dfoliatR provides dendrochronologists with tools for identifying and analyzing the signatures of insect defoliators preserved in tree rings. The methods it employs closely follow (or exactly replicate) OUTBREAK, a FORTRAN program available from the Dendrochronological Program Library.
dfoliatR is not yet on CRAN, to install it use the devtools function:
Once installed, dfoliatR can be called like any other R package.
The package includes two sets of tree-ring data for examples and exploration.
For the full range of usage in dfoliatR, please visit the introduction vignette.
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. 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. These standardized ring-width series should be 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 dplR with 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 dplR.
Begin using dfoliatR by applying the defoliate_trees() function that calls for these host tree series and a non-host site chronology. Note that the non-host chronology cannot include the “samp.depth” column commonly included in chronology files (e.g., .crn) and created by the dplr::chron() function.
Analyses of the tree series (termed defol objects) can be done via:
plot_defol()defol_stats()get_defol_events()sample_depth()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:
plot_outbreak()outbreak_stats()Please contact the author, Chris Guiterman