javawrapper.DisperseBehaviors Class Reference

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

Inheritance diagram for javawrapper.DisperseBehaviors:

javawrapper.BehaviorTypeBase javawrapper.WorkerBase List of all members.

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.

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)


Constructor & Destructor Documentation

javawrapper.DisperseBehaviors.DisperseBehaviors ( GUIManager  oManager  ) 

Constructor.

Parameters:
oManager GUIManager object.

Edit history:
------------------
April 28, 2004: Submitted in beta version (LEM)


Member Function Documentation

void javawrapper.DisperseBehaviors.DoSetup ( TreePopulation  oPop  )  throws ModelException [virtual]

Does setup.

Sets up the substrate favorability grid.

Parameters:
oPop TreePopulation object.
Exceptions:
ModelException if there's a problem setting behavior use data.
Todo:
Something for stump removal?

Edit history:
------------------
April 28, 2004: Submitted in beta version (LEM)

Implements javawrapper.WorkerBase.

void javawrapper.DisperseBehaviors.ValidateData ( TreePopulation  oPop  )  throws ModelException [virtual]

Validates the data in preparation for parameter file writing or some such.

Parameters:
oPop TreePopulation object.
Exceptions:
ModelException if:
  • Any value in mp_fMinDbhForReproduction is 0
  • There aren't values for all species for any disperse function used
  • Max search radius for anisotropic disperse is not a positive number and less than half the shortest plot distance
  • The direction of max dispersal is not between 0 and 2PI
  • Spatial disperse is used and a value for weibull theta (masting or non-masting) is greater than or equal to 50
  • Spatial disperse is used and a value for beta (masting or non-masting) is greater than 25
  • Fraction participating, either masting or non-masting, is not a proportion
  • All values for X0 and Xb are not 0

Edit history:
------------------
April 28, 2004: Submitted in beta version (LEM)

Implements javawrapper.WorkerBase.


Member Data Documentation

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.

ModelVector javawrapper.DisperseBehaviors.mp_fSlopeOfLambda [protected]

Initial value:

 new ModelVector(
      "Slope Mean Non-Spatial Seed Rain, seeds/m2/ha of BA/yr",
      "di_nonSpatialSlopeOfLambda",
      "di_nssolVal", 0,
      ModelVector.FLOAT)
Slope of lambda for non spatial dispersal for each species.

ModelVector javawrapper.DisperseBehaviors.mp_fInterceptOfLambda [protected]

Initial value:

 new ModelVector(
      "Intercept of Mean Non-Spatial Seed Rain, seeds/m2/yr",
      "di_nonSpatialInterceptOfLambda",
      "di_nsiolVal", 0, ModelVector.FLOAT)
Intercept of lambda for non spatial dispersal for each species.

ModelVector javawrapper.DisperseBehaviors.mp_fMinDbhForReproduction [protected]

Initial value:

 new ModelVector(
        "Minimum DBH for Reproduction, in cm", "di_minDbhForReproduction",
        "di_mdfrVal", 0, ModelVector.FLOAT)
Minimum DBH for reproduction for each species.

ModelVector javawrapper.DisperseBehaviors.mp_fStumpSTR [protected]

Initial value:

 new ModelVector("STR/n for Stumps",
      "di_suckerSTR", "di_ssVal", 0, ModelVector.FLOAT)
STR for stump dispersal for each species.

ModelVector javawrapper.DisperseBehaviors.mp_fStumpBeta [protected]

Initial value:

 new ModelVector("Beta for Stumps",
      "di_suckerBeta", "di_sbVal", 0, ModelVector.FLOAT)
Beta for stump dispersal for each species.

ModelVector javawrapper.DisperseBehaviors.mp_fDirectionMaxDispersal [protected]

Initial value:

 new ModelVector(
      "Azimuth Direction of Max Dispersal Distance, in rad",
      "di_directionMaxDispersal", "di_dmdVal", 0, ModelVector.FLOAT)
Azimuth direction of maximum dispersal distance, in radians.

ModelVector javawrapper.DisperseBehaviors.mp_fAnisotropicAmplitude [protected]

Initial value:

 new ModelVector(
      "Amplitude of Anisotropic Effect", "di_anisotropicAmplitude", "di_aaVal",
      0, ModelVector.FLOAT)
Amplitude of anisotropic effect.

ModelVector javawrapper.DisperseBehaviors.mp_fStandardDeviation [protected]

Initial value:

 new ModelVector(
      "Seed Dist. Std. Deviation (Normal or Lognormal)",
      "di_standardDeviation", "di_sdVal", 0, ModelVector.FLOAT)
Standard deviation if seed distribution method is normal or lognormal.

ModelVector javawrapper.DisperseBehaviors.mp_fClumpingParameter [protected]

Initial value:

 new ModelVector(
      "Seed Dist. Clumping Parameter (Neg. Binomial)",
      "di_clumpingParameter", "di_cpVal", 0, ModelVector.FLOAT)
Clumping parameter if seed distribution is negative binomial.

ModelVector javawrapper.DisperseBehaviors.mp_fMastingA [protected]

Initial value:

 new ModelVector(
      "Masting Disperse - Masting CDF \"a\"",
      "di_mastCDFA", "di_mcdfaVal", 0, ModelVector.FLOAT)
Masting spatial disperse - "a" for masting CDF.

ModelVector javawrapper.DisperseBehaviors.mp_fMastingB [protected]

Initial value:

 new ModelVector(
      "Masting Disperse - Masting CDF \"b\"",
      "di_mastCDFB", "di_mcdfbVal", 0, ModelVector.FLOAT)
Masting spatial disperse - "b" for masting CDF.

ModelVector javawrapper.DisperseBehaviors.mp_iMastSTRDrawPDF [protected]

Initial value:

 new ModelVector(
      "Masting Disperse - STR Draw PDF",
      "di_mastSTRPDF", "di_mstrpdfVal", 0, ModelVector.MODEL_ENUM)
Masting spatial disperse - Probability distribution for STR draw.

ModelVector javawrapper.DisperseBehaviors.mp_fMastNonMastSTRMean [protected]

Initial value:

 new ModelVector(
              "Masting Disperse - Non-Masting STR/n Mean",
              "di_spatialSTR", "di_sstrVal", 0, ModelVector.FLOAT)
Masting spatial disperse - Non-mast STR mean.

ModelVector javawrapper.DisperseBehaviors.mp_fMastNonMastSTRStdDev [protected]

Initial value:

 new ModelVector(
              "Masting Disperse - Non-Masting STR/n Standard Deviation",
              "di_spatialSTRStdDev", "di_sstrsdVal", 0, ModelVector.FLOAT)
Masting spatial disperse - Non-mast STR draw standard deviation, if PDF = normal or lognormal.

ModelVector javawrapper.DisperseBehaviors.mp_fMastMastSTRMean [protected]

Initial value:

 new ModelVector(
              "Masting Disperse - Masting STR/n Mean",
              "di_mastingSTR", "di_mstrVal", 0, ModelVector.FLOAT)
Masting spatial disperse - Masting STR mean.

ModelVector javawrapper.DisperseBehaviors.mp_fMastMastSTRStdDev [protected]

Initial value:

 new ModelVector(
              "Masting Disperse - Masting STR/n Standard Deviation",
              "di_mastingSTRStdDev", "di_mstrsdVal", 0, ModelVector.FLOAT)
Masting spatial disperse - Masting STR draw standard deviation, if PDF = normal or lognormal.

ModelVector javawrapper.DisperseBehaviors.mp_fMastNonMastBeta [protected]

Initial value:

 new ModelVector(
              "Masting Disperse - Non-Masting Beta",
              "di_spatialBeta", "di_sbVal", 0, ModelVector.FLOAT)
Masting spatial disperse - Non-masting beta.

ModelVector javawrapper.DisperseBehaviors.mp_fMastMastBeta [protected]

Initial value:

 new ModelVector(
              "Masting Disperse - Masting Beta",
              "di_mastingBeta", "di_mbVal", 0, ModelVector.FLOAT)
Masting spatial disperse - Masting beta.

ModelVector javawrapper.DisperseBehaviors.mp_fMastMastWeibDisp [protected]

Initial value:

 new ModelVector(
              "Masting Disperse - Masting Weibull Dispersal",
              "di_weibullMastingDispersal", "di_wmdVal", 0, ModelVector.FLOAT)
Masting spatial disperse - Weibull masting dispersal.

ModelVector javawrapper.DisperseBehaviors.mp_fMastMastWeibTheta [protected]

Initial value:

 new ModelVector(
              "Masting Disperse - Masting Weibull Theta",
              "di_weibullMastingTheta", "di_wmtVal", 0, ModelVector.FLOAT)
Masting spatial disperse - Weibull masting theta.

ModelVector javawrapper.DisperseBehaviors.mp_fMastMastLognormalX0 [protected]

Initial value:

 new ModelVector(
              "Masting Disperse - Masting Lognormal X0",
              "di_lognormalMastingX0", "di_lmx0Val", 0, ModelVector.FLOAT)
Masting spatial disperse - Lognormal masting X0.

ModelVector javawrapper.DisperseBehaviors.mp_fMastMastLognormalXb [protected]

Initial value:

 new ModelVector(
              "Masting Disperse - Masting Lognormal Xb",
              "di_lognormalMastingXb", "di_lmxbVal", 0, ModelVector.FLOAT)
Masting spatial disperse - Lognormal masting Xb.

ModelVector javawrapper.DisperseBehaviors.mp_iMastGroupID [protected]

Initial value:

 new ModelVector(
              "Masting Disperse - Masting Group",
              "di_mastGroup", "di_mgVal", 0, ModelVector.MODEL_ENUM)
Masting spatial disperse - Group identification for each species.

ModelVector javawrapper.DisperseBehaviors.mp_iMastDrawPerSpecies [protected]

Initial value:

 new ModelVector(
      "Masting Disperse - Stochastic STR Draw Frequency",
      "di_mastDrawPerSpecies", "di_mdpsVal", 0, ModelVector.MODEL_ENUM)
Masting spatial disperse - Whether to draw STR once per species (1) or once per tree (0).

ModelVector javawrapper.DisperseBehaviors.mp_fMastMastPropParticipating [protected]

Initial value:

 new ModelVector(
      "Masting Disperse - Mast Proportion Participating (0-1)",
      "di_mastPropParticipating", "di_mppVal", 0, ModelVector.FLOAT)
Masting spatial disperse - Proportion trees participating in disperse for mast event.

ModelVector javawrapper.DisperseBehaviors.mp_fMastNonMastPropParticipating [protected]

Initial value:

 new ModelVector(
      "Masting Disperse - Non-Mast Proportion Participating (0-1)",
      "di_spatialPropParticipating", "di_sppVal", 0, ModelVector.FLOAT)
Masting spatial disperse - Proporton trees participating in disperse for non-mast event.

ModelEnum javawrapper.DisperseBehaviors.m_iSeedDistributionMethod [protected]

Initial value:

      new ModelEnum(new int[] {0, 1, 2, 3, 4}
                    ,
                    new String[] {"Deterministic", "Poisson", "Lognormal",
                    "Normal", "Negative binomial"}
                    , "Seed Distribution", "di_seedDistributionMethod")
Seed distribution.

ModelFloat javawrapper.DisperseBehaviors.m_fMaxSearchRadius [protected]

Initial value:

 new ModelFloat(0,
                                          "Maximum Search Distance for Neighbor Parents, in m",
                                          "di_maxSearchRadius")
Maximum search radius, in meters, for neighbors for isotropic and anisotropic disperse.

ModelInt javawrapper.DisperseBehaviors.m_iMaxGapDensity [protected]

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.


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