DESIGNING A DECISION-SUPPORT TOOL FOR HARVEST MANAGEMENT OF GREAT
LAKES LAKE WHITEFISH (COREGONUS CLUPEAFORMIS) IN A CHANGING CLIMATE
By
Abigail Julia Lynch

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
Fisheries and Wildlife – Doctor of Philosophy
Ecology, Evolutionary Biology, and Behavior – Dual Degree
2013

ABSTRACT
DESIGNING A DECISION-SUPPORT TOOL FOR HARVEST MANAGEMENT OF GREAT
LAKES LAKE WHITEFISH (COREGONUS CLUPEAFORMIS) IN A CHANGING CLIMATE
By
Abigail Julia Lynch

Fisheries are a vitally important renewable resource if managed sustainably (i.e., with
harvest at a rate that does not deplete population levels and allows for future use). Climate
change is expected to impact fish, fisheries, and the communities dependent upon them by
altering fish habitat which will shift the distribution and abundance of fish populations. Changes
to fish distribution and abundance will challenge current fisheries management practices and
highlight the need for new adaptive approaches to manage the ecological, social, and economic
impacts of climate change on fisheries. Decision-support tools can assist fishermen and fisheries
managers make more informed management choices related to climate change. Using the
Laurentian Great Lakes as a case-study, and specifically the Lake Whitefish (Coregonus
clupeaformis) fishery in the 1836 Treaty Waters of Lakes Huron, Michigan, and Superior, the
objectives of this dissertation were to:
1)

Review the physical and biological mechanisms by which cold-, cool-, and warm-

water fish species will be affected by climate change in the Great Lakes;
2)

Examine the feasibility of decision-support tools for fishery management in the

context of climate change;
3)

Survey Lake Whitefish fishermen, fishery researchers, and fishery managers to

document need and willingness to implement a decision-support tool for harvest management of
Lake Whitefish and climate change; and,

4)

Develop a model of Lake Whitefish recruitment including climatic relationships

and project recruitment with climate change.
By the end of the 21st century, the Great Lakes will be warmer, wetter, winder, with less
ice cover. Changes to the Great Lakes climate will change habitat for Great Lakes fishes,
including Lake Whitefish. Lake Whitefish recruitment has been linked to climate variables,
specifically temperature, wind speed, and ice cover. A mechanistic model confirmed a positive
relationship between Lake Whitefish recruitment and temperature and ice cover and a negative
relationship between Lake Whitefish recruitment and wind speed using corrected Akaike’s
Information Criterion for model selection.
Surveying Lake Whitefish fishermen, researchers, and managers showed that those
affiliated with the fishery support the use of decision-support tools can assist this fishery
integrate science into management. The survey recommendations were used to develop the
decision-support tool for the Lake Whitefish climate-recruitment relationship with climate
projections. Some management units will expect up to a 50% decline and others up to a 220%
increase in Lake Whitefish recruitment because of spatial variability in the climate-recruitment
relationships and climate projections.

ACKNOWLEDGMENTS

While I could never have imagined what was in store for me as I read over the half page
position description at a Thai restaurant in Arlington, Virginia, I knew the graduate position at
Michigan State University (MSU) was an opportunity to seize. Looking back on my doctoral
program at MSU, this document cannot come close to representing all that it entailed. I am so
grateful for the amazing perspective-changing experiences and all that I have learned.
I am sincerely grateful to my advisor, Dr. Bill Taylor, and my committee members, Drs.
Jack Liu, Aaron McCright, and Doug Beard for their guidance and support through my
dissertation project. I thank Bill for all he has taught me and taught me to teach others – “a no is
a yes in waiting” and so much more. I feel that I have matured from an unsure, but enthusiastic,
student to, hopefully, a capable world-changer. I thank Jack for his very insightful
recommendations linking the human and natural world; he is a great model to emulate. I thank
Aaron for his social perspective; he helped significantly increase the applicability and impact of
my work. I thank Doug for providing a great reality check, providing pragmatic context for my
project and external opportunities.
While I may never have envisioned myself a Michigander, I thank Becky Humphries and
Kelley Smith for welcoming me into the Michigan Department of Natural Resources
immediately upon my arrival. I thank Rique Campa, Maya Fischhoff, Ian Gray, Kelly
Millenbah, Chuck Pistis, and President Simon for opportunities to engage in activities on
campus. And, I thank Richard Christian the U.S. Fish and Wildlife’s Branch of Partnerships and
Communication for accommodating my non-traditional student appointment and for the
opportunity to be a remote member of the branch.

iv

I will always have fond memories of Michigan thanks to my many colleagues and friends
that have helped me through this process. Jim Bence, Bo Bunnell, Dave Caroffino, Arthur
Cooper, Ian Cowx, Mark Ebener, Dan Hayes, Mark Holey, Ron Kinnunen, Brent Lofgren, Jared
Myers, Paul Ripple, Iyob Tsehaye, Yin-Phan Tsang, Dan Weiferich, So-Jung Youn, the Center
for Systems Integration and Sustainability (CSIS), the Quantitative Fisheries Center, the 1836
Treaty Waters Technical Fishery Committee and Modeling Sub-Committee, the Michigan
Department of Natural Resources, the Chippewa Ottawa Resource Authority, Bay Mills Indian
Community, and all the survey participants have been integral in providing data, discussion, and
direction to the project. I have great gratitude for my FW cohort, CSIS folks, Murphy-InfanteRoth-Taylor-Hayes (MIRTH) folks, housemates, and friends for making the experience so
enjoyable and entertaining: Andrea Bowling, John Burke, Neil Carter, Ryan Fletcher, Amber
Goguen, Marissa Hammond, Marta Jarzyna, Sam Keeney, Hanna Kruckman, Nancy Léonard,
Michelle Lute, Ayman Mabrouk, Colleen Matts, Kevin McDonnell, Kyle Molton, Joe Nohner,
Christin O’Brien, Marielle Peschiera, Kelsey Schlee, Abigail Schroeder, Damien Sheppard,
Shikha Singh, Kiira Sitarii, Jenna Smith, Ragnar Stroberg, Darren Thornbrugh, Ralph Tingley
III, Yin-Phan Tsang, James Vatter, Kerry Weaver (née Waco), So-Jung Youn, and Chiara
Zuccarino-Crowe, among others.
While my family may never have believed I would ever be finished with school, they
have been a constant source of support and encouragement through all of my educational
pursuits. Many thanks to them all, especially my parents, Dennis and Debbie Lynch, my sisters,
Nora Krasowski and Annie Lynch, my grandmother, Jean Hall, and my aunts and uncles, Grace
and Ed Simpson, Pat and Dick Chastonay, and Loretta and Pete Haste. And, of course, Eric
MacMillan.

v

Funding for this project was provided by: a Dr. Howard A. Tanner Fisheries Excellence
Fellowship; a Ecology, Evolutionary Biology, and Behavior Summer Fellowship; a Graduate
School Research Enhancement Award; the Great Lakes Integrated Sciences and Assessments
Center; an International Studies and Programs Pre-dissertation Award; a J. Frances Allen
Scholarship; a Janice Lee Fenske Excellence in Fisheries Management Fellowship; the Michigan
Department of Natural Resources; Michigan Sea Grant; a Paul W. Rodgers Scholarship; a Red
Cedar Fly Fishers Graduate Fellowship in Fisheries Management; a University Distinguished
Fellowship; and a William W. And Evelyn M. Taylor Endowed Fellowship for International
Engagement in Coupled Human and Natural Systems; Michigan Department of Natural
Resources; Great Lakes Integrated Sciences and Assessments Center; Red Cedar Fly Fishers
Graduate Fellowship in Fisheries Management; Ambrose Pattullo Fund for Environmental Issues
Graduate Fellowship in Literary Works; William W. And Evelyn M. Taylor Endowed
Fellowship for International Engagement in Coupled Human and Natural Systems; International
Studies and Programs Pre-dissertation Award; Ecology, Evolutionary Biology, and Behavior
Summer Fellowship; Graduate School Research Enhancement Award; Theodore Roosevelt
conservation and Environmental Leadership Fellowship; Janice Lee Fenske Excellence in
Fisheries Management Fellowship; and University Distinguished Fellowship.

vi

TABLE OF CONTENTS

LIST OF TABLES ......................................................................................................................... X
LIST OF FIGURES ..................................................................................................................... XII
INTRODUCTIORY SUMMARY ............................................................................................... 1
Designing a climate change decision-support tool for Great Lakes Lake Whitefish ......... 2
“A better fish cannot be eaten!” .......................................................................................... 2
Aiming for 20/20 vision of lake whitefish recruitment ...................................................... 2
Could warmer temperatures be good for a coldwater fish? ................................................ 3
Predicting the Monopoly board .......................................................................................... 4
Dissertation format.............................................................................................................. 5
CHAPTER 1: THE INFLUENCE OF CHANGING CLIMATE ON THE ECOLOGY
AND MANAGEMENT OF SELECTED LAURENTIAN GREAT LAKES
FISHERIES ................................................................................................................................... 6
Abstract ............................................................................................................................... 7
A changing global climate .................................................................................................. 8
Climate projections for the Great Lakes Basin ................................................................... 9
Effects on Great Lakes fish habitat ..................................................................................... 9
Temperature .......................................................................................................... 14
Dissolved oxygen .................................................................................................. 16
Food web dynamics............................................................................................... 17
Potential Consequences of Climate Change on Fish Populations .................................... 18
Cold water: Coregonus clupeaformis ................................................................... 20
Cool water: Sander vitreus ................................................................................... 22
Warm water: Micropterus dolomieu..................................................................... 23
Future of Fisheries Management ...................................................................................... 24
Learning from Aquatic Invasive Species Management ......................................... 26
Climate Change Decision Support........................................................................ 28
Acknowledgements ........................................................................................................... 31
LITERATURE CITED ................................................................................................................. 32
CHAPTER 2: THE NEED FOR DECISION-SUPPORT TOOLS FOR A CHANGING
CLIMATE: APPLICATION TO INLAND FISHERIES MANAGEMENT ........................ 40
Abstract ............................................................................................................................. 41
Introduction ....................................................................................................................... 42
Factors that influence fisheries management decisions .................................................... 42
Society ................................................................................................................... 43
Politics .................................................................................................................. 44
Economics ............................................................................................................. 45
Scientific uncertainty ............................................................................................ 46
Decision support ............................................................................................................... 47
Decision-support tools .......................................................................................... 48
Application of science-based decision support to inland fisheries management.. 51
vii

Climate change and inland fisheries ................................................................................. 52
Potential impacts of climate change on inland fisheries ...................................... 53
Managing inland fisheries in a changing climate................................................. 55
Harvest management of lake whitefish with climate change............................................ 57
Potential impacts of climate change on lake whitefish ......................................... 57
Potential impacts on lake whitefish management ................................................. 58
Need for decision support ..................................................................................... 60
Acknowledgements ........................................................................................................... 61
LITERATURE CITED ................................................................................................................. 62
CHAPTER 3: PERCEPTIONS OF MANAGEMENT AND WILLINGNESS TO USE
DECISION SUPPORT: INTEGRATING THE POTENTIAL IMPACTS OF CLIMATE
CHANGE ON THE LAKE WHITEFISH (COREGONUS CLUPEAFORMIS) FISHERY
INTO HARVEST MANAGEMENT IN THE 1836 TREATY WATERS OF LAKES
HURON, MICHIGAN, AND SUPERIOR................................................................................ 70
Abstract ............................................................................................................................. 71
Introduction ....................................................................................................................... 72
Lake Whitefish and climate change decision support ........................................... 72
Methods............................................................................................................................. 74
Study location fishery management ...................................................................... 74
Lake Whitefish management and decision-support survey design ....................... 74
Lake Whitefish management and decision-support survey analysis ..................... 76
Results ............................................................................................................................... 76
Perceptions of Lake Whitefish management ......................................................... 82
Willingness to use decision-support tools ............................................................. 83
Discussion ......................................................................................................................... 90
Perceptions of Lake Whitefish management ......................................................... 91
Willingness to use decision-support tools ............................................................. 93
Integration into climate change decision support ................................................ 94
Acknowledgments............................................................................................................. 95
APPENDICES .............................................................................................................................. 96
LITERATURE CITED ............................................................................................................... 102
CHAPTER 4: PROJECTED CHANGES IN LAKE WHITEFISH (COREGONUS
CLUPEAFORMIS) RECRUITMENT WITH CLIMATE CHANGE IN THE 1836
TREATY WATERS OF LAKES HURON, MICHIGAN, AND SUPERIOR .................... 105
Abstract ........................................................................................................................... 106
Introduction ..................................................................................................................... 107
Temperature ........................................................................................................ 107
Wind and waves .................................................................................................. 108
Ice cover .............................................................................................................. 109
Climate change ................................................................................................... 110
Lake Whitefish in the 1836 Treaty Waters .......................................................... 112
Methods........................................................................................................................... 113
Spawning stock biomass and recruitment ........................................................... 115
Climate variables ................................................................................................ 115
Pearson correlation ............................................................................................ 117
viii

Variance inflation factors ................................................................................... 118
Lake Whitefish recruitment model selection ....................................................... 119
Projecting recruitment with climate ................................................................... 120
Results ............................................................................................................................. 121
Climate variable selection .................................................................................. 121
Lake Whitefish recruitment model selection ....................................................... 122
Lake Whitefish climate-recruitment projection .................................................. 122
Discussion ....................................................................................................................... 135
Lake Whitefish recruitment model selection ....................................................... 135
Lake Whitefish climate-recruitment projection .................................................. 139
Implications for Lake Whitefish management..................................................... 140
Acknowledgments........................................................................................................... 141
APPENDICES ............................................................................................................................ 142
LITERATURE CITED ............................................................................................................... 157
SYNTHESIS .............................................................................................................................. 162
Climate change will affect the Great Lakes .................................................................... 163
Climate influences the productivity of the Lake Whitefish fishery ................................ 163
Modeling can project changes in Lake Whitefish recruitment with climate change ...... 165
Decision-support tools can help integrate Lake Whitefish climate change projections into
harvest management........................................................................................................ 165
LITERATURE CITED ............................................................................................................... 167

ix

LIST OF TABLES

TABLE 1.1. Selected climate change projections grouped by feature class (air temperature,
precipitation and lake level, ice cover, wind speed, water temperature, stratification and
dissolved oxygen, thermal habitat and bioenergetics) for the Laurentian Great Lakes region with
ecological relevance to fisheries. GCMs = General Circulation Models; 2×CO2 = 2×present CO2
concentration; IPCC = Intergovernmental Panel on Climate Change. ......................................... 10
TABLE 2.1. Select decision-support tools, their approaches, strengths, and weaknesses with
relation to fisheries and climate change. Modified from NRC (2010) Table 4.1. ....................... 49
TABLE 2.2. Examples of potential effects of climate change and impacts on inland fish
production. .................................................................................................................................... 49
TABLE 3.1. Survey respondent recommendations for improving management of Lake Whitefish
(Coregonus clupeaformis) in the 1836 Treaty Waters, grouped by topic..................................... 80
TABLE 3.2. Survey respondent listed barriers implementing decision-support tools in Lake
Whitefish (Coregonus clupeaformis) management in the 1836 Treaty Waters, grouped by topic.
....................................................................................................................................................... 88
TABLE 4.1. Projected impacts of changes in ice cover, wind and waves, fall temperature, and
spring temperature on Lake Whitefish (Coregonus clupeaformis)............................................. 111
TABLE 4.2. Pearson correlation coefficients (below diagonal) and p-values (above diagonal;
<0.05 bolded) for potentially relevant climate variables by 1836 Treaty Waters Lake Whitefish
(Coregonus clupeaformis) management unit: ice cover (December 10m depth contour), thermal
index (April temperature deviation – November temperature deviation), rate index (spring
warming rate – fall cooling rate), November wind speed (monthly average), and November wave
height (monthly average). Note WFM-03 temperature data unavailable................................... 123
TABLE 4.3. Variance Inflation Factors for potentially relevant climate variables by 1836 Treaty
Waters Lake Whitefish (Coregonus clupeaformis) management unit: density-dependent ice cover
(S:ice; December 10m depth contour), thermal index (April temperature deviation – November
temperature deviation), rate index (spring warming rate – fall cooling rate), and November wind
speed (monthly average). Note WFM-03 temperature data unavailable. ................................. 126
TABLE 4.4. The difference between corrected Akaike’s Information Criterion (AICc) values
between the Lake Whitefish (Coregonus clupeaformis) stock-recruitment (S-R) model and the
best fit model including climate variables: ice cover (December 10m depth contour), thermal
index (t_index; April temperature deviation – November temperature deviation), rate index
(r_index; spring warming rate – fall cooling rate), and November wind speed (wind; monthly
average) for each of the 13 management units of the 1836 Treaty Waters evaluated. Parameter
estimates are listed (blue = positive; red = negative). Management units with AICc comparisons
< 3 are gray. Note WFM-03 temperature data unavailable. ...................................................... 127

x

TABLE 4.5. Variables used in best fit linear regression models for the 1836 Treaty Waters Lake
Whitefish (Coregonus clupeaformis) management units. Values in parentheses indicate
management units with a difference between corrected Akaike’s Information Criterion (AICc)
values between the stock-recruitment (S-R) model and the best fit model of > 3, indicating
significant improvement in model fit. Note that some models contain more than one variable.
..................................................................................................................................................... 128
TABLE 4.6. Comparison of Lake Whitefish (Coregonus clupeaformis) recruitment estimates from
the best fit linear regression models including climate variables for 2007 with the projected
estimates for 2052-2070, by management unit. Values are displayed as a proportion of the 2007
estimate for each management unit (blue = projected increase; red = projected decrease). ....... 129

xi

LIST OF FIGURES

FIGURE 2.1. Factors that contribute to fisheries management decisions....................................... 43
FIGURE 2.2. Schematic of the ecological inputs and anticipated outputs of a mechanistic decisionsupport tool to sustainably manage lake whitefish (Coregonus clupeaformis) production in a
changing climate ........................................................................................................................... 59
FIGURE 3.1. Land and water territories ceded by the Chippewa and Ottawa nations in the 1836
Treaty of Washington and Lake Whitefish (Coregonus clupeaformis) management units
managed under the 2000 Consent Decree. For interpretation of the references to color in this and
all other tables and figures, the reader is referred to the electronic version of this dissertation. .. 73
FIGURE 3.2. Age distribution of survey respondents by primary affiliation. ............................... 77
FIGURE 3.3. Lake Whitefish (Coregonus clupeaformis) fishery affiliation of survey respondents
by affiliation. Note that respondents could select more than one affiliation. .............................. 78
FIGURE 3.4. Satisfaction level of survey respondents with current management of the Lake
Whitefish (Coregonus clupeaformis) fishery in the 1836 Treaty Waters by primary affiliation. 79
FIGURE 3.5. Heat map of survey responses to the importance level (not important, moderately
important, very important) for 11 issues to the future management of Lake Whitefish in the 1836
Treaty waters: allocation, bycatch, climate change, communication, habitat loss or modification,
human population growth, invasive species, land-use changes, market forces, overexploitation,
and water quality and quantity. A heat map is three dimensional with the height and color
indicating intensity of importance for each issue: green = 20-30 respondents, red = 10-20
respondents, and blue = 0-10 respondents. ................................................................................... 84
FIGURE 3.6. Survey responses to how well integrated science is into Lake Whitefish (Coregonus
clupeaformis) management in the 1836 Treaty Waters (very well; well; moderately; poorly; very
poorly; don’t know/no opinion) by primary affiliation................................................................. 85
FIGURE 3.7. Heat map of survey responses to the importance level (not important, moderately
important, very important) for seven factors to facilitate integration of science into Lake
Whitefish management in the 1836 Treaty Waters: addressing significant management problems;
being transparent with research methods and analyses; communicating clearly to fishers and/or
managers; creating decision-support tools; ensuring incorporation into long-term management;
involving fishers and/or managers in the research process; and, providing recommendations
within the structure of current management. A heat map is three dimensional with the height and
color indicating intensity of importance for each issue: green = 20-30 respondents, red = 10-20
respondents, and blue = 0-10 respondents. ................................................................................... 86
FIGURE 3.8. Survey responses to the usefulness of decision-support tools to Lake Whitefish
(Coregonus clupeaformis) management in the 1836 Treaty Waters (completely agree; somewhat
agree; neither agree nor disagree; somewhat disagree; completely disagree; don’t know/no
opinion) by primary affiliation...................................................................................................... 87
xii

FIGURE 4.1. Lake Whitefish (Coregonus clupeaformis) management units for the 1836 Treaty
Waters of Lakes Huron, Michigan, and Superior color coded by the best fit linear regression
model for recruitment as selected by Corrected Akiake’s Information Criterion. ..................... 114
FIGURE 4.2. Lake Whitefish (Coregonus clupeaformis) recruitment estimates from the 1836
Treaty Waters Modeling Subcommittee Statistical Catch-at-Age (SCAA) models (2007 and
earlier) and projections using CHARM inputs into the best fit linear regression models including
climate variables (2052-2070) by management unit. A) management units with a difference
between corrected Akaike’s Information Criterion (AICc) values between the stock-recruitment
(S-R) model and the best fit model of > 3, indicating significant improvement in model fit; B) all
Lake Huron management units; C) all Lake Michigan management units; and D) all Lake
Superior management units. Note that stock size in projection years was held constant at 2007
levels to isolate climate effects and WFM-02 was removed because of high variance (σ2 =
30.64). ......................................................................................................................................... 130
FIGURE 4.3. Lake Whitefish (Coregonus clupeaformis) management units for the 1836 Treaty
Waters of Lakes Huron, Michigan, and Superior color coded by the 2052-2070 mean projected
change in recruitment (blue = projected increase; red = projected decrease). ............................ 134
FIGURE 4.4. A) Current and B) Projected change in Lake Whitefish (Coregonus clupeaformis)
recruitment with climate conditions: temperature, wind, and ice cover (Todd Marsee, Michigan
Sea Grant). .................................................................................................................................. 137
FIGURE 4.5. Plots of Pearson correlation coefficients for potentially relevant climate variables:
ice cover (December 10m depth contour), thermal index (April temperature deviation –
November temperature deviation), rate index (spring warming rate – fall cooling rate),
November wind speed (Nov_WSPD; monthly average), and November wave height
(Nov_WVHT; monthly average) by 1836 Treaty Waters Lake Whitefish (Coregonus
clupeaformis) management units. Confidence ellipses (above diagonal) demonstrate correlation
magnitude and direction where a circle corresponds to zero correlation and as the correlation
increases, the ellipse narrows, and finally collapses into a line segment as the correlation
approaches ±1, a perfect linear relationship. Pie graphs (below diagonal) indicate the magnitude
of the pairwise correlation (blue = positive; red = negative). ..................................................... 144

xiii

INTRODUCTIORY SUMMARY

Lynch, A. J. 2013. WINNER: One Fish, Two Fish, Where Fish for Whitefish? Fisheries
38(8):356.

The content of the introductory summary contains updated results from the publication cited
above but still reflects journal specifications (e.g. formatting).
The publication cited above won the 2013 American Fisheries Society Student Writing Contest.
For the contest, students are “asked to submit a 500- to 700-word article explaining their own
research or a research project in their lab or school. The article must be written in language
understandable to the general public (i.e., journalistic style).”

1

Designing a climate change decision-support tool for Great Lakes Lake Whitefish
Imagine you are playing a game of Monopoly and are investing wisely for the future. You
have numerous hotels on “Boardwalk” and are raking in the dough any time another player lands
on your valuable property. Then, the rules of the game unexpectedly change. “Baltic Place” is
the hot commodity and all of your painstaking investments in “Boardwalk” are for naught. Now,
imagine this is not a game and your actual livelihood and family depend on your success.
Currently, the Great Lakes Lake Whitefish fishery is the most economically valuable
commercial fishery in the upper Great Lakes. But, like a modified Monopoly, this fishery could
face new “rules of the game” from climate change. My dissertation research developed a
decision-support tool to ensure that the fish, the fishery, and the livelihoods dependent upon
them remain sustainable in the face of climate change.
“A better fish cannot be eaten!”
Lake Whitefish, a member of the salmon family, are found in coldwater lakes throughout
much of northern North America. Like many salmon species, they are highly valued as food
fish: fresh fillets, smoked fillets, frozen fillets, fish cakes, spread, and sausage. Lake Whitefish
have been a staple of native communities in the Great Lakes for thousands of years and were a
particular favorite of early French explorers—one even wrote that “a better fish cannot be
eaten!” They are a favorite still today; over 15 million pounds of Lake Whitefish are consumed
each year in the Great Lakes region alone.
Aiming for 20/20 vision of lake whitefish recruitment
To reach someone’s dinner plate, a Lake Whitefish must survive a treacherous journey
from an egg to a larvae to a juvenile and, finally, recruit to the fishery. Ultimately, we want to
know how many Lake Whitefish enter the fishery so that we can determine how many can be

2

harvested without negatively impacting future populations and harvest. But, it is next to
impossible to know how many Lake Whitefish are actually out there. So, we estimate the
population size using mathematical modeling.
You can think of mathematical modeling of fish populations like a visit to the eye doctor.
For many of us, perfect 20/20 vision is as unobtainable as knowing true population abundance is
for fishery managers. But, with corrective lenses and modeling approaches, we can get pretty
close to estimating (or seeing) those realities. Like adjusting the lenses in an eye exam, including
biologically relevant variables in the model can often improve our ability to predict fish
populations.
My dissertation research did just that. I examined climate factors, specifically
temperature, wind, and ice cover, which have been shown to influence recruitment of Lake
Whitefish to the commercial fishery. Because Lake Whitefish spawn in the fall and hatch as
larvae in the spring, these time periods are particularly critical to the survival of Lake Whitefish.
I used historical data to model how changes in these climate variables affected recruitment.
Could warmer temperatures be good for a coldwater fish?
Earlier research has observed positive relationship between recruitment and spring
temperatures and ice cover and a negative relationship between recruitment and fall temperatures
and fall wind speed. My research confirmed these same patterns. Warmer spring temperatures
may improve survival of larval Lake Whitefish, if food resources are available, and increase
Lake Whitefish production in the Great Lakes. However, warmer fall temperatures, more wind,
and less ice cover may inhibit egg survival and, consequently, Lake Whitefish production.
The relationship between climate variables and Lake Whitefish recruitment has
significant implications for the fishery in the context of climate change. By the end of this

3

century, the Great Lakes region will be warmer, windier, with less ice cover. Surface
temperatures for the Great Lakes, for example, are expected to increase by as much as 7°F. So,
is this just another “doom and gloom” climate change story where a species will be ousted by
habitat changes? Or, perhaps could warmer temperatures be good for this coldwater fish?
Using the climate-recruitment model, I was able to project anticipated impacts on Lake
Whitefish recruitment using my climate-recruitment model and a downscaled climate model
developed for the Great Lakes for the 1836 Treaty Waters of Lakes Huron, Michigan, and
Superior. The 1836 Treaty Waters currently sustain a highly productive Lake Whitefish fishery,
approximately 25% of the whole fishery in the upper Great Lakes. Recruitment projections
varied between management units; some had up to a 50% decline and others had as much as a
220% increase. Overall, my research suggests that there is potential for increased Lake
Whitefish recruitment in the Great Lakes with climate change and some shift in the distribution
of the fishery.
Predicting the Monopoly board
These potential changes in Lake Whitefish populations have significant repercussions for
fishermen and the communities dependent upon this fishery. Returning to the Monopoly analogy,
if you could predict changes to the game, you would change your strategy and invest differently.
Likewise, my research aims to help the Lake Whitefish fishery adapt to anticipated climate
change. I hope my climate-recruitment model and projections will serve as a decision-support
tool to assist fishermen and fishery managers. This tool, which is being housed on the Michigan
Sea Grant website, will tell fishermen if it’s better to give up on the “Boardwalk” fishery
locations and focus their investments on “Baltic Place” for a more sustainable and prosperous
fishery. Because, ultimately, who doesn’t want to win Monopoly?

4

Dissertation format
This dissertation is composed of four central chapters, bounded by this introductory
summary and a final synthesis. I studied the potential impacts of climate change on Laurentian
Great Lakes fish and fisheries (Chapter 1) and investigated the potential use of decision-support
tools in fisheries in the context of climate change (Chapter 2), using Lake Whitefish (Coregonus
clupeaformis) as a case-study. I surveyed fishermen, managers, and researchers affiliated with
the Lake Whitefish fishery to understand their perceptions of Lake Whitefish management and
willingness to use decision-support tools (Chapter 3). Using the recommendations from the
survey, I developed a decision-support tool for harvest management of Lake Whitefish in a
changing climate by modeling the relationship between climate variables, specifically fall and
spring temperature, fall wind speed, and winter ice cover, and Lake Whitefish recruitment then
projecting that climate-recruitment relationship forward with climate change (Chapter 4).
Climate change will influence recruitment of Lake Whitefish in the Great Lakes. Some
management units will increase in productivity and others will decrease, as a result of the climate
influences on recruitment and the projections for climate change in each of the management
units. The objective of the tool developed in this dissertation is to communicate the potential
impacts of climate change on the Lake Whitefish fishery with fishermen, fishery managers,
researchers, students, and the public to help them anticipate changes to the fishery (synthesis).
Ultimately, the goal of this tool is to support an ecologically sustainable, prosperous fishery and
promote the well-being of associated communities.

5

CHAPTER 1: THE INFLUENCE OF CHANGING CLIMATE ON THE ECOLOGY
AND MANAGEMENT OF SELECTED LAURENTIAN GREAT LAKES
FISHERIES

Lynch, A. J., W. W. Taylor, and K. D. Smith. 2010. The Influence of Changing Climate on the
Ecology and Management of Selected Great Lakes Fisheries. Journal of Fish Biology 77:
1964-1982.

The content of this chapter is intended to be identical to the publication cited above and reflects
journal specifications (e.g. formatting, British spelling). Any differences should be minor and
are unintended.

6

Abstract
The Laurentian Great Lakes Basin provides an ecological system to evaluate the potential
effect of climate change on dynamics of fish populations and the management of their fisheries.
This review describes the physical and biological mechanisms by which fish populations will be
affected by changes in timing and duration of ice cover, precipitation events and temperature
regimes associated with projected climate change in the Great Lakes Basin with a principal focus
on the fish communities in shallower regions of the basin. Lake whitefish Coregonus
clupeaformis, walleye Sander vitreus and smallmouth bass Micropterus dolomieu were
examined to assess the potential effects of climate change on guilds of Great Lakes cold, cool
and warm-water fishes, respectively. Overall, the projections for these fishes are for the
increased thermally suitable habitat within the lakes, though in different regions than they
currently inhabit. Colder-water fishes will seek refuge further north and deeper in the water
column and warmer-water fishes will fill the vacated habitat space in the warmer regions of the
lakes. While these projections can be modified by a number of other habitat elements (e.g.
anoxia, ice cover, dispersal ability and trophic productivity), it is clear that climate-change
drivers will challenge the nature, flexibility and public perception of current fisheries
management programmes. Fisheries agencies should develop decision support tools to provide a
systematic method for incorporating ecological responses to climate change and moderating
public interests to ensure a sustainable future for Great Lakes fishes and fisheries.

KEYWORDS: adaptive management; climate change; decision support; fisheries conservation.

7

A changing global climate
Scientific evidence suggests global air and ocean temperatures are rising at a relatively
rapid pace with increased melting of snow and ice and rising sea levels (IPCC 2007). While
some climatic variability is expected and cooler years have occurred, temperature increases over
the last 50 years (1955 to 2005) have been nearly twice what they were in the 100 years
preceding and are anthropogenically induced (IPCC 2007).
The effects of climatic warming are predicted to be significant on the distribution and
abundance of freshwater fishes as water temperature, quantity and quality are all factors
influenced by the atmosphere with direct implications for the structure of fish communities
(Regier and Meisner, 1990). In particular, measures of sustained fish yields in North America
have been empirically related to water temperature with increased yields at lower latitudes and
warmer water systems (Schlesinger and Regier, 1982; Meisner et al., 1987). In the Laurentian
Great Lakes region, climate change is estimated to alter the hydrographic and geographic
distributions of freshwater fishes (Regier and Meisner, 1990), their year-class strength
(Casselman, 2002), growth and bioenergetics (Brandt et al., 2002) and trophic dynamics
(Jackson and Mandrak, 2002). For example, Regier & Meisner (1990) suggest that cold-water
habitat for lake trout Salvelinus namaycush (Walbaum) and lake whitefish Coregonus
clupeaformis (Mitchill) will be shifted deeper within each lake, particularly during the warmer
summer months.
Climate change will challenge the current practices and tenets of fisheries management
within the basin. It is important for fisheries managers to understand the implications for fish
communities and their productivity within these lakes in order to implement strategies that
accommodate climate change (i.e. focus conservation efforts on populations capable of persisting
in a changing climate). This paper reviews the climate change literature pertinent to Great Lakes
8

fisheries, with focused assessments on three key species from different thermal guilds, cold: C.
clupeaformis, cool: walleye Sander vitreus (Mitchill), and warm: smallmouth bass Micropterus
dolomieu (Lacépède). In addition, this paper suggests models for managing Great Lakes fisheries
in a dynamic, adaptive manner based on lessons learned via aquatic invasive species
management to ameliorate the impact of climate change on Great Lakes fishes.
Climate projections for the Great Lakes Basin
The regional climate of the Laurentian Great Lakes Basin is predicted to be warmer with
increased precipitation and less ice cover by the end of the 21st century (Table 1.1). Air
temperatures in the Great Lakes region are projected to increase by 0-11oC in the summer and
0.5-9.1oC in the winter (Mortsch and Quinn, 1996; Sousounis and Albercook, 2000; Sousounis
and Grover, 2002; Kling et al., 2003; Wuebbles and Hayhoe, 2004).
Concurrent with temperature increases, Sousounis & Albercook (2000) estimate a 1525% increase in summer precipitation across much of the region. This increase in precipitation,
however, will not necessarily result in higher lake levels because higher temperature and
evaporation rates will occur with less ice cover during the winter months (Smith, 1991a); Angel
& Kunkel (2010) modelled a range of −3 to +1.5 m changes in lake level, depending on emission
conditions. These changes will contribute to an increase in water temperature and changes in
Great Lake morphometry, which will influence resident fish distribution and production.
Effects on Great Lakes fish habitat
As one of the largest bodies of surface fresh water in the world, representing c. 20% of
the world’s supply (Lehman et al., 2000), the Great Lakes provide a diverse set of fish habitats:
wetlands, embayments, nearshore, and open water. Climate change will alter the structure and
dynamics of these habitats and affect the distributions of resident fishes. This will principally
9

TABLE 1.1. Selected climate change projections grouped by feature class (air temperature, precipitation and lake level, ice cover, wind
speed, water temperature, stratification and dissolved oxygen, thermal habitat and bioenergetics) for the Laurentian Great Lakes region
with ecological relevance to fisheries. GCMs = General Circulation Models; 2×CO2 = 2×present CO2 concentration; IPCC =
Intergovernmental Panel on Climate Change.
Air temperature
Declines from -3 m to increases of +1.5 m in lake level for all lakes using 23 GCMs and three IPCC
(Angel and Kunkel, 2010)
emission scenarios
4-11oC increase using three GCMs with 2×CO2.

(Croley, 1990)
st

3-8oC increase (winter); 3-9oC increase (summer) by the end of the 21 century using two GCMs
and three IPCC emission scenarios.
3.4-9.1oC increase (winter); 2.7-8.6oC increase (summer) with 2×CO2.

(Kling et al., 2003)
(Mortsch and Quinn, 1996)

Minimum summer temperature increase by 1-2oC and maximum temperature increase by 0-1oC;
minimum winter temperature increase by 0.5-6oC and maximum temperature increase by 0.5-3oC
using two GCMs and steady CO2 increase for the period 2025-2034.

(Sousounis and Albercook,
2000)

3-7oC increase (winter); 4-11oC increase (summer) by the end of the 21st century using two GCMs
and four IPCC emission scenarios.

(Wuebbles and Hayhoe, 2004)

Precipitation and lake level
Reduction between 23 and 51% of water supply to the Great Lakes using three GCMs with 2×CO2.

(Croley, 1990)

10-20% increase in precipitation by the end of the 21st century using two GCMs and three IPCC
emission scenarios.

(Kling et al., 2003)

10

TABLE 1.1 (cont’d).
Precipitation and lake level, continued
Declines by 0.06 m – 0.94 m in lake level for all lakes with a 3.2-4.8oC increase in average annual
air temperatures for the Great Lakes Basin.

(Meisner et al., 1987)

Declines from -0.23 to -2.48 m in lake level for all lakes with most scenarios using four GCMs and
2×CO2.

(Mortsch and Quinn, 1996)

Precipitation increases throughout large portions of the basin but declines in southwestern portion
of the basin (Ohio, Indiana) using four GCMs and 2×CO2.

(Mortsch and Quinn, 1996)

Water supply decreases due to warmer air temperatures, higher evapotranspiration and evaporation,
and decreased runoff using four GCMs and 2×CO2.

(Mortsch and Quinn, 1996)

Summer precipitation increases by 15-25% using two GCMs and steady CO2 increase for the
period 2025-2034.

(Sousounis and Albercook,
2000)

Ice cover
Ice cover virtually absent in Lake Erie’s central and eastern basins and reduced from 4 months to 11.5 months in Lake Superior using three GCMs with 2×CO2.

(Assel, 1991)

All but Lake Erie ice-free year round; Lake Erie with a 50% decline in ice cover using one GCM
with 2×CO2.

(Howe et al., 1986)

Substantially reduced ice cover duration in Lake Erie and Whitefish Bay, Lake Superior by the end
of the 21st century using two GCMs with 2×CO2.

(Lofgren et al., 2002)

Ice-free winters between 0 and 17% of simulated years for Lake Erie and between 7 and 43% of
simulated years for Lake Superior using four GCMs and multiple emission scenarios.

(Magnuson et al., 1997)

11

TABLE 1.1 (cont’d).
Wind speed
Average wind speed decline; more frequent easterly wind events using two GCMs and a gradual
increase in CO2 concentrations.

(Sousounis and Grover, 2002)

Water temperature
As much as 5oC increase (bottom temperature) by the end of the 21st century using two GCMs with
2×CO2.

(Lehman, 2002)

As much as 6oC increase (summer surface temperature) by the end of the 21st century using one
GCM and two emission scenarios.

(Trumpickas et al., 2009)

Stratification and dissolved oxygen
Declines of 1 mgl-1 dissolved oxygen in upper layers and 1-2 mgl-1 in deeper layers of Lake Erie
using three GCMs with 2×CO2.

(Blumberg and Di Toro, 1990)

Longer length of thermal stratification, stronger stability of stratification, and deeper depth of daily
mixing during peak thermal stratification using two GCMs with 2×CO2.

(Lehman, 2002)

Increased intensity and duration of summer stratification in Lake Michigan (by up to two months)
using three GCMs with 2×CO2.

(McCormick, 1990)

No thorough winter turnover in Lake Michigan using three GCMs with 2×CO2.

(McCormick, 1990)

12

TABLE 1.1 (cont’d).
Thermal habitat
Habitat increases for all three thermal guilds in southern Lake Michigan and for cool and warm
water fishes in central Lake Erie with three GCMs and 2×CO2.

(Magnuson et al., 1990)

Increases in thermal habitat for all three thermal guilds in the deep, stratified lakes; decreases in
thermal habitat for cold water species in Lake Erie using four GCMs and multiple emission
scenarios.

(Magnuson et al., 1997)

Twenty-seven of 58 fish species with high potential for expanding their range to the Great Lakes
found to be likely invaders as a result of climatic warming using discriminate function and principal
(Mandrak, 1989)
component analyses comparing ecological characteristics of potential invaders with recently
established species.
Bioenergetics
Year-class strength of M. dolomieu increase by 2-5 times with a 1oC increase in temperature and six
(Casselman, 2002)
times with a 2oC increase in temperature at the northern extent of the species current distribution.
Increased growth of fishes if factors currently limiting growth also increase using three GCMs with
2×CO2.

(Hill and Magnuson, 1990)

Increases in growth for species currently below their thermal optimum; decreases in growth for
species at or above their thermal optimum using four GCMs and multiple emission scenarios.

(Magnuson et al., 1997)

Faster development and time to maturity with climate change.

(Regier et al., 1990)

13

entail a northward shift of colder-water species in the longitudinally-oriented lakes (Michigan
and Huron) and changing dominance in many assemblages towards warmer-water fishes in the
southern and nearshore regions. For some species, the altered state will provide opportunities to
expand their range, increase growth and reproductive rates and reduce over-winter mortality. For
others, however, it will contract their niches. Because the shallower regions of these lakes will
be the first to experience impact from climatic warming, this review focuses principally on the
effects within shallower areas of the basin. In long-term scenarios, though, these factors are also
predicted to have significant influence on the deep, open water regions of the lakes (Kling et al.,
2003).
Temperature
Temperature is an important abiotic factor governing the distribution (Shuter and Post,
1990), growth and survival of fishes in the Great Lakes and is directly linked to climate change
(Christie and Regier, 1988; Brandt et al., 2002). Because the northern and southern edges of the
range for many species are largely influenced by temperature (Shuter and Post, 1990), there is
greater variability in abundance and growth rates at the edges of their range than in the middle
(Shuter et al., 2002). Populations at these margins, consequently, show the most pronounced
correlations with global climate signals (King et al., 1999). For example, as climate warming
shifts the southern limit of a species’ range northward in the Great Lakes and deeper in the water
column, previously stable populations may become more variable because they will no longer be
in their optimal thermal habitat, which provides ideal conditions for maximal survival, growth
and reproduction.
In the Great Lakes, fishes have been grouped into three broad thermal guilds according to
their recorded approximate optimal temperatures (cold water: 15 degrees C; cool water: 24

14

degrees C; and warm water: 28 degrees C; Hokanson, 1977). Though it may appear
counterintuitive, in a warmer climate, optimal thermal habitat is expected to expand
volumetrically for all three thermal guilds in the Great Lakes. The reason for this is that fish will
have the opportunity to move both northward (in the longitudinally oriented lakes) or deeper (in
the deep lakes) to maintain their preferred temperature (Magnuson et al., 1997). It is important
to note, however, that while this analysis considered the deeper depth strata fairly depauperate of
fish fauna (i.e. currently free habitat space), recent deep water surveys have revealed higher than
expected abundances of siscowet, the deepwater morphoptype of S. namaycush, among other
species, in depths exceeding 200 m (Sitar et al., 2008).
Nonetheless, overall projections of warmer temperatures in the Great Lakes are predicted
to increase growth and survival for most cold, cool and warm-water species (Shuter and Post,
1990). Additionally, fishes in the Great Lakes are often transition species, living at the edge of
their thermal range. As such, they generally live in temperatures where their metabolic rate is
not optimal; thus exhibiting lower growth and reproduction rates. Increased temperature, and
consequently metabolic rates, will allow for greater growth, higher fecundity and generally better
survival rates. This is particularly true for Great Lakes cool and warm-water species. Assuming
prey abundance is non-limiting, productivity of fishes increases with time spent at optimal
temperature with optimal metabolic rates (Christie and Regier, 1988).
Increased optimal temperature alone, however, does not necessarily equate to increased
optimal habitat space for all fishes. Lake morphomentry also has a significant influence on the
suitability of habitat available to fish (Regier and Meisner, 1990). Micropterus dolomieu, for
example, require sheltered environments to build nests. Though a habitat may have temperatures
in their optimal range, if it is turbulent, it will not be suitable for high M. dolomieu nest success
(Goff, 1986).
15

Dissolved oxygen
While temperature is generally predicted to expand the amount of optimal thermal fish
habitat space in the Great Lakes with climatic warming, dissolved oxygen may well be a limiting
factor to fish productivity, particularly in Lake Erie and warm nearshore bays such as Saginaw
Bay (Lake Huron) and Green Bay (Lake Michigan) (Stefan et al., 1996). With warmer water
temperatures, the thermocline is expected to sharpen, the duration of stratification is predicted to
increase and the timing, extent and duration of winter mixing is expected to decrease (Lehman,
2002). When light levels are too low in the hypolimnion to allow dissolved oxygen levels to be
replenished via photosynthesis, oxygen consumed in respiratory activities of the biotic
community, including fishes, zooplankton, phytoplankton and bacteria, cannot be readily
replaced (Lehman et al., 2000). This generally leads to hypoxic (e.g. 2 mgl-1 dissolved oxygen
or less) or even anoxic conditions. Some species and age classes of fish can avoid these harmful
areas by being mobile and can relocate to suitable living conditions elsewhere. But as
temperatures warm and fish move deeper in the water column to maintain their optimal thermal
habitat, loss of dissolved oxygen could become another factor reducing optimal habitat. Lower
dissolved oxygen could also increase competition for food and space within the remaining
livable habitat, further reducing overall fish production of the current assemblage of fishes.
Current summer oxygen levels in Lake Erie’s central basin, for example, range between
8 and 9.5 mgl-1 in the epilimnon and between 2 and 6 mgl-1 in the hypolimnion (Rao et al.,
2008). Climate warming simulations for this location predict central basin summer oxygen
declines by 1 mgl-1 in the epilimnon and 1-2 mgl-1 in the hypolimnion (Blumberg and Ditoro,
1990). These declines are expected to lead to increases in anoxic dead zones, or areas that do not
contain sufficient oxygen levels to sustain aquatic organisms. Similarly, McCormick (1990)

16

modelled an increase in summer stratification by up to two months and a permanent deep zone of
isolated water below the thermocline because of minimal winter mixing in Lake Michigan.
These studies suggest that climate-related reductions in dissolved oxygen will significantly limit
the availability of suitable habitat for some cold-water fishes, including C. clupeaformis and S.
namaycush (Magnuson et al., 1990; Stefan et al., 1996).
Food web dynamics
Plankton biomass is the foundation of the Great Lakes food chain. Phytoplankton
supports the productivity of higher trophic levels, including zooplankton and fishes (Lehman et
al., 2000). Though increasing temperatures are unlikely to increase the standing biomass of
phytoplankton, annual productivity and diversity are likely to increase with a longer ice-free
season (Magnuson et al., 1997). This is expected to occur because phytoplankton production
depends principally upon water temperature, sunlight, oxygen and nutrients (i.e. nitrogen and
phosphorus). Nutrients, rather than temperature, however, are the principal limiting factor for
phytoplankton abundance in the Great Lakes (Hecky and Kilham, 1988). A shallower
epilimnion is expected to affect the nutritional value of phytoplankton because of a reduced
residence time of nutrients in the mixed layer where they can be incorporated into the
phytoplankton and be transferred to higher trophic levels (Magnuson et al., 1997).
Zooplankton species are also expected to be impacted by climatic warming. Because
temperature provides important cues for maturity stages of zooplankton, particularly
overwintering stages (Magnuson et al., 1997), some species of zooplankton may be
physiologically more sensitive to warmer summer temperatures or lower oxygen levels
(Stemberger et al., 1996). However, the overall projection is for zooplankton biomass to
increase in the Great Lakes with warming (Regier et al., 1990).

17

Climate change is projected to increase primary production and has the potential to
translate through the intermediate zooplankton trophic levels to increase fish production in the
Great Lakes overall. Rainbow smelt Osmerus mordax (Mitchill), as one example, are an
important prey species for salmonids in the Great Lakes. With warmer spring water temperatures
and greater plankton production, juvenile O. mordax abundances should increase (Bronte et al.,
2005), providing a larger forage base that could translate into increased salmonid production.
Potential Consequences of Climate Change on Fish Populations
Overall, climate change projections for the Great Lakes fishes should result in an increase
in optimal thermal habitat for cold, cool and warm-water species (Magnuson et al., 1990).
However, habitat increases will be largest for warmer-water species moving in to occupy the
more southern and shallower habitat space vacated by the cool and cold-water species. Because
the cool and cold-water fishes are expected to move to more northern and deeper, offshore
regions and not gain habitat, there should be a predominant shift of species types from the
current cold-water dominated community towards a warmer-water assemblage (Mandrak, 1989).
Further exacerbating this trend is the probable ecological consideration that cold-water species,
such as S. namaycush and C. clupeaformis, may have difficulty competing with cooler-water
adapted species at the warmer, southern edges of their current distributions.
Translation of this potential for greater optimal thermal habitat may not, however,
directly transfer into greater overall fish production. A number of limiting habitat elements,
namely anoxia, ice cover, dispersal ability and food-web dynamics need to be considered. For
instance, while McLain et al.(1994) predicted that deep-water refuges over large latitudinal
ranges for the Great Lakes would be maintained in the face of climate warming, they did not
factor in effects from increases in anoxia that would be expected with warmer temperatures and

18

higher phytoplankton production. These latter two factors will likely reduce suitable habitat. In
open water, however, phytoplankton productivity is not expected to increase as much as in
shallow areas and embayments because primary production in the open water is still heavily
influenced by the establishment of the thermocline and nutrient availability (Lehman, 2002).
Climate warming may also directly impact fish production through physiological means,
particularly for fish species adapted to cold water. Some species, including yellow perch Perca
flavescens Mitchill, require cold temperatures for full gonadal development (Jones et al., 1972).
Others, like C. clupeaformis, need ice cover to protect over-wintering eggs in marginal nursery
habitat to increase year-class strength (Taylor et al., 1987a). While suitable habitat may exist in a
theoretical context, realised habitat is only possible if a species can travel there, namely if eggs
or larvae can physically reach suitable habitat (Sharma et al., 2007). S. vitreus larvae, for
example, are passively transported large distances with surface currents. Their survival is
dictated in part by drift into productive habitats that provide them with appropriate temperature
and food for growth and survival (Roseman, 1997). Fish growth is also strongly dependent on
biological factors, particularly production at lower trophic levels. Annual fish growth may
decrease if prey availability is insufficient for the increased metabolic costs associated with
living at higher temperatures (Hill and Magnuson, 1990).
Influx of new species is another extensive threat to current fish communities in the Great
Lakes. Mandrak (1989) predicted that 19 warm-water fish species from Atlantic coastal basins
and the Mississippi may extend their range to Lakes Ontario, Erie, and Michigan and that 8
warm water species currently in these three lakes could expand to Lakes Huron and Superior.
These 27 new species could additionally introduce up to 83 parasites that currently do not exist
in the Great Lakes (Marcogliese, 2001).

19

To examine the potential effects of climate change on a smaller scale, three species were
evaluated in this study as representatives of the three thermal guilds in the Great Lakes: C.
clupeaformis (cold), S. vitreus (cool), and M. dolomieu (warm):
Cold water: Coregonus clupeaformis
Since 1980, populations of C. clupeaformis have supported the most economically
valuable commercial fishery in the upper Great Lakes (Madenjian et al., 2006). Commercial
landings have fluctuated over the last half century with variation in population abundance caused
by overfishing, habitat degradation, sea lamprey Petromyzon marinus L. parasitism and
competition with exotic species (Taylor et al., 1987a). Coregonus clupeaformis populations have
rebounded since the 1960s, with a 10-fold increase in Great Lakes commercial harvest between
1959 and 1995 (Ebener, 1997).
The C. clupeaformis recovery has been principally attributed to control of P. marinus
(Ebener, 1997), but the species’ recruitment variability has been linked with climatic influences,
including water temperature, wind speed and ice cover (Miller, 1952; Christie, 1963; Lawler,
1965; Taylor et al., 1987a; Freeberg et al., 1990). As a result, C. clupeaformis production varies
with the amount of thermally suitable habitat (Christie and Regier, 1988), which is likely to be
modified significantly by climate change. In particular, C. clupeaformis year-class strength has
been found to be directly related to the timing and duration of ice cover (i.e. egg survival) and
temperature of spring plankton blooms (i.e. larval growth and survival) (Taylor et al., 1987a;
Freeberg et al., 1990).
While climate warming should increase suitable thermal habitat volume for C.
clupeaformis (Magnuson et al., 1997) in most of the Great Lakes, predictions for realised habitat
space are not entirely positive. There are projections for significant reductions in ice cover

20

(Marchand et al., 1988) and higher mortalities at the southern boundary of the range (Meisner et
al., 1987) because of reduced egg and larval survival (Taylor et al., 1987a). In Lake Erie, for
example, cold water habitat will shrink between the thermocline and either the bottom of the lake
or the anoxic “dead zone” (Magnuson et al., 1990). However, in the deeper lakes, such as Lake
Michigan, C. clupeaformis will not experience the same loss in potential habitat space because
they can shift with the thermocline to deeper regions which have livable temperatures and
oxygen levels (Regier and Meisner, 1990).
Additionally, the survival of eggs is largely contingent upon substrate size and the
amount of ice cover during the winter (Taylor et al., 1987a). When winter ice cover is extensive,
C. clupeaformis eggs are protected from wave and current damage and their survival is greater
for all depths and substrates up to 6 m (Hayes et al., 1996). With predictions for substantial
reductions in annual lake ice cover (surface area and duration) (Lofgren et al., 2002; Assel et al.,
2003), protection, and hence survival, for over-wintering C. clupeaformis eggs will decline. This
will particularly be the case in sub-optimal spawning habitat, which is essential for strong year
classes.
On a basin-wide scale, abundance and distribution of C. clupeaformis adults are expected
to shift northward and deeper in the water column (Regier and Meisner, 1990). Though they
may experience some decreases in habitat space at the southern edge of their range (Meisner et
al., 1987), mortality and reduced scope for growth will not be significant as the available deep
habitat for these fish should increase. While distribution changes are likely, overall C.
clupeaformis production in the Great Lakes is expected to remain stable, if not increase.

21

Cool water: Sander vitreus
Sander vitreus is a very popular nearshore, shallower water recreational species
throughout the Great Lakes and is also a commercially captured species in Canada (Knight,
1997). Commercial landings increased precipitously until catches collapsed around the basin in
the first half of the 20th century due to over exploitation, pollution and degraded habitat
(Roseman, 1997) but have since made significant recoveries. Though S. vitreus can disperse to
open water in the summers, it is primarily restricted to the shallow waters and embayments of the
Great Lakes and is prolific in Lake Erie and connecting waterways (i.e. Lake St. Clair, Detroit
River), Saginaw Bay (Lake Huron), and Green Bay (Lake Michigan). As mentioned earlier,
these shallower areas of the lakes will be the first to experience significant impacts from climate
change. A number of key abiotic factors that influence S. vitreus recruitment will certainly be
affected by climatic warming. The rate of spring warming and variability of May water
temperature, for instance, both play important roles in structuring year-class strength during early
life-history stages (Nate et al., 2001). These abiotic factors serve principally as proxies for the
presence and abundance of quality food sources for larval S. vitreus (Roseman, 1997).
Additionally, adult S. vitreus need an extended period where temperatures are below 10oC for
initiation and successful completion of their gonadal maturation cycle (Hokanson, 1977). Given
the forecast for warmer (i.e. when temperatures do not stay below 10oC for extended periods of
time) and shorter winters (Trumpickas et al., 2009), S. vitreus reproductive success, and hence
abundance, may be inhibited in the extreme southern edge of their range.
Nonetheless, populations of S. vitreus are expected to expand to more northern regions
(Shuter et al., 2002) and deeper depths throughout much of their present range (Chu et al., 2005).
The resulting increase in fish production and change in distribution of S. vitreus will have major

22

implications for fisheries management because recreational and commercial fisheries come
principally from different jurisdictions (i.e. recreational from U.S.A. states and commercial from
Ontario) (Roseman et al., 2008). With climate warming, management authorities could be faced
with potentially contentious policy issues because of a shift northward in the abundance of S.
vitreus populations, thus favoring stakeholders from some jurisdictions (i.e. Ontario) over others
(i.e. U.S.A. states) (Roseman et al., 2008).
Warm water: Micropterus dolomieu
Micropterus dolomieu is currently found in the southern regions of the Great Lakes Basin
and inhabit warm water habitats. Like S. vitreus, it is a particularly popular recreational species
but, unlike S. vitreus, its commercial harvest is not permitted. As a result, there have been no
large scale surveys to monitor population distributions and abundances of this species within the
Great Lakes. Micropterus dolomieu colonised the Great Lakes via multiple sequential dispersal
events following Pleistocene glaciation (Borden and Krebs, 2009) and is expected to increase its
range within the basin as a result of climate warming (Casselman, 2002).
Micropterus dolomieu is particularly sensitive to climatic events, particularly with respect
to growth rates and nesting behaviour. Changes in growth rates, for example, are known to be
associated with other global climate events, such as El Niño warming periods (King et al., 1999)
and changes in nesting behaviour are related to storm events (Steinhart et al., 2005). Warming
periods are conducive to recruitment while high intensity storms can hinder recruitment success.
With climate change, warmer water temperatures and a longer growing season are predicted to
lead to higher production of M. dolomieu because of a greater scope for growth (Shuter and Post,
1990). Casselman (2002) predicted that climatic warming would strongly favor M. dolomieu
over northern pike Esox lucius L. by relating abundance indices to temperature variables for

23

Lake Ontario populations of both species. However, Steinhart et al. (2005) found that storms
reduce M. dolomieu reproductive (i.e. nest) success. With greater numbers of extreme storm
events predicted with climate change (Kling et al., 2003), there is the potential for decreased M.
dolomieu production due to this interference with successful nest recruitment.
The ability of this species to increase its range northward will thus be limited by its
ability to build and protect nests in a more turbulent, high wave environment (Goff, 1986). If
this does not become a major recruitment bottleneck, M. dolomieu is expected to extend its
distribution substantially northwards to inhabit shallow water embayments and riverine systems.
As its abundance increases in these areas, there is also potential for the species to exhibit
competitive and predatory pressure on the current fish communities in the nearshore zones of the
Great Lakes (Vander Zanden et al., 2004); which may be severe enough to further change the
current fish community in these regions.
Future of Fisheries Management
Climate change compounds the uncertainty of Great Lakes fisheries management,
making the already difficult task more complex. With climate change, fisheries managers must
consider potentially greater abundances of some fish populations, possible collapses of others
and likely expanded warm-water habitat in their decision making process. These changes will,
ultimately, affect opportunities for commercial and recreational fisheries in these lakes and
impact the value they have in the public mindset. Management in a changing environment must
be adaptive and decisive in the face of uncertainty. While improving data sets, ecological
modelling, and predictions will surely aid decision makers with more precise planning (Smith,
1991a), management initiatives often need to be implemented before such improvements to the

24

predictions can be fully achieved. The question for managers is how to implement measures that
effectively sustain Great Lakes fisheries using the available science.
Site-based management is, ultimately, ineffective and inappropriate, given the scale at
which the threats from climate change act upon Great Lakes fisheries and their ecosystems. The
application for this type of management paradigm, which has been used as the standard in
addressing many 20th century concerns in fisheries management (e.g. overfishing in specific
areas, point source pollution), is clearly not adequate for broad-scale threats such as climate
change. Stabilising a segment of shoreline on Lake Erie will not, for example, ensure that the
habitat is suitable for S. vitreus if the winter temperature exceeds 10oC. To address issues, such
as climate change, at a broad scale, management must shift from site-based to regional-based;
higher levels of governance are needed to prioritise landscape-level actions for rehabilitation
efforts (see Liu and Taylor, 2002 for examples). By considering Great Lakes fisheries
management from a basin-wide scale, managers can act strategically, comprehensively, and in a
coordinated fashion so as to better address key elements. This approach will increase the
resiliency of the fisheries for the entire basin.
The second issue that needs to be recognised for effective Great Lakes fisheries
management in the face of a changing climate is that there are few realistic opportunities for
mitigating its effects. If there is no change in greenhouse gas emissions, it is estimated that up to
one-third of plant and animal species worldwide will be “committed to extinction” by 2050
(IPCC 2007). It is important to take responsibility for the consequences of anthropogenic
changes to biodiversity; but, even if some remediating changes are implemented, chances are it
will not be enough to protect all Great Lakes species. Thus fisheries managers must gauge their
ability to rehabilitate, maintain, or enhance these ecosystems and the expense of such action in

25

relation to its benefits and likelihood of success. As optimistic as fisheries managers might like
to remain, pragmatic management strategies will serve the resources, and the public better. As
management ethics have the goal of conserving natural resources for future generations, fisheries
managers must focus their efforts on populations and species of fish that are capable of being
conserved in the face of changing climate in lieu of those, such as the cold water C. clupeaformis
in Lake Erie, that are not likely not to persist.
Learning from Aquatic Invasive Species Management
The spread of aquatic species beyond their native ranges, be it intentionally or
unintentionally, is considered one of the most ubiquitous and detrimental processes to natural
ecosystems (Ricciardi and Rasmussen, 1998). It can also serve as a model, of what should and
should not be done, for designing management methods to address climate change. Despite the
often devastating consequences of invasions, forecasting aquatic species invasions and taking
precautionary measures are almost always difficult to implement because of tracking the
potential paths of invasion (Cooney, 2005). Management of invasive species is often
reactionary; a response to successfully established threats. This approach to management is
inherently inefficient, expensive, (OTA, 1993) and almost always unsuccessful (i.e. does not
eradicate the threat).
Because of its large-scale causes and implications, the effects of climate change may be
orders of magnitude greater than the effects of aquatic invasive species observed to date.
Reactionary management measures may have less potential to ‘restore’ fish populations to preclimate change conditions than is even possible when dealing strictly with aquatic invasive
species. The Great Lakes will probably never return to a prior state, but the term ‘restore’ brings
exactly that connotation to the public. Fisheries and ecosystems may be rehabilitated to some

26

level of former state and function, such as a given spawning stock biomass or specific water
quality variables; but ecosystems evolve and the managers and the public must be prepared to
cope with that change.
Natural resource managers are increasingly aware of the importance of human values in
the process of achieving management goals (Decker et al., 1996). Jacobson & McDuff (1998)
state that people must be considered ‘the beginning, middle, and end of all management issues.
Recognition of this central role will improve our ability to conserve.’ The public can inform and
improve sustainable strategies for managing effects on natural resources related to climate
change in comparison with what has been used to manage effects of invasive species. Coping
with change is difficult for the general public. Managers and researchers often struggle to
prepare the public for inevitable changes that are bound to occur.
A prime example of this is the introduction of predatory Chinook salmon Oncorhynchus
tshawytscha (Walbaum) in Lake Huron to control invasive alewife Alosa pseudoharengus
(Wilson). A manufactured byproduct of this fishery management strategy has been the creation
of a highly valued recreational fishery for O. tshawytscha (Whelan, 2004). Subsequent decreases
in biomass of A. pseudoharengus, a function of O. tshawytscha predation, climatic conditions
and other invasive species (i.e. Dreissena spp. mussels), have caused the O. tshawytscha
population and the recreational industry dependent upon it to crash in recent years (Johnson et
al., 2007).
Concurrently, populations of recreationally viable native species of fish including S.
vitreus, S. namaycush, M. dolomieu and E. lucius have rebounded (Johnson et al., 2007). These
species, however, are not perceived by the public to have the same value as O. tshawytscha.
This is somewhat ironic as residents on Lake Huron three-quarters of a century ago did not have
the productivity of native species that is present today and they would likely have found the
27

recreational and commercial opportunities provided by the current fish communities in Lake
Huron to be outstanding and highly valuable. This highlights the importance of perception of
value in fisheries management. The recreational fishery for O. tshawytscha was nonexistent mere
decades ago. But, as the salmonid fishing industry grew and boomed, people came to rely upon
its economic outputs and set expectations that were unrealistic for the Lake Huron fishery
ecosystem.
Managers preparing strategies for climate change have an advantage over those dealing
with aquatic invasive species in that effects from climate change will likely be gradual. While
people are resistant to change and the change associated with aquatic invasive species is
generally rapid and drastic, climate change will occur over a much longer period of biological
time. As such, managers will have time to educate the public on predictions for ecosystems
changes, mitigating the negative perceptions by giving the public time to adjust and accept the
changes.
Climate Change Decision Support
Forecasting the effects of climate change on Great Lakes fisheries, as with aquatic
invasive species, will be a difficult task because the projections have high uncertainty and also
because fisheries management needs to effectively integrate differing perspectives and
competing objectives (Clemen and Reilly, 2001). Good decisions require good information, but
in the absence of perfect knowledge about a fishery and its ecosystem, managers can use
adaptive management practices in the decision making process (Enck and Decker, 1997). In the
context of Great Lakes fisheries management, climate change poses to have a significant, longterm impact, affecting the biological, economic and social functioning of this system. By
integrating these analyses into the management process, decision support tools can facilitate the

28

communication of the most current scientific, economic and social data and management
outcomes. An understanding of the interactions between these factors will improve the prospect
for implementing appropriate conservation action that is feasible, cost-efficient and sustainable.
Jones et al. (2006) argued that mechanistic modelling of habitat changes, which
incorporates the interactions of multiple climate-induced changes to thermal habitat with fish
population dynamics, is a useful, though by no means perfect, approach to fisheries management.
As a working example of this approach to decision support, Jones et al. (2006) developed a
series of models linking habitat parameters with population dynamics for S. vitreus in Lake Erie
and applying five climate change scenarios. This study found that warmer temperatures led to
increased habitat space for S. vitreus, primarily in the central and eastern basins of Lake Erie, but
that lower lake levels counteracted that increase to produce a net decline in habitat space in the
western and central basins. While high uncertainty limits the predictive powers of this and other
modelling exercises, Jones et al. (2006) revealed potentially important interactions between S.
vitreus habitat (i.e. basin hydrology and lake levels) and population dynamics (i.e. larval
recruitment) which can help inform management decisions.
For these large-scale impacts, decision support tools can be particularly useful because
ecosystem and regional-level issues are dynamic and operate at large spatial scales (Gavaris,
2009). Fisheries management also includes multiple considerations (e.g. biological, economic,
social and political) involving many participants (Lane and Stephenson, 1998). In the context of
the three thermal guild case studies, decision support tools can assist in defining policies that
increase the resiliency of fish populations in the Great Lakes to the impacts of climate change.
Building from the Jones et al. (2006) example, when setting harvest allocations for Lake Erie S.
vitreus, managers could potentially take climate change into consideration by lowering catch
quotas in the western and central basins while maintaining quotas in the eastern basin. In the
29

case of M. dolomieu, a biological understanding of future habitat usage could allow for the
management of extended seasons for recreational fisheries. With regards to C. clupeaformis,
because it has a particularly important commercial fishery, managers could use predicted habitat
and population changes to allocate quotas appropriately among the multiple jurisdictional
interests (i.e. state, provincial and tribal). With the integration of interdisciplinary
considerations, decision support tools can assess multiple decision alternatives (Lane and
Stephenson, 1998) and can help objectively compare potential policies and their outcomes for the
fish, their ecosystems and society (Azadivar et al., 2009).
As helpful as it sounds to have a decision support tool simplify these complexities, it is
important to note that these decision support tools are just that, i.e. decision support. They will
not ‘fix’ the Great Lakes and their limitations must be taken into account (Shim et al., 2002).
Models of natural systems, for example, are rarely very precise or reliable; but, they can examine
proposed management actions and suggest which options are the most feasible to carry forward
through the policy process (Riley et al., 2003). When carefully applied, they can assist with
making better decisions (Azadivar et al., 2009) but may not necessarily give a manager the
‘correct’ answer in an unpredictable environment. The managers and decision-makers cannot
shirk the responsibility for the management of the resources to a support tool (Taylor and
Dobson, 2008).
Climate change will surely challenge the flexibility of current Great Lakes fisheries
management programs and require enlisting public support to set realistic expectations.
Learning from past experience and the public’s perception of invasive species management, a
precautionary, adaptive approach to managing Great Lakes fisheries is essential. Decision
support tools provide a platform for integrating the best and most current science with

30

management needs to craft appropriate fisheries conservation action in the face of a changing
climate.

Acknowledgements
The authors thank Michigan Department of Natural Resources and Environment Director
Becky Humphries, Dan Hayes, Betsy Puchala, Dennis Lynch, Eric MacMillan, Chiara
Zuccarino-Crowe, the Taylor Lab, and the Fenske Fellowship Committee for encouraging and
supporting these efforts. Additionally, the authors would like to thank Ian Winfield and two
anonymous reviewers who provided insightful recommendations for focusing and strengthening
this manuscript. This research was funded by the 2009 Janice Lee Fenske Excellence in
Fisheries Management Fellowship and a Michigan State University Distinguished Fellowship.

31

LITERATURE CITED

32

LITERATURE CITED

Angel, J. R. and K. E. Kunkel (2010). "The Response of Great Lakes Water Levels to Future
Climate Scenarios with an Emphasis on Lake Michigan-Huron." Journal of Great Lakes
Research 36(sp2): 51-58.
Assel, R., K. Cronk, et al. (2003). "Recent trends in Laurentian Great Lakes ice cover." Climatic
Change 57(1-2): 185-204.
Assel, R. A. (1991). "Implications of CO2 global warming on Great-Lakes ice cover." Climatic
Change 18(4): 377-395.
Azadivar, F., T. Truong, et al. (2009). "A decision support system for fisheries management
using operations research and systems science approach." Expert Systems with
Applications 36(2): 2971-2978.
Blumberg, A. F. and D. M. Di Toro (1990). "Effects of climate warming on dissolved-oxygen
concentration in Lake Erie." Transactions of the American Fisheries Society 119(2): 210223.
Blumberg, A. F. and D. M. Ditoro (1990). "Effects of climate warming on dissolved-oxygen
concentration in Lake Erie." Transactions of the American Fisheries Society 119(2): 210223.
Borden, W. C. and R. A. Krebs (2009). "Phylogeography and postglacial dispersal of
smallmouth bass (Micropterus dolomieu) into the Great Lakes." Canadian Journal of
Fisheries and Aquatic Sciences 66(12): 2142-2156.
Brandt, S. B., D. M. Mason, et al. (2002). "Climate change: Implications for fish growth
performance in the Great Lakes." Fisheries in a Changing Climate 32: 61-75.
Bronte, C. R., M. P. Ebener, et al. (2005). "Fish community change in Lake Superior, 19702000." Canadian Journal of Fisheries and Aquatic Sciences 62(2): 482-482.
Casselman, J. M. (2002). "Effects of temperature, global extremes, and climate change on yearclass production of warmwater, coolwater, and coldwater fishes in the Great Lakes
Basin." Fisheries in a Changing Climate 32: 39-59.
Christie, G. C. and H. A. Regier (1988). "Measures of optimal thermal habitat and their
relationship to yields for four commercial fish species." Canadian Journal of Fisheries
and Aquatic Sciences 45(2): 301-314.
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.

33

Chu, C., N. E. Mandrak, et al. (2005). "Potential impacts of climate change on the distributions
of several common and rare freshwater fishes in Canada." Diversity and Distributions
11(4): 299-310.
Clemen, R. T. and T. Reilly (2001). Making Hard Decisions, 2nd Edition. Belmont, CA,
Duxbury Press.
Cooney, R. (2005). From Promise to Practicalities: The Precautionary Principle in Biodiversity
Conservation and Sustainable Use. Biodiversity and the Precautionary Principle: Risk
and Uncertainty in Conservation and Sustainable Use. R. Cooney and B. Dickson.
London, Earthscan: 3-18.
Croley, T. E. (1990). "Laurentian Great-Lakes double-CO2 climate change hydrological
impacts." Climatic Change 17(1): 27-47.
Decker, D. J., T. L. Brown, et al. (1996). Human dimensions research: Its importance in natural
resource management. Natural Resource Management: The Human Dimension. A. W.
Ewert. Boulder, CO, Westview Press: 29-52.
Ebener, M. P. (1997). "Recovery of lake whitefish populations in the Great Lakes." Fisheries 22:
18-22.
Enck, J. W. and D. J. Decker (1997). "Examining assumptions in wildlife management: a
contribution of human dimensions inquiry." Human Dimensions of Wildlife 2(3): 56-72.
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.
Gavaris, S. (2009). "Fisheries management planning and support for strategic and tactical
decisions in an ecosystem approach context." Fisheries Research 100(1): 6-14.
Goff, G. P. (1986). "Reproductive success of male smallmouth bass in Long Point Bay, Lake
Erie." Transactions of the American Fisheries Society 115(3): 415-423.
Hayes, D. B., C. P. Ferreri, et al. (1996). "Linking fish habitat to their population dynamics."
Canadian Journal of Fisheries and Aquatic Sciences 53: 383-390.
Hecky, R. E. and P. Kilham (1988). "Nutrient limitation of phytoplankton in fresh-water and
marine environments - A review of recent evidence on the effects of enrichment."
Limnology and Oceanography 33(4): 796-822.
Hill, D. K. and J. J. Magnuson (1990). "Potential effects of global climate warming on the
growth and prey consumption of Great-Lakes fish." Transactions of the American
Fisheries Society 119(2): 265-275.

34

Hokanson, K. E. F. (1977). "Temperature requirements of some percids and adaptations to the
seasonal temperature cycle." Journal of the Fisheries Research Board of Canada 34:
1524-1550.
Howe, D. A., D. S. Marchand, et al. (1986). Socio-economic assessment of the implications of
climatic change for commercial navigation and hydro-electric power generation in the
Great Lakes-St. Lawrence River system. Windsor, Canada, Great Lakes Institute,
University of Windsor.
Intergovernmental Panel on Climate Change (IPCC) Core Writing Team (2007). Climate Change
2007: Synthesis Report. R. K. Pachauri and A. Reidinger. Geneva, IPCC: 104.
Jackson, D. A. and N. E. Mandrak (2002). "Changing fish biodiversity: Predicting the loss of
cyprinid biodiversity due to global climate change." Fisheries in a Changing Climate 32:
89-98.
Jacobson, S. K. and M. D. McDuff (1998). "Training idiot savants: The lack of human
dimensions in conservation biology." Conservation Biology 12(2): 263-267.
Johnson, J. E., S. P. DeWitt, et al. (2007). Causes of variable survival of stocked Chinook
salmon in Lake Huron. Michigan Department of Natural Resources, Fisheries Research
Report 2086. Ann Arbor, Michigan, Michigan Department of Natural Resources: 54.
Jones, B. R., K. E. F. Hokanson, et al. (1972). Winter temperature requirements for maturation
and spawning of yellow perch, Perca flavenscens (Mitchell). Proceedings, World
Conference Towards a Plan of Action for Mankind. M. Marois. New York, Pergamon
Press. 3: 189-192.
Jones, M. L., B. J. Shutter, et al. (2006). "Forecasting effects of climate change on Great Lakes
fisheries: models that link supply to population dynamics can help." Candian Journal of
Fisheries and Aquatic Sciences 63(2): 457-468.
King, J. R., B. J. Shuter, et al. (1999). "Empirical links between thermal habitat, fish growth, and
climate change." Transactions of the American Fisheries Society 128(4): 656-665.
Kling, G. W., K. Hayhoe, et al. (2003). Confronting Climate Change in the Great Lakes Region:
Impacts on our Communities and Ecosystems. Washington, D.C., Union of Concerned
Scientists and Ecological Society of America: 92.
Knight, R. L. (1997). "Successful interagency rehabilitation of Lake Erie walleye." Fisheries
22(7): 16-17.
Lane, D. E. and R. L. Stephenson (1998). "Fisheries co-management: Organization, process, and
decision support." Journal of Northwest Atlantic Fishery Science(23): 251-265.
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.

35

Lehman, J. T. (2002). "Mixing patterns and plankton biomass of the St. Lawrence Great Lakes
under climate change scenarios." Journal of Great Lakes Research 28(4): 583-596.
Lehman, J. T., A. S. Brooks, et al. (2000). Water Ecology. Great Lakes Regional Assessment
Report- Preparing for a Changing Climate: The Potential Consequences of Climate
Variability and Change in the Great Lakes Region. P. J. Sousounis and J. M. Bisanzm.
Ann Arbor, Michigan, University of Michigan Atmospheric, Oceanic, and Space
Sciences Department: 43-50.
Liu, J. and W. W. Taylor (2002). Integrating Landscape Ecology into Natural Resource
Management. Cambridge, Cambridge University Press.
Lofgren, B. M., F. H. Quinn, et al. (2002). "Evaluation of potential impacts on Great Lakes water
resources based on climate scenarios of two GCMs." Journal of Great Lakes Research
28(4): 537-554.
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., D. J. Meisner, et al. (1990). "Potential Changes in the Thermal Habitat of Great
Lakes Fisher after Global Climate Warming." Transactions of the American Fisheries
Society 119: 253-264.
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.
Mandrak, N. E. (1989). "Potential invasions of the Great Lakes by fish species associated with
climate warming." Journal of Great Lakes Research 15: 306-316.
Marchand, D., M. Sanderson, et al. (1988). "Climate change and great lakes levels the impact on
shipping." Climate Change 12: 107-133.
Marcogliese, D. J. (2001). "Implications of climate change for parasitism of animals in the
aquatic environment." Canadian Journal of Zoology-Revue Canadienne De Zoologie
79(8): 1331-1352.
McCormick, M. J. (1990). "Potential changes in thermal structure and cycle of Lake-Michigan
due to global warming." Transactions of the American Fisheries Society 119(2): 183-194.
McLain, A. S., J. J. Magnuson, et al. (1994). "Latitudinal and longitudinal differences in thermal
habitat for fishes influences by climate warming: expectation from simulations."
International Association of Theoretical and Applied Limnology 25: 2080-2085.
Meisner, J. D., J. L. Goodier, et al. (1987). "An assessment of the effects of climate warming on
Great-Lakes basin fishes." Journal of Great Lakes Research 13(3): 340-352.

36

Miller, R. B. (1952). "The relative sizes of whitefish year classes as affected by egg planting and
the weather." Journal of Wildlife Management 16: 39-50.
Mortsch, L. D. and F. H. Quinn (1996). "Climate change scenarios for Great Lakes Basin
ecosystem studies." Limnology and Oceanography 41(5): 903-911.
Nate, N. A., M. A. Bozek, et al. (2001). "Variation of adult walleye abundance in relation to
recruitment and linmological variables in northern Wisconsin lakes." North American
Journal of Fisheries Management 21(3): 441-447.
Office of Technology Assessment (OTA) (1993). Harmful nonindigenous species in the United
States. Washington, D.C., U.S. Government Printing Office: 391.
Rao, Y. R., N. Hawley, et al. (2008). "Physical processes and hypoxia in the central basin of
Lake Erie." Limnology and Oceanography 53(5): 2007-2020.
Regier, H. A., J. A. Holmes, et al. (1990). "Influence of temperature changes on aquatic
ecosystems: an interpretation of empirical data." Transactions of the American Fisheries
Society 119(2): 374-389.
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.
Ricciardi, A. and J. B. Rasmussen (1998). "Predicting the identity and impact of future biological
invaders: a priority for aquatic resource management." Canadian Journal of Fisheries and
Aquatic Sciences 55(7): 1759-1765.
Riley, S. J., W. F. Siemer, et al. (2003). "Adaptive Impact Management: An Integrative
Approach to Wildlife Management." Human Dimensions of Wildlife 8: 81-95.
Roseman, E. F. (1997). Factors Influencing the Year-Class Strength of Reef-Spawned Walleye in
Western Lake Erie (Ph.D. Dissertation) Ph.D. Dissertation, Michigan State University.
Roseman, E. F., R. L. Knight, et al. (2008). Ecology and international governance of Lake Erie's
percid fisheries. International governance of fisheries ecosystems: learning form the past,
finding solutions for the future. M. G. Schechter, N. J. Leonard and W. W. Taylor.
Bethesda, Maryland, American Fisheries Society: 145-169.
Schlesinger, D. A. and H. A. Regier (1982). "Climatic and morphoedaphic indicies of fish yields
from natural lakes." Transactions of the American Fisheries Society 11: 141-150.
Sharma, S., D. A. Jackson, et al. (2007). "Will northern fish populations be in hot water because
of climate change?" Global Change Biology 13(10): 2052-2064.
Shim, J. P., M. Warkentin, et al. (2002). "Past, present, and future of decision support
technology." Decision Support Systems 33(2): 111-126.

37

Shuter, B. J., C. K. Minns, et al. (2002). "Climate change, freshwater fish, and fisheries: Case
studies from Ontario and their use in assessing potential impacts." Fisheries in a
Changing Climate 32: 77-87.
Shuter, B. J. and J. R. Post (1990). "Climate, population viability, and the zoogeography of
temperate fishes." Transactions of the American Fisheries Society 119(2): 314-336.
Sitar, S. P., H. M. Morales, et al. (2008). "Survey of siscowet lake trout at their maximum depth
in Lake Superior." Journal of Great Lakes Research 34(2): 276-286.
Smith, J. B. (1991). "The potential impacts of climate change on the Great-Lakes." Bulletin of
the American Meteorological Society 72(1): 21-28.
Sousounis, P. J. and G. M. Albercook (2000). Potential Futures. Great Lakes Regional
assessment report- Preparing for a changing climate: The potential consequences of
climate variability and change in the Great Lakes Region. P. J. Sousounis and J. M.
Bisanzm. Ann Arbor, Michigan, University of Michigan Atmospheric, Oceanic, and
Space Sciences Department: 19-24.
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.
Stefan, H. G., M. Hondzo, et al. (1996). "Simulated long-term temperature and dissolved oxygen
characteristics of lakes in the north-central United States and associated fish habitat
limits." Limnology and Oceanography 41(5): 1124-1135.
Steinhart, G. B., N. J. Leonard, et al. (2005). "Effects of storms, angling, and nest predation
during angling on smallmouth bass (Micropterus dolomieu) nest success." Canadian
Journal of Fisheries and Aquatic Sciences 62(11): 2649-2660.
Stemberger, R. S., A. T. Herlihy, et al. (1996). "Climatic forcing on zooplankton richness in
lakes of the northeastern United States." Limnology and Oceanography 41(5): 10931101.
Taylor, W. W. and C. Dobson (2008). Interjurisdictional Fisheries Governance: Next Steps to
Sustainability. International Governance of Fisheries Ecosystems: Learning from the
Past, Finding Solutions for the Future. W. W. Taylor, N. J. Leonard and M. G. Schechter.
Bethesda, Maryland, American Fisheries Society: 431-440.
Taylor, W. W., M. A. Smalle, 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.

38

Vander Zanden, M. J., J. D. Olden, et al. (2004). "Predicting occurrences and impacts of
smallmouth bass introductions in north temperate lakes." Ecological Applications 14(1):
132-148.
Whelan, G. E. (2004). "A historical perspective on the philosophy behind the use of propagated
fish in fisheries management: Michigan's 130-year experience." Propagated Fish in
Resource Management 44: 307-315.
Wuebbles, D. J. and K. Hayhoe (2004). Climate change projections for the United States
Midwest. International Conference on Climate Change and Environmental Policy,
University of Illinois at Urbana-Champaign, USA, November 2002., Kluwer Academic
Publishers.

39

CHAPTER 2: THE NEED FOR DECISION-SUPPORT TOOLS FOR A CHANGING
CLIMATE: APPLICATION TO INLAND FISHERIES MANAGEMENT

Lynch, A. J., E. Varela-Acevedo, and W. W. Taylor. 2012. The need for decision-support tools
for a changing climate: application to inland fisheries management. Journal of Fisheries
Management and Ecology. DOI: 10.1111/fme.12013.

The content of this chapter is intended to be identical to the publication cited above and reflects
journal specifications (e.g. formatting, British spelling). Any differences should be minor and
are unintended.

40

Abstract
Large-scale environmental impacts, such as those of climate change on fisheries, require
policy and management action not only at the local level, but at regional, national and
international levels. Fisheries biology and ecology, along with social, political and economic
considerations, can influence policy design and implementation. Decision-support tools can
integrate these sciences to distil often complex, mechanistic and synergistic processes into a
format that the public, policy makers and managers can use when designing strategies to ensure
fisheries sustainability in the face of large-scale environmental perturbations, such as climate
change. Harvest management of lake whitefish, Coregonus clupeaformis (Mitchill), in the
Laurentian Great Lakes provides an excellent case study to examine the value and utility of a
decision-support tool for inland fisheries management when considering the effects of climate
change because this fishery is expected to be impacted by future changes in water temperature,
ice cover and wind speed.

KEYWORDS: climate change, decision support, inland fisheries management

41

Introduction
Fisheries policy makers, in concert with managers, set fishing regulations, ideally to
balance the ecological productivity of fish populations with the current and future needs of
fisheries resource users. A primary goal of fisheries management decisions is sustainable use of
the resource (i.e. continued use with minimal ecological impact). Fisheries research, historically
external to this decision-making process, can provide information to assist policy makers in
forming decisions if reliable and clearly articulated information is available. It is common for
researchers to complete a study or develop a new approach and then feel frustrated when it is not
implemented into management (Roux et al., 2006). Rather than reflect on this fact, it is more
productive to question why the research was not implemented into management. In this article,
discussions within human dimensions are drawn upon to explore factors that influence fisheries
management decisions with the purpose of providing context for the utility of decision-support
tools, particularly for inland fisheries management impacted by a changing climate.
Incorporating policy implications into research is a valuable exercise that will result in more
relevant research and management strategies that lead to sustainability given a changing climate,
as demonstrated in the concluding case study on Laurentian Great Lakes lake whitefish,
Coregonus clupeaformis (Mitchill).
Factors that influence fisheries management decisions
Often, researchers fail to acknowledge that other factors besides fisheries biology and
ecology are involved in guiding management decisions (Fig. 1). Today, there are multiple ways
to approach problems and decisions are revisable (Beck et al., 2003). While science is
acknowledged as an important consideration for fisheries policy makers, it is not the only
influence on decisions (Lahsen, 2005). Policansky (1998) observed that in many controversial

42

topics studied by the United States (US) National Research Council, such as wetlands
delineation, anadromous salmon declines and the US Endangered Species Act, science was
generally not even relevant to the issues in dispute (Policansky, 1998). Ultimately, effective
decisions are made by a shared commitment to a particular line of action (Sarewitz, 2004). This
commitment comes from an integration of factors related to society, economics, politics and
scientific uncertainty (Figure 2.1).

Management Decision
Society

Social ties

Conforming
to
community
value
system

Politics

Competing
interests

Economics

Timescale
to results

Economic
viability

Timescale
to results

Science

Clarity

Uncertainty

FIGURE 2.1. Factors that contribute to fisheries management decisions.

Society
Societies exert a tangible influence on and are influenced by their environment, be it
through industries, voluntary associations or governing bodies (Dunlap and Catton, 1979).
Fisheries systems, from subsistence fishing communities to international governing institutions,
are no exception; strong social ties are important at all scales of management. In self-managed
43

fishing communities, for example, social networks often maintain social norms and behaviours
of fishers (Frank et al., 2011). If a fisher does not conform to the value system of the community,
he or she may be excluded from benefits of local fishing knowledge and may have lower yields.
Conversely, if a fisher is well-integrated into the social network, he or she will have access to
expert knowledge, high social capital and likely higher yields. For example, Leonard et al.
(2011) found that a well-integrated social network supported the effectiveness of a Joint
Strategic Plan for Management of Great Lakes Fisheries. Participants in the Joint Strategic Plan
formed strong social ties, benefitted from an easy exchange of information and their ability to
share resources facilitated the implementation of the Plan. Understanding the role of people in
these fisheries systems is, therefore, important to understanding how social forces drive
management decisions. Translating these factors into policy action requires consideration not
only of how people have acted in the past, but also their future outlook (Peterson, 2000). Values,
attitudes, beliefs, intentions and behaviours are personal motivators which, when scaled up to a
societal level, influence fisheries management decisions. Fishing families and fishing
communities, with strong social bonds, can be a powerful force in support of, or opposition to,
the management process (Arlinghaus et al., 2002). For example, US walleye, Sander vitreus
(Mitchill), anglers on Lake Erie were integral in converting the US fishery, once dominated by
commercial harvest, to solely recreational harvest (Koonce et al., 1999).
Politics
Political dynamics can be a major motivation for human action and decision making, and
both are heavily influenced by the political framework in which they exist (Peterson, 2000; Beck
et al., 2003). Because fisheries management is prescribed at a governmental level, managers are
often tasked with producing results on the timescale of political appointments, which often are

44

not biologically meaningful. As a result, political actors may prioritise short-term interests over
long-term sustainability at regional, national and local levels. For example, Axelrod (2011)
examined the conditions under which regional fisheries management organisations adopted
climate actions (i.e. included climate change in their research and management plans). He found
that member countries were more apt to favour climate action not when it aligned with scientific
recommendations, but rather when it coincided with avoiding catch regulations (Axelrod, 2011).
Also to circumvent catch regulations, O’Leary et al. (2011) found that European Union Fisheries
Ministers engage in competitive bargaining driven by immediate national interest when setting
total allowable catch (TAC) regulations. Competitive bargaining for Atlantic bluefin tuna,
Thunnus thynnus (L.) quotas is an oft-cited cautionary tale of the impacts of quota overinflation
(Safina and Klinger, 2008), but it is far from the only case. In 68% of the TAC decisions
analysed by O’Leary et al. (2011), for example, quotas were set higher than the scientific
recommendation for catch limits. Tan-Mullins (2007) evaluated fisheries management
enforcement on a smaller governance scale, in Pattani Province, Thailand, but found similar
motives (e.g. personal interests and gains) that led to unsustainable behaviours. Weak
enforcement of regulations at any level of governance allows local enforcement officers to act in
personal interest (e.g. accept bribes for non-compliance with ordinances) rather than enforce
regulations (Tan-Mullins, 2007).
Economics
Economic influence often drives political motivations of fisheries management decisions
(Beck et al., 2003). Fishing, at all scales, is a livelihood and contributor to quality of life and,
hence, is economically driven (Valdimarsson and Metzner, 2011). Fishers attempt to maximise
profit and minimise inter-annual variation in effort, catch and market value (Christensen, 1997).

45

Market dynamics can be very powerful; maximizing profits and value while reducing the cost of
‘doing business’ is often a high priority in how decisions are rationalised (Mohai et al., 2009;
Valdimarsson and Metzner, 2011). Differing economic and political objectives, as a result of
different governing structures, frequently weaken fisheries legislation, particularly with respect
to inter-jurisdictional fisheries (Collares-Pereira and Cowx, 2004). For example, the commercial
fishers who sit on the Chilean National Fisheries Council ultimately represent the interests of
their industries. Appealing to potential impacts of fish processing plant closures and losses of
jobs, these council members vote in favour of TAC regulations that generate higher levels of
employment and perceived greater, at least on the short term, economic value, potentially at the
expense of population-level sustainability (Leal et al., 2010).
In effect, policy makers must often consider trade-offs between political, economic (i.e.
market value) and ecological (i.e. biodiversity) services, in selecting cost-effective management
options that are conscious of needs for predictability (Farber et al., 2006), although one need
may not be exclusive of the other. Often, conservation action is beyond the economic scope of a
region (Collares-Pereira and Cowx, 2004). However, there are other institutional processes, such
as subsidies and incentives, which may decouple the economic viability and ecological
sustainability of fishing. If fisheries management is realigned with resource and market realities,
for example through rights-based systems, the sector can become attractive to fishers, investors
and consumers (Valdimarsson and Metzner, 2011).
Scientific uncertainty
Often it is appropriate for fisheries managers to weigh other factors as much as fisheries
biology and ecology. But, it is not appropriate for them to claim scientific rationale for decisions
when there is none (Policansky, 1998) or defer decision making until a given level of scientific

46

certainty is achieved (McCright and Dunlap, 2010). For example, casting doubt on complex
stock assessment methods has been used as a shield for many fisheries, including Inter-American
Tropical Tunas (Oh, 2011), European fish stocks under the Common Fisheries Policy (O'Leary et
al., 2011) and Chilean fisheries (Leal et al., 2010). As in these cases, scientific uncertainty can
aggravate management controversy (Policansky, 1998) and is often used as a justification for not
adhering to scientific advice (O'Leary et al., 2011).
While reduction of uncertainty may be the central goal of scientific research conducted
for management purposes (Sarewitz, 2004), predictive sciences cannot capture all stochasticity in
both human and natural systems. The greater the uncertainty in a system, the less managers are
able to predict the consequences of their conservation action. Decision making under these
conditions must be flexible and adaptive and able to incorporate new information and
circumstances into its processes so that management and policy are implemented most
effectively and efficiently (Grafton, 2010). Reducing uncertainty narrows the range of potential
strategies and likely increases certainty of resultant policy outcomes.
Decision support
While improved data sets, modelling and predictions will surely aid decision makers with
more precise planning (Smith, 1991b), many fisheries management decisions must be
implemented before such improvements to the predictions can be achieved (de Bruin and Hunter,
2003). For example, the International Commission for the Conservation of Atlantic Tunas is
responsible for maintaining stock levels of highly migratory species at sustainable levels in the
Atlantic Ocean. While in many cases, incidental catch of non-target species also under their
purview is largely unknown, the Commission has to determine the harvest limits on both the
target fisheries and associated bycatch species (Lynch et al., 2011). Often such decisions lack

47

scientific input because the science is not available or highly uncertain to the decision makers at
the time of need (Klein et al., 2008; Lynch et al., 2010).
Decision-support tools
The question policy makers and managers often grapple with is how to determine
regulations that ensure sustainability using currently available science. It is the role of fisheries
scientists to ensure that fisheries biology and ecology are understood and not misrepresented in
the decision-making arena (Policansky, 1998). One way to do this is to use decision-support
tools, which can come in many forms including economic models, integrated assessment models,
policy simulations and mechanistic models of ecosystem processes. Each approach has strengths,
weakness and limits when applied to fisheries management (see Table 2.1). The overall goal of
any decision- support tool is to identify policy options within the range of a desired outcome in
the face of uncertainty. The different types of tools deal with uncertainty in different ways;
uncertainty can be considered resolved prior to decision making (i.e. deterministic), random and
in need of an iterative approach to management (i.e. stochastic) or as a likelihood where policy
recommendations are determined through optimisation procedures (i.e. integrated assessment
models). Understanding how uncertainty is accounted for is important to increase the
transparency, objectivity and inclusiveness of management decisions (Jones and Bence, 2009),
and the likelihood of voluntary compliance with those decisions.
Integrated assessment models typically link a climate model with models of the economic
system, land use, agriculture or ecosystems, depending on which is applicable to the question
being addressed (NCR 2010). These models can examine proposed management actions and
suggest which options are likely to reach the most desired policy outcomes as defined by the
managers or stakeholders involved (Riley et al., 2003). Particularly for large-scale impacts, such

48

TABLE 2.1. Select decision-support tools, their approaches, strengths, and weaknesses with relation to fisheries and climate change.
Modified from NRC (2010) Table 4.1.
Tool

Modelling approach

Strengths

Weaknesses

Economic models

-Cost-effectiveness/cost-benefit
analysis
-Agent based models

-Estimates the costs and benefits
of policies

-Difficult to measure beyond
economic value

Integrated assessment
models

-links relevant sub-models:
climate, economic system, land
use, agriculture, and/or
ecosystems

-Examines proposed management
actions in the context of predefined desired policy outcomes

-Complex
-Difficult to validate
-Do not account for tradeoffs

Policy simulations

-Heuristic methods

-Compares alternative policies
-Accounts for tradeoffs

-Simple; may not capture full
implications

-Ecosystem processes

-Analyzes the impact of changes
in climate on the environment and
human activity
-Capable of capturing synergistic
effects

-Complex
-Difficult to validate
-Do not account for tradeoffs

Mechanistic models

TABLE 2.2. Examples of potential effects of climate change and impacts on inland fish production.
Direct effects
↑ water temperatures
↑ evaporation
↑ extreme storm events

Indirect effects
↑ eutrophication
Δ in location of optimal thermal habitat
↓ river discharge
↑ groundwater extraction
↑ runoff
↑ flash flooding

Inland fisheries impacts
↓ dissolved oxygen
Δ in species abundance and distribution
possible ↑ in invasive species
↓ habitat space
↑ habitat contamination

49

as climate change, which are experienced at local and regional scales, management decisions
need a method to evaluate management options for wide geographic ranges. This requires
governance at a regional or higher level and a thorough understanding of landscape-level impacts
on a system (for examples, see (Liu and Taylor, 2002). While they are helpful for examining the
synergies of these dynamic systems, integrated assessment models are difficult to validate and do
not allow for value trade-offs (i.e. different stakeholder values of what should be conserved,
enhanced or sacrificed).
Mechanistic models are often the ecosystem component (e.g. risk analysis of ecosystem
indicators) of integrated assessment models. They are ecological (i.e. involve population or food
web dynamics) and consequently tend to be complex (i.e. parameter rich) and difficult to
validate. When modelling fish movement, for example, spatial processes can be inferred from
recreating spatial patterns rather than from actual observed movement behaviour (Humston et al.,
2004). By examining and integrating these ecological responses, these models can be informative
tools for decision support to fisheries management. However, decision makers must understand
that most models are specific and do not address all ramifications of actions (e.g. while a fish
population may rebound under a certain harvest regime, that regime may have other negative
impacts to the ecosystem). The strength in these models is that they allow scientists and decision
makers to recognise possibilities that may not be inferred from more empirical, but less
integrated, approaches (Jones et al., 2006). For example, Jones et al. (2006) found that the
projected impact of climate change on walleye population dynamics was quite different using
multiple factors (temperature, river hydrology, lake levels and light penetration) than just
considering temperature alone.
It is important to note, however, that decision-support tools are just that – decision
support. They will not fix problems, and their limitations must be taken into account (Shim et al.,
50

2002). Fisheries science is an important process that provides predictable information and
answers questions but does not make decisions (Sarewitz, 2004). These tools aid decision
making by systematically incorporating information, accounting for uncertainties and facilitating
evaluation of trade-offs between different choices (NRC, 2010). By formalizing the complexities
of a system into a modelling framework, decision-support tools can provide managers with a
quantitative comparison of potential policy outcomes (Azadivar et al., 2009). Decision-support
tools cannot make policy choices, but rather assess the implementation of those choices
(Sarewitz, 2004). The onus of the decision still resides with the decision maker (Taylor and
Dobson, 2008), not a support tool.
Application of science-based decision support to inland fisheries management
Decision-support tools may help inform successful inland fisheries management
strategies as they can be designed to assist management at a range of geographic scales.
Arlinghaus et al. (2002) suggested that decision-support tools can improve decision making for
the management of inland fisheries resources by providing options that maximise societal
welfare without compromising the integrity of aquatic ecosystems. By capturing synergies of
multiple types of information (e.g. economic, social, biological), decision-support tools can
ensure a more transparent, objective and inclusive management process (Azadivar et al., 2009;
Jones and Bence, 2009). To be effective, decision-support systems should involve individuals,
organisations and institutions with decision-relevant information and be readily communicable to
decision makers and stakeholders (NRC, 2010). Citizen involvement needs to be a key
component in the design of a decision-support tool because more ownership generally equates to
higher implementation success (Irvin and Stansbury, 2004) and voluntary compliance.

51

Effective and efficient river management, for example, must connect monitoring and
assessment of the water cycle to ensure that the approach produces the desired outcome
(Goethals and De Pauw, 2001). Restoration projects whose objectives are narrowly focused may
be incomplete, and consequently, they likely will not accomplish their goals because they do not
consider the impact of the key factors driving system processes (Verdonschot and Nijboer,
2002). For example, Lynch and Taylor (2010) found that small-scale restoration projects for
brook charr, Salvelinus fontinalis (Mitchill), could not always fulfill their proposed objectives,
likely because of larger-scale perturbations. To incorporate these important components into a
model requires considering large-scale before small-scale influences. Addressing large-scale
problems, like upstream agricultural run off, through which moving water may spread waste and
disease over a wide distance, will strengthen the success of localised efforts (Verdonschot and
Nijboer, 2002), such as restoration of habitat structure for brook charr further downstream.
Climate change and inland fisheries
Managing inland fisheries is a complex task, with or without the added potential effects
of climate change. Addressing climate-related risks proactively, whether the impacts are mild or
severe, will be beneficial to fisheries because these actions may buffer against other ecological
changes (Hay and Mimura, 2006; Grafton, 2010). For example, climate change will manifest
itself in more ways than just temperature increases in aquatic habitats (e.g. precipitation patterns,
evapotranspiration, wind patterns, ground water and surface water inputs and dissolved oxygen
content). As a result, models regarding fish production that account only for thermal habitats
may not be sufficient to predict the full suite of consequences of climate change to these
populations and their fisheries (Jones et al., 2006).

52

Potential impacts of climate change on inland fisheries
Fish stocks continually face stress associated with human transformation of the land, air
and waterscapes, including fishing, loss of habitat, pollution, invasive species and pathogens
(Brander, 2007). These factors lead to changes in the production dynamics of affected waterways
and their biotic communities, impeding resiliency of these communities to environmental
changes (Planque et al., 2010). For instance, global air temperature increases over the past 50
years (1955–2005) have been nearly twice what they were in the preceding 100 years (IPCC
2007). As water temperature, quantity and quality are all influenced by climate, the effects of this
warming have been predicted to affect the distribution, production and abundance of freshwater
fishes (Regier and Meisner, 1990).
Although surface fresh water accounts for only 0.01% of global water supplies and 0.8%
of the earth’s surface, it provides habitat for approximately 40% of global fish diversity, 25% of
global vertebrate diversity (Dudgeon et al., 2006) and 23% of global aquatic production (in
2004; (Brander, 2007), as well as being an essential component to human life and well-being.
Inland waters are particularly sensitive to landscape-level changes because they have a direct tie
to terrestrial inputs and experience the compounded effects from perturbations further upstream
in a watershed. Freshwater ecosystems are highly vulnerable to land use alterations, invasive
species and climate change because of the proximity to and impacts from people (see Table 2.2).
Additionally, these effects impact both the quantity and quality of ground water and surface
water delivered to these environments that influence fish distribution and production. Terrestrial
runoff of nutrients and sediments from human use have the potential to impact freshwater
ecosystems, contributing to eutrophication and loss of fish species (Sala et al., 2000). Climate
change may potentially exacerbate land use alterations by directly modifying the aquatic
environment (i.e. change thermal regime, habitat volume and food resources; (Jones et al., 2006).
53

In addition to the direct effects of climate change, many fish populations and associated
fisheries will be indirectly threatened by the associated environmental changes. These impacts
include alterations to water regimes through water use (i.e. agricultural, domestic and industrial
use and alterations; (Wilby et al., 2010). Lake Tanganyika, Africa, for example, supported a
productive fishery in the 1990s with annual harvests ranging from 165 000 to 200 000 t (Molsa
et al., 1999), providing up to 40% of the animal protein consumed in its surrounding countries
(O'Reilly et al., 2003) . As a result of climate change and human alteration of the landscape,
warmer waters have caused the Lake Tanganyika water column to become stratified (O'Reilly et
al., 2003), limiting nutrient circulation between the hypolimnion and epilimnion in lakes, which,
in turn, limits primary production in the pelagic zone and the trophic chain dependent upon it.
O’Reilly et al. (2003) estimated that decreasing primary production in Lake Tanganyika by 20%
has the potential to reduce fisheries yields by up to 30%.
The effects of climate change will also likely increase some inland fish populations and
decrease others. For example, if smallmouth bass, Micropterus dolomieu (Lacepède), extends its
range as projected to inhabit more northern inland lakes of North America (Chu et al., 2005),
these fish will likely negatively impact the diverse cyprinid communities that will serve as their
forage base (Jackson and Mandrak, 2002). Conversely, cold water stenotherms (i.e. able to
survive in a narrow range of cold temperatures) are predicted to retract north as waters warm.
Ultimately, ecosystem-scale changes will alter waterscape productivity, opportunities for
subsistence, commercial and recreational fisheries to exist and how those fisheries can be
managed in sustainable ways.

54

Managing inland fisheries in a changing climate
The regional to global impacts of climate change will require drastically different
approaches to fisheries management than are currently used at local levels (Lynch et al., 2010).
Widely used methods, such as site-based management, are largely inefficient and ineffective at
addressing regional disturbances because their fragmented approach often does not target the
source of large-scale problems because they are beyond the scope of understanding or geopolitical jurisdiction. As a result, these methods do not generally provide a solution to regional
problems (Verdonschot and Nijboer, 2002). Liu and Taylor (2002) suggested that management
should be coordinated through higher levels of governance for landscape-level conservation
action. For example, rehabilitation efforts for brook charr in the Eastern US have historically
focused on site-specific habitat restoration and these efforts have been generally unsuccessful at
reversing population declines (Lynch and Taylor, 2010). In response to these continued declines
of brook charr populations, the Eastern Brook Trout [Charr] Joint Venture (EBTJV) formed as a
multiorganisation partnership of state and federal agencies, non-governmental organisations and
academic institutions to identify and address range-wide threats, such as agricultural practices,
climate change and urbanisation to brook charr populations. The EBTJV is an important model
for collaborative regional aquatic management because it considers broader scales than sitespecific habitat (i.e. stream segment) to manage brook charr across its entire Eastern US range.
The Great Lakes Fishery Commission (GLFC) is another success story in multi-jurisdictional
management. Although not a regulating body itself, the GLFC facilitates the cooperation among
management agencies throughout the Great Lakes basin and is a forum for scientific exchange
and basin-wide strategic fisheries planning (Gaden et al., 2012).
Even when attempts are made to address large-scale impacts such as climate change, the
question remains of how to incorporate the uncertainty of climate variability into policy (Wilby
55

et al., 2010). Managers must adapt their strategies for organisms and habitats of concern in the
face of uncertainty regarding their future states. In addition to the ecosystem impacts, fisheries
managers must also consider the social and economic effects on subsistence, commercial and
recreational fisheries in the communities they manage. In regards to potential changes,
management policies and practices must be realigned for the conservation objectives to be
feasible in the face of climate change while still fulfilling societal priorities and needs (Lynch et
al., 2010; Wilby et al., 2010). Sustainability of inland fisheries can only be achieved when there
is balance between economic development to meet changing human needs and the conservation
of natural resources and their habitats to absorb the stressors resulting from these human
activities (Hay and Mimura, 2006).
One way of approaching sustainability is the use of adaptive management protocols.
These treat management action as an iterative, experimental approach with adjustments to
policies and management practices based on the ecological and social responses to initial action
(Prato, 2003). Adaptive management considers people an integral part of any system, and as
such, their impacts and influence cannot be ignored. As a result, adaptive measures will have the
greatest acceptance when they have the greatest benefit to multiple stakeholders over a long
period of time (Wilby et al., 2010). Adaptive management is well suited to address the future
effects of climate change on fisheries sustainability because it is designed to provide a buffer
against ecological and socio-economic uncertainties by adjusting strategies based on informative
monitoring systems (Walters, 1986). Management can, consequently, be adaptive but decisive in
the face of uncertainty and the current limitations of climate and fisheries projections.

56

Harvest management of lake whitefish with climate change
The following case study from the Laurentian Great Lakes exemplifies the utility of the
emerging interdisciplinary field of fisheries decision support to inland fisheries management in a
changing climate. The purpose of its inclusion is to identify the need to develop new tools to
address objectives from multiple stakeholders and help optimise management strategies in
diverse fisheries ecosystems.
Potential impacts of climate change on lake whitefish
Since 1980, populations of lake whitefish have supported the most economically valuable
commercial fishery in the upper Laurentian Great Lakes (Lakes Huron, Michigan and Superior;
annual catch value US$16.6 million, averaged over years between 1994 and 2004) (Madenjian et
al., 2006; Ebener et al., 2008). Climate change is expected to impact the economic value of this
fishery because the success of recruitment to the fishery has been linked with climatic influences,
including water temperature, wind speed and ice cover (Miller, 1952; Christie, 1963; Lawler,
1965; Taylor et al., 1987a; Freeberg et al., 1990; Lynch et al., 2010). Climate change is expected
to increase surface temperatures of the Great Lakes by as much as 6 °C (Trumpickas et al.,
2009), average wind speed is expected to decline (Sousounis and Grover, 2002), and ice cover is
expected to be substantially reduced (Assel et al., 2003). In their current habitat space, increased
water temperature, decreased wind speed and decreased ice cover are projected to inhibit the
success of recruitment to the lake whitefish fishery (Lynch et al., 2010). However, the warming
trends associated with predicted climate change should increase suitable thermal habitat volume
for lake whitefish (Magnuson et al., 1997) because the species could shift northwards and deeper
in the water column to maintain optimal thermal habitat (Regier and Meisner, 1990). Given these
changes, the overall amount of new thermal habitat space for lake whitefish in the Great Lakes is
57

projected to exceed reductions that are coincident with the warming of their more nearshore and
southern extremes.
Potential impacts on lake whitefish management
Climate change impacts on lake whitefish production dynamics add ecological and social
dimensions for consideration in designing and implementing sustainable management
programmes. Currently, lake whitefish management spans at least 35 Native American
governments, eight US states and the Province of Ontario, Canada (Ebener et al., 2008). Most of
the management in the Great Lakes occurs on a stock-by-stock basis without cross-jurisdictional
cooperation (Ebener et al., 2008). This type of management is not adequate for addressing largescale environmental threats such as climate change; management must shift to more regional
governance that encourages landscape-level conservation efforts (Liu and Taylor, 2002). Such
landscape approaches to fisheries management will help avoid fragmentation of fisheries policies
in each jurisdiction, which have historically resulted in the demise of fish populations and their
associated fisheries (see (Taylor et al., 2013)). As lake whitefish move deeper and more
northerly in these lakes to find optimal habitat as a result of changes in climate, the stock
distributions and production dynamics will not remain in their current jurisdictional structure.
Managers and society, like the fish themselves, must therefore adapt their strategies to the
ecological realities that come with a changing climate. Decision-support tools, based on reliable
monitoring and evaluation systems, will be a key feature of future adaptive management
strategies for this fisheries ecosystem and will assist policy makers, managers and society in
adjusting their behaviours and expectations to allow for productive, sustainable fisheries.

58

Temperature

Ice Cover

Wind

Survival

Climatic
Conditions

Population
Dynamics

Growth

Reproduction

Lake Whitefish
Distribution and
Abundance

Effort

Management
Strategies

Fishing
Pressure

Method

Location

FIGURE 2.2. Schematic of the ecological inputs and anticipated outputs of a mechanistic decision-support tool to sustainably manage
lake whitefish (Coregonus clupeaformis) production in a changing climate.

59

Need for decision support
As the public demands a greater voice in decisions over management of natural resources
(Lord and Cheng, 2006), it is essential that management incorporates stakeholders into the
decision-making process through integrated assessment approaches (MI Sea Grant & Graham
Environmental Sustainability Institute 2009). Without general acceptance, management measures
have a low probability of acceptance and, consequently, adherence (Decker et al., 2006). Lord
and Cheng (2006) argued the main barrier to stakeholder involvement is the lack of public
support and understanding of the science, costs and benefits of management options, the
decision-making process and monitoring and evaluation systems. By seeking public input on the
design of decision-support tools, these tools can better address objectives from multiple
stakeholders on an ecosystem level. They can help optimize the most effective management
strategies (Azadivar et al., 2009) and likely enable agencies to implement effective monitoring
systems to gauge the success of their actions and need for adjustments. With meaningful public
integration into the process, decisions will be culturally and socially acceptable while ensuring
the resilience and sustainability of the fish populations and their ecosystems.
For lake whitefish, decision-support tools will be informative for managers, policy
makers and stakeholders (e.g. commercial fishermen, seafood consumers and community
residents). Integrating these key players into the process increases a sense of ownership and
accountability (Irvin and Stansbury, 2004) and ensures that proposed solutions address
fundamental problems (Roux et al., 2006). As such, decision-support tools can be effective ways
to simplify complex ecological processes and social structures (Figure 2.2). They inform the
public and the diverse, often non-scientific, audience who is tasked with allocating scarce
fisheries and financial resources (Sarkar et al., 2006), allowing for a more informed decision60

making dialogue. By comparing scenarios of lake whitefish production with climate projections
over ecologically relevant (i.e. generational) timescales, these tools can use science and
stakeholder input to assist decision makers with making more informed choices that should
increase the sustainability of this species and related human prosperity for current and future
generations.

Acknowledgements
The authors thank I. Cowx and two anonymous reviewers for constructive
recommendations to strengthen this manuscript and D. Lynch and E. MacMillan for final review.

61

LITERATURE CITED

62

LITERATURE CITED

Arlinghaus, R., Mehner, T. & Cowx, I.G. (2002) Reconciling traditional inland fisheries
management and sustainability in industrialized countries, with emphasis on Europe. Fish
and Fisheries, 3, 261–316.
Assel, R., Cronk, K. & Norton, D. (2003) Recent trends in Laurentian Great Lakes ice cover.
Climatic Change, 57, 185-204.
Axelrod, M. (2011) Climate Change and Global Fisheries Management: Linking Issues to
Protect Ecosystems or to Save Political Interests? Global Environmental Politics, 11, 6484.
Azadivar, F., Truong, T. & Jiao, Y. (2009) A decision support system for fisheries management
using operations research and systems science approach. Expert Systems with
Applications, 36, 2971-2978.
Beck, U., Bonss, W. & Lau, C. (2003) The theory of reflexive modernization - Problematic,
hypotheses and research programme. Theory Culture & Society, 20, 1-33.
Brander, K.M. (2007) Global fish production and climate change. Proceedings of the National
Academy of Sciences of the United States of America, 104, 19709-19714.
Christensen, S. (1997) Evaluation of management strategies - A bioeconomic approach applied
to the Greenland shrimp fishery. Ices Journal of Marine Science, 54, 412-426.
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,
597-646.
Chu, C., Mandrak, N.E. & Minns, C.K. (2005) Potential impacts of climate change on the
distributions of several common and rare freshwater fishes in Canada. Diversity and
Distributions, 11, 299-310.
Collares-Pereira, M.J. & Cowx, I.G. (2004) The role of catchment scale environmental
management in freshwater fish conservation. Fisheries Management and Ecology, 11,
303-312.
de Bruin, S. & Hunter, G.J. (2003) Making the trade-off between decision quality and
information cost. Photogrammetric Engineering and Remote Sensing, 69, 91-98.
Decker, D.J., Jacobson, C.A. & Brown, T.L. (2006) Situation-specific "Impact dependency" as a
determinant of management acceptability: Insights from wolf and grizzly bear
management in Alaska. Wildlife Society Bulletin, 34, 426-432.
Dudgeon, D., Arthington, A.H., Gessner, M.O., Kawabata, Z.I., Knowler, D.J., Leveque, C.,
Naiman, R.J., Prieur-Richard, A.H., Soto, D., Stiassny, M.L.J. & Sullivan, C.A. (2006)
63

Freshwater biodiversity: importance, threats, status and conservation challenges.
Biological Reviews, 81, 163-182.
Dunlap, R.E. & Catton, W.R. (1979) Environmental Sociology. Annual Review of Sociology, 5,
243-273.
Ebener, M.P., Kinnunen, R.E., Schneeberger, P.J., Mohr, L.C., Hoyle, J.A. & Peeters, P. (2008)
Management of Commercial Fisheries for Lake Whitefish in the Laurentian Great Lakes
of North America. In: M.G. Schechter, N.J. Leonard & W.W. Taylor (eds.) International
Governance of Fisheries Ecosystems: Learning from the Past, Finding Solutions for the
Future. Bethesda, Maryland: American Fisheries Society Press, pp. 99-143.
Farber, S., Costanza, R., Childers, D.L., Erickson, J., Gross, K., Grove, M., Hopkinson, C.S.,
Kahn, J., Pincetl, S., Troy, A., Warren, P. & Wilson, M. (2006) Linking ecology and
economics for ecosystem management. Bioscience, 56, 121-133.
Frank, K.A., Maroulis, S., Belman, D. & Kaplowitz, M.D. (2011) The Social Embeddedness of
Natural Resource Extraction and Use in Small Fishing Communities. In: W.W. Taylor,
A.J. Lynch & M.G. Schechter (eds.) Sustainable Fisheries: Multi-Level Approaches to a
Global Problem. Bethesda, MD: American Fisheries Society, pp. 309-331.
Freeberg, M.H., Taylor, W.W. & Brown, R.W. (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, 92-100.
Gaden, M., Goddard, C. & Read, J. (2013) Multi-Juisdicitional Management of the Shared Great
Lakes Fishery: Transcending Conflict and Diffuse Political Authority. In: W.W. Taylor,
A.J. Lynch & N.J. Leonard (eds.) Great Lakes Fisheries Policy and Managment: A
Binational Perspective. Second ed. East Lansing, MI: MSU Press, pp. 305–337.
Goethals, P. & De Pauw, N. (2001) Development of a concept for integrated ecological river
assessment in Flanders, Belgium. Journal of Limnology, 60, 7-16.
Grafton, R.Q. (2010) Adaptation to climate change in marine capture fisheries. Marine Policy,
34, 606-615.
Hay, J. & Mimura, N. (2006) Supporting climate change vulnerability and adaptation
assessments in the Asia-Pacific region: an example of sustainability science.
Sustainability Science, 1, 23-35.
Humston, R., Olson, D.B. & Ault, J.S. (2004) Behavioral assumptions in models of fish
movement and their influence on population dynamics. Transactions of the American
Fisheries Society, 133, 1304-1328.
Intergovernmental Panel on Climate Change (IPCC) Core Writing Team (2007) Climate Change
2007: Synthesis Report. No. 104 pp.

64

Irvin, R.A. & Stansbury, J. (2004) Citizen participation in decision making: Is it worth the effort?
Public Administration Review, 64, 55-65.
Jackson, D.A. & Mandrak, N.E. (2002) Changing fish biodiversity: Predicting the loss of
cyprinid biodiversity due to global climate change. Fisheries in a Changing Climate, 32,
89-98.
Jones, M.L. & Bence, J.R. (2009) Uncertainty and Fishery Management in the North American
Great Lakes: Lessons from Applications of Decision Analysis. In: C.C. Krueger & C.E.
Zimmerman (eds.) Pacific Salmon: Ecology and Management of Western Alaska's
Populations. Bethesda, MD: American Fisheries Society, pp. 1059-1082.
Jones, M.L., Shutter, B.J., Zhao, Y. & Stockwell, J.D. (2006) Forecasting effects of climate
change on Great Lakes fisheries: models that link supply to population dynamics can
help. Candian Journal of Fisheries and Aquatic Sciences, 63, 457-468.
Klein, C.J., Steinback, C., Scholz, A.J. & Possingham, H.P. (2008) Effectiveness of marine
reserve networks in representing biodiversity and minimizing impact to fishermen: a
comparison of two approaches used in California. Conservation Letters, 1, 44-51.
Koonce, J.F., Locci, A.B. & Knight, R.L. (1999) Contribution of Fisheries Management in
Walleye and Yellow Perch Populations of Lake Erie. In: W.W. Taylor & C.P. Ferreri
(eds.) Great Lakes Fisheries Policy and Management: A Binational Perspective. First ed.
East Lansing, MI: MSU Press, pp. 397-416.
Lahsen, M. (2005) Technocracy, democracy, and US climate politics: The need for
demarcations. Science Technology & Human Values, 30, 137-169.
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,
1197-1227.
Leal, C.P., Quinones, R.A. & Chavez, C. (2010) What factors affect the decision making process
when setting TACs?: The case of Chilean fisheries. Marine Policy, 34, 1183-1195.
Leonard, N.J., Taylor, W.W., Goddard, C.I., Frank, K.A., Krause, A.E. & Schechter, M.G.
(2011) Information Flow within the Social Network Structure of a Joint Strategic Plan for
Management of Great Lakes Fisheries. North American Journal of Fisheries
Management, 31, 629-655.
Liu, J. & Taylor, W.W. (2002) Integrating Landscape Ecology into Natural Resource
Management, Cambridge: Cambridge University Press, 520 pp.
Lord, J.K. & Cheng, A.S. (2006) Public Involvement in State Fish and Wildlife Agencies in the
U.S.: A Thumbnail Sketch of Techniques and Barriers. Human Dimensions of Wildlife:
An International Journal, 11, 55 - 69.

65

Lynch, A.J. & Taylor, W.W. (2010) Evaluating a science-based decision support tool used to
prioritize brook charr conservation project proposals in the eastern United States.
Hydrobiologia, 650, 233-241.
Lynch, A.J., Taylor, W.W. & Smith, K.D. (2010) The influence of changing climate on the
ecology and management of selected Laurentian Great Lakes fisheries. Journal of Fish
Biology 8, 174–1784.
Lynch, P.D., Graves, J.E. & Latour, R.J. (2011) Challenges in the Assessment and Management
of Highly Migratory Bycatch Species: A Case Study of the Atlantic Marlins. In: W.W.
Taylor, A.J. Lynch & M.G. Schechter (eds.) Sustainable Fisheries: Multi-Level
Approaches to a Global Problem. Bethesda, MD: American Fisheries Society, pp. 197–
225.
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. (2006) Evaluation of a lake whitefish
bioenergetics model. Transactions of the American Fisheries Society, 135, 61-75.
Magnuson, J.J., Webster, K.E., Assel, R.A., Bowser, C.J., Dillon, P.J., Eaton, J.G., Evans, H.E.,
Fee, E.J., Hall, R.I., Mortsch, L.R., Schindler, D.W. & Quinn, F.H. (1997) Potential
effects of climate changes on aquatic systems: Laurentian Great Lakes and Precambrian
Shield Region. Hydrological Processes, 11, 825-871.
McCright, A.M. & Dunlap, R.E. (2010) Anti-reflexivity The American Conservative
Movement's Success in Undermining Climate Science and Policy. Theory Culture &
Society, 27, 100-133.
Michigan Sea Grant and Graham Environmental Sustainability Institute (2009). Tackling
Wicked
Problems through Integrated Assessment. [MICHU-09-506] University of Michigan, Ann Arbor,
MI. Available at: www.miseagrant.umich.edu/downloads/research/tackling-wickedproblems.pdf.
Miller, R.B. (1952) The relative sizes of whitefish year classes as affected by egg planting and
the weather. Journal of Wildlife Management, 16, 39-50.
Mohai, P., Pellow, D. & Roberts, J.T. (2009) Environmental Justice. Annual Review of
Environment and Resources, 34, 405-430.
Molsa, H., Reynolds, J.E., Coenen, E.J. & Lindqvist, O.V. (1999) Fisheries research towards
resource management on Lake Tanganyika. Hydrobiologia, 407, 1-24.
National Research Council (NRC) (2010) Informing an effective response to climate change.
National Academies Press, (Washington, D.C.). 9780309145947, http://www.nap.edu/
openbook.php?record_id=12784.

66

Oh, S. (2011) Role of Science in the Management of Tunas by the Inter-American Tropical Tuna
Commission: Limitations to Sustainability. In: W.W. Taylor, A.J. Lynch & M.G.
Schechter (eds.) Sustainable Fisheries: Multi-Level Approaches to a Global Problem.
Bethesda, MD: American Fisheries Society.
O'Leary, B.C., Smart, J.C.R., Neale, F.C., Hawkins, J.P., Newman, S., Milman, A.C. & Roberts,
C.M. (2011) Fisheries mismanagement. Marine Pollution Bulletin, 62, 2642-2648.
O'Reilly, C.M., Alin, S.R., Plisnier, P.D., Cohen, A.S. & McKee, B.A. (2003) Climate change
decreases aquatic ecosystem productivity of Lake Tanganyika, Africa. Nature, 424, 766768.
Peterson, G. (2000) Political ecology and ecological resilience: An integration of human and
ecological dynamics. Ecological Economics, 35, 323-336.
Planque, B., Fromentin, J.M., Cury, P., Drinkwater, K.F., Jennings, S., Perry, R.I. & Kifani, S.
(2010) How does fishing alter marine populations and ecosystems sensitivity to climate?
Journal of Marine Systems, 79, 403-417.
Policansky, D. (1998) Science and decision making for water resources. Ecological Applications,
8, 610-618.
Prato, T. (2003) Multiple-attribute evaluation of ecosystem management for the Missouri River
system. Ecological Economics, 45, 297-309.
Regier, H.A., Holmes, J.A. & Pauly, D. (1990) Influence of temperature changes on aquatic
ecosystems: an interpretation of empirical data. Transactions of the American Fisheries
Society, 119, 374-389.
Regier, H.A. & Meisner, J.D. (1990) Anticipated effects of climate change on fresh-water fishes
and their habitat. Fisheries, 15, 10-15.
Riley, S.J., Siemer, W.F., Decker, D.J., Carpenter, L.H., Organ, J.F. & Berchielli, L.T. (2003)
Adaptive Impact Management: An Integrative Approach to Wildlife Management.
Human Dimensions of Wildlife, 8, 81-95.
Roux, D.J., Rogers, K.H., Biggs, H.C., Ashton, P.J. & Sergeant, A. (2006) Bridging the sciencemanagement divide: Moving from unidirectional knowledge transfer to knowledge
interfacing and sharing. Ecology and Society, 11 (1), 4
http://www.ecologyandsociety.org/vol11/iss1/art4/.
Safina, C. & Klinger, D.H. (2008) Collapse of bluefin tuna in the Western Atlantic. Conservation
Biology, 22, 243-246.
Sala, O.E., Chapin, F.S., Armesto, J.J., Berlow, E., Bloomfield, J., Dirzo, R., Huber-Sanwald, E.,
Huenneke, L.F., Jackson, R.B., Kinzig, A., Leemans, R., Lodge, D.M., Mooney, H.A.,
Oesterheld, M., Poff, N.L., Sykes, M.T., Walker, B.H., Walker, M. & Wall, D.H. (2000)
Biodiversity - Global biodiversity scenarios for the year 2100. Science, 287, 1770-1774.
67

Sarewitz, D. (2004) How science makes environmental controversies worse. Environmental
Science & Policy, 7, 385-403.
Sarkar, S., Pressey, R.L., Faith, D.P., Margules, C.R., Fuller, T., Stoms, D.M., Moffett, A.,
Wilson, K.A., Williams, K.J., Williams, P.H. & Andelman, S. (2006) Biodiversity
conservation planning tools: Present status and challenges for the future. Annual Review
of Environment and Resources, 31, 123-159.
Shim, J.P., Warkentin, M., Courtney, J.F., Power, D.J., Sharda, R. & Carlsson, C. (2002) Past,
present, and future of decision support technology. Decision Support Systems, 33, 111126.
Smith, J.B. (1991) The potential impacts of climate change on the Great Lakes. Bulletin of the
American Meteorological Society, 72, 21-28.
Sousounis, P.J. & Grover, E.K. (2002) Potential future weather patterns over the Great Lakes
region. Journal of Great Lakes Research, 28, 496-520.
Tan-Mullins, M. (2007) The state and its agencies in coastal resources management: The
political ecology of fisheries management in Pattani, southern Thailand. Singapore
Journal of Tropical Geography, 28, 348-361.
Taylor, W.W. & Dobson, C. (2008) Interjurisdictional Fisheries Governance: Next Steps to
Sustainability. In: W.W. Taylor, N.J. Leonard & M.G. Schechter (eds.) International
Governance of Fisheries Ecosystems: Learning from the Past, Finding Solutions for the
Future. Bethesda, Maryland: American Fisheries Society, pp. 431–440.
Taylor, W.W., Lynch, A.J. & Leonard, N.J. (2013) Great Lakes Fisheries Policy and
Management: A Binational Perspective, East Lansing, MI: MSU Press, 865 pp.
Taylor, W.W., Smalle, M.A. & Freeberg, M.H. (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., Shuter, B.J. & Minns, C.K. (2009) Forecasting impacts of climate change on
Great Lakes surface water temperatures. Journal of Great Lakes Research, 35, 454-463.
Valdimarsson, G. & Metzner, R. (2011) Inside the Framework: Making a Living from Fisheries.
In: W.W. Taylor, A.J. Lynch & M.G. Schechter (eds.) Sustainable Fisheries: Multi-Level
Approaches to a Global Problem. Bethesda, MD: American Fisheries Society.
Verdonschot, P.F.M. & Nijboer, R.C. (2002) Towards a decision support system for stream
restoration in the Netherlands: an overview of restoration projects and future needs.
Hydrobiologia, 478, 131-148.
Walters, C. (1986) Adaptive Management of Renewable Resources, New York, N.Y.: McMillan
Press 374 pp.

68

Wilby, R.L., Orr, H., Watts, G., Battarbee, R.W., Berry, P.M., Chadd, R., Dugdale, S.J., Dunbar,
M.J., Elliott, J.A., Extence, C., Hannah, D.M., Holmes, N., Johnson, A.C., Knights, B.,
Milner, N.J., Ormerod, S.J., Solomon, D., Timlett, R., Whitehead, P.J. & Wood, P.J.
(2010) Evidence needed to manage freshwater ecosystems in a changing climate: Turning
adaptation principles into practice. Science of the Total Environment, 408, 4150-4164.

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CHAPTER 3: PERCEPTIONS OF MANAGEMENT AND WILLINGNESS TO USE
DECISION SUPPORT: INTEGRATING THE POTENTIAL IMPACTS OF CLIMATE
CHANGE ON THE LAKE WHITEFISH (COREGONUS CLUPEAFORMIS) FISHERY
INTO HARVEST MANAGEMENT IN THE 1836 TREATY WATERS OF LAKES
HURON, MICHIGAN, AND SUPERIOR

Lynch, A. J., W. W. Taylor, A. M. McCright. In Prep. Perceptions of management and
willingness to use decision support: Integrating the potential impacts of climate change
on the Lake Whitefish (Coregonus clupeaformis) fishery into harvest management in the
1836 Treaty Waters of Lakes Huron, Michigan, and Superior.

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Abstract
Decision-support tools are designed to aid decision making by systematically
incorporating multiple sources of information, accounting for uncertainty in estimates, or
facilitating evaluation of trade-offs between alternatives. However, if they are not implemented
with investment from the users, decision-support tools fail to achieve their intended goal. This
study investigated the perceptions of fishery management and the willingness to use decisionsupport tools for fishery management. The survey recommendations informed the development
of a decision-support tool for the potential impacts of climate change Lake Whitefish
(Coregonus clupeaformis) recruitment in the 1836 Treaty Waters of Lakes Huron, Michigan, and
Superior, which hosts a significant portion of the economically, socially, and ecologically
important Lake Whitefish fishery. Climate change is expected to influence Lake Whitefish
recruitment because recruitment has been linked to temperature, wind, and ice cover, variables
all projected to alter with climate change. We surveyed researchers, fishery managers, and
fishermen affiliated with the fishery to document perceived barriers and opportunities to
developing a decision-support tool from a Lake Whitefish climate-recruitment projection model.
Survey respondents indicated that decision-support tools can be useful to inform management.
But, they highlighted a number of barriers for implementation of decision-support tools,
including lack of political will and uncertainty in decision-support outputs. These considerations
were incorporated into the design of a decision-support tool for Lake Whitefish in the 1836
Treaty Waters which will provide guidance on anticipated changes in recruitment with a
changing climate to ensure a prosperous and sustainable fishery, now and in the future.

KEYWORDS: decision-support tools, fishery management, Lake Whitefish (Coregonus
clupeaformis), recruitment, climate change, 1836 Treaty Waters

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Introduction
The purpose of decision-support tools is to make scientific knowledge more accessible to
decision makers (Moser, 2012). Management decisions will be made, with or without the input
of adequate science. In order to be useful, decision-support tools must addresses managementinformative questions and communicate information to decision makers in a clear, logical
manner. To do this most effectively, decision-support tools must be documented and designed
with the input from the potential users.
Lake Whitefish and climate change decision support
Lynch et al. (2012) suggested that decision-support tools could be useful for informing
managers, fishers, and other stakeholders about the potential impacts of climate change on the
Lake Whitefish (Coregonus clupeaformis) fishery in the Laurentian Great Lakes. This fishery is
the largest and most economically valuable commercial fishery in the upper Laurentian Great
Lakes (Madenjian et al., 2006; Ebener et al., 2008) and there is concern that climate change
could impact the fishery because recruitment of fish to a harvestable size has previously been
linked to climatic conditions (Miller, 1952; Christie, 1963; Lawler, 1965; Taylor et al., 1987a;
Freeberg et al., 1990; Lynch et al., 2010).
Approximately one quarter of the total Lake Whitefish harvest in the upper Great Lakes
comes from The 1836 Treaty Waters of Lakes Huron, Michigan, and Superior (Figure 3.1; M.
Ebener, Chippewa Ottawa Resource Authority, personal communication). Lynch et al. (Chapter
3) suggest that including climate variables, specifically temperature, wind, and ice cover, in
stock-recruitment models results in better model fit to the recruitment data than models without
climate variables for a majority of the 1836 Treaty Waters management units. Projecting those
climate recruitment relationships with the Coupled Hydrosphere-Atmosphere Research Model

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FIGURE 3.1. Land and water territories ceded by the Chippewa and Ottawa nations in the 1836
Treaty of Washington and Lake Whitefish (Coregonus clupeaformis) management units
managed under the 2000 Consent Decree. For interpretation of the references to color in this and
all other tables and figures, the reader is referred to the electronic version of this dissertation.
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(Lofgren, 2004), Lynch et al. (Chapter 4) found potential for increased Lake Whitefish
recruitment with climate change, if stock size was held constant.
The goal of this study was to investigate the perceptions of Lake Whitefish management
and willingness to use decision-support tools. The outcomes informed the design of a decisionsupport tool from the Lynch et al. (Chapter 4) model to inform management of Lake Whitefish
in the 1836 Treaty Waters of the potential implications of climate change.
Methods
Study location fishery management
The 1836 Treaty Waters of Lakes Huron, Michigan, and Superior are managed by the
Chippewa Ottawa Resource Authority (CORA), a cooperative agency among the Bay Mills
Indian Community, Grand Traverse Band of Ottawa and Chippewa Indians, Little River Band of
Ottawa Indians, Little Traverse Bay Band of Odawa Indians, and the Sault Ste. Marie Tribe of
Chippewa Indians. In accordance with the 2000 Consent Decree, CORA is advised by a
Technical Fisheries Committee and Modeling Sub-Committee to set harvest quotas. In some of
the management units, CORA co-manages with the Michigan Department of Natural Resources.
The 2000 Consent Decree directs managers in the 1836 Treaty Waters to maintain profitable and
sustainable harvest of Lake Whitefish.
Lake Whitefish management and decision-support survey design
We designed a two pronged survey to document perceived barriers and opportunities for
implementing a decision-support tool for Lake Whitefish given changes to climate variables,
specifically temperature, wind, and ice cover, in the 1836 Treaty Waters. We chose to use a
detailed consent form (Appendix 3.1) to fully explain the purposes of the project to participants
and a short survey to encourage broader participation (Appendix 3.2). In addition to five
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demographic questions, we included five modified Likert-scale questions (Likert, 1932) and two
open-ended questions to allow respondents the opportunity to elaborate, if desired. The survey
included a sequence of questions related to current Lake Whitefish management:
•

How satisfied are you with the management of Lake Whitefish in the 1836 Treaty
Waters?

•

What could improve current management of Lake Whitefish in the 1836 Treaty
Waters?

•

What issues are important for the future management of Lake Whitefish in the
1836 Treaty Waters?

and a sequence of questions on decision-support tools:
•

Can decision-support tools be useful for fisheries management?

•

What are barriers to use of decision-support tools in fisheries management?

•

How well is science integrated into Lake Whitefish management in the 1836
Treaty Waters?

•

What factors are important for integrating science into Lake Whitefish
management in the 1836 Treaty Waters?

We specifically designed the survey to target Lake Whitefish biologists, managers, and
fishers as survey respondents because they are the most likely potential users of a decisionsupport tool related to Lake Whitefish management in the 1836 Treaty Waters because they
influence, define, and accept Lake Whitefish management decisions. We distributed the surveys
to the 1836 Treaty Waters Technical Fisheries Committee, Modeling Sub-Committee, Bay Mills
Indian Community Conservation Committee, and Great Lakes Fishery Commission Upper Great
Lakes Committee Meeting participants. We distributed the survey at events where these

75

potential participants were present, in concert with a presentation on the results of the Lynch et
al. (Chapter 4) projection model of Lake Whitefish recruitment with climate change. We
intentionally linked the survey with this modeling project to provide context for the type of
scientific information that could be used in a climate change decision-support tool.

Lake Whitefish management and decision-support survey analysis
The quantitative data from the Likert-scale questions on perceptions of Lake Whitefish
management and willingness to use decision-support tools were compiled and evaluated using
descriptive statistics (e.g., count, mean, mode). Descriptive statistics are useful for examining
the patterns in the data and summarizing the survey samples (Mann, 2012). The qualitative data
from the survey complimented the quantitative data by putting the quantitative responses in
context. We grouped the open-ended comments by topic and used them to assist with
explanatory patterns in the quantitative survey responses. These comments provide rationale for
quantitative survey responses which can inform management of Lake Whitefish in the 1836
Treaty Waters and the development of a climate change decision-support tool for Lake Whitefish
in the 1836 Treaty Waters.
The Michigan State University Committee on Research involving Human Subjects (IRB#
x12-1284e) reviewed the methods and questions posed in this study and deemed them exempt
status in accordance with federal regulations.
Results
The survey was completed by 31 individuals between April 2013 and October 2013.
Thirty of the 31 survey participants were male. Seven percent of respondents were 18-29; 42%
were 30-49; 35% were 50-64; and 9% were 65+ (Figure 3.2; 7% did not indicate age).

76

Age distribution of survey respondants
14

number of survey respondents

12
10
8

not listed
fishery

6

management
research

4
2
0
18-29

30-49

50-64
Age

65+

FIGURE 3.2. Age distribution of survey respondents by primary affiliation.

77

not listed

Affiliation with the Lake Whitefish fishery
12

number of survey respondents

10

8

6

fishery educator
extension
management
attorney

4

fishery
biologist
research
2

0

FIGURE 3.3. Lake Whitefish (Coregonus clupeaformis) fishery affiliation of survey respondents by affiliation. Note that respondents
could select more than one affiliation.
78

Satisfaction with Lake Whitefish management
18

number of survey respondents

16
14
12
10

not listed
fishery

8

management

6

research

4
2
0
Mostly
Slightly
Neither
dissatisfied dissatisfied satisfied nor
dissatisfied

Slightly
satisfied

Mostly
satisfied

Completely
satisfied

No opinion

FIGURE 3.4. Satisfaction level of survey respondents with current management of the Lake Whitefish (Coregonus clupeaformis)
fishery in the 1836 Treaty Waters by primary affiliation.

79

TABLE 3.1. Survey respondent recommendations for improving management of Lake
Whitefish (Coregonus clupeaformis) in the 1836 Treaty Waters, grouped by topic.
Research needs
Population models
• "Better population models, if possible."
• "Collect data from all management units."
• "Functioning population models."
• "Knowledge of stock-specific characteristics including size-at-age, maturity
schedules, weight at age, age composition structure to compliment mixed
stocks analysis results."
• "More accurate models."
• "More comprehensive population level data."
Recruitment estimation
• "A better understanding of recruitment dynamics for all Coregonines."
• "A good pre-recruit survey for scaling the SCAA predictions."
• "Ability to plan ahead in terms of management based on predictions of year
class would be great."
• "Better and more timely estimates of year class strength."
• "Better estimates of recruitment!"
• "Better estimates/predictions of recruitment."
• "Better knowledge of early life histories (young fish)."
• "Better recruitment estimates."
• "Better understanding of recruitment indices."
• "Better understanding of recruitment."
• "Better understanding of recruitment."
• "Better understanding of what controls recruitment now that ice cover is
infrequent."
• "Without question, a reliable predictive model of recruitment (and I'm not
just saying this)."
Additional data needs
• "[Consideration for] multi-species fisheries!"
• "Better estimates of mature mortality lakewide."
• "Better understanding of mechanistic relationships between fisheries
population and environmental/food web variables."
• "Fishery independent survey data."
• "Improved/effective fishery independent lake whitefish surveys to track
annual changes in abundance and age structure (in some areas)."
Management recommendations
Cooperation
• "[Add a] state fisher person on TFC."
• "Continue cooperation between the tribal and state fisheries management
agencies [to] plan ahead for the next consent agreement."
• "Enhanced state tribal regulations and cooperation."
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TABLE 3.1 (cont’d).
Cooperation (cont’d)
• "Political will to pursue sustainable management."
• "Stakeholder buy-in on scale and severity of issue."
• "While the biologists get along well, once you bring the attorneys and the
various party leaders in, things become more contentious."
Allocation
• "Allocating adequately high TAC while still preserving stock."
• "Application of conditional constant catch policies."
• "Backing off 'the edge' of sustainability to a more 'optimal' yield rather than
'maximum' yield approach."
• "Expand the fishery itself."
• "To be assured that we get all of our treaty water returned."
Funding considerations
• "More funding for research and studies to increase staff and equipment for
biological staff."
• "Having our own [tribal] hatchery."
• "There are data gaps which need to be addressed; Staffing reductions are
causing [data gaps]."
• "Increased sampling."
• "More funding for fisheries support staff."
• "Less costly methods than SCAA for estimating allowable catch."
Invasive species control
• "Ballast water exchanges farther down river."
• "Controlling the invasive species - like lamprey, zebra mussels, Eurasian Ruffes."
• "Reduce lamprey mortality in some management units."
• "Better understanding of the influence of invasive species on sustainability of
stocks."
• "Better estimates of lamprey mortality lakewide."

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Eleven individuals self-identified as fishery managers; eight as fishery biologists; and 21
identified as being affiliated with subsistence or commercial fishing (Figure 3.3; note that survey
respondents could identify with more than one category). Among the survey participants,
experience with the Lake Whitefish fishery ranged from less than a year to over 60 years (fourth
generation in the fishery).
Perceptions of Lake Whitefish management
Survey respondents were predominately satisfied with current management of the Lake
Whitefish fishery in the 1836 Treaty Waters. Twenty-three of the 31 respondents identified with
the slightly, mostly, or completely satisfied categories (Figure 3.4). When asked what could
improve current management of Lake Whitefish in the 1836 Treaty Waters, respondents made
suggestions that fit broadly in the following categories: 1) research needs; 2) management
recommendations; 3) funding considerations; and, 4) invasive species control (Table 3.1).
The survey asked participants to indicate the importance of 11 issues selected by
researchers and managers as potentially relevant to the future management of Lake Whitefish in
the 1836 Treaty Waters:
•

Allocation

•

Bycatch

•

Climate change

•

Communication

•

Habitat loss or modification

•

Human population growth

•

Invasive species

•

Land-use changes
82

•

Market forces

•

Overexploitation

•

Water quality and quantity

There was an overall tendency in the survey responses towards listing categories as more
important than not important on a four item Likert scale but there was consistency across
participants in the designation of important and non-important issues (Figure 3.5). Some of the
issues that were stressed in current management, specifically allocation and invasive species
control, were also highlighted by survey respondents as issues of future importance. Invasive
species was listed as the most important factor for the future management of Lake Whitefish (27
respondents listed it as very or moderately important), followed by bycatch and market forces
(26 respondents, each); and allocation, climate change, communication between managers and
fishermen (25 respondents, each). Human population growth was overwhelming considered the
least important issue (12 respondents listed it as not important), followed by land-use change (8
respondents), and overexploitation (5 respondents).
Willingness to use decision-support tools
While a large majority of survey respondents (26) believed that science is moderately,
well, or very well integrated into the management process (Figure 3.6), respondents suggested
that improvements can be made. Again, showing a tendency towards listing categories as more
important than not important, all but one of the respondents listed all seven factors identified to
facilitate the integration of science into Lake Whitefish management (addressing significant
management problems; being transparent with research methods and analyses; communicating
clearly to fishers and/or managers; creating decision-support tools; ensuring incorporation into
long-term management; involving fishers and/or managers in the research process; and,
83

Importance of issues to Lake Whitefish management
Don't know

No opinion

20-30
10-20

Not important

0-10
Moderately important

Very important

FIGURE 3.5. Heat map of survey responses to the importance level (not important, moderately important, very important) for 11
issues to the future management of Lake Whitefish in the 1836 Treaty Waters: allocation, bycatch, climate change, communication,
habitat loss or modification, human population growth, invasive species, land-use changes, market forces, overexploitation, and water
quality and quantity. A heat map is three dimensional with the height and color indicating intensity of importance for each issue: green
= 20-30 respondents, red = 10-20 respondents, and blue = 0-10 respondents.
84

Integration of science into Lake Whitefish management
12

number of survey respondents

10

8
not listed

6

fishery
management
research

4

2

0
Very well

Well

Moderately

Poorly

Very poorly

Don’t know/no
opinion

FIGURE 3.6. Survey responses to how well integrated science is into Lake Whitefish (Coregonus clupeaformis) management in the
1836 Treaty Waters (very well; well; moderately; poorly; very poorly; don’t know/no opinion) by primary affiliation.
85

Factors to integrate science into Lake Whitefish management
Don't know/no opinion

Not important

20-30
10-20

Moderately important

0-10

Very important

FIGURE 3.7. Heat map of survey responses to the importance level (not important, moderately important, very important) for seven
factors to facilitate integration of science into Lake Whitefish management in the 1836 Treaty waters: addressing significant
management problems; being transparent with research methods and analyses; communicating clearly to fishers and/or managers;
creating decision-support tools; ensuring incorporation into long-term management; involving fishers and/or managers in the research
process; and, providing recommendations within the structure of current management. A heat map is three dimensional with the height
and color indicating intensity of importance for each issue: green = 20-30 respondents, red = 10-20 respondents, and blue = 0-10
respondents.
86

Usefulness of decision-support tools
for Lake Whitefish management
16

number of survey respondents

14
12
10
not listed

8

fishery
management

6

research

4
2
0
Completely
agree

Somewhat agree Neither agree
nor disagree

Somewhat
disagree

Completely
disagree

Don't know/no
opinion

FIGURE 3.8. Survey responses to the usefulness of decision-support tools to Lake Whitefish (Coregonus clupeaformis) management
in the 1836 Treaty Waters (completely agree; somewhat agree; neither agree nor disagree; somewhat disagree; completely disagree;
don’t know/no opinion) by primary affiliation.
87

TABLE 3.2. Survey respondent listed barriers implementing decision-support tools in Lake
Whitefish (Coregonus clupeaformis) management in the 1836 Treaty Waters, grouped by topic.
Political will
Communication
• "Communication and understanding."
• "Difficult to communicate with fishers."
• "Direct interaction with the fishers [to] give more "real time" data on what
is going on with fishing capacity."
• "Poor communication."
Control
• "I think people are generally unwilling to relinquish control and allow
objective tools to weigh in on decisions. Management is largely politics,
and the objective decision is often not the preferred decision."
• "Political process."
• "Reliance on single method for decision-making."
Participation
• "Acceptance of the process."
• "Agency buy-in."
• "Buy-in."
• "Lack of participation at all levels of interested parties."
• "Making sure participants are objective in their thinking."
• "Making sure you get management and fishermen buy in before getting too
far. Don't want to finish only to have them reject it for lack of involvement."
• "Participation by certain stakeholder groups."
Unfamiliarity
• "Has not been widely used in the past and may not be readily accepted in
the future."
• "Misunderstanding about function and application of tools."
• "Understanding of process and data/fisheries management, etc."
Data issues
Uncertainty
• "Adequate fisheries information system (information system does not equal
common or even centralized database)."
• "Appropriate underlying models."
• "Do they address real world situation?"
• "It's sometimes easier to assume we have one outcome; it makes action
easier. Uncertainty is messy and often requires making qualitative
judgments, which is hard."
• "Lack of consensus on 'unknowns'."
• "Model assumptions and over generalities."
• "Models represent a larger area than what is actually being used."
88

TABLE 3.2 (cont’d).
Uncertainty (cont’d)
• "Over parameterization."
• "Requir[ing] a lot of information."
• "The utility of such tools is somewhat dependent on the data inputs used to
design the tool. If appropriate data are used, then the tool can be robust."
• "There are still data limitations (data gaps) to deal with in the current models."
• "Too variable."
• "Unclear objectives."
• "Whether you have the equipment for the right places; whether the fish are
going to be where you think they should be."
Logistical considerations
• "Huge investment to run models/tool."
• "Implementation (no agency expertise in decision-support tools)."
• "Time."
• "Using SCAA is almost too costly for agencies; [They require] intense
annual levels of stock assessment."

89

providing recommendations within the structure of current management) as moderately or very
important (Figure 3.7). Clear communication of research was the most important factor (23
respondents listed it as very important) followed by addressing significant management problems
(19 respondents).
Decision-support tools can assist with both clearer communication and addressing
significant management problems. In the listing of factors identified to facilitate integration of
science into Lake Whitefish management, 5 respondents also identified decision-support tools as
very important and 20 listed them as moderately important. When directly asked, survey
respondents overwhelmingly agreed that decision-support tools can assist management for Lake
Whitefish in the 1836 Treaty Waters. Twenty-four of the 31 respondents somewhat or
completely agreed that decision-support tools can be useful for Lake Whitefish management and
no respondents disagreed (Figure 3.8). The respondents qualified the utility of decision-support
tools with potential barriers to implementation that fit broadly into the following categories: 1)
political will, including communication, decision control, participation, and uneasiness with
using decision-support tools, and, 2) data issues, including uncertainty and logistical
considerations, such as time and cost to develop harvest allocations (Table 3.2).
Discussion
The survey responses were well representative of the three major potential user groups
for a decision-support tool concerning Lake Whitefish in the 1836 Treaty Waters: fishers,
managers, and researchers. Our targeted survey distribution ensured almost complete
representation of managers in addition to representative samples of fishermen and researchers.
While researchers and managers are well distributed among the younger age brackets, 80% of
those identifying primarily with the fishery were over 50 years old (Figure 3.2). This age

90

distribution may be a significant concern for management engagement and the longevity of the
fishery because the younger fishers are either not actively involved in management or not present
in the fishery.
Perceptions of Lake Whitefish management
Overall, survey participants were more satisfied than dissatisfied with management
(Figure 3.4). Those affiliated with the fishery had the widest range (from “mostly dissatisfied”
to “completely satisfied”) and, perhaps not surprisingly, managers were the most satisfied with
the work they were conducting (73% of surveyed managers were “mostly satisfied”).
Nonetheless, all survey respondents had recommendations for ways to improve Lake Whitefish
management in the 1836 Treaty Waters (Table 3.1). These recommendations fell broadly into
four broad categories:
•

Research needs

•

Management recommendations

•

Funding considerations

•

Invasive species control

Research recommendations primarily focused on the need for better population models
and better estimates of recruitment. “Without question, a reliable predictive model of
recruitment,” wrote one respondent; “better population models,” wrote another.
The management recommendations focused on the need for better cooperation between
managers and fishers and some even gave specific suggestions for allocation changes. These
comments well-aligned with the importance of allocation and communication indicated in Figure
3.5. Survey respondents cited “political will” and “stakeholder buy-in” to support more effective
management. Without public input, management measures have low probability of acceptance

91

(Decker et al., 2006). The additional layer of complication for the 1836 Treaty Waters is the
tribal-state management dynamic. “Continued cooperation between the tribal and state fisheries
management agencies” will be particularly necessary with the upcoming reauthorization of the
2000 Consent Decree. Allocation will also surely be a topic with reauthorization of the Consent
Decree. Survey recommendations for allocation were diverse; from “expand[ing] the fishery” to
focusing on “more ‘optimal’ yield rather than ‘maximum’ yield;” with optimal yield, effort is
maximized rather than yield. As shown from these recommendations, meaningful public
integration and management cooperation will be necessary to make culturally and socially
acceptable allocation decisions which also ensure the resilience and sustainability of Lake
Whitefish populations and their ecosystems through long-term, rather than short-term planning.
As with many management needs, changes generally require funding. The survey
recommendations emphasized the need for more “staff and equipment for biological staff,”
“increased sampling,” and even suggested considering adding a tribal hatchery or considering
less costly methods than statistical catch-at-age models to determine harvest quotas. While the
survey respondents agreed on the need for funding, the diverse suggestion of needs highlighted
that they do not all agree on the same management objectives.
One item the survey respondents could agree on was that invasive species is an important
concern to Lake Whitefish management. Invasive species was the most important issue listed in
Figure 3.5 and numerous comments in Table 3.1 concerned invasive species. The survey
comments underscored that invasive species are still an unknown with respect to Lake Whitefish
management. Sea Lamprey (Petromyzon marinus), in particular, parasitize Lake Whitefish and
the estimates of Sea Lamprey induced mortality are poor. M. Ebener (Chippewa Ottawa
Resource Authority, personal communication) hypothesized, for example, that increased
mortality on Lake Whitefish in Lake Huron is a result of stocking an alternative strain of Lake
92

Trout (Salvelinus namaycush), which has a depth preference beyond that of Sea Lamprey. Lake
Whitefish may serve as an alternative host in the absence of Lake Trout availability and, as a
result, may be subject to greater parasitism, reduced health, reduced fitness, reduced recruitment
and, ultimately, reduced harvest.
Willingness to use decision-support tools
As evidenced by the survey recommendations for improvement, Lake Whitefish
management in the 1836 Treaty Waters is no simple task, with or without considering climate
change. Perhaps not surprisingly, researchers thought science is well integrated into
management more than managers do and fishers were split on their perception (Figure 3.6).
Nonetheless, they all recognized the importance of considering science and they overwhelming
agree that decision-support tools can be useful in assisting management (Figure 3.8).
Respondents cited political will and data issues as broad-scale potential barriers to the use of
these tools (Table 3.2).
Political will pertains to the support needed for acceptance of decision-support tools by
users, namely managers and fishers. The research cannot be applied to management if it remains
only in the research arena. To effectively garner this political will, decision-support tools must
overcome control barriers, lack of participation, poor communication, and the uneasiness of
potential users because of unfamiliarity with the tools. Survey participants continuously noted
the importance of communication between fishers and managers (Figure 3.7; Table 3.2).
Managers can express an “unwillingness to relinquish control and allow objective tools to weigh
in on decisions,” especially if the developed tool is poorly communicated and they do not
understand the “function and application.”

93

Unfamiliarity can often result from data barriers to the development of decision-support
tools, in particular uncertainty and logistical considerations in designing decision-support tools.
One respondent questioned if decision-support tools can “address real world situations.”
Another believed that the utility of decision-support tools is “dependent upon the data inputs
used to design the tool[s].” And logistically, decision-support tools require “time,” a “huge
investment to run,” and “expertise” to implement. These are all very important concerns to
effective implementation.
Communicating the objectives and process to design a decision-support tool to managers
and fishermen so that they can participate in the design process will help ensure proper
utlization. While uncertainty in the outputs and assumptions in the methods may be broad,
uncertainty can sometimes serve as an impetus for contingency planning (Marx and Weber,
2012). An informed decision, even if it is qualified by significant assumptions, is generally
better than an uninformed decision.
Integration into climate change decision support
Climate change poses to be a significant, long-term influence on the biological,
economic, and social functioning of the Great Lakes fisheries ecosystems (Lynch et al., 2010).
But, there is no “clear, natural, or easy fit” between climate change research and decision making
because climate change impacts will be diverse (Moser, 2012). Unlike, for example, aquatic
invasive species, which have immediate and obvious effects on ecosystems and economies,
climate change effects will be long-term (Lynch et al., 2010). The timescale of these effects
makes climate change a particularly difficult concept for the public to grasp. This is where
decision support can be most useful. Decision-support tools can translate and communicate
available science to improve the abilities of decision makers to make informed decisions

94

(Scheraga, 2012). For example, Winkler et al. (2012) developed a climate change decisionsupport tool, through the Pileus Project, to examine the potential impacts of climate change on
the yield of Michigan tart cherries. Through this online tool, farmers and municipal managers
can make long-term decisions in anticipation of the potential impacts of climate change on the
industry.
The recommendations from this study are being incorporated into the development of a
climate change decision-support tool, the Lynch et al. (Chapter 4) climate-recruitment model.
This tool is housed on the Michigan Sea Grant website (http://www.miseagrant.umich.edu/) with
an interactive, user-friendly interface to display the recruitment projections with anticipated
climate change. We anticipate that this tool will be used to inform adaptive decision making for
Lake Whitefish fishers and fishery managers as well as to educate the public about the potential
impacts of climate change on this important fishery to the Great Lakes region. It may also serve
as a model for other climate change issues in fisheries management beyond the Great Lakes
basin. Ultimately, this tool aims to support informed decision making for sustainable and
prosperous fishery resources and coastal communities by providing guidance on the potential
impacts of climate change to recruitment of Lake Whitefish.

Acknowledgments
Dave Caroffino, Mark Ebener, Mark Holey, Ron Kinnunen, Eric MacMillan, Paul
Ripple, So-Jung Youn, CSIS, MIRTH, 1836 Treaty Waters Technical Fisheries Committee and
Modeling Subcommittee, and the survey participants without whom this analysis could not exist.
Funding for this project was provided by a Michigan Sea Grant Coastal Communities
Development Grant.

95

APPENDICES

96

Appendix 3.1. Survey Consent Form

Lake whitefish and climate change: On-Site CONSENT FORM
Improving decision-support tool design: case study on lake whitefish (Coregonus
clupeaformis) and climate change
You are being asked to take part in a research study on how to improve the design of fisheries
decision-support tools. Decision-support tools aid decision making by systematically
incorporating information, accounting for uncertainties, and/or facilitating evaluation of tradeoffs between alternatives. However, if they are not implemented properly, decision-support
tools can fail to achieve their intended goal.
Please read the information listed below carefully.
RESEARCH OBJECTIVE: This project will investigate perceptions and recommendations
for how to successfully develop a fisheries decision-support tool. We seek to document
perceived barriers and opportunities to implementing a decision-support tool for lake whitefish
and climate change.
YOUR ROLE: If you choose to participate, you will be asked to fill out a voluntary,
anonymous, 10 minute survey on your perceptions of lake whitefish management and decision
support. Your answers will be confidential. Your participation in the project is completely
anonymous, voluntary, uncompensated, and will NOT impact your involvement with the lake
whitefish fishery and its management. Your participation will assist with the development of
a decision-support tool to inform lake whitefish management given a changing climate. There
is no penalty or loss of benefits if you chose not to participate.
QUESTIONS? Please ask any questions you have now. If you have any questions later
about the research study, please contact Abby Lynch (lynchabi@msu.edu), Bill Taylor
(taylorw@msu.edu), or Aaron McCright (mccright@msu.edu). If you have any questions or
concerns about your role and rights as a research participant, would like to obtain information
or offer input, or would like to register a complaint about this research study, you may contact,
anonymously if you wish, the Michigan State University Human Research Protection Program at
PHONE: 517-355-2180, FAX: 517-432-4503, EMAIL: irb@msu.edu, or REGULAR MAIL: 207 Olds
Hall, MSU, East Lansing, MI 48824. Please ask any questions you may have before agreeing to
participate in this study. Thank you for your contribution to this important study.
STATEMENT OF CONSENT: I have read the above information and have received answers to any
questions I asked. I consent to take part in this study.
Your Signature ___________________________________ Date ________________________
Your Name (printed) ____________________________________________________________
Are you willing to be contacted for a project follow-up? If so, what is the best way to reach you?
□ email: _______________________________
□ phone: ______________________________
□ mail: ________________________________
97

Appendix 3.2. Survey instrument

SURVEY
LAKE WHITEFISH MANAGEMENT:
1. How satisfied are you with the management of lake whitefish in the 1836 Treaty Waters?

No
opinion

Completely
dissatisfied

Mostly
dissatisfied

Slightly
dissatisfied

□

□

□

□

Neither
satisfied nor
dissatisfied
□

Slightly
satisfied

Mostly
satisfied

Completely
satisfied

□

□

□

2. What issues are important for the FUTURE management of lake whitefish in the 1836 Treaty Waters?

Allocation
Bycatch
Climate change
Communication between
managers and fishermen
Habitat loss or modification
Human population growth
Invasive species
Land-use change
Market forces
Overexploitation
Water quality and quantity
issues

No opinion

Don’t know

Not important

□
□
□

□
□
□

□
□
□

Moderately
important
□
□
□

□

□

□

□

□

□
□
□
□
□
□

□
□
□
□
□
□

□
□
□
□
□
□

□
□
□
□
□
□

□
□
□
□
□
□

□

□

□

□

□

98

Very important
□
□
□

3. What could improve CURRENT management of lake whitefish in the 1836 Treaty Waters?

DECISION SUPPORT:
*Decision-support tools aid decision making by systematically incorporating information, accounting for uncertainties, and/or
facilitating evaluation of trade-offs between alternative choices.
4. Can decision-support tools be useful for fisheries management?
Don’t know/
no opinion
□

Completely
disagree
□

Somewhat
disagree
□

Neither agree nor
disagree
□

Somewhat agree

Completely agree

□

□

5. What are barriers to use of decision-support tools in fisheries management?

6. How well is science integrated into lake whitefish management in the 1836 Treaty Waters?
Don’t know/
no opinion
□

Very poorly

Poorly

Moderately

Well

Very well

□

□

□

□

□

99

7. What factors are important for integrating science into lake whitefish management in the 1836 Treaty Waters?

Addressing significant
management problems
Being transparent with
research methods and
analyses
Communicating clearly to
fishers and/or managers
Creating decision-support
tools
Ensuring incorporation into
long-term management
Involving fishers and/or
managers in the research
process
Providing recommendations
within the structure of
current management

Don’t know/
no opinion

Not important

Moderately
important

Very important

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

1) _____________________________________________
2) _____________________________________________

Other (please list):

3) _____________________________________________

100

DEMOGRAPHICS:
1. Gender:
Female
Male
2. Age:
18-29
30-49

5. Occupation? Check all that apply.

50-64

Fish distributor

65+

Fishery manager
Fish processor

3. How many years have you lived in the Great Lakes
basin?

Fish retailer

_______years

Gill-net fisher
Trap-net fisher

4. How many years have you worked with lake whitefish?

Other: ________________

_______years

101

LITERATURE CITED

102

LITERATURE CITED

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,
597-646.
Decker, D. J., Jacobson, C. A. & Brown, T. L. (2006). Situation-specific "Impact dependency" as
a determinant of management acceptability: Insights from wolf and grizzly bear
management in Alaska. Wildlife Society Bulletin 34, 426-432.
Ebener, M. P., Kinnunen, R. E., Schneeberger, P. J., Mohr, L. C., Hoyle, J. A. & Peeters, P.
(2008). Management of Commercial Fisheries for Lake Whitefish in the Laurentian Great
Lakes of North America. In International Governance of Fisheries Ecosystems: Learning
from the Past, Finding Solutions for the Future (Schechter, M. G., Leonard, N. J. &
Taylor, W. W., eds.), pp. 99-143. Bethesda, Maryland: American Fisheries Society Press.
Freeberg, M. H., Taylor, W. W. & Brown, R. W. (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, 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,
1197-1227.
Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology 22 140,
55.
Lofgren, B. M. (2004). A model for simulation of the climate and hydrology of the Great Lakes
basin. Journal of Geophysical Research-Atmospheres 109.
Lynch, A. J., Taylor, W. W., Beard, T. D. & Lofgren, B. M. (Chapter 4). Projected changes in
Lake Whitefish (Coregonus clupeaformis) recruitment with climate change in the 1836
Treaty Waters of Lakes Huron, Michigan, and Superior.
Lynch, A. J., Taylor, W. W. & Smith, K. D. (2010). The influence of changing climate on the
ecology and management of selected Laurentian Great Lakes fisheries. Journal of Fish
Biology.
Lynch, A. J., Varela-Acevedo, E. & Taylor, W. W. (2012). The need for decision-support tools
for a changing climate: application to inland fisheries management. Fisheries
Management and Ecology.
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. (2006). Evaluation of a
lake whitefish bioenergetics model. Transactions of the American Fisheries Society 135,
61-75.

103

Mann, P. S. (2012). Introductory Statistics. Hoboken, NJ: Wiley.
Marx, S. M. & Weber, E. U. (2012). Decision Making under Climate Uncertainty: The Power of
Understanding Judgement and Decision Processes. In Climate Change in the Great Lakes
Region (Dietz, T. & Bidwell, D., eds.), pp. 99-128. East Lansing, Michigan: Michigan
State University Press.
Miller, R. B. (1952). The relative sizes of whitefish year classes as affected by egg planting and
the weather. Journal of Wildlife Management 16, 39-50.
Moser, S. (2012). The Contextual Importance of Uncertainty in Climate-Sensitive DecisionMaking: Toward an Integrative Decision-Centered Screening Tool. In Climate Change in
the Great Lakes Region (Dietz, T. & Bidwell, D., eds.), pp. 179-212. East Lansing,
Michigan: Michigan State University Press.
Scheraga, J. D. (2012). Linking Science to Decision Making in the Great Lakes Region. In
Climate Change in the Great Lakes Region (Dietz, T. & Bidwell, D., eds.), pp. 213-230.
East Lansing, Michigan: Michigan State University Press.
Taylor, W. W., Smale, M. A. & Freeberg, M. H. (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.
Winkler, J. A., Bisanz, J. M., Guentchev, G. S., Piromsopa, K., van Ravensway, J., Prawiranata,
H., Torre, R. S., Min, H. K. & Clark, J. (2012). The Development and Communication of
an Ensemble of Local-Scale Climate Scenarios: An Example from the Pileus Project. In
Climate Change in the Great Lakes Region (Dietz, T. & Bidwell, D., eds.), pp. 231-248.
East Lansing, Michigan: Michigan State University Press.

104

CHAPTER 4: PROJECTED CHANGES IN LAKE WHITEFISH (COREGONUS
CLUPEAFORMIS) RECRUITMENT WITH CLIMATE CHANGE IN THE 1836
TREATY WATERS OF LAKES HURON, MICHIGAN, AND SUPERIOR

Lynch, A. J., W. W. Taylor, T. D. Beard, and B. M. Lofgren. In Prep. Projected changes in Lake
Whitefish (Coregonus clupeaformis) recruitment with climate change in the 1836 Treaty
Waters of Lakes Huron, Michigan, and Superior.

105

Abstract
Lake whitefish (Coregonus clupeaformis) is an ecologically, culturally, and economically
important species to the Laurentian Great Lakes. Lake Whitefish have been a staple food source
for those in the region for thousands of years and, since 1980, have supported the most
economically valuable (annual catch value ≈ US$16.6 million) and productive (annual harvest ≈
15 million lbs.) commercial fishery in the upper Great Lakes (Lakes Huron, Michigan, and
Superior). Climate change, specifically change in temperature, wind, and ice cover, is expected
to impact the ecology, production dynamics, and value of this fishery, because the success of
recruitment to the fishery has been linked with these climatic factors. We used linear regression
to determine the relationship between fall and spring temperature indices, fall wind speed, winter
ice cover, and Lake Whitefish recruitment in 13 management units located in the 1836 Treaty
Waters. Corrected Akaike’s Information Criterion comparisons indicated that the inclusion of
selected climate variables significantly improved model fit in eight of the 13 management units.
Isolating the climate-recruitment relationship and projecting recruitment using the Coupled
Hydrosphere-Atmosphere Research Model (CHARM) suggested increased Lake Whitefish
recruitment in the majority of the 1836 Treaty Waters management units. These results can
inform adaptive management strategies to ensure a sustainable and prosperous Lake Whitefish
fishery, now and in the future.

KEYWORDS: Lake Whitefish (Coregonus clupeaformis), recruitment, climate change, 1836
Treaty Waters.

106

Introduction
Lake Whitefish (Coregonus clupeaformis) are an ecologically, culturally, and
economically important species in the upper Laurentian Great Lakes (Lakes Huron, Michigan,
and Superior). Ecologically, Lake Whitefish transfer energy from lower, benthic food webs to
the upper, pelagic food webs (Nalepa et al., 2005). Culturally, they have been a staple food
source and traditional icon for aboriginal people in the region for thousands of years (Cleland,
1982). Economically, Lake Whitefish support the largest and most valuable commercial fishery
in the upper Laurentian Great Lakes (annual catch value ≈ US$16.6 million; annual harvest ≈ 15
million lbs.; Madenjian et al., 2006; Ebener et al., 2008).
Observational studies of Great Lakes Lake Whitefish indicate that climatic factors are
also important drivers of recruitment, but these field studies have yet to be scaled up to a
management unit scale (Christie, 1963; Lawler, 1965; Taylor et al., 1987a; Freeberg et al.,
1990). According to these studies, the most influential of climate factors on Lake Whitefish
recruitment include: fall and spring temperature, fall wind, and ice cover.
Temperature
Within the Great Lakes, which are located at either the southern or northern limits for
many resident fish species, temperature is considered one of the most important abiotic factors
governing their distribution and growth (Shuter et al., 2002). The Great Lakes serve as a glacial
refuge for coldwater fish and an expansion zone for warmer water fish (Magnuson et al., 1990).
In comparison to other variables, temperature can have a disproportionate influence on
production, biomass, and abundance of fish within the Great Lakes (Hayes et al., 2009). For
example, in his re-examination of published environment-recruitment correlations, Myers (1998)
found that nearly all of the temperature-recruitment correlations at the southern and northern

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limits of a species range were verified, whereas the re-test of other environment variablerecruitment correlations were not.
Lake Whitefish are no exception, the Great Lakes are at the southern extent of their range
and observational studies suggest that recruitment variability may be linked to fall and spring
temperatures (Christie, 1963; Lawler, 1965; Freeberg et al., 1990; Brown et al., 1993). Christie
(1963) found that cold fall temperatures and warm spring temperatures were correlated with
strong year classes in Lake Ontario and the reverse combination produced weak year classes. He
hypothesized that fast cooling in the fall may encourage peak concentrations of spawning fish at
an optimum temperature (generally below 6oC; Hooper et al., 2001) and slow spring warming
may increase the likelihood of readily available food for hatchlings (Christie, 1963). Lawler
(1965) found a similar correlation in Lake Erie and attributed the relationship to optimal
spawning temperature, incubation, and development, but suggested that a slow increase in spring
temperatures would allow for a prolonged incubation period and full absorption of the yolk sac
so that larvae are larger and more proficient feeders. Freeberg et al. (1990) proposed that the
correlation between recruitment and spring temperatures in Lake Michigan was more indirect,
related to the timing and production of copepod zooplankton, prey for larval Lake Whitefish.
Wind and waves
Wind and wave circulation patterns are transport pathways for ecological systems
(Beletsky et al., 1999), including nutrients and larval fish. While larval fish can have some
directional mobility, fish eggs and larvae are plankton and, consequently, subject to large-scale
wind events, waves, and circulation. Because of their large size, the Great Lakes circulation
patterns more closely resemble a marine system than many smaller freshwater systems. The
Great Lakes have greater thermal inertia and longer wind fetches than smaller lakes (Magnuson

108

et al., 1997). As a result, wind and the resultant waves and current have a larger influence on the
physical environment and biota of the Great Lakes than they likely would on smaller systems.
Wind intensity has been shown to influence Lake Whitefish egg deposition, larval
movement, and recruitment (Brown et al., 1993). Wind and wave action during the late fall and
winter can cause physical, potentially fatal, trauma to eggs (Taylor et al., 1987a). This impact
has been shown to be particularly pronounced when eggs are deposited in marginal rearing
habitat (Freeberg et al., 1990). Freeberg et al. (1990) further hypothesized that currents could
influence egg survival by shifting eggs from good to poor incubation habitat.
Ice cover
Ice cover can mediate some of the impacts of wind and waves in the Great Lakes. It can
dampen the magnitude of wind-driven waves and turbulence by reducing friction over the lake
surface. Ice cover can also affect mass and energy exchanges between the lakes and atmosphere
(Assel et al., 2003) and can protect the lakes from winter evaporation and helps maintain lake
levels (Lofgren et al., 2002).
Lake Whitefish are fall spawners with peak aggregations generally occurring in
November; the eggs overwinter before hatching in the spring with peak hatching in April
(Ebener et al., 2008). Lake Whitefish spawn in nearshore (< 2km) waters over small to
moderate-sized cobble and, less preferably, over sand (Ebener et al., 2008). Observational
studies of Lake Whitefish suggest that recruitment variability may be linked to ice cover which
can moderate the impacts of wind-driven waves over recently deposited eggs (Taylor et al.,
1987a; Freeberg et al., 1990). Brown et al. (1993) found that ice cover was the most significant
factor predicting recruitment between two Lake Whitefish spawning areas of Lake Michigan. In

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high recruitment years with egg deposition in marginal habitat, ice cover can dampen currents
and wave action, reduce overall egg mortality, and increase recruitment (Freeberg et al., 1990).
Climate change
By the end of this century, the Great Lakes are projected to be warmer, with more wind
and less ice cover. Climate change is expected to increase surface temperatures of the Great
Lakes by as much as 6oC (Trumpickas et al., 2009). Ice cover is expected to be substantially
reduced from these projected temperature increases (Lofgren et al., 2002; Assel et al., 2003).
With warmer temperatures and a smaller air-to-lake temperature gradient, there is less friction at
the water surface and wind speeds have already been increasing by nearly 5% each decade
(Desai et al., 2009).
Climate change is hypothesized to impact the magnitude and value of the Lake Whitefish
fishery, because the success of recruitment to the fishery has been linked with climatic
influences, including fall and spring water temperature, fall wind and waves, and ice cover
(Table 4.1). Increased water temperature and decreased ice cover could inhibit the success of
recruitment to the Lake Whitefish fishery with greater egg mortality (Lynch et al., 2010).
However, the warming trends associated with predicted climate change could increase overall
suitable thermal habitat volume for Lake Whitefish (Magnuson et al., 1997) because the species
is expected to shift northwards and deeper in the water column to maintain optimal thermal
habitat (Regier and Meisner, 1990). While thermal suitability is an important component of
habitat, Lake Whitefish stocks are characterized by spatial and temporal variation (Deroba and
Bence, 2012). For example, Brenden et al. (2010) found substantial variability in the relative
abundance and size of Lake Whitefish recruits within even the same sampling sites. As a result,

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forecasts based only on temperatures are likely to be ecologically incomplete projections (Jones
et al., 2006).

TABLE 4.1. Projected impacts of changes in ice cover, wind and waves, fall temperature, and
spring temperature on Lake Whitefish (Coregonus clupeaformis).
Climate factor

Ice cover

Wind and waves

Fall temperature

Spring temperature

Projected change

Anticipated impact on Lake Whitefish

↓ ice cover

Lake Whitefish spawn in the fall and their eggs
stay through the winter, hatching in the spring. Ice
cover has been shown to protect eggs in suboptimal spawning habitat. Reduced ice cover
could lead to lower lake whitefish recruitment
(survival) from habitat that is considered suboptimal.

↑ wind and waves

Wind and waves can damage lake whitefish eggs
and increase egg mortality. Strong storm events
before the onset of ice cover have been linked to
reduced survival of eggs to hatching. Increased
wind and waves could lead to lower Lake
Whitefish recruitment.

↑ temperature

Warmer fall temperatures are often associated
with increased wind and waves. Because storm
events reduce egg survival, warmer fall
temperatures could lead to lower Lake Whitefish
recruitment.

↑ temperature

Lake Whitefish hatch into larvae in the spring.
The survival of larvae is very dependent on
finding food (i.e., plankton). Warmer spring
temperatures generally lead to higher densities of
plankton and have been linked with stronger Lake
Whitefish survival because of increased
availability of food resources. Warmer spring
temperatures could lead to higher Lake Whitefish
populations.

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Lake Whitefish in the 1836 Treaty Waters
The 1836 Treaty Waters are regions of Lakes Huron, Michigan, and Superior that were
ceded from the Ottawa and Chippewa nations to the United States of America. Until the 1970s,
the Treaty Waters were managed by the Michigan Department of Natural Resources because the
Michigan Supreme Court declared that the tribes had no special fishing or hunting rights in this
region, though the Treaty did not specifically cede Tribal fishing rights to the state. When the
state began limiting entry into the commercial fishery, the tribes challenged the court ruling and,
in 1979, United States v. State of Michigan (the Fox Decision) reaffirmed the rights of the tribes
to fish for Lake Whitefish. Fishing rights were not a negligible concession; the harvest from the
1836 Treaty Waters currently comprises approximately a quarter of the total harvest of Lake
Whitefish in the upper Great Lakes (M. Ebener, Chippewa Ottawa Resource Authority, personal
communication).
To ensure that the fishery is managed for long-term profitable yields and ecosystem
integrity, the 2000 Consent Decree established guidelines for management under the purview of
the Chippewa Ottawa Resource Authority (CORA), a cooperative tribal management agency.
Currently, a Technical Fisheries Committee recommends total allowable catches and harvest
regulations for the 15 Lake Whitefish management units located in these waters using the
guidance from a Modeling Sub-Committee. The Modeling Sub-Committee fits statistical catchat-age (SCAA) models to the commercial fishery data to estimate population metrics, including
population abundance and recruitment (Deroba and Bence, 2009).
For these models, recruitment is defined as the number of individuals in a population that
reach the legally defined fishable size (17 inch total length; Ebener et al., 2008). Recruitment is
a particularly important metric to estimate because “the regenerative process of a population is
critical to the maintenance of the population” (Quinn and Deriso, 1999). The SCAA models use
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a Ricker (1954) stock-recruitment relationship because Lake Whitefish recruitment is density
dependent (Henderson et al., 1983) and the Ricker model accounts for density dependence.
Using fishery dependent data, the models estimate population abundance, mortality (natural,
lamprey, trap net, and gill net), fishery harvest, among other population parameters (Deroba and
Bence, 2009) , but do not include environmental factors, which may influence the productivity of
these fish.
The goal of this study was to examine the relationship between climate variables and
Lake Whitefish recruitment in the 1836 Treaty Waters of the Great Lakes. Specifically, this
study investigated the relationship between recruitment and temperature indices, wind, and ice
cover, which have all been cited as influential in Lake Whitefish recruitment dynamics.
Projecting the relationship between these climate variables and recruitment forward with climate
change will help the fishery and fishery managers anticipate changes in recruitment and prepare
adaptive management strategies to maintain sustainable harvest of the fishery into the future.
Methods
SCAA models have been developed to establish total allowable catches and designate
harvest regulations for 13 of the 15 Lake Whitefish management units in the 1836 Treaty Waters
by the Modeling Subcommittee to the Technical Fisheries Committee (Figure 4.1). Using
fishery data, the models estimate population abundance, mortality (natural, lamprey, trap net, and
gill net), fishery harvest, and other population parameters (Deroba and Bence, 2009). The details
of this modeling approach are described in Ebener et al. (2005).

This study examined if the

inclusion of climate variables could significantly improve recruitment estimates, accounting for
the increase in parameters.

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FIGURE 4.1. Lake Whitefish (Coregonus clupeaformis) management units for the 1836 Treaty
Waters of Lakes Huron, Michigan, and Superior color coded by the best fit linear regression
model for recruitment as selected by Corrected Akiake’s Information Criterion.
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Spawning stock biomass and recruitment
Spawning stock biomass and recruitment estimates were calculated using the Modeling
Sub-Commitee SCAA models for each management unit. Data spanned from 1976-2011,
depending on the management unit (Appendix 4.1). We truncated the dataset at 2007 as SCAA
models perform inconsistently with recent data due to insufficient population data inputs to run
the analysis (J. Bence, Michigan State University, personal communication). Spawning stock
biomass was measured as spawning stock biomass per kg recruit and recruitment is measured as
number of individuals that reach a fishable size (17 inch total length; Ebener et al., 2008), which
generally occurs at age 3 or age 4, depending on the management unit (Appendix 4.1). For the
purposes of this analysis, we calculated recruitment without the penalty for recruitment
deviations used in harvest quota estimation. In the SCAA models, spawning stock biomass is
used as a constraining parameter to minimize recruitment fluctuation and stabilize estimation.
Because our linear regression analysis was outside of the SCAA framework, we decoupled the
interaction between spawning stock biomass and recruitment so that the variables were
independently considered in our analysis.
Climate variables
To determine if key climate variables improve the SCAA recruitment estimates, we
examined the following variables for inclusion in multiple linear regressions: Temperature, wind
speed and wave height, ice cover.

Temperature
In order to compare recruitment with temperature, we used composite indices of
temperature because indices reduce the likelihood of multicollinearity (Farrar and Glauber,

115

1967). To calculate the composite temperature indices, we first calculated mean, minimum, and
maximum monthly air temperature estimates (oF) from the land-based National Climate Data
Center station data within a five mile buffer of each management unit in ArcMap 10 (ESRI,
2011). Using available data, this analysis spanned from 1980-2010 (Appendix 4.1). To match
recruitment values with the temperature conditions the recruits experienced as eggs and
hatchlings (i.e., their most vulnerable life stages; Freeberg et al., 1990), we linked recruitment
estimates for a given year with fall (October-December) temperatures during the year those
recruits were spawned (three or four years prior, depending on management unit) and spring
temperatures (March-May) during the year they hatched (two or three years prior, depending on
management unit). These temperature values were then converted to the following composite
temperature indices: thermal index and rate index.
Thermal index is the deviation of a given year’s April mean temperature from the
dataset’s mean of all April temperatures minus the deviation of the previous year’s November
mean temperature from the dataset’s mean of all November temperatures (Christie, 1963). April
and November were chosen as representative seasonal indicators because Lake Whitefish
spawning peaks in November and hatching peaks in April. Positive thermal index values occur
when a cooler-than-average November is followed by a warmer-than-average April. Rate index
is the deviation of a given year’s spring warming rate (maximum May temperature – minimum
March temperature) from the data set’s mean spring warming rate minus the deviation of the
previous year’s fall cooling rate (maximum October temperature – minimum December
temperature) from the data set’s mean spring warming rate.
Wind speed and wave height
In order to compare recruitment with wind intensity, we examined wind speed and wave
height from 1983-2011 (Appendix 4.1). Mean November wind speed (m/s) and wave height (m)
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estimates were calculated for each management unit from the closest National Data Buoy Center
offshore buoy (buoy id: 45002, 45003, 45004, and 45007). Wind and wave action are correlated
over large spatial scales (2,500 km+; Koenig, 2002). November was chosen because it is the
peak of the spawning season when the majority of eggs are deposited. To match recruitment
values with the wind conditions the recruits experienced as eggs, we linked recruitment for a
given year to wind speed and wave height from the year fish were spawned (two or three years
prior, depending on management unit).
Ice cover
In order to compare recruitment with ice cover, we clipped ice cover estimates by
management unit from the National Oceanic and Atmospheric Administration (NOAA) Great
Lakes Ice Atlas (Assel et al., 2003) using ArcMap 10 (ESRI, 2011). Data used in our analysis
spanned from 1972-2008 (Appendix 4.1). We calculated the proportion of ice cover at the 10m
depth contour as close to December 1st as possible based on available data. We chose the 10m
depth contour because this generally represents the outer margin of Lake Whitefish spawning
habitat at the end of the spawning season. To match recruitment values with the ice cover the
recruits experienced as eggs, we linked recruitment for a given year to ice cover from the year
fish were spawned (two or three years prior, depending on management unit).
Pearson correlation
We used the Pearson correlation coefficient, r, to examine pairwise correlation between
the thermal index, rate index, ice cover, wind speed, and wave height. The Pearson correlation
coefficient compares two variables, xi and yi, by using contour ellipse of a two dimensional
normal distribution to describe the relationship:

117

𝑟=

∑(𝑥𝑖 − 𝑥̅ )(𝑦𝑖 − 𝑦�)

�∑(𝑥𝑖 − 𝑥̅ )2 ∑(𝑦𝑖 − 𝑦�)2

The Pearson correlation coefficient evaluates linear relationships, like the linear Ricker
stock-recruitment model (Ricker, 1954) used for Lake Whitefish because they exhibit density
dependent recruitment (Henderson et al., 1983). If both variables are scaled to have a variance
of 1, then a correlation of zero corresponds to circular contours, as the correlation increases, the
ellipses narrow, and finally collapse into a line segment as the correlation approaches ±1, a
perfect linear relationship (Dalgaard, 2008).
To determine if any of the examined variables were correlated, we calculated the Pearson
correlation coefficient using pairwise complete observations for ice cover, thermal index, rate
index, November wind speed, and November wave height with the Rcmdr package (Fox, 2005)
and plotted them using the corrgram package (Wright, 2006) in Tinn-R GUI 2.4.1.7 (Faria,
2013).
Variance inflation factors
While pairwise collinearity can be determined with the Pearson correlation coefficient,
we calculated Variance inflation factors (VIF) to examine higher-order collinearity (Zuur et al.,
2009):

𝑉𝐼𝐹𝑖 =

1
1 − 𝑅𝑖2

For a given independent variable, i, VIFi is the comparison of the proportion of variance a
variable shares with the other independent variables to the situation in which it shares none of its
variance with the other independent variables (O'Brien, 2007). A VIF of 10, for example,
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indicates that the variance of the regression coefficient, Ri, is 10 times greater than if the variable
had been linearly independent of the other independent variables in the analysis. VIF values of 4
or 10 are often cited as “rules of thumb” to consider variables for removal from an analysis
(O'Brien, 2007). We calculated VIF values using multiple linear regression including the
following predictor variables: stock-dependent ice cover, thermal index, rate index, November
wind speed, and November wave height with the car package (Fox and Weisberg, 2011) in TinnR GUI 2.4.1.7 (Faria, 2013).
Lake Whitefish recruitment model selection
Lake Whitefish exhibit density dependent recruitment (Taylor et al., 1987a) and,
consequently, a standard Ricker stock-recruitment model (Ricker, 1954) is used as the
foundation for the Modeling Subcommittee’s SCAA modeling efforts. Because lognormal
variability is appropriate for stock-recruitment relationships (Hilborn and Walters, 1992), we
transformed the Ricker model into a linear function by taking the natural log of both sides of the
equation. We used linear regression techniques to evaluate the relationship between recruitment
(R), stock (S), and additional climate variables (Var), where α is a productivity parameter, β is a
density dependent shape parameter, and ε is normally distributed random error:

𝑙𝑜𝑔

𝑅𝑖
= 𝑙𝑜𝑔𝛼𝑖 − 𝛽1 𝑆𝑖 + 𝛾1 𝑉𝑎𝑟𝑖 … + 𝛾𝑖 𝑉𝑎𝑟𝑖 + 𝜖𝑖
𝑆𝑖

The full model used in this study included density-dependent ice cover (ice), densityindependent thermal index (t_index), rate index (r_index), and wind speed (W):

𝑙𝑜𝑔

𝑅𝑖
=
𝑆𝑖

𝑙𝑜𝑔𝛼𝑖 − 𝛽1 𝑆𝑖 + 𝛽2 𝑖𝑐𝑒𝑖 𝑆𝑖 + 𝛾1 𝑡_𝑖𝑛𝑑𝑒𝑥𝑖 + 𝛾2 𝑟_𝑖𝑛𝑑𝑒𝑥𝑖 + 𝛿1 𝑊𝑖 + 𝜖𝑖
119

To determine the best fitting model for each management unit, we compared all possible
combinations of models including climate variables to the standard stock-recruitment Ricker
model (without the addition of any climate variables) using corrected Akaike’s Information
Criterion (AICc) in R 2.4.1.7 (R Core Management Team, 2008). We used corrected AIC to
avoid overparameterization for small sample sizes, with k parameters, an L likelihood of the
model representing the data, and n observations:

𝐴𝐼𝐶𝑐 = 2𝑘 − 2 ln(𝐿) + 2𝑘
Projecting recruitment with climate

𝑘+1
𝑛−𝑘−1

To project the relationships described by the best fitting models of climate and
recruitment, we used the Coupled Hydrosphere-Atmosphere Research Model (CHARM), a
simulation model of climate and water resources in the Great Lakes Region (Lofgren, 2004).
CHARM uses the Regional Atmospheric Modeling System (Pielke et al., 1992) with lake
thermodynamics, surface temperature, heat transfer, and a model of land processes specifically
for the Great Lakes. The regional approach allows for enhanced spatial resolution at the
atmosphere-water interface. The model is resolved to 40 km grids (smaller than the smallest
management unit) and simulated at six hour intervals for two twenty year periods, 1981-2000
and 2050-2070. We extracted the following CHARM outputs: fall and spring air temperatures,
November wind speed, and December ice cover, for each management unit using the stringr
package (Wickham, 2012) in Tinn-R GUI 2.4.1.7 (Faria, 2013). We calculated thermal index
and rate index using the annual deviation from the 20 year monthly mean, maximum, and
minimum CHARM temperature simulation, depending on the month and metric. We calculated
wind speed from the U (east) and V (north) vector components generated by the CHARM model.
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The CHARM model simulated ice cover in mean meters thickness. While this metric is different
than what we used in the climate-recruitment regression model, the proportional relationship
(i.e., amount of ice cover) is still analogous for the purposes of this comparison.
We projected Lake Whitefish recruitment for each management unit using these CHARM
outputs as inputs into the models identified through AICC selection for each management unit to
generate projections of Lake Whitefish recruitment for 2050-2070. To constrain simulated
variability to only climate causes, we held spawning stock size constant at the 2007 estimate.
Since simple back-transformation of log-transformed linear regression estimates is biased,
producing the geometric rather than arithmetic mean (MacCall and Ralston, 2002), the value is
not analogous to recruitment. We corrected for this back-transformation bias by including the
addition of recruitment variance, σ2, as a variable to project recruitment:

Results

𝑅𝑖 = 𝑆𝑖 𝑒

2

𝛼𝑖 −𝛽1 𝑆𝑖 𝛽2 𝑖𝑐𝑒𝑖 𝑆𝑖 +𝛾1 𝑡_𝑖𝑛𝑑𝑒𝑥𝑖 +𝛾2 𝑟_𝑖𝑛𝑑𝑒𝑥𝑖 +𝛿1 𝑊𝑖 +𝜎 �2

Climate variable selection
We used the Pearson correlation coefficient and VIF values to determine if there was any
reason to remove a climate variable from our climate-recruitment model. For each of the 13
management units, the Pearson correlation coefficient was significant (p < 0.05) between
November wind speed and November wave height; 10 other pairwise comparisons resulted in a
significant correlation (p < 0.05) but not consistently across management units (Table 4.2;
Appendix 4.2). Because wind speed and wave height are both measures of storm intensity and
wind speed is often more readily available, wave height was removed from subsequent analyses.
Though O’Brien (2007) cautions against using a “rule of thumb” VIF value to remove variables
121

from an analysis, all of the VIF values in this analysis were below 10 and most of them were
below 4 (Table 4.3). Consequently, these results did not indicate than any variables should be
removed from use in this analysis because of higher order collinearity. The climate variables
included in the AICC model comparisons were thermal index, rate index, wind speed, and ice
cover.
Lake Whitefish recruitment model selection
The AICc comparisons between the stock-recruitment model and the best fit model
including selected climate variables ranged between 0 (where the stock-recruitment model,
alone, was the best fit) and 20.91 (Table 4.4). In eight management units, the AICc comparisons
were higher than three, indicating significant improvement of model fit when climate variables
were included (Burnham and Anderson, 2002). For six of those eight management units across
all three lakes, November wind speed was an included variable; rate index was an included
variable in four management units across all three lakes; ice cover was included in two Lake
Superior management units; and thermal index was included in one Lake Superior management
unit (Table 4.5; Figure 4.1).
Lake Whitefish climate-recruitment projection
Of the eight management units identified to have improved model fit with the inclusion
of climate variables, six (WFH-05, WFH-Northern Huron, WFM-01, WFM-02, WFS-04, and
WFS-07) are projected to have increases in Lake Whitefish recruitment and two units (WFM-06
and WFS-05) are projected to have decreases in Lake Whitefish recruitment (Figure 4.2). The
WFM-06 model includes wind speed and the WFS-05 model incudes ice cover and thermal
index. Projected recruitment changes range from over 250% increase to almost 80% declines
(Table 4.6; Figure 4.3).
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TABLE 4.2. Pearson correlation coefficients (below diagonal) and p-values (above diagonal; <0.05 bolded) for potentially relevant
climate variables by 1836 Treaty Waters Lake Whitefish (Coregonus clupeaformis) management unit: ice cover (December 10m depth
contour), thermal index (April temperature deviation – November temperature deviation), rate index (spring warming rate – fall cooling
rate), November wind speed (monthly average), and November wave height (monthly average). Note WFM-03 temperature data
unavailable.
WFH_05

ice

ice
thermal index
rate index
wind speed
wave height
WFH_Northern_Huron
ice
thermal index
rate index
wind speed
wave height
WFM_01

-0.2967
0.1268
0.094
-0.2757
ice
-0.1696
-0.4066
0.1251
-0.1923
ice

ice
thermal index
rate index
wind speed
wave height
WFM_02

-0.0209
-0.2561
0.5878
0.0442
ice

ice
thermal index
rate index
wind speed
wave height

-0.4082
-0.3291
0.5878
0.0442

thermal index
0.1498
-0.2722
0.0222
0.3372
thermal index
0.4282
-0.3348
-0.3283
-0.1285
thermal index
0.9209
-0.4578
0.1274
0.0367
thermal index
0.0593
-0.025
-0.0267
0.1126
123

rate index
0.5458
0.188
-0.3648
-0.4185
rate index
0.0486
0.1098
0.1847
0.0607
rate index
0.2382
0.0281
-0.2256
-0.3722
rate index
0.1452
0.9142
-0.1495
0.2909

wind speed
0.6853
0.9239
0.1039
0.7495
wind speed
0.5888
0.1463
0.4228

wave height
0.2265
0.135
0.059
<.0001
wave height
0.4036
0.5787
0.7939
<.0001

0.7495
wind speed
0.0103
0.6145
0.4008

wave height
0.8492
0.8746
0.1165
0.0128

0.5739
wind speed
0.0103
0.9162
0.5668
0.5739

wave height
0.8491
0.627
0.2134
0.0128

TABLE 4.2 (cont’d).
WFM_03

ice
ice
wind speed
wave height

WFM_04

0.6209
0.0764
ice

ice
thermal index
rate index
wind speed
wave height
WFM_05

-0.4265
-0.0889
0.6018
0.002
ice

ice
thermal index
rate index
wind speed
wave height
WFM_06

0.0003
-0.1397
0.553
-0.0173
ice

ice
thermal index
rate index
wind speed
wave height
WFM_08

-0.3776
-0.1332
0.5859
-0.0227
ice

ice
thermal index
rate index

-0.1472
0.0785

wind speed
0.0078

wave height
0.7489
0.0206

0.5555
thermal index
0.0335
-0.1142
-0.055
0.1247
thermal index
0.9988
-0.2039
0.3942
0.2913
thermal index
0.0756
0.0386
0.1786
0.1819
thermal index
0.5027
-0.0998
124

rate index
0.6727
0.5867
-0.1314
-0.1845
rate index
0.5053
0.3282
-0.1512
-0.2706
rate index
0.5546
0.8647
-0.0191
-0.1853
rate index
0.7218
0.6504

wind speed
0.0082
0.8283
0.6034

wave height
0.9931
0.5902
0.4233
0.0128

0.5739
wind speed
0.0173
0.1055
0.5493

wave height
0.9407
0.2002
0.2355
0.0128

0.5739
wind speed
0.0106
0.4782
0.942

wave height
0.922
0.4299
0.4342
0.0128

0.5739
wind speed
0.0222
0.7762
0.7803

wave height
0.7043
0.4973
0.8978

TABLE 4.2 (cont’d).
ice
0.496
-0.0905

thermal index
0.066
0.1612

rate index
-0.0648
0.0307

wind speed

WFM_08 (cont’d) wind speed
wave height
WFS_04

ice

thermal index
0.1158

rate index
0.1976
0.1898

wind speed
0.1162
0.7419
0.2853

ice
thermal index
rate index
wind speed
wave height
WFS_05

-0.3451
-0.2929
0.4229
-0.0183
ice

ice
thermal index
rate index
wind speed
wave height
WFS_07

-0.2385
-0.2465
0.4201
-0.0197
ice

ice
thermal index
rate index
wind speed
wave height
WFS_08

-0.2888
0.1661
0.4093
-0.0278
ice

ice
thermal index
rate index
wind speed
wave height

-0.0369
-0.0342
0.2961
-0.2013

-0.2978
0.0929
0.3398
thermal index
0.3566
-0.3358
0.068
0.2295
thermal index
0.1711
-0.4454
0.0783
0.3268
thermal index
0.8642
-0.3771
-0.0383
0.3026
125

-0.3072
-0.2941
rate index
0.3574
0.221
-0.3967
-0.3584
rate index
0.4489
0.0332
-0.2623
-0.3642
rate index
0.8738
0.0693
-0.1997
-0.4256

wave height
0.0032

0.6259
wave height
0.9463
0.1979
0.2873
0.0005

0.8064
wind speed
0.119
0.8425
0.2564

wave height
0.9424
0.473
0.2527
0.0005

0.8064
wind speed
0.1298
0.7815
0.3449

wave height
0.9185
0.2167
0.1656
0.0005

0.8064
wind speed
0.284
0.8922
0.4756
0.8064

wave height
0.4548
0.2547
0.1003
0.0005

TABLE 4.3. Variance Inflation Factors for potentially relevant climate variables by 1836 Treaty Waters Lake Whitefish (Coregonus
clupeaformis) management unit: density-dependent ice cover (S:ice; December 10m depth contour), thermal index (April temperature
deviation – November temperature deviation), rate index (spring warming rate – fall cooling rate), and November wind speed (monthly
average). Note WFM-03 temperature data unavailable.
WFH_05
WFH_Northern_Huron
WFM_01
WFM_02
WFM_03
WFM_04
WFM_05
WFM_06
WFM_08
WFS_04
WFS_05
WFS_07
WFS_08

thermal index
1.23
1.19
2.27
1.57

rate index
1.52
1.24
2.33
1.46

1.67
1.39
1.32
1.19
2.97
2.17
2.38
1.56

1.55
1.08
1.06
1.26
2.94
4.16
1.9
1.07

126

wind speed
1.25
1.54
1.7
1.74
1.9
1.99
1.98
1.71
1.35
1.54
1.4
1.45
1.17

S:ice
1.16
1.26
1.96
2.83
1.68
3.76
1.81
1.86
1.79
12.74
3.51
4.1
2.43

TABLE 4.4. The difference between corrected Akaike’s Information Criterion (AICc) values between the Lake Whitefish (Coregonus
clupeaformis) stock-recruitment (S-R) model and the best fit model including climate variables: ice cover (December 10m depth
contour), thermal index (t_index; April temperature deviation – November temperature deviation), rate index (r_index; spring warming
rate – fall cooling rate), and November wind speed (wind; monthly average) for each of the 13 management units of the 1836 Treaty
Waters evaluated. Parameter estimates are listed (blue = positive; red = negative). Management units with AICc comparisons < 3 are
gray. Note WFM-03 temperature data unavailable.
Δ AICC

variables
included
rate index,
wind speed

WFH_05

15.33

WFH_Northern
_Huron

12.28

WFM_01

20.01

WFM_02

20.91

WFM_03

0.00

S-R only

WFM_04

0.00

S-R only

WFM_05

1.83

wind speed

WFM_06

11.31

wind speed

WFM_08

0.00

S-R only

WFS_04

4.77

wind speed

wind speed
rate index,
wind speed
rate index,
wind speed

Intercept

S

5.91E08
3.12E-0.9742
08
1.48E0.0567
07
8.03E-0.7606
07
-3.24E1.69E+00 08
-5.90E2.20E01
07
4.92E1.10E+00 07
1.57E-1.41
06
-7.36E1.97E+00 08
-1.13E-2.601
06
-0.9621

127

t_index

residual
SE

variance

-0.02713 -0.2728

0.5217

0.27217
1

-0.1455

0.4429

0.19616

-0.01889 -0.2439

0.5649

0.31911
2

-0.08493 -1.599

5.536

30.6473

r_index

wind

S:ice

0.3428
0.3008
-8.21E02

0.2953

-0.2401

0.7009
0.6479

0.07233

0.3129

0.11751
2
0.09048
1
0.08720
2
0.49126
1
0.41977
4
0.09790
6

TABLE 4.4 (cont’d).
Δ AICC

variables
included

Intercept

WFS_05

13.60

ice, thermal
index

-1.319

WFS_07

11.53

ice, rate index

0.5525

WFS_08

0.85

thermal index,
rate index

-7.79E01

S
1.13E06
1.58E06
1.79E06

t_index

r_index

wind

9.29E04

2.99E02

S:ice
2.782
E-16
5.778
E-17

0.00043
2
8.68E03

residual
SE

variance
0.06574
1
0.04613
9

0.2564
0.2148
0.5772

0.33316

TABLE 4.5. Variables used in best fit linear regression models for the 1836 Treaty Waters Lake Whitefish (Coregonus clupeaformis)
management units. Values in parentheses indicate management units with a difference between corrected Akaike’s Information
Criterion (AICc) values between the stock-recruitment (S-R) model and the best fit model of > 3, indicating significant improvement in
model fit. Note that some models contain more than one variable.
Lake Huron
stock-recruitment only
ice
thermal index
rate index
wind speed

Lake Michigan
3

1 (1)
2 (2)

2 (2)
4 (3)

128

Lake Superior
2 (2)
2 (1)
2 (1)
1 (1)

Total
3
2 (2)
2 (1)
5 (4)
7 (6)

TABLE 4.6. Comparison of Lake Whitefish (Coregonus clupeaformis) recruitment estimates from the best fit linear regression models
including climate variables for 2007 with the projected estimates for 2052-2070, by management unit. Values are displayed as a
proportion of the 2007 estimate for each management unit (blue = projected increase; red = projected decrease).

2007
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
2063
2064
2065
2066
2067
2068
2069
2070

WFH-Northern_Huron
1
2.23
1.94
2.58
2.5
2.09
2.39
2.04
2.29
1.84
2.82
2.48
1.94
2
1.96
2.16
2
2.06
1.59
2.08

WFH-05
1
2.22
2.08
3.29
3.41
1.63
2.72
1.34
2.39
1.5
3.59
2.35
1.57
3.45
1.89
2.19
1.77
2.14
1.19
1.95

WFM-01
1
1.54
1.48
2.22
2.75
1.35
1.97
1.13
1.57
1.26
3.05
2.05
1.23
1.65
1.23
1.32
1.62
1.61
1
1.2

WFM-02
1
1.29E+13
1.59E+13
1.71E+14
3.38E+14
7.33E+12
9.60E+13
5.10E+12
2.38E+13
3.67E+12
6.28E+14
8.12E+13
6.49E+12
2.36E+13
9.42E+12
8.09E+12
1.29E+13
1.90E+13
1.46E+12
3.61E+12

129

WFM-06
1
0.43
0.36
0.62
0.77
0.42
0.56
0.43
0.53
0.34
0.84
0.66
0.34
0.42
0.36
0.38
0.41
0.45
0.27
0.4

WFS-04
1
1.41
1.48
1.38
1.3
1.45
1.35
1.48
1.37
1.6
1.26
1.38
1.5
1.5
1.55
1.45
1.48
1.48
1.68
1.54

WFS-05
1
0.78
0.78
0.78
0.78
0.78
0.78
0.78
0.78
0.78
0.78
0.78
0.78
0.78
0.78
0.78
0.78
0.78
0.78
0.78

WFS-07
1
1.22
1.22
1.22
1.22
1.22
1.22
1.22
1.22
1.22
1.22
1.22
1.22
1.22
1.22
1.22
1.22
1.22
1.22
1.22

A)

SCAA and climate projected recruitment
(significant only)

6000000
5000000
Northern Huron

recruitment

4000000

WFH-05
WFM-01

3000000

WFM-06
WFS-04

2000000

WFS-05
WFS-07

1000000

1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2052
2054
2056
2058
2060
2062
2064
2066
2068
2070

0

FIGURE 4.2. Lake Whitefish (Coregonus clupeaformis) recruitment estimates from the 1836 Treaty Waters Modeling Subcommittee
Statistical Catch-at-Age (SCAA) models (2007 and earlier) and projections using CHARM inputs into the best fit linear regression
models including climate variables (2052-2070) by management unit. A) management units with a difference between corrected
Akaike’s Information Criterion (AICc) values between the stock-recruitment (S-R) model and the best fit model of > 3, indicating
significant improvement in model fit; B) all Lake Huron management units; C) all Lake Michigan management units; and D) all Lake
Superior management units. Note that stock size in projection years was held constant at 2007 levels to isolate climate effects and
WFM-02 was removed because of high variance (σ2 = 30.64).
130

FIGURE 4.2 (cont’d).
B)

SCAA and climate projected recruitment
(Lake Huron)

6000000

4000000
3000000

Northern Huron
WFH-05

2000000
1000000

131

2070

2068

2066

2064

2062

2060

2058

2056

2054

2052

2006

2004

2002

2000

1998

1996

1994

1992

1990

1988

1986

1984

1982

1980

1978

0
1976

recruitment

5000000

FIGURE 4.2 (cont’d).
C)

SCAA and climate projected recruitment
(Lake Michigan)

7000000
6000000
WFM-01
WFM-03

4000000

WFM-04

3000000

WFM-05
WFM-06

2000000

WFM-08

1000000

132

2070

2068

2066

2064

2062

2060

2058

2056

2054

2052

2006

2004

2002

2000

1998

1996

1994

1992

1990

1988

1986

1984

1982

1980

1978

0
1976

recruitment

5000000

FIGURE 4.2 (cont’d).
D)

SCAA and climate projected recruitment
(Lake Superior)

1000000
900000
800000
600000

WFS-04

500000

WFS-05

400000

WFS-07

300000

WFS-08

200000
100000

133

2070

2068

2066

2064

2062

2060

2058

2056

2054

2052

2006

2004

2002

2000

1998

1996

1994

1992

1990

1988

1986

1984

1982

1980

1978

0
1976

recruitment

700000

FIGURE 4.3. Lake Whitefish (Coregonus clupeaformis) management units for the 1836 Treaty
Waters of Lakes Huron, Michigan, and Superior color coded by the 2052-2070 mean projected
change in recruitment (blue = projected increase; red = projected decrease).
134

Discussion
Lake Whitefish recruitment model selection
Differences in variables included in the best fit model are expected between the
management units because Great Lakes Lake Whitefish stock-recruitment dynamics are
characterized by spatial and temporal variation (Deroba and Bence, 2012). Because of spatial
variability and population dynamics, climate factors will not have the same influence on
recruitment in different management units that have different conditions. For five of the 13
management units, climate variables did not improve recruitment estimation; other population
drivers, not investigated in this study, are more strongly coupled with recruitment in these
management units.
However, the results of this study support including climate variables in the Lake
Whitefish stock recruitment models in the 1836 Treaty Waters of the Great Lakes. The addition
of climate variables in eight of the 13 management units assessed improved model fit, meaning
that climate is an important driver of recruitment in these management units. November wind
speed was the most commonly included climate variable, present in six of the eight significant
management units, followed by rate index in four, ice cover in two, and thermal index in one.
While previous site-based correlational studies have also indicated that climate variables
influence year class strength and future recruitment (Christie, 1963; Lawler, 1965; Taylor et al.,
1987a; Freeberg et al., 1990; Brown et al., 1993), this analysis was important because it
expanded upon these historical studies by integrating climate variables and recruitment on a
much larger spatial scale that is more applicable to the current methods of how Lake Whitefish
are managed.

135

Ecologically, our modeling results indicated that climate variables influence the
magnitude of Lake Whitefish recruitment in the 1836 Treaty Waters. Our analysis suggested
that, across all three lakes wind events during the November peak spawning period have a
negative relationship with recruitment. High wind events can lead to physical trauma, burial in
sediments, and higher mortality for Lake Whitefish eggs after deposition (Taylor et al., 1987a).
The rate index, the rate at which temperatures cool in the fall compared with the rate at
which temperatures warm in the spring (spring warming – fall cooling), also influenced
recruitment in all three lakes. Fast fall cooling followed by slow spring warming, measured by
the rate index, promotes strong Lake Whitefish year classes. This scenario concentrates
spawning at optimal temperatures in the fall (generally below 6oC; Hooper et al., 2001) and
allows larval Lake Whitefish to absorb their yolk sac more slowly in the spring so that they are
larger, faster feeders when the yolk sac is fully absorbed (Lawler, 1965). Warming rate has also
been shown to be influential in recruitment of walleye in western Lake Erie and is hypothesized
to be a result of shortening the period of vulnerability of walleye eggs to storm events
(Madenjian et al., 1996; Roseman et al., 1996).
In this study, ice cover and thermal index influenced recruitment in management units in
Lake Superior only. This relationship is likely because these management units are the farthest
north in the study and more often have ice cover present over the spawning grounds (due to cold
temperatures) before spawning occurs. When ice is present, particularly over marginal spawning
habitat, it can diminish the impacts of wind, current, and wave action improving Lake Whitefish
egg survivability (Freeberg et al., 1990) and, hence, recruitment.

136

A)

FIGURE 4.4. A) Current and B) Projected change in Lake Whitefish (Coregonus clupeaformis) recruitment with climate conditions:
temperature, wind, and ice cover (Todd Marsee, Michigan Sea Grant).

137

FIGURE 4.4 (cont’d).
B)

138

Lake Whitefish climate-recruitment projection
Using the CHARM model of future climatic conditions in the Great Lakes region, our
results indicated that climate change has the potential to increase Lake Whitefish recruitment in
the 1836 Treaty Waters (Figure 4.4). It is important to note that these projections were
simulations given a constant stock size. This approach isolated the change in projected
recruitment to only change directly related to climate. Stock-recruitment relationships are highly
complex and the influence of stock size on recruitment is not negligible (Taylor et al., 1987a).
Nonetheless, this study suggests that Lake Whitefish recruitment in the 1836 Treaty Waters is
affected by variables that will be influenced by changes in climate.
Of the eight management units where the addition of climate variables significantly
improved model fit, six of them saw increases in recruitment, though the projection for WFM-02
has a large amount of variance in the estimates (σ2 = 30.64), indicating that other factors drive its
recruitment. The projected increase in recruitment within the 1836 Treaty Waters aligns with the
hypothesis that climate change will increase optimal thermal habitat for Lake Whitefish at all life
stages (Magnuson et al., 1990; Magnuson et al., 1997). Though rate index, a composite
temperature variable, was included in the best fit models for four of these six management units,
it is important to note that other climate variables, namely wind and ice cover, were also
important variables. Wind was included in five of the six models and ice cover was included in
one Lake Superior management unit model.
Our modeling analysis suggests recruitment declines for the remaining two management
units, WFM-06 and WFS-05, given projected climate conditions (Figure 4.1). The best fit model
for WFM-06 included wind and the best fit model for WFS-05 included ice cover and thermal
index. Recruitment in WFM-06 has a negative relationship with wind speed and the negative

139

impacts on recruitment may be a result of projected increases in wind. Because WFS-05 is a
Lake Superior unit, ice cover and cold temperatures before spawning occurs are likely to be
important influences on recruitment. As a result, reduced ice cover and warmer temperature
changes may result in a decline in recruitment. WFS-05, in particular, is management unit with
high harvest rates. Projected decreases of almost 78% will likely severely change the Lake
Whitefish population dynamics and dependent fisheries in the area.
Implications for Lake Whitefish management
The potential changes in Lake Whitefish recruitment have significant implications for the
ecosystem, Lake Whitefish fishers, and the communities dependent upon the fishery. These
results are not aimed at providing exact estimates of Lake Whitefish abundance in a given year
but rather provide information to help managers allocate resources in a sustainable manner with
changing conditions in the future. Management of Lake Whitefish in the 1836 Treaty Waters,
using the Modeling Sub-Committee guidance for setting harvest limits in tribal and shared (tribal
and state) zones, provides an example of the type of collaborative management that will likely
become more necessary as Lake Whitefish populations shift locations to maintain optimal
environmental conditions. Most management units are projected to have increased recruitment
but two management units, WFM-06 and WFS-05, are projected to have decreased recruitment
because of changing climate conditions. Managers can use the results of this study to anticipate
general changes to the resource and adjust harvest strategies from management units that will
decrease in productivity to those that will increase, ensuring that the fishery is sustainable and
profitable now and in the future.

140

Acknowledgments
The authors thank Jim Bence, Bo Bunnell, Dave Caroffino, Arthur Cooper, Mark Ebener,
Eric MacMillan, Jared Myers, Yin-Phan Tsang, Iyob Tsehaye, CSIS, MIRTH, QFC, 1836 Treaty
Waters Modeling Sub-Committee, and Michigan Department of Natural Resources and
Chippewa Ottawa Resource Authority personnel who collected and provided the data for this
project.

141

APPENDICES

142

Appendix 4.1. Data ranges by 1836 Treaty Water Lake Whitefish (Coregonus clupeaformis)
management unit.
Spawning stock biomass (SSB) and recruitment (R) are from the Modeling Sub-Committee
statistical catch-at-age models; temperature is from land-based National Climate Data Center
station data within a five mile buffer of each management unit; ice cover is from the National
Oceanic and Atmospheric Administration (NOAA) Great Lakes Ice Atlas; and wind speed and
wave height are from the closest National Data Buoy Center offshore buoy. Note that fall
climate variables are linked to recruitment corresponding to the year fish were spawned and
spring climate variables are linked to the year fish hatched.
Recruitment age

SSB/R

Temperature

Ice cover

Wind/wave

WFH_05

3

1981-2011

1980-2010

1972-2012

1983-2011

WFH_Northern_Huron

4

1976-2011

1980-2010

1972-2012

1983-2011

WFM_01

3

1976-2011

1980-2010

1972-2012

1983-2011

WFM_02

3

1986-2011

1980-2010

1972-2012

1983-2011

WFM_03

4

1986-2011

1980-2010

1972-2012

1983-2011

WFM_04

3

1981-2011

1980-2010

1972-2012

1983-2011

WFM_05

3

1981-2011

1980-2010

1972-2012

1983-2011

WFM_06

3

1985-2011

1980-2010

1972-2012

1983-2011

WFM_08

3

1985-2011

1980-2010

1972-2012

1983-2011

WFS_04

4

1986-2011

1980-2010

1972-2012

1983-2011

WFS_05

4

1986-2011

1980-2010

1972-2012

1983-2011

WFS_07

4

1976-2011

1980-2010

1972-2012

1983-2011

WFS_08

4

1981-2011

1980-2010

1972-2012

1983-2011

143

Appendix 4.2. Plots of Pearson correlation coefficients for potentially relevant climate
variables by 1836 Treaty Waters Lake Whitefish (Coregonus clupeaformis) management unit.

FIGURE 4.5. Plots of Pearson correlation coefficients for potentially relevant climate variables:
ice cover (December 10m depth contour), thermal index (April temperature deviation –
November temperature deviation), rate index (spring warming rate – fall cooling rate),
November wind speed (Nov_WSPD; monthly average), and November wave height
(Nov_WVHT; monthly average) by 1836 Treaty Waters Lake Whitefish (Coregonus
clupeaformis) management units. Confidence ellipses (above diagonal) demonstrate correlation
magnitude and direction where a circle corresponds to zero correlation and as the correlation
increases, the ellipse narrows, and finally collapses into a line segment as the correlation
approaches ±1, a perfect linear relationship. Pie graphs (below diagonal) indicate the magnitude
of the pairwise correlation (blue = positive; red = negative).

A)

144

FIGURE 4.5 (cont’d).
B)

145

FIGURE 4.5 (cont’d).
C)

146

FIGURE 4.5 (cont’d).
D)

147

FIGURE 4.5 (cont’d). Note WFM-03 temperature data unavailable.
E)

148

FIGURE 4.5 (cont’d).
F)

149

FIGURE 4.5 (cont’d).
G)

150

FIGURE 4.5 (cont’d).
H)

151

FIGURE 4.5 (cont’d).
I)

152

FIGURE 4.5 (cont’d).
J)

153

FIGURE 4.5 (cont’d).
K)

154

FIGURE 4.5 (cont’d).
L)

155

FIGURE 4.5 (cont’d).
M)

156

LITERATURE CITED

157

LITERATURE CITED

Assel, R., Cronk, K. & Norton, D. (2003). Recent trends in Laurentian Great Lakes ice cover.
Climatic Change 57, 185-204.
Beletsky, D., Saylor, J. H. & Schwab, D. J. (1999). Mean circulation in the Great Lakes. Journal
of Great Lakes Research 25, 78-93.
Brenden, T. O., Ebener, M. P., Sutton, T. M., Jones, M. L., Arts, M. T., Johnson, T. B., Koops,
M. A., Wright, G. M. & Faisal, M. (2010). Assessing the health of lake whitefish
populations in the Laurentian Great Lakes: Lessons learned and research
recommendations. Journal of Great Lakes Research 36, 135-139.
Brown, R. W., Taylor, W. W. & Assel, R. A. (1993). Factors affecting the recruitment of lake
whitefish in two areas of northern Lake Michigan. Journal of Great Lakes Research 19,
418-428.
Burnham, K. P. & Anderson, D. R. (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,
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, 761-784.
Dalgaard, P. (2008). Introductory Statistics with R. New York, NY: Springer.
Deroba, J. J. & Bence, J. R. (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, 50-63.
Deroba, J. J. & Bence, J. R. (2012). Evaluating harvest control rules for lake whitefish in the
Great Lakes: Accounting for variable life-history traits. Fisheries Research 121, 88-103.
Desai, A. R., Austin, J. A., Bennington, V. & McKinley, G. A. (2009). Stronger winds over a
large lake in response to weakening air-to-lake temperature gradient. Nature Geoscience
2, 855-858.
Ebener, M. P., Bence, J. R., Newman, K. R. & Schneeberger, P. J. (2005). Application of
Statistical catch-at-age models to assess lake whitefish stocks in the 1836 treaty-ceded
waters of the upper Great Lakes. In Proceedings of a workshop on the dynamics of lake
whitefish (Coregonus clupeaformis) and the amphipod Diporeia spp. in the Great Lakes
(Mohr, L. C. & Nalepa, T. F., eds.), pp. 271-309. Ann Arbor, MI: Great Lakes Fishery
Commission.

158

Ebener, M. P., Kinnunen, R. E., Schneeberger, P. J., Mohr, L. C., Hoyle, J. A. & Peeters, P.
(2008). Management of Commercial Fisheries for Lake Whitefish in the Laurentian Great
Lakes of North America. In International Governance of Fisheries Ecosystems: Learning
from the Past, Finding Solutions for the Future (Schechter, M. G., Leonard, N. J. &
Taylor, W. W., eds.), pp. 99-143. Bethesda, Maryland: American Fisheries Society Press.
ESRI (2011). ArcGIS Desktop: Release 10. Redlands, CA: Environmental Systems Research
Institute.
Faria, J. C. (2013). Resources of Tinn-R GUI/Editor for R Environment. Ilheus, Brasil: UESC.
Farrar, D. E. & Glauber, R. R. (1967). Multicollinearity in Regression Analysis: The Problem
Revisited. The Review of Economics and Statistics, 49, 92-107.
Fox, J. (2005). The R commander: A basic-statistics graphical user interface to R. Journal of
Statistical Software 14.
Fox, J. & Weisberg, S. (2011). An R Companion to Applied Regression. Thousand Oaks, CA:
Sage.
Freeberg, M. H., Taylor, W. W. & Brown, R. W. (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, 92-100.
Hayes, D., Jones, M., Lester, N., Chu, C., Doka, S., Netto, J., Stockwell, J., Thompson, B.,
Minns, C. K., Shuter, B. & Collins, N. (2009). Linking fish population dynamics to
habitat conditions: insights from the application of a process-oriented approach to several
Great Lakes species. Reviews in Fish Biology and Fisheries 19, 295-312.
Henderson, B. A., Collins, J. J. & Reckahn, J. A. (1983). Dynamics of An Exploited Population
of Lake Whitefish (Coregonus clupeaformis) In Lake Huron. Canadian Journal of
Fisheries and Aquatic Sciences 40, 1556-1567.
Hilborn, R. & Walters, C. J. (1992). Quantitative Fisheries Stock Assessment: Choice, Dynamics
& Uncertainty. New York, Springer. 570pp.
Hooper, G., S. J. Kerr, et al. (2001). Lake whitefish culture and stocking: An annotated
bibliography and literature review. Fisheries Section, Ontario Ministry of Natural
Resources.
Jones, M. L., Shutter, B. J., Zhao, Y. & Stockwell, J. D. (2006). Forecasting effects of climate
change on Great Lakes fisheries: models that link supply to population dynamics can
help. Candian Journal of Fisheries and Aquatic Sciences 63, 457-468.
Koenig, W. D. (2002). Global patterns of environmental synchrony and the Moran effect.
Ecography 25, 283-288.

159

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,
1197-1227.
Lofgren, B. M. (2004). A model for simulation of the climate and hydrology of the Great Lakes
basin. Journal of Geophysical Research-Atmospheres 109.
Lofgren, B. M., Quinn, F. H., Clites, A. H., Assel, R. A., Eberhardt, A. J. & Luukkonen, C. L.
(2002). Evaluation of potential impacts on Great Lakes water resources based on climate
scenarios of two GCMs. Journal of Great Lakes Research 28, 537-554.
Lynch, A. J., Taylor, W. W. & Smith, K. D. (2010). The influence of changing climate on the
ecology and management of selected Laurentian Great Lakes fisheries. Journal of Fish
Biology.
MacCall, A. D. & Ralston, S. (2002). Is logarithmic transformation really the best procedure for
estimating stock-recruitment relationships? North American Journal of Fisheries
Management 22, 339-350.
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. (2006). Evaluation of a
lake whitefish bioenergetics model. Transactions of the American Fisheries Society 135,
61-75.
Madenjian, C. P., Tyson, J. T., Knight, R. L., Kershner, M. W. & Hansen, M. J. (1996). Firstyear growth, recruitment, and maturity of walleyes in western Lake Erie. Transactions of
the American Fisheries Society 125, 821-830.
Magnuson, J. J., Meisner, D. J. & Hill, D. K. (1990). Potential Changes in the Thermal Habitat of
Great Lakes Fisher after Global Climate Warming. Transactions of the American
Fisheries Society 119, 253-264.
Magnuson, J. J., Webster, K. E., Assel, R. A., Bowser, C. J., Dillon, P. J., Eaton, J. G., Evans, H.
E., Fee, E. J., Hall, R. I., Mortsch, L. R., Schindler, D. W. & Quinn, F. H. (1997).
Potential effects of climate changes on aquatic systems: Laurentian Great Lakes and
Precambrian Shield Region. Hydrological Processes 11, 825-871.
Myers, R. A. (1998). When do environment-recruitment correlations work? Reviews in Fish
Biology and Fisheries 8, 285-305.
Nalepa, T. F., Mohr, L. C., Henderson, B. A., Madenjian, C. P. & Schneeberger, P. J. (2005).
Lake whitefish and Diporeia spp. in the Great lakes: an overview. In Proceedings of a
workshop on the dynamics of lake whitefish (Coregonus clupeaformis) and the amphipod
Diporeia spp. in the Great Lakes (Mohr, L. C. & Nalepa, T. F., eds.), pp. 3-20. Ann
Arbor, Michigan.
O'Brien, R. M. (2007). A caution regarding rules of thumb for variance inflation factors. Quality
& Quantity 41, 673-690.

160

Pielke, R. A., Cotton, W. R., Walko, R. L., Tremback, C. J., Lyons, W. A., Grasso, L. D.,
Nicholls, M. E., Moran, M. D., Wesley, D. A., Lee, T. J. & Copeland, J. H. (1992). A
comprehensive meteorlogical modeling system - RAMS. Meteorology and Atmospheric
Physics 49, 69-91.
Quinn, T. J. & Deriso, R. B. (1999). Quantitative Fish Dynamics. New York: Oxford University
Press.
Regier, H. A. & Meisner, J. D. (1990). Anticipated effects of climate change on fresh-water
fishes and their habitat. Fisheries 15, 10-15.
Ricker, W. E. (1954). Stock and Recruitment. Journal of the Fisheries Research Board of Canada
11, 559-623.
Roseman, E. F., Taylor, W. W., Hayes, D. B., Haas, R. C., Knight, R. L. & Paxton, K. O. (1996).
Walleye egg deposition and survival on reefs in Western Lake Erie (USA). Annales
Zoologici Fennici 33, 341-351.
Shuter, B. J., Minns, C. K. & Lester, N. (2002). Climate change, freshwater fish, and fisheries:
Case studies from Ontario and their use in assessing potential impacts. Fisheries in a
Changing Climate 32, 77-87.
Taylor, W. W., Smale, M. A. & Freeberg, M. H. (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.
Team, R. D. C. (2008). R: A language and environment for statistical computing. Vienna,
Austria: R Foundation for Statistical Computing.
Trumpickas, J., Shuter, B. J. & Minns, C. K. (2009). Forecasting impacts of climate change on
Great Lakes surface water temperatures. Journal of Great Lakes Research 35, 454-463.
Wickham, H. (2012). Package 'stringr': Make it easier to work with strings.
Wright, K. (2006). corrgram: Plot a Correlogram.
Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A. & Smith, G. M. (2009). Mixed Effects
Models and Extensions in Ecology with R. New York, NY: Springer.

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SYNTHESIS

162

Climate change will affect the Great Lakes. The habitat will be different for Great Lakes
fishes, including Lake Whitefish. Because Lake Whitefish recruitment is linked to climate
variables, including temperature, wind, and ice cover, climate change will impact the
productivity of the Lake Whitefish fishery. Modeling can project these changes in Lake
Whitefish recruitment with climate change. Decision-support tools can help integrate these Lake
Whitefish climate change projections into harvest management.
Climate change will affect the Great Lakes
Research evaluating the long-term changes in climate patterns project that the Laurentian
Great Lakes region will be warmer, windier, with less ice cover by the end of the 21st century
(Lynch et al., 2010). Temperature, wind, and ice cover are all defining factors of fish habitat.
Air temperatures are expected to increase by 3-7oC in winter and 4-11oC in summer (Wuebbles
and Hayhoe, 2004) which will impact water temperatures. Surface temperatures of the Great
Lakes are expected to increase by as much as 6oC (Trumpickas et al., 2009). With increased air
and water temperatures, there will be less difference in the temperatures of the atmosphere and
aquatic environments which will likely result in the more frequent occurrence of stronger winds
and wind-driven waves (Desai et al., 2009). All of the Great Lakes are expected to be ice-free
year round, except for Lake Erie, the shallowest lake (Howe et al., 1986) and Lake Erie is
expected to have substantial reductions in ice cover (Lofgren et al., 2002); certain coldwater
species of fish depend on ice cover for protection of vulnerable life stages.
Climate influences the productivity of the Lake Whitefish fishery
Since 1980, populations of lake whitefish (Coregonus clupeaformis) have supported the
most economically valuable commercial fishery in the upper Laurentian Great Lakes (Lakes
Huron, Michigan, and Superior; Madenjian et al., 2006; Ebener et al., 2008). The success of
163

recruitment to the fishery has been linked with climatic influences, including temperature, wind,
and ice cover (Miller, 1952; Christie, 1963; Lawler, 1965; Taylor et al., 1987b; Freeberg et al.,
1990). Lynch et al. (Chapter 4) found that including temperature, wind speed, and ice cover as
variables in stock-recruitment modelling improves model fit and estimation of recruitment for
Lake Whitefish in the majority of management units examined. Namely, temperature, wind
speed, and ice cover are important drivers of Lake Whitefish recruitment in these management
units (see Figure 4.4).
Lake Whitefish spawn in the fall and the eggs overwinter before hatching in the spring.
As a result, fall and spring are critical periods to determining recruitment success because high
levels of mortality can occur at these life-stages, depending on environmental conditions.
Warmer spring water temperatures have been linked to Lake Whitefish larval growth and
survival through increased availability of plankton prey resources (Brown et al., 1993). Ice
cover has been linked to Lake Whitefish egg survival by mediating the damaging impacts of
wind and wave action to overwintering eggs, particularly eggs in sub-optimal rearing habitat
(Taylor et al., 1987a; Freeberg et al., 1990). Lynch et al. (Chapter 4) confirmed the positive
relationship between recruitment and temperature and ice cover and the negative relationship
between recruitment and wind speed using corrected Akaike’s Information Criterion for model
selection. In eight of the 13 management units examined in the 1836 Treaty Waters of Lakes
Huron, Michigan, and Superior, climate variables significantly improved model estimation of
Lake Whitefish recruitment (see Figure 4.1). In the remaining units, climate did not explain
recruitment variability; this is likely because other factors not considered in this analysis are
more strongly coupled with recruitment.

164

Modeling can project changes in Lake Whitefish recruitment with climate change
Climate change is expected to impact the Lake Whitefish fishery because temperature,
wind, and ice cover are important drivers of recruitment (Lynch et al., 2012). Thermal niche
modeling for the Great Lakes indicates that there will be a greater volume of optimal thermal
habitat for Lake Whitefish at all life stages (Magnuson et al., 1990). It is important to note that
realized habitat is composed of abiotic and biotic elements and interactions beyond just
temperature (Hudson et al., 1992). Less ice cover (Lofgren et al., 2002) and stronger winds
(Desai et al., 2009) may result in lower survival of Lake Whitefish eggs to hatching.
Lynch et al. (Chapter 4) evaluated the combined impact of the conflicting influences of
temperature, wind, and ice cover on Lake Whitefish recruitment in the 1836 Treaty Waters of
Lakes Huron, Michigan, and Superior. The climate-recruitment relationships and climate
projections for the Great Lakes indicate the potential for increase in Lake Whitefish recruitment
with climate change and the potential for a change in the distribution of the fishery. Some
management units will expect up to a 50% decline and others up to a 220% increase because of
spatial variability in the climate-recruitment relationships and climate projections (see Figure
4.3).
Decision-support tools can help integrate Lake Whitefish climate change projections into
harvest management
Decision-support tools can aid decision making by systematically incorporating
information, accounting for uncertainties, and facilitating evaluation of trade-offs between
alternative choices. Lynch et al. (Chapter 3) examined perceptions of the Lake Whitefish fishery
management and the willingness of Lake Whitefish fishermen, researchers, and managers to
utilize decision-support tools, such as the model developed by Lynch et al. (Chapter 4), to
increase the availability of research to assist harvest management decisions for Lake Whitefish in
165

the Great Lakes. The survey participants (Lake Whitefish fishermen, researchers, and managers)
indicated that they agreed with the statement that decision-support tools can be useful for
fisheries management (see Figure 3.8). The survey participants were given the opportunity to
provide suggestions for successful implementation of decision-support. The recommendations
included the following general categories: 1) fostering communication between managers and
fishermen; 2) addressing significant management questions; and, 3) using a user-friendly, lowmaintenance format.
The survey recommendations were used to develop the online user-interface for the
Lynch et al. (Chapter 4) decision-support tool, which is housed on the Michigan Sea Grant
website (http://www.miseagrant.umich.edu/) for ease of access and use by fishermen, fishery
managers, scientists, students, and the public. This tool will be a means to communicate the
potential impacts of climate change on the Lake Whitefish fishery with fishermen, fishery
managers, and scientists to help them anticipate changes to the distribution and abundance of the
fishery. The aim of the tool is to support an ecologically sustainable, prosperous Lake Whitefish
fishery and promote the well-being of associated coastal communities. Further, this tool can be
used to educate students and the public on potential impacts of climate change to the Great Lakes
region using Lake Whitefish as a case-study. More broadly, it can serve as a model for other
fisheries that have the potential to be impacted by global environmental processes, such as
climate change. The ultimate goal of this research is to support scientifically-informed decision
making and ensure sustainable use of fisheries resources, now and in the future.

166

LITERATURE CITED

167

LITERATURE CITED

Brown, R. W., Taylor, W. W. & Assel, R. A. (1993). Factors affecting the recruitment of lake
whitefish in two areas of northern Lake Michigan. Journal of Great Lakes Research 19,
418-428.
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,
597-646.
Desai, A. R., Austin, J. A., Bennington, V. & McKinley, G. A. (2009). Stronger winds over a
large lake in response to weakening air-to-lake temperature gradient. Nature Geoscience
2, 855-858.
Ebener, M. P., Kinnunen, R. E., Schneeberger, P. J., Mohr, L. C., Hoyle, J. A. & Peeters, P.
(2008). Management of Commercial Fisheries for Lake Whitefish in the Laurentian Great
Lakes of North America. In International Governance of Fisheries Ecosystems: Learning
from the Past, Finding Solutions for the Future (Schechter, M. G., Leonard, N. J. &
Taylor, W. W., eds.), pp. 99-143. Bethesda, Maryland: American Fisheries Society Press.
Freeberg, M. H., Taylor, W. W. & Brown, R. W. (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, 92-100.
Howe, D. A., Marchand, D. S. & Alpaugh, C. (1986). Socio-economic assessment of the
implications of climatic change for commercial navigation and hydro-electric power
generation in the Great Lakes-St. Lawrence River system. Windsor, Canada: Great Lakes
Institute, University of Windsor.
Hudson, P., Griffiths, R. & Wheaton, T. (1992). Review of habitat classification schemes
appropriate to streams, rivers, and connecting channels in the Great Lakes drainage basin.
The development of an aquatic habitat classification system for lakes. CRC Press, Boca
Raton, Florida, 73-107.
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,
1197-1227.
Lofgren, B. M., Quinn, F. H., Clites, A. H., Assel, R. A., Eberhardt, A. J. & Luukkonen, C. L.
(2002). Evaluation of potential impacts on Great Lakes water resources based on climate
scenarios of two GCMs. Journal of Great Lakes Research 28, 537-554.
Lynch, A. J., Taylor, W. W., Beard, T. D. & Lofgren, B. M. (Chapter 4). Projected changes in
Lake Whitefish (Coregonus clupeaformis) recruitment with climate change in the 1836
Treaty Waters of Lakes Huron, Michigan, and Superior.

168

Lynch, A. J., Taylor, W. W. & McCright, A. M. (Chapter 3). Perceptions of management and
willingness to use decision-support: Integrating the potential impacts of climate change
on the Lake Whitefish (Coregonus clupeaformis) fishery into harvest management in the
1836 Treaty Waters of Lakes Huron, Michigan, and Superior.
Lynch, A. J., Taylor, W. W. & Smith, K. D. (2010). The influence of changing climate on the
ecology and management of selected Laurentian Great Lakes fisheries. Journal of Fish
Biology.
Lynch, A. J., Varela-Acevedo, E. & Taylor, W. W. (2012). The need for decision-support tools
for a changing climate: application to inland fisheries management. Fisheries
Management and Ecology.
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. (2006). Evaluation of a
lake whitefish bioenergetics model. Transactions of the American Fisheries Society 135,
61-75.
Magnuson, J. J., Meisner, D. J. & Hill, D. K. (1990). Potential Changes in the Thermal Habitat of
Great Lakes Fisher after Global Climate Warming. Transactions of the American
Fisheries Society 119, 253-264.
Miller, R. B. (1952). The relative sizes of whitefish year classes as affected by egg planting and
the weather. Journal of Wildlife Management 16, 39-50.
Taylor, W. W., Smale, M. A. & Freeberg, M. H. (1987a). Biotic and abiotic determinants of lake
whitefish (Coregonus clupeaformis) recruitment in northeastern Lake Michigan.
Canadian Journal of Fisheries and Aquatic Sciences 44, 313-323.
Taylor, W. W., Smalle, M. A. & Freeberg, M. H. (1987b). 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., Shuter, B. J. & Minns, C. K. (2009). Forecasting impacts of climate change on
Great Lakes surface water temperatures. Journal of Great Lakes Research 35, 454-463.
Wuebbles, D. J. & Hayhoe, K. (2004). Climate change projections for the United States
Midwest. In International Conference on Climate Change and Environmental Policy,
University of Illinois at Urbana-Champaign, USA, November 2002., pp. 335-363:
Kluwer Academic Publishers.

169