Spatial variability in forest growth - climate relationships in the Olympic Mountains, Washington.
Authors:
Nakawatase, J.M. Peterson, D.L. USDA, FS
Source:
Canadian journal of forest research. 2006 Jan., v. 36, no. 1, p. 77-91.
NALT Subjects:
forest growth climatic factors spatial variation seasonal variation basal area tree and stand measurements altitude precipitation snowpack temperature coniferous forests conifers watersheds climate change growing season forest types Washington
For many Pacific Northwest forests, little is known about the spatial and temporal variability in tree growth - climate relationships, yet it is this information that is needed to predict how forests will respond to future climatic change. We studied the effects of climatic variability on forest growth at 74 plots in the western and northeastern Olympic Mountains. Basal area increment time series were developed for each plot, and Pearson's correlation analysis and factor analysis were used to quantify growth-climate relationships. Forest growth in the Olympic Mountains responds to climatic variability as a function of mean climate and elevation. Low summer moisture limits growth across all elevations in the dry northeastern Olympics. Growth at low elevations in the wet western Olympics is associated with phases of the Pacific Decadal Oscillation and with summer temperature. Heavy winter snowpack limits growth at high elevations in the western Olympics. In the warmer greenhouse climate predicted for the Olympic Mountains, productivity at high elevations of the western Olympics will likely increase, whereas productivity at high elevations in the northeastern region and potentially in low elevations of the western region will likely decrease. This information can be used to develop adaptive management strategies to prepare for the effects of future climate on these forests. Because growth-climate relationships on the Olympic Peninsula vary at relatively small spatial scales, those relationships may assist modeling and other efforts to provide more accurate predictions at local to regional scales.
