i

Identifying Hotspots of Sewage Pollution in Coastal Areas with Coral

Reefs

Presented to the Faculty of the

Tropical Conservation Biology and Environmental Science Graduate Program

University of Hawaiʻi at Hilo

In partial fulfillment of the requirement for the degree of

Master of Science

In Tropical Conservation Biology and Environmental Science

August 2016

By

Leilani M. Abaya

Approved by:

Tracy N. Wiegner

Karla J. McDermid

Jonathan D. Awaya

i

Acknowledgments

I express my sincere gratitude to my advisor, Tracy Wiegner, and committee

members, Jonathan Awaya and Karla McDermid. Their assistance and guidance over the

last two years have helped tremendously in my growth as a scientist. I also sincerely

thank all my family and friends who supported my academic pursuits over the last few

years. Without your continuing inspiration, love, and guidance I would not be the person

I am today. I also thank the funding agencies that made this project possible: National

Oceanic and Atmospheric Administration (NOAA NA14NOS4820087), Puakō

Community Association (PCA), Kamehameha Schools, Pacific Internship Program for

Exploring Sciences (PIPES), Center for Microbial Oceanography: Research and

Education (CMORE), Sigma Xi, STEM Honors Program, and the Marine Science

Department at the University of Hawaiʻi at Hilo. I would also like to thank the following

co-authors: Steven Colbert, Kaileʻa Carlson, Lindsey Kramer, and Jim Beets.

I am grateful to all the interns, fellows, volunteers, undergraduate students, and

graduate students who assisted in the different aspects of this project: Maile Aiwohi,

Evelyn Braun, Ricky Tabandera, Carrie Soo Hoo, Devon Aguiar, Jazmine Panelo, Cherie

Kauahi, Serina Kiʻili, Chelsie Wung, Bryan Tonga, and Louise Economy.

In addition to those mentioned above, I thank the University of Hawaiʻi at Hilo

Analytical Laboratory, Northern Arizona University Stable Isotope Laboratory, and Craig

Nelson who all had a part in the sample analysis. I am thankful for my collaborators: The

Nature Conservancy (TNC) and the Coral Reef Alliance (CRA) who made it possible to

share this work with community members, and assisted in fieldwork and support.

* This thesis is written as two separate manuscripts which will be submitted for

publication*

ii

TABLE OF CONTENTS

PAGE

Overview abstract………………………………………………………………………....1

Chapter 1:

A multi-indicator approach to identify hotspots of shoreline pollution in coral reefs

Abstract…………………………………………………………………………………....3

Introduction………………………………………………………………………………..4

Methods……………………………………………………………………………………7

Results……………………………………………………………………………………10

Discussion………………………………………………………………………………..12

Conclusion……………………………………………………………………………….17

Tables…………………………………………………………………………………….19

Figures…………………………………………………………………………………....23

Literature Cited…………………………………………………………………………..30

Chapter 2:

Spatial distribution of sewage pollution on a Hawaiian coral reef

Abstract…………………………………………………………………………………..37

Introduction………………………………………………………………………………38

Methods…………………………………………………………………………………..41

Results……………………………………………………………………………………44

Discussion………………………………………………………………………………..45

Conclusion……………………………………………………………………………….49

Figures……………………………………………………………………………………50

Tables………………………………………………………………………………….....56

Literature Cited…………………………………………………………………………..57

iii

LIST OF TABLES

Chapter 1 Tables PAGE

1. Average ± SE and range of sewage indicators...............................................................19

2. Average ± SE of δ15N - NO3-, NO3

-+NO2-, NH4

+, and PO43- of N sources...................20

3. Average ± SE and range of nutrient concentrations and salinity...................................21

4. Summary of sewage score indicators.............................................................................22

Chapter 2 Tables

1. Average ± SE and range of nutrient concentrations and salinity...................................56

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LIST OF FIGURES

Chapter 1 Figures PAGE

1. Sewage indicator stations, dye tracer sites, and N sources in Puakō, Hawaiʻi………..23

2. Sewage indicator data from shoreline stations………………………………………...24

3. Summary of δ15N in macroalgal tissue relative to N sources…….…………………...25

4. Mixing plots of salinity and nutrients………………………………………….……...26

5. Correlation between C. perfringens and NH4+…………………………………….….27

6. Correlations between δ15N in macroalgal tissue and nutrients…….………………….28

7. Puakō sewage pollution score map……...…………..………………………………...29

Chapter 2 Figures

1. Offshore sampling stations in Puakō, Hawaiʻi………………………………………..50

2. Purging experiment results……………………………………………………………51

3. Sewage indicator data from surface and benthic waters ………………………...........52

4. Summary of δ15N in macroalgal tissue relative to N sources..….…………….………53

5. Pre- and post-deployment δ15N values in Ulva fasciata………….……………..…….54

6. Puakō sewage pollution score map……………………………………………………55

1

Overview Abstract

Sewage pollution threatens human and coral reef health. Study goals were to

identify sewage pollution hotspots through dye tracer tests, measurements of sewage

indicators, and development of a sewage pollution score, along Puakō’s (Hawaiʻi) reef.

Sewage was localized within 10 m of the shoreline and reached it within 9 hours to 3

days. Shoreline nutrient concentrations were two times higher than upland groundwater.

Sewage indicators were higher and more variable along the shoreline than on the reef,

and often greater than water quality standards. Shoreline δ15N macroalgal values were

indicative of sewage, while offshore values were indicative of soil or groundwater nitrate.

A sewage pollution score was created using several indicators that accurately identified

sewage pollution hotspots, as three dye tracer locations had the highest scores. Results

highlight the need for a multi-indicator approach and scoring system for identifying

sewage pollution hotspots to improve water quality.

2

A multi-indicator approach to identify hotspots of shoreline sewage

pollution in coral reef areas.

Chapter 1

3

Abstract

Globally, coral reefs are declining because of various stresses, with sewage

pollution being one of the most concerning. This study used a combination of sewage

indicators to investigate the presence of sewage pollution along the Puakō coastline in

Hawaiʻi. Fecal indicator bacteria (FIB) counts and nutrient concentrations were high and

variable along the Puakō shoreline, and stable nitrogen isotope (δ 15N) levels in

macroalgal tissue were within the sewage range. These indicators together with results

from dye tracer tests, documented sewage pollution in Puakō’s coastal waters. However,

sewage indicator data were not always in agreement in terms of the intensity and location

of sewage pollution. A sewage pollution score was created using a combination of

sewage indicators and nutrients to identify pollution hotspots. The scoring system

allowed locations of sewage pollution to be visualized in a holistic manner. The approach

used here will be useful to other coastal communities around the world who are

challenged with documenting sewage pollution in their nearshore waters.

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Introduction

With more than 50% of the world’s population living near coastal areas, sewage

pollution has become a growing global concern. Sewage is introduced into coastal

environments from cesspools, septic tanks, injections wells, and sewage treatment plants

(Peterson & Oberdorfer 1985; Hunt 2006; Knee et al. 2008; Zimmerman 2010). Sewage

pollution can result in elevated levels of pathogens, hydrocarbons, nutrients, toxins, and

endocrine disruptors in nearshore waters (Wear & Thurber 2015), which can have

devastating consequences on the health of recreational water users. For example, human

exposure to sewage can to result in skin and urinary tract infections, hepatitis, and

gastroenteritis illnesses (Pinto 1999). In fact, globally, over 120 million gastroenteric

illnesses annually are associated with sewage contaminated waters (Shuval 2003). In

addition, sewage pollution can have detrimental effects on coastal ecosystems, especially

coral reefs. Coral reefs are one of the most valuable and biologically diverse ecosystems

in the world, contributing billions of dollars in ecosystem services worldwide (Wear &

Vega Thurber 2015). However, coral reefs are steadily declining from multiple stresses

including, one of the most harmful, sewage pollution (Wear & Vega Thurber 2015).

Sewage pollution contributes to coral disease (Vega Thurber et al. 2014; Yoshioka et al.

2016). For example, Serratia marcescens, a human pathogen in sewage, caused ~88%

decline in coral cover in the Caribbean (Sutherland & Ritchie 2004). Nutrient enrichment

from sewage alters coral growth rates, species distribution and abundance, diversity, and

prevalence and severity of disease (Pastorok & Bilyard 1985; Dollar & Grigg 2004;

Parsons et al. 2008; Vega et al. 2014). These nutrients also stimulate benthic algal

growth, which can result in a benthic phase shift from coral to macroalgal reefs (Hunter

& Evans 1995; Lapointe et al. 2005; Paytan et al. 2006).

As the human population continues to grow along the coast, monitoring water

quality for sewage pollution is essential. Measurements of fecal indicator bacteria (FIB)

are a widely used method to detect sewage. FIB are used to evaluate potential risk to

human health. Enterococcus is monitored in marine recreational waters by the United

States Environmental Protection Agency (USEPA) (USEPA 2003), and in the tropics, a

secondary indicator, Clostridium perfringens, is used (Davies et al. 1995; Fung et al.

5

2007). Clostridium perfringens is an anaerobic, spore-forming bacterium that does not

multiply in coastal waters, nor grow in tropical soils like Enterococcus (Hardina &

Fujioka 1991; Fung et al. 2007). Measurements of stable nitrogen (N) isotopic

composition (δ15N) in macroalgal tissues is another method to detect sewage pollution in

coastal waters (Umezawa et al. 2002; Savage 2005; Hsing-Jun Lin et al. 2007; Dailer et

al. 2012; Wiegner et al. 2016). Macroalgae only minimally discriminate between 14N and

15N. Therefore, they have similar isotopic compositions relative to their N sources

(Savage 2005). Sewage, in particular, has a very distinct stable N isotopic composition

compared to other sources, i.e, fertilizers, soils, groundwater, and ocean water (reviwed

in Wiegner et al 2016). Additionally, the macroalgal species often used in these studies as

bioindicators are opportunistic ones whose growth is stimulated by increases in N (Littler

& Littler 1980; Dailer 2010; Dailer 2012).

Measuring a single sewage indicator to assess sewage pollution in a location

could be misleading due to the variability associated with the different indicators. For

example, one study found that when using Enterococcus, the authorities would be more

likely to post beach advisories than if using C. perfringens (Shibata et al. 2004). In

addition, δ15N in macroalgal tissues can be highly variable due to inputs of N from many

sources with different N isotope compositions (Ochua-Izaquirre and Soto-Jimenez 2015).

Hence, it is imperative to measure multiple indicators of sewage to determine spatial and

temporal patterns of pollution with greater confidence. Few studies have done this to

date, and at most, only two indicators have been measured simultaneously.

Hawaiʻi is an ideal location to develop a multiple indicator sewage detection

approach. Hawaiʻi’s coastal waters and coral reefs have been plagued by sewage

pollution for decades, initially from outfalls in enclosed bays and today from more

diffuse and prevalent sources – cesspools (Pastorok & Bilyard 1985). Cesspools are a

major source of sewage pollution especially in rural areas, which comprise most of the

state. Hawaiʻi uses cesspools more widely than any other state (USEPA 2013), and has

only recently banned the installment of new cesspools. Over 110,000 individual

wastewater systems exist in the state, with the Hawaiʻi Island having nearly 49,000

(Whittier & El-Kadi 2014). A high-risk area where wastewater systems are likely

6

impacting nearshore waters on the Hawaiʻi Island is Puakō (Whittier & El-Kadi 2014).

Puakō is a coastal community that is home to some of the richest, most diverse reefs in

the state (Hayes et al. 1982). However, coral coverage has decreased from 80% in 1975

to 33% in 2010 at Puakō (Minton et al. 2012), with concurrent decreases in fish

abundance (49% - 69%), and increases in turf and macroalgal cover (38%) (HDAR

2012). Declining coral health and increases in disease prevalence and severity have also

been documented (Couch et al. 2014). While sewage pollution is thought to be one of the

culprits contributing to these ecosystem changes, the link between these conditions and

the presence of presence has not been made.

The goal of this study was to develop a multiple sewage indicator approach to

more accurately detect the presence of sewage in nearshore waters. To do this, four

indicators were measured along the Puakō coastline: 1) FIB, 2) nutrients (up mountain

and near the ocean), 3) δ15N in macroalgal tissue, and 4) fluorescein dye tracer releases

from cesspools. Dye tracer studies have been used to effectively illustrate the connection

between hydrogeologic features (Gaspar 1987). Based on the concentration, dye tracer

studies can be used to calculate transit time, flow rates, and dilution in the aquifer during

transport between two points. Fluorescein, a non-toxic organic dye, has strong

fluorescence, which can be used as a tracer with detection levels as low as 1 ppb (Gaspar

1987; Reich et al 2001). In addition, to identify sewage pollution hotspots along the

coast besides homes where dye tracer studies were conducted, a sewage pollution score

was developed.

This is the first study to use a combination of techniques and indicators to

examine shoreline sewage pollution, as most studies to date have used a single one, or

two at most, to assess sewage pollution. Increasing human population in coastal areas is

causing sewage to be a worldwide water quality issue (Wear & Thurber 2015).

Developing an approach to assess sewage pollution is imperative for improving human

and nearshore ecosystem health. The multiple sewage indicator approach and sewage

pollution score developed through this research will be an invaluable management tool

for coastal areas. Information gathered through this approach will allow managers to

7

identify hotspots of sewage pollution and develop effective wastewater treatment

solutions for their coastal communities.

Methods

Site Description

This study was conducted along the Puakō coast (Fig. 1), which is primarily

comprised of basalt from the flow of Mauna Loa Volcano (Kaniku Flow in 1859) and is

mostly aʻa with some pahoehoe. The annual rainfall in this region ranges from 250 – 750

mm. Infiltration of rainwater into the aquifer is high due to the permeable substratum, and

average submarine groundwater discharge (SGD) ranges from 2083 - 2730 L m-1 h-1

(Paytan et al. 2006).

Puakō is a residential community consisting of 207 lots, of which 163 have

homes, along a 3.5 km stretch of the south Kohala coastline. The population is growing at

a rate of 6.9% per year (Minton et al. 2012). At Puakō, 47 homes have cesspools and 139

have conventional septic tanks with leach fields (Schott 2010). The entire coastline is

accessible to the public and is frequently used for recreational activities such as fishing,

surfing, diving, and snorkeling. Presently, there is one development up-slope of Puakō,

Waikoloa Village, with over 4,800 people, whose homes have onsite sewage disposal

systems (OSDS) (U.S. Census Bureau 2000).

Station Selection

To select shoreline stations for sampling, a salinity survey was conducted using a

YSI 6600 V2 multi-parameter sonde interfaced with a Garmin etrex Global Positioning

System (GPS). This was completed in the summer of 2014 during low tides to capture

maximum groundwater input. From this survey, sixteen shoreline stations were chosen

with varying salinity (Fig. 1).

FIB & Nutrient Analyses

Water samples were collected four times at all 16 stations and analyzed for FIB,

nutrients, and salinity. Samples were taken between November 2014 and July 2015

during low tide conditions near sunrise as sunlight has been shown to affect the survival

of bacteria (bactericidal effect) (Fujioka et al. 1981). FIB Enterococcus was analyzed

using the Enterolert MPN method (IDEXX Laboratories Inc) and C. perfringens was

8

quantified using a membrane filtration technique (Bisson & Cabelli 1979). Water samples

were filtered through pre-combusted (500°C for 6 hours), 0.7-µm pore size GF/F filters

(WhatmanTM), and stored frozen until analysis at University of Hawaiʻi at Hilo Analytical

Laboratory. Nutrients were analyzed on a Pulse TechniconTM II autoanalyzer using

standard methods (NO3- + NO2

- [Detection Limit (DL) 0.07 µmol L-1, USEPA 353.4],

NH4+ [Detection Limit (DL) 0.36 µmol L-1, USGS I-2525], PO4

3- [DL 0.03 μmol L-1,

Technicon Industrial Method 155-71 W], total dissolved phosphorous (TDP) [DL 0.5

μmol L-1, USGS I-4650-03], and H4SiO4 [DL 1 μmol L-1, USEPA 366]) and reference

materials (NIST; HACH 307-49, 153-49, 14242-32, 194-49). Total dissolved nitrogen

(TDN) was analyzed by high-temperature combustion, followed by chemiluminescent

detection of nitric oxide (DL 5 μmol L-1, ASTM D5176, Shimadzu TOC-V, TNM-1)

(Sharp et al. 2002). Salinity was measured at the time of water collection using a YSI Pro

2030 multi-parameter probe.

δ15N Analyses

At the time of water sample collection, the most abundant macroalgae were

collected at all stations and analyzed for δ15N (Fig. 1). Macroalgal tissues were placed on

ice during transport to the laboratory, where tissues were rinsed with deionized water.

Subsamples of macroalgae were preserved as voucher specimens and identified using an

OlympusTM CH30 microscope and identification books (Abbott 1999; Abbott & Huisman

2004). The remainder of the samples were dried at 60° C until a constant weight was

achieved, ground and homogenized using a Wig-L-Bug grinding mill, and ~2 mg of the

macroalgal tissue were folded in 4x6 mm tin capsules for stable isotope analysis.

Macroalgal tissues were analyzed for δ15N using a Thermo-FinniganTM Delta V

Advantage isotope ratio mass spectrometer (IRMS) with a Conflo III interface and a

CostechTM ECS 4010 Elemental Analyzer located at the University of Hawaiʻi at Hilo

Analytical Laboratory. Data were normalized to USGS standard NIST 1547. Isotopic

signatures were expressed as standard (δ) values, in units of parts per mil (‰), and

calculated as [(Rsample – Rstandard) / Rstandard] x 1000, where R = 15N/14N.

To determine the sources of N being used by the macroalgae, δ15N - NO3- of

potential sources were measured. Nutrient concentrations (NO3- + NO2

-, NH4+, and PO4

3-)

9

in these sources were also measured. Sources sampled included cesspools (n = 3), high

elevation groundwater wells (n = 3), low elevation groundwater wells (n =7), ambient

seawater (n = 2), and soil (n = 3) from under Kiawe trees (Prosopis pallida). Kiawe, an

introduced N2-fixing tree, found widely on the leeward coasts on the Hawaiʻi Island, and

contributes N to soil and groundwater (Gallaher & Merlin 2010; Dudley et al. 2014). Soil

was collected directly under the Kiawe trees, dried, and then shaken overnight with

reagent-grade water. N source water samples were collected at several locations to assess

spatial variability. All N source samples were filtered through a 0.22-μm cellulose acetate

filter (WhatmanTM) and frozen until analysis. δ15N - NO3- samples were analyzed on a

Thermo-FinniganTM Delta Plus IRMS with data normalized to U.S. Geological Survey

(USGS) standards (USGS32, USGS34, USGS53) at Northern Arizona University Stable

Isotope Laboratory. IAEA-NO3 was used as a check standard. Fertilizer values were also

used in this study that was collected from a previous sewage study on the Hawaiʻi Island

(Wiegner et al 2016). To determine their N sources, the δ15N macroalgal tissue values

were plotted relative to δ15N source values (Derse et al. 2007; Wiegner et al. 2016).

Dye Tracer Tests

Dye tracer tests were conducted to determine the hydraulic connectivity between

the OSDS at four oceanfront homes. Tests were conducted along the southern portion of

Puakō’s coastline as nearshore waters here were relatively fresh. Three homes had

cesspools, and one had a fractured aerobic treatment unit tank. Two homes were occupied

during the tests and the other two were not. At each home, the closest point where dye

could be delivered to the OSDS was identified, and 500 - 1000 g of high purity

fluorescein dye (Amresco Fluorescein Sodium Salt) were injected over approximately 10

h. Each hour, 50 or 100 g of dye were mixed with 20 L of tap water and slowly added to

the OSDS. Additional tap water was added throughout the day and its volume recorded

to calculate an initial dye concentration.

Five to six stations were identified in front of each home and adjacent properties,

representing three to four springs of varying salinity and two stations with higher salinity

and no apparent freshwater input. Samples for fluorescence were collected at each station

before and during dye tracer tests in an opaque brown high density polyethylene (HPDE)

10

bottle, pre-rinsed with sample water, to prevent photodegradation, and stored at 4°C until

analysis. During the first 12 h of the dye test, samples were collected every two h to

identify any fast-flow pathways. Afterwards, two samples were collected at each station

within an hour of the lowest-low water each day for up to 14 days.

To quantify the concentration of fluorescein, samples were brought to room

temperature, filtered (Whatman GF/F), and analyzed using a Turner AU10 fluorometer in

the dark. The detection limit for our analysis was 0.95 ppb (USEPA 40 CFR 2011).

When salinity was not measured in the field, conductivity of samples was measured in

the laboratory (Orion Star) and converted to PSS-78 salinity (Unesco 1981).

Data Analyses

To determine if FIB, δ15N values, and nutrients in macroalgal tissue differed

among stations, a one-way analysis of variance (ANOVA) was used. Correlations were

used to evaluate associations between FIB, δ15N values, nutrients, and other water quality

parameters. Data were tested for normality and equal variances. If assumptions were not

met for parametric analyses, log transformations were used. Statistical analyses were

conducted using Minitab17 (2010) with an α=0.05.

Mixing plots of nutrient concentrations and salinity were also used to determine

nutrient sources (freshwater vs. ocean) to coastal water bodies, and to examine their

behavior as freshwaters and ocean waters mix. Nutrient concentration data on mixing

plots were compared to a theoretical mixing line connecting the freshwater and ocean end

members in that system. When nutrient concentration data fell on the mixing line, the

nutrient was described as behaving conservatively – that is dilution was the only thing

affecting the nutrient concentration in the nearshore waters. When data fell above or

below the mixing line, the nutrient was described as behaving non-conservatively, with

some source adding the nutrient to the water or some process removing it during mixing.

Results

Sewage Indicators

Contrasting patterns were seen among sewage indicators along shoreline stations.

Enterococcus ranged from 18 to 2777 MPN/100 mL and counts significantly differed

among stations (p =0.04), with station 13 (Average ± SE) (2777 ± 1806 MPN/100 mL)

11

having the highest counts (Fig. 2, Table 1). Clostridium perfringens values ranged from 2

to 12 CFU/100 mL and counts were similar among stations, averaging 5 ± 3 CFU/100

mL across all stations (p =0.06) (Fig. 2, Table 1). The most prevalent macroalgal species

along the shoreline were Ulva fasciata, Cladophora spp., and Gelidiella acerosa. The δ

15N in macroalgal tissue ranged from 4.23‰ to 11.88‰ across all 16 shoreline stations,

and δ 15N significantly differed among stations (p <0.0001) (Fig. 2,Table 1), with stations

3 (10.55‰ ± 0.15) and 4 (11.88‰ ± 0.32) being the most enriched (Fig. 2). Six out of 16

stations fell within the sewage δ 15N - NO3- range (Table 2), including stations 3, 4, as

well as 5 (7.21‰ ± 2.01), 6 (7.77‰ ± 1.29), 7 (8.35‰ ± 0.73), and 13 (7.74‰ ± 1.93)

(Fig. 3). The remaining stations fell within the high and low elevation groundwater

ranges (Fig. 3). NO3- + NO2

- concentrations were lower in high elevation wells (93.9

µmol/L ± 4.4) compared to the lower elevation wells (130.1 µmol/L ± 6.7) (Table 2). In

addition, PO43- and NH4

+ concentrations were similar between high (2.5 µmol/L ± 0.2; 5

µmol/L ± 1, respectively) and low elevation wells (2.5 µmol/L ± 0.5; 5 µmol/L ± 1,

respectively) (Table 2). NO3- + NO2

-, TDN, PO43-, TDP, and H4SiO4 concentrations

significantly differed among shoreline stations (p <0.001) (Table 3). Station 4 had the

highest concentrations, and station 15 had the lowest concentrations for all nutrients,

except H4SiO4. H4SiO4 had the highest concentrations at station 14 (652 µmol/L ± 174),

while station 6 (95 µmol/L ± 43) had the lowest (Table 3). NH4+ concentrations were

similar across all shoreline stations (p >0.06). Salinity also varied across stations (p

<0.01), with stations 2 (7.12 ± 0.61) and 14 (6.43 ± 0.63) being the freshest (Table 3).

Nutrient concentrations (NO3- + NO2

-, TDN, PO43-, TDP, and H4SiO4,) were also

inversely correlated with salinity (p <0.01). Mixing plot analysis revealed conservative

mixing of groundwater-derived nutrients (NO3- + NO2

-, TDN, PO43-, TDP, and H4SiO4)

with seawater, except for a few stations that consistently fell well above the theoretical

mixing line (Fig. 4). These included stations 3, 4, and 7. NH4+ displayed non-

conservative mixing (Fig. 4).

Correlations Among Sewage Indicators

Most sewage indicators were not correlated with each other; however, δ15N in

macroalgal tissue values were correlated with C. perfringens and nutrient concentrations.

12

Specifically, C. perfringens was positively correlated with NH4+ (p =0.02) (Fig. 5) and δ

15N in macroalgal tissue was positively correlated with NO3- + NO2

- (p <0.001), TDN (p

<0.001), and PO43- concentrations (p <0.001) (Fig. 6).

Dye Tracer Tests

Dye was visually observed at the shoreline at all four sites. For each test, there

was only one spring with dye, which was located on the beach in front of the property.

The groundwater discharge at these springs dispersed over an area between 0.25 and 4

m2. Initial breakthrough of dye at the shoreline, calculated based on the start of the dye

tracer test until the first appearance of dye at the shoreline, ranged from 9 h to 3 d. Three

of the homes had comparable flow rates between 4 and 14 m/day; the OSDS at one home

was remarkably faster, where dye in groundwater traveled 76 m/day. Based on the

dilution of the dye, the maximum fraction of sewage in the freshwater at the shoreline

varied from <0.02% to 0.14%, depending on how much mixing occurred before

discharge at the shoreline.

Discussion

Sewage Indicators

FIB are used by federal and state regulatory agencies to monitor impaired

recreational waters. At Puakō, Enterococcus counts differed among shoreline stations,

with station 13 having the highest counts. Additionally, when comparing our average

values of Enterococcus counts to Hawaiʻi Department of Health’s (HDOH) single sample

maximum of 104 CFU/100 mL, 13 out of 16 stations exceeded this threshold (Fig 2).

However, Enterococcus has been shown to vary spatially, temporally, seasonally, and to

be tidally influenced (Shibata et al. 2004; Maiga et al. 2009; Shibata et al. 2010; Nnane et

al. 2011; Converse et al. 2012). In addition, Enterococcus has been found to persist in

tropical soils (Hardina & Fujioka 1991; Byappanahalli & Fujioka 1998; Byappanahalli &

Fujioka 2004), and thus, may not be indicative of sewage, but possibly of a soil source.

However, soils are an unlikely source of Enterococcus at Puakō as the area generally

lacks soil and the substratum is primarily basalt. This, in combination with the above

factors, indicate that Enterococcus may not be the best indicator because of its high

variability. However, the levels that were found along the shoreline were extremely high

13

compared to state standards and may be indicative of sewage. While C. perfringens

counts did not vary among stations, 11 of the 16 stations fell above the recommended

standard to HDOH for marine recreational waters (5 CFU/100 mL) (Fujioka et al. 1997).

Additionally, using the Fung/Fujioka C. perfringens scale for sewage pollution based on

single sample maximums (Fung et al. 2007), only four of our stations (stations 7, 11, 14,

and 15) were indicative of non-point sewage contamination (>10 CFU/100 mL). The

remaining five stations fell below this range, and are classified as not being polluted by

sewage. While only certain stations had C. perfringens counts within the range for non-

point sewage pollution, the correlation between C. perfringens and NH4+ suggest that the

sewage pollution may be more pervasive, as these two parameters are associated in

anaerobic conditions, which are often found in OSDS.

While FIB are used to detect sewage, their application is primarily for assessing

human health hazards for recreational water users. δ 15N values in macroalgal tissue, on

the other hand, are used to determine N sources to coastal waters including sewage

(Costanzo et al. 2005; Lapointe et al. 2005; Savage 2005; Derse et al. 2007; Dailer et al.

2012). Typical sewage values range from +5‰ to +20‰ (Kendall 1998; Hunt 2006;

Dailer et al. 2010; Derse et al. 2007; Xue et al. 2009), and values from the cesspools in

our study fell within this range (Table 3). δ 15N in macroalgal tissue along the Puakō

shoreline ranged from 4.23‰ to 11.88‰, with six out of 16 shoreline stations falling

within the range for sewage (Table 4, encompassing SE of averages). Stations 3 and 4

had the most enriched δ 15N macroalgal tissues, highlighting two potential sewage

pollution hotspots. However, past studies have found that macroalgae assimilate N more

rapidly under low NO3- concentrations (Fujita 1985), and that δ 15N in macroalgal tissue

can be underestimated by up to 6‰ in waters with high NO3- concentrations (>10

μmol/L) (Swart et al. 2014). All of the stations had NO3- + NO2

- concentrations

exceeding 10 µmol/L, suggesting that the δ 15N macroalgal values may be

underestimated. If this is the case, then all 16 stations fall within the sewage range. In

contrast, other studies have found that tissue from opportunistic macroalgal reflects the

nutrient concentrations in the water column (Fong et al. 1994). Two out of the three taxa

(Ulva fasciata and Cladophora spp.) collected along the Puakō shoreline were

14

opportunistic macroalgae; however, during sample processing, tissue from all three

macroalgae species were combined for δ 15N analysis, and G. acerosa is not considered

an opportunistic species. Additionally, NO3- + NO2

-, TDP, PO43-, and TDN

concentrations were nearly 8x greater at station 4 compared to all stations, this pattern

was also seen with δ 15N in macroalgal tissue. These patterns further suggest that station

4 is a hotspot of non-point sewage pollution at Puakō. Correlations between δ 15N in

macroalgae and nutrient concentrations also suggest that some portion of the nutrients’

concentrations are derived from sewage (Fig. 6).

Hydrology

At Puakō, a large portion of the nutrient concentration data for NO3-+NO2

-, TDN,

PO43-, and TDP fell on the theoretical mixing line, with highest concentrations at the

lowest salinities. This result suggests that high elevational groundwater is a source of

nutrients to Puakō’s coastal waters and that they are behaving conservatively as

groundwater and ocean water mix at the shoreline. This pattern, in part, explains the lack

of associations between sewage indicators and salinity, as a large portion of groundwater

nutrients discharging at the shoreline is from a nutrient source other than sewage. The

conservative mixing nutrient patterns observed at Puakō have been documented

elsewhere on Hawai‘i Island in coastal areas with groundwater inputs (Knee et al. 2008).

In contrast to the majority of our shoreline stations, data for stations 3, 4, and 7

consistently fell above the theoretical mixing line, suggesting there is a localized source

of nutrients in those areas. The likely source is the OSDS as our dye tracer tests

demonstrated that OSDS at these stations were leaking, and that the travel time from the

homes to the shoreline was 9 h to 3 d. Additionally, dye was only observed seeping out

during low tide and was localized within 10 m of the shoreline. δ15N-NO3- at these

shoreline stations clearly fell within our measured δ15N-NO3- sewage range at Puakō

(Table 2), as did the δ15N in the macroalgal tissue (Fig. 3). These results provide insight

to the hydrology and geology at Puakō, where the fracture system within the basalt

determines the flow path of the sewage from the OSDS to the shoreline, and affects the

time of travel. Two other factors affecting sewage inputs are weather and house

occupancy. On the only rainy sampling day during this study (March 4, 2015), all three

15

stations (3, 4, and 7) fell above the mixing line. This result illustrates that precipitation

inputs enhanced the connection between the OSDS with the shoreline seeps through

increased groundwater discharge. In contrast, there were some sampling dates on which

nutrient concentrations for stations 3 and 7 fell on the mixing line. We suspect that on

these dates, homes at these stations were not occupied, and therefore, there the OSDS

were not being used.

The δ15N-NO3- and NO3

- concentration data from the groundwater wells and

shoreline stations also provided another insight into the hydrology of the Puakō

watershed and coastal nutrient sources. The δ15N-NO3- became increasing enriched in

15N moving downslope to the Puakō shoreline. The change in the δ15N-NO3- from the

high to low elevational groundwater wells suggests a change in NO3- source from forest

soil to sewage. It is possible that sewage is contaminating the lower elevational

groundwater as an upslope development (Waikoloa Village) has over 4,800 people whose

homes have OSDS (U.S. Census Bureau 2000). Additionally, NO3- concentrations

increase ~40 µmol/L from the high to low elevational groundwater wells (Table 3).

Lastly, the 15N enrichment in the δ15N-NO3- from the lower elevational groundwater

wells to the shoreline seeps suggests that additional sewage from Puakō homes is

contaminating the groundwater before it is discharged along the shoreline. To understand

the relative percent contributions of these two different communities to sewage pollution

along Puakō’s shoreline, more δ15N-NO3- data and a mixing model capable of

determining source contributions is needed (Wiegner et al. 2016). With this additional

information, informed decisions about management actions can be made.

Development of a Sewage Pollution Score

As this study and others have shown, sewage indicators can provide conflicting

information on the intensity and location of sewage pollution along the shoreline. In this

study, Enterococcus counts were highly variable among stations, with some exceeding

HDOH standards, and station 13 having the highest counts. In contrast, C. perfringens

counts were similar among stations, but averages for stations 7, 11, 14, and 15 were in the

non-point source sewage pollution range (Fung et al. 2007). Additionally, δ 15N in

macroalgal tissue were found to be highly variable along the shoreline, with six stations

16

falling within the range of our sewage source value (Fig. 2, Table 3). Previous studies

have confronted similar issues with their sewage indicator data (Shibata et al. 2004;

Yoshioka et al. 2016). Hence, creating a sewage pollution score using sewage indicators

may be a way to more holistically assess sewage pollution in coastal waters. Water

quality scores and indices have been used successfully in the past to assess healthy water

quality conditions for both humans and ecosystems (Zambrano et al. 2009; Wang et al

2015). One successful study was able to examine four different regions utilizing

physiochemical and nutrient parameters to relate human activities to water quality,

highlighting areas in need for better wastewater management.

To better assess sewage pollution conditions along the Puakō shoreline, a scoring

system was developed using sewage indicators (FIB, δ15N macroalgae, and nutrients).

The scoring system had three levels for each indicator: level 1 = low, level 2 = medium,

and level 3 = high. Levels for each indicator were based on established standards or

literature information (Table 4). Specifically, the scoring system used HDOH’s single

sample maximum for Enterococcus counts in marine waters (HDOH 2014), the

Fung/Fujioka C. perfringens scale for sewage pollution (Fung et al. 2007), δ 15N values

in macroalgal tissue for different N sources (reviewed in Wiegner et al. 2016), and

HDOH’s water quality standards for nutrient concentrations in open coastal waters (NO3-

+ NO2-, NH4

+, TDP) (HDOH 2014) (Table 4). Nutrient concentration standards for the

wet criteria were used because the freshwater inputs along the Puakō shoreline ranged

from 2083-2730 L m-1 h-1 (Paytan et al. 2006), an order of magnitude larger than the

baseline for the wet criteria (>294 L m-1 h-1). Two dissolved inorganic forms of N were

chosen for the score system rather than TDN because it contains DON and there are no

well-established sewage pollution patterns with this constituent. TDP was used as the

phosphorous water quality indicator since HDOH has no PO43- water quality standard for

open coastal waters (HDOH 2014). It should also be noted that a ‘medium’ score in

nutrient concentrations exceeds HDOH standards for open coastal waters wet criteria.

Once each indicator was assigned a level (1-3) based on its measured value and

our scoring system (Table 4), its level was multiplied by a weight factor (1-3), with the

most reliable sewage indicators having the greatest weight. The greatest weight (weight =

17

3) was given to C. perfringens and δ 15N in macroalgal tissue because these indicators are

more specific to sewage pollution, more integrative measurements of environmental

conditions, and do not fluctuate as much as Enterococcus counts and nutrient

concentrations (Fung et al 2007; Dailer et al. 2010; Viau et al. 2011; Yoshioka et al.

2016). Enterococcus received a medium weight (weight = 2) as HDOH uses this FIB to

assess marine recreational water safety specifically for sewage pollution, but not the

highest weight because counts fluctuate over short time scales (min to h) and have other

sources, like soils, in tropical areas (Hardina & Fujioka 1991; Byappanahalli & Fujioka

1998; Byappanahalli & Fujioka 2004). Nutrient concentrations received the lowest

weight (weight = 1) since sewage pollution is known to increase nutrient concentrations,

but nutrients can also come from other sources within the watershed and concentrations

can vary over short time scales (Lapointe et al. 1990; David et al. 2013; Nelson et al.

2015). The equation for deriving the overall sewage pollution score for each station was:

(C. perfringens level x 3) + (δ15N macroalgae level x 3) + (Enterococcus level x 2) +

(NO3-+NO2

- level x 1) + (NH4+ level x 1) + (TDP level x 1). Sewage pollution score

categories were: ‘low’ = 11-15, ‘medium’ = 16-20, ‘high’ = 21-30.

The stations with highest pollution sewage scores were station 7 (score =30) and 4

(score =30) (Fig. 7). Note, that based on dye tracer data, these two stations are known

locations of OSDS leakage. Station 3 (score = 27), another location of known OSDS

leakage, had the third highest pollution score. These results provide confirmation of the

effectiveness of our sewage pollution score in identifying hotspots of sewage pollution.

Overall, 13 stations fell in the high category, two were medium, and one was low. This

integrated approach identified sewage hotspots along the Puakō coastline, and locations

where it is critical for homes to remove their cesspools and employ better sewage

treatment technology. This map also provides information to the community on areas

where community members may want to limit water exposure during recreational

activities until sewage treatment is improved.

Conclusion

Globally, coral reefs are declining from multiple stressors, with sewage pollution

being one of the most devastating (Wear & Vega Thurber 2015). FIB counts and nutrient

18

concentrations were high and variable along the Puakō shoreline, and δ 15N in macroalgal

tissue were within the determined range for sewage at Puakō. Dye tracer tests further

confirmed locations of sewage pollution and provided information on the time of travel

of the sewage from the homes to the shoreline. However, data from different sewage

indicators were not always in agreement with one another on the intensity and locations

of sewage pollution. Hence, a novel sewage pollution score was developed to use these

indicators to identify hotspots of sewage pollution. This approach allows a holistic

visualization of locations of sewage pollution. With sewage becoming a growing global

threat in nearshore waters, being able to effectively assess sewage pollution is crucial for

both human and marine ecosystem health. A multi-indicator approach for detecting

sewage pollution and this sewage pollution scoring system will allow other coastal

communities to assess their water quality and take appropriate management actions to

improve safety of recreational waters users and coastal ecosystem health.

19

Station Enterococcus

Clostridium

perfringens δ15N

1 18 ± 8b

[9-43] 2 ± 1 [0-4]

6.38 ± 0.15a-c

[6.03-6.65]

2 74 ± 25ab

[37-143] 2 ± 1 [0-4]

7.54 ± 0.18a-c

[7.04-7.90]

3 349 ± 162ab

[37-739] 5 ± 2 [1-10]

10.55 ± 0.15ab

[10.37-11.00]

4 237 ± 178ab

[47-770] 6 ± 2 [3-13]

11.88 ± 0.32a

[11.27-12.78]

5 1107 ± 861ab

[94-3674] 4 ± 1 [0-6]

7.21 ± 2.01a-c

[1.29-10.26]

6 1051 ± 570ab

[72-2546] 6 ± 2 [2-10]

7.77 ± 1.30a-c

[4.15-10.18]

7 170 ± 32ab

[104-257] 12 ± 5 [3-27]

8.35 ± 0.74a-c

[6.48-9.80]

8 738 ± 603ab

[62-2544] 7 ± 1 [3-10]

5.48 ± 0.37a-c

[5.06-6.58]

9 216 ± 120ab

[27-563] 3 ± 0 [2-3]

4.79 ± 0.53bc

[3.85-6.18]

10 122 ± 30ab

[66-202] 5 ± 1 [2-7]

4.54 ± 0.70c

[3.57-6.56]

11 315 ± 107ab

[15-495] 8 ± 3 [2-14]

6.02 ± 0.30a-c

[5.59-6.91]

12 676 ± 251ab

[121-1323] 5 ± 2 [2-8]

6.43 ± 0.65a-c

[4.90-8.04]

13 2777 ± 1806a

[17-7985] 2 ± 1 [0-3]

7.74 ± 1.92a-c

[4.80-13.12]

14 80 ± 36ab

[24-185] 10 ± 5 [1-20]

5.94 ± 0.47a-c

[5.08-7.28]

15 454 ± 132ab

[180-816] 9 ± 3 [2-13]

4.24 ± 0.51c

[3.62-5.77]

16 699 ± 554ab

[17-2338] 3 ± 1 [0-6]

4.23 ± 0.44c

[3.53-5.50]

Table 1. Average ± SE and [range] of Enterococcus (MPN/100 mL), C.

perfringens (CFU/100 mL), and δ15N in macroalgal tissue (‰) for shoreline stations at Puakō, Hawaiʻi. Superscript letters indicate significant groupings from One-way ANOVA and post-hoc Tukey’s tests. α = 0.05; n = 4.

20

N Source n δ15N in NO3- NO3

- + NO2- NH4

+ PO43-

Cesspools 3 10.45 ± 0.58 20.7 ± 10.5 6370 ± 806 378.6 ± 16.6 Soil 3 2.13 ± 2.37 6366.7 ± 3682.5 595 ± 93 193.6 ± 141.6

Ocean 2 3.02 ± 0.79 1.4 ± 0.1 3 ± 1 0.1 ± 0.1 High elevation

groundwater wells 3 4.76 ± 0.43 93.9 ± 4.4 5 ± 1 2.5 ± 0.2

Low elevation groundwater wells

7 7.03 ± 0.50 130.1 ± 6.7 5 ± 1 2.5 ± 0.5

Shoreline 3 11.95 ± 1.13 133.9 ± 64.7 n/a n/a

Table 2. Average ± SE of δ15N - NO3- (‰) and NO3

- + NO2-, PO4

3-, and NH4+ concentrations

(µmol/L) of N sources collected in the Puakō watershed. (n = sample size)

21

Station NO3- + NO2

- NH4+ TDN PO4

3- TDP H4SiO4 Salinity

1 27.87 ± 4.09b-e

[18.10-36.79] 20.83 ± 0.15 [0.78-1.23]

41 ± 7c-f

[25-58] 0.44 ± 0.04fg

[0.33-0.51] 0.70 ± 0.12fg

[0.51-1.04] 133 ± 23a-c

[87-195] 27.58 ± 1.44a-c

[23.63-30.37]

2 149.94 ± 12.79ab

[129.62-187.09] 0.49 ± 0.11 [0.18-0.72]

159 ± 13ab

[139-195] 2.24 ± 0.24a-d

[1.62-2.73] 2.86 ± 0.26a-e

[2.21-3.45] 581 ± 155ab

[187-876] 7.12 ± 0.61e [5.77-8.70]

3 137.12 ± 35.39a-c

[36.22-190.37] 1.95 ± 0.30 [1.04-2.29]

154 ± 39a-c

[41-217] 3.81 ± 0.92ab

[1.34-5.37] 4.28 ± 0.72ab

[2.42-5.09] 377 ± 124a-c

[112-646] 16.26 ± 3.96b-e [9.50-25.73]

4 196.05 ± 28.14a

[125.66-263.07] 1.3 ± 0.05 [1.24-1.47]

221 ± 26a

[153-267] 7.42 ± 1.11a

[4.12-9.0] 8.25 ± 1.36a

[4.45-10.84] 501 ± 113ab

[172-683] 15.25 ± 2.30c-e

[9.10-20.20]

5 46.92 ± 8.73a-e

[23.44-65.52] 1.32 ± 0.16 [0.86-1.57]

70 ± 12a-f

[42-87] 1.34 ± 0.17b-f

[0.90-1.71] 1.74 ± 0.28b-f

[0.90-2.13] 179 ± 41a-c

[85-278] 24.98 ± 2.35a-d

[19.70-31.07]

6 26.78 ± 11.48de

[2.50-54.16] 1.22 ± 0.10 [1.03-1.46]

44 ± 16d-f

[23-86] 0.66 ± 0.21e-g

[0.25-1.17] 0.85 ± 0.22fg

[0.25-1.26] 95 ± 43c

[22-219] 30.77 ± 2.31a [24.53-35.53]

7 134.56 ± 54.94a-d

[42.27-285.74] 1.69 ± 0.65 [0.46-2.90]

131 ± 43a-d

[53-241] 3.08 ± 0.44a-c

[2.12-3.83] 3.41 ± 0.50a-c

[2.19-4.51] 447 ± 132ab

[164-804] 21.98 ± 0.97a-d [19.87-24.03]

8 39.15 ± 14.53c-e

[0.99-67.10] 2.40 ± 0.97 [1.00-0.33]

59 ± 19b-f

[12-99] 0.70 ± 0.23e-g

[0.52-1.07] 1.01 ± 0.21e-g

[0.56-1.55] 253 ± 83a-c

[31-416] 20.60 ± 4.90a-d

[14.10-35.17]

9 69.74 ± 9.06a-e

[47.81-91.92] 1.00 ± 0.33 [0.89-1.77]

85 ± 7a-e

[74-105] 1.37 ± 0.13b-f [1.15-1.73]

1.80 ± 0.17b-f

[1.48-2.30] 342 ± 90a-c

[219-609] 15.28 ± 2.31cd [8.53-18.53]

10 56.72 ± 17.48a-e

[11.59-94.94] 0.95 ± 0.27 [0.47-1.51]

73 ± 19b-f

[20-106] 1.14 ± 0.31c-g

[0.34-1.84] 1.48 ± 0.16b-f

[1.18-1.84] 354 ± 76a-c

[129-445] 15.03 ± 3.60de [4.90-21.90]

11 16.52 ± 1.21de

[14.08-18.73] 0.96 ± 0.30 [0.18-1.45]

29 ± 4ef

[23-41] 0.49 ± 0.04e-g

[0.40-0.58] 0.76 ± 0.22fg

[0.25-1.33] 108 ± 27bc

[53-173] 28.30 ± 0.93ab

[26.07-30.60]

12 35.80 ± 4.37a-e

[25.62-46.59] 1.34 ± 025 [0.78-1.88]

46 ± 5b-f

[34.2-55.6] 0.99 ± 0.11c-g

[0.40-1.31] 1.26 ± 0.29c-g

[0.91-2.11] 260 ± 105a-c

[112-568] 24.50 ± 0.96a-d [22.57-27.13]

13 34.89 ± 4.73a-e [22.54-44.18]

1.21 ± 0.19 [0.73-1.56]

49 ± 7b-f

[35-67] 1.64 ± 0.28b-e

[0.91-2.29] 1.89 ± 0.17b-f

[1.66-2.38] 207 ± 23a-c

[167-267] 23.96 ± 2.00a-d [19.90-28.27]

14 89.08 ± 5.48a-d [75.93-101.22]

1.15 ± 0.29 [0.64-1.54]

101 ± 7a-d

[84-117] 2.61 ± 0.17a-c

[2.22-2.98] 2.91 ± 0.27a-d

[2.35-3.61] 652 ± 174a

[359-1018] 6.43 ± 0.63e [5.33-8.07]

15 13.37 ± 2.80e

[5.73-19.24] 1.07 ± 0.17 [0.75-1.44]

22 ± 3f

[15-27] 0.39 ± 0.09g

[0.16-0.55] 0.57 ± 0.21g

[0.25-1.12] 120 ± 24a-c

[52-158] 29.94 ± 0.70a [28.67-31.27]

16 38.5 ± 7.2a-e

[17.35-47.44] 0.63 ± 0.31 [0.18-1.51]

46 ± 4c-f

[34-52] 0.81 ± 0.13d-g

[0.45-1.09] 1.14 ± 0.30d-g

[0.60-1.99] 323 ± 86a-c

[142-552] 17.13 ± 3.44b-e [7.94-24.53]

Table 3. Average ± SE and [range] of NO3- + NO2

-, NH4+, TDN, PO4

3-, TDP, H4SiO4 concentrations (µmol/L), and salinity for shoreline stations at Puakō, Hawaiʻi. Superscript letters indicate significant groupings from One-way ANOVA and post-hoc Tukey’s tests. α = 0.05; n = 4.

22

Sewage Indicator Weight Factor

Low (1)

Medium (2)*

High (3) Reference

C. perfringens 3 0 – 10 11 - 100 101 – 505+ Fung et al. 2007 δ15N in macroalgae 3 +2 - +7 -5 - +1.9 +7 - +20 Wiegner et al. 2016

Enterococcus 2 0 - 35 36 - 104 105+ HDOH 2014 NO3

- + NO2- 1 0 – 0.4 0.5 – 1 1.1 – 1.8+ HDOH 2014

NH4+ 1 0 – 0.25 0.26 – 0.61 0.61 – 1.07+ HDOH 2014

TDP 1 0 – 0.7 0.8 – 1.3 1.4 – 1.9+ HDOH 2014

Table 4. Indicators (FIB = CFU/100 mL, δ15N = ‰, and nutrients = µmol/L) used to evaluate water quality health along the Puakō coastline. Sewage indicators were ranked (low = 1, medium = 2, high = 3), multiplied by a weight factor, and summed for a final sewage pollution score. * “Medium” nutrient concentration ranks exceed HDOH standards.

23

Figure 1. FIB, nutrients, δ15 N in macroalgae and water quality paraemeters were taken from 16 stations along the Puakō coastline, Hawaiʻi, USA (black circles). Dye tracer tests were conducted in proximity to the stations with squares. Nitrogen sources sampled included cesspools, high and low elevation groundwater wells, and soil.

Up-mountain

Cesspool

High elevation groundwater wells

Low elevation groundwater wells

Soil

24

0

2

4

6

8

10

12

14

δ15

N(‰

)

*

*Ap <0.0001

1

10

100

1000

10000

Ente

roco

ccus

(MP

N/1

00

mL)

*Bp =0.04

0

5

10

15

20

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16C. p

erfr

ing

ens

(CF

U/1

00

mL)

Cp =0.06

Figure 2. Average ± SE of sewage indicators: (A) δ15 N (‰) of residential macroalgae, (B) Enterococcus, and (C) C. perfringens at 16 shoreline stations at Puakō, Hawaiʻi. Single sample maximum for Enterococcus (104 CFU/100 mL) and a recommended standard for C. perfringens (5 CFU/100 mL) is indicated by black lines. Dashed black line respresents non-point sewage pollution (10 CFU/100 mL). Results from One-way ANOVA and Tukey’s tests are shown on figure, with * indicating significant differences (α = 0.05).

Station

25

-2

0

2

4

6

8

10

12

14

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

δ1

5N

(‰

)

Station

Soil

Ocean

High elevation GW

Low elevation GW

Sewage

Fertilizer

Figure 3. Average ± SE δ15 N (‰) of residental macroalgae found at 16 stations in Puakō, Hawaiʻi. Background areas represent (average ± SE) δ15 NO3

- of the N sources (fertilizer, soil, ocean, high elevation groundwater wells, low elevation groundwater wells, and sewage) measured as part of this study. Fertilizer values are from previous sewage study on Hawaiʻi Island (Wiegner et al. 2016).

26

Figure 4. Mixing plots of nutrient concentrations along the Puakō shoreline, Hawaiʻi: (A) NO3

- + NO2-, (B) NH4+, (C) TDN, (D) PO4

3-, (E) TDP, and (F) H4SiO4. Line represents theoretical mixing line, connecting freshest and saltiest shoreline samples. Groundwater samples were only analyzed for NO3

- + NO2-, NH4

+, and PO43- (orange triangles).

0.0

2.0

4.0

6.0

8.0

10.0

12.0

0 10 20 30

TD

P (

µm

ol/L

)

E

0

50

100

150

200

250

300

0 10 20 30

TD

N (

µm

ol/L

)

C

0.0

2.0

4.0

6.0

8.0

10.0

0 10 20 30

PO

43-(µ

mol

/L)

D

0

200

400

600

800

1000

1200

0 10 20 30

H4S

iO4

(µm

ol/L

)

F

0.0

50.0

100.0

150.0

200.0

250.0

300.0

350.0

0 10 20 30

A

0

1

2

3

4

5

6

0 10 20 30

NH

4+ (u

mol

/L)

B

NO

3- + N

O2-

(µm

ol/L

)

Salinity (‰)

Salinity (‰)

Station 3

Station 4

Station 7

Other stations

GW Wells

27

Figure 5. Correlation between C. perfringens and NH4+along the

Puakō shoreline, Hawaiʻi.

0

1

2

3

4

5

6

0 5 10 15 20 25 30

NH

4+(µ

mol

/L)

C. perfringens (CFU/100 mL)

p =0.02r =0.28

28

0.0

50.0

100.0

150.0

200.0

250.0

300.0

350.0

NO

3-+

NO

2-(µ

mol

/L)

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

10.0

PO

43-(µ

mol

/L)

0

50

100

150

200

250

300

0 2 4 6 8 10 12 14

TD

N (

µm

ol/L

)

δ15 N (‰)

Figure 6. Correlations between δ15 N in macroalgal tissue and nutrient concentrations: (A) NO3

- + NO2-, (B) PO4

3-, and (C) TDN along the Puakō shoreline, Hawaiʻi.

A p <0.001 r =0.47

B p <0.001 r =0.61

C p <0.001 r =0.37

29

Figure 7. Sewage pollution scores for 16 stations along Puakō’s shoreline were created based on established and recommended water quality standards and literature values for sewage indicators (FIB, δ15 N in macroalgae, and nutrients). Sewage pollution score catergories are: Low = 11 - 15; Medium = 16 - 20; High = 21 - 30.

Medium

Low

High

30

Literature Cited

Abbott IA, Huisman JM (2004) Marine green and brown algae of the Hawaiian Islands. Honolulu, Hawaii: Bishop Museum Press, Honolulu, Hawaii

Abbott IA (1999) Marine red algae of the Hawaiian Islands. Bishop Museum Press, Honolulu, Hawaiʻi

Bisson JW, Cabelli VJ (1979) Membrane filter enumeration method for Clostridium perfringens. Appl Environ Microbiol 37:55-66

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Spatial distribution of sewage pollution on a Hawaiian coral reef

Chapter 2

37

Abstract

Sewage pollution has been shown to affect both human health and nearshore ecosystems,

and is especially a concern in tropical regions with coral reefs. Puakō, located on Hawaiʻi Island,

is one area of concern because sewage pollution has been detected along on its shoreline;

however, the spatial distribution of sewage pollution offshore in surface and benthic waters is

unknown. This study examined the spatial extent of sewage pollution using algal bioassays and a

combination of sewage indicators (fecal indicator bacteria (FIB), stable nitrogen isotopes (δ15N)

in macroalgal tissue, nutrients). FIB counts and nutrients were spatially variable in both surface

and benthic waters, with shoreline concentrations exceeding water quality standards. δ15N in

macroalgal tissue, along the shoreline were the most enriched and within the range for sewage.

However, sewage indicators were not always in agreement on the location and intensity of

pollution. To assess water quality with regards to these indicators, a sewage pollution score was

created, allowing for locations of sewage pollution hotspots to be identified. This approach for

identifying sewage pollution hotspots is valuable for other coastal communities with documented

sewage pollution so that appropriate management actions can be taken to improve water quality,

and reduce human and ecosystem health hazards.

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Introduction

Worldwide, sewage pollution impacts coastal waters. Sewage can enter coastal waters

from accidental spills from sewage treatment plants, injection wells, and leaks from cesspools

and septic tanks (Hunt 2006, Knee et al. 2008, Zimmerman 2010). Sewage pollution introduces

pathogens (bacteria and viruses), nutrients, hydrocarbons, toxins, and endocrine disruptors to

nearshore waters, threatening human health and coastal ecosystems (USEPA 2012 1990, Wear &

Thurber 2015). Over 120 million gastroenteric illnesses have been reported globally within a

year in relation to sewage-contaminated coastal waters (Shuval 2003). Exposure to sewage,

either from ingestion, inhalation, and direct contact, may result in hepatitis and skin and urinary

tract infections (Pinto et al. 1999, USEPA 2011). The United States Environmental Protection

Agency (USEPA) estimates that nationwide 61% of small communities (<10,000 people) use

cesspools or septic tanks for sewage disposal (USEPA 2012), which can contaminate

groundwater and nearshore waters with untreated sewage (Bruno et al. 2003; Friedlander et al.

2005; Paytan et al. 2006; Fung et al. 2007; Boehm et al. 2010).

Coral reefs are one coastal ecosystem particularly sensitive to sewage pollution. Sewage

affects the prevalence and severity of coral disease (Sutherland et al. 2010; Vega et al. 2014). In

addition, high levels of sewage-derived nutrients can alter coral calcification rates, reshape

microbial communities, and affect coral symbionts (Wear & Thurber 2015). Nutrient enrichment

has also been shown to trigger elevated levels of algal growth and phytoplankton blooms,

resulting in eutrophication (Hunter & Evans 1995; Lapointe et al. 2005; Paytan et al. 2006;

Rabalais et al. 2009). This increase in algal growth can cause detrimental ecological shifts on

coral reefs, such that macroalgal density increases and fish abundance and coral cover decreases

(Reopanichkul et al. 2009; Wear & Thurber 2015).

Measuring levels of fecal indicator bacteria (FIB) is a widely used method to detect

sewage pollution in coastal waters. FIB are used to assess the potential risk to human health in

recreational waters. Enterococcus is the most commonly used FIB in marine waters, but it has

been found to naturally thrive in tropical soils due to ideal temperatures and rich nutrient

substrates, and can multiply in seawater (Hardina & Fujioka 1991; Byappanahalli & Fujioka

1998; Byappanahalli & Fujioka 2004). Thus, in tropical states like Hawaiʻi, Clostridium

perfringens has been adopted as an additional FIB for sewage pollution because it does not

39

multiply in aerobic seawater (Davies et al. 1995; Fung et al. 2007). In addition, Enterococcus has

been shown to be an indicator of recent sewage contamination, whereas C. perfringens reflects

continuous inputs (Curiel-Ayala et al. 2012), and thus, both FIB should be used concurrently to

assess sewage pollution.

In addition to FIB, some studies measure stable nitrogen (N) isotopes (δ15N) in

macroalgal tissue to detect sewage in coastal environments (Umezawa et al. 2002; Savage 2005;

Hsing-Jun Lin et al. 2007;Wiegner et al. 2016). Stable N isotopes are used to determine N

sources to coastal waters as N sources can have distinct isotopic signatures, and macroalgal

tissues reflect the N they uptake due to their minimal discrimination between 14N and 15N during

uptake (Savage 2005). For example, treated sewage gets highly enriched due to denitrification

with δ15N values for NO3- ranging from +7‰ to +20‰, differing from fertilizers (-5‰ to +5‰),

soil NO3- (-10‰ to +15‰), seawater NO3

- (+2‰ to +7‰), and atmospheric N2 (0‰) (reviewed

in Wiegner et al. 2016). Often, the macroalgal species chosen in sewage-related studies as

bioindicators are opportunistic ones whose growth is stimulated by increases in N (Littler &

Littler 1980; Dailer 2010; Dailer 2012). Ulva fasciata is a common species used because it is

commonly found along the shoreline and has rapid nutrient uptake rates (Abbott & Huisman

2004). More recently, the use of plant tissues to evaluate sewage pollution (macroalgal

bioassays) was established to examine sewage within water bodies, and to encompass offshore

transport patterns (Costanzo et al. 2001; Costanzo et al. 2005.) This technique has been used to

map improvements in water quality following sewage treatment plant upgrades (Costanzo et al.

2005), nearshore sewage plumes (Dailer et al. 2010; Dailer et al. 2012), nutrient inputs from

local fish farms (Garcia-Sanz et al. 2010; Garcia-Sanz et al. 2011), and to detect cruise ship

wastewater inputs (Kaldy et al. 2011).

Usually, studies focus on a single indicator to assess sewage pollution in coastal waters,

with only a few studies using two or more (Shibata et al. 2004; Knee et al. 2008; Dailer et al.

2010; Lenart-Boron et al. 2015; Yoshioka et al. 2016). Using a single indicator to assess sewage

pollution may be misleading as different indicators can vary greatly over time and space, and

different factors affect different indicators differently. For example, studies that have evaluated

both FIB Enterococcus and C. perfringens to monitor sewage pollution found that Enterococcus

was more likely to exceed recreational water quality standards in more locations compared to C.

40

perfringens (Griffin et al. 1999; Shibata et al. 2014; Weisz 2014). Thus, beach closures and

advisories would be more likely if Enterococcus levels were used instead of C. perfringens. In

addition, monitoring δ15N in macroalgal tissue has been shown to be highly variable depending

on contributing N sources in the area (Ochua-Izaquirre & Soto-Jimenez 2015). Therefore,

measuring more than a single sewage indicator is vital for accurately assessing sewage pollution.

Furthermore, most studies that have sampled FIB and δ15N in macroalgae have been restricted to

the shoreline (Bonkosky et al. 2009; Dailer et al. 2010; Shibata et al. 2010; Yoshioka et al.

2016). However, sewage can be transported offshore or enter coastal waters through benthic

seeps. For example, elevated Enterococcus levels that are indicative of sewage have been

detected nearly a mile offshore (Weisz 2014). Sewage has also been detected in both offshore

and benthic waters using δ15N in macroalgal tissues (Dailer et al. 2012; Dailer et al. 2010). These

studies highlight that determining the spatial distribution of sewage offshore and in the benthos is

critical to improving water quality for humans and ecosystems.

Hawaiʻi is one state dealing with human and coral reef health threats from sewage

pollution. Hawaiʻi uses cesspools more widely than any other state in the United States (USEPA

2013), and a ban on new installments were only recently signed into state legislation (2015). The

greatest number of cesspools in Hawaiʻi State are located on Hawaiʻi Island, totaling ~49,000

(Whittier & El-Kadi 2014). Puakō, a coastal community located in the South Kohala district on

Hawaiʻi Island, is a known “area of concern” in the state. It is considered a high-risk area, where

the presence of onsite sewage disposal systems (OSDS) can affect nearshore environments

(Whittier & El Kadi 2014), and it has also been designated by Hawaiʻi State as a priority site for

site-based action due to its rich diversity of corals (Hayes et al. 1982; NOAA 2010). However,

over the last 40 years, decreases in coral coverage and fish abundances have been documented

(Minton et al. 2012). Increases in coral disease prevalence and severity have also been

documented (Couch et al. 2014; Yoshioka et al. 2016). These ecosystem changes are thought in

part to be due to sewage pollution.

To understand the spatial distribution of sewage pollution offshore of the Puakō

coastline, a combination of techniques were used. To assess the offshore spatial distribution of

sewage pollution in surface and benthic waters, FIB and nutrients were measured, and bioassays

quantifying δ15N in macroalgal tissues were deployed. Only a handful of macroalgal bioassay

41

studies have examined sewage pollution offshore in surface and benthic waters, and even fewer

that used both FIB in combination with macroalgal bioassays (Dailer et al. 2010; Dailer et al.

2012; Yoshioka et al. 2016). The presence of sewage along the coast of Puakō has been

documented (Yoshioka et al. 2016; Chapter 1 thesis); however, the spatial extent of sewage

offshore in surface and in the benthos was not determined. Sewage pollution is a global concern

and assessing the presence of sewage in coastal waters is vital to human and ecosystem health

(Wear & Thurber 2015). This study assessed whether sewage was present offshore in surface and

benthic waters. Other coastal communities worldwide will be able to use this approach to

determine the spatial distribution of sewage offshore, allowing them to accurately monitor water

quality and develop solutions and management tools to improve environmental conidtions for

both human and coastal ecosystems health.

Methods

Site Description

The Puakō community has more than 200 homes along the coast (<0.001 km) and relies

heavily on cesspools (~47) and septic tanks (~139) for wastewater disposal (Schott 2010). The

Puakō area is mostly comprised of basalt from the Mauna Loa Volcano flow in 1859, and thus,

infiltration is high at the ground’s surface due to the porous nature of the substratum. Puakō

receives 250 – 750 mm of rainfall annually and groundwater discharge ranges from 2083 to 2730

L m-1 h-1 (Paytan et al. 2006). The hydrological connectivity between cesspools and the

nearshore environment in Puakō ranges from 9 h to 3 d (Chapter 1 thesis). The shoreline

substratum consists mostly of turf algae, basalt with little limestone, and coral coverage that

increases with increasing distance from the shoreline; however, turf algae is the most dominant

benthic species in both shallow and deeper benthic regions.

Study Design

To determine the spatial extent of sewage pollution offshore, as well as possible inputs

from benthic seeps that could directly impact the coral reefs, water was sampled for FIB and

nutrients. Additionally, the green macroalga, Ulva fasciata, was deployed during bioassays for

δ15N analysis at five stations (Fig. 1). These stations encompassed three zones (shoreline, bench,

and slope) and two depths (surface and benthic) (Fig. 1). Benthic zones were chosen based on

physiography features. The bench zone was ~7 m deep, and ~196 m from the shoreline. The

42

slope zone was ~15 m in depth, and ~267 m from the shoreline. The bench and slope zones were

~65 m apart. Collection of water samples and algal cage deployments were conducted in June

and July 2015. There was one sample collection and cage deployment per month.

FIB & Nutrient Analyses

Water samples were collected once during each deployment at all zones and depths, and

analyzed for FIB, nutrients, and salinity. FIB Enterococcus was analyzed using the Enterolert

MPN method (IDEXX Laboratories Inc) and C. perfringens was quantified using a membrane

filtration technique (Bisson & Cabelli 1979). Water samples for nutrient analysis were filtered

through pre-combusted (500°C for 6 hours), 0.7-µm pore size GF/F filters (WhatmanTM) and

stored frozen until analysis at the University of Hawaiʻi at Hilo (UHH) Analytical Laboratory.

Nutrients in water samples were analyzed on a Pulse TechniconTM II autoanalyzer using standard

methods and reference materials (NIST; HACH 307-49, 153-49, 14242-32, 194-49). These

samples were analyzed for NO3- + NO2

- [Detection Limit (DL) 0.07 µmol L-1, USEPA 353.4],

NH4+ [DL 0.36 μmol L-1, USGS I-2525], PO4

3- [DL 0.03 μmol L-1, Technicon Industrial Method

155-71 W], total dissolved phosphourous (TDP) [DL 0.5 μmol L-1, USGS I-4650-03], and

H4SiO4 [DL 1 μmol L-1, USEPA 366]. Total dissolved nitrogen (TDN) was analyzed by high-

temperature combustion, followed by chemiluminescent detection of nitric oxide (DL 5 μmol L-

1, Shimadzu TOC-V, TNM-1). Salinity was assessed using an YSI Pro 2030.

Ulva fasciata Deployments & Analysis

Ulva fasciata (Chlorophyta) was collected locally and acclimated to low nutrient water to

ensure N stores within the thalli were depleted prior to deployments. A sample of the U. fasciata

was collected and preserved as a voucher for identification. Preliminary studies determined that

Instant-Ocean (33‰-35‰) was the most suitable low-nutrient medium, with an acclimation

period of 3 d for U. fasciata (Fig. 2), during which water was changed every 24 h. Thalli of U.

fasciata were deployed in netted plastic cages (mesh size ~ 5 mm x 5 mm) creating a protective

barrier from herbivores, while allowing sunlight and water movement within the cages. These

cages were incubated in a grid-like pattern at the three zones for seven days offshore of the

Puakō coastline (Fig. 1). Within each zone, excluding the shoreline, three cages containing U.

fasciata were placed at two depths: ~1 m near the surface with subsurface buoys (surface) and

three above the reefs (~1 m) secured by weights (benthic) (Fig. 1). Approximately 4 g of U.

43

fasciata were rinsed with reagent-grade water, spun-dried, and weighed before being placed in

cages. In addition, to determine if there was a difference between deployed U. fasciata and

nearby in situ macroalgae, wild algae were also collected at all benthic zones during the June

deployment.

Following deployments, U. fasciata and adjacent wild algae were retrieved, rinsed with

reagent-grade water, dried to 60° C until a constant weight was achieved, ground, and

homogenized using a Wig-L-Bug grinding mill. For stable isotope analysis, 2 mg of the

macroalgal tissues were folded in 4x6 mm tin capsules. Macroalgal tissues were analyzed for

δ15N using a Thermo-FinniganTM Delta V Advantage isotope ratio mass spectrometer (IRMS)

with a Conflo III interface and a CostechTM ECS 4010 Elemental Analyzer located at the UHH

Analytical Laboratory. Data were normalized to USGS standard NIST 1547. Isotopic signatures

were expressed as standard (δ) values, in units of parts per mil (‰), and calculated as: [(Rsample –

Rstandard) / Rstandard] x 1000, where R = 15N/14N.

Statistical Analyses

To examine differences among sewage indicators among zones, general linear models

(GLM) were used. One model examined surface water patterns extending from the shoreline to

offshore. The second model examined benthic patterns from the shoreline to offshore. To

determine differences between depths, a nested GLM was used, where depth was nested within

zones to account for variability at each station. In addition, to determine differences between

initial and final δ15N in U. fasciata, a one-way analysis of variance (ANOVA) was used with

initial values compared with shoreline, as well as surface and benthic offshore zones. δ15N in U.

fasciata was also compared to N sources that were taken in a companion study (Chapter 1

thesis). To determine if differences in δ15N existed between adjacent wild macroalgae and

deployed U. fasciata, a two-sample t-test was used. Data were tested for normality and equal

variances; if assumptions for parametric analyses were not met, log, log + 1, and rank

transformations were used (Potvin & Roff 1993). Statistical analyses were conducted using

Minitab 16TM (α=0.05).

44

Results

Surface and Benthic Waters Spatial Patterns

Enterococcus counts were similar among zones in surface waters (p =0.29; Fig. 3);

however, counts did differ significantly among benthic zones (p =0.04; Fig. 3). The greatest

differences in the benthos were detected between shoreline (302 MPN/100 mL ± 306) and slope

(35 MPN/100 mL ± 22) zones, which were almost an order of magnitude different. In contrast,

C. perfringens differed significantly among zones in both surface (p =0.01) and benthic waters (p

<0.01). In surface waters, the largest differences were detected between shoreline (8 CFU/100

mL ± 4) and slope (2 CFU/100 mL ± 1) zones (Fig. 3B). Shoreline C. perfringens counts were

also significantly higher (8 CFU/100 mL ± 4) compared to benthic bench (1 CFU/100 mL ± 1)

and benthic slope (1 CFU/100 mL ± 0.22) waters (Fig. 3E). Nutrient concentrations (NO3- +

NO2-, NH4

+, TDN, PO43-, TDP, and H4SiO4) were highest on the shoreline in both surface (p

<0.02) and benthic (p <0.01) waters (Table 1). Nutrient concentrations among zones in surface

and benthic waters were similar between bench and slope zones. Salinity concentrations also

varied among zones in both surface (p<0.01) and benthic waters (p<0.01), with the shoreline

having the freshest (lowest) value (18.52 ± 3.08). δ15N in U. fasciata varied significantly by zone

in both surface (p =0.01) and benthic waters (p<0.01) (Fig. 3). Shoreline values were the highest

(5.61‰ ± 0.77), followed by slope (surface = 4.32‰ ± 0.34, benthic = 4.18‰ ± 0.34), and bench

(surface = 3.92‰ ± 0.46, benthic = 3.71‰ ± 0.29). Both δ15N for surface and benthic U. fasciata

samples fell within the δ15N - NO3- range for soil, seawater, and low elevation groundwater at all

zones determined in a companion study (Chapter 1 thesis) (Fig. 4).

Surface vs. Benthic Waters

No differences in depth were detected among sewage indicators: Enterococcus (p >0.43),

C. perfringens (p >0.10), nutrient concentrations [NO3- + NO2

- (p >0.71), NH4+ (p >0.17), TDN

(p >0.29), PO43- (p >0.66), and TDP (p >0.32)], and δ15N in U. fasciata (p >0.08). H4SiO4

concentrations did vary, with the greatest differences detected between surface waters at the

bench and benthic waters at the slope (p <0.01). Salinity levels between surface and benthic

waters were also similar (p >0.90).

45

Initial vs. Post Deployment δ15N values

Initial (3.49‰ ± 0.44) and post-deployment δ15N U. fasciata values differed (p <0.01),

with the greatest differences occurring at the shoreline (5.69‰ ± 0.23)(Fig. 5). The slope zone in

surface waters (4.32‰ ± 0.17) and benthic waters (4.19‰ ± 0.21) showed smaller differences in

initial and post deployment δ15N, followed by the bench zone in surface waters (4.08‰ ± 0.45)

and benthic waters (3.83‰ ± 0.22).

Wild Macroalgae vs. Deployed U. fasciata

Adjacent wild collected macroalgae had δ15N values similar to those deployed in the

benthic zones; however, differences were detected at the shoreline. δ15N in wild algae in the

bench zone ranged from -0.57‰ to +4.02‰, with an average of +2.90‰ ± 1.96. Deployed U.

fasciata in the bench zone ranged from +3.23‰ to +4.27‰, with an average of +3.83‰ ± 0.49.

In the slope zone, δ15N in wild algae ranged from +3.48‰ to +8.92‰ (+6.09‰ ± 2.31) and

deployed U. fasciata ranged from +3.50‰ to +4.78‰ (+4.19‰ ± 0.48). However, no significant

difference in δ15N values between wild algae and deployed U. fasciata were observed (p =0.58).

At the shoreline, wild algae ranged from +5.07‰ to +10.18‰, with an average of +7.75‰ ±

1.25. Deployed shoreline U. fasciata ranged from +3.37‰ to +7.27‰, with an average of

+5.61‰ ± 1.08. Significant differences between wild macroalgae and deployed U. fasciata were

also detected (p =0.01). In both cases at the shoreline, the highest δ15N values were observed at

the same station (station 2).

Discussion

FIB counts are often spatially variable within a water body, with highest levels generally

observed closer to the shoreline (Paul et al. 1995; Griffen et al. 1999; Shibata et al. 2004;

Bonkosky et al. 2009; Lisle et al. 2014). Similarly, our FIB counts were spatially variable in both

surface and benthic waters. Enterococcus concentrations in the surface waters were similar

across zones. However, in the benthic waters, Enterococcus concentrations were significantly

higher along the shoreline compared to bench and slope zones. Clostridium perfringens in both

surface and benthic waters were greatest at the shoreline, with the greatest differences being

between shoreline and slope zones. The spatial variability observed at Puakō is similar to FIB

spatial differences reported for other water bodies (Shibata et al. 2004). For example, in the

Florida Keys, Enterococcus and C. perfringens levels were highly variable according to distance

46

from the shoreline, with the highest counts being reported nearshore (Paul et al. 1995). Similar

patterns in Hawai’i have also been observed, where high counts of Enterococcus were

consistently seen over a 1.6 km from shore, and C. perfringens counts were highest nearshore

(Weisz 2014).

FIB concentrations at the shoreline exceeded established and recommended recreational

water quality standards in Hawaiʻi. Enterococcus counts in surface waters at all three zones

(inclusive of SE) exceeded the Hawaiʻi Department of Health (HDOH) single sample maximum

(104 CFU/100mL). At the shoreline, counts were three times greater than HDOH’s single sample

maximum. The benthic waters at the shoreline and bench zones also exceeded the HDOH

maximum, suggesting these waters may be impaired. Additionally, in surface and benthic waters,

C. perfringens counts at the shoreline exceeded recommendations of 5 CFU/100mL for marine

recreational waters, and fell above the Fung/Fujioka scale for non-point source sewage

contamination levels (> 10 CFU/100mL) (Fujioka et al. 1997; Fung et al. 2007). This suggests

that the shoreline may be polluted by sewage.

Similar to FIB, nutrient concentrations were observed to be spatially variable among

zones and between depths. Nutrient concentrations were highest at the shoreline and decreased

with increasing distance from shore. In the Guangzhou Sea, China, a similar inshore to offshore

nutrient gradient was seen in an area with significant sewage inputs (Peng & Liangmin 2010).

Similar spatial nutrient patterns have been observed in Hilo Bay, Hawaiʻi, a known impaired

water body (Weisz 2014). Furthermore, in a similar sewage study on Hawaiʻi Island, in which

nutrient concentrations were assessed in tide pools adjacent to homes with cesspools, a

comparable decreased level onshore to offshore was observed (Wiegner et al. 2016). Compared

to other studies conducted in Hawaiʻi, NO3- + NO2

- values were 10x greater than those values

observed on Kauaʻi in Hanalei Bay, an area known for sewage contaminated groundwater (Knee

et al. 2008). In addition, our nutrient data were consistently higher than Hilo Bay and Hanalei

Bay, with some nutrient concentrations nearly five times greater at the shoreline. In addition,

Puakō nutrient concentrations (NO3- + NO2

-, NH4+, PO4

3+) were the highest that have been

reported, and in some cases, 60x greater than those previously reported for Puakō (Knee et al.

2010). Salinity levels indicated the freshest seeps occurred near the shoreline where nutrients are

highest, suggesting that freshwater may be a source of nutrients.

47

Elevated shoreline δ15N macroalgal values in comparison to offshore sites have been

documented in many locations impacted by sewage (Yoshioka et al. 2016; Dailer et al. 2010;

Chapter 1 thesis). Lower values offshore probably reflect smaller direct inputs of sewage near

benthic seeps or dilution of nearshore inputs. In this study, the shoreline had the highest δ15N

macroalgal values, followed by slope and bench zones in both surface and benthic waters. This is

similar to a study conducted in Maui, which found δ15N in macroalgal tissue to be most enriched

near a wastewater reclamation facility (WWRF), decreasing with increasing distance from the

shoreline and the WWRF (Dailer et al. 2010). However, our study indicated a slight gradient, in

which shoreline was significantly more enriched compared to bench and slope zones. This

similar gradient trend was seen in Hanalei Bay, Kauaʻi, highlighting the shoreline as a potential

hotspot compared to offshore sites (Derse et al. 2007). In contrast, a study detected fish farm

waste up to 2450 m offshore in Murcia, Spain, using macroalgal bioassays (Garcia-Sanz et al.

2011), suggesting fish farm waste was transported offshore. However, Puakō results indicate that

sewage transport offshore may not be occurring and is limited.

δ15N macroalgal values were found to be fall in the range for soil, seawater, and low

elevation groundwater. In addition, sewage transport offshore seems to be limited. The shoreline

δ15N values in U. fasciata may be underestimated by up to ~6‰ due to the high nutrient

concentration environment (NO3- concentrations >10 μmol/L). Under these conditions,

discrimination between 15N and 14N during nutrient uptake is enhanced (Swart et al. 2014). If this

occurred in our study, then all of our shoreline δ15N macroalgal values fall within local sewage

range. At several locations along the Puakō shoreline, the presence of sewage was confirmed

through dye tracer studies in which sewage hotspots were identified (Chapter 1 thesis). These are

thought to occur due to local geological features such as fractures and lava tubes. Input of

sewage at the shoreline could be a reason why nutrient concentrations are highly elevated

compared to offshore zones and other coastal locations within the state of Hawaiʻi.

Similarities between depths among sewage indicators were found in this study. A

previous study found sewage to rise from the benthos to surface waters, reflecting a stronger

δ15N signal in surface waters, and to follow a general direction away from the sewage source

(Dailer et al. 2012). However, during large wave mixing events, sewage was detected in the

benthos more so than the surface waters (Dailer et al. 2010). In contrast, no differences among

48

sewage indicators between depths were detected in our study. δ15N values in U. fasciata in

surface waters fell within the range of low elevation groundwater, which may be contaminated

with sewage given the numerous OSDS above the wells (Chapter 1 thesis). In contrast, the

benthic δ15N values in U. fasciata were within the range of a soil source. These differences could

be due in part to the macroalgae being exposed to different light levels and nutrient

concentrations and forms (NH4+) between two depths, as these parameters have been shown to

influence the growth rates in macroalgae, and therefore their capability to uptake N (Xu et al.

2014). The amount of available light at our deep depths could have also influenced the ability of

the macroalgae to uptake N. During deployments macroalgae were suspended near the benthos at

15 m in the slope zones. The ability of light to reach these deeper areas may not have been as

significant compared to those on the surface and may have influenced our δ15N values. However,

past studies have found Ulva spp. capable of taking up N as they were exposed to different N

forms and concentrations (Gartner et al. 2002; Dailer et al. 2010), which may indicate why δ15N

in U. fasciata values significantly increased from initial δ15N values compared to post-

deployment values.

Similar to chapter 1, our results found that sewage indicators provided conflicting

information on the intensity and location of sewage pollution among zones and depths. To date,

various sewage-related studies have examined a single sewage indicator to assess sewage

pollution such as FIB (Fujioka & Shizumura 1985; Fung et al. 2007), macroalgal bioassays using

δ15N (Kaldy 2011; Dailer et al. 2010; Dailer et al. 2012; Garcia-Sanz et al. 2010), and/or

combination of macroalgal bioassays using δ15N and FIB (Moynihan et al. 2012; Yoshioka et al.

2016), without assessing nutrient data. This study demonstrated that elevated levels of FIB,

nutrients, and δ15N in U. fasciata along the shoreline were indicative of sewage. However,

patterns among sewage indicators were not consistent. To assess potential sewage hotspots, a

sewage pollution score developed in a companion study was employed (Chapter 1 thesis). This

scoring system used the following sewage indicators according to water quality standard and/or

literature values for sewage contamination: Enterococcus (104 CFU/100 mL), C. perfringens (10

CFU/100 mL), δ15N in macroalgae tissue (> +7‰), and nutrients (NO3- + NO2

-, NH4+, TDP).

These indicators were then weighted based on their reliability as sewage indicators and used in

the following equation to determine a sewage pollution score for each station. Sewage pollution

49

score = (C. perfringens level x 3) + (δ15N macroalgae level x 3) + (Enterococcus level x 2) +

(NO3-+NO2

- level x 1) + (NH4+ level x 1) + (TDP level x 1) [Eq. 1]. Sewage pollution score

categories were: ‘low’ = 11-15, ‘medium’ = 16-20, ‘high’ = 21-30.

The shoreline zone fell within the low and medium sewage pollution scores, with stations

1 and 3 being potential hotspots (Fig. 6). This is similar to the results from Chapter 1 of this

study. However, in the first part of the study, majority of the shoreline stations fell within a high

sewage pollution score. Offshore, the majority of the stations fell within a low sewage pollution

score category with only stations 1 and 4 possibly having a sewage pollution contamination (Fig.

6). This holistic approach identified potential shoreline sewage pollution hotspots, and provides

additional information to the community about areas where onshore and offshore sewage

pollution may exist. In addition, our data in combination with coral health data can assist in

examining relationships between sewage and disease prevalence and severity.

Conclusion

An approach using multiple indicators is needed to detect sewage pollution in coastal

waters to accurately assess water quality conditions for both human and coral reef health. This is

the first study that employed the use of FIB, δ15N in macroalgae, and nutrients to assess the

spatial distribution of sewage offshore and in the benthos of a coral reef. However, because

sewage indicators didn’t agree on the locations and intensity of sewage pollution, a sewage

pollution score was utilized. From this, we were able to identify areas of concern in Puakō, due

to possible sewage pollution. By combining our sewage pollution score with coral disease and

prevalence we may be able to establish a link between them. Using multiple techniques to assess

sewage pollution will provide other coastal communities around the world an application to

holistically determine spatial patterns of sewage pollution and to identify areas in need of

management action to improve water quality for human and ecosystems health.

50

Figure 1. Location of water sample collection (for FIB and nutrients) and algal cage deployments (for δ15N in U. fasciata) in Puakō, Hawaiʻi. Water and macroalgal samples were taken at three zones (shoreline, bench, deep) to determine the spatial extent of sewage pollution in surface and benthic waters offshore, Diagram of algal cage deployment design is shown in lower right corner of figure.

Benthic Weight

Buoy

Surface 1 m

1 m

51

0

1

2

3

4

5

6

7

1 2 3 7 14 21 28

Oce

an

Wate

r δ

15

N (

‰)

Time (days)

B - Ocean Water

0

1

2

3

4

5

6

7

1 2 3 7 14 21 28

Inst

an

t O

cean

δ1

5N

(‰

)

Time (days)

A - Instant Ocean

Figure 2. Changes in δ15N of U. fasciata during purging experiments with Instant Ocean (A) and ocean water (B).

52

Ben

thic

Wat

ers

0

1

2

3

4

5

6

7

Shoreline Bench Slope

δ15N

(‰

)

C p =0.01 a

b b

1

10

100

1000

Shoreline Bench Slope

En

tero

cocc

us*

(MP

N/1

00 m

L)

Ap =0.29

0

2

4

6

8

10

12

14

Shoreline Bench Slope

C. P

erfr

ing

ens

(CF

U/1

00 m

L)

B p =0.01 a

ab b

Sur

face

Wat

ers

0

1

2

3

4

5

6

7

Shoreline Bench Slope

δ15N

(‰

)

Fp <0.01 a

b b

1

10

100

1000

Shoreline Bench Slope

Ente

roco

ccus*

(MP

N/1

00 m

L)

Dp =0.04 a

ab

b

0

2

4

6

8

10

12

14

Shoreline Bench Slope

C. p

erfr

ing

ens

(CF

U/1

00 m

L)

Ea

b b

p <0.01

Figure 3. Average ± SE of sewage parameters (A, D) Enterococcus (*logged scale), (B, E) C. perfringens, and (C, F) δ15N in U. fasciata

collected within three zones (shoreline, bench, slope) in both surface and benthic waters in Puakō. Black lines represent the HDOH single sample maximum for Enterococcus (104 CFU/100 mL) and Fujioka’s recommendation (1997) for C. perfringens in marine recreational waters (5 CFU/100mL). Dashed lines represent non-point source sewage contamination level of 10 CFU/100 mL for C. perfringens (Fung et al. 2007) Results from GLM and Tukey’s test are shown, with different letters indicating significant differences (α =0.05). FIB n =10. Sample size varied for δ15N in U. fasciata in both surface waters (shoreline, n =9; bench, n =6; slope, n =10) and benthic waters (shoreline, n =9; bench, n =8; slope, n =10).

53

-2

0

2

4

6

8

10

12

δ1

5N

(‰

)

Benthic Surface

Soil

Ocean

Low Elevation GW

High Elevation GW

Sewage

Fertilizer

Shoreline Bench Slope

Surface Benthic

Figure 4. Average ± SE δ15 N (‰) of U. fasciata deployed within three benthic zones (shoreline, bench, slope) in Puakō, Hawaiʻi. Background areas represent average ± SE of δ15 N – NO3

- of the N sources taken in a companion study at Puakō (Chapter 1 thesis) and fertilizer from another study on Hawaiʻi Island (Wiegner et al. 2016). Surface samples are represented by grey triangles and benthic samples by black circles.

54

Figure 5. Average ± SE δ15 N (‰) of U. fasciata pre-(initial) and post-deployments within three benthic zones (shoreline, bench, slope) and two depths (surface and benthic) in Puakō, Hawaiʻi. GLM was used and shared lettering indicates no significant differences in Tukey’s post hoc test. Sample size varied (initial, n =11; shoreline, n =5; surface bench, n =4; surface slope, n =5; benthic bench, n =5; benthic slope, n =5). α =0.05.

0

1

2

3

4

5

6

7

Initial Shoreline Bench Slope Bench Slope

δ1

5N

(‰

)

p <0.01

b

a

ab abb

b

Surface Benthic

55

Figure 6. Map of sampling stations at Puakō with their sewage pollution scores. Larger circles represent benthic stations and smaller circles represent surface stations. Scores were created based on established and recommended water quality standards and literature values for sewage indicators. Sewage pollution scores represent the following categories: Low = 11 – 15; Medium = 16 – 20; High = 21 – 30.

Low

Medium

High

56

Zone NO3- + NO2

- NH4+ TDN PO4

3- TDP H4SiO4 Salinity Shoreline 66.87 ± 11.47a

[11.59 – 139.72] 1.52 ± 0.16a [0.18 – 3.05]

73 ± 11a [21 – 121]

1.67 ± 0.22a [0.47 – 2.56]

1.98 ± 0.22a [0.70 – 3.25]

439 ± 74a [154 – 617]

18.52 ± 3.08a

[3.78 – 29.63] Surface

Bench 1.43 ± 0.26b [0.83 – 1.84]

0.57 ± 0.14b [0.18 – 1.56]

10 ± 1b [8 – 12]

0.14 ± 0.03b [0.02 – 0.27]

0.64 ± 0.13b [0.25 – 1.23]

7 ± 3b

[1 – 21] 33.26 ± 1.11b

[29.95 – 34.47] Slope 1.23 ± 0.18b

[0.40 – 2.14] 0.38 ± 0.11b [0.18 – 1.06]

9 ± 1b [7 – 13]

0.12 ± 0.02b [0.02 – 0.24]

0.59 ± 0.11b [0.25 – 0.96]

5 ± 1b

[1 – 11] 34.24 ± 0.41b

[33.75 – 34.62] Benthic

Bench 1.10 ± 0.13b

[0.53 – 2.06] 0.50 ± 0.12b [0.18 – 1.23]

10 ± 1b [7 – 13]

0.18 ± 0.05b [0.02 – 0.49]

0.58 ± 0.11b [0.25 – 0.94]

2 ± 1b

[1 – 5] 33.55 ± 0.95b

[31.03 – 35.0] Slope 1.57 ± 0.51b

[1.01 – 6.09] 1.10 ± 0.53ab [0.18 – 5.58]

9 ± 1b [7 – 13]

0.24 ± 0.11b [0.02 – 1.13]

0.94 ± 0.29b [0.25 – 3.25]

1 ± 0b

[1 – 1] 34.46 ± 0.30b

[34.22 – 34. 85]

Table 1. Average ± SE and [range] of nutrient concentrations (μmol/L) and salinity for surface and benthic water samples among zones (shoreline, bench, slope) in Puakō, Hawaiʻi. A GLM was used to determine differences among zones and between depths, and superscript letters indicate grouping from post hoc Tukey’s test. α = 0.05; n = 10.

57

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