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.
1.1: Introduction to Monitoring
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What is Monitoring?
The word “monitor” comes from the root term meaning “to warn“. Monitoring allows land managers and scientists the opportunity to detect trends in resource conditions and apply knowledge to improve resource management (Monitoring Manual Herrick et al. 2009). We expect the vegetation around us to change over time and across space. Monitoring efforts provide an early-detection system to see if land management is headed in the right direction and enable people to take appropriate actions or change course if needed. The ability to accurately monitor natural resource conditions enables us to make informed management decisions and provide sound scientific knowledge.
After a wildfire, a land manager of a big sagebrush (Artemisia tridentata) steppe area in Southern Idaho may implement a monitoring program to determine if the plant community in the burned area is recovering and how closely it resembles a similar, unburned area. Likewise, specific criteria may be defined to determine when a burned area has reached conditions adequate for a sage-dependent species, such as sage thrashers (Oreoscoptes montanus). In Figure 1, a fire burned to a fence where grazing had occurred. The fire stopped when it reached the area of lower fuel loads.
Figure 1. A big sagebrush (Artemisia tridentata) steppe site with recent prescribed-fire burn.
A rancher may implement a monitoring protocol to determine if a new grazing system is affecting establishment of sedges or willows along a stream to improve the stability of stream banks (Figure 2). If the grazing system is not leading to the desired condition, the grazing plan could be changed or the stream could be fenced to remove grazing.
Figure 2. An initial photo was taken of a stream in Oregon a) that was monitored after the season of grazing was changed. After 10 years, improvements were seen, b), in the plant composition of the streambank. The number of animals in the pasture was not reduced, yet repeat photos showed the management change was effective.
The Powell County Weed District in Montana implemented a targeted grazing project near Deer Lodge to control leafy spurge (Figure 3). The monitoring program revealed that the treatment was effective and targeted grazing projects have been implemented for weed control across Montana.
Figure 3. A yellow-green colored leafy spurge (Euphorbia esula) patch is shown in a) which was monitored to determine if a new weed management plan was effective. In b) we see that the plan was effective in reducing the amount of leafy spurge.
In natural resources, monitoring is the repeated measurement and analysis of data to evaluate changes in the characteristics of a given feature with the goal of meeting a particular resource management objective or to document conditions over time (See chapter 1 of Elzinga et al., 1998). Alternatively, we may monitor a resource to determine if specific conditions exist that might create opportunities for certain management practices, or to determine whether the resource is approaching or has reached a specific, target threshold value determined by resource objectives. For example, we may design a monitoring protocol to determine whether canopy cover of perennial grasses increased following a reduction in stocking rate: in this case, the management objective is to detect changes or trends. Alternatively, we could monitor to determine whether the density of a rare plant reaches a critical threshold value: in this case, target/threshold management objectives direct monitoring to detect a condition.
Types of Monitoring: There are four basic categories of monitoring that reflect the reason for monitoring:
- Trend monitoring – to document changes over time
- Effectiveness monitoring – to document the effectiveness of management over time
- Implementation monitoring – to document the implementation of specific management actions
- Validation monitoring – to corroborate agency assessments and determinations
When deciding whether monitoring is appropriate for a particular situation, two concepts must be addressed. First, monitoring is driven by objectives! Specifically, monitoring protocols are designed to gather sufficient information to determine whether the desired resource objectives are being met by management. Second, monitoring should only be conducted if management solutions are available, or if alternate management opportunities are possible. If monitoring is not directly related to resource objectives, if it is not intended to inform management decisions, or if alternate management opportunities are not possible, then monitoring should not be conducted.
Ideally, management activities implemented in response to monitoring should be flexible. Both the methods and the objectives should be sufficiently flexible so that if new conditions become apparent changes in the planned actions are still possible. Also, the selected methodology, or simply what we measure and monitor, may not be what was actually needed to meet the management objectives. In this case, both the objectives and methods should be carefully reviewed and revised as necessary.
Monitoring is an element of the adaptive management cycle. The adaptive management cycle includes four primary steps in which: 1) management objectives are developed to define desired resource conditions, 2) a management system is created and implemented to meet these objectives, 3) monitoring is conducted to determine whether the resource response meets the desired objectives, and 4) if the objectives are achieved, then management is maintained, but if the objectives are not met, alternative management is implemented (Figure 4). Thus, management is adapted as needed depending on the response of the resource, and monitoring is the key step that provides information about how the resource responded to management.
Figure 4. Diagram of a successful adaptive management cycle. Note that monitoring provides the critical link between objectives and adaptive (alternative) management (original figure published as 1.1 in Elzinga et al., 1998).
In this cycle, it is imperative for monitoring data to be collected from appropriately designed monitoring protocol and interpreted in the context of management objectives! If the data are inconclusive and cannot provide sufficient information about the resource’s response to management, then we lose the ability to make decisions informed by the data. Therefore, in the absence of monitoring data that are appropriately collected and interpreted, the adaptive management cycle is incomplete and will fail to inform management decisions (Figure 5).
Figure 5. Diagram of monitoring that fails to close the adaptive management cycle. Because monitoring data is inconclusive, the management response is unknown and the cycle is unsuccessful (original figure published as 1.2 in Elzinga et al., 1998).
Related Data Gathering Activities
Many of the techniques used for monitoring are applied in other data-gathering activities that are commonly used to evaluate rangeland and wildland resources, or to conduct scientific investigations of natural phenomena. While monitoring has a very specific role relative to resource management, inventory, natural history studies, and experimental research are also forms of data-gathering activities that play important roles in management and/or increasing our scientific understanding of the natural world.
Unlike monitoring, which involves repeated observation, an inventory is a point-in-time measurement of one or multiple resources. Inventories may be used to develop a baseline assessment of a population of interest (for example, gathering information about a rare plant species). Inventories may also be conducted to develop a baseline assessment of all the resources on a management unit. In this latter case, the inventory is used for planning purposes, and often includes measurement of vegetation, soil, and water resources, and an accounting of structural improvements such as fences, water developments, stock-handling facilities, etc. Measurements taken for inventories are used to determine location or condition of a resource at one point in time, not specifically to detect change over time.
Inventories offer a broad array of information types. Several national inventories exist, including the National Resources Inventory (NRI) of the US Department of Agriculture-Natural Resources Conservation Service (USDA-NRCS). Use this link to view an example of NRI inventory data. These natural inventories are used to guide broad-scale planning and policy decisions. Inventories of specific management units are generally used to inform decisions made by the owners and/or managers of that specific parcel of land.
Natural History Study
Natural history studies involve the investigation of ecological or biological questions through observation and measurement. These investigations attempt to describe natural phenomena in a single location, to compare locations, or along ecological gradients. For example, we may study whether the phenological development of a perennial forb changes along a natural precipitation gradient, or whether nest fledging success rates are higher in northern latitudes compared to southern latitudes. In general, natural history studies involve observational data that are collected without imposing treatments or manipulating the environment or organisms.
Experimental research involves the application of the scientific method to test hypotheses about natural phenomena, specifically through the imposition of treatments followed by data collection to determine the effect of the treatments on the variable(s) of interest. One distinguishing feature of experimental research is that the study area is usually divided into relatively similar experimental units before treatments are applied, and each experimental unit is randomly assigned to a treatment group or a control group. Replication is another common feature of experimental research, meaning that the various treatments and non-treated controls are assigned to multiple experimental units. While scientists use many of the same measurement techniques that are used in monitoring, the reasons for collecting data and how the results are interpreted is fundamentally different. Experimental research focuses on determining cause and effect, whereas monitoring is conducted to provide information about resource conditions in the context of resource management.
It is useful to consider how the complexity of data-gathering activities changes as we incorporate additional replications or impose treatments and non-treated control in the area being studied. Figure 6 provides an excellent illustration of a continuum or gradient of data-gathering activities that may be used to detect the effects of a prescribed fire. In scenario A, the fire is imposed and no data are collected to examine the effect of the fire on the resource. In scenario B, monitoring data are only collected after the fire (post-management). Although monitoring is repeated over time, the data-gathering activities are only conducted in one location. In scenario C, monitoring is still restricted to only one location, but data were collected before and after the fire treatment was imposed. In scenarios B and C, the measurements are only taken in the burned areas, so it is difficult to determine whether any detected change in the resource was due to the fire or to some other factor. By adding an untreated control in scenario D, we can compare the burned area to the unburned control, which improves our ability to attribute changes in the resource to the management action (prescribed fire). However, none of the scenarios B-D include replication, and this prevents us from making statistical inferences about cause and effect. By adding more experimental units, as shown in scenarios E and F, we are able to make statistical inferences about cause and effect. The main difference between scenario E and F is that greater replication increases statistical power to detect differences. Obviously, adding multiple replications increases costs associated with time and labor for imposing treatments on multiple areas and increased data collection.
Figure 6. A comparison of monitoring and research approaches for detecting a treatment effect from a prescribed burn (original figure published as 1.3 in Elzinga et al., 1998).
The intensity and scale of vegetation measurements aimed at assessing management actions will differ from those designed for scientific research. A manager may only want a best-guess or rough estimate of vegetation conditions, whereas scientists often seek to take many measurements that are very precise. Scientists will expend considerable resources to assess the effects of a treatment. For example, a good scientific study may have 40 sites for “each” pre-event control, pre-event treatment, post-event control, and post-event treatment, which would then be repeated multiple times in subsequent years to ensure that what they observed wasn’t a fluke in a given year. Although such research may provide detailed information about cause and effect, or why an observed response occurred, this level of data-gathering requires large commitments of time and resources. The research approach is often not in the interests of land managers who may have more immediate needs for monitoring information.
It is important to note that it is not possible to establish cause and effect in the absence of controls or replication. However, data gathering activities that lack controls or replications can still provide useful information about present conditions at a site.
The following questions are designed to test your knowledge and understanding of vegetation measurements for monitoring. These questions are for your own benefit: scores are not recorded.