Vegetation Measurement and Monitoring

5.1: Indicators and Methods

Video Presentation

Learning Guide

This module explores the concepts of indicators and methods in measuring vegetation and plant communities. These are important concepts to understand for designing and implementing monitoring programs and for using and interpreting monitoring data.

 

Ecosystem Attributes: building blocks for management

In order to understand the importance of indicators and methods, it is necessary to first know about ecosystem attributes. Simply put, an ecosystem attribute is a complex variable that represents the status of a suite of related ecological properties (e.g., species composition) and processes (e.g.,water or nutrient cycling) that are essential to ecosystem function.

Figure 5.2.1 – Assessing and monitoring natural systems requires consideration of the major ecosystem attributes (boxes) and their functional relationships (solid arrows). Biotic integrity, soil and site stability, hydrologic function, and landscape integrity have been identified as key attributes that can be evaluated to determine ecosystem function. Reprinted from Toevs et al. (2011).

Ecosystem attributes capture the components and dynamics that define an aspect of an ecosystem. For example, growth and reproduction of vegetation in a rangeland is tied to the existing plant community on a site, interactions from insects (e.g., pollination) and wildlife (e.g., herbivory), and influences from soils and available water. The three attributes that generally define ecosystem health, particularly in rangelands, include biotic integrity, soil and site stability, and hydrologic function.

In many cases, management is focused on maintaining or enhancing ecosystem attributes. However, because of their complex nature, ecosystem attributes can be challenging to completely describe and difficult to measure.

 

Conceptual Models: understanding ecosystem attributes

Successful monitoring programs are focused on measurements that are sensitive to changes in key ecosystem attributes. For this to happen, we must be able to identify what those important indicators are and determine how to interpret changes in the indicator values over time. To do this effectively, we need to understand how management and other disturbances, such as drought, affect the land. Conceptual ecosystem models are helpful for organizing this knowledge and information so that it can be applied to selecting and interpreting indicators.  While not statistical or predictive, conceptual models should contain enough detail to document the known (or hypothesized) impacts of management and other disturbances on plant communities and soils. Conceptual models can also highlight knowledge gaps in ecosystem structure or function.

Conceptual models are graphical representations of our knowledge of how ecosystems function. They describe the components and function of an ecosystem attribute. Reprinted from Miller et al. (2010).

Types of Conceptual Models

Any system can be described using a number of different conceptual models. One model may emphasize the events or processes of a system and their interactions while another may focus on the components themselves and how the processes or stressors cause changes in them. Conceptual models can also be created at different scales to describe the same system. Sometimes several different types models or models at different scales can be helpful in developing monitoring programs.

 

One type of conceptual model is the Control Model. Control models describe our best knowledge about how an ecosystem is organized and functions, and how it responds to different ecosystem drivers. Control models describe the predominant drivers and stressors of a system and how they interact. Control models may be very general for broadly-defined ecosystems like the model in that describes the dynamics of terrestrial dryland ecosystems, or much more specific for narrowly defined systems. Control models focus on the overall influence and interactions of ecosystem drivers on the components of an ecosystem.

Control Models describe ecosystem organization and function by focusing on overall influence and interaction of ecosystem components and processes (i.e., drivers). From Miller et al. (2010)

In some systems, components can be considered discrete elements relative to the influence of drivers and stressors. When this is the case, the components of a system can be represented as a series of interrelated states that are linked by transitions defined by one or more drivers. This state-and-transition approach to modeling clearly illustrates possible outcomes of natural or human-caused processes and events. State-and-transition models are particularly useful for developing a monitoring program because of their management-oriented focus on the causes of change in an ecosystem.

 

State-and-transition models are commonly used to illustrate possible changes in terrestrial plant communities and soil properties and their interactions. They can be used to help decide which areas to monitor based on where change is most likely to occur. They can also be used to help decide what to monitor, because they often provide information on soil and vegetation changes that are likely to precede a change in state. States are distinguished by transitions, which may be relatively irreversible, reflecting a significant increase in energy required to shift back to the previous state. State and transition models generally include at least two states, and one or more plant community within each state. Plant communities within a state are similar in their species compositions.

State and Transition Models describe discrete states of an ecosystem and the processes that can lead to transitions between states. State-and-transition models often have a management-oriented focus. This example is for a “Breaks” ecological site in west-central New Mexico (Major Land Resource Area 36).

Plant communities within a state are generally functionally similar in their capacity to limit soil loss, cycle water and produce vegetative biomass. Changes among plant communities within states are considered to be reversible through simple changes in grazing management (in grazed ecosystems) or fluctuating climatic conditions. The state-and-transition model diagrams show possible transitions between states. The diagrams also illustrate the factors that increase the probability that changes will occur. Transitions between states are reversible only through generally costly, intensive practices such as shrub removal or soil modification.

 

Regardless of what form of conceptual model you use, however, a good conceptual model will help you define what aspects of an ecological attribute may be important to measure for monitoring.

Using Conceptual Models for Monitoring

Applying conceptual models to monitoring program design helps define a) ecological potential, benchmarks, or reference conditions and b) predictions about the possible future change of different land units in a landscape. This allows monitoring plot selection to be based on objectives and the ecological processes involved in land change. Designing a monitoring program within a conceptual model framework helps specify the ecosystem attributes to be monitored and other details that may vary among states and ecological sites.

 

For example, if impacts of grazing management is an objective of monitoring on a “Breaks” ecological site (shown in the model above), the state-and-transition model above provides several important pieces of information for selecting indicators of potential transitions between states. First, the model predicts that competition for water and resources leads to a transition between the mixed-grass savannah and woody/succulent-dominated states, and that this competition is influenced by grazing intensity, fire frequency, and precipitation. Second, the transition between states is characterized by changes in bare ground and cover of litter and perennial grasses.

 

This information can help you determine what aspects of the ecosystem to measure and how to interpret changes in those measurements. Armed with this information, we can now formally define monitoring indicators.

 

Indicators: windows into ecosystem attributes

An indicator is something that can be observed or measured that is correlated with an ecosystem attribute or process that is too difficult, inconvenient, or expensive to measure directly. Sometimes direct measurements of ecosystem processes and properties are impossible. Others are simply too expensive. For example, the amount of bare ground on a plot and its arrangement can be an indicator of potential for soil erosion and soil nutrient loss, decreased water infiltration, and species invasion. Indicators are what you are measuring, and there may be more than one important indicator for an ecosystem attribute. When selecting indicators, it is important to think carefully about what you need to learn from your monitoring program, compatibility with other monitoring programs, and how precise the data need to be. Conceptual models, policies, and regulations are good places to look for what are appropriate indicators for a monitoring program.

Vegetion height or structure and the distribution or amount of bare ground are examples of potential indicators of ecosystem attributes.

Properties of Good Indicators

So what makes a good indicator for a study or for monitoring? Here is a list of desirable traits for an indicator. While it may not be possible to realize all these traits for your indicators, priority should be given to indicators that meet as many as possible. Useful and informative indicators have the following traits (from Karl et al. 2017):

  • Relevant to ecosystem structure or function – Indicators must relate in a known way (e.g., documented in a conceptual model) to the structure or function of an ecosystem of interest.
  • Usable – meaning sufficient documentation exists to select appropriate methods and calculate indicators from measurements or observations.
  • Cost-effective – The cost of collecting indicator data is either low or lower than for other competing indicators.
  • Known cause/effect – A clear understanding exists of how changes in ecosystem attributes will result in changes to the indicator.
  • High signal-to-noise ratio – Changes in indicator values are primarily related to the intended ecosystem attribute and not natural variability or other factors.
  • Established Quality assurance and quality control procedures – Quality assurance and control procedures are available for the indicator and adequate.
  • Anticipatory – Indicator provides early warning of widespread ecosystem changes.
  • Historical record – Information on the indicator has been collected over a period of time such that a reference set of data exist.
  • Retrospective – The indicator provides information about historic conditions (e.g., tree rings), over extended time periods (e.g., soil carbon), or can be applied to previously collected data (e.g., remote-sensing imagery).
  • Provides new information – The indicator provides new information to your study or monitoring (i.e., not redundant with other indicators).
  • Minimal environmental impact – Collection of information for measuring the indicator causes the least amount of disturbance to the environment.
  • Used by other monitoring programs – Priority should be given to indicators that are in use by other (especially regional to national) monitoring programs to facilitate cross-program data combination.
  • Easy to understand and explain – Indicators that are intuitive are likely to be more effective at informing and influencing management decisions.
  • Applicable to policy and management – Indicators that relate to aspects of an ecosystem that can be managed or that are tied to range management policies should be prioritized.

 

Core and Supplemental Indicators

For local monitoring needs, indicators can be selected for each monitoring or assessment project. However, for monitoring at regional and national scales where monitoring data may be used to address multiple resource objectives, indicators can be divided into two sets: core and supplemental indicators.

 

Core Indicators are classes of indicators that are informative of many aspects of range health and are useful for answering many other resource management questions. Core indicators are based on land health concepts, can be measured consistently in many ecosystems, are scalable, and apply to many different objectives. Ideally, core indicators should be measured whenever you are collecting monitoring data, and should always be measured in a consistent way.

 

Examples of terrestrial core indicators for the Bureau of Land Management’s Assessment, Inventory, and Monitoring (AIM) program are: vegetation composition, cover and presence of plant species of management concern, cover and presence of invasive species, vegetation height, canopy gaps, and plant species diversity. Similar core indicators have been developed for aquatic (lotic) systems and lentic and riparian areas.

 

Core indicators, however, are not sufficient to answer every management question. In this case, supplemental indicators are added to help meet a local or resource-specific objective. Supplemental indicators may be:

  • Related to a specific land use (e.g., utilization for grazing management)
  • Related to a resource concern (e.g., density of plants in a restoration area)
  • Applicable only to specific ecosystems or circumstances (e.g., depth of active layer in tundra systems.
  • It is important to remember, though, that supplemental indicators are used in conjunction with core indicators.

 

Examples of supplemental indicators include measuring active layer depth (i.e., depth to permafrost) in tundra systems or measurements of tree density or size in forested systems.

Indicators vs. Methods

Indicators are characteristics of an ecosystem that can be measured or observed. But for any given indicator there may be many different ways of measuring it. A method is a specific technique for measuring an indicator. In other words, an indicator defines what you want to measure, and the method describes how you’re going to measure it. For each indicator you must specify a method to go with it. Keep in mind, though, that there may be more than one appropriate method for measuring an indicator. For example, cover can be measured by point-intercept methods, along continuous transects, or using Daubenmire frames. Also, some methods may be able to generate measures for multiple indicators. A good example of this is the line-point intercept method which records data that can be used to calculate numerous cover and species composition indicators.

 

Multiple methods for indicators: an example

The example shown here illustrates the distinction between indicators and methods. The indicator of shrub cover can be measured via any vegetation cover method including Line-point intercept (LPI), Continuous-line intercept (CI), point-frames, nested frequency, or ocular estimates (see Bonham (2013) for more information on the different vegetation cover methods). While each of these methods measure the same indicator, they can have differences in how they define that indicator. This can lead to inconsistencies in indicator measurements or even incompatibility of data.

 

Different ways of defining plant cover can lead to different measures that may not be compatible. From Toevs et al. (2012).

Foliar Cover* (A) – The percentage of ground covered by the vertical projection of the aerial portion of plants. Small openings in the canopy and intraspecific overlap are excluded.

CanopyCover* (B or C) – The percentage of ground covered by a vertical projection of the outermost perimeter of the natural spread of foliage of plants. Small openings within the canopy are included.

*Definitions from the SRM Glossary

 

It is important when selecting methods that you understand how the method defines the characteristic being measured, and how it is measuring that characteristic. For example, LPI and CI are both measures of cover, but they use different definitions of what cover is (Figure 1.1). The LPI method uses a foliar cover definition where only exposed plant area is included in the cover calculation. That is equivalent to example A here. Alternatively, the CI method uses a total canopy cover definition where any area within the perimeter of a plant counts toward the cover calculation – similar to examples B or C. While in practice the difference between these different definitions of cover may be small, it is an important distinction that makes data from the two methods incompatible (even though they are both measures of “cover”).

 

Properties of Good Methods

So what makes a good method? Like the criteria for selecting indicators, there are several desirable properties of methods for research or monitoring. These include:

  • Quantitative – A method should record measurements or direct observations of a site’s biophysical features.
  • Repeatable and efficient – measurements should be repeatable within a stated margin of error and should be able to be collected at minimal cost.
  • Low potential for non-sampling error – Methods that minimize sources of error (e.g., inter-observer variability) and perform consistently across a wide range of environments.
  • Objective – Methods should minimize the opportunity for observer bias to influence the results.
  • Established and validated – Methods implemented for monitoring programs should be well documented and tested. Quality assurance and quality control procedures should be well defined. Be wary of creating new methods or using methods that have not been well vetted.
  • Implementable with minimal training – Ideally methods should be able to be learned quickly and reliable data
  • Collectable by individuals without extensive experience. Comprehensive training and calibration programs should accompany any method implemented in a monitoring program.
  • Can be used to calculate many indicators – The more indicators that can be derived from a method’s data, the more value it can offer as a core method.
  • Used in other monitoring programs – Methods that are already implemented in other (especially large-scale) monitoring programs should be prioritized.

 

Method Documentation and Modification

Monitoring manuals like those shown here provide detailed descriptions of methods and QA/QC procedures. Documenting methods is a time-consuming process – good monitoring manuals are often the product of years of work editing and revising.

Documenting your methods in excruciating detail is critical. This includes the definition of what the indicators are, the specifics of how the methods are implemented, and quality control procedures. This step is often overlooked or poorly done. But consider that the repeatability of a study hinges on the ability to replicate its methods. Additionally, monitoring data sets from different times or places can only be compared or combined if the methods were well described and compatible.

Limited adjustments to methods can be made if needed to fit local needs or conditions or to improve compatibility with another data set. However, changes that affect only the precision of the indicator measurement can be made. Changes that would affect either the accuracy of the indicator measurement or the definition of what is being measured cannot be made because they would compromise the ability of the data to be used or combined with other datasets. For example, we can change the number of transects at a monitoring location or number of LPI pin-drops along a transect because it only affects the precision of the cover estimates. The more transects or pin-drops per transect you have, the more precise your estimates of plant cover will be. However, reducing the lower size limit of what is considered a rock versus soil effectively increases what is reported as “rock cover”. In other words, it affects the definition of the indicator, so it cannot be changed.