Original Paper

Tree Genetics & Genomes

, Volume 7, Issue 2, pp 399-408

First online:

Open Access This content is freely available online to anyone, anywhere at any time.

Developing seed zones and transfer guidelines with multivariate regression trees

  • Andreas HamannAffiliated withDepartment of Renewable Resources, University of Alberta Email author 
  • , Tim GylanderAffiliated withDepartment of Renewable Resources, University of Alberta
  • , Pei-yu ChenAffiliated withDepartment of Renewable Resources, University of Alberta


Managing seed movement is an important component of forest resource management to minimize maladaptation of planting stock in forest plantations. Here, we describe a new approach to analyze geographic patterns of adaptive and neutral genetic variation in forest trees and to link this genetic information to geographic variables for the delineation of seed zones and the development of seed transfer guidelines. We apply multivariate regression trees to partition genetic variation, using a set of environmental or geographic predictor variables as partitioning criteria in a series of dichotomous splits of the genetic dataset. The method can be applied to any type of genetic data (growth, adaptive, or marker traits) and can simultaneously evaluate multiple traits observed over several environments. The predictor variables can be categorical (e.g., ecosystem of seed source), continuous (e.g., geographic or climate variables), or a combination of both. Different sets of predictor variables can be used for different purposes: In two case studies for aspen and red alder, we show (1) how latitude, longitude, and elevation of seed sources in a provenance trial can be used to develop simple seed transfer guidelines; (2) how ecosystem classes and elevation as predictor variables can be used to delineate seed zones and breeding regions; and (3) how climate variables as predictors can reveal adaptation of genotypes to the environments in which they occur. Partitioning of genetic variation appears very robust regarding the choice of predictor variables, and we find that the method is a powerful aid for interpreting complex genetic datasets.


Genetic diversity Tree improvement Ecological genetics Genecology Aspen Red alder