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.
6.1: Density Overview
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Introduction to Density
Density is a measure of the number of individuals within a unit area. Since density measurements may be done on plant parts or other counting units, a more inclusive definition is the number of counting units per unit area. For example density may be the number of plants/m2, the number of flowers/m2, or the number of trees/ha. We often use density to determine the effect of management practice or vegetation treatments targeting a specific plant. A measure of the target plant density is taken before and after treatment to determine the degree of control achieved by the treatment.
Example 1: Researchers in Montana used density measurements to determine what effect spotted knapweed control had in the Bitterroot Valley. They found that the density of spotted knapweed (Figure 1) declined 99%, from 25 plants/m2 to 0.4 plants/m2, following the release of a biological control agent that attacked the roots of the plant (Story et al. 2006).
Density measurements are often applied when we want to monitor changes in a given plant species over long periods. Density can be especially useful to detect the response of plants to a given management action. For example, density measures can reveal the success of reseeding efforts by quantifying the number of seedlings present within management areas.
We may also use density to identify threshold densities in community dynamics, and to provide information needed to make timely decisions concerning vegetation management. Researchers measured Douglas-fir (Pseudotsuga menziesii) densities in foothill grasslands with various levels of encroachment (Figure 2). They identified a threshold density for management: herbaceous biomass (i.e. forage) was substantially reduced once the tree density surpassed 2,200 trees / ha (Selensky et al. 2009), a critical threshold at which Douglas-fir control would be warranted.
Figure 2. Images of foothills grassland with differing densities of Douglas-fir. Density was measured by counting individuals (including seedlings) in 30 m2 belt transects, which were randomly distributed through multiple plots.
Advantages of Density Measurements:
- Easy to conceptualize. Density is very straightforward, easy to grasp and easy to explain to others.
- Measurements are quick. This is because the measure is simply a count of species and does not require more extensive measurements.
- Counts of plants are not highly affected by seasonal or yearly variation due to weather fluctuations or other factors. This property makes inter-year comparisons relatively easy. By contrast measurements of cover or biomass can vary quite substantially within or between years.
- Easy to measure in xeric ecosystems or for specific life forms such as trees, shrubs, and bunch grasses.
Disadvantages of Density Measurements:
- Only a count of the number of plants present. It does not provide information about plant health, quality of forage, or productivity. For example, 4 healthy individuals per unit area will produce the same data as 2 healthy and 2 unhealthy individuals per unit area.
- Difficult to measure density of plants that do not have distinct individuals such as fungi, mosses, lichens, or sod-forming grasses. For clonal plants, such as aspen, it is also difficult to identify individuals. In this case, density of stems is often recorded rather than density of individuals.
- Measuring density can be very time consuming in dense or complex environments. For example, imagine measuring the density of shrubs in a highly heterogeneous and complex plant community (Figure 3a) compared to a homogeneous plant community (Figure 3b). The greater the heterogeneity, the more difficult density measurements can be to collect.
- Density does not relate to dominance or resource use by the individuals. To illustrate this, a single tree, (density = 1/unit area), may use far greater amounts of water, nutrients, light, and rooting volume than 100 annual forbs in the same unit area. Consequently, biomass and cover are much better indicators of plant dominance in vegetation plant communities.
Common Ground Rules
Several methods are available for measuring density. All methods share two essential ground rules:
- Define counting units
- Determine level of plant identification
Density is often measured by counting individual plants. Alternative counting units such as flowers or tillers may be measured, depending on the management objectives and the goals of the monitoring protocol. Counting units should directly relate to the management goals or monitoring objectives and should be easily identifiable by all observers.
Level of Plant Identification
The level of plant identification must be clearly defined before gathering information. In most cases, plants need to be identified at the species level (Figure 4a). Certainly identification to species provides the most information. However, this may not be feasible when flowers and inflorescences are not present or with observers of different skill levels, or if all species within a genus tend to respond similarly to management actions or environmental fluctuations. In these cases, it may be sufficient to identify plants to genus (Figure 4b). Plants in the same functional group often respond similarly to management. Therefore, identification to the functional group may be sufficient for many monitoring protocols, depending on management objectives (Figure 4c).
Figure 4. Varying levels of plant identification: a) species level, such as Juniperus occidentalis Hook.; b) genus level, such as Artemisia spp L.; and c) functional group level, such as perennial grasses.
There are three main approaches to measuring density:
- Counting Methods
- Distance Methods
- Abundance Classes
The most common way to determine density is by counting individuals or plant parts within a known area, such as a plot. Plots vary in size and shape; the selection of plot dimensions depends on the vegetation being sampled. With counting methods, plants are counted in plots distributed in the study area, and mean plant density is calculated by summing the number of individuals counted and dividing by the number of plots sampled (Figure 5).
Figure 5. Density measured in five 1m2 plots. The number of plants counted is summed (12 plants), and divided by the number of plots sampled (5 plots) to determine the mean plant density (2.4 plants/m2).
Depending on the plot size, we often need to adjust the reported mean to a standard unit, such as number per m2 (#/m2) or number per hectare (#/ha). Counting methods are covered in detail in Lesson 2 of this module.
Distance methods provide a way to estimate density without using plots. Distance methods are based on the concept of mean area and plant spacing: as density increases, the average area occupied by each individual plant decreases (Figure 6).
Figure 6. Conceptual illustration of the mean area concept. a) When few plants are present in a given area, the average area that plants occupy is larger, and the spacing between plants (distance between plants) is larger. b) As plant density increases, the average area that each plant occupies is reduced, and the spacing between plants (distance between plants) is reduced.
There is an inverse relationship between mean area and plant density. Both describe the relationship of individual plants on an area basis:
So, D = 1/MA and MA = 1/D
Distance methods are often used to estimate the density of large individual that are easy to distinguish, such as trees and large shrubs. A major advantage of distance methods is that there are no plot boundaries, which eliminates the need to make decisions about counting plants that occur at the edge of the plot. Distance methods are covered in detail in Lesson 3 of this module.
Abundance classes are used to rapidly assess the relative abundance of plants. This approach usually reserved for large scale surveys in which it is more important to estimate how relatively scarce or common plants are. This approach involves a visual rating system. In one commonly use classification system, plants are classified as:
- Very rare
- Very abundant
The abundance class approach does not provide quantitative data, although there are underlying assumptions that the classes are determined by plant quantities. The classes themselves are qualitative evaluations. In addition, the ratings are relatively subjective, which introduces the potential for biased results. Therefore it is important that observers are well-trained and calibrated to ensure consistent evaluations.
This activity is designed to test your knowledge and understanding of Best Data Management Practices. This self-check is provided for you to evaluate your own learning. Answers are not recorded.