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 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...
maxent.log
Jun 9, 2020 - Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 1.0
Plain Text - 118.1 KB - MD5: 0d90b68c95f849dbbf28eb3157e43d5c
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 three variables and regularization multiplier of 1.0
Comma Separated Values - 12.1 KB - MD5: c044dc0faf0609dda53d244114913829
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 three variables and regularization multiplier of 1.0
ZIP Archive - 11.5 MB - MD5: 66a8c9990f40577027bcfaff455c9d8f
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 three variables and regularization multiplier of 1.0
ZIP Archive - 11.4 MB - MD5: ae4b142e7ce405a34cb062f90556a097
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 three variables and regularization multiplier of 1.0
ZIP Archive - 11.5 MB - MD5: 4dfb7659d104ae085c8efc5285a117f4
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 three variables and regularization multiplier of 1.0
ZIP Archive - 11.4 MB - MD5: eb52522332d97288684e5c3f85c87740
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 three variables and regularization multiplier of 1.0
ZIP Archive - 11.4 MB - MD5: 67edf476dd1961f3b1e6b7d1cc7153a7
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 three variables and regularization multiplier of 1.0
ZIP Archive - 11.6 MB - MD5: 1177884646883e54011535f64201025d
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 three variables and regularization multiplier of 1.0
ZIP Archive - 11.4 MB - MD5: 30b0a6272716a8d99bef0ab5fe57bf69
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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