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Abstract• The performance of ten commonly used taper equations for predicting both stem form and volume in balsam fir [Abies balsamea (L.) Mill], red spruce[Picea rubens (Sarg.)], and white pine[Pinus strobus (L.)] in the Acadian Region of North America was investigated.• Results show that the Kozak (2004) and Bi (2000) equations were superior to the other equations in predicting diameter inside bark for red spruce and white pine, while the Valentine and Gregoire (2000) performed well when those measurements were unavailable.• The incorporation of crown variables substantially improved stem volume predictions (mean absolute bias reduction of 7-15%; root mean square error reduction of 10-15%) for all three species, but had little impact on stem form predictions.• The best taper equation reduced the predicted root mean square error by 16, 39, and 45% compared to estimates from the widely used Honer (1965) regional stem volume equations for balsam fir, red spruce, and white pine, respectively.• When multiple taper equations exist for a certain species, the use of the geometric mean of all predictions is an attractive alternative to selecting the "best" equation. • L'incorporation de variables de couronne a amélioré sensiblement les prédictions du volume des troncs (réduction moyenne des biais absolu de 7-15 % ; réduction de l'erreur quadratique moyenne de 10-15 %) pour les trois espèces, mais avait peu d'impact sur les prédictions de la forme du tronc. Mots• La meilleure équation de décroissance a réduit l'estimation de l'erreur quadratique moyenne de 16, 39, et 45 % par rapport aux estimations largement utilisées avec les équations régionales d'Honer (1965) pour l'estimation du volume de la tige respectivement pour le sapin baumier, l'épinette rouge et le pin Weymouth.• Lorsque plusieurs équations de défilement existent pour certaines espèces, l'utilisation de la moyenne géométrique de toutes les prédictions est une alternative intéressante pour la sélection de la "meilleure" équation.

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.

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Assessing forest productivity is important for developing effective management regimes and predicting future growth. Despite some important limitations, the most common means for quantifying forest stand-level potential productivity is site index (SI). Another measure of productivity is gross primary production (GPP). In this paper, SI is compared with GPP estimates obtained from 3-PG and NASA’s MODIS satellite. Models were constructed that predict SI and both measures of GPP from climate variables. Results indicated that a nonparametric model with two climate-related predictor variables explained over 68% and 76% of the variation in SI and GPP, respectively. The relationship between GPP and SI was limited (R2 of 36%–56%), while the relationship between GPP and climate (R2 of 76%–91%) was stronger than the one between SI and climate (R2 of 68%–78%). The developed SI model was used to predict SI under varying expected climate change scenarios. The predominant trend was an increase of 0–5 m in SI, with some sites experiencing reductions of up to 10 m. The developed model can predict SI across a broad geographic scale and into the future, which statistical growth models can use to represent the expected effects of climate change more effectively.

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