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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131 to 140 of 223 Results
Tree_7_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 - 11.1 MB - MD5: 365f8fbb3c35d010f7bc99971d48a7b6
Results from a Maxent model based on eight 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_8_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: ece8dea9b9c715e979ad22fc976b22a6
Results from a Maxent model based on nine 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_9_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: 87e7392f54f83847e671536be167bb1f
Results from a Maxent model based on ten 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_Statistics.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 - 54.5 MB - MD5: b924bd680a20fd7bb18347d3a052559c
Maps (*.asc) of statistics (resolution of 1.0 m) for probability of occurrence from ten replicate runs based on subsamples of data (min, max, median, average, standard deviation) of model output (probability of downed tree occurrence).
Variable_Responses.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 - 32.7 KB - MD5: e1f5ce9e22ed36415bb0952c7752cada
Tables consisting of mean responses of ten replicate model runs based on subsamples of data, associated with each environmental variable and a model formed with only that specific variable (only).
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...
maxent.log
Jun 9, 2020 - Maximum Entropy machine learning model output using digitized downed trees (structure) with six variables and regularization multiplier of 1.0
Plain Text - 213.7 KB - MD5: 285d657177372c0f565d294f864cc880
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 1.0
Comma Separated Values - 14.6 KB - MD5: 461a8462730c4baa79e0dfa146c0a6ca
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 1.0
ZIP Archive - 112.4 MB - MD5: 7bb5b44305653d7ed2dca5c3f59944bb
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 1.0
ZIP Archive - 109.1 MB - MD5: 451b42eaaf432a50ba70667af62c904f
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...
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