Myriophyllum Spicatum L.) and Milfoil Weevil (Euhrychiopsis Lecontei Dietz)

Effects of in-lake and shoreland variables on Eurasian watermilfoil

(Myriophyllum spicatum L.) and milfoil weevil (Euhrychiopsis lecontei Dietz)

abundance in Wisconsin lakes

Paul M. Skawinski

A thesis submitted in partial fulfillment of the requirements for the degree of

MASTER OF SCIENCE

Natural Resources

College of Natural Resources

University of Wisconsin

Stevens Point, Wisconsin

May 2014

APPROVED BY THE GRADUATE COMMITTEE OF:

Dr. Ronald L. Crunkilton, Chainnan Professor of Fisheries and Water Resources College of Natural Resources

£z:~Dr. met J. Judziewic Pic>fessor of Biology College of Letters and Science

Dr. Robert W. Freckmann Professor Emeritus of Biology College of Letters d Science

. Ginnett Professor of Wildl ife College of Natural Resources

ii ABSTRACT

The invasive aquatic macrophyte Eurasian watermilfoil (Myriophyllum spicatum L.)

(Haloragaceae) is a serious nuisance in water bodies throughout its non-native range. Studies seeking a feasible biological control agent for M. spicatum have focused on an aquatic weevil,

Euhrychiopsis lecontei Dietz (Coleoptera: Curculionidae), which is native to much of the United

States. The objectives of this study were to assess the density of E. lecontei in 14 Wisconsin lakes, assess the relationships between a wide variety of limnological and geographical variables and densities of E. lecontei or M. spicatum, and assess whether density of E. lecontei or abundance of the M. spicatum fungal pathogen Mycoleptodiscus terrestris (Gerd.) Ostazeski correlated with the M. spicatum density index in the study lakes.

Lakewide E. lecontei average densities varied between zero and 1.37 per stem, and substantial variation within lakes was observed. Out of a total of 27 weevil surveys conducted on 14 lakes over two years, 17 surveys found live individuals of E. lecontei. Evidence of E. lecontei feeding activity was observed on the M. spicatum stems collected during all 27 surveys of the 14 study lakes, indicating a widespread distribution of E. lecontei throughout Wisconsin.

The majority of limnological and geographical characteristics were not statistically correlated with E. lecontei density or the M. spicatum density index. E. lecontei was negatively correlated with coarseness of substrate texture. All other variables tested were insignificant.

M. spicatum showed significant positive correlations with variables related to human disturbance, suggesting that preservation of natural habitat around lakes and minimization of disturbance could reduce the likelihood of nuisance M. spicatum populations. Neither abundance of M. terrestris nor density of E. lecontei correlated significantly with the M.

iii spicatum density index.

The host-specificity and broad distribution of E. lecontei in Wisconsin make this insect a strong candidate for biological control of M. spicatum. Further research into predation of E. lecontei is needed to better understand the ideal conditions in which to employ E. lecontei as a biological control tool.

ACKNOWLEDGEMENTS

I would like to thank the Wisconsin Department of Natural Resources (WDNR) and the University of Wisconsin – Stevens Point College of Natural Resources for funding this study. The support from WDNR staff was wonderful and very much appreciated. I cannot imagine a better place to complete this project than the College of Natural Resources.

My graduate committee members, Dr. Ronald Crunkilton, Dr. Robert Freckmann, Dr. Emmet Judziewicz, and Dr. Tim Ginnett provided insight and guidance throughout my graduate career. Thank you all for your help and friendship.

Thank you to Dr. Judy Shearer, U.S. Army Corps of Engineers, who examined our M. spicatum samples for presence of Mycoleptodiscus terrestris.

The support of lake residents was incredible throughout my entire study. I am sincerely grateful to Bill Santner of Crystal Lake for allowing us to use his cabin and boat during our visits, and for showing us the bog adjacent to the lake. I’m sure that my field assistant Andy will never forget his first trip to a bog, though his primary memory may be the swarm of wasps that found him while he admired a glistening sundew on a Sphagnum hummock. Also, I cannot express enough appreciation to Steve Fleming of Archibald Lake. His generosity in providing us with dinner and lodging during our Oconto/Marinette County sampling trip cannot be overstated. I struggle to think of another place where I’ve ever felt so welcome.

Thank you to my hard-working, dedicated field assistants Andrew Teal and Erik Hendrickson. Getting stuck at sandy boat ramps and using Eckman dredges from kayaks in 3-foot waves were no match for our collective determination. I should also thank my poor old 1.6L Mazda Protege for towing my research boat and gear around the state like an eco-friendly champ.

Lastly, I express my sincere gratitude to my beautiful wife Allie and our son Bradley for their support during this project. Your smiles and encouragement kept me going through many long nights of stem examination, data entry/analysis, and thesis construction.

TABLE OF CONTENTS

TITLE PAGE...... i COMMITTEE SIGNATURE PAGE...... ii ABSTRACT...... iii ACKNOWLEDGEMENTS...... v TABLE OF CONTENTS...... vi LIST OF FIGURES...... viii LIST OF TABLES...... ix I. INTRODUCTION...... 1 General information...... 1 Myriophyllum spicatum introduction and biology...... 2 Impacts of M. spicatum...... 4 Options for biological control of M. spicatum...... 6 Euhrychiopsis lecontei biology...... 7 Use of E. lecontei as a management tool...... 9 Concurrent management...... 11 Purpose of study...... 11 Study objectives...... 12 II. METHODS...... 16 Study site descriptions...... 16 Point-intercept aquatic macrophyte surveys...... 18 Sediment chemistry...... 19 M. spicatum and E. lecontei sampling...... 26 M. spicatum density index...... 27 Timing of M. spicatum and E. lecontei sampling...... 27 Sample storage and examination...... 28 Data analysis...... 30 III. RESULTS...... 31 Observed densities of E. lecontei in 14 study lakes...... 31 Effects of lake and land use variables on the M. spicatum density index (2011 & 2012)...... 37 Variables that influenced M. spicatum density index values...... 39 Effects of lake and land use variables on E. lecontei density (2011 & 2012)...... 41

Effects of lake and land use variables on M. terrestris abundance (2011)...... 43 IX. DISCUSSION...... 44 E. lecontei distribution across study area...... 44 E. lecontei density...... 44 Effects of lake and land use variables on M. spicatum density index values...... 46 Effects of lake and land use variables on E. lecontei density...... 48 Significance of M. terrestris abundance...... 48 Anecdotal lake observations...... 49 Bear Paw Lake M. spicatum crash...... 49 Two genera of weevils in Lake Wingra, Dane County...... 50 Lake Joanis, Portage County...... 50 Lake Emily, Portage County...... 51 Chironomids observed attacking M. spicatum...... 52 V. NEED FOR FURTHER STUDY...... 53 LITERATURE CITED...... 56 APPENDIX A. Abbreviations and descriptions of independent variables collected between 2001 and 2012 and provided by the Wisconsin Department of Natural Resources for use in our data analysis...... 64

vii

List of Figures

Figure 1. Euhrychiopsis lecontei adult...... 13 Figure 2. E. lecontei adult and two E. lecontei eggs on apical meristem of M. spicatum...... 13 Figure 3. E. lecontei larva...... 14 Figure 4. Three E. lecontei larvae on M. spicatum...... 14 Figure 5. E. lecontei larva tunneling inside stem of M. spicatum...... 15 Figure 6. “Blast hole”, where an adult E. lecontei emerged from its puparium...... 15 Figure 7. Diagram of transect method used during lake sampling, with 15 total points per bed, placed along three transects. Two stem samples were collected at each point...... 20 Figure 8. Average E. lecontei densities per stem, as totals of all life stages, for 14 study lakes. Data were collected in 2011 and 2012...... 31 Figure 9. Plot of percent area of deciduous forest in watersheds of study lakes correlated with the EWM density index...... 36 Figure 10. Plot of percent area of undisturbed land in the watersheds of study lakes correlated with the M. spicatum density index...... 36 Figure 11. Scatter plot of study lakes relative to principal components PC1 and PC2. Data were collected between 2001 and 2012. Variables with the highest loadings on "PC1" include "% area of deciduous forest in watershed", "% area of undisturbed land in watershed", and "% area of forest in watershed". Variables with the highest loadings on "PC2" include "% littoral rock", "% vegetated rock", and "% littoral points with an emergent species, including visuals"...... 41

viii

List of Tables

Table 1. Study lakes sampled in 2011 and 2012, arranged in order of sampling...... 17 Table 2a. Physical, in-lake measures provided by the Wisconsin Department of Natural Resources for 14 study lakes, included as independent variables in our principal components analysis examining the effects of limnological and land use variables on the Myriophyllum spicatum density index, Euhrychiopsis lecontei density, and Mycoleptodiscus terrestris presence. Data were collected between 2001 and 2012...... 24 Table 2b. Physical, shoreland measures provided by the Wisconsin Department of Natural Resources for 14 Wisconsin lakes, and included as independent variables in our principal components analysis examining the effects of limnological and land use variables on the Myriophyllum spicatum density index, Euhrychiopsis lecontei density, and Mycoleptodiscus terrestris presence. Data were collected between 2001 and 2012...... 25 Table 3a. Biological, in-lake measures provided by the Wisconsin Department of Natural Resources for 14 Wisconsin lakes, and included as independent variables in our principal components analysis examining the effects of limnological and land use variables on the Myriophyllum spicatum density index, Euhrychiopsis lecontei density, and Mycoleptodiscus terrestris presence. Data were collected between 2001 and 2012...... 26 Table 3b. Biological, shoreland measures provided by the Wisconsin Department of Natural Resources for 14 Wisconsin lakes, and included as independent variables in our principal components analysis examining the effects of limnological and land use variables on the Myriophyllum spicatum density index, Euhrychiopsis lecontei density, and Mycoleptodiscus terrestris presence. Data were collected between 2001 and 2012...... 27

Table 4a. Chemical measures provided by the Wisconsin Department of Natural Resources for 14 Wisconsin lakes, and included as independent variables in principal components analysis examining the effects of limnological and land use variables on the Myriophyllum spicatum density index, Euhrychiopsis lecontei density, and Mycoleptodiscus terrestris presence. Data were collected between 2001 and 2012...... 28

Table 4b. Chemical and physical characteristics of lake sediment samples analyzed by the University of Wisconsin-Madison Soil & Forage Analysis Laboratory from 14 study lakes, included as independent variables in our principal components analysis examining the effects of limnological and land use variables on the Myriophyllum spicatum density index, Euhrychiopsis lecontei density, and Mycoleptodiscus terrestris presence. Data were collected in 2011 and 2012...... 24 Table 5. E. lecontei densities per stem observed in 2011, as totals of all life stages, in each M. spicatum bed across 14 Wisconsin lakes...... 32 Table 6. E. lecontei densities per stem observed in 2012, as totals of all life stages, in each M. spicatum bed across 14 Wisconsin lakes...... 33 Table 7. Number of each life stage of E. lecontei observed in samples from 14 Wisconsin lakes in 2011 and 2012...... 34 Table 8. Spearman rank coefficients (ρ) describing significant correlations between combined density of E. lecontei in all life stages and individual life stages or feeding damage indicator variables. Variables were collected between 2011 and 2012 from 14 Wisconsin lakes. Alpha was set at

0.05...... 35 Table 9. Spearman rank coefficients (ρ) describing significant correlations between M. spicatum (EWM) density index and limnological and geographical variables collected between 2001 and 2012 from 14 Wisconsin lakes. M. spicatum densities used in the index calculation were averages of 2011 and 2012 calculated density indices. Alpha was set at 0.05 ...... 37 Table 10. Results of principal components analysis showing individual and cumulative variance explained by each component...... 39 Table 11. Component matrix of independent variables significantly correlated with average M. spicatum density (2011 & 2012), included in principal components analysis. Data from eight lakes were included in this analysis; these data were collected between 2001 and 2012...... 39 Table 12. Spearman rank coefficients (ρ) describing significant correlations between E. lecontei density and limnological and geographical variables collected between 2001 and 2012 from 14 Wisconsin lakes. E. lecontei density here represents an average of 2011 and 2012 observed densities. Alpha was set at 0.05...... 40

Table 13. Spearman rank coefficients (ρ )describing the seven strongest correlations between M. terrestris abundance (2011) and limnological and geographical variables collected in 14 Wisconsin lakes between 2001 and 2012. Alpha was set at 0.05...... 42

I. INTRODUCTION

General information

Eurasian watermilfoil (Myriophyllum spicatum L.) is an exotic, aquatic macrophyte that causes severe nuisance conditions in many lakes throughout North America. M. spicatum has

been documented in 44 of the lower 48 United States. With its fast growth rate and ability to tolerate many habitat conditions, it is quick to displace native macrophytes by outcompeting them for available resources. Madsen (1998) found M. spicatum abundance to be inversely correlated with native vegetation abundance. Nuisance populations of M. spicatum can be controlled with herbicides and mechanical harvesting, which tend to be quick, short-term solutions. Alternatively, the feasibility of a sustainable, biological control approach is being investigated. An aquatic weevil (Euhrychiopsis lecontei Dietz (Coleoptera: Curculionidae)) has shown promise in causing declines in M. spicatum populations (Creed & Sheldon 1994a; Lillie

2000; Newman & Biesboer 2000; Reeves et al. 2008a).

Other biocontrol agents, including an aquatic midge (Cricotopus myriophylli Oliver), a

Pyralid moth (Acentria ephemerella Denis & Schiffermuller), and another weevil (Phytobius leucogaster Marsham) have been shown to feed on M. spicatum (Creed & Sheldon 1994b;

Harms & Grodowitz 2009), but have failed to significantly impact the plants, or are difficult to rear in mass quantity, making them less promising options for biological control studies

(Buckingham et al. 1981; Kangasniemi 1983).

Some M. spicatum populations in Wisconsin have been experiencing significant declines without any active management (Nault et al. 2011), and these declines could be related to in- lake or near-lake characteristics, or feeding activity of E. lecontei. Some lakes may provide

conditions that are unfavorable for growth and/or reproduction of M. spicatum. The

relationship of physical, chemical, and biological variables and E. lecontei to M. spicatum

density were considered during this project. These variables were examined for important correlations that may show potential to predict a water body’s susceptibility to invasion by M. spicatum. Understanding these relationships is critical to the efficient management of M.

spicatum.

Myriophyllum spicatum Introduction and Biology

M. spicatum is an opportunistic, perennial aquatic plant which first arrived in waters of

the United States in Chesapeake Bay in the late 1800s (Aiken et al. 1979). Since then, it has

spread to 44 of the lower 48 United States, and several southern Canadian provinces (Van der

Meijden & Caspers 1971; USDA Plants Database). Lake groups and governmental units often

spend tens of thousands of dollars or more to control M. spicatum. The Tennessee Valley

Authority (TVA) spent over $4 million by 1979 to control M. spicatum in their reservoirs (Aiken

et al. 1979), and continues to battle the plant today.

M. spicatum prefers nutrient-rich, eutrophic lakes, but can grow on sediments ranging

from silt to sand to peat, and can even tolerate salinity levels up to 15ppt (Aiken et al. 1979).

The upper threshold of percent sand appears to be around 75% (Barko & Smart 1986). It can

tolerate pH levels from 5.4-11, and is most successful in depths of 0.5-3.5m (Aiken et al. 1979).

M. spicatum has shown the strongest growth on fine sediments with sediment organic matter

content between 10-25% (Pearsall 1920; Misra 1938; Nichols 1971; Chapman et al. 1974; Reed

1977; British Columbia Ministry of Environment 1981; Madsen 1982). Similarly, Barko & Smart

(1986) and McComas (2009) found 20% organic matter to be an upper threshold where M.

spicatum growth quickly decreased. Barko (1983) suggested that sediments too high in organic

matter inhibited growth, possibly due to increased metal availability, low pH, accumulation of

organic acids, low redox potential, or other factors detrimental to M. spicatum growth. In lakes

with abundant M. spicatum, deposition of its own biomass during senescence could lead to

increased organic matter, therefore degrading its own habitat quality (Barko & Smart 1986).

Colonization success of M. spicatum appears to be related to sediment characteristics.

In Lake Takern, Sweden, Forsberg (1964) found colonization of M. spicatum to be inhibited by

soft and flocculent sediments. The majority of nutrient uptake in M. spicatum needs to come

from the sediments (Carignan & Kalff 1979, 1980; Barko & Smart 1981), but it can increase its

foliar uptake of nutrients if the sediment concentrations are inadequate (Bole & Allan 1978;

Carignan & Kalff 1980). Barko (1983) and Anderson & Kalff (1986) suggested that sediment

nitrogen availability is the most important limiting factor for M. spicatum growth. McComas

(2009) suggested that sediment ammonium (NH4) concentration over 10ppm NH4 indicates potential for heavy growth (250+ stems/m2). Indeed, Anderson & Kalff (1986) demonstrated that the addition of NH4 to lake sediments can significantly increase the growth of M. spicatum.

Water quality characteristics can affect growth potential of M. spicatum. Calcium is important for its uptake of inorganic carbon during photosynthesis (Lowenhaupt 1956; Stanley

1970; Smart and Barko 1986). Inorganic carbon availability, especially as bicarbonate, can be limiting to M. spicatum growth (Stanley 1970; Adams et al. 1978; Titus and Stone 1982; Smart and Barko 1986). Optimal growth of M. spicatum is in alkaline waters, with high concentrations of dissolved inorganic carbon, leading some researchers to suggest that lake alkalinity can

3 provide an approximation of M. spicatum’s growth potential (Spence 1967; Hutchinson 1970;

Stanley 1970).

Impacts of M. spicatum

In large colonies, M. spicatum can quickly develop a surface canopy, growing up to 15cm per week under ideal conditions (Newman et al. 1997). It can also alter the temperatures of localized areas by up to 10 degrees Celsius in shallow water (Dale & Gillespie 1977). This large increase in water temperature can have a profound impact on biological communities and processes within this zone. Smith & Adams (1986) studied M. spicatum in Lake Wingra,

Wisconsin and determined that M. spicatum takes up a total of 3.0g of phosphorus per m2 per year, mostly from the sediments, and about 2.8g P/m2/year is released to the water column in autumn when the stems undergo senescence. This contribution of increased temperature and available phosphorus makes M. spicatum a potentially important factor contributing to nuisance algal blooms.

M. spicatum plants growing in moderately turbid water more frequently form dense, tangled canopies, because poor light penetration promotes stem elongation. These dense canopies can quickly interfere with many forms of recreation. M. spicatum growing in deep, clear water rarely becomes an issue because it tends to form bushy stems rather than long ones

(Smith and Barko 1990). This relationship could explain some of the variability in M. spicatum density among Wisconsin lakes.

Borawa et al. (1979) found that yellow perch and sunfishes increased in number within

M. spicatum beds, but not in biomass, indicating stunting of the fish populations. Successful

predation of bluegills by largemouth bass was found to be inversely correlated with M.

spicatum stem density, as the bluegills simply hid among the dense vegetation, out of reach of

the predators (Savino & Stein 1982).

M. spicatum offers some value to the aquatic community. It has moderate food value for wildlife, as Fassett (1969) determined that it is sparingly consumed by muskrats and moose, and some waterfowl will eat the fruits (Elser 1969). Menzie (1980) found up to 196,000

Chironomid larvae per square meter associated with M. spicatum and the underlying sediment, supporting the suggestions of Krull (1970) that the macrophyte per se may not be of tremendous food value to herbivores, but the surrounding animal protein component likely is.

Reproduction is primarily through fragmentation. As the plants are broken up by

external forces such as boat propellers or wave action, or as they separate during a period of

autofragmentation, the fragments develop adventitious roots at the nodes that allow for

successful dispersal throughout and between water bodies (Grace & Wetzel 1978). Seedlings

have not been found in natural water bodies, including the Tennessee Valley Authority (TVA)

reservoirs, where M. spicatum seed densities have been observed as high as 4 million per

hectare (Aiken et al. 1979). Seed germination rates of about 30% have been accomplished in

the laboratory by drying the seeds for seven years (Davis et al. 1973), and Patten (1955) determined that the most successful way of germinating M. spicatum was to split or scarify the endocarp of the fruit.

Options for biological control of M. spicatum

Many different insect pests of M. spicatum have been studied for use as possible

biological control agents (Buckingham et al. 1981; Kangasniemi 1983; Painter & McCabe 1988;

Creed & Sheldon 1994b; Newman 1997; Creed 1998; Jester 2000; Newman & Biesboer 2000;

Anderson 2002; Harms & Grodowitz 2009). These include an aquatic midge (Cricotopus

myriophylli), which feeds on the meristems; an aquatic moth (Acentria emphemerella), which

feeds on the stems and leaves (Batra 1977; Buckingham & Ross 1981; Creed & Sheldon 1995);

and two aquatic weevils (Euhrychiopsis lecontei and Phytobius [Litodactylus] leucogaster).

Euhrychiopsis feeds on the stems and leaves as adults, while the larvae tunnel through the stem

and feed on vascular tissues. Phytobius feeds on the emergent, flowering portion of the stem

at all life stages (Anderson 2002). The focus of current research is primarily on Euhrychiopsis, as

the other three have been difficult to rear in mass quantity, or have failed to cause significant

impacts to M. spicatum in laboratory tests (Buckingham et al. 1981). Euhrychiopsis has been

successfully reared in laboratory and field tests, and has been associated with significant

declines in M. spicatum populations (Creed 1998; Jester 2000; Lillie 2000; Newman & Biesboer

2000). Creed (1998) noted that when plotting locations of all unexplained declines in M.

spicatum populations across North America, the majority of the declines were located in the

northern United States and southern Canada, which corresponds to the native range of E.

lecontei and its primary native host, Myriophyllum sibiricum.

In addition to insects, several fungal pathogens were also noted to infect M. spicatum:

Rhizoctonia soloni (Joyner & Freeman 1973), Fusarium sporotrichoides (Andrews & Hecht 1981),

Acremonium curvulum (Andrews et al. 1982), and Mycoleptodiscus terrestris (Shearer 2009).

The former three have been found to infect M. spicatum, but cause little to no effect on the

plant’s health (Nichols & Shaw 1986). M. terrestris does not typically affect the plant’s health

unless it is concurrently being impacted by other stressors (Shearer 2009). For example, when

M. spicatum is growing in nutrient-poor conditions, it is already stressed. M. terrestris requires its host to provide its nutritional requirements, and the plant may not be able to survive the additional stress (Shearer 2009). Indeed, Shearer found that when infected M. spicatum was rooted in nitrogen-poor sediments, leaves and stems turned brown and flaccid within three weeks. Shearer suggests that E. lecontei could even be a vector for spreading M. terrestris via

its feeding habits.

Euhrychiopsis lecontei biology

The milfoil weevil, Euhrychiopsis lecontei Dietz (Coleoptera: Curculionidae) (Fig. 1), is

indigenous to North America, distributed widely across the northern United States and

southern Canada (Creed & Sheldon 1994, Creed 1998, Tamayo et al. 1999). E. lecontei is a

specialist herbivore, feeding only on watermilfoils (Myriophyllum spp.). Out of seven native species of Myriophyllum occurring in the Upper Midwest region of the United States (Skawinski

2011), E. lecontei is known to use two of them as hosts: M. sibiricum and M. verticillatum.

However, eggs and larvae exhibit poor survival on these species (Sheldon & Creed 2003). If M. spicatum is introduced where the weevils are present, they will often switch hosts to attack the

M. spicatum (Solarz & Newman 1996). This is due to increased levels of glycerol and uracil exuded from M. spicatum compared to other watermilfoils, both of which are detected by E. lecontei’s chemosensory organs (Marko et al. 2005). Not only is the plant more attractive to

weevils, it also tends to support faster growth rates of weevils, and greater egg and larval

survival (Sheldon & Creed 2003).

E. lecontei adults overwinter in organic material on the shoreline (Newman et al. 2001).

Between March and May, weevils migrate back to the water, using vision to initially locate likely

M. spicatum plants or appropriate habitat, and then use chemosensory abilities to lead them to

the plants (Reeves et al. 2008b). After about one week of feeding at 15°C, females lay eggs on

the plant’s apical meristem(s) (Mazzei et al. 1999) (Fig. 2). Upon hatching, larvae move down

the stem, cut a hole in the stem (“pin hole”), and bore through the vascular tissues of the plant

(“tunneling”) (Fig. 3, 4, 5). This process of feeding on the internal tissues disrupts carbohydrate

transport from the canopy to the roots (Creed & Sheldon 1995; Newman et al. 1997), and also

compromises the structural integrity of the stem, causing it to fall down in the water column

(Creed & Sheldon 1994b). Creed (2000) observed that the larvae often exit the stem, crawl

slightly further down, and create a new tunnel. He hypothesized that this is due to accumulation of excrement within the tunnel, which results in unfavorable conditions for the larva; a possible side effect of this is creating favorable conditions for bacteria and fungi, which may have a pathogenic effect on the plant. The wounds, or “blast holes”, in the stem produced by the mature pupa breaking out of its puparium may also promote pathogenic attack (Creed

2000) (Fig. 6). Surveys attempting to quantify weevils or damaged plants should be completed by early September in the Upper Midwest, to avoid influence of plant senescence (Reeves et al.

2008a).

The ideal temperature for weevil development occurs between 27 and 31°C. At this temperature, complete development from egg to adult takes an average of 17.3 days, and the

lowest mortality rate can be expected – 25% die before reaching adulthood (Mazzei et al.

1999). At 15°C, complete development takes nearly 62 days, and mortality rates increase to

81% dying before reaching adulthood (Mazzei et al. 1999).

Use of E. lecontei as a management tool

E. lecontei has been documented as having a significant negative effect on M. spicatum populations, but this does not happen in all cases. An accurate model has not yet been developed for predicting conditions in which weevils will cause and sustain declines in M. spicatum abundance (Creed 2000; Newman & Biesboer 2000). In-lake weevil populations tend

to be positively correlated with percent natural shoreline, and negatively correlated with sandy

shores (Jester et al. 2000; Thorstenson 2011). Weevils are found in lower numbers at the deep

edges of M. spicatum beds, or in beds at greater depths (Jester et al. 2000; Johnson et al. 2000;

Lillie 2000). However, distance from shore does not appear to be a limiting factor; Getsinger et al. (2002) found weevils in M. spicatum beds as far as 3.8 kilometers from shore on Houghton

Lake, Michigan.

E. lecontei is more likely to have a significant effect on M. spicatum if other conditions are favorable. Lillie (2000) found that weevils were most abundant when M. spicatum was also at high density. When the M. spicatum bed decreased, the weevils also decreased. Lillie suggested two hypotheses: the lower stem density may have presented the larvae with difficulty moving between stems to feed or find suitable pupation habitat, or the reduced stem density may have allowed predator fishes more opportunities to feed on weevil adults and larvae. When a reduction in M. spicatum density occurs, it is important that conditions favor

the re-colonization by native aquatic macrophytes. In the best-documented studies of

persistent decline, native macrophytes have quickly returned to the area previously colonized

by M. spicatum (Sheldon 1997; Newman & Biesboer 2000).

Sediment nutrient availability could be closely tied to M. spicatum’s ability to re-grow

damaged plant material faster than the damage can occur. Sediment exchangeable nitrogen is

considered to be important in influencing how well native macrophytes return to the area, and

whether M. spicatum can outgrow or repair the weevil damage (Getsinger et al. 2002).

Newman and Biesboer (2000) observed a decline in Cenaiko Lake, Minnesota and attributed it to E. lecontei. They discounted poor sediment nutrient levels as the single culprit for the collapse, but suggested that the sediments may have helped facilitate it. Ammonium levels increased after the M. spicatum had crashed, suggesting that the plants may have had inadequate ammonium supplies to outgrow damage caused by weevil predation. Creed (1998) found that a decline in M. spicatum from 11 hectares to <0.5 hectares was caused by weevils, and this effect was monitored from 1989 to 1995. Weevils averaged 1 weevil per stem during this decline.

Augmentation of weevils has been explored as a way to hasten the decline of M. spicatum due to weevil damage. Jester (2000) noted significant declines in many lakes just one year after augmentation with weevils. At the highest augmentation level, four weevils per stem, 100% of treatments showed a significant decline in M. spicatum biomass. However, estimates of necessary weevil densities for M. spicatum control are as low as 0.25 weevils per stem (Newman et al. 2001).

Concurrent management

A combination of biological control and mechanical or chemical control methods can be

counter-productive (Getsinger et al. 2002). Since weevils depend exclusively on watermilfoil for

survival, elimination of the plants would inherently eliminate the weevils as well. Integrating

mechanical harvesting and biocontrol would likely not be effective because the weevils tend to

live in the top 60 centimeters of the stem, and harvesting removes this portion of the plants.

Not only could the weevils be directly removed by this process, but remaining weevils will have

fewer apical meristems for oviposition. Sheldon and O’Bryan (1996) found that harvested areas contained much lower densities of weevils than non-harvested areas.

Purpose of study

Populations of M. spicatum undergo unexplained wide fluctuation in density and extent

within individual lakes that are unmanaged for this nuisance aquatic plant. In addition, many managed lakes contain abundant populations of M. spicatum, despite repeated herbicide

applications to reduce these populations (WDNR, unpublished). Lake managers would greatly

benefit from a better understanding of the environmental variables that might explain why

some lakes support a balanced community of aquatic macrophytes including M. spicatum, and

why in others the species dominates and becomes invasive. In particular, the role of in-lake

conditions that facilitate population of E. lecontei, and which conditions may effectively reduce

the invasive nature of M. spicatum needs to be assessed. For example, if a manager knew that

M. spicatum generally remained in low densities in lakes with high levels of sediment calcium,

he/she could inform residents of these lakes that the M. spicatum is of little concern, and

11 would likely become a harmless part of the aquatic macrophyte community. Similarly, if managers better understood the variable or set of variables that favor abundant populations of

E. lecontei, they could promote biological control as a viable option for lakes exhibiting favorable conditions. Stocking E. lecontei into lakes that are naturally unfavorable to their success would be a waste of resources. Proponents of stocking large numbers of reared E. lecontei into natural lakes should first know that the lakes under consideration (and their near- shore habitats) provide favorable conditions for E. lecontei to survive and reproduce.

Furthermore, lake residents and managers of shorelands could encourage certain habitats or types of vegetation if those are more favorable for E. lecontei and/or unfavorable for growth of

M. spicatum.

Study objectives

This study examined the relation between chemical and physical characteristics of study lakes and watershed characteristics and abundance of M. spicatum and E. lecontei in 14 study lakes in Wisconsin. The objectives of this study were to:

1) Assess density of E. lecontei within 14 Wisconsin lakes.

2a) Assess the relationships between limnological and geographical variables of study lakes and watersheds and M. spicatum density.

2b) Assess the relationships between limnological and geographical variables of study lakes and watersheds and E. lecontei density.

3) Determine if E. lecontei density or abundance of M. terrestris correlated with M. spicatum density.

Figure 1. Euhrychiopsis lecontei adult (scale in millimeters). Photo by Paul Skawinski.

Figure 2. E. lecontei adult and two E. lecontei eggs on apical meristem of M. spicatum. Photo by Paul Skawinski.

Figure 3. E. lecontei larva (scale in millimeters). Photo by Paul Skawinski.

Figure 4. Three E. lecontei larvae on M. spicatum (note larva in background entering stem). Photo by Paul Skawinski.

Figure 5. E. lecontei larva tunneling inside stem of M. spicatum (note intact vascular tissue in the left half of the stem and dying leaves on the right). Photo by Paul Skawinski.

Figure 6. “Blast hole”, where an adult E. lecontei emerged from its puparium. Photo by Paul Skawinski.

II. METHODS

Study site descriptions

Study lakes were selected from a group of lakes that have been extensively monitored

by Wisconsin Department of Natural Resources (WDNR) staff, including annual aquatic

macrophyte surveys using the point-intercept methodology described in Aron et al. (2010). An

“unmanaged” subset of lakes, those with no active aquatic plant control efforts, was used for

this study (Table 1). Many lakes in Wisconsin are treated with herbicides to reduce aquatic

plants, or are mechanically harvested to remove vegetation within about 1 - 1.5 meters of the

water surface. These or other aquatic plant reduction techniques would negatively influence the reliability of the data by confounding the effects of E. lecontei on the M. spicatum population. Therefore, lakes where these techniques were being employed could not be included in our study.

The study lakes included in this project were widely scattered across Wisconsin, and occurred in the Southeastern Wisconsin Till Plains ecoregion (Ivanhoe, Gibbs, Wingra, Fish), the

North Central Hardwood Forests ecoregion (Crystal, Emily, Thomas, Joanis, Bear Paw, and

Montana), and the Northern Lakes and Forests ecoregion (Hancock, Little Bearskin, Manson, and Round) (Omernik et al. 2000).

Two lakes had herbicides applied for aquatic plant control in spring of 2011 and were dropped from the study. Two additional lakes were removed from the study in 2012 for the same reason.

Table 1. Study lakes sampled in 2011 and 2012, arranged in order of sampling. Surface Area Lake County (hectares) Maximum Depth (m) Ivanhoe Walworth 17 3 Gibbs Rock 28.7 7 Wingra Dane 139.6 6.4 Fish* Dane 80.5 18.9 Crystal Marquette 50.2 18.3 Thomas Portage 12.9 8.5 Emily Portage 42.5 11 Joanis Portage 9.7 8.2 Bear Paw Oconto 19.8 6.1 Montana Marinette 54.6 8.5 Manson Oneida 95.5 16.5 Hancock Oneida 104.8 6.7 Little Bearskin Oneida 66.4 8.2 Round** Burnett 82.6 8.2 Wingra Dane 139.6 6.4

* Not sampled in 2011. Added to study to replace Round Lake. ** Not sampled in 2012 due to herbicide application.

Point-intercept aquatic macrophyte surveys

Aquatic macrophyte surveys were performed annually in our study lakes from 2008-

2012. The purpose was to map the position and relative abundance of macrophytes in each

lake. The results of the surveys were used to determine the density and position of M. spicatum

within each lake. These aquatic macrophyte surveys were conducted by Wisconsin Department

of Natural Resources staff, using a point-intercept method as outlined in Aron et al. (2010). A

GPS grid was overlaid on each lake by using ArcMap 10.0 (Environmental Systems Research

Institute, 2010), with sampling points typically 30-40m apart and spaced equally throughout the lake. At each sample location, a 35.6cm, double-headed steel rake was spun 360° and pulled straight up. Total rake fullness and abundance of each individual species was given a numerical rating of 0-3, following the same protocol described for M. spicatum bed density sampling. For individual species ratings, the amount of rake coverage pertained only to the amount of the rake covered by that species.

One hundred and ninety limnological and land use variables from the study lakes were provided by the Wisconsin Department of Natural Resources. Land use measurements were collected in 2001, and in-lake data was collected between 2010 and 2012. These variables were used in correlative analyses with M. spicatum abundance, E. lecontei density, and sediment nutrient concentrations. These analyses were conducted to determine if M. spicatum abundance, E. lecontei density, or M. terrestris presence was significantly influenced by physical characteristics of the lake (Table 2a) and shorelands (Table 2b), biological characteristics of the lake (Table 3a) and shorelands (Table 3b), or water chemistry (Table 4a).

Sediment chemistry

Samples of sediments were collected from within beds of M. spicatum in each lake to determine if sediment chemistry or bulk density affected the M. spicatum density index or E. lecontei density. These sediment variables were included in a multivariate statistical analysis.

Sediment samples were collected with an Eckman dredge in M. spicatum beds concurrently with E. lecontei sampling efforts along the same transects. A 237mL sub-sample of sediment was taken from each sampling point. Sediment samples were collected at the first, third, and fifth sample point of each transect to ensure representation of the bed as a whole.

Each sample was placed into a 3.78L bucket lined with a 3.78L plastic freezer bag, and composited with subsamples from other sampling points within the same bed. When sampling was complete for that bed, the sediment sample was sealed, marked accordingly, and chilled for transport to the lab. Samples were then stirred using a cordless drill and paint-stirring attachment to homogenize the samples. A 1.19L subsample of this mixture was analyzed by the University of Wisconsin - Madison Soil & Forage Analysis Laboratory (2611 Yellowstone

Drive, Marshfield, Wisconsin 54449). Samples were air-dried and analyzed for physical and chemical characteristics listed in Table 4b. These data were included in our correlative analysis and principal components analysis.

Table 2a. Physical, in-lake measures provided by the Wisconsin Department of Natural Resources for 14 study lakes, included as independent variables in our principal components analysis examining the effects of limnological and land use variables on Myriophyllum spicatum abundance, Euhrychiopsis lecontei density, and Mycoleptodiscus terrestris presence. Data were collected between 2001 and 2012.

Register of Waterbodies (ROW) Waterbody type Latitude Secchi disc depth % littoral muck % littoral sand % littoral rock % vegetated muck % vegetated sand % vegetated rock

Table 2b. Physical shoreland measures provided by the Wisconsin Department of Natural Resources for 14 Wisconsin lakes, included as independent variables in our principal components analysis examining the effects of limnological and land use variables on Myriophyllum spicatum abundance, Euhrychiopsis lecontei density, and Mycoleptodiscus terrestris presence. Data were collected between 2001 and 2012.

Area of land in watershed Area of open water in watershed Area of developed, open space in watershed Area of low-intensity development in watershed Area of medium-intensity development in watershed Area of high-intensity development in watershed Area of barren land in watershed Area of deciduous forest in watershed % area of undisturbed land in 100m buffer % area of undisturbed land in the watershed % area of disturbed land in the watershed % area of urbanland in the watershed % area of agricultural land in the watershed % area of forest in the watershed % area of disturbed land in 100m buffer % area of urbanland in 100m buffer % area of agricultural land in the watershed % area of forest in 100m buffer % of land in 100m buffer that is covered by canopy % of land in 100m buffer that is impervious surface Land:water ratio in buffer Land:water ratio in watershed m2 of canopy in 100m buffer m2 of impervious surface

Table 3a. Biological, in-lake measures provided by the Wisconsin Department of Natural Resources for 14 Wisconsin lakes, included as independent variables in our principal components analysis examining the effects of limnological and land use variables on Myriophyllum spicatum abundance, Euhrychiopsis lecontei density, and Mycoleptodiscus terrestris presence. Data were collected between 2001 and 2012.

Simpson's Diversity Index for aquatic macrophytes Simpson's Diversity Index, submersed plants Natives per littoral point Submersed natives per littoral point Natives per vegetated point Submersed natives per vegetated point Species richness Native species richness Submersed species richness Submersed native species richness Floristic Quality Index Floristic Quality Index, submersed species Aquatic macrophyte community index Chlorophyll-a Fluorescence Omernick’s ecoregion Secchi depth Color Chlorophyll-A Presence of rusty crayfish

Table 3b. Biological shoreland measures provided by the Wisconsin Department of Natural Resources for 14 Wisconsin lakes, included as independent variables in our principal components analysis examining the effects of limnological and land use variables on Myriophyllum spicatum abundance, Euhrychiopsis lecontei density, and Mycoleptodiscus terrestris presence. Data were collected between 2001 and 2012.

% of Area of deciduous forest in 100m buffer % of Area of evergreen forest in 100m buffer % of Area of mixed forest in 100m buffer % of Area of scrub/shrub in 100m buffer % of Area of grassland/herbaceous in 100m buffer % of Area of pasture/hay in 100m buffer % of Area of cultivated crops in 100m buffer % of Area of woody wetlands in 100m buffer % of Area of emergent herbaceous wetland in 100m buffer Area of deciduous forest in 100m buffer Area of evergreen forest in 100m buffer Area of mixed forest in 100m buffer Area of scrub/shrub in 100m buffer Area of grassland/herbaceous vegetation in 100m buffer Area of pasture/hay in 100m buffer Area of cultivated crops in 100m buffer Area of woody wetlands in 100m buffer Area of emergent herbaceous wetland in 100m buffer Area of deciduous forest in watershed Area of evergreen forest in watershed Area of mixed forest in watershed Area of scrub/shrub in watershed Area of grassland/herbaceous in watershed Area of pasture/hay in watershed Area of cultivated crops in watershed Area of woody wetlands in watershed Area of emergent herbaceous wetland in watershed Area of deciduous forest in 100m buffer Area of evergreen forest in 100m buffer Area of mixed forest in 100m buffer Area of scrub/shrub in 100m buffer Area of grassland/herbaceous in 100m buffer Area of pasture/hay in 100m buffer Area of cultivated crops in 100m buffer Area of woody wetlands in 100m buffer Area of emergent herbaceous wetland in 100m buffer

23 26

Table 4a. Chemical measures provided by the Wisconsin Department of Natural Resources for 14 Wisconsin lakes, included as independent variables in our principal components analysis examining the effects of limnological and land use variables on Myriophyllum spicatum abundance, Euhrychiopsis lecontei density, and Mycoleptodiscus terrestris presence. Data were collected between 2001 and 2012.

Water source Alkalinity as total [CaCO3] Hypolimnetic alkalinity* Epilimnetic alkalinity* Lake water pH Conductivity Total Kjehldahl Nitrogen Summer phosphorus [NO2+NO3] dissolved [NH3-N] Total Kjehldahl Nitrogen Conductivity Alkalinity pH Calcium Magnesium Phosphorus

* Water samples to determine alkalinity were collected at the time of M. spicatum sampling.

24 26

Bulk density of dry sediments % phosphorus % potassium % calcium % sulfur % magnesium Zinc (mg/kg) Boron (mg/kg) Manganese (mg/kg) Iron (mg/kg) Copper (mg/kg) Aluminum (mg/kg) Sodium (mg/kg) pH % organic matter Exchangeable NH4 (ppm) Phosphorus (Bray-P) Phosphorus (Olsen-P)

25 26

M. spicatum and E. lecontei sampling

Lake sampling proceeded from south to north latitude in the state to follow the

reported 2011 “ice-out” data provided by the Citizen Lake Monitoring Network, a database of

water quality measurements taken by volunteer citizen scientists. M. spicatum and E. lecontei

sampling was completed for all lakes between mid-June and mid-August of 2011 and again

between mid-June and early August of 2012. All life stages of E. lecontei are present during this

time, so it offered the best estimate of population density. This also helped to minimize bias

associated with length of the growing season at the time of sampling.

The four largest, most dense beds of M. spicatum were marked on a map of the most

recent point-intercept survey of the aquatic macrophyte community by the Wisconsin

Department of Natural Resources (2010-2012). These beds were examined by boat to

determine if the M. spicatum was in sufficient abundance for sampling. If not, visual surveying

continued until four sufficient beds had been located, or the entire lake had been searched,

which, in two lakes, resulted in fewer than four beds being delineated and sampled. There was

no minimum size requirement for a bed, as long as the largest, most dense beds were chosen,

and M. spicatum was present in sufficient abundance and density for sampling to occur. Two

plant stems were collected at five roughly equidistant points along three transects, for a total of

120 stems/lake (4 beds x 3 transects x 5 sample points x 2 stems per sample point). In lakes with very steep littoral zones where M. spicatum beds were in narrow bands following the lake contours, transects were run parallel to shore to better represent the bed as a whole. Stem

collection occurred by snagging stems from a small boat with a 40.6cm wide, double-sided,

steel rake. In cases where M. spicatum was growing near or along the surface, the first two

60cm stems were grabbed by hand, regardless of plant condition. Collected stem sections of this length were chosen, because all life stages of E. lecontei live primarily in this area of the plant (Sheldon & O’Bryan 1996). Most transects extended through the M. spicatum bed perpendicular to the shoreline, to sample across all depth zones (Fig. 7).

M. spicatum density index

Density of M. spicatum in each bed was estimated with a rating of 0-3. A rating of 0

indicated there was no vegetation on the rake. A rating of 1 was given if less than 10% of the

tines were covered by vegetation, a 2 was if 10-90% of the tines were covered, and a 3 was

given when all of the tines were covered by vegetation.

An "EWM density index" was calculated for each study lake in our data analysis. M.

spicatum rake fullness values of 0-3 were recorded at each sample point from the point-

intercept aquatic macrophyte surveys on each lake. The sum of these values across all

vegetated points in a lake was divided by the maximum possible sum (if all vegetated points

had rake fullness ratings of "3") to create our index variable, which ranged from 0-1. This index

variable then describes the vegetated area-wide density of M. spicatum compared to the

maximum density that it could achieve.

27 26

Figure 7. Diagram of transect method used during lake sampling, with 15 total points per bed, placed along three transects. Two stem samples were collected at each point.

Sample storage and examination

Stem samples from each point were stored in 3.78L plastic freezer bags and labeled

accordingly with a permanent marker. Samples in 2011 were preserved with 80% isopropyl alcohol and refrigerated at 4°C until they could be examined. All 2011 samples were examined by January 2012. During the 2012 sampling season, all samples were refrigerated and examined within 3 days of collection and were not preserved in alcohol.

M. spicatum stems were placed into a glass pan containing enough water to float the plants. Underneath the pan, a light table provided backlighting to aid in spotting E. lecontei, particularly individuals existing inside of a stem. Magnifying goggles (10X) were also used to help spot E. lecontei. Dissecting probes and tweezers were used to manipulate the plant samples and extract any E. lecontei found. All life stages of E. lecontei found were extracted, identified, and retained in a 20mL glass vial with 80% isopropyl alcohol, which was labeled

28 26

according to the lake and sampling location. M. spicatum stem length was measured with a standard ruler, and presence/absence of any evidence of E. lecontei feeding activity (e.g. tunneling, pinholes, or blast holes) was also recorded. Relative abundance of E. lecontei was estimated for each bed by its mean density (in total weevils of all life stages per stem), and a mean E. lecontei density value for each lake was determined by adding the mean densities in weevils per stem of each bed and dividing by the number of beds.

In 2011, an additional 2L bulk sample of M. spicatum was collected and bagged from each bed of each lake to determine abundance of the endophyte Mycoleptodiscus terrestris.

Entire stem samples were gathered at several different points within the bed to ensure that the

overall sample was representative of the bed as a whole. Samples were preserved on ice, and sent to the U.S. Army Engineer Research and Development Center in Vicksburg, Mississippi for analysis. Dr. Judy Shearer examined the samples to determine abundance of M. terrestris,

following methodology described in Shearer (2009).

29 26 Data Analysis

Variable reduction

Because an initial large number of variables were assessed, a correlation matrix was run

on all independent variables (SPSS, International Business Machines, Inc., 2012). When two

variables showed a correlation coefficient of greater than 0.70, the variable considered to be of lesser relevance to lake managers was eliminated. A second correlation matrix was used to find which of the remaining independent variables were significantly correlated (P < 0.05) with the

M. spicatum density index or total density of all life stages of E. lecontei. These matrices

reduced the number of independent variables from 123 to 17 for M. spicatum and from 123 to

only 2 for E. lecontei. A principal components analysis (PCA) was performed on the M. spicatum

density index and its correlated independent variables to extract and test an appropriate

number of components. Since E. lecontei density was correlated with just two independent

variables, a PCA was not performed for this dependent variable.

III. RESULTS

Observed densities of E. lecontei in 14 study lakes

E. lecontei density was highly variable between lakes (Fig. 8, Tables 5,6). Densities, as totals of all life stages in each lake, ranged from zero weevils per stem (Crystal, Gibbs, Hancock,

Ivanhoe, and Thomas) to 1.37 weevils per stem (Bear Paw and Wingra) in 2011 surveys. In

2012, densities were again as low as zero (Crystal, Gibbs, Joanis, Thomas) and as high as 1.03 weevils per stem (Fish). The greatest range in weevil density between beds was observed at

Bear Paw Lake in 2011, with a range of 0 - 1.37.

Out of a total of 27 weevil surveys conducted on 14 lakes over two years, 17 surveys found live individuals of E. lecontei. All life stages of E. lecontei were observed at 10 lakes.

Although in several lakes, E. lecontei were not detected, all lakes showed indications of stem tissue damage that is unique to E. lecontei feeding activity. Ten of the lakes also showed a higher abundance of the larval life stage compared to other life stages. Adult E. lecontei were dominant in eight lakes, pupae in two lakes, and eggs in one lake (Table 7).

Overall density of E. lecontei (as a total of all life stages) was not significantly correlated

(p < 0.05) with the M. spicatum density index within our study lakes. Similarly, M. spicatum density was not correlated with larval E. lecontei, density of adult E. lecontei, frequency of pinholes, blast holes, tunnels, or damaged meristems. Total E. lecontei density was correlated with average adult E. lecontei density, average frequency of pinholes, average frequency of blast holes, average frequency of tunnels, and average frequency of damaged meristems (p =

0.007, 0.007, 0.011, 0.006, 0.005, respectively) (Table 8).

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0.9

0.8

0.7

0.6

0.5

0.4 July '11 July '12 0.3 Weevil density (avg. weevils/stem) (avg. density Weevil 0.2

0.1

0 Ivanhoe Gibbs Wingra Fish Crystal Thomas Emily Joanis Bear Montana Hancock L. Manson Round Paw Bearskin Lake

Figure 8. Average E. lecontei densities per stem, as totals of all life stages for 14 study lakes. Data were collected in 2011 and 2012.

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Table 5. E. lecontei densities per stem observed in 2011, as totals of all life stages, in each M. spicatum bed across 13 Wisconsin lakes. Weevils per apical stem (2011)

Lake Bed 1 Bed 2 Bed 3 Bed 4 Mean ± 1SD

Bear Paw 0 1.27 1.37 0.63 0.82 ± 0.63 Crystal 0 0 0 0 0 Emily 0.47 0.6 Na Na 0.53 ± 0.09 Gibbs 0 0 0 0 0 Hancock 0 Na Na Na 0 Ivanhoe 0 0 0 0 0 Joanis 0.03 0 0.1 0.03 0 0.03 ± 0.04 Little Bearskin 0.3 0 0.1 0.067 0.12 ± 0.13 Manson 0.03 0 0 0.13 0.04 ± .06 Montana 0.07 0.4 0.07 0.57 0.28 ± 0.25 Round 0.07 0.03 0.03 0 0.03 ± 0.03 Thomas 0 0 0 0 0 Wingra 1.37 0.23 0.23 0.4 0.56 ± 0.54

a Due to a lack of contiguous "beds" of M. spicatum, only two beds were sampled. b Fish Lake was added to the study in 2012 and therefore was only sampled once. c Due to a lack of contiguous beds of M. spicatum, only one bed was sampled. d Lake Joanis was sampled using five beds instead of four. e Round Lake was only sampled in 2011 due to an herbicide treatment in spring 2012.

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Table 6. E. lecontei densities per stem observed in 2012, as totals of all life stages, in each M. spicatum bed across 14 Wisconsin lakes. Weevils per apical stem (2012)

Lake Bed 1 Bed 2 Bed 3 Bed 4 Mean ± 1SD

Bear Paw 0.1 0 0 0.9 0.25 ± 0.44 Crystal 0 0 0 0 0 Emilya 0.23 0.83 Na Na 0.53 ± 0.42 Fishb 0.83 1.03 1.03 0.27 0.79 ± 0.36 Gibbs 0 0 0 0 0 Hancockc 0.77 Na Na Na 0.77 Ivanhoe 0 0.17 0.03 0.17 0.09 ± 0.09 Joanisd 0 0 0 0 0 0 Little Bearskin 0.07 0 0.3 0.17 0.13 ± 0.13 Manson 0.03 0 0 0.2 0.058 ± 0.10 Montana 0.43 0.5 0.2 0.17 0.33 ± 0.17 Rounde Na Na Na Na Na Thomas 0 0 0 0 0 Wingra 0.9 0.1 0 0 0.25 ± 0.44

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Table 7. Number of each life stage of E. lecontei observed in samples from 14 Wisconsin lakes in 2011 and 2012. Lake Average % eggs % larvae % pupae % adults weevils/stem Bear Paw 0.53 ± 0.59 0.04 0.81 0.14 0.02 Crystal 0 0 0 0 0 Emily 0.53 ± 0.25 0.13 0.395 0.175 0.3 Fish 0.79 ± 0.36 0.33 0.19 0.03 0.45 Gibbs 0 0 0 0 0 Hancock 0.77 0.3 0.04 0.13 0.52 Ivanhoe 0.05 ± 0.08 0.05 0.18 0.09 0.68 Joanis 0.02 ± 0.03 0 0.8 0.2 0 Little Bearskin 0.13 ± 0.12 0.1 0.55 0.21 0.16 Manson 0.05 ± 0.08 0.1 0.69 0.07 0.145 Montana 0.3 ± 0.2 0.19 0.13 0.26 0.415 Round 0.03 ± 0.03 0.5 0.25 0 0.25 Thomas 0 0 0 0 0 Wingra 0.40 ± 0.49 0.19 0.17 0.315 0.33

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Table 8. Spearman rank coefficients (ρ) describing significant correlations between combined density of E. lecontei in all life stages and individual life stages or feeding damage indicator variables. Variables were collected between 2011 and 2012 from 14 Wisconsin lakes. Alpha was set at 0.05.

Variable ρ P Adult E. lecontei density 0.855 0.007 Average frequency of pinholes 0.850 0.007 Average frequency of blast holes 0.826 0.011 Average frequency of tunnels 0.862 0.006 Average frequency of damaged meristems 0.874 0.005

3644

Effect of lake and land use variables on the M. spicatum density index (2011 & 2012)

Fifteen variables were significantly correlated (P < 0.05) with the M. spicatum density index. The two most strongly correlated variables were "% area of deciduous forest in the watershed" (ρ = -0.905) (Fig. 9), and "% area of undisturbed land in watershed" (ρ = -0.881) (Fig.

10). The majority of significant variables related to the intensity of human impact in the watershed. Variables representing natural habitat conditions were negatively correlated with the M. spicatum density index, while those representing habitat disturbance or alteration were positively correlated with the M. spicatum density index. Spearman correlation coefficients are shown for all significant variables in Table 9.

0.45 0.4 0.35 0.3 y = -0.3914x + 0.2407 0.25 R² = 0.3856 density index density 0.2 0.15 0.1 0.05 M. spicatum 0 0.0000 0.1000 0.2000 0.3000 0.4000 0.5000 0.6000 % area of deciduous forest in watershed

Figure 9. Plot of percent area of deciduous forest in watersheds of study lakes correlated with the M. spicatum density index.

3744

0.45

0.4 0.35 0.3

ensity index ensity 0.25 y = -0.2721x + 0.2753 0.2 R² = 0.4729 0.15 0.1 0.05 M. spicatum d 0 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 % area of undisturbed land in watershed

Figure 10. Plot of percent area of undisturbed land in the watersheds of study lakes correlated with the M. spicatum density index.

Table 9. Spearman rank coefficients (ρ) describing significant correlations between the M. spicatum density index and limnological and geographical variables collected between 2001 and 2012 from 14 Wisconsin lakes. M. spicatum density indices were averages of 2011 and 2012 values. Alpha was set at 0.05.

Variable ρ P % area of deciduous forest in 100m buffer -0.714 0.047 % area of mixed forest in 100m buffer -0.781 0.022 % area of undisturbed land in watershed -0.881 0.004 % area of forest in watershed -0.857 0.007 % area of forest in 100m buffer -0.762 0.028 % littoral rock -0.710 0.048 % vegetated rock -0.761 0.028 % points with an emergent species -0.733 0.039 % points with emergent sp, including visuals -0.805 0.016 % area of deciduous forest in watershed -0.905 0.002 Area of deciduous forest in 100m buffer -0.810 0.015 Area of mixed forest in 100m buffer -0.830 0.011 Aquatic macrophyte community index -0.755 0.031 % disturbed land in watershed 0.881 0.004 % disturbed land in 100m buffer 0.786 0.021

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Variables that influenced M. spicatum density index values

Principal components analysis was used to determine the variables that had the most influence on the M. spicatum density index. Results indicate that over 75% of the variance in the dataset can be explained by two components, and a scree plot confirmed that a maximum of two components should be retained (Table 10). Component PC1 was significantly correlated with the M. spicatum density index (ρ = -0.756, p = 0.030). Variables loading high on this component include “% area of deciduous forest” (0.980), “area of forest in watershed” (0.947),

“% area of disturbed habitat in watershed” (-0.937), “% area of undisturbed habitat in watershed” (0.937), and “% area of deciduous forest in 100m buffer” (0.931). Component PC2 was not significantly correlated with the M. spicatum density index (ρ = -0.126, p = 0.767).

Variables loading high on PC2 included "% littoral rock" and "% vegetated rock" (Table 11).

Lakes Wingra and Gibbs fell lowest on the X-axis (Fig. 11). They also contained the highest density index values of M. spicatum within our study lakes, with values of 0.399 and

0.157, respectively. Lakes Hancock and Bear Paw fell highest on the X-axis. These lakes had the lowest density index values of M. spicatum in our study, with values of 0.003 and 0.044, respectively.

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Table 10. Individual and cumulative variance explained by each component of the principal components analysis.

Component % of Variance Cumulative % 1 59.61 59.61 2 15.406 75.016 3 8.571 83.587 4 6.351 89.938 5 5.357 95.295 6 2.758 98.053 7 1.947 100

Table 11. Component matrix of independent variables significantly correlated with the average M. spicatum density index values (2011 & 2012), included in principal components analysis. Data from eight lakes (2001-2012) were included in this analysis.

Variable Component 1 2 Aquatic macrophyte community index 0.792 0.073 % littoral rock 0.404 -0.818 % vegetated rock 0.489 -0.737 % of sample points containing emergent species 0.745 0.545 % of sample points in littoral zone containing emergent species, including visuals 0.578 0.578 Frequency of occurrence of Stuckenia pectinata -0.738 0.112 Frequency of occurrence of Stuckenia pectinata, including visuals -0.817 0.052 % area of deciduous forest in watershed 0.980 -0.105 Area of deciduous forest in 100m buffer 0.716 0.122 Area of mixed forest in 100m buffer 0.882 -0.256 % area of deciduous forest in 100m buffer 0.504 0.546 % area of deciduous forest in 100m buffer 0.931 -0.118 % area of undisturbed habitat in watershed 0.937 -0.213 % area of disturbed habitat in watershed -0.937 0.212 % area of forest in the watershed 0.947 -0.013 % area of disturbed habitat in 100m buffer -0.629 -0.332 % area of forest in 100m buffer 0.765 0.388

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Effect of lake and land use variables on E. lecontei density (2011 & 2012)

Only two variables were significantly correlated (p < 0.05) with the E. lecontei density

(Table 12). "% muck in littoral zone" was positively correlated (ρ = 0.790,

p = 0.02), and "% sand in littoral zone" was negatively correlated (ρ = -0.790, p = 0.02).

Presence or absence of Mycoleptodiscus terrestris was not significantly correlated with E.

lecontei density. E. lecontei data was not analyzed with PCA because only two independent

variables were correlated with E. lecontei density.

Table 12. Spearman rank coefficients (ρ) describing significant correlations between E. lecontei density and limnological and geographical variables collected between 2001 and 2012 from 14 Wisconsin lakes. E. lecontei density here represents an average of 2011 and 2012 observed densities. Alpha was set at 0.05.

Variable Correlation Coefficient (ρ) P % sand in littoral zone -0.790 0.02 % muck in littoral zone 0.790 0.02

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1.5 Bear Paw

Ivanhoe Gibbs 0.5 Hancock

PC1 0 -1.5 -1 -0.5 Crystal 0 0.5 1 1.5 Montana Wingra -0.5

-1

-1.5

-2 Manson

-2.5 PC2

Figure 11. Scatter plot of study lakes relative to principal components PC1 and PC2. Data were collected between 2001 and 2012. Variables with the highest loadings on "PC1" include "% area of deciduous forest in watershed", "% area of undisturbed land in watershed", and "% area of forest in watershed". Variables with the highest loadings on "PC2" include "% littoral rock", "% vegetated rock", and "% littoral points with an emergent species, including visuals".

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Effect of lake and land use variables on M. terrestris abundance (2011)

Thirty-three variables were significantly correlated with M. terrestris abundance. The seven most strongly correlated variables are shown in Table 13.

Table 13. Spearman rank coefficients (ρ) describing the seven strongest correlations between M. terrestris abundance (2011) and limnological and geographical variables collected in 14 Wisconsin lakes between 2001 and 2012. Alpha was set at 0.05.

Variable ρ P % area in 100m buffer covered by canopy 0.932 0.001 EWM/hybrid frequency of occurrence (relative) -0.856 0.007 EWM/hybrid frequency of occurrence (littoral) -0.856 0.007 % area of forest in watershed 0.894 0.003 Area of pasture/hay in watershed -0.890 0.003 % area of pasture/hay in watershed -0.890 0.003 % area of mixed forest in 100m buffer 0.877 0.004

43 26 IV. DISCUSSION

E. lecontei distribution across study area

Newman and Maher (1995) surveyed 19 lakes in Minnesota and Wisconsin for

herbivores of M. spicatum and only found E. lecontei in 52% of these lakes, but we observed live individuals or feeding evidence of E. lecontei throughout our study area of fourteen lakes.

Live individuals or feeding damage unique to E. lecontei occurred at 100% of our study lakes.

Our data support the apparent widespread distribution of E. lecontei noted by Jester et al.

(2000) in a study of E. lecontei distribution throughout Wisconsin. Latitude within our study area (349km or 3.135 degrees of latitude) had no effect on E. lecontei density. Of the five lakes with the highest densities, two were in southern Wisconsin, one in central, and two in northern

Wisconsin.

E. lecontei density

Density of E. lecontei was highly variable between lakes. In Crystal, Gibbs, Hancock, and

Joanis lakes, E. lecontei of any life stage were undetectable. However, evidence of E. lecontei feeding/tunneling activity was observed in each of these lakes. Some feeding activity measures

were correlated with total E. lecontei density in other lakes. In future studies, measures of

feeding activity may be suitable for use as proxies to estimate E. lecontei populations, as

specific feeding damage to stems can be attributed to this species.

Newman (1996) noted that the life cycle of E. lecontei is more rapid in warm water,

taking 60 days at 15°C and accelerating to only 17 days at 27°C. The cold water of Crystal Lake may have reduced reproductive potential and accounted for low weevil density, particularly in

beds 3 and 4. Our data showed the highest abundance of weevil damage in the shallowest of our sampling areas, which contained a dark, organic sediment that may have helped to warm the M. spicatum bed in this area. These characteristics combined with the canopy-forming M. spicatum in this area of Crystal Lake could have increased ambient water temperatures, as

reported by Dale & Gillespie (1977) in a study of temperature fluctuation caused by

submergent aquatic plants. These conditions may favor colonization by weevils, though numbers of weevils in all areas of Crystal Lake were very low.

Neither M. spicatum density nor E. lecontei density were correlated with water transparency. However, Smith and Barko (1990) found that deep, clear lakes tend to support more short, bushy M. spicatum plants because they have little need for stem elongation with

high availability of light. Short and deep beds could also reduce the risk of weevil predation,

because E. lecontei abundance tends to decrease as water depth increases (Jester et al. 2000).

Also, the high water transparency of Crystal Lake may allow for high predation of weevils by

bluegills (Lepomis macrochirus) and other small fishes, which was observed in an enclosure

study by Newbrough (1993). This could explain why damage from the early life stages was

fairly common, but no live weevils could be found. An abundance of panfish was noted near one of our sampling beds in Crystal Lake where E. lecontei was undetectable during our 2012 survey. Sutter and Newman (1997) examined stomach contents of bluegills and pumpkinseeds

(Lepomis gibbosus) in Lake Auburn and Cedar Lake in Minnesota, and found up to 28.6% of total

stomachs examined to contain E. lecontei adults and/or larvae. Panfish populations across our study lakes may have played some role in E. lecontei population variability.

In-lake limnological characteristics may be less important in predicting E. lecontei than

the effect of predation on E. lecontei. There were few in-lake characteristics identified in this

study that were correlated with E. lecontei density or the M. spicatum density index. Barko and

Smart (1983) examined the effects of organic matter additions to sediments and also found few

variables to be significantly correlated with M. spicatum density. The effects of fishes, insects, and other predatory organisms on E. lecontei could be significant and should be further explored.

Effects of lake and land use variables on M. spicatum density index values

The data indicate that M. spicatum was positively correlated with several variables relating to human impact around the lakes, including "% disturbed land in watershed"

(ρ = 0.881, p = 0.004) and "disturbed land in 100m buffer" (ρ = 0.786, p = 0.021). These correlations suggest that anthropogenic alteration of the landscape anywhere in the watershed may result in conditions that favor growth of M. spicatum to nuisance levels. Alteration of the landscape can result in higher surface runoff, warmer lake temperatures, and increased nutrient loading, all of which can favor M. spicatum growth (Barko and Smart 1981b, Madsen et al. 1989).

Habitat variables such as area of forest in the watershed and aquatic macrophyte community index score, which reflects higher diversity and overall quality of the aquatic macrophyte community, were negatively correlated with M. spicatum. This suggests that preservation of healthy, diverse plant communities both in and around the lakes may create a less favorable habitat for M. spicatum. Madsen (1998) also found M. spicatum to be inversely

correlated with native macrophyte abundance. This preservation of native plant communities

not only results in a reduction in M. spicatum, but also favors the colonization by E. lecontei

(Thorstenson 2011), which may further reduce the density and vigor of nearby M. spicatum.

The vast majority of limnological and physical characteristics measured in this study

were not correlated with M. spicatum or E. lecontei abundance. The M. spicatum density index was not significantly correlated with total E. lecontei density or density of blast holes (exit holes from E. lecontei adults), but these relationships may require a study of longer duration to follow

E. lecontei and M. spicatum populations through a typical predator-prey cycle. Several lakes underwent drastic population changes in both E. lecontei and M. spicatum during our two-year study. These fluctuations could be showing evidence of predator-prey fluctuations that could not be tied to the within-year comparisons of populations. Future studies on E. lecontei’s impacts to M. spicatum may benefit from a longer duration, during which the predator-prey interactions between these species could be tracked from year to year.

Results of our principal components analysis indicated that the variables “% area of deciduous forest”, “area of forest in watershed”, “% area of disturbed habitat in watershed”,

“% area of undisturbed habitat in watershed”, and “% area of deciduous forest in 100m buffer” had the most impact on the M. spicatum density index. The lake which had the highest M. spicatum density is in the center of a highly developed, urban landscape. The second highest

M. spicatum density index value was at Gibbs Lake, which is surrounded by a watershed of intense agriculture. Interestingly, both of these lakes have large expanses of undisturbed shoreline, but our analyses indicate that disturbance elsewhere in the watershed, such as clearing of forests, may have important implications on the likelihood of M. spicatum growing

to high density. Many of the variables most strongly correlated with M. spicatum in our study represented anthropogenic alteration to the landscape, whether by urbanization or agricultural development. Our study suggests that alteration of natural landcover anywhere in the

watershed may favor M. spicatum. Anthropogenic disturbance and excessive nutrients have been shown to affect aquatic macrophyte communities (Barko and Smart 1986, Nilsson and

Keddy 1990; Richardson et al. 2005). These variables should be included in future studies involving the preferred habitats of M. spicatum and E. lecontei.

Effects of lake and land use variables on E. lecontei density

Euhrychiopsis lecontei density was highly correlated with "% muck in littoral zone" (ρ =

0.790, p = 0.02) and "% sand in littoral zone" (ρ = -0.790, p = 0.02). E. lecontei density was very low in areas of sandy substrate. This may correspond to adjacent sandy shorelines with less available duff material for overwintering. Thorstenson (2011) and Newman et al. (1997) observed that E. lecontei requires a substantial duff layer on the shoreline to be suitable for E. lecontei overwintering. Thorstenson (2011) found a total absence of E. lecontei on sites containing bare sand, and a general increase in weevil occupancy as leaf cover or depth of organic material increased. Since damage to M. spicatum appears to begin closest to suitable areas of overwintering habitat and move outward as the growing season progresses (Newman and Ragsdale 1995), preservation of natural shoreland habitat with intact organic duff layers is important to attract E. lecontei to nearby M. spicatum beds and favor successful control.

Significance of M. terrestris abundance

Shearer (2009) suggested that E. lecontei could be a vector for M. terrestris movement throughout M. spicatum beds. In this study, the relationship of M. terrestris abundance to both

E. lecontei density and the M. spicatum density index was insignificant during both years of sampling. Thus, it appears that the presence of this fungal pathogen of M. spicatum is not

important in predicting within-year density of this species.

Anecdotal lake observations

Bear Paw Lake M. spicatum crash

Bear Paw Lake in Oconto County experienced a drastic reduction in the M. spicatum density index between 2011 and 2012, especially in sampling beds 2 and 3, both of which were nearly gone in 2012. The crash in M. spicatum and E. lecontei suggests a classic predator-prey

interaction. In 2011, the M. spicatum density index was 0.216 (which indicates that 21.6% of the available habitat for M. spicatum is currently occupied by that species), and lake-wide E. lecontei density was 0.81 weevils/stem. Beds 2 and 3 contained an average of 1.27 and 1.37 weevils/stem, respectively. In 2012, the M. spicatum density index was 0.047, and lake-wide E. lecontei density was 0.25 weevils/stem. Average 2012 E. lecontei densities in beds 2 and 3 dropped to 0.0 weevils/stem in both cases. These data suggest that the E. lecontei population in

Bear Paw Lake had climbed well beyond the 0.25 weevils/stem threshold for M. spicatum control as described by Newman et al. (2001). This high population of E. lecontei appears to have caused a crash in M. spicatum density, followed by its own crash due to a lack of appropriate food and habitat.

Bear Paw Lake is surrounded by mostly undeveloped land within the Chequamegon-

Nicolet National Forest. According to the recommendations for E. lecontei overwintering

habitat from Thorstenson (2011), Bear Paw Lake provides ideal conditions. This characteristic

could help explain the very high numbers of E. lecontei encountered during our 2011 survey.

The undeveloped, undisturbed conditions that prevail at Bear Paw Lake may allow the

native vegetation to successfully compete with M. spicatum. The combined effects of multiple

stressors, including a strong native macrophyte community, high weevil numbers, and the

second highest presence of Mycoleptodiscus terrestris in our study, could help explain the crash

in M. spicatum (Shearer 2009). It is also possible that release from predation could have been

responsible for a resurgence in weevil numbers.

Two genera of weevils in Lake Wingra, Dane County

Our sampling at Lake Wingra found large populations of both E. lecontei and another aquatic specialist herbivore, Phytobius leucogaster (Coleoptera: Curculionidae). P. leucogaster also feeds on watermilfoils including M. spicatum, but it attacks the emergent, flowering portion of the plant (Harms and Grodowitz 2009). Despite an abundance of both of these predators, the M. spicatum population was also abundant and dominant in Lake Wingra during

2011 and 2012, with M. spicatum index values of 0.405 and 0.388, respectively.

Average weevil density was fairly high in 2011 at 0.56 E. lecontei weevils/stem, dropping to only 0.27 E. lecontei / stem in 2012. Prior to our 2012 sampling at Lake Wingra, the City of

Madison and Friends of Lake Wingra operated an aquatic plant harvester to open up several

navigational lanes. One of these lanes was in close proximity to two of our sampling areas, and

may have decreased the abundance of E. lecontei in those areas (Sheldon & O'Bryan 1996).

Lake Joanis, Portage County

Lake Joanis (9.7 hectares) has had annual E. lecontei density surveys since 2008, as well as supplemental stocking of E. lecontei in 2008 and 2009 as described by Thorstenson (2011). A total of 23,000 weevils were stocked into Lake Joanis during these two years. E. lecontei

density increased slightly from 0.018 weevils/stem in 2008 to 0.035 weevils/stem in 2009, but

quickly dropped below pre-stocking levels by 2010 (0.012 weevils/stem). Densities of E.

lecontei remained low in 2011 and 2012, as 0.013 and 0.006 weevils/stem, respectively.

Lake Joanis is a man-made lake, created in the 1970s. Its substrate is primarily sand,

which was found to negatively correlate with E. lecontei density in our study. We also noted an

abundance of panfish (Lepomis spp.), damselflies (Odonata: Zygoptera), and mayflies

(Ephemeroptera: Caenidae) in our sampling beds. The influence of these organisms on E.

lecontei should be investigated.

Lake Emily, Portage County

Lake Emily received a whole-lake application of 2,4-dichloro-phenoxyacetic acid (2,4-D)

in 2009. The bay where our samples were collected was left out of the treatment, but the

majority of M. spicatum in the bay was still killed, presumably by the chemical dissipating from

the treatment area. Despite a drastic reduction in M. spicatum in our sampling area, E. lecontei

was still found during a 2010 survey at an average density of 0.05 weevils/stem. These samples

were collected by the Center for Watershed Science & Education at the University of Wisconsin

- Stevens Point, and preserved until our study began. In 2011, average E. lecontei density had

risen sharply to 0.52 weevils/stem. In 2012, our sampling indicated a similar density of 0.44

weevils/stem. These data suggest that E. lecontei populations can quickly recover from 2,4-D applications. While it is known that 2,4-D can have negative effects on water fleas (Daphnia spp.) and midges (Diptera: Chironomidae), little is known regarding its effects on weevils

(Wisconsin Department of Natural Resources 2012).

Sheldon & O'Bryan (1996) found that mechanical harvesting removes the top 1-1.5m of

M. spicatum, which is the primary region where E. lecontei lives and feeds. However, these data from Lake Emily suggest that a different strategy for integrated M. spicatum management--one that combines 2,4-D herbicide treatments and use of E. lecontei (through

natural or stocked populations)--could be a viable alternative.

Chironomids observed attacking M. spicatum

It is known that some chironomids are stem-mining, aquatic herbivores (Copeland et al.

2012). In Lake Thomas, Portage County, and Round Lake, Burnett County, we documented

chironomids of the genera Endochironomus and Glyptotendipes, respectively. Both of these

genera were observed burrowing through stems of M. spicatum.

Round Lake contained a low density of E. lecontei (0.03 weevils/stem), but stem samples

contained an abundance of Glyptotendipes larvae and feeding damage. Round Lake's M.

spicatum density index score was very low, at 0.011, even though Wisconsin Department of

Natural Resources records indicate it has been known to occur in Round Lake since 2003.

Something appears to be keeping the M. spicatum at low densities, and the effects of

Glyptotendipes could be worth further investigation.

One M. spicatum stem collected from Lake Thomas contained an Endochironomus larva inside. Kornijów (1996) describes Endochironomus as a generalist herbivore, and as such, it may have minimal impacts on any given species, such as M. spicatum. Lake Thomas had an average M. spicatum density index score of 0.153, and indeed, M. spicatum forms a dense ring around most of the lake, with no apparent control occurring due to natural predation.

V. Need for further study

Live E. lecontei or feeding damage was observed in all of the 14 study lakes. Intra-lake E. lecontei density also varied, and could have been influenced by depth and temperature of the

M. spicatum beds. Shallow, warmer beds of M. spicatum allow for more rapid life cycle completion by E. lecontei (Newman 1996), and could therefore lead to a higher local population of E. lecontei in a given season.

Lakes with abundant panfish populations may decimate E. lecontei, as these fishes consume large numbers of E. lecontei (Newbrough 1993; Sutter and Newman 1997). Weevil densities dropped to pre-stocking levels within one year. An abundance of bluegills (Lepomis macrochirus) and damselflies (Odonata: Zygoptera) observed during our sampling could have had a negative influence on weevil density. Further research on possible weevil predation by these two groups is warranted.

Neither E. lecontei density (as total density, larval density, adult density, or density of blast holes) or M. terrestris abundance was significantly related to M. spicatum density.

Variables that showed significant correlations with M. spicatum density were primarily related to human disturbance. Other significant variables included "% area of deciduous

forest in watershed" (ρ = -0.905, p = 0.002) and "% area of undisturbed land in watershed"

(ρ = -0.881, p = 0.004). Our data suggest that minimization of disturbance to near-shore areas, and retention of natural vegetative communities can discourage infestation of M. spicatum.

The only variable significantly correlated with E. lecontei density in our study was substrate texture. E. lecontei density and "% sand in littoral zone" were negatively correlated

(ρ = -0.790, p = 0.02).

Mycoleptodiscus terrestris was significantly inversely correlated with epilimnetic alkalinity. M. terrestris showed significant positive correlations with variables representing measurements of undisturbed habitat.

Due to the large number of variables included in our study, it is possible that one or more type 1 errors occurred during our analysis, indicating significant relationships that may have been considered significant by mere chance. We also realize that some significant independent variables may not have direct significance to our dependent variables, but rather may be part of a multi-step relationship. For example, consider watershed disturbance and its possible result of sedimentation in a nearby stream. This stream may deposit those sediments into a lake downstream, which may decrease native aquatic macrophytes, and in turn, favor opportunistic species like M. spicatum. While the watershed disturbance would not directly cause an increase in M. spicatum density, it could be an important link in a series of events that leads to this increase in M. spicatum density.

We observed two genera of midges (Diptera: Chironomidae) mining stems of M.

spicatum. It is possible that Endochironomus or Glyptotendipes could serve as additional

stressors to M. spicatum. The presence of multiple stressors in a system could compound their effects and allow for greater impact to M. spicatum populations.

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Appendix A. Abbreviations and descriptions of independent variables collected between 2001 and 2012 and provided by the Wisconsin Department of Natural Resources for use in our data analysis.

VARIABLE DESCRIPTION lake Lake Name county Lake County WBIC Waterbody Identification Code detect.year Year EWM first detected survey.year Year of PI Survey timeEWM Time between EWM detect and survey EWM.pa EWM Presence (Y) or absence (N) ref Reference reservoir ROW Waterbody type reservoir2 Waterbody type - classified by dam/hydraulic height eco Omernick's ecoregion lat Latitude rieraNum Stream order - connectivity to surface water drainage network reiraType Stream order - connectivity to surface water drainage network wtrSrc Water source class DNR class ha Lake area littha Littoral area maxdm Maximum depth avgdm Average depth mdc Maximum depth of colonization Secchi.mdc Secchi depth:Max depth of colonization ratio sAvgdm Average depth of submersed plants litt.p % Littoral veg.p % Vegetated littveg.p % Littoral zone that is also vegetated ehFull Mean rake fullness of EWM/Hybrid samples ehHighrel Frequency of high-density EWM/Hybrid sample (relative) ehHighlit Frequency of high-density EWM/Hybrid sample (littoral areas) ehHighveg Frequency of high-density EWM/Hybrid sample (vegetated areas) ehLitt EWM/Hybrid frequency of occurrence (littoral area) ehRel EWM/Hybrid frequency of occurrence (relative) ehLake EWM/Hybrid frequency of occurrence (whole lake) ehVeg EWM/Hybrid frequency of occurrence (vegetated area) eFull Mean rake fullness of EWM samples eRel EWM frequency of occurrence (relative) eLake EWM frequency of occurrence (whole lake) eLitt EWM frequency of occurrence (littoral area) eVeg EWM frequency of occurrence (vegetated area) hyb Hybrid is present hFull Mean rake fullness of Hybrid samples hRel Hybrid frequency of occurrence (relative) hLake Hybrid frequency of occurrence (whole lake) hLitt Hybrid frequency of occurrence (littoral area) hVeg Hybrid frequency of occurrence (vegetated area)

64 simp Simpson's Diversity Index sSimp Simpson's Diversity Index, submersed plants nLittp Natives per littoral point snLittp Submersed natives per littoral point nVp Natives per vegetated point snVp Submersed natives per vegetated point rich Species richness nRich Native species richness sRich Submersed species richness snRich Submersed native species richness fqi Floristic Quality Index sfqi Floristic Quality Index, submersed species amci Aquatic macrophyte community index chla.1 Chlorophyll-a fluorescence p.1 Summer phosphorus secM.1 Secchi depth alk.1 Alkalinity as total [CaCO3] ph.1 pH cond.1 Conductivity tkn.1 Total Kjehldahl Nitrogen p.2 Summer phosphorus no2.2 [NO2+NO3] dissolved nh3.2 [NH3-N] tkn.2 Total Kjehldahl Nitrogen col.2 Color cond.2 Conductivity alk.2 Alkalinity ph.2 pH ca.2 Calcium mg.2 Magnesium chla.2 Chlorophyll-a Fluorescence secM.2 Secchi depth tsiP.1 Trophic state index (Phosphorus) tsiChl.1 Trophic state index (Chlorophyll-a) tsiSec.1 Trophic state index (Secchi) tsiP.2 Trophic state index (Phosphorus) tsiChl.2 Trophic state index (Chlorophyll-a) tsiSec.2 Trophic state index (Secchi) m % littoral muck s % littoral sand r % littoral rock mV % vegetated muck sV % vegetated sand rV % vegetated rock flLitt % points with a floating leaf species (littoral) emergLitt % points with an emergent species (littoral) emergVLitt % points with an emergent species (with visuals, littoral) rtLitt % species rooted (littoral) nrtLitt % species non-rooted (littoral) isoLitt % species isoetids (littoral)

65 rtV % species rooted (vegetated) nrtV % species non-rooted (vegetated) isoV % species isoetids (vegetated) potill Littoral frequency of occurrence of Potamogeton illinoensis stupec Littoral frequency of occurrence of Stuckenia pectinata potgra Littoral frequency of occurrence of Potamogeton gramineus najfle Littoral frequency of occurrence of Najas flexilis spaang Littoral frequency of occurrence of Sparganium angustifolium elocan Littoral frequency of occurrence of Elodea canadensis cerdem Littoral frequency of occurrence of Ceratophyllum demersum potillV Vegetated frequency of occurrence of Potamogeton illinoensis stupecV Vegetated frequency of occurrence of Stuckenia pectinata potgraV Vegetated frequency of occurrence of Potamogeton gramineus najfleV Vegetated frequency of occurrence of Najas flexilis spaangV Vegetated frequency of occurrence of Sparganium angustifolium elocanV Vegetated frequency of occurrence of Elodea canadensis cerdemV Vegetated frequency of occurrence of Ceratophyllum demersum mgt Management score strat How strategic is management? othmgt Other management trtSurv Treatment occurred year of survey datSurv Time between treatement and plant survey rust Whether rusty crayfish are present msRat mix-stratify ratio natcom Natural community warea Area of land in watershed delineated by Chris Smith wha Area of land watershed delineated by Chris Smith w11 Area of Open Water in watershed w21 Area of Developed, Open Space in watershed w22 Area of Developed, Low Intensity in watershed w23 Area of Developed, Medium Intensity in watershed w24 Area of Developed, High Intensity in watershed w31 Area of Barren Land in watershed w41 Area of Deciduous Forest in watershed w42 Area of Evergreen Forest in watershed w43 Area of Mixed Forest in watershed w52 Area of Scrub/Shrub in watershed w71 Area of Grassland/Herbaceous in watershed w81 Area of Pasture/Hay in watershed w82 Area of Cultivated Crops in watershed w90 Area of Woody Wetlands in watershed w95 Area of Emergent Herbaceous Wetland in watershed wlandwat.rat land:water ratio in watershed w21.p % of Area of Developed, Open Space in watershed w22.p % of Area of Developed, Low Intensity in watershed w23.p % of Area of Developed, Medium Intensity in watershed w24.p % of Area of Developed, High Intensity in watershed w31.p % of Area of Barren Land in watershed w41.p % of Area of Deciduous Forest in watershed w42.p % of Area of Evergreen Forest in watershed

66 w43.p % of Area of Mixed Forest in watershed w52.p % of Area of Scrub/Shrub in watershed w71.p % of Area of Grassland/Herbaceous in watershed w81.p % of Area of Pasture/Hay in watershed w82.p % of Area of Cultivated Crops in watershed w90.p % of Area of Woody Wetlands in watershed w95.p % of Area of Emergent Herbaceous Wetland in watershed barea Area of 100m buffer (outside) of lake polygon bha Area of 100m buffer (outside) of lake polygon b11 Area of Open Water in 100m buffer b21 Area of Developed, Open Space in 100m buffer b22 Area of Developed, Low Intensity in 100m buffer b23 Area of Developed, Medium Intensity in 100m buffer b24 Area of Developed, High Intensity in 100m buffer b41 Area of Deciduous Forest in 100m buffer b42 Area of Evergreen Forest in 100m buffer b43 Area of Mixed Forest in 100m buffer b52 Area of Scrub/Shrub in 100m buffer b71 Area of Grassland/Herbaceous in 100m buffer b81 Area of Pasture/Hay in 100m buffer b82 Area of Cultivated Crops in 100m buffer b90 Area of Woody Wetlands in 100m buffer b95 Area of Emergent Herbaceous Wetland in 100m buffer blandwat.rat land:water ratio in buffer b21.p % of Area of Developed, Open Space in 100m buffer b22.p % of Area of Developed, Low Intensity in 100m buffer b23.p Area of Developed, High Intensity in 100m buffer b24.p % of Area of Developed, High Intensity in 100m buffer b31.p % of Area of Barren Land in 100m buffer b41.p % of Area of Deciduous Forest in 100m buffer b42.p % of Area of Evergreen Forest in 100m buffer b43.p % of Area of Mixed Forest in 100m buffer b52.p % of Area of Scrub/Shrub in 100m buffer b71.p % of Area of Grassland/Herbaceous in 100m buffer b81.p % of Area of Pasture/Hay in 100m buffer b82.p % of Area of Cultivated Crops in 100m buffer b90.p % of Area of Woody Wetlands in 100m buffer b95.p % of Area of Emergent Herbaceous Wetland in 100m buffer bcan m2 of canopy in 100m buffer bimp m2 of impervious surface bundist.p % of Area of undisturbed land in 100m buffer wundist.p % of Area of undisturbed land in the watershed wdist.p % of Area of disturbed land in the watershed wurb.p % of Area of urbanland in the watershed wag.p % of Area of agricultural land in the watershed wfor.p % of Area of forest in the watershed bdist.p % of Area of disturbed land in the buffer burb.p % of Area of urbanland in the buffer bag.p % of Area of agricultural land in the watershed bfor.p % of Area of forest in the buffer

67 bcan.p % of land in 100m buffer that is covered by canopy bimp.p % of land in 100m buffer that is impervious surface