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Effect of Fishing Success on Angler Satisfaction on a
Central Oregon Rainbow Trout Fishery: Implications for
Establishing Management Objectives
Joshua L. McCormicka & Timothy K. Porterb
a Oregon Department of Fish and Wildlife, 4304 Fairview Industrial Drive Southeast, Salem,
Oregon 97302, USA
b Oregon Department of Fish and Wildlife, 2042 Southeast Paulina Highway, Prineville,
Oregon 97754, USA
Published online: 13 Aug 2014.
To cite this article: Joshua L. McCormick & Timothy K. Porter (2014) Effect of Fishing Success on Angler Satisfaction on a
Central Oregon Rainbow Trout Fishery: Implications for Establishing Management Objectives, North American Journal of
Fisheries Management, 34:5, 938-944
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North American Journal of Fisheries Management 34:938–944, 2014
C
American Fisheries Society 2014
ISSN: 0275-5947 print / 1548-8675 online
DOI: 10.1080/02755947.2014.932869
MANAGEMENT BRIEF
Effect of Fishing Success on Angler Satisfaction on a Central
Oregon Rainbow Trout Fishery: Implications for Establishing
Management Objectives
Joshua L. McCormick*
Oregon Department of Fish and Wildlife, 4304 Fairview Industrial Drive Southeast, Salem,
Oregon 97302, USA
Timothy K. Porter
Oregon Department of Fish and Wildlife, 2042 Southeast Paulina Highway, Prineville,
Oregon 97754, USA
Abstract
Understanding the relationship between fish populations, fish-
ing success, and angler satisfaction is critical for effective fisheries
management. Our objectives were to quantify angler satisfaction
in a fishery for Rainbow Trout Oncorhynchus mykiss in central
Oregon and examine the factors influencing angler satisfaction.
Multinomial logistic regression models were used to determine the
effect of several variables, including fishing success, on angler sat-
isfaction. Measures of fishing success were present in all of the top
candidate models. The probability of an increase in angler satis-
faction rating was positively related to mean length and number
of fish caught per hour. However, younger anglers tended to have
higher satisfaction ratings at lower mean length and catch rates of
fish than did older anglers. These models provided information on
the expected percentage of anglers that will be satisfied given the
average length of fish caught and the number of fish caught per
hour. These results can be used to establish quantitative, measur-
able, management objectives that will satisfy desired percentages
of anglers and lead to more effective fisheries management.
The primary objective when managing recreational fisheries
is often to improve the quality of fishing or optimize human
benefit (Pollock et al. 1994; Weithman 1999). This is frequently
equated with maximizing or optimizing angler catch rates or
size of fish for anglers to catch. By doing so, the ultimate goal
is to increase angler satisfaction. Many variables have been re-
ported to contribute to angler satisfaction (Hudgins and Davies
1984; Holland and Ditton 1992; Miko et al. 1995; Arlinghaus
2006; Schultz and Dodd 2008), and anglers can still be satis-
fied with their fishing trips even if they were dissatisfied with
their fishing success or vice versa (Weithman and Katti 1979;
*Corresponding author: joshua.l.mccormick@state.or.us
Received February 9, 2014; accepted May 27, 2014
Graefe and Fedler 1986). Although angler trip satisfaction can
be influenced by factors other than fishing success, managing
fish populations for fishing success may be one of the only
tools that managers can use to achieve their objectives (Hicks
et al. 1983; Spencer 1993; Weithman 1999). Therefore, under-
standing the relationship between fish populations, fishing suc-
cess, and angler satisfaction is critical to providing high quality
recreational fisheries. The first step to understanding this rela-
tionship is to quantify the effect of fishing success on angler
satisfaction.
Several studies have shown that angler satisfaction is
positively related to fishing success (Hicks et al. 1983;
Graefe and Fedler 1986; McMichael and Kaya 1991: Spencer
1993; Mostegl 2007; Hunt et al. 2012). If maximizing angler
satisfaction is a fishery management goal and given that some
studies have shown the importance of fishing success on
angler satisfaction, it is logical to assume that fishery managers
should establish objectives for fishing success that maximize
angler satisfaction. For example, if a manager decides that it
is desirable to have 90% of anglers satisfied with their fishing
trip (i.e., that is their management objective for satisfaction),
what catch rates or average size of fish do they need to catch to
meet that objective given various motivations and expectations
of anglers in the population? However, we are unaware of any
published studies that have directly attempted to quantify the
probability of an angler being satisfied as a function of fishing
success. Therefore, our objectives were to examine the effect of
fishing success and other available variables on angler trip sat-
isfaction in a fishery for Rainbow Trout Oncorhynchus mykiss
938
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MANAGEMENT BRIEF 939
in central Oregon and quantify the probability that an angler
would report a given satisfaction level as a function of these
variables.
STUDY SITE
The Crooked River is located in central Oregon and is the
largest tributary to the Deschutes River. The Crooked River is
impounded by Bowman Dam to form Prineville Reservoir at
river kilometer 113.9. We focused on a 12.9-km section of river
downstream from Bowman Dam, which is a popular fishery for
Rainbow Trout and Mountain Whitefish Prosopium williamsoni.
The fishery is primarily for natural-origin fish; however, summer
steelhead (anadromous Rainbow Trout) fry are stocked in this
section of the Crooked River. This section of the Crooked River
is adjacent to a road that provides public access to the fishery and
is managed by the U.S. Bureau of Land Management. This sec-
tion of the Crooked River is open to fishing the entire year; how-
ever, bait can only be used between May 25 and October 31. The
daily bag limit for Rainbow Trout is two and is managed with
a harvest slot limit where only fish between 203 and 508 mm
may be harvested. There are no size or bag limits for Mountain
Whitefish.
METHODS
Creel survey.—A probabilistic roving–roving creel survey
(Pollock et al. 1994) was conducted on the Crooked River to
estimate angler catch rates, angling effort, and total catch and to
evaluate angler satisfaction. The creel survey followed a strat-
ified two-stage cluster sampling design, in which days were
selected as the primary sampling unit and an 8-h shift (i.e.,
cluster) was selected for sampling (Cochran 1977). Days were
stratified by weekend and weekday. Three randomly selected
weekdays and all weekend days were selected to be sampled
from early May through mid-December 2013. The majority of
angler interviews were conducted while the angler was still
fishing (i.e., incomplete trips). Anglers were asked to report the
number and species of fish harvested and released and the num-
ber of hours they fished. They were also asked to estimate the TL
of all fish released. Total length of harvested fish was measured
or estimated to the nearest millimeter by creel surveyors. Catch
rate for each angler was estimated as the ratio of fish caught to
the number of hours fished (Jones et al. 1995; McCormick et al.
2012).
In addition to common creel survey metrics, anglers were
asked to rate their satisfaction level with their overall fishing
experience on a scale of one to five with one being very dissat-
isfied and five being very satisfied. Anglers were also asked
to provide their age range in 10-year age-groups, zip code
of their primary residence, and their primary terminal tackle
type. Although satisfaction is commonly defined as the differ-
ence between expectations and realized outcomes (Brunke and
Hunt 2007), only posttrip satisfaction was quantified using the
sum-of-satisfaction approach (Graefe and Fedler 1986; Arling-
haus 2006). We assumed, given our probabilistic sampling de-
sign, that a representative sample was obtained from the pop-
ulation of expectations of anglers; thus, satisfaction was rep-
resentative of the entire population of Crooked River anglers
regardless of what those expectations were.
Statistical analysis.—The probability of an angler reporting a
given satisfaction level was modeled using multinomial logistic
regression models (Fox 2008). Multinomial logistic regression
is similar to logistic regression but allows probabilities for more
than two categorical responses to be estimated. Thirteen a priori
candidate models were created using various combinations of
covariates such as catch rate of Rainbow Trout, mean length
of Rainbow Trout caught, angler age-class, terminal gear type,
and distance from residence. Angler age-class was treated as a
categorical covariate and was included in the candidate set of
models due to hypothesized changes in expectations or levels
of angling specialization with age (Bryan 1977; Russel 1990;
Aas 1996; Mostegl 2007). Terminal gear was evaluated as a sur-
rogate for level of angling specialization, which was hypothe-
sized to account for differences in catch expectations among an-
glers (Bryan 1977; Ditton et al. 1992). Distance from residence
was evaluated because spatial trends in angler expectations may
exist (Hutt and Neal 2010). Coefficients in multinomial logistic
regression models are expressed as natural log odds ratios of a
TABLE 1. Comparison of multinomial logistic regression models that esti-
mated satisfaction levels of anglers in a Rainbow Trout fishery on the Crooked
River, Oregon. Number of parameters (K), Akaike’s information criteria (AIC),
change in AIC value (AIC), and AIC weights (wi) were used to select the
top models from a set of a priori candidate models. Variables considered in the
models included angler age, average length of fish caught, catch rate, gear type,
and distance of angler’s residence from the fishery.
Model KAIC AIC wi
Age +Catch rate
+Length
40 2,160.7 0.0 0.75
Age +Length 36 2,163.0 2.3 0.24
Age +Catch rate 36 2,172.6 11.8 0
Length 8 2,177.6 16.9 0
Catch rate +
Length
12 2,177.8 17.1 0
Catch rate +
Length +
(Catch rate ×
Length)
16 2,179.3 18.6 0
Catch rate 8 2,186.9 26.2 0
Length +Gear 20 2,187.7 26.9 0
Catch rate +Gear 20 2,197.0 36.2 0
Age 32 2,208.5 47.8 0
Intercept only 4 2,214.4 53.7 0
Distance 8 2,215.6 54.9 0
Gear 16 2,225.3 64.6 0
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940 McCORMICK AND PORTER
TABLE 2. Parameter estimates, standard errors, and 95% confidence limits for the top multinomial logistic regression model that estimated angler satisfaction
levels in a Rainbow Trout fishery on the Crooked River, Oregon. Coefficients are interpreted as odds ratios relative to a satisfaction level of one (i.e., the reference
category).
Confidence limits
Variable Estimate SE Lower Upper
Satisfaction level two
Intercept 1.0625 1.5622 0.4432 2.5470
10–20 age-group 0.0000 1.0000 0.0000 0.0000
21–30 age-group 24,634,350.0 1.5329 10,664,429.7 56,903,964.1
31–40 age-group 19,051,790.0 1.5341 8,234,576.9 44,078,798.1
41–50 age-group 0.0000 1.0000 0.0000 0.0000
51–60 age-group 0.3833 2.2182 0.0804 1.8269
61–70 age-group 0.7750 1.7968 0.2457 2.4441
70 +age-group 1.0879 2.7203 0.1530 7.7347
Catch rate 2.8195 2.7419 0.3905 20.3591
Average length 1.0235 1.1288 0.8072 1.2979
Satisfaction level three
Intercept 12.0679 1.4744 5.6384 25.8288
10–20 age-group 0.0540 3.4643 0.0047 0.6161
21–30 age-group 6,443,671.0 1.3285 3,692,875.9 11,243,535.5
31–40 age-group 4,429,118.0 1.3423 2,487,308.8 7,886,871.6
41–50 age-group 0.9234 2.5142 0.1516 5.6253
51–60 age-group 0.2833 1.8596 0.0840 0.9556
61–70 age-group 0.2514 1.7084 0.0880 0.7183
70 +age-group 0.7543 2.4868 0.1265 4.4976
Catch rate 2.8639 2.6948 0.4103 19.9891
Average length 1.0235 1.1142 0.8280 1.2653
Satisfaction level four
Intercept 1.4961 1.4323 0.7398 3.0255
10–20 age-group 1.9777 2.5856 0.3073 12.7287
21–30 age-group 206,430,700.0 1.2098 142,110,583.9 299,865,298.9
31–40 age-group 146,742,800.0 1.2141 100,322,765.0 214,641,725.7
41–50 age-group 10.8053 2.4601 1.8509 63.0800
51–60 age-group 4.6016 1.7877 1.4736 14.3690
61–70 age-group 2.7797 1.6563 1.0339 7.4731
70 +age-group 6.9775 2.4383 1.2163 40.0293
Catch rate 3.0438 2.6870 0.4386 21.1246
Average length 1.1183 1.1120 0.9082 1.3769
Satisfaction level five
Intercept 7,626,695.0 1.4269 3,799,477.9 15,309,072.4
10–20 age-group 0.0000 2.5018 0.0000 0.0000
21–30 age-group 41.6449 1.2038 28.9506 59.9056
31–40 age-group 31.1174 1.2071 21.5162 45.0028
41–50 age-group 0.0000 2.4500 0.0000 0.0000
51–60 age-group 0.0000 1.7779 0.0000 0.0000
61–70 age-group 0.0000 1.6515 0.0000 0.0000
70 +age-group 0.0000 2.4471 0.0000 0.0000
Catch rate 3.3110 2.6865 0.4773 22.9697
Average length 1.1487 1.1117 0.9333 1.4136
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MANAGEMENT BRIEF 941
given response type to the reference category. All coefficients
were exponentiated so the odds ratios could be interpreted on the
real number scale. For all analyses the reference category was an
angler satisfaction rating response of one. Because satisfaction
rating is ordinal instead of nominal, these data are better suited
for ordinal multinomial logistic regression. However, such mod-
els assume the relationship between each pair of outcomes is
proportional (i.e., proportional odds: Agresti 2002). This as-
sumption was not met with these data. As such, we proceeded
with multinomial logistic regression analysis.
Akaike’s information criterion (AIC) was used to evaluate
candidate models (Akaike 1973; Burnham and Anderson 2002).
Akaike weights (wi) were used to assess the relative plausibility
of each candidate model. All statistical analyses were conducted
using the R statistical computing language (R Development
Core Team 2009).
RESULTS
A total of 1,073 anglers were interviewed during the 2013
Crooked River creel survey. Interviewed anglers fished for a
total of 3,441 h, caught and released 4,471 Rainbow Trout, and
harvested 57 Rainbow Trout. The overall angler catch rate was
1.32 ±1.95 fish/h (mean ±SD). Eighty-seven percent of an-
glers were male. One percent were under 10 years of age, 4%
of anglers were in the 10–20-year age-group, 14% were in the
21–30 age-group, 13% were in the 31–40 age-group, 15% were
in the 41–50 age-group, 21% were in the 51–60 age-group, 25%
were in the 61–70 age-group, and 8% were in the over 70 age-
group. The majority of anglers (88%) reported using flies as
their primary terminal tackle type, 7% reported using bait, 5%
reported using lures, and less than 1% reported using a combi-
nation of gears. The mean length of Rainbow Trout harvested
was 295 ±40.64 mm TL and the mean estimated length of
Rainbow Trout released by anglers was 249 ±50.55 mm. A
majority of anglers (55%) rated their overall fishing trip satis-
faction level as five, 33% rated it as four, 9% rated it as three,
2% rated it as two, and 1% of anglers rated their satisfaction
level as one.
The most parsimonious model of angler satisfaction rating
probability included the additive effects of age-group, mean
length of fish caught, and catch rate (Table 1). This model
accounted for 75% of the total wi. The only other model that
accounted for any of the wiwas a model that included age-
group and mean length of fish caught. All of the top models
included covariates for fishing success and age (i.e., catch rate
or average length). The coefficients (i.e., odds ratios with ex-
perience rating category one as a reference), standard errors,
and confidence intervals for the top model are displayed in
Table 2. The probability of an increase in angler satisfaction
rating was positively related to the mean length and number
of fish caught per hour (Figure 1). However, younger anglers
tended to have higher satisfaction ratings with lower fishing
success than did older anglers (Figure 1). In general, models
that included mean length of fish caught had more support
than models that included catch rate as a measure of fishing
success, suggesting that size of fish was more important than
numbers of fish caught in this fishery (Table 1). However, their
effect sizes appeared to be similar and varied among age-groups
(Figures 1–3).
DISCUSSION
Results of this study are consistent with others in that the
majority of anglers were satisfied with their overall fishing ex-
perience regardless of their fishing success (Weithman and Katti
1979; Hudgins and Davies 1984; Miko et al. 1995; Hutt and
Neal 2010). The apparent disconnect between fishing success
and angler satisfaction can be problematic when establishing
management objectives and evaluating the status of fish popu-
lations. However, fishing success was still an important factor
in determining overall satisfaction as evidenced by the pres-
ence of mean length of fish caught and catch rate in all of the
top models. Angler catch rates were relatively high and may
explain why the length of fish appeared to be slightly more im-
portant to angler satisfaction than catch rates. Additionally, a
majority of anglers were fly anglers suggesting that they have
a higher specialization level than nonfly anglers, and thus are
more size-oriented than less specialized anglers (Bryan 1977;
Ditton et al. 1992). Petering et al. (1995) also suggested that
anglers preferred catching larger fish than more fish in Ohio
crappie Pomoxis spp. fisheries, and Hunt et al. (2012) found
that the size of catfish had the strongest influence on overall
catfish angler satisfaction in Texas. Although size was most
important, catch rates are commonly used as a management
objective as a proxy for angler satisfaction (Hicks et al. 1983;
Miko et al. 1995; Schramm et al. 1998; Schultz and Nygren
1999). Additionally, angler age also had an influence on angler
satisfaction.
There is a general consensus in the literature that factors other
than fishing success affect overall fishing satisfaction (Weithman
1999; Hunt and Grado 2010). Several studies suggested that
angler motivations can influence satisfaction and individuals
can have different motivations in the same fishery (Spencer
1993; Fedler and Ditton 1994; Schramm et al. 1998; Arlinghaus
2006). Although angling motivation or expectations were not
quantified, older anglers tended to be less satisfied with their
fishing trips at similar fishing success levels than younger
anglers. The reason for the disparity in satisfaction levels
among age-groups is unknown, but it is reasonable to assume
that angling motivations and expectations change with age
(Aas 1996). Other studies have shown that, in general, older
individuals tended to be less satisfied with their recreational
activities than were younger individuals (Russel 1990; Mostegl
2007). Managers should be aware that changes in angler
demographics may result in changes in motivations or expec-
tations, and changes in fish populations or regulations may
influence angler demographics. For instance, fly-fishing-only
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942 McCORMICK AND PORTER
FIGURE 1. Predicted probability that an angler would report a satisfaction level of four or five depending on the angler’s age-group, and catch rate (fish per hour)
and mean length (mm TL) of Rainbow Trout caught in the Crooked River, Oregon. Anglers under 10 years of age were not included in this figure because they
all reported a trip satisfaction level of five. Also, anglers in age groups of 21–30, 41–50, and 61–70 years are not shown, but they had intermediate probabilities
compared with their adjacent age-classes.
or catch-and-release regulations may attract specialized anglers
who have different expectations than anglers who currently fish
a given fishery (Bryan 1977; Ditton et al. 1992). Additionally,
angler specialization and expectations may change with angling
experience and ultimately angler age. This may also explain
why younger anglers were satisfied with lower levels of
fishing success. As such, repeated angler satisfaction surveys
may be necessary to evaluate changes in regulations or fish
population dynamics.
Our results suggested that the length of fish caught was a
more important factor in predicting angler satisfaction than were
catch rates in this fishery. Because evaluation of the status of fish-
eries is commonly conducted using fishery-independent surveys
(Guy and Brown 2007), the next step in the management of fish
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MANAGEMENT BRIEF 943
FIGURE 2. Predicted probability that an angler would report a satisfaction
level of four or five given the age-group of the angler and the mean length of
Rainbow Trout caught in the Crooked River, Oregon. Anglers under 10 years of
age were not included in this figure because they all reported a trip satisfaction
level of five.
populations is to determine how fishery-independent sampling
data or management of the biota relates to angler satisfaction.
If the desired percent of anglers with a given satisfaction level
can be quantified in relation to the size of fish caught, objectives
may be set at that level, and management actions can be taken to
achieve such objectives. For instance, our models suggested that
approximately 85% of anglers will rate their satisfaction level as
four or greater if the average length of fish they catch is 265 mm.
If managers believe that this level of satisfaction is reasonable,
FIGURE 3. Predicted probability that an angler would report a satisfaction
level of four or five given the age-group of the angler and the catch rate of
Rainbow Trout in the Crooked River, Oregon. Anglers under 10 years of age
were not included in this figure because they all reported a trip satisfaction level
of five.
then a management objective of 265 mm for mean length of
fish that are catchable by anglers is prudent. Minimum-length
or harvest-slot limits, bag limits, and, if applicable, stocking
rates can be adjusted to achieve this objective, based on pop-
ulation dynamic rates that can be estimated through fisheries
independent sampling. Although Rainbow Trout in the Crooked
River were very lightly exploited, mortality caps can be used to
determine the effect that minimum length limits can have on the
average size of fish caught in exploited populations (Miranda
2002). For instance, Quist et al. (2004) used mortality caps to
establish management objectives where the desired mean length
of Walleye Sander vitreus varied from 500 to 600 mm. Although
this approach is sound, a more precise management approach
could include quantifying angler satisfaction levels based on
fish length, then using mortality caps to determine appropriate
growth and mortality rates that meet that objective rather than
evaluating a range of desired mean lengths with no connection to
angler satisfaction levels. Similarly, Peterson and Evans (2003)
used a population model to evaluate the effects of changing
length limits for Largemouth Bass Micropterus salmoides in a
Georgia reservoir. Those authors attempted to quantify angler
satisfaction probability indirectly by weighting preference an-
swers in an off-site survey and converting them to probabilities.
An alternative approach may have been to directly measure an-
gler satisfaction probability as a function of fishing success in
an on-site survey. However, their use of a quantitative decision
analysis once satisfaction probabilities were estimated is a logi-
cal next step and will likely lead to more efficient sport fisheries
management.
Angler satisfaction data are commonly analyzed using ex-
ploratory multivariate techniques such as cluster analysis or
hypothesis tests (Holland and Ditton 1992; Pollock et al. 1994;
Miko et al. 1995). Although these methods are effective at de-
termining differences among groups, they often fail to provide
information about effect sizes. The coefficients (i.e., odds ra-
tios) in multinomial logistic regression models allow managers
to predict the percentage of anglers that will be satisfied as a
function of continuous or categorical covariates. This interpre-
tation permits managers to set specific management objectives
for angler satisfaction in relation to fishing success. Further-
more, Pollock et al. (1994) suggested that, in addition to general
surveys of angler satisfaction, more detailed surveys should be
conducted to evaluate specific components of angler satisfac-
tion (e.g., catch rates, size of fish). However, this can lead to
issues when determining the effect of fishing success on over-
all satisfaction that includes nonfishing success-related factors.
The methods we used allow for evaluation of both components
simultaneously and will hopefully lead to more effective fish-
eries management.
ACKNOWLEDGMENTS
Funding for this project was provided by a grant from
the Oregon Department of Fish and Wildlife Restoration and
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944 McCORMICK AND PORTER
Enhancement Board. We thank Jill Lukacs for conducting the
creel survey. We thank Michael Gauvin, Michael C. Quist,
Richard Eades, and three anonymous reviewers for providing
comments on earlier versions of this manuscript.
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