This section is made up of adapted excerpts from the paper “Best Practices for Integrating Ecosystem Services into Federal Decision Making.”
A causal chain—also known as a path model or means-end diagram—is a logical model that declares how current conditions, desired conditions, or a management action or policy is expected to propagate through the ecosystem to the provision of ecosystem services and benefits to various segments of society (Figure 1). An ecosystem services assessment requires a well-crafted causal chain whereby the indicators used to quantify the supply of services are defined as BRIs. The initial conceptual diagrams created in the scoping process consist of high-level (or preliminary) causal chains. Before analysis, each chain for a selected service must be elaborated and verified. Indicators are added or refined as needed to make the concepts more measurable in terms of societal benefits (e.g., hospital visits). Ultimately, the chains may be implemented as data-driven models that are used to estimate current provision of services or changes in services expected to result from management or policy actions.
Answering the following sequential questions will help assessors build causal chains for decision making (Figure 1):
- How does a policy, management decision, or program action affect ecological conditions and related human behaviors?
- How do these changes affect the delivery of ecosystem services (defined as ecological changes that directly influence people)?
- How do those changes in the delivery of ecosystem services affect benefits or costs to individuals or groups?
Causal chains that connect current ecological conditions to current delivery of ecosystem services and societal benefits without any changes or alternatives introduced into the system would be represented by a three-box diagram without the initial action.
Figure 1. Components of an ecosystem service causal chain
An ecosystem services assessment must consider how and which changes in the environment affect benefits to people. When causal connections to people are not made explicit, it is unclear whether and how each ecological change is related to changes in social benefits, and important changes to societal benefits may be left out of the analysis.
Figure 2 compares an ecological assessment with ecological indicators that are not explicitly linked to things people value, to an ecosystem services assessment using BRIs. In this example, resource managers are assessing mechanical thinning of forests to reduce the intensity of fire. An ecological assessment of this option might consider changes in the fuel load, which affects fire intensity (Figure 2a), along with a variety of other biophysical implications. An ecosystem services assessment, in contrast, would extend these causal chains to specific benefits to people that would result from mechanical thinning and the consequent management of fire risk (Figure 2b). There are many ways that people might be influenced by this action. For example, by reducing fire intensity the management action would reduce the incidence of smoke and the extent of poor air quality and exposure, reducing adverse health impacts from fire for nearby residents (e.g., as hospital visits, missed work days, or actual health care costs).1 These considerations extend the ecological assessment to an ecosystem services assessment by including the interaction of people with the ecology (Figure 2b). Best practice for ecosystem services assessment will focus on estimation of changes in ecosystem service values or preferences (blue text in Figure 2b), but when time or resources are limited, the minimum standard for assessment is to focus on BRIs (red text in Figure 2b).
Figure 2. Differences between ecological and ecosystem services assessments and indicators
Note: Causal chains consider expected outcomes from forest fire management activities like mechanical thinning. Black text indicates an ecological assessment and indicators, red text indicates extension to an ecosystem services assessment, indicators within ovals illustrate BRIs, and blue text indicates measures of social benefit and value. The demarcation among ecology, ecosystem services, and social benefits is not absolute (the lines between categories are drawn differently by different people), as represented by the tricolored arrow.
Adding these details (and subsequent quantification) requires expertise. Practitioners should engage experts from all relevant fields to ensure that the maps are as complete and accurate as possible. This group may include (but is not limited to) physical and biological scientists (hydrologists, wildlife biologists, botanists, ecologists, fisheries managers, ecological modeling experts, and foresters), social scientists, and economists.
Spatial context also plays an important role in many natural resource decision processes. Since the delivery of ecosystem services is not uniform across the landscape, the use of spatial information can be particularly important to ecosystem services assessments in addressing, for example, whether to conserve an upland rather than a lowland site or which management practices work best for reducing fire risk in an upland versus a lowland site.
One reason that causal chains are useful is that they can help to prevent double counting. Double counting occurs when an estimate of a value for an output (something further to the right on a causal chain) is added to the estimated value of an input along the same chain (something further to the left on the causal chain). A comprehensive value estimate for any element on a causal chain will capture some of the values associated with all of the elements to the right of it on the same causal chain—even if one ecological outcome leads to multiple social benefits. In principle, a causal chain diagram helps identify the logical endpoints of how the system responds to management, with each endpoint (on the right side of the diagram in Figure 2b) representing a single service or benefit that is meaningful to an identified beneficiary or stakeholder population. If a single indicator is selected to capture each meaningful endpoint to each affected beneficiary group, double counting is unlikely to occur. Hence, a properly constructed causal chain can be used to minimize double counting in an ecosystem services assessment because it clearly illustrates these input-output relationships.2 However, no causal chain can eliminate all possibility of double counting. Hence, if the analysis is to proceed to values assessment, it is important to involve experts in monetary or nonmonetary valuation to ensure that double counting is eliminated or minimized and that all major sources of value are considered (i.e., to avoid under counting as well as double counting).
Once causal chains are constructed, changes in the benefit-relevant indicators can be quantified, with or without an assessment of value, and used in a decision process.
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