SORTIE Java Interface  1
Public Member Functions | Static Public Attributes | Protected Attributes | List of all members
sortie.tools.parfileupdater.DisperseBehaviors Class Reference

This is the organizer class for all disperse behaviors. More...

Inheritance diagram for sortie.tools.parfileupdater.DisperseBehaviors:
sortie.tools.parfileupdater.GroupBase

Public Member Functions

 DisperseBehaviors ()
 Constructor.
 
- Public Member Functions inherited from sortie.tools.parfileupdater.GroupBase
void writeBehaviorsList (BufferedWriter jOut) throws IOException
 
void writeDataToFile (BufferedWriter jOut) throws IOException
 Writes all data to an XML file.
 
ModelData findObjectByXMLTag (String sXMLTag, String sXMLParentTag)
 Finds an object based on its XML tag.
 
boolean setSingleValueByXMLTag (String sXMLTag, String sXMLParentTag, Attributes oAttributes, String sData) throws ModelException
 Sets a data object's value.
 
ArrayList< BehaviorgetBehaviorByXMLTag (String sXMLTag)
 Finds a behavior by its XML tag.
 
boolean readXMLParentTag (String sXMLTag, Attributes oAttributes) throws ModelException
 Accepts an XML parent tag (empty, no data) from the parser.
 
void endXMLParentTag (String sXMLTag)
 
boolean parentTagOKForQueue (String sTag)
 Some tags get everything messed up in a parsing queue.
 

Static Public Attributes

static final int WEIBULL = 0
 Weibull disperse function.
 
static final int LOGNORMAL = 1
 Lognormal disperse function.
 
static final int CANOPY = 0
 Canopy forest cover status for cells.
 
static final int GAP = 1
 Gap forest cover status for cells.
 
static final int NUMBER_OF_DISPERSE_FUNCTIONS = 2
 Total number of disperse functions.
 
static final int NUMBER_OF_FOREST_COVERS = 2
 Total number of forest cover statuses.
 

Protected Attributes

ModelData[][] mp_fSTR
 STR for disperse function.
 
ModelData[][] mp_fBeta
 Beta for disperse function.
 
ModelData[][] mp_fThetaOrXb
 Theta (if weibull) or Xb (if lognormal) for disperse function.
 
ModelData[][] mp_fDispOrX0
 Dispersal (if weibull) or X0 (if lognormal) for disperse function.
 
ModelData[] mp_iWhichFunctionUsed
 Which disperse function to use under each forest cover - valid values are WEIBULL and LOGNORMAL - this is a vector of ModelEnums.
 
ModelData mp_fSlopeOfLambda = new ModelData("di_nonSpatialSlopeOfLambda", "di_nssolVal")
 Non-spatial disperse - slope of lambda for each species.
 
ModelData mp_fInterceptOfLambda = new ModelData("di_nonSpatialInterceptOfLambda", "di_nsiolVal")
 Non-spatial disperse - intercept of lambda for each species.
 
ModelData mp_fMinDbhForReproduction = new ModelData("di_minDbhForReproduction", "di_mdfrVal")
 Minimum DBH for reproduction for each species.
 
ModelData mp_fStumpSTR = new ModelData("di_suckerSTR", "di_ssVal")
 STR for stump dispersal for each species.
 
ModelData mp_fStumpBeta = new ModelData("di_suckerBeta", "di_sbVal")
 Beta for stump dispersal for each species.
 
ModelData mp_fStandardDeviation = new ModelData("di_standardDeviation", "di_sdVal")
 Standard deviation if seed distribution method is normal or lognormal.
 
ModelData mp_fClumpingParameter = new ModelData("di_clumpingParameter", "di_cpVal")
 Clumping parameter if seed distribution is negative binomial.
 
ModelData mp_fSpatialMastMastingA = new ModelData("di_mastCDFA", "di_mcdfaVal")
 Masting spatial disperse - "a" for masting CDF.
 
ModelData mp_fSpatialMastMastingB = new ModelData("di_mastCDFB", "di_mcdfbVal")
 Masting spatial disperse - "b" for masting CDF.
 
ModelData mp_iSpatialMastSTRDrawPDF = new ModelData("di_mastSTRPDF", "di_mstrpdfVal")
 Masting spatial disperse - Probability distribution for STR draw.
 
ModelData mp_fSpatialMastNonMastSTRMean = new ModelData("di_spatialSTR", "di_sstrVal")
 Masting spatial disperse - Non-mast STR mean.
 
ModelData mp_fSpatialMastNonMastSTRStdDev = new ModelData("di_spatialSTRStdDev", "di_sstrsdVal")
 Masting spatial disperse - Non-mast STR draw standard deviation, if PDF = normal or lognormal.
 
ModelData mp_fSpatialMastMastSTRMean = new ModelData("di_mastingSTR", "di_mstrVal")
 Masting spatial disperse - Masting STR mean.
 
ModelData mp_fSpatialMastMastSTRStdDev = new ModelData("di_mastingSTRStdDev", "di_mstrsdVal")
 Masting spatial disperse - Masting STR draw standard deviation, if PDF = normal or lognormal.
 
ModelData mp_fSpatialMastNonMastBeta = new ModelData("di_spatialBeta", "di_sbVal")
 Masting spatial disperse - Non-masting beta.
 
ModelData mp_fSpatialMastMastBeta = new ModelData("di_mastingBeta", "di_mbVal")
 Masting spatial disperse - Masting beta.
 
ModelData mp_fSpatialMastMastWeibDisp = new ModelData("di_weibullMastingDispersal", "di_wmdVal")
 Masting spatial disperse - Weibull masting dispersal.
 
ModelData mp_fSpatialMastMastWeibTheta = new ModelData("di_weibullMastingTheta", "di_wmtVal")
 Masting spatial disperse - Weibull masting theta.
 
ModelData mp_fSpatialMastMastLognormalX0 = new ModelData("di_lognormalMastingX0", "di_lmx0Val")
 Masting spatial disperse - Lognormal masting X0.
 
ModelData mp_fSpatialMastMastLognormalXb = new ModelData("di_lognormalMastingXb", "di_lmxbVal")
 Masting spatial disperse - Lognormal masting Xb.
 
ModelData mp_iSpatialMastGroupID = new ModelData("di_mastGroup", "di_mgVal")
 Masting spatial disperse - Group identification for each species.
 
ModelData mp_iSpatialMastDrawPerSpecies = new ModelData("di_mastDrawPerSpecies", "di_mdpsVal")
 Masting spatial disperse - Whether to draw STR once per species (1) or once per tree (0)
 
ModelData mp_fSpatialMastMastPropParticipating = new ModelData("di_mastPropParticipating", "di_mppVal")
 Masting spatial disperse - Proportion trees participating in disperse for mast event.
 
ModelData mp_fSpatialMastNonMastPropParticipating = new ModelData("di_spatialPropParticipating", "di_sppVal")
 Masting spatial disperse - Proportion trees participating in disperse for non-mast event.
 
ModelData mp_iNonSpatialMastNonMastFunction = new ModelData("di_nonSpatialNonMastFunction", "di_nsnmfVal")
 Masting non-spatial disperse - distribution function to pick seeds in non-mast conditions.
 
ModelData mp_fNonSpatialMastBinomialP = new ModelData("di_nonSpatialMastBinomialP", "di_nsmbpVal")
 Masting non-spatial disperse - P parameter for binomial distribution for deciding whether to mast.
 
ModelData mp_iNonSpatialMastMastFunction = new ModelData("di_nonSpatialMastMastFunction", "di_nsmmfVal")
 Masting non-spatial disperse - distribution function to pick seeds in mast conditions.
 
ModelData mp_fNonSpatialMastMastInvGaussMu = new ModelData("di_nonSpatialMastInvGaussMu", "di_nsmigmVal")
 Masting non-spatial disperse - mu parameter for inverse gaussian distribution - mast conditions.
 
ModelData mp_fNonSpatialMastMastInvGaussLambda = new ModelData("di_nonSpatialMastInvGaussLambda", "di_nsmiglVal")
 Masting non-spatial disperse - lambda parameter for inverse gaussian distribution - mast conditions.
 
ModelData mp_fNonSpatialMastNonMastInvGaussMu = new ModelData("di_nonSpatialNonMastInvGaussMu", "di_nsnmigmVal")
 Masting non-spatial disperse - mu parameter for inverse gaussian distribution - non-mast conditions.
 
ModelData mp_fNonSpatialMastNonMastInvGaussLambda = new ModelData("di_nonSpatialNonMastInvGaussLambda", "di_nsnmiglVal")
 Masting non-spatial disperse - lambda parameter for inverse gaussian distribution - non-mast conditions.
 
ModelData mp_fNonSpatialMastMastNormalMean = new ModelData("di_nonSpatialMastNormalMean", "di_nsmnmVal")
 Masting non-spatial disperse - mean for normal distribution - mast conditions.
 
ModelData mp_fNonSpatialMastMastNormalStdDev = new ModelData("di_nonSpatialMastNormalStdDev", "di_nsmnsdVal")
 Masting non-spatial disperse - standard deviation for normal distribution.
 
ModelData mp_fNonSpatialMastNonMastNormalMean = new ModelData("di_nonSpatialNonMastNormalMean", "di_nsnmnmVal")
 Masting non-spatial disperse - mean for normal distribution - non-mast conditions.
 
ModelData mp_fNonSpatialMastNonMastNormalStdDev = new ModelData("di_nonSpatialNonMastNormalStdDev", "di_nsnmnsdVal")
 Masting non-spatial disperse - standard deviation for normal distribution.
 
ModelData mp_iNonSpatialMastMastGroupID = new ModelData("di_nonSpatialMastGroup", "di_nsmgVal")
 Masting non-spatial disperse - group identification for each species.
 
ModelData mp_fTempDepNeighM = new ModelData("di_tempDepNeighFecM", "di_tdnfmVal")
 Temperature dependent neighborhood disperse - fecundity function M.
 
ModelData mp_fTempDepNeighN = new ModelData("di_tempDepNeighFecN", "di_tdnfnVal")
 Temperature dependent neighborhood disperse - fecundity function N.
 
ModelData mp_fTempDepNeighPresM = new ModelData("di_tempDepNeighPresM", "di_tdnpmVal")
 Temperature dependent neighborhood disperse - presence function M.
 
ModelData mp_fTempDepNeighPresB = new ModelData("di_tempDepNeighPresB", "di_tdnpbVal")
 Temperature dependent neighborhood disperse - presence function B.
 
ModelData mp_fTempDepNeighPresThreshold = new ModelData("di_tempDepNeighPresThreshold", "di_tdnptVal")
 Temperature dependent neighborhood disperse - presence threshold.
 
ModelData mp_fTempDepNeighA = new ModelData("di_tempDepNeighA", "di_tdnaVal")
 Temperature dependent neighborhood disperse - A.
 
ModelData mp_fTempDepNeighB = new ModelData("di_tempDepNeighB", "di_tdnbVal")
 Temperature dependent neighborhood disperse - B.
 
ModelData m_fTempDepNeighMaxRadius = new ModelData("di_tempDepNeighRadius")
 Temperature dependent neighborhood disperse - maximum search radius, in meters, for neighbors.
 
ModelData m_iSeedDistributionMethod = new ModelData("di_seedDistributionMethod")
 Seed distribution.
 
ModelData m_iMaxGapDensity = new ModelData("di_maxGapDensity")
 Max number of parent trees that can be in a grid cell for it to still be marked as gap.
 
- Protected Attributes inherited from sortie.tools.parfileupdater.GroupBase
ArrayList< ModelDatamp_oAllData
 All data for this object.
 
String m_sXMLTag
 Highest-level XML tag for this object.
 
Behavior[] mp_oChildBehaviors
 

Additional Inherited Members

- Protected Member Functions inherited from sortie.tools.parfileupdater.GroupBase
void loadDataMember (ModelData oData)
 Takes some data and adds it to all applicable behaviors in their string buffer.
 

Detailed Description

This is the organizer class for all disperse behaviors.

Copyright: Copyright (c) Charles D. Canham 2003

Company: Institute of Ecosystem Studies

Author
Lora E. Murphy
Version
1.0


Edit history:
---------------—
April 28, 2004: Submitted in beta version (LEM)
April 24, 2007: Added masting spatial disperse (LEM)
August 19, 2008: Added masting non-spatial disperse (LEM)

Constructor & Destructor Documentation

sortie.tools.parfileupdater.DisperseBehaviors.DisperseBehaviors ( )

Constructor.

Member Data Documentation

final int sortie.tools.parfileupdater.DisperseBehaviors.CANOPY = 0
static

Canopy forest cover status for cells.

final int sortie.tools.parfileupdater.DisperseBehaviors.GAP = 1
static

Gap forest cover status for cells.

final int sortie.tools.parfileupdater.DisperseBehaviors.LOGNORMAL = 1
static

Lognormal disperse function.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.m_fTempDepNeighMaxRadius = new ModelData("di_tempDepNeighRadius")
protected

Temperature dependent neighborhood disperse - maximum search radius, in meters, for neighbors.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.m_iMaxGapDensity = new ModelData("di_maxGapDensity")
protected

Max number of parent trees that can be in a grid cell for it to still be marked as gap.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.m_iSeedDistributionMethod = new ModelData("di_seedDistributionMethod")
protected

Seed distribution.

ModelData [][] sortie.tools.parfileupdater.DisperseBehaviors.mp_fBeta
protected

Beta for disperse function.

Array is 3D - first index is which disperse function is used - weibull or lognormal. The second index is cover - canopy or gap. The third index is species.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_fClumpingParameter = new ModelData("di_clumpingParameter", "di_cpVal")
protected

Clumping parameter if seed distribution is negative binomial.

ModelData [][] sortie.tools.parfileupdater.DisperseBehaviors.mp_fDispOrX0
protected

Dispersal (if weibull) or X0 (if lognormal) for disperse function.

Array is 3D - first index is which disperse function is used - weibull or lognormal. The second index is cover - canopy or gap. The third index is species.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_fInterceptOfLambda = new ModelData("di_nonSpatialInterceptOfLambda", "di_nsiolVal")
protected

Non-spatial disperse - intercept of lambda for each species.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_fMinDbhForReproduction = new ModelData("di_minDbhForReproduction", "di_mdfrVal")
protected

Minimum DBH for reproduction for each species.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_fNonSpatialMastBinomialP = new ModelData("di_nonSpatialMastBinomialP", "di_nsmbpVal")
protected

Masting non-spatial disperse - P parameter for binomial distribution for deciding whether to mast.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_fNonSpatialMastMastInvGaussLambda = new ModelData("di_nonSpatialMastInvGaussLambda", "di_nsmiglVal")
protected

Masting non-spatial disperse - lambda parameter for inverse gaussian distribution - mast conditions.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_fNonSpatialMastMastInvGaussMu = new ModelData("di_nonSpatialMastInvGaussMu", "di_nsmigmVal")
protected

Masting non-spatial disperse - mu parameter for inverse gaussian distribution - mast conditions.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_fNonSpatialMastMastNormalMean = new ModelData("di_nonSpatialMastNormalMean", "di_nsmnmVal")
protected

Masting non-spatial disperse - mean for normal distribution - mast conditions.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_fNonSpatialMastMastNormalStdDev = new ModelData("di_nonSpatialMastNormalStdDev", "di_nsmnsdVal")
protected

Masting non-spatial disperse - standard deviation for normal distribution.

  • mast conditions
ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_fNonSpatialMastNonMastInvGaussLambda = new ModelData("di_nonSpatialNonMastInvGaussLambda", "di_nsnmiglVal")
protected

Masting non-spatial disperse - lambda parameter for inverse gaussian distribution - non-mast conditions.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_fNonSpatialMastNonMastInvGaussMu = new ModelData("di_nonSpatialNonMastInvGaussMu", "di_nsnmigmVal")
protected

Masting non-spatial disperse - mu parameter for inverse gaussian distribution - non-mast conditions.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_fNonSpatialMastNonMastNormalMean = new ModelData("di_nonSpatialNonMastNormalMean", "di_nsnmnmVal")
protected

Masting non-spatial disperse - mean for normal distribution - non-mast conditions.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_fNonSpatialMastNonMastNormalStdDev = new ModelData("di_nonSpatialNonMastNormalStdDev", "di_nsnmnsdVal")
protected

Masting non-spatial disperse - standard deviation for normal distribution.

  • non-mast conditions
ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_fSlopeOfLambda = new ModelData("di_nonSpatialSlopeOfLambda", "di_nssolVal")
protected

Non-spatial disperse - slope of lambda for each species.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_fSpatialMastMastBeta = new ModelData("di_mastingBeta", "di_mbVal")
protected

Masting spatial disperse - Masting beta.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_fSpatialMastMastingA = new ModelData("di_mastCDFA", "di_mcdfaVal")
protected

Masting spatial disperse - "a" for masting CDF.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_fSpatialMastMastingB = new ModelData("di_mastCDFB", "di_mcdfbVal")
protected

Masting spatial disperse - "b" for masting CDF.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_fSpatialMastMastLognormalX0 = new ModelData("di_lognormalMastingX0", "di_lmx0Val")
protected

Masting spatial disperse - Lognormal masting X0.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_fSpatialMastMastLognormalXb = new ModelData("di_lognormalMastingXb", "di_lmxbVal")
protected

Masting spatial disperse - Lognormal masting Xb.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_fSpatialMastMastPropParticipating = new ModelData("di_mastPropParticipating", "di_mppVal")
protected

Masting spatial disperse - Proportion trees participating in disperse for mast event.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_fSpatialMastMastSTRMean = new ModelData("di_mastingSTR", "di_mstrVal")
protected

Masting spatial disperse - Masting STR mean.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_fSpatialMastMastSTRStdDev = new ModelData("di_mastingSTRStdDev", "di_mstrsdVal")
protected

Masting spatial disperse - Masting STR draw standard deviation, if PDF = normal or lognormal.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_fSpatialMastMastWeibDisp = new ModelData("di_weibullMastingDispersal", "di_wmdVal")
protected

Masting spatial disperse - Weibull masting dispersal.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_fSpatialMastMastWeibTheta = new ModelData("di_weibullMastingTheta", "di_wmtVal")
protected

Masting spatial disperse - Weibull masting theta.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_fSpatialMastNonMastBeta = new ModelData("di_spatialBeta", "di_sbVal")
protected

Masting spatial disperse - Non-masting beta.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_fSpatialMastNonMastPropParticipating = new ModelData("di_spatialPropParticipating", "di_sppVal")
protected

Masting spatial disperse - Proportion trees participating in disperse for non-mast event.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_fSpatialMastNonMastSTRMean = new ModelData("di_spatialSTR", "di_sstrVal")
protected

Masting spatial disperse - Non-mast STR mean.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_fSpatialMastNonMastSTRStdDev = new ModelData("di_spatialSTRStdDev", "di_sstrsdVal")
protected

Masting spatial disperse - Non-mast STR draw standard deviation, if PDF = normal or lognormal.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_fStandardDeviation = new ModelData("di_standardDeviation", "di_sdVal")
protected

Standard deviation if seed distribution method is normal or lognormal.

ModelData [][] sortie.tools.parfileupdater.DisperseBehaviors.mp_fSTR
protected

STR for disperse function.

Array is 3D - first index is which disperse function is used - weibull or lognormal. The second index is cover - canopy or gap. The third index is species.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_fStumpBeta = new ModelData("di_suckerBeta", "di_sbVal")
protected

Beta for stump dispersal for each species.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_fStumpSTR = new ModelData("di_suckerSTR", "di_ssVal")
protected

STR for stump dispersal for each species.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_fTempDepNeighA = new ModelData("di_tempDepNeighA", "di_tdnaVal")
protected

Temperature dependent neighborhood disperse - A.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_fTempDepNeighB = new ModelData("di_tempDepNeighB", "di_tdnbVal")
protected

Temperature dependent neighborhood disperse - B.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_fTempDepNeighM = new ModelData("di_tempDepNeighFecM", "di_tdnfmVal")
protected

Temperature dependent neighborhood disperse - fecundity function M.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_fTempDepNeighN = new ModelData("di_tempDepNeighFecN", "di_tdnfnVal")
protected

Temperature dependent neighborhood disperse - fecundity function N.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_fTempDepNeighPresB = new ModelData("di_tempDepNeighPresB", "di_tdnpbVal")
protected

Temperature dependent neighborhood disperse - presence function B.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_fTempDepNeighPresM = new ModelData("di_tempDepNeighPresM", "di_tdnpmVal")
protected

Temperature dependent neighborhood disperse - presence function M.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_fTempDepNeighPresThreshold = new ModelData("di_tempDepNeighPresThreshold", "di_tdnptVal")
protected

Temperature dependent neighborhood disperse - presence threshold.

ModelData [][] sortie.tools.parfileupdater.DisperseBehaviors.mp_fThetaOrXb
protected

Theta (if weibull) or Xb (if lognormal) for disperse function.

Array is 3D - first index is which disperse function is used - weibull or lognormal. The second index is cover - canopy or gap. The third index is species.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_iNonSpatialMastMastFunction = new ModelData("di_nonSpatialMastMastFunction", "di_nsmmfVal")
protected

Masting non-spatial disperse - distribution function to pick seeds in mast conditions.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_iNonSpatialMastMastGroupID = new ModelData("di_nonSpatialMastGroup", "di_nsmgVal")
protected

Masting non-spatial disperse - group identification for each species.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_iNonSpatialMastNonMastFunction = new ModelData("di_nonSpatialNonMastFunction", "di_nsnmfVal")
protected

Masting non-spatial disperse - distribution function to pick seeds in non-mast conditions.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_iSpatialMastDrawPerSpecies = new ModelData("di_mastDrawPerSpecies", "di_mdpsVal")
protected

Masting spatial disperse - Whether to draw STR once per species (1) or once per tree (0)

ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_iSpatialMastGroupID = new ModelData("di_mastGroup", "di_mgVal")
protected

Masting spatial disperse - Group identification for each species.

ModelData sortie.tools.parfileupdater.DisperseBehaviors.mp_iSpatialMastSTRDrawPDF = new ModelData("di_mastSTRPDF", "di_mstrpdfVal")
protected

Masting spatial disperse - Probability distribution for STR draw.

ModelData [] sortie.tools.parfileupdater.DisperseBehaviors.mp_iWhichFunctionUsed
protected

Which disperse function to use under each forest cover - valid values are WEIBULL and LOGNORMAL - this is a vector of ModelEnums.

final int sortie.tools.parfileupdater.DisperseBehaviors.NUMBER_OF_DISPERSE_FUNCTIONS = 2
static

Total number of disperse functions.

final int sortie.tools.parfileupdater.DisperseBehaviors.NUMBER_OF_FOREST_COVERS = 2
static

Total number of forest cover statuses.

final int sortie.tools.parfileupdater.DisperseBehaviors.WEIBULL = 0
static

Weibull disperse function.


The documentation for this class was generated from the following file: