Malus Diversity in Wild and Agricultural Ecosystems
Item Type text; Electronic Dissertation
Authors Routson, Kanin Josif
Publisher The University of Arizona.
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Link to Item http://hdl.handle.net/10150/223381
MALUS DIVERSITY IN WILD AND AGRICULTURAL ECOSYSTEMS
By
Kanin J. Routson
______
A Dissertation Submitted to the Faculty of the
GRADUATE INTERDISCIPLINARY PROGRAM IN ARID LANDS RESOURCE SCIENCES
In Partial Fulfillment of the Requirements For the Degree of
DOCTOR OF PHILOSOPHY
In the Graduate College
THE UNIVERSITY OF ARIZONA
2012
2
THE UNIVERSITY OF ARIZONA GRADUATE COLLEGE
As members of the Dissertation Committee, we certify that we have read the dissertation prepared by Kanin J. Routson, entitled “Malus Diversity in Wild and Agricultural Ecosystems” and recommend that it be accepted as fulfilling the dissertation requirement for the Degree of Doctor of Philosophy.
______Date: April 20, 2012 Gary Paul Nabhan
______Date: April 20, 2012 Gayle M. Volk
______Date: April 20, 2012 Steven Smith
______Date: April 20, 2012 Paul F. Robbins
______Date: April 20, 2012 Stuart E. Marsh
Final approval and acceptance of this dissertation is contingent upon the candidate’s submission of the final copies of the dissertation to the Graduate College.
I hereby certify that I have read this dissertation prepared under my direction and recommend that it be accepted as fulfilling the dissertation requirement.
______Date: April 20, 2012 Dissertation Director: Gary Paul Nabhan
3
STATEMENT BY AUTHOR
This dissertation has been submitted in partial fulfillment of requirements for an advanced degree at the University of Arizona and is deposited in the University Library to be made available to borrowers under rules of the Library.
Brief quotations from this dissertation are allowable without special permission, provided that accurate acknowledgment of source is made. Requests for permission for extended quotation from or reproduction of this manuscript in whole or in part may be granted by the author.
SIGNED: Kanin J. Routson
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ACKNOWLEDGMENTS
I sincerely thank all who have contributed to this research and helped me navigate through my doctoral degree. Specifically, I would like to thank my dissertation chair, Gary Nabhan, and my PhD committee members Gayle Volk, Steven Smith, Stuart Marsh, and Paul Robbins for their guidance and mentoring of my education and research. Thank you Stuart Marsh, Marylou Meyers, and Alicia Canett of Arid Land Resources Sciences and GDIP for help with tuition, teaching assistantships, and in meeting deadlines and the necessary paperwork. I am grateful to the GIDP office for funding much of my research and education through teaching assistantships and professors, George Gehrels, Katie Hirschboek, and Pat Willerton whose classes I assisted. Thank you Joe Wilder, Lupita Cruz and other folks at the Southwest Center for providing assistance, a workspace, and funding. I am indebted to all who have contributed to the diverse chapters of my research. Dendrochronology of historic apple trees would not have been possible without help from Cody Routson and Paul Sheppard. The M. fusca research as well was made possible through the generosity and contributions of numerous people. Thanks to British Columbia collaborators Victoria Wyllie de Echeverria, Nancy Turner, Leslie Main Johnson, and Ken Downs for their insights and contributions to the project and for providing samples from culturally managed harvesting sites. Thanks to University of Arizona Herbarium (ARIZ) staff Phillip Jenkins and Sarah Hunkins for assistance in acquiring herbarium records and Steffi Ickert-Bond and other staff at the University of Alaska Museum of the North Herbarium (ALA), staff at the University of British Columbia Herbarium (UBC) and the University of Alberta Herbarium (ALTA) for sending samples. Sarah Hayes and Joseph Postman were very helpful in collecting field samples of M. fusca. Thank you Christopher Richards for help with analyses and for use of the lab facilities and Adam Henk for technical assistance in the lab. Thanks Ned Garvey and Karen Williams for assistance in securing Plant Germplasm Collection funds. Thank you Kellogg Program of the University of Arizona Southwest Center for additional support. Lastly I am grateful for my family and friends, Mom, Dad, Rafael, and Cody for who have supported me throughout my life and education. 5
TABLE OF CONTENTS
LIST OF FIGURES...... 6 LIST OF TABLES...... 7 ABSTRACT...... 8 CHAPTER 1 ...... 10 INTRODUCTION……...... 10 Explanation of the Problem and Its Context ...... 10 Apple Trees as a Perennial Crop …...... 12 Explanation of Dissertation Format ...... 13 CHAPTER 2 ...... 15 PRESENT STUDY...... 15 1. Resilience of Feral Domestic Apple Trees ...... 15 2. Collecting Crop Wild Relatives to Capture Genetic Variation ...... 20 A. Species-Level Collections ...... 21 B. Population-Level Collections ...... 22 C. Family-Level Collections ...... 23 Informed Subsequent Collections …………...... 24 3. Genetic Variation in the Pacific Crabapple ...... 25 Conclusions and Recommendations for Future Research ...... 29 REFERENCES ...... 31 APPENDIX A: DENDROCHRONOLOGY REVEALS PLANTING DATES OF HISTORIC APPLE TREES IN THE SOUTHWESTERN UNITED STATES (Published in The Journal of American Pomological Society, 66(1): 9-15, January 2012) ...... 37 APPENDIX B: COLLECTING CROP WILD RELATIVES TO CAPTURE GENETIC VARIATION (Intended for Crop Science) ...... 46 APPENDIX C: GENETIC VARIATION AND DISTRIBUTION OF PACIFIC CRABAPPLE, MALUS FUSCA, (RAF.) C.K. SCHNEID. (Intended for The Journal of the American Society for Horticultural Science) ...... 71
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LIST OF FIGURES
Figure 1: Farms growing apples in Arizona over the last century ...... 16 Figure 2: Abandoned historic orchard sites analyzed ...... 17 Figure 3: Raw ring-width series of individual trees …………………...... 18 Figure 4: Tree-ring chronologies of four historic apple orchards ………………………... 19 Figure 5: Collection informed in situ and ex situ conservation ...... 24 Figure 6: Potential habitat for Pacific crabapple ...... 28 7
LIST OF TABLES
Table 1: A framework for collecting genetic variation in wild relatives ...... 21 8
ABSTRACT
Human-induced land degradation and climate change can reduce agricultural productivity and increase susceptibility to food shortages at local and global scales.
Planting perennial crop species, such as fruit and nut crops, may be an intervention strategy because of their beneficial contributions to sustainable agriculture and human nutrition. Many perennial temperate fruit and nut species are however, particularly vulnerable to frost events, drought, insufficient chill hours, and disease and insect outbreaks. Modifying these species to yield harvests under a wider range of biotic and abiotic conditions may increase the value and long-term viability of perennials in agroecosystems. This dissertation examines adaptation and ecogeography in temperate perennial fruit crops, using apple (Malus sensu lato) as an example for case studies. The resilience of feral domestic apple trees in abandoned farmstead orchards throughout the southwestern U.S. indicates plasticity in adapting to local environmental conditions. Dendrochronology reveals these trees tend to persist where they have access to supplemental water, either as shallow groundwater or irrigation. While domestic apples are cultivated under a range of growing conditions, wild relatives of agricultural crops may further expand the cultivable range of the species. Crop wild relatives are species closely related to agricultural species, including progenitors that may contribute beneficial traits to crops. Sampling the genetic variation in crop wild relatives may benefit from ecological genetics and GIS theory to reveal genetic structure. The Pacific crabapple is an example of a wild apple relative that may contain genetic variation useful in apple breeding. Species distribution modeling of the Pacific crabapple identifies a 9 narrow climatic window of suitable habitat along the northern Pacific coast, and genetic fingerprinting reveals a highly admixed genetic structure with little evidence of natural or cultural selection. While the moist coastal Pacific Northwest is not necessarily characteristic of many apple-growing regions, the species may have useful adaptations transferable to domestic apples. Genetic resources offer a promising source of raw material for adapting crops to future agricultural environments; their characterization, conservation, and use may offer important contributions to adaptation and use of perennial crops in agro-ecosystems.
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CHAPTER 1
INTRODUCTION
Explanation of the Problem and Its context
Perennial crops are considered an essential component of human nutrition worldwide (Janick and Paull 2008), and offer important services in agro- ecosystems. Currently planted on over 178 million acres, perennial crops (excluding perennial forage species) account for around 5% of global agricultural production and include more than 75 commercially important fruit, nut, fiber, and plantation crops grown on six continents (Monfreda et al. 2008). Perennials in agro- ecosystems have been shown to be a more sustainable food source for humanity than annual crops because they require fewer fossil fuel inputs, and they may reduce erosion, improve water quality and infiltration, increase soil organic matter, enhance carbon storage and sequestration, and reduce greenhouse gas emissions in agricultural production (Post and Kwon 2000; Boody et al., 2005; Tilman et al.,
2006; Glover et al., 2007; Jordan et al., 2007; Monfreda et al. 2008). Perennial agro- ecosystems may lend themselves to higher on-farm biodiversity through providing habitat and food resources for pollinators and insect predators (Schulte et al. 2006).
Tradeoffs typical between crop yields and maintaining ecosystem services may not be absolute, with similar yields possible between the two systems with higher potential ecological sustainability in perennial fields (Foley et al. 2005, Glover et al.
2010).
Many perennial crops however, are adapted to a narrow range of growing conditions and are vulnerable to shifting and erratic climate trends. Many temperate 11 fruit and nut species (including apples and other Rosaceae, pecans, pistachios, etc.) require sufficient winter chill hours to break dormancy and initiate fruit set
(Cesaraccio et al. 2004). Insufficient winter chill hours can decrease total yields in perennial fruit and nut crops (Luedeling et al. 2009). Erratic climate processes, such as late spring freezes, more extreme droughts, and more frequent and severe storms can reduce crop yields and cause food vulnerability (Lin et al. 2008).
Climate change, natural or human-induced shifts in the climatic mean or variability that persist over extended time periods of decades or longer (IPCC 2007), may negatively affect agriculture across multiple scales. The Intergovernmental
Panel on Climate Change (IPCC) Fourth Assessment Report predicts an increase in mean global temperatures of up to 6.4 degrees Celsius by the year 2100 (IPCC,
2007). Altered precipitation regimes could reduce soil moisture by upwards of 20% in some areas (Scheiermeier 2008) potentially compounding effects of rising temperatures on bioproductivity. The frequency and duration of drought in combination with higher average temperatures are also expected to increase in many dryland areas affected by climate change. Increases in the frequency and intensity of extreme events and the overall variability of the climate system in combination with varying rates of change are key components in climate change vulnerability and adaptation (Habash et al. 2009).
Agricultural systems may need to adapt to conditions of marginal lands
(defined here areas unable to support permanent or intensive agriculture without substantial improvement) and in response to altered climate in order for food 12 production to continue at or approach current levels (Howden et al. 2007). Breeding stress tolerance in agricultural crop plants is one solution that may increase our ability to grow food on marginal lands or in changing environments. Genetic variation enables evolution and adaptation to changing environmental conditions
(Frankel et al. 1995), and genetic diversity in agricultural crops is a source of variation that is considered necessary for maintaining agricultural production and buffering against disease and pest outbreaks and environmental influences (Harlan
1975; Ehrlich et al. 1993; Qualset and Shands 2005; Damania 2008).
This dissertation focuses on genetic variability as a mechanism for adapting agricultural plants species in perennial agro-ecosystems to a wider range of environmental growing conditions. The premise of this dissertation is that we need to understand how diversity can better work in the service of adaptation. Genetic diversity and selection may allow adaptation of agricultural crops to marginal lands and climate change. Apple trees are one example of an important perennial species and are utilized as a lens through which this dissertation focuses to address adaptive capacity in perennial crops.
Apples as a Perennial Crop
Domesticated apples (Malus × domestica Borkh.) are a global agricultural crop and one of the most important temperate fruit crops (Janick et al. 1996; Brown 2012).
The $2.5 billion U.S. apple industry (Brown 2012) is predicated on a very narrow genetic base. Modern commercial apple cultivars are increasingly derived from progeny from the crosses of four primary seedling parents, Cox’s Orange Pippin, 13
Golden Delicious, Jonathan, and McIntosh (Noiton and Alspach 1996; Luby 2003;
Brown 2012). Just 11 apple cultivars account for 90% of production and commercial sales in the U.S. (Dennis 2008). Worldwide, a disproportionate amount of apple production is based on just two cultivars, Red Delicious and Golden Delicious and their derivatives: Gala, Mutsu, Jonagold, Empire, and Fuji (Hokanson et al. 2001).
Biotic stress including fire blight, apple scab, and cedar apple rust and abiotic stresses that include late spring frost events, reduced chill hours, droughts, and hail damage reduce apple harvests worldwide (Forte et al. 2002; Qualset and Shands
2005; Erdin et al. 2006). Achieving a broad genetic base for cultivated apples may increase adaptation in apples for mitigating biotic and a-biotic stresses and may improve the long-term viability of the world’s apple industry.
Explanation of Dissertation Format
This dissertation represents the culmination of my doctoral studies in Arid Lands
Resource Sciences at the University of Arizona. Drawing from wide my range of training in the natural and social sciences, I explore the ecological, genetic, and cultural aspects associated with the geography, collection, and use of apples and their wild relatives. All research presented here was conducted by myself as primary researcher and author with guidance and contributions from committee members and collaborating authors.
I focus on genetic diversity in the service of adapting agricultural crops to a wider range of environmental growing conditions using apple (Malus) an example genus. This research is addressed through three questions: 1) Do apple trees in 14 historic orchards in the southwestern U.S. show response to local climate conditions? 2) How can wild relatives of agricultural crops be sampled to capture variation? 3) Do populations of Pacific crabapple show evidence of natural selection across the species range or cultural selection associated with First Nation harvesting areas? These questions are addressed in separate manuscripts appended to the end of this dissertation. Appendix A utilizes dendrochronology to determine the age of historic orchards and to understand climatic affects on tree growth. This work was published in The Journal of American Pomological Society, 66(1): 9-15,
January 2012. Appendix B outlines a strategy for sampling crop wild relatives to capture genetic variation. This manuscript is intended for Crop Science. Appendix C utilizes the Pacific crabapple as an example case study for both sampling genetic variation in a wild crop relative and to assess the genetic structure and natural and cultural selection of a wild apple relative. This manuscript is intended for The
Journal of the American Society for Horticultural Science. A summary of key findings is presented in Chapter 2, Present Study.
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CHAPTER 2
PRESENT STUDY
Methods, results, and conclusions of this research are presented in manuscript format appended to this dissertation. Important findings from this research are summarized below.
1. Resilience of Feral Domestic Apple Trees
Remnant historic apple orchards can be found throughout higher elevations of the southwestern United States (where sufficient winter chill hours enable fruit set) and are relicts of past agricultural endeavors in the region. These alien exotics were able to establish only through human tending that modified their local environment by providing additional water and protection against biotic and abiotic stresses that enabled these domesticates to survive.
The decline in southwestern farms growing apples (Fig. 1) resulted in numerous abandoned orchards throughout the region. Many of these orchards likely succumbed to drought upon abandonment, yet trees remain despite high climatic variability. This leads to the question: Do apple trees in historic orchards in the southwestern U.S. show response to local climate conditions? Or on the other hand, do surviving orchards have access to external water, such as high groundwater or close proximity to surface water that have enabled their survival in an arid environment?
16 2000 1000 Farms growing apples 0 1930 1950 1969 1987 2007 Year
Figure 1: Farms growing apples in Arizona over the last century. Note the sharp decline in farms between the late 1930s through the 1960s. Data compiled from USDA agricultural censuses.
To determine the age of historic orchards and response of these trees to environmental variability, standard dendrochnological methods were used to analyze increment cores from apple trees in three abandoned orchards in Arizona, one from an abandoned farmstead near Big Bug Creek in the Prescott National
Forest and another at Bottle Spring in the Tonto National Forest, and from Colorado in an abandoned orchard on Crossfire Ranch (Fig. 2). Orchards in the historic
Pendley Homestead at Slide Rock State Park, Arizona, are currently being maintained but were included because of known oral history planting dates and helped to validate the use of dendrochronology methods for apple trees.
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Figure 2: Abandoned historic orchard sites analyzed. Big Bug Orchard in the Bradshaw Mountains, AZ (BBO), Bottle Springs Orchard near Young, AZ (BSO), Crossfire Ranch Orchard, near Ignacio, CO (CRF), and the currently maintained orchards at Slide Rock State Park, AZ (SLR).
Graphical (Stokes and Smiley 1968) and statistical (Holmes 1983; Grissino-
Mayer 2001) methods were used to cross-date trees in the orchards. Cross-dating is a technique to match common growth patterns among trees to identify missing rings and double rings. Ring-widths were measured and compiled into raw ring- width series (Fig. 3). 18
Figure 3: Raw ring-width series of individual trees. Trees were cored in the Bottle Springs, Big Bug, Crossfire, and Slide Rock Orchards.
These series were standardized into individual orchard chronologies (Fig. 4).
Tree growth (using ring width as a proxy) was compared within each orchard to local climate records of precipitation and maximum summer temperatures using correlation and partial correlation analysis (Meko et al. In review) on gridded
PRISM data (Daly et al. 2002).
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Figure 4: Tree-ring chronologies of four historic apple orchards. Annual precipitation (Daly et al. 2002) is plotted with a dashed gray line behind the orchard chronologies. Tree-ring and precipitation series are normalized to z-scores (normalized departures from the mean). All series have a mean of zero and a standard deviation of 1, and are therefore comparable to each other.
The year 1903 is the likely planting date for the Big Bug Orchard. The Bottle
Springs Orchard was planted in 1946, and the Crossfire Orchard dates to at least the
1920s, though heart rot (caused by a fungus) in the trees prevented precise-dating of the orchard.
Trees in the abandoned historic orchards showed a slight response to climate
(Bottles Springs had the highest correlation between ring growth and precipitation with r = 0.43), and trees still being maintained in the Slide Rock Orchards showed no response to climate. Dry years were often recorded by narrow annual rings in all orchards, but average precipitation years had more variable ring growth. Orchards that remain on the landscape appear to have access to exogenous resources such 20 supplemental water in the form of irrigation or high ground water that have assisted in their survival. This study indicates that domestic apples can respond to a limited amount of environmental variability, but may not be adapted to large environmental changes possible in future. Wild apple relatives may contain raw genetic material for improving domestic apple trees.
2. Collecting Genetic Variation in Crop Wild Relatives
Wild relatives of agricultural crops are undomesticated species closely related to their domesticated counterparts. They may offer a promising source of novel genetic variation for increasing adaptive capacity and stress tolerance in agricultural crops
(Tanksley and McCouch 1997; Hoisington et al. 1999 Gur and Zamir 2004; Hajjar and Hodgkin 2007; Damania 2008; Maxted and Kell 2009; Guarino et al. 2011).
Natural population structure and unique genetic histories complicate sampling genetic variability in wild plants. How a collector chooses to sample for genetic variation can limit future uses and analysis options. Through conscious methodological sampling decisions a collector can leverage geospatial and genomic data to increase analytical and utilitarian potential of the collection. For instance, genomics-assisted plant breeding and other recent techniques enable researchers to draw from vast quantities of genetic variation (Varshney et al. 2005, 2009, Elshire et al. 2011). Habitat loss that threatens the existence wild relatives further necessitates the collection, characterization, and conservation of crop wild relatives, and leads to the question: How can wild relatives of agricultural crops be sampled to capture variation? To address a gap in methodology for collecting 21 genetic variation in wild relatives, we provide a framework (Table 1) that outlines three levels of sampling (species, population, and family) and the collections units, logistical effort, analytical potential, and conservation value associated with each sampling level. We draw upon theory in landscape and ecological genetics, GIS technologies, and existing incidence records to inform sampling decisions when a- priori information on a species is lacking.
Table 1: A framework for collecting genetic variation in wild relatives. Sampling level and the corresponding logistical requirements, analyses options and end use- value of the germplasm being collected are presented.
Sample Sampling Site Unit of Collection Logistical Analytical Impact to Target Hierarchy Selection Effort Potential Collection
Single site Single sample Sites with Individual or Site Point Taxonomic known bulked accessibility; reference &
taxonomic collecting sample. phylogenetic coverage; voucher Species level interests Overlaps with specimens ** comparisons among other species species for increased Species level efficiency Multiple Representative Select Seeds/propagules; Between site Coarse scale Capture sites sampling of multiple DNA samples; transportation; estimates of allelic individuals individuals in distinct sites; Random vs GIS mapping structure- diversity of not each of Multiple Specific; and spatial dependent on the species mapped multiple sites; habitat Individuals * distribution of sampling maintain ranges; GIS sample sites; intensity. progeny from informed, models to Basic collection sites Gradient predict diversity separately sampling estimation. intensity; General sample patterns of Population level tracking ** diversity Multiple Within site Consideration Separate maternal Number of Fine scale Within sites with sampling of transects, lines; within site individuals per structure, population individuals plant mapping and site; demographic species mapped demography, habitat data * Demographic history, dynamics abundance record keeping isolation by
distance estimates, population history and biogeographic Family level influences * DNA to sample plants for which propagules are unavailable **Herbaria opportunities?
A. Species-Level Collections
Single-point collections of a species (Species level in Table 1) offer opportunities to identify species and resolve taxonomic relationships from species and higher 22 taxonomic levels. These single collections are the most basic level of collecting both in terms of collecting logistics (permits, travel and site access, and sampling requirements) but may not yield much in the way of genetic variation or offer many analytical options beyond species-level inquiries. Occurrence records can inform sampling when little is known about a species. Resources for identifying suitable collection areas include floras, The Global Biodiversity Information Facility (GBIF, gbif.org) and other online resources, botanical garden and herbarium specimens, previous collections in gene-bank accessions, and local experts (see Nabhan 1990).
Botanical gardens and herbaria are examples of institutions that may be dedicated to conserving, studying, and spreading awareness of species-level biodiversity (e.g. Dosmann 2006; Powledge 2011). The 2,500 botanic gardens around the world maintain an estimated 80,000 taxa as living collections of biodiversity (Wyse Jackson 2001, Maunder et al. 2004, Pautasso and Parmentier
2007; Golding et al. 2010).
B. Population-Level Collections
Population-scale collections within a species (Population level in Table 1) are efficient at capturing allelic diversity. Analyses can reveal broad patterns in the range of variation within a species and population-genetic processes such as population demography, dispersal, and regional differences and patterns of genetic variation. These intra-species collections may be fundamental in determining selection processes across environmental gradients and in defining species ranges through habitat suitability modeling. Sampling multiple individuals across multiple 23 populations within a species involves more pre-trip planning and logistics than single point collections. These can include GIS mapping and predictive modeling to determine the spatial distribution of sampling sites and sampling intensity, in addition to field collecting methods and record keeping. Numbers of individuals per site and numbers of sites are determined in part by a species’ taxonomy, distribution, and breeding system.
C. Family-Level Collections
Collecting variation within a population of individuals at the family level (Family level in Table 1) provide additional analytical and use options for the germplasm.
Fine-scale genetic structure and local population genetic processes including gene flow, genetic relatedness and non-random mating can be estimated when sampling within a population to capture family classes. Sampling this way requires keeping individuals separate and precise mapping of how the individuals are distributed on the landscape in addition to the logistics associated with population-level sampling.
Small ecotones, topographical features, and mating systems can impart fine-scale structure on a population and influence sampling methods.
Combining levels of sampling (across rows in Table 1) such as sampling families within populations across multiple populations can yield highly detailed descriptions of population of population history, structure, and within and among population dynamics.
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Informed Subsequent Collections
Sampling genetic variation on multiple collection trips over a period of time can accomplish a thorough collection. The first collection trips can inform future collections as knowledge of genetic structure and population dynamics is determined. Gap analyses methods (Maxted et al. 2008b; Ramírez-Villegas et al.
2010; Parra-Quijano et al. 2012) similarly utilize data from herbaria and genebanks in combination with GIS to find species that may not be fully represented in germplasm collections because of previously overlooked gaps sampling, geographic, or environmental representation. This iterative sampling and mapping can be highly effective for ex situ sampling and in designing in situ reserves that include areas of highest diversity for conservation (Fig. 5). Sampling strategies discussed here can be applied to sampling adaptive variation in wild apple species.
Figure 5: Collection informed in situ and ex situ conservation. This can be applied to each row in Table 1. 25
3. Genetic Variation in the Pacific Crabapple
The Pacific crabapple (Malus fusca (Raf.) C.K. Schneid.) is one of four Malus species native to North America (Dickson et al. 1991). The species occurs in moist coastal habitats in the Pacific Northwest (Viereck and Little 1986). Like other wild Malus, it is a relative of domesticated apples, yet little is known about this crabapple’s genetic diversity, distribution or potential use in apple breeding. Species distribution modeling (SDM) is used to describe potential Pacific crabapple habitat using MaxEnt software (V. 3.3.3; Phillips et al 2006) and 208 occurrence records from GBIF
(gbif.org, Accessed 02/05/12) and WorldClim 1.4 spatially interpolated climate grids (Hijmans et al. 2005) using bioclimatic variables of altitude, mean diurnal temperature range, temperature seasonality, mean temperature of wettest quarter, mean temperature of warmest quarter, annual precipitation, precipitation of driest month, and precipitation of coldest quarter as model elements. Mapping was done using Arcmap (ArcGIS 10, ESRI, Redlands, California, USA) and revealed 338,700 square kilometers of potential habitat (Fig. 6), primarily restricted to coastal areas.
Cluster analysis of six of the bioclimatic variables used in the SDM (excluding altitude and mean diurnal temperature range) revealed two distinct climate clusters
(Fig. 6), a colder, drier “northern” cluster (mean annual temp. = 4.2 degrees Celsius; annual precip. = 1702 mm) and a warmer, wetter “southern” (mean annual temp. =
9.1 degrees Celsius; annual precip. = 2267 mm).
Malus fusca remains a culturally important species for First Nations of the
Pacific Northwest (Moerman 1998, Turner and Turner 2008), who use the fruits as 26 food, the leaves and bark in medicine, and the wood for tools and building. Haida,
Tsimshian, Tlingit, and Wakashan peoples in British Columbia and southeast Alaska were known to tend small orchards of M. fusca (Deur and Turner 2005; McDonald
2005; Turner and Peacock 2005; Turner et al. 2005; Downs 2006; Turner and
Turner 2008). It is not known, however, if tending of M. fusca has resulted in cultural selection (meaning a change in allele frequencies as a result of active selection for desirable genotypes) at these sites.
Differences in climate across the M. fusca range and tending of the species by
First Nations leads to the question: Do populations of Pacific crabapple show evidence of natural selection across the species range or cultural selection associated with First Nation harvesting areas? To answer this question we genetically fingerprint individuals from across the species range and in harvesting sites and wild areas and compare differences using analysis of molecular variance and Chi-square tests.
A total of 157 individuals were included in the genetic analyses differentiating between climate types. Of these, 110 individuals were obtained through field collections, from the USDA-ARS Plant Genetic Resources Unit collection in Geneva, New York, and from herbarium records to represent the southern portion of the Pacific crabapple range. These individuals originated from northern California into British Columbia. Another 47 individuals from herbarium specimens were included from the northern portion of the species range. The
University of Alaska Museum of the North Herbarium and University of British 27
Columbia Herbarium contributed these specimens. Fifty individuals were collected in seven currently utilized First Nation harvesting sites in British Columbia near
Hartley Bay, BC (Gitga’at First Nation) and in the Kitsumkalum, and mid-
Skeena/Kitimat regions (Kitsumkalum First Nation).
Individuals were compared genetically using six microsatellite markers following procedures described in Volk et al. (2005). Unlinked primers (GD12,
GD15, GD96, GD142, GD147, and GD162) described in Hokanson et al. (1998) and
Hemmat et al. (2003) were used in this study. The primers amplified 50 alleles. Chi- square tests on the individual alleles revealed low but significant differences in allele frequency between the northern and southern clusters for markers GD96 and
GD147. Analysis of Molecular Variance (Excoffier et al. 1992; Michalakis and
Excoffier 1996) also revealed a low but significant FST value for GD96 (FST = 0.025;
P = 0.007).
FST values among all loci between the two regions were extremely low (FST =
0.009; P = 0.023), and include zero when results were bootstrapped. This indicates very similar populations between the two regions. A subsample of 27 individuals randomly selected from the northern (15 individuals) and southern (12 individuals) regions were run across an additional seven microsatellite markers to increase the within-genome sampling on a subset of the samples. Markers CH01d09, CH02b12,
CH02d08, CH05e03, NH009b, NH015a, and NZ28f4 (Liebhard et al. 2002) were added to the six markers listed above. These individuals showed no significant differences between the two regions across all 13 markers (FST = -0.001; P = 0.5). 28
While there is some evidence of selection between the two environments and additional sampling may reveal additional differences, the two climate clusters appear very similar with respect to M. fusca diversity.
Figure 6: Potential habitat for Pacific crabapple. Collection localities are denoted in “+” signs and in “×” signs. Northern (“+” signs) and Southern (“×” signs) collection localities denote two significantly different climate regions derived from clustering six WorldClim bioclimatic variables for Pacific crabapple collections. 29
Small but significant differences were observed between First Nation harvesting sites (FST = 0.063, P = 0.004) and between wild B.C. M. fusca (herbarium collections) and harvesting sites (FST = 0.042, P = 0.005). However, variation was much higher within sites than among them (FIS = 0.127). While this indicates that the harvesting sites are not under active selection, neutral markers may not reveal expressed variation or selection present in these sites. To fully answer this question, a revised sampling strategy and marker choice may be needed.
While adaptation of M. fusca into agricultural production is unlikely to be feasible or desirable, and coastal environments are generally not large apple growing regions, the species may contain novel variation that may be useful for apple improvement. The Pacific crabapple is one example of a wild apple species.
Wild apples offer promising sources of genetic variability for maintaining orchard productivity in agro-ecosystems through mitigating pests and diseases and in adapting to changing environmental conditions.
Conclusions and Recommendations for Future Research
This dissertation examines genetic diversity in the genus Malus for adapting apple crops in perennial agro-ecosystems to a wider range of environmental conditions. Through assessing climate response in historic apple trees in the
Southwest and collecting and characterizing genetic diversity in the wild apple relative, the Pacific crabapple, this study reveals domestic apples can persist in an arid environment with access to supplemental water, but may not be well adapted 30 to marginal growing conditions. Genetic diversity in crop wild relatives may be useful in adapting agro-ecosystems to a broader range of environments, even if the species themselves are not well suited for domestication. The Pacific crabapple may contain useful genetic variation for coastal regions, but additional research is necessary to identify, assess, and incorporate these traits into domesticated apples.
Many of the 25-30 wild apple species remain largely unstudied, and additional research to collect, characterize, and describe this genetic diversity would be valuable for the conservation and use of these species. Additionally, considerable research may be needed in adapting apple crops to climate change. Issues breeders will face include increases in diseases associated with warmer climatic conditions, higher incidences of hail damage, and more frost damage with earlier bloom times
(Brown 2012). Through breeding increased stress tolerance into agricultural crop species, agro-ecosystems may be better adapted to produce on marginal lands and under future climate change scenarios. 31
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APPENDIX A
DENDROCHRONOLOGY REVEALS PLANTING DATES OF HISTORIC APPLE TREES IN THE SOUTHWESTERN UNITED STATES
OF ! J O U R N A L THE A M E R I C A N P O M O L O G I C A L S O C I E T Y /'0$-123"425#'56'47.'/,.%23"#'05,515823"1'9532.4& !"#$"%&'()*( :51$,.';; +$,-.%'* <=+>?+>9
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5HYLHZLQJ3RWHQWLDO/RFDO)UXLW0DUNHWV $8WDK&DVH6WXG\±7DXQ\D(UQVW 6HDQ'5RZOH\%UHQW/%ODFNDQG7HU\O55RSHU>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
7KH3HUIRUPDQFHRI)RXU9LQHODQG$SSOH5RRWVWRFNVLQ%ULWLVK&ROXPELD &DQDGD±&KHU\O5+DPSVRQ">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
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7KH(IIHFWRI7UDLQLQJ6\VWHPDQG
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
39
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!"#$%&'$ # +LVWRULFDSSOHRUFKDUGVJURZWKURXJKRXWWKH8QLWHG6WDWHV 5HFRQVWUXFWLQJKLVWRULHVRIWKHVHRUFKDUGVFDQ RIIHUYDOXDEOHLQVLJKWVLQWRORFDODJULFXOWXUDOKLVWRU\DQGFDQEHLPSRUWDQWGRFXPHQWDWLRQIRUKLVWRULFDOO\VLJQL¿FDQW ODQGVFDSHV%\DSSO\LQJVWDQGDUGGHQGURFKURQRORJ\WHFKQLTXHVWRKLVWRULFDSSOHRUFKDUGVLQFHQWUDO$UL]RQDDQG VRXWKHUQ&RORUDGRZHVXFFHVVIXOO\FURVVGDWHGULQJJURZWKEHWZHHQWUHHVLQWKHRUFKDUGVFRPSDUHGDQQXDOWUHH $%&'()#'*()#+&,-+#,+*.-(/#,&01*(*&023#-01#1/(/%.*0/1#.*0*.4.#5+-0(*0$#1-(/2#&6#()/#)*2(&%*,#&%,)-%127#84%#%/24+(2# LQGLFDWH DQGODWH VSODQWLQJGDWHVIRUWZRKLVWRULFRUFKDUGVLQ$UL]RQD$QRUFKDUGQHDU,JQDFLR&RORUDGR ZDVSODQWHGSULRUWRWKHODWH VWKRXJKKHDUWURWSUHFOXGHG¿QGLQJWKHDFWXDOSODQWLQJGDWH7UHHULQJGDWHV IURPDSSOHWUHHVLQWKHKLVWRULF3HQGOH\2UFKDUG6OLGH5RFN6WDWH3DUN$UL]RQDJHQHUDOO\FRPSDUHGFORVHO\ZLWK 5+-0(*0$#1-(/2#1/2,%*9/1#*0#-0#&%-+#)*2(&%:#&6#()/#5%&5/%(:3#94(#2)&'/1#2&./#1*2,%/5-0,*/2#'*()#()/#&+1/%#(%//23#.&2(# +*;/+:#2(/..*0$#6%&.#%/5+-0(*0$#/
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'! -+%#$*!(),,+()*'+.'!"",)'!.5'")!#1'C$>#.!,'$0' ()*+&,-.#%RUNK ,Q0RKDQ-DLQ6DQG30 $PHULFDQ6RFLHW\RI+RUWLFXOWXUDO6FLHQFH 3UL\DGDUVKDQ(GV %UHHGLQJ3ODQWDWLRQ 7UHH&URSV 7HPSHUDWH6SHFLHV6SULQJHU1HZ +RNDQVRQ6&$.6]HZF0F)DGGHQ:) H$#;1'&(("@II9991*"#+./)#,+.;1%$-I%$.().(I /DPER\DQG-50F)HUVRQ 0LFURVDWHOOLWH Y 665 PDUNHUVUHYHDOJHQHWLFLGHQWLWLHVJHQHWLF 3ROODQ07KH%RWDQ\RI'HVLUH $3ODQW¶V 5+<)#*+(?'!.5'#),!(+$.*&+"*'+.'"#$%&!!'!()*+&,-.#' (\H9LHZRIWKH:RUOG5DQGRP+RXVH1HZ %RUNKFRUHVXEVHWFROOHFWLRQ7KHRUHWLFDO$SSOLHG 46 APPENDIX B COLLECTING CROP WILD RELATIVES TO CAPTURE GENETIC VARIATION (This manuscript is intended for Crop Science) Kanin J. Routson1 University of Arizona, Arid Lands Resource Sciences 1955 East Sixth Street PO Box 210184 Tucson, AZ 85719 Gayle M. Volk National Center for Genetic Resources Preservation U.S. Department of Agriculture, Fort Collins, CO 80521 Christopher M. Richards National Center for Genetic Resources Preservation U.S. Department of Agriculture, Fort Collins, CO 80521 Gary Paul Nabhan The Southwest Center and School of Geography and Development University of Arizona, Tucson, AZ 85721 1 Corresponding author: e-mail [email protected] Abstract Wild relatives of agricultural crops may represent a valuable source of alleles for crop improvement, adaptation, and food security. Many wild relatives, especially for minor crops are, however, under-represented in ex situ collections. Efforts to sample crop wild relatives are complicated by natural population structures and genetic histories that are unique to each species. The methods by which original genetic resources are collected can limit the future analytical potential and utility of ex situ collections. By making conscious decisions about a collection’s structure, curators 47 are now able to leverage geospatial and genomic data in fundamentally new ways. Using specific techniques when sampling germplasm increases the analytical and utilitarian options available to the user community as well as the overall effectiveness of germplasm conservation. For example, recent advancements in genomics-assisted plant breeding are enabling researchers to utilize vast quantities of genetic variation, which in turn increases the value of wild collections. With rapid advancements in plant breeding and threats to plant genetic resources, well- designed sampling strategies for crop wild relatives will be paramount to future germplasm collection. Additional Index Words: Genetic resources, crop wild relatives, ex situ collections 1. Introduction Crop genetic resources are considered essential for maintaining agricultural production and food security by providing materials that may show resistance to disease and pest epidemics and adaptation to environmental changes (Harlan 1975; Ehrlich et al. 1993; Qualset and Shands 2005; Damania 2008). Habitat fragmentation, land use change, climate change, and agricultural intensification and commoditization reduce plant biodiversity and threaten many crop genetic resources (Young et al. 1996; Meilleur and Hodgkin 2004; Thuiller et al. 2005; Brooks et al. 2006; Jarvis et al. 2008). The collection, characterization, and conservation of crop genetic resources are consequently of high priority. 48 Wild relatives of agricultural crops may be integral to plant genetic resource development for current and future food security (Maxted et al. 2008a). Crop wild relatives are undomesticated species that include congenic and conspecific progenitors as well as interbreeding associates of agricultural crops. They are a valuable source of novel alleles and genetic traits for crop improvement, most notably for resistance to biotic and aboitic stressors (Tanksley and McCouch 1997; Hoisington et al. 1999 Gur and Zamir 2004; Hajjar and Hodgkin 2007; Damania 2008; Maxted and Kell 2009; Guarino et al. 2011). As their importance to global agriculture has become more widely recognized, so has the need for their conservation (Hammer et al. 2003; Meilleur and Hodgkin 2004; Rubenstein et al. 2005; Heywood et al. 2007; Damiana 2008). Botanic gardens and gene banks conserve plant genetic resources ex situ as living plants, seeds, budwood, cuttings, rhizomes, and tissue culture in vitro, or pollen. Ex situ collections of crop wild relatives represent a subset of the total variation present in natural populations (Frankel 1970; Maunder et al. 2004) and comprehensive sampling remains a challenge for field collectors because their wild phenotypes are less useful to breeding (Lockwood et al. 2007). Consequently, many crop wild relatives remain neglected and absent from germplasm collections (Maxted and Kell 2009; Khoury et al 2010). Crop wild relatives are also more difficult to conserve ex situ than land races. Higher species numbers, varying and often unknown seed storage and germination requirements, and more stringent environmental requirements for regeneration and multiplication are some of the additional requirements for conserving crop wild relatives ex situ. Per accession 49 costs are estimated to be five to ten times higher for wild species (Guarino, L. pers. comm.). Additionally, large collections of plant genetic resources often lack adequate evaluation, characterization, and utilization of accessions (Scarascia- Mugnozza & Perrino 2002). A combination of in situ and ex situ conservation strategies is necessary for adequately conserving the genetic resources of crop wild relatives (Hawkes et al. 2000; Maxted and Kell 2009; Dulloo et al. 2010). And while genetic characterization of crop wild relatives is essential for both collection and maintenance of ex situ collections and in designing and maintaining in situ reserves, we focus on sampling techniques for ex situ conservation of plant genetic resources that maximize analytical potential, conservation, and value of the germplasm to the end user. 2. Maximize Benefits from Wild Collection Expeditions Collection missions for crop wild relatives often target economically important species in what Harlan and Wet (1971) identify as secondary genepools (wild relatives closely related to agricultural crops) and tertiary genepools (distantly related to agricultural crops). From these genepools, collectors focus on finding useful adaptations, adaptive variation, and identify promising new sources of material for plant breeding. Ex situ germplasm collections will increasingly need to anticipate the use of genetic resources by future technologies. Genomics, spatial analyses, and new statistical algorithms together contribute valuable information that can be used to inform plant breeders. New collections will need to provide the necessary 50 associated data to conform to advanced analytical methods. Understanding genetic variation and its distribution in the target taxon become critical to efficiently meet the goals of a collection mission. Advancements in molecular mapping enable assessment of the quality and completeness of ex situ collections. Next-generation sequencing technologies are revolutionizing plant breeding through increasing efficiency and use of genetic mapping (Association mapping, genotyping by sequencing, linkage mapping, QTL mapping, etc.), and enabling large-scale identification and tracking of genetic variation (Varshney et al. 2005, 2009). New approaches of genomics assisted breeding including advanced back-cross QTL analysis, functional genomics, genetical genomics, allele mining, genotype-by- sequencing, TILLING and EcoTILLING act to increase accuracy in prediction of a phenotype from a genotype and enable the development of improved cultivars with increased stress-tolerance and higher agronomic performance (Varshney et al. 2005, 2009, Elshire et al. 2011). Conservation targets and collection units. Selecting a conservation target, which can range from a specific gene to an individual, population, or entire community, should begin with the end-use of the germplasm in mind. Selection of the conservation target in turn, affects the choice in sampling strategy (Walters et al. 2008). The collection strategy also defines future possibilities to measure, characterize, and capture the genetic variation of a species. Broad-scale, representative sampling is often necessary for understanding underlying evolutionary processes affecting genetic diversity of a conservation target. The unit 51 of collection selected to capture the conservation target influences the collection strategy. Seeds are generally more efficient at capturing diversity than clonal material such as propagules or vegetative cuttings, and are more easily stored and transferred across international boundaries with less chance of introducing diseases. Collecting DNA samples of the maternal parent can further aid in characterizing genetic variation and will also yield data on the paternal pollen source. DNA samples (leaves dried in desiccant) can provide data on non-fruiting genotypes during later analyses. How collection units are sampled also influences future analysis and use options. Sampling individual genotypes in a population (family collections) or bulking multiple individuals from a population into a single sample (bulk collections) lead to different future options. Generally individuals sampled and maintained separately will retain more genetic information and future potential than bulked samples but come at greater expenditures of time and energy. Maintaining separate maternal lines can give insights into family and sibling relationships at a collection locality. Genetic structure in wild populations. Crop wild relatives occur in natural populations. While they hypothetically conform to Hardy-Weinberg (HW) gene frequencies, many factors can cause individual populations to exhibit non-normal distribution. Genetic variation present in natural populations of wild plants may allow for evolution and adaptation to changing environments (Frankel et al. 1995). The genetic structure of a population reflects population history and underlying 52 processes of dispersal and adaptation, differential selection pressures, gene flow and drift, migration, and mutation (Escuderoa et al. 2003). The exchange of alleles between populations acts to homogenize allele frequencies between populations and determines the relative affects of selection and genetic drift. High gene flow reduces local adaptation and fixation (Barton and Hewitt 1985; Slatkin 1985; Balloux and Lugon-Moulin 2002). An organism’s biology and life history can influence genetic variation and its distribution (Hamrick and Godt 1996), as can landscape features such as geographical barriers to gene flow and ecological habitat niches (Manel et al. 2003; Holderegger et al. 2010). A species biology and range influence genetic variation and distribution. Non-random gene flow can result in spatial genetic structure of natural plant populations (Wright 1943, 1978; Turner et al. 1982). Breeding system, life form, seed dispersal mechanism, and geographic range have significant effects on the partitioning of genetic diversity within and among plant populations (Hamrick and Godt 1996). Pollen is important in mediating gene flow within and among plant populations (Latta et al. 1998; Streiff et al. 1998; Imbert and Lefevre 2003). Out- crossing species for instance, tend to be genetically diverse but show low differentiation among populations (Hamrick and Godt 1989, 1996), whereas mating by proximity can result in localized inbreeding within demes and spatial grouping of allele frequencies (Wright 1943, Epperson 1989). Seed dispersal is another mechanism of gene movement, enabling colonization of new sites and migration between subpopulations (Ouborg et al. 1999; Cain et al. 2000). Higher levels of genetic variation are associated with long-distance dispersal mechanisms such as 53 animal-mediated dispersal rather than shattering or gravity-mediated dispersal that tend to be highly localized. A species range also influences genetic variation and partitioning with widespread species showing higher levels of genetic diversity than species with narrow or regional ranges (Hamrick and Godt 1989, 1996). Describing variation. Numerous descriptive strategies have been developed for describing genetic diversity including pedigree (e.g., Noiton and Alspach 1996), morphological data (e.g., Forsline et al. 2003), biochemical (e.g., Hamrick and Godt 1997), and DNA-based markers are among the most important (for summary see Spooner et al. 2005). Application of DNA fingerprinting in the plant sciences has become widespread, as techniques and technologies have improved over the past two decades (Weising et al. 2005). Molecular traits are desirable for characterization in combination with phenotypic ones because they do not exhibit phenotypic plasticity whereas morphological and biochemical markers can vary depending on environment. In addition, the high abundance, independence, and selective neutrality add to the utility of molecular markers in genetic studies (Spooner et al. 2005; Gepts 2006). Frequently used molecular mapping techniques include DNA sequencing, allozymes, restriction fragment length polymorphisms (RFLPs), amplified fragment length polymorphisms (AFLPs), variable number tandem repeats (VNTRs), microsatellites (SSRs), and single-nucleotide polymorphisms (SNPs). These techniques are now widely used for assessing diversity in plant species and of the underlying genetic structure of breeding populations, bottlenecks, and the biogeographic structure (Luikart et al. 2003). 54 Increasingly, gene specific markers are being used to quantify diversity of relevant traits for breeding purposes. Methods for describing genetic structure in natural populations primarily rely on allele or genotype frequencies. Typical descriptive measurements include number of alleles per locus, number of polymorphic alleles, rare alleles, and observed and expected heterozygosities. The partitioning of genetic variation within and among populations (Wright 1965, Nei 1973), multivariate clustering (e.g., Pritchard et al. 2000) and isolation by distance (Mantel 1967) are frequently drawn from the researchers statistical toolbox. Various statistical approaches for detecting, testing, and quantifying spatial genetic structure are reviewed in Guillot et al. (2009); earlier reviews include Manel et al. (2003), Holdregger and Wagner (2006), Storfer et al. (2007) and Møller-Hansen & Hemmer-Hansen (2007). Sampling variation. Lacking a-priori knowledge of a species’ genetic structure, sampling can be done across the geographical or ecological range of a species, either using GIS-informed, transects along an ecological or geographic gradient, or sampling distinct sites across habitat ranges (Hawkes et al. 2000). Regions of high species richness offer collection opportunities for multiple species (species overlap) and increase the efficiency of collection trips (Nabhan 1990). Occurrence records can also inform sampling. For species with little taxonomic or population genetics data available, initial sources useful in identifying suitable collection areas: 1) botanical literature and local floras; 2) online resources such as the USDA PLANTS Database (plants.usda.gov) and The Global Biodiversity Information Facility (GBIF, 55 gbif.org), 3) notes and journal entries from previous collectors; 4) botanical garden and herbarium specimens; 5) genebank accessions and passport data; and 6) local experts (see Nabhan 1990). Herbaria offer a wealth of genetic data in their specimen collections. DNA sampling of herbarium records can yield insights into the genetic diversity and structure of a species a-priori and increase the efficiency and productivity of subsequent field collection missions and can be useful in determining the number of samples and distribution of samples needed to capture the genetic variation of a species or population. Voucher specimens provide physical proof the desired species was collected, where it was collected, and in what phonological stage (Lowe et al. 2004). GIS (climate, elevation, gradients) can be used to differentiate habitat types. Species distribution modeling, (also referred to as ecological niche modeling or predictive distribution modeling) utilizes environmental data derived from GIS at georeferenced natural population localities to build models of a species ecological requirements to predict suitable habitat and potential geographic area (Graham et al. 2004; Peterson et al. 2007; Kozak et al. 2008 Nakazato et al. 2010). Recording habitat data with the samples provides valuable information for future analyses and research. Standards are defined in DarwinCore (tdwg.org); key data to include are location data (Latitude and longitude, country/state/region), altitude, habitat, population notes, dominant plant associations, local names and uses (if known), collectors and collection date, and additional notes (Hawkes et al. 2000; Volk and Richards 2011). 56 3. Selection of Collection Units to Meet a Conservation Target The conservation target, including the desired collection unit (taxa, classes, genes, alleles), germplasm type (seeds, tubers, vegetative material), and end use of the germplasm dictate the collection methods (Walters et al. 2008). With limited resources available to germplasm collectors and the repositories that store the collected material, it is imperative that collection efforts maximize efficiency in collecting germplasm while at the same time maximizing the genetic variation obtained (Marshall and Brown 1983). A-priori knowledge of the species, its taxonomy, ecogeographic distribution, breeding system (inbreeding versus out- crossing), genetic variation and its location (both physically and how it is partitioned within and among populations) will greatly increase efficiency and resource allocation during collection trips. Nevertheless, there is often a lack of a- priori knowledge of the genetic structure to be found across a species’ habitat. As a result, sampling decisions are often based on other factors including morphological variation, pilot studies or basic knowledge of breeding system or gene flow. Recognizing such potential tradeoffs and limitations we provide a framework (Table 1) to help future collectors visualize the tradeoffs, analytical options, and limitations associated with specific sampling techniques. Using this framework, a collector can make better-informed decisions for balancing sampling strategies with logistical effort, resource allocation and management. 57 Table 1: A framework of sampling level with corresponding tradeoffs and benefits to the collector, collection, and end user of the germplasm being collected. Sample Sampling Site Unit of Collection Logistical Analytical Impact to Target Hierarchy Selection Effort Potential Collection Single site Single sample Sites with Individual or Site Point Taxonomic known bulked accessibility; reference & taxonomic collecting sample. phylogenetic coverage; voucher Species level interests Overlaps with specimens ** comparisons among other species species for increased Species level efficiency Multiple Representative Select Seeds/propagules; Between site Coarse scale Capture sites sampling of multiple DNA samples; transportation; estimates of allelic individuals individuals in distinct sites; Random vs GIS mapping structure- diversity of not each of Multiple Specific; and spatial dependent on the species mapped multiple sites; habitat Individuals * distribution of sampling maintain ranges; GIS sample sites; intensity. progeny from informed, models to Basic collection sites Gradient predict diversity separately sampling estimation. intensity; General sample patterns of Population level tracking ** diversity Multiple Within site Consideration Separate maternal Number of Fine scale Within sites with sampling of transects, lines; within site individuals per structure, population individuals plant mapping and site; demographic species mapped demography, habitat data * Demographic history, dynamics abundance record keeping isolation by distance estimates, population history and biogeographic Family level influences * DNA to sample plants for which propagules are unavailable **Herbaria opportunities? A. Species-level collections. Single-point collections of a species (Species level in Table 1) offer opportunities to identify species and resolve taxonomic relationships and phylogenies among species, genera and higher taxonomic levels. These single collections constitute the most basic level of collecting both in terms of collecting logistics (permits, travel and site access, and sampling requirements) but also may not yield much in the way of genetic variation or offer many analytical options beyond species-level inquiries. With high numbers of point sampling, as is often the case with herbarium collections, within species genetic variation and genetic structure may be discernable. Yet while herbaria are useful for informing 58 germplasm sampling decisions, point sampling is not in and of itself well suited to capturing within-species genetic diversity for germplasm collections. Site selection is often based on species presence/absence or in areas overlapping with other desired species for increased collection trip efficiency. Collections can be individual or bulked, and should include making a voucher specimen for species identification. Botanical gardens and herbaria are examples of institutions dedicated to conserving, studying, and spreading awareness of species-level biodiversity (e.g. Dosmann 2006; Powledge 2011). An estimated 80,000 taxa are maintained as living collections in 2500 botanic gardens around the world (Wyse Jackson 2001, Maunder et al. 2004, Pautasso and Parmentier 2007; Golding et al. 2010). Botanic Gardens Conservation International (BGCI) is an organization that promotes worldwide conservation of threatened plants with over 700 members in 118 countries (Powledge 2011). B. Population-level collections. Local populations, or mating demes, are groups of inter-mating individuals that share a common gene pool (Dobzhansky 1950). Mating demes (Population level in Table 1) provide local sources of allelic variation and are important in understanding and capturing the spread of genetic variation within a species. Analyses can, depending on sampling intensity, reveal broad patterns in genetic variation and population-genetic processes including population demography, dispersal, and regional patterns of genetic differentiation. Intra- species collections are also fundamental in determining genetic differentiation by habitat (selection) and in refined niche estimation (habitat suitability modeling). 59 Inter-population sampling involves more pre-trip planning and logistics than single point collections. These can include GIS mapping and predictive modeling to determine the spatial distribution of sampling sites and sampling intensity, in addition to field collecting methods and record keeping. The sample target involves multiple collection sites. Sites are selected across habitat range, either through GIS and predictive modeling, or by sampling across gradients. Sample hierarchy includes collecting representative samples of individuals in each of multiple sites and maintaining progeny from collection sites separately. Collection units can be seeds or propagules and within sites can be maintained separately or bulked. DNA collections enable assessment of variation in non-fruiting genotypes. Numbers of individuals per site and numbers of sites are determined in part by a species’ taxonomy, distribution, breeding system and genetic history. In addition to revealing coarse scale-estimates of genetic structure, inter-population sampling is an important method for sampling allelic diversity for ex situ conservation. Inter-population sampling methods of genetic variation are widely used for crop genetic resources. For example Hübner et al. (2009) identified temperature and rainfall gradients as significant selective forces acting on the population genetic structure of wild barley (Hordeum spontaneum) in the Fertile Crescent using microsatellite loci. For instances such as this, where biogeography influences the population genetic structure of a species, GIS and predictive modeling can greatly increase the efficiency and effectiveness of sampling germplasm. 60 C. Family-level collections. Collecting variation within a local population deme (Family level in Table 1) provides additional analytical and use options for germplasm collections. Small ecotones, topographical features, and localized mating can impart fine-scale structure on a population. The sample target and hierarchy include within site mapping and sampling of individuals at multiple sites. As with inter-population sampling, sites are selected across a species habitat, either through GIS and predictive modeling or gradient sampling. Collection units are often seed from separate maternal lines. DNA or vegetative propagules of the maternal parent can provide additional information. Sampling requires keeping maternal units separate and precise mapping of how the individuals are distributed on the landscape. In addition to the logistics associated with population-level sampling, family-level collections require additional site mapping, demographic record keeping, and sample tracking. Analyses can reveal fine scale structure, demographic history, isolation by distance, population history, and biogeographic influences and can be important in understanding within species dynamics. As an example, Arnaud et al. (2011) studied microspatial and temporal genetic structuring in weedy crop- wild hybrid beets the sugar beet producing region of northern France. They identified spatial genetic structure of weed beets to be consistent with a gravity- based seed dispersal mechanism and limited pollen flow among demes. The substantial Ne (number of effective alleles) and outcrossing mating system promote gene flow and dispersal with implications for managing the spread of introduced genes into wild beet populations in northern France. 61 D. Combined benefit. Sampling across multiple scales (across rows in Table 1), including at intra-population and inter-population levels, can, depending on sample- size, enable analyses capable of detailed descriptions of population history, structure, within and among population dynamics, estimations of gene dispersal parameters from individual to population, regional, to species-wide scales. Understanding population dynamics is important for comprehensive ex situ sampling and can increase effectiveness of conservation planning and design of in situ reserves by targeting levels and areas of highest diversity for conservation. In some cases, it may be practical to sample some sites in-depth and others more broadly. 4. Informed Subsequent Collections Multiple collection trips over a period of time enable comprehensive sampling via an iterative process (Fig. 1). Early collection efforts can be used to calibrate and refine future collections, and as information becomes available on the genetic structure of a taxon in the wild, subsequent sampling can increase the thoroughness and completeness of an ex situ collection. This is illustrated in over half a century of mapping Teosinte populations in Mexico and Central America by Garrison Wilkes and others (e.g. Wilkes 1967, 1972; Sánchez González and Ruiz Corral 1997; Heerwaarden et al. 2010; Warburton et al. 2011). Gap analyses methods developed by Andy Jarvis and others (Maxted et al. 2008b; Ramírez- Villegas et al. 2010; Parra-Quijano et al. 2012) also utilize information derived from herbarium and genebank data (previous collections) in combination with GIS to 62 inform subsequent missions to fill sampling, geographic, and environmental gaps in germplasm collections. Timing collection with propagule availability (e.g. early season versus late season genotypes) may also require multiple collection efforts to effectively sample a species’ diversity. While not all species are as economically important or as well studied as wild maize, even less well known crop relatives are important for conservation and through iterative sampling and mapping, can effective conservation of genetic resources be obtained. Figure 5. Balancing in situ and ex situ conservation. This is applicable to each row in table 1. 5. Conclusions Genetic variation of agricultural crops, landraces, and their wild relatives remain vital to maintaining agricultural productivity, disease and pest resistance, 63 and in adapting crops to changing environmental growing conditions and stresses. With habitat loss and climate change potentially threatening populations of crop wild relatives, there is paramount need for both in situ conservation and ex situ collection of these resources. However, with limited funds available to both collect and conserve crop genetic resources, efficient use of resources and effective collection strategies will be critical. The strategies and methods employed by germplasm collectors define future possibilities to measure, characterize, and capture the genetic variation of a species. Sampling strategies that are effective at capturing genetic diversity rely on well organized logistical planning and knowledge of the desired taxon. 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Richards National Center for Genetic Resources Preservation, U.S. Department of Agriculture, Fort Collins, CO 80521 Steven E. Smith School of Natural Resources and the Environment, University of Arizona Gary Paul Nabhan The Southwest Center and School of Geography and Development, University of Arizona Victoria Wyllie de Echeverria School of Environmental Studies, University of Victoria 1 Corresponding author: e-mail [email protected] Abstract The Pacific crabapple (Malus fusca (Raf.) C.K. Schneid.) occurs in moist coastal habitats in the Pacific Northwest. It is one of four apple species native to North America. Malus fusca is culturally important to First Nations of the region who value 72 and use the fruits of this species as food, bark and leaves for medicine, and wood for making tools and in construction. Little is known, however, about either distribution or genetic diversity of this species. To correct this deficiency, we used habitat suitability modeling to map M. fusca habitat types using species occurrence records. The species apparently occupies at least two distinct climate regions: a colder, drier northern region and a warmer, wetter southern region. Total area of modeled habitat encompasses approximately 338,700 square kilometers of low-lying areas along the Pacific coast. A total of 239 M. fusca individuals sampled from across its native range were genetically compared using six microsatellite markers to assess for possible geographic structuring of genotypes associated with different climate regions. We also tested whether indigenous peoples’ horticultural practices resulted in measurable changes to the genetic structure of populations in known harvesting sites. The primers amplified 50 alleles. Low but significant Chi-square tests for two markers may indicate some natural selection occurring between the two climate regions, but evidence was not detected for cultural selection in harvesting sites used by First Nations, although neutral markers may not be well suited for identifying selection or expressed variation. Significant isolation by distance was identified across the approximately 2600 (straight line) kilometers where samples were distributed. These results may help establish priorities for M. fusca conservation with regard to both ex situ and in situ conservation. Key Words: Species distribution modeling; Genetic diversity; Malus fusca; Ex situ gene conservation 73 Introduction Wild apple species (Malus spp.; Rosaceae) are native throughout temperate climes of Asia, Europe, and North America (Luby 2003; Brown 2012). They offer promising sources of genetic diversity for apple breeding (Brown 2012); they also provide ecological services, wildlife habitat, and serve as a direct food source for humans (Duncan and Duncan 1988; Moerman 1998). Pacific crabapple, M. fusca (Raf.) C.K. Schneid, is a small, deciduous, often multi-trunked tree or thicket-forming shrub that occurs naturally in mesic environments along river bottoms, meadow and muskeg fringes at low to mid elevations along the Pacific coast of North America from northern California to the Kenai Peninsula in Alaska (Viereck and Little 1986). Four Malus species are native to North America. Three species occur in eastern and midwestern U.S. and eastern Canada. Malus angustifolia (Aiton) Michx. is native from southern New Jersey to Florida, M. coronaria (L.) Mill. from Ontario to South Carolina, and M. ioensis (Alph. Wood) Britton ranges from Minnesota to Texas. These have been determined to be close relatives to one another using isozymes (Dickson et al. 1991). The Pacific Northwest species, M. fusca, is the sole geographic, morphological (van Eseltine 1933), chemical (Williams 1982), and genetic outlier among the North American taxa. It groups genetically with the species native to central Asia and China rather than the other North American taxa, according to AFLP data (Qian et al. 2006), nuclear ribosomal and chloroplast DNA (Robinson et al. 2001). Because of genetic grouping with central Asia, the species is hypothesized to be a recent migrant across the Bering Strait (Williams 1982). 74 Malus fusca can serve as a rootstock for domesticated apple trees in waterlogged sites (Justice 2000; Bob Duncan, Saanich Peninsula, Vancouver Island, BC) and remains a culturally important species for First Nations of the Pacific Northwest (Moerman 1998; McDonald 2005; Downs 2006; Turner and Turner 2008). All parts of the tree have been used including the fruits as food, the bark and leaves for medicines, and the dense wood for implements and in construction (Moerman 1998; Turner 1998). It is well documented that Haida, Tsimshian, Tlingit, and Wakashan peoples in British Columbia and southeast Alaska have tended small orchards of M. fusca that were often owned and managed in ways that were passed down between generations (Deur and Turner 2005; McDonald 2005; Turner and Peacock 2005; Downs 2006; Turner and Turner 2008). It is not known, however, if wild materials were specifically selected for cultivation as managed populations at these sites. We assessed geographic distribution of M. fusca using species distribution modeling (SDM) of known occurrence records and genetic variation in 239 M. fusca individuals. These individuals were sampled from northern California to southeast Alaska, and then compared using six microsatellite markers to address the following questions: 1) How is genetic variation in M. fusca distributed or structured across the species range? 2) Is there evidence for natural selection associated with climate region as determined by cluster analysis? 3) Do gene frequencies and genetic variation differ between truly wild populations of M. fusca and managed populations in traditional First Nation harvesting sites that may be a result of cultural selection? 75 Materials and Methods Sample collections. Malus fusca individuals were sampled across its native range from southeast Alaska to northern California. Samples were obtained from field collections (138 individuals from CA, OR, WA and BC), herbarium specimens (65 individuals from AK and BC), and USDA germplasm accessions (36 individuals from CA, OR, and WA). The latter were obtained from the USDA-ARS Plant Genetic Resources Unit collection in Geneva, New York. The University of Alaska Museum of the North Herbarium and University of British Columbia Herbarium provided herbarium specimens. Of the 239 individuals, a total of 189 were sampled from presumably wild populations (collected in areas not known to be associated with cultural sites) while 50 individuals (of the 138 from CA, OR, WA, and BC) were sampled from seven currently utilized First Nation harvesting sites in British Columbia in the vicinity of Hartley Bay, BC (Gitga’at First Nation) and in the Kitsumkalum, and mid-Skeena/Kitimat areas (Kitsumkalum First Nation). All seven sites in cultural landscapes were from Tsimshian and possibly Wakashan cultural areas, although other groups, including the Haida and Tlingit peoples, may have also practiced horticultural management of M. fusca in areas we did not sample. Field collections in CA, OR, and WA took place October 2010. Collections were made in publicly accessible parks, natural areas, and roadsides in moist, coastal habitats. Fresh leaf tissue was sampled (generally five leaves per tree) for genetic analysis. The fresh leaves were stored in plastic bags and kept cool until tissue samples were sent to the lab. There they were loaded into DNA extraction plates, after which they were frozen at -80 degrees Celsius for long-term storage. 76 General physical site characteristics, habitat type, associated vegetation, and locality descriptions, were documented and photos taken at each site. Latitude/Longitude coordinates and elevation were recorded for individual trees during field collections using a Garmin eTrex-Vista handheld unit, (Olathe, KS). Herbarium vouchers were collected at six sites and sent to the University of Arizona and University of Washington herbariums. Locations for herbarium specimens and GRIN accessions were verified by comparing collection notes with Google Earth (V.6.2, Google Inc. Mountain View, CA) and biogeoreferencing web application GEOLocate Web (Rios and Bart 2005). Points were selected based on habitat characteristics and descriptions of the localities. Specimens with less than a 5000-m uncertainty in GEOLocate were included in the species distribution modeling. Species distribution modeling. A species distribution model (SDM) for M. fusca was created using MaxEnt software (V. 3.3.3; Phillips et al 2006) and WorldClim 1.4 spatially interpolated climate grids (Hijmans et al. 2005) and M. fusca collection localities (n = 205 with less than 5000-m uncertainty) and M. fusca occurrence records (n = 152 with less than 5000-m uncertainty) from GBIF (gbif.org, Accessed 02/05/12). We used 1950-2000 data at a 30-arc second resolution (approximately 0.56 km2 at 49°N.) for elevation and 19 bioclimatic variables based on monthly precipitation and temperature. ASCII files were generated in DIVA-GIS (V. 7.5.0, Hijmans et al. 2001) from WorldClim data for the Pacific Northwest region (121.0°W-151.0°W by 40.0°N-62.0°N). Multiple sample occurrences in grid cells (n = 149) were deleted using ENMTools (Warren et al. 2010) resulting in a total of 208 77 occurrence locations to build the model. A target group background occurrence file (Phillips et al. 2009; Elith et al. 2011) was constructed using 10,000 randomly selected occurrence records for flowering plants from GBIF (gbif.org, Accessed 02/05/12). Multiple occurrences within a grid were removed in ENMTools. We ran MaxEnt using default settings. A model was selected that A) minimized Akaike information criterion scores (AICc) (Warren and Seifert 2011) calculated in ENMTools over 10 replicate runs; B) used environmental variables whose correlation was less than 0.60; C) showed relatively high AUC scores; and D) appeared consistent with known species occurrences. AUC scores relate to the probability of the model correctly scoring random presence and absence sites (Fielding and Bell 1997; Philips et al. 2009). We conducted hierarchical cluster analysis to assess the environmental variability using PROC CLUSTER (Ward's minimum-variance method) in SAS software (SAS Institute, Inc., Cary, North Carolina). Observations were based on individual occurrences and clustering was performed on a subset of environmental variables selected to maximize variation and standardized to mean = 0 and standard deviation = 1. We selected environmental variables by first performing a hierarchical of clustering of the variables themselves using PROC VARCLUS in SAS to identify correlation among variables and identify variables from non-overlapping clusters that maximized variation. Intrataxon clusters were identified by examining the cubic clustering criterion and the pseudo F and t2 statistics where P ≤ 0.01 (Cooper and Milligan, 1988). Two percent of the observations with the lowest 78 estimated probability density were omitted from clustering. In cases where more than one cluster was identified, the omitted observations were grouped into the nearest cluster. Suitable habitat was quantified using Arcmap (ArcGIS 10, ESRI, Redlands, California, USA). Probability of presence/background ASCII files produced by MaxEnt were converted into raster files in Arcmap. The map projection was converted from WGS1984 into Albers equal-area conic projection to calculate area of suitable habitat. The percent of suitable habitat sampled in the genetic analyses was calculated by applying a 1km buffer to each individual included in the genetic analyses, then merging buffered areas and clipping to exclude non-suitable habitat using the Buffer and Clip functions in Arcmap. Microsatellite analysis. The genetic analysis of the 239 samples was performed following procedures described in Volk et al. (2005). The six un-linked microsatellite markers used in this study can only represent 35 percent of the 17 linkage groups in the Malus genome. They have, however, been used to successfully differentiate between individuals and identify structure in other Malus spp. (Richards et al. 2009; Volk et al. 2008). We extracted genomic DNA from frozen leaf tissue (field collections) and dried leaf tissue (herbarium records) using Qiagen DNeasy 96 plant kits (Qiagen, Valencia, CA). DNA was extracted from the frozen leaves using 50 mg tissue frozen in liquid nitrogen during initial tissue grinding, 10- 12 mg tissue was utilized for the dry samples and was extracted using the same 79 protocol but without liquid nitrogen and a lower temperature of reagent AP1. Unlinked primers (GD12, GD15, GD96, GD142, GD147, and GD162) described in Hokanson et al. (1998) and Hemmat et al. (2003) were utilized to amplify microsatellite loci. Infrared florescent dye IRD700 or IRD800 (MWG-Biotech, High Point, NC) labeled forward primers. Reverse primers were unlabeled (IDT, Coralville, IA). Polymerase chain reactions (PCR) were carried out in 15 μL reactions. Each reaction contained: 0.25 μL GoTaq® Flexi Taq Polymerase (Promega, Madison, WI) (5 units/μL), 3 μL Promega 5x Colorless GoTaq® Flexi Buffer (10 mM Tris-HCl, 50 mM KCl, and 0.5% Triton X-100), 1.5 μL of 0.25 mM MgCl2, 1.5 μL of 0.25 mM dNTPs, and 0.25 μL forward and reverse primers. We added 5 μL of undiluted genomic DNA product obtained from the Qiagen DNeasy 96 plant kits to each reaction. Addition of sterile distilled H20 brought reaction volumes to 15 μL. Primer pairs GD96, GD15; GD142, GD147, and GD162 were multiplexed. GD12 was run independently. PCR was run on a MJ Research PTC 200 Thermocycler (Reno, NV), using touchdown PCR. The annealing temperature was reduced 1 degree Celsius at each cycle, beginning at 63 degrees Celsius and ending at 54 degrees Celsius, followed by annealing at 55 degrees Celsius for 18 cycles, and ending with a 72 degrees Celsius extension for 2 minutes. PCR products were diluted 1:1 with a formamide bromophenol blue loading buffer and denatured at 95 degrees Celsius for 5 minutes. The denatured products were run on gels (6.5% LI-COR KB Plus acrylamide; Lincoln, NB) in 1x TBE buffer (89 mM Tris, 89 mM boric acid, and 20 mM EDTA) for 1 hour, 45 minutes at 1500V, 80 40W, 40mA, and 45 degrees Celsius in a LI-COR 4200 DNA Sequencer. Digital images of the gels imported to LI-COR Saga Generation2 software were visually analyzed in Saga. Over-loaded gels were diluted 1:10 with additional loading buffer and re-run. Genetic data analysis. Allele frequencies, observed and expected heterozygosities, allelic richness, and Wright’s F-statistics were calculated in GDA (Lewis and Zaykin 2002), FSTAT (Goudet 1995), and GENODIVE (Meirmans and Van Tienderen, 2004) programs. Isolation by distance was determined by a Mantel test (1967) using Rousset’s linearized FST, FST (Rousset 1997) over 5,000 permutations in 1" FST GENODIVE. ! We utilized STRUCTURE software (Pritchard et al. 2000) to identify possible evidence of population structure using posterior probabilities of possible populations. The number of populations was estimated from the natural log of the probability of allele frequencies over the posterior probability of 1-12 possible populations (Evanno et al. 2005). An ancestry model of admixture and correlated allele frequencies between populations enabled fractional assignment of individual genotypes to multiple populations by probability of membership. We ran a Markov chain Monte Carlo (MCMC) method in STRUCTURE using an iteration burn-in period of 500,0000 runs followed by 100,000 iterations per chain. We ran each Markov chain 100 times for 1-12 possible populations. STRUCTURE output was captured and analyzed by Structure Harvester (Earl and vonHoldt 2011). CLUMPP software produced population membership coefficients based on matrices of multiple 81 STRUCTURE runs to average multiple population assignment runs for individuals belonging to more than one cluster (Jakobsson and Rosenberg 2007). Individual genotypes were assigned to the population cluster in which they had the highest membership. Results and Discussion Species distribution modeling. A species distribution model (SDM) for M. fusca was developed using eight WorldClim bioclimatic variables (Fig. 1). Bioclimatic variables of altitude, mean diurnal temperature range, temperature seasonality, mean temperature of wettest quarter, mean temperature of warmest quarter, annual precipitation, precipitation of driest month, and precipitation of coldest quarter were included as model elements. This model was selected based on low Akaike information criterion scores (AICc = 5476) calculated over 10 replicate runs, less than 0.60 correlation among environmental variables, high AUC scores (AUC = 0.976) and consistency with known species occurrences. Under this SDM, there are 338,700 square kilometers of suitable habitat for M. fusca (Fig. 2). Using a buffer area of 1km, samples from just 150km2 of suitable habitat were utilized in the genetic analyses, equating to less than a half percent (0.04%) of the suitable habitat. Cluster analysis of six bioclimatic variables on 157 georeferenced wild individuals revealed two significant climatic clusters (Fig. 1), revealing a colder and drier “northern” cluster with 47 individuals (mean annual temperature = 4.2 degrees Celsius, annual precipitation = 1702 mm) and a warmer and wetter 82 “southern” cluster with 110 individuals (mean annual temperature = 9.1 degrees Celsius, annual precipitation = 2267 mm). Bioclimatic variables of temperature seasonality, mean temperature of wettest quarter, mean temperature of warmest quarter, annual precipitation, precipitation of driest month, and precipitation of coldest quarter were utilized in the cluster analyses. These variables were selected from the PROC VARCLUS hierarchical of clustering on the variables themselves to identify variables from non-overlapping clusters that maximized variation (see Materials/Methods above). The two climatic clusters are not perfectly segregated in space. Two species occurrences on the north end of Haida Gwaii, BC were characterized as being climatically grouped with “southern” cluster, even though they are generally well within the “northern” climatic region. Ocean currents or some “island” phenomenon associated with Haida Gwaii could be responsible for the mixing of clusters. 83 Figure 1: Malus fusca collection localities are denoted in “+” signs and “×” signs. A habitat suitability model derived from presence only data in MaxEnt indicates predicted suitable habitat in the shaded areas. Northern (“+” signs) and Southern (“×” signs) denote two significantly different habitat types derived from clustering of six WorldClim bioclimatic variables for M. fusca presence data (collection localities). 84 Genetic data analysis. The six primers amplified a total of 50 alleles (Table 1). When individuals collected within 1km of each other were assumed to be from the same population and grouped together, we found significant differences between populations using Analysis of Molecular Variance (Excoffier et al. 1992; Michalakis and Excoffier 1996) with an (FST = 0.074, P = 0.001). There is however, a much higher within population variability (FIS = 0.135, P = 0.001) and differences between populations include zero when bootstrapped. This indicates there is little difference between the “populations” with respect to species diversity. Table 1. Descriptive statistics by locus. Six markers were run across 239 individuals; and 13 markers were run on 27 randomly selected individuals. Reported values include number of individuals (n), number of Polymorphic alleles (Alleles), Expected heterozygosity (He), observed heterozygosity (Ho), and linkage group (L.G.) for each marker (Celton et al. 2009). Locus n Alleles He Ho L.G. GD12 237/27 5/5 0.538/0.567 0.544/0.481 3 GD15 237/27 2/2 0.159/0.307 0.127/0.296 unk GD96 237/27 7/6 0.311/0.361 0.309/0.37 17 GD142 236/27 10/2 0.122/0.037 0.122/0.037 9 GD147 236/27 20/13 0.859/0.866 0.410/0.556 13 GD162 239/27 6/5 0.580/0.633 0.544/0.444 4 CH01d09 27 16 0.936 0.741 12 CH02b12 27 9 0.846 0.852 5 CH02d08 26 5 0.545 0.577 11 CH05e03 21 24 0.962 0.905 2 NH009b 27 4 0.64 0.37 13 NH015a 22 15 0.933 0.636 unk NZ28f4 27 6 0.676 0.593 12 Mean 237/26.1 10.2/8.6 0.428/0.639 0.343/0.528 -- Total 239/27 50/112 ------ 85 The six primers were sufficient to differentiate between all individuals except nine duplicate pairs of genotypes. Seven of these were from adjacent trees and two from herbarium records. Two duplicate genotypes were found in the University of Alaska herbarium specimens, one from the vicinity of Katalia, AK and from the Yakutat Foreland, AK, which are approximately 300 km apart. Another herbarium specimen’s genotype from the Alexander Archipelago, AK matched a genotype from the Skeena River, near Kitwanga, BC, also more than 300 km distant. An additional seven markers, described by Liebhard et al. (2002) were run on the 9 pairs of duplicates and 27 individuals randomly selected from the northern (15 individuals) and southern clusters (12 individuals) based on percent of individuals sampled from unique grid cells in each habitat to compare to the results of the six markers (Table 1). These additional markers resolved the geographically isolated duplicates into unique genotypes. Two of the duplicates from adjacent trees were not resolved, and are presumed to have been collected from single individuals (the shrub-nature of M. fusca, possible suckering, and often densely vegetated habitat make differentiating between individual trees difficult in some cases). A neighbor-joining dendrogram (Fig. 2) was computed in DARwin (v.5.0.158) (Perrier and Jacquemoud-Collet 2006) from dissimilarity among genotypes (Perrier et al. 2003). Both dissimilarity calculations and neighbor joining were bootstrapped over 10,000 replications. 86 Figure 2: Nearest-neighbor dissimilarity among M. fusca. Genotypes are labeled by state where each individual was collected. The lack of distinct geographic groupings signifies high admixture in M. fusca. STRUCTURE results identified four significant clusters. The composition of individuals within clusters is highly admixed both genetically and geographically. Each individual was assigned to multiple clusters (the highest percent of any individual belonging to a single cluster was 41 percent with an average highest contribution of 33 percent across all samples). Clusters showed no geographic separation when individuals were plotted on Google Earth (V.6.2, Google Inc. Mountain View, CA). 87 Pleistocene ice sheets in the Pacific Northwest (30,000-10,000C yr BP) (Clague and James 2002) resulted in strong geographic structuring of many species now found in the region (reviewed in Shafer et al. 2010). While this may have influenced the genetic structure of M. fusca, either through the extirpation of some isolated populations or through recolonization from isolated refugia, the high admixture and lack of discernable geographic patterning in STRUCTURE results suggest a lack of geographic structuring of M. fusca populations, lending support to a recent introduction of the species in the last ca 10K years. A Chi-square test on the individual alleles showed significant differences in allele frequency between the northern and southern clusters for markers GD96 and GD147. FST for GD96 was low but significant (FST = 0.025; P = 0.007), and FST values among all loci between the two regions were extremely low (FST = 0.009; P = 0.023). The subsample that was run across 13 microsatellite markers showed no significant differences between the two regions (FST = -0.001; P = 0.5). Differences between the two climate regions are very slight. Neutral markers may not be well suited for identifying selection. We found slight, but significant differences between First Nation harvesting sites (FST = 0.063, P = 0.004) and between wild B.C. collections and harvesting sites (FST = 0.042, P = 0.005). In both cases, within-population variability was much higher than among population variability (FIS = 0.127 within all populations). We used herbarium records represent wild populations of M. fusca, though they were not sampled from within 1km and hence are unlikely to be from the same biological 88 population. Samples from wild populations in close proximity to the harvesting areas would be ideal for testing for selection, but were not available for inclusion in this study. Individuals within the harvesting areas are generally consistent with wild populations of M. fusca. We note that neutral markers may not be well suited for detecting selection events. We found low, but significant isolation by distance (IBD) for FST by 1" FST kilometers between populations (defined above as all individuals within 1km of ! each other). Mantel’s r was calculated at 0.395 (P = 0.007, error SS = 0.483, and R- square = 0.156). Isolation by distance reported in other species of woody Rosaceae range from marginal isolation by distance (R-square = 0.17, P = 0.091) across ~10km in Prunus mahaleb populations in southeast Spain (Jordano and Godoy 2000) to high isolation by distance (R-square = 0.67, P = 0.01) across ~2.5km in island populations of wild flowering cherry (Prunus lannesiana) from the Izu Islands in Japan (Kato et al. 2011). Grouping of samples in an IBD plot indicates geographic barriers to gene flow (Guillot et al. 2009). The IBD plot for M. fusca (Fig. 3) does not show distinct groupings between samples, suggesting a single, continuous population under isolation by distance. While significant, these values of IBD seem very low compared to the magnitude of the distance surveyed (2600km). This is possibly a result of a previous founder event or high habitat-related selection that are (or were) pushing the species towards uniformity. Another possibility is that not enough of the genome was sampled or too few individuals were sampled to 89 accurately capture IBD structure. Malus fusca is an animal-pollinated and dispersed species with the potential for effective long-distance dispersal. This, in combination with a relatively continuous current distribution of suitable habitat (Fig. 1), lends support to the genetic findings of a single continuous population with low isolation by distance across the species range. 0.45 0.35 0.25 0.15 FST/(1-FST) 0.05 R² = 0.16205 -0.05 0 500 1000 1500 2000 2500 3000 Kilometers Figure 3: Malus fusca shows significant isolation by distance when individuals collected 1km or less apart are grouped into populations, using pairwise comparisons of FST by kilometers (R-square = 0.162, P = 0.0019). 1" FST The USDA Plant Genetic Resources Unit, Geneva, NY conserves ! M. fusca germplasm ex situ as 40 living accessions obtained from CA, OR, WA, and one from AK. Thirty-four of the 50 alleles found in the wild populations and harvesting sites are present in the USDA accessions. An additional 11 alleles present in the USDA 90 accessions were not identified in the wild or harvesting site samples. These additional alleles could be present in wild populations not sampled or could be hybrid introgressions. FST between USDA collections and the herbarium specimens and the field collections showed slight, significant differences between the sample sets (FST = 0.006; P = 0.043), which suggest that USDA PGRU M. fusca collections accurately represent wild populations, at least in the regions sampled. The USDA collection, however, contains only a single representative sample from the northern climatic cluster, and while large differences in allele frequencies between the climatic clusters were not identified in the neutral markers used in this study, a comprehensive collection effort of the species may warrant additional collections from the northern climatic cluster. Conclusions We estimate suitable Malus fusca habitat to be geographically limited to approximately 338,700 square kilometers. The habitats within this range consist of low-lying, moist areas along the northern Pacific coast. In the regions sampled (less than half a percent of suitable habitat), M. fusca shows high admixture with little population differentiation, though it does show significant isolation by distance across the approximately 2600 (straight line) kilometers sampled. We found some limited evidence for differential selection between the colder northern and warmer southern regions of the M. fusca range. Additional analyses using quantitative traits are, however, needed to substantiate this finding. We were not able to identify a genetic signal for cultural selection by indigenous foragers in First Nation 91 harvesting sites, possibly because neutral markers are not well suited for identifying expressed variation, or because a limited number of samples were included. This research can be brought to bear on ex situ and in situ conservation management of the species. USDA germplasm collection currently contains only a single representative sample of M. fusca from the northern regions of its range. Differences in the climate regions could justify additional collections in the northern region, though the PGRU does capture much of the identified variability. A lot of the M. fusca range covers regions not readily threatened by immediate coastal development or urban expansion. On the other hand, warmer temperatures and higher summer moisture deficits predicted under climate model simulations (Mote and Salathé 2010, Littell et al. 2010) could negatively affect M. fusca distribution in the future. Acknowledgments We sincerely thank all who have contributed to the success of this research. We specifically acknowledge our British Columbia collaborators Nancy Turner, Leslie Main Johnson, and Ken Downs for their insights samples from cultural areas. 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