1. Modeling Species Distribution with MaxEnt Bryce Maxell, Acting Director, Montana Natural Heritage Program & Scott Story, Nongame Data Manager, Montana Fish, Wildlife and Parks

2. Agenda - Wednesday 8-9 Introduction to MaxEnt 9:05-10Reptile and Amphibian Model Examples 10:05-11 Installation and Walkthrough of MaxEnt 11:05-12 Preparation of Data 12-1 Lunch 1-1:55 Thresholds & Model Validation 2-3 Using models in your DSS 3 - 5Hands-on Session Tomorrow 8-11 Hands-on, Data Prep, Questions & Discussion

3. About to start again folks on the phone.

4. INSTALLATION Installing and Running MaxEnt

5. Download & Install http://www.cs.princeton.edu/~schapire/maxent/ Current MaxEnt Version = 3.3.3e Requires Java Version 1.4 or later Type java –version at command prompt http://www.java.com Extract the.zip file to a very simple directory – No spaces, no strange characters, short – C:\maxent Three files are installed – Maxent.bat – Maxent.jar – Readme.txt – Download the tutorial Word document

6. Check Java Version

7. Set PATH and customize.bat file My Computer  Properties  Advanced  Environment Variables  System Variables  PATH  Edit Add to end of the PATH  ;c:\maxent Change the maxent.bat file –Change the extension to.txt so that you can edit it with Notepad –Change line reading java -mx512m -jar maxent.jar to… –java -mx512m -jar c:\maxent\maxent.jar –Change the extension back to.bat –Note that changing the 512 to another number allocates more memory 512 Mb = 0.5 Gb 1024 = 1 Gb 1536 = 1.5 Gb 2048 = 2 Gb

8. BASIC MODELING RUN Running MaxEnt

9. Required Inputs Species presence localities (“samples”) file Environmental feature layers Output directory

10. MaxEnt – Main Screen

11. Supply presence localities

14. Supply folder containing environmental feature layers

16. Change variable types as necessary Supply an output directory

17. Ready to Run

18. What MaxEnt Does Reads through each layer to – Determine type – Create.mxe file for each layer in maxent.cache Extracts the random background and sample data – You will get warnings about points that are “missing some environmental data” Calculates the gain until a threshold is reached Creates the output grids for each species (this takes the longest) Creates the thumbnail.png images

19. Time Required Ten feature layers (3 categorical) – 46 million pixels 2 Species Intel Core 2 Quad CPU (2.83 GHz) 4.00 GB RAM Windows 7 32-bit Operating System 512Mb of memory specified Without maxent.cache = 38 minutes With maxent.cache = 24 minutes

20. EXAMINING OUTPUT Running MaxEnt

22. Output plots folder logfile maxentResults.csv For each species –.asc –.html –.lambdas – _omission.csv – _sampleAverages.csv – _samplePredictions.csv

23. Logfile Timestamp Version of MaxEnt Samples file name Warnings Command line to repeat Species Layers Layertypes Directories for: samples file, layers, output Number of samples Maximum gain

24. Gain Closely related to deviance, a measure of GOF in GAM and GLM Starts at zero and heads toward an asymptote MaxEnt trying to come up with best fit Average log probability of presence samples minus a constant Gain indicates how closely the model is concentrated around presence samples Avg likelihood of presence samples = exp(gain)

25. Gain Examples McCown’s Longspur – Resulting gain: 2.275 – Average likelihood for presence points = 9.728 Olive-sided Flycatcher – Resulting gain: 1.297 – Average likelihood for presence points = 3.658 Average likelihood of the presence sample is X times higher than that of a background pixel

26. Html Analysis of omission/commission Receiver Operating Curve (AUC calculated) Preset Thresholds Pictures of the Model Analysis of Variable Contributions Raw Outputs

27. Omission Rate vs. Cumulative Threshold

28. Receiver Operating Curve

29. Sample Predictions File Coordinates for all points Test or Training Predicted values – Raw – Cumulative – Logistic Use this file to calculate deviance Use samples procedure in ArcMap to extract the ones and zeros (above threshold or not)

30. Sample Predictions File

31. Logistic Ouput High probability of suitable conditions Low predicted probability of suitable conditions White dots = training (1059 points or 75%) Purple dots = test (352 points or 25%)

32. Viewing Data in ArcMap Build Raster Attribute Table (Categorical) –.vat.dbf Build Histograms (Classified) –.aux Build Pyramids –.rrd –.xml For species output grids – Convert ASCII to Raster (Output Data Type = FLOATING) – Output as.bil ( Band interleaved by line)

34. MORE ADVANCED PARAMETERS Running MaxEnt

35. REPLICATE RUNS Running MaxEnt

36. BATCH MODE Running MaxEnt

37. Preparation of Data Scott Story

38. Required Inputs Species presence localities (“samples”) file Environmental feature layers Output directory

39. Getting Feature Data Ready Same projection (coordinate system, units, datum) Same resolution Same extent ESRI ascii format

40. Two Raster Datasets Land cover Source = Montana Natural Heritage Program Type = IMAGINE Image Cell size = 30 meters Columns & Rows =33005, 24008 Spatial Reference = Montana State Plane (NAD83) Pixel Type = Unsigned Integer (8-bit) Precipitation Source = PRISM Climate Center Type = ASCII grid Cell size = 0.0083333333 Columns & Rows = 7025, 3105 Spatial Reference = undefined (see metadata) Pixel Type = Signed Integer (32-bit)

41. Two Raster Datasets Land coverPrecipitation

42. Making Rasters Match Define coordinate systems for both Set some environment variables – Tools  Options  Geoprocessing Tab  Environments – General Settings: Extent and Snap Raster – Raster Analysis Settings: Cell Size, Mask Project Raster – Select target raster to match for output cell size

43. Precipitation Reprojected & Resampled Same exact extent Same exact number or rows & columns Same exact cell size Real test is…does Maxent throw any errors? In this case…it worked! Getting all your data layers squared away will take some time!

44. Deriving New Raster Data - Ruggedness

45. Types of Environmental Features Continuous (Quantitative) – Interval-scale (interval data, order, linear scale) – Ordinal variables (scale unknown-transformed?, rank clear) – Ratio-scale (interval data, ordered, not on linear scale, e. g. temp on F or C scale) Categorical (Qualitative) – Nominal (e.g. gender) – Ordinal (has order, e.g. low to great) – Dummy variables from quantitative (classes) Name the ASCII files with CONT or CAT prefix

46. Preparing Point Data Create a separate file for each species Combine them all\groups of them into one file Probably want to retain a unique identifier May want to setup scripts in ArcGIS to extract presence data Might also want more control of how background data is selected Let’s look at an example script - ExtractModelInputData.py

47. Other “Feature” Layers Masks – useful if you want to train a model using only a subset of the region – mask.asc – containing a constant value (1, for example) in area of interest and no-data values everywhere else. Bias – assumption that species occurrence data are unbiased – good understanding of the spatial pattern – values should indicate relative sampling effort

48. THRESHOLDS Representing the output

49. Logistic Output (Ranges 0-1)

50. Reclassify with ArcGIS

51. Preset MaxEnt Thresholds Cumulative Threshold Logistic Threshold Fractional Predicted Area Training Omission Rate Test Omission Rate Fixed Cumulative Value 110.0430.3440.0020.000 Fixed Cumulative Value 550.1720.2550.020 Fixed Cumulative Value 10100.2600.2100.0440.082 Minimum Training Presence0.6990.0290.3650.000 10 Percentile Training Presence17.5220.3510.1670.0990.151 Equal Training Sensitivity & Specificity 21.9890.3930.1490.1480.205 Maximum Training Sensitivity Plus Specificity 9.2010.2480.2160.0350.065 Equal test sensitivity & specificity18.6030.3610.1620.1060.162 Maximum test sensitivity plus specificity 7.7290.2250.2280.0290.043 Balance Training Omission, Predicted Area, &Threshold Value 1.0540.0470.3420.0020.000 Equate Entropy of Thresholded & Original Distributions 5.4650.1820.2500.0210.026

52. Thresholds – Ends of Spectrum Balance Training Omission, Predicted Area, &Threshold Value Equal Training Sensitivity & Specificity

53. MODEL VALIDATION Model Validation

54. Validation Metrics Receiver Operating Curve – obtained by plotting, for each threshold in this range, the proportion of true positive against the proportion of false positive Area Under Curve – computed by computing the area under the above described curve Deviance – 2 times the log probability of the test data. Absolute Validation Index - the proportion of presence evaluation points falling above the threshold or within the GAP predicted distribution Point Biserial Correlation - The correlation between a model’s predictions and presence/absence in test data (regarded as a 01 variable)

55. _samplePredictions.csv

57. Discussion Point

59. Topics Left Data Prep Output Thresholds Validation Batch Replicates