Exploitation of Burbot Lota Lota in the Wind River

Home , Burbot, Hucho taimen, Lake chub

EXPLOITATION OF BURBOT LOTA LOTA IN THE

WIND RIVER DRAINAGE, WYOMING

Sean Alois Lewandoski

A thesis submitted in partial fulfillment of the requirements for the degree

Master of Science

Fish and Wildlife Management

MONTANA STATE UNIVERSITY Bozeman, Montana

November 2015

©COPYRIGHT

Sean Alois Lewandoski

2015

ACKNOWLEDGMENTS

Funding for this research was provided by the Wyoming Game & Fish

Department. The collective expertise and energy of many people allowed us to conduct this research successfully. First, many thanks to Rob Beattie, Colby Clark, Joe

Deromedi, Paul Gerrity, Jeff Glaid, Pat Hnilicka, Kevin Johnson, Colin Manning, Mike

Mazur, Dan Schermerhorn, Michael Schilz, and Dave Skates for providing the support needed to complete three seasons of burbot netting. I spent a lot of hours on the water picking Catostomus spp. out of trammel nets in good company. Thanks to Mark Smith,

Dr. Alexander Zale, and Dr. Robert Garrott for critically reviewing this manuscript and offering insight on the study design. Also, I’d like to thank Dr. Christopher Guy for his mentorship throughout this research process and challenging me to develop as a fisheries professional. Finally, I thank my parents for their enduring encouragement, my fellow graduate students in the Ecology Department for their advice and friendship, and Natalie

Jackson for her patience and support during this process.

iii

TABLE OF CONTENTS

1. INTRODUCTION ...... 1

2. MATERIALS AND METHODS ...... 3

Study Site and Field Methods ...... 3 Data Analysis and Parameter Estimation ...... 6 Population Model ...... 10 Fishery Dynamics Simulations ...... 13

3. RESULTS ...... 16

Exploitation and Demographic Parameters ...... 16 Population Projection Model ...... 21

4. DISCUSSION ...... 25

Status of Burbot in the Wind River Drainage ...... 25 Burbot Exploitation and Population Model ...... 26 Multistate Mark-Recapture Model Assessment ...... 28 Fishery Model Assessment and Further Research ...... 30 Management Recommendations ...... 32

REFERENCES CITED ...... 35

APPENDIX A: Length Distributions, Catch per Unit Effort Statistics, and Simulation Results ...... 42

LIST OF TABLES

Table Page

1. Tagged Burbot Length Statistics and Sample Size ...... 16

2. Model Selection Results for Exploitation ...... 19

3. Exploitation and Population Demographic Parameters ...... 20

4. Burbot Stage-Structured Model Parameters ...... 22

LIST OF FIGURES

Figure Page

1. Map of Study Lakes ...... 4

2. Capture Date of Returned Burbot Tags ...... 17

3. Distribution of Angler Residency ...... 18

4. Fishery Dynamics Simulation Results: Spawning Biomass ...... 23

5. Fishery Dynamics Simulation Results: CPUE ...... 24

ABSTRACT

Regionally important recreational fisheries exist for burbot (Lota lota) in the Wind River drainage of Wyoming; however, burbot populations in the region may not be stable and exploitation could be limiting these populations. We addressed this hypothesis by estimating exploitation from tagging data. Bias in our exploitation estimates was minimized by using a multistate capture-recapture model that accounted for incomplete angler reporting and tag loss. We determined that exploitation varied from 0.02–0.32 (95% CI: 0.00–0.67) in the study lakes. Burbot populations are suspected to be vulnerable to exploitation due to elevated density-dependent catchability caused by burbot aggregating behavior during the fishery; therefore, fishery resilience assuming different levels of density-dependent catchability was investigated using demographic parameters from our tagging study and the literature. Fisheries with low initial exploitation (0.02–0.11) were resilient in all simulation scenarios, whereas the resilience of fisheries with intermediate exploitation depended on the assumed level of inverse density-dependent catchability. Based on these results, the importance of exploitation as a limiting factor on burbot populations in the region is variable and, for some populations, instability in population abundance is due to population stressors other than exploitation. 1

INTRODUCTION

Burbot (Lota lota) are the only freshwater members of the predominantly marine

Gadidae family and are able to inhabit a wide variety of freshwater ecosystems due to variable life history strategies (Jude et al. 2013) and physiological adaptations (Holker et al. 2004). As top predators, they are an indicator of freshwater ecosystem function and drivers of trophic interactions (Cott et al. 2011). Globally, many burbot populations appear to be stable (Stapanian et al. 2010); however, some populations have been reduced to critically low levels (Hardy et al. 2013) or extirpated (Krueger and Hubert 1997;

Worthington et al. 2010). Habitat alteration and pollution are the primary stressors on burbot populations (Stapanian et al. 2010), but over exploitation can lead to population declines (Bernard et al. 1993; Ahrens and Korman 2002). Interestingly, these stressors are also the main drivers of population declines in freshwater species globally (Collen et al. 2014). Burbot are exploited throughout the majority of their Holarctic range, though their popularity as a sport fish varies considerably by geographic location (Quinn 2000;

Stapanian et al. 2010). Furthermore, the influence of exploitation on burbot populations is largely unknown because few stock assessments targeting burbot have been conducted

(Stapanian et al. 2010).

The Wind River drainage, Wyoming, is the southwestern extent of the native range for burbot. Within the Wind River drainage, burbot are an important cultural resource for the Eastern Shoshone and Northern Arapahoe tribes and are commonly harvested by anglers (Hubert et al. 2008). Burbot populations in the region have been extirpated due to anthropogenic influences (Krueger and Hubert 1997) and the stability of 2

remaining populations is uncertain (Hubert et al. 2008). A number of factors are hypothesized to be limiting populations in the region; high mortality from entrainment in

irrigation canals, habitat fragmentation, loss of spawning habitat, and exploitation

(Hubert et al. 2008). We specifically addressed the exploitation hypothesis, though

population demographic information from this study provides the basis for understanding

how other limiting factors may influence burbot populations. Additionally, burbot are

targeted during the winter when they form spawning aggregations and, as a result, the

level of inverse density-dependent catchability (increasing catchability as population

density decreases) of burbot by anglers is suspected to be high (Hubert et al. 2008).

Inverse density-dependent catchability can substantially alter the dynamics of a

recreational fishery (Shuter et al. 1998; Post et al. 2002; Hunt et al. 2011); therefore, we

investigated the effects of this mechanism in our fishery assessment. The objectives of

our study were to: 1) estimate exploitation, natural mortality, individual growth rate, and

abundance from tagging data, 2) develop a population projection model that would

provide insight into the effects of exploitation on burbot populations in the Wind River

drainage, and 3) assess how inverse density-dependent catchability influences the resiliency of recreational burbot fisheries.

MATERIALS AND METHODS

Study Site and Field Methods

We sampled burbot in six glacially formed lakes located in the upper Wind River

drainage from September through November of 2011, 2012, and 2013 (Fig. 1). The

Dinwoody lakes were modified by the construction of a low head dam at the outlet of the

lower lake and the construction of a large dam on Bull Lake in 1938 raised the water

level at full pool by 15 m (Hubert et al. 2008), whereas water level in Torrey, Ring, and

Trail lakes is unaltered. In addition to burbot, the native fish assemblage in the region

includes lake chub (Couesius plumbeus), longnose sucker (Catostomus catostomus),

white sucker (Catostomus commersonii), mountain sucker (Catostomus platyrhynchus),

longnose dace (Rhinichthys cataractae), and mountain whitefish (Prosopium williamsoni). Angling opportunities are supported by stocked and naturalized populations of rainbow trout (Oncorhynchus mykiss), cutthroat trout (Oncorhynchus clarkii), brown trout (Salmo trutta), and lake trout (Salvelinus namaycush). The nearest population centers (Dubois, Fort Washakie, and Lander) have small to moderate population sizes (971–7,487), but anglers from outside the region also travel to the Wind

River drainage to recreate (Miller 1970).

Random sampling was facilitated by dividing each lake into areas of approximately equal size using scaled maps with an overlaid grid. A random subset of the areas was selected during each sampling occasion and a trammel net was placed in each selected area. Trammel nets were 48.8-m long, 1.8-m deep, had outer panels of 4

25.4-cm bar mesh, and inner panels of 2.5-cm bar mesh. Trammel nets were set in the morning and allowed to fish for 24 hours. We set a maximum sampling depth of 22.9 m to decrease the probability of injury to captured burbot because, as physoclistous fish, they are susceptible to barotrauma if forced to the surface from deep depths (Neufeld and

Spence 2004). Additionally, after processing burbot a weighted wire basket was used to return burbot to the lake bottom (Neufeld and Spence 2004).

Fig. 1. Map of study lakes located in the upper Wind River drainage, Wyoming. 5

Captured burbot were measured to the nearest millimeter (total length) and tagged. A Carlin tag (2.54 cm x 0.95 cm) was attached behind the anterior dorsal fin of each captured burbot > 389 mm not injured from the netting process. Tags were blaze orange and inscribed with the Montana Cooperative Fishery Research Unit phone number, a unique index number, and Reward $10. Additionally, in 2012 and 2013 high reward tags (same as standard tags but fluorescent yellow and inscribed with Reward

$100) were used in Bull Lake concurrently with standard reward tags. Burbot receiving high-reward tags were systematically selected (every other fish until all high reward tags were used) and efforts were made to ensure that length distributions of standard-tagged and high-reward-tagged burbot were similar. Burbot that had previously lost their Carlin tag were identified by examining all captured burbot for distinctive scars resulting from tags pulling through the dorsal musculature. In 2012, a small section of the pectoral fin was removed from all burbot that received a Carlin tag. Using these marking methods, untagged burbot with a tag-loss scar were assigned to either the 2011 or 2012 tagging cohort.

The effects of our tagging and handling procedures on short-term survival were measured by holding tagged burbot in sealed cod traps placed on the lake bottom

(Miranda et al. 2002). Cod traps had 2.5-cm bar mesh panels and were 60-cm tall with a

96-cm diameter base and a 66-cm diameter top. We placed one to five burbot in each cage and after a 48-hour holding period the percentage of surviving fish was recorded.

Short-term survival was estimated using nonparametric bootstrapping with cage as the 6

sampling unit and confidence intervals were constructed with the percentile method

(Efron 1979).

Anglers were informed about the research using local media outlets and signs

around the study lakes. Tag return envelopes were made available at each lake access

along with information on how to return tags. Tags could be returned by mail to the

Montana Cooperative Fishery Research Unit, local natural resource management offices, or tag information could be entered online at www.montana.edu/burbot. Along with tag

information, anglers were requested to provide the population center they reside in and

the date the tagged burbot was captured. Using these data, the timing of the burbot

fishery and the spatial distribution of angler residency was investigated.

Data Analysis and Parameter Estimation

A multistate model developed in program MARK was used to estimate

exploitation and natural mortality (White and Burnham 1999). With this modeling

approach, data from live recaptures of marked burbot, tag returns, tag loss, and tag

reporting rate information could be integrated (Lebreton and Pradel 2002). The

motivation for this approach was to account for the uncertainty in our exploitation and

natural mortality estimates due to tag loss and incomplete angler reporting (Miranda et al.

2002), and efficiently use multiple sources of information to increase the precision of our

parameter estimates (Kendall et al. 2006). Multistate capture-recapture models generate

maximum-likelihood estimates of apparent survival, detection probability, and transition

probability (Lebreton and Pradel 2002; Horton et al. 2011). The model had four states — 7 live, live-untagged (tagged burbot that became untagged), harvested, and harvested- untagged (unobservable) — and transition probabilities from live to live-untagged (tag loss) and from live to harvested (exploitation) were estimated. All other state transitions were fixed at zero.

We assumed an instantaneous pulse fishery that occurred immediately after the tagging period. This is a reasonable approximation for the fishery because burbot are targeted by anglers when lakes are frozen (typically December through February) and are rarely targeted during the remainder of the year. Given this model structure and assuming no emigration or immigration, natural mortality (M) is equal to the complement of survival because our survival estimate is conditioned on not being harvested. Natural mortality and tag loss are assumed to occur between the end of the fishing season and the beginning of the next tagging period.

There were two capture occasions per year, a live capture occasion and a tag- recovery period for harvested fish. We note that the capture probability of harvested fish is equivalent to the probability a tag is reported (i.e., tag-reporting rate). By alternatively fixing capture probability and tag reporting to zero, live fish are only observable during live capture occasions; similarly, harvested fish are only observable during tag-recovery periods. During tag-recovery periods the high-reward tag reporting rate was fixed at 1.0

(in recreational fisheries, a $100 reward value is enough to elicit a 100% reporting rate

[Meyer et al. 2012]) and the standard-tag reporting rate was estimated by assuming that standard and high-reward tagged burbot were exploited at the same rate (Pollock et al. 8

2001). High-reward tags were only released in Bull Lake; consequently, the reporting

rates estimated from the Bull Lake fishery were applied to all standard tags.

Recaptured burbot that lost their tag could be assigned to a lake and tagging

cohort, but individual identification was not possible. Accordingly, tag-loss data were

incorporated into the MARK input file by adding recapture events of burbot in the untagged-live state to capture histories from the corresponding lake and cohort. Using these data and assuming symmetrical survival and capture probability for tagged and

untagged burbot, we estimated annual tag loss as the probability of transitioning from a

live to untagged-live state (Conn et al. 2004; Lebreton et al. 2009).

The most general model had a single natural morality parameter and time varying

capture probability. Exploitation was allowed to vary by lake and year. Due to low tag return rates from the lakes in the Torrey Creek drainage (Torrey Lake, Ring Lake, and

Trail Lake) and their close proximity (Fig. 1), data from these lakes were pooled and exploitation was estimated for the drainage. Annual tag loss was modeled based on tagging cohort and separate tag-reporting rates for the 2012 and 2013 seasons were estimated. The 2012 tag-reporting rate was applied to the 2011 data because tag- reporting information was not collected during the first year of the study. Goodness-of- fit for the most general model was estimated using the median c-hat method in program

MARK (Cooch and White 2006; Horton et al. 2011) and the resulting estimate (c-hat =

2.27) was used to adjust standard errors for overdispersion. Reduced models with a single tag loss rate for both cohorts and time-invariant exploitation and tag-reporting rates 9

were constructed. Model selection was conducted using AICC corrected for

overdispersion bias (QAICc) (Burhnam and Anderson 2002).

An abundance estimation model that grouped data by year and constrained

abundance to be equal among years was constructed in program MARK using the closed

captures full likelihood option to estimate mean abundance during the study period (the

parameter constrained was the number of animals never caught [Cooch and White

2006]—the bias this introduces is minimal because the variation in number of burbot

captured each year was much smaller than the estimated population size). There were too

few recaptures in the Dinwoody lakes to reliably estimate abundance using a closed

population estimator; therefore, abundances were calculated from the multistate model

capture probabilities using the formula:

(1) , = where, n is abundance of burbot >389 mm, p is capture probability, and C is the number of captured burbot >389 mm. Abundance was not estimated for Ring Lake or Trail Lake

Lake because very few fish were recaptured.

Individual growth rates were modeled by calculating growth increments and time- at-large of recaptured burbot from the tagging data and fitted to a modified version of the von Bertalanffy growth curve (Fabens 1965). Burbot in the Torrey Creek drainage had a vastly different size structure than other populations in the region; thus, two growth models (i.e., slow and fast) were developed—one using Torrey Creek drainage recaptures and one using data from Bull Lake, Lower Dinwoody Lake, and Upper Dinwoody Lake recaptures. 10

Population Model

We modeled burbot populations in the Wind River drainage and projected

population dynamics using a stage-structured matrix model (Lefkovitch 1965; Caswell

2001). Using this modeling approach, population abundance can be projected using the

formula:

(2) = , where n is a vector containing the number of individuals in each stage at time t and A is a population projection matrix. The four length stages were: 1) 100-199 mm, 2) 200-389 mm, 3) 390-529 mm, and 4) 530-955 mm. A prespawning census was used and the first stage contained age-1 burbot (estimated length-at-age 1 from Taylor and Arndt [2013]).

The second stage contained burbot older than age 1 that have not recruited to the fishery and the final two stages contain burbot recruited to the fishery. The parameterization of

A was:

(1 − )(1 − ,) A= (1 − ), (1 − )(1 − ,) 0 0 , 0 (1 − ), (1 − )(1 − ,) 0 0 0 (1 − ) (1 − ) ,

where Mjuv is annual juvenile natural morality, φi,j is the probability of transitioning from

stage i to j after a year of growth, fj is the mean weight (kg) of burbot in stage j, mj is the

probability of maturity in stage j, a is the number of age-1 recruits per kg of spawning

burbot, and M is annual adult natural morality. All parameters were estimated from our

tagging study or from estimates reported in the literature. Transition probabilities were

generated by calculating the proportion of burbot that would transition into a different 11

stage after a year of growth based on the von Bertalanffy growth model estimated from

our tagging data. Adult natural mortality was estimated from our tagging study and we

assumed that the survival rate of burbot in the juvenile stage (100-199 mm) was half of

the adult rate. Estimates of size-at-maturity were based on published size and maturity

data on burbot in the Wind River drainage (Bjorn 1940; Miller 1970). Mean weight per

stage class was estimated from length data using the weight-length relationship

developed from a lentic burbot population in Moyie Lake, British Columbia (Neufeld et

al. 2011). Population models describing two distinct burbot life-history strategies observed in the Wind River drainage were developed. Specifically, a fast-growth

population with a large size-at-maturity, which was characterized by Bull Lake, Lower

Dinwoody Lake, and Upper Dinwoody Lake demographic parameters, and a small size-

at-maturity, slow-growth population characterized by Torrey Lake demographic

parameters were modeled.

We assumed density-dependent recruitment to age 1 (Neubert and Caswell 2000)

using the formula:

(3) = ∗ ,

where a* is the recruitment rate as spawner biomass approaches zero, N is spawner biomass, and b is a density-dependent parameter. With an estimate of the intrinsic

* population growth rate (λint), b was calculated by solving for a and the recruitment rate

at carrying capacity aK (at carrying capacity population growth rate is 1.0). Because all

other parameters in population projection matrix A were estimated, recruitment rates 12

could be iteratively solved by using the methods in Vaughan and Saila (1976).

Incorporating recruitment values into equation 3, b can be solved using the formula:

∗ (4) = − ,

where Ksbm is the carrying capacity of the spawning burbot population in kilograms.

The time series stock and recruitment data from unfished and heavily exploited conditions needed to estimate λint for burbot populations in the Wind River drainage were unavailable (Ricker 1975; Tsehaye et al. 2013). Therefore, we followed the advice of

Myers et al. (2002) and incorporated data from stock assessments of a taxonomically similar species in our population projections. Specifically, the upper and lower bounds for λint were 1.284 and 1.786 based on the inter-quartile range of reported values by

Myers et al. (1997) for Atlantic cod Gadus morhua, a taxonomically similar species to

* burbot. From these rates, maximum recruitment rates for the lower (a min) and upper

* bound (a max) of λint were estimated.

Current spawning biomass (Bsbm) was used as an estimate of carrying capacity.

Stable stage distributions from the population projection matrices and abundance

estimates of burbot >389 mm were used to determine the number of burbot in each

length-based stage. Abundance was converted to spawning biomass by multiplying each

stage by mean weight and probability of maturity. Finally, with density dependence

operating on a, recruitment of age-1 burbot at time t with stages j was calculated as

(5) ∑ . = ,

Fishery Dynamics Simulations

To assess the resilience of burbot fisheries in the Wind River drainage, burbot

population dynamics were projected with simulation scenarios that included the range of

inverse density-dependent catchability levels found in the literature for recreational

fisheries (Hunt et al. 2011). Inverse density-dependent catchability can lead to non-stable

exploitation rates over time and influence fishery resilience (Post et al. 2002). Thus,

variable exploitation rates were modeled by assuming fixed effort and allowing

catchability to vary as a function of fish density. Specifically, based on the relationship

between exploitation effort and catchability (µ=Eq; Arreguín-Sánchez 1996), exploitation was modeled following methods Hunt et al. (2011) used to model density-dependent catchability with the formula:

∗ (6) = ,

* where E is angler effort, q is area-specific catchability, A is lake area, nt is burbot abundance at time t, and c is the level of density dependence. At c=0 there is no density- dependence and when c > 0 catchability increases as population density decreases. At low population densities and elevated c this equation can produce µ values greater than the logical upper limit of one; therefore, all values exceeding this limit were constrained to one. We estimated q* for each value of c based on the highest density lake in our study

and maximum CPUE (0.8·hr-1) (Hunt et al. 2011). Limited data on angler catch rates for burbot are available; therefore, data from walleye (Sander vitreus) fisheries were used and suggest that 0.8 fish·hr-1 is a high catch rate (Hansen et al. 2005). For each lake, 14

simulations were run with the amount of effort required to generate initial exploitation

varying from 0.0 to 1.0. Annual exploitation in year t was modeled by removing the percentage of the population recruited to the fishery equal to µt (calculated from eq. 6).

Mid-range (40 years) population projections were used because we were interested in

fishery resilience on time scales relevant to natural resource managers rather than long-

term population dynamics.

A simulation scenario comprised a density-dependent catchability level and an

initial exploitation rate. Variability in the maximum recruitment rate was included by

conducting 500 Monte Carlo iterations of each simulation scenario and sampling a* from

* * the uniform distribution [loga min, loga max] (sampling on a log scale resulted in a more uniform distribution for λint). The starting number of burbot recruited to the fishery was

equal to the corresponding abundance estimate from the tagging study. Initial size

structure and number of age-1(100-199 mm) and pre-recruited burbot (200-389 mm)

were calculated using stable-stage distributions from the population projection matrices.

Depensatory mechanisms that occur at low population densities can lead to

fishery collapse (Mullon et al. 2005). Maintaining population densities above the

threshold that these mechanisms take place is imperative for sustainable fishery

management (Post et al. 2002). Using this heuristic principle, we established a simple

biological reference point (Blim) as 30% of carrying capacity. For each population

projection, the number of years Bsbm was below Blim was tracked, the mean and coefficient of variation ( ) for exploitation and burbot spawning biomass was = evaluated, and mean CPUE (burbot·hr-1) of burbot >389 (mm) was calculated. Spawning 15 biomass metrics are presented by population model rather than lake because results were very similar among lakes with the same population model (see Appendix A for lake- specific output). The CPUE metrics were sensitive to population density at carrying capacity and are presented for each lake with an abundance estimate.

RESULTS

Exploitation and Demographic Parameters

Of the 1852 burbot tagged in this study, the majority were tagged in Bull Lake—

including the 221 burbot with high-reward tags (Table 1). Mean length of tagged burbot

varied by lake with the largest burbot in the Dinwoody Lakes and smallest burbot in the

Torrey Creek drainage (Table 1). The length distributions of standard-tagged and high-

reward-tagged burbot in Bull Lake were similar (Table 1); therefore, the reporting rate

estimates were not biased by length-dependent catchability.

Table 1. Number and mean (2.5 and 97.5 quantiles in parenthesis) total length (mm) of tagged burbot and burbot captured by anglers (Returned) by lake. Burbot tagged in Bull Lake are separated by tag type (i.e., standard and high-reward (HR)). Tagged Returned Lake N Length N Length Bull 809 577 (393–856) 17 598 (425–756) Bull (HR) 221 562 (392–875) 19 593 (392–840) Lower Dinwoody 162 694 (336–963) 19 656 (395–920) Upper Dinwoody 155 689 (414–940) 5 799 (529–905) Torrey Creek 505 500 (325–756) 4 436 (404–493) Drainage

Based on data submitted by anglers, all reported tagged burbot were captured

during the winter or early spring, except for two captured in August. Most burbot were captured between 31 December and 16 February (Fig. 2). All anglers who reported tags were from population centers in Wyoming. Anglers who captured burbot in Bull Lake were primarily from Fort Washakie, Lander, Kinnear, and Riverton, whereas anglers who captured burbot in the Dinwoody lakes were primarily from Crowheart, Fort Washakie, 17

and Lander (Fig. 3). Only four anglers reported tags from the Torrey Creek drainage and

they were from Jackson, Dubois, and Hudson (Fig. 3).

Fig. 2. Number of reported tags plotted by capture date. Data are binned by half-month time periods and shading corresponds to the fishing season the tag was returned.

Fig. 3. Distribution of angler residency for Bull Lake, Dinwoody lakes, and Torrey Creek drainage.

The estimated 48-hour survival probability from tagging and handling was 0.98

(95% CI: 0.95–1.00; N = 46). Thus, the mortality estimation model was not biased from short-term tagging and handling related mortality and we did not include a correction for 19

short-term mortality in our multistate mortality estimation model. The most

parsimonious mortality estimation model (smallest QAICC) included a single natural mortality parameter, time varying lake-specific capture probability, a single annual tag loss parameter, time varying tag reporting rates, and lake-specific exploitation rates

(Table 2). Using this model for making inferences, exploitation varied from 0.02 to 0.32 in the study lakes and estimates were most precise for Bull Lake and Torrey Creek drainage (Table 3). Lower Dinwoody had the highest exploitation and the least precise estimate (Table 3). The combined annual natural mortality estimate for all of the study lakes was 0.43; however, the precision of this estimate was low (Table 3). The tag- reporting rate was 0.33 in 2011-2012 (95% CI: 0.12–0.63) and 0.13 in 2013 (95% CI:

0.03–0.42). The annual tag loss rate was 0.15 (95% CI: 0.08–0.27) for both tagging cohorts.

Table 2. Model selection results for burbot tagging data from the Wind River drainage. Constant natural mortality (M) was assumed for all models; lake-specific annual exploitation rates (µ) were constant or allowed to vary by year (yr); angler tag reporting (ψ) was constant (.) or varied by year; and annual tag loss (φ) was constant or varied by tagging cohort (cohort).

Model df QAICc ∆QAICc Likelihood M(.) + µ(lake) + ψ(yr) + φ(.) 16 678.9612 0 1 M(.) + µ(lake) + ψ(.) + φ(.) 15 679.4294 0.4682 0.7913 M(.) + µ(lake) + ψ(yr) + φ(cohort) 17 679.6648 0.7036 0.7034 M(.) + µ(lake) + ψ(.) + φ(cohort) 16 680.2446 1.2834 0.5264 M(.) + µ(lake·yr) + ψ(.) + φ(.) 23 683.8152 4.854 0.0883 M(.) + µ(lake·yr) + ψ(.) + φ(cohort) 24 684.3261 5.3649 0.0684 M(.) + µ(lake·yr) + ψ(yr) + φ(.) 24 685.5832 6.622 0.0365 M(.) + µ(lake·yr) + ψ(yr) + φ(cohort) 25 686.1105 7.1493 0.028

Table 3. Lake area, exploitation, and demographic parameters estimated from tagging data for burbot in the Wind River drainage, Wyoming. Values in parenthesis are the 95% confidence intervals.

Lake Lower Upper Dinwoody Dinwoody Parameter Bull Lake Lake Lake Torrey Lake Unit Lake Area 1300 149 77 94 ha Exploitation (µ) 0.06 0.32 0.08 0.02 year-1 (0.03–0.11) (0.10–0.67) (0.02–0.32) (0.00–0.11) Natural mortality 0.43 0.43 0.43 0.43 year-1 (M) (0.20–0.70) (0.20–0.70) (0.20–0.70) (0.20–0.70) Abundance (N) 6,433 262 177 966 fish (3,924–10,582) (84–1,183) (92–450) (616–1558) Density 4.95 1.76 2.30 10.28 fish·ha-1 (3.01–8.14) (0.56–7.94) (1.19–5.84) (6.55–16.57) Spawning biomass 10119 379 256 1582 kg (Bsbm) Spawning biomass 7.78 2.54 3.32 16.83 kg·ha-1 density Annual harvest 0.30 0.56 0.18 0.21 fish·ha-1 von Bertalanffy 0.12 0.12 0.12 0.09 year-1 growth rate (k) (0.08–0.17) (0.08–0.17) (0.08–0.17) (0.04–0.15) von Bertanlanffy 923 923 923 782 mm asymptotic (837–1091) (837–1091) (837–1091) (668–1142) length (L∞)

Bull Lake had the highest burbot abundance, but Torrey Lake had the highest density (Table 3). Similarly, Bull Lake had the highest spawning biomass and Torrey

Lake had the highest spawning biomass density (Table 3). The highest estimated mean annual harvest rate (fish·ha-1) occurred in Lower Dinwoody Lake, which also had the lowest density (Table 3). The lowest estimated mean harvest rate occurred in Upper

Dinwoody Lake (Table 3). Burbot in Torrey Lake had a lower von Bertalanffy growth rate (k) and asymptotic length (L∞) compared to the other study populations (Table 3).

Population Projection Model

Under the slow-growth burbot population model, a portion of the population matured before recruiting to the fishery, while all mature burbot in populations following the fast-growth model were vulnerable to exploitation (Table 4). The slow-growth population model required a higher maximum recruitment rate to achieve the same deterministic intrinsic population growth rate as the fast-growth model (Table 4).

Additionally, the recruitment rate at carrying capacity was higher for the slow-growth model (Table 4). For both population models, mean relative spawning biomass for the

40-year simulation period declined as initial exploitation increased (Fig. 4). The slow- growth population model did not collapse at high initial exploitation rates because some burbot matured before they recruited to the fishery (Fig. 4). The effects of inverse density-dependent catchability (c) on mean relative spawning biomass were minimal at low to intermediate initial exploitation values (0.0-0.25), but became more substantial as exploitation was increased (Fig. 4). The variability of spawning biomass over the 40- year simulation period was increased at elevated levels of c. For the fast-growth model, at an initial exploitation rate of 0.25 the mean spawning biomass CV was 0.01 with c=0, while at c=0.6 the mean CV increased to 0.16. At low to intermediate exploitation the effects of inverse density-dependent catchability on spawning biomass variability were similar for the two population models; however, as initial exploitation was increased mean spawning biomass CV began to increase at a faster rate for the fast-growth model

(Fig. 4). For constant catchability (c=0), spawning biomass did not fall below Blim at initial exploitation rates below 0.29 for the fast-growth model or initial exploitation 22 below 0.39 for the slow-growth model (Fig. 4). However, with c=0.6 lower initial exploitation rates (0.24 and 0.27 respectively) caused spawning biomass to fall below Blim

(Fig. 4).

Table. 4. Parameters for stage-structured burbot population models. The fast-growth model characterizes the Bull Lake, Lower Dinwoody Lake, and Upper Dinwoody Lake populations. The slow-growth model is characteristic of the Torrey Creek drainage. The maximum recruitment rate was calculated from deterministic population growth estimates from the literature. Fast-growth Slow-growth Parameter population population Source Adult natural mortality Mark-recapture (yr-1) 0.43 0.43 model Transition probability (yr-1) Tagging data

, 0.89 0.62

, 0.35 0.21

, 0.37 0.17 Bjorn (1940); Probability of maturity Miller (1970) 200-389 (mm) 0.00 0.18 390-529 (mm) 0.41 1.00 530-955 (mm) 1.00 1.00 Neufeld et al. Mean weight (kg) (2011) 200-389 (mm) 0.33 0.33 390-529 (mm) 0.78 0.78 530-955 (mm) 4.17 4.17 Maximum recruitment 29.95 50.78 rate (age-1·kg)a (13.06–59.60) (24.85–85.02) See Methods Deterministic intrinsic 1.51 1.52 Myers et al. population growth ratea (1.30–1.78) (1.29–1.77) (1997) Recruitment rate at carrying capacity (age- 1·kg) 3.07 6.92 See Methods a Mean value from 500 Monte Carlo simulations, numbers in parentheses are the 2.5 and 97.5 quantiles.

Fig. 4. Simulation results for the fast-growth model (A) and slow-growth model (B) depicting the effects of a range of initial exploitation rates on relative mean spawning biomass, mean spawning biomass CV, and mean proportion of the simulation period below 30% of carrying capacity (Blim) at varying levels of density-dependent catchability (0, 0.3, and 0.6).

Catch per unit effort (CPUE) was highest in the Torrey Creek population at all

levels of initial exploitation and the influence of inverse density-dependent catchability

on CPUE was minimal compared to the other populations (Fig. 5). For the other

populations, CPUE was higher for scenarios with elevated levels of c at low to 24

intermediate initial exploitation (Fig. 5). The difference between CPUE with linear

catchability (c=0) and c=0.6 was the largest for the Lower Dinwoody population (Fig. 5).

Fig. 5. Simulation results for mean catch per unit effort (burbot·hr-1) by exploitation rate for varying catchability (0, 0.3, and 0.6) for burbot populations in the Wind River Drainage, Wyoming.

DISCUSSION

Status of Burbot in the Wind River Drainage

In the Wind River drainage it was hypothesized that burbot populations were at low population density due to overexploitation (Kruger and Hubert 1997; Hubert et al.

2008). Burbot abundance was low in Lower Dinwoody Lake and Upper Dinwody Lake.

The highest exploitation rate occurred in Lower Dinwoody Lake, though the precision of this estimate was low. Conversely, Bull Lake and Torrey Lake had low exploitation rates and burbot abundance was high. Based on these data, the burbot populations in Torrey

Lake and Bull Lake have a low risk of overexploitation, but there is a potential for overexploitation in Lower Dinwoody Lake.

Similar to a previous study using trammel nets to sample burbot (Abrahamse

2009), we captured few burbot in Ring Lake and CPUE was low. These CPUE values contrast with earlier data that suggested burbot density in the Torrey Creek drainage was highest in Ring Lake and lowest in Torrey Lake (Kruger and Hubert 1997). One explanation for the different CPUE rates is that burbot population density decreased in

Ring Lake and increased in Torrey Lake. However, Kruger and Hubert (1997) used hoop nets to sample burbot and relative differences in CPUE may be due to an interaction between lake and gear type (i.e., hoop net capture efficiency could be high in Ring Lake and low in Torrey Lake, whereas trammel net capture efficiency could be low in Ring

Lake and high in Torrey Lake). We determined that exploitation was low in the Torrey

Creek drainage, though the majority of our tagged fish were capture in Torrey Lake and 26

Trail Lake and we had insufficient data to robustly estimate exploitation for the Ring

Lake population. If density in Ring Lake is low, as indicated by low CPUE rates, a

minimal amount of angler effort could produce high exploitation rates and the Ring Lake

population could be vulnerable to overexploitation.

Burbot Exploitation and Population Projection Model

Previous work has suggested that burbot populations are vulnerable to

overexploitation (Bernard 1993; Ahrens 2002), but to our knowledge this study

represents the first empirical estimates of burbot exploitation from a recreational fishery.

Exploitation of burbot in the Wind River drainage varied from 0.02 to 0.32 and is

comparable to other commonly exploited freshwater fish species. For example,

exploitation for northern pike (Esox lucius), another widely distributed top-predator,

varied from 0.04 to 0.22 in seven Minnesota lakes (Pierce et al. 1995). Additionally,

estimated mean natural mortality for northern pike in Minnesota lakes (M=0.48; Pierce et

al. 1995) was similar to our burbot natural morality estimate (M=0.43). Mean

exploitation for white bass (Morone chrysops) populations in Kansas reservoirs varied

from 0.11 to 0.40 and the estimated natural mortality rate was high (M=0.56) (Schultz and Robinson 2002). The mean exploitation estimate for 46 North American walleye populations was 0.20 (Baccante and Colby 1996), though estimates as high as 0.68 have been made (Quist et al. 2010). Exploitation rates estimated for several black crappie

(Pomoxis nigromaculatus) and white crappie (Pomoxis annularis) populations (0.33– 27

0.68) were all higher than our burbot exploitation estimates (Larson et al. 1991; Maceina

et al. 1998; Dotson et al. 2009).

Given that recreational fisheries can exert high exploitation rates, what level of

exploitation warrants a management action? High exploitation rates (0.50) were

determined to be sustainable for black crappie because this species is suspected to have a

large compensation potential (Dotson et al. 2009). However, unregulated exploitation

exceeding 0.30 was deemed unsustainable for walleye (Quist et al. 2010). Recovery of

sauger (Sander candensis) in the Yellowstone River was not considered to be inhibited by intermediate (0.19) exploitation (Jaeger et al. 2005). Comparatively, low levels of exploitation on sturgeon populations can lead to population collapse (Rieman and

Beamesderfer 1990; Quist et al. 2002) and an exploitation rate of 0.10 on taimen (Hucho taimen) was estimated to be capable of collapsing the population (Jensen et al. 2009).

At current estimated exploitation rates, the Bull Lake and Torrey Lake populations appear resilient to exploitation at all levels of inverse density-dependence considered. The mean estimated exploitation rate for Upper Dinwoody Lake was low, but the variance for the estimate was high and exploitation could be a concern near the upper bounds of the estimate. The highest exploitation for burbot occurred in Lower

Dinwoody Lake (mean=0.32). Assuming linear catchability, this exploitation rate suppressed spawning biomass to critically low levels for 10% of the 40-year simulation.

Furthermore, assuming a high level of density-dependent catchability, the population was suppressed for 31% of the 40-year simulation. Exploitation rates that reduce spawning biomass to a small proportion of carrying capacity could result in population collapse if 28 depensatory mechanisms that operate at low densities are present in the population

(Mullon et al. 2005).

We assumed constant angler effort in all of our simulation scenarios; however, the numeric response in effort by anglers to changes in catch rate is thought to be an important mechanism regulating fishery resiliency (Johnson and Carpenter 1994; Post et al. 2002). Fisheries could ‘self-regulate’ if angler effort dissipates as population density decrease due declining CPUE. However, inverse density-dependent catchability can cause CPUE to remain high at low population densities, and these effects are largest in populations with low-density carrying capacities. If a numeric response exists, elevated

CPUE could cause effort to remain high as population density declines. The interaction between this functional response to population density and numeric response to CPUE could lead to fishery collapse. However, angler behavior is a complex process driven by catch related and non-catch related aspects of angling (Johnston et al. 2010; Hunt et al.

2011). Reliably modeling angler behavior requires both accurate characterization of angler preferences and an understanding of how exploited populations will respond to harvest (Johnston et al. 2013).

Multistate Mark-Recapture Model Assessment

The multistate mark-recapture model we used to estimate exploitation and apparent natural mortality allowed us to incorporate supporting data on tag reporting and tag loss into our modeling framework and efficiently use information from live recapture, catch data, and tag returns (Kendall et al. 2006). Data on angler-reporting rates are 29

difficult to obtain and estimates are often imprecise, but this rate is important for obtaining reliable exploitation estimates (Pollock et al. 2001; Miranda et al. 2002). The high-reward tag method proved to be an effective way to measure angler reporting.

Though variances for the reporting estimates were large, the exploitation estimates for

Bull Lake and the Torrey Creek drainage were relatively precise. Exploitation estimates for Upper and Lower Dinwoody were less precise, but exploitation estimates for all populations provided the necessary data needed to assess fishery resilience.

The annual natural mortality estimate in this study was higher than the majority of natural mortality rates reported in the literature for lentic burbot populations (M=0.33–

0.43; Worthington et al. 2011). Our natural mortality estimate could be biased if

permanent emigration is occurring in these populations or if our tagging procedures

reduced long-term survival. Substantial permanent emigration from Bull Lake and

Lower Dinwoody Lake is likely because burbot are commonly observed entrained in

irrigation canals and barriers constructed on the outlets of the lakes prevent return

migrations (Hubert et al. 2008). We did not sample outside of lentic systems during our

study and had to rely on angler tag returns to obtain data on emigration rates (Barker

1997). However, due to low reporting rates, low angler effort in areas outside of the

study lakes, low emigration rates, or a combination of these factors, only one tagged

burbot captured outside of our study area was reported (a tagged burbot was harvested

from the irrigation canal near the outlet of Lower Dinwoody lake). Thus, we were unable

to estimate emigration rates given the low sample size. Future tagging studies with

concurrent sampling in connected waterways or fixed receiver arrays at the outlets 30 combined with electronic tags could provide higher resolution data on emigration rates

(Horton et al. 2011; Weber et al. 2013).

Fishery Model Assessment and Further Research

The biological component of our fisheries model was parametrized with data from our tagging experiment and estimates based on data from the literature. The maximum recruitment rate of age-1 burbot was derived from estimates of intrinsic population growth rates of Atlantic cod (Myers et al. 1997); however, it is not known how similar intrinsic growth of burbot is to this taxonomically related marine gadoid. Model projections could be inaccurate if burbot intrinsic population growth rates in the Wind

River drainage are substantially different than the values we considered from the literature. Data on burbot stock-specific recruitment dynamics would increase the utility of our model, but recreational fishery managers seldom have access to stock-specific recruitment data (Post et al. 2002) and modeling strategies that incorporate data from other stock assessments are often useful (Jensen et al. 2009).

Many forms of density-dependence have been used in stock assessments including density-dependent growth (Lester et al. 2014) and models with multiple density-dependent parameters (Bardos et al. 2006). We chose a simple model with density-dependent recruitment to age 1 based on the critical-period hypothesis, which suggests that juvenile survivorship dictates the strength of a year class and subsequent mortality is density-independent (Armstrong 1997; Diana 2004). Researchers applied this hypothesis to Baltic cod stocks and found that larval abundance was strongly 31

associated with adult cod recruitment and the inclusion of environmental parameters

associated with larval survival improved the performance of stock-recruitment models

(Koster et al. 2003). Evidence for density-dependence operating on survival and growth

through the juvenile stage has also been reported in freshwater ecosystems (Einum et al.

2006). The early life history of burbot has been investigated (Ryder and Pesendorfer

1992, Donner and Eckmann 2011, Jude et al. 2013), but studies investigating juvenile

survivorship are lacking. Further research and empirical modeling investigating

relationships between biological and physical processes and juvenile survivorship would

improve understanding of burbot recruitment processes and facilitate effective

management of burbot fisheries (Ludsin et al. 2014).

Some degree of inverse density-dependent catchability is expected in all

recreational fisheries (Hunt et al. 2011) and should be accounted for in stock assessment

(Shuter et al. 1998). In burbot recreational fisheries, the majority of angler effort occurs

during the winter when lakes are iced over and coincides with burbot aggregating

behavior associated with pre-spawning and spawning activities. Anglers can likely target

these areas more frequently than would be expected by chance due to local knowledge of

the spatial distribution of catch rates and lake bathymetry. The combination of fish and

angler behavior comprises a probable mechanism for elevated inverse density-dependent

catchability (Erisman et al. 2011). Accordingly, we believe our simulation scenarios with

c > 0.0 are the more realistic representations of burbot recreational fisheries. Quantifying angler effort in addition to exploitation rates and population density would provide the data needed to estimate the level of inverse density-dependent catchability occurring in 32 burbot recreational fisheries. Additionally, different functional forms of density- dependent catchability have been proposed (Wilberg et al. 2010) and investigations into the mechanisms that cause density-dependent catchability could further understanding of the function form of density-dependent catchability occurring in burbot recreational fisheries. However, even at elevated levels of density-dependent catchability, populations in the Wind River drainage appear to not be overexploited. Thus, the cause of burbot population declines in the region may be related to other hypothesized population stressors such as entrainment, habitat fragmentation, and loss of spawning habitat.

Burbot are a native top-predator that support recreational fisheries in the Wind

River drainage. Based on their aggregating behavior and the popularity of burbot as a sport fish in the region, it was suspected that populations could be vulnerable to overharvest. We discovered that exploitation rates in the region varied from 0.02–0.32.

Based on these exploitation estimates, natural resource managers can identify fisheries that are likely resilient to exploitation and those that would benefit from conservative harvest limits.

Management Recommendations

Based on current exploitation rates, further conservative harvest limits on the

Torrey Creek drainage burbot fishery are unnecessary, whereas the Dinwoody lakes may benefit from conservative harvest limits. If overharvest is occurring, more restrictive regulations might include reducing the number of lines that can be used by non-tribal 33

members (current regulations allow for non-tribal members to harvest two burbot and use

six lines with a single hook). Anglers will be less likely to harvest a limit at low

population density with fewer lines, but at high population density the probability of

harvesting a limit would likely remain high. Effort for tribal members is unregulated.

Encouraging tribal anglers to limit the number of lines set in the Dinwoody lakes would further lower the risk of overexploitation in this system. The Bull Lake population

supports an active burbot fishery, but is currently not overexploited. Thus, no immediate

management action is warranted, but given the high level of angler effort in this system,

it is an important burbot fishery in the region to monitor.

A major difficulty in assessing the sustainability of burbot fisheries in the Wind

River drainage is a lack of time-series data on angler effort, harvest, and burbot

recruitment. Information on angler effort and harvest could be obtained by instigating a

volunteer angler logbook program for burbot anglers. The effort and harvest data would

be self-reported and likely have biases, but it could be effective in obtaining information on the fishery. Spawning biomass can be estimated using mark-recapture methods and

length-at-maturity estimates. However, it is not practical to regularly estimate population

size because these methods require considerable effort. Alternatively, age-0 burbot

density could be estimated using standardized transects to provide an index of year-class

strength (Taylor and Arndt 2013). Tracking individual year-class strength combined with periodic netting of adult fish to obtain age-structure and growth data would provide insight into burbot population dynamics and data useful for managing sustainable burbot

fisheries. Additionally, a declining age-0 density estimate could indicate that spawning 34 biomass is declining. Linking declining age-0 density estimates with other management actions could be an effective strategy for ramping up data collection when population declines are suspected (e.g., conducting a creel survey to obtain detailed angler effort and harvest data if age-0 density is low for consecutive surveys). 35

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APPENDIX A

LENGTH DISTRBUTIONS, CATCH PER UNIT EFFORT STATISTICS, AND SIMULATION RESULTS

Table S1. Number of tags reported from each lake by individual anglers. The angler city of origin is listed but names are omitted. Lower Upper Bull Dinwoody Dinwoody Torrey Ring City Angler Lake Lake Lake Lake Lake Total Crowheart 1 0 1 0 0 0 1 2 0 2 0 0 0 2 3 0 1 0 0 0 1 Dubois 4 0 1 0 0 0 1 5 0 0 0 1 0 1 6 0 0 0 0 1 1 Fort Washakie 7 3 0 0 0 0 3 8 2 0 0 0 0 2 9 1 0 0 0 0 1 10 0 1 0 0 0 1 11 1 0 0 0 0 1 12 0 3 0 0 0 3 13 1 0 0 0 0 1 14 2 0 0 0 0 2 Hudson 15 1 0 0 1 0 2 Jackson 16 0 0 0 1 0 1 Kinnear 17 1 0 0 0 0 1 18 1 0 0 0 0 1 19 3 0 0 0 0 3 20 4 0 0 0 0 4 Lander 21 1 0 0 0 0 1 22 2 5 5 0 0 12 23 1 1 0 0 0 2 24 1 0 0 0 0 1 25 0 1 0 0 0 1 26 2 0 0 0 0 2 27 1 0 0 0 0 1 Riverton 28 1 0 0 0 0 1 29 3 3 0 0 0 6 30 1 0 0 0 0 1 31 1 0 0 0 0 1 32 2 0 0 0 0 2

Table S2. Catch per unit effort (CPUE) quantiles and effort (N) by lake and year. CPUE quantiles Lake Year N 5% 25% 50% 75% 95% Bull Lake 2011 218 0 1 2 4 6 2012 373 0 1 2 3 6

2013 308 0 0 2 3 7

Lower Dinwoody 2011 112 0 0 1 2 3 2012 166 0 0 0 1 2

2013 126 0 0 0 1 2

Ring 2011 24 0 0 0.5 2 3 2012 16 0 0 0 1 2

Torrey 2011 78 0 2 5 10 18 2012 134 0 2 3 5 9

2013 129 0 1 2 4 7

Trail 2011 35 0 1 1 2 5 2012 67 0 0 1 2 4

2013 45 0 0 1 2 3

Upper Dinwoody 2011 56 0 0 1 2 3 2012 120 0 0 0 1 2

2013 87 0 0 1 2 4

Fig. S1. Length-relative frequency histogram of burbot captured in Bull Lake with 20- mm bin width. 46

Fig. S2. Length-relative frequency histogram of burbot captured in Lower Dinwoody Lake with 20-mm bin width. 47

Fig. S3. Length-relative frequency histogram of burbot captured in Upper Dinwoody Lake with 20-mm bin width. 48

Fig. S4. Length-relative frequency histogram of burbot captured in Torrey Lake with 20- mm bin width. 49

Fig. S5. Length-relative frequency histogram of burbot captured in Trail Lake with 20- mm bin width. 50

Fig. S6. Length-relative frequency histograms of burbot captured 2011-2013 with 20- mm length bins. 51

Fig. S7. Simulation results for Lower Dinwoody Lake (A) and Upper Dinwoody Lake (B) depicting the effects of a range of initial exploitation rates on mean spawning biomass, the CV of spawning biomass, and the proportion of the simulation period below 30% of carrying capacity (Blim). The solid line depicts linear catchability, while the broken lines and dashed lines show moderate (0.3) and high (0.6) inverse density- dependent catchability.