Landscape Assessment - Overview

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

Landscape Prioritization

Species distribution models like the one we created in the previous section are often key ingredients in landscape planning as they allow us to include a species range in the prioritization of areas for conservation. It’s rare, however, that we are able to protect the entire range of a given species, so we need to ask ourselves whether some areas of a species’ range might be more worth conserving than others. The answer, of course, is yes, and we’ll spend the next few weeks looking at different ways to examine and prioritize areas within a species range for conservation.

More specifically, we’ll establish a number of criteria for prioritizing portions of habitat to protect. First, we’ll compute statistics related to patch geometry, i.e., the size, shape of a given patch, and discuss how these relate to conservation importance. Then, we’ll examine suites of patches from a connectivity perspective: are there sets of habitat patches that, if selected, can act effectively as a single patch since they are close enough to each other? And finally, we’ll examine factors related to a patch’s viability or protection importance given the likelihood and severity of various threats to that patch.

In examining all these concepts, we’ll have a chance to examine a number of new geospatial tools and approaches that are also applicable to other scenarios, not just habitat patches. The full set of lesson objectives are listed below.


Learning Objectives

Topic Learning Objectives
§ 4.0 - Section Intro
[PPT] [Recording]
• Explain the term Geodesign as it applies to landscape assessment
• Outline key topics in the Craighead’s Conservation Planning book
• Describe the central question of landscape assessment
§ 4.1 - Habitat Patch Geometry
[PPT] [Recording]
• List some example habitat requirements for various species
• Create a dataset of habitat patches from modeled habitat data
• Compute patch areas and patch perimeters from a dataset of patches
• Explain shape complexity: why it is a useful measure, and how it is calculated
• Discuss fragmentation in terms of landscape changes and impacts on populations
• Define the concepts of “habitat cores” and “edge effects”
• Create a dataset of habitat patch cores from a habitat patch dataset
• Use ArcGIS to quantify patch geometry values for a given landscape
• Use FRAGSTATS software to quantify patch geometry values for a given landscape
• Compare the ArcGIS and FRAGSTATS approach and outputs
• Explain the proper usage of patch geometry in describing a landscape
§ 4.2 - Habitat Patch Connectivity
[PPT] [Recordings (x6)]
• Define connectivity and explain the importance of connectivity in conservation
• Explain connectivity in terms of populations and metapopulation dynamics
• Describe the relevance of connectivity in climate change resilience
• Compare ways in which connectivity can be quantifiably measured or modeled
• Describe the challenges in creating cost surfaces for connectivity modeling
• Explain the pros and cons of using least cost paths in measuring connectivity
• Calculate least cost paths among patches in a landscape using GIS
• Calculate least cost corridors among patch pairs in a landscape using GIS
• Explain the basics of graph theory and how it applies to connectivity
• Describe how graph theory is implemented in GIS
• Describe the components of graph theory and how they are computed
§ 4.3 - Mapping Habitat Threats
[PPT] [Recordings (x3)]
• Examine and critically evaluate global/region threat maps
• Define a what a threat is in terms of biodiversity loss
• Explain the challenges in mapping and evaluating threats to biodiversity
• Decompose threats into “stresses” and “sources”
• List the 5 practical steps to threat mapping and describe the role of GIS in that effort
• Describe the challenges in mapping threats and how those challenges are met
§ 4.4 - Fuzzy Analysis
[PPT] [Recordings (x3)]
• Explain what “fuzzy logic” is, as compared to binary logic
• Use different membership functions to compute fuzzy membership
• Use fuzzy inference to assign binary probabilities
• Use fuzzy logic in GIS analyses
• Relate out fuzzy logic alters our use of assumptions in making yes/no decisions