Role of population genetics in guiding ecological responses to climate.

Abstract: 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 <italic>Pinus contorta</italic> var. <italic>latifolia</italic>. 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 (MINDD0^1/2), 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 MINDD0^1/2 and using a function suited to skewed normal distributions, regressions were developed for predicting growth from the distance in climate space (MINDD0^1/2) 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. [ABSTRACT FROM AUTHOR]