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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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...
maxent.log
Jun 9, 2020 - Maximum Entropy machine learning model output using all digitized downed trees with three variables and regularization multiplier of 6.0
Plain Text - 112.1 KB - MD5: f9388a3ff256d9205c7534fcc81a1f61
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 all digitized downed trees with three variables and regularization multiplier of 6.0
Comma Separated Values - 12.0 KB - MD5: a7e35235d770ef361542f8f8d274cac7
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 all digitized downed trees with three variables and regularization multiplier of 6.0
ZIP Archive - 11.3 MB - MD5: 668449fa5de53cd9ea39bc3f0f14c34f
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 all digitized downed trees with three variables and regularization multiplier of 6.0
ZIP Archive - 11.3 MB - MD5: adea21955816075517ae710ab5f6684c
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 all digitized downed trees with three variables and regularization multiplier of 6.0
ZIP Archive - 11.4 MB - MD5: bb72d6246760a1d89696448766bfaf14
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 all digitized downed trees with three variables and regularization multiplier of 6.0
ZIP Archive - 11.4 MB - MD5: 31e3267cb00b7ed09170ef3e40f03194
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 all digitized downed trees with three variables and regularization multiplier of 6.0
ZIP Archive - 11.3 MB - MD5: 83b18db8c0d84e129a4e05adf496a2fb
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 all digitized downed trees with three variables and regularization multiplier of 6.0
ZIP Archive - 11.4 MB - MD5: c5e1d3ef74d9607692b406e5f8404e9b
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 all digitized downed trees with three variables and regularization multiplier of 6.0
ZIP Archive - 11.3 MB - MD5: d9e8c31fcabd398f1a88a2729e9839ea
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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