riparian and wetland ecosystems throughout the southwestern united states are being threatened by the invasion of tamarisk ( tamarix spp . ) , a non - native woody shrub introduced from eurasia in the 1800s .
tamarisk has many physiological mechanisms that allow the genus to exploit water resources , out - compete native species , and alter ecosystem processes .
mapping tamarisk distributions for assessing environmental impacts and formulating effective control strategies are high priorities for resource managers . although ground surveys remain regularly used , they are impractical for extremely large areas due to the associated costs of labor , time , and logistics .
satellite remote sensing has played an important , but limited , role in the detection and mapping of tamarisk infestations . conventional classification analyses and remote sensing software
several recent studies have explored non - traditional approaches to detect invasive plants using remote sensing data .
tamarisk , like many invasive plants , exhibits phenological variation throughout the growing season that differs from native riparian species ' phenology . in some areas , for example
, tamarisk leaf - out is before some native riparian plants , and tamarisk retains its foliage longer than other native species . by using spectral bands and spectral indices derived from a time - series of satellite data throughout the growing season
, we can distinguish tamarisk from native plants based on these phenological differences . building on the work of evangelista et al .
2009 , in this study we incorporated individual bands 1 - 7 from a time - series of landsat 5 thematic mapper ( tm ) satellite imagery and derived normalized difference vegetation index ( ndvi ) , soil - adjusted vegetation index ( savi ) , and tasseled cap transformations from these bands .
normalized difference vegetation index ( ndvi ) is one of the most commonly used spectral indices for estimating vegetation biomass , canopy cover , and leaf area indices , and is a non - linear transformation of the ratio between the visible ( red ) and near - infrared bands .
soil - adjusted vegetation index ( savi ) is a modified ndvi used to minimize the effects of soil background on vegetation indices .
tasseled cap transformations are weighted composites of the six landsat bands into three orthogonal bands that measure soil brightness ( tasseled cap , band 1 ) , vegetation greenness ( tasseled cap , band 2 ) , and soil / vegetation wetness ( tasseled cap , band 3 ) and are often used to distinguish vegetation composition , age class , and structure .
we used the coefficients reported in crist ( 1985 ) for all tasseled cap transformations . in this study , we test five species distribution models with a time - series of spectral bands and vegetation indices derived from landsat 5 tm to map tamarisk along the lower arkansas river in southeastern colorado , usa .
the arkansas river , spanning 2,364 km ( 1,469 mi ) , is the second largest tributary in the missouri - mississippi system .
its watershed covers 435,123 km ( 168,002 mi ) with headwaters in the colorado rocky mountains . from its origin at 2,965 m ,
the arkansas drops considerably in elevation , leveling out near pueblo , co , and meandering through agricultural lands and short - grass prairie .
the river is subject to seasonal flooding and is relied on for municipal and agricultural water use in rocky ford , la junta , and lamar , before continuing into kansas , oklahoma , and arkansas where it flows into the mississippi river .
tamarisk was first observed on the arkansas river by r. niedrach in 1913 near the present - day town of lamar .
today , it has been estimated that tamarisk covers more than 100 km between pueblo and the kansas state line , with an additional 60 km along the tributaries of the arkansas river .
the study area includes irrigation ditches , wetlands , agricultural land , and the confluences of several tributaries ; all with varying degrees of tamarisk infestation .
ranching and agriculture are the primary land - uses adjacent to the riparian corridors consisting largely of alfalfa , hay , corn , and winter wheat . species distribution models rely on geo - referenced occurrences ( i.e. , latitude , longitude ) to identify relationships between a species ' occurrence and its environment .
the five species distribution models we tested include boosted regression trees ( brt ) , random forests ( rf ) , multivariate adaptive regression splines ( mars ) , a generalized linear model ( glm ) , and maxent .
these five model algorithms are among the most commonly employed for species distribution modeling , and a number of studies have demonstrated their efficacy .
we used the software for assisted habitat modeling ( sahm ) v. 2.0 modules to execute the five models , which are contained in vistrails v.2.2.2 visualization and processing software .
in addition to the formalization and tractable recording of modeling processes , sahm allows users to work with multiple species distribution model algorithms that , individually , have disparate interfaces , software and file formatting .
sahm produces consistent threshold - independent and threshold - dependent evaluation metrics to evaluate model performance .
one of these is area under the receiver operating characteristic curve ( auc ) , a threshold independent metric that evaluates ability of a model to discriminate presence from background .
an auc value of 0.5 or less indicates model predictions are not better or worse than random ; values between 0.5 and 0.70 indicate poor performance ; and values increasing from 0.70 to 1.0 indicate progressively higher performance .
another metric is percent correctly classified ( pcc ) , a threshold dependent metric that weighs sensitivity and specificity based on a user - defined threshold metric ; sensitivity measures the percentage of observed presences classified as suitable and specificity measures the percentage of background locations classified as unsuitable . yet another
metric is true skill statistic ( tss = sensitivity + specificity - 1 ) , which places more weight on model sensitivity than specificity , with values ranging between -1 and 1 where values > 0 indicate better model performance than chance . to map tamarisk using model output , we constructed binary classifications using the threshold that equalizes sensitivity and specificity to define the presence or absence of tamarisk .
ensemble maps combine the predictions of individual species distribution models to produce a classified map that ranks the collective agreement of the models tested .
for example , an ensemble cell value of one indicates that only one model classified that cell as suitable habitat , whereas a value of five indicates that all five models classified the cell as suitable habitat .
one advantage to this approach is that ensemble maps yield a lower mean error than any individual model
our overall goal was to provide a detailed description of these methods that can be tailored to model the current distribution of species on the landscape .
derive field data for tamarisk from a vector polygon dataset collected by the tamarisk coalition in a state - wide survey in 2005 and 2006 . note : the data were obtained through an intensive ground survey , where field technicians mapped all tamarisk stands along the arkansas river using global positioning systems ( gps ) and aerial photographs . within these polygons , generate 499 random points ( i.e. , presences ) to train the models .
place two sets of constraints on the random points : ( 1 ) each point is required to be > 30 m from any polygon edge to ensure it is within the tamarisk stand at a landsat 5 tm resolution ; and ( 2 ) each point is required to be  60 m from any neighboring point , to ensure a robust distribution across the study site and minimize spatial autocorrelation . compile field data in a ms excel file in three columns with the headings labeled " response " , " x " , and " y " , where the response values are ( 1 ) for presence , utm easting for x , and utm northing for y. save this file in .csv format for use in sahm ( fielddata module ) .
generate an additional 100 random points within the tamarisk coalition 's tamarisk polygons and another 100 random points outside the polygons as independent test data to evaluate model results .
download landsat 5 surface reflectance l4 - 5 tm images ( path 32 , row 34 ) from the u.s .
the scenes include the extent sampled by the colorado tamarisk coalition in 2005 and 2006 .
to determine the months used for the models , select scenes that are largely cloud - free ( i.e. , < 10% cloud cover ) and representative of months thought to have distinguishable tamarisk phenology ; these were october 2004 , april 2005 , may 2005 , june 2005 , july 2005 , september 2005 , april 2006 , may 2006 , june 2006 , july 2006 , august 2006 , september 2006 , and november 2006 .
run the python script in the tool , either gdal or arcpy version ; gdal is recommended .
select the appropriate satellite sensor , desired indices , and set the input image file and the output folder where files will be stored ( figure 1 ) .
we exported the individual bands and used the ndvi , savi , and tasseled cap brightness , greenness , and wetness indices derived from each of the landsat tm scenes .
run the tool and verify the output files visually in arcmap v. 10.0 ( esri , redlands , ca ) or another geographic information system software .
the entire sahm workflow includes input data , preprocessing , preliminary model analysis and decision , correlative models , and output routines . for running sahm , first download files ( including vistrails ) from the u.s .
refer to the user s guide at the same website for detailed instructions on downloading and installing sahm .
note that the website also has a sahm tutorial and data for additional instructions . to develop the tamarisk species distribution models , use the sahm_tutorial_2.0.vt file that comes with the package download ( in the sahm examples folder ) . within the history view , select the independent locations workflow .
the other workflow examples can be selected and depend on study objectives ; descriptions are provided with each . select pipeline . set the output folder , by going to packages and then sahm > change session folder . throughout the workflow development process , detailed descriptions of each step and options may be found by selecting the documentation tab located on the right side of the sahm viewer screen .
all modules listed in the following methods can be found on the left side of the sahm window under the sahm tab .
next , direct sahm to the field data that will be used to train species distribution models .
browse to the raster that will be used as a mask and to define the projection , cell size and extent of the analyses.click on the fielddata module in the left side of the workflow .
( i.e. , training.csv ) file of field data ( presence points or presence and absence points ) within the field data module.click on the predictorlistfile module and browse to .csv file list ( e.g. , file containing the full path to all predictors to use in the model- refer to user guide ) . 
 
browse to the raster that will be used as a mask and to define the projection , cell size and extent of the analyses .
( i.e. , training.csv ) file of field data ( presence points or presence and absence points ) within the field data module .
click on the predictorlistfile module and browse to .csv file list ( e.g. , file containing the full path to all predictors to use in the model- refer to user guide ) .
next , conduct preprocessing steps . click on the fielddataquery module and fill in response column with column heading for response ( i.e. , the column names in the fielddata.csv ) , x and y columns.click on the mdsbuilder module . set the backgroundpointfield to 10,000 .
note : if you are using presence and absence data for the species being modeled , you do not need to change the backgroundpointfield ; you would include these locations with a response ( 0 ) in the field data.csv .
it is optional to set a backgroundprobsurf if you want to constrain background point selection within an area by pointing to a raster surface with values ranging from 0 to 100 ( these values represent the probability that a randomly generated point will be retained should it fall within a particular cell ) . for this study , use a backgroundprobsurf with values of 100 within a 5,000 m buffer of the arkansas river and 0 for areas outside this buffer ( based on the total area sampled by the tamarisk coalition ) . 
 
click on the fielddataquery module and fill in response column with column heading for response ( i.e. , the column names in the fielddata.csv ) , x and y columns .
note : if you are using presence and absence data for the species being modeled , you do not need to change the backgroundpointfield ; you would include these locations with a response ( 0 ) in the field data.csv .
it is optional to set a backgroundprobsurf if you want to constrain background point selection within an area by pointing to a raster surface with values ranging from 0 to 100 ( these values represent the probability that a randomly generated point will be retained should it fall within a particular cell ) .
for this study , use a backgroundprobsurf with values of 100 within a 5,000 m buffer of the arkansas river and 0 for areas outside this buffer ( based on the total area sampled by the tamarisk coalition ) .
note that the boostedregressiontree , glm , mars , and randomforest modules are already set up in the independent locations workflow .
note : begin with default settings for all models ; these can be modified based on study objectives ( see model documentation for more details).add a modeloutputviewer module and connect it to the maxent module ; change column to 5 and row to 1 .
the modeloutputviewer produces a spreadsheet that can be used for model result comparisons.click on the outputname module and type a subfolder name . 
 
note that the boostedregressiontree , glm , mars , and randomforest modules are already set up in the independent locations workflow .
note : begin with default settings for all models ; these can be modified based on study objectives ( see model documentation for more details ) . add a modeloutputviewer module and connect it to the maxent module ; change column to 5 and row to 1 .
this module produces two output maps ; one with the average continuous probability of all the included outputs and a second with the count of the number of models with a positive binary probability .
it is optional to set a threshold metric ; for this study , choose auc and leave the threshold value at the default value of 0.75 .
this will ensure that only models with an auc value greater than or equal to 0.75 are included in the ensemble map outputs .
connect the boostedregressiontree , glm , mars , randomforest , and maxent modules to the ensemblebuilder . 
 
it is optional to set a threshold metric ; for this study , choose auc and leave the threshold value at the default value of 0.75 .
this will ensure that only models with an auc value greater than or equal to 0.75 are included in the ensemble map outputs .
connect the boostedregressiontree , glm , mars , randomforest , and maxent modules to the ensemblebuilder .
click on the other fielddata module ( right side of the workflow ) and browse to the .csv file that contains model validation data .
these are the 200 presence and absence points generated in step 1.4 of the protocol.click on the fielddataquery module and make sure the response , x , and y columns match the columns in the field data .csv.add an applymodel module and connect it to the maxent module .
add a modeloutputviewer module and connect it to this applymodel module ; change the column to 5 and the row to 1 . in the menu , select packages - > sahm - > change processing mode .
since you want to run more than one model , select single models sequentially ( n - 1 cores each ) .
this will expedite the execution time of the models by taking advantage of multiple computer cores . 
 
click on the other fielddata module ( right side of the workflow ) and browse to the .csv file that contains model validation data .
these are the 200 presence and absence points generated in step 1.4 of the protocol .
click on the fielddataquery module and make sure the response , x , and y columns match the columns in the field data .csv .
add a modeloutputviewer module and connect it to this applymodel module ; change the column to 5 and the row to 1 .
since you want to run more than one model , select single models sequentially ( n - 1 cores each ) .
this will expedite the execution time of the models by taking advantage of multiple computer cores .
save the file .vt and then click on execute.when the covariatecorrelationandselection widget ( figure 3 ) appears , de - select one of each correlated pair of variables where the correlation coefficient is |r|  0.7 ( based on % deviance explained from a univariate generalized additive model seen in on the left side of this widget ( figure 3 ) and informed ecological decisions ; in this study prioritize the selection of at least one covariate for each month to capture phenological variation in tamarisk ) .
the number of plots in the viewing window can be changed by typing in a number ( the default is 8) and clicking on update.after finalizing covariate selection , select ok at the bottom of the covariatecorrelationandselection widget ( figure 3 ) . for this study ,
the following 9 variables were retained : july_30_2006_brightness , june_09_2005_savi , sept_16_2006_savi , may_24_2005_b4 , oct_28_2004_ndvi , april_22_2005_brightness , april_09_2006_savi , aug_31_2006_b4 , and nov_19_2006_savi . the species distribution models will execute after selecting ok . 
 
when the covariatecorrelationandselection widget ( figure 3 ) appears , de - select one of each correlated pair of variables where the correlation coefficient is |r|  0.7 ( based on % deviance explained from a univariate generalized additive model seen in on the left side of this widget ( figure 3 ) and informed ecological decisions ; in this study prioritize the selection of at least one covariate for each month to capture phenological variation in tamarisk ) .
the number of plots in the viewing window can be changed by typing in a number ( the default is 8) and clicking on update . after finalizing covariate selection , select ok at the bottom of the covariatecorrelationandselection widget ( figure 3 ) . for this study ,
the following 9 variables were retained : july_30_2006_brightness , june_09_2005_savi , sept_16_2006_savi , may_24_2005_b4 , oct_28_2004_ndvi , april_22_2005_brightness , april_09_2006_savi , aug_31_2006_b4 , and nov_19_2006_savi .
note : after the models have completed , a vistrails spreadsheet appears for model comparison ( figure 4 ) compare auc plots , text outputs , response curves , calibration , confusion matrix , residuals , and response curves across models .
note : output from sahm models include a covariatecorrelationoutputmds , confusion matrix , residual plot , calibration plot , model evaluation plot , variable importance plot , a response curves folder , an expanded output folder , bin map , mess map , mod map , prob map , residual map , and an output.txt ; see the sahm user guide for more description . 
 
compare auc plots , text outputs , response curves , calibration , confusion matrix , residuals , and response curves across models .
note : output from sahm models include a covariatecorrelationoutputmds , confusion matrix , residual plot , calibration plot , model evaluation plot , variable importance plot , a response curves folder , an expanded output folder , bin map , mess map , mod map , prob map , residual map , and an output.txt ; see the sahm user guide for more description .
this is the auc model comparison for the training data ; from left to right the models are brt , glm , mars , rf , and maxent , respectively .
derive field data for tamarisk from a vector polygon dataset collected by the tamarisk coalition in a state - wide survey in 2005 and 2006 . note : the data were obtained through an intensive ground survey , where field technicians mapped all tamarisk stands along the arkansas river using global positioning systems ( gps ) and aerial photographs . within these polygons , generate 499 random points ( i.e. , presences ) to train the models .
place two sets of constraints on the random points : ( 1 ) each point is required to be > 30 m from any polygon edge to ensure it is within the tamarisk stand at a landsat 5 tm resolution ; and ( 2 ) each point is required to be  60 m from any neighboring point , to ensure a robust distribution across the study site and minimize spatial autocorrelation . compile field data in a ms excel file in three columns with the headings labeled " response " , " x " , and " y " , where the response values are ( 1 ) for presence , utm easting for x , and utm northing for y. save this file in .csv format for use in sahm ( fielddata module ) .
generate an additional 100 random points within the tamarisk coalition 's tamarisk polygons and another 100 random points outside the polygons as independent test data to evaluate model results .
download landsat 5 surface reflectance l4 - 5 tm images ( path 32 , row 34 ) from the u.s .
the scenes include the extent sampled by the colorado tamarisk coalition in 2005 and 2006 .
to determine the months used for the models , select scenes that are largely cloud - free ( i.e. , < 10% cloud cover ) and representative of months thought to have distinguishable tamarisk phenology ; these were october 2004 , april 2005 , may 2005 , june 2005 , july 2005 , september 2005 , april 2006 , may 2006 , june 2006 , july 2006 , august 2006 , september 2006 , and november 2006 .
run the python script in the tool , either gdal or arcpy version ; gdal is recommended .
select the appropriate satellite sensor , desired indices , and set the input image file and the output folder where files will be stored ( figure 1 ) .
we exported the individual bands and used the ndvi , savi , and tasseled cap brightness , greenness , and wetness indices derived from each of the landsat tm scenes .
run the tool and verify the output files visually in arcmap v. 10.0 ( esri , redlands , ca ) or another geographic information system software .
the entire sahm workflow includes input data , preprocessing , preliminary model analysis and decision , correlative models , and output routines . for running sahm , first download files ( including vistrails ) from the u.s .
refer to the user s guide at the same website for detailed instructions on downloading and installing sahm .
note that the website also has a sahm tutorial and data for additional instructions . to develop the tamarisk species distribution models , use the sahm_tutorial_2.0.vt file that comes with the package download ( in the sahm examples folder ) . within the history view , select the independent locations workflow .
the other workflow examples can be selected and depend on study objectives ; descriptions are provided with each .
select pipeline . set the output folder , by going to packages and then sahm > change session folder . throughout the workflow development process , detailed descriptions of each step and options may be found by selecting the documentation tab located on the right side of the sahm viewer screen .
all modules listed in the following methods can be found on the left side of the sahm window under the sahm tab .
next , direct sahm to the field data that will be used to train species distribution models .
browse to the raster that will be used as a mask and to define the projection , cell size and extent of the analyses.click on the fielddata module in the left side of the workflow .
( i.e. , training.csv ) file of field data ( presence points or presence and absence points ) within the field data module.click on the predictorlistfile module and browse to .csv file list ( e.g. , file containing the full path to all predictors to use in the model- refer to user guide ) . 
 
browse to the raster that will be used as a mask and to define the projection , cell size and extent of the analyses .
( i.e. , training.csv ) file of field data ( presence points or presence and absence points ) within the field data module .
click on the predictorlistfile module and browse to .csv file list ( e.g. , file containing the full path to all predictors to use in the model- refer to user guide ) .
next , conduct preprocessing steps . click on the fielddataquery module and fill in response column with column heading for response ( i.e. , the column names in the fielddata.csv ) , x and y columns.click on the mdsbuilder module .
note : if you are using presence and absence data for the species being modeled , you do not need to change the backgroundpointfield ; you would include these locations with a response ( 0 ) in the field data.csv .
it is optional to set a backgroundprobsurf if you want to constrain background point selection within an area by pointing to a raster surface with values ranging from 0 to 100 ( these values represent the probability that a randomly generated point will be retained should it fall within a particular cell ) . for this study , use a backgroundprobsurf with values of 100 within a 5,000 m buffer of the arkansas river and 0 for areas outside this buffer ( based on the total area sampled by the tamarisk coalition ) . 
 
click on the fielddataquery module and fill in response column with column heading for response ( i.e. , the column names in the fielddata.csv ) , x and y columns .
note : if you are using presence and absence data for the species being modeled , you do not need to change the backgroundpointfield ; you would include these locations with a response ( 0 ) in the field data.csv .
it is optional to set a backgroundprobsurf if you want to constrain background point selection within an area by pointing to a raster surface with values ranging from 0 to 100 ( these values represent the probability that a randomly generated point will be retained should it fall within a particular cell ) . for this study , use a backgroundprobsurf with values of 100 within a 5,000 m buffer of the arkansas river and 0 for areas outside this buffer ( based on the total area sampled by the tamarisk coalition ) .
note that the boostedregressiontree , glm , mars , and randomforest modules are already set up in the independent locations workflow .
note : begin with default settings for all models ; these can be modified based on study objectives ( see model documentation for more details).add a modeloutputviewer module and connect it to the maxent module ; change column to 5 and row to 1 .
the modeloutputviewer produces a spreadsheet that can be used for model result comparisons.click on the outputname module and type a subfolder name . 
 
note that the boostedregressiontree , glm , mars , and randomforest modules are already set up in the independent locations workflow .
note : begin with default settings for all models ; these can be modified based on study objectives ( see model documentation for more details ) . add a modeloutputviewer module and connect it to the maxent module ; change column to 5 and row to 1 .
this module produces two output maps ; one with the average continuous probability of all the included outputs and a second with the count of the number of models with a positive binary probability .
it is optional to set a threshold metric ; for this study , choose auc and leave the threshold value at the default value of 0.75 .
this will ensure that only models with an auc value greater than or equal to 0.75 are included in the ensemble map outputs .
connect the boostedregressiontree , glm , mars , randomforest , and maxent modules to the ensemblebuilder . 
 
it is optional to set a threshold metric ; for this study , choose auc and leave the threshold value at the default value of 0.75 .
this will ensure that only models with an auc value greater than or equal to 0.75 are included in the ensemble map outputs .
connect the boostedregressiontree , glm , mars , randomforest , and maxent modules to the ensemblebuilder .
click on the other fielddata module ( right side of the workflow ) and browse to the .csv file that contains model validation data .
these are the 200 presence and absence points generated in step 1.4 of the protocol.click on the fielddataquery module and make sure the response , x , and y columns match the columns in the field data .csv.add an applymodel module and connect it to the maxent module .
add a modeloutputviewer module and connect it to this applymodel module ; change the column to 5 and the row to 1 .
in the menu , select packages - > sahm - > change processing mode . since you want to run more than one model , select single models sequentially ( n - 1 cores each ) . this will expedite the execution time of the models by taking advantage of multiple computer cores . 
 
click on the other fielddata module ( right side of the workflow ) and browse to the .csv file that contains model validation data .
these are the 200 presence and absence points generated in step 1.4 of the protocol .
click on the fielddataquery module and make sure the response , x , and y columns match the columns in the field data .csv .
add a modeloutputviewer module and connect it to this applymodel module ; change the column to 5 and the row to 1 . in the menu , select packages - > sahm - > change processing mode . since you want to run more than one model , select single models sequentially ( n - 1 cores each ) .
this will expedite the execution time of the models by taking advantage of multiple computer cores .
save the file .vt and then click on execute.when the covariatecorrelationandselection widget ( figure 3 ) appears , de - select one of each correlated pair of variables where the correlation coefficient is |r|  0.7 ( based on % deviance explained from a univariate generalized additive model seen in on the left side of this widget ( figure 3 ) and informed ecological decisions ; in this study prioritize the selection of at least one covariate for each month to capture phenological variation in tamarisk ) .
the number of plots in the viewing window can be changed by typing in a number ( the default is 8) and clicking on update.after finalizing covariate selection , select ok at the bottom of the covariatecorrelationandselection widget ( figure 3 ) . for this study ,
the following 9 variables were retained : july_30_2006_brightness , june_09_2005_savi , sept_16_2006_savi , may_24_2005_b4 , oct_28_2004_ndvi , april_22_2005_brightness , april_09_2006_savi , aug_31_2006_b4 , and nov_19_2006_savi .
when the covariatecorrelationandselection widget ( figure 3 ) appears , de - select one of each correlated pair of variables where the correlation coefficient is |r|  0.7 ( based on % deviance explained from a univariate generalized additive model seen in on the left side of this widget ( figure 3 ) and informed ecological decisions ; in this study prioritize the selection of at least one covariate for each month to capture phenological variation in tamarisk ) .
the number of plots in the viewing window can be changed by typing in a number ( the default is 8) and clicking on update .
after finalizing covariate selection , select ok at the bottom of the covariatecorrelationandselection widget ( figure 3 ) . for this study ,
the following 9 variables were retained : july_30_2006_brightness , june_09_2005_savi , sept_16_2006_savi , may_24_2005_b4 , oct_28_2004_ndvi , april_22_2005_brightness , april_09_2006_savi , aug_31_2006_b4 , and nov_19_2006_savi .
results output . note : after the models have completed , a vistrails spreadsheet appears for model comparison ( figure 4 ) compare auc plots , text outputs , response curves , calibration , confusion matrix , residuals , and response curves across models .
note : output from sahm models include a covariatecorrelationoutputmds , confusion matrix , residual plot , calibration plot , model evaluation plot , variable importance plot , a response curves folder , an expanded output folder , bin map , mess map , mod map , prob map , residual map , and an output.txt ; see the sahm user guide for more description . 
 
compare auc plots , text outputs , response curves , calibration , confusion matrix , residuals , and response curves across models .
note : output from sahm models include a covariatecorrelationoutputmds , confusion matrix , residual plot , calibration plot , model evaluation plot , variable importance plot , a response curves folder , an expanded output folder , bin map , mess map , mod map , prob map , residual map , and an output.txt ; see the sahm user guide for more description .
this is the auc model comparison for the training data ; from left to right the models are brt , glm , mars , rf , and maxent , respectively .
statistical evaluations of brt , rf , mars , glm , and maxent based on the independent test dataset indicated all five models performed relatively well in detecting tamarisk ; there was little difference between threshold independent and threshold dependent evaluation metrics among models .
auc values were > 0.88 , percent correctly classified values were  > 77% , sensitivities and specificities were  > 0.77 , and tss were  > 0.54 ( table 1 ) .
an ensemble of binary model outputs revealed much model agreement in areas along the arkansas river ( figure 5 ) .
the mess ( multivariate environmental similarity surface ) map outputs for each model indicated the available environment of the study area was well sampled ( figure 6 ) , further increasing our confidence in the ensemble approach .
threshold independent ( auc ) and threshold dependent ( pcc , sensitivity , specificity , and tss ) evaluation metrics for brt , rf , mars , glm , and maxent models fit to an independent test dataset of tamarisk presence and absence .
ensemble results combining brt , glm , mars , rf , and maxent binary output maps in arcgis .
areas are colored by number of models in agreement , from 0 ( no color ) to 5 ( red ) .
note the colored area in the northwest corner of the prediction ; this line is an artifact of landsat imagery ; therefore model results should be taken with caution in this region .
of the nine predictors used , june 30 2006 brightness was the most important variable for all five models ( table 2 ) .
this was the only variable retained by glm based on stepwise akaike information criterion ( aic ; this is the default for glm model selection in sahm ) , however it is important to note this model also included a squared term of this variable .
our results demonstrate fitting brt , rf , mars , glm , and maxent with presence points for tamarisk and a time - series of remotely sensed landsat satellite imagery data can distinguish tamarisk on the landscape and is an effective alternative to traditional single - scene classification methods .
it is clear from our results that june is a particularly important time for detecting tamarisk within our study area ; this agrees with evangelista et al .
2009 which indicated june wetness was the most important predictor for tamarisk occurrence in this area based on a maxent model fit with a time series of landsat imagery .
the other spectral indices and bands that were included in the brt , rf , mars , and maxent models may further distinguish tamarisk from soil substrate , other deciduous trees including cottonwood ( populus spp . ) and willow ( salix spp . ) , or irrigated agriculture that is common in the lower arkansas river basin .
other gis layers , such as topography , soil types , or climate data could also be considered as covariates and included in these models , but we recommend keeping these to a minimum if the goal is to detect current species distribution on the landscape rather than predict potential occurrence or suitable habitat .
the models tested for our research provided strong analytical capacity and multiple options for evaluating results . having all of these correlative models within a single framework , such as sahm , allows the formalization and tractable recording of the modeling process .
pre- and post - processing of response and predictor variables are standardized in sahm , allowing better and efficient model comparisons , while workflows record each step of the analyses facilitating modification , iteration and replication .
ensemble mapping aims to combine the strengths of several correlative models , while minimizing the weakness of any one model .
we believe this was the case in our study ; however , we caution that models that underperform ( i.e. , under - predict or over - predict ) can weaken the overall results .
the limited use of ensemble mapping in the literature has had favorable results , but most of these approaches have attempted to " predict " species occurrence rather than " detect . "
furthermore , ensemble mapping allows for a visual assessment of uncertainty among the different modeling methods , identifying levels of model agreement .
most often it is the choice of modeling method ( e.g. , glm versus brt ) that has greatest quantifiable impact on model results rather than other decisions in the modeling process such as location data uncertainty .
although we believe that our best tamarisk map is where all five models are in agreement , further testing and using various methods of ensemble mapping is recommended ( e.g. , weighted by auc ) , and best validated through independent field observations . in summary , these methods can easily be tailored to model the distribution of other species using environmental variables derived for a given study region in sahm .
 early detection of invasive plant species is vital for the management of natural resources and protection of ecosystem processes . 
 the use of satellite remote sensing for mapping the distribution of invasive plants is becoming more common , however conventional imaging software and classification methods have been shown to be unreliable . in this study , we test and evaluate the use of five species distribution model techniques fit with satellite remote sensing data to map invasive tamarisk ( tamarix spp . ) along the arkansas river in southeastern colorado . the models tested included boosted regression trees ( brt ) , random forest ( rf ) , multivariate adaptive regression splines ( mars ) , generalized linear model ( glm ) , and maxent . 
 these analyses were conducted using a newly developed software package called the software for assisted habitat modeling ( sahm ) . 
 all models were trained with 499 presence points , 10,000 pseudo - absence points , and predictor variables acquired from the landsat 5 thematic mapper ( tm ) sensor over an eight - month period to distinguish tamarisk from native riparian vegetation using detection of phenological differences . from the landsat scenes , we used individual bands and calculated normalized difference vegetation index ( ndvi ) , soil - adjusted vegetation index ( savi ) , and tasseled capped transformations . 
 all five models identified current tamarisk distribution on the landscape successfully based on threshold independent and threshold dependent evaluation metrics with independent location data . to account for model specific differences 
 , we produced an ensemble of all five models with map output highlighting areas of agreement and areas of uncertainty . 
 our results demonstrate the usefulness of species distribution models in analyzing remotely sensed data and the utility of ensemble mapping , and showcase the capability of sahm in pre - processing and executing multiple complex models . 
Introduction
Protocol
1. Field Data Collection
2. Predictor Variables
3. Software for Assisted Habitat Modeling (SAHM) (Figure 2)
Representative Results
Discussion
Disclosures