Genetic diversity and structure of Siberian Stone Pine (Pinus sibirica Du Tour) populations

Approval of the List of Areas of Forest Growth and Forest Areas of the Russian Federation; Order no. 367 (2014) Ministry of Natural Resources and Environment of the Russian Federation [online]. Available at <https://www.mnr.gov.ru/> [cited 2/17/2023] Search in Google Scholar

Bekh IA, Krivets SA, Bisirova EM (2009). Siberian pine - pearl of Siberia. Tomsk: Pechatnaya manufaktura, 49 p. SBN 978-5-94476-164-4 Search in Google Scholar

Belokon MM, Politov DV, Mudrik EA, Polyakova TA, Shatokhina AV, Belokon YuS, Oreshkova NV, Putintseva YuA, Sharov VV, Kuzmin DA, Krutovsky KV (2016) Development of microsatellite genetic markers in Siberian stone pine (Pinus sibirica Du Tour) based on the de novo whole genome sequencing. Russian Journal of Genetics 52:1263-1271. https://doi.org/10.1134/s1022795416120036 Search in Google Scholar

Blacket MJ, Robin C, Good RT, Lee SF, Miller AD (2012) Universal primers for fluorescent labelling of PCR fragments - an efficient and cost-effective approach to genotyping by fluorescence. Molecular Ecology Resources 12:456-463. https://doi.org/10.1111/j.1755-0998.2011.03104.x Search in Google Scholar

Brown AHD, Weir BS (1983) Measuring genetic variability in plant populations. In: Tanksley SD and TJ Orton (eds) Isozymes in Plant Genetics and Breeding, Part A. Amsterdam, Netherlands: Elsevier Science Publishing Amsterdam. pp. 219-239. https://doi.org/10.1016/b978-0-444-42226-2.50016-5 Search in Google Scholar

Debkov NM (2019) Accelerated formation of Siberian pine (Pinus sibirica Du Tour) stands: a case study from Siberia. Journal of Forest Science 65:291-300. https://doi.org/10.17221/48/2019-jfs Search in Google Scholar

Dieringer D, Schlötterer C (2003) MICROSATELLITE ANALYSER (MSA): A platform independent analysis tool for large microsatellite data sets. Molecular Ecology Notes 3:167-169. https://doi.org/10.1046/j.1471-8286.2003.00351.x Search in Google Scholar

Dumolin S, Demesure B, Petit RJ (1995) Inheritance of chloroplast and mitochondrial genomes in pedunculate oak investigated with an efficient PCR method. Theoretical and Applied Genetics 91(8):1253-6. https://doi.org/10.1007/bf00220937 Search in Google Scholar

Earl DA, VonHoldt BM (2012) STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conservation Genetics Resources 4:359-361. https://doi.org/10.1007/s12686-011-9548-7 Search in Google Scholar

Ellstrand NC, Elam DR (1993) Population genetic consequences of small population size: Implications for plant conservation. Annual Review of Ecology and Systematics 24:217-242. http://dx.doi.org/10.1146/annurev.es.24.110193.001245 Search in Google Scholar

Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Molecular Ecology 14:2611-2620. https://doi.org/10.1111/j.1365-294x.2005.02553.x Search in Google Scholar

Goudet J (2005) Hierfstat, a package for R to compute and test hierarchical F-statistics. Molecular Ecology Notes 5(1):184-6. https://doi.org/10.1111/j.1471-8286.2004.00828.x Search in Google Scholar

Graudal L, Loo J, Fady B, Vendramin G, Aravanopoulos FA, Baldinelli G, Bennadji Z, Ramamonjisoa L, Changtragoon S, Kjær ED (2020) Indicators of the genetic diversity of trees - State, Pressure, benefit and response. State of the World’s Forest Genetic Resources - Thematic study. Rome. FAO. https://doi.org/10.4060/cb2492en ISBN 978-92-5-133759-2 Search in Google Scholar

Gribkov AV, Shchur AV, Kuzminki DV (2014) Altai cedar forests under threat: problems of protection and use, recommendations for sustainable forest management. WWF, Moscow. pp. 64. ISBN: 978-5-906599-09-4 Search in Google Scholar

Hamrick JL, Godt MJ, Sherman-Broyles SL (1992) Factors influencing levels of genetic diversity in woody plant species. New Forests 6(1-4):95-124. https://doi.org/10.1007/bf00120641 Search in Google Scholar

Hartl DL, Clark AG (1997) Principles of Population Genetics 3rd Ed. Sunderland, Massachusetts: Sinauer Associates, Inc. pp. 519. https://doi.org/10.1002/(sici)1521-1878(199812)20:12%3C1055::aid-bies14%3E3.0.co;2-x Search in Google Scholar

Hoban S, Bruford M, D’Urban JJ, Lopes-Fernandes M, Heuertz M, Hohenlohe PA, Paz-Vinas I, Sjögren-Gulve P, Segelbacher G, Vernesi C, Aitken S, Bertola LD, Bloomer P, Breed M, Rodríguez-Correa H, Funk WC, Grueber CE, Hunter ME, affe R, Liggins L, Mergeay J, Moharrek F, O’Brien D, Ogden R, Palma-Silva C, Pierson J, Ramakrishnan U, Simo-Droissart M, Tani N, Waits L, Laikre L (2020) Genetic diversity targets and indicators in the CBD post-2020 Global Biodiversity Framework must be improved. Biological Conservation 248:108654. https://doi.org/10.1016/j.biocon.2020.108654 Search in Google Scholar

Ivetić V, Devetaković J, Nonić M, Stanković D, Šijačić-Nikolić M (2016) Genetic diversity and forest reproductive material - from seed source selection to planting. iForest 9:801-812. https://doi.org/10.3832/ifor1577-009 Search in Google Scholar

Jakobsson M, Rosenberg NA (2007) CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 23(14):1801-6. https://doi.org/10.1093/bioinformatics/btm2333 Search in Google Scholar

Jombart T (2008) adegenet: a R package for the multivariate analysis of genetic markers, Bioinformatics 24 (11):1403-1405, https://doi.org/10.1093/bioinformatics/btn129 Search in Google Scholar

Kerchev IA, Mandelshtam MY, Krivets SA, Ilinsky YY (2019) Small spruce bark beetle Ips amitinus (Eichhoff, 1872) (Coleoptera, Curculionidae: Scolytinae): a new alien species in West Siberia. Entomological Review 99 (5):639-644. https://doi.org/10.1134/s0013873819050075 Search in Google Scholar

Lê S, Josse J, Husson F (2008) FactoMineR: an R package for multivariate analysis. Journal of Statistical Software 25(1):1-18. https://doi.org/10.18637/jss.v025.i01 Search in Google Scholar

Manni F, Guerard E, Heyer E (2004) Geographic Patterns of (Genetic, Morphologic, Linguistic) Variation: How Barriers Can Be Detected by Using Monmonier’s Algorithm. Human Biology 76:173-190. https://doi.org/10.1353/hub.2004.0034 Search in Google Scholar

Mantel N (1967) The detection of disease clustering and a generalized regression approach. Cancer Research 27:209-220. Search in Google Scholar

Monmonier MS (2010) Maximum-Difference Barriers: An Alternative Numerical Regionalization Method. Geographical Analysis 5:245-261. https://doi.org/10.1111/j.1538-4632.1973.tb01011.x Search in Google Scholar

Nei M (1972) Genetic distance between populations. American Naturalist 106:283-92. https://doi.org/10.1086/282771 Search in Google Scholar

Oliveira EJ, Pádua JG, Zucchi MI, Vencovsky R, Vieira MLC (2006) Origin, evolution and genome distribution of microsatellites. Genetic and Molecular Biology 29: 294-307. https://doi.org/10.1590/s1415-47572006000200018 Search in Google Scholar

Oreshkova NV, Sedel’nikova TS, Efremov SP, Pimenov AV (2020) Genetic polymorphism of Siberian Stone Pine (Pinus sibirica Du) in Kuznetsk Alatau. Contemporary Problems of Ecology 13(6):569-576. https://doi.org/10.1134/s1995425520060116 Search in Google Scholar

Peakall R, Smouse PE (2006) GenAlEx 6: Genetic analysis in Excel. Population genetic software for teaching and research. Molecular Ecology Notes 6:288-295. https://doi.org/10.1111/j.1471-8286.2005.01155.x Search in Google Scholar

Petrova ЕA, Velisevich SN, Belokon MM, Belokon YuS, Politov DV, Goroshkevich SN (2014) Genetic diversity and differentiation of Siberian stone pine populations at the southern edge in lowland part of West Siberia. Ecological Genetics 12(1):48-61. https://doi.org/10.17816/ecogen12148-61 Search in Google Scholar

Petit RJ, El Mousadik A, Pons O (1998) Identifying populations for conservation on the basis of genetic markers. Conservation Biology 12(4):844-855. https://doi.org/10.1046/j.1523-1739.1998.96489.x Search in Google Scholar

Pritchard J, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945-959. https://doi.org/10.1093/genetics/155.2.945 Search in Google Scholar

Rajora OP, Mosseler A (2001) Challenges and opportunities for conservation of forest genetic resources. Euphytica 118(2):197-212. https://doi.org/10.1023/a:1004150525384 Search in Google Scholar

R Core Team (2013) R: a Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Search in Google Scholar

Selkoe KA, Toonen RJ (2006) Microsatellites for ecologists: a practical guide to using and evaluating microsatellite markers. Ecology Letters 9(5): 615-629. https://doi.org/10.1111/j.1461-0248.2006.00889.x Search in Google Scholar

Shah S, Qijing L, Jian Y, Shengwang M, Guang Z, Yuanyuan L, Khan D, Ahmad A, Saeed S, Mannan A (2019) Potential geo-distribution of Pinus sibirica demonstrated by climatic similarity between Western Siberia and Northeast China. Journal of Animal and Plant Sciences 29(4). Search in Google Scholar

Shuvaev DN, Ibe AA (2021) Genetic structure and postglacial recolonization of Pinus sibirica Du Tour in the West Siberian Plain, inferred from nuclear microsatellite markers. Silvae Genetica 70:99-107. https://doi.org/10.2478/sg-2021-0008 Search in Google Scholar

Șofletea N, Mihai G, Ciocîrlan E, Curtu AL (2020) Genetic Diversity and Spatial Genetic Structure in Isolated Scots Pine (Pinus sylvestris L.) Populations Native to Eastern and Southern Carpathians. Forests 11:1047. https://doi.org/10.3390/f11101047 Search in Google Scholar

Slatkin M (1995) A measure of population subdivision based on microsatellite allele frequencies. Genetics 139(1):457-462. https://doi.org/10.1093/genetics/139.1.457. Search in Google Scholar

Talantsev NK, Pryazhnikov AN, Mishukov NP (1978) Siberian stone pine forests. Forest industry. Moscow, 176 p. Search in Google Scholar

Timoshok E, Timoshok E, Skorokhodov S (2014) Ecology of Siberian stone pine (Pinus sibirica Du Tour) and Siberian larch (Larix sibirica Ledeb.) in the Altai mountain glacial basins. Russian journal of ecology 45:194-200. https://doi.org/10.1134/s1067413614030138 Search in Google Scholar

Titov EV (2007) Siberian stone pine - the king of the Siberian taiga. Kolos. Moscow. 152 p. Search in Google Scholar

Van Oosterhout C, Weetman D, Hutchinson WF (2006) Estimation and adjustment of microsatellite null alleles in nonequilibrium populations. Molecular Ecology Notes 6:255-256. https://doi.org/10.1111/j.1471-8286.2005.01082.x Search in Google Scholar

Voronin VI, Morozova TI, Stavnikov DYu, Nechesov IA, Oskolkov VA, Buyantuev VA, Mikhailov YuZ, Govorin YaV, Seredkin AD, Shuvarkov MA (2013) Bacterial damage of Siberian stone pine forests in the Baikal region. Forestry 3:39-41. Search in Google Scholar

Yeh FC, Yang RC, Boyle TB, Ye ZH, Mao JX, Yeh C, Timothy B, Mao X (1999) Popgene version 1.32: the user friendly software for population genetic analysis. Molecular biology and biotechnology Centre. Canada: University of Alberta, 29 p. Search in Google Scholar