Informing the Debate: Examining Options for Management and Stewardship
Setting policy goals for the conservation of ecosystem services requires going beyond simply managing for fisheries yield and the reversibility of fisheries-related depletion. The term ecosystem-based management has been used by the U.S. Commission on Ocean Policy (2004) and the Pew Oceans Commission (2003) to mean developing ecosystem-level goals that are multi-species focused and that consider multiple kinds of human activities that are tied to healthy marine ecosystems. This means that both consumptive and nonconsumptive uses are weighed when deciding management actions and regulations for the oceans and coasts as a whole.
However, even if the overarching policy goal for marine ecosystems is to manage both consumptive and nonconsumptive uses in an integrated approach, successfully carrying out the objective ultimately requires determining what the overall ecosystem goals should be. What level of productivity is desirable? How much risk of irreversible change is acceptable? What do we—as a community—want our ocean environments to be? These are all questions about social values.
One possible goal is to strive for pristine ecosystems that resemble natural conditions with no human impact. This is neither possible nor practical, for it would be impossible for humans not to have an impact on ocean ecosystems. Indeed humans are part of marine ecosystems. Even if fishing pressure were eliminated, there would still be impacts from coastal development, tourism, transportation, and many other uses. Thus, it must be decided what mix of these uses is most desirable based on measured or perceived benefits. These are not easy decisions. Adequate scientific knowledge from both the natural and social sciences is important for delineating options and illuminating choices, but science alone
cannot address these issues. The path forward will require melding of the understanding of ecosystem processes, the human dimension, and the possible policy options. Tradeoffs between conflicting goals can then be decided with input from diverse users, including tradeoffs needed to implement restoration or rehabilitation activities when deemed necessary.
This chapter discusses the application of model-based scenario analysis for shaping fishery management goals and the current capabilities for conducting such analyses. Also presented is a discussion of environmental ethics and the public involvement needed to make sound decisions for our future and ensure that all uses of ocean resources are represented when setting fishery management policies.
EVALUATING STRATEGIC MANAGEMENT OPTIONS
Fisheries management in the United States in recent years has tended to follow prescriptive policies defined in terms of nonspecific biological reference points used to set targets and limit harvest rates and to specify biomass thresholds to be avoided. In this management setting, the scientific support for management decisions is largely couched in terms of how different levels of catch or effort controls fare against the accepted generic standards. For example, annual quotas are estimated so as to meet a target exploitation fraction with a low probability of exceeding the thresholds. A stock assessment is conducted annually and the resulting estimate of stock size is normally multiplied by the target exploitation fraction to calculate the allowable catch. In terms of management goals, a de facto decision about the “best” policy is implied in the choice of the generic reference points. Thus, the role of the annual stock assessment is largely limited to informing tactical decisions, such as the choice of an annual catch quota, instead of being concerned with evaluating the consequences of different strategic policy choices for the ecosystem and for all different stakeholders. Ecosystem considerations are discussed in regular stock assessments, and environmental impact statements are required for major fishery management actions, but in general these actions do not involve a comprehensive evaluation of management strategies and there is no legal requirement to account for these ecosystem interactions.
Certainly science—including social and economic science—has a much larger role to play in informing strategic policy choices. In some countries and for other U.S. resource management organizations, fishing strategies or management procedures are designed by taking into account the nature of the system and the specific management issues involved. Different candidate strategies are tested using simulation models, and their performance is evaluated by examining graphical outputs of model projections and various performance statistics that measure policy outcomes according to different, often conflicting, management goals. The role of fishery scientists in these cases is to integrate all available information (scientific and empirical) to help assess the likely consequences of alternative
policy choices. This is best done formally using simulation models to test the performance of candidate policies across a variety of scenarios/hypotheses that represent all possible futures in light of all available evidence. The challenge for scientists is to identify the series of scenarios that capture the uncertainty existing about the system dynamics and to assign each a relative plausibility.
A formal approach for the evaluation of alternative management strategies was pioneered at the International Whaling Commission (IWC 2005) and has been mostly applied to the design of catch-control rules for industrial fisheries (e.g., Butterworth and Punt 1999, Parma 2002a, Smith et al. 1999). The approach involves: (1) formal specification of scenarios used to represent the dynamics of the exploited system and their relative plausibility, (2) identification of candidate policies considered feasible a priori, and (3) identification of graphical output and definition of a series of performance statistics used to measure policy outcomes that reflect the interests of all stakeholders.
The first component is strictly a scientific endeavor. Assessments are conducted based on various competing assumptions about key processes in the dynamics of the stock and its fishery, and the results of these assessments provide the basis for identifying alternative scenarios to be used for policy evaluation. However, the role of the assessment is not to come up with the best estimate of stock size, but to “condition” the simulation models to the available historic information. The different scenarios may represent a range of productivity, maximum stock size and depletion levels, and/or different structural models of the system dynamics, as well as different relationships between the fishery indicators used for monitoring the underlying stock dynamics.
Management strategies then go well beyond the specification of reference points. They not only specify the feedback rules used to calculate input or output controls, but also specify the data needed to implement those rules. In this way, the evaluation of management strategies places due emphasis on the information gaps and uncertainties specific to the fishery in question, as well as on the problems that need to be addressed for the implementation of the different candidate policies. The results of the policy evaluation can be summarized in the form of a decision table that provides the outcomes of different candidate policies across a range of model scenarios. Some policies tend to be more robust to the uncertainties than others, but ultimately some fundamental tradeoffs between conflicting management objectives need to be made.
The goal of the analytical exercise is not to build models that are able to predict what will happen, but to build a series of models and hypotheses of what may happen and to assign relative plausibilities to them so that tradeoffs between conflicting management objectives will be explicit when decisions are made. These tradeoffs must be decided not only between competing fisheries, but also between fisheries and other uses of marine resources.
The last two components of the approach described involve societal values and choices, and therefore require input from all parties so that the different
perspectives are reflected in the selection of candidate policies. (Greater stakeholder participation is discussed in a later section.) Although experience with formal evaluation of fisheries management strategies around the world has mostly focused on single-species management as described above, the design framework is flexible and could be applied to assess the consequences of alternative policies in multi-species systems (Sainsbury et al. 2000).
Using Ecosystem Models for Policy Screening
The strength of single-species policy analysis is its strong reliance on past data to develop the model scenarios. Conditioning is critical because by fitting the models to historical data, the universe of plausible models is constrained to those that can reproduce historical trends. Conditioning of single-species models is supported by significant development of statistical techniques and specialized software for estimating model parameters efficiently, quantifying uncertainty using modern Bayesian techniques, and incorporating this uncertainty into the decision analysis (Parma 2002b, Punt and Hilborn 2001).
To evaluate policies that take into account inter-specific interactions, similar capabilities will be required from ecosystem, species-interaction, and food-web models. The challenges for scientists here are more difficult, as the uncertainty present in single-species models is compounded by uncertainty about the parameters and relationships that govern interactions among species, habitat, and the physical environment.
The simplest approach to incorporate the effects of trophic interactions into the evaluation of policies is to link the dynamics of just a few key species. An example of this approach is the model of the hake-seal system developed by Punt and Butterworth (1995) to evaluate the impact of culling the predator fur seals (Arctocephalus pusillus pusillus) based on the abundance and catches of the Cape hakes (Merluccius capensis and M. paradoxus). Even in such relatively simple models, the uncertainty is large. Furthermore, the decision about how to bound the system and determine which components to include is not trivial, as model predictions may be misleading due to oversimplification. For example, simple analyses of predator-prey relationships may overestimate the impact of fishing the prey species on the sustainable yield from the predator species by ignoring the complex compensatory dynamics of the entire food web. Indeed, models of the whole ecosystem tend to predict much less severe bottom-up fishing impacts than do predator-prey models because the availability of alternative prey species for piscivores buffers the reduction of some of their prey by fishing (Walters et al. 2005).
At the opposite extreme along a gradient of increasing complexity are models that represent whole ecosystems as they vary in space and time, including the environmental processes that force changes in productivity. An example of this type of approach is the model being developed to represent the California Current
for the western United States (Levin 2005). The model represents marine ecosystem dynamics through spatially explicit submodels that simulate hydrology, biogeochemistry, food-web dynamics, fishing, and management. The first model of this kind was created in Australia and has already been used to help define management scenarios (Fulton et al. 2004). A socioeconomic submodel is also currently being crafted (Smith 2005) that will allow tradeoff determinations not based solely on biological concerns.
However, such models are extremely complex. The one being developed for the California Current has 1,600 parameters for each depth/spatial stratum of the model (including parameters such as growth rates, fecundity, and mortality rates). There are 64 spatial regions in the model and for each region there are 10 depth strata. Obviously, the creation of such a model, even without the addition of socioeconomic data, is an enormously data-intensive process and requires extensive knowledge of the ecosystem and its components.
Models of intermediate complexity include the increasingly popular Ecopath-with-Ecosim (EwE) models (Walters et al. 1997). These models are a dynamic extension of the ECOPATH models (Polovina 1984, Christensen and Pauly 1992), which provide a static description of the interactions between a series of functional groups. In the simplest versions of Ecosim, rates of biomass change are predicted as efficiencies multiplied by food intakes minus losses due to predation, harvest, and unaccounted mortality agents. In most applications, at least a few species are also simulated with much more elaborate “multi-stanza” accounting for size-age structure over time. EwE software is a widely used tool for the quantitative analysis of food webs and ecosystem dynamics (e.g., Pauly et al. 2000), and a number of models have been constructed to represent major ecosystems (e.g., Christensen et al. 2002; Cox et al. 2002a, 2002b; Martell et al. 2002; NRC 2003; Olson and Watters 2003; Shannon et al. 2004). The capabilities of the models and software are being constantly expanded and the impact of the various assumptions made to model trophic interactions are being scrutinized (Walters and Martell 2004, Plagányi and Butterworth 2004). Critical recent developments include the capability to input historical trends in fishing mortality or effort, productivity indices (e.g., upwelling), recruitment indices, and biomass of other, nonmodeled species to drive the dynamics of different model components. Also, predicted trends can be fitted to observed trends in relative or absolute abundances, direct estimates of total mortality rate, and historical catches (Walters and Martell 2004). Experience with fitting the model to time-series data using formal statistical methods is being gained in a few study cases, but this method is in its infancy compared to single-species approaches.
Despite growing experience with the use of multi-species models to reconstruct historical changes in ecosystems, the potential for these models to predict ecosystem responses to complex harvest management policies cannot yet be assessed (Walters et al. 2005). Still, multi-species models have evolved to a point where they can begin to provide useful tools for policy screening. For example,
the Ecosim model of the central north Pacific developed by Cox et al. (2002a, 2002b) has been used to evaluate the effects of alternative strategies for harvesting tuna on the pelagic food web and the rebuilding of billfish and shark populations (Hinke et al. 2004, Kitchell et al. 2004). Management options examined include removing shallow hooks from longlines to reduce bycatch of marlins, banning of shark finning, and overall reduction of longline and purse-seine fishing effort. Simulations over 30 years indicated that elimination of shallow gear and shark finning was more effective at recovering marlins and sharks than a reduction of longline effort (Hinke et al. 2004). However, the resulting increase in predation had a negative impact on the simulated abundance and catches of yellowfin tuna, bringing about important economic tradeoffs between gains due to increased recreational catches of billfishes and major losses in tuna fishery revenues (Kitchell et al. 2004). Although several sources of uncertainty were identified, the sensitivity of the specific results to them was not examined.
A systematic evaluation of alternative model scenarios consistent with historical data would be needed if such model-based projections were to be used to inform management choices. Usually, the available data do not allow discrimination between several competing hypotheses about the structural relationships in the ecosystem. As a result, models that fit the historic data equally well may make widely different predictions about future policy outcomes. The importance of evaluating the sensitivity of predictions to model structure and parameter uncertainties is well illustrated by Koen-Alonso and Yodzis (2005) using a relatively simple interactive system involving four key species in the ecosystem of the Argentine Patagonian shelf. Several models of the predator functional responses of hake (Merluccius hubbsi) and sea lions (Otaria flavescens) preying on squid (Illex argentinus) and anchovy (Engraulis anchoita), and sea lions preying on hake, provide adequate fits to time series of abundances and catches, but make very different predictions under some exploitation scenarios. Clearly, in this example, a lack of time-series data on prey mortality rates and predator diets contributes to substantial uncertainty.
In general, food-web models may provide useful tools to simulate possible ecosystem responses as long as users are conscious of their limited predictive capabilities and therefore place due emphasis on evaluating the robustness of candidate policies relative to alternative model assumptions (Christensen and Walters 2004, Essington 2004).
Harvesting policies need to be conceived as adaptive experiments, with a requirement to implement monitoring programs to evaluate system responses and to detect unexpected consequences should they happen (Walters et al. 2005). It is only through adaptive management that our understanding of ecosystem dynamics and our ability to design robust harvesting strategies will improve. Building ecosystem models to design harvesting policies requires the cooperation of many specialists and the integration of information from many sources. This may best be achieved by a series of workshops that bring together people with different
expertise. Here again, just as in the case of single-stock policy evaluation, input from stakeholders is essential for identifying important tradeoffs when evaluating feasible candidate policies.
PROJECTING RECOVERY STRATEGIES AND THE EFFECTS OF SHIFTING BASELINES
When the Magnuson-Stevens Fishery Conservation and Management Act (MSFCMA) underwent Congressional reauthorization in 1996, greater emphasis was placed on the ecological aspects of fisheries management, including the protection of essential fish habitat, the reduction of bycatch, and the rebuilding of overfished fisheries. The Sustainable Fisheries Act (the amendment to the MSFCMA) requires not only that fisheries be managed according to the best available scientific data but also that overfished stocks be rebuilt. However, it is not clear whether the rebuilding requirement for each stock should be based on its most recent population dynamics or to some earlier dynamics. Should it be restoration to a pristine level akin to pre-Columbian contact? Pre-human contact? Or some level immediately prior to recent overfishing?
It is not known if New England cod stocks can recover to the high levels inferred from historical logbook records by Rosenberg et al. (2005), but substantially reducing fishing pressure on cod to enable recovery is a reasonable policy. Such recovery actions may be achieved by many different regulatory controls, but the critical feature of a good rebuilding strategy is its capacity to adjust in response to new information about ecosystem and food-web status by imposing more or less stringent regulations, depending on system responses.
But to be able to test the feedback capacity of any candidate rebuilding plan, it is important that a wide range of scenarios be considered in simulation trials. A major simulation study of this type has just been completed under the umbrella of the Commission for the Conservation of Southern Bluefin Tuna (CCSBT). Different decision rules proposed by teams of scientists from several countries have been tested using simulation models chosen over a series of workshops. The models span a wide range of uncertainty and imply a variety of plausible rebuilding levels (CCSBT 2005).
Simulation trials serve to quantify tradeoffs between the short-term pain inflicted by imposing immediate major quota cuts and the long-term benefits derived from possible stock rebuilding. Also, the range of likely impacts of stock rebuilding on other ecosystem components may need to be examined. For example, rebuilding for top predators may have negative consequences for their prey (e.g., the cod-lobster example discussed in the previous chapter). These are just examples of the kinds of tradeoffs that need to be explicitly evaluated to inform management choices. More conservative or less conservative decision rules can be tested, each achieving a different rebuilding target, depending on the scenario.
However, care must be taken not to limit the possibilities. As mentioned before, current models are based or “conditioned” on historical information. But what happens when our historical data has already been subject to shifting baselines? Ultimately, recovery plans should not be determined based on upper limits of population abundances estimated within the period defined arbitrarily by the date at which baseline data collection began. Scientists will need to take a long-term view when identifying the range of plausible scenarios and make use of all sources of information to protect against the shifting baselines phenomenon. In many cases, this may involve looking at unconventional sources of information that predate the establishment of regular fisheries monitoring programs and scientific databases. The “back-to-the future” approach developed by Pitcher (2001) provides a structured method for reconstructing models of past ecosystems using information about the presence and abundance of species from historical documents, archaeology, and local and traditional environmental knowledge.
STRATEGIES FOR INFORMED AND INCLUSIVE DECISION MAKING
The previous chapter discussed mostly choices between species in managing fisheries. However, the ecosystem impacts of fishing can, and do, affect others beyond just fishermen. For centuries, humans have viewed the natural environment as a source of material resources and services as well as a source of spiritual, cultural, and aesthetic experiences. Marine ecosystems generate a diverse set of goods and services: these must be evaluated based upon consumptive uses, nonconsumptive uses, and public-good “existence” values (see below). Consumptive uses are, of course, those that rely on the removal or harvest of ocean resources, such as fishing. These uses and their value are easily quantifiable, based almost purely on market values. The value of the other two goods and services are much more difficult to measure, but that does not mean they are any less important. The most common nonconsumptive values are those related to tourism, research, and education, where their relative value depends in part on the presence of a healthy ecosystem, as well as on the cultural and economic integrity of coastal communities. Existence values are even more difficult to quantify, but are proportionally more important. Ocean and coastal ecosystems provide many services to Earth, such as climate and atmospheric regulation. These services are experienced equally by all, but few truly realize this value. This is in contrast to fisheries and other consumptive uses where a select few monetarily benefit at a much higher rate.
Making Value Judgments
There are two main roles for public policy regarding public natural resources. One is to set regulations that define use rights for those resources. The second
role is to decide macroscale allocations of public resources when different user groups come into conflict. This role has become more important recently in disputes between different consumptive users, between different nonconsumptive users, and between consumptive and nonconsumptive users.
Public policy pertaining to fisheries resources in the United States is conducted within a setting circumscribed by law. Fisheries law and other applicable environmental laws reflect ongoing and changing assertions of interest between various claimants of coastal ecosystem services. The fisheries policy process is contentious precisely because policies are not generally between right and wrong decisions, but between decisions that create both winners and losers. It is convenient to use the metaphor of a pie. Some policies (e.g., allocation across gear types) determine which group is going to get which slice of a fixed pie, namely the economic and biological yield from a system. These kinds of decisions dominate fisheries policy and they are largely zero-sum in that any policy that benefits one group will impose costs on another group. Selecting the best policy in allocation decisions primarily must reflect value judgments involving tradeoffs among different user groups. These are not science questions. However, as discussed in the previous sections, science and scientists can play useful roles by providing predictions of the likely impacts on various user groups of alternative policies, thereby making it easier for policy makers to take into account the implications of their allocation decisions.
Alternatively, some policy decisions may involve increasing or decreasing the size of the pie. These kinds of policies are particularly difficult to implement because their associated policy options have the possibility of generating win-win (or lose-lose) scenarios. For example, policies that reduce the overall productivity of an ecosystem so that sustainable harvest of all species is reduced are options that many would view as unacceptable. On the other hand, policies that enhance all dimensions of a system’s productivity make it at least physically feasible to make all user groups better off without harming any particular group. These appear to be easy public policy cases because the best decisions seem straightforward. Unfortunately, win-win policy decisions are rare. Instead, most policy decisions involve gains by one group at the expense of another.
What role should science play in this decision arena where various policies affect not only the system’s overall health and productivity but also who gets what share of the services provided? This is a contentious issue among ecologists and biologists and there is considerable disagreement, particularly over whether scientists ought to be recommending policies or just informing the policy process about the likely outcomes. Is it important for scientists to avoid promoting, recommending, or selecting policies? Recommendations about the “best” policy inevitably reflect value judgments about who ought to receive various slices of the public pie, whether these judgments are explicit or implicit. Judgments about which user group ought to win and which ought to lose are not scientific questions, but rather questions answerable only by adding layers of value judgments.
The Role of Social Science and Ethics in Stewardship
Since 2002, the National Oceanic and Atmospheric Administration (NOAA) has convened a series of workshops to develop a social science research strategy to understand the human dimensions of marine protected areas (MPAs). Although the focus of the workshops was specifically on the creation of MPAs, some of the facets of planning, management, and evaluation are relevant to the development of a broad strategy for addressing ecosystem effects of fishing.
As a result of the 2002 workshop held in Monterey, California, the MPA Center identified six priority themes in social science: governance institutions and processes; use patterns; economics of MPAs; communities; cultural heritage and resources; and a category called attitudes, perceptions, and beliefs (Walhe et al. 2003). In other words, what are the variations in attitudes, perceptions, and beliefs of different actors (user groups, stakeholders, and decision makers) toward marine resources as well as toward other users? The attitudes, perceptions, and beliefs (APB) theme covers the underlying motivations that may influence human preferences, choices, and actions.
In subsequent workshops, the APB theme was elaborated more fully and generated suggestions for work germane to the topic of ecosystem effects of overfishing (National Marine Protected Areas Center [MPA Center] 2003a, 2003b, 2004). Participants suggested that it would be valuable to evaluate local knowledge about resource quality, use, access, and protection (MPA Center 2004). This inquiry should encompass perceptions of the relative condition of marine resources compared to available scientific information, and it should document elder fishermen’s knowledge about changes in fishing resources over time (MPA Center 2003a). Participants suggested examining how to integrate local, traditional knowledge with scientific knowledge and vice versa. Saenz-Arroyo et al. (2005) show that these sources of knowledge are important in assessing the shifting baseline phenomenon.
Additionally, the NOAA workshops called for a determination of the differences in APBs between managers and the affected communities and stakeholder groups (MPA Center 2003a). The workshops identified the need for analysis of public participation methods in the process and how the methods themselves may affect perceptions of both the process and the outcomes (MPA Center 2003b). Participants also highlighted the need to assess perceptions of ownership of common resources between users (e.g., commercial and recreational fishermen) and nonusers (at least nonextractive users) (MPA Center 2003a, 2003b).
Two major challenges face the management community: developing social science tools, and accepting social science data as an important component of decision making (Walhe et al. 2003). While fisheries science is essential for management, by itself it is not enough. What terrestrial conservation biologists find to be true—that they manage not so much the organisms, but human behavior—holds true for the marine environment as well. To produce truly com-
prehensive management plans, fisheries managers must further incorporate social and economic sciences, as well as the natural sciences, in their deliberations. And part of the social science aspect involves explicit consideration of the values that underlie the decision-making process—consideration of environmental ethics.
Defining the Role of Ethics in Management
Bringing ethics into the discourse on fisheries policy will produce a paradigm shift in the way that fisheries policy is both designed and justified. In other areas of applied ethics there have been similar shifts, the best example of which is the inclusion of ethics training in the medical school curriculum. Hargrove (1995, p. 17) noted:
Environmental ethicists have not succeeded in developing the kind of relationship, for example, which medical ethicists have with doctors, lawyers, and policy makers…. Medical ethicists generally are asked to participate in the resolution of tough decisions which members of the medical community do not want to make themselves…. Environmental professionals have little interest in having philosophers make tough decisions for them.
Crises in natural resources management are directing scientists into the realms of ethicists and resource managers, where they now often seek ethical advice in ways similar to those experienced by the medical profession. Indeed the report of the Pew Oceans Commission (2003) called for the creation of an “ocean ethic” to guide U.S. policymaking.
Despite decades of development, the body of environmental ethics literature examining our relationship with the marine environment remains slim. Pioneers of U.S. environmental philosophy such as Pinchot, Muir, and Leopold expressed some regard for marine environments in their resource management writings, but their focus, conscious or unconscious, was on terrestrial environments. Indeed Leopold (1949, p. 251) characterized the sum of his views as “the land ethic” and wrote that “we can be ethical only in relation to what we can see, feel, understand, love, or otherwise have faith in.” Where does that leave policymaking with respect to the sea? Can or should we extend the “land ethic” to the sea (Safina 2003) and to the animals who live there (Box 4.1)? How do we go about formulating and defending ethical marine policy?
Environmental ethicists who have examined the moral foundations for preservation of biodiversity suggest a shift of emphasis away from saving nature on a species-by-species basis (Callicott 1995a, 1995b; Norton 1986). This approach might prove to be even more useful for the marine environment than it is for the land. First, marine biologists may have greater difficulty than terrestrial biologists in determining which marine species to protect, particularly if they are bycatch whose population dynamics are not closely tracked. Second, by protecting habitat
One of the most dramatic shifts in extending moral regard to nonhuman species is our changing view of whales (Kellert 2003). Formerly hunted by the hundreds of thousands to produce commodities such as lamp oil and margarine, whales and other cetaceans have attained an extraordinary level of ethical concern within the time span of a single human generation. Granting some important exceptions, whale killing has now been supplanted by whale watching as a source of income as well as an aesthetic and naturalistic experience (Russow 1981). In the United States, the Marine Mammal Protection Act of 1972 created a moratorium on the taking of marine mammals not only in U.S. waters but also outside U.S. jurisdiction—prohibiting such activity by any U.S. citizen on the high seas. This law provides a stringent code of ethics with strong penalties for violators and with very narrow exceptions. Additionally, consumer demand for dolphin-safe tuna led the U.S. government to impose bans on importation of tuna caught using fishing techniques that resulted in high dolphin mortality.
But what about other marine organisms? Compared with their terrestrial counterparts, marine species are much less protected. Few truly marine animals and plants appear on the U.S. endangered and threatened species list, and there seems to be even less support at the international level for adding marine species to international lists such as those promulgated by the Convention on International Trade in Endangered Species (CITES) of Wild Fauna and Flora (CITES 2005).
integrity, a whole range of organisms is protected as well as the integrity of their interconnectedness.
There are two aspects of utilitarian ethics that could be used to modify behavior in ways that could result in more favorable outcomes for the sea: One is how inclusively the “self” in “self-interest” is defined and the other is the time frame considered (Tam 1992). The definition of self can be thought of as an issue of scale, a concept familiar to conservation biologists who study ecological scales that range from genes to the biosphere. A multi-scalar analysis reveals that human impacts occur at many different spatial scales (Norton 2003). If the ethical scale is extended to include a moral regard for the biotic community, then it is possible to consider not only impacts of human activities on other humans but also on marine domains of all sizes. As a consequence, an opportunity opens for a wider-ranging discussion of the multiple ethical values which are underpinning the duties of stewardship that humans owe the marine environment.
Changing one’s scale in time, as well as space, also can modify the perception of what best advances self-interest. Leopold (1949) counseled his readers to “think like a mountain,” and thereby extend the time reference from experiential
to that of ecological (or even geological) scale. Extending the time horizon requires users of the marine environment to balance perceptions of what is currently in our self-interest against our potential foreclosing of environmental options that might be available to future generations (Knecht 1992). This concept, one of intergenerational equity, is grounded in part on an ethical stance requiring persons to act in such a way that they would want their actions to be universalized. As Rawls (1971) characterized it, how would our actions change if we did not know which generation we are in?
Including consideration of future generations in current policy decisions does not mean we try to forecast their preferences. But we can scientifically assess the fragility of marine ecosystems, and we do have the technology to alter them irreversibly. According to Norton (1991, p. 219), “The lesson of ecology is that one cannot care for the future of the human race without caring for the future of its context…. Context gives meaning to all experience; consequently, it is a shared context that allows shared meaning—what we call culture—to survive across generations.” In Norton’s sense, then, the sea ethic we create must link past, present, and future generations in a culture that recognizes and respects limits on our actions in the sea.
Public Representation in Fisheries Management Decisions
The Magnuson-Stevens Fishery Conservation and Management Act created eight Fishery Management Councils as administrators for the living marine resources within the U.S. exclusive economic zone. One of the principal goals of the Act was to link the fishing community more directly to the management process. In fact, the Councils are a principal avenue for public involvement in deciding fishing limits and regulations. To date, the majority of public appointees are fishermen and fishing industry representatives (both commercial and recreational); there have been few appointments from outside the fishing community. Granted, this “unequal” representation might not have been such an issue when the resources were thought to be boundless and there was no recognition of the role fisheries could play in shaping the functioning of whole ecosystems. However, armed with this new knowledge, methods for bringing other interests into the decision-making process may be warranted.
Recently, NMFS has been experimenting in some regions with a system designed to improve access and involvement of multiple user groups and stakeholders as stock assessments are developed. For example, in the Gulf of Mexico and South Atlantic regions, this process is called SEDAR (Southeast Data and Assessment Review) and in the Northeast, SAW-SARC (Stock Assessment Workshop-Stock Assessment Review Committee). The new process employed by these groups includes a series of workshops where NOAA scientists, fishermen, nongovernmental organizations, industry consultants, academics, state biologists, economists, and others meet to discuss the data sets needed to develop
an assessment. This is followed by a second workshop where a similar diverse group (with some overlap of members) builds on the output from the first workshop and produces the assessment. Each assessment is reviewed by an independent group of experts before it is passed to the Council’s Science and Statistical Committee. While this process has increased the number of meetings—and therefore the time—it takes to produce final products, the assessments created through this process are generally better received by the Councils and their advisory panels, and skepticism about the assessments is reduced. The apparent success of these new organizations supports the implementation of a similar approach to incorporate stakeholder input when discussing ecosystem impacts of fishing and when examining alternative model-based scenarios for management actions.
Creating a formal approach for evaluating alternative management strategies using food-web models, as discussed in a previous section, will not be a productive exercise if those affected by alternative actions are not involved in the decision-making process. Obviously, choosing among tradeoffs and settling conflicts between two competing fish species is not the greatest issue. If we are to include in our scenarios ecosystem recovery options to develop other uses, or to decide tradeoffs between consumptive and nonconsumptive uses, the proponents of these uses cannot be excluded from the discussion. This is particularly true if cascading effects or trophic interactions caused by fishing will prevent or compromise these other uses or services.
MAJOR FINDINGS AND CONCLUSIONS FOR CHAPTER 4
More extensive use of food-web and other ecosystem simulation analyses are needed to explore possible consequences of different candidate harvesting strategies under alternative scenarios representing the state and dynamics of marine ecosystems. Fisheries management advice has tended to follow prescriptive policies defined in terms of generic biological reference points for individual populations. However, within an ecosystem context, tradeoffs between conflicting management objectives need to be made explicitly by evaluating policy consequences in terms of different measures of performance that reflect policy impacts on various ecosystem components and uses, including consumptive and nonconsumptive uses.
Owing to their inherent complexity and associated uncertainties, ecosystem models are unlikely to provide numerical tactical advice on fisheries regulations. The main use of ecosystem models in the near future will be to build alternative scenarios to test strategic policy choices. The challenge for scientists and managers is to identify and assign probabilities to a range of scenarios that captures existing uncertainties about the food-web dynamics and the responses of fished food webs to various fishing strategies.
Food-web and other ecosystem models currently exist that provide useful tools for policy screening. Not all interactions need to be known to begin creating and applying existing models of key species interactions and food-web components and to aggregate and manage the underlying data.
Scenario analyses and the corresponding management actions are best applied in an iterative and adaptive process. As management is applied and knowledge about marine ecosystems increases, some scenarios will be seen to be less plausible while other new scenarios may emerge. In addition, new information about how humans respond to regulations may favor some policies more than others. The models themselves will improve as more is learned and greater levels of complexity are added, requiring an adaptive approach to management.
A diverse cross-section of constituents may be needed to weigh the varied ecosystem values and uses involved in model-based scenario analysis. An important public policy issue is how to assure that nonconsumptive and public-good values receive proper consideration when making tradeoffs among ocean services. Commercial and recreational uses are represented through the Fishery Management Councils, but the current composition of the constituents “at the table” does not represent concerns of potential ecosystem services beyond fisheries extraction.