"Characterizing the sensitivity of tree species and forest types to past weather variability using tree ring data"
Many tree species in North American are experiencing increased stress from changing climatic conditions, with uncertain effects on forest productivity. Tree ring data provide valuable information on past growth responses to climate variability. We used tree-ring reconstructions of biomass growth to ask (a.) how tree, stand, and weather interact to predict forest biomass growth, and (b.) whether the variance in growth due to climatic variability is as important as other factors.
We reconstructed tree, species and stand biomass growth using a census dataset of tree-rings from eight forest communities covering the temperate and boreal forests in northern Minnesota. We used mixed models to predict annual biomass growth from tree size and age, stand density and aggregate growth trends, species and local site characteristics, and variations in annual precipitation, temperature and summer moisture deficit using PRISM climate data (1895-2009).
We found that mean tree biomass growth depended most strongly (and positively) on tree size, age, and competitive status, while weather variability typically explained less than 5% of total variance in growth. Growth relationships varied significantly by species. Variations in biomass growth around the mean were significantly related to the summer ratio of precipitation to PET. While weather fluctuations significantly affect growth, our results suggest that changes in forest composition and structure may be more important to predict short-term productivity responses to climate change.