NOAA Technical Memorandum NMFS-NWFSC-124

Washington and Oregon 

Saltwater Sportfishing Surveys

Methodology and Results

September 2013

U.S. DEPARTMENT OF COMMERCE

National Oceanic and Atmospheric Administration

National Marine Fisheries Service

Northwest Fisheries Science Center
NOAA Technical Memorandum

NMFS-NWFSC Series

The Northwest Fisheries Science Center of NOAA’s

National Marine Fisheries Service uses the NOAA Technical 

Memorandum NMFS-NWFSC series to issue scientific and 

technical publications. Manuscripts have been peer reviewed 

and edited. Documents published in this series can be cited 

in the scientific and technical literature.

The Northwest Fisheries Science Center’s NOAA Technical 

Memorandum NMFS-NWFSC series continues the NMFSF/NWC series established in 1970 by the Northwest and

Alaska Fisheries Science Center, which subsequently was 

divided into the Northwest Fisheries Science Center and the 

Alaska Fisheries Science Center. The latter center uses the 

NOAA Technical Memorandum NMFS-AFSC series.

Reference throughout this document to trade names does not 

imply endorsement by the National Marine Fisheries Service.

Reference this document as follows:

Anderson, L.E., and S.T. Lee. 2013. Washington and 

Oregon saltwater sportfishing surveys: Methodology and 

results. U.S. Dept. Commer., NOAA Tech. Memo. NMFSNWFSC-124.
NOAA Technical Memorandum NMFS-NWFSC-124

Washington and Oregon 

Saltwater Sportfishing Surveys

Methodology and Results

Leif E. Anderson and S. Todd Lee

Northwest Fisheries Science Center

Fishery Resource Analysis and Monitoring Division

2725 Montlake Boulevard East

Seattle, Washington 98112

September 2013

U.S. DEPARTMENT OF COMMERCE

National Oceanic and Atmospheric Administration

National Marine Fisheries Service
Most NOAA Technical Memorandums

NMFS-NWFSC are available at the 

Northwest Fisheries Science Center

Web site, http://www.nwfsc.noaa.gov

Copies are also available from the

National Technical Information Service

5285 Port Royal Road

Springfield, VA 22161

Phone orders: 1-800-553-6847

E-mail orders: orders@ntis.fedworld.gov

ii
iii

Contents

List of Tables................................................................................................................................................v

Executive Summary....................................................................................................................................vii

Acknowledgments........................................................................................................................................ix

1. Introduction...............................................................................................................................................1

2. Survey Instrument Design and Administration.........................................................................................2

2.1. Pretesting ...........................................................................................................................................2

2.2. Administration...................................................................................................................................3

3. Survey Responses .....................................................................................................................................5

3.1. Response Rate....................................................................................................................................5

3.2. Type of Fishing..................................................................................................................................6

3.3. Most Recent Trip...............................................................................................................................8

3.4. Demographics..................................................................................................................................11

3.5. General Survey Results: Representativeness...................................................................................12

4. Experimental Design...............................................................................................................................14

4.1. Definition of Attributes and Levels.................................................................................................14

4.2. Design Considerations.....................................................................................................................14

4.3. Candidate Set...................................................................................................................................16

4.4. Choice Sets......................................................................................................................................17

Tables 1–31.................................................................................................................................................20

References...................................................................................................................................................29

Appendix A: State Recreational Data .........................................................................................................31

Appendix B: Saltwater Sportfishing Survey Documents............................................................................37

PC ECAP Telephone Screening Survey (Washington/Oregon).............................................................37

Notice Letter for the Mail Survey...........................................................................................................41

Cover Letter for First Mailing of the Complete Survey Package ...........................................................42

Mail Survey Instrument (16 pages)........................................................................................................43

Reminder Postcard..................................................................................................................................59

Cover Letter for Second Mailing of the Complete Survey Package.......................................................60

Cover Letter for Third and Final Mailing of the Complete Survey Package..........................................61
iv
v

Tables

Table 1. Survey timeline ............................................................................................................................20

Table 2. Detailed effective response rates .................................................................................................20

Table 3. Fishing versus other recreation ....................................................................................................20

Table 4. Saltwater fishing versus freshwater fishing .................................................................................20

Table 5. Most often used mode from mail survey responses.....................................................................21

Table 6. Most often used mode from telephone survey responses ............................................................21

Table 7. Boat access or ownership.............................................................................................................21

Table 8. Average trips last 12 months from the mail survey responses.....................................................21

Table 9. Average trips last 12 months from telephone survey responses..................................................21

Table 10. Average trips by species group in Oregon.................................................................................22

Table 11. Average trips by species group from telephone survey responses.............................................22

Table 12. Importance of keeping fish for Oregon respondents..................................................................22

Table 13. Importance of keeping fish for Washington respondents ..........................................................22

Table 14. Average daily catch per person..................................................................................................23

Table 15. Average daily rockfish bycatch per person................................................................................23

Table 16. Primary purpose of trip ..............................................................................................................23

Table 17. Average daily expenditures per person on the most recent trip.................................................24

Table 18. Likelihood of saltwater fishing next 12 months from mail survey responses............................24

Table 19. Likelihood of saltwater fishing next 12 months from telephone survey responses...................24

Table 20. Sex distribution from mail survey responses.............................................................................25

Table 21. Sex distribution through use of the sample frame for Oregon...................................................25

Table 22. Age distribution from mail survey responses ............................................................................25

Table 23. Age distribution from telephone survey responses....................................................................25

Table 24. Mean household size..................................................................................................................26

Table 25. Highest level of education .........................................................................................................26

Table 26. Annual household income from mail survey responses.............................................................26

Table 27. Annual household income from telephone survey responses....................................................27

Table 28. Work for pay or profit................................................................................................................27

Table 29. Personal hourly wage.................................................................................................................27

Table 30. Time off work for fishing ..........................................................................................................27
vi

Table 31. Experimental design attributes and attribute levels ...................................................................28

Table A-1. Sources of lengths and weights in simulation data by species and area ..................................34

Table A-2. Outlier threshold for catch per angler by species and area ......................................................35
vii

Executive Summary

The Northwest Fisheries Science Center administered surveys to saltwater anglers in 

Washington and Oregon during 2006 and 2007. These surveys gathered data on the preferences, 

trips, expenditures, and demographic characteristics of saltwater anglers in the two states. These 

data are essential to economic models that measure the value of the fishery. In addition, the data 

collected here can be used to measure the economic impact on regional economies, as well as 

provide a basis for predicting angler effort. This document describes the survey methods 

employed and uses these data to characterize the fishery participants.

The surveys were administered by mail in 2-month waves to approximately 8,000 anglers 

in each state. Participants were selected randomly from the population of adult anglers who 

purchased any license that enabled them to fish in salt water during the license year. We 

implemented the data collection following the first edition of Dillman’s book, Mail and Internet 

Surveys, and used a modified protocol with up to six total contacts: an initial telephone screening 

survey, a notice letter for the mail survey, the first mailing of the complete survey package, a 

reminder postcard, the second mailing of the survey package, and the third and final mailing of 

the survey package.

The surveys collected data in four sections: 1) recent fishing trips, 2) detailed information 

on the last trip taken, 3) discrete choice experiment questions, and 4) demographic information. 

Focus groups and one-on-one interviews were used to select the attributes and levels of the 

discrete choice experiment questions in order to ensure that the fishing trips presented in those

questions were contextually realistic and presented the respondents with no excessive cognitive 

difficulties.

Due to an oversampling of nonresident anglers in each state, results are provided 

separately for population segments determined by residency status and state. Fishing is the most 

important recreational activity for 35–45% of each segment of the population, and 86–93% 

consider fishing either the most important activity or are indifferent between fishing and other 

recreational activities. When comparing freshwater fishing to saltwater fishing, there is a 

noticeable difference between the two states. In Oregon, more anglers prefer freshwater fishing,

whereas in Washington, more anglers prefer saltwater fishing.

Overall, the general characteristics and preferences of anglers indicate that anglers are a 

very heterogeneous group. For example, preferences for fishing relative to other recreational 

activities show an almost equal split of respondents who prefer fishing and respondents who are 

indifferent. Preferences for saltwater fishing relative to freshwater fishing show a similar degree 

of variation, as answers are split almost equally between the three categories: prefer salt water, 

prefer freshwater, and indifferent. This heterogeneity is surprisingly similar across states and 

residency status.
viii

The majority of anglers in the two states use a private boat most often to fish in salt

water. This proportion is largest in Washington and is likely due to the greater ease and safety of 

saltwater access in the state. The other two fishing modes—charter boat and shore—receive a 

roughly equal share of primary use among respondents.

The effective overall response rates for Oregon and Washington saltwater anglers are 

48% and 51%, respectively. We used a brief telephone survey administered to the same 

population in order to examine differences to questions asked on both the phone survey and mail 

survey. Variables that could reliably be gathered over the phone and which had the potential for

differences between respondents and nonrespondents were collected from the same underlying 

population. In most instances, telephone respondent characteristics match closely to mail survey 

respondents, indicating a low probability of nonresponse bias.
ix

Acknowledgments

The saltwater sportfishing surveys described in this document were developed through 

collaboration and consultation with numerous individuals. The following people made important 

contributions to the survey design or the fielding protocol: Brad Gentner, Gentner Consulting 

Group; Cindy Thomson, Southwest Fisheries Science Center; and David Layton, Daniel 

Huppert, Robert Halvorsen, and Gardner Brown, University of Washington. The Oregon 

Department of Fish and Wildlife and the Washington Department of Fish and Wildlife were 

extremely helpful in providing data from license databases in order to draw the sample of 

licensed anglers. The Northwest Fisheries Science Center also thanks all of the sportfishing 

individuals who volunteered their time for the surveys.
x
1. Introduction

The Washington Sportfishing Survey and the Oregon Sportfishing Survey were 

administered during 2006 and 2007 by the Northwest Fisheries Science Center. The surveys 

were mixed-mode surveys, with a short screening survey administered by telephone followed by 

a more substantial mail survey instrument. These data are essential to economic models that 

measure the value of the fishery. In addition, the data collected here can be used to measure the 

economic impact on regional economies, as well as provide a basis for predicting angler effort. 

This technical memorandum describes the survey methods employed and uses these data to 

characterize the fishery participants.

Section 2 describes the design, pretesting, and administration of the survey instruments. 

Responses from the mail survey are presented alongside responses from the telephone survey in 

Section 3. Section 4 discusses the experimental design in detail. Appendix A, State Recreational 

Data, describes the sources and characteristics of an available set of simulation data taken from 

the marine recreational fisheries in Washington and Oregon. Appendix B, Saltwater Sportfishing 

Survey Documents, contains seven documents including the initial telephone screening survey 

and one example of the 16-page mail survey instrument.
2

2. Survey Instrument Design and Administration

The Oregon Sportfishing Survey and Washington Sportfishing Survey gathered new data

on the preferences, trips, expenditures, and demographic characteristics of saltwater anglers in 

Oregon and Washington. These data are intended to increase the level of understanding about

fishery participants as well as quantify the effects associated with potential changes in the state 

of the fishery. In particular, understanding the current fishery participants requires a 

characterization of the level of effort along with information depicting the species targeted, the 

number and type of trips, general fishing preferences, and trip expenditures.

The Oregon Sportfishing Survey and the Washington Sportfishing Survey collected data 

in four sections: 1) recent fishing trips, 2) detailed information on the last trip taken, 3) discrete 

choice experiment questions, and 4) demographic information. The surveys cover all of the 

saltwater fishing areas of Washington and Oregon. Washington is further broken out into two 

regions: the Ocean Area and the Inside Area. The Inside Area includes the areas commonly 

referred to as the Strait of Juan de Fuca, Puget Sound, and San Juan Islands. This distinction was

necessary due to the nature of the fishing regulations; while Washington saltwater fishing is 

separated into 13 Marine Areas for regulatory purposes by the Washington Department of Fish 

and Wildlife (WDFW), the most significant differences appear when comparing Marine Areas 

1–5 (the Ocean Area) with Marine Areas 6–13 (the Inside Area). Estimating the effect of 

regulatory changes using the discrete choice experiment questions independent of the effect of 

area fished required explicit acknowledgement of the area fished.1 See the map in the 16-page

mail survey instrument in Appendix B for more detail. The species chosen to be covered in the 

surveys represent the most commonly targeted species in Oregon and Washington. Both states 

included Pacific halibut (Hippoglossus stenolepis), rockfish (Sebastes spp.), lingcod (Ophiodon 

elongatus), Chinook salmon (aka king salmon, Oncorhynchus tshawytscha), and coho salmon 

(aka silver salmon, O. kisutch). Washington also included pink salmon (O. gorbuscha) in the 

survey instrument, which become important during the more prolific run in odd-numbered years.

2.1. Pretesting

The surveys went through extensive pretesting. During 2005, anglers who held a license 

within the previous 12 months were sampled in order to recruit participants for survey testing.

2

Though focus groups and one-on-one interviews could be used to gather much of the same 

information, we chose a sequential process, using focus groups first to provide broad definitions 

and identify larger issues with the survey instrument. Focus groups were conducted across the 

 1 This can be seen more clearly with an example. If the discrete choice experiment questions contained an 

alternative with a daily limit for rockfish equal to 10, respondents may have automatically assumed that the trip was 

set somewhere in the Ocean Area; daily limits in the Inside Area were, in general, an order of magnitude lower 

when the survey instruments were being designed. Without an explicit distinction between the Ocean Area and the 

Inside Area in the discrete choice questions, attributes related to the area of the trip would have been confounded 

with the rockfish limit.

2 Participants were given a participation incentive based on industry standard practices.
3

two states in order to ensure that differences in regional dialect did not cause inconsistencies 

with respect to question understanding. Specifically, two focus groups were conducted at each 

of the following locations: Seattle, Spokane, Astoria, and Portland. The focus groups helped to 

establish general definitions, determine which attributes were important to anglers, set 

appropriate levels for these attributes, and refine the layout associated with the mail survey 

instrument.

Next, one-on-one interviews were conducted with anglers in both states at the following 

locations: Seattle, Bellingham, Aberdeen, and Portland. Anglers were asked to think aloud in the 

one-on-one interviews, helping to ensure that questions were answered in a consistent manner 

across respondents. These interviews also help to identify outliers by eliminating the group 

dynamics that are potential in focus groups. In total, 56 one-on-one interviews were used to test 

the two surveys.

2.2. Administration

The Oregon Sportfishing Survey and Washington Sportfishing Survey were administered 

by mail in waves during 2006 and 2007 to approximately 8,000 anglers in each state. 

Participants were selected randomly from the population of adult anglers who purchased any 

license that enabled them to fish in salt water during the license year.3 The mailings were 

designed to allow the detection of seasonal influences on fishing choices and preferences by 

sending the surveys out in 2-month waves. Based on a low occurrence of saltwater fishing trips

during the winter months, wave one (January and February) and wave six (November and 

December) were skipped and mailings were focused during higher use periods. The samples 

were drawn with nonresident license holders constituting 30% of each wave’s mailings in order 

to allow the potential to estimate different preferences, expenditures, and willingness to pay 

(WTP) for fishing trip attributes for resident and nonresident respondents. We implemented the 

data collection following Dillman (2000) and used a modified protocol with up to six total 

contacts: an initial telephone screening survey, a notice letter for the mail survey, the first 

mailing of the complete survey package, a reminder postcard, the second mailing of the survey

package, and the third and final mailing of the survey package. Table 1 provides an outline of 

the time frame used for mailings.

The first contact was the telephone screening survey (Appendix B). The initial telephone 

survey was administered to the full sample, and was first used to refine the sample further; as 

many types of fishing licenses allow for both saltwater and freshwater use, the telephone survey 

 3 In Washington, sampled licenses include 1) the annual combination license, 2) the annual saltwater license, and 

3) the 1, 2, 3, 4, or 5-day combination licenses. In Oregon, sampled licenses include 1) the annual angling license, 

2) the combined angling/hunting license, 3) the senior citizen annual angling license, 4) the senior citizen combined 

angling/hunting license, 5) the sports pac license, 6) the nonresident annual angling license, 7) 1, 2, 3, 4, and 7-day 

angling licenses, 8) the resident disabled war veteran angling and hunting license, and 9) the resident pioneer 

angling and hunting permanent license. For our purposes, it is unfortunate that there are no saltwater licenses in 

Oregon; all fishing licenses allow use in freshwater and salt water. Angling tags are the closest tie to saltwater 

fishing records. Anglers are required to purchase a tag to fish for salmon, steelhead (Oncorhynchus mykiss), 

sturgeon (Acipenser spp.), or halibut. Since this list does not include two species in our survey, lingcod and 

rockfish, sampling from the angling tags would potentially lead to a nonrandom sample, so we chose to draw 

samples instead from the broader category of license holders.
4

screened anglers in the initial sample who do not fish in salt water. In addition to verifying 

respondent addresses for later mailings and screening for anglers who have fished in salt water in 

the last year, the initial telephone survey served the important purpose of collecting information 

to later test for (and potentially correct for) nonresponse bias. Testing for nonresponse bias 

requires collecting data on variables thought to vary with saltwater fishing preferences. As these 

preferences were expected to vary by the frequency with which respondents fish in salt water, the 

relative importance of substitute recreational activities including freshwater fishing, and the 

demographic characteristics of respondents, the telephone survey collected these variables. Later 

mail contacts were sent to anglers who were unable to be reached by phone or those who had 

fished in salt water within the last 12 months and agreed to a follow-up mail survey.

Within 2 weeks of the telephone survey, a notice letter (Appendix B) for the pending mail 

survey was mailed. The letter concisely described the survey project, promised confidentiality, 

and encouraged participation by stressing the importance of every response.

Three days after the notice letter was mailed, the first complete survey package 

(Appendix B) was mailed. All survey mailings contained a cover letter (which reminded the 

respondents of the earlier contacts, stressed the importance of the project, and repeated the 

assurance of confidentiality), one of 50 versions of the survey instrument, and a business reply 

envelope.

Seven days after the notice letter was mailed, all respondents received a reminder 

postcard (Appendix B). The postcard provided a gentle reminder to those respondents who had

not yet completed the survey and a thank you to respondents who had completed the process.

Three weeks after the notice letter was mailed, the second mailing (cover letter in 

Appendix B) of the survey package went to those respondents who had not yet returned a 

completed survey.

The last contact was made 6 weeks after the notice letter was mailed, with the third and 

final mailing (cover letter in Appendix B) of the survey package to those who had not yet 

returned a completed survey.
5

3. Survey Responses

3.1. Response Rate

The effective response rate is calculated as the number of completed surveys received 

divided by the total number of surveys sent to saltwater anglers, where our definition of saltwater

anglers is limited to those who have fished in salt water within the last 12 months. Based on the 

sample frame, we did not know whether a given respondent was a valid saltwater angler until we 

established a contact, as a number of license types allow saltwater fishing while also allowing 

other uses. License holders who were not reached by the initial telephone survey were also sent 

mail surveys. Since we did not know how many of these license holders fish in salt water, the 

total number of surveys sent to saltwater anglers must therefore be estimated. We used the 

telephone screening survey to determine the percentage of eligible saltwater anglers within the 

sample frame, then applied this percentage to the calculation of the base in the effective response 

rates. For each state, this calculation is simply given as

Response Rate = �

Completedtotal−Completedfw

αsw(Senttotal−Sentsw)+Sentsw

�, (1)

where αsw = Nsw⁄Ntotal, total refers to the total sample, sw (fw) refers to the portion of the 

sample who did (did not) fish in salt water in the last 12 months, and N is the size of these 

subgroups. In order to make the calculation more transparent, we note that Senttotal − Sentsw

represents the number of mail surveys sent to license holders who were not reached by the 

telephone survey. The percentage of saltwater anglers in the sample frame is given by αsw and 

was estimated as 22.6% for Oregon and 48.5% for Washington.

The effective overall response rates for Oregon and Washington are 48% (N = 4,681) and 

51% (N = 4,925), respectively. State-level aggregate measures can mask potential differences 

between segments of these populations and, more importantly, are not valid without first 

correcting for oversampling of nonresident anglers described earlier. Therefore, in Table 2 and 

throughout the rest of the document, we list separate results for resident and nonresident anglers 

within the two states. As expected, response rates for those who were first contacted by 

telephone and determined to be eligible are significantly higher than the estimated response rates 

for those who were unable to be reached by phone for all groups of the population. The 

difference between Washington residents and nonresidents is also expected; the response rate for 

residents is significantly higher.

While response rates are the standard measure of whether or not the sample is 

representative of the population of interest, a true test of representativeness requires testing for

nonresponse bias.4 Low response rates increase the likelihood of nonresponse bias, but are not 

 4 Nonresponse and nonresponse bias both produce undesirable effects, but these effects differ in severity. 

Nonresponse that is random within the population is only responsible for inflating the variance of parameter 

estimates. If nonresponse is systematic within a population and is left uncorrected, the result is more severe: biased 

estimates. For a good overview of survey nonresponse, see Groves et al. 2002.
6

by themselves a direct cause for concern; high response rates are typically the result of a 

carefully administered survey, but are neither necessary nor sufficient to show that nonresponse 

bias has been eliminated. Based in part on the potential for response rates to be misleading, there 

seems to be no universally accepted response rate in the literature above which nonresponse bias

is no longer a concern, so we must look beyond a simple response rate calculation in order to test 

the representativeness of our survey data.

As mentioned above, the telephone survey provided the data necessary to test for

differences between respondents and nonrespondents. Variables that could reliably be gathered 

over the phone and which had ex ante expected differences between respondents and

nonrespondents were collected from the same population. For example, anglers who fish more 

often might be more likely to respond to the subsequent mail survey and the resulting estimates 

of effort, expenditures, and value might therefore be biased. We note that the telephone survey 

might not be a perfect representation of the true population because refusals are still possible. 

However, refusals on a short telephone survey are likely to be a subset of the potential refusals 

on a longer mail survey and, for this reason, the telephone survey is likely a better measure of the 

underlying population. We use the telephone data set as a comparison to the mail data set 

wherever possible throughout the rest of this document.

3.2. Type of Fishing

In order to understand the relative importance of saltwater fishing in the context of the 

full set of recreational activities, respondents were asked to compare fishing to other recreational 

activities and saltwater fishing to freshwater fishing. Table 3 and Table 4 show these results.5

Fishing is chosen as the most important recreational activity for 35–45% of each segment

of the population, and 86–93% consider fishing either the most important activity or are 

indifferent between fishing and other recreational activities. When comparing freshwater fishing 

to saltwater fishing, there is a noticeable difference between the two states. In Oregon, more 

anglers prefer freshwater fishing, whereas in Washington, more anglers prefer saltwater fishing.

6

 

Washington has a greater ease and safety of saltwater access due to Puget Sound and this may

increase the relative utility of a saltwater fishing trip for the average angler.

Three different modes of fishing are possible in Oregon and Washington: private, charter,

or shore. We use the term charter fishing generally to refer to the hiring of a fishing guide and 

do not, in general, distinguish between the sizes and characteristics of these trips. Our definition 

of a private boat includes personal property owned directly by the respondent and also includes a 

boat owned by a family or friend that is accessed by the respondent. The majority of anglers in 

the two states use a private boat most often to fish in salt water (Table 5 and Table 6). This 

 5 Results are comparable between Oregon and Washington only after dropping all respondents who did not fish in 

salt water within the last 12 months. This is necessary due to differences in the licensing systems in the two states. 

Oregon does not have a saltwater fishing license; therefore, the random sample is drawn from anyone in Oregon 

who purchased a fishing license. Since the population of interest is saltwater anglers, we first drop all anglers who 

did not fish in salt water. This step is applied in all tables uniformly throughout the rest of the document. 6 However, it is important to keep in mind that the sampled populations are anglers who have fished in salt water in 

the past 12 months, thus the relative preferences we describe should not be assumed to hold in the larger population 

of all anglers.
7

proportion is largest in Washington and is likely due to the greater ease and safety of saltwater 

access in the state. The other two fishing modes receive a roughly equal share of use among 

respondents. This question also provides the means for our first examination of nonresponse. 

There is a great degree of similarity between the telephone and mail survey respondents, 

indicating a low potential for being affected by nonresponse bias.7

Closely related to the choice of mode for an angler is the accessibility of a private boat. 

Table 7 shows the degrees of private boat ownership and access. Large differences between

residents and nonresidents are apparent in the table. As expected, a greater share of residents 

own their boat (48–61%) than nonresidents (22–34%).8 This relative difference remains when 

considering the proportion of anglers who either own or have access to a private boat. It is also 

evident in Table 7 that a greater share of Washington residents own their boat than Oregon 

residents.9

 Again, this is likely due to factors like ease and safety of saltwater access.

The frequency of use provides another useful depiction of the fishery. Table 8 shows the 

average number of saltwater fishing trips taken over the last 12 months from the mail survey, and 

Table 9 provides a nonresponse comparison by examining the same information from the 

telephone survey. The distribution of trips is very skewed, with a few anglers fishing almost 

every day. Therefore, we also include a trimmed mean, as this measure is less sensitive to 

outliers.10 The answers in Table 8 and Table 9 are comparable, but it should be noted that the 

questions were asked in a different format; the telephone survey asked for an aggregate number 

and the later mail survey asked for this same information broken out by species and geographic 

location. It is possible for a trip in which an angler targeted both salmon and bottom fish to be 

listed under both categories, and to therefore be double counted during aggregation. While 

respondents to the mail survey appear to have taken more trips on average than respondents to 

the telephone survey in most of the population segments, we cannot say how much of this 

difference can be attributed to nonresponse and how much of this difference can be attributed to 

double counting.

Table 10 and Table 11 provide the average number of trips with more detail about the 

species group targeted. More trips were made targeting salmon than bottom fish across all 

population segments. However, the size of this difference is largest within the resident 

populations. The distributions of all trip types are skewed, with considerable differences 

between calculated means and trimmed means.

11

 7 A close examination of the data shows a higher percentage of shore anglers in the Oregon mail survey data and a 

lower number of charter boat anglers in the Washington survey. There is very little difference observed between the 

mail and phone samples for private boat anglers. Though these differences do not seem to be very large in practical 

magnitude, the differences are statistically significant, as the overall test results for Oregon and Washington are χ2 = 

15.08 (P < .001) and χ2 = 9.63 (P = .008), respectively. 8 In both states, these differences are significant. Tests were made comparing the frequency of boat ownership 

versus the other categories after dropping the missing responses. In Oregon, χ2 = 8.09 (P = .005). In Washington, 

χ2 = 122.81 (P < .001). 9 This test was made comparing the frequency of boat ownership versus the other categories after dropping the 

missing observations. The difference was significant: χ2 = 27.00 (P < .001). 10 The trimmed mean listed in Table 8 and Table 9 is the mean calculated after dropping the top and bottom 5%

from the tails of the distribution.

11 Again, the trimmed means are calculated as simple means after dropping the bottom and top 5% of the trips.
8

Space constraints on the Washington survey did not allow us to include this level of 

detail, so the nonresponse comparison is only possible for Oregon. However, it seems likely that

any differences between respondents and nonrespondents would be the same between the two 

states. While this information provides a more detailed comparison of respondents and

nonrespondents, it also suffers from the potential bias of aggregating over different levels of

detail. The mail data for Oregon explicitly acknowledged that trips could be for multiple species 

and asked for saltwater fishing trips broken out by those targeting bottom fish only, salmon only, 

bottom fish together with salmon, and other trips. In contrast, the telephone survey only asked 

for trips broken out by those targeting bottom fish and those targeting salmon. Allocating trips 

in which anglers targeted both bottom fish and salmon to either or both categories is problematic;

therefore, we refrain from attempting to make such a comparison here. The only valid

comparison we can safely make is one for Oregon nonresidents. Respondents to the mail survey 

made more salmon-only trips as well as more bottom fish–only trips, so even without allocating 

the combined bottom fish and salmon trips, it seems they made more trips than nonrespondents.

One question of direct relevance to fishery managers is the relative importance of 

keeping certain species of fish. For each of the five major saltwater species in our survey, 

anglers were asked whether they would keep all, most, some, or none of the fish.12 Table 12 and 

Table 13 provide this information for Oregon and Washington, respectively.13 It is apparent that

the most important species to keep are Chinook (king) salmon, coho (silver) salmon, and halibut. 

Among the two remaining species, keeping lingcod is more important than keeping rockfish to 

most anglers. There are no significant differences between the two states or between residents

and nonresidents in terms of this preference ordering.

3.3. Most Recent Trip

The highest level of detail for fishing trips is only available for the most recent trip.

14 

This is primarily due to memory recall limitations uncovered during survey pretesting. Anglers 

were unable to recall detailed trip information over the past 12 months, especially the number 

and weights of fish caught, with any reliable level of confidence.

Angler success on the most recent trip, as measured by the average daily catch per 

person, is provided in Table 14. In addition to calculating simple means across all trips, the 

survey also asked anglers to indicate which species were targeted on the most recent trip. This 

allowed us to also calculate the average catch, conditional on targeting a given species. This can 

be seen as a more accurate measurement of success. Large differences between the simple and 

conditional averages are evident, providing a strong justification for eliciting the target species 

for these surveys. Catch rates are quite consistent across population segments. The highest 

 12 We did not collect this information for hatchery and wild salmon separately for a few reasons: 1) the issue of 

hatchery and wild salmon was in the media prior to sending this survey and we did not want respondents to feel like 

this was the primary reason for the survey, and 2) in some seasons and areas anglers are not allowed to keep any 

wild king or silver salmon; asking for information that appears to be self-incriminating is not likely to generate 

meaningful data.

13 In Table 12 and throughout the rest of this document, instances in which frequencies do not fully sum to 100 

reflect missing values (item nonresponse). 14 A trip is defined in the survey as the time from when an angler leaves their residence until they return (see third 

page in the 16-page survey instrument in Appendix B).
9

catch rates are those for rockfish, with average daily catch rates per person as high as 4.4 fish for 

Washington residents, conditional on targeting rockfish on the trip. The lowest catch rates are 

those for halibut and Chinook (king) salmon. It is interesting to note that halibut and king 

salmon are two of the species for which the importance of keeping a landed fish is relatively high 

compared with other species, as was seen earlier in Table 12 and Table 13.

15

The targeted species data also enables us to examine another issue relevant to fishery 

management: recreational bycatch. Bycatch is often examined in the context of commercial 

fishing but less commonly measured in a recreational context, primarily because catch and target 

data are often not asked in conjunction. Fishery managers are extremely aware of recreational 

bycatch issues, though, as evidenced by full temporal and spatial closures designed to keep 

sportfishing mortality levels within harvest caps. If bycatch were not seen as an issue, a greater 

share of recreational closures would instead be species specific. In the data we collect, nonzero 

catch data for a species that is not targeted represents bycatch. We do not include catch levels 

above the daily bag limit in our definition of bycatch. An important context for recreational 

bycatch is bottom fish trips on which rockfish are not targeted but still caught, because rockfish 

often do not survive being released due to barotrauma.

16 Rockfish bycatch numbers are provided 

in Table 15. There is some amount of accidental catch in both states and this bycatch is seen 

while targeting lingcod and halibut. However, the magnitude of bycatch is very small in most 

cases. The largest bycatch appears for nonresidents in Washington targeting halibut: 1.14 

rockfish are caught per person per day.17

In order to determine what share of the total expenses and value from the most recent trip 

can be attributed to saltwater fishing, we asked respondents whether the primary purpose of this 

trip was for saltwater fishing (Table 16). It is evident that fishing was a secondary concern on 

some of these trips. In particular, nonresidents may have visited the state on a family vacation or 

business trip and decided to fish while there. This is true for a large number of trips in 

Washington, as more trips taken by nonresidents were primarily for reasons other than saltwater 

fishing. For residents, the vast majority of respondents indicated that the last trip was for 

saltwater fishing: 82% in Oregon and 83% in Washington.

Next we turn to the expenditures from the most recent saltwater fishing trip. Anglers 

were asked to provide their personal or household expenditures for a detailed series of 

categories. They were also asked to provide the number of people covered by each expense. 

This follow-up question enabled us to more accurately calculate expenditures per person in cases 

where expenses are shared between people on the fishing trip. Using these data, we calculated

the average daily expenditures per person (Table 17).

18 As expected, there were substantial 

 15 This is in line with economic intuition: from an angler’s perspective, targeted species that are more difficult to 

successfully land are more important species to retain.

16 For example, see Jarvis and Lowe 2008. 17 The difference between residents and nonresidents here is due to one of two reasons. First, residents are probably 

more likely to realize that when targeting halibut, rockfish are also caught; they are therefore more likely to indicate 

that trips targeting halibut are also targeting rockfish. Second, this difference could be due to real differences in 

catch; residents could in fact have more experience and skill in avoiding bycatch. 18 In Table 17, all means are trimmed at the 5% level. The mode is taken as the mode used most often when a 

respondent indicated using more than one mode on the last fishing trip. Ties are treated as missing and not included 

in the table. The fishing cost is divided by the number of days fishing, whereas the travel cost is divided by the total

number of days on the trip. Mean lodging costs are calculated conditional on being greater than zero.
10

differences between the calculated daily fishing cost for charter boat trips, private boat trips, and 

shore trips; charter boat trips are the most expensive, followed by private boat trips, then shore 

trips. Travel costs were broken out into three categories determined by the length of the trip: 

one-day trips, two-day trips, and trips longer than two days. Many trips have an associated fixed 

cost component and a simple average has a tendency to mask the inverse relationship between 

the daily transportation cost and the length of the trip. This fixed cost is largest for anglers 

traveling the longest distances, thus the difference between one-day and multiple-day trips is 

primarily evident in the nonresident populations.

The stability of the saltwater fishing population base determines, in part, the temporal 

relevance of these data. The population of interest in this study is all anglers who fished in salt

water in the last 12 months and the proportion of anglers who remain in this fishery provides us 

with a static measure of stability. Specifically, we collected data on the likelihood of fishing in 

salt water within the state in the next 12 months (Table 18 and Table 19). The overall population 

is quite stable, as a majority of anglers are likely to remain active in the population over the next 

12 months. Washington nonresidents represent the least stable population segment, as more than 

20% will probably not saltwater fish in the next 12 months. Without more information, it is 

difficult to tell whether this truly represents a dynamic population; it is possible that for some 

nonresidents, saltwater fishing in Washington is a vacation taken once every few years.

The likelihood of saltwater fishing within the state in the next 12 months may influence

the individual decision to respond to the survey, so we used the telephone survey to address 

potential nonresponse. A comparison of Table 18 and Table 19 shows that respondents to the 

mail survey are more certain than respondents to the phone survey that they will saltwater fish 

within the state in the next year; the entire distribution of responses is shifted upward toward the 

case in which respondents are certain to fish.19 This question represents an instance in which the 

difference between phone survey respondents and mail survey respondents has the potential to 

indicate a concern for nonresponse bias. However, a difference in the likelihood of saltwater 

fishing in the next year may be more closely related to past success (or lack thereof) than to

differences in the underlying preferences for standardized levels of fishing trip attributes or 

expenditures.20 In addition, the differences observed here are small in practical magnitude. If 

the noted differences in the likelihood of saltwater fishing were to translate into WTP, it seems 

likely that the alternative specific constants related to saltwater fishing trips would be most 

affected in the subsequent choice modeling. Whether or not these differences are large enough 

to warrant complex reweighting techniques requires a judgment call.21 We think that 

reweighting should be reserved for addressing the nonrandom sampling procedure used to draw 

residents and nonresidents.

 19 For one example of differences between the phone survey and mail survey, we provide test results comparing the 

proportion of respondents who said they were certain to fish relative to the other categories of responses. For 

Oregon residents, χ2 = 31.43 (P < .001), for Oregon nonresidents, χ2 = 5.51 (P = .0189), for Washington residents, 

χ2 = 57.39 (P < .001), and for Washington nonresidents, χ2 = 2.22 (P = .1362). 20 Survey pretesting in focus groups and one-on-one interviews suggested that a number of the respondents who are 

unlikely to fish in saltwater in the next 12 months make that decision based in part on past success. 21 For a discussion of reweighting techniques, see Gelman and Carlin 2002.
11

3.4. Demographics

The anglers in this fishery are overwhelmingly male, with a total of 78–90% of anglers in

the population segments. As this number seemed somewhat high, we sought an external check

of validity. Sex was not asked in the telephone survey and was only contained in the sample 

frame for Oregon.22 Table 20 provides the frequencies for sex from the mail survey and Table

21 provides a nonresponse comparison through use of the sample frame for Oregon. For 

Oregon, the proportion of male respondents is slightly higher than the proportion in the 

population.23

The age of the respondents within the sample is truncated below 18, due to a desire to 

sample only adult respondents. Therefore, the sample average is less useful than simple 

frequencies. We provide Table 22 to fully illustrate the age distribution within the sample. This 

question was also asked in the telephone survey and these data are provided in Table 23. 

Comparing Table 22 to Table 23, there appear to be no significant differences in the age 

distribution between respondents and nonrespondents.24

The average household size in the sample is 2.0 adults and 0.7 children for Oregon and

2.0 adults and 0.6 children for Washington (Table 24). These numbers are very consistent across 

population segments.25

The education levels for the sample are provided in Table 25. Specifically, these 

frequencies represent the highest level of education completed by the respondent. For all 

population segments, the greatest share of responses are from respondents who had completed a 

college degree or more, followed by some college, a high school degree, technical school, and 

the smallest share of responses among the education categories were respondents who attended 

high school but did not complete the degree. These rankings are consistent across the population 

segments.

Multiple questions were provided to collect information on household and personal 

income. First, categorical household income data were obtained from both the mail survey and 

telephone survey, and are provided in Table 26 and Table 27, respectively. A visual comparison 

of these tables shows that the income distribution from the telephone survey closely matches that 

 22 It was decided that asking whether the respondent was male or female over the phone could have the undesired 

effect of alienating some respondents. 23 Calculated confidence intervals for the mail survey proportions do not include the population proportions. The 

confidence interval for Oregon residents is (.8127, .8717) and the confidence interval for Oregon nonresidents is 

(.8514, .9474). However, since the population included many licenses bought without the intention of fishing in salt

water, we cannot firmly say this is evidence of nonresponse and the difference between these two proportions is very 

small in practical magnitude.

24 A formal test comparing the mail sample to the phone sample indicates that there are no significant differences 

across the age distribution for Washington, χ2 = 3.20 (P = 0.78). In Oregon, there are slightly fewer returns in the 

18–29 age group and slightly higher returns in the 30–39 age group in the mail returns, χ2 = 18.36 (P = .005). 25 It should be noted that one respondent indicated that 47 adults lived in a single household. In order to preserve a 

meaningful average, this respondent was dropped from the calculations in Table 24.
12

of the mail survey.26 These two tables show income distributions that are right-skewed, most 

likely reflecting the fact that in many populations, income distributions are commonly found to 

be log-normal. The majority of households have annual incomes that fall between $40,000 and 

$125,000. Income data appears to also be the most sensitive question, as these questions have

the greatest level of item nonresponse among all demographic questions.

Next the mail survey collected information necessary to estimate the personal income of 

the respondent by first asking whether the respondent worked for pay or profit (Table 28). This 

question provides more detail than if we had used the age distribution and assumed that 

respondents who are 65 or older are retired and therefore do not work, as seen by the differences 

between the proportions of those who do not work to those age 65 or older in Table 22.

A follow-up question to respondents who confirmed that they personally worked for pay

or profit elicited an approximate hourly wage (Table 29). These data should represent more 

accurate estimates of hourly wage rates than alternative calculations that rely on household 

income, the number of adults in the household, and employment status.

An important goal of this survey was to understand the opportunity cost of a fishing day;

therefore, in addition to asking the standard income questions mentioned above, we also

collected information on the use of paid or unpaid time off work devoted to fishing (Table 30). 

Taking time off work to fish could be an indicator of an increased relative preference for fishing, 

a lower opportunity cost of time off work (lower hourly wage), a family structure that

encourages devoting weekends and other time off work to family pursuits other than fishing, or 

could be the result of several day-of-week regulations which, in some areas, may not allow 

fishing for certain species on weekends. Table 30 very clearly shows that recreational fishing is 

an important activity for these respondents, as approximately 50% take paid time off for fishing, 

25% take unpaid time off for fishing, and 12% take both unpaid and paid time off for fishing.

3.5. General Survey Results: Representativeness

The results from these surveys provide a thorough description of recreational saltwater 

anglers in the Northwest. The Oregon and Washington surveys were successful in achieving

relatively high response rates—48% and 51%, respectively—providing a degree of confidence 

when applying sample results to the overall population.

We examined the potential for nonresponse bias in more detail through the use of a series 

of brief questions administered to a wider segment of the population. Differences between 

respondents and nonrespondents, when found, were documented and used to make a judgment 

on the necessity of using complex nonresponse correction techniques in order to make inferences

about the population. We found this comparison to be a very efficient and simple means to 

evaluate the degree to which the sample represents the population at large and recommend the 

inclusion of a brief initial telephone survey to researchers conducting similar mail surveys.

 26 The Oregon mail survey exhibits slightly more responses in the upper end of the income distribution: χ2 = 13.97 

(P = .052). The Washington mail survey shows the opposite pattern, with more responses in the lower end of the 

distribution, and these differences are more statistically significant: χ2 = 43.08 (P < .001).
13

In most instances, characteristics of telephone survey respondents match those of mail 

survey respondents, indicating a low probability of nonresponse bias. The greatest potential for

bias is evident in responses to the question asking for the likelihood of fishing in salt water

within the next 12 months. Respondents are more likely to be certain to fish. Other differences 

in variables are apparent in the classification of sex (respondents are slightly more likely to be 

male) and the number of trips (respondents take a greater number of trips). While results 

indicate that some of the differences between respondents and nonrespondents are statistically 

significant, none seem to be significant in practical magnitude.
14

4. Experimental Design

This section of the document provides detail on the experimental design used to select the 

attributes and levels for the discrete choice experiment questions of the surveys. The basic steps 

involved with the design of our choice experiment were 1) the definition of relevant attributes 

and attribute levels for the study and 2) the creation of the experimental design.

4.1. Definition of Attributes and Levels

Perhaps the most important tools we used to select the attributes for our choice 

experiment were focus groups and one-on-one interviews with recreational anglers. Gathering 

information on the relevant attributes that affect trip choice ensured that the appropriate tradeoffs were represented in the choice tables given to respondents. Unbelievable attribute levels can 

cause respondents to lose interest or question the credibility of the survey instrument, which in 

turn can lead to the use of simplified decision making heuristics, increased item and survey 

nonresponse, or answers not grounded in reality. Therefore, the attributes and levels selected for 

inclusion in the experimental design were selected to mimic the types of trips available to 

saltwater recreational anglers as closely as possible.

In the context of our design, it was necessary to have knowledge of the distribution of

weights and catch for each included species, and the fishing costs associated with different types 

of trips. Focus groups and one-on-one interviews also provided a medium with which to gather 

information on the possibility of multiple-species trips that should be included in the design. 

Including the appropriate multiple-species trips serves the dual purpose of adding a degree of 

realism as well as allowing for the estimation of interactions in the utility function. Finally, oneon-one interviews were used to test the attributes and levels we selected to ensure the fishing 

trips in the design were contextually realistic and presented the respondents with no excessive 

cognitive difficulties. Table 31 provides a description of the final attributes and levels we use in 

the experimental design.

One unique aspect of our choice experiment that can be seen in Table 31 is the large 

number of attributes. We know of no other choice experiment in the environmental economics 

literature that attempts to measure the effect of such a large number of attributes. Clearly,

estimation becomes increasingly difficult as the number of estimated parameters increases,

ceteris paribus, and this puts increasing pressure on the experimental design.

4.2. Design Considerations

The process of experimental design refers to systematic selection of the choice sets to 

include in the survey instrument. The experimental design was generated in two distinct stages: 

1) we generated a candidate set consisting of feasible saltwater fishing trips and 2) we paired
15

members of this candidate set optimally based on the criteria of maximizing the D-efficiency27 of 

a choice model. While optimal selection of choice sets in the literature typically refers to some 

statistical criteria, such as minimizing the variance of the resulting parameter estimates, we treat 

the phrase more generally and include consideration of the trade-offs that statistical efficiency 

can have on the cognitive complexity of the choice task, as well as the believability of the choice 

sets to respondents. In the rest of this section, we compare some of the experimental design 

techniques in the literature before moving to a detailed description of the methods we used to 

generate our designs. In all but the most simple of experimental designs, a full factorial 

approach is not feasible.28 Some subset from the full combination of possible trips must 

therefore be selected.

Many strategies have been proposed in the literature for optimally selecting a subset of 

the full factorial design. A quick review of the environmental economics literature reveals that 

the majority of the experimental designs are orthogonal main effects designs for linear models, in 

contrast to the D-optimal designs29 built for choice models. The bias toward linear designs is 

perhaps best seen by looking at Lusk and Norwood (2005) who, while testing the performance of 

a large set of competing experimental designs, completely omit a choice design from 

consideration. Ferrini and Scarpa (2007) suggest that the profession might not have missed out 

on a great deal of efficiency in the slow acceptance of the more theoretically correct choice 

designs. Nonetheless, their work shows that small efficiency gains are a very probable result of 

using choice designs, as opposed to linear designs, even when lacking good information on the 

data generating process. This is very much in agreement with the findings from the Monte Carlo 

experiments in Anderson (2009).

One important decision in the process of experimental design is the consideration of

interaction effects. Many applications ignore interaction effects for one reason or another. 

Main-effects-only designs will tend to perform poorly at the edges of the design space, which 

might be an important region with respect to changing policies. Lusk and Norwood (2005) show 

that main-effects designs lead to unbiased welfare estimates, even when the underlying 

preferences include significant interactions. However, the simulations used to show these results 

focus on the middle of the design space and assume a utility function with an increasing marginal 

 27 D-optimal designs attempt to maximize the efficiency of the resulting parameter estimates by using a criterion 

referred to as D-efficiency, which uses a function of the geometric mean of the eigenvalues of the information 

matrix to gauge the relative size of the variance matrix of the resulting parameter estimates (see Equation 2). Using 

D-efficiency to examine candidate experimental designs is a useful general approach for design selection in choice 

experiments.

28 A full factorial design is simply the full representation of all attributes and attribute levels used in the choice 

experiment to describe the alternatives. They are typically not seen in the literature because the problem blows up 

quite quickly with even a small design space. For example, in the design we generated for Oregon, the full 21

·37·422

factorial involves 76,948,221,758,275,584 total combinations.

29 See footnote 27.
16

rate of substitution.30 We feel that the theoretical justification for including interactions is strong 

enough to warrant their explicit use in our design selection.31

4.3. Candidate Set

One common approach to constructing a candidate set is to simply generate the full 

factorial design and drop unrealistic combinations of attributes.32 In our context, however, the 

size of the full factorial design was a limiting factor. In order to overcome this computing 

limitation, the candidate set was constructed from many smaller designs. These designs included

single species trips and all desired combination trips. We use the term combination trip to refer 

to a trip where anglers target or catch more than one of the following: halibut, rockfish, lingcod, 

coho (silver) salmon, and Chinook (king) salmon. Combination trips were selected to match the 

types of trips commonly observed in practice while satisfying space constraints imposed by the 

survey instrument.33 Each of these smaller designs was created by maximizing the D-efficiency 

of a linear model, as given by

D-efficiency = �|Ω|

1⁄K�

−1

, (2)

where K is the number of parameters and the covariance matrix , Ω, is given by

Ω = σ2(X′X)−1. (3)

In these small initial designs, we defined the design matrix X to include all potential crosseffects.34 Following Kuhfeld et al. (1994), a computerized search algorithm was used to find

 30 Interactions become increasingly important as you move closer to the corners of the design space and an 

increasing marginal rate of substitution is not in general a characteristic of the typical attributes valued in a discrete 

choice experiment. For these reasons, the conclusions of Lusk and Norwood might not fully generalize. 31 Omission of interaction terms in the experimental design simply refers to the case in which D-efficiency is 

calculated using only main effects. This does not preclude the estimation of utility functions containing interactions. 

However, the variance of interaction parameter estimates will be higher if these terms are not included in the design 

stage. We think that, in general, preserving the ability to test for interactions during model specification with some 

degree of power is important. If significant interaction effects are falsely tested out of a model, the accuracy of 

welfare estimates calculated at the edges of the design space can be compromised. Since one of the major benefits 

of using discrete choice experiment data is to predict changes in welfare beyond attribute levels that have been 

observed historically, this limitation seems to outweigh the very low cost of including interactions in the design 

stage.

32 We note that in some contexts, a candidate set is not explicitly necessary. If a researcher chooses a linear design 

from a list of known orthogonal designs, there is no need for a candidate set. If a researcher is using the more 

theoretically correct choice design, then the most commonly used search algorithms require a candidate set (even if 

this is simply a full factorial design).

33 We used the Recreational Fisheries Information Network data (RecFIN 2006) to choose the most important 

multiple-species trips, which led to selecting all of the possible bottom fish–bottom fish combination trips and all of 

the salmon-salmon combination trips, but no salmon–bottom fish combination trips. We used a threshold of 15% to 

signify an important combination trip; if species A was caught on more than 15% of the trips targeting species B, we 

included combination trips catching both A and B together in the candidate set. Space constraints on the survey 

instruments allowed no more than two of the following fish types to appear within a single choice set: halibut, 

lingcod, rockfish, and salmon.

34 Even if the true model turns out to be linear, Lusk and Norwood (2005) have shown that using designs that 

include cross-effects results in more accurate WTP estimates. Potential cross-effects were somewhat restricted 

based on the decision to leave salmon–bottom fish combination trips out of the design. The result is that no bottom

fish–salmon cross-effects are identified.
17

designs that are efficient, but not necessarily orthogonal.35 We used these fractional factorial 

designs as building blocks for the candidate set because the full factorial design was too large to 

implement.

Next we appended these smaller designs into an initial candidate set and eliminated any 

undesirable or unrealistic trips, defined as trips with catch levels falling outside the credible 

range derived from focus group testing.36 To illustrate the procedure, note that trips containing 

halibut in the candidate set come from a halibut-only design, a halibut-rockfish design, or a 

halibut-lingcod design. Trips were eliminated at this point if a duplicate trip existed in the 

candidate set. The last step in creating the candidate set was to eliminate the subset of trips in 

which it was impossible to uniquely identify which salmon would be kept. The uncertainty in 

determining which salmon would be kept is a result of the salmon regulations used by fishery 

managers; grouped regulations do not allow for a unique characterization of the salmon which 

could be kept on a given trip without additional information.37 This deleted subset included

roughly 30% of the salmon trips in the candidate set. This last step ensured that we could

estimate catch and release separately for the discrete choice scenarios for all respondents without 

gathering separate information on preferences for keeping salmon species and sizes.

4.4. Choice Sets

After the candidate set was completed for each state, we turned to the process of pairing 

the alternatives as choice sets. A search algorithm was used to optimally select 200 choice sets,

using the candidate set of trips. Since there are many alternative ways to rank candidate

experimental designs, we turn now to the criteria used in the selection process. We followed

Zwerina et al. (2005) by using a computerized iterative search algorithm to optimally pair

members of this candidate set based on maximizing the D-efficiency of a choice model.38

Standard discrete choice models, such as the multinomial logit, require knowledge of parameter 

values to assess the efficiency of a design. McFadden (1974) showed that the maximum

likelihood methods for the conditional logit model yield a covariance matrix given by

Ω = (Z′PZ)−1 = �∑ ∑ �z′

jnPjnzjn� Jn

j=1

N

n=1 �

−1

, (4)

where

zjn = xjn − ∑ (xinPin) Jn

i=1 , (5)

Jn is the number of alternatives in the choice set, and N is the number of choice sets. The

covariance matrix described above is a function of the choice probabilities, Pin, which in turn are 

a function of the true unknown utility parameters, β. To see this more clearly, note that in the 

case of the conditional logit model, the choice probabilities are given by

 35 These linear designs were generated in SAS software using the %mktex macro. 36 For example, a combined salmon catch above six salmon per person per day was described as very rare in focus 

group testing and was therefore eliminated from the candidate set.

37 As an example, knowing which fish would be released under a binding total salmon limit would be impossible 

when catching both Chinook (king) and coho (silver) salmon on the same trip unless one of the more narrowly 

defined regulations (king salmon limit, wild king salmon limit, silver salmon limit, wild silver salmon limit) had the 

effect of bringing total salmon catch below the total salmon limit. 38 The choice sets were formed in SAS software using the %choiceff macro.
18

Pin(Yn|β) = eβ′xin

∑ eβ′x Jn jn j=1

. (6)

We must therefore incorporate a set of prior expected values for the parameters of the 

utility function that vary in the design. These prior values can come from past research or the 

process of experimental design can be iterated with expected values derived from an earlier 

iteration.39 Alternatively, the case in which the sign and magnitude of true parameter values are 

unknown can be represented as the case where prior values are assumed to be zero. In this case, 

the covariance matrix for the multinomial logit model simplifies to

Ω = (Z′PZ)−1 = �∑ 1

Jn

∑ �z′

jnzjn� Jn

j=1

N

n=1 �

−1

, (7)

where

zjn = xjn − 1

Jn

∑ xin

Jn

i=1 . (8)

Several authors, including Huber and Zwerina (1996) and Carlsson and Martinsson (2003), have 

tested the consequences of this simplification. The result was an experimental design which 

required 10–50% more respondents to achieve the level of precision resulting from nonzero 

unbiased parameter values. Without any prior knowledge of parameter values and a large list of 

attributes, this is the path we chose. Other work (Anderson 2009) has shown that maximizing 

the D-efficiency of a choice model with no information about parameter values still provides 

more accurate WTP estimates than maximizing the D-efficiency of a linear model when the data 

generating process is unknown to the researcher at the time of the design and typical model 

selection or model averaging criteria are used. As in the construction of the candidate set, we 

again included all relevant cross-effects in the design matrix X.

D-efficiency can be misleading when used as the only measure of an optimal design. As

mentioned above, the D-efficiency of a given design is a function of the unknown parameter

values and, perhaps more problematic when working with choice models, the unknown data

generating process. A hypothesized model must be specified in the search algorithm in order to 

make this calculation. Different hypothesized models are likely to lead to different optimal

designs. We sought a robust design that was capable of providing efficient parameter estimates

under an array of model specifications, rather than a design that only performed well under a

single specification. For this purpose, we turned to simulation to provide additional criteria with 

which to evaluate competing designs.

For each design we 1) constructed a set of theoretically appealing utility functions, 

2) generated independent and identically distributed errors from the Type I extreme value 

distribution,40 3) simulated choices, 4) estimated multinomial logit models for each true utility 

function, 5) stored parameter estimates and t-statistics, 6) repeated steps 1–5 100 times, and 

 39 In the context of our surveys, we could have used the first wave of survey mailings to provide expected parameter 

values that would feed into the experimental design for the second and subsequent waves of survey mailings. 

However, this sort of updating is quite costly in practice.

40 To generate the unobservable portion of utility in a realistic manner, we generated error terms from a distribution 

chosen to mimic fit statistics from prior discrete choice experiment recreational fishing choice studies. Specifically, 

we added variance to the distribution from which the error terms are drawn until the resulting McFadden’s R2 from 

the estimated logit model was approximately 0.3.
19

7) stored the bottom 5th, 10th, and 50th percentiles of the (absolute value of) t-statistics. This 

output was used to focus on key model parameters that proved more difficult to efficiently 

estimate. While the designs considered at this stage had D-efficiency values that were nearly 

indistinguishable, there were noticeable differences in the precision of certain parameter values;

therefore, the final design was chosen to minimize the number of parameters with low 5th and 

10th percentile t-statistics. Results from a simulation such as this help to provide a design robust 

to potential intricacies in the true data generating process.

The final design was blocked into sets of four, as each of the 50 survey versions has four

choice questions. In order to ensure that every respondent would receive trips representing as 

many trade-offs as possible, we created a blocking variable that was held orthogonal to a set of

indicator variables representing the broadly defined trips being compared in each choice set. 

Each choice set may compare, for example, bottom fishing trips to other bottom fishing trips,

bottom fishing trips to salmon fishing trips, or salmon fishing trips to other salmon fishing trips,

so an indicator variable representing these choices was held orthogonal to the blocking variable. 

Other factors held orthogonal included indicator variables representing the species present 

(halibut, rockfish, lingcod, silver salmon, king salmon) and the three-level factor of cost. Last, 

each survey version was examined by hand, and some choices that were strictly dominated were 

slightly edited in order to provide more meaningful trade-offs.

The experimental design discussed here provides the discrete choice experiment 

questions intended to estimate preferences for fishing trip attributes. While we do not directly 

estimate preferences here, we note that the intended use of these data is to estimate a model that 

can simulate and measure the economic effect of changes in the recreational fishery. See 

Anderson and Lee (2013) and Anderson et al. (2013) for examples of these models. Model 

simulations require baseline catch data—the most complete of which are collected through creel 

sampling programs in the two states. We include a description of the sources and characteristics 

of the creel data that can be used for similar model simulations in Appendix A.
20

Tables 1–31

Table 1. Survey timeline.

Contact Time between contacts

Telephone screening survey —

Notice letter for mail survey 2 weeks

First survey mailing 3 days

Reminder postcard 4 days

Second survey mailing 2 weeks

Third survey mailing 3 weeks

Table 2. Detailed effective response rates.

Oregon Washington

Resident Nonresident Resident Nonresident

Overall sample 48 52 56 41

N = 3084 N = 1597 N = 3412 N = 1513

No phone contact 46 45 48 35

N = 2700 N = 1505 N = 2398 N = 1300

Phone contact 55 71 66 52

N = 384 N = 92 N = 1,014 N = 213

Table 3. Fishing versus other recreation.

Preferred recreational activity

Oregon Washington

Resident Nonresident Resident Nonresident

Fishing 45 45 41 35

Other activity 4 7 10 10

Indifferent 48 45 47 51

Did not answer 2 3 2 3

N = 469 N = 159 N = 1,194 N = 248

Table 4. Saltwater fishing versus freshwater fishing.

Preferred fishing 

type

Oregon Washington

Resident Nonresident Resident Nonresident

Saltwater fishing 25 22 46 32

Freshwater fishing 34 34 19 22

Indifferent 36 40 32 42

Did not answer 4 4 3 4

N = 469 N = 159 N = 1,194 N = 248
21

Table 5. Most often used mode from mail survey responses.

Mode

Oregon Washington

Resident Nonresident Resident Nonresident

Private 57 50 71 66

Charter 14 19 8 13

Shore 19 18 15 15

Did not answer 10 13 6 6

N = 469 N = 159 N = 1,194 N = 248

Table 6. Most often used mode from telephone survey responses.

Mode

Oregon Washington

Resident Nonresident Resident Nonresident

Private 66 57 75 67

Charter 23 17 12 15

Shore 10 22 12 17

Did not answer 1 4 1 1

N = 384 N = 92 N = 1,014 N = 213

Table 7. Boat access or ownership.

Ease of access

Oregon Washington

Resident Nonresident Resident Nonresident

Own boat 48 34 61 22

Access boat 29 32 22 40

Neither 22 31 16 33

Did not answer 1 3 2 4

N = 469 N = 159 N = 1,194 N = 248

Table 8. Average trips last 12 months from mail survey responses.

Trips

Oregon Washington

Resident Nonresident Resident Nonresident

Mean 8.8 5.9 9.3 2.5

Trimmed mean 6.9 4.3 7.6 1.6

N = 469 N = 159 N = 1,194 N = 248

Table 9. Average trips last 12 months from telephone survey responses.

Trips

Oregon Washington

Resident Nonresident Resident Nonresident

Mean 7.1 3.2 7.8 2.1

Trimmed mean 4.4 2.1 5.8 1.5

N = 384 N = 92 N = 1,014 N = 213
22

Table 10. Average trips by species group in Oregon.

Species group targeted Resident Nonresident

Bottom fish

Mean 1.8 1.0

Trimmed mean 1.2 0.6

Salmon

Mean 4.4 2.6

Trimmed mean 3.2 1.6

Bottom fish and salmon

Mean 1.7 1.4

Trimmed mean 1.0 0.7

N = 448 N = 155

Table 11. Average trips by species group from telephone survey responses.

Species group 

targeted

Oregon Washington

Resident Nonresident Resident Nonresident

Bottom fish

Mean 2.8 0.9 1.8 1.0

Trimmed mean 1.2 0.6 1.0 0.4

Salmon

Mean 5.7 1.4 5.7 1.1

Trimmed mean 2.8 1.0 3.9 0.7

N = 384 N = 92 N = 1,014 N = 213

Table 12. Importance of keeping fish for Oregon respondents.

Species

Resident: I would keep... Nonresident: I would keep...

All Most Some None All Most Some None

Halibut 63 15 13 1 57 16 20 3

Rockfish 37 20 29 6 37 18 29 11

Lingcod 49 20 19 4 45 16 25 8

King salmon 64 18 11 1 60 21 12 4

Silver salmon 62 18 13 2 55 23 15 4

N = 469 N = 159

Table 13. Importance of keeping fish for Washington respondents.

Species

Resident: I would keep... Nonresident: I would keep...

All Most Some None All Most Some None

Halibut 64 15 14 2 50 15 21 4

Rockfish 38 19 28 10 29 15 29 14

Lingcod 53 18 18 6 40 15 25 10

King salmon 67 18 12 1 56 15 19 4

Silver salmon 67 18 12 1 53 15 19 5

N = 1,194 N = 248
23

Table 14. Average daily catch per person.

Species caught 

or targeted

Oregon Washington

Resident Nonresident Resident Nonresident

Halibut

Targeted 0.65 0.87 0.56 0.42

All trips 0.05 0.09 0.04 0.06

Rockfish

Targeted 3.79 3.92 4.42 1.76

All trips 0.97 1.20 0.76 0.47

Lingcod

Targeted 1.02 1.12 1.32 0.70

All trips 0.24 0.28 0.18 0.14

King salmon

Targeted 0.47 0.48 0.70 0.52

All trips 0.24 0.23 0.39 0.26

Silver salmon

Targeted 0.92 1.35 0.98 1.04

All trips 0.30 0.39 0.58 0.41

N = 469 N = 159 N = 1,194 N = 248

Table 15. Average daily rockfish bycatch per person.

Rockfish bycatch for 

species targeted

Oregon Washington

Resident Nonresident Resident Nonresident

Halibut 0 0 0 1.14

Lingcod 0.06 0 0 0.08

N = 43 N = 11 N = 74 N = 24

Table 16. Primary purpose of trip.

Purpose

Oregon Washington

Resident Nonresident Resident Nonresident

Fishing 82 60 83 47

Other 12 36 12 48

Did not answer 6 4 5 6

N = 469 N = 159 N = 1,194 N = 248
24

Table 17. Average daily expenditures per person on the most recent trip.

Expense type

Oregon Washington

Resident Nonresident Resident Nonresident

Fishing cost

Charter boat $106.99 $123.13 $126.38 $175.00

N = 65 N = 26 N = 107 N = 33

Private boat $45.83 $36.77 $44.98 $29.67

N = 245 N = 72 N = 743 N = 146

Shore $18.73 $33.58 $18.90 $15.94

N = 73 N = 31 N = 152 N = 31

Travel cost

1-day trip $18.10 $59.98 $12.53 $72.23

N = 166 N = 26 N = 445 N = 50

2-day trip $16.09 $33.40 $16.23 $33.84

N = 87 N = 17 N = 163 N = 18

Longer trip $11.50 $20.60 $13.14 $32.03

N = 166 N = 98 N = 464 N = 152

Lodging cost $26.29 $28.25 $21.71 $28.17

N = 152 N = 78 N = 301 N = 95

Food cost $14.74 $20.61 $11.58 $16.33

N = 421 N = 143 N = 1,074 N = 222

Table 18. Likelihood of saltwater fishing next 12 months from mail survey responses.

Likelihood

Oregon Washington

Resident Nonresident Resident Nonresident

Certain to fish 61 48 63 23

Very likely 22 26 21 28

Somewhat likely 9 18 11 23

Very unlikely 4 5 2 19

Definitely will not 0 1 1 5

Did not answer 3 3 3 3

N = 469 N = 159 N = 1,194 N = 248

Table 19. Likelihood of saltwater fishing next 12 months from telephone survey responses.

Likelihood

Oregon Washington

Resident Nonresident Resident Nonresident

Certain to fish 42 33 47 17

Very likely 32 26 32 28

Somewhat likely 16 17 12 21

Very unlikely 6 16 6 19

Definitely will not 3 5 2 14

Did not answer 2 2 1 1

N = 384 N = 92 N = 1,014 N = 213
25

Table 20. Sex distribution from mail survey responses.

Sex

Oregon Washington

Resident Nonresident Resident Nonresident

Male 84 90 84 78

Female 10 8 12 15

Did not answer 6 3 4 7

N = 469 N = 159 N = 1,194 N = 248

Table 21. Sex distribution through use of the sample frame for Oregon.

Sex Resident Nonresident

Male 77 83

Female 23 17

N = 411,282 N = 94,737

Table 22. Age distribution from mail survey responses.

Age

Oregon Washington

Resident Nonresident Resident Nonresident

18–29 6 7 4 6

30–39 13 12 11 13

40–49 22 20 23 19

50–59 27 27 24 20

60–69 18 21 23 25

70–79 6 8 8 8

80 + 1 3 2 2

Did not answer 6 3 5 8

N = 469 N = 159 N = 1,194 N = 248

Table 23. Age distribution from telephone survey responses.

Age

Oregon Washington

Resident Nonresident Resident Nonresident

18–29 6 10 6 8

30–39 13 12 13 14

40–49 24 16 23 21

50–59 24 20 23 23

60–69 21 25 19 17

70–79 7 11 9 9

80 + 1 1 3 2

Did not answer 4 5 4 5

N = 384 N = 92 N = 1,014 N = 213
26

Table 24. Mean household size.

Oregon Washington

Resident Nonresident Resident Nonresident

Adults 2.00 1.98 1.95 1.96

Children 0.70 0.70 0.57 0.57

N = 469 N = 159 N = 1,194 N = 248

Table 25. Highest level of education.

Education

Oregon Washington

Resident Nonresident Resident Nonresident

Some high school 5 3 3 3

High school graduate 19 14 18 17

Technical school 11 9 9 6

Some college 28 19 26 26

College graduate or more 30 52 39 42

Did not answer 6 2 5 7

N = 469 N = 159 N = 1,194 N = 248

Table 26. Annual household income from mail survey responses.

Income ($)

Oregon Washington

Resident Nonresident Resident Nonresident

Less than 20,000 4 4 2 4

20,000–39,999 16 6 9 8

40,000–59,999 20 20 18 13

60,000–79,999 18 17 22 17

80,000–99,999 13 20 15 13

100,000–124,999 8 8 11 14

125,000–149,999 5 8 5 8

150,000 or more 7 16 8 12

Did not answer 10 2 11 13

N = 469 N = 159 N = 1,194 N = 248
27

Table 27. Annual household income from telephone survey responses.

Income ($)

Oregon Washington

Resident Nonresident Resident Nonresident

Less than 20,000 6 4 5 6

20,000–39,999 13 13 12 10

40,000–59,999 18 20 18 18

60,000–79,999 17 14 16 15

80,000–99,999 11 11 13 11

100,000–124,999 10 12 8 9

125,000–149,999 3 7 4 6

150,000 or more 4 5 6 9

Did not answer 17 14 20 15

N = 384 N = 92 N = 1,014 N = 213

Table 28. Work for pay or profit.

Status

Oregon Washington

Resident Nonresident Resident Nonresident

Do not work 22 26 27 27

Work part time 7 8 7 8

Work full time 63 63 58 56

Did not answer 8 3 8 8

N = 469 N = 159 N = 1,194 N = 248

Table 29. Personal hourly wage.

Wage ($)

Oregon Washington

Resident Nonresident Resident Nonresident

5.00–9.99 4 2 1 3

10.00–14.99 13 8 8 12

15.00–19.99 18 18 13 14

20.00–29.99 29 23 31 23

30.00–49.99 17 22 27 21

50.00 or more 14 23 13 21

Did not answer 6 4 7 6

N = 326 N = 113 N = 781 N = 159

Table 30. Time off work for fishing.

Time off

Oregon Washington

Resident Nonresident Resident Nonresident

No time off 21 15 21 13

Paid time off 38 53 46 48

Unpaid time off 24 25 22 28

Both paid and unpaid 15 7 10 9

Did not answer 2 0 1 2

N = 326 N = 113 N = 781 N = 159
28

Table 31. Experimental design attributes and attribute levels.

Attribute

Washington 

inside levela

Washington 

ocean levela

Oregon 

levela

Small halibut catch (15 lb) 1, 2 1, 2 1, 2

Medium halibut catch (25 lb) 1, 2 1, 2 1, 2

Large halibut catch (50 lb) 1, 2 1, 2 1, 2

Halibut limit 1, 2 1, 2 1, 2

Small lingcod catch (5 lb) 1, 2, 3 1, 2, 3 1, 2, 3

Medium lingcod catch (10 lb) 1, 2, 3 1, 2, 3 1, 2, 3

Large lingcod catch (15 lb) 1, 2, 3 1, 2, 3 1, 2, 3

Lingcod limit 1, 2, 3 1, 2, 3 1, 2, 3

Small rockfish catch (2 lb) 1, 2 8, 10, 12 4, 6, 8

Medium rockfish catch (4 lb) 1, 2 8, 10, 12 4, 6, 8

Large rockfish catch (6 lb) 1, 2 8, 10, 12 4, 6, 8

Rockfish limit 1, 2 8, 10, 12 4, 6, 8

Small wild silver catch (5 lb) 1, 2, 3 1, 2, 3 1, 2, 3

Medium wild silver catch (10 lb) 1, 2, 3 1, 2, 3 1, 2, 3

Large wild silver catch (15 lb) 1, 2, 3 1, 2, 3 1, 2, 3

Wild silver salmon limit 0, 1, 2 0, 1, 2 0, 1, 2

Small hatchery silver catch (5 lb) 1, 2, 3 1, 2, 3 1, 2, 3

Medium hatchery silver catch (10 lb) 1, 2, 3 1, 2, 3 1, 2, 3

Large hatchery silver catch (15 lb) 1, 2, 3 1, 2, 3 1, 2, 3

Silver salmon limit NA NA 0

Small wild king catch (10 lb) 1, 2, 3 1, 2, 3 1, 2, 3

Medium wild king catch (20 lb) 1, 2, 3 1, 2, 3 1, 2, 3

Large wild king catch (30 lb) 1, 2, 3 1, 2, 3 1, 2, 3

Wild king salmon limit 0, 1, 2 0, 1, 2 0, 1, 2

Small hatchery king catch (10 lb) 1, 2, 3 1, 2, 3 1, 2, 3

Medium hatchery king catch (20 lb) 1, 2, 3 1, 2, 3 1, 2, 3

Large hatchery king catch (30 lb) 1, 2, 3 1, 2, 3 1, 2, 3

King salmon limit 1, 2 1, 2 1, 2

Pink salmon catch (3 lb) 2, 4 2, 4 NA

Pink salmon limit 2, 4 2, 4 NA

Total salmon limit 2, 4 2, 4 2, 4

Fishing costb 20, 40, 80 20, 40, 80 20, 40, 80

a All attributes listed in table also include a level of “missing” in the design except fishing cost. b Levels shown are for private boat cost. Charter boat cost levels were 85, 125, 175.
29

References

Anderson, L. 2009. Fishing for angler preferences: Nonmarket valuation of saltwater recreational 

fishing. Doctoral thesis. Univ. Washington, Seattle.

Anderson, L. E., and S. T. Lee. 2013. Untangling the recreational value of wild and hatchery salmon. 

Mar. Resour. Econ. 28(2):175–197.

Anderson, L. E., S. T. Lee, and P. S. Levin. 2013. Costs of delaying conservation: Regulations and the 

recreational values of rockfish and co-occurring species. Land Econ. 89(2):371–385.

Anderson, L. G. 1993. Toward a complete economic theory of the utilization and management of 

recreational fisheries. J. Environ. Econ. Manag. 24:272–295.

Carlsson, F., and P. Martinsson. 2003. Design techniques for stated preference methods in health 

economics. Health Econ. 12:281–294.

Dillman, D. A. 2000. Mail and Internet surveys: The tailored design method. First edition. Wiley & 

Sons, New York.

Ferrini, S., and R. Scarpa. 2007. Designs with a priori information for nonmarket valuation with choice 

experiments: A Monte Carlo study. J. Environ. Econ. Manag. 53(3):342–363.

Gelman, A., and J. B. Carlin. 2002. Poststratification and weighting adjustments. In R. M. Groves, 

D. A. Dillman, J. L. Eltinge, and R. J. A. Little (eds.), Survey nonresponse, p. 289–302. Wiley & 

Sons, New York.

Groves, R. M., D. A. Dillman, J. L. Eltinge, and R. J. A. Little. 2002. Survey nonresponse. Wiley & 

Sons, New York.

Huber, J., and K. Zwerina. 1996. The importance of utility balance in efficient choice design. J. Mark.

Res. 33:307–317.

Jarvis, E. T., and C. G. Lowe. 2008. The effects of barotrauma on the catch-and-release survival of 

southern California nearshore and shelf rockfish (Scorpaenidae, Sebastes spp.). Can. J. Fish.

Aquat. Sci. 65(7):1286–1296.

Kuhfeld, W. F., R. Tobias, and M. Garrat. 1994. Efficient experimental design with marketing research 

applications. J. Mark. Res. 31:545–557.

Lusk, J. L., and F. B. Norwood. 2005. Effect of experimental design on choice-based conjoint valuation 

estimates. Am. J. Agric. Econ. 87:771–785.

McFadden, D. 1974. Conditional logit analysis of qualitative choice behavior. In P. Zarembka (ed.), 

Frontiers in econometrics, p. 105–142. Academic Press, New York.

RecFIN (Recreational Fisheries Information Network). 2006. Pacific States Marine Fisheries 

Commission. Online at http://www.psmfc.org/recfin [accessed January 2006].
30

Scrogin, D., K. Boyle, G. Parsons, and A. J. Platinga. 2004. Effects of regulations on expected catch, 

expected harvest, and site choice of recreational anglers. Am. J. Agric. Econ. 86(4):963–974.

Woodward, R. T., and W. L. Griffin. 2003. Size and bag limits in recreational fisheries: Theoretical and 

empirical analysis. Mar. Resour. Econ. 18:239–262.

Zwerina, K., J. Huber, and W. F. Kuhfeld. 2005. A general method for constructing efficient choice 

designs. SAS Institute Paper TS-722E. SAS Institute, Cary, NC.
31

Appendix A: State Recreational Data

The discrete choice experiment questions in the Washington Sportfishing Survey and the 

Oregon Sportfishing Survey are intended to provide the data necessary to estimate preferences 

for fishing trip attributes. These preferences, in turn, can be used to measure the effect of 

changes in management or ecological conditions. However, the degree of realism in any 

assessment of changes depends directly on the extent to which the data used in model 

simulations match the actual conditions in the fishery. This appendix describes the sources and 

characteristics of an available set of simulation data taken from the marine recreational fisheries 

in Washington and Oregon.

The data needed for model simulations are the same attributes contained in the 

experimental design: catch by species and size. Bag limits can be directly simulated for analysis. 

Taken together with catch, simulated bag limits provide the necessary components from which to 

calculate release.

Additional detail on the required catch data is necessary. Most theoretical models of 

recreational fishing in the economic literature feature bag limits that affect utility only when 

binding.41 Equivalently, slack bag limits have no effect. Using the average catch per angler over 

all trips will not, in general, allow measurement of the effect from a change in bag limits, as the 

average catch per angler is less than the bag limit. In order to conduct simulations under such a 

framework, a distribution of catch over anglers is needed.

Angler interviews conducted by Washington Department of Fish and Wildlife (WDFW) 

and Oregon Department of Fish and Wildlife (ODFW) formed the basis of the simulation data. 

ODFW’s Ocean Recreational Boating Survey (ORBS) is the source of all Oregon data. 

WDFW’s saltwater recreational data collection is split into the Puget Sound Sampling Program

and the Ocean Sampling Program, consisting of creel interviews, voluntary trip reports (VTRs), 

onboard observers, test boats, and biological data. Creel interviews provide the fishing mode, 

the number of fish caught by species, and the number of fish released by species. Weight data 

come primarily from a combination of length data and length-to-weight relationships we 

estimate. All sample lengths for Oregon come from the creel interviews conducted for ORBS. 

Sample lengths for Washington come from a combination of VTRs, onboard observers, test 

boats, and creel interviews.

VTRs are submitted by salmon anglers in both the Ocean Area and Inside Area. For our 

purposes, VTR data provided a source of lengths for coho salmon (aka silver salmon, 

Oncorhynchus kisutch) and Chinook salmon (aka king salmon, O. tshawytscha). WDFW places 

observers on charter boats in the Ocean Area in order to monitor retained and released fish. 

Observer data provide an additional source of lengths for coho and Chinook salmon caught in the 

 41 For examples, see Anderson 1993, Scrogin et al. 2004, and Woodward and Griffin 2003.
32

Ocean Area. Test boat anglers employed by WDFW use the same fishing methods and fish in 

the same general locations as the recreational fleet in Puget Sound. For our purposes, test boats 

provided a source of lengths for Chinook salmon caught in the Inside Area.

Fish sizes in the experimental design (and the variables describing the size of fish in the 

resulting models estimated on the discrete choice experiment data) are given by weight in 

pounds. Fish size is only available on a subset of catch data from WDFW and ODFW. Most of 

the records with size characteristics are limited to length, with a subset containing both length 

and weight. We used the existing length-weight pairs to construct relationships, through 

regression, that were then used to impute missing weights.

Table A-1 provides the sources of all lengths and weights in the simulation data. For all 

combinations of species and areas, there exist length data. In a few cases, the sampling program 

for a particular species and area combination does not collect any weight data. In these cases, 

length-to-weight relationships from a neighboring area were used to translate lengths into 

weights. Length-to-weight relationships borrowed from a neighboring area are denoted as L~W 

in the table. As an example, the length-to-weight relationship for Pacific halibut in the Ocean 

Area of Washington was borrowed from the Inside Area.

Next we describe the calculation of released fish. For all bottom fish species, the 

assumption that larger fish are preferred within a species was sufficient to calculate the number 

of released fish for any desired daily bag limit. Grouped salmon regulations complicated this

calculation for salmon species and, when binding, required knowledge of the trade-offs anglers 

are willing to make between different sizes and species of salmon that must be released. These 

trade-offs were determined with the estimated parameters of the econometric model. We 

assumed that anglers minimized the disutility of releasing fish subject to the set of regulations 

that are imposed. This assumption, combined with the model estimates for release parameters, 

uniquely identified release in our simulation data.

The choice experiment data were framed in terms of individual angler attributes, whereas 

all catch data collected from WDFW and ODFW were framed in terms of a fishing vessel. In 

order to ensure that randomly drawn simulation data were representative of the levels of catch

for an individual angler, we expanded all boat-level data by the number of anglers on the boat in 

order to arrive at the average catch per angler for each interviewed boat.

In the Oregon data, port sites have unequal sampling rates. Since the choice experiment 

data were gathered at the state level, we sought a catch distribution representative of marine 

fishing trips at the same level. Differences in the proportion of sampled trips across ports would 

have led to a bias as calculated at the state level. In order to correct the unequal port sampling, 

we expanded all Oregon interviews by the ratio of ODFW-estimated trips to ODFW-sampled 

trips.

For management purposes, individual rockfish species are broken out for the creel 

interviews in both states. However, focus group testing indicated that anglers do not commonly 

differentiate between species of rockfish when forming preferences (independent of the effect of 

size); therefore, the choice experiment data aggregated all rockfish species together. In order to 
33

match the choice experiment data, we aggregated all rockfish species together to create the 

simulation data.

Creel interviews with unrealistically high levels of catch were dropped. We defined trips 

with the top 1% of catch per angler as outliers and removed them from the simulation data set.42 

Table A-2 contains the thresholds we used to qualify outlier levels of catch; creel interviews 

containing catch per angler exceeding the entries in the table were eliminated from the 

simulation data set.

The mark status of released salmon in Oregon is not tracked. While all retained Chinook 

salmon and coho salmon on creel interviews, VTR, onboard observer, and test boat trips are 

characterized as either marked or unmarked in both states, released salmon are only 

characterized in Washington. In order to impute the mark rate of released salmon in Oregon, we 

assumed that the mark rate of released Chinook salmon was the same as the mark rate of retained 

Chinook, and that all released coho salmon were marked.

43

There are several limitations to the source data not mentioned above that we 

acknowledge. Though any increase in the proportion of fish that are characterized by length or 

weight would be a valuable improvement to the simulation data we gathered, this is especially 

true with respect to released fish. For a majority of the species and areas in the source data, there 

were no sizes collected for fish that are released. This required us to assume that the size 

distribution of released fish was the same as the size distribution of retained fish.44 While this 

might not greatly affect an analysis of a change in bag limits, the size of released fish would 

become increasingly important in an analysis of a change in, for example, minimum size 

regulations.

Another limitation of the source data is that all creel interviews are conducted at the boat 

level. Using the boat-level data to create angler-level data masks some of the catch 

heterogeneity that would be present in data collected at the angler level. While the effect of a 

change in bag limits measured under this approach will tend to be attenuated, relative to the true 

individual-level effect, the bias will likely be small in magnitude due to regulations allowing 

individual anglers to remain fishing until the limits are reached for all anglers on the boat.

In Oregon, the Shore and Estuary Boat Survey was originally designed to sample shorebased fishing trips and boat trips that launch at estuary locations where a majority of trips remain 

in the estuary and do not proceed to the ocean. That survey is no longer fielded by ODFW and, 

as a result, some estuary locations are likely to be undersampled. In addition, while there are 

estuary trips in the sample frame, there are not current effort estimates for estuary trips, requiring 

the port-level sampling correction described above to rely solely on the ratio of sampled to 

estimated ocean trips.

 42 As a somewhat extreme example, a trip in one of the creel interviews for the Ocean Area of Washington reported 

rockfish catch per angler of 110. 43 These assumptions were based on the recommendations of ODFW sampling program personnel (E. Schindler, 

ODFW, Newport, OR. Pers. commun., 15 June 2010). 44 There are some exceptions to this requirement, including VTR, onboard observer, and test boat data.
34

Figure A-1 is a flowchart illustrating the basic steps used to generate the simulation data 

described to this point. Length and weight data were combined to form length-to-weight 

relationships that were used to impute missing weights and create a size distribution for each 

species. Catch and size distributions were (randomly) combined in order to provide a set of 

simulated catch, broken out by the sizes in our experimental design. Bag limits and, in the case 

of some salmon trips, parameters from an econometric model describing the utility of release 

were applied to the simulated catch in order to produce the final element of the simulation data: 

catch that must be released.

In order to provide further detail to the general approach we used to conduct simulations 

with these data, we describe a general example: willingness to pay (WTP) for a change in bag 

limits. As a starting point, we take the set of simulated catch by weight data, where each row 

corresponds to a sampled trip, and column entries contain the catch for three sizes of each 

species. With these data, we 1) draw randomly from the distribution of catch for each 

respondent who completed our mail surveys, 2) impose baseline bag limits, 3) calculate release, 

4) estimate WTP for each type of fishing trip, and 5) repeat the entire process under the changed 

bag limits, then for any desired statistic of interest (e.g., mean WTP), we must 6) average the 

results of this process over repeated draws from the distribution of catch. WTP for a change in 

catch rates is simulated with the same approach, except bag limits are held constant and catch 

rates are varied.

Table A-1. Sources of lengths and weights in simulation data by species and area.

Species

Washington ocean Washington inside Oregon

Length Weight Length Weight Length Weight

Pacific halibut (Hippoglossus stenolepis)

Creel L~Wa

inside creel

Creel Creel ORBS L~W 

inside creel

Rockfish

(Sebastes spp.)

Creel Creel Creel Creel ORBS ORBS

Lingcod (Ophiodon 

elongatus)

Creel Creel Creel Creel ORBS ORBS

Coho salmon 

(Oncorhynchus kisutch)

VTR and 

observer

L~W 

ORBS

VTR L~W 

ORBS

ORBS ORBS

Chinook salmon 

(O. tshawytscha)

VTR and 

observer

L~Wb

NWFSC

Test boat 

and VTR

L~W 

NWFSC

ORBS L~W 

NWFSC

a L~W denotes use of a length-to-weight relationship from a neighboring area. For example, L~W inside creel

indicates that the length-to-weight relationship estimated on the Inside Area creel interviews was used to translate 

length data to weight data. b Length-weight pairs for 243 Chinook salmon collected in marine waters from 6 regions (northern coastal British 

Columbia, northern Georgia Basin, southern Georgia Basin, Puget Sound, Columbia River, northern coastal 

California), provided by G. Ylitalo and S. O’Neill of the NWFSC, were used to estimate the relationship that 

allowed us to convert length data to weight data in all three regions. This relationship is represented as L~W 

NWFSC.
35

Table A-2. Outlier threshold for catch per angler by species and area.

Species Washington ocean Washington inside Oregon

Pacific halibut 3.0 1.0 1.7

Rockfish 18.5 5.0 8.5

Lingcod 6.0 4.0 2.7

Coho salmon 9.5 4.5 7.0

Chinook salmon 3.5 6.0 1.5

Figure A-1. Steps used to prepare simulation data.

Catch Length-to-weight 

relationships Weights

Simulated catch 

by weight

Calculated 

release

Bag limits Salmon release 

parameters

Final simulation 

data

Lengths
36
37

Appendix B: Saltwater Sportfishing 

Survey Documents

This appendix contains the following documents from the survey package: 1) the initial 

telephone screening survey, 2) the notice letter for the mail survey, 3) the cover letter for the first 

mailing of the complete survey package, 4) one example of the mail survey instrument, 5) the 

reminder postcard, 6) the cover letter for the second mailing of the complete survey package, and 

7) the cover letter for the third and final mailing of the complete survey package. The mail 

survey instrument is in its original format; the other documents are not.

PC ECAP Telephone Screening Survey (Washington/Oregon)

INTRO:

Hello, my name is _____________________________. I’m calling on behalf of NOAA 

Fisheries to conduct a brief survey about recreational fishing activities of residents in 

Washington and Oregon State. Your answers will be used to help fishery managers make 

informed decisions.

[If asked: NOAA stands for National Oceanic and Atmospheric Administration.]

May I please speak with _____________________________?

01 (SKIP TO Intro 2:) YES, RESPONDENT AVAILABLE/TRANSFERRING

02 (SKIP TO EXIT1) NO, RESPONDENT UNAVAILABLE

03 DK/REFUSED [Schedule callback 1 day out]/SCHEDULE CALLBACK/

EXIT1: Thank you, I will call back later. When would be a good time to reach [Respondent]? 

/SCHEDULE CALLBACK/

[Q: What is NOAA Fisheries? A: NOAA Fisheries is the federal agency responsible for the 

stewardship of the nation’s living marine resources and their habitat.]

[Q: How did you get my name/phone number? A: Your name/telephone number was drawn in a 

random sample of people who purchased a fishing license in [Washington/Oregon]

[Q: Is this interview confidential? A: This interview is completely confidential. Your name will 

never be linked to your responses in any way.]

[Q: How long will this interview take? A: The interview takes 4–5 minutes.]
38

Intro. 2: Before we begin, I want to assure you that your answers will be kept completely 

confidential and this call may be monitored for quality assurance. This is a voluntary statewide 

study and we appreciate your assistance.

PHI: I’m going to read you a few short questions about your sportfishing activities in 

[Washington/Oregon]. /Insert appropriate state based on area code/

1. How many saltwater fishing trips have you taken in [Washington/Oregon] in the last 

12 months? /Insert appropriate state based on area code/

01 [Enter number of trips] /IF TRIP NUMBER IS 1–365/

02 NO TRIPS

98 DON’T KNOW/NOT SURE

99 REFUSED

[Q: What do you mean by salt water? A: Salt water is defined as waters that include 

oceans, bays, estuaries and brackish portions of rivers.]

2. How many freshwater fishing trips have you taken in [Washington/Oregon] in the last 

12 months? /Insert appropriate state based on area code/

01 [Enter number of trips] /IF TRIP NUMBER IS 1–365/

02 NO TRIPS

98 DON’T KNOW/NOT SURE

99 REFUSED

/IF BOTH SALTWATER TRIPS = 0 AND FRESHWATER TRIPS = 0, or 01 AND 

Q2 = DK/REFUSED, SKIP TO ENDING1./

PH2: The next few questions will be about your saltwater fishing.

3. How many saltwater fishing trips have you taken in [Washington/Oregon] in the last 

12 months where you “targeted” bottom fish, such as halibut, rockfish, or lingcod? 

/Insert appropriate state based on area code/

01 [Enter number of trips] /IF TRIP NUMBER IS 1–365/

02 NO TRIPS

98 DON’T KNOW/NOT SURE

99 REFUSED

[Q: What do you mean by rockfish? A: Rockfish are also called sea bass, red snapper, 

rock cod, black bass.]

[Q: What do you mean by “targeted?” A: This means you were trying to catch a 

particular species of fish.]
39

4. How many saltwater fishing trips have you taken in [Washington/Oregon] in the last 

12 months where you “targeted” salmon? /Insert appropriate state based on area code/

01 [Enter number of trips] /IF TRIP NUMBER IS 1–365/

02 NO TRIPS

98 DON’T KNOW/NOT SURE

99 REFUSED

5. When you saltwater fish in [Washington/Oregon], do you usually fish from...? /Insert 

appropriate state based on area code/

01 A private boat

02 A charter boat

03 The shore

98 DON’T KNOW/NOT SURE

99 REFUSED

6. How likely is it that you will saltwater sport fish in [Washington/Oregon] during the 

next 12 months? /Insert appropriate state based on area code/

01 Certain to fish

02 Very likely

03 Somewhat likely

04 Very unlikely

05 Definitely will not fish

98 DON’T KNOW/NOT SURE

99 REFUSED

PH3: So that we can see how your fishing activities compare with those of other people in your 

state, I’d like to ask you a few demographic questions. Again, please remember all your answers 

are kept completely confidential.

7. In what year were you born?

01 [Record year] /1907–1990/

98 DON’T KNOW/NOT SURE

99 REFUSED

8. I’m going to read you some income categories. For classification purposes only, 

please tell me, which of the following income categories best describes your 

household’s total annual income before taxes in 2005.

01 Less than $10,000

02 $10,000 to $14,999

03 $15,000 to $24,999

04 $25,000 to $34,999
40

05 $35,000 to $49,999

06 $50,000 to $74,999

07 $75,000 to $99,999

08 $100,000 to $149,999

09 $150,000 to $199,999

10 $200,000 or more

98 DON’T KNOW/NOT SURE

99 REFUSED

[If respondent refuses: Your answers are completely confidential and will only be used 

for classification purposes. You will never be identified with your response.]

► GO TO ENDING 2

______________________________________________________________________________

ENDING1:

Thank you very much for your help today.

______________________________________________________________________________

ENDING2:

Thank you very much for your help today. This call is part of a larger research project to help 

fishery managers learn more about the likes and dislikes of anglers like you. I’d like to send you 

a short survey in the mail if I could just verify the address I have from your license. I have:

Name _______________________________________________

Street Address.________________________________________

City _______________________ State ______ Zip __________

Phone _______________________________________________

[If respondent refuses: Very few anglers were selected for this survey, so your help is critical to 

its success. Your participation will help provide important information to fishery managers to 

improve your fishing opportunities. The survey should not take more than 20 minutes to 

complete. We really appreciate your help.]

Thank you, you will be receiving a short survey in the mail in the next few weeks.
41

Notice Letter for the Mail Survey

Washington Sportfishing Survey

<Month, Day, Year>

<First Last>

<Street Address>

<City, State Zip>

Dear <First Last>:

A few days from now, you will receive a short questionnaire for an important study being 

conducted by NOAA Fisheries (National Marine Fisheries Service).

The Washington Sportfishing Survey will help us learn more about your interest and success in 

fishing for bottom fish and saltwater salmon in Washington. This study will be used to improve 

the management of Washington’s recreational fishery through a better understanding of the 

activities and preferences of anglers like you. Even if you have only fished once, it is important 

that we hear from you.

We need your help. Your response will provide important information to fishery managers to:

• Improve your sportfishing experience and opportunities, and

• Enhance sound fishery management practices.

Your name was selected at random from anglers who purchased a Washington sportfishing 

license. Very few anglers were chosen for the study, so your help is critical to its success. We 

will send you a questionnaire through ORC Macro, a nationally recognized survey research firm 

who is our partner in conducting the survey. Simply complete the questionnaire and return it in 

the postage paid envelope provided.

If you would like to learn more about this important survey or have any questions, please call me 

toll free at 1-866-791-3726.

Thank you very much for your help!

Sincerely,

Todd Lee

Project Director

NOAA Fisheries—Northwest Fisheries Science Center
42

Cover Letter for First Mailing of the Complete Survey Package

Washington Sportfishing Survey

<Month, Day, Year>

<First Last>

<Street Address>

<City, State Zip>

Dear <First Last>:

Enclosed is the survey we mentioned in our previous letter to you. The Washington 

Sportfishing Survey is being conducted by NOAA Fisheries (National Marine Fisheries 

Service). This is your chance to help improve Washington’s recreational fishery.

Your answers will be used to:

• Help fishery managers understand what anglers like and dislike,

• Enhance your sportfishing experience, and

• Improve fishery management.

Your answers are completely confidential and will be released only as summaries in which no 

individual’s answers can be identified. There are no right or wrong answers and, even if you’ve 

only fished one time, it is important that we hear your opinions.

If you have any questions, please call me toll free at 1-866-791-3726.

Thank you very much for your help!

Sincerely,

Todd Lee

Project Director

NOAA Fisheries—Northwest Fisheries Science Center
43

Mail Survey Instrument (16 pages)
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47
48
49
50
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Reminder Postcard

Washington Sportfishing Survey Reminder

Sponsored by NOAA Fisheries

Last week a questionnaire seeking your opinion about Washington saltwater sportfishing was 

mailed to you. Your name was selected at random from anglers who purchased a Washington 

fishing license.

If you have already completed and returned it to us, please accept our sincere thanks. If not, 

please do so today. The questionnaire has been sent to only a small, but representative, sample 

of anglers. It is extremely important that yours also be included in the study if the results are to 

accurately represent the opinions of anglers.

If by some chance you did not receive the questionnaire or it got misplaced, please call 1-866-

791-3726
60

Cover Letter for Second Mailing of the Complete Survey Package

Washington Sportfishing Survey

<Month, Day, Year>

<First Last>

<Street Address>

<City, State Zip>

Dear <First Last>:

About three weeks ago, we sent you a questionnaire that asked you about saltwater sportfishing 

in Washington. To the best of our knowledge, it has not yet been returned.

The comments of other people who have already responded include a wide variety of fishing 

experiences and preferences. Many have told us about the fishing they enjoy and about trips 

they have taken. We think the results are going to be very useful to fishery managers.

We are writing you again because of the importance that your questionnaire has for helping to 

get accurate results. Your name was drawn through a scientific sampling process in which every 

individual who purchased a 2005 or 2006 Washington sportfishing license had an equal chance 

of being selected. Because only a small number of anglers were chosen for the study, your 

participation is essential if the results are to be truly representative of the opinions, preferences, 

and activities of all anglers.

• It doesn’t matter how often you’ve fished, your answers are valuable.

• Even if you’ve never fished in salt water, please return the survey so we can more 

accurately measure fishing participation.

We hope you will fill out and return the questionnaire soon, but if for any reason you prefer not 

to answer it, please let us know by returning a note or blank questionnaire in the enclosed 

stamped envelope.

Thank you very much for your help.

Sincerely,

Todd Lee

Project Director

NOAA Fisheries—Northwest Fisheries Science Center

P.S. If you have any questions, please call me toll free at 1-866-791-3726.
61

Cover Letter for Third and Final Mailing of the Complete 

Survey Package

Washington Sportfishing Survey

<Month, Day, Year>

<First Last>

<Street Address>

<City, State Zip>

Dear <First Last>:

During the last two months, we have sent you several mailings about an important research study 

we are conducting on sportfishing in Washington. As of today, we have not received your 

questionnaire. If you have already mailed it to us, we thank you for your assistance.

The purpose of this study is to improve fishery management by providing a more complete 

picture of participation rates and angler preferences.

The study is drawing to a close, and this is the last contact that will be made with the random 

sample of people who purchased a Washington sportfishing license in 2005 or 2006. We are 

sending this final contact because of our concern that people who have not responded may have 

different experiences and preferences than those who have responded. In order for our results to 

be accurate, we need to hear from you, regardless of whether you sport fished in salt water or the 

number of times you fished.

We also want to assure you that your response to this study is voluntary and any responses you 

give us are confidential.

Finally, we appreciate your willingness to consider our request as we conclude this effort to 

better understand sportfishing in Washington. Thank you very much.

Sincerely,

Todd Lee

Project Director

NOAA Fisheries—Northwest Fisheries Science Center

P.S. If you have any questions, please call me toll free at 1-866-791-3726.

Recent NOAA Technical Memorandums

published by the

Northwest Fisheries Science Center

NOAA Technical Memorandum NMFS-NWFSC123 Ward, E.J., M.J. Ford, R.G. Kope, J.K.B. Ford, L.A. Velez-Espino, C.K. Parken, L.W. LaVoy, 

M.B. Hanson, and K.C. Balcomb. 2013. Estimating the impacts of Chinook salmon abundance and 

prey removal by ocean fishing on Southern Resident killer whale population dynamics. U.S. Dept. 

Commer., NOAA Tech. Memo. NMFS-NWFSC-123, 71 p. NTIS number PB2013-110079.

122 Frame, E., and K. Lefebvre. 2013. ELISA methods for domoic acid quantification in multiple marine 

mammal species and sample matrices. U.S. Dept. Commer., NOAA Tech. Memo. NMFS-NWFSC-122, 

20 p. NTIS number PB2013-109341.

121 Lian, C.E. 2012. West Coast limited entry groundfish cost earnings survey: Protocol and results for 

2008. U.S. Dept. Commer., NOAA Tech. Memo. NMFS-NWFSC-121, 62 p. NTIS number PB2013-

104899.

120 Pollock, M.M., J.M. Wheaton, N. Bouwes, C. Volk, N. Weber, and C.E. Jordan. 2012. Working 

with beaver to restore salmon habitat in the Bridge Creek intensively monitored watershed: Design 

rationale and hypotheses. U.S. Dept. Commer., NOAA Tech. Memo. NMFS-NWFSC-120, 47 p. NTIS 

number PB2013-101722.

119 Waples, R.S., K. Hindar, and J.J. Hard. 2012. Genetic risks associated with marine aquaculture. 

U.S. Dept. Commer., NOAA Tech. Memo. NMFS-NWFSC-119, 149 p. NTIS number PB2013-101344.

118 Stout, H.A., P.W. Lawson, D.L. Bottom, T.D. Cooney, M.J. Ford, C.E. Jordan, R.G. Kope, 

L.M. Kruzic, G.R. Pess, G.H. Reeves, M.D. Scheuerell, T.C. Wainwright, R.S. Waples, E. Ward, 

L.A. Weitkamp, J.G. Williams, and T.H. Williams. 2012. Scientific conclusions of the status review 

for Oregon coast coho salmon (Oncorhynchus kisutch). U.S. Dept. Commer., NOAA Tech. Memo. 

NMFS-NWFSC-118, 242 p. NTIS number PB2012-113458.

117 Maynard, D.J., T.A. Flagg, W.C. McAuley, D.A. Frost, B. Kluver, M.R. Wastel, J.E. Colt, and 

W.W. Dickhoff. 2012. Fish culture technology and practices for captive broodstock rearing of ESAlisted salmon stocks. U.S. Dept. Commer., NOAA Tech. Memo. NMFS-NWFSC-117, 65 p. NTIS 

number PB2012-110667.

116 Lian, C.E. 2012. West Coast open access groundfish and salmon troller survey: Protocol and results 

for 2005 and 2006. U.S. Dept. Commer., NOAA Tech. Memo. NMFS-NWFSC-116, 52 p. NTIS 

number PB2012-107486.

Most NOAA Technical Memorandums NMFS-NWFSC are available at the

Northwest Fisheries Science Center Web site, http://www.nwfsc.noaa.gov