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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151 to 160 of 223 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...
maxent.log
Jun 9, 2020 - Maximum Entropy machine learning model output using digitized downed trees (structure) with six variables and regularization multiplier of 2.0
Plain Text - 214.4 KB - MD5: 9a78d49de59bc10e70c05a058876481e
This is a log that consists of the time model is run, maxent software version, command line to invoke model, list of input environmental variables, list of input features, number of replicate runs, computational activities for each replicate, brief description of outputs
maxentResults.csv
Jun 9, 2020 - Maximum Entropy machine learning model output using digitized downed trees (structure) with six variables and regularization multiplier of 2.0
Comma Separated Values - 14.6 KB - MD5: e675a31d23ad3706c6866d602fc47e69
Contains performance metrics for each model run based on each sample replicate as well as their average. Notable Performance metrics include 1) Variable importance (contribution) for each environmental variable. 2) Variable permutation importance for each environmental variable....
Tree_0_Results.zip
Jun 9, 2020 - Maximum Entropy machine learning model output using digitized downed trees (structure) with six variables and regularization multiplier of 2.0
ZIP Archive - 111.4 MB - MD5: 8a7af4b2746887bbea27ab6144244235
Results from a Maxent model based on one of ten replicates for a subset (50% training, 50% testing) of all downed trees. Consists of the following files: (*.asc) - Probability (map) based on training data from replicate sample and generated from Maxent model informed by subset of...
Tree_1_Results.zip
Jun 9, 2020 - Maximum Entropy machine learning model output using digitized downed trees (structure) with six variables and regularization multiplier of 2.0
ZIP Archive - 111.3 MB - MD5: 69f1c3d7845a7aa0ca391977882cddc4
Results from a Maxent model based on two of ten replicates for a subset (50% training, 50% testing) of all downed trees. Consists of the following files: (*.asc) - Probability (map) based on training data from replicate sample and generated from Maxent model informed by subset of...
Tree_2_Results.zip
Jun 9, 2020 - Maximum Entropy machine learning model output using digitized downed trees (structure) with six variables and regularization multiplier of 2.0
ZIP Archive - 111.9 MB - MD5: 50977a6cbb008d99250a6ba126a994ab
Results from a Maxent model based on three of ten replicates for a subset (50% training, 50% testing) of all downed trees. Consists of the following files: (*.asc) - Probability (map) based on training data from replicate sample and generated from Maxent model informed by subset...
Tree_3_Results.zip
Jun 9, 2020 - Maximum Entropy machine learning model output using digitized downed trees (structure) with six variables and regularization multiplier of 2.0
ZIP Archive - 110.0 MB - MD5: 1a726d9b9751ff2945700d79eeef8ab3
Results from a Maxent model based on four of ten replicates for a subset (50% training, 50% testing) of all downed trees. Consists of the following files: (*.asc) - Probability (map) based on training data from replicate sample and generated from Maxent model informed by subset o...
Tree_4_Results.zip
Jun 9, 2020 - Maximum Entropy machine learning model output using digitized downed trees (structure) with six variables and regularization multiplier of 2.0
ZIP Archive - 111.2 MB - MD5: 7a4d0d29ddaeec9626fbed3473993cc8
Results from a Maxent model based on five of ten replicates for a subset (50% training, 50% testing) of all downed trees. Consists of the following files: (*.asc) - Probability (map) based on training data from replicate sample and generated from Maxent model informed by subset o...
Tree_5_Results.zip
Jun 9, 2020 - Maximum Entropy machine learning model output using digitized downed trees (structure) with six variables and regularization multiplier of 2.0
ZIP Archive - 112.5 MB - MD5: d3a969a97ffb1133ac790bfb456a8372
Results from a Maxent model based on six of ten replicates for a subset (50% training, 50% testing) of all downed trees. Consists of the following files: (*.asc) - Probability (map) based on training data from replicate sample and generated from Maxent model informed by subset of...
Tree_6_Results.zip
Jun 9, 2020 - Maximum Entropy machine learning model output using digitized downed trees (structure) with six variables and regularization multiplier of 2.0
ZIP Archive - 110.0 MB - MD5: 5dbecd0e3b9059c9e1a7209159c2a09a
Results from a Maxent model based on seven of ten replicates for a subset (50% training, 50% testing) of all downed trees. Consists of the following files: (*.asc) - Probability (map) based on training data from replicate sample and generated from Maxent model informed by subset...
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