Landscape Ecology Vignette #3 — Landscape Metrics


>>John Humphreys: Hello, my name is John
Humphreys. I am the Florida Department of Environmental
Protection’s Mitigation Banking and Uniform Mitigation Assessment Method Coordinator. This video is the third in a four part series
dealing with landscape ecology and terms and concepts often discussed as part of the Uniform
Mitigation Assessment Method’s Location and Landscape Support scoring attributes. Metrics discussed in this video include Patch
Richness, Patch Richness Density, Class Area Proportion, Mean Patch Area, and Patch Shape. Prior to applying any landscape metric, several
points should be considered. Firstly, landscape metrics should only be
used to conduct an “alternatives analysis.” Alternatives analysis simply refers to the
process of comparing a given patch under various conditions or states. The Uniform Mitigation Assessment Method is
also designed as an alternatives analysis procedure, in that rather than comparing two
different assessment areas located at distance, it is designed to measure an assessment area’s
Current Condition with that assessment area’s future, or “With Mitigation” condition. So, just as UMAM is not designed to evaluate
the “appropriateness” of a proposed mitigation site, landscape metrics are also not designed
to compare different sites. Rather, metrics are designed to evaluate the
same patch under various land use or project activity scenarios. Secondly, because individual landscape measures
are highly specific, no single metric should be used to determine a patch’s total landscape
support value. Rather, a suite of different landscape metrics
should be applied in combination to quantify a patch’s landscape value from multiple perspectives. Lastly, it is important to point out that
although landscape metrics can be automatically calculated using GIS and freeware available
online, all metrics can be calculated manually using a standard maps, rulers, and desktop
resources. The first metric to be discussed is Patch
Richness. Patch Richness merely describes the type and
number of patches over a given extent. For example, the landcover map shown on this
slide displays several different colors, each of which represents a different landcover
type. Light green represents forest, dark blue signifies
rivers and streams, light blue stands for lakes and ponds, brown and red colors symbolize
developed areas, and so forth. The total number of different map colors pictured
here represents the total number of different landcover types in the given extent. If each landcover type is thought of as a
patch, then the total number of landcover types is equivalent to this map’s Patch Richness. Another way to understand Patch Richness is
to consider it analogous to species richness. Both Patch and species richness quantify the
number and types of classes in the environment, but neither provide information as to the
distribution or configuration of those types or classes. Patch Richness Density is a metric that can
be performed by dividing Patch Richness by the areal extent of the study area. This metric builds on Patch Richness by describing
the number of patch types per unit area. For example, if the total patch richness of
the depicted map is determined to be twenty-five, because it displays twenty-five different
colors, then Patch Richness Density could be calculated by dividing twenty-five by the
total acreage of the extent to determine how many patch types are found per acre of area. Patch Richness Density can also be further
refined to determine the “Relative” Patch Density for a particular habitat type of interest. For example, if one wanted to quantify the
availability of isolated amphibian breeding ponds in the region north of the river pictured
here, they could divide the total number of isolated ponds by the total extent and determine
that one isolated pond exists for every 400 acres of area. The Class Area Proportion Metric describes
the relative proportion of area occupied by each patch type for a given extent. This metric can be calculated by dividing
the area covered by each patch type by total areal extent. Once gain looking to the pictured landcover
map, we could perform the Class Area Proportion metric to determine the percentile of the
total area occupied by forest, occupied by lakes and ponds, etcetera. Just as Patch Richness is analogous to species
richness, Class Area Proportion is analogous to species distribution. Having analogous values for both species richness
and distribution, we could apply standard biological indices, such as the Shannon’s
and Simpson’s diversity indices, to Patch Richness and Class Area Proportion to quantify
landscape diversity for a given area. Mean Patch Area describes the average patch
size for a given patch type. It can be calculated by first summing the
total area occupied by a patch type and then dividing that value by the total number of
that patch type in the study area. Mean Patch Area is a useful metric in weighing
the degree of habitat fragmentation anticipated under various land use project scenarios. As pictured here, the group of circles at
left occupy a total of seventy-five acres and have a Mean Patch Area of twenty-five
acres. The circles at right also occupy a total of
seventy-five acres, but exhibit a Mean Patch Area of only nineteen acres. If the circles at left represent current condition
and those at right display the future condition, we can see that those in the future condition
distribute total habitat area over a greater number of individual patches. This future configuration results in a net
increase in perimeter area and thus, its patches may be subject to intensified edge effects. Shape is the final metric to be discussed. Shape is a measure of geometric complexity
for a given patch. Shape can be calculated by measuring the total
perimeter of an assessment area and then comparing that length to the perimeter of a theoretically
optimum shape having the same areal extent as the assessment area. The most compact shape in most cases is a
circle; because, circles have the least circumference for a given area than do any other shapes. Knowing that the area of our theoretical circle
is the same as the assessment area, we can approximate Pi and determine the optimum perimeter
length for any assessment area using the formulas Area equals Pi times the radius squared, and
Circumference equals two times Pi times the radius. Dividing the assessment area’s perimeter length
by the circumference of our theoretical circle results in an index value of one or greater. A value of one would be ideal as it would
mean that the assessment area’s perimeter is the same length as the circumference of
the theoretically optimum shape. As the resulting value increases above one,
it signifies a growing perimeter length and complexity and an associated increased susceptibility
to edge effects. For additional information, please visit the
Uniform Mitigation Assessment Method website, thank you.