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A likelihood-based time series modeling approach for application in dendrochronology to examine the growth-climate relations and forest disturbance history
Lee, EHenry, C. Wickham, P. Beedlow, Ron Waschmann, AND D. Tingey. A likelihood-based time series modeling approach for application in dendrochronology to examine the growth-climate relations and forest disturbance history. Dendrochronologia. Elsevier, Shannon, Ireland, 45:132-144, (2017).
Tree-ring width chronologies covering decades to several millennia are key sources for dendrochronological studies to understand the climate relations on tree growth, reconstruct past climate patterns, date natural disasters (e.g. eruptions of volcanoes, floods) and forest disturbance events (pests, diseases, fires), and track ecological processes (e.g., tree-line movement). But, identifying the key climatic factors influencing tree growth is difficult because multiple climate factors interact and are highly seasonal and are confounded with forest disturbances. Autocorrelation, seasonality, confounding of age-related and climate growth trends, and the confounding effects of climate and disturbance on growth pose problems in the dendrochronological study of growth-climate relations and forest disturbance history. WED scientists have developed a unified statistical modeling approach to detect outliers (i.e., outbreaks of forest disturbance agents) in dendrochronological time series to infer the growth-climate-disturbance relations and forest disturbance history. The time series intervention analysis methodology represents a significant advance in the field of dendrochronology and should have broad application to a wide range of forest types, climate and disturbance regimes. Our work is important for providing a rigorous statistical modeling approach to better understand the complex interactions of temperature, water, and biotic disturbance agents on conifer forests in the PNW under climate change scenarios using dendrochronological time series data in the presence of outliers. As temperatures rise, tree pathogens, phytophagous insects, and fires will likely increase in range, frequency, and intensity, affecting tree growth in sensitive forested ecosystems which are becoming more susceptible to these forest disturbance agents. These interactions of climate and forest disturbances raise concerns of the ability of forested watersheds to maintain a supply of high quality water for human use under a changing climate. This paper is a deliverable under ACE 130.
A time series intervention analysis (TSIA) of dendrochronological data to infer the tree growth-climate-disturbance relations and forest disturbance history is described. Maximum likelihood is used to estimate the parameters of a structural time series model with components for climate and forest disturbances (i.e., pests, diseases, fire). The statistical method is illustrated with a tree-ringwidth time series for a mature closed-canopy Douglas-fir stand on the west slopes of the Cascade Mountains of Oregon, USA that is impacted by Swiss needle cast disease caused by the foliar fungus, Phaecryptopus gaeumannii (Rhode) Petrak. We propose the TSIA method for the field of dendrochronology to understand the interaction of temperature, water, and forest disturbances that are important in forest ecology and climate change studies.
Record Details:Record Type: DOCUMENT (JOURNAL/PEER REVIEWED JOURNAL)
Organization:U.S. ENVIRONMENTAL PROTECTION AGENCY
OFFICE OF RESEARCH AND DEVELOPMENT
NATIONAL HEALTH AND ENVIRONMENTAL EFFECTS RESEARCH LABORATORY
WESTERN ECOLOGY DIVISION
ECOLOGICAL EFFECTS BRANCH