Genetic Variation and Distribution of Pacific Crabapple

in Journal of the American Society for Horticultural Science
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Kanin J. RoutsonUniversity of Arizona, Arid Lands Resource Sciences, 1955 East Sixth Street, P.O. Box 210184, Tucson, AZ 85719

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Gayle M. VolkNational Center for Genetic Resources Preservation, U.S. Department of Agriculture, 1111 S. Mason Street, Fort Collins, CO 80521

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Christopher M. RichardsNational Center for Genetic Resources Preservation, U.S. Department of Agriculture, 1111 S. Mason Street, Fort Collins, CO 80521

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Steven E. SmithSchool of Natural Resources and the Environment, University of Arizona, P.O. Box 210043, Tucson, AZ 85721

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Gary Paul NabhanThe Southwest Center and School of Geography and Development, University of Arizona, 1052 N. Highland Avenue, Tucson, AZ 85721

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Victoria Wyllie de EcheverriaSchool of Environmental Studies, University of Victoria, P.O. Box 3060 STN CSC, Victoria, British Columbia V8W 3R4, Canada

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Pacific crabapple [Malus fusca (Raf.) C.K. Schneid.] is found in mesic coastal habitats in Pacific northwestern North America. It is one of four apple species native to North America. M. fusca is culturally important to First Nations of the region who value and use the fruit of this species as food, bark and leaves for medicine, and wood for making tools and in construction. However, little is known about either distribution or genetic diversity of this species. To correct this deficiency, we used habitat suitability modeling to map M. fusca habitat types with 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 ≈356,780 km2 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. The primers amplified 50 alleles. Significant isolation by distance was identified across the ≈2600 km (straight line) where samples were distributed. These results may help establish priorities for in situ and ex situ M. fusca conservation.

Abstract

Pacific crabapple [Malus fusca (Raf.) C.K. Schneid.] is found in mesic coastal habitats in Pacific northwestern North America. It is one of four apple species native to North America. M. fusca is culturally important to First Nations of the region who value and use the fruit of this species as food, bark and leaves for medicine, and wood for making tools and in construction. However, little is known about either distribution or genetic diversity of this species. To correct this deficiency, we used habitat suitability modeling to map M. fusca habitat types with 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 ≈356,780 km2 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. The primers amplified 50 alleles. Significant isolation by distance was identified across the ≈2600 km (straight line) where samples were distributed. These results may help establish priorities for in situ and ex situ M. fusca conservation.

Wild apple species (Malus Mill.) are native throughout temperate climes of Asia, Europe, and North America (Brown, 2012; Luby, 2003). They offer promising sources of genetic diversity for apple breeding (Brown, 2012) and also provide a wildlife habitat and serve as a direct food source for humans. Four Malus species are native to North America. Three species occur in eastern and midwestern United States and eastern Canada: M. 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 closely related based on isozymes (Dickson et al., 1991). The species native to the Pacific northwestern North America, M. fusca, is the sole geographic, morphological (Van Eseltine, 1933), chemical (Williams, 1982), and genetic outlier among the North American taxa.

Pacific crabapple is a small, deciduous, often multitrunked tree or thicket-forming shrub that occurs naturally in mesic environments along river bottoms, meadows, 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). Yellow to red fruit are oblong and 1 to 2 cm in length (Fig. 1). It groups genetically with the species native to central Asia and China rather than the other North American taxa, according to amplified fragment length polymorphism data (Qian et al., 2006) and nuclear ribosomal and chloroplast DNA (Robinson et al., 2001). M. fusca is considered to be in section Kansuenses Rehd. along with M. kansuensis (Batalin) C.K. Schneid., M. toringoides (Rehder) Hughes, and M. transitoria (Batalin) C.K. Schneid. (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).

Fig. 1.
Fig. 1.

Images of Malus fusca fruit collected from trees in (A) Washington and (B) California.

Citation: Journal of the American Society for Horticultural Science J. Amer. Soc. Hort. Sci. 137, 5; 10.21273/JASHS.137.5.325

Malus fusca can serve as a rootstock for domesticated apple trees in waterlogged sites (R. Duncan, personal communication) and remains a culturally important species for First Nations of the Pacific northwestern North America (Downs, 2006; McDonald, 2005; Turner and Turner, 2008). All parts of the tree have been used including the fruit as food, the bark and leaves for medicines, and the dense wood for implements and in construction (Turner and Bell, 1971a, 1971b). It is well documented that Haida, Tsimshian, Tlingit, and Wakashan peoples in British Columbia and southeastern 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; Downs, 2006; McDonald, 2005; Turner and Peacock, 2005; Turner and Turner, 2008). Diploid M. fusca hybridizes with M. ×domestica Borkh. (Hartman, 1929) and has been a source of fire blight (Erwinia amylovora Burrill) resistance in apple breeding programs that use transgenic early flowering methods to reduce the length of the generation cycles (Flachowsky et al., 2011).

We describe genetic diversity and distribution of M. fusca through species distribution modeling (SDM) of known occurrence records and genetic fingerprinting of 239 M. fusca individuals sampled from northern California to southeastern Alaska using six microsatellite markers.

Materials and Methods

Sample collections.

Two hundred thirty-nine M. fusca individuals were sampled across the species’ native range from southeastern Alaska to northern California. Samples were obtained from field collections (138 individuals from California, Oregon, Washington, and British Columbia), herbarium specimens (65 individuals from Alaska and British Columbia), and U.S. Department of Agriculture (USDA) germplasm accessions (36 individuals from California, Oregon, and Washington). The latter were obtained from the USDA Agricultural Research Service Plant Genetic Resources Unit (PGRU) collection in Geneva, NY. The University of Alaska Museum of the North Herbarium and University of British Columbia Herbarium provided herbarium specimens.

Field collections took place Oct. 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 laboratory. There they were loaded into DNA extraction plates, after which they were frozen at –80 °C for long-term storage. General physical site characteristics, habitat type, associated vegetation, and locality descriptions were documented and photographs taken at each site. Latitude/longitude coordinates and elevation were recorded for individual trees during field collections using a handheld GPS (eTrex-Vista; Garmin, 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 USDA accessions were verified by comparing collection notes with Google Earth (Version 6.2; Google, 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 SDM for M. fusca was created using MaxEnt software [Version 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 the Global Biodiversity Information Facility (2001). We used 1950–2000 data at a 30-arc second resolution (≈0.56 km2 at lat. 49° N) for elevation and 19 bioclimatic variables based on monthly precipitation and temperature. American Standard Code for Information Interchange (ASCII) files were generated in DIVA-GIS [Version 7.5.0 (Hijmans et al., 2001)] from WorldClim data for the Pacific northwestern North America region (long. 121.0° W to 151.0° W, lat. 40.0° N to 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 occurrence locations to build the model. A target group background occurrence file (Elith et al., 2011; Phillips et al., 2009) was constructed using 10,000 randomly selected occurrence records for flowering plants from the Global Biodiversity Information Facility (2001). Multiple occurrences within a grid were removed in ENMTools. We ran MaxEnt using default settings. A model was selected that 1) minimized Akaike information criterion scores [AICc (Warren and Seifert, 2011)] calculated in ENMTools over 10 replicate runs; 2) used environmental variables whose correlation was less than 0.60; 3) showed relatively high area under curve (AUC) scores; and 4) 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; Phillips et al., 2009).

Malus fusca occurs across a wide latitudinal range and may occupy fundamentally different environments across this range (Nakazato et al., 2010). We assessed environmental variability across the M. fusca range by conducting a hierarchical cluster analysis using PROC CLUSTER (Ward's minimum-variance method) in SAS (Version 9.3; SAS Institute, Cary, NC). 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 sd = 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 estimated probability density were omitted from clustering. In cases in which more than one cluster was identified, the omitted observations were grouped into the nearest cluster. Separate SDMs were developed for significantly distinct climate clusters.

Suitable habitat was quantified using Arcmap (ArcGIS 10; Esri, Redlands, CA). Probability of presence/background ASCII files produced by MaxEnt were converted into raster files in Arcmap. Map projections were converted from WGS1984 into Albers equal-area conic projection to calculate area of suitable habitat. The percentage of suitable habitat sampled in the genetic analyses was calculated by applying a 10-km 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 unlinked microsatellite markers used in this study can only represent 35% of the 17 linkage groups in the Malus genome. However, they have been used to successfully differentiate between individuals and identify structure in other Malus sp. (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 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 to 12 mg tissue was used for the dry samples and was extracted using the same protocol but without liquid nitrogen and a lower temperature of reagent AP1. Unlinked primers (GD12, GD15, GD96, GD142, GD147, and GD162) described in Hemmat et al. (2003) and Hokanson et al. (1998) were used 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 (PCRs) were carried out in 15-μL reactions. Each reaction contained: 0.25 μL GoTaq® Flexi Taq Polymerase (Promega, Madison, WI) (5 U/μL), 3 μL Promega 5× 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 H2O 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 PTC 200 Thermocycler (MJ Research, Reno, NV) using touchdown PCR. The annealing temperature was reduced 1 °C at each cycle, beginning at 63 °C and ending at 54 °C, followed by annealing at 55 °C for 18 cycles, and ending with a 72 °C extension for 2 min. PCR products were diluted 1:1 with a formamide bromophenol blue loading buffer and denatured at 95 °C for 5 min. The denatured products were run on gels (6.5% KB Plus acrylamide; LI-COR, Lincoln, NE) in 1× TBE buffer (89 mm Tris, 89 mm boric acid, and 20 mm EDTA) for 1 h, 45 min at 1500 V, 40 W, 40 mA, and 45 °C in a LI-COR 4200 DNA Sequencer. Digital images of the gels imported to LI-COR Saga Generation 2 software were visually analyzed in Saga. Overloaded gels were diluted 1:10 with additional loading buffer and rerun.

Genetic data analysis.

Allele frequencies, observed heterozygosity (Ho) and expected heterozygosity (He), 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. Neighbor joining was computed in DARwin [Version 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. Isolation by distance was determined by a Mantel (1967) test using Rousset’s linearized F-statistic (FST), (Rousset, 1997), over 5000 permutations in Genodive.

We used 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 one to 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 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 one to 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 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.

Cluster analysis of six bioclimatic variables on 157 georeferenced individuals identified two significant climatic clusters (Fig. 2), revealing a colder and drier “northern” cluster with 47 individuals (mean annual temperature = 4.2 °C, annual precipitation = 1702 mm) and a warmer and wetter “southern” cluster with 110 individuals (mean annual temperature = 9.1 °C, 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 used 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 and Methods”). The two climatic clusters are not perfectly segregated in space. Two species occurrences on the north end of Haida Gwaii, British Columbia, Canada, were characterized as being climatically grouped with the “southern” cluster, although 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.

Fig. 2.
Fig. 2.

A habitat suitability model derived from presence only data in MaxEnt indicates predicted suitable habitat for Malus fusca in the shaded areas. Northern (“x” signs) and Southern (“+” signs) denote two significantly different habitat types derived from clustering of six WorldClim bioclimatic variables for M. fusca presence of data based on collection localities.

Citation: Journal of the American Society for Horticultural Science J. Amer. Soc. Hort. Sci. 137, 5; 10.21273/JASHS.137.5.325

Potential M. fusca habitat was mapped using two SDMs for the northern and southern climatic clusters (Fig. 2). The SDM for the northern cluster for M. fusca was developed using eight WorldClim bioclimatic variables. Bioclimatic variables of altitude, mean diurnal temperature range, minimum temperature of coldest month, mean temperature of driest quarter, mean temperature of warmest quarter, annual precipitation, precipitation of driest month, and precipitation of coldest quarter were included as model elements. The SDM for the southern climatic cluster used seven bioclimatic variables including: altitude, annual mean temperature, mean diurnal temperature range, mean temperature of wettest quarter, annual precipitation, precipitation of warmest quarter, and precipitation of coldest quarter as model elements. The models were selected based on low AICc scores [6134 (northern), 5708 (southern)] calculated over 10 replicate runs, less than 0.60 correlation among environmental variables, high AUC scores (AUC = 0.985 in the northern cluster, AUC = 0.986 in the southern cluster), and consistency with known species occurrences. When the two SDMs were merged in ArcMap, there is 356,780 km2 of potential habitat for M. fusca (Fig. 2). Using a buffer area of 10 km, samples representing 15,306 km2 of potential habitat were used in the genetic analyses, equating to 4.29% of the potential habitat.

Genetic data analysis.

The six primers amplified a total of 50 alleles (Table 1). Polymorphic alleles observed in the 239 individuals ranged from a low of two for GD15 to a maximum of 20 for GD147. Observed heterozygosity (0.343) was found to be lower than expected heterozygosity (0.428) suggesting slight inbreeding may be occurring (Table 1). In another apple species, M. sieversii (Ledeb.) M. Roem., Richards et al. (2009) report much higher values of observed and expected heterozygosities (He = 0.749; Ho = 0.693). This may indicate higher total genetic diversity in M. sieversii compared with M. fusca.

Table 1.

Descriptive statistics by locus.z

Table 1.

The six primers were sufficient to differentiate among all individuals except nine 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 ≈300 km apart. Another herbarium specimen’s genotype from the Alexander Archipelago, AK, matched a genotype from the Skeena River, near Kitwanga, British Columbia, Canada, also more than 300 km distant.

An additional seven markers, described by Liebhard et al. (2002), were run on the nine pairs of duplicates and 27 individuals randomly selected from the northern (15 individuals) and southern clusters (12 individuals) based on percentage of individuals sampled from unique grid cells in each habitat to confirm 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).

When individuals collected within 1 km 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 (range, 0.05 to 0.099). However, there is a much higher within-population variability (F-statistic FIS = 0.135, P = 0.001). Overall, these values are similar to those reported for other species (Table 2), except that fewer polymorphic alleles were identified in M. fusca than either M. sieversii (Richards et al., 2009) or M. orientalis Uglitzk. (Volk et al., 2008). This may indicate a lower total genetic variation in M. fusca compared with other wild Malus species.

Table 2.

Diversity comparisons with other Malus sp. Geographic range (maximum distance), number of markers in the analyses, number of amplified alleles, within population (pop.) variance (FIS), and among population variance (FST) are presented for M. fusca, M. sieversii (Richards et al., 2009), and M. orientalis (Volk et al., 2008).

Table 2.

A neighbor-joining dendrogram (Fig. 3) of the 47 individuals from the northern climatic region and 47 individuals randomly selected individuals from the southern climatic cluster show no distinct separation of clusters by region. This suggests high admixture among M. fusca and a general lack of genetic structure.

Fig. 3.
Fig. 3.

Interindividual distances among Malus fusca genotypes. Individuals are labeled by northern (squares) and southern (circles) climatic region where each individual was collected. The lack of distinct grouping between climatic region by cluster signifies high admixture in M. fusca.

Citation: Journal of the American Society for Horticultural Science J. Amer. Soc. Hort. Sci. 137, 5; 10.21273/JASHS.137.5.325

We found low but significant isolation by distance (IBD) for by kilometers between populations (defined as all individuals within 1 km of each other). Mantel’s r was calculated at 0.395 (P = 0.007, error sum of squares = 0.483, R2 = 0.156). Isolation by distance reported in other species of woody Rosaceae range from marginal isolation by distance (R2 = 0.17, P = 0.091) across ≈10 km in Prunus mahaleb L. populations in southeastern Spain (Jordano and Godoy, 2000) to high isolation by distance (R2 = 0.67, P = 0.01) across ≈2.5 km in island populations of wild flowering cherry [Prunus lannesiana (Carriere) E.H. Wilson] from the Izu Islands in Japan (Kato et al., 2011). Grouping of samples in an IBD plot indicates geographic barriers to gene flow (Guillot, 2009). The IBD plot for M. fusca (Fig. 4) does not show distinct groupings between samples, suggesting a single, continuous population under IBD. Although significant, these values of IBD seem very low compared with the magnitude of the distance surveyed (2600 km). This may be a product of the high admixture observed in the species. Another possibility is that habitat-related selection or a previous founder event drove the species toward uniformity. It is also possible that not enough of the genome was sampled or too few individuals were sampled to accurately capture IBD structure. M. 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. 2), lends support to the genetic findings of a single continuous population with low IBD across the species range.

Fig. 4.
Fig. 4.

Isolation by distance regression. Malus fusca shows significant isolation by distance as demonstrated in this biplot displaying geographical distance between sampling sites and genetic distance using Rousset’s distance measure (Rousset, 1997). Individuals collected 1 km or less apart were grouped into populations. Fst is a measure of genetic differentiation between groups of gentoypes (R2 = 0.162, P = 0.0019).

Citation: Journal of the American Society for Horticultural Science J. Amer. Soc. Hort. Sci. 137, 5; 10.21273/JASHS.137.5.325

The 239 individuals were grouped into “population” clusters using posterior probabilities of allele frequencies in STRUCTURE software (Pritchard et al., 2000). 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 percentage of any individual belonging to a single cluster was 41% with an average highest contribution of 33% across all samples). Clusters showed no geographic separation when individuals were plotted on Google Earth (Version 6.2; Google).

Pleistocene ice sheets in the Pacific northwestern North America [30,000 to 10,000 C years BP (uncalibrated radiocarbon date); Clague and James, 2002] resulted in strong geographic structuring of many species now found in the region (reviewed in Shafer et al., 2010). Although 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 discernible 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 ≈10,000 years. Other mechanisms could also account for a lack of geographic structuring and high admixture including effective dispersal mechanisms or movement of plant material by humans or animals.

The USDA PGRU conserves M. fusca germplasm ex situ as 40 living accessions obtained from California, Oregon, Washington, and one from Alaska. Thirty-four of the 50 alleles found in the field collections and herbarium specimens are present in the USDA accessions. An additional 11 alleles present in the USDA accessions were not identified in field collections or herbarium records. 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 generally represent wild populations, at least in the regions sampled. However, the USDA collection contains only a single representative sample from the northern climatic cluster, and although 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 M. fusca habitat to be geographically limited to ≈356,780 km2. The habitats within this range consist of low-lying, moist areas along the northern Pacific coast. In the regions sampled (less than 5% of suitable habitat), M. fusca shows high admixture with little population differentiation, although it does show significant IBD across the ≈2600 km (straight line) sampled. High admixture in M. fusca could be a result of a continuous habitat distribution, effective pollen and seed dispersal mechanism, recent introduction and rapid dispersal of the species, trade or movement of M. fusca plant material by humans, or a combination of factors. 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, although the PGRU does capture much of the identified variability. Much 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 (Littell et al., 2010; Mote and Salathé, 2010) could negatively affect M. fusca distribution in the future.

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  • Hemmat, M., Brown, S.K. & Weeden, N.F. 2003 Mapping and evaluation of Malus × domestica microsatellites in apple and pear J. Amer. Soc. Hort. Sci. 128 515 520

  • Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G. & Jarvis, A. 2005 Very high resolution interpolated climate surfaces for global land areas Intl. J. Climatol. 25 1965 1978

    • Search Google Scholar
    • Export Citation
  • Hijmans, R.J., Guarino, L., Cruz, M. & Rojas, E. 2001 Computer tools for spatial analysis of plant genetic resources data: 1. DIVA-GIS Plant Genet. Resour. Newsl. 127 15 19

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hokanson, S.C., Szewc-McFadden, A.K., Lamboy, W.F. & McFerson, J.R. 1998 Microsatellite (SSR) markers reveal genetic identities, genetic diversity and relationships in a Malus × domestica Borkh. core collection Theor. Appl. Genet. 97 671 683

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jakobsson, M. & Rosenberg, N.A. 2007 CLUMPP: A cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure Bioinformatics 23 1801 1806

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jordano, P. & Godoy, J.A. 2000 RAPD variation and population genetic structure in Prunus mahaleb (Rosaceae), an animal-dispersed tree Mol. Ecol. 9 1293 1305

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kato, S., Tsumura, Y., Iwata, H. & Mukai, Y. 2011 Genetic structure of island populations of Prunus lannesiana var. speciosa revealed by chloroplast DNA, AFLP and nuclear SSR loci analyses J. Plant Res. 124 11 23

    • Search Google Scholar
    • Export Citation
  • Lewis, P.O. & Zaykin, D. 2002 GDA user’s manual. 12 July 2012. <http://www.eeb.uconn.edu/people/plewis/software.php>

    • Crossref
    • Export Citation
  • Liebhard, R., Gianfranceschi, L., Koller, B., Ryder, C.D., Tarchini, R., Weg, E. & Gessler, C. 2002 Development and characterization of 140 new microsatellites in apple (Malus × domestica Borkh.) Mol. Breed. 10 217 241

    • Search Google Scholar
    • Export Citation
  • Littell, J.S., Oneil, E.E., McKenzie, D., Hicke, J.A., Lutz, J.A., Norheim, R.A. & Elsner, M.M. 2010 Forest ecosystems, disturbance, and climatic change in Washington State, USA Clim. Change 102 1 2

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Luby, J.J. 2003 1. Taxonomic classification and brief history, p. 1–14. In: Ferree, D.C. and I.J. Warrington (eds.). Apples: Botany, production and uses. CABI, Cambridge, MA

  • Mantel, N. 1967 The detection of disease clustering and a generalized regression approach Cancer Res. 27 209 220

  • McDonald, J.A. 2005 Cultivating in the northwest: Early accounts of Tsimshian horticulture, p. 240–273. In: Deur, D. and N.J. Turner (eds.). Keeping it living. University of Washington Press, Seattle, WA

    • Crossref
    • Export Citation
  • Meirmans, P.G. & Van Tienderen, P.H. 2004 Genotype and genodive: Two programs for the analysis of genetic diversity of asexual organisms Mol. Ecol. Notes 4 792 794

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Michalakis, Y. & Excoffier, L. 1996 A generic estimation of population subdivision using distances between alleles with special reference for microsatellite loci Genetics 142 1061 1064

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mote, P.W. & Salathé, J.E.P. 2010 Future climate in the Pacific Northwest Clim. Change 102 29 50

  • Nakazato, T., Warren, D.L. & Moyle, L.C. 2010 Ecological and geographic modes of species divergence in wild tomatoes Amer. J. Bot. 97 680 693

  • Perrier, X., Flori, A. & Bonnot, F. 2003 Data analysis methods, p. 43–76. In: Hamon, P., M. Seguin, X. Perrier, and J.C. Glaszmann (eds.). Genetic diversity of cultivated tropical plants. Enfield, Montpellier, France

  • Perrier, X. & Jacquemoud-Collet, J.P. 2006 DARwin software. 5 July 2012. <http://darwin.cirad.fr/>

    • Crossref
    • Export Citation
  • Phillips, S.J., Anderson, R.P. & Schapire, R.E. 2006 Maximum entropy modeling of species geographic distributions Ecol. Modell. 190 231 259

  • Phillips, S.J., Dudik, M., Elith, J., Graham, C.H., Lehmann, A., Leathwick, J. & Ferrier, S. 2009 Sample selection bias and presence-only distribution models: Implications for background and pseudo-absence data Ecol. Appl. 19 181 197

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pritchard, J.K., Stephens, M. & Donnelly, P. 2000 Inference of population structure using multilocus genotype data Genetics 155 945 959

  • Qian, G.Z., Lui, L.F. & Tang, G.G. 2006 A new selection of Malus (Rosaceae) from China Ann. Bot. Fenn. 43 68 73

  • Richards, C.M., Volk, G.M., Reilley, A.A., Henk, A.D., Lockwood, D., Reeves, P.A. & Forsline, P.L. 2009 Genetic diversity and population structure in Malus sieversii, a wild progenitor species of domesticated apple Tree Genet. Genomes 5 339 347

    • Search Google Scholar
    • Export Citation
  • Rios, N.E. & Bart H.L. Jr 2005 GEOLocate. Georeferencing software for natural history collections. 5 July 2012. <http://www.museum.tulane.edu/geolocate/web/WebGeoref.aspx>

    • Crossref
    • Export Citation
  • Robinson, J.P., Harris, S.A. & Juniper, B.E. 2001 Taxonomy of the genus Malus Mill. (Rosaceae) with emphasis on the cultivated apple, Malus domestica Borkh Plant Syst. Evol. 226 35 58

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rousset, F. 1997 Genetic differentiation and estimation of gene flow from F-statistics under isolation by distance Genetics 145 1219 1228

  • Shafer, A.B.A., Cullingham, C.I., Coltman, D.W. & Cote, S.D. 2010 Of glaciers and refugia: A decade of study sheds new light on the phylogeography of northwestern North America Mol. Ecol. 19 4589 4621

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Turner, N.C. & Bell, M.A.M. 1971a The ethnobotany of the Coast Salish Indians of Vancouver Island Econ. Bot. 25 63 104

  • Turner, N.C. & Bell, M.A.M. 1971b The ethnobotany of the Coast Salish Indians of Vancouver Island Appendix I. Econ. Bot. 25 335 339

  • Turner, N.J. & Peacock, S. 2005 Solving the perennial paradox: Ethnobotanical evidence for plant resource management on the northwest coast, p. 101–150. In: Deur, D. and N.J. Turner (eds.). Keeping it living. University of Washington Press, Seattle, WA

  • Turner, N.J. & Turner, K.L. 2008 ‘Where our women used to get the food’: Cumulative effects and loss of ethnobotanical knowledge and practice; case study from coastal British Columbia Botany 86 103 115

    • Search Google Scholar
    • Export Citation
  • Van Eseltine, G.P. 1933 Notes on the species of apples. I. The American crabapples. Agr. Expt. Sta. New York Tech. Bul. No. 208

  • Viereck, L.A. & Little, E.L. 1986 Oregon crab apple (Malus fusca), p. 205–207. In: Alaska trees and shrubs. University of Alaska Press, Fairbanks, AK

    • Crossref
    • Export Citation
  • Volk, G.M., Richards, C.M., Reilley, A.A., Henk, A.D., Forsline, P.L. & Aldwinckle, H.S. 2005 Ex situ conservation of vegetatively propagated species: Development of a seed-based core collection for Malus sieversii J. Amer. Soc. Hort. Sci. 130 203 210

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Volk, G.M., Richards, C.M., Reilley, A.A., Henk, A.D., Reeves, P.A., Forsline, P.L. & Aldwinckle, H.S. 2008 Genetic diversity of wild Malus orientalis from Turkey and southern Russia J. Amer. Soc. Hort. Sci. 133 383 389

    • Search Google Scholar
    • Export Citation
  • Warren, D.L., Glor, R.E. & Turelli, M. 2010 ENMTools: A toolbox for comparative studies of species distribution models Ecogeography 33 607 611

  • Warren, D.L. & Seifert, S.N. 2011 Species distribution modeling in Maxent: The importance of model complexity and the performance of model selection criteria Ecol. Appl. 21 335 342

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Williams, A.H. 1982 Chemical evidence from the flavonoids relevant to the classification of Malus species Bot. J. Linn. Soc. 84 31 39

Contributor Notes

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 the region. Thanks to Phillip Jenkins and Sarah Hunkins at the University of Arizona Herbarium (ARIZ) for assistance with herbarium records and Steffi Ickert-Bond at the University of Alaska Museum of the North Herbarium (ALA) and staff at the University of British Columbia Herbarium (UBC) and University of Alberta Herbarium (ALTA) for specimens. Thanks to Sarah Hayes, Joseph Postman, and all who helped with sample collection. Thanks to Adam Henk for technical assistance in the laboratory and Ned Garvey and Karen Williams for securing Plant Germplasm Collection funds. Finally, thanks to the Kellogg Program of the University of Arizona Southwest Center for funding.

Corresponding author. E-mail: kjr53@e-mail.arizona.edu.

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    Images of Malus fusca fruit collected from trees in (A) Washington and (B) California.

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    A habitat suitability model derived from presence only data in MaxEnt indicates predicted suitable habitat for Malus fusca in the shaded areas. Northern (“x” signs) and Southern (“+” signs) denote two significantly different habitat types derived from clustering of six WorldClim bioclimatic variables for M. fusca presence of data based on collection localities.

  • View in gallery

    Interindividual distances among Malus fusca genotypes. Individuals are labeled by northern (squares) and southern (circles) climatic region where each individual was collected. The lack of distinct grouping between climatic region by cluster signifies high admixture in M. fusca.

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    Isolation by distance regression. Malus fusca shows significant isolation by distance as demonstrated in this biplot displaying geographical distance between sampling sites and genetic distance using Rousset’s distance measure (Rousset, 1997). Individuals collected 1 km or less apart were grouped into populations. Fst is a measure of genetic differentiation between groups of gentoypes (R2 = 0.162, P = 0.0019).

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  • Guillot, G. 2009 On the inference of spatial structure from population genetics data Bioinformatics 25 1796 1801

  • Hartman, H. 1929 Hybrids between Pyrus malus and Pyrus fusca J. Hered. 20 379 390

  • Hemmat, M., Brown, S.K. & Weeden, N.F. 2003 Mapping and evaluation of Malus × domestica microsatellites in apple and pear J. Amer. Soc. Hort. Sci. 128 515 520

    • Search Google Scholar
    • Export Citation
  • Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G. & Jarvis, A. 2005 Very high resolution interpolated climate surfaces for global land areas Intl. J. Climatol. 25 1965 1978

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hijmans, R.J., Guarino, L., Cruz, M. & Rojas, E. 2001 Computer tools for spatial analysis of plant genetic resources data: 1. DIVA-GIS Plant Genet. Resour. Newsl. 127 15 19

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hokanson, S.C., Szewc-McFadden, A.K., Lamboy, W.F. & McFerson, J.R. 1998 Microsatellite (SSR) markers reveal genetic identities, genetic diversity and relationships in a Malus × domestica Borkh. core collection Theor. Appl. Genet. 97 671 683

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jakobsson, M. & Rosenberg, N.A. 2007 CLUMPP: A cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure Bioinformatics 23 1801 1806

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jordano, P. & Godoy, J.A. 2000 RAPD variation and population genetic structure in Prunus mahaleb (Rosaceae), an animal-dispersed tree Mol. Ecol. 9 1293 1305

    • Search Google Scholar
    • Export Citation
  • Kato, S., Tsumura, Y., Iwata, H. & Mukai, Y. 2011 Genetic structure of island populations of Prunus lannesiana var. speciosa revealed by chloroplast DNA, AFLP and nuclear SSR loci analyses J. Plant Res. 124 11 23

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lewis, P.O. & Zaykin, D. 2002 GDA user’s manual. 12 July 2012. <http://www.eeb.uconn.edu/people/plewis/software.php>

  • Liebhard, R., Gianfranceschi, L., Koller, B., Ryder, C.D., Tarchini, R., Weg, E. & Gessler, C. 2002 Development and characterization of 140 new microsatellites in apple (Malus × domestica Borkh.) Mol. Breed. 10 217 241

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Littell, J.S., Oneil, E.E., McKenzie, D., Hicke, J.A., Lutz, J.A., Norheim, R.A. & Elsner, M.M. 2010 Forest ecosystems, disturbance, and climatic change in Washington State, USA Clim. Change 102 1 2

    • Search Google Scholar
    • Export Citation
  • Luby, J.J. 2003 1. Taxonomic classification and brief history, p. 1–14. In: Ferree, D.C. and I.J. Warrington (eds.). Apples: Botany, production and uses. CABI, Cambridge, MA

  • Mantel, N. 1967 The detection of disease clustering and a generalized regression approach Cancer Res. 27 209 220

  • McDonald, J.A. 2005 Cultivating in the northwest: Early accounts of Tsimshian horticulture, p. 240–273. In: Deur, D. and N.J. Turner (eds.). Keeping it living. University of Washington Press, Seattle, WA

    • Crossref
    • Export Citation
  • Meirmans, P.G. & Van Tienderen, P.H. 2004 Genotype and genodive: Two programs for the analysis of genetic diversity of asexual organisms Mol. Ecol. Notes 4 792 794

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Michalakis, Y. & Excoffier, L. 1996 A generic estimation of population subdivision using distances between alleles with special reference for microsatellite loci Genetics 142 1061 1064

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mote, P.W. & Salathé, J.E.P. 2010 Future climate in the Pacific Northwest Clim. Change 102 29 50

  • Nakazato, T., Warren, D.L. & Moyle, L.C. 2010 Ecological and geographic modes of species divergence in wild tomatoes Amer. J. Bot. 97 680 693

  • Perrier, X., Flori, A. & Bonnot, F. 2003 Data analysis methods, p. 43–76. In: Hamon, P., M. Seguin, X. Perrier, and J.C. Glaszmann (eds.). Genetic diversity of cultivated tropical plants. Enfield, Montpellier, France

    • Crossref
    • Export Citation
  • Perrier, X. & Jacquemoud-Collet, J.P. 2006 DARwin software. 5 July 2012. <http://darwin.cirad.fr/>

    • Crossref
    • Export Citation
  • Phillips, S.J., Anderson, R.P. & Schapire, R.E. 2006 Maximum entropy modeling of species geographic distributions Ecol. Modell. 190 231 259

  • Phillips, S.J., Dudik, M., Elith, J., Graham, C.H., Lehmann, A., Leathwick, J. & Ferrier, S. 2009 Sample selection bias and presence-only distribution models: Implications for background and pseudo-absence data Ecol. Appl. 19 181 197

    • Search Google Scholar
    • Export Citation
  • Pritchard, J.K., Stephens, M. & Donnelly, P. 2000 Inference of population structure using multilocus genotype data Genetics 155 945 959

  • Qian, G.Z., Lui, L.F. & Tang, G.G. 2006 A new selection of Malus (Rosaceae) from China Ann. Bot. Fenn. 43 68 73

  • Richards, C.M., Volk, G.M., Reilley, A.A., Henk, A.D., Lockwood, D., Reeves, P.A. & Forsline, P.L. 2009 Genetic diversity and population structure in Malus sieversii, a wild progenitor species of domesticated apple Tree Genet. Genomes 5 339 347

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rios, N.E. & Bart H.L. Jr 2005 GEOLocate. Georeferencing software for natural history collections. 5 July 2012. <http://www.museum.tulane.edu/geolocate/web/WebGeoref.aspx>

    • Crossref
    • Export Citation
  • Robinson, J.P., Harris, S.A. & Juniper, B.E. 2001 Taxonomy of the genus Malus Mill. (Rosaceae) with emphasis on the cultivated apple, Malus domestica Borkh Plant Syst. Evol. 226 35 58

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rousset, F. 1997 Genetic differentiation and estimation of gene flow from F-statistics under isolation by distance Genetics 145 1219 1228

  • Shafer, A.B.A., Cullingham, C.I., Coltman, D.W. & Cote, S.D. 2010 Of glaciers and refugia: A decade of study sheds new light on the phylogeography of northwestern North America Mol. Ecol. 19 4589 4621

    • Search Google Scholar
    • Export Citation
  • Turner, N.C. & Bell, M.A.M. 1971a The ethnobotany of the Coast Salish Indians of Vancouver Island Econ. Bot. 25 63 104

  • Turner, N.C. & Bell, M.A.M. 1971b The ethnobotany of the Coast Salish Indians of Vancouver Island Appendix I. Econ. Bot. 25 335 339

  • Turner, N.J. & Peacock, S. 2005 Solving the perennial paradox: Ethnobotanical evidence for plant resource management on the northwest coast, p. 101–150. In: Deur, D. and N.J. Turner (eds.). Keeping it living. University of Washington Press, Seattle, WA

  • Turner, N.J. & Turner, K.L. 2008 ‘Where our women used to get the food’: Cumulative effects and loss of ethnobotanical knowledge and practice; case study from coastal British Columbia Botany 86 103 115

    • Search Google Scholar
    • Export Citation
  • Van Eseltine, G.P. 1933 Notes on the species of apples. I. The American crabapples. Agr. Expt. Sta. New York Tech. Bul. No. 208

    • Crossref
    • Export Citation
  • Viereck, L.A. & Little, E.L. 1986 Oregon crab apple (Malus fusca), p. 205–207. In: Alaska trees and shrubs. University of Alaska Press, Fairbanks, AK

    • Crossref
    • Export Citation
  • Volk, G.M., Richards, C.M., Reilley, A.A., Henk, A.D., Forsline, P.L. & Aldwinckle, H.S. 2005 Ex situ conservation of vegetatively propagated species: Development of a seed-based core collection for Malus sieversii J. Amer. Soc. Hort. Sci. 130 203 210

    • Search Google Scholar
    • Export Citation
  • Volk, G.M., Richards, C.M., Reilley, A.A., Henk, A.D., Reeves, P.A., Forsline, P.L. & Aldwinckle, H.S. 2008 Genetic diversity of wild Malus orientalis from Turkey and southern Russia J. Amer. Soc. Hort. Sci. 133 383 389

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Warren, D.L., Glor, R.E. & Turelli, M. 2010 ENMTools: A toolbox for comparative studies of species distribution models Ecogeography 33 607 611

  • Warren, D.L. & Seifert, S.N. 2011 Species distribution modeling in Maxent: The importance of model complexity and the performance of model selection criteria Ecol. Appl. 21 335 342

    • Search Google Scholar
    • Export Citation
  • Williams, A.H. 1982 Chemical evidence from the flavonoids relevant to the classification of Malus species Bot. J. Linn. Soc. 84 31 39

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