thin {rconifers}R Documentation

Thin a CONIFERS sample.data object with user-defined settings.

Description

Reduces the expansion factors for tree records in sample.data list object, to the specification of the user.

Usage

thin( x,control=list(type=2,target=50.0,target.sp=NULL ) )

Arguments

x an of type sample.data.
control a list that controls how the sample is thinned. See details.

Details

type
integer indicator of thinning type.
0 Thin trees to a target sdi, does not remove shrubs (not used directly)
1 Proportional thin for selected tree species, does not remove shrubs
2 Proportional thin across all tree species
3 Thin from below, by dbh, all species
4 Thin from below, by dbh, species identified with sp.code
target
target thinning level. The value is the targeted value for the thinning operation. For an SDI thin, 0, the target is SDI in English units. For remaining thinning operations, target is expressed in stems per acre.
target.sp
is a character vector that defines the target thinning species. This value should be in the species map (see swo and smc). For proportional thinning (i.e. type=2 or 3), the species must be set to NULL (i.e. sp=NULL)
.

The default thinning regime is to remove 50 percent of all trees across all diameter classes.

For thinning from below, the trees are ranked in increasing diameter, and stems are removed (i.e. expf=0.0) for all stems until the target percent removal has been achieved or all stems have been removed.

The function prints out a diagnostic statement reporting the number of stems removed and the total basal area removed during the operation.

To reduce the shrubs, use a type 4 thinning, which thins from below, by dbh, which here can be zero, for a single species (i.e. sp="COCO")

The sp element of the control list is examined only is the thinning type is one (1) (DO_EXPF_SP_THIN) or four (4) (DO\_EXPF_SP_THIN_FROM_BELOW), otherwise the argument is ignored and assumed to be zero, which includes all species.

Value

The function returns a list object of type sample.data.

Author(s)

Jeff D. Hamann jeff.hamann@forestinformatics.com,
Martin W. Ritchie mritchie@fs.fed.us

References

Ritchie, M.W. 2008. User's Guide and Help System for CONIFERS: A Simulator for Young Conifer Plantations Version 4.10. See http://www.fs.fed.us/psw/programs/ecology_of_western_forests/projects/conifers/

See Also

calc.max.sdi, impute, plants, plots project, rand.seed, rconifers, sample.data, set.species.map, set.variant, smc, summary.sample.data, swo, thin

Examples

library( rconifers )

## set the variant to the SWO variant
set.variant( 0 )

# load the Southwest-Oregon species coefficients into R as a data.frame object
data( swo )

# set the species map 
sp.map <- list(idx=swo$idx,
                 fsp=swo$fsp,
                 code=as.character(swo$code),
                 em=swo$endemic.mort,
                 msdi=swo$max.sdi,
                 b=swo$browse.damage,
                 m=swo$mechanical.damage,
                 gwh=swo$genetic.worth.h,
                 gwd=swo$genetic.worth.d)
set.species.map( sp.map )

## grow the data that was originally swo in the smc variant
# load and display CONIFERS example plots
data( plots )
print( plots )

# load and display CONIFERS example plants
data( plants )
print( plants )

# randomly remove 10 crown.width observations to test
# the impute function
blanks <- sample( 1:nrow( plants ), 10, replace=FALSE )
plants[blanks,]$crown.width <- NA

# create the sample.data list object
sample.3 <- list( plots=plots, plants=plants, age=3, x0=0.0 )
class(sample.3)  <- "sample.data"

# fill in missing values
sample.3.imp <- impute( sample.3 )

# print the maximum stand density index for the current settings
print( calc.max.sdi( sample.3.imp ) )

# print a summary of the sample
print( sample.3.imp )

# now, project the sample forward for 20 years
# with all of the options turned off
sample.23 <- project( sample.3.imp, 
          20, 
          control=list(rand.err=0,rand.seed=0,endemic.mort=0,sdi.mort=0))

## print the projected summaries
print( sample.23 )

## plot the diagnostics from the fit a linear dbh-tht model 
## before thinning
opar <- par( mfcol=c(2,2 ) )
plot( lm( sample.23$plants$tht ~ sample.23$plants$dbh ) )
par( opar )

## thin the stand to capture mortality, redistribute growth, 
## and possibly generate revenue
## Proportional thin for selected tree species, does not remove shrubs
sample.23.t1 <- thin( sample.23, 
             control=list(type=1, target=50.0, target.sp="DF" ) )
print( sample.23.t1 )

## Proportional thin across all tree species
sample.23.t2 <- thin( sample.23, 
             control=list(type=2, target=50.0 ) )
print( sample.23.t2 )

## Thin from below, by dbh, all species
sample.23.t3 <- thin( sample.23, 
             control=list(type=3, target=50.0 ) )
print( sample.23.t3 )

## Thin from below, by dbh for species "PM"
sample.23.t4 <- thin( sample.23, 
             control=list(type=4, target=50.0, target.sp="PM" ) )
print( sample.23.t4 )

## plot the diagnostics from the fit a linear dbh-tht model
## after proportional thinning
opar <- par( mfcol=c(2,2 ) )
plot( lm( sample.23.t2$plants$tht ~ sample.23.t2$plants$dbh ) )
par( opar )

## print the differences, by species
print( sp.sums( sample.23.t4 ) - sp.sums( sample.23 ) )


[Package rconifers version 0.0-9 Index]