Blair, A. C. & Hufbauer, R. A. 2009. Hybridization and invasion: one of North America's most devastating invasive plants shows evidence for a history of interspecific hybridization. Evolutionary Applications 3: 40-51.

Briggs, D. 1997. Plant variation and evolution. Cambridge University Press.

Burgarella, C., Lorenzo, Z., Jabbour-Zahab, R., Lumaret, R., Guichoux, E., Petit, R., Soto, A. & Gil, L. 2009. Detection of hybrids in nature: application to oaks (Quercus suber and Q. ilex). Heredity 102: 442-452.

Burger, W. C. 1975. The species concept in Quercus. Taxon 24(1): 45-50.

Coyne, J. & Orr, H. 2004. Speciation. Sinauer Associates Sunderland, MA.

Darwin, C. 2009. On the Origin of Species: By Means of Natural Selection, 6th edition (1872). p. 34. The Floating Press.

Department of the Interior. 1988. Endangered and Threatened Wildlife and Plants; Determination of Threatened Status for Quercus hinckleyi (Hinckley Oak). Vol. 53 (Fish and Wildlife Service, ed.). Federal Register 50 CFR Part 17.

Dow, B., Ashley, M. & Howe, H. 1995. Characterization of highly variable (GA/CT) n microsatellites in the bur oak, Quercus macrocarpa. Theoretical and Applied Genetics 91: 137-141.

Earl, D. A. & vonHoldt, B. M. 2012. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conservation Genetics Resources 4: 359-361.

Evanno, G., Regnaut, S. & Goudet, J. 2005. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Molecular Ecology 14: 2611-2620.

Haig, S. M. & Allendorf, F. W. 2006. Hybrids and policy. In: The endangered species act at thirty: conserving biodiversity in human-dominated landscapes, (eds. J. M. Scott, D. D. G., and F. Davis), pp. 150-163. Island Press, Chicago, IL.

Kampfer, S., Lexer, C., Glössl, J. & Steinkellner, H. 1998. Characterization of (GA)n microsatellite loci from Quercus robur. Hereditas 129: 183-186.

Kennedy, K. & Poole, J. 1992. HlNCKLEY OAK (Quercus hinckeyi) RECOVERY PLAN. U.S. Fish and Wildlife Service Region 2, Albuquerque, New Mexico.

Kothera, L., Ward, S. M. & Carney, S. E. 2007. Assessing the threat from hybridization to the rare endemic Physaria bellii Mulligan (Brassicaceae). Biological Conservation 140: 110-118.

Lee, J.-H. & Choi, B.-H. 2014. Genetic Differentiation and Introgression Among Korean Evergreen Quercus (Fagaceae) are Revealed by Microsatellite Markers. Annales Botanici Fennici 51: 39-48.

López-Pujol, J., Garcia-Jacas, N., Susanna, A. & Vilatersana, R. 2012. Should we conserve pure species or hybrid species? Delimiting hybridization and introgression in the Iberian endemic Centaurea podospermifolia. Biological Conservation 152: 271-279.

Mayr, E. 1996. What Is a Species, and What Is Not? Philosophy of Science 63: 262- 277.

Miller, H. & Lamb, S. 2006. Oaks of North America. Naturegraph Publishers, Inc., Happy Camp, CA.

Muir, G., Fleming, C. C. & Schlatterer, C. 2000. Species status of hybridizing oaks. Nature (London) 405: 1016.

Muller, C. 1970. Quercus. In Manual of the vascular plants of Texas, pp. 467-492. University of Texas at Dallas, Richardson, TX.

Muller, C. H. 1951. The oaks of Texas. Texas Research Foundation.

Nixon, K. C. 1997. Fagaceae In: Flora of North America Editorial Committee, eds. 1993+ Flora of North America North of Mexico. Vol. 3. pp. 496-497. Oxford University Press., New York and Oxford.

Nixon, K. C. 2002. The oak (Quercus) biodiversity of California and adjacent regions. USDA Forest Service General Technical Report PSW-GTR-184.

Ortego, J. & Bonal, R. 2010. Natural hybridisation between kermes (Quercus coccifera L.) and holm oaks (Q. ilex L.) revealed by microsatellite markers. Plant Biology 12: 234-238.

Peakall, R. & Smouse, P. E. 2012. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research -- an update. Bioinformatics 28: 2537- 2539.

Peakall, R. O. D. & Smouse, P. E. 2006. GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Molecular Ecology Notes 6: 288-295.

Petit, R. J., Bodénès, C., Ducousso, A., Roussel, G. & Kremer, A. 2004. Hybridization as a mechanism of invasion in oaks. New Phytologist 161: 151-164.

Poole, J. M., Carr, W. R., Price, D. M. & Singhurst, J. R. 2007. Rare plants of Texas. Texas A&M University Press, College Station.

Powell, A. M. 1998. Trees and shrubs of the Trans-Pecos and adjacent areas. University of Texas Press.

Terry, M. & Scoppa, S. 2010. Quercus hinckleyi— Q. vaseyana, a putative hybrid from Presidio County, Texas. Phytologia 92: 400-406.

Thompson, J. D., Gaudeul, M. & Debussche, M. 2010. Conservation value of sites of hybridization in peripheral populations of rare plant species. Conservation Biology 24: 236-245.

USFWS. 2009. Hinckley Oak (Quercus hinckleyi) 5-Year Review: Summary and Evaluation. (Trans-Pecos-Sub-Office, ed.). Alpine, TX.

Valbuena-Carabana, M., Gonzalez-Martinez, S. C., Sork, V. L., Collada, C., Soto, A., Goicoechea, P. G. & Gil, L. 2005. Gene flow and hybridisation in a mixed oak forest (Quercus pyrenaica Willd. and Quercus petraea (Matts.) Liebl.) in central Spain. Heredity 95: 457-465.

Van Devender, T. R. 1990. Late Quaternary Vegetation and Cllimate of the Chihuahuan Desert, United States and Mexico. In: Packrat Middens: The last 40,000 Years of Biotic Change, (Betancourt, J. L., Devender, T. R. V. & Martin, P. S., eds.). pp. 104-133. The University of Arizona Press, Tucson.

Van Valen, L. 1976. Ecological species, multispecies, and oaks. Taxon: 233-239.

Vila, M., Weber, E. & Antonio, C. M. 2000. Conservation implications of invasion by plant hybridization. Biological Invasions 2: 207-217.

Willis, K. J. & McElwain, J. C. 2002. The evolution of plants. Oxford University Press.

Zaya, D. N. 2013. Genetic Characterization of Invasion and Hybridization: A Bittersweet (Celastrus spp.) (Doctoral dissertation, University of Illinois at Chicago).

3. GENETIC CLUSTERING AND GENE FLOW IN AN ENDEMIC CHANNEL ISLAND

OAK: QUERCUS PACIFICA K. NIXON & C.H. MULLER (FAGACEAE)

3.1 Introduction

There are about 100 endemic plant species on the California Channel Islands,

approximately 20 of which are shared between the Northern (Anacapa, San Miguel,

Santa Rosa, and Santa Cruz) and Southern (San Clemente, San Nicolas, Santa

Barbara, and Santa Catalina) Island groups (Raven, 1967, Philbrick, 1980, Moody,

2001). The origin of a given endemic, whether through speciation on the islands or as a

relict population of a species that has disappeared from the mainland, is often not clear

(Moody, 2001). Quercus pacifica is endemic to two of the northern islands, Santa Rosa

and Santa Cruz, and the southern island of Santa Catalina. While there have been

studies related to conservation efforts and diseases of Q. pacifica (Knapp, 2002,

Stratton, 2001), this is the first study to examine its genetic diversity and population

structure using microsatellite DNA analysis, a method that has been used extensively to

answer population questions including variability, clone identification and gene flow in

oaks (Dow & Ashley, 1998, Steinkellner et al., 1997b, Ashley et al., 2010, Ashley, 2010).

The overall objective of this research was to examine the evolutionary history of Q.

pacifica by evaluating evidence for genetic drift and/or genetic differentiation due either to

separate colonization histories or limited gene flow among islands.

Interpreting this evidence requires an understanding of the geologic and settlement history of the islands and of oak reproductive biology. Since their emergence, the islands have been sculpted by continued uplift, volcanism, varying sea levels due to changes in climate, soil formation and plant settlement, and erosion events caused by herbivory. The uplift of the Channel Islands began about five million years ago

(Schumann et al., 2012, Atwater, 1998). Evidence of geological and geophysical

activity over the last 2.6 million years indicates that there were no land bridges between

the islands and the mainland, or between the northern and southern islands (Vedder &

Howell, 1980, Junger & Johnson, 1980), although during that time there were a series of

glaciation events which resulted in changes in sea level. As recently as the last ice age

(20–12,000 CYBP) Santa Rosa and Santa Cruz, located off the Santa Barbara coast,

were joined with Anacapa and San Miguel islands into a larger landmass, known as

Santarosae. Because of the lower sea levels, Santarosae had an area approximately

2.5 times that of the current separate islands and was within 9 km of the mainland. In

this period Santa Catalina, located farther south off the coast from Los Angeles, was

approximately 1.3 times its current area and was within 24 km of the mainland and over

100 km from Santarosae. Today Santa Cruz is approximately 15 km from Santa Rosa,

40 km from the mainland, and approximately 125 km from Santa Catalina, and Santa

Catalina is approximately 40 km from the mainland (Porcasi et al., 1999, Moody, 2001).

While plant species were present on the islands well before humans arrived (Rick et al.,

2014), settlers may have played a role in gene flow of Quercus species and certainly were responsible for habitat transformation on the islands. Native Americans and more recently non-native settlers have continuously occupied the California Channel Islands for over 10,000 years (Erlandson et al., 2008). The Chumash and the Gabrielino,

sophisticated maritime cultures, made use of the resources of the islands for millennia

(Collins, 1991). Marine travel followed established routes and facilitated exchange of

goods, including acorns (Arnold, 1992). Humans likely affected the natural plant

communities before European settlement, possibly transplanting oaks as well as other

organisms (Erlandson et al., 2004). Acorns are a food source still used by mainland

Native Americans (Anderson, 2005) and the palatability of acorns of Q. dumosa, the

scrub oak group with which Q. pacifica was originally identified, has been documented

(Bainbridge, 1987). Recent archaeological excavations conducted on Santa Cruz have

found few acorn remains or in-ground grinding sites, as well as little isotopic evidence

for an herbivorous diet in analysis of human remains, suggesting acorns may not have

been a major trade item among the Chumash (Fauvelle, 2013). This does not preclude,

however, limited exchange during ritual cycles such as mourning ceremonies which

brought together diverse communities albeit for short periods of time (Hollimon, 2001).

When Europeans began settling the islands in the mid-19th century, they brought non-

native herbivores to Santa Rosa, Santa Cruz, and Santa Catalina. Over the past 200

years, introduced sheep, cattle, pigs, and horses, as well as elk and mule deer on

Santa Rosa and goats and bison on Santa Catalina, have led to over-grazing and

habitat destruction (Westman, 1983, Knowlton et al., 2007). The human footprint

continues to be evident on the islands. Santa Catalina, for example, has been used as

a resort area for over 100 years and annually attracts thousands of visitors (Sweitzer et

al., 2005, Moody, 2001). Recently, however, efforts to restore and preserve native

ecosystems through removal of non-indigenous herbivores have been successfully

undertaken. For example, eradication of feral sheep and pigs on Santa Cruz has resulted

in a rapid recovery of native tree and shrub communities (Klinger et al., 2003), and cattle, feral goats, feral pigs, and sheep have been eliminated from Santa Catalina (Catalina

Island Conservancy, 2014).

Quercus pacifica, like all oaks, is monoecious and wind-pollinated, and self-pollination is rare (Buiteveld et al., 2001, Yacine & Bouras, 1997). Both maternal seed and paternal pollen play a part in gene flow. Acorns, the fertilized seeds, can be transported by rodents (Iida, 1996, Miyaki & Kikuzawa, 1988, Li & Zhang, 2003), by birds (Gómez,

2003, Scofield et al., 2010, Darley-Hill & Johnson, 1981), by humans (Toumi & Lumaret,

1998, Anderson, 2005, Abrams & Nowacki, 2008) or by mechanical means as when gravity moves them down slopes. Quercus adaptation and spread is generally facilitated by short-distance dispersal of acorns and long distance dispersal of pollen. A number of parentage studies have documented a high proportion of pollen coming from outside the near vicinity of the maternal trees (Chybicki & Burczyk, 2010, Dow & Ashley,

1998, Dutech et al., 2005, Nakanishi et al., 2008, Streiff et al., 1999, Ashley, 2010,

Koenig & Ashley, 2003), including distances of more than 80 km (Buschbom et al.,

2011). In addition to sexual reproduction, some oak species may also reproduce

clonally, so that stands of trees which may appear to be multiple individuals have only

one unique molecular genotype.

3.2 Materials and Methods

3.2.1 Study Species

Prior to 1994, Q. pacifica was identified as Quercus dumosa Nuttall var. polycarpa

Greene. At that time, based on morphology, ecology and distribution, the scrub oak

growing on the Channel Islands was renamed Q. pacifica Nixon and C.H. Muller and

designated an endemic species. Leaves are leathery with either smooth or irregularly

toothed margins. A key component of the scrub oak chaparral, it is found at elevations

between 50 – 150 m on ridges and open slopes as well as in canyons and can cover

relatively large areas. The species occurs in both a shrub and tree form up to 5 m in

height. Quercus pacifica may be closely related phylogenetically to Q. berberidifolia, or

possibly be a nothospecies resulting from hybridization between Q. douglassii and Q.

berberidifolia or another species no longer on the islands (Nixon, 2002, Nixon & Muller,

1994). While not listed as a threatened or endangered species, as an insular endemic

Q. pacifica is a conservation concern. Evolving for some time without native herbivores

other than insects, it exhibits significantly reduced morphological defenses as

exemplified by fewer and shorter spines on leaves compared with those of its closely

related mainland counterparts (Bowen & Vuren, 2003). Current threats to the species

include episodic oak die-back, root rot fungus, and herbivory by insects and non-native

animals (Knapp, 2002). Although Q. pacifica has been negatively impacted by human

disturbance, it benefits from conservation efforts to rid the islands of non-native

herbivores and to monitor oak health (Knapp, 2002), as well as to restore the chaparral

ecosystem (Stratton, 2001).

3.2.2 Genetic Analysis

Leaf samples from a total of 133 Q. pacifica trees were collected across the three

islands on which it is found (Table VI). Forty-six trees were sampled on Santa Rosa, 39 on Santa Cruz, and 48 on Santa Catalina. Approximately 20 mg of dry leaf material

was homogenized to a fine powder using Talboys High Throughput Homogenizer

(Troemner). DNA was extracted with DNeasy Plant MiniKit (Qiagen). Microsatellite loci and primers used in this study were developed and applied successfully with other oaks

in the white oak group (Quercus subgenus Quercus) (Dow et al., 1995, Isagi &

Suhandono, 1997, Craft & Ashley, 2007, Ashley et al., 2010). Each tree was

genotyped at eight neutral microsatellite loci: QpZAG1/5, QpZAG15, and QpZAG110,

originally developed for Q. petraea (Steinkellner et al., 1997a); QpZAG15 and

QpZAG11, developed for Q. robur (Kampfer et al., 1998); MSQ4 and MSQ13,

developed for Q. macrocarpa (Dow et al., 1995); and QM69–2M1, developed for Q.

myrsinifolia (Isagi and Suhandono 1997). PCR amplification followed the protocol

described previously by Abraham et al. (2011) using labeled forward primers as

described by Schuelke (2000). PCR products (0.9 – 1.5 µL) were genotyped on an ABI

3730 DNA Analyzer using GeneScan ™ - 500 LIZ500® Size Standard (Applied

Biosystems). All genotypes were scored using Applied Biosystems GeneMapper,

version 3.7.

Table VI: Quercus pacifica sample (ID) locations.

Santa Rosa Santa Cruz Santa Catalina ID latitude longitude ID latitude longitude ID latitude longitude 101 33.99672 -120.061 151 33.99898 -119.734 202 33.35467 -118.355 102 33.99792 -120.061 152 33.99962 -119.738 203 33.35473 -118.353 103 33.98234 -120.073 153 34.00148 -119.740 204 33.35720 -118.362 104 33.98288 -120.073 154 33.97883 -119.722 205 33.36227 -118.362 105 33.98317 -120.072 155 33.97717 -119.717 206 33.35388 -118.375 106 33.98336 -120.073 156 33.97662 -119.719 207 33.36008 -118.370 107 33.98309 -120.073 157 34.00688 -119.764 208 33.36583 -118.375 108 33.98255 -120.074 158 34.00798 -119.769 209 33.35595 -118.389 109 33.98211 -120.074 159 34.00592 -119.759 210 33.35595 -118.445 110 33.99472 -120.060 160 34.01088 -119.776 211 33.37025 -118.469 111 33.99501 -120.060 161 34.01265 -119.782 212 33.41442 -118.471 112 33.99535 -120.061 162 34.01455 -119.790 213 33.42723 -118.472 113 33.99603 -120.061 163 34.01812 -119.821 214 33.43680 -118.479 114 33.95651 -120.008 164 34.02030 -119.851 215 33.43765 -118.515 115 33.95626 -120.008 165 34.02312 -119.865 216 33.44193 -118.524 116 33.95626 -120.008 166 34.00332 -119.650 217 33.46587 -118.587 117 33.95590 -120.008 167 34.00172 -119.643 218 33.47065 -118.591 118 33.95588 -120.009 168 34.00198 -119.643 219 33.46908 -118.583 119 33.95566 -120.009 169 34.00467 -119.705 220 33.47118 -118.578 120 33.95757 -120.009 170 34.00623 -119.700 221 33.47042 -118.569 121 33.95770 -120.008 171 33.98859 -119.743 222 33.46363 -118.526 122 33.95768 -120.008 172 33.98723 -119.741 223 33.45373 -118.511 123 33.95739 -120.008 173 33.98871 -119.735 224 33.44927 -118.502 124 33.95711 -120.008 174 33.98910 -119.733 225 33.37660 -118.464 125 33.94315 -120.109 175 34.01052 -119.805 226 33.37540 -118.452 126 33.94315 -120.109 177 34.00886 -119.807 227 33.37167 -118.459 127 33.94315 -120.109 178 34.01196 -119.817 228 33.36632 -118.445 128 33.94315 -120.109 179 34.01400 -119.820 229 33.36612 -118.430 129 33.94315 -120.109 180 34.01507 -119.823 230 33.36312 -118.454 130 33.90973 -120.121 181 34.01592 -119.825 231 33.36027 -118.461

Table VI (continued)

Santa Rosa Santa Cruz Santa Catalina ID latitude longitude ID latitude longitude ID latitude longitude 131 33.90933 -120.121 182 34.01687 -119.830 232 33.35617 -118.466 132 33.90911 -120.121 183 34.01708 -119.838 233 33.34780 -118.469 133 33.90871 -120.121 184 34.01732 -119.842 234 33.33988 -118.452 134 33.90871 -120.121 185 34.01719 -119.846 235 33.33775 -118.442 135 33.90898 -120.121 186 34.01848 -119.687 236 33.34143 -118.424 136 33.98329 -120.017 187 34.01921 -119.688 237 33.34777 -118.446 137 33.98329 -120.017 188 34.02497 -119.693 238 33.35313 -118.447 138 33.98329 -120.017 189 34.00428 -119.706 239 33.35767 -118.424 139 33.98329 -120.017 190 34.00960 -119.661 240 33.37755 -118.407 140 33.98329 -120.017 241 33.38770 -118.407 141 33.98329 -120.017 242 33.38727 -118.422 142 34.00481 -120.089 243 33.39200 -118.437 143 34.00481 -120.089 244 33.39957 -118.428 144 34.00481 -120.089 245 33.38942 -118.404 145 34.00481 -120.089 246 33.39073 -118.391 146 34.00481 -120.089 247 33.38992 -118.391 117CAT 33.41015 -118.464 118CAT 33.40657 -118.419

3.2.3 Data analysis

Statistical analyses such as allele frequency, observed and expected heterozygosity,

and fixation index were analyzed using GenAlEx 6.501 (Peakall & Smouse, 2012,

Peakall & Smouse, 2006). Testing for clones was done using ALLELEMATCH version

2.03 (Galpern et al., 2012), an R-package designed to identify unique multilocus genotypes where the number of individuals is not known a priori and where limited genotyping error and missing data may be present as is often the case in sampling and genotyping natural populations. The R-package DEMEtics (Gerlach et al., 2010) was

used to examine population differentiation giving fixation indices GST (Nei, 1973) and

DJOST (Jost, 2008). To reduce the possibility of Type I errors caused by multiple

pairwise comparisons of a single data set, this package uses bootstrapping to calculate

Bonferroni corrected p-values. While GST has been widely used to measure genetic

differentiation, it has been shown that DJOST is a better indicator for polymorphic loci with

more than two alleles (Jost, 2009, Gerlach et al., 2010). This R-package also checks

for Hardy-Weinberg Equilibrium (HWE) in the input populations, and uses either alleles or genotypes as appropriate based on HWE for a given locus (Goudet 1996).

Population structure, both within and across islands, was analyzed using four tools:

STRUCTURE (Pritchard et al., 2000, Falush et al., 2003, Falush et al., 2007, Hubisz et

al., 2009), GENELAND (Guillot, 2008, Guillot et al., 2005a, Guillot et al., 2005b, Guillot

et al., 2008), Principal Coordinates Analysis (GenAlEx 6.501), and DAPC (Jombart &

Ahmed, 2011, Jombart et al., 2010). Both STRUCTURE and GENELAND use

Bayesian clustering algorithms based on genotypes of individual samples.

GENELAND, however, includes geographic locations of samples to infer population structure and potential physical barriers to gene flow, even in populations with low genetic differentiation (Bech et al., 2014, Vernesi et al., 2012, Blair et al., 2012, Rieux et al., 2013). Principal Coordinates Analysis (PCoA) provides a visual representation of patterns resulting from a multivariate analysis of multiple loci and multiple samples.

Discriminant Analysis of Principal Components (DAPC) is a non-model based method

that uses a two-step PCA and Linear Discriminant Analysis multivariate process to infer

genetic clusters. For STRUCTURE, the following procedure was used: 50K burnin with

100K for MCMC for potential K of 1 to 7 for 10 reps each, using the Admixture Model

with NOLOCPRIOR. Best K was determined by calculating l(K) and ∆ K (Evanno et al.,

2005). With GENELAND, the GUI interface was used to run the correlated frequency,

spatial model at 100K MCMC, thin rate 100, and burnin 200 for 10 runs at K of 1 to 10.

This procedure was repeated five times. For each repetition, the run with the highest

posterior probability was chosen, and from these five results best K was inferred from

the modal value of K with the highest posterior probability. For DAPC analysis, 70

Principal Components (PCs) were retained to determine clusters and, based on the PC

results, three clusters were indicated; in order to ensure stable results, 44 PCs were

used for discriminant analysis of principal components and, based on results, two

discriminant functions were retained.

3.3 Results

All loci used in this study were polymorphic with the mean Na (number of alleles) per

locus across the islands varying from 15.3 to 16.8, mean Ho (observed heterozygosity)

from 0.795 to 0.828, and mean He (expected heterozygosity) was 0.832 to 0.862 (Table

VII).

Table VII: Descriptive statistics for Quercus pacifica by island on which it is found.

Santa Rosa Santa Cruz Locus N Na Ho He FIS N Na Ho He FIS MSQ13 46 19 0.826 0.782 -0.056 39 18 0.897 0.826 -0.087 MSQ4 45 16 0.911 0.900 -0.012 38 19 0.763 0.900 0.152 Q1/5 46 12 0.543 0.821 0.338 36 17 0.639 0.877 0.271 Q11 46 17 0.761 0.763 0.003 38 15 0.684 0.868 0.212 Q15 46 14 0.978 0.883 -0.108 38 12 0.737 0.824 0.106 Q110 46 18 0.848 0.844 -0.005 38 15 0.921 0.887 -0.038 Q9 46 18 0.913 0.887 -0.030 38 15 0.868 0.884 0.018 QM69 44 9 0.841 0.775 -0.086 37 11 0.892 0.827 -0.079 Mean 45.6 15.4 0.828 0.832 0.006 37.8 15.3 0.800 0.862 0.069

Santa Catalina All Island Mean Locus N Na Ho He FIS N Na Ho He FIS MSQ13 48 21 0.875 0.869 -0.007 44.3 19.3 0.866 0.826 -0.050 MSQ4 44 19 0.750 0.914 0.179 42.3 18.0 0.808 0.905 0.106 Q1/5 48 16 0.688 0.817 0.159 43.3 15.0 0.623 0.839 0.256 Q11 48 14 0.729 0.813 0.104 44.0 15.3 0.725 0.815 0.106 Q15 47 15 0.872 0.889 0.019 43.7 13.7 0.862 0.865 0.005 Q110 48 18 0.896 0.854 -0.050 44.0 17.0 0.888 0.861 -0.031 Q9 48 21 0.750 0.902 0.169 44.0 18.0 0.844 0.891 0.052 QM69 45 10 0.800 0.823 0.028 42.0 10.0 0.844 0.808 -0.046 Mean 47.0 16.8 0.795 0.860 0.075 43.5 15.8 0.808 0.851 0.050

N = number of samples, Na = number of alleles, Ho = observed heterozygosity, He = expected heterozygosity, FIS = fixation index (GenAlEx 6.501).

This level of genetic diversity is high and comparable to values found for mainland

California oaks (Craft et al., 2002, Dutech et al., 2005, Abraham et al., 2011). In spite of short distances between some of the samples, there was no evidence of cloning,

although scrub oak is given to asexual reproduction through underground rhizomes resulting in stands of genetically identical plants (AlleleMatch psib < .001). This suggests

that recruitment is primarily sexual rather than asexual. The fixation index (FIS) was low

for each island: 0.006 for Santa Rosa, 0.069 for Santa Cruz, and 0.075 for Santa

Catalina. Mean FIS overall was 0.051. As suggested by the low fixation index, no

significant deviation from HWE was found. Pairwise differentiation as measured using

GST and DJOST for Q. pacifica on Santa Rosa, Santa Cruz and Santa Catalina showed

low but significant values with p = .003 (Bonferroni corrected) for each result (Table

VIII).

Table VIII: Pairwise differentiation index using GST and DJOST for Q. pacifica on Santa Rosa, Santa Cruz and Santa Catalina.

SROSA SCRUZ SCAT SROSA -- 0.008 0.014 SCRUZ 0.106 -- 0.014 SCAT 0.155 0.188 --

p = 0.003 (Bonferroni corrected) GST results above diagonal; DJOST results below diagonal (DEMEtics).

STRUCTURE results were analyzed with STRUCTURE HARVESTER (Earl & vonHoldt,

2012) to compute mean LnP(K), and ∆K. ∆K peaked sharply at K=3, indicating three

distinct genetic clusters. Output from STRUCTURE HARVESTER for K=3 was run

through CLUMPP (Jakobsson & Rosenberg, 2007) to group individuals into genotypes

and those results were input into DISTRUCT (Rosenberg, 2004), producing a summary

image of the runs (Fig. 3-1). Although three clusters were indicated, they did not

coincide with the separate islands; rather, each individual had substantial percentages

of each genetic cluster.

Figure 3-1: Q. pacifica inferred admixture proportions in each genetic cluster for individual plants (columns).

Admixture Model with NOLOCPRIOR (STRUCTURE 2.3.4). Best K= 3 (per Evanno method).

Discriminant Analysis of Principal Components (DAPC) also found three clusters of genetically related individuals (Fig. 3-2). However, samples from each genetic cluster

were located on each of the islands: Santa Rosa had 13 individuals in Cluster 1, 7 in

Cluster 2, and 26 in Cluster 3; Santa Cruz had eight individuals in Cluster 1, 12 in

Cluster 2, and 19 in Cluster 3; and Santa Catalina had 15 individuals in Cluster 1, 29 in

Cluster 2, and 4 in Cluster 3.

Figure 3-2: Scatterplot of individuals on the two principal components.

Ellipses indicate genetic clusters. Dots represent individuals. (DAPC)

Principal Coordinates Analysis (Fig.3- 3) showed individuals from each island

intermixed across the coordinates, with no discernible grouping. Each of these models

inferred three distinct genetic clusters among the samples with membership in the

clusters across the islands.

Figure 3-3: Q. pacifica Principal Coordinates Analysis of variance.

Principal coordinate 1 and 2 account for 5.48% and 5.37% of the variation, respectively (GenAlEx 6.501).

GENELAND results indicated the individuals fell into five clusters (Table IX). Using the

assumption that there is often spatial dependence among individuals and making use of

the spatial input to infer origins of samples, GENELAND has the capability of detecting

underlying population structure on a more granular level (Coulon et al., 2006). Although

geographic barriers restricted gene flow among the sampling locations, there was still

porosity in the barriers. Members of the same cluster were found on Santa Rosa, Santa

Cruz and Santa Catalina, and two clusters were found on both Santa Cruz and Santa

Catalina, suggesting gene flow between the northern islands and Santa Catalina. Both

Santa Rosa and Santa Cruz had clusters found on no other island (Fig. 3-4).

Table IX: Q. pacifica inferred cluster membership based on spatial clustering analysis.

Inferred Cluster Santa Rosa Santa Cruz Santa Catalina 1 11 19 28 2 0 4 5 3 0 6 15 4 35 0 0 5 0 10 0 (GENELAND)

Figure 3-4: Five inferred spatial clusters based on GENELAND analysis: results plotted at locations from which samples were collected.

Source: “Channel Islands.” 33°39'12.77" N 119°15'54.63" W. Google Earth 7.1.2.2041. Accessed: 08/14/2014.

3.4 Discussion

There is no geological or geophysical evidence that land bridges between the California

Channel Islands and the mainland, or between the northern and southern island groups,

were present over the last 2.6 million years (Vedder & Howell, 1980, Junger &

Johnson, 1980). The islands have been wooded since well before humans arrived, and

pollen from Santa Rosa shows that Quercus species grew there at least 10,000 years ago (Rick et al., 2014). Given these findings, this research examined the evolutionary history of Q. pacifica by evaluating evidence for genetic drift and/or genetic differentiation due either to separate colonization histories or limited gene flow within or among islands.

The study found levels of genetic diversity comparable to that observed in mainland oaks and little genetic differentiation in Q. pacifica among the islands on which it is found,

Santa Rosa, Santa Cruz, and Santa Catalina. Structure analyses revealed overlap of

genetic clusters across the three islands. This high level of genetic variability and lack of

genetic differentiation among the islands precludes a history of population bottlenecks or

founder effects. Founder effects have often been suggested as the underlying factor for

low genetic diversity in island populations and especially island endemics (Frankham,

1997, Templeton, 2008, DeJoode & Wendel, 1992). Recent interpretation, however,

points to the fact that endemics have existed on their islands through many generations

during which time founder effects could be erased (Stuessy et al., 2014), and so lack of

evidence of founder effects in existing Q. pacifica may not be surprising. The current

genetic state of Q. pacifica indicates extensive gene flow has kept the diversity of the

species rich, a finding in agreement with a study of another Channel Island endemic,

Acmispon (Fabaceae), leading the authors to conclude that the islands are close

enough to each other and to the mainland to facilitate repeated gene flow (McGlaughlin

et al., 2014). Several interesting questions regarding the origins of Q. pacifica and the

mechanisms contributing to the high degree of genetic sharing remain.

Gene flow in oaks is accomplished through movement of acorns and spread of pollen.

Given the geologic history of the islands, initial oak colonization had to occur through

overwater dispersal of acorns, probably by water currents or birds. This event or series

of events occurred well before humans appeared on the islands. Genetic and

ecological investigation indicates that a colonization event of species of Acacia has

taken place between Pacific islands over 18,000 km apart (Le Roux et al., 2014), so it is

reasonable to assume that the relatively close proximity of the Channel Islands to the

mainland would not present a barrier to acorn colonization. Once oaks were

established, how did the species Q. pacifica evolve on the islands? There are several possible explanations. One is that it arrived on the islands as a result of acorn colonization of a mainland species. Although another endemic, Q. tomentella, has been

found in fossil remains on the mainland (Philbrick, 1980), there is no evidence of Q.

pacifica on the mainland, so this hypothesis is not supported. The other explanation is

that Q. pacifica is a true island species that evolved from another scrub oak species or

emerged through hybridization. It has been suggested that Q. pacifica is actually a

hybrid of Q. douglassii and another scrub oak, perhaps Q. berberidifolia (Nixon, 2002).

Although this hypothesis has not been genetically verified, such interspecific gene

exchange has been suggested as a mechanism for bypassing colonization as oaks

establish new territories (Petit et al., 2003). If Q. pacifica is the product of hybridization,

this study found no indication of locations at which it may have initially occurred.

There is evidence that long distance genetic continuity has been maintained within and among the islands through gene exchange. Two potential participants in acorn translocation among the islands are humans and birds. Given the sophisticated maritime cultures of the Native Americans who inhabited the islands, there was potentially a human role in moving acorns from island to island as part of trade or ceremonial exchanges. Birds can also act as acorn transporters (Scofield et al.,

2010,Caldwell et al. 2013).

Pollen has been found to travel long distances and studies have found a significant percentage of paternal material comes from outside oak stands. Oak phenology combined with weather conditions determines the amount of pollen available for transport in any given year, but winds and fog banks along the coast both have the potential of carrying pollen. Spatial cluster analysis conducted as part of this study indicates genetic barriers between the islands, although these are not absolute. The presence of physical barriers is suggested by the existence of unique genetic clusters on both Santa Rosa and Santa Cruz, while the porosity of the barriers is implied by the clusters common to these two islands and by the clusters they share with Santa

Catalina. Pollen flow carried by prevailing northwesterly winds appears to be indicated by the findings that three clusters from the northern islands are found on Santa Catalina and there are no clusters unique to Santa Catalina.

In summary, the genetic variability and lack of structure found in Q. pacifica on the

California Channel Islands suggests a dynamic history for the species, affected by weather, climate, landscape, humans and other animals, and Quercus biology. This research contributes to the overall understanding of island endemics and suggests that geographical, climatic, and biological dynamics must be considered when attempting to characterize them.

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4. UNRAVELLING THE TAXONOMIC PUZZLE OF THREE CALIFORNIA SHRUB

OAK SPECIES: QUERCUS DUMOSA NUTT., Q. BERBERIDIFOLIA LIEBM. AND

Q. PACIFICA NIXON & C.H. MULLER

4.1 Introduction

Species of oaks (Quercus L., Fagaceae) retain genetic and ecological boundaries

(Gailing & Curtu, 2014, Muir et al., 2000, Cavender-Bares & Pahlich, 2009) in spite of well-documented interspecific gene flow (Burgarella et al., 2009, Petit et al., 2003, Craft et al., 2002, Abraham et al., 2011, Valbuena-Carabana et al., 2005, Lepais et al., 2009).

This apparent disparity between genetic admixture and species integrity challenges those seeking to understand how species boundaries in oaks are formed and are maintained, as well as those investigating oak phylogeny.

Clarification of taxonomic designations of California oaks has been ongoing for over a century (Fryer, 2012). During this time the name ‘Quercus dumosa’ was applied to a number of shrub oaks and the ‘Q. dumosa complex’ included at least five species that are now recognized as separate taxa based on acorn morphology, leaf vestiture, and habitat (Nixon, 2002). Three of those species are the subject of this study. Quercus berberidifolia (common name: California scrub oak) is widespread in the Central and

South Coast ranges of California at elevations of 100-1800 m. It grows on a variety of soils in chaparral plant communities. From Santa Barbara southward, it does not

descend to the low elevations where Q. dumosa is found. Quercus dumosa Nutt. sensu

stricto (common name: coastal sage scrub oak) is found only in Southern California and

northern Baja California, growing in open chaparral on coastal bluffs and hillsides near

the sea, often on loose sandstone or granitics. Its habitat is dwindling due to human

encroachment into the desirable real estate locations it occupies. Misclassification of

other species as ‘dumosa’ may have contributed to lack of appreciation of the scarcity of

this species. Quercus pacifica (common name: island scrub oak) is endemic to three of

the Channel Islands, Santa Rosa, Santa Cruz, and Santa Catalina, but is not found on

the mainland. It is a key component of the island scrub oak chaparral and can cover relatively large areas, growing on ridges and open slopes as well as in canyons at

elevations between 50-150 m. It occurs in both a shrub and tree form up to 5 m in

height (Nixon, 1997, Nixon, 2002, Fryer, 2012) (Fig. 4-1).

Figure 4-1: Range maps of Q. berberidifolia, Q. dumosa sensu stricto, and Q. pacifica.

Maps: adapted from The Jepson Herbarium. 2012, 2014. Jepson online interchange for California floristics, [Online]. In: Jepson Flora Project. Berkeley, CA: University of California, The University and Jepson Herbaria (Producers). Available: http://ucjeps.berkeley.edu/interchange.html

To examine genetic structure across the three species this study used eight neutral nuclear microsatellite markers previously applied successfully in studies of white oak species (Craft et al., 2002, Dow et al., 1995, Abraham et al., 2011, Dow & Ashley, 2008,

Isagi & Suhandono, 1997, Ashley et al., 2010). Microsatellite analysis continues to be appropriate for evaluating population differentiation and resolving ecological questions

(Guichoux et al., 2011, Ashley, 2010, Selkoe & Toonen, 2006). While the work of Nixon and others (Tucker, 1993, Nixon & Muller, 1994, Nixon, 2002) was based on morphological and ecological characteristics, this is the first genetic analysis to examine the reclassification of these three recently identified species.

4.2 Materials and Methods

4.2.1 Genetic Analysis

Leaf samples were collected from individuals of each species: 60 Q. berberidifolia from

the western Santa Monica Mountains and near Topanga; 24 Q. dumosa from the Point

Loma Peninsula and Torrey Pines State Park; and 133 Q. pacifica from the three

Channel Islands on which it is found (46 from Santa Rosa, 39 from Santa Cruz, and 48

from Santa Catalina) (Fig.4-2).

Figure 4-2: Q. berberidifolia, Q. dumosa, and Q. pacifica collection sites.

Source: “Southern California collection sites”. 33°31’37.71” N and 118°42’35.11” W. GOOGLE EARTH. April 9, 2013. July 7, 2014.

Samples were stored in desiccant for transport and prior to DNA extraction. DNA was extracted from approximately 20 mg of the dry leaf material using DNeasy Plant MiniKit

(Qiagen). DNA concentrations and quality were verified on a NanoDrop spectrophotometer (Thermo Scientific). Genotyping was completed using eight primer pairs: QpZAG1/5, QpZAG15, and QpZAG110 which were developed for Q. petraea

(Steinkellner et al., 1997), QpZAG15 and QpZAG11 developed for Q. robur (Kampfer et al., 1998), MSQ4 and MSQ13 developed for Q. macrocarpa (Dow et al., 1995), and

M69–2M1 developed for Q. myrsinifolia (Isagi and Suhandono 1997). PCR amplification was completed according to the protocol described in (Abraham et al.,

2011). Genotyping of PCR products (0.9 – 1.5 µL) was done on the ABI 3730 DNA

Analyzer using LIZ500 ladder (Applied Biosystems). Applied Biosystems GeneMapper version 3.7 was used for genotype scoring of the raw data.

4.2.2. Data analysis

All samples were tested for clones with ALLELEMATCH version 2.03 (Galpern et al.,

2012), an R-package that identifies unique multilocus genotypes (UMGs). Clones were

collapsed into UMGs for all additional analyses. Allele frequency (Na), observed (Ho)

and expected (He) heterozygosity, and fixation index (FIS) were determined with

GenAlEx 6.501 (Peakall & Smouse, 2012, Peakall & Smouse, 2006).

Three methods of analysis were used to examine population differentiation and levels of

introgression among the three species. Differentiation indices GST (Nei, 1973) and

DJOST (Jost, 2008) were examined with the R-package DEMEtics (Gerlach et al., 2010)

which employs bootstrapping to provide Bonferroni corrected p-values to test

significance of differentiation. GST is a widely employed measure of genetic

differentiation, but DJOST is reportedly a more accurate measure for polymorphic loci with

more than two alleles (Jost, 2009, Gerlach et al., 2010). DEMEtics checks for Hardy-

Weinberg Equilibrium (HWE) in the input populations, randomizing either alleles or

genotypes depending on whether or not all populations are in HWE for a given locus

(Goudet 1996). GenAlEx 6.501 was used for Principal Coordinates Analysis (PCoA).

This method provides a visual representation of patterns resulting from a multivariate analysis of multiple loci and multiple samples. Bayesian analysis was performed with

STRUCTURE (Pritchard et al., 2000, Falush et al., 2003, Falush et al., 2007, Hubisz et al., 2009) using the Admixture Model with LOCPRIOR and a 50K burnin and 100K

MCMC for potential K of 1 to 7 for 10 reps each. Optimum number of genetic clusters

(K) was determined by calculating l(K) and delta K (Evanno et al., 2005) using

STRUCTURE HARVESTER (Earl & vonHoldt, 2012). Using the best K value, the

STRUCTURE project was rerun with 50K burnin and 250K MCMC to determine q- values (posterior probability), representing the percentage of individual genotypes in each K cluster. These were utilized to infer the degree of admixture among the species.

The q-value threshold selected to evaluate significant introgression, for example to identify F1 hybrids or to substantiate conservation management decisions, varies

according to the questions being asked (Burgarella et al., 2009, Vähä & Primmer,

2006). For purposes of this research, the goal of which is to detect genetic

introgression among the three study species, the criteria of q < 0.80 was used to identify

individuals with significant admixture.

4.3. Results

ALLELEMATCH (psib < .001) showed that the original set of 60 Q. berberidifolia samples

contained 43 UMGs. No clones were found among the 24 Q. dumosa and 133 Q.

pacifica samples. Clones were collapsed for Q. berberidifolia and from that point only

unique genotypes were used for analysis. All loci were polymorphic in each species

(Table X). Allelic diversity is comparable to that found for other California oaks (Dutech et al., 2005, Craft et al., 2002, Abraham et al., 2011). For Q. berberidifolia, Na (number of alleles) varies from 14 to 24 per locus, Ho (observed heterozygosity) varies from

0.558 to 0.977, and He (expected heterozygosity) from 0.812 to 0.928; for Q. dumosa,

Na varies from 5 to 16 per locus, Ho from 0.542 to 0.917, and He from 0.597 to 0.903;

and for Q. pacifica, Na varies from 15 to 31 per locus, Ho from 0.623 to 0.886, He from

0.830 to 0.915 (Table X). Although some loci show homozygous excess, others show

heterozygous excess; overall, there is no consistent pattern across species or loci.

Each species has alleles not found in other species: Q. berberidifolia has 26, Q.

dumosa has five, and Q. pacifica has 49. Mean FIS (fixation index) for Q. berberidifolia

is 0.039, for Q. dumosa it is 0.029, and for Q. pacifica it is 0.072. These low values

suggest an absence of inbreeding (Table X).

Table X: Q. berberidifolia, Q. dumosa, and Q. pacifica descriptive statistics.

Q. berberidifolia Q. dumosa Q. pacifica Locus N Na Ho He FIS N Na Ho He FIS N Na Ho He FIS S15 43 18 0.977 0.892 -0.095 24 16 0.917 0.903 -0.015 133 31 0.865 0.864 -0.001 S16 43 18 0.860 0.868 0.009 24 14 0.792 0.858 0.077 127 23 0.811 0.915 0.114 Q1/5 43 20 0.744 0.883 0.157 24 10 0.542 0.794 0.318 130 21 0.623 0.851 0.267 Q11 43 14 0.558 0.812 0.313 24 5 0.875 0.710 -0.232 132 24 0.727 0.831 0.124 Q15 43 16 0.930 0.869 -0.071 24 10 0.750 0.835 0.102 131 16 0.870 0.892 0.025 Q110 43 22 0.837 0.928 0.098 24 11 0.875 0.837 -0.046 132 23 0.886 0.880 -0.007 Q9 42 24 0.905 0.914 0.011 24 13 0.875 0.883 0.009 132 26 0.841 0.906 0.072 QM69 42 17 0.952 0.861 -0.106 24 10 0.583 0.597 0.023 127 15 0.843 0.830 -0.016 MEAN 42.8 18.6 0.846 0.878 0.039 24.0 11.1 0.776 0.802 0.029 130.4 22.5 0.808 0.871 0.073

N = number of samples, Na = number of alleles, Ho = observed heterozygosity, He = expected heterozygosity, FIS= fixation index (GenAlEx 6.501).

The highest level of genetic differentiation is between the two mainland species, Q. berberidifolia and Q. dumosa, with GST = 0.029 and DJOST = 0.341, Bonferroni corrected

p = 0.003. Comparison between mainland and island species shows lower but

significant levels of differentiation, with that between Q. berberidifolia and Q. pacifica

(GST = 0.014, DJOST =0.219, Bonferroni corrected p = 0.003) less than that between Q.

dumosa and Q. pacifica (GST = 0.022, DJOST =0.274, Bonferroni corrected p = 0.003).

Principal coordinates analysis using PCoA supports these patterns (Fig. 4-3). The

greatest degree of separation is between Q. dumosa and Q. berberidifolia. Quercus

pacifica genotypes overlap both Q. berberidifolia and Q. dumosa, although there is

more overlap with Q. berberidifolia.

Figure 4-3: Q. berberidifolia, Q. dumosa, and Q. pacifica Principal Coordinates Analysis of variance.

Principal Coordinates (PCoA)

Q. berberidifolia Q. dumosa Coord. 2 Coord. Q. pacifica

Coord. 1

Principal coordinate 1 and 2 account for 4.84% and 4.50% of the variation, respectively (GenAlEx 6.501).

In STRUCTURE HARVESTER, ∆K peaked sharply at K=3, indicating three distinct genetic clusters which corresponded closely to the three named species (Fig. 4-4).

Figure 4-4: Q. berberidifolia, Q. dumosa, and Q. pacifica inferred admixture proportions in each genetic cluster for individual plants (columns).

Admixture Model with LOCPRIOR grouped by species (STRUCTURE 2.3.4). Best K = 3 (per Evanno method). Species: 1 = Q. berberidifolia, 2 = Q. dumosa, 3 = Q. pacifica

The proportion of inferred ancestry of each pre-defined population (LOCPRIOR) in each

of the three clusters was high: 0.905 for Q. berberidifolia, 0.849 for Q. dumosa, and

0.938 for Q. pacifica. Using the criterion of q < 0.80 for admixture, there was limited

introgression. Five Q. berberidifolia, four Q. dumosa, and four Q. pacifica, all identified

as these species in the field, met this threshold (Table XI).

Table XI: Q. berberidifolia, Q. dumosa and Q. pacifica introgression.

Inferred cluster Sample Species Q. pacifica Q. berberidifolia Q. dumosa QUBE120 Q. berberidifolia 0.171 0.679 0.150 QUBE123 Q. berberidifolia 0.330 0.653 0.017 QUBE126 Q. berberidifolia 0.361 0.604 0.036 QUBE142 Q. berberidifolia 0.155 0.775 0.070 QUBE157 Q. berberidifolia 0.541 0.451 0.008 QUDU113 Q. dumosa 0.645 0.025 0.330 QUDU114 Q. dumosa 0.317 0.095 0.588 QUDU118 Q. dumosa 0.231 0.028 0.741 QUDU124 Q. dumosa 0.234 0.035 0.732 QUPA211 Q. pacifica 0.758 0.010 0.232 QUPA218 Q. pacifica 0.794 0.006 0.200 QUPA229 Q. pacifica 0.774 0.015 0.211 QUPA239 Q. pacifica 0.716 0.011 0.273

Three of the five Q. berberidifolia have over 30% of inferred ancestry in the Q. pacifica

cluster. Over 20% of the inferred ancestry in the four Q. dumosa also falls in the Q.

pacifica cluster, with one sample having over 60% in that cluster. Both of these cases

suggest island-to-mainland gene flow. The Q. pacifica samples that show significant

introgression each have over 20% of inferred ancestry that fall into the Q. dumosa

cluster. All of these Q. pacifica individuals are from Santa Catalina, which is the island

closest to the locations at which Q. dumosa was sampled, suggesting a mainland-to-

island gene flow.

4.4 Discussion

Quercus maintains species integrity while readily exchanging interspecific genetic material. These two seemingly conflicting findings have challenged those trying to

define a universal species concept since the time of Darwin (Coyne & Orr, 2004,

Darwin, 1872), and have presented difficulties to those trying to clarify oak taxonomies

(Nixon, 2002). This study substantiates the taxonomic reclassification of three species

that had been grouped within a complex of California scrub white oaks. The limited

introgression among the three differentiated species leads to questions regarding

barriers to gene flow that contribute to retention of species boundaries.

California scrub oaks have long been a taxonomic challenge with species ‘blurred’ by

introgression and hybridization (Fryer, 2012). The group known as the ‘Q. dumosa

complex’ included a number of similar species. Reclassification of this group, based on

more stringent criteria, identified a number of species that had been lumped together,

including Q. berberidifolia, Q. dumosa sensu stricto, and Q. pacifica. The genetic

analysis of this study supports the reclassification that was based on morphology,

habitat, and associated plant communities.

While confirming the three species’ genetic boundaries, the data also reveal limited

introgression and gene flow among them. Results suggest directions in which gene flow has occurred. There was evidence of limited genetic exchange between the two mainland species, Q. berberidifolia and Q. dumosa. This low level of introgression

between the mainland species may be explained by the distances of 160-220 km and

physical barriers, both natural and human-induced, between the collected samples.

There were higher levels of introgression between the two mainland species and the island species. Quercus pacifica and the mainland species are geographically separated by at least 24 km of open ocean. Quercus is wind pollinated, and studies

have shown that pollen often comes from well outside the stand of oaks in which the

acorns sprout (Dow & Ashley, 1998, Buschbom et al., 2011, Koenig & Ashley, 2003), so

gene flow through pollen movement across this distance is not unreasonable.

Researchers continue to examine mechanisms that may play a role in the conservation

of species integrity in the face of interspecific gene flow. To this end, they have looked

at an array of possible pre-mating and post-mating reproductive barriers in oaks,

including differences in phenology (Cavender-Bares & Pahlich, 2009), pollen tube

formation, floral abortion, fertilization success, progeny fitness (Abadie et al., 2012), the

impact of ecological and climatic niches (Ortego et al., 2014), and the impact of

landscape barriers (Herrera-Arroyo et al., 2013). New genome sequencing methods

are emerging and beginning to be used in the search to clarify species-related

questions (Butlin, 2010, Gailing & Curtu, 2014) as well as to untangle taxonomic

puzzles (Hipp et al., 2014). Reproductive barriers in oaks are diverse and are affected

by abiotic as well as biotic factors (Abadie et al., 2012). This is a compelling reason to

believe that there will not be a simple, universal explanation for the maintenance of

species integrity in the genetically porous Quercus genus.

4.5 References

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5. VITA

NAME: Janet Rizner Backs

EDUCATION: B.A., Social Studies, Loretto Heights College, Denver, Colorado, 1968

M.A., Ibero-American Studies, University of Wisconsin at Madison, 1971

M.S., Chemistry, Northeastern Illinois University, Chicago, Illinois, 1985

M.S., Natural Resources/Environmental Sciences, University of Illinois at Urbana-Champaign, 2008

Ph.D., Biological Sciences, University of Illinois at Chicago, 2014

HONORS: Iota Sigma Pi, National Honor Society for Women in Chemistry – Aurum Iodide Chapter

Gamma Sigma Delta, The Honor Society of Agriculture – Illinois Chapter

PROFESSIONAL Botanical Society of America MEMBERSHIP: International Oak Society

Union of Concerned Scientists

The Torrey Botanical Society

Association of Computer Machinery (ACM)

PUBLICATIONS: J.R. Backs, et al. “Urban greening: a case study to determine the efficacy of using low maintenance planters as a means of growing plants in Chicago urban settings.” J. Environ. Hort. 31(3):145–152. September 2013.

“DB2 for OS/390 Version 5: Tools of the Trade”, an IBM White Paper by DB2 Consultants, July 1997

“Time to Look at IBM’s DB2 Utilities Again”, IDUG Solutions Journal, Issue 2, 1996

“DB2 Version 4: Our Dreams Come True?”, IDUG Solutions Journal, October 1994

“The Magic of Materialization”, Database Programming & Design, March 1994

“Distributed Relational Database Architecture (DRDA): What’s It All About?”, DB2 Journal, August 1992

POSTER: ‘The story of a tough little West Texas oak, the threatened Quercus hinckleyi C.H. Mull.’ at ICCB 2013 (International Congress for Conservation Biology), Baltimore, Maryland

PRESENTATIONS: ‘Using microsatellite analysis to assess viability of a rare, isolated Quercus species: Q. hinckleyi C.H. Mull.’, at Botany 2012, Columbus, Ohio

‘Using microsatellite analysis to assess viability and genetic distribution of an endemic Channel Island oak: Quercus pacifica K. Nixon & C.H. Muller’ at 8th California Islands Symposium (2012), Ventura, California

“Beyond the Magic: Locking and Data Materialization”, IDUG 9th Annual North American Conference, May 1997

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