Mercury concentration and aquatic food web alterations associated with zebra mussel invasion in
Minnesota lakes
A THESIS SUBMITTED TO THE FACULTY OF THE UNIVERSITY OF MINNESOTA BY
Denver J. Link
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER
OF SCIENCE
Dr. Gretchen J.A. Hansen
August 2023
© Denver J. Link 2023
Acknowledgments
Local knowledge of the DNR area offices and their cooperation in sample collection made this
research possible. I especially thank Carl Pedersen, Jody Derks, and the entire Walker Area
Fisheries Office for accommodating us into busy sampling schedules and their dedication in
finding benthic invertebrates. Similarly, many DNR research staff provided data, support, and
feedback throughout the process.
Many hands ensured smooth sailing on lakes and provided countless hours in the lab. Jenna
Nelson, Chris Rounds, Ashley LaRoque, Nikayla Barnes, Leah Sterns, Tristan Blechinger, and
Amanda Van Pelt each provided a unique skill set essential for productive and fun sample
collection and processing.
I owe many thanks to my advisor Dr. Gretchen Hansen for taking a chance on me. Her expertise
and knowledge of aquatic science is only the base of what I gathered during my time. I have a
new appreciation for the entire scientific process and its application in a changing world. I look
forward to using these skills to address challenges our freshwater systems are facing.
Last, but certainly not least, I would like to acknowledge the sacrifices of my family. My mom,
dad, and brother allowed me to chase my fish fever. They graciously listened to me talk about
stable isotopes and Bayesian statistics, while providing me an escape from the daily grind. My
Australian shepherd border collie, Hazel, greeted me with tail wags everyday when I came home.
Most importantly, I am forever grateful for the sacrifices made by my soon-to-be wife, Alaina.
She pushed me the entire way, making the process feel like it was a team effort.
i
Abstract
Zebra mussels (Dreissena polymorpha) are an invasive species documented in 299
Minnesota lakes, with 231 of those lakes managed as walleye fisheries. Many ecological changes
are associated with zebra mussel invasion, including increased water clarity, depleted pelagic
energy resources, expanded littoral zones, deposition of benthic organic matter causing anoxic
sediments, and increased benthic energy reliance of food webs. However, the effects of zebra
mussels vary among lakes, and few opportunities exist to examine lake ecosystems and food
webs pre- and post- zebra mussel invasion. Furthermore, recent evidence has suggested that
Dreissenid mussels may impact contaminant bioaccumulation in higher trophic levels, but these
effects have not been well-studied in inland lakes. I took two distinct approaches to
understanding zebra mussel impacts on food webs and contaminants in fishes. I combined
historic Minnesota statewide mercury monitoring data from 1997-2021 coupled with stable
isotope data to provide insight into food web dynamics and mercury concentration alterations
with zebra mussel invasion. I found the probability of exceeding the Minnesota safe threshold for
safe eating of average sized northern pike (Esox Lucius) and walleye (Sander vitreus) ranged
from 60%-70%, with zebra mussel lakes having an increased chance. Specifically, mercury
concentrations analyzed using Before-After Control-Impact (BACI) study design increased by
8.2% in northern pike and 15.4% in walleye for invaded lakes, while uninvaded systems were
stagnant or decreased. To quantify resource use and community structure, food webs in Leech
Lake were analyzed pre- and post- invasion using stable isotope analysis of δ13C and δ15N. Fish
community response to zebra mussel invasion varied spatially in Leech Lake. Bays on the
western shoreline of Leech Lake with heterogeneous habitat increased niche size by 39.81% and
fish in those areas relied more heavily on benthic resources following invasion. In contrast to the
western bays and to hypothesized effects of zebra mussels, fish from the main basin of Leech
Lake, containing mostly homogenous pelagic resources, decreased in niche size by 32.26% and
relied more heavily on offshore resources. Taken together, high mercury concentrations in
Minnesota northern pike and walleye are exacerbated in zebra mussel lakes. Food web dynamics
are variable within the same lake, suggesting preexisting food web structure and access to
benthic resources are important to community resilience with zebra mussel invasion.
ii
Table of Contents
Acknowledgments........................................................................................................................... i
Abstract...........................................................................................................................................ii
Table of Contents.......................................................................................................................... iii
List of Tables............................................................................................................................. iv
List of Figures............................................................................................................................ v
Chapter 1 - Invasive species play a role in mercury bioavailability: How zebra mussels
affect mercury concentrations in Minnesota fish........................................................................1
Introduction................................................................................................................................1
Methods......................................................................................................................................4
Results........................................................................................................................................9
Discussion................................................................................................................................19
Chapter 2 - Zebra mussel invasion has varying food web effects on different Leech Lake
habitats..........................................................................................................................................24
Introduction..............................................................................................................................24
Methods....................................................................................................................................28
Results......................................................................................................................................34
Discussion................................................................................................................................45
Bibliography................................................................................................................................. 49
Appendix.......................................................................................................................................62
iii
List of Tables
Table 1. Summary of samples and lakes within comprehensive analysis....................................... 9
Table 2. Summary of fish lengths..................................................................................................10
Table 3. Summary of samples and lakes within the BACI analysis..............................................10
Table 4. Posterior summary of zebra mussel effects on mercury concentrations in comprehensive
analysis...........................................................................................................................................11
Table 5. Probability of crossing state thresholds within the comprehensive analysis...................12
Table 6. Bayesian R2 for models................................................................................................... 12
Table 7. Posterior summary of zebra mussel effects on mercury concentration in BACI analysis..
17
Table 8. Zebra mussel invasion sample summary for Lake Mille Lacs and Lake Winnibigoshish..
18
Table 9. Probability of crossing state thresholds within BACI analysis....................................... 19
Table 10. Summary of baseline isotopic values............................................................................ 33
Table 11. Summary of standard ellipse area by grouping............................................................. 37
Table 12. Standard ellipse area comparisons by grouping............................................................ 40
Table 13. Benthic reliance and trophic position summary for fish groupings.............................. 43
Table S1. Summary of posterior estimates for northern pike comprehensive mercury analysis.. 63
Table S2. Summary of posterior estimates for walleye comprehensive mercury analysis........... 64
Table S3. Summary of posterior estimates for northern pike BACI mercury analysis................. 65
Table S4. Summary of posterior estimates for walleye BACI mercury analysis including Mille
Lacs................................................................................................................................................66
Table S5. Summary of posterior estimates for walleye BACI analysis excluding Mille Lacs..... 67
Table S6. Summary of fish length in millimeters for each fish species in the respective
community......................................................................................................................................68
Table S7. Lakes included in BACI analysis for Northern Pike.....................................................71
Table S8. Lakes included in BACI analysis for Walleye............................................................ 103
iv
List of Figures
Figure 1. Distribution of mercury samples..................................................................................... 5
Figure 2. Spatial distribution of lakes in BACI analysis.................................................................8
Figure 3. Mercury concentration zebra mussel effects plot comprehensive analysis................... 13
Figure 4. Mercury concentration zebra mussel effects plot for northern pike BACI analysis......15
Figure 5. Mercury concentration zebra mussel effects plot for walleye BACI analysis...............16
Figure 6. Lake Mille Lacs and Lake Winnibigoshish mercury concentrations with length before
and after zebra mussel invasion..................................................................................................... 21
Figure 7. Map of Leech Lake sampling sites................................................................................ 27
Figure 8. Baseline isotope plot......................................................................................................32
Figure 9. 90% standard ellipses comparing across lake basins within the same year.................. 35
Figure 10. Direction of ellipses changes.......................................................................................36
Figure 11. Standard ellipse area boxplot for each area and invasion grouping.............................38
Figure 12. 90% standard ellipses comparing within lake basins across invasion years............... 39
Figure 13. Benthic reliance and trophic position population-level changes within each basin.... 41
Figure 14. Population level changes within the main basin..........................................................42
Figure 15. Population level changes within the western bays...................................................... 43
Figure S1. Isotopic values of fish groupings with their means and baselines.............................. 62
v
Chapter 1 - Invasive species play a role in mercury bioavailability: How zebra
mussels affect mercury concentrations in Minnesota fish
Introduction
Primarily ingested through fish consumption, mercury affects brain and neurological
development (Mahaffey, 1999). Subsistence anglers and indigenous communities with cultural
importance to fish consumption are disproportionately affected by mercury contamination from
fish (Patterson, 2002). Mercury naturally occurs in the environment, but mining and fossil fuel
combustion have emitted mineral reservoirs into the atmosphere. Mercury emissions have caused
atmospheric concentrations to triple relative to pre-industrial times, with anthropogenic sources
accounting for over 50% of the mercury found in the ocean (Amos et al., 2013). Since the 1970s,
decreased dependence on coal-powered energy and pollution control technologies have
significantly reduced deposition (Engstrom & Swain, 1997). Atmospheric deposition remains the
primary mercury source for the surface water of lakes, while watershed inputs from terrestrial
particles dominate sediment sources (Chen et al., 2016). Despite over an 80% reduction in
atmospheric deposition of mercury over the past thirty years in the United States, trends of
mercury concentration in biota are variable. Mercury in Minnesota fish initially decreased by
4.6% from 1982-1992, but increased by 14% from 1990-2019 (Hemken & Kovacevic, 2022;
Monson, 2009). Similarly, trends in mercury concentrations in fish varied across latitudes in
Wisconsin from 1982-2005, decreasing by 0.5% per year in northern lakes but increasing by
0.8% per year in southern lakes (Rasmussen et al., 2007). Understanding variable biological
response to depositional reduction of mercury will protect sensitive populations from
consumption of fish with unsafe mercury levels.
Inorganic mercury directly deposited into the environment is not typically biologically
available to organisms and thus not biomagnified in food webs (Morel et al., 1998). Instead,
sulfate reducing bacteria in anoxic sediments create methylmercury, the most bioavailable form
(Gilmour et al., 2013; Molina et al., 2010). Mercury concentrations in fish vary at individual,
lake, and catchment levels (Moslemi-Aqdam et al., 2022). At an individual-level, growth rate,
foraging area, and trophic position are important determinants of bioaccumulation (Burke et al.,
1
2020; Power et al., 2002). Environmental factors within a lake strongly influence methylation
and thus mercury available for uptake. Water chemistry, including DOC, pH, DO, SO4, and
chlorophyll-a, has been linked to available mercury in lakes (Paranjape & Hall, 2017; Pickhardt
et al., 2002; Winfrey & Rudd, 1990). Physical attributes of catchment areas, including total area,
watershed to lake area ratio, and vegetation type, are contributing factors for mercury in fish at a
large scale (Denkenberger et al., 2020; Kritzberg et al., 2020). Changes to these factors at any
level affects incorporation of mercury into the aquatic food web. Divergent trends of atmospheric
deposition and fish concentrations likely result from legacy mercury stored in aquatic
environments and local processes that increase the bioavailability (Wang et al., 2019). Globally,
climate change poses a massive threat to mercury concentration increases due to changes in food
web structure, permafrost melt, and bioenergetics of primary consumers (Wang et al., 2019), but
any environmental disturbance affecting food webs and/or water chemistry could influence
mercury availability and bioaccumulation in fishes.
Invasive species affect ecosystems, economies, and human health. Aquatic invasive
species (AIS), in particular, threaten freshwater systems by altering their hydrology, biodiversity,
production, and biogeochemical cycling (Strayer, 2010). The zebra mussel (Dreissena
polymorpha), an aquatic invasive species native to eastern Europe, was initially reported in the
Great Lake Basin in 1988 and has continued to spread to inland lakes across North American
(Nalepa & Schloesser, 2013). Zebra mussels deplete pelagic energy resources by filtering large
volumes of water, removing phytoplankton and depositing waste in benthic areas of lakes
(Karatayev et al., 2002; Stewart et al., 1998). Zebra mussels may influence mercury
bioaccumulation in fish by several processes. First, zebra mussels remove phytoplankton from
the water column and deposit organic matter on lake bottoms, increasing benthic secondary
production (Stewart et al., 1998). Zebra mussels can form dense mats on lake bottoms, and the
deposition of their psuedofeces can result in anoxic sediments where sulfate reducing bacteria
produce methylmercury (Berg et al., 1996; Watras et al., 1995). Thus, zebra mussels could
potentially increase mercury methylation in the littoral zone of lakes. Increased water clarity
caused by zebra mussels simultaneously expands littoral areas through enhanced light
penetration. This expansion leads to an increased proportion of the lake where benthic
production occurs, while also boosting benthic production itself. Finally, many fish species may
2
increase their reliance on benthic energy sources following zebra mussel invasion (Miehls et al.,
2009; Strayer, 2009). In combination, the potential to increase mercury methylation in the
nearshore zone while also increasing fish reliance on food resources from that zone could
increase mercury concentrations in fish tissues independent of mercury atmospheric deposition.
These patterns have been noted in predatory fish species in Lake Michigan, as they experienced
greater reliance on benthic energetic pathways with elevated mercury concentrations following
the invasion of dreissenid mussels (Lepak et al., 2019; Turschak et al., 2014). Similarly, fish in
smaller inland lakes rely more heavily on benthic resources in lakes with zebra mussels
compared to lakes without (McEachran et al., 2019; Morrison et al., 2021). Despite records of
mercury increases in fish from small-scale cross-lake studies, it is unknown if invasive zebra
mussels are playing a role across the landscape.
Federal and state agencies issue fish consumption advisories to protect the public and
sensitive populations from mercury contamination. In 2001, the food and drug administration
provided consumption guidelines for pregnant women, women of child bearing age, and young
children (Shimshack et al., 2007). Since the establishment of the general guideline, the
Environmental Protection Agency (EPA) delegates responsibility to state, tribal, and local
governments for the protection of people eating contaminated fish by monitoring and issuing fish
consumption advisories (Scherer et al., 2008). The state of Minnesota maintains Minnesota's Fish
Contaminant Monitoring Program as a collaboration between the Minnesota Department of
Natural Resources (MDNR), Health (MDH), and the Minnesota Pollution Control Agency
(MPCA) to select lakes and rivers for fish collection and analysis. Following protocols outlined
by the Great Lakes Consortium for Fish Consumption Advisories, Minnesota develops fish
consumption advisories for specific populations of people by outlining meal serving frequency
for certain fish species (Minnesota Department of Health). The established protocols use a fish
mercury concentration of 0.22ppm as a threshold to increase meal frequency restriction from one
meal per week to one meal per month (Great Lakes Consortium for Fish Consumption
Advisories, 2007). Similarly, the MPCA considers a body of water impaired by mercury when
10% of fish species in a lake or river has a mercury concentration in filets that exceeds 0.2ppm
(Minnesota Pollution Control Agency).
3
In this study, we use mercury concentrations collected from fish in lakes throughout the
state of Minnesota from 1997-2021 from over 1,000 lakes to assess the impact of zebra mussel
invasion on mercury concentrations in fish tissue. Zebra mussel impact on mercury
concentrations is quantified in walleye (Sander vitreus) and northern pike (Esox Lucius) using
Bayesian hierarchical models. A comprehensive analysis for each species explores trend
overviews from the collective dataset. A smaller subset of data for both species contains samples
from lakes before and after invasion, allowing for a pseudo Before-After Control-Impact (BACI)
analysis. Although long term trend data in individual lakes are sparse, this dataset offers a
powerful tool for examining the potential impact of zebra mussels on fish tissue mercury in
Minnesota lakes.
Methods
Data. The Minnesota Department of Natural Resources (MDNR) conducted regular monitoring
of fish in lakes using gill nets and trap nets. Over the past 50 years, more than 4,400 unique lakes
have been sampled, with 500-700 lakes sampled each year (Monson, 2009). The surveys are
rotated each year based on lake size and angling pressure. Larger lakes that experience heavy
angling are surveyed every 1-3 years, smaller lakes with lighter angling pressure are surveyed
every 5 years, and some remote lakes are surveyed every 20 years.
The Minnesota Department of Health and the Minnesota Pollution Control Agency
collaborate with the MDNR to select a subset of lakes from which to collect fish for mercury
analysis. The subset contains new and resampled sites that help guide agency decisions of
impaired waters and management of fish consumption. Fish samples selected for mercury
analysis are wrapped in aluminum foil and frozen (Monson, 2009). Thawed fish are fileted with
the skin on, ground, and refrozen in clean 125 mL glass jars until analyzed. Prior to 1997, fish
with similar lengths were usually combined into several samples per site. After 1997, fish were
analyzed individually to improve statistical power. Mercury data were collected for northern pike
(Esox Lucius), walleye (Sander vitreus), black crappie (Pomoxis nigromaculatus), blue gill
(Lepomis macrochirus), largemouth bass (Micropterus salmoides), smallmouth bass
4
(Micropterus dolomieu), yellow perch (Perca flavescens), and white sucker (Catostomus
commersonii), but 77% of the data were northern pike (1,000 unique lakes) and walleye (819
unique lakes). Due to the lack of time-series data for zebra mussel lakes, data were only
sufficient for statistical analysis of northern pike and walleye. To avoid confounding results due
to shifting sample processing, analysis was restricted to fish collected in 1997-2021 (Figure 1).
The refined time frame specifically targets the period of zebra mussel invasion in Minnesota,
avoiding initial declines due decreased atmospheric deposition. Total mercury concentrations
were not obtained in 2020 due to the global COVID-19 pandemic.
Figure 1. Spatial distribution and temporal coverage of mercury tissue concentration for zebra
mussel invaded and uninvaded lakes for walleye and northern pike in Minnesota lakes from 1997
through 2021.
5
Statistical analysis. We quantified the effect of zebra mussels on fish tissue mercury
concentrations using Bayesian hierarchical models implemented by the R program ‘brms’
(Bürkner, 2020). First, we analyzed the full dataset using separate models for each species using
diffuse priors from a lognormal distribution for a comprehensive analysis. As zebra mussels
typically reach peak densities around 2-3 years after initial invasion (Burlakova et al., 2006),
samples are considered invaded if collected 3 years after the lake had been designated. Each
model contained four chains of 10,000 iterations with 5,000 burn-in steps. Gelman and Rubin
tests and visual inspection of trace plots confirmed model convergence (Rhat values ≤ 1.01).
Fixed effects included scaled length (mean of 0 and standard deviation of 1) and invasion status
when the fish sample was collected. Random factors included lake and year level intercepts. The
model is described as follows:
Let Yijk = the mercury concentration for fish i, lake j, in year k:
Yi | (bj, bk) ~ lognormal(μi, σ2)
log(μi) = β0 + bj + bk + β1scaled.lengthi + β2I(Invasion = ZM)i
bj ~ N(0, σ2)
bk ~ N(0, σ2)
In this model, β0 is the overall intercept, bj is the intercept for each lake, bk is the intercept for
each year, and I is an indicator variable set to 1 when the condition is true or 0 when false. The
ZM variable distinguishes if the sample was collected when the lake was invaded with zebra
mussels. The model fit was inspected using posterior predictive checks and Bayesian estimation
of R2 values.
To control for lake-level variables that can influence mercury deposition, methylation, and
biotic uptake, we conducted a second analysis on a subset of lakes for which we had repeat
samples (Figure 2). For lakes invaded by zebra mussels, we included lakes that were sampled at
least once before and once after zebra mussel invasion. Samples were again considered invaded
if collected 3 years after the lake had been designated. We established a set of reference systems
that were never invaded with zebra mussels and contained at least one year of data before and
after 2017 (3 years after the median zebra mussel infestation). The sensitivity of choosing the
6
reference invasion year was explored. In this model, ZM is a lake level variable that
distinguishes between lakes that become invaded (ZM=1) and uninvaded lakes (ZM=0). The
time period variable indicates if the sample was collected post (Time Period = 1) or pre (Time
Period = 0). Each species model contained four chains of 10,000 iterations with 5,000 burn-in
steps. Gelman and Rubin tests and visual inspection of trace plots confirmed model convergence
(Rhat values ≤ 1.01). Preliminary analysis showed that a single lake comprised a large proportion
of data (22%) for walleye from invaded lakes, and we therefore analyzed the data both with and
without this lake included. The model is described as follows:
Let Yijk = the mercury concentration for fish i, lake j, and year k:
Yi | (bj, bk) ~ lognormal(μi, σ2)
log(μi) = β0 + bj + bk + β1scaled.lengthi + β2I(ZM = Y)ij + β3I(Time Period = Y)ij + β4I(ZM =
Y)ij*I(Pseudo = Y)ij
bj ~ N(0, σ2)
bk ~ N(0, σ2)
In this model, β0 is the overall intercept, bj is the intercept for each lake, bk is the intercept for
each year, and I is an indicator variable set to 1 when the condition is true or 0 when false. The
model fit was inspected using posterior predictive checks and Bayesian estimation of R2 values.
7
Figure 2. Spatial distribution of BACI analysis lakes. Reference systems are displayed as uninvaded systems. Zebra mussel lakes are
displayed as invaded systems.
8
Results
Comprehensive analysis of northern pike and walleye contained a large range of lakes
and samples. Analysis for northern pike contained 1,000 unique lakes, with 976 being uninvaded
and 64 with zebra mussels (Table 1). For walleye, we analyzed 819 lakes, with 795 uninvaded
and 67 invaded (Table 1). Northern pike ranged from 191mm to 1,156mm with an average size
of 563mm (Table 2). Walleye measured from 163mm to 772mm with an average size of 444mm
(Table 2).
Before-After Control-Impact (BACI) analysis for northern pike contained 270 unique
lakes, 40 zebra mussel lakes and 230 reference lakes (Table 3). Zebra mussel lakes contained a
total of 1,272 total samples, 814 before invasion and 458 after (Table 3). Reference lakes had
6,101 total samples, 4,090 before pseudo invasion and 2,011 after (Table 3). Northern pike in this
analysis were a minimum of 249mm and maximum of 1,041mm for an average of 565mm (Table
2). BACI analysis for walleye contained 218 unique lakes, 43 zebra mussel and 175 reference
systems (Table 3). Zebra mussel lakes contained 1,132 total samples, 603 before invasion and
529 after (Table 3). Reference lakes contained 3,677 total samples, 2,187 samples before pseudo
invasion and 1,490 after (Table 3). Walleye in this analysis ranged from 173mm to 772mm for an
average of 448 mm (Table 2).
Table 1. Summary of lakes, time-series, and sample count for each species and invasion status for
comprehensive modeling. Unique lakes correspond to the overall distinctive lakes for each species
included in the analysis. Lakes may contain data for both or only one invasion category.
Species Unique
Lakes
Invasion
Status
Lakes Samples Years
Northern
Pike
1000 Invaded 64 669 10
Northern
Pike
Uninvaded 976 15813 24
Walleye 819 Invaded 67 732 11
Walleye Uninvaded 795 9898 24
9
Table 2. Summarized total length statistics in millimeters for each dataset. Lengths for fish were
scaled for each model fit. BACI analysis is the subset of lakes that contains data before and after
zebra mussel invasion, while the comprehensive analysis is the larger inclusive data set.
Species Analysis Mean SD Min Max
Northern
Pike
BACI 565.21 112.68 248.92 1041.40
Walleye BACI 447.94 99.41 172.72 772.16
Northern
Pike
Comprehensive 563.04 114.36 190.50 1155.70
Walleye Comprehensive 443.75 99.08 162.56 772.16
Table 3. Summary of lakes, years, and samples for each species and invasion status for the BACI
modeling. Progressing from left to right, the levels shift from entire species, lake, and lake-invasion.
Species Unique
Lakes
Lake
Grouping
Lakes Samples Time
Period
Samples Years
Northern
Pike
270 Invaded 40 1272 Post 458 8
Northern
Pike
Invaded Pre 814 21
Northern
Pike
Reference 230 6101 Post 2011 4
Northern
Pike
Reference Pre 4090 20
Walleye 218 Invaded 43 1132 Post 529 11
Walleye Invaded Pre 603 20
Walleye Reference 175 3677 Post 1490 4
10
Walleye Reference Pre 2187 20
Zebra mussel invasion was associated with higher total mercury concentrations in both
northern pike and walleye in Minnesota lakes. Specifically, the median mercury concentration of
average-sized fish collected following zebra mussel invasion was 12.6% (7.5%-17.9%) higher
for northern pike and 3.7% (-0.01%-8.7%) higher for walleye than uninvaded collection (Table
4). The median mercury concentration of average size northern pike (563mm) and walleye
(444mm) from uninvaded lake-years was 0.24ppm (0.23ppm-0.26ppm) for both species while
those from invaded lake-years were 0.27ppm (0.25ppm-0.29ppm) and 0.25ppm
(0.23ppm-0.27ppm), respectively (Figure 3, Table 4).
The probability of exceeding the 0.22ppm state threshold for safe eating was calculated
using posterior prediction from lengths within the data range. Regardless of invasion status,
mercury in walleye and northern pike of average size was on average above the 0.22ppm
threshold triggering more stringent consumption advisories, although the probability of
exceeding this threshold was higher for both walleye and northern pike of average size when
zebra mussels were present. Northern pike of average size from uninvaded lake years had a 63%
chance of surpassing the state threshold, compared to a 70% probability of exceeding 0.22ppm in
invaded lake-years (Table 5). Similarly, walleye of average size from uninvaded lake-years had a
60% chance of surpassing the threshold, while invaded samples increased to 63% (Table 5).
Bayesian R2 values of 0.739 and 0.779 for northern pike and walleye indicate a strong model fit,
suggesting a high level of explained variability (Table 6).
Table 4. Posterior summary of comprehensive analysis for the zebra mussel effect on mercury
concentrations. Lakes were either invaded or uninvaded at the time of collection. The estimates
represent the median mercury value (ppm) when length is set to the average value.
Species Invasion Status Estimate 95% CI Percentage Increase
Walleye Invaded 0.25 0.233-0.272 3.7 (-0.01-8.7)
Walleye Uninvaded 0.24 0.228-0.258
11
Northern
Pike
Invaded 0.27 0.251-0.293 12.6 (7.5-17.9)
Northern
Pike
Uninvaded 0.24 0.226-0.257
Table 5. The probability of exceeding the 0.22ppm safe eating threshold set by the Minnesota
Department of Heath for average sized fish (walleye 444mm and northern pike 563mm) in the
comprehensive analysis. Fish invasion corresponds to invasion status of a lake at the time of
collection. Probability was calculated using posterior predictions at each length within the data
range.
Species Invasion Status Probability Exceeding
Threshold
Northern
Pike
Uninvaded 0.630
Northern
Pike
Invaded 0.701
Walleye Uninvaded 0.603
Walleye Invaded 0.629
Table 6. Bayesian proxy R2 values for each model run indicating the relative amount of variation
explained in the data. Bayesian R2 is interpreted as a data-based estimate of the proportion of
variance explained for new data, where the denominator can be interpreted as an estimate of the
expected variance of predicted future data from the model under the assumption that the predictors
X are fixed (Gelman et al., 2019).
Model R2 95% CI
Walleye - Comprehensive 0.78 0.773-0.785
Northern Pike - Comprehensive 0.74 0.732-0.745
Walleye - BACI (Mille Lacs included) 0.73 0.723-0.742
12
Walleye - BACI (Mille Lacs excluded) 0.73 0.717-0.739
Northern Pike - BACI 0.69 0.676-0.697
13
Figure 3. Mercury concentrations in walleye and northern pike for lakes invaded or uninvaded with
zebra mussels from comprehensive statewide analysis. The estimated mercury concentration for
each group is for a fish with mean total length for each species (walleye = 444mm and northern pike
= 563mm). Dots represent the median value with heavy lines and thin lines representing the 66%
and 95% credible intervals, respectively.
Mercury concentrations in fish from invaded lakes increased while uninvaded lakes were
stagnant after invasion when using BACI subset analysis with lakes containing repeat samples
(Figures 4 and 5). Mercury concentrations increased in northern pike from invaded lakes by
8.2% (-0.8%-18.1%), from 0.26ppm (0.21ppm-0.31ppm) to 0.28ppm (0.23ppm - 0.33ppm)
(Figure 4, Table 7). Mercury in fish from uninvaded lakes decreased by -5.2% (-13.8%-3.9%)
from 0.23ppm (0.21ppm-0.26ppm) to 0.22ppm (0.20ppm - 0.25ppm) (Figure 4, Table 7). For
walleye, the increase in mercury concentrations varied depending on whether fish from Mille
Lacs were included in the analysis. Samples from Mille Lacs comprised 22% of invaded walleye
14
observations from lakes containing zebra mussels (Table 8). When included, we observed no
difference between invaded and uninvaded lakes in the change in mercury concentrations from
the two time periods. In invaded lakes, mercury concentrations increased from 0.26ppm
(0.22ppm-0.32ppm) to 0.27ppm (0.22ppm-0.33ppm), an increase of 2.7% (-5.1%-11.1%) (Figure
5, Table 7). In uninvaded lakes, mercury concentrations decreased from 0.24ppm
(0.22ppm-0.27ppm) to 0.23ppm (0.21ppm-0.36ppm), a change of -3.5% (-11.5%-4.8%) (Figure
5, Table 7). However, when fish from Mille Lacs were excluded from the analysis, zebra mussel
invasion was associated with higher fish tissue mercury; mercury concentrations increased from
0.26ppm (0.21ppm-0.31ppm) to 0.30ppm (0.24ppm-0.36ppm), a 15.4% (4.4%-27.7%) increase
in invaded systems. Uninvaded lakes were unchanged from 0.24ppm (0.21ppm-0.28ppm) to
0.24ppm (0.22ppm-0.26ppm), a 1.6% (-8.4%-12.6%) average difference (Figure 5, Table 7).
The probability of average size northern pike exceeding the state threshold increased
from 61% to 78% following invasion in zebra mussel lakes (Table 9). The probability of average
size walleye exceeding the state threshold increased from 62% to 72% following invasion in
zebra mussel lakes (Table 9). Uninvaded systems for both species only increased their
probability of crossing the threshold by 5% in northern pike and 2% in walleye following
invasion (Table 9).
15
Figure 4. A.) Mercury concentrations of northern pike in uninvaded (U-Pre/Post) and invaded
(I-Pre/I-Post) systems of BACI analysis. Lakes are considered invaded after 2017 (3 years after
median zebra mussel infestation) for the reference systems and 3 years after zebra mussel invasion
for invaded systems. B). The difference in mercury concentrations for each lake grouping, reference
or zebra mussel. The effect of zebra mussel invasion is represented by the mean total length
(565mm), with dots representing median posterior values and lines representing 95% credible
intervals.
16
Figure 5. A.) Mercury concentrations of walleye in uninvaded (U-Pre/Post) and invaded
(I-Pre/I-Post) systems of BACI analysis with Lake Mille Lacs included. Lakes are considered
invaded after 2017 (3 years after median zebra mussel infestation) and 3 years after zebra mussel
invasion for the invaded systems. B.) The difference in mercury concentrations for each lake
grouping, reference or zebra mussel in BACI analysis with Lake Mille Lacs included. The effect of
invasion is represented by the mean total length, 448mm. Dots represent the median value with
95% credible interval error bars. C.) Mercury concentrations of walleye in uninvaded (U-Pre/Post)
and invaded (I-Pre/I-Post) systems of BACI analysis with Lake Mille Lacs excluded. D.) The
difference in mercury concentrations for each lake grouping, reference or zebra mussel in BACI
analysis with Lake Mille Lacs excluded. The effect of zebra mussel invasion is represented by the
mean total length (448mm), with dots representing median posterior values and lines representing
95% credible intervals. Light blue color represents uninvaded lakes, while salmon color represents
zebra mussel invaded lakes. Open dots in panel A and C represent before invasion while solid dots
represent after invasion.
17
Table 7. The posterior summary for the BACI analysis of zebra mussel invasion includes two
factors: lake grouping -indicating if a lake was ever invaded by zebra mussels, and fish invasion -
representing the individual sample invasion status. Fish in the reference lake grouping are invaded
when the sample was collected in 2017 or after, whereas zebra lakes are invaded after zebra mussel
invasion. Each estimate represents the median mercury value at average length (walleye 448mm
and northern pike 565mm).
Species Lake
Grouping
Fish
Invasion
Estimate 95% CI Percent
Difference
Northern Pike Invaded Post 0.28 0.23-0.33 8.2 (-0.8-18.1)
Northern Pike Invaded Pre 0.26 0.21-0.31
Northern Pike Uninvaded Post 0.22 0.20-0.25 -5.2 (-13.8-3.9)
Northern Pike Uninvaded Pre 0.23 0.21-0.26
Walleye (Mille
Lacs Included)
Invaded Post 0.27 0.22-0.33 2.7 (-5.1-11.1)
Walleye (Mille
Lacs Included)
Invaded Pre 0.26 0.22-0.32
Walleye (Mille
Lacs Included)
Uninvaded Post 0.23 0.21-0.36 -3.5 (-11.5-4.8)
Walleye (Mille
Lacs Included)
Uninvaded Pre 0.24 0.22-0.27
Walleye (Mille
Lacs Excluded)
Invaded Post 0.30 0.24-0.36 15.4 (4.4-27.7)
Walleye (Mille
Lacs Excluded)
Invaded Pre 0.26 0.21-0.31
Walleye (Mille
Lacs Excluded)
Uninvaded Post 0.24 0.22-0.26 1.6 (-8.4-12.6)
Walleye(Mille
Lacs Excluded)
Uninvaded Pre 0.24 0.21-0.28
18
Table 8. Total number of zebra mussel invaded samples from BACI walleye analysis from Lake
Winnibigoshish and Lake Mille Lacs.
Lake Invaded
Samples
Percentage of
Total
Winnibigoshish 20 3.80
Mille Lacs 115 21.70
Invaded Sample
Total
529 -
19
Table 9. The probability of exceeding the 0.22ppm safe eating threshold set by the Minnesota
Department of Heath for average sized fish (walleye 448mm and northern pike 565mm) in the
BACI analysis. Probability was calculated using posterior predictions at each length within the data
range.
Species Invasion
Grouping
Time
Period
Probability Exceeding
Threshold
Northern Pike Invaded Post 0.78
Northern Pike Invaded Pre 0.61
Northern Pike Uninvaded Post 0.64
Northern Pike Uninvaded Pre 0.59
Walleye (Mille Lacs
Included)
Invaded Post 0.57
Walleye (Mille Lacs
Included)
Invaded Pre 0.56
Walleye (Mille Lacs
Included)
Uninvaded Post 0.60
Walleye (Mille Lacs
Included)
Uninvaded Pre 0.59
Walleye Invaded Post 0.72
Walleye Invaded Pre 0.62
Walleye Uninvaded Post 0.60
Walleye Uninvaded Pre 0.58
Discussion
Overall high mercury concentrations in walleye and northern pike in Minnesota lakes
were exacerbated by zebra mussel invasion. Median mercury concentrations in average-size fish
20
from zebra mussel lakes in comprehensive analysis were 12.6% (7.5%-17.9%) higher for
northern pike and 3.7% (-0.01%-8.7%) higher for walleye than samples collected in uninvaded
systems (Figure 3). Similarly, mercury concentrations in zebra mussel lakes that contained data
before and after invasion were higher after the invasion (8.2%, -0.8%-18.1% in pike; 15.4%,
4.4%-27.7% in walleye with Mille Lacs excluded) (Figures 4 and 5, Table 7). Increases in zebra
mussel lakes were higher than reference systems that were never invaded with zebra mussels
(Figures 4 and 5). In each analysis, we considered variation in mercury concentrations due to
year, lake, and fish length. Despite some limitations due to opportunistic sampling and small
time-series, comprehensive models for both species demonstrated higher overall mercury
concentrations in zebra mussel lakes (Figure 3). To minimize variation not accounted for
between lakes, Bayesian application of the BACI approach was utilized. BACI is an effective
tool when natural events are not randomly chosen, allowing for quantification of effect size using
probability (Conner et al., 2016). As such, subset BACI analysis of mercury concentrations in
northern pike and walleye uses only lakes with repeat samples and thus limits confounding
factors across lakes. In those lakes, we demonstrate higher increases in mercury concentrations
after invasion than reference systems.
As is the case with many large scale analyses of system responses to drivers of global
change, the responses of individual lakes are not homogenous across the landscape (Hansen et
al., 2020). Using the combination of comprehensive and BACI analysis, we expose
landscape-level trends of mercury concentration increases associated with zebra mussel invasion,
while demonstrating uneven distribution of impact. Results of the BACI analysis differed
depending on whether data from Mille Lacs were included or excluded. Lake Mille Lacs was
sampled 17 years for mercury and is the single lake with the highest proportion (22%) of walleye
samples from invaded lake-years (Table 8). In contrast to the overall average patterns when data
from Mille Lacs was excluded, walleye in Mille Lacs decreased in mercury concentrations
following zebra mussel invasion (Figure 6). Ironically, the invasion of Lake Mille Lacs with
zebra mussels and spiny water flea (Bythotrephes cederströmii) results in symmetrically
antagonistic effects on water clarity (Rantala et al., 2022). Despite energetic shifts due to zebra
mussel invasion in Mille Lacs, the effect of multiple invasive species likely confounds zebra
mussel impact on mercury concentrations. Mercury concentrations from another well-sampled
21
lake with zebra mussels (Lake Winnibigoshish, 6 years of data and 3.8% of zebra mussel invaded
data points) increased following zebra mussel invasion (Figure 6), consistent with overall
statewide patterns.
Figure 6. Mercury concentrations trends before and after zebra mussel invasion in Lake Mille Lacs
and Winnibigoshish with respect to total length. The line represents the mean value while bands
represent the 95% confidence interval.
Minnesota has generally high mercury concentrations in fish throughout the state.
Mercury increases since 2000 have been observed in the Midwest that are variable with region
(Monson, 2009; Rasmussen et al., 2007). Prenatal and postnatal exposure to mercury causes
adverse neurological impacts in children and adults with increased risk for psychiatric symptoms
(Harada, 1995; Yorifuji et al., 2011). Consumption of contaminated fish disproportionately
affects populations in society that rely on fish consumption (Shimshack et al., 2007).
22
Specifically, indigenous communities rely on fish consumption for subsistence and culture (Roe,
2003). Mercury consumption guidelines force decisions that balance the protection of health and
culture. Accurate fish consumption advisories are essential for the public health protection from
mercury contamination. As atmospheric deposition of mercury decreases, legacy mercury within
sediments will be made available via methylation from several landscape drivers, like climate
change and invasive species. Our study shows relatively high mercury concentrations in walleye
and northern pike throughout the state of Minnesota and demonstrates zebra mussel influence in
these systems. Continual monitoring that accounts for these drivers is essential for public health
protection. Similarly, a holistic sample collection of fish communities is required for broad
generalization across species. Foraging type and food web structure are important factors in
variation of mercury concentrations in fish populations (Zhou & Wong, 2000). Our study
contains apex predatory species. Zebra mussel effects on mercury concentrations in lower trophic
level or herbivorous species might vary. Exploring lake-level parameters of several species will
allow for the development of mechanisms of mercury increases, providing better fish
consumption guidance to Minnesota residents.
Results of samples collected across the state of Minnesota reveal comparable patterns of
response to those experienced on smaller scales. Studies conducted in Lake Michigan and inland
Minnesota lakes indicate zebra mussel induced mercury concentrations in fish, likely stemming
from increased food web reliance on benthic resources (Blinick, et al., in review; Lepak et al.,
2019; Turschak et al., 2014). Zebra mussels are known to be ecosystem engineers that
completely alter physical conditions and energy pathways in lake ecosystems (Karatayev et al.,
2002). Methylation in anoxic sediment makes mercury available for biotic organisms. Thus,
zebra mussels alter lake biogeochemistry and physical conditions, exposing fish food webs to
increased bioaccumulation of methylmercury typically stored as legacy mercury in sediments.
Minnesota’s Fish Contaminant Monitoring Program is designed to construct fish consumption
advisories and designate water impairment using protocol from the Great Lakes Consortium for
Fish Consumption Advisories (Great Lakes Consortium for Fish Consumption Advisories,
2007). As these data are not constructed to explore zebra mussel trends, we were not able to test
the mechanisms of mercury increase in fish. Although mechanistic understanding would build
23
deeper understanding, these results establish a clear pattern that will aid in designing monitoring
and fish consumption advisories.
Our study suggests that invasive species, particularly zebra mussels, influence variable
biotic responses to decreases in atmospheric deposition of mercury. Environmental processes,
like increased anoxia and expanded littoral zones caused by global drives, make legacy mercury
biologically available through methylation. Zebra mussels alter lake characteristics and energy
pathways, posing a human-health concern of fish consumption. Various populations who rely on
fish for their diet might be disproportionately affected by zebra mussel impact on mercury.
Therefore, zebra mussels, or aquatic invasive species more broadly, should be considered as an
important factor when constructing fish consumption advisories and warrant mechanistic
research into zebra mussel induced mercury concentration increases. Future mechanistic research
will build understanding in how heterogeneous lake systems are impacted by zebra mussel
invasion, providing accurate and precise fish consumption advisories while targeting lake
systems of most concern.
24
Chapter 2 - Zebra mussel invasion has varying food web effects on different Leech
Lake habitats
Introduction
Zebra mussels (Dreissena polymorpha) are an invasive bivalve in North America that
have spread in lakes and rivers throughout the Great Lakes region since their initial discovery in
the 1980s (Nalepa & Schloesser, 2013). Zebra mussels quickly colonize hard surfaces in lakes,
covering a large proportion of benthic areas (Karatayev et al., 2002). Zebra mussels cause
numerous alterations in lakes, such as increased water clarity, expanded littoral zones, and
enhanced benthic secondary production (Karatayev et al., 2002; Stewart et al., 1998). As zebra
mussels filter feed, they deplete pelagic food resource availability and deposit organic material in
bethinc regions, stimulating secondary production in the nearshore zones (Higgins & Vander
Zanden, 2010). Lake changes imposed by zebra mussels influence how biological communities
interact with the ecosystem.
Within lake systems, pelagic production is supported by energy resources from
planktonic energy sources (phytoplankton/zooplankton) while littoral production is supported by
periphyton, macrophytes, and allochthonous energy. Fish communities in lakes access energy
from each system depending on behavior and availability. Mobile predators integrate energy
resources from both habitats through food web connections with prey or maintenance of a
generalist diet (Vander Zanden & Vadeboncoeur, 2002). As zebra mussels deplete pelagic energy
production and stimulate benthic energy pathways (Higgins & Vander Zanden, 2010), decreases
in recruitment and production would be expected if fish are unable to adapt to habitat changes or
altered food web structure. Fish population responses to zebra mussel invasion relates to their
capacity to access benthic prey resources in an altered environment. Fish of increasing trophic
level typically assimilate energy resources from benthic and pelagic resources using mobility
between habitats (McMeans et al., 2016). Habitat coupling of omnivorous predators makes them
resilient to subtle changes in food scarcity. With environmental disturbance, mobile generalist
fish species disperse to unfamiliar environments to consume new prey or shift relative energy use
between habitats (Bartley et al., 2019). Ecological disturbances that make one of these habitats
inaccessible, however, decreases their ability to respond to change (McMeans et al., 2016). For
25
example, lake trout in Ontario demonstrated increased pelagic feeding on invertebrates and
zooplankton when smallmouth bass depleted littoral minnow populations (Vander Zanden et al.,
2004).
Within Minnesota, three fish species in midwestern lakes demonstrate a variety in
adaptation and behavior: yellow perch (Perca flavescens) typically maintain a generalist diet of
macroinvertebrates and fish while foraging in various habitats and trophic levels (Cobb &
Watzin, 1998; Hayes & Taylor, 1990), walleye (Sander vitreus) are a mobile predator adapted to
foraging in pelagic regions (Vandenbyllaardt et al., 1991), and cisco (Coregonus artedi) maintain
a planktivorous diet in offshore regions (Ahrenstorff & Hrabik, 2016). Wide ranging habitat and
diet restriction in generalist species, like yellow perch, could make them more fit to handle
ecological changes imposed by zebra mussels. Pelagic energy resources are especially important
in early life stages of fish, as zooplankton comprise a large portion of their diet (Wu & Culver,
1992). Age-0 walleye collected from zebra mussel invaded Minnesota lakes were associated with
a 14% decrease in length than uninvaded lakes, likely minimizing the chance for recruitment
(Hansen et al., 2020). Similar to changing energy production, fish adapted to offshore lake areas
may struggle accessing nearshore habitat. Minnesota walleye abundance decreases have been
associated with increased water clarity and temperature (Hansen et al., 2019). Population
response to zebra mussel invasion likely depends upon species specific adaptations in response
to increased light penetration, expanded littoral zones, and boosted secondary production in
benthic areas.
As each fish species responds to change, community dynamics and structure shift. Large
changes in community composition and function have been associated with climate change,
invasive species, and introduction of predators (Genner et al., 2004; Solomon et al., 2016; Walls
et al., 1990). As specific fish populations respond to zebra mussel invasion, entire fish
communities will interact differently. Niche space overlap of fish populations in a Minnesota
lake grew as they increased reliance on benthic sources after zebra mussel invasion (McEachran
et al., 2019; Morrison et al., 2021). Similarly, Wisconsin lakes experienced declining walleye
populations with higher largemouth bass abundances that are associated with habitat changes due
to climate change (Hansen et al., 2017). Changing lake habitats create advantageous
26
environments for fish species better adapted for the environment. Increased light penetration in
lakes makes nearshore benthic food sources difficult to assimilate for pelagic species, like
walleye. Lakes that experience increased competition for benthic resources would favor fish
communities adapted to warmer, benthic environments (ex. centracids) while limiting growth
and recruitment of pelagic fish species (ex. walleye and cisco).
Aquatic ecosystem changes are often variable across landscapes at spatial and temporal
scales (Hansen et al., 2022; Soranno et al., 2019). Despite documented increased food web
reliance on benthic resources following zebra mussel invasion in Minnesota lakes (Blinick et al.,
in review.; McEachran et al., 2019; Morrison et al., 2021), the change is likely variable across
lake types (Bethke et al., 2023). Leech Lake is one of the 10 largest lakes in Minnesota (417 km2
total area) that account for almost 40% of walleye harvest (Miller et al., 2019), and was
determined to be infested with zebra mussels by the Minnesota Department of Natural Resources
in 2016 (Pedersen, 2020). Our study presents a unique opportunity to conduct a pre-post invasion
analysis of the Leech Lake system following the introduction of zebra mussels. Unlike many
previous studies that rely on cross-lake comparisons of similar morphology (Bethke et al., 2023;
Blinick, et al., in review.; McEachran et al., 2019), our approach studying before and after the
zebra mussel invasion enables a direct and robust understanding of response within a large and
complex system. Stemming from wide ranging morphology and bathymetry, Leech lake is made
up of diverse habitats for fish communities (Perleberg & Loso, 2010). The most notable
difference in habit splits the lake into western bay and main basin areas (Figure 7). The western
bays range in depth from 15 feet bays in the north to 150 feet oligotrophic bay areas. Conversely,
the main basin of the lake contains several large and shallow mesotrophic bays, with a maximum
depth of 40 feet. Quantifying zebra mussel impact in food webs across habitat types before and
after invasion in the same lake will broaden understanding of population and community
response.
27
Figure 7. Sample locations of fish and invertebrates before (2017) and after (2022) zebra mussel
invasion in Leech Lake split into Western Bays and Main Basin. Adult fish were collected at
standardized gillnetting sites by the Minnesota Department of Natural Resources (MDNR). Littoral
and age-0 fish were collected at standardized MNDNR seining sites. Zooplankton tows were
completed at MDNR water quality sites. Littoral invertebrates were typically collected at seining
sites, but supplemental sampling was completed in 2022 to improve baseline coverage.
Stable isotopes of carbon and nitrogen, δ13C and δ15N track energy sources and transfer
within ecosystems (Post, 2002). In aquatic systems, δ13C is used to differentiate between benthic
and pelagic energy resources. Primary benthic energy resources are enriched in δ13C relative to
primary pelagic resources (France, 1995). Thus, food web reliance on benthic resources is
determined by comparing δ13C values of fish and primary consumers from each aquatic habitat.
Although fish do not necessarily consume the food sources directly, relatively long-lived primary
consumers provide representative signals of basal δ13C sources (Post, 2002; Vander Zanden &
Rasmussen, 1999). On the other hand, δ15N increases with trophic position due to differential
28
association of lighter isotopes during metabolism (Post, 2002). Taken together δ13C and δ15N
tracks energy flow and community dynamics in fish communities, allowing insight into how a
system changes after ecological disturbance. Mixing models using the isotopes estimate trophic
position and reliance of benthic carbon. Estimates of energy use and trophic position for each
fish species allows for the creation of standard ellipses, representing isotopic community niche
space. Standard ellipse area (SEA) calculates the isotopic space, relating the variability of
resource use and trophic position in a community (M. C. Jackson et al., 2012). Ecological
disturbances, like increased benthic production with zebra mussel invasion, could shrink niche
space, representing increased competition for similar resources within the community.
In this study, we use δ13C and δ15N to assess fish population and community changes in
Leech Lake before and after zebra mussel invasion. The Minnesota Department of Natural
Resources collected stable isotope data in 2017 in Leech Lake, when zebra mussels were not
established in large densities. Data were collected again in 2022, when adult zebra mussels were
present throughout the lake.We quantify benthic reliance and trophic position before and after
zebra mussel invasion to track population-level effects. Baseline-corrected values for each fish
population are used to compare fish communities before and after zebra mussel invasion. The
western bays and main basin of Leech lake are analyzed separately to distinguish community
response in unique environments. Population and community level food web conclusions in
various lake habitats builds understanding of changes associated with zebra mussel invasion
across ecosystem types.
Methods
Study Location
Leech is a large lake with 417 km2 total area (235 km2 as littoral area). The lake has a
maximum depth of 150 feet, but 80% of Leech is under 35 feet deep. Leech has considerable
variability in morphology, notability split between western bays and the main basin. Within the
western bay region, northern areas have a maximum depth of 15 feet that expands to oligotrophic
areas of over 100 feet. The mesotrophic main basin, consisting of a maximum depth of 40 feet,
mixes with heavy wind events. Plant vegetation surveys from 2002-2009 reported 80%
29
vegetation coverage in northern regions of the western bays, while the main basin and deep
sections of the western bays had vegetation at less than 30% of sites (Perleberg & Loso, 2010).
The 201 miles of shoreline contains approximately 23% of a gravel/rubble mixture ideal for
walleye spawning. The diversity in habitat offers wide-ranging fish species, including: walleye
(Sander vitreus), northern pike (Esox lucius), cisco (Coregonus artedi), smallmouth (Micropterus
dolomieu) and largemouth bass (Micropterus salmoides), yellow perch (Perca flavescens),
musky (Esox masquinongy), and many panfish species. Several invasive species are present in
the lake, including zebra mussels, rusty crayfish (Orconectes rusticus), banded mystery snail
(Viviparus georgianus), heterosporosis (Heterosporis sutherlanda), curly-leaf pondweed
(Potamogeton crispus), Eurasian watermilfoil (Myriophyllum spicatum), and starry stonewort
(Nitellopsis obtusa).
Field Collection
Littoral Fishes. During mid-July 2017 and 2022, littoral fish were collected via standardized
MNDNR seines at seven locations throughout the lake. Age-0 walleye, age-0 yellow perch,
centrarchids, and a diversity of minnows were collected at each location. Due to ontogenetic diet
shifts in early life stages of walleye, shallow water seines can be unreliable for capture. Age-0
walleye collected in trawls were used to supplement sample size. Fish were flash frozen on dry
ice for further processing in the lab.
Adult Fishes. Standardized MNDNR 250-foot gill net sets during the first two weeks in
September in 2017 and 2022 provided adult fish samples. The sampling focused on the western
bays in the first week, shifting to the main lake during the second week. Samples were taken
from two net clusters from each of the western bays and main lake basins for 2017 and 2022. A
small chuck of dorsal muscle from walleye, yellow perch, and centrarchids was collected and
stored on ice for further processing in the lab.
Littoral Invertebrates. Benthic macroinvertebrates were collected in 2017 and 2022 near littoral
fish seining sites in mid-July. Opportunistic approaches (kick nets, snorkeling, direct collection
from rocks, etc.) were used in collection. Seining sites were typically sandy without great benthic
macroinvertebrate habitat. To supplement invertebrate sample size and explore temporal baseline
30
isotopic patterns, an additional sampling took place in mid-august of 2022. Organisms were
collected alive, placed in glass vials with lake water, and stored in a cooler with ice for several
hours to allow for gut clearance. Invertebrates were then sorted to identify functional feeding
groupings (Merritt & Cummins, 1996). Identical taxa were combined at each site for maximum
material during stable isotope processing.
Pelagic Invertebrates. Zooplankton samples were collected at standardized MNDNR water
quality sites using an 80μm net. Vertical tows of roughly 3⁄4 of the water column were conducted
to capture variation with depth. Samples were stored directly in ethanol for further processing in
the lab. Zooplankton samples from July – September were analyzed to capture potential baseline
isotopic shifts throughout the sampling period.
Lab Processing
Stable isotopes. A dorsal muscle chunk was collected from fish > 50 mm in length. Whole body
processing was used for fish under the threshold after first removing the head, fins, and internal
organs. Zooplankton samples were sorted, ensuring only non-predatory zooplankton were in each
sample (daphnia, copepod, etc.).
All fish, macroinvertebrates, and zooplankton samples were dried at 60°C for 24-48
hours. The samples were ground to ensure homogenization. Each sample was weighted to 1 mg
+/- 0.2 mg and encapsulated in a tin capsule. Mass spectrometry was conducted at the Stable
Isotope Facility, UC Davis, CA for δ13C and δ15N using MICRO cube elemental analyzer with a
precision of +/- 0.07 and +/- 0.06, respectively. Calibration was conducted using pure CO2 or N2
reference gas to calculate provisional isotopic values of the sample peaks. After, isotopic values
were adjusted for changes in linearity and instrumental drift using Nylon Powder and Chitin or
Alfalfa Flour. Measurements are scale-normalized to the primary reference materials using the
reference materials Glutamic Acid and Enriched Alanine or Caffeine. Isotopic data is expressed
in delta notation (δ) represented by the equation ((Rsample/Rstandard)-1) x 1000‰, where R is
the ratio of light to heavy isotopes of each element. Rstandard is the Vienna Pee Dee Belemnite
(VPDB) for carbon and nitrogen.
31
Statistical Analysis
Lipids have more negative δ13C values relative to other major biochemical compounds,
and variable lipid content in biological tissues can impact mixing model results (Logan et al.,
2008). Lipid correction models correct for the potential bias in results due to varying lipid
content of organisms (Post et al., 2007). It has been suggested, however, to avoid lipid
normalization as the correction introduces more uncertainty and potential biases (Silberberger et
al., 2021). Thus, we did not correct samples for lipid to avoid undesired uncertainty. Although
lipid content in tissue was not normalized, ethanol storage for zooplankton was required. Ethanol
storage depletes δ13C relative to fresh samples and could have a profound impact on mixing
model results (Feuchtmayr & Grey, 2003). Zooplankton values were corrected using methods
developed for ethanol storage from Minnesota lakes (Blechinger et al., in review).
The tRophicPosition package (Quezada-Romegialli et al., 2018) in R estimated benthic
reliance and trophic position for each fish. A two-baseline modeling approach was used,
incorporating a mixing model for δ13C and including heterogeneity derived from alternative
sources of δ15N. Separate models were fit for each lake area: main lake and western bays. Each
model was grouped by species and year of sample collection. The model fit included 4 chains of
100,000 iterations, and 50,000 burn-in steps. Inspection of trace plots and Gelman-Rubin
diagnostics confirmed model convergence (Rhat < 1.05). Zooplankton were used as pelagic
baseline taxa (Figure 8, Table 10). Amphipoda, Gastropoda and shredding families of
Ephemeroptera and Trichoptera make up the benthic baselines (Figure 8, Table 10). Adult and
age-0 groupings distinguished fish of different life stages. Fish of the following families were
grouped as such: Notropis as “Shiners”, Etheostoma as “Darters”, and Pimephales as
“Bluntnose”. Pairwise comparisons of trophic position and littoral reliance were used to estimate
the difference probability between invasion years for each fish species.
32
Figure 8. Raw isotope values for the pelagic and benthic baselines (secondary consumers) before and after zebra mussel invasion. Dots
represent mean values with 95% confidence intervals. Shapes denote taxa represented in each baseline community aggregation.
33
Table 10. Mean and standard deviation δ13C and δ15N values for baseline taxa used in trophic
position and littoral reliance modeling. Taxa within the same lake part, year, and baseline type were
aggregated to create pelagic and benthic baselines within each respective community. Zooplankton
values were corrected for ethanol storage (Blechinger et al., in review).
Lake
Part
Year Taxa Baseline
Type
Mean
δ13C
SD
δ13C
Mean
δ15N
SD
δ15N
n
Bays 2017 Amphipoda benthic -23.98 - 4.62 - 1
Bays 2017 Ephemeroptera benthic -25.86 - 4.46 - 1
Bays 2017 Trichoptera benthic -22.98 5.40 5.12 0.30 2
Bays 2017 Zooplankton pelagic -32.91 2.14 7.16 1.89 9
Bays 2022 Amphipoda benthic -23.74 1.24 4.19 0.37 5
Bays 2022 Zooplankton pelagic -31.73 1.94 7.83 0.89 8
Main 2017 Amphipoda benthic -21.99 0.70 5.08 0.53 5
Main 2017 Ephemeroptera benthic -22.91 1.27 5.14 1.14 7
Main 2017 Zooplankton pelagic -27.37 0.53 7.66 1.29 6
Main 2022 Amphipoda benthic -21.37 0.65 4.81 0.73 8
Main 2022 Gastropoda benthic -20.28 0.57 4.81 1.02 3
Main 2022 Zooplankton pelagic -27.59 0.39 7.42 0.55 5
The SIBER package (Jackson et al., 2011) in R estimated community level impacts. Fish
communities were composed of species from each lake part (western bays/main basin) and year
(invaded/uninvaded). Graphical representation of each community isotopic niche space was
analyzed using 90% confidence ellipses of the bivariate mean. Models included 3 chains of
100,000 interactions with 10,000 burn-in steps and a thinning rate of 20. Standard ellipse area
was used to calculate niche size and overlap for each grouping. To quantify changes between
34
groups, the angles and distances between centroids of ellipses were calculated for each posterior
draw.
Results
We observed differences in fish community structure and species-level resource use
between both time periods and regions of Leech Lake. Fish from the western bays of Leech Lake
relied more heavily on benthic resources than those in the main basin in both collection events
(Figure 9, Table 11). The main basin and western bay communities experienced opposite change
in resource use post invasion, as the main basin used more pelagic and western bays more
benthic (Figure 10).
35
Figure 9. The 90% confidence ellipse for each lake area faceted by sampling year. 90% of data are
expected to fall in the ellipse, given the covariation of δ13C and δ15N. Each point represents the
median posterior estimates of littoral reliance and trophic position value for a unique species in the
community.
36
Figure 10. Summary of ellipse change represented by vectors for one centroid of the 90% ellipse to
another. The line represents the median vector, with the density of vector starting points from the
position distribution displayed in the background. The “Bays” and “Main” contrast the invaded to
uninvaded years. The lower plots contrast the main basin to the western bays within each sampling
year. The median vector arrow was flipped to show orientation of change from uninvaded to
invaded years.
37
Table 11. Standard ellipse area for each fish community with the 90% credible interval.
Community Median Lower
CI
Upper
CI
2017 Bays 0.12 0.08 0.22
2022 Bays 0.17 0.11 0.31
2017 Main 0.22 0.14 0.39
2022 Main 0.15 0.10 0.26
Post invasion, the standard ellipse area (SEA) for the western bay community increased
by 39.81% (-34.49%-199.58%) but decreased for the main basin community by -32.26%
(-66.62%-38.42%) (Figures 11 and 12, Table 12). When comparing between lake parts within the
same year, the SEA of the western bays was -81.89% (-278.76%-13.22%) smaller than the main
basin before invasion but 12.10% (-83.34%-58.26%) bigger after invasion. Western bay SEA
had an 82.2% probability of being larger during uninvaded years. Main basin SEA had an 81.8%
of being smaller during uninvaded years. Before invasion, there was a 93.7% chance that the
main basin ellipse was smaller than the western bays with a 61.3% chance that the western bays
were smaller than the main basin after invasion.
38
Figure 11. Summarized posterior draws of the standard ellipse area for each fish community. The
middle line represents the median, with the lower and upper bounds of the box reaching the 25th
and 75th percentiles, respectively.
39
Figure 12. The 90% confidence ellipse for each sampling year faceted by lake area. 90% of data are
expected to fall in the ellipse, given the covariation of δ13C and δ15N. Each point represents the
median posterior estimates of littoral reliance and trophic position value for a unique species in the
community.
40
Table 12. Standard ellipse area comparisons for specific fish communities. Comparisons “Bays” and
“Main” contrast the invaded community to the uninvaded community within each lake part. The
“between” groups contrast the main basin to bays for each sampling year. Each contrast contains
the upper and lower 90% credible interval.
Group Percent
Difference
SEA
Lower
CI
Upper
CI
Bays 39.81 -34.49 199.58
Invaded Between 12.10 -83.34 58.26
Main -32.26 -66.62 38.42
Uninvaded Between -81.89 -278.76 13.22
Population level effects were variable among lake area and species. Within the main
basin, most fish species increased their use of pelagic resources after invasion (Figure 13). The
difference in littoral resource use between time periods was significant (the 90% credible interval
of the difference did not overlap 0) for several fish populations in the main basin, including age-0
walleye (estimated difference in littoral resource use =-0.28, CI= -0.42- -0.15), shiners (-0.24,
-0.42- -0.07), log perch (-0.23, -0.44, 0.00), age-0 perch (-0.21, -0.35 - -0.08) and adult walleye
(-0.16, -0.32 - -0.01) (Figure 14, Table 13). Using a 90% credible interval, no differences were
detected for littoral reliance in the western bays pre and post invasion, although in general fish
increased littoral resource use (Figure 15, Table 13). Few fish populations differed in trophic
position when comparing pre and post invasion; adult perch in the western bays was the only
statistical difference detected for trophic position (0.41, 0.01-0.82). (Figures 13 and 14, Table
13).
41
Figure 13. The trophic position and littoral reliance of each fish species before and after zebra
mussel invasion separated between western bays and main basin. Dots represent the median value
of their respective position distribution. 90% ellipses are overlaid for each community and invasion
grouping. Lines display the change in littoral reliance and trophic position for each fish grouping,
pointing towards invasion. Species acronyms are as follows: walleye (WAE), northern pike (NOP),
cisco (TLC), darters (DRT), yellow perch (YEP), logperch (LGP), rock bass (RKB), bluegill (BGL),
shinners (SHN), bluntnose minnow (BNM) where uppercase letters correspond to adult fish and
lowercase letter to age-0 fish.
42
Figure 14. Littoral reliance and trophic position for each fish grouping in the main basin. Dots
represent median values with 95% confidence intervals. Arrows point from uninvaded to invaded
years.
43
Figure 15. Littoral reliance and trophic position for each fish grouping in the western bays. Dots
represent median values with 95% confidence intervals. Arrows point from uninvaded to invaded
years.
Table 13. Summary of trophic position and littoral reliance modeling for fish species within the four
fish communities with the 90% credible interval.
Fish
Species
Year Area Trophic
Position
Lower
CI
Upper
CI
Littoral
Reliance
Lower
CI
Upper
CI
Adult Bluegill 2022 Bays 2.66 2.00 4.12 0.76 0.55 1.00
Adult Bluegill 2017 Bays 2.50 2.17 2.81 0.78 0.51 1.00
Adult Bluegill 2022 Main 2.67 2.21 3.12 0.49 0.12 0.89
Adult Bluegill 2017 Main 2.68 2.00 4.13 0.55 0.12 1.00
44
Adult Cisco 2022 Bays 3.27 2.00 6.81 0.39 0.00 0.89
Adult Cisco 2017 Bays 3.09 2.67 3.52 0.32 0.06 0.74
Adult Cisco 2022 Main 3.33 2.00 6.34 0.23 0.00 0.87
Adult Cisco 2017 Main 2.77 2.31 3.18 0.06 0.00 0.18
Adult Perch 2022 Bays 3.12 2.79 3.48 0.80 0.64 0.98
Adult Perch 2017 Bays 2.72 2.35 3.05 0.77 0.50 1.00
Adult Perch 2022 Main 3.18 2.88 3.49 0.59 0.41 0.77
Adult Perch 2017 Main 3.11 2.87 3.37 0.65 0.50 0.81
Adult Pike 2022 Bays 3.36 2.68 4.03 0.87 0.73 1.00
Adult Pike 2017 Bays 3.23 2.77 3.66 0.71 0.44 1.00
Adult Pike 2022 Main 3.51 3.18 3.85 0.41 0.28 0.55
Adult Pike 2017 Main 3.55 3.17 3.94 0.50 0.32 0.68
Adult Rock
Bass
2022 Bays 2.93 2.25 3.64 0.92 0.69 1.00
Adult Rock
Bass
2017 Bays 3.00 2.56 3.41 0.66 0.37 1.00
Adult Walleye 2022 Bays 3.74 3.42 4.07 0.77 0.60 0.96
Adult Walleye 2017 Bays 3.56 3.18 3.92 0.64 0.38 0.99
Adult Walleye 2022 Main 3.73 3.50 3.97 0.42 0.31 0.53
Adult Walleye 2017 Main 3.78 3.50 4.08 0.57 0.43 0.73
Age0 Perch 2022 Bays 2.90 2.61 3.20 0.47 0.27 0.67
Age0 Perch 2017 Bays 3.15 2.79 3.49 0.73 0.47 1.00
Age0 Perch 2022 Main 3.00 2.79 3.22 0.28 0.19 0.36
45
Age0 Perch 2017 Main 2.83 2.55 3.11 0.49 0.35 0.63
Age0 Walleye 2022 Main 3.24 3.04 3.47 0.32 0.23 0.42
Age0 Walleye 2017 Main 3.25 3.01 3.50 0.60 0.47 0.73
Darters 2022 Bays 3.18 2.73 3.55 0.77 0.48 1.00
Darters 2017 Bays 3.00 2.00 4.70 0.77 0.39 1.00
Darters 2022 Main 3.09 2.71 3.46 0.35 0.09 0.61
Darters 2017 Main 3.10 2.72 3.48 0.69 0.46 0.93
Bluntnose
Minnow
2022 Main 2.86 2.25 3.49 0.42 0.14 0.70
Bluntnose
Minnow
2017 Main 2.76 2.50 3.01 0.59 0.46 0.73
Logperch 2022 Bays 3.06 2.34 3.71 0.83 0.45 1.00
Logperch 2017 Bays 3.14 2.00 7.80 0.70 0.15 1.00
Logperch 2022 Main 2.89 2.46 3.31 0.64 0.41 0.88
Logperch 2017 Main 3.07 2.76 3.39 0.86 0.73 1.00
Shiners 2022 Bays 2.80 2.48 3.13 0.63 0.39 0.87
Shiners 2017 Bays 2.96 2.53 3.35 0.59 0.28 0.99
Shiners 2022 Main 2.69 2.47 2.92 0.19 0.09 0.28
Shiners 2017 Main 2.72 2.39 3.03 0.43 0.25 0.62
Discussion
Variable food web changes in diverse habitats in Leech Lake are associated with zebra
mussel invasion. In general, the western bay community increased benthic energy reliance after
46
invasion, while the main basin increased pelagic reliance (Figure 10). Although the standard
ellipse area (SEA) increased for the western bay fish community, it decreased within the main
basin (Figures 11 and 12, Table 12). The western bay community's increase in benthic reliance is
consistent with other studies conducted on fish communities following zebra mussel invasion
(Blinick, et al., in review; McEachran et al., 2019; Morrison et al., 2021). Based on known zebra
mussel effects, however, decreases in niche size associated with boosted benthic energy
production was unexpected. A before-after zebra mussel study noted increased niche overlap
after invasion, decreasing community niche size (Morrison et al., 2021). Interestingly, the region
that experienced increased benthic energy reliance also underwent expanded niche size.
Although a general increase in benthic littoral reliance was noted in the western bays at the
community level (Figures 9 and 10), some individual fish groupings were unimpacted or
increased pelagic reliance (Figures 14 and 15). Thus, individual variation in response at the
population level may have expanded the niche size of the community. Furthermore, of the largest
lakes in Minnesota, those invaded by zebra mussels generally had more benthic energy reliance
than uninvaded systems, but contained significant variation in littoral reliance (Bethke et al.,
2023). Thus, changes in fish communities are likely to vary across habitat structures.
Invasive species impacts vary across landscapes due to heterogeneity of response in
habitat types (Thiele et al., 2010). Local impacts from invasive species, like zebra mussels,
depend on the abundance and abundance-impact relationship to the environment (Vander Zanden
et al., 2017). Furthermore, abundance-impact relationships are often non-linear, making density
of invasion an important factor. High degree of heterogeneity builds inversely related
frequency-magnitude relationships with invasive species invasion. Many low-impact invasions
are observed, with less frequent high-impact invasions (Ricciardi et al., 2013). Significant habitat
variations within Leech Lake, notably split between western bays and the main basin, contributes
to the abundances of zebra mussels variable across the lake. Zebra mussels facilitate complex
interactions across the biotic and abiotic environments, likely making the abundance-impact
relationship vary between habitats. Thus, the per-capita zebra mussel impact likely affects food
web response differently across habitats, contributing to species variation in response.
47
In general, most fish species in the western bays had over 50% benthic reliance before
zebra mussel invasion (Figure 15). The western bay area has vast access to benthic resources,
with over 75% plant coverage in shallow northern areas and under 30% in the main basin
(Perleberg & Loso, 2010). Opposite to the western bays, fish in the main basin were more
pelagically reliant prior to zebra mussel invasion (Figure 9). Inter-habitat omnivory allows
mobile fish to couple benthic and pelagic energy resources (Post et al., 2000). Pelagic fish
species, like walleye, incorporate energy resources from both habitats by complex food web
connections and mobility into benthic areas. A walleye study in Canadian lakes exhibited
increased benthic energy dependence in systems with higher littoral prey populations and
decreased light penetration (Tunney et al., 2018). Similarly, lake trout from lakes with increased
thermal refuge demonstrated higher habitat coupling than lakes with more littoral production
(Dolson et al., 2009). Thus, habitat restrictions constrain fish populations in their ability to
couple resources from benthic and pelagic habitats. Despite the effects of zebra mussels,
heterogeneity of habitat in the western bays allows fish species to interact between the benthic
and pelagic communities. Increased light penetration in the main basin and general lack of
benthic production limits the community to respond to zebra mussel changes. Decreases in
benthic reliance were observed in several main basin fish populations (Figure 13), opposite of
the general western bay trend. Age-0 walleye, age-0 perch, shiners, logperch, and adult walleye
in the main basin increased pelagic reliance between invasion years when using a 90%
confidence interval (Figure 14). Zebra mussel invasion likely creates a habitat that is difficult for
pelagic species, like walleye, to consume benthic energy. The impact of zebra mussel invasion
depends upon habitat restrictions of the spatial area.
Outside of population level adaptation, several differences between lake areas correspond
to heterogeneous response of food webs. Each unique lake part comprises different trophic
classification, preexisting food web structure, and habitat (Perleberg & Loso, 2010). The western
bays contain a large range in morphology and habitat, while the main basin contains relatively
similar structure throughout the area. In general, fragmentation of a habitat affects the
persistence of populations by preventing their movements to places of refuge during changing
conditions (Allen et al., 2016). In general, the resilience of a system in response to environmental
change depends on connectivity to suitable habitat (Uden et al., 2014). The western bay
48
community contains heterogeneity of habitat where fish, especially mobile fish, would be able to
access benthic and pelagic resources. The general high availability and connectivity of benthic
energy resources in the western bays compared to the main basin enabled the community to
adapt to depleted pelagic energy sources. On the other hand, the shallow habitat of the main
basin, coupled with increased light penetration from zebra mussel invasion, could make benthic
energy pathways inaccessible to pelagic fish species. Notably, zebra mussels initially invaded the
western bays (Figure 7), taking several years for adults to establish in eastern reaches of the main
basin. Maximum densities of adults are typically reached 2-3 years after initial detection, with
food web changes likely taking longer to manifest (Burlakova et al., 2006). A delayed ecosystem
response may be related to the relatively short time period in which the main basin has been
invaded. Overall, if the main basin fish community is not able to assimilate to known ecosystem
changes imposed by zebra mussels, production and recruitment could suffer.
As zebra mussel eradication is not plausible in most systems, fisheries managers face
difficult decisions in supporting fish communities. In this study, we demonstrate variable
response to zebra mussel invasion based on lake habitat. As pelagic production is important for
recruitment and growth of fish (Hansen et al., 2020), communities unable to adapt to depleted
pelagic energy resources will not be able to maintain production. Typically, changes in aquatic
systems lead fisheries managers to make decisions that: 1.) resist change to maintain historical
conditions, 2.) accept change and manage within the new conditions, or 3.) direct change to
produce new conditions desirable by resource users (Rahel, 2022). Careful monitoring of fish
communities in Leech Lake, noting zebra mussel induced changes, will be important for future
management. Our analysis demonstrates the importance of spatial and food web characteristics
that guide socially, culturally, and economically appropriate decisions. Noting variation in
response to invasive species response is important for understanding landscape level changes and
risk (Vander Zanden et al., 2017). Stable isotope analysis of fish communities before and after
zebra mussel invasion in Leech Lake offers insight into variable response to zebra mussel
invasion and guidance for future management decisions.
49
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Appendix
Figure S1. Raw isotope values of each fish species before and after zebra mussel invasion in a.) the main basin and b.) western
bays of Leech Lake. Benthic and pelagic baselines are represented black, benthic with greater δ13C values.
63
Table S1. Summary of posterior estimates for northern pike comprehensive mercury analysis.
64
Table S2. Summary of posterior estimates for walleye comprehensive mercury analysis.
65
Table S3. Summary of posterior estimates for northern pike BACI mercury analysis.
66
Table S4. Summary of posterior estimates for walleye BACI mercury analysis including Mille Lacs.
67
Table S5. Summary of posterior estimates for walleye BACI analysis excluding Mille Lacs.
68
Table S6. Summary of fish length in millimeters for each fish species in the respective community.
Lake
Part
Year Species Mean
Length (mm)
SD n
Bays 2017 Adult Bluegill 136.64 35.92 28
Bays 2017 Adult Cisco 342.88 108.01 16
Bays 2017 Adult Perch 231.00 50.47 25
Bays 2017 Adult Pike 550.60 142.25 15
Bays 2017 Adult Rock Bass 212.60 45.43 10
Bays 2017 Adult Walleye 490.23 130.64 22
Bays 2017 Age0 Perch 36.12 5.33 8
Bays 2017 Darters 31.33 11.85 3
Bays 2017 Logperch 66.50 0.71 2
Bays 2017 Shiners 45.75 2.50 4
Bays 2022 Adult Bluegill 140.33 27.75 3
Bays 2022 Adult Cisco 290.00 79.20 2
Bays 2022 Adult Perch 199.24 44.97 17
Bays 2022 Adult Pike 529.00 67.94 5
Bays 2022 Adult Rock Bass 235.40 47.70 5
Bays 2022 Adult Walleye NA NA 16
Bays 2022 Age0 Perch 37.93 1.98 15
Bays 2022 Darters 49.25 2.63 4
Bays 2022 Logperch 71.00 2.58 4
Bays 2022 Shiners 55.33 14.32 9
69
Main 2017 Adult Bluegill 127.00 52.00 3
Main 2017 Adult Cisco 319.89 93.81 18
Main 2017 Adult Perch 177.32 70.82 28
Main 2017 Adult Pike 536.83 68.55 6
Main 2017 Adult Walleye 442.94 111.03 16
Main 2017 Age0 Perch 40.28 6.83 18
Main 2017 Age0 Walleye NA NA 30
Main 2017 Bluntnose
Minnow
62.44 4.39 9
Main 2017 Darters 43.00 11.44 8
Main 2017 Logperch 77.50 5.32 4
Main 2017 Shiners 45.59 23.76 17
Main 2022 Adult Bluegill 140.20 62.11 5
Main 2022 Adult Cisco 437.50 9.19 2
Main 2022 Adult Perch 231.92 52.22 13
Main 2022 Adult Pike 540.43 69.59 7
Main 2022 Adult Walleye 442.62 132.03 21
Main 2022 Age0 Perch 38.46 2.18 13
Main 2022 Age0 Walleye 93.26 19.06 23
Main 2022 Bluntnose
Minnow
53.44 11.95 9
Main 2022 Darters 48.38 3.25 8
Main 2022 Logperch 79.00 5.39 5
70
Main 2022 Shiners 50.27 5.04 15
71
Table S7. Lakes included in BACI analysis for northern pike. The years with data from each lake are provided along with the
number of samples before and after invasion.
Dow Lake
Name
Lak
Category
Data
Years
Year
Invaded
Pre
Invasion
Samples
Post
Invasion
Samples
Lake
Area
(ac)
Littoral
Area
(ac)
Max
Depth
(FT)
01003300 Minnewawa Reference 2012, 2018 NA 8 7 2355.19 2286.00 21.00
01004200 Glacier Reference 2011, 2021 NA 8 10 134.98 59.00 60.00
01004600 Ball Bluff Reference 2011, 2021 NA 8 10 168.23 32.00 78.00
01006200 Big Sandy Reference 2010, 2016,
2021
NA 27 10 6088.13 3085.00 84.00
01008700 Sugar Reference 2011, 2021 NA 6 10 416.09 267.00 45.00
01009600 Dam Reference 2012, 2019 NA 8 10 597.66 256.00 48.00
01010500 Fleming Reference 2008, 2017 NA 6 6 314.48 314.48 15.00
72
01012300 Elm Island Reference 2008, 2013,
2019
NA 13 10 518.13 389.00 25.00
01012500 Lone Reference 2010, 2019 NA 8 10 433.24 148.00 60.00
01014600 Ripple Reference 2008, 2018 NA 5 8 629.24 295.00 39.00
01017800 Spirit Reference 2010, 2019 NA 8 10 523.60 260.00 49.00
01020900 Cedar Reference 2014, 2021 NA 15 10 1745.27 405.00 105.00
01021200 Moulton Reference 2011, 2021 NA 8 9 257.87 220.00 24.00
02000300 Otter Reference 2007, 2019 NA 6 5 302.21 298.82 21.00
02002600 Linwood Reference 1999, 2009,
2015, 2021
NA 28 10 572.10 475.00 42.00
02008400 Crooked Reference 2004, 2021 NA 5 6 118.30 86.00 26.00
73
03001700 Two Inlets Reference 2001, 2011,
2016, 2021
NA 23 10 577.98 169.00 60.00
03003000 Boot Reference 2001, 2006,
2011, 2016,
2021
NA 65 10 377.63 81.00 109.00
03010700 Toad Reference 2001, 2006,
2011, 2016,
2021
NA 59 10 1716.17 561.00 29.00
03015300 Island Reference 2007, 2017 NA 4 6 1179.48 457.00 38.00
03019500 Height of
Land
Reference 2010, 2021 NA 8 10 3789.50 3190.00 21.00
03028600 Cotton Reference 2016, 2021 NA 4 10 1783.10 781.00 28.00
03038300 Long Reference 2011, 2021 2020 8 10 408.73 152.00 61.00
03057500 Leif Reference 2008, 2018 NA 7 8 520.60 316.00 26.00
74
04001100 Moose Reference 2006, 2016,
2021
NA 13 11 600.71 239.00 71.00
04003200 Pimushe Reference 2008, 2017 2019 8 8 1203.26 654.00 40.00
04003400 Rabideau Reference 2008, 2021 NA 5 12 680.35 515.00 112.00
04006900 Blackduck Reference 2001, 2012,
2018
NA 36 15 2686.26 1374.00 28.00
04011100 Turtle River Reference 2011, 2021 NA 8 11 1740.29 668.60 63.00
04013202 Big Bass (east
basin)
Reference 2002, 2017 NA 3 8 336.99 315.00 17.00
04015900 Turtle Reference 2003, 2021 NA 5 10 1606.29 718.00 45.00
04016600 Julia Reference 2003, 2018 NA 24 15 511.26 167.00 43.00
04019600 Campbell Reference 2016, 2021 NA 7 10 461.92 222.00 25.00
04023000 Deer Reference 2004, 2016,
2021
NA 13 10 297.79 118.00 42.00
75
06000200 Artichoke Reference 1999, 2007,
2008, 2013,
2019
NA 40 8 1970.22 1970.22 15.50
07004400 Madison Reference 2008, 2009,
2019
NA 19 10 1446.16 722.00 59.00
07004700 George Reference 2000, 2005,
2010, 2021
NA 16 3 88.24 60.70 28.00
09006700 Tamarack Reference 2007, 2016,
2021
NA 14 10 234.68 176.00 48.00
10001900 Bavaria Reference 2002, 2007,
2017
NA 9 5 166.46 65.00 66.00
10004500 Steiger Reference 2003, 2019 NA 5 10 165.88 103.00 37.00
11002600 Sugar Reference 2009, 2019 NA 7 10 702.35 325.00 44.00
11002900 Vermillion Reference 2010, 2018 NA 7 7 410.35 230.00 27.00
76
11005900 Washburn Reference 2003, 2009,
2015, 2021
NA 45 10 1590.19 748.00 111.00
11006200 Thunder Reference 2012, 2017 NA 7 8 1346.87 226.00 95.00
11014200 Long Reference 2010, 2021 NA 8 10 1008.77 356.00 115.00
11016700 Little Boy Reference 2011, 2015,
2021
NA 16 10 1451.67 466.00 74.00
11017100 Wabedo Reference 2009, 2021 NA 8 10 1226.41 295.00 95.00
11020100 Woman Reference 2009, 2016,
2017
NA 14 7 5519.65 1953.00 54.00
11020400 Portage Reference 2007, 2021 NA 6 3 1538.61 674.00 55.00
11027400 Blackwater Reference 2008, 2019 NA 5 10 766.67 336.00 67.00
11028300 Baby Reference 2012, 2019 NA 7 10 737.32 248.00 69.00
11030400 Sylvan Reference 2014, 2021 2020 8 10 894.02 367.00 57.00
77
11030800 Big Portage Reference 2007, 2017 NA 6 7 902.34 901.00 23.00
11031100 Webb Reference 2010, 2018 NA 8 9 744.05 277.80 84.00
11031300 Lower Sucker Reference 2011, 2021 NA 8 10 591.84 301.00 35.00
11037100 Stony Reference 2008, 2018 NA 5 6 576.46 178.50 50.00
11038300 Pleasant Reference 2001, 2012,
2017
NA 21 15 1099.17 410.00 72.00
11041300 Ten Mile Reference 2006, 2008,
2014, 2021
2019 40 10 5080.41 1316.00 208.00
13002700 South Center Reference 2008, 2019 NA 3 10 835.33 561.00 109.00
13008300 Goose Reference 2007, 2021 NA 5 10 719.43 370.00 55.00
16008900 Northern
Light
Reference 2000, 2007,
2021
NA 25 10 378.10 378.10 7.50
78
16009600 Elbow Reference 2000, 2010,
2015, 2021
NA 14 8 408.33 408.33 9.00
16019300 Mit Reference 2005, 2011,
2017
NA 10 1 87.21 53.80 40.00
16023500 McDonald Reference 2001, 2004,
2007, 2017
NA 44 8 85.70 85.70 8.00
16023800 Hand Reference 2001, 2016,
2021
NA 13 10 80.45 69.50 22.00
16025200 Pike Reference 2006, 2009,
2019
NA 18 1 814.43 218.00 45.00
16034200 East Pope Reference 2008, 2018 NA 8 7 35.53 19.00 28.00
16034700 Little Cascade Reference 2005, 2010,
2017
NA 37 8 262.44 262.44 9.00
16036500 Clara Reference 2007, 2019 NA 4 10 387.64 387.64 15.00
79
16036600 Holly Reference 2007, 2018 NA 1 8 75.94 75.94 6.00
16040600 Homer Reference 2004, 2019 NA 11 9 433.96 400.00 22.00
16062900 Sea Gull Reference 2003, 2009,
2018
NA 22 10 3957.72 927.00 145.00
16064600 Finger Reference 2011, 2018 NA 8 6 203.85 203.85 14.00
16080800 Phoebe Reference 1998, 2018 NA 10 8 610.01 388.00 25.00
18002000 Borden Reference 2003, 2014,
2021
NA 13 10 1011.95 304.00 84.00
18003800 Clearwater Reference 2007, 2019 NA 8 10 900.19 252.00 54.00
18009301 Rabbit (East
Portion)
Reference 1997, 2009,
2015, 2021
NA 24 10 666.67 242.00 337.00
18009302 Rabbit (West
Portion)
Reference 2009, 2021 NA 7 10 534.75 165.00 50.00
80
18009600 Upper South
Long
Reference 2013, 2019 NA 8 10 795.38 283.00 47.00
18013600 South Long Reference 2013, 2019 2021 8 10 1294.89 461.00 47.00
18029700 West Fox Reference 2008, 2019 NA 7 10 449.42 138.00 55.00
18037100 Perch Reference 2009, 2021 NA 4 10 274.61 165.00 40.00
19002600 Marion Reference 2011, 2017 2017 8 8 530.30 454.00 21.00
19005700 Fish Reference 1998, 2002,
2013, 2018
NA 36 1 29.89 23.70 33.50
19008000 Rogers Reference 2008, 2018 NA 2 4 101.76 101.76 8.00
21001600 Smith Reference 2010, 2018 NA 8 7 666.33 270.00 30.00
21014500 Chippewa Reference 2009, 2017 2017 8 8 1175.04 505.00 95.00
21029100 Red Rock Reference 2010, 2019 NA 8 10 902.70 595.00 22.00
81
24001800 Fountain Reference 2011, 2021 NA 4 10 521.29 521.29 14.00
26000200 Pelican Reference 2009, 2017 NA 8 8 3760.62 3460.00 21.00
26009700 Pomme de
Terre
Reference 2011, 2017 2018 3 4 1815.67 1568.00 23.00
27000100 Snelling Reference 2003, 2018 NA 5 3 101.43 101.43 9.50
27003700 Wirth Reference 2007, 2012,
2018
NA 17 3 39.88 23.00 25.00
27006700 Bryant Reference 1999, 2012,
2017
2015 11 6 177.62 64.00 45.00
27010400 Medicine Reference 2004, 2012,
2017
2017 21 10 925.24 399.00 49.00
27010700 Parkers Reference 2001, 2007,
2017
NA 31 8 100.17 67.70 37.00
27011800 Fish Reference 2003, 2009,
2019
NA 7 10 237.73 45.00 48.00
82
27014900 Spurzem Reference 2003, 2009,
2019
NA 6 15 81.88 37.00 38.00
27018400 Whaletail Reference 2005, 2016,
2021
NA 13 3 510.03 469.00 22.00
27019100 Sarah Reference 2013, 2017 NA 8 8 561.20 373.00 59.00
29003600 Eleventh
Crow Wing
Reference 2011, 2021 NA 8 12 750.97 173.41 80.00
29010101 East Crooked Reference 2007, 2017 NA 5 3 379.05 120.00 96.00
29014300 Big Stony Reference 2002, 2012,
2017
NA 11 3 343.38 223.00 24.00
29014600 Belle Taine Reference 2011, 2021 NA 7 10 1496.66 771.00 56.00
29014800 Upper Bottle Reference 2008, 2018 NA 7 8 459.14 176.00 55.00
29017800 Pickerel Reference 2006, 2016,
2021
NA 14 10 310.29 270.00 26.00
29024200 Fish Hook Reference 2012, 2017 NA 8 5 1642.56 661.00 76.00
83
29025000 Portage Reference 2002, 2017 NA 5 14 429.32 410.00 17.00
29031300 Little
Mantrap
Reference 2007, 2017 NA 6 8 380.95 197.00 54.00
31033400 Deer Reference 2010, 2018 NA 9 8 1854.59 1332.00 50.00
31053200 Pokegama Reference 2010, 2021 2019 12 10 6709.61 1978.00 112.00
31054000 Clubhouse Reference 2014, 2021 NA 8 10 264.71 32.00 103.00
31057100 Loon Reference 1999, 2006,
2017
NA 48 5 230.99 48.00 69.00
31057500 Little Bass Reference 1997, 2017 NA 45 10 160.50 55.00 62.00
31065300 North Star Reference 2009, 2019 2017 6 7 831.62 314.00 90.00
31071700 Rice Reference 2002, 2011,
2018
NA 13 8 852.37 229.00 68.00
31081300 Bowstring Reference 2007, 2014,
2021
2020 22 10 9528.13 4736.00 32.00
84
31091300 Island Reference 1997, 2021 NA 10 10 3108.15 1195.00 35.00
33002800 Knife Reference 2011, 2019 NA 8 9 1259.24 1259.24 15.00
33004000 Ann Reference 2015, 2021 NA 8 9 653.49 600.00 17.00
34004400 Diamond Reference 1997, 2003,
2008, 2013,
2018
2018 81 15 1606.90 635.00 27.00
34006600 Long Reference 1997, 2003,
2008, 2013,
2018
NA 82 15 328.98 127.00 46.00
34014200 George Reference 1999, 2004,
2006, 2008,
2013, 2018
2017 94 15 228.14 112.00 34.00
34022400 Games Reference 2010, 2019 2018 7 5 521.10 242.00 42.00
85
38003300 Ninemile Reference 2004, 2013,
2019
NA 35 10 296.75 288.00 40.00
38004200 Wye Reference 2010, 2019 NA 6 8 51.69 51.69 13.00
38004700 Wilson Reference 2011, 2019 NA 8 11 650.23 239.90 53.00
38004800 Harriet Reference 2009, 2017 NA 15 12 264.98 194.00 37.00
38005100 Little Wilson Reference 2001, 2019 NA 6 3 54.40 42.50 22.00
38006600 T Reference 2011, 2019 NA 4 9 295.00 294.50 15.00
38008000 Kawishiwi Reference 2003, 2010,
2017
NA 38 12 371.65 371.65 12.00
38023200 Nipisiquit Reference 2000, 2009,
2018
NA 21 5 58.94 29.00 21.00
38040600 Lax Reference 2006, 2008,
2018
NA 10 8 295.10 192.00 35.00
38052600 Parent Reference 2012, 2019 NA 6 3 450.97 91.00 50.00
86
38052900 Snowbank Reference 2000, 2010,
2017
NA 25 6 4654.83 879.00 150.00
38055000 Surprise Reference 2000, 2019 NA 7 10 37.67 37.67 10.00
38055700 Grouse Reference 2004, 2017 NA 5 2 119.23 119.23 11.00
38057300 Gegoka Reference 2008, 2011,
2018
NA 10 6 145.13 145.13 10.00
38065600 Greenwood Reference 2000, 2009,
2019
NA 21 10 1329.21 1329.21 7.00
38069100 August Reference 2002, 2007,
2013, 2019
NA 24 10 228.68 219.00 19.00
39000200 Lake of the
Woods
Reference 1997, 2002,
2009, 2018
2019 22 13 305488.6
4
79253.00 210.00
40003100 Tetonka Reference 2009, 2017 NA 3 5 1357.80 548.00 35.00
40003200 Gorman Reference 2009, 2019 NA 8 10 521.12 521.12 14.00
87
40006300 German Reference 2008, 2021 NA 5 6 791.64 521.00 51.00
40011700 Washington Reference 1997, 2008,
2013, 2019
NA 28 2 1519.46 783.00 51.00
41008900 Shaokotan Reference 1999, 2013,
2018
NA 2 8 996.28 996.28 10.00
41011000 Hendricks Reference 2002, 2005,
2008, 2013,
2019
NA 58 7 656.34 656.34 9.00
43010400 Stahl's Reference 1997, 2008,
2018
NA 58 15 140.57 85.00 37.00
44002300 North Twin Reference 2009, 2019 NA 7 10 965.96 900.00 16.00
47001500 Jennie Reference 2007, 2012,
2018
NA 27 8 1064.05 1064.05 15.00
47002300 Arvilla Reference 2004, 2018 NA 4 8 137.85 137.85 9.00
88
47004200 Betty Reference 2007, 2017 NA 6 8 153.66 89.80 29.00
47008200 Dunns Reference 1999, 2003,
2008, 2018
NA 29 15 151.89 85.00 20.00
47008800 Richardson Reference 1999, 2003,
2008, 2018
NA 43 8 119.37 45.00 47.00
47009500 Clear Reference 2013, 2017 NA 2 8 529.07 441.00 18.00
47012900 Star Reference 2013, 2017 NA 8 8 556.69 556.69 15.00
49003500 Green Prairie
Fish
Reference 2000, 2017 NA 6 5 179.74 82.00 22.00
49014000 Cedar Reference 2000, 2016,
2021
NA 30 10 235.64 66.00 88.00
51001800 Buffalo Reference 2015, 2021 NA 4 10 126.90 126.90 8.50
56016000 Spitzer Reference 2007, 2018 NA 6 7 713.89 631.00 33.00
89
56024300 Marion Reference 2003, 2012,
2021
NA 14 10 1623.77 685.00 62.00
56038300 Dead Reference 1997, 2009,
2015, 2021
2019 40 10 7534.81 6537.00 65.00
56038601 Big
McDonald
Reference 2008, 2021 2019 5 10 991.70 368.00 46.00
58006700 Sturgeon Reference 2011, 2019 NA 7 10 1705.91 495.20 40.00
60030500 Maple Reference 2010, 2015,
2021
NA 17 10 1575.61 1575.61 14.00
61002300 Grove Reference 1997, 2002,
2007, 2012,
2017
NA 111 11 344.55 265.00 31.00
61004100 Scandinavian Reference 2001, 2006,
2012, 2018
NA 60 15 415.63 237.00 49.00
61006400 Amelia Reference 2008, 2019 2018 5 10 934.40 370.00 69.00
90
62000700 Gervais Reference 2000, 2011,
2017, 2018
NA 11 10 235.01 91.00 41.00
62001300 Phalen Reference 2009, 2015,
2018
NA 14 4 197.69 80.00 91.00
62004800 Bennett Reference 2006, 2019 NA 7 5 28.45 28.45 9.00
62005600 Owasso Reference 2001, 2006,
2012, 2018
NA 59 5 374.96 292.90 37.00
62006100 Turtle Reference 1997, 2002,
2007, 2013,
2019
NA 96 5 450.02 245.10 28.00
62006700 Long Reference 2008, 2021 2019 5 9 172.63 110.00 30.00
66002700 Circle Reference 2007, 2017 NA 5 1 837.58 837.58 14.00
66003800 French Reference 2012, 2017 NA 1 6 875.81 397.50 56.00
91
66003900 Mazaska Reference 2002, 2007,
2012, 2019
NA 51 8 672.73 336.00 50.00
66004700 Hunt Reference 2001, 2017 NA 7 5 176.36 134.00 27.00
69000400 White Iron Reference 2001, 2004,
2008, 2010,
2017
NA 75 11 3246.08 1603.00 47.00
69006900 Shagawa Reference 2004, 2017 NA 24 15 2344.50 711.00 48.00
69007000 Low Reference 2000, 2005,
2010, 2017
NA 30 10 316.39 211.00 40.00
69016100 Wolf Reference 2014, 2019 NA 15 10 288.94 145.00 28.00
69025400 Bear Head Reference 2004, 2008,
2018
NA 63 8 661.74 371.00 46.00
92
69037200 Island Lake
Reservoir
Reference 1998, 2003,
2006, 2012,
2021
NA 88 10 8000.51 3260.00 94.00
69037300 Boulder Reference 1997, 2008,
2013, 2018
NA 66 15 3259.66 3206.59 18.00
69037500 Whiteface
Reservoir
Reference 2001, 2007,
2012, 2017
NA 61 10 4567.47 4480.00 35.00
69037800 Vermilion Reference 2009, 2018 NA 6 9 39272.25 15006.00 76.00
69051100 Grand Reference 2010, 2019 NA 8 10 1658.57 1510.00 24.00
69052100 Leora Reference 1998, 2011,
2021
NA 18 10 262.76 110.00 35.00
69054400 Dinham Reference 2010, 2018 NA 7 8 201.54 132.30 25.00
93
69068400 Mukooda Reference 2002, 2007,
2012, 2017
NA 43 13 773.76 151.00 78.00
69074400 Elbow Reference 2003, 2008,
2013, 2018
NA 21 7 1695.11 664.00 60.00
69074800 Kjostad Reference 2002, 2007,
2012, 2017
NA 25 6 437.15 286.00 50.00
69074900 Myrtle Reference 2007, 2017 NA 6 8 876.43 862.00 20.00
69075000 Moose Reference 2011, 2019 NA 8 19 227.97 227.97 8.00
69075500 Marion Reference 2012, 2018 NA 6 8 182.93 182.93 13.00
69076000 Little Johnson Reference 1998, 2003,
2017
NA 10 8 566.12 471.00 28.00
69083300 Peary Reference 2001, 2018 NA 24 9 116.12 116.12 15.00
94
69084500 Kabetogama Reference 1997, 2002,
2004, 2006,
2009, 2019
NA 92 10 24034.00 7440.00 80.00
69086400 Ash Reference 2002, 2008,
2013, 2018
NA 38 7 689.62 195.00 25.00
69129100 St. Louis
River Estuary
Reference 2000, 2012,
2013, 2017,
2018, 2019
NA 70 48 5415.66 10242.07 34.00
70005400 Spring Reference 2006, 2008,
2018
NA 10 4 591.85 290.00 37.00
71008200 Big Reference 2004, 2017 2020 6 8 253.66 110.00 48.00
73003700 Pearl Reference 2009, 2017 NA 6 8 753.34 511.00 17.00
73013300 Cedar Island Reference 2003, 2013,
2018
2018 9 15 953.51 755.00 75.00
95
73013900 Long Reference 2013, 2018 NA 5 8 487.12 304.00 35.00
73014700 North
Brown's
Reference 2013, 2018 NA 6 7 312.23 130.00 41.00
77001900 Mary Reference 2000, 2006,
2012, 2017
NA 40 12 124.20 39.00 58.00
77015000 Sauk Reference 2007, 2019 2018 22 10 2125.69 1380.00 61.00
77018100 Maple Reference 2004, 2021 NA 6 10 388.30 150.00 23.00
78002500 Traverse Reference 2000, 2009,
2013, 2019
NA 26 10 5683.03 5683.03 12.00
80003000 Lower Twin Reference 2008, 2018 NA 5 5 251.91 175.00 26.00
80003400 Blueberry Reference 2007, 2017 NA 6 8 532.54 532.54 15.00
80003700 Stocking Reference 2011, 2021 NA 5 10 356.94 326.00 22.00
96
82002300 Lily Reference 2000, 2011,
2021
NA 8 2 42.60 19.70 51.00
82005200 Big Marine Reference 1999, 2010,
2018
NA 16 5 1799.22 1152.00 60.00
82005400 Bone Reference 2012, 2018 2019 8 5 221.45 124.00 30.00
82009200 Powers Reference 2007, 2018 NA 4 1 58.21 26.00 41.00
82010100 DeMontrevill
e
Reference 2011, 2018 NA 8 5 157.06 129.00 24.00
82010400 Jane Reference 2002, 2007,
2019
NA 30 10 152.75 104.01 39.00
82010600 Elmo Reference 2001, 2018 NA 21 5 281.21 44.50 140.00
82011500 Tanners Reference 2000, 2005,
2011, 2018
NA 17 1 74.39 28.00 46.00
97
82015900 Forest Reference 1998, 2009,
2015, 2017
2015 18 8 2270.93 1531.00 37.00
83004300 St. James Reference 2013, 2021 NA 13 10 202.76 202.76 15.40
86009000 Buffalo Reference 2013, 2021 NA 8 10 1551.90 760.00 33.00
86011400 Waverly Reference 2004, 2019 NA 5 10 491.96 141.00 70.50
86019300 Mary Reference 2001, 2011,
2017
NA 10 8 189.97 85.00 46.00
86022900 Mink Reference 2016, 2021 NA 4 10 279.74 276.00 39.00
86025100 Pleasant Reference 1997, 2017 2020 10 7 597.00 260.00 74.00
86029800 Union Reference 2007, 2017 NA 6 8 92.93 27.40 35.00
03038700 Floyd Zebra
Mussel
2016, 2021 2018 5 10 1177.84 861.00 34.00
03050000 Maud Zebra
Mussel
2010, 2016,
2021
2016 16 10 517.01 300.00 32.00
98
03057600 Big
Cormorant
Zebra
Mussel
2008, 2014,
2021
2015 35 10 3657.12 812.00 75.00
11014700 Winnibigoshi
sh
Zebra
Mussel
1997, 2002,
2006, 2010,
2018, 2019
2013 43 25 56471.40 18904.00 69.80
11020300 Leech Zebra
Mussel
1997, 2002,
2006, 2009,
2019
2016 63 10 110311.0
0
57994.00 150.00
11030500 Gull Zebra
Mussel
2001, 2004,
2013, 2017
2010 31 30 9947.21 2825.00 80.00
11050400 Steamboat Zebra
Mussel
2003, 2014,
2021
2017 14 10 1755.67 532.00 93.00
18003400 Bay Zebra
Mussel
2014, 2021 2018 8 10 2319.88 1005.00 74.00
18021200 Ruth Zebra
Mussel
2009, 2015,
2021
2015 15 10 599.20 200.00 39.00
99
18031100 Rush-Hen Zebra
Mussel
2011, 2018 2014 8 8 857.82 499.00 105.00
18031200 Cross Lake
Reservoir
Zebra
Mussel
2005, 2011,
2018
2014 11 8 1786.97 879.00 84.00
18031500 Big Trout Zebra
Mussel
2011, 2018 2014 8 8 1363.04 369.00 128.00
18037200 North Long Zebra
Mussel
2012, 2018 2014 8 8 6144.05 3905.00 97.00
18037500 Hubert Zebra
Mussel
2013, 2019 2016 8 10 1287.74 465.00 83.00
18037800 Lower Hay Zebra
Mussel
2011, 2018 2014 8 8 693.10 215.00 100.00
18040300 Lower Cullen Zebra
Mussel
1998, 2009,
2015, 2021
2016 21 10 559.97 180.00 39.00
21005200 Geneva Zebra
Mussel
2008, 2016 2009 5 8 639.81 265.00 63.00
21005700 Carlos Zebra
Mussel
2000, 2008,
2013
2009 47 12 2605.11 910.00 163.00
100
21007900 Maple Zebra
Mussel
2000, 2009,
2013, 2019
2014 43 10 830.87 387.00 78.00
21008300 Miltona Zebra
Mussel
2011, 2019 2013 8 10 5724.28 2802.00 105.00
21010600 Latoka Zebra
Mussel
2009, 2021 2014 8 10 752.55 155.00 108.00
21014400 Lobster Zebra
Mussel
2009, 2013,
2017
2014 20 15 1329.02 667.00 65.00
21018000 Mill Zebra
Mussel
2012, 2018 2014 8 8 450.30 240.00 40.00
27001600 Harriet Zebra
Mussel
2003, 2009,
2014, 2021
2017 28 2 341.22 85.00 87.00
27013300 Minnetonka Zebra
Mussel
2000, 2009,
2014, 2019
2010 38 21 14729.56 5849.00 113.00
27013700 Christmas Zebra
Mussel
2001, 2007,
2018
2014 50 5 267.17 77.00 87.00
101
27017600 Independence Zebra
Mussel
2006, 2010,
2019
2014 18 10 832.02 425.00 58.00
29004800 Benedict Zebra
Mussel
2006, 2016,
2021
2017 15 10 464.36 172.00 91.00
34007900 Green Zebra
Mussel
1999, 2005,
2007, 2012,
2017, 2018
2014 72 23 5560.63 2035.00 110.00
34021700 Florida Zebra
Mussel
2010, 2019 2016 8 10 705.39 267.00 40.00
48000200 Mille Lacs Zebra
Mussel
1997, 2003,
2008, 2013,
2014
2006 37 28 128226.1
7
33129.00 42.00
56038700 Sybil Zebra
Mussel
2004, 2021 2016 5 10 681.63 423.00 74.00
56074702 South Lida Zebra
Mussel
2015, 2021 2014 1 10 775.37 356.00 48.00
102
56091500 Prairie Zebra
Mussel
2001, 2013 2009 7 15 1002.66 801.00 22.00
62007800 Johanna Zebra
Mussel
1997, 2014,
2018, 2021
2018 43 5 211.91 95.93 43.00
70002600 Lower Prior Zebra
Mussel
2006, 2008,
2015
2009 6 8 956.16 373.00 60.00
70007200 Upper Prior Zebra
Mussel
2006, 2018 2009 1 13 386.26 329.00 50.00
77008900 Little Birch Zebra
Mussel
2007, 2019 2016 6 10 839.44 277.00 89.00
86023300 Sugar Zebra
Mussel
1998, 2012,
2021
2018 22 10 1019.99 357.00 69.00
86025200 Clearwater Zebra
Mussel
1997, 2005,
2019
2015 16 10 3158.26 1595.60 73.00
103
Table S8. Lakes included in BACI analysis for walleye. The years with data from each lake are provided along with the
number of samples before and after invasion.
Dow Lake
Name
Lak
Category
Data
Years
Year
Invaded
Pre
Invasion
Samples
Post
Invasion
Samples
Lake
Area
(ac)
Littoral
Area
(ac)
Max
Depth
(FT)
01003300 Minnewawa Reference 2012, 2018 NA 6 9 2355.19 2286.00 21.00
01008700 Sugar Reference 2011, 2021 NA 7 10 416.09 267.00 45.00
01009600 Dam Reference 2012, 2019 NA 7 10 597.66 256.00 48.00
01012300 Elm Island Reference 2008, 2019 NA 5 5 518.13 389.00 25.00
01012500 Lone Reference 2010, 2019 NA 10 10 433.24 148.00 60.00
01014600 Ripple Reference 2008, 2018 NA 5 6 629.24 295.00 39.00
01014700 Esquagamah Reference 2007, 2019 NA 6 10 837.05 520.00 31.00
01017800 Spirit Reference 2010, 2019 NA 4 7 523.60 260.00 49.00
104
01021200 Moulton Reference 2006, 2011,
2021
NA 13 5 257.87 220.00 24.00
02002600 Linwood Reference 1999, 2021 NA 8 1 572.10 475.00 42.00
03001700 Two Inlets Reference 2001, 2011,
2016, 2021
NA 21 10 577.98 169.00 60.00
03003000 Boot Reference 2001, 2021 NA 17 10 377.63 81.00 109.00
03008500 Bad Medicine Reference 2012, 2017 NA 5 8 745.68 288.00 84.00
03010700 Toad Reference 2006, 2016,
2021
NA 15 10 1716.17 561.00 29.00
03015300 Island Reference 2007, 2017 NA 5 4 1179.48 457.00 38.00
03019500 Height of
Land
Reference 2010, 2021 NA 8 10 3789.50 3190.00 21.00
03028600 Cotton Reference 2016, 2021 NA 5 10 1783.10 781.00 28.00
03038300 Long Reference 2011, 2021 2020 8 10 408.73 152.00 61.00
105
03057500 Leif Reference 2008, 2018 NA 8 3 520.60 316.00 26.00
04001100 Moose Reference 2006, 2021 NA 6 10 600.71 239.00 71.00
04003400 Rabideau Reference 2008, 2021 NA 2 12 680.35 515.00 112.00
04003501 Red (Upper
Red)
Reference 1997, 2002,
2007, 2009,
2012, 2018
NA 62 23 48000.0
0
47681.20 15.00
04011100 Turtle River Reference 2011, 2016,
2021
NA 17 10 1740.29 668.60 63.00
04013002 Bemidji (main
lake)
Reference 1998, 2012,
2017, 2019
NA 16 13 6580.57 1862.00 76.00
04015900 Turtle Reference 2003, 2016,
2021
NA 14 10 1606.29 718.00 45.00
06000200 Artichoke Reference 2008, 2019 NA 8 3 1970.22 1970.22 15.50
106
06002900 Long Tom Reference 1999, 2014,
2017
NA 14 8 147.21 147.21 15.00
07004400 Madison Reference 2008, 2009,
2019
NA 10 10 1446.16 722.00 59.00
09006700 Tamarack Reference 2016, 2021 NA 1 10 234.68 176.00 48.00
11002600 Sugar Reference 2009, 2019 NA 8 10 702.35 325.00 44.00
11002900 Vermillion Reference 2010, 2018 NA 8 4 410.35 230.00 27.00
11005900 Washburn Reference 2003, 2015,
2021
NA 10 10 1590.19 748.00 111.00
11006200 Thunder Reference 2012, 2017 NA 7 8 1346.87 226.00 95.00
11014200 Long Reference 2010, 2021 NA 6 10 1008.77 356.00 115.00
11016700 Little Boy Reference 2011, 2015,
2021
NA 11 10 1451.67 466.00 74.00
11017100 Wabedo Reference 2009, 2021 NA 8 10 1226.41 295.00 95.00
107
11020100 Woman Reference 2009, 2016,
2017
NA 12 8 5519.65 1953.00 54.00
11020400 Portage Reference 2007, 2021 NA 6 10 1538.61 674.00 55.00
11027400 Blackwater Reference 2008, 2019 NA 5 10 766.67 336.00 67.00
11028300 Baby Reference 2012, 2019 NA 8 10 737.32 248.00 69.00
11030800 Big Portage Reference 2007, 2017 NA 6 7 902.34 901.00 23.00
11031100 Webb Reference 2010, 2018 NA 8 8 744.05 277.80 84.00
11031300 Lower Sucker Reference 2011, 2021 NA 4 10 591.84 301.00 35.00
11037100 Stony Reference 2008, 2018 NA 5 6 576.46 178.50 50.00
11041300 Ten Mile Reference 2006, 2008,
2021
2019 10 10 5080.41 1316.00 208.00
13002700 South Center Reference 2004, 2008,
2019
NA 19 10 835.33 561.00 109.00
108
13003201 North Center
Lake
Reference 2010, 2017 NA 8 7 749.26 608.00 46.00
13008300 Goose Reference 1999, 2007,
2021
NA 17 10 719.43 370.00 55.00
15006800 Long Lost Reference 2005, 2017 NA 5 8 501.20 275.00 63.00
16002700 McFarland Reference 2013, 2021 NA 7 10 385.77 203.00 49.00
16009600 Elbow Reference 2000, 2010,
2015, 2021
NA 18 10 408.33 408.33 9.00
16019300 Mit Reference 2005, 2011,
2017
NA 5 4 87.21 53.80 40.00
16023800 Hand Reference 2001, 2016,
2021
NA 9 10 80.45 69.50 22.00
16025200 Pike Reference 2006, 2009,
2019
NA 7 10 814.43 218.00 45.00
16034200 East Pope Reference 2008, 2018 NA 4 2 35.53 19.00 28.00
109
16036600 Holly Reference 2007, 2008,
2018
NA 17 8 75.94 75.94 6.00
16040600 Homer Reference 2004, 2019 NA 10 12 433.96 400.00 22.00
16045300 Rice Reference 2001, 2021 NA 2 10 222.52 222.52 10.00
16062200 Alton Reference 2003, 2008,
2018
NA 48 12 968.63 320.00 72.00
16062900 Sea Gull Reference 2003, 2018 NA 4 3 3957.72 927.00 145.00
16064600 Finger Reference 1999, 2011,
2018
NA 16 8 203.85 203.85 14.00
16080800 Phoebe Reference 1998, 2018 NA 10 8 610.01 388.00 25.00
18002000 Borden Reference 2003, 2021 NA 5 10 1011.95 304.00 84.00
18003800 Clearwater Reference 2007, 2019 NA 6 10 900.19 252.00 54.00
18009301 Rabbit (East
Portion)
Reference 2009, 2015,
2021
NA 10 10 666.67 242.00 337.00
110
18009302 Rabbit (West
Portion)
Reference 2009, 2021 NA 7 10 534.75 165.00 50.00
18009600 Upper South
Long
Reference 2013, 2019 NA 7 10 795.38 283.00 47.00
18013600 South Long Reference 2004, 2013,
2015, 2019
2021 35 10 1294.89 461.00 47.00
18029700 West Fox Reference 2008, 2019 NA 6 7 449.42 138.00 55.00
21001600 Smith Reference 2010, 2018 NA 7 5 666.33 270.00 30.00
21014500 Chippewa Reference 2009, 2017 2017 5 8 1175.04 505.00 95.00
21029100 Red Rock Reference 2010, 2019 NA 8 9 902.70 595.00 22.00
24001800 Fountain Reference 1999, 2006,
2011, 2018,
2021
NA 21 16 521.29 521.29 14.00
26000200 Pelican Reference 2009, 2017 NA 8 8 3760.62 3460.00 21.00
111
26009700 Pomme de
Terre
Reference 2003, 2011,
2017
2018 13 8 1815.67 1568.00 23.00
27001600 Harriet Reference 2000, 2009,
2014, 2019
2017 25 1 341.22 85.00 87.00
27003100 Calhoun Reference 2000, 2019 2018 24 2 419.41 123.00 82.00
29003600 Eleventh
Crow Wing
Reference 2011, 2021 NA 8 10 750.97 173.41 80.00
29007200 Eighth Crow
Wing
Reference 2008, 2018 NA 5 4 502.97 153.00 30.00
29007500 Kabekona Reference 2003, 2017 NA 5 8 2433.39 532.00 133.00
29008700 Palmer Reference 2011, 2021 NA 7 10 144.46 92.00 21.00
29010101 East Crooked Reference 2007, 2017 NA 6 7 379.05 120.00 96.00
29014300 Big Stony Reference 2002, 2012,
2017
NA 8 7 343.38 223.00 24.00
112
29014600 Belle Taine Reference 2011, 2021 NA 7 10 1496.66 771.00 56.00
29014800 Upper Bottle Reference 2008, 2018 NA 10 8 459.14 176.00 55.00
29015600 Plantagenet Reference 1999, 2012,
2017
NA 16 8 2530.81 986.00 65.00
29017800 Pickerel Reference 2006, 2016,
2021
NA 11 10 310.29 270.00 26.00
29018400 Blue Reference 1999, 2014,
2019
NA 12 10 336.35 81.00 84.00
29024200 Fish Hook Reference 2012, 2017 NA 8 6 1642.56 661.00 76.00
29031300 Little Mantrap Reference 2007, 2017 NA 6 5 380.95 197.00 54.00
31021600 Trout Reference 2001, 2013,
2018
NA 12 8 1854.21 438.00 135.00
31053200 Pokegama Reference 2005, 2021 2019 11 10 6709.61 1978.00 112.00
31065300 North Star Reference 2009, 2019 2017 7 10 831.62 314.00 90.00
113
31072200 Moose Reference 2010, 2018 NA 8 9 1273.81 345.00 61.00
31072500 Turtle Reference 2010, 2017 NA 5 8 2103.01 556.00 137.00
31078600 Jessie Reference 2004, 2018 NA 8 9 1740.06 455.00 42.00
31081300 Bowstring Reference 2007, 2021 2020 7 9 9528.13 4736.00 32.00
31091300 Island Reference 1997, 2021 NA 10 10 3108.15 1195.00 35.00
32006900 Round Reference 2009, 2019 NA 8 10 929.85 929.85 9.00
33002800 Knife Reference 2004, 2011,
2019
NA 18 9 1259.24 1259.24 15.00
33004000 Ann Reference 2015, 2021 NA 6 10 653.49 600.00 17.00
38003300 Ninemile Reference 2013, 2019 NA 12 10 296.75 288.00 40.00
38004200 Wye Reference 2010, 2019 NA 8 4 51.69 51.69 13.00
114
38004700 Wilson Reference 2001, 2011,
2019
NA 38 9 650.23 239.90 53.00
38005100 Little Wilson Reference 2001, 2008,
2012, 2019
NA 21 10 54.40 42.50 22.00
38006000 Whitefish Reference 2000, 2010,
2018
NA 15 8 345.50 191.00 49.00
38006600 T Reference 2011, 2019 NA 8 10 295.00 294.50 15.00
38008000 Kawishiwi Reference 2010, 2017 NA 15 15 371.65 371.65 12.00
38040600 Lax Reference 2006, 2018 NA 5 8 295.10 192.00 35.00
38055700 Grouse Reference 2004, 2017 NA 5 3 119.23 119.23 11.00
38065600 Greenwood Reference 2000, 2019 NA 6 10 1329.21 1329.21 7.00
38069100 August Reference 2002, 2007,
2013, 2019
NA 40 10 228.68 219.00 19.00
115
38071400 Section
Twelve
Reference 2000, 2017 NA 8 8 49.24 11.40 52.00
40003100 Tetonka Reference 2009, 2017 NA 4 5 1357.80 548.00 35.00
40003200 Gorman Reference 2009, 2019 NA 5 10 521.12 521.12 14.00
40011700 Washington Reference 1997, 2013,
2019
NA 31 10 1519.46 783.00 51.00
41004300 Benton Reference 2000, 2006,
2009, 2013,
2019
NA 72 10 2699.47 2699.47 9.00
41011000 Hendricks Reference 2008, 2019 NA 3 11 656.34 656.34 9.00
42002000 Lady Slipper Reference 2002, 2012,
2017
NA 11 7 286.15 286.15 11.00
44000300 Tulaby Reference 2011, 2021 NA 7 10 832.07 281.00 43.00
44002300 North Twin Reference 2009, 2019 NA 6 9 965.96 900.00 16.00
116
46014500 Fish Reference 2015, 2021 NA 8 9 155.63 155.63 5.00
47001500 Jennie Reference 2012, 2018 NA 8 9 1064.05 1064.05 15.00
47008200 Dunns Reference 2008, 2018 NA 1 15 151.89 85.00 20.00
47008800 Richardson Reference 2008, 2018 NA 5 7 119.37 45.00 47.00
47009500 Clear Reference 2013, 2017 NA 4 8 529.07 441.00 18.00
47012900 Star Reference 2013, 2017 NA 8 5 556.69 556.69 15.00
49012700 Shamineau Reference 2010, 2021 NA 8 11 1434.02 746.00 52.00
56013800 East Battle Reference 2013, 2021 2019 8 10 1985.09 825.00 87.00
56024300 Marion Reference 2003, 2021 NA 6 10 1623.77 685.00 62.00
56038300 Dead Reference 1997, 2021 2019 11 10 7534.81 6537.00 65.00
56052300 East Loon Reference 2013, 2019 2018 8 10 1044.33 592.00 105.00
117
56065800 Wall Reference 2013, 2017 NA 8 8 727.72 229.00 34.00
60030500 Maple Reference 2010, 2015,
2021
NA 15 10 1575.61 1575.61 14.00
62000200 Bald Eagle Reference 2008, 2018 2018 6 4 1046.63 751.00 36.00
62000700 Gervais Reference 2005, 2017 NA 9 6 235.01 91.00 41.00
66002700 Circle Reference 2007, 2017 NA 6 3 837.58 837.58 14.00
66003800 French Reference 2012, 2017 NA 8 7 875.81 397.50 56.00
66003900 Mazaska Reference 2002, 2012,
2019
NA 35 4 672.73 336.00 50.00
69025400 Bear Head Reference 2008, 2018 NA 48 8 661.74 371.00 46.00
69037200 Island Lake
Reservoir
Reference 1998, 2003,
2006, 2021
NA 108 10 8000.51 3260.00 94.00
118
69037500 Whiteface
Reservoir
Reference 2001, 2007,
2012, 2017
NA 87 15 4567.47 4480.00 35.00
69037600 Whitewater Reference 1997, 2002,
2019
NA 18 10 1212.25 564.00 73.00
69037800 Vermilion Reference 1997, 2002,
2006, 2009,
2012, 2018
NA 57 6 39272.2
5
15006.00 76.00
69051100 Grand Reference 2010, 2019 NA 7 10 1658.57 1510.00 24.00
69052100 Leora Reference 1998, 2011,
2021
NA 14 10 262.76 110.00 35.00
69056500 Esquagama Reference 1999, 2018 NA 8 8 452.61 81.50 90.00
69067100 Pfeiffer Reference 1997, 2017 NA 10 3 57.92 34.20 26.00
69074800 Kjostad Reference 1997, 2017 NA 20 6 437.15 286.00 50.00
119
69076000 Little Johnson Reference 1998, 2003,
2010, 2017
NA 22 8 566.12 471.00 28.00
69084500 Kabetogama Reference 2002, 2012,
2019
NA 11 10 24034.0
0
7440.00 80.00
69129100 St. Louis
River Estuary
Reference 2000, 2004,
2006, 2012,
2013, 2017,
2021
NA 87 36 5415.66 10242.07 34.00
70005400 Spring Reference 2002, 2006,
2012, 2018
NA 13 8 591.85 290.00 37.00
71008200 Big Reference 2004, 2017 2020 5 7 253.66 110.00 48.00
73003700 Pearl Reference 2009, 2017 NA 3 8 753.34 511.00 17.00
73013900 Long Reference 2013, 2018 NA 4 2 487.12 304.00 35.00
120
73014700 North
Brown's
Reference 2013, 2018 NA 3 4 312.23 130.00 41.00
73019600 Rice Reference 2012, 2017 NA 6 8 1509.38 958.00 41.00
73020002 Koronis (main
lake)
Reference 2007, 2012,
2017
NA 14 8 2968.28 1176.00 132.00
76014600 Oliver Reference 1998, 2010,
2019
NA 16 10 671.37 300.00 35.00
78002500 Traverse Reference 2005, 2019 NA 8 10 5683.03 5683.03 12.00
80003000 Lower Twin Reference 2008, 2018 NA 5 2 251.91 175.00 26.00
80003400 Blueberry Reference 2007, 2017 NA 6 7 532.54 532.54 15.00
80003700 Stocking Reference 2011, 2021 NA 2 2 356.94 326.00 22.00
82005400 Bone Reference 2012, 2018 2019 1 5 221.45 124.00 30.00
82010600 Elmo Reference 2008, 2018 NA 9 1 281.21 44.50 140.00
121
82015900 Forest Reference 1998, 2009,
2015, 2017
2015 21 8 2270.93 1531.00 37.00
82016300 Clear Reference 1999, 2015,
2018
NA 19 5 428.95 267.00 28.00
85001100 Winona Reference 2007, 2021 NA 5 7 306.88 278.00 38.00
86009000 Buffalo Reference 2013, 2021 NA 6 10 1551.90 760.00 33.00
86011400 Waverly Reference 2004, 2019 NA 5 12 491.96 141.00 70.50
86019300 Mary Reference 2011, 2017 NA 6 8 189.97 85.00 46.00
86022900 Mink Reference 2016, 2021 NA 3 10 279.74 276.00 39.00
86025100 Pleasant Reference 1997, 2017 2020 5 6 597.00 260.00 74.00
87018000 Del Clark Reference 2001, 2019 NA 6 10 156.30 88.00 30.00
03038700 Floyd Zebra
Mussel
2016, 2021 2018 5 10 1177.84 861.00 34.00
122
03050000 Maud Zebra
Mussel
2010, 2016,
2021
2016 12 10 517.01 300.00 32.00
04003000 Cass Zebra
Mussel
2002, 2006,
2012, 2018
2014 35 17 15958.2
5
3119.00 120.00
05001300 Little Rock Zebra
Mussel
2004, 2014 2006 7 8 1310.60 1240.12 17.00
11014700 Winnibigoshis
h
Zebra
Mussel
1997, 2002,
2006, 2012,
2018, 2019
2013 30 20 56471.4
0
18904.00 69.80
11020300 Leech Zebra
Mussel
1997, 2002,
2006, 2012,
2019
2016 41 10 110311.
00
57994.00 150.00
11050400 Steamboat Zebra
Mussel
2003, 2014,
2021
2017 12 11 1755.67 532.00 93.00
18003400 Bay Zebra
Mussel
2014, 2021 2018 8 10 2319.88 1005.00 74.00
123
18021200 Ruth Zebra
Mussel
2009, 2015,
2021
2015 14 8 599.20 200.00 39.00
18031100 Rush-Hen Zebra
Mussel
2011, 2018 2014 8 3 857.82 499.00 105.00
18031200 Cross Lake
Reservoir
Zebra
Mussel
2005, 2011,
2018
2014 14 8 1786.97 879.00 84.00
18031500 Big Trout Zebra
Mussel
2011, 2018 2014 5 8 1363.04 369.00 128.00
18037200 North Long Zebra
Mussel
2012, 2018 2014 8 8 6144.05 3905.00 97.00
18037500 Hubert Zebra
Mussel
2013, 2019 2016 8 10 1287.74 465.00 83.00
18037800 Lower Hay Zebra
Mussel
2011, 2018 2014 8 8 693.10 215.00 100.00
18040300 Lower Cullen Zebra
Mussel
1998, 2009,
2015, 2021
2016 18 10 559.97 180.00 39.00
21005200 Geneva Zebra
Mussel
2008, 2016 2009 4 8 639.81 265.00 63.00
124
21007900 Maple Zebra
Mussel
2009, 2019 2014 6 10 830.87 387.00 78.00
21008000 Darling Zebra
Mussel
2004, 2012 2009 7 6 1049.99 477.00 62.00
21008300 Miltona Zebra
Mussel
2011, 2019 2013 7 10 5724.28 2802.00 105.00
21009200 Mary Zebra
Mussel
2009, 2017 2014 8 16 2450.43 1020.00 40.00
21010600 Latoka Zebra
Mussel
2009, 2021 2014 8 10 752.55 155.00 108.00
21018000 Mill Zebra
Mussel
2012, 2018 2014 8 8 450.30 240.00 40.00
27001900 Nokomis Zebra
Mussel
2007, 2018 2010 2 5 201.24 100.00 33.00
27013300 Minnetonka Zebra
Mussel
2000, 2019 2010 29 4 14729.5
6
5849.00 113.00
27017600 Independence Zebra
Mussel
2001, 2019 2014 10 10 832.02 425.00 58.00
29004800 Benedict Zebra
Mussel
2006, 2016,
2021
2017 14 10 464.36 172.00 91.00
125
31085000 Little
Winnibigoshis
h
Zebra
Mussel
2007, 2017 2013 7 8 1001.62 209.00 28.00
31085700 Cut Foot
Sioux
Zebra
Mussel
2007, 2017 2013 6 8 2807.79 1324.00 78.00
38002400 Crooked Zebra
Mussel
2002, 2010,
2014, 2019
2014 35 9 271.90 258.00 18.00
48000200 Mille Lacs Zebra
Mussel
1997, 1998,
2000, 2002,
2003, 2004,
2006, 2008,
2010, 2011,
2012, 2013,
2014, 2016,
2017, 2018,
2019
2006 103 115 128226.
17
33129.00 42.00
126
51006300 Sarah Zebra
Mussel
2010, 2021 2018 8 10 1209.24 1209.24 11.00
56036000 Rose Zebra
Mussel
2009, 2019 2011 7 10 1200.47 465.00 137.00
56038700 Sybil Zebra
Mussel
2004, 2013,
2021
2016 12 10 681.63 423.00 74.00
56074701 North Lida Zebra
Mussel
2012, 2021 2014 8 10 5513.57 2380.00 58.00
56074702 South Lida Zebra
Mussel
2009, 2015,
2021
2014 16 10 775.37 356.00 48.00
56074900 Crystal Zebra
Mussel
2006, 2014 2009 6 8 1412.30 674.00 55.00
56078600 Pelican Zebra
Mussel
2011, 2015,
2021
2009 8 18 3962.88 1625.00 55.00
56094500 Orwell Zebra
Mussel
1999, 2016 2013 8 8 607.58 607.58 25.00
62004600 Pleasant Zebra
Mussel
2000, 2012,
2018, 2021
2007 6 15 607.23 273.00 58.00
127
69049000 Pike Zebra
Mussel
1999, 2009,
2014
2009 16 15 488.26 134.70 60.00
77008900 Little Birch Zebra
Mussel
2007, 2019 2016 5 10 839.44 277.00 89.00
86025200 Clearwater Zebra
Mussel
1997, 2005,
2019
2015 16 9 3158.26 1595.60 73.00
128