Building Topographically Modified Tree‐Ring Chronologies from High Elevation Bristlecone Pine in the White Mountains of California, USA
Andrew G. Bunn1, Malcolm K. Hughes2, Matthew W. Salzer2
1. Department of Environmental Sciences, Huxley College, Western Washington University, Bellingham, WA, 98225, USA.
2. Laboratory of Tree‐Ring Research, University of Arizona, Tucson, AZ 85721, USA (ID# GC51D‐0781)
I. The Problem
Abstract
II. An Opportunity
Mean Ring Width Chronologies with a 50‐yr smooth
We analyze growth rates of dozens of high elevation (~3400 m.a.s.l.) bristlecone pine trees (Pinus longaeva D.K. Bailey) from the White Mountains of California, USA by exploiting the biophysical position of the individual trees. Recent work has shown that 20th century growth in the highest elevation bristlecone pine is greater than at any point in recent millennia. This phenomenon is coincident with increased temperatures over the instrumental climate record. While both temperature and moisture availability appear to influence growth, the trade‐off of these limiting factors is not well understood. The White Mountains are dominated by steep, rugged terrain. We suggest that even in relatively small areas, the complexity of terrain in high mountain systems can alter the limiting growth factors of individual trees and that these differences may be of value to better understand bristlecone pine growth. In a multivariate analysis we are able to isolate different patterns of growth based that are associated with topographic indices reflecting variations in local temperature lapse rates and soil moisture anomalies. When we build topographically modified mean ring‐width chronologies along these gradients we find substantial variation between the chronologies in the time and frequency domains. We also find that the correlations with the instrumental climate record vary and that we are partially able to unmix temperature versus precipitation signals. These differences appear consistent with mechanistic understanding of the control of tree‐ring variability. If these results are robust, the calculation of climate reconstructions using ring‐width data from these trees could be placed on a firmer, clearer, basis.
VI. Climate Correlates
V. Chronologies
The chronologies correlate differently with climate data (PRISM 1896‐
2005). Note the separation of temperature and precipitation between clusters two and three. Although an incredible paleoclimate resource, decades of research shows that bristlecone pine growth in the White Mountains is sensitive to both temperature and precipitation. Thus, paleoclimate reconstructions based on simple associations (rclimate|growth)might be flawed. These data (based on Salzer et al. 2009 PNAS) show trees on a elevational transect in the White Mountains have different growth patterns and different correlations with the instrumental climate record (PRISM 1896‐
2005).
We use 69 trees from the Salzer et al. (2009) dataset for which we have differentially corrected GPS points to sample topographic indices that might influence tree growth. These trees were not originally sampled to maximize landscape heterogeneity which makes this a post‐hoc description of topography and growth.
IV‐A. Ordination
We reduced tree growth from 69 dimensions to 2 dimensions via non‐
metric multidimensional scaling and plotted each tree’s location in this ordination space using weighted averaging. The data reduction produced very low stress (14.24) and the non‐metric R2 between the ordination distance and observed dissimilarity is 0.98. Using the topographic data on each tree we display the topographic variables as vectors for the significantly correlated variables (p≤0.01). III. The Data
Topographic Convergence Index (TCI)
Potential Relative Radiation (PRR)
Dark
Dry
Wet
Bright
IV‐B. Cluster Analysis
Bold symbols are significant at p≤0.01 and n is penalized for autocorrelation.
Acknowledgements
We gratefully acknowledge support from the National Science Foundation’s Paleoclimatology Program (Grant ATM‐0551986). We thank the US Forest Service for site access, Chris Van de Ven, and Justin Lewis. Further Reading
Bunn et al. (In press) Topographically modified tree‐ring chronologies as a potential means to improve paleoclimate inference. Climatic Change Letters
Salzer et al. (2009) Recent unprecedented tree‐ring growth in bristlecone pine at the highest elevations and possible causes. Proceedings of the National Academy of Sciences. doi10.1073pnas.0903029106.
The White Mountains are steep and rugged. Instead of viewing this complex topography as noise we suggest that it’s advantageous to exploit biophysical setting.
Cluster‐chronologies vary significantly in the time and frequency domains (shown by wavelet transforms)
We elected to separate the 69 trees into three clusters based on optimum average silhouette width. We assessed the stability of the clusters via bootstrapping. The Jaccard
similarities indicated that clusters two and three are robust (Clusterwise
Jaccard means of 0.81 and 0.73 respectively) while cluster one is less stable (0.64). Coloring between the clusters and the ordination points are the same.
Topographic Position Index (TPI)
Slope
↑ cold anomaly
↓ cold anomaly
Flat
Steep
We took 69 bristlecone pines growing from ~3100 m to ~3500 m and performed cluster analysis and ordination. We associated each tree’s position in the ordination space to four topographic indices derived from a digital elevation model. Though rough, they provide a first cut at describing the physical setting that the tree experiences.