Abstract
Understanding the genetic basis of how species respond to changing environments is essential to the conservation of species. However, the molecular mechanisms of adaptation remain largely unknown for long-lived tree species which always have large population sizes, long generation time, and extensive gene flow. Recent advances in landscape genomics can reveal the signals of adaptive selection linking genetic variations and landscape characteristics and therefore have created novel insights into tree conservation strategies. In this review article, we first summarized the methods of landscape genomics used in tree conservation and elucidated the advantages and disadvantages of these methods. We then highlighted the newly developed method “Risk of Non-adaptedness,” which can predict the genetic offset or genomic vulnerability of species via allele frequency change under multiple scenarios of climate change. Finally, we provided prospects concerning how our introduced approaches of landscape genomics can assist policymaking and improve the existing conservation strategies for tree species under the ongoing global changes.
Highlights
INTRODUCTIONForest trees cover ca. 30% of the terrestrial surface of the earth from boreal to tropical latitudes and contain approximately three-quarters of the terrestrial biomass of the earth, which tightly links them with the global carbon cycle (Holliday et al, 2017; Isabel et al, 2020)
Rapid climate change can break this association and create a mismatch between population climatic optima and current climate (Jump and Peñuelas, 2005; Aitken et al, 2008). Additional challenges such as gene flow, eco-evolutionary dynamics on the species range margins, and variation in climate changes across the landscape may impact the adaptation of species (Savolainen et al, 2007; Alberto et al, 2013; Aitken and Bemmels, 2016)
The genetic structure is incorporated as a random factor, allele frequencies are defined as response variables, and environmental factors are used as fixed factors. In this “Mixed-effects models” section, we illuminated the principles and methodologies using mixed models to detect signals of local adaptation based on BAYENV (Coop et al, 2010), Bayesian population association analysis (BayPass) (Gautier, 2015; Olazcuaga et al, 2020), latent factor mixed models (LFMMs) (Frichot et al, 2013), and spatial analysis method (SAM) (Joost et al, 2007, 2008)
Summary
Forest trees cover ca. 30% of the terrestrial surface of the earth from boreal to tropical latitudes and contain approximately three-quarters of the terrestrial biomass of the earth, which tightly links them with the global carbon cycle (Holliday et al, 2017; Isabel et al, 2020). The genetic structure is incorporated as a random factor, allele frequencies are defined as response variables, and environmental factors are used as fixed factors In this “Mixed-effects models” section, we illuminated the principles and methodologies using mixed models to detect signals of local adaptation based on BAYENV (Coop et al, 2010), Bayesian population association analysis (BayPass) (Gautier, 2015; Olazcuaga et al, 2020), latent factor mixed models (LFMMs) (Frichot et al, 2013), and spatial analysis method (SAM) (Joost et al, 2007, 2008). The BAYENV is a method under the Bayesian framework employed to evaluate correlations between loci and environmental variables, and it can incorporate the uncertainty of allele frequencies from uneven sample sizes (Coop et al, 2010) The advantage of this program is that it applies a covariance matrix to take account for population structure, which is similar to an FST or kinship matrix.
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