Showing papers by "Tongli Wang published in 2012"

ClimateWNA—High-Resolution Spatial Climate Data for Western North America

Abstract: This study addresses the need to provide comprehensive historical climate data and climate change projections at a scale suitable for, and readily accessible to, researchers and resource managers. This database for western North America (WNA) includes over 20 000 surfaces of monthly, seasonal, and annual climate variables from1901to2009;severalclimatenormalperiods; andmultimodelclimateprojectionsforthe2020s, 2050s, and 2080s. A software package, ClimateWNA, allows users to access the database and query point locations, obtain time series, or generate custom climate surfaces at any resolution. The software uses partial derivative functions of temperature change along elevation gradients to improve medium-resolution baseline climate estimates and calculates biologically relevant climate variables such as growing degree-days, number of frost-free days, extreme temperatures, and dryness indices. Historical and projected future climates are obtained by using monthly temperature and precipitation anomalies to adjust the interpolated baseline data for the location of interest. All algorithms used in the software package are described and evaluated against observations from weather stations across WNA. The downscaling algorithms substantially improve the accuracy of temperature variables over the medium-resolution baseline climate surfaces. Climate variables that are usually calculated from daily data are estimated from monthly climate variables with high statistical accuracy.

Predicting adaptive phenotypes from multilocus genotypes in Sitka spruce (Picea sitchensis) using random forest.

Abstract: Climate is the primary driver of the distribution of tree species worldwide, and the potential for adaptive evolution will be an important factor determining the response of forests to anthropogenic climate change. Although association mapping has the potential to improve our understanding of the genomic underpinnings of climatically relevant traits, the utility of adaptive polymorphisms uncovered by such studies would be greatly enhanced by the development of integrated models that account for the phenotypic effects of multiple single-nucleotide polymorphisms (SNPs) and their interactions simultaneously. We previously reported the results of association mapping in the widespread conifer Sitka spruce (Picea sitchensis). In the current study we used the recursive partitioning algorithm ‘Random Forest’ to identify optimized combinations of SNPs to predict adaptive phenotypes. After adjusting for population structure, we were able to explain 37% and 30% of the phenotypic variation, respectively, in two locally adaptive traits—autumn budset timing and cold hardiness. For each trait, the leading five SNPs captured much of the phenotypic variation. To determine the role of epistasis in shaping these phenotypes, we also used a novel approach to quantify the strength and direction of pairwise interactions between SNPs and found such interactions to be common. Our results demonstrate the power of Random Forest to identify subsets of markers that are most important to climatic adaptation, and suggest that interactions among these loci may be widespread.