NR 422 Lab 6: Habitat Modeling
In this lab you will be constructing a habitat suitability index (HSI) model. HSI models are only
one of many types of niche models (generalize linear models, occupancy Models, resource
selection functions, etc.) to predict suitable habitat for a species and perhaps their presence. HSI
models are generally used for rapid assessment (ex: Environmental Impact Assessments) and
have many applications. Some models are relatively simple while others are quite complicated.
Goals:
 Understand how to construct Habitat Suitability Index (HSI) models
 Use raster calculator to produce a suitability index for model variables
 Produce a habitat suitability map for a species
 Build a model of the analysis process using model builder
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1. Obtaining Temperature and Precipitation Data
To get started you will want to have a variety of environmental layers based on the type of
species you are modeling. For plants you will probably want temperature and precipitation
layers. There are a number of sources for this type of data, Worldclim,
http://www.worldclim.org/, developed at the University of California, Berkeley, is just one.
What Worldclim did was compile discrete global weather data and derives continuous raster
layers of those data including temperature and precipitation. You will be provided a few of these
layers for the United States, but here is how you would download additional data of this type.
1.1 Downloading Data
a. Go to http://www.worldclim.org/.
b. Click the Download link in the middle of the page
c. From here you can choose to download current, future or past data. Click which ever data
set you are interested in.
d. At this page you can download data based on what type of data (Mean Temperature
Precipitation, etc.) and the resolution (30arc-seconds to 10arc-minutes). You can also
choose to download generic grids or specific ESRI grids to get your data.
e. After making your selection, you will be prompted to save a zip file of your data.
Remember these data sets are for the world so they will be large files.
2. The Process
To construct a HIS model you will need to first select a species to model. Then you will need to
decide what variables you will include in your model (Ex: Land cover, Temperature, Distance to
roads, etc.). These variables should be environmental conditions your species responds to and be
life history requisites. The next step is to think about the relationship between each variable you
picked and the species you’re trying to model. Using your own knowledge and literature
searches, you will need to construct suitability indexes (SI) for each of your variables (see figure
1 below). These relationships can be categorical or continuous and for each value your variable
make take on you should have a suitability index between 0-1.
Once you have the suitability indices for each variable, you will need to construct the final
habitat suitability equation combining all the variables. There are many ways to do this and you
may have to think a bit how you will go about this process. You may choose to have an additive,
multiplicative or logical model. For instance, if you choose a multiplicative form then any SI
value of 0 will equate to a HIS of 0 for that area (pixel). So think hard about how you wish to
build your model.
Summary:
1. Pick a species to model
2. Select the variables to model that species
3. Estimate and/or research the relationship between the suitability of the variables and your
species
4. Combine your variable to produce a final Habitat Suitability Model
3. Motivating Example
Lets say we are interested in modeling pika habitat in Rocky Mountain National Park. One of the
variables we have chosen to include in this model is elevation. We do a quick literature search
and find that pika rarely exist below 8,000ft (about 2500m) and we think the suitability of pika
habitat increases linearly from 0.0 at 8,000ft to 1.0 at 12,000 (about 3600m) where it plateaus. In
addition we found some literature on cover types for pika. We hypothesize that rocks have a SI
of 0.9, perennial snow and ice (which we are assuming has rock underneath) has a SI of 0.7,
herbaceous vegetation has a SI of 0.5, evergreen forests have a SI of 0.1 and all other cover types
have a SI of 0.0. Lastly, we want to include temperature in our model. Again we do some
research and some thinking and hypothesize that pika can only tolerate a narrow rage of mean
annual temperatures, so we decide that the SI for mean annual temperature is 1.0 between 0-5
degrees Celsius and 0.0 everywhere else.
Before you start your model I would highly recommend doing two things:
1) Draw a graph for each the variables and their suitability index (like the graphs in Fig. 1)
2) Create a flow diagram of the step you are going to take to produce your final model.
NLCD
DEM
Temp
Reclass
DEM-2500
Temp<= 5
Temp>= 0
LowTemp
HighTemp
DEM >= 2500
DEM<= 3600
Offset
Offset/1100
LowE
HighE
SlopeLine
LowTemp * HighTemp
NLCD_SI
Temp_SI
SlopeLine*LowE+HighE
(Ele_SI * Temp_V * SI_NLCD)/10
Ele_SI
HSI
As practice you could draw the graphs for this example then check them with the instructor to
make sure you are on the right track. You could do the same for the flow diagram.
3.1 NLCD variable(Categorical)
First we will calculate the suitability indexes for the land cover variable
To do this we will do a reclass of the NLCD raster. We have use this tool before in class. We
will reclass the values of our land cover to be suitability indexes. Note that we cannot easily
change integer values of the NLCD to be decimal values in Arc so we will reclass the NLCD to
our hypothesized suitability indexes*10 to make them integers. We will account for this by
dividing our final equation by 10.
Now we have the first piece of our Habitat Suitability Model.
3.2 Temperature variable (Box)
Next, we are going to calculate the suitability indexes for the mean annual temperature. We want
to classify the value between 0 and 5 to be 1.0 and everything else to be 0.0.
3.2.1 Raster Calculator!
We are now going to dive head first into Raster Calculator (I hope you brought your swimsuit).
It’s important to be patient and to help each other as you try to understand what works and what
doesn’t for raster calculator. Also, double check your output layers you create and make sure the
raster you create make sense before moving to the next step.
Raster Calculator is located in Spatial Analyst→Raster Calculator. Raster calculator is really
finicky about how you enter equations. Spaces are especially important and there should be
spaces between each item in your equation. Use the buttons provided in the calculator as much as
possible to avoid syntax errors.
To start, we will select all the values less then our maximum to get the low range of our
temperature layer. First think of a name to call this new raster (LowTemp in the example) then
set this new raster to equal the statement
Breaking this down…We are creating a new raster called LowTemp and using a Boolean
operator (<=) to select the cells in our temperature layer that are equal to or less than 5. Examine
the result and this may make more sense.
The next step is to select everything greater than our minimum value to get our high range.
You will notice that we have created 2 rasters with values of 0 and 1. What does each of these
rasters mean? How would we combine them to just pull out the range we are interested in?
Answer: if we multiply these two layers together this will cause any areas that have a 0
associated with them to be 0 (false) and everywhere both these conditions are true (have a value
of 1) will be 1. This is our “Box/Window”. What this gives us is values of 1 in the box/window
we defined and 0 for all other values.
3.3 Elevation variable (Linear)
Finally, we want to calculate suitability index for our elevation variable. This is going to be a bit
trickier then previous two examples but just think about the steps and it will make sense.
For this variable we hypothesized a linear relationship which then plateaus. This will require
multiple steps (and raster) in order to produce the SI for elevation.
First we want to calculate the offset (this is where our line crosses the x axis).
Now we want to calculate the slope of the line using the offset and the range (3600-2500).
The next steps are similar to what we did for the temperature variable.
LowE = [rmnp_dem30] <= 3600
HighE = [rmnp_dem30] >= 2500
Lastly, we are going to put these all together to get our SI for Elevation
Ele_SI = [Slope] * [LowE] + [HighE]
Note that we added [HighE] instead of multiplying it. This accounted for the plateau portion of
our variable.
3.4 Putting it all together
At this point we have the SI for each of our variables. Now we want to combine them in a final
habitat suitability model. There are many ways to do this but commonly combined
multiplicatively.
Here is an example for the variable created above (hopefully you will be more consistent in your
naming then I was for the variable):
HIS = ([Ele_SI] * [Temp_V] * [SI_NLCD])/10
Remember to dived by 10 to account for the Arc obstacle we had to get around to get our SI for
the NLCD.
Now take a look at your model…What did you get? If your ambitious (and I know you are, you
can apply a threshold to your final output.
FINAL TURN IN:
A professional report describing your Habitat Suitability Model: Use at least three variables to
construct you’re HIS model .Include a final map of your model, descriptions of your variables
and the equations you used to calculate the suitability index for each variable. Also include the
final equation you used to produce your model. You can add any supplementary information or
maps that may help to illustrate your model.