Zooplankton As Indicators of Lake Change in a Dredged Southwest Florida Lake

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Ecological Indicators of Restoration Success: Zooplankton as Indicators of Lake Change in a Dredged Southwest Florida Lake

A Thesis Presented to

The Faculty of the College of Arts and Sciences

Florida Gulf Coast University

In Partial Fulfillment

Of the Requirements for the Degree of

Master of Science in Environmental Science

John A Ferlita II

May 2014 2

Florida Gulf Coast University Thesis

APPROVAL SHEET

This thesis is submitted in partial fulfillment of

the requirements for the degree of

Masters of Science

______

John A Ferlita II

Approved: May 2014

______Edwin M. Everham, III, Ph.D. Committee Chair/Advisor

______David W. Ceilley, MS Co-Chair/Advisor

______Serge Thomas, Ph.D. Committee Member

ACKNOWLEDGEMENTS

I would like to thank the Big Cypress Basin Board of the South Florida Water Management

District for providing the funding to Florida Gulf Coast University which allowed me to conduct this research; FGCU’s Inland Ecology Research Group for providing me with a graduate research assistantship and all equipment, materials, and vehicles necessary; the College of Arts and Science and Coastal Watershed institute for the use of field vehicles; all the wonderful people of the Lake Trafford Marina; and of course my parents for encouraging me to pursue a

Master’s degree.

I would particularly like to express my appreciation to my committee members for being professors, mentors, employers, and friends throughout my time at FGCU; to my committee

Chair Win Everham with FGCU for teaching me how to be a scientist, keeping me focused, and providing insurmountable knowledge and experience; to my co-chair and mentor David W.

Ceilley for guidance, support, and teaching me all the knowledge and skills needed to complete my degree; and to committee member Serge Thomas providing extensive limnological knowledge and making me a better and more confident scientist and researcher.

ABSTRACT

Eutrophication, caused by excess inputs of nutrients in lakes, rivers, estuaries, and coastal oceans, is a worldwide problem. Although the addition of nutrients may lead to abrupt increases in eutrophication, immediate decreases of such inputs do not always cause rapid or complete reversal of eutrophic conditions. Internal nutrient loading will often drive the eutrophication status of the lake and delay its recovery. Internal loading can often be vastly decreased by sediment removal through sediment dredging, which has been used in many lake restoration projects as an eco-engineering technology. However, it is still a controversial technique.

Biological assessments are currently the chief method used to determine the integrity or

“bio-integrity” of an ecosystem. Aquatic invertebrates are an integral part of freshwater biotic communities and can be used to indicate disturbance or recovery of aquatic systems.

Zooplankton is a major contributor to the importance of invertebrates within aquatic systems.

Zooplankton has potential value as indicators of changing trophic state since community structure and composition are greatly affected by disturbed conditions such as eutrophication.

The objectives of this study were to: i) examine the spatial and temporal patterns of zooplankton in Lake Trafford, ii) explore possible controlling factors for changes in the zooplankton community (including water parameters and phytoplankton), and iii) evaluate the potential use of zooplankton community characteristics as a measure of lake health in a post dredged southwest

Florida lake.

Our findings indicate spatial distribution of zooplankton is highly variable within Lake

Trafford and the influence of wind and wind driven waves seem to be the driving factors for this water body. Seasonal patterns of zooplankton abundance are opposite of the normal summer peaks and winter lows. In addition the seasonal peaks appear to be becoming less severe over 5 time thus, potentially indicating a more stable lake ecosystem subsequent to dredging. The stabilization and indication of the altered community structure is apparent and may be a precursor to major lake change. It appears that a transitional period is taking place and continued monitoring should ideally reveal a definitive lake change. Temperature was shown to be the most important abiotic factor driving zooplankton abundance. Zooplankton was indeed negatively correlated with temperature in Lake Trafford. Water temperature and conductance were key factors during the spring and summer seasons while other abiotic factors (DO, pH, light penetration, and wind) were more important in the winter and fall. A significant correlation between phytoplankton and zooplankton was found; however, their relationship is weak. Further study into alternative bottom-up control is suggested.

Unique spatial and temporal patterns of zooplankton abundance exit in Lake Trafford.

Continued monitoring of zooplankton may help illuminate post-dredging biotic dynamics and guide management decisions. A deeper understanding of Lake Trafford dynamics may help inform management decision on other eutrophic subtropical lakes.

TABLE OF CONTENTS

Page

Approval Sheet....………………………………………………………………………………... 2

Acknowledgments.………………………………………………………………………………. 3

Abstract...... ……………………………………………………………………………………....4

Table of Contents…………..……………………………………………………………………. 6

List of Figures..…………………………………………………………………………………...7

List of Tables……………………………………………………………………………………...8

Introduction....………………………………………………………………………………….... 9

Objectives………………………………………………………………………………………..21

Methods…………………….…………………………………………………………………....23

Results……….……….....……….………………………………………………………………27

Discussion……………….……….……………………………………………………………....59

Summary Conclusions and Recommendations...…………………………….………………….69

Literature Cited....……………...………………………………………………………………..71

APPENDIX A: Surfer Maps of Zooplankton Distribution.....………...... ……………………..77

APPENDIX B: Zooplankton MDS and Zooplankton-phytoplankton MDS Ordinations...……..96

APPENDIX C: Results of SIMPER and ANOSIM Analysis..………………………………….97

APPENDIX D: Zooplankton Density and Temperature Bar Graph…………………………....100

APPENDIX E: Phytoplankton Bubble Overlays……………………………………………… 102

LIST OF FIGURES

Page

Figure 1. Map of Southwest Florida, location of Lake Trafford and sampling sites……….…..20

Figure 2a. Surfer map of zooplankton spatial variation, June 2010 ……………………………27

Figure 2b. Surfer map of zooplankton spatial variation, December 2010………………………28

Figure 3. Mean densities for major zooplankton groups …………………………………….....29

Figure 4. Cluster diagram of zooplankton sampling points grouped by year…………………..30

Figure 5. MDS ordination of the biweekly sampling zooplankton population data……………31

Figure 6. MDS ordination of zooplankton points by year and trajectory overlay………………32

Figure 7. 2D bubble overlay of Cladoceran zooplankton abundance for all data points……….33

Figure 8. 2D bubble overlay of Calanoid zooplankton abundance for all data points………….34

Figure 9. 2D bubble overlay of Cyclopoid zooplankton abundance for all data points………...35

Figure 10. 2D bubble overlay of Ostracod zooplankton abundance for all data points………..36

Figure 11. 2D bubble overlay of Rotifer zooplankton abundance for all data points.………….37

Figure 12. Principle Componant Analysis (PCA) of abiotic parameters……….……………….43

Figure 13. Principle Componant Analysis (PCA) of abiotic parameters. Overlayed is data points by year in mutlidimentional space………………...………………………44 Figure 14. Principle Componant Analysis (PCA) of abiotic parameters. Overlayed is data points by season in mutlidimentional space………………...……………………45 Figure 15. Correlation scatter plot of zooplanton mean abundance and temperature....………..46

Figure 16. Correlation scatter plot of zooplanton mean abundance and dissolved oxygen…….47

Figure 17. Correlation scatter plot of zooplanton mean abundance and secchi depth………….48

Figure 18. Correlation scatter plot of zooplanton mean abundance and conductance………….49

Figure 19. Correlation scatter plots of zooplankton and temperature for each sampling year….50

Figure 20. Mean densities for major zooplankton groups and temperature over time………….51 8

Figure 21. Cluster analysis of zooplankton-phytoplankton sampling points grouped by year…52

Figure 22. MDS ordination of the biweekly sampling zooplankton and phytoplankton data….53

Figure 23. MDS ordination of the biweekly sampling zooplankton and phytoplankton data with trajectory overlay....……..…………………………………………………………54

Figure 24. Mean total phytoplankton and total zooplankton biomass over time……………….55

Figure 25. Zooplankton biomass volume and phytoplankton mean totals as ratio over time…..56

Figure 26. Correlation scatter plot of zooplankton biomass (tot_biov) vs phytoplankton concentration (C_tot) (fall-winter data)…….…………………………………………..57 Figure 27. Correlation scatter plot of zooplankton biomass (tot_biov) vs phytoplankton Concentration (C_tot) (spring-summer data)……………..…………………………….58

LIST OF TABLES

Table 1. SIMPER results of zooplankton group comparison for 2010 and 2011……………….38

Table 2. SIMPER results of zooplankton group comparison for 2010 and 2012……………….39

Table 3. SIMPER results of zooplankton group comparison for 2010 and 2013……………….39

Table 4. SIMPER results of zooplankton group comparison for 2011 and 2012……………….40

Table 5. SIMPER results of zooplankton group comparison for 2011 and 2013……………….40

Table 6. SIMPER results of zooplankton group comparison for 2012 and 2013……………….41

Table 7. ANOSIM results for study year comparison of mean zooplankton data.………………42

CHAPTER 1

INTRODUCTION

Eutrophication

Eutrophication, caused by excess inputs of nutrients in lakes, rivers, estuaries, and coastal oceans, is a worldwide problem (Smith, 1998; Carpenter et al., 1999). Limnetic systems have undergone significant human-driven changes in recent decades. Increased development and urban sprawl has led to a rise in eutrophication of freshwater systems and downstream estuaries which profoundly affects future development of a sustainable society and economy.

Negative effects of eutrophication include increased plant growth, shifts in phytoplankton blooms to toxic or unpalatable species for grazers, increased turbidity, dissolved oxygen depletion leading to fish kills, and issues with wastewater and subsequent treatment (Smith,

1998; Carpenter et al., 1999). Furthermore, increased phosphorus, nitrogen, and decreased dissolved oxygen, can have economic consequences with respect to watersports, tourism, quality of drinking water, and the quantitative and qualitative yield of fisheries (Lotter et al., 1998).

Eutrophication in the United States is estimated to account for 60% of the impaired river reaches, roughly 50% of the impaired lakes, and is subsequently the largest contributor to estuarine pollution (USEPA 1996; Carpenter et al., 1999). Most of the excess nutrient input to U.S. waters is caused by nonpoint pollution from agricultural and urban lands (Carpenter et al., 1999). Other causes of eutrophication include - but are not limited to - soil erosion, industrial effluent, and improper wastewater-sewage disposal. However, these sources are diffuse and such pollution is difficult to measure and or regulate.

Although the addition of nutrients may lead to abrupt increases in eutrophic effects, immediate decreases of such inputs does not always cause rapid or complete reversal of eutrophication (Carpenter et al., 1999; Sas, 1989; Cooke et al., 1993). The delayed responses and or lack of responses are often linked to nutrient recycling, commonly phosphorus (P). Where P is accumulated in sediments, a decrease in external loading may result in an increase in rates of recycling from sediments back to the water column (Carpenter et al., 1999). The release of nutrients from sediments or “internal loading” is commonly present in shallow, eutrophic lakes

(Sondergaard et al., 2001). This is unlike oligotrophic lakes, where the microbial loop and their subsequent nutrient cycling tend not to have the same influence on water columns (Cotner and

Biddanda, 2002). This has much to do with competition with larger autotrophs for available nutrient and carbon forms and phytoplankton’s slower growth rates (Cotner and Biddanda,

2002). It has been proposed that organic carbon is less available to bacterio-plankton in eutrophic systems, compared to oligotrophic systems, due to reduced relative phytoplankton excretion and higher sedimentation (Gasol and Duarte 2000; Cotner and Biddanda, 2002). Additionally, grazing by protozoan bacterivores is generally high in eutrophic lakes as they generally ingest the largest, fastest growing cells (Sanders et al., 1992). Hence, the role of the microbial loop on nutrients tends to be less critical in eutrophic systems as opposed to oligotrophic ones.

Internal nutrient loading often drives the eutrophication status of the lake and delay its recovery even though external nutrient loading reductions are implemented (Pettersson, 1998).

Internal nutrient loading is especially important in summer months where nutrient concentration and primary production in the form of phytoplankton blooms co-occur (Pettersson, 1998). Source

P held in surficial sediments is quite large in comparison with those of the water column, meaning that even a very small percentage released will have a significant impact on phosphorus 11 lake water concentrations. Phosphorus (P) release can occur via two different mechanisms: release at the sediment-water interface during anoxic or hypoxic conditions; and re-suspension of sediments releasing P from pore water or desorbing from sediment particles (Selig, 2003).

Whole lake experiments show that rates of P can increase to significant levels in a matter of years as recycling from sediments to water of eutrophic lakes commonly exceeds inputs

(Carpenter et al., 1999; Schindler et al. 1987; Soranno et al. 1997). Eutrophication cannot often be reversed by input reductions alone and eutrophy can be sustained long after reduction of inputs which will inherently interfere with restoration efforts (Sas 1989; Carpenter et al., 1999;

Cooke et al. 1993). Therefore, lakes which fall under such characterization will likely require additional steps towards reduced recycling from lake sediments. One such intervention involves large scale removal of excess nutrient bearing sediments, referred to as “restoration dredging”.

Lake Restoration through Sediment Dredging

Sediment dredging has been used in many lake restoration projects as an eco-engineering technology and yet, it is still a controversial technique (Jing et al., 2013). It is one of many restoration tools commonly used in coastal and estuarine habitats. Dredging can be used as a method to control eutrophication rather than to simply deepen passage ways as in marine ports and waterways. For example, dredging has been widely used for the treatment of eutrophic lakes in China because of its positive effect on water quality (Zhang et al., 2010). However, the effects of sediment dredging can also be negative (Zhong and Fan, 2007; Zhang et al., 2010). Some common negative impacts include the release of toxic chemicals, increased turbidity, mortality of benthic organisms, altered food webs, and issues with disposal of the dredged material.

For applications regarding eutrophic control, internal loading can often be vastly decreased by sediment removal through dredging activity. However, some dredging projects 12 have failed to achieve the desired positive effect (Jing et al., 2013; Zhong and Fan, 2007).

Sediment dredging may be detrimental to lake ecosystems as it can potentially alter internal balances in lakes (Jing et al., 2013; Pu et al., 2000). Nutrient cycling in lakes is directly and significantly influenced by disturbance at the sediment-water interface which ultimately occurs with dredging. Sudden shifts of nutrient balance could negatively impact primary productivity, but the effects and mechanisms of dredging on nutrient cycling are still unclear (Jing et al.,

2013).

Sediment dredging is costly and the occurrence of unanticipated effects is always a concern with regards to maintaining “restored” lakes. Consideration of impacts to nutrient recycling, particularly phosphorus, from exposed sediment layers should be made prior to dredging (Kleeberg and Kohl, 1999). Currently there are only a limited number of studies on P release from post dredged sediments (Ruley and Rusch, 2002; Kleeberg, 1999; Ryding, 1982;

Peters & van Liere, 1985) and data on future sediment-water interface and P remobilization are lacking (Kleeberg, 1999). Studies suggest determining optimum dredging depth by identifying sediment layers which contain less readily exchangeable forms of P, and thus reducing potential for remobilization of P (Kleeberg, 1999). However, changes in forms of sediment P and prolonged restriction of P releases remain to be validated.

It thus appears essential to understand both positive and potential negative effects before making decisions to dredge. Some studies have performed experiments to assess dredging effects using sediment cores with simulated dredging disturbance but, these were done in the laboratory and cannot reflect longer timescales and or incorporate unique variability found in the field

(Reddy et al., 2007 and Zhong et al., 2010; Jing et al., 2013). The interactions between dredged and un-dredged areas also make it difficult to understand the effectiveness of dredging projects 13

(Jing et al., 2013). Hence, there is a need for additional research examining the success of restoration dredging projects.

Monitoring Lake Recovery Post-Dredging

Biological assessments are currently the chief method used to determine the integrity or

“bio-integrity” of an ecosystem. Frey (1977) defines bio-integrity specifically as “the capability of supporting and maintaining a balanced, integrated, adaptive community of organisms having a species composition, diversity, and functional organization comparable to that of the natural habitat of the region”. The assessment of ecosystem health cannot proceed without measuring the level of biological integrity (Gibson et al. 1996; Gerritsen et al., 2000). Bio-assessments evaluate ecosystem condition by measuring present flora and fauna and incorporate impacts of biotic and abiotic stressors as indications of aquatic system health (EPA, 2011). They can function as on- going measures of quality and respond to habitat alteration and both episodic and cumulative pollution effects (Gerritsen et al., 2000). The data gathered from these assessments ultimately provide the ability to set future goals for restoration if needed, and environmental impact analysis (EPA, 2002). However, seldom are the resources available to document “a balanced, integrated, and adapted community of organisms”. Monitoring programs typically focus on a single or limited number of taxa as “indicators” of ecosystem health. In lakes, these have typically included plants, fish, and benthic organisms. Florida is one of only nine states with lake bio-assessment programs in place and one of only three states developing numeric bio-criteria for lakes (USEPA, 2007). Florida’s Department of Environmental Protection (DEP) has established specific bio-assessments to determine the biological state of lakes and other aquatic systems. The major indices used are the Lake Vegetation Index (LVI) and Lake Condition Index

(LCI). 14

The Lake Vegetation Index (LVI) evaluates lake plant communities and compares how similar or dissimilar they are to those of condition with minimal human disturbance (DEP-SAS-

002, 2011). It was developed by relating plant metrics with indicators of human disturbance, so that the index responds to the effects of human disturbance rather than natural variability among lake plant communities (DEP-SAS-02, 2011). Such disturbance can include introduction of exotics, physical anthropogenic alterations, and chemical and nutrient inputs (DEP-SAS-002,

2011). The LVI procedure divides the lake into twelve sections, of which four are randomly selected for each sampling event and the dominant and or co-dominant macrophyte species are determined. With the information collected, calculations are then used to determine the percent of native species, percent of invasive species, and the percent of sensitive species. Numeric scores for each sampling section are created and then averaged together for a total lake score; 0-

37 is impaired, 38-77 is healthy, and 78-100 is exceptional.

Similarly, the Lake Condition Index (LCI) is a bio-assessment which uses the sampling of macro-invertebrates within the benthic zone to determine the biological status of lake ecosystems. This method was developed in the late 1990’s based on “best professional judgment” to assign reference and non-reference lakes (Gerritsen et al., 2000; Fore, 2007).

Macroinvertebrates are classified as animals which have no backbone and are visible with the naked eye; usually larger than ½ mm in size or retained in a 500 micron sieve. Here, the macroinvertbrate community’s biological integrity at the lake bottom is used to gauge the status of the entire lake. As with the LVI, the lake is divided into twelve sections, benthic samples are taken from each section using a standard petite Ponar dredge and species are identified and counted. Using these data the percent of pollution tolerant, intolerant, and sensitive species are 15 calculated. Results are then averaged together for an overall score divided into four categories;

Very good (≥55), Good (35-54), Poor (18-34), and Very poor (<18) (FDEP, 2000).

While both the LVI and LCI have been used for years in determining the status of Florida lakes, it has recently come in to question whether these measures are truly accurate (Fore, 2007).

Some suggest they are an incomplete measure of the system as a whole and therefore confidence is lacking (Fore, 2007). This is especially true in lakes where the vegetation has been managed for many years with the benthic sediment removed as part of a restoration effort. Other methods of monitoring and assessment can and should be used in addition, to validate LVI and LCI indices and provide greater confidence. For example, through “biological monitoring”, analysis of organisms such as phytoplankton, zooplankton, and fish communities can lead to the establishment of bio-indicators and indices which can be used to assess pollution and trophic status (Kumari et al., 2008). Currently bio-monitoring has become an integral part of pollution studies and water quality assessments. It is possible that using these other methods of assessing biological integrity in place of LVI and LCI indices can provide an accurate and dependable ecosystem health status, and moves us closer to the ideal of determining if the system is maintaining a “balanced, integrated, adaptive community of organisms having a species composition, diversity, and functional organization comparable to that of the natural habitat of the region” (Frey 1977).

Zooplankton

Aquatic invertebrates are an integral part of freshwater biotic communities. Most are the primary food source for forage fish and function as decomposers and recycle nutrients for use by primary producers (Watkins et al., 1983). They can also be used to indicate disturbance or recovery of aquatic systems (Havens, 2002). Invertebrate abundance, composition, and 16 distribution seem to be regulated by a number of biotic and abiotic factors (Watkins et al., 1983) including: temperature; wind; conductivity; turbidity; and predator/prey interactions; and even presence or absence of aquatic vegetation and their roles as possible refugia.

Zooplankton are a major contributor to the importance of invertebrates within aquatic systems. Zooplankton communities are the result of growth, reproduction, competition for resources and predation pressures. However, community structure is primarily shaped by physico-chemical factors and biotic factors, predation and interspecific competition for resources, being dominant forces (Blancher, 1984). Rapidly changing environments favor opportunistic highly reproductive species while more consistent conditions favor less flexible competitively superior species. Highly eutrophic lakes usually exhibit large populations of herbivorous zooplankton, such as cladocerans, which have short life cycles (Blancher, 1984;

Havens 2002). Oligotrophic systems tend to be dominated by copepod zooplankton that exhibit longer life cycles (Blancher, 1984). Zooplankton populations have potential value as indicators of changing trophic state since community structure and composition are greatly affected by disturbed conditions, including eutrophication. The effect of eutrophication on individual species has been well described but, the use of species indicators can be limited by regional specificity and taxonomic uncertainty (Gannon and Stemberger, 1978; Blancher, 1984). Alternatively, changes in major zooplankton groups have been proposed as being a valuable indicator of trophic condition (Jose and Sanalkumar, 2012; Blancher, 1984; Gannon and Stemberger, 1978).

In the wide range of uncertain lake types, quantitative data on zooplankton community composition offer more potential than qualitative information on species presence or absence.

For instance, the ratio of calanoid copepods to other major groups of zooplankton appears to be 17 of value in identifying relative differences in trophic conditions (Gannon and Stemberger, 1978;

Wang et al., 2007).

Compared to well-studied zooplankton of northern temperate environments, freshwaters of subtropical and tropical environments are believed to exhibit lower zooplankton species diversity, but composition and behavior of tropical zone species are far less investigated

(Guevara et al., 2009; Sarma et al., 2005). Despite the lack of thorough studies, there are species present that are suggested to be good indicators of lake trophic status (Wang et al., 2007).

Zooplankton abundance is commonly high in shallow lakes and hence, are indicators of ecosystem health in shallow lakes systems (Cozar et al., 2003; Garcia, 2009). Major zooplankton groups likely respond differentially to influences of phytoplankton biomass which vary with trophic status (Wang et al., 2007).

Generally, food resources that are available to zooplankton undergo qualitative changes in composition as trophic state increases. For instance, oligotrophic lakes in Florida may express phytoplankton communities that are dominated by algal species which are a good food source for zooplankton species (Havens, 2002). Larger adult zooplankton are relatively more efficient at using these larger algae but, are not able to fully utilize filamentous or colonial blue-green algae especially when toxic or poorly palatable. As a result, these algal groups are less capable of supporting healthy and stable macro-zooplankton populations (Porter, 1977). Macrozooplankton are also more subject to predation with increased trophic state due to the abundance of zooplanktivorous fish in these systems (Drenner et al., 1982). Macrozooplankton tends to show inconsistent response to nutrient enrichment due to decreased food availability and increased fish predation. Additionally, algal populations in shallow eutrophic lakes have been predicted to be 18 correlated with nutrients and either non-correlated or positively correlated with zooplankton biomass (Havens, 2002).

It has been well documented that natural zooplankton abundances are also related to fish production; for example, predation pressure on cladocerans is often high in the presence of larval and juvenile fish (Kerfoot and Sih 1987). Physicochemical variables such as temperature, pH and dissolved oxygen also regulate the density and diversity of zooplankton (Berzins and Pejler,

1987; 1989), and since these variables change annually, there is significant interest to quantify seasonal changes in zooplankton community structure (Garcia, 2009).

Top-down influences can be just as important as bottom-up forces in structuring aquatic ecosystems (Carpenter et al. 1985). Zooplankton communities play an important role and reflect the influence of both bottom-up and top-down processes because they represent the link between predators and primary producers. However, the relative influence of these forces on zooplankton can vary considerably with; for example, nutrient availability, lake depth, and opportunities for refuge (Schriver et al., 1995; Gyllstrom et al., 2005). Warmer climates are associated with a shift toward omnivorous fish species which may exert a strong, negative effect on zooplankton such as increased predation resulting in a lowered proportion of predation-sensitive species like large

Daphnia (Hansson et al., 2004). Bottom-up effects caused by changes in temperature may be expected as the contribution of cyanobacteria, to the total phytoplankton biomass, has been shown to increase with temperature (Gyllstrom et al., 2005). Because cyanobacteria are characterized as being less edible, their increased proportion would be expected to result in a negative impact on the zooplankton community of warmer climates (Gyllstrom et al., 2005).

Ratios between trophic levels are suggested as a possible method for explaining differences in zooplankton communities (Gyllstrom et al, 2005). For instance, the ratio between zooplankton 19 and phytoplankton biomass is sensitive to top-down control through cascading trophic interactions from fish to zooplankton to phytoplankton and therefore a low ratio may be indicative of top-down control (Jeppeson et al., 2000; Hessen et al., 2003).

Lake Trafford

Lake Trafford is a shallow, subtropical lake located in southwest Florida (Fig. 1). With a surface area of about 600 ha and an average depth of 2 m, it is the largest freshwater lake south of Lake Okeechobee. The lake has no defined tributaries, but instead expands (flooded adjacent marsh habitat) and contracts based on inflow. It receives drainage from its sub-basin surface and groundwater drainage area and typically has little outflow (Flaig, 2000). Deposits of the Talbot terrace, which make up the lake watershed, are characterized by very fine to coarse sand and some silt and clay; this provides for fairly rapid water infiltration (Kang, 2008; Florida

Geological Survey, 1962). Historically the lake bed has been dominated by sand and the bed supported a healthy population of native aquatic plants. However, it has been subjected to increasing anthropogenic nutrient loading primarily from nearby agricultural lands, and urban areas associated with the city of Immokalee. 20

Figure 1. Map of Southwest Florida, location of Lake Trafford and sampling sites.

Since its introduction in the late 1960s, excessive growth of Hydrilla verticillata, an invasive exotic submerged aquatic rooted macrophyte, had out competed native aquatic plant species and negatively impacted the lake (Everham, 2007). It has similarly impacted many other

Florida lakes (Bowes, 1979). As a result of using herbicide treatments throughout the 1970s - 90s to eradicate H. verticillata, the lake bottom had accumulated large amounts of decaying plant matter (Lake Trafford and You, 2000), which consequently released nutrients back into the water column (i.e. “internal loading”). At high nutrient levels in the water column, lake systems are dominated by algal growth (Bachmann et al., 2002) and the buffering mechanisms resist change to more macrophyte-dominated systems (Everham, 2007). Due to these circumstances, pre- 21 restoration Lake Trafford has been a phytoplankton dominated system with frequent algal blooms and fish kills.

A restoration project involving hydraulic dredging of excess sediments and decaying vegetation from the bottom removed 6.3 million cubic yards (SFWMD, 2011). Initially, turbidity and decreased water clarity from the re-suspension of excess sediments by wind generated waves and resulting algal blooms, has prevented natural recruitment of native aquatic macrophytes.

Although Secchi disk depth remains shallow, replanting efforts and some natural recruitment has increased the macrophyte coverage in the lake to approximately 29 ha as of September 2013

(Ceilley et al., 2013).

Research Objectives This study is intended to develop a post restoration baseline of the spatial and temporal variation that occurs within the zooplankton community of Lake Trafford, toward the possible use of zooplankton community data as an indicator of lake health. Preliminary data has shown significant differences between winter and summer seasons. Hence, it is expected that seasonal abundance of zooplankton populations will vary considerably. Abiotic environmental factors can play an important role in controlling the biotic communities of lakes, so their influence on zooplankton will be explored here. Data should provide information on the current trophic state within the lake and to reveal changes in lake health. The following research objectives are:

Objective 1: To examine the spatial and temporal patterns of the zooplankton population

within Lake Trafford. 22

Objective 2: To explore possible controlling factors for changes in the zooplankton community.

2A. Correlate zooplankton abundance to various abiotic water quality parameters.

2B. Quantify the phytoplankton-zooplankton relationships.

Objective 3: To evaluate the potential use of zooplankton community data as a measure of lake health.

CHAPTER 2 METHODS

Sampling Design A total of twelve geo-referenced sampling stations were chosen for this study (Fig. 1).

These stations were equally distributed between all the regions of Lake Trafford to best capture spatial variation. Water quality parameters were collected at five of twelve sampling sites (T1,

T4, T6, T11, T12). Measurements included water temperature, conductivity, dissolved oxygen, depth, and Secchi disk depth (Havens, 2002). Water profiles were conducted at station T6 which is deep and centrally located. Water temperature, dissolved oxygen and conductivity were measured every 0.25m increments until the lake bed was reached with an YSI™ 85 meter and probe. The probe was allowed to equilibrate at each measuring depth. Irradiance profiles were conducted with a LICOR® 1400 meter connected to a 4Pi LI193 Quantum sensor. Irradiance measurements were taken at every 0.25m from the side of the boat facing the sun to avoid boat shading and when the sky was uniformly clear or cloudy. The pH was measured in subsurface with a Hach HQ40 meter connected to an Intellical PHC281 pH probe. The secchi disk depth was measured to the nearest cm at all stations to estimate water clarity (Havens, 2002). At each station and the disk was lowered to the bottom to assess the water depth. Sampling stations were reached by means of 18 foot aluminium (“Sea Ark”) boat from the Inland Ecology Research

Group (IERG) at FGCU.

Zooplankton Bi-monthly diurnal surveys of zooplankton communities were performed at the twelve stations from March 2010 until March 2013. Diurnal subsurface and bottom sample collection were conducted using a standard 363-micron mesh plankton net (diameter of 20cm) which 24 excludes nano-plankton. Tow samples, of five meter length, were transferred into individual

125ml Nalgene bottles with volumes measured in laboratory. Samples were then taken back to the laboratory and were either immediately identified or refrigerated for later identification

(within 24 hrs). Organisms were identified to the lowest practical taxonomic level with density estimates for major taxa prepared based on length of tow, diameter of the net aperture, and

Sedgewick-Rafter cell counts. Numeric densities were calculated from counts as animals per litre

(Havens, 2002). When applicable or definable, percent gravidity of each sub group was recorded as well.

High Frequency Sampling In order to better understand zooplankton spatial dynamics, two 24 hour sampling events were performed to gain high temporal frequency data and assess both nocturnal and transitional zooplankton behaviours. For the 24 hour events, five sites were chosen for best spatial distribution and time management (T1, T4, T6, T11, T12). Top and bottom zooplankton tows were taken at each site every three hours over a period of twenty four hours. Collected samples were placed on ice and returned to the laboratory for identification.

Phytoplankton Phytoplankton samples were collected from all 12 sampling locations each month. At each station, the phytoplankton sample was hand collected at 0.25 meter deep using a 500 ml opaque bottle. The capped bottle was kept in the dark between 2-24 hours at room temperature in a cooler with no ice until being analyzed.

Phytoplankton analysis: As an alternative to photic microscopy, the various types of algae and their biomass as assessed by their total chlorophyll concentration per liter were determined with a WALZ™ Phytopam (www.walz.com). The phytopam is a tetra fluorometer which separates 25 the various algae groups using a small 3-ml dark adapted water sample. The total chlorophyll concentration of all three algae groups, Cyanophyceae, Chlorophyceae, and Bacillariophycaea were thus determined by a complex algorithm deconvoluting the fluorescence emitted by the algae upon excitation at four wavelengths. These excitation wavelengths are 620 nm to excite the phycocyanine found in Cyanophyceae, 470 nm and 650 nm to excite the chlorophyll b found in

Chlorophyceae and 535nm to excite the chlorophyll c and carotenoids found in

Bacillariophyceae.

Data analysis Zooplankton abundances were compared between sampling sites for spatial distribution analysis. Surfer version 8 (www.goldensoftware.com) was used to create zooplankton abundance event maps for visual interpretation. Primer V6 (Clarke and Gorely, 2006) was utilized to perform multivariate analyses on distribution and abundance of major zooplankton groups and major phytoplankton groups. Data were standardized to alleviate the inconsistency in number of sampling sites per sampling period. In addition, 4th root transformation of the data was applied so that important plankton groups would not be overshadowed by extreme differences in variable densities. Multidimensional scaling (MDS) ordinations based on Bray-Curtis similarities were also performed to explore patterns and or identify movement in multidimensional space regards to environmental changes. Bubble overlays were created for each major organismal group and distinguished by year, season, and density for visual interpretation. Similarity Percentage Test

(SIMPER) was used to identify which major taxonomic groups contribute the most to the similarity within a group and the dissimilarity between groups throughout study years. Analysis of similarity (ANOSIM) was used to determine similarity between sampling years and was performed using fourth route transformed data of zooplankton. ANOSIM can further be 26 described as a multivariate analogue of analysis of variance between groups through permutated randomization tests.

Principle component analysis (PCA) was used to test relationships of the abiotic variables between one another as well as the relationships between those abiotic variables and phytoplankton to illuminate factors which might affect zooplankton. Correlation analyses of zooplankton abundance and water quality parameters were performed in Microsoft Excel and

SPSS to identify significant relationships. Correlation of zooplankton and phytoplankton were performed using SPSS 20 statistical software (www.ibm.com/SPSS-Statistics). SPSS was used to explore possible significant zooplankton-phytoplankton coupling. For this, zooplankton was transformed to biomass data, by methods of Binggeli (2011), and expressed as a ratio relative to phytoplankton concentration.

CHAPTER 3

RESULTS

Objective 1“Spatial and Temporal Patterns”

Zooplankton populations show both spatial and temporal variation. Figures 2a and 2b illustrate Surfer maps of peak seasonal change in average zooplankton population data from sampling sites (see appendix A for all maps). The series of maps chronologically highlight spatial variation for each sampling date and temporal changes between dates. Note how winter months exhibit higher zooplankton density and greater spatial variation as compared to summer months which exhibit lower density and more monotypic distribution.

6/25/10 Wind – E 5-8mph

Organisms/Liter

Temp °C 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16

Figure 2a. Surfer map of zooplankton spatial variation in June 2010. Black dots depict the sampling stations. 28

12/22/10 Wind – E 10mph

Organisms/Liter

Temp°C 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16

Figure 2b. Surfer map of zooplankton spatial variation from December 2010. Black dots depict the sampling stations.

Figure 3 shows the mean densities of major zooplankton groups (cladocerans, copepods, and cyclopoids) for each sampling date from March 2010 to March 2013. Population data indicate repeating patterns or “cycles” of increase and decrease abundance throughout each year and their subsequent seasonality. A cyclic pattern exists where all major zooplankton groups drastically increase during fall-winter months and decrease in late spring-summer months. Additionally, major zooplankton groups begin to increase abundance earlier and decrease later within sampling years. The zooplankton “eruptions” or “wave like successions” express less intensity with each progressive year and season. Additionally, the ratios or differences of major groups in relation to one another have become closer overtime. (see appendix D for larger graph)

Figure 3.Mean densities for major zooplankton groups (cladocerans, calanoids, and cyclopoids) from March 2010 to March 2013.

Figure 4 shows a cluster diagram of sampling events and their associated zooplankton population data. Sampling years are identified by unique color and symbol. Cluster analysis lump the majority of 2010-2011 data together and 2012-2013 together indicating difference in zooplankton between these time periods. There are two main grouping and one outlier. Each main grouping has a similarity of about 70 percent.

Figure 4. Cluster diagram of zooplankton sampling points grouped by year.

Figure 5 shows the MDS ordination of each sampling event, using the zooplankton adundance data. Yearly data is identified by a unique color and symbol. Note that the two- dimensional stress – a measure of the degree to which the image accurately represents the multidimensional pattern – is 0.14, below the 0.2 recommended level (Clarke and Gorely, 2006) indicating an accurate representaion of the relationship in multi-dimensional space. Although the scatter within the ordination space probably reflects the seasonal patterns shown in Figure 3, the ordination also shows 2012 and 2013 sampling events grouped more toward the lower left of the figure with less scatter, and 2010 and 2011 grouped more toward the upper right with more scatter. (see additional graphs in appendix B)

Figure 5. MDS ordination of the biweekly sampling zooplankton population data. 32

Figure 6 shows MDS ordination of the bi weekly sampling zooplankton population data but, includes trajectory overlay of all data points. Sampling points are distinguished by year with a unique symbol and color. The two dimensional stress is 0.14. The ordination shows 2012 and

2013 sampling events grouped more toward the lower left of the figure, and the earlier two years grouped more toward the upper right. Note how trajectory begins at one point in dimensional space but, does not return to the initial point.

Figure 6. MDS ordination of zooplankton points by year and trajectory overlay.

Figure 7 shows bubble overlays of cladoceran zooplankton abundance for all data points.

The top graph shows the zooplankton abundance by year and the bottom graph shows abundance by season. Data points arrange from year 2010 in the upper right toward 2013 in lower left in this ordination. Note how abundance overlays in both graphs are larger on the left side and smaller on the right. Cladoceran density is significantly higher in fall and winter months compared to spring and summer which also exhibits more scatter. This pattern is consistent through time.

Figure 7. 2D bubble overlay of mean cladoceran zooplankton abundance for all data points. Top graph shows abundance by year. Bottom graph shows abundance by season. 34

Figure 8 shows bubble overlay of calanoid zooplankton abundance for all data points.

Calanoid copepod density is more widespread and consistent throughout seasons but, highest densities occur in mid-winter peak winter and lowest in the heart of summer.

Figure 8. 2D bubble overlays of mean calanoid zooplankton abundance for all data points. Top graph shows abundance by year. Bottom graph shows abundance by season. 35

Figure 9 shows bubble overlay of cyclopoid zooplankton abundance for all data points.

The top graph shows abundance by year and the bottom graph shows abundance by season. Data points arrange from year 2010 in upper right toward 2013 in lower left in this ordination. Note how abundance overlays in both graphs are larger on the left side and much smaller or nonexistent on the right. Cyclopoid copepod density is significantly higher is fall and winter months compared to spring and summer whereas they are extremely low or nonexistent.

Figure 9. 2D bubble overlays of mean cyclopoid zooplankton abundance for all data points. Top graph shows abundance by year. Bottom graph shows abundance by season. 36

Figure 10 shows bubble overlay of ostracod zooplankton abundance for all data points.

The top graph shows abundance by year and the bottom graph shows abundance by season. Data points arrange from year 2010 in upper right toward 2013 in lower left in this ordination.

Ostracod abundance is relatively consistent throughout seasons of 2010 and 2011 (fall, winter, spring, and summer). However, in late 2011 ostracods largely disappear from the zooplankton community with their absence proceeding through 2013.

Figure 10. 2D bubble overlays of mean ostracod zooplankton abundance for all data points. Top graph shows abundance by year. Bottom graph shows abundance by season. 37

Figure 11 shows bubble overlay of rotifer zooplankton abundance for all data points. The top graph shows abundance by year and the bottom graph shows abundance by season. Data points arrange from year 2010 in upper right toward 2013 in lower left in this ordination. Rotifer abundance is nonexistent in years 2010, 2011, and early 2012. However, in late 2012 rotifers appear as part of the zooplankton community through 2013.

Figure 11. 2D bubble overlays of mean rotifer zooplankton abundance for all data points. Top graph shows abundance by year. Bottom graph shows abundance by season.

SIMPER analysis (similarity percentage test) was performed using fourth root transformed zooplankton data (Clarke and Warwick, 2001). Here, SIMPER test was used to identify which major taxonomic groups contribute the most to the similarity within a group and the dissimilarity between groups. The results are presented by rank order of importance.

Average dissimilarity of major zooplankton taxonomic groups between years 2010 and

2011 was 28.47% (Table 1). Contributions of each are as follows in order of importance: cladocerans 36.25%, calanoids 26.1%, cyclopoids 21.88%, and ostracods 15.76%. Average abundances in all groups increased between 2010 and 2011.

Table 1. SIMPER results of zooplankton group comparison for 2010 and 2011.

Groups 2010 & 2011 Average dissimilarity = 28.47 Group 2010 Group 2011 Species Av.Abund Av.Abund Av.Diss Diss/SD Contrib% Cum.% Cladacerans 2.63 2.90 10.32 1.46 36.25 36.25 Calanoid 2.07 2.70 7.43 1.46 26.10 62.36 Cyclopoid 0.84 0.91 6.23 1.42 21.88 84.24 Ostracoda 0.43 0.48 4.49 1.13 15.76 100.00

Average dissimilarity of major zooplankton taxonomic groups between years 2010 and

2012 was 29.03% (Table 2). Contributions of each are as follows in order of importance: cladocerans 29.26%, calanoids 23.49%, cyclopoids 17.55%, rotifers 16.41%, and ostracods

11.59%. Average abundances in all groups increased between 2010 and 2012 except for ostracods which decreased. Noteworthy is the first appearance of rotifers in zooplankton tows in

2012, with a16.41% contribution to the overall dissimilarity between communities.

Table 2. SIMPER results of zooplankton group comparison for 2010 and 2012.

Groups 2010 & 2012 Average dissimilarity = 29.03 Group 2010 Group 2012 Species Av.Abund Av.Abund Av.Diss Diss/SD Contrib% Cum.% Cladacerans 2.63 2.98 8.49 1.41 29.26 29.26 Calanoid 2.07 2.86 6.82 1.52 23.49 52.75 Cyclopoid 0.84 1.12 5.09 1.29 17.55 70.30 Rotifer 0.00 0.65 4.76 1.01 16.41 86.71 Ostracoda 0.43 0.10 3.36 0.83 11.59 98.30

Average dissimilarity of major zooplankton taxonomic groups between years 2010 and

2013 was 29.19% (Table 3). Contributions of each are as follows in order of importance: cladocerans 27.91%, calanoids 23.77%, rotifers 22.58%, cyclopoids 11.78%, and ostracods

10.07%. Average abundances increased overall between 2010 and 2013 except in ostracods which decreased to zero. Most notable were calanoids and rotifers which accounted for a combined 46.35% of total contribution.

Table 3. SIMPER results of zooplankton group comparison for 2010 and 2013.

Groups 2010 & 2013 Average dissimilarity = 29.19 Group 2010 Group 2013 Species Av.Abund Av.Abund Av.Diss Diss/SD Contrib% Cum.% Cladacerans 2.63 3.39 8.15 1.45 27.91 27.91 Calanoid 2.07 3.00 6.94 1.52 23.77 51.68 Rotifer 0.00 0.96 6.59 1.41 22.58 74.26 Cyclopoid 0.84 1.13 3.44 1.04 11.78 86.04 Ostracoda 0.43 0.00 2.94 0.76 10.07 96.11

Average dissimilarity of major zooplankton taxonomic groups between years 2011 and

2012 was 29.94% (Table 4). Contributions of each are as follows in order of importance: cladocerans 31.22%, calanoids 20.81%, cyclopoids 19.95%, rotifers 15.01%, and ostracods 40

11.45%. Average abundances increased in all zooplankton groups except for ostracods which decreased markedly. Change in rotifers and ostracods accounted for 26.46% contribution.

Table 4. SIMPER results of zooplankton group comparison for 2011 and 2012.

Groups 2011 & 2012 Average dissimilarity = 29.94 Group 2011 Group 2012 Species Av.Abund Av.Abund Av.Diss Diss/SD Contrib% Cum.% Cladacerans 2.90 2.98 9.35 1.41 31.22 31.22 Calanoid 2.70 2.86 6.23 1.39 20.81 52.03 Cyclopoid 0.91 1.12 5.97 1.37 19.95 71.98 Rotifer 0.00 0.65 4.50 1.00 15.01 86.99 Ostracoda 0.48 0.10 3.43 1.05 11.45 98.44

Average dissimilarity of major zooplankton taxonomic groups between years 2011 and

2013 was 29.61% (Table 5). Contributions of each are as follows in order of importance: cladocerans 28.08%, rotifers 21.09%, calanoids19.09%, cyclopoids 17.12%, and ostracods

10.98%. Average abundances increased markedly in all groups except ostracods which decreased to zero in 2013. The largest contributions to dissimilarity were via change in rotifers and ostracods with a combined contribution of 32.07%.

Table 5. SIMPER results of zooplankton group comparison for 2011 and 2013.

Groups 2011 & 2013 Average dissimilarity = 29.61 Group 2011 Group 2013 Species Av.Abund Av.Abund Av.Diss Diss/SD Contrib% Cum.% Cladacerans 2.90 3.39 8.31 1.26 28.08 28.08 Rotifer 0.00 0.96 6.25 1.39 21.09 49.17 Calanoid 2.70 3.00 5.65 1.31 19.09 68.26 Cyclopoid 0.91 1.13 5.07 1.56 17.12 85.39 Ostracoda 0.48 0.00 3.25 1.07 10.98 96.36

Average dissimilarity of major zooplankton taxonomic groups between years 2012 and

2013 was 19.64% (Table 6). Contributions of each are as follows in rank order of importance: cladocerans 33.47%, rotifers 23.85%, cyclopoids 16.69%, and calanoids 16.01%. Average abundances increased slightly overall groups between 2012 and 2013.

Table 6. SIMPER results of zooplankton group comparison for 2012 and 2013.

Groups 2012 & 2013 Average dissimilarity = 19.64 Group 2012 Group 2013 Species Av.Abund Av.Abund Av.Diss Diss/SD Contrib% Cum.% Cladacerans 2.98 3.39 6.57 1.37 33.47 33.47 Rotifer 0.65 0.96 4.69 1.25 23.85 57.32 Cyclopoid 1.12 1.13 3.28 1.32 16.69 74.01 Calanoid 2.86 3.00 3.15 1.13 16.01 90.03

ANOSIM analysis was performed using fourth root transformed data of zooplankton.

Analysis was used to determine similarity between sampling years. The results of ANOSIM produced a global R of 0.101 and a significance level of sample statistic of 0.7% (interpreted as

P value = 0.007). The number of permutations was 999 with only 6 permuted statistics greater than or equal to global R (Table 7). The data exhibits a normal distribution (Appendix C). The

Pairwise Test in ANOSIM showed no significant differences between years 2010 and 2011 (sig level 23.7% or P=0.237), 2011 and 2013 (significance level 41.6% or P=0.416), or 2012 and

2013 (significance level 94.8% or P=0.948). However, significant differences existed between years 2010 and 2012 (significance level 0.1% or P=0.001), 2010 and 2013 (significance level

5.6% or P=0.056), and 2011 and 2012 (significance level 0.9% or P=0.009) (Table 7).

Table 7. ANOSIM results for study year comparison of mean zooplankton data.

Global Test Sample statistic (Global R): 0.101 Significance level of sample statistic: 0.7% Number of permutations: 999 (Random sample from a large number) Number of permuted statistics greater than or equal to Global R: 6

Pairwise Tests R Significance Possible Actual Number >= Groups Statistic Level % Permutations Permutations Observed 2010, 2011 0.024 23.7 Very large 999 236 2010, 2012 0.23 0.1 Very large 999 0 2010, 2013 0.213 5.6 33649 999 55 2011, 2012 0.129 0.9 Very large 999 8 2011, 2013 0.011 41.6 98280 999 415 2012, 2013 -0.199 94.8 65780 999 947

Objective 2A“Abiotic Factors and Zooplankton”

Figure 12 shows the results of the Principal Component Analysis (PCA) of the water quality data for all samling events. Note that the first Principal Component loads both secchi disk depth (SD) and Zeu negatively and K positively, which is normal (Zeu calculated from K).

The second Principle Component loads both temperature and conductivity negatively, and DO and wind postively.

Figure 12. Results of Principle Componant Analysis (PCA) of all sampling events using water parameters. The symbols are: TEMP for temperature, COND for conductivity, SD for secchi disk depth, ZEU foreuphotic zone depth, K for inverse of zeu, DO for dissolved oxygen, pH for pH. 44

Data points plotted by year (figure 13) indicates a shift from 2010 to 2013 in relative importance of abiotic factors within Lake Trafford over time. Data points encompasing years

2011 and 2012 exhibit less clear separation as they are widespread and mixed in multidimensional space.

Figure 13. Results of Principle Componant Analysis (PCA) of all sampling events using abiotic parameters. Overlayed is individual sampling events grouped by year in mutlidimentional space.

Data points are plotted according to season (fall, winter, spring, summer) in Figure 14, which exhibit separation between seasons whereas spring/summer points follow factors of temperature and conductance and fall/winters points arrange oppositely toward all other factors.

Figure 14. Results of Principle Componant Analysis (PCA) of all sampling events using abiotic parameters. Overlayed is individual sampling events grouped by season in mutlidimentional space.

Figure 15 shows a correlation scatter plot of zooplankton mean abundance and temperature for all sampling data points. The linear regression has an R squared value of 0.327.

Note that a negative correlation coefficient of -0.572 exists which is statistically significant

(P<0.001).

Zooplankton and Temperature

1600

1400

1200 P < 0.001

Cor. Co = -0.572 R² = 0.327 1000

800

Organisms/Liter 600

400

200

0 15 17 19 21 23 25 27 29 31 33 35 Temperature °C

Figure 15. Correlation scatter plot of zooplanton mean abundance and temperature with linear trend line. P-value less than 0.001, Correlation coefficient of -0.572, and R² of 0.327.

Figure 16 shows a correlation scatter plot of zooplankton mean abundance and dissolved oxygen measurements for all sampling data points. The linear regression has an R squared value of 0.005. Note that a positive correlation coefficient of 0.07499 exists; however, it is not statistically significant (P= 0.562).

Zooplankton and Dissolved Oxygen 1600

1400

1200 P = 0.562

Cor. Co = 0.07499 R² = 0.005 1000

800

600 Organisms/Liter

400

200

0 5 6 7 8 9 10 11 12 13 14 Dissolved Oxygen mg/L

Figure 16. Correlation scatter plot of zooplanton mean abundance and dissolved oxygen with linear trend line. P-value equal to 0.562, Correlation coefficient of 0.0749, and R² of 0.005.

Figure 17 shows a correlation scatter plot of zooplankton mean abundance and Secchi disk depth measurements for all sampling data points. The linear regression has an R squared value of 0.000. Note that a positive correlation coefficient of 0.02009 exists; however, it is not statistically significant (P= 0.878).

Zooplankton and Secchi Depth 1600

1400

1200 P = 0.878 Cor. Co = 0.02009

R² = 0.000 1000

800

600 Organisms/Liter 400

200

0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Secchi Depth (m)

Figure 17. Correlation scatter plot of zooplanton mean abundance and secchi depth with linear trend line. P-value equal to 0.878, Correlation coefficient of 0.02009, and R² of 0.000.

Figure 18 shows a correlation scatter plot of zooplankton mean abundance and conductance for all sampling data points. The linear regression has an R squared value of 0.108.

Note that a negative correlation coefficient of -0.32903 exists which is statistically significant

(P= 0.009).

Zooplanton and Conductivity 1600

1400

P = 0.009 1200

Cor. Co = -0.32903

R² = 0.108 1000

800

600 Organisms/Liter 400

200

0 200 250 300 350 400 450 500 550 Conductance (uS)

Figure 18. Correlation scatter plot of zooplanton mean abundance and Conductivity with linear trend line. P-value equal to 0.009, Correlation coefficient of -0.32903, and R² of 0.108.

Figure 19 shows correlation scatter plots of zooplanton mean abundance and temperature broken down into each sampling year. Years 2010-2011 and 2011-2012 exhibit statistically significant P-values (P= 0.001 and P= 0.005) while Year 2012-2013 does not (P= 0.012).

Correlation coefficients and R squared values have a decreasing trend from each year to the next. 50

(2010-2011) 900 P-value = 0.001 800 Cor. = -0.66189 700

R² = 0.4381 600 500 400 300

Organisms/Liter 200 100 0 15 20 25 30 35 Temperature °C

(2011-2012) 900 P-value = 0.005 800 Cor. = -0.55793 700

R² = 0.3113 600 500 400 300

Organisms/Liter 200 100 0 15 20 25 30 35 Temperature °C

(2012-2013) 1600 P-value = 0.012 1400 Cor. = -0.53744 1200 R² = 0.2888 1000 800 600

400 Organisms/Liter 200 0 15 20 25 30 35 Temperature °C

Figure 19. Correlation scatter plots of zooplankton and temperature for each sampling year. Top is 2010-2011, middle is 2011-2012, and bottom is 2012-2013. 51

Figure 20 shows mean densities of major zooplankton groups (cladocerans, copepods, and cyclopids) for each sampling date from March 2010 to March 2013. A cyclic pattern exists where all major zooplankton groups drastically increase during fall-winter months and oppositely decrease in late spring-summer months. Note that these seasonal increases and decreases in zooplankton abundance coincide with temperature. As temperature increases, zooplankton decreases. As temperature decreases, zooplankton increases. (See appendix D for larger graph)

Figure 20. Mean densities for major zooplankton groups (cladocerans, copepods, and cyclopids) from March 2010 to March 2013. Temperature is plotted as the red line. Temperature scale is on the right side.

Objective 2B “Zooplankton-phytoplankton Relationships”

Figure 21 shows the results of cluster analysis of combined zooplankton and phytoplankton for all events where both were sampled. Sampling events are distinguished by year and indicated by distinct colors and symbols. Note how 2010 -2011 data points cluster together and 2012-2013 data cluster together in these apparent groupings.

Figure 21. Cluster analysis of combined zooplankton-phytoplankton sampling points grouped by year.

Figure 22 shows the MDS ordination of the biweekly sampling zooplankton and phytoplankton data. Data points are grouped by year and identified with different color symbols.

Note how the stress value is low at 0.09. The 2010-2011 data points are arranged in the upper portion of the graph and exhibit an apparent grouping with wide scatter. The 2012-2013 data points are located toward the lower left portion and also exhibit an apparent grouping, but have less scatter. (additional graphs in Appendix B)

Figure 22. MDS ordination of the biweekly sampling zooplankton and phytoplankton data.

Figure 23 shows the MDS ordination of the biweekly sampling zooplankton and phytoplankton data. Data points are grouped by year and identified with different color symbols.

As before 2010-2011 data points appear in the upper right portion of the graph and the 2012-

2013 data points are located toward the lower left portion. The overlaid trajectory shows that data points over time (years) move away from the initial point in multidimensional space.

Figure 23. MDS ordination of the biweekly sampling zooplankton and phytoplankton data. Graph shows trajectory for all data points.

Zooplankton-phytoplankton Interactions

Figure 24 shows the mean total phytoplankton (blue line) and the mean total zooplankton biomass volume (green line) for Lake Trafford sampling points from 2010 to 2013. Note how both phytoplankton and zooplankton decrease for the remainder of the study after a large spike in late 2010 into early 2011. No distinguishable relationship, as to the expected Lotka Volterra oscillation patterns of predator prey interaction, was found here.

Figure 24. Mean total phytoplankton and total zooplankton biomass plotted from 2010-2013.

Figure 25 shows the zooplankton biomass volume and phytoplankton mean totals expressed as a ratio between the two over time. The zooplankton biomass – phytoplankton concentration ratio (ratio_biov_div_chltot) is plotted on the Y axis. Time (or sampling dates) is plotted on X axis. Note how this ratio oscillates over time as a function of the seasons. Peaks in zooplankton-phytoplankton biomass ratio occur in the fall and winter months while valleys occur in the spring and summer.

Figure 25. Zooplankton biomass volume and phytoplankton mean totals as ratio over time.

Figure 26 shows a correlation scatter plot of zooplankton biomass (tot_biov) to phytoplankton concentration (C_tot) over time. Points are taken from yearly “peak” (fall-winter) biomass ratio data. Note how zooplankton biomass increases as phytoplankton concentration increases toward the right of the graph. A significant and positive correlation between zooplankton and algal biomass is present (P < 0.001) but, the relationship is weak (r2= 0.285).

Figure 26. Correlation scatter plot of zooplankton biomass (tot_biov um^3) vs phytoplankton concentration (C_tot ug chl/L). Data points used included the fall-winter data only. Significant positive correlated (P<0.001) but, weak linear regression is expressed.

Figure 27 shows a correlation scatter plot of zooplankton biomass (tot_biov) to phytoplankton concentration (C_tot) over time. Points are taken from yearly “trough” (spring- summer) biomass ratio data. Note how zooplankton biomass increases as phytoplankton concentration increases toward the right of the graph. A significant and positive correlation between zooplankton and algal biomass is present (P < 0.001) but, the relationship is weak (r2=

0.126).

Figure 27. Correlation scatter plot of zooplankton biomass (tot_biov um^3) vs phytoplankton concentration (C_tot ug chl/L). Data points used included the spring-summer data only. Significant positive correlated (P<0.001) but, weak linear regression is expressed.

CHAPTER 4

DISCUSSION

Limitations

Net size

One apparent limitation to our study deals with zooplankton tow net size. The net mesh size used for our study was 363 µm. Crustacean zooplankton are most frequently collected with conical nets of 156µm and 76µm although coarser (363µm) and finer (64 µm) are used as well

(Evans and Sell, 1985). Larger mesh sizes may exclude important indicators within smaller crustacean zooplankton taxa, species of rotifers, and or other micro-zooplankton. However, in lakes with sufficiently high seston concentrations, as in eutrophic waters, smaller mesh size are especially prone clogging and reduced filtration, creating an inherent bias (Evans and Sell,

1985). Studies done in other Florida lakes report relatively consistent zooplankton body size of

600-800µm (Havens, 2002). Hence, the decision to use 363µm was done to reduce problems with clogging-filtration bias, to best capture the macrozooplankton population and retain optimal sampling efficiency.

As there has not been any previous work published on zooplankton in Lake Trafford, our work here can be considered as baseline data. It was not the objective to link specific species as indicators of specific factors or stressors but, to examine the zooplankton community in relation to overall lake health/change. Gannon and Stemberger (1978) and Jose and Sanalkumar (2012) suggest the use of larger zooplankton groups as practical means for indicating lake status especially with taxonomic uncertainty generally associated with tropical and subtropical lakes. It may have been optimal to use a smaller mesh size and to determine species to the lowest 60 practical taxon, but this would have greatly increased the level of effort and associated costs, reduced the plankton tow sample lengths due to clogging, and likely not have added much more confidence in results drawn by the currently collected data. Sample tow lengths of five meters were consistently possible using the 363µm mesh size without clogging and appeared to provide good representation of the temporal fluctuations in macrozooplankton community structure.

Taxonomic resolution

Zooplankton were not identified to species level, due to taxonomic uncertainty and the time associated with identification. Monitoring changes in ecosystems is challenged by balancing effort with obtaining a signal. Observing major groups of zooplankton to obtain general lake status or overall change seems a viable and cost effective method if multiple annual cycles can be observed. To link specific system stressors on shorter time scales or to identify specific indictor taxa, a higher taxonomic resolution may be required. .

Spatio-temporal resolution

To explore patterns in the zooplankton community, subjective decisions were made about averaging abundance across spatial (entire lake) and temporal (monthly averages) were made.

The initial monitoring plan set the sampling as bi-monthly. It is possible that some phytoplankton blooms may be occurring at shorter timeframes. The data indicated finer spatial patterns, probably linked to wind, of zooplankton abundance, but long-term dynamics and correlations to other environmental factors were based on averaging values across the lake. Some additional sampling to explore 24 hour patterns and possible vertical and horizontal movements on finer temporal scales were added to the monitoring plan.

Zooplankton as Indicators of Lake Change

Spatio-temporal patterns

Zooplankton distribution patterns are influenced by wind-induced water movements and possibly dispersion of particulate materials from the lake bottom (Gorham and Boyce. 1989;

George and Edwards, 1976). However, Lake Trafford is relatively shallow and very well mixed throughout all of its regions and zones year round, so attempts to deduce the role of organic particulate would be difficult. It seems likely that wind-induced hydrodynamics physically moved zooplankton throughout the lake as well as their secondary behavioral movements to actively feed on phytoplankton which are susceptible to water movements (George and Edwards,

1976). Annual trends exist in examining spatial and temporal dynamics whereas zooplankton density increases in winter months which coincide with greater, more frequent wind shear and slightly increased food sources. The opposite is true in summer months when zooplankton density decreases in conjunction with lesser wind forces, higher temperature, and low food availability. Phytoplankton could bloom in certain regions where nutrients, possibly from sediments or ground water, are pluming but, wind speed and direction is still a driving factor.

There is a clear link between zooplankton spatial distribution, in regards to phytoplankton distribution and wind induced water movement (George and Edwards, 1976). It is likely that as wind generated waves force bottom sediments into the water column, internal loading occurs and nutrients are available for primary production (Cyr and Coman, 2012). Wind driven suspension of fine particulate matter was frequently observed during sampling events over the three year period. While not quantified in this study, Secchi depths were often observed to decrease during the day as winds increased from early morning through afternoon hours. Phytoplankton then 62 bloom according to nutrient availability and zooplankton tends to follow suit. This is a common linkage found in many lakes including Lake Trafford. However, Lake Trafford exhibits the seasonal patterns opposite to the norm. Other eutrophic lakes tend to have blooms in summer months where phytoplankton concentration increases and zooplankton density increases as they have higher respiration rates and reproduce quickly. Winter months see a decline in zooplankton density as their metabolic rate is significantly slowed by cooler waters (Shayestehfar et al, 2010;

Havens, 2002; Beisner, 1997) and is less influenced by phytoplankton concentration.

In Lake Trafford intense blooms in both phytoplankton and zooplankton occur in winter months. Excluding intense blooms, phytoplankton is plentiful year round but, zooplankton densities exhibit consistent lows in summer months. This is believed to be linked to temperature regimes reaching the maximum tolerable limits of zooplankton (Beisner, 1997). For example the fecundity of Daphnia ambigua is known to reach its maximum value at a water temperature of near 25°C and then decline rapidly as temperature approaches 30°C (Havens, 2002; Beisner,

1997). A similar pattern was observed in Lake Trafford with respect to Cladaceran density or abundance with an apparent optimal temperature range between 16°C and 26°C. The subtropical- tropical location and relatively shallow depth of Lake Trafford seems to allow for temperature to be another major driving or limiting factor especially when lack of suitable refugia exists due to inadequate thermo-cline or vegetative thermo buffering.

Additional data on zooplankton encompassing bottom sampling, high frequency 24 hour sampling, and vegetative sampling were performed during this study to gain a better overall understanding of Lake Trafford zooplankton dynamics. In general, bottom zooplankton density was slightly higher than subsurface density but, exhibited no change in composition. Hence, it is possible that even a slight temperature change could provide some refuge and active vertical 63 movement takes place even in very shallow lakes. High frequency data collected over 24 hours showed that zooplankton density increases significantly during night hours likely as a photic response to predator avoidance. However, zooplankton community structure also changes slightly with the addition of predatory Chaoborus larvae and increased number of cyclopoid copepods. The presence of Chaoborus was not unexpected as they reside in sediments during the day and come out to feed at night. On the other hand, the higher abundance of cyclopoid copepods in nighttime samples was surprising.

Cyclopoid abundance in subsurface and bottom daytime sampling was extremely low to nonexistent consistently throughout the study. Nighttime bottom samples also yielded relatively low numbers of cyclopoids as well. However, nighttime subsurface abundance of cyclopoids was abnormal and significant which led question their origins. The possibility of horizontal migration by zooplankton from recovering submerged aquatic vegetation (SAV) beds, led to sampling in these areas. Cyclopoids were found here to be in the highest numbers relative to study sites and were also highly gravid. No other zooplankton groups where associated with submerged vegetation beds. It seems that cyclopoids may be selecting this habitat as possible refugia from predators or in response to temperature on a daily basis. Therefore, not only is the presence of submerged aquatic vegetation important for buffering wind generated waves and sedimentation rates but, apparently may play an important role for certain zooplankton groups and overall recovery of impacted lakes. The restoration target for SAV coverage in Lake Trafford is currently being refined by the Lake Trafford Management Team but ranges between 30% and

40% based on both sport fisheries management objectives and water clarity targets (Ceilley et al.,

2013).

Zooplankton and WQ parameters

Zooplankton showed no direct correlations to water quality parameters of dissolved oxygen. This is likely due to the fact that zooplankton in subtropical-tropical lakes, tend to exhibit tolerance to variable extremes in DO. For instance Daphnia, which mainly compose the cladoceran group in Lake Trafford, are tolerant of poor water quality with dissolved oxygen ranges from near zero to super-saturation. Their ability to survive in an oxygen poor environment lies in their ability to create hemoglobin which may be associated with high temperatures

(Takayuki and Dodson, 1995). Additionally, their low D.O. tolerance is a function of their vertical migration adaptations moving from the hypolimnion with low D.O. upward to metalimnion and epilimnion at night to forage (U.S. EPA, 2002). However, Daphnia spp are widely used in freshwater acute toxicity testing by the U.S. EPA and are generally considered as sensitive to, and intolerant of pollutant discharges to freshwater ecosystems.

Calanoid and cyclopoid copepods are also tolerant of DO variation as they can actively seek out better conditions. Therefore, DO variation appears to have little effect on zooplankton abundance. Water clarity decrease due to suspended solids also tends to have minimal ill effects on zooplankton in Lake Trafford. The pH of Lake Trafford was found to vary between pH-7 and pH-9.5, of which zooplankton had no significant correlation. Zooplankton had a significant negative correlation with temperature and subsequently conductivity. Correlation between zooplankton and temperature was examined by separate years from March 2010 to March 2013.

Each consecutive year saw a less significant correlation of zooplankton to temperature compared to the year before. It is possible that over time as the lake is changing and progressing to a new state, in which temperature is becoming less of a defining factor and that zooplankton will be more noticeably influenced by other abiotic and biotic parameters. 65

Phytoplankton-zooplankton relationships

Generally, in freshwater lakes primary production of phytoplankton and growth/abundance of zooplankton populations coincide. As edible phytoplankton increases zooplankton follows suit and subsequently suppress phytoplankton. Phytoplankton then decrease followed by a decrease in zooplankton as food availability drops. Hence, such Lotka Volterra fluctuations of predator-prey interactions of somewhat independent and interdependent communities create a cyclic pattern exhibited in many freshwater systems (Murdoch et al.,1998;

Sarnelle, 1992). However, peak phytoplankton may not necessarily dictate zooplankton populations in highly eutrophic lake ecosystems that exhibit some unforeseen variability in primary production. Lack in identifying clear correlation between the two, however, may be explained by zooplankton grazing rates and their ability or inability to effectively suppress a large spike in primary production that eutrophic lakes, such as Lake Trafford, commonly exhibit.

Spikes in phytoplankton production of eutrophic lakes can commonly be attributed to cyanobacterial blooms which greatly mask chlorophyceae because they act as a “nutrient sponge” (Murdoch et al., 1998). These cyanophyceae tend to be less palatable, too small to capture, or too large for the zooplankton to consume when they form colonies and even toxic to zooplankton, whereas chlorophyceae are the preferred food source for zooplankton (Wilson et al., 2006). Hence, zooplankton’s lack of ability to control phytoplankton blooms in eutrophic lakes where cyanophyceae growth can far exceed that of chlorophyceae.

Comparing raw data of total zooplankton and total phytoplankton over time exhibited no visible cycles or lagging trends where zooplankton population should increase or decrease coinciding with an increase or decrease of primary production (Figure 24). However, concentration of chlorophyceae and cyanophyceae algae has decreased in the lake over time 66

(Figure 24). Zooplankton data was converted to bio-volumes (methods adopted from Binggeli,

2011) and then compared with total phytoplankton as a ratio. The ratio between zooplankton and phytoplankton through time indicated that they both exhibit annual cycles relative to one another

(Figure 25). Data was analyzed by comparing annual peaks and annual troughs to one another.

Extreme outliers, specifically two points in 2010 and 2012 were excluded as they skewed normalcy of the data. Analysis showed that mean ratio between zooplankton and phytoplankton predictably fluctuates annually relative to one another (i.e. blooms). Correlation analysis of peaks (fall-winter) data had a significant P-value of less than 0.001 but, a linear regression R² of

0.285 (Figure 26). This means it showed a strong correlation but, a weak linear relationship.

Similarly correlation of troughs (spring-summer) data had a significant P-value of 0.001 but, a linear regression R² value of 0.126 (Figure 27). Again, this means it showed a strong correlation but, a weak linear relationship

Chlorophyceae (green algae) density was widespread over all seasons and all study years

(Figure 1 and 2, appendix E). Concentrations of chlorophyceae were also consistent throughout seasons with the exception of high spikes in winter 2011. Cyanophyceae (blue-green algae) density was also widespread throughout all seasons and all study years as well (Figure 3 and 4, appendix E). Concentrations of cyanophyceae were also consistent throughout consecutive seasons with the exception of very high spikes is winter 2011. In general, cyanophyceae was consistently found at higher concentrations in relation to chlorophyceae. In comparing phytoplankton bubble overlays with all major zooplankton group overlays, it was found that there was no lack of food source for zooplankton throughout the studied years. In other words, food source availability was consistent in all years through all seasons. Hence, the zooplankton abundance decreases in spring and summer months are more likely related to abiotic factors, 67 specifically temperature. Temperature frequently reached or surpassed the maximum tolerable limits for zooplankton survival and reproduction (Havens, 2002). However, as suggested by

Havens, 2002, bacterio-plankton in Florida lakes might be an important alternate and underlying driving food source for zooplankton as seen in other studies (Kankaala et al., 2010). The quicker response of bacterio-plankton to nutrient could potentially explain the weak relationship and lack of Lotka Voltera predator prey interaction between zooplankton and larger phytoplankton groups.

Excessive primary production of algal groups which cannot be controlled through grazing by zooplankton creates an inherent problem. When phytoplankton peaks as “carrying capacity” or maximal growth load is reached and insufficient top down control exist, the algae blooms die and then settle on the lake bottom. These dead algae add to lake sediment accumulation, buildup of nutrients, and subsequent “internal loading”. Stable lakes tend to display a balance of zooplankton grazing and phytoplankton growth, and can exhibit clear waters and good quality.

This is not the case with Lake Trafford as issues of algal blooms, lake sediment accumulation, and internal loading influence ecosystem imbalance. However, this study has shed light on the fact that these processes have decreased in intensity over the last three years since dredging was completed. Ultimately, this could mean that Lake Trafford is recovering and moving toward a healthier, balanced ecosystem. However, Secchi disk depths and light attenuation curves do not indicate improvements in water clarity. Continued monitoring will be necessary to track biotic and abiotic trends at Lake Trafford over time to determine if restoration targets, goals and objectives are obtained.

Primary production is either driven or limited by nutrient loads within the system but, pathways for altering these loads for restorative purposes are not always straight forward. For 68 instance, the presence or absence of emergent/submerged aquatic vegetation plays a key role in buffering wind generated waves and subsequently influencing turbidity, water clarity, light attenuation, and internal loading. In addition, this vegetation may provide critical habitat for some zooplankton taxa. Currently, Lake Trafford remains turbid and phytoplankton dominated despite efforts in transplanting and restoring aquatic vegetation. As of April 2013, approximately

29.5 hectares (73 acres) of native submerged aquatic vegetation has been restored in Lake

Trafford. Restoration targets for the Lake currently range between 180-240 hectares and so restoration success for this target was between 16.7% and 12.5% respectively depending on total

SAV coverage desired (Ceilley et al., 2013). Despite indications of previously poor health, the lake does seem to be moving in a direction toward a restored system. However, additional and continued restoration efforts and monitoring are important to ensure Lake Trafford continues towards a healthy lake ecosystem.

Indicators of lake health

Another indication that Lake Trafford is altering “status” is that the composition and stability of both plankton communities has changed even when observing major groups.

Phytoplankton has seen a noticeable decrease in excessive blooms, especially of cyanophyceae, and has become more stable in comparison to the start of this study. Both cyanophyceae and chlorophyceae have shown a considerable decrease in relative concentration overtime. This could be an indication that nutrients in the lake, previously coming from internal loading, have dropped as a result of the dredging project. However, it is difficult to determine at this time as many factors could have contributed to this. For example, the extent of SAV and emergent plant restoration efforts, have likely contributed to nutrient reduction through buffering and uptake. 69

Wind intensity as also slightly decreased overtime which, in theory, should minimize wave action and subsequently re-suspension of nutrient rich sediment. Zooplankton composition has become slightly more diverse. In general, greater biodiversity is a good indication of ecosystem stability but, can also signify change. The 2D bubble overlays of zooplankton groups overtime give a clear picture at how the community diversity has expanded over the time of the study

(Figures 7 – 11). They not only show a stabilizing of initial zooplankton found but, also the change in community structure over time.

No comparisons of plankton dynamics to LVI or LCI indices for lake health were performed as these index values were not available for all years of this study. However, future measures of these indices and plankton dynamics should be examined in order to determine if all these measures trend in similar, or different, ways. Phytoplankton and zooplankton offer an important “window” into lake changes as they are two of the first groups to react to variation and are a major trophic link. As LVI and LCI measures have come to question in recent years, measures of plankton dynamics offer alternative ways to measure lake health. Examination and continued monitoring over the next few years could play a role in determining restoration success of Lake Trafford. Determining the dynamics of post-restoration dredging in Lake

Trafford will have a profound impact on the future management of similarly impacted lakes.

Summary conclusions and recommendations

Conclusions:

 Lake Trafford is a complex system with a unique history that makes predictions of future

dynamics difficult

 Heavily managed systems pose a challenging for determining casual factors

 Unique spatial and temporal patterns of zooplankton exist in Lake Trafford 70

 Zooplankton and phytoplankton dynamics are closely linked but abiotic factors, particularly wind

and temperature, express greatest influence

 It is difficult to assess and predict lake condition using singularly based observational studies.

On-going monitoring of multiple taxonomic groups will be necessary to track post-dredging

dynamics and guide management decisions

 Zooplankton may be used in determining future lake health/trophic status of Lake Trafford

 In depth research on Lake Trafford should help guide future dredging restoration projects

Future areas of study might include:

 Continued monitoring of zooplankton and phytoplankton dynamics to quantifying and

changing patterns in algal blooms or zooplankton population peaks.

 Development of a lake nutrient budget to differentiate between external and internal

 Relating nutrient loading to zooplankton dynamics and possible bottom-up control

through bactio-plankton

 Exploring possible top-down controls on zooplankton

 Investigating the horizontal migration of zooplankton in and out of SAV beds and their

potential as refugia in relation to temperature tolerance or predator prey interactions

LITERATURE CITED

Bachmann, R.W., C.A. Horsburgh, M.V. Hoyer, L.K. Mataraza. and D.E. Canfield, Jr. 2002. Relations between trophic state indicators and plant biomass in Florida lakes.Hydrobiologia.470: 219-234.

Beisner, B. E., E. McCauley, and F. J. Wrona. 1997. The influence of temperature and food chain length on plankton predator-prey dynamics. Canadian Journal of Fisheries and Aquatic. Science, 54: 586-595.

Berzins, B., and B. Pejler. 1987. Rotifer occurrence in relation to pH. Hydrobiologia, 147: 107- 116.

Berzins, B., and B. Pejler. 1989. Rotifer occurrence in relation to oxygen content. Hydrobiologia, 183: 165-172.

Bowes, G. A., S. Holiday, and W.T. Haller.1979. Seasonal Variation in the Biomass, Tuber Density, and Photosynthetic Metabolism of Hydrilla in three Florida Lakes. Aquatic Plant Management, 17: 61-65.

Carpenter, S. R., D. Ludwig, and W. A. Brock. 1999. Management of eutrophication for lakes subject to potentially irreversible change. Ecological Applications, 9(3): 751–771.

Carpenter, S. R., J. F. Kitchell, and J. R. Hodgso n. 1985. Cascading trophic interactions and lake productivity. BioScience, 35: 634-639.

Ceilley, D. W., E. M. Everham, S. E. Thomas, and J. A. Ferlita II. 2013. Lake Trafford Biological Monitoring Report. To: Big Cypress Basin Board of South Florida Water management District. Unpublished manuscript, Florida Gulf Coast University, Fort Myers, FL

Ceilley, D. W., S. Thomas, and E. M. Everham III. 2013. Lake Trafford Management Action Plan. Unpublished manuscript, Florida Gulf Coast University, Fort Myers, FL

Clarke, K. R. and R. N. Gorley. 2006. Primer v6: user manual/tutorial. PRIMER-E, Plymouth.

Clarke, K. R. and R. M. Warwick. 2001. Change in marine communities: an approach to statistical analyses and interpretation, 2nd ed. PRIMER-E, Plymouth.

Cooke, G. D., E. B. Welch, S. A. Peterson, and P. R. Newroth.1993.Restoration and anagement of lakes and reservoirs. Lewis Publishers, Boca Raton, Florida, USA.

Cotner, J. B. and B. A. Biddanda. 2002. Small players, large role: microbial influence on biogeochemical processes in pelagic aquatic ecosystems. Ecosystems, 5(2): 105-121.

Cozar, A., C. M. Garcia, and J. A. Galvez.2003.Analysisofplanktonsizespectra irregularities in two subtropical shallow lakes (EsterosdelIbera, Argentina).Canadian Journal of Fisheries and Aquatic Science, 60: 411-420.

Cyr, H., and M. A. Coman. (2012). Wind-driven physical processes and sediment characteristics affect the distribution and nutrient limitation of nearshore phytoplankton in a stratified low-productivity lake. Limnology & Oceanography: Fluids & Environments, 2: 93-108.

Evans, M. S., and D. W. Sell. 1985. Mesh size and collection characteristics of 50-cm diameter conical plankton nets. Hydrobiologia, 122(2): 97-104

Everham, E. 2007. Lake Trafford Watershed Management Plan – Phase I Final Report. Florida Gulf Coast University, Fort Myers, Florida.

Flaig, E. 2000.Lake Trafford. Available at http://naples.net/presents/trafford/

Florida Department of Environmental Protection.DEP-SAS-002. 2011. Sampling and Use of the LVI for Assessing Lake Plant Communities: A Primer. Print.

Florida Department of Environmental Protection. 2000. Development of lake condition indexes (LCI) for Florida. Florida Department of Environmental Protection, Contracts WM 565 and WM 655.

Florida Geological Survey. 1962. Ground-Water Resources of Collier County, Florida. U.S. Geological Survey, Tallahassee, Florida. Fore, L. S. 2007. Evaluation of Benthic Macroinvertebrate Assemblages as Indicators of Lake Condition. Final Report for FDEP. Frey, D. 1975. The Integrity of Water, ASymposium.March 10-12, 1975 Washington D.C. Environmental Protection Agency Office of Water and Hazardous Materials. Print. Gannon, J. E. and R. S. Stemberger. 1978. Zooplankton (especially crustaceans and rotifers) as indicators of water quality. Transactions of the American Microscopical Society, 97:16- 35. Garcia C. E., S. Nandini, and S.S.S. Sarma. 2009. Seasonal dynamics of zooplankton in Lake Huetzalin, Xochimilco (Mexico City, Mexico). Limnologica – Ecology and Management of Inland Waters, 39(4): 283-291. Gasol, J. M.and C. M. Duarte. 2000. Comparative analyses in aquatic microbial ecology: how far do they go? FEMS Microbiol Ecology, 31: 99-106.

George, D. G. and R. W. Edwards. 1976. The effect of wind on the distribution of chorophyll A and crustacean plankton in a shallow eutrophic reservoir. Journal of Applied Ecology, 13(3): 667-690

Gerritsen, J., B. Jessup, E. W. Leppo, and J. White. 2000.Development of Lake Condition Indexes (LCI) for Florida. Florida Department of Environmental Protection. Print.

Gibson, G.R. (ed.), M.T. Barbour, J.B. Stribling, J. Gerritsen, and J.R. Karr. 1996. Biological criteria: Technical guidance for streams and small rivers. USEPA, Office of Science and Technology, Washington, DC. EPA 822-B-96-001.

Gorham E., and F. M. Boyce. 1989. Influence of lake surface area and depth upon thermal stratification and the depth of the summer thermocline. Journal of Great Lakes Research, 15(2): 233-245. Guevara, G., P. Lozano, G. Reinoso, and F. Villa. 2009. Horizontal and seasonal patterns of tropical zooplankton from the eutrophic Prado Reservoir (Colombia). Limnologica - Ecology and Management of Inland Waters, 39(2): 128-139. Gyllstrom, M., and L. A. Hansson. 2005. The role of climate in shaping zooplankton communities in shallow lakes. Limnology and Oceanography, 50: 2008-2021

Hansson, L. A., M. Gyllstrom, A. Stahl-Deblanco, and M. Svensson. 2004. Responses to fish predation and nutrients by plankton at different levels of taxonomic resolution. Freshwater Biology, 49:1538–1550.

Havens, K. E. 2002. Zooplankton structure and potential food web interactions in the plankton of a subtropical chain-of-lakes. The Scientific World Journal, 2: 926-942.

Hessen, D., B. A. Faafeng, and P. Brettum. 2003. Autotroph: Herbivore biomass ratios: Carbon deficits judged from plankton data. Hydrobiologia, 491: 167-175.

Jeppeson, E., J. P. Jensen, M. Søndergaard, T. L. Lauridsen, and F. Landkildehus. 2000. Trophic structure, species richness, and biodiversity in Danish lakes: Changes along a phosphorus gradient. Freshwater Bioliogy, 45: 201-218.

Jose, R., and M. G. Sanalkumar. 2012. Seasonal Variations in the Zooplankton Diversity of River Achencovil. International Journal of Scientific and Research Publications. 2(11)

Kankaala, P., S. Taipale. L. Li, and R. I. Jones. 2010. Diets of crustacean zooplankton, inferred from stable carbon and nitrogen isotope analyses, in lakes with varying allochthonous dissolved organic carbon content. Aquatic Ecology, 44: 781–795.

Kang, W. and D. Gilbert. 2008. TMDL Report Nutrient, Un-ionized Ammonia, and DO TMDLs for Lake Trafford. Floirda Department of Environmental Protection, South District.

Kerfoot, W. C. and A. Sih. 1987. Predation: direct and indirect impacts on aquatic communities. Univ. Press New England, Hanover. pp. 386.

Kleeberg, A., and J. G. Kohl. 1999. Assessment of the long-term effectiveness of sediment dredging to reduce benthic phosphorus release in shallow Lake Muggelsee (Germany). Hydrobiology, 394: 153-161. Kumari, P., S. Dhadse, P. R. Chaudhari, and S. R. Wate. 2008. A Biomonitoring of Plankton to assess quality of water in lakes of Nagpur city. In The 12th World Lake Conference, pp. 160-164.

Lake Trafford and You: Community Opinion. 2000. ISS 4935 Senior Seminar in Social Science. Florida Gulf Coast University. http://itech.fgcu.edu/&/issues/vol3/issue2/ISSSrSem/ index.html

Lotter, A. F., H. J. B. Birks, W. Hofmann, and A. Marchetto. 1998. Modern diatom, cladocera, chironomid, and chrysophyte cyst assemblagesas quantitative indicators for the reconstruction of past environmentalconditions in the Alps. II. Nutrients. Kluwer Academic Publishers.Printed in Belgium.Journal of Paleolimnology, 19: 443–463.

Marmorek, D. R., and J. Korman. 1993. The use of zooplankton in a biomonitoring program to detect lake acidification and recovery. Water, Air, and Soil Pollution, 69(3-4): 223-241.

Murdoch, W. W., R. M. Nisbet, E. McCauley, A. M. Deroos, and W. S. C. Gurney. 1998. Plankton abundance and dynamics across nutrient levels: tests of hypothesis. Ecology, 79(4): 1339-1356

Pettersson, K. 1998. Mechanisms for internal loading of phosphorus in lakes. Hydrobiologia, 373/374: 21–25.

Pu, P., G. Wang, C. Hu, W. Hu, and C. Fan. 2000. Can we control lake eutrophication by dredging? Journal of Lake Science, 12: 269-279.

Reddy, K., M. Fisher, Y. Wang, J. White, and R. T. James. 2007. Potential effects of sediment dredging on internal phosphorus loading in a shallow, subtropical lake. Lake and Reservoir Management, 23: 27–38.

Ruley, J. E., and K. A. Rusch. 2002. An assessment of long-term post-restoration water quality trends in a shallow, subtropical, urban hypereutrophic lake. Ecological Engineering, 19(4): 265–280.

Sanders, R. W., D. A. Caron, and U. G. Berninger. 1992. Relationships between bacteria and heterotrophic nanoplankton in marine and fresh waters: an inter-ecosystem comparison. Marine Ecology Progress Series, 86: 1–14.

Sarma, S. S. S., S. Nandini and R. D. Gulati. 2005. Life history strategies of cladocerans: comparisons of tropical and temperate taxa. Hydrobiologia, 542: 315–333. Sarnelle, O. 1992. Nutrient enrichment and grazer effects on phytoplankton in lakes. Ecology, 551-560. 75

Sas, H. 1989. Lake restoration by reduction of nutrient loading: expectations, experiences, extrapolations. Academia Verlag, Richarz, St. Augustin, Germany.

Schindler, D. W., R. H. Hesslein, and M. A. Turner. 1987. Exchange of nutrients between sediments and water after 15 years of experimental eutrophication. Canadian Journal of Fisheries and Aquatic Sciences, 44: 26-33.

Schriver, P., J. Bøgestrand, E. Jeppesen, and M. Søndergaard. 1995. Impact of submerged macrophytes on fish-zooplankton-phytoplankton interactions: Large-scale enclosure experiments in a shallow eutrophic lake. Freshwater Biology, 33: 255-270.

Selig, U. 2003. Particle size-related phosphate binding and P-release at the sediment- waterinterface in a shallow German lake. Hydrobiologia, 492: 107-118.

Sellami, I., W. Guermazi, A. Hamza, L. Aleya, and H. Ayadi.2010.Seasonal dynamics of zooplankton community in four Mediterranean reservoirs in humid area (BeniMtir: north of Tunisia) and semi-arid area (Lakhmes, Nabhana and SidiSaâd: center of Tunisia). Journal of Thermal Biology, 35(8): 392-400. Shayestehfar, A., M. Noori , and F. Shirazi. 2010. Environmental factor effects on the seasonally changes of zooplankton density in Parishan Lake (Khajoo Spring site), Iran. Asian Journal of Exp. Biology, 1(4): 840- 844

Smith, V. H. 1998. Cultural eutrophication of inland, estuarine and coastal waters. M. L. Pace and P.M. Groffman, editors. Successes, limitations and frontiers of ecosystem science. Springer-Verlag, New York, NewYork, USA. pp. 7-49.

Søndergaard, M., J. P. Jensen and E. Jeppesen. 2001. Retention and internal loading of phosphorus in shallow, eutrophic lakes. The Scientific World Journal, 1: 427–442.

Soranno, P. A., S. R. Carpenter, and R. C. Lathrop. 1997.Internal phosphorus loading in Lake Mendota: response to external loads and weather. Canadian Journal of Fisheries and Aquatic Sciences, 54: 1883–1983.

South Florida Water Management District (SFWMD). 2011. Quick facts on… Lake Trafford Restoration. Accessed at http://www.sfwmd.gov/portal/page/portal/xrepository/sfwmd_repository_pdf/spl_lake_tr afford.pdf

Takayuki H., and S. I. Dodson. 1995. Synergistic effects of low oxygen concentration, predator kairomone, and a pesticide on the cladoceran Daphnia pulex. Limnology and Oceanography, 40(4): 700-709

U.S. Environmental Protection Agency. 2011. “Biological Integrity.” Biological Indicators of Watershed Health. Web.

U. S. Environmental Protection Agency (EPA). 2007. Status of State and Tribal Programs for Lakes and Reservoirs. Retrieved from: www.epa.gov/waterscience/biocriteria/States/lakes/lakes0.html. U.S. Environmental Protection Agency. 2002. Biological Assessments and Criteria: Crucial Components of Water Quality Programs. Print. U.S. Environmental Protection Agency. 2002. Methods for Measuring the Acute Toxicity of Effluents and Receiving Waters to Freshwater and Marine Organisms. EPA-821-R-012. 5th Edition. USEPA Office of Water (4303T) Washington, DC.

U.S. EPA Environmental Protection Agency. 1996. Environmental indicators of water quality in the United States. USEPA, Office of Water (4503F), U.S. Government Printing Office, Washington, D.C., USA.EPA 8410R-96-002.

Watkins II, C. E., J. V. Shireman, and W. T. Haller. 1983. The influence of aquatic vegetation upon zooplankton and benthic macroinvertebrates in Orange Lake, Florida. Journal of Aquatic Plant Management, 21: 78-83.

Wang, S., P. Xie, S. Wu, and A. Wu. 2007. Crustacean zooplankton distribution patterns and their biomass as related to trophic indicators of 29 shallow subtropical lakes. Limnologica - Ecology and Management of Inland Waters, 37(3): 242-249.

Wilson, A. E., O. Sarnelle, and A. R. Tillmanns. 2006. Effects of cyanobacterial toxicity and morphology on the population growth of freshwater zooplankton: Meta-analyses of laboratory experiments. Limnology and Oceanography, 51(4): 1915–1924

Zhang, S., Q. Zhou, D. Xu, J. Lin, S. Cheng, and Z. Wu. 2010. Effects of sediment dredging on water quality and zooplankton community structure in a shallow of eutrophic lake. Journal of Environmental Sciences, 22(2): 218-224.

Zhong, J., C. Fan, L. Zhang, E. Hall, S. Ding, B. Li, and G. Liu. 2010. Significance of dredging on sediment denitrification in Meiliang Bay, China: a yearlong simulation study. Journal of Environmental Science China, 22: 68-75.

Zhong, J., and C. Fan. 2007. Advance in the study on the effectiveness and environmental impact of sediment dredging. Journal of Lake Science, 19: 1-10.

APPENDIX A

3/23/10 Wind – NW 10mph

Organisms/Liter

Temp °C 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16

4/30/10 Wind – SE 9mph

Organisms/Liter

Temp °C 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16

5/14/10 Wind – E 9mph

Organisms/Liter

Temp °C 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16

6/25/10 Wind – E 5-8mph

Organisms/Liter

Temp °C 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16

7/26/10 Wind – SSE 0-3mph

Organisms/Liter

Temp °C 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16

8/23/10 Wind – SW 0-5mph

Organisms/Liter

Temp °C 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16

9/27/10 Wind – SSE 6mph

Organisms/Liter

Temp °C 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16

10/27/10 Wind – SE 10-15mph

Organisms/Liter

Temp °C 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16

11/22/10 Wind – ESE 10-15mph

Organisms/Liter

Temp °C 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16

12/22/10 Wind – E 10mph

Organisms/Liter

Temp °C 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16

1/24/11 Wind – ESE 10-15mph

Organisms/Liter

Temp °C 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16

2/21/11 Wind – SSE 15-20mph

Organisms/Liter

Temp °C 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16

3/4/11 Wind – SE 20mph

Organisms/Liter

Temp °C 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16

4/4/11 Wind – S 10mph

Organisms/Liter

Temp °C 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16

5/3/11 Wind – ENE 7mph

Organisms/Liter

Temp °C 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16

6/6/11 Wind – ESE 5-10mph

Organisms/Liter

Temp °C 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16

7/7/11 Wind – S 0-6mph

Organisms/Liter

Temp °C 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16

8/18/11 Wind – SSE 0-5mph

Organisms/Liter

Temp °C 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16

9/28/11 Wind – NE 0-3mph

Organisms/Liter

Temp °C 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16

10/20/11 Wind – NNE 5-10mph

Organisms/Liter

Temp °C 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16

11/8/11 Wind – NE 6mph

Organisms/Liter

Temp °C 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16

12/14/11 Wind – ENE 15mph

Organisms/Liter

Temp °C 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16

1/9/12 Wind – SSE 5mph

Organisms/Liter

Temp °C 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16

2/16/12 Wind – SSW 5mph

Organisms/Liter

Temp °C 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16

3/13/12 Wind – ESE 5-10mph

Organisms/Liter

Temp °C 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16

4/10/12 Wind – 0-2mph

Organisms/Liter

Temp °C 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16

5/8/12 Wind – SSW 0-5mph

Organisms/Liter

Temp °C 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16

6/4/12 Wind – W 0-2mph

Organisms/Liter

Temp °C 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16

7/19/12 Wind – SE 5mph

Organisms/Liter

Temp °C 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16

8/30/12 Wind – SE 10mph

Organisms/Liter

Temp °C 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16

9/14/12 Wind – SE 5mph

Organisms/Liter

Temp °C 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16

10/26/12 Wind – N 20-30mph

Organisms/Liter

Temp °C 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16

11/29/12 Wind – NE 10mph

Organisms/Liter

Temp °C 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16

12/27/12 Wind – N 0-5mph

Organisms/Liter

Temp °C 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16

1/15/13 Wind – SE 5mph

Organisms/Liter

Temp °C 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16

2/2/13 Wind – E 10mph

Organisms/Liter

Temp °C 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16

2/28/13 Wind – N 7-10mph

Organisms/Liter

Temp °C 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16

3/28/13 Wind – ENE 10mph

Organisms/Liter

Temp °C 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16

APPENDIX B

APPENDIX C

SIMPER Similarity Percentages - species contributions One-Way Analysis

Data worksheet Name: Data1 Data type: Abundance Sample selection: All Variable selection: All

Parameters Resemblance: S17 Bray Curtis similarity Cut off for low contributions: 90.00%

Group 2010 Average similarity: 74.69 Species Av.Abund Av.Sim Sim/SD Contrib% Cum.% Cladacerans 2.63 34.43 4.54 46.10 46.10 Calanoid 2.07 29.65 4.74 39.70 85.80 Cyclopoid 0.84 8.43 1.18 11.28 97.09

Group 2011 Average similarity: 70.26 Species Av.Abund Av.Sim Sim/SD Contrib% Cum.% Calanoid 2.70 31.21 4.43 44.42 44.42 Cladacerans 2.90 29.94 2.98 42.62 87.04 Cyclopoid 0.91 5.14 0.73 7.32 94.36

Group 2012 Average similarity: 76.59 Species Av.Abund Av.Sim Sim/SD Contrib% Cum.% Calanoid 2.86 32.82 7.14 42.85 42.85 Cladacerans 2.98 30.36 4.62 39.65 82.50 Cyclopoid 1.12 9.47 1.47 12.37 94.87

Group 2013 Average similarity: 85.07 Species Av.Abund Av.Sim Sim/SD Contrib% Cum.% Cladacerans 3.39 35.11 4.88 41.27 41.27 Calanoid 3.00 32.76 10.17 38.51 79.78 Cyclopoid 1.13 11.44 13.06 13.45 93.23

ANOSIM Analysis of Similarities One-Way Analysis

Resemblance worksheet Name: Resem1 Data type: Similarity Selection: All

Factor Values Factor: Year 2010 2011 2012 2013

Outputs Plot: Graph20

100

APPENDIX D

101

102

APPENDIX E

103