Plants on the move: Assisted migration of forest trees in the face of climate change

• Published in: Forest ecology and management Vol. 344; pp. 30 - 37
• Main Authors: Koralewski, Tomasz E., Wang, Hsiao-Hsuan, Grant, William E., Byram, Thomas D.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.05.2015
• Subjects: Assisted migration; Categorical Universal Response Function; Categories; Climate change; Forest management; Forests; Mathematical models; Pine; Pinus taeda; Seed deployment; Seeds; Trees; Universal Response Function; Pinus taeda; Categorical Universal Response Function; Climate change; Assisted migration; Universal Response Function; Seed deployment
• ISSN: 0378-1127; 1872-7042
• DOI: 10.1016/j.foreco.2015.02.014

•We propose a novel method to guide seed deployment and assisted migration decisions.•The method assesses the probability of family performance under altered climate conditions.•The approach can be applied to categorical data which covers many environmental metrics.•The approach accounts for both genetic and environmental effects.•The developed models can be readily implemented in Decision Support Systems. Climate change is impacting distributions of both fauna and flora, including many crops. In order to ensure the health and vigor of the species we depend upon for food and fiber, assisted migration strategies may need to be implemented. This is especially true for long-lived forest trees. Multinomial logit regression was used to develop a Categorical Universal Response Function (CURF) to delineate deployment zones for loblolly pine (Pinus taeda L.) using 15-year measurements from the Western Gulf Forest Tree Improvement Program (WGFTIP) Geographic Seed Source Study (GSSS). The CURF uses performance categories for the response variable, and the model assigns the probabilities with which the performance for a given seed source will fall into these various categories. First and second powers of minimum temperature of the coldest month, summer precipitation, and variation measures of these two metrics at both the seed source site of origin and the test location were used as independent variables. Planted tree volume, accounting for both survival and growth, was used as the response variable. Model performance was good, with the AUC score ranging from 0.785 to 0.808, depending on (1) whether or not the variable interactions were included and (2) the variable selection criterion used (AIC or BIC). Resulting models were then applied to historic weather patterns to illustrate inferred deployment zones for three seed sources. The projected performance generally agreed with the current consensus on loblolly pine seed movement guidelines. The models developed here can be readily implemented in a Decision Support System as they (1) suggest sets of adapted loblolly pine families from which foresters can choose based on local knowledge, (2) can be easily expanded to include other variables, and (3) can be applied to outputs from projected climate scenarios to extrapolate into the future.