Angler preferences and satisfaction in a high-threshold bucket-list recreational fishery

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Fisheries Research

journal homepage: www.elsevier.com/locate/ﬁshres

Angler preferences and satisfaction in a high-threshold bucket-list

recreational ﬁshery

Abigail S. Golden

a,b,

⁎

, Christopher M. Free

b,c

, Olaf P. Jensen

Graduate Program in Ecology and Evolution, Rutgers University, New Brunswick, NJ, USA

Department of Marine and Coastal Sciences, Rutgers University, New Brunswick, NJ, USA

Bren School of Environmental Science and Management, University of California, Santa Barbara, Santa Barbara, CA, USA

ARTICLE INFO

Handled by B. Morales-Nin

Keywords:

Angler preferences, motivation, and satisfaction

Hucho taimen

Mongolia

Catch-and-release ﬂy ﬁshing

Discrete choice experiment

Mixed quantitative-qualitative methods

ABSTRACT

It is important to understand recreational anglers’ motivations for ﬁshing in order to predict when, where, and

how they interact with species that can be sensitive to overﬁshing. So far, few studies have investigated angler

motivation in recreational ﬁsheries that are extremely distant from their angler population, require specialized

angler skill, and pose other barriers to participation like high travel and equipment costs. We collectively refer to

these as “high-threshold” ﬁsheries and explore angler motivation and its implications for anglers’ decision-

making in one particularly remote example, the Mongolian ﬂy ﬁshery for endangered taimen, Hucho taimen, the

largest salmonid in the world. We used a mixed-methods approach that enriched discrete choice experiments

with in-depth qualitative interview data to investigate anglers’ motivations for participating in the taimen

ﬁshery, their satisfaction with the ﬁshing experience, and their stated interest in participating in the ﬁshery in

the future. We found that anglers preferred fewer high-quality, trophy-sized ﬁsh to a higher catch rate of smaller

taimen, but that activity-general factors like the opportunity to travel to an “exotic” wilderness destination were

also highly motivating. The anglers we sampled were all ﬁrst-time taimen ﬁshermen and many were bucket-list

anglers who sought a wide variety of ﬁshing tourism experiences throughout their lifetime and therefore and had

no intention to return to the taimen ﬁshery. Instead, these ﬁshermen selected their future trips from among a set

of similarly remote, specialized, and costly ﬁsheries throughout the world, especially in developing countries.

We argue that these high-threshold ﬁsheries should not be studied in isolation but instead would beneﬁt from a

uniﬁed research approach that accounts for their unique traits and shared angler population.

1. Introduction

Recreational anglers are the primary users of many freshwater ﬁsh

stocks and can provide signiﬁcant economic, social, and environmental

beneﬁts (Arlinghaus et al., 2002; Parkkila et al., 2010). Recreational

ﬁsheries can be important economic engines at the local and regional

scale (Hyder et al., 2018) and they engage an estimated 220 million

people worldwide (World Bank, 2012), not to mention accounting for a

signiﬁcant fraction of the global ﬁsh harvest (Cooke and Cowx, 2004).

They also present unique management challenges. Fishing eﬀort in

recreational ﬁsheries tends to be geographically diﬀuse and to include

diverse targets and gear types even within a single ﬁshery (Arlinghaus

et al., 2014; Post et al., 2002). These factors can make it diﬃcult to

collect accurate catch statistics, enforce regulations, and predict how

biological and regulatory changes might impact future eﬀort. A large

body of literature and theory has emerged to address these challenges

and especially to understand the dynamics that control angler eﬀort.

For instance, many researchers have modeled angler behavior as a

predator-prey interaction in which anglers intensify their eﬀort when

they encounter rich patches of “prey,” causing eﬀort to equalize across

a landscape (Johnson and Carpenter, 1994; Post et al., 2008; Wilson

et al., 2016).

This understanding of anglers as human predators works well in

ﬁsheries where anglers are uniformly catch-oriented to the exclusion of

other goals, but many ﬁsheries cater to a spectrum of anglers who range

from catch-oriented to trophy-seeking to casual recreationists

(Arlinghaus et al., 2008; Bryan, 1977; Magee et al., 2018). In these

ﬁsheries, the connection between current ﬁshery status and future

ﬁshery participation is more complex. Anglers are motivated by a di-

verse set of factors that include not only their preferred catch rate and

target species but also activity-general elements that are common to

many forms of outdoor recreation, such as experiencing nature,

https://doi.org/10.1016/j.ﬁshres.2019.105364

Received 26 February 2019; Received in revised form 3 September 2019; Accepted 5 September 2019

⁎

Corresponding author at: Department of Marine and Coastal Sciences, Rutgers University, 71 Dudley Rd, New Brunswick, NJ 08901, USA.

E-mail address: [email protected] (A.S. Golden).

Fisheries Research 220 (2019) 105364

enjoying solitude, and socializing (Arlinghaus, 2006; Oh and Ditton,

2008; Oh et al., 2013). These preferences and motivations set the ex-

pectations that determine anglers’ satisfaction and consequently their

future ﬁshing eﬀort (Arlinghaus, 2006). Therefore, if anglers are mo-

tivated to ﬁsh partly or primarily by activity-general characteristics of

the activity, their satisfaction may depend heavily on factors other than

the number and perceived quality (size, species, etc.) of the ﬁsh they

catch (Curtis and Breen, 2017; Fedler and Ditton, 1994; Greiner et al.,

2016). For instance, a recent study of angler satisfaction in a German

multispecies ﬁshery found that the contribution of catch-related factors

to satisfaction plateaued above a certain threshold for most species,

demonstrating that catch provides diminishing returns in satisfaction

(Beardmore et al., 2015). In another case, 90% of surveyed anglers

reported that they would be satisﬁed with a trip even if they did not

catch any ﬁsh. These minimally catch-oriented anglers instead valued

“relaxing in the outdoors at the water side and ﬁshing in pleasant

company” (Arlinghaus, 2006).

Random utility theory provides one framework for understanding

this heterogeneity in preferences and weighing the relative importance

of diﬀerent catch-related and activity-general factors (Aas et al., 2000;

Hunt, 2005). The theory states that anglers choose ﬁshing options by

subconsciously integrating the costs and bene ﬁts of diﬀerent options in

a way that maximizes their overall utility, or beneﬁt, from the ﬁshing

opportunity. This utility metric includes a set of deterministic compo-

nents that can be measured by the researcher and a stochastic term that

represents individual variation in taste, missing variables, and other

unmeasured factors (Train, 2002). Studies that use the framework of

random utility theory draw on stated or revealed preference data to

characterize the utility of diﬀerent options and the marginal contribu-

tion of each aspect of an option to its overall utility (Hunt, 2005).

Discrete choice experiments used to elicit anglers’ stated preferences

can incorporate a wide variety of catch-related preferences and activity-

general factors like travel costs and crowding by other recreationists

(e.g. Beardmore et al., 2015). Combining these stated preference sur-

veys with qualitative methods such as in-depth interviews and focus

groups is less common, but this mixed-method approach can draw an

even more nuanced picture of ﬁ

shermen’s

preferences and behavior

(Carr and Heyman, 2016; Magee et al., 2018).

The angler preference literature that uses this framework is domi-

nated by studies that investigate anglers’ decision-making in local- or

regional-scale ﬁsheries that include a range of generalist and specia-

lized anglers (e.g. Duﬃeld et al., 2012; Arlinghaus et al., 2014; Curtis

and Breen, 2017). However, few studies investigate the behavior of

anglers in distant, high-cost recreational ﬁsheries that require specia-

lized angler skill, where one can expect diﬀerent relationships between

catch and participation simply because the commitment required to

participate is so high (but see Nguyen et al., 2013; Pinder and

Raghavan, 2013). In many of these ﬁsheries, the targeted ﬁsh are highly

desirable for their size, appearance, or behavior, attracting avid and

generally wealthy anglers from around the world. These ﬁsheries often

require highly skilled ﬁshing techniques and specialized gear and have

either formal regulations or informal norms that enforce catch-and-re-

lease practices. One example is the boneﬁsh (Albula vulpes) ﬁsheries of

the Caribbean, which provide millions of dollars annually to local

economies and are primarily catch-and-release (Adams et al., 2014).

Boneﬁsh are highly valued by international sport ﬁshermen for their

speed and aggressive behavior when hooked, such that a single boneﬁsh

has been estimated to be worth US$3500 to the local economy (Santos

et al., 2017). Other examples include peacock bass (Cichla spp.) in the

Amazon (Holley et al., 2008), tigerﬁsh (Hydrocynus vittatus ) in Africa

(Smit et al., 2009), mahseer (Tor spp.) in India (Pinder and Raghavan,

2013), and high-cost pelagic trophy ﬁsheries for marlin, tuna, and other

highly migratory species (Duﬃeld et al., 2012). Collectively, we cate-

gorize these ﬁsheries as “

high-threshold” ﬁsheries,

where economic,

biological, and social factors combine to raise the barriers required to

participate.

In northern Mongolia, abundant populations of taimen (Hucho

taimen) attract wealthy ﬂy ﬁshermen willing to pay up to US$7000 per

week, excluding travel costs, for the chance to catch the largest sal-

monid in the world (Jensen et al., 2009). Taimen can reach lengths of

up to two meters and weigh up to 100 kg, and at about one meter total

length they undergo an ontogenetic shift that enlarges the head and jaw

disproportionately to the body (Holcik et al., 1988). This large size,

unusual appearance, and status as the world’s largest salmonid make

them a valued target for some highly specialized recreational anglers.

Many of these anglers travel to Mongolia with dedicated ﬂy-ﬁshing

outﬁtting companies. The large size and selective feeding behavior of

taimen combine to make them hard to catch on ﬂy ﬁshing gear, in-

spiring ﬁshing guides to call them “the ﬁsh of a thousand casts.” The

high cost, low catch rate, and specialized skills required of anglers in

the international taimen ﬁshery make it an excellent place to in-

vestigate the preferences and behavior that characterize anglers in such

high-threshold recreational ﬁsheries.

This study investigates the preferences, satisfaction, and behavior of

international anglers in the taimen ﬁshery in northern Mongolia. Using

a mixed-methods approach that enriches discrete choice methodology

with in-depth qualitative interview data, we ask the following three

general questions: 1) What factors contribute to anglers’ motivation to

participate in the taimen ﬁshery, and what preferences do they have for

their taimen ﬁshing experience? 2) Based on these motivating factors,

are sampled anglers generally satisﬁed with their experience in the

taimen ﬁshery, and what factors drive satisfaction most strongly? And

ﬁnally, 3) How does satisfaction translate into future participation;

namely, do satisﬁed anglers express more interest in a return trip?

2. Methods

2.1. Study system

This study focuses on recreational taimen ﬁshing in the Eg-Uur

watershed in northern Mongolia, which is remote and relatively pristine

(Gilroy et al., 2010). The watershed is located primarily in the

Khövsgöl, Bulgan, and Selenge provinces in the transition zone between

Mongolia’s steppe ecosystem and Siberian taiga forest. The Eg River,

and its tributary, the Uur River, drain into the transboundary Selenge

River, the largest tributary of Lake Baikal (Fig. 1). The rivers support a

ﬁ

sh community dominated by the salmonids lenok (Brachymystax

lenok)

and Arctic grayling (Thymallus arcticus arcticus), with taimen as

the apex predator (Mercado-Silva et al., 2008). Taimen are relatively

long-lived, large-bodied omnivorous ﬁsh with extensive habitat needs;

some individuals have been observed to traverse home ranges of over

100 km (Gilroy et al., 2010; Kaus et al., 2016). Mongolian rivers like the

Eg, Uur, and Selenge have historically served as refuge habitat for

taimen because of Mongolia’s limited culture of ﬁshing and ﬁsh con-

sumption (FAO, 2007). However, in the mid-1990s, the ﬁrst foreign

outﬁtting companies began bringing ﬂy ﬁshermen to Mongolia to ﬁsh

for taimen (D. Vermilion, pers. comm.). Now, taimen are the primary

object of a growing recreational ﬁshery that includes both foreign ﬂy

ﬁshermen (Vander Zanden et al., 2007) and, increasingly, Mongolian

ﬁshermen (Chandra et al., 2005). The American and European-based

outﬁtting companies that cater to many of these foreign ﬂy ﬁshermen

enforce strict catch-and-release policies, while Mongolian recreational

anglers vary in their adherence to catch-and-release practices.

Taimen are IUCN Red Listed throughout their range because of

overﬁshing and habitat degradation (Hogan and Jensen, 2013), and

Mongolian law prohibits the harvest and consumption of taimen. Re-

search has shown that purely catch-and-release ﬁshing for taimen can

be sustainable, but that even limited consumptive ﬁshing could lead to

local extirpation of taimen populations (Jensen et al., 2009). As a result,

foreign outﬁtting companies and Mongolian conservation activists have

found common cause in supporting catch-and-release angling by for-

eigners, which provides economic revenues to rural, isolated areas, and

A.S. Golden, et al.

Fisheries Research 220 (2019) 105364

thus discourages consumptive ﬁshing by local herders and urban,

middle-class Mongolians. The outﬁtting companies provide funding to

several non-proﬁt conservation organizations for research and en-

forcement of Mongolia’s ﬁshing laws and permitting regulations. These

eﬀorts have been concentrated in the Eg-Uur watershed, which was also

one of the earliest areas of Mongolia to see foreign ﬂy-ﬁshing eﬀort. As

a result, the watershed has become known for its abundant taimen

population.

2.2. Research sites and study population

Fieldwork was conducted throughout the six-week 2017 ﬁshing

season (August to October) at two ﬁshing camps operated by the

Mongolian-run Hovsgol Travel Company in partnership with the

American-based Sweetwater Travel Company. One camp is located on

the Uur River, 9 km above its conﬂuence with the Eg, and the other is

located 89 km downstream of the Eg-Uur conﬂuence on the Lower Eg

(Fig. 1). Data were collected for approximately three weeks at each

camp to ensure roughly equal sampling of respondents. The two ﬁshing

camps hosted 60 clients over the 6-week ﬁ shing season, of which 38

overlapped with the research team. Of these 38 anglers, 30 completed

paper surveys and 26 participated in semi-structured interviews for a

79% and 68% response rate, respectively. An additional angler who had

recently completed a ﬁshing trip at another camp completed a survey

and interview at the travel company’s headquarters in Ulaanbaatar.

Although the 60-person sampling frame is small in absolute numbers, so

is the overall population of foreign anglers who ﬂy-ﬁsh for taimen in

Mongolia each year. This research protocol was approved by the Rut-

gers Institutional Review Board (Protocol #E17-714).

2.3. Survey design and implementation

Anglers were surveyed using a brief (15 min) paper survey

instrument that included two components: 1) a series of six discrete

choice scenarios designed to elicit preferences and motivations for

participating in a Mongolian ﬁshing trip, and 2) questions about re-

spondent's demographic characteristics (e.g., age, location, and past

ﬁshing trips) and ﬁshing experience. Discrete choice experiments

(DCEs) present respondents with multiple hypothetical scenarios in-

volving tradeoﬀs between desired attributes of an experience and ask

them to rank or choose their most preferred option (Train, 2002). These

data allowed us to assess the relative importance of diﬀerent attributes

of the experience to respondents, analyze decision-making, and eval-

uate the utility of diﬀerent options. In this case, anglers were presented

with three options within each scenario: two alternative ﬁshing trips

that varied in the number of taimen caught and the size of the largest

ﬁsh, and a third option in which they did not travel to Mongolia to ﬁsh,

following the survey design of Carter

and Liese (2012). The size attri-

bute had six levels, ranging from 0 cm (i.e., no ﬁsh caught) to 150 cm,

which were presented in both centimeters and inches (Table S1). The

catch attribute had seven levels ranging from zero taimen (no ﬁsh

caught) per week to eighteen, representing a maximum catch rate of

about three ﬁsh per day. Attribute levels were developed to represent

the whole range of outcomes possible on a taimen ﬁshing trip based on

our knowledge of the ﬁshery. We chose to include only two attributes

because we anticipated a small pool of respondents, which would limit

our sample size and force us to focus on a small number of attributes of

high interest. Although including a cost attribute would have enabled

us to estimate anglers’ willingness to pay (WTP) for a taimen ﬁshing

experience, we deemed the tradeoﬀ between size and catch rate in

anglers’ preferences to be a higher research priority for this ﬁshery

given the high WTP already demonstrated by taimen anglers.

Anglers were asked to select the best and worst alternatives (Fig. 2),

allowing for a full ranking of the three alternatives as implemented by

Lew and Larson (2012). Discrete choice experiments often do not use a

full factorial design because the number of unique combinations would

Fig. 1. Study sites. Map of the Eg and Uur Rivers in northern Mongolia’s Eg-Uur watershed showing the two ﬁshing camps where research was conducted (black

triangles). The Sweetwater/Hovsgol Travel companies have exclusive rights to ﬁsh the river section emphasized in grey, which occupies a total of 219 river

kilometers. The black dotted line marks the boundary between waters ﬁshed by the upper camp (109 river km) and lower camp (110 river km).

A.S. Golden, et al.

Fisheries Research 220 (2019) 105364

be prohibitively large (Fox, 2007; Kuhfeld, 2010). In this case, a frac-

tional factorial design was developed from a full factorial design re-

presenting the entire set of combinations of attribute levels. Illogical

choices within the full factorial design (i.e., a catch rate of zero with a

non-zero maximum size) were then discarded. Since most of the re-

maining scenarios were non-informative in that they possessed a logi-

cally superior option that was better in terms of both size and catch

(e.g., a choice between 10 ﬁsh with a maximum size of 100 cm versus

ﬁve ﬁsh at 50 cm), we followed Train et al. (1987) in narrowing down

our fractional design to focus on the scenarios that presented in-

formative choices. Scenarios were divided into “control” scenarios

(those with an obviously superior option as described above) and “ex-

perimental” ones that present a meaningful tradeoﬀ (e.g., a choice

between 10 ﬁsh with a maximum size of 50 cm and two ﬁsh with a

maximum size of 100 cm). Thirty unique survey variants were devel-

oped in order to include all of the experimental scenarios. Each survey

variant included ﬁ ve experimental scenarios and one control scenario,

selected from the large pool of potential control scenarios, to screen for

survey fatigue and respondent disengagement.

Survey disengagement is a well-established concern in the im-

plementation of discrete choice experiments (Petrik et al., 2013), and

the interview portion of this project, combined with the inclusion of

control questions, provided a unique opportunity to address this po-

tential source of bias. To take advantage of this opportunity, re-

spondents who failed the control scenario (n = 3) or displayed pre-

ferences that were notably di ﬀerent from the majority of survey

responses (n = 7 out of 31) were asked about their DCE responses

during the interview stage. One angler revealed that he had failed the

control due to disengagement and his responses were removed from the

data. The other two anglers who failed the control revealed that they

had misunderstood the survey questions in an easily resolved way

(circled their best and second-best choices instead of best and worst),

and their responses were amended to reﬂect this. The remainder were

asked about their responses because their choices suggested unusual

preferences which could be proﬁtably explored in the qualitative in-

terview phase, like the frequent choice of the “no trip” option even

when catch rates were high. These respondents’ clariﬁcations were

included in the interview data but did not aﬀect the DCE analysis in any

way.

Surveys were distributed at the beginning of the anglers’ trip and

anglers completed them at their leisure throughout the ﬁshing week.

Some completed and returned them immediately, and some returned

them when they left a week later. The day that anglers returned the

survey did not have a signiﬁcant eﬀect on their choices and did not

improve the model ﬁt(Table 1).

2.4. Semi-structured interviews

In-person,

semi-structured interviews were conducted with anglers

to expand on discrete choice experiment results. An interview guide

was designed based on 10+ years of experience working on the

Mongolian taimen ﬁshery and following best practices in qualitative

research techniques (Roller and Lavrakas, 2015). The guide was mod-

iﬁed several times in the ﬁeld to address unforeseen circumstances and

saturation of some interview questions (that is, reaching a point when

no new insights emerge from further responses) (Appendix). Interviews

were conducted at both ﬁshing camps and during breaks on the river

during the ﬁshing day. Most interviews lasted 15 to 30 min, with out-

liers ranging from 8 min to over an hour. All interviews were audio

Fig. 2. Sample discrete choice scenario.

Table 1

Candidate models with explanatory variables, number of parameters (K), log

likelihood (LL), Akaike Information Criterion (AIC) score, and diﬀerence in AIC

score relative to the model with the lowest AIC score (ΔAIC). Models with a

ΔAIC < 2 are similarly supported.

Model K LL AIC ΔAIC

size + ln(catch) + size * ln(catch) 5 −163.1 336.1 0

size + catch + size * catch 5 −164.6 339.1 3

size 3 −167.3 340.6 4.5

size + catch + trip-day + size* catch 7 −163.4 340.7 4.6

size + ln(catch) 4 −167.2 342.3 6.2

size + catch 4 −167.2 342.4 6.3

catch 3 −188.4 382.8 46.7

A.S. Golden, et al.

Fisheries Research 220 (2019) 105364

recorded and transcribed by a professional transcriber, following which

the transcripts were checked for accuracy by the researchers. Twenty-

six interviews were conducted in English and one was conducted in

French; this was translated and transcribed by a native English speaker

with an advanced degree in French.

2.5. Additional data sources

A researcher accompanied anglers and guides throughout the

ﬁshing day for 26 of the 43 days of the ﬁshing season and recorded

catch and eﬀort data in units of angler hours per day. Limited catch logs

were also maintained by some ﬁshing guides. These were used to es-

timate the size structure of captured taimen. Unstructured key in-

formant interviews were conducted with ﬁshing guides and outﬁtting

company staﬀ members (n = 8) to assess longer-term ﬁshery dynamics.

2.6. Data analysis

2.6.1. Modeling angler preferences

A multinomial logit model was ﬁt to anglers’ ranked choices using

the mlogit package (Croissant, 2018) in R v.3.4.3 (R Core Team, 2017).

Candidate models included catch, size, both catch and size, and an

interaction term between catch and size as parameters. One candidate

model included a natural log-transformed catch rate and natural log

transformation of the catch component in the catch-size interaction

term because the observed data showed a diminishing eﬀect of catch

rate on utility (Fig. 3) and other studies have found diminishing returns

of high catch rate (Beardmore et al., 2015). We also included trip day as

an individual-speciﬁc variable to test whether the day the survey was

returned aﬀected anglers’ responses. Models were competed using

Akaike’s Information Criterion (AIC; (Akaike, 1974)(Table 1). In-

dividual-speciﬁc parameters like ﬂy ﬁshing experience and days ﬁshed

per year (a proxy for angler avidity) were not included because the

interview portion of the project allowed us to explore the importance of

these characteristics in a richer, albeit more qualitative, way, and be-

cause drawing valid conclusions about these parameters in DCE

analysis generally requires larger sample sizes (Louviere et al., 2000:

110).

2.6.2. Qualitative analysis of interview responses

Interviews were coded for themes using the qualitative coding

software NVivo 12.1.0 (NVivo qualitative data analysis Software; QSR

International Pty Ltd. Version 12, 2018).

Themes used for this analysis were divided into the general cate-

gories of angler demography, angler preferences/satisfaction, and trip

choice, and sub-themes were developed inductively during the coding

process; that is, guided by the themes that emerged throughout parti-

cipants’ responses rather than a priori assumptions about which factors

would be important (Roller and Lavrakas, 2015). References to angler

preference subthemes were quanti

ﬁed

by their frequency in responses

to questions about angler motivation (e.g., “What appealed to you about

coming to Mongolia to ﬁsh for taimen?”) and satisfaction (e.g, “How do

you feel about the trip so far?”). These preference themes were iden-

tiﬁed as either catch-related or activity-general. Anglers were also

classed into groups based on their expressed interest in returning to the

ﬁshery in future years.

3. Results

3.1. Angler demography

Surveyed anglers were primarily male (87%), white (100%), and

middle-aged, with a median age of 52. The majority (51%) had a pro-

fessional degree, with another 32 percent possessing a bachelor’s de-

gree or some college education. Forty percent of anglers were

American, 23 percent were European, and the rest were from Canada,

Australia, and Morocco. Key informant interviews with ﬁshing guides

suggest that the majority of Sweetwater’s clientele are American, al-

though the outﬁtter sometimes hosts large groups of anglers from

countries not represented in this sample, such as Russia. Angler ex-

perience and avidity, as measured by frequency of ﬁshing (Beardmore

et al., 2015; Ferter et al., 2013), varied widely among surveyed anglers.

Fig. 3. Discrete choice model results. The marginal eﬀect at the mean of (a) size of largest ﬁsh available and (b) expected catch rate per week on the predicted

probability that anglers will choose a trip option in a discrete choice experiment. The black points show the observed proportion of trips selected with each level of

the size and catch rate attributes. Panel c) shows the predicted probability of trip choice based on the interaction between size of largest ﬁsh and catch rate per week

(deeper blues indicate higher probabilities of selection).

A.S. Golden, et al.

Fisheries Research 220 (2019) 105364

The median angler had 30 years of ﬁshing experience and 18 years of

ﬂy-ﬁshing experience; however, several were novices who had never ﬂy

ﬁshed before and a few had over 50 years of experience as ﬂy ﬁsh-

ermen. The surveyed anglers spent an average of 29 days per year

ﬁshing (interquartile range = 14–38 days). All the anglers in the sur-

veyed population were ﬁrst-time taimen ﬁshermen, although guide

interviews indicate that typically about 25 percent of Sweetwater’s

clientele are repeat visitors.

3.2. Angler preferences and motivation

The best ﬁt model included size of largest ﬁsh, natural log-trans-

formed catch rate, and an interaction term between size and natural

log-transformed catch (Table 1). The size term, catch term, and the

interaction term were statistically signiﬁcant (p = 0.000, p = 0.048,

and p = 0.005 respectively), as was the intercept for the no-trip option

(p = 0.002) (Table 2). The no-trip intercept represents the utility of not

traveling to Mongolia to ﬁsh compared to a trip with the lowest levels

of each attribute—that is, a trip in which no ﬁsh are caught. The mean

probability of choosing a trip with a catch rate of six ﬁsh per week and a

maximum size of 50 cm was 0.32, while the mean probability of

choosing a trip with a catch rate of 12 and a largest ﬁsh of 100 cm was

0.63, an increase of 31%. Doubling only the maximum ﬁsh size while

maintaining the catch rate at six ﬁsh per week moderated this impact to

some degree, increasing the probability of choosing a trip by only 24%

instead of 31%. In contrast, doubling the expected catch rate while

maintaining the size of largest ﬁsh constant at 50 cm increased the

probability of choosing an option by only two percent. Overall, the

eﬀect of catch rate was low at small maximum ﬁsh size and stronger at

high maximum ﬁsh size ( Fig. 3c). The best ﬁtting model had a

McFadden’s Pseudo R

value of 0.475. McFadden’s Pseudo R

is ana-

logous to a standard R

statistic but generally produces lower values

(Ben-Akiva and Lerman, 1985). This is considerably better than the

20% standard of ﬁt proposed as a benchmark in Hensher and Johnson

(1981:50).

In interviews, anglers’ discussion of their motivations for traveling

to Mongolia conﬁrmed and expanded on this result. Anglers were mo-

tivated by catch quality over catch rate, with quality deﬁned mostly by

size but also by the interaction between size and other factors, such as

the way the ﬁsh fought on the line. As one 49-year-old Canadian angler

said, “If the largest you catch is 30 inches and you get a dozen ﬁsh, that

wouldn't be enough to motivate me…they’re cool ﬁsh, but there’s not a

spectacular run, there’s not a spectacular jump. Frankly, you’re hand-

lining in a 30-inch ﬁsh…you’re basically just dragging them in.”

Overall, anglers expressed a desire for 40- to 50-inch (100- to 125-cm)

“trophy”-sized ﬁsh in interviews; this size class represents the 98th

percentile of the ﬁsh caught during the season (Supplemental Fig. 1).

Catch rate expectations were more heterogeneous, but most anglers

expected to hook one or two ﬁsh per 8 -h ﬁshing day. Anglers were split

between being frustrated with these low catch rates and valuing the ﬁsh

more highly because they were diﬃcult to catch. One 53-year-old

British angler who typiﬁed the latter view said that landing a ﬁsh is

more enjoyable when “you [have] to work a bit harder, wade a bit

deeper, cast a bit further, ﬁght the weather.”

More broadly, interviews reﬂected anglers’ interest in a variety of

catch-related motivators beyond size and catch rate, as well as in fac-

tors related to the overall experience of the trip. Slightly over half of

anglers’ references to their motivation for traveling to Mongolia men-

tioned these catch-related factors, which included not only taimen size

and catch rates, but also being able to catch a diversity of species and

the desire to catch speci

ﬁc species (primarily taimen, but also lenok,

Arctic grayling, and northern pike) (Fig. 4). Anglers valued taimen in

particular for its size, its aggressiveness, its “interesting-looking” mor-

phology, its evolutionary lineage as an “ancient ﬁsh,” and its status as

the “largest salmonid in the world.” Although a few anglers mentioned

taimen’s rarity and endemism as a draw, many did not know that the

species was endangered, and its conservation status was not a factor in

their ﬁshing decision: “I didn’t know if there were a million ﬁsh a mile,

or two, no idea. I don’t know any more now.” Some even assumed that

if anglers knew taimen’s conservation status, it would be a less ap-

pealing target. As one 48-year-old British ﬁsherman speculated, it

would hurt the operators’ interest to share the information because

“they’re going to want people to think, there’s loads of them: you’re

going to come, you’re going to catch loads every day, and they’re going

to be huge.”

Interview responses revealed that a nuanced interaction between

Table 2

List of parameters in best-ﬁt model with the parameter estimates, standard error, p-values, and signiﬁcance.

Parameter Attribute β Std. Error Pr(> |z|)

Alternative-speciﬁc coeﬃcients generic trip intercept 0.0712 0.1673 0.6703

no-trip intercept no trip −2.2739 0.9778 0.0200

Generic coeﬃcients size size of largest ﬁsh caught 0.0140 0.0039 0.0003

***

Ln(catch) catch rate per week −0.2195 0.1108 0.0475

size x ln(catch) interaction between catch rate and size 0.0066 0.0024 0.0054

N = 180 McFadden R

: 0.475

* Signiﬁcant to p < 0.05.

** Signiﬁcant to p < 0.01.

*** Signiﬁcant to p < 0.001.

Fig. 4. Interview themes related to angler motivation and satisfaction.

Proportion of interview text that refers to catch-related (blue) and activity-

general (orange) themes in responses to questions about angler motivation and

satisfaction.

A.S. Golden, et al.

Fisheries Research 220 (2019) 105364

catch-related factors and aspects of the broader trip experience com-

bined to draw anglers to the taimen ﬁshery. These activity-general

motivating factors included the social aspect of ﬁshing with friends, the

lure of travel to an exotic or “pristine” wilderness destination, and the

experience of a novel culture. Anglers mentioned these activity-general

factors almost as often as their catch-related expectations and motiva-

tions for the trip; approximately 44 percent of motivation-related re-

ferences fell in this group (Fig. 4). Thus, the very aspects of the taimen

ﬁshery that create barriers to participation—its distance, its remote-

ness, and its skill requirements—were also strong motivators for many

anglers. As one 72-year-old American angler said, “Probably, if these

taimen were in Bakersﬁeld, California, I wouldn’t go there. But because

they’re in Mongolia—there’s a certain ﬂair where people say, ‘Where’re

you going?’ and you say, ‘I’m going to Mongolia’—their eyes kind of

light up and they go, ‘Oh, that’s cool. ’” For some anglers, these factors

even outweighed their interest in trophy-sized taimen. One 56-year-old

American ﬁsherman had recently begun ﬂy ﬁshing and spoke passio-

nately about his new sport. He said, “It’s not about catching ﬁsh, it’s

about ﬁshing. It’s about the challenge. Can you get everything lined up

to where you can get that hit? You know, get the ﬂy in the right place,

in the right fashion, at the right time? So it’s cool [that] there’s big ﬁsh

here, but I would have come if there weren’t big ﬁsh. I would have

come if it were just lenok and grayling.”

3.3. Angler satisfaction

iﬀerent set of catch-related factors emerged in interviews when

anglers spoke about their satisfaction with their ongoing trip, rather

than their motivation for taking it. The size and number of ﬁsh that they

had caught still mattered, but for many, catching a single taimen was

enough to fulﬁl their goals for the trip: one seventy-year-old British

ﬁsherman said, “If I don’t catch another taimen, I’m not going to go

home and say to friends and family, ‘Well, that was a waste of time, I

only caught one taimen.’” A minority were frustrated by the pace or

diﬃculty of the ﬁshing, and especially by “blanking” or “ skun-

king”—the experience of spending a day on the water without en-

countering any ﬁsh. One Australian angler, who came with a group of

friends who were all ﬁnding the ﬁshing more diﬃcult than they ex-

pected, said on his fourth evening: “After the ﬁrst day, I didn’t take a

ﬁsh. And I thought, my god, these are the ﬁsh of ten thousand casts.

People said to me, how’s your day? And I said, look, it was a beautiful

day, in that you’re out there and enjoying the environment, the wild-

erness, but it was really a hard day’s ﬁshing…very, very diﬀerent to my

anticipation and expectation of coming here.” For him, the frustration

of going multiple days without seeing a ﬁsh on the line outweighed the

experience of “enjoying the environment, the wilderness.” Overall, as

typiﬁed by this interview, catch-related themes like this one dominated

anglers’ responses to questions about satisfaction, while activity-general

themes were secondary. This contrasted with their responses about

motivation,

where catch-related and activity-general themes were al-

most

equally prominent (Fig. 4).

3.4. Anglers' intentions to return

The model indicated signiﬁcant negative utility for the alternative

of not traveling to Mongolia to ﬁsh for taimen (p = 0.02), showing that

anglers strongly preferred even an unsuccessful taimen ﬁshing trip to

no trip (Table 2). This would be a surprising result in many catch-or-

iented recreational ﬁsheries, but the high prevalence of activity-general

motivations in the taimen ﬁshery revealed by the interview data sug-

gest that it is realistic for taimen anglers to have a positive utility for a

Mongolia ﬁshing trip even if they catch no ﬁsh. However, this tolerance

for unsuccessful trips did not translate into equivalent interest in future

taimen trips. In interviews, only one angler expressed a deﬁnite in-

tention to return to Mongolia to ﬁsh. Of the rest, thirteen said they

would not return, eleven said they might under the right circumstances,

and one was undecided (Fig. 5).

The anglers who stated some intention to return mostly did so in

vague terms, or only if certain conditions were met; for instance, if their

children or friends became interested in the trip, or if they could expect

better ﬁshing in the future. Some weighed their mixed satisfaction with

the experience against the longer-term population dynamics described

by the guides, like one 53-year-old Welsh ﬁsherman who had been

frustrated with several “blank” days before catching four taimen in the

span of an hour, including a 42-inch trophy ﬁsh: “I get the sense that

this week has been quite slow compared to what the guides normally

expect. And so I wouldn’t come back to repeat the week, but I would be

interested in coming back to something that [had] more action, and

bigger ﬁsh around.”

The anglers who said they would not return were mostly bucket-list

travelers; that is, those who wished to collect a variety of experiences

and achievements during their lifetime (Thurnell-Read, 2017), and who

therefore rarely traveled to the same place twice regardless of their

satisfaction with a particular ﬁshing destination. One 53-year-old

Englishman typiﬁed the bucket-list view: “Probably, I won’ t ever come

back. Too big a world, too many places. And there’saﬁnite amount of

time physically I can do it, because this [kind of ﬁshing] is quite a

physical job; I won’t be on this water when I’m seventy years old. [So] I

don't go on holiday anywhere twice in my life.” While overcoming lo-

gistical and physical challenges was part of his motivation for this ﬁrst

trip—“the ﬁshing

is the bit you do on the river, [but] there’s [also] the

traveling to it, preparing for it, planning for it, trying new places, new

airports”—those same challenges posed a high barrier to a second trip.

As evident above, many anglers felt that the time and resources for a

second trip could just as well go toward another bucket-list destination.

They had many destinations to choose from; the more avid and ex-

perienced anglers in the study group participated in a wide range of

recreational ﬁsheries around the world. Common targets were boneﬁsh

and tarpon (Megalops atlanticus) in the Caribbean, sea run brown trout

(Salmo trutta) in Patagonia, tropical reef ﬁsh like milkﬁsh (Chanos

chanos) and giant trevally (Caranx ignobilis) in the Seychelles, and a

variety of salmonids in Alaska, Canada, and northern Europe. Some

bucket-list anglers saved money to travel every two or three years,

while others took expensive international trips multiple times a year to

Fig. 5. Angler attitudes to a return trip. Bar plot showing the frequency of

diﬀerent attitudes to taking a return trip to Mongolia as expressed in interviews.

On the right are selected quotes representative of each attitude.

A.S. Golden, et al.

Fisheries Research 220 (2019) 105364

a mix of old and new destinations. Though these anglers’ target species

were diverse, they were united in being large, visually unusual, ag-

gressive, and best caught on specialized ﬂy-ﬁshing gear. They were also

found in a range of scenic destinations in remote parts of low-income

countries where anglers could experience a “human culture that is

outside your normal experience, a long way from civilization.” These

traits matched the activity-general factors that motivated anglers to ﬁsh

for taimen in Mongolia.

4. Discussion

This paper aims to investigate anglers’ motivations for participating

in a remote, high-threshold recreational ﬁshery and to understand how

these motivations could impact future ﬁshery participation. We found a

strong link to the “bucket list” cultural phenomenon (Thurnell-Read,

2017), where anglers were motivated largely by the prospect of accu-

mulating a novel and exotic travel experience to catch an unusual ﬁsh.

The size and morphological traits of the ﬁsh contributed to its appeal,

while its perceived abundance and catchability were less important.

This focus on size over expected catch rate adds to a growing literature

assessing the catch-related preferences of anglers across a diverse range

of ﬁsheries (Hunt et al., 2019) and provides important information for

guiding ﬁshery-speciﬁc management, because strategies that maximize

catch often require giving up size and vice versa (Hansen et al., 2015).

More broadly, the importance of this “one and done” travel and ﬁshing

experience to anglers’ motivations meant that satisfaction with the

ﬁshing itself was, for many anglers, a poor predictor of future partici-

pation in the ﬁshery. The majority of anglers did not intend to return to

the taimen ﬁshery, and even those who expressed some interest in fu-

ture participation were not strongly motivated to do so. We found that

the high barriers to entry in the taimen ﬁshery, especially its diﬃcult

logistics and physical demands, contributed to the appeal of a single

trip but discouraged anglers from seeking out further experiences.

This ﬁnding contrasts with our usual understanding of angler par-

ticipation in recreational ﬁsheries as a functional response by in-

dividual anglers to their past experiences of catching ﬁsh “prey,”

mediated by the mechanism of satisfaction or dissatisfaction with prior

ﬁshing trips (

Johnson and Carpenter, 1994). There has been a trend

toward emphasizing speciﬁc catch-related goals and catch satisfaction,

rather than general ﬁshing motivations, as the primary drivers of future

ﬁshing eﬀort (e.g. Robert Arlinghaus, 2006; Beardmore et al., 2011).

However, our results show that in some contexts, anglers’ general

motivations for ﬁshing, or for taking a particular kind of ﬁshing trip,

can be crucial to understanding their future ﬁshery participation. In

settings like the Mongolian taimen ﬁshery, the desire to experience a

bucket-list vacation and encounter a unique ﬁsh can outweigh expected

catch rates and the availability of trophy ﬁsh in driving participant

behavior. Therefore, we must tailor our theoretical assumptions about

angler behavior and participation to the individual ﬁsheries we study

and especially to the goals and demography of participants, as pre-

viously argued by Beardmore et al. (2015).

Our study represents a ﬁrst step toward ﬁlling a gap in the con-

ceptual scope of the angler satisfaction literature. There is a large body

of research on the relationship between angler site choice and travel

distance, recently reviewed by Hunt et al. (2019), but few studies focus

on ﬁsheries characterized by extreme travel distances, diﬃcult logistics,

high costs, and intensive skill requirements. The Mongolian taimen

ﬁshery provides an excellent example of this high-threshold dynamic,

given that the average angler surveyed here traveled thousands of

kilometers to participate and paid approximately US$7000 plus travel

expenses for a week of ﬁshing. Additionally, its participants have access

to a network of similar high-threshold bucket-list ﬁsheries throughout

the world, including sea run brown trout in Argentina, the salmon

ﬁsheries of Alaska and Canada, and tropical reef ﬁsheries in the Car-

ibbean and the Indian Ocean. The variety of options available to these

anglers suggests that participation in a ﬁshery like Mongolia’s taimen

ﬁshery is best modeled not as a binary choice between participation and

non-participation but as a multivariate decision about how to allocate

ﬁshing days among a set of globally distributed ﬁshing options. Con-

sequently, future participation in the taimen ﬁshery, and other ﬁsheries

like it, may depend just as much on diﬃcult-to-measure climatological

and geopolitical factors as they do on maintaining abundant popula-

tions of the target species, as posited in the metacoupling framework

proposed by Liu (2017). Water conditions in Argentina, the length of

hurricane season in the Bahamas, and volatility in global

ﬁnancial

markets,

to give a few examples, could all have unseen ramiﬁcations for

the year-to-year ﬁshing eﬀort in the Mongolian taimen ﬁshery halfway

around the world.

These dynamics are worth investigating because ﬁshery participa-

tion by international anglers can have important local-scale economic

and conservation consequences. Many high-threshold ﬁsheries are in

remote rural areas, meaning that the ﬁsheries are signiﬁcant economic

drivers in places with few other sources of employment and income

(Zwirn et al., 2005). But if ﬁshery participation is in fact driven by

global forces, these economic beneﬁts could be unstable, causing un-

intended consequences like the employment precarity observed among

tourist industry employees in the Seychelles (Lee et al., 2015). In ad-

dition, these specialized catch-and-release sport ﬁsheries are particu-

larly suited to being channeled into conservation solutions, as described

by Cooke et al. (2016), because of the wealth and avidity of their

participants and the resource stewardship norms that often accompany

catch-and-release practices. One example is the growing ﬁshery for

mahseer (Tor spp.) in southern India, where catch-and-release angling

organizations that place high value on this large and aggressive species

have supported research and retrained former poachers as ﬁshing

guides (Pinder et al., 2015; Pinder and Raghavan, 2013; Raghavan

et al., 2011). But if ﬁshery participation by wealthy international an-

glers varies greatly from year to year, funding and human capital for

these eﬀorts will ﬂuctuate as well, and the community of anglers may

be too fragmented to share the social norms and best practices that

enable good conservation outcomes.

Fisheries that attract bucket-list anglers pose particular sampling

and generalizability challenges in human dimensions research because

they possess a high proportion of anglers who, by deﬁnition, are un-

likely to return. Much of the angler satisfaction and recreational ﬁshing

participation literature describes—or assumes—a relationship between

future eﬀort and aspects of the angler experience on previous ﬁshing

trips (Johnson and Carpenter, 1994). The bucket list angler has no plans

to return, regardless of the experience, and the current pool of bucket

list anglers will not necessarily reﬂect the demography and decision-

making process of future participants. Therefore, the population of

potential anglers for a

ﬁshery of this type is inherently diﬀuse and

poorly deﬁned, requiring researchers to reach for creative sampling and

data collection methods to understand and anticipate future ﬁshery

participation. One possible avenue is to compensate by collecting richer

data on the sampling pool that is available, for instance by combining

conventional surveys with qualitative techniques like focus groups and

interviews. This approach has been used extensively to inform and

enrich discrete choice experiment results in health (Coast and Horrocks,

2007), transportation (Pineda Jaramillo et al., 2016), and other ﬁelds

(Que et al., 2017). However, there are still relatively few examples of

research combining discrete choice methods and semi-structured in-

terviews in ﬁsheries, especially in a recreational context (Carr and

Heyman, 2016). In addition, the examples that do exist tend to use

interviews mostly to gather quantitative data or guide survey devel-

opment (e.g Ward et al., 2013). While this is a useful approach, it does

not take advantage of the key strength of qualitative data: the rich in-

sight it provides into the thoughts and motivations of others (Weiss,

1995). Our approach of pairing surveys with simultaneous in-depth

interviews allowed us to harness this strength and compensate for the

limited sample size by gathering rich, in-depth data from each in-

dividual participant using mixed quantitative-qualitative methods.

A.S. Golden, et al.

Fisheries Research 220 (2019) 105364

5. Conclusion

In summary, we found that anglers are motivated to ﬁsh for taimen

in Mongolia by a mix of catch-related and activity-general factors,

particularly the size and morphology of the species and the chance to

ﬁsh in a destination perceived as an exotic, pristine wilderness. Many

anglers were bucket-list travelers who rarely ﬁshed in the same place

twice but instead allocated their ﬁshing eﬀort among a variety of ex-

pensive, remote, and specialized ﬁsheries for unusual ﬁsh, often in

developing countries. We collectively label these ﬁsheries as “high-

threshold” ﬁsheries because of the barriers to participation posed by

their location, cost, skill requirements, and other factors. We argue that

the populations of anglers who surmount these barriers are qualita-

tively diﬀerent from their counterparts in more accessible ﬁsheries and

that ﬁsheries with these traits require research approaches that re-

cognize the unique niche they occupy. Such study is especially urgent

since many of these high-threshold ﬁsheries are in developing countries

with little funding for proactive research and ﬁshery management,

leaving scientists at risk of “following ﬁsheries around” (Neis, 2011)

and focusing attention only where crises have already occurred.

CRediT authorship contribution statement

Abigail S. Golden: Conceptualization, Methodology, Software,

Validation, Formal analysis, Investigation, Data curation, Writing -

original draft, Visualization, Project administration, Funding acquisi-

tion. Christopher M. Free: Methodology, Software, Validation, Writing

- review & editing, Visualization. Olaf P. Jensen: Conceptualization,

Writing - review & editing, Supervision, Funding acquisition.

Acknowledgements

This project would not have been possible without the help of the

many anglers and ﬁshing guides who shared their time with the au-

thors, and without the support of the leadership and staﬀ at the

Sweetwater Travel Company and Hovsgol Travel Company. Special

thanks are due to Bazartseren Boldgiv, Batsaikhan Ganzorig, and Bud

Mendsaikhan for scientiﬁc expertise and in-country support. Ben

Beardmore provided key feedback and advice during the analysis

phase. We also thank Holly Kindsvater, Kiva Oken, Mattea Berglund,

and the Jensen and Pinsky labs for their comments on this manuscript.

This research was funded by a U.S.A.I.D. Research and Innovation

Fellowship to ASG. Additional support was provided by NSF grants

OISE #1658251, CNH #1716066, and DEB #1442436 to OPJ and a

U.S. State Department Title VIII fellowship in Mongolian language to

ASG. ASG is supported by the National Science Foundation Graduate

Research Fellowship Program under Grant No. NSF DGE-1842213. Any

opinions, ﬁndings, conclusions, or recommendations expressed in this

paper are those of the authors and do not necessarily reﬂect the views of

the National Science Foundation.

Appendix A. Supplementary data

Supplementary material related to this article can be found, in the

online version, at doi:

https://doi.org/10.1016/j.ﬁshres.2019.105364.

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