Developing seed zones and transfer guidelines with multivariate regression trees |
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Authors: | Andreas Hamann Tim Gylander Pei-yu Chen |
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Institution: | (1) Department of Renewable Resources, University of Alberta, 739 General Services Building, Edmonton, AB, T6G 2H1, Canada |
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Abstract: | 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. |
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