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IMPROVING THE COLLECTION, MANAGEMENT, AND USE OF MARINE FISHERIES DATA 1 Introduction Some people fish to provide food for themselves and their families, others for commerce. Some fish for recreation, whereas others fish to carry on traditional ways of life and ceremonial customs. In the sense most relevant to management—and according to the Magnuson-Stevens Fishery Conservation and Management Act of 1976—fisheries include the humans who harvest fish. In addition to the direct benefits to humans, fisheries management must also consider ecosystem-level objectives such as preserving essential fish habitat and maintaining diversity of marine organisms. Because fishing by its nature causes some mortality to fish—even if the fish escape the gear or are caught and released —and because the summed activities of all individuals who fish may impact fish populations, it is critically important to understand and manage fisheries to ensure continuing benefits to all. The Magnuson-Stevens Act provides a set of 10 national standards for fishery conservation and management (Sec. 301 of the Act), to which the fishery management plans developed by regional fishery management councils1 must adhere. Some of these standards can be formulated in quantifiable terms (although this is not always done). For example, National Standard 1 requires estimation of optimum yield and overfishing thresholds, National Standard 7 mandates minimizing costs of management, and National Standard 9 requires minimizing bycatch mortality (so these must be measured). Adherence to the national standards can be evaluated on the basis of data collected to characterize biological, economic, and social aspects of fisheries and the effects of management on these characteristics. Even with the best data, fisheries and fisheries management are subject to uncertainty. We can never be completely sure of the current population abundance of a stock or how it will change. Environmental variables that affect the growth and reproduction of fish stocks are frequently unknown and are always difficult to both measure and predict. Even if the environment were 1 The exclusive economic zone of the United States is divided among eight regions by the Magnuson-Stevens Act for the purposes of fisheries management. The fisheries of each region are managed by a separate fishery management council, as specified in Section 302 of the act. The regional councils are composed of designated state and federal fishery officials, as well as commercial fishermen, recreational fishermen, and environmental advocates nominated by state governors.
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IMPROVING THE COLLECTION, MANAGEMENT, AND USE OF MARINE FISHERIES DATA predictable, the effects of interactions of fish stocks with each other and with the environment are largely unknown. The expectations of fishermen, scientists, and managers can differ quite drastically from reality because of such uncertainties. The major goal of data collection is to support and enable biological, economic, and social analysis that will reduce these uncertainties, so that harvest can be sustained at the highest level that is commensurate with other management goals, such as maximizing long-term potential yield. NMFS (1999) estimates recent average yield from U.S. fisheries is 33% below the long-term potential yield. Fisheries management includes three activities on an ongoing basis: (1) assessing the condition of a fish stock2 in the context of its place in the ecosystem and in connection with the fishery it supports; (2) developing and implementing regulations to use and sustain the fish stock and the fishery; and (3) monitoring the biological, economic, and social effects of regulations. These activities may each require biological, economic, and social data. Because fish are publicly owned and publicly managed resources, the government has a stake in collecting biological, economic, and social data needed to encourage effective management. However, collection of private and proprietary social and economic data is sometimes viewed by the industry as being too intrusive (PFMC, 1998b). The legal precedents for the government's role in protecting resources for the good of the nation, the government's public trust responsibilities, are not as developed for marine resources as for terrestrial resources. A good summary of the legal precedents is given in NRC (1999b). A fundamental goal of data collection and quantitative stock assessment processes is to estimate current and future stock abundance and the effects of fishing activities. Data management is an important link between the collection and assessment processes, because data of high quality must be available in a timely manner and accessible form to be useful for assessment scientists. High-quality biological, economic, and social data are essential for evaluating the effectiveness of regulations and, when necessary, designing new regulations. A gulf commonly exists between the beliefs of fishermen and those of managers and scientists in terms of the current status of marine fisheries, the existence of problems in the fisheries, and how such problems can be solved. The committee will explore the nature of these beliefs in the following chapters. Differences in viewpoints related to fisheries problems frequently arise because3 different fisheries stakeholders operate with different time horizons. Commercial fishermen often have to focus on cash flow within a given year to meet current expenses. Managers may evaluate the costs and benefits of different management options based on discount rates set by the government at 7% (OMB Circular A-94). Scientists and environmental groups focus on sustaining stocks indefinitely for both biological and economic reasons. fishermen take pride in their ability to catch fish and in their good working knowledge about fish behavior and distribution. They are frustrated by scientists who seem to be unwilling or unable 2 “A fish stock can be defined as all fish belonging to a given species that live in a particular geographic area at a particular time, that is, all individuals actually capable of interbreeding. For practical management purposes, a stock is often further defined by political boundaries. That is, the management unit, often still called a stock, includes those members of a biological stock that are under management by a single governmental agency. Units so defined, however, do not necessarily reflect meaningful biological entities or the spatial heterogeneity of fish distributions.” (NRC, 1998a, p. 8) 3 These points are obviously generalizations and are based on statements made by participants at the committee's meetings, not on systematic sociological research on fishery stakeholders.
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IMPROVING THE COLLECTION, MANAGEMENT, AND USE OF MARINE FISHERIES DATA to utilize this knowledge and expertise. Scientists, on the other hand, frequently consider data from commercial and recreational fisheries to be biased (i.e., collected where fish density is high rather than representative of the full range of abundance); are skeptical of estimates of stock abundance that may be inflated due to technical innovation in fishing gear and methods; and believe that data from fisheries are qualitative (rather than quantitative) in nature. Scientists may have personal experience of receiving incorrect information from fishermen. Fishermen suspect that scientists present the most pessimistic results in assessments. Scientists are reluctant to include potentially biased data in their assessments because they have learned through their training and experience that such data may result in incorrect and misleading assessments. Fishermen are sometimes reluctant to share their information, including accurate logbook data, because they have learned through their experience that such data often are ignored or can be used against them. even when scientists use commercial data (e.g., catch, fishing effort, and catch-at-age data) these data are filtered through mathematical models in ways that make it difficult for non-scientists to understand the resulting output and its interpretation. Even in the cases in which industry generally understands the use to which their data are put, a knowledge of the likely errors in the data (e.g., suspected misreporting by other fishermen)—as well as the length of time between data gathering and the assessment—may make them suspicious of the assessment results. scientists treat an assessment as an accurate representation of a stock that can provide a basis for action; some fishermen appear to consider assessments as no more than an opening bid in negotiations to set total allowable catch. fishermen are made acutely aware daily of the variability of nature and usually ascribe stock changes to such variability, rather than to constant fishing pressure. Conversely, scientists interpret declining trends in fish populations as being exacerbated by fishing pressures. sustaining individual stocks and maintaining ecosystem structure and function may require that large standing stocks be left in the sea for reasons that may not be obvious to fishermen. different users may hold different goals for the fishery. For example, commercial fishermen may seek to maximize total biomass harvested (for the food market), whereas recreational fishermen may seek to maximize size of individual fish harvested (for trophies) and tend to want to maintain high standing stocks so they are more likely to catch fish on any particular fishing trip. These differences in viewpoint among commercial fishermen, recreational fishermen, and scientists are often central to problems of stock management and conservation. Fishermen tend to be most vocal when management is most restrictive. Because of the uncertainty that always exists in stock assessments, fishermen may attack both the science and the stock assessments, arguing for risk-prone management decisions and total allowable catches (TACs) at the high end of assessment ranges (Sissenwine and Rosenberg, 1993). This can result in a downward spiral in the fishery, as increasingly dire warnings are met with increasingly strident objections until, ultimately, all doubt is removed by a fishery collapse. Some complaints from fishermen are valid and have a basis in fact, and in some cases fishermen are correct and NMFS could learn from them. In other cases, different opinions of fishermen and scientists arise from fishermen's misunderstanding of fish population dynamics that needs to be addressed if fishermen and managers are to become partners in sustainable fisheries management. However, even if all participants in fisheries have the same understanding of the problems, differing incentives will lead participants to argue for different solutions. This report examines existing practice in fisheries data collection, management, and use in the United States and recommends how these important functions of fisheries management can be carried out more effectively.
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IMPROVING THE COLLECTION, MANAGEMENT, AND USE OF MARINE FISHERIES DATA DATA COLLECTION Biological Data A common problem in understanding and managing marine fisheries is that the fish populations are not directly observable. As a consequence, key pieces of information that determine the type of management, such as the size of the stock and the rate at which fish are being removed, rarely can be observed directly and often can only be determined in a relative sense. For example, a 10,000 metric-ton catch of a species might have been taken by removing 1 percent of a 1 million metric-ton stock or 50 percent of a 20,000 metric-ton stock. Fisheries assessment science concerns itself with determining which is most likely the case from the available data. It uses the resulting estimates to provide biological advice to fishery managers. Many kinds of biological data are useful for fisheries management. An integrated age-based4 assessment (see Box 1-1), for example, requires accurate estimates of the following biological information characterizing the population and the way the fishery interacts with it: Total fishing mortality = landed catch + discarded catch*percent discard mortality + mortality of unlanded fish caused by gear Proportion of catch comprising each age of fish Relationship of weight and age in the population Proportion of fish of each age in the population that are mature Recruitment indices5 Indices of abundance such as catch per unit effort from commercial vessels or from a scientific survey Mortality rate from causes other than fishing (natural mortality) The effort required to catch a given number or weight of fish is critical to understanding how catch is related to fish population abundance. Data are collected directly from commercial and recreational fisheries (these provide fishery-dependent data)6 and through various statistically designed surveys (these provide fishery-independent data) conducted by state and federal fishery agencies and some international commissions. Fishery-independent data are collected to provide measures of relative abundance that are not confounded by the commercial and recreational strategies of targeting areas where fish densities are highest. Collection procedures are designed according to sound sampling principles. Fishery-dependent and fishery-independent data collection methods will be discussed in greater detail later in the report. Other auxiliary information about fish populations can be used in assessments. These include egg surveys, which count the numbers of fish eggs of a species from a specific area of the ocean and use these numbers to estimate the size of the population of spawning females. Acoustic 4 Age-0 fish are less than 12 months old; age-1 fish are 13-24 months old; and so on. 5 Recruitment is the addition of new individuals to the population and is typically viewed as individuals becoming vulnerable to capture as they grow or change their behavior as they mature. 6 Fishery-dependent data can be collected from anyone who harvests fish—recreational, commercial, ceremonial, and subsistence users. These groups have very different motivations, and if we are to make use of data collected in a fishery, we must understand and account for these motivations. More specifically, fishery-dependent data include catch (and appropriate descriptions of size, age, sex, and location of the catch), effort (and appropriate descriptions of the fleet or anglers applying the effort), catch per unit effort, fish prices, information on the costs of fishing, the technology employed in fishing, the number of jobs generated, the net economic value of the fishery, the distribution of these items by geographic area and time, and the effects of fishing on habitat. Fishery-dependent data can be obtained from onboard observers, vessel monitoring systems, logbooks, and port agents, as described in detail in Chapter 3.
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IMPROVING THE COLLECTION, MANAGEMENT, AND USE OF MARINE FISHERIES DATA surveys use echosounders or sonars to count numbers of fish or, more commonly, estimate the biomass. Both of these methods can be valuable because they estimate absolute rather than relative biomass or numbers of fish. Egg surveys can be expensive because they often require a number of scientific cruises to span the spawning season of a fish (often several months long) and work best for fish that spawn over a short time period (summer flounder spawn several times over a period of four to six months). Acoustic surveys tend to work best for species that give strong echos (associated with swim bladders), aggregate in single-species schools, and are found either at the surface or in mid-water. (Summer flounder, like other flatfish, have no swim bladder and being bottom-dwelling fish are poor subjects for acoustic surveys.) Another approach to estimating mortality and population size is to use tagging experiments. In practice, it is usually difficult to design such experiments to be wholly representative of a stock, but they can provide data to help test the assumptions used in stock assessment models. Social and Economic Data Traditionally, biological data, such as those described above, have been collected and used most commonly for fisheries management, although it has long been recognized that economic and social data related to fisheries, fishermen, and communities are also needed. However, economic and social data have not been routinely collected for fishery purposes. The success of fisheries management depends on knowing how management may affect individuals and communities as well as fish stocks. Management measures may intentionally or unintentionally change how fish are harvested. Social and economic data are needed to understand catch, catch at age, and catch per unit effort because social and economic factors affect these variables. Also, some of the intended benefits of fisheries management are social and economic in nature. Several of the Magnuson-Stevens Act's national standards concern social and economic issues, so relevant data must be collected to assess the performance of fishery management plans in relation to these standards. Social and economic data are necessary for understanding fishery-dependent information and comparing the predictions of stock assessment models with the observations of fishermen, constructing regulatory impact review sections in fishery management plans, evaluating the effects of management actions, understanding multispecies fisheries in which fishermen switch among species based on both biological and economic factors, and designing incentives and disincentives that are likely to result in compliance with regulations intended to encourage responsible fishing. For example, economic data are needed to understand the age composition of the catch if certain age/size classes are being targeted for special markets (e.g., large fish being highly prized for the sushi market). The West Coast Fisheries Economic Data Plan produced by the Pacific Fishery Management Council (PFMC, 1998b) provides a list of the kinds of economic data that should be collected (Table 1-1). The next generation of stock assessment models may include social and economic factors that can affect fishing activities. Fisheries assessment biologists are accustomed to observing a variation of 20% or more in their estimates. The magnitude of this observational variation depends on sample coverage, ecosystem variation, and the longevity of the species. Scientists have been frustrated in the past because even an increase in sample size did not guarantee an increase in the precision7 of current biomass levels, due to the variation occurring in the driving forces of the environment and commerce. Now, however, there has been an information explosion and the rapid development of methods and technology for dealing with environmental and industrial variations. Managers are just beginning to acknowledge the uncertainty of scientists ' predictions and the risks associated with overexploitation. Scientists will continue to refine their estimates and predictions, but managers and stakeholders must account for risk and uncertainty in their decisionmaking. Managers and stakeholders should be able to call on scientists and economists to help evaluate the risks and set reasonable objectives. 7 Precision is a measure of the variability of data around its mean value, whereas accuracy is a measure of the closeness of a measured or computed value to its true value.
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IMPROVING THE COLLECTION, MANAGEMENT, AND USE OF MARINE FISHERIES DATA TABLE 1-1 Economic Data Needed for Fisheries Management Harvesters Processors Charter Vessels Recreational Anglers Communities Revenue and efforta data Revenue data Revenue data Effort and catchc by target species Tax revenues Fixed and variable cost data Fixed and variable cost data Fixed and variable cost data Cumulative per angler catch and effort Fishery-related economic infrastructure Cost of harvesting Cost of processing Cost of doing business Wages paid and jobs provided Wages paid and jobs provided Wages paid and jobs provided Trip costs and angler demographics Fishery-related income and employment Capacityb information Angler values and preferences Geographic and physical characteristics SOURCE: PFMC (1998b), p. ES-2, modified by committee (changes initalics). a Effort is a measure of the resources devoted to catching fish, most often hours or days after fishing occurs. b Capacity is a measure of the vessel and gear resources applied to a specific fishery, such as the net capacities, number of fixed gear units carried, and horsepower of the vessel. c Catch is the number or weight of fish captured, including fish discarded. DATA MANAGEMENT After data are collected, they must be processed to make them useful for stock assessments and fisheries management. Data processing often includes some degree of quality control. As computing and communication resources have advanced, attempts have been made to link data collection more closely to management systems. Presently, data are managed at state, regional, national, and international levels, not always with appropriate standardization and communication structures in place to allow sharing among organizations and levels. Incipient efforts have begun to standardize data collection among regions, and NMFS has produced a plan for a nationwide fisheries information system in response to a request of Congress. Important issues of fisheries data management relate to the appropriate level of data confidentiality, how fisheries data can be made compatible with other types of environmental data collected by the government, and the timeliness of data for decisionmaking.
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IMPROVING THE COLLECTION, MANAGEMENT, AND USE OF MARINE FISHERIES DATA DATA USE Biological data are used primarily to understand the effects of fishing on fish populations and marine ecosystems. Social and economic data are used mainly to address the human components of fisheries and to develop and implement management methods that sustain fishermen and fishing communities and ensure future stock viability. Assessments Fisheries management, particularly when it is based on total allowable catch, relies heavily on assessments of stock abundance, which in turn rely on accurate and precise information. This information often comes in the form of data collected from commercial fisheries (e.g., logbooks, observers, and landing receipts), from the Marine Recreational Fishery Statistics Survey (e.g., intercept and telephone surveys), and from scientific field observations (e.g., annual state and federal trawl surveys). Clearly, the quality8 and quantity of data directly affects the accuracy of assessments and resulting predictions. The National Research Council (NRC) (1998a) found that the quality of data and soundness of assumptions employed by stock assessment scientists are major factors influencing the performance of stock assessment models. Scientists use a number of approaches to stock assessment, depending on the nature of a species' population dynamics, the value of the fishery, and the management requirements for advice. Thorough reviews of the theory of population dynamics and stock assessment are found in Hilborn and Walters (1992) and Quinn and Deriso (1999). Because most commercial fish species have life spans of several years and because there is often substantial variation in the numbers recruited each year, it is often appropriate to estimate the number of individuals in each year-class.9 Such approaches are called age-based or age-structured assessments and are often developed within a broader assessment framework sometimes referred to as an integrated or fully analytical assessment (Box 1-1). Assessments for summer flounder are integrated assessments but other approaches are appropriate for other fisheries (see NRC, 1998a, for more details). Age-based methods require a finer level of detail in the data than required by other methods—for example, those based on stock production theory or those using equilibrium concepts— and thus tend to be more expensive than methods that do not distinguish among ages of individual fish. Traditionally, an age-based assessment proceeds with the application of sequential population analysis to the catch-at-age data (together with an assumed rate of natural mortality). This approach provides estimates of the numbers of fish in the sea in earlier years. It also provides estimates of the removal rate (measured by assessment scientists as fishing mortality rate, F) for these earlier years. These estimates are possible due to the convergence of the estimates backward in time. Put simply, the life of every fish ends in either catch or natural death. At any given level of natural mortality, the total number of fish caught from a cohort through a cohort's lifetime must be the number of that cohort recruited, minus adjustments for deaths due to natural mortality. These techniques are often collectively referred to in the literature as cohort analysis or virtual population analysis (VPA), although both of these terms correspond to specific types of sequential population analyses (Megrey, 1989). It was recognized early in the development of assessment methods based only on catch data 8 The concept of data quality includes such attributes as bias, precision, accuracy, comparability, completeness, and representativeness (EPA, undated). 9 Many fish species produce young at specific times of year and thus the population is formed of distinct age cohorts often called year-classes.
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IMPROVING THE COLLECTION, MANAGEMENT, AND USE OF MARINE FISHERIES DATA BOX 1-1 Age-based or Integrated Models Age-based models use equations based on initial year-class size, natural mortality rate, and fishing mortality rates to determine the abundance of each year class. Because of the flexible nature of such general models, integration of a variety of data types is feasible, thus the models are often referred to as integrated. Estimation can be accomplished by maximum likelihood or least-squares procedures applied to age-specific indices of abundance, age-specific catch, and other types of auxiliary information. The main advantage of these models is that they make almost full use of available age-specific information. The primary disadvantage is that such models require many observations and include numerous parameters, thereby increasing the cost of using them. ADAPT, CAGEAN, and Stock Synthesis are all age-based models; the first two were used by the committee to run assessments on summer flounder data. ADAPT (Gavaris, 1988; Conser and Powers, 1989) is an age-structured assessment method based on least-squares comparison of observed catch rates (generally age-specific) and those predicted by a tunablea sequential population analysis. CAGEAN (Deriso et al., 1985) is an age-structured assessment method based on forward-recursion population equations, a least-squares objective function (although other objective functions are also described), and lognormal distributions for catch at age and fishing mortality. Stock Synthesis (Methot, 1989, 1990) is an age-structured assessment technique based on maximum likelihood methods, but with more flexibility to include auxiliary information and fitting criteria. The Stock Synthesis program allows inclusion of “environmental proxies” that might influence parameters such as natural mortality or growth. A length-based, age-structured model that has been used for several highly migratory pelagic species is MULTIFAN-CL (Fournier et al., 1998). Somewhat simpler, so-called ad hoc tuning methods, such as the Laurec-Shepherd technique (Laurec and Shepherd, 1983; Pope and Shepherd, 1982; Darby and Flatman, 1994), adjust the last age/last year assumptions of sequential population analysis to give the closest fit to auxiliary information. Because the Laurec-Shepherd method is relatively simple in its formulation and its behavior is well understood, the committee used it to analyze the potential problems and questions associated with the summer flounder assessments. It is significant that these models typically do not include information about environmental conditions and how fish populations respond to different conditions, although, in principle, there is no reason that such information could not be considered. The assessment models applied in this review were chosen for their generality, ease of application, and the fact that they should be familiar to most in the fisheries community. These models represent a range of assumptions and are sensitive to different characteristics of a fishery. Such model attributes actually make these approaches useful for diagnosing problematic areas in an assessment, and thus they are useful tools to have on hand. Models better suited for a full assessment should deal with the complexities of these systems on a fishery-by-fishery basis. The software and technical methodology for dealing with these complexities are now available, making the need for good information more important. a Tuning of a model involves adjusting parameter estimates to minimize differences between predicted population estimates and observations from indices of population (e.g., catch rate, survey index of abundance).
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IMPROVING THE COLLECTION, MANAGEMENT, AND USE OF MARINE FISHERIES DATA that such methods could not provide estimates of population abundance or mortality for the most recent years, because more recent cohorts have not yet been subject to the full extent of fishing or natural mortality. Abundance and mortality rates in the most recent years have to be related back to estimates for earlier years by using relative indices of abundance, such as catch per unit effort (CPUE)10 of the fishery or relative trends from surveys.11 In the case of summer flounder, only survey data are used to tune the assessments. Although some commercial catch, catch-at-age, and CPUE data are available for this stock, they are not used because concern has been expressed that CPUE indices from fishery logbooks are incomplete, inaccurate, or too variable. In part, this is because of changes in regulations for the summer flounder fishery over time. More recent approaches use both catch-at-age data and the auxiliary data in the overall assessment. Such integrated approaches normally proceed by simultaneously fitting all data sources using least squares or maximum likelihood techniques. Traditional sequential population analysis techniques typically assume that catch-at-age data are precise and accurate and provide population estimates through a back-calculation technique. Integrated analyses assume that the catch observations may have error and that the parameters of the model should be estimated by balancing good fits of the model to the catch data and to auxiliary effort or survey abundance data. Estimates of the numbers of fish in the most recent year, obtained from tuned sequential population analysis or integrated analyses combined with estimates of the recruitment, expected fishing mortality rate, natural mortality rate, and weight-at-age data, allow predictions to be made of the future catch. This is the basis for estimating TACs compatible with management plans for a sustained harvest. Regulations and Management Because fish typically are free-access common property resources, commercially valuable stocks tend to be harvested in an unsustainable or uneconomic fashion unless they are managed. Fisheries management uses stock assessment results and other data for decisionmaking. Managers can use only a few instruments to ensure a sustained harvest. Typically, managers employ rules that directly govern the actions of fishermen. Managers can attempt to control landings by imposing catch quotas, bag limits, or trip limits; limiting fishing effort (e.g., restrictions on the amount of time and gear that can be applied in a fishery); closing key fishing areas; closing seasons when a certain level of catch is achieved; and/or restricting the kind or quantity of gear that may be used and the time and place it may be used or by limiting the number of users. In principle, fiscal measures such as taxes on catches, fishing licenses, or fuel could also be used to manage fisheries. Frequently, a combi- 10 Catch per unit effort (CPUE) is a measure of the average number or biomass of fish caught per unit of effort (e.g., fisherman-hours, length of tow, number of hooks). CPUE is often used as a measurement of relative abundance for particular fish stocks (e.g., Pacific halibut), although it can be misleading because fishermen are selective rather than random samplers of fish populations and because the nature and quality of the fishing gear and targeting practices may change over time, resulting in a CPUE that can be stable or increasing in the presence of an actual decline in a fish stock (Rose and Kulka, 1999). 11 The major objective of fishery-independent surveys is to monitor temporal and spatial changes in the relative or absolute abundance of a target fish population or a particular component of that population (e.g., larvae, juveniles, spawning adults) in a manner that is not subject to the biases inherent in commercial or recreational fishery data, which are subject to changes in gear and targeting practices. Surveys usually use either fixed stations or randomly selected stations within the geographic range of the stock and often use gear that can be applied consistently year after year. More information is provided in Chapter 2 and Appendix C regarding the surveys used for summer flounder.
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IMPROVING THE COLLECTION, MANAGEMENT, AND USE OF MARINE FISHERIES DATA BOX 1-2 Biological Reference Points Biological reference points are calculated quantities that describe a population's state and are used to evaluate objectively the consequences of management. They are used as targets for optimal fishing and for setting overfishing thresholds. (Councils must include measures in fishery management plans to prevent or end overfishing) (Magnuson-Stevens Act, Sec. 301[a], Sec. 303[a]). Biological reference points are calculated from the life-history characteristics of a given stock and are used to define harvest control rules. Biological reference points can be based on fishing mortality rate (F) (related to yield per recruita or spawning biomass per recruitb) or biomass and often assume the existence of some optimal level for abundance. F0.1 and Fmax are defined from the relationship between the yield per recruit and fishing mortality. Fmax is the fishing mortality rate that provides maximum yield per recruit; it is a threshold level. Levels of fishing mortality higher than Fmax constitute growth overfishing because individual fish are harvested before they have had a chance to grow to a size that will maximize the yield per recruit. Fmax is used as an overfishing definition for summer flounder. F0.1 is the fishing mortality rate beyond which increases in yield per recruit relative to increases in fishing effort are marginal. In technical terms, F0.1 is defined as the rate of fishing mortality for which the increase in yield resulting from a small increase in fishing mortality is one-tenth the increase that would have resulted if the same small increase in fishing mortality had been applied to the unexploited stock (Gulland, 1969). F0.1 is generally preferred over Fmax, because F0.1 is a target reference point that is lower than Fmax and provides a buffer to avoid growth overfishing. It is widely used as a target F. FX% and Fmed are based on the relationship between the spawning biomass per recruit and fishing mortality. FX% is the fishing mortality calculated to reduce the spawning biomass per recruit to X percent of its unfished level. Many of the overfishing definitions for U.S. fish stocks are based on this biological reference point (Rosenberg et al., 1994). Values from 20-30% have been used to define overfishing, and values from 35% to 50% have been used for target values. Fmed is the fishing mortality rate that allows the adult fish to be adequately replaced by new generations of fish. It theoretically gives the replacement spawning stock biomass per recruit that is equivalent to the median historic level. Sissenwine and Shepherd (1987) suggested Fmed as an overfishing threshold because a stock harvested at this rate (or lower) should be able to replace itself. However, Fmed is based on observed survival ratios, which depend on knowledge of the exploitation history of the stock, which is often unavailable. a Yield per recruit is the total yield in weight harvested from a year-class of fish over its lifetime, divided by the number of fish recruited into the stock. b Spawning biomass per recruit is “the ratio of the total weight of mature fish in a fish stock to the total weight that would exist if the stock were unfished” (Roberts et al., 1991).
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IMPROVING THE COLLECTION, MANAGEMENT, AND USE OF MARINE FISHERIES DATA nation of measures is employed to protect the long-term sustainability of the stock and long-term interests of users. However, most regulations ultimately control the current catch, and this may limit the income of fishermen in any given year. Stock assessment scientists support the management process by advising on the nature of long-term sustainable harvest plans and the measures that are needed to accomplish such plans. They estimate the numbers of fish or biomass and a corresponding fishing mortality at each age in each year. Such estimates can be used to examine the rate of recruitment, current recruitment levels, and spawner-recruitment relationships and then to predict what might occur under various management scenarios. Biological reference points are pre-specified management control objectives developed from observations and assumptions based on population characteristics (e.g., annual mortality rates, growth, maturity at age) that often prove to be useful statistics for comparing the output of stock assessment models (Box 1-2). CONTENT OF REPORT The committee summarizes its review of the 1996 and 1999 summer flounder assessments in Chapter 2 and provides additional detail about the assessments in Appendix C and Appendix D. Chapter 3 provides background information for the more general data issues examined by the committee and Chapter 4 contains the committee's findings and recommendations.
Representative terms from entire chapter: