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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121 to 130 of 223 Results
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...
maxent.log
Jun 9, 2020 - Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 7.0
Plain Text - 118.5 KB - MD5: 72fbad568a97560e55530c9e0395520b
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 7.0
Comma Separated Values - 12.0 KB - MD5: a1cea428060e45303e6734ed42d6840f
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 7.0
ZIP Archive - 11.0 MB - MD5: 9a7c6ba7a22cde5f016736f87cc45c01
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 7.0
ZIP Archive - 10.7 MB - MD5: b81b8f97e37b27acaffc4723a1cfd52c
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 7.0
ZIP Archive - 10.9 MB - MD5: 21399bdbb16ffa51f560c4dd851f7d17
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 7.0
ZIP Archive - 11.0 MB - MD5: 5e2ed492534be207d335959f7a96f93e
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 7.0
ZIP Archive - 10.1 MB - MD5: cb9ee2206fe31e4f9ba07b90cfbd1bc3
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 7.0
ZIP Archive - 10.0 MB - MD5: 6fc1062bb4fae8e32d3c2da6ce374a91
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 7.0
ZIP Archive - 9.8 MB - MD5: 31789c366fb8d59d80f0b261ac4f0303
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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