Dynamics of Daphnia Lumholtzi in Pueblo Reservoir

Dynamics of Daphnia lumholtzi in Pueblo Reservoir

A Master’s Thesis

Presented to the Faculty of the

College of Science and Mathematics

Colorado State University-Pueblo Pueblo, Colorado

In Partial Fulfillment of the Requirements for the Degree of

Master of Science in

BIOLOGY

Candace Walker

Colorado State University – Pueblo

Spring 2014

CERTIFICATE OFACCEPTANCE

This Thesis is Presented in Partial Fulfillment of the Requirements for the Degree

Master of Science in Biology

Candace Walker

Has Been Accepted By the Graduate Faculty of the

College of Science and Mathematics

Colorado State University-Pueblo

APPROVAL OF THESIS COMMITTEE

______Graduate Advisor (Dr. Scott Herrmann) Date

______Committee Member (Dr. Brian Vanden Heuvel) Date

______Committee Member (Dr. DelWayne Nimmo) Date

______Graduate Director (Dr. Jeff Smith) Date

ACKNOWLEDGEMENTS

I would like to thank the following for their contributions in funding and support: James

Melby, John Beaver, Board of Pueblo County Commissioners, Board of Water Works of Pueblo,

Lower Arkansas Valley Water Conservancy District, U.S. Bureau of Reclamation, Regional

Access Graduate Education, Arkansas River Basin Water Forum, Dr. Richard Kreminski, Dr.

Scott Herrmann, Dr. DelWayne Nimmo, and Jim Carsella. Without each of these people and organizations, it would not have been possible to carry out the research and statistical analyses that were the essence of the thesis.

Arriving at this point in my education took the help of all of my professors to whom I would like to offer my gratitude: Dr. Scott Herrmann, Dr. Igor Melnykov, Dr. Moussa Diawara,

Dr. Brian Vanden Heuvel, Dr. Jeff Smith, Dr. Perry Cabot, Dr. Frank Zizza, Dr. Dan Caprioglio, and Dr. Lee Anne Martinez. Taking courses under these instructors opened my eyes to a number of new interests and passions and for that I am immensely grateful.

There are several additional people that went out of their way to be helpful on a regular basis: Dr. Helen Caprioglio, Theresa Jimenez, Stacy Righini, Derek Moore, and Kenneth

McKenzie. These are some of the most prompt and efficient individuals at aiding students. They are blessed with the unique ability of making a person feel like they are of the utmost importance and do so without hesitation, even with busy schedules of their own. To say that I appreciate them would barely scratch the surface.

I have to give special recognition to Roy Jo Sartin and Erin DeCuir of the Graduate

Student Support Center (GSSC). A large amount of my time was spent in the support center where I benefited from a quiet place to write and run statistical analyses. During this time, these two women took every opportunity to help with manuscript editing and presentation delivery and

iii offered much encouragement on a daily basis. Stressful times were instantly alleviated by their laughter and uplifting presence. They have become dear friends to me and I will always cherish my time spent with them.

There is no way to begin to thank my thesis committee. In my book, Dr.’s Herrmann,

Nimmo, and Vanden Heuvel are nothing short of legendary in their careers. To have my name anywhere associated with this group of distinguished gentlemen is a privilege beyond words.

Getting to work with Dr. Herrmann and Dr. Nimmo was a joy and I know I am one of the precious few that can use the word “joy” in the context of a master’s thesis. Their expertise and knowledge was tremendous, and I will always be overwhelmed with appreciation for their willingness to pass it down to my level. Thank you.

I cannot complete this section without thanking my family. My husband is the eternal optimist, always offering unwavering encouragement, always instilling his faith in me. Thank you for inspiring me to be resilient and positive. To my sweet-hearted children, my most favorite people in the world, thank you for giving me a reason to want to better myself and thank you for tolerating the time spent doing so. Thank you to my mom, dad, and brother for all of the times you offered listening ears and words of reassurance. There is no way I could have done this without you.

ABSTRACT

Ours is the first report of the occurrence of Daphnia lumholtzi, an invasive zooplankter,

in Pueblo Reservoir, Colorado. Six sites were surveyed from 2008-2010 and four were revisited in 2013. Our study involved the investigation of dynamics pertaining to D. lumholtzi’s presence, including density changes from 2008 to 2013, community composition, and variation in abiotic parameters. Sites with abundant D. lumholtzi showed significant differences in species assemblage and physical properties and an increase in population density for the zooplankter was observed. The observations of this study can be used to generate a plethora of experimental hypotheses for future work as well as provide valuable baseline data for managing the lake to better serve the needs of wildlife and utilities.

Table of Contents

Page Numbers

BACKGROUND and SIGNIFICANCE 1-18 Pueblo Reservoir 1 Invasive Species 4 Daphnia lumholtzi: Native Range 7 Daphnia lumholtzi: North America Invasion, Dispersal and Success 10 Previous Colorado Plankton Studies 17

HYPOTHESES 19-20

SPECIFIC AIMS 20

MATERIALS and METHODS 21-29 Study Site 21 Zooplankton Collection 21 Phytoplankton Collection 25 Water Quality Parameters 25 Statistical Analyses 28

RESULTS 30-54

DISCUSSION 55-62

CONCLUSION 63-64

REFERENCES 65-79

APPENDICES 80-110 A: Additional Methods Information 80 B: Additional Statistical Analyses 98 C: Data Concerning Daphnia lumholtzi 105

LIST of TABLES Page Numbers

Table 1. Zooplankton sample dates and sites in Pueblo Reservoir 24

Table 2. Phytoplankton sample dates and sites in Pueblo Reservoir 26

Table 3. The 95% confidence interval for the slope of regression lines fit to log transformed density of D. lumholtzi in 2008, 2009, 2010, and 2013 32

Table 4. Chi square goodness-of-fit homogeneity tests for comparing density of D. lumholtzi among the four sample sites. 34

Table 5. PERMANOVA table of results for environmental variables by date and site factors 35

Table 6. Canonical analysis of principal coordinates for environmental parameters grouped by site for samples taken in 2013 35

Table 7. Canonical analysis of principal coordinates for environmental parameters grouped by date for samples taken in 2013 36

Table 8. PERMANOVA table of results for phytoplankton variables by date and site factors 38

Table 9. Canonical analysis of principal coordinates for phytoplankton grouped by site for samples taken in 2013 38

Table 10. Canonical analysis of principal coordinates for phytoplankton grouped by date for samples taken in 2013 41

Table 11. PERMANOVA table of results for zooplankton variables by date and site factors 45

Table 12. Canonical analysis of principal coordinates for zooplankton grouped by site for samples taken in 2013 46

Table 13. Canonical analysis of principal coordinates for zooplankton grouped by date for samples taken in 2013 48

Table 14. Zooplankton occurrence in Pueblo Reservoir 2013 52

Table 15. Chi square goodness-of-fit tests comparing proportions of cladocerans, copepods, and rotifers in the month of September 53

vii

LIST of FIGURES Page Numbers

Figure 1. Historic end of month content of Pueblo Reservoir 22

Figure 2. Site map of Pueblo Reservoir 23

Figure 3. Average density of Daphnia lumholtzi and native daphnids 30

Figure 4. Average biomass of Daphnia lumholtzi and native daphnids 31

Figure 5. Log transformed density of D. lumholtzi from 2008-2010 and 2013 32

Figure 6. Abundance of D. lumholtzi: distribution among sites in each of the sampling years 33

Figure 7. Canonical analysis of principal coordinates according to site of environmental data 36

Figure 8. Canonical analysis of principal coordinates according to date of environmental data 37

Figure 9. Canonical analysis of principal coordinates according to site of phytoplankton data with Bacillariophyta vector overlay 39

Figure 10. Canonical analysis of principal coordinates according to site of phytoplankton data with Chlorophyta vector overlay 39

Figure 11. Canonical analysis of principal coordinates according to site of phytoplankton data with other classification vector overlay 40

Figure 12. . Canonical analysis of principal coordinates according to date of phytoplankton data with Bacillariophyta vector overlay 42

Figure 13. Canonical analysis of principal coordinates according to date of phytoplankton data with Chlorophyta vector overlay 43

Figure 14. Canonical analysis of principal coordinates according to date of phytoplankton data with other classification vector overlay 44

Figure 15. Canonical analysis of principal coordinates according to site of zooplankton data with cladoceran vector overlay 46

Figure 16. Canonical analysis of principal coordinates according to site of zooplankton data with copepod vector overlay 47

viii

Figure 17. Canonical analysis of principal coordinates according to site of zooplankton data with rotifer vector overlay 47

Figure 18. Canonical analysis of principal coordinates according to date of zooplankton data with cladoceran vector overlay 49

Figure 19. Canonical analysis of principal coordinates according to date of zooplankton data with copepod vector overlay 50

Figure 20. Canonical analysis of principal coordinates according to date of zooplankton data with rotifer vector overlay 51

Figure 21. Relative biomass proportions of zooplankton in Pueblo Reservoir during peak D. lumholtzi occurrence 54

Figure 22. Canonical analysis of principal coordinates according to site of only cladoceran biological data 99

Figure 23. Canonical analysis of principal coordinates according to site of only copepod biological data 100

Figure 24. Canonical analysis of principal coordinates according to site of only rotifer biological data 101

Figure 25. Canonical analysis of principal coordinates according to date of only cladoceran biological data 102

Figure 26. Canonical analysis of principal coordinates according to date of only copepod biological data 103

Figure 27. Canonical analysis of principal coordinates according to date of only rotifer biological data 104

BACKGROUND and SIGNIFICANCE

When initiated in 2008, the aim of this research was to survey Pueblo Reservoir for

the presence of nuisance zebra mussels, Dreissena polymorpha and quagga mussels,

Dreissena rostriformis bugensis. While no forms of either mussel were found during the

collection efforts from 2008-2010, other surprising results surfaced. This report will be used

to provide insight of the results from the initial survey plus an additional sampling effort in

2013. While comprehensive plankton data were collected, special emphasis will be placed on the seasonal dynamics of the invasive cladoceran, Daphnia lumholtzi (Sars, 1885).

Pueblo Reservoir

Pueblo Reservoir is a multipurpose facility that was completed in 1975 as a part of

the U. S. Bureau of Reclamation effort called the “Fryingpan-Arkansas Project” (Ferrari

1994). This transmountain development diverts water from the western part of the state to the

Arkansas River on the eastern side of the Rocky Mountains (Ferrari 1994). Pueblo Reservoir

is located outside of the city of Pueblo in the south-central portion of Colorado (Ferrari

1994). When full, it stretches approximately 14.5 km in length with widths ranging from 0.5–

3.5 km and a maximum depth of over 47 m at the dam (Mast and Krabbenhoft 2010).

Drainage from 12092.65 km2 (4,669 square miles) occurs in the Arkansas River above the dam (Ferrari 1994). When reservoir storage began in 1974, the reservoir’s surface area was

5,671 acres with a 441,193,293 m3 (357,821 acre-feet) capacity (Ferrari 1994). Almost

entirely fed by the Arkansas River, the inflow majority occurs from May – July (Mast and

Krabbenhoft 2010). Streamflow regimes can be broken into three categories: runoff from

snowmelt (May-June), post runoff (July-September), and low flow (October-April) (Ortiz et

al. 1998). Because the reservoir is located at a plains elevation (dam crest elevation 1501.14

m amsl) but receives cold water influx from snow runoff, multiple temperature niches are

available throughout the year. Annual storage peaks in April, then declines through summer

and autumn due to high downstream irrigation demands (Mast and Krabbenhoft 2010).

While the main uses in the Arkansas River basin are for flood control and agricultural

purposes, many municipalities have become more and more reliant on the river and reservoir for water supply (Ortiz et al. 1998). The residents and businesses of Pueblo obtain water from the Whitlock Treatment Plant which receives water diverted from Pueblo Reservoir via pipeline. The Fountain Valley Conduit, also a component of the “Fryingpan-Arkansas

Project”, carries approximately 24,792,985.29 m3 (20,100 acre-feet) of water north to

Fountain, CO, where it is utilized by Fountain, Widefield, Security, and Stratmoor Hills, CO

(U.S. Department of the Interior Bureau of Reclamation, 2013). Upon its completion, the

Southern Delivery System (SDS) Project will deliver additional water from the reservoir to

municipalities such as Colorado Springs, Fountain, Security, and Pueblo West (U.S.

Department of the Interior Bureau of Reclamation 2008).

Having knowledge of the planktonic community can aid in utilities management as

control of algal blooms, which can impact the quality of the water, can be supported with

high diversity of the zooplankton that graze upon the algae (Dzialowski and Smith 2008). On

the other hand, if these zooplankton are reduced or removed from the food web or if

eutrophication increases from the addition of nutrients from sewage or other pollutants, algal

conditions can pose a tremendous hassle for those managing a lake system. One such

example is Lake Mendota, Wisconsin, where expensive efforts to control levels of algae were

implemented (Lathrop et al. 2002). Costly biomanipulation initiatives were applied by

stocking the lake with large fish species and enforcing strict harvesting regulations, with the

hopes that this would reduce the population of small fish that feed on the zooplankton

(Lathrop et al. 2002). Not until the levels of zooplankton were restored was water clarity

improved (Lathrop et al. 2002). While deemed a success in the end, Lake Mendota serves as

a clear reminder that changes in the food web, especially those affected by the phytoplankton

and zooplankton, can be highly problematic and expensive to remedy (Lathrop et al. 2002).

Not only can plankton levels disrupt the water quality for drinking water, but they can also affect the fisheries in the lake. Zooplankton serve as efficient transmitters of energy up the food chain (Havel and Graham 2006). By feeding on the phytoplankton, they act as primary consumers and are in turn a food source for the planktivorous fish (Frisch and

Weider 2010; Dodson and Hanazato 1995). These planktivorous fish are then prey for top- predators and serve as the basis for the production of commercial fish (Dejen et al. 2004).

Additionally, most fish larvae and juveniles are heavily dependent upon zooplankton in their early growth (Dejen et al. 2004). The biomass of edible zooplankton thus proves critical for both forage fish and young game fish in a self-sustaining fishery (Dobberfuhl and Elser

2002). The abundance of algae can also create conditions of anoxia for fish if allowed to go

unchecked by the removal or reduction of zooplankton species (Dodson and Hanazato 1995).

Fisheries of both cold- and warm-water species are supported in Pueblo Reservoir with the routine stocking of walleye, trout, bass, channel catfish, and wiper (Mast and

Krabbenhoft 2010). A reservoir’s ability to sustain populations of fish is important

economically with fishing bringing in $1.26 x 109 yr-1 and providing over 14,000 jobs in

Colorado (BBC Research and Consulting 2008). Pueblo Reservoir alone sees roughly 40,000

boats yr-1 launched from its ramps (B. Henley, personal communication, June 13, 2013).

Pueblo Reservoir also provides water directly to the Pueblo Hatchery. This hatchery is the

only warm and cold water facility in the state, raising catfish, largemouth bass, trout (brown

and rainbow), grass carp, wiper, saugeye, and walleye (Colorado Parks and Wildlife 2013).

Current knowledge of the food web dynamics, especially those pertaining to the planktonic

community, can help provide insight for wildlife and utilities management concerning Pueblo

Reservoir.

Invasive Species

Evaluating the planktonic community is clearly important for wildlife and utility

management; however, the need for research can be further exaggerated with the presence of

invasive species. The incidence of invasive species may have discernible consequences for

both planktonic communities and the production of fish (Dobberfuhl and Elser 2002), and in

turn result in severe economic concerns (Engel and Tollrian 2009). These species pose a principal threat to the diversity of natural communities and can alter key processes in ecosystems (Havel et al. 2002). The occurrence of alien species invasions is noted as having the highest impact on aquatic ecosystems, ranking second only to loss of habitat as a threat to diversity on a global scale (Green and Figuerola 2005).

Invasion has three main stages: introduction, establishment, and spread (Engel et al.

2011). The first step may be completed accidentally or deliberately when the organism is transferred to a location outside its native range. In the second step, the introduced organism must survive and reproduce to become a self-sustaining population in order to be considered established. The final step of spreading denotes whether the species is considered “invasive”.

If the species becomes established in locations considerably beyond the initial site of

introduction then it is given this term (Engel et al. 2011). Only a small percentage of introduced species are successful at all three steps (Gonzales et al. 2010). Establishment and spread depends on the magnitude of new arrivals, invader traits, conditions of the potential site, and interactions between the native communities and the invading species (Gonzales et al. 2010).

Human activity is often blamed for the success of invasive organisms’ range expansions (Dzialowski et al. 2000). Activities such as modification of habitats and introductions, both deliberate for fisheries and accidental release from aquaculture, greatly increase the rate of invasions (Havel and Shurin 2004). Recreational boating can also transport organisms in their live wells to and from frequented lakes. Studies have shown that live wells may harbor a variety of live zooplankton and that these organisms may live up to three days, plenty of time to be transported to another lake (Havel and Shurin 2004).

Transport in ships via ballast water can be responsible for introduction from other continents

(Green and Figuerola 2005).

Many successful invaders also possess life history traits that aid in their rapid dispersal (Havel and Shurin 2004). Several use asexual reproduction to get around mate limitation. Many organisms can also produce resting egg stages which are resistant to freezing and desiccation, and can pass through the guts of fish or birds if ingested (Havel and

Shurin 2004). These diapausing eggs have been found to remain viable in the sediment for extended periods of time or survive transport overland. Some egg cases even possess barbs or spines that can attach to feathers or fur. Other vectors such as wind and stream corridors may also provide possible means of dispersal of these eggs (Havel and Shurin 2004).

Once introduced, the species must possess traits that make it successful at

establishing new populations. Most have a broad tolerance of environments, generation times

that are comparatively short, and generalized preferences of food and habitat (Havel et al.

1995). The ability to reproduce asexually as mentioned above can allow for one individual to

colonize a new environment (Havel et al. 1995). Successful invaders may have the ability to utilize an otherwise unexploited resource or more efficiently consume a resource than its

native counterparts (Fey and Cottingham 2011). Climate change can also act in favor of the

broad tolerances of non-native species, i.e. climate warming has enabled many species to

shift distribution in a poleward or elevational direction (Engel et al. 2011). Many native

species experience stress with alterations of ecosystem properties such as changes in

temperature and alien species possessing phenotypic plasticity may be better adapted to such

changes (Engel and Tollrian 2009).

Even if a species becomes established and has many dispersal mechanisms in its

favor, the rate of spread is greatly determined by the conditions of potential new sites. The

proximity of an un-invaded site to the source populations can increase chances of invasion

(Havel et al. 2002). Some suggest that the local species dictate the invasability of a water

body with competition and predation playing key roles in invasive resistance (Havel et al.

2002). Most agree that organisms are more successful if they can occupy a vacant niche

where or when native competition is low (Dzialowski et al. 2000). The nutrient levels of a

water system may also play a role. Some studies have found that invaded locations have

lower nutrient concentrations and chlorophyll than non-invaded sites (Dzialowski et al.

2007).

Many studies agree that reservoirs in general are more prone to invasion (Havel et al.

2005). These water bodies experience regular disturbance from fluctuation of water levels

and manipulation of food webs (Havel et al. 1995). These disturbances can create temporary reductions in populations of native species which can make them susceptible to invaders

(Havel et al. 2005). Reservoirs tend to be highly connected to one another through their sequential placement along river systems, thus increasing the potential for downstream passage of organisms (Havel et al. 2005). Larger reservoirs tend to be advantageous for invasive species as well. Dispersal vectors such as recreational boaters or waterfowl have greater chances of encountering lakes with larger surface area (Havel et al. 1995). Larger reservoirs have a high degree of spatial heterogeneity due to their relationships with watershed areas and incoming rivers. These inputs result in high loading of nutrients, solutes, and organic materials at the riverine end (Havel et al. 2005). Towards the dam, the reservoir takes on a more lacustrine form with greater water clarity, stratification, and lower nutrients

(Havel et al. 2005). This longitudinal gradient can provide multiple niches and lower extinction probability (Havel et al. 2005; Havel et al. 1995). A reservoir’s age may also contribute to the likelihood of invasion. If community succession is in its early stages, successful colonization may be more likely (Havel et al. 2005).

Daphnia lumholtzi: Native Range

Daphnia lumholtzi is a cladoceran zooplankter endemic to Africa, Asia, and

Australia, where it occupies lakes, small ponds, stagnant pools, and large impoundments

(Dzialowski et al. 2000). Many of these waters are often ephemeral due to high evaporation rates (Sarma et al. 2005). Because there is little fluctuation in water temperatures, most tropical water bodies have year round cyanobacteria blooms and constant predation pressure

from numerous fishes that remain planktivorous their whole life (Fey and Cottingham 2011;

Dumont 1994). The persistence of cyanobacteria and predation often results in a reduction of cladoceran density and diversity, especially in large bodied species (Sarma et al. 2005).

Following are some accounts of specific native ranges of D. lumholtzi.

Lake Albert, Uganda

In Lake Albert, consistently high temperature (typically 25 - 29°C) and presence of

planktivorous fish can be found in line with what is mentioned above for most tropical lakes;

however, variation in predator intensity occurs in different parts within the lake itself (Green

1967). The key planktivore African silverside, Alestes baremose, remains in the shallow

waters, rarely found deeper than 20 m. In Green’s (1967) study, the first observation of

different morphs of D. lumholtzi were reported with the deeper central waters inhabited by

larger individuals with rounded helmets and shallower peripheral waters occupied by sleeker

members with very elongated helmet and tail spines (Green 1967). Thought at first to be a

mechanism to increase buoyancy, Green (1967) determined the spines instead were used to

deter consumption by A. baremose. Upon analysis of stomach contents, thousands of the round-helmeted forms were found, comprising 89% of the diet, and only 0.1% of spiny-

helmeted forms contributed to the contents (Green 1967).

Lake Chivero, Zimbabwe

A survey done by Elenbaas and Grundel (1994) provided insight to zooplankton

species that typically co-exist with D. lumholtzi in its native range. Lake Chivero was found

to have two calanoid species, five to six cladoceran species (two to three of those of the

Daphnia genus), and a diverse assemblage of rotifers with 17 different species. The main

species included the dominant cladocerans Bosmina longirostris and Ceriodaphnia dubia as

well as rotifers Keratella cochlearis, Keratella quadrata, Polyarthra dolichoptera, and

Filinia opoliensis (Elenbaas and Grundel 1994).

Lake Tana, Ethiopia

In Lake Tana, Dejen et al. (2004) also reported common species in the zooplankton

assemblage; however, the dominant species was the calanoid copepod Thermodiaptomus

galebi lacustris instead of a cladoceran. The assemblage also included three cyclopoid

copepod species, with the most abundant being Thermocyclops ethiopiensis. The dominant

cladocerans were B. longirostris, Daphnia hyalina, D. lumholtzi, and Diaphanosoma sarsi.

Dejen et al. (2004) also examined the effects of turbidity on the zooplankton community finding a negative correlation especially in Daphnia. Dejen et al. (2004) contribute this negative correlation directly to filtration inhibition or indirectly through light limitation affecting their autotrophic food supply.

Daphnia lumholtzi is one of the few cladocerans to have a native range that spans continents. Fernando and Kanduru (1984) found D. lumholtzi to be widely distributed in equatorial, tropical and temperate zones of the sub-continent of India, showing little difference in abundance with respect to latitude.

Lake Kinneret (Sea of Galilee), Israel

Daphnia lumholtzi was found in Lake Kinneret, its northern-most limit at the time, on multiple occasions from 1894 to 1956 (Gophen 1979). In 1958, Lake Kinneret was stocked with blue tilapia, Oreochromis aureus and muglid fingerlings, after which D. lumholtzi was

9 no longer found in the samples. Gophen (1979) deemed the D. lumholtzi extinct with the likeliest cause the stocking of these fish species.

Daphnia lumholtzi: North America Invasion, Dispersal, and Success

In 1991, Sorensen and Sterner (1992) reported the first North American occurrence of

D. lumholtzi in Fairfield Reservoir, Texas. Since then it has been reported in much of the southeastern U.S., extending north to Lake St. Clair, near Detroit, Michigan, and west to

California (Havens et al. 2011). The non-native cladoceran was most likely introduced as an accidental ‘hitchhiker’ when the Nile perch, Lates niloticus, was imported from Lake

Victoria, Africa, to stock Fairfield Reservoir in 1983 (Havel and Hebert 1993). It is considered highly successful due to its rapid establishment in a growing number of lakes

(Havens et al. 2011) and persistence over several years in most of the invaded locations (East et al. 1999).

Daphnia lumholtzi’s invasion is peculiar as most cladoceran species are rarely dispersed between continents, and unlike other invertebrates that use ships’ ballast waters to do so, its inland reservoir distribution suggests the alternative vector mentioned above (Havel et al. 1995). Not only has it appeared in North America, but it also has invaded multiple sites within a short time span (Havel et al. 1995). The native range of Africa, Asia, and Australia from a wide variety of habitats may have aided in its success (Havel et al. 1995). This rapid spread has generated much attention and research largely because of the key role native

Daphnia play as substantial algae grazers and sources of food for fish (Havens et al. 2011).

As previously mentioned, D. lumholtzi was most likely introduced with exotic fish

(East et al. 1999; Havel and Stelzleni-Schwent 2000). Even though the fish were properly

quarantined, D. lumholtzi’s resistant egg stages, ephippia, likely survived (Havel and Hebert

1993). Its rapid spread is largely credited to the recreational boaters whose live wells may

provide a transport mechanism across long distances (Havel et al. 2002).

Daphnia lumholtzi’s success upon arrival is attributed to many factors. One such

factor is that the organism appears to occupy a vacant niche characterized by four key

features: elevated temperatures (Yurista 2004), abundance of cyanobacteria (Fey and

Cottingham 2011), high turbidity (Schulze et al. 2006), and complementary dynamics with

native populations (Havel and Graham 2006). The most convincing of the three is

temperature. In most studies, D. lumholtzi shows brief periods of abundance that coincide

with warm temperatures of summer months: Kansas (Dzialowski et al. 2000), Missouri

(Havel and Graham 2006), Lake Texoma of Oklahoma and Texas (Work and Gophen 1999),

and Florida (East et al. 1999; Havens et al. 2011), to name a few. While locomotion and

filtering can be enhanced by increasing temperature, these activities are more energetically

demanding, and elevated temperatures usually result in a decline of native species (Frisch

and Weider 2010). Conversely, Daphnia lumholtzi has a temperature optimum of 25°C

(Havel et al. 2002) and shows positive growth rates in temperatures up to 30°C while temperate zooplankton are typically supressed at 25°C (Havel and Graham 2006). Daphnia

lumholtzi is likely well adapted to these temperatures due to its tropical origin and warming

climate trends that stress temperate species and may alter interactions between native and exotic species (Engel et al. 2011).

Elevated temperature is often associated with blooms of cyanobacteria (Fey and

Cottingham 2011) and peak abundances of D. lumholtzi coincide with both (Havel and

Graham 2006). Cyanobacteria can be problematic for daphnids in several ways: filamentous

forms can interfere with filtering and inhibit growth (East et al. 1999) and they offer little in

the way of nutritional value, and certain species can produce toxins (Sarnelle et al. 2010).

Once again, conditions of its native range, including sustained levels of cyanobacteria, may preadapt D. lumholtzi to high concentrations often detected in summertime of temperate lakes. In experiments with the toxin producing blue-green Microcystis aeruginosa, D. lumholtzi showed high growth rates if competitors were not present and similar growth rates were found with a high quality food (Scenedesmus acutus) and a non-toxic blue green

(Anabaena flos-aquae), suggesting its resistance to M. aeruginosa toxins (Fey and

Cottingham 2011). Sarnelle et al. (2010) found that smaller neonates were produced when given diets of toxin-producing M. aeruginosa, but no effect on surviving adults’ individual fecundities were detected. Positive growth of the population may occur even in the presence of high cyanobacterial toxin concentrations (Sarnelle et al. 2010).

Another key feature that can determine open niches is turbidity. In most cases, increased turbidity is disrupting as it can limit the amount of light available for primary production and interfere with feeding filtration of zooplankters (Dejen et al. 2004). On the other hand, turbidity can reduce visual planktivores’ reactive distance by reducing the light intensity under water (Dejen et al. 2004). In Lake Texoma, Daphnia lumholtzi appears to take advantage of this refuge as spined forms were frequently found in the turbid waters of the more riverine locations (Schulze et al. 2006). Schulze et al. (2006) suggested that the suspended organic components may also provide nutrients and minerals that can enhance reproduction, as D. lumholtzi often carried large egg clutches in the turbid areas.

While temperature, cyanobacteria, and turbidity contribute to open niches, the dynamics of the native populations may be the biggest determinants (Havel and Graham

2006). Daphnia lumholtzi shows maximum abundance that is offset temporally from native

peaks in abundance and in cases of overlap with native species, population growth rates are

negatively correlated (Havel and Graham 2006). Dzialowski et al. (2007) suggest that D.

lumholtzi may be prevented from occupying the communities until competitive interactions

subside with native species decline.

While many agree with the vacant niche theory, several others credit the phenotypic

plasticity of both morphological and behavioural adaptations to be the main attribute in D.

lumholtzi’s success (Sarma et al. 2005). Daphnids display high degrees of morphological

ecotypic plasticity by producing neck teeth, crests, and helmets in response to kairomones

(chemical cues) released by predators (Laforsh and Tollrian 2004). Daphnia lumholtzi

produces long helmet and tail spines, sharp pointed fornices, and prominent spines on its

carapace margin, making it easily distinguishable from native North American species

(Havel and Hebert 1993). Larger daphnids are particularly susceptible to fish predators who

hunt by sight so it is not surprising that helmet formation is also accompanied by a reduction

in carapace size (Green 1967). The substantial spines may offer protection against gape-

limited predators, who consume their prey whole (Metzke and Pederson 2006). If attacked,

the helmet and tail spines may also disrupt handling efficiency of the predator and allow for

escape (Effert and Pederson 2006; Lienesch and Gophen 2005).

Development of such thorn-like features poses an interesting conundrum where it is

advantageous to develop such traits prior to birth as often times, neonates are threatened by

predators early in their development; however, space is limited in the mother’s brood pouch and these morphological defenses may mechanically interfere with or harm the mother or other siblings (Laforsh and Tollrian 2004). Interestingly, Laforsh and Tollrian (2004) found

13 that D. lumholtzi has a thin membrane that keeps the spines and fornices folded against the embryo. At birth, the membrane bursts and absorption of water leads to dramatic helmet elongation. The neonate transitions from embryonic in appearance to adult-like in a matter of seconds (Laforsh and Tollrian 2004). A similar membrane could not be detected in D. pulex,

D. longicephala, D. cucullata, or D. ambigua (Laforsh and Tollrian 2004). Having folded, compact embryos also allows for high fecundity which may be required to remain competitive (Laforsh and Tollrian 2004).

Late stage embryos exposed to predator-conditioned media displayed helmets that were significantly longer than those of unexposed individuals (Laforsh and Tollrian 2004).

When examined closely, Laforsh and Tollrian (2004) found that all daphnid embryos have in common three membranes that get shed before leaving the mother’s brood pouch. Once the third membrane is shed, chemosensillae at the end of the first antennae are exposed that may be able to detect the kairomones released by predators. While detection is likely possible at this stage, the sensitivity to kairomones is highest once the nervous and glandular systems are fully developed just before being released from the brood pouch (Laforsh and Tollrian 2004).

While advantageous in the presence of predation, these defenses occur at a cost and are only used when needed (Laforsh et al. 2006). Because of this, species have adapted to show responses to specific chemical cues released by predators and prey (Tollrian 1994;

Laforsh et al. 2006). Altered morphology was elicited in D. lumholtzi, D. cucullata, and D. longicephala when exposed to cues released from macerated conspecifics. More pronounced effects were seen when exposed to kairomones from feeding predators, and helmet length was greatest when predators were fed conspecifics, suggesting that daphnids have the ability to distinguish between predators feeding on different prey (Laforsh et al. 2006). The specific

nature of the chemicals released by predators and prey is often unknown; however, a non-

olefinic hydroxycarboxylic acid with low molecular weight was purified from water

containing the phantom midge larvae, Chaoborus flavicans, a known predator of many

daphnids (Tollrian and von Elert 1994); and kairomones released by cyprinids have been characterized as low-molecular-weight anions that are non-olefinic and of intermediate lipophilicity (von Elert and Loose 1996).

Predator-released kairomones are not the only cues that can stimulate these changes in morphology (cyclomorphosis). High temperatures (31°C) induced helmet and tail spines of D. lumholtzi in laboratory cultures (Yurista 2000), and chemicals such as the insecticide carbaryl can mimic kairomones eliciting similar responses (Dodson and Hanazato 1995).

Chemical cues do not always result in elongation of spines in D. lumholtzi. Burns (2000)

found that D. lumholtzi responded with a reduction in tail spine length (nearly 40%) to

chemicals released from crowded D. magna.

Kairomones can also influence behavior of cladocerans by stimulating diel vertical

migration (von Elert and Loose 1996; Sarma et al. 2005). By dropping to lower depths in the

daytime, zooplankton may avoid predation from sight-feeding fish and then return to the

surface layers at night (Dodson and Hanazato 1995). Williams and Pederson (2004) found that even though D. lumholtzi shows cyclomorphosis to deter predators, it also undergoes vertical migration patterns. In mesocosm experiments carried out by Havel and Lampert

(2006) it was found that D. lumholtzi showed strong migration even when kairomones were absent, suggesting that this behavior may not be an induced response but rather one that evolved in the constant predation pressure of its native habitat.

As mentioned previously, many zooplankton have the ability to produce a resistant

resting egg that can aid in invasion success. In daphnids, these are produced sexually and are

called ephippia (de Senerpont Domis et al. 2007). Daphnia lumholtzi produces ephippia that

are very well adapted for easy dispersal (Dzialowski et al. 2000). Long points on the ends

and small hairs on the dorsal edge can act like hooks enabling attachment to boats, ropes, or

entangle macrophytes (Dzialowski et al. 2000). These highly resistant ephippia can be

deposited in sediment, reaching densities exceeding 9.0 x 103/m2 (Dzialowski et al. 2000).

The production of ephippia can be cued by a variety of stimuli including predator presence, crowding, photoperiod, and food availability, and typically peaks during late spring or autumn for most species (de Senerpont Domis et al. 2007). In diet quantity and quality studies by Acharya et al. (2006), D. lumholtzi showed similar life history responses as D. pulicaria and D. magna when given different quantities/qualities diets; however, D. lumholtzi had significantly higher percentage-RNA which may have contributed to its ability to make

10 times more ephippia compared to the other species (Acharya et al. 2006). Smith et al.

(2009) found that D. lumholtzi did not produce ephippia in low quality (low phosphorus) diets. Instead, they produced them only during high phosphorus diets (Smith et al. 2009).

This behavior suggests that D. lumholtzi may not wait until conditions deteriorate but takes advantage of optimal conditions to maximize ephippia production (Smith et al. 2009). The two studies together suggest that D. lumholtzi allocates its resources to ephippia production when other species may use them for increased growth rates. Species that show lower standard growth rates when given high phosphorus diets may be less susceptible to changes in food quality than those exhibiting higher growth rates (Seidendorf et al. 2010). This lower

16 sensitivity to food changes may provide resistance to more harsh conditions (Seidendorf et al.

2010).

Previous Colorado Plankton Studies

In 1957, Pennak reported the composition of species in the limnetic communities of zooplankton of 27 north-central and western Colorado lakes over a 14 year period. Vertical samples in the deepest parts of the lakes were analysed for cladocerans, copepods, and rotifers (Pennak 1957). He found that at any one point in a year, the limnetic zooplankton communities were relatively simple on a species level with one 1-3 cladoceran species, 1-2 copepod species, and 3-7 rotifer species (Pennak 1957). Of these, one species in each was usually more dominant: 78% of all cladocerans, 80% of all copepods, and 64% of all rotifers were comprised by single species of each (Pennak 1957). If two species of the same genera were present at the same time they usually differed in numerical dominance as mentioned, occurred at different depths, or were of a great enough size difference to minimize interspecific competition in food selectivity (Pennak 1957). A similar compilation of cladoceran and copepod data from 25 north-central and western Colorado lakes from 1991-

2009 was completed by Martinez et al. (2010) with special emphasis on the presence of

Mysis diluviana, a mysid shrimp that could negatively impact daphnids. The invasive water flea, Daphnia lumholtzi was not reported in either study (Pennak 1957, Martinez et al. 2010).

While comprehensive, the Martinez et al. (2010) survey efforts concentrated on north-central and western lakes and did not include Pueblo Reservoir. Studies specific to

Pueblo Reservoir were done in the late 1970’s and 1980’s (Herrmann and Mahan 1977,

Edelmann et al. 1991, Lewis and Edelmann 1994). While no D. lumholtzi were reported in

17 those studies we believe Pueblo Reservoir to be a good candidate for the invasive daphnid for reasons such as warm seasonal temperatures, multiple temperature niches, and high boat traffic similar to known invaded lakes in other regions. Because of this likelihood and the possible implications that may result from invasions of D. lumholtzi, we feel the results of our study could be critical in management decisions for both wildlife and utilities.

HYPOTHESES

As mentioned previously, the initial reservoir sampling effort from 2008-2010 was with the

goal of determining whether benthic or planktonic forms of zebra (Dreissena polymorpha)

and quagga (Dreissena rostriformis) mussels showed recurring presence in Pueblo Reservoir.

While no forms of either mussel were found, Daphnia lumholtzi, an invasive water flea, was

positively identified in the planktonic samples. This came as a surprise upon completion of

sampling from 2008-2010; however, hypotheses and methods for the survey efforts of 2013

were geared specifically toward D. lumholtzi.

H1: We hypothesize that Daphnia lumholtzi shows recurring presence in Pueblo Reservoir from 2008-2013.

H1 null: Daphnia lumholtzi does not show recurring presence in Pueblo Reservoir

from 2008-2013.

H2: We hypothesize that the population of D.lumholtzi has increased significantly in density

from 2008 to 2013.

H2A null: The population of D. lumholtzi has decreased significantly in density from

2008 to 2013.

H2B null: The population of D. lumholtzi shows no significant change in density from

2008 to 2013.

H3: We hypothesize that D. lumholtzi will not show uniform distribution among the four

sample sites in Pueblo Reservoir in 2013.

H3 null: Daphnia lumholtzi will show uniform distribution among the four sample

sites in Pueblo Reservoir in 2013.

H4: We hypothesize that sites with abundant D. lumholtzi will differ significantly from sites without or with reduced abundance of D. lumholtzi in 2013.

H4 null: Sites with abundant D. lumholtzi will not be significantly different from

those without or with reduced abundance of D. lumholtzi in 2013.

SPECIFIC AIMS

Aim1: We aim to determine whether Daphnia lumholtzi shows recurring presence in Pueblo

Reservoir from 2008-2013.

Aim2: We aim to investigate whether the population of D. lumholtzi has increased significantly in density from 2008 to 2013.

Aim3: We aim to examine whether D. lumholtzi shows uniform distribution amongst the four sample sites in Pueblo Reservoir in 2013.

Aim4: We aim to test whether sites with abundant D. lumholtzi differ significantly from sites without or with reduced abundance of D. lumholtzi in 2013.

MATERIALS and METHODS

Aim1: We aim to determine whether Daphnia lumholtzi shows recurring presence in Pueblo

Reservoir.

Aim2: We aim to test whether the population of D. lumholtzi has increased significantly in

density and from 2008 to 2013.

Aim3: We aim to examine whether D. lumholtzi shows uniform distribution amongst the four sample sites in Pueblo Reservoir in 2013.

Study Site

As mentioned before, Pueblo Reservoir is located outside of the city of Pueblo in the south- central portion of Colorado (Ferrari 1994). When full, it stretches approximately 14.5 km in length with widths ranging from 0.5–3.5 km and a maximum depth of over 47 m at the dam

(Mast and Krabbenhoft 2010). Historic water storage content from the Bureau of

Reclamation is shown in Figure 1 for each of the sample years.

Zooplankton Collection

Six sites were sampled for zooplankton from 2008 – 2010 in Pueblo Reservoir (Figure 2). In

2013, four sites were sampled in triplicate from May –November (Figure 2). Dates and GPS coordinates for these samples can be seen in Table 1. Vertical hauls were taken from 15 m depth when lake depth would allow. If less than 15 m deep, the entire water column from just above the bottom was sampled. A Wildco® plankton net (500 mm mouth, 2 m length, 63 µm mesh) was used for all zooplankton samples and was used exclusively for Pueblo Reservoir so as to prevent contamination to other water bodies. Between each month’s use, the net was

21 dried for two weeks. The net was lowered by hand off the side of the boat and allowed to sink to the desired depth, after which it was pulled vertically at a speed of approximately 0.1 m sec-1. Roughly 125 ml of collected material was transferred from the net collection chamber to 250 ml clear plastic bottles which were then filled immediately in the field with

100% ethanol for preservation. After allowing the material to settle, the uppermost 125 ml were aspirated in the lab and replaced with fresh 100% ethanol for a final preservation in approximately 70% ethanol. These samples were then sent to Dr. John Beaver, BSA

Environmental Services Inc., for identification and enumeration (Beaver et al. 2010).

Figure 1. Historic end of month content (m3) of Pueblo Reservoir for the water years pertaining to each of the sampled years. Water years begin in October of the preceding year and end in September. Values were obtained from the databases maintained by the Bureau of Reclamation and Division of Water Resources.

1 - PR Four sites

2010.

from 2008 from

2 5 - - PR PR

4 - PR

. Site map of Pueblo Reservoir, Colorado in the Arkansas River basin (shown in blue). Six sites were sampled for zooplankton zooplankton for sampled were sites Six blue). in (shown basin River Arkansas the in Colorado Reservoir, Pueblo of map Site . Figure 2 Figure 2013. in sampled were

Table 1. Zooplankton sample dates and sites in Pueblo Reservoir. GPS coordinates are as follows for each site: PR-1 (13 523965E, 4235561N); PR-2 (13 520861E, 4234811N); PR-3 (13 517771E, 4236498N); PR-4 (13 516705E, 4237776N); PR-5 (13 520658E, 4235802N); PR-6 (13 522893E, 4234541N).

Sites Sampled Sample Date PR-1 PR-2 PR-3 PR-4 PR-5 PR-6 9-Jun-08 X X X X X X 30-Jun-08 X X X X X

8-Jul-08 X X X X X X 6-Aug-08 X X X X X X 4-Sep-08 X X X X X X 9-Oct-08 X X X X X X 20-Nov-08 X X X X X X 30-Dec-08 X

31-Jan-09 X

22-Feb-09 X

29-Mar-09 X

29-Apr-09 X

4-May-09 X X X X X X 31-May-09 X

3-Jun-29 X X X X X X 18-Jun-09 X

30-Jun-09 X X X X X X 16-Jul-09 X

5-Aug-09 X X X X X X 31-Aug-09 X

23-Sep-09 X X X X X X 27-Sep-09 X

31-Oct-09 X

24-Nov-09 X

28-Dec-09 X

30-Jan-10 X

27-Feb-10 X

23-Mar-10 X

26-Mar-10 X X X X X X 27-Apr-10 X

3-May-10 X X X X X X 19-May-10 X

26-May-10 X X X X X X 16-Jun-10 X

23-Jun-10 X X X X X X 20-Jul-10 X

26-Jul-10 X X X X X X 25-Aug-10 X X X X X X 26-Aug-10 X

27-Sep-10 X X X X X X 30-Sep-10 X

21-Oct-10 X X X X X

26-Oct-10 X

29-May-13 X X X X 24-Jun-13 X X X X 17-Jul-13 X X X X 21-Aug-13 X X X X 19-Sep-13 X X X X 12-Oct-13 X X X X 9-Nov-13 X X X X

Aim4: We aim to test whether sites with abundant D. lumholtzi differ significantly from sites

without or with reduced abundance of D. lumholtzi in 2013.

Phytoplankton Collection

Once in 2008, monthly from May-September in 2009, and monthly from January-September in 2010 phytoplankton were collected in Pueblo Reservoir (Table 2). In 2013, phytoplankton were collected from four sites (same as the zooplankton) monthly from May-November

(Table 2). Phytoplankton were collected using a Wildco® model 1520-A45 Kemmerer bottle

(2.2 L) at 1 m intervals from 1-5 m. At each site, the samples from 1-5 m were combined in a

19 L bucket and gently swirled to ensure mixing. A 250 ml sample was drawn from the bucket and stored on ice in amber plastic bottles. Once in the lab, each was preserved with 4 ml of Lugol’s iodine solution. These were then sent to Dr. John Beaver, BSA Environmental

Services, Inc., for identification and enumeration (Beaver et al. 2013).

Water Quality Parameters

Temperature and Conductance

In 2013, surface temperature and conductance was measured at the same sites and dates as

sampled for zooplankton (Table 1) using a Yellow Springs Conductivity Instrument 30M-

100 at 0.2 m.

Turbidity/Water Clarity

Turbidity/water clarity was measured using a Secchi disk (20 cm) at each of the sample sites

mentioned above for 2010 and 2013. The disk was lowered by hand off the shadowed side of

Table 2. Phytoplankton sample dates and sites in Pueblo Reservoir. GPS coordinates are as follows for each site: PR-1 (13 523965E, 4235561N); PR-2 (13 520861E, 4234811N); PR-3 (13 517771E, 4236498N); PR-4 (13 516705E, 4237776N); PR-5 (13 520658E, 4235802N); PR-6 (13 522893E, 4234541N).

Sites Sampled Sample Date PR-1 PR-2 PR-3 PR-4 PR-5 PR-6 30-Jun-2008 X X X X X 4-May 2009 X X X X X X 3-Jun-2009 X X X X X X 30-Jun-2009 X X X X X X 5-Aug-2009 X X X X X X 23-Sep-2009 X X X X X X 30-Jan-10 X 27-Feb-10 X 23-Mar-10 X 27-Apr-10 X 19-May-10 X 16-Jun-10 X 20-Jul-10 X 26-Aug-10 X 30-Sep-10 X 29-May-13 X X X X 24-Jun-13 X X X X 17-Jul-13 X X X X 21-Aug-13 X X X X 19-Sep-13 X X X X 12-Oct-13 X X X X 9-Nov-13 X X X X

the boat just until it disappeared from sight and that depth was recorded. The recorded depth

is a measurement of water clarity but represents an indirect method for expressing turbidity

and concentration of suspended solids (Bhargava and Mariam 1991).

Nutrients

Inorganic anions were determined by ion chromatography according to EPA method 300.1.

Water samples were collected from 0.2 m in 250 ml plastic bottles and stored on wet ice from

each of the four sites sampled for zooplankton in 2013 (Table 1). Upon returning to the lab,

30 ml of the water samples were drawn into a 60 ml syringe. A Phenex TM RC membrane

filter (26 mm, pore size 0.45 µm) was fixed to the end of the syringe and each water sample

was filtered into plastic vials and refrigerated at approximately 6°C until ready to be analyzed

with the ion chromatograph DIONEX ICS-5000m AS-DV. Samples were transferred to 5 ml

Thermo Scientific™ Dionex™ AS-DV Autosampler PolyVials and capped prior to placing in the machine. A field blank of deionized water that was taken in the field in the same manner as the samples served as a control. Also, two intentional spikings of known concentrations of solutes served as positive controls. Average retention times and detection limits are as follows for each analyte: fluoride (4.6-4.7 min., 0.009 mg/L), chloride (7.7 – 7.9 min., 0.004 mg/L), nitrite-N (9.9 – 10.0 min., 0.001 mg/L), bromide (12.4 – 12.7 min., 0.014 mg/L), nitrate-N (14.6 – 14.9 min., 0.008 mg/L), ortho-phosphate-P (19.4 – 19.7 min., 0.019 mg/L), and sulfate (21.8 -23.0 min., 0.019 mg/L).

Trace elements in the water samples were determined by inductively coupled plasma

– mass spectrometry (ICP-MS) according to EPA method 200.8. Similar to the above procedure, water samples were collected in 250 ml plastic bottles and stored on wet ice from each of the four sites sampled for zooplankton in 2013 (Table 1). Upon returning to the lab,

30 ml of the water samples were drawn into a 60 ml syringe. To determine total recoverable elements, 30 ml of each sample was transferred to Nalgene® 30 ml bottles (#2006-0001) and immediately acidified with three drops of Ultrex nitric acid (HNO3) and refrigerated at ~6°C until analysis. To determine dissolved elements, a Phenex TM RC membrane filter (26 mm,

pore size 0.45 µm) was fixed to the end of the syringe and the water samples (30 ml) were

filtered into plastic vials. These samples were acidified in the same manner as above with

nitric acid and refrigerated until analyzed with the Agilent 7500ce Inductively Coupled

Plasma Mass Spectrometer (ICP-MS). Detectable limits (ppb) are as follows: Be

(0.087286133), Na (6.185468204), Mg (6.688199748), Al (0.218390676), P (8.012609896),

K (4.133849667), 40 Ca (9.062294475), 44 Ca (1.791007483), Ti (0.182610305), V

(0.03607287), Cr (0.046008829), Mn (0.038589591), Fe (9.949770146), Co (0.072259447),

Ni (0.094110125), Cu (0.100669076), Zn (0.206178536), As (0.032568997), 78 Se

(0.110473171), 82 Se (0.052167441), Kr (241.3753537), Mo (0.100284926), Ag

(0.104005843), Cd (0.053896275), Sb (0.068875928), Ba (0.113247081), Hg (0.004240195),

Ti (0.080751937), 206 Pb (0.14156323), 207 Pb (0.129315023), 208 Pb (0.13020687), Th

(0.067924995), and U (0.066158913).

Statistical Analyses

Microsoft Excel ™ 2010 was used to calculate confidence intervals for slopes of linear

regression lines found in Figure 5. Minitab® 17 was used to perform Chi-square goodness-

of-fit tests for testing the uniformity of D. lumholtzi in Pueblo Reservoir (Figure 6) along

with comparing the relative proportions of cladocerans to copepods and rotifers in each of the

study years (Figure 21) when expected frequencies were greater than five and Chi-square

goodness-of-fit tests were appropriate. For examining the homogeneity of sites in 2008 and

2010, expected frequencies were below five and randomization tests for goodness-of-fit were

performed in Microsoft Excel TM 2010 according to McDonald (2009) with 2.0 x 104

replications. PRIMER 6 Version 6.1.16 and PERMANOVA Version 1.0.6 from PRIMER-E

Ltd. software with manual was used to examine multivariate data (Anderson et al. 2008;

Clarke and Gorley 2006). Draftsman plots were generated for environmental data to see which parameters required log transformation. After log transformation, the data were then normalized as a pretreatment and resemblance matrices based on Euclidian distances were created. PERMANOVA tests were run to check for significant differences between samples according to date and site. PERMANOVA analysis tests the null hypothesis that site and date factors have no effect on the sample values and therefore randomly shuffles the factor group

labels a large number of times to obtain a pseudo-F statistic distribution and a P-value. By using this permutation procedure data normality need not be assumed. Canonical analyses of principle coordinates (CAP) tests were performed by date and site to examine which variables contributed to the variance. For biological data, zooplankton abundance was pretreated with a square root transformation and phytoplankton abundance was pretreated with log transformation. Resemblance matrices based on Bray Curtis similarity were generated. PERMANOVA tests were again performed to detect significant differences between samples according to date and site and CAP tests were performed for both date and site.

RESULTS

Pueblo Reservoir is the first recorded site in Colorado for the invasive water flea, Daphnia

lumholtzi (Walker et al. 2013). The zooplankter shows recurring presence in the reservoir each of the sample years with peak abundance/biomass in September (Figures 3 and 4). In

2008, 2009, and 2010, D. lumholtzi showed peak densities of approximately 5.0 L-1 (Figure

3A) and average biomass between 12.0-40.0 µg d.w. L-1 (Figure 4A). In 2013, D. lumholtzi average density was considerably higher at around 25.0 L-1 (Figure 3B) and average biomass

reached 43.8 µg d.w. L-1 (Figure 4B).

Figure 3. A. Average density (#/L) of Daphnia lumholtzi and native daphnids (shown in grey shades) from 2008-2010 (n = 6 unless otherwise noted in Table 1). B. Average density (#/L) of same species in 2013 (n = 12).

Figure 4. A. Average biomass (µg d. w. /L) of Daphnia lumholtzi and native daphnids (shown in grey shades) from 2008-2010 (n = 6 unless otherwise noted in Table 1). B. biomass (µg d. w. /L) of same species in 2013 (n = 12).

The population density (individuals L-1) was log transformed and data were fit with regression lines for comparison of growth rates from early summer to peak abundance in

September for each of the sample years 2008, 2009, 2010, and 2013 (Figure 5). Slopes of the regression lines were compared by generating 95% confidence intervals for each year which can be seen in Table 3. When comparing our earliest sampling effort in 2008 to the most recent in 2013, the confidence intervals do not overlap, suggesting a significant increase in population density growth rate from 2008 to 2013.

Figure 5. Log (x+1) transformed density (#/L) of D. lumholtzi from 2008-2010 (n = 6 unless otherwise noted in Table 1) and 2013 (n = 12).

Table 3. The 95% confidence interval for the slope of regression lines fit to log transformed (x+1) density of D. lumholtzi in 2008, 2009, 2010, and 2013.

Sampling Year 95% Confidence Interval lower bound upper bound 2008 0.04606 0.160677

2009 0.065415 0.218712 2010 0.074974 0.195359 2013 0.168558 0.311724

September peak values were analyzed for site to site comparison to investigate homogeneity of D. lumholtzi in the lake (Figure 6). In 2008, sites PR-2 and PR-4 show similar densities of approximately 1.6 individuals L-1 and a slightly higher density in PR-1 of

3.317 individuals L-1 (Figure 6). In 2009, 2010, and 2013, the site with the highest density was PR-4 (Figure 6).

Results from the Chi-square goodness-of-fit tests for homogeneity in 2009 and 2013 samples along with the randomization test of goodness-of-fit tests in 2008 and 2010 samples are shown in Table 4. In all but 2008, highly significant p-values were found suggesting that the population of D. lumholtzi is not uniformly distributed throughout the lake.

Figure 6. Abundance of D.lumholtzi: distribution among sites in each of the sampling years. *Values in 2013 are averaged from the triplicate samples taken at each site.

Table 4. Chi-square goodness-of-fit homogeneity tests for comparing density of D. lumholtzi among the four sample sites in 2009 and 2013. *For 2008 and 2010, expected values were below appropriate levels for Chi- square goodness-of-fit tests. Reported are the observed χ2 and the 4 randomization p-value generated from 2.0 x 10 replications.

Sampling Year χ2 P 2008* 3.33523* 0.177*

2009 27.2532 0.000 2010* 11.5270* 0.007* 2013 81.1184 0.000

Environmental and biological parameters were compared for the sample sites and dates so as to elucidate any key differences in samples with abundant D. lumholtzi. In order to do so the data from these parameters were used to generate resemblance matrices for the samples according to both site and date. PERMANOVA tests were significant for both site and date (Table 5). The eigenvalues produced by the canonical analysis of principal coordinates according to site yielded high correlations, indicating the strength of the model

(Table 6). When analyzing the environmental variables according to site differences, our results indicated that PR-4 sites were correlated to higher amounts of trace elements aluminum (total - ppb), vanadium (total and dissolved – ppb), phosphorus (total – ppb), iron

(total – ppb), manganese (total – ppb), and titanium (total – ppb) and lower amounts of molybdenum (total – ppb) and lower Secchi depths (m) when compared to the other three sites (Figure 7).

When analyzing the environmental variables according to date differences, the eigenvalues again showed high correlation (Table 7) signifying high strength in the model clusters. May samples show correlation to greater amounts of dissolved and total uranium 34

(ppb), sulfate (ppm), chloride (ppm), and dissolved manganese (ppb). June samples are

correlated to greatest Secchi depths (m) and dissolved iron (ppb). The July PR-1, PR-2, and

PR-5 samples as well as the June PR-4 sample are correlated to high amounts of dissolved aluminum (ppb). The August samples and the July PR-4 sample are correlated to high temperatures (°C). September samples are separated from the rest by correlation to nitrate

(ppm) and several trace elements: dissolved vanadium (ppb), dissolved and total arsenic

(ppb), total and dissolved antimony (ppb), total and dissolved krypton (ppb), total and dissolved molybdenum (ppb), total potassium (ppb), and total and dissolved barium (ppb).

October and November share many similar correlations such as: fluoride (ppm), isotopes

40Ca (total – ppb) and 44Ca (total and dissolved – ppb), dissolved potassium (ppb), and nickel (total and dissolved – ppb) (Figure 8).

Table 5. PERMANOVA table of results for environmental variables by date (zone) and site (loc) factors.

Source df SS MS Pseudo- P(perm) Unique F perms Zo 6 719.89 119.98 4.1479 0.001 998 Lo 3 271.44 90.48 3.128 0.001 998 Res 18 520.67 28.926 Total 27 1512

Table 6 . Canonical analysis of principal coordinates (CAP) from resemblance based on Euclidean distance of environmental parameters grouped by site for samples taken in 2013.

Eigenvalue Correlation Correlation Squared 1 0.9898 0.9797 2 0.9387 0.8812 3 0.7231 0.5229

Table 7. Canonical analysis of principal coordinates (CAP) from resemblance based on Euclidean distance of environmental parameters grouped by date for samples taken in 2013. Eigenvalue Correlation Correlation Squared 1 0.9919 0.9838 2 0.9851 0.9704 3 0.9102 0.8285 4 0.277 0.0767 5 0.0213 0.0005

T- Mn T- Fe T- P T- V T- Al D- V

Figure 7. Canonical analysis of principal coordinates according to site of environmental data from a resemblance matrix based on Euclidian distance. Only the highest correlation vectors are shown. Units are as follows: Secchi (m), total Mo (ppb), total Ti (ppb), total P (ppb), total Fe (ppb), total Mn (ppb), total and dissolved V (ppb). 36

D- Ba T- Ba T- K T- Ni D- Ni D- Mo D- K D- Sb Fluoride T- Sb

Chloride D- As T- As

Temp

Figure 8. Canonical analysis of principal coordinates according to date of environmental data from a resemblance matrix based on Euclidian distance. Only the highest correlation vectors are shown. Units are as follows: temp. (°C), chloride (ppm), fluoride (ppm), total and dissolved K (ppb), total and dissolved Ni (ppb), total and dissolved Ba (ppb), dissolved Mo (ppb), total and dissolved Sb (ppb), and total and dissolved As (ppb).

In addition to the environmental variables, we surveyed biological features such as phytoplankton and other zooplankton which likely play large roles in the abundance of D. lumholtzi. The results of PERMANOVA tests for phytoplankton show significant differences in site and date factors (Table 8). The canonical analysis of principal coordinates for site difference in phytoplankton elicited an eigenvalue with high correlation, showing separation of PR-4 sites from the remaining sites (Table 9). The analysis was performed on all of the

37 phytoplankton but Figures 9-11 show the results with three different overlaying vectors so as to see the species more clearly. Daphnia lumholtzi was included in the analysis to illustrate any similarities.

Table 8. PERMANOVA table of results for biological phytoplankton variables by date (zone) and site (loc) factors.

Source df SS MS Pseudo-F P(perm) Unique perms Zo 6 12893 2148.9 3.135 0.001 998 Lo 3 6584 2194.7 3.2018 0.001 998 Res 18 12338 685.45 Total 27 31816

Table 9. Canonical analysis of principal coordinates (CAP) from resemblance based on Bray Curtis similarity of phytoplankton grouped by site for samples taken in 2013.

Eigenvalue Correlation Correlation Squared 1 0.9273 0.8599 2 0.4772 0.2278 3 0.3664 0.1343

B- N. cryptotenella

B- C. placentula CL- D. lumholtzi B- N. lanceolata B- G. parvulum B- N. dissipata B- D. moniliformis B- D. mesodon B- S. pinnata B- A. pediculus B- C. cistula B- R. curvata B- S. ulna B- S. hantzschii B- Nitzschia spp. B- N. fruticosa B- N. fruticosa

Chl- Cosmarium spp. Chl- S. dimorphus Chl- S. quadricauda Chl- S. bijuga CL- D. lumholtzi Chl- Q. lacustris Chl- Staurastrum spp. Chl- Chlamydomonas spp. Chl- S. schroeteri Chl- C. microporum Chl- P. tetrarhynchus Chl- B. braunii Chl- P. morum

Chl- Closterium spp. Chl- M. mirabile Chl- D. pulchellum

Figures 9 and 10. Canonical analysis of principal coordinates according to site of phytoplankton biological data from a resemblance matrix based on Bray Curtis similarity. Shown are the vectors for members of phylum Bacillariophyta (B-) in Figure 9 (top) and members of phylum Chlorophyta (Chl-) in Figure 10 (bottom) with the highest correlation. 39

Mallomonas spp. Chr- Pyr- G. quadridens CL- D. lumholtzi Chr- M. pseudocoronata

Cya- M. aeruginosa

Cry- Rhodomonas spp. C. hirudinella Cry- Cryptomonas spp. Pyr-

Figure 11. Canonical analysis of principal coordinates according to site of phytoplankton biological data from a resemblance matrix based on Bray Curtis similarity. Shown are the vectors for and Chrysophyta (Chr-), Cryptophyta (Cry-), Pyrophyta (Pyr-), Cyanobacteria (Cya-), and Euglena (Eug-).

When looking specifically at the diatoms (Bacillariophyta), there are several species shown to be correlated with PR-4, coinciding with the presence of D. lumholtzi (Figure 9).

Some of the species that are correlated with the same sites as D. lumholtzi include: Navicula cryptotonella, Cocconeis placentula, Navicula lanceolata, and Gomphonema parvulum.

When examining the members of Chlorophyta, Cosmarium spp. and Chlamydomonas spp. show correlation to sites where D. lumholtzi is present. Mallomonas pseudocoronata

(Chrysophyta), Mallomonas spp. (Chrysophyta), and the dinoflagellate, Glenodinium quadridens, were also found to be correlated to associated sites.

The canonical analysis of principal coordinates for date difference in phytoplankton

was also completed, resulting in multiple eigenvalues with high correlations showing

separation of samples according to their dates (Table 10). Again, the analysis was performed

on all of the phytoplankton but Figures 12-14 show the results with three different overlaying vectors including Daphnia lumholtzi for comparison purposes. Similar to the environmental factors, the phytoplankton show clear distinctions among dates, with dominant species changing monthly. In September, during peak D. lumholtzi density, the dominant diatom is

Synedra tenera (Figure 12). The most prominent green algae correlation in September is S. quadricauda (Figure 13). At the same time, Cryptomonas spp. from phylum Cryptophyta and

G. quadridens of Pyrophyta contribute correlations to the September samples as well (Figure

14).

Table 10. Canonical analysis of principal coordinates (CAP) from resemblance based on Bray Curtis similarity of phytoplankton grouped by date for samples taken in 2013.

Eigenvalue Correlation Correlation Squared 1 0.9851 0.9703 2 0.9488 0.9002 3 0.9085 0.8253 4 0.8670 0.7517 5 0.7425 0.5513 6 0.5496 0.3021

CL- D. lumholtzi CL- S. tenera

Figure 12. Canonical analysis of principal coordinates according to date of phytoplankton biological data from a resemblance matrix based on Bray Curtis similarity. Shown are the vectors for members of phylum Bacillariophyta (B-).

Chl- S. quadricauda CL- D. lumholtzi

Chl- C. quadrata Chl- P. morum Chl- Staurastrum spp.

Figure 13. Canonical analysis of principal coordinates according to date of phytoplankton biological data from a resemblance matrix based on Bray Curtis similarity. Shown are the vectors for members of phylum Chlorophyta (Chl-).

CL- D. lumholtzi

Cry- Cryptomonas spp. Pyr- G. quadridens

Figure 14. Canonical analysis of principal coordinates according to date of phytoplankton biological data from a resemblance matrix based on Bray Curtis similarity. Shown are the vectors for members of phyla Chrysophyta (Chr-), Cryptophyta (Cry-), Pyrophyta (Pyr-), Cyanobacteria (Cya-), and Euglena (Eug-).

In site comparisons for zooplankton similarities, PERMANOVA tests were

significant for both site and date (Table 11). CAP tests resemble that of the phytoplankton

where a single eigenvalue yields high correlation and PR-4 sites are separated from the remaining sites (Table 12). Vectors are shown in three ways to provide better clarity of species: Cladocerans (Figure 15), Copepods (Figure 16), and Rotifers (Figure 17).

Table 11. PERMANOVA table of results for biological zooplankton variables by date (zone) and site (loc) factors.

Source df SS MS Pseudo-F P(perm) Unique perms Zo 6 12893 2148.9 3.135 0.001 998 Lo 3 6584 2194.7 3.2018 0.001 998 Res 18 12338 685.45 Total 27 31816

Cladoceran species that show similar correlations as D. lumholtzi include: Bosmina longirostris, Chydorus sphaericus, and Diaphanosoma brachyurum (Figure 15). In Figure

16, where D. lumholtzi is plotted with copepods, it really stands apart from the copepod species. Two species show some vector similarity: Ergasilus spp. and Mesocyclops edax.

This is sharply contrasted when D. lumholtzi is shown with the rotifer species (Figure 17).

The majority of the vectors for rotifers are skewed toward the PR-4 sites with Conochiloides spp., Conochilus unicornis, Keratella earlinae, K. earlinae var. tecta, Trichocerca multicrinis, and Gastropus stylifer, overlaying with the vector for D. lumholtzi.

Table 12. Canonical analysis of principal coordinates (CAP) from resemblance based on Bray Curtis similarity of zooplankton grouped by site for samples taken in 2013.

Eigenvalue Correlation Correlation Squared

1 0.8311 0.6308 2 0.219 0.048 3 0.0958 0.0092

CL- D. lumholtzi CL- B. longirostris

Figure 15. Canonical analysis of principal coordinates according to site of zooplankton biological data from a resemblance matrix based on Bray Curtis similarity. Shown are the vectors for Cladoceran (CL-) species.

CL- D. lumholtzi

Figure 16. Canonical analysis of principal coordinates according to site of zooplankton biological data from a resemblance matrix based on Bray Curtis similarity. Shown are the vectors for Copepod (CO-) species.

CL- D. lumholtzi

Figure 17. Canonical analysis of principal coordinates according to site of zooplankton biological data from a resemblance matrix based on Bray Curtis similarity. Shown are the vectors for Rotifer (R-) species.

The samples show discrete clustering when analyzed by date with each month’s samples clustering distinctly (Figures 18-20) and multiple eigenvalues with high correlation

(Table 13). In Figure 18, the vectors displaying cladoceran species show similar date correlations of D. lumholtzi, Ceriodaphnia spp. and D. brachyurum while many of the native daphnid vectors are opposite D. lumholtzi, associated with earlier month’s samples. Of the copepods, only the vector for Ergasilus spp. shows close resemblance to that of D. lumholtzi, lining up with the month of September (Figure 19). In Figure 20, we see again the large association of samples correlated to both rotifers and D. lumholtzi, namely C. unicornis, T. multicrinis, Conochiloides spp., P. major, and P. vulgaris. A complete list of zooplankton species occurrence in the Pueblo Reservoir is found in Table 14.

Table 13. Canonical analysis of principal coordinates (CAP) from resemblance based on Bray Curtis similarity of zooplankton grouped by date for samples taken in 2013.

Eigenvalue Correlation Correlation Squared 1 0.9925 0.985 2 0.9773 0.955 3 0.9717 0.9442 4 0.8523 0.7265 5 0.7373 0.5436 6 0.6794 0.4616

CL- D. lumholtzi CL- Ceriodaphnia spp. CL- D. brachyurum

Figure 18. Canonical analysis of principal coordinates according to date of zooplankton biological data from a resemblance matrix based on Bray Curtis similarity. Shown are the vectors for Cladoceran (CL-) species.

CL- D. lumholtzi CO- Ergasilus spp.

Figure 19. Canonical analysis of principal coordinates according to date of zooplankton biological data from a resemblance matrix based on Bray Curtis similarity. Shown are the vectors for Copepod (CO-) species.

R- P. vulgaris R- P. major CL- D. lumholtzi R- K. cochlearis var. tecta

R- C. unicornis R- T. multicrinis R- Conichiloides spp.

Figure 20. Canonical analysis of principal coordinates according to date of zooplankton biological data from a resemblance matrix based on Bray Curtis similarity. Shown are the vectors for Rotifer (R-) species.

Table 14. Zooplankton occurrence in Pueblo Reservoir 2013.

Division Name May June July Aug Sep Oct Nov Cladocera Alona guttata (Sars, 1862) X Bosmina longirostris (O. F. Müller, 1776) X X X X X X X Ceriodaphnia lacustris (Birge, 1893) X X X Ceriodaphnia spp. X X X X Chydorus sphaericus (O. F. Müller, 1776) X X X Daphnia galeata (G. O. Sars, 1864) X X X X X X Daphnia lumholtzi (G. O. Sars, 1885) X X X X X Daphnia pulex complex* (Leydig, 1860) X X X X Daphnia retrocurva (Forbes, 1882) X X X X X X X Daphnia spp. X X Diaphansoma brachyurum (Liévin 1848) X X Copepoda Acanthocyclops vernalis (Fischer, 1853) X Diacyclops thomasi (S. A. Forbes, 1882) X X X X X Ergasilus spp. X Eurytemora affinis (Poppe, 1880) X X X X X Leptodiaptomus siciloides (Lilljeborg in X X X X Guerne and Richard, 1889) Mesocyclops edax (Forbes, 1891) X X X X X X X Skistodiaptomus pallidus (Herrick, 1879) X X X X X X X Rotifera Asplanchna priodonta (Gosse, 1850) X X Asplanchna spp. X Brachionus angularis (Gosse, 1851) X X Brachionus bidentatus (Anderson , 1889) X Cephalodella spp. X Collotheca libera (Zacharias, 1894) X X Collotheca spp. X X X X X Conochiloides coenobasis (Hudson, 1885) X Conochiloides spp. X X Conochilus unicornis (Rousselet, 1892) X X X X Gastropus stylifer (Imhof, 1891) X Hexarthra mira (Hudson, 1871) X Keratella americana (Carlin, 1943) X Keratella cochlearis (Gosse, 1851) X X X X X X X Keratella cochlearis var. tecta (Gosse, 1851) X X X X Keratella crassa (Ahlstrom, 1943) X X X X X X X Keratella earlinae (Ahlstrom, 1943) X X X X X X X Keratella quadrata (O. F. Müller, 1786) X X X Lepadella ovalis (O.F. Muller, 1896) X Monostyla bulla (Ehrenberg, 1930) X Monostyla stenroosi (Meissner, 1908) X Plationus patulus (Müller, 1786) X X Polyarthra major (Burckhardt, 1900) X X X X X Polyartrha remata (Skorikov, 1896) X X X X X X X Polyarthra vulgaris (Carlin, 1943) X X X X X X X Pompholyx sulcata (Hudson, 1885) X Synchaeta pectinata (Ehrenberg, 1832) X X X X X X X Synchaeta spp. X X X X X X X Trichocerca multicrinis (Kellicott, 1897) X X X Trichocerca similis (Wierzejski, 1893) X X X Trichocerca spp. X X

The final analyses were done to see if the proportions of zooplankton during the month of September for each of the sampling years showed significant flux from 2008 to

2013. Figure 21 shows the proportions of zooplankton in September when the population of

D. lumholtzi was at its peak. In each of the sample years, D. lumholtzi comprised the majority of the available biomass, making up 61% in 2008 and as much as 89% in 2009 (Figure 21A and B). In both 2010 and 2013, D. lumholtzi contributed approximately 80% of the biomass

(Figure 21C and D). Other cladoceran species, B. longirostris, D. galeata mendotae, and

Ceriodaphnia spp., made up between 6-14% each year. The rotifers and copepods represent the remaining 5-29%, with rotifers fluctuating considerably between the sample years.

Rotifers made up as much as 26% of the available biomass in 2008 but only 1-2% in 2009 and 2010 (Figure 21A, B and C). Copepods accounted for 3-8% each year. Chi-square goodness-of-fit tests reveal significant differences (even with Bonferroni adjusted α =

0.0083) between 2008 assemblage and each of the other sample years, and 2009 composition compared to both 2010 and 2013 (Table 15). The ratios for 2010 and 2013 were calculated by be congruent (p = 0.596); however, rotifers switched places with copepods as the dominant non-cladoceran component in 2013.

Table 15. Chi-square goodness-of-fit tests comparing proportions of cladocerans, copepods, and rotifers in each of the sample years for the month of September when D. lumholtzi was prevalent.

Sampling Year χ2 P 2008 vs. 2009 63.6299 0.000 2008 vs. 2010 24.5725 0.000 2008 vs. 2013 22.6643 0.000 2009 vs. 2010 7.72103 0.005 2009 vs. 2013 12.9879 0.000 2010 vs. 2013 0.280490 0.596 53

occurrence. A. 2008. B. 2009. C. 2010. D. 2013. D. 2010. C. 2009. B. 2008. A. occurrence.

D. lumholtzi

D. B. Relative biomass proportions of zooplankton in Pueblo Reservoir during peak peak during Reservoir Pueblo in zooplankton of proportions biomass Relative .

A. C. Figure 21Figure

DISCUSSION

Daphnia lumholtzi appears to be established in Pueblo Reservoir which supports our first hypothesis that D. lumholtzi shows recurring presence year to year. Continued monitoring of its occurrence is of great importance. Similar to findings of Havel and Graham (2006), the occurrence of D. lumholtzi is temporally offset from its native daphnid counterparts, displaying peak abundance and biomass in September instead of April-May. From 2008-

2010, the peak densities show similar amounts; however, in 2013, a nearly five-fold increase in abundance can be seen (Figure 3). Upon determining the 95% confidence intervals for the slopes of regression lines fit to the log transformed density data, D. lumholtzi had a significantly higher rate of population density growth in 2013 when compared to the growth rate in 2008 as the confidence intervals do not overlap (Figure 5 and Table 3). Therefore, this provides evidence to reject the null hypotheses that D. lumholtzi has either decreased in density or that no change in density was detected.

These results differed from those of Havens et al. (2011) in Lake Okeechobee, FL, who found that after many established years, D. lumholtzi had yet to become a substantial constituent of the zooplankton assemblage and had not impacted the native D. ambigua. Lake

Okeechobee is much larger and shallower than Pueblo Reservoir with a surface area of over

400,000 acres and mean depth of 2.7 m (Havens et al. 2011) compared to 5,671 acres and mean depth of approximately 16 m in Pueblo Reservoir (Ferrari 1994). On the other hand,

Kolar et al. (1997) found that D. lumholtzi had increased in Lake Springfield, IL, a lake closer to Pueblo Reservoir’s size (3909 acres, mean depth 4.0 m (lake parameters from Kolar and Wahl 1998). This resulted in an order-of-magnitude decrease in the native daphnid population abundance found prior to D. lumholtzi invasion of Lake Springfield even with the

temporal offset in occurrence (Kolar et al. 1997). The impact of D. lumholtzi invasion may be

more exaggerated in smaller lakes. While year-round surveillance was not the focus of the

current study, continued survey efforts of D. lumholtzi are of the utmost importance so as to

determine the outcome of the increased population abundance in Pueblo Reservoir.

The above findings are based upon averages of the lake samples taken together as a

whole. Since Pueblo Reservoir offers a variety of micro-habitats, from a riverine environment at the Arkansas River inlet to the more lacustrine setting found at the dam, it is necessary to investigate whether D. lumholtzi shows homogeneity in its distribution throughout the lake. Figure 5 shows that D. lumholtzi density was highest in PR-4, where the river enters the reservoir (Figure 1), for all years except 2008. The Chi-square goodness-of- fit and randomization tests for goodness-of-fit show highly significant p-values for the years

2009, 2010, and 2013 providing enough evidence to reject the null hypothesis that D. lumholtzi is uniformly distributed throughout the lake for those years.

Because D. lumholtzi has differential densities among the sites, it is important to examine any differences there may be between the sites to provide clues to the conditions best suited for high abundance of the zooplankter. In our sampling efforts, we surveyed both environmental and biological parameters for each of the sites. Of the environmental parameters analyzed by site, site PR-4 showed correlation to higher amounts of total aluminum (ppb), total and dissolved vanadium (ppb), total phosphorus (ppb), total iron (ppb), total manganese (ppb), and total titanium (ppb) but lower correlation to amounts of total molybdenum (ppb) and Secchi depths (m) than the other three sample sites. Schulze et al.

(2006) suggests that riverine sites may have more micronutrients available that are used for metabolism and reproduction and reduced water clarity may offer protection from site-

feeding predators for D. lumholtzi. When environmental parameters were analyzed according

to dates, September samples showed correlations with total and dissolved barium (ppb), total

potassium (ppb), dissolved molybdenum (ppb), nitrate (ppm), total and dissolved antimony

(ppb) and total and dissolved arsenic (ppb). These findings along with the PERMANOVA

test results support our last hypothesis that sites with abundant D. lumholtzi are significantly

different from the other sites.

Lewis and Edelmann (1994) credit acid mine drainage in the headwaters, minerals

from sedimentary rock, return flows from irrigation, inflow from saline ground-water, and

water treatment plant discharges as playing roles in the quality of water in the Arkansas

River and Pueblo Reservoir. Iron and manganese are especially attributed to acid mine

drainage and sedimentary rock (Lewis and Edelmann 1994). Antimony (Sb) can occur

naturally or through mining for use in alloy production (Nam et al. 2009). Arsenic can arise

from natural weathering or through agricultural practices (Shaw et al. 2007). Khangarot and

Ray (1989) show 48-hour EC50 concentrations in D. magna for the following metals: As3+,

6.23 ppm; Fe2+, 7.20 ppm; Mn2+ 8.28 ppm; Ba2+, 32.00 ppm; Al3+, 59.60 ppm; K+,

141.46 ppm; and Sb3+, 423.45 ppm. Our trace element results were well below these limits.

Beusen and Neven (1987) state that vanadium can originate from land erosion, fall out as particles, or dissolved in rain due to atmospheric emission with the combustion of coal and crude oils. Concentrations of at least 3.4 ppm were needed to reach LC50 and 2.9 ppm to reach EC50 in 48 hours when tested on D. magna (Beusen and Neven 1987). Reproduction was unaltered in chronic vanadium exposure of concentrations up to 1.6 ppm. These are well above the values found in our samples in Pueblo Reservoir with the highest concentration found to be 1.55 ppb.

Wiench et al. (2009) suggests that titanium can enter a freshwater system via swimming or effluents from wastewater as it is commonly found in sunscreens as TiO2 but

Heinlaan et al. (2008) found bulk and nano suspensions of TiO2 not to be toxic to D. magna.

Molybdenum is associated with industrial production of finished metals, used in

pigments, corrosion inhibition, and flame retardancy (Diamantino et al. 2000). One of the

biggest molybdenum mines is located in Climax, CO, north of the headwaters of the

Arkansas River (Eisler 1989). Acute and chronic toxicity tests of molybdenum on D. magna

showed 48-hour LC50 = 2847.5 ppm and EC50 = 255.1 ppm, and up to 50 ppm with no

reproductive effects (Diamantino et al. 2000). Our samples did not exceed concentrations of

4.48 ppb Mo. Studies on D. lumholtzi and optimal amounts of these trace elements would

help provide insight on how they affect the invasive water flea.

Even though temperature was not a key correlating parameter for PR-4 or September

sites in our statistical analyses, it is worth noting that D. lumholtzi was found in PR-4 samples with surface temperatures ranging from as high as 24.7°C in August to as low as

9.8°C in November. When peak density occurred in September in PR-4, surface water temperature was 21.2°C. Lennon et al. (2001) found D. lumholtzi in waters as warm as 31°C in Clinton Reservoir, KS, and determined optimal temperature ranges between 20-30°C, with reduced fecundity and low survivorship below 10°C. While most agree that D. lumholtzi is highly associated with seasonally high temperatures, Havel and Graham (2006) suggest that

D. lumholtzi may be capable of thermal niche expansion due to its appearance in winter in

Stockton Lake, MO, and the range expansion into the Great Lakes. Frisch and Weider (2010) found members of D. lumholtzi populations that remained throughout the winter in Lake

Texoma who possessed a different genotype from those that thrived in the warm summer

months. Experiments with our population of D. lumholtzi are needed to determine optimum

temperature ranges. Since Pueblo Reservoir’s temperatures did not reach the upper optimal

ranges determined by Lennon et al. (2001) in our study, it is possible that the population may

have adapted to a cooler regime.

Results of statistical analysis based on phytoplankton composition showed that sites

were significantly different and those correlated with abundant D. lumholtzi were also

correlated to a wide variety of diatoms. Carotenuto and Lampert (2004) found single-celled

diatoms to be ingested by D. pulex at rates comparable to edible algae and credited them with

possibly providing essential fatty acids that are generally lacking in better carbon sources

such as the green algae, Scenedesmus. Though algae, including Scenedesmus spp.,

Selenastrum spp., and Cryptomonas spp. do occur during these sample times as well.

Interestingly, while D. lumholtzi occurred during the same time as cyanobacteria, sites with

these were not statistically correlated. It appears that the cyanobacteria were more correlated

with the July samples and more lacustrine sites (Figures 11 and 14). Gophen (1979) found

gut contents of D. lumholtzi to consist primarily of green algae species such as Scenedesmus

and Selenastrum even in the presence of high cyanobacteria. Daphnia lumholtzi appeared to

tolerate high concentrations of cyanobacteria, Anabaena flos-aquae and Microcystis

aeruginosa, but consumes other phytoplankton if available.

The cladoceran that shows the most similarity to D. lumholtzi in site correlation is

Bosmina longirostris. This is not surprising as B. longirostris is published alongside D. lumholtzi as a common cladoceran of its native ranges (Swar and Fernando 1979; Sarma et

al. 2005; Elenbaas and Grundel 1994); however, little has been published on their relationship to each other. Magadza (1994) compared diet preferences of D. lumholtzi to B.

59 longirostris, showing that larger particles were preferred by D. lumholtzi and smaller were the choice of B. longirostris. This food partitioning may be part of the reason these two can coexist. Daphnia lumholtzi was not statistically similar to any copepods other than Ergasilus spp. This copepod is of great concern, not for its occurrence with D. lumholtzi, but for its known parasitic capabilities on fish. Otherwise known as “fish lice”, Bhuthimethee et al.

(2005) suggest that its presence may be an indicator of unhealthy environments due to pollution or disturbances. Sites with abundant Daphnia lumholtzi also showed correlation to a variety of rotifer species. Communities of rotifers have been found to graze at rates equal to or even superseding those of other crustaceans (Williamson 1983) and are consumed by members of all major taxa (Williamson 1983). Their relationship to D. lumholtzi, if one exists, is not known.

While much information is provided here in the way of environmental and biological parameters, we did not examine the effects of predators on D. lumholtzi. Several studies have found that D. lumholtzi is consumed by a variety of invertebrates and fish. In studies done with the predatory cladoceran, Leptodora kindtii, Effert and Pederson (2006) found that D. lumholtzi was at a disadvantage to non-spined daphnids as the spines made D. lumholtzi easier to retain once grasped. Contrary to these findings, Celik et al. (2002) determined that the spines were advantageous against the phantom midge larvae, Chaoborus punctipennis who was unable to ingest the spined D. lumholtzi but was able to consume D. magna. Smith and Alexander (2008) found that the freshwater jellyfish, Craspedacusta sowerbii, can also utilize D. lumholtzi as a prey item quite effectively. Neither L. kindtii nor

C. puntipennis are known to occur in Pueblo Reservoir; however, C. sowerbii does occur in the lake.

Many studies suggest that D. lumholtzi can be an important prey item for fish. Lemke

et al. (2003) found D. lumholtzi was consumed by white bass, juvenile bluegill, and white

and black crappie; however, emerald shiner, freshwater drum, and gizzard shad selected

against it. Kolar and Wahl (1998) found that even though juvenile bluegill are capable of

consuming D. lumholtzi, they select against it if given an alternative like D. pulex. Metzke

and Pederson (2006) showed that D. lumholtzi are readily consumed by the mosquitofish,

Gambusia affinis. Lienesch and Gophen (2005) found them to be taken by inland silversides,

Menidia beryllina. Of these species, bluegill, Lepomis macrochirus, and black crappie,

Pomoxis nigromaculatus, contribute 1% each of the relative fish abundance in Pueblo

Reservoir and gizzard shad, Dorosoma cepedianum, makes up 11% (McGree 2012). The fish

with the greatest relative abundance in Pueblo Reservoir is the walleye, Sander vitreus, though no work to date has been published on whether larval or juveniles of this species consume D. lumholtzi.

Since D. lumholtzi may provide another source of food to the reservoir system it is important to look at the proportion of zooplankton biomass it contributes. When looking at the changes in biomass from 2008-2010 and then again in 2013, the same increase as was seen in density is not found (Figure 4). Biomass proportions of D. lumholtzi were actually slightly lower in 2013 at 81% when compared to 2009’s 89% (Figure 21). This finding is concerning as D. lumholtzi seems to be capable of increasing considerably in numbers while at the same time not contributing a proportional increase to the available biomass of the system. Dobberfuhl and Elser (2002) stated that D. lumholtzi had a slower individual somatic growth rate and lower fecundity when compared to other daphnids. These results are in line with the studies done by Dzialowski et al. (2003) who determined that D. lumholtzi devoted

energy toward defenses in the form of elongated spines while reducing carapace size in order

to avoid predation from sight predators. In a study where D. lumholtzi was provided with a

nutrient rich diet, D. lumholtzi produced a ten-fold increase in ephippia production over other daphnids, suggesting that it expended more resources towards diapausing eggs (Acharya et al. 2006). Future studies are needed to investigate the spine to body ratios of D. lumholtzi and

ephippia production in Pueblo Reservoir.

It is also important to note that site specific environmental and phytoplankton

composition data for the four sample sites were not collected until 2013 when lake water

levels were substantially lower than the other sampling years (Figure 1). It is critical then to

consider the findings of our study in the context of drought-like conditions, understanding

that in years of surplus, the results may be different.

CONCLUSION

Daphnia lumholtzi is an invasive water flea that has become established in Pueblo Reservoir.

It has the potential to impact the reservoir system in both positive and negative ways. In

either case, the management of the lake for wildlife and utilities will be better served with

continued monitoring of the zooplankter’s presence. Changes in plankton composition can result in expensive remediation (Lathrop et al. 2002) and can impact the economy if populations of higher trophic level fish are affected. Recall that $1.26 x 109 yr-1 and over

14,000 jobs are contributed to fishing in Colorado (BBC Research and Consulting 2008).

Each of our hypotheses was supported with our results. There was evidence that D. lumholtzi reoccurred in each of the sampled years, that the population density had significantly increased from 2008 to 2013, that the zooplankter is not uniformly distributed throughout the lake, and that sites with abundant D. lumholtzi differ significantly from other

sites. Our study serves as baseline data from which we have gathered that D. lumholtzi

shows recurrence in the warm summer months, once native populations have dwindled. It

shows greatest abundance in our riverine study site which is characterized by relatively lower

Secchi depths, and greater amounts of trace elements such as aluminum, vanadium,

phosphorus, iron, manganese and titanium when compared to other sites.

Concurring phytoplankton include several diatoms such as Nitzschia cryptonella,

Rhoicosphenia curvata, and Synedra tenera; a variety of green alga like Cosmarium spp.,

Scenedesmus quadricauda and S. dimorphus, and Selenastrum gracile; and other algae such

as Cryptomonas spp. While cyanobacteria are present at the same time, our findings do not show any correlation to sites with abundant D. lumholtzi. Daphnia lumholtzi coincides

regularly with the cladoceran, B. longirostris, but rarely with other cladocerans. Likewise,

our data show a general separation of D. lumholtzi from copepod species in site preference.

On the other hand, rotifers appear heavily skewed toward sites with high D. lumholtzi densities to include Conochiloides spp., Conochilus unicornis, Keratella earlinae and K. earlinae var. tecta, and Polyarthra major and P. vulgaris.

Much work remains to be completed in order to better understand the invasive water flea. Laboratory studies are needed to examine optimal amounts of the trace elements mentioned above. More competitive experiments comparing D. lumholtzi and B. longirostris are necessary. Diet studies for D. lumholtzi are desirable to better understand its role in the maintenance of the primary producers. Studies involving potential consumers of D. lumholtzi, namely the abundant species, walleye, Sander vitreus, are required. Since vertical migration has been shown in previous studies (Williams and Pederson 2004, Havel and

Lampert 2006), depth profile analysis would also be beneficial. Spine to body ratios need to be examined as well as ephippia production for our population of D. lumholtzi. It is likely that D. lumholtzi is not restricted to Pueblo Reservoir alone but may exist in several similar

Colorado lakes such as John Martin Reservoir, and Trinidad Reservoir, which have not been surveyed specifically for D. lumholtzi. Precise evaluation of its potential impact in the soon approaching completion of the Southern Delivery System also needs to be a high priority. All

in all, our work with Daphnia lumholtzi serves as the starting point for many additional

research efforts that are sure to follow on this intriguing new member of Pueblo Reservoir.

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APPENDIX A: Additional Methods Information

Plankton Analysis

Identification of zooplankton was done according to Edmundson (1959), Ruttner-Kolisko

(1974), Stemberger (1979), and Pennak (1989) (Beaver et al. 2010). For density

determination, aliquots of the samples were put in an Utermohl chamber and examined at

100X magnification using a Wilovert inverted microscope (Beaver et al. 2010). Zooplankton

were counted until a minimum of 200 animals were tallied (Beaver et al. 2010). To

determine biomass estimates, lengths and widths of the species were measured and used in

accordance with McCauley (1984) to calculate a mean biomass (Beaver et al. 2010). These

means were then multiplied by the determined abundance for each sample to produce the

species’ biomasses (Beaver et al. 2010).

Phytoplankton analysis was done using the membrane filtration technique of McNabb

(1960) (Beaver et al. 2013). A Leica DMLB compound microscope was used to count the

phytoplankton until at least 400 natural units (single cells, colonies, or filaments) had been

examined (Beaver et al. 2013). Estimates of biovolumes were calculated according to the

formulas that best fit the cell shapes according to Hillebrand et al. (1999) (Beaver et al.

2013). Ten organisms of each taxon were measured and mean biovolumes were computed

(Beaver et al. 2013).

Canonical Analysis of Principal coordinates (CAP)

Canonical Analysis of Principal coordinates (CAP) was chosen based on the method’s

allowance of constrained ordination to be carried out on the basis of both Euclidian distance

measures for environmental data as well as Bray-Curtis similarity measures for biological data. The following steps illustrate how PRIMER 6 version 6.1.16 & PERMANOVA+

an example.

To begin, data were compiled in a Microsoft Excel TM spreadsheet with the first three columns labeled “Code”, “X”, and “Y” (shown below). In these columns, the “Code” column contained the sample labels, including the sampling month and site. The “X” and “Y” columns were filled with the corresponding GPS coordinates for each sample.

The subsequent columns contained the values for the measured variables for each sample.

The variables were labeled accordingly across the first row.

The final columns were necessary for proper uploading into the PRIMER software with the factors “Zone” and “Loc” corresponding to the sample months and sites, separated by a blank column. The spreadsheet was also named according the type of data to be uploaded: “ENV” for environmental data and “BIO” for biological data.

Once the PRIMER software was opened, “Open File” was selected and Excel Files from the dropdown menu were indicated in order to upload the Excel document.

The ENV worksheet was selected from the dropdown menu and “Sample data” was indicated. Data setup was designated with samples in rows and as environmental in type.

Once uploaded, all variable were selected except the GPS coordinates. In order to examine the data graphically, draftsman plots were generated by selecting this option from the

Analyze menu.

The zoom feature was selected to examine the Draftsman plots for any variables that would need transformation.

Temperature (°C), Secchi depth (m), fluoride (ppm), nitrite (ppm), bromide (ppm), and Al

(ppb) were chosen to undergo log transformation based upon the scatterplots.

Log transformation was accomplished by highlighting the desired columns, and selecting

“Transform Individuals” from the “Tools” menu.

Since environmental data was collected in a wide range of measurements (i.e. – depth in m, ppm, ppb, °C, etc.), it was necessary to normalize the data so as to be able to compare the values. To do so, Analyze>Pre-treatment>Normalize variables was selected.

In order to run PERMANOVA or CAP tests, a resemblance matrix must be generated. This was accomplished by selecting “Resemblance” from the “Analyze” menu. .

Euclidian distance is the default setting for worksheets named ENV.

With the resemblance matrix selected, a CAP analysis was achieved by selecting it from the “PERMANOVA+” menu. The factor “loc” was chosen to examine variation between sites.

The font was changed to a smaller size and symbols were selected to make the chart more legible.

To overlay vectors, the worksheet corresponding to the log transformed, normalized, environmental data was selected. Only those with highest correlations were displayed by clicking on “Select”.

The axes represent eigenvalues from the CAP analysis. The eigenvalue that extracts the

highest variance in the data is displayed on the x-axis and will have the highest correlation.

The eigenvalue with the second highest correlation is that which shows the next highest

variance independent of the first eigenvalue and that is displayed on the y-axis. In this figure, the most variance is between PR-4 sites and the other remaining sites, as indicated by their position on the x-axis. The y-axis shows some clustering effects as well, separating PR-1,

PR-2, and PR-5. PR-2 and PR-5 show less separation which is to be expected due to their

geographical proximity (Figure 2). These eigenvalues can be verified in the CAP report that

is generated at the same time. Shown below was the report generated for the CAP test for

environmental variables according to site variation.

APPENDIX B: Additional Statistical Analyses

CAP Canonical analysis of principal coordinates

Resemblance worksheet Name: Resem-BIO-sqrt-BC-Cladoceran Data type: Similarity Selection: All Transform: Square root Resemblance: S17 Bray Curtis similarity

Factor for groups: Loc

Number of samples: 28 Choice of m: 4

CANONICAL ANALYSIS Correlations Eigenvalue Correlation Corr.Sq. 1 0.6318 0.3992 2 0.3782 0.143 3 0.1801 0.0324

Figure 22. Canonical analysis of principal coordinates according to site of Cladoceran biological data from a resemblance matrix based on Bray Curtis similarity.

CAP Canonical analysis of principal coordinates

Resemblance worksheet Name: Resem-BIO-sqrt-BC-Copepods Data type: Similarity Selection: All Transform: Square root Resemblance: S17 Bray Curtis similarity

Factor for groups: Loc

Number of samples: 28 Choice of m: 4

CANONICAL ANALYSIS Correlations Eigenvalue Correlation Corr.Sq. 1 0.7434 0.5526 2 0.5844 0.3415 3 0.4802 0.2306

Figure 23. Canonical analysis of principal coordinates according to site of Copepod biological data from a resemblance matrix based on Bray Curtis similarity.

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CAP Canonical analysis of principal coordinates

Resemblance worksheet Name: Resem-BIO-sqrt-BC-Rotifers Data type: Similarity Selection: All Transform: Square root Resemblance: S17 Bray Curtis similarity

Factor for groups: Loc

Number of samples: 28 Choice of m: 3

CANONICAL ANALYSIS Correlations Eigenvalue Correlation Corr.Sq. 1 0.813 0.661 2 0.1356 0.0184 3 0.0997 0.0099

Figure 24. Canonical analysis of principal coordinates according to site of Rotifer biological data from a resemblance matrix based on Bray Curtis similarity.

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CAP Canonical analysis of principal coordinates

Resemblance worksheet Name: Resem-BIO-sqrt-BC-Cladoceran Data type: Similarity Selection: All Transform: Square root Resemblance: S17 Bray Curtis similarity

Factor for groups: Zone

Number of samples: 28 Choice of m: 6

CANONICAL ANALYSIS Correlations Eigenvalue Correlation Corr.Sq. 1 0.9619 0.9253 2 0.8387 0.7035 3 0.7809 0.6098 4 0.6801 0.4625 5 0.499 0.249 6 0.1003 0.0101

Figure 25. Canonical analysis of principal coordinates according to date of Cladoceran biological data from a resemblance matrix based on Bray Curtis similarity. 102

CAP Canonical analysis of principal coordinates

Resemblance worksheet Name: Resem-BIO-sqrt-BC-Copepods Data type: Similarity Selection: All Transform: Square root Resemblance: S17 Bray Curtis similarity

Factor for groups: Zone

Number of samples: 28 Choice of m: 3

CANONICAL ANALYSIS Correlations Eigenvalue Correlation Corr.Sq. 1 0.7725 0.5968 2 0.5768 0.3327 3 0.3768 0.142

Figure 26. Canonical analysis of principal coordinates according to date of Copepod biological data from a resemblance matrix based on Bray Curtis similarity.

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CAP Canonical analysis of principal coordinates

Resemblance worksheet Name: Resem-BIO-sqrt-BC-Rotifers Data type: Similarity Selection: All Transform: Square root Resemblance: S17 Bray Curtis similarity

Factor for groups: Zone

Number of samples: 28 Choice of m: 4

CANONICAL ANALYSIS Correlations Eigenvalue Correlation Corr.Sq. 1 0.9587 0.919 2 0.9187 0.844 3 0.6079 0.3695 4 0.3502 0.1227

Figure 27. Canonical analysis of principal coordinates according to date of Rotifer biological data from a resemblance matrix based on Bray Curtis similarity. 104

APPENDIX C: Data Concerning Daphnia lumholtzi

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