Climate change risk management in tree improvement programs: selection and movement of genotypes
- 431 Downloads
Tree improvement programs usually consist of multiple breeding populations that target different climatic or ecological regions. Parent breeding material normally originates from and is deployed within the same breeding region, assuming optimal local adaptation of populations. Given the climate trends observed over the last several decades in western Canada, this assumption is unlikely to still be valid. This problem needs to be addressed either by delineating new deployment areas for improved planting stock or by selecting genotypes suitable for changed climatic environments. In a case study for white spruce, we analyzed height data from 135,000 trees grown in 44 genetic tests established and evaluated over a period of 35 years by industry and government agencies in Alberta. We show how the risk of planting maladapted trees can be minimized by moving planting stock to new areas, or by eliminating genotypes from breeding programs that are sensitive to anticipated future climate environments. Transfers that outperformed local sources consistently originated from locations with higher temperatures, suggesting north or northwest transfers. However, adaptation to cold appears to be a prevalent driver for genetic population differentiation in spruce, thus limiting how far material may be moved in current reforestation efforts to address future climate change.
KeywordsReforestation Climate change White spruce Seed transfer Breeding programs
This analysis includes data from every white spruce tree improvement region in Alberta and would not have been possible without participation and data sharing by Alberta Newsprint Company, Canadian Forest Products Ltd., Manning Diversified Forest Products, Millar Western Forest Products Ltd., Northlands Forest Products, Timber Ltd., Tolko Industries Ltd., West Fraser Mills Ltd. (including its divisions: Hinton Wood Products and Blue Ridge Lumber), Weyerhaeuser Company Ltd. (Grande Prairie and Pembina Timberlands), and the provincial ministry of Agriculture and Forestry (AAF). Funding was provided by the Tree Species Adaptation Risk Management project, managed by Tree Improvement Alberta (TIA) and funded by Climate Change and Emissions Management (CCEMC) Corporation and AAF (formerly Alberta Environment and Sustainable Resource Development). We would like thank Daniel Chicoine from Incremental Forest Technology Ltd. for his contribution as the CCEMC project Manager. Finally, we would like to acknowledge the commitment and tireless effort and support of Bruce Macmillan, who was instrumental in helping to secure the CCEMC project funding and ensuring the success of this work.
Data from genetic field trials were provided by private companies and government agencies (see Acknowledgements), and so are not owned by the authors and thus not available in the archive.
- Beaulieu J (1996) Breeding program and strategy for white spruce in Quebec. Natural Resource Canada, Canadian Forest Service, Sainte-Foy, Quebec. Inf. Rep. LAU-X-117E, 25 pGoogle Scholar
- Beaulieu J, Perron M, Bousquet J (2004) Multivariate patterns of adaptive genetic variation and seed source transfer in Picea mariana. Canadian J For Res 34:531–545Google Scholar
- De'Ath G (2002) Multivariate regression trees: a new technique for modeling species-environment relationships. Ecology 83:1105–1117Google Scholar
- De'ath G (2014) mvpart: Multivariate partitioning. R Package Version 1.6.2. Available at: https://cran.r-project.org/web/packages/mvpart/mvpart.pdf.
- Fettig CJ, Reid ML, Bentz BJ, Sevanto S, Spittlehouse DL, Wang T (2013) Changing climates, changing forests: a Western North American perspective. J For 111:214–228Google Scholar
- Field CB, Mortsch LD, Brklacich M, Forbes DL, Kovacs P, Patz JA, Running SW, Scott MJ (2007) Climate change 2007: impacts, adaptation and vulnerability. In: Parry ML, Canziani OF, Palutikof JP, van der Linden PJ, Hanson CE (eds) Contribution of working group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, pp 617–652Google Scholar
- Gilmour AR, Thompson R, Cullis BR (1995) Average information REML: an efficient algorithm for variance parameter estimation in linear mixed models. Biometrics 1440–1450Google Scholar
- Gilmour AR, Gogel BJ, Cullis BR, Thompson R (2009) ASReml User Guide Release 3.0. VSN International Ltd, Hemel Hempstead, HP1 1ES, UKGoogle Scholar
- O’Neill GA, Ukrainetz NK, Carlson MR, Cartwright CV, Jaquish BC, King JN, Krakowski J, Russell JH, Stoehr MU, Xie C, Yanchuk AD (2008) Assisted migration to address climate change in British Columbia: recommendations for interim seed transfer standards. BC Ministry Forest Range, Research Branch, Victoria, BC. Technical Rep, 48Google Scholar
- Oksanen J, Kindt R, Legendre P, O’Hara B, Stevens MHH, Oksanen MJ, Suggests M (2013) vegan: R functions for community ecology. R Package Version 2.0.7. Available at: https://cran.r-project.org/web/packages/vegan/vegan.pdf
- R Development Core Team (2015) R: a language and environment for statistical computing. R Foundation for Statistical Computing Version 3.1.3, Vienna, AustriaGoogle Scholar
- Rweyongeza D, Yang R, Dhir N, Barnhardt L, Hansen C (2007a) Genetic variation and climatic impacts on survival and growth of white spruce in Alberta, Canada. Silvae Genetica 56:117–126Google Scholar
- Rweyongeza D, Barnhardt L, Dhir N, Hansen C (2010) Population Differentiation and Climatic Adaptation for Growth Potential of White Spruce (Picea glauca) in Alberta, Canada. Silvae Genetica 59:158Google Scholar
- SRD (2009) Alberta Forest Genetic Resource Management and Conservation Standards (FGRMS). Alberta Sustainable Resource Development, EdmontonGoogle Scholar
- Williams ER, Matheson AC, Harwood CE (2002) Experimental design and analysis for tree improvement Second Ed. CSIRO publishing, CollingwoodGoogle Scholar