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example of the use of RCS, because the original sample set comprises data from
various species from widely separated locations, and even the use of sub-RCS
curves will not account for the large site-to-site differences in growth rates. Each
sub-RCS curve will be bracketed by very wide confidence limits, leaving scope for
cates the use of multiple RCS, in effect dividing the data on the basis of relative
growth rate into a number of RCS curves, each of which is then applied to its corre-
sponding group of measurement series to produce (where the data are continuous)
multiple parallel sub-RCS chronologies. If these chronologies are then averaged
together, contemporaneous-growth-rate bias will be reduced. However, this pro-
cess will also remove the potential to preserve some long-timescale variance that
is contained in the relative differences of the sub-RCS chronology means. There is
a particular requirement for a practical way to distinguish genuine long-timescale
climate signals from spurious trends that may arise in RCS, solely as a result of
non-climate-related differences in the growth rates of sample trees.
Basal area increment (BAI) is a more direct measure of wood production (espe-
cially in mature trees after height increase has reduced) and BAIs are used widely in
forestry. A number of researchers have used BAI chronologies as an alternative to
likely reduce some of the problems of RCS, such as the tendency for negatively
sloping indices from relatively faster-grown trees in the recent ends of chronologies
and positively sloping indices from slower-growing trees, but BAI data still suf-
fer from differing-contemporaneous-growth-rate bias and modern-sample-bias. The
use of signal-free methods and the diagnostic value in examining sub-RCS curves
and chronologies are equally applicable to chronologies of BAI data and, of course,
to other tree-growth parameters.
5.8 Conclusions
The conceptual and practical examples of the implementation of RCS presented
here are intended to demonstrate how problematic the application of a simple
concept can be in practice. The recognition of the presence of bias within a chronol-
ogy and the routine exploration of the magnitudes of different biases in RCS can
only provide a better foundation for quantifying and expressing RCS chronology
uncertainty.
The net effect of potential biases in the application of the RCS method will
vary according to the specific makeup of the samples in a chronology. Much of the
potential bias may average out, especially when sample replication is high, but the
particular problems associated with the reliability of the start and end of chronolo-
gies may affect chronology calibration and hinder the study of recent tree-growth
forcing trends. Where, by coincidence, a chronology starts around 1000 years ago,
similar problems may be associated with gauging the accurate level of tree growth
