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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
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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-
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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
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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
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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
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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?
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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
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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.
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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
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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
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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
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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).
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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
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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)
Representative terms from entire chapter:
fishery management
