Modeling Species Distribution with MaxEnt

Bryce Maxell, Acting Director, Montana Natural Heritage Program

&Scott Story, Nongame Data Manager, Montana Fish,

Wildlife and Parks

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

• About to start again folks on the phone.

INSTALLATIONInstalling and Running MaxEnt

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

Check Java Version

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 Gb1024 = 1 Gb1536 = 1.5 Gb2048 = 2 Gb

BASIC MODELING RUNRunning MaxEnt

Required Inputs

• Species presence localities (“samples”) file

• Environmental feature layers

• Output directory

MaxEnt – Main Screen

Supply presence localities

Supply folder containing

environmental feature layers

Change variable types as necessary

Supply an output directory

Ready to Run

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

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 minutesWith maxent.cache = 24 minutes

EXAMINING OUTPUTRunning MaxEnt

Output• plots folder• logfile• maxentResults.csv• For each species

– .asc– .html– .lambdas– _omission.csv– _sampleAverages.csv– _samplePredictions.csv

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

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)

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

Html

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

Omission Rate vs. Cumulative Threshold

Receiver Operating Curve

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)

Sample Predictions File

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%)

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)

MORE ADVANCED PARAMETERSRunning MaxEnt

REPLICATE RUNSRunning MaxEnt

BATCH MODERunning MaxEnt

Preparation of Data

Scott Story

Required Inputs

• Species presence localities (“samples”) file

• Environmental feature layers

• Output directory

Getting Feature Data Ready

• Same projection (coordinate system, units, datum)

• Same resolution• Same extent• ESRI ascii format

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)

Two Raster Datasets

Land cover Precipitation

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

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!

Deriving New Raster Data - Ruggedness

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

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

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

THRESHOLDSRepresenting the output

Logistic Output (Ranges 0-1)

Reclassify with ArcGIS

Preset MaxEnt ThresholdsCumulative 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 0.020

Fixed Cumulative Value 10 10 0.260 0.210 0.044 0.082

Minimum Training Presence 0.699 0.029 0.365 0.000 0.000

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 0.162

Maximum test sensitivity plus specificity

7.729 0.225 0.228 0.029 0.043

Balance Training Omission, Predicted Area, &Threshold Value

1.054 0.047 0.342 0.002 0.000

Equate Entropy of Thresholded & Original Distributions

5.465 0.182 0.250 0.021 0.026

Thresholds – Ends of SpectrumBalance Training Omission, Predicted Area, &Threshold Value

Equal Training Sensitivity & Specificity

MODEL VALIDATIONModel Validation

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)

_samplePredictions.csv

Discussion Point

Topics Left

• Data Prep• Output• Thresholds• Validation• Batch• Replicates