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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1 to 10 of 17 Results
Maximum Entropy machine learning model output using all digitized downed trees with three variables and regularization multiplier of 1.0
Jun 9, 2020
FLUD Research Group, 2020, "Maximum Entropy machine learning model output using all digitized downed trees with three variables and regularization multiplier of 1.0", https://doi.org/10.18738/T8/GK5DBZ, Texas Data Repository, V1
This dataset (multiple outputs files in a compressed folder) is output from a run of the Maxent ML Model (Phillips et.al., 2006, url: https://biodiversityinformatics.amnh.org/open_source/maxent/) using all digitized downed trees (N=9505) and includes the three environmental varia...
Maximum Entropy machine learning model output using all digitized downed trees with three variables and regularization multiplier of 3.0
Jun 9, 2020
FLUD Research Group, 2020, "Maximum Entropy machine learning model output using all digitized downed trees with three variables and regularization multiplier of 3.0", https://doi.org/10.18738/T8/EQUZO5, Texas Data Repository, V1
This dataset (multiple outputs files in a compressed folder) is output from a run of the Maxent ML Model (Phillips et.al., 2006, url: https://biodiversityinformatics.amnh.org/open_source/maxent/) using all digitized downed trees (N=9505) and includes the three environmental varia...
Maximum Entropy machine learning model output using all digitized downed trees with three variables and regularization multiplier of 6.0
Jun 9, 2020
FLUD Research Group, 2020, "Maximum Entropy machine learning model output using all digitized downed trees with three variables and regularization multiplier of 6.0", https://doi.org/10.18738/T8/WHS3BR, Texas Data Repository, V1
This dataset (multiple outputs files in a compressed folder) is output from a run of the Maxent ML Model (Phillips et.al., 2006, url: https://biodiversityinformatics.amnh.org/open_source/maxent/) using all digitized downed trees (N=9505) and includes the three environmental varia...
Maximum Entropy machine learning model output using all digitized downed trees with six variables and regularization multiplier of 1.0
Jun 9, 2020
FLUD Research Group, 2020, "Maximum Entropy machine learning model output using all digitized downed trees with six variables and regularization multiplier of 1.0", https://doi.org/10.18738/T8/4TVY7S, Texas Data Repository, V1
This dataset (multiple outputs files in a compressed folder) is output from a run of the Maxent ML Model (Phillips et.al., 2006, url: https://biodiversityinformatics.amnh.org/open_source/maxent/) using all digitized downed trees (N=9505) and includes all environmental variables (...
Maximum Entropy machine learning model output using all digitized downed trees with six variables and regularization multiplier of 2.0
Jun 9, 2020
FLUD Research Group, 2020, "Maximum Entropy machine learning model output using all digitized downed trees with six variables and regularization multiplier of 2.0", https://doi.org/10.18738/T8/VMKLOK, Texas Data Repository, V1
This dataset (multiple outputs files in a compressed folder) is output from a run of the Maxent ML Model (Phillips et.al., 2006, url: https://biodiversityinformatics.amnh.org/open_source/maxent/) using all digitized downed trees (N=9505) and includes all environmental variables (...
Maximum Entropy machine learning model output using all digitized downed trees with six variables and regularization multiplier of 3.0
Jun 9, 2020
FLUD Research Group, 2020, "Maximum Entropy machine learning model output using all digitized downed trees with six variables and regularization multiplier of 3.0", https://doi.org/10.18738/T8/AJFW6L, Texas Data Repository, V1
This dataset (multiple outputs files in a compressed folder) is output from a run of the Maxent ML Model (Phillips et.al., 2006, url: https://biodiversityinformatics.amnh.org/open_source/maxent/) using all digitized downed trees (N=9505) and includes all environmental variables (...
Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 1.0
Jun 9, 2020
FLUD Research Group, 2020, "Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 1.0", https://doi.org/10.18738/T8/GMU4XY, 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 the three environmental variables...
Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 3.0
Jun 9, 2020
FLUD Research Group, 2020, "Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 3.0", https://doi.org/10.18738/T8/F2U3QV, 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 the three environmental variables...
Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 7.0
Jun 9, 2020
FLUD Research Group, 2020, "Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 7.0", https://doi.org/10.18738/T8/ILRKFQ, 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 the three environmental variables...
Maximum Entropy machine learning model output using digitized downed trees (structure) with six variables and regularization multiplier of 1.0
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 1.0", https://doi.org/10.18738/T8/8UXWKH, 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...
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