Introduction

Basic Usage

treeclim is an R package for the numerical calibration of proxy-climate relationships, developed with a focus on tree-ring data.

The workhorse function of treeclim is dcc, shorthand for “dendroclimatic calibration”. In its most simplest case, a growth-climate analysis can be performed like this:

dc1 <- dcc(muc_spruce, muc_clim)
## Running for timespan 1950 - 2007...

For this first example, we use data that comes with treeclim: a chronology of Picea abies growing near Munich (Bavaria, Germany) together with corresponding monthly climate data (temperature and precipitation). The default configuration of dccis to perform a bootstrapped response function analysis (Guiot 1991), using monthly climate variables from previous June to current September. The main difference here to other software (e.g., Dendroclim2002, Biondi and Waikul 2004) is that treeclim uses stationary bootstrapping (Politis and Romano 1994) by default, to account for potentially autocorrelated tree-ring data.

The resulting coefficients can be viewed by calling:

dc1
## Coefficients (significance flags correspond to p < 0.05):
##               id varname month   coef significant ci_lower ci_upper
## prec.prev.jun  1    prec   jun  0.014       FALSE   -0.195    0.241
## prec.prev.jul  2    prec   jul -0.032       FALSE   -0.250    0.166
## prec.prev.aug  3    prec   aug -0.037       FALSE   -0.255    0.183
## prec.prev.sep  4    prec   sep  0.050       FALSE   -0.151    0.223
## prec.prev.oct  5    prec   oct  0.086       FALSE   -0.124    0.297
## prec.prev.nov  6    prec   nov -0.032       FALSE   -0.231    0.144
## prec.prev.dec  7    prec   dec  0.143       FALSE   -0.084    0.341
## prec.curr.jan  8    prec   JAN  0.090       FALSE   -0.138    0.345
## prec.curr.feb  9    prec   FEB  0.113       FALSE   -0.077    0.271
## prec.curr.mar 10    prec   MAR  0.036       FALSE   -0.226    0.325
## prec.curr.apr 11    prec   APR -0.130       FALSE   -0.347    0.067
## prec.curr.may 12    prec   MAY  0.070       FALSE   -0.145    0.257
## prec.curr.jun 13    prec   JUN  0.042       FALSE   -0.186    0.248
## prec.curr.jul 14    prec   JUL  0.122       FALSE   -0.088    0.379
## prec.curr.aug 15    prec   AUG -0.008       FALSE   -0.239    0.219
## prec.curr.sep 16    prec   SEP  0.076       FALSE   -0.177    0.291
## temp.prev.jun 17    temp   jun -0.081       FALSE   -0.272    0.125
## temp.prev.jul 18    temp   jul -0.022       FALSE   -0.217    0.165
## temp.prev.aug 19    temp   aug -0.112       FALSE   -0.323    0.098
## temp.prev.sep 20    temp   sep -0.028       FALSE   -0.245    0.191
## temp.prev.oct 21    temp   oct  0.212       FALSE   -0.070    0.423
## temp.prev.nov 22    temp   nov -0.109       FALSE   -0.306    0.140
## temp.prev.dec 23    temp   dec  0.163       FALSE   -0.062    0.419
## temp.curr.jan 24    temp   JAN -0.070       FALSE   -0.280    0.160
## temp.curr.feb 25    temp   FEB  0.134       FALSE   -0.125    0.363
## temp.curr.mar 26    temp   MAR  0.017       FALSE   -0.182    0.255
## temp.curr.apr 27    temp   APR -0.154       FALSE   -0.349    0.010
## temp.curr.may 28    temp   MAY  0.027       FALSE   -0.172    0.226
## temp.curr.jun 29    temp   JUN -0.028       FALSE   -0.343    0.297
## temp.curr.jul 30    temp   JUL  0.044       FALSE   -0.193    0.315
## temp.curr.aug 31    temp   AUG -0.086       FALSE   -0.320    0.236
## temp.curr.sep 32    temp   SEP  0.004       FALSE   -0.216    0.255

We see a table listing the monthly climatic predictors that have been generated by treeclim with the value of their corresponding coefficients, significance flags and confidence intervals. In this example, none of the climatic predictors turned out to be significant at a level of p < 0.05. (One important notice: the object returned by dcc is actually more complex than the table we get when print it. To access the coefficients, e.g. for writing your own plotting functions, you could use coef(dc1).)

A basic plot is obtained by issueing:

plot(dc1)