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Modeling Climate Change Effects on the Height Growth of Loblolly Pine

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We present a statistical model to predict the effects of climate change on the height growth of loblolly pine (Pinus taeda L.) families in the southeastern United States. Provenance-progeny trials were used for assessing the response of loblolly pine seed sources to environmental change. Ordinary least squares, ridge regression, and LASSO regression were used to develop height growth prediction models. The approach integrates both genetic and environmental effects and is meant to overcome the critical limitations of population response function and transfer function methods by making full use of data from provenance trials. Prediction models were tested using a hypothetical future climate scenario with 5% decrease in precipitation and 0.5° C increase in maximum and minimum temperatures, relative to historical average values. Under this scenario, local families from the coastal plains of Georgia, Florida, and South Carolina showed the highest performance relative to the current climate in their native environments. As these seed sources were moved to colder northern and inland regions from their origin, we observed declines in their height growth. Similarly, the climatic change scenario suggested that performance of northern seed sources declined significantly when they were moved to more southern warmer regions. The statistical model can be used as a quantitative tool to model the effect of climatic variables on the performance of loblolly pine seed sources and may help to develop sound breeding deployment strategies.
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Keywords: Pinus taeda; climate change; provenance test; statistical model; universal response function

Document Type: Research Article

Publication date: 2015-08-05

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  • Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
    Forest Science is published bimonthly in February, April, June, August, October, and December.

    2016 Impact Factor: 1.782 (Rank 17/64 in forestry)

    Average time from submission to first decision: 62.5 days*
    June 1, 2016 to Feb. 28, 2017

    Also published by SAF:
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