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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# dfoliatR <img src="man/figures/logo.png" align="right" width="120" />
<!-- badges: start -->
[](https://travis-ci.org/chguiterman/dfoliatR)
[](https://coveralls.io/github/chguiterman/dfoliatR?branch=master)
[](https://cran.r-project.org/package=dfoliatR)
[](https://doi.org/10.5281/zenodo.3626135)
<!-- badges: end -->
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](https://www.ltrr.arizona.edu/pub/dpl/).
When using `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.](https://authors.elsevier.com/sd/article/S1125786520300898) _Dendrochronologia_. DOI: 10.1016/j.dendro.2020.125750.
## Installation
You can install the released version of dfoliatR from [CRAN](https://CRAN.R-project.org) with:
``` r
install.packages("dfoliatR")
```
And the development version from [GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("chguiterman/dfoliatR")
```
## Usage
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 `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`.
## Example
Here we briefly explore defoliation and outbreaks patterns for a Douglas-fir site in New Mexico. These data are included in the package
```{r }
library(dfoliatR)
## load the data
data("dmj_h")
data("dmj_nh")
```
To start out, we identify *defoliation* events on individual trees,
```{r }
## 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
```{r }
## Identify site-level outbreak patterns
dmj_obr <- outbreak(dmj_defol)
## Plot those results
plot_outbreak(dmj_obr)
```
## Further resources
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()`
For the full range of usage in `dfoliatR`, please visit the [introduction vignette](https://chguiterman.github.io/dfoliatR/articles/intro-to-dfoliatR.html).
#### Questions, concerns, problems, ideas, or want to contribute?
Please contact the author, Chris Guiterman