Habitat Modeling Project: Overview

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

Contents

Recordings

Overview

Last week we examined a few techniques for deriving several useful datasets from digital elevation model data. These derived datasets gave us clues to where we are likely to find higher biodiversity (hot and wet areas), but by themselves they don’t reveal much about what particular species are likely to be found in any given location. In this exercise, we focus on the spatial analyses (and to some degree the statistical analyses) used to do just that: to map out the habitat for a given species.

Habitat mapping, also called species distribution modeling, is an indispensable tool for conservation since we usually want to focus efforts where species of concern actually exists or where biodiversity is high. The techniques learned here, however, have additional applications, as they can reveal not only where species are likely to be found, but also where other natural resources might occur or even thrive. For example, the Pennsylvania TNC used habitat modeling techniques to predict where new hydraulic fracturing wells are likely to be placed so they could model their impact on forests (source).

Approaches to habitat modeling

There are a few approaches to habitat modeling; which one is appropriate for a given situation depends on what is known about the species and the quality and abundance of data available for an analysis.

Perhaps the most historically widespread approach to habitat modeling is expert based mapping. This is where people who are very familiar with the known range of the species (i.e. experts) can draw that range on a map with a reasonable degree of confidence. These maps are common in biological atlases and can be viewed for many species through NatureServe’s / InfoNatura’s portal (LINK). While the quality of expert based maps is only as good as the knowledge held by the expert(s), and some may be of dubious accuracy, the maps are often the best information available and can lead to better conservation decisions than without them.

A second approach to habitat modeling is generative or rule-based mapping. This approach is also informed by expert knowledge, but instead of knowledge where the species has been observed, the knowledge is more about the requirements and limitations of the species. For example, perhaps it’s known that the species lives within a certain elevation range, is seldom found too far from water, and needs trees to build its nests. With that information, we can create a set of spatial rules that meet these criteria with the result being a map of that species’ habitat. As above, this approach is only as good as the knowledge that goes into it -- and the spatial data on which these rules are applied. However, the result can be an improvement over the expert drawn maps as it may pick up nuance that is overlooked by the drawn map.

The third approach, discriminative or statistical based mapping, is the most complex and the most objective. This approach is more useful when little is known about the species, but it requires at least a few known locations where species has been observed, and is most accurate when a thorough survey is conducted to provide data on both known locations and known absences of the species. These locations are used to run statistical analyses on various spatial datasets representing environmental factors thought to influence habitat preferences to tease out (1) which of those environmental factors is important and (2) what values of those factors deemed important define suitable habitat for the species in question.

The statistical approach to habitat mapping continues to evolve as more methods are developed and as computational power improves. We will not be going too deep into the various statistical approaches in this lab exercise - that is more a topic for a statistics course than a spatial analysis one. Instead we will concentrate more on the overall structure of the statistical based analysis and the role GIS plays in generating the data for these statistical models as well as interpreting and evaluating the results.

Context of our analysis:
The pygmy salamander (Desmognathus wrighti)

In this exercise we will be developing a habitat model for the pygmy salamander found in the southwest corner of North Carolina. However, our overall goal will not be so much to generate a “bullet proof” habitat map for this species as it will to (1) explore what environmental factors influence this species, and (2) understand the role GIS/spatial analysis plays in modeling this species’ habitat.

The exercise consists of three parts. The first part involves getting to know the species. Any clues as to what biophysical factors might shape the salamander’s habitat can help us narrow our search for supporting data and improve our results. Second, we will develop a rule-based model informed by “expert knowledge” provided to us. And third, we will use MaxEnt, a popular statistics-based habitat modeling package - to generate a habitat suitability map based on occurrence locations and environmental data.

We will be following this lab up next week with further analysis of our salamander habitat models. In that exercise we will be evaluating how well we think our models did and interpreting the result to improve our understanding of the salamander and ultimately arrive at a habitat map of the species.

Data used in this exercise

The SDM_Exercise.zip file includes both a set of known locations where the pygmy salamander has been observed as well as a set of spatial datasets that can be used as environmental layers (or to generate environmental layers) for both deductive and statistic-based habitat mapping.

SDM_Exercise.zip contents:

  • A raster mask marking the extent of your analysis

  • 1 arc-second DEM raster (NED)

  • 2011 land cover raster (NLCD)

  • GAP land cover raster (SE Regional Gap Analysis Program)

  • 1 km modeled precipitation and temperature data raster (PRISM)

  • Pygmy salamander element occurrence data (NC Wildlife Commission)

  • Some useful geoprocessing tools to kick start your analysis

  • MaxEnt software folder

While the spatial data are public domain and you are welcome to re-distribute these data freely, the salamander occurrence records are provided by permission only and should not be used outside this lab exercise.

Deliverables (Part 1- ungraded)

While there is nothing to submit for this week’s lab assignment (it will all be submitted next week, after we’ve done our model assessment), the following products will be good milestones for your analysis.

  • A short description of the biophysical features that may be relevant in modeling your species.

  • A listing of the spatial datasets that are useful proxies for these biophysical features

  • A geoprocessing toolbox used to run a rule-based model for your species

  • A geoprocessing toolbox used to generate the inputs formatted for MaxEnt

  • Your MaxEnt results

  • Habitat range maps for the species derived from the rule-based and MaxEnt models

References

Austin, M.P. (2002), Spatial prediction of species distribution: an interface between ecological theory and statistical modeling. Ecological Modelling, 157: 101-118. http://www.sciencedirect.com/science/article/pii/S0304380002002053

Elith, J., et al. (2006), Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29: 129–151. (http://onlinelibrary.wiley.com/doi/10.1111/j.2006.0906-7590.04596.x/abstract)

Elith, J, et al. (2010), A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17: 43 - 55. http://onlinelibrary.wiley.com/doi/10.1111/j.1472-4642.2010.00725.x/pdf

Phillips, S. (2006), A brief tutorial on Maxent. AT & T Research. (http://www.cs.princeton.edu/~schapire/maxent/tutorial/tutorial.doc)