The goal of
dfoliatR is to provide dendrochronologists with tools for identifying and analyzing the signatures of insect defoliators preserved in tree rings. The methods it employs closely follow (or in some cases exactly replicate) OUTBREAK, a FORTRAN program available from the Dendrochronological Program Library.
dfoliatR in a publication, please cite the paper:
Guiterman, CH, AM Lynch, and JN Axelson (2020)
dfoliatR: An R package for detection and analysis of insect defoliation signals in tree rings. Dendrochronologia. DOI: 10.1016/j.dendro.2020.125750.
You can install the released version of dfoliatR from CRAN with:
And the development version from GitHub with:
# install.packages("devtools") devtools::install_github("chguiterman/dfoliatR")
The package requires users to input two sets of tree-ring data: standardized ring widths of individual host trees and a standardized tree-ring chronology from a local non-host tree species or climate series.
dfoliatR combines these to remove the climate signal represented by the non-host chronology from the host tree series. What’s left should represent a disturbance signal. Then
dfoliatR identifies defoliation events in the host tree series.
We recommend that the input tree-ring data be standardized in either ARSTAN or the
dplR R package. If there is more than one ring-width series from the same tree, these should be standardized and averaged to the tree level. In ARSTAN, make sure to output ‘.TRE’ files and read them into R with the
read.compact() function in
dplR. If you choose to standardize raw ring widths in
detrend(), then use the
treeMean() function to generate tree-level series. All data input to
dfoliatR needs to be an
rwl object as defined in
Here we briefly explore defoliation and outbreaks patterns for a Douglas-fir site in New Mexico. These data are included in the package
To start out, we identify defoliation events on individual trees,
## Identify defoliation signals dmj_defol <- defoliate_trees(host_tree = dmj_h, nonhost_chron = dmj_nh) ## Plot the results plot_defol(dmj_defol)
And then scale up to outbreaks by compositing across the site via
## Identify site-level outbreak patterns dmj_obr <- outbreak(dmj_defol) ## Plot those results plot_outbreak(dmj_obr)
Analyses of the tree series (termed
defol objects) can be done via:
To identify ecologically-significant outbreak events, use the
outbreak() function. Various filters are available to aid users in defining outbreak thresholds. Analyses of outbreak series (termed
obr objects) can be done via:
For the full range of usage in
dfoliatR, please visit the introduction vignette.