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Fisheries Research 181 (2016) 163–171

Contents lists available at ScienceDirect

Fisheries Research

journa l homepage: www.e lsev ier .com/ locate / f i shres

ull length article

emand for fishery regulations: Effects of angler heterogeneity andatch improvements on preferences for gear and harvest restrictions

cott Knoche a,∗, Frank Lupi b

Department of Fisheries and Wildlife, Michigan State University, USADepartment of Agricultural, Food, and Resource Economics and Department of Fisheries and Wildlife, Michigan State University, USA

r t i c l e i n f o

rticle history:eceived 30 June 2015eceived in revised form 12 April 2016ccepted 15 April 2016andled by A.E. Punt

eywords:ngler heterogeneityegulation preferenceshoice experimentandom parameters logitrout fishery management

a b s t r a c t

Allocating limited funds across competing management actions to generate the fisheries improvementsmost desired by anglers requires in-depth knowledge of angler preferences for aspects of the fishingexperience. Characterizing angler preferences in terms of a probability distribution, rather than simplya population mean, provides fisheries managers with insights into how preferences vary across the pop-ulation of anglers. This is particularly important with respect to fishing regulations, for which anglersmay have strong yet diverse preferences. To examine angler preferences and how these preferences varyacross the population of trout anglers, we estimated a random parameters logit model of trout fishingsite choice using data from a mail and internet administered choice experiment of Michigan trout anglers(response rate—44.6%). We found that, even though on average trout anglers do not prefer fishing at siteswith the most strict fishing regulations (Catch and Release Only and Artificial Flies Only; p < 0.01), about18% and 26% of anglers do prefer sites with these regulations (all else equal). We then exploit positive

and statistically significant (p < 0.01) mean preferences for trout catch rates and sizes to examine howsites with regulations that result in offsetting catch-related improvements can increase the proportion ofanglers preferring such sites. These results can help improve trout fishery management by focusing scarceresources on the improvement of the most-preferred catch-related attributes, while understanding howanglers are affected by gear and harvest regulations.

© 2016 Elsevier B.V. All rights reserved.

. Introduction

Trout species such as brown trout (Salmo Trutta) and rain-ow trout (Onchorhynchus mykiss), endemic to Europe and Northmerica respectively, have been successfully introduced andre now targeted by recreational anglers across six continentsnd throughout dozens of countries (Elliott, 1994). Other troutpecies, including brook trout (Salvelinus fontinalis) and cutthroatrout (Oncorhynchus clarkii), provide regional fishing opportunitiesithin their native watersheds. Fishing for trout is particularly pop-

lar in the U.S. (7.2 million trout anglers and 76 million days in011) and in U.K./Wales (843,000 trout and salmon anglers and3.5 million trips in 1995) (USDOI, 2013; Hickley, 1995). To gen-

rate and sustain high-quality trout fishing experiences, managersndertake costly management actions such as trout stocking, pol-

ution mitigation, and restoration of streams and riparian zones.

∗ Corresponding author at: Morgan State University, Patuxent Environmental &quatic Research Laboratory, 10545 Mackall Road, Saint Leonard, MD 20685, USA.

E-mail address: scott.knoche@morgan.edu (S. Knoche).

ttp://dx.doi.org/10.1016/j.fishres.2016.04.010165-7836/© 2016 Elsevier B.V. All rights reserved.

Further, managers seek to prevent the over-exploitation of troutfisheries through the implementation of site- and season-specificregulations that restrict trout harvest and permissible fishing gear.Achieving fishery improvements desired by anglers while stay-ing within budget and being cognizant of angler tolerance forregulations requires an in-depth understanding of the types oftrout fishing experiences preferred by anglers. Characterizing meanangler preferences for trout fishing site attributes in terms of thewillingness to incur increased costs (such as increased travel dis-tance to reach a fishing destination) to obtain fishing site qualityimprovements provides a convenient and relevant metric for com-paring angler preferences for different site attributes. Moreover,management decision-making can be greatly aided by informationon how these preferences for fishing site attributes vary across thepopulation of anglers. For example, fisheries managers seeking toachieve catch-related improvements through the implementationof more restrictive regulations such as higher minimum size, lower

daily harvest limit, and restrictions on certain types of fishing gearwould do well to understand how heterogeneous angler prefer-ences for regulations affect whether and to what extent anglers

164 S. Knoche, F. Lupi / Fisheries Research 181 (2016) 163–171

out st

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re made better off via regulation-induced catch-related improve-ents.

To examine angler preferences and preference heterogeneityor fishing site attributes, we use the survey-based stated prefer-nce discrete choice experiment approach (Louviere et al., 2000).his approach allows for the identification of angler preferenceshrough measuring angler choices of a preferred fishing site from

set of fishing sites, with each site consisting of different catchates, regulations, and travel distance. Angler choice of a fishing sites statistically analyzed using the random parameters logit model,

hich produces both mean and standard deviation parameter esti-ates that characterize the distribution of preferences for fishing

ite attributes across the angling population. This provides insightnto angler preferences and preference heterogeneity for fishingite attributes, thus providing an additional dimension of analysiselative to the widely used conditional logit model which only pro-ides information on mean preferences. Subsequently, we examinehe extent to which trout anglers switch from being negativelympacted by trout regulations to being positively impacted whenhe regulations generate catch-related improvements in the troutshery. Finally, we describe the potential use of these results inuiding fisheries management decisions.

. Study area

There are approximately 12,500 miles of coldwater trouttreams in Michigan (MDNR, 1994), with the majority of theseaterways distributed throughout Michigan’s Upper Peninsula and

orthern Lower Peninsula (Fig. 1). Trout fishing opportunities areore limited in the south-central and southeastern portions of the

ower Peninsula. However, this region is the most populous, withhe nine-county Detroit-Ann Arbor-Flint combined statistical area

reams in Michigan.

constituting about 54% of the state’s population. Brown trout andbrook trout comprise the majority of the wild, non-anadromoustrout fishery, although there are some localized populations of wildrainbow trout.

Throughout these coldwater trout fishing streams, fisheriesmanagers utilize a variety of regulations to protect and enhancetrout fishing experiences. The Michigan Department of NaturalResources (MDNR) classifies the majority of Michigan (∼1600)trout streams into different regulations categories, and regulationregimes can vary. We focus on two major elements that vary acrossstreams: harvest limits and restrictions on lures and bait. Absentspecial regulations, trout streams have a harvest limit of five fishand do not have any restrictions on bait or lures. However, onsome sections of trout streams, the MDNR has implemented spe-cial regulations restricting the use of natural bait/artificial lures andthe number of trout that may be harvested. On three sections ofstreams totaling 21.8 miles, catch and release is required duringthe standard (i.e., last Saturday in April through September 30)trout fishing season. A two trout harvest limit is imposed on tensections of stream totaling 105.4 miles. Michigan trout anglers alsoface restrictions on the types of lures/baits that may be used oncertain portions of streams. Trout anglers fishing in eight streamsections totaling 78.2 miles are restricted to the use of artificial flies,while anglers fishing in seven stream sections totaling 53.4 milesare restricted to using artificial lures (including artificial flies). Addi-tionally, five stream sections totaling 34.6 miles have restrictionson lures/bait which vary throughout the year.

Special harvest and gear restrictions during the standard fishing

season occur on about 260 miles of trout streams, only about 2% ofthe 12,500 miles of coldwater trout streams in Michigan. The mostwidespread regulation, mandatory catch and release for browntrout and brook trout outside of the standard trout fishing season,

s Research 181 (2016) 163–171 165

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ccurs only on about 8% of trout streams in Michigan. However,hese regulations exist on highly regarded trout streams whicheceive substantial fishing pressure, such as the Au Sable, Manis-ee, and Pere Marquette rivers. This includes an 8.7 mile stretchf the Au Sable known to many fly anglers as the “Holy Waters”Bain, 1987; Gigliotti, 1989), a highly regulated (artificial flies onlynd catch & release only) portion of the Au Sable renowned for itsxcellent trout fishing.

Privately and publicly funded stocking and habitat restora-ion efforts are used to enhance trout fisheries in Michigan.xcluding Great Lakes and Great Lakes connecting waterways (i.e.,etroit River and St. Clair River), about 1.5 million brown troutnd about 100,000 brook trout were stocked in rivers/streamsn Michigan in 2012 (MDNR, 2012). The MDNR Fisheries Divi-ion undertakes numerous habitat restoration projects that benefitoldwater species such as trout—in 2011 the MDNR Fisheries Divi-ion reported carrying out 17 dam removal projects resulting in 167iles of rehabilitated stream habitat, and ten additional restoration

rojects which reconnected and improved 161 miles of streams andivers (MDNR, 2011). The MDNR Fisheries Division also manages aabitat restoration and research fund that is funded from penalties

evied on hydropower operators due to damages from dam opera-ion. This fund supports habitat restoration and research efforts on

ajor trout fishing streams in Michigan such as the Au Sable andanistee rivers. In 2011, these mitigation funds helped fund the

emoval of six dams, facilitated three woody habitat improvementtructures, and helped reduce sedimentation via two road-streamrossing projects (MDNR, 2011).

. Material and methods

.1. Stated preference discrete choice methods and survey

Stated preference discrete choice methods employed within aecreational fisheries context use surveys to elicit angler prefer-nces through choice scenarios that explicitly and quantitativelyefine attributes levels available at alternative fishing locations.here has been substantial growth in the use of stated preferenceiscrete choice modeling efforts to examine angler preferences forecreational fishing attributes. While a Hunt (2005) meta-analysisf discrete choice recreational fishing studies identified only threetated preference discrete choice studies, we have identified over0 peer-reviewed research papers using the stated preferencepproach since Hunt’s publication. Three of these efforts focusedn stream trout fishing, with Ahn et al. (2000) examining anglerreferences for catching brown trout and grayling in Norway, andeville and Kerr (2008, 2009) analyzing stream trout fishing in Newealand.

To analyze angler preferences for aspects of a stream trout fish-ng experience, we developed and implemented a stated preferenceiscrete choice experiment survey of trout anglers in Michigan.he survey instrument presented anglers with four choice sce-arios, with each scenario consisting of two stream trout fishingptions described by the attributes and attribute levels availablet each option. The attributes themselves are constant across fish-ng options, whereas the attribute levels vary across options. Forach choice scenario, the survey respondent was instructed to com-are the attribute levels associated with the two fishing options and

dentify the preferred fishing option. Fig. 2 is an example of a choicecenario that was included in the survey.

The process of identifying management-relevant attributes for

se in describing Michigan stream trout fishing locations beganith a comprehensive review of peer-reviewed literature, grey lit-

rature, and additional materials which could provide insight intomportant aspects of the recreational fishing experience. Reviewing

Fig. 2. Example Michigan stream trout fishing site choice scenario.

50 discrete choice recreational fishing studies, Hunt (2005) foundthat the following site attributes influence an individual’s fishingsite selection decision; (a) costs relating to travel or access, (b)fishing quality (e.g., size, catch rate, and species availability), (c)environmental quality, (d) facility development, (e) encounter lev-els (with anglers and other individuals), and (f) regulations. Usingthese general attributes as a starting point, we used the 2013 Michi-gan Fishing Guide (MDNR, 2013) to identify the types of troutspecies targeted in Michigan and types of fishing regulations facedby trout anglers. Ultimately we identified seven stream trout fish-ing site attributes as being management-relevant and potentiallyimportant drivers of fishing site choice. Each fishing site optionin a choice scenario is described by four catch-related variables(hourly catch rate and length-frequency for both brown trout andbrook trout), two regulation variables (gear restriction and har-vest restriction), and distance to the fishing site. All attributes andattribute levels are detailed in Table 1. Discussions with MDNRpersonnel and Trout Unlimited (a coldwater fisheries conservationorganization) helped us identify realistic levels for catch-relatedattributes. For both brown trout and brook trout, we employ a rangeof typical angler hourly catch that ranged from four trout per hour(high end) to one trout every eight hours (low end). As brown trouttypically grow larger than brook trout, it was necessary to havelength distributions which accounted for this size difference. The25th percentile lengths ranged from 7′′ to 10′′ for brown trout and6′′–8′′ for brook trout. The 75th percentile lengths ranged from 9′′

to 18′′ for brown trout and 7′′–13′′ for brook trout.Most anglers living in the south-central or southeastern portion

of Michigan’s Lower Peninsula do not have stream trout fishinglocations within close proximity to their residence. To ensure thattravel distance levels in the choice experiment reflected realistictravel distances an angler might face on an actual trout fishingtrip, we separated anglers into three distinct geographic regionsby region of residence (Fig. 1). Trout anglers residing in the North-ern Lower Peninsula or Upper Peninsula of Michigan likely havenearby stream trout fishing options, and thus might see travel dis-tances as low as 10 miles in choice scenarios, whereas anglers livingin southeastern Michigan likely have few or no nearby stream trout

fishing options, and thus do not see travel distances less than 100miles.

166 S. Knoche, F. Lupi / Fisheries Research 181 (2016) 163–171

Table 1Fishing site attributes and attribute levels for choice experiment of stream trout anglers in Michigan.

Category Attribute # of Levels Attribute Levels

Regulations Harvest Limit 3 Catch and Release2 Trout5 Trout

Lure/Bait Restrictions 3 NoneArtificial Lures & Flies OnlyFlies Only

Brown Trout CatchAttributes

Typical Angler CatchSize

9 25% under 7”, 50% 7”–9”, 25% over 9”25% under 7”, 50% 7”–11”, 25% over 11”25% under 7”, 50% 7”–15”, 25% over 15”25% under 8”, 50% 8”–10”, 25% over 10”25% under 8”, 50% 8”–12”, 25% over 12”25% under 8”, 50% 8”–16”, 25% over 16”25% under 10”, 50% 10”–12”, 25% over 12”25% under 10”, 50% 10”–14”, 25% over 14”25% under 10”, 50% 10”–18”, 25% over 18”

Typical Angler CatchRate

6 1 trout per 15 min 1 trout per 2 h1 trout per 30 min 1 trout per 4 h1 trout per hour 1 trout per 8 h

Brook Trout CatchAttributes

Typical Angler CatchSize

9 25% under 6”, 50% 6”–7”, 25% over 7”25% under 6”, 50% 6”–8”, 25% over 8”25% under 6”, 50% 6”–11”, 25% over 11”25% under 7”, 50% 7”–8”, 25% over 8”25% under 7”, 50% 7”–9”, 25% over 9”25% under 7”, 50% 7”–12”, 25% over 12”25% under 8”, 50% 8”–9”, 25% over 9”25% under 8”, 50% 8”–10”, 25% over 10”25% under 8”, 50% 8”–13”, 25% over 13”

Typical Angler CatchRate

6 1 trout per 15 min 1 trout per 2 h1 trout per 30 min 1 trout per 4 h1 trout per hour 1 trout per 8 h

Travel Travel Distance 6 Region 1 (miles) Region 2 (miles) Region 3 (miles)

10 60 10020 70 11035 85 12550

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Experimental design procedures allow for the control of fac-ors within a choice experiment that affect parameter estimation,

odel flexibility, and the statistical efficiency of estimated parame-ers (Johnson et al., 2007). We used the software package NGENE toenerate stream fishing site choice sets, with each choice set con-isting of two fishing site alternatives that vary by attribute levels.o generate the most efficient experimental design (the design thatroduces unbiased parameter estimates with the smallest possibletandard errors), we use the D-Optimality criterion. That is, we useGENE to identify the experimental design with the lowest D-errorstimate (i.e., highest efficiency). The resulting experimental designonsists of seventy-two distinct choice scenarios.

To evaluate angler comprehension and pretest the survey prioro actual implementation, we recruited individuals with a his-ory of fishing in Michigan to participate in one-hour individualnterviews, which incorporated internet meeting software along

ith phone discussion (following the approach of Weicksel, 2012).atherPlace screen sharing software was used which allowed indi-iduals to take the survey while we visually monitored surveyrogress from a remote location. A phone connection was main-ained throughout the process to address immediate comments,uestions or concerns an individual might have regarding specificspects of the survey instrument. A thorough assessment of respon-ent comprehension occurred after the survey was completed, withach individual asked a series of questions which were designed to

dentify potential issues with survey instrument design or content.or survey instrument pretesting, we used a convenience sample ofeven anglers associated with the Trout Unlimited organization and

100 140125 165

150 190

15 individuals recruited for a previous Michigan fishing survey. Atotal of 22 people completed the survey one-on-one pretesting ses-sions. The survey was also reviewed by a MDNR steering committeethat advises MDNR and consists of fishery managers and represen-tatives from a range of trout angling interests. Though no majorissues were identified in the pretesting process, we received help-ful comments and suggestions that allowed us to improve aspectsof the survey layout and design.

Survey recipients consisted of a randomly drawn sample of4099 Michigan residents who purchased an “Unrestricted” fishinglicense (which allows for the holder to target trout and salmon) forthe 2012–2013 Michigan fishing season. We used a mixed modeinternet and mail survey approach with up to four contacts for eachpotential respondent. The first three contacts consisted of an enve-lope with letter (first contact) and two postcards (second and thirdcontacts), which explained the nature and purpose of the surveyand provided a website address which the individual could typeinto their web browser to access the survey. Individuals who didnot attempt to complete the online version of the survey were senta fourth contact, which included both the website address as wellas a printed 12-page questionnaire.

4. Theory and model estimation

Random utility theory (McFadden, 1974) is the underlyingbehavioral theory supporting our discrete choice analysis of streamtrout fishing site selection. According to random utility theory, anindividual selects the fishing location which maximizes his/her

S. Knoche, F. Lupi / Fisheries Research 181 (2016) 163–171 167

Table 2Fishing site attribute variables in indirect utility function.

Fishing Site Attribute Variables Fishing Site Attribute Variable Definition Variable Type Min, Max

Distance One-way distance from individual’s residence (in miles) Continuous 10, 190Catch&Release All trout caught must be released Categorical 0, 1Harvest 2 Harvest limit of two trout Categorical 0, 1Lures&Flies Only Only lures and flies may be used; no natural bait Categorical 0, 1Flies Only Only flies may be used; no other lures or natural bait Categorical 0, 1Brown Rate # of brown trout caught per hour by a Typical Angler Continuous 0.125, 4Brook Rate # of brook trout caught per hour by a Typical Angler Continuous 0.125, 4Brown 25 25th percentile of brown trout catch length (in inches) Continuous 7, 10Brown 75 75th percentile of brown trout catch length (in inches) Continuous 9, 18Brook 25 25th percentile of Brook Trout catch length (in inches) Continuous 6, 8Brook 75 75th percentile of Brook Trout catch length (in inches) Continuous 7,13

Table 3Mean coefficient estimates and statistical significance for Michigan trout fishing site attributes.

Variable Parameter Value Robust Std. Error P-Value

Distance (negative) Mean of ln (coefficient) −4.763 0.284 0.000Catch&Release Mean coefficient −2.318 0.277 0.000Harvest 2 Mean coefficient −0.517 0.160 0.001Lures&Flies Only Mean coefficient −0.267 0.125 0.032Flies Only Mean coefficient −1.451 0.209 0.000Brown 25 Mean coefficient −0.034 0.052 0.513Brook 25 Mean coefficient 0.018 0.083 0.825Brown Rate Mean coefficient 0.277 0.040 0.000Brook Rate Mean coefficient 0.155 0.041 0.000Brown 75 Mean coefficient 0.095 0.021 0.000Brook 75 Mean coefficient 0.048 0.031 0.130

Model StatisticsLog likelihood −1532.4Wald chi sqd (11) 646.28# of observations 5070

Table 4Variance-covariance matrix of coefficient estimates and statistical significance for Michigan trout fishing site attributes with random distributions.

Variable Catch&Release Harvest 2 Lures&Flies Only Flies Only Brown 25 Brook 25 Distance (negative)

Catch&Release 6.347c

(1.839)Harvest 2 2.753c 1.423a

(1.039) (0.779)Lures&Flies Only 0.469 −0.178 0.717a

(0.577) (0.345) (0.383)Flies Only 1.930b −0.134 1.827c 5.068c

(0.894) (0.655) (0.707) (1.552)Brown 25 −0.076 −0.022 −0.014 −0.058 0.151a

(0.251) (0.123) (0.077) (0.197) (0.089)Brook 25 −0.628a −0.332 0.113 0.313 −0.029 0.274

(0.331) (0.210) (0.164) (0.326) (0.086) (0.199)Distance (negative) −1.289c −0.555b −0.286 −0.378 0.269a 0.000 2.916c

(0.409) (0.239) (0.254) (0.427) (0.150) (0.396) (0.852)

a Statistically significant at 10% level.

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b Statistically significant at 5% level.c Statistically significant at 1% level.

ndirect utility on a given choice occasion. In Eq. (1), Vnjt is the indi-ect utility from alternative j in choice occasion t by individual n,njt is a vector of site attributes for individual i at alternative j inhoice occasion t, ̌ is a vector of parameters associated with theseite attributes, and εijt is the stochastic error term unobservable tohe researcher.

njt = ˇnxnjt + εnjt (1)

The indirect utility function we estimate consists of the fishingite attribute variables defined and described in Table 2.

4.1. Random parameters logit model

The random parameters logit modeling approach accountsfor preference heterogeneity of individuals via the estimationof parameter distributions for specified random variables (Train,2009). While the random parameters logit is often referred to as amixed logit in the literature, McFadden and Train (2000) show thatthe mixed logit is a more flexible model that can be constrainedto produce the random parameters logit. As such, we employ therandom parameters logit terminology throughout this paper. This

method has been applied to discrete choice analysis since the early1980s (e.g., Boyd and Mellman, 1980; Cardell and Dunbar, 1980),and has now been used by a number of researchers to examine pref-

168 S. Knoche, F. Lupi / Fisheries Research 181 (2016) 163–171

Table 5Per-trip mean marginal rate of substitution for statistically significant (p < 0.01) fishing site attribute variables.

Fishing Site Attribute Variables Mean Marginal Rate of Substitution (in miles)

Catch&Release −63.1Harvest 2 −14.1Flies Only −39.5Brown Rate (Catch increase of 1 Brown Trout/hour) 7.6Brown 75 (Increase in 75th percentile Brown Trout catch size by 1”) 2.6

N og of t

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Brook Rate (Catch increase of 1 Brook Trout/hour)

ote: To calculate mean marginal rate of substitution, we convert the mean of the l

rences for angling site attributes (e.g., Train, 1998; McConnell andseng, 1999; Breffle and Morey, 2000; Kerkvliet and Nowell, 2000;rovencher and Bishop, 2004; Massey et al., 2006; Oh and Ditton,006; Wielgus et al., 2009 Wielgus et al., 2009).

In the random parameters logit model with repeated choices,he probability that an angler n makes a sequence of alternativehoices i, where i =

{i1, . . ., iT

}, is estimated by integrating the

roduct of standard logit probabilities (McFadden, 1974) over aensity of ̌ parameters (Train, 2009).

ni =∫ T∏

t=1

exp(ˇ′nxnit t

)∑

jexp(ˇ′nxnjt

) f (ˇ)d (ˇ)

(2)

Random parameters logit estimation proceeds with the objec-ive of estimating mean and standard deviation parameters for thearameters specified to have a random distribution. The researcherakes distributional assumptions for parameters—commonly

ssumed distributions include normal, lognormal, and triangularistributions. We also explore the possibility of fixed parametershat do not vary over the population. We explore a variety of ran-om and fixed parameter distributions in our model specification,long with the potential for covariation among random param-ters, and we use Akaike Information Criterion (AIC) to assesselative model quality. For each of the random parameters in this

odel, we estimate the mean b and standard deviation s whichharacterize the probability distribution f

(ˇ)

. Parameters are esti-ated via maximum simulated likelihood estimation, in which

andom parameters logit probabilities in Eq. (2) are approximatedy repeating draws of a value ̌ from f

(ˇ|�

), where � refers to the

ean and covariance of the distribution. Halton sequences (200)re used for the draws from distribution f

(ˇ|�

)f(ˇ|�

), which sub-

tantially reduces model run time and simulation error relative toandom draws (Train, 2000; Bhat, 2001). For each draw of ̌ from(ˇ|�

), the random parameters logit probability in Eq. (2) is cal-

ulated, and the average of these probabilities is the simulatedrobability (Train, 2009).

.2. Estimating marginal rates of substitution and preferenceeterogeneity

Once the random parameters logit model has been used tostimate mean and standard deviation parameters for fishing sitettributes, we examine the willingness of stream trout anglers toake trade offs between the distance traveled to reach a fishing site

nd the attributes available at that fishing site. To do this, we useq. (3) to estimate the mean marginal rates of substitution (MRS)f trout anglers to obtain different levels of fishing site attributesonditional on taking a trip to one of the sites.

arginal Rate of Substitutionq,distance = −b̂q/b̂distance (3)

The estimated mean parameter b̂q in Eq. (3) refers to a fishing sitettribute other than distance, and b̂distance is the estimated parame-er associated with the distance attribute. That is, MRS reflects theillingness of trout angler to drive further distances to obtain qual-

4.2

he distance coefficient (in Table 3) to the mean of the distance coefficient.

ity improvements at a trout fishing site. For example, if b̂q = hourlycatch rate = 5 and b̂distance = −1, an individual would be willing todrive up to an additional 5 miles to obtain an increase in the hourlycatch rate by one fish per hour.

Finally, when the standard deviation on the normally dis-tributed random variables is statistically significant (p < 0.01), wecalculate the proportion of individuals who derive positive andnegative utility from these site attributes. We then examine howthe proportion of individuals deriving positive and negative utilityfrom a regulation might change given a corresponding change incatch-related attributes such as catch size and catch rate.

5. Results

A total of 1038 individuals completed the internet survey and743 individuals completed the mail survey for a final response rateof 44.6%, with responses to the choice experiment used to modelangler site choice. We used AIC to compare and evaluate the rela-tive quality of a variety of model specifications that incorporateddifferent distributional assumptions for the independent variables.Ultimately, the model with the best fit according to AIC incorpo-rated a lognormal distribution for the distance variable, normaldistributions for the four regulation variables and the two 25thpercentile catch size variables, and covariation among the sevenrandom parameters, with the four other catch-related variablesfixed. Mean coefficient estimates, standard errors and significancelevels are presented in Table 3. As expected, the parameter on Dis-tance was negative and statistically significant (p < 0.01), meaningthat the net utility an individual receives from a site decreases asthe distance to the site increases. Mean coefficient estimates forthe most restrictive regulation levels in the gear and harvest reg-ulation categories were also negative and significant (p < 0.01). Allelse equal, an average trout angler was less likely to select fishingsites where trout may not be harvested (Catch&Release) or whereanglers are restricted to harvesting a maximum of two trout (Har-vest 2), relative to sites where there is a harvest limit of five trout(p < 0.01). Similarly, sites where only artificial flies may be used(Flies Only) were less likely to be chosen than sites where there arenot restrictions on natural bait or artificial lures (p < 0.01). A prohi-bition on the use of natural bait (Lures&Flies Only) is negative, butwith weaker evidence than the other three regulations (p = 0.032).As expected, anglers preferred to fish at locations where catch ratesare higher, and this result was statistically significant (p < 0.01) forboth brown and brook trout catch rates. While anglers preferredhigher catch rates of both trout species, the mean coefficient onBrown Rate was close to double that of the mean coefficient onBrook Rate, indicating a stronger preference for brown trout catchrates. On average, anglers did not appear to have strong prefer-ences for increasing the 25th percentile of trout size for eitherspecies; mean coefficients on Brown 25 and Brook 25 were nega-

tive, but not statistically significant at conventional measurementlevels. Finally, our results indicated a preference for greater browntrout size (p < 0.01), whereas we could not reject the null hypothesisthat the mean coefficient on Brook 75 is different than zero.

S. Knoche, F. Lupi / Fisheries Research 181 (2016) 163–171 169

Table 6Current proportion of anglers receiving positive utility from regulations (Scenario 1) and the proportion of anglers receiving positive utility when the regulation is accompaniedby a catch-related site quality improvement (Scenarios 2–4).

Regulations

Mandatory Catch andRelease

Artificial FliesOnly

Scenario 1:Add regulations to a site without catch-related site quality improvements better for

worse for17.9%82.1%

26.0%74.0%

Scenario 2: size increase:Add regulations to a site with an increase in 75th percentile length of 3” for

brown troutbetter forworse for

21.0%79.0%

30.2%69.8%

Scenario 3: catch rate increase:Add regulations to a site with an increase in catch rate of 1 brown trout/hour

and 1 brook trout/hourbetter forworse for

22.7%77.3%

32.5%67.5%

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Scenario 4: size & catch rate increase:Add regulations to a site with increase in 75th percentile length of 3” for brown

trout and increase in catch rate of 1 brown trout/hour and 1 brook trout/hour

The diagonal components of the variance-covariance matrixoefficients in Table 4 identify the variance across the angler pop-lation with respect to the relevant fishing site attribute. There isvidence for heterogeneous preferences for fishing regulations, ashe estimated variance coefficients for the two moderate regula-ions (Harvest 2 and Lures&Flies Only) are statistically significant athe 10% level and the most strict regulations (Catch&Release andlies Only) are statistically significant at the 1% level.

The off-diagonal components of the variance-covariance matrixrovided in Table 4 identify whether individuals have correlatedreferences for attributes. For example, we expect that individualsho have preferences against a specific regulation are more likely

o have preferences against other regulations. Within-regulationovariance is identified between Catch&Release and Harvest 2p < 0.01) as well as between Flies Only and Lures&Flies Onlyp < 0.01). This is intuitive; an individual’s preferences for one levelf a gear (harvest) restriction are likely to be correlated with theirreferences for another level of the same type of gear (harvest)estriction. Additionally, there is covariance of preferences for thewo most strict regulations (p < 0.05), whereas there is no evidenceor covariance across the two less strict harvest and gear regula-ions.

The MRS figures in Table 5 are interpreted as the number of milesn average individual would be willing to travel to obtain a changen fishing site quality attribute. For the two most restrictive regula-ions, we see very large negative estimates of mean MRS, implyinghat individuals would travel large distances to avoid these reg-lations. For brown trout catch-related attributes, we see that anngler would be willing to travel an additional 7.6 miles to increaserout catch rate by one brown trout per hour, and would be willingo travel an additional 2.6 miles to increase the 75th percentile ofrown trout length by 1′′. Together, the MRS for Brown Rate andrown 75 indicate that an angler would be roughly indifferent to ahange in brown trout catch of one per hour and a change in the5th percentile of brown trout length of about 3′′. Regarding brookrout catch attributes, Table 5 shows that the average angler woulde willing to drive an additional 4.2 miles to catch one more brookrout every hour. We do not provide the MRS for Brook 75, as we arenable to reject the null hypothesis that the coefficient is zero usingonventional significance levels. In general, it appears that anglersave a stronger preference for increases in brown trout attributeselative to brook trout attributes.

.1. Management regulation policy scenarios

The proportion of the angling population who supports restric-ive regulations such as mandatory catch and release and artificial

better forworse for

26.3%73.7%

37.2%62.8%

flies only may be influenced by the effectiveness of these regula-tions in improving catch-related angling outcomes. To understandthe potential for changes in angler support for regulations resultingfrom regulation-induced catch improvements at fishing sites, weexamine the change in the proportion of anglers who benefit froma regulation when the regulation occurs along with a catch-relatedquality change. Take, for example, the mandatory catch and releaseregulation. Some anglers who realize low to moderate disutilitywith respect to mandatory catch and release may derive positiveutility from a scenario in which mandatory catch and release isaccompanied by catch-related quality improvements. That is, theseanglers go from being worse off with the regulation to being betteroff with both the regulation and the catch improvements.

Within Table 6, we examine regulation-induced fishing sitequality change scenarios involving the three catch-related vari-ables that have mean coefficients which are statistically significant(p < 0.01); Brown 75, Brown Rate, and Brook Rate. In Table 6 “betterfor” refer to the proportion of anglers for whom the scenario makesthem better off (i.e., generates positive utility for the angler) while“worse for” refers to the proportion of anglers for whom the sce-nario makes them worse off (i.e., generates negative utility for theangler). The scenarios described in Table 6 show that regulation-induced catch-quality improvements increase the proportion ofanglers who receive positive utility, relative to the scenario inwhich regulations are not accompanied by catch-quality improve-ments. These results also indicate that catch-related improvementsoccurring from the “artificial flies only gear” restriction result ina greater number of anglers with positive utility. However, it isworth noting that none of Table 6 scenarios involving regulationsand catch-related changes make a majority of anglers better-off. Inpractice, these regulations would not need to be applied at all fish-ing locations, thereby permitting sorting of anglers by sites withdifferent regulations and fishing quality based on their preferences

6. Discussion

Results of our discrete choice study of stream trout fishing inMichigan generally conform with results from other discrete choiceresearch on recreational fishing. Similar to the review of recre-ational fishing discrete choice studies in Hunt (2005), we foundthat on average, anglers prefer larger fish, higher catch rates, lessrestrictive regulations, and lower travel distances. In particular, theaverage angler is substantially and negatively impacted by the most

highly restrictive regulations, with the average angler willing todrive many miles to avoid mandatory catch and release and artifi-cial flies only fishing regulations at trout fishing sites. Importantly,we do find that the less strict harvest regulations (two trout per

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ay) and gear restrictions (artificial lures and flies permitted; noatural bait) have less impact on trout angler site choice. Managersesiring to mitigate negative impacts on the fishery while minimiz-

ng impacts to anglers should strongly consider implementing suchess strict regulations. Further, the literature on hooking mortalityas largely confirmed that the use of natural baits leads to greatersh mortality than the use of artificial lures (e.g., Taylor and White,992; Bartholomew and Bohnsack, 2005; Huehn and Arlinghaus,011), while more limited and equivocal evidence exists regard-

ng a reduction in trout mortality through the use of artificial fliess opposed to artificial lures (e.g., Wydoski, 1977; Schisler andergersen, 1996; Risley and Zydlewski, 2010 find evidence for such

mortality reduction whereas Mongillo, 1984 does not). This sug-ests that the greatest reductions in fish mortality with least impacto anglers can be achieved by banning the use of natural baits,hereas mortality reductions achieved by mandating the use of

rtificial flies are more uncertain and would have negative impactsn a large percentage of trout anglers.

The results also underscore the critical role that accountingor angler heterogeneity plays for understanding the diversity ofreferences among anglers. Despite strongly negative mean pref-rences for the most restrictive regulations our analysis shows thatven while holding all other site characteristics (such as catch-elated attributes) constant, 26.0% of anglers (17.9% of anglers)refers fishing in areas with flies only gear restrictions (mandatoryatch and release). This result suggests that the targeted estab-ishment of restricted areas can benefit a sizable minority of therout angler population. To minimize impacts on the majority ofnglers, it may be prudent to locate such restricted fishing sitesn areas that have a number of substitute fishing sites with less-estrictive regulations. The proportions of anglers preferring theseegulations (with or without catch-related quality improvements)re far greater than the proportion of coldwater trout streams inichigan having mandatory catch and release (0.2%) and artificial

ies only restrictions (0.6%) throughout the trout fishing season.Importantly for fisheries managers, our analysis shows that

egulation-induced catch quality changes can increase the numberf anglers better off with otherwise undesirable regulations. How-ver, the negative effects of strict regulations to the mean anglerre such that very substantial catch-related increases would beecessary to make these anglers better-off. For example, Table 5hows that an increase in brown trout catch rate (brook trout catchate) of 8.4 brown trout (14.9 brook trout) per hour would beecessary to fully compensate for the mean angler for implement-

ng catch and release harvest regulations, whereas an increase inrown trout catch rate (brook trout catch rate) of 5.2 brown trout9.3 brook trout) per hour would be necessary to fully compen-ate for the implementation of flies only gear restrictions. Thesencreases in catch rates are beyond the highest attribute level ofourly trout catch rates in the choice experiment (four trout perour) and are probably not a realistic fisheries outcome from imple-enting regulations. It should also be noted that strongly negativeean angler preferences for strict regulations likely means that

roposed new regulations will generate disproportionate anglerpposition. However, such regulation changes are possible anday require operating through the political structure. For exam-

le, gear-restricted streams in Michigan were legislatively cappedt 212 miles in 2002 (PA 434, 2002), an increase from the 100 milesimit in 1994 (PA 451, 1994).

Our research also sheds light on angler preferences with respecto catch rate and catch size for brown trout and brook trout. Whilenglers benefit from improvement in catch rates for both species,

e find stronger mean preferences for brown trout. This result

uggests greater benefits for anglers from stocking and habitatanagement programs which improve brown trout catch rate. We

nd that anglers have stronger positive preferences for increases in

arch 181 (2016) 163–171

the 75th percentile of brown trout size distribution, while there islimited evidence that anglers are concerned with upper end brooktrout size. This difference may be due to angler preferences for thechallenge of the fight associated with catching a large brown trout(which can reach lengths of up to 30 in.) relative to brook trout (forwhich 13 in. would be considered particularly large).

Ultimately, effective management of stream trout fisheriesinvolves allocating scarce budgetary funds to management actionswhich provide the greatest benefit to anglers. The results of ouranalysis provide important information necessary to better under-stand angler preferences for stream trout fishing attributes such ascatch size, catch rate, and gear and harvest regulations, and high-light the importance of understanding the distribution of thesepreferences. Further, we provide managers with a quantitativemethod for evaluating potential increases in angler support forscenarios in which regulations are accompanied by catch-relatedsite quality improvements, relative to scenarios in which regu-lations do not result in such improvements. The research withincan lead to improved resource allocation by allowing managers todirect resources to the improvement of attributes most preferredby anglers and thus improve the quality of stream trout fishingexperiences.

Acknowledgements

Funding was provided by the Michigan State UniversityAgBioResearch, Michigan Department of Natural Resources andMichigan Trout Unlimited (MTU), with a special thanks to MTUExecutive Director Bryan Burroughs for supporting, encouraging,and informing this research. Many thanks to Jody Simoes for hisassistance with project initiation, survey development and surveyimplementation. Jonathan Siegle, with support from Michigan StateUniversity Honors College, provided superior assistance with dataentry, cleaning and organization. Additional thanks to Troy Zornof the Michigan Department of Natural Resources and the Michi-gan Coldwater Resources Steering Committee for reviewing andproviding comments on an earlier draft of the survey.

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