predicted climate change effects on fisheries habitat and...
TRANSCRIPT
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A. Miehls
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Collaborative team:
David “Bo” Bunnell Chuck Madenjian
David Warner
Paris Collingsworth
Yu-Chun Kao
Randy Claramunt
Brent Lofgren
Marjorie Perroud
Michael Murray Carlo DeMarchi
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IPCC WG1 2013
A warming climate: especially at higher latitudes and over land:
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Laurentian Great Lakes:
Home to more than 30 million US and Canadian citizens within its watershed.
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Laurentian Great Lakes:
Contain about 20% of the world’s surface freshwater (drinking water to tens of millions).
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Laurentian Great Lakes:
17,000 km of shoreline (half way around the equator). Supports numerous recreational activities.
Michigan Sea Grant, Todd Marsee
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Laurentian Great Lakes:
Fisheries are a key economic driver. Anglers directly spent $2.5 billion in 2006. Multiplicative impact = $7 billion (Thayer & Loftus 2013)
Michigan Sea Grant, Todd Marsee
“Fishtown”, USA Leland, Michigan
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Number that survive to the fishery
Less ice cover Less protection for eggs incubating overwinter
Climate-driven Effects on lakes
Effects on fish Effects on fish management
Ice
Michigan Sea Grant
Freeberg et al. 1990; Brown et al. 1993
Our approach: understand how climate affects fish… Our mechanistic approach:
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Fish growth
Falling water levels
Less ice cover
Earlier & longer thermal stratification
Changes in fish food
Less protection
Climate-driven Effects on lakes
Effects on fish Effects on fish management
Warming temperatures Metabolism
Reduced nearshore spawning habitat
Lake productivity
Follows Jones et al. 2006
Our mechanistic approach:
Number that survive to the fishery
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Overall conceptual framework:
Mechanistic approach for understanding climate-driven impacts on fisheries.
Looking backwards- Can we use historical data to detect climate signal
on biotic variables?
Looking forward- Develop localized climate predictions.
Where i) we find climate signals in historical biotic
data or ii) have previous understanding of climate impacts on biota: Forecast fish responses (i.e., growth)
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Outline for today’s talk: 1. Intro Climate-driven effects on large lake ecosystems Our conceptual framework
2. Looking backward: climate-driven effects on: Algal production Prey-fish recruitment
3. Looking forward: climate-driven effects on: Water temperature, precipitation, water level, ice
cover
4. Looking forward: climate-driven effects on: Growth rates of recreationally, commercially
important fisheries.
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Outline for today’s talk: 1. Intro Climate-driven effects on large lake ecosystems Our conceptual framework
2. Looking backward: climate-driven effects on: Algal production Prey-fish recruitment
3. Looking forward: climate-driven effects on: Water temperature, precipitation, water level, ice
cover
4. Looking forward: climate-driven effects on: Growth rates of recreationally, commercially
important fisheries.
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David Warner and Barry Lesht
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Why Worry About Algal Production?
• Lake productivity affects ecosystem services: – Availability of clean, safe drinking water – Desirability of recreational opportunities – Production of harvestable fish
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Why timing matters….
Ideally, overlap in the timing….
Less ideal: algae respond differentially… and zooplankton (and fish larvae) starve
Time
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Why the amount matters…
Algae
Zooplankton
Prey fish
Fish-eating fish (piscivores)
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Declining nutrients in Lakes Michigan & Huron
1975 1980 1985 1990 1995 2000 2005 2010
Tota
l pho
spho
rus
load
ings
(ton
nes)
2000
3000
4000
5000
6000
7000
sprin
g to
tal p
hosp
hurs
(ug/
L)
2
3
4
5
6
7
8
EPA-GLNPO data
Lake Michigan
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Research Questions 1. Has the timing and amount of algal production
changed in lakes Michigan and Huron since 1998?
2. If so, what role does climate play, relative to other variables?
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Data Response – Satellite derived estimates of algae- measured as
chlorophyll-a. Note primary production was also estimated. Method validated in Lesht et al. 2013 March – May: when lake is not thermally layered. March – November: entire year without ice. May 20, 1998
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Data Response – Satellite derived estimates of algae- measured as
chlorophyll-a. Note primary production was also estimated. Method validated in Lesht et al. 2013 March – May: when lake is not thermally layered. March – November: entire year without ice.
Explanatory variables: Climate ○ Air and water temperature ○ Precipitation
Nutrients ○ Spring TP concentrations in the offshore (EPA monitoring) ○ Annual total phosphorus (TP loading)
Dreissenid mussels ○ Annual density
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1. Timing–does algae bloom earlier in warmer years?
Analysis
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Timing: Lake Michigan 1998-2008
o Chl a (solid line)- o Spring bloom evident
from 1998~2001. o 2002 to 2008 “flatter”
time series across the year; no clear spring bloom.
o No evidence of the spring
bloom being related to water temperature between 1998-2008.
Stratification date line
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1. Timing–does algae bloom earlier in warmer years?
2. Amount of algae produced- Developed regression statistical models Identified most reasonable model(s) using AICc.
Analysis
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Spring: r2 =0.72 Nutrients (Spring TP, +0.77) Dreissenid mussel density (-0.31) No climate variable included in best model(s)
Results- Amount of algae produced
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Annual: r2 =0.66 Dreissenid mussel density (-0.55) Water temperature (-0.47) Air temperature (+0.40)
Temperature
Alga
e
Algal growth increases with temperature
Colder years, later stratification; increases mussel access to algae.
Temperature
Alga
e
Zooplankton feeding increases with temperature.
Results- Amount of algae produced
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Timing of algal “blooms”: Spring bloom is not necessarily earlier in warmer years.
How much algae is produced: Influenced by nutrients, mussels and temperature. Future research needs to tease out these interacting effects on
algal production.
Conclusions
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Outline for today’s talk: 1. Intro Climate-driven effects on large lake ecosystems Our conceptual framework
2. Looking backwards: climate-driven effects on: Algal production Prey-fish recruitment
3. Looking forward: climate-driven effects on: Water temperature, precipitation, water level, ice
cover
4. Looking forward: climate-driven effects on: Growth rates of recreationally, commercially
important fisheries.
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Paris D. Collingsworth, David B. Bunnell, Charles P. Madenjian, Stephen C. Riley
Transactions of the American Fisheries Society, 2014 doi:10.1080/00028487.2013.833986
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Why fish recruitment (number that are produced in a given year) is important:
Leads to sustainable fish populations (& fisheries).
Recruitment is unpredictable.
The number of mature adults rarely, if ever, singularly predicts recruitment.
Other environmental predictors (including climate) are key to predicting recruitment.
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Recruitment of prey fish in the Great Lakes
Prey fish support recreational fisheries (and limited commercial fisheries) with highest economic value.
Prey fish recruitment (and biomass) has been declining in several Great Lakes.
Box of Lake Michigan prey fish sampled during USGS annual
bottom trawl survey
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Lake Michigan
Bloater and alewife are key prey fishes and near record-lows.
A tale of two lakes: Michigan and Huron
USGS-GLSC
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A tale of two lakes: Michigan and Huron
Despite prey fish decline, salmonine fishery holds steady.
Year1998 2000 2002 2004 2006 2008 2010
Pisc
ivor
e bi
omas
s (to
nnes
)
0
2000
4000
6000
8000
10000
12000
14000
16000Chinook salmonLake trout
Lake Michigan
Tsehaye et al., Michigan State University
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0
50
100
150
200
250
300
350
400
1976 1981 1986 1991 1996 2001 2006 2011
Mai
n ba
sin
biom
ass
estim
ate
(kilo
tonn
es)
Year
ROUND GOBY
TROUT PERCH
NINESPINE STICKLEBACK
DEEPWATER SCULPIN
SLIMY SCULPIN
YAO BLOATER
YOY BLOATER
YAO SMELT
YOY SMELT
YAO ALEWIFE
YOY ALEWIFE
A tale of two lakes: Michigan and Huron
Lake Huron
USGS-GLSC
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Year1998 2000 2002 2004 2006 2008 2010
Pisc
ivor
e bi
omas
s (to
nnes
)
0
1000
2000
3000
4000Chinook salmonLake trout
A tale of two lakes: Michigan and Huron
Chinook salmon collapsed with alewife collapse.
Lake Huron
Brenden et al., Michigan State University
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Develop statistical models to understand drivers in alewife and bloater recruitment. Traditional Ricker stock-recruit models Dynamic Linear Models (Bayesian)
Analysis
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Explanatory variable Hypothesis Source
Spring-summer water temperature
(+) Facilitates faster larval growth Madenjian et al. 2005
Length of winter (-) First year overwinter mortality O’Gorman et al. 2004
Water level (+) Greater access to spawning habitat
This study
Alewife
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Explanatory variable Hypothesis Source
Spring-summer water temperature
(+) Facilitates faster larval growth Madenjian et al. 2005
Length of winter (-) First year overwinter mortality O’Gorman et al. 2004
Water level (+) Greater access to spawning habitat
This study
Salmonine biomass (-) Predation on young recruits Madenjian et al. 2005
Lake productivity (+) More food for larvae O’Gorman et al. 2004
Alewife
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Salmon Summer Winter Level
Sum
wi
0.0
0.2
0.4
0.6
0.8
1.0 Lake MichiganLake Huron
Alewife
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Explanatory variable Hypothesis Source
Winter-spring water temperature
(+) Accelerates egg incubation and reduces predation by egg predators
Rice et al. 1987
Bloater
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Explanatory variable Hypothesis Source
Winter-spring water temperature
(+) Accelerates egg incubation and reduces predation by egg predators
Rice et al. 1987
Alewife biomass (-) Predation on larval recruits Eck and Wells 1987
% female (sex ratio) (-) Balanced sex ratio increases egg fertilization rate
Brown et al. 1987; Bunnell et al. 2006
Adult condition (+) Produce eggs with more lipids to enhance larval survival
Bunnell et al. 2009
Bloater
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Sex raio Condition Temp Alewife
Sum
wi
0.0
0.2
0.4
0.6
0.8
1.0Lake MichiganLake Huron
Bloater
Sex ratio
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Conclusions Using historical time series, climate signals were difficult to
detect for as drivers of Lake Michigan and Huron prey fish.
Alewife- Recruitment in Lake Michigan explained by salmon predation. Lake Huron- weak explanatory power.
Bloater- Recruitment in Lakes Michigan and Huron were explained by common factors (sex ratio, alewife). No evidence of a climate signal.
Limits our ability to forecast recruitment of alewife or bloater based on future climate scenarios.
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Outline for today’s talk: 1. Intro Climate-driven effects on large lake ecosystems Our conceptual framework
2. Looking backward: climate-driven effects on: Algal production Prey-fish recruitment
3. Looking forward: climate-driven effects on: Water temperature, precipitation, water level, ice
cover
4. Looking forward: climate-driven effects on: Growth rates of recreationally, commercially
important fisheries.
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Brent Lofgren
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Simplistic chain of causality in many studies that evaluate climate impacts
Greenhouse gases
Ecological impact (e.g., lake
temperature)
Air temperature, perhaps precipitation
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Temperature Pressure
Clouds
Humidity
Wind
Sfc temperature Sfc roughness Sfc moisture
Sensible heat
Upward longwave Friction
Downward longwave
Sfc albedo
Latent heat/evap
Upward solar
Downward solar Upward longwave
Precip
More accurate chain of causality
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Based on Regional Atmospheric Modeling System (RAMS) 40 km grid, with domain reaching into the Great Plains,
Hudson Bay, Atlantic Ocean, and near the Gulf of Mexico 24 vertical levels up to 17 km Array (on the same horizontal grid) of 1-D lake column
models based on Hostetler and Bartlein (1990) LEAF3 formulation of land surface Mellor-Yamada level 2.5 atmospheric boundary layer Driven by:
Canadian CRCM3 GCM SRES A2 scenario GHG concentrations
Coupled Hydrosphere-Atmosphere Research Model (CHARM)
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Changes in air temperature (°C): 2057 (2043-2070) minus 1985 (1978-1993)
Winter (Dec-Feb) Summer (Jun-Aug) 4.0
3.0
2.0
1.0
4.0
3.0
2.0
1.0
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Changes in Lake Michigan water temperature: 1964-1977 vs. 2058-2070
-200
-150
-100
-50
00 5 10 15 20
Feb
-200
-150
-100
-50
00 5 10 15 20
Apr
-200
-150
-100
-50
00 5 10 15 20
Jun
-200
-150
-100
-50
00 5 10 15 20
Aug
-200
-150
-100
-50
00 5 10 15 20
Oct
-200
-150
-100
-50
00 5 10 15 20
Dec
1971 2062
Dep
th (m
)
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Changes in precipitation (mm/day): 2057 (2043-2070) minus 1985 (1978-1993)
Winter (Dec-Feb) Summer (Jun-Aug)
0.5
0
0.25
-0.25
-0.5
0.5
0
0.25
-0.25
-0.5
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Net water balance lake water levels: 2057 (2043-2070) minus 1985 (1978-1993)
0.03
0
-0.02
0.02
0.01
-0.01
Inches/day
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Changes in Lake Michigan ice thickness (m):
February (1979-1993)
1.0
0
0.5
0.25
0.75
1.0
0
0.5
0.25
0.75
February (2043-2070)
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Preliminary forecasts from CHARM
Water temperatures (warmer): Summer-Fall: 2-3 °C increase throughout water column. February: deepest waters can be warmer than 4 °C.
Precipitation (wetter):
Winter: even snowier within lake effect regions. Summer: patchy, but generally more rain.
Ice cover (less):
North to south gradient.
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Outline for today’s talk: 1. Intro Climate-driven effects on large lake ecosystems Our conceptual framework
2. Looking backward: climate-driven effects on: Algal production Prey-fish recruitment
3. Looking forward: climate-driven effects on: Water temperature, precipitation, water level, ice
cover
4. Looking forward: climate-driven effects on: Growth rates of recreationally, commercially
important fisheries.
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Yu-Chun Kao, Chuck Madenjian
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How does a warming climate affect fish growth?
Temperature
Consumption
Metabolism Waste loss
Fish growth
Prey quantity and quality
Food web
Fish physiology
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Physiological optimal temperature for lake whitefish = 12 °C
Fish are ectothermic (cold-blooded)
Where they live balances competing factors: physiological optima, habitat quality, food availability, avoiding predators.
How can fish respond to warmer temperatures?
Gorskyet al. 2012
Clear Lake, Maine
They find their ideal temperatures within the lake…
13 °C
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Today’s results
Forecast growth of yellow perch (cool-water fish) and lake trout (cold-water fish) in 2043 to 2070.
Simulate growth and consumption within lakes Michigan and Huron.
Assume fish occupy physiological optimum temperature, if available.
Assume different prey densities (high, baseline, low).
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Yellow perch Lake trout
Effectively, fish have more days when optimal temperature is available in future….
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How did we predict future fish growth?
Used the Wisconsin bioenergetics models, a mass-balance approach to account for energy
Realized consumption (PCmax) =
Metabolic cost + Waste loss + Growth
Cmax PCmax = M + W + G
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Ye Yellow perch Lake trout
High prey availability
Limited prey availability
Baseline
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Lake trout forecasted to grow better (relative to baseline) than yellow perch in both prey scenarios.
o Despite the number of days with “optimal temperature” increasing for yellow perch more than lake trout.
o In the future, yellow perch predicted to have higher energetic costs (metabolism, waste).
o Lake trout prey (alewife, rainbow smelt) have more calories in the fall.
Unexpected results:
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Have similar predictions of future growth for:
Lake whitefish Rainbow trout Chinook salmon
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Conclusion
Unless food resources increase with temperature, yellow perch and lake trout growth will likely decrease in a warming climate.
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Overall take-home messages 1. Climate change will affect key fish habitat
variables (water temperature, ice cover, water level).
2. We recommend a mechanistic approach for translating those effects to fish responses.
3. Those mechanisms should include factors beyond climate. Our results revealed non-climate factors had an even greater impact: Mussels, nutrients affected algae Salmon affected alewife recruitment Prey densities affected fish growth
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Overall take-home messages 4. This does not mean that forecasted climate
change effects should be ignored. Rather, these changes should be considered within a broader context. For example, use models that include the entire food-web to best understand how climate directly and indirectly influences fisheries.
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Acknowledgements
USGS Patty Armenio, Roger Bergstedt , Jeff Schaeffer US FWS Greg Jacobs University of Michigan Michael Wiley, James Breck, James Diana Michigan State University Amber Peters, Iyob Tsehaye Michigan Department of Natural Resources Dave Caroffino, Ji He Ontario Ministry of Natural Resources Lloyd Mohr
Funding:
National Climate Change Wildlife Science Center
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Questions? David “Bo” Bunnell [email protected]
David Warner [email protected]
Paris Collingsworth [email protected]
Brent Lofgren [email protected]
Yu-Chun Kao [email protected]
Chuck Madenjian [email protected]
Lars Jensen
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Climate Warming Influence on Large Lakes
Consistent physical changes have been observed among large, deep lakes Increased water temperature, especially in surface
waters. Decrease in ice cover, lengthening of stratified period.
Fewer consistent chemical/biological observations
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Lake Baikal, Siberia. Over last century:
Moore et al. 2009 BioScience
Ice-free season ~ 18 days longer. Annual surface waters warmed ~2 °C.
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Lake Superior, United States & Canada
o Largest, o deepest, o northernmost Laurentian Great Lake
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Star
t of
stra
tific
atio
n M
ean
sum
mer
w
ater
tem
pera
ture
% ice cover, 1979-2006
Lake Superior; Austin and Colman (2007)
Less ice = Earlier stratification & warmer summer temperatures.
% ice cover, 1979-2006
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Cline et al. 2013
Changes in Lake Superior fish habitat: 1979-2006
Walleye, Chinook salmon, lean lake trout each have enjoyed more water of their preferred temperature.
Siscowet lake trout has found less preferred water.
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Pothoven and Fahnenstiel 2013
J. Allen, USGS
Early 2000s: proliferation of quagga mussels in Lake Michigan
Declining nutrients in Lakes Michigan & Huron