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
 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
  • mast conditions.

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
  • non-mast conditions.

ModelVector mp_iNonSpatialMastMastGroupID
 Masting non-spatial disperse - group identification for each species.
ModelVector mp_fTempDepNeighM
 Temperature dependent neighborhood disperse - fecundity function M.
ModelVector mp_fTempDepNeighN
 Temperature dependent neighborhood disperse - fecundity function N.
ModelVector mp_fTempDepNeighPresM
 Temperature dependent neighborhood disperse - presence function M.
ModelVector mp_fTempDepNeighPresB
 Temperature dependent neighborhood disperse - presence function B.
ModelVector mp_fTempDepNeighPresThreshold
 Temperature dependent neighborhood disperse - presence threshold.
ModelVector mp_fTempDepNeighA
 Temperature dependent neighborhood disperse - A.
ModelVector mp_fTempDepNeighB
 Temperature dependent neighborhood disperse - B.
ModelFloat m_fTempDepNeighMaxRadius
 Temperature dependent neighborhood disperse - maximum search radius, in meters, for neighbors.
ModelEnum m_iSeedDistributionMethod
 Seed distribution.
ModelInt m_iMaxGapDensity
 Maximum search radius, in meters, for neighbors for isotropic and anisotropic disperse.


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

javawrapper.DisperseBehaviors.DisperseBehaviors ( GUIManager  oManager  ) 

Constructor.

Parameters:
oManager GUIManager object.


Member Function Documentation

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

Does setup.

Parameters:
oPop TreePopulation object.
Exceptions:
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.

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
  • Non-spatial masting disperse - inverse gaussian mus and lambdas are negative

Implements javawrapper.WorkerBase.


Member Data Documentation

final int javawrapper.DisperseBehaviors.WEIBULL = 0 [static]

Weibull disperse function.

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.

Total number of disperse functions.

Total number of forest cover statuses.

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.

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.

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.

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.

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

Initial value:

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

Initial value:

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

Initial value:

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

Initial value:

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

Initial value:

 new ModelVector(
      "Seed Dist. Std. Deviation (Normal or Lognormal)",
      "di_standardDeviation", "di_sdVal", 0, ModelVector.FLOAT)
Azimuth direction of maximum dispersal distance, in radians.

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)
Clumping parameter if seed distribution is negative binomial.

Initial value:

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

Initial value:

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

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.

Initial value:

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

Initial value:

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

Initial value:

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

Initial value:

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

Initial value:

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

Initial value:

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

Initial value:

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

Initial value:

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

Initial value:

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

Initial value:

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

Initial value:

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

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).

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.

Initial value:

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

Initial value:

 new ModelVector(
      "Mast NS Disperse - PDF Non-Masting Conditions", 
      "di_nonSpatialNonMastFunction", "di_nsnmfVal", 0, 
      ModelVector.MODEL_ENUM)
Masting non-spatial disperse - distribution function to pick seeds in non-mast conditions.

Initial value:

 new ModelVector(
        "Mast NS Disperse - Binomial P (Mast Chance)",
        "di_nonSpatialMastBinomialP", "di_nsmbpVal", 0, ModelVector.FLOAT)
Masting non-spatial disperse - P parameter for binomial distribution for deciding whether to mast.

Initial value:

 new ModelVector(
      "Mast NS Disperse - PDF Masting Conditions", 
      "di_nonSpatialMastMastFunction", "di_nsmmfVal", 0, 
      ModelVector.MODEL_ENUM)
Masting non-spatial disperse - distribution function to pick seeds in mast conditions.

Initial value:

 new ModelVector(
        "Mast NS Disperse - Mast Inv. Gauss. Mu",
        "di_nonSpatialMastInvGaussMu", "di_nsmigmVal", 0, ModelVector.FLOAT)
Masting non-spatial disperse - mu parameter for inverse gaussian distribution - mast conditions.

Initial value:

 new ModelVector(
        "Mast NS Disperse - Mast Inv. Gauss. Lambda",
        "di_nonSpatialMastInvGaussLambda", "di_nsmiglVal", 0, 
        ModelVector.FLOAT)
Masting non-spatial disperse - lambda parameter for inverse gaussian distribution - mast conditions.

Initial value:

 new ModelVector(
        "Mast NS Disperse - Non-Mast Inv. Gauss. Mu",
        "di_nonSpatialNonMastInvGaussMu", "di_nsnmigmVal", 0, 
        ModelVector.FLOAT)
Masting non-spatial disperse - mu parameter for inverse gaussian distribution - non-mast conditions.

Initial value:

 new ModelVector(
        "Mast NS Disperse - Non-Mast Inv. Gauss. Lambda",
        "di_nonSpatialNonMastInvGaussLambda", "di_nsnmiglVal", 0, 
        ModelVector.FLOAT)
Masting non-spatial disperse - lambda parameter for inverse gaussian distribution - non-mast conditions.

Initial value:

 new ModelVector(
        "Mast NS Disperse - Mast Normal Mean",
        "di_nonSpatialMastNormalMean", "di_nsmnmVal", 0, ModelVector.FLOAT)
Masting non-spatial disperse - mean for normal distribution - mast conditions.

Initial value:

 new ModelVector(
        "Mast NS Disperse - Mast Normal Standard Deviation",
        "di_nonSpatialMastNormalStdDev", "di_nsmnsdVal", 0, ModelVector.FLOAT)
Masting non-spatial disperse - standard deviation for normal distribution
  • mast conditions.

Initial value:

 new ModelVector(
        "Mast NS Disperse - Non-Mast Normal Mean",
        "di_nonSpatialNonMastNormalMean", "di_nsnmnmVal", 0, ModelVector.FLOAT)
Masting non-spatial disperse - mean for normal distribution - non-mast conditions.

Initial value:

 new ModelVector(
        "Mast NS Disperse - Non-Mast Normal Standard Deviation",
        "di_nonSpatialNonMastNormalStdDev", "di_nsnmnsdVal", 0, 
        ModelVector.FLOAT)
Masting non-spatial disperse - standard deviation for normal distribution
  • non-mast conditions.

Initial value:

 new ModelVector(
        "Mast NS Disperse - Masting Group", "di_nonSpatialMastGroup", 
        "di_nsmgVal", 0, ModelVector.MODEL_ENUM)
Masting non-spatial disperse - group identification for each species.

Initial value:

 new ModelVector(
        "Temp Dep Neigh Disperse - M", "di_tempDepNeighFecM", "di_tdnfmVal", 0, 
        ModelVector.FLOAT)
Temperature dependent neighborhood disperse - fecundity function M.

Initial value:

 new ModelVector(
        "Temp Dep Neigh Disperse - N", "di_tempDepNeighFecN", "di_tdnfnVal", 0, 
        ModelVector.FLOAT)
Temperature dependent neighborhood disperse - fecundity function N.

Initial value:

 new ModelVector(
        "Temp Dep Neigh Disperse - Presence M", "di_tempDepNeighPresM", 
        "di_tdnpmVal", 0, ModelVector.FLOAT)
Temperature dependent neighborhood disperse - presence function M.

Initial value:

 new ModelVector(
        "Temp Dep Neigh Disperse - Presence B", "di_tempDepNeighPresB", 
        "di_tdnpbVal", 0, ModelVector.FLOAT)
Temperature dependent neighborhood disperse - presence function B.

Initial value:

 new ModelVector(
        "Temp Dep Neigh Disperse - Presence Threshold (0-1)", 
        "di_tempDepNeighPresThreshold", "di_tdnptVal", 0, ModelVector.FLOAT)
Temperature dependent neighborhood disperse - presence threshold.

Initial value:

 new ModelVector(
        "Temp Dep Neigh Disperse - A", "di_tempDepNeighA", "di_tdnaVal", 0, 
        ModelVector.FLOAT)
Temperature dependent neighborhood disperse - A.

Initial value:

 new ModelVector(
        "Temp Dep Neigh Disperse - B", "di_tempDepNeighB", "di_tdnbVal", 0, 
        ModelVector.FLOAT)
Temperature dependent neighborhood disperse - B.

Initial value:

 new ModelFloat(0,
      "Temp Dep Neigh Disperse - Max Distance for Conspecific Adults (m)",
      "di_tempDepNeighRadius")
Temperature dependent neighborhood disperse - maximum search radius, in meters, for neighbors.

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.

Initial value:

 new ModelInt(0,
      "Maximum Parent Trees Allowed in Gap Cell", "di_maxGapDensity")
Maximum search radius, in meters, for neighbors for isotropic and anisotropic disperse.

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


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

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