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PLANT GENETIC RESOURCES

Genetic Variation in Three Native Plant Species across the State of Minnesota

^a Dep. of Agronomy and Plant Genetics, Univ. of Minnesota, 411 Borlaug Hall, 1991 Upper Buford Cir., St. Paul, MN 55108
^b The Land Institute, 2440 E. Water Well Rd., Salina, KS 67401. This research was funded by the Minnesota Department of Transportation

^* Corresponding author (monc0003{at}umn.edu).

Restoration of native plant communities requires adapted germplasm; thus, information is needed to guide native seed collection and production. Analysis of genetic variation has potential to provide insight into diversity and relatedness of natural populations. Our objectives were to examine genetic diversity of native species in Minnesota to discover if variation is related to biomes or distance, and possibly to develop seed collection zones. Our study included prairie cordgrass (Spartina pectinata), purple prairie clover (Dalea purpurea), and spotted joe-pye weed (Eupatorium maculatum). Using amplified fragment length polymorphisms, we analyzed 100 samples from eight populations for prairie cordgrass, 152 samples from nine populations for purple prairie clover, and 127 samples from 10 populations for spotted joe-pye weed. We found moderate to high levels of genetic diversity within each species. Small populations were not necessarily lower in diversity than larger ones. Analysis of molecular variance results indicate clear population differentiation. However, rather than displaying geographic or ecological associations, the variation had discontinuous patterns. Therefore, we were not able to develop unambiguous recommendations for seed collection. We discuss the applicability of molecular markers in detecting adaptive potential.

Abbreviations: AFLP, amplified fragment length polymorphism • AMOVA, analysis of molecular variance • ECS, Ecological Classification System • FTA, fast technology for analysis • RAPD, random amplified polymorphic DNA • RFLP, restriction fragment length polymorphism • UPGMA, unweighted arithmetic average clustering algorithm