Prevalence of Cryptosporidium, Giardia, Salmonella, and ... · Canada goose fecal samples were collected from 5 peri-urban and 7 urban sites throughout the Greater Columbus, Ohio

Prevalence of Cryptosporidium, Giardia, Salmonella, and Cephalosporin-Resistant E. coli Strains in Canada goose Feces at Urban

and Peri-Urban Sites in Central Ohio

THESIS

Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University

By

Laura E. Binkley

Graduate Program in Environment and Natural Resources

The Ohio State University

2015

Thesis Committee:

Robert Gates, PhD Advisor

Michael S. Bisesi, PhD

Stephen Matthews, PhD

Copyright by

Laura E. Binkley

2015

ii

ABSTRACT

Large populations of resident geese can pose a pathogen exposure hazard and

disease risk to humans and animals in urban areas. Evidence suggests that waterfowl play

a role in pathogen dissemination and disease transmission to humans, however, more

definitive data are often needed. This exploratory study sought to identify potential

exposure hazards, the first step in risk assessment. This research also discusses dose-

response for protozoan organisms and initiated the exposure assessment process by

measuring environmental variables that may be associated with exposure. A total of 199

Canada goose fecal samples were collected from 5 peri-urban and 7 urban sites

throughout the Greater Columbus, Ohio area. Samples were collected during two time

periods: during 4-11 June, 2013 when geese had just begun their molt, and 16-30 August,

2013 after geese regained flight. Juveniles were distinguished from adults only during the

first sample period. Antigen capture enzyme-linked immunosorbent assay (ELISA) was

used to detect presence of Giardia and Cryptosporidium. Selective media were used to

culture Salmonella and cephalosporin-resistant E. coli strains. Cryptosporidium was the

most prevalent pathogen with 44.7% of samples testing positive. Feces collected from

urban sites during the first period were 1.86 times more likely to be positive for

Cryptosporidium than peri-urban sites (P = 0.10). Forward model selection methods

determined that prevalence was positively associated with human population density

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surrounding collection sites, proportion of each site defined as pasture by the National

Land Cover Database (NLCD), and distance of each site from nearest wastewater

treatment plant (WWTP). Feces collected from urban sites during the second period were

no more likely to be positive for Cryptosporidium than peri-urban sites (P = 1.00, Odds

Ratio= 1.00). Forward stepwise model selection determined that prevalence was

positively associated with human population density within study sites, distance of each

site from nearest livestock farm, and distance of each site from nearest wastewater

treatment plant.

Giardia was present in only 3.5% of samples. None were positive for Giardia in

the first period. Feces collected from urban sites during the second period were 1.9 times

more likely to be positive for Giardia than peri-urban sites (P = 0.74). Prevalence of

cephalosporin-resistant E. coli strains was 10.8%. Only one positive was detected during

the first period. Feces collected from urban sites during the second period were 6.7 times

more likely to be positive for cephalosporin-resistant E.coli than peri-urban sites (P =

0.10). No Salmonella was detected in the fecal samples. All pathogens tested, with the

exception of Salmonella, showed a similar trend where a greater percentage of positives

were detected at urban sites than peri-urban sites.

The exposure potential of goose feces to the human population appears to be high

for Cryptosporidium but low for Giardia and Salmonella. The discovery of

cephalosporin-resistant strains of E.coli in fecal samples poses a potential exposure

hazard because antibiotic-resistant strains are difficult to treat if infection occurs.

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ACKNOWLEDGEMENTS

I want to thank my primary advisor Dr. Robert J. Gates for his time, patience, guidance, and support as well as for taking me on as an advisee and giving me the chance to conduct a successful wildlife study.

I also want to thank the highly regarded members of my thesis committee Drs. Michael S. Bisesi and Dr. Stephen N. Matthews for their additional support and guidance. I would especially like to thank Dr. Michael S. Bisesi for making this project possible and allowing me the use of his lab and resources.

I would like to thank Dr. Thomas E. Wittum for offering me his lab, resources, and insight, so that I could add a whole new and exciting dimension to my project. I also thank him for his time when helping me with my analysis.

I thank Dr. Gabriel Karns for his time, patience, and guidance, when helping me carry out my GIS analysis.

Lastly, I would like to thank the Ohio State University Terrestrial Wildlife Ecology Laboratory for helping to provide me with the necessary resources to carry out my study.

5

VITA

2009.................................................. .B.S. Biology, The Ohio Wesleyan University

2014....................................................MPH at The Ohio State University

2011-2015...........................................SENR Graduate Student at The Ohio State University

PUBLICATIONS

Cunningham, D., DeBarber, A.E., Bir, N., Binkley. L., Merkens, L.S., Steiner, R.D., and Herman, G.E. (February, 2015). Analysis of Hedgehog Signaling in Cerebellar Granule Cell Precursors in a Conditional Nsdhl Allele Demonstrates an Essential Role for Cholesterol in Postnatal CNS Development. Human Molecular Genetic. First published online February 4, 2015 doi:10.1093/hmg/ddv042

FIELDS OF STUDY

Major Field: Environment and Natural Resources-Wildlife

Public Health- Environmental Health Sciences-Veterinary Preventive Medicine

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TABLE OF CONTENTS

Abstract. ..................................................................................................................... ii

Acknowledgments. ..................................................................................................... iv

Vita. ............................................................................................................................ v

List of Tables ............................................................................................................. ix

List of Figures ........................................................................................................... xi

Chapter 1- Introduction………… .............................................................................. 1

Goose Population Trends in Ohio. ................................................................. 5

Life History .................................................................................................... 6

Migration…………. ........................................................................................ 7

Resident Populations ...................................................................................... 8

Human Conflicts With Resident Canada Geese ............................................ 10

Protozoa ........................................................................................................ 11

Cryptosporidium ........................................................................................... 13

Giardia ................................................................................................................17

Bacteria ......................................................................................................... 21

Antimicrobial Resistance .............................................................................. 22

Pathogen Transmission Environments. ......................................................... 23

Literature Cited. ............................................................................................ 25

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Chapter 2- Prevalence of Cryptosporidium, Giardia, Salmonella, and Cephalosporin-Resistant E. coli Strains in Canada goose Feces Urban and Peri-Urban Sites in Central Ohio. ................................................................ 35

Introduction .................................................................................................. 35

Study Objectives and Hypotheses. ............................................................... 37

Study Sites ..................................................................................................... 41

Sample Collection. ........................................................................................ 46

Pathogen Detection ....................................................................................... 51

Immuno Assay. .................................................................................. 51

Bacteria Culture ................................................................................ 52

Data Analysis ................................................................................................ 53

Age, Landscape, and Season. ........................................................... 53

Environmental Factors ...................................................................... 55

GIS Analysis ..................................................................................... 56

Results .......................................................................................................... 58

Pathogen Prevalence ........................................................................ 58

Age, Landscape, and Season ............................................................ 58


Discussion .................................................................................................... 69

Pathogen Prevalence .................................................................................... 69

Age, Landscape, and Season ............................................................ 76


Future Research and Management Recommendations. .................... 87

Literature Cited ................................................................................ 91 CHAPTER 3- HAZARD IDENTIFICATION SUMMARY .................... 102

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Literature Cited. ............................................................................. 109

BIBLIOGRAPHY ...................................................................................... 112

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LIST OF TABLES

Table 2.1 Land cover categories selected from the National Land Cover Database Program Legend. .......................................................................................... 42

Table 2.2 Proportion of each land cover type within sites where Canada goose

fecal samples were collected in the greater Columbus area during the molt and post-molt periods in the year 2013. ....................................................... 43

Table 2.3 Descriptions for sites where Canada goose fecal samples were collected

during the molt and post-molt periods in the year 2013 ............................... 44

Table 2.4 Number of resident Canada goose fecal samples collected per site by period in the greater Columbus area during the molt and post-molt periods in the year 2013 ........................................................................................... 49

Table 2.5 Pathogen prevalence in Canada goose fecal samples collected in the

greater Columbus area during the molt and post-molt periods in the year 2013 by period .............................................................................................. 59

Table 2.6 Percentage of Cryptosporidium positives in Canada goose fecal samples

collected in the greater Columbus area during the year 2013 out of number of samples collected per site for Period 1 and Period 2 .................................... 61

Table 2.7 Percentage of Giardia and cephalosporin-resistant E. coli positives in

Canada goose fecal samples collected in the greater Columbus area during the year 2013 out of number of samples collected per site for period 2 ....... 61

Table 2.8 Summary prevalence for Cryptosporidium, Giardia, and cephalosporin-

resistant E. coli by age and landscape variables for Canada goose fecal samples collected in the greater Columbus area during the molt and post -molt periods in 2013 for Period 1 and Period 2 .......................................... 62

Table 2.9 Environmental variable measurements and odds ratios by site for Period

1 and Period 2 Cryptosporidium data from Canada goose fecal samples collected in the greater Columbus area during the molt and post-molt periods in the year 2013 ............................................................................... 65

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Table 2.10 Univariate linear regression results for variable vs. odds ratio during period 1 and period 2 for Cryptosporidium data collected from Canada goose fecal samples collected in the greater Columbus area during the molt and post-molt periods in the year 2013 ................................................ 66

Table 2.11 Linear regression output for Cryptosporidium first and second period best-fit models from Canada goose fecal samples collected in the greater Columbus area during the molt and post-molt periods in the year 2013 ... 67

Table 2.12 Prevalence of Giardia and Cryptosporidium in Canada goose fecal samples among studies ................................................................................. 71

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LIST OF FIGURES

Figure 1.1 Cryptosporidium life cycle with summary (Center for Disease Control and Prevention. 2013. Cryptosporidium. Parasites. Retrieved 2014 from: http://www.cdc.gov/parasites/crypto/ biology.html). ......................................................................................... 16

Figure 1.2 Giardia life cycle with summary (Center for Disease Control and

Prevention. 2011.Giardia. Parasites. Retrieved 2014 from: http://www.cdc.gov/parasites/Giardia/). ....................................................... 20

Figure 2.1 Study sites used for resident Canada goose fecal sample collection

during the molt and post-molt periods in the Greater Columbus area during the year 2013. ............................................................................... 45

Figure 2.2. Univariate linear regressions for best-fit model variables of

Cryptosporidium odds ratio vs. a.) Period 1 human population density b.) Period 2 human population density c.) Period 1 proportion pasture d.) Period 2 distance to livestock farm e.) Period 1 distance to WWTP f.) Period 2 distance to WWTP in Canada goose fecal samples collected in the greater Columbus are during the molt and post-molt periods in the year 2013. ........................................................................ 68

http://www.cdc.gov/parasites/crypto/%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20biology.html)...........................................................................................16

http://www.cdc.gov/parasites/crypto/%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20biology.html)...........................................................................................16

http://www.cdc.gov/parasites/Giardia/)


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CHAPTER 1

INTRODUCTION

Resident Canada goose (Branta canadensis maxima) populations have increased

eight-fold in North America over the last 20 years where they concentrate in urban areas

and have close contact with human populations (Clark 2003). Large populations of

resident geese in urban areas can pose an exposure hazard and disease risk to humans and

animals. Potential risks stem from large populations of resident geese that produce large

quantities of fecal matter that accumulates in urban areas used by humans. This raises

concern considering the potential for fecal-oral pathogen transmission via direct or

indirect routes. Canada geese serve as reservoirs and vectors for numerous pathogens

including Giardia, Cryptosporidium, Salmonella, Avian Influenza, Campylobacter jejuni,

Listeria, Legionella, Newcastle Disease, E. coli, and a variety of other pathogens and

microbes (Clark 2003). This thesis addresses prevalence of enteric pathogens including

Cryptosporidium, Giardia, Salmonella, and E. coli in Canada goose fecal samples

collected throughout the Greater Columbus area during the early molt period (4-11 June)

and late post-molt period (16-30 August) in the year 2013. Juveniles were distinguished

from adults during the first period but not the second due to a more fully developed

immune system by August which could have introduced confounding.

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The role of Canada geese in transmission of antimicrobial resistant strains of

bacteria is another human health concern. Of particular interest is the role of Canada

geese in transmission of cephalosprin-resistant E. coli strains. Close contact with

livestock may result in transmission to wildlife populations, thus contributing to the

spread of resistant strains to humans. Evidence in many cases suggests the importance of

waterfowl in pathogen dissemination and pathogen transmission, however quantitative

data are often lacking and more data are needed before any quantitative risk assessments

are possible (Clark 2003).

Thus far, information on the molecular diversity of Canada goose fecal microbiota

is scarce, although Lu (2009) hypothesized that waterfowl gut microbial communities

differ from other birds. Fogarty and Voytek, (2005) found lower levels of the anaerobic

bacterial group, Bacteroides-Prevotella in geese, chickens, and gulls compared to cows,

dogs, horses, humans, deer, and pigs. That microbial composition of waterfowl feces

remains poorly studied is a barrier for developing methods to distinguish goose fecal

sources from other animal fecal sources (Lu et al. 2009). This becomes important for

source-tracking studies to identify causes of contamination in the water supply and food

systems. More studies are needed to better assess what risk of exposure the public may

incur in areas inhabited by geese (Clark 2003). More needs to be understood about

microbial communities and microbial loads in excrement from resident Canada geese.

My central hypothesis was that prevalence of enteric pathogens including

Cryptosporidium, Giardia, Salmonella, and cephalosporin-resistant E. coli strains is

higher in urban than peri-urban areas. This is based on accounts of resident Canada geese

feeding on human refuse and habituation to being hand-fed by humans, which could

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serve as an indirect route of transmission from humans to geese. There simply are more

opportunities for contact between humans and geese in urban areas. This is in addition to

exposure from contact with goose feces in water and grass as well as other wildlife,

domestic animals, and fomites. I also hypothesized that there will be higher pathogen

prevalence during the molt because resident geese often become more localized and

concentrated during the flightless period of their annual molt. My third major hypothesis

is that cephalosporin-resistant E. coli strains will be present in peri-urban Canada goose

fecal samples in the Greater Columbus area. This is thought to come from exposure to

livestock waste where antimicrobial resistance has previously been found (Cole et al.

2005, Wittum et al. 2010, Allen et al. 2011, Seiffert et al. 2013).

Seven urban sites and five peri-urban sites in the Greater Columbus area were

selected for study (Ohio Department of Natural Resources 2012c, Ohio Department of

Natural Resources 2013, and Columbus Metro Parks 2009a). Sites were confirmed as

being either urban or peri-urban based on Geographical Information System (GIS)

analysis in combination with U.S. census data from 2010 and an NLCD overlay (NLCD

2011, Environmental Systems Research Inst. 2011, U.S. Census Bureau 2010, U.S.

Geological Survey 2011).

This research was conducted to assist with hazard identification and

characterization (the first step in the risk assessment process) by determining prevalence

of specific pathogens in Canada goose fecal samples throughout the Greater Columbus

area during the molt and post-molt periods. It also discusses dose-response for protozoan

parasites from previous studies, and initiated the exposure assessment process by

measuring factors such as human population density within study sites, distance to

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potential sources of contamination, and time of year when exposure may be highest.

Future analysis should focus on quantifying dose-response and exposure. This may rely

on molecular methods such as PCR in order to create a dose-response model. In order to

confirm exposure, more variables that may be associated with pathogen exposure should

be measured, surveys should be conducted, behaviors should be observed and analyzed,

and epidemiological databases should be utilized.

My goal was to provide information that will improve understanding of the

ecology of zoonotic pathogens and the transmission pathways between human and animal

populations in addition to determining the effects of urbanization on pathogen

transmission. Ultimately this will facilitate identification of critical control points to

apply preventive measures. Results from this study can be used to guide management

decisions about the most effective ways to reduce exposure and transmission whether by

establishing ecological barriers or baiting with resins that bind bile (cholestyramine) to

prevent proliferation of pathogens (Erlandsen 2005).

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Goose Population Trends in Ohio

The Canada goose, a characteristic species of the Nearctic avifauna, is the most

widely distributed goose in North America (Mowbray et al. 2002). Canada geese thrive in

a broad range of habitats in temperate to low-arctic regions including tundra, boreal

forest, prairies and parklands, high mountain meadows, managed refuges and, of

particular significance here, areas of human habitation (Mowbray et al. 2002). Giant

Canada geese (Branta canadensis maxima) were rarely seen in Midwestern states

including Ohio in the early nineteenth century (Ohio Department of Natural Resources

2012a). This was the result of many years of egg harvesting, over-hunting, and habitat

loss from draining wetlands for crop production (Titchenell and Lynch 2010, Mowbray et

al. 2002). Population levels became so low that recovery programs were initiated.

The Ohio Division of Wildlife initiated a Giant Canada goose restoration program

in 1956 when 10 pairs were released on each of 3 state-owned wetland areas (Ohio

Department of Natural Resources 2012a). Canada geese were nesting in 49 of Ohio’s 88

counties in 1979 with a state population of 18,000 geese (Ohio Department of Natural

Resources 2012a). Recent surveys found goose nests in every county with a population

estimate of 84,000 geese (Ohio Department of Natural Resources 2012a). The Giant

Canada goose is now the most abundant subspecies of Canada goose in Ohio (U.S. Fish

and Wildlife Service 2012). Efforts to re-establish the giant Canada goose in Midwestern

states have been so successful that many of these birds have been translocated from areas

where high populations have become a nuisance to areas outside the original breeding

range of the species (Mowbray 2002). The result of this translocation has been an

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expansion southward of the natural range of giant Canada geese which now includes the

southern and southwestern United States (Nelson and Oetting 1998).

Life History

The resident Canada goose, (Branta canadensis maxima), forages primarily on

grasses, sedges, and berries on breeding grounds and on grasses and agricultural crops on

wintering areas (Mowbray et al. 2002). Additionally, geese are known to feed on human

food such as bread and popcorn in urban areas (Titchenell and Lynch 2010).

Branta canadensis nest individually or semi-colonially, preferring sites on small

islands in tundra lakes and ponds or on margins of lakes and rivers (Mowbray et al.

2002). Breeders form life-long monogamous pair bonds, generally during their second

year (Mowbray et al. 2002). Most begin nesting in substantial numbers as 2-yr-olds and

reach peak breeding in their fourth or fifth year (Mowbray et al. 2002). The nesting

season may start as early as mid-March or as late as June (U.S. Fish and Wildlife Service

2012). Average clutch size for female B. c. maxima is 5.6 eggs (Mobray et al. 2002). The

nest is a large open cup made of dry grasses and sedges, lichens, mosses, or other plant

material typically placed on the ground usually near water. The nest is commonly lined

with down and some contour feathers (Mowbray et al. 2002). Offspring are precocial,

however most remain with their parents throughout the first year of life, traveling

together in large flocks of family groups (Afton and Paulus 1992). Once the goslings

reach one year of age they join other yearlings and move to new areas such as the Arctic

barrens (Hanson 1965).

http://bna.birds.cornell.edu.proxy.lib.ohio-state.edu/bna/species/682/articles/species/682/biblio/bib275

7

Canada geese also molt all of their feathers once each year. Though seemingly

risky, this behavior has proven successful among many Anseriformes (Gates et al. 1993).

Molting usually occurs between June and late July followed by a 4-5 week flightless

period when they shed and re-grow their wing feathers. Most birds resume flight by

August (Mowbray et al. 2002). The rest of the summer is spent preparing for migration

which may start as early as late September or as late as December-January for late

migrants in the Mississippi Valley population (Gates et al. 2001). In general the geese

typically spend summers in northern North America and fly south when cold weather

limits availability of food resources and roosting sites (Mowbray et al. 2002).

Migration

The current study focuses primarily on the population of B. c. maxima in Ohio

which is included in the Mississippi Flyway. The Mississippi Flyway includes all

Canada geese nesting in Mississippi Flyway States (Alabama, Arkansas, Indiana, Illinois,

Iowa, Kentucky, Louisiana, Michigan, Minnesota, Mississippi, Missouri, Ohio,

Tennessee, and Wisconsin), the Canadian Provinces of Saskatchewan, Manitoba, and

Ontario as well as Canada geese nesting south of latitude 50°N in Ontario and 54° N in

Manitoba (U.S. Fish and Wildlife Service 2012).

The general fall migration pattern from the most northerly nesting areas begins in

late August and continues through September with most geese reaching the northern U.S.

by late September to early October. Those wintering farther south arrive at their

wintering grounds by early to mid-November (Mowbray et al. 2002). Spring migration

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begins around late January and peak arrival at breeding ground occurs in April (Mowbray

et al. 2002).

Molt migrations are undertaken mostly by first and second year nonbreeding

geese and adult geese that did not breed or that lose nests or broods. These migrations are

always northward to take advantage of plants in earlier phenological stages of growth and

larger water bodies (Mowbray et al. 2002). Giant Canada geese undertaking molt

migrations from breeding areas in the U.S. and southern Canada migrate in late May to

mid-June to the shores of Hudson and James Bays, between Québec and Manitoba, to

molt (Mowbray et al. 2002). Luukkonen et al. (2008) found that 80% of females in

Michigan whose nests were experimentally destroyed and 51% whose nests failed due to

natural causes undertook molt migrations. Of females with failed nests residing in urban

parks, only 23% migrated north to molt while >65% of females with failed nests in other

areas undertook molt migrations. The geese began to return to their former nesting areas

during late August through early September, with the majority migrating before or during

October (Luukkonen et al. 2008).

Resident Populations

According to the Code of Federal Regulations, the resident Canada goose

population is defined as Canada geese that nest within the lower 48 States and the District

of Columbia in the months of March, April, May, or June, or reside within the lower 48

States and the District of Columbia in the months of April, May, June, July, or August

(50 C.F.R. part 20 2007). This is in contrast to migrant populations that nest in arctic and

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subarctic breeding grounds (Giant Canada goose Committee 1996). Resident Canada

goose populations have increased eight-fold over the past 20 years in North America

(Clark 2003).

Resident geese include two subspecies, the giant Canada goose (B. c. maxima)

and the western Canada goose (B. c. moffitti), and possible hybrids between these and

other subspecies. These populations remain in one area year-round and undertake short-

distance or no migrations during spring and fall (U.S. Fish and Wildlife Service 2012,

Nelson and Oetting 1998). Groepper et al. (2008) found that the mean home range of

resident geese in Nebraska was ~25 km² and the mean maximum distance moved

between study areas of use over the course of 30 months was 13 km. Rutledge et al.

(2013) reported that the mean distance traveled by geese during the molt season (1 Jun-15

July) was 2 km for males and 2.1 km for females. They found that mean distance traveled

per goose was 4.8 km for males and 4.1 km for females during the post-molt (Rutledge et

al. 2013).

Human-modified landscapes provide resident geese with two main habitat

requirements: permanent bodies of freshwater and open areas where they can land, view

their surroundings, and are provided with abundant of vegetation for feeding and nesting

(Titchenell and Lynch 2010). Many urban and suburban areas contain bodies of water

and marshes with islands that provide safe nesting sites for geese (Gosser and Conover

1999). Resident geese have access to growing crops, pastures, lawn, waste grains, and

natural wetland vegetation before, during, and after nesting. They also benefit from

tender new grass growth stimulated by mowing, urban gardens that provide a variety of

native and exotic plants, and human handouts (U.S. Fish and Wildlife Service 2012,

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Conover 1998). Not only is food abundant, but resident geese have few natural predators.

Hunting is restricted in most urban areas where geese can seek refuge to avoid peak

harvest periods (U.S. Fish and Wildlife Service 2012, Nelson and Oetting 1998).

Human Conflicts With Resident Canada Geese

Large populations of resident geese in urban areas pose a potential threat to

environmental and human health. The majority of problems stem from the fact that large

populations of resident geese produce large quantities of fecal matter that accumulate in

urban areas where there is close contact with humans. Resident geese are frequently seen

near picnic areas, in parking lots, and anywhere where there is green vegetation to forage

on. This is cause for concern, considering the potential for fecal-oral pathogen

transmission to humans. Canada geese excrete about one dropping per minute when they

are feeding, producing nearly 907 g/day of feces (Dieter et al. 2001). Studies of urban-

suburban populations have implicated Canada goose feces in the eutrophication and

contamination of school yards, parks, and boating and swimming areas, introducing the

possibility of pathogen transmission to humans from direct contact with fecal material or

indirect contact with contaminated water (Conover and Chasko 1985, Feare et al.1999,

Allan et al. 1995).

Canada geese are known to serve as reservoirs and vectors for numerous

pathogens including Giardia, Cryptosporidium, Salmonella, Avian Influenza,

Campylobacter jejuni, Listeria, Legionella, Newcastle Disease, and E. coli, and a variety


11

of other pathogens and microbes (Clark 2003). The combination of exotic and domestic

waterfowl along with resident Canada geese in high densities within urban areas is

conducive to transmission of enzootic pathogens, such as Duck Virus Enteritis, and other

zoonotic pathogens such as Avian Influenza. Additionally, coliform bacteria counts

increase dramatically in sewage treatment plants when large numbers of Canada geese

are present and decline when geese are removed (U.S. Fish and Wildlife Service 2012).

The risk of humans contracting infections from Canada goose droppings depends

on: 1) presence of pathogenic organisms in feces, 2) survival of pathogens in feces after

deposition, and 3) deposition frequency and distribution of Canada goose droppings in

areas where humans are likely to contact them (Feare et al. 1999). An additional factor to

consider is the frequency of human contact. This study focuses on addressing the first

factor, presence of pathogenic organisms (Giardia, Cryptosporidium, Salmonella, and

cephalosporin resistant E. coli) in resident Canada goose feces. These pathogens can

survive for long periods in the environment. Giardia can survive for up to two months in

water while Cryptosporidium can survive up to 1 year (Graczyk et al.2008). Salmonella

can survive for weeks in water and years in soil, while E. coli can survive from days to

weeks in the environment (U.S. Environmental Protection Agency 2009).

Protozoa

Protozoan parasites such as Cryptosporidium parvum and Giardia lamblia are

human anthropozoonotic (animal to human transmission/direct zoonoses) pathogens that

inflict considerable morbidity (due to diarrheal disease) on healthy people and can cause

mortality in immunosuppressed individuals (Clark 2003, Graczyk et al. 2008). These

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pathogens are category B biodefense agents on the National Institutes of Health (NIH)

list because they are “moderately easy to disseminate, result in moderate morbidity and

low mortality rates, and require specific enhancements for diagnostic capacity and

enhanced disease surveillance” (National Institute of Allergy and Infectious Disease

2012). Giardia and Cryptosporidium are also the most common enteric parasites of

humans and domestic animals, and are increasingly recognized as parasites of a diverse

range of wildlife species (Hunter and Thompson 2005). The major routes of transmission

include ingestion of contaminated food and/or water and direct contact with infected

people, animals, or contaminated environmental surfaces (Kassa et al. 2004). Cases of

Giardia and Cryptosporidium in the human population tend to peak in early summer

through early fall, coinciding with the summer water-based recreational season (Xiao and

Fayer 2008, Yoder et al. 2012a).

A model of pathogen deposition by waterfowl in water was created by Graczyk et

al. (2008) to facilitate exposure assessment for recreational water users. This model

considers the number of fecal pellets deposited on a 1 x 100 m shore-line segment by a

single visitation of an average flock of waterbirds including gulls, geese, and ducks.

Using this model, researchers concluded that when one considers the average

concentrations of Giardia and Cryptosporidium shed in bird feces, an average waterbird

feces pellet weighing 17.2 g, and 8.6% of birds shedding human pathogens, a single

visitation by an average size flock of waterfowl can introduce approximately 9.3 x 106

infectious Cryptosporidium oocysts and 1.0 x 107 Giardia cysts to the water (Graczyk et

al. 2008, Kirschner et al. 2004). However, this model needs verification from research

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and only addresses exposure for recreational water users and not the human population as

a whole.

The circumstances under which transmission cycles interact to cause zoonotic

transfer of these protozoa is poorly understood, although zoonotic transmission of

Giardia and Cryptosporidium has been documented (Hunter et al. 2005, Graczyk et al.

2008). Evidence of human-to-animal transmission (zooanthroponotic or reverse

zoonoses) is found in the fact that resident geese can acquire Cryptosporidium hominis, (a

human-adapted genotype) oocysts from local garbage and other unhygienic places

(Graczyk et al. 1998, Zhou et al. 2004). Though these forms are not infective to geese, the

geese act as biological vectors. Additionally, geese can become exposed to pathogens

through livestock, domestic pets, and other wildlife.

Cryptosporidium

Cryptosporidium is one of the most frequent causes of waterborne disease among

humans (Centers for Disease Control 2013). The transmissive stage, the oocyst, is

environmentally resilient and therefore ubiquitous in the environment. Oocysts can retain

their infectivity for up to a year in water and can retain viability and infectivity after

freezing (Graczyk et al. 2008, Fayer and Nerad 1996). Cryptosporidium is also highly

chlorine-resistant (Yoder et al., 2012a). As a result, identification, removal, and

inactivation of oocysts in drinking water can be a challenge.

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The genus Cryptosporidium comprises 26 species and over 40 genotypes (Xiao

and Fayer 2008, Ryan et al., 2014a). As of 2011 there were 21 recognized species of

Cryptosporidium with several host groups susceptible to more than one species (Power et

al. 2011). Only C. hominis and C. parvum are of major significance to public health by

causing the majority of human cases (Zhou et al. 2004). However, other species and

genotypes have been associated with human illness as well (Ryan et al. 2014a). For

example, C. meleagridis is generally considered to be an avian pathogen and is

increasingly recognized as an important human pathogen (Appelbee et al. 2005). Birds in

general are known to be susceptible to infection with C. meleagridis, C. baileyi, and C.

galli along with 12 other genotypes (Ryan et al. 2014b). Five of these genotypes are

known as the “goose genotypes I-V.” Only Canada geese become infected by these

genotypes (Ryan et al. 2014b). Geese also act as biological vectors for other species such

as C. parvum (Graczyk et al. 1998). Oral inoculations of Canada geese with infectious C.

parvum oocysts have shown that the oocysts remain infective after passing through the

gastrointestinal tract (Graczyk et al. 1997).

Cryptosporidium parvum is the most widespread cause of cryptosporidiosis in all

mammals, in addition to being the most prevalent zoonotic species (Xiao and Fayer

2008). Two genotypes of Cryptosporidium parvum, the animal-adapted or zoonotic

genotype (also known as C. parvum senso stricto), and the human-adapted genotype (also

known as C. hominis), have been identified with molecular techniques (Xiao and Fayer

2008, Hunter et al. 2005). Both genotypes can produce life-threatening infections in

immunocompromised and immunosuppressed people (Graczyk et al. 1998). Although C.

parvum most often produces a self-limited diarrheal illness, symptoms can be severe and

15

last for several weeks. Kassa et al. (2001) reported that as few as 30 Cryptosporidium

oocysts can cause illness in a healthy adult human (DuPont et al. 1995). The mean

concentration of oocysts found in the average Canada goose fecal sample is 370 ± 197 (±

standard deviation) oocysts/g (Graczyk et al. 1998).

The Cryptosporidium lifecycle is complex (Figure 1.1). Oocysts must be ingested

or inhaled which is followed by excystation during which sporozoites are released and

attach to epithelial cells in the gastrointestinal tract or other tissues (Centers for Disease

Control 2013). Once this is achieved, asexual multiplication and then sexual

multiplication occurs producing microgamonts (male) and macrogamonts (female).

Oocysts develop after macrogamonts are fertilized by the microgamonts. Thick-walled

oocysts are generally excreted from the host while thin-walled oocysts are involved in

autoinfection. Oocysts are infective after excretion (Centers for Disease Control 2013).

Even though cryptosporidiosis has been a Nationally Notifiable Disease since

1995, estimates of prevalence in the human population vary greatly because of low

reporting rates, inconsistent diagnostic methods, and many people have no access to

medical care or do not seek it (Dietz and Roberts 2000, Xiao and Fayer 2008). The

largest outbreak of Cryptosporidium occurred in Milwaukee during spring 1993. An

estimated 403,000 people were affected and over 600 confirmed cases of gastrointestinal

illness were associated with the outbreak. The outbreak was caused by a failure in the

filtration system at a municipal water-treatment plant (MacKenzie et al. 1994). A total of

11,612 laboratory-confirmed cases of cryptosporidiosis were reported to the Centers for

Disease Control during 1995-1998 (Dietz and Roberts 2000). All ages and both sexes

16

Figure 1.1 Cryptosporidium life cycle with summary (Center for Disease Control and Prevention. 2013. Cryptosporidium. Parasites. Retrieved 2014 from: http://www.cdc.gov/parasites/crypto/biology.html).

http://www.cdc.gov/parasites/crypto/biology.html

17

were affected. An increase in case reporting was observed in late summer during each

year of surveillance for people <20 years of age (Dietz and Roberts 2000). The greatest

proportion of case patients were in the 0-9 year age group each year (Dietz and Roberts

2000). People younger than age 20 accounted for the highest percentages of cases during

early summer through early fall (Dietz and Roberts 2000). There were 3,505 cases

reported in the USA in 2003, 3,911 in 2004, and 8,269 in 2005 (Yoder and Beach 2007,

Xiao and Fayer 2008). The large increase in reporting in 2005 was primarily attributed to

a single recreational water-associated outbreak (Xiao and Fayer 2008). The most recent

available report from the Centers for Disease Control covers surveillance during 2009-

2010. There were 7,656 confirmed and probable cases of cryptosporidiosis reported in

2009 (2.5 per 100,000 population) and 8,951 confirmed and probable cases in 2010 (2.9

per 100,000 population) (Yoder et al. 2012a).

Giardia

Giardia, a Nationally Notifiable disease since 2002, is the most common human

intestinal parasitic infection reported in the United States (Centers for Disease Control

2014, Centers for Disease Control 2011). The disease is thought to be highly

underreported with an estimated 1.2 million infections occurring annually (Centers for

Disease Control 2014, Adams et al. 2012). The transmissive stage, or cyst, is tolerant to

chlorine disinfection and survivability of the cysts decreases as temperature increases

(Centers for Disease Control 2011, U.S. Environmental Protection Agency 2000). Cysts

can retain their infectivity for up to two months in water (Graczyk et al. 2008).

18

Giardia cysts are common in water and can be acquired by birds from the

environment (Graczyk et al. 2008). Graczyk et al. (2008) and Upcroft et al. (1997) found

that a single avian isolate of Giardia was virulent to mammals, indicating that birds can

serve as reservoir hosts in addition to being biological vectors. The average concentration

of Giardia sp. cysts found in a Canada goose fecal sample was 405 cysts/g (Gatczyk et al.

1998). Genotyping and subtyping data suggest that zoonotic transmission is not as

important in the epidemiology of giardiasis as in cryptosporidiosis (Xiao and Fayer

2008). Most types of Giardia are adapted to a single host species which reduces the

likelihood that zoonotic transmission pathways play a major role in human disease

(Hunter et al. 2005). However, zoonotic transmission has been documented and still

remains poorly understood (Hunter et al. 2005, Xiao and Fayer 2008).

There are currently 6 recognized species of Giardia based on cyst and trophozoite

morphology and host occurrence. These include Giardia agilis (amphibians), Giardia

ardeae (birds) Giardia psittaci (birds) Giardia duodenalis or lamblia (most mammals),

Giardia microti (voles and muskrats), and Giardia muris (rodents) (Xiao and Fayer 2008,

Appelbee et al. 2005). Giardia duodenalis is recognized to have a broader host range than

the other species. There are seven variants of G. duodenalis that are currently recognized,

each with a varying degree of host specificity (Xiao and Fayer 2008). Variants A and B

are often referred to as zoonotic variants because they have the ability to infect a broad

range of mammals, including humans. Variants C and D primarily infect dogs while

variant E infects livestock. The F variant infects cats while the G variant infects rodents

(Appelbee et al. 2005, Xiao and Fayer 2008).

19

Taxonomy has been the most controversial area of Giardia research due to its

wide host range and lack of association with morphological features to measure host

specificity (Thompson and Monis 2011). Molecular tools are only now helping to resolve

this uncertainty (Thompson and Monis 2011). Studies of Giardia in Canada geese have

mostly looked for Giardia spp. or Giardia lamblia. Although Giardia has been clearly

detected in Canada goose fecal samples (Kassa et al. 2001, Roscoe 2001, Kostroff and

Rollender 2001, Ayers et al. 2014, Binkley 2014 unpublished), it is unknown whether

there is a specific isolate that is able to infect and reproduce in Canada geese.

The Giardia lifecycle is relatively complex (Figure 1.2). Once cysts are ingested

by the host, excystation occurs and trophozoites are released into the small intestine

(Centers for Disease Control 2011). Trophozoites then multiply by longitudinal binary

fission and remain in the lumen of the proximal small bowel. Encystation occurs as the

parasites move toward the colon. Cysts are infectious upon excretion by the host (Centers

for Disease Control 2011).

Reporting of giardiasis to the Centers for Disease Control and Prevention

Surveillance Systems during 1998 - 2003 indicated that the total number of cases varied

between 20,075 and 24,226 annually in the USA (Xiao and Fayer 2008). The total

number of reported cases increased slightly from 19,239 for 2006 to 19,794 for 2007 and

decreased slightly to 19,140 in 2008 (Yoder et al. 2010). The total number of cases

increased from 19,403 for 2009 to 19,888 in 2010 (Yoder et al. 2012b). A larger number

of cases were reported for children ages 1-9 (Yoder et al. 2012b).

20

Figure 1.2 Giardia life cycle with summary (Center for Disease Control and Prevention. 2011.Giardia. Parasites. Retrieved 2014 from: http://www.cdc.gov/parasites/Giardia/)


21

Bacteria

Bacterial infections, such as Salmonella and E. coli are also a cause for concern.

Major routes of transmission for these two pathogens include ingestion of contaminated

food or water and direct contact with infected people, animals, or contaminated

environmental surfaces (National Institute of Allergy and Infectious Disease 2011a,

National Institute of Allergy and Infectious Disease 2011b).

Salmonella is a large group of rod-shaped, gram negative bacteria comprising

>2000 known serotypes in the family Enterobacteriaceae (Maier et al. 2009). All

serotypes are pathogenic to humans (Maier et al. 2009). As a gram negative bacterium,

its survival strategy relies on a cell envelope that facilitates interaction with mineral

surfaces and solutes in the environment to obtain nutrients required for metabolism

(Maier et al. 2009). Salmonellosis is the second leading cause of foodborne illness in the

USA. Most common symptoms include nausea, vomiting, abdominal cramps, diarrhea,

fever, and headache (Maier et al. 2009).

E. coli, also a gram-negative rod bacteria, is found in the gastrointestinal tract of

all warm-blooded animals and is usually considered to be harmless, although several

strains are capable of being pathogenic (Maier et al. 2009). These strains include:

enterotoxigenic, enteropathogenic, enteroinvasive, and entercohemorrhagic E .coli (Maier

et al. 2009). Enterohemorrhagic strains can cause renal failure and hemolytic anemia

(Maier et al. 2009).

22

Antimicrobial Resistance

The importance of Canada geese as reservoirs and sources of transmission of drug

resistant strains of bacteria is an important issue for pathogen management.

Antimicrobial-resistant organisms in domestic animals such as poultry, beef, and swine,

are well documented and these animals have been implicated as reservoirs for multidrug-

resistant foodborne pathogens (Cole et al. 2005). In particular, ESC-R E. coli (ESC-R-

Ec), ESC-R Salmonella (ESC-R-Sal) and MDR Acinetobacter spp. have been isolated

from farm, wild, and companion animals (Seiffert et al. 2013). The massive and

indiscriminate use of antibiotics, especially cephalosporins, has contributed to selection

and spread of multi-drug resistant organisms (Seiffert et al. 2013).

Cephalosporins are a group of antibiotics classified by four generations, with later

generations being more resistant to β-lactam destruction. The drug is rendered

ineffective when the β-lactam ring in the molecular structure is destroyed (Merck 2010-

2013). CTX-M extended-spectrum β-lactamases are enzymes produced by bacteria that

are capable of inhibiting the antimicrobial effects of cephalosporin drugs. Extended

spectrum cephalosporins (ESCs) are used to treat serious infections caused by pathogens

such as E. coli, Salmonellaspp. and Acinetobacter spp. (Seiffert et al. 2013). They are the

treatment of choice for invasive gram-negative infections (Wittum et al. 2010). CTX-M

type extended-spectrum β-lactamases from fecal E. coli of both sick and healthy dairy

cattle in Ohio were reported in 2010 (Wittum et al. 2010).

23

Bacterial resistance to β-lactam antibiotics including cephalosporins has risen

dramatically in human pathogens. The use of extended-spectrum cephalosporins in

human health institutions contributes to the emergence of resistance (Bradford et al.

1999). Interaction with waste materials from livestock species may transfer resistant

pathogens and genetic elements to free-ranging wildlife such as Canada geese, therefore

creating an additional environmental reservoir of resistant organisms (Cole et al. 2005).

Pathogen Transmission Environments

The potential for zoonotic transmission is largely influenced by the type of

environment along with the ecology of host organisms within that environment.

Therefore, it is useful to examine the structure of these environments to predict where

transmission may occur. Comparisons of urban and peri-urban environments may reveal

factors that provide the greatest potential for pathogen transmission. Urbanization is a

process where human habitation increases in a particular area along with increased per

capita energy consumption and extensive modification of the landscape. The result is a

system that does not depend principally on local natural resources to persist (McDonnell

et al. 1997).

The greater Columbus area contains a mixture of both urban and peri-urban

environments which provides ideal study conditions to compare pathogen prevalence in

these two environments. The study area allows comparisons of pathogen prevalence in

Canada goose feces between urban resident and peri-urban resident populations. One of

24

the major gaps in knowledge is the effect that urbanization has on zoonotic pathogen

transmission and what pathogens are present that can potentially be transmitted to

humans. It is important to establish what risk factors within the urban environment makes

some sites a higher risk for pathogen exposure than others. For example, a likely risk

factor for exposure to pathogens is high human density. This allows more opportunities

for exposure to occur.

25

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CHAPTER 2

PREVALENCE OF CRYPTOSPORIDIUM, GIARDIA, SALMONELLA, AND CEPHALOSPORIN-RESISTANT E.COLI STRAINS IN CANADA GOOSE FECES AT

URBAN AND PERI-URBAN SITES IN CENTRAL OHIO

Introduction

Resident Canada goose populations have increased eight-fold over the past 20

years in North America. These populations are concentrated in urban areas (Clark 2003)

resulting in resident geese becoming so numerous in some areas that they are considered

a nuisance (Nelson and Oetting, 1998).

Human-modified landscapes provide these birds with significant advantages. Two

main habitat requirements are easily fulfilled in urban environments, including a

permanent body of freshwater and an open area where they have a good view of their

surroundings and are provided with an abundance of vegetation for feeding and nesting

(Titchenell and Lynch 2010). Because these resident geese are able to remain in areas

associated with human activity and a longer growing season year round, they are

provided with a consistently available source of food (Nelson and Oetting 1998).

36

The major issue is that large populations of resident geese in urban areas pose a

threat to both environmental health, through eutrophication, and human health. The

majority of issues stem from the fact that large populations of resident geese result in

large quantities of fecal matter that accumulate in urban areas where there is close contact

with the human population. Studies of urban-suburban populations have implicated

Canada goose feces in the contamination of school yards, parks, and boating and

swimming areas thus introducing the possibility for pathogen transmission to humans

from either direct or indirect contact (Conover and Chasko 1985, Feare et al.1999, Allan

et al. 1995). Canada geese are known to serve as reservoirs and vectors for numerous

pathogens including Giardia, Cryptosporidium, Salmonella, Avian Influenza,

Campylobacter jejuni, Listeria, Legionella, Newcastle Disease, and E. coli, among other

pathogens and microbes (Clark 2003). This study focuses solely on Giardia,

Cryptosporidium, Salmonella, and cephalosporin resistant E. coli strains.

Cryptosporidium and Giardia cause self-limited diarrheal illness however

symptoms can be severe and last for several weeks. They can also cause life-threatening

infections in immunocompromised and immunosuppressed individuals (Graczyk et al.

1998). Zoonotic transmission of both Giardia and Cryptosporidium has been documented

(Hunter et al. 2005, Graczyk et al. 2008). Evidence of human-to-animal transmission

(zooanthroponotic /reverse zoonoses) can be found in the fact that residential geese can

acquire Cryptosporidium hominis, the human-adapted genotype, oocysts from local

garbage and other unhygienic places (Graczyk et al. 2008).

Presence of Salmonella and antimicrobial-resistant bacteria in Canada goose fecal

samples is also of great concern. Salmonella is one of the leading causes of foodborne


37

illness in the U.S. (Maier et al. 2009). Antimicrobial-resistant bacteria and genetic

elements are thought to be transferred to free-ranging wildlife, such as Canada geese,

through interaction with waste materials from livestock species therefore creating an

additional environmental reservoir of resistant organisms (Cole et al. 2005).

The potential for zoonotic transmission is largely influenced by the type of

environment as well as the ecology of the organisms within that environment. High

densities of humans in urban areas typically results in large-scale modification of the

environment and a tremendous concentration of food, water, energy, materials, sewage,

pollution, and waste in one area (McDonnell et al. 1997). Therefore, urbanization tends

to degrade environmental quality and disrupt natural processes.

Study Objectives and Hypotheses

This was an exploratory study that sought to assist with hazard identification (the

first step in the risk assessment process) by determining prevalence of specific enteric

pathogens in Canada goose fecal samples throughout the Greater Columbus area during

the molt and post-molt period. It also initiated the exposure assessment process by

measuring specific environmental variables that may be associated with exposure. My

central hypothesis was that if greater environmental contamination and more interactions

at the animal-human interface occur in urban locations (McDonnell et al. 1997, Zhou et

al. 2004, Graczyk et al. 2008) while rural sites have less contamination but more

interactions at the wildlife-livestock interface where antimicrobial resistance has been

known to occur (Cole et al. 2005, Wittum et al. 2010, Allen et al. 2011, Seiffert et al.

38

2013). Consequently prevalence of Giardia, Cryptosporidium, and Salmonella will be

higher at urban sites while prevalence of cephalosporin-resistant E. coli strains will be

higher at peri-urban sites. Prevalence of pathogens will be more concentrated at urban

sites during the molt period and will become more evenly distributed during the post-

molt period.

This study sought to provide information to explain several major gaps in

knowledge including: 1) The effects of urbanization and landscape factors on zoonotic

pathogen prevalence 2) The prevalence of pathogens and the molecular diversity of

Canada goose fecal microbiota, 3) The presence of antimicrobial-resistant bacteria in the

Canada goose population in the Greater Columbus area. The overall goal was to provide

information that will improve understanding of the ecology of infectious zoonotic

pathogens and the transmission pathways between human and animal populations as well

as to determine the effects of urbanization on pathogen transmission. Ultimately this will

allow critical control points for prevention to be identified. Study objectives and

hypotheses included:

Objective 1. Determine the prevalence of Giardia, Cryptosporidium, Salmonella, and

cephalosporin-resistant E. coli strains in resident Canada goose fecal samples collected

from the Greater Columbus area.

Hypothesis 1. If both Giardia and Cryptosporidium have an outer shell (cyst/oocyst) and

are environmentally robust (Centers for Disease Control 2011, Centers for Disease

Control 2013) while Salmonellaand E. coli have no outer protection, then there will be

39

higher prevalence of Giardia and Cryptosporidium than Salmonella and cephalosporin-

resistant E. coli strains observed in resident Canada goose fecal samples collected at

various sites throughout the Greater Columbus area.

Objective 2. Examine the effects of age, landscape, and season, on prevalence of the

selected organisms in resident Canada goose fecal samples.

Hypothesis 2a. If juvenile Canada geese lack a fully developed immune system during

the first collection period and have not had enough time to acquire resistance, then

prevalence of pathogens will be higher in juvenile geese than adult geese during the first

collection period.

Hypothesis 2b: If greater environmental contamination and more interactions at the

animal-human interface occur in urban locations (McDonnell et al. 1997, Zhou et al.

2004, Graczyk et al. 2008) while peri-urban sites have less contamination but more

interactions at the wildlife-livestock interface where antimicrobial resistance has been

known to occur (Cole et al. 2005, Wittum et al. 2010, Allen et al. 2011, Seiffert et al.

2013), then the prevalence of Giardia, Cryptosporidium, and Salmonella will be higher at

urban sites while prevalence of cephalosporin-resistant E. coli strains will be higher at

peri-urban sites.

Hypothesis 2c: If geese residing at urban sites are exposed to more indirect and direct

contact with the human population (McDonnell et al. 1997, Zhou et al. 2004, Graczyk et

40

al. 2008), then a greater pathogen prevalence will be observed at urban sites than at peri-

urban sites during the molt period (June) while prevalence will become more even

between urban and peri-urban sites once flight is resumed (August).

Objective 3: Determine the effects of environmental factors on prevalence of the selected

pathogens in Canada goose fecal samples.

Hypothesis 3a: If contact with the human population introduces more opportunities for

pathogen transmission between the two populations (Rolland et al. 1985, McDonnell et

al. 1997, Zhou et al. 2004, Graczyk et al. 2008, Allen et al. 2011), then pathogen

prevalence will be positively associated with human population density and the number

of humans present at each site.

Hypothesis 3b: If antimicrobial resistance is introduced by livestock (Cole et al. 2005,

Wittum et al. 2010, Allen et al. 2011, Seiffert et al. 2013), then sites that contain a greater

proportion of livestock farms or are located in close proximity to a livestock farm will

have a higher prevalence of cephalosporin-resistant E.coli.

Hypothesis 3c. If wastewater treatment plants contain contaminated water, then sites

located in close proximity to wastewater treatment plants will have higher pathogen

prevalence.

41

Hypothesis 3d. If a larger number of geese present at a site provides more opportunities

for horizontal transmission within the Canada goose community, then pathogen

prevalence will be greater at sites with larger Canada goose populations.

Study Sites

Canada geese were studied in 5 peri-urban and 7 urban sites throughout the

Greater Columbus area in Ohio. Sites were selected based on descriptions provided by

the Ohio Department of Natural Resources and Columbus Metro Parks (Ohio Department

of Natural Resources 2012a, Ohio Department of Natural Resources 2013, Columbus

Metro Parks 2009a, Columbus Metro Parks 2009b) as well as general knowledge of the

area. Sites were confirmed as being urban or peri-urban based on GIS analysis in

combination with a National Land Cover Database overlay (Environmental Systems

Research Inst. 2011, NLCD 2011). Land cover categories were based on the NLCD 2011

program legend (Table 2.1). Each site was classified by the land cover category that made

up the greatest percentage of the site (≥35%) (Table 2.2). The grassland, open water,

barren, deciduous forest, evergreen forest, mixed forest, forested wetland, and herbaceous

wetland categories were combined into an “undeveloped” category for linear regressions

and modeling of Cryptosporidium prevalence. Analysis included a 1km radius around

each site (Rodewald and Shustack, 2008). Site descriptions and spatial distribution can be

seen in Table 2.3 and Figure 2.1.

42

Table 2.1 Land cover categories selected from the National Land Cover Database Program Legend

Land cover category

Grassland

Description

Areas dominated by gramanoid or herbaceous vegetation, generally greater than 80% of total vegetation.

Open Water Areas of open water, generally with less than 25% cover of vegetation or soil.

Developed Open Space

Areas with a mixture of some constructed materials, but mostly vegetation. Impervious surfaces account for less than 20% of total cover.

Developed Low Intensity

Areas with a mixture of constructed materials and vegetation. Impervious surfaces account for 20% to 49% percent of total cover.

Developed Medium Intensity

Areas with a mixture of constructed materials and vegetation. Impervious surfaces account for 50% to 79% of the total cover.

Barren Land Areas of bedrock, desert pavement, scarps, talus, slides, volcanic material, glacial debris, sand dunes, strip mines, gravel pits and other accumulations of earthen material. Vegetation accounts for less than 15% of total cover.

Deciduous Forest

Areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. More than 75% of the tree species shed foliage simultaneously in response to seasonal change.

Evergreen Forest Areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. More than 75% of the tree species maintain their leaves all year.

Mixed Forest Areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. Neither deciduous nor evergreen species are greater than 75% of total tree cover.

Pasture Areas of grasses, legumes, or grass-legume mixtures planted for livestock grazing or the production of seed or hay crops, typically on a perennial cycle. Pasture/hay vegetation accounts for greater than 20% of total vegetation.

Forested Wetland Area where forest or shrubland vegetation accounts for greater than 20% of vegetative cover and the soil or substrate is periodically saturated with or covered with water.

Emergent Herbaceous Wetland

Area where perennial herbaceous vegetation accounts for greater than 80% of vegetative cover and the soil or substrate is periodically saturated with or covered with water.

1

Table 2.2 Proportion of each land cover type within sites where Canada goose fecal samples were collected in the greater Columbus area during the molt and post-molt periods in the year 2013

Site % Landcover

Prairie Oaks

Three Creeks

Big

Island

Heritage

Horse- shoe Rd.

Scioto

ODNR Morse Rd.

Bob Evans

Franklin Park

Beekman

Chadwick

Olentangy

Grassland *

0.00

0.14

0.57

0.20

0.46

0.00

0.00

1.06

0.00

0.00

0.00

0.00

Open Water * 18.3 5.16 21.3 0.00 1.43 10.3 0.00 0.00 0.80 0.00 1.06 3.38

Developed Open Space 4.47 11.4 3.07 12.1 3.47 30.2 6.91 16.9 15.5 27.7 30.7 4.61

Developed Low Intensity 1.69 6.22 2.09 12.3 3.04 46.3 27.9 21.1 32.0 28.2 23.7 16.20

Developed Medium Intensity 0.00 7.20 0.00 27.6 0.14 12.7 65.2 40.3 51.3 44.1 44.5 75.80

Barren * 5.07 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Decidious Forest * 14.3 37.7 8.57 4.35 31.3 0.32 0.00 15.4 0.37 0.00 0.00 0.00

Evergreen Forest * 0.00 3.56 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Mixed Forest * 0.00 0.00 0.00 0.00 0.00 0.20 0.00 0.00 0.00 0.00 0.00 0.00

Cultivated Pasture/Hay 18.5 1.66 7.02 6.59 8.23 0.00 0.00 5.19 0.00 0.00 0.00 0.00

Cultivated Crops 36.3 11.5 45.9 36.8 51.6 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Forested Wetland * 0.97 15.1 0.14 0.00 0.29 0.00 0.00 0.00 0.00 0.00 0.00 0.000

Emergent Herbaceous Wetland *

0.40

0.32

11.3

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.000

*Combined to create an "undeveloped" category for analysis. Bold Font: Greatest percentage of land cover per site.

43

44

Table 2.3 Descriptions for sites where Canada goose fecal samples were collected during the molt and post-molt periods in the year 2013

Site UTM Coordinates County Prairie Oaks 17T0302136 m E

4430395 m N

Franklin- Madison

Three Creeks 17S0336515 m E 4417355 m N

Franklin

Big Island 17T0308116 m E 4493327 m N

Marion

Heritage 17T0314148.09 m E 4437080.22 m N

Franklin

Horseshoe Rd. 17T0328823 m E 4475450 m N

Delaware

Scioto 17T0321386.89 m E 4432626.20 m N

Franklin

Morse Road 17T0332336 m E 4436008 m N

Franklin

Restaurant

17T 0336001 m E 4445931 m N

Delaware

Olentangy 17S 0325942 m E 4429654 m N

Franklin

Franklin Park 17S0333049.58 m E 4425676.04 m N

Franklin

Beekman 17T0325908 m E 4430189 m N

Franklin

Chadwick 17T0326694.84 m N 4430790.06 m N

Franklin

45

Figure 2.1 Study sites used for resident Canada goose fecal sample collection during the molt and post-molt periods in the Greater Columbus area during the year 2013

= Peri-urban

= Urban

46

Sample Collection

The goal was to sample only resident Canada geese. My study population was

Canada geese that were present consistently at each site during collection periods and had

been observed at the sites at least one week before collection began. Flightless geese were

restricted to sites during the molt period and therefore any pathogens of study would most

likely have been obtained on or near the site where the geese were found

considering these pathogens are self-limiting, meaning they tend to resolve without

treatment, and usually short-lived. I observed that the geese were present at the sites for

at least one week before sample collection, so geese would have likely cleared any

previously acquired pathogens. This assumption cannot be applied to the post-molt period

however, during the second period photos were taken during the observation period in an

attempt to sample from individuals that had been observed at sites the week prior to

collection. As a result my sample population was considered representative of resident

populations in central Ohio. Once the geese were able to resume flight, the population

was likely composed of geese that had been living in the area over the past few months

based on estimates of distance traveled by geese during the post-molt season (4.8 km for

males and 4.1 km for females) (Rutledge et al. 2013).

Additionally, hunting season, which begins in October in Ohio (Ohio Department

of Natural Resources, 2015), and migration had not yet begun. Molt migrants would only

just be starting to return and would not be likely to influence results (Luukkonen et al.

2008).

47

Samples were collected during two separate time periods (period 1 and period 2).

The first period was during June 4-June11 when geese had just begun their molt. The

second occurred August 16-30 when geese had resumed flight. Samples were collected

two separate times at each site within each period to increase validity by attempting to

ensure that results from fecal samples were not specific to a confounding factor that may

have been present on a single day of sampling (ex: wind, temperature). Events that affect

geese, such as flooding between sampling days, could not be controlled. Peri-urban sites

sampled during the first period included Prairie Oaks, Big Island, Horseshoe Road, and

Three Creeks (Table 2.3, Figure 2.1). Urban sites included Beekman Park, Olentangy

River, Morse Road, and a restaurant. Additional samples were collected from the

Franklin Park Conservatory and the Scioto River sites during the first period because

insufficient numbers of samples were collected from adults and juveniles. Additional

samples were needed in case samples from other sites could not be analyzed. The period

2 peri-urban sites did not include the Horseshoe Road or Three Creeks sites because

geese were no longer present. The Heritage site was added as a replacement site for the

Horseshoe Road site because it had similar land cover (≥35% cultivated crops). A

replacement for the Three Creeks site was not found within the Greater Columbus area.

The geese also left the Olentangy River site in the second sampling period. The

Chadwick Arboretum site was used as a replacement and was located less than 1.6 km

from the Olentangy site.

Juveniles were distinguished from adults during the first sampling period based

on small body size, presence of down feathers alone, grey color with little brown

contrast, and lighter colored necks (Sibley, 2000). My goal was to obtain fecal samples

48

from 3 adults and 3 juveniles during each visit in the first period and samples from geese

(both age classes) at each site during the second period with a goal of 96 samples

collected during each period. A total 199 samples were collected and analyzed (Table

2.4). One extra sample was collected from the Prairie Oaks site and 3 samples from the

Chadwick Arboretum site were eliminated because they had insufficient masses of

material to analyze.

Geese were approached slowly after ~15 minutes of standing to allow geese to

become acclimated to my presence. Grazing geese were followed until they defecated.

Droppings were marked with a twig, or the sample was collected immediately. Samples

were collected in 15ml sterile screw cap centrifuge tubes (Corning, Tewksbury, MA,

USA). Attempts to avoid

introduction of any sources of contamination were made by wearing nitrile gloves

and using a sterile tongue depressor.

Samples were stored on ice until returning to the Ohio State University campus

where they were frozen at -80°C until processing. All samples were processed within 9

month after collection. Enteropathogens can survive in fecal samples as long as 12

months when stored at very low temperatures (Dan et al. 1989). Samples were collected

to avoid sampling defecations from the same goose. If the geese were resting, a mental

note was made of their positions and once they moved away, droppings were collected

from where individuals were loafing. Samples were collected from individuals that were

separated by ≥ 0.30 m to avoid collecting from the same goose. Attempts were made to

collect from different family groups during the second visit. If this was not possible,

49

Table 2.4 Number of resident Canada goose fecal samples collected per site by period in the greater Columbus area during the molt and post-molt periods in the year 2013

Site

Period 1

Age

Adult/ Juvenile

Period 2

Prairie Oaks 14 6/8

13

Three Creeks 12 6/6 0 Big Island 12 6/6 12 Heritage 0 - 12 Horseshoe Road. 12 6/6 0 Scioto 6 6/0 12 Morse Road 12 6/6 12 Restaurant 12 6/6 12 Olentangy 13 6/7 0 Franklin Park 6 5/1 0 Beekman 12 6/6 12 Chadwick 0 - 3 Total 111 59/52 88

50

memory of physical markings or individual characteristics were used in order to avoid

sampling from the same individuals. There were seven occasions during the second

period when samples were collected when geese were not present. In this situation,

moisture content of the fecal samples was used to identify fresh fecal deposits. This

means that the samples had to be visibly moist, soft, malleable, and dark in color. Resting

sites were usually identified where droppings and feathers remained and droppings were

collected from fecal deposits that were separated by ≥ 0.30 m. Information was obtained

from bystanders at several urban sites about the time and location where the geese had

last been seen to find fresh droppings (≤ 24 hrs.). However, 11 slightly older (≥ 24 hrs)

and more desiccated droppings were used when necessary.

Data were collected using 6 minute point count surveys to obtain information on

time, date, weather, GPS location, total number of people present before and after

collection, and total numbers of adult and juvenile geese before and after collection.

Nearby landmarks such as paths to or near the water or refuse container were recorded.

Human behaviors that could influence results such as water activities or hand-feeding

geese were recorded along with goose behaviors such as aggression and swimming.

Number and direction of geese landing in or leaving a site during the second period also

were noted .

51

Pathogen Detection

Immuno Assay

Antigen capture enzyme-linked immunosorbent assay (ELISA) was used to detect

presence of Giardia and Cryptosporidium. ProSpecT Giardia microplate assay (Thermo

Fisher Scientific Remel Products, Lenexa, KS, USA) testing for Giardia specific antigen

65 (Giardia lamblia) and ProSpecT Cryptosporidium microplate assay (Thermo Fisher

Scientific Remel Products, Lenexa, KS, USA) testing for Cryptosporidium specific

antigen (not species specific) were used to conduct the assay. Each sample was cut in

half lengthwise on a sterilized cutting board with a sterilized knife. Each half was

weighed to ensure that there were at least 5.00 g of sample. One half was then placed into

a sterile Whirl-Pak bag while the other half of each sample was placed into a 15ml sterile

screw cap centrifuge tube (Corning, Tewksbury, MA, USA). Samples placed in

centrifuge tubes were then stored at 4°C until bacterial culture within 48 hours. The

samples in the Whirl-Pak bags to be used for the assay were left to thaw at 4°C overnight.

Five milliliters of deionized water were added to each bag and each sample was manually

homogenized on the following day. Sterile swabs were inserted into each Whirl-Pak bag

until the tip was covered with a sufficient amount of sample. The swabs were then placed

into 15ml centrifuge tubes filled with 1.00 ml of specimen dilution buffer provided by the

manufacturer. Positive and negative controls were used. Manufacturer’s instructions

were used to conduct the remainder of the assay. Positives and negatives were

determination based on visual interpretation of a color reaction.

52

Bacterial Culture

Methods used for bacterial culture of Salmonella and cephalosporin-resistant E.

coli strains were standard lab practices for the lab of Dr. Thomas E. Wittum at the Ohio

State University and can be found in Mollenkopf et al. (2012). Samples were first

removed from -80°C and allowed to thaw at 4ºC where they were kept for no more than

48 hours before culturing. Four grams of each sample was added into 36ml of

Tetrathionate broth for Salmonella culture. The samples were mixed and left to incubate

for 24-hours at 37°C. One-hundred microliters of sample solution from each sample were

mixed with 10ml of Rappaport-Vassiliadis (RV) enrichment broth the following day.

Samples were then incubated at 42°C for 24 hours. The RV sample solution was

inoculated to XLT-4 agar for each sample with a sterile swab and streaked for isolation.

The plates were incubated for 24 hours at 37°C. Suspect Salmonella were to be

inoculated to MacConkey agar after which they would be streaked for isolation and

allowed to incubate at 37°C for 24 hours. Isolated Salmonella colonies would then be

inoculated to a nutrient slant and allowed to incubate for 24 hours at 37°C. An antisera

test would be performed for final results.

One gram of each fecal sample was added into 9 ml of nutrient broth containing 2

µg/ml of cefotaxime (a third-generation cephalosporin) and allowed to incubate for 24

hours at 37°C to identify cephalosporin-resistant E. coli strains,. Two sets of plates were

prepared. One set consisted of selective MacConkey agar mixed with 4 µg/ml of

cefoxitin (a second-generation cephalosporin). The other consisted of selective

53

MacConkey agar mixed with 4 µg/ml of cefepime (a fourth generation cephalosporin).

Sample solution was inoculated to a MacConkey agar with cefoxitin plate and a

MacConkey agar with cefepime plate using a sterile swab. Samples were then streaked

for isolation and allowed to incubate for 24 hours at 37°C. If phenotypic E. coli was

present, then an isolated colony was inoculated to both a nutrient slant and to Tryptophan

broth and allowed to incubate at 37°C for 24 hours. The nutrient slant was then checked

for growth and the Tryptophan-sample solution was tested using an Indole test for final

verification.

Data Analysis

Age, Landscape, and Season

Each period was examined separately and then compared. For analysis of

cephalosporin-resistant E.coli strains, all sites used for the first period were represented in

the analyses of adult samples. All sites used for the second period, with the exception of

the Chadwick Arboretum site, were also represented. The Chadwick Arboretum samples

were not used due to small mass of fecal samples. No juvenile samples were obtained

from the Big Island, or Beekman Park sites because they were too small to analyze. Forty

peri-urban and 53 urban samples were tested. There were 13 peri-urban and 14 urban

samples that were not analyzed due to desiccation or small sample mass.

Logistic regression was used to examine the effects of age on prevalence of

Cryptosporidium during the first period. An odds ratio was then calculated from logistic

54

regression coefficients. Age differences were not analyzed during the post-molt

collection because age was not distinguished during this period. No statistical tests could

be carried out to examine the effects of age on prevalence of Giardia or cephalosporin-

resistant E.coli strains during either period due to low numbers of positives and lack of

distinguishing adults from juveniles during the second period.

Logistic regressions were also conducted to determine the effects of landscape on

prevalence of Cryptosporidium. A model that evaluated the effect of landscape alone on

prevalence of Cryptosporidium during the first period was used followed by the same

analysis for the second period. Odds ratios were calculated from the regression

coefficients

Two-tail Fisher’s Exact tests were used to examine presence of Giardia and

cephalosporin-resistant E. coli strains in urban vs. peri-urban sites during the second

period. Fisher’s Exact was used due to low number of positives. Only the second period

could be examined because there were no Giardia and only one cephalosporin-resistant

E.coli strain detected during the first period. An odds ratio was then calculated for each

pathogen during the second period.

Although statistical tests were not performed across the molt and post-molt

periods, prevalence was used to compare Giardia and cephalosporin-resistant E.coli

between periods. For Cryptosporidium data, prevalence, logistic regression results, and

odds ratios were used for comparison. Statistical significance was set at the alpha= 0.10

level for all tests. Statistical programs used included Program R and the Statistical

Package for the Social Sciences (R Core Team 2013, International Business Machines

Corp. 2013).

55

Environmental Factors

Linear regression was used with Cryptosporidium data in order to examine the

relationship between the odds ratio (dependent variable) at each site and seven

independent variables including: population density within study sites, mean number of

geese at each site, distance of each site from nearest livestock farm, distance of each site

from nearest wastewater treatment plant, proportion of site defined by the NLCD as

developed medium intensity land (highest level of development detected in the area),

pasture, and undeveloped space (combined category). Linear regressions were not

conducted for other pathogens because few positives were detected. Variables that

measured the same or similar properties were grouped together into human factors,

livestock factors, and other factors. The human factors group included population density

within study sites, proportion of each site defined by NLCD as developed medium

intensity, and proportion of each site defined by NLCD as undeveloped space. Livestock

factors included proportion of each site defined by NLCD as pasture and distance of each

site from nearest livestock farm. Other factors included distance of each site from nearest

wastewater treatment plant and average number of geese total. Univariate linear

regression analysis was then run on each variable. Collection periods were analyzed

separately. The variable with the lowest P- value within each group during each period,

regardless of statistical significance, was selected for inclusion in a model. Once a model

had been created for each period, forward stepwise selection was used to determine the

56

best model for each period. Multivariate linear regression was then carried out for the

first period best-fit model and again for the second period best-fit model.

The number of humans counted at each site was averaged by sample period.

Human population density within study sites along with proportion of site defined by the

NLCD as developed medium intensity land were factored into the linear regressions run

on the Cryptosporidium data.

Number of adults and juveniles present at each site were averaged together to

produce site averages. Afterward, these averages were grouped by landscape to get an

overall urban and overall peri-urban average of adults and juveniles.

GIS Analysis

GIS analysis was used to calculate values for the seven environmental variables to

examine the effects of these variables on pathogen prevalence. Population density within

study sites was measured with 1km-radius buffers from 2010 U.S. census data at the

census tract level for the following counties: Marion, Madison, Franklin, Delaware, and

Union (U.S. Census Bureau 2010). Human population density was calculated by

multiplying total human population within the census tract by the proportion of the

census tract area within the 1km radius. Where a 1 km-radius surrounding a site

intersected multiple census tracts, the proportion of each census tract area that fell within

the radius was multiplied by the total human density within each census tract. These

values were then added together to obtain the area-weighted sum of human density for

each site.

57

The distance of each site from the nearest livestock farm was determined by

obtaining parcel shape files and land use attributes data from every county containing or

bordering a study site. This included Marion (Burns Marion County Auditor Office

2014), Madison (Madison County Auditor Office 2014), Franklin (Franklin County

Auditor Office 2014), Union (Xiaong Union County Auditor Office 2014), and Delaware

(Delco 2014) counties. The land use attributes used to determine distance to nearest

livestock farm for all counties except Madison county included: livestock farms, dairy

farms, poultry farms, livestock farms qualified Current Agricultural Use Value (CAUV),

dairy farms qualified CUAV, and poultry farms qualified CAUV. The Madison county

data set used a different set of land use codes than the other counties. The “pasture”

attribute was used to determine distance to nearest livestock farm for Madison county.

The distance of each site from the nearest wastewater treatment plant was

measured by using a shape file with attributes that included the location of all wastewater

treatment plants in the area from the Ohio Environmental Protection Agency (Bouder

Ohio Environmental Protection Agency 2014).

Proportion of each site defined by the NLCD as developed medium intensity land,

pasture, and undeveloped space was determined by dividing the number of km made up

by each category at each site and dividing by the 1km radius.

58

Results

Pathogen Prevalence

Overall pathogen prevalence for samples that tested for all pathogens was 55/93

(59%). Cryptosporidium had the highest prevalence in fecal samples 89/199 (44.7%)

followed by cephalosporin-resistant E. coli 10/93 (10.8%) and Giardia 7/199 (3.52%)

(Table 2.5). Prevalence of Cryptosporidium was higher during the first period than the

second period. Prevalence of both cephalosporin-resistant E. coli and Giardia was higher

during the second period than the first period (Table 2.5). No Salmonella was detected in

any fecal samples.


There were no effects of age on prevalence of Cryptosporidium during the first

period (P = 0.77). The odds of detecting a Cryptosporidium positive in adults were 0.89

times the odds of detecting a Cryptosporidium positive in juveniles.

Among 61 samples collected from urban areas during the first period, 35 (57.4%)

were positive for Cryptosporidium, compared to 21 of 50 (42%) samples collected from

peri-urban sites during the first period. Among 51 samples collected from urban areas

during the second period, 21 (41.2%) were positive for Cryptosporidium, compared to 12

of 37 (32.4%) samples collected from peri-urban sites during the second period. The

Morse Road site had the highest percentage of positives detected during both periods

59

Table 2.5 Pathogen prevalence in Canada goose fecal samples collected in the greater Columbus area during the molt and post-molt periods in the year 2013 by period

Pathogen

Period 1 Number Positive

N

Period 2 Number Positive

N

Cryptosporidium

56

111

33

88

Ceph. Resistant E. coli

1

40

9

53

Giardia 0 111 7 88

Salmonella 0 111 0 88

60

(Table 2.6). Prevalence of Cryptosporidium was higher in urban than peri-urban sites

during the first period (P = 0.10). The odds of detecting a Cryptosporidium positive at an

urban site were 1.86 times the odds of detecting a positive at a peri-urban site during the

first period. There was no difference in prevalence of Cryptosporidium between

landscapes during the second period (P = 1.0). The odds of detecting a Cryptosporidium

positive at an urban site were 1.00 times the odds of detecting a positive at a peri-urban

site during the second period.

No Giardia was detected during the first period. Among 51 samples collected

from urban areas during the second period, 5 (9.8%) were positive for Giardia compared

to 2 of 37 (5.4%) samples collected from peri-urban sites during the second period. The

site with the greatest percentage of positives detected during the second period for both

Giardia and cephalosporin-resistant E.coli was the Morse Road site (Table 2.7). The

Fisher’s Exact analysis for presence of Giardia in urban vs. peri-urban sites during the

second period showed no statistically significant difference with a P- value of 0.74. The

odds of detecting Giardia at an urban site during the second period were 1.9 times the

odds of detecting a positive at a peri-urban site during the second period.

Only one cephalosporin-resistant E. coli strain was detected during the first

period. Among 32 samples collected from urban areas during the second period, 8 (25%)

were positive for cephalosporin-resistant E. coli compared to 1 of 21 (4.8%) samples

collected from peri-urban sites during the second period. There was no difference in

prevalence of cephalosporin-resistant E. coli between urban and peri-urban sites during

the second period (P = 0.11). The odds ratio shows that the odds of detected a

cephalosporin-resistant E. coli positive at an urban site are 6.67 times the odds of

61

Table 2.6 Percentage of Cryptosporidium positives in Canada goose fecal samples collected in the greater Columbus area during the year 2013 out of number of samples collected per site for Period 1 and Period 2

Period 1

Period 2

Site

Number Positive

N

Site

Number Positive

N

Prairie Oaks 3 14 Prairie Oaks 9 13 Three Creeks 9 12 Big Island 1 12 Big Island 3 12 Scioto 0 12 Franklin 2 6 Beekman 2 12 Scioto 3 6 Morse Road 10 12 Beekman 6 12 Restaurant 7 12 Olentangy 5 13 Chadwick 2 3 Horseshoe 6 12 Heritage 2 12 Morse Road 10 12 Restaurant 9 12

Table 2.7 Percentage of Giardia and cephalosporin-resistant E. coli positives in Canada goose fecal samples collected in the greater Columbus area during the year 2013 out of number of samples collected per site for period 2

Giardia

Site

Number Positive

Prairie Oaks Big Island

1 0

Scioto 0 Beekman 0 Morse Road 4 Restaurant

Chadwick 1

0

Heritage 1

1

Table 2.8 Summary prevalence for Cryptosporidium, Giardia, and cephalosporin-resistant E. coli by age and landscape variables for Canada goose fecal samples collected in the greater Columbus area during the molt and post-molt periods in 2013 for Period 1 and Period 2

Period 1 Period 2

Variable

Crypto.

Giardia

Resistant E. coli

Variable

Crypto.

Giardia

Resistant E. coli

Number Positive

N

Number

Positive

N

Number Positive

N

Number

Positive

N

Number Positive

N

Number Positive

N Adult

29

59

0

59

1

25

Peri-urban

9

24

1

24

1

21

Juvenile 27 52 0 52 0 15 Urban 24 64 6 64 8 32

Peri-urban 21 50 0 50 1 19

Urban 35 61 0 61 0 21

62

63

detecting a positive at a peri-urban site. The only positive detected during the first period

was at a peri-urban site. A summary of positives and negatives for each pathogen during

each period was created (Table 2.8)

Prevalence of Cryptosporidium was slightly higher during the first period (50%)

than the second period (38%). Prevalence of both Giardia and cephalosporin-resistant

E.coli was higher during the second period [Giardia: 7/88 (7.95%), Cephalosporin-

resistant E. coli: 9/53 (17%)] .


Univariate linear regressions of site-specific odds ratios for Cryptosporidium on

environmental factors (Table 2.9) during the first period show the population density

within study sites variable had the lowest P-value within the human factors group, the

proportion of each site defined by NLCD as pasture variable had the lowest P- value

within the livestock factors group, and the distance of each site from nearest wastewater

treatment plant variable had the lowest P-value within the other factors group (Table

2.10, Figure 2.2a,-d). Forward selection of a model that included the three selected

variables supported a model containing all three variables (Table 2.11).

Population density within study sites was again selected from the human factors

group the second period, and distance of each site from nearest livestock farm variable

was selected from the livestock factors group, and the distance of each site from nearest

wastewater treatment plant variable was selected from the other factors group (Table

64

2.10, Figure 2.2b- f). Forward selection of a model that included the three selected

variables supported a model that contained all three variables (Table 2.11).

There were more humans present at collections sites during period 1 (~18/site)

than in period 2 (~3/site). Estimates of geese present at each sampling site showed that a

greater number of adult geese were present at urban sites on average (~15 adults; ~25

overall on average) than at peri-urban sites (~9 adults on average; ~23 overall on

average) during period 1. Average ratio of adults to juveniles during the first period was

~0.76.

65

Table 2.9. Environmental variable measurements and odds ratios by site for Period 1 and Period 2 Cryptosporidium data from Canada goose fecal samples collected in the greater Columbus area during the molt and post-molt periods in the year 2013

Period 1 Site

Odds Ratio

Pop. Density

Proportion Developed Medium Intensity

Proportion Un- Developed

Farm Dist. Km

Proportion Pasture

WWTP Dist.Km

Mean

No. Geese

Beekman

1.00

2737

0.44

0.00

4.27

0.00

10.1

11.0 Big Island 0.33 62.2 0.00 0.42 17.6 0.07 4.27 14.0 Bob Evans 3.00 2122 0.40 0.17 11.9 0.05 4.55 25.5 Franklin 0.50 5278 0.51 0.01 8.21 0.00 7.8 19.0 Horseshoe Rd.

1.00

237

0.00

0.33

4.53

0.08

5.84

15.0

ODNR Morse Rd.

5.00

8438

0.65

0.00

3.03

0.00

5.08

26.0

Olentangy 0.63 4548 0.76 0.03 5.33 0.00 9.8 44.0 Prairie Oaks

0.27 111 0.00 0.39 1.46 0.19 2.57 42.0

Scioto 1.00 3406 0.13 0.11 4.78 0.00 13.6 24.0 Three Creeks

3.00

806

0.07

0.62

14.6

0.02

4.22

19.5

Site

Odds Ratio

Pop.

Density

Proportion Developed Medium

Intensity

Proport Un- Develop

Farm Dist.

Km

Proportion Hay Pasture

WWTP

Dist. Km

Beekma 0.20 2737 0.44 0.00 4.27 0.00 10.1 Big Islan 0.09 62.2 0.00 0.42 17.6 0.07 4.27 Bob Eva 1.40 2122 0.40 0.17 11.9 0.05 4.55 Chadwic 2.00 1285 0.45 0.01 5.2 0.00 10.8 Heritage 0.20 865 0.28 0.05 2.97 0.07 8.04 ODNR

Morse R

5.00

8438

0.65

0.00

3.03

0.00

5.08

Prairie

Oaks

2.25

111

0.00

0.39

1.46

0.19

2.57

Scioto 0.00 3406 0.13 0.11 4.78 0.00 13.6

1

Table 2.10 Univariate linear regression results for variable vs. odds ratio during period 1 and period 2 for Cryptosporidium data collected from Canada goose fecal samples collected in the greater Columbus area during the molt and post-molt periods in the year 2013

Variable

Period 1

Period 2

Β

Std. Error

R²

P- Value

β

Std. Error

R²

P- Value

Population Density 2.87 x 10-4 0.00 0.25 0.14 4.27 x 10-4

0.00 0.46 0.06 Developed Medium

Int.

1.73

1.80

0.10

0.37

4.13

2.46

0.32

0.14 Undeveloped -0.47 2.52 0.00 0.86 -1.97 3.99 0.04 0.64 Pasture -8.54 8.71 0.11 0.36 -0.37 10.9 0.00 0.97 Distance from Farm

in Km.

0.01

0.10

0.00

0.92

-0.11

0.12

0.12

0.40 Distance from WWTP

in Km.

-0.12

0.15

0.07

0.44

-0.18

0.16

0.17

0.31 Average Number of

Geese

-8.37 x 10-3

0.05

0.00

0.87

0.01

0.06

0.01

0.82

66

2

Table 2.11 Linear regression output for Cryptosporidium first and second period best-fit models from Canada goose fecal samples collected in the greater Columbus area during the molt and post-molt periods in the year 2013

Period 1 Period 2

Coefficient

Β Std. Error

P- value

Coefficient

β Std. Error

P- Value

(Intercept) 3.90 1.65 0.06 (Intercept) 2.86 1.25

0.08 Population Density 2.32 x 10-4

0.00 0.28 Population Density 4.05 x 10-4 0.00 0.06

Distance from WWTP in Km.

-0.35

0.15

0.06

Distance from WWTP in Km.

-0.25

0.11

0.09

Pasture

-14.6

10.9

0.23

Distance from Farm in Km

-0.09

0.08

0.31

Residual standard error: 1.22 on 6 DF Residual standard error: 1.09 on 4 DF

Multiple R-squared: 0.60 Multiple R-squared: 0.77

Adjusted R-squared: 0.40 Adjusted R-squared: 0.60

F-statistic: 2.95 on 3 and 6 DF F-statistic: 4.44 on 3 and 4 DF

P-value: 0.12 P-value: 0.09

67

68

Figure 2.2. Univariate linear regressions for best-fit model variables of Cryptosporidium odds ratio vs. a.) Period 1 human population density b.) Period 2 human population density c.) Period 1 proportion pasture d.) Period 2 distance to livestock farm e.) Period 1 distance to WWTP f.) Period 2 distance to WWTP in Canada goose fecal samples collected in the greater Columbus are during the molt and post-molt periods in the year 2013

Period 1 Period 2 a.) b.)

c.) d.)

e.) f.)

69

Discussion

Pathogen Prevalence

Prevalence of Cryptosporidium was consistent with the hypothesis that parasites

would have the highest prevalence. This indicates that Cryptosporidium was the highest

hazard potential of the pathogens examined. It should be noted that pseudoreplication of

samples during repeat visits to sites could have resulted in an underestimation of

variability of prevalence results, however attempts were made to avoid pseudoreplication

when possible.

Previous studies of Cryptosporidium prevalence in resident Canada geese vary

widely. A study in Northwest Ohio at 16 different urban sites including public parks, golf

courses, an industrial site, cemeteries, and health care facilities, found Cryptosporidium

spp. in 77.8% of fecal samples (Kassa et al. 2001). A second study in Northern Ohio

found a prevalence of 43.9% in fecal samples collected at a wildlife refuge on Lake Erie

(Rea 2013 unpublished, Binkley 2014 unpublished). These results are consistent with the

results found in this study. This may be influenced by the fact that both studies took place

at a similar time and place in addition to using the same methods for Cryptosporidium

detection. A New Jersey study sampled 500 resident geese at various locations including

lakes, rivers, zoos, and parks, and found Cryptosporidium in only 10% of fecal samples

(Roscoe 2001). Zhou et al. (2004) found Cryptosporidium in 49/209 (23.4%) of resident

and migratory Canada goose fecal samples combined in both urban and peri-urban parts

70

of Illinois and Northern Ohio. Kostroff and Rollender (2001) found Cryptosporidium in

81% of samples after examination of 100 Canada goose fecal samples collected from 2

parks in New York, NY. Lastly, Graczyk et al. (1998) detected Cryptosporidium at 78%

of sites they examined near Chesapeake Bay in Maryland however there was no mention

of how many individual samples tested positive. (Table 2.12)

Prevalence of Giardia were not consistent with my hypothesis the protozoa would

have a higher pathogen prevalence. Previous studies on the prevalence of Giardia in

resident Canada geese are scarce, however reports suggest relatively low prevalence.

Kassa et al. (2001) found overall prevalence of Giardia was 16.7% in fecal samples

collected in urban Northern Ohio. Binkley (2014 unpublished) reported 3.5% prevalence

in fecal samples collected at a wildlife refuge in Northern Ohio. Again results from

Binkley (2014 unpublished) agreed with results from this study. Roscoe et al. (2001)

found Giardia in 15% of fecal samples collected in urban and peri-urban sites throughout

New Jersey. However, Kostroff and Rollender (2001) found 74% prevalence in fecal

samples collected from New York, NY. Lastly, a study that collected resident Canada

goose fecal samples from Raleigh, Durham, and Chapel Hill, North Carolina found no

positive fecal samples out of 234 total samples (Ayers et al. 2014) (Table 2.12).

Though there is great variability in existing data on occurrence of both

Cryptosporidium (10%-81% ) and Giardia (0%-74%) in resident Canada goose fecal

samples, three out of the four studies (Table 2.12) that examined both Cryptosporidium

and Giardia in Canada goose fecal samples report a higher percentage of

Cryptosporidium positive samples. The only study that showed a higher prevalence of

71

Table 2.12 Prevalence of Giardia and Cryptosporidium in Canada goose fecal samples among studies

Study

%Cryptosporidium

%Giardia

Location

Method

Kassa et al. 2001

77.8 (7/18) 16.7 (3/18) Northern Ohio/Toledo

Solid phase enzyme immunoassay (EIA) using monoclonal antibodies specific for Cryptosporidium (CSA) and Giardia (GSA 65) antigens (ProSpect Microplate Assay kits).

Binkley 2014 (Unpublished) Rea 2013 (Unpublished)

41.5 (44/106) 2.8 (3/106) Central Ohio EIA/Enzyme Linked Immunosorbent Assay (ELISA) using monoclonal antibodies specific for Cryptosporidium (CSA) and Giardia (GSA 65) antigens (ProSpect Microplate Assay kits).

Roscoe 2001 10 (49/500) 15 (75/500) New Jersey Direct immunofluorescent antibody test. Detection reagent is FITC labeled anti- Cryptosporidium cell wall and anti-Giardia cell wall monoclonal antibodies (Meridian Diagnostics, Inc). Not species specific.

Zhou et al. 2004

23.4 (49/209) N/A Ohio and Illinois

PCR-restriction fragment length polymorphism (RFLP) analysis of the SSU rRNA gene. Testing for all species.

Ayers et al. 2014

N/A 0 (0/234) North Carolina

Enzyme Linked Immunosorbent Assay (ELISA) ProSpecT® Giardia EZ Microplate Assay (Remel Inc., Lenexa, Kan., USA). (GSA 65).

Kostroff and Rollender 2001

81 (81/100) 74 (74/100) New York Premier Cryptosporidium and Giardia EIA using monoclonal antibodies (Meridian Diagnostics, Inc).

The present study 2012- 2013

41 (44/107) 2.8 (3/107) Northern Ohio

EIZ/Enzyme Linked Immunosorbent Assay (ELISA) using monoclonal antibodies specific for Cryptosporidium (CSA) and Giardia (GSA 65) antigens. (ProSpect Microplate Assay kits).

72

Giardia than Cryptosporidium was Roscoe (2001) study however, even in this case there

was only a 5% difference.

One factor that sets these pathogens apart is that Cryptosporidium can retain

infectivity in water for up to a year while Giardia can only retain infectivity for up to two

months (Graczyk et al., 2008). Cryptosporidium is also highly chlorine-resistant (Yoder

et al. 2012a) while Giardia is considered to be chlorine-tolerant (Centers for Disease

Control 2011) (U.S. Environmental Protection Agency 2000). LeChevallier and Norton

(1995) found that in studies of surface waters, the major route of Giardia and

Cryptosporidium exposure for Canada geese, showed a slightly higher percentage of

Cryptosporidium positives (60.2%) than Giardia positives (53.9%). However, results

from previous studies do not show a difference as significant as the 44.7% positive for

Cryptosporidium and 3.5% positive for Giardia found in this study.

The most likely contributor to such a large difference in prevalence between

Cryptosporidium and Giardia in resident Canada goose fecal samples observed in this

study is a difference in species detection between the ELISA assays. The ELISA assay

used to detect Cryptosporidium was able to detect any species of Cryptosporidium that

was active within the goose at the time of sampling. Conversely, the ELISA assay used to

detect Giardia was only able to detect Giardia specific antigen 65 which means only

Giardia lamblia, the species that can infect humans, could be detected. This is a

limitation for comparisons of prevalence as well as for examination of human exposure to

species of Cryptosporidium that infect humans.

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However, out of the previous studies mentioned in table 2.12, only Roscoe (2001)

and Kostroff and Rollender (2001) did not look specifically for Giardia lamblia. Kostroff

and Rollender (2001) reported the highest Giardia prevalence by far suggesting that this

result may be related to the lack of species identification. Therefore, prevalence in this

study may have been higher had the ELISA assay not been species specific. Though there

is truth to the finding that Cryptosporidium occurs more frequently in resident Canada

goose fecal samples than Giardia based on previous studies and differences in viability,

the effect is most likely not as dramatic as the difference that was observed in this study.

Prevalence of cephalosporin-resistant E. coli strains, a bacteria, in Canada goose

fecal samples collected during this study was unexpectedly higher than prevalence of

Giardia. It was hypothesized that prevalence of the protozoa would be higher. Total

number of positives detected for both Giardia (7 positives samples) and cephalosporin-

resistant E. coli (10 positive samples) were low making any conclusions only speculation.

However, Cole et al. (2005) reported that 11% of 46 E. coli isolates found in Canada

goose fecal and cloacal samples collected in Georgia and North Carolina were resistant to

cephalothin (a cephalosporin). This is close to the 10.8% prevalence found in this study.

Because a higher prevalence provides more opportunities for some of those organisms to

be either cephalosporin-resistant E. coli or a specific species of Giardia, it is possible that

overall prevalence of E. coli was simply greater than overall prevalence of Giardia.

Reports of overall E. coli shedding in the Canada goose population are generally higher

than reports of Giardia shedding (Table 2.12). Fallacara et al. (2001) found 67%

prevalence when surveying fecal shedding of E. coli in free-living waterfowl throughout

metropolitan parks in central Ohio. Feare et al. (1999) surveyed 12 parks throughout

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London, England and found pathogenic E. coli strains in 55% of Canada goose feces. The

majority of studies show that prevalence of Giardia, even though it contains a hardy

outer cyst, is much lower than E.coli prevalence. This study did not test specifically for

E. coli because small sample mass was limiting and presence of resistant bacteria was

considered to be a priority.

A seasonal effect is also possible due to bacterial replication by binary fission

which is faster at warmer temperatures than protozoa (Maier et al. 2009). Bacteria grow

most rapidly in the range of temperatures between 4.4 °C and 60°C and can double in

number in as little as 20 minutes (U.S. Department of Agriculture 2013). Samples were

collected in the summer when bacteria prevalence would be at its peak. Kullas et al.

(2002) found that overall prevalence of E. coli ranged from 2% during the coldest time of

the year to 94% during the warmest months with an average of ~41% in a study that took

place in Fort Collins, Colorado over the course of 1 year (Kullas et al. 2002). In addition

to temperature, the researchers believe that the behavior of the geese may have

contributed to the prevalence patterns. Daily movement patterns of geese during the fall

and winter are often concentrated in areas where they are not likely to come into contact

with habitats contaminated with mammalian sources of E. coli (Kullas et al. 2002). In

contrast, during the spring and summer, geese do not move far from their nests and their

habitat consists of small water impoundments and littoral zones that easily become fouled

(Kullas et al. 2002). When the birds range from their nest area they are more likely to

visit outlying agricultural areas that are surface-treated with manure from local feedlots

and dairies. This may help explain a greater prevalence of E. coli than Giardia as well as

a greater prevalence of Cryptosporidium during period 1 than period 2.

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Lastly, exposure of geese to an undetected source of contamination, such as a

nearby livestock farm in an urban area, could have resulted in greater exposure to

cephalosporin-resistant E. coli than Giardia. Because geese were able to fly during the

second period when the majority of cephalosporin-resistant E. coli were detected, they

could have received high exposure to antimicrobial-resistant bacteria at an outside site

that was not accompanied with exposure to Giardia. The ability of geese to fly acts as a

limitation here and will be further discussed.

The fact that no Salmonella were detected is consistent with previous studies.

Clark (2003) reported that no Salmonella were isolated in studies by Hussong et al.

(1979), Roscoe (2001), and Fallacara et al. (2001). Prevalence of only 2.5% was found by

Feare et al. (1999), and <0.5% by Converse et al. (1999). It is possible that Salmonella is

simply difficult to culture from Canada geese; however, different methods were carried

out in these studies that produced the same results. Freezing the samples before analysis

may have resulted in die off of a portion of existing Salmonella; however,

enteropathogens can survive in fecal samples for as long as 12 months when stored at

very low temperatures (Dan et al. 1989). Additionally, not all of the previous studies

froze samples before analysis yet they produced similar results. Desiccation could also

have affected survivability of the Salmonella, however most desiccated samples were not

used for this analysis and there were only a total of 11 desiccated samples. Though

Canada geese can serve as vectors of Salmonella, prevalence is very low in the

population and therefore introduces a minimal hazard to the human population in the

Greater Columbus area.

76


The results showed no difference in prevalence of Cryptosporidium between adult

and juvenile populations during the June sampling period (P = 0.77). This finding was

not consistent with hypothesis 2a pathogen prevalence would be higher in adult than

juvenile geese. Roscoe (2001) found that Cryptosporidium was twice as prevalent in

juvenile geese than in adult geese sampled in New Jersey. However, there were many

factors that could have contributed to the unexpected results. The first explanation is that

the sample size was too small to detect an effect. Though the total sample size for the first

period was 111 individuals, only 52 were juveniles and 59 were adults. The study in New

Jersey sampled 500 individuals consisting of 250 juveniles and 250 adults. A second

possibility is that the exact age of the juveniles varied allowing older juveniles to acquire

more resistance than younger juveniles. Wehr and Herman (1954) found that goslings

acquire most parasitic infections during their first week of life. However, this conclusion

was largely based on evidence from worm parasites and Cryptosporidium was not one of

the parasites examined. A third possibility is that some of the goslings were able to

acquire passive immunity from their mothers. Though no studies could be found

examining passive immunity to Cryptosporidiosis in Canada geese, Buckland and Gerard

(2002) showed that female geese that have acquired immunity are capable of giving

passive immunity to their offspring from infections such as spirochetosis and goose

enteritis (Lastly, variation in exposure due to location could have skewed results and

prevented a trend from being observed.

77

There was a difference in prevalence of Cryptosporidium between urban and peri-

urban sites during the first period, supporting hypothesis 2b that if greater environmental

contamination and more interactions at the animal-human interface occur in urban

locations while peri-urban sites have less contamination but more interactions at the

wildlife-livestock interface where antimicrobial resistance has been known to occur, then

the prevalence of Giardia, Cryptosporidium, and Salmonella will be higher at urban sites

while prevalence of cephalosporin-resistant E. coli strains will be higher at peri-urban

sites. The results for Cryptosporidium prevalence during the second period however do

not support hypothesis 2b. This implies that because the geese were able to move freely

between urban and peri-urban sites during the second period and receive exposure from

both urban and peri-urban landscapes, a mixing of pathogens occurred resulted in a more

even distribution of Cryptosporidium between urban and peri-urban sites. However, when

the geese are restricted to sites during the first period and only receive exposure to one

type of landscape, prevalence of Cryptosporidium was higher in geese residing at urban

sites than at peri-urban sites.

It was expected that a greater prevalence of cephalosporin-resistant E. coli would

be detected at peri-urban sites as opposed to urban sites due to exposure to livestock

being treated with cephalosporins. Cole et al. (2005) found that the number of

antimicrobial resistant E.coli isolates recovered from resident Canada goose fecal and

cloacal samples was higher among geese in agricultural areas compared to other land use

types. Additionally, the proportion of isolates resistant to antimicrobial agents was

greater among isolates where interaction with swine waste lagoons was observed, while

little or no resistance was observed among isolates recovered from Canada geese in

78

regions with no known direct contact with liquid wastes (Cole et al. 2005). Allen et al.

(2011) found greater antimicrobial resistance isolates from small mammals trapped on

swine farms when examining E.coli and Salmonella isolates from swine farms,

residential areas, landfills, and natural woodland habitats, throughout Ontario, Canada.

However, study showed that prevalence was higher at urban sites. Proximity to

humans has been associated with higher prevalence of antimicrobial resistance. Rolland

et al. (1985) found that resistant bacteria were detected more frequently in baboons

feeding on human refuse than in animals living in more remote areas with no human

contact in Kenya. This suggests that exposure to anthropogenic sources, such as human

waste, contribute to development and maintenance of antimicrobial resistance in bacteria

of wildlife (Rolland et al. 1985, Allen et al. 2011). It is likely that both factors play a role

in prevalence and distribution of antimicrobial resistant bacteria. The odds of detecting a

positive at an urban site during the second period in this study were unusually high

(6.67). This result is largely due to the fact that 83% of the cephalosporin-resistant E. coli

were discovered at the Morse Road site again suggesting a random exposure or exposure

event.

Because 9 of 10 positives were detected during the second period when flight was

resumed, the geese could have acquired the cephalosporin-resistant E. coli from either an

urban or a peri-urban location. The only positive that was detected during the first period

was at a peri-urban site, however. It appears that both urban and peri-urban sites have the

potential to expose Canada geese to antimicrobial-resistant bacteria. The U.S. Food and

Drug Administration has been working on legislation to limit the use of antibiotics in

79

food animals, however current legislation relies on companies to voluntarily cut back on

use (United States Food and Drug Administration 2014).

The effects of season on pathogen prevalence were most evident with

Cryptosporidium. A difference in prevalence between urban and peri-urban sites was

observed when geese were unable to fly and thus fixed to study sites. No differences were

observed after geese were able to move freely between urban and peri-urban sites. This is

due to the fact that geese were exposed to both landscapes instead of being localized in

one or the other. The pathogens that they acquired could have come from either landscape

resulting in a mixing of pathogens at the study sites. Additionally, prevalence wa

s lower during the second period making any differences more difficult to detect.

Though it was hypothesized that prevalence would become more even between urban and

peri-urban sites once flight was resumed, it was unexpected to see the effect of landscape

entirely diminished. Therefore the effects of regaining flight had a greater impact on

pathogen distribution than expected.

No Giardia and only one cephalosporin-resistant E. coli strain were detected

during the first period while most were detected during the second period. This is most

likely due to the fact that so few positives were detected overall for both organisms.

However, it is important to consider the possibility of a random event or exposure that

was occurred in the second period and not the first. This was supported by the finding

that the majority of both Giardia and cephalosporin-resistant E. coli were detected at the

Morse Road site which suggests that an unknown source of contamination was present at

this site during the second period. Lastly, it is possible that once geese resumed flight

during the second period, they were able to acquire pathogens at other locations

80

throughout the Greater Columbus area that they then brought back with them to the study

sites.


Human factors were analyzed to determine whether presence of humans and

urbanization had an effect on prevalence of Cryptosporidium, with the belief that a higher

human population density would result in higher prevalence of Cryptosporidium. The

statistically significant univariate analysis of the human population density variable for

the first period indicates that this variable played a role in the distribution of

Cryptosporidium (Table 2.10). The Morse Road site had the highest odd ratio during the

first period and was the site with the greatest human population density. Three of four

peri-urban sites had the lowest population densities accompanied with the lowest odds

ratios. The Three Creeks site had an unexpectedly high odds ratio, however it did happen

to be the peri-urban site with the greatest population density of all the peri-urban sites.

Additionally, the presence of portable toilets was observed at this site which could have

provided an extra source of contamination.

The Morse Road site had the highest odds ratio for the second period and was the

site was the site with the greatest human population density as well as the greatest

proportion of developed medium intensity land. Two out of the three peri-urban sites had

the lowest odds ratios combined with the lowest human population densities. However,

the third peri-urban site, Prairie Oaks, had the second highest odds ratio. This was the

only other site where the presence of portable toilets was noted which may imply that the

81

portable toilets serve as a confounding factor. Additionally, the geese could have

obtained pathogens from another site.

There were many more people present on average at sampling sites during first

period sample collection (≈18 per site), when Cryptosporidium prevalence was higher,

than during the second period sample collection. Not only do the geese contribute to

further contamination in these areas, but two-way transmission causes geese to acquire

pathogens from anthropogenic sources which they can then transmit back to the human

population. Zhou et al. (2004) discovered that 10.2% of positive resident Canada goose

fecal specimens collected from Ohio and Illinois had C. parvum and C. hominis; the two

genotypes that can infect humans. These authors believed that the human form of the

pathogen was acquired by several geese from a human-contaminated environment after

determining that the sites where these strains were found were more intensely used than

other sites and were often littered with garbage. Since humans are the predominant source

of C. parvum and C. hominis, it is likely that finding these pathogens in goose feces

suggests mechanical transfer. Similarly, Kullas et al. (2002) isolated E. coli strains

consistent with those associated with human illness from 24.5% of fecal samples during

March-July when non-migratory geese predominate in local goose populations. Since

geese were molting during this time, those located in urban areas were more likely to

come into contact with humans. In urban areas there are simply more opportunities for

pathogen transmission.

Livestock factors were examined to determine whether presence and proximity of

a site to a livestock farm influenced prevalence of Cryptosporidium, with the belief that

sites that sites that contain a greater proportion of livestock farms or were located in close

82

proximity to a livestock farm would have a higher prevalence of Cryptosporidium. The

livestock factors had no significant association with the odds of detecting

Cryptosporidium at sites during either period. (Table 2.10). The proportion of pasture

variable had an opposite effect than what was expected. The site with the greatest

proportion of pasture within a site for the first period, the Prairie Oaks site, had the lowest

odds ratio (Table 2.9, Figure 2.2c). However, the distance to livestock farm variable

showed that the site located furthest from a livestock farm at 17.6 km, the Big Island site,

had the second lowest odds ratio (0.09) while the site with the highest odds ratio (5.00) ,

Morse Road, was the second closest site to a livestock farm at 3.30 km away (Table 2.9).

Other than these associations however, there does not seem to be much of a trend

between odds ratios and distance to livestock farms among sites.

Stronger support for this variable was found in the second period which may be a

result of the change in study sites. The site located closest to a livestock farm, Prairie

Oaks, was the site with the second highest odds ratio. It was also the site with the greatest

proportion of pasture within a site (Table 2.9). This may partially explain why a higher

prevalence than expected was observed at this peri-urban site when the human density

was low and there was no medium intensity developed land. The Morse Road site had the

highest odds ratio and was the third closest site to a livestock farm. The site located

farthest away from a livestock farm, Big Island, had the second lowest odds ratio.

The other factors were analyzed to determine whether proximity to a WWTP or

number of geese present at a site had an effect on prevalence of Cryptosporidium. I

hypothesized that close proximity to a WWTP or high numbers of geese present at a site

would result in a greater prevalence of Cryptosporidium (hypothesis 3c, 3d). No

83

differences were observed when looking at the linear regressions for the other factors vs.

odds ratios for either period.

The average number of geese at each site variable did not have any detectable

effect on prevalence of Cryptosporidium. It is possible that because a portion of

individuals may be immune, the disease cannot be effectively transmitted to these

individuals and therefore the number of individuals would have a small impact on

prevalence. However, this would not explain cases of mechanical transfer. Further

investigation of this topic may provide a more comprehensive explanation.

The linear regression for distance to WWTP vs. odds ratios for the first period

(Table 10, Figure 2.2e) provides little support for the hypothesis (3c). It was unexpected

that the site with the shortest distance to a WWTP, the Prairie Oaks site, would have the

lowest odds ratio. It is possible that management of this site, being a metro-park, was

effective at reducing pathogens during this time.

Examination of the second period linear regression (Table 2.9, Figure 2.2f)

provides more support for the hypothesis than the period 1 regression. The Prairie Oaks

site was located closest to a WWTP and had the second highest odds ratio. The Scioto

site, the farthest site from a WWTP, was the site with the lowest odds ratio. Following

this trend, the third and fourth farthest sites from a WWTP were the sites with the third

and fourth lowest odds ratios.

The site with the highest prevalence of all pathogens is the Morse Road site

(Table 2.6, 2.7). This site had the highest human population density among sites (Table

2.9), was only located only 5.39 km from the Olentangy River (Google Earth 2013), 5.1

km from a WWTP, and 3.03 km from the nearest livestock farm (Table 2.9). Therefore,

84

this site most likely received the strongest influence of all three factors combined. This

supports the results of the multivariate analysis.

The best-fit model’s for both periods show that all three factors play a role in the

variation observed in prevalence of Cryptosporidium in fecal samples observed between

urban and peri-urban sites (Table 2.11). The first period best-fit model produced a low P-

value and a high coefficient of determination indicating that variation in Cryptosporidium

prevalence is well explained by this model. Within this model, the distance to WWTP

variable had the lowest P-value (Table 2.11). The model selected for the second period

produced an even lower P- value with a higher coefficient of determination. Within the

model, both the human population density and distance to WWTP variables had

statistically significant P-values (Table 2.11).

Because this is an exploratory analysis, even though there are fundamental

differences between the first period and second period data due to sample collection, a

linear regression was run across both periods including the variables that were selected

for both periods. The linear regression was run on odds ratios against human population

density within study sites, distance of each site from nearest livestock farm, proportion of

each site defined by NLCD as pasture, and distance of each site from nearest wastewater

treatment plant. The results of this regression showed a highly significant P-value (P =

0.01) with an F-statistic of 4.1. The coefficient of determination shows that 67% (R2 =

0.67) of the variability in prevalence of Cryptosporidium among sites during both periods

is explained by the model. Consistent with results from the regressions run on period 1

and period 2 separately, the distance to WWTP variable was the strongest explanatory

variable with a P-value of 0.01. These results strongly support the previous results and

85

indicate that the selected variables were significant factors influencing the variability

observed in Cryptosporidium prevalence between urban and peri-urban sites.

Wastewater treatment plants have great potential to serve as a source of

contamination to both human and wildlife populations. The Columbus wastewater

collection system consists of 2,782 miles of sanitary sewers and 167 miles of combined

sewers that collect domestic and industrial wastewater and rainwater from the combined

system (Jackson Pike Wastewater Treatment Plant 2014). There are also 2,537 miles of

storm sewers which discharge untreated storm water to creeks and rivers (Jackson Pike

Wastewater Treatment Plant 2014). When combined sewer overflows (CSOs) occur, they

discharge storm water as well as untreated human and industrial waste, toxic materials,

and debris (U.S. Environmental Protection Agency 2014). All of these sources of

contamination are ultimately combined at the wastewater treatment plants where they are

collected and treated. Treatment includes periods when the wastewater sits out to settle.

Geese often take advantage of this source of open water and not only contribute to further

contamination, but also expose themselves to the contaminants in the wastewater.

Wastewater treatment plants find that coliform bacteria counts increase dramatically

when large numbers of Canada geese are present and decline dramatically when the geese

are removed (U.S. Fish and Wildlife Service 2012).

Though it was hypothesized that contact with livestock waste would result in a

greater prevalence of cephalosporin-resistant E. coli, it is also a significant contributor of

Cryptosporidium. Livestock can have a high prevalence of infection with both

Cryptosporidium and Giardia. During peak shedding, infected animals, particularly

young animals, can excrete 107 Cryptosporidium oocysts and Giardia cysts per gram of

86

feces for several days (Budo-Amoako et al. 2011). Olson et al. (1997) sampled a variety

of livestock species throughout various farms in Canada for Cryptosporidium and found

overall prevalence to be 20% in cattle, 23% in sheep, 11% in swine, and 17% in horse

samples. These results are supported by similar studies (Coklin et al. 2007, Helmy et al.

2013).

Livestock factors play a strong role in the contamination of the watershed which

becomes centralized at the WWTP. Concentrations of Cryptosporidium tend to be higher

in surface waters during periods of high precipitation combined with manure application

on agriculture farms. A study by Hansen et al. (1991) found that seasonal factors,

including runoff of land drainage, may affect oocyst concentrations in rivers by 10-fold,

with concentrations in drier periods being significantly lower than those in wetter periods

(Hansen et al. 1991). Sischo et al. (2000) looked at 11 dairy farms in the northeastern

United States and concluded that the single largest risk factor for detecting

Cryptosporidium in surface water was increased frequency of spreading of manure on

fields. The researchers state that frequent manure spreading was associated with an 8-fold

increase in the odds of detecting oocysts in stream water (Sischo et al. 2000). Numerous

studies all over the world have found similar results (Bodley-Tickell et al. 2002, Hörman

et al. 2004, Wilkes et al. 2009). Not only do geese receive exposure to infected livestock

waste from surface water, but Canada geese have been observed wandering behind

livestock and picking up undigested waste from their feces (Graczyk et al. 1998).

Graczyk et al. 1998 found C. parvum in Canada goose fecal samples on the eastern shore

of Maryland, where land use was 30% agricultural (Maryland Department of Agriculture

2011) with scattered cattle farms. In this case, migratory geese were observed to follow

87

cattle and consume undigested corn from feces (Graczyk et al. 1998). This demonstrates

transmission to Canada geese from livestock.

In Ohio, most farms will apply manure in the spring before planting, then again in

the fall after harvest (Wicks 2014). Small farms, which may not have any manure

storage, may apply daily, but that is overall a minority (Wicks 2014). Total annual

precipitation for the Greater Columbus area during the year 2013 was highest in the

month of June (17.5 cm) which was when the first sampling period took place (National

Oceanic and Atmospheric Administration 2013). Prevalence of Cryptosporidium was

slightly higher during the first period (50%) than the second period which is not only

consistent with observing a greater number of people at each site during this period, but

also agrees with the precipitation patterns and a shorter duration after manure application

on agriculture fields for the first period than the second period. Therefore, surface water

could have contained especially high concentrations of pathogens during this period that

would ultimately be centralized at wastewater treatment plants.

Future Research and Management Recommendations

This study is an exploratory study that deals with hazard identification and

characterization. The next step in a microbial risk assessment is the dose-response

assessment followed by exposure assessment, and finally risk characterization (U.S.

Environmental Protection Agency/U.S. Department of Agriculture 2012). These

assessments are quantitative analyses that would involve examination of the exact

number of cysts, oocysts, or colony forming units per unit of measurement. This would

88

require the use of more molecular methods. For example, it would be useful to use

methods that estimate gene copies or cell equivalents (U.S. Environmental Protection

Agency/U.S. Department of Agriculture 2012). The most important analysis to include

would be identification of the pathogen species and genotypes using PCR or other similar

molecular methods. This will help quantify which pathogens within each fecal sample are

able to infect the human population. Taking into account the various exposure pathways,

this information would then be applied to a dose-response model. This study does initiate

the exposure assessment process by measuring individual environmental variables that

have the potential to influence exposure, such as distance of each site from nearest

livestock farm and WWTP. Future exposure assessment steps should focus on

confirmation of exposure through surveys, observation of behaviors, and use of

epidemiological databases. Information from the initial hazard identification and

characterization, such as the need to focus on the molt period vs. the most-molt period, is

necessary to carry out these next steps.

There are many methods to be considered when it comes to the control and

management of Canada goose populations. Traditional methods include: hunting and

lengthening the hunting season, poisoning or immobilization, repellent chemicals and

grasses (the Alaskan Native Medical Center in Anchorage, Alaska uses grape Kool-Aid

powder with much success), egg sterilization, physical programs including fences around

ponds and overhead placement of lines or grid wires to prevent landing, vegetative or

rock barriers, reduced mowing, sterilization by surgical neutering or oral contraception,

creating single-sex flocks, hazing and scaring techniques, translocation of predators, and

translocation of the geese (Dieter et al. 2001, Smith et al. 1999). One beneficial method

89

that is often overlooked is the use of riparian buffers or alternative feeding areas.

Creating these buffers can draw the geese away from human population centers and

provide the geese with their land requirements thus preventing conflict.

New methods are being experimented with such as induced cholestasis

(interrupting the flow of bile from the liver to the intestine) (Erlandsen 2005). For

example, bile is a major growth factor for the proliferation of Giardia spp. trophozoites in

the small intestine and it stimulates encystment of trophozoites at high concentrations

(Erlandsen 2005). A study on mice found that cholestasis induced through surgery

produced a 3 log reduction in excretion of Giardia cysts within 24 hours of surgery and a

4 log reduction after 3 days (Erlandsen 2005). Cholestasis induced through adding bile-

binding resins to the diet was associated with reductions in cyst excretion of 3 logs within

1 day (Erlandsen 2005).The bile-binding diet is much less expensive than surgery and

produces nearly the same reductions in excretion of cysts and therefore should be pursued

further, especially in other organisms.

Another newer method being developed is vaccination. The goal is to vaccinate

mothers so that their offspring acquire passive immunity. Currently most of these studies

focus on livestock species. For example, a recombinant plasmid encoding the 15 kDa

surface sporozoite protein of Cryptosporidium parvum was developed in adult pregnant

goats (Sagodira et al. 1999). The study found that nasal immunization of pregnant goats

with the plasmid led to a transfer of immunity to offspring that resulted in protection

against C. parvum infection (Sagodira et al. 1999). Offspring from vaccinated dams shed

significantly fewer oocysts and over a shorter period of time than did offspring from non-

vaccinated dams (Sagodira et al. 1999). Further development of these new pathogen

90

control methods, especially in other species such as B. Canadensis, could have huge

implications for zoonotic pathogen management.

The human population can take preventive measures as well such as making sure

to clean up waste and storing or disposing of it in a way that does not allow access to

Canada goose populations. A simple step such as ensuring that dumpster lids are always

closed properly can prevent geese from feeding on food remnants. Human food handouts

are another issue that attract Canada goose populations. Informative signs and posters can

be used to educate the public about the negative effects of human food handouts. Better

management of livestock waste could be practiced as well. All farms are encouraged to

have a nutrient management plan which gives guidelines specific to that farm as to when

and where (which fields) to apply (Wicks 2014). These plans usually recommend that

manure should not be applied if there is a 50% chance or greater of rainfall of more than

½ inch within 24 hours after application (Natural Resources Conservation Service 2012).

These plans need to be enforced rather than recommended. Similarly, legislation to limit

the use of antimicrobials in livestock, as well as in hospitals, needs to be enforced as

opposed to voluntary.

91

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CHAPTER 3

HAZARD IDENTIFICATION SUMMARY

The first step in a pathogen risk assessment is hazard identification and

characterization. This chapter is a qualitative examination that reviews information

related to the epidemiological, surveillance, clinical, and microbial aspects of the hazard.

It seeks to describe the mechanisms involved in causing harm and the pathogen’s ability

or potential to cause harmful effects (U.S. Environmental Protection Agency/U.S.

Department of Agriculture 2012). The stressors examined in this study were enteric

pathogens in fecal samples collected from Canada geese in the greater Columbus, Ohio

area. The adverse health effects examined were cryptosporidiosis, giardiasis,

salmonellosis, and infection with cephalosporin-resistant E. coli.

The National Institute of Health classifies Giardia, Cryptosporidium, Salmonella,

and E. coli as category B biodefense agents, the second highest priority biological agents,

meaning they are “moderately easy to disseminate, result in moderate morbidity rates and

low mortality rates, and require specific enhancements for diagnostic capacity and

enhanced disease surveillance” (National Institute of Allergy and Infectious Disease

2012). They are also all considered to be American Biological Safety Association Risk

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Group 2 Agents that are associated with human disease, that are rarely serious, and for

which preventive or therapeutic interventions are often available (American Biological

Safety Association 2014). The National Institute of Health has recently classified

antimicrobial resistant bacteria as a category C biodefense agent which includes

“emerging pathogens that could be engineered for mass dissemination in the future

because of availability, ease of production and dissemination, and potential for high

morbidity and mortality rates and major health impact” (National Institute of Allergy and

Infectious Disease 2012). All of these pathogens are nationally notifiable diseases

(Centers for Disease Control 2015). All produce diseases of significance and high levels

of exposure result in an exposure hazard to the human population.

Major routes of transmission for all of these pathogens include ingestion of

contaminated food and/or water and by direct contact with infected people, animals, or

contaminated environmental surfaces (Kassa et al. 2004, National Institute of Allergy and

Infectious Disease, 2011a, National Institute of Allergy and Infectious Disease 2011b).

Canada goose droppings containing these pathogens have the greatest potential for hazard

to human health where there is close contact with the human population such as

recreational sites, near restaurants, or local sources of open water. Canada goose

droppings may also present a greater hazard than other species because of their

widespread distribution and large body size which produces larger droppings that take up

more space and increase the potential for exposure (Feare et al. 1999). Variation in

susceptibility to infection is largely influenced by human behavior in addition to location.

For example, children who play with balls are more likely to contact fecal material with

their hands than adults who do not play in parks. Additionally, children with

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contaminated hands may be more likely to transfer some of this contamination to the

mouth than are adults (Feare et al., 1999).

Giardia and Cryptosporidium are both persistent pathogens environmentally and

can survive harsh conditions such as freezing and chlorine treatment (Yoder et al. 2012,

Centers for Disease Control 2011, Environmental Protection Agency 2000). Infectivity is

high for both organisms with as few as 30 Cryptosporidium oocysts capable of causing

illness in healthy adults (DuPont et al. 1995, Kassa et al. 2001). A single avian isolate of

Giardia is virulent to mammals (Graczyk et al. 2008, Upcroft et al. 1997). Shedding is

also high in both pathogens. The mean concentration of Cryptosporidium oocysts found

in the average Canada goose fecal sample is 370 ± 197 oocysts/g while the average

concentration of Giardia sp. cysts found in a Canada goose fecal sample is 405 cysts/g

(Gatczyk et al. 1998).

Results from this study showed that 55 of 93 (59%) Canada goose fecal samples

that were tested for all pathogens were positive. Resident Canada goose droppings pose a

minimal exposure hazard to the human population in the Greater Columbus area for

exposure to Giardia lamblia, the only species known to infect humans, with an overall

prevalence of 3.52%. Prevalence in other studies is generally low (Table 2.12). Kostroff

and Rollender (2001) reported a prevalence of 74% in a Canada goose population in New

York but did not test specifically for Giardia lamblia (Kostroff and Rollender, 2001).

The Giardia assay used in this study was specific for the species that can infect humans

so the prevalence of Giardia from this study is more informative about potential hazards

to public health than from Cryptosporidium prevalence.

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Conversely, results from this study showed that resident Canada goose fecal

samples in the Greater Columbus area have a high potential to expose the human

population to Cryptosporidium with an overall prevalence of 44.7%. Though the

Cryptosporidium assay was not specific for species that can infect humans it is likely that

a significant portion of positives detected were species that are able to infect humans.

Unlike Giardia that tends to be highly host-specific and has only six known species, only

one of which can infect humans, (Hunter et al. 2005, Xiao and Fayer 2008, Appelbee et

al. 2005), there are at least 21 of 26 recognized species of Cryptosporidium that are

capable of infecting multiple species (Xiao and Fayer 2008, Ryan et al. 2014, Power et al.

2011). Though only C. hominis and C. parvum are currently of major significance to

public health by causing the majority of human cases (Zhou et al. 2004), other species

and genotypes, such as C. meleagridis (Appelbee et al. 2005), are increasingly associated

with human illness (Ryan et al. 2014). Additionally, genotyping and subtyping data

suggest that zoonotic transmission is more important in the epidemiology of

cryptosporidiosis than in giardiasis because most types of Giardia are adapted to a single

host species in addition to the fact that only 1 of 6 species of Giardia is capable of

infecting humans (Xiao and Fayer, 2008). Because multiple species of Cryptosporidium

have the potential to infect humans, it is likely that a significant proportion of the total

Cryptosporidium species detected would infect humans.

Results from this, and numerous other studies (Hussong et al. 1979, Converse et

al. 1999, Feare et al.1999, Roscoe 2001, Fallacara et al. 2001), showed that Salmonella in

Canada goose fecal samples in the Greater Columbus area has very low exposure

potential to infect the human population. This study did not detect Salmonella in any of

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the fecal samples. It appears that although Canada geese can serve as vectors of

Salmonella, prevalence is very low in the population and therefore introduces a minimal

hazard to the human population in the greater Columbus area.

Detection of any antimicrobial-resistant bacteria in wildlife populations represents

a major concern because bacterial strains are very difficult to treat if infection occurs.

This study detected cephalosporin-resistant E. coli in 10.8% of samples in both urban and

peri-urban environments. Though antimicrobial resistance has been documented in

several other wildlife species (Wang et al. 2014), the discovery of cephalosporin-resistant

E. coli in Canada geese is significant because of geese can fly long distances and transmit

antimicrobial-resistant bacteria strains acting as highly efficient vectors.

This study also showed greater overall pathogen prevalence at urban sites 69/199

(35%) compared to peri-urban sites 37/199 (19%). The environmental variables within

the urban landscape that appeared to have the greatest influence on percentage of

Cryptosporidium detected at each site were: distance of site to nearest WWTP, human

population density within study sites, proportion of land defined by the NLCD as pasture,

and distance to nearest livestock farm. The distance to WWTP variable was the only

variable that was significant at the alpha=0.1 level for both periods. Distance to WWTP

was also significant at the alpha=0.05 level for the experimental regression run on the

best fit model between periods.

Wastewater treatment plants collect untreated water as well as human, livestock,

and industrial waste, toxic materials, and debris. Not only do geese contribute

significantly to contamination of wastewater treatment ponds where they are known to

congregate (U.S. Fish and Wildlife Service 2012), but they are also exposed to

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contaminants in the water that they can then transmit to other locations. It would be

beneficial in some areas to use a grid wire system to prevent geese from landing in ponds,

however this may not be economical for larger ponds. Replacing storm and combined

sewers with sanitary sewers may also be beneficial by preventing contaminants from

reaching the surface water.

High densities of humans in urban areas result in high concentrations of food,

water, energy, materials, sewage, pollution, and waste (McDonnell et al. 1997). Geese

that inhabit these urban areas further contribute to this accumulation by introducing large

quantities of fecal matter. Additionally, they are more likely to be exposed to

anthropogenic waste that they transmit back to the human population or to other

reservoirs including domestic animals, wildlife, and fomites. Deterring geese from

concentrating in high risk urban areas will help eliminate this problem. A successful

strategy that has been used by Suburban Commercial and Residential Animal

Management (SCRAM), a humane wildlife removal organization in central Ohio, has

been the use of border collies that patrol areas where geese tend to congregate. Where

possible, creating riparian buffers and alternative feeding areas can not only be effective

but may also offer other benefits such as water purification through filtration. People can

also work to clean up waste and store it properly to avoid access to geese.

Livestock waste and manure on agricultural fields are a major source of pathogens

that are easily disseminated throughout the environment through surface water. Geese can

acquire these pathogens from surface waters as well as from feeding in agricultural fields

treated with manure. Proper barriers that prevent waste from reaching surface water may

be effective. Creating nutrient management plans for farms is a valuable method to

108

reduce this problem and these plans must be enforced. Similarly, in order to reduce

antimicrobial resistant bacteria in livestock, as well as human populations, legislation to

limit the use of antibiotics need to be enforced as opposed to voluntary.

Future steps should focus on quantitative analysis including dose-response

assessment followed by exposure assessment (U.S. Environmental Protection

Agency/U.S. Department of Agriculture 2012). This would require the use of more

molecular methods such as identification of the pathogen species and genotypes using

PCR. This will help quantify which pathogens within each fecal sample are able to infect

the human population. This study initiates the exposure assessment process by measuring

individual environmental variables that have the potential to influence exposure, such as

distance of each site from nearest livestock farm and WWTP. Future exposure

assessment steps should focus on confirmation of exposure and measurement of

additional environmental variables that may influence exposure. For example, samples

could be taken from multiple wastewater treatment plants. Information from the initial

hazard identification and characterization, such as the need to focus on the molt period

vs. the most-molt period, is necessary to carry out these next steps making this study

necessary.

109

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