Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
5 Human Dimensions Especially in recent decades, fisheries management considers eco- logical, political, economic, and sociocultural factors. From the national standards of the current Magnuson-Stevens Fishery Conservation and Management Act (16 U.S.C. 1801 et seq.), it should be readily apparent that fisheries management decision making, whether for recreational or commercial fisheries, requires a diversity of valid and reliable data well beyond "estimating the impact of recreational fishing on marine resources" (the stated purpose of the Marine Recreational Fisheries Statistics Survey [MRFSS] and other National Marine Fisheries Service [NMFS] surveys) (National Oceanic and Atmospheric Administration, 2005b). Most available data are biological and ecological in orientation. The political domain has been dominated by established rules and regulations, by policies of agencies and administrations, and by the values of those involved in the task of fisheries management. There has been a paucity of data on human dimensions available to decision makers in fisheries management. Part of the lack of data on human dimensions flows from a lack of recognition among fishery managers of the importance of those data, and part has to do with agency tasks. Management councils are tasked with conservation first (i.e., identifying available yield) and optimum use second (i.e., how to best serve the nation with the yield available). This has dictated the priorities of recreational surveys, but the surveys have not evolved along with advances in sociocultural and economic information. The difficulties facing fishery management agencies are as often sociocultural and economic as they are biological, and failure to incorporate sociocultural and economic information into fishery man- agement increases the likelihood of management failure. In the statement 93
94 REVIEW OF RECREATIONAL FISHERIES SURVEY METHODS of task, the committee was asked to consider the match or mismatch between options for collecting recreational fisheries data and alternative approaches for managing recreational fisheries. Determining whether there is a match or mismatch between how data are collected and alternative approaches to fisheries management greatly depends on the human element. Identifying and evaluating alternative management approaches and their intended benefits, and ascertaining the relevant data needed to support them, means tracking the human dimensions of the programs. To provide one example, shifts in management actions result in both expected and unexpected shifts in angler behavior. Anticipating potential shifts in data collection needs to match changes in management requires an understanding of this behavior. This chapter discusses the reasons why these other requirements need to be addressed. In addition, the recommendations that follow address more than just human dimensions; some will strengthen the survey, some will derive additional value from the survey, and some will add to what is done now. The various surveys currently conducted by NMFS have the potential to provide critical insight to the human dimensions of recreational fisheries on a direct (i.e., during the survey) or indirect (i.e., after the survey) basis. While most of the surveys presently are designed to produce insight to the extent of catch and catch per unit effort (CPUE), it is also possible to gather social and economic data simultaneously or independently as per the requirements of the Magnuson-Stevens Fishery Conservation and Management Act, the national standards therein, the National Environmental Policy Act, and the host of other regulatory requirements addressed by fishery management plans. Currently, the MRFSS collects some sociocultural data (e.g., number of days fishing per year and angler state, zip code, and county of residence), but the focus of the MRFSS and most other NMFS surveys is on catch, harvest, discards, and effort. Collection of human dimensions information, such as angler attitudes, motivations, management preferences, expenditures, and demographics, can take place onsite during an intercept survey if they serve the objectives of the survey (Green et al., 1991; Pollock et al., 1994). Alternatively, the sampled anglers' information, collected during the creel intercept, can be used to facilitate follow-up or add-on surveys via telephone or mail if, for example, data collection requires more time than is available at the intercept interview (Pollock et al., 1994). This latter approach would be a combination of an onsite fishery-dependent survey with an offsite human dimensions survey. This approach has been used previously in conjunction with MRFSS sampling to collect socio-
HUMAN DIMENSIONS 95 cultural data from anglers on a species-targeted basis in the southeast and northeast United States. Yet, socioeconomic data on recreational fishing through the MRFSS are collected only rarely; the most recent data collection effort was completed in 2000. In addition to the nationwide valuation and expenditure surveys associated with the MRFSS, several other surveys have been conducted to gather additional information regarding angler behavior and characteristics, such as Northeast Recreational Anglers: Preferences for Fishing and Management Alternatives, the Gulf Reef Survey, and Tackle Retailer Profiles (National Oceanic and Atmospheric Administration, 2000a). In addition, the Large Pelagic Survey collects some socioeconomic information, which is used to estimate the demand for and value of the large pelagic fishery among anglers. But the infrequent, inconsistent timing of these surveys does not provide the ongoing monitoring of the recreational sector that is needed to better inform management decisions. MANAGEMENT USES FOR DATA The purpose of evaluation of research efforts is to determine whether agency programs targeting anglers are working and producing the intended benefits. An agency's ability to lead and serve the public depends to a great extent on its ability to continue, modify, or terminate its programs when necessary. An evaluation of the NMFS programs, including its various survey efforts, would be useful to understand angler sentiment in a systematic way and whether intended benefits are being achieved. Other topics would include an evaluation of how open the angler public feels the fishery management process is, how they rank NMFS and the fishery management councils as sources of information and educational materials, and finally, how they rate the effectiveness of the various angler programs. Human dimensions research can be the basis for changing existing agency efforts to be more effective, devel- oping new program elements, or reducing support in favor of alternative efforts. Occasionally, evaluation efforts reveal unanticipated outcomes that agencies should be aware of so they can take appropriate action. Because of the diversity of angler motivations, the product of recreational fishing is not necessarily the number or size of fish caught but rather anglers' satisfaction level with recreational fishing overall or on the particular day they were intercepted. If NMFS seeks to maximize angler satisfaction as a management goal, they must know something
96 REVIEW OF RECREATIONAL FISHERIES SURVEY METHODS about the importance of various motivations to anglers and the extent to which they are achieved in their fishing experience. Human dimensions information about marine recreational fishing would include anglers' characteristics, such as age, income, boat owner- ship, information about their choice of fishing destination (e.g., cost, availability of friends or family, and fishing experience), and how often they fish. These data would be used to form angler profiles for the population and to develop or to provide input data for economic models. These models could be used to evaluate the effects of management pol- icies and to simulate the possible effects of proposed management policies. The identification and characterization of various stakeholders, including marine anglers, is perhaps where most human dimensions research has focused previously. Angler profiles provide managers with the most basic of information on their clientele. These profiles have seen a shift away from simple means and other measures of central tendency for the angler population (i.e., the "average angler" approach) to identifying groups using market segmentation techniques. This involves partitioning anglers into groups with similar characteristics (e.g., coastal residents and nonresidents, tournament and non-tournament participants, private boat and for-hire anglers); thus, the anglers within each group are likely to be more similar in fishing behavior and attitudes. Once the angler population is partitioned to form subgroups of managerial con- cern, profiles can cross-tabulate groups by demographic characteristics; participation frequency measures; motivations for participation; attitudes, beliefs, and knowledge; management expectations and preferences; and satisfaction measures. There are various measures of onsite angler participation including fishing frequency, location, angler expenditures, and mode of fishing. Fishing frequency (or avidity as it is often described) is a measure of fishing experience along with number of years of previous participation. (In the MRFSS, number of days fishing in the past two months is used.) Fishing participation begins with a point of origin (location of primary residence) and ends with a location where the angler was intercepted, as well as mode of fishing that day (e.g., shore, for-hire sector, private and rental boat). See Chapter 3 for a more detailed discussion. Additionally, some anglers participate in fishing tournaments, and some do not; some belong to fishing clubs and organizations, and some do not. Other useful participation measures include self-perceived assessments of fishing skill, as well as fishing-related knowledge and an assessment of how
HUMAN DIMENSIONS 97 important recreational fishing is to them compared to their other outdoor recreational activities. Understanding angler preferences for various management measures prior to implementation is important to understanding compliance probabilities. Previous approaches for understanding angler preferences for various management measures have depended mostly on opinion measurement techniques, whereby it was not possible for anglers to consider fully the tradeoffs involved, an approach that yields many socially acceptable "yes" answers. In contrast, stated preference models make use of hypothetical scenarios to derive individuals' preferences for various management components (Äas et al., 2000). This approach assumes that complex decisions are based not on one factor or criterion but on several considered jointly. Results allow managers to understand how anglers combined their preferences for various management measures under consideration and the relative influence of each management attribute (Louviere et al., 2000). Using a mail questionnaire format, Hicks (2002), for example, identifies anglers' stated preferences for summer flounder regulatory alternatives as an add-on survey to the MRFSS in the northeastern United States. There are many important human dimensions questions today that involve change over time and require longitudinal study designs. They include questions about trends for anglers joining clubs and associations to gain a voice in management, about rates of participation in fishing tournaments, and about annual fishing frequency. Also, to what extent are attitudes toward catch and release changing over time? These questions will require longitudinal measures using the same questions over time with the saltwater angler population or with angler panel studies. ECONOMIC DATA AND MODELS As noted above, marine recreational fishing data often include an economic component--indeed, more broadly, there is sociocultural information as well. The economic data include information on the characteristics of anglers (e.g., age, income, boat ownership), trip choice, expenditures, and other related information. These data are used to form profiles of the angler population and to develop economic models for analyzing fisheries policies. The data may be gathered in an expanded version of an existing recreational fishing survey focused on catch and
98 REVIEW OF RECREATIONAL FISHERIES SURVEY METHODS species information, as a follow-up to such a survey at a later time (possibly using a different survey mode), or as an independent survey with a new sample. While there are a variety of economic models that use recreational fishing data, the two most common are economic valuation models and economic impact models. Other applications, such as bioeconomic mod- els, participation rate studies, marketing studies, and recreation supply studies, for the most part, use similar data. The purpose of the valuation and impact models and their data requirements is discussed below. There is also a specific set of recommendations at the end of this chapter for accommodating economic data in recreational fishing surveys. Economic valuation models consider the behavior of anglers and, as their name suggests, are used to value fishery resources. For example, they may be used in costbenefit analyses of fisheries and environmental regulations, in damage assessment, and in setting management priorities. The following are some examples of the types of questions valuation models can address: · What is the economic value of an increased catch rate for a specific fish species or group of species in a given region? · What is the short-term economic loss of closing a recreational fishery? What is the long-term gain if a fishery recovers? · What is the economic loss to anglers due to a consumption ad- visory? · What is the economic value of improved coastal access for anglers? · What are the relative values of additional recreational versus commercial catch? Also, valuation models are used to predict the response of anglers to regulatory changes, which in turn may be useful for management and planning at local and regional levels. The following are some examples of behavioral response questions that may be considered: · How will anglers respond if a recreational fishery is closed? Will they fish fewer days in total? Target another species of fish? Delay fishing until the fishery reopens? Substitute another form of outdoor recreation? · How will anglers respond to a consumption advisory?
HUMAN DIMENSIONS 99 · How will anglers respond to catch limits or other restrictions on a fishery? Will they change fishing effort, targeted species, chosen fishing site, or mode of fishing? Valuation models come in two basic forms: revealed preference and stated preference. Revealed preference models infer values from fishing choices actually made by anglers. Anglers implicitly reveal economic values in the sites they choose, the species they target, the modes they select, and the frequency with which they fish. Revealed preference models are designed to measure implicit values for fishing using data on observed choices. Stated preference models, on the other hand, ask individuals to state their values directly in a survey. The former has the advantage of being based on actual behavior; the latter has more flexibility in the scenarios it can consider. Additionally, the models may be combined. There are numerous revealed preference models of recreational fishing, but the travel-cost random utility model is the standard. McFadden's (2001) Nobel lecture provides an excellent exposition on random utility theory. Parsons (2003) presents a review of the model as it is used in recreation demand. There are numerous applications to marine recreational fishing (e.g., Huppert, 1989; McConnell et al., 1994; Gautam and Steinback, 1998; Haab et al., 2001). The travel-cost random utility model uses data on actual trips to fishing sites to model where anglers fish, how often they fish in a season, what fish they target, what mode of fishing they use, and how long they stay onsite. The model is designed so that choices depend on the characteristics of the site and the characteristics of the angler. A model may include one or more of these choices. The model predicts outcomes in a probabilistic form. For example, the probability that an angler visits a site might increase with the quality of fishing at the site, ease of access to the water, amenities at the site, proximity to the angler's home, and the angler's years of fishing experience. The basic data requirements for estimating a travel-cost random utility model using marine recreational fishing data are the following: · A probability sample of anglers and potential anglers · The location of each angler's residence · The characteristics of anglers believed to influence site choice, mode choice, and species targeted · The location and a clear definition of each fishing site
100 REVIEW OF RECREATIONAL FISHERIES SURVEY METHODS · The characteristics of sites believed to influence anglers' choice of sites over a season · Each angler's choice of sites visited, species targeted, and mode used over given season A probability sample of anglers is needed to make accurate inference on the complete fishing population. This is critical if the economic analysis is to be used in policy--the values and behavioral results should be representative of the population. The location of the angler's residence and the location of the fishing sites are necessary in the calculation of travel cost by each angler to each site. The model bears the title "travel cost" for a good reason--travel cost is invariably an excellent predictor of site choice, and it is the factor that anglers use to trade off money and time for better sites and better fishing. Ultimately, travel cost is the way values for sites and their attributes are inferred. Site and angler characteristics are also needed in the behavioral models as predictors to create a realistic model of how anglers make decisions. Finally, actual choices of the site visited, species targets, and mode used are needed as the dependent variables to be modeled. The data should distinguish between primary purpose and side trips, which can be done easily as part of the survey design. In the analysis, the side trips can be handled by changing the origin of the trip. In many instances, the side trips are set aside entirely. In general, clear definition of trip length, purpose, and activities will make for a richer data set from which better analysis can be done. The data requirements then are twofold: angler-specific characteristic data and site-specific characteristic data. The former are gathered in a recreational fishing survey, and the latter usually are gathered separately as an inventory of relevant sites. While it is difficult to make generalizations about the angler-specific data required for estimating a travel-cost random utility model, Table 5.1 provides some guidance for marine recreational fishing surveys hoping to accommodate economic analysis; the list also includes information that would be useful in a variety of sociocultural analyses as well. This list is not meant to be exhaustive, nor is it meant to be a necessary list for doing an analysis. Rather, it is meant to be representative--one that incorporates most of the important characteristics that show up in contemporary analyses.
HUMAN DIMENSIONS 101 TABLE 5.1 Angler-Specific Characteristic Data to Accommodate Economic Valuation Models of Marine Recreational Fishing Data Type Characteristics Angler-specific Location of residence (city and zip code) Demographics Gender Family size Years of fishing experience Age Boat ownership (yes/no and size) Location of vacation home Favorite and preferred species of fish Income Occupation Employment status (e.g., retired) Education level Trip-specific Destination (launch point and at-sea location) Mode (e.g., shore, for-hire sector, private boat) Species targeted Species caught Time onsite Day trip (hours onsite) Overnight trip (days away from home) Expense of bait, tackle, and other supplies Stated preference data Behavioral response questions to support management needs Trip and catch recall is always an issue in recreational fishing sur- veys. At one extreme, the survey may ask for detail only about the last trip taken; at the other, it may ask for detail on all trips in a season. Somewhere in between these extremes is preferred: trips in the last month or two. Since revealed and stated models are often combined, a data element called "stated preference data" has been included. As discussed earlier, these are data elements in which individuals are asked to respond to hypothetical questions, such as changes in fishing laws and
102 REVIEW OF RECREATIONAL FISHERIES SURVEY METHODS TABLE 5.2 Site-Specific Characteristic Data to Accommodate Economic Valuation Models of Marine Recreational Fishing Quality of fishing--catch, abundance, and success rate Size of site (length of coastline) Type of water body (e.g., ocean, bay, river) Number of boat ramps and lifts Population density at site (e.g., urban, rural) Type of site (e.g., pier, beach) Availability of facilities (e.g., bathroom, food, bait shops, boating, gas/repair, camping) Level of regulatory control Availability of natural cover Availability of parking catch rates. To model participation in marine recreational fishing, it is important that data be gathered on those who choose to fish as well as those who choose not to fish. Also, if the data are gathered as a panel (so anglers respond to a survey that reoccurs every two months over one year), there is a single initial collection of the demographic data. There- after, each angler is asked only about trips in the preceding two months, which shortens the later surveys. Site-characteristic data, which are gathered separately, are an inven- tory of characteristics that can be compiled using existing state agency data, field visits, tourist guides, and fishing guides. The catch data may be obtained separately from a creel survey or in the angler survey, but some calibration using both is preferred. Again, it is difficult to make generalizations about the site-specific data required for estimating a travel-cost random utility model, but Table 5.2 provides some guidance. The other economic valuation models mentioned above use stated preference analyses. In these analyses, values are not inferred from actual behavior. Instead, analysts pose hypothetical trip or valuation (willing- ness to pay) questions to anglers. As noted above, this approach has more flexibility but is less conducive to a general template or guide for data collection since the valuation questions are likely to vary with region and to be specific to policy needs. In a national study, there may be some merit in considering a rotating set of stated preference questions for loss of a specific site or sites and for change in the catch of specific fish species. A time series on values such as these may be useful for policy
HUMAN DIMENSIONS 103 makers. In addition, state preference questions can be adjusted regionally and over time to meet specific policy needs. Mitchell and Carson (1989) provide an excellent background on the application of surveys to value public goods using stated preference methods. For some applications to marine recreational fisheries, see a study of summer flounder (Hicks, 2002), an analysis of fisheries management options (Oh et al., 2005), a study of salmon and striped bass in San Francisco Bay (Cameron and Huppert, 1989), and a study of saltwater and freshwater fisheries in Washington State (Layton et al., 1999). The other analyses conducted with marine recreational fishing data are studies using economic impact models. These are sometimes called inputoutput models and attempt to track the impact of regulatory changes through a local or regional economy. They are used to answer questions, such as the following: · What is the local and regional economic impact on different user groups and industries of a catch limit or area restriction for a specific fish species or group of species? · What is the impact of improved access to a site or of a new marina at a given site? · What is the impact of the complete collapse of a recreational fishery? Consider the collapse of a fishery. The local economy would be affected through a drop in the sales of bait and fishing equipment, sales of gasoline, visits to restaurants, visits to nearby attractions, stays at hotels, and so forth. These declines in economic activity, in turn, lead to decline in demand for other goods and services by the producers of these goods and services. Therefore, in turn, summer employment may decline, groceries sales may fall, and so on. In this way, the impacts of the fishery ripple through the economy. The shortcoming of these models is that they ignore the impacts outside the local economy and region. A declining fishery, for example, may leave an inland angler at home to spend his money with a positive (and ignored) impact there. Still, local and regional regulators often demand impact studies. Unlike the travel-cost random utility model, impact studies rarely, if ever, develop models specifically to study fisheries impacts. Instead, existing impact or inputoutput models are used. The most widely
104 REVIEW OF RECREATIONAL FISHERIES SURVEY METHODS known model is IMPLAN (Impact Analysis for Planning).1 A number of trip-specific expenditure variables (e.g., transit costs to site, food, bait and tackle, launch and boat fees, fuel and rental costs, lodging) and dur- ables data (e.g., value of boat, electronics, and rods and reels) could be targeted to accommodate a typical impact study. Like stated preference studies, data collection efforts for impact analyses could be flexible enough to be adjusted regionally and over time to meet specific policy needs. CONCLUSIONS AND RECOMMENDATIONS The current MRFSS was not designed with human dimensions data in mind. Much of the data is collected through add-on surveys that suffer from many of the same design problems associated with collection of catch and effort data. There is the potential to collect high-quality human dimensions data, but this has never been a traditional component of the MRFSS and other NMFS surveys. Despite the numerous important human dimensions questions identified earlier in this chapter, a human dimensions perspective on catch and effort has not been a priority of NMFS data collection efforts. However, with the amount of money currently allocated to support the MRFSS and the amount that might be necessary to support a redesigned MRFSS, an integrated approach to fisheries management and the collection of requisite data is essential. With respect to the economic models, add-on surveys for human dimensions should be continued but in a more focused way than is done currently to target specific management needs and to supple- ment the national data as needed. Traditional add-ons are "choice- based" onsite samples (i.e., access-point intercept surveys for CPUE) that make extrapolation to the population of users unreliable. Add-on surveys that build on the samples to develop effort estimates (i.e., offsite random digit dialing surveys) provide a better sampling frame for the choice component of the data. Unfortunately, these data have been constrained to the population of anglers within 25 miles of the coast, which severely limits the ability of the models to make inference about the relevant population of anglers. Also, surveys that gather biological and economic data simultaneously place a large burden on respondents, which may 1Refer to Dietzenbacher and Lahr (2004) for more information on the structure, theory, and history of impact models. For some examples of impact studies, see Steinback (1999) and Bohnsack et al. (2002).
HUMAN DIMENSIONS 105 lower the quality of both data sets through lower response rates and interviewer fatigue. Simultaneous surveys also can remove flexibility in timing, design, and sampling, which may vary in the economic and bio- logical components. Finally, the data set (or inventory) of marine recreational fishing sites and their characteristics lacks some needed data. For this reason, analyses often use limited site characteristics in the models (such as catch and travel cost only), collect the site data independently, focus on more limited policy needs, and estimate less defensible models. Economic valuation studies, marketing studies, busi- ness interests, and even data collection efforts regarding catch would benefit from a carefully designed data set on marine recreational fishing sites that is updated regularly for accuracy. If the number of marine fishing trips increases, it is likely that additional fishing access sites will be developed. In addition, social and environmental changes (e.g., changes in the distribution and numbers of people, a major hurricane) also can affect the availability and use of access sites. To ensure adequate coverage of the recreational fishery, a periodic updating of lists and descriptions of fishing locations and access sites is needed. An independent national trip and expenditure survey should be developed to support economic valuation studies, impact analyses, and other social and attitudinal studies. This survey should follow these guidelines: · Use a random sample of anglers from the national registry or license frame (see earlier recommendation) and collect the data independent of the catch and effort survey · Gather data on anglers and their choices (see Table 5.1 as a guide) · Conduct the survey continuously and as an annual panel for trip data, and every five years for expenditure data · Use multiple survey modes--mail, phone, internet, in-person-- to gather data · Target response to exceed 50 percent · Target annual sample size of respondents to be at least 1,000 anglers in each fishery council region · Include behavioral response questions for verification and to meet specific policy needs
106 REVIEW OF RECREATIONAL FISHERIES SURVEY METHODS The design of the national human dimensions survey should be inde- pendent of the MRFSS catch and effort survey to better align the surveys to their respective purposes, to give adequate flexibility on both the economic and biological sides, and to reduce respondent burden. However, the sites sampled should be the same for the national economic and the MRFSS surveys, as described below. The survey should be conducted throughout the year to develop good seasonal profiles. Survey questions should ask about trips no more than two months prior to avoid recall problems and to keep the survey short to avoid interviewer fatigue. The questions on expenditures should focus on the last trip only for the same reasons. If time and other resource constraints are limiting, less frequent sampling (every other year or every third year, for example) would be preferred to a lower sample size, lower response rate, and "convenient" sampling strategies tied to onsite surveys. High response rate and probability sampling should be high priorities because they maintain the quality of the survey. If the survey must be conducted as an add-on, it should be part of the effort survey, not the onsite CPUE survey. Also, information on the angler's hometown and destination of each trip is essential for conducting the valuation models. The other data elements in Table 5.1 are of next importance, and expenditure data would have lowest priority. In the absence of a national registry or license frame, the same data outlined above using a sampling frame that covers the entire country but stratified to oversample coastal counties using a combined telephonemailinternet survey would be the best alternative. The national database on marine recreational fishing sites and their characteristics should be enhanced to support social, eco- nomic, and other human dimensions analyses. The database should: · Geo-code and define sites at levels as fine as possible · Gather data on site characteristics (see Table 5.2 as a guide) · Use multiple resources, such as field visits, travel guides, and state agency data files, to gather the data · Be updated periodically · Coordinate with other surveys on catch and species information · Include historic trip counts and fish catch · Develop an "on-the-water" site inventory (i.e., document where people fish on the water)
