Public Member Functions | |
DisperseBehaviors (GUIManager oManager) | |
Constructor. | |
void | DoSetup (TreePopulation oPop) throws ModelException |
Does setup. | |
void | ValidateData (TreePopulation oPop) throws ModelException |
Validates the data in preparation for parameter file writing or some such. | |
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 | |
ModelVector[][] | mp_fSTR |
STR for disperse function. | |
ModelVector[][] | mp_fBeta |
Beta for disperse function. | |
ModelVector[][] | mp_fThetaOrXb |
Theta (if weibull) or Xb (if lognormal) for disperse function. | |
ModelVector[][] | mp_fDispOrX0 |
Dispersal (if weibull) or X0 (if lognormal) for disperse function. | |
ModelVector[] | mp_iWhichFunctionUsed |
Which disperse function to use under each forest cover - valid values are WEIBULL and LOGNORMAL - this is a vector of ModelEnums. | |
ModelVector | mp_fSlopeOfLambda |
Non-spatial disperse - slope of lambda for each species. | |
ModelVector | mp_fInterceptOfLambda |
Non-spatial disperse - intercept of lambda for each species. | |
ModelVector | mp_fMinDbhForReproduction |
Minimum DBH for reproduction for each species. | |
ModelVector | mp_fStumpSTR |
STR for stump dispersal for each species. | |
ModelVector | mp_fStumpBeta |
Beta for stump dispersal for each species. | |
ModelVector | mp_fStandardDeviation |
Azimuth direction of maximum dispersal distance, in radians. | |
ModelVector | mp_fClumpingParameter |
Clumping parameter if seed distribution is negative binomial. | |
ModelVector | mp_fSpatialMastMastingA |
Masting spatial disperse - "a" for masting CDF. | |
ModelVector | mp_fSpatialMastMastingB |
Masting spatial disperse - "b" for masting CDF. | |
ModelVector | mp_iSpatialMastSTRDrawPDF |
Masting spatial disperse - Probability distribution for STR draw. | |
ModelVector | mp_fSpatialMastNonMastSTRMean |
Masting spatial disperse - Non-mast STR mean. | |
ModelVector | mp_fSpatialMastNonMastSTRStdDev |
Masting spatial disperse - Non-mast STR draw standard deviation, if PDF = normal or lognormal. | |
ModelVector | mp_fSpatialMastMastSTRMean |
Masting spatial disperse - Masting STR mean. | |
ModelVector | mp_fSpatialMastMastSTRStdDev |
Masting spatial disperse - Masting STR draw standard deviation, if PDF = normal or lognormal. | |
ModelVector | mp_fSpatialMastNonMastBeta |
Masting spatial disperse - Non-masting beta. | |
ModelVector | mp_fSpatialMastMastBeta |
Masting spatial disperse - Masting beta. | |
ModelVector | mp_fSpatialMastMastWeibDisp |
Masting spatial disperse - Weibull masting dispersal. | |
ModelVector | mp_fSpatialMastMastWeibTheta |
Masting spatial disperse - Weibull masting theta. | |
ModelVector | mp_fSpatialMastMastLognormalX0 |
Masting spatial disperse - Lognormal masting X0. | |
ModelVector | mp_fSpatialMastMastLognormalXb |
Masting spatial disperse - Lognormal masting Xb. | |
ModelVector | mp_iSpatialMastGroupID |
Masting spatial disperse - Group identification for each species. | |
ModelVector | mp_iSpatialMastDrawPerSpecies |
Masting spatial disperse - Whether to draw STR once per species (1) or once per tree (0). | |
ModelVector | mp_fSpatialMastMastPropParticipating |
Masting spatial disperse - Proportion trees participating in disperse for mast event. | |
ModelVector | mp_fSpatialMastNonMastPropParticipating |
Masting spatial disperse - Proportion trees participating in disperse for non-mast event. | |
ModelVector | mp_iNonSpatialMastNonMastFunction |
Masting non-spatial disperse - distribution function to pick seeds in non-mast conditions. | |
ModelVector | mp_fNonSpatialMastBinomialP |
Masting non-spatial disperse - P parameter for binomial distribution for deciding whether to mast. | |
ModelVector | mp_iNonSpatialMastMastFunction |
Masting non-spatial disperse - distribution function to pick seeds in mast conditions. | |
ModelVector | mp_fNonSpatialMastMastInvGaussMu |
Masting non-spatial disperse - mu parameter for inverse gaussian distribution - mast conditions. | |
ModelVector | mp_fNonSpatialMastMastInvGaussLambda |
Masting non-spatial disperse - lambda parameter for inverse gaussian distribution - mast conditions. | |
ModelVector | mp_fNonSpatialMastNonMastInvGaussMu |
Masting non-spatial disperse - mu parameter for inverse gaussian distribution - non-mast conditions. | |
ModelVector | mp_fNonSpatialMastNonMastInvGaussLambda |
Masting non-spatial disperse - lambda parameter for inverse gaussian distribution - non-mast conditions. | |
ModelVector | mp_fNonSpatialMastMastNormalMean |
Masting non-spatial disperse - mean for normal distribution - mast conditions. | |
ModelVector | mp_fNonSpatialMastMastNormalStdDev |
Masting non-spatial disperse - standard deviation for normal distribution
| |
ModelVector | mp_fNonSpatialMastNonMastNormalMean |
Masting non-spatial disperse - mean for normal distribution - non-mast conditions. | |
ModelVector | mp_fNonSpatialMastNonMastNormalStdDev |
Masting non-spatial disperse - standard deviation for normal distribution
| |
ModelVector | mp_iNonSpatialMastMastGroupID |
Masting non-spatial disperse - group identification for each species. | |
ModelEnum | m_iSeedDistributionMethod |
Seed distribution. | |
ModelInt | m_iMaxGapDensity |
Maximum search radius, in meters, for neighbors for isotropic and anisotropic disperse. |
Copyright: Copyright (c) Charles D. Canham 2003
Company: Institute of Ecosystem Studies
javawrapper.DisperseBehaviors.DisperseBehaviors | ( | GUIManager | oManager | ) |
void javawrapper.DisperseBehaviors.DoSetup | ( | TreePopulation | oPop | ) | throws ModelException [virtual] |
Does setup.
oPop | TreePopulation object. |
ModelException | if there's a problem setting behavior use data. |
Implements javawrapper.WorkerBase.
void javawrapper.DisperseBehaviors.ValidateData | ( | TreePopulation | oPop | ) | throws ModelException [virtual] |
Validates the data in preparation for parameter file writing or some such.
oPop | TreePopulation object. |
ModelException | if:
|
Implements javawrapper.WorkerBase.
final int javawrapper.DisperseBehaviors.WEIBULL = 0 [static] |
Weibull disperse function.
final int javawrapper.DisperseBehaviors.LOGNORMAL = 1 [static] |
Lognormal disperse function.
final int javawrapper.DisperseBehaviors.CANOPY = 0 [static] |
Canopy forest cover status for cells.
final int javawrapper.DisperseBehaviors.GAP = 1 [static] |
Gap forest cover status for cells.
final int javawrapper.DisperseBehaviors.NUMBER_OF_DISPERSE_FUNCTIONS = 2 [static] |
Total number of disperse functions.
final int javawrapper.DisperseBehaviors.NUMBER_OF_FOREST_COVERS = 2 [static] |
Total number of forest cover statuses.
ModelVector [][] javawrapper.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.
ModelVector [][] javawrapper.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.
ModelVector [][] javawrapper.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.
ModelVector [][] javawrapper.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.
ModelVector [] javawrapper.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.
Initial value:
new ModelVector( "Slope Mean Non-Spatial Seed Rain, seeds/m2/ha of BA/yr", "di_nonSpatialSlopeOfLambda", "di_nssolVal", 0, ModelVector.FLOAT)
Initial value:
new ModelVector( "Intercept of Mean Non-Spatial Seed Rain, seeds/m2/yr", "di_nonSpatialInterceptOfLambda", "di_nsiolVal", 0, ModelVector.FLOAT)
Initial value:
new ModelVector( "Minimum DBH for Reproduction, in cm", "di_minDbhForReproduction", "di_mdfrVal", 0, ModelVector.FLOAT)
Initial value:
new ModelVector("STR/n for Stumps", "di_suckerSTR", "di_ssVal", 0, ModelVector.FLOAT)
Initial value:
new ModelVector("Beta for Stumps", "di_suckerBeta", "di_sbVal", 0, ModelVector.FLOAT)
Initial value:
new ModelVector( "Seed Dist. Std. Deviation (Normal or Lognormal)", "di_standardDeviation", "di_sdVal", 0, ModelVector.FLOAT)
Amplitude of anisotropic effectStandard deviation if seed distribution method is normal or lognormal
Initial value:
new ModelVector( "Seed Dist. Clumping Parameter (Neg. Binomial)", "di_clumpingParameter", "di_cpVal", 0, ModelVector.FLOAT)
Initial value:
new ModelVector( "Masting Disperse - Masting CDF \"a\"", "di_mastCDFA", "di_mcdfaVal", 0, ModelVector.FLOAT)
Initial value:
new ModelVector( "Masting Disperse - Masting CDF \"b\"", "di_mastCDFB", "di_mcdfbVal", 0, ModelVector.FLOAT)
Initial value:
new ModelVector( "Masting Disperse - STR Draw PDF", "di_mastSTRPDF", "di_mstrpdfVal", 0, ModelVector.MODEL_ENUM)
Initial value:
new ModelVector( "Masting Disperse - Non-Masting STR/n Mean", "di_spatialSTR", "di_sstrVal", 0, ModelVector.FLOAT)
Initial value:
new ModelVector( "Masting Disperse - Non-Masting STR/n Standard Deviation", "di_spatialSTRStdDev", "di_sstrsdVal", 0, ModelVector.FLOAT)
Initial value:
new ModelVector( "Masting Disperse - Masting STR/n Mean", "di_mastingSTR", "di_mstrVal", 0, ModelVector.FLOAT)
Initial value:
new ModelVector( "Masting Disperse - Masting STR/n Standard Deviation", "di_mastingSTRStdDev", "di_mstrsdVal", 0, ModelVector.FLOAT)
Initial value:
new ModelVector( "Masting Disperse - Non-Masting Beta", "di_spatialBeta", "di_sbVal", 0, ModelVector.FLOAT)
Initial value:
new ModelVector( "Masting Disperse - Masting Beta", "di_mastingBeta", "di_mbVal", 0, ModelVector.FLOAT)
Initial value:
new ModelVector( "Masting Disperse - Masting Weibull Dispersal", "di_weibullMastingDispersal", "di_wmdVal", 0, ModelVector.FLOAT)
Initial value:
new ModelVector( "Masting Disperse - Masting Weibull Theta", "di_weibullMastingTheta", "di_wmtVal", 0, ModelVector.FLOAT)
Initial value:
new ModelVector( "Masting Disperse - Masting Lognormal X0", "di_lognormalMastingX0", "di_lmx0Val", 0, ModelVector.FLOAT)
Initial value:
new ModelVector( "Masting Disperse - Masting Lognormal Xb", "di_lognormalMastingXb", "di_lmxbVal", 0, ModelVector.FLOAT)
Initial value:
new ModelVector( "Masting Disperse - Masting Group", "di_mastGroup", "di_mgVal", 0, ModelVector.MODEL_ENUM)
Initial value:
new ModelVector( "Masting Disperse - Stochastic STR Draw Frequency", "di_mastDrawPerSpecies", "di_mdpsVal", 0, ModelVector.MODEL_ENUM)
Initial value:
new ModelVector( "Masting Disperse - Mast Proportion Participating (0-1)", "di_mastPropParticipating", "di_mppVal", 0, ModelVector.FLOAT)
Initial value:
new ModelVector( "Masting Disperse - Non-Mast Proportion Participating (0-1)", "di_spatialPropParticipating", "di_sppVal", 0, ModelVector.FLOAT)
Initial value:
new ModelVector( "Mast NS Disperse - PDF Non-Masting Conditions", "di_nonSpatialNonMastFunction", "di_nsnmfVal", 0, ModelVector.MODEL_ENUM)
Initial value:
new ModelVector( "Mast NS Disperse - Binomial P (Mast Chance)", "di_nonSpatialMastBinomialP", "di_nsmbpVal", 0, ModelVector.FLOAT)
Initial value:
new ModelVector( "Mast NS Disperse - PDF Masting Conditions", "di_nonSpatialMastMastFunction", "di_nsmmfVal", 0, ModelVector.MODEL_ENUM)
Initial value:
new ModelVector( "Mast NS Disperse - Mast Inv. Gauss. Mu", "di_nonSpatialMastInvGaussMu", "di_nsmigmVal", 0, ModelVector.FLOAT)
Initial value:
new ModelVector( "Mast NS Disperse - Mast Inv. Gauss. Lambda", "di_nonSpatialMastInvGaussLambda", "di_nsmiglVal", 0, ModelVector.FLOAT)
Initial value:
new ModelVector( "Mast NS Disperse - Non-Mast Inv. Gauss. Mu", "di_nonSpatialNonMastInvGaussMu", "di_nsnmigmVal", 0, ModelVector.FLOAT)
Initial value:
new ModelVector( "Mast NS Disperse - Non-Mast Inv. Gauss. Lambda", "di_nonSpatialNonMastInvGaussLambda", "di_nsnmiglVal", 0, ModelVector.FLOAT)
Initial value:
new ModelVector( "Mast NS Disperse - Mast Normal Mean", "di_nonSpatialMastNormalMean", "di_nsmnmVal", 0, ModelVector.FLOAT)
Initial value:
new ModelVector( "Mast NS Disperse - Mast Normal Standard Deviation", "di_nonSpatialMastNormalStdDev", "di_nsmnsdVal", 0, ModelVector.FLOAT)
Initial value:
new ModelVector( "Mast NS Disperse - Non-Mast Normal Mean", "di_nonSpatialNonMastNormalMean", "di_nsnmnmVal", 0, ModelVector.FLOAT)
Initial value:
new ModelVector( "Mast NS Disperse - Non-Mast Normal Standard Deviation", "di_nonSpatialNonMastNormalStdDev", "di_nsnmnsdVal", 0, ModelVector.FLOAT)
Initial value:
new ModelVector( "Mast NS Disperse - Masting Group", "di_nonSpatialMastGroup", "di_nsmgVal", 0, ModelVector.MODEL_ENUM)
Initial value:
new ModelEnum(new int[] {0, 1, 2, 3, 4}, new String[] {"Deterministic", "Poisson", "Lognormal", "Normal", "Negative binomial"}, "Seed Distribution", "di_seedDistributionMethod")
Initial value:
new ModelInt(0, "Maximum Parent Trees Allowed in Gap Cell", "di_maxGapDensity")
Max number of parent trees that can be in a grid cell for it to still be marked as gap