1. Introduction to Vegetation Inventory, Assessment, and Monitoring
The purpose of this section is to explore steps in designing and conducting vegetation monitoring projects. Specific concepts and tools will complete the story in subsequent sections of this course.
2. Sampling Principles
This unit focuses on the principles of sampling: why we sample, the relationship between population parameters and sample statistics, accuracy and precision, types of error and their causes, and using confidence intervals to make inferences about populations. Very simply, we sample so that we can gather accurate and precise information about populations, and to make inferences about populations with confidence.
3. Sampling Design
This module focuses on the elements of sampling design. Sampling design encompasses all of the practical components of a sampling endeavor: where to sample, what to sample, and how to sample!
4. Monitoring Implementation, Data Quality, and Best Practices
Data management is fundamental to any type of data gathering activity. It is a process that includes many steps, each of which provide opportunities to introduce non-sampling errors related to human error. This module focuses on the best management practices that can be used to reduce or eliminate potential errors associated with data management.
5. Indicators, Methods, Descriptors, and Covariates
This section explores the distinctions between indicators and methods, introduces the concepts of site descriptors and covariates that are used to help classify and interpret monitoring data.
This module focuses on plant density: what it is, how it is measured, and how density data are used by land managers to inform resource management decisions. Very simply, density is defined as the number of individuals per unit area, and reflects the closeness of individuals.
This module focuses on plant frequency: what it is, how it is measured, and how frequency data are used by land managers to inform resource management decisions. Very simply, frequency measurements record the presence of species in quadrats or plots placed repeatedly across a stand of vegetation. Frequency reflects the probability of finding a species at any location in the vegetated area.
This module focuses on cover: what it is, how it is measured, and how cover data are used by land managers to inform resource management decisions.
9. Vegetation Height and Structure
This module focuses on vegetation structure: what structure represents, how it is measured, and how information about vegetation structure is used to inform resource management decisions. Very simply, vegetation structure refers to the three-dimensional arrangement of plants and plant materials on a site or across a landscape. Vegetation structure is primarily influenced by plant cover on horizontal and vertical planes.
10. Biomass and Production
This module focuses on plant biomass: what it is, how it is measured, and how biomass data are used by land managers to inform resource management decisions.
This module focuses on plant utilization: what it is, how it is measured, and how utilization data are used by land managers to inform resource management decisions.
12. Composition, Diversity, Similarity
This module focuses on plant community diversity: how it is described, how it is measured, and how diversity is interpreted by land managers to inform management decisions.
13. Remote Sensing for Vegetation Monitoring and Assessment
Remote sensing techniques offer many opportunities to inform, supplement, and sometimes replace traditional field-based aproaches to vegetation assessment and monitoring. This module explores ways in which remote sensing can be used in monitoring and provides example applications.
14. Assessment and Monitoring Programs
This module explores some established rangeland assessment and monitoring programs, describes their protocols, and discusses how the collected data are used in management decision making.
12.1 Plant Community Composition, Diversity, and Similarity
Watch this video presentation for an overview and discussion of species composition and diversity.
To add captions to this video click the CC icon on the bottom right side of the YouTube panel and select English: Corrected captions.
The goal of vegetation assessment is to interpret field data and make decisions about the status or condition of the plant community relative to management plans or conservation goals. However, it is often difficult to get a clear picture of what is going on in a landscape based solely on descriptive statistics derived from raw field data.
In order to draw interpretations and make decisions, we use summarized field data from vegetation attributes (density, cover, or biomass) to calculate new variables or indices that describe diversity. Three ways of measuring and describing diversity are through species composition, diversity indices, and similarity indices.
What is Diversity?
Diversity is the variation within and among life forms on a site, in an ecosystem, or on a landscape. Diversity is defined as the distribution and abundance of different plants and animals within an area. Thus, it is an attribute comprised of two components — richness and evenness.
Richness is the number of groups of genetically or functionally related individuals. In most vegetation surveys, richness is expressed as the number of plant species in a quadrat, area, or community, and is usually called species richness.
Evenness refers to how equally abundant species are in a particular area. The more equally species occur in proportion to each other, the greater the evenness of the site. A site with low evenness indicates that one or more species dominate the site, while other species are relatively “under-represented”.
Diversity can be used to describe variation at several scales:
- Genetic (allele frequency, etc.)
- Taxonomic (families, species, sub-species and varieties, etc.)
- Life form (grasses, forb, trees, mosses, etc.)
- Functional group (deep rooted, nitrogen-fixing, soil crust, evergreen, etc.)
- Landscape ( spatial patterns of vegetation at landscape scale )
Note: The terms biodiversity and biological diversity are also commonly used to refer to the variety of biological organisms. In this lesson, the term diversity is used for the sake of simplicity, and not to imply any distinction or preference between the terms diversity, biodiversity and biological diversity.
Why is diversity measured?
Diversity is often measured because high diversity is perceived as being synonymous with ecosystem health. In general, diverse communities are believed to have increased stability, productivity, and resistance to invasion and other disturbances.
Advantages attributed to diverse ecosystems
- Diverse habitats provide forage for a variety of invertebrate and vertebrate species.
- System stability results from species in the plant community being able to survive drought, insect plagues, and/or disease outbreaks, so that the site will have some level of soil protection, forage, resistance and resilience to perturbation during those events.
- Genetic diversity represents the potential for species to adapt to changing environments and conditions, which is considered an important aspect of community stability and resilience.
- The community benefits from a mixture of plants in that:
>> soils improve with nitrogen fixers, deep rooted plants translocate nutrients from soil layers below other plants roots.
>> interactions between some species are mutually beneficial, or at least not detrimental (e.g., either through commensalism or symbiosis) and therefore, diverse communities can be more stable.
- Theoretically, diverse plant communities generally have all niches filled and are less likely to be invaded by noxious or opportunistic introduced species.
Potential disadvantages to ecosystem diversity
- Diverse communities may be an indicator of fragmented or somewhat degraded sites where species richness is affected by disturbance species.
- Plant communities with high diversity can be more difficult to manage for grazing because different species of plants have different grazing tolerances and different phenological development.
Diversity can be expressed at several scales
Diversity can be measured and monitored at several spatial scales (Figure 1).
Alpha Diversity: refers to richness and evenness of species within a plot, or site. Alpha diversity reflects both the number of species and their relative abundance, and can be considered “within-habitat” diversity.
Beta Diversity: is the difference in species diversity between disparate areas or communities, and also reflects species turnover along a gradient. Beta diversity can be considered “between-habitat” diversity.
Gamma Diversity: is the diversity of a landscape, or the overall diversity of a set of sites, habitats, areas or communities.
Figure 1. The three images are conceptual representations of: a) alpha diversity (singular community), b) beta diversity (between sites or along a gradient of communities), and c) gamma diversity (mosaic of multiple communities throughout a landscape).
Measuring and Describing Variation
There are several ways to measure, quantify, or describe the diversity or variation of species within a community. We will focus on three types of measures: species composition, diversity indices, and similarity indices. Species composition and diversity indices are calculated values that describe individual sites (or plots or quadrats). Similarity indices are used to compare sites in terms of their composition, and so are calculated using data from pairs of sites.
Species composition is a composite variable that is not measured in the field, but is calculated based on other plant attribute measurements. Species composition is defined as the proportion (or percent) of various plant species in relation to the total on a given area. Species composition is also known as “botanical composition”, or simply “composition”.
Why estimate species composition
- Species composition is an intuitive measurement. It is an expression of the relative contribution that each species makes as a percent of the total community, which people can easily visualize.
- Traditional guidelines used to set stocking rates for livestock on rangelands are based on the availability of forage, and consider the relative contribution of forage species to total biomass.
- Species composition has been used extensively to describe ecological sites and to evaluate rangeland condition, or similarity to a desired plant community.
- Measurements of composition over time can be used to characterize trend or changes in rangeland condition.
- Allows comparison of dominance of individual plants across plant communities. For example, two sites may be very different in terms of total biomass produced but they could both have about 50% mesquite by weight.
- Composition can be calculated based on individual species or groups, such as percent of noxious weeds, or percent forbs, grasses, and shrubs.
Value of Calculating and Comparing Composition
- Allows for “relative” comparison of individual species across sites, or times, that vary significantly.
- Composition reflects the relative contribution of a species to a community and the dominance of a specific species on a site.
- Many management objectives are focused on the assessment or manipulation of species composition. For example, a land manager may want to:
>>minimize the composition of noxious weeds in a community.
>>increase the relative abundance of desirable forage species in a pasture.
>>manipulate the relative abundance of warm-season or cool-season plants.
>>alter the relative contribution of various species that provide shelter or food for wildlife.
Calculating species composition
Species composition is generally expressed as a percent, so all species components add up to 100%. Composition can be calculated with measures of density, biomass, or cover (Table 1). It is NOT appropriate to estimate composition based using frequency data!
Table 1. Calculations of species composition by plant attribute.
For example, assume we have collected density data for shrub species on Clay Loam sites in southern Texas using 200 quadrats. We calculated mean density and reported that on a hectare basis (Table 2). To analyze the data for species composition, we sum the mean density for all species, which equals 472 plants per hectare, and then divide the mean density of each individual species by the density of all species combined to determine the contribution of each species to the species composition for the site. Note that when the composition percentages of the individual species are added up, it always adds up to 100 percent!
Table 2. Calculation of species composition using summarized data of shrub density on Clay Loam sites in southern Texas.
Let’s examine one more example, this time calculating species composition using cover data. Cover data are reported in units of percent, and the absolute value of total vegetation cover rarely adds up to 100%. Let’s assume we also measured canopy cover of shrub species in the Clay Loam site, and the mean cover values are reported in Table 3.
Table 3. Calculation of species composition using summarized data of shrub canopy cover on Clay Loam sites in southern Texas.
The approach is the same. The total cover of shrubs on the site added to 40%. By calculating the species composition based on cover, we see that blackbrush, which had an absolute canopy cover of 10%, contributes to 25% of the total canopy cover of shrub species on the site. Again, the composition percentages of the individual species sums to 100%.
Sometimes it is convenient to express diversity using a single number. This makes it easy to describe the diversity of sites without listing species, and enables us to compares sites that occur in different biomes and do not have any species in common.
The simplest metric of diversity is species richness. Species richness is simply the number of species on a site, and is often represented by “s”. While counting species is intuitive, richness alone doesn’t clearly reflect diversity because diversity is also influenced by evenness, or the relative abundance of species. Consider the “idealized” plant communities in Figure 2.
Figure 2. Graphical representation of two plant communities that exhibit identical species richness (s = 5), but different evenness.
Both communities have 50 individuals, distributed among five species. The evenness of the two sites is very different: in Site 1, each species is represented equally, by 10 individuals, but Site 2 is dominated by one species (green cross), and the other 4 species are relatively rare. We use diversity indices that incorporate both richness and evenness to express diversity. Two commonly used diversity indices are the Shannon-Wiener Index (H’) and Simpson’s Index (D). The Shannon-Wiener index is also called the Shannon Diversity Index, and is sometimes incorrectly presented as the Shannon-Weaver Index.
Shannon-Wiener Index (H’)
- Most commonly used index of diversity in ecological studies
- Values range from 0 to 5, usually ranging from 1.5 to 3.5
Advantages of the Shannon-Wiener Index
- Relatively easy to calculate
- Fairly sensitive to actual site differences
Disadvantages of the Shannon-Wiener Index
- Assumes that all of the species in the community are represented
- It is possible to produce similar values of H’ for sites that do not have similar diversity
Calculating the Shannon-Wiener Index
Calculation of H’ is based on the following formula:
The calculation of H’ based on density data is demonstrated in Table 4.
Table 4. Calculation of Shannon-Weiner Index (H’) using density data.
Simpson’s Index (D)
- D is a measure of dominance. Therefore, (1-D) reflects species diversity; however, H’ and D are not on the same scale, so the absolute value of the two cannot be directly compared.
- Gives the probability that any two individuals drawn at random from an infinitely large community belong to different species.
Advantages and Disadvantages
- Not as sensitive to species richness
- More strongly influenced by evenness, heavily weighted towards the most abundant species
- Generally less sensitive than Shannon-Weiner H’ to real changes in diversity
Calculating the Simpson’s Index
Calculation of D is based on the following formula:
The calculation of D based on density data is demonstrated in Table 5.
Table 5. Calculation of Simpson’s Index (D) using density data.
In vegetation studies, it is often desirable to compare two plant communities and determine how similar they are. This can be accomplished with a similarity index. Similarity indices measure the degree to which species composition in plots, quadrats, or sites is alike.
Measures of similarity can be used to examine:
- Differences between two sites within a landscape or management unit.
- Differences between similar sites that have been managed differently.
- Changes that may have occurred because of a natural or human caused disturbance (e.g., similarity between burned and unburned sites).
- Variation between different study times on the same site. (e.g., determine how similar the communities are now compared to what they were 10 years ago)
A very important application of similarity indices is to compare a site that we are managing to a reference site or reference state, in order to see if we are achieving management objectives. Usually the reference state is considered an “ideal” or “targeted” community that exhibits a certain species composition, which could reflect relative amounts of density, cover, or biomass. On ecological sites, we assess range condition by calculating the percent similarity to a desired plant community. Conversely, we can compare a site to an untreated “control” to see whether implemented treatments produced real change in the community.
There are many similarity indices that may be used to compare sites. The four most common include:
- Jaccard Coefficient,
- Sorensen Coefficient,
- Czekanowski Coefficient
- Coefficient of Squared Euclidean Distance (SED).
The Jaccard and Sorensen coefficients typically use presence/absence data, which makes them a rapid way to assess similarity based on relatively little detail. The Czekanowski coefficient and SED are most commonly used and preferred because they can be constructed using more detailed, quantitative data, such as density, cover and biomass.
The Czekanowski coefficient values range from zero (complete dissimilarity) to one (total similarity). The values are calculated using the following formula:
The resulting coefficient can be converted to units of percent, expressing percent similarity between sites. An example showing how to calculate a Czekanowski coefficient to compare 2 sites based on canopy cover is given in Table 6.
Table 6. Calculations for the Czekanowski coefficient are demonstrated with canopy cover data (%) from two different sites.
For each species, the cover values at each site are compared, and the smaller value, or lesser score, is recorded. The lesser scores are summed, and divided by the sum of the scores for all species in both sites.
Coefficient of Squared Euclidean Distance
The coefficient of Squared Euclidean Distance (SED) draws upon the mathematical properties of a right-angled triangle and defines the similarity of species between quadrats based on geometric space. The values are calculated using the following formula:
The values for the coefficient of SED range from zero to infinity. A coefficient of zero indicates total similarity, but theoretically there is no upper limit for dissimilarity. An example showing how to calculate the coefficient of SED using the previous data set is given in Table 7.
Table 7. Calculations for the coefficient of SED are demonstrated with canopy cover data (%) from two different sites.
Putting Similarity Indices into Practice
Similarity indices are particularly useful to make comparisons among multiple sites, to see which sites are more similar and which are not. The indices are calculated using all pairwise comparisons. Let’s expand the example given above to include one additional site, and calculate similarity using both types of similarity indices.
In this example, we compare Site 1, Site 2, and Site 3. There are 3 possible pairwise comparisons, between Sites 1 and 2, Sites 2 and 3, and Sites 1 and 3.
Table 8. Summarized canopy cover field (raw) data from three range sites in South Dakota.
When we compare the similarity indices for the three possible pairwise comparisons (Table 9), we can see that both indices show that Sites 1 and 2 are the most similar because this pair produced the largest Czekanowski coefficient (0.80) and the smallest coefficient of SED (10.30). In addition, we see that Site 3 is slightly more similar to Site 1 than to Site 2, but by a small margin. Of the three sites, Sites 3 could be considered an outlier compared to Sites 1 and 2.
Table 9. Two half-matrices showing pairwise comparisons for our three sites from South Dakota using both Czekanowski coefficient and coefficient of SED.
the following questions and activity are designed to test your knowledge and understanding of plant community composition and diversity concepts and data analysis.