Modeling Species Distribution with MaxEnt

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 • 9:05-10 Reptile 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 - 5 Hands-on Session • Tomorrow 8-11 Hands-on, Data Prep, Questions & Discussion

3. About to start again folks on the phone.

4. Installing and Running MaxEnt INSTALLATION

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. Running MaxEnt Basic modeling run

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. Running MaxEnt Examining output

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. Running MaxEnt MORE Advanced parameters

35. Running MaxEnt Replicate runs

36. Running MaxEnt BATCH MODE

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 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) • 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)

41. Two Raster Datasets Land cover Precipitation

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. Representing the output THRESHOLDS

49. Logistic Output (Ranges 0-1)

50. Reclassify with ArcGIS