# 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.