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.
1.3: Setting Priorities, Avoiding Pitfalls, and Problems
A Question of Priority
The majority of monitoring programs are going to be limited by resources and funding, and monitoring program objectives will probably not be able to address all species of interest. This means priorities will need to be set defining which species or management issue should be given the most attention, and scale and intensity of sampling determined. We will first discuss how to prioritize monitoring and management objectives, and then address the issue of how to decide upon scale and intensity of sampling.
Consider the scenario that you have been hired by the Bureau of Land Management to manage 20,000 acres that include forests and rangelands intersected by riparian areas. Within these areas you have an endangered species of owl (Figure 1), and 3 fish species of concern. You may also have considerable fine fuel build-up or areas where soil erosion is making slopes unstable above roads and streams. In summary, you have many management goals and concerns. If you are asked to produce a monitoring protocol you first need to set your management priorities.
Figure 1. Western Burrowing Owl (Athene cunicularia hypugaea; photo by Scott Martin)
Thankfully, several organizations have produced guidelines on how to go about defining the priority of one species or management objective over another.
Established Priority Systems:
An example of a ranking system is one developed to rank the abundance of species created by the Natural Heritage Program. The main levels of this system are:
- Critically Imperiled
- Very Rare and Local
- Apparently Secure
- Demonstrably Secure
- Status Uncertain
In this system, the first 3 priority levels are defined by the number of individuals. For example, critically imperiled refers to < 5 occurrences or <1000 individuals; whereas imperiled refers to 6-20 occurrences and <3000 individuals
The level “Status Uncertain” refers to instances where not enough information about that species exists to determine whether the species is at risk.
Inventory methods could be used to assess the current status of a species. Monitoring protocols could be subsequently applied to evaluate whether the populations of those species are increasing or declining.
An example of how matrices could be developed using several biological and management criteria to rank importance of monitoring among species and situations is presented on page 32 and 33 in Measuring and Monitoring Plant Populations by Elzinga et al. (1998). In this example, the following biological and management criteria have been included:
• taxonomic status
• known decline
• extent of threats
• immediacy of threats
• existing conflict
• monitoring difficulty
• availability of management actions
• recovery potential
• public interest
• potential for crisis
Establish Your Own Priorities
There are many cases where a defined priority system does not exist for a specific monitoring task and you will need to set your own priorities. When selecting monitoring sites and activities it is imperative that they are POSSIBLE to accomplish and give USEFUL information.
Here are a few points that should be considered when initiating monitoring activities:
- Are important legal or policy considerations driving monitoring, such as sensitive or endangered species that occur in the area?
- Are there management activities pending for which it will be necessary to evaluate impact or effectiveness?
- Are there growing changes in land use activities, such as recreation, that might cause changes to the plant community?
- Are management decisions being made, such as a grazing permit renewal, for which monitoring information would be valuable to monitor outcomes?
- Is there growing public interest in a specific area, activity, or species for which monitoring data might be valuable to inform or resolve potential conflicts?
- Can areas be identified for which management activities can affect change in the plant community?
- What social, political, regulatory, or financial obstacles exist in relation to monitoring activities?
Scale and Intensity
There is rarely, if ever, enough time or money to conduct exhaustive monitoring activities. The extent to which we conduct monitoring will be limited in both space and time. As such, it is important before starting any monitoring protocol to know what our scale of assessment and level of intensity are going to be. Clearly this will depend on many factors beyond our available resources. For example, monitoring the annual foraging range of sheep may require measurements over a larger spatial extent than if you were interested in monitoring the feeding habitat of a deer mouse. Level of intensity reflects the frequency of sampling and the amount of detail in the information gathered, as determined by the monitoring protocol. For example, photo monitoring represents a lower level of intensity than a protocol calling for canopy cover, ground cover, and canopy gap measurements.
Scale can be simply defined as the spatial extent of your monitoring program. Clearly, the scale at which our measurements are taken should correspond to our management objectives and include the necessary spatial extent. The scale at which we are making measurements should always be explicitly defined. For example, is sampling to be conducted at the local or landscape scale? Local scale generally refers to measurements taken at the scale of individual plants to patches or stands of small populations, while landscape scale generally encompasses measurements that are taken across larger areas, and may extend to include many large populations or even the geographic range of the species (Figure 2). Similarly, if the monitoring objective is management oriented, local scale measurements may be taken within a distinct stretch of a riparian corridor (e.g., a 0.5 km stretch used for livestock crossings), compared to addressing questions about management impacts at a landscape scale (e.g., monitoring effects of salt cedar control over a 100 km stretch of the river).
Figure 2. The scale of various ecosystem elements is depicted above. The scale of plant parts, such as a leaf, would be at the centimeter scale, while the landscape scale is in the kilometers and hundreds of kilometers.
In a similar way, the level of intensity in our measurements will vary depending on our specific management objectives. Intensity refers to the frequency of sampling and the level of detail at which sampling is conducted. Frequency of sampling will largely be determined by whether we are collecting qualitative or quantitative data (refer to Lesson 2 of this module). However, the greatest determinants for sampling frequency are time and financial resources. The same is true for the level of sampling detail. This is because the greater the detail of sampling, the longer the time to collect samples from each location. For example, if we take a photo-point and conduct ocular estimates of grass cover in two 1 m x 1 m frames, the total time of equipment set-up and sample collection might be 10 minutes. If we increase our measurement detail by taking four photo-points, and five 50 m line-point intercept transects, we may increase the time it takes us for set-up and sample collection to two hours.
Pitfalls and Problems
Some sage advice — Once monitoring has started, be willing to adapt the objectives and methods with reality. No matter what you plan for, it will invariably be different once you arrive in the field!
This unforeseen condition could be as simple as planning for 20 days of work and because of weather or transportation problems you only actually work 12 days (Figure 3). Another example could be once you start collecting data you find that one of the measurements you want to make at each plot takes twice as long as you thought it would. Therefore, if the measurement is not of high value to your dataset, it may not be worth the extra collection time. This is especially true if, without the additional measurement, you would be able to sample more plots. Another common and unforeseen occurrence is that during preliminary analysis of your data (before data collection is completed), you may find that the measurements you have collected do not provide the necessary information to answer your monitoring questions. In such cases, you will need to re-evaluate and revise your measurements and methodology.
Figure 3. Vehicle troubles, equipment malfunction, treatment implementation and other unforeseen circumstances can delay, stall or cancel monitoring plans. Be prepared for potential adaptions (photo by Karen Launchbaugh).
In general, monitoring is not perfect and many factors can ruin your hard sought efforts. A few of the more common reasons include:
- Poor planning such as arranging fieldwork during late fall, when plants are dormant and difficult to identify therefore obscuring what you are trying to monitor.
- Poor design by not taking measurements in all representative areas or not including an unaffected area (i.e., a control) and thus not being able to assess whether certain factors are responsible for what you are observing.
- Inconsistent observations arise by having more than one observer taking measurements, when a standard protocol has not been agreed upon or is not being consistently applied. Clearly, observers need to be well-trained on how to conduct the measurement methods, and calibration is needed to ensure that any measurements based on ocular estimation are consistent (see Observer Training and Calibration below).
- Problems can occur when data are entered incorrectly, either when recording on data sheets or when data are typed incorrectly into the spreadsheets or analysis software.
- Incorrect inferences about the meaning of the results can occur when the data are not analyzed correctly. This is more likely to occur if the person analyzing the data does not have sufficient knowledge of applied statistics.
- Finally, nature sometimes behaves in a way that we don’t expect. We could have the best plan and design in the world, yet a wildfire could pass through the study area shortly before we planned to sample.
The Results are In… Or are They?
There are a few specific pitfalls that are sometimes associated with results, specifically when results are not reported, or if results are not accepted by the parties involved. Inconclusive results may be obtained if sampling is insufficient to clearly show whether changes have occurred or not. Usually this can be remedied by reviewing the sampling protocol and determining whether a different design might have greater statistical power. In certain situations, particularly when multiple parties concerned with the outcome may have divergent views on management priorities, the reported results may be disputed if there is conflict or opposing interests among the concerned individuals. This undesirable situation can be avoided to a certain degree if all parties are engaged, a priori, in the planning, design, and execution of monitoring protocols.
** An excellent overview of common problems encountered in monitoring is presented in Appendix 1 of Elzinga et al., 1998.
Implement Monitoring as a Pilot Study
It is always a good idea to envision difficulties in data collection or analysis. A successful monitoring protocol ensures that:
- Necessary and useful information will be collected.
- Time and resources will not be wasted by collecting data that are not related to the objectives or does not give the intended information.
To avoid these pitfalls, remember the critical importance of pilot studies in the monitoring plan! Conducting a “real world” trial of the monitoring protocol helps to expose problems at a time when the protocol can be revised to address these problems (Figure 4).
Figure 4. Two groups conduct a trial of monitoring protocols for land in the sage steppe (photo by Karen Launchbaugh).
FOUR steps to testing out a field protocol in a Pilot Study (Elzinga et al. 1998; pages 20-21):
1. Collect field data and evaluate field methods.
- Is the sampling unit we selected the right size or shape?
- Is our transect the correct length?
- Is it difficult to determine the species of interest?
- Can we get to the sites we want to examine?
2. Analyze pilot study data.
- Are objectives of power and precision met?
- How many sampling units or sites will you need to examine differences between sites or repeated measures?
- Is the level of difference or change you expected to see realistic?
3. Reassess time and resources.
- Will we be able to conduct the study in the time you have allotted?
- Do we have enough people or other resources to meet the objectives?
- Can technologies or other resources be secured to meet the goals of the project?
4. Review – Solicit review of the results of our pilot study.
- Do the parties involved still agree with the way the monitoring is proposed?
- Will those involved be able to abide by the results?
- Are there better ways to accomplish your monitoring objective?
Record Keeping for Monitoring
There are two primary record keeping elements that must be considered for success of a monitoring program: documenting the plan and protocol, and recording plot metadata.
Properly document the monitoring plan, protocol, and methods used for collecting data. This is especially important for long-term monitoring projects. The likelihood of having changes in personnel and observers is very high, and if protocols are not clearly and articulately defined, inaccurate data may be collected with severe consequences to management.
Plot metadata is the basic information regarding the collection of data at individual monitoring (Figure 5 and 6). Basically, metadata includes information about sampling location within a study area, the date that data were collected and the names of the observers, often specifying who had the role of observer and who recorded the data. Metadata sometimes includes additional site-specific information related to soils or ecological site, or measurement information such as the bearing or direction of transect placement, verification of transect length and distance between points measured. Essentially, each data sheet needs to include sufficient metadata to link the data on the sheet to the time and place where the information was recorded: without this basic level of identification, a data sheet could quickly be rendered useless if it can’t be properly identified! It is also important to remember that both qualitative and quantitative data collection require adequate detail and completeness of metadata.
Figure 5. Example of a partial data sheet with properly completed metadata (adapted from Sampling Vegetation Attributes, 1996).
Figure 6. Partial datasheet showing completed sample-site metadata (adapted from Interpreting Indicators of Rangeland Health, 2005).
Observer Training and Calibration
In order to assure sufficient accuracy and consistency in the data, observers need to be properly trained. Observer training should ideally be led by expert trainers and assure that everyone collecting data understands the sampling methodologies and monitoring protocol. Observations by all people collecting samples should be calibrated amongst themselves and with objectively collected measurements. Calibration of all data gatherers and trainers is necessary in assuring data quality. It improves consistency in the data collected between different gatherers over varying time periods and increases the integrity of the data by eliminating bias as much as possible. Calibration should be carried out at frequent intervals throughout the monitoring season, or whenever measurements are to be collected in a new ecosystem.
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.