Population responses to climate were assessed using 3-7 years height growth data gathered for 266 populations growing in 12 common gardens established in the 1980s as part of five disparate studies of Pinus contorta var. latifolia. Responses are interpreted according to three concepts: the ecological optimum, the climate where a population is competitively exclusive and in which, therefore, it occurs naturally; the physiological optimum, the climate where a population grows best but is most often competitively excluded; and growth potential, the innate capacity for growth at the physiological optimum. Statistical analyses identified winter cold, measured by the square root of negative degree-days calculated from the daily minimum temperature (MINDD0 ), as the climatic effect most closely related to population growth potential; the colder the winter inhabited by a population, the lower its growth potential, a relationship presumably molded by natural selection. By splitting the data into groups based on population MINDD0 and using a function suited to skewed normal distributions, regressions were developed for predicting growth from the distance in climate space (MINDD0 ) populations had been transferred from their native location to a planting site. The regressions were skewed, showing that the ecological optimum of most populations is colder than the physiological optimum and that the discrepancy between the two increases as the ecological optimum becomes colder. Response to climate change is dependent on innate growth potential and the discrepancy between the two optima and, therefore, is population-specific, developing out of genotype-environment interactions. Response to warming in the short-term can be either positive or negative, but long term responses will be negative for all populations, with the timing of the demise dependent on the amount of skew. The results pertain to physiological modeling, species distribution models, and climate-change adaptation strategies.
a b s t r a c tApproximately, 123,500 forest inventory and ecological ground plots representing eastern North America were used to predict the contemporary distribution of eastern white pine (Pinus strobus L.) from climate. The random forests classification tree procedures produced an 8-variable algorithm that had an 8% overall error rate. Erroneous predictions of presence, or errors of commission, were 13%, while falsely predicting absence, or errors of omission were 1%. Climate-based multiple regression models were developed to describe patterns of genetic variation among 112 populations representing the range of P. strobus in Ontario, Canada east of Lake Superior. Degree days >5°C was the best predictor of variation in phenology and growth potential, with 5-year height providing the greatest resolution of inter-population variation (R 2 = 0.68). Cold hardiness in the fall was most closely associated with mean minimum temperature (R 2 = 0.26). Height growth data from four disparate provenance test series that together included a total of 354 provenances corroborated the range-wide applicability of the regional genetic models. Although variation in growth potential in the central Appalachian Mountains was most closely associated with mean minimum temperature, degree days >5°C remained the best predictor of range-wide variation in growth potential (R 2 = 0.41). The contemporary distribution and inter-population genetic variation were projected into future climates predicted by three General Circulation Models, two scenarios, and three time steps. All projections indicate early and sustained deterioration in the contemporary habitat. Concurrence among projections regarding the redistribution of suitable habitat to the north of the contemporary distribution identifies geographic locations with the highest probability of supporting vigorous stands of P. strobus. Concurrences among genetic projections clarify the intraspecific redistribution required to conserve adaptive variation. The projections have direct relevance in developing management strategies for accommodating the changing climate.
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