Habitat Modeling Project - Part 2: Rule-Based Modeling

ENV 761 - Landscape GIS   |   Spring 2024   |   Instructors: Peter Cada & John Fay  |  

Contents

Recordings

Overview - Rule-based modeling

Rule-based habitat models, also referred to as generative or envelope models, are the oldest and simplest habitat models. They consist of defining the limits of different environmental conditions in which one would expect to find a species - the so-called “Hutchinsonian hypervolume”. The trickiest part of these rule based models is defining the limits. This is most often done using expert knowledge about the species, but could also be done using observation data to chart - in parameter space - the ranges in which the species has been observed. Envelopes or Mahalanobis distance metrics can be applied to these values to confine the hypervolume, but once the boundaries are specified, the spatial component is relatively easy.

In our exercise, we will rely on “expert knowledge” about the pigmy salamander. We’ll use this knowledge to set the rules and then use some raster algebra to apply those rules to come up with habitat map for our species.

Step 2.1. Setting the rules

From our background research as well as meetings with pigmy salamander experts, we’ve deduced the following constraints on our salamander.

  • Salamanders are found above 762 m in elevation and below 2012 m.

  • Salamanders prefer areas that are within 400 m of the following GAP cover classes:

    • Class #63 - Central and Southern Appalachian Northern Hardwood Forest

    • Class #84 - Southern and Central Appalachian Oak Forest

    • Class #96 - Central and Southern Appalachian Spruce-Fir Forest

  • Salamanders require places where the max monthly temperature never exceeds 18° C.

  • Salamanders occur in places where the driest month gets at least 96mm of precipitation.

Step 2.2. Applying the rules

Given this information, we can fairly easily extract the pixels that meet these criteria using raster calculations in a geoprocessing model:

  1. Create a new geoprocessing model. Name it “Rule Based Model”

  2. Add the elevation, focal forest (400m), PRISM max monthly temperature, and PRISM driest month precipitation layers to the model.

  3. Add the Raster Calculator tool and assemble the following calculation. You may want to do this piecemeal, running the tool after each chunk to make sure you have no typos. You should end up with a raster output containing 1’s (where all the criteria are true) and 0’s where any of the criteria are false. If you end up with only zeros, check your statement.

    ([Elevation] > 762) & ([Elevation] < 2012) & 
([FocalForest400] > 0.5) & 
([TMax] <= 1800) & 
([PPTmin] > 9600)

The output is your rule-based prediction. It is the spatial representation of the conditions that the experts believed to be real constraints on the salamander’s habitat. Your result should resemble this:

Summary

Not much to rule based modeling. We simply isolate the pixels that match the rules. Its elegance is in its simplicity, but the result is a pretty stark binary map. We have no indication of how close a pixel was to being ruled out or ruled in, no way of tuning our result to be more sensitive to including possibly misclassified habitat or excluding possibly misclassified non-habitat.

And this is why we continue with a second way of modeling habitat, so called discriminant analysis where we look beyond applying rules in a binary fashion, but rather look at the likelihood that a pixel meets a given criteria as well as how important that particular criteria is in determining whether the pixel is actually habitat or not.