Successful monitoring programs are focused on measuring indicators 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.
The level of detail that is necessary in a conceptual model depends on the management and monitoring objectives as well as the scale at which those objectives are defined.
Types of Conceptual Models
Any system can be described using a number of different conceptual models. One model may emphasize the drivers of a system and their interactions while another may focus on the components themselves and how the drivers 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. Several different types of conceptual models that focus on different aspects of the structure and function of a system are described below. These different types of models are can be used together in developing monitoring programs.
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 a model that describes the dynamics of terrestrial dryland ecosystems, or much more specific for narrowly defined systems like the model here. Control models focus on the overall influence and interactions of ecosystem drivers on the components of an ecosystem.
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 (Figure 2.1). 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.
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 (Figure 2.4) 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.
Mechanistic driver or stressor models
At the finest level of detail, mechanistic models describe the specific ways that a driver or stressor affects a system component. Mechanistic models may also incorporate interactions between different drivers. Because of their level of detail, mechanistic models typically incorporates only the most relevant components, drivers, and interactions. Mechanistic models are also sometimes used to derive testable hypotheses for scientific research. Mechanistic models can be useful for identifying monitoring indicators and for interpreting the meaning of indicator values, but it may not be necessary to create mechanistic models for monitoring programs if the effects of drivers and stressors are well understood and communicated in other conceptual models.
Using Conceptual Models to Identify Key Attributes and Select Indicators
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 site 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, a state-and-transition model 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.
Using conceptual models to guide monitoring site selection minimizes monitoring expenditures in highly degraded states where all available evidence suggests they will not change; and focuses monitoring efforts in ‘at risk’ states and plant communities where management has the potential to limit degradation or promote recovery. With this logic in place, monitoring can be treated as a series of tests matched to specific parts of a landscape. Key components of this test are the steps used to apply state-and-transition models to a monitoring program design.
Sources of Conceptual Models
The NRCS, BLM, The Nature Conservancy and other organizations are currently developing state-and-transition models, and other types of conceptual models for grassland, shrubland, and savanah ecosystems. For example, in the United States, state-and-transition conceptual models are part of an Ecological Site Description. The USDA-ARS Jornada Experimental Range maintains a comprehensive website on using and developing ESDs and their accompanying state-and-transition models.
In areas where a suitable conceptual model is not available, existing models from similar areas may be a good starting point. You should consult soil scientists, ecologists, wildlife biologists, and local knowledge experts to develop conceptual models that can serve your needs for monitoring design. The list of sources below provide conceptual ecosystem models for many different types of systems:
- NRCS Ecological Dynamics Interpretive Tool – Repository for ecological site descriptions in the United States, many of which contain state-and-transition models.
- BLM Rapid Ecoregional Assessments
Bestelmeyer, B. T., K. Moseley, P. L. Shaver, H. Sanchez, D. D. Briske, and M. E. Fernandez-Gimenez. 2010. Practical Guidance for Developing State-and-Transition Models. Rangelands 32:23–30.
Bestelmeyer, B. T., A. J. Tugel, G. L. Peacock, D. G. Robinett, P. L. Shaver, J. R. Brown, J. E. Herrick, H. Sanchez, and K. M. Havstad. 2009. State-and-transition models for heterogeneous landscapes: a strategy for development and application. Rangeland Ecology and Management 62:1–15.
Elzinga, C. L., D. W. Salzer, and J. W. Willoughby. 1998. Measuring and monitoring plant populations. U.S. Department of the Interior, Bureau of Land Management. National Applied Resource Sciences Center, Denver, Colorado.
Karl, J.W. and J.E. Herrick. 2010. Monitoring and Assessment Based on Ecological Sites. Rangelands, 32, 60–64.
Miller, M. E. 2005. The structure and functioning of dryland ecosystems: conceptual models to inform long-term ecological monitoring. Page 73. U.S. Geological Survey, Reston, VA.
Miller, D. M., S. P. Finn, Andrea Woodward, Alicia Torregrosa, Mark E. Miller, D. R. Bedford, and A. M. Brasher. Conceptual Ecological Models to Guide Integrated Landscape Monitoring of the Great Basin. Scientific Investigations Report. Reston, VA: U.S. Geological Survey, 2010.