A Closer Look at Regional Curve Standardization of Tree-Ring Records: Justification of the Need, aWarning of Some Pitfalls, and Suggested Improvements in Its Application - Dendroclimatology - page 126

This result demonstrates the strong likelihood of sampling bias implicit in anal-
yses of ring width, density, or basal area increments, where the data are stratified
within discrete age classes (e.g., Briffa et al. 1992 ; Nicolussi et al. 1995 ) . Age
band decomposition (ABD) as proposed by Briffa et al. ( 2001 ) , although originally
envisaged as a means of circumventing the need to define a statistical model of
expected growth as a function of tree age, is in effect similar to applying RCS. This
conclusion follows because the mean value for each age band, when plotted as a
function of ring age, will form a stepped version of the RCS curve. In ABD, the
mean value of the time series for each age band is subtracted from each average
yearly value for that band, and these differences are divided by the standard devi-
ation of the band time series to transform the data into normalized series. These
series are then summed across all (or selected) age bands to form an ABD chronol-
ogy. When this method is applied in this way to the data for a single species and
location (e.g., see Briffa et al. 2001 ) , it is similar to applying the RCS at a site level
to a set of living-tree core samples. The results may, therefore, be affected by a
modern-sample bias, bearing in mind the common practice of sampling dominant
or codominant trees (Schweingruber and Briffa 1996 ) .
Where this sampling bias exists, it is difficult to gauge the extent to which it
amplifies or obscures the accompanying influence of climate variability. Note that
in Fig. 5.7b , the range of the bias is only 0.14 units, likely considerably less than the
range for typical chronology variances, for example, as is shown in Fig. 3 of Becker
( 1989 ) or in Fig. S4b of Esper et al. ( 2007 ) , where chronologies display similar
shapes (in their time variance) to that of the bias in Fig. 5.7b .
Figure 5.8 is a dramatic example of how the selection of samples, based on
a minimum size criterion, can lead to a large potential bias at the ends of, even
long subfossil, chronologies. Again we use measurement data provided by the
ADVANCE-10 K project (Eronen et al. 2002 ; Grudd et al. 2002 ) , this time including
all subfossil (from lakes and dry land) and modern core data for the last 2000 years.
These (more than 1000) sample series were combined to produce an RCS chronol-
ogy (based on a single RCS curve) shown as the solid black line in Fig. 5.8a , with
the temporal distribution of the sample series shown by gray shading. The data were
sub-sampled to simulate eight hypothetical samplings during the last 2000 years,
separated by 200-year intervals, the most recent of which was in the year 1980.
Only trees that would have been alive and that had achieved a minimum diameter
of 14 cm are included in each subsample. The subsamples form a large proportion
(43%) of all rings. This is a realistic simulation of common dendroclimatic sampling
strategies.
The temporal distribution of each 'sampled' group of trees is shown in Fig. 5.8b .
The eight individual sub-chronologies, each produced as the average of the index
series generated in the production of the overall mean RCS chronology, are shown
superimposed on the RCS chronology produced from all 1024 series. The sub-
chronologies generally underestimate the mean chronology in their early years
(mainly composed of older and generally slower-growing trees) and overestimate
the overall chronology in their later years (when the younger, more vigorous trees
dominate the sample). This result demonstrates that sampling bias, when allied