Title: | Selecting the Best Set of Relevant Environmental Variables along with the Optimal Regularization Multiplier for Maxent Niche Modeling |
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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. |
Authors: | Alexander Jueterbock |
Maintainer: | "Alexander Jueterbock" <[email protected]> |
License: | GPL (>= 2) |
Version: | 1.0-3 |
Built: | 2025-03-13 04:01:52 UTC |
Source: | https://github.com/alj1983/maxentvariableselection |
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.
Package: | MaxentVariableSelection |
Type: | Package |
Version: | 1.0-3 |
Date: | 2018-01-23 |
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. | |
To cite the package 'MaxentVariableSelection' in publications use:
Jueterbock A, Smolina I, Coyer JA and Hoarau, G (2016)
The fate of the Arctic seaweed Fucus distichus under climate change:
an ecological niche modelling approach
Ecology and Evolution 6(6), 1712-1724
Alexander Jueterbock
Maintainer: Alexander Jueterbock, <[email protected]>
Longitude and latitude values, as well as values of four environmental variables (from the Bio-ORACLE dataset; Tyberghein et al., 2012) for each of 10,000 background points. The background points were selected randomly along shorelines of all continents in the northern hemisphere.
A data frame that specifies environmental conditions and geographic locations of 10,000 background sites.
species
The species name is here set to 'bg', which stands for background
longitude
longitudinal coordinate
latitude
latitudinal coordinate
calcite
Mean calcite concentration (mol/m3)
parmean
Mean photosynthetically active radiation (Einstein/m2/day)
salinity
Mean salinity (PSS)
sstmax
Maximum sea surface temperature (degree celsius)
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.
backgroundlocations <- system.file("extdata", "Backgrounddata.csv", package="MaxentVariableSelection") backgroundlocations <- read.csv(backgroundlocations,header=TRUE) head(backgroundlocations)
backgroundlocations <- system.file("extdata", "Backgrounddata.csv", package="MaxentVariableSelection") backgroundlocations <- read.csv(backgroundlocations,header=TRUE) head(backgroundlocations)
Longitude and latitude values, as well as values of four environmental variables (from the Bio-ORACLE dataset; Tyberghein et al., 2012) for each of 98 occurrence sites (locations where a species was recorded).
A data frame that specifies geographic locations and environmental conditions of 98 occurrence sites.
species
The species name is here set to 'bg', which stands for background
longitude
longitudinal coordinate
latitude
latitudinal coordinate
calcite
Mean calcite concentration (mol/m3)
parmean
Mean photosynthetically active radiation (Einstein/m2/day)
salinity
Mean salinity (PSS)
sstmax
Maximum sea surface temperature (degree celsius)
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.
occurrencelocations <- system.file("extdata", "Occurrencedata.csv", package="MaxentVariableSelection") occurrencelocations <- read.csv(occurrencelocations,header=TRUE) head(occurrencelocations)
occurrencelocations <- system.file("extdata", "Occurrencedata.csv", package="MaxentVariableSelection") occurrencelocations <- read.csv(occurrencelocations,header=TRUE) head(occurrencelocations)
This is the core function of the package in which a set of environmental variables is reduced in a stepwise fashion in order to avoid overfitting the model to the occurrence records. This can be done for a range of regularization multipliers. The best performing model, based on AICc values (Akaike, 1974) or AUC.Test values (Fielding and Bell, 1997), identifies then the most-important uncorrelated environmental variables along with the optimal regularization multiplier.
VariableSelection(maxent, outdir, gridfolder, occurrencelocations, backgroundlocations, additionalargs, contributionthreshold, correlationthreshold, betamultiplier)
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 |
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. |
For further details on the model selection process and the variable settings, please have a look at the vignette that comes with this package.
The following result files are saved in the directory specified with the outdir
argument.
ModelPerformance.txt |
A table listing the performance indicators of all created Maxent models
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 |
ModelWithMinAICc.txt |
Subset of the table
|
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
|
VariableSelectionMaxAUCTest.txt |
Subset of
|
VariableSelectionMinAICc.txt |
Subset of
|
Depending on the number of environmental variables and the range of different betamultipliers you want to test, variable selection can take several hours so that you might want to run the analysis over night.
Alexander Jueterbock, [email protected]
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.
## 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) )
## 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) )