UNIVERSiTY OF H.~,\V,ll,1'1 LIBRARY

EFFECTS OF INTRODUCED FISH ON AQUATIC INSECT

ABUNDANCE: A CASE STUDY OF HAMAKUA MARSH. OAHU

HAWAI'I

A THESIS SUBMI'nED TO THE GRADUATE DIVISION OF THE UNIVERSITY OF HAWAI'I IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF

MASTER OF SCIENCE

IN

BOTANY

DECEMBER 2006

By: Christina McGuire

Thesis Committee:

David Duffy. Chairperson Donald Drake Joan Canfield Richard Mackenzie We certify that we have read this thesis and that, in our opinion, it is satisfactory in scope

and quality as a thesis for the degree of Master of Science in Botany.

THESIS COMMITfEE

ii Acknowledgements

I would like to give my utmost thanks to Dr. David Duffy for all of his support and guidance, and who made this arduous process easier. I am indebted to Dr. Joan

Canfield for all her encouragement and editorial assistance, and who kept me on-track during the toughest times. I wish to thank Dr. Richard Mackenzie, who spent many hours helping me to create the solid foundation of this project and helping me along every step.

Much gratitude is extended to Dr. Don Drake for all of his advice and time. lowe sincere thanks to Micah Ryder, who spent many Saturdays in Hamakua Marsh, and without his love and support none of this would have been possible.

iii Abstract

The African blackchin tilapia (Sarotherodon melanotheron) was introduced to

Hawaii in 1951 for use as a baitflsh and to control aquatic weeds; poeciliid mosquitoflsh

(Gambusia affinis, Poecilia latipinna) were introduced in 1905 for biocontrol. Since their introductions, these fish have rapidly colonized the Hawaiian Islands, maintaining abundant populations along low-energy coastlines as well as in streams and drainage channels. No study to date has documented the ecological impacts of these invasive fish on aquatic invertebrate assemblages within Hawaii's aquatic ecosystems.

To examine the effects of fish predation on aquatic insect populations, insect emergence was measured within Hamakua Marsh, a brackish wetland on the island of

Oahu, Hawaii. Fish access to invertebrate communities was manipulated through the use of exclosures, creating areas of fish free habitat. Emergence was sampled every week over a six month period, encompassing both wet and dry seasons. Results revealed that abundance and biomass of emergent insects were indirectly affected by fish presence. 1n addition, many environmental and temporal factors affected aquatic insect populations at

Hamakua Marsh. The presence of widgeon grass, Ruppia maritima positively correlated with increased aquatic insects. Decreased water levels and dry season conditions also promoted increased aquatic insect populations. My results suggest that the presence of invasive fish can change the aquatic environment by decreasing populations of aquatic insects, in tum decreasing forage for the endangered waterbirds that inhabit wetland areas throughout the Hawaiian Islands.

iv Table of Contents Page

Sl~ature· Pa~e •.••.•.•...... •...... 11 ..

Acknowledgements ...... •••.•.••...•.•...... iii

Abstract ...... •...... •...... •...... •...... •...... iv

Table of Contents ..•...... •.•...•.•..•.•.•..•••.•.•.•..•.•...... •..v

List of Tables •.•...... •...... •...... •...... •...... •..viii

List of Figures ...... •...... •...... •...... •....ix

Chapter 1. Research Rationale ...... 1

Section 1.1 Thesis Outline ..•....•...•...... •...... 1

Section 1.2 Research Rationale ...... •.•.•...•..•...... 1

Chapter 2. Research Objectives and Hypotheses ...... 3

Section 2.1 Rationale for Methods ...... •...... •...... •.•.•..•....3

Section 2.2 Objectives and Hypotheses .••••••••.•..••..•...... •...... •.....7

Chapter 3. Literature Review .•...... •••.•9

Section 3.1 Overview ...... •...... 9

Section 3.2 Introduction ...... •...... •...... 9

Section 3.3 Aquatic Insects •.•...... •.•••.•....•.•...... •...... •.•...... •12

Section 3.4 Factors Affects Aquatic Insect Populations ...... •...... •..•••••.•• 13

Section 3.4.1 Anthropo~enic Disturbance ...... •...... •••.. 14

Section 3.4.2 Hydroperiodl Water Level ...... •...... 14

Section 3.4.3 Substarte ..•...... ••.••••.• 16

Section 3.4.4 Water Chemistry ...... •...... •...... 18

v Section 3.4.4a Dissolved Oxygen .•...... •.•...... 19

Section 3.4.4b Salinity ...... 20

Section 3.4.4c Temperature •.•...... •...... •.•.....•••21

Section 3.4.4d Nutrients ...... •.....•.....•••••.•.....22

Section 3.4.5 Vegetation ...... •...... ••.••••.•.•.•.•....•.23

Section 3.4.6 Fish ...... •.•...... •.•...... •..24

Section 3.5 Invasive Species ...... •...•...... •...•••.•.•.•....•...... • 27

Section 3.5.1 Blackchin Tilapia, Sarotherodon melanotheron •..•...... 27

Section 3.5.2 Mosquitoflsh, Gambusia affinis ...•...... •...... 29

Section 3.6 Importance of Aquatic Insects as a Food Source for Waterbirds ... 31

Section 3.7 Ruppia maritima •••...... 34

Section 3.8 Future Research ...... 36

Chapter 4. Materials and Methods •...... •...... •.•...... •.•.•..•.•..•....37

Section 4.1 Study Site...... •...... •...... •...... •...... •...... 37

Section 4.2 Experimental Design ...... ••..•...... 40

Section 4.3 Aquatic Insect Emergence Sampling .....•...... •...... •...... •...••.43

Section 4.4 Water Quality Parameters ...... •...... •...••.•...... •...••...... 46

Section 4.5 Fish Density Sampling ...... •.....••.•...... 47

Section 4.6 Vegetation Experiment Sampling .....•...... 47

Section 4.7 Statistical Analysis ...... •...... •...... •...... 49

Chapter 5. Results ..••..•••.•...... •...... •.•.••.•••.••...•...... 51

Section 5.1 Introduction .....•...... •...... •.•...... 51

Section 5.2 Rainfall ...... o ...... 51

vi Section 5.3 Water Deptll ...... 53

Section 5.4 Water Quality ...... 54

Section 5.5 Aquatic Insect Occurrence and Frequency ...... 55

Section 5.6 Aquatic Insect Abundance ...... 59

Section 5.7 Aquatic Insect Biomass ...... 61

Section 5.8 Seasonality ...... 62

Section 5.9 Vegetation ...... 63

Section 5.10 Fish Community ...... 65

Chapter 6. Discussion ...... 67

Section 6.1 Introduction ...... 67

Section 6.2 Environmental Conditions ...... 67

Section 6.2.1. Rainfall ...... 68

Section 6.2.2. Water Depth ...... 69

Section 6.2.3 Water Quality ...... 71

Section 6.3 Aquatic Insect Populations and Treatment Regime ...... 73

Section 6.3.1. Aquatic Insect Abundance ...... 74

Section 6.3.2 Aquatic Insect Biomass ...... 75

Section 6.4 Vegetation ...... 75

Section 6.5 Fish Community ...... 76

Section 6.6 Aquatic Insect as a Food Source for Waterbirds ...... 78

Chapter 7. Conclusions ...... 80

Chapter 8. Recommendations and Future Research ...... 83

Section 8.1 Management Recommendations ...... 83

vii Chapter 1. Research Rationale

Section 1.1 Thesis Outline

This thesis is composed of eight chapters and their associated sections. The first

chapter introduces the subject and the research rationale used. The second chapter

reviews the objectives and hypotheses that guided the study design and data analysis.

The third chapter is an overview of the literature related to this work. The literature review provides an understanding of the importance of aquatic insects in wetland systems

and the biotic and abiotic factors that can affect their communities. The fourth chapter

covers the materials and methods used in conducting the study. The fifth chapter reports

the results of the field and laboratory investigations and the statistical analysis.

Discussion of the study results is presented in chapter six. Conclusions from the study

are stated in chapter seven. This manuscript concludes with chapter eight which gives

recommendations for future research endeavors.

Section 1.2 Research Rationale

Aquatic insects within wetland ecosystems have been studied extensively outside

Hawaii and have been shown to be a critical component of wetland food chains. An

extensive literature review revealed little to no information regarding aquatic insect

communities within Hawaiian wetlands, or how introduced fish and plant species affect

these communities. There is a need to evaluate the effects of introduced species on

wetland trophic structure and how this effect may cascade throughout a system affecting

higher trophic levels, particularly Hawaii's endangered waterbird populations.

The main goal of this study was to evaluate the effects introduced fish species

have on aquatic insect emergence within Hamakua Marsh, located on Oahu's windward

1 side. The majority of wetland management in Hawaii is undertaken for the recovery and conservation of endangered, endemic waterbird species. Insects are an important food source for these birds, and fish may be competing with waterbirds for insect forage.

Furthermore, this study was intended to identify environmental factors that may affect differences in aquatic insect emergence rates.

Conclusions from this study will be of interest to local, state, and private land managers who are looking to restore wetland ecosystems as well as provide improved habitat for Hawaii's endangered waterbird populations. This study's fmdings suggest non-native fish species are having a detrimental effect on the aquatic environment of

Hamakua Marsh and management actions should be considered to reduce their populations.

2 Chapter 2: Research Objectives and Hypotheses

Section 2.1 Rationale for Methods

Wetlands are places of incredible biological productivity, species diversity and habitat transition, providing an important interface between terrestrial and aquatic systems. At one time Hawaii contained an estimated 23,876 hectares of wetlands

(Stedman and Hanson 1997). Hawaii has lost over 12 percent of its total wetland acreage and over 30 percent of its naturaI lowland wetlands (Dahl 1990). Although the remaining wetlands cover less than three percent of Hawaii's surface area, they are extremely important because they support a suite of plant and animal species endemic to the

Hawaiian Islands (Dahl 1990). Hawaii's hydrological conditions-heavy rainfall, porous volcanic soil, and steep terrain-create wetlands that are different from those found in any other continental land masses, where most wetland research has been conducted. Urban sprawl, agriculture, and invasive species have been and are currently the biggest threats to Hawaiian wetlands (Meier et al. 1993).

Hawaii's wetlands are inhabited by five endangered endemic waterbird species and most wetland management is executed to enhance their populations. The Hawaiian Duck

CAnas wyvilliana), the Hawaiian Stilt (Hi11Ul1ltopus mexicanus), the Hawaiian Moorhen

(Ga/linula chloropus sandvicensis), the Hawaiian Goose (JJranta sandvicensisl and the

Hawaiian Coot (Fulica alai) all reside in Hawaiian wetlands and utilize insects as a food source during some point of their life cycle. The availability of food is of primary importance to waterbirds, helping to ensure they store energy for over-winter survival and for laying eggs in the spring. Research has shown that aquatic insect abundance positively correlates with waterfowl and shore bird distributions (Fredrickson and Reed

3 1988). Cox etaL (1998) showed that growth and survival of mallard ducklings (Anas plathyrhynchos) were directly related to total abundance of aquatic invertebrates. Insect abundances can influence wetland use and feeding behavior by waterfowl (Fredrickson and Reed 1988). Despite the vital importance of aquatic insects to waterbird populations, few investigations have examined aquatic insect community structure and densities in

Hawaiian Island wetlands.

Aquatic insects play many roles in the ecosystem function of wetlands. They provide valuable ecological services, like detrital recycling, and are also an important food source for both aquatic and terrestrial organisms. Aquatic insect communities can be impacted by environmental dynamics, habitat availability and type, as well as predation. For example, the presence of aquatic macrophytes can have significant positive effects on aquatic insect community structure. Aquatic macrophytes provide structural habitat for aquatic insects, as well as improving food availability, the physical environment (Le. shading, physical space for attachment, stabilization of sediments), and the chemical environment (Le. dissolved oxygen, pH) (Evans et aL 1999). However, the presence of fish can negatively affect insect communities through direct predation and competition for resources (i.e. food, habitat). Influences of fish on insect communities may be particularly important, given the negative effects of fISh documented in other aquatic habitats (Zimmer et a1. 2000). Hanson and Riggs (1995) found that stocking fathead minnows Cl'imephales prome(as) in prairie wetlands reduced abundance, biomass, and taxon richness of invertebrates that would otherwise be available for waterfowl.

Therefore, identifying variables that influence insect abundance and community structure

4 in Hawaii should be a high research and management priority when considering approaches to recovery of endangered waterbird populations.

One particular variable of concern in Hawaiian wetlands are invasive fish. While invasive plants have contributed significantly to the loss of functionality within wetland ecosystems in Hawaii (Stemmermann 1981), several different fish species have also become well established in these systems (Maciolek 1984) through purposeful or accidental introductions (Englund 1999). The present study focuses on two introduced aquatic species. The blackchin tilapia (Sarotherodon melanotheron) was introduced in the 1970's, while mosquitofish (Gambusia affinis) were introduced in 1905. Both species are now well established in the Hawaiian Islands (Maciolek 1984). Several studies have highlighted the negative impacts that these introduced fish species can have on an aquatic system (Batzer 1998; Englund 1999; Hanson and Riggs 1995). In some cases the presence of Gambusia affinis has all but extirpated local fish and insect populations through competition and predation (Lee et al. 1980). Scholdt (1972) warned "the impact of the fish on the aquatic environment cannot be underestimated as there is good evidence that the indiscriminate use of mosquitofish can be as detrimental as the misuse of pesticides." Detrimental effects of tilapia were documented in the case of the near extinction of the small endemic goby (or "sinarapan", Mistichthys luzonensis) in Lake

Buhi in the Philippines (Baluyut 1983). Adverse impacts of introduced fishes have not been well documented in Hawaii, but it is understood that these species may have a direct impact on ecosystems through competition for food resources and predation (Maciolek

1984).

5 Hamakua Marsh Wildlife Sanctuary is a 9.19 hectare brackish wetland, located on the windward side of the island of Oahu. which is intensively managed in order to preserve its populations of endangered waterbird species. To investigate the effects of invasive fish species on aquatic insects, experiments were conducted at Hamakua Marsh for a six­ month period from February through July of 2005. During this study, preservative-free emergence traps were used to sample insect populations exposed to three treatment regimes in 25 experimental plots. Ten plots were randomly chosen to be treatment plots where fish were excluded using cage exclosures, while 10 other plots were sampled as controls where fish were allowed access. Five plots were procedural controls where fish were allowed access while testing for artifactual effects of the cages.

The collection of aquatic insect density and biomass samples tested for any effects that fish predation or environmental perturbations may have on aquatic insect emergence.

Prior studies with similar research objectives have tested their hypotheses through the use of insect emergence traps (Batzer 1998; Hunener and Kadlec 1992; Mackenzie and

Kaster 2002; Mackenzie and Kaster 2004). Recently Mackenzie and Kaster (2002) designed a low-cost preservative-free emergent trap which provides uncontaminated samples and can be used for elemental and stable isotope analysis, as well as minimized organism disfigurement, allowing for easy identification.

To document potential environmental effects, ecological variables were measured at the study site, including water quality, water depth, and rainfall. Water quality data (i.e. temperature, pH, dissolved oxygen, and salinity) were collected using an electronic multi probe sampling device. while water depth was measured using a ruler. and rainfall was recorded by a National Weather Service rain gauge.

6 Section 2.2 Objectives and Hypotheses

Invasive fish populations may negatively affect food availability for endangered waterbirds through increased competition or degradation of water quality. This investigation focused on understanding the effects of introduced invasive fISh species on aquatic insect emergence in a restored wetland system to address management concerns regarding endangered waterbird habitat quality at Hamakua Marsh Wildlife Sanctuary.

The specific objectives of the study included:

1. To evaluate the effects of introduced invasive fISh on aquatic insect

population biomass and abundance.

2. To evaluate whether fISh presence affected water quality.

3. To test whether the native sea grass Ruppia mariti11Ul is negatively affected by

the presence of invasive fISh.

4. To compare different types of aquatic vegetation and their associated aquatic

insect communities.

5. To quantify the response of aquatic insect emergence to varying wetland

water levels.

6. To understand the effect of seasonality on aquatic insect emergence rates.

Six hypotheses based on these objectives guided this investigation, providing the framework for this study.

1. The areas which exclude fish (fish absent treatments) will have significantly

higher aquatic insect biomass and abundance. in comparison to the areas in

which fish are present (control treatments).

7 2. Areas without fish will have significantly higher dissolved oxygen levels than

areas in which fish were present (control treatments).

3. Ruppia maritima abundance will be significantly higher in areas where fish

are absent, in comparison to the areas in which fish are presenL

4. Areas containing aquatic vegetative cover (algae or emergent plants) will have

higher aquatic insect emergence rates in comparison to areas without aquatic

vegetative cover (bare substrate).

5. Aquatic insect emergence rates will be significantly higher in areas with lower

water levels in comparison to areas with higher water levels.

6. Aquatic insect emergence rates will be significantly higher in the dry season

months (May through July) than in the rainy season months (February through

April).

8 Chapter 3. Literature Review

Section 3.1 Overview

To date there have been few studies on the aquatic insect biota of tropical wetland systems in Hawaii. This literature review aims to give a better understanding of the importance of aquatic insects in wetland systems and of the factors. biotic and abiotic. which can affect their communities. My review begins with a summary of information relating to wetland ecosystems and their diverse invertebrate communities. In order to better understand the complexity of aquatic insects. I include a brief discussion on their taxonomy. morphology and physiology. I discuss factors that affect aquatic insect community abundance and diversity within ecosystems. including anthropogenic disturbance. hydroperiod. substrate. water chemistry (dissolved oxygen. salinity. temperature. and nutrients). vegetation, and fish predation. I then provide information on two abundant alien fish predators at Hamakua Marsh. Sarotherodon melanotheron and

Gambusia afftnis. Next I summarize the importance of wetland insects to birds. including how insects fulfill the dietary needs of many adult and juvenile bird species. I also discuss the plant Ruppia maritima. which is utilized by birds and insects in wetland systems. and is found at Hamakua Marsh. I conclude with a discussion about future research needs in Hawaii. emphasizing wetland and insect aspects.

3.2 Introduction

Wetlands are defmed by the U.S. Army Corps of Engineers as "areas that are inundated or saturated by surface or ground water at a frequency and duration sufficient to support, and under normal circumstances do support, a prevalence of vegetation typically adapted for life in saturated soil conditions" (USACOE 1987). Topographically

9 and ecologically, wetlands are transition zones between uplands and deepwater aquatic systems and include swamps, bogs, marshes, mires, fens, and other wet ecosystems found throughout the world (Mitsch & Gosselink 1993).

Wetlands are one of the most important ecosystems on Earth. These ecosystems provide a suite of ecological functions including: flood control, improved water quality, habitat for unique flora and fauna, and stabilization of the global levels of available nitrogen, carbon dioxide, atmospheric sulfur, and methane (Mitsch & Gosselink 1993).

Wetlands have some of the highest primary productivity rates of any ecosystem and can be compared only to tropical rain forests and coral reefs in their high species diversity

(Kentula & Kusler 1990; Mitsch & Gosselink 1993; Zedler 2001).

Besides environmental values, wetlands also have numerous economic values, opportunities for recreation, research, and education. For example, wetlands provide habitat for many commercially harvested fish and shellfISh species, waterfowl species, and fur animals. Greater than 95% of commercially harvested fISh and shellfISh in the

United States are wetland dependent (Mitsch & Gosselink 1993). Eighty percent of

America's breeding bird population and over fifty 50% of the over 800 species of protected migratory birds rely on wetlands (Mitsch & Gosselink 1993). Fur bearing mammals (muskrats, mink, beaver, and nutria) and alligators are harvested for their pelts and spend most of their lives in wetlands (Mitsch & Gosselink 1993). During the past century, the value of wetland ecosystems was recognized by the U.S. government, resulting in the implementation of wetland protection laws and regulations, as well as long-term management plans for restoring degraded wetlands (Kusler & Kentula 1990).

10 Recent advances in our understanding of food web dynamics, nutrient cycling, and overall productivity of wetlands have drawn increasing attention to the important role filled by aquatic invertebrates in the ecology and function of these systems. In the 1960's and 1970's, wildlife biologists discovered that invertebrates, particularly insects, were an important waterbird food, and today most research on wetland insects has been driven by wildlife-focused research (Batzer & Wissinger 1996). Invertebrates playa vital role in all wetland communities. and are one of the components of aquatic systems most widely used in biological monitoring (Merritt & Cummins 1996; Thorp & Covich 2001). This is due to the diversity of invertebrates, particularly insects, which make up 54% of all described species of organisms (Wilson 1988).

Most wetland macroinvertebrates (invertebrates that can be seen with the naked eye) fall into three groups: annelid worms, mollusks. and arthropods (Hammer 1989).

Worms (platyhelminthes, nematodes, and annelids) are the most common group of macroinvertebrates in wetlands. Wetland mollusks include several genera of snails and bivalves, while wetland arthropods are most often crustaceans (i.e. amphipods, isopods) and insects such as stoneflies, dragonflies, midges, mayflies, aquatic beetles, and springtails (Hammer 1989).

Invertebrates are critical components of the energy dynamics of wetlands, and are integral parts of wetland food chains (Hammer 1989). Invertebrates play significant roles as nutrient recyclers, primary and secondary consumers, as food for wildlife, and as indicators of ecosystem function (Keiper et aL 2002). Aquatic invertebrates, especially insects. are usually the main staple in the diets of waterfowl, fISh and other wetland invertebrates (Nelson et aL 2000). For instance dipterans make up a large portion of

11 dabbling duck diets during reproduction and molting (Keiper et al. 2002). Invertebrates can also impact sediment characteristics like compaction, water content and texture

(Zedler 2001). Surprisingly little is known about macroinvertebrate communities in natural wetlands, and even less is known about those in constructed wetlands (Nelson et al.2000). Due to the important role invertebrates play within wetland ecosystems, numerous restoration projects have added design components specifically to accommodate these organisms.

3.3 Aquatic Insects

This study will focus on the aquatic insect assemblage within Hamakua Marsh, a

Hawaiian wetland ecosystem. For the purpose of this study, aquatic insects will be defmed as any insect species whose larval and/or juvenile forms live within the water­ based environment of Hamakua Marsh.

Aquatic insects exhibit a wide array of morphological, physiological, and behavioral adaptations that allow them successful survival in all types of aquatic habitats

(Ward 1992). Aquatic stages of insects occur in numerous habitats including riparian buffers, hot and cold springs, lentic and lotic wetlands, intertidal pools, intermittent streams, fresh and sllline lakes, prairie potholes, and temporary and permanent ponds

(Ward 1992). The morphological and physiological change in structure and form during the life of an insect is termed metamorphosis. Metamorphosis in aquatic insects differs in detail among the various taxa, but the characteristics involved are primarily related to the change from an aquatic larval stage to a terrestrial adult stage (especially respiration and flight) (Merritt & Cummins 1996). The final event in metamorphosis is eclosion, or emergence, which is the escape of the adult insect from the cuticle of the pupa or last

12 larval instar (Merritt & Cummins 1996). Emergence of adult aquatic insects represents the culmination of their production in the aquatic environment and a potential transfer of energy and nutrients from aquatic to terrestrial systems (Whiles & Goldwitz 2001).

Although widely considered an important process. the significance of aquatic insect emergence to consumers within a system remains poorly understood (Whiles & Goldwitz

2001).

3.4 Factors Affecting Aquatic Insect Populations

Aquatic insect populations can be influenced by multiple biotic and abiotic factors including: anthropogenic disturbance. hydrologic regime. substrate. nutrient loading. temperature. vegetation community. habitat structure (vegetation type and diversity). water physiochemical characteristics (i.e. salinity. dissolved oxygen. temperature). presence of predators. and human disturbance. All of these factors can influence the abundance and diversity of invertebrate communities that may colonize a habitat (Batzer

& Wissinger 1996; Merritt & Cummins 1996; Minshall 1984; Neckles et at. 1990;

Nelson et aL 2000; Ward 1992; Whiles & Goldwitz 2001; Wissinger 1999; Zedler 2001).

Because aquatic insects are sensitive to physical and chemical conditions in their environment, as well as to top-down and bottom-up biotic controls. their communities reflect land use changes on both geographical and ecological scales (Thorp & Covich

2001). Distribution of an aquatic insect population is ultimately set by the physical­ chemical tolerance of the individuals in the population to an array of environmental factors (Merritt & Cummins 1996). which are linked and make it difficult to discern singular effects when changes in community structure or abundance occur.

13 3.4.1 Anthropogenic Disturbance

The effects of anthropogenic stresses (Le. pollution. urban and agricultural development, hydrologic changes, invasive species introductions) on aquatic systems can have detrimental effects on aquatic insect communities. Aquatic insects can be extremely sensitive to environmental changes. Urbanization and agricultural development contribute to increased runoff, adding excess amounts of toxins, sediments and nutrients into aquatic systems. Diversion of water for development causes changes in hydroperiod and hydrologic regime of aquatic systems. Accidental and intentional invasive species introductions can disrupt ecosystem processes and food web structures. Aquatic insect abundance and diversity respond to all of these perturbations. Urban sprawl, agriculture, and invasive species have been, and are currently, the biggest threats to Hawaiian wetlands and their inhabitants (Meier et al. 1993).

Englund (1999) discussed the consequences of degraded habitats in Hawaii's

Pearl Harbor (Oahu) estuary and the surrounding wetlands where alien invertebrate species have almost completely displaced native species. During a benthic invertebrate and fish survey in 1997-1998, nonindigenous species dominated the Pearl Harbor estuary and comprised 48% of the species composition. whereas only 33% of the species surveyed were native and another 19% were cryptogenic (of uncertain origin) (Englund

2002). Englund concluded that nonindigenous species are an escalating threat to

Hawaiian stream, wetland, estuarine, and anchialine pond ecosystems.

3.4.2 Hydroperiod / Water Level

Water level and hydroperiod in wetland systems playa major role in determining aquatic insect abundance and community structure (Batzer & Wissinger 1996; Necldes et

14 aL 1990; Whiles & Goldowitz 2001). Surface water levels in most wetlands (even those that are pennanent in most years) fluctuate seasonally. and many wetlands dry completely on a seasonal cycle or as a result of interannual variation in climate (i.e. drought) (Wissinger 1999). Variation in the degree of permanence. duration, seasonal timing. and predictability of drying and filling affects invertebrate colonization strategies and community composition (Wissinger 1999).

Whiles and Goldowitz (2001) found that in Nebraska. temporal patterns of insect emergence show that sites with different hydrology generate peaks of adult insect biomass at different times of the year. In Florida Evans et aL (1999) concluded that water level fluctuation was the most important factor influencing seasonal abundance of fISh and macroinvertebrates. Neckles eta! (1990) observed the influence of seasonal flooding on macro invertebrate abundance in wetland habitats. and found that regular flooding and drying were essential to maintaining high densities of invertebrates in seasonal wetlands.

The duration a habitat contains water determines the types of invertebrates which will constitute the community; hence only taxa with compatible life histories are able to inhabit areas with varying hydroperiods. Short-duration habitats support species with rapid growth and development or desiccation-resistant phases (Schneider 1999). Long­ duration habitats support taxa with longer development times. like larger predacious species. Highly ephemeral habitats tend to be dominated by beetles and mosquitoes while midges and odonates predominate in habitats which flood for longer durations

(Batzer & Wissinger 1996). Wetlands which are flooded for short durations or infrequently tend to have lower invertebrate productivity (Batzer & Wissinger 1996).

15 Schneider (1999) found that as wetland hydroperiod increases (i.e. water is held longer) species diversity and abundance within ecological guilds also increases. This means that the longer a wetland contains water, the more diverse its invertebrate assemblage could become (i.e. a longer hydroperiod accommodates species with more complex life cycles).

Schneider (1999) studied the effects of hydroperiod on invertebrate communities of temporary pond communities in Wisconsin. Seven wetland ponds dependent upon snow melt and precipitation were sampled pre and post drought for invertebrates.

Schneider observed that as pond duration increased, taxa number also increased, ranging from four in short duration ponds to 65 in long duration ponds. In addition as pond duration increased, the more important interactions among taxa (predation and competition) became in predicting the abundance of the taxa. For instance predator taxa increased from one in the shortest duration pond to thirty-four in the longest duration pond (Schneider 1999). The study concluded that the more hydrologically variable a pond, the more biological change was observed. Besides structuring trophic relationships and changing habitat characteristics, water fluctuations can affect invertebrate communities indirectly by eliminating aquatic plants, altering chemical conditions, and increasing erosion, thereby altering the substrate and water clarity (Ward 1992).

3.4.3 Substrate

The majority of aquatic invertebrates exluoit an intimate association with the substrate of a habitat during at least a portion of their lives (Ward 1992). Therefore it is not surprising that substrate type has a major influence over the distribution and abundance of aquatic insects (Minshall 1984). The substratum consists of various types of organic and inorganic materials, and can be anything stable enough for insects to crawl

16 on, cling to. or burrow in (Minshall 1984). Substrate variables of ecological importance include heterogeneity. stability. physical structure. and organic content (Ward 1992;

Minshall 1984). Differing substrates may harbor different assemblages of aquatic insects. as well as dictate the density and biomass of fauna within a system (Ward 1992).

The eight categories which Ward (1992) offers as a general classification of benthic insects include: phytophilous fauna (insects associated with living aquatic macrophytes). xylophilous fauna (insects that occur on or are associated with submerged wood). lithophilous fauna (insects associated with large mineral particles. rocks or stones). psephophilous fauna (insects associated with gravel substrate). sammophilous fauna

(aquatic insects which burrow into sand). and leophilous fauna (insects which dwell in muddy habitats where silt and clay sized particles predominate).

A study conducted by Sanderson et aL (2005) looked at macroinvertebrate populations at 188 sites along the River Rede in northeast England. The researchers hypothesized that invertebrate assemblages at differing sites would be primarily influenced by the variation in dispersal abilities of insects from neighboring sites.

However. the study found that the physical structure of in-stream habitat (i.e. streambed composition) was one of the driving variables affecting invertebrate assemblage composition. The stream descriptors found to affect invertebrate assemblage included the percent of sand, silt, clay. pebbles. cobbles. boulders. and the presence and location of vegetation (Sanderson et aL 2005).

In a study looking at substrates in Michigan streams. Tarzwell (1936) discovered that a sand substrate harbored the lowest faunal crops. The general lack of aquatic insects in sand is directly related to its low organic content, as well as to its instability (Ward

17 1992). Organic materials in soils, particularly fme particulate organic matter and associated microfauna. are an important source of food for macroinvertebrates, as well as physical habitat (Evans et al. 1999). Low soil organic matter concentrations are generally associated with reduced levels of wetland function, including the poor establishment and growth of vegetation, poor habitat and food chain support for invertebrates and fish, and an altered nutrient cycle (Shaffer & Ernst 1999).

Besides the physical attributes of a substrate, there are interactive ways in which the substratum can act to modify the microhabitat in which aquatic insects live (Minshall

1984). The most important modifiers of an insect's response to substratum are water temperature, water chemistry (pH, dissolved oxygen), nutrient levels, light, flow velocity, and interactions with other organisms (Merritt & Cummins 1996; Minshall 1984; Ward

1992).

3.4.4 Water Chemistry

Aquatic invertebrate communities are affected by hydrologic variables that determine the presence, abundance, and size of specific taxa (Bolduc & Afton 2004).

Physiochemical factors that are most often citied as having an effect on community composition and invertebrate abundance in wetlands include dissolved oxygen, salinity, pH, suspended sediment and nutrient levels (Wissinger 1999). For this review I focused on levels of dissolved oxygen, salinity, and temperature which affect osmoregulation and respiration in aquatic invertebrates (Bolduc & Afton 2004). Generally it is the extremes in any of these parameters which result in a change to aquatic insect communities (Thorp

& Covich 2001).

18 3.4.4a Dissolved Oxygen

In natural water bodies, dissolved oxygen (DO) is determined by competition between organisms supplying DO (via diffusion and photosynthesis) and organisms requiring DO (simple chemical oxidation and biological consumption) (Hall et aL 1999), as well as water temperature, pressure and salinity (Ward 1992). DO concentrations are highly variable over time and space, and oxygen may be totally lacking from some aquatic habitats, a condition known as anoxia (Thorp & Covich 2001). DO is paramount to aquatic insect survival, as most insects depend on oxygen in solution to meet their respiratory needs (Ward 1992). However, many aquatic insects have evolved mechanisms to cope with low oxygen waters. For instance dragonfly nymphs can rectally ventilate, while some midges and water bugs posses hemoglobin which acts as an oxygen-transport pigment during hypoxia (Ward 1992). Despite these adaptations, many studies have shown that habitats containing low DO levels harbor poor invertebrate diversity and abundance (Rader 1999; Ward 1992). Rader (1999) found that invertebrate densities in the Northern Everglades were five times higher in open-water sloughlwet prairie habitat than in sawgrass plains. The study concluded that compared to dense sawgrass plains, open-water habitats offered more food and oxygen because of an abundance of algae and submerged macrophytes (Rader 1999).

Seasonal and spatial variation in oxygen concentrations greatly restricts the types and diversity of insects found in aquatic environments (Thorp & Covich 2001). In a

Florida study by Rader and Richardson (1994), it was concluded that the depletion of oxygen caused by high biological oxygen demand is the dominant mechanism causing a decline in species diversity within most enriched lakes and streams. In 2000 Nelson et aL

19 found that DO concentrations affected invertebrate community development within wastewater treatment wetlands. Data collected showed that the low DO of wastewater wetlands decreased invertebrate taxa richness (Nelson et aL 2000). These results confirm a direct relationship between DO concentrations and invertebrate density and abundance.

3.4.4b Salinity

Another important water chemistry variable that can affect invertebrate populations is the salt content of water. Salinity gradients that fonn in coastal wetlands can affect insects, most of which are salt-intolerant (Thorp & Covich 2001). Compared to freshwater ecosystems relatively few species of aquatic insects live submerged in salt or brackish water. Aquatic insect orders represented in saline environments include the

Ephemeroptera, Plecoptera, and Megaloptera found in oligosaline habitats (0-5.0 ppt), the

Odonata, Lepidoptera, Trichoptera found in mesosaline habitats (5.0-18 ppt) and the

Hemiptera, Coleoptera and Diptera found in polysaline (18-30 ppt) or hypersaline (> 40 ppt) habitats (Ward 1992). Some insects such as brine flies (Diptera: Ephydridae) actually thrive in warm, saline waters, where they have few competitors (Thorp & Covich

2001).

Lovvorn et aL (1999) looked at invertebrate communities within stands of aquatic plants in lakes of differing salinities in Wyoming. Core substrate samples were taken • from an oligosaline lake and a mesosaline lake, and invertebrate biomass and diversity were compared. Total invertebrate biomass did not differ between oligosaline and mesosaline lakes. However, the study found taxonomic composition of invertebrates to be strongly affected by salinity. Despite the observed effects salinity has on

20 invertebrates. few studies have researched the impact of salinity gradients on aquatic insects.

3.4.4c Temperature

Water temperature fluctuations in wetland ecosystems have been shown to affect aquatic insect communities. The temperatures of temperate. polar and even tropical waters fluctuate seasonally and are highly predictable (Williams 1992). With thermal heterogeneity occurring in most aquatic habitats. aquatic insects respond to the entire temperature regime. which includes absolute levels. seasonal and diel ranges. rate functions. and the timing and duration of thermal events (Ward 1992). Temperature characteristics can influence properties of both individual and entire insect communities

(Williams 1992).

Research has indicated that temperature may playa major role in influencing life­ history patterns of aquatic insects (Sweeney 1984; Ward 1992; Williams 1992). Most aquatic insect life-history parameters. especially larval growth and fecundity. are affected significantly by temperature (Sweeney 1984; Ward 1992). Temperature may influence other life history parameters such as egg incubation period. hatching success. duration of hatching. and the induction and termination of diapause (Sweeney 1984; Ward 1992). At higher temperatures. larvae develop faster due to decreased time spent in each larval instar. or a reduction in instar number can occur (Sweeney 1984; Ward 1992; Williams

1992). In addition. larval growth may be affected by temperature through its effects on ingestion and assimilation rates of food (Sweeney 1984; Ward 1992). Generally. ingestion rates increase with increasing temperature but a leveling out or even a decrease may occur above temperature thresholds (Sweeney 1984; Ward 1992; Williams 1992).

21 Fecundity is influenced by temperature and is positively correlated with adult body size; therefore deviations from optimal thermal conditions will affect the competitive potential ofpopuIations (Sweeney 1984; Ward 1992; Williams 1992).

The timing and duration of aquatic insect emergence is partially determined by response to temperature changes (Ward 1992). In both lab and field experiments, increasing water temperature resulted in early emergence, whereas decreasing temperature delayed emergence (Sweeney & Vannote 1981: Ward 1992). Peters et at.

(1987) found that the mayfly, Dolania americana, used rising spring water temperatures as an emergence cue. The study concluded that nymphs responded to water temperatures

24-48 hours before emergence occurred. These findings help illustrate how closely linked the lives of aquatic insects are-with the temperature of their environment

3.4.4d Nutrients

Excessive nutrient loading and anaerobic conditions can often limit the success of wetlands as invertebrate habitat (King & Brazner 1999). Eutrophication has been shown to alter macroinvertebrate communities in wetlands (King & Brazner 1999; Rader &

Richardson 1994). Urban runoff typically contains high levels of suspended solids and organic matter that increases oxygen demand of recipient waters, often increasing mortality rates of sensitive organisms (Wolf et aL 1999).

To examine the effects of increased nutrients, King and Brazner (1999) looked at insect densities and diversity along a trophic gradient in Green Bay, Lake Michigan.

Insect density and diversity were sampled from a oligotrophic marsh (upper bay), a mesotrophic wetland (middle bay), and a eutrophic marsh (lower bay). The study found smaller springtime numbers of insects in lower bay wetlands (eutrophic areas) than in

22 upper bay wetlands, suggesting that eutrophication was negatively impacting wetland insect communities.

However in a similar study in the Florida Everglades, excessive nutrient loading was found to increase invertebrate diversity (Rader & Richardson 1994). Invertebrate samples were collected across eight sites along a nutrient enrichment gradient within the

Northern Everglades wetlands. The study found increased abundance and diversity of invertebrates in all functional feeding groups within enriched areas, versus unenriched areas. This may have been caused by an increase in primary production, resulting in increases in macrophyte and algal biomass (Rader & Richardson 1994). Thus, nutrient enrichment within the Everglades may not be harmful to invertebrates and their food webs, but instead could indirectly affect invertebrates by facilitating the monotypic invasion of TypJuz lati/olia, common cattail. These contradicting studies demonstrate the difficulties associated with making generalizations about the impact of increased nutrient levels on aquatic insect communities.

3.4.5 Vegetation

Numerous studies have shown that the abundance and diversity of invertebrates which occur in vegetated habitats are greater than in non-vegetated habitats (e.g. Evans et aL 1999: Schramm etaL 1987). This positive relationship is based on the fact that macrophytes influence food availability, vertebrate predation, the physical environment

(i.e. shading, physical space for attachment, stabilization of sediments), and the chemical environment (i.e. dissolved oxygen, pH) (Evans et aL 1999). The greatest macroinvertebrate densities and diversity are usually found in vegetated areas that are structurally heterogeneous, on plants that have large surface areas, and in vegetation that

23 covers a large spatial area or persists throughout the year. Additional influences include the position of the plant within the water column and the amount of organic matter that accumulates on plant surfaces (Chilton 1990; Findlay et aL 1989; Minshall 1984; Parsons

& Matthews 1995; Schramm et aL 1987; Voigts 1976).

Schramm et al. (1987) studied two Florida lakes, looking at differences in benthic invertebrate populations between vegetated and non-vegetated areas. The study concluded that diversity and abundance of benthic macroinvertebrates were greater in vegetated communities compared to open water communities.

In a study examining the effects of specific macrophytes on macroinvertebrates,

Parsons and Matthews (1995) compared macroinvertebrate populations among six separate macrophyte species. The study found that there were significant differences in macroinvertebrate assemblages associated with different macrophyte species. and that those differences were most strongly related to whether the plant was submerged or emergent. By measuring the biomass of the invertebrate specimens collected, they concluded that plant features (i.e. macrophyte surface area, structural stability, etc.) have the greatest influence on macroinvertebrate associations. Submerged plants generally supported the greatest density and biomass of invertebrates (all submerged specimens within the study had large surface areas per unit weight).

3.4.6 Fish

Many species of wetland fish can impact aquatic insect communities directly through predation or indirectly through competition of resources or degradation of habitat quality. Fish are often a major determinant of aquatic invertebrate abundance and community structure (Hanson & Riggs 1995). FIsh compete with invertebrates for

24 resources (Hanson & Riggs 1995; Hunter et al. 1986; Whiles & Goldwitz 2001). Several studies have shown that wetlands with fIsh have lower insect biomass than those without fIsh (Diehl 1992, Hunter et aL 1986, Mallory et al. 1994). While it seems likely that high population densities of invasive fIsh species depress wetland invertebrate abundance, these potential effects have not yet been thoroughly investigated in Hawaiian wetlands.

The impacts of fISh on aquatic insect populations can cascade throughout the entire community (Williams 1992). Many species of aquatic insects on which fISh prey are also important in the diets of migratory waterfowl (Batzer et aI. 1993). However, the hypothesis that fISh may compete with wetland bird species for food resources has been tested in only a handful of studies. Hunter et aI. (1986) observed the effects of acidifIcation and fISh on black ducks,Anas rubripes, in four Maine wetlands. The two acidic ponds lacked fIsh, where as the two circumneutral ponds had fISh. Invertebrate biomass and density were significantly greater in the two acidic ponds. The study revealed that acidifIcation was benefIcial for black ducklings, increasing their growth rates by extirpating fISh competitors (Huntet et aL 1986). These results support the idea that fISh and ducklings may compete for invertebrates, and that the negative effect of acidifIcation on fISh may be advantageous for ducklings.

Hanson and Riggs (1995) studied the potential effects of fathead minnows,

Pimephales promelas, on wetland invettebrate populations. They found that the presence of a non-native minnow was associated with lower density, biomass, and taxon richness of invettebrates in their study wetlands. Their experiment concluded that predation by fathead minnows may intensify food limitation and, in combination with seasonal

25 invertebrate population trends. may have the potential to limit waterfowl productivity

(Hanson & Riggs 1995).

Marklund et aL (2002) studied the effects of waterfowl and fish on submerged vegetation and macroinvertebrates. Exclosures were used to exclude fish. birds. and both fish and birds from foraging in sampling areas. Macroinvertebrate populations were sampled from each exclosure area using a core sampler. The results indicated that the presence of fish led to macroinvertebrate biomasses that were 40-60% lower than in treatments where fish were excluded. In contrast waterfowl did not affect macroinvertebrate densities in this experiment. This study concluded that even low densities of fISh seemed to reduce macroinvertebrate biomass. while waterfowl assemblages rarely had a significant effect on macroinvertebrate biomass.

In Utah researchers looked at macroinvertebrate response to differing management strategies. one being the presence of carp. Cyprinus carpo. in managed impoundments (Huener & Kadlec 1992). Macroinvertebrate populations were sampled in pools with and without carp. In areas with carp. there were reduced populations of both emergent vegetation and macroinvertebrates. The results suggested a negative effect on waterfowl via reduced standing crops of both submerged vegetation and aquatic invertebrates. The study concluded that carp affect invertebrates through direct predation, as well as by destroying submerged vegetation, which acts as microhabitat for many phases of aquatic invertebrate life (Hunener & Kadlec 1992). These are just a few of the studies that demonstrate the biologically significant impact flsh can have on aquatic insect communities.

26 Section 3.5 Invasive Species

In the previous section. fish predation was shown to negatively impact aquatic insects. However, aquatic insects have developed numerous behavioral and morphological anti-predation defenses including camouflage, chemical cues, thanatosis

(death feigning), deimatic behavior (bluff), reflex bleeding (autohaemorrhaging), and predator avoidance (Williams 1992). Despite these adaptations, many aquatic insects have not evolved rapidly enough to combat ever-changing predator densities and assemblages such as those associated with the invasion of alien fish species. Two species which seem to be exhibiting a deleterious effect on the aquatic insect community at

Hamakua Marsh are the blackchin tilapia. Sarotherodon melanotheron. and the Western mosquitoflsh Gambusia affinis.

Section 3.5.1 Blackchin Tilapia. Sarotherodon melanotheron

The blackchin tilapia. Sarotherodon melanotheron, is the dominant species within the aquatic environment of Hamakua Marsh. Sarotherodon melanotheron belongs to the family Cichlidae, and is native to estuarine areas of West Africa. ranging from Senegal to

Zaire (Trewavas 1983). Because the blackchin tilapia has a tropical distribution. low temperatures are believed to be the primary environmental factor controlling its range

(Jennings 1991). Tilapia can tolerate water temperatures between 17 and 32'l C, but can only successfully spawn and rear young at temperatures above 23° C (Lee et aL 1980;

Trewavas 1983). Jennings (1991) found that blackchin tilapia ceased all behavioral activity between 10-12° C, and 6.9" C caused death for all test subjects.

Tilapia frequent brackish estuaries. lagoons. and wetlands and rarely inhabit fresh or salt water (Trewavas 1983). Jennings (1991) found that blackchin tilapia spawned at

27 salinities of up to 35 ppt and survived brief exposure to hypersaline waters of up to 100 ppt under experimental conditions, although they can also inhabit waters of low salinities

(Shaw & Aronson 1954; Trewavas 1983). Experiments performed by Shaw and Aronson

(1954) demonstrated that blackchin tiIapia can live, breed, and produce viable offspring in freshwater aquaria.

In its natural range, tiIapia spawn throughout the year but with diminished frequency during periods of heavy rain (Trewavas 1983). Sexual dimorphism is minimal in the blackchin tilapia, mating is monogamous and the male broods the eggs in the mouth (Jennings 1991; Lee et al. 1980; Trewavas 1983). During the mouth-brooding period, males remain inactive in the nest while females remain with the male and actively defend the nesting territory (Jennings 1991). Shaw and Aronson (1954) found incubation in the mouth of the male to last six to 22 days, with a mean of 14 days, and females to produce approximately 50 eggs per clutch.

Adult tilapias are primarily detritus feeders with a diet consisting of fIlamentous algae, benthic diatoms, and submerged vegetation (Trewavas 1983). However, juvenile tilapias tend to be more carnivorous, consuming invertebrates and small fish (Lee et aL

1980; Trewavas 1983). In Florida, where tiIapias have invaded some inland waterways,

Courtenay et aL (1974) reported grazing on green and blue-green algae, with aquatic vegetation of all types being greatly reduced in areas with tilapia. Trewavas (1983) observed tilapia in their native range (Lagos, Africa) browsing on the surface of submerged plants.

Blackchin tilapia were introduced to Hawaii in the 1970's for aquatic weed control within ditch and reservoir areas (Maciolek 1984). Despite limited introductions,

28 four tilapia species are now well distributed throughout all the main Hawaiian Islands

(Maciolek 1984). New populations continue to appear and are likely a significant predator on native aquatic fauna. especially in streams and estuaries. Maciolek concluded that tilapia are the most prevalent exotic fish species in Hawaii, based on abundance and population numbers. Maciolek (1984)

Section 3.5.2 MosquitofIsh, Gambusia affinis

Another important alien fish species threatening Hawaii's wetlands is the Western mosquitofIsh, G. a/finis, an abundant member of the fish community at Hamakua Marsh.

Gambusia affinis is in the family Poeciliidae and native to the Southern United States

(Lee et al. 1980). This species has been introduced throughout most of the temperate and tropical regions of the world as a mosquito control agent (Otto 1973).

G. affinis are remarkably adaptive, surviving in waters with little dissolved oxygen, in high salinities and temperatures of up to 42Q C for short periods of time

(McCullough 1998). Like tilapia, the primary factor inhibiting range expansion to the north (409 N Latitude being the critical habitat boundary) by mosquitofIsh populations has been the inability to adapt physiologically to cold (Otto 1973). Otto (1973) found that the lower lethal temperatures for mosquitofJSh were between 3-59 C, and upper lethal temperatures were between 32-33" C. Otto (1973) also found that thermal tolerance of mosquitofJSh responds to environmental selection, meaning fISh from colder climates had lower lethal temperatures than fISh sampled from warmer climates.

Mosquitofish have been called "possibly the single most abundant freshwater fISh in the world" (Minckley eta! 1991). G. affinis are most abundant in lower reaches of streams, where they inhabit brackish standing to slow-moving waters, but can be

29 common to abundant in vegetated ponds, lakes, drainage ditches, backwaters and streams, where they tend to swim near the surface (Lee etaL 1980; Minckley eta!. 1991).

This species can also tolerate waters with high salinities. Fish could withstand salinities of 20-50 ppt with little mortality, but at salinities between 50-75 ppt, 100% mortality of all test subjects occurred (Al-Daham & Bhatti 1977). 1n Hawaii this species can tolerate a wide range of salinity tolerance, between 0-44 ppt, but is usually found in areas having salinities of 16 ppt or less (Englund 1999).

Females of the species are much larger than males, averaging 31-59 mm, while males only average 19-36 mm in length (Lee et al. 1980). G. affinis bear live young, producing an average of three to four broods per summer at intervals of three to six weeks (Lee et al. 1980). The number of fry per brood averages between 25 and 125, and in native ranges, reproduction usually occurs during the warmer spring and summer months (Lee et al. 1980). 1n Hawaii mosquitofish reproduce year round (Haynes &

Cashner 1995).

G. affinis is omnivorous, preferring mosquito larvae and pupae, but will consume other aquatic insects, zooplankton, fIsh, snai1s, and algae (Harrington & Harrington

1961). G. affinis is well known for its high feeding capacity. Chips and Wahl (2004) observed maximum consumption rates of 42-167% of their body weight per day.

Stocking this fIsh in the American Southwest has resulted in the extirpation of many rare, localized populations of native aquatic invertebrate species (Lee et al. 1980).

G. affinis was imported from Texas and introduced to all the main Hawaiian

Islands in 1905 (Maciolek 1984). MosquitofIsh were introduced to control mosquito larvae in urban ponds and ditches, and reservoirs (Maciolek 1984). Today, G. affinis are

30 common and can be found in every low elevation and some coastal aquatic habitats on

Oahu. with some populations reaching an elevation of nearly 400 m (Englund 1999).

Despite the beneficial attributes of mosquito control. Englund (1999) found that mosquitoflSh and other introduced poeciliids were causing declines in native damselfly populations around Oahu. Through sampling endemic Megalagrion damselfly populations in Oahu streams. wetlands and estuaries. Englund concluded there was a negative correlation between the distributions of introduced poeciliids and native damselfly populations.

Section 3.6 Importance of Aquatic Insects as a Food Source for Waterbirds

Birds exploit a great variety of food resources. Aquatic insects are an invaluable food source for many waterfowl and waterbird species that utilize wetland habitats.

Breeding and wintering waterfowl feed heavily upon invertebrates. and pre-fledging waterfowl depend on invertebrates for their survival (Batzer et at. 1993; Krapu &

Reinecke 1992; King & Wrobleski 1998). While some taxa, such as shorebirds. feed almost exclusively on invertebrates. others such as large wading birds utilize aquatic invertebrate resources seasonally {Bolduc & Afton 2004; Skagen & Oman 1996).

Several studies have documented waterfowl nesting preferences based on invertebrate aVailability (de Szalay et at. 2003; King & Wrobleski 1998). Wetland bird communities are most likely influenced by the composition and abundance of aquatic invertebrate communities.

Despite the fact that protein aVailability may fluctuate seasonally and daily. waterfowl are unable to store protein in a concentrated form (Krapu & Reinecke 1992).

During egg formation in females. protein, fat and calcium needs increase because these

31 essential nutrients are a major component of waterfowl eggs. Waterfowl satisfy these nutrient requirements for proteins, fats, and calcium by feeding on macroinvertebrates during the breeding season (Krapu & Reinecke 1992). Insects, primarily larvae and nymphs of aquatic species in the orders Diptera, Coleoptera, Odonata, and Trichoptera, are the principal animal foods ingested by nesting female dabbling ducks (Krapu &

Reinecke 1992). Invertebrates are also essential for ducklings, and other waterbird young, because they provide the majority of the necessary protein and energy during the pre-fledging period (de Szalay et al. 2003). As a result, knowledge of factors affecting aquatic invertebrate abundance is important for quantifying and managing wetland productivity and fledging success in waterfowl.

Bolduc and Afton (2004) investigated relationships between sediment and hydrologic characteristics of wetlands and how they may indirectly affect waterbird communities through their direct effects on invertebrate communities. The study concluded that water depth was the primary factor affecting food accessibility for bird species. Food availability for birds is limited through their physical abilities (i.e. bill length, size. leg length, etc); therefore, bird species are limited to areas where they can exploit food resources. Bolduc and Afton (2004) suggested maintaining impoundments at different water depths to create habitat heterogeneity· in order to maximize invertebrate diversity (i.e. prey items) and waterbird species diversity.

Many studies have provided habitat management recommendations for wetlands where bird productivity is the main objective. Fredrickson and Reed (1998) present three management implications stemming from their research in wetland ecology. Their research suggests timing water movements to coincide with the exploitation of leaf litter

32 by invertebrates. Fredrickson and Reed (1998) state that management for specific aquatic or semi-aquatic plant communities may be the most practical way to increase invertebrate productivity. In addition managers can enhance the potential for invertebrate consumption by waterfowl, if peak periods of waterfowl use of wetlands coincide with reduced water level (Fredrickson & Reed 1998).

Wetlands in Hawaii are inhabited by four endemic waterbird species: the

Hawaiian Duck (Anas wyvilliana), the Hawaiian Stilt (JIimantopus mexicanus), the

Hawaiian Moorhen (Gallinula chloropus sandvicemis), and the Hawaiian Coot (Fulica alai). All of these species are federally and state listed as endangered and all depend on invertebrate forage at some point in their life cycle. Engilis et al. (2002) report that the major food items of the Hawaiian Duck are wetland macrophyte seeds and invertebrate dipterans and mollusks. Robinson et al. (1999) report the Hawaiian Stilt's diet consisting primarily of aquatic invertebrates and fish. No study to date has documented the dietary preferences of the Hawaiian Coot or the Hawaiian Moorhen. Mainland America adult coots and moorhens are primarily herbivorous: however, consumption of anintal foods, which include a variety of insects, may be taken opportunistically during emergence peaks (Alisauskas & Arnold 1994: Greij 1994). Aquatic invertebrates can comprise a large portion (45-85%) of the diet of young coot chicks (Alisauskas & Arnold 1994).

Despite the dwindling numbers of native Hawaiian waterbirds, no study to date has looked at avian food resources for Hawaiian waterbirds, or how to maximize invertebrate prey items in managed wetland habitats.

33 Section 3.7 Ruppia maritima

Ruppia maritima is a submerged perennial herb. occurring in alkaline. subs aline, or brackish waters along coasts or sometimes inland waters (Wagner et aL 1999). Ruppia is a genus of a single highly variable species in the family Ruppiaceae (Wagner et aL

1999). Ruppia is cosmopolitan, but is especially common in Eurasia, Africa and the

Americas; in Hawaii it occurs in brackish ponds and estuaries at 0-9 m in depth on all the main islands except Kahoolawe (Wagner et aL 1999).

Throughout the world, communities of submerged macrophytes support diverse communities of waterbirds, fish and invertebrates, by providing habitat and food resources. Submerged macrophytes affect abiotic variables such as light, temperature and oxygen concentrations and provide habitat and resources for many rnacroinvertebrates (Jeppesen 1998). Ruppia provides cover for numerous invertebrate species, as well as a detrital food source for estuarine and marine invertebrates (Kantrud

1991). Ruppia has a mutually beneficial relationship with some invertebrates, which through grazing pressure can stimulate fruit production in the plant (Kantrud 1991). In

Western Europe, invertebrate communities associated with Ruppia numbered 43.800 individuals per mZ with a biomass of up to 22.9 glmz dry weight (Verhoeven 1980).

Many waterbirds eat Ruppia as well as its invertebrate inhabitants. In subtropical regions, wintering waterfowl can consume entire stands of Ruppia (Kantrud 1991). Ruppia is known to be a favorite food of coots (Fu/ica spp.). Verhoeven (1980) calculated individual coot consumption of Ruppia at 70 gI dry weight per day.

Some studies have suggested that population densities of waterbirds are often closely related to the abundance of macrophytes. Hanson and Butler (1994) studied food

34 web effects in Lake Christina in Minnesota and found that populations of Coots and

Canada Geese declined throughout a progressive ten-year decline in macrophyte cover.

However, waterbird populations increased along with macrophyte populations once fish were removed from the lake (Hanson & Butler 1994). Studies all over the world have shown the attractiveness of Ruppia-dominated wetlands to waterbirds and have demonstrated that all parts of the plant are consumed by birds (Kantrud 1991).

Many biotic and abiotic factors can negatively impact populations of Ruppia maritima. Twilley et aL (1985) found that excessive amounts of the major nutrients (N,

P, K) can cause phytoplankton blooms and epiphytic algae growth that can decrease sunlight availability within the water column, reducing photosynthesis within Ruppia populations. Ruppia takes up nutrients and essential gases from the water column; therefore epiphytes can hinder nutrient uptake and photosynthesis, resulting in decreased propagule formation and Ruppia biomass (Richardson 1980). Fish can raise turbidity through movement, re-suspending bottom sediments, decreasing water clarity and thus limiting plant growth. In addition, herbivorous fISh feed directly on macrophytes and add nutrient rich excrement to the aquatic system (Kantrud 1991; Williams et aI. 2002).

Williams et al. (2002) looked at the causes of macrophyte losses in mesocosm experiments. Mesocosms are experimental enclosures designed to mimic naturaJ conditions, and in which environmental factors can be manipulated. Top-down and bottom-up ecological pressures were imposed to study macrophyte decline. Top-down effects studied were the impact of fISh removing zooplankton and macroinvertebrate grazers, leading to macrophyte shading through reduced grazing and increased epiphyte and algal populations (Williams et aL 2002). Bottom-up effects included simulating

35 increased nutrients from both fish excrement and sediment disturbance, which can facilitate phytoplankton and epiphytic algal blooms, again causing macrophyte shading.

The study concluded that decreases in macrophyte abundance were a result of an increase in epiphytic load and a subsequent reduction in available light and COz (Williams et a/.

2002). However, they stressed that both top-down and bottom-up pressures play important roles in aquatic systems and can vary considerably from year to year due to environmental changes.

Section 3.8 Future Research

In Hawaii, factors influencing wetland invertebrate communities are poorly understood, especially in constructed and restored wetlands. Invertebrates provide an important component in the food web, recycle nutrients and contribute to the breakdown of organic matter. Further research is needed to understand their intricate role in the wetland environment. Research on the trophic structures of Hawaiian wetlands will provide crucial information that could be used to restore the populations of Hawaii's four endangered waterbirds.

36 Chapter 4. Materials and Methods

Section 4. 1 Study Site

This study was conducted at Hamakua Marsh (2 10 24' N, 1570 45' W), a 9.1 9 hectare, low elevation, brackish marsh situated 1.5 m above sea level within the

Ko'olaupoko watershed on the windward side of the island ofO'ahu, Hawai'i (Figure

4.1) (Athens and Ward 1993). Historically, Hamakua Marsh was a sedge-dominated wetland that connected to Kawainui Marsh to its west and Kaelepulu Pond to its east

(Athens and Ward 1993). More recently urbanization and agricultural activities have resulted in fragmentation and tidal restrictions between the two marshes and the pond.

Today, Hamakua Marsh is a remnant floodplain which, due to its separation from

Kawainui Marsh and Kaelepulu Pond, exists as a discrete wetland ecosystem.

.4,,_, ... I .(l =-=-_ i 1 + ( North

Hamakua Marsh

-=-

Fih'1lre 4.1. Geographic location of Hamakua Marsh

37 Hamak:ua Marsh is characterized by brackish water conditions with salinity levels fluctuating seasonally from 1-35 parts per thousand. The average annual temperature at

Hamakua Marsh is 23.7 DC, while the mean annual rainfall is 1,931 mm (Westem

Regional Climate Center). The soil environment within the marsh is best described by the Marsh soil series (MZ) established by the U.S. Department of Agriculture's Natural

Resource Conservation Services. The wetland is bounded to the southwest by a mostly grass-covered ridge of volcanic origin (Pu'u 0 'Ehu, or "Hill of Spray"), and on the north by Kawainui Stream, which is the marsh's natural drainage outlet (Athens and Ward

1993). To the northeast, the marsh is bordered by a calcareous sand berm on which

Kailua town sits. The present vegetation is a mixture of species which are predominantly non-native. The upland areas surrounding the wetland are covered with kiawe (prosopis pallidal, Christmas berry (Schinus terebinthi/olus), and koa haole CLeucaena leucocephala). The low-lying areas within the marsh are dominated by pickleweed

(Batis maritima), red mangrove (Rhizophora mangle), and sourbush (J>luchea indica).

Pockets of native vegetation, including water hyssop (Bacopa monnieri), bulrush

(Bolhoschoenus maritimus), and akulikuli (Sesuviumportulacastrum), form small patches throughout the marsh.

In 1992 the Kaneohe Ranch gave Hamakua Marsh to the State of Hawaii,

Department of Land and Natural Resources which designated a wildlife sanctuary to

protect populations of four endemic endangered waterbirds that inhabit the wetland:

Hawaiian Stilt (JIimantopus mexicanus knudseni), Hawaiian Duck (Anas wyvilliana),

Moorhen (Gallinu/a chloropus sandvicensis), and Coot (Fulica alai). Currently, the marsh is managed by the Hawaii Department of Land and Natural Resources, Division of 38 Forestry and Wildlife which have spearheaded the restoration of this fragmented wetland ecosystem. To make management easier. the Marsh was divided into four basins. Basin

A at 2.3 hectares is the lowest and wettest within the marsh; Basin B at 3.3 hectares is the largest basin with extensive mudflats and a deep pond in its center; Basins C at 2 hectares and D at 0.7 hectares are higher in elevation than A and B. thus they flood less frequently. Management activities have included the eradication of non-native mangrove

(J1hizophora mangle) which once dominated the marsh vegetation community. The removal of mangroves has changed the landscape of the marsh. creating improved habitat for waterbird species by decreasing cover for predators. Removal of debris and other human-related trash has been part of the restoration process. In addition, the out-planting of native plants is transforming Hamakua Marsh back into a Hawaiian wetland landscape.

The natura1 wetland functions of Hamakua Marsh have been impaired due to invasions of non-native species. hydrology changes. and urbanization. Presently. the hydrology of Hamakua Marsh is influenced by precipitation inputs. and man-made flood protection structures. Kawainui Stream. which runs adjacent to Hamakua Marsh. becomes swollen with runoff from the surrounding urban and mountainous area during precipitation events. and then overflows into Hamakua Marsh. This urban runoff. combined with the runoff from the bordering "Hill of Spray". provides the marsh with the majority of its water. Previously. Kawainui Stream (historically fed by Kahana lId and

Maunawili Streams) flowed uninterrupted from the Ko'olau Mountains through

Kawainui Marsh out into Kailua Bay. Today Kawainui Stream is separated from the

Ko'olau Mountains by a man-made levee which bisects Kawainui Marsh (this levee provides flood protection for the Coconut Grove community). This levee has made

39 Kawainui Stream a waterway, flowing from the levee parallel to Hamakua Marsh and ending at Kailua Bay (see Figure 4.1). At Kailua Beach Park. Kawainui Stream enters

Kailua Bay, however the stream is often blocked by a sandbar. The sandbar forms as a result of sand deposition from tidal action and storm waves, as well as siltation from

Kaelepulu and Kawainui streams. When formed, the bar blocks drainage from these streams, resulting in a backwatering effect. The sand bar blocking the stream's entrance into the Bay is now controlled by heavy equipment, and the stream system is only opened when it poses a flooding threat to nearby communities. This closed canal system has dramatic effects on the amount and level of water within Hamakua Marsh.

Hamakua Marsh is directly affected by the commercial and light industrial development of Kailua town that now surrounds the marsh. The small businesses along

Kawainui Stream adjacent to the marsh are a source of point pollution, contributing polluted runoff into the stream and marsh. Storm drain discharge from surrounding urban areas also carries pesticides, nutrients, and pollutants into Kawainui Stream. In addition, a small family ranch lies directly upslope of Hamakua Marsh. The ranch houses ten head of livestock which add high levels of nutrients into the marsh via runoff.

Section 4.2 Experimental Design

During the winter and spring of 2005, a line transect experiment, using ftsh exclosures, was used to determine the impacts of invasive ftsh on benthic invertebrate communities in Hamakua Marsh (Figure 4.2). This experiment was designed to measure aquatic insect population emergence rates within a brackish wetland, and to understand the impact ftsh predation has upon these populations. A 165 m transect, running west to east, was established within the B basin of Hamakua Marsh. Basin B was chosen due to

40 its habitat heterogeneity and because it is the largest basin within the marsh. The sampling transect was placed parallel to Kawainui Stream in an area of Basin B that would remain partially covered with water throughout the year. It was important to place the transect in an area which would contain water most of the year so that fish presence could be observed and quantified.

Hamakua Marsh Experimental Design

0 5 01 1 .• 2015 0 23 . 26 · 29 c 32 " 39 0 47 • 53 • 59 o 65 · 71

•o 8176 o 86 " 89 o 93 0 99 c 119

• 129 • No Fish Cl 147 o Cage effect o 156 (] Fish • 161

Figure 4.2. Transect location in Hamakua Marsh

Twenty-five sampling stations were randomly created along the transect. The total number of sampling stations was determined using a power analysis, and benthic invertebrate densities reported from a Pearl Harbor mudflat (Demopolous 2000). Each

41 station was then randomly assigned one of three treatments: 1) fish-absent, exclosures restricted fish access to the station (n = 10),2) control, exclosures absent, fish could freely access station (n = 10), and 3) procedural control, exclosures present, but rai sed 20 cm off of the marsh sediments, allowing fish access while testing for artifact effects of the exclosures (n = 5). The fish absent treatments were designed to exclude all fish from within a 2.25 m2 area, and to measure aquatic insect emergence in the created "fish-free" zone. Procedural control treatments were designed to measure insect emergence in a 2.25 m2 area where some structure was present. This was important since many studies have demonstrated that fish are attracted to structure and that structure can increase emergence rates by providing habitat for insects as well. The control treatments were designed to sample aquatic insect emergence in areas where fish could move freely into and out of a

2.25 m2 area without any structural artifact. (Figure 4.3 illustrates the transect location as well as placement of treatment along the transect).

Figure 4.3. Emergence trap in treatment plot in Hamakua Marsh

42 Treatments were established during the fllSt week of January 2005. Ten fish­ absent treatments were created by enclosing a 1.5 x 1.5 m2 area with 2-mm mesh fiberglass window screening attached to 8 PVC support poles, spaced 0.75 m apart. Prior to securing of the screening to the PVC poles, the bottom 10 em of screening was buried in the underwater substrate, to ensure that no openings were present. Adult fish (> length

2Omm) were then removed from the exclosures using large scoop nets, lift nets, and baited minnow traps. Because these fishing techniques could not effectively remove ftsh smaller than 10 em in length and occasional high water levels resulted in fish re-entering these treatments, exclosures were ftshed at least once a month throughout the experiment.

Five procedural control treatments were constructed similarly to ftsh-absent treatments, except that the bottom of the screening was raised 20 em off of the substrate, allowing access by fish, while testing for artifact effects of the exclosures. Ten control plots were randomly placed along the transect; these plots contained no structure except for the emergence trap. The control plots allowed ftsh to access the area freely, therein demonstrating the effects of ftsh presence. Emergence traps (MacKenzie and Kaster

2003) were then constructed for each of the 25 sampling locations. Fifty nets were made

in order to keep the emergence traps constantly deployed over the six month sampling period.

Section 4.3 Aquatic Insect Emergence Sampling

Aquatic insect emergence was sampled once a week from 2116/2006 until

7115/2005 at each of the 25 stations in Hamakua Marsh using emergence traps

(MacKenzie and Kaster 2003) that had been modified to rise and fall with the fluctuating water levels at Hamakua Marsh. The frames of the traps were constructed out of child's

43 plastic hula-hoops with a mean diameter of 85.3 cm, 2cm diameter PVC spokes, and a 72 em high, 2cm diameter PVC center pole (Figure 4.4). Attached to the frame was l-mm­ mesh mosquito netting that had been sewn together at each end to create a cylindrical net.

The nets had a diameter of 87 em and were 1.5 m tall. The bases of the l-mm-mesh mosquito netting were clamped around the hula-hoops using 10-cm long pieces of plastic garden hose that had been slit down the middle. The top edge of the mosquito mesh was then bunched together above the 2cm diameter PVC center pole and closed off using a cable tie to prevent the escape of any insects. Pieces of foam pipe insulation were then cable tied to the PVC spokes and the hula-hoops. to increase the buoyancy of the traps.

Two 10-em long pieces of 2cm diameter PVC pipe were also hose-clamped parallel to the PVC center pole. which were lowered over lcm diameter PVC poles that had been previously driven into the wetland sediment at each station. This stabilized the emergence traps during periods of high wind, yet allowed them to rise and fall with fluctuating water levels. The hula-hoop bases were slightly submerged, creating a seal at

2 the air-water interface. and sampled an area of 0.5 m •

44 I 1mm mosquito netting 136 an

1hem"""lll ....-- drawstring

r ",Cpipe_

bose clamp

~..... -- SOan-- /

Figure 4.4. Emergence trap design (after MacKenzie and Kaster 2003)

Emergence traps were collected once a week by removing the garden hose clamps while the traps were still submerged. Once the hose was removed, a drawstring in the base of the mosquito mesh was pulled to cinch the net around the center pole of the trap while underwater. The nets were then pushed up the center pole. while being closed off around the base to prevent any insects from escaping. Traps were then reassembled with a new. clean net and re-deployed. Net samples were returned to the lab and placed in a freezer at ooe for a period of 24 hours. After freezing. nets were hung upside down and allowed to dry for at least 24 hours. Once dried, insects were removed from the nets by gently shaking the nets over white paper and placing all insects collected in glass vials.

After processing. nets were inspected for wear and repaired as needed, as well as cleaned in a washing machine prior to re-deployment.

45 Aquatic insects collected were identified to lowest possible taxonomic resolution using an Olympus SZ40 microscope and several insect identification guides. including

Merritt and Cummins 1996. Throp and Covich 2001. Lehmkuhl etal. 1979 and

McAlpine et al. 1981. Individuals were counted and then dried at 61l"C for a 24 hour period before being weighed to the nearest 0.001 mg. In addition all insects sampled were weighed using a Fisher Scientific Aceu-series 124 digital scale. giving weights to 4 decimal places in grams. For each sampling date. numbers of emerging insects were converted to daily densities by dividing the number of insects by the number of days that the emergence trap was deployed and the area that was sampled. Total numbers of insects emerging from each plot were then calculated by summing up insect densities over the time period sampled.

Section 4.4 Water Quality Parameters

Water quality parameters were collected during the six month duration of the study using a YSI 556 Multiprobe System. (ySI Incorporated. Yellow Springs. OH).

Measurements of dissolved oxygen (mgll). conductivity (microsiemens per centimeter, flS/em), temperature (0'). pH, and total dissolved solid (gil) were taken at mid-depth in the west corner of six of the 25 stations. The six randomly selected stations were 1. 3. 12.

13, 18, and 22 (see Figure 4.2 for station locations along the experimental transect). The six experimental stations sampled included three control stations and three treatment exclosures stations.

Water depth was also measured to the nearest 0.1 em at each station each week using a ruler. Three depth measurements were taken in each plot and averaged.

46 Section 4.5 Fish Density Sampling

Six additional stations were established, parallel to the experimental transect on the northern side closest to Kawainui Stream. to sample fish densities within Hamakua

Marsh. Each station was sampled once a month for 4 months during the project, using

2.8 m2 lift nets (Mackenzie and Dionre. unpublished data). Lift nets were deployed at each sampling station by laying the nets flush with the bottom and then pushing the nets down into the top layer of the sediment. partially burying them. The nets were then left for an undisturbed 30 minute period. To collect fIsh. the nets were quickly lifted out of the water. trapping any fIsh that were present in the 2.8 m2 area. Trapped fIsh were then identifIed to species. counted. weighed to the nearest 0.1 mg. measured to the nearest mm in length. and then released back into Hamakua Marsh. Densities were then calculated by

2 dividing the number of fIsh by the area of the nets (2.8 m ).

Section 4.6 Vegetation Experiment Sampling

An additional sampling transect was established in March 2005. on the up-land side of the marsh and parallel to the experimental transect. to investigate 1) the out­ planting feasibility of Ruppia maritima and 2) the effects fIsh have on out-planted Ruppia populations. Twelve sampling stations were randomly placed along the transect and two types of experimental treatments were employed. a treatment or "fIsh-free" plot and a control or "fish present" plot. Eight plots selected at random were assigned a treatment regime and were constructed by enclosing a 1.5 x 1.5 m2 area with 2-mm mesh fIberglass window screening attached to 8 PVC support poles spaced 0.75 m apart. Prior to the securing of the screening to the PVC poles. the bottom ten em of screening was buried in the underwater substrate. to ensure that no openings were present. Adult fish (>2Omm)

47 were then removed from the exclosures using large scoop nets, lift nets, and baited minnow traps. Four control plots were randomly placed along the transect and were denoted by the presence of a PVC pole at the center of the plot. Once plots were established, Ruppia was transplanted into the plots from donor beds found within Basin C in Hamakua Marsh. A 5.7 cm diameter Plexiglas core with one end sharpened was used to extract cores of Ruppia from the donor bed (Figure 4.5). Ruppia cores were then placed in peat pots and out-planted into each of the 12 randomly selected plots. Each control plot received one Ruppia core, while each treatment plot received two Ruppia cores. Cores were out-planted by plunging the Plexiglas core into the sediment and creating a hole, then the peat pot was placed into the hole and the excess sediment from the hole was used to anchor the peat pot to the bottom. Plots were then monitored in

April and June of 2005. during which measurements of Ruppia canopy height and reproductive phase were recorded. Canopy height was measured by selecting 10 different blades of Ruppia within each plot and then using a ruler to measure the length of each blade in centimeters. Reproductive phase was noted by the observation of fruits or flowers associated with each blade measured for canopy height.

48 Figure 4.5. Plexiglas core used for harvesting and out-planting Ruppia

Section 4.7 Statistical Analysis

After all samples and measurements were taken and recorded, data were analyzed to compare relationships between the three treatment regimes and the measured environmental variables. Results of aquatic insect biomass and abundance, water depth, rainfall amounts, fish densities, and Ruppia canopy heights over the study period were compiled in a spreadsheet. Analysis of variance was used to compare relationships between sampled variables. The analysis of treatment effects was done for all dependent variables and for each sampling date, as well as over the entire sampling period. Any differences detected in statistical analysis were considered highly significant at or below the 0.05 probability level and significant from 0.05-0.10. Mean comparisons were done using a Tukey's test to compare the detected differences between means.

49 Exploratory data analysis was utilized to review the data and histograms were used to look at the distribution of insect abundance and biomass data. They revealed that the data were not normally distributed; both data sets showing kurtosis when graphed.

Insect density data were transformed using a log base 10. and kurtosis was significantly reduced, but there was still some skewness to the righL The insect biomass data set was made more normal by using a square root transformation. After transforming the data, I then assumed the data were normally distributed and used parametric statistical methods. mainly analysis of variance. to look at relationships between data variables. Table 2.5 in appendix 2 illustrates the raw data and data transformations used to obtain normality in the data seL

50 Chapter 5. Results

Section 5.1 Introduction

This study was designed to evaluate the effects that two introduced fish species.

Gambusia affinis and Sarotherodon melanotheron, exert on the Hamakua Marsh environment and the aquatic insect community. Direct measurements used to observe insect populations were percent abundance, biomass, and population abundance of adult insects. In addition. environmental data including rainfall. water depth, water quality, and vegetation presence and absence were collected to help understand the degree to which fish influence the aquatic insect community structure. Data on the fish community were collected in order to understand fish reproduction and population dynamics within

Hamakua Marsh. All raw data collected for each parameter described in this section is contained within the appendices.

Section 5.2 Rainfall

Rainfall at the study site was inferred from data collected at the nearest recording station at Olomana Fire Station in Kailua, Oahu (3.7 km inland (south) of the study site).

March had the highest rainfall total, while June had the lowest (Table 5.1). Rainfall totals were calculated for each sampling event by totaling daily rainfall values during each 2- week sampling period (Figure 5.1) (Appendix 1). The maximum rainfall event (15.65 cm) occurred from 21112005 - 2116/2005, while the minimum rainfall event (0.03 em) occurred from 4/17/2003 - 4/2312005. Mean rainfall (± 1 STDEV) over the six month study period was 2.12 ± 3.81 em per 2-week period. Figure 5.1 illustrates the seasonal rainfall patterns during the 6 month sampling period. The overall trend shows a transition from wet winter to dry summer conditions at the end of March.

51 Table 5.1 Rainfall totals for each month during sampling period

Month Rainfall total (em) February 2005 16.13 March 2005 17.37 April 2005 2.08 Mav2005 8.18 June 2005 1.52 July 2005 2.97

Rainfall Over Sampling Period (2-week totals)

18 16 14 .gE 12 10 .. /I, J : I 4 I 2 I / "- r, o l...1 V ...... A '- -""" ~ .p... ro ~.., ~... ro ~t§\ ~~ ~.o. fI;j~'" f$.~

2005 Figure 5.1 Rainfall distribution for sampling period, each data point represents the sum of the previous 2 weeks.

Section 5.3 Water Depth

Water depth ranged from 0 to 48.9 em during the six month sampling period.

Table 5.2 gives the average water depth by month. Water depth minima, maxima, averages. and standard deviations for all 25 plots are listed in appendix 1. Because the bottom topography was sloped over the length of the transect. the deepest water occurred at the eastern and western ends of the transect while the middle plots had the shallowest water depths (Figure 5.2). Water depth fluctuated temporally. with a drying trend occurring during late April 2005. Some plots dried out completely during the sampling 52 period, resulting in a water depth of 0 cm. Table 5.3 denotes 0 water depth events by dates for particular plots. Figure 5.2 illustrates water fluctuation for each plot over the sampling period.

Table 5.2 Average water depth by month

Month Average Water Depth (em) February 2005 17.34 Mareh 2005 22.68 Ap ri l 2005 17.73 May 2005 17.69 June 2005 15.56 Jul y 2005 9.14

Table 5.3 Zero water depth events by date and plot

Plot Treatment Type 4130/2005 51712005 5114/2005 71812005 7/15/2005

10 Procedural Control 0 0 0 0 0 11 Treatment 0 0 0 0 0 12 Treatment 4.6cm 0 2cm 0 0 13 Control 0.5cm 0 0 0 0 14 Treatment 0 0 0 0 0 15 Treatment 0 0 0 0 0

Average Water Depth by Month Along Transect

_ 35 r------~ E .!!. 30 .c --February-05 -0. 25 " --March-05 ~ 20 t-"\!i~~r..:'t:~~----_n April-05 ~ 15 ~::;:;;IJ.t""'\,;:;-o;d ---- May-05

"g> :;; 1 05 !~~_===~~==:s~~~i;~~:s::::z:==~----~ L-----__....: J:.::uun"IY~05....:-0:.::5__ -' > < O +.-.~.-~-..-~~~~L,.-~_,.-,,_,~ 3 5 7 9 11 13 15 17 19 21 23 25 Plot

Figure 5.2 Average water depth (cm) by month along transect

53 Aquatic insect abundance was significantly affected by water depth fluctuation

within the wetland. Comparisons were made between treatment plots which reached a

zero water depth, and treatment plots which were covered with water during the entire

sampling period to observe insect abundance fluctuations. Transformed aquatic insect

abundance totals (see Table 2.5 in Appendix 2) were compared from "wet plots" (plots 3,

4, 6, 7, 22 and 25) and from plots "dry plots" (plots 11, 12, 14, and 15). Aquatic insect

abundance was significantly higher in treatment plots which dried (ANOYA, F =94.04,

P =< 0.01).

Section 5.4 Water quality

Water quality variables sampled within treatment plots (plots 3, 12,22), and

control plots (plots I, 13, 18) showed little to no variation between treatment regimes.

Water quality did vary over the seasonal gradient and is depicted graphically in appendix

1. Table 5.4 illustrates the range of values sampled for each parameter. Dissolved

oxygen content was not found to be significantly different between treatment and control

plots (ANOYA, F = 23.05, P = 0.163).

54

------Table 5.5 List of families collected during sampling period

Order - Famllv Habitat Common Name No. CoReeted Coleoptera - Anobiidae terrestrial Cil!arette Beetle 1 Coleoptera - Anthribidae terrestrial Fungus weevil 1 Coleoptera- Anthicidae terrestrial Ant-like flower beetle 1 Coleoptera - Carabidae aquatic Gnrundbeetles.T~~beetles 89 Coleoptera - Dytiscidae aquatic Predaceous divinl! beetles 8 Coleoptera - Hydropbiloidea aquatic Water scavenl!er beetles 137 Coleoptera - Scirtidae aquatic Marsh beetles 1 Coleoptera - Scolytidae terrestrial Bark beetles 1 Coleoptera - Tenebrioniilae terrestrial Darkling beetles 1 Coleoptera - Staphylinidae semi-aquatic Rove beetles 114 Collembola - EntomobrYidae aquatic Sprinl! tail 1 Diptera - Cecidomyiidae terrestrial Gall midl!es 1 Diptera - Ceratopol!onidae aquatic PunkiesJ no see-urns 3 Diptera - Chironomidae aquatic larva Midl!es 316 Diptera - Culicidae aquatic larva Mosquitoes 1 Diptera - Dolichopodidae aquatic larva Long lel!l!ed flies 328 Diptera - Ephvdridae aquatic larva Salt marsh flies 5019 Diptera - Muscidae aquatic larva House flies 556 Diptera - Psychodidae aquatic larva Moth flies 32 Diptera - Sciomyzidae semi-aquatic Snail-killing flies 15 Diptera - Sphaeroceridae semi c Small dung flies 9574 Diptera - Stratiomyiidae aquatic larva Soldier flies 4 Diptera - Tipulidae aquatic larva Crane flies 13 Diptera - Unknown unknown 49 Hemiptera -Heteroptera - Anthocoridae terrestrial Minute pirate bUll 1 Hemiptera -Heteroptera - Lygaeidae terrestrial Seed bugs 2 HemiPtera -Heteroptera - Mesoveliidae aquatic Water treaders 94 HemiPtera -Heteroptera - Miridae terrestrial Plantbul! 1 Hemiptera -Heteroptera - Reduviidae terrestrial Assassin bul! 1 Hemiptera -Heteroptera - Saldidae aquatic ShorebuJ!s 59 Hemiptera -Heteroptera - TinlZidae terrestrial Lacebu2S 15 Hemiptera -Homoptera - Cicadellidae semi-aquatic Cicadas 1 Hemiptera -Homoptera- Delphacidae terrestrial Plant hODoers 1 Hymenoptera- Eucoilidae 'aauatic' Wasp 98 Hymenoptera- Eulophidae "aquatic" Wasp 3 Hymenoptera- Formicidae terrestrial Ants 12 Hymenoptera- Ichneumonidae terrestrial Wasp 1 Lepidoptera - Gracillariidae terrestrial Leaf miner 1 Lepidoptera - Tineidae terrestrial Clothes moths 2 Odonata - ZVl!optera- CoenalZrionidae aauatic larva Damselflies 21 Psocoptera - Perientomidae terrestrial Bark lice 6 Psocoptera - Peripsocidae terrestrial Bark lice 12 Psocoptera - Psocidae terrestrial Bark lice 2 Psocoptera: Ectopsocidae terrestrial Bark lice 20

56 Examination of frequency occurrence data revealed that two dipterans were the

most frequently collected insects. The Diptera family Ephydridae was collected most

frequently over the study period. occurring in 95% of the sampling events. while

Chrionomidae occurred in 86% of the sample events (Table 5.6).

Table 5.6 Percent occurrence by family over the six month sampling period

~L- 59% L -. 27% r L - 68% r. L -, 5% L - 41% ,- 5% nint,,"\_ 5% nint,,"\_ 86% nin',,"\ _ c. 5% 1- 55% 1- 23% 1- 95% n;nt,,"\_ . 59IJ 1- 27~ nint,,"\_ 68% 1- 14% 1- 32% 1- 82% 1- ~ 77% 1- 41% 5% 8: ~% ~ 9% 1- ,r 36%

Insect abundance by treatment regime is shown in Table 5.7. Of the 24 aquatic

and semi-aquatic families collected over the study period, 21 families occurred in

treatment plots. 19 families in control plots and 19 in procedural control plots. Two

families of dipterans accounted for the majority of insect collected in all treatment

regimes. Sphaeroceridae or dung flies were the most abundant in treatment plots (70% of

57 insects collected in treatment plots). Ephydridae or salt marsh flies were the most

abundant in control plots (46% of insects collected in control plots) and procedural

controls plots (43% of insects were collected in procedural control plots).

Table 5.7 Total abundance and percent abundance of insect families that emerged over the entire six-month sampling period from each treatment regime.

~. ~_ • Famlly Total Treatment Percent of Control Control Proeedural & ._ Individuals Dim' treatment Dim' Percent ~n~l control ~ total· total Blm- % total . -(' 19 ~9 18 0.70% . -. nMDZ. Coleoptera - .37 18 .3 16 3.33%

C'n .- 1 o onnD}), o n MDZ. 1 n nADZ. (' 114 55 0.61% 34 0.69% 25 0.97%

C'nl 1 1 0.01% o 0.00% o o OOD}),

3 o 0.00% 1 0.02% 2

I­ 316 219 , A'D}), 70 1.43% 27 1.05% I­ 1 1 0.01% o n nnDZ. o IOODJi, I­ 328 90 n 0Q0Jl. 143 '01 D}), 95 : II. I­ 15 9 ln~ 1 IO'D}), 5 (.1' ~ I­ 5019 1597 .7.6.5% 2304 II> O'D}), 1118 A":I ":I":IDZ. All, I- 556 405 110 , ?ADZ. 41 1.59% I­ 32 16 0.1 RD}), 13 n '''DZ. 3 0.12% I­ 9574 6371 70.4lD}), 2094 A? ".c:DZ. 1109 I­ 4 4 n nADZ. o OOODJi, o I- 13 8 n nODZ. 5 0.10DJi, o n ?nDZ. n A":IDZ. l-~ 49 18 21 10 94 64 0.71% 15 0.31% 15 ". 59 47 0.52% 8 0.16% 4 0.16%

1 o 0.00% o 0.00% 1 0.04%

98 52 0.57% 37 0.75% 9

3 3 0.03% o o 0.00%

Odonata - Zygoptera- 21 4 0.04% 7 01.4D}), 10 0.39% (" IutAL 16536 9046 100% 4910 100% 2580 100% ... Percent was obtained by dividing the genus total by the respective treatment regune total. 58 Section 5.6 Aquatic Insect Abundance

Total insect emergence was generally higher from treatment plots compared to either control or procedural control plots (Figure 5.3), although this difference was not significant (ANOVA, F = 1.18, P = 0.3 14). Figure 5.3 illustrates total insect abundance by treatment regime (see also Appendix 2). Treatment plots had the highest aquatic insect total abundance per meter squared, while procedural control plots had the lowest

(Table 5.8).

Table 5.8 Total insect abundance by treatment regime.

Total Insect Abundance (no of Treatment Control Procedural 2 insect per m ) Plots Plots Control

N 10 10 5 Range 0-6,371 0-2,304 0 - 1,118 Mean ± 1 Standard deviation 411 + 894 223 ± 578 117 + 327 Total Abundance 9,046 4,910 2,508

Insect Total Abundance by Treatment Regime (± 1 STDEV)

"0 ~12000 .------, ~10000 +------,------en !!l 8000 <.> ~ 6000 c '0 4000 ~ 2000 0 ..1..--­ Treatment Control ProceduralControl Figure 5.3 Total insect abundance by treatment regime ± 1 standard deviation

Comparisons of temporal data sets revealed that May of 2005 (n =14,057), had the largest number of insects collected, while February (n = 72) had the least (Table 5.9). 59 Abundance for each sampling date is listed in Table 2.2 in appendix.2. The greatest

abundances occurred during the 4/30/2005 sampling period, while the lowest occurred during the 3/13/2005 sampling period (Figure 5.4). February through mid April had the

lowest abundance of insects.

Table 5.9 Aquatic insect abundance totals sampled by month

Month Total Insect Abundance February 2005 72 March 2005 105 April 2005 488 May 2005 14,057 June 2005 297 July 2005 1,517

Average Abundance Over Time by Treatment Reg ime (± 1 STDEV)

600.00

-c 500.00 ~ ~ Co 400.00 _T E til '" 300.00 -.-Treatment '"() 200.00 P. Control -Ql .!:;'" 100.00 Control -0 0.00 , , 0 z -100.00 .::...... ==---==---==--:- .. ------•• -200.00 2116/2005 4/23/2005 7/15/2005

Figure 5.4 Average insect abundance over time ± 1 standard deviation

60 Section 5.7 Aquatic lnsect Biomass

Aquatic insect biomass measured from each plot over the entire sampling period totaled 5.52 g. Figure 5.5 illustrates total insect biomass by treatment regime. Table 5.10 illustrates the range of biomass values sampled for each of the three treatment regimes. lnsect biomass was similar between treatment and control plots. which were both higher than procedural control plots (Figure 5.5). Insect biomass was not found to be significantly different between treatment regimes (ANOVA. F = 0.01. P = 0.993).

Table 5.10 Total insect biomass (g) by treatment regime

Total Insect Biomass (g) Treatment Plots Control Plots Procedural Control

N 10 10 5 Range 0-0.8404 0-1.9816 0-0.8080 Mean ± 1 Standard deviation 0.2081 ±0.2969 0.2262± 0.6172 0.2359 ± 0.3222 Total Biomass 2.0815 2.2622 1.1797

Total Biomass by Treatment Regime (± 1 STDEV) 3.5 -,-~------....., ~ 3 E 2.5 i-T ----;:=:I=::;----- ~ 2 co E 1.5 iil 1 0.5 +--­ o +--- Treatment Control Procedural Control

Figure 5.5 Total insect biomass (g) by treatment regime ± 1 standard deviation

The most total biomass was collected on 5/14/2005 (2.10 g). while the lowest total biomass was collected on 6/10/2005 (0 g) (Appendix 2). Figure 5.6 illustrates insect biomass fluctuation over the sampling period. Biomass showed the same temporal

61 pattern as insect abundance, with biomass peaking in the dry season months of May and

June and then decreasing in late June and early July.

Average Biomass (g) Over Time (± 1 STDEV)

0.2 -,------,

~ 0.15 +------1 .!:!J --Treatment Control Procedural Control ------'- '- -0.05 211612005 412312005 711512005

Figure 5.6 Average biomass by treatment regime over the sampling period ± 1 standard deviation

Section 5.8 Seasonality

Insect abundance by date was compared to determine if mean insect abundance was similar between wet and dry seasons. Only treatment plots were compared, and all plots which reached 0 water depths (plots 11 , 12, 14, and 15) were removed before analysis. According to the rainfall data collected during the study period, February through April (2/16/2005- 4/30/2005) constituted the wet season, while May through July

(51712005-7/15 /2005) constituted the dry season. Table 5.11 illustrates the number of insects sampled per meter squared during each season. Aquatic insect abundance within areas without fish were significantly higher during the dry season (ANOVA, F = 5.28, P

= 0.03).

62 Table 5.11 Insect abundance in treatment plots by season

Insect Abundance (no. of Insects per m~ Wet Season Dry Season N 11 11 Range 2-18 4-49 Mean ± 1 Standard deviation 9.45 ± 4.74 22.45 ± 15.10

Section 5.9 Vegetation

In plots from which fISh were excluded, aquatic vegetation started to appear one month after fish were removed. In treatment plots 3, IS, 22 and 25, Ruppia maritima, a native aquatic vascular plant (Ruppiaceae), was present and produced reproductive structures throughout the duration of the experiment. Enteromorpha flexuosa, a native green alga species (Phylum Chlorophyta), was present in plots 7, 11, 12, 14 and 15.

Treatment plot 12 contained a second native species of green alga, Pithophara sp.

Control and procedural control plots contained fragmented, mobile pieces of Gracilaria tikvahiae cf. (Kim Peyton, pers. comm. 2006), a red algae species (Phylum Rhodophyta) that was introduced to Hawaii for aquaculture and is now the dominant aquatic vegetative cover in Hamakua Marsh.

Insect abundance was not significantly different between plots with vegetation and without vegetation (ANOVA, F = 0.86, P =0.364). Table 5.12 displays the range of insects sampled from plots with and without vegetations. To further investigate any relationship between insect abundance and vegetation presence, insect abundance was compared between treatment plots with (3, 22, and 25) and without (4, 6, and 7) Ruppia martiti11Ul. There was no signiftcant difference between mean aquatic insect abundance in plots with or without Ruppia (ANOV A, F = 1.49, P = 0.289).

63 Table 5.12 Insect abundance in treatment plots with and without vegetation

Insect Abundance (no. of Present Absent F P insects per m~ Vegetation N 8 17 Range 9 -19 10-15 Mean ± 1 Standard deviation 13.25 ± 3.37 12.35 ± 1.53 F=O.86 P=0.364 Ruppia N 3 3 Range 59-71 40-72 Mean ± 1 Standard deviation 65±6.00 52±17.4 F= 1.49 P=0.289

A separate experimental transect was created and out-planted Ruppia maritima

was monitored on April 25 and June 24, 2005. During the April 25 sampling event, no

Ruppia was present in the four control plots (plots 1,3, 6, and 8), but was present in all

eight treatment plots (plots 2, 4, 5, 7.9, 10. 11. 12). On June 25th Ruppia was again

absent in all control plots and present only in five treatment plots. Mean canopy height

varied between April and June (Tables 3.1. 3.2. and 3.3, in Appendix 3). Table 5.131ists

mean canopy heights for the two treatment regimes for the two sampling periods. Figure

5.7 illustrates the difference between mean canopy heights for each sampling event by

treatment regime. Mean canopy heights for both April and June were significantly higher

in areas where fISh were absent (ANOVA. table 5.13).

Table 5.13 Mean canopy height (em) by treatment regime and sampling event

Mean Canopy HeiKht (em) Treatment Plots Control Plots F P N 8 4 AprU Range 5.17 -27.13 0-0 Mean ± 1 Standard deviation 16.34 ± 7.34 0-0 F= 18.84 P=O.OOl June Range 0-34.20 0-0 Mean ± 1 Standard deviation 16.34 ± 14.60 0-0 F=4.77 P=O.054

64 Ruppia Mean Canopy Height (± 1 Standrad Deviation)

35 Q,>- -- 30 o E c: u 25 ftI~ o April o:E 20 c: .2' 15 • June ftI CII 10 CIIJ: ::::IE 5 0 Treatment Control

Figure 5.7 Mean canopy height of Ruppia maritima in treatment regimes ± 1 standard deviation

Section 5.10 Fish Community

Fish community data was collected from four sampling plots once a month during the months of February, April, May and June 2005 (n= 4X4). A total of 952 fish were sampled, of which only two individuals were native fish species. The fish community was dominated by the poeciliid, Gambusia affinis, and cichlid, Sarotherodon melanotheron, (Figure 5.8). The goby, Mugilogobius cavifrons, made up only a small part of the fish community. Tilapia (Sarotherodon melanotheron) were most abundant in

February, while mosquito fish (Gambusia affinis) and the mangrove goby (Mugilogobius cavifrons) were both most abundant in May (Figure 5.9).

65 Fish Community Composition at Hamakua Marsh

3%

c Cichlidae (Sarotherodon meianotheron)

46% • Poeciliidae (Gambus/a affinis)

o Gobiidae (Mlgilogobius cavifrons)

Figure 5.8 Fish community composition

Mean Fish Density Sampled by Month (+ 1 Standard Deviation)

30 _ Cichlidae (ns) [==:J Poeciliidae (ns) N 25 -E [==:J Gobiidae (ns) 0 --c: 20 ->- ~en 15 c: Q) C 10 .s::. en u:: 5 c: CO Q) 0 :E Feb April May June 2005

Figure 5.9 Mean fish densities by month (n= 4 for each month) 66 Chapter 6. Discussion

Section 6.1 Introduction

Wetlands are extremely dynamic ecosystems that are influenced by many factors. and Hamakua Marsh is no different. The wetland environment at Hamakua Marsh is affected by seasonal variations in rainfall and temperature. by chemical and biological cycles. and by human influence. The complexity of interactions from both the terrestrial and aquatic interface makes the environment at Hamakua Marsh highly variable and unpredictable. Aquatic insect communities within the marsh are affected by many factors. including predation, their immediate environment, and overall habitat qUality. The position of the marsh within the tidally influenced zone creates an environment that varies from highly saline to slightly brackish. Many studies have shown that aquatic insect communities in other brackish environments tend to be less diverse than their freshwater counterparts (Thorp & Covich 2001). The aquatic insect community at

Hamakua Marsh reflected this fmding and was comprised of relatively few aquatic insect families. with five families accounting for the majority of aquatic insects sampled:

Sphaeroceridae (dung flies). Ephydridae (marsh flies). Muscidae (house flies).

Dolichopodidae. (long-legged flies) and the Chironomidae (midges). The presence and abundance of these insect families varied and appeared to be directly influenced by environmental conditions more than the presence or absence of fish predators.

Section 6.2 Environmental Conditions

A number of environmental factors were monitored over the six-month study period which influenced conditions at Hamakua Marsh. Rainfall affected water levels and the overall hydroperiod of the wetland. The topography of Hamakua Marsh also played a

67 factor in water depth. Along the sampling transect, some plots were higher in elevation

(plots 10 through 15) and dried out completely (twice) during the sampling period. The rest of the transect plots remained submerged throughout the study. and had much lower aquatic insect densities.

Section 6.2.1 Rainfall

Rainfall is one of the major water inputs in Hamakua Marsh and the sole natural cause of increases in water level. The collection and interpretation of rainfall data was a necessary step for determining any treatment effects on aquatic insect communities. while separating out environmental effects. Rainfall data were provided by the National

Oceanic and Atmospheric Administration from a rain gauge three kIn inland of the study site. In future studies. it would be best to have a rain gauge on site to alleviate apparent discrepancies between rainfall amounts at the two locations.

The biological data collected at Hamakua Marsh displayed seasonal patterns of aquatic insect emergence associated with water depth and thus rainfall. Seasonal trends in rainfall were observed during the study period: highest during February and March. and lowest during April. June and July. Aquatic insect densities were highest during the dry months of May and July. In comparing weekly and monthly rainfall totals to insect abundance totals. patterns appear in insect emergence with peak emergence events occurring after periods of drying and low rainfall. For instance the highest peak in aquatic insect abundance occurred in May 2005. Rainfall events and water depth displayed declining trends leading up to this emergence peak (Appendix 1). The data illustrate a trend of increasing aquatic insect emergence as water levels decline and recede.

68 Studies have revealed that some aquatic insect species will not emerge during rainfall events (Dudgeon 1996: Perng et al. 2005). Additionally, inclement weather can reduce emergence of adult chironomids and other insects (Wrubleski 1991). This is most likely because immediately following emergence most aquatic insects need a "drying" period during which their exoskeleton can harden. During this time, aquatic insects are extremely vulnerable to the environment and predation. For this reason, many aquatic insects wait for suitable environmental conditions for emergence. Insect emergence data at Hamakua Marsh support these findings: insect emergence in a tropical wetland on

Oahu is similar in that insect emergence decreases during rainy periods and increases during dry periods.

Section 6.2.2 Water Depth

Water levels at Hamakua Marsh were measured to observe patterns between water depth and emergence rates and to discern whether the marsh's water levels should be managed. Since water depth is directly affected by the water inputs into Hamakua

Marsh, a correlation between rainfall and water depth was observed at the site. The depth of water in each plot was affected by the amount of rainfall the marsh received, the number of dry days which allowed the marsh surface to dry out, and the opening and closing of the adjacent Kawainui Canal that effectively "flushes" the system. The canal is a man-made drainage channel and is opened to Kailua Bay monthly in order to flush out storm water which has accumulated in the canal. The opening and closing of the canal is dictated by the need for flood control, therefore the canal is also opened following large rainfall events. This combination of monthly and sporadic opening of the drainage canal has profound effects on the water depth within Hamakua Marsh. Water

69 level at Hamakua Marsh can drop up to 30 cm in just over an hour after the canal was opened (pers. obs.).

Rainfall also varied in its effects on water depth. Rainfall often can have a delayed effect on water depth, with a lag time between the water input event and the resulting increase in water level, as runoff from the surrounding area filters into the marsh. Water depth also varied seasonally as a result of wet versus dry season inputs.

As expected, water levels were higher in the wet season months, and decreased during the dry season months.

Water depth varied tremendously over the course of the study period and had a significant affect on aquatic insect emergence. Aquatic insect densities were highest in plots which dried out completely on multiple dates, while areas with higher water had the lowest aquatic insect emergence rates during the six-month sampling period. Six experimental plots dried out completely during the sampling period (plots 10 through 15).

These six plots accounted for 92 % of the aquatic insects sampled during the six-month study period. The trend in aquatic insect emergence shows a peak in insect abundance during low water levels (around mid-May), then a dramatic decrease as water levels increased (early June), and then a second increase (mid-July) when water levels began to recede again (Figure 6.1). These drying periods may have allowed aquatic insects additional opportunities for the ovipostion of eggs, and may account for the higher densities in plots that dried out (see Section 6.2.2). In addition the fact that plots dried out also excluded fish completely from these areas, removing the potential for predation.

70 60 7OOO .c ->- -E 50 6000 ~ u 0 - 5000 I: .c.. 40 Q. -41 41 4000 u c I: 30 41 -41 3000 CI 'Iii 41 3: 20 -E 41 41 2000 CI 10 ..u 01 1000 41 41 III -> I:

Many studies have found regular flooding and drying to be essential to maintaining high densities of aquatic insects in seasonal wetlands (Evans et al. 1999;

Neckles et at. 1990; Whiles & Goldowitz 2001). If water depth and hydroperiod optimal for aquatic insect emergence could be mimicked at a wetland restoration site, then conditions creating abundant waterbird forage could be attained. Data from this study show trends over a relatively short (six month) time period and revealed emergence peaks during times of low water. However if more effective water management strategies are to be developed, decisions should be based on long term data detailing seasonal water depth and aquatic insect emergence rates.

Sections 6.2.3 Water Quality

Water quality observations were included in this study to determine whether water quality variations affected aquatic insect emergence rates and population densities.

71 The thought was that if certain parameters varied enough (i.e. pH, DO, or temperature steeply increased or decreased), aquatic insects would time emergence to avoid deleterious conditions. Water quality parameters were measured in selected plots (1,3,

12, 13, 18, and 22) and had similar values despite their varying locations along the sampling transect.

A high variation in DO concentrations were observed in all plots sampled.

Because photosynthesis occurs during the day when there is light, DO often increases in the day. At night, DO drops as organisms metabolize and photosynthesis decreases. Low

DO often occurs when organic matter, nutrient inputs, or temperature are high, and light inputs are low. Most of the variation in DO can be accounted for by the fact that DO readings were taken at different times of the day during sampling. However the data tends to indicate declining dissolved oxygen levels as summer approaches. During summer, water temperature increases and water concentrates in small pools when no new water enters Hamakua Marsh. Fish trapped in these isolated pools, usually in large numbers, add excess nutrients from excretion and require more oxygen than is available.

Low DO in these pools causes fish to die, and the dead fISh elevate nutrient levels in the water as their bodies decay.

The remaining water quality parameters (pH, temperature, salinity, and total dissolved solids) showed much less variation between treatment regimes. However, all of these parameters showed similar temporal variation over the six-month sampling period. Salinity and total dissolved solids had identical trends over the sampling period, primarily because the YSI 556 multi-probe used to measure water quality bases these two measurements on one equation used to calculate the electro-conductivity of water. As

72 expected, temperature increased during the summer months, causing water to heat and evaporate at an accelerated rate. As water evaporated. salinity increased due to the concentration of salt molecules. In addition total dissolved solids increased during the summer months due to the reduced amount of water, causing increases in minerals and salts in the water column. Water pH also increased during summer months, but only slightly. Again due to the evaporation of water, cations and anions increased in concentration in the water column, increasing pH during the warmer summer months. causing the water in the marsh to become more basic in the summer.

The intention in this study was to compare aquatic insect data with measured water quality parameters. However, weekly water quality data were insufficient to track any trends in emergence or abundance with water quality parameters. In future studies of aquatic insects in Hawaiian wetlands. it would be useful to measure water quality continuously every 15 minutes and have the sampling duration last longer than six­ months. In addition nutrient levels in the water column should be observed to look at eutrophication and its effects on aquatic insects.

Section 6.3 Aquatic Insect Populations and Treatment Regimes

Insect abundances and biomass were sampled within treatment plots at Hamakua

Marsh to determine whether the presence of non-native fISh species affected aquatic insect populations. Fish predation has been shown to reduce aquatic insect diversity and productivity in wetlands (Huener & Kadlec 1992; Mallory et aL 1994), and fISh and aquatic insects can also compete for resources (Morin et al. 1988).

The effects of non-native fISh upon aquatic insects compared over the three treatment regimes showed no statistical significance between the means of aquatic insect

73 abundances or biomass. Despite no statistical difference general trends illustrated higher insect abundance in treatment plots. However, temporal differences (i.e. seasonal variations) in aquatic insect abundance were observed, which could be driving differences in aquatic insect populations over the sampling period. Difficulty in keeping fish out of treatment plots, and the weekly fishing of treatment plots (to remove fish) may have artificially reduced aquatic insect abundances in treatment plots. During high water levels after heavy rainfall events, fish may have been able to jump over barriers designed to keep them out of treatment plots. Fish also began to feed on algae that accumulated along portions of the netted treatment plots, and created holes in the nets through which they were able to gain access.

Section 6.3.1 Aquatic Insect Abundance

The presence or absence of fish proved to be insignificant as a predictor of aquatic insect abundance. However, aquatic insect abundance was highest during low water depth events and minimal rainfall periods. The data showed highest emergence rates from April 7 to May 21 2005. This event followed dwindling rainfall totals as the dry season continued, and culminated in a zero water depth occurrence. These low and zero water depths which seem to trigger aquatic insect emergence could be an adaptation to living with introduced fish species. As water levels decreased in Hamakua Marsh, fish were unable to access certain portions of the marsh and this allowed aquatic insects to emerge safely without the threat of predation. More data are needed to understand the effects fish are exerting on aquatic insect populations at Harnakua Marsh.

74 Section 6.3.2 Aquatic Insect Biomass

Aquatic insect biomass was also measured to observe variations in aquatic insect populations between treatment regimes. Biomass was used to measure the amount of insects collected as a measure of productivity. Abundance data allow for a more general look at the trends of insect populations. while biomass gives a more specific look at insect individuals. accounting for size and differences between families and individuals collected. Biomass relates to productivity and this measure gave a better indication of which plots were more productive. an important implication for managing waterbirds that forage on aquatic insects.

When compared statistically over the three treatment regimes. no significant difference was found between mean aquatic insect biomass measurements. Biomass data mirrored that of insect abundance. showing similar trends in relation to water depth within the treatment plots. As with aquatic insect abundance. fISh did not impact insect biomass. and temporal variations accounted for variations in biomass.

Section 6.4 Vegetation

Eight of the ten treatment plots which excluded fish had vegetation appear one month after the plots were installed. Ruppia maritima is present in some areas of

Hamakua Marsh but was not observed in Basin B where the experimental transect was located. The populations of Ruppia in Hamakua tend to be isolated toward the upland fringes of the wetland. which tend to be shallower and shorter in hydroperiod. However

Ruppia maritima began to appear in experimental plots after one month of excluding fISh.

An observational experiment was conducted to see if Ruppia was being eaten by fish in

Basin B. Additional plots were established. and a second experiment conducted in which

75 Ruppia maritima was planted in areas with fish present and then in areas where fish were excluded. In areas where fish were not excluded. the planted Ruppia was only present for one week post planting. In plots where fish were excluded, Ruppia persisted for three or more weeks and even started to produce reproductive structures. This led to the supposition that the herbivorous tilapias were feeding on Ruppia in the deeper areas of the marsh. Due to the high density of tilapia sampled in Basin B, Ruppia populations appeared to decline in areas where large tilapia can forage. Invasive, non-native ftsh appear to contribute to the decline of Ruppia populations in Hamakua Marsh.

Areas with aquatic vegetation have been reported to support higher numbers of aquatic insects than bare areas (Gerking 1957; Krull 1970). When compared statistically, plots in this study that contained vegetation were not signiftcantly different in mean aquatic insect densities than plots without vegetation. Because this study only lasted six months, positive impacts form the presence of vegetation may not have been observed in insect emergence as insect life cycles are often longer then six months. At Hamakua

Marsh, aquatic insect densities were observed to be higher in areas containing Ruppia, but tilapia eat Ruppia and could be limiting the plant's distribution within Hamakua

Marsh. This study demonstrated that Ruppia is a viable species to out-plant during wetland restoration, flourishing in the absence of ftsh.

Section 6.5 Fish Community

Populations of mosquito ftsh, Gambusia afJinis, and the mangrove goby,

Mugilogobius cavijrons, were highest in May 2005. It may be no coincidence that these insectivores are most abundant during the time when aquatic insects were most abundant.

These two species appear to be foraging in high densities within the marsh around the

76 same time aquatic insect emergence is peaking. In contrast, tilapia, Sarotherodon melanotheron. were abundant in February when water levels were highest and aquatic insect numbers were at their lowest. Due to their larger size. tilapia may only be able to access the marsh when water levels are highest. Additionally. tilapia tend to be more herbivorous, while mosquito f'lSh tend to be insectivorous. These population density trends correlate with the aVailability of forage for each species.

High densities of tilapia and mosquito fish may consume food sources that are important for many native Hawaiian birds (e.g., Hawaiian Stilt, Hawaiian Duck,

Hawaiian Coot, and Hawaiian Moorhen). Foraging in sediments by large numbers of tilapia could also decrease water quality in these systems. Fish that forage for food in sediments increase turbidity levels, the concentration of nutrients, and frequency of phytoplankton blooms. Decreased water quality can affect submerged vegetation by decreasing light penetration and slowing vegetative growth. Slowed vegetative growth decreases plant surface area, providing less area for aquatic insects to inhabit and eventually decreasing aquatic insect populations. Decreased aquatic insect populations mean less forage for waterbirds and perhaps reduce waterbird fledging success.

This study demonstrated that the absence of fish resulted in an increase in insect abundance and in the presence of submergent vegetation. Although not significant, these results demonstrated that non-native fish can affect waterbird forage through the elimination of aquatic submergent vegetation. However, this study was only six-months in length, a time period which is shorter in duration than some insects' life cycles, and therefore it was not possible to detect long term impacts associated with f'lSh removal.

77 Section 6.6 Aquatic Insects as a Food Source for Waterbirds

Aquatic insects. especially insects. are usually the main staple in the diet of waterfowl and many other wetland inhabitants (Nelson et aL 2000). The endangered endemic waterbird populations of Hamakua Marsh are thought to rely heavily on aquatic insect forage at different points of their life cycle. The literature suggests that young birds need higher fat and calories than older birds in order to grow and survive through the period of adolescence and fledging. There is increasing evidence to suggest that birds time egg-laying to ensure that the peak period of nestling energetic demand coincides with the period of maximum prey insect abundance (Krapu & Reinecke 1992).

At Hamakua Marsh. birds begin nesting in late February or early March (Ethan

Shiinoki. State Wildlife Biologist, pers. comm. 2005) and continue through June depending on rainfall events. Stilts have two cycles of fattening and losses each year in regard to nutrition and energetics: fattening periods occur pre-breeding and post­ breeding. In Hawaii. stilts nest any time between mid-February and late August with peak nesting varying from year to year (Robinson et aL 1999). Since stilts only produce one brood per season (but nest several times until successful). timing is everything. Eggs incubate 23-26 days. and young are capable of sustained flight 27-31 days post hatching

(Robinson et al. 1999). This means that stilt chicks need 50-61 days in orderfor successful fledging to occur. Based on the data collected during 2005. the Hawaiian Stilt would have needed to start nesting around mid-March in order for YQung to take advantage of aquatic insect emergence peaks in May. No data was collected on stilts nesting during the study period. however stilt presence was noted in Basin B and one chick was noted as fledged around June 6.

78 Environmental factors of rainfall and water depth. which combine to create a wetland hydroperiod. should be monitored intensively as a predictor of aquatic insect abundance and emergence timing. as well as a way to determine nesting success in endangered waterbird populations. Instituting intensive monitoring programs at wetlands specifically managed to increase endangered waterbird populations could allow managers to create optimal conditions for waterbirds. Once all parameters at a site are better understood. then adaptive management can help guide the restoration process.

79 Chapter 7. Conclusions

This study was conducted at Hamakua Marsh to determine how introduced fish species affect insect communities. densities. abundance and biomass. and thelaquatic environment overall. and to discern whether introduced fish species are competing with waterbird populations for food resources. The most abundant insects were the dipterans. particularly the Sphaeroceridae family or dung flies. The most plentiful fish species were the non-native mosquitoflsh Gambusia affinis and tilapia Sarotherodon melanotheron.

By understanding the impacts and interactions of these two invasive species. we can determine the extent of their effect on native bird habitat. This information may help managers at the local. state. and national level identify cost-effective strategies to eradicate or control invasive species in current or future wetland restoration projects.

The first experimental hypothesis stated that areas containing aquatic vegetative cover (algae and/or emergent plants) would have higher aquatic insect abundances compared to areas without aquatic vegetative cover (bare substrate). This hypothesis was rejected at a p-value of 0.364. revealing that areas with vegetative cover did not have significantly different aquatic insec} community abundances.

The second hypothesis stated that areas which excluded ftsh would have significantly higher aquatic insect biomass and abundances than areas in which fish were present (control plots). A one-way ANOVA was used to compare aquatic insect biomass and abundance between areas with and without fish. In testing the difference between aquatic insect abundances a p-value of 0.314 was calculated, leading to a rejection of the hypothesis of any difference between treatments. When comparing biomass a p-value of

80 0.993 was obtained, leading to the rejection of the hypothesis of any significant difference.

The third hypothesis stated that aquatic insect abundance would be significantly higher in areas with lower water levels in comparison to areas with higher water levels.

Control plots that contained water during the entire study period were compared to treatment plots which reached a zero water depth during the study period. A one-way

ANOVA was employed and yielded a p-value of 0.01, leading to acceptance of the hypothesis of a significant difference between areas of differing water depths. The average aquatic insect abundance was much higher in plots which dried then in those which retained water for the duration of the sampling period.

The fourth hypothesis stated that areas where fish were excluded would have significantly higher dissolved oxygen (DO) levels then areas in which fish were present

(control treatments). This hypothesis could not be tested due to the inconsistency of dissolved oxygen sampling times on the individual sampling dates. There was more temporal variation observed between samples than observed between treatment regimes.

Dissolved oxygen levels in aquatic systems show a large variation in concentration at differing times in the diurnal cycle. Because photosynthesis occurs during the day when there is light, DO often increases in the day, while at night, DO drops as organisms metabolize.

The fifth hypothesis assumed that aquatic insect abundance would be significantly higher in the dry season when compared to the rainy season. This hypothesis was tested by comparing aquatic insect abundance in control plots during the two sampling periods.

The dry season of May - July had significantly higher emergence rates compared to the

81 wet season of February - April. A one-way ANOV A was used to compare the two seasons and yielded a p-value of 0.03. leading to acceptance of this hypothesis.

The sixth hypothesis stated that the presence of Ruppia maritima would increase in areas where fish were excluded in comparison to areas in which fish were present. In a complementary study. we observed thatRuppia thrived in areas where fish were excluded. while being consumed in areas where fish were present. Ruppia was observed having higher mean canopy heights and higher stem densities in areas where fish were excluded. A one-way ANOVA was used to compare the two treatment regimes and yielded a p-value of 0.001. leading us to accept the hypothesis of increased Ruppia in areas without fish. These fmdings suggest that Ruppia has better survivorship and is more robust in fish-free areas.

82 Chapter 8. Recommendations and Future Research

Section 8.1 Management Recommendations

This study shows the potential negative impacts that invasive species can exert upon native wetland ecosystems. The data demonstrate that invasive fish can change community trophic structure as well as vegetative cover in aquatic systems. This study reveals trends within the wetland environment which could provide natural resource managers insight into how to better manage these ecosystems

Vegetative cover in wetlands is an important predictor of species richness within that system. The majority of Hawaii's wetland ecosystems are overrun by alien plant species, often creating monotypic stands which alter ecosystem function. In 1981, 55% of the plant species identified in Hawaii's wetlands were non-native (Stemmermann

1981).

To combat non-native species invasions. many wetland restoration projects incorporate the return of native plant species to degraded sites. Restoration practitioners need to consider restoring both terrestrial and aquatic plant assemblages during restoration. This study demonstrated the viability of Ruppia maritima as an out-planting species. and that it should be considered when planning wetland aquatic habitat. Once established. aquatic vegetation allows a diverse array of invertebrates to colonize an area.

Invertebrates. especially aquatic insects. in turn act as food for macroinvertebrate and vertebrate species.

Non-native fish species influence the functionality of wetland systems. These fish disrupt natural processes in wetlands .sometimes causing poor water quality and reducing the value of those wetlands for wildlife. This study demonstrated the negative effects

83 fish can exert on aquatic insect populations. Invasive fish have displaced native fish in

Hamakua Marsh through competition, while decimating aquatic vegetation by grazing, and reducing aquatic insect abundance through predation. During the design phase of wetland restoration and creation projects, measures should be taken to try to exclude non­ native fish from entering wetland and water-filled areas. Before wetland managers decide on water level manipulations or fish introductions, initial monitoring should be conducted to better understand the site's aquatic environment and how these actions could affect the community. Once non-native fish have colonized a wetland, removal measures should be researched and implemented. Eradication of fish populations could be achieved through water level draw dOWDS, physical barriers (i.e. culverts), or Rotenone applications. More research is needed into the most effective means for eradicating fish from wetland areas in the Hawaiian Islands.

In addition, the data collected from this type of study can be used as a tool for managing endangered waterbird populations. In combining the trends observed in the data with environmental trends and observations regarding the Hawaiian Stilt,

Himantopus knudseni, from previous studies (Robinson et al. 1999), wetland managers can design effective strategies for increasing fledgling success. For example, if wetland managers could perform predator control and invasive weed control around the end of the rainy season, during a period of rainfall and water level decline, they could create a pest free environment for nesting. This would allow stilts to lay eggs during the most environmentally opportune time for young to hatch and forage. We can assume that if stilts are forced to nest during periods of high water in order to avoid predators, their young mayor may not hatch out during invertebrate emergence peaks. These peaks are

84 the optimal time for a young bird to forage and are more likely to occur during periods of low water.

Section 8.2 Future Research Recommendations

Little scientific information is available regarding tropical wetlands and in particular Hawaiian wetlands. In order to understand how these ecosystems are affected by invasions, fragmentation, increased nutrient inputs, and other perturbations, more research and monitoring needs to be completed. Increased interest in restoration and management of wetland ecosystems has created a need for research into how to best establish these highly productive and variable ecosystems. Numerous studies within the continental U.S. have documented the wetland restoration process, along with its success and failures (Kusler & Kentula 1990; Kentulaetal. 1992; Kentulaetal. 1996; Weiheret aL 1996; Mitsch et aL 1998; Kirkman et al. 2000: Zedler 2000; Shuwen et aL 2001), but there remains a paucity of literature about wetland restoration in the Hawaiian Islands and other tropical island settings.

A study using stable isotope analysis to descnbe food web structures in Hawaiian wetlands would provide useful scientific data on the complexity of wetland food webs and insight into their structure. Different primary producers. and thus food sources. can have distinctive isotope signatures, which are calculated from the ratio of the heavy (1lC) to light isotope (IZC). These signatures are determined by using mass spectrometers. The signature for a particular food source is translated up the food web to primary and secondary consumers. This type of study could reveal much needed information on the diets of endangered waterbirds, as well as how introduced species affect trophic

85 structures within wetlands. To date, no study has been undertaken to discern the dietary requirements and composition of Hawaii's endangered waterbirds.

Research is needed to examine and better understand the physical environment of wetlands in Hawaii. Numerous abiotic components of wetlands warrant research, including nutrient dynamics from urban, commercial, and agricultural runoff. In addition more information is needed to determine annual and long term variability in ground water, including volume and nutrient concentrations. Detailed soil analyses at each wetland in the state could reveal insight into soil texture, nutrients, and ground water.

These components are the foundation of wetland systems, yet virtually no literature exists for them regarding Hawaiian wetlands.

The biotic community within Hawaii's wetlands also warrants further investigation. It is critical that invasive species are documented and their life histories and cycles studied. This information is imperative if we ever hope to eradicate these species from Hawaii's wetland habitats. For invasive plants, more information is needed on germination requirements, energetics, and seed production. For non-native fish and reptiles, additional information is needed in regard to reproduction, niche occupation, and population dynamics. This study provides much needed baseline information regarding

Hawaii's wetland insect communities, however for wetland restoration and management to succeed more research into Hawaii's wetlands will need to be undertaken in the future.

86 Appendix 1. Environmental Conditions

Table 1.1 Rainfall totals for each sampling event

Sample Date Days Sampled Rainfall (em) 2116/2005 17 15.65 212612005 10 0.30 3/4/2005 7 0.20 3/13/2005 9 10.21 3/1812005 5 0.13 3/2612005 8 3.91 41312005 9 3.56 41912005 6 1.45 4117/2005 8 0.08 4/23/2005 6 0.03 4/30/2005 7 0.08 5nt2005 7 0.05 5114/2005 7 0.86 5/21/2005 7 3.58 5/2812005 7 3.63 6/4/2005 7 0.08 6/1012005 6 0.13 6/17/2005 7 0.20 6/2412005 7 0.71 71112005 7 0.46 7/8/2005 7 0.28 7115/2005 7 1.24

87 Table 1.2 Water depth descriptive statistics by plot

Plot Treatment Type Min Deptb MaxDeptb Mean Deptb Std. Deviation

1 Control 13.5 47.8 24.59 9.33 2 Control 6.9 42.5 18.43 9.75 3 Treatment 8.3 40.8 21.02 9.03 4 Treatment 9.9 42.1 22.61 9.03 5 Procedural 7.1 42.6 19.99 9.75 6 Treatment 14.6 43.2 25.58 8.07 7 Treatment 13.2 44.9 24.47 8.32 8 Control 7.4 41.2 20.48 9.1 9 Control 5.5 36.8 17.17 8.74 10 Procedural 0 28.6 7.17 7.77 11 Treatment 0 26.1 6.75 7.85 12 Treatment 0 30.3 9.45 8.14 13 Control 0 27.4 7.57 8.38 14 Treatment 0 24.6 6.76 7.56 15 Treatment 0 24.8 6.09 7.51 16 Procedural 8.9 39.1 18.35 7.63 17 Procedural 2.1 31.2 13.19 8.05 18 Control 2.2 31.2 13.86 7.73 19 Procedural 3.3 35.2 13.33 8.61 20 Control 7.4 36.8 17.26 8.8 21 Control 14.5 48.9 25.08 8.79 22 Treatment 9.3 43.9 22.77 8.89 23 Control 10.5 42.2 20.18 8.89 24 Control 12.2 45.2 23.07 8.87 25 Treatment 9.6 42.2 20.94 8.88

88 Total Dissolved Solids by Plot ::::: OJ III ~ o -+- Plot 1, Control (/) ___ Plot 3, Treatment "C GI Pl ot 12, Treatment ~o -M- Plot 13, Control III ...... Plot 18. Control .!!! c ~ Plot 22, Treatment so I-

Sample Collection Date

Figure 1.1 Total dissolved solids over sampling period by plot

Specific Conductivity by Plot

~ > 80 :;; -+-- Plot 1, Control (.) 70 - :::J 60 - / __ Plot 3, Treatment "C E 1----- ~ ~ c: (.) 50 ,- Plot 12, Treatment 0- 40 Jt... ./,.... u(/) ..... ~ Plot 13, Control 30 J! :\ -, (.) E ~ '\\ /" _____ Plot 18, Control - ---' 20 ~ r !€: - (.) 10 -+-Plot 22, Treatment GI Co o (/) II) II) II) II) II) II) II) II) II) II) II) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 N ~ ~ ~ N ~ ~ ~ N ~ ~ II) ,... CO ~ CO ... - en M 0 '

89 u Water Temperature by Plot t/) Q) ....Q) 40 C) --+- Plot 1, Control Q) 35 "0 30 --Plot 3, Treatment c: 25 Plot 12, Treatment Q) 20 .... -><-- Plot 13, Control ::::1 15 10 .....- Plot 18, Control ....co 5 -Q) --Plot 22, Treatment Co 0 E 10 10 10 10 10 10 10 10 10 10 10 Q) 0 0 0 0 0 0 0 0 0 0 0 ~ 0 0 0 0 0 0 0 0 0 0 0 N N N N N N N N N N N co '

Figure 1.3 Water temperature over sampling period by plot

Salinity by plot

Q. 50 Co 40 +--- Z. 30 'c 20 -+- Plot 1, Control co 10 --Plot 3, Treatment 00 0 +--'-''-'--.--r-.--'--r--r-'~ 10 10 10 10 10 10 10 10 10 10 10 Plot 12, Treatment 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 --Plot 13, Control N N N N N N ~ ~ ~ ~ ~ -ill- Plot 18, Control co co .... 10 01 ,... 0 '

Figure 1.4 Salinity over sampling period by plot

90 pH by plot 10 ....,- 8 J: 6 - Q. 4 -- Plot 1, Control 2 - -- Plot 3, Treatment 0 , , , , Plot 12, Treatment It) It) It) It) It) It) It) It) It) It) It) -- Plot 13, Control 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -ill- Plot 18, Control N N N N N N N N N N N -- Plot 22, Treatment CIO .., CIO .... It) en M ,.... 0 .., CIO -.... - -.... - -.... -N -.... -N -.... -N -,.... M .., -N - -M - ----.., .., It) It) -10 -10 - Sample CQllection Date

Figure I .S pH over sampling period by plot

Dissolved Oxygen Concentration by Plot Cl -E c:: 0 20 :;; 15 ...C\l c:: 10 --Plot 1, Cont rol -GI u 5 --Plot 3, Treatment c:: ~ Plot 12, Treatment 0 0 u It) It) It) It) It) It) It) It) It) It) It) -- Plot 13, Control 0 0 0 0 0 0 0 0 0 0 0 0 -ill- Plot 18, Control 0 0 0 0 0 0 0 0 0 0 0 0 N N N N N N N N N N N --Plot 22, Treatment CIO .., CIO .... It) en M ,.... 0 ~ CIO - - -.... - --N - -N - N .... M .., ...... -,.... -N - -M - ----.., .., It) It) CO -10 - Sample Collection Date

Figure 1.6 Dissolved oxygen over sampl ing period by plot

91 Appendix 2. Aquatic Insect Data

Table 2.1. Family of highest occurrence by plot

Plot Treatment Family of Highest % Occurrence % Occurrence

1 Control Diptera - Chironomidae 22.7% 2 Control Hemiptera -Heteroptera- 13.6% Mesoveliidae Diptera - Muscidae Diptera - Ephydridae

3 Treatment Diptera - Chironomidae 27.2% 4 Treatment Diptera - Ephydridae 31.8% 5 Procedural Control Diptera - Ephydridae 22.7% 6 Treatment Diptera - Ephydridae 36.3% 7 Treatment Diptera - Ephydridae 27.2% 8 Control Diptera - Sphaeroceridae 27.2% 9 Control Diptera - Sphaeroceridae 18.1% 10 Procedural Control Diptera - Ephydridae 36.3% 11 Treatment Diptera - Ephydridae 45.4% Diptera - Sphaeroceridae 12 Treatment Diptera - Ephydridae 50% 13 Control Diptera - Ephydridae 40.9% 14 Treatment Diptera - Ephydridae 45.4% 15 Treatment Diptera - Ephydridae 45.4% 16 Procedural Control Diptera - Sphaeroceridae 22.7% Diptera - Chironomidae 17 Procedural Control Diptera - Ephydridae 31.8% 18 Control Diptera - Sphaeroceridae 27.2% 19 Procedural Control Diptera - Ephydridae 27.2% 20 Control Diptera - Chironomidae 22.7% 21 Control Diptera - Chironomidae 22.7% 22 Treatment Diptera - Chironomidae 31.8% 23 Control Diptera - Chironomidae 31.8% 24 Control Diptera - Ephydridae 40.9% 25 Treatment Diptera - Ephydridae 36.3% Diptera - Chironomidae

92 Table 2.2. Invertebrate emergence rates by sampling period

Sampling Date Total Inverts Sampled Days Sampled Invert Emergence Rate by Day for .56 m2 area 211612005 45 17 2.65 2/26/2005 27 10 2.70 3/4/2005 35 7 5.00 3/13/2005 23 9 2.56 3/18/2005 20 5 4.00 312612005 27 8 3.38 41312005 26 9 2.89 41912005 28 6 4.67 4/1712005 54 8 6.75 4/2312005 22 6 3.67 4/3012005 358 7 51.14 5n12005 5488 7 784.00 5/1412005 6130 7 875.71 5/2112005 2365 7 337.86 5/28/2005 74 7 10.57 6/4/2005 36 7 5.14 6/10/2005 26 6 4.33 6/17/2005 74 7 10.57 6/24/2005 161 7 23.00 71112005 55 7 7.86 7/8/2005 184 7 26.29 7/1512005 1278 7 182.57

2 Table 2.3. Total biomass (gIm ) by treatment regime

Treatment Control Procedural Control 0.0258 0.0113 0.1447 0.0172 0.0554 0.8080 0.0179 0.0139 0.0336 0.0212 0.0167 0.1052 0.8404 1.9816 0.0882 0.1457 0.0198 0.5942 0.0914 0.3788 0.0114 0.0233 0.0359 0.0170 0.0248

93 Table 2.4 Biomass (g) statistics by plot

Plot Treatment Max Biomass Mean Biomass Std. Dev. Total Biomass

1 Control 0.0021 0.0005 0.0007 0.0113 2 Control 0.0308 0.0025 0.0068 0.0554 3 Treatment 0.0055 0.0012 0.0013 0.0258 4 Treatment 0.0030 0.0008 0.0009 0.0172 5 Procedural 0.1280 0.0066 0.0271 0.1447 6 Treatment 0.0027 0.0008 0.0007 0.0179 7 Treatment 0.0050 0.0010 0.0013 0.0212 8 Control 0.0042 0.0006 0.0009 0.0139 9 Control 0.0057 0.0008 0.0014 0.0167 10 Procedural 0.6249 0.0367 0.1339 0.8080 11 Treatment 0.3548 0.0382 0.0873 0.8404 12 Treatment 0.0519 0.0066 0.0135 0.1457 13 Control 0.9509 0.0901 0.2468 1.9819 14 Treatment 0.1626 0.0270 0.0486 0.5942 15 Treatment 0.1348 0.0172 0.0318 0.3788 16 Procedural 0.0239 0.0015 0.0051 0.0336 17 Procedural 0.0334 0.0048 0.0090 0.1052 18 Control 0.0047 0.0009 0.0014 0.0198 19 Procedural 0.0651 0.0040 0.0140 0.0882 20 Control 0.0584 0.0042 0.0126 0.0914 21 Control 0.0031 0.0005 0.0008 0.0114 22 Treatment 0.0089 0.0011 0.0021 0.0233 23 Control 0.0204 0.0016 0.0044 0.0359 24 Control 0.0076 0.0011 0.0018 0.0248 25 Treatment 0.0050 0.0008 0.0011 0.0170

94 Table 2.5 Raw data and data transfonnations for aquatic insect biomass and abundance data

Treatment Plot # Biomass (g) Biomass square root Abundance Abundance log Regime transformed (g) base 10 transformed Treatment 3 0.0258 0.16062 71 1.851258 Treatment 4 0.0172 0.13115 40 1.60206 Treatment 11 0.0179 0.13379 3837 3.583992 Treatment 12 0.0212 0.14560 610 2.78533 Treatment 14 0.8404 0.91673 2923 3.465829 Treatment 15 0.1457 0.38171 1325 3.122216 Treatment 22 0.5942 0.77084 65 1.812913 Treatment 25 0.3788 0.61547 59 1.770852 Treatment 6 0.0233 0.15264 72 1.857332 Treatment 7 0.017 0.13038 44 1.643453 Control 1 0.0113 0.10630 34 1.531479 Control 2 0.0554 0.23537 33 1.518514 Control 8 0.0139 0.11790 41 1.612784 Control 9 0.0167 0.12923 45 1.653213 Control 13 1.9816 1.40769 4499 3.653116 Control 18 0.0198 0.14071 38 1.579784 Control 20 0.0914 0.30232 67 1.826075 Control 21 0.0114 0.10677 38 1.579784 Control 23 0.0359 0.18947 50 1.69897 Control 24 0.0248 0.15748 65 1.812913 Procedural Control 5 0.1447 0.38039 55 1.740363 Procedural Control 10 0.808 0.89889 2069 3.31576 Procedural Control 16 0.0336 0.18330 112 2.049218 Procedural Control 17 0.1052 0.32435 190 2.278754 Procedural Control 19 0.0882 0.296985 154 2.187521

95 Appendix 3. Ruppill Maritima data

Table 3.1 April Ruppill Canop Heights in em by plot (n=10 per plot) Sample! 1 2 3 4 5 6 7 8 9 10 11 12 Plot 0 13 0 29.5 11.5 0 6 0 26 23.2 24 12 0 12 0 31 11 0 6.5 0 27 33 3.5 23 0 18.5 0 6 7.9 0 5 0 27 20 15.1 21 0 17.7 0 19.6 10 0 4 0 10 33 28.5 10 0 15.9 0 34 8.2 0 4 0 15 25.1 16.1 24 0 10.4 0 27 6.5 0 5.5 0 27 30 31 10 0 9 0 24 7.5 0 0.19 0 7 28 15 10.4 0 13.5 0 28.6 0 0 0 8 24 10 22 0 12.5 0 22 0 0 0 28 33 11 14

Table 3.2 June Ru, 1pia Canop~ Heights in em by plot (n=10 ~ er plot) Sample! 1 2 3 4 5 6 7 8 9 10 11 12 Plot 0 10 0 26 0 0 0 0 26 0 30 35 0 9 0 27 0 0 0 0 17 0 37 32 0 12 0 36 0 0 0 0 28 0 29 29 0 85 0 30 0 0 0 0 24 0 32 27 0 5 0 29 0 0 0 0 23 0 28 26 0 8.5 0 46 0 0 0 0 20 0 30 23 0 7.5 0 38 0 0 0 0 22 0 30 33 0 8.5 0 39 0 0 0 0 17 0 29 24 0 6 0 27 0 0 0 0 22 0 30 30

Table 3.3 Ruppia Maritima Mean CanoPJ Height in em by sam ling event Plot 1 2 3 4 5 6 7 8 9 10 11 12 April mean 0 13.61 0 24.58 8.94 0 5.16 0 18.8 27.13 16.32 16.14 canopy height June Mean 0 16.15 0 34.2 0 0 0 0 22 0 30 30 Canopy Height

96 Average Canopy Height of Ruppia maritima (± 1 Standrad Deviation) 60 .c- 50 'OJw I 40 >- TI Co 30 r-- o~ 0 4/25/2005 c E CIS u 20 t- r- I- . 6/24/2005 o~ Q) 10 i- l- i- OJ CIS L ~ 0 Q) ~ -10 -20 1 ') . 'l JI 0:; I>. .. 7 . 1'1 . a in 11 1') Plot (n = 12)

Figure 3.1 Average canopy heights of Rupp ia maritima

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