designing’a’decision’support’systemfor harvest(management...

Decision Support and Great Lakes Lake Whitefish in a Changing Climate

www.glisa.msu.edu

Designing a Decision Support System for Harvest Management of Great Lakes Lake Whitefish in a Changing Climate

Abigail J. Lynch, Principal Investigator, University Distinguished Fellow William W. Taylor, University Distinguished Professor in Global Fisheries Systems

Center for Systems Integration and Sustainability Michigan State University East Lansing, Michigan

This project was funded by the Great Lakes Integrated Sciences + Assessments Center

through a 2011 Great Lakes Climate Assessment Grant. Recommended Citation:

Lynch, A.J., W.W. Taylor, 2013. Designing a Decision Support System for Harvest Management of Great Lakes Lake Whitefish in a Changing Climate. In: GLISA Project Reports. D. Brown, D. Bidwell, and L. Briley, eds. Available from the Great Lakes Integrated Sciences and Assessments (GLISA) Center.

For further questions, please contact Abigail Lynch at [email protected].

PROJECT BRIEF: GREAT LAKES LAKE WHITEFISH IN A CHANGING CLIMATE

2 www.glisa.umich.edu Last updated: 6/9/2015

Contents Project Objectives .............................................................................................................................................................................................................................. 3 Importance of lake whitefish ........................................................................................................................................................................................................ 3 Lake whitefish recruitment ........................................................................................................................................................................................................... 3 Potential impacts of climate change .......................................................................................................................................................................................... 3 Modeling climate relationships with lake whitefish recruitment in the 1836 Treaty waters ......................................................................... 4 Modeling methods ............................................................................................................................................................................................................................. 4 Results to date ..................................................................................................................................................................................................................................... 5 Modeling implications ..................................................................................................................................................................................................................... 5 Decision-‐maker engagement ........................................................................................................................................................................................................ 5 Anticipated outcomes ...................................................................................................................................................................................................................... 6 References ............................................................................................................................................................................................................................................. 6



Project Objectives The goal of this project was to design a decision support system for sustainable harvest management of Lake Whitefish in the upper Laurentian Great Lakes in a changing climate. Upon completion, this project will synthesize the current state of lake whitefish ecological knowledge and provide an opportunity to assist decision makers allied with the fisheries to adaptively respond to climate change processes in the Great Lakes region (Figure 1). Our research that was funded by GLISA emphasized the development of a quantitative fishery model which will serve as the foundation for decision support tool. The full project objectives were to: • Develop a lake whitefish production model, including

influential climatic factors; • Engage scientists, managers, and fishermen to assess

decision-‐support needs and ensure the climate-‐lake whitefish production model is relevant to their management needs; and,

• Design an interactive, user-‐friendly interface to display the outcomes of the climate -‐ lake whitefish production modeling outcomes.

Importance of lake whitefish Lake whitefish (Coregonus clupeaformis) are an economically, culturally, and ecologically important species in the upper Laurentian Great Lakes (Lakes Huron, Michigan, and Superior). Ecologically, lake whitefish are primarily a benthivores and serve as a key energy transfer vector from lower, benthic food webs to the upper, pelagic food webs (Nalepa, Mohr et al. 2005), and are an important

food source for humans. They are and have been a staple food source and focus of livelihoods for aboriginal people in the region (Cleland 1982). Today, populations of lake whitefish support the most robust and economically valuable commercial fishery in the upper Laurentian Great Lakes (Lakes Huron, Michigan, and Superior; annual catch value = US$16.6 million; Mohr and Ebener 2005; Madenjian, O'Connor et al. 2006; Ebener, Kinnunen et al. 2008).

Lake whitefish recruitment Stock-‐recruitment relationships estimates are inherently stochastic but much of that variability can be attributed to variation in spawning stock size and environmental influences on growth and survival of young fish (Quinn and Deriso 1999). However, one of the most difficult problems in fisheries science is determining the influence of the environment upon recruitment (Myers 2001). For lake whitefish, both stock size and environmental factors have been shown to influence recruitment (Taylor, Smale et al. 1987). In particular, previous studies of Great Lakes lake whitefish indicate that key environmental drivers that will be impacted by climate change, water temperature, ice cover, and storm intensity, affect recruitment (Christie 1963; Lawler 1965; Taylor, Smale et al. 1987; Freeberg, Taylor et al. 1990).

Potential impacts of climate change Climate change is expected to increase surface temperatures of the Great Lakes by as much as 6oC (Trumpickas, Shuter et al. 2009) while average wind speed is expected to decline (Sousounis and Grover 2002), and ice

Figure 1. Conceptual model of the design of a decision support system for Great Lakes Lake Whitefish harvest management strategies in a changing climate.



cover is expected to be substantially reduced (Assel, Cronk et al. 2003). In their current habitat space, increased water temperature, increased wind speed, and decreased ice cover are projected to inhibit the success of recruitment to the lake whitefish fishery (Lynch, Taylor et al. 2010). However, the warming trends associated with predicted climate change could increase suitable thermal habitat volume for lake whitefish (Magnuson, Webster et al. 1997) because the species will be able to shift northwards in the lake system as well as live deeper in the water column to maintain access to optimal thermal habitats (Regier and Meisner 1990).

Modeling climate relationships with lake whitefish recruitment in the 1836 Treaty waters The 1836 Treaty waters are regions of Lakes Huron, Michigan, and Superior that were ceded from the local tribes to the United States. The tribes, however, did not relinquish their right to fish in the ceded waters. In 1979, United States v. State of Michigan (Fox Decision) reaffirmed the rights of the tribes for commercial fishing of lake whitefish and other species. To ensure that the fishery is managed and regulated cooperatively, the 2000 Consent Decree established guidelines for management of this fishery under the purview of the Chippewa Ottawa Resource Authority (CORA) and the Michigan Department of Natural Resources when the management units are shared. Currently, a Technical Fisheries Committee and a Modeling Subcommittee conduct stock assessments, establish total allowable catches, and designate harvest regulations for the 14 lake whitefish management units in these waters. The harvest from the 1836 Treaty waters management units comprise approximately a quarter of the total harvest of lake whitefish in the entire Great Lakes (M. Ebener, CORA, personal communication). For lake whitefish in the 1836 Treaty waters, harvest objectives have not yet been formalized but the co-‐managers (the State of Michigan and the Chippewa-‐Ottawa tribes) desire high yields, low interannual variability in yield, and avoidance of low stock sizes (Deroba and Bence 2012). To achieve these goals, the Modeling subcommittee uses statistical catch-‐at-‐age models to estimate lake whitefish population metrics, including recruitment (Deroba and Bence 2009) in order to optimally regulate harvest rates.

Modeling methods We chose the 1836 Treaty Waters of Lakes Huron, Michigan, and Superior as our study area because they have a latitudinal gradient, comprise approximately a quarter of the total lake whitefish harvest in the entire Great Lakes (M.

Ebener, personal communication), and have established statistical catch-‐at-‐age models to determine total allowable catches which are used to designate harvest regulations for 13 management units (Figure 2). We used the spawning stock biomass and recruitment estimates from 1976-‐2011 to integrate 5 climate variables into a Ricker stock-‐recruitment model because lake whitefish recruitment is density dependent (Taylor, Smale et al. 1987). The Ricker model was transformed to a linear function by taking the natural log:

𝑙𝑛𝑅!𝑆!= 𝑙𝑛𝛼! − 𝛽!𝑆! + 𝜖!

For a given yearclass, i, Ri is recruitment, Si is spawning stock, αi is the productivity parameter, β1 is the density dependent shape parameter, and ϵi is normally distributed random error. We developed 25 climate variable combination models, with the full model including three climate variables:

𝑙𝑛𝑅!𝑆!= 𝛼! + 𝛽!𝑆! + 𝛽!𝑖𝑐𝑒! + 𝛾!𝑓𝑎𝑙𝑙! + 𝛾!𝑠𝑝𝑟𝑖𝑛𝑔! + 𝜖!

Where: • icei is the proportion of mid-‐December ice cover over spawning habitat for a given management unit and β1 is its density dependent shape parameter,

Figure 2. Management units.



• falli is the mean fall (October – December) air temperature for land-‐based stations within 5 miles of a given management unit and γ1 is its density independent shape parameter,

• springi is the mean spring (March – May) air temperature for land-‐based stations within 5 miles of a given management unit and γ2 is its density independent shape parameter.

We used Akaike’s Information Criterion (AIC), which examines goodness of fit with a penalty for number of parameters, to compare the different model combinations of variables for each of the 13 management units.

Results to date In three of the 13 management units, data were not sufficient to complete model comparison. In three other units, the standard stock-‐recruitment model was the best model fit. In the remaining seven units, AIC comparisons between the standard stock-‐recruitment model and the models indicated that climate variables improved model fit (Table 1). These results indicate that seasonal temperature, ice cover, and the interaction between ice cover and stock size improve the ability of the stock-‐recruitment model to represent recruitment data and that these variables are able to explain a proportion of the variability in lake whitefish recruitment beyond what is

explained by stock size alone.

Modeling implications Our findings show that seasonal temperatures and ice cover improves stock-‐recruitment model fit and suggest that fall temperatures and ice cover are important for egg development, spring temperatures are important for larval survival, and all three are important for determining year-‐class strength of lake whitefish in the 1836 Treaty Waters. Great Lakes lake whitefish stocks are characterized by spatial and temporal variation (Deroba and Bence 2012) and previous site-‐based correlational studies have also indicated indicate seasonal temperature and ice cover influences recruitment (Christie 1963; Lawler 1965; Taylor, Smale et al. 1987; Freeberg, Taylor et al. 1990; Brown, Taylor et al. 1993). Christie (1963) found that cold autumn temperatures and warm spring temperatures correlated to strong year-‐classes in Lake Ontario and the reverse combination produced weak year-‐classes. Lawler (1965) found a similar correlation in Lake Erie and attributed the relationship to optimal spawning temperature, incubation, and development while Freeberg et al. (1990) proposed a different mechanism for correlations with spring temperatures for Lake Michigan, that temperature influences the timing and production of copepod zooplankton, prey for larval lake whitefish. The relationship between seasonal temperatures, ice cover, and lake whitefish recruitment have significant implications for the fishery in the context of climate change. Climate change is expected to increase surface temperatures of the Great Lakes by as much as 6oC (Trumpickas, Shuter et al. 2009). The positive relationship between spring temperatures and recruitment with climate change suggests the potential for increased lake whitefish production in the Great Lakes, if habitat is not limiting and sufficient food resources are available when the larvae hatch. However, the negative relationship between fall temperatures, ice cover, and recruitment may inhibit egg survival and, consequently, lake whitefish production. Future research will investigate the impact of water temperature and storm intensity on recruitment and use the full suite of climate variable to project our model analysis using potential climate scenarios.

Decision-‐maker engagement In presenting our climate-‐lake whitefish production model to state, tribal, provincial, and federal managers, fishery scientists, and tribal fishermen, we are soliciting their input through in-‐person and online surveys on how they would recommend that this model be best integrated into a decision-‐support tool that could be used to inform adaptive

Management unit Selected model

WFH_Northern_Huron S + ice + fall WFH_05 S WFM_01 S + ice + fall + S:ice WFM_02 Spring WFM_03 Data insufficient WFM_04 S WFM_05 S + ice + S:ice WFM_06 Fall WFM_08 Data insufficient WFS_04 Data insufficient WFS_05 S + ice + spring + S:ice WFS_07 S + ice WFS_08 S

Table 1. The management units and model selected through Akaike’s Information Criterion (AIC) comparisons. S = stock size; ice = the proportion of mid-‐December ice cover over spawning; fall = the mean fall (October – December) air temperature for land-‐based stations within 5 miles; spring = the mean spring (March -‐ May) air temperature for land-‐based stations within 5 miles; and S:ice = the interaction between stock and ice cover.



decision making for fishermen and fishery managers while at the same time being useful to educate the students and public of potential impacts of climate change to the Great Lakes region and its fisheries resources. To date, we have surveyed over 31 individuals (managers, scientists, and fishermen). Initial results indicate that survey participants believe that decision-‐support tools can be useful for fisheries management (77% somewhat or completely agree; none disagree; Figure 3). The participants suggest that invasive species (87%), bycatch (84%), market forces (84%), allocation (81%), climate change (81%), and communication between fishermen and managers (81%) are among the most important issues for the future of lake whitefish management. Using these results, we will incorporate their recommendations into our tool design to ensure that the tool, which will be housed on the Michigan Sea Grant website, is practical and useful to the users and, ultimately, lead to a more sustainable and prosperous fishery resources and coastal communities in the future.

Anticipated outcomes This decision-‐support tool developed will: • Provide assistance for sustainable management to lake whitefish managers including: the 1836 Treaty Waters Technical Fisheries Committee, the Michigan Department of Natural Resources, and the Chippewa-‐Ottawa Resource Authority and other management agencies by example;

• Communicate the potential impacts of climate change on the ecology and management of Great Lakes fisheries and coastal communities to fishermen, managers, students, and the public;

• Serve as an example regional climate change case-‐study for Michigan Sea Grant Extension Educators, the Great Lakes Integrated Sciences and Assessments Center, and other educational users;

• Inform future proposals for National Oceanic and Atmospheric Administration climate change funding initiatives, including the Great Lakes Integrated Sciences and Assessment;

• Help build prosperous and adaptive coastal Michigan communities by encouraging adaptive strategies; and,

• Support the completion and dissemination of student research (Abigail Lynch’s dissertation).

References Assel, R., K. Cronk, et al. (2003). "Recent trends in Laurentian

Great Lakes ice cover." Climatic Change 57(1-‐2): 185-‐204. Brown, R. W., W. W. Taylor, et al. (1993). "FACTORS AFFECTING

THE RECRUITMENT OF LAKE WHITEFISH IN 2 AREAS OF NORTHERN LAKE-‐MICHIGAN." Journal of Great Lakes Research 19(2): 418-‐428.

Burnham, K. P. and D. R. Anderson (2002). Model Selection and Multimodel Inference: A Practical Information-‐Theoretic Approach. New York, NY, Springer.

Christie, W. J. (1963). "Effects of artificial propagation and their weather on recruitment in the Lake Ontario whitefish fishery." Journal of the Fisheries Research Board of Canada 20(3): 597-‐646.

Cleland, C. E. (1982). "The inland shore fishery of the northern Great Lakes: it development and importance in prehistory." Society for American Archaeology(47): 761-‐784.

Deroba, J. J. and J. R. Bence (2009). "Developing Model-‐Based Indices of Lake Whitefish Abundance Using Commercial Fishery Catch and Effort Data in Lakes Huron, Michigan, and Superior." North American Journal of Fisheries Management 29(1): 50-‐63.

Deroba, J. J. and J. R. Bence (2012). "Evaluating harvest control rules for lake whitefish in the Great Lakes: Accounting for variable life-‐history traits." Fisheries Research 121: 88-‐103.

Ebener, M. P., R. E. Kinnunen, et al. (2008). Management of Commercial Fisheries for Lake Whitefish in the Laurentian Great Lakes of North America. International Governance of Fisheries Ecosystems: Learning from the Past, Finding Solutions for the Future. M. G. Schechter, N. J. Leonard and W.

Figure 3. Survey response from 31 lake whitefish (Coregonus clupeaformis) fishermen, managers, and scientists to the question: Can decision-‐support tools be useful for fisheries management?



W. Taylor. Bethesda, Maryland, American Fisheries Society Press: 99-‐143.

Freeberg, M. H., W. W. Taylor, et al. (1990). "Effect of egg and larval survival on the year-‐class strength of lake whitefish in Grand Traverse Bay, Lake Michigan." Transactions of the American Fisheries Society 119(1): 92-‐100.

Lawler, G. H. (1965). "Fluctuations in the success of year-‐classes of whitefish populations with special reference to Lake Erie." Journal of the Fisheries Research Board of Canada 22(5): 1197-‐1227.

Lynch, A. J., W. W. Taylor, et al. (2010). "The influence of changing climate on the ecology and management of selected Laurentian Great Lakes fisheries." Journal of Fish Biology.

Madenjian, C. P., D. V. O'Connor, et al. (2006). "Evaluation of a lake whitefish bioenergetics model." Transactions of the American Fisheries Society 135(1): 61-‐75.

Magnuson, J. J., K. E. Webster, et al. (1997). "Potential effects of climate changes on aquatic systems: Laurentian Great Lakes and Precambrian Shield Region." Hydrological Processes 11(8): 825-‐871.

Mohr, L. C. and M. P. Ebener (2005). Description of the fisheries. The state of Lake Huron. M. P. Ebener. Ann Arbor, MI, Great Lakes Fishery Commission Special Publication. 05-‐02: 19-‐26.

Myers, R. A. (2001). "Stock and recruitment: generalizations about maximum reproductive rate, density dependence, and variability using meta-‐analytic approaches." Ices Journal of Marine Science 58(5): 937-‐951.

Nalepa, T. F., L. C. Mohr, et al. (2005). Lake whitefish and Diporeia spp. in the Great lakes: an overview. Proceedings of a workshop on the dynamics of lake whitefish (Coregonus clupeaformis) and the amphipod Diporeia spp. in the Great Lakes. L. C. Mohr and T. F. Nalepa. Ann Arbor, Michigan: 3-‐20.

Quinn, T. J. and R. B. Deriso (1999). Quantitative Fish Dynamics. New York, Oxford University Press.

Regier, H. A. and J. D. Meisner (1990). "Anticipated effects of climate change on fresh-‐water fishes and their habitat." Fisheries 15(6): 10-‐15.

Sousounis, P. J. and E. K. Grover (2002). "Potential future weather patterns over the Great Lakes region." Journal of Great Lakes Research 28(4): 496-‐520.

Taylor, W. W., M. A. Smale, et al. (1987). "Biotic and abiotic determinants of lake whitefish (Coregonus clupeaformis) recruitment in northeastern Lake Michigan." Canadian Journal of Fisheries and Aquatic Sciences 44: 313-‐323.

Trumpickas, J., B. J. Shuter, et al. (2009). "Forecasting impacts of climate change on Great Lakes surface water temperatures." Journal of Great Lakes Research 35(3): 454-‐463.

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