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Changes in the Salmonine Community of Lake Michigan
and Their Implications for Predator–Prey Balance
a
a
a
a
Iyob Tsehaye , Michael L. Jones , Travis O. Brenden , James R. Bence & Randall M.
Claramunt
b
a
Quantitative Fisheries Center, Department of Fisheries and Wildlife , Michigan State
University , 293 Farm Lane, Room 153, East Lansing , Michigan , 48824 , USA
b
Michigan Department of Natural Resources , Charlevoix Fisheries Research Station , 96
Grant Street, Charlevoix , Michigan , 49720 , USA
Published online: 07 Mar 2014.
To cite this article: Iyob Tsehaye , Michael L. Jones , Travis O. Brenden , James R. Bence & Randall M. Claramunt (2014)
Changes in the Salmonine Community of Lake Michigan and Their Implications for Predator–Prey Balance, Transactions of the
American Fisheries Society, 143:2, 420-437
To link to this article: http://dx.doi.org/10.1080/00028487.2013.862176
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Transactions of the American Fisheries Society 143:420–437, 2014
C American Fisheries Society 2014
ISSN: 0002-8487 print / 1548-8659 online
DOI: 10.1080/00028487.2013.862176
ARTICLE
Changes in the Salmonine Community of Lake Michigan
and Their Implications for Predator–Prey Balance
Iyob Tsehaye,* Michael L. Jones, Travis O. Brenden, and James R. Bence
Quantitative Fisheries Center, Department of Fisheries and Wildlife, Michigan State University,
293 Farm Lane, Room 153, East Lansing, Michigan 48824, USA
Downloaded by [Michigan State University] at 09:09 07 March 2014
Randall M. Claramunt
Michigan Department of Natural Resources, Charlevoix Fisheries Research Station, 96 Grant Street,
Charlevoix, Michigan 49720, USA
Abstract
We combined statistical stock assessment methods with bioenergetic calculations to assess historical changes
in abundance and consumptive demand of the hatchery-supported salmonine community in Lake Michigan, with
the goal of providing information needed to examine the lake’s predator–prey balance. Especially for Chinook
Salmon Oncorhynchus tshawytscha, the most dominant salmonine predator in the lake, our analysis revealed densitydependent changes in growth, survival, production levels, consumptive demand, and fishery characteristics, suggesting
that increased salmonine abundance possibly had substantial impacts on prey abundance that led to predators being
food limited. Indeed, the estimated changes in the salmonine community were consistent with historical changes in
prey abundances that were previously documented for Lake Michigan. Specifically, higher salmonine abundance
and consumption were estimated for the early 1980s, during which time Alewife Alosa pseudoharengus abundance
experienced a marked decline, leading to a Chinook Salmon mass mortality event in 1987. Similarly, increased
salmonine abundance and consumption were estimated for the years since the early 2000s, and the Alewife population
in Lake Michigan has been driven to historically low levels during these years. Increased salmonine abundance
estimates in recent years were attributable to improved survival rates and natural reproduction of Chinook Salmon.
Although past revisions to stocking rates may have been reasonable measures taken to stabilize the predator–prey
system, our analysis suggests that recent reductions in stocking have not been sufficient to reduce predatory pressure
on the Alewife population; however, they may have ameliorated potential effects of increased natural reproduction
of Chinook Salmon. Along with a complementary assessment of the production dynamics of key prey species, our
retrospective assessment of the dynamics of the Lake Michigan predator community and their consumptive demands
can provide the basis for making future fishery management decisions from an ecosystem perspective.
Given that predator–prey interactions are a major ecological
process regulating the trophic structure of food webs in many
aquatic ecosystems (Goodrich and Buskirk 1995; Christensen
1996; Bax 1998), altering the abundance of predatory fishes
through fishing or stocking has the potential to shift predator–
prey balance of a system and lead to major trophic restructuring (Carpenter et al. 1985; Pauly et al. 1998). However, because of the single-species nature of most fishery assessments,
the ecosystem-level effects of altering predator abundance in
*Corresponding author: [email protected]
Received March 19, 2013; accepted October 25, 2013
420
aquatic systems can be hard to predict (Link and Garrison 2002).
Therefore, adopting approaches that combine stock assessment
methods with explicit or implicit consideration of predator–
prey interactions can give better insights into potential ecosystem effects of fishery management decisions (Link and Garrison
2002). In view of the recurrent changes in predator and prey populations in the Laurentian Great Lakes (Madenjian et al. 2002;
Bence et al. 2008; Claramunt et al. 2012), this study considers
the importance of predator–prey balance for the management
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SALMONINE COMMUNITY CHANGES IN LAKE MICHIGAN
of salmonine fisheries in these lakes by focusing on the Lake
Michigan fish community.
Owing to combined effects of overfishing, habitat degradation, eutrophication, and invasive species, predator–prey communities of the Great Lakes have undergone considerable
changes over the last century. Many important native species
have been either greatly reduced or extirpated, resulting in introduced and invasive species taking on more important roles in
fish communities (Mills et al. 1993; Eshenroder and BurnhamCurtis 1999). Most notably, abundances of the once-dominant
top predator, Lake Trout Salvelinus namaycush, were greatly reduced in the middle of the 20th century due to predation by Sea
Lamprey Petromyzon marinus and overfishing, leading to the
expansion of invasive Alewife Alosa pseudoharengus and Rainbow Smelt Osmerus mordax populations (Hansen et al. 1996;
O’Gorman and Stewart 1999; Bence et al. 2008). Where they
have become abundant, these invasive species have adversely affected native fish species through competition for food and predation on eggs and larvae (Crowder 1980; Eck and Wells 1987;
Krueger et al. 1995; Mason and Brandt 1996). To reduce the
abundance of exotic prey fishes and rehabilitate native species,
millions of native (Lake Trout) and nonnative salmonines, including Chinook Salmon Oncorhynchus tshawytscha, steelhead
O. mykiss (anadromous Rainbow Trout), Brown Trout Salmo
trutta, and Coho Salmon O. kisutch, have been stocked into the
Great Lakes since the 1960s (Claramunt et al. 2012). Due to
its fast growth, high angling quality, low production cost, and
high rate of Alewife predation, Chinook Salmon became the
preferred species for stocking by both fishery managers and
anglers (Hansen and Holey 2002). The stocking of salmonines
led to reductions in Alewife and Rainbow Smelt densities and
the recovery of several formerly depressed native species, such
as Deepwater Sculpin Myoxocephalus thompsoni, Yellow Perch
Perca flavescens, and Burbot Lota lota (Madenjian et al. 2002;
Dobiesz et al. 2005) and created opportunities for the development of new recreational fisheries, bringing considerable economic benefits to the region (Kotchen et al. 2006; Fenichel et al.
2010; Dettmers et al. 2012).
Although the stocking of salmonines is credited with having
led to the reduction of invasive species and re-establishment
of native species in the Great Lakes, fishery managers have
been concerned about the risk of overstocking as they sought
to achieve a delicate balance between rehabilitating native
fishes (mainly Lake Trout) by maintaining invasive prey species
(mainly Alewife) at relatively low levels and sustaining an
economically important recreational fisheries by maintaining
adequate prey fish for the nonnative predators (i.e., Pacific
salmonines) (Stewart et al. 1981; Madenjian et al. 2002;
Dettmers et al. 2012). Throughout the years, there have been
several instances across the Great Lakes suggesting that the
predator–prey systems have been “out of balance.” In Lake
Michigan, a massive die-off of Chinook Salmon, associated
with an outbreak of bacterial kidney disease, occurred in the late
1980s following declines in Alewife abundances (Holey et al.
421
1998; Madenjian et al. 2002; Benjamin and Bence 2003). In
Lake Huron, Alewife populations collapsed in the early 2000s,
which was followed by a major decline in Chinook Salmon
abundance (Riley et al. 2008; Roseman et al. 2008; Brenden
et al. 2012). Even in Lake Superior, which is considered the
least perturbed of the Great Lakes, Lake Trout abundance declines have occasionally been observed during periods of increased Rainbow Smelt densities (Mason et al. 1998; Kitchell
et al. 2000; Cox and Kitchell 2005). Seeking to achieve a better predator–prey balance, Great Lakes fishery managers have
adjusted stocking rates when evidence of limited predator food
emerged, mostly in the form of reduced Chinook Salmon growth
rates and low Alewife abundance (Bence et al. 2008; Jones and
Bence 2009; Fenichel et al. 2010; Murry et al. 2010).
The Lake Michigan salmonine community has undergone
some of the most striking changes in the Great Lakes, posing serious challenges to management (Madenjian et al. 2002; Claramunt et al. 2012). In the early years of the stocking program,
salmonine abundance increased steadily along with stocking
rates, leading many to conclude that the lake’s prey populations
could support more stocking (Stewart et al. 1981). Conversely,
the Alewife decline and the subsequent Chinook Salmon dieoff in the 1980s suggested that stocking levels had exceeded
prey fish production capacity (Stewart and Ibarra 1991; Hansen
and Holey 2002; Benjamin and Bence 2003). Although both
the salmonine and Alewife populations rebounded following
reductions in stocking rates after the Chinook Salmon die-off,
salmonine and prey abundances have continued to fluctuate,
which necessitated continuing adjustments to stocking (e.g., in
1998) (Madenjian et al. 2002; Claramunt et al. 2012). Similarly,
major declines in Alewife abundance in the years since 2000
suggested a possible recurrence of poor feeding conditions for
salmonines, prompting a 25% reduction in stocking rates in
2005 (Jones and Bence 2009; Claramunt et al. 2012). However,
despite these adjustments in predator stocking levels, Alewife
abundance in Lake Michigan remained low (Warner et al.
2011).
The predator–prey system of Lake Michigan was assessed
quantitatively at several points during the last 30 years. Early
studies, including those of Stewart et al. (1981), Stewart and
Ibarra (1991), Koonce and Jones (1994), and Rutherford (1997),
estimated historical predatory demand by combining results
from simple age-structured population and bioenergetic models. As part of an effort to explore optimal salmonine stocking
options for Lake Michigan (Jones et al. 2008), Szalai (2003) developed updated predator assessments and bioenergetics models to estimate lake-wide predatory demand using data collected
through 1999. Although these studies led to a better understanding of the Lake Michigan fish communities, the predator
and prey populations in the lake have undergone several major
changes since the most recent study. First, as previously indicated, adult Alewife abundance remains at historically low levels
(Warner et al. 2011). Second, due to the decreased importance
of Diporeia in the diet of adult Alewives after the invasion of
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422
TSEHAYE ET AL.
dreissenid mussels, adult Alewife energy density in Lake Michigan has declined by about 23%, increasing the amount of
Alewife consumption needed to maintain preinvasion growth
rates by about the same percentage (Madenjian et al. 2006;
Nalepa et al. 2006). Third, just as in Lakes Huron (Johnson
et al. 2010) and Ontario (Connerton et al. 2009), a large proportion of the Chinook Salmon population in Lake Michigan
is now derived from natural reproduction (Williams 2012). In
light of these changes, quantitative reassessment of the dynamics of Lake Michigan’s salmonine populations and their consumptive demands is a prerequisite for the development of effective stocking or fishery management policies. By combining
modern statistical stock assessment methods with bioenergetics
calculations, we aimed to assess historical changes in the Lake
Michigan salmonine community and their consumptive demand
and examine their implications for predator–prey balance in the
lake.
METHODS
We developed updated statistical catch-at-age (SCA) models for Chinook Salmon, steelhead, Brown Trout, and Coho
Salmon in Lake Michigan using data collected through 2008
and by adding new model components (e.g., time-varying catchability) to previous assessments. A monthly time step was also
incorporated in the SCA models to account for seasonal fluctuations in fishing effort and semelparous spawning by some of
the species. Depending on the life history characteristics and
evidence regarding population processes for individual species,
the SCA models accounted for several sources of mortality,
including (1) fishing mortality, (2) baseline natural mortality,
(3) maturation (spawning) mortality, and (4) time-varying natural mortality. Estimated parameters were time-varying catchability (for all species), age-specific selectivities (for all species
except Coho Salmon), and age-specific, time-varying natural
mortality and maturation parameters (for Chinook Salmon). For
Lake Trout, stock assessment models already existed for multiple management areas in portions of Lake Michigan ceded in the
1836 Treaty (Jonas 2011). We aggregated abundance estimates
from these models, expanded estimates to cover unassessed areas, and calculated weighted averages of parameter estimates
to provide lake-wide estimates. These existing SCA models for
Lake Trout accounted for fishing, Sea Lamprey-induced, and
baseline natural mortality. Finally, we generated time series of
species-specific and lake-wide consumption estimates by combining age-specific abundance estimates from our salmonine
SCA and the Lake Trout Treaty-ceded-water models with time
series of growth data.
Model inputs and background.—Annual fishery-dependent
data, collected as part of creel survey programs administered by
the Michigan (MDNR), Indiana (INDNR), Illinois (ILDNR),
and Wisconsin (WDNR) Departments of Natural Resources,
were used as input to the stock assessment models for Chinook
Salmon, steelhead, Brown Trout, and Coho Salmon. Depend-
ing on the information available for individual species, fisherydependent data used in fitting the SCA models were: (1) total
harvest from 1985 to 2008 (all species individually), (2) fishing
effort directed at all salmonine species as a group from 1985
to 2008 (all species), (3) age composition of total harvest from
1985 to 2008 (all species except Coho Salmon), (4) age composition of harvest of mature fish from 1985 to 2008 (Chinook
Salmon only), and (5) age composition of weir harvest (fish
captured during the spawning run) from 1985 to 2008 (Chinook
Salmon only). Because fishing mostly occurs during the summer months, annual summaries of the age composition of harvest were obtained using harvest data for July and August. Total
salmonine targeted effort was used as an input for all models
because this was consistently recorded across jurisdictions and
time, and because many trips were directed more generally at the
group rather than at a single species. We believe this was the best
index of fishing pressure available for the salmonine fisheries.
In addition to fishery-dependent data, other types of data
were used as model input: (1) natural mortality (for steelhead,
Brown Trout, and Coho Salmon) (2) stocking numbers and/or
recruitment, (3) weight at age, and (4) diet composition. While
natural mortality was estimated as a parameter for Chinook
Salmon, we used existing natural morality estimates as input
into the models for the other salmonines because previous studies found no evidence of increased natural mortality rates of
these salmonines as prey abundance declines (Rutherford 1997;
Szalai 2003) (Table A.1). Recruitment (Figure 1) was assumed
to occur at the beginning of the year for all species. Because
of shorter stream residency of Chinook Salmon juveniles, and
thus better survival, and the production costs involved (Cochrane
et al. 1992), Chinook Salmon are typically stocked as fingerlings
and the other salmonines as yearlings. Therefore, recruitment
was incorporated into the models as the number of age-0 fish for
Chinook Salmon and age-1 fish for the other species. Total annual recruitment was calculated as the sum of wild recruitment
and stocking numbers, and stocking numbers were adjusted for
FIGURE 1. Salmonine recruitment in Lake Michigan in numbers stocked
(lower solid line) and sum of stocked and wild (upper solid line) for Chinook
Salmon and numbers stocked for all other species.
423
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SALMONINE COMMUNITY CHANGES IN LAKE MICHIGAN
poststocking survival rates of 0.75 for Chinook Salmon, 0.5 for
steelhead and Coho Salmon, and 0.75 for Brown Trout (Rutherford 1997). Annual salmonine stocking numbers were extracted
from a database compiled by the U.S. Fish and Wildlife Service (USFWS 2009). Estimates of wild recruitment of Chinook
Salmon from 1967 to 2008 were obtained from the Salmonid
Working Group (SWG) of the Lake Michigan Technical Committee. These data were obtained using stream surveys conducted in 1979 and 1983 and oxytetracyclene (OTC) marking
studies from 1992 to1995, 2001–2004, and 2007 to the present
(Jonas et al. 2008; Williams 2012). Recruitment levels for the
intervening years were obtained through linear interpolation.
Natural recruitment was assumed to be negligible for all other
salmonines; there is no evidence of substantial natural recruitment for any of the other salmonines including Lake Trout. The
difference in recruitment success between Chinook Salmon and
the other nonnative salmonines is believed to be because the
other salmonines have longer stream residency that extends to
the summer months, when streams may be too warm for their
juveniles. The inability of Lake Trout to reproduce naturally in
Lake Michigan is believed to be due to Sea Lamprey predation,
fishing, and negative impacts from nonnative species (Jonas
et al. 2008).
Salmonine stocking into Lake Michigan increased steadily
during the 1960s and 1970s, reaching peak levels in the mid
1980s (Figure 1). Since 1975, Chinook Salmon stocking rates
have exceeded those of all other species. Principally as the result
of lake-wide management decisions in 1991, 1998, and 2005 to
reduce stocking rates, Chinook Salmon stocking has generally
declined since 1990. The other salmonine species have experienced variable stocking rates since the mid-1970s without any
clear trends. Estimates of wild Chinook Salmon recruitment
have risen steadily since the early 1970s, and data from recent
years indicate that annual wild smolt production has exceeded
3 million fish (Figure 1).
Weight-at-age data (Tables A.2, A.3), collected from recreational fishers as part of the MDNR creel survey program, were
used as model inputs for the estimation of consumption rates.
For Chinook Salmon, we generated mean weight at age at time
of harvest and at annulus formation (1985–2008) as weights
on August 1 and January 1, respectively, using a linear regression of the natural log (ln) of weight on date of capture (Table
A.2). Weight-at-age data for Chinook Salmon between 1965 and
1978, which were obtained based on weight data from fish during spawning runs, were derived from Rybicki (1973) and Wesley (1996). Weight-at-age estimates for the period between 1978
and 1985 were obtained through linear interpolation. For Lake
Trout, weight-at-age estimates, which showed slight changes
over time, were calculated as weighted averages of management area-specific, weight-at-age values from the assessment
models for the 1836 Lake Michigan Treaty-ceded waters. For
steelhead, Brown Trout, and Coho Salmon, we assumed constant weights over time as in previous studies (Table A.3) given
that there has been no evidence of variable growth rates that can
be associated with changes in prey abundance in Lake Michigan
(Rutherford 1997; Madenjian et al. 2002; Szalai 2003).
Diet composition data were also used as a model input during
the estimation of prey consumption. For years before 1999, we
used diet composition estimates from earlier studies, including
Stewart et al. (1981), Stewart and Ibarra (1991), and Rand et al.
(1993). The diet composition data for Chinook Salmon, Coho
Salmon, and Lake Trout were given as the proportion of small
Alewives (<8 g), large Alewives (≥8 g), and other fish, using
diet categories defined by Stewart et al. (1981). The diet composition data for Brown Trout and steelhead were taken from Szalai
(2003). For years after 1999, we generated annual diet composition data for Chinook Salmon and Lake Trout using stomach
content data compiled by the SWG. These data include weight
and length category of major prey species (Alewife, Rainbow
Smelt, and others) consumed by individual predators collected
from multiple management areas and over several months covering the open-water fishing season. The SWG assembles lakewide Lake Michigan salmonine diet data from fish collected
from gill-net surveys conducted by MDNR as well as from
angler-caught fish according to the Lake Michigan Technical
Committee lake-wide diet assessment protocol (Elliott et al.
1996). Diet compositions for steelhead, Brown Trout, and Coho
Salmon were assumed to remain the same as that observed for
years before 1999 because more recent data were not available.
Population submodel.—Fish abundance was predicted using
an exponential population model in monthly time steps, in which
spawning mortality was assumed to occur as a pulse event and
fishing mortality was assumed to occur throughout the year, with
fishing effort concentrated in the summer months. Based on the
timing of weir returns, spawning was assumed to occur at the end
of September (after fishing and natural mortality had occurred)
for Chinook Salmon, Coho Salmon, and Brown Trout, and at
the end of November for steelhead. To account for the assumed
discrete-spawning mortality event, each year was divided into
two time periods: the first time period represented the first nine
months (or 11 months for steelhead) prior to maturation, and the
second time period represented the remainder of the year. Age1
),
specific abundances at the end of the first time period (Na,y
2
at the beginning of the second time period (Na,y ), and at the
beginning of the subsequent year (Na+1,y+1 ) were calculated as
1
Na,y
= Na,y e
s
s
− 12
Ma,y +
Fa,y,m
m=1
2
1
1
Na,y
= Na,y
(1 − θa,y ) + Na,y
θa,y ϕa ,
2
e
Na+1,y+1 = Na,y
12
− 12−s
M
+
F
a,y
a,y,m
12
(1)
m=s+1
where θa,y is the proportion at age (a) of the population that matures and returns to the streams to spawn every year (y) in month
s, (s = 11 for steelhead; s = 9 for all other modeled salmonines),
ϕ is the fraction of repeat spawners for each species, and Fm,a,y is
monthly instantaneous fishing mortality rate. With recruitment
424
TSEHAYE ET AL.
to the first age-class assumed known in each year, the models
projected abundances for ages 0–5 for Chinook Salmon, 1–5
for steelhead and Brown Trout, and 1–2 for Coho Salmon as in
Rutherford (1997) and Szalai (2003).
Natural mortality (Ma,y ) was modeled as an instantaneous
process occurring throughout the year. While natural mortality
rates for all species other than Chinook Salmon were assumed
known based on Rutherford (1997) (Table A.1), natural mortality rates for Chinook Salmon were assumed to vary over time
and were modeled as the sum of an age-specific background
component (Ma ) and a time-varying (TVMa,y ) component:
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Ma,y = Ma + TVMa,y .
(2)
The age-specific background components were set equal to the
values used in Benjamin and Bence (2003) (Table A.1). The
time-varying component was predicted by combining age (λ)
and year (γ) effects using a separable model, which reduced the
total number of estimated parameters:
TVMa,y = λa γ y ,
(3)
Age-specific instantaneous fishing mortality rates were assumed
to vary by year and month for all species, and were calculated
as
Fm,a,y = f y Sa gm ,
(4)
where fy is annual fishing intensity, Sa is age-specific fishery
selectivity (i.e., relative vulnerability), and gm is the relative
proportion of fishing that occurs in each month (Table A.4).
Selectivities were estimated for the first three age-classes, and
older fish were assumed to be fully vulnerable to fishing gear
used in the recreational fisheries (i.e., selectivities were assumed
to be 1.0). Because data on the age composition of harvest were
not available for Coho Salmon, we assumed known selectivities
for this species based on Rutherford (1997). Annual fishing intensity was modeled as a function of fishing effort (E), measured
in angler-hours, and catchability coefficient (q), which was estimated as a time-varying parameter based on a random walk
process (Wilberg and Bence 2006; Wilberg et al. 2010); i.e.,
f y = qy E y ,
(5)
where
ln(q y ) =
ln(q y )
ln(q y−1 ) + ε y−1
for y = 1985
for y > 1985
(6)
The random walk deviations, ε, were assumed to be normally distributed with a mean of zero and a SD of σε . These
deviations combine both measurement error, representing the
difference between measured and actual fishing effort, and process error, representing deviations from the direct proportionality assumption between fishing mortality and fishing effort.
All mature Chinook and Coho salmon were assumed to die
upon spawning (ϕ = 0.0), while a fraction of the spawning
steelhead (ϕ > 0.5) and Brown Trout (ϕ = 0.3) were assumed
to survive spawning and thus would contribute to harvests, prey
consumption, and spawning runs in subsequent years. Because
age composition of harvest of mature fish was only available
for Chinook Salmon, and Chinook Salmon size at age changed
over time, maturation probabilities were allowed to vary temporally for this species, while a constant maturity schedule was
assumed for all other salmonines using values presented in
Koonce and Jones (1994). Maturation probabilities for age-0
and age-5 Chinook Salmon were set equal to 0% and 100%,
respectively. Maturation probabilities for Chinook Salmon ages
1–4 were estimated as a logistic function of weight at age as
follows:
θa,y =
1
1+
e(−αa +βa Wa,y )
,
(7)
where the values for α were estimated as free parameters and
those for β were estimated as a function of age using a linear
equation:
βa = ρ1 + ρ2 (a − 1),
(8)
where ρ1 and ρ2 are the intercept and slope, respectively. Equations (7) and (8) were used to estimate Chinook Salmon maturation probabilities for the years for which maturation data were
available (1985–2008). For years prior to 1981, a constant maturity schedule was applied using results drawn from (Rutherford
1997). From 1981 through 1984, maturation probabilities were
calculated by linear interpolation.
Observation submodel.—Monthly age-specific harvest
(Ĉa,y,m ) for all modeled species was predicted using the Baranov
catch equation,
Ĉa,y,m =
Fm,a,y
1
M
+ Fm,a,y
12 a,y
1
Nm,a,y 1 − e−( 12 Ma,y +Fm,a,y ) . (9)
Annual harvest (Ĉa,y ) was calculated by summing monthly valmat
) was calculated
ues. Age-specific harvest of mature fish (Ĉa,y
by multiplying age-specific harvest by the predicted maturation
probability as follows:
mat
Ĉa,y
= Ĉa,y θa,y .
(10)
and
ε ∼ N (0, σε ).
Predicted age composition of total harvest was calculated by dividing age-specific harvest by total harvest. Similarly, predicted
425
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SALMONINE COMMUNITY CHANGES IN LAKE MICHIGAN
age composition of the harvest of mature fish was calculated for
Chinook Salmon by dividing harvest at age of mature fish by
total harvest of mature fish. Age-specific spawning run abunsp
dances (Na,y ) of Chinook Salmon were predicted as the product
1
) and
of abundances at age at the end of the first period (Na,y
age-specific maturation probabilities:
and the corresponding negative log-likelihood components were
calculated based on effective sample size (n) and observed (P)
and predicted ( P̂) proportions at age using:
sp
1
Na,y
= Na,y
Pa,y .
The effective sample size was assumed known for each likelihood component and was set at n = 100 for age composition of total harvest, n = 50 for age composition of mature
fish harvest, and n = 50 for age composition of weir harvest.
The effective sample sizes weight the relative importance of
likelihood components corresponding to age compositions. The
use of effective sample size, rather than actual sample size, is
based on the recognition that the number of independent sample units is smaller than the actual number of fish aged due to
nonrandomness of samples. The effective sample sizes we used
were based on analyses from other systems (e.g., Crone and
Sampson 1998) and our impressions regarding the relative qualities of the age-composition data sets. Similar approaches are
widely used (Merritt and Quinn 2000) given the challenges in assessing effective sample sizes analytically (Crone and Sampson
1998).
We conducted a retrospective examination of model fits,
which showed that the standard deviations and effective sample sizes we assumed were reasonable. In particular, the mean
square error of model fits (for harvest and catchability) closely
matched the prespecified standard dispersions (σc = 0.08, σε
= 0.04), and the deviations between observed and predicted
proportions at age were consistent with the variances of the
multinomial distributions given the effective sample sizes. This
latter approach for evaluating effective sample sizes was generally endorsed by Francis (2011).
The fitted SCA models were considered to have converged
on a highest posterior density of parameter estimates when the
gradient of the objective function was less than 1.0 × 10−4 with
respect to each parameter. To assess uncertainty associated with
parameter estimates, posterior probability distributions for parameter estimates were obtained by Markov chain Monte Carlo
(MCMC) simulations through a Metropolis Hastings algorithm
(Fournier et al. 2012). The MCMC chain was run for 1 million steps and sampled every 100th step. The initial 1,000 saved
steps were discarded as a burn-in period. The MCMC chains
were evaluated for adequacy (convergence and sufficient information) using trace plots for each estimated parameter and
derived variable as a visual check to ensure the chain was well
mixed and did not show long-term patterns, the effective sample
size of the saved MCMC chains, and similarity of the first 10%
and last 50% of the saved chains using Geweke’s (1992) tests.
All MCMC chain diagnostics were conducted in R (R Development Core Team 2010) using the CODA package (Plummer
et al. 2010).
Estimation of consumption.—Based on our salmonine SCA
and the Treaty-ceded-water Lake Trout models, we generated
(11)
Predicted age composition of spawning runs was calculated by
dividing the age-specific spawning run estimates by the spawning run total. We did not include total spawning run estimates
in the objective function when fitting the Chinook Salmon SCA
model as the observed weir harvest data only included a small
subset of spawning tributaries.
Model fitting.—The SCA models were fit by minimizing the
negative log posterior probability of parameter estimates using
Automatic Differentiation Model Builder (Fournier et al. 2012).
This approach to estimation is also known as highest posterior
density or penalized likelihood estimation (Schnute 1994). Estimated parameters consisted of q1985 , εy (for all species), Sa (for
all species except Coho Salmon), and λa , γy , βα , ρ1 , and ρ2 (for
Chinook Salmon). Uniform priors (on a ln scale) were specified
for all parameters so that the resulting posterior distributions
were mainly influenced by observed data. Given the number of
data types used in model fitting for each species, the objective
function used in the optimization of model fits consisted of five
log-likelihood or log-prior (penalty) components for Chinook
Salmon, three for Brown Trout, and steelhead, and two for Coho
Salmon.
The different likelihood components were assumed to follow
different error structures depending on the type of observed data.
Total annual harvest was assumed to be lognormally distributed,
so the negative log-likelihood (−logL) was calculated as
− log L =
n
i=1
[ln(Ci ) − ln(Ĉi )]2
ln(σc ) +
.
2σc2
(12)
A lognormal distribution was also assumed for the catchability
random walk deviations; therefore,
− log L =
n i=1
εi2
ln(σε ) + 2 .
2σε
(13)
The standard dispersion of observation error for total annual harvest (σc ) and catchability deviations (σε ) were assumed known
for all species and were set equal to 0.08 and 0.04, respectively.
Benjamin and Bence (2003) used these same values for previous
assessments of Chinook Salmon. We chose to use these same
values for all other species because no previous estimates were
available.
Age composition of total harvest, mature fish harvest, and
weir harvest was assumed to follow a multinomial distribution,
− log L = −n
y
Pa,y ln P̂a,y .
(14)
a
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TSEHAYE ET AL.
time series of species-specific and lake-wide consumption estimates by combining age-specific abundance estimates with time
series of mortality rates and growth data using the production–
conversion efficiency method (Ney 1990), where gross production (Pra,y ) is divided by gross conversion efficiency (GCE)
to estimate consumption. Gross production was estimated as a
function of fishery yield (Ya,y ), production lost to natural mortality (Da,y ), production lost to spawning mortality (Ra,y ) (which
applies to all species other than Lake Trout), and change in
biomass of the standing stock (Ba+1,y+1 − Ba,y ), i.e.,
Pra , y = Ya,y + Da,y + Ra,y + Ba+1,y+1 − Ba,y .
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(16)
For species other than Lake Trout, production lost to spawning mortality was calculated as the product of spawning run
abundances, the fraction of the populations that are not repeat
s
) as
spawners, and weight at age at the time of spawning (Wa,y
follows:
sp
s
(1.0 − ϕ) Wa,y
.
Ra,y = Na,y
(17)
Because spawning mortality was modeled as a pulse event,
production lost to natural mortality was first estimated for the
1
) and then for the months after
months before spawning (Da,y
2
spawning (Da,y ) as follows:
1
Da,y
=
s (G 1 −Ma,y )
Ma,y
e 12 a,y
− 1 Na,y Wa,y
1 − M
G a,y
a,y
2
Da,y
=
12−s (G 2 −Ma,y )
2
Ma,y
s
a,y
12
.
e
−
1
Na,y Wa,y
2 − M
G a,y
a,y
(18)
For the months before spawning, the instantaneous growth rate
1
(G a,y
) was calculated as
1
G a,y
= ln
h
Wa,y
ann
Wa,y
s
,
12
(19)
ann
where Wa,y
is mean weight at age at annulus formation, defined
as the start of the year weight. For the months after spawning,
2
) was calculated as
the instantaneous growth rate (G a,y
2
G a,y
= ln
ann
Wa+1,y+1
h
Wa,y
12 − s
.
12
G a,y = ln
Wa+1,y+1
Wa,y
.
(21)
Because no pulse spawning mortality was assumed for Lake
Trout, Da,y was calculated as
Da,y =
(G −Ma,y )
Ma,y
e a,y
− 1 Na,y Wa,y .
G a,y − Ma,y
(22)
(15)
Yield was estimated as the product of harvest and mean weight
h
):
at age at time of harvest (Wa,y
h
Ya,y = Ĉa,y Wa,y
.
1
2
and G a,y
For all salmonines other than Chinook Salmon, G a,y
were assumed equal and calculated as
(20)
Age-specific consumption rates were calculated by dividing agespecific production estimates by age-specific GCEs, which represent the fraction of consumed biomass that is converted to
growth. To account for the observed drop in Alewife energy
density after the dreissenid mussel invasion (Madenjian et al.
2006), we used lower GCE values for the periods after (post1995) the invasion (Table A.5). Finally, to obtain the proportion
of consumption by prey type, the total consumption calculated
using the production–conversion efficiency method was partitioned into different prey types based on stomach-content data
for the different salmonines (see Model inputs and background
section above).
RESULTS
Model Fits
Each of the predator SCA models successfully converged
on a solution, in which the maximum gradient of the objective
function was less than 1.0 × 10−4. Predicted values generally
matched observed data quite well for both the harvest and age
compositions, and the mean absolute percent error between observed and predicted values was less than 5% for all species.
Based on all criteria used to evaluate convergence, the MCMC
chain for each of the parameter estimates was judged to have
converged to the underlying posterior probability distribution
and to contain enough information to characterize the distribution. Trace plots showed no “stickiness,” effective sample sizes
were similar to actual number of saved MCMC samples, and the
means of the first 10% and last 50% of the saved samples were
similar based on Geweke’s (1992) Z-score. Parameters were estimated with varying degrees of uncertainty, with estimates of
catchability, selectivity, and maturation parameters having narrower 95% Bayesian credible intervals than estimates of year
and age effects on natural mortality (Table 1). Similarly, annual
abundance, biomass, and consumption were estimated with low
degree of uncertainty, and CV (100·SD/mean) was estimated at
less than 10% for each derived quantity.
Changes in Salmonine Fisheries
Model fits as well as observed data showed large temporal
fluctuations in total salmonine harvest, and Chinook Salmon
427
SALMONINE COMMUNITY CHANGES IN LAKE MICHIGAN
TABLE 1. Highest posterior density estimates (HPD) and lower and upper 95% Bayesian credible limits (CLs) of year [ln(γ)] and age [ln(λ)] effects on natural
mortality, maturity parameters (ρ1 and ρ2 ), catchability [ln(q)], catchability deviations [ln(ε)], and age-specific selectivity [ln(Sa )] (note: for vector parameters,
only minimum and maximum values are shown; empty cells of parameter–species combinations are because no such parameters were estimated for the given
species).
Parameters
Species
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Chinook
Salmon
Estimate
HPD
Lower CL
Upper CL
Steelhead HPD
Lower CL
Upper CL
Brown
HPD
Trout
Lower CL
Upper CL
Coho
HPD
Salmon Lower CL
Upper CL
ln(γ)
ln(λ)
ρ1
ρ2
α
ln(q)
ln(ε)
−29.80, 0.61 −22.21, −0.85 0.38 −0.09
0.08, 2.29 −16.59 −0.01, 0.17
−34.95, 0.48 −23.71, −2.05 0.28 −0.16 −0.05, 1.86 −16.83 −0.07, 0.07
−11.36, 1.52 −3.31, −0.75 0.62 −0.01
0.30, 2.49 −16.43
0.05, 0.22
−19.67 −0.15, 0.25
−19.80 −0.27, 0.12
−19.54 −0.03, 0.36
−19.69 −0.15, 0.05
−19.83 −0.22, −0.02
−19.54 −0.08, 0.12
−17.45 −0.23, −0.42
−17.87 −0.61, 0.10
−16.98
0.20, 0.80
experienced greater levels of change than the other species.
Chinook Salmon harvest declined sharply between the mid1980s and the early 1990s from approximately 4.5 million kg
(∼900,000 fish) to less than 1 million kg (∼200,000 fish) (Figure 2). Since the early 1990s, Chinook Salmon harvest has
increased steadily to a mean of approximately 4 million kg
(∼800,000 fish) between 2005 and 2008 (Figure 2). Brown
Trout harvest has declined steadily throughout the modeled
time period from approximately 300,000 kg in the 1980s to
100,000 kg in the late 2000s. Steelhead harvest increased from
approximately 200,000 kg in the mid-1980s to approximately
500,000 kg in the early 2000s, but declined to the mid-1980
levels by the late 2000s (Figure 2). Coho Salmon harvest has
fluctuated without much of a trend (Figure 2). Lake Trout harvest has sharply declined since 2000 (Figure 2), which reflected
the decline in recreational fishing effort (Figure 3), but may also
have been due to an increase in the minimum size limit for Lake
Trout established for fishing in some management areas (Jonas
2011). The proportion by weight of Chinook Salmon in total
salmonine harvest steadily fell from over 60% in the early1980s
to about 30% by the mid-1990s, but has since increased annually
and reached over 70% in the late 2000s.
Recreational fishing effort declined throughout the late
1980s, from a peak of over 8 million angler-hours to a low
of approximately 2 million angler-hours (Figure 3), which paralleled the decline in Chinook Salmon and Lake Trout harvests.
Total fishing effort has been relatively low (∼4 million anglerhours) for the past 15 years, while Chinook Salmon harvest
increased throughout the 1990s and 2000s. These increases in
Chinook Salmon harvest may be attributable to increased fish
abundance, but there was also a large increase in catchability
ln(Sa )
−3.81, −0.93
−3.91, −1.18
−3.49, −0.82
−3.23, −0.74
−3.46, −0.83
−3.05, −0.65
−3.94, −0.08
−4.19, 0.22
−3.69, 0.06
from the 1980s to the late 2000s (Figure 3). Chinook Salmon
weight at age in the recreational fishery harvest was the highest
in the early years of the time series and dropped sharply in the
mid-1980s, primarily for older age-groups (Figure 4). Chinook
Salmon weight at age rose again in the 1990s, albeit not to historic levels, but has decreased somewhat since 2004. Lake Trout
weight at age fluctuated with no clear trends (Figure 4).
Model fits as well as observed data indicated that harvestage composition has fluctuated over time for most of the species
(Figures 4, 5). For Chinook Salmon, recreational harvest of age1 fish was proportionally higher in the early 1990s than it was
in the mid-1980s, but has declined in recent years (Figure 5).
Proportions of recreational harvest of age-1 and age-2 mature
fish increased from the mid-1980s to the late 1980s and have
since been relatively stable, with fish of ages 2 through 4 comprising 97% of the recreational harvest of mature fish (Figure 5).
Age composition in weir harvest fluctuated slightly over time,
with age-2 and age-3 fish alternately comprising the highest proportion of harvest (Figure 5). The differences between the age
compositions of mature fish from the recreational fisheries and
weir returns largely reflect the varying selectivities of the two
fisheries. For steelhead and Brown Trout, age compositions of
the recreational harvest were fairly stable. For steelhead, age-3
and older fish comprised between 70% and 80% of the recreational harvest (Figure 6). For Brown Trout, the proportion of
age-2 fish was consistently greater than age-3 and older fish
(Figure 6).
Changes in Salmonine Abundance
Model estimates indicated that total Chinook Salmon abundance increased steadily from near zero in 1968 to over 10
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428
TSEHAYE ET AL.
FIGURE 2. Observed (symbols) and predicted (lines) values of recreational fishery harvest of (a) Chinook Salmon, (b) steelhead, (c) Brown Trout, (d) Coho
Salmon, and (e) Lake Trout in Lake Michigan.
million fish by the early 1980s (Figure 7). From the early 1980s
to the late 1990s, Chinook Salmon abundance has generally
fluctuated without a trend between 10 million and 14 million
fish. Overall, Chinook Salmon abundance has remained high
and relatively stable despite a sharp reduction in older Chinook Salmon abundance in the late 1980s (Figure 7), indicating
that total Chinook Salmon abundance during these years was
dominated by age-0 and age-1 fish. Thus, fluctuations in their
abundance largely reflected changes in recruitment (Figures 1,
7). During these years, total Chinook Salmon recruitment has
remained relatively high at approximately 8 million fish, with
increases in natural recruitment compensating for declines in the
stocking of hatchery-reared fish (Figure 1). Since 2000, there
has been a general downward trend in Chinook Salmon abundance as well as total recruitment. Although there has been a
decline in total Chinook Salmon recruitment during these years,
declines in natural mortality (particularly for age-1 and older
fish) from the elevated rates seen during the late 1980s and
1990s and increased wild recruitment have helped ameliorate
changes in total abundance as a result of lower stocking rates
SALMONINE COMMUNITY CHANGES IN LAKE MICHIGAN
429
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FIGURE 3. Recreational fishery effort in Lake Michigan in millions of anglerhours targeted at salmonines and average salmonine catchability.
(Figures 1, 7). Age-2 and older Chinook Salmon experienced
more drastic declines than age-1 fish, indicating that the die-off
event mostly affected older fish (Figure 7). For steelhead, Coho
Salmon, and Brown Trout, total abundance estimates increased
from the beginning of the stocking program in the late 1960s
until the early 1980s, and appear to have been fairly stable
ever since. For these salmonines, changes in abundance generally reflected changes in stocking numbers, given that their
natural mortalities were assumed to be constant over time. Estimated abundance levels for steelhead, Brown Trout, and Coho
Salmon in 2008 were approximately 3.5, 2.5, and 2.0 million
fish, respectively (Figure 7). For Lake Trout, abundance was
predicted to have increased from approximately 4 million fish
in the late 1960s to approximately 8 million fish throughout the
late 1980s and mid-1990s. Lake Trout abundance subsequently
declined during the late 1990s. This decline was attributable to
increased mortalities during these years (Figure 7) and slightly
lower stocking numbers in the preceding few years. Lake Trout
abundance has since increased to approximately 8 million fish
as stocking numbers increased.
FIGURE 4. Average weight of age-3 Chinook Salmon and age 5 and older
Lake Trout in the Lake Michigan recreational fishery harvest.
FIGURE 5. Predicted (bars) and observed (dots) age composition of (a) total
recreational fishery harvest, (b) mature recreational fishery harvest, and (c) weir
harvest of Chinook Salmon in Lake Michigan.
Changes in Salmonine Biomass, Production
and Consumption
Estimated salmonine biomass increased steadily from less
than 1 kilotonne (kt; 103 metric tons) in 1965 to more than
25 kt in 1986 (Figure 8), which is in concert with stocking
numbers (Figure 1). Since the mid-1980s, salmonine biomass
has remained relatively high, averaging approximately 23 kt,
with Chinook Salmon comprising, on average, over 40% of the
total salmonine biomass. Owing to the declines in abundance
and weight at age of older Chinook Salmon (Figure 4), total
salmonine biomass declined during the late 1980s. However,
total salmonine biomass did not decline as much as Chinook
Salmon biomass, indicating that increases in the abundance of
other salmonines (especially Lake Trout and steelhead) made up
for the decline in Chinook Salmon abundance. Chinook Salmon
biomass increased moderately during 1994–1998, apparently
due to the recovery of the population after the mass mortality event in the late 1980s. Although there has been a general
downward trend in total Chinook Salmon abundance since 2000,
Chinook Salmon biomass remained high (around 10 million kt),
principally because there has been a greater proportion of older
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430
TSEHAYE ET AL.
FIGURE 6. Predicted (bars) and observed (dots) age composition of recreational fishery harvest of (a) steelhead and (b) Brown Trout in Lake Michigan.
Chinook Salmon (Figure 7). Total salmonine biomass has remained relatively stable since the late 1990s despite fluctuations
in biomass of Chinook Salmon and Lake Trout (Figure 8). For
the other salmonines, trends in biomasses reflected changes in
their abundance, given that the weight at age of these salmonines
did not change over time.
In concert with total biomass, estimated salmonine production increased steadily from less than 1 kt in 1965 to close to 20
kt in the mid-1980s (Figure 8). Estimated lake-wide salmonine
production has averaged around 17 kt/year since 1980. Compared with the major decline in biomass that occurred in the
late 1980s as a result of the Chinook Salmon mass mortality
event, declines in production were not as pronounced, mainly
because the mass mortality event primarily affected the older,
less productive segments of the population. Compared with the
other salmonines, Chinook and Coho salmon showed relatively
higher levels of production per biomass, meaning that these
species have faster growth rates.
Estimated total salmonine consumption increased steadily
from less than 1 kt in 1965 to about 100 kt in the mid-1980s
(Figure 8), which also largely reflected changes in stocking numbers (Figure 1). In contrast to total production, total salmonine
consumption did show a decline in concert with the Chinook
Salmon mass mortality event, suggesting that older age-classes
accounted for a larger proportion of total consumption. Yet,
the decline in total salmonine consumption was not as steep as
the decline in Chinook Salmon consumption, indicating that the
FIGURE 7. (a) Estimated lake-wide salmonine abundance, (b) abundances of
age-1 and age-2 and older Chinook Salmon and average natural mortality of
age-1 and older Chinook Salmon, and (c) average natural mortality of age-1 and
older Lake Trout in Lake Michigan.
other salmonines played a considerable role as predators. Total
salmonine consumption increased to historic high levels (∼100
kt/year) in the mid-1990s and remained relatively high since
that time (Figure 8). This more recent increase in estimated
total consumption is partly a consequence of the lower GCE
we used to account for the decline in Alewife energy density
that occurred after 1995. Just as with production, Chinook and
Coho salmon showed relatively higher levels of consumption
per biomass compared with the other salmonines. Finally, large
Alewives represented the most common prey item (by weight)
for all salmonines combined throughout the time series, with
the exception of a brief period in the late 1980s when smaller
Alewives became relatively more important (Figure 8). Small
Alewives accounted for an increasingly larger proportion of
salmonine diets in recent years.
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SALMONINE COMMUNITY CHANGES IN LAKE MICHIGAN
431
FIGURE 8. Estimated lake-wide salmonine (a) biomass, (b) production, (c) consumption, and (d) prey composition in Lake Michigan, 1965–2008, total and
apportioned by species (note: total values are on the secondary y-axes).
Density-dependent Demographic Changes
Our analysis revealed evidence for two density-dependent
demographic processes operating for Chinook Salmon: growth
and mortality. Growth rate of Chinook Salmon appeared to be inversely related to numbers stocked (Figure 9). Similarly, growth
rate of Chinook Salmon as inferred from mean weight of age-3
fish (Figure 4) appeared to be inversely related to total Chinook Salmon abundance (Figure 7). The highest weights at age
for Chinook Salmon were observed during the early years of
the stocking program (Figure 4; Table A.2), when their abundance was relatively low (Figure 7). The lowest weights at age
for Chinook Salmon were observed prior to the mass mortality of the late 1980s, when their abundance was the highest.
While there were no changes in the growth rates of the other
salmonines modeled (given their constant weight at age over
time), Lake Trout experienced periods of depressed growth rates
(Figure 4). In contrast to growth rate, natural mortality rate of
age-1 and older Chinook Salmon increased as stocking rates
increased, suggesting an inverse relationship between survival
rates of Chinook Salmon and number of fish stocked (Figure 9).
Most significantly, the mass mortality of the late 1980s occurred
during a time period when Chinook Salmon stocking rates and
abundance were the highest (Figure 7). Conversely, the recent
declines in natural mortality corresponded with reduced stocking rates in the 2000s.
FIGURE 9. (a) Changes in average natural mortality of age-1 and older Chinook Salmon in Lake Michigan and (b) changes in average weight of age-3
Chinook Salmon in relation to stocking rate in the preceding year.
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TSEHAYE ET AL.
DISCUSSION
By incorporating historical and contemporary biological data
into stock assessment and bioenergetics models, we assessed
historical changes in the Lake Michigan salmonine community
and their consumptive demand to examine their implications
for the stability of the lake’s predator–prey balance. Primarily due to a substantially longer time series of data than what
was available for previous assessments, we were able to obtain
better model fits to historical harvest data and to estimate abundances for all major salmonine predators. Our results revealed
substantial changes in the abundance, biomass, and consumptive
demand of predatory salmonines in Lake Michigan since the beginning of the stocking program in the mid-1960s. In light of its
abundances and associated consumptive demands, the Chinook
Salmon population has been a major driver of lake-wide
salmonine community dynamics in Lake Michigan. Given that
changes in lake-wide salmonine consumption were less pronounced than the fluctuations in Chinook Salmon consumption,
the other salmonines (especially Lake Trout and steelhead) also
played a considerable role as predators in the Lake Michigan
fish community. On the other hand, while the changes in the
abundance and biomass of the other salmonines generally reflected changes in their stocking rates because of their constant
mortality and weigh at age over time, Chinook Salmon abundance and biomass changed more independently of stocking
numbers due to density-dependent changes in growth and survival rates. Density-dependent changes in growth and survival
rates of Chinook Salmon (Figure 9) suggested that changes in
salmonine abundance and consumption had substantial impacts
on the predator–prey balance. Specifically, the sharp decline in
survival and growth rate that accompanied increasing salmonine
abundances prior to the mass mortality of the late 1980s (Figure 7), as well as the decline in Chinook Salmon growth in the
late 1990s and in the most recent years, possibly stemmed from
reduced prey availability resulting from an excess of predators.
Conversely, some of the improvements in Chinook Salmon survival and growth rates following reductions of stocking levels
in the 1990s was likely due to improved prey availability arising
from reduced predation pressure (Figure 7).
Indeed, the estimated changes in the salmonine community
were consistent with historical changes in prey abundances that
had previously been documented for Lake Michigan (Madenjian et al. 2002, 2005; Warner et al. 2008, 2011). Most notably, increased salmonine abundance and consumption were
estimated for the early 1980s (Figure 8), during which time
Alewife, the most dominant prey species, experienced a marked
decline, with trawl-survey lake-wide abundance indices of age3 and older fish falling from over 400 million fish in the early
1980s to below 200 million fish in the late 1980s (Stewart and
Ibarra 1991; Madenjian et al. 2002; Warner et al. 2008, 2011).
Before the Chinook Salmon mass mortality event in 1987, the
decline in Alewife abundances apparently caused Chinook and
Coho salmon to shift from feeding primarily on large Alewives
to small Alewives, which are less preferred (Stewart and Ibarra
1991). Although the proximate cause of death for many of the
fish during the mass mortality event was bacterial kidney disease, the low weight at age of Chinook Salmon in the years
leading up to the event suggested that Chinook Salmon faced
food limitation, which potentially increased the risk of epizootic
events. Even if it was also believed that the initial outbreak of
bacterial kidney disease stemmed from poor hatchery practices
during the 1980s (Claramunt et al. 2012), it is likely that nutritional stress exacerbated the risk of mortality events.
Just as for the 1980s, increased salmonine abundance and
consumption were estimated for the years since the early 2000s,
and during this time the Alewife population in Lake Michigan has been estimated to be at very low levels, and trawlsurvey lake-wide abundance estimates of age-3 and older fish
have been well below 200 million fish (Warner et al. 2008,
2011). Total abundance, biomass, consumption, and production
of salmonines during these years have remained high despite
considerable reductions in Chinook Salmon stocking rates. This
is most likely because increases in Chinook Salmon natural reproduction have made up for the stocking reductions. Based
on analyses of a multiyear mass-marking program conducted
throughout Lake Michigan, it has been estimated that since
2006 the proportion of naturally reproduced Chinook Salmon
in the recreational fishery harvest has consistently exceeded 50%
(Williams 2012). In addition to increases in wild reproduction,
survey data and our assessment modeling results suggest that
Chinook Salmon survival rates have changed since 1990 in a
manner that has compensated for reduced stocking. Accompanying these changes in salmonine abundance, there have been
fewer and smaller Alewives and fewer alternative prey species,
especially during the late 2000s (Jacobs et al. 2012). Though
recent levels of salmonine abundance may be large enough to
have caused declines in Alewife numbers, these declines may
have been exacerbated by the decline in Alewife energy density
after the dreissenid mussel colonization, which led to a 22.1%
increase in per capita consumption of Alewives by Chinook
Salmon (Madenjian et al. 2006; Nalepa et al. 2006).
Though improvements in survival rates of Chinook Salmon
in recent years could be a result of changes in environmental conditions or hatchery management practices, it is possible
that they are in response to the decline in Chinook Salmon
abundance, which points to the existence of a potentially important feedback mechanism that could limit the ability of
managers to control the predator populations through stocking adjustments, as was suggested by Johnson et al. (2010).
Similarly, though improvements in natural reproduction in recent years may be coincidental to cuts in stocking rates, increased natural reproduction could also undermine the ability
of fishery managers to exert control over the predator–prey system (Claramunt et al. 2009). Conversely, it may be argued
that feedback mechanisms could ultimately operate with increased Chinook Salmon abundance wherein nutritional stress
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SALMONINE COMMUNITY CHANGES IN LAKE MICHIGAN
leads to lower salmonine survival, thereby reducing predation
pressure on Alewives and allowing prey fish recovery (Johnson
et al. 2010). However, considering the 2004 Alewife collapse
in the Lake Huron (Riley et al. 2008; Johnson et al. 2010; Brenden et al. 2012), we cannot be certain if self regulation will
really prevent salmonine predators from overexploiting their
forage base in Lake Michigan. Nevertheless, while improved
survival and natural reproduction rates of Chinook Salmon may
increase the risk of Alewife collapse, the resurgence of native
species such as Lake Trout and Walleye Sander vitreus following periods of suppressed Alewife abundances in Lake Huron
suggests some positive outcomes may result from increased
Chinook Salmon predation. Furthermore, though current trends
in survival and natural recruitment suggest increasing Chinook
Salmon abundance, our assessment of the recreational fishery
catchability (showing a large increase since the 1980s) suggests
that if angler effort were to return to the levels observed in the
mid-1980s, which may not be very likely in the near future given
current trends in the culture of recreational fishing (O’Keefe and
Miller 2011), then the Chinook Salmon population could actually decline, potentially leading to lowered predation pressure
on prey fish (Claramunt et al. 2009).
Our analysis supports earlier assessments (Madenjian et al.
2002) that Chinook Salmon have been responsible for over 60%
of the predation on Alewives by Lake Michigan salmonines
since 1980, which is due to not only their higher abundance and
higher consumption per biomass but also their heavy reliance on
Alewife as a food source. The heavy reliance of Chinook Salmon
on Alewife, which has also been documented by a multitude of
other studies (Hagar 1984; Brandt 1986; Jude et al. 1987; Diana
1990; Elliott 1993; Warner et al. 2008), is in contrast to the
more diverse diets of Brown Trout, steelhead, and Lake Trout,
which tend to include a wider variety of prey fish (including
Yellow Perch, Rainbow Smelt, and Bloater Coregonus hoyi)
and macroinvertebrates (Rand et al. 1993). The tighter coupling
between Chinook Salmon and Alewife abundance suggests that
Chinook Salmon are more sensitive to declines in Alewife abundance in Lake Michigan. Given their constant annual weight at
age, growth rates of the other salmonines have not been affected by Alewife abundance, supporting the contention that
the other salmonines have sufficient alternative prey to make
up for declines in Alewife abundance. While Chinook Salmon
are the preferred species for stocking due in part to their faster
growth rates, these varying feeding habits among the different
salmonines point to the need to optimize the species composition
of salmonines stocked in Lake Michigan.
In conclusion, fisheries managers for Lake Michigan, as for
the other Great Lakes, face an interesting dilemma of whether to
manage in the short term for a popular and economically important recreational fisheries or to aim for long-term sustainability
by allowing recovery of native fish species that are presumed
to be more suited to the ecosystem (Stewart and Ibarra 1991;
Fenichel et al. 2010; Claramunt et al. 2012; Dettmers et al.
2012). While the stocking of nonnative salmonines is believed
433
to have led to the reduction of invasive species and the creation of recreational fisheries, Lake Michigan fishery managers
needed to implement major reductions stocking rates to maintain adequate prey supply for smaller, but healthier, salmonine
populations. To this end, they implemented stocking reductions
in 1991, 1998, 2006, and 2012 in light of evidence of predator
food limitation, such as reduced Chinook Salmon growth rates
or low Alewife abundance. Although the revisions to stocking
rates implemented in the past may have been reasonable measures aimed at stabilizing the predator–prey system (Claramunt
et al. 2012), our analysis of the salmonine community suggests
that recent reductions in stocking have not been sufficient to reduce predatory pressure on the Alewife population and prevent
the food limitation experienced by Chinook Salmon in Lake
Michigan, although they may have ameliorated potential effects
of increased natural reproduction of Chinook Salmon.
Our combined retrospective assessment of the Lake Michigan predator community and their consumptive demands can
provide the basis for making future stocking decisions from
an ecosystem perspective. However, the choice of appropriate
stocking rates will also depend on the production capacity of the
prey populations and what fraction of prey production is consumed by stocked and wild predators. This will require assessment of stock–recruitment relationships for the Lake Michigan
prey species and an understanding of how consumption rates
for major predator species in the lake would change depending on prey densities. Along with complementary assessments
by Tsehaye et al. (in press), our retrospective assessment of the
Lake Michigan predator community could ultimately contribute
toward formal development of a decision model that objectively
forecasts the consequences of alternative stocking options in
terms of their expected success at achieving management objectives.
ACKNOWLEDGMENTS
Funding for this project was provided by the Great Lakes
Fishery Trust (project number 2007.950). Additional funding
was provided through the U.S. Fish and Wildlife Service Sportfish Restoration Project F-80R and a grant from Federal Aid in
Sport Fish Restoration and the MDNR Game and Fish Protection
Fund. The authors acknowledge the Lake Michigan Salmonid
Working Group of the Lake Michigan Technical Committee
for their willingness to share data and input on the project.
Personnel at the MDNR Charlevoix Fisheries Research Station
were instrumental in data collection and interpretation. This
is manuscript 2014-04 of the Quantitative Fisheries Center at
Michigan State University.
REFERENCES
Bax, N. J. 1998. The significance and prediction of predation in marine fisheries.
ICES Journal of Marine Science 55:997–1030.
Bence, J. R., N. E. Dobiesz, C. P. Madenjian, R. Argyle, R. Barbiero, J. N.
Bowlby, R. M. Claramunt, R. O’Gorman, and T. Schaner. 2008. Top-down
Downloaded by [Michigan State University] at 09:09 07 March 2014
434
TSEHAYE ET AL.
effects of open-water salmonine predators in the Great Lakes. Quantitative
Fisheries Center, Technical Report T2008-7, East Lansing, Michigan.
Benjamin, D. M., and J. R. Bence. 2003. Statistical catch-at-age framework for
Chinook Salmon in Lake Michigan, 1985–1996. Michigan Department of
Natural Resources, Fisheries Research Report 2066, Ann Arbor, Michigan.
Brandt, S. B. 1986. Food of trout and salmon in Lake Ontario. Journal of Great
Lakes Research 12:200–205.
Brenden, T. O., J. R. Bence, and E. B. Szalai. 2012. An age-structured integrated
assessment of Chinook Salmon population dynamics in Lake Huron’s main
basin since 1968. Transactions of the American Fisheries Society 141:919–
933.
Carpenter, S. R., J. F. Kitchell, and J. R. Hodgson. 1985. Cascading trophic
interactions and lake productivity. BioScience 35:634–639.
Christensen, V. 1996. Managing fisheries involving predator and prey species.
Reviews in Fish Biology and Fisheries 6:417–442.
Claramunt, R. M., T. L. Kolb, D. F. Clapp, D. B. Hayes, J. L. Dexter, and D. M.
Warner. 2009. Effects of increasing Chinook Salmon bag limits on Alewife
abundance: implications for Lake Michigan management goals. North American Journal of Fisheries Management 29:829–842.
Claramunt, R. M., C. P. Madenjian, and D. F. Clapp. 2012. Pacific salmonines in
the Great Lakes basin. Pages 609–650 in W. W. Taylor, A. J. Lynch, and N. J.
Leonard, editors. Great Lakes fisheries policy and management: a binational
perspective, 2nd edition. Michigan State University Press, East Lansing.
Cochrane, J. A., R. C. Bishop, and D. B. Ives. 1992. Lake Michigan salmonid
stocking costs in Wisconsin. Marine Resource Economics 7:169–185.
Connerton, M. J., B. A. Murry, N. H. Ringler, and D. J. Stewart. 2009. Majority
of age-3 Chinook Salmon Oncorhynchus tshawytscha in Lake Ontario were
wild from 1992 to 2005, based on scale pattern analysis. Journal of Great
Lakes Research 35:419–429.
Cox, S., and J. F. Kitchell. 2005. Lake Superior ecosystem, 1929–1998: simulating alternative hypotheses for recruitment failure of Lake Herring Coregonus
artedi. Bulletin of Marine Science 74:671–683.
Crone, R. M., and D. B. Sampson. 1998. Evaluation of assumed error structure in
stock assessment models that use sample estimates of age composition. Pages
315–338 in F. Funk, T. J. Quinn II, J. Heifetz, J. N. Ianelli, J. E. Powers, J. J.
Schweigert, P. J. Sullivan, and C. I. Zhang, editors. Fishery stock assessment
models. University of Alaska, Fairbanks.
Crowder, L. B. 1980. Alewife, Rainbow Smelt and native fishes in Lake Michigan: competition or predation? Environmental Biology of Fishes 5:225–
233.
Dettmers, J. M., C. I. Goddard, and K. D. Smith. 2012. Management of Alewife
using Pacific salmon in the Great Lakes: whether to manage for economics
or the ecosystem? Fisheries 37:495–501.
Diana, J. S. 1990. Food habits of angler-caught salmonines in western Lake
Huron. Journal of Great Lakes Research 16:271–278.
Dobiesz, N. E., D. A. McLeish, R. L. Eshenroder, J. R. Bence, L. C. Mohr, M.
P. Ebener, T. F. Nalepa, A. P. Woldt, J. E. Johnson, R. L. Argyle, and J. C.
Makarewicz. 2005. Ecology of the Lake Huron fish community, 1970–1999.
Canadian Journal of Fisheries and Aquatic Sciences 62:1432–1451.
Eck, G. W., and L. Wells. 1987. Recent changes in Lake Michigan’s fish community and their probable causes, with emphasis on the role of the Alewife
Alosa pseudoharengus. Canadian Journal of Fisheries and Aquatic Sciences
44:53–60.
Elliott, R. F. 1993. Feeding habits of Chinook Salmon in eastern Lake Michigan.
Master’s thesis. Michigan State University, East Lansing.
Elliott, R. F., P. J. Peeters, M. P. Ebener, R. W. Rybicki, P. J. Schneeberger,
R. J. Hess, J. T. Francis, G. W. Eck, and C. P. Madenjian. 1996. Conducting
diet studies of Lake Michigan piscivores—a protocol. U.S. Fish and Wildlife
Service, Report for the Lake Michigan Technical Committee, Ann Arbor,
Michigan.
Eshenroder, R. L., and M. K. Burnham-Curtis. 1999. Species succession and
sustainability of the Great Lakes fish community. Pages 489–513 in W. W.
Taylor and C. P. Ferreri, editors. Great Lakes fisheries policy and management:
a binational perspective. Michigan State University Press, East Lansing.
Fenichel, E. P., R. D. Horan, and J. R. Bence. 2010. Indirect management of
invasive species through bio-controls: a bioeconomic model of salmon and
Alewife in Lake Michigan. Resource and Energy Economics 32:500–518.
Fournier, D. A., H. J. Skaug, J. Ancheta, J. Ianelli, A. Magnusson, M. Maunder,
A. Nielsen, and J. Sibert. 2012. AD model builder: using automatic differentiation for statistical inference of highly parameterized complex nonlinear
models. Optimization Methods and Software 27:233–249.
Francis, R. I. C. C. 2011. Data weighting in statistical fisheries stock assessment models. Canadian Journal of Fisheries and Aquatic Sciences 68:1124–
1138.
Geweke, J. 1992. Evaluating the accuracy of sampling-based approaches to the
calculation of posterior moments. Pages 169–193 in J. M. Bernardo, J. O.
Berger, A. P. Dawid, and A. F. M. Smith, editors. Bayesian statistics 4. Oxford
University Press, Oxford, UK.
Goodrich, J. M., and S. W. Buskirk. 1995. Control of abundant native vertebrates
for conservation of endangered species. Conservation Biology 9:1357–1364.
Hagar, J. M. 1984. Diets of Lake Michigan salmonids: an assessment of the dynamics of predator-prey interaction. Master’s thesis. University of Wisconsin,
Madison.
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., and M. E. Holey. 2002. Ecological factors affecting sustainability
of Chinook and Coho salmon populations in the Great Lakes. Pages 155–179
in K. D. Lynch, M. L. Jones, and W. W. Taylor, editors. Sustaining North
American salmon: perspectives across regions and disciplines. American
Fisheries Society, Bethesda, Maryland.
Holey, M. E., R. F. Elliott, S. V. Marcquenski, J. G. Hnath, and K. D. Smith.
1998. Chinook Salmon epizootics in Lake Michigan: possible contributing
factors and management implications. Journal of Aquatic Animal Health
10:202–210.
Jacobs, G. R., C. P. Madenjian, D. B. Bunnell, D. M. Warner, and R.
M. Claramunt. 2012. Chinook Salmon foraging patterns in a changing
Lake Michigan. Transactions of the American Fisheries Society 142:362–
372.
Johnson, J. E., S. P. DeWitt, and D. J. A. Gonder. 2010. Mass-marking reveals
emerging self regulation of the Chinook Salmon population in Lake Huron.
North American Journal of Fisheries Management 30:518–529.
Jonas, J. 2011. Statistical catch-at-age models used to describe the status of lean
Lake Trout populations in the 1836-treaty ceded waters of Lakes Michigan,
Huron, and Superior at the inception of the 2000 consent decree. Michigan
Department of Natural Resources, Modeling Subcommittee to the Technical
Fisheries Committee, Sault Ste. Marie.
Jonas, J., R. M. Claramunt, and E. S. Rutherford. 2008. Salmonine reproduction
and recruitment. Pages 65–70 in D. F. Clapp and W. Horns, editors. The state
of Lake Michigan in 2005. Great Lakes Fishery Commission, Ann Arbor,
Michigan.
Jones, M. L., and J. R. Bence. 2009. Uncertainty and fishery management in the
North American Great Lakes: lessons from applications of decision analysis.
Pages 1059–1082 in C. C. Krueger and C. E. Zimmerman, editors. Pacific
salmon: ecology and management of western Alaska’s populations. American
Fisheries Society, Symposium 70, Bethesda, Maryland.
Jones, M. L., J. R. Bence, E. B. Szalai, and Wenjing Dai. 2008. Assessing stocking policies for Lake Michigan salmonine fisheries using decision analysis.
Pages 81–88 in D. F. Clapp and W. Horns, editors. The state of Lake Michigan
in 2005. Great Lakes Fishery Commission, Ann Arbor, Michigan.
Jude, D. J., F. J. Tesar, S. F. Deboe, and T. J. Miller. 1987. Diet and selection of
major prey species by Lake Michigan salmonines, 1973–1982. Transactions
of the American Fisheries Society 116:677–691.
Kitchell, J. F., S. P. Cox, C. J. Harvey, T. B. Johnson, D. M. Mason, K. K.
Schoen, K. Aydin, C. Bronte, M. Ebener, M. Hansen, M. Hoff, S. Schram, D.
Schreiner, and C. J. Walters. 2000. Sustainability of the Lake Superior fish
community: interactions in a food web context. Ecosystems 3:545–560.
Downloaded by [Michigan State University] at 09:09 07 March 2014
SALMONINE COMMUNITY CHANGES IN LAKE MICHIGAN
Koonce, J. F., and M. L. Jones. 1994. Sustainability of the intensively managed
fisheries of Lake Michigan and Lake Ontario. Final Report of the SIMPLE
Task Group, Board of Technical Experts, Great Lakes Fishery Commission,
Ann Arbor, Michigan.
Kotchen, M. J., M. R. Moore, F. Lupi, and E. S. Rutherford. 2006. Environmental
constraints on hydropower: an ex post benefit-cost analysis of dam relicensing
in Michigan. Land Economics 82:384–403.
Krueger, C. C., D. L. Perkins, E. L. Mills, and J. E. Marsden. 1995. Predation
by Alewives on Lake Trout fry in Lake Ontario: role of an exotic species in
preventing restoration of a native species. Journal of Great Lakes Research
21:458–469.
Link, J. S., and L. P. Garrison. 2002. Changes in piscivory associated with
fishing induced changes to the finfish community on Georges Bank. Fisheries
Research 55:71–86.
Madenjian, C. P., G. L. Fahnenstiel, T. H. Johengen, T. F. Nalepa, H. A. Vanderploeg, G. W. Fleischer, P. J. Schneeberger, D. M. Benjamin, E. B. Smith,
J. R. Bence, E. S. Rutherford, D. S. Lavis, D. M. Robertson, D. J. Jude, and
M. P. Ebener. 2002. Dynamics of the Lake Michigan food web, 1970–2000.
Canadian Journal of Fisheries and Aquatic Sciences 59:736–753.
Madenjian, C. P., T. O. Höök, E. S. Rutherford, D. M. Mason, T. E. Croley II, E.
B. Szalai, and J. R. Bence. 2005. Recruitment variability of alewives in Lake
Michigan. Transactions of the American Fisheries Society 134:218–230.
Madenjian, C. P., S. A. Pothoven, J. M. Dettmers, and J. D. Holuszko. 2006.
Changes in seasonal energy dynamics of Alewife Alosa pseudoharengus in
Lake Michigan after invasion of dreissenid mussels. Canadian Journal of
Fisheries and Aquatic Sciences 63:891–902.
Mason, D. M., and S. B. Brandt. 1996. Effect of Alewife predation on survival
of larval Yellow Perch in an embayment of Lake Ontario. Canadian Journal
of Fisheries and Aquatic Sciences 53:1609–1617.
Mason, D. M., T. B. Johnson, and J. F. Kitchell. 1998. Consequences of prey
fish community dynamics on Lake Trout Salvelinus namaycush foraging efficiency in Lake Superior. Canadian Journal of Fisheries and Aquatic Sciences
55:1273–1284.
Merritt, M. F., and T. J. Quinn II. 2000. Using perceptions of data accuracy
and empirical weighting of information: assessment of a recreational fish
population. Canadian Journal of Fisheries and Aquatic Sciences 57:1459–
1469.
Mills, E. L., J. H. Leach, J. T. Carlton, and C. L. Secor. 1993. Exotic species in
the Great Lakes: a history of biotic crises and anthropogenic introductions.
Journal of Great Lakes Research 19:1–54.
Murry, B. A., M. J. Connerton, R. O’Gorman, D. J. Stewart, and N. H. Ringler.
2010. Lake-wide estimates of Alewife biomass and Chinook Salmon abundance and consumption in Lake Ontario, 1989–2005: implications for prey
fish sustainability. Transactions of the American Fisheries Society 139:223–
240.
Nalepa, T. F., D. L. Fanslow, A. J. Foley III, G. A. Lang, B. J. Eadie, and M. A.
Quigley. 2006. Continued disappearance of the benthic amphipod Diporeia
spp. in Lake Michigan: is there evidence for food limitation? Canadian Journal
of Fisheries and Aquatic Sciences 63:872–890.
Ney, J. J. 1990. Trophic economics in fisheries: assessment of demand-supply
relationships between predators and prey. Reviews in Aquatic Sciences 2:55–
81.
O’Gorman, R., and T. J. Stewart. 1999. Ascent, dominance, and decline of the
Alewife in the Great Lakes: food web interactions and management strategies.
Pages 489–513 in W. W. Taylor and C. P. Ferreri, editors. Great Lakes fisheries
policy and management: a binational perspective. Michigan State University
Press, East Lansing.
O’Keefe, D. M., and S. R. Miller. 2011. 2009 Michigan Charter fishing
study. Michigan State University, Michigan Sea Grant, MICHU-11-200, East
Lansing.
Pauly, D., V. Christensen, J. Dalsgaard, R. Froese, and F. Torres. 1998. Fishing
down marine food webs. Science 279:860–863.
435
Plummer, M., N. Best, K. Cowles, and K. Vines. 2010. Output analysis and diagnostics for MCMC, R package version 0.14-2. Available: http://cran.r-project.
org/web/packages/coda/index.html. (January 2014).
R Development Core Team. 2010. R: a language and environment for statistical
computing. R Foundation for Statistical Computing, Vienna.
Rand, P. S., D. J. Stewart, P. W. Seelbach, M. L. Jones, and L. R. Wedge. 1993.
Modeling Steelhead population energetics in lakes Michigan and Ontario.
Transactions of the American Fisheries Society 122:977–1001.
Riley, S. C., E. F. Roseman, S. J. Nichols, T. P. O’Brien, C. S. Kiley, and J.
S. Schaeffer. 2008. Deepwater demersal fish community collapse in Lake
Huron. Transactions of the American Fisheries Society 137:1879–1890.
Roseman, E. F., T. P. O’Brien, S. C. Riley, S. Farha, and J. R. P. French III. 2008.
Status and trends of the Lake Huron deepwater demersal fish community,
2008. Great Lakes Fishery Commission, Ypsilanti, Michigan.
Rutherford, E. S. 1997. Evaluation of natural reproduction, stocking rates, and
fishing regulations for steelhead Oncorhynchus mykiss, Chinook Salmon O.
tshawytscha, and Coho Salmon O. kisutch in Lake Michigan. Michigan Department of Natural Resources, Fisheries Division, Final Report, Project F35-R-22, Ann Arbor.
Rybicki, R. W. 1973. Michigan’s Great Lakes trout and salmon fishery (1969–
1972). Michigan Department of Natural Resources, Fisheries Division, Fisheries Management Report 5, Lansing.
Schnute, J. T. 1994. A general framework for developing sequential fisheries models. Canadian Journal of Fisheries and Aquatic Sciences 51:1676–
1688.
Stewart, D. J., and M. Ibarra. 1991. Predation and production by salmonine
fishes in Lake Michigan, 1978–88. Canadian Journal of Fisheries and Aquatic
Sciences 48:909–922.
Stewart, D. J., J. F. Kitchell, and L. B. Crowder. 1981. Forage fishes and their
salmonid predators in Lake Michigan. Transactions of the American Fisheries
Society 110:751–763.
Szalai, E. B. 2003. Uncertainty in the population dynamics of Alewife Alosa
pseudoharengus and Bloater Coregonus hoyi and its effects on salmonine
stocking strategies in Lake Michigan. Doctoral dissertation. Michigan State
University, East Lansing.
Tsehaye, I., M. L. Jones, J. R. Bence, T. O. Brenden, C. P. Madenjian, and
D. M. Warner. In press. A multispecies statistical age-structured model to
assess predator–prey balance: application to an intensively managed Lake
Michigan pelagic fish community. Canadian Journal of Fisheries and Aquatic
Sciences.
USFWS (U.S. Fish and Wildlife Service). 2009. Stocking summary for Lake
Michigan 1976–2008. USFWS, New Franken, Wisconsin.
Warner, D. M., R. M. Claramunt, D. Hansen, and S. A. Farha. 2011. Status of
pelagic prey fishes in Lake Michigan, 2011. Great Lakes Fishery Commission,
Windsor, Ontario.
Warner, D. M., C. S. Kiley, R. M. Claramunt, and D. F. Clapp. 2008. The
influence of Alewife year-class strength on prey selection and abundance
of age-1 Chinook Salmon in Lake Michigan. Transactions of the American
Fisheries Society 137:1683–1700.
Wesley, J. K. 1996. Age and growth of Chinook Salmon in Lake Michigan:
verification, current analysis, and past trends. Master’s thesis. University of
Michigan, Ann Arbor.
Wilberg, M. J., and J. R. Bence. 2006. Performance of time-varying catchability
estimators in statistical catch-at-age analysis. Canadian Journal of Fisheries
and Aquatic Sciences 63:2275–2285.
Wilberg, M. J., J. T. Thorson, B. C. Linton, and J. Berkson. 2010. Incorporating
time-varying catchability into population dynamic stock assessment models.
Reviews in Fisheries Science 18:7–24.
Williams, M. 2012. Spatial, temporal, and cohort-related patterns in the contribution of wild Chinook Salmon Oncorhynchus tshawytscha to total Chinook
harvest in Lake Michigan. Master’s thesis. Michigan State University, East
Lansing.
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TSEHAYE ET AL.
Appendix: Additional Input Data Used in the
Construction of the Lake Michigan Salmonine Assessment
Models
TABLE A.1. Estimates of natural mortality and maturation probabilities derived from Rutherford (1997).
Age
Species
0
2
3
Natural mortality
0.7
0.3
0.1
0.1
0.1
0.1
0.1
0.3
0.1
0.1
0.5
0.1
Maturation probabilities
Chinook Salmonb 0.00 0.12 0.33 0.99
Steelhead
0.04 0.14 0.42
Brown Trout
0.05 0.50 0.65
Coho Salmon
0.05 0.99
Chinook Salmona
Steelhead
Brown Trout
Coho Salmon
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1
a
Background natural mortality.
For years prior to 1981.
b
4
5
0.1
0.5
0.1
0.1
1.0
0.1
0.99
0.62
0.70
0.99
0.62
0.99
TABLE A.2.
Weight at age at annulus formation of Chinook Salmon (kg).
Age
Year
0
1
2
3
4
5
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.586
0.586
0.586
0.586
0.586
0.586
0.586
0.586
0.586
0.586
0.586
0.586
0.586
0.586
0.586
0.586
0.586
0.586
0.650
0.770
0.760
0.750
0.710
0.780
0.810
0.890
0.800
0.770
0.820
0.840
0.720
0.730
0.840
0.870
0.800
0.780
0.830
0.760
0.720
0.780
0.740
0.740
3.176
3.176
3.176
3.176
3.176
3.176
3.176
3.176
3.176
3.176
3.176
3.176
3.063
2.936
2.810
2.683
2.557
2.430
2.640
1.730
2.160
2.230
2.020
2.110
2.250
2.610
2.600
2.240
2.210
2.380
2.290
1.850
2.220
2.850
2.400
2.240
2.180
2.330
1.920
2.110
2.250
1.870
8.193
7.485
7.485
7.485
7.485
7.485
7.485
7.485
7.485
7.485
7.485
7.485
7.200
6.766
6.332
5.898
5.463
5.029
2.830
4.260
3.510
4.260
4.010
4.080
4.140
4.880
5.010
4.700
4.330
4.330
4.380
3.820
3.900
5.160
5.010
4.460
4.140
4.200
3.840
3.840
4.180
3.730
9.830
9.300
9.630
9.630
9.630
9.630
9.630
9.630
9.630
9.630
9.630
9.630
9.270
8.650
8.110
7.570
7.030
6.490
5.950
4.470
6.400
5.920
6.290
6.590
6.490
7.350
7.630
7.420
7.220
6.810
6.600
5.990
6.320
7.500
7.510
7.450
6.620
6.480
5.750
6.140
6.300
5.710
11.800
11.560
12.400
12.400
12.400
12.400
12.400
12.400
12.400
12.400
12.400
12.400
11.930
11.070
10.400
9.730
9.050
8.380
7.850
7.670
6.630
9.100
7.990
9.170
9.150
10.100
10.210
10.100
10.080
9.790
9.130
8.090
8.690
10.530
9.820
10.020
9.570
9.060
7.870
8.200
8.850
7.690
437
SALMONINE COMMUNITY CHANGES IN LAKE MICHIGAN
TABLE A.3.
Average weight at age at annulus fromation of other salmonines (kg).
Age
Species
Lake Trout
Steelhead
Brown Trout
Coho Salmon
2
3
4
5
6
7
8
9
10 +
0.05
0.08
0.48
0.23
0.26
1.45
1.50
1.61
0.81
3.16
3.03
1.42
4.69
3.72
2.06
5.44
3.72
2.74
3.32
3.91
4.39
5.33
TABLE A.4.
Proportion of fishing effort that occurs each month relative to effort in August.
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
0.000
0.001
0.047
0.350
0.187
0.622
1.000
0.520
0.296
0.006
0.000
0.000
Downloaded by [Michigan State University] at 09:09 07 March 2014
1
TABLE A.5. Gross conversion efficiency values by age used in the estimation of consumption for the years before and after dreissenid invasion (1995 was used
as the cutoff year).
Age
Chinook Salmon
Lake Trout
Steelhead
Brown Trout
Coho Salmon
Before
After
Before
After
Before
After
Before
After
Before
After
0
1
2
3
4
5
0.271
0.243
0.205
0.199
0.277
0.224
0.192
0.180
0.238
0.222
0.231
0.216
0.302
0.285
0.186
0.152
0.162
0.148
0.221
0.196
0.221
0.196
0.234
0.199
0.092
0.075
0.144
0.132
0.213
0.185
0.213
0.185
0.037
0.030
0.144
0.131
0.154
0.134
0.154
0.134
0.037
0.030
0.128
0.116
0.144
0.125
0.154
0.134
APPENDIX REFERENCE
Rutherford, E. S. 1997. Evaluation of natural reproduction, stocking rates, and
fishing regulations for steelhead Oncorhynchus mykiss, Chinook Salmon O.
6
7
8
9
10
0.116
0.105
0.105
0.095
0.095
0.087
0.085
0.077
0.205
0.199
tshawytscha, and Coho Salmon O. kisutch in Lake Michigan. Michigan Department of Natural Resources, Fisheries Division, Final Report, Project F35-R-22, Ann Arbor.