R: Basal Area Increment (Inside Out)

bai.in {dplR} | R Documentation

## Basal Area Increment (Inside Out)

### Description

Convert multiple ring-width series to basal area increment (i.e., ring area) going from the pith to the bark.

### Usage

    bai.in(rwl, d2pith = NULL)

### Arguments

`rwl` | a `data.frame` with series as columns and years as rows such as that produced by `read.rwl`
`d2pith` | an optional `vector` containing two variables. If present, then variable one (`series` in the example below) gives the series ID as either `characters` or `factors`. These must exactly match `colnames(rwl)`. Variable two (`d2pith` in the example below) gives the distance from the innermost measured ring to the pith of the tree in mm. If `d2pith` is `NULL` then the distance to pith is assumed to be zero for each series (column) in `rwl`.

### Details

This converts ring-width series (mm) to ring-area series (mm squared) (aka basal area increments) based on the distance between the innermost measured ring and the pith of the tree. It is related to `bai.out`, which calculates each ring area starting from the outside of the tree and working inward. Both methods assume a circular cross section (Biondi 1999). See the references below for further details.

### Value

A `data.frame` containing the ring areas for each series with column names, row names and dimensions of `rwl`.

### Note

DendroLab website: http://dendrolab.org/

### Author(s)

Code by Andy Bunn based on work from DendroLab, University of Nevada Reno, USA. Patched and improved by Mikko Korpela.

### References

Biondi, F. (1999) Comparing tree-ring chronologies and repeated timber inventories as forest monitoring tools. Ecological Applications, 9(1), 216–227.

Biondi, F. and Qeadan, F. (2008) A theory-driven approach to tree-ring standardization: Defining the biological trend from expected basal area increment. Tree-Ring Research, 64(2), 81–96.

### See Also

`bai.out`

### Examples

    library(graphics)
    library(stats)
    library(utils)
    ## Toy
    n <- 100
    ## Make three fake tree-ring series to show that these funcs work on rwl objects
    base.series <- 0.75 + exp(-0.2 * 1:n)
    rwl <- data.frame(x1 = base.series + abs(rnorm(n, 0, 0.05)),
                      x2 = base.series + abs(rnorm(n, 0, 0.05)),
                      x3 = base.series + abs(rnorm(n, 0, 0.05)))

    ## The inside out method
    foo <- bai.in(rwl = rwl)
    ## The outside in method
    bar <- bai.out(rwl = rwl)

    ## Identical
    head(bar)
    head(foo)

    ## Use gp data
    data(gp.rwl)
    data(gp.d2pith)
    foo <- bai.in(rwl = gp.rwl, d2pith = gp.d2pith)
    foo.crn <- chron(foo)
    yrs <- time(foo.crn)
    plot(yrs, foo.crn[, 1], type = "n",
         xlab = "Year", ylab = expression(mm^2))
    lines(yrs, foo.crn[, 1], col = "grey", lty = "dashed")
    lines(yrs, ffcsaps(foo.crn[, 1], nyrs = 32), col = "red", lwd = 2)

[Package dplR version 1.7.1 Index]