great lakes fishery commission 2011 project ......great lakes fishery commission 2011 project...
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GREAT LAKES FISHERY COMMISSION
2011 Project Completion Report1
A Decision Analysis for Multispecies Harvest Management of Lake Huron Commercial
Fisheries
by:
Michael L. Jones
2 and Brian J. Langseth
2
2 Quantitative Fisheries Center Department of Fisheries & Wildlife Michigan State University East Lansing, MI, 48824-1222
January 2012
1 Project completion reports of Commission-sponsored research are made available to the Commission’s
Cooperators in the interest of rapid dissemination of information that may be useful in Great Lakes fishery
management, research, or administration. The reader should be aware that project completion reports have not
been through a peer-review process and that sponsorship of the project by the Commission does not
necessarily imply that the findings or conclusions are endorsed by the Commission. Do not cite findings
without permission of the author.
2
ABSTRACT:
Tradeoffs between achieving desired yield objectives for lake whitefish (Coregonus clupeaformis) and restoration
objectives for lake trout (Salvelinus namaycush) were assessed for harvest policies affecting coldwater
commercial fisheries in Lake Huron. Lake whitefish are targeted in the majority of commercial fisheries operating
in Lake Huron, but these fisheries also capture lake trout as bycatch. Lake trout were nearly extirpated from Lake
Huron by the 1950s, and substantial stocking efforts have been underway for decades to aid in recovery. Ongoing
or expanded harvest of lake whitefish may negatively affect rehabilitation efforts for lake trout. Additionally,
Lake Huron has undergone substantial changes to its food web in the last two decades. Dreissenid mussels and
round gobies (Neogobius melanostomus) have invaded and thrived in Lake Huron. Abundance of several prey
fish species has declined as has abundance of Diporeia, a primary food source for lake whitefish. These
ecosystem changes may also affect tradeoffs for coldwater commercial fishing policy in Lake Huron. To assess
these tradeoffs, we developed a food-web model (Ecopath with Ecosim: EwE) for the coldwater community in the
main basin of Lake Huron and used this model to compare harvest policies and evaluate the importance of key
system uncertainties to policy rankings. We engaged Lake Huron fishery stakeholders in two workshops to help
guide model development. Obtaining a balanced EwE model, and appropriately including invasive species in the
dynamic simulations both proved difficult, and prompted additional simulation studies. We found that in general,
dynamic simulations in Ecosim are not highly sensitive to ad hoc balancing adjustments, but that sensitivity
increases as the strength of trophic interaction among groups increases. We compared four methods for
incorporating invasive species into the EwE model and concluded that initializing invasive species biomasses
before actual invasion at very low biomasses, and maintaining them at low levels by imposing an ad hoc mortality
until the time of invasion was reasonably good at reproducing observed time series of all groups. The completed
EwE model was used to simulate changes to fishing mortality targets, to the season in which fishing occurred, and
to the type of gear used. Conversions of gill nets to trap nets resulted in the maintenance of lake whitefish harvest
and 15% increases in lake trout biomass over the status quo policy. Changes in fishing seasons varied among
policies, but resulted in at most a 14% increase in lake trout biomass, and a 39% increase in lake whitefish
harvest. Changing fishing mortality targets revealed the expected tradeoffs between lake whitefish harvest and
lake trout biomass. In general, changes in harvest were greater than changes in biomass as fishing mortality
targets changed, suggesting increases in harvest could be achieved without large decreases in biomass, but raising
questions about the model’s representation of compensatory processes for both species. Our assessment of the
significance of uncertainties about future environmental productivity, diet, and strength of trophic interactions
revealed that the first of these had the greatest effect on model outcomes, but did not alter the relative
performance of the policies. The other two uncertainties had lesser effects than changes in productivity, and
influenced lake whitefish harvest and biomass much more than that of lake trout. We found little evidence for
substantial indirect interactions between lake trout and lake whitefish, leading us to conclude that the commercial
fishery is the primary factor that links these two groups. Future work on balancing tradeoffs in the commercial
fisheries should therefore focus on direct interactions with the fishery (i.e. bycatch reduction), rather than on
indirect interactions through the food web.
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INTRODUCTION:
This report describes work completed on a project to evaluate harvest options for multispecies commercial
fisheries in Lake Huron, using a food-web modeling approach. Our work focused on interactions between lake
whitefish (Coregonus clupeaformis) and lake trout (Salvelinus namaycush) exploitation. Lake whitefish are the
primary exploited coldwater species in Lake Huron, and lake trout are commonly harvested as bycatch in the lake
whitefish fishery. Lake trout are also the object of a native-species restoration program in Lake Huron, so impacts
of the lake whitefish fishery on lake trout recovery, either directly through bycatch, or indirectly through food-
web interactions, are a significant management concern. Our research sought to identify whether certain harvest
policies would be preferable to others in simultaneously meeting the objectives of commercial fishers and of other
stakeholders who view lake trout restoration as a top priority.
Consideration of direct and indirect interactions among exploited species is a hallmark of multispecies and
ecosystem-based management (EPAP 1999; Pikitch et al. 2004). Ecosystem-based approaches have received
increased attention within the past decades and often build upon the techniques of single-species management. A
variety of quantitative approaches and modeling tools have been developed (and continue to be developed) for
considerations of such multispecies and ecosystem-based management (Jackson et al. 2000; Smith et al. 2007).
Within the Great Lakes region an ecosystem approach to management is central to the guiding principles of the
FCOs (DesJardine et al. 1995), the fundamental concept of the Strategic Vision of the Great Lake Fishery
Commission for the First Decade of the New Millenium (GLFC 2001).
A number of studies have looked at interactions among multiple species in Great Lakes fish communities
(Fontaine and Stewart 1992; Jones et al. 1993; Jones et al. 1995; Hebert and Sprules 2002), including implications
for management. Fewer studies have attempted to understand Great Lakes ecosystems as a whole (but see Flint
1986; Halfon et al. 1996; Kitchell et al. 2000; Jaeger 2006), and these studies often place less emphasis on
management options (with the exception of Kitchell et al. 2000). Elsewhere, harvest strategies incorporating
interactions among multiple species and the fisheries that target them have also been discussed, including issues
of bycatch (De Oliviera et al. 1998; Pascoe 2000), competing management objectives (Cochrane et al. 1998;
Matsuda and Abrams 2006) and trophic interactions (Arreguin-Sanchez et al. 2004; Zetina-Rejon et al. 2004; Eby
et al. 2006).
Viewing fisheries management at an ecosystem level may be particularly important given the substantial changes
that have occurred recently in Lake Huron (Ebener 2005). One of the most noticeable recent changes to Lake
Huron’s fish community has been the collapse of the alewife (Alosa psuedoharengus) population (Riley et al.
2008). The potential for native species to fill the void left by alewives has yet to be realized in Lake Huron,
however recent pulses of lake herring (Coregonus artedi; Schaeffer and Warner 2008) and bloater (Coregonus
hoyi; Roseman and Riley 2009) are promising. In the meantime, the alewife collapse has raised serious concerns
about the ability of the forage base in Lake Huron to support predators, possibly including lake trout, at desired
levels. Consequently, efforts to rehabilitate lake trout may necessitate reducing other forms of mortality on the
prey fish community, including fishing. In addition, increased densities of Dreissenid mussels (Nalepa et al. 2007)
raise concerns that Lake Huron’s energy pathways have changed, leading to uncertain consequences for growth
and production of harvested fish populations, especially lake whitefish (Nalepa et al. 2009). Dreissenids have
been implicated in the declines of the macroinvertebrate, Diporeia (Nalepa et al. 2007). Diporeia are a primary
food source for lake whitefish, and thus declines in Diporeia density could result in changes to diet content and
production of lake whitefish (Pothoven and Nalepa 2006). This context provides a strong argument in favor of
using a food-web approach to consider the performance of harvest strategies applied to the multiple fisheries
operating in Lake Huron.
OBJECTIVES:
Our project was guided by five objectives:
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1. Determine an appropriate set of management objectives and harvest strategy options for Lake Huron’s
coldwater commercial fishery and identify the key uncertainties about this system that limit our ability to
make confident predictions. These are essential components of any decision analysis;
2. Develop a food-web model for the Lake Huron coldwater fish community that reflects our understanding of
trophic interactions within this community as well as key environmental drivers of these interactions;
3. Incorporate a multispecies commercial fishery into the food-web model to simulate the effects of alternative
harvest strategies that result from completion of objective 1;
4. Critically assess the consequences of uncertainties identified in objective 1 on expected harvest policy
performance by using alternative parameterizations of the food-web model;
5. Provide stakeholders with an evaluation of the relative performance of a range of harvest strategy options
measured in terms of their success at achieving the management objectives identified through completion of
project objective 1.
As detailed below, we have completed four of these five objectives. Completion of objective 2 (food-web model)
proved very challenging, and led to us addressing two other unanticipated sub-objectives:
2a. Evaluate the sensitivity of Ecosim projections to the assumptions made in developing a balanced Ecopath
model; and
2b. Assess alternative strategies for incorporating invasive species into an Ecopath with Ecosim model when
these species are not present in the ecosystem at the initial time step of the model.
The credibility of our food-web model for Lake Huron depended on us addressing these two sub-objectives
before moving on to objectives 3 and 4.
The fifth objective will be completed in part by distribution of this completion report to the stakeholders we
engaged earlier in the project. However, we also intend to convene a final workshop during spring 2012, where
we will share our findings with stakeholders, and discuss implications for management.
METHODS:
Objective 1. We convened two workshops with Lake Huron fishery stakeholders to discuss management
objectives, management options, and food-web model development issues. The first workshop was held on April
20-21, 2009 and was attended by 15 stakeholder participants. The second workshop was held on April 22, 2010,
and was attended by 13 stakeholder participants. The workshops followed a Structured Decision Making (SDM)
format (Irwin et al. 2011). At the first workshop we focused on introducing the participants to the food-web
modeling approach we proposed to undertake, and developed an initial list of management objectives and options.
At the second workshop we presented preliminary model results, and refined our list of management objectives
and options. We also discussed key areas of uncertainty to consider in our decision analysis. Lists of participants
at both meetings are provided in Appendix 1.
Objective 2. We developed a food-web model for the main basin of Lake Huron using the Ecopath with Ecosim
(EwE) modeling platform (Christensen and Walters 2004). Graduate student Brian Langseth visited the
University of British Columbia for several weeks in 2009 and 2010 to collaborate with EwE experts (Carl Walters
and Villy Christensen) on model development. We used an extensive literature search and consultation with Great
Lakes food-web experts on all trophic levels to obtain estimates of biomass, production, and diet for all trophic
groups included in the model, as well as time series estimates of biomass for model fitting. The model
development methods are described in detail in Appendices 2 and 3 which also describe the methods for sub-
objectives 2a and 2b, respectively.
Objective 3. We included five commercial fisheries in the food web model: US treaty water lake whitefish (trap
and gill nets); Canadian (non-treaty) lake whitefish gill net fishery; Canadian (non-treaty) lake whitefish trap net
fishery; US treaty water Chinook salmon (Oncorhynchus tshawytscha) fishery; and Canadian bloater fishery. We
focused our policy analysis on lake whitefish fisheries and lake trout bycatch in these fisheries. We evaluated
three types of policies: varying levels of a fixed fishing mortality control rule; conversion of gill net fisheries to
5
trap nets; and restrictions on seasons during which fishing occurred. The gear conversion and seasonal fishing
policies included extreme scenarios that were unrealistic but allowed examination of whether these policies have
much impact on the achievement of lake trout restoration objectives. The implementation of these policies is
described in detail in Appendix 4.
Objective 4. We focused on three key areas of uncertainty: future ecosystem productivity; strengths of trophic
interactions; and diet contributions for relatively rare prey. The EwE modeling strategy did not allow a statistical
assessment of these uncertainties, so we used an ad hoc approach to evaluate how sensitive our policy results
were to each of these uncertainties. We used retrospective model fitting results, which included estimates of
“production anomalies”, to bound possible future ecosystem productivities. Specifically, we projected into the
future with three different productivity levels, representing the first quartile, the median, and the third quartile of
the range of production anomalies estimated for the period 1981-2008. We represented uncertainty concerning the
strength of trophic interactions by deliberately modifying values of vulnerability (parameters that describe the
strength of trophic interaction). We changed vulnerability values for the oldest age groups of lake whitefish, lake
trout, and Chinook salmon to values that differed as much as possible from those estimated in the retrospective
model fitting process without leading to substantially worse model fits (i.e., vulnerability values that were
plausible, given the data). Finally, we evaluated sensitivity to diet by including young lake whitefish as a small
(2%) component of older lake trout diets, and by including age 1+ alewife and age 1+ rainbow smelt as small (1%
each) components of older lake whitefish diets. Our choice of which modifications to make to our “best guess”
diets was based on discussions with stakeholders at our second workshop. When we modified the vulnerabilities
or the diets to explore sensitivity, we re-fit the remaining model parameters to the historical data before projecting
forward. Further details are provided in Appendix 4.
Objective 5. As noted earlier, this objective will be partially addressed by distribution of this report, in addition to
a third stakeholder workshop planned for spring 2012.
RESULTS:
Objective 1. Management objectives and harvest strategy options were identified during the first stakeholder
workshop and refined during the second workshop (Table 1). Details of these objectives and options are in
Appendix 1.
Table 1: Refined management objectives and harvest strategy options from the two stakeholder workshops. Order
in table does not suggest order of preference.
Management objectives Harvest strategy options
Minimize operating costs of fishing Constant catch harvest control rule
High catch rates for fish for sports fishermen Constant fishing mortality (F) harvest control rule
Diversity of the fishery Seasonal adjustments incorporating both control
rules
Minimum bycatch induced restrictions on harvest of
target species
Gear conversions from gill net to trap incorporating
both control rules; constant catch and constant F
Maintain lake whitefish yield objective from FCOs (3.8
million kg yield of lake whitefish, bloater, and herring)
Market development to improve demand and
therefore price
Maximize landed value of catch incorporating species
mix
Policies based on desired species ratios in catches,
biomass, trends, or reference points
Stability of yield, but not at the expense of foregone
harvestable surplus
Maximize harvest while maintaining lake trout
biomass above a threshold.
Lake trout annual mortality < 40%
Key uncertainties in the system were also discussed and refined during the two stakeholder workshops (Table 2).
A subset of the options and uncertainties were then implemented in the modeling work and are discussed further
under objective 4.
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Table 2: Key uncertainties in the understanding of food-web dynamics in Lake Huron.
Key uncertainties Extent that fouling by filamentous algae limits lake whitefish harvest
Diet proportions of rare items in lake whitefish and lake trout
Diets of difficult to monitor groups, e.g. larval fish
Lamprey predation trends based on biomass estimates or marking rates
Indirect effects of alewife on predators, predatory zooplankton on zooplankton,
and Dreissenids on predator-prey interactions
Predatory impact of walleye
Lake whitefish dynamics in US, non-treaty waters
Production of age 0 lake whitefish
Stochasticity in recruitment
Future levels of environmental productivity
Effects of balancing on model results
Effects of accounting for invasive species on model results
Effects of vulnerabilities on model results
Reasons for declines in key trophic groups (alewife, Diporeia, other prey fish)
Extent of predation mortality by round gobies
Response of recreational fishery to future biomass levels
Are FCO targets achievable given new food-web?
Objective 2a (see Appendix 2 for supporting details). Developing a balanced food-web model proved more
challenging than expected given the wide availability of data for Great Lakes aquatic organisms. The initial model
was unbalanced, and in general consumption by predators exceeded the available production of their prey. A
balanced model was required before policy simulations could be run and balancing required ad hoc changes to
model inputs (i.e., assumptions about initial biomass and consumption). It was uncertain how great an effect these
changes would have on future biomass dynamics. To assess the effect of balancing on future biomass dynamics,
the model was balanced in two different ways, simple policy simulations were run for both cases, and the
resulting biomass dynamics were compared. The first balancing approach preferentially adjusted consumption of
predators downward, whereas the second preferentially adjusted production of prey upward. Alternative values of
vulnerabilities were also used in each balanced model to explore the effect of vulnerabilities on future biomass
dynamics. We found that changes in model input and changes in vulnerabilities both affected future biomass
dynamics. The proportional difference in biomass dynamics between the two balanced models was 4% for default
values of vulnerabilities, but changed approximately four-fold when vulnerabilities were adjusted. The
proportional difference was 15% when vulnerabilities were increased and 1% when vulnerabilities were
decreased. To conclude, differences in model inputs affected biomass dynamics most when vulnerabilities of prey
to their predators were large, however the overall affect of these changes appeared small.
Objective 2b (see Appendix 3 for supporting details). An additional challenge encountered while developing the
food-web model was how to treat recent invasive species. Dreissena sp., predatory zooplankton (Bythotrephes
longimanus), and round goby (Neogobius melanostomus) invaded Lake Huron after 1981, the year in which the
food-web model was initialized, and would therefore have zero biomass at the time the model was initialized.
Ecopath with Ecosim models require modeled groups to have positive biomasses, which presented a challenge for
these groups. These three invasive species have considerably altered food-web dynamics in Lake Huron since
their invasion, so it was essential to incorporate their dynamics in the model. With the help of additional
collaborators, we developed four methods of incorporating invasive species into our EwE model. These methods
included 1) forcing invasive species biomass to observed levels, 2) starting invasive species biomass at very low
levels and allowing them to increase, 3) starting invasive species biomass at recent (high) levels and artificially
removing them until the time at which they invade, and 4) adjusting vulnerabilities of invasive species over time
to match biomass dynamics. The ability of each method to reproduce the observed dynamics of each invasive
species, as well as of non-invasive groups was assessed. All methods could reproduce “invasion”, i.e. an increase
in biomass, while maintaining reasonable fits to other groups. Method 2, however, did this somewhat better than
the other methods and with greater simplicity. We recommend using this method for including invasive species in
EwE models.
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Objective 3 (see Appendix 4 for supporting details). As fishing mortality targets were incrementally increased
from the lowest rate (-75% of status quo) to the highest (+100% of status quo), biomass of lake whitefish and lake
trout declined by 54% and 28%, respectively, while harvest increased by 271% and 266%, respectively. Increases
in harvests were greater for both species than declines in biomasses, suggesting compensatory responses to
fishing. Biomass and harvest of lake whitefish remained unchanged in the gear conversion policy, while lake
trout biomass increased 15% above status quo levels for a total conversion of gill nets to trap nets. Partial
conversions of gill nets to trap nets resulted in smaller increases in biomass of lake trout. Fishing only in winter
resulted in the greatest changes to biomass and harvest of lake whitefish and lake trout. Biomass of lake trout
increased by at most 14% over the status quo policy when lake whitefish targets were maintained. Harvest of lake
whitefish increased by at most 39% over the status quo policy when lake whitefish targets were increased while
lake trout harvest was maintained at current levels. Harvest policy simulations therefore suggested that doubling
fishing mortality targets for lake whitefish best achieved lake whitefish yield objectives, whereas total conversion
of gill nets to trap nets best achieved lake trout biomass objectives. Multiple objectives exist for the policies
however, and although lake whitefish harvest was increased under changes to fishing mortality targets, lake trout
biomass declined. In contrast, gear conversion and seasonal adjustment policies balanced higher lake trout
biomass with unchanged or greater lake whitefish harvests. Consequently, although extreme, gear conversion and
seasonal adjustment policies show promise in meeting the competing objectives of high lake whitefish harvest
and high lake trout biomass.
Objective 4 (see Appendix 4 for supporting details). Uncertainties in future levels of environmental productivity
caused the largest changes in expected levels of harvest and biomass of all the uncertainties considered. Increased
productivity positively affected lake whitefish biomass to a greater extent than lake trout biomass. This is likely
due to lake whitefish feeding on a greater proportion of lower trophic organisms than do lake trout. Although the
magnitude of policy outcomes changed with environmental productivities, the relative performance of each policy
did not change. In addition, alewife biomass was predicted to recover when productivity was increased, whereas
under median or low productivity, alewife biomass remained very low. Changes in diet and trophic interaction
strengths resulted in less change to policy outcomes than did changes in productivity, and similarly affected lake
whitefish more than lake trout. Changes to diet and trophic interaction strengths resulted in greater sensitivity of
lake whitefish harvest and less sensitivity of lake whitefish biomass to changes in fishing mortality. Uncertainties
in diet also altered the extent of direct and indirect interactions between lake whitefish and lake trout. In general,
increases in the biomass of one species resulted in small decreases in biomass to the other. Changes in biomass of
lake trout minimally impacted biomass of lake whitefish; when diet of lake trout was adjusted (see Methods,
Objective 4), the interaction become slightly stronger. Changes in biomass of lake whitefish affected biomass of
lake trout more than vice versa, likely due to the greater absolute biomass of lake whitefish in the system.
Adjusting lake whitefish diets mitigated some of the effect on lake trout biomass, but overall the indirect
interactions between these two species were found to be weak.
DISCUSSION:
This project sought to examine harvest policy options for Lake Huron commercial fisheries by developing a
food-web model that would allow consideration of both direct and indirect effects on targeted and incidentally
harvested species. We focused our analysis on lake whitefish and lake trout, because the former is the primary
target for coldwater commercial fisheries in Lake Huron and the latter is incidentally harvested in lake whitefish
fisheries and is the object of a native species restoration program. We developed the food web model using
Ecopath with Ecosim (EwE), which uses a mass-balance approach to describe the food web and its constituent
trophic interactions, and a foraging-arena predation model to enable dynamic simulations that project food-web
changes over time. Despite the large amounts of data that exist for the Lake Huron food web, development of a
model proved very challenging, and considerable effort in this project was devoted to addressing two key model
development issues: obtaining a balanced Ecopath model, and incorporating invasive species into the dynamic
Ecosim model. Nevertheless, we were successful in developing a model that allowed evaluation of a variety of
harvest policies for lake whitefish commercial fisheries and consideration of three key areas of uncertainty
regarding food-web dynamics.
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Our EwE model included 20 groups of species, several of which were divided into age-based stanzas, resulting in
a total of 36 modeled groups. Initial attempts to balance the Ecopath model using available data were
unsuccessful, primarily due to estimated predator consumption demands greatly exceeding estimates of available
prey production. This made it necessary for us to make ad hoc adjustments to the Ecopath model inputs, raising
concerns about the sensitivity of our results to these ad hoc choices. Our comparison of two contrasting strategies
for ad hoc model balancing indicated that in general dynamic simulation results are not highly sensitive to the
choice of strategy, although the sensitivity is substantially greater if the strength of trophic interactions among
species is high (i.e., high vulnerabilities, top-down control). We concluded from this analysis that correctly
determining the degree of top-down control in a food web was more important than the choice of strategy for
making ad hoc adjustments to model inputs (Appendix 2).
Our EwE model was initialized in 1981. We chose this early period to allow use of as much historical biomass
time series data as possible to fit the Ecosim model. Between 1981 and the present, three new taxa have invaded
Lake Huron and had substantial food web effects: Dreissenid mussels, Bythotrephes, and round gobies. EwE is
not well-suited to additions of new species groups to the model after the initial period, so we needed to develop a
method for including these species in the model. Working in collaboration with other Great Lakes EwE modelers,
we developed and evaluated four alternative methods for incorporating invasive species (Appendix 3). We
concluded that a method which initializes the biomass of each invasive species at a very low level in 1981
resulted in the best fit of estimated Ecosim biomass dynamics to observed data. This method required
adjustments of predator diets so that the invasive species’ contribution to consumption is negligible until the
species abundance reaches levels observed during the actual invasion/establishment period. The other methods
also performed reasonably well, but implementation of this method was comparatively simple and allowed the
model fitting process to estimate the strength of trophic interactions between the invasive species and their prey.
After addressing these two key model development issues, we fitted an EwE model for the main basin of Lake
Huron to time series of biomass estimates for 18 species groups. These time series included any data that were
available during the period 1981-2008. The best-fit model suggested that control in Lake Huron is mostly
bottom-up. Estimated vulnerabilities for most major predator groups were relatively low implying that there is
little evidence for strong trophic interactions among these groups. Instead, we found that the best model fits
resulted when we allowed for substantial changes to environmental productivity during the 1981-2008 period.
When we considered a range of possible future productivity levels during our harvest policy analysis, we found
that the outcomes were strongly influenced by this uncertainty. If productivity remains low in the future, our
simulations suggest that alewife biomass is very unlikely to recover, and that future biomass and yield of
harvested species will be much lower than if productivity returns to higher levels observed prior to the early
2000s.
Our analysis of harvest policy alternatives revealed the expected trade-off between harvest and biomass.
Increased exploitation rates for the targeted lake whitefish resulted in higher harvests of both lake whitefish and
lake trout, together with reduced biomasses of both groups. Harvests tended to increase more with increased
fishing mortality than biomass was reduced, suggesting some form of compensation in both species. For lake
trout this is at least partially due to the maintenance of recruitment through stocking, regardless of the removal of
spawning stock biomass through harvest. Our model was not able to consider the potential linkage between the
biomass of hatchery-derived lake trout that reach maturity and subsequent recruitment of wild lake trout – rather
hatchery and wild lake trout were treated as distinct groups in the model. The model results also did not reveal a
decline in harvest of either species at the highest fishing mortality level considered (double the current levels),
suggesting that there is little risk of overfishing at current harvest rates. However, we are concerned that the EwE
model may be overestimating the capacity for compensation in these species, especially lake whitefish. This
result warrants further examination. For lake whitefish, the pattern of declining biomass and increasing harvest
with increasing fishing mortality was sensitive to our uncertainties about diet and vulnerabilities, with biomass of
lake whitefish declining less when additional fish prey were added to lake trout and lake whitefish diets, and
when vulnerabilities for lake trout and lake whitefish were reduced. This sensitivity was much less evident for
lake trout.
Our simulations of gear conversion and seasonal restrictions on fishing indicated that these policies can provide
9
benefits for lake trout restoration by either reducing lake trout bycatch while maintaining lake whitefish harvests
at current levels, or increasing lake whitefish harvests without increasing lake trout removals. However the
predicted effects on lake trout biomass of complete conversion to trap nets or complete seasonal closures were
not very large (less than 20% increases in biomass). These estimated ecological benefits would need to be
weighed against the economic consequences of these policies in terms of gear conversion costs and market
consequences of shifting the seasonal patterns of lake whitefish harvests.
We also used the EwE model to assess the importance of indirect effects of harvest policies on non-target species.
In general the indirect interactions between lake trout and lake whitefish were quite weak. Substantially
increasing harvest of lake whitefish (and thus reducing lake whitefish biomass) without changing lake trout
harvest resulted in only small (< 10%) increases in lake trout biomass. Changes to lake trout harvest had even
less of an indirect effect on lake whitefish biomass. The magnitude of these indirect effects was sensitive to our
assumptions about diets – in particular whether lake trout consume lake whitefish – but remained small
regardless of the diet assumptions. From this we conclude that harvest policies for lake whitefish and lake trout in
Lake Huron should be more concerned with direct effects – through bycatch – than with indirect effects.
Our analysis represents the first attempt to explicitly link an evaluation of alternative harvest strategies with a
food-web model for a Great Lakes commercial fishery. The large changes that have occurred to the Lake Huron
food web in recent years point to considerable uncertainty about what commercial fishery yields and coldwater
fish biomasses can be expected in the future. If productivity remains low, expectations for future yields should be
adjusted downward to reflect this. Clearly this type of change in expectations has already occurred for the Lake
Huron recreational Chinook salmon fishery. On the other hand, our finding suggest that comparisons of
alternative harvest policies for balancing lake whitefish yield objectives with lake trout recovery objectives
should focus on direct effects – through management of incidental harvest of lake trout – rather than on indirect
effects manifested through food-web interactions.
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11
ACKNOWLEDGEMENTS:
This project depended on contributions from many individuals and organizations that we would like to
acknowledge. The participants at both of our stakeholder workshops, listed in Appendix 1, helped to guide model
development and the focus of our analysis. Valuable data and insights about these data were provided by Richard
Barbiero, Travis Brenden, Dave Caroffino, Adam Cottrill, Norine Dobiesz, Mark Ebener, Darryl Hondorp, Ji He,
Ora Johannsson, Jim Johnson, Dave Jude, Tracy Kolb, Ann Krause, Lloyd Mohr, Charles Madenjian, Andrea
Miehls, Steve Pothoven, Stephen Riley, Edward Roseman, Jeffrey Schaeffer, and Mike Siefkes. Brian Irwin
assisted in the early stages of the project and in writing the original grant. Matt Catalano assisted with analyses of
time series data. Carl Walters and Villy Christensen provided invaluable assistance to BJL in understanding the
EwE modeling software. David McLeish, Tammy Newcomb, Jim Bence, Yu-Chun Kao, Hongyan Zhang, Mark
Rogers, provided numerous helpful comments and insight during discussions of the model. Joe Buszowski and
Jeroen Steenbeek helped with refinements to EwE code. Our work was funded by a grant from the Great Lakes
Fishery Commission to MLJ, a fellowship (William E. Ricker Fellowship) from Michigan State University to BJL
to support his graduate research, and travel support from the Foreign Affairs and International Trade Canada to
BJL for travel to the University of British Columbia.
DELIVERABLES:
Progress reports in Dec 2009 and Dec 2010
Final report (this document)
Final EwE Lake Huron main basin model – available from Quantitative Fisheries Center
Participation in four EwE modeling workshops funded by the Great Lakes Fishery Commission Science
Transfer Program (Jun & Nov 2010, Feb & July 2011)
Oral presentations (IAGLR 2010 – Toronto; AFS 2010 – Pittsburgh; AFS 2011 – Seattle) – all by Brian
Langseth, PhD student and Ricker Fellow, Quantitative Fisheries Center
Three manuscripts (Appendices 2-4, this document): Appendix 2 is under revision for re-submission to
Ecological Modeling; Appendix 3 will be merged with results from a Lake Michigan EwE for submission
in 2012; Appendix 4 will be revised based on further analyses and submitted in 2012.
PhD dissertation – Brian Langseth, to be submitted in 2012.
PRESS RELEASE:
Researchers at Michigan State University have completed a three year study that uses a food-web model to
explore commercial fishing policies in Lake Huron. Mussels, gobies, and other invasive species have increased in
Lake Huron, while other prey of fished species have declined. These changes increase uncertainty about future
numbers of fish in the lake, as well as future fishery yields. The researchers used the food-web model to account
for these changes and forecast how different fishing policies might perform.
Lake whitefish (Coregonus clupeaformis) is the main species caught by the fisheries, but lake trout (Salvelinus
namaycush) is caught as well. Lake trout is being restored in the Great Lakes, adding objectives other than
harvest to the analysis. The researchers found that policies which change the type of fishing gear or change
fishing seasons maintained or increased lake whitefish harvest while at the same time maintaining or increasing
lake trout biomass, thereby balancing the tradeoff between objectives.
Future environmental productivity was found to be a very important source of uncertainty. Expected yields of
lake whitefish as well as expected biomass of lake trout and other species were much lower if productivity
remained low than if productivity increased to levels experienced in the 1990s. The researchers also found little
evidence that lake trout and lake whitefish interact indirectly through food-web dynamics. This led them to
conclude that the commercial fishery is the primary factor linking these two important species. Future work on
balancing tradeoffs in the commercial fisheries should therefore focus on direct interactions within the fishery –
that is, reducing bycatch – rather than on indirect interactions through the food web. Even so, the food-web model
can still be useful to managers for exploring other questions related to Lake Huron and its changing food web.
12
APPENDICES:
Appendix 1: Notes from first two stakeholder workshops
Appendix 2: Draft manuscript describing the effects of balancing on Ecosim output. To be resubmitted to Ecological
Modeling.
Appendix 3: Modeling species invasions in an Ecopath with Ecosim model of a Laurentian Great Lake, Lake Huron.
Appendix 4. Evaluation of harvest policies for Lake Huron coldwater commercial fisheries using an Ecopath with
Ecosim model.
1 - 1
Appendix 1: Notes from first two stakeholder workshops
Workshop 1
Context of this project: How to manage commercial fisheries that harvest multiple species to ensure long-term
sustainability of the fisheries?
Purpose of this project: Explore what harvest policies for Lake Huron commercial fisheries are optimal given trade-
offs.
Objective of this project: Determine possible management objectives and harvest options to consider. These do not
reflect objectives and options which should actually be done, but rather objectives and options which can be
considered in an exploratory modeling exercise. Determine key uncertainties in our knowledge of the system. Use a
spatially structured food-web model using Ecopath with Ecosim that makes forecasts into the future given a range of
possible management options. Evaluate the performance of the model forecasts in meeting the possible management
objectives.
Objective of this workshop: List possible management objectives and options. Discuss the spatial resolution and
species to include in the model.
Stakeholders and their affiliation:
Michael Jones Michigan State University (MSU)
Brian Irwin MSU
Brian Langseth MSU
Frank Krist Michigan Lake Huron Citizens Fishery Advisory Committee
Chris McLaughlin McMaster University
David Reid Ontario Ministry of Natural Resources (OMNR)
Adam Cottrill OMNR
Stephen Riley United States Geological Survey
David McLeish OMNR
Lloyd Mohr OMNR
David Carlson Commercial fisherman
Peter Meisenheimer Ontario Commercial Fisheries Association (OCFA)
Kevin Reid OCFA
Dennis Morrison Lake Huron Georgian Bay Fisheries Stewardship Council
Tim Purdy Commercial fisherman
Forrest Williams Michigan Fish Producers Association
Milford Purdy Commercial fisherman
George Purvis Commercial fisherman, President Algoma-Manitoulin Commercial
Fishing Association
Schedule of events:
Day one - April 20
1. Welcome and introductions
2. Background to project
3. Overview of project goals, objectives and approach
4. Discussion of management objectives
Break
5. Discussion of management options
6. Wrap-up, plan for day two
Day two – April 21
1. Foodweb/management models – Ecopath with Ecosim
1 - 2
2. Discussion of key model components and uncertainties
Break
3. Continued discussion
4. Wrap-up, review of next steps
Outcome of discussions pertinent to meeting the objectives of this workshop:
Management objectives – note that this list is a summary of ideas presented by workshop participants without further
interpretation or refinement of the MSU team. This list of objectives will be used by the MSU team to define
quantities that our forecasting model will need to include, so that we can assess the predicted performance of
management options.
Stability over time of TAC’s, but not at the expense of forgone harvestable
surplus
Lake trout total annual mortality < 40%
50% of lake trout are wild
Minimum bycatch induced restrictions on harvest of target species
Maximize landed value of catch incorporating species mix
Minimize operating costs of fishing
Diversity of the fishery (more harvestable species)
Maintain lake whitefish yield objective from Fish Community Objectives
Production of valued species not limited by native food base (but prefer low
abundance of alewives)
Higher yields of chubs (bloater), cisco, walleye, and yellow perch
Reduced uncertainty
Maintain high catch rates for fish for sports fishermen
Management options (things in the system that can be changed, particularly by fisheries management agencies) –
similarly, this list has not been interpreted or refined by the MSU team and will be used to inform the development of
our model..
Quotas
Seasonal closures/openings
Area closures/openings
Gear conversion when feasible (e.g. trap nets can’t keep up with lake whitefish
movements on Canadian side of southern main basin)
Gillnet length
Effort
Buy outs
Stocking - only stock natives, stock by ratios
Policies based on species ratios in catches, biomasses, reference points, trend
response
Quota zonation
Increased assessment
Active adaptive management – allow targeted effort for lake trout
Bycatch reduction
Market development
Enhanced sea lamprey control
Risk-based harvest control rules when species of importance influence each other
either positively or negatively (e.g. cisco harvest related to lake trout restoration)
Description of the fishery
Gillnet offshore in Ontario, trapnet nearshore in southern main basin (4-5) and off
Manitoulin (4-2)
No commercial operations in southern main basin on Michigan side up to tip of
1 - 3
thumb. Trapnets from tip to Hammond Bay, and mix of trapnet and gillnet in northern main basin.
Recreational fishing for lake trout, walleye, yellow perch, salmon, cisco in north
channel and northern main basin.
How to represent the spatial structure of Lake Huron in the model:
Can model all three basins, however focus should be on main basin.
Test the model using a single basin and then build other basins in if model is
useful
Nearshore and offshore zones are different in nutrients and species composition.
Should consider separate models for each
Saginaw bay species are not as important to the cold-water fishery, and thus likely
do not need to be included in the model. Exception would be if they play a significant role in the diet
of cold-water species
Species to be included in the model and relative uncertainty about them:
Species Uncertainty Species Uncertainty
lake whitefish low alewife Low
lake trout low/med smelt Low
bloater low/med slimy/deepwater sculpin Low
cisco med/high gobies low/med
walleye low/med emerald shiners High
yellow perch low/med spottail shiners Med
round whitefish med/high sticklebacks med/high
lamprey low trout perch med/high
cormorant low burbot med
salmon species med/high dreissenids med
pinks native molluscs high
chinook diporeia med
steelhead mysis high
coho zooplankton med/high
atlantic phytoplankton high
brown cladophora high
Potential drivers of recent changes in Lake Huron
Mussels – reduce diporeia and contribute to poor food quality, therefore reducing
prey availability
Gobies – predation pressure
Salmon and trout – predation pressure
Thiamine deficiency syndrome
--------------------------------------------------------------------------------------------------------------------------------------------
Workshop 2
Objectives of this workshop:
1) Review and comment on current EwE model
2) Detail the metrics we will use to report outcomes of the modeling work.
3) Discuss viable management options.
Attendees:
Michael Jones Michigan State University (MSU)
Brian Irwin MSU
Brian Langseth MSU
1 - 4
Frank and Teresa Krist Michigan Lake Huron Citizens Fishery Advisory Committee
David Reid Ontario Ministry of Natural Resources (OMNR)
Adam Cottrill OMNR
David McLeish OMNR
Lloyd Mohr OMNR
David and Paul Carlson Commercial fisherman
Peter Meisenheimer Ontario Commercial Fisheries Association (OCFA)
Kevin Reid OCFA
Dennis Morrison Lake Huron Georgian Bay Fisheries Stewardship Council
George Purvis Commercial fisherman, President Algoma-Manitoulin Commercial
Fishing Association
Jim Johnson Michigan Dept. of Natural Resources and the Environment
Agenda:
1) Update on food-web model (Langseth)
2) Discussion of management objective, what metrics should be reported (Irwin)
3) Discussion of management options (Jones)
Summary of agenda item 1:
-Main basin model built, parameterized in 1981.
-Reasonable fits of model dynamics to biomass time series can be generated by fitting vulnerabilities only, as
well as both vulnerabilities and primary production anomalies.
-Invasive species (round gobies, zebra and quagga mussels, and bythotrephes) are included in the model.
Mortalities of these groups are set very high in earlier years when invasives were not present, and reduced for
periods when they invade.
-Using values from the literature suggest an overconsumption by prey fish on lower trophic groups
(invertebrates and plankton). Consequently, to achieve mass balance, production of lower trophic groups was
increased and consumption of prey fish was decreased.
Summary of agenda item 2
Objectives (in no particular order) and initial identification of corresponding performance metrics:
1. Maintain lake whitefish yield objective from FCOs
Metrics: harvest of bloater, lake whitefish, and cisco.
2. High catch rates for fish for sports fishermen
Metrics: number per effort, with some measure of quality.
3. Minimum bycatch induced restrictions on harvest of target species
Bycatch defined as catch above that which is allowed, or catch of non-marketable fish.
Metrics: biomass of bycatch species.
4. Diversity of the fishery
Want to be able to pursue other species when beneficial to do so.
Metrics: age composition of harvest, and fishable biomass.
5. Lake trout annual mortality < 40%
Metrics: total mortality, spawning stock biomass, for fully selected ages
6. Maximize landed value of catch incorporating species mix
Value dependent on supply, also exchange rate.
Metrics: dockside values and harvest.
7. Minimize operating costs of fishing
1 - 5
Metrics: effort and price per effort for trap net and gill net.
8. Stability of yield, but not at the expense of foregone harvestable surplus
Metrics: total net income (harvest x price – costs).
Additional discussion item:
Need understanding that this model operates on a lake-wide scale but that an individual local effect could still
influence results.
Summary of agenda item 3:
Management Options (in no particular order) to focus on in model simulations.
a. Quotas
Include quotas in policy searches
b. Effort
Output controls (e.g. quotas) generally preferred over input controls (e.g. effort)
c. Gear adjustments or conversions
Where physically able to use gear
d. Seasonal closures/openings
Seasons where lake whitefish price is highest (winter) are preferable, but requires gill net
e. Market development
Market forces impact price, and thus increasing available harvest may not always be as profitable
Strategies on implementing these options were also discussed and include
f. Policies based on species ratios in catches, biomass, reference points, trends.
Explicit rule that says harvest related to some population measure
g. Risk-based harvest control rules when species of importance influence each other either + or - (e.g. cisco
harvest related to LT restoration)
(Maximize harvest without reducing lake trout biomass to some low level)
2 - 1
Appendix 2: Draft manuscript describing the effects of balancing on Ecosim output. To be resubmitted to Ecological
Modeling.
Title: The effect of different approaches to achieve mass balance on a food-web model of Lake Huron
Authors: Brian J. Langsetha*
([email protected]), Michael L. Jonesa ([email protected]), Stephen C. Riley
b
Addresses: a Quantitative Fisheries Center, 153 Giltner, Michigan State University, East Lansing, MI, USA, 48824.
b Great Lakes Science Center, United States Geological Survey, 1451 Green Rd., Ann Arbor, MI, USA, 48105.
*Corresponding author: Tel: 1-517-355-0126, Fax: 1-517-355-0138
Highlights:
Ecopath models are rarely balanced. Imbalances are often in lower-trophic groups.
Biomass dynamics in Ecosim can also be used to assess the effect of balancing.
The effect of balancing is greatest when vulnerabilities are high.
The effect of balancing is greatest when changes in biomass through time are small.
Abstract:
The Ecopath with Ecosim (EwE) software is an increasingly popular modeling tool in fishery research and
management. Ecopath requires a mass-balanced snapshot of a food-web at a particular point in time, which Ecosim
then uses to simulate biomass through time. Initial inputs to Ecopath, including available estimates of biomass,
production, consumption, and diets, rarely produce mass balance, and thus changes to the inputs are required to
balance the model. There has been little discussion of whether these changes to achieve mass balance affect model
results. We constructed two EwE models for the offshore community of Lake Huron, balanced in two contrasting but
realistic ways. One placed more confidence in estimates of consumption; levels of production were increased to
achieve mass balance. The other placed more confidence in estimates of production; levels of predation were
decreased. To assess the effect of balancing approach on model results, we compared ecosystem metrics within
Ecopath (ascendency and system omnivory index (SOI)), as well as biomass dynamics within Ecosim. Within
Ecosim, we compared simulations given alternative assumptions about the type of control (top-down or bottom-up)
under two scenarios of (1) increased fishing mortality or (2) increased environmental production. Ascendency for the
first balancing approach was approximately four times that of the second, and was mostly due to greater assumed
production in planktonic groups. Values of SOI were nearly identical, suggesting very little difference between the
two approaches. Differences in overall biomass between the two balancing approaches were greatest under scenarios
assuming top-down control, and differed by at most a factor of 1.15. When expressed relative to the overall change in
biomass for each scenario, the differences between balancing approach represented at most 41%, but were much
lower for scenarios where biomass changed substantially. The importance of these differences appears to be small,
and comparisons with other models would be helpful to compare significance. Our findings suggest that approaches
to balancing Ecopath models have the greatest effect on model results when top-down control is prevalent in the
system and when simulated biomass dynamics are stable through time.
Keywords: Ecosystem models, Ecopath, Ecosim, mass balance, vulnerabilities, Great Lakes
2 - 2
1. Introduction:
Mathematical models are tools to explain complex processes in simple and understandable ways. Trophic
interactions within most aquatic ecosystems are complex, and attempts to understand these interactions for
applications to fisheries management can be aided by models. A variety of modeling frameworks have been used to
describe ecosystem processes, and even within an individual framework, the processes themselves can be described in
multiple ways (e.g. alternative assumptions about the strength of trophic interactions). When models are used to
inform decisions, and thereby influence management actions, it is important to determine whether alternative
descriptions of ecosystem processes within a modeling framework affect conclusions.
The use of models to inform fisheries management decisions is likely to increase in the future. This is due
partly to increased computing power, which allows for more explicit accounting of multiple species and the processes
that govern their interactions (Hilborn and Walters, 1992; Robinson and Frid, 2003), and also to greater awareness of
the need to base management on an understanding of the ecosystems within which fisheries operate (Bundy et al.,
2001; Walters et al., 2008). These have contributed towards the progression from single-species management to
ecosystem-based fisheries management (EBM), which incorporates objectives for many components within an
ecosystem, not just for harvested species (Marasco et al., 2007). Examples of ecosystem objectives include prey
availability for non-human components of the ecosystem, maintaining important habitats, and managing bycatch
(Ruckelshaus et al., 2008). Ecosystem models are a valuable tool for the assessment of EBM policies in meeting
desired objectives (Pikitch et al., 2004; Smith et al., 2007; Walters and Martell, 2004). Policies for Laurentian Great
Lakes fisheries have incorporated attributes of EBM, such as the Fish Community Objectives for Lake Huron
(DesJardines et al., 1995) and the Joint Strategic Plan (Gaden et al., 2008). As the use of ecosystem models increases,
the value of understanding their sensitivity to alternative process descriptions within the model will as well.
A variety of ecosystem models have been used for fisheries applications (Hollowed et al., 2000; Latour et al.,
2003; Robinson and Frid 2003; Whipple et al., 2000), all with strengths and weaknesses. The modeling framework
used here is the Ecopath with Ecosim (EwE) software. EwE consists of two modules, Ecopath and Ecosim. Ecopath
allows for a mass-balanced description of a food-web at a single point in time, which is parameterized with biomass,
growth, and consumption data for each modeled group (Christensen and Walters, 2004). The Ecopath model, along
with values reflecting the strength of interspecific interactions is then used as input to Ecosim, which simulates the
dynamics of the modeled groups through time (Christensen and Walters, 2004). Published Ecopath models in the
Great Lakes exist for Lake Superior (Cox and Kitchell, 2004; Kitchell et al., 2000) and Lake Ontario (Halfon et al.,
1996; Koops et al., 2006; Stewart and Sprules, 2011), while unpublished work exists for Lake Michigan (Ann Krause,
University of Toledo (UT), pers. comm.). Work is also underway on building models for other Great Lakes (David
Bunnell, United States Geological Survey (USGS), pers. comm.; Sara Adlerstein and Ed Rutherford, University of
Michigan, pers. comm.).
A challenge when constructing Ecopath models is obtaining representative average data inputs across a year
and system that meet the physical constraints of mass balance. Mass balance is a requirement in Ecopath models; the
amount of food consumed must be less than what is available, and the gain in weight from consumption must be less
than the weight of food consumed. Achieving mass balance in Ecopath models invariably requires modifying input
parameters (Christensen et al., 2005), but EwE users rarely discuss this process. Modifying input parameters can be
done in many ways, and thus multiple descriptions of the food web are possible as a result of the balancing process.
Studies that used Ecosim models of the Great Lakes focused on time dynamic simulations and only briefly discussed
balancing procedures (Cox and Kitchell, 2004; Kitchell et al., 2000). Studies without time dynamic simulations
focused on ecosystem descriptions, and discussed the balancing process only briefly (Halfon et al., 1996; Koops et al.,
2006), with the exception of Stewart and Sprules (2011).
There are two primary procedures to balance Ecopath models. Model inputs can be adjusted subjectively by
the modeler, based on their judgment of which inputs are least reliable or most likely to achieve mass balance
(Christensen et al., 2005). Alternatively, changes can be made objectively by the software, based on a quantification
of user-perceived reliability for input parameters and a formal objective function, such as minimizing changes to
initial inputs (Kavanagh et al., 2004). In one of the few publications in which the impact of balancing was discussed,
Essington (2007) found that randomly balancing a model produced a balanced model as close to the “true” model
(one in which all parameters were known) as when balancing was done using an objective function. Although the
overall variation in input parameters among balanced models was similar when using either balancing procedure, the
differences in model outputs such as ecosystem metrics or simulated biomasses in Ecosim were not assessed. When
using the subjective procedure, the modeler, rather than the software, makes changes to input parameters which can
2 - 3
provide a better understanding of the linkages within the system. Although more time consuming, consideration of
such linkages may reveal unexpectedly important groups or gaps in the understanding of the system.
Subjective balancing was used to balance the model described herein. Through iterative attempts to achieve
mass balance, a major source of imbalance was found to be due to a perceived overconsumption of intermediate
trophic-level groups. Conversations with other EwE modelers suggest this is a common problem, but the observation
has not been discussed in standard balancing documentation and has only recently been discussed in the literature
(Stewart and Sprules, 2011).
The objective of this research was to assess how alternative strategies for balancing an Ecopath model
affected EwE results. An Ecopath model of the offshore fish community of Lake Huron was constructed and
subjectively balanced using two approaches. The first approach assumed data for upper trophic-level groups were
more reliable than for lower trophic-level groups, whereas the second assumed the opposite. Differences in model
outputs of ecosystem metrics in Ecopath and time dynamic simulation results in Ecosim were used to compare the
two balancing approaches. The strength of trophic interactions, and the level of environmental productivity and
fishing mortality were adjusted within Ecosim to assess the impact of ecosystem conditions on model results. Possible
reasons for observed imbalances in the model are also described.
2. Methods:
2.1 Study site
Lake Huron is the second largest of the Laurentian Great Lakes with a surface area of 59,570 km2
(DesJardines et al., 1995), and is divided by the border between the United States and Canada (Fig. 1). Both
commercial and recreational fisheries exist in the lake; the majority of commercial effort occurs in Canadian waters,
whereas the majority of recreational activity occurs in US waters. Management responsibilities for the fisheries are
shared among Michigan, Ontario, and Native American agencies. These agencies collect information pertinent to
fisheries management, and are assisted in collection and coordination by the USGS, US Fish and Wildlife Service,
Environmental Protection Agency, Canadian Department of Fisheries and Oceans, and Great Lakes Fishery
Commission. Data to parameterize the model were provided by these agencies as well as from published sources, and
are described in detail in the online appendix (Table A.1).
2.2 The model
Ecoppath with Ecosim (EwE) version 6.1.1 was used to construct a food-web model of the offshore fish
community in the main basin of Lake Huron (Fig. 2). It is a freely downloadable software package that describes a
snapshot of an ecosystem at a particular point in time (Ecopath), and simulates the dynamics of modeled groups
through time (Ecosim). Details of the modeling software have been discussed previously (Christensen and Pauly,
1992; Christensen and Walters, 2004), but a summary of key equations and parameters is provided below. The mass
balance equation in Ecopath for each group i is
Pi = Yi + Ei + (BA)i + Bi(M2)i + Bi(M0)i , (1)
where P is production; Y is total harvest; E is net migration (emigration - immigration); BA is biomass accumulation;
B is biomass; M2 is predation mortality rate; and M0 is other mortality rate, which represents sources of mortality not
included in the model. For simplicity, both E and BA were assumed to be zero for this research. The Ecopath user
does not provide estimates for all parameters in equation (1) directly, but rather provides inputs from which
parameters in equation (1) are calculated. When expressed in terms of Ecopath user inputs, equation (1) becomes
ii
j
ijjji
iBBP
DCBBQY
EE)/(
/
, (2)
where EE is ecotrophic efficiency, which equals 1-M0i/(P/B)i and represents the proportion of total mortality
explained by sources in the model; Q/B is the consumption to biomass ratio; DCij is the percent contribution of prey i
to the diet of predator j; and P/B is the production to biomass ratio. Allen (1971) found that under standard
assumptions of exponential mortality and von Bertalanffy growth, P/B is equivalent to the total instantaneous
mortality rate, assuming BA is zero. It is assumed that all components of mortality are included in equation (1), and
thus conservation of matter requires that the sum of these terms equals the assumed level of production. In relation to
equation (2), this means total production (denominator) must be greater than the loses due to consumption and fishing
described in the model (numerator) so that EE<1. When this occurs, the Ecopath model is said to be balanced and can
be used as input into Ecosim.
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Ecosim is governed by two primary equations. For groups with a single age stanza, the change in biomass of
each group is modeled as
iiiii
j
ij
j
jii
i BeFMIQQgdt
dB0 , (3)
where t is time in months; g is the gross food-conversion efficiency ratio (GCE), which is the ratio of P/B to Q/B; Qij
is consumption of prey i by predator j; I is immigration; e is the emigration rate; and F is the fishing mortality rate.
The solution to the differential equation is estimated using an Adams-Bashford method (Christensen et al., 2005).
More complicated versions of equation (3) are used for groups with multiple age stanzas (see Walters et al., 2008 for
details). The second Ecosim equation defines the value of consumption, which in its simplest form is modeled as
jijij
jiijij
ijBav
BBvaQ
2, (4)
where aij is the effective search rate of predator j for prey i, and vij is the vulnerability of prey i to predator j.
Vulnerabilities define the strength of trophic interaction between a predator and its prey and can be adjusted within
Ecosim. The user does not actually adjust vij, but rather a constant (Kij) from which vij is calculated using vij =
Kij(Qij/Bi) (Walters and Christensen, 2007).
2.2.1 Building an Ecopath model
The Ecopath model of Lake Huron was constructed in a series of three steps: choice of groups, choice of time
period, and choice of data sources. For choosing which groups to include in the model, Lake Huron biologists were
consulted, and 21 species or groups of species were identified based on their assumed importance to the offshore
community (Table 1). Individual species were further separated into age stanzas when appropriate data were
available. Each age stanza consisted of a set of age classes and reflected a difference in trophic ontogeny or mortality
schedule. The total number of modeled groups increased from 21 to 36 when age stanzas were included (Table 1).
Special considerations were required for modeling stocked and semelparous species. Ecopath requires the age
of the first stanza for each group to begin at 0 months. Species are stocked into Lake Huron at either 6 months
(fingerlings) or 12 months (yearlings) so a pre-stocking stanza with age either 0-6 or 0-12 months was included in the
model with a very low mortality rate and import-only diet. An import-only diet represents feeding outside the
modeled system and allows a stanza that is not actually in the system, but is required by the model, to be included
without affecting other species. Within Ecosim, the biomass of the first age stanza for stocked species (lake trout
Salvelinus namaycush, steelhead Oncorhynchus mykiss, and Chinook salmon Oncorhynchus tshawytscha) was set to
the initial Ecopath value for each simulation year. This was also done for sea lamprey Petromyzon marinus, whose
recruitment is heavily influenced by management (pest control). Ecopath also assumes that all species are iteroparous.
Therefore, the semelparous Chinook salmon was modeled with a terminal age stanza (age 6+) that had an import-only
diet. A terminal age stanza (age 6+) for steelhead was also included in the model.
The second step was to choose a representative time period, which was dictated by the availability of data.
Biomass estimates for nearly all modeled groups were available for 1999. The choice of time period should reflect a
period of stability in the food-web. Lake Huron has undergone substantial changes in recent years; zebra Dreissena
polymorpha and quagga mussels Dreissena bugensis have proliferated in the 1990s and early 2000s (Nalepa et al.,
2007) and alewife Alosa pseudoharengus abundance collapsed in 2003 (Riley et al., 2008). Despite the proximity of
these events, 1999 was chosen as the modeled year due to the greater availability of biomass estimates, particularly
for lower trophic-level groups.
Step three was to find estimates for Ecopath parameters centered around 1999 for the chosen groups. Ecopath
requires estimates of B, P/B, Q/B, and diet for all groups. For groups with age stanzas, B and Q/B are needed for only
one stanza. Ecopath then calculates B and Q/B for all other age stanzas based on an age-structured model that assumes
a stable age distribution and von Bertalanffy growth (Walters et al., 2008). Consequently, von Bertalanffy growth
coefficients (K) were also required for groups with multiple age stanzas. When estimates of parameters were not
available for Lake Huron in 1999, estimates were obtained from as similar a system and time period as possible.
Recent time periods in Lake Huron are generally divided by the invasion of dreissenid mussels in the early 1990s and
the collapse of alewife in 2003. Consequently, when data were not available explicitly for 1999, sources from 1990-
2003 were preferentially used. When estimates were not available for Lake Huron, estimates for other Great Lakes
were used. Given the geographical and biological similarities between Lake Huron and Lake Michigan, parameter
estimates were preferentially taken from Lake Michigan data, and as near to 1999 as possible. When Lake Michigan
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estimates were not available, preference was given to studies on Lake Ontario, Lake Superior, and finally Lake Erie.
When estimates were not available from studies within the Great Lakes, estimates were taken from Ecopath models
built for Lake Michigan (Ann Krause, UT, pers. comm.), Lake Superior (Cox and Kitchell, 2004; Kitchell et al.,
2000), or from regression relationships (e.g. Pauly, 1980).
2.2.2 Balancing an Ecopath model
Once the Ecopath model was built, parameter estimates were adjusted so that the model was balanced. We are
not aware of any Ecopath model that met the requirement of mass balance without some adjustment to initial
parameter estimates. To achieve mass balance, initial parameter estimates were changed by an iterative process
following recommended practices (Christensen et al., 2005). Estimates of P/B, Q/B, and diet from other Great Lakes
and other time periods were consulted to supply a range of plausible parameter estimates as additional guidelines to
inform the balancing process. Lake Huron biologists were consulted throughout the balancing process to ensure that
only plausible estimates of parameters were considered.
The order in which parameters were changed to achieve mass balance reflected their assumed reliability. Diet
studies for Lake Huron species are not common, and studies within the Great Lakes themselves range over many
years, particularly for lower trophic-level groups. The inherent nature of diet studies also results in poor reliability.
Diet varies both temporally and spatially, and thus diet observations may differ substantially among samples, even
within the same system and year. Consequently, diet information was assumed to be least reliable and was changed
first during balancing. After reasonable adjustments to diets were made, Q/B and P/B ratios, and B were changed.
Changing Q/B ratios was particularly relevant when many groups consumed by the same predator were all
unbalanced.
The balancing process can produce many different, but balanced, models. Two contrasting but realistic
approaches to balancing were compared. The first approach was based on the assumption that data for top trophic-
level groups were most reliable. Therefore, to achieve mass balance, production (P/B or B) of lower trophic-level
groups was preferentially increased to meet the consumption demands of their predators. This approach will be
referred to as the “consumption-based” approach throughout this paper, reflecting the confidence placed on the
estimates of consumption by top trophic-level groups. The second approach was based on the assumption that data for
prey fish and other lower trophic-level groups were most reliable. Consumption (Q/B or B) by predators was therefore
lowered to meet the production of their prey. This approach will be referred to as the “production-based” approach,
reflecting the confidence placed on estimates of production by lower trophic-level groups.
2.3 Assess impacts of balancing approaches
Ecosystem metrics within Ecopath and biomass dynamics within Ecosim were used to compare the two
balancing approaches. Measures of ecosystem maturity (sensu Odum, 1969) have been used to compare ecosystem
models (Christensen, 1995). For this research, ascendency (as described in Christensen and Pauly, 1992) was used to
assess ecosystem maturity. Ascendency describes the size and organizational structure of an ecosystem, and is argued
to reflect ecosystem maturity where high values represent a mature ecosystem and low values represent an immature
ecosystem (Ulanowicz, 1986). The second ecosystem metric was system omnivory index (SOI) which is the average
of each consumer’s omnivory index weighted by the logarithm of their food intake (Christensen et al., 2005). This
index also describes the structure of the food web while accounting for the magnitude of consumption for each group,
and is therefore influenced by changes to parameters that define either food-web structure (diet) or food intake (B,
Q/B).
Ecosim dynamics were also used to compare the two balancing approaches. Ecosim was run for a duration of
40 years, and the proportional difference in biomass between the two balancing approaches was calculated as
index 1 =mtim
mtim
tiB
Bmedmed
,,
,,
min
max, (5)
where medi is the median value taken over the index (in this case i groups); maxm and minm are the maximum and
minimum values between the m balancing approaches, respectively; and Bi,t,m is the biomass of group i in month t for
balancing approach m. To assess the sensitivity of balancing, the median change in relative biomass across balancing
approach, time, and then groups was calculated as
index 2 = mti
mtimti relBmedmedmed ,,
,, , (6)
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where relBi,t,m is the biomass of group i in month t for balancing approach m relative to the biomass in month 1, and
ζi,t,m is an indicator variable that was 1 if relBi,t,m > 1 and -1 if relBi,t,m < 1, and then the ratio of the two indices
(adjusted to reflect percentage changes) was calculated as
index 3 = (index 1 – 1)/(index 2 – 1). (7)
For all indices, sea lamprey and the first age stanza for stocked species were not included in the calculations because
the biomasses of these groups were forced at their initial Ecopath values. The last age stanzas for steelhead and
Chinook salmon were also excluded because they were modeling artifacts and were not of interest.
In the absence of any perturbation, the biomass of each group in Ecosim does not change from its initial
Ecopath value. Therefore, to compare the two balancing approaches, either the underlying productivity of the
environment (modeled as the P/B ratio of phytoplankton) or the total fishing mortality on all groups was doubled
during simulations. This caused the biomass for each group to vary across years in both balancing approaches. The
changes made to environmental productivity and fishing mortality were based on observed variability from past
estimates. Estimates based on chlorophyll a suggest that primary production during the late 1980s and early 1990s
was approximately twice that during 1999 (Barbiero et al., 2009). Similarly, estimates of fishing mortality for lake
whitefish Coregonus clupeaformis (Adam Cottrill, Onatrio Ministry of Natural Resources, unpublished data) and
other salmonines have varied more than two-fold through time, although doubling F produced greater values than
those observed in the past for lake trout (Ji He, Michigan Department of Natural Resources, unpublished data) and
Chinook salmon (Travis Brenden, Michigan State University, unpublished data).
Ecosim dynamics are heavily influenced by assumptions about the strength of species interaction
(vulnerabilities) (Christensen and Walters, 2004). Therefore, three different values for the vulnerabilities of prey to
their predators were also used: 1.01, 2, and 10. These values reflect low (1.01), intermediate (2), and high (10) impact
of predators on their prey. Low impact can be thought to represent bottom-up control, whereas high impact can be
thought to represent top-down control. Each vulnerability value was used on both types of forcing (environmental or
fishing) for a total of six different Ecosim scenarios.
3. Results:
As expected, the initial Ecopath model was unbalanced. Unbalanced groups included age 0 bloater
Corregonus hoyi (EE=2.1) and smelt Osmerus mordax (EE=3.2); less abundant prey fish including round goby
Neogobius melanostomus (EE=28.4), ninespine stickleback Pungitius pungitius (EE=7.4), and slimy Cottus cognatus
(EE=60) and deepwater sculpins Myoxocephalus thompsoni (EE=1.2); yearling lake trout (EE=2.1); age 1+ alewife
(EE=1.5); and phytoplankton (EE=3.2). Except for phytoplankton, these groups occupied intermediate trophic levels,
where demand on them was entirely predatory. Groups with demand from commercial or recreational harvest, on the
other hand, were all balanced.
To achieve mass balance, simple changes in diet contributions were made first. The final diet matrix for the
“consumption-based” approach describes the changes made during balancing (Table 2). The diet matrix for the
“production-based” approach was similar and is not shown. Predation on age 0 bloater was nearly entirely (99%) from
benthic prey-fish. Consumption of bloater eggs by slimy sculpin, deepwater sculpin, and stickleback was modeled as
consumption of age 0 bloater. Removing the contribution of age 0 bloater from the diets of these three species
resulted in mass balance for bloater (Table 2). Slimy sculpin had the largest pre-balance value of EE, and thus was the
most unbalanced group. Approximately 80% of its predation mortality was from age 1+ rainbow smelt. Lantry and
Stewart (1993) assumed that smelt consumed slimy sculpin, and although supported by Brandt and Madon (1986),
few other studies report predation on this species (Storch et al., 2007; Walsh et al., 2008). Consequently, slimy
sculpin was removed from the diet of age 1+ smelt (Table 2). This change alone, however, did not result in mass
balance.
Changes to more than just diet were required for balancing some species. Values of B, P/B, and Q/B for all
species are provided for models balanced with both the “consumption-based” (Table 3) and “production-based”
(Table 4) approaches. Slimy sculpin were particularly difficult to balance. Once predation by age 1+ smelt was
removed, the remaining mortality was from steelhead and burbot Lota lota. Moderate changes to slimy sculpin in the
diet of these species were made (Table 2), but slimy sculpin were still unbalanced. Balance was achieved by either
increasing slimy sculpin biomass (Table 3), or reducing consumption by its predators (Table 4). Stickleback and
round gobies also required more than just changes in diet to balance. Most consumption of stickleback (94%) and all
consumption of round goby was by adult lake whitefish. Although contributions of round goby and stickleback to the
diet of lake whitefish were small, the large biomass of lake whitefish resulted in high levels of consumption. Changes
to the diet of lake whitefish lowered the EE of these groups, but, as was the case for slimy sculpin, balance was
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achieved only after either increasing biomass of round goby and stickleback (Table 3) or decreasing consumption by
lake whitefish (Table 4). Changes in diet were not used to balance phytoplankton. Zooplankton were responsible for
99% of mortality of phytoplankton, and because they fed nearly exclusively on phytoplankton, balance was achieved
by either increasing phytoplankton production (Table 3) or decreasing zooplankton consumption (Table 4).
Values for the ecosystem metrics showed some difference between the two balancing approaches.
Ascendency was 28884 for the “consumption-based” approach and 7840 for the “production-based” approach. The
nearly four-fold difference in ascendency between the two models was mostly due to the different levels of
production of phytoplankton and zooplankton. For the “consumption-based” and “production-based” balancing
approaches, phytoplankton and zooplankton contributed 56 and 47% of the total ascendency, respectively. Detritus
made up an additional 40% for each approach. Although the scale of the models as estimated by ascendency was
different, the structure was nearly identical; SOI was 0.085 for the “consumption-based” approach and 0.084 for the
“production-based” approach.
Differences in biomass dynamics between the two balancing approaches (index 1) also suggested that
balancing affected model results. The magnitude of the difference depended on the assumed strength of interaction
(vulnerabilities) between predators and prey. Ecosim dynamics in the two balancing approaches were least similar
under high vulnerabilities and most similar under low vulnerabilities (Fig. 3). Although this pattern was maintained
under both types of forcing, it was more pronounced when environmental productivity was doubled than when fishing
mortality was doubled. At most, the difference was 15% (Fig. 3).
Median changes in relative biomass (index 2) followed a similar trend as index 1. Doubling environmental
productivity produced the greatest change in relative biomass, especially when vulnerabilities were high (Fig. 4).
Although index 2 reflected both decreases and increases from initial biomass levels, biomass of nearly all groups
increased when productivity was doubled. Doubling fishing mortality resulted in much smaller changes, but these
were still greatest when vulnerabilities were high (Fig. 4). Index 2 was used to calculate index 3, which expressed the
difference in biomass between the balancing approaches relative to the change in biomass due to other sources of
perturbation. Values for index 3 were at most 41% and were again greatest under high vulnerabilities (Table 5), but in
contrast to other indices, were greatest when fishing mortality was doubled than when environmental productivity was
doubled (Table 5).
Indices were also calculated for each modeled group. The largest values for indices 1 and 2 were observed
when environmental productivity was doubled and vulnerabilities were high. Under these conditions, index 1 was
greatest for dreissenids (7.39) and stickleback (5.31), and followed by slimy sculpin (1.60). Index 2 was greatest for
age 3+ and age 0-3 burbot (21.2 and 21.0, respectively), and followed by dreissenids (12.3). In scenarios with low
vulnerabilities, index 1 was greatest for age 0 alewife when environmental productivity and fishing mortality were
doubled (1.19 and 1.27, respectively) and was followed by age 0.5-1 Chinook salmon (1.06 and 1.12, respectively). In
scenarios with intermediate vulnerabilities, index 1 was greatest for age 1-5 Chinook salmon (1.10) when fishing
mortality was doubled, and stickleback (1.19) when environmental productivity was doubled.
Values for index 3 were greatest when fishing mortality was doubled, but contrary to the pattern in overall
medians, the greatest individual group value occurred under intermediate vulnerabilities. Under intermediate and high
vulnerabilities, respectively, age 0 lake whitefish (203 and 161%) and Diporeia (200 and 170%) had the greatest
values when fishing mortality was doubled. Under low vulnerabilities, age 0 alewife (12.7 and 119%) and age 0.5-1
Chinook salmon (3.3 and 102%) had the greatest values when both environmental productivity and fishing mortality
were doubled, respectively. Stickleback had the greatest values (8.65 and 70%) under intermediate and high
vulnerabilities, respectively, when environmental productivity was doubled.
Biomass dynamics for age 4+ lake whitefish, Diporeia, age 1+ alewife, and sticklebacks illustrate the general
patterns observed among modeled groups (Fig. 5). Lake whitefish represent 80% of commercial harvest in Lake
Huron (Ebener et al., 2008), Diporeia has been their primary prey, and alewife are a major component of Chinook
salmon and lake trout diets. Stickleback was included because of its large values for index 1 and 3. Because indices
were greatest when vulnerabilities were high, biomass dynamics are shown only for these scenarios (Fig. 5).
4. Discussion:
4.1 Effect of balancing approach
The effect of balancing depended on the strength of trophic interactions and the magnitude of biomass change
in the system. Under both types of forcing, all indices were greatest when vulnerabilities were high. Although the
overall difference between balancing approaches (index 1) was smallest when fishing mortality was doubled (Fig. 3),
the effect of balancing when expressed relative to the overall change in biomass (index 3) was greatest (Table 5). This
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occurred because changes in relative biomass were small when fishing mortality was doubled (Fig. 4), and therefore
differences between the two balancing approaches were also small in absolute terms (Fig. 3). When expressed relative
to the change in biomass, however, the effect of these differences appeared greater (Fig. 5). In other words, when
small changes to biomass occurred in the system, the effect of changes produced by differences in balancing becomes
more important. Based on these results, we conclude that the way in which our Ecopath model was balanced most
affected model conclusions when vulnerabilities were high and when biomass dynamics in the system were stable.
The significance of our results remains difficult to determine. Although the effect of balancing is different
among the six scenarios, and influenced by whether absolute or relative effects we considered, the question remains;
“Is a 41% effect important?”. Based on our results, the answer to this questions appears to be no. Under scenarios
with greater biomass change (i.e. environmental productivity was doubled) the absolute difference between balancing
approaches was at most 1.15 (Fig. 3). When put into the context of the overall change in biomass, the difference
between balancing approaches was 3.5% (Table 5). Although a 41% effect is greater than a 3.5% effect, the overall
change in biomass when fishing mortality was doubled was at most 1.08 and therefore small in absolute terms (Fig.
4). We understand that our findings reflect a single ecosystem, and that other ecosystems may have greater changes in
biomass, or require more contrast between balancing approaches when modeled. Additional comparisons would be
helpful to determine the significance of these results, and whether the general patterns we indentify hold.
Indices for some individual species (i.e. not averaging over i groups) were larger than those when averaging
over all groups. Age 0 lake whitefish, Diporeia, age 0 alewife, age 0.5-1 Chinook salmon, and dreissenids were often
the groups with the greatest values in various scenarios. Biomass dynamics of age 0 alewife and age 0.5-1 Chinook
salmon were highly oscillatory when vulnerabilities were low. A similar pattern existed for age 0 lake whitefish at all
vulnerabilities when environmental productivity was doubled, and can also be seen for age 4+ lake whitefish (Fig. 5).
A slight shift in phase for oscillating biomass trajectories inflated the difference between the two balancing
approaches, and thus these indices were large. Similarly, as biomass dynamics from either balancing approach neared
zero, the differences between the two approaches increased greatly. This occurred for dreissenids, and to a lesser
extent sticklebacks (Fig. 5), when vulnerabilities were high and environmental productivity was doubled. Although
biomass dynamics for sticklebacks did approach zero briefly, the large effect of balancing was likely because of high
predation mortality by lake whitefish. Under high vulnerabilities, oscillations in lake whitefish biomass likely
amplified oscillations in stickleback biomass.
Ecopath metrics were less informative in determining the effects of balancing on model results than indices
within Ecosim. Of the two Ecopath metrics that were used, ascendency changed the most. Values of ascendency for
both models were within the range (approximately 1000-100000) of those for 41 other Ecopath models (Christensen,
1994), and similar to Lake Superior (5221.3; Kitchell et al., 2000) and Lake Ontario (25630; Halfon and Schito,
1993). Christensen (1994) found that ascendency was highly correlated with total system throughput, which is a
measure of ecosystem size. Because production by lower trophic-level groups was increased in the “consumption-
based” approach, and consumption of higher trophic-level groups was decreased in the “production-based” approach,
the sizes of the models balanced by the two approaches were expected to be different. However, much of the
difference in size was due to differences in biomass of planktonic and detrital groups. Although the sizes of the Lake
Huron models were different, the values for SOI were very similar. This was likely because SOI is a metric for
consumers, and thus does not account for differences in biomass estimates of phytoplankton and detritus, which were
substantial. The values of SOI for Lake Huron were between those for Lake Ontario (0.0633; Halfon and Schito 1993)
and Lake Superior (0.108; Kitchell et al., 2000), and less than two models of the Bay of Quinte in Lake Ontario
(0.114 and 0.147; Koops et al., 2006).
4.2 Effect of vulnerabilities
Vulnerability parameters influenced the effect of balancing on Ecosim results. Values for each index were
greatest under scenarios with high vulnerabilities, and smallest under scenarios with low vulnerabilities. When
vulnerabilities are high, the effect that a predator can have on its prey is greater, and small changes to predator
biomass can cause large changes in the biomass of their prey. It makes sense then that differences in biomass
dynamics between the balanced models would be amplified when vulnerabilities were high. Vulnerabilities are known
to influence results when exploring management scenarios within Ecosim (Cox and Kitchell, 2004), but have not
previously been examined for their influence on balancing.
Vulnerabilities are commonly associated with the type of control in the food web, either top-down, bottom-
up, or a combination of the two. Because high vulnerabilities reflect top-down control, Ecopath models of systems
where predators control overall dynamics would be most affected by the way in which balancing is achieved. There is
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no consensus on the type of control operating in Great Lakes food-webs (Bence et al., 2008). Lake trout were
historically the top-piscivore within the Great Lakes and were believed to exert strong top-down control on the food
web (Ryder et al., 1981). Through a combination of increased fishing mortality and predation mortality from the
invasive sea lamprey, lake trout were nearly extirpated from the Great Lakes (Eshenroder et al., 1995). Ongoing
stocking of introduced Pacific salmonines and lake trout has increased the number of species occupying top trophic
levels in several Great Lakes and has raised concerns about levels of consumption (Dobiesz et al., 2005; Jones et al.,
1993). This concern continues today, especially given the high level of wild Chinook salmon recruitment in Lake
Huron (Johnson et al., 2010) and because predation by salmonines was found to be an important factor in alewife
recruitment in Lake Michigan (Madenjian et al., 2005). Evidence of top-down control also exists at lower trophic
levels. Increased predation mortality by predatory zooplankton has been suggested by Bunnell et al. (2011) to have
caused declines in zooplankton abundance in Lake Huron (Barbiero et al., 2009).
Bottom-up control has also been hypothesized as a driver of recent changes in Lake Huron. Dreissenid
mussels, which are hypothesized to remove nutrients from the offshore community and bind them in nearshore areas
(Hecky, 2004) have been implicated in the decline of Diporeia (Nalepa et al., 2007) and consequently the reduction in
lake whitefish growth (Pothoven and Madenjian, 2008). The clearing of the water column by mussels has also been
suggested as a possible driver for large declines in cladoceran zooplankton biomass (Barbiero et al., 2009; 2011).
Significant declines in primary production have been observed in Lake Michigan (Fahnenstiel et al., 2010) and Lake
Erie (Depew et al., 2001), and both have been attributed to filtering of dreissenids. The declines in zooplankton are
implicated in the decline in the abundance of alewife as well as other deepwater prey-fish in Lake Huron (Riley et al.,
2008). Consequently, community dynamics within Lake Huron are likely a combination of top-down and bottom-up
control.
4.3 Model imbalance
A common observation in Ecopath models is that based on initial input values, consumption of intermediate
and lower-trophic groups often exceeds production by those groups, and that various adjustments must be made to
achieve mass balance. The model presented here supports these observations. Stewart and Sprules (2011) discuss
trophic imbalances in an Ecopath model of Lake Ontario and compare this to observations from stream ecology (see
Huryn, 1996). An informal poll of other EwE modelers revealed that they had imbalances in the lower-trophic groups
in their models. Apart from Stewart and Sprules (2011), little has been discussed in the EwE literature about whether
these common observations reveal a larger issue with the modeling framework or with sampling of aquatic
ecosystems in general.
If intermediate and lower trophic-level groups are commonly overconsumed in Ecopath models, it makes
sense to discuss possible reasons for why this should occur. An obvious reason is uncertainty in input parameters. A
critical assumption of sampling is that it is both spatially and temporally representative of the system being modeled.
Biomass estimates of prey fish were taken from area-swept methods based on bottom trawl surveys. These surveys
are best suited for benthopelagic species, and are likely to underestimate biomass of benthic species, such as
deepwater and slimy sculpins, and pelagic species such as rainbow smelt (Riley et al., 2008). Comparisons with
acoustic surveys however, suggest that trawl estimates are generally similar (Roseman and Riley, 2009; Warner et al.,
2009). Biomass estimates of phytoplankton were taken within the top 20 m of the water column and therefore ignored
the contribution in the deep chlorophyll layer, which has been estimated to provide a substantial contribution to
primary production (Barbiero and Tuchman, 2001). Biomass estimates of zooplankton were taken from surveys done
once in August, which likely captured a peak in consumptive demand on phytoplankton (Barbiero et al. 2009).
Increasing biomass of benthic and pelagic fish and phytoplankton (Table 3), or decreasing biomass of zooplankton
(Table 4) addressed some model imbalances as reflected by the two balancing approaches.
Although spatial and temporal constraints on sampling can be addressed by more representative techniques,
there still exist challenges that may contribute to imbalances when combining parameter estimates into a single
model. Often, the offshore zone is less productive than the nearshore, although some studies have found the opposite
(Depew et al., 2001). Estimates taken only from offshore sites ignore the greater contribution of food resources
provided in the nearshore zone. Consequently, resources are available to predators in the system that are not included
in an offshore model. In addition, predators rarely feed within an average area of a system, so predation is likely to be
concentrated in areas were food resources are at their highest. Thus, although a sampler sees an average value of food
availability, a predator may see an above-average value.
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4.4 Subjective balancing
We elected to balance the Ecopath model using the subjective balancing procedure, despite the ease at which
balancing can be reproduced by the objective procedure (Kavanagh et al., 2004). Although two different objective
functions could have been used to perform the analyses for this research, we chose to balance the model in ways with
more biological appeal. Initial discussions with Lake Huron biologists about ways to address model imbalance often
focused on increasing production or decreasing consumption, and thus were used as the two approaches. While
subjectively balancing the model, problems in the modeling of diet contributions were found such as the consumption
of bloater eggs being attributed as age 0 bloater, and consumption of slimy sculpin by age 1+ smelt. These balancing
problems could have also been noticed when using the objective procedure, however, because changes can be made
automatically within the software it does not assist in their possible discovery.
4.5 Conclusions
Estimates used to parameterize Ecopath models commonly result in imbalance. The process of balancing an
Ecopath model is rarely described in the literature, especially with respect to its effect on simulations in Ecosim. This
research assessed the effect of contrasting but realistic approaches to balancing on biomass dynamics in Ecosim and
ecosystem metrics in Ecopath. Indices in Ecosim were more informative at assessing differences between balancing
approaches than were metrics in Ecopath. The difference in biomass dynamics between the two balancing approaches
was greatest when control of the ecosystem was top-down, providing further evidence that vulnerabilities are
important parameters in Ecosim. The effect of balancing was also greatest when changes in biomass caused by other
perturbations in the model were small. We encourage EwE modelers to consider the sensitivity of their models to
balancing when dynamics within their system are stable through time or when top-down control is expected, but
otherwise to focus their efforts on understanding the sensitivity of model results to vulnerability parameters rather
than alternative ways to achieve mass balance.
Acknowledgements:
This research was made possible by a grant from the Great Lakes Fishery Commission to MLJ, and with the
assistance of the Government of Canada for BJL to travel to the University of British Columbia (UBC). We’d also
like to thank members of the Fisheries Centre at the UBC for help in understanding the modeling software, members
of the Lake Huron Technical Committee for providing data and assistance during model construction, participants at
stakeholder meetings for assistance in model construction, and members of the Quantitative Fisheries Center (QFC) at
Michigan State University for assistance in model construction.
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2 - 14
Table 1: Names of species (with scientific names) or groups of species that were included in the Ecopath model, and
their corresponding group numbers. Specific age stanzas are listed for groups modeled with age structure.
Group name Scientific Name Group number
Sea lamprey Petromyzon marinus 1
Lake whitefish Corregonus clupeaformis
age 0, age 1-3, age 4+ 2, 3, 4
Lake trout Salvelinus namaycush
age 0, age 1, age 2-4, age 5+ 5, 6, 7, 8
Chinook salmon Oncorhynchus tshawytscha
age 0, age 0.5, age 1-5, age 6+ 9, 10, 11, 12
Steelhead Oncorhynchus mykiss
age 0, age 1, age 2-5, age 6+ 13, 14, 15, 16
Burbot Lota lota
age 0-3, age 3+ 17, 18
Alewife Alosa pseudoharengus
age 0, age 1+ 19, 20
Rainbow smelt Osmerus mordax
age 0, age 1+ 21, 22
Bloater Corregonus hoyi
age 0, age 1+ 23, 24
Round Goby Neogobius melanostomus 25
Slimy sculpin Cottus cognatus 26
Deepwater sculpin Myoxocephalus thompsoni 27
Ninespine Stickleback Pungitius pungitius 28
Diporeia Diporeia hoyi 29
Mysis Mysis diluviana 30
Benthic invertebrates 31
Dreissenid mussels Dreissena polymorpha
Dreissena bugensis
32
Predatory zooplankton Bythotrephes longimanus 33
Zooplankton 34
Phytoplankton 35
Detritus 36
2 - 15
Table 2: Contributions of prey (rows) to the diet of predators (columns) from the model balanced with the “consumption-based” approach. When
changed, initial contributions are provided in parenthesis and bolded. Numbers in the leading row and column correspond to group numbers
provided in Table 1. Groups not preyed on, or predators that fed outside the system are not shown. Predator group numbers
Pre
y g
rou
p n
um
ber
s
1 2 3 4 6 7 8 10 11 14 15 17 18
2 0.004 0.015
4 0.05
6 0.01 (0.029)
8 0.50
11 0.20
15 0.10
18 0.10
19 0.44 0.54 0.16 0.66 (0.19) 0.37 0.15 (0) 0.16
20 0.28 0.70 0.74 0.50 (0.85) 0.26
21 0.50 0.08 0.19 (0.81) 0.03
22 0.04 0.031 (0.012) 0.26 (0.24) 0.32 (0.09) 0.14 0.35
23 0.015
24 0.05 0.015 0.083 0.03
25 0.001 (0.032)
26 0.03 0.009 0.005
0.005
(0.03)
0.04
(0.115)
0.02
(0.06)
27 0.025 0.018 0.011
0.19
(0.115)
0.10
(0.06)
28 0.005 (0.01) 0.005 (0.048) 0.01 0.01 (0.03) 0.004 0.004
29 0.015 (0.01) 0.074 (0)
30 0.02 0.06 0.003
31 0.11 0.36 0.34 0.15 (0) 0.60 0.03 0.40 0.19
32 0.24 0.56 0.07 0.031
33 0.12 0.02
34 0.87 0.20
35
36
2 - 16
Table 2 cont.
Predator group numbers
Pre
y g
rou
p n
um
ber
s
19 20 21 22 24 25 26 27 28 29 30 31 32 33 34
2
4
6
8
11
15
18
19 0.01
20
21 0.05 (0.18)
22
23 0 (0.064) 0 (0.057) 0 (0.02)
24
25
26 0 (0.09)
27 0 (0.064) 0 (0.057)
28
29 0.035 0.09 0.065 0.20 0.095 0.024 0.72 (0.63) 0.37 (0.32) 0.65 0.03
30 0.21 0.31 0.18 0.56 (0.28) 0.40 0.009 0.17 (0.15) 0.62 (0.55) 0.22
31 0.015 0.02 0.015 0.02 0.004 0.034 0.11 0.011 0.026 0.05
32 0.93
33 0.13 0.05 0.006 0.02 (0.017) 0.005
34 0.61 0.53 0.73 0.14 0.50 0.10 0.67 1.0 0.05
35 0.01 0.30
0.30
(0.51) 0.95
36 0.99 0.95
0.70
(0.49)
2 - 17
Table 3: Parameter values from the Ecopath model balanced with the “consumption-based” approach. Proportional
adjustments from initial parameter estimates that were changed are in parentheses and bolded. Names of group
numbers are provided in Table 1.
Group
number
B (g/m2) P/B (/yr) Q/B (/yr) EE Harvest
(g/m2)
1 0.000470 0.860 16.0 0
2 0.0116 2.00 18.4 0.09
3 0.464 0.355 5.09 0
4 0.96 0.500 2.80 0.19 0.090
5 0.000398 0.00100 13.2 0
6 0.00363 0.410 6.91 0.72
7 0.00782 0.330 4.81 0.30 0.001
8 0.0318 0.600 3.35 0.53 0.006
9 0.00167 0.00100 27.1 0
10 0.0101 3.60 14.2 0
11 0.0500 1.40 6.22 0.29 0.019
12 0.000321 1.40 3.48 0
13 0.00166 0.00100 16.5 (0.75) 0
14 0.0122 0.500 9.16 (0.75) 0
15 0.142 0.106 5.50 (0.75) 0.10 0.001
16 0.475 0.106 4.49 (0.75) 0
17 0.00357 0.745 5.02 0
18 0.0647 0.149 2.00 0.08
19 0.263 4.00 33.7 0.31
20 0.667 1.25 13.6 0.88
21 0.0751 2.64 12.9 0.77
22 0.462 1.17 4.60 0.70
23 0.0147 2.33 31.9 0.02
24 0.309 1.02 8.60 0.06 0.004
25 0.0096 (2) 0.640 4.70 0.44
26 0.0113 (2.5) 1.00 (1.18) 7.50 (0.63) 0.78
27 0.117 0.850 (1.42) 7.50 (0.75) 0.19
28 0.0240 (2) 1.77 12.0 0.66
29 14.7 1.43 25.0 0.25
30 3.53 2.80 25.0 0.80
31 3.92 2.50 8.60 0.40
32 11.0 0.315 8.60 0.60
33 0.610 10.0 86.0 0.33
34 67.8 26.3 110 0.28
35 23.8 (3) 365 (1.31) 0.82
36 147 0.08
2 - 18
Table 4: Parameter values from the Ecopath model balanced with the “production-based” approach. Proportional
adjustments from initial parameter estimates that were changed are in parentheses and bolded. Names of group
numbers are provided in Table 1.
Group
number
B (g/m2) P/B (/yr) Q/B (/yr) EE Harvest
(g/m2)
1 0.000470 0.860 16.0 0
2 0.00886 (0.75) 2.00 13.8 (0.75) 0.06
3 0.348 (0.75) 0.355 3.82 (0.75) 0
4 0.72 (0.75) 0.500 2.10 (0.75) 0.25 0.090
5 0.000398 0.00100 13.2 0
6 0.00363 0.410 6.91 0.72
7 0.00782 0.330 4.81 0.30 0.001
8 0.0318 0.600 3.35 0.53 0.006
9 0.00125 (0.75) 0.00100 20.3 (0.75) 0
10 0.00756 (0.75) 3.60 10.6 (0.75) 0
11 0.0375 (0.75) 1.40 4.66 (0.75) 0.39 0.019
12 0.000241 (0.75) 1.40 2.61 (0.75) 0
13 0.000832 (0.5) 0.00100 16.5 (0.75) 0
14 0.00611 (0.5) 0.500 9.16 (0.75) 0
15 0.0710 (0.5) 0.106 5.50 (0.75) 0.20 0.001
16 0.238 (0.5) 0.106 4.49 (0.75) 0
17 0.00179 (0.5) 0.745 5.02 0
18 0.0324 (0.5) 0.149 2.00 0.16
19 0.263 4.00 33.7 0.17
20 0.667 1.25 13.6 0.63
21 0.0751 2.64 12.9 0.70
22 0.462 1.17 4.60 0.22
23 0.0147 2.33 31.9 0.02
24 0.309 1.02 8.60 0.05 0.004
25 0.00480 0.630 4.70 0.50
26 0.00450 1.00 (1.18) 7.50 (0.63) 0.78
27 0.117 0.850 (1.42) 7.50 (0.75) 0.11
28 0.0120 1.77 12.0 0.90
29 14.7 1.43 25.0 0.24
30 3.53 2.80 25.0 0.79
31 3.92 2.50 8.60 0.32
32 11.0 0.315 8.60 0.34
33 0.610 10.0 86.0 0.31
34 16.8 (0.25) 26.3 100 (0.91) 0.48
35 7.94 278 0.75
36 80.6 0.27
2 - 19
Table 5: Values of index 3 for each scenario. Index 3 is the percentage change in biomass due to balancing, averaged
across all groups, relative to the percentage change in biomass due to other sources, averaged across all groups.
Ranges of values for individual groups are provided in parentheses. Details of vulnerabilities and forcing type are
explained in Fig. 2.
Vulnerabilities Forcing type Index 3 (%) (range)
Low Environ 0.059 (0.00-12.7)
Med Environ 1.40 (0.11-8.65)
High Environ 3.46 (0.19-70.0)
Low Fishing 21.1 (0.00-119)
Med Fishing 23.1 (0.00-203)
High Fishing 41.3 (1.79-170)
2 - 20
Fig. 1: Map of Lake Huron with neighboring countries and lake basins labeled.
Fig. 2: Simplified food-web of the offshore community in the main basin of Lake Huron. “Pacific salmon” represent
groups 9-16 in Table 1, “Main prey fish” groups 19-24, “Other prey fish” groups 25-28, “Main inverts” groups 29-30,
“Other inverts” groups 31-32, and “Plankton” groups 33-35.
Fig. 3: Median proportional differences in biomass between the two balancing approaches taken across groups and 40
years of simulation (index 1) for each of six scenarios. Values above the bars reflect the range of proportional
differences over groups. “Low”, “med”, and “high” represent the magnitudes of trophic interactions (vulnerabilities)
used in each scenario (1.01, 2, and 10, respectively). “Environ” and “Fishing” represent scenarios where
environmental productivity was doubled and fishing mortality was doubled, respectively. The vertical dashed lines
separates scenarios between “Environ” and “Fishing”.
Fig. 4: Median change in relative biomass taken across groups, 40 years of simulation, and balancing approach for
each of six scenarios. Values above the bars reflect the range of proportional differences over groups. Details of each
scenario are explained in Fig. 2.
Fig. 5: Relative biomass for 40 years of simulation from the “consumption-based” (solid line) and “production-based”
(dashed line) balancing approaches for age 4+ lake whitefish, Diporeia, age 1+ alewife, and ninespine stickleback,
under high vulnerabilities and environmental (Environ) and fishing (Fishing) forcing types. Note the different scales.
2 - 21
Figure 1:
2 - 22
Figure 2:
Lake trout
(4 groups)
Plankton
(3 groups)
Sea lamprey
Lake whitefish
(3 groups)
Main prey fish
(6 groups)
Burbot
(2 groups)
Pacific salmon
(8 groups)
Other prey fish
(4 groups)
Other inverts
(2 groups)
Main inverts
(2 groups)
Detritus
2 - 23
Figure 3
2 - 24
Figure 4:
2 - 25
Figure 5:
2 - 26
Appendix A
Table A. 1: Sources of data for biomass (B), production to biomass (P/B) and consumption to biomass (Q/B) ratios, diet, and
when appropriate commercial or recreational harvest harvest. Brody growth coeffecients (K) and age of 50% maturity (Amat) are
given for multistanza groups. When applicable, location of data is listed, LH = lake huron, LM = lake Michigan, LS = lake
superior, LE = lake erie, and LO = lake Ontario. RE = estimates taken from published regression relationships. Group numbers
for pre-stocking stanzas for lake trout (5), Chinook (9), and steelhead (13), as well as age 6+ stanzas for Chinook (12) and
steelhead (16), are not included. Names of group numbers are provided in Tables 1.
Group
#
Parameter Data description Years Source
1 B LH number of spawners
Survival of parasite to spawning = 0.75
Average wet weight (WW) of parasitic
phase
1999 Mike Siefkes (Great Lakes Fishery
Commission, pers. comm.)
Jones et al. (2009)
Bergstedt and Swink (1994)
P/B LM ecopath model estimate Ann Krause (University of Toledo-UT,
pers. comm.)
Q/B Great Lakes bioenergetics study Madenjian et al. (2003)
Diet LM ecopath model estimate Ann Krause (UT, pers. comm.)
2 P/B Assumed value
Diet LH spring-summer sampling 2000, 2003 Nalepa et al. (2009)
3 P/B LH Ontario catch-at-age (SCA) models
LH 1836 treaty waters SCA models
1999
1999
Adam Cottrill (Ontario Ministry of
Natural Resources-OMNR, pers.
comm.)
Ebener et al. (2005), Modeling
subcommittee (2009)
Diet LH spring summer sampling 2000, 2003 Nalepa et al. (2009)
Harvest LH Ontario catch-at-age (SCA) models
LH 1836 treaty waters SCA models
1999
1999
Adam Cottrill (OMNR, pers. comm.)
Ebener et al. (2005), Modeling
subcommittee (2009)
4 B LH Ontario catch-at-age (SCA) models
LH 1836 treaty waters SCA models
1999
1999
Adam Cottrill (OMNR, pers. comm.)
Ebener et al. (2005), Modeling
subcommittee (2009)
P/B LH Ontario catch-at-age (SCA) models
LH 1836 treaty waters SCA models
1999
1999
Adam Cottrill (OMNR, pers. comm.)
Ebener et al. (2005), Modeling
subcommittee (2009)
Q/B Bioenergetics on laboratory fish Madenjian et al. (2006a)
Diet LH spring summer sampling 2000, 2003 Nalepa et al. (2009)
Harvest LH Ontario catch-at-age (SCA) models
LH 1836 treaty waters SCA
1999
1999
Adam Cottrill (OMNR, pers. comm.)
Ebener et al. (2005), Modeling
subcommittee (2009)
K LH Canadian index sampling = 0.229 1999-2008 Adam Cottrill (OMNR, pers. comm.)
Amat LH Canadian index sampling = 6 yrs 1999-2008 Adam Cottrill (OMNR, pers. comm.)
6 P/B LH SCA models 1999 Ji He (Michigan Department of Natural
Resources-MDNR, pers. comm.)
Diet LH bioenergetics model 1984-1998 Dobiesz (2003)
7 P/B LH SCA models 1999 Ji He (MDNR, pers. comm.)
Diet LH offshore spring/summer diet 1998-2003 Madenjian et al. (2006b)
Harvest LH SCA models 1999 Ji He (MDNR, pers. comm.)
8 B LH SCA models 1999 Ji He (MDNR, pers. comm.)
P/B LH SCA models 1999 Ji He (MDNR, pers. comm.)
Q/B LH bioenergetics model 1998 Dobiesz (2003)
Diet LH offshore spring/summer diet 1998-2003 Madenjian et al. (2006b)
Harvest LH SCA models 1999 Ji He (MDNR, pers. comm.)
K LH sampling = 0.303 1975-2007 Ji He (MDNR, pers. comm.)
Amat LH SCA models = 7 yrs 1984-2008 Ji He (MDNR, pers. comm.)
10 P/B LH stocking model 1999 Travis Brenden (Michigan State
University–MSU, pers. comm.)
Diet LM spring-fall diet 1973-1982 Jude et al. (1987)
2 - 27
11 B LH stocking model 1999 Travis Brenden (MSU, pers. comm.)
P/B LH stocking model 1999 Travis Brenden (MSU, pers. comm.)
Q/B LH bioenergetics model 1998 Dobiesz (2003)
Diet LH spring-fall diets 1984-1998 Dobiesz (2003)
Harvest LH US recreational and tribal commercial
harvest estimates
1999 Jim Johnson (MDNR, pers. comm.)
K LH stocking model = 0.37 Travis Brenden (MSU, pers. comm.)
Amat Assumed value = 3 yrs
14 P/B LH stocking model 1999 Travis Brenden (MSU, pers. comm.)
Diet LM spring-fall diet 1973-1982 Jude et al. (1987)
15 B LH stocking model 1999 Travis Brenden (MSU, pers. comm.)
P/B LH stocking model 1999 Travis Brenden (MSU, pers. comm.)
Q/B LM bioenergetics study 1978-1988 Stewart and Ibarra (1991)
Diet LM spring-fall diet 1973-1982 Jude et al. (1987)
Harvest LH US recreational estimates 1999 Jim Johnson (MDNR, pers. comm.)
K LH stocking model = 0.466 Travis Brenden (MSU, pers. comm.)
Amat Assumed value = 3 yrs
17 P/B LH bioenergetics model 1998 Dobiesz (2003)
Diet LH bioenergetics model 1984-1998 Dobiesz (2003)
18 B LH bioenergetics model 1998 Dobiesz (2003)
P/B LH bioenergetics model 1998 Dobiesz (2003)
Q/B LH bioenergetics model 1998 Dobiesz (2003)
Diet LH bioenergetics model 1984-1998 Dobiesz (2003)
K LH sampling = 0.238 1987-2008 Ji He (MDNR, pers. comm.)
Amat Assumed value = 3 yrs
19 P/B LM bioenergetics model 1987 Rand et al. (1995)
Diet LM spring-fall diet study 1998-2004 Pothoven and Madenjian (2008)
20 B LH bottom trawl estimates with fishing
power correction (FPC)
1999 provided by S.R.
P/B LM bioenergetics model 1987 Rand et al. (1995)
Q/B LM bioenergetics model 1987 Rand et al. (1995)
Diet LM spring-fall diet study 1998-2004 Pothoven and Madenjian (2008)
K LH index survey = 0.625 1999-2004 Adam Cottrill (OMNR, pers. comm.)
Amat Assumed value = 2 yrs
21 P/B LH bioenergetics model late 1980s Lantry and Stewart (1993)
Diet LH bioenergetics model late 1980s Lantry and Stewart (1993)
22 B LH bottom trawl estimates with FPC 1999 provided by S.R.
P/B LH bioenergetics model late 1980s Lantry and Stewart (1993)
Q/B LH bioenergetics model late 1980s Lantry and Stewart (1993)
Diet LH bioenergetics model late 1980s Lantry and Stewart (1993)
K LH bioenergetics model = 0.477 late 1980s Lantry and Stewart (1993)
Amat Assumed value = 2 yrs
23 P/B LM bioenergetics model 1987 Rand et al. (1995)
Diet LM fall diet study 1979-1980 Crowder and Crawford (1984)
24 B LH bottom trawl estimates with FPC 1999 provided by S.R.
P/B LM bioenergetics model 1987 Rand et al. (1995)
Q/B LM bioenergetics model 1987 Rand et al. (1995)
Diet LM august sampling 1995-1996 TeWinkel and Fleischer (1999)
Harvest LH fishery harvests 1999 Baldwin et al. (2002)
K LH Canadian index sampling = 0.147 1999-2008 Adam Cottrill (OMNR, pers. comm.)
Amat Assumed value = 2 yrs
25 B LH bottom trawl estimates with FPC 1999 provided by S.R.
P/B RE estimates
Lmax = 11.8 from Detroit River
K = 0.4 from Detroit River
T = 6 from LH SCA model for group 8
1996
1996
Pauly (1980)
MacInnis and Corkum (2000)
Fishbase (www.fishbase.org)
Ji He (MDNR, pers. comm.)
Q/B LE bioenergetics study 2000-2001 Lee and Johnson (2005)
2 - 28
Diet LH fall diet study by number
WW for zooplankton
WW for benthic invertebrates
WW for dreissenids
WW for diporeia
WW for mysis
WW for bythotrephes
2000-2001 Schaeffer et al. (2005)
Hawkins and Evans (1979)
Nalepa and Quigley (1980), Nalepa et
al. (2002)
Mills et al. (1999)
Landrum (1988)
Sell (1982)
Barbiero and Tuchman (2004),
Johannsson et al. (2000)
26 B LH bottom trawl estimates with FPC 1999 provided by S.R.
P/B LS ecopath model Kitchell et al. (2000)
Q/B LS ecopath model Kitchell et al. (2000)
Diet LM diet study in dry weights (DW)
DW:WW for Diporeia = 0.2
DW:WW for Mysis = 0.15
DW:WW for other (chironomids) = 0.14
DW:WW for fish eggs (standard
zoobenthos) = 0.166
2000-2001 Hondorp et al. (2005)
Landrum (1988), Johnson (1988)
Landrum et al. (1992)
Smit et al. (1993)
Jørgensen (1979)
27 B LH bottom trawl estimates with FPC 1999 provided by S.R.
P/B LS ecopath model Kitchell et al. (2000)
Q/B LS ecopath model Kitchell et al. (2000)
Diet LM diet study Hondorp et al. (2005)
28 B LH bottom trawl estimates 1999 provided by S.R.
P/B RE with
Lmax = 7.6 from Canada
K = 1.6 from England
T = 6 from LH SCA model for group 8
Pauly (1980)
Fishbase (www.fishbase.org)
Fishbase (www.fishbase.org)
Ji He (MDNR, pers. comm.)
Q/B Assumed the same as group 26
Diet LS diet study 1968-1969 Griswold and Smith (1973)
29 B LH sampling study in numbers
Average WW per individual
1999 Richard Barbiero (Environmental
Protection Agency-EPA, pers. comm.)
Landrum (1988)
P/B LH sampling study in profundal zone 1980-1982 Johnson (1988)
Q/B LS ecopath model Kitchell et al. (2000)
Diet LM spring-fall sampling 1986-1987 Evans et al. (1990)
30 B LH sampling study 1971 Sell (1982)
P/B LH sampling study 1971 Sell (1982)
Q/B LS ecopath model Kitchell et al. (2000)
Diet LO diet study 1995 Johannsson (2001)
31 B LH numbers
Conversions to WW
2000
1987-1996
Nalepa et al. (2007)
Nalepa et al. (2002)
P/B LO study 1967-1968 Johnson and Brinkhurst (1971)
Q/B LM ecopath model Ann Krause (UT, pers. comm.)
Diet LM ecopath model Ann Krause (UT, pers. comm.)
32 B LH numbers
LO WW
LE conversion to shell free WW
2000
1995
1993-1994
Nalepa et al. (2007)
Mills et al. (1999)
Johannsson et al. (2000)
P/B LO study 1967-1968 Johnson and Brinkhurst (1971)
Q/B LO network model Jaeger (2006)
Diet LM ecopath model Ann Krause (UT, pers. comm.)
33 B LH sampling in DW
DW:WW ratio = 0.1
Average depth = 76 m
1999 Richard Barbiero (EPA, pers. comm.)
Richard Barbiero (EPA, pers. comm.)
Barbiero et al. (2001)
P/B LE RE
LH sampling for average length
LH surface temperature data
1993-1994
1983-1999
Johannsson et al. (2000)
Barbiero and Tuchman (2004)
www.ndbc.noaa.gov
Q/B LM ecopath model Ann Krause (UT, pers. comm.)
Diet LH summer diet study 1988 Vanderploeg et al. (1993)
2 - 29
34 B LH sampling in DW
DW:WW ratio = 0.1
Average depth = 76 m
1999 Richard Barbiero (EPA, pers. comm.)
Richard Barbiero (EPA, pers. comm.)
Barbiero et al. (2001)
P/B LE RE
RE
LH surface temperature data
1993-1994 Johannsson et al. (2000)
Shuter and Ing (1997)
www.ndbc.noaa.gov
Q/B LM ecopath model Ann Krause (UT, pers. comm.)
Diet LM ecopath model Ann Krause (UT, pers. comm.)
35 B LH sampling
Depth of sampling = 20 m
1999
Richard Barbiero (EPA, pers. comm.)
Richard Barbiero (EPA, pers. comm.)
P/B LM ecopath model Ann Krause (UT, pers. comm.)
36 B RE
LH spring euphotic depth = 26
WW:Carbon (C) for phytoplankton = 42
DW:C for detritus = 2.22
DW:WW for detritus = 0.08 (assuming
same as for phytoplankton)
1993-1995
Pauly et al. (1993)
Fahnenstiel et al. (2000)
Cushing (1958)
Jørgensen (1979)
Cushing (1958)
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Johnson, M.G., 1988. Production by the amphipod Pontoporeia hoyi in South Bay, Lake Huron. Canadian Journal of Fisheries
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the Fisheries Reserach Board of Canada 28, 1699-1714.
Jones, M.L., Irwin, B.J., Hansen, G.J.A., Dawson, H.A., Treble, A.J., Liu, W., Dai, W., Bence, J.R., 2009. An operating model
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Jude, D.J., Tesar, F.J., Deboe, S.F., Miller, T.J., 1987. Diet and selection of major prey species by Lake Michigan salmonines,
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M., Hoff, M., Schram, S., Schreiner, D., Walters, C.J., 2000. Sustainability of the Lake Superior fish community:
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MacInnis, A.J., Corkum, L.D., 2000. Age and growth of round goby Neogobius melanostomus in the upper Detroit river.
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Madenjian, C.P., Cochran, P.A., Bergstedt, R.A., 2003. Seasonal patterns in growth, blood consumption, and effects on hosts by
parasitic-phase sea lampreys in the Great Lakes: An individual-based model approach. Journal of Great Lakes Research
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Madenjian, C.P., Holuszko, J.D., Desorcie, T.J., 2006a. Spring-summer diet of lake trout on six fathom bank and yankee reef in
Lake Huron. Journal of Great Lakes Research 32, 200-208.
Madenjian, C.P., O'Connor, D.V., Pothoven, S.A., Schneeberger, P.J., Rediske, R.R., O'Keefe, J.P., Bergstedt, R.A., Argyle, R.L.,
Brandt, S.B., 2006b. Evaluation of a Lake Whitefish Bioenergetics Model. Transactions of the American Fisheries
Society 135, 61-75.
Mills, E.L., Chrisman, J.R., Baldwin, B., Owens, R.W., O'Gorman, R., Howell, T., Roseman, E.F., Raths, M.K., 1999. Changes in
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report_288026_7.pdf
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species composition of benthic macroinvertebrate populations in Saginaw Bay, Lake Huron, 1987-96. NOAA Technical
Memorandum 122, Great Lakes Environmental Research Laboratory, Ann Arbor.
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populations in Lake Huron over the past four decades. Journal of Great Lakes Research 33, 421-436.
Nalepa, T.F., Pothoven, S.A., Fanslow, D.L., 2009. Recent changes in benthic macroinvertebrate populations in Lake Huron and
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River, 1976-1977. NOAA Data Report ERL GLERL 17, Great Lakes Environmental Research Laboratory, Ann Arbor.
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2 - 31
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3 - 1
Appendix 3: Modeling species invasions in an Ecopath with Ecosim model of a Laurentian Great Lake, Lake Huron.1
Introduction:
Non-native species are a continual threat to the maintenance of Great Lakes ecosystems. Approximately 185
non-native species have invaded the Laurentian Great Lakes, and of these 10% have caused considerable ecological
change (Environment Canada and EPA 2009). Not all non-native species cause ecological change; effects range from
negligible, when the species fails to proliferate, to severe, when the species becomes established and alters ecosystem
processes (Williamson and Fitter 1996). Non-native species that become established and alter system processes are
called “invasive species” and will be referred to as such throughout this paper.
Over the past three decades, several non-native species from the Ponto-Caspian region of Eurasia have
invaded the Great Lakes. Those considered to be invasive include the spiny-water flea (Bythotrephes longimanus),
zebra (Dreissena polymorpha) and quagga (Dreissena bugensis) mussels, and round goby (Neogobius melanostomas).
The spiny water flea invaded the Great Lakes during the 1980s (Vanderploeg et al. 2002), and preys heavily on large
zooplankton (Bunnell et al. 2011). Zebra and quagga mussels were first observed in Lake St. Clair in 1988
(Vanderploeg et al. 2002), and have been suggested to contribute to shifts in the zooplankton community (Barbiero et
al. 2009; Bunnell et al. 2011), declines in prey fish abundances (Riley et al. 2008), reductions in the spring
phytoplankton bloom (Barbiero and Tuchman 2004), and declines in abundances of the native benthic amphipod
(Diporeia) (Nalepa et al. 2007). Round goby were first observed in the St. Clair River in 1990 (Vanderploeg et al.
2002) and compete with other benthic fish, predominantly sculpins.
Ecosystem-based approaches to fisheries management are becoming more prevalent in the management of
aquatic resources (Pikitch et al. 2004). Such approaches take into account objectives for multiple species within an
ecosystem, and are useful for exploring possible management scenarios. A popular tool in assessing multi-species
objectives in fisheries management is the Ecopath with Ecosim (EwE) computer software (Robinson and Frid 2003).
Ecopath with Ecosim requires a mass-balance description of a food-web at a single point in time (Ecopath) which is
then used as the basis for running time dynamic simulations of the future (Ecosim; Christensen and Walters 2004).
Published EwE models in the Great Lakes exist for Lake Superior (Kitchell et al., 2000; Cox and Kitchell, 2004) and
Lake Ontario (Halfon and Schito 1993; Halfon et al. 1996; Stewart and Sprules 2011), and models are currently being
developed for the other three lakes.
Ecopath with Ecosim models can help to assess the implications of recent changes in Great Lakes food-webs
for management. Invasive species have played prominent roles in shaping the ecosystems in the Great Lakes and need
to be incorporated into EwE models. Properly simulating the dynamics of invasions is not straightforward. All
modeled species, including invasive species, must have positive biomass when the model is initialized. This presents
a problem for modeling invasive species that have not yet entered the system by the year the model is initialized. One
solution to account for the effect of invasive species on a food-web has been to use Ecopath to construct two models
that describe a system before and after species invasion (Jaeger 2006; Stewart and Sprules 2011). Such an approach
can only be done with Ecopath, and precludes the use of dynamic simulations. A second approach is to initialize an
Ecopath model in a year after all invasive species are present, and therefore have positive biomass values. However,
because EwE fits time-series of data to tune model parameters, initializing Ecopath in a later year would sacrifice
information about species dynamics prior to the invasion that can contribute to the model-fitting process.
Methods for simulating the invasion process in EwE models without reducing the length of time series
available for model fitting have been proposed. These methods initialize invasive species at some positive biomass
value prior to actual invasion, artificially maintain the invasive at negligibly low biomasses until the year in which it
invades, and then afterwards allow the species to proliferate. Pine et al. (2007) simulated the invasion of catfish into
an inland reservoir by artificially increasing its mortality through fishing prior to the time of invasion, and then
reducing fishing mortality. Similarly, Espinosa-Romero et al. (2011) kept sea otter levels low, prior to introduction,
through an artificial cull. Forcing biomass of invasive species (i.e., specifying a time series rather than dynamically
modeling it) has also been successfully employed to model lionfish invasion in the Caribbean (V. Christensen,
Fisheries Centre, University of British Columbia, pers. comm.). In addition to adjusting biomass, Cox and Kitchell
(2004) also adjusted diets and mortalities to properly account for the effect of invasive rainbow smelt in Lake
Superior. Such methods are ad hoc but represent practical attempts to account for the effect of invasive species.
1 Substantial contributions to the ideas in this appendix were provided by Mark Rogers, United States Geological Survey,
Sandusky, OH, and Hongyan Zhang, Cooperative Institute for Limnology and Ecosystem Research, Ann Arbor, MI.
3 - 2
Different methods of accounting for invasive species have not been compared nor summarized. In this paper,
four methods of incorporating invasive species into an EwE model of Lake Huron, one of the Laurentian Great Lakes,
are discussed. These methods included 1) forcing invasive species biomass to observed levels, 2) starting invasive
species biomass at very low levels and allowing them to increase, 3) starting invasive species biomass at recent (high)
levels and artificially removing them until the time at which they invade, and 4) adjusting vulnerabilities of invasive
species over time to match biomass dynamics. We present the advantages and disadvantages of each method, based
on subjective testing and exploration of the models, and provide a quantitative comparison as to which method best
reproduced observed time series of biomasses. Rather than providing a rule that all other EwE modelers should use,
we instead hope to provide guidance about the strengths and weaknesses of our four methods. Future EwE modelers
can then use these strengths and weaknesses to make decisions about how best to include invasive species in EwE
models for their system.
Methods:
Models
Data inputs taken primarily from Lake Huron were used to construct an Ecopath models. The model was
parameterized from data collected around 1981 for 20 unique species or groups of species. When data were available,
multiple age-stanzas were included for biologically important species as well as those targeted by fisheries, which
increased the total number of modeled groups to 36 (Table 1). This model focused on the offshore fish community, so
important nearshore groups were excluded. Five fisheries were included in the model: trap nets and gill nets for the
lake whitefish commercial fisheries in Canadian and US waters; combined trap nets and gill nets for the lake
whitefish commercial fishery in tribal waters; the commercial fishery for bloater (Coregonus hoyi) in Canadian
waters; and the recreational fishery for Pacific salmonines and lake trout (Salvelinus namaycush) throughout the lake.
Sea lamprey (Petromyzon marinus) biomass was forced at observed levels because much of its biological control
occurs outside the model in the form of chemical treatments. Additionally, Chinook and steelhead salmon, and lake
trout are stocked in the model and thus recruitment dynamics were driven by stocking. Bythotrephes were assumed to
enter into Lake Huron in 1984 (Makarewicz 1988), round goby in 1997 (S. Riley, United State Geological Survey,
Ann Arbor, unpublished data), and Dreissenids in 1997. Dreissenids were established prior to 1997, but data were
available beginning in 2000 (Nalepa et al. 2007, French et al. 2009) and thus the same year of “invasion” as round
goby was chosen.
Estimates of biomass (B), production to biomass (P/B), consumption to biomass (Q/B), diet components
(DC), harvest, and biomass accumulation (BA) from the literature and previously published models were used to
define interactions among modeled groups and fisheries within Ecopath (Christensen and Pauly, 1992). Important
parameters governing these interactions which are calculated within Ecopath are search rates (aji) of predator j on
group i, and other mortality (M0i) of group i. Other mortality is the mortality not explained by modeled sources, and is
the difference between total mortality entered into the model (P/B) and the sum of predation mortality, fishing
mortality, and BA. When M0 was negative, the model was unbalanced, and data inputs were adjusted following
recommended practices (Christensen et al. 2005, see also Appendix 2).
After species interactions were defined in Ecopath, Ecosim was used to calculate biomass estimates through
time. Descriptions of the calculations are outlined in Christensen and Walters (2004). Perhaps the most important
parameters governing interactions in Ecosim are vulnerabilities. Vulnerabilities control the strength of the trophic
interaction between a predator and its prey. Although unique vulnerabilities for each predator-prey interaction are
possible, it is recommended that a single vulnerability be used for all prey of a single predator. Vulnerabilities are
difficult to estimate in the field, however general ranges can be specified based on the assumed direction of control in
the system (top-down versus bottom-up) or the observed variability in biomass of a group (where large variability
would suggest high vulnerabilities) (Christensen et al. 2005; Ahrens et al. 2012). Alternatively, Ecosim has a fitting
procedure to estimate vulnerability parameters so that modeled biomass dynamics match time series of user-provided
biomass data as close as possible, and which is recommended (Ahrens et al. 2012). Fitting to time series also provides
a way to estimate the level of relative productivity (measured as the P/B of phytoplankton) in the system for each
year, called “production anomalies”. These anomalies often reduce the deviations in fit by about a third from
estimating vulnerabilities alone (C.J. Walters, Fisheries Centre, University of British Columbia, pers. comm.).
Quantitative criteria for assessing performance
The fits of predicted Ecosim dynamics to observed time series provided objective criteria to compare the four
methods of incorporating invasive species in EwE models. Ecosim calculates residual sum of squares (RSS) between
3 - 3
the Ecosim calculated biomass dynamics and the observed data inputs, calculated on a log scale. Data inputs can be
relative or absolute values. Because our four methods utilized different starting values of biomass for the invasive
species, absolute biomass time series of invasive species were used. Time series of biomasses, fishing mortalities,
stocking levels, growth, and total mortality for some modeled groups were available from published and unpublished
sources and ranged from 1981-2008 (Table 2). Unique vulnerability parameters for every modeled predator were
estimated as well as yearly production anomalies.
Methods of incorporating invasive species:
Overview:
Four methods of incorporating invasive species into EwE models were compared. The methods differed in
terms of whether or not the observed invasive species biomass time series was used to fit the model, assumed initial
values of biomass, and the method used to “release” the invasive species and allow its biomass to increase at the time
of invasion (Table 3). A low initial biomass is arguably more representative of the pre-invasion state, whereas high
initial biomasses facilitate the biomass reaching recent (post-invasion) levels in the later years of the time series.
Forcing the biomass time series for an invasive species (i.e., not requiring the model to predict values that fit the data)
allows model fitting to be focused on non-invasive groups; alternatively the biomass of the invasive species could be
fit along with all other groups. When the modeled invasive species biomass was fit to data, the model was adjusted to
mimic the observed invasion dynamics (low before invasive and increasing after invasion) by artificially increasing
mortality through fishing before invasion, and then removing (“releasing”) fishing after invasion, or by changing the
strength of predator-prey interactions (i.e., vulnerabilities).
Method 1 – Forcing biomass
The simplest way in which invasive species were included in the EwE model was through biomass forcing.
Time series of invasive species biomass were used to overwrite simulated values based on the Ecosim equations.
Time series of all other species groups were used to estimate vulnerability parameters and production anomalies.
Biomass time series of round gobies, Dreissenids, and Bythotrephes were available. Time series for Dreissenids and
Bythotrephes were incomplete, and did not extend all the way to early the years of invasion (Table 2). For years
before data availability, but after species invasion, biomasses were assumed to be zero. Small gaps in the time series
were not a problem because Ecosim estimates biomass values at each time step, and therefore gaps were filled in with
Ecosim estimates.
Using time series of biomass to force dynamics in Ecosim did not solve the issue of initial biomass estimates
in Ecopath, which should be zero because the model was initialized before species invaded. The way in which Ecosim
fills in gaps in the time series was found to be affected by the choice of initial biomass estimate. Low initial biomass
estimates resulted in Ecosim estimating lower biomasses than observed in the time series. On the other hand, high
initial biomass estimates resulted in Ecosim estimates that more closely matched the observed biomasses for years
later in the time series. Thus we chose to use a high initial biomass for this method, as described in detail for method
3.
Method 2 – Starting biomass at low levels
For this method, initial biomass was set at a low value. During an actual invasion, biomass starts at a low
value, and then builds to higher levels. Starting biomass at low levels therefore has the advantage that it more closely
resembles initial biomass values. However, starting with a very low, positive biomass can result in unrealistically high
predation mortality by predators of invasive species. Consequently, diet contributions of invasive species to their
predators must also be low.
Entering biomass or diet at arbitrary low levels could lead to grossly incorrect descriptions of trophic
interactions between groups. To address the concerns of reasonably describing interactions between invasive species
and their prey and predators, we took the following approach when starting invasive species biomass low. We picked
a year, 2002, in the invasive species time series during which the invasive species was well established and which diet
information for its predators were available. The biomass for invasive species at this year was then divided by 1000
and used as an initial biomass value. This scaling factor was large enough to make initial biomass estimates of the
invasive species realistically small, but the factor was also small enough so that diet estimates could be entered into
Ecopath at appropriate precision. Because the invasive species biomass was reduced by a factor of 1000, diet
contributions of the invasive species to its predators had to be reduced as well to maintain search rates at calculated
levels. This assumes that predator search rates for the invasive species do not change over time. To allow all diet
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contributions to sum to one, other components of the predators’ diets were proportionally adjusted. Although the
invasive species was also feeding on prey items during the pre-invasions period which in reality do not yet experience
this mortality, the mortality levels were very small initially due to the low initial invasive species biomass, and thus
no corrections were required.
To simulate an increase in biomass during the time at which invasion actually occurred, fishing mortality was
applied to each invasive species initially, and then removed in the year of invasion. The level of fishing mortality was
based on the instantaneous rate of population increase (r) from the time series of data as calculated by B0ert, where B0
is the first biomass estimate in the time series, and t is the number of years after the year for B0. To offset the added
fishing mortality rate, P/B ratios of the invasive species were increased by an amount equivalent to the level of fishing
mortality. This ensured that estimates for M0 were taken only from trophic interactions with other species.
Method 3 – Starting biomass at high levels
Rather than choosing an initial biomass estimate that is much lower than the earliest levels, an alternative is to
initialize the biomass at a future observed level, and then artificially reduce invasive species biomass (by applying
fishing mortality) until the time of invasion, generally around 2002. A biomass estimate was selected from a year in
which diets for predators of invasive species were available, and used as the initial biomass. Diet contributions of
invasive species in that year were also added into their predators’ diets for the initial modeled year. Other diet items
were proportionally adjusted so that total diet proportions summed to one. The initial biomass calculated this way was
substantial for Dreissenids and caused imbalance in groups Dreissenids prey on. To offset the (artificial) predation
mortality by Dreissenids, as well as other invasive species that occurred when the model was initialized, we added a
negative biomass accumulation to prey items that was equivalent to the initial level of consumption of the prey item
by the invasive species. Adding negative biomass accumulation allowed Ecopath to calculate appropriate levels of M0
based on groups actually present in the system in 1981.
Once the invasive species were entered into the initial Ecopath model, it was important to remove their
biomass as quickly as possible during the time dynamic simulations. This was done by a combination of adding large
fishing mortality and removing that mortality with negative BA. Fishing mortality was applied to each invasive group
so that biomass would become zero within the first year. To account for this artificial fishing mortality, biomass
accumulation equivalent to the level of fishing was added to each invasive species so that other mortality estimates
would be based solely on trophic dynamics. This allowed the invasive species to be present in the time dynamic
simulation prior to its actual invasion, but without having any effect on the rest of the food web.
Method 4 – Adjusting trophic interactions by vulnerability mediation
The previous 3 methods required some form of artificial reduction or increase in biomass of invasive groups.
Ecosim controls the strength of interaction between two groups through vulnerability parameters. For the fourth
method, assumptions about changes in the strength of species interactions were used to mediate vulnerabilities, and
therefore influence the response of invasive species. Two forcing functions were used to mediate vulnerabilities: one
to mediate vulnerabilities of invasive species to their predators, and the other to mediate the vulnerabilities of prey to
invasive species. These functions forced vulnerabilities to zero in years prior to the actual invasion, which implies no
effect of the invasive species on either their predators or their prey. Once invasion occurs, prey species were assumed
to become more susceptible to the invasive predator, which was simulated by forcing vulnerabilities to increase to a
peak. Over time, we assumed that prey defense mechanisms would be developed and vulnerabilities would reach
stable levels (i.e. would return to a relative value of 1). Similarly, in the initial stages of an invasion, predators on
invasive species may not have developed a search image, and therefore vulnerabilities of invasive species to its
predators were assumed to be very low. Over time during the invasion, the vulnerabilities of invasive species to their
predators were assumed to increase to stable levels. The shape (Figure 1) of the forcing functions (relative changes to
vulnerabilities over time) was chosen using an ad hoc procedure that reasonably matched the model to the invasive
species time series prior to searching over vulnerabilities and production anomalies. For this method we used the
same approach as for method 3 for setting initial biomasses and diets.
Results:
All four methods performed reasonably in matching basic dynamics of invasive species for the Lake Huron
model (Figure 2). The approach of forcing invasive species or fitting invasive species to biomass time series could
capture increases and decreases in biomass around the same time as the data suggested. Starting biomass of invasive
species at both low and high levels also resulted in increases in biomass in later years.
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Fits to non-invasive species ranged from adequate to poor, depending on the species and method employed
(Figure 2). Fits to lake whitefish and Diporeia were poorest among non-invasive groups across all four methods. In
general, each method tended to underestimate lake whitefish biomass and overestimate Diporeia biomass. Dreissenids
are a large proportion of the diet of lake whitefish and thus the way in which this invasive species was modeled may
have contributed to low lake whitefish biomass. Overall, method 2 resulted in the most reasonable fits for invasive
and non-invasive groups (Figure 2).
Quantitative measures of model fit could not be directly compared between methods because the data used to
fit the models differed among methods. All methods except method 1 used invasive time series in the fitting
procedure to estimate vulnerability and environmental productivity parameters. In contrast, method 1 forced (i.e., did
not fit) the invasive time series. Since the goal of estimating vulnerabilities was to match as many of the modeled
groups’ dynamics, which included invasive species, fits to all groups were important.
Among methods where all time series were used to estimate vulnerabilities and production anomalies, method
2 had the lowest sum of squares, followed by method 4 and lastly by method 3 (Table 4). Method 2 performed nearly
as well in terms RSS as did method 1 despite a greater number of data points contributing to the RSS. Method 3
performed substantially poorer than the other methods (Table 4). The RSS for method 3 could actually be improved if
the invasive species time series were not included when searching over vulnerabilities and environmental
productivities, but was then included when calculating RSS. This occurred because the fitting routine worked to
reduce the very high RSS for the invasive species time series at the expense of improving the RSS in other time series
(i.e., the predicted invasive species time series data were very inconsistent with the other groups in the model).
The presence of invasive species in the diet of their predators was a second important attribute that we wanted
the model to capture. In addition to reasonably reproducing biomass time series, estimated diet proportions for
predators of invasive species reproduced observed data for all methods (but are not shown). As expected, the increase
in contributions of invasive species to their predators’ diet matched the increase in abundance.
Discussion:
Methods 1 and 2 had the lowest RSS among the four methods considered (Table 4). However fits were
reasonable for important groups only for method 2 (Figure 2), and is therefore recommended for future EwE
modelers. We also judged method 2 to be the simplest of the three non-forcing methods. Because biomass starts low,
method 2 doesn’t require large changes to predation mortalities of invasive species on their prey, or to diet
contributions of invasive species to their predators.
In addition to quantitative comparisons among methods, another objective of this study was to summarize the
advantages and disadvantages of each method. Below we discuss each method in turn.
Method 1 - Forcing biomass:
The primary advantage for using this method is that forcing biomass of invasive species is the best way to
match observed dynamics of invasive species. Although observed dynamics are not necessarily the dynamics that
actually occurred in the system due to measurement error, they are the best available information on the invasion
dynamics. This method is also simple, in that there is no need for artificial mechanisms to keep calculated biomass
levels low until the time of invasion. The main disadvantage of this method is the requirement for a complete time
series of observed data. Time series for both Bythotrephes and Dreissenids did not extend to the year of actual
invasion. Biomass time series are required for all methods, but having a complete time series is more critical for
method 1. When the time series are incomplete, interpolation between years can be used, but this is only reliable if a
small fraction of years are missing and there are data for the beginning and end of the time series, or the invasion can
be assumed to start later than it actually occurred (as was the case here for Bythotrephes and Dreissenids). Another
important disadvantage of method 1 is that future dynamics for invasive species are not informed by past dynamics
because biomass was forced, rather than having the model estimate vulnerabilities based on the observed patterns of
change in the biomass of the invasive species and their predators/prey.
Method 2 - Starting biomass low:
The primary advantage for starting invasive species low is that this method best mirrors the process of actual
invasion. In addition, a species with very low biomass has very little impact on other species in the model, and thus
few adjustments are needed. With low invasive species biomass, contributions of invasive species to the diets of their
predators are low, resulting in few changes to the contributions of other groups. Similarly, initial predation mortality
by invasive species on prey is minimal, and thus mortalities do not need to be offset. Combined, these attributes
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simplify the process of modeling invasive species. The primary disadvantage of this method is that species dynamics
must be maintained by an artificial fishery. The fishery kick-starts the invasion, and thus depends on the time of
release from fishing as well as on the level of fishing. For fishing mortalities that are too low, invasive species
biomass fails to reach observed levels, even with high vulnerabilities. For mortalities that are too high, invasive
species biomass increases very quickly, and vulnerabilities are lower. This method also assumes, simply because
invasive species must increase their biomass greatly during the invasion period, that their vulnerabilities will be high.
Method 3 - Starting biomass high:
This method is the most complicated of all methods. Consequently, the advantages are minimal. The primary
advantage is that invasive species are able to increase very rapidly due to the removal of a very substantial fishing
mortality. This makes this method fairly suitable for species which invade very quickly. The disadvantages are that
because invasive biomass begins at a high level, the impact of invasive groups on other groups is large, and thus must
be accounted for. The high initial biomass must also be removed quickly. The combination of high diet contributions
of invasive species to their predators, and quick removal of invasive biomass, leaves predators without a substantial
proportion of their diet. Although diet proportions are adjusted, substantial time is spent searching for a now non-
existent invasive prey item. Such an effect results in substantial drops in predator biomass and is affected by the value
of vulnerabilities. One potential way to address this effect is to increase the predator search rate on prey other than the
invasive so that once the invasive species biomass becomes zero, the predator spends the same amount of time
searching for existing prey. This effect is most serious for predators with a large proportion of their diets contributed
by invasive species after the invasion has taken place.
Method 4 - Mediating vulnerabilities:
The primary benefit of mediating vulnerabilities is that biomasses of invasive species are altered by biological
interactions rather than artificial fishing. Adjusting vulnerability also provides greater plasticity in biomass dynamics
than does simply removing fishing mortality. Unfortunately, the sensitivity of biomass to the shape of the mediation
function is also the greatest disadvantage of this method. Although the overall shape of the mediation functions we
used made theoretical sense, there was no way to know whether the shape was correct. Although changing the shape
would improve biomass fits, this is not substantially different from forcing the fits. Another disadvantage with this
method is that although artificial fishing mortalities were not used to adjust biomasses, ad hoc changes in diet were
included. Consequently, many of the disadvantages from method 3 exist here as well.
Other considerations:
Of the four methods we examined, one was based on forcing invasive species, and the other three involved
using past data on invasive species to fit a model. The choice between forcing and fitting depends on the objectives
for which the model was developed. We recommend that if there are complete time series of data on invasive species
biomass, and if the objectives of the work are primarily to assess the effects of invasive species on the system, rather
than to predict future interactions, then forcing biomasses may be the best approach. If the objectives of the work are
to account for the effect of invasive species and consider future outcomes of management strategies, then using one of
the fitting methods is preferable.
Sum of squares fitting to data provides an easy metric from which to assess performance between methods
and choose the “best” approach. However, caution with sum of squares data is warranted. When multiple time series
are used, the weighting of such time series to the overall sum of squares can be important. Poorer fits to groups that
are less important to overall objectives may be more acceptable than poor fits to important groups. Similarly, although
scales in Ecosim are relative and residual sum of squares are fit on a log-scale, differences among groups in the
magnitude of change in the biomass time series can bias the fitting procedure toward fitting one group over another.
In general, careful attention should be given to judging which data sources are most informative about food-web
dynamics, and which groups warrant priority in model fitting given the project’s objectives. It is unlikely that giving
all data sets equal weight will result in the best possible model fit.
The methods we examined here emerged from a working group discussion of options for including invasive
species in EwE models. We do not mean to suggest that our list is exhaustive, but we suspect other methods will be
related to one or more of the methods examined here. As the EwE software moves towards more user-developed
plug-ins (Christensen and Lai 2007), additional ways to include invasive species will likely be developed. In addition,
the above recommendations were based on a single model of the Laurentian Great Lakes. Although method 2 was
preferred, it is not possible to say that this method would always out-perform others if tested in different systems. Our
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comparison is not meant to be a single recommendation for all modelers, but instead will hopefully elucidate ways to
account for invasive species, and foster new approaches to best account for the interactions of these important groups.
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Table 1: Species or groups of species used in the Lake Huron Ecopath model. For multi-stanza groups, the beginning
age (in years) of each stanza is provided.
Group/species name (age stanzas) Scientific name
Sea lamprey Petromyzon marinus
Lake whitefish (0, 1-3, 4+) Coregonus clupeaformis
Lake trout (0, 1, 2-4, 5+) Salvelinus namaycush
Chinook salmon (0, 0.5, 1-5, 6+) Oncorhynchus tshawytscha
Steelhead (0, 1, 2-5, 6+) Oncorhynchus mykiss
Burbot (0-3, 3+) Lota lota
Alewife (0, 1+) Alosa pseudoharengus
Rainbow smelt (0, 1+) Osmerus mordax
Bloater (0, 1+) Coregonus hoyi
Round Goby Neogobius melanostomus
Slimy sculpin Cottus cognatus
Deepwater sculpin Myoxocephalus thompsoni
Ninespine Stickleback Pungitius pungitius
Diporeia Diporeia hoyi
Mysis Mysis diluviana
Benthic invertebrates
Dreissenid mussels Dreissena polymorpha
Dreissena bugensis
Predatory zooplankton Bythotrephes longimanus
Zooplankton
Phytoplankton
Detritus
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Table 2: Time series used for comparing performance of methods to incorporate invasive species.
Type of time series # of data points
Fishing mortalities
Age 3 lake whitefish 26
Age 4+ lake whitefish 26
Yearling lake trout 25
Age 2-4 lake trout 25
Age 5+ lake trout 25
Age 1-5 Chinook salmon 28
Stocking
Lake trout 27
Chinook salmon 25
Steelhead salmon 24
Biomass
Sea lamprey 25
Age 3 lake whitefish 26
Age 4+ lake whitefish 26
Age 2-4 lake trout 25
Age 5+ lake trout 25
Age 1-5 Chinook salmon 28
Age 1-5 steelhead salmon 25
Age 1+ alewife 23
Age 1+ rainbow smelt 23
Age 1+ bloater 23
Round goby 9
Slimy sculpin 23
Deepwater Sculpin 23
Ninespine stickleback 12
Diporeia 10
Dreissenid spp. 8
Bythotrephes 8
Zooplankton 8
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Table 3: Summary of each method for incorporating invasive species into Ecopath with Ecosim models. Details of
each method are described in the text.
Method Invasive
time series
forced or
fit?
Biomass
high or
low?
Reasoning
1-Forcing Forced High Forcing time series allows fitting routine to
match dynamics to other species, while
invasive species dynamics are fit without
error.
2-Biomass low Fit Low Invasive species begin their invasive at low
biomass levels, and thus should be initialized
as such
3-Biomass high Fit High Starting biomass at levels more similar to
recent years allows invasive species to reach
high levels of biomass
4-Mediating
vulnerabilities
Fit High Biological processes keep invasive species
biomass suppressed until the time in which
they invade
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Table 4: Comparison of fits to time series data for each method for incorporating invasive species into Ecopath with
Ecosim models. The number of data point used to fit method 1 differs from those of methods 2-4 and thus cannot be
compared.
Method
Model 1 - Forcing 2 – Biomass low 3 – Biomass high 4 – Mediating
vulnerabilities
Lake Huron 132.3 134.6 3260 167.7
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Figure 1: Shapes of vulnerability forcing function for a) prey of invasive species, and b) invasive species to their
predators used in method 4. For vulnerabilities of prey to invasive species, vulnerabilities begin very low for the early
simulation years, and increase to a peak (Y) after the species invades (time period X1), then stabilizes to 1 once the
species begins to become established (time period X2). For vulnerabilities of invasive species to their predators,
vulnerabilities begin very low for the early simulation years, and increase to 1 once the species invades.
X2 X1
1
a)
b)
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Figure 2: Fits to key species for each method from the Lake Huron model from 1981-2008. The solid black line
represents model predicted biomass, and the open circles represent observed biomass. Key species include age 4+
lake whitefish (whitefish), age 5+ lake trout (lake trout), age 1+ alewife (alewife), Diporeia (diporeia), round goby
(goby), Dreissenids (dreiss), and Bythotrephes (bytho).
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Appendix 4. Evaluation of harvest policies for Lake Huron coldwater commercial fisheries using an Ecopath with
Ecosim model.
Introduction:
Lake trout (Salvelinus namaycush), lake whitefish (Coregonus clupeaformis), bloater (Coregonus hoyi) and
introduced Pacific salmonines comprise the majority of targeted fishing effort within Lake Huron’s cold water fish
community. Lake trout were the primary target of commercial fisheries in the early part of the 1900’s (Baldwin et al.
2002) but increased fishing pressure in combination with the invasive sea lamprey (Petromyzon marinus) resulted in
their near extirpation by the 1960’s. Lake whitefish (Coregonus clupeaformis) harvest has replaced lake trout harvest
since the 1980s (Dobiesz et al. 2005). Current yields of lake whitefish have now surpassed historical levels, and the
lake whitefish commercial fishery on Lake Huron produces the second largest commercial fishery harvest among all
Laurentian Great Lakes in terms of yield (Mohr and Ebener 2005). Bloater harvest has been lower than either lake
trout or lake whitefish, but is not inconsequential (Baldwin et al. 2002). Both bloater and lake whitefish fisheries are
concentrated in Canadian waters, although there are substantial tribal and non-tribal commercial fisheries for lake
whitefish in U.S. waters.
Fishery management goals for a “desirable” fish community have been established for Lake Huron
(DesJardine et al. 1995). These management objectives are called fish community objectives (FCOs) and include
sustainable yields of both lake whitefish and lake trout. In addition, lake trout populations should be self-sustaining,
meaning not dependent on hatchery production. Current lake trout production within the lake is generated almost
entirely through stocking although natural reproduction has increased since it was first observed in 1984 (Riley et al.
2007), providing evidence that management goals are beginning to be realized. In light of the FCO targets, managers
would like to develop a harvest strategy framework that would maintain harvests of coregonines while also increasing
the sustainable production and harvest of lake trout. If this ideal solution is unattainable, then managers wish to
evaluate strategies that represent a compromise between coregonine and lake trout objectives.
Recent large-scale changes in the Lake Huron food-web may have an important influence on interactions
between the coldwater fish community and its fisheries. Abundances of several species of prey fish and an important
benthic amphipod, Diporiea, have declined since the late 1990s (Nalepa et al. 2007, Riley et al. 2008). These species
contribute significantly to the diet of lake trout and lake whitefish (Madenjian et al. 2006, Pothoven and Nalepa
2006). Invasive Dreissenid mussels and round gobies (Neogobius melanostomus) have proliferated in recent years,
further contributing to changes in Lake Huron (Nalepa et al. 2007). Clearly, improved understanding of ecosystem
processes becomes even more important in light of these recent changes, as does appreciation of their implications for
the performance of harvest strategies applied to the multiple fisheries operating in Lake Huron.
We developed a food-web model of Lake Huron’s main basin coldwater fish community using the Ecopath
with Ecosim (EwE) modeling software to explore the effects of alternative harvest policies on management objectives
for lake whitefish and lake trout. Our policy analysis included three types of management options; 1) incremental
adjustments to fishing mortality targets, 2) conversions of the gill net fishery to trap nets, and 3) adjustments to the
seasons in which fishing occurred. The importance of uncertainties surrounding future levels of environmental
productivity, the strength of trophic interactions between predators and prey, and contributions of potentially
important but low frequency prey items were assessed as well. Overall, this analyses should help establish a more
transparent management framework that considers multiple objectives, system uncertainties, and potential tradeoffs
connected to the decision making process.
Methods:
Model
A food-web model of coldwater fish community in the main basin of Lake Huron was constructed using the
Ecopath with Ecosim software package. This model was similar to those described in Appendices 1 and 2 but
contained an additional group, wild lake trout, divided into the same age stanzas as hatchery lake trout in the other
models. The model was parameterized for the year 1981, with data from Lake Huron or as similar of a system as
possible. When data were not available from 1981, data from other time periods were used. Once parameterized,
Ecopath requires the data inputs to balance, meaning the sum of consumption from predators or fisheries on a single
group can not exceed the entered value of production for that group. Given the diversity of data sources used to
parameterize the model, balance rarely occurs without some manipulation of the data inputs. Data inputs were
adjusted following recommended practices (Christensen et al. 2005, but see also Appendix 2).
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After balancing was achieved, Ecosim was used to project calculated state variables from Ecopath forward
through time. To assess the accuracy of these projections, Ecosim-estimated biomass dynamics were compared to
observed biomasses. Parameters governing the strength of trophic interaction between predators and their prey, called
vulnerabilities, were adjusted to improve model fits to observed biomasses. Following conventional practice for EwE
models, vulnerabilities to a predator for all prey items of that predator were assumed the same. Vulnerabilities control
the extent to which an increase in predator biomass causes an increase in predation mortality on its prey, with low
vulnerabilities reflecting very little increase and high vulnerabilities reflecting a large increase (Christensen and
Walters 2004). Parameters governing the relative annual level of primary productivity available in the system, called
production anomalies, were also tuned to improve model fits to observed biomasses. Production anomalies are yearly
deviations from the initial productivity entered in Ecopath (Christensen and Walters 2004). These two types of
parameter adjustments were important for matching biomass dynamics estimated in Ecosim to those observed in Lake
Huron (Figure 1).
Policies
After adjusting vulnerability and production anomaly parameters to achieve a reasonable fit to observed
biomasses in 1981-2008, simulations were run to forecast the outcomes of various management policies. Simulations
were run for 50 years, and estimates over the last five years were used to compare policy outcomes. We used a
constant fishing mortality harvest control rule for our simulations, where fishing mortality targets were set for the
primary species harvested in coldwater commercial fisheries, lake trout and lake whitefish. Fishing effort for fisheries
that targeted lake whitefish were then adjusted so that these mortality targets were achieved. The commercial fisheries
included a treaty fishery for lake whitefish, which was a combination of gill nets and trap nets in the 1836 treaty
waters of Lake Huron; a gill net fishery for lake whitefish in non-treaty waters; a trap net fishery for lake whitefish in
non-treaty waters; a Chinook salmon (Oncorhynchus tshawytscha) fishery in treaty waters; and a bloater fishery in
Canadian waters. A recreational fishery for salmonines was also included in the model.
Three types of policy comparisons were used in our simulations. These policies were specified to reflect
earlier discussions with stakeholders (Objective 1). The first policy simply incrementally adjusted lake whitefish
fishing mortality targets above and below the fishing mortality estimated from catch-at-age models in 2006. The
percentage adjustments we considered were -75%, -50%, -25%, 0%, +25%, +50%, and +100 percent. Our purpose
here was to examine how biomass and harvest of both the target species (lake whitefish) and the bycatch species (lake
trout) were affected by changing fishing mortality rates.
The second policy comparison represented a conversion of gill gets to trap nets for non-treaty fisheries only.
The proportion of gill nets converted ranged from no conversion (0%) to complete conversion (100%) in increments
of 25 per cent. Gill nets capture and kill more lake trout than do trap nets (Johnson et al 2004). Consequently, the
purpose of this policy comparison was to explore ways to maintain harvest of lake whitefish while minimizing the
death of lake trout. Harvest of lake whitefish was maintained for each level of conversion; the only change was to the
amount of harvest of lake trout.
The third policy comparison adjusted harvest of lake whitefish or lake trout based on assumed changes to the
seasons in which fishing occurred. We considered two seasonal adjustments to fishing patterns: a) fishing occurred
only in winter; and b) fishing did not occur in summer. Adjustment “a” was considered because stakeholders stated
that lake whitefish prices were greatest in winter, when the supply of lake whitefish was limited. Bycatch of lake trout
in winter was less than the yearly average, and thus fishing only in winter would reduce lake trout harvest for the
same total lake whitefish harvest, or could allow greater lake whitefish harvest while maintaining lake trout harvest at
current levels. We considered both scenarios, referring to the one where the lake whitefish target was achieved and
lake trout were under target as “WF wint”, and the one where the lake trout target was achieved thereby allowing lake
whitefish to be above target as “LT wint”. Adjustment “b” was considered because Johnson et al. (2004) reported
high bycatch of lake trout in gill nets in spring, and high bycatch of lake trout in trap nets in summer. Additionally,
analysis of seasonal observer data in the Canadian commercial fishery suggested bycatch was a greater issue in
summer (Adam Cottrill, unpublished data). As done for adjustment “a”, we considered two scenarios, one where the
lake whitefish target was achieved with lake trout below target (“WF no sum”), and one where the lake trout target
was achieved with lake whitefish above target (“LT no sum”).
For both the gear conversion and seasonal fishing policy comparisons, we allowed for scenarios that were
more extreme than would likely be possible in Lake Huron. We recognize that there are areas of the lake where
complete conversion to trap nets will not be feasible. Likewise it is unrealistic to expect all lake whitefish fishing to
occur in one season (winter). Our rationale for simulating these extreme scenarios was to determine whether direct
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and indirect effects of these policies on non-target species (i.e., lake trout) would be great enough to warrant further
discussion.
Uncertainties:
We discussed potential sources of uncertainty with stakeholders during our workshops, and as expected many
candidate areas of uncertainty were identified. For this analysis we decided to focus on three key areas of uncertainty:
(1) future environmental productivity; (2) the strength of trophic interactions between fished groups and their prey
(vulnerabilities): and (3) alternative assumptions about the contributions of particular fish species to the diets of lake
trout and lake whitefish.
Environmental production anomalies played an important role in allowing the Lake Huron food-web model to
reproduce observed dynamics of the system from 1981-2008. The model that best fit observed biomasses included
production anomalies that varied considerably over the time series. In general, anomalies were high during the 1990s,
corresponding to increased biomass of many groups during that time, and declined after 2000 (Figure 2). It remains
very uncertain, however, what future production levels will be. To consider a range of possible futures we used the
first and third quartiles from the estimated time series of past environmental productivities to simulate future
productivity, and compared this with the initial estimate of environmental productivity, that is the initial value in
Ecopath, which was very close to the median of past environmental productivities.
Vulnerabilities are widely recognized as important parameters in Ecosim models (Christensen and Walters
2004), but are difficult to estimate with much precision (Ahrens et al. 2012). Every feeding group in the EwE model
has an estimated vulnerability from the model fitting process. To make an analysis of model sensitivity to
vulnerability uncertainty tractable, we needed to concentrate on a subset of these vulnerabilities. We chose to focus on
vulnerability uncertainty on the oldest age groups of species targeted by the primary commercial and recreational
fisheries in Lake Huron, the rationale being that these age groups are of greatest interest to us, and that vulnerabilities
can influence the level of compensation by fished groups (Ahrens et al. 2012). Species targeted by the primary
fisheries include lake trout (both hatchery and wild), lake whitefish, and Chinook salmon. Vulnerabilities operate, in
effect, on a log scale, ranging from 1 to 109, so order-of-magnitude adjustments to these parameters were appropriate.
Estimated vulnerabilities for lake whitefish and lake trout were very high, and therefore reduced to either 10 or 100;
in contrast estimated vulnerabilities for Chinook salmon were close to 1, so they were increased to 10 or 100. Once
we had adjusted the vulnerabilities for these species, we re-fit production anomalies and vulnerabilities of other
groups to observed biomasses.
Diet contributions of rare prey are difficult to accurately estimate, particularly over long time periods and
large spatial scales. During our workshops, stakeholders argued for the existence of diet contributions that may be
important, but were not supported by our assessment of existing diet data. For example, stakeholders noted that they
sometimes observe lake whitefish in small quantities in lake trout diets, but the diet data we examined (Ji He,
Michigan Department of Natural Resources, unpublished data, Madenjian et al. 2006, Dobiesz 2003; Diana 1990) did
not indicate lake whitefish as a prey item for lake trout. This potential predator-prey interaction could increase the
effect lake trout have on lake whitefish, so to explore this possibility we added 2% of the diet of age 5+ lake trout to
come from age 1-3 lake whitefish. Similarly, stakeholders suggested lake whitefish sometimes show greater levels of
piscivory than suggested by the diet data we used (Pothoven and Madenjian 2008, McNickle et al. 2006, Nalepa et al.
2009). Accordingly, we added small contributions (1% each) of age 1+ alewife and age 1+ smelt to age 4+ lake
whitefish diets. As done when vulnerabilities were adjusted, after making these changes to diets, the model was re-fit
to best match observed biomasses.
Results and discussion:
Lake whitefish and lake trout were the primary species of concern for this project. Critical objectives for these
species included maintenance of acceptable levels of biomass and harvest. As expected, lower fishing mortalities
resulted in increased biomass of both lake trout and lake whitefish while increasing fishing mortality led to consistent
declines in biomass across the range of effort levels considered (Figure 3, left panels). A doubling of fishing mortality
(100% increase) resulted in a 30% decline in lake whitefish biomass and a 17% decline in lake trout biomass.
Harvests increased consistently for both species across the range of fishing mortality targets considered, suggesting
that both species can sustain higher exploitation rates than they currently experience. The relatively large increases in
harvests compared to the corresponding declines in biomasses as fishing mortality increased suggest a high degree of
compensation for both species. Lake trout seem to have even greater compensation than lake whitefish, which may be
simply a consequence of their recruitment being sustained by stocking for the hatchery group. Over the range of
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fishing mortalities explored, biomass declined by 54% for lake whitefish and 23% for lake trout while harvest
increased by 271% for lake whitefish and 266% for lake trout (Figure 3, left panels). These findings suggest that the
EwE model is overestimating the steepness of the stock-recruitment relationship for both species at low spawning
stock biomass levels.
Other policies also resulted in expected results. Under gear conversion scenarios, lake whitefish biomass and
harvest remained relatively stable whereas biomass of lake trout increased and harvest decreased with the amount of
gill nets converted (Figure 3, middle panels). Complete conversion of non-treaty gill nets to trap nets resulted in a
15% increase in lake trout biomass. When fishing was limited to winter and total lake whitefish harvests remained at
status quo levels, lake trout biomass was 14% greater (Figure 3, right panels – “WF wint”). In contrast, allowing lake
whitefish targets to increase while maintaining lake trout harvest at status quo levels resulted in a 29% reduction in
lake whitefish biomass, and a 39% increase in harvest (Figure 3, right panels – “LT wint”). Interestingly, under this
scenario the biomass of lake trout increased by 4.4%, most likely due to the decrease in lake whitefish biomass (see
paragraph about assessment of species interactions below). The results were similar but less extreme for the scenarios
where only summer fishing was eliminated (Figure 3, right panels – “WF no sum” and “LT no sum”).
Diet and vulnerability uncertainties had a greater effect on biomass and harvest of lake whitefish than on lake
trout (Figure 4). Under the standard model, variation across all changes to targets was greatest for biomass (Figure 4).
Changes to diet lowered the absolute biomass and harvest for all changes in fishing mortality except 100%. Changes
to vulnerability increased absolute biomass and harvest for lake whitefish, with assumed vulnerabilities of 10 showing
the greatest increase. In contrast, lower vulnerabilities for lake whitefish and lake trout, but higher vulnerabilities for
Chinook salmon, had an opposite, if smaller, effect on lake trout. Lake whitefish biomass was less sensitive to
changes in fishing pressure when diets were changed and vulnerabilities decreased than under the standard model
(Figure 4). Ahrens et al. (2012) stated that greater compensation to fishing pressure when vulnerabilities are low is
expected. Across the range of fishing mortalities explored, lake whitefish biomass declined by 54% under the
standard model; 29% when vulnerabilities for lake trout, lake whitefish, and Chinook salmon were set to 10; 20%
when vulnerabilities were set to 100; and 17% when diets were changed (Figure 4). For lake trout the effect was much
smaller, but in the opposite direction, with biomass being more sensitive when diets were changed and vulnerabilities
decreased (Figure 4). This is possibly due to the effect of changes in lake whitefish biomass on lake trout (see final
paragraph in this section).
Changes to environmental productivities had a far greater effect on biomass and harvest than did changes to
diet or vulnerabilities (Figure 5). Median environmental productivities were the same as initial levels, whereas high
and low environmental productivities represented a 13% increase and 10% decrease from initial levels, respectively.
Lake whitefish responded more to increases in production than did lake trout, presumably because greater production
resulted in greater biomass increases in their prey than the prey of lake trout. Lake whitefish feed at a lower trophic
level than lake trout (3.2 versus 4.2) which implies their prey may be more directly affected by changes in system
productivity. Interestingly, alewife biomass does not recover from 2008 levels unless productivity in the system
increases above initial levels (Figure 6). As expected, alewife biomass increases as fishing on lake trout increases
(Figure 6).
Our goal in building a food-web model was to allow assessment of both direct and indirect interactions
among exploited species, particularly lake trout and lake whitefish. The policies presented above reflect adjustments
to fishing effort that affected both lake trout and lake whitefish. To assess indirect interactions between lake trout and
lake whitefish, resulting from food-web changes, we adjusted fishing mortality on one species without affecting the
fishing mortality of the other. We ran these simulations with the original and modified diets as discussed above. When
the biomass of the exploited species declined due to increased harvest, the biomass of the other species tended to
increase (Figure 7). The only exception was when the original diets were used and lake trout harvest was modified –
this resulted in negligible changes to lake whitefish biomass. More generally, changes in harvest of lake whitefish had
a far greater indirect effect on lake trout biomass than vice versa (Figure 7), probably reflecting the far greater
biomass of lake whitefish than lake trout in the system. Changes to the diet increased the magnitude of the indirect
effect of lake trout harvest on lake whitefish biomass (Figure 7 – “LTH-WF”). In contrast, these same changes
reduced the magnitude of the indirect effect on lake trout biomass of increased lake whitefish harvest (Figure 7 –
“WF-LTH”). When lake whitefish contribute to the diet of lake trout, any competitive release for lake trout that
results from reduced lake whitefish biomass is offset by a reduction in a lake trout prey item.
References:
Ahrens, R.N.M., Walters, C.J., Christensen, V., 2012. Foraging arena theory. Fish and Fisheries. 13, 41-59.
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Baldwin, N.A., Saalfeld, R.W., Dochoda, M.R., Buettner, H.J., Eshenroder, R.L., 2002. Commercial Fish Production
in the Great Lakes 1867-2000. (available online at http://www.glfc.org/databases/commercial/commerc.php).
Christensen, V., Walters, C., 2004. Ecopath with Ecosim: methods, capabilities, and limitations. Ecol. Model. 172,
109-139.
Christensen, V, Walters C.J., Pauly, D., 2005. Ecopath with Ecosim: a User’s Guide. Fisheries Centre, University of
British Columbia, Vancouver. November 2005 edition, 154 p. (available online at www.ecopath.org).
DesJardine, R.L., Gorenflo, T.K., Payne, N.R., Schrouder, J.D., 1995. Fish-community objectives for Lake Huron.
Great Lakes Fish. Comm. Spec. Pub. 95-1. 38 p.
Diana, J.S., 1990. Food habits of angler-caught salmonines in western Lake Huron. J. Great Lakes Res. 16, 271-278.
Dobiesz, N.E., 2003. An evaluation of the role of top piscivores in the fish community of the main basin of Lake
Huron, Ph.D. Dissertation, Michigan State University, Department of Fisheries and Wildlife, East Lansing,
Michigan
Dobiesz, N.E., McLeish D.A., Eshenroder R.L., Bence J.R., Mohr L.C., Ebener M.P., Nalepa T.F., Woldt A.P.,
Johnson J.E., Argyle R.L., Makarewicz J.C., 2005. Ecology of the Lake Huron fish community, 1970-1999.
Can. J. Fish. Aquat. Sci. 62, 1432-1451.
Johnson, J.E., Ebener M.P., Gebhardt K., Bergstedt R., 2004. Comparison of catch and lake trout bycatch in
commercial trap nets and gill nets targeting lake whitefish in northern Lake Huron. Mich. Dept. Nat. Res.
Rep. 2071. 25 p.
Madenjian, C.P., Holuszko, J.D., Desorcie, T.J., 2006. Spring-summer diet of lake trout on Six Fathom and Yankee
Reef in Lake Huron. J. Great Lakes Res. 32, 200-208.
McNickle, G.G., Rennie, M.D., Sprules, W.G., 2006. Changes in benthic invertebrate communities of South Bay,
Lake Huron following invasion by zebra mussels (Dreissena polymorpha), and potential effects on lake
whitefish (Coregonus clupeaformis) diet and growth. J. Great Lakes Res. 32, 180-193.
Mohr, L.C., Ebener, M.P., 2005. Description of the fisheries. p. 19-26. In: Ebener, M.P. (ed.) The state of Lake Huron
in 1999. Great Lakes Fish. Comm. Spec. Pub. 05-02. 140 p.
Nalepa, T.F., Pothoven, S.A., Fanslow, D.L., 2009. Recent changes in benthic macroinvertebrate populations in Lake
Huron and impact on the diet of lake whitefish (Coregonus clupeaformis). Aquat. Ecosyst. Health 12, 2-10.
Nalepa, T.F., Fanslow D.L., Pothoven S.A., Foley III A.J., Lang G.A., 2007. Long-term trends in benthic
macroinvertebrate populations in Lake Huron over the past four decades. J. Great Lakes Res. 33, 421-436.
Pothoven, S.A., Nalepa T.F., 2006. Feeding ecology of lake whitefish in Lake Huron. J. Great Lakes Res. 32, 489-
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Pothoven, S.A., Madenjian, C.P., 2008. Changes in consumption by alewives and lake whitefish after dreissenid
mussel invasions in lakes Michigan and Huron. North American Journal of Fisheries Management 28, 308-
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Riley, S.C., He, J.X., Johnson, J.E., O’Brien, T.P., Schaeffer, J.S., 2007. Evidence of widespread natural reproduction
by lake trout Salvelinus namaycush in the Michgian waters of Lake Huron. J. Great Lakes Res. 33, 917-921.
Riley, S.C., Roseman, E.F., Nichols, S.J., O’Brien, T.P., Kiley, C.S., Schaeffer, J.S., 2008. Deepwater demersal fish
community collapse in Lake Huron. Trans. Am. Fish. Soc. 137, 1879-1890.
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Figure 1: Ecosim fits to time series of observed biomasses. In all subpanels, the black line represents estimated
biomasses in Ecosim and the open circles represent observed data.
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Figure 2: Relative annual production anomalies during 1981-2008 for the “standard” model. The grey line at 1 is for
reference to the initial primary productivity in the initialized model.
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Figure 3: Average biomass and harvest from the last five years of simulation for age 4+ lake whitefish (whitefish) and
age 5+ hatchery lake trout (lake trout) for three policy options in the standard model. For each figure, lake whitefish is
on the primary y-axis and lake trout is on the secondary y-axis.
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Figure 4: Average biomass and harvest of age 4+ lake whitefish (whitefish) and age 5+ hatchery lake trout (lake trout)
from the last five years of simulation under uncertainties around diet (diet), and vulnerabilities (vuln 10 and vuln
100).
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Figure 5: Average biomass and harvest of age 4+ lake whitefish (whitefish) and age 5+ hatchery lake trout (lake trout)
from the last five years of simulation for three levels of environmental productivities in the standard model.
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Figure 6: Average biomass of age 1+ alewife in the last five years of simulation under three different assumptions
about the future level of environmental productivity in the standard model. Biomass is at zero under both low and
medium levels of productivity.
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Figure 7: Average biomass in the last five years of simulation of age 4+ lake whitefish at various levels of fishing
mortality on age 5+ hatchery lake trout (LTH-WF) and of age 5+ hatchery lake trout at various levels of adjustment to
fishing mortality on age 4+ lake whitefish (WF-LTH). Biomass values were plotted relative to the biomass when
fishing mortalities were unchanged (0%). The second row of the x-axis reflects the model under standard assumptions
(Standard) or with greater direct and indirect interactions between the two groups (Diet).