Few dendrochronological studies have been carried out in Spain or Portugal. Mountain pine (Pinus uncinata Ram.) may be especially suitable for investigation because of its broad altitudinal range and great age. Samples from a site in the Sierra de Cebollera were prepared and dated using several cross-dating techniques. The dated series were used to develop a ring-width index chronology that was compared with local climate data. Ring-width variability is related to precipitation, but temperature can also be important, indicating a complex climate response. Future studies of this species will be important for dendroclimatology and for study of ecophysiology of subalpine plants in the Mediterranean area.

X-ray densitometry has proven useful in dendroclimatic research on relatively fast growing, complacent trees in mesic climates. The best dendrochronological materials, however, come from semiarid-site conifers that grow very slowly, have missing rings, are extremely sensitive to climate, and attain advanced ages. This study presents the first evaluation of X-ray densitometry of Douglas-fir, ponderosa pine, and pinyon from four semiarid sites in the eastern San Juan River Basin (northwestern New Mexico and southwestern Colorado). The relationship of climate with intra-annual tree-ring anatomy is anlyzed. Moving slit X-ray densitometry definest earlywood and latewood zones, yielding eight data types for each annual ring: total ring width, earlywood and latewood width, mean ring density, mean earlywood and latewood density, and minimum earlywood and maximum latewood density. response functions using regional averages of monthly mean temperature and total precipitation indicate that climate may strongly influence all eight types of data, depending on species and site conditions. Low moisture stress (cooler, wetter climate) increases total ring width, earlywood and latewood width, and ring, latewood, and maximum latewood density. High moisture stress increases earlywood and minimum earlywood density. The climate response of the density parameters differs from that reported for conifers in more mesic environments, although selected density parameters from a relatively mesic southwestern site are strongly related to climate. Site selection has nevertheless proven to be an important factor in getting the most climatically sensitive densitometric data. This study demonstrates that densitometry is feasible with conifers from semiarid sites. The intra-annual width and density data derived can increase the climate information available from these dry-site trees and should lead to improved seasonal and annual reconstructions of paleoclimate. Practical constraints imposed by current X-ray densitometric techniques may be removed with promising new procedures such as surface image analysis of cell anatomy.

Tree rings have long been used to make inferences about the environmental factors that influence tree growth. Great Basin bristlecone pine is a long-lived species and valuable dendroclimatic resource, but often with mixed growth signals; in many cases, not all trees at one location are limited by the same environmental variable. Past work has identified an elevational threshold below the upper treeline above which trees are limited by temperature, and below which trees tend to be moisture limited. This study identifies a similar threshold in terms of temperature instead of elevation through fine-scale topoclimatic modeling, which uses a suite of topographic and temperature-sensor data to predict temperatures across landscapes. We sampled trees near the upper limit of growth at four high-elevation locations in the Great Basin region, USA, and used cluster analysis to find dual-signal patterns in radial growth. We observed dual-signal patterns in ring widths at two of those sites, with the signals mimicking temperature and precipitation patterns. Trees in temperature-sensitive clusters grew in colder areas, while moisture-sensitive cluster trees grew in warmer areas. We found thresholds between temperature- and moisture-sensitivity ranging from 7.4 degrees C to 8 degrees C growing season mean temperature. Our findings allow for a better physiological understanding of bristlecone pine growth, and seek to improve the accuracy of climate reconstructions.

Thesis (M.A.)--West Virginia University, 2009. / Title from document title page. Document formatted into pages; contains vi, 67 p. : ill. (some col.), col. maps. Includes abstract. Includes bibliographical references (p. 39-42).

A principal components model for analyzing tree-ring data is presented which allows one to examine site heterogeneity and to compose chronologies of a new kind in a conceptually unified and computationally efficient manner. Using this model, one can develop chronologies that correlate better with local climate data than the standard chronology for a site and which can be tested for time stability within the framework of the model. The numerical procedures are described and applied to a specific tree site to illustrate their use and their usefulness. The tree-ring width data tested is from a white spruce (Picea glauca [Moench.] Voss) site in the forest-tundra ecotone of Yukon Territory. The results from the study of this and other sites indicate that more climatic information can be extracted using these techniques.

This paper is concerned with the overall significance and reproducibility of the response function. A test of significance is devised which is based on the Binomial distribution. Combined with other tests, the method is then used to compare two different response functions to examine the reproducibility of climate-chronology response. Two approaches are used: the first compares two response functions covering the same period from the same site, based on independent chronologies of the same species; the second compares the response of a single chronology over two equal non-overlapping time periods. The results suggest that the response in the examples used is statistically reproducible on a site, and statistically stable over periods of time.

A new approach to removing the non-climatic variance of forest interior tree-ring width series, using the smoothing spline, is described. This method is superior to orthogonal polynomials because it makes no assumptions about the shape of the curve to be used for standardization. Also, because the spline curve can range continuously from a linear least squares fit to cubic interpolation through the data, it is far more flexible than polynomials and provides a more "natural" fit. For computing the spline, we found that specifying the Lagrange multiplier p which appears in the calculus of variation solution rather than the residual variance as suggested by Reinsche was both practical and more efficient. In effect, the smoothing spline is a one-parameter family of low-pass filters defined by p. We describe the general characteristics of these filters in the time and frequency domains and compute the response functions for several of them. The smoothing spline is an excellent tree-ring standardization method because its filtering characteristics are well defined. Its utility for dendroclimatology should be considerable since, outside of semiarid environments, sites similar to forest interiors predominate.

Tests of the compound increment function, introduced by Warren (1980) as a means for removing the growth trend from dendrochronological data, are herein reported. In particular, the inter- and intra-site correlations of the residuals generated by the new method are compared with those generated by standard exponential fits. It is also shown that, in the presence of non-climatically induced responses, such as might arise from thinning, exponential fits can lead to spuriously high intra-site correlations. Accordingly, and because the new method's virtual elimination of negative and very low positive correlations, it appears to be the more satisfactory for portraying the growth trend.

The average ring-width index of two published chronologies from the eastern Tennessee climatic division was used as a single predictor variable in linear regression to reconstruct May June precipitation. Regression equations obtained using regionally averaged precipitation data from stations within the climatic division were compared with regressions obtained using single-station (Knoxville, Tennessee) data for comparable periods. The (regionally averaged) division data always provided the better calibration statistics for the regression equations. When the regressions calibrated using division data were verified with independent data for the climatic division and for Knoxville, the better results were always obtained for the division data. When regressions calibrated using single-station data were verified, the independent division data once again provided better results than the independent Knoxville data. Regionally averaged precipitation data also provided more satisfactory results than single- station data in a similar experiment for central Iowa, and probably provides better results in general for this type of dendroclimatic experiment.

The use of orthogonalized climatic variables in regression to specify treegrowth/climate relationships, commonly known as response function analysis, involves several a priori decisions and a posteriori interpretations, any of which maybe open to question. Decisions about the number of climatic variables to include, confidence limits, number of eigenvectors to allow as candidate predictors in regression, etc., can affect the response function in unpredictable ways and lead to possible errors in interpretation. To demonstrate the nature of these effects, we compared response functions for particular chronologies with the correlation function, which is simply the series of correlation coefficients between a tree-ring chronology and each of several sequential monthly climatic variables. The results indicate that response functions including high-order eigenvectors should be interpreted cautiously, and we recommend using the correlation function as an interpretive guide. Prior tree-growth variables in regression can mask climatic effects, and the correlation function can also be useful in detecting this masking. Statistical significance is more often attained in response functions than in correlation functions, possibly due to differences in the statistical testing procedures, to the statistical efficiency of eigenvectors in spending degrees of freedom, or to the filtering effects on the climatic data that result from eliminating high-order eigenvectors (noise) from the response function. These filtering effects plus the orthogonalization make response function analysis an efficient method for specifying tree-growth/climate relationships. The examples and guidelines presented here should enhance the usefulness of the method.