Type: Package

Title: Selecting the Best Set of Relevant Environmental Variables along with the Optimal Regularization Multiplier for Maxent Niche Modeling

Version: 1.0-3

Date: 2018年01月23日

Author: Alexander Jueterbock

Maintainer: "Alexander Jueterbock" <Alexander-Jueterbock@web.de>

Description: Complex niche models show low performance in identifying the most important range-limiting environmental variables and in transferring habitat suitability to novel environmental conditions (Warren and Seifert, 2011 <doi:10.1890/10-1171.1>; Warren et al., 2014 <doi:10.1111/ddi.12160>). This package helps to identify the most important set of uncorrelated variables and to fine-tune Maxent's regularization multiplier. In combination, this allows to constrain complexity and increase performance of Maxent niche models (assessed by information criteria, such as AICc (Akaike, 1974 <doi:10.1109/TAC.1974.1100705>), and by the area under the receiver operating characteristic (AUC) (Fielding and Bell, 1997 <doi:10.1017/S0376892997000088>). Users of this package should be familiar with Maxent niche modelling.

License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]

Imports: ggplot2, raster

Depends: R (≥ 3.1.2)

Suggests: knitr, rmarkdown

VignetteBuilder: knitr

SystemRequirements: maxent.jar file

LazyData: true

Packaged: 2018年01月23日 20:22:35 UTC; alj

RoxygenNote: 6.0.1

NeedsCompilation: no

Repository: CRAN

Date/Publication: 2018年01月23日 21:56:31 UTC

backgroundlocations <- system.file("extdata",
 "Backgrounddata.csv",
 package="MaxentVariableSelection")
backgroundlocations <- read.csv(backgroundlocations,header=TRUE)
head(backgroundlocations)

Package: MaxentVariableSelection

Type: Package

Version: 1.0-3

Date: 2018年01月23日

Depends: R (>= 3.1.2)

Imports: ggplot2, raster

Suggests: knitr, rmarkdown

VignetteBuilder: knitr

License: GPL (>= 2)

Literature: Akaike H (1974)

A new look at the statistical model identification

IEEE Transactions on Automatic Control 19:6 716--723.

Fielding AH and Bell JF (1997)

A review of methods for the assessment of prediction

errors in conservation presence/absence models

Environmental Conservation 24:1 38--49.

Jimenez-Valverde A (2012)

Insights into the area under the receiver operating characteristic curve

(AUC) as a discrimination measure in species distribution modelling

Global Ecology and Biogeography 21:4 498--507.

Tyberghein L, Verbruggen H, Pauly K, Troupin C, Mineur F and De Clerck, O (2012)

Bio-ORACLE: a global environmental dataset for marine species distribution modelling

Global Ecology and Biogeography 21:2 272--281.

Warren DL, Glor RE, and Turelli M (2010)

ENMTools: a toolbox for comparative studies of environmental niche

models

Ecography 33:3 607--611.

Warren DL and Seifert SN (2011)

Ecological niche modeling in Maxent: the importance of model

complexity and the performance of model selection criteria

Ecological Applications 21:2 335--342.

occurrencelocations <- system.file("extdata",
 "Occurrencedata.csv",
 package="MaxentVariableSelection")
occurrencelocations <- read.csv(occurrencelocations,header=TRUE)
head(occurrencelocations)

VariableSelection(maxent, outdir, gridfolder, occurrencelocations,
backgroundlocations, additionalargs, contributionthreshold,
correlationthreshold, betamultiplier)

maxent

String specifying the filepath to the maxent.jar file (download from here: https://www.cs.princeton.edu/~schapire/maxent/). The package was tested with maxent.jar version 3.3.3k.

outdir

String specifying the path to the output directory to which all the result files will be written.Please don't put important files in this folder as all files but the output files of the VariableSelection function will be deleted from this folder.

gridfolder

String specifying the path to the directory that holds all the ASCII grids (in ESRI's .asc format) of environmental variables. All variables must have the same extent and resolution.

occurrencelocations

String specifying the filepath to the csv file with occurrence records. Please find the exact specifications of the SWD file format in the details section below.

backgroundlocations

String specifying the filepath to the csv file with background/pseudoabsence data. Please find the exact specifications of the SWD file format in the details section below.

additionalargs

String specifying additional maxent arguments. Please see in the details section below.

betamultiplier

Vector of beta (regularization multipliers) (positive numerical values). The smaller this value, the more closely will the projected distribution fit to the training data set. Overfitted models are poorly transferable to novel environments and, thus, not appropriate to project distribution changes under environmental change. The model performance will be compared between models created with the beta values given in this betamultiplier vector. Thus, providing a range of beta values from 1 (the default in Maxent) to 15 or so, will help you to spot the optimal beta multiplier for your specific model.

correlationthreshold

Numerical value (between 0 and 1) that sets the threshold of Pearson's correlation coefficient above which environmental variables are regarded to be correlated (based on values at all background locations). Of the correlated variables, only the variable with the highest contribution score will be kept, all other correlated variables will be excluded from the Maxent model. Correlated variables should be removed because they may reflect the same environmental conditions, and can lead to overly complex or overpredicted models. Also, models comiled with correlated variables might give wrong predictions in scenarios where the correlations between the variables differ.

contributionthreshold

Numerical value (between 0 and 100) that sets the threshold of model contributions below which environmental variables are excluded from the Maxent model. Model contributions reflect the importance of environmental variables in limiting the distribution of the target species.

ModelPerformance.txt

A table listing the performance indicators of all created Maxent models

Model

Unique model number

betamultiplier

Maxent's regularization multiplier

variables

Number of environmental variables

samples

Number of occurrence sites

parameters

Number of parameters estimated from the model

loglikelihood

log likelihood value

AIC

Akaike Information Criterion

AICc

sample size corrected AIC

BIC

Bayesian information criterion

AUC.Test

Area under the receiver operating characteristic fro test data

AUC.Train

Area under the receiver operating characteristic fro training data

AUC.Diff

Difference between AUC.Test and AUC.Train

The information criteria (AIC, AICc, and BIC) are set to 'x' if the number of parameters is lower than the number of variables in the model.

ModelSelectionAICc_MarkedMaxAUCTest.png

A figure showing the AICc values of all models, which are ordered along the x-axis based on the applied beta-multiplier. The number of environmental variables included in each model is coded by dot color and size. The model with highest AUC.Test value is marked in red.

ModelSelectionAICc_MarkedMinAICc.png

A figure showing the AICc values of all models, which are ordered along the x-axis based on the applied beta-multiplier. The number of environmental variables included in each model is coded by dot color and size. The model with highest minimum AICc value is marked in red.

ModelSelectionAUCTest_MarkedMaxAUCTest.png

A figure showing the AUC.Test values of all models, which are ordered along the x-axis based on the applied beta-multiplier. The number of environmental variables included in each model is coded by dot color and size. The model with highest AUC.Test value is marked in red.

ModelSelectionAUCTest_MarkedMinAICc.png

A figure showing the AUC.Test values of all models, which are ordered along the x-axis based on the applied beta-multiplier. The number of environmental variables included in each model is coded by dot color and size. The model with highest minimum AICc value is marked in red.

ModelWithMaxAUCTest.txt

Subset of the table ModelPerformance.txt, which shows only the model with the highest AUC.Test value.

ModelWithMinAICc.txt

Subset of the table ModelPerformance.txt, which shows only the model with the lowest AICc value.

VariableSelectionProcess.txt

Table listing model contributions for and correlations between each of the environmental variables for all created Maxent models. The numbers of the models refer to the unique model numbers in the table ModelPerformance.txt (see above). The following entries describe the content row-wise (not column-wise)

Test

Either 'Contributions' or 'Correlation. Informs if the numbers for each of the environmental variables refers to model contribution coefficients or to correlation coefficients.

Model

The unique model number (the same unique model number as in ModelPerformance.txt.)

betamultiplier

The (regularization multipliers) used to compile the respective model.

X

'X' stands here for the name of an environmental variable. The Test row above informs whether the values in this row refer to the model contribution of this environmental variable or to its coefficient of correlation with another environmental variable. The variable to which it is compared is recognizable by a correlation coefficient of 1. If this environmental variable was excluded from the model, the value in this row is 'NA', which stands for 'Not Available'.'

VariableSelectionMaxAUCTest.txt

Subset of VariableSelectionProcess.txt that shows only those models which lead directly to the model with the highest AUC.Test value.

VariableSelectionMinAICc.txt

Subset of VariableSelectionProcess.txt that shows only those models which lead directly to the model with the lowest AICc value.

## Not run: 
# Please find a workflow tutorial in the vignette of this package. It
# will guide you through the settings and usage of the
# 'VariableSelection' function, the core function of this package.
## End(Not run)
VariableSelection(
maxent="C:/.../maxent.jar",
outdir="OutputDirectory",
gridfolder="BioORACLEVariables",
occurrencelocations=system.file("extdata", "Occurrencedata.csv", package="MaxentVariableSelection"),
backgroundlocations=system.file("extdata", "Backgrounddata.csv", package="MaxentVariableSelection"),
additionalargs="nolinear noquadratic noproduct nothreshold noautofeature",
contributionthreshold=5,
correlationthreshold=0.9,
betamultiplier=seq(2,6,0.5)
)