Science to Enable Future Management
Many ecological and fisheries publications have raised the issues of ecosystem effects of fishing on populations, food webs, and communities. While the evidence presented in Chapter 2 is compelling, it is by no means conclusive about all possible effects. There is still much that we do not know.
To comprehensively understand the ecosystem effects of fishing, we must know the current state of the ecosystem, the state of the system at earlier stages (preferably pre-exploitation), and what factors contribute to stability or change. Additional research is needed to underpin the incorporation of ecosystem effects of fishing into fishery management and to help decide the allocation tradeoffs discussed in the previous chapters. If we are to make tradeoffs between uses and between species, we must try to anticipate the possible outcomes of candidate management policies. Given our present understanding, not enough is known to “steer” exploited marine ecosystems. The ability to move a whole ecosystem toward a desired dynamic state by altering fishing targets, catches, and effort is limited because many variables are unknown and many ecosystem drivers are not under our control. These variables can be unique to each system; managers must appreciate the level of ignorance about the systems, the limits of our forecasting capability, and the consequent uncertainty generated by capricious, extreme events.
Data perform a central and dual role as indicators of ecosystem performance, while also providing input variables to complex models, such as those representing ecosystem dynamics and management scenarios used to assess tradeoffs in decision making. New data can challenge existing theory and facilitate wider application of model-based scenario analyses; but changes in what we collect and
how we manage data will be necessary. Finite resources require us to reassess available data and to revisit existing monitoring programs and data resources. However, we must prioritize data needs both for near- and long-term efforts with complex, multiple objectives.
Promising results have come from analyses and models at levels of synthesis above individual populations and individual food-web components. Some of the data, models, and knowledge are now sufficiently developed to be applied to evaluating ecosystem approaches to management. However, the derived management actions inevitably will be experiments in themselves—adding to a growing body of knowledge and creating an environment of adaptive management.
Clearly, moving forward requires science, management, and policy interacting constructively in synergy. We need to think outside of the box: incorporate new ideas, new analyses, new models, and new data, and perhaps most importantly, establish the social and institutional climates that will catalyze creative, long-term, comparative, and synthetic science of food webs and communities applied to exploited ecosystems. Data needs in support of ecosystem-based management will likely be more than the simple sum of currently available single-species information. Diet data and strengths of linkages between species and life-history stages will be as important as population abundance data. A rich array of social science, economic science, and policy considerations is essential because many more tradeoffs among ecosystem components and stakeholders are likely to be apparent. Science will be challenged to provide policy-relevant options in this new context; managers will be challenged to broaden their concerns and experiment openly; and policy makers will be challenged to act unselfishly in behalf of the broader community of people who value and depend on ocean ecosystems.
This chapter presents the need for research on food-web interactions, spatially explicit data, complete historical time series, and scientifically useful definitions of ecosystem boundaries. There is also discussion of future needs in valuation of nonmarket services, fishing behavior, and integrated bioeconomic modeling.
IMPROVING ECOSYSTEM MODELS AND SCENARIO ANALYSIS
Choosing goals and standards that would be appropriate for food-web and community management is a worthy but formidable challenge. As discussed in the previous chapter, such approaches will be important in any comprehensive fisheries management planning and will likely include consideration of a wider variety of ecosystem services than the food and economics of fish yields. However, if model-based scenario analysis is to be used more extensively in fisheries management applications, many variables must be better defined and understood to reduce the inherent uncertainty.
Models in marine science and fisheries range from whole-system ecosystem models to single-species population models; many have been around for decades,
at least in some form. A variety of modeling approaches are now available to address the dynamics of marine food webs from multi-species fisheries models (May et al. 1979, Hollowed et al. 2000), to Ecopath/Ecosim/Ecospace (Polovina 1984; Walters et al. 1999; Pauly et al. 2000; Christensen and Pauly 1992, 2004). While some of these have received more attention and application than others, there will certainly be use for many different kinds of models, including those based on the underlying knowledge of the systems, the extent of resource exploitation, the assumptions made within the model architecture, and the goals for management.
Currently, the most popular multi-species models focus on community ecology and food-web interactions (Crowder et al. 1996, Mangel and Levin 2005). This approach avoids the problems of managing single-species populations as though they can be isolated from key species with which they interact, particularly their prey and predators. But the emphasis on community ecology and food-web interactions also avoids building in the overwhelming complexity of marine ecosystems, including interactions and linkages that may or may not be important to the dynamics of species under management.
Food-web modeling approaches can focus on all food-web connections, or on those that transfer the largest proportion of energy and materials, or on those that exhibit strong interactions (sensu Paine 1980) in which the “unit” is made up of individuals. Although interaction strength in the field initially was quantified only by manipulative experiments (Paine 1980), insights from a large number of experiments and “natural experiments” driven by climatic variation (Francis et al. 1998) or heavily exploited fisheries have revealed strong interactions in some marine ecosystems (Frank et al. 2005). It is currently unknown whether the pelagic trophic cascades of Estes et al. (1998) and Frank et al. (2005) could be modeled, and hence predicted. But with increased understanding of per capita effects or population effects, it may be possible to account for the dynamical changes at a variety of trophic levels, and thus legitimize the concept of ecosystem-based management.
Estimates of per capita interaction strengths have been made often over the years. They were mathematically formalized by Lotka and Volterra in the 1920s (Lotka 1925, Volterra 1926), led directly to Gause’s (1934) experiments, and then led to a discussion of application in MacArthur (1972), Paine (1992), and Laska and Wootton (1998). In a completely described trophic interaction, the reciprocal effects of predator on prey and prey on predator are quantified. Such information would permit questions on how changes in predator and prey abundance influence their respective fecundities and mortalities. Numerical and functional responses can be considered as can the consequences on predator performance or predator switching (Murdoch 1969). Further, it is plausible that
the ecologically important consequences of ontogenetic changes in predator diet (Hardy 1924, Hutchinson 1959) can enter quantitative ecosystem models.
Can the necessary estimates be developed in a manner useful to the management of fisheries? Most signs are positive. Wootton and Emmerson (2005) review the evidence that studies on per capita effects typically identify a few strong and many weak interactions. The question of whether such information justifies the simplification of food-web studies to a limited number of “important” species is not trivial. More significantly, two recent studies suggest that per capita estimates can be obtained from combined field observations and measurements; experimental manipulation is not necessary. Wootton (1997) develops this approach for a rocky intertidal assemblage in which three bird species consume at least twenty-three taxonomically varied prey. Bascompte et al. (2005) examine a Caribbean food web of 249 species dominated by fishes; interaction strength is calculated as prey biomass consumed per predator per day. However, theses two studies were only possible because the web architecture and species diets were known. Both of the studies involved a significant benthic component and were accessible to direct observation at multiple trophic levels. As such, many of the limitations imposed by equally complex but entirely pelagic or less well studied systems were avoided.
But it is important to note that these food-web interactions can change drastically over time. In fact, the Bascompte et al. study (2005) analyzes stomach contents of fishes from the 1950s and 1960s. Yet species commonly used as food by large predators such as sharks and groupers have virtually disappeared from Caribbean reefs, so the topology of links as well as interaction strengths are possibly very different today. Thus, even if these large predators recover, their diets will most likely be different. These concerns underscore the need for improved data on evolving diets due to changes in connected species.
Many models simply ignore, of necessity, the generally unknown ecological complexities characteristic of all lower trophic levels. The consequences of aggregating many seemingly comparable taxa into “trophospecies” are unknown, but it artificially simplifies the specific relationships in the food web and potentially disguises important factors. Yodzis (1982) and Martinez (1991) explore the consequences of aggregation in real food webs. The dynamics of lower trophic levels on which the fished species depend is often poorly known. For instance, Ward and Myers (2004) report increases in squid and fishes (pomfrets and mid-water sting rays) in mid-Pacific areas where apex predator presence has been reduced. Does this hint at a possible cascade? Is there a measurable ecological effect of an increase in these secondary prey species? There can be no answer in the absence of data on these species, such as population abundances or their individual natural histories.
Data on age specific diet continue to be a priority because this information is essential to identifying significant trophic linkages and per capita interaction
strengths. Equally vital are natural (and fished) mortality rates obtained from standard surveys and tagging studies. In addition, the per capita interaction strength often needs to be calculated in biomass units to reflect the changes in size structure imposed by fisheries or simply interspecific differences. So another concern is that although trend data are available for landings of many fished components, relating landings to biomass in the ocean remains a problem.
Tying Data to Spatial Scales
Interactions in the marine environment occur in a spatial-temporal matrix, and strengths of food-web interactions depend on the species being in the same space at the same time. Fisheries scientists usually deal with large spatial scales, whereas the relevant data are obtained from finer, spatially explicit regions. While it may have been sufficient to substitute variability observed over time in matters concerning often relatively local single-species management approaches, it will be necessary to characterize variability both in time and space to reduce the uncertainty at the ecosystem level. Space has been called the final frontier in ecological theory (Kareiva 1994), and spatial analyses may be one of the greatest obstacles faced by fishery managers (Caddy and Garcia 1986, Garcia and Hayashi 2000).
Developments in measurement and methods of analysis make explicit consideration of spatial variability possible. The general availability of global positioning systems permits much greater accuracy in determining where samples are collected than previously possible. Geographic Information Systems (GIS) provide a means for storage and interpretation of spatially explicit data (Keleher and Rahel 1996, Johnson and Gage 1997, Wiley et al. 1997, Clark et al. 2001). Remote sensing allows simultaneous measurement of environmental variables over large scales (Cole and McGlade 1998, Polovina et al. 1999). The recognition that population dynamics are influenced by large-scale oceanic patterns and decadal climate patterns such as the El Niño-Southern Oscillation and the Pacific Decadal Oscillation has aided in explaining variation and trends that may appear random at a local level. At smaller scales, animal positions and movements can be related to fixed and dynamic oceanographic features (Magnuson et al. 1981), but improvements in current tools for marine geospatial analysis are needed to achieve these goals.
Ecological data show that context is extremely important. The ability to tie landings and fisheries-independent data to finer spatial scales will allow researchers to examine local population trends and localized depletions. Furthermore, better understanding of the spatial distribution of stocks, life histories, and species interactions may allow for a new generation of management techniques based on this new knowledge, including that gained from the creation of transient protected areas.
Defining Ecosystem Boundaries
Complex biological systems cannot be reduced to a single diagnostic scale (Levin 1992), thus multiple spatial scales are applicable in research and management endeavors. Ecosystem-based management acknowledges the significance of a multi-species perspective and implies that the spatial boundaries defining the system of choice can be identified. The concept of an “ecosystem”—roughly defined as a biotic assemblage and its environment—was originated by the English botanist A.G. Tansley and given its modern face by R.L. Lindeman (Golley 1993). This conceptual evolution derived from the study of partially closed, physically bounded environments such as lakes, ponds, grasslands, and forests. But application of this definition remains problematic in the marine environment due to physically large systems that lack distinctive boundaries and are “open” in the sense that nutrients and species can be exchanged over great distances.
The growing recognition of important exchanges between distinctive ecosystems further blurs the concept. Landscape ecology (Turner et al. 2003) recognizes the importance of interactions among “patches,” which may be a more reasonable construct. For example, migratory species move through and between identifiable parts of the ocean. Nutrient-laden terrestrial run-off impacts marine coastal ecosystems (Rabalais et al. 2002), and marine nutrients transported by birds to Aleutian Islands power the productivity of those systems (Croll et al. 2005). The increasingly recognized reality of such “subsidies” (Polis and Hurd 1996), transported across the bentho-pelagic boundary, represents yet another challenge to management of marine resources.
The importance of the term “ecosystem,” as in ecosystem-based management, is that it acknowledges numerous interacting species that must be managed simultaneously. Defining precise marine ecosystem boundaries is a biologically unrealistic, unattainable goal. Instead, ecosystems may be better defined using knowledge of food-web structure and embedded interactions, with the boundaries determined by management goals, financial constraints, and other realities imposed by political considerations.
Applying Models to Management
Testing new modeling approaches in food webs and communities where fishing occurs is an important endeavor. The challenge in ecology and fisheries is to forecast ecosystem behaviors in response to management manipulations when we are unsure of the baseline and have little idea of what the future will look like. However, the alternative to no action and walking away from the issues owing to limited data or inadequate models is not acceptable. Iterative development—that includes trial, monitoring, and eventually feedback—will most certainly be needed.
Building models relevant for fisheries management requires the cooperation of many specialists and the integration of information from many sources. Most
likely, this is best done over series of workshops that bring together people with different expertise. Workshop participants with interests beyond the fishing industry will help to bring broader management objectives to the design of ecosystem management approaches. Furthermore, just as is the case of single-stock policy evaluation, seeking input from a wide variety of stakeholders is essential for identifying important tradeoffs to evaluate the feasible candidate policies.
Similar working groups can be used as a template for the formation of such modeling endeavors. For example, the National Center for Ecological Analysis and Synthesis (NCEAS) is currently developing a modeling and data integration framework for ecosystem-based management and applying that framework to a case study from coastal California. NCEAS is connecting experts in the modeling of natural and human systems with policy specialists to forward the goal of developing a policy-relevant modeling approach that includes the dynamics of social, biophysical, and economic components of the ecosystem and critical feedbacks among them. The Long-Term Ecological Research system sponsored by the National Science Foundation has also conducted similar workshops, which gathered investigators from several distinct sites to model and test new ecological analytical techniques.
ANALYZING HISTORICAL TIME-SERIES DATA
Current data gathering and analytical approaches for fisheries management will be necessary but not sufficient to support effective management of fisheries impacts on food webs and communities. Further progress is being made through studies that are reconstructing the history of exploited ecosystems, using sophisticated and creative data analysis and synthesis tools to extend data well back in time (Pauly et al. 1998a, 2005; Myers and Worm 2003, 2005b; Jennings and Blanchard 2004; Rosenberg et al. 2005). The focus of these studies has been on the history of fishery impacts, which provides only part of the information required to develop a greater mechanistic understanding of ecosystem function.
Because of their availability and length of time series, landings data are most often used for both historical, retrospective analyses and current multi-species, dynamical ecosystem models. Often the sampling extent is long; for instance, landings of five species of Alaskan salmon date back to 1880 (NRC 2003). However, numerous problems are recognized; landings may or may not reflect population numbers in the wild, can be subject to often unknown effort, and will be influenced by changing demand and catch technologies. Nonetheless, such data sets are increasingly acquired, resolved to varying degrees, and made accessible to interested parties.
Historical data—both long time-series and specific snapshots—have recently been used to examine severely reduced components of food webs and to evaluate the potential linkages of these components to consequent population changes. These data can allow glimpses into the past, possibly even pre-exploitation. Thus
the roles particular species played in food-web dynamics prior to their decline can be examined. Including these time-series or snapshot data in ecosystem and food-web models may provide the best approach to synthesizing long-term data and identifying alternative future scenarios to evaluate policy choices.
Frank et al. (2005) provide a second example of research integrating disparate time-series data in their description of combining a trophic cascade involving Atlantic cod (data from landings), pelagic fishes, shrimp and snow crab (catch numbers standardized by effort), zooplankton size structure (from plankton recorders), phytoplankton (from a color index), and nutrients (nitrogen). Analysis and integration of these types of data will not only continue to inform managers about the dynamic challenges they face, and the consequences of mismanagement, but will also be invaluable for the application of ecosystem models to novel systems.
Some of the other efforts to recover information of past population sizes and distributions have generated controversy (e.g., Springer et al. 2003, Palumbi and Roman 2004). Questions always remain about their interpretation; for instance, how accurate are Jackson et al.’s (2001) historical reconstructions, or how abundant were Atlantic cod 200 years ago (Rosenberg et al. 2005)? A variety of techniques are involved, some qualitative (e.g., site photographs), others quantitative (e.g., standardized time series). None are without flaws, but the quest to reveal historical levels of exploited populations is vital to determining baselines around which to establish fisheries management and recovery goals.
Conserving and Accessing Data
An information system is needed that increases access to historical data, incorporates data from disparate sources, and supports the policy-making process. Access to data from diverse sources will facilitate the transformation of these data into useful information that leads to model-based scenario analysis and informed decisions. Large-scale modeling of marine ecosystems requires facile integration with data from multiple sources. In addition, new sensors are being incorporated into ocean observing systems deployed across large scales that are capable of generating massive data streams. The use of these data will demand the appropriate information technology infrastructure for data storage, management, access, and analysis, often in near-real time. Data streams that incorporate fine-scale, spatially explicit data will be particularly useful to improve understanding and to enhance management. Synthetic scientific endeavors can be hindered by the difficulties inherent in discovering and accessing data across heterogeneous systems.
Furthermore, collaboration technology is needed to support interactions in science and management. Information technology infrastructure is an essential component for generating ecosystem and food-web analyses for policy scenarios. Ideally, information systems would provide the scientific community with data
discovery and access capabilities as well as the documentation of the data that fosters the interpretation and integration of multiple data sets.
Development of an information technology infrastructure that would provide a highly functional platform for scientific endeavors requires meeting challenges in a number of areas: data discovery, access, evaluation, and integration; a framework for modeling that provides repositories and documentation for models and model output; frameworks for designing and executing complex workflows; and advanced analysis and visualization tools, particularly for spatially explicit data. A prior group that examined future data needs of ocean science identified the following issues that need to be addressed: (1) technical support for maintenance and upgrade of local information technology infrastructure resources; (2) model, data, and software curatorship; and (3) facilitation of advanced applications programming (OITI Working Group 2004).
CONTRIBUTIONS FROM SOCIAL AND ECONOMIC SCIENCE
In the United States and in other countries with modern fisheries management systems, the task of managing just a single species should require as much information about socioeconomic aspects as it does information about biological mechanisms. As managers gradually adopt multi-species and ecosystem-based management methods, the social and economic information needs evident in current single-species systems will be amplified. Thus, there is a need for new research from the social and economic sciences to better inform future management processes. Three areas of particular interest include valuation studies, integration of biological and economic models, and examination of governance options for managing ecosystem goals.
Valuing Nonmarket Ecosystem Services
One of the most important under-researched areas pertaining to marine ecosystems is the issue of ecosystem services valuation. The notion of ecosystems services is a broad and encompassing term, intended to include more familiar marketed commercial services such as the value of fish harvested, but also the value of components and characteristics of ecosystems that are not consumed or marketed (for a taxonomy, see Grafton et al. 2001). Understanding and measuring the values of actual and prospective portfolios of services is critical to making sound policy decisions about the use of the marine environment (Lange 2003). Various social science disciplines have focused attention on how humans form values, how those values change, and how values from different experiences compare and scale vis-à-vis one another. Economics has a well-developed methodology for measuring and scaling economic values that borrows from cognitive psychology and survey research theory (Freeman 1993, NRC 2004). Much of the valuation research over the past decade has been devoted to measuring nonmarket
values, or values that humans place on ecosystem services that are not tied to any market transactions.
While there have been numerous applications of nonmarket value methods to the services provided by terrestrial systems, there are few studies devoted to marine systems (Barbier 1994, Swallow 1994). The kinds of questions that need addressing are: how do the values provided by on-site, nonconsumptive services (e.g., diving, tourism, education, research) compare with on-site consumptive services (e.g., commercial and recreational harvesting, oil and gas production, seabed mining)? What are the determinants of off-site nonconsumptive service values (e.g., existence values, posterity benefits)? How do humans perceive the value of intact and relatively pristine marine environments? What intrinsic characteristics of systems do humans value; what losses are perceived when systems lose those functions and characteristics? How do human values relate to the rarity and uniqueness of ecosystems and/or assemblages of species? What human values are associated with stability, resilience, diversity, and other similar characteristics of intact systems? These are but a few of the issues that have not been explored but that need further investigation in order to make informed social decisions about the possible outcomes of fisheries policies.
Fishermen’s Behavior and Fleet Dynamics
As ecosystem-based management methods are implemented, there will be a significant need to model and predict the behavior of fishermen and to integrate this information into ecosystem-based biological models. The “second generation” of ecosystem models must account for changes in fishing capacity—including the effects of economic conditions, changes in climate, changes in market and trade conditions, advances in and increased costs of technology, and changes in abundance (e.g., those changes associated with biological interactions and regulatory changes).
Minimal research exists on determinants and mechanisms of fleet behavior. There is some work on coarse-scale, long-horizon choices made by fishermen and some research on fishermen’s aggregate entry/exit behavior in fisheries (Wilen 1976, Bjorndal and Conrad 1987). Results from empirical studies of entry/exit (fleet size) behavior under open access confirm that fishermen enter when current and recent profits are positive and exit when profits are negative. Even fewer studies have examined capacity creep or “capital stuffing” when access is closed because of limited entry (Pearse and Wilen 1979).
Economists have studied the determinants of and speed at which fishermen switch target species and gear use over various time scales from daily decisions to between-season decisions (Bockstael and Opaluch 1983). These studies show that the main determinants of switching decisions are profits, so that price changes play as much a role as catch rates in determining target species and gear. The studies also find sluggishness in the adjustment between species/gear combina-
tions in the sense that targeting and gear switches take time to fully unfold. Individual micro-level behavior at fine time scales (e.g., daily or weekly) is modestly responsive to short-term catch and price changes, but much more responsive over longer periods when catch rates and price changes persist.
Understanding the spatial behavior of fishermen will also become increasingly important. In one of the first empirical studies of spatial behavior, Hilborn and Ledbetter (1979) conclude that a British Columbia purse seine salmon fleet was composed of a mobile fleet that adjusted rapidly to changes in revenues over space and a sedentary fleet that seemed to enter and participate when revenues exceeded some threshold level. Eales and Wilen (1985) show that, in repeated daily decisions, pink shrimp fishermen select patches in a manner consistent with forecasts of average revenues based on the previous day’s data. The same study shows shrimp concentrations to be ephemeral and that fishermen use information-sharing mechanisms to expand their spatial search. A few studies use panel data on a very fine scale (daily or hourly) to study short-term location and fishing participation choices. Smith and Wilen (2003a) also estimate a model of daily location and participation choice by sea urchin divers, exploring differences between short (daily) and longer-term (monthly) spatial behavior. Spatial responsiveness increases in the long run as fishermen switch home ports and home regions (Smith and Wilen 2003b).
Understanding how fishermen react and behave as a result of different management actions is essential when developing ecosystem-based management methods and solutions. Investigations of behavior, changing in both time and space, are greatly needed in order to parameterize models of fishing effort determination. The social and economic models within ecosystem-based management need to become as robust as the biological models in that system. Furthermore, better understanding is needed of how behavior and choice affect the interactions of fisheries and other sectors. This knowledge could lead to better decision making on the tradeoffs between sectors and uses and could create greater acceptance of regulatory measures.
Integrated Bioeconomic Modeling
Moving toward rational assessment of various ecosystem-based management alternatives requires understanding both the biological linkages connecting various species and the environmental medium, and also the manner in which humans have an impact on systems and the values placed on various impacts. This understanding will emerge only by developing and examining integrated biological-social-economic models, or models that explicitly link biological and human modules. Two different modeling approaches need further development. The first is what might be called impact analysis modeling. Impact analysis attempts to measure the first-order consequences on humans of various policy-induced changes in ecosystem characteristics. An example would be a simple
cost/benefit computation of the effects of various restoration strategies in a multi-species system where the interconnections are reasonably well known. Impact analysis generally assumes passive response of humans to policies.
A second, more sophisticated kind of integrated bioeconomic modeling approach incorporates active behavioral assumptions about humans (Wilen et al. 2002). For example, one might model fisherman’s fishing location choices with a model that incorporates the economic motivations behind those choices, and then use that model to predict how fishermen would reallocate after establishment of a marine reserve (Holland 2000, Smith and Wilen 2003a). Behavioral models allow analysts to predict the biological and economic consequences of policy measures by understanding how human behavior is altered with policy changes, and how those behavioral changes impact ecosystems. Few fully integrated bioeconomic modeling studies have analyzed policy options using behavioral modeling approaches (Carpenter et al. 1999, Carpenter and Brock 2004).
Furthermore, little integrated bioeconomic analysis has been based on the fundamental production relationships among interacting species and between species’ health and habitat conditions and characteristics. We know little about how disturbance and destruction affect the fundamental production relationships among dependent organisms. For example, how does urchin harvesting affect groundfish via its affects on kelp forests? This kind of understanding is critical to more effectively manage competing consumptive service production from ecosystems. Aside from habitat connections, we know little about even simple multi-species interrelationships in systems that produce multiple and competing marketed services. For example, how does the commercial harvesting of menhaden for fish oil affect participation and valuation per recreation day in the valuable sports fishery for striped bass? How does nutrient pollution and runoff from agriculture affect higher-level trophic organisms via their link to lower level organisms in dead zones? These are all questions that involve linkages between components of marine ecosystems, which then translate into (mostly) on-site consumptive market value services. Understanding them requires integrated bioeconomic modeling and calibration in ways that capture important biophysical linkages, translated through to human impacts via economic market valuation methods (Söderqvist et al. 2003).
Institutional Options for Ecosystem-Based Management
Little research effort has been devoted to answering what kinds of governance and management institutions are best for conducting ecosystem-wide management for marine systems (Rudd 2004). As the Pew Oceans Commission (2003) and the U.S. Commission on Ocean Policy (2004) reports suggest, governance issues will be critical for adopting ecosystem objectives and for implementing policies to carry them out. A wealth of related thought in the social science and management science literature addresses components of this question, but less
research puts issues in the context of marine ecosystems specifically. In essence, marine ecosystems can be thought of as having the capability to generate an almost infinite variety of ecosystem services, depending upon the structure and characteristics of the system. But society can alter ecosystems and ultimately choose the portfolio of services provided from among a feasible mix associated with direct consumptive services and nonconsumptive services (van Kooten and Bulte 2000).
The more important dimensions of this question for fisheries are those that deal with accounting for species interactions, incorporating nonconsumptive use values, making tradeoffs among and between user groups, and generating stewardship values (Ferraro and Kiss 2003) among users of consumptive services. These, for the most part, are extensions and elaborations of issues currently confronted by managers guided by single-species approaches. It would be speculative and probably of no use to point to specific, finely defined research that would solve these institutional design problems. On the other hand, a variety of institutions currently operating in various parts of the world are tackling many of these problems, with varying degrees of success. Many of these are conventional top-down systems, but a large number of experiments with decentralized systems are also in progress, including individual transferable quotas (ITQs), individual quotas (IQs), harvester cooperatives, community cooperatives, and territorially defined cooperatives. At a minimum, a sensible first step would be to carefully summarize experience with other management structures, including those that are devolved down to varying levels of local control. Much can be learned from existing examples of rights-based systems, many of which are currently also incorporating nonmarket values (like their top-down counterparts) and addressing issues such as interspecies conflicts and quota setting, voluntary interspecies quota trading, market based bycatch schemes, and habitat damage reduction measures.
MAJOR FINDINGS AND CONCLUSIONS FOR CHAPTER 5
Greater knowledge of food-web interactions, including interactions at lower trophic levels, will be essential to improving ecosystem and food-web models. Model development is based on knowing species interactions and the strength of these interactions. By necessity, many species are ignored in current models or are grouped together based on trophic level. More research is needed to determine whether these simplifications are sound, and whether current models have ignored species with strong interactions in the system. Collecting baseline data on a number of non-target and lower trophic level species may also aid in model development. Without these data it is difficult to determine the role of these species in the ecosystem when their abundances and interactions change due to fishing pressure.
The development of new ecosystem and food-web models will be a highly interactive process that will require input from many disciplines. Collaborations of numerous scientists, managers, and stakeholders in a workshop setting will most likely be the best approach to develop, test, and apply new models. The incorporation of social and economic scientists at the beginning of this process will ensure that these issues are not left until after the biological model is created.
Combining biological and spatial data will allow both large-scale population trends and changes at finer scales to be monitored and understood. Patterns of interaction, and the strength of these interactions, vary in time and space. If data are collected in both dimensions, the models created for management scenarios will better account for this variability. In addition, understanding interactions on various scales can help to define what the ecosystem might be. Although the term ecosystem is in general use, specific boundaries in the marine environment are difficult to identify. Rather, defining an “interaction space” with boundaries determined by nonecological considerations may be a more workable solution.
Assessing historical data can continue to lead to new insights about former species abundances and interactions. Comprehensive analyses of existing data can reveal ongoing changes in target species as well as habitat and non-target species that are thought to indicate ecosystem status. Landings data, narratives and descriptions, fisheries-independent data, phytoplankton records, satellite data, and archived specimens can all be evaluated to develop insights about prior ecosystem status. Data need to be made available at the highest possible spatial resolution. Highly aggregated fisheries data do not allow appropriate integration with environmental data.
Currently, fisheries data are fragmented and dispersed, which is slowing the use of these data in comprehensive analyses. If historical data are to be combined and reassessed in new ways, it is essential to collect and provide open access to these data. It will be particularly important to access a wide variety of data and information when developing and applying model-based scenario analysis. It is important not to underestimate the need for information management support for collaborative efforts to create workable models.
Future protocols for ecosystem-based fisheries management will place new demands on social and economic analyses to determine tradeoffs and make strategic decisions. If value-based tradeoffs are to be made when determining fishing harvest strategies, there must be an understanding of the value assigned to non-target species, ecosystem functioning, nonconsumptive uses, and large-scale processes such as climate regulation and nutrient cycling. Furthermore, combining biological and socioeconomic information into integrated models will allow for the explicit consideration of these sometimes conflicting values.
Various management institution options are available that change the race-to-fish incentives for fishermen, and encourage stewardship in single-species systems. Individual quotas, harvester cooperatives, community cooperatives, and territorially defined cooperatives exist in a handful of fisheries in the United States and in other countries. But research is needed to understand how these systems affect incentives in a multi-species setting, and how they might be adapted to handle more inclusive goals associated with fisheries management in the United States.