Inheritance diagram for javawrapper.DisperseBehaviors:

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 |
| Slope of lambda for non spatial dispersal for each species. | |
| ModelVector | mp_fInterceptOfLambda |
| Intercept of lambda for non spatial dispersal 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_fDirectionMaxDispersal |
| Azimuth direction of maximum dispersal distance, in radians. | |
| ModelVector | mp_fAnisotropicAmplitude |
| Amplitude of anisotropic effect. | |
| ModelVector | mp_fStandardDeviation |
| Standard deviation if seed distribution method is normal or lognormal. | |
| ModelVector | mp_fClumpingParameter |
| Clumping parameter if seed distribution is negative binomial. | |
| ModelVector | mp_fMastingA |
| Masting spatial disperse - "a" for masting CDF. | |
| ModelVector | mp_fMastingB |
| Masting spatial disperse - "b" for masting CDF. | |
| ModelVector | mp_iMastSTRDrawPDF |
| Masting spatial disperse - Probability distribution for STR draw. | |
| ModelVector | mp_fMastNonMastSTRMean |
| Masting spatial disperse - Non-mast STR mean. | |
| ModelVector | mp_fMastNonMastSTRStdDev |
| Masting spatial disperse - Non-mast STR draw standard deviation, if PDF = normal or lognormal. | |
| ModelVector | mp_fMastMastSTRMean |
| Masting spatial disperse - Masting STR mean. | |
| ModelVector | mp_fMastMastSTRStdDev |
| Masting spatial disperse - Masting STR draw standard deviation, if PDF = normal or lognormal. | |
| ModelVector | mp_fMastNonMastBeta |
| Masting spatial disperse - Non-masting beta. | |
| ModelVector | mp_fMastMastBeta |
| Masting spatial disperse - Masting beta. | |
| ModelVector | mp_fMastMastWeibDisp |
| Masting spatial disperse - Weibull masting dispersal. | |
| ModelVector | mp_fMastMastWeibTheta |
| Masting spatial disperse - Weibull masting theta. | |
| ModelVector | mp_fMastMastLognormalX0 |
| Masting spatial disperse - Lognormal masting X0. | |
| ModelVector | mp_fMastMastLognormalXb |
| Masting spatial disperse - Lognormal masting Xb. | |
| ModelVector | mp_iMastGroupID |
| Masting spatial disperse - Group identification for each species. | |
| ModelVector | mp_iMastDrawPerSpecies |
| Masting spatial disperse - Whether to draw STR once per species (1) or once per tree (0). | |
| ModelVector | mp_fMastMastPropParticipating |
| Masting spatial disperse - Proportion trees participating in disperse for mast event. | |
| ModelVector | mp_fMastNonMastPropParticipating |
| Masting spatial disperse - Proporton trees participating in disperse for non-mast event. | |
| ModelEnum | m_iSeedDistributionMethod |
| Seed distribution. | |
| ModelFloat | m_fMaxSearchRadius |
| Maximum search radius, in meters, for neighbors for isotropic and anisotropic disperse. | |
| ModelInt | m_iMaxGapDensity |
| Max number of parent trees that can be in a grid cell for it to still be marked as gap. | |
Copyright: Copyright (c) Charles D. Canham 2003
Company: Institute of Ecosystem Studies
| javawrapper.DisperseBehaviors.DisperseBehaviors | ( | GUIManager | oManager | ) |
Constructor.
| oManager | GUIManager object. |
| void javawrapper.DisperseBehaviors.DoSetup | ( | TreePopulation | oPop | ) | throws ModelException [virtual] |
Does setup.
Sets up the substrate favorability grid.
| 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( "Azimuth Direction of Max Dispersal Distance, in rad", "di_directionMaxDispersal", "di_dmdVal", 0, ModelVector.FLOAT)
Initial value:
new ModelVector( "Amplitude of Anisotropic Effect", "di_anisotropicAmplitude", "di_aaVal", 0, ModelVector.FLOAT)
Initial value:
new ModelVector( "Seed Dist. Std. Deviation (Normal or Lognormal)", "di_standardDeviation", "di_sdVal", 0, ModelVector.FLOAT)
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 ModelEnum(new int[] {0, 1, 2, 3, 4}
,
new String[] {"Deterministic", "Poisson", "Lognormal",
"Normal", "Negative binomial"}
, "Seed Distribution", "di_seedDistributionMethod")
Initial value:
new ModelFloat(0, "Maximum Search Distance for Neighbor Parents, in m", "di_maxSearchRadius")
Initial value:
new ModelInt(0, "Maximum Parent Trees Allowed in Gap Cell", "di_maxGapDensity")
1.5.2