A new toolkit for modeling: SDMtoolbox (www.SDMtoobox.org)

[Jason Brown]

Hi Maxent'rs
   I wanted to notify this group that I recently published a GIS toolkit to assist many advanced practices in species distribution modeling. This is a toolbox for ArcGIS 10.1 (or greater) and consists of a series python scripts (68 and growing) designed to automate complicated ArcMap analyses. A large set of the tools were created to complement MaxEnt species distribution models (SDMs) or to improve the predictive performance of MaxEnt models.


Overview of my suggestions of the 'best practices' modeling pipeline and how SDMtoolbox will facilitate them (link to document with more detail).
    1. Batch clip environmental data and convert to ASCIIs
    2. Automatically test for correlations in input environmental data-- then remove highly correlated variables
    3. Spatially rarefy (or filter) occurrence localities to remove spatial autocorrelation (this greatly affects model tuning)
    4. Create regional background selection bias file to fine-tune background selection (this also affects model tuning)
    5. Run Maxent: Spatial jackknife and independent tests of model parameters (see text below, Why use SDMtoolbox for MaxEnt analyses?)

Why use SDMtoolbox for MaxEnt analyses:
I. Spatial Jackknifing
Spatial jackknifing (or geographically structured k-fold cross-validation) tests evaluation performance of spatially segregated spatially independent localities. SDMtoolbox automatically generates all the GIS files and batch files necessary to spatially jackknife your MaxEnt Models. The script splits the landscape into 3-5 regions based on spatial clustering of occurrence points (e.g, for 3 regions: A,B,C). Models are calibrated with k-1 spatial groups and then evaluated with the withheld group. For example if k=3, then models would be run with following three subgroups:

1. Model is calibrated with localities and background points from region AB and then evaluated with points from region C
2. Model is calibrated with localities and background points from region AC and then evaluated with points from region B
3. Model is calibrated with localities and background points from region BC and then evaluated with points from region A

II. Independent Tests of Model Feature Classes and Regularization Parameters
Equally important, this tool allows for testing different combinations of five model feature class types (FC) and many regularization multipliers (RM) to optimize your MaxEnt model performance. For example, if a RM was input (here 5), this tool kit would run MaxEnt models on the following parameters for each species:

1. RM: 5 & FC: Linear
2. RM: 5 & FC: Linear and Quadratic
3. RM: 5 & FC: Hinge
4. RM: 5 & FC: Linear, Quadratic and Hinge
5. RM: 5 & FC: Linear, Quadratic, Hinge, Product and Threshold

III. Automatic Model Selection and batch running of Maxent
Finally, the script chooses the best model by evaluating each model’s: 1. omission rates (OR),2. AUC, and 3. model feature class complexity. It does this in order, choosing the model with the lowest omission rates on the test data. If many models have the identical low OR, then it selects the model with the highest AUC. Lastly if several models have the same low OR and high AUC, it will choose the model with simplest feature class parameters in the following order(1. linear; 2. linear and quadratic; 3. hinge; 4. linear, quadratic, and hinge; and 5. linear, quadratic, hinge, product, and threshold).  Once the best model is selected, SDMtoolbox will run the final model using all the occurrence points. If desired, at this stage models will be projected into other climates, environmental variables will be jackknifed to measure importance, and response curves will be created.

Brief overview of other relevant tools
Batch data preparation:
       Batch raster processing (i.e. preparing Worldclim data for MaxEnt): ASCII to raster files, raster to ASCII files, project to any projection, clip to a particular extent, re-sampling resolution, reclassifying and summing many rasters

Maxent Analyses:

• Creation of MaxEnt bias files for sampling biases associated with latitudinal changes in the area encompassed by decimal degree units
• Creation of MaxEnt bias files (to function as a mask) to limit background point selection to a maximum distance from presence points or within a buffered minimum-convex polygon (MCP) of a species’ distribution
• SDM over-prediction correction: clip by buffered MCP
  
• Limit dispersal in future SDMs
• Create a friction layer from a species distribution model (a.k.a. ecological niche model or environmental niche model)
• Calculate area of habitat contraction, expansion and other distribution changes between current and future SDMs (see image to right)
• Calculate vectors of core distributional changes between current and future SDMs
• Explore summary statistics and correlations between environmental rasters before running a SDM

Citation: Brown J.L. 2014, SDMtoolbox: a python-based GIS toolkit for landscape genetic, biogeographic, and species distribution model analyses. Methods in Ecology and Evolution  DOI: 10.1111/2041-210X.12200
Webpage: http://www.sdmtoolbox.org

[Manuel Spínola]

Dear Jason,

Any chance to implement these tools in QGIS?

Best,

Manuel

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[Jason Brown]

Hi Manuel-
Unfortunately it is not compatible with QGIS.   I completely support the development of free open-source software.  However, this toolkit was mostly a compilation of my personal scripts generated for my research and was not initially created under the idea that it would be shared with others.  To clarify, there are no plans to port this to any other platform. 
Cheers,
Jason

[Manuel Spínola]

Jason, I understand.  Thank you very much.

Manuel

[Jesus]

Dear Jason,

I have been working with the tool kit. It is very interesting and great fro SDM analysis! Would you have any idea why when I investigate the "centroid changes (lines)" with your sample data using the three species I get the three arrows representing these changes in location, but when I use three of my species (same coordinates and extent) I only get one arrow?

The tool kit is great, thanks you for making it available.

Regards,

Jesus

Op maandag 2 juni 2014 17:52:32 UTC+2 schreef Jason Brown:

[Jason Brown]

Hi Jesus,

Thanks for using SDMtoolbox, I am sorry you are having issues.  Please send me your data (directly to my email account). And then I will try to troubleshoot your issue.

Cheers,

Jason

[Will Smith]

Hi Jason,

I'm currently working with SDMtoolbox and have found it very useful so far, however when using the spatially rarefy occurrence data tool it fails. I have tried this with my data in csv and shapefile format and am unsure of what the issue may be. Any help would be greatly appreciated.

Many thanks

Will

[Louis Phipps]

Hi Jason,

Thanks for developing what looks to be a very useful tool.

Unfortunately I am having installation issues - the toolbox is incomplete / missing some vital tools - please see attached screenshot. I have reinstalled a few times and have updated Arc service packs (sp 5). I'm sure I'm missing a very basic step somewhere!

Thanks in advance,

Louis

On Monday, June 2, 2014 4:52:32 PM UTC+1, Jason Brown wrote:

[Jason Brown]

SDMtoolbox v1.1 has been released. This is our first major update!  Thus, if you are a current user, please update!
Cheers,
Jason

[Dimitris Poursanidis]

Hi Jason

The new release has issues. A lot of tools are missing.

[Thomas Starnes]

Same issue as Will Smith with "1. Spatially Rarefy Occurrence Data for SDMs (reduce spatial autocorrelation)".

I fill in all the parameters, all in WGS_1984, using a resolution of 500 metres. When I set "Equidistance Projection" as "Europe: Equidistant Conic", I get an "ERROR 000365 Invalid geographic transformation". When I try again using "World: Equidistant Conic", I get an "ERROR 999999: Error executing function" and "The maximum record length has been exceeded".

The species shapefile was originally in British National Grid but I have converted this to WGS_1984 as well as the map document Data Frame and Datum, and I am using two new fields DDLat and DDLong calculated from feature geometry for Latitude and Longitude fields.

Any ideas?

Great toolbox, I can see this is a labour of love! I wonder how many man-hours this has saved already?

[Jason Brown]

Hi Dimitris,
    Know I just checked the release and everything is there.  Further no other users are having this issue. Are you sure you have ArcMap 10.1 or 10.2 installed?  Please check this. If you do have one of these versions, re-download and reinstall.  If both do not work, let me know.
Cheers,
Jason

[Ramamoorthy Suganthasakthivel]

Dear Jason,

I could not download the version 1.1, on clicking the link i have got this screen

Please fix this

Suganthan

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[Jason Brown]

Hi Suganthan,
       Everything seems to be working here, perhaps my server was updating. Also, please check your firewall and internet connection.
Cheers,
Jason 

[Jason Brown]

Hi Thomas,
  I suspect something is wrong with your projection here.  First, I would use the 'define projection WGS1984' tool, to re-write the projection (sometimes this gets messed up during transformations).  Then rerun the tool.  If that doesn't work send me another email.
  Best,
Jason

[Virginia Moreno]

Hi Jason,

I have the same problem. My presence points and all layers are in UTM 18S coordinates. I can't run the tools... Do I have to convert my UTM coordinates to lat long coordinates, and reproject all my rasters in order to run SDM tools? 

Thanks,

Virginia

[daryan kaky]

Dear all when I want to do Spatially Rarefy Occurence Data for SDMs (reduce spatial autocorrelation) every time I can not do this step and I get message say failed execute any can help me to avoid this failed please. 

[daryan kaky]

Dear Jason when I want to do (Spatially Rarefy Occurence Data for SDMs (reduce spatial autocorrelation) every time I failed to do this step can you help me.

On Monday, 2 June 2014 16:52:32 UTC+1, Jason Brown wrote:

[Jason Brown]

Dear Daryan,
  Please go to SDMtoolbox.org and read the 'contact us' tab.   I cannot solve your issue with the limited info provided, this link will help you provide the useful info. Cheers,
Jason

p.s. If I were to guess, the most common issue with this (and most tools) are the input shapefiles are not projected in WGS1984-- the tool will stop running if not this.

[daryan kaky]

Dear Jason

ExecuteError: Failed to execute. Parameters are not valid. ERROR 000824: The tool is not licensed. Failed to execute (DeleteIdentical).

The above message I got when run it again and I am sorry about this silly question.

regards 

[Jason Brown]

Hi Daryan,

  You overlooked a large part of on the contact page where I suggested reading  ‘a first run’ suggestion (link here: http://www.jasonleebrown.org/SDMtoolbox/current/a_first_run.pdf). In that document it would ask if the ‘spatial analyst’ extension is turned on.  In your case, this is causing the error you are seeing.  Please see that document for detail overview of how to fix this.   Thanks again for using SDMtoolbox!

Jason

p.s. also please delete you double posting of this error (you asked the previous question in the main forum)---you do not want to waste others' time answering the same question.

[Jason Brown]

Hi Viriginia-
  As stated in the tool menu, the input to the spatially rarefy points tool must be in WGS1984 projection.  Thus, you need project the data to WGS1984,  run the tool and then project points back to 18S. Thanks for using SDMtoolbox. Good luck!
Cheers,
Jason

[Rajiv]

Hi! Jason,

I ran the 'Spatially Jackknife' tool in SDMToolbox and everything worked without any error. Then I executed 'Step1_Optimize_MaxEnt_Model_Parameters.bat' file and it completed without any error. Subsequently, the next bat file i.e. 'Step2_Run_Optimized_MaxEnt_Models.bat' did not run and when I opened it with the text editor, it was empty and showed zero byte size. What could be wrong here. I would greatly appreciate your help and am giving the text report below.

Thanks and best wishes.

Rajiv

Rajiv Kalsi


Executing: RunMaxentModel2 "C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar" "C:\Users\Rajiv\Backup\Working Folder\GIS\Data\cheer\cheer.csv" Species Longitude Latitude "C:\Users\Rajiv\Backup\Working Folder\GIS\Data\cheer_asc" 'C:\Users\Rajiv\Backup\Working Folder\GIS\Data\cheer_asc\glc2000.asc';'C:\Users\Rajiv\Backup\Working Folder\GIS\Data\cheer_asc\aspect.asc' 'C:\Users\Rajiv\Backup\Working Folder\GIS\Data\cheer_asc\bio_10.asc';'C:\Users\Rajiv\Backup\Working Folder\GIS\Data\cheer_asc\bio_11.asc';'C:\Users\Rajiv\Backup\Working Folder\GIS\Data\cheer_asc\bio_13.asc';'C:\Users\Rajiv\Backup\Working Folder\GIS\Data\cheer_asc\bio_15.asc';'C:\Users\Rajiv\Backup\Working Folder\GIS\Data\cheer_asc\bio_16.asc';'C:\Users\Rajiv\Backup\Working Folder\GIS\Data\cheer_asc\bio_18.asc';'C:\Users\Rajiv\Backup\Working Folder\GIS\Data\cheer_asc\bio_19.asc';'C:\Users\Rajiv\Backup\Working Folder\GIS\Data\cheer_asc\bio_3.asc';'C:\Users\Rajiv\Backup\Working Folder\GIS\Data\cheer_asc\bio_4.asc';'C:\Users\Rajiv\Backup\Working Folder\GIS\Data\cheer_asc\bio_5.asc';'C:\Users\Rajiv\Backup\Working Folder\GIS\Data\cheer_asc\bio_6.asc';'C:\Users\Rajiv\Backup\Working Folder\GIS\Data\cheer_asc\bio_7.asc';'C:\Users\Rajiv\Backup\Working Folder\GIS\Data\cheer_asc\bio_9.asc' "C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer" "C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models" Logistic asc true true true false true "Automatic Settings" "10 percentile training presence" # false false 1 true true 5 15 1 3 false 5 subsample 20 true false false false false false 5 crossvalidate 20
Start Time: Thu Feb 12 12:27:24 2015
Running script RunMaxentModel2...
*******************************************
*************Spatial Jackknife*************
Input grid used for spatial environment template: alt.asc
Environmental Cell Size: 0.008333333768
Projection in: GCS_WGS_1984
version 10.0

  Adding toolbox: c:/program files/arcgis/desktop10.1/ArcToolbox/Toolboxes/Data Management Tools.tbx

  Checking for appropriate field type(  string, decimal (0 scale) or integer)
FID        OID        0      Y     
Shape      Geometry   0      N     
FID_       Integer    0      Y     
Species    String     0      Y     
Longitude  Double     0      Y     
Latitude   Double     0      Y     
UN_ID      SmallInteger 0      Y     
Species is being queried for unique values.
Unique values:
 cheer

  Processing: C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models/All_Occ.shp
Output and query: C:/Users/Rajiv/Backup/Working Folder/GIS/Analysis/cheer_models/TEMP_ilUlii/cheer.shp  Species = 'cheer'

  Processing complete

Preparing data for: C:/Users/Rajiv/Backup/Working Folder/GIS/Analysis/cheer_models/TEMP_ilUlii/cheer.shp
************************************************
************************************************
***Step 1 of 2**********************************
Creating GIS layers for spatial jackknifing: cheer
Generating spatial groups for spatial jackknifing: cheer
Generating regional bias files for spatial jackknifing: cheer
Generating test and training CSV files for spatial jackknifing: cheer
Writing test and training CSV files for spatial jackknifing: cheer
Successfully created GIS layers for: cheer
************************************************
************************************************
***Step 2 of 2 *********************************
Generating batch code for running MaxEnt models: cheer
Creating batch files for Beta value: 1
Compiling batch code for cheer: Group AB and Beta 1
Compiling batch code for cheer: Group AC and Beta 1
Compiling batch code for cheer: Group BC and Beta 1
Compiling batch code for cheer: Final Optimized Run
start C:\Python27\ArcGIS10.1\pythonw.exe C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\importMask.py
java -mx512m -jar C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar -e C:\Users\Rajiv\Backup\Working Folder\GIS\Data\cheer_asc -s C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\AB_Sp_Occ.csv -o C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\AB\b1\L noautofeature -q -h -p nothreshold pictures=true biasfile=C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\BiasFileAB.asc biastype=3 betamultiplier=1 "applythresholdrule=10 percentile training presence" -N bio_10. -N bio_11. -N bio_13. -N bio_15. -N bio_16. -N bio_18. -N bio_19. -N bio_3. -N bio_4. -N bio_5. -N bio_6. -N bio_7. -N bio_9. -t glc2000. -t aspect. -z warnings=false -a
java -cp C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar density.AUC C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\C_Sp_Occ.csv C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\AB\b1\L\cheer.asc >> C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\tempAUC.csv
java -cp C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar density.Getval C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\C_Sp_Occ.csv C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\AB\b1\L\cheer_thresholded.asc >> C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\tempOC.csv
java -mx512m -jar C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar -e C:\Users\Rajiv\Backup\Working Folder\GIS\Data\cheer_asc -s C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\BC_Sp_Occ.csv -o C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\BC\b1\L noautofeature -q -h -p nothreshold pictures=true biasfile=C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\BiasFileBC.asc biastype=3 betamultiplier=1 "applythresholdrule=10 percentile training presence" -N bio_10. -N bio_11. -N bio_13. -N bio_15. -N bio_16. -N bio_18. -N bio_19. -N bio_3. -N bio_4. -N bio_5. -N bio_6. -N bio_7. -N bio_9. -t glc2000. -t aspect. -z warnings=false -a
java -cp C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar density.AUC C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\A_Sp_Occ.csv C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\BC\b1\L\cheer.asc >> C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\tempAUC.csv
java -cp C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar density.Getval C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\A_Sp_Occ.csv C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\BC\b1\L\cheer_thresholded.asc >> C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\tempOC.csv
java -mx512m -jar C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar -e C:\Users\Rajiv\Backup\Working Folder\GIS\Data\cheer_asc -s C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\AC_Sp_Occ.csv -o C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\AC\b1\L noautofeature -q -h -p nothreshold pictures=true biasfile=C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\BiasFileAC.asc biastype=3 betamultiplier=1 "applythresholdrule=10 percentile training presence" -N bio_10. -N bio_11. -N bio_13. -N bio_15. -N bio_16. -N bio_18. -N bio_19. -N bio_3. -N bio_4. -N bio_5. -N bio_6. -N bio_7. -N bio_9. -t glc2000. -t aspect. -z warnings=false -a
java -cp C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar density.AUC C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\B_Sp_Occ.csv C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\AC\b1\L\cheer.asc >> C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\tempAUC.csv
java -cp C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar density.Getval C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\B_Sp_Occ.csv C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\AC\b1\L\cheer_thresholded.asc >> C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\tempOC.csv
start C:\Python27\ArcGIS10.1\pythonw.exe C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\ABC\b1\L\cheer.py
java -mx512m -jar C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar -e C:\Users\Rajiv\Backup\Working Folder\GIS\Data\cheer_asc -s C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\AB_Sp_Occ.csv -o C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\AB\b1\LQ noautofeature -h -p nothreshold pictures=true biasfile=C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\BiasFileAB.asc biastype=3 betamultiplier=1 "applythresholdrule=10 percentile training presence" -N bio_10. -N bio_11. -N bio_13. -N bio_15. -N bio_16. -N bio_18. -N bio_19. -N bio_3. -N bio_4. -N bio_5. -N bio_6. -N bio_7. -N bio_9. -t glc2000. -t aspect. -z warnings=false -a
java -cp C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar density.AUC C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\C_Sp_Occ.csv C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\AB\b1\LQ\cheer.asc >> C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\tempAUC.csv
java -cp C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar density.Getval C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\C_Sp_Occ.csv C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\AB\b1\LQ\cheer_thresholded.asc >> C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\tempOC.csv
java -mx512m -jar C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar -e C:\Users\Rajiv\Backup\Working Folder\GIS\Data\cheer_asc -s C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\BC_Sp_Occ.csv -o C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\BC\b1\LQ noautofeature -h -p nothreshold pictures=true biasfile=C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\BiasFileBC.asc biastype=3 betamultiplier=1 "applythresholdrule=10 percentile training presence" -N bio_10. -N bio_11. -N bio_13. -N bio_15. -N bio_16. -N bio_18. -N bio_19. -N bio_3. -N bio_4. -N bio_5. -N bio_6. -N bio_7. -N bio_9. -t glc2000. -t aspect. -z warnings=false -a
java -cp C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar density.AUC C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\A_Sp_Occ.csv C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\BC\b1\LQ\cheer.asc >> C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\tempAUC.csv
java -cp C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar density.Getval C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\A_Sp_Occ.csv C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\BC\b1\LQ\cheer_thresholded.asc >> C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\tempOC.csv
java -mx512m -jar C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar -e C:\Users\Rajiv\Backup\Working Folder\GIS\Data\cheer_asc -s C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\AC_Sp_Occ.csv -o C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\AC\b1\LQ noautofeature -h -p nothreshold pictures=true biasfile=C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\BiasFileAC.asc biastype=3 betamultiplier=1 "applythresholdrule=10 percentile training presence" -N bio_10. -N bio_11. -N bio_13. -N bio_15. -N bio_16. -N bio_18. -N bio_19. -N bio_3. -N bio_4. -N bio_5. -N bio_6. -N bio_7. -N bio_9. -t glc2000. -t aspect. -z warnings=false -a
java -cp C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar density.AUC C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\B_Sp_Occ.csv C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\AC\b1\LQ\cheer.asc >> C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\tempAUC.csv
java -cp C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar density.Getval C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\B_Sp_Occ.csv C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\AC\b1\LQ\cheer_thresholded.asc >> C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\tempOC.csv
start C:\Python27\ArcGIS10.1\pythonw.exe C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\ABC\b1\LQ\cheer.py
java -mx512m -jar C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar -e C:\Users\Rajiv\Backup\Working Folder\GIS\Data\cheer_asc -s C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\AC_Sp_Occ.csv -o C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\AC\b1\H noautofeature -l -q -p nothreshold pictures=true biasfile=C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\BiasFileAC.asc biastype=3 betamultiplier=1 "applythresholdrule=10 percentile training presence" -N bio_10. -N bio_11. -N bio_13. -N bio_15. -N bio_16. -N bio_18. -N bio_19. -N bio_3. -N bio_4. -N bio_5. -N bio_6. -N bio_7. -N bio_9. -t glc2000. -t aspect. -z warnings=false -a
java -cp C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar density.AUC C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\B_Sp_Occ.csv C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\AC\b1\H\cheer.asc >> C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\tempAUC.csv
java -cp C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar density.Getval C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\B_Sp_Occ.csv C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\AC\b1\H\cheer_thresholded.asc >> C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\tempOC.csv
java -mx512m -jar C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar -e C:\Users\Rajiv\Backup\Working Folder\GIS\Data\cheer_asc -s C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\BC_Sp_Occ.csv -o C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\BC\b1\H noautofeature -l -q -p nothreshold pictures=true biasfile=C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\BiasFileBC.asc biastype=3 betamultiplier=1 "applythresholdrule=10 percentile training presence" -N bio_10. -N bio_11. -N bio_13. -N bio_15. -N bio_16. -N bio_18. -N bio_19. -N bio_3. -N bio_4. -N bio_5. -N bio_6. -N bio_7. -N bio_9. -t glc2000. -t aspect. -z warnings=false -a
java -cp C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar density.AUC C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\A_Sp_Occ.csv C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\BC\b1\H\cheer.asc >> C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\tempAUC.csv
java -cp C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar density.Getval C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\A_Sp_Occ.csv C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\BC\b1\H\cheer_thresholded.asc >> C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\tempOC.csv
java -mx512m -jar C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar -e C:\Users\Rajiv\Backup\Working Folder\GIS\Data\cheer_asc -s C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\AB_Sp_Occ.csv -o C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\AB\b1\H noautofeature -l -q -p nothreshold pictures=true biasfile=C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\BiasFileAB.asc biastype=3 betamultiplier=1 "applythresholdrule=10 percentile training presence" -N bio_10. -N bio_11. -N bio_13. -N bio_15. -N bio_16. -N bio_18. -N bio_19. -N bio_3. -N bio_4. -N bio_5. -N bio_6. -N bio_7. -N bio_9. -t glc2000. -t aspect. -z warnings=false -a
java -cp C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar density.AUC C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\C_Sp_Occ.csv C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\AB\b1\H\cheer.asc >> C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\tempAUC.csv
java -cp C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar density.Getval C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\C_Sp_Occ.csv C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\AB\b1\H\cheer_thresholded.asc >> C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\tempOC.csv
start C:\Python27\ArcGIS10.1\pythonw.exe C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\ABC\b1\H\cheer.py
java -mx512m -jar C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar -e C:\Users\Rajiv\Backup\Working Folder\GIS\Data\cheer_asc -s C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\AB_Sp_Occ.csv -o C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\AB\b1\LQH noautofeature -p nothreshold pictures=true biasfile=C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\BiasFileAB.asc biastype=3 betamultiplier=1 "applythresholdrule=10 percentile training presence" -N bio_10. -N bio_11. -N bio_13. -N bio_15. -N bio_16. -N bio_18. -N bio_19. -N bio_3. -N bio_4. -N bio_5. -N bio_6. -N bio_7. -N bio_9. -t glc2000. -t aspect. -z warnings=false -a
java -cp C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar density.AUC C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\C_Sp_Occ.csv C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\AB\b1\LQH\cheer.asc >> C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\tempAUC.csv
java -cp C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar density.Getval C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\C_Sp_Occ.csv C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\AB\b1\LQH\cheer_thresholded.asc >> C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\tempOC.csv
java -mx512m -jar C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar -e C:\Users\Rajiv\Backup\Working Folder\GIS\Data\cheer_asc -s C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\BC_Sp_Occ.csv -o C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\BC\b1\LQH noautofeature -p nothreshold pictures=true biasfile=C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\BiasFileBC.asc biastype=3 betamultiplier=1 "applythresholdrule=10 percentile training presence" -N bio_10. -N bio_11. -N bio_13. -N bio_15. -N bio_16. -N bio_18. -N bio_19. -N bio_3. -N bio_4. -N bio_5. -N bio_6. -N bio_7. -N bio_9. -t glc2000. -t aspect. -z warnings=false -a
java -cp C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar density.AUC C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\A_Sp_Occ.csv C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\BC\b1\LQH\cheer.asc >> C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\tempAUC.csv
java -cp C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar density.Getval C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\A_Sp_Occ.csv C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\BC\b1\LQH\cheer_thresholded.asc >> C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\tempOC.csv
java -mx512m -jar C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar -e C:\Users\Rajiv\Backup\Working Folder\GIS\Data\cheer_asc -s C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\AC_Sp_Occ.csv -o C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\AC\b1\LQH noautofeature -p nothreshold pictures=true biasfile=C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\BiasFileAC.asc biastype=3 betamultiplier=1 "applythresholdrule=10 percentile training presence" -N bio_10. -N bio_11. -N bio_13. -N bio_15. -N bio_16. -N bio_18. -N bio_19. -N bio_3. -N bio_4. -N bio_5. -N bio_6. -N bio_7. -N bio_9. -t glc2000. -t aspect. -z warnings=false -a
java -cp C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar density.AUC C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\B_Sp_Occ.csv C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\AC\b1\LQH\cheer.asc >> C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\tempAUC.csv
java -cp C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar density.Getval C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\B_Sp_Occ.csv C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\AC\b1\LQH\cheer_thresholded.asc >> C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\tempOC.csv
start C:\Python27\ArcGIS10.1\pythonw.exe C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\ABC\b1\LQH\cheer.py
java -mx512m -jar C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar -e C:\Users\Rajiv\Backup\Working Folder\GIS\Data\cheer_asc -s C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\AB_Sp_Occ.csv -o C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\AB\b1\LQHPT noautofeature threshold pictures=true biasfile=C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\BiasFileAB.asc biastype=3 betamultiplier=1 "applythresholdrule=10 percentile training presence" -N bio_10. -N bio_11. -N bio_13. -N bio_15. -N bio_16. -N bio_18. -N bio_19. -N bio_3. -N bio_4. -N bio_5. -N bio_6. -N bio_7. -N bio_9. -t glc2000. -t aspect. -z warnings=false -a
java -cp C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar density.AUC C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\C_Sp_Occ.csv C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\AB\b1\LQHPT\cheer.asc >> C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\tempAUC.csv
java -cp C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar density.Getval C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\C_Sp_Occ.csv C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\AB\b1\LQHPT\cheer_thresholded.asc >> C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\tempOC.csv
java -mx512m -jar C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar -e C:\Users\Rajiv\Backup\Working Folder\GIS\Data\cheer_asc -s C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\AC_Sp_Occ.csv -o C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\AC\b1\LQHPT noautofeature threshold pictures=true biasfile=C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\BiasFileAC.asc biastype=3 betamultiplier=1 "applythresholdrule=10 percentile training presence" -N bio_10. -N bio_11. -N bio_13. -N bio_15. -N bio_16. -N bio_18. -N bio_19. -N bio_3. -N bio_4. -N bio_5. -N bio_6. -N bio_7. -N bio_9. -t glc2000. -t aspect. -z warnings=false -a
java -cp C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar density.AUC C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\B_Sp_Occ.csv C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\AC\b1\LQHPT\cheer.asc >> C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\tempAUC.csv
java -cp C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar density.Getval C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\B_Sp_Occ.csv C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\AC\b1\LQHPT\cheer_thresholded.asc >> C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\tempOC.csv
java -mx512m -jar C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar -e C:\Users\Rajiv\Backup\Working Folder\GIS\Data\cheer_asc -s C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\BC_Sp_Occ.csv -o C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\BC\b1\LQHPT noautofeature threshold pictures=true biasfile=C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\BiasFileBC.asc biastype=3 betamultiplier=1 "applythresholdrule=10 percentile training presence" -N bio_10. -N bio_11. -N bio_13. -N bio_15. -N bio_16. -N bio_18. -N bio_19. -N bio_3. -N bio_4. -N bio_5. -N bio_6. -N bio_7. -N bio_9. -t glc2000. -t aspect. -z warnings=false -a
java -cp C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar density.AUC C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\A_Sp_Occ.csv C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\BC\b1\LQHPT\cheer.asc >> C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\tempAUC.csv
java -cp C:\Users\Rajiv\Backup\Working Folder\GIS\Data\maxent\maxent.jar density.Getval C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\A_Sp_Occ.csv C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\BC\b1\LQHPT\cheer_thresholded.asc >> C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\GISinputs\tempOC.csv
start C:\Python27\ArcGIS10.1\pythonw.exe C:\Users\Rajiv\Backup\Working Folder\GIS\Analysis\cheer_models\cheer\ABC\b1\LQHPT\cheer.py

Batch Code successfully compiled and output for cheer: Beta 1
Successfully created GIS layers for: cheer
************************************************
************************************************
Generating final python scripts for spatial jackknifing MaxEnt models: cheer
************************************************
************************************************
************************************************
************************************************
***************IMPORTANT************************
Batch code, python scripts, and GIS layers were successfully created for all input species.
                                                 
To run batch code, go to the specified output folder and execute the:
                                                 
       'Step1_Optimize_MaxEnt_Model_Parameters.bat' batch file
              (this script will optimize model parameters)
                                                 
Once the Step 1 batch file is finished, to generate your final SDMs execute:
                                                 
       'Step2_Run_Optimized_MaxEnt_Models.bat' batch file
                                                 
************************************************
************************************************
Completed script RunMaxentModel2...
Succeeded at Thu Feb 12 12:29:18 2015 (Elapsed Time: 1 minutes 54 seconds)

[Stephanie Auer]

Rajiv, 

Have you found a solution to your problem? I am having the same issue. "Step2_Run_Optimized_MaxEnt_Models" has 0KB. 

When I run the spatial jackkife tool it runs successfully, however sometimes when I run the "Step1_Optimize_maxent_model_paramters" I get multiple error messages that say the same thing:

I don't know what to do. Any suggestions from the community would be appreciated. 

Thanks, 

Stephanie

[tahmine tavanpour]

 Dear Jason Brown

I'm currently working with SDM toolbox and it's very interesting and helpful for SDM analysis. During the program, I was faced with an error of Background Selection and I could not solve the problem. I hope you can help me on this issue.

I selected  Sample by Buffered MCP, my error is:

Running script PseudoabsenceSelectionSBufferedPtsFOLDER...

...setting up environment

Traceback (most recent call last):

  File "C:\Program Files (x86)\sdm toolbox\SDM_Toolbox_v1.1_ArcMap10.1.tbx#PseudoabsenceSelectionSBufferedPtsFOLDER.py", line 30, in <module>

  File "c:\program files (x86)\arcgis\desktop10.1\arcpy\arcpy\geoprocessing\_base.py", line 515, in set_

    self[env] = val

  File "c:\program files (x86)\arcgis\desktop10.1\arcpy\arcpy\geoprocessing\_base.py", line 567, in __setitem__

    ret_ = setattr(self._gp, item, value)

RuntimeError: Object: Error in accessing environment <extent>

Failed to execute (PseudoabsenceSelectionSBufferedPtsFOLDER).

Failed at Tue Feb 17 19:16:46 2015 (Elapsed Time: 1.00 seconds)  

Best Regards

[Rajiv]

Hi Stephanie,
My problems with the sdmtoolbox were due to the following reasons:

1. Environment was not correctly set.
2. The directory structure was too complex and had folder names with spaces.
I corrected these and everything ran smoothly.

In your case there are no spaces in names so it does not seem to be the problem. So set up environment again and try. Also try with a different threshold setting when running the spatial jackknifing and see if the first batch file runs properly. When the first batch file does not complete its run, the second batch file always shows 0 bytes.

Meanwhile, I have found another problem:

The spatial jackknifing tool includes the given bias file as one of the variables in the run and this variable shows maximum percent contribution (98 - 99 %) in the variable contributions table of the final model. All other variables show zero % contributions.

All the best with your model runs and hope we find an explanation to the second problem it listed.

Rajiv

[kaky]

Dear Brown

Please i have some problems with SDMstool when i want to estimate species richness, when put every things in its place the tool is run but automatically the arcGIS shutdown i dont know what is the problem. second when i want to create bias file by  sample by Distance from Obs. Pts or by Buffered MPC every thing going well and the tool said processed successful but when i go back for output folder is empty please can you advice my to cover these problems.

Regards

Emad