Decision Support and Great Lakes Lake Whitefish in a Changing Climate

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].

www.glisa.msu.edu

PROJECT BRIEF: GREAT LAKES LAKE WHITEFISH IN A CHANGING CLIMATE

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

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Project Objectives food source for humans. They are and have been a staple food source and focus of livelihoods for aboriginal people in The goal of this project was to design a decision support the region (Cleland 1982). Today, populations of lake system for sustainable harvest management of Lake whitefish support the most robust and economically Whitefish in the upper Laurentian Great Lakes in a changing valuable commercial fishery in the upper Laurentian Great climate. Upon completion, this project will synthesize the Lakes (Lakes Huron, Michigan, and Superior; annual catch current state of lake whitefish ecological knowledge and value = US$16.6 million; Mohr and Ebener 2005; Madenjian, provide an opportunity to assist decision makers allied with O'Connor et al. 2006; Ebener, Kinnunen et al. 2008). 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 Lake whitefish recruitment development of a quantitative fishery model which will serve as the foundation for decision support tool. The full Stock-recruitment relationships estimates are inherently project objectives were to: stochastic but much of that variability can be attributed to variation in spawning stock size and environmental • Develop a lake whitefish production model, including influences on growth and survival of young fish (Quinn and influential climatic factors; Deriso 1999). However, one of the most difficult problems • Engage scientists, managers, and fishermen to assess in fisheries science is determining the influence of the decision-support needs and ensure the climate-lake environment upon recruitment (Myers 2001). For lake whitefish production model is relevant to their whitefish, both stock size and environmental factors have management needs; and, been shown to influence recruitment (Taylor, Smale et al. • Design an interactive, user-friendly interface to display 1987). In particular, previous studies of Great Lakes lake the outcomes of the climate - lake whitefish production whitefish indicate that key environmental drivers that will modeling outcomes. be impacted by climate change, water temperature, ice cover, and storm intensity, affect recruitment (Christie 1963; Lawler 1965; Taylor, Smale et al. 1987; Freeberg, Importance of lake whitefish Taylor et al. 1990). Lake whitefish (Coregonus clupeaformis) are an economically, culturally, and ecologically important species Potential impacts of climate change in the upper Laurentian Great Lakes (Lakes Huron, Michigan, and Superior). Ecologically, lake whitefish are Climate change is expected to increase surface primarily a benthivores and serve as a key energy transfer temperatures of the Great Lakes by as much as 6oC vector from lower, benthic food webs to the upper, pelagic (Trumpickas, Shuter et al. 2009) while average wind speed food webs (Nalepa, Mohr et al. 2005), and are an important 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.

www.glisa.umich.edu Last updated: 6/9/2015 3 PROJECT BRIEF: GREAT LAKES LAKE WHITEFISH 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 Figure 2. Management units. fishery under the purview of the Chippewa Ottawa Resource Authority (CORA) and the Michigan Department of Natural Resources when the management units are Ebener, personal communication), and have established shared. Currently, a Technical Fisheries Committee and a statistical catch-at-age models to determine total allowable Modeling Subcommittee conduct stock assessments, catches which are used to designate harvest regulations for establish total allowable catches, and designate harvest 13 management units (Figure 2). We used the spawning regulations for the 14 lake whitefish management units in stock biomass and recruitment estimates from 1976-2011 these waters. The harvest from the 1836 Treaty waters to integrate 5 climate variables into a Ricker stock- management units comprise approximately a quarter of the recruitment model because lake whitefish recruitment is total harvest of lake whitefish in the entire Great Lakes (M. density dependent (Taylor, Smale et al. 1987). The Ricker Ebener, CORA, personal communication). For lake whitefish model was transformed to a linear function by taking the in the 1836 Treaty waters, harvest objectives have not yet natural log: 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 For a given yearclass, i, Ri is recruitment, Si is spawning Modeling subcommittee uses statistical catch-at-age models stock, αi is the productivity parameter, β1 is the density to estimate lake whitefish population metrics, including dependent shape parameter, and ϵi is normally distributed recruitment (Deroba and Bence 2009) in order to optimally random error. We developed 25 climate variable regulate harvest rates. combination models, with the full model including three climate variables:

�! Modeling methods �� = �! + �!�! + �!��! + �!��! + �!��! + �! �! We chose the 1836 Treaty Waters of Lakes Huron, Where: Michigan, and Superior as our study area because they have • icei is the proportion of mid-December ice cover over a latitudinal gradient, comprise approximately a quarter of spawning habitat for a given management unit and β1 is the total lake whitefish harvest in the entire Great Lakes (M. its density dependent shape parameter,

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• falli is the mean fall (October – December) air explained by stock size alone. temperature for land-based stations within 5 miles of a given management unit and γ1 is its density independent shape parameter, Modeling implications • springi is the mean spring (March – May) air temperature for land-based stations within 5 miles of a given Our findings show that seasonal temperatures and ice cover management unit and γ2 is its density independent improves stock-recruitment model fit and suggest that fall shape parameter. temperatures and ice cover are important for egg development, spring temperatures are important for larval We used Akaike’s Information Criterion (AIC), which survival, and all three are important for determining year- examines goodness of fit with a penalty for number of class strength of lake whitefish in the 1836 Treaty Waters. parameters, to compare the different model combinations Great Lakes lake whitefish stocks are characterized by of variables for each of the 13 management units. 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, Results to date Smale et al. 1987; Freeberg, Taylor et al. 1990; Brown, In three of the 13 management units, data were not Taylor et al. 1993). Christie (1963) found that cold autumn sufficient to complete model comparison. In three other temperatures and warm spring temperatures correlated to units, the standard stock-recruitment model was the best strong year-classes in Lake Ontario and the reverse model fit. In the remaining seven units, AIC comparisons combination produced weak year-classes. Lawler (1965) between the standard stock-recruitment model and the found a similar correlation in Lake Erie and attributed the models indicated that climate variables improved model fit relationship to optimal spawning temperature, incubation, (Table 1). These results indicate that seasonal and development while Freeberg et al. (1990) proposed a temperature, ice cover, and the interaction between ice different mechanism for correlations with spring cover and stock size improve the ability of the stock- temperatures for Lake Michigan, that temperature recruitment model to represent recruitment data and that influences the timing and production of copepod these variables are able to explain a proportion of the zooplankton, prey for larval lake whitefish. variability in lake whitefish recruitment beyond what is The relationship between seasonal temperatures, ice cover, and lake whitefish recruitment have significant implications Management unit Selected model for the fishery in the context of climate change. Climate WFH_Northern_Huron S + ice + fall change is expected to increase surface temperatures of the WFH_05 S Great Lakes by as much as 6oC (Trumpickas, Shuter et al. 2009). The positive relationship between spring WFM_01 S + ice + fall + S:ice temperatures and recruitment with climate change suggests WFM_02 Spring the potential for increased lake whitefish production in the WFM_03 Data insufficient Great Lakes, if habitat is not limiting and sufficient food WFM_04 S resources are available when the larvae hatch. However, WFM_05 S + ice + S:ice the negative relationship between fall temperatures, ice cover, and recruitment may inhibit egg survival and, WFM_06 Fall consequently, lake whitefish production. Future research WFM_08 Data insufficient will investigate the impact of water temperature and storm WFS_04 Data insufficient intensity on recruitment and use the full suite of climate WFS_05 S + ice + spring + S:ice variable to project our model analysis using potential WFS_07 S + ice climate scenarios. WFS_08 S

Table 1. The management units and model selected through Decision-maker engagement Akaike’s Information Criterion (AIC) comparisons. S = stock size; ice = the proportion of mid-December ice cover over spawning; In presenting our climate-lake whitefish production model fall = the mean fall (October – December) air temperature for to state, tribal, provincial, and federal managers, fishery land-based stations within 5 miles; spring = the mean spring scientists, and tribal fishermen, we are soliciting their input (March - May) air temperature for land-based stations within 5 through in-person and online surveys on how they would miles; and S:ice = the interaction between stock and ice cover. recommend that this model be best integrated into a decision-support tool that could be used to inform adaptive

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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 Figure 3. Survey response from 31 lake whitefish Assel, R., K. Cronk, et al. (2003). "Recent trends in Laurentian (Coregonus clupeaformis) fishermen, managers, and scientists Great Lakes ice cover." Climatic Change 57(1-2): 185-204. to the question: Can decision-support tools be useful for Brown, R. W., W. W. Taylor, et al. (1993). "FACTORS AFFECTING fisheries management? THE RECRUITMENT OF LAKE WHITEFISH IN 2 AREAS OF NORTHERN LAKE-MICHIGAN." Journal of Great Lakes decision making for fishermen and fishery managers while Research 19(2): 418-428. at the same time being useful to educate the students and Burnham, K. P. and D. R. Anderson (2002). Model Selection and public of potential impacts of climate change to the Great Multimodel Inference: A Practical Information-Theoretic Lakes region and its fisheries resources. To date, we have Approach. New York, NY, Springer. surveyed over 31 individuals (managers, scientists, and Christie, W. J. (1963). "Effects of artificial propagation and their weather on recruitment in the Lake Ontario whitefish fishermen). fishery." Journal of the Fisheries Research Board of Canada 20(3): 597-646. Initial results indicate that survey participants believe that Cleland, C. E. (1982). "The inland shore fishery of the northern decision-support tools can be useful for fisheries Great Lakes: it development and importance in prehistory." management (77% somewhat or completely agree; none Society for American Archaeology(47): 761-784. disagree; Figure 3). The participants suggest that invasive Deroba, J. J. and J. R. Bence (2009). "Developing Model-Based species (87%), bycatch (84%), market forces (84%), Indices of Lake Whitefish Abundance Using Commercial allocation (81%), climate change (81%), and Fishery Catch and Effort Data in Lakes Huron, Michigan, and communication between fishermen and managers (81%) Superior." North American Journal of Fisheries Management 29(1): 50-63. are among the most important issues for the future of lake Deroba, J. J. and J. R. Bence (2012). "Evaluating harvest control whitefish management. rules for lake whitefish in the Great Lakes: Accounting for variable life-history traits." Fisheries Research 121: 88-103. Using these results, we will incorporate their Ebener, M. P., R. E. Kinnunen, et al. (2008). Management of recommendations into our tool design to ensure that the Commercial Fisheries for Lake Whitefish in the Laurentian tool, which will be housed on the Michigan Sea Grant Great Lakes of North America. International Governance of website, is practical and useful to the users and, ultimately, Fisheries Ecosystems: Learning from the Past, Finding lead to a more sustainable and prosperous fishery Solutions for the Future. M. G. Schechter, N. J. Leonard and W. resources and coastal communities in the future. www.glisa.umich.edu Last updated: 6/9/2015 6 PROJECT BRIEF: GREAT LAKES LAKE WHITEFISH IN A CHANGING CLIMATE

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.