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: Reptile and Amphibian Model Examples
10: Installation and Walkthrough of MaxEnt
11: Preparation of Data
Lunch
1-1:55 Thresholds & Model Validation
Using models in your DSS
Hands-on Session
Tomorrow 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
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
Note that coordinate systems other than Lat/Long are permitted but the samples file coordinate system must match that of the environmental feature layers
We will talk more about preparing feature datasets in the next discussion

10. MaxEnt – Main Screen

11. Supply presence localities

12. The file can have a .txt or a .csv extension.
The file should have a header
Multiple species are permitted
The x coordinate must come before the y coordinate
Note that duplicate points will be dropped (duplicates are those that fall in same grid cell)
Keep track of the points that you use in a database if you want (might want to preserve a unique identifier from your point observation database)
You will get warnings for any points that fall outside of any of the input feature layers

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) 2 Species
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
maxentResults.csv contains all of the variable importantce, threshold information, one row per species
Html has pointers to a variety of plots
omission
receiver operating curve
table of threshold values
pictures of the model
analysis of variable contribution
raw data outputs and control parameters

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 Olive-sided Flycatcher
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
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
Note that coordinate systems other than Lat/Long are permitted but the samples file coordinate system must match that of the environmental feature layers
We will talk more about preparing feature datasets in the next discussion

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 = 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)
Source = PRISM Climate Center
Type = ASCII grid
Cell size =
Columns & Rows = 7025, 3105
Spatial Reference = undefined (see metadata)
Pixel Type = Signed Integer (32-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

51. Preset MaxEnt Thresholds
Cumulative Threshold
Logistic Threshold
Fractional Predicted Area
Training Omission Rate
Test Omission Rate
Fixed Cumulative Value 1 1
0.043
0.344
0.002
0.000
Fixed Cumulative Value 5 5
0.172
0.255
0.020
Fixed Cumulative Value 10 10
0.260
0.210
0.044
0.082
Minimum Training Presence
0.699
0.029
0.365
10 Percentile Training Presence
17.522
0.351
0.167
0.099
0.151
Equal Training Sensitivity & Specificity
21.989
0.393
0.149
0.148
0.205
Maximum Training Sensitivity Plus Specificity
9.201
0.248
0.216
0.035
0.065
Equal test sensitivity & specificity
18.603
0.361
0.162
0.106
Maximum test sensitivity plus specificity
7.729
0.225
0.228
Balance Training Omission, Predicted Area, &Threshold Value
1.054
0.047
0.342
Equate Entropy of Thresholded & Original Distributions
5.465
0.182
0.250
0.021
0.026
Purple row/column are only calculated in test data is specified.
Column of p-values is also present at far right. One-sided p-value for test of null hypothesis that test points are predicted no better than by a random prediction with the same fractional predicted area
The “balance” threshold minimizes 6 * training omission rate * cumulative threshold * fractional predicted area

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