This dataverse contains data inputs and products from two complimentary analyses: a spatial analysis and a machine learning approach. Both analyses were geared towards understanding relative importance of ecological factors within the Mission River Floodplain. These datasets are broken down as follows:

Digitized Trees:

    Digitized Downed/Standing Trees in the Mission River Floodplain

Spatial Analysis

    Environmental Variables Used in Spatial Analysis

    Counts of Downed Trees for Levels of Environmental Variables: Results of Spatial Analysis

ML Approach:

    Environmental Variables Used in Maxent Machine Learning Approach

    MaxentVariableselection Inputs/Outputs

    Maxent Models (digitized downed trees with structure):

        Model output with six variables and regularization multiplier of 1.0

        Model output with six variables and regularization multiplier of 2.0

        Model output with six variables and regularization multiplier of 3.0

        Model output with three variables and regularization multiplier of 1.0

        Model output with three variables and regularization multiplier of 3.0

        Model output with three variables and regularization multiplier of 7.0

    Maxent Models (all digitized downed trees):

        Model output with six variables and regularization multiplier of 1.0

        Model output with six variables and regularization multiplier of 2.0

        Model output with six variables and regularization multiplier of 3.0

        Model output with three variables and regularization multiplier of 1.0

        Model output with three variables and regularization multiplier of 3.0

        Model output with three variables and regularization multiplier of 6.0

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11 to 17 of 17 Results
Maximum Entropy machine learning model output using digitized downed trees (structure) with six variables and regularization multiplier of 2.0
Jun 9, 2020
FLUD Research Group, 2020, "Maximum Entropy machine learning model output using digitized downed trees (structure) with six variables and regularization multiplier of 2.0", https://doi.org/10.18738/T8/GQYIX2, Texas Data Repository, V1
This dataset (multiple outputs files in a compresssed folder) is output from a run of the Maxent ML Model (Phillips et.al., 2006, url: https://biodiversityinformatics.amnh.org/open_source/maxent/) using trees with structure (N=4283) and includes all environmental variables (veget...
Maximum Entropy machine learning model output using digitized downed trees (structure) with six variables and regularization multiplier of 3.0
Jun 8, 2020
FLUD Research Group, 2020, "Maximum Entropy machine learning model output using digitized downed trees (structure) with six variables and regularization multiplier of 3.0", https://doi.org/10.18738/T8/0PQLIB, Texas Data Repository, V1
This dataset (multiple outputs files in a compresssed folder) is output from a run of the Maxent ML Model (Phillips et.al., 2006, url: https://biodiversityinformatics.amnh.org/open_source/maxent/) using trees with structure (N=4283) and includes all environmental variables (veget...
Environmental Variables Used in Spatial Analysis
Jun 8, 2020
FLUD Research Group, 2020, "Environmental Variables Used in Spatial Analysis", https://doi.org/10.18738/T8/O8GYRJ, Texas Data Repository, V1
Polygon shapefiles that comprise levels of six environmental variables are used in conjunction with downed trees to inform the spatial analysis. A census of downed trees is created in each region within each environmental variable. Four environmental variables (elevation, general...
Environmental Variables Used in Maxent Machine Learning Approach
Jun 8, 2020
FLUD Research Group, 2020, "Environmental Variables Used in Maxent Machine Learning Approach", https://doi.org/10.18738/T8/MJNKOW, Texas Data Repository, V1
Six environmental variables are represented as raster maps, and they are used in conjunction with locations of downed trees to inform the variable importance and relationships derived from results of the Maxent ML approach. These variables are processed as ESRI ASCII raster maps...
Digitized Downed/Standing Trees in the Mission River Floodplain
Jun 8, 2020
FLUD Research Group, 2020, "Digitized Downed/Standing Trees in the Mission River Floodplain", https://doi.org/10.18738/T8/UUAQT7, Texas Data Repository, V1
Downed trees (N=9505) were digitized by members of the FLUD Research group (faculty and student researchers). Student researchers consist of graduate and undergraduate students. Undergraduate student researchers worked at the supervision and expertise of faulty and graduate stude...
Counts of Downed Trees for Levels of Environmental Variables: Results of Spatial Analysis
Jun 8, 2020
FLUD Research Group, 2020, "Counts of Downed Trees for Levels of Environmental Variables: Results of Spatial Analysis", https://doi.org/10.18738/T8/YHNJAP, Texas Data Repository, V1
The following archives consists of polygon shapefiles that contain single and combinations of two environmental variables. Within each of these shapefiles are stratified levels of values, where each level contains counts of ecologically significant points that are associated with...
MaxentVariableselection Inputs/Outputs
Jun 8, 2020
FLUD Research Group, 2020, "MaxentVariableselection Inputs/Outputs", https://doi.org/10.18738/T8/ACTHJO, Texas Data Repository, V1
The following files are inputs and outputs to the MaxentVariableSelection procedure (Jueterbock et.al. 2016) that is used to judge which combinations of environmental variables and regularization multiplier (beta parameter) are most effective. MaxentVariableSelection was conducte...
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