pointer: Calculates Pointer Years from a Group of Ring-Width Series

In dplR: Dendrochronology Program Library in R

Calculates Pointer Years from a Group of Ring-Width Series

Description

This function calculates pointer years on a data.frame of ring-width series using the Becker algorithm. The pointer years are computed with adjustable thresholds of relative radial growth variation and number of series displaying similar growth pattern (i.e. positive or negative variations).

Usage

pointer(rwl, rgv.thresh = 10, nseries.thresh = 75, round.decimals = 2)

Arguments

rwl	a data.frame with ring-width series as columns and years as rows such as that produced by read.rwl.
rgv.thresh	a numeric giving the minimum absolute relative radial growth variation (in percentage) above which the growth change from the year t-1 to t is considered as significant. Must be > 0. Values > 100 are possible but highly unusual. See references. Defaults to 10.
nseries.thresh	a numeric giving the minimum percentage of series displaying significant relative radial growth variations of a same sign above which the year t is considered as a pointer year. Positive significant variations will results in a positive pointer year, negative ones in a negative pointer year. This number ranges from 1 to 100. Defaults to 75.
round.decimals	an integer indicating the number of decimal places to be used for outputs. This number must be positive. Defaults to 2.

Details

This calculates pointer years from ring-width series for each year t of the time period covered by the series using the Becker algorithm. This algorithm is based on, first, the calculation of the individual relative radial growth variation by comparison of ring-width of year t to that of year t-1 for each series, and second, the inter-series comparison of both sign and magnitude of these variations.

For example, if rgv.thresh and nseries.thresh are set at 10 and 75 respectively, pointer years will be defined as those years when at least 75% of the series present an absolute relative radial growth variation higher than 10%.

Users unfamiliar with the Becker algorithm should refer to Becker et al. (1994) and Mérian and Lebourgeois (2011) for further details.

Value

A data.frame containing the following columns (each row corresponds to one position of the window):

Year	Considered year (t).
Nb.series	Number of available series.
Perc.pos	Percentage of series displaying a significant positive radial growth variation.
Perc.neg	Percentage of series displaying a significant negative radial growth variation.
Nature	Number indicating whether the year is a positive pointer year (1), a negative pointer year (-1) or a regular year (0).
RGV_mean	Mean radial growth variations over the available series.
RGV_sd	Standard deviation of the radial growth variations over the available series.

Author(s)

Pierre Mérian. Improved by Mikko Korpela and Andy Bunn.

References

Becker, M., Nieminen, T. M., and Gérémia, F. (1994) Short-term variations and long-term changes in oak productivity in northeastern France – the role of climate and atmospheric CO2. Annals of Forest Science, 51(5), 477–492.

Mérian, P. and Lebourgeois, F. (2011) Size-mediated climate–growth relationships in temperate forests: A multi-species analysis. Forest Ecology and Management, 261(8), 1382–1391.

See Also

skel.plot

Examples

## Pointer years calculation on ring-width series. Returns a data.frame.
library(utils)
data(gp.rwl)
py <- pointer(rwl=gp.rwl, rgv.thresh=10, nseries.thresh=75,
              round.decimals=2)
tail(py)

dplR documentation built on June 22, 2024, 9:59 a.m.