Predicting climate change-related genetic offset for the endangered southern South American conifer Araucaria araucana

被引:14
|
作者
Varas-Myrik, Antonio [1 ]
Sepulveda-Espinoza, Francisco [1 ]
Fajardo, Alex [2 ]
Alarcon, Diego [3 ]
Toro-Nunez, Oscar [4 ]
Castro-Nallar, Eduardo [5 ]
Hasbun, Rodrigo [1 ]
机构
[1] Univ Concepcion, Fac Ciencias Forestales, Dept Silvicultura, Lab Epigenet Vegetal, Victoria 631, Concepcion, Chile
[2] Univ Talca, Inst Invest Interdisciplinario I3, Campus Lircay, Talca 3460000, Chile
[3] Univ Chile, Inst Ecol & Biodiversidad, Dept Ciencias Ecol, Las Palmeras 3425, Santiago, Chile
[4] Univ Concepcion, Fac Ciencias Nat & Oceanog, Dept Bot, Barrio Univ S-N, Concepcion, Chile
[5] Univ Andres Bello, Fac Ciencias Vida, Ctr Bioinformat & Integrat Biol, Av Republ 330, Santiago, Chile
关键词
Local adaptation; Climate change; Landscape genomics; Population structure; Conservation genetics; RAD-Seq; LOCAL ADAPTATION; R PACKAGE; LANDSCAPE; PATTERNS; DIVERSITY; DROUGHT; RESPONSES; PLANTS; FLOW;
D O I
10.1016/j.foreco.2021.119856
中图分类号
S7 [林业];
学科分类号
0829 ; 0907 ;
摘要
Understanding adaptive genetic variation is key for predicting the evolutionary response of species and populations to climate change, decisively influencing management and conservation decisions. Landscape genomics provides a framework to disentangle the effects of local adaptation from those of geographic distance and demographic history, through genomic analysis and the modeling of genotype-environment relationships. This approach can inform how evolutionary forces shape the neutral and adaptive genetic structure, helping to identify those populations subject to a greater risk of maladaptation due to anthropogenic climate change, i.e., the "genetic offset". Using restriction-site associated DNA sequencing (RAD-Seq) and more than 49,000 single nucleotide polymorphisms screened from 12 locations of Araucaria araucana in Chile, we assessed the genetic structure and predicted the genetic offset of this emblematic tree species under two future climate scenarios. Using generalized dissimilarity modeling (GDM) we found that the temperature annual range was the most important variable shaping the observed patterns of adaptive divergence. Our results show that populations living in the piedmont of the southern Andes Mountain range are at the greatest risk of maladaptation, while populations living in the high elevation zones in the Andes Mountain range are at the lowest risk. This study constitutes an important tool for forestry management and conservation of A. araucana forests.
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页数:11
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