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Statistical methodologies for tree-ring research to understand the climate-growth relationships over time and space
Citation:
Lee, EHenry, P. Beedlow, Ron Waschmann, D. Tingey, AND C. Wickham. Statistical methodologies for tree-ring research to understand the climate-growth relationships over time and space. Presented at Spatial Statistics Conference, Columbus, OH, June 04 - 07, 2013.
Impact/Purpose:
The International Tree-Ring Database is a valuable resource for studying climate change and its effects on terrestrial ecosystems over time and space. The climatic signal in a tree-ring time series may be confounded with non-climatic signals such as disease or pest disturbances at unknown times in the past, which obscure the underlying climate-growth relationships. WED scientists have developed a statistical model to decompose a tree-ring time series into components for the climatic and disturbance signals in order to study the effects of climate change on forest ecosystems and their interactions with geography. Our work is important for filling in the gaps of knowledge in understanding the climatic and disease effects on conifer forests in the Pacific Northwest under climate change scenarios and establishing a database of tree-ring and meteorological data across spatial scales.
Description:
The International Tree-Ring Database is a valuable resource for studying climate change and its effects on terrestrial ecosystems over time and space. We examine the statistical methods in current use in dendroclimatology and dendroecology to process the tree-ring data and make inferences of past climate and the spatiotemporal climate-growth relationships. The tree-ring time series are often used to reconstruct past climate from the year-to-year changes in annual ring-width and ring density. However, the climatic signal may be confounded with non-climatic signals such as disease or pest disturbances at unknown times in the past. We present a time series intervention analysis approach to extract the relevant signals in tree-ring data to either reconstruct the history of disturbance adjusting for the climatic signal or reconstruct past climate adjusting for disturbances. Signal extraction in tree-ring research is considered as a general structural time series problem with components for age-related and climate trends, climatic effects, and disturbances. We present a time series intervention analysis approach to solve this decomposition problem and present an example of the technique.