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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21 to 30 of 206 Results
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 3.0
ZIP Archive - 11.4 MB - MD5: 9c60a074f7c0bf18497132f752055377
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 3.0
ZIP Archive - 11.5 MB - MD5: 36c1f5a66bc674ff5c444a0c6710fc51
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 3.0
ZIP Archive - 11.6 MB - MD5: fc2ce0e1a866fa8792b2c057700aee15
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...
Tree_7_Results.zip
Jun 9, 2020 - Maximum Entropy machine learning model output using all digitized downed trees with three variables and regularization multiplier of 3.0
ZIP Archive - 11.5 MB - MD5: 058937da9a3e4d1fea5d6bfa2c87a6b2
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 all digitized downed trees with three variables and regularization multiplier of 3.0
ZIP Archive - 11.5 MB - MD5: d0bf5f3374f9b4c7877d233bafd60c49
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 all digitized downed trees with three variables and regularization multiplier of 3.0
ZIP Archive - 11.5 MB - MD5: 59bec32adedcf9f61c08c9fda09eaddb
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 all digitized downed trees with three variables and regularization multiplier of 3.0
ZIP Archive - 57.4 MB - MD5: 404f6d093275146a0574d8934ab802b4
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 all digitized downed trees with three variables and regularization multiplier of 3.0
ZIP Archive - 33.4 KB - MD5: f0dc6ec41deb69eb3c5afb565455457e
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).
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....
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