Dynamics of a Recovering Lake Trout Population in Eastern
Wisconsin Waters of Lake Superior, 1980–2001
BRIAN C. LINTON*
AND MICHAEL J. HANSEN
College of Natural Resources, University of Wisconsin–Stevens Point,
800 Reserve Street, Stevens Point, Wisconsin 54481, USA
STEPHEN T. SCHRAM
Wisconsin Department of Natural Resources, Lake Superior Office,
141 South 3rd Street, Box 589, Bayfield, Wisconsin 54814, USA
SHAWN P. SITAR
Michigan Department of Natural Resources, Marquette Fisheries Research Station,
484 Cherry Creek Road, Marquette, Michigan 49855, USA
Abstract.—Lake trout Salvelinus namaycush are important in Lake Superior because of their economic and
ecological value. Lake trout populations collapsed in the early 1950s due to overexploitation by the
commercial fishery and predation by sea lampreys Petromyzon marinus. Efforts to rehabilitate a self-
sustaining lake trout population included stocking of hatchery-reared lake trout, control of sea lamprey
populations, and closure of the lake trout fishery. To quantify and describe the dynamics of the recovering
lake trout population in eastern Wisconsin waters of Lake Superior between 1980 and 2001, we used
statistical catch-at-age analysis to estimate abundance, recruitment, mortality, and fishery selectivity of wild
and stocked lake trout. We found that estimated wild lake trout abundance increased, whereas estimated
stocked lake trout abundance decreased. Estimated wild lake trout recruitment was erratic, while estimated
stocked lake trout recruitment decreased until stocking was discontinued in 1996. Natural mortality was the
largest component of estimated wild lake trout total mortality, where commercial fishing mortality was the
largest component of estimated stocked lake trout total mortality. Wild lake trout abundance in Wisconsin
waters of Lake Superior is on par with precollapse abundance levels, and total mortality rates are below
rehabilitation target levels; however, wild lake trout recruitment is still below the level thought necessary to
sustain the population.
Population dynamics is the engine of fisheries
management because resource managers must under-
stand underlying processes that drive a fish population
if they hope to effectively solve problems that arise
within the population. Current theories of f ish
population dynamics postulate that changes in abun-
dance are caused by recruitment balanced against
fishing and natural mortality (Hilborn and Walters
1992). Kn owledge of each of these processes is
therefore required to understand the abundance and
structure of a population.
The lake trout Salvelinus namaycush is a species of
great interest to Lake Superior fisheries managers
because of its importance as a food source, as the focus
of a recreational fishery, and as the top predator in the
Lake Superior ecosystem. Lake Superior supported an
average annual lake trout harvest of 2 million kg from
1913 to 1950 (Baldwin et al. 1979). Lake trout stocks
collapsed in the early 1950s due to the combined
effects of fishery exploitation and predation by sea
lampreys Petromyzon marinus (Pycha and King 1975;
Pycha 1980; Swanson and Swedberg 1980). The
Wisconsin commercial fishery for lake trout was
closed in 1962 as a result of the collapse (Pycha and
King 1975). At the same time, a free permit system was
initiated to regulate the Wisconsin recreational fishery
for lake trout.
Efforts to rehabilitate lake trout populations in Lake
Superior began soon after stocks collapsed. Interagency
management goals specific to lake trout initially were
formulated by the Lake Superior Lake Trout Technical
Committee and were based on use of historic stock
sizes as recovery ref erence points (LSLTT 1986;
Hansen 1996; Horns et al. 2003). Fisheries managers
in Wisconsin and other areas of Lake Superior
attempted to meet these goals by stocking hatchery-
reared lake trout to bolster natural recruitment,
* Corresponding author: firstname.lastname@example.org
Present address: Department of Fisheries and Wildlife,
Quantitative Fisheries Center, Michigan State University, 13
Natural Resources Building, East Lansing, Michigan 48824,
Received February 3, 2006; accepted November 13, 2006
Published online August 2, 2007
North American Journal of Fisheries Management 27:940–954, 2007
Ó Copyright by the American Fisheries Society 2007
reducing sea lamprey-based mortality through control
of the parasite’s abundance, and reducing fishing
mortality by setting stricter regulations on fisheries.
Initial reports showed that wild lake trout abundance in
Wisconsin waters of Lake Superior remained relatively
constant and then increased slowly after 1970 (Hansen
1990, 1999; Hansen et al. 19 94b). Reports also
revealed a decline in stocked lake trout abundance in
Wisconsin waters despite consistent stocking rates
between 1963 and 1986 (Hansen et al. 1994a, 1994b);
the decline was later attributed to commercial large-
mesh gill-net fishing effort (Hansen et al. 1996). Early
stock–recruitment analyses showed that stocked fish in
Wisconsin waters had fueled recovery and that wild
fish had contributed little between 1959 and 1993
(Hansen et al. 1995, 1997). This finding concerned
fisheries managers because wild lake trout were
expected to support a self-sustaining population as
recovery progressed. In contrast, Schram et al. (1995)
found that wild adult lake trout abundance explained
most of the recruitment variation in Wisconsin’s Gull
Island Shoal refuge during that same period. The
progress and mechanisms of lake trout recovery in
Wisconsin waters of Lake Superior were uncertain
owing to the mixed results of rehabilitation efforts.
Statistical catch-at-age analysis (SCAA) is a stock
assessment technique that can provide insight into the
recovery of the lake trout population in Wisconsin
waters of Lake Superior. S tatistical catch-at- age
analysis is already being used to manage stocks of
lake trout and lake whitefish Coregonus clupeaformis
in some Michigan waters of the upper Great Lakes
(MSC 2005) and can be a versatile tool to manage lake
trout in Wisconsin. The primary advantage of SCAA is
that it provides estimates of absolute abundance at age
and key demographic characteristics, such as age-
specific mortality rates, using fishery catch-at-age data
(Fournier and Archibald 1982; Megrey 1989). Statis-
tical catch-at-age analysis can be improved by
incorporating additional information such as fishery-
independent survey abundance indices and fishery
effort data (Deriso et al. 1985). By use of SCAA,
Wisconsin fisheries managers will be able to explicitly
account for uncertainty in their data and determine the
effect of that uncertainty on the estimation of important
lake trout population attributes (Megrey 1989). The
Wisconsin waters of Lake Superior provided us with a
unique opportunity to compare the dynamics of wild
and stocked lake trout by separately analyzing these
two components of the population. Such a comparison
between wild and stocked lake trout has not been
conducted before using SCAA.
Our objective was to quantify and describe the
dynamics of the recovering lake trout population in
eastern Wisconsin waters of Lake Superior between
1980 and 2001 by addressing two questions. First, how
have abundance, recruitment, and mortality of wild and
stocked lake trout changed over time? Second, how do
current levels of abundance, recruitment, and mortality
of wild lake trout relate to established recovery goals?
To answer these questions, we used SCAA to estimate
abundance, recruitment, mortality, and fishery selec-
tivity of wild and stocked lake trout.
Study area.—Lake Superior is highly oligotrophic;
Wisconsin waters are among the more productive areas
of the lake. The southern shore of Lake Superior has
surface water temperatures ranging from 28Cto118C
annually (Bennett 1978). Wisconsin waters are shallow
(depth , 100 m) relative to other areas of Lake
Superior; the substrate is primarily sandy, and the
habitat is highly complex due to the presence of the 22
Apostle Islands (Johnson et al. 2004). Concentrations
of major ions are lower than lakewide averages (Weiler
1978), and mean phytoplankton biomass is 196.65 mg/
(Munawar and Munawar 1978).
The Wisconsin waters of Lake Superior are divided
into two lake trout management units (Figure 1). Most
fishing activity, stocking, and surveys occur within the
eastern management unit (WI-2). The WI-2 unit has a
surface area of 4,474 km
and includes the Apostle
Islands. Shallow, rocky reefs (depth ¼ 3–30 m) along
the shoreline of the mainland and islands provide
spawning grounds for lake trout (Coberly and Horrall
1980). The WI-2 unit contains two lake trout refuges
that are closed to commercial and recreational fishing.
The Gull Island Shoal refuge has a surface area of 336
, and the Devils Island Shoal refuge has a surface
area of 283 km
Data collection.—We compiled harvest, effort, and
age distribution data for commercial and recreational
lake trout fisheries in WI-2 from unpublished records
maintained by the Wisconsin Department of Natural
Resources (WDNR), and the Red Cliff and Bad River
bands of Lake Superior Chippewa for use in SCAA.
We used age distribution and catch-per-unit-effort
(CPUE) data from WDNR large-mesh and graded-
mesh gill-net surveys to provide fishery-independent
indices of relative abundance for our SCAA.
In Wisconsin waters of Lake Superior, the WDNR
surveyed the angling fishery annually to estimate effort
and harvest, whereas state and tribal commercial gill-
net fisheries were required to report harvest and effort
as part of license requirements. The WDNR and the
Red Cliff Band also monitored state and tribal
commercial fisheries. We used the percentage of wild
fish caught in the large-mesh gill-net survey to estimate
LAKE TROUT POPULATION DYNAMICS 941
separate harvests of wild and stocked lake trout. We
applied year-specific age–length keys from the large-
mesh gill-net survey to recreational and commercial
monitoring data to develop age distributions for the two
fisheries because no age data were collected from the
fisheries (Ricker 1975). We included commercial and
recreational fishery data in our SCAA from 1980 to
2001 for ages 4–15þ, where the age-15þ group
included all fish ages 15 and older. Data for age-15þ
fish were pooled because those fish constituted a small
proportion of the fishery harvest. The WDNR also
provided yearling stocking data from 1977 to 1995,
when yearlings were last stocked, and fall fingerling
stocking data from 1976 to 1985, when fall fingerlings
were last stocked.
The WDNR conducted large-mesh gill-net surveys
to provide an index of relative abundance and age
distribution data for lake trout in Wisconsin waters of
Lake Superior. This survey was conducted using
standardized bottom-set gill nets (114-mm stretched
mesh; 210/2 multifilament nylon twine; 18 meshes
deep). The nets were hung on the one-half basis, set for
an average of three nights, and fished from late April to
early June. We defined CPUE as the number of fish
caught per 305 m (1,000 ft) of net because nets were
not of uniform length. We calculated mean annual
large-mesh survey CPUE as a geometric mean. The
WDNR estimated ages from scales or otoliths removed
from a subsample of fish caught in gill nets; we then
used year-specific age–length keys to expand estimated
ages to the entire catch (Ricker 1975; Hansen et al.
1994a, 1995). Prior to 1987, the WDNR collected only
scales from lake trout, whereas from 1987 onward, they
collected scales from lake trout shorter than 58.4 cm
and otoliths from lake trout 58.4 cm and longer.
Stocked fish were identified by year-class-specific fin
clip patterns. We calculated separate survey CPUEs
and age distributions for wild and stocked lake trout.
We used large-mesh gill-net survey data from 1981 to
2001 for ages 4–15þ in our SCAA.
The WDNR conducted graded-mesh gill-net surveys
to provide another index of relative abundance and age
distribution data for lake trout in Wisconsin waters of
Lake Superior. The graded-mesh survey tended to
sample the younger fish in the lake trout population.
The WDNR used nets with stretched mesh ranging
from 38 to 178 mm in 12.7-mm increments; nets were
hung on the one-half basis, set for approximately 24 h,
and fished during July–August. The WDNR used
multifilament nylon nets prior to 1991 and monofila-
ment nylon nets from 1991 to 2001. We assumed the
difference in efficiencies between the two net types to
be negligible when calculating graded-mesh survey
CPUE. The graded-mesh survey was conducted in WI-
2 in alternate years beginning in 1980. We defined
CPUE as the number of fish caught per 305 m of net.
We calculated mean annual graded-mesh survey CPUE
as a geometric mean. The WDNR estimated ages from
scales or otoliths removed from a subsample of fish
caught in gill nets; we used year-specific age–length
keys to expand estimated ages to the entire catch. We
calculated separate CPUEs and age distributions for
wild and stocked lake trout. We used graded-mesh gill-
net survey data from 1980 to 2001 for ages 4–10þ in
Statistical catch-at-age models.—Separate SCAA
models were built for wild and stocked lake trout in
WI-2 using AD Model Builder software (Otter
Research Ltd. 2002). Models were not constructed
for WI-1 due to insufficient data. We initially
attempted to model lake trout within the WI-2 refuges
as separate subpopulations because of differences in
FIGURE 1.—Map of Lake Superior, showing lake trout management units. The U.S. management units are marked by state (MI
¼ Michigan; MN ¼ Minnesota; WI ¼ Wisconsin). Canadian management units are marked only by numbers.
LINTON ET AL.
observed lake trout age composition between refuge
and nonrefuge areas. The resulting model failed to
converge to a solution; the model was overparame-
terized because survey CPUE and age distribution were
the only data sources for refuge lake trout. Therefore,
we estimated parameters for the nonrefuge portion of
WI-2 by only including survey data from large-mesh
and graded-mesh gill-net survey sites outside of the
refuges. We know that lake trout move between
management units (Kapuscinski et al. 2005), but we
lack sufficient data to explicitly account for that
movement. Consequently, we assumed net lake trout
movement between the refuge and nonrefuge portions
of WI-2 and between WI-2 and adjacent management
units to be nil (i.e., immigration and emigration were
assumed to be equal). If emigration exceeded immi-
gration, then our analysis would underestimate recruit-
ment or overestimate mortality to account for fish
leaving the population. If immigration exceeded
emigration, then our analysis would overestimate
recruitment or underestimate mortality to account for
fish entering the population. The models were used to
estimate abundance, recruitment, mortality, and fishery
selectivity from 1980 to 2001 for lake trout of ages 4–
15þ by fitting predicted fishery harvest, survey CPUE,
and age dist ribu tion s to ob ser ved dat a. For t he
following model descriptions, symbols used in the
equations are found in Table 1.
The heart of SCAA is the simultaneous estimation of
age-specific fishery harvest and the abundance required
to produce that harvest. We calculated commercial and
recreational fishery harvests using Baranov’s catch
equation (Ricker 1975):
We calculated age distributions of the commercial and
recreational harvests as proportions at age by dividing
the catch at age by the total annual harvest. We
calculated abundance using the exponential population
equation (Ricker 1975; Quinn and Deriso 1999),
for all years after 1980 and all ages greater than age 4.
We estimated a series of deviations around a mean of
zero for recruitment of the age-4 fish in each year and
for each additional age-class in 1980. We constrained
these deviations to sum to zero. We estimated a
population scaling parameter to scale the deviations to
an appropriate population size. In the stocked lake trout
model, we based predicted numbers of age-4 fish on
the observed number of yearlings stocked 3 years
before and the observed number of fall fingerlings
stocked 4 years before. We assumed fall fingerling
overwinter survival to be 40% (Elrod et al. 1988) based
on the approach of Sitar et al. (1999). We estimated
mortality from age 1 to 4 as yearly deviations around a
mean instantaneous mortality, which scaled the number
of yearlings stocked to account for age-4 predicted
We partitioned total mortality into natural mortality,
sea lamprey-based mortality, and fishing mortality:
¼ M þ M
We calculated an initial value of 0.19 for natural
mortality (from which the final natural mortality was
estimated) by use of Pauly’s equation (Pauly 1980;
Quinn and Deriso 1999). We estimated the von
Bertalanffy growth parameters (asymptotic length L
TABLE 1.—Description of equation symbols used in analysis
of lake trout population dynamics in Wisconsin waters of
Mean coded age
A True age
First inflection point of selectivity curve
First slope of selectivity curve
Second inflection point of selectivity curve
Second slope of selectivity curve
Vector of predicted catch at age
Vector of predicted catch at age adjusted for
age estimation error
C Observed fishery catch
Predicted fishery catch
Predicted fishery catch adjusted for age
E Fishing effort
F Instantaneous fishing mortality
Instantaneous commercial fishing mortality
Instantaneous recreational fishing mortality
L Log-likelihood component
k Log-likelihood emphasis factor
M Instantaneous natural mortality
Instantaneous sea lamprey-based mortality
N Numbers of fish
n Number of years in model
Effective sample size of age-estimated fish
p Probability of surviving sea lamprey attack
P Observed proportion of fish at age
Predicted proportion of fish at age
s Fishery selectivity
t Time of survey as proportion of year
Transpose of age estimation error matrix
U Observed survey CPUE
Predicted survey CPUE
W Estimated average sea lamprey wounding rate
Z Instantaneous total mortality
a Shape paramet er for true versus coded age curve
b Shape paramet er for true versus coded age curve
e Deviations in fishing mortality–effort relationship
r Log-scale SD
a Age index
i Fishery–survey index
l Length index
y Year index
LAKE TROUT POPULATION DYNAMICS 943
¼ 83.3 cm; K ¼ 0.15 per year) for Pauly’s equation
separately from the SCAA models using mean length-
at-age data from the large-mesh gill-net survey and the
lakewide average water temperature of 68C (Bennett
1978). We separated sea lamprey-based mortality from
natural mortality due to the impact that sea lampreys
have on lake trout abundance (Pycha and King 1975;
Pycha 1980; Swanson and Swedberg 1980). We used a
logistic model to estimate the number of sea lamprey
wounds on lake trout as a function of lake trout length
(Eshenroder and Koonce 1984; Ebener et al. 2003;
Rutter and Bence 2003). The logistic model was used
separately from the SCAA models because of the small
sample sizes of large lake trout used for wounding rate
estimation. The predicted wounding rates were then
used to estimate length-specific sea lamprey-based
mortality separately from the SCAA model (Sitar et al.
1999; Bence et al. 2003):
The probability of surviving a sea lamprey attack is
summarized in Greig et al. (1992) from a laboratory
study conducted by Swink (1990). Sea lamprey-based
mortality was converted from length-specific to age-
specific values using large-mesh survey age–length
We separated fishing mortality into two components
for the commercial and recreational fisheries:
We assumed fishery-specific fishing mortality to be
separable into age and year effects (Doubleday 1976;
Quinn and Deriso 1999):
The age effect was fishery selectivity, and the year
effect consisted of observed fishery effort and annual
deviations in the fishing mortality–effort relationship.
Catchability represented the overall scalar between
fishing mortality and observed effort. Annual devia-
tions in the fishing m ortality–effort rel ationship
represented a combination of observation error in
fishery effort and annual variation in catchability. We
constrained annual deviations in the fishing mortality–
effort relationship to sum to zero. We estimated fishery
selectivity using a double logistic function (Bence et al.
1 þ e
1 þ e
We normalized the selectivity curve to the age of
estimated maximum selectivity to uniquely parameter-
ize the age and year effects of fishing mortality
(Doubleday 1976; Bence et al. 1993).
We calculated CPUEs and age distributions for the
large-mesh and graded-mesh surveys as
where population abundance at the time of the survey
was employed. We estimated selectivity for the surveys
using the double logistic function described for the
commercial and recreational fisheries. We calculated
large-mesh and graded-mesh survey age distributions
as proportions at age by dividing the age-specific
CPUE by the total annual CPUE for the year.
In our SCAA, a method developed by Weeks (1997)
was used to account for lake trout age estimation
errors, which affect the observed fishery catch-at-age
and age-specific survey CPUE data. We built two age
estimation error matrices separately from the SCAA
models to account for the change in age estimation
techniques that occurred in 1987. We used age
estimation data from fin-clipped hatchery lake trout,
whose ages were known with a high degree of
confidence. The WDNR assigned coded ages to each
fin-clipped fish by use of both scales and otoliths. We
calculated the observed mean coded age assigned to
each true age (i.e., ages as determined by fin clips). We
used a simple power function (a ¼ 1.53; b ¼ 0.79) to
estimate mean coded ages from true ages:
A ¼ aA
This method was used because of the small sample
sizes available for calculating observed mean coded
ages of older fish. We assumed that the probability of
assigning a coded age to a given fish in our SCAA was
normally distributed around our estimated mean coded
age for that fish’s true age. We developed separate
distributions for the probability of assigning coded ages
for each age in our model. We determined a single SD
for all of the coded age assignment distributions by
averaging the SDs of all observed mean coded ages.
We built the first matrix using only coded ages
estimated from scales and applied the matrix to the
model time series when only scales were used to assess
ages (1980–1986). We built the second matrix using
coded ages estimated from scales for ages 4–8 and
coded ages estimated from otoliths for ages 9–15þ.
Age 9 was chosen for the transition to otolith-estimated
ages because the mean length of age-9 lake trout was
approximately 58.4 cm. The second matrix was applied
to the model time series when both scales and otoliths
were used to assess ages (1987–2001). We applied the
age estimation error matrices to predicted catch at age
within the models as follows (using matrix notation):
944 LINTON ET AL.
We used the same age estimation error matrices in both
wild and stocked lake trout models.
Model parameters were estimated on the log scale
using a quasi-Newton iterative algorithm with Bayes-
ian-based likelihood methods (i.e., prior densities were
assigned to some parameters) that fit model predictions
to observed data (Tables 2, 3). We obtained parameter
estimates by minimizing the negative log-likelihood
function, which was formulated as
where an emphasis factor ( k
) was used to adjust the
weight of each negative log-likelihood component and
TABLE 2.—Log-scale estimated parameter values and asymptotic SDs for a statistical catch-at-age model of wild lake trout in
Wisconsin waters of Lake Superior. Model parameters include the population scaler (N
) and population deviations (f
other parameters are described in Table 1.
Parameter Value(s) (SD)
1.14 (0.22), 0.83 (0.23), 0.75 (0.22), 1.13 (0.22), 1.51 (0.20), 0.96 (0.22), 0.79 (0.23), 1.38 (0.20), 0.77 (0.24),
1.47 (0.21), 1.11 (0.24), 1.35 (0.22), 1.20 (0.22), 0.98 (0.22), 0.84 (0.23), 1.30 (0.23), 1.04 (0.24), 1.65 (0.24),
0.79 (0.29), 1.80 (0.28), 1.00 (0.32), 1.17 (0.61)
0.19 (0.29), 0.37 (0.31), 1.69 (1.75), 0.59 (0.52), 4.31 (2.97), 1.07 (0.45), 4.47 (2.92), 3.81 (3.04),
2.30 (1.78), 3.04 (2.80), 3.87 (2.94)
1.87 (0.01), 0.74 (0.04), 5.00 (0.19), 1.16 (0.12)
0.60 (0.20), 0.37 (0.18), 0.13 (0.17), 0.16 (0.16), 0.02 (0.16), 0.10 (0.16), 0.08 (0.15), 0.12 (0.15), 0.28 (0.15),
0.49 (0.15), 0.50 (0.15), 0.25 (0.15), 0.13 (0.14), 0.10 (0.14), 0.04 (0.15), 0.04 (0.15), 0.24 (0.15),
0.13 (0.16), 0.06 (0.17), 0.02 (0.18), 0.02 (0.19), 0.08 (0.20)
1.83 (0.01), 1.11 (0.06), 1.50 (0.001), 1.19 (0.13)
0.11 (0.37), 0.81 (0.36), 0.80 (0.36), 0.86 (0.36), 0.93 (0.36), 0.52 (0.36), 0.02 (0.35), 0.07 (0.36),
0.14 (0.35), 0.06 (0.35), 0.12 (0.35), 0.40 (0.36), 0.22 (0.35), 0.18 (0.35), 0.22 (0.36), 0.16 (0.35), 0.39 (0.35),
0.53 (0.35), 0.59 (0.36), 0.07 (0.36), 0.52 (0.37), 0.80 (0.37)
M 1.71 (0.06)
1.89 (0.01), 0.76 (0.04), 1.00 (0.001), 0.86 (0.10)
1.44 (0.01), 2.00 (0.001), 5.00 (0.45), 0.03 (0.07)
TABLE 3.—Log-scale estimated parameter values and asymptotic SDs for a statistical catch-at-age model of hatchery lake trout
in Wisconsin waters of Lake Superior. Model parameters included the population scaler (N
), population deviations (f
instantaneous mortality of stocked yearlings (S
), and yearly deviations in mortality of stocked yearlings (w
). All other
parameters are described in Table 1.
Parameter Value(s) (SD)
1.85 (1.07), 5.40 (2.67), 5.34 (2.67), 0.76 (0.30), 0.54 (0.30), 1.23 (0.29), 0.77 (0.29), 1.67 (0.29), 1.36 (0.29),
0.72 (0.29), 1.17 (0.29), 1.70 (0.29), 1.00 (0.29), 1.54 (0.29), 1.32 (0.29), 0.90 (0.29), 1.27 (0.28), 0.12 (0.27),
3.56 (0.73), 1.94 (2.07), 0.81 (5.19), 2.08 (1.37), 1.55 (4.08), 1.81 (0.90), 0.99 (4.26), 0.26 (2.23), 2.26 (3.62),
2.07 (2.90), 3.07 (3.21)
1.39 (5.14), 4.59 (1030.2), 2.59 (0.03), 0.89 (0.41)
0.30 (0.14), 0.48 (0.13), 0.55 (0.13), 0.39 (0.12), 0.17 (0.12), 0.06 (0.12), 0.07 (0.13), 0.33 (0.13), 0.11
(0.13), 0.03 (0.13), 0.17 (0.13), 0.03 (0.14), 0.38 (0.14), 0.26 (0.14), 0.39 (0.14), 0.85 (0.14), 1.07 (0.14), 0.69
(0.15), 0.14 (0.16), 0.37 (0.15), 0.25 (0.16), 0.49 (0.16)
1.46 (0.003), 2.00 (0.0002), 2.00 (4.51), 3.00 (0.002)
0.96 (0.35), 1.58 (0.35), 1.34 (0.34), 1.18 (0.33), 0.99 (0.33), 0.51 (0.32), 0.05 (0.33), 0.14 (0.33),
0.01 (0.33), 0.47 (0.34), 0.36 (0.35), 0.58 (0.35), 0.64 (0.34), 0.48 (0.35), 0.57 (0.36), 0.87 (0.36), 1.11 (0.37),
1.02 (0.36), 0.43 (0.38), 0.45 (0.36), 0.25 (0.36), 0.44 (0.36)
M 1.84 (0.05)
1.45 (0.003), 2.00 (0.0002), 2.55 (0.04), 0.11 (0.35)
1.41 (0.01), 2.00 (0.002), 5.00 (0.34), 0.10 (0.08)
LAKE TROUT POPULATION DYNAMICS 945
negative log-prior density (Methot 1990). We included
eight negative log-likelihood components in the wild
lake trout model for commercial and recreational
fishery harvests and age distributions and large-mesh
and graded-mesh gill-net survey CPUEs and age
distributions. We included four negative log-prior
densities in the wild lake trout model for commercial
and recreational fishing effort, natural mortality,
recruitment, and abundance in the first year. The
stocked lake trout model also included a negative log-
prior density for poststocking mortality. We assumed
that negative log-likelihood components for fishery
harvest followed lognormal distributions of the form
þ n log
Negative log-likelihood components for commercial
and recreational fishery harvest were weighted by their
associated variances (r
¼ 0.02; r
¼ 0.16), which
were derived from the expert opinion of WDNR and
tribal managers. We a ssumed tha t negativ e log-
likelihood components for survey CPUE followed
lognormal distributions of the form
Negative log-likelihood components for large-mesh
and graded-mesh survey CPUE were weighted by their
associated variances, which were calculated for each
year from the raw survey data. We assumed that
negative log-likelihood components for fishery and
survey age distributions followed multinomial distri-
butions of the form
as was recommended by Methot (1990). Negative log-
likelihood components for fishery and survey age
distributions were weighted by the effective sample
size, which was the number of fish for which ages were
estimated each year up to a maximum of 200 fish (Sitar
et al. 1999). Negative log-prior densities for commer-
cial and recreational fishing effo rt assumed th at
variability in the relationship between fishing mortality
and fishing effort was lognormally distributed. The
negative log-prior density for natural mortality as-
sumed that the deviation between the prior natural
mortality value (i.e., from Pauly’s equation) and the
predicted natural mortality value was lognormally
distributed. The negative log-prior density for recruit-
ment of the first age in each year and abundance of
each age in the first year assumed that variability in
recruitment and abundance in the first year was
lognormally distributed. In the stocked lake trout
model, the negative log-prior density for poststocking
mortality was lognormally distributed. All negative
log-prior densities were weighted by their associated
variances, which were derived from the expert opinion
of WDNR and tribal managers.
Negative log-likelihood components and negative
log-prior densities are naturally weighted by the
variances or effective sample sizes of the individual
components. Emphasis factors provide a convenient
means of adjusting these natural weights. We set all
emphasis factors to a value of 1.0, which assumes that
the variances and effective sample sizes were correctly
specified, except for the emphasis factors for commer-
cial and recreational fishing effort. Observed fishing
effort was not directly related to lake trout fishing
mortality: commercial fishing effort targeted lake
whitefish while avoiding lake trout as bycatch, whereas
recreational fishing effort targeted several other species
in addition to lake trout. The poor fit of predicted to
observed commercial and recreational harvest when the
fishing effort emphasis factors were set to 1.0 was
evidence that we had underestimated the fishery effort
variances. Therefore, we allowed estimated fishing
mortality to deviate further from observed fishing effort
by setting commercial and recreational fishing effort
emphasis factors to 0.1, which effectively increased the
variances associated with commercial and recreational
fishing effort by a factor of 10 (r
¼ 0.22; r
We judged a model run to have converged on a
solution when the maximum gradient component was
less than 1 3 10
, which is the default convergence
criterion for AD Model Builder. To evaluate model
robustness, we tested the sensitivity of SCAA model
output to changes in model assumptions. We individ-
ually doubled and halved each likelihood emphasis
factor and estimated model parameters after each
adjustment. We measured model sensitivity as percent
difference between adjusted and unadjusted model
values for total abundance, fully selected commercial
fishing mortality, and fully selected recreational fishing
mortality averaged over the most recent 3 years of data.
Wild and stocked lake trout models successfully
converged on solutions. Predictions of commercial and
recreational fishery harvests, commercial and recrea-
tional fishery age distributions, large-mesh and graded-
mesh survey CPUEs, and large-mesh and graded-mesh
survey age distributions for wild and stocked lake trout
from the SCAA models were consistent with observed
data. Predicted commercial harvest for wild lake trout
946 LINTON ET AL.
and recreational harvest for wild and stocked lake trout
closely fit observed harvest (Figure 2). Predicted
commercial harvest for stocked lake trout consistently
underestimated observed harvest prior to 1990 and
closely fit observed harvest the reafter. Trends in
predicted large-mesh and graded-mesh gill-net surveys
matched observed trends in both surveys (Figure 3).
Predicted mean age did not systematically differ from
observed mean ag e for the commercial fishery,
recreational fishery, large-mesh survey, or graded-
mesh survey for either wild or stocked lake trout
(Figures 4, 5). Predicted average total abundance, fully
selected commercial fishing mortality, and fully
selected recreational fishing mortality for wild and
stocked l ake tr out were most sensitive (.17%
difference) to adjustments in the likelihood emphasis
factors for commercial and recreational fishing effort,
commercial and recreational fishery age distributions,
and the graded-mesh gill-net survey age distribution.
Changes in remaining likelihood emphasis factors led
to only small changes ( , 10% difference) in wild and
stocked lake trout model output quantities. The stocked
lake trout model failed to converge to a solution when
adjustments were made in the likelihood emphasis
factors for commercial fishery harvest and age
distribution, and the graded-mesh gill-net survey age
For wild lake trout, commercial fishery harvest
declined from 1980 to 2001, while recreational fishery
harvest increased over the same period (Figure 2).
Predicted c ommercial harvest of wild l ake trout
declined from 31,160 fish in 1980 to 23,991 fish in
2001; the maximum harvest of 37,672 fish occurred in
1990. Predicted recreational harvest of wild lake trout
increased from 1,130 fish in 1980 to a maximum of
11,774 fish in 2001.
For stocked lake trout, commercial fishery harvest
declined form 1980 to 2001, while recreational fishery
harvest remained relatively constant over the same
period (Figure 2). Predicted commercial harvest of
stocked lake trout declined from 80,829 fish in 1980 to
8,224 fish in 2001. Predicted recreational harvest of
stocked lake trout was highest in 2000 (5,756 fish) and
lowest in 1992 (1,749 fish).
FIGURE 2.—Commercial fishery harvest (observed ¼ solid
circles; predicted ¼ solid line) and recreational fishery harvest
(observed ¼ open circles; predicted ¼ dashed line) of wild
(upper panel) and stocked lake trout (lower panel) in
Wisconsin management unit 2 of Lake Superior between
1980 and 2001.
FIGURE 3.—Wild (upper panel) and stocked (lower panel)
lake trout catch per unit effort in large-mesh (observed ¼ solid
squares; predicted ¼ solid line) and graded-mesh (observed ¼
open squares; predicted ¼ dashed line) gill-net surveys
conducted within Wisconsin management unit 2 of Lake
Superior between 1980 and 2001.
LAKE TROUT POPULATION DYNAMICS 947
Estimated abundance of age-4þ wild lake trout more
than doubled between 1980 and 2001, while estimated
abundance of stocked lake trout decreased steadily from
1980 to 2001 (Figure 6). Estimated abundance of age-
4þ wild lake trout increased from 670,358 fish in 1980
to 1,589,600 fish in 2001; the maximum of 1,797,000
fish occurred in 1999. Estimated abundance of age-4þ
stocked lake trout decreased from a maximum of
630,897 fish in 1980 to 126,928 fish in 2001; a peak in
abundance was observed in 1998 (301,345 fish).
Recruitment of age-4 wild lake trout was erratic from
1980 to 2001, while recruitment of age-4 stocked lake
trout declined through time except for a brief, sharp
increase in 1998 (Figure 6). Recruitment of wild lake
trout was lowest in 1982 (212,055 fish) and highest in
1999 (603,887 fish). Recruitment of stocked lake trout
decreased from 200,487 fish in 1980 to no fish by
1999; a peak of 182,389 fish was apparent in 1998.
Instantaneous total mortality of age-7 (i.e., fully
selected in the commercial and recreational fisheries)
wild lake trout declined from 0.58 in 1980 to 0.28 in
2001 (Figure 7). Instantaneous natural mortality was
estimated as a constant (0.18) and made up the largest
component of total mortality in every year except 1980
and 1981 (when it was surpassed by commercial
fishing mortality) and 1987 (when it was surpassed by
sea lamprey-based mortality). Instantaneous sea lam-
prey-based mortality declined from 0.16 in 1980 to
0.04 in 2001; a peak of 0.19 occurred in 1987.
Instantaneous commercial fishing mortality declined
steadily from 0.22 in 1980 to 0.04 in 2001.
Instantaneous recreational fishing mortality was the
only mortality source to increase (from 0.009 in 1980
to 0.022 in 2001), but it still made up the smallest
proportion of total mortality except in 1999, when it
surpassed sea lamprey-based mortality.
Instantaneous total mortality of age-7 stocked lake
trout was relatively constant from 1980 to 1996 and
then declined from 0.69 in 1996 to 0.31 in 2001
(Figure 7). Instantaneous natural mortality was esti-
FIGURE 4.—Mean age (observed ¼ solid circles; predicted ¼
solid line) of wild lake trout caught in the commercial and
recreational fisheries and large-mesh and graded-mesh gill-net
surveys within Wisconsin management unit 2 of Lake
Superior between 1980 and 2001.
FIGURE 5.—Mean age (observed ¼ solid circles; predicted ¼
solid line) of stocked lake trout caught in the commercial and
recreational fisheries and large-mesh and graded-mesh gill-net
surveys within Wisconsin management unit 2 of Lake
Superior between 1980 and 2001.
LINTON ET AL.
mated as a constant (0.16) from 1980 to 2001.
Instantaneous sea lamprey-based mortality declined
from 0.16 in 1980 to 0.04 in 2001; sea lamprey-based
mortality peaked in 1987 at 0.19. Instantaneous
commercial fishing mortality increased from 0.25 in
1980 to 0.44 in 1996 and then declined to 0.08 in 2001.
Instantaneous recreational fishing mortality was 0.01 in
1980, attained a maximum of 0.08 in 1996, and then
declined to 0.04 in 2001.
The commercial fishery, recreational fishery, and
large-mesh gill-net survey exhibited similar trends in
selectivity for wild lake trout (Figure 8). Predicted
selectivity for commercial and recreational fisheries
and the large-mesh gill-net survey was highest at age 7,
whereas predicted selectivity for the graded-mesh gill-
net survey was highest at age 5. Predicted selectivity
decreased after the age of maximum selectivity for all
Fishery selectivity patterns for stocked lake trout
were similar among the commercial fishery, recrea-
tional fishery, and large-mesh gill-net survey (Figure 9)
but differed from those of wild lake trout (Figure 8).
The graded-mesh gill-net survey selectivity pattern was
similar between stocked and wild lake trout. For
stocked fish, predicted selectivity was highest at age 5
for all four gears. Predicted selectivity was relatively
constant from age 5 to 12 for the commercial fishery
and to age 10 for the large-mesh gill-net survey; it
declined sharply at older ages for both, declined
slightly after age 5 for the recreational fishery, and
declined sharply after age 5 for the graded-mesh gill-
For wild lake trout, large-mesh and graded-mesh
gill-net survey CPUE increased from 1980 to 2001
(Figure 3). Large-mesh gill-net CPUE increased from
1.92 fish/305 m of net in 1981 to 6.86 fish/305 m of net
in 2001. Graded-mesh gill-net CPUE increased from
0.38 fish/305 m of net in 1980 to 0.76 fish/305 m of net
For stocked lake trout, large-mesh and graded-mesh
gill-net survey CPUE decreased steadily from the early
1980s to the mid-1990s and peaked in the late 1990s
(Figure 3). Large-mesh gill-net CPUE decreased from
16.0 fish/305 m of net in 1981 to 1.69 fish/305 m of net
in 1994; the CPUE then increased to 9.41 fish/305 m of
net in 1999. Graded-mesh gill-net CPUE decreased
from 2.59 fish/305 m of net in 1980 to 0.23 fish/305 m
of net in 1991; a subsequent increase to 1.65 fish/305
m of net was observed in 1998.
FIGURE 6.—Predicted age-4þ abundance (upper panel) and
age-4 recruitment (lower panel) of wild (solid line) and
stocked (dashed line) lake trout in Wisconsin management
unit 2 of Lake Superior between 1980 and 2001.
FIGURE 7.—Predicted instantaneous mortality rates (natural,
sea lamprey-base d, commercial fishery, and recreational
fishery mortality) of age-7 wild (upper panel) and stocked
(lower panel) lake trout in Wisconsin management unit 2 of
Lake Superior between 1980 and 2001.
LAKE TROUT POPULATION DYNAMICS 949
The stocked lake trout model was not as stable as the
wild lake trout model. Lack of stability in the stocked
lake trout model was demonstrated by a poor fit
between predicted commercial catch and observed
catch, which normally fit closely, and by the model’s
failure to converge when commercial fishery harvest
and age distribution and graded-mesh gill-net survey
age distribution likelihood emphasis factors were
changed. The stocked lake trout model’s poor stability
is probably due to the relatively low fishery catch
during the latter half of the time series. Statistical catch-
at-age analysis requires sufficiently high levels of
fishery catch to estimate parameters and fit observed
data accurately. The lack of fit for the commercial
catch data is due to our assumption of 40% fall
fingerling overwinter survival. Fall fingerlings were
stocked from 1976 to 1985 and recruited to age 4
between 1980 and 1989, which represents the period
when commercial harvest was underestimated. The
model’s inability to estimate abundances that were high
enough to support observed catches suggests that fall
fingerling overwinter survival was actually higher than
40%. The Elrod et al. (1988) value for overwinter
survival was based on stocked lake trout from Lake
Ontario, but fall fingerling overwinter survival may be
different in Lake Superior.
According to SCAA model predictions, wild lake
trout abundance increased and stocked lake trout
abundance decreased from 1980 to 2001 in eastern
Wisconsin waters, as was found by earlier studies of
lake trout population dynamics in Lake Superior. These
results are similar to the earlier recovery of lake trout in
Michigan waters of Lake Superior, where stocked fish
abundance decreased while wild fish abunda nce
increased to near-historic levels (Hansen et al. 1994b,
1995; Wilberg et al. 2003). In eastern Wisconsin
waters, the decline in stocked lake trout abundance was
temporarily reversed in the late 1990s. This increase
was due to low pos tstocking mortality rates for
yearlings stocked in 1994 and 1995 (the last two
year-classes stocked). Earlier studies revealed that lake
trout abundance increased in the 1960s after closure of
the commercial fishery (Pycha and King 1975).
FIGURE 8.—Predicted selectivity of wild lake trout by
commercial and recreational fisheries and large-mesh and
graded-mesh gill-net surveys within Wisconsin management
unit 2 of Lake Superior, 1980–2001.
FIGURE 9.—Predicted selectivity of stocked lake trout by
commercial and recreational fisheries and large-mesh and
graded-mesh gill-net surveys within Wisconsin management
unit 2 of Lake Superior, 1980–2001.
LINTON ET AL.
Abundance decreased from the 1970s to the early
1980s with the advent of tribal commercial fisheries
and excessive lake trout bycatch in fisheries targeting
other species (Hansen et al. 1994b). Stocked lake trout
drove trends in abundance prior to 1970, whereas wild
lake trout increased erratically from the 1970s to the
1990s (Hansen et al. 1995). Hansen et al. (1995)
predicted that lake trout abundance would increase in
the late 1990s, as we found, due to effort limitations on
the commercial fishery, which would decrease lake
trout bycatch and increase lake trout survival.
Wild lake trout recruitment fluctuated erratically
from 1980 to 2001, whereas stocke d lake trout
recruitment declined to low levels before disappearing
altogether with the cessation of stocking in 1996.
Earlier studies also showed a decline in the survival of
stocked lake trout as wild lake trout reproduction
increased in the late 1980s (Hansen 1988, 1989; M. J.
Powell and J. Atkinson, Ontario Ministry of Natural
Resources, unpublished). Hansen et al. (1996) later
attributed the decline in stocked lake trout survival in
Wisconsin waters between 1963 and 1986 to increased
mortality in the commercial fishery, which corresponds
to our findings that stocked lake trout commercial
fishing mortality increased from 1980 to 1996. The
fluctuations without trend in wild lake trout recruitment
are due to how we modeled recruitment (i.e., as a series
of deviations around a mean value).
Total mortality of wild lake trout declined from 1980
to 2001; an increase in 2000 was caused by increased
sea lamprey-based mortality. Pollock et al. (2007)
found that stocked lake trout had higher mortality rates
than wild lake trout spawning at nearby Gull Island
Shoal. Our analysis confirmed that total mortality of
stocked lake trout was higher than that of wild lake
trout because of higher commercial fishing mortality
on stocked fish. Stocked lake trout may have
experienced higher fishing mortality than wild lake
trout for two reasons. First, stocked lake trout may be
distributed inshore, where the commercial fishery is
more active, whereas wild lake trout may be distributed
offshore, where they experience lower commercial
fishing mortality (Krueger et al. 1986; Mattes 2004).
Second, less-abundant stocks, like stocked lake trout,
within a mixed fishery composed of stocked and wild
fish will experience higher fishing mortality than the
more abundant stocks (Ricker 1958; Paulik et al.
1967). Natural mortality estimates for wild and stocked
lake trout in WI-2 were similar to natural mortality
estimates (0.16–0.21) for lake trout in the Michigan
management units of Lake Superior (MSC 2005),
which suggests that our estimates of natural mortality
Our study provides a quantitative description of wild
and stocked lake trout populations in eastern Wisconsin
waters of Lake Superior. Our results may be informa-
tive for managers that deal with similar sources of
fishery exploitation and sea lamprey-based mortality in
the lower Great Lakes, where lake trout are sustained
by hatchery stocking. We found that stocked lake trout
were disproportionately more vulnerable to commercial
fisheries than wild lake trout, resulting in higher
mortality rates on a broad range of age-classes. Great
Lakes fisheries managers may want to be more
restrictive w ith commercial fisheries that harvest
stocked lake trout by use of large-mesh gill nets.
Although the bottlenecks for lake trout recovery in the
lower Great Lakes may include many factors besides
fishing mortality (e.g., low early-life survival), main-
taining sufficient spawning stock biomass of stocked
lake trout is a prerequisite for the eventual transition to
self-sustaining wild populations, as has occurred in
By 2001, wild lake trout abundance for WI-2 was on
par with precollapse abundance levels. The primary
goal for lake trout rehabilitation in Lake Superior is to
reestablish self-sustaining populations at precollapse
abundance levels (Horns et al. 2003). Pycha and King
(1975) used commercial fishery CPUE data from 1929
to 1943 for Michigan management unit 2 (MI-2),
which is adjacent to WI-2 (Figure 1), as an index of
historic lake trout abundance for Wisconsin waters of
Lake Superior because no fishery effort data from
Wisconsin are available for that period. Similarly, we
compared our predicted large-mesh gill-net survey
CPUE with the 1929–1943 average large-mesh gill-net
CPUE for MI-2 (Wilberg et al. 2003) to evaluate
current and historic abundance levels. We converted
our predicted CPUE (fish/305 m of net) to fish per
kilometer of net and standardized CPUE to 1.0 net-
night using the gill-net saturation curve developed by
Hansen et al. (1998) to facilitate comparison. We found
that our predicted large-mesh gill-net CPUE for wild
lake trout exceeded the historic average of 7.2
each year from 1999 to 2001.
Wild lake trout recruitment in WI-2 has yet to
consistently reach the level thought necessary to sustain
a recovering pop ulation. The number of recruits
produced by the spawning stock is another measure of
progress towards the goal of rehabilitating lake trout
stocks to historic abundance levels. Hansen (1996)
estimated numbers of wild yearling recruits needed to
sustain the recovering lake trout population in WI-2. We
used the natural mortality rate of 0.12 (Ebener et al.
1989; Hansen 1996), which was used in the recruitment
LAKE TROUT POPULATION DYNAMICS 951
estimate, to calculate the number of wild age-4 recruits
needed for sustainability. Our estimated recruitment of
age-4 wild lake trout only exceeded the target of 389,231
age-4 recruits in 1987, 1989, 1997, and 1999 but was
within 150,000 fish of the target in most other years.
Wild lake trout in WI-2 experienced mortality rates
consistent with the recovery of sustainable populations
as of 2001. Excessive mortality due to either fishery
exploitation or sea lamprey predation would be
detrimental to lake trout recovery. Hansen (1996)
recommended that total annual mortality for adult lake
trout should be less than 45% to promote self-
sustaining populations. We found that estimated total
mortality for wild lake trout o nly exceeded this
mortality limit in 1987 for age-8þ fish.
By 2001, wild lake trout in WI-2 had made
considerable progress towards recovery. Wild lake
trout abundance wa s above the his toric ave rage
abundance and total mortality was below the recom-
mended limit, but recruitment was still below the level
thought necessary for sustainability. We recommend
that a stock–recruitment model be developed for WI-2,
as has been done for Michigan management units
(Richards et al. 2004), to assess factors that are currently
affecting wild lake trout recruitment. Such a study
would help Wisconsin fisheries managers understand
why recruitment is still below target levels and what
might be done to improve wild lake trout recruitment.
We believe that improvement of our SCAA models
will lead to a better understanding of population
dynamics governing lake trout in WI-2 and will allow
Wisconsin fisheries managers to better measure
population recovery. Two areas of potential improve-
ment are the recreational fishery age distributions in the
wild and stocked lake trout models and selectivity
functions in the stocked lake trout model. The
recreational fishery age distributions would be better
estimated if age data could be collected from the angler
creel survey. At present, the age distribution for the
recreational fishery may be misrepresented by using
the age–length key from the large-mesh gill-net survey,
which is based on a different sampling gear. The
unusual selectivity patterns for the stocked lake trout
commercial fishery, recreational fishery, and large-
mesh gill-net survey (where a wide range of age-
classes is highly vulnerable to the gears) suggest that
selectivity might be misspecified. Selectivity may be
better est imated using a different function or by
allowing one or more of the selectivity function’s
parameters to vary over time.
We would like to thank the WDNR Lake Superior
Office; Bill Mattes and the Great Lakes Indian Fish and
Wildlife Commission; Tom Fratt and the Red Cliff
Band of Lake Superior Chippewa; and Rick Huber and
the Bad River Band of Lake Superior Chippewa for
collecting and making available the data used in this
analysis and for sharing their knowledge of our study
system. Jim Hardin and Tim Ginnett of the University
of Wisconsin–Stevens Point provided advice on the
development of this study. Michael Rutter estimated
the rates of sea lamprey-based mortality used in this
analysis. Our associate editor and three anonymous
reviewers provided comments that improved the
quality of this manuscript. This project was funded
by the University of Wisconsin Sea Grant Institute
under grants from the National Sea Grant College
Program, National Oceanic and Atmospheric Admin-
istration (Grant Number NA86RG0047, Project Num-
ber R/LR-84), and the State of Wisconsin. This
research was completed in partial fulfillment of the
requirements for a Master of Science degree at the
University of Wisconsin–Stevens Point.
Baldwin, N. S., R. W. Saalfeld, M. A. Ross, and H. J.
Buettner. 1979. Commercial fish production in the Great
Lakes 1867–1977. Great Lakes Fishery Commission
Technical Report 3.
Bence, J. R., R. A. Bergstedt, G. C. Christie, P. A. Cochran,
M. P. Ebener, J. F. Koonce, M. A. Rutter, and W. D.
Swink. 2003. Sea lamprey (Petromyzon marinus)
parasite–host interactions in the Great Lakes. Journal of
Great Lakes Research 29(Supplement 1):253–282.
Bence, J. R., A. Gordoa, and J. E. Hightower. 1993. Influence
of age-selective surveys on the reliability of stock
synthesis assessments. Canadian Journal of Fisheries
and Aquatic Sciences 50:827–840.
Bennett, E. B. 1978. Characteristics of the thermal regime of
Lake Superior. Journal of Great Lakes Research 4:310–
Coberly, C. E., and R. M. Horrall. 1980. Fish spawning
grounds in Wisconsin waters of the Great Lakes.
University of Wisconsin Sea Grant Institute, Madison.
Deriso, R. B., T. J. Quinn II, and P. R. Neal. 1985. Catch-age
analysis with auxiliary information. Canadian Journal of
Fisheries and Aquatic Sciences 42:815–824.
Doubleday, W. G. 1976. A least squares approach to
analyzing catch-at-age data. International Commission
for the Northwest Atlantic Fisheries Research Bulletin
Ebener, M. P., J. R. Bence, R. A. Bergstedt, and K. M.
Mullett. 2003. Classifying sea lamprey marks on Great
Lakes lake trout: observer agreement, evidence on
healing times between classes, and recommendations
for reporting of marking statistics. Journal of Great Lakes
Research 29(Supplement 1):283–296.
Ebener, M. P., J. H. Selgeby, M. P. Gallinat, and M. Donofrio.
1989. Methods for determining total allowable catch of
lake trout in the 1842 treaty-ceded area within Michigan
waters of Lake Superior, 1990–1994. Great Lakes Indian
LINTON ET AL.
Fish and Wildlife Commission, Administrative Report
89–11, Odanah, Wisconsin.
Elrod, J. H., D. E. Ostergaard, and C. P. Schneider. 1988.
Comparison of hatchery-reared lake trout stocked as fall
fingerlings and as spring yearlings in Lake Ontario.
North American Journa l of F ishe rie s Manage ment
Eshenroder, R. L., and J. F. Koonce. 1984. Recommendations
for standardizing the reporting of sea lamprey marking
data. Great Lakes Fishery Commission Special Publica-
tion 84–1, Ann Arbor, Michigan.
Fournier, D., and C. P. Archibald. 1982. A general theory for
analyzing catch at age data. Canadian Journal of
Fisheries and Aquatic Sciences 39:1195–1207.
Greig, L., D. Meisner, and G. Christie. 1992. Manual for the
management protocol for the implementation of integrat-
ed management of sea lamprey in the Great Lakes basin,
version 1.1. Report to the Great Lakes Fishery Commis-
sion, Ann Arbor, Michigan.
Hansen, M. J. 1988. Report to the Lake Superior Committee
by the Lake Superior Technical Committee. Pages 9–22
in C. R. Bronte, editor. Lake Superior Committee annual
meeting (minutes): March 15–16, 1988. Great Lakes
Fishery Commission, Ann Arbor, Michigan.
Hansen, M. J. 1989. Report to the Lake Superior Committee
by the Lake Superior Technical Committee. Pages 43–64
in C. R. Bronte, editor. Lake Superior Committee annual
meeting (minutes): March 15, 1989. Great Lakes Fishery
Commission, Ann Arbor, Michigan.
Hansen, M. J., editor. 1990. Lake Superior: the state of the
lake in 1989. Great Lakes Fishery Commission, Special
Publication 90-3, Ann Arbor, Michigan.
Hansen, M. J., editor. 1996. A lake trout restoration plan for
Lake Superior. Great Lakes Fishery Commission,
Miscellaneous Publication, Ann Arbor, Michigan.
Hansen, M. J. 1999. Lake trout in the Great Lakes: basinwide
stock collapse and binational restoration. Pages 417–454
in W. W. Taylor and C. P. Ferreri, editors. Great Lakes
fisheries policy and management. Michigan State
University Press, East Lansing.
Hansen, M. J., J. R. Bence, J. W. Peck, and W. W. Taylor.
1997. Evaluation of the relative importance of hatchery-
reared and wild fish in the restoration of Lake Superior
lake trout. Pages 492–497 in D. A. Hancock, D. C.
Smith, A. Grant, and J. P. Beumer, editors. Developing
and sustaining world fisheries resources: the state of
science and management. Proceedings of the 2nd World
Fisheries Congress. CSIRO Publishing, Collingwood,
Hansen, M. J., M. P. Ebener, R. G. Schorfhaar, S. T. Schram,
D. R. Schreiner, and J. H. Selgeby. 1994a. Declining
survival of lake trout stocked during 1963–1986 in U.S.
waters of Lake Superior. North American Journal of
Fisheries Management 14:395–402.
Hansen, M. J., M. P. Ebener, R. G. Schorfhaar, S. T. Schram,
D. R. Schreiner, J. H. Selgeby, and W. W. Taylor. 1996.
Causes of declining survival of lake trout stocked in U.S.
waters of Lake Superior in 1963–1986. Transactions of
the American Fisheries Society 125:831–843.
Hansen, M. J., M. P. Ebener, J. D. Shively, and B. L.
Swanson. 1994b. Lake trout. Pages 13–34 in M. J.
Hansen, editor. The state of Lake Superior in 1992. Great
Lakes Fishery Commission, Special Publication 94-1,
Ann Arbor, Michigan.
Hansen, M. J., J. W. Peck, R. G. Schorfhaar, J. H. Selgeby,
D. R. Schreiner, S. T. Schram, B. L. Swanson, W. R.
MacCallum, M. K. Burnham-Curtis, G. L. Curtis, J. W.
Heinrich, and R. J. Young. 1995. Lake trout (Salvelinus
namaycush) populations in Lake Superior and their
restoration in 1959–1993. Journal of Great Lakes
Research 21(Supplement 1):152–175.
Hansen, M. J., R. G. Schorfhaar, and J. H. Selgeby. 1998.
Gill-net saturation by lake trout in Michigan waters of
Lake Superior. North American Journal of Fisheries
Hilborn, R., and C. J. Walters. 1992. Quantitative fisheries
stock assessment: choice, dynamics, and uncertainty.
Chapman and Hall, New York.
Horns, W. H., C. R. Bronte, T. R. Busiahn, M. P. Ebener
R. L. Eshenroder, T. Gorenflo, N. Kmiecik, W. Mattes
J. W. Peck, M. Petzold, and D. R. Schreiner. 2003. Fish–
community objectives for Lake Superior. Great Lakes
Fishery Commission, Special Publication 03-01, Ann
Johnson, T. B., M. H. Hoff, A. S. Trebitz, C. R. Bronte, T. D.
Corry, J. F. Kitchell, S. J. Lozano, D. M. Mason, J. V.
Scharold, S. T. Schram, and D. R. Schreiner. 2004.
Spatial patterns in assemblage structures of pelagic
forage fish and zooplankton in western Lake Superior.
Journal of Great Lakes Research 30(Supplement 1):395–
Kapuscinski, K. L., M. J. Hansen, and S. T. Schram. 2005.
Movements of lake trout in U.S. waters of Lake Superior,
1973–2001. North American Journal of Fisheries Man-
Krueger, C. C., B. L. Swanson, and J. H. Selgeby. 1986.
Evaluation of hatchery-reared lake trout for reestablish-
ment of populations in the Apostle Islands region of Lake
Superior, 1960–84. Pages 93–107 in R. H. Stroud, editor.
Fish culture in fisheries management. American Fisheries
Society, Bethesda, Maryland.
LSLTTC (Lake Superior Lake Trout Technical Committee).
1986. A lake trout rehabilitation plan for Lake Superior.
In Lake Superior Committee. Lake Superior Committee
1986 annual meeting (minutes). Great Lakes Fishery
Commission, Ann Arbor, Michigan.
Mattes, W. P. 2004. Temperature and depth profiles of
namaycush (lake trout) in Lake Superior. Great Lakes
Indian Fish and Wildlife Commission, Project Report
04-01, Odanah, Wisconsin.
Megrey, B. A. 1989. Review and comparison of age-
structured stock assessment models from theoretical
and applied points of view. Pages 8–48 in E. F. Edwards
and B. A. Megrey, editors. Mathematical analysis of fish
stock dynamics. American Fisheries Society, Symposium
6, Bethesda, Maryland.
Methot, R. D. 1990. Synthesis model: an adaptable framework
for analysis of diverse stock assessment data. Pages 259–
277 in L. Low, editor. Proceedings of the symposium on
applications of stock assessment techniques to gadids.
International North Pacific Fisheries Commission, Bul-
letin 50, Vancouver, British Columbia.
MSC (Modeling Subcommittee, Technical Fisheries Commit-
tee). 2005. Summary status of lake trout and lake
LAKE TROUT POPULATION DYNAMICS 953
whitefish populations in the 1836 treaty-ceded waters of
Lakes Superior, Huron and Michigan in 2003, with
recommended yield and effort levels for 2004. Report to
the Technical Fisheries Committee, 1836 treaty-ceded
waters of Lakes Superior, Huron and Michigan.
Munawar, M., and I. F. Munawar. 1978. Phytoplankton of
Lake Superior 1973. Journal of Great Lakes Research
Otter Research Ltd. 2002. AD model builder, version 6.0.4.
Otter Research Ltd., Nanaimo, British Columbia.
Paulik, G. J., A. S. Hourston, and P. A. Larkin. 1967.
Exploitation of mixed stocks by a common fishery.
Journal of the Fisheries Research Board of Canada
Pauly, D. 1980. On the interrelationships between natural
mortality, growth parameters, and mean environmental
temperature in 175 fish stocks. Journal du Conseil,
Conseil International pour l’Exploration de la Mer
Pollock, K. H., J. Yoshizaki, M. C. Fabrizio, and S. T.
Schram. 2007. Factors affecting survival rates of a
recovering lake trout population estimated by mark–
recapture in Lake Superior, 1969–1996. Transactions of
the American Fisheries Society. 136:185–194.
Pycha, R. L. 1980. Changes in mortality of lake trout
(Salvelinus namaycush) in Michigan waters of Lake
Superior in relation to sea lamprey (Petromyzon marinus)
predation, 1968–78. Canadian Journal of Fisheries and
Aquatic Sciences 38:1113–1119.
Pycha, R. L., and G. R. King. 1975. Changes in the lake trout
population of southern Lake Superior in relation to the
fishery, the sea lamprey, and stocking, 1950–70. Great
Lakes Fishery Commission, Technical Report 28, Ann
Quinn, T. J., II, and R. B. Deriso. 1999. Quantitative fish
dynamics. Oxford University Press, New York.
Richards, J. M., M. J. Hansen, C. R. Bronte, and S. P. Sitar.
2004. Recruitment dynamics of the 1971–1991 year-
classes of lake trout in Michigan waters of Lake Superior.
North American Journal of Fisherie s Management
Ricker, W. E. 1958. Maximum sustainable yields from
fluctuating environments and mixed stocks. Journal of
the Fisheries Research Board of Canada 15:991–1006.
Ricker, W. E. 1975. Computation and interpretation of
biological statistics of fish populations. Fisheries Re-
search Board of Canada Bulletin 191.
Rutter, M. A., and J. R. Bence. 2003. An improved method to
estimate sea lamprey wounding rate on hosts with
application to lake trout in Lake Huron. Journal of Great
Lakes Research 29(Supplement 1):320–331.
Schram, S. T., J. H. Selgeby, C. R. Bronte, and B. L.
Swanson. 1995. Population recovery and natural recruit-
ment of lake trout at Gull Island Shoal, Lake Superior,
1964–92. Journal of Great Lakes Research 21(Supple-
Sitar, S. P., J. R. Bence, J. E. Johnson, M. P. Ebener, and
W. W. Taylor. 1999. Lake trout mortality and abundance
in southern Lake Huron. North American Journal of
Fisheries Management 19:881–900.
Swanson, B. L., and D. V. Swedberg. 1980. Decline and
recovery of the Lake Superior Gull Island Reef lake trout
(Salvelinus namaycush) population and the role of sea
lamprey (Petromyzon marinus). Canadian Journal of
Fisheries and Aquatic Sciences 37:2074–2080.
Swink, W. D. 1990. Effect of lake trout size on survival after a
single sea lamprey attack. Transactions of the American
Fisheries Society 119:996–1002.
Weeks, C. T. 1997. Dynamics of lake trout (Salvelinus
namaycush) size and age structure in Michigan waters of
Lake Superior, 1971–1995. Master’s thesis. Michigan
State University, East Lansing.
Weiler, R. R. 1978. Chemistry of Lake Superior. Journal of
Great Lakes Research 4:370–385.
Wilberg, M. J., M. J. Hansen, and C. R. Bronte. 2003. Historic
and modern abundance of wild lean lake trout in
Michigan waters of Lake Superior: implications for
restoration goals. North American Journal of Fisheries
LINTON ET AL.