In many forests around the world, episodes of relatively synchronous tree mortality affecting large populations of trees have been shown to have complex causes involving interactions of biotic and abiotic factors. In the temperate rainforests of southern Chile, such forest diebacks have long been reported for the coniferous tree Pilgerodendron uviferum. This doctoral dissertation project will investigate the previously unexplained stand-level mortality in this threatened tree species across a large part of the range of this species in southern Chile. The doctoral candidate will test the primary hypothesis that the timing of forest dieback is related to climatic variation, which is the most likely predisposing factor operating at a regional scale. The student will examine differential impacts of climatic variation in relation to local site factors (such as elevation, topographic position, soil drainage, and other soil variables) and local disturbance factors (such as earthquakes and fire) in causing stand level tree mortality. He will use a combination of geographic information system-based methods, remote sensing, tree ring analysis, and field sampling to classify the types of dieback affecting these forests and to elucidate the predisposing and triggering factors of dieback. Based on quantifying spatial relationships of dieback to a large range of environmental factors, logistic regression models will be developed for predictive mapping of habitats susceptible to future Pilgerodendron dieback.
The results of this study will enhance basic understanding regarding how regional climatic variation interacts with local site factors and disturbance events to produce stand-level tree mortality in Pilgerodendron populations. Many of the Pilgerodendron forests affected by dieback are in national parks and forest reserves, and resource managers currently lack information about the causes and consequences of dieback. The findings of this project will inform management decisions in the context of understanding whether the dieback is due to natural or human-related influences. The GIS layers of present dieback patterns and susceptibility to future dieback will be particularly useful to resource managers. Understanding such complexity in the causes of forest dieback will help to predict threats to forest resources and biodiversity from climate change, and may aid in mitigating their effects. As a Doctoral Dissertation Research Improvement award, this award also will provide support to enable a promising student to establish a strong independent research career.