THE WATERMILFOIL WEEVIL (EUHRYCHIOPSIS LECONTEI): DEVELOPING A
SAMPLING PROGRAM AND INVESTIGATING THE INFLUENCE OF BIOTIC
AND ABIOTIC FACTORS ON ITS DISTRIBUTION

By

Thomas G. Alwin

A THESIS
Submitted to
Michigan State University
In partial fulfillment of the requirements
for the degree of
MASTER OF SCIENCE
Fisheries and Wildlife
2010

ABSTRACT

THE WATERMILFOIL WEEVIL (EUHRYCHIOPSIS LECONTEI): DEVELOPING A
SAMPLING PROGRAM AND INVESTIGATING THE INFLUENCE OF BIOTIC
AND ABIOTIC FACTORS ON ITS DISTRIBUTION

By

Thomas G. Alwin

Euhrychiopsis lecontei is used in North America as a biocontrol agent for the invasive
aquatic plant Myriophyllum spicatum. E. lecontei can suppress M. spicatum and lakewide M. spicatum declines have been attributed to E. lecontei. However, investigations at
the whole lake scale are lacking. The aim of my field research was to quantify minimum
sample size for lake-wide E. lecontei estimation and investigate the influence of M.
spicatum metrics and abiotic factors on E. lecontei presence and density. Four Michigan
lakes were sampled. Power analysis revealed precise lake-wide E. lecontei abundance
estimation (µ 20%) required a large sample size (>300 quadrats) and sizeable effort
(261 hrs) and lower precision (µ 50%) required 49 samples and 41.8 hrs. Zero-inflated
binomial regression found abiotic factors positively influenced and M. spicatum metrics
negatively influenced E. lecontei presence (p<0.014), and density was positively
influenced by M. spicatum metrics and negatively influenced by abiotic factors
(p<0.001). My research illustrates the importance of sample size when estimating E.
lecontei populations and suggests that both M. spicatum metrics and abiotic factors
influence E. lecontei presence and abundance.

ACKNOWLEDGEMENTS

There are so many people that deserve recognition for their contributions to this research.
Thank you to my committee members: Drs. Doug Landis and Mary Bremigan, and
especially my advisor Dr. Kendra Spence Cheruvelil. Your guidance and expertise was
essential to the completion of this research. Thank you to Marty Hilovsky and Cortney
Marquette at EnviroScience Inc. for helping to fund this project and allowing me access
to E. lecontei stocking records. Thank you to Emi Fergus, Matt Fox, Jerek Gutierrez,
Emily Jacobson and Liz Throckmorton for your assistance with field and laboratory
work. Without you I’d still be processing samples! I’d also like to thank the Bear, Clear
and Hackert lake associations and Tim Machowicz at Lake Ovid for allowing me access
to their lakes and their interest in my research, Dr. Pat Sorrano for entrusting me with her
boat and other field equipment, Dr. Travis Brendon for assistance with statistical analysis
and all of my peers in the Limnology Lab at Michigan State University for your
comradery and your insightful nuggets of knowledge. Lastly, I want to thank my wife,
Christe, for her support, encouragement, patience and love. Without you I would not be
where I am today.

iii

TABLE OF CONTENTS

LIST OF TABLES……………………………………………………………..…….……v
LIST OF FIGURES……………………………………………………………..……......vi
INTRODUCTION
Invasive species………………………………………………………..…….........1
Myriophyllum spicatum……………….…………………… …………………….....……1
Controlling M. spicatum……………........................................................................3
E. lecontei as a biological control agent..................................................................5
Sampling to estimate E. lecontei population density...............................................9
CHAPTER 1
Estimating lake-wide watermilfoil weevil (Euhrychiopsis lecontei) density: the roles of
quadrat size, sample size and effort……………………………………………….…….12
Abstract………………………………………………………………….………12
Introduction…………………………………………………………….………..13
Materials and Methods…………………………………………………....……..17
Results and Discussion……………………………………………….………….21
CHAPTER 2
Investigating the influence of habitat characteristics on lake-wide distribution and
abundance of the native milfoil weevil (Euhrychiopsis lecontei dietz) in three Michigan
lakes…………………………...…………………………………………….………..….26
Abstract……………………………………………………………..……………26
Introduction………………………………………………………….…….……..27
Materials and Methods………………………………………………….………..35
Results…………………………………………………………………...……….38
Discussion………………………………………………………………...……...40
Conclusions………………………………………………………………...…….46
THESIS CONCLUSIONS………………………………………………………...…......49
APPENDICES……………………………………………………………………..…….55
Appendix A…………………………………………………………………..…..56
Appendix B…………………………………………………………………..…..64
Appendix C…………………………………………………………………..…..69
LITERATURE CITED...………………………………………………………....…...…95

iv

LIST OF TABLES

Table 1.1 Results of composite quadrat analysis of E. lecontei (n = 15) and M. spicatum
(n = 6) samples. Lab time is a proxy for cost. Standard errors for estimated E. lecontei
and M. spicatum stem densities are in parenthesis and the letters in each column represent
the results of post hoc Tukey test for significance. The same letter denotes no significant
difference found (α=0.05). *=quadrat selected for further
use…...………………………………...……………………………………...………….57
2

Table 1.2 Projected time in hours (weeks) needed to collect and process 0.10m quadrats
to reach optimal sample size (N) to estimating lake-wide E. lecontei population density. d
= fixed portion of the mean α = significance level………………………………………58
Table 2.1 Sample lakes and the criteria used to select them for the study. N/A = not
available. Bear Lake is a private lake and does not have an official bathymetric map.
2
“Samples” refer to the number of 0.1 m quadrats taken to estimate E. lecontei and M.
spicatum densities………………………………………………….…………………….65
Table 2.2 Zero-inflated negative binomial regression results for individual E. lecontei
samples collected during 2008 from three study lakes………………………….……….66
Table 3.1 2008 raw data for Bear Lake. Site letters represent M. spicatum beds and the
numbers represent individual quadrats. Depth is reported in meters. E. lecontei are life
stages are coded as A=adult, L=larva, P=pupa and E=egg. Biomass is dry M. spicatum
2
weight in g/m .……………………..……………..……………………………………...70
Table 3.2 2008 raw data for Clear Lake. Site letters represent M. spicatum beds and the
numbers represent individual quadrats. Depth is reported in meters. E. lecontei are life
stages are coded as A=adult, L=larva, P=pupa and E=egg. Biomass is dry M. spicatum
2
weight in g/m ..……………………………………………..……………....……………78
Table 3.3 2008 raw data for Hackert Lake. Site letters represent M. spicatum beds and the
numbers represent individual quadrats. Depth is reported in meters. E. lecontei are life
stages are coded as A=adult, L=larva, P=pupa and E=egg. Biomass is dry M. spicatum
2
weight in g/m ..………………………………………………………….……………....86

v

LIST OF FIGURES

Figure 1.1 Map of Lake Ovid with M. spicatum sample locations and sampled beds for
determining lake-wide weevil density (a) and lake-wide quadrat sample site locations (b).
Point intercept sample locations used to determine lake-wide M. spicatum cover: • = no
M. spicatum found, ○ = M. spicatum found.
= mapped M. spicatum beds.
= bed
sampled for quadrat size testing. 1a inset depicts the M. spicatum beds sampled during
June to determine quadrat size. Composite quadrats were collected at three points along
five transects…..................................................................................................................59
Figure 1.2 Composite quadrat containing four quadrat sizes and depicting diagonal
support wire.……………………………………………………………...……………...62
Figure 1.3 Optimal sample size (N) of quadrats and M. spicatum stems to estimate lakewide E. lecontei population density using equation 1 (see page 26). Calculated using a
2
2
range of precision (d) and both 90 and 95% confidence intervals. s = 4144.9 and m =
1315.6. CI = confidence interval……………………………………………………...…63
Figure 2.1 Bed-level power analysis for each study lake using data from all three sample
dates. The d value is the fixed portion of the mean calculated using equation 1 (see page
26). Power is generally considered adequate when d ≤
0.50…………………………………………………………….………………...………67
Figure 2.2 Summary of lake-wide E. lecontei and M. spicatum density estimates based on
324, 309 and 351 quadrats collected from Bear, Clear and Hackert Lakes, respectively.
Error bars represent standard deviation………………………………………………….68
.

vi

INTRODUCTION
Invasive species
Non-indigenous species (NIS) are considered one of the top threats to both native
biodiversity and ecosystem function (Kolar & Lodge 2001). While many non-indigenous
species are beneficial (e.g., food crops and livestock) others can have negative economic
and ecological impacts (Pimentel et al. 2005). A widespread NIS that has negative
impacts on an ecosystem or the services it provides is considered an invasive species.
Invasive species are expected to severely impact all ecosystems in time (U.S. Congress
1993).
Numerous invasive species have invaded the aquatic ecosystems of North
America. Among these, aquatic plants can have both ecological and economic impacts on
the water bodies they invade, resulting in them being termed “weeds”. Efforts to control
aquatic weed species in the United States have been estimated to be $100 million
annually (U.S. Congress 1993). Of the invasive aquatic weeds, Myriophyllum spicatum is
one of the most troublesome in temperate North America.

Myriophyllum spicatum
Eurasian watermilfoil (Myriophyllum spicatum L.) is an invasive aquatic weed
that has become a widespread problem throughout lakes and rivers of North America. M.
spicatum is native to Europe, Asia and northern Africa, and became established in North
America just prior to 1950 (Smith and Barko 1990). Since its introduction, this plant has
spread to 45 states and three Canadian provinces (as reviewed by: Newman 2004).
The biology of M. spicatum makes it well suited to dominate many north

1

temperate lakes. M. spicatum, a rooted submersed perennial plant, is able to grow at
water depths of up to ten meters (Aiken et al. 1979). Spring shoot and overwintering stem
growth begin once water temperatures reach 15oC, which is prior to native macrophyte
growth. Stem elongation continues until the surface is reached, where extensive
branching then takes place (Aiken et al. 1979), often resulting in the formation of
expansive M. spicatum beds covering the littoral zone of a lake. In addition to dense
growth, M. spicatum’s reproductive capacity facilitates rapid spread both within and
among lakes.
The rapid spread of M. spicatum is in part due to its capacity to reproduce
asexually. Stolon growth, an asexual method for localized expansion, is the process by
which new plants are formed on horizontal stems growing outward from the parent plant
(Smith and Barko 1990). Autofragmentation is a plant-induced process by which apical
portions of the plant are shed to colonize new areas of a lake. Allofragmentation is the
mechanical breakage of M. spicatum by external forces, such as wave action, boats, and
swimming (Madsen and Smith 1997). Unintentional transport of M. spicatum fragments
attached to boats and trailers is its main dispersal method between lakes (Madsen et al.
1988). Once established, fragmentation enables M. spicatum to disperse and form new
beds across long distances (Madsen and Smith 1997). The ability of M. spicatum to
spread, both among and within lakes, is a trait that enables it to reach nuisance levels in
many aquatic ecosystems.
Once introduced, M. spicatum’s ecology often enables it to out-compete native
macrophyte species (Smith and Barko 1990). Where established, M. spicatum commonly
grows to form a dense monoculture surface canopy in the littoral zone of mesotrophic to

2

moderately eutrophic lakes (Madsen 1998). In fact, M. spicatum can comprise up to 95%
of the plant cover in a lake (e.g., Cheruvelil et al. 2005). This dense M. spicatum surface
canopy can alter the physical, chemical and biological conditions of the lake. Physical
and chemical impacts can include elevated temperature and depressed dissolved oxygen
levels of the surface waters within a bed (Unmuth et al. 2000). These conditions can alter
the habitat suitability of the littoral zone for many organisms. Biological impacts can
include reduced biodiversity of littoral macrophyte beds (Madsen et al. 1991, Cheruvelil
et al. 2001), interruptions of food web interactions (Smith and Barko 1990), and a
reduced overall biodiversity of aquatic organisms (Keast 1984).
Dense M. spicatum growth can also affect the utility of the lake for human uses,
which can result in negative economic impacts (Johnson and Blossey 2002). High
densities of M. spicatum in the littoral zone of lakes can impede swimming, fishing and
recreational boating. Reductions in these activities may reduce revenue coming into the
local economy surrounding an infested lake as well as decrease lake property values
(Horsch and Lewis 2009). Due to its potentially negative impacts on human uses and lake
ecology, much time and money is spent on researching and conducting effective M.
spicatum management techniques.

Controlling M. spicatum
Current lake-wide management efforts focus on the application of chemical
herbicides to control M. spicatum. Chemical herbicides are widely used and are either
applied to the whole lake or to targeted beds of M. spicatum within a lake (Getsinger et
al. 1997, Madsen et al. 2002). When properly applied, a California study found limited

3

short-term and no long-term toxicity of aquatic herbicide applications (Siemering et al.
2008). However, herbicide use can be expensive, is not a permanent or long-lasting
solution, and if incorrect dosages are used or excessive herbicide exposure times occur,
there can be negative ecological impacts on non-target species (Netherland et al.1997,
Pothoven et al. 1999, Wagner et al. 2007). These drawbacks led to the search for a
biological control agent for M. spicatum.
Biological control has the potential to be a cost effective, long-term solution for
many invasive plants (Hajek 2004). The process for determining an appropriate
biocontrol agent includes agent selection, host-specificity testing, and evaluation after
release (McFadyen 1998). When testing a potential agent, it is important that the agent
has a density-dependent relationship with the target pest population in addition to host
specificity. Evaluation, which is often a neglected component of biocontrol programs, is
important for monitoring to make necessary adjustments and justify the expense of the
program (McFadyen 1998). Proper evaluation requires the use of an appropriate sampling
technique for both the biocontrol agent and the weed species.
The search for a suitable control agent for M. spicatum led to an uncommon
organism. Most examples of successful biocontrol have been with classical biocontrol
agents (those in which the pest species and its control agent are both exotic species;
Eilenberg et al. 2001). Initial attempts to locate a natural enemy of M. spicatum in its
native range were unsuccessful because the potential classical biocontrol agents were
generalist herbivores that lacked host specificity (Sheldon and Creed 1995). However,
several native or naturalized North American insect species have been found to feed on
M. spicatum (Painter & McCabe 1988, Newman and Maher 1995): Acentria ephemerella,

4

a naturalized pyralid caterpillar, Cricotopus myriophylli, a native midge, and
Euhrychiopsis lecontei, a native watermilfoil weevil. Employing a native herbivore as a
biocontrol agent has the benefit of reducing the risks associated with introducing another
non-native organism. In addition, when using a native biocontrol agent, there may be
fewer negative ecological impacts as compared to traditional control methods (Sheldon
and Creed 1995). Research on E. lecontei has shown the most promise among the native
and naturalized herbivores. Therefore, the majority of M. spicatum biocontrol research
has been focused on this native herbivore.

E. lecontei as a biological control agent
The native range of E. lecontei spans from coast to coast in the northern United
States and southern Canada (Aiken et al. 1979). Within this wide geographic range, E.
lecontei is believed to be ubiquitous; however only a few quantitative studies have been
performed throughout its range. For example, 25 of 27 Wisconsin lakes (Jester et al.
2000), 8 of 19 Minnesota lakes (Newman and Maher 1995), and 14 of 50 Washington
lakes sampled had E. lecontei populations (Tamayo et al. 2000). This potentially large
native range and natural abundance of E. lecontei helps make their employment as a
management tool more acceptable to stakeholders and management agencies than
chemical treatments (Madsen et al. 2000).
E. lecontei’s life cycle is closely coupled with M. spicatum. As adults, E. lecontei
reach a length of three millimeters and are dark-colored with brown to yellow stripes on
the dorsal side. Ovary development begins in the spring once water temperatures reach
15°C and within one to two weeks, females begin laying eggs (Newman et al. 2001).

5

Females lay an average of 1.9 eggs per day on the apical meristem of M. spicatum, and a
maximum of four to six generations can be completed each summer (Newman et al.
2001). Once the larvae hatch, they initially feed on meristem tissue and then burrow into
the stem of M. spicatum, where they mine the plant’s central cortex. The larvae hollow
out an individual pupation chamber in the upper portion of the plant (0.5 to 1 m from the
apical meristem) to complete development. When the adult beetle emerges from the
pupal chamber, it moves back to the M. spicatum bed canopy to feed and reproduce. Each
season, the last generation of adults foregoes reproduction and instead allocates energy
toward flight muscle development and fat storage for overwintering, which occurs in
near-shore soil and leaf litter. During the spring, adults emerge from the soil and return to
the water where their flight muscles are reduced and energy is once again allocated
toward reproduction (Newman et al. 2001). Therefore, E. lecontei is particularly wellsuited for M. spicatum biocontrol because all active life stages depend on watermilfoil
plants as a food source and multiple generations are completed in a season.
Grazing by E. lecontei is believed to impact M. spicatum in several ways. Grazing
by E. lecontei reduces stem elongation and suppresses M. spicatum bed development
(Creed and Sheldon 1993, Creed and Sheldon 1995, Newman et al. 1996). Adults feed on
the leaves and stems of M. spicatum, reducing its ability to photosynthesize (Johnson and
Blossey 2002). Although adult grazing has negative impacts on M. spicatum, larval
feeding activity has a larger impact (Creed and Sheldon 1993). Each larva will tunnel
through approximately 15 cm of M. spicatum stem during this stage (Johnson and
Blossey 2002). The larvae damage the plant by hollowing out the cortex, which interrupts
the flow of nutrients through the plant and reduces the buoyancy of the upper portions of

6

the plant (Newman 2004, Creed and Sheldon 1993). The reduced flow of nutrients results
in lower concentrations of root nutrient stores that may be linked to reduced overwinter
survival of M. spicatum (Newman and Maher 1995). Reduced buoyancy of the upper
portions of M. spicatum can decrease the standing stock of M. spicatum in floating beds,
and can potentially lead to the upper portions sinking out of the photic zone (Creed and
Sheldon 1995). The presence of the larval pupa chamber and larval stem mining can also
decrease structural integrity of the stem, resulting in increased breakage (Newman et al.
1996). Stem fragments from larval grazing also have reduced viability relative to other
fragments and rarely succeed in developing into new plants (Creed and Sheldon 1995).
The distribution, abundance, life history, and ecology of E. lecontei all suggest that it is a
suitable biocontrol agent for M. spicatum.
Research on E. lecontei has shown promise as a biocontrol agent for M. spicatum.
Small-scale tank and mesocosm experiments have determined that E. lecontei are able to
2

suppress M. spicatum at densities of 267–410 E. lecontei per m (Sheldon & Creed 1995,
Newman et al. 1996). Field observations and augmentation studies have found that
2

densities of just 23–200 E. lecontei per m were able to suppress M. spicatum (Newman
2004). In addition, several natural lake-wide declines of M. spicatum in Vermont,
Wisconsin and Minnesota have been attributed to the E. lecontei (Sheldon and Creed
1995, Creed 1998, Lillie 2000, Newman and Biesboer 2000). In fact, it has been
suggested that E. lecontei may act as a keystone species in North American lakes by
having a disproportionally large impact on littoral trophic interactions (Creed 2000a).
However, it is uncommon for natural E. lecontei populations to reach adequate densities
to exert sufficient M. spicatum control (Solarz and Newman 2001). To overcome this
7

issue, augmentation or introduction of E. lecontei has become a commercially-available
option for lake managers. However, results from E. lecontei stocking efforts have been
inconsistent (Reeves et al. 2008). Therefore, a better understanding of the factors that
affect E. lecontei effectiveness as a biocontrol agent is needed.
Although the life history and host specificity of E. lecontei suggest that it shows
promise as a biocontrol agent for M. spicatum, effective M. spicatum suppression is
dependent on E. lecontei grazing rates, which are primarily determined by population
density. Therefore, achieving and maintaining adequate E. lecontei population density is
critical for successful M. spicatum control (Newman 2004). However, research on E.
lecontei population dynamics, food web interactions and the minimum density required to
impart M. spicatum control at the lake-wide scale are lacking (Creed 2000b). In order to
conduct such research, a sampling program that can estimate E. lecontei density at this
spatial scale with sufficient precision must be employed (Buntin 1994).
The thesis research described herein examined sample size and statistical power
when sampling E. lecontei at the lake-wide scale as well as relationships among E.
lecontei distribution and density, several M. spicatum metrics, and lake abiotic factors.
Understanding E. lecontei population dynamics at the lake-wide scale is important for
advancing scientific comprehension of insect herbivory in freshwater systems as well as
for making informed management decisions. For example, an understanding of E.
lecontei demography at this scale may enable lake managers to better predict which lakes
will benefit from stocking and how to most effectively stock E. lecontei within lakes.

8

Sampling to estimate E. lecontei population density
An appropriate sampling technique that is employed at the spatial scale that
control is desired is critical for determining the impact of a biocontrol agent on the
decline of a pest species. Intensity of sampling, sample unit, sample strategy, and
statistical power all need to be considered and are dictated by the objectives of the
sampling program. Intensive sampling should be used for scientific study where high
precision estimates are needed while less intensive sampling may be acceptable for some
pest management efforts (Bunting 1994).
There are two sample units that are commonly employed for E. lecontei
population density estimation: individual stem sampling and quadrat sampling. Both are
subcategories of absolute population estimation. Pedigo and Buntin (1994) defines an
absolute estimate as “presenting information on numbers of arthropods, usually per unit
of land surface area, in the habitat (e.g., number per square meter) or providing data that
can be easily converted to such”. Quadrat sampling is a proper absolute estimate that is
2

reported as number of individuals per land area (#/m ), whereas individual stem sampling
is a basic estimate that is reported as number of individuals per standard unit of habitable
area (#/stem) (Pedigo and Buntin1994). Individual stem estimates can be converted to
proper absolute estimates if M. spicatum stem densities per unit of area are also
determined.
Sample strategy is the method used for determining sample site locations and
includes: transects, randomly determined points, and point intercept methods. Each
strategy has benefits and drawbacks associated with it and deciding which to use should
be based on sampling objectives. Sample strategy for lake-wide E. lecontei population
9

estimation was not investigated in this study but deserves investigation. We chose to
focus instead on statistical power, which should be considered regardless of sample
strategy (Pedigo and Buntin 1994).
Adequate statistical power can ensure that study results have the precision
necessary to detect differences in population densities over time or between experimental
manipulations. Precision is a function of the number of samples collected and of the
sample program (sample unit and sample strategy) employed (Peterman 1990). Low
precision will result in poor confidence in the population estimate and may bring into
question the study conclusions or monitoring effort (Cheruvelil et al. 2000). Acceptable
levels of precision are, in part, up to the researcher or manager, but Bunting (1994)
suggests confidence intervals of 0.05, 0.10 or 0.25 (with 0.025 preferred for pest
management sampling). However, as the number of samples collected increases, and
power increases, so do the costs associated with estimating population density.
An effective and efficient sampling program will accurately estimate the
population density of the species of interest, with the desired level of precision, without
incurring excessive costs (Hutchins 1994). Both scientific and management-oriented
sampling efforts have budgetary constraints and finding a balance between precision and
cost is essential. A better understanding of the influence of abiotic and M. spicatum
metrics on E. lecontei presence and abundance may help focus sampling efforts, which
will reduce the effort and cost for estimating lake-wide populations with sufficient
precision. For example, if it is determined that E. lecontei presence has a negative
relationship with distance from shore, then sampling too far from shore would be
unnecessary.

10

The chapters of this thesis describe 1) the sampling effort necessary to achieve a
statically precise lake-wide E. lecontei population density estimate and 2) the influence of
abiotic and M. spicatum metrics on presence and abundance of E. lecontei. The
conclusion from the first chapter seems disheartening due to the amount of effort required
to confidently estimate lake-wide density. However, these results exemplify the need to
account for power when conducting in situ research of E. lecontei. The results for each
lake sampled in the second chapter contradict one another and preclude definitive
conclusions regarding the influences of the measured abiotic and M. spicatum metrics on
E. lecontei presence and abundance. However, the methodology and analytical tools
presented are an improvement on previous studies. Therefore, it is important for us to
recognize how widely E. lecontei distributions vary from lake-to-lake, future study of E.
lecontei will benefit from my work.

11

CHAPTER 1

ESTIMATING LAKE-WIDE WATERMILFOIL WEEVIL (EUHRYCHIOPSIS
LECONTEI) DENSITY: THE ROLES OF QUADRAT SIZE, SAMPLE SIZE AND
EFFORT

Thomas G. Alwin, M. G. Fox, K. Spence Cheruvelil

Abstract
Eurasian watermilfoil (Myriophyllum spicatum L.), a non-indigenous macrophyte in
North America, can reach nuisance levels in freshwater systems. The watermilfoil weevil
(Euhrychiopsis lecontei) has been employed as a biological control agent for Eurasian
watermilfoil. If lake-wide watermilfoil suppression is desired, then E. lecontei research
needs to be focused at this spatial scale. However, previous studies and current
management methods estimate E. lecontei density with limited sampling, and lake-wide
studies are rare. In a single watermilfoil-infested lake, previously stocked with E.
lecontei, we 1) determined which of four quadrat sizes was most appropriate for sampling
E. lecontei, 2) calculated the optimum number of samples required to estimate E. lecontei
abundance at a lake-wide scale with varying precision, and 3) estimated the cost
associated with each required sample size. Analysis of variance showed that differences
between quadrat sizes were not significant (p > 0.9775), likely due to E. lecontei’s highly
2

variable distribution. Next, we collected lake-wide samples with a 0.1 m quadrat to
estimate E. lecontei density. Power analysis concluded that highly precise estimation
12

(20% of the true mean) of lake-wide E. lecontei abundance required a large sample size
(>300) and substantial effort (261 hours) and that reduced precision (50% of the true
mean) required 49 samples and 41.8 hours. The patchy distribution of E. lecontei makes
highly precise lake-wide density estimation difficult, implying that researchers and
managers will need to either accept lower precision associated with their sampling or
largely increase sample size and effort when estimating lake-wide E. lecontei density.
Key Words: Biological control, Myriophyllum spicatum, power, optimum sample size,
Michigan, precision
Introduction
Non-indigenous species (NIS) are considered one of the top threats to both native
biodiversity and ecosystem function (Kolar and Lodge 2001). Controlling the spread and
reducing the impacts of NIS is important for preserving biodiversity, protecting
ecosystem function, and minimizing negative economic impacts (Lovell et al. 2005). The
aquatic plant Eurasian watermilfoil (Myriophyllum spicatum L.), hereafter referred to as
EWM, is not indigenous to North America and, once introduced, can reach nuisance
levels in lakes, rivers and reservoirs. Dense EWM growth can alter the physical and
chemical conditions of water bodies and change fish and wildlife habitat (Keast 1984,
Smith and Barko 1990, Madsen et al.1991, Cheruvelil et al. 2001 Unmuth et al. 2000).
Dense EWM growth can also negatively affect human uses by impeding swimming,
fishing and boating. When EWM accumulates at the water surface (i.e. forms a surface
canopy) or broken EWM stems collect on shore, the aesthetic quality of the lake may also
be reduced (Smith and Barko 1990). Therefore, there is much interest in effectively

13

stopping the spread of this species into new aquatic ecosystems, and for control in
colonized systems.
Biological control, the use of biological means (such as parasites, viruses, or
predators) to suppress a pest population by reducing its numbers or the damage it causes
(Eilenberg et al. 2001), is one EWM control option for infested aquatic systems. The
benefits of this approach are the potential for long-term control and increased selectivity,
and it is generally considered a more natural and sustainable method of control than
alternatives such as chemical or mechanical control (Madsen et al. 2000). Although
several biological control agents have been suggested for EWM control, this paper
focuses on the watermilfoil weevil (Euhrychiopsis lecontei), which has shown biocontrol
promise (see below) and is commercially available for stocking.
E. lecontei is an aquatic beetle that is native to North America. All E. lecontei life
stages are dependent on watermilfoil plants for food and habitat (Sheldon and O’Bryan
1996a). Research has found that although its natural host plant is a native variety of
Myriophyllum, E. lecontei prefers EWM once exposed to it and is highly host-specific to
EWM (Sheldon and Creed 1995, Solarz and Newman 2001, Sheldon and Creed 2003).
These facts mean that E. lecontei has little negative impact on other aquatic plants (see
Newman 2004 for a review of E. lecontei). Although the life history and host specificity
of E. lecontei suggest that it shows promise as a biocontrol agent for EWM, effective
EWM suppression is dependent on E. lecontei grazing rates, which are primarily
determined by population density, and EWM response to herbivory (Newman 2004).

14

Research of E. lecontei suggests that it is well-suited for EWM biocontrol, yet it
does not always reach population densities high enough to control EWM. A number of
studies have investigated factors that may be limiting E. lecontei density (Sheldon and
O’Bryan 1996b, Sutter and Newman 1997, Newman and Biesboer 2000, Tamayo et al.
2000, Newman et al. 2001, Ward and Newman 2006); however the reasons for
incomplete control remain unclear. One problem with our current state of knowledge is
that previous studies have sampled E. lecontei densities at a different spatial scale than
EWM control is desired, often sampling a limited number of EWM beds in a lake or
making lake-wide population inferences from a relatively small number of EWM stems.
If we wish to control EWM at the whole-lake scale, we need to understand E. lecontei
population dynamics and the factors that may limit E. lecontei effectiveness at that scale,
and therefore must sample and estimate E. lecontei density at the whole-lake scale. Our
knowledge of E. lecontei, however, is based mainly on small-scale studies. Therefore, in
situ studies of E. lecontei at the lake-wide scale are essential to improve our
understanding of lake-wide population dynamics and the use of E. lecontei as a
biocontrol agent.
When developing a sampling program, there are three main components that need
to be addressed: sample units (i.e., what will you collect (quadrats or individual stems)),
sample strategy (i.e., how will you collect samples (transects, randomly determined
points, point intercept, ect…)), and statistical power (i.e., how many samples will you
collect), which determines your ability to detect differences among treatments (Peterman
1990). When conducting scientific study, it is important to ensure that the results estimate
the population density of the species of interest, with the desired level of precision,
15

without incurring excessive costs (Hutchins 1994). Adequate statistical power can ensure
that study results have the precision necessary to detect differences in population
densities over time or between experimental manipulations. Therefore, regardless of the
sample unit selected or the sampling strategy employed, accounting for power for in situ
population studies is important and should be addressed. Collecting preliminary samples
and running power analysis can determine the number of samples required to reach the
desired level of precision, known as optimal sample size, and sample collection and
processing times can be used to estimate cost.
Our study selected the sample unit and sampling strategy we felt most appropriate
to explore optimal sample size and the cost necessary to estimate lake-wide E. lecontei
population density using a variety of desired precision levels. Although many previous
studies collected individual stems and reported E. lecontei densities as number/EWM
stem, we chose to use quadrats to collect and estimate E. lecontei and EWM densities.
The use of quadrats allowed us to collect a large number of EWM stems quickly. By
counting EWM stems in these samples, we were able to present E. lecontei density in
both aerial and per stem basis. Our objectives were to determine:
1. The quadrat size that most minimizes cost while maximizing accuracy for
estimating lake-wide E. lecontei abundance.
2. The number of quadrats required (using the chosen quadrat size from above) to
estimate lake-wide E. lecontei density across a range of confidence intervals and
detection levels.
3. The amount of effort needed to collect and process the required number of
quadrats for each of the scenarios in objective 2.
16

Based on past research of epiphytic invertebrates (Downing and Anderson 1985), we
expected that although using a smaller quadrat would require a larger number of samples
to be collected, this scenario would more accurately estimate E. lecontei abundance than
taking fewer samples with a larger quadrat. We also expected that smaller quadrats would
minimize cost (measured as processing time in the lab) (Downing and Anderson 1985).
In order to meet our objectives, we conducted an intensive field study of one lake infested
with EWM and previously stocked with E. lecontei in Michigan, USA. The information
gained from this study will provide a useful tool for future scientific study of E. lecontei
population ecology and inform whole-lake EWM management.

Materials and Methods
Study Lake
We collected all M. spicatum and E. lecontei from Lake Ovid. This lake, with a
mean depth of 2.3 meters and a maximum depth of six meters, is located within Sleepy
Hollow State Park in Clinton County, Michigan, U.S.A. The 147 ha lake is a man-made
reservoir created by a dam on the Little Maple River. Lake Ovid, with a Secchi depth of
< 1.5m, is hypereutrophic (Kalff 2002), with a natural shoreline except for a public
camping area, boat launch and beach. During July of 2006, a team from the Michigan
Department of Natural Resources stocked 23,000 weevils at six locations in Lake Ovid.
An additional 14,000 weevils were added in 2007 (Tim Machowicz pers comm.). Using
the point intercept method (Madsen 1999), we determined lake-wide M. spicatum cover
to be 39.5% during July 2008. Only two other macrophyte species were found and they
were uncommon.

17

Prior to quadrat collection, we visited Lake Ovid to determine extent and location
of EWM beds. For the purposes of this study, M. spicatum beds are defined as any area
where M. spicatum growth is within 50 cm of the water surface and thus could impede
recreation. We collected waypoints using a handheld GPS unit (Garmin GPSmap 76S)
around the perimeter of M. spicatum beds. GPS data were uploaded to ArcMap (ESRI
v9.2) to digitize and map M. spicatum beds using a MNDNR Garmin extension
(www.dnr.state.mn.us, last accessed June 2008).

Determining quadrat size
We constructed a composite quadrat that consisted of four different quadrat sizes
within the outer frame (Figure 2). We used 1½ inch PVC pipe for the outer frame and
steel wire for the inner quadrats. A steel wire was connected from the bottom left to the
upper right corner of the PVC frame to stabilize the upper right corner of each inner
quadrat. We used this quadrat to gather M. spicatum samples of each quadrat size. These
samples were collected from a single bed selected for this composite quadrat study
(Figure 1a inset).
We visited Lake Ovid on June 17, 2008 to collect composite quadrat samples. We
established five evenly-spaced transects perpendicular to shore from west to east along
the bed. Three samples, each containing the four quadrat sizes, were taken per transect
for a total of 15 M. spicatum composite quadrats that provided us with 60 total samples
(Figure 1a inset). When taking samples, we submerged the quadrat a minimum of one
half meter below the water surface, and to the substrate when possible, and used the wires
and visual estimates to separate and clip the M. spicatum stems. We did not record field

18

collection time for composite quadrats, since summing smaller quadrat times to estimate
the effort of larger quadrats would not accurately represent collecting the larger
individual quadrat. Minimum stem length was decided based on three factors: 1) our
working definition of a M. spicatum bed, 2) E. lecontei inhabit the upper portions M.
spicatum (Creed and Sheldon 1993), and 3) use of this stem length in previous E. lecontei
research (e.g., Sheldon 1997, Jester et al. 2000 and Tamayo et al. 2000). Individual
quadrat samples were placed in separate pre-labeled re-sealable 3.8 L bags while
underwater and stored on ice until returned to the lab where they were kept refrigerated
until processed.
We processed each sample individually using 37.9 Liter tubs and white dissecting
trays. We placed one to two stems of M. spicatum into a tray and carefully inspected
them for all life stages of E. lecontei. We also recorded the number of M. spicatum
strands and meristems from transects four and five. We separated M. spicatum strands
into two categories: stems (strands that contained an apical meristem and were at least 10
cm long), and fragments (strands that had two broken ends or were less than 10 cm long).
2

Because individual samples larger than 0.05m represented a portion of the total area for
a quadrat, we summed values of the smaller sections to get values for the larger desired
quadrat sizes. We kept track of processing time, in minutes, as a proxy for sampling cost.
We natural log transformed the quadrat size data to achieve more normal
distributions. We used these transformed data to run ANOVA and Tukey post-hoc tests
using SAS 9.1 to determine whether or not the four quadrat sizes produced significantly
different estimates of E. lecontei density (alpha < 0.05).

19

Determining number of quadrats
Based on the results of above, we used one quadrat size to collect 118 lake-wide
E. lecontei samples on July 22, 2008. Quadrat samples were collected from M. spicatum
beds previously mapped (above). To determine the appropriate number of samples per
bed, we divided the individual bed areas by the sum of all bed areas and multiplied these
proportions by 100. All beds comprising less than 3% of the total sampling area were
assigned a minimum sampling value of three for two reasons: 1) E. lecontei density and
bed size were not correlated (unpublished data) and 2) three is the minimum number that
allows statistical analysis. We randomly generated sample points for each M. spicatum
bed using Hawth’s Tools Extension (ArcMap 9.2) (Figure 1b). We used GIS maps and
GPS coordinates to locate our sample points in the field. The same techniques were used
to collect, store, and process the M. spicatum and E. lecontei samples as were described
for the quadrat size study above. For this stage of sampling we kept track of both sample
collection and processing time as a proxy for cost.
We used Power analysis (eq. 1) on the data collected from the lake-wide sampling
to calculate the optimum sample size (N) required to reliably estimate lake-wide E.
lecontei density using the selected quadrat.
2 2

2

N= (tα/2/d) (s /m ),

(1)

with tα/2 = t value for a given probability (α), d = desired fixed proportion of the mean, s
= sample variance, and m = mean density (Buntin 1994). Sample variance and mean
density were determined from the lake-wide samples collected. We calculated optimal

20

2

sample size for a range of α and d to represent common confidence intervals and levels
for pest management sampling programs (Buntin 1994).

Results and Discussion
Quadrat size
There were no differences in estimated E. lecontei density (p = 0.9775), M.
spicatum stem densities per square meter (0.0864), or estimated E. lecontei per M.
spicatum stem (p = 0.8525) between the four quadrat sizes collected from Lake Ovid
during June 2008 (Table 1). We noted that the variation among samples was quite high,
which likely attributed to there being no difference among the four different sizes of
quadrat. Previous foodweb studies of the littoral zone have found that epiphytic
invertebrates are highly spatially variable and this may account for our high sample
variation (Downing and Cyr 1985, Cheruvelil et al. 2000). In fact, when we later ran
2

power analysis to detect differences among the 0.05, 0.1, 0.2 and 0.3 m quadrat sizes,
we found that our power was low (0.82, 0.75, 0.69 and 0.70, respectively). Therefore,
compared to the 15 samples we collected, we would have needed to take 39, 33, 29 and
30 samples, respectively, to determine a 50% difference in E. lecontei density between
the quadrat sizes. Some other research has shown that a large number of epiphytic
invertebrate samples are likely needed to achieve the high levels of precision (Downing
and Cyr 1985). In addition, edge effects of the composite quadrat may have introduced
some bias in our results and deserves further investigation.
Average lab processing time increased as quadrat size increased (Table 2).
2

Processing time would have been reduced most using a 0.05m quadrat. This increase in
21

samples would have, however, required more sample transport space and increased total
sample collection time by increasing the amount of time spent entering/exiting the water
and locating additional sample sites. Therefore, based on the costs associated with each
quadrat size, the increased number of samples determined by the post hoc power analysis
for the 0.05 quadrat, and because there was no difference in weevil densities among
quadrat sizes, we chose to use the size that we felt would be most practical for the next
2

step of our study. We used a 0.1m quadrat for four reasons: 1) We wished to minimize
cost (processing time, above), 2) Previous E. lecontei scientific studies had been done
2

using 0.1m quadrats (Newman and Biesboer 2000), 3) Extrapolating weevil densities to
represent abundance per square meter would be mathematically simple when beginning
2

with a 0.1m quadrat, and 4) It is often better to collect a larger number of samples using
smaller quadrats than a smaller number of large quadrats (Green and Young 1993).

Required sample size
2

We estimated that the lake-wide E. lecontei density was 36.3 per m with a
2

sample variance (s ) of 4145 and the average number of M. spicatum stems per sample
was 33.4 with a variance of 262, resulting in an estimated average of 1.09 E. lecontei per
stem. Based on our estimated EWM bed area, we would estimate that there were 264,000
E. lecontei in Lake Ovid at the time of sampling. This estimate suggests a substantial
increase from the 37,000 E. lecontei stocked since 2007. Several factors should be
considered, however, when comparing stocking numbers to our estimate: 1) Lake Ovid
likely had an existing population of E. lecontei prior to stocking, 2) since 2007, some E.
22

lecontei have been removed for culturing purposes (with similar numbers being returned
at the end of each season), and 3) using equation one and solving for d, our estimated
2

number per m is within 32% of the true mean, therefore the true lake-wide E. lecontei
population lies somewhere between 179,520 and 348,480 individuals. In fact, power
analyses of the July 22, 2008 samples demonstrated that with an alpha of 0.05 and d
value (desired fixed proportion of the mean) of 0.2, the optimal sample size is 309
quadrats or 10,321 stems. At the other end of the spectrum, using a d value of 0.5 would
require 49 quadrat samples or 1651 stems. Additional analysis with the same d values and
an alpha of 0.10 resulted in optimum samples needed ranging from 34-214 quadrats and
1147-7167 stems (Figure 3). It is interesting to note that although we cannot determine
whether M. spicatum biocontrol is being achieved from our single-season of sampling,
according to previous studies, our estimate of E. lecontei density is within the range
needed to bring about significant M. spicatum decline and/or suppression (Newman
2004).
An additional factor that needs to be considered is our differentiation between M.
spicatum stems and fragments. We used only M. spicatum stem data for data analysis.
This has two main implications: 1) underestimation of M. spicatum stem densities since
stems broken during collection increased fragment counts and decreased stem counts, and
2) overestimation of E. lecontei per stem results. However, there was no way for us to
take the fragment information and accurately transform it into a stem count. Future
studies could overcome this by measuring individual stem and fragment lengths and
divide the total fragments lengths by a predefined minimum strand length.

23

2

The estimated time to collect and process a 0.10m quadrat from Lake Ovid was
50.2 minutes. Using this estimate, the total time needed to process the optimum number
of samples for each of the previous scenarios ranged from 29-261 hours (Table 2). These
results demonstrate that achieving high precision lake-wide E. lecontei estimates requires
a large number of samples and considerable cost, but that by looking for just large
differences between treatments (i.e., requiring lower power) it is possible to reduce
samples and costs. For example, if we were interested in being 95% confident of our
estimates of lake-wide E. lecontei density in Lake Ovid during the summer of 2008
within 50% of the true mean, we would need to spend one week to collect and process 49
2

0.01m quadrats (or 1637 stems).
The decision of minimum acceptable power is dependent on the questions being
asked and the resources available. In addition, power will be highly variable among lakes
and years. For example, Lake Ovid has a relatively high E. lecontei density, which
influences the optimum sample size calculations. A lake with low E. lecontei population
density would require a larger number of samples to achieve similar levels of power.
Therefore, power should be calculated for each project on an individual lake basis and
likely reevaluated at the beginning of each sampling season. Although the optimum
sample size values we calculated for Lake Ovid should not be directly applied to other
lakes, they can provide general guidance on the likely range of samples needed when
sampling E. lecontei.
Our results suggest that previous research that used fewer samples (quadrats or
stems) from fewer M. spicatum beds likely did not achieve adequate power to precisely
estimate lake-wide E. lecontei density or detect differences in E. lecontei densities across
24

lakes with a high level of confidence. Some of these studies may not have been
attempting to estimate lake-wide E. lecontei populations, and we are not suggesting that
all in situ research of E. lecontei needs to be performed at the whole-lake scale. The issue
of statistical power is important, however, when estimating E. lecontei population
densities regardless of spatial scale and should be considered and reported. In addition, if
we wish to use E. lecontei to manage M. spicatum at the whole-lake scale, we need to
understand E. lecontei population dynamics at that scale; therefore we must sample,
estimate, and study E. lecontei density at the whole-lake scale. Future research should
conduct a priori power analysis when investigating in situ E. lecontei density and use the
results to ensure sufficient power is realized.

25

CHAPTER 2

INVESTIGATING THE INFLUENCE OF HABITAT CHARACTERISTICS ON
LAKE-WIDE DISTRIBUTION AND ABUNDANCE OF THE NATIVE MILFOIL
WEEVIL (EUHRYCHIOPSIS LECONTEI DIETZ) IN THREE MICHIGAN LAKES

Abstract
Eurasian watermilfoil (Myriophyllum spicatum L.), a non-indigenous macrophyte in
North America, reaches nuisance levels in many U.S. freshwater systems. The
watermilfoil weevil (Euhrychiopsis lecontei) has been employed as a biological control
agent for M. spicatum. A better understanding of the influence of habitat conditions on
the presence and abundance of E. lecontei will advance the scientific understanding of in
situ population dynamics and may improve the use of this herbivorous insect for
biological control of M. spicatum. I investigated the influence of a number of M.
spicatum metrics and abiotic factors on the presence and abundance of E. lecontei in
three Michigan lakes using zero-inflated binomial regression. E. lecontei presence was
positively influenced by abiotic factors and negatively influenced by M. spicatum metrics
(p<0.014) and density was positively influenced by M. spicatum metrics and negatively
influenced by abiotic factors (p<0.001). These findings suggest that both M. spicatum
metrics and abiotic factors influence E. lecontei presence and abundance.

26

Introduction
Eurasian watermilfoil (Myriophyllum spicatum L.), is a non-indigenous invasive
aquatic weed found throughout North America (Smith and Barko 1990). Once introduced
to a water body, M. spicatum may reach nuisance levels. When M. spicatum forms dense
surface canopies, habitat suitability for native organisms, lake recreational uses, and the
aesthetic value of the water body can be negatively affected (Keast 1984, Smith and
Barko 1990, Madsen et al. 1991, Unmuth et al. 2000, and Cheruvelil et al. 2002). Due to
the potential negative impacts of M. spicatum, much time and effort is put into stopping
its spread and controlling it in waters already infested.
Biological control, defined as the use of a biological means (such as parasite,
virus, or predator) to suppress a pest population and reduce its numbers or the damage it
causes (Eilenberg et al. 2001), is one option for combating M. spicatum infestations. The
aim of such biological control would be achieving sustained control of M. spicatum.
Several organisms have been found to feed on M. spicatum (as reviewed by Newman
2004), but Euhrychiopsis lecontei, the milfoil weevil, is a native North American beetle
that has shown the most potential as a biocontrol agent. In fact, E. lecontei is now
commercially available as a biological control agent for introduction and augmentation in
M. spicatum infested lakes, and it has been associated with multiple M. spicatum declines
(Newman 2004)
E. lecontei’s life cycle is closely coupled with M. spicatum. Adult E. lecontei
spend the summer on M. spicatum, feeding on stem and leaf tissue and reducing its
ability to photosynthesize (Johnson and Blossey 2002). Females lay their eggs on the
apical meristem of M. spicatum and can produce an average of 1.9 eggs per day. A

27

maximum of four to six generations can be completed each summer (Newman et al.
2001). Early instar larvae feed on meristem tissue, and after several days, burrow into the
stem of M. spicatum, where they mine the plant’s central cortex. Each larva will tunnel
through approximately 15 cm of M. spicatum stem before entering the pupal stage
(Johnson and Blossey 2002). Individual larva hollow out a chamber in the upper portion
of the plant (0.5 to 1 meter from the apical meristem) where they pupate. When the adult
beetle emerges, it returns to the M. spicatum bed canopy to feed and reproduce (Sheldon
and O’Bryan 1996b). Each season, the last generation of adults foregoes reproduction and
instead allocates energy toward flight muscle development and fat storage for
overwintering, which occurs in near-shore soil and leaf litter (Newman et al. 2001).
Adults emerge from the soil in the spring and return to the water where flight muscles are
reduced and energy is once again allocated toward reproduction. The cycle is completed
when E. lecontei spend the entire growing season feeding and reproducing on M.
spicatum (Newman et al. 2001).
E. lecontei is particularly well-suited for M. spicatum biocontrol because all
active life stages are dependent on watermilfoil plants as a food source, they have high
fecundity, and multiple generations can be completed in a season. Larval feeding has the
largest impact on M. spicatum through interrupting nutrient flow and reducing the
buoyancy of the upper portions of the plant (Newman 2004, Creed and Sheldon 1993).
Reduced nutrient flow may negatively affect M. spicatum’s ability to overwinter
(Newman and Maher 1995), and reduced stem buoyancy can decrease the standing stock
of M. spicatum in floating beds and potentially cause the upper portions of the plant to
sink out of the photic zone (Creed and Sheldon 1995). Damage from the pupation

28

chamber and larval stem mining can also result in increased stem breakage (Newman et
al. 1996). Stem fragments resulting from larval grazing have low viability and rarely
succeed in developing into new plants (Creed and Sheldon 1995). Grazing by E. lecontei
reduces stem elongation and suppresses M. spicatum bed development, but overall plant
biomass remains relatively unchanged (Creed and Sheldon 1993, Creed and Sheldon
1995, Newman et al. 1996, and Cofrancesco et al. 2004). For all of these reasons, E.
lecontei should be a suitable biocontrol agent for M. spicatum.
Research using tank and field enclosures has shown that E. lecontei densities of
2

267– 410 per m are capable of causing M. spicatum declines (Newman 2004).
Anecdotal evidence from stocking efforts (ranging from several thousand to tens of
thousands of individuals) suggests that E. lecontei stocking is able to suppress M.
spicatum at the whole-lake scale, but few scientific studies have been conducted at this
scale. A recent study using data collected from stocking and monitoring efforts found no
correlation between changes in E. lecontei density and M. spicatum density (Reeves et al.
2008). However, based on the low number of samples collected and my results presented
in chapter one, this lack of correlation may be due to low power. Therefore, there remains
uncertainty about how well one can extrapolate small-scale studies to the spatial scale
that control is desired (i.e., the whole lake).
E. lecontei and M. spicatum population densities should follow typical predatorprey oscillations when in equilibrium. Evidence of predator-prey dynamics for E. lecontei
and M. spicatum have been seen in a number of previous studies (e.g., Creed and Sheldon
1995, Sheldon 1997, Lillie 2000, and Newman and Biesboer 2000). However, it is more
common for lakes to have low E. lecontei population density and be overrun with M.
29

spicatum (personal observation). When E. lecontei densities are below the critical
threshold for M. spicatum suppression, the disparity between the two populations may
prevent equilibrium from being realized. Augmenting E. lecontei is one way to help bring
M. spicatum and E. lecontei populations into equilibrium and keep M. spicatum below
nuisance levels. Due to the costs associated with producing and releasing control agents,
most augmentative biocontrol efforts cannot directly release sufficient agent numbers to
achieve this equilibrium; therefore it may require several years before an agent
population increases to a level at which impacts on the pest species can are realized
(Hajek 2004). Therefore, long-term in situ research dedicated to the predator-prey
interactions between E. lecontei and M. spicatum are needed to gain a better
understanding of the conditions necessary for equilibrium.
Attaining and maintaining sufficient E. lecontei density is necessary for
successful suppression of M. spicatum. However, not all lakes achieve sufficient control,
and several hypotheses about the factors that limit E. lecontei density have been
suggested. These can be lumped into three categories: M. spicatum density limitation,
trophic interactions, and abiotic influences.
First, E. lecontei density can be limited by M. spicatum. The rate of growth and
spread of M. spicatum when introduced to many aquatic systems may outpace the growth
capacity of an existing E. lecontei population, thereby eliminating the possibility of a
balanced predator/prey relationship becoming established. In addition, at high M.
spicatum densities, low E. lecontei population growth may be stifled by the allee effect
(inability to find a mate). The allee effect has been suggested as a culprit for a number of
unsuccessful terrestrial classical biocontrol attempts (Hopper and Roush 1993). This

30

potential negative influence of M. spicatum on low density E. lecontei populations needs
additional research. If mate limitation is limiting population growth of E. lecontei, then
augmentation of existing populations may facilitate reaching a balanced predator/prey
relationship, and allow M. spicatum stem and tip densities to be directly and negatively
influenced by E. lecontei density.
Second, trophic interactions, both top-down and bottom-up, influence E. lecontei
density, and some of these interactions may be mediated by M. spicatum. Although the
impact of invertebrate predators is not well studied, it is not believed to be sizeable (as
reviewed by Newman 2004). However, several studies have shown that sunfish will
consume E. lecontei (Sutter and Newman 1997, Newman and Biesboer 2000, and Ward
and Newman 2006). Adult E. lecontei are most susceptible to fish predation, and high
predation rates of adults will result in reduced egg production that could limit E. lecontei
population numbers (Sutter and Newman 1997).
Using a predictive model, Sutter and Newman (1997) looked at the relationship
between E. lecontei and sunfish densities and how that relationship may in turn influence
2

sunfish predation rates. They predicted that high sunfish density (0.5/m ) could
2

negatively impact already low densities of E. lecontei (<5/m ) but have little to no effect
2

on medium and high E. lecontei densities (30 and 70/m , respectively). They suggest that
M. spicatum density could be a confounding factor for sunfish predation of adult E.
lecontei by providing E. lecontei refuge at higher M. spicatum density. Therefore, the
impact of fish predation is, in part, dependant on M. spicatum density. As E. lecontei
reduce M. spicatum, sunfish predation may in turn reduce E. lecontei numbers below the

31

critical threshold for M. spicatum control. Lake-wide studies of top-down impacts on E.
lecontei population density, and how those are mediated by M. spicatum, are limited and
deserve additional attention for us to better understand this plant-herbivore interaction
and improve biocontrol efforts.
Plant quality, in the form of physical and chemical plant characteristics, can also
have bottom-up effects on E. lecontei population density. Stem width has been proposed
as a potential limiting factor for pupal development, and nutritional quality may influence
larval and adult growth rates and fecundity (Creed and Sheldon 1995, and Newman
2004). It has been hypothesized that M. spicatum possesses higher nutritional value and
lower chemical defense mechanisms for E. lecontei than native milfoil species (Newman
et al. 1997). However, little research has been performed on the effects of chemical plant
characteristics on E. lecontei growth and reproduction. Recent speculation about
hybridization between native milfoil species and M. spicatum suggest that these plants
may possess morphological and nutritional deficits and chemical defenses, therefore
being less favorable for E. lecontei growth and reproduction. Much work is still needs to
be done to improve understand how trophic interactions influence E. lecontei
populations.
Third, a number of abiotic variables have been suggested as factors that may limit
E. lecontei populations and/or distribution within a lake. Jester et al. (2000) found a
negative correlation between percent sandy shoreline and E. lecontei
presence/abundance, and suggested that non-natural shoreline may limit overwintering
success. However, further research on overwintering has found that natural shoreline was
not limiting (Newman et al. 2001), and anecdotal reports suggest that overwintering can

32

be successful around highly developed lakes. Jester et al. (2000) also found a positive
correlation between E. lecontei abundance and distance from the middle and deep edges
of M. spicatum beds to shore and no correlation with the shallow edge. Cooler water
temperature, increased wave action, and greater exposure to fish predation have all been
suggested as underlying reasons behind an association with distance from shore (Jester et
al. 2000, Lillie 2000, and Tamayo et al. 2000). Water depth has also been found by a
number of researchers to negatively influence E. lecontei density (Lillie 2000, Jester et al.
2000, Tamayo et al. 2000, and Johnson et al. 2000). Generally, shallower M. spicatum
beds have been found to be better suited for E. lecontei and tend to have higher densities
of the herbivore (Jester et al. 2000, Johnson et al. 2000, Lillie 2000, and Tamayo et al.
2000). However, more research on the relative effects of overwintering habitat, water
depth, and M. spicatum bed location for determining E. lecontei density is needed.
Previous studies of E. lecontei may also have been limited by their methods of
data analysis. Spearman’s rank and Pearson correlations were commonly used in the past
to determine associations between E. lecontei and biotic and abiotic factors. Advances in
and new applications of statistical analyses, in this case zero-inflated negative binomial
regression (ZINB), offer improved techniques to investigate relationships in populations
of such rare, patchy, and difficult to find species (Cunningham and Lindenmayer 2005).
ZINB allowed us to perform non-parametric analysis to determine which variables are
related to E. lecontei density rather than using ranked information. Additionally, ZINB
separately analyzes the relationships between habitat characteristics and E. lecontei
presence/absence and abundance. A regression analysis is run using all of the data that
focuses on which characteristics influence E. lecontei presence/absence. A second

33

regression analysis is then run using all of the non-zero data to determine which
characteristics influence abundance. The output of the analysis is which characteristics
were statistically significantly and whether they have a positive or negative influence on
presence/absence and/or abundance (Cunningham and Lindenmayer 2005).
I investigated several factors that may influence E. lecontei distribution and
density at the lake-wide scale during a single season. The previous chapter of this thesis
outlined the importance of and demonstrated the difficulties associated with quantifying
E. lecontei populations at the lake-wide scale, mainly a lack of statistical power.
Therefore, I combined data across sample dates in order to achieve adequate power to
investigate several relationships between E. lecontei density and habitat characteristics.
Using field surveys of lakes previously stocked with E. lecontei, I addressed the question:
How is E. lecontei abundance influenced by abiotic characteristics (distance from shore
and depth) and M. spicatum metrics (stem density, biomass, and tip count)?
Based on the research reviewed above, I expected to find a positive relationship
between M. spicatum tips and E. lecontei density because higher tip density will allow for
unfettered oviposition opportunities (Jester et al. 2000). Stem density should decrease as
E. lecontei density increases as a result of stems sinking from the surface canopy.
However, I expected to find no relationship between lake-wide M. spicatum biomass and
E. lecontei based on a previous study that found little to no impact of E. lecontei
herbivory on M. spicatum biomass (Cofrancesco et al 2004). Additionally, I expected that
increased water depth at sample locations would negatively influence E. lecontei
abundance and that distance from shore would not limit E. lecontei density (Jester et al.
2000). Understanding the influence of lake abiotic conditions and M. spicatum metrics on

34

E. lecontei density and distribution will provide information on this important plantherbivore interaction. In addition, our results can improve our choice of in situ E. lecontei
sample site location for both scientific study and biocontrol efforts. Finally, this
information may inform management decisions on which lakes E. lecontei are most
suitable for use as a control agent.

Materials and Methods
Study Lakes
Lakes were chosen from 45 Michigan lakes previously stocked with E. lecontei
(EnviroScience Inc.). Final lake selection was based on the following criteria: lakes
located in Michigan’s Lower Peninsula (in order for drive time to be limited), lakes
stocked with E. lecontei during 2005 or later, lakes with surface areas of less than 50 ha
(in order to be sampled in a single day), lakes with no recorded aquatic plant herbicide
applications post-2002, and lakes that vary in degree of M. spicatum infestation. Clear
Lake in Mecosta County, Hackert Lake in Mason County, and Bear Lake in Clare County
were selected and sampled (Table 1). Lakes were visited four times during 2008 (June
through August) for data collection.

Data Collection and Processing
Lake-wide M. spicatum cover was determined for each lake at the start of the field
season using the point intercept method (Madsen 1999). M. spicatum beds (defined as
any area where M. spicatum growth was within 50 cm of the water surface and therefore
could impede recreation) were also mapped using a Garmin 76Smap GPS unit during the

35

first visit. These maps were then converted to polygon layers in ArcMap. The area of
each M. spicatum bed was estimated using the “add area/perimeter” function in Hawth’s
tools extension (ESRI Version 9.2).
2

Three subsequent sampling visits were made to collect 0.10 m quadrats of M.
spicatum for determining E. lecontei densities and M. spicatum metrics (number of stems,
fragments, meristems (tips) and dry biomass) (Table 1; see figure 1.1b from chapter one
as an example of how samples were collected from a lake). Sample locations were
assigned using the random point generator in ArcMap’s Hawth’s Tools Extension
(www.spatialecology.com, last accessed February 24, 2009; ESRI version 9.2). One
hundred samples were targeted as the minimum number of quadrats per lake per visit.
The target number of samples to be collected per bed was calculated by dividing
individual bed areas by the sum of all bed areas and multiplying these proportions by
100. All beds comprising less than 3% of the total sampling area were assigned a
sampling value of three for two reasons: 1) E. lecontei density and bed size were not
correlated (unpublished data) and 2) three is the minimum number that allows statistical
analysis. Shortest distance from shore to each sample site was determined using
ArcMap’s ET Geowizard extension (www.ian-ko.com, last accessed March 12, 2009).
At each lake, GIS-generated maps and GPS sample coordinates were used to
locate the quadrat sample points. I collected samples by hand using a 3-sided quadrat
constructed of PVC piping. The quadrat was lowered a half meter into the M. spicatum
bed open-end down, rotated to horizontal, and all strands within the quadrat collected.
This design and approach was used to minimize the displacement of M. spicatum strands,
either in or out of the quadrat, caused by the quadrat edges. Additional data collected at
36

each sample point included sample water depth and collection time for individual
samples. In the upper 10 cm of each M. spicatum bed, I recorded water temperature and
dissolved oxygen with a YSI Probe. Individual samples were placed, while underwater,
in 3.8 L sealable bags, kept on ice in the field, and later transferred to and stored in
refrigerators until processed.
Samples were processed in the laboratory where all E. lecontei life stages were
enumerated and M. spicatum metrics (number of stems, fragments, meristems (tips) and
dry biomass) were determined. Stems were defined as any length of M. spicatum greater
than 10 cm in length with an apical meristem. Fragments were strands shorter than 10 cm
with a meristem or any length strand without an apical meristem. A tip was defined as a
meristem greater than one millimeter in length where E. lecontei could opviposit. Dry M.
spicatum biomass was determined by weighing dried samples that were placed in pie tins
and dried in a convection oven at 65 degrees Celsius for a minimum of 24 hours (Prusty
et al. 2007).

Data Analysis
I first conducted Post hoc power analyses to determine our ability to confidently
estimate E. lecontei density at different spatial and temporal scales. To do this I used
Power analysis (eq. 1) to calculate d (fixed proportion of the mean) at both the individual
bed and whole-lake scales.
2 2

2

N= (tα/2/d) (s /m ),

37

(1)

2

with N=number of samples collected, tα/2 = t value for a given probability (α), s =
sample variance, and m = mean density (Buntin 1994). Sample variance and mean
density were determined from the lake-wide samples collected, and α was set at 0.05.
I tested for the influence of M. spicatum metrics and abiotic factors on E. lecontei
distribution using zero-inflated negative binomial regression (ZINB) conducted with SAS
(version 9.1). This approach was necessary to account for the high proportion of zeros in
the dataset (due to the patchy distribution of E. lecontei). ZINB uses two models to
perform the regression analysis. The first model is used to determine what variables
predict presence/absence of E. lecontei and the second model determines what variables
should be included in a predictive model of E. lecontei using the non-zero sample points
only (Minami et al. 2007). ZINB models were developed separately for each lake.
Correlation analysis was run to determine if variables were highly correlated (r > 0.70).
All M. spicatum metrics, and distance from shore and depth were included as covariates
for both models. Models were run multiple times with different covariates to determine
which model had the lowest Akaike’s information criterion (AIC) value (indicating better
model fit that accounts for the number of variables included in the model; Burnham and
Anderson 2004).

Results
Post hoc power analyses
Statistical power at the individual bed level for each visit to each lake was
insufficient to enable further analysis. I composited the bed level data from all three visits
for single-season bed level estimates for each lake. Statistical power remained low (Fig
38

2.1), however, adequate power was reached for two of the three lakes by combining
individual samples collected from each lake over the entire summer into a single-season,
lake-wide E. lecontei density estimate. After compiling the data, the fixed portion of the
mean (d) for each lake, was 0.66, 0.22 and 0.24 for Bear, Clear, Hackert Lakes,
respectively. Although adequate power is considered to be indicated by d ≤ 0.50 (Buntin
1994), I analyzed the data from Bear Lake, knowing that interpretations would be limited.
Using the lake-wide compiled data, I then investigated the influence of biotic and abiotic
factors on E. lecontei population distribution and density.

Influence of abiotic and M. spicatum metrics on E. lecontei presence and density
2

Lake-wide M. spicatum density estimates ranged from 61.4-112.6 stems/ m and
2

E. lecontei density from 1.3-8.2/m (0.02-0.07/stem) in the three lakes (Fig 2).
Correlation coefficients among predictor variables were low for Bear Lake and Hackert
Lake (r < ±0.21 and 0.34, respectively). However, distance and depth as well as tips and
biomass were moderately correlated in Clear Lake (r = 0.66 and 0.63, respectively).
I used ZINB to determine how abiotic factors and M. spicatum metrics influence
where and how many E. lecontei are found in each lake. For Bear Lake, the best model
for determining whether E. lecontei was present or absent from the sample included water
depth and stem density (positive and negative relationships, respectively; p < 0.014;
Table 2). In the second step of the model-building process, distance from shore and M.
spicatum biomass were related to E. lecontei density (not significant and positive
relationships, respectively; p < 0.001; Table 2). The best model for E. lecontei
presence/absence in Clear Lake included distance from shore, water depth, M. spicatum
39

tip density and biomass (not significant, positive, negative, and negative relationships,
respectively; p < 0.001; Table 2), and depth and M. spicatum tip density were related to
E. lecontei density (negative and positive relationships, respectively; p < 0.05; Table 2).
The Hackert Lake model used M. spicatum biomass as the predictor for E. lecontei
presence/absence (negative relationship; p < 0.001; Table 6) and distance from shore,
sample depth, and M. spicatum stem density for E. lecontei density (not significant,
negative, and positive relationships, respectively; p <0 .001; Table 6).
At least one of the three M. spicatum metrics (stems, tips, or biomass) was
negatively related to the presence/absence of E. lecontei and one of these same metrics
was positively related to E. lecontei density across all lakes. In addition, water depth
and/or distance to shore was positively related to E. lecontei presence/absence in two of
the three lakes, and negatively related to E. lecontei density across all lakes (Table 2.2).
Therefore, my findings suggest that both M. spicatum metrics and abiotic factors
influence E. lecontei presence and abundance.

Discussion
Factors influencing E. lecontei abundance and distribution
Little is known about in situ population dynamics of E. lecontei, a biological
control agent for the invasive macrophyte M. spicatum. Previous studies have found
evidence suggesting that E. lecontei density is positively related to M. spicatum tips and
distance from shore, and negatively related to the depth of the outer edge and center of
the M. spicatum beds (Jester et al. 2000). Lillie (2000) also reported decreased E. lecontei
stem damage at deeper sample locations in Fish Lake, WI, and research in Washington

40

State found a significant negative relationship between E. lecontei density and depth
(Tamayo et al. 2000).
However, this previous research sampled limited M. spicatum beds with a
relatively low number of samples per lake. I revisited these factors and their relationship
to E. lecontei population densities using larger sample sizes and by sampling all M.
spicatum beds in three lakes. The small size of E. lecontei and its patchy distribution
(Sheldon 1997) within and across M. spicatum beds resulted in many samples with no
individuals found. This high variance and overall low density estimates resulted in low
Post hoc power at the individual bed scale, and precluded my initial plans to explore
population dynamics at the bed level, as well as changes in E. lecontei density over time.
Had the results of chapter one been known at the time of data collection, I may have
reallocated resources to intensely sample a single lake. However, I was still able to
investigate some lake-wide influences on E. lecontei populations by compositing samples
within each lake across the sampling period.
I investigated the effect of distance from shore on E. lecontei at the whole-lake
scale by sampling all M. spicatum beds. Distance from shore was included as a covariate
in just one of the three lake-wide models (Table 2.2); however, it did not significantly
influence E. lecontei presence or density. Therefore, our results agree with the
conclusions of Jester et al. (2000), and suggest that distance from shore does not limit E.
lecontei density within the range that I collected (0.1 to 155 m from shore).
Previous research has found that lake depth is negatively correlated with E.
lecontei density. Cooler water temperature, increased wave action and greater exposure to
fish predation have all been suggested as underlying reasons behind this association

41

(Jester et al. 2000, Lillie 2000, and Tamayo et al. 2000). My study provides additional
support for a relationship between E. lecontei and lake depth; however, my results were
more complicated than expected. Sample depth was included in Bear and Clear Lake
models for determining E. lecontei presence. In both lakes, depth was significant but
positively related to E. lecontei presence. However, depth was also included in Clear and
Hackert Lake models for determining E. lecontei density, and in both lakes depth was
significant and negatively related to density. Our results suggest that E. lecontei were
more likely to be present as depth increased but at lower densities than at shallow depths.
This phenomenon may be caused by differences in M. spicatum metrics as depth
increases. However, I found no consistent trends between increased depth and M.
spicatum metrics in the three study lakes. Additionally, the moderate correlation between
distance and depth in Clear Lake further limits my ability to make confident inferences
about their impact on E. lecontei presence and abundance. It is likely that factors not
tested in our study (e.g., fish predation) may have been responsible for this trend. Further
study of the factors that limit E. lecontei density at deeper sites is justified.
During each 2008 field-sampling visit, I collected a temperature measurement
from each bed. I had planned to test the influence of temperature on E. lecontei density at
the bed level. However, since there was only one data point per bed and per visit, I was
unable to run ZINB regression using these data compiled across sample dates. It is
important to note, however, that temperatures in our lakes did not exceed the critical
thresholds for E. lecontei (~10 – 31oC; Mazzei et al. 1999). Therefore, it is not likely that
there would have been a significant influence of temperature on E. lecontei density.
Future study of E. lecontei and the factors that may influence its density should collect

42

such parameter measurements of interest at each sample point.
Our results provide evidence that M. spicatum metrics can influence E. lecontei
presence and abundance within a lake. However, no clear trend was found to suggest that
any one metric has more influence than any other metric. Certainly I did not expect to
find a single variable that could precisely predict E. lecontei presence or abundance;
however, I had hoped that a clear trend would have been discernable among the three
lakes. It is likely that, in addition to the three M. spicatum metrics I measured (stem
density, tip densities, biomass), additional biotic and/or abiotic factors not measured may
have influenced the E. lecontei population densities (e.g., M. spicatum nutrient levels,
fish predation, interspecific competition). If the factors that influence E. lecontei presence
and abundance can be determined, then models can be developed to predict where E.
lecontei is most likely to be found within a lake, which could be used to improve lakewide population estimation (Chapter 1) and where E. lecontei introduction or
augmentation may be most effective.

Implications: E. lecontei and M. spicatum control
My E. lecontei density estimates are within or below the 0.0-4.5 individuals per
M. spicatum stem reported in previous field observation studies (as reviewed by Newman
2004). However, directly comparing the results of our sampling to previous studies
should be done with caution. Previous E. lecontei sampling often collected many fewer
M. spicatum stems (48-779), often did not collect samples lake-wide or were targeting
stocked beds (Creed and Sheldon 1995, Jester et al. 2000, Johnson et al. 2000, Lillie
2000, Newman et al. 2001, Reeves et al. 2008, Tamayo et al. 2000), and did not report

43

statistical power for their density estimates. Therefore, past estimates may not accurately
represent lake-wide E. lecontei densities and may not be able to detect E. lecontei
population changes over time. These differences between my methods and those of
previous studies further exemplifies the need for E. lecontei research at the lake-wide
scale and the importance of quantifying statistical power when estimating population
densities.
There are a number of things to bear in mind when considering my results and
their implications for control of M. spicatum. First, although some of my results differed
from previous study results, it is important to consider the difference in the spatial scales
of these studies. Densities of E. lecontei and M. spicatum reported from small scale
studies may not appropriately extrapolate to the whole lake scale (Carpenter 1999).
Second, lake-to-lake variation, both natural and anthropogenic in origin, affects M.
spicatum growth and spread. For example, Bear Lake is a private lake with limited boat
traffic and a high percentage of wooded or wetland riparian areas, whereas Clear Lake
has a public boat launch and mostly residential riparian area, including a golf course.
These differences among lakes may affect rates of M. spicatum growth and spread,
especially through fragmentation spread caused by boat traffic. Therefore, lakes may
require different E. lecontei densities to impart control.
Finally, short-term studies need to consider whether predator-prey equilibrium has
been reached. For example, Jester et al. (2000) investigated the initial stage of applying
E. lecontei, when equilibrium was not likely reached and Newman and Biesboer (2000)
studied a natural decline of M. spicatum that was attributed to E. lecontei, where
equilibrium was likely present. In my study lakes, I do not know whether E. lecontei and

44

M. spicatum were in equilibrium. E. lecontei augmentations were initiated two to four
years prior to this study, which may not have been enough time for equilibrium to be
achieved, especially in Clear Lake that had a single augmentation in 2006. To
demonstrate how such dynamics might affect the interpretation of results; consider two
2

2

lakes, one with 1000 M. spicatum stems/m and 100 E. lecontei/m and the other with 10
2

stems and 1 E. lecontei/m . Although both lakes have the same E. lecontei/stem ratio, the
former lake may not have reached the critical threshold for control while the latter lake
may have achieved control and be in equilibrium. Therefore, additional lake-wide studies
that follow E. lecontei and M. spicatum from pre-decline through suppression and beyond
are needed to provide more information on the minimum E. lecontei density necessary to
cause declines and maintain suppression.
The estimated lake-wide E. lecontei population densities in my study lakes are
below the range of 0.1-4.5 individuals per M. spicatum stem that was been found to cause
significant M. spicatum decline in previous field enclosure and tank studies (Newman
2004). At first, this fact might be viewed as evidence that E. lecontei is not effectively
controlling M. spicatum in our study lakes. However, property-owners on two of the three
study lakes stated that a dramatic reduction in M. spicatum occurred after E. lecontei
were stocked (I did not have contact with property-owners on the third lake). Achieving
success with E. lecontei augmentation is matter of reaching the point where E. lecontei
density controls M. spicatum. Since lakes are heterogeneous, the time required to achieve
control is difficult to predict. Some lakes have seen dramatic results after a single season
of stocking; however, it is more common that several years are needed before widespread
impacts are seen. As previously suggested, this equilibrium point is likely different for
45

each lake and may depend on a number of biotic and abiotic factors. Lake managers and
stakeholders interested in employing E. lecontei as a biological control for M. spicatum
should understand that there are two components to consider when trying to achieve M.
spicatum control: the number of E. lecontei stocked and the time within which control is
desired. Essentially, the greater the number of E. lecontei stocked, the more quickly
control can be expected (and stocking small numbers of E. lecontei in lakes with dense
M. spicatum will likely not produce dramatic results in a single season). However,
satisfied lake managers and property owners who use E. lecontei as a control method will
be those that understand at the outset that biocontrol will likely take time to achieve, with
the promise of long-term M. spicatum control.

Conclusions
My conclusions from chapter one suggest that previous research on the factors
that influence E. lecontei population density lacked sufficient power to accurately
estimate lake-wide population densities. In addition to ensuring adequate statistical power
for our research, I also used zero inflated negative binomial regression to investigate
relationships between E. lecontei presence and abundance and habitat characteristics.
This technique for data analysis allowed me to look both at E. lecontei presence/absence
and density relationships separately, which has not previously been done in E. lecontei
research. Previous analysis was not able to determine whether factors were influencing
presence, abundance or both. The methodologies presented in these two chapters should
be used as a guide for future E. lecontei research as they will ensure rigorous results.
I examined some of the factors that had previously been found to influence

46

population density using larger sample sizes from multiple, whole lakes taken over the
course of one growing season. I investigated population dynamics at the lake-wide scale
to determine if E. lecontei presence and density coincided with M. spicatum metrics
and/or abiotic conditions, which might suggest a cause-effect relationship. Similar to
previous less intensive or small-scale research, I found that both M. spicatum metrics and
abiotic conditions were related to E. lecontei densities. However, neither specific M.
spicatum metrics nor particular abiotic conditions had consistent influences on E. lecontei
presence or density across the lakes sampled. These lake-to-lake differences make it
impossible for me to suggest a single model for general prediction of E. lecontei presence
or density based on M. spicatum metrics and abiotic factors.
At first, I was disheartened by my findings. However, I now realize that these
results have important insights. If I had sampled just a single lake, I would have
developed and promoted a model that would not be widely applicable. The lake-to-lake
variation I found suggests that either all of the metrics I tested have an impact, that the
metrics are correlated enough to be confounded, and/or that metrics not measured were
influential in determining E. lecontei presence and abundance. Therefore, the approach
and methods described in this paper can be used and expanded upon to test additional
parameters to increase scientific understanding of this plant-herbivore interaction and
improve the application of E. lecontei as a biocontrol agent.
Further investigation of lake-wide and bed level E. lecontei population dynamics
is needed to better understand this important aquatic biocontrol agent. Additional multilake studies should be performed that continue to work toward the goal of understanding
the population dynamics of E. lecontei at the lake-wide scale. Other parameters that

47

should be investigated include fish predation, availability of overwintering habitat, and a
number of M. spicatum characteristics including; stem width, nutritional and chemical
defense profiles, growth rates (which would include a number of water and sediment
parameters), and plant genetics. Although the number of samples and effort necessary to
make conclusions with high precision is substantial, without them, sound scientific
conclusions on the efficacy of E. lecontei cannot be reached, and the optimal E. lecontei
density for M. spicatum for control at the lake-wide scale will remain unknown.

48

THESIS CONCLUSIONS

The goal of this research was to explore lake-wide E. lecontei population
abundance and distribution. Several factors may influence where and how many E.
lecontei are found in a lake. In order to study E. lecontei at this scale, researchers first
needed to determine the minimum number of samples necessary to estimate population
density with sufficient precision. Only then can the relationships between E. lecontei
abundance and distribution and M. spicatum metrics and abiotic factors be truly
understood. Increasing scientific knowledge of this biological control agent at this spatial
scale will inform scientific study of this herbivore-insect relationship and can improve
biocontrol efforts at the scale pest control is desired.
Using power analysis, I found that a large number of samples, and corresponding
effort, are needed to obtain high precision, lake-wide E. lecontei estimates. Therefore,
fewer samples, such as the number often previously used to assess E. lecontei population,
provide lower precision estimates. Using a single study lake as an example, the
magnitude of samples and effort required for high precision population estimation (see
Table 1.2) is likely cost prohibitive for single sampling events. However, if lower
precision can be accepted (d ≥ 0.5) then single sample events may be feasible.
Alternatively, using several sample dates to produce a single season high power estimate
(as done in Chapter 2) may be an option. I strongly recommend that all future E. lecontei
research determine the desired level of precision and perform a priori power analysis to
determine the minimum number of samples needed to obtain desired precision and be
able to confidently estimate lake-wide E. lecontei population densities.
49

Most previous in-lake studies of E. lecontei have focused on a few M. spicatum
beds in a lake. Studies at this scale can be used to answer many important unanswered
questions regarding E. lecontei. However, my findings suggest that sampling in this
manner may result in low statistical power to estimate lake-wide population densities.
Studies at the bed-level scale should also consider optimum sample size using a priori
power analysis, and should refrain from extrapolation from the bed to lake-scale without
testing to make sure that such inference is appropriate.
Current sampling for biological control efforts also samples a limited number of
M. spicatum stems to estimate E. lecontei population density. This is particularly
troubling as the purpose of this sampling is often to estimate lake-wide E. lecontei
population density. Although high precision estimates are not always necessary for
monitoring and management purposes (Buntin 1994), the number of stems currently
being collected (30 to 300 stems) should be increased to improve precision. An increase
in sampling effort will result in an increase in cost. This cost could be passed on to the
consumers, but that practice may negatively affect the number of lakes that chose to
employ E. lecontei as a biological control agent. However, collecting too few samples
will result in low precision estimates, which questions the ability to compare estimates
over time and the reliability of such estimates to characterize lake-wide E. lecontei
populations.

Lake-wide understanding can improve biocontrol efforts
Study of E. lecontei at the lake-wide scale will advance scientific understanding
in a number of ways (e.g., aquatic insect herbivory, host-shift dynamics, metapopulation

50

dynamics), but it can also be used to improve the use of E. lecontei as a biological control
agent. Investigations of the interactions between E. lecontei and M. spicatum and abiotic
conditions could improve sampling efficiency, predictability of successful biocontrol, and
stocking site selection. In addition, a better understanding of minimum E. lecontei density
for M. spicatum control could guide stocking rates, and help determine the efficacy of E.
lecontei as a biological control agent for M. spicatum. While the diversity of biotic and
abiotic conditions in lakes will preclude precise and widely applicable density values for
control, it should be possible to establish guidelines that use a few key variables to
characterize a lake (e.g., M. spicatum stem density, bed area, or accumulated seasonal
water temperatures) and provide more direction for E. lecontei stocking.
The interactions between E. lecontei and M. spicatum are poorly understood.
Native populations of E. lecontei are already present in many M. spicatum-infested
waters. Yet, existing populations rarely reach densities capable of suppressing M.
spicatum. In addition to top-down and bottom-up influences, the allee effect (difficulty
finding a mate) may also be at work keeping E. lecontei population densities low.
Augmenting naturally occurring populations may enable E. lecontei to overcome the allee
affect. This augmentation might allow E. lecontei populations to increase to a point
where they can suppress M. spicatum and enter into a balanced predator-prey cycle.
The timeframe for effective suppression of M. spicatum by E. lecontei is currently
unknown. Using terrestrial biocontrol efforts as an example, it is possible that several
years of increasing population density is required before M. spicatum control is achieved.
One example is the black-margined loosestrife beetle (Galerucella calmariensis L.) as a
biocontrol agent for purple loosestrife (Lythrum salicaria L.). Research on the

51

establishment and impact of releases in Michigan found that it took three to five years
post stocking for impacts to be seen in L. salicaria (Landis et al. 2003). Therefore, longterm, lake-wide studies that monitor E. lecontei and M. spicatum densities are needed.
Integrated pest management (IPM) is a management technique commonly
employed in terrestrial biocontrol that may benefit M. spicatum management (Alwin and
Cheruvelil 2009). Incorporating other management techniques (e.g., herbicide
applications) with E. lecontei augmentation (either spatially or temporally) may reduce
the time to achieve lake-wide control, while still establishing a long-term means of
maintaining control. However, although E. lecontei will not negatively impact the
efficacy of other management options, the same cannot be said about the reverse.
Therefore, careful execution of IPM is critical to ensure that E. lecontei are not negatively
impacted by the other management techniques.

What our findings mean for the application of E. lecontei as a biocontrol
Our research was not trying to draw conclusions as to the effectiveness of E.
lecontei as a management tool for M .spicatum control. Rather, I aimed to begin the
process of investigating E. lecontei at the same spatial scale that M. spicatum control is
desired. Without multi-year data for comparison, I cannot say whether M. spicatum was
or was not being reduced or controlled by E. lecontei in our study lakes. However, the
current status of scientific understanding of this plant-insect interaction at smaller spatial
scales and under semi-controlled conditions is that E. lecontei is an effective biocontrol
agent for M. spicatum. Anecdotal evidence suggests that E. lecontei is capable of longterm, lake-wide suppression of M. spicatum; however, long-term scientific investigation

52

at this scale is lacking. The information and methods presented in this thesis can serve as
the foundation for research to provide insights of E. lecontei efficacy as a biocontrol
agent at the lake-wide scale.

Looking toward future E. lecontei and M. spicatum research
My study was an ambitious project that collected a large number of samples from
several lakes to investigate E. lecontei populations at the whole-lake scale. While I
collected a large amount of data, I feel it is important to discuss the implications of how I
used several important variables in order to inform future research.
One idea to consider is how I dealt with the various E. lecontei life stages. For our
studies, although I enumerated the E. lecontei life stages separately, I combined them all
before data analysis. I did this to most accurately represent the E. lecontei population
present at the time of sampling. However, if the goal of our research was to investigate
feeding efficacy, I might have performed data analysis using only the life stages that feed
on M. spicatum (larvae and adults) or created a system that would weight each life stage
according to the impact and or potential impact they would have on M. spicatum. Future
research should consider E. lecontei life stage and the impact on M. spicatum when
designing and performing studies.
Second, I classified M. spicatum strands as either a stem or a fragment (≥10cm =
stem, <10cm = fragment). I distinguished between stems and fragments because
considering each piece of M. spicatum in a sample as a stem would have inflated our
stem counts but I could not simply dismiss the small pieces of M. spicatum material
created in the sample collection process. However, this classification may have resulted

53

in an inflated fragment count and conservative stem count for our samples. Similarly, our
designation of a M. spicatum tip may have influenced our conclusions. I did not include
tips fewer than two millimeters in length in our counts because these were too small for
E. lecontei oviposition. Therefore, if I had counted the minute meristems that were newly
forming at the time of collection then our tip count would have been larger. Our decision
to classify and analyze strands and tips in these ways certainly influenced our results and
conclusions. Perhaps these classifications are partly responsible for why I failed to see a
clear indication of a particular M. spicatum metric being related to E. lecontei presence
and density across the lakes.
Finally, I classified M. spicatum beds as those growing within 50cm of the water
surface and comprising the majority of the plant community in the area. By explicitly
defining a bed this way, I missed portions of the lake where M. spicatum was present but
did not meet both criteria. Therefore, areas where E. lecontei may have been present in
the lake were not sampled, leading to conservative population estimates. However, I
classified beds this way for several reasons: 1) management is mainly concerned with M.
spicatum when it is impeding recreation in a water body (matting at the water’s surface
and/or forming a dense monoculture, 2) our inability to effectively sample M. spicatum
beyond this depth while snorkeling. Future research should take the above designations
into consideration when designing their studies, especially if they intend to compare their
findings to ours.

54

APPENDICES

55

APPENDIX A

Chapter One Tables and Figures

56

Table 1.1
Quadrat
2
size (m )

Lab time
(minutes)

E. lecontei
2
density (m )

M. spicatum
stem density
2
(m )

0.05

46

135 (51.72)A

427 (163.30)B

0.32C

0.1*

88

95 (33.19)A

362 (106.47)B

0.26C

0.2

135

73 (23.75)A

283 (80.66)B

0.26C

0.3

210

64 (21.02)A

261 (93.08)B

0.24C

# E. lecontei per M.
spicatum stem

Results of composite quadrat analysis of E. lecontei (n = 15) and M. spicatum (n = 6)
samples. Lab time is a proxy for cost. Standard errors for estimated E. lecontei and M.
spicatum stem densities are in parenthesis and the letters in each column represent the
results of post hoc Tukey test for significance. The same letter denotes no significant
difference found (α=0.05). *=quadrat selected for further use.

57

Table 1.2
d



Time
N
Collection

Processing

Total time

0.2

0.05

309

18.3

242.8

261.0 (6.5)

0.2

0.10

214

12.7

168.6

181.3 (4.5)

0.3

0.05

137

8.1

107.9

116.0 (2.9)

0.3

0.10

95

5.6

74.9

80.6 (2.0)

0.4

0.05

77

4.6

60.7

65.3 (1.6)

0.4

0.10

54

3.2

42.1

45.3 (1.1)

0.5

0.05

49

2.9

38.8

41.8 (1.0)

0.5

0.10

34

2.0

27.0

29.0 (0.7)

2

Table 1.2 Projected time in hours (weeks) needed to collect and process 0.10m quadrats
to reach optimal sample size (N) to estimate lake-wide E. lecontei population density. d =
fixed portion of the mean α = significance level

58

Figure 1.1 Map of Lake Ovid with M. spicatum sample locations and sampled beds for
determining lake-wide weevil density (a) and lake-wide quadrat sample site locations (b).
Point intercept sample locations used to determine lake-wide M. spicatum cover: • = no
M. spicatum found, ○ = M. spicatum found.
= mapped M. spicatum beds.
= bed
sampled for quadrat size testing. 1a inset depicts the M. spicatum beds sampled during
June to determine quadrat size. Composite quadrats were collected at three points along
five transects

59

Figure 1.1

a

60

Figure 1.1 (cont’d)

b B

61

Figure 1.2

Figure 1.2 Composite quadrat containing four quadrat sizes and depicting diagonal
support wire.

62

Figure 1.3

Figure 1.3 Optimal sample size (N) of quadrats and M. spicatum stems to estimate lakewide E. lecontei population density using equation 1 (see page 26). Calculated using a
2
2
range of precision (d) and both 90 and 95% confidence intervals. s = 4144.9 and m =
1315.6. CI = confidence interval.

63

APPENDIX B

Chapter Two Tables and Figures

64

Table 2.1.
Lake
area
(ha)

Max
depth
(m)

Mean
depth
(m)

# E. lecontei
stocked
(years)

County

Coordinates

% M. spicatum
cover

# samples
collected

July 30-08

Lake

Sample
dates

21

107

Clare

43o 53'19.76" N
84o 57'06.23" W

35

4

N/A

28,000
(04-06)

Aug 11-08

111

Aug 19-08

106

Total

Bear

324

Aug 1-08

66

103

Mecosta

43o 40'43.60" N
85o 23'34.83" W

50

9

3.1

14,000
(2006)

Aug14-08

103

Aug 27-08

103

Total

Clear

309

Jul 24-08

13

121

Mason

43o 58'58.25" N
86o 19'29.30" W

49

5

9,000
(05-06)

2

Aug 8-08

117

Aug 21-08

113

Total

Hackert

351

Table 2.1 Sample lakes and the criteria used to select them for the study. N/A = not available. Bear Lake is a private lake and does not
2
have an official bathymetric map. “Samples” refer to the number of 0.1 m quadrats taken to estimate E. lecontei and M. spicatum
densities

65

Table 2.2
Bear Lake
Parameter Estimate
Presence/absence
intercept
0.007
depth
0.942
stems
-0.015
If present
intercept
distance
biomass

2.619
-0.011
0.032

Standard Error t Value Pr > |t|
0.826
0.225
0.006

0.01
4.18
-2.47

0.993
<0.001
0.014

0.332
0.008
0.009

7.89
-1.38
3.4

<0.001
0.168
0.001

Clear Lake
Presence/absence
intercept
0.332
distance
0.010
depth
0.364
tips
-0.002
biomass
-0.016

0.387
0.015
0.113
0.001
0.005

0.86
0.65
3.23
-2.02
-2.89

0.391
0.516
0.001
0.045
0.004

If present
intercept
depth
tips

0.174
0.040
<0.001

16.14
-1.99
5.72

<0.001
0.048
<0.001

0.261
0.002

7.63
-4.33

<0.001
<0.001

2.803
-0.080
0.002

Hackert Lake
Presence/absence
intercept
1.992
biomass
-0.007

If present
intercept
3.090
0.220
14.05
<0.001
distance
-0.005
0.006
-0.83
0.409
depth
-0.091
0.041
-2.21
0.028
stems
0.005
0.001
5.35
<0.001
Table 2.2 Zero-inflated negative binomial regression results for individual E. lecontei
samples collected during 2008 from three study lakes.

66

Figure 2.1

Figure 2.1 Bed-level Power analysis for each study lake using the data from all three
sample dates. The d value is the fixed portion of the mean calculated using equation 1
(see page 26). Power is generally considered adequate when d ≤0.50

67

Figure 2.2

Figure 2.2 Summary of lake-wide E. lecontei and M. spicatum density estimates based on
324, 309 and 351 quadrats collected from Bear, Clear and Hackert Lakes, respectively.
Error bars represent standard deviation.

68

APPENDIX C

Raw Data

69

Table 3.1

Date
7/30
7/30
7/30
7/30
7/30
7/30
7/30
7/30
7/30
7/30
7/30
7/30
7/30
7/30
7/30
7/30
7/30
7/30
7/30
7/30
7/30
7/30
7/30
7/30
7/30
7/30
7/30
7/30
7/30
7/30
7/30
7/30
7/30
7/30
7/30
7/30
7/30
7/30
7/30

Site
A0
A1
A2
C0
C1
C2
C3
C4
C5
C6
D0
D1
D2
E0
E1
E2
F0
F1
F2
F3
F4
F5
F6
F7
F8
G0
G1
G2
H0
H1
H1
J0
J1
J2
K0
K1
K2
K3
K4

Bear Lake
E. lecontei
M. spicatum
Depth A L P
E Total Stem Frag Tip Biomass
0.9
0
70
10 110
20.3
1.2
0
20
110 360
72.6
0.9
0
70
60 100
31.6
1.5
0
90
40
80
15.1
1.2
0
120 110
10.7
1.5
0
60
80
18.1
0.9
0
20
20
30
3.2
1.2
0
20
80
70
14.6
0.9
0
60
40
50
13.0
1.5
0
110
20 100
23.0
1.5
0
70
60
80
17.6
1.5
0
40
40
40
13.7
1.5
0
80
30 130
19.5
1.5
0
60
80
90
14.0
1.5
0
80
30 110
24.6
1.5
0
120
80 290
55.8
1.2
0
50
20
50
21.7
1.2
0
140
40 160
50.4
1.2
10
20
30
110
40 100
21.8
1.2
0
50
30
30
9.5
1.2
0
40
50
90
18.2
1.2
0
50
20
60
21.2
1.2
0
120
20 180
46.7
1.2
0
30
50
9.5
1.2
0
60
30
90
23.2
1.2
0
70
10 120
21.8
1.2
10
10
140
120 320
43.6
1.2
0
60
30
80
21.3
1.2
60
60
60
90 140
26.5
0.9
20 10
30
40
30
40
14.0
1.2
0
10
20 190
19.4
1.2
0
70
140 160
25.9
1.2
0
120
80 150
44.1
1.2
0
80
10 130
23.8
1.5
0
80
120 210
27.2
1.5
0
130
80 250
49.8
1.2
0
90
20
80
30.0
1.2
0
70
50 140
21.1
1.2
0
90
10 120
28.2
70

Table 3.1 (cont’d)
7/30 K5
1.5
7/30 L0
1.2
7/30 L1
1.2
7/30 L2
1.5
7/30 L3
1.2
7/30 L4
1.2
7/30 L5
1.2
7/30 M0
0.9
7/30 M1
0.9
7/30 M2
0.6
7/30 M3
0.9
7/30 M4
0.9
7/30 M5
0.9
7/30 M6
0.6
7/30 M7
0.9
7/30 M8
0.6
7/30 M9
0.9
7/30 M10
0.9
7/30 M11
0.9
7/30 M12
0.9
7/30 M13
0.6
7/30 M14
0.6
7/30 M15
0.9
7/30 M16
0.9
7/30 M17
0.6
7/30 M18
0.9
7/30 M19
0.9
7/30 M20
0.6
7/30 M21
0.6
7/30 M22
0.9
7/30 M23
0.9
7/30 O0
1.8
7/30 O1
1.8
7/30 O2
1.8
7/30 R0
>1.8
7/30 R1
>1.8
7/30 R2
>1.8
7/30 R3
>1.8
7/30 R4
>1.8
7/30 R5
>1.8
7/30 R6
>1.8
7/30 R7
>1.8

30

10

10
10
10

0
0
0
0
0
0
0
30
0
0
0
0
0
0
0
10
0
0
10
0
10
0
10
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
71

90
30
60
50
110
80
10
160
30
70
60
30
80
50
60
80
80
50
40
20
70
80
80
30
30
120
50
130
50
30
80
90
130
120
40
30
20
60
50
40
50
20

70
10
20
10
30
40
50
20
10
10
20
40
10
80
70
60
50
10
40
30
50
20
30
60
20
150
20
50
20
90
20
20
20
30
50
10
10
20
10

90
20
60
60
190
100
80
100
90
120
60
50
90
60
60
110
120
80
40
30
110
90
100
60
60
190
60
130
60
40
70
110
320
290
40
30
40
100
80
50
100
20

18.2
8.6
14.5
15.1
35.1
19.9
22.4
34.9
15.1
16.4
13.2
10.9
21.3
10.2
16.2
17.0
21.3
12.1
15.8
21.9
1.8
23.6
21.2
4.7
5.8
34.3
11.2
35.4
12.4
6.7
27.4
25.0
49.0
43.1
21.2
21.6
8.1
29.2
30.3
15.4
34.1
8.9

Table 3.1 (cont’d)
7/30 R8
>1.8
7/30 R9
>1.8
7/30 R10 >1.8
7/30 R11 >1.8
7/30 R12 >1.8
7/30 R13 >1.8
7/30 R14 >1.8
7/30 R15 >1.8
7/30 R16 >1.8
7/30 R17 >1.8
7/30 S0
>1.8
7/30 S1
>1.8
7/30 S2
>1.8
7/30 S3
>1.8
7/30 S4
>1.8
7/30 S5
>1.8
7/30 S6
>1.8
7/30 S7
>1.8
7/30 S8
>1.8
7/30 S9
>1.8
7/30 S10 >1.8
7/30 S11 >1.8
7/30 S12 >1.8
7/30 T0
>1.8
7/30 T1
>1.8
7/30 T2
>1.8
8/11 A0
1.5
8/11 A1
1.5
8/11 A2
1.5
8/11 C0
0.6
8/11 C1
0.3
8/11 C2
0.6
8/11 C3
1.2
8/11 C4
0.9
8/11 D0
1.8
8/11 D1
1.8
8/11 D2
1.8
8/11 E0
1.8
8/11 E1
1.8
8/11 E2
1.8
8/11 F0
1.2
8/11 F1
1.2

10

20
10

0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
10
0
0
0
0
0
0
0
0
20
0
10
0
0
0
0
0
0
0
0
72

50
20
30
10
30
10
30
30
20
20
20
60
30
50
40
70
40
10
10
20
10
40
70
30
40
30
140
60
90
30
130
60
30
20
90
90
50
50
60
40
20
60

10
10
10
20
30
10
10
10
50
20
10
50
50
10
0
10
10
30
30
20
10
10
140
60
80
30
80
10
30
60
30
70
20
60
30
40
10
70

50
30
40
10
20
10
30
30
30
30
30
80
30
60
70
80
90
10
20
40
10
140
80
50
50
90
180
90
90
60
140
120
40
40
130
210
120
80
110
40
20
100

25.6
22.2
20.7
8.1
11.0
5.9
11.8
12.0
12.7
7.2
8.0
16.0
10.5
12.3
15.5
17.3
19.2
5.0
3.3
8.1
6.3
24.4
34.0
15.0
11.7
12.1
66.1
30.9
27.3
11.7
39.0
21.2
6.9
15.0
22.2
27.4
12.6
16.2
17.6
11.0
4.8
21.6

Table 3.1 (cont’d)
8/11 F2
1.5
8/11 F3
1.2
8/11 F4
1.2
8/11 F5
1.2
8/11 F6
1.2
8/11 F7
1.2
8/11 G0
1.5
8/11 G1
1.5
8/11 G2
1.5
8/11 H0
1.5
8/11 H1
1.5
8/11 H2
1.5
8/11
J0
1.8
8/11
J1
1.5
8/11
J2
1.8
8/11 K0
1.5
8/11 K1
1.8
8/11 K2
1.5
8/11 L0
1.2
8/11 L1
1.5
8/11 L2
1.5
8/11 M0
0.6
8/11 M1
0.9
8/11 M2
0.9
8/11 M3
0.9
8/11 M4
0.6
8/11 M5
0.6
8/11 M6
0.9
8/11 M7
0.6
8/11 M8
0.6
8/11 M9
0.6
8/11 M10
0.9
8/11 M11
0.9
8/11 M12
0.9
8/11 M13
0.9
8/11 M14
0.6
8/11 M15
0.9
8/11 M16
0.9
8/11 M17
0.9
8/11 M18
0.6
8/11 O1
2.1
8/11 O2
>1.8

20
10
10

100
10
10

0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
20
0
110
20
0
10
0
0
0
0
0
0
0
73

40
90
90
90
10
140
50
40
40
40
50
50
60
70
30
50
120
110
30
90
30
20
30
60
60
50
90
30
40
90
90
170
70
50
80
50
30
70
100
20
110
90

20
40
50
60
10
50
0
0
50
10
40
20
50
80
20
20
30
70
10
20
30
0
20
0
60
50
50
10
30
60
50
270
30
80
130
40
40
120
40
20
120
50

50
110
110
130
10
170
70
40
110
50
100
60
140
150
50
70
160
180
30
110
70
20
40
80
170
60
160
30
70
110
100
270
70
80
90
80
40
140
110
20
220
140

15.2
35.3
29.1
23.0
5.5
38.7
9.7
8.4
10.6
10.1
25.2
16.9
19.9
15.7
14.0
12.7
22.4
29.4
5.9
20.8
8.1
3.3
9.0
11.5
17.5
11.6
14.8
4.5
11.5
13.4
14.8
66.0
13.2
14.5
21.1
10.4
9.1
25.9
16.8
3.9
32.4
26.6

Table 3.1 (cont’d)
8/11 O3
>1.8
8/11 O4
>1.8
8/11 O5
>1.8
8/11 O6
>1.8
8/11 O7
>1.8
8/11 O8
>1.8
8/11 O9
>1.8
8/11 O10 >1.8
8/11 O11 >1.8
8/11 O12 >1.8
8/11 O13 >1.8
8/11 O14 >1.8
8/11 O15 >1.8
8/11 O16 >1.8
8/11 O17 >1.8
8/11 O18 >1.8
8/11 O19 >1.8
8/11 O20 >1.8
8/11 O21 >1.8
8/11 O22 >1.8
8/11 O23 >1.8
8/11 O24 >1.8
8/11 O25 >1.8
8/11 P0
>1.8
8/11 P1
>1.8
8/11 P2
>1.8
8/11 R0
>1.8
8/11 R1
>1.8
8/11 R2
>1.8
8/11 R3
>1.8
8/11 R4
>1.8
8/11 R5
>1.8
8/11 R6
>1.8
8/11 R7
>1.8
8/11 R8
>1.8
8/11 R9
>1.8
8/11 R10 >1.8
8/11 R11 >1.8
8/11 R12 >1.8
8/11 R13 >1.8
8/11 S0
>1.8
8/11 S1
2.1

0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
74

150
200
50
70
160
170
90
100
120
70
50
40
10
80
30
60
40
70
170
100
50
70
20
30
110
30
60
40
10
20
20
20
10
30
10
20
10
70
20
20
70
40

240
260
20
1230
100
90
10
150
80
40
50
30
20
20
20
60
20
40
70
100
20
50
40
10
40
0
30
10
0
10
0
20
10
0
10
0
0
10
10
0
50
20

210
300
110
140
210
290
130
210
210
130
120
90
20
140
90
130
50
80
250
210
100
110
40
60
210
50
70
80
10
20
40
30
30
60
10
40
10
10
20
20
80
70

61.4
111.5
17.5
31.6
32.4
85.9
25.5
31.7
45.2
16.2
19.4
10.8
2.0
20.8
10.9
17.1
17.9
19.1
46.5
39.7
13.7
22.9
11.7
6.6
31.3
7.2
20.8
12.0
3.5
11.6
4.0
6.4
5.2
7.3
3.7
6.8
4.5
6.5
6.1
6.4
23.9
12.6

Table 3.1 (cont’d)
8/11 S2
2.1
8/11 S3
2.1
8/11 S4
2.1
8/11 S5
2.1
8/11 S6
2.1
8/11 S7
2.1
8/11 S8
2.1
8/11 S9
2.1
8/11 T0
0.6
8/11 T1
0.6
8/11 T2
0.6
8/19 A0
1.8
8/19 A1
1.8
8/19 A2
1.8
8/19 A3
1.8
8/19 A4
1.8
8/19 A5
1.8
8/19 A6
1.8
8/19 A7
1.8
8/19 C0
0.6
8/19 C1
0.6
8/19 C2
0.6
8/19 D0
1.5
8/19 D1
1.5
8/19 D2
1.5
8/19 D3
1.5
8/19 D4
1.8
8/19 D5
1.8
8/19 D6
1.5
8/19 D7
1.5
8/19 E0
2.1
8/19 E1
2.1
8/19 E2
2.1
8/19 E3
2.1
8/19 E4
2.1
8/19 F0
1.5
8/19 F1
1.5
8/19 F2
1.5
8/19 F3
1.5
8/19 F4
1.5
8/19 G0
1.2
8/19 G1
1.2

10

0
0
0
0
0
0
0
0
10
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
75

10
30
10
20
10
10
20
50
320
130
190
90
70
20
40
30
90
60
30
50
70
50
20
120
140
100
20
40
80
20
70
80
40
70
80
20
40
30
70
80
50
50

0
40
0
0
0
10
30
30
100
80
90
50
60
40
40
10
30
70
40
70
90
60
20
160
170
50
60
0
420
20
230
140
40
80
150
60
60
30
120
40
30
40

30
60
10
20
20
10
30
70
360
240
310
150
110
30
70
30
150
90
90
50
80
80
50
170
340
160
70
50
180
40
160
110
80
90
130
20
60
30
110
110
70
70

9.5
20.8
2.2
7.1
4.5
3.8
16.3
24.6
59.4
30.0
49.3
29.8
28.6
11.5
11.2
6.1
24.4
21.1
9.8
18.6
23.0
15.3
3.6
21.1
55.8
22.9
8.5
3.5
36.0
5.9
34.5
19.0
19.6
28.3
22.6
3.9
12.5
9.5
25.0
20.0
17.8
18.2

Table 3.1 (cont’d)
8/19 G2
1.2
8/19 H0
1.2
8/19 H1
1.2
8/19 H2
1.2
8/19
J0
>1.8
8/19
J1
>1.8
8/19
J2
>1.8
8/19 K0
>1.8
8/19 K1
>1.8
8/19 K2
>1.8
8/19 K3
>1.8
8/19 K4
>1.8
8/19 K5
>1.8
8/19 K6
>1.8
8/19 L0
>1.8
8/19 L1
>1.8
8/19 L2
>1.8
8/19 L3
>1.8
8/19 L4
>1.8
8/19 L5
>1.8
8/19 L6
>1.8
8/19 L7
>1.8
8/19 L8
>1.8
8/19 L9
>1.8
8/19 L10 >1.8
8/19 L11 >1.8
8/19 L12 >1.8
8/19 M0
0.9
8/19 M1
0.9
8/19 M2
0.9
8/19 M3
0.9
8/19 M4
0.9
8/19 M5
0.9
8/19 M6
0.9
8/19 M7
0.9
8/19 O0
1.5
8/19 O1
1.5
8/19 O2
1.5
8/19 O3
1.5
8/19 O4
1.5
8/19 O5
1.5
8/19 O6
1.5

0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
76

30
10
30
60
80
70
40
120
100
50
20
30
90
100
40
70
70
50
20
30
60
60
50
40
60
20
30
30
20
20
40
50
60
20
30
20
260
80
50
80
180
140

0
0
70
50
30
90
170
100
40
50
0
40
70
60
80
20
30
40
20
90
30
110
30
0
50
50
50
60
50
50
80
170
110
50
60
50
170
90
150
80
220
170

40
10
30
90
110
130
90
140
150
110
20
60
120
150
50
80
130
80
40
60
70
90
50
40
110
40
70
90
20
20
70
160
80
20
30
40
410
230
130
100
310
380

6.9
3.0
19.3
19.2
22.5
23.8
30.9
41.5
19.1
11.4
3.2
6.1
26.2
27.6
15.0
14.2
23.9
15.1
6.2
11.3
10.8
12.4
8.8
11.6
2.9
12.0
12.9
12.6
6.0
8.4
19.2
17.0
4.8
9.7
4.9
56.3
36.0
29.4
20.2
58.7
21.6
31.1

Table 3.1 (cont’d)
8/19 O7
1.5
8/19 O8
1.5
8/19 O9
1.5
8/19 O10
1.5
8/19 O11
1.5
8/19 O12
1.5
8/19 O13
1.5
8/19 P0
2.1
8/19 P1
2.1
8/19 P2
2.1
8/19 R0
2.1
8/19 R1
2.1
8/19 R2
2.1
8/19 R3
2.1
8/19 R4
2.1
8/19 R5
2.1
8/19 R6
2.1
8/19 R7
2.1
8/19 R8
2.1
8/19 R9
2.1
8/19 R10
2.1
8/19 S0
2.1
8/19 S1
2.1
8/19 S2
2.1
8/19 S3
2.1
8/19 S4
2.1
8/19 S5
2.1
8/19 S6
2.1
8/19 S7
2.1
8/19 S8
2.1
8/19 S9
2.1
8/19 S10
2.1
8/19 S11
2.1

0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

130
170
70
90
170
160
160
80
150
110
30
10
10
20
10
10
30
10
0
30
10
20
20
30
10
10
30
50
30
10
30
70
100

120
120
120
40
150
300
120
40
100
90
10
10
40
10
20
0
10
0
20
40
20
30
20
20
40
40
40
40

10
30
90

190
250
120
130
230
610
260
170
230
190
30
10
20
30
20
20
40
10
0
30
20
30
30
40
30
10
40
80
60
10
40
90
200

49.5
24.3
114.5
41.7
54.2
67.3
40.7
26.3
47.0
30.8
6.9
2.8
7.7
8.2
5.0
5.6
11.8
2.7
2.0
8.1
5.5
3.6
6.7
8.9
7.2
5.7
10.2
17.4
10.1
3.2
7.4
32.7
37.3

Table 3.1 2008 raw data for Bear Lake. Site letters represent M. spicatum beds and the
numbers represent individual quadrats. Depth is reported in meters. E. lecontei are life
stages are coded as A=adult, L=larva, P=pupa and E=egg. Biomass is dry M. spicatum
2
weight in g/m .

77

Table 3.2

Date
8/1
8/1
8/1
8/1
8/1
8/1
8/1
8/1
8/1
8/1
8/1
8/1
8/1
8/1
8/1
8/1
8/1
8/1
8/1
8/1
8/1
8/1
8/1
8/1
8/1
8/1
8/1
8/1
8/1
8/1
8/1
8/1
8/1
8/1
8/1
8/1
8/1
8/1
8/1

Site
A0
A1
A2
A3
A4
B0
B1
B2
B3
B4
B5
C0
C1
C2
C3
C4
C5
C6
C7
D0
D1
D2
E0
E1
E2
E3
E4
E5
E6
E7
F0
F1
F2
G0
G1
G2
G3
G4
G5

Depth
0.9
0.9
0.9
0.9
0.9
0.9
0.9
0.9
0.9
0.9
0.9
0.9
0.9
0.9
0.9
0.9
0.9
0.9
0.9
0.3
0.3
0.3
0.9
0.9
1.2
0.9
1.5
1.5
1.2
0.9
0.9
0.9
0.9
0.6
0.6
0.6
0.6
0.6
0.6

Clear Lake
E. lecontei
A L P E Total
0
20
20
10
30
40
0
0
10
10
30
30
0
0
0
10
10
0
0
0
0
0
0
0
0
10
10
0
10
10
20
10
10
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
78

Stem
50
50
70
30
10
70
60
100
110
180
100
80
120
70
10
40
70
40
40
150
40
80
120
130
110
150
120
80
80
170
50
60
30
50
130
60
80
10
50

M. spicatum
Frag Tip
Biomass
60
90
9.6
40
50
5.1
40
120
12.1
50
80
5.7
20
90
9.9
80
180
28.5
50
100
18.3
60
260
29.7
150
160
40.0
80
110
30.8
40
180
24.9
240
150
29.9
70
130
32.0
110
230
18.3
210
110
21.1
260
190
18.6
140
190
18.7
250
180
23.7
60
110
12.6
260
210
56.7
140
130
22.2
170
300
30.2
220
360
67.0
290
260
69.2
130
200
50.7
240
350
55.5
140
370
38.4
130
80
23.6
120
170
26.7
320
170
66.5
220
210
20.0
190
280
21.7
30
80
15.1
120
240
17.0
70
330
37.9
50
180
27.6
110
240
13.6
60
50
2.9
90
180
12.8

Table 3.2 (cont’d)
8/1
H0
0.9
8/1
H1
0.9
8/1
H2
0.9
8/1
I0
1.5
8/1
I1
0.9
8/1
I2
0.9
8/1
I3
0.9
8/1
J0
0.9
8/1
J1
1.8
8/1
J2
1.2
8/1
J3
0.9
8/1
J4
0.9
8/1
J5
1.5
8/1
J6
1.2
8/1
J7
>1.8
8/1
K0
>1.8
8/1
K1
>1.8
8/1
K2
>1.8
8/1
L0
0.9
8/1
L1
1.2
8/1
L2
0.9
8/1
L3
1.2
8/1
L4
1.5
8/1
L5
>1.8
8/1
L6
1.2
8/1
L7
0.9
8/1
M0
0.3
8/1
M1
0.6
8/1
M2
0.6
8/1
M3
0.3
8/1
M4
0.6
8/1
N0
>1.8
8/1
N1
>1.8
8/1
N2
>1.8
8/1
N3
>1.8
8/1
N4
>1.8
8/1
O0
0.9
8/1
O1
0.9
8/1
O2
1.2
8/1
P0
>1.8
8/1
P1
>1.8
8/1
P2
>1.8

10

10
0
10
10
0
10
10
0
0
20
0
0
10
10
0
0
20
0
20
10
0
10
20
0
10
0
20
0
10
20
40
0
0
0
0
0
0
0
0
0
0
0

10
10
10
10

10

10

10
10

20
20
10
10
10

10
10
20
10
20

10

30

79

140
170
120
30
130
190
150
40
120
110
80
130
110
110
90
130
170
80
150
80
40
70
200
50
50
90
110
50
70
70
170
90
110
70
20
50
120
40
140
70
120
130

210
250
190
90
230
120
300
40
220
260
100
110
170
230
120
40
220
130
190
120
140
120
320
70
180
70
200
70
30
100
340
140
110
80
160
70
180
40
110
30
240
90

230
170
140
120
170
280
170
80
290
310
180
170
200
190
230
260
300
100
550
200
190
210
610
170
190
170
140
140
250
230
450
260
300
340
150
270
130
110
240
190
190
360

64.3
16.3
29.0
60.2
47.8
66.2
66.0
44.7
74.4
65.9
42.0
34.5
31.0
40.6
23.7
57.3
91.8
31.0
57.4
25.5
22.6
23.3
71.0
15.1
18.5
19.7
48.6
19.4
32.0
14.9
43.9
23.2
29.5
33.3
21.6
17.3
39.4
12.0
28.0
22.5
41.5
36.9

Table 3.2 (cont’d)
8/1
Q0
1.2
8/1
Q1
0.9
8/1
Q2
0.9
8/1
Q3
0.9
8/1
Q4
0.6
8/1
Q5
0.9
8/1
Q6
0.9
8/1
R0
0.6
8/1
R1
0.6
8/1
R2
0.9
8/1
S0
0.9
8/1
S1
0.9
8/1
S2
1.2
8/1
S3
0.9
8/1
S4
0.9
8/1
S5
0.6
8/1
S6
0.9
8/1
S7
0.9
8/1
S8
0.9
8/1
S9
0.6
8/1 S10
1.2
8/1 S11
0.6
8/14 A0
0.9
8/14 A1
0.9
8/14 A2
0.9
8/14 A3
0.9
8/14 A4
0.9
8/14 B0
0.9
8/14 B1
0.9
8/14 B2
0.9
8/14 B3
0.9
8/14 B4
0.9
8/14 B5
0.9
8/14 C0
0.9
8/14 C1
0.9
8/14 C2
0.9
8/14 C3
0.9
8/14 C4
0.9
8/14 C5
0.9
8/14 C6
0.9
8/14 C7
0.9
8/14 D0
0.3

10
10

10
30
10
0
10
0
10
10
10
10
0
10
10
0
0
0
60
0
70
30
40
0
0
0
0
0
0
0
0
20
0
40
0
10
0
0
0
0
0
0
20
0

20

10
10
10
10
10
10
10
10

30
10
20
40

30
20

40
10

20
10

30
10

10

10
80

170
260
120
160
160
210
140
220
140
130
50
110
150
70
40
20
100
40
180
80
150
130
20
20
30
40
20
80
230
90
180
240
80
280
210
200
80
180
260
120
220
130

150
280
70
200
100
80
260
120
90
90
30
70
320
220
130
180
70
240
60
220
110
60
40
20
50
60
140
390
190
330
360
300
280
210
120
50
240
420
130
130
150

350
550
230
400
500
440
510
380
260
320
270
240
360
180
140
40
120
130
440
370
370
160
50
50
40
80
50
180
510
170
360
390
140
500
530
360
140
370
340
190
260
260

55.8
82.2
34.4
47.4
62.0
55.1
46.3
48.1
29.7
28.6
21.8
22.0
49.6
24.7
15.4
4.9
32.7
10.8
43.3
31.2
56.1
33.8
11.4
7.0
9.4
10.5
5.2
29.7
90.0
34.0
75.2
89.6
39.9
59.4
61.2
29.6
19.1
70.8
106.3
41.5
87.7
30.3

Table 3.2 (cont’d)
8/14 D1
0.3
8/14 D2
0.3
8/14 E0
0.9
8/14 E1
0.9
8/14 E2
1.2
8/14 E3
0.9
8/14 E4
1.5
8/14 E5
1.5
8/14 E6
1.2
8/14 E7
0.9
8/14 F0
0.9
8/14 F1
0.9
8/14 F2
0.9
8/14 G0
0.6
8/14 G1
0.6
8/14 G2
0.6
8/14 G3
0.6
8/14 G4
0.6
8/14 G5
0.6
8/14 H0
0.9
8/14 H1
0.9
8/14 H2
0.9
8/14
I0
1.5
8/14
I1
0.9
8/14
I2
0.9
8/14
I3
0.9
8/14
J0
0.9
8/14
J1
1.8
8/14
J2
1.2
8/14
J3
0.9
8/14
J4
0.9
8/14
J5
1.5
8/14
J6
1.2
8/14
J7
2.1
8/14 K0
2.1
8/14 K1
2.1
8/14 K2
2.1
8/14 L0
0.9
8/14 L1
1.2
8/14 L2
0.9
8/14 L3
1.2
8/14 L4
1.5

20

20
10
0
0
0
10
0
0
0
10
0
20
10
20
50
10
30
30
0
50
20
10
0
0
0
0
0
0
0
10
10
0
10
0
0
0
0
50
0
0
0
0

10

10

10
10

10
10

20
20

20

10
10
30
30

20
10

30
10

10

10
10
10

30

20

81

320
60
50
70
70
50
110
240
70
60
180
80
60
260
140
200
140
110
130
220
100
30
70
30
100
100
80
30
30
0
20
40
30
70
80
70
180
240
70
130
130
130

350
120
110
80
100
150
110
210
100
180
130
110
430
140
220
260
240
200
220
190
440
260
150
30
90
120
140
280
50
180
70
140
60
100
190
80
290
660
80
250
460
240

360
300
130
260
250
140
200
410
130
110
330
260
190
460
180
320
280
210
240
310
120
70
120
70
150
180
190
270
60
60
60
50
110
180
150
110
200
360
110
290
230
170

88.4
19.5
13.8
27.6
27.5
27.3
36.3
56.3
19.4
26.2
46.7
33.2
41.5
58.4
36.9
52.2
39.9
21.1
43.6
70.7
68.5
29.5
40.2
7.6
22.8
30.9
34.8
28.7
20.0
19.4
16.8
27.8
10.9
20.4
31.9
27.1
70.2
99.4
25.5
70.3
85.6
53.9

Table 3.2 (cont’d)
8/14 L5
>1.8
8/14 L6
1.2
8/14 L7
0.9
8/14 M0
0.3
8/14 M1
0.6
8/14 M2
0.6
8/14 M3
0.3
8/14 M4
0.6
8/14 N0
>1.8
8/14 N1
>1.8
8/14 N2
>1.8
8/14 N3
>1.8
8/14 N4
>1.8
8/14 O0
0.9
8/14 O1
0.9
8/14 O2
1.2
8/14 P0
>1.8
8/14 P1
>1.8
8/14 P2
>1.8
8/14 Q0
1.2
8/14 Q1
0.9
8/14 Q2
0.9
8/14 Q3
0.9
8/14 Q4
0.6
8/14 Q5
0.9
8/14 Q6
0.9
8/14 R0
0.6
8/14 R1
0.6
8/14 R2
0.9
8/14 S0
0.9
8/14 S1
0.9
8/14 S2
1.2
8/14 S3
0.9
8/14 S4
0.9
8/14 S5
0.6
8/14 S6
0.9
8/14 S7
0.9
8/14 S8
0.9
8/14 S9
0.6
8/14 S10
1.2
8/14 S11
0.6
8/27 A0
0.3

10

10
0
0
0
10
0
0
0
10
0
0
0
0
0
0
0
0
10
0
0
0
10
0
0
0
0
0
10
0
0
0
0
0
0
40
70
80
10
20
0
40
20

10

10

10

10

10

10
10

10
50
10

10
10

20
10
60
10
10
40
20
82

140
190
150
80
100
100
120
90
160
190
50
80
160
60
70
50
120
20
40
50
60
150
180
60
70
30
180
180
150
160
110
150
60
100
0
190
170
110
240
110
100
40

100
110
210
240
130
230
140
140
160
140
50
210
160
150
80
100
60
40
80
70
200
260
200
10
70
50
60
280
330
240
100
170
160
190
370
260
310
350
270
300
160
210

210
230
260
170
300
200
190
220
330
70
140
250
110
200
110
290
90
50
110
220
300
90
230
80
480
260
260
250
330
380
100
200
350
530
420
310
470
210
240
130

33.4
35.7
25.7
45.0
36.6
32.3
31.0
37.7
61.9
59.5
34.0
38.8
61.2
21.5
22.4
19.9
35.9
15.1
27.4
14.1
17.1
42.3
32.4
16.8
22.2
17.6
61.5
65.5
59.8
53.5
66.6
36.1
27.1
30.3
83.3
71.6
63.4
49.6
79.6
43.5
33.5
19.4

Table 3.2 (cont’d)
8/27 A1
0.3
8/27 A2
0.6
8/27 B0
1.5
8/27 B1
1.5
8/27 B2
0.3
8/27 C0
1.2
8/27 C1
0.9
8/27 C2
0.9
8/27 C3
1.5
8/27 C4
1.5
8/27 C5
0.3
8/27 C6
1.2
8/27 C7
0.9
8/27 C8
1.5
8/27 C9
1.2
8/27 C10
1.5
8/27 C11
1.5
8/27 C12
0.9
8/27 C14
1.5
8/27 C15
1.5
8/27 C16
1.8
8/27 C17
1.5
8/27 C18
1.5
8/27 C19
1.5
8/27 D0
1.2
8/27 D1
1.2
8/27 D2
1.2
8/27 D3
1.5
8/27 D4
1.2
8/27 D5
1.5
8/27 D6
1.5
8/27 D7
1.5
8/27 D8
1.5
8/27 D9
1.2
8/27 D10
1.5
8/27 D11
1.2
8/27 D12
1.2
8/27 F0
1.2
8/27 F1
1.5
8/27 F2
1.2
8/27 G0
2.1
8/27 G1
2.1

10

10
20

10
10

0
20
50
30
0
0
20
0
0
0
0
10
0
0
10
0
0
0
10
0
0
20
0
0
10
30
0
0
0
0
10
50
0
10
0
0
10
20
0
0
20
20

10
10
20

20

10

10

10

10

10
10

20

10

20

10
20

10

10

10
20

10

10
20
83

60
260
180
120
50
150
60
180
130
140
260

270
380
290
240
110
220
190
230
400
160
340

90
140
90
170
60
150
210
270
290
180
30

190
220
80
200
220
330
230
260
540
120
590

140
200
110
240
130
210

130
300
210
350
170
340

170
240
230

280
440
260

130
60
120
110
70

210
240
160
170
260

160
170
770
340
300
200
440
120
380
320
230
530
310
500
320
180
580
230
340
340
390
700
370
130
320
290
230
290
990
200
440
420
470
1090
430
500
530
260
90
300
150
520

50.5
25.0
84.3
100.1
43.6
45.7
95.5
26.0
82.1
70.2
48.5
124.8
22.8
58.9
70.3
30.1
52.4
62.0
84.8
51.4
78.8
168.1
66.6
152.8
61.7
52.7
191.8
41.8
77.8
50.4
115.4
186.8
76.4
130.3
106.6
79.9
157.4
53.1
49.7
36.7
57.3
47.2

Table 3.2 (cont’d)
8/27 G2
2.1
8/27 G3
2.1
8/27 G4
2.1
8/27 H0
1.5
8/27 H1
1.2
8/27 H2
0.9
8/27
I0
1.5
8/27
I1
1.5
8/27
I2
1.5
8/27
I3
1.5
8/27
J0
1.5
8/27
J1
1.5
8/27
J2
1.5
8/27
J3
1.2
8/27
J4
1.5
8/27
J5
1.5
8/27 K0
2.1
8/27 K1
2.1
8/27 K2
2.1
8/27 L0
1.5
8/27 L1
1.5
8/27 L2
1.8
8/27 L3
0.9
8/27 L4
2.1
8/27 L5
1.2
8/27 L6
0.9
8/27 M0
2.1
8/27 M1
2.1
8/27 M2
1.5
8/27 M3
1.8
8/27 M4
0.6
8/27 N0
2.1
8/27 N1
2.1
8/27 N2
2.1
8/27 N3
2.1
8/27 N4
2.1
8/27 P0
2.1
8/27 P1
2.1
8/27 P2
2.1
8/27 Q0
2.1
8/27 Q1
2.1
8/27 Q2
2.1

0
0
0
0
10
0
20
40
10
0
0
0
0
10
80
10
0
0
0
10
0
10
0
0
10
10
0
0
10
0
20
0
10
0
0
30
0
0
0
10
0
0

10
10

10

10
40
10

40

10
10
10

20

10
10

10
10

10
20
10

10

20

10

84

150
70
110

240
100
330

140
130
80
140
80
60

150
270
320
230
140
120

70
60
60

50
100
120

120
10
60
190
100

370
260
220
340
130

150

370

110

360

30
90
200
310

240
120
380
540

90
180
100
180

160
330
650
1020

90

240

490
290
310
180
260
280
160
300
220
220
50
80
320
150
780
280
380
350
520
270
210
420
340
220
600
150
510
310
550
220
330
210
100
250
830
390
280
110
280
230
680
210

120.7
55.8
63.7
66.5
103.2
73.4
66.6
63.7
47.8
36.7
38.4
24.1
42.4
21.7
116.7
71.6
59.8
31.4
115.2
34.7
36.8
164.4
28.5
92.5
68.3
42.3
57.6
95.4
141.7
125.0
54.5
56.6
84.7
131.4
91.1
79.0
69.2
41.6
60.7
53.9
172.1
45.4

Table 3.2 (cont’d)
8/27 Q3
1.5
8/27 Q4
1.5
8/27 R0
0.9
8/27 R1
0.9
8/27 R2
0.9
8/27 S0
0.9
8/27 S1
0.9
8/27 S2
1.2
8/27 S3
0.9
8/27 S4
1.2
8/27 S5
1.5
8/27 T0
>1.8
8/27 T1
1.2
8/27 T2
>1.8
8/27 T3
1.5
8/27 T4
>1.8
8/27 T5
1.5
8/27 T6
1.5

30
10

20
30

10
40
40
10

20
10
10

20
10
80
20

30
0
10
0
0
40
20
80
150
30
0
0
0
0
0
0
0
0

140

220

80
130
140
150

210
680
80
430

230
40
60

390
160
30

60

40

170

320

330
100
520
200
350
1000
310
980
1020
630
440
100
120
350
140
180
700
100

102.3
56.3
93.2
10.8
42.4
139.3
72.4
100.9
126.6
84.7
89.1
24.9
19.0
104.2
24.7
54.4
78.7
20.1

2008 raw data for Clear Lake. Site letters represent M. spicatum beds and the numbers
represent individual quadrats. Depth is reported in meters. E. lecontei are life stages are
coded as A=adult, L=larva, P=pupa and E=egg. Biomass is dry M. spicatum weight in
2
g/m .

85

Table 3.3

Date
7/24
7/24
7/24
7/24
7/24
7/24
7/24
7/24
7/24
7/24
7/24
7/24
7/24
7/24
7/24
7/24
7/24
7/24
7/24
7/24
7/24
7/24
7/24
7/24
7/24
7/24
7/24
7/24
7/24
7/24
7/24
7/24
7/24
7/24
7/24
7/24
7/24
7/24
7/24

Site
A0
A1
A2
B0
B1
B2
C0
C1
C2
E0
E1
E2
E3
E4
E5
E6
E7
E8
E9
E10
F0
F1
F2
F3
F4
G0
G1
G2
H0
H1
H2
H3
H4
H5
H6
H7
H8
H9
H10

Depth
2.1
2.1
2.1
2.1
2.1
2.1
2.1
2.1
2.1
2.1
1.5
2.1
2.1
2.1
1.5
2.1
2.1
2.1
2.1
2.1
2.1
2.1
2.1
2.1
2.1
1.5
2.1
1.8
1.2
1.5
1.5
0.9
0.6
0.9
1.2
1.2
1.5
1.5
0.9

A

10

10

10
40

Hackert Lake
E. lecontei
L P E Total Stem
10 20
30
140
10 10
20
210
10
10
90
0
110
0
120
0
90
0
150
0
230
0
200
40
40
110
0
150
0
120
10
10
120
10
20
90
0
130
0
120
10
130
20
20
120
0
120
0
110
10
10
140
0
170
0
30
0
110
0
140
10
10
100
10
10
160
0
70
0
110
0
60
0
160
0
200
20
20
60
10
10
90
0
100
10 20
30
160
20
30
200
20 30
50
160
10 10
60
140
86

M. spicatum
Frag Tip Biomass
110 370
187.5
80
420
344.7
20
180
103.0
80
170
161.4
30
140
173.5
120 260
NA
50
270
156.6
110 200
191.9
110 230
303.1
160 230
122.2
80
240
128.4
150 160
123.1
100 190
168.4
40
140
138.8
130 330
189.4
20
250
95.5
80
170
201.8
230 280
196.8
100 240
97.5
180 400
259.1
190 330
135.6
200 290
190.6
60
70
71.5
40
160
92.3
70
220
227.0
80
100
145.1
80
190
184.9
110 120
96.7
130 160
258.1
70
180
40.1
150 190
144.5
70
430
181.4
20
120
142.3
30
200
91.7
120 180
122.0
120 400
279.2
100 400
324.4
130 150
209.5
100 240
155.7

Table 3.3 (cont’d)
7/24 H11
1.2
7/24 H12
0.9
7/24 H13
1.5
7/24 H14
1.2
7/24 H15
1.2
7/24 H16
1.2
7/24 H17
0.9
7/24 H18
1.2
7/24 H19
0.9
7/24 H20
1.8
7/24 H21
0.9
7/24 H22
1.5
7/24 H23
1.2
7/24 H24
1.5
7/24 H25
0.6
7/24 H26
0.9
7/24 H27
1.2
7/24 H28
1.8
7/24
I0
>1.8
7/24
I1
>1.8
7/24
I2
>1.8
7/24
J0
1.5
7/24
J1
1.2
7/24
J2
1.2
7/24 K0
1.5
7/24 K1
0.9
7/24 K2
0.9
7/24
L0
>1.8
7/24
L1
>1.8
7/24
L2
>1.8
7/24
L3
>1.8
7/24
L4
>1.8
7/24
L5
>1.8
7/24
L6
>1.8
7/24
L7
>1.8
7/24
L9
>1.8
7/24 L10
>1.8
7/24 L11
>1.8
7/24 L12
>1.8
7/24 L13
>1.8
7/24 L14
>1.8
7/24 L15
>1.8

0
0
40
0
0
20
0
10
80
0
0
0
0
0
0
0
50
30
0
0
0
0
30
110
0
10
10
0
0
0
0
0
0
0
10
0
0
0
20
0
0
10

40

20

10

20

10
60

10

20

10

30

70

10

30
30

10
10

10

20

10
87

110
110
200
160
260
70
60
60
260
130
50
150
200
100
110
40
150
130
50
40
100
160
120
200
90
50
60
80
130
300
80
150
500
80
190
110
110
80
70
100
120
220

120
110
250
180
140
30
20
50
320
160
10
30
150
100
60
80
120
60
30
50
60
150
60
60
10
30
10
120
120
180
30
80
130
50
70
50
80
70
60
20
190
160

290
270
580
290
490
200
80
90
440
260
80
210
410
260
190
100
330
180
110
90
160
230
260
480
60
130
110
230
330
430
170
240
320
90
300
120
220
110
200
200
380
240

87.9
136.7
227.2
137.4
265.8
119.8
99.5
140.4
352.8
331.6
40.8
146.5
316.8
82.6
190.1
134.5
327.5
219.8
74.7
70.0
147.8
122.9
127.0
227.9
117.4
80.5
102.1
69.3
107.0
249.1
60.9
151.2
287.6
63.3
178.2
111.3
98.1
54.6
94.3
79.9
170.0
171.4

Table 3.3 (cont’d)
7/24 L16
2.1
7/24 L17
2.1
7/24 L18
2.1
7/24 L19
2.1
7/24 M0
0.9
7/24 M1
0.6
7/24 M2
0.9
7/24 M3
0.9
7/24 M4
1.5
7/24 M5
1.2
7/24 M6
1.5
7/24 M7
1.2
7/24 M8
0.6
7/24 M9
0.9
7/24 M10
0.6
7/24 N0
2.1
7/24 N1
2.1
7/24 N2
2.1
7/24 O0
1.2
7/24 O1
2.1
7/24 O2
1.5
7/24
P0
1.2
7/24
P1
1.2
7/24
P2
1.5
7/24
P3
2.1
7/24
P4
2.1
7/24
P5
2.1
7/24
P6
2.1
7/24
P7
2.1
7/24
P8
2.1
7/24
P9
2.1
7/24 Q0
2.1
7/24 Q1
2.1
7/24 Q2
2.1
7/24 R0
1.5
7/24 R1
1.5
7/24 R2
1.5
7/24
S0
1.2
7/24
S1
1.5
7/24
S2
2.1
8/8
A0
2.1
8/8
A1
2.1

20

20
50
0
0
10
20
0
0
0
0
0
40
10
0
0
0
40
0
10
30
0
10
0
0
60
0
0
0
0
0
0
0
0
0
30
0
0
0
10
10
30
0

50

10
20

20

10

10

20

10
10

10
10
10

20

10

20

20

10

10

10

10

10

10
10
10

20
88

100
170
230
160
30
90
20
30
20
60
30
40
50
30
40
40
250
140
70
70
100
220
120
130
420
120
270
80
210
120
210
180
110
70
40
70
110
80
130
260
110
140

160
170
160
150
20
30
30
10
40
20
30
60
20
50
210
40
20
90
40
170
80
160
260
40
220
40
100
80
130
160
140
10
40
30
20
30
70
70
180
160

240
280
340
210
40
140
110
120
50
60
60
120
150
30
80
90
360
100
80
120
130
410
140
210
620
170
270
110
390
170
320
280
220
60
40
90
120
250
290
200
130
280

185.8
210.5
156.0
24.9
87.0
49.5
37.7
30.9
37.8
25.3
54.6
44.8
7.9
42.7
35.5
221.9
115.1
54.6
75.9
77.8
217.1
79.4
108.6
451.7
85.6
226.1
52.4
266.8
110.6
188.6
206.2
109.7
45.1
52.0
77.5
74.9
130.5
226.4
197.5
129.9
152.4

Table 3.3 (cont’d)
8/8
A2
>1.8
8/8
B0
>1.8
8/8
B1
>1.8
8/8
B2
>1.8
8/8
C0
>1.8
8/8
C1
>1.8
8/8
C2
>1.8
8/8
E0
>1.8
8/8
E1
>1.8
8/8
E2
>1.8
8/8
E3
>1.8
8/8
E4
>1.8
8/8
E5
>1.8
8/8
E6
>1.8
8/8
E7
>1.8
8/8
E8
>1.8
8/8
E9
>1.8
8/8
E10
>1.8
8/8
E11
>1.8
8/8
E12
>1.8
8/8
E13
>1.8
8/8
E14
>1.8
8/8
E15
>1.8
8/8
F0
>1.8
8/8
F1
>1.8
8/8
F2
>1.8
8/8
F3
>1.8
8/8
F4
>1.8
8/8
F5
>1.8
8/8
F6
>1.8
8/8
G0
>1.8
8/8
G1
>1.8
8/8
G2
>1.8
8/8
H0
1.2
8/8
H1
1.5
8/8
H2
0.6
8/8
H3
1.2
8/8
H4
1.2
8/8
H5
1.2
8/8
H6
1.5
8/8
H7
1.2
8/8
H8
1.2

10

10
0
0
0
0
0
0
0
20
0
0
0
0
0
0
30
0
0
10
10
0
10
0
0
0
0
0
10
0
0
0
0
0
0
0
0
0
0
70
0
0
0

20

30

10
10
10

10

10

10

20

30

89

100
140
40
140
30
30
70
70
80
40
40
30
20
50
20
120
40
10
70
140
60
50
50
200
130
70
100
80
70
60
190
130
90
170
70
50
20
160
320
40
270
30

90
100
100
180
80
130
50
140
130
80
120
130
10
30
50
70
190
30
100
270
70
20
40
110
40
160
250
60
260
120
290
110
100
250
190
110
0
110
510
60
210
100

150
250
60
160
70
80
110
90
90
70
80
120
30
60
30
190
110
10
120
310
80
80
90
250
160
100
160
130
180
80
350
230
200
190
100
60
20
290
740
80
380
100

78.1
210.1
106.3
150.9
47.5
83.5
84.2
61.7
87.8
66.5
73.5
90.6
32.8
59.3
84.1
120.2
88.0
30.5
137.5
221.1
117.8
40.0
102.1
241.4
71.8
145.0
174.5
170.6
130.2
104.3
246.3
267.0
95.2
216.2
146.6
81.8
42.6
92.4
858.8
90.5
401.2
109.1

Table 3.3 (cont’d)
8/8
H9
0.9
8/8 H10
1.2
8/8 H11
0.6
8/8 H12
0.6
8/8 H13
1.2
8/8 H14
1.5
8/8 H15
0.9
8/8 H16
0.6
8/8 H17
1.5
8/8 H18
0.9
8/8 H19
0.9
8/8 H20
1.2
8/8 H21
0.9
8/8 H22
1.2
8/8 H23
1.2
8/8 H24
1.2
8/8 H25
0.9
8/8 H26
1.2
8/8 H27
1.2
8/8 H28
1.2
8/8 H29
0.6
8/8 H30
1.5
8/8
I0
1.8
8/8
I1
1.5
8/8
I2
1.8
8/8
J0
1.5
8/8
J1
>1.8
8/8
J2
1.5
8/8
K0
0.6
8/8
K1
0.6
8/8
K2
1.2
8/8
K3
0.9
8/8
L0
>1.8
8/8
L1
>1.8
8/8
L2
>1.8
8/8
L3
>1.8
8/8
L4
>1.8
8/8
L5
>1.8
8/8
L6
1.5
8/8
L7
1.5
8/8
L8
1.2
8/8
L9
1.5

20

0
50
0
30
0
10
0
0
0
0
0
0
0
10
0
0
20
60
0
0
20
0
0
0
0
0
0
0
0
0
0
30
0
10
0
0
0
0
10
0
0
10

30
10

20

10

10

10
60

10

20

20

10
10

10

10
90

40
130
130
90
80
130
50
110
40
60
60
110
100
110
160
100
210
110
120
100
130
100
100
170
60
220
140
90
170
150
160
80
50
120
120
170
60
120
130
80
140
60

100
210
190
190
140
110
110
80
110
140
60
110
60
170
60
50
190
60
130
50
190
130
30
150
40
150
110
140
120
90
40
90
60
210
20
80
130
80
190
10
110
90

100
260
320
210
150
360
70
290
100
110
80
160
110
170
280
190
260
180
250
160
370
140
110
260
100
370
160
150
220
330
310
150
80
190
310
250
160
170
310
130
140
100

84.3
359.3
151.3
147.6
87.6
340.4
77.2
181.8
129.1
106.1
69.9
107.2
90.6
229.7
100.6
75.5
197.1
237.0
101.7
208.7
167.5
141.1
44.7
101.5
71.0
276.2
123.3
157.3
155.0
174.7
131.5
113.8
98.6
235.0
69.5
272.2
132.8
186.3
177.1
136.0
145.2
110.0

Table 3.3 (cont’d)
8/8
L10
1.5
8/8
L11
>1.8
8/8
L12
>1.8
8/8
L13
>1.8
8/8
L14
>1.8
8/8
M0
0.6
8/8
M1
0.6
8/8
M2
0.6
8/8
M3
0.6
8/8
N0
>1.8
8/8
N1
>1.8
8/8
N2
>1.8
8/8
O0
>1.8
8/8
O1
>1.8
8/8
O2
>1.8
8/8
P0
>1.8
8/8
P1
>1.8
8/8
P2
>1.8
8/8
P3
>1.8
8/8
P4
>1.8
8/8
P5
>1.8
8/8
Q0
>1.8
8/8
Q1
>1.8
8/8
Q2
>1.8
8/8
R0
1.5
8/8
R1
1.5
8/8
R2
1.5
8/8
R3
1.8
8/8
S0
1.2
8/8
S1
1.2
8/8
S2
1.2
8/21 A0
>1.8
8/21 A1
>1.8
8/21 A2
>1.8
8/21 B0
>1.8
8/21 B1
>1.8
8/21 B2
>1.8
8/21 C0
>1.8
8/21 C1
>1.8
8/21 C2
>1.8
8/21
E0
1.5
8/21
E1
1.8

0
0
0
0
0
20
0
0
20
0
0
0
0
0
10
50
20
0
0
0
0
0
0
10
0
0
20
0
0
0
0
20
0
10
0
10
0
10
0
0
0
0

20

20

10
10
20

40

10

20

20
10
10
10

91

40
100
120
150
50
20
40
40
30
40
60
80
120
90
80
80
180
70
60
40
90
60
60
40
70
100
130
90
90
80
110
150
70

30
90
120
130
90
20
20
40
50
70
140
140
30
80
60
350
200
40
80
30
260
50
50
80
20
80
220
120
80
90
130
720
160

20

120

40
70
50
40
30
50

160
150
170
100
110
100

40
170
130
250
100
40
40
40
70
100
140
100
170
160
220
150
250
120
80
60
180
120
140
100
160
210
270
230
130
100
240
290
130
240
100
320
60
100
140
200
90
170

50.4
106.1
127.3
203.1
107.1
17.6
26.9
56.2
39.3
48.9
94.2
87.6
167.8
70.4
138.0
244.1
93.3
53.4
108.2
43.2
209.4
71.2
63.9
55.6
116.3
140.7
208.4
241.8
163.5
144.1
250.5
438.7
93.9
218.0
46.8
141.8
71.1
80.9
54.8
48.9
107.7
99.8

Table 3.3 (cont’d)
8/21
E2
1.5
8/21
E3
1.5
8/21
E4
>1.8
8/21
E5
1.8
8/21
E6
1.5
8/21
E7
>1.8
8/21
E8
1.5
8/21
E9
1.5
8/21 E10
>1.8
8/21 E11
1.5
8/21 E12
1.5
8/21 E13
1.5
8/21 E14
1.5
8/21 E15
>1.8
8/21
F0
>1.8
8/21
F1
>1.8
8/21
F2
>1.8
8/21
F3
>1.8
8/21
F4
>1.8
8/21
F5
>1.8
8/21 G0
>1.8
8/21 G1
1.5
8/21 G2
>1.8
8/21 H0
0.6
8/21 H1
0.6
8/21 H2
0.9
8/21 H3
0.9
8/21 H4
1.5
8/21 H5
0.6
8/21 H6
>1.8
8/21 H7
1.2
8/21 H8
1.5
8/21 H9
0.9
8/21 H10
1.2
8/21 H11
1.2
8/21 H12
1.5
8/21 H13
0.9
8/21 H14
0.9
8/21 H15
0.9
8/21 H16
1.5
8/21 H17
0.9
8/21 H18
1.5

0
0
0
0
0
0
0
0
10
0
0
10
0
0
0
0
0
0
0
0
10
10
10
0
0
0
0
0
0
0
10
0
0
0
0
10
0
0
0
0
0
10

10

10

10
10
10

10

10

10
92

40
10
140
20
40
40
60
30
40
60
10
20
80
40
60
60
50
20
90
30
110
40
70
60
20
40
80
70
40
30
40
50
10
70
50
60
120
20
30
50
0
50

200
10
20
160
70
470
90
150
60
50
70
100
320
80
30
40
70
80
70
70
300
230
120
40
160
60
130
120
20
50
90
20
20
110
100
80
220
30
40
70
200
20

400
60
80
80
150
170
160
60
120
130
90
160
260
70
80
140
190
40
200
40
410
180
160
170
80
130
190
330
60
100
100
90
10
160
220
170
390
20
70
100
10
90

196.7
18.8
87.8
111.7
101.0
182.5
79.0
66.2
85.0
58.1
41.5
72.3
196.0
56.6
81.8
43.8
131.3
45.9
58.3
52.4
186.8
187.2
97.0
11>1.8
98.3
111.8
145.4
86.4
75.9
111.1
90.4
30.4
29.9
164.1
177.8
78.1
292.8
60.1
100.1
49.6
147.0
93.5

Table 3.3 (cont’d)
8/21 H19
1.2
8/21 H20
1.2
8/21 H21
1.2
8/21 H22
>1.8
8/21 H23
1.8
8/21 H24
0.9
8/21 H25
0.6
8/21 H26
1.8
8/21 H27
1.5
8/21 H28
1.2
8/21 H29
1.5
8/21 H30
1.2
8/21
I0
>1.8
8/21
I1
>1.8
8/21
I2
>1.8
8/21
J0
1.2
8/21
J1
1.5
8/21
J2
1.2
8/21 K0
1.5
8/21 K1
0.9
8/21 K2
0.9
8/21 K3
0.6
8/21
L0
>1.8
8/21
L1
>1.8
8/21
L2
>1.8
8/21
L3
>1.8
8/21
L4
>1.8
8/21
L5
>1.8
8/21
L6
>1.8
8/21
L7
>1.8
8/21
L8
>1.8
8/21
L9
>1.8
8/21 L10
>1.8
8/21 L11
>1.8
8/21 L12
>1.8
8/21 L13
>1.8
8/21 L14
>1.8
8/21 M0
1.2
8/21 M1
1.2
8/21 M2
1.2
8/21 M3
1.2
8/21 N0
>1.8

0
0
10
0
0
0
0
0
0
0
0
0
10
0
0
10
0
20
0
0
0
30
0
30
0
0
0
0
0
0
0
0
0
0
10
0
0
0
0
0
0
0

10

10

10
10

20

10

10
10

20

10

93

40
70
50
50
80
140
30
10
40
30
30

40
30
280
20
80
310

60
30
50
80
50
110
30
90
150
40
100
120
40
70
80
120
30
50
80
40
30
20
50
40
20
70

220
30
100
130
270
240
260
170
190
450
110
60
70
80
100
120
60
90
100
20
20
40
80
130
60
90

120
100
120

30
60
390

30
20
50
70

90
70
250
160
160
200
70
20
100
50
100
290
140
40
70
150
260
220
110
190
300
140
270
290
60
150
140
280
50
100
120
50
30
50
130
100
40
100
150
30
100
250

51.6
108.5
380.1
33.1
81.1
353.1
29.2
64.5
57.1
35.6
39.1
414.6
171.8
35.2
78.4
275.9
91.1
300.6
128.2
120.5
167.4
144.7
87.1
132.4
58.9
89.4
124.2
100.3
42.7
61.1
88.3
38.0
23.6
86.1
105.8
42.0
24.2
95.1
113.8
96.5
93.5
94.5

Table 3.3 (cont’d)
8/21 N1
>1.8
8/21 N2
>1.8
8/21 O0
1.5
8/21 O1
1.8
8/21 O2
1.5
8/21
P0
0.6
8/21
P1
>1.8
8/21
P2
>1.8
8/21
P3
0.9
8/21
P4
>1.8
8/21
P5
1.2
8/21 R0
1.5
8/21 R1
1.5
8/21 R2
1.5
8/21 R3
1.5
8/21
S0
1.2
8/21
S1
1.2
8/21
S2
1.5

0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
30
0
0

30

40
20
90
0
50

90
220
180
140
180

70
70
10

150
180
40

20
50

190
50

140
130

100
160

120

200

120
40
120
70
170
10
260
250
130
70
80
80
110
450
180
210
210
180

93.5
94.3
92.5
32.4
93.9
85.2
261.7
99.8
50.5
27.6
63.0
109.1
86.6
263.7
262.3
187.6
242.8
237.2

Table 3.3 2008 raw data for Hackert Lake. Site letters represent M. spicatum beds and the
numbers represent individual quadrats. Depth is reported in meters. E. lecontei are life
stages are coded as A=adult, L=larva, P=pupa and E=egg. Biomass is dry M. spicatum
2
weight in g/m .

94

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101