Abstract

Trembling aspen (Populus tremuloides Michx.) is experiencing increased drought stress resulting from climate change together with increases in damage by forest tent caterpillar and other defoliators. Coupled with effects of intraspecific and interspecific competition this could result in an overall decrease in survival and growth. In order to improve our understanding of the key limiting factors affecting trembling aspen we investigated survival probability and tree growth using data from an extensive network of permanent sample plots in Alberta (Canada). We developed mixed-effect non-linear models which included: tree size, competition, climate, pest incidence and time elapsed between consecutive measurements as predictor variables. Tree data from 1144 Permanent Sample Plots representing 39,394 trees for a total of 52,522 growth interval observations were used to develop these models. Annual data on aspen defoliation were available for the period 1990–2010, and the average Climate Moisture Index (CMI) was calculated for each measured interval by location. Intraspecific competition and competition from conifer species (i.e. spruce/fir and pine), had a strong negative effect on survival and growth. Aspen defoliators such as the forest tent caterpillar (Malacosoma disstria Hbn.), the large aspen tortrix (Choristoneura conflictana) and the Bruce spanworm (Operophtera bruceata) had a negative impact on survival, but a strong positive effect on growth of surviving trembling aspen (i.e. compensatory growth). Increasing levels of CMI, which are associated with relatively cooler and wetter conditions, had a positive effect on survival and growth. Model validation results indicated good performance for survival and tree growth predictions. This study indicates that trembling aspen is very sensitive to competition, insect damage, and climate (i.e. drought); respectively, and this behavior will likely intensify as climate warms. The models developed in this study enhance our understanding of trembling aspen survival and growth in boreal North America and could be used to improve the predictive ability of existing growth and yield models.

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