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Population Biology |
United States Department of Agriculture, Agriculture Research Service, Forage and Range Research Laboratory, Logan, Utah 84322-6300 USA
Received for publication February 18, 2004. Accepted for publication August 6, 2004.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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Key Words: AFLP • bluebunch wheatgrass • genetic diversity • North America • Poaceae • population structure • Pseudoroegneria spicata
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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). Once dominant on millions of acres of semi-arid grass and sagebrush sites, this species is prized for drought tolerance and palatability to many grazing animals, including a variety of livestock and wildlife species (Daubenmire, 1942
). Despite its ecological importance and widespread use in large-scale rangeland revegetation, relatively little is known about population structure in this species. Variation in awn structure and growth habit has been examined (Daubenmire, 1960
; Passey and Hugie, 1963). Cultivars of P. spicata display relatively high rates of overall nucleotide sequence variation (within varieties), but surprisingly little sequence divergence (6.9%) was detected between varieties once thought to represent different species or subspecies (Larson et al., 2000
). No true-breeding amplified fragment length polymorphisms were detected between these cultivars (Larson et al., 2000
). Most forms of P. spicata are diploid (2n = 2x = 14), but autotetraploid forms exist in the northern geographic range.
Pseudoroegneria (Nevski) Á. Löve is a genus of the Northern Hemisphere, with its species occurring from the Middle East and Transcaucasia, across Central Asia and Northern China to western North America (Dewey, 1984
). Based on chromosome pairing during meiosis, Jensen et al. (1995)
suggested that Eurasian and North American Pseudoroegneria taxa may form one continuous polymorphic species complex. The chloroplast DNA sequences of Eurasian and North American Pseudoroegneria taxa are very similar (Redinbaugh et al., 2000
; Mason-Gamer, 2002). However, there is evidence from chloroplast DNA restriction site variation that North American P. spicata is different from Eurasian P. strigosa (M. Bieb.) Á. Löve and P. libanotica (Hack.) D. R. Dewey (Mason-Gamer, 2002). A survey of New World and Old World species of Pseudoroegneria, including P. spicata, indicates that this genus is highly self-sterile (Jensen et al., 1990
). Although P. spicata has many close relatives, this species has no recognized congeners in North America. North American Elymus wawawaiensis J. R. Carlson and Barkworth was previously misidentified as P. spicata (Carlson and Barkworth, 1997
). However, the genomically defined Elymus genus is derived from diploid Pseudoroegneria and Hordeum (formerly Critesion) progenitors (Dewey, 1984
; Löve, 1984
; Mason-Gamer, 2001
). The chloroplast genome of Elymus derives from Pseudoroegneria (Redinbaugh et al., 2000
). Thus, allotetraploid E. wawawaiensis is genetically distinct from diploid or autotetraploid P. spicata. Moreover, E. wawawaiensis and P. spicata can be distinguished by glume morphology and rachis internode length (Carlson and Barkworth, 1997
).
This study used amplified fragment length polymorphism and model-based Bayesian clustering to investigate genetic diversity and population structure among 565 P. spicata plants from 82 localities in western North America. Bayesian clustering (Pritchard et al., 2000
) was used to evaluate models of population structure by (1) allocating individual genotypes into K populations, where K is a series of numbers and (2) determining the likelihood for each model of K populations. The best resulting model of population structure will be further evaluated using analysis of molecular variance and other methods of hypothesis testing. This study tested and compared the relative importance of genetic panmixia vs. genetic structure, in P. spicata, over a broad geographic region of western North American. We investigated relationships among genetic distance, geographic distance, and population structure. For comparison, E. wawawaiensis and Old World P. cognata (Hack.) Á. Löve, P. libanotica, P. stipifolia (Czern. Ex Nevski) Á. Löve, and P. strigosa species were also included.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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The 86 P. spicata accessions included single-origin accessions from 81 mapped collection sites (Fig. 1), one single-origin accession from Alaska, one single-origin P. spicata accession from an unknown collection site, and one multiple-origin accession. The 86 P. spicata accessions also included the OR3.1 and OR3.2 accessions replicated from the same DNA samples (technical replicates) in addition to the WA9a and WA9b accessions collected from the same site (biological replicates). Awnless bluebunch wheatgrass (P. spicata [Pursh] Á. Löve ssp. inermis Scribn. & J. G. Sm. = A. inerme [Scribn. & J. G. Sm.] Rydb.) was distinguished from awned bluebunch wheatgrass (P. spicata ssp. spicata [Pursh] Á. Löve = A. spicatum Pursh), however no other dispersion of characters between these taxa have been observed (Holmgren and Holmgren, 1977
; Carlson and Barkworth, 1997
). Hybrids of these taxa are meiotically regular (Stebbins and Pun, 1953
), form fertile hybrids, and hybridize freely when found growing together (Holmgren and Holmgren, 1977
). Our P. spicata accessions display awns as a continuously variable trait evident (including awnless forms), both within and among accessions, but we have not classified the accessions according to this characteristic.
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AFLP analysis
Amplified fragment length polymorphisms (AFLPs) were assayed as described by Vos et al. (1995)
, except that EcoRI selective amplification primers included a fluorescent 6-FAM (6-carboxy fluorescein) label on the 5' nucleotide. Selective amplifications were performed using six EcoRI + 3/MseI + 3 primer pairs (e.g., E36M59, E36M62, E37M48, E37M49, E40M49, E40M59, and E41M47), described by Larson et al. (2000)
. The amplified DNA fragments were size fractionated using an ABI3100 instrument with 50-cm capillaries, POP-6 polymer, GeneScan 400HD ROX (rhodamine X) internal size standards, and GeneScan software (PE Applied Biosystems, Foster City, California, USA). The GeneScan sample files were visually analyzed for the presence and absence of DNA fragments, between 50 and 400 base pairs, using Genographer version 1.5 (Benham et al., 1999
).
Data analyses
Neighbor-joining genetic distance analysis (Saitou and Nei, 1987
), model-based Bayesian clustering (Pritchard et al., 2000
), analyses of variance (Excoffier et al., 1992
), and other methods of hypothesis testing (Leonard et al., 1999
) were used to investigate genetic diversity and population structure. Genetic distances between individual plants were determined by the numbers of amplified fragment length polymorphisms (Euclidean distance) and similarity coefficients computed as 2Nm/(Nx + Ny) where Nm is the number of pairs of bands matching between individuals, and Nx and Ny are the total number of bands amplified from the individuals (Dice, 1945
; Nei and Li, 1979
; Lynch, 1990
). Neither of these Euclidean distances nor similarity coefficients factor null alleles as shared characteristics. Pairwise comparisons of genetic distance were computed using SAS (SAS Institute, Cary, North Carolina, USA).
The average number of differences between accessions (PXY), average number of pairwise differences within accessions (PX and PY), corrected average pairwise difference among accessions (PXY – (PX + PY)/2), and corresponding analysis of molecular variance based on Euclidean distances was computed using Arelequin (Excoffier et al., 1992
). Likewise, analyses of variances based on pairwise comparisons of similarity coefficients were performed using programs described by Leonard et al. (1999)
.
The neighbor-joining genetic distance analysis (Saitou and Nei, 1987
) was based on a user defined Euclidean distance matrix of the corrected average pairwise difference among accessions (PXY – (PX + PY)/2), from Arelequin (Excoffier et al., 1992
), using PAUP* version 4.0b8 (Swofford, 2000
). The neighbor-joining tree (Fig. 2) was rooted a priori using allotetraploid E. wawawaiensis as the outgroup. A graphic display of the neighbor-joining tree was developed using TREEVIEW (Page, 1996
). Bayesian cluster analysis was performed using the Structure program (Pritchard et al., 2000
) on a Linux cluster at the Virginia Bioinformatics Institute (Blacksburg, Virginia, USA), using locality as prior population information as describe by Pritchard et al. (2000)
.
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test statistic (Z), using the MxComp procedure of NTSYS-pc (Rohlf, 1998
). Significance tests for these correlations were determined by comparing observed values to values obtained by 9999 random permutations (Smouse et al., 1986
). Therefore, the upper-tail probability (P) that 1000 random Mantel test statistic (Z) values are (by chance) less than observed values of Z equals 0.0002 or greater. Geographical distance (in kilometers) matrices were computed from geographical coordinates, using a SAS program developed by the first author, based on the formula: km = Arccos[cos(LATX)cos(LONGX)cos(LATy)cos(LONGy) + cos(LATX)sin(LONGX)cos(LATy)sin(LONGy) + sin(LATX)sin(LATy)]r, where LATX, LONGX and LATy, LONGy are the latitude, longitude (expressed in radians) for the two accessions (X and Y) and r is 6378 km, the radius of Earth. |
RESULTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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The number of polymorphic fragments, average number of fragments per plant, average similarity coefficient, and average number DNA polymorphisms within accessions varied by locality and species (Table 1). As might be expected, the average (±1 SE) number of fragments per diploid P. spicata plant (242 ± 0.5) was significantly less than the average number of fragments per tetraploid plant (259 ± 3.6). Moreover, the allotetraploid Elymus accessions displayed substantially more fragments per plant compared with the diploid or autotetraploid Pseudoroegneria plants (Table 1). The average similarity coefficient within the AK1 accession (0.622) was significantly lower than for any other accession. Nevertheless, the average similarity coefficients within AK1 and other P. spicata accessions (Table 1) were significantly greater than the average similarity coefficient among P. spicata localities (0.587). Conversely the average number of differences within P. spicata localities (Table 1) was substantially less than the average number of differences among P. spicata localities (210).
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) partitioned 31.9% of the DNA polymorphism among 80 diploid sample populations. The apportionment of DNA polymorphism among accessions was significant (P < 0.0001, 1023 permutations), indicating that genotypes are subdivided among the 80 sample populations. This analysis of variance excluded three tetraploid populations (MT1, ID1, and WA4), OR3.2 (technical replicate), P7 (multiple origin), and WA9b (biological replicate). Individual P. spicata plants group almost strictly by locality, based on neighbor-joining distance analysis (results not shown). One of the few exceptions was the multiple-origin P7 accession (Larson et al., 2000
; Jones et al., 2002
), which did not form a cohesive group of plant genotypes. Moreover, neighbor-joining analysis of the average number of polymorphisms among localities (corrected for polymorphism within localities) revealed a substantial number of regional (geographic) groups (Fig. 2). The neighbor-joining tree (Fig. 2) was rooted using E. wawawaiensis as the outgroup. The Mantel test of the correlation between the average number of polymorphisms and geographical distances among 78 diploid populations resulted in an r value of 0.58412, significant (P = 0.0002) among 9999 random permutations. Likewise, correlations between geographical distance and the corrected number of polymorphisms (r = 0.57526) was significant among 9999 random permutations (P = 0.0002) as was the correlation between geographical distance and FST (r = 0.54474). These correlation tests excluded three tetraploid populations (MT1, ID1, and WA4), OR3.2 (replicated sample population), P1 (unknown origin), P7 (multiple origin), WA9b (duplicate sample population), and the AK1 accession from Alaska. Although AK1 appears to be the most unique P. spicata germplasm, genetic identity between the AK1 population and other P. spicata populations evidently transcends wide geographical distances, which substantially diminishes the overall correlations between DNA polymorphism and geographical distances among sample populations.
Models of population structure among the 81 diploid accessions (531 genotypes) were evaluated from K = 1 to K = 40, where K equals the number of populations (model) as determined by the investigator (Fig. 3). These evaluations excluded three tetraploid populations (MT1, ID1, and WA4), OR3.2 (replicated sample population), WA9b (duplicate sample population). We initially evaluated two or three runs of the Gibbs sampler using 10 000 iterations with a burn-in of 10 000, at each level of K from K = 1 to K = 24 (Fig. 3). The model choice criteria, log P(X|K), were relatively low and stable where K < 5. The estimated likelihood generally improved from K = 1 up to about K = 15. Although the model choice criteria improved beyond K = 15, the likelihood estimates became highly unstable (Fig. 3). The model choice criterion is a function of the mean likelihood and variance of likelihood over multiple iterations of the Gibbs sampler (Pritchard et al., 2000
). Thus, we evaluated another two runs using 100 000 iterations at each level of K from K = 4 to K = 30, plus one run at each level of K from K = 31 to K = 40 (Fig. 3). Finally, we evaluated seven additional runs with 200 000 iterations (plus 20 000 burn-in) where K equals 16, 19, 20, 22, 26, 27, and 31 (Fig. 3). Altogether, these evaluations required approximately 15 000 h using Pentium III and Pentium IV processors (>2 Ghz). Thus we were practically limited in the number of iterations and models that could be tested. However, judging from Fig. 3, one seemingly good model among these computationally intensive searches was K = 31. With 100 000 iterations of the Gibbs sampler, the estimated likelihood of the K = 31 model was substantially greater than any other model (Fig. 3). The K = 31 model was also evaluated in another run with 200 000 iterations of the Gibbs sampler, which also produced one of the highest estimated likelihoods (Fig. 3). The K = 31 model included 21 nontrivial groups (Figs. 1 and 2) and 10 trivial groups composed of three individual plants, including one from WY3, one from ID7, and one from OR6. These three plants group with other P. spicata plants by locality based neighbor-joining and UPGMA searches (data not shown). It is not clear why these three plants formed 10 trivial groups (i.e., groups with less than one plant), especially under the no-admixture model (Pritchard, 2000
). Nevertheless, samples from a given locality were usually allocated within Bayesian clusters, which show reasonable congruence with the distance-based neighbor-joining tree (Fig. 2).
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The significance of population structure among the 21 regional (Bayesian) groups was reexamined using several well-recognized methods. Analysis of molecular variance (AMOVA) partitioned 18.3% of the DNA polymorphism among the 21 regional groups, 14.9% among localities within regional groups, and 66.8% within localities. Irrespective of localities, AMOVA partitioned 21.9% of the DNA polymorphism among regional groups and 78.1% of the DNA polymorphism within regional groups (Table 2). All of these regional and locality factors were highly significant (P < 0.0001, 1023 permutations). Diversity within and among Bayesian groups was also evaluated based on variances of the similarity index (Table 3). The average similarity coefficients within groups ranged from 0.622 for group U (composed of the AK1 accession) and 0.733 for group O (composed of the P1 accession, unknown locality). The average similarity coefficients among groups ranged from 0.524 between groups B and U to 0.633 between groups H and F (Table 3). Permutation tests (Leonard et al., 1999
) indicate that most comparisons of similarity coefficients among groups were significantly less (P < 0.05) than similarity coefficients within groups (Table 3). However, in some comparisons, the number of genotypes allocated in one and/ or the other group limits the power of the permutation test. In any case, significant differences among the 21 groups were detectable in pair-wise comparisons of DNA polymorphism and similarity matching coefficients, based on permutations of individuals within and among groups. Although DNA polymorphism and similarity matching coefficients, between individual plants, are very much related (inversely), the permutation test based on similarity matching coefficients (Leonard et al., 1999
) was more conservative than the permutation test based on Euclidean distances (Excoffier et al., 1992
).
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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As expected, P. spicata plants show a relatively high degree of intraspecific identity (i.e., greater than interspecific comparisons). As shown in the neighbor-joining tree (Fig. 2) P. cognata, P. strigosa, and P. stipifolia displayed more genetic relatedness than E. wawawaiensis to P. spicata on the basis of the absolute number of differences (amplified length fragment polymorphisms). In particular, P. cognata displayed a relatively strong genetic similarity to P. spicata as determined by amplified fragment length polymorphism. Although chromosome pairing in P. cognata (syn = Agropyron ferganene Drobow) x P. spicata hybrids averaged 6.21 bivalents per cell, compared to 6.98 in P. cognata, these hybrids failed to produce seed (Dewey, 1981
). Hybrids of P. spicata x E. wawawaiensis are sterile and display irregular pairing (Dewey, 1982
; Carlson and Barkworth, 1997
). The neighbor-joining tree (Fig. 2) was rooted a priori using allotetraploid E. wawawaiensis as the outgroup. However, P. libanotica and P. spicata actually displayed the greatest number of interspecific differences and the lowest interspecific similarity coefficients. Thus, interpretation of interspecific relationships as depicted by the neighbor-joining tree (Fig. 2) should be reviewed with caution. Allotetraploid E. wawawaiensis displayed many differences from P. spicata, but relatively strong similarity coefficients with P. spicata. It is quite conceivable that North American E. wawawaiensis and P. spicata have introgressed, or that P. spicata is a relatively recent diploid progenitor of E. wawawaiensis. Morphological similarities between E. wawawaiensis and P. spicata also suggest a very close relationship. In any case, comparisons of similarity coefficients and the absolute number of differences within and between species, demonstrate a relatively high degree of genetic relatedness and cohesiveness in North American P. spicata (Fig. 2).
Although our sampling strategy emphasized sampling of localities rather than individual plants, DNA profiles from 4–7 plants per locality were sufficient to demonstrate a relatively high degree of genetic identity within localities and significant differences in the average similarity coefficients within accessions (Table 1). Of particular interest, the P7 accession was the third most polymorphic accession in terms of similarity coefficients and the absolute number of differences (Table 1). The P7 germplasm was developed as a multiple-origin synthetic, which may be appropriate for large-scale revegetation uses in western North America (Larson et al., 2000
; Jones et al., 2002
). Only two other accessions, AK1 and WA3, displayed more DNA polymorphism than P7. The AK1 accession, in particular, displayed substantially more variation than any other accession. The explanation for this is not certain, but it may have cross-pollinated with other P. spicata accessions. The AK1 seed accession was collected in 1971 and probably regenerated two or three times, whereas most of the other P. spicata seed accessions were collected from wildland plants or regenerated once with reasonable pollen isolation (see Supplemental Data accompanying the online version of this article). The AK1 collection site is dislocated from other P. spicata collection sites examined in this study. Thus, we might expect to find highly elevated levels of DNA variation in AK1 if it was outcrossed with P. spicata from the lower latitudes. Moreover, we would also expect to find reduced DNA polymorphism between AK1 and other P. spicata accessions from lower latitudes, had AK1 been outcrossed. For example, divergence among Pascopyrum smithii (Rydb.) Barkworth & D. R. Dewey accessions from the Northern Great Plains was reduced by intentional cross breeding (Larson et al., 2003
). Likewise, it is possible that many of our P. spicata seed collections, some of which have been regenerated (see Supplemental Data accompanying the online version of this article), were inadvertently biased toward sibling relationships that may or may not fully reflect genetic diversity within localities. Therefore, sibling relationships and the spatial limitations of most seed collections (usually <1 ha) probably affect the apportionment of genetic variation observed within and among P. spicata sample localities. Nevertheless, the apportionment of DNA polymorphism among P. spicata sample locations (approximately 32%) compares very closely to the apportionment of P. smithii DNA polymorphism among natural germplasm sources from the Northern and Central Rocky Mountain regions (28 and 33%, respectively) (Larson et al., 2003
). Moreover, the correlation of DNA polymorphism and geographic distance among P. smithii localities (r = 0.66) (Larson et al., 2003
) is very similar to P. spicata (0.58). Thus, significant effects of geographic provenance can be detected within and among localities of allogamous range grasses in western North America.
A primary objective of this study was to determine whether broad regional population structure exists in North American P. spicata. Distance-based neighbor-joining, model-based Bayesian clustering, analysis of molecular variance, Mantel correlation tests, and other methods of hypothesis testing based on amplified fragment length polymorphism and associated similarity coefficients provide substantial evidence of population structure and geographic origin in P. spicata. In particular, Bayesian clustering (Pritchard et al., 2000
) separated the P. spicata samples from 81 localities into 21 groups, which display significant geographic dispersion. Thus, dispersal among sampling locations was not sufficient to group genotypes into one unstructured population. The apportionment of DNA polymorphism among the 21 regional groups of P. spicata (approximately 18%) was threefold greater than the apportionment of DNA polymorphism among the Northern Great Plains, Northern Rocky Mountain, and Central Rocky Mountain groups of P. smithii (approximately 6%) (Larson et al., 2003
). However, the separation of 21 regional P. spicata groups may result from insufficient sampling across the species distribution. For example, group U was strictly composed of samples from one locality (AK1) in Alaska, which is dislocated from other collection sites examined in this study. In actuality, genetic similarity between the AK1 and other P. spicata accessions transcends this large geographic distance (see Results above). Conversely, wide gaps in the geographic sampling of P. spicata (Fig. 1) exist where one might expect to find new sources of genetic variation such as Oregon, Arizona, Colorado, eastern Montana, western North Dakota, western South Dakota, and vast areas of western Canada. Thus, other geographically significant populations may have been overlooked as a result of insufficient sampling. In many instances, conspicuous geographic boundaries exist between several genetically distinct lineages (compare Figs. 1, 2, and 4). In particular, two distinct lineages converge between the Central Great Basin and Bonneville Basin Floristic sections of the Great Basin (Holmgren, 1972
). A lineage of related groups A, B, C, and E (Bonneville Basin) interface with a distinct lineage of groups K and J (Central Great Basin) along the Nevada-Utah border. The overall correspondence of population structure in P. spicata with floristic sections of the Intermountain region is tenuous. Nevertheless, we postulate that distinct lineages of P. spicata have admixed between the Central Great Basin and Bonneville Basin Floristic sections of the Great Basin. The Structure program can detect admixture (Rosenberg et al., 2002
), however the procedure is not recommended for dominant marker data (Pritchard et al., 2000
). In any case, more continuous and expansive geographic collections of P. spicata genotypes are needed to fully elucidate the population structure of this North American species.
Most of the P. spicata AFLPs examined in this study were nuclear DNA sequences. The nuclear DNA content of P. spicata is virtually identical to Hordeum vulgare (Vogel et al., 1999
), which has an approximate DNA content of 4873 Mbp (Arumuganathan and Earle, 1991
) compared to the 134 545 bp chloroplast genome of Triticum aestivum (Ogihara et al., 2002
) and 490 520 bp mitochondrial genome of Oryza sativa (Notsu et al., 2002
). Although plastid genome sizes vary among grasses, the combined plastid genomes of wheat and rice are nearly four orders of magnitude smaller than the nuclear genome of P. spicata. Moreover, the vast majority of polymorphic AFLP markers in full-sib plant families display Mendelian segregation and map into well-defined linkage groups that correspond to nuclear chromosomes (Wu et al., 2003
). Thus, AFLPs provide a good overall measure of genetic identity among plants that should reflect patterns of seed and pollen dispersal. The Bayesian cluster analysis of AFLP genotypes provide a useful starting point and framework to investigate population structure and adaptive variation in P. spicata.
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FOOTNOTES |
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2 E-mail: stlarson{at}cc.usu.edu
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LITERATURE CITED |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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Benham J. J.-U. Jeung M. Jasieniuk V. Kanazin T. Blake 1999 Genographer: a graphical tool for automated AFLP and microsatellite analysis. Journal of Agricultural Genomics 4: article 4. Available at http: //www.cabi-publishing.org/gateways/jag/index.html
Carlson J. R. M. E. Barkworth 1997 Elymus wawawaiensis: a species hitherto confused with Pseudoroegneria spicata (Triticeae, Poaceae). Phytologia 83: 312-330
Daubenmire R. F. 1942 An ecological study of the vegetation of southeastern Washington and adjacent Idaho. Ecological Monographs 12: 53-78[CrossRef]
Daubenmire R. F. 1960 An experimental study of variation in the Agropyron spicatum-A. inerme complex. Botantical Gazette (Chicago) 122: 104-108[CrossRef]
Dewey D. R. 1981 Cytogenetics of Agropyron ferganense and its hybrids with six species of Agropyron, Elymus, and Sitanion. American Journal of Botany 68: 216-225[CrossRef][Web of Science]
Dewey D. R. 1982 Genomic and phylogenetic relationships among North American perennial Triticeae. In J. R. Estes, R. J. Tyrl, and J. N. Brunken [eds.], Grasses and grasslands: systematics and ecology, 51–88. University of Oklahoma Press, Norman, Oklahoma, USA
Dewey D. R. 1984 The genomic system of classification as a guide to intergeneric hybridization within the perennial Triticeae. In J. P. Gustafson [ed.], Gene manipulation in plant improvement. Proceedings of the 6th Stadler genetics symposium, 209–279. Columbia University Press, New York, New York, USA
Dice L. R. 1945 Measures of the amount of ecologic association between species. Ecology 26: 297-302[CrossRef][Web of Science]
Excoffier L. P. E. Smouse J. M. Quattro 1992 Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics 131: 479-491[Abstract]
Holmgren N. H. 1972 Plant geography of the Intermountain region. In A. Cronquist, A. H. Holmgren, N. H. Holmgren, and J. L. Reveal [eds.], Intermountain flora, vol. 1, 77–161. Hafner, New York, New York, USA
Holmgren A. H. N. H. Holmgren 1977 Poaceae. In A. Cronquist, A. H. Holmgren, N. H. Holmgren, and J. L. Reveal [eds.], Intermountain flora, vol. 6, 175–464. New York Botanical Garden, New York, New York, USA
Jensen K. B. Y. F. Zhang D. R. Dewey 1990 Mode of pollination of perennial species of the Triticeae in relation to genomically defined genera. Canadian Journal of Plant Science 90: 215-225
Jensen K. B. M. Curto K. Asay 1995 Cytogenetics of Eurasian bluebunch wheatgrass and their relationship to North American bluebunch and thickspike wheatgrasses. Crop Science 35: 1157-1162
Jones T. A. S. R. Larson D. C. Nielson S. A. Young N. J. Chatterton A. J. Palazzo 2002 Registration of P-7 bluebunch wheatgrass germplasm. Crop Science 42: 1754-1755
Larson S. R. T. A. Jones Z.-M. Hu C. L. McCracken A. Palazzo 2000 Genetic diversity of bluebunch wheatgrass cultivars and a multiple-origin polycross. Crop Science 40: 1142-1147
Larson S. R. A. J. Palazzo K. B. Jensen 2003 Identification of western wheatgrass cultivars and accessions by DNA fingerprinting and geographic provenance. Crop Science 43: 394-401
Leonard A. C. S. E. Franson V. S. Hertzberg M. K. Smith G. P. Toth 1999 Hypothesis testing with the similarity index. Molecular Ecology 8: 2105-2114[CrossRef][Medline]
Löve A. 1984 Conspectus of the Triticeae. Feddes Repertorium 95: 425-521[CrossRef]
Lynch M. 1990 The similarity index and DNA fingerprinting. Molecular Biology and Evolution 7: 478-484[Abstract]
Mantelo N. A. 1967 The detection of disease clustering and a generalized regression approach. Cancer Research 27: 209-220
Mason-Gamer R. J. 2001 Origin of North American Elymus (Poaceae: Triticeae) allotetraploids based on granule-bound starch synthase gene sequences. Systematic Botany 26: 757-768[Web of Science]
Mason-Gamer R. J. N. L. Orme C. M. Anderson 2002 Phylogenetic analysis of North American Elymus and the monogenomic Triticeae (Poaceae) using three chloroplast DNA data sets. Genome 45: 991-1002[Medline]
Nei M. W. H. Li 1979 Mathematical model for studying genetic variation in terms of restriction endonucleases. Proceedings of the National Academy of Sciences, USA 76: 5269-5273
Notsu Y. S. Masood T. Nishikawa N. Kubo G. Akiduki M. Nakazono A. Hirai K. Kadowaki 2002 The complete sequence of the rice (Oryza sativa L.) mitochondrial genome: frequent DNA sequence acquisition and loss during the evolution of flowering plants. Molecular Genetics and Genomics 268: 434-445[CrossRef][Web of Science][Medline]
Ogihara Y. T. Kojima A. Endo M. Hanaoka T. Shiina T. Terachi S. Utsugi M. Murata N. Mori S. Takumi K. Ikeo T. Gojobori R. Murai K. Murai Y. Matsuoka Y. Ohnishi H. Tajiri K. Tsunewaki 2002 Structural features of a wheat plastome as revealed by complete sequencing of chloroplast DNA. Molecular Genetics and Genomics 266: 740-746[CrossRef][Web of Science][Medline]
Page R. D. 1996 TREEVIEW: an application to display phylogenetic trees on personal computers. Computer Applications in the Biosciences 12: 357-358
Passey H. B. V. K. Hugie 1996 Variation in bluebunch wheatgrass in relation to environment and geographic location. Ecology 44: 158-161[CrossRef]
Pritchard J. K. M. Stephens P. Donnelly 2000 Interference of population structure using multilocus genotype data. Genetics 155: 945-959
Redinbaugh M. G. T. A. Jones Y. Zhang 2000 Ubiquity of the St chloroplast genome in St-containing Triticeae polyploids. Genome 43: 846-852[Medline]
Rohlf F. J. 1998 NTSYSpc numerical taxonomy and multivariate analysis system version 2.0. Exeter Software, Setauket, New York, USA
Rosenberg N. A. J. K. Pritchard J. L. Weber H. M. Cann K. K. Kidd L. A. Zhivotovsky M. W. Feldman 2002 Genetic structure of human populations. Science 298: 2381-2385
Saitou N. M. Nei 1987 The neighbor-joining method: a new method for reconstructing phylogenetic trees. Molecular Biology and Evolution 4: 406-425[Abstract]
Smouse P. E. J. C. Long R. R. Sokal 1986 Multiple regression and correlation extensions of the Mantel test of matrix correspondence. Systematic Zoology 35: 627-632[CrossRef][Web of Science]
Stebbins G. L. F. T. Pun 1953 Artificial and natural hybrids in the Gramineae tribe Hordeae. VI. Chromosome pairing in Secale cereale x Agropyrum intermedium and the problem of genome homologies in the Triticeae. Genetics 38: 600-608
Swofford D. L. 2000 PAUP*. Phylogenetic analysis using parsimony (*and other methods). Version 4b10. Sinaur Associates, Sunderland, Massachusetts, USA
Vogel K. P. K. Arumuganathan K. B. Jensen 1999 Nuclear DNA content of perennial grasses of the Triticeae. Crop Science 39: 661-667
Vos P. R. Hogers M. Bleeker M. Reijans T. Van De Lee M. Hornes A. Frijters J. Pot J. Peleman M. Kuiper M. Zabeau 1995 AFLP: a new technique for DNA fingerprinting. Nucleic Acids Research 23: 4407-4414
Wu X.-L. S. R. Larson Z.-M. Hu A. J. Palazzo T. A. Jones R. R.-C. Wang K. B. Jensen N. J. Chatterton 2003 Molecular genetic linkage maps for allotetraploid Leymus wildryes (Gramineae: Triticeae). Genome 46: 627-646[Medline]
Zlatnik E. 1999 Fire effects information system, [Online]. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory (Producer). Available at http://www.fs.fed.us/database/feis
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