swo {rconifers}R Documentation

Species table for the SWO variant of the CONIFERS growth model

Description

These are species codes used for the SWO variant of the CONIFERS growth model.

The swo data frame has 38 rows and 13 columns.

Usage

data(swo)

Format

This data frame contains the following columns:

idx
The index of the species code that is used within the CONIFERS source code.
code
The alpha-numeric code that is used during data input, normally in the field data recorder. These are used in the summaries and outputs rather than the species names.
fsp
The functional species code (found in the coefficients) that is used to project the plant record. Every species code is mapped to a single functional species code. See another manual page.
name
The long name for the plant species.
organon
The code that should be used to export the species to the ORGANON growth and yield model. See http://www.cof.orst.edu/cof/fr/research/organon/index.htm for more details.
cactos
The code that should be used to export the species to the CACTOS growth and yield model. See http://www.cnr.berkeley.edu/~wensel/cactos/cactoss.htm for more details.
fvs
The code that should be used to export the species to the FVS growth and yield model. See http://www.fs.fed.us/fmsc/index.php for more details.
endemic.mort
is a percent for the endemic mortality, where (0.002) means that two tenths of a percent of the trees of that functional species type will be "killed" for each year of projection.
max.sdi
is the maximum stand density index the functional species can obtain before competition induced mortality is applied.
browse.damage
is a percent for the browse damage, where (0.002) means that two tenths of a percent of the trees of that species type will be "killed" as a result of browse damage.
mechanical.damage
is a percent for the mechanical damage, where (0.002) means that two tenths of a percent of the trees of that species type will be "killed" as a result of mechanical damage.
genetic.worth.h
is the average expected level of gain, expressed as percentage, for diameter at a given age. Values are bounded by 0 and 20.
genetic.worth.d
is the average expected level of gain, expressed as percentage, for diameter at a given age. Values are bounded by 0 and 20.

Details

The dataset is similar to the swo.txt file that is distirbuted with the GUI version of CONIFERS. A species lookup table is nothing more than a data.frame with specific column names.

Author(s)

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

References

Ritchie, M. and J. Hamann. 2006. Modeling dynamics of competing vegetation in young conifer plantations of northern California and southern Oregon, USA. Canadian Journal of Forest Research 36(10): 2523-2532.

Ritchie, M. and J. Hamann. 2008. Individual-tree height-, diameter- and crown-width increment equations for young Douglas-fir plantations. New Forests 35(2):173-186.

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]