The Ecosystem Services Approach
An ecosystem services approach to damage assessment, which fully accounts for an event’s impact on all aspects of human well-being, can improve efforts to remediate the damage to natural resources caused by events such as the Deepwater Horizon (DWH) oil spill. This approach shows promise for both phases of remediation: assessment and restoration. With respect to assessment, conceiving of a resource as a part of a system that supplies valuable goods and services to people provides a different way to measure losses—that is, in terms of changes in the flows of those goods and services. With respect to restoration, an ecosystem services approach can expand the menu of projects used to restore the public owners of the resource to their pre-event position.
This chapter first explains the concept of ecosystem services and summarizes the various definitions of the term that have been developed in an extensive literature. Next, the chapter discusses the role of “ecological production functions” in producing ecosystem services and the challenges of assigning an economic value to ecosystem services, such as uncertain and dynamic baselines for ecological and human components of the ecosystem, multiple stressors, inadequacies of data and modeling tools, and “making the public whole” in response to complex system-level disturbances. These ideas are further explored in Chapter 5, which takes a more detailed look at ecosystem services specific to the Gulf of Mexico (GoM) and uses four case studies to explore how current and expanded impact studies can be used to enable an ecosystem services approach to damage assessment.
This chapter also provides an overview of the steps required to translate the concept of ecosystem services into practical tools that can aid in remediation efforts, and it explores three major obstacles to building and applying these tools. First, baseline measures of goods and services produced by the system just prior to the harmful event are needed to assess damages; but differences in the pre- and post-spill state of the GoM ecosystem are difficult to establish because the pre-spill state is not fully known. Associated with this challenge is the added complication of dynamic or shifting baselines.
Assuming that a baseline is established, a second major obstacle is the difficulty of developing a model that can provide defensible estimates of the full impact and costs of reduced goods and services production resulting from the release of a given amount and kind of pollutant (or any other human-induced stressor or stressors) into the ecosystem. Existing ecosystem models are capable of measuring some, but not all, of the complex and intertwined socialecological impacts of an event such as the DWH oil spill; however, these models might serve as the basis for models that can.
The third obstacle relates to the relative values of various ecosystem services. Some ecosystem goods and services are more easily priced than others. For example, economists have
a much better idea of how to price a decrease in a system’s ability to produce fish for a commercial fishery than a decrease in a system’s ability to provide socioeconomic stability to small, rural communities. Furthermore, even when services can be measured, policymakers will have to prioritize among restoration options because of resource limitations. The first scenario raises concerns that more-difficult-to-price services will be discounted or ignored in decision making; the second highlights an important limitation to any approach to remediation.
The committee concludes that the key to feasible application of an ecosystem services approach is the development of tools capable of establishing and quantifying causal links among the event, an injury to an ecosystem, the resulting decrease in goods and services provided by that system, and the cost of that decreased production of goods and services to individual communities and society at large.
WHAT ARE ECOSYSTEM SERVICES?
Society benefits from a wide variety of resources and processes that are provided by ecosystems. These benefits are known as ecosystem services, and they result from the functioning of an ecosystem—the interactions of plants, animals, and microbes with the environment (NRC, 2011).
A rich and evolving literature on ecosystem services offers a variety of definitions of ecosystem services (e.g., Barbier, 1994; Costanza et al., 1997; Daily, 1997; de Groot, 1987; de Groot et al., 2002; Ehrlich and Mooney, 1983; EPA, 2009; MEA, 2005; NRC, 2005b; TEEB, 2010; Westman, 1977; Wilson and Carpenter, 1999). The common thread through all of these definitions is the concept of a relationship between ecosystems and the value humans derive from them. In 2000, the United Nations commissioned the Millennium Ecosystem Assessment (MEA) to summarize the current status and future conditions of biodiversity and ecosystems, and to describe the consequences of ecosystem change for human well-being, including secure livelihoods, social cohesion, security, and freedom of choice and action (MEA, 2005).
The MEA defines ecosystem services as “the benefits provided by ecosystems to humans, which contribute to making human life both possible and worth living” (MEA, 2005, p. 23). Moreover, the MEA defines explicit categories of ecosystem services, including:
• Provisioning services (e.g., material goods such as food, feed, fuel, and fiber);
• Regulating services (e.g., climate regulation, flood control, water purification);
• Cultural services (e.g., recreational, spiritual, aesthetic); and
• Supporting services (e.g., nutrient cycling, primary production, soil formation).
These service categories are now widely accepted and form the basis for the discussion of ecosystem services throughout this report. When applied to the GoM, discussions of ecosystem services must take into account the geographic, oceanographic, ecological, and social context of the GoM. Details of the GoM ecosystem and the regional context for the GoM were presented in the committee’s Interim Report (NRC, 2011).
APPLYING THE ECOSYSTEM SERVICES APPROACH
Measurement of the impact of human actions on the environment—either intentional actions brought about by a policy or management change, or unintentional actions, such as an oil spill—requires an understanding of three important linkages: environmental impacts, ecological production functions (i.e., models for ecosystem interactions), and valuation (Figure 2.1).
Understanding of the linkages will be informed by answers to three overarching questions. First, what are the impacts of decisions and disturbances (whether or not caused by humans) on the structure (composition, physical, biological, and social organization) or on the basic functioning (interaction of humans, plants, animals, microorganisms and their environment) on the social-ecological systems?
Second, how do these changes in the structure and function of ecosystems lead to changes in the potential provisioning of ecosystem services? An important step in this sequence of questions is underpinned by the development of ecological production functions. Harm to ecological production functions may lead to reductions in the ecosystem’s ability to generate ecosystem services. And, because most ecosystems have the ability to “right” themselves from harm, an attribute termed resilience, it is important to determine the strength of that ability when response and other management actions are taken. Chapter 3 discusses the importance of resilience in natural systems such as the GoM ecosystem and, in particular, its relevance to the DWH oil spill.
Third, how do changes in the provisioning of ecosystem services affect human well-being, and if the value of those changes should be estimated, then how should it be estimated (see Figure 2.1)? Economic valuation is sometimes used to estimate the value of ecosystem services in monetary terms. Summation of the estimated values across all individuals affected by the change in services can identify the overall societal value of the change in services. However, the values of some ecosystem services are difficult to measure in monetary terms. The decisionmaking process does not require that all ecosystem services be measured in monetary terms to be of use (Polasky and Segerson, 2009). Aspects of these three linkages are illustrated below.
Environmental Impacts, NRDA, and Ecosystem Services
In the United States, the legal context for measuring environmental impacts of certain oil or chemical spills is defined through the Natural Resource Damage Assessment (NRDA) process and implemented under the Oil Pollution Act of 1990, a topic discussed in the Interim Report (NRC, 2011). The National Oceanic and Atmospheric Administration (NOAA) is one of several federal and state groups involved in the NRDA process; additional details on the status of its efforts can be found at NOAA (2012a).1
NOAA has a three-phase approach to NRDA:
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FIGURE 2.1 The three important links from human actions to human well-being through ecosystems: (1) environmental impacts, (2) ecological production functions, and (3) valuations. SOURCE: Adapted from NRC (2005b, 2011).
1. pre-assessment,
2. restoration planning, and
3. restoration implementation.
The phases and methods are generally described in the applicable regulations2 and in NOAA’s Damage Assessment, Remediation, and Restoration Program (DARRP) guidance documents (Huguenin et al., 1996; Reinharz and Michel, 1996) (Figure 2.2).
Individuals charged with representing the public in an assessment of the damages from an oil spill (the Trustees) first need to determine if a pathway exists between the releases of contaminants or response actions (e.g., oil, dispersants, or stranded boom in tidal marshes) and potential impacts. Once a pathway is established, the Trustees will proceed to the restoration
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2 15 C.F.R. § 990 (2012).
FIGURE 2.2 Illustration of the damage assessment process. SOURCE: NOAA, 2012b.
planning phase, which includes assessing injury, soliciting public input, and scoping for appropriate restoration projects. After the injury is quantified and the appropriate amount and types of restoration activities are identified, a final restoration plan (in some cases, multiple plans) is implemented and monitored for success. Emergency and early restoration actions can occur prior to injury quantification and implementation of a final restoration plan(s) (NOAA, 2012b).
Under current NRDA practice, injuries to natural resources and losses are generally measured in ecological terms (e.g., number of acres damaged or number of fish killed), and restoration generally follows relatively straightforward equivalency approaches (e.g., acres of habitat restored or fish stocks replaced). These equivalency approaches can be applied to the DWH oil spill (NOAA, 2012c; NRC, 2011).
Since the DWH oil spill, numerous studies conducted under the NRDA process have focused on better understanding the impact of the spill on the GoM ecosystem (NOAA, 2012c). Many thousands of samples and observations have been, and continue to be, taken and analyses are under way; some of the results have already been published. Certain impacts may not become apparent until well into the future, if at all. Yet, against this backdrop, the government is legally obligated to conduct a timely assessment of the damages associated with the event.3
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3 15 C.F.R. § 990 (2012).
In the Interim Report (NRC, 2011), the committee discussed the potential advantages of using an ecosystem services approach to damage assessment for the DWH oil spill and suggested possible mechanisms for its use within the existing NRDA process. For example, for the ecosystem service of hazard moderation (through reduction of storm surges) provided by wetlands, the committee recommended that damage assessment sampling should include documentation of plant type, height, and density, as well as estimates of the vegetation likely to experience salt burn and dieoff, measurements of the areal extent of wetlands harmed, and estimates of the ability of wetlands to recover with and without human intervention. Such a suite of measurements would then allow for the determination of an ecological production function that could relate the plant height, density, and areal extent to wave energy reduction. This approach focuses not only on restoring damaged resources (as per current damage assessment practice), but also on establishing and maintaining the usefulness of those resources to the public. It is this broader view that may be particularly applicable to an event of the magnitude and complexity of the DWH oil spill, which is the focus of this report.
Ecological Production Functions
An important underpinning of an ecosystem services approach is the ecological production function (Box 2.1). In simple terms, an ecological production function represents the chemical, physical, and biological processes or elements that work collectively to provide the service that is valued by people.
BOX 2.1
Ecological Production Functions
Production functions are tools used by economists to describe how inputs can be transformed into outputs. A production function gives the feasible output of goods and services that can be produced from a given set of inputs. For example, what is the maximum amount of steel (output) that can be produced from a given amount of iron ore, energy, machinery, and labor (inputs)? The notion of production functions applied to ecological systems has a long history in agricultural economics (e.g., crop yield functions) and resource economics (e.g., bioeconomic modeling of fisheries and forestry).
Production functions have been applied recently to the provision of ecosystem services (e.g., Barbier, 2007; Daily et al., 2009; NRC, 2005b; Tallis and Polasky, 2009). An ecological production function specifies the output of ecosystem services generated by an ecosystem given its current condition. Changes in ecosystem conditions, either from natural disturbances such as hurricanes or from human disturbances such as an oil spill, can alter the amount and quality of the various ecosystem services supplied. For example, degradation of coastal marshes may reduce protection from storm surges and reduce nursery habitat for fish, in addition to other service losses.
For some ecosystem services, ecological production functions are fairly well understood and data exist that can be used to quantify the amount of a service provided. A good example of a fairly well-understood and well-studied ecosystem service is carbon sequestration in the biomass of terrestrial ecosystems, particularly forests. The U.S. Forest Service collects data on biomass in forests by stand age and tree species for different areas of the country (Gan and Smith, 2006). These data, along with knowledge of the carbon ratio in biomass, can be used to calculate carbon sequestered in forests.
In marine systems, production function approaches have been used to study the productivity of fisheries as a function of ecosystem conditions (Barbier, 2000, 2003; Barbier and Strand, 1998; Barbier et al., 2002; Ellis and Fisher, 1987; Kahn and Kemp, 1985; Lynne et al., 1981; McConnell and Strand, 1989; Parks and Bonifaz, 1994; Sathirathai and Barbier, 2001; Swallow, 1994) although there is far greater uncertainty in the functional relationship between habitat conditions and fishery productivity. The Interim Report (NRC, 2011) provided some examples of the data and analyses required to establish ecological production functions for different resource categories, expanding upon the basic NRDA approach to include information about key ecosystem services. Additional examples of production functions and the types of data required for their characterization are provided in the case studies detailed in Chapter 5.
Valuation: Current Understanding of the Value of Ecosystem Services
As illustrated in Box 2.1, ecological production functions can be used to estimate reductions in ecosystem services. A challenge arises in attempting to quantitatively determine the impact of reductions in ecosystem services on human well-being. Economists often do this through a process that attempts to determine the monetary value of ecosystem services.
The most fully developed approach for valuing ecosystem services comes from economics. Economic analysis of ecosystem services can generate estimates of the value of services in terms of a common (monetary) metric. An economic analysis provides decision makers with clear comparisons of the benefits and costs of alternative choices. Given preferences, the value of an ecosystem service can be measured in terms of what the individual would be willing to give up to get more of the ecosystem service. By measuring what an individual is willing to give up in terms of a monetary metric, or “willingness to pay,” the economic approach to valuation generates measures of the relative value of goods and services.
Valuation of ecosystem services does not need to be in monetary terms. This is simply a useful and well-recognized approach to quantify the willingness to trade one thing for another. For goods and services that are traded in markets, an individual’s willingness-to-pay, or price, for a marginal change in the good or service is reflected in an individual’s demand curve. Aggregating across all individuals who purchase the good or service generates an estimate of the societal value of the good or service.
A fundamental assumption of this approach is that individuals have well-defined and stable preferences. Most ecosystem services, however, are not directly traded in markets, so the direct approach to estimating value via observing market transactions is not possible.
Environmental economists have developed a set of approaches for nonmarket valuation that are applicable for estimating the value of many ecosystem services. These methods have been widely applied to value such things as carbon sequestration (Tol, 2009), water quality improvements (Johnston et al., 2005; Smith and Desvouges, 1986), wetlands (Boyer and Polasky, 2004; Woodward and Wui, 2001), and endangered species (Richardson and Loomis, 2009). The application of nonmarket methods to valuing ecosystem services has been discussed extensively in Champ et al. (2003), EPA (2009), Freeman (2003), NRC (2005b), TEEB (2010), and Chapter 4 of the Interim Report (NRC, 2011). In the case of the DWH oil spill, of primary relevance in applying the valuation approaches is estimating the value of the change of ecosystem services attributable to the spill.
It should be noted that there are critics of these economic approaches to valuation. Some psychologists, for example, question the assumption that people have well-defined, stable, and consistent preferences that they bring to decision making. A body of work in both psychology and behavioral economics has documented systematic departures from classic assumptions of rational behavior (e.g., Ariely, 2009; Kahneman and Tversky, 1979). A body of experimental evidence suggests that people often construct their preferences when called upon to make decisions and are therefore sensitive to the context and framing of decisions (e.g., Lichtenstein and Slovic, 2006). Sociologists question the central focus on individuals and individual decisions, which they posit does not give proper consideration to how values are shaped by larger groups, norms, and culture. Despite these criticisms and the fact that other approaches to valuation do exist, virtually all valuations of ecosystem services to date have used the economic approach.4
Obstacles to Application of the Ecosystem Services Approach
Thus far, this report has focused on discussing the elements of an ecosystem services approach, illustrated in Figure 2.1, and the potential benefits of using this approach to assess damages resulting from an event such as the DWH oil spill. The approach has the potential to capture a more holistic picture of impacts on the ecosystem and on human well-being and thereby provide a more realistic view of the overall range of damages, as well as the potential to increase the number of restoration options. An examination of the challenges to application of this approach to the GoM and DWH oil spill follows.
The Interim Report (NRC, 2011) briefly identified some of the major challenges to application of the ecosystem services approach. The following section addresses three major challenges in detail, with particular focus on the GoM and the DWH oil spill: (1) shifting baselines of physical, chemical, and biological processes and socioeconomic conditions; (2) the lack of complete or validated models that capture the full complexity of ecosystem interactions in the GoM; and (3) understanding the tradeoffs between restoration options.
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4 See EPA (2009) for a review of both economic and noneconomic approaches.
Establishing the Baseline
Quantification of the effects of, and recovery from, an event such as an oil spill is difficult, particularly when the effects must be measured against a changing marine environmental background (NRC, 2003; Peterson, 2001; Spies et al., 1996; Wiens, 1995). The baseline may reflect the fact that recovery from a prior disturbance is under way, which must somehow be taken into account when determining the pre-spill status.
Assessing initial damage from the acute effects of the spill, especially those of socioeconomic importance, is difficult, but assessing recovery is perhaps even more challenging. Recovery is removed in time from the acute phase of the damage and therefore may be occurring in a different environmental and socioeconomic framework (and location) than that existing at the time of the accident.
Figure 2.3 presents a simplified illustration of how stressors on an ecosystem, in this case a salt marsh, may result in short- and long-term changes in the system and ultimately in the services it provides. Under Scenario A, baseline ecological services are produced at a variable rate over time, but that variability falls within a range that is likely dependent on factors that influ-
FIGURE 2.3 Hypothetical baseline of an ecosystem (salt marsh) service pre- and post-DWH oil spill and possible recovery responses. Baseline A is the hypothetical trajectory in the absence of the spill. Trajectory B is a hypothetical negative response to DWH, while D is a hypothetical positive response. Trajectory C is the possible response following restoration. SOURCE: Committee.
ence the ecological production functions and the resilience innate to the system. After a major stress to the system—Hurricane Katrina, for example, in the case of the GoM—two outcomes are likely: the level of services continues, but is reduced (Scenario A), or another stressor like the DWH oil spill provides additional stress that the system is unable to withstand (Scenario B). Under both scenarios, it is possible that ecosystem services will decline and, depending on conditions that influence the underpinning ecological production functions, may or may not recover to their original level over time.
Other potential outcomes of the type of stress rendered by Hurricane Katrina and the DWH oil spill include Scenario D, where restoration and response actions enable the system to recover, but not to the baseline condition, and Scenario C, where restoration and response actions are highly beneficial to the extent that the ecosystem services recover to levels similar to those existing prior to the two major stressors. Figure 2.3 illustrates the importance of prestressor monitoring data. Without such data, it would be difficult to isolate the impact of the spill as well as to determine the success of the restoration actions.
Quantitative assessment of recovery from a spill event requires either a well-designed Before and After Control Impact (BACI) approach or an approach that compares measurements of the environmental variable of interest along a gradient of perturbation (Wiens, 1995). This gradient can be across space or time. When numbers of organisms are compared for assessment of recovery, attributes such as age or reproductive potential must be taken into account. Using the example of marine birds, young, inexperienced birds do not have the same value to the population as experienced, breeding adults.
The natural variability inherent in estimates of populations introduces considerable uncertainty in assessing impact and recovery following major stressor events such as oil spills or loss of habitat. Confidence limits in excess of 20 percent of the mean size are typical in wildlife censuses (Geissler, 1990). Such variability in the estimated mean makes it certain that population changes will be difficult to detect without a high degree of replication spatially and temporally before and after an event. More importantly, under some circumstances estimates of recovery based on the population returning to a “window” of natural fluctuation could minimize the time to true recovery. If there are no data to implement a BACI approach, then it may be possible to compare suitable data on the measurable parameters during and immediately after the spill to any suitable long-term database that may provide evidence of change in relatively well-monitored ecosystem components (e.g., the long-term fisheries data collected by NOAA Fisheries, as well as their marine mammal surveys and sea turtle surveys).
Changing baselines also make assessment of the spill impacts on the social structure and its recovery equally difficult. Economic conditions outside the control of a community, such as fuel prices, may constrain the community’s ability to harvest fishery resources unless they are sufficiently abundant to cover the cost of fuel. Therefore, a spill-related reduction in the abundance of fishery resources may make harvesting economically infeasible. Underlying trends of economic stressors may mask or compound the impact of stressors that resulted from the DWH oil spill.
For example, after the DWH oil spill, the federal government imposed a moratorium on deep-water drilling pending further study of blowout prevention and response. It was reported
that, during this time, deep-water drilling vessels and parts of the service fleet and logistics industry moved to Brazil to operate.5 Such vessels and other equipment would not likely return to the initial location immediately after expiration of the moratorium. It will take more than a few years to determine the long-term economic impacts of the DWH spill on this sector. During this time, a global economic effect (e.g., oil embargo or new discoveries of oil elsewhere worth exploring) could readily shift the “average” baseline.
Models and Data Needs
As briefly discussed in the Interim Report (NRC, 2011) perhaps the most notable challenge to our ability to apply an ecosystem services approach to the GoM is the lack of comprehensive models for assessing conditions in the GoM. These computer models, or simulations, are needed to better understand the potential impacts of the DWH oil spill on GoM ecosystem services. Ideally, a thorough ecosystem services approach would be based on a mechanistic understanding of, and model for, the complex linkages and interdependencies of the ecosystem being studied, socioeconomic factors, and the linkages of the natural ecosystem processes to sociological processes that represent the local coastal communities and the broader U.S. and global economies. Such a model would enable prediction of the provision of ecosystem services given the state of the ecosystem (i.e., the ecological production functions). Development of such a model requires either a comprehensive empirical database (from which interactions can be derived and predicted) or a thorough theoretical understanding of the complex interactions of the system—both of which are currently lacking. An ongoing challenge is that the data collected under the NRDA process are not always the most appropriate for developing a basic understanding of variability in biological and other processes, which is essential for developing appropriate ecosystem models for the GoM.
An Example of a Thorny Modeling Issue: Multiple Stressors
Multiple stressors often affect ecosystems, populations, or communities, and present extra challenges to individuals tasked with understanding the impacts of stressors on the ecosystem (Ferenc and Foran, 2000). For instance, a Louisiana bay or wetland may be subject to changes in salinity due to freshwater diversions, eutrophication due to excess nutrients, changes in inorganic nutrient regimes, or changes in turbidity with wind mixing. The ability to determine the effects of oil on these ecosystems is complicated by these and other stressors.
The northern GoM and especially the Louisiana coast are experiencing relatively rapid sea level rises compared to other locations in the world (Donoghue, 2011). This is due to a combination of global (eustatic) sea level rise superimposed on (a) the sediment-limited and extensively manipulated Mississippi River deltaic system and (b) an isostatically sinking continental margin. Factors such as regional fluid withdrawal and sediment compaction are also thought
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5http://www.reuters.com/article/2010/06/11/brazil-oil-rigs-idUSN1115006620.
to contribute to the relative sea level rise (Walker et al., 1987). The net result is a dramatic transition of estuarine habitats, the natural resources they support, and the human communities that depend upon them.
Multiple or single stressors may also be cumulative over time. For instance, the sediment contaminant levels of polycyclic aromatic hydrocarbons (PAHs) from produced water discharges in inshore and offshore waters represent the discharge of these materials over many years (Rabalais et al., 1992, 1998). In addition, although the range of exposure of sediments and fauna to PAHs produced from waters around oil platforms on the continental shelf of the GoM may be limited to the periphery of the production platform, the cumulative effect of many such discharges may be more concerning. Likewise, although a pulse of high nitrate-nitrogen loading from a freshwater diversion into a salt marsh may seem to stimulate aboveground biomass, longer-term exposure endangers belowground root biomass, leading to its subsequent decomposition and eventual erosion (Darby and Turner, 2008; Deegan et al., 2012; Turner, 2011).
Stressors may include socioeconomic factors that would create variability around a baseline that is a goal for recovery. The coastal county population of the GoM was approximately 14.2 million in 2010 (NOAA, 2013b). Between 1960 and 2008, the Gulf coastline counties realized an increase in population of greater than 150 percent (Wilson and Fischetti, 2010). Increases in population are not uniform across the landscape. Human populations are also shifting as more individuals move from flood-prone areas to higher elevation. Shifts in population, socioeconomics, and the ability of human communities to provide adequate support for new populations with new needs can interact as stressors that might affect the baseline sought for “recovery” of social systems. These coastline populations receive considerable economic benefit from the GoM, as evidenced by an estimated employment of more than 6.2 million people in the region in 2010.6 Many important ecosystem services are now threatened by multiple stressors on the functions and processes of natural ecosystems.
Oil and gas development currently has the greatest economic value in the GoM ecosystem (NOAA, 2011f) through its extraction, transportation, and transformation into petroleum-based products. Fisheries, both commercial and recreational, are the most valuable, marketable living resource produced by the GoM system. In 2011, the market value of the catch from commercial fishing was worth $818 million just off the vessel; the value of the fish increases as it moves up through the value chain and is processed and sold at retail. In 2011, approximately 3 million recreational anglers took 23 million fishing trips in the GoM, worth $9.8 billion in terms of sales (NOAA, 2013a). Seafood from the GoM also supports artisanal and subsistence fishing, although data on these activities are rare or nonexistent.
It is clear that many local communities, especially in the north-central GoM coastal region, depend on a combination of energy-related economic development and natural resource harvesting. These economic pursuits often support and supplement harvesting, but they are antagonistic because of the environmental impacts on fisheries of energy sector operations in the GoM. As the fortunes of the oil and gas development and production industry wax and
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wane in the northern GoM, the economies of many coastal communities follow suit. Similarly, as the economic viability of fishing fluctuates, communities may switch employment opportunities or suffer the same economic declines as the fishery resources. Chapter 3 further explores these issues under the topic of community resilience.
The northern GoM social-ecological system that was impacted by the DWH oil spill is also subject to multiple external forces over which the region has, in reality, little control. A suite of global and domestic economic forces impact the region, including fluctuating fuel prices, an influx of imported goods such as cultured shrimp, and the loss of social services through federal and state budget cuts. These forces further challenge a social structure that depends on healthy, functioning ecosystems for food, jobs, well-being, and a sense of identity. An episodic impact, such as the loss of 5 acres of salt marsh and its ecosystem services, is more easily quantifiable, and the baseline more obvious, than is a suite of interacting chronic impacts amid a shifting set of baselines further influenced by global market forces.
As noted in this section, baseline conditions within the GoM, or any ecosystem for that matter, change over time, which presents a challenge to individuals attempting to parse out the natural variability in a system from the potential impacts associated with stressors originating from the DWH oil spill. This challenge can be alleviated through the development of models that can approximate biotic and abiotic conditions in the system and then inform understanding of how stressors impact the system and the services it can provide. As discussed in the following section, such models are currently being developed.
Existing Models
Over the past 15 years, marine ecosystem models have been developed for a variety of purposes, but, more recently, attempts have been made to use these models to evaluate the consequences of external events on the dynamics of ecosystems. Ecosystem models can be used to study and predict future changes in the ecosystem because direct experimentation on the ecosystem is seldom possible, especially when the subject ecosystem is large and has open boundaries such as the GoM ecosystem. Few of the existing marine ecosystem models have been developed with the objective of enhancing an understanding of the dynamics in a holistic ecosystem services approach; however, many of them are useful to evaluating a particular subset of services. This section discusses some of the models that have been developed to support management of marine ecosystems and how they might be used to support an ecosystem services approach. Models that were designed for fishery applications are considered first, followed by others aimed at broader segments of marine systems.
Fishery Ecosystem Models (FEMs)
Fishery Ecosystem Models (FEMs) are distinct from purely ecological ecosystem models in that they consider not only the biological components of the ecosystem but also the fishery, which includes a human component. The structure of a FEM reflects the priorities of
fishery managers and harvest activities. Thus, the model includes the monitoring and management process related to fisheries, but often ignores components representing other marine industries.
FEMs allow researchers to narrow the set of possible hypotheses to be tested when an ecosystem is disturbed by human actions. Although typically these disturbances are related to the possible cascading effects of increases or declines in harvests (Fulton, 2010), they can also be disturbances of the type caused by direct and indirect mortality resulting from pollution (Yanez-Arencibia and Day, 2004). By reducing the set of possible hypotheses, FEMs can assist in the design of studies that will collect the necessary data to more precisely estimate the effects of pollution on future fishery and ecosystem productivity. In that regard, they can also assist in the development of effective responses to oil spills (Freire et al., 2006). Although the models’ ability to predict the precise magnitude of changes is still questioned (Butterworth and Plagányi, 2004), they have proven useful in predicting the direction and order of magnitude of the change, and they have been used recently to inform management decisions related to fisheries ecosystems (Fulton, 2010; Fulton et al., 2007).
One type of FEM, Ecopath with Ecosim (EwE), is a modeling framework that integrates a wide range of biological and fisheries dynamics for multiple species and functional groups (biomass pools) over long time periods, using a trophic mass-balance approach (Christensen and Walters, 2004). Although designed as a FEM, EwE has been used to address many aspects of marine ecosystem dynamics, including the impacts of the 1989 Exxon Valdez oil spill in the Prince William Sound ecosystem (Okey and Pauly, 1999). Oil spill impacts can be represented as both direct mortality events on susceptible biomass pools and as bioaccumulation of oil-related contaminants, using EwE’s Ecotracer routine (Christensen and Walters, 2004). EwE models have been developed at three spatial scales within the GoM: (1) bay-estuary (Rosado-Solórzano and Guzmán Del Próo, 1998; Vega-Cendejas and Arreguin-Sanchez, 2001); (2) regional shelf (Arreguin-Sanchez and Manickchand-Heilman, 1998; Arreguin-Sanchez et al., 2004; Okey et al., 2004); and (3) basin (Walters et al., 2008). Within the context of the DWH event, EwE could at least serve to identify the components of the model ecosystem most likely to be affected by the oil spill.
Whole-Ecosystem Models
Atlantis is an ecosystem modeling framework with submodels that simulate oceanography, ecology, fish population dynamics, fishing fleet dynamics, economics, fisheries stock assessments, management decisions, and exploitation (Fulton et al., 2004a, b, 2011). Atlantis provides a detailed model of higher trophic–level dynamics, fisheries, and socioeconomics, which may enable tracing of the impacts of spills like the DWH all the way to the human impacts. The model is driven by strong oceanographic data that could be used to project the distribution of oil following a spill in the GoM. It can also interface with finer-scale and more detailed models applied to some components of the ecosystem. Plagányi (2007) found that Atlantis is the best whole-ecosystem model currently available for evaluating the management strategy for ma-
rine ecosystems, in part because its modular structure allows for great flexibility in modeling a range of ecosystems. However, one significant limitation of the current model is that modelers cannot apply a sensitivity analysis to show how the uncertainty in the output can be attributed to different sources of uncertainty in the inputs.
Several integrated models are capable of modeling the joint provision of multiple ecosystem services. These models have been developed primarily for terrestrial systems (e.g., Kareiva et al., 2011), but some models have also been applied to coastal and marine systems.
One integrated model built specifically for coastal and marine ecosystems is the Marine Integrated Valuation of Ecosystem Services and Tradeoffs (Marine InVEST). Marine InVEST is a flexible tool that is spatially explicit, can be run at different levels of complexity to account for different data availability and system knowledge, and maps the provision and value of multiple ecosystem services as a function of social and ecosystem conditions (Guerry et al., 2012). The model has been applied in a variety of sites, including Mobile Bay, Alabama, and Galveston, Texas, as well as the West Coast of Vancouver Island, British Columbia, Canada, and Belize. Marine InVEST is designed primarily for use with coastal ecosystems and does not yet model deep-sea ecosystem services. The model currently includes the ability to assess habitat susceptibility to various environmental stressors, but also includes wave energy, aquaculture, coastal protection, coastal erosion, aesthetic quality, and a simple fisheries model. Soon-to-be-released modules will cover water quality, shellfish, wind energy, recreation, and tourism. Further applications allow for valuing ecosystem services when tradeoffs are made—for example, differences in construction for rebuilding a barrier island intended for wave surge protection versus one intended for tourism.
The Multi-Scale Integrated Models of Ecosystem Services (MIMES) were developed to fully account for several relevant factors that contribute to human well-being, including built, social, and natural capital. These are conceptually complex, spatially explicit models that were created by the Gund Institute for Ecological Economics at the University of Vermont.7 MIMES are a suite of simulation models that are designed to quantify the implications of human uses in the natural environment. These models integrate socioecological relationships, analyses, strategies, and policies to comprehend the links and interdependencies that exist between complex subsystems. The five subsystems are atmosphere, lithosphere, hydrosphere, biosphere, and “anthroposphere” (human habitats), which reflect an integrated approach to assessing the capacity of complex systems to deliver a healthy stream of benefits to people.
The Artificial Intelligence for Ecosystem Services (ARIES) tool constitutes a suite of applications that maps concrete, spatially explicit beneficiaries of ecosystem services and quantifies their demand for each service.8 ARIES is a modeling platform, rather than a single model or collection of models, delivered to end users through an online Web tool. The ARIES approach is to map benefits, beneficiaries, and service flows to allow managers and conservationists to visualize, value, and manage the ecosystems on which the human economy and well-being depend.
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By accounting for biophysical flows of ecosystem services across the landscape, ARIES can link marine and terrestrial habitats. For example, ARIES is being used to model flows of sediment, nutrients, and freshwater from land to near-shore ecosystems, allowing users to model changes in provision of marine ecosystem services based on changing land use practices in Madagascar (Wendland et al., 2010). In addition, ARIES has been implemented in several project sites located in America, Europe, and Africa.
Application of Marine Ecosystem Models to GoM Services
Although each of the models described above offers some potential for providing critical insight into the impact of an event such as the DWH oil spill on GoM ecosystem services, the ability of a model to accurately predict outcomes is a function of the degree to which the model captures the physics and complex interactions of a particular ecosystem, including human communities, and the accuracy of the data used to parameterize the model. Data needed to parameterize ecosystem models to the GoM typically come from published data (e.g., Collins and Wlosinski, 1983; Leidy and Jenkins, 1977; Leidy and Plosky, 1980) from the National Marine Fisheries Service (NMFS) databases such as the NMFS recreational fishery statistics,9 NMFS Fishery-Independent Survey System,10 and NMFS Southeast Data Assessment and Review (a fishery stock assessment process),11 as well as from the expertise of individuals who live and work in the GoM. Additional data on the ecosystem produced by the Gulf of Mexico Research Initiative consortia12 and the NRDA process are likely to facilitate implementation of these models for the GoM. Input from stakeholders will also be necessary, so that the models correctly capture the components of the system that are relevant to them. Nevertheless, baseline data may be insufficient.
One of the limitations of the ecosystem services approach is the lack of socioeconomic data needed to implement a more robust and complex understanding of the human dependencies on natural systems. Although models exist to explore some of these dependencies and the capacity of systems to provide a healthy stream of benefits to citizens (e.g., InVEST models,13 and MIMES14), no comprehensive model has been developed that integrates the biological, physical, and socioeconomic dimensions, and data are often lacking on social aspects of the system such as the multigenerational linkages described earlier.
Emerging ecosystem models, such as Atlantis, could be modified to support an ecosystem services approach, because they include shallow- and deep-water components of the ecosystem, chemical and physical processes, and certain components of human dimensions. The University of South Florida, the University of Miami, and NOAA are leading an effort to develop
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9http://www.st.nmfs.noaa.gov/st1/recreational/queries/index.html.
10https://www.st.nmfs.noaa.gov/finss/si/siMain.jsp.
11http://www.sefsc.noaa.gov/sedar/.
12http://www.gulfresearchinitiative.org/2011/rfp-i-consortia-grant-awards-gri-years-2-4/.
such a model for the GoM with the purpose of evaluating fishery management strategies.15 The model can be thought of as a snapshot of the best currently available information about the fishery ecosystem of the GoM. Current model implementations, including the one being developed for the GoM, however, fall short of considering all ecosystem services.
The Marine InVEST group of the Natural Capital Project consortium16 (a consortium of institutions developing tools for facilitating the inclusion of natural capital into policy and business decisions), including The Nature Conservancy, began implementing the InVEST models of ecosystem services in the Galveston Bay area in early 2012. This project is developing methods for assessing nature-based and engineered adaptation solutions to climate change. The project is funded by the Climate and Societal Interactions Program of NOAA.
Although there are no structural impediments, neither the MIMES nor the ARIES models have been applied to GoM ecosystems. Thus, while several ecosystem models under development are relevant to the GoM, none of them incorporates all of the relevant processes describing the dynamics of the provision of ecosystem services in the GoM. Lack of a comprehensive model for the GoM ecosystem remains a major challenge to the ecosystem services approach as well as to fuller application of the NRDA process to the DWH oil spill. The models presented above, however, are appropriate for the evaluation of the impacts of the spill on various subsets of ecosystem services. Until a more comprehensive model for the GoM is developed, it will be necessary to consider an approach that uses multiple models to assess changes in GoM ecosystem services related to the DWH oil spill.
Outside-the-Model Obstacles to the Ecosystem Services Approach
The ecosystem services approach can be thought of as a lens that allows us to view natural systems in a different way: as sources of services and goods for people (Figure 2.4). Use of the lens conceptually deconstructs each natural system into a set of ecosystem services and goods, which provide insight into its value. Identification of the goods and services provided by a natural system can also change the way we think about approaches to making the public whole when the natural system has been damaged.
Figure 2.4 helps to illustrate some high-level problems inherent in using an ecosystem services approach to remediation. These problems are not intractable; rather, they are denoted as high level because they will arise even if effective methods to identify baselines and to model the linkages between impacts and costs are developed. This section highlights three issues. First, as noted in Figure 2.4 in the valuation question related to the ecosystem service of “community stability,” some services are difficult, if not impossible, to measure. The committee described this broad category of services as “indirect benefits” and provided examples of these benefits as they were brought to its attention by community representatives from around the GoM. The important point is that, even though a service may be difficult to measure, it may still
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15http://cimage.rc.usf.edu/presentations?destination=presentations.
FIGURE 2.4 An example (a salt marsh) of a natural system viewed as a resource of goods and services via an ecosystem services approach. SOURCE: Committee.
have enormous value. Policymakers should take steps to ensure that difficult-to-measure but important services are given adequate consideration in decision-making processes.
Second, even when the value of all services can be measured, policymakers will still be faced with evaluating difficult tradeoffs among restoration options. Resources are limited, and choices may have diverse impacts on different sectors of the public in different regions. Finally, using an ecosystem services approach to restoration can raise concerns about replacing nature with human-engineered substitutes. Policymakers should be clear that using such substitutes is not an objective of the ecosystem services approach, but rather a last resort—that is, an option to be employed only when there is no other way to make the public whole.
Indirect Benefits
Economic metrics can be used to evaluate many types of ecosystem services, but they are not effective at measuring the value of important services such as the contribution of a healthy
ecosystem to the social stability of rural communities. The relationship between ecosystems and communities is particularly relevant throughout the GoM region. Many coastal communities depend on access to the GoM ecosystems and the diverse services they provide, including the economic benefits that result from oil and gas exploration and the extraction and production of hydrocarbons.
It is also important to note that populations and communities are themselves diverse, and the perceptions of these ecosystem services can vary greatly and consequently influence their value and utility at quite local scales. The result of these interactions between communities and their environment over generations is a social-ecological fabric underpinned by the GoM and its multitude of ecosystem services. Hence it is not unexpected that impacts to these services would also translate, directly or indirectly, into impacts on the social-ecological fabric of this region. These impacts are explored further in Box 2.2.
By definition, ecosystems are linked to human well-being through the regulating, provisioning, supporting, and cultural services they provide. Figure 2.5 (MEA, 2005) “depicts the strength of linkages between categories of ecosystem services and components of human well-being that are commonly encountered, and includes indications of the extent to which it is possible for economic, social, and technological factors to mediate the linkage. The strength of the linkages and the potential for mediation differ in different ecosystems and regions. In addition to the influence of ecosystem services on human well-being depicted here, other factors including other environmental factors as well as economic, social, technological, and cultural factors influence human well-being.” Thus, ecosystem services provide both direct and indirect benefits to humans, which in turn collectively contribute to the various human systems that support social cohesion, security, adequate livelihoods, individual and community health, and ultimately freedom of choice and action in pursuing a lifestyle of choice.
Events that disrupt or interfere with the normal functioning of ecosystems may impair ecosystem services, which has the potential to cause short- and long-term harm and loss of benefits to the individuals and communities that depend on those services (see also Adger, 2000). Understanding and quantifying the nature and level of these impacts are difficult and complex tasks, but ones that are essential for establishing the appropriate procedures for recovery, restoration, and management, and when applicable, for seeking compensation for damages caused. This latter point is particularly relevant to the ongoing NRDA process being undertaken by state and federal natural resource agencies and described in the Interim Report (NRC, 2011).
Considering Tradeoffs in an Ecosystem Services Approach
The goal of the NRDA process is to “make the environment and the public whole for the injuries to natural resources and services” (NOAA, 1996). Disturbances to ecosystems such as the DWH oil spill lead to changes in ecosystems and the provision of at least some services in some locations for some period of time. This is true even if the ecosystem eventually fully recovers. Recovery that aims to “make the environment and the public whole” will therefore necessarily
BOX 2.2
Additional Impacts of the Deepwater Horizon Oil Spill on Coastal Communities in the Gulf of Mexico
The Gulf region comprises diverse human communities, each with its own cultural dynamics and values and each reliant on a suite of cultural, provisioning, regulating, and supporting ecosystem services. Ecosystem services, such as fisheries, not only provide commercial and recreational value, but also are integral to the lives of people within these communities. Many traditions and lifestyles of Gulf residents are based on their relationship with the waterways and land around them and have defined their way of life for generations. Because these services are often difficult to evaluate and quantify, they may be overlooked when assessing the impacts of disasters such as the Deepwater Horizon (DWH) oil spill.
During the public session of its fifth meeting, held on April 25, 2012, in Mobile, Alabama, the committee heard a series of presentations by invited community leaders and representatives on the impacts of the DWH oil spill. In addition to the direct economic effects, many of the presenters discussed lifestyle changes that the spill has imposed on their communities. Natalie Bergeron, Executive Director of Project LEARN–LaTerre in Chauvin, Louisiana, defined “lifestyle” as a “composite of motivations, needs, and wants … influenced by factors such as culture, family, reference groups, and social class.” Bergeron further stated that lifestyle is “expressed in both work and leisure behavior patterns, in activities, attitudes, interests, opinions, values, and allocation of income” and “reflects people’s self-image or self-concept: The way they see themselves and believe they are seen by others.”
She explained that jobs in the bayou communities, which in large part involve shrimping, crabbing, oyster harvesting, and charter fishing, are intertwined with the cultural history, values, and ultimately the overall lifestyles of the people in these communities. Many fishing enterprises, particularly in the shrimping industry, are family operations that were severely impacted by the fisheries closures in the GoM. Closures not only affect the people who make a living catching fish, but also impact other community members who benefit from fishing activities. As part of the social fabric of many small coastal communities, fishers routinely set aside a portion of their catch to share with community members in need—the elderly, the disabled, and the unemployed. This seafood provides much of the protein in these individuals’ diets, and the closures forced them to spend a larger portion of their very limited income on other protein sources.
Bergeron also reported that the market for Gulf seafood has remained depressed since the DWH oil spill because of low demand due to public perception that Gulf seafood is not safe. She concluded by emphasizing that the spill’s impacts compounded existing stressors on the communities such as limited access to employment, adequate housing, health care, and education.
Maryal Mewherter, representing the United Houma Nation, described the tribe’s historical reliance on Louisiana’s coastal environment for food, materials for handicrafts, traditional medicine, and other cultural practices, specifically for weaving marsh grasses into baskets, making jewelry from fish scales and seashells, and collecting assorted medicinal plants and mosses. The Houma Nation also relies on commercial fishing as the “economic foundation” of its community. Referring to the DWH oil spill, Mewherter stated that “[w]ith fishing being a fundamental component of tribal living, the cultural impacts of this disaster on our indigenous communities are difficult to assess.” She emphasized that cumulative impacts—from multiple hurricanes, the recent global economic downturn, loss of wetlands due to erosion and subsidence, and the DWH oil spill—have collectively imposed severe challenges to the tribe’s way of life. As with the other bayou communities, the oiling of the wetlands and the fishery closures greatly reduced the tribe members’ ability to feed themselves and generate income from traditional crafts and medicines.
A primary challenge facing the Houma Nation is the loss of coastal wetlands; families and villages are being displaced, and the availability of land further inland and at higher elevations is limited. Although
wetland loss is a historic and ongoing challenge, the DWH oil spill is an exacerbating factor. The Houma Nation also struggles to influence state and federal agencies—especially in the development of land use policies and restoration efforts in the GoM wetlands and barrier islands. Mewherter concluded by pointing to a tribal crest bearing the image of the red crawfish, which serves as the Houma Nation’s war emblem and as an identifying symbol within its community, another example of how important local resources are to the tribe’s culture.
Khai Nguyen, a business development counselor for the Mary Queen of Viet Nam Community Development Corporation, highlighted the impacts of the DWH oil spill on the local Vietnamese-American community. According to Nguyen, many of the first Vietnamese settlers in the region arrived in 1975 as war and political refugees after the conclusion of the Vietnam War. They were, in large part, from three fishing villages in Northern Vietnam, and they found the New Orleans area attractive because of its similar climate and proximity to rich fishing grounds. In addition to support for their families, both in the United States and in Vietnam, the GoM fisheries provide the local Vietnamese-American community with subsistence catch. As with the other coastal communities, Vietnamese-American fishermen historically have saved a portion of their catch to feed their families and neighbors, as well as to trade for vegetables and other farm products with the gardeners in their community. Nguyen stated that many of the fishermen have sought compensation for their loss of subsistence since the DWH oil spill, but without proof of subsistence use, they have found it nearly impossible to succeed with their claims. Still, Nguyen says that the “fishermen are willing to go through the process to stand up for their way of life.”
As with the other communities, the fishery closures and subsequent loss of income forced a number of Vietnamese-Americans to apply for social services for the first time in their lives. Facing these difficulties and others, many Vietnamese-American fishermen have reiterated their commitment to see their children acquire better education and new opportunities in different fields.
In contrast to the other presentations, which focused on the loss of coastal and marine ecosystem services and subsequent community impacts, the presentation by Jackie Antalan, Director of Building Lasting Organizations in Communities, Inc., and a family services advocate on behalf of rural African-American communities, focused on the impacts of the DWH oil spill response and cleanup on inland communities. According to Antalan, nearly 40,000 tons of oil and dispersant-laden debris from the spill cleanup efforts were dumped in nine rural landfills across Louisiana, Mississippi, Alabama, and Florida. Public health concerns were primarily twofold: air quality (volatiles from the oiled debris were detectable in many neighborhoods) and contamination from the oil and dispersants leaching from the debris in these dumps into local water tables and supplies. Antalan asserted that a significant percentage of the debris was sent to dumps in communities with predominantly African-American, Latino, and Native-American populations. Secondary to the public health concerns was the lack of communication among the spill’s responsible parties and the public-sector agencies involved in the cleanup efforts and the impacted communities. This lack of communication led to the perception within these communities that their fears and concerns—real or perceived—were neither being acknowledged nor addressed by local and regional officials.
Collectively, these presentations highlight some of the pressing, and difficult-to-measure and address, challenges that communities can face after a disaster of the magnitude of the DWH oil spill. The loss or degradation of ecosystem services results not only in income losses, but also in losses to community self-sufficiency, sense of identity, and independence. That several of these communities engage in traditional but not commercial transactions to provide or share resources provides an example of how typical quantitative economic impact metrics may be inadequate to capture the real value of the transaction or its loss. It is also clear that the communities that are particularly reliant on GoM ecosystem services are as vulnerable as the GoM itself. Just as estimating the impacts of the DWH oil spill on the diverse suite of GoM ecosystem services presents challenges, so does estimating the impacts of the spill on these diverse GoM communities and their traditions, cultures, and values.
FIGURE 2.5 Linkages between ecosystem services and human well-being. SOURCE: MEA, 2005.
involve tradeoffs. Is restoration aimed at increases in ecosystem services in the future, perhaps involving a different mix of services and locations, sufficient to offset the damages to ecosystem services? The question of “benefits for whom?” further complicates issues of making the public whole. For example, mitigation efforts that restore ecosystem processes and enhance provision of services in a location different from the one that incurred damages can generate benefits for communities that weren’t negatively impacted by the disturbance.
Assessing whether recovery efforts are adequate to make the environment and the public whole is easiest when ecosystem conditions and services are restored as close as possible to original conditions. Then, the pre- and post-disturbance conditions and services can be compared to determine the success of the restoration. As discussed above, baseline conditions may not have been accurately measured or may be continually changing, which complicates the setting of restoration goals. Restoration efforts may then aim to restore proximal ecosystem conditions such as equivalent amounts of “healthy marsh habitat” under the hypothesis that restoring such conditions will lead to full restoration of pre-disturbance services.
The traditional approach under NRDA has been to restore equivalent acres of habitat, populations of species, or other resources known to have been harmed. However, it may not
be possible to restore pre-disturbance conditions, or at the very least, doing so will take time. Large-scale disturbances to ecosystems can cause fundamental shifts in ecosystem processes that can make it time-consuming, resource intensive, or in some instances impossible to recover pre-disturbance conditions (see the discussion of resilience in Chapter 3). In this case, restoration efforts may by necessity aim to replace ecosystem conditions in other locations or replace damaged services via other means, raising questions about what are adequate replacements to “make the environment and the public whole.”
Ecosystems provide multiple services that benefit a range of societal groups. For example, coastal ecosystems provide fish and shellfish, recreational opportunities, aesthetic beauty, habitat for species, storm protection, carbon sequestration, and other services. Ecosystem services cannot be simultaneously maximized; as one service is optimized, other services may be reduced or lost (Holling and Meffe, 1996). Tradeoffs will be inevitable when decisions are made about which ecosystem services to restore or make more resilient, even with the influx of research and resources of the scope committed to the GoM region since the DWH oil spill. Dynamic societal objectives combined with heterogeneous human communities make selecting or predicting the highest priority services difficult. Decision making often shows a preference for provisioning services first, then regulating, cultural, and supporting services in that order (Pereira et al., 2005), potentially disadvantaging human communities dependent on cultural services.
Even if a set of preferred ecosystem services could be agreed upon, no one service is likely to benefit all people equally; often there will be disconnects between where the benefits are experienced and where the costs are borne (Carpenter et al., 2009). Inherently, the distribution of benefits will be determined based on a wide variety of variables, including but not limited to spatial dimensions such as the geographic proximity to the ecosystems providing the services and temporal variables (Rodriguez et al., 2006) (see also Chapter 5). For example, in the case of fisheries, individuals in the fishing industry in the GoM are likely to be the most immediate beneficiaries of a recovered fishery, but fishery product consumers across the globe may also benefit. In contrast, the benefits associated with storm mitigation from wetlands are almost entirely determined by one’s geographic position relative to the specific GoM wetland that has been maintained (or degraded or lost).
From a practical perspective, it is important to consider who will reap the benefits and who will endure the costs associated with particular ecosystem services. Identification of the primary stakeholders for each service assists in determining who will support and who will oppose restoration efforts. More importantly, however, it allows decision makers to consider whether or not particular groups or communities are being fairly or disproportionately harmed or enriched.
Use of an ecosystem services approach raises a potentially problematic question about what it means to “make the environment and the public whole.” For example, suppose that a wetland that provided coastal protection for a community is damaged and restoration efforts are directed toward building a seawall for that community that provides equal coastal protection. In this case, the particular ecosystem service has been replaced and it could be argued that the community has been made whole. This argument, however, makes many stakeholders
nervous. Policymakers should be aware that the goal of an ecosystem services approach to remediation is not to replace the lost natural system with a set of constructed replacements or a long-term period of payment to users for the lost ecosystem service value. Rather, the goal should be framed in a much more limited manner. Replacement projects for services will not “make the environment whole” but may be appropriate when there is no feasible way to restore the natural system and when the replacement is acceptable to the public. Often, the restoration of natural systems is not feasible even in a world of unlimited resources, because of our inability to clean or reconstruct ecosystem components such as salt marsh or benthic habitats. In this case, it is better to implement a publicly acceptable project that restores lost services rather than to deny any compensation to the public simply because it could not be complete.
Summary
An ecosystem services approach to evaluating the impact of events such as the DWH oil spill involves measuring the impact of the event on the structure and function of the socialecological system (typically through comparisons to baseline data), understanding how changes in the structure and function affect the provision of ecosystem services (typically through modeling), and understanding how changes in the provision of ecosystem services affect human well-being (typically through an economic valuation process). Such an approach can offer a more holistic view of impacts as well as expand the possibilities for restoration actions aimed at “making the public whole.”
An ecosystem services approach to damage assessment is not incompatible with the current Oil Pollution Act’s NRDA process. However, data sets more appropriate for the development of ecological production functions and for measurement of human dependencies on natural systems may be required.
Although the ecosystem services approach offers clear advantages over more traditional approaches, it presents many challenges, including:
1. The lack of adequate baseline data for some parameters, particularly for those related to the human dimension of impacts. In the absence of adequate baseline data, it will be difficult to determine the full impact of an event such as the spill, particularly in an environment such as the GoM where the baselines are dynamic and where economic conditions are often impacted by events far-removed from the local community.
2. It is difficult to parse out the impact of a single event such as the DWH oil spill in an environment such as the GoM that is subject to multiple natural and anthropogenic stressors. The key to addressing this challenge is the development of validated models that can be used to understand how individual stressors impact the ecosystem and the services it provides. Although numerous models for various system components are currently under development, fully comprehensive models for social-ecological linkages and interactions are not currently available.
3. Although using an economic valuation process as a proxy for impact is convenient, it does not necessarily capture all of the goods and services provided by a natural system, and replacement sources may not be a perfect substitute for the services provided by the natural system (e.g., a seawall may replace the storm protection of a destroyed marsh, but it may also have negative impacts). These tradeoffs must be carefully weighed when applying an ecosystem services approach.
Despite these challenges, the ecosystem services approach, when used prudently, can offer an enhanced opportunity to more fully achieve the goal of making the public whole in response to an event such as the DWH oil spill because it may provide otherwise unavailable information on the value of lost goods and services.
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