ABSTRACT

CHARACTERIZING THE FECAL MICROBIOTA AND RESISTOME OF CORVUS BRACHYRHYNCHOS (AMERICAN CROW) IN FRESNO AND DAVIS, CALIFORNIA

American Crows are common across the United States, well adapted to human habitats, and congregate in large winter roosts. We aimed to characterize the bacterial community (microbiota) of the crows’ feces, with an emphasis on human pathogens. The antibiotic resistance (AR) of the bacteria was analyzed to gain insight into the role crows may play in the spread of AR genes. Through 16S rRNA gene and metagenomic sequencing, the microbiota and antibiotic resistance genes (resistome) were determined. The core microbiota (taxa found in all crows) contained Lactobacillales (22.2% relative abundance), Enterobacteriales (21.9%) and Pseudomonadales (13.2%). Among the microbiota were human pathogens including Legionella, Camplycobacter, Staphylococcus, Streptococcus, and Treponema, among others. The Fresno, California crows displayed antibiotic resistance genes for multiple drug efflux pumps, macrolide-lincosamide- streptogramin (MLS), and more. Ubiquitous, urban wildlife like the American Crow may play a role in the spread of AR pathogens to the environment and human populations.

Rachel Lee Nelson August 2018

CHARACTERIZING THE FECAL MICROBIOTA AND RESISTOME OF CORVUS BRACHYRHYNCHOS (AMERICAN CROW) IN FRESNO AND DAVIS, CALIFORNIA

by Rachel Lee Nelson

A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Biology in the College of Science and Mathematics California State University, Fresno August 2018 APPROVED For the Department of Biology:

We, the undersigned, certify that the thesis of the following student meets the required standards of scholarship, format, and style of the university and the student's graduate degree program for the awarding of the master's degree.

Rachel Lee Nelson Thesis Author

Tricia Van Laar (Chair) Biology

John V. H. Constable Biology

Krish Krishnan Chemistry

For the University Graduate Committee:

Dean, Division of Graduate Studies AUTHORIZATION FOR REPRODUCTION OF MASTER’S THESIS

X I grant permission for the reproduction of this thesis in part or in its entirety without further authorization from me, on the condition that the person or agency requesting reproduction absorbs the cost and provides proper acknowledgment of authorship.

Permission to reproduce this thesis in part or in its entirety must be obtained from me.

Signature of thesis author: ACKNOWLEDGMENTS Python and Linux expertise - Wesley Leach Guidance - Tricia Van Laar and committee Funding - California State University, Fresno: Graduate Net Initiative, Faculty Student Research, Graduate Research and Creative Activities CSU Council on Ocean Affairs San Joaquin Valley Chapter of the Wildlife Society

TABLE OF CONTENTS Page

LIST OF TABLES ...... vii

LIST OF FIGURES ...... ix

INTRODUCTION ...... 1

Corvus brachyrhynchos (American Crow) ...... 1

Gut Microbiota ...... 7

Antibiotic Resistance (AR) ...... 9

Aims, Hypotheses, and Significance ...... 15

METHODS ...... 17

Field Work ...... 17

Experimental Design ...... 21

DNA Extraction and Sequencing ...... 23

Microbiota Analysis ...... 24

Resistome Determination ...... 29

RESULTS ...... 34

American Crow Microbiota ...... 34

Diversity Analyses of Fecal Microbiota ...... 47

American Crow Resistome ...... 52

DISCUSSION ...... 56

American Crow Microbiota ...... 56

Pathogens ...... 62

American Crow Resistome ...... 64

Future Directions ...... 66

CONCLUSION ...... 67 vi vi Page

REFERENCES ...... 68

APPENDICES ...... 84

APPENDIX A: QIIME2 SYSTEM DETAILS AND COMMANDS ...... 85

APPENDIX B: MICROBIOTA DETAILS ...... 89

APPENDIX C: RESISTOME RESULTS VIA ARGS-OAP ...... 108

LIST OF TABLES

Page

Table 1. FeatureTable (table.qzv) Displaying Number of Aequences Per Sample Remaining After Quality Control (DADA2)...... 25

Table 2. Isolation Agars Used to Select Organisms from Fecal Samples ...... 30

Table 3. Test Antibiotics Modes of Action ...... 31 Table 4. 96 Well Plate Set Up Used to Determine Minimal Inhibitory Concentrations ...... 31

Table 5. Taxonomic Richness of Fresno, Davis, and Critter Creek Microbiota .... 34 Table 6. Relative Abundance of Phyla and Orders in the American Crow Microbiota ...... 35 Table 7. Taxa Identified in All Fecal Samples, Comprising the Core Fecal Microbiota of the American Crow ...... 40

Table 8. Fresno American Crow Core Fecal Microbiota ...... 41

Table 9. Critter Creek American Crow Core Fecal Microbiota ...... 42

Table 10. Davis American Crow Core Fecal Microbiota ...... 43

Table 11. Fresno American Crow Variable Fecal Microbiota ...... 44

Table 12. Critter Creek American Crow Variable Fecal Microbiota ...... 44

Table 13. Davis American Crow Variable Fecal Microbiota ...... 45

Table 14. Number of Pathogen-Containing Taxa in American Crow Microbiota . 46

Table 15. Summary of Microbiota Diversity Statistical Analyses ...... 48

Table 16. Antibiotic Resistance Genes Identified in Fresno Resistome ...... 52 Table 17. Minimum Inhibitory Concentrations (µg/mL) of Critter Creek Isolates ...... 53 Table 18. Minimal Inhibitory Concentration (µg/mL) Observed in Fresno, California Fecal Microbiota ...... 54

Table 19. Antibiotic Resistant Organisms Isolated from Fresno Crow MIC ...... 54 viii viii Page

Table 20. Minimum Inhibitory Concentrations (µg/mL) of CSU, Fresno Farm Animals Isolates ...... 55

Table 21. Full Microbiota of American Crow Feces ...... 90 Table 22. Complete Relative Abundance of Phyla in the Fresno Crow Microbiota ...... 105 Table 23. Complete Relative Abundance of Phyla in the Critter Creek Crow Microbiota ...... 106 Table 24. Complete Relative Abundance of Phyla in the Davis Crow Microbiota ...... 107 Table 25. Complete List of Antibiotic Resistance Genes Identified by ARGs-OAP...... 109

LIST OF FIGURES

Page

Figure 1. Map of American Crow staging and roosting locations in Fresno, California...... 18 Figure 2. Map of American Crow staging and roosting locations in Davis, California...... 20

Figure 3. Forward and reverse reads quality (Demux.qzv)...... 26

Figure 4. Average relative abundance of bacterial taxa in American Crow feces. 36

Figure 5. Relative abundance of phyla and orders in each sample...... 37 Figure 6. Unweighted and weighted UniFrac analysis of beta diversity between collection sites...... 50 Figure 7. Unweighted and weighted UniFrac analysis of beta diversity between dynamic diet samples (Fresno and Davis) and consistent diet samples (Critter Creek)...... 51

INTRODUCTION

In this study, we investigated the bacterial communities (microbiota) present in the feces of the American Crow (Corvus brachyrhynchos) and analyzed the antibiotic resistance these bacteria possessed (resistome). American Crows served as the focus of our study because of their close proximity to humans in the urban environment, diverse diet, and roosting behavior. These conditions make it possible for crows to acquire and potentially transfer antibiotic resistant pathogens to humans. We aimed to characterize the microbiota and resistome of the American Crow, with an emphasis on pathogenic bacteria.

Corvus brachyrhynchos (American Crow) The American Crow (Corvus brachyrhynchos, henceforth referred to as “crow”) is a member of the corvid family, along with ravens, jays, magpies, rooks and others [1]. The corvids have relatively advanced cognitive abilities such as basic problem solving and tool creation [2]. They also have complex social behaviors including non-relatives participating in raising offspring (called cooperative breeding) [3], forming multi-generational family units, and congregating in large winter communities called roosts [4].

Diet Crows are highly opportunistic foragers and will eat from many food sources. The natural crow diet includes invertebrates and small animals including hatchling tortoises, grains and small nuts, avian eggs and hatchlings, and frogs and fish [5]. At the beginning of the roosting period (about November) grapes, almonds, pistachios, strawberries, and other human food crops are consumed [6]; whereas towards the end of the roosting season (around February) the diet is 2 2 compositionally different including citrus, broccoli, collard greens, beets, and others [6]. In human-altered habitats, crow diets can include a wide range of refuse originating in landfills and urban streets [7, 8], and agricultural (farms, orchards, ranch feeding areas, and pastures) sources [9]. Heiss (2009) observed urban dwelling crows eating more frequently from anthropogenic sources, such as human food waste, even when more nutritious, natural options (e.g., invertebrates) were available [10]. When placed in the context of the optimal foraging theory [11], anthropogenic sources commonly provide less energy but have a relatively low energetic cost to obtain, suggesting the loss of nutrients from their diet was outweighed by the gains urban foraging provides including protection (against Great Horned Owls and armed farmers) or steady food supply [12]. These urban food sources may contain antibiotic resistant organisms [13] derived from human activities which can colonize the avian gastrointestinal tract (see Territories - Urban below).

Territories Crows are found year-round throughout most of the United States, except along the border between the US and Mexico [14]. Crows prefer semi-open land like woods, towns, cities, farms, and shorelines, and avoid deserts [14]. Increasing numbers of crows have made suburbs and cities their homes since 1950, before which roosts were only seen in rural areas [15]. Human created habitats are attractive to crows because these locations provide more consistent food supplies (e.g., food wastes, and roadkill [16]), and protection from predators such as owls [12]. 3 3

Urban. Crows that forage primarily in urban areas favor shopping centers, residential neighborhoods, freeways, hospitals, schools, parks, water basins, and landfills. Typically, wastes are disposed of in dumpsters or trashcans, but crows can gain access if the lids are left open or the trash disposed of incorrectly. Urban environments are structurally and temporally complex but exhibit a commonality between cities resulting in urban homogeneity, which is caused by the common distribution of parking lots, shopping centers, and parks that create cities with similar layouts [17]. Common cultural practices, such as waste disposal practices and the types of material thrown away, also add to urban homogeneity. Since such large swaths of land are very similar, it reduces the niches available, therefore making overall biodiversity lower (fewer phylogenetic lineages) [17]. Generalists, such crows and raccoons, take advantage of multiple food sources, allowing them to thrive over specialists that may not be capable of foraging in the limited urban landscape [18, 19]. Landfills are known foraging environments for crows [14], and likely contain unrecorded levels of antibiotics [20] due to the disposal of unused or expired prescriptions. This problem is not addressed by the Federal Drug Administration (FDA) which has no recommendations for proper disposal of antibiotics [21]. Consumption of residual antibiotics may cause disruption to the microbiota (dysbiosis) or allow bacteria to develop resistance. Hrenovic et al. (2017) [22] recovered both extensively and multi drug-resistant Acinetobacter baumannii, an opportunistic infectious bacterium which has been increasing in incidence and drug resistance [23], from a landfill in Croatia.

Rural. Crows that travel from rural areas could have had opportunity to feed on natural sources (see Diet above) including access to the San Joaquin or 4 4

Sacramento Rivers which may provide insects, small fish, eggs or amphibians for the crows to eat. Various agricultural environments like dairy operations, farms, orchards, and field crops may supplement these sources. Due to the growth promoting effects of some antibiotics [24], and to preempt infection, livestock producers routinely dose their livestock, such as giving chlortetracycline to poultry [25], to increase profit [26]. The continual use of antibiotics selects for antibiotic resistant gastrointestinal (GI) microbes [27], which may be ingested crows foraging at the facilities.

Roosts Crows defend the territories where they forage and raise their young [28]. Territories range from 1-3500 hectares (0.01 km2 to 35 km2) [28]. Urban territories generally comprise less area when compared to rural territories [28]. In the fall, after the young have fledged and are capable of prolonged flight, adult and young crows start to leave their territory to join communal roosts at night [29]. There may be multiple, smaller roosts in the beginning of the season (around November), but the members of these smaller roosts usually merge into one large roost by late December. During roosting season crows commute from their daily foraging territories to the roost (up to 40 miles [30]) at dusk [15]. When the roost is congregating for the night, the crows do not all group together at once. Generally, smaller groups of crows will form in various locations around the final roosting site for that night, called “staging” or “pre-roosting” sites [31], commonly trees and power lines. As the pre-roosts develop there is significant interaction among individuals, especially as new birds arrive, with loud vocalizations and chasing and tumbling behaviors between individuals. After dark, the crows leave the staging sites for the primary roost, which may be a group of 5 5 trees or rooftops and which point interactions and vocalizations among the birds decline. It is hypothesized darkness provides cover to evade potential predation from raccoons, hawks, or humans [32]. The reason for this roosting behavior is unclear, but hypotheses are available. They could be using the roost for protection, most likely from Great Horned Owls [16] which are the crows’ top predator. They could be using this opportunity to share information regarding the locations of food sources and danger. It has been shown that crows recognize faces that they perceive as threatening and share this information with crows in the area [33]. The process of roost selection and the site properties used to identify an appropriate roost location are unclear but access to water may be a primary factor. Seattle, Washington where ocean and fresh water sources are abundant may have been attractive to the very large crow roost that has formed over the years and has been the focus of studies including Marzluff et al. (2001) [28]. The roosts we collected from, Fresno and Davis, California, also have access to the San Joaquin and Sacramento Rivers respectively. Water may be a more important factor on the location of a roost in California than in Washington, as California is drier on average. The UC Davis and downtown Fresno roosts, which generate fecal deposits probably containing numerous, possiblly pathogenic bacteria, are located in areas trafficked by thousands of people on a standard day.

American Crows as Reservoirs of Infection The frequency with which urban crows create their roost in highly populated areas serves as the launch point for this study building on the idea that crow feeding on human refuse may contain antibiotic resistant (AR) organisms which in turn may lead to AR pathogens transferring to humans, posing a public 6 6 health risk. We used American Crows as the focus of our study due to their roosting behavior, diet, and proximity to humans. The probability of crows hosting pathogenic bacterial taxa in their gastrointestinal (GI) tract and feces is high due to their urban diet which can include landfills and urban streets [7], in addition to agricultural sources [9]. Evening coalescence into the roosts subsequently concentrates individuals from the surrounding feeding territories and deposition of feces presents a potential risk of the fecal bacteria, including pathogens, being transferred to humans and other animals. Avian fecal microbiota possesses many pathogen-containing taxa and organisms, both enteric (including Campylobacter and Salmonella) and non- enteric (such as Mycoplasma and Clostridium). Shorebird and waterfowl microbiota display C. botulinum [34] and Chlamydia [35] and gull (Larus) species specifically contain Listeria [36, 37]. Game birds such as turkeys (Meleagris gallopavo) [38], chukar partridges (Alectoris chukar), and peafowl (Pavo cristatus) [39], along with eastern house finches (Carpodacus mexicanus) [40] harbor Mycoplasma species in their GI tract. Poultry, common focuses of avian fecal microbiota studies for commercial interests, shed C. perfringens [41] and Chlamydia [35] in their feces. Psittacine birds, common household pets such as parrots, harbor Chlamydia [42] and Borrelia species [43]. The enteric bacteria Campylobacter (C. jejuni), Salmonella (S. enterica and S. typhiruium), Listeria, and E. coli are common fecal microbiota members of waterfowl [44], shorebirds [45-48], pigeons [49], and songbirds [50-52]. Several focused studies have identified specific pathogens in crow feces including C. jejuni [9], which causes gastroenteritis. More alarming is the antibiotic resistant (AR) pathogens that have been characterized such as plasmid-mediated quinolone resistant (PMQR) 7 7

Enterobacteriaceae [53], vancomycin-resistant Enterococcus [54], and MDR Bacillus [55]. The goals of this study are to describe the complete microbiota present in crow feces and provide insight into how the gut microbiota of these pervasive birds may be altered by the urban environment. Additionally, a characterization of the potential for crows to serve as bacterial reservoirs that in urban systems may contain human pathogens. Finally, the crow gut may provide an ideal environment to facilitate the transfer of AR genes among bacterial species that may subsequently be transferred to other animals including human pathogen that present a concern to public health and the epidemiology of pathogen outbreaks. We hope the results of this study can serve as a model for other urban city crow populations and a stepping-stone for future works towards examining the role of avian microbiota in public health.

Gut Microbiota A microbiota is the collection of microorganisms (bacteria, archaea, viruses, fungi, and protozoans) living together in a community. We were specifically investigating the fecal bacterial microbiota of the crow. Investigating the fecal microbiota provides information about the gut microbiota without invasive sampling of the host. Microbial communities in the GI tract have been shown to convey physiological importance, such as improving metabolic function [56], contributing nutrients to the host [57], aiding in the development of the host immune system [58], and preventing host colonization by transient and possibly pathogenic bacteria [59]. Avian fecal microbiota contain bacteria from various phyla, typically Bacteroidetes, Firmicutes, Proteobacteria, and more [60]. Microbiota are complex multi-species communities, yet there are certain taxa 8 8 found in nearly all crows, termed the “core microbiota” [61]. The unique microbes found in individual or groups of crows comprise the “variable microbiota” [62]. Argument persists as to whether the taxonomic core microbiota is responsible for providing essential functions to the host, or if multiple taxa can fill the same functional niche, creating a “functional core microbiota” [62]. When the relative abundance of the microbiota is altered (inclusion of exogenous taxa, or elimination, increase or decrease of current microbiota), termed dysbiosis, the host may develop diseases [62]. In humans, dysbiosis of the gut plays a role in inflammatory bowel diseases [63], obesity [61], cancer [64], malnutrition [65], and neurological disorders [66]. Having knowledge of the microbiota can give insight to which taxa, when disturbed, may lead to disease. We use the term “healthy” to define a microbiota that provides necessary function and does not cause disease in the host. Not all members of the microbiota are valuable to the host. Commensal bacteria benefit from the gut, as the gut provides a stable environment (water balance and temperature) coupled with an abundance of nutrients, but the host does not receive any advantage from the microbes’ presence. Mutualistic bacteria play important functional roles in the host like improved amino acid and carbohydrate metabolism [61]. If the taxa responsible for providing these functions are present, this mutually symbiotic system is healthy. A stable microbiota displays a degree of functional redundancy where more than one taxa may provide the same function [62]. This way, if the primary taxa were greatly reduced the redundant taxa would still be present to provide the function. Currently, it is believed that greater microbial species diversity is beneficial in a healthy microbiota because low diversity in the gut microbiota of humans can lead to inflammatory bowel disease and obesity [67], and increased frailty in 9 9 elderly patients [57]. Species diversity helps guard against the colonization by exogenous, possibly pathogenic, microbes because most of the available niches are occupied by members of the native microbiota [68]. For example, Clostridium difficile can dominate the colon causing a range of colonic issues such as diarrhea or fatal colitis [69]. Such cases are commonly caused by the destruction of the GI microbiota by antibiotics and subsequent nosocomial acquisition of C. difficile [70]. Diversity also maintains intra-microbiota competition which keeps endogenous taxa from dominating the population [62]. Dysbiosis of the GI microbiota can lead to potentially fatal pathology resulting from altered competitive balance between microbial species and upset functional redundancy.

Antibiotic Resistance (AR) Antibiotics are compounds synthesized and secreted by microorganisms that kill or inhibit the growth of other microbes. The first antibiotic, penicillin G, was discovered by Alexander Fleming in 1928 and mass produced in 1944. The discovery saved millions of lives, but resistance to the drug was observed within Fleming’s lifetime [71]. There are five major modes of action by which antibiotics are characterized. Theses mechanisms are (i) the inhibition of cell wall synthesis, (ii) the inhibition of protein synthesis, (iii) the alteration of lipid membranes that changes membrane permeability, (iv) inhibition of nucleic acid synthesis of nucleic acids, and (v) antimetabolite activity. Antibiotics can kill microorganisms through the disruption of various metabolic pathways and cellular structures, yet microbes have evolved mechanisms to counteract the disruptive action of many antibiotics. A key mechanism for minimizing the impacts of antibiotics includes production of enzymes to break down the antibiotic. For example, some 10 10

Enterobacteriaceae secrete extended-spectrum β-lactamases, exoenzymes that can break down penicillins before they are taken up by the bacteria [72]. Alternatively, C. jejuni [73] and Streptococcus pneumoniae [74] can target specific antibiotic molecules and pump them into the extracellular space using efflux pumps [75]. C. jejuni expresses an efflux system that is effective against multiple drugs (chenodeoxycholic acid, taurocholic acid, trimethoprim, and more) [73]. In addition, some bacteria can change the target of the antibiotic sufficiently to limit the ability of the antibiotic to disrupt the target. For example, rifampin resistance is usually due to a mutation in the gene for the β subunit for RNA polymerase in this situation rifampin is unable to bind and inhibit RNA polymerase action [76]. Many organisms resistant to the groups of antibiotics macrolides, lincosamides, and streptogramin B, alter their shared target, the 50S ribosome [77, 78].

Gene Exchange Occurrence in Bacterial Populations The diversity of the microbiota provides the host with a certain number of benefits, however, in some cases pathogens be present as minor components of the community. As bacteria are adept at exchanging genetic information through a variety of mechanisms, this diversity creates the possibility that non-pathogenic bacteria with AR genes may exchange those genes with pathogens creating a pathogen with AR characteristics. In some pathogens, like Shigella, Helicobacter pylori, and Vibrio cholerae, important virulence factors called “pathogenicity islands” are encoded in the genome [79]. These virulence factors, like antibiotic resistance [80], hemolysin and P-related fimbriae [81] can be spread to other bacteria through the processes of horizontal gene transfer (HGT). One mode of HGT is transformation where bacteria uptake naked fragments of DNA from their environment [82, 83]. The classic experiment 11 11 performed by Frederick Griffith in 1928 demonstrated that S. pneumoniae could express traits obtained from DNA present in the environment [84]. When a non- encapsulated strain of S. pneumoniae was mixed with lysate from a heat-killed strain of virulent, encapsulated S. pnuemoniae, the non-encapsulated strain gained the ability to form a capsule and became pathogenic through uptake of DNA released from the heat-killed strain [84]. Transduction is a process by which viruses transport genetic information obtained from one organism to the next cell they infect. The gene responsible for methicillin resistance (mecA) in Staphylococcus aureus (MRSA) has been identified in bacteriophages isolated from wastewater treatment plants [13], demonstrating opportunity for viral vectors to facilitate the spread of antibiotic resistance. As bacteriophages must recognize the surface proteins of suitable bacteria and attach to specific receptors, transduction is commonly limited to closely related bacterial species [85]. The most effective (successful incorporation of genetic material and greatest number of organisms effected) method of HGT is conjugation, by which bacteria directly share genetic material, even with bacteria from different bacterial clades through specially constructed pilus [86]. Not all bacteria possess the ability to conjugate, as the donor cell must carry the fertility factor (F-factor) which are the genes required to form the pilus [87] For conjugation to occur physical contact is made between the two cells, at donor and a recipient, in the form of a pilus, constructed of pilin proteins, and genetic material, usually a plasmid, is transferred from the donor cell to the recipient cell [87]. Enterococcus faecium is capable of transferring vancomycin resistance to E. faecalis by conjugation [54]. E. faecium isolated from chickens can transfer the vanA resistance gene to human-isolated E. faecium while in the human GI tract 12 12

[88], demonstrating the spread of avian-borne AR to humans. Since conjugation rates are dependent on population density [89], this process may occur in the crows’ gut or feces where bacterial numbers are high [90]. Discerning the microbiota of a host can provide insight into which bacteria are commonly found together and may participate in conjugative transfer of virulence factors [91]. As crows can act as bacterial reservoirs within which genes may be transferred among bacteria, including AR genes, and their feeding habits in urban locations may allow them to acquire human pathogens there is the potential crows to serve as facilitators of the spread of AR. Furthermore, the wide distribution of crows and their ability to travel long distances rapidly raises concerns that pathogens can be easily transferred among urban centers and affect human health.

Public Health Concern Antibiotic resistant (AR) organisms were first identified ~20 years after the first mass produced antibiotic, penicillin, was widely used [92]. The frequency of antibiotic resistant infections have increased steadily ever since with estimated 2 million people per year acquiring antibiotic resistant infections, with a mortality of approximately 23,000 annually [93]. The Centers for Disease Control and Prevention (CDC) stated these conditions may result in the entry to a “post- antibiotic era” and The World Health Organization (WHO) declared the AR crisis to be “dire” [94]. The following organisms have been classified by the CDC as an urgent public health risk: drug-resistant Neisseria gonorrhoeae, carbapenem- resistant Enterobacteriaceae, and Clostridium difficile [93]. Prompt action in treatment and containment of these antibiotic resistant pathogens is required to minimize increases in mortality and morbidity as well and containing rising public health costs [93]. 13 13

In regions of the world where antibiotics can be obtained without a prescription, improper use and disposal are causing AR to develop at alarming rates [94]. For example, in 2016, a patient was infected with a strain of Klebsiella pneumoniae that was resistant to all available antibiotics in the United States (pan- resistant) [95]. This strain of K. pneumoniae was contracted in New Delhi, India where controls over antibiotic usage are limited [96]. The rapid rate and frequency at which bacteria are developing resistance can, in part, be attributed to the processes of horizontal gene transfer (HGT), improper use of antibiotics in humans (e.g., a treatment for viral infections), overuse of antibiotics in agricultural livestock facilities [97], lack of proper regulation, and poor public understanding of prescription instructions. For some forms of HGT to occur, physical contact between bacteria is required. Birds may be contributing to the spread of AR [98, 99] as they both may create suitable gut conditions to promote HGT and fly over long distances. Many bird species migrate to warmer climates in winter, possibly depositing AR organisms along the way.

Transfer of Microbes The fecal microbiota of animals may be transferred to other animal populations, including humans [100, 101]. Crows are found across the United States, in both urban and rural environments and may fly up to 40 miles during roosting season [32]. If they harbor AR pathogens in their gut microbiota, they may transfer them to other environments as they forage and travel to the roost. Livestock may acquire pathogens left by crows foraging in pastures and poultry houses [102]. Children are especially vulnerable to bacterial colonization as they encounter feces in parks, schools, and playgrounds [49, 103]. 14 14

Thousands of crows [15] can occupy a single roost and are commonly found in cities. Human habitation has been shown to attract urban wildlife such as crows by providing more consistent food sources and protection from predation [12] than in a non-urban location. In Portage Bay, Oregon, neighbors are engaged in a $200,000 civil lawsuit over the accumulation of fecal matter due to the attraction of crows by feeding them [104]. Though no infections have been reported to date, with so many birds defecating in a small area overnight, large fecal deposits are produced on the ground which may have sufficiently high bacterial numbers to pose an infection risk to humans. A similar occurrence can be seen in Rome, Italy when millions of starlings migrate to the city in the winter. Fecal deposits become so vast the city officials close-off roads due to reduced friction [105] and have attempted to relocate the flock with trained falcons [106]. Smaller groups of crows could potentially be problematic as well. If individuals with compromised immune systems are exposed to roosting locations with abundant fecal matter, it is reasonable to consider potential transfer of crow fecal microbes to humans. Persons with weak immune systems are of particular concern including infants (<6 months old), the elderly (>70) [107], and people with primary or secondary immune deficiencies. When healthy people are infected with a pathogen, they mount a robust immune response and, often with the assistance of antibiotics, can clear the infection. An immune compromised person is less likely to clear the infection and must rely heavily on antibiotics to recover [108]. If the pathogen is AR the immunocompromised patient may develop chronic or latent infections, or not recover at all [109]. No studies to date have demonstrated the probability of contracting a pathogen from wild bird feces, but zoonotic acquisition of C. psittaci via aerosolization [110] by pet owners, zoo keepers, and poultry house workers [111] 15 15 have been documented. Another possible route of infection could be walking though the fecal deposits and tracking it into the house where it may be transferred to mucous membranes. If the feces landed in a pool with low chlorination the microbes may survive and attach to mucous membranes. Because crows are ubiquitous, so are their feces. Infection facilitated by crow fecal deposits may result in significant health care costs and loss of income for individuals, however, if the pathogen is AR the infection could last longer, increase treatment costs, or be life threatening.

Aims, Hypotheses, and Significance

Aims 1. Describe the composition of the fecal, bacterial microbiota of the American Crows in Fresno, Davis, and Critter Creek Wildlife Station, Squaw Valley, CA. a. Compare the core fecal microbiota of the three locations. b. Compare the variable fecal microbiota of the three locations. 2. Assess changes in the composition of microbiota in the Fresno roost, over the roosting period. 3. Evaluate the antibiotic resistance in the crow fecal microbiota.

Hypotheses 1. The central hypothesis of our study was that fecal samples in the Fresno and Davis, CA., roosts would have (1a) similar core microbiota but significantly different (1b) variable microbiota, due to factors including, Davis being a university campus, the different agriculture between the 16 16

two cities, and human population. Alternatively, the urban roosts may have similar microbiota due to urban homogeneity. 2. We hypothesized the microbiota may change in relative abundance over a roosting period due to changes in available food sources. Alternatively, the relatively short period (November to February) may not be adequate to observe a microbiota shift in microbiota composition and community structure. 3. We also hypothesized the crows from urban roosts will have more antibiotic resistance in their fecal microbiota than the Critter Creek population resulting from differences in diet, roosting behavior, geographic range.

Significance With the information from this study, we gain a better understanding of the American Crows’ core and variable microbiota, how the microbiota changes over a roosting season, and if the microbiota varies between cities. The knowledge of possible human pathogens and antibiotic resistance may contribute to questions about urban public health and the growing intersection between human and animal populations.

METHODS

Field Work We collected samples from a roost in Fresno, California and the roost in Davis, California with the assumption they represented isolated populations of crows as the typical flight range peaks around 40 miles and Central California crows are not characterized as being migratory. As a contrasting “consistent diet” population, samples were obtained from Critter Creek Wildlife Station in Squaw Valley, California, a privately funded organization that rehabilitates injured wildlife from the San Joaquin Valley.

Collection Sites

Fresno, California Roost. Fresno is the fifth largest city in California with 184.6 km2 (114.7 square miles), and an estimated population of 979,915 (4,418 persons per square mile), not including neighboring communities such as Clovis and Cecile. The roost in downtown Fresno is located at or near the Community Regional Medical Center and could include hundreds to thousands of crows. Though the roosting location is urban, when accounting for a range [32], this roost probably includes crows that forage within Fresno’s urban center and those that feed on the aquatic resources of the San Joaquin River [112] and expansive agricultural sites (>1,231,324 acres of agricultural crops [113]) in Fresno County Collections took place within an approximate area of 3 square miles (8 km2) centered on the Veterans Memorial Museum in downtown. The exact roosting and staging sites varied, with Veterans Memorial Museum (Roost 1) and Community Regional Medical Center (Roost 2) being identified as reliable roosts 18 18 for this study coupled to several staging areas where crows congregated before settling in a roost for the night (Figure 1).

Figure 1. Map of American Crow staging and roosting locations in Fresno, California. Roost 1 was the Veterans Memorial Museum and Root 2 was Community Regional Medical Center. Numerous staging locations were identified and varied over collection events.

Davis, California Roost. The University of California, Davis roost is well- known and has been the focus of American Crow studies in the past [4, 15]. This roost probably included crows consuming the urban refuse on the university campus and Sacramento, and those feeding on the crops in Yolo County and along the Sacramento River. The crows had access to urban sources on the UC Davis campus and surrounding community [114] in addition to the larger Sacramento 19 19 area, a short flight away (approximately 15 miles or 24 kilometers). The cities of Sacramento and Davis had an estimated 2016 population of 495,000 and 68,000 respectively [115]. Like the Fresno roost, members of the Davis roost may have commuted from surrounding rural and agricultural areas. Davis is in Yolo County which contained tomatoes, rice, almonds crops for the crows [116]. Yolo County also had 18,700 head of cattle, and 12,700 head of sheep and lambs [116] at facilities at which crows could forage. These crows also had access to the Sacramento River which may have had insects, small fish, eggs or amphibians. There were thousands of crows in the staging and roosting areas nightly. The largest and most easily accessible staging area at UC Davis was in the parking lot and surrounding trees, indicated on the map as “Stage 1,” so this is where we collected samples. The crows also reliably roosted in the same location, the trees around the Segundo Dining Commons of Student Housing, labeled “Roost 1” (Figure 2).

Critter Creek Wildlife Station. The use of Critter Creek Wildlife Station in Squaw Valley, California began as a way for us to continue working though the non-roosting periods when getting fecal samples from free-range crows was difficult. Critter Creek serves as a shelter for a variety of injured wildlife with the goal of treating and releasing the animals. However, animals injured beyond reasonable release to the wild become permanent residents at the facility. Critter Creek is used in this study as a consistent diet comparison because they were not actively foraging and fed an unchanging diet allowing a comparison of free-range crows at roosts to those that were not free-range. 20 20

Figure 2. Map of American Crow staging and roosting locations in Davis, California. Roost 1 was the Segundo Dining Commons of Student Housing. eBird, and Staging and Roosting Sites We identified the general area of our fecal collection sites using the online resource eBird [117]. The website and phone application provide information on are bird sightings across the U.S. citing bird species, the number of individuals, and the time and location seen. These data are then aggregated in real-time into an on-line map with multiple records at a single location being identified as a “hotspot” with a special icon on the map. 21 21

Once we identified the general area of the roost, on site field observations were used to identify the specific location of individual staging areas and roosts. On any given collection day, the crows congregated in two or three locations which varied between collection events. At the beginning of the study, we misidentified staging sites as roosting sites and collected samples from multiple staging sites per collection event. Subsequently, it was determined that the crows were moving to a central roost location after dark. We then collected from the roost as well as the staging sites. Since the staging sites contained hundreds of individuals and we collected from various staging sites, the microbiota determined from these sites are representative of the roost.

Field Sample Collection New, plastic shower curtains (about 1.8 m x 1.8 m) were used for each collection event to ensure the samples were not contaminated by soil microbes [118]. The plastic was placed under trees or powerlines where crows perched, and the corners weighted. After 10-30 minutes the fecal samples on the shower curtain (2-10 samples on each curtain) were collected using sterile toothpicks and placed in 1.5 mL Eppendorf tubes. The samples were placed on ice for 2-4 hours and then at -20° C until DNA extraction. The number of samples collected per outing ranged from 7-50 samples per collection event.

Experimental Design

Determining Early and Late Roosting Period (Blocking) Beyond characterizing the microbiota between two distinct roosts, we wanted to determine if the bacterial community changed over a roosting season. 22 22

We were unable to obtain early roosting season data from Critter Creek or UC Davis, but both early and late in the roosting season were obtained at Fresno. We collected as many fecal samples as we could, every weekend for 10 weeks. We took the first three collections (12/3/16, 12/10/16, and 12/17/16) that provided enough DNA samples and labeled these the “early roosting period.” Approximately 43 samples were collected in the early roosting period, and ~15 were confirmed to possess bacterial DNA. The last three collections (1/21/17, 2/6/17, and 2/21/17) were designated “late roosting period.” We had an idea of when the end of the roosting season was due to personal observations of the roost the season prior. It was possible the roost continued passed this point and we were unable to locate them. Over all collection sites, approximately 114 samples were collected in the late roosting period and ~45 were confirmed to possess bacterial DNA. For a weeks’ worth of samples to be considered for inclusion of the study, five individual fecal samples had to produce enough DNA to pass a 16S rRNA gene PCR check (see Selection of DNA extracts). With sequences from the “early roosting period” and “late roosting period” we were able to observe changes in the microbiota over this period. The beginning of the roosting season is considered “Block 1” and the end considered “Block 2.” In each block, we collected samples once a week for 3 weeks. Within each week we extracted the DNA from five fecal samples and combined them to represent the roost’s microbiota for that week. Note that Block 2 is over a 4-week period rather than a 3-week period. This was because one of the weeks (2/12/17) did not yield five pure (without contaminates such as proteins), high quality DNA extractions. To compensate, we took the closest week of samples that was not already included (1/21/17) and placed that in the series. 23 23 Selection of DNA Extracts The five extracted samples from each week were chosen based on the concentration and purity of the DNA, as determined by spectrophotometry at 260 nm (Nanodrop model or Qubit model). The five samples were diluted to a common DNA concentration of 2.5 µg/20 µL with molecular grade water to ensure each sample contributed equally to the relative abundance of bacterial taxa when they were pooled into one 1.5 mL microfuge tube for sequencing.

DNA Extraction and Sequencing We extracted DNA from the collected fecal samples using the DNeasy® PowerSoil® Kit from Qiagen (previously the PowerSoil DNA Extraction Kit from MoBio) [119]. We generally retrieved low DNA concentrations, so we eluted in lower volumes (30 µL of molecular grade water rather than 100 µL) and passed the elutant through the column filter twice. Some samples needed to be precipitated to increase DNA concentration or remove impurities such as proteins. To precipitate the DNA, we added 1 µL tRNA, 10 µL of 3M NaOAc and 277 µL ice-cold 95% ethanol. The mixture was stored at -20°C overnight. It was then centrifuged (Sorvall Legend Micro 21 Centrifuge, serial number 41823648) at 14,000 rpm (21.1 times gravity) for 30 minutes, the supernatant was discarded, and the pellet was washed with 100 µL 70% ethanol. After 15 minutes of centrifugation (14,000 rpm), the supernatant was discarded, the pellet was air dried to remove the residual ethanol, and the purified DNA pellet was rehydrated in molecular grade water. Purified DNA was amplified using primers specific for the 16S rRNA gene (27F-1391R) [120, 121] to confirm the presence of bacterial DNA. The DNA samples were then sent to the University of Connecticut Microbial Analysis, Resources, and Services: Center for Open Research Resources and Equipment 24 24

(UConn MARS) where the DNA was amplified using primers for the V4 region of the 16S rRNA gene with the Illumina adapter sequence and 12-base pair barcode sequence [120, 121]. DNA sequencing was performed on the Illumina MiSeq platform.

Microbiota Analysis We used the bioinformatics pipeline Quantitative Insights Into Microbial Ecology (QIIME2) [122] to analyze our 16S rRNA gene sequencing data. With this software the microbial diversity within a roost (alpha diversity) was determined and compared to the diversity between roosts (beta diversity). The complete list of commands and the system details can be found in Appendix A. UConn MARS sequencing data was received in the fastq file format and included forward and reverse paired-end reads with no barcode sequences. To match the sequencing data to the sample identities, we created a CSV (comma separated values) manifest file including sample ID, the file path to the fastq sequencing data file, and read direction.

Operational Taxonomic Unit (OTU) Assignment Data were visualized to illustrate sequence frequency and quality (Figure 3) using the demux plugin (https://github.com/qiime2/q2-demux). The demux.qzv visualization showed the quality scores of the reads, which allowed us to remove those with low scores in DADA2 [123]. The quality control method, DADA2 detects and, if possible, corrects errors made during sequencing. DADA2 can rectify chimeric sequences and removes phiX reads, which was a common gene marker used in Illumina sequencing [123]. The DADA2 function requires two parameters: how many nucleotides to trim and truncate [123], both of which were 25 25 obtained from the demux.qzv visualization (Figure 3). The forward reads were trimmed 13 nucleotides from the left and truncated 251 nucleotides from the right. The reverse reads were trimmed 17 nucleotides from the left and truncated 250 nucleotides from the right. The number of sequences remaining after this quality control step varied between samples (Table 1) from Fresno W2 (63,130 sequences) to Davis W3 (0 sequences).

Table 1. FeatureTable (table.qzv) Displaying Number of Sequences Per Sample Remaining After Quality Control (DADA2). Sample ID Sequence Count Fresno W2 63,130 Fresno W3 62,268 CCW5 59,278 CCW7 59,080 FresnoW8 56,599 DavisW2 53,891 CCW4 53,877 FresnoW7 53,183 FresnoW10 50,738 FresnoW4 41,922 DavisW1 27,398 DavisW3 0

Sequences were binned according to similarity, resulting in operation taxonomic units (OTUs), followed by the generation of a representative sequence for each OTU [124]. Representative sequences were >3% different from each other, commonly cited as the species level of microbial taxonomy [124] and decreases the chance of multiple OTUs being assigned to the same taxa. The resulting representative sequences, hereinafter referred to as phylotypes, were used for downstream taxonomic assignment and diversity metrics. Phylotypes were assigned taxonomy using the QIIME implemented pre- trained Naïve Bayes classifier, trained on the Greengenes 13_8 99% OTUs. 26 26

Figure 3. Forward and reverse reads quality (Demux.qzv). Visualization to determine parameters for quality control of (A) forward and (B) reverse reads using the DADA2 function. These visualizations are before the forward reads were trimmed to base pair (bp) 13 and truncated to bp 251, and the reverse reads trimmed to bp 17 and truncated to bp 250.

27 27

Microbial community diversity metrics utilized a rooted phylogenetic tree, generated by adding midpoints to the longest tip-to-tip distance of the unrooted phylogenetic tree produced from the FASTTREE program [125]. The diversity analytic plugin, q2-diversity, used a sampling depth of 27,398 to ran multiple alpha and beta diversity metrics. This depth was determined by selecting the minimum number of sequences out of the samples (Table 1). To verify the richness of the samples was fully observed using 27,398 sampling depth, rarefaction of the alpha diversity was performed. Rarefaction generated tables at each of the sampling depth steps, these tables were then combined, and the average values were plotted. By default, 10 rarefied tables were generated but this could be changed using the iterations parameter. In this step, we specified the minimum and maximum sampling depth to demonstrate that the selected sampling depth was sufficient to accurately identify the species richness of the samples. Alpha diversity is the species diversity within a population. To observe differences in alpha diversity, between the collection sites, we made visualizations of the alpha diversity using Shannon’s diversity index.

푠 퐻′ = − ∑ 푝𝑖 ln 푝𝑖 푖=1

Shannon’s diversity considers the total number of species in the population (“S”), the relative abundance of the species (pi), and evenness to calculate the biodiversity of a population. To observe the differences in the microbiota between populations we calculated beta diversity. We examine if the urban roosts, with dynamic diets, were more alike than the constant diet control (Critter Creek); if the microbiota 28 28 over the roosting period were significantly different; and if the microbiota from each collection site were significantly different from each other. Beta diversity was determined using weighted and unweighted UniFrac (Unique Fraction) analysis [126]. 훴 푈푛𝑖푞푢푒 푏푟푎푛푐ℎ 푙푒푛𝑔푡ℎ푠

훴 퐴푙푙 푏푟푎푛푐ℎ 푙푒푛𝑔푡ℎ푠 UniFrac measures the diversity between microbial communities by evaluating similarities and differences in phylogenetic trees [126]. The program builds phylogenetic trees for each sample and determines the number of shared phylogenetic branches among samples and the number of unique branches in each sample. Greater UniFrac values indicate greater dissimilarity among samples. In cases where multiple samples are being compared simultaneously, a matrix is formed which can be used as an input for multivariate statistical analyses such as principal coordinate analysis. To determine the likelihood the phylogenetic trees could have been created due to random chance, the program ran randomization steps. The shape or scaffold of the tree was kept the same and the positions of the samples on the tree were randomized. The P (p-value) was the fraction of randomly generated trees that had the same or more unique branches as our experimental trees. There are two main types of UniFrac analysis, weighted UniFrac and unweighted UniFrac. In the weighted Unifrac analysis, the relative abundance of taxa found in the sample is considered, so less abundant taxa have less weight in the analysis. Unweighted UniFrac, conversely does not weight by abundance but rather absence or presence of taxa and as such rare and less abundant taxa carry the same weight as the most abundant taxa in the development of the phylogenetic trees. 29 29

The statistical significance of each type of UniFrac analysis was evaluated using the PERMANOVA “permutational multivariate analysis of variance” command that estimates dissimilarity without assuming a normal distribution [127]. The P resulting from analyses comparing multiple groups (Fresno, Critter Creek, and Davis) were adjusted using Benjamini-Hochberg correction [128] to reduce the Type I error rate. Within each UniFrac analysis the sample conditions were compared pairwise. Metrics were determined for collection site (Fresno, Davis, or Critter Creek), diet fluctuation type (Dynamic vs. Consistent diet), and the relative roosting period (early vs. late).

Resistome Determination

Identifying Resistance Genes in the Metagenome To investigate the antibiotic resistance genes (ARG) present in the Fresno, California roost (unable to collect from Critter Creek or UC Davis), we sent a consolidated sample for shotgun metagenomic sequencing at the biotechnology company, Mr. DNA. The sequencing data were processed by the antibiotic resistance gene pipe line ARGs-OPA [129], which integrates the ARG databases, Antibiotic Resistance Genes Database (ARDB) [130] and The Comprehensive Antibiotic Resistance Database (CARD) [131]. The quality of sequences was improved by removal of non-ARG, redundant, and single nucleotide polymorphism variant ARG sequences, resulting in the Structured Antibiotic Resistance Genes Database (SARG) [129], from which the Fresno resistome ultimately was generated. 30 30 Minimum Inhibitory Concentrations (MICs)

Critter Creek. Samples were examined to determine the minimal inhibitory concentration of selected antibiotics on bacteria isolated from fresh crow feces collected (2/24/18) from Critter Creek Wildlife Station. Samples were immediately put on ice and brought back to lab where the samples were mixed with Luria-Bertani LB broth and plated on isolation agars to select for potential human pathogens (Table 2). After incubated for 24 hours at 37°C, isolated colonies were selected for testing.

Table 2. Isolation Agars Used to Select Organisms from Fecal Samples Name of product Target organisms Catalog number BBL™ Bile Esculin Agar Group D Streptococci 299068 Difco™ Pseudomonas Pseudomonas 292710 Isolation Agar Vibrio cholerae and other Difco™ TCBS Agar 265020 Enteropathogenic Vibrios Difco™ SS Agar Salmonella and some Shigella 274500 Difco™ MacConkey Agar Gram-negative Enterics 212123 EM Science® Orange Acid tolerant, putrefactive 1.10673.0500 Serum Agar Isolation agars were chosen to select for possible human pathogens.

We chose one organism from the Bile Esculin Agar, Pseudomonas Isolation Agar, and SS Agar plates to test against a variety of antibiotics (Table 3). The isolates were challenged to determine the minimal inhibitory concentrations (Table 4). Prior to testing, the isolates were inoculated into fresh LB broth and incubated for 2 hours in a shaking incubator at 37°C. Cultures were diluted (1:100) and 100 µL was added to each well. The 96 well plates were incubated at 37°C in a shaking incubator for 24 hours. 31 31

Table 3. Test Antibiotics Modes of Action Mode of Action Antibiotic Tested Cell wall synthesis inhibition (Cef) cefotaxime (Amp) ampicillin (Mero) meropenem (Met) methicillin (Van) vancomycin Protein synthesis inhibition (Spec) spectinomycin (Apr) apramycin (Cam) chloramphenicol (Ery) erythromycin (Hyg) Hygromycin (Gent) gentamicin (Strep) streptomycin (Tet) tetracycline (Kan) kanamycin Nucleic acid synthesis inhibition (Oflx) ofloxacin (Novo) novobiocin (Rif) rifampicin Lipid membrane disruption (PolyB) polymyxin B

Table 4. 96 Well Plate Set Up Used to Determine Minimal Inhibitory Concentrations Antibiotics (µg/mL) Cef Spect Gent PolyB Strep Oflx Amp Kan Cam Mero Rif No AB A 400 B 200 C 100 D 50 E 25 F 12.5 G 6.25 H No antibiotic or organism The last row (H) had no antibiotic or organism to check for contamination of the LB broth and the last column (12) had no antibiotic to check the health of the test organism. Rows A-G were used to perform a two-fold serial dilution. 32 32

Fresno, California. A fecal sample was collected on 11/21/2015 from Staging site 1 (Figure 1, p. 18), placed on ice and returned to lab where approximately 2.5g of the sample was mixed into 50 mL of LB. MICs were determined after 100 µL of fecal mixture was dispensed into a 96 well plate with varying concentrations (same as Table 4) of antibiotics (Amp, Apr, Cef, Cam, Ery, Gent, Hyg, Kan, Mero, Meth, Novo, PolyB, Tet, and Van; Table 3). The 96 well plates were incubated at 37°C in a shaking incubator for 24 hours. Wells containing growth at the highest antibiotic concentration (half MIC concentration) were streaked for isolation on isolation agars (Table 3). Morphologically distinct isolated colonies were chosen from the plates.

AR Organisms in Fresno Animal Husbandry We performed a rough screening of fecal samples acquired from livestock on the California State University, Fresno campus. Results from such analysis provided insight into potential AR pathogens present at a possible crow feeding location. We mixed fecal samples (~1 g) from a dairy cow (#410), a pig (in pen #8 from the left), and a juvenile female sheep in LB broth and followed the same protocol outlined above (Critter Creek).

Identification of test organisms. To identify the isolates from Fresno, Critter Creek Fresno, and CSU, Fresno, we extracted DNA from overnight cultures, PCR amplified the 16S rRNA region, and sent for sequencing. The DNA was extracted using the EasyPrep protocol or, if that failed, using Promega’s Wizard® Genomic DNA Purification Kit. The extracts were then PCR amplified using the 27F-1391R primers for the entire16S rRNA gene [120, 121] and visualized on a 0.8% agarose gel. The positive products were cleaned using the Promega’s Wizard® SV Gel and 33 33

PCR Clean-Up System and sent to Eurofins for 16S rRNA gene sequencing on Illumina’s MiniSeq platform. The data was returned as .abi files and was converted to FASTA format using the application SeqTrace [132]. The forward and reverse reads were identified by the primer sequences, 27F (AGAGTTTGATCMTGGCTCAG) and 1391R (GACGGGCGGTGTGTRCA), and merged into consensus reads [132]. The resulting FASTA files were entered into the BLAST program to assign taxonomy [133].

RESULTS

American Crow Microbiota

Taxonomic Richness Microbial analysis resulted in 1,638 phylotypes, 44.0% were assigned genus level taxonomy, followed in percentage by family (29.2%), species (12.5%), order (9.7%), class (3.2%), phylum (0.79%), then kingdom (0.55%). Following results focus heavily on phyla and orders, providing insight into the majority of the microbiota (99.4% coverage with phyla and 95.4% coverage with orders). The fecal microbiota of the American Crows sampled in Fresno, California; Critter Creek Wildlife Station in Squaw Valley, California; and Davis, California, presented 26 phyla, 85 orders, and 163 families (Table 5). All collection sites contained similar richness in taxa with the Fresno microbiota possessing slightly more taxa at each level.

Table 5. Taxonomic Richness of Fresno, Davis, and Critter Creek Microbiota Fresno Critter Creek Davis All sites Taxonomic Level (n=6) (n=3) (n=2) (n=11) Kingdom 2 1 2 2 Phylum 23 20 14 26 Class 53 35 33 61 Order 71 50 52 85 Family 143 102 95 163 Genus 185 134 102 239 Species 56 34 31 78 The table above displays the number of each taxonomic level identified in each site’s microbiota. For example, 53 classes were identified in the Fresno microbiota.

Average Relative Abundance of Taxa The most abundant phyla in the crow microbiota, averaged over all collection sites (N=11), were Proteobacteria 44.5% (ranging from 73.9% in Davis 35 35 to 27.9% in Fresno), Firmicutes 38.6% (54.5% in Fresno to 17.8% in Davis), and Actinobacteria 10.7% (14.6% in Critter Creek to 6.9% in Davis) (Table 6, Figure 4). These three phyla, identified in all 11 fecal samples, were observed in differing relative abundance at each collection site and individual sample (Firmicutes between 91.8% in FW10 and 20.7% in FW2) but were always found in the top three most abundant phyla. An average of 2.3% of the microbiota were comprised of unclassified bacteria.

Table 6. Relative Abundance of Phyla and Orders in the American Crow Microbiota Average relative Average relative Phyla abundance ± SD Orders abundance ± SD Proteobacteria 44.5 ± 28.8 Lactobacillales 22.2 ± 23.3 Firmicutes 38.6 ± 28.2 Enterobacteriales 21.9 ± 24.0 Actinobacteria 10.7 ± 7.2 Pseudomonadales 13.2 ± 13.2 Unclassified 2.3 ± 6.3 Actinomycetales 10.0 ± 6.6 Cyanobacteria 1.6 ± 1.8 Clostridiales 10.5 ± 25.4 Bacteroidetes 1.0 ± 1.6 Bacillales 5.1 ± 8.3 Chloroflexi 0.71 ± 0.68 Aeromonadales 3.5 ± 10.5 Tenericutes 0.33 ± 0.40 Unclassified 2.3 ± 6.0 Phyla less than 0.1% Orders less than each 0.35 ± 0.41 2.0% each 11.4 ± 0.46 Table displays the average relative abundance of phyla of all fecal samples (N=11). SD is standard deviation of samples. See Appendix B for complete microbiota (to species level) and relative abundances (phyla level).

The most abundant orders identified in the American Crow fecal microbiota, also identified in all fecal samples (N=11), were Lactobacillales (22.2%), Enterobacteriales (21.9%), Pseudomonadales (13.2%), Clostridiales (10.5%), and Actinomycetales (10.0%) (Table 6, Figure 4). 36 36

Figure 4. Average relative abundance of bacterial taxa in American Crow feces. Phyla relative abundance is to the left and order to the right. The top row displays the relative abundances averaged over all collection sites. The following rows present the relative abundance of the fecal microbiota in each collection site alone (Fresno, Critter Creek, and Davis). Note this figure does not display variation between samples within a collection site. For sample relative abundance see Figure 5. 37 37

Variation between samples. Relative abundance of taxa varied, in some cases dramatically, between samples (Figure 5). For example, Lactobacillales, Clostridiales, and Aeromondales represented drastically different relative abundance over samples. These cases are expanded on in the following sections.

Figure 5. Relative abundance of phyla and orders in each sample. Displays relative abundance of phyla and orders in all samples (N=11) individually. Graphs are arranged from greatest to lowest relative abundance. 38 38

Fresno microbiota relative abundance. The Fresno microbiota consisted of 24 phyla (Table 5, p. 34), the most abundant of which were Firmicutes (54.5%), Proteobacteria (27.9%), and Actinobacteria (10.0%) (Table 6, Figure 4). Samples FresnoW10 (FW10) and FW3 contributed large relative abundances to Firmicutes with 91.8% and 82.1% respectively, while FW8 (56.8%) and FW4 (51.3%) displayed moderate abundance, and FW7 (24.0%) and FW2 (20.7%) possessed a relatively low abundance of Firmicutes (Figure 5). Sample FW8 had 21.1% Unclassified phyla which could have been any bacterial taxa. FW2 (5.9%), FW7 (3.2%), and FW10 (2.9%) were the largest sources of Cyanobacteria (2.0% of Fresno microbiota) in the microbiota. The majority of the abundance (82.4%) originated from only two phyla (8.7% of the total phyla) while the least abundant phyla (>0.01%) was responsible for 47.8% of the phyla diversity. Relative abundance of the order Lactobacillales ranged from 0.87% (FW10) to 77.8% (FW3) resulting in a large sample standard deviation (76% ± 28.3). Samples FW4 (34.0%) and FW8 (45.4%) contained moderate levels of Lactobacillales and FW7 (18.0%) and FW2 (14.2%) accommodated lower abundances. Large sample standard deviations were produced by the orders Enterobacteriales (15.9% ± 18.6), Clostridiales (16.9% ± 36.0), and Pseudomonadales (7.0% ± 11.3). Enterobacteriales was found in relatively high abundance (Figure 5) in two samples (47.6% in FW7, 28.8% in FW2) and to a lesser extent in the remaining samples (10.8% in FW4 to 2.2% FW10. Clostridiales was only found in high abundance in FW10 (90.4%) and represented minute fractions of the other samples (3.7% in FW3 to 0.47% in FW8). Pseudomonadales was only moderate in FW4 (29.6%) and low in the remaining samples (5.8% in FW2 to 0.49% in FW10). 39 39

Critter Creek microbiota relative abundance. Critter Creek’s microbiota was comprised of 19 phyla (Table 5, p. 34), most abundant of which was was Proteobacteria (57.9%), Firmicutes (20.7%), and Actinobacteria (14.6%) (Figure 4). Relative abundance of Proteobacteria ranged from 75.5% (CCW5) to 29.2% (CCW7), Firmicutes from 35.3% (CCW7) to 11.1% (CCW5), and Actinobacteria from 26.0% (CCW7) to 6.8% (CCW4) (Figure 5). The Critter Creek microbiota contributed the highest abundance of Bacteroidetes (2.8%) to the average abundance (1.0%). Many phyla (66.1% of all phyla; 9) were only present in low abundance (< 0.01%), and 93.2% of the relative abundance originated from the top three phyla which represents 15% of the phyla richness. The orders Aeromonadales and Bacillales varied between samples, present at high abundance in only one sample each. Aeromonadales represented 36.7% of CCW4 compared to 0.37% (CCW5) and 1.3% (CCW7), and Bacillales comprised 30.0% of CCW7 while representing low abundances CCW4 (9.9%) and CCW5 (1.4%).

Davis Microbiota relative abundance. The Davis microbiota constituted 14 phyla (Table 5, p. 34), the fewest of the collection sites, with Proteobacteria at 73.9% abundance (Figure 4), four times more abundant than the next phyla, Firmicutes with 17.8%. Actinobacteria (6.9%) was the third most abundant and less abundant that in the Critter Creek and Fresno microbiota. The majority of the relative abundance (91.7%) originated from only Proteobacteria and Firmicutes (13.3% of the phyla diversity) while the majority of the phyla (60%) represented only 0.32% of the microbiota. Enterobacteriales was the most abundant (55.7%) order at 55.7% in DW1 and 23.6% in DW2, followed by Pseudomonadales (14.4%; DW2: 28.5%, DW1: 0.31%), and Lactobacillales (12.15%; DW1:12.5%, DW2: 11.8%). 40 40 Core Microbiota

Core microbiota identified in all collection sites. The core microbiota, defined as the taxa identified in all fecal samples (N=11), included six phyla (Actinobacteria, Bacteroidetes, Chloroflexi, Firmicutes, Plantomycetes, and Proteobacteria), 11 classes (Actinobacteria,, Acidimicrobiia, Thermoleophilia, Thermomicrobia, Bacilli, Clostridia, Planctomycetia, and Alpha-, Beta-, Delta-, and Gamma-proteobacteria), 18 orders, 18 families, and five genera (Table 7). The core microbiota contained notable, potential human pathogens such as Enterococcus, Acinetobacter, and Pseudomonas.

Table 7. Taxa Identified in All Fecal Samples, Comprising the Core Fecal Microbiota of the American Crow Phyla Class Order Family Genus Actinobacteria Actinobacteria Actinomycetales Geodermatophilaceae Intrasporangiaceae Microbacteriaceae Micrococcaceae Nocardiaceae Rhodococcus Nocardioidaceae Acidimicrobiia Acidimicrobiales Thermoleophilia Gaiellales Bacteroidetes Chloroflexi Thermomicrobia JG30-KF-CM45 Solirubrobacterales Firmicutes Bacilli Bacillales Bacillaceae Planococcaceae Clostridia Clostridiales Clostridiaceae Candidatus Arthromitus Lactobacillales Enterococcaceae Enterococcus Planctomycetes Planctomycetia Pirellulales Pirellulaceae Proteobacteria Alphaproteobacteria Caulobacterales Caulobacteraceae Rhizobiales Phyllobacteriaceae Rhizobiaceae Rhodobacterales Rhodobacteraceae Rhodospirillales Rickettsiales Betaproteobacteria Burkholderiales Deltaproteobacteria Gammaproteobacteria Enterobacteriales Enterobacteriaceae Legionellales Pseudomonadales Moraxellaceae Acinetobacter Pseudomonadaceae Pseudomonas Table above contains the taxa identified in all fecal samples from all collection sites (N=11). 41 41

Fresno American Crow core fecal microbiota. The taxa identified in all Fresno fecal samples (N=6) included eight phyla, 13 classes, 20 orders, 25 families, seven genera, and one species (Table 8). Two phyla were not included in all collection site fecal samples (N=11) including Cyanobacteria and Verrucomicrobia, two classes (Chloroplast and Spartobacteria), two orders (Streptophyta and Chthoniobacterales), seven families, two genera (Curtobacterium and Bacillus) and the species B. flexus were also not identified in all samples (N=11).

Table 8. Fresno American Crow Core Fecal Microbiota Phyla Class Order Family Genus Species Actinobacteria Actinobacteria Actinomycetales Micromonosporaceae Curtobacterium Streptomycetaceae Thermoleophilia Solirubrobacterales Conexibacteraceae Solirubrobacteraceae Cyanobacteria Chloroplast Streptophyta Firmicutes Bacilli Bacillales Bacillaceae Bacillus flexus Proteobacteria Alphaproteobacteria Rickettsiales mitochondria Betaproteobacteria Burkholderiales Oxalobacteraceae Deltaproteobacteria Myxococcales Verrucomicrobia Spartobacteria Chthoniobacterales Chthoniobacteraceae The table above contains the taxa identified in all Fresno fecal samples (N=6). Core taxa present in all samples (N=11; Table 6) were only included in this table when displaying Fresno core taxa at lower levels. For example, the genus Bacillus is core in Fresno and under the family Bacillaceae which is a member of the core microbiota of all sites. Fresno specific core taxa are underlined.

Critter Creek American Crow core fecal microbiota. The Critter Creek core fecal microbiota contained nine phyla, 19 classes, 30 orders, 46 families, 47 genera, and 10 species (Table 9). Three phyla (Cyanobacteria, Tenericutes, and Verrucomicrobia) identified in the Critter Creek core microbiota were not found in all sites (N=11), along with eight classes (including Flavobacteriia and Verrucomicrobiae), 12 orders (Gemmatales and Mycoplasmatales), 28 families (Weeksellaceae and Alcaligenaceae), 42 genera (Aeromicrobium and Clostridium) 42 42 and all 10 species (E. coli and S. faecium). The core microbiota contained notable human pathogens including Corynebacterium, Staphylococcus, and Streptococcus.

Table 9. Critter Creek American Crow Core Fecal Microbiota Phylum Class Order Family Genus Species

Actinobacteria Actinobacteria Actinomycetales Bogoriellaceae Georgenia

Brevibacteriaceae Brevibacterium Cellulomonadaceae Cellulomonas

Corynebacteriaceae Corynebacterium

Dermabacteraceae Brachybacterium Dietziaceae Dietzia

Microbacteriaceae Cryocola

Leucobacter Micrococcaceae Arthrobacter

Kocuria

Rothia mucilaginosa Nocardioidaceae Aeromicrobium

Sanguibacteraceae Sanguibacter

Bacteroidetes Cytophagia Cytophagales Flavobacteriia Flavobacteriales Flavobacteriaceae Aequorivita

Gelidibacter

Weeksellaceae Chryseobacterium Saprospirae Saprospirales Chitinophagaceae

Sphingobacteriia Sphingobacteriales Sphingobacteriaceae Sphingobacterium faecium 7 other phyla 14 other classes 41 other orders 32 other families 30 other genera 8 other species The table above contains the taxa identified in all Critter Creek fecal samples (N=3). Core taxa present in all samples (N=11; Table 6) were only included in this table when displaying Critter Creek core taxa at lower levels. Table contents are listed alphabetically.

Davis American Crow core fecal microbiota. The Davis fecal core microbiota contained 11 phyla (five not included in all sites, N=11), 16 classes (five not in all sites), 27 orders (9 not in all sites), 38 families (20 not in all sites), 26 genera (21 not in all sites), and three species (Table 10). Notable human pathogen-containing taxa found in the core microbiota included Mycobacterium, Clostridium, and Pseudomonas. 43 43 Table 10. Davis American Crow Core Fecal Microbiota Phylum Class Order Family Genus Species

Acidobacteria Acidobacteria-6 iii1-15 Actinobacteria Actinobacteria Actinomycetales Cellulomonadaceae Cellulomonas Intrasporangiaceae Phycicoccus Microbacteriaceae Curtobacterium Micrococcaceae Arthrobacter Mycobacteriaceae Mycobacterium Nocardioidaceae Nocardioides Sanguibacteraceae Sanguibacter Streptomycetaceae Streptomyces Williamsiaceae Thermoleophilia Gaiellales Gaiellaceae Solirubrobacterales Solirubrobacteraceae Solirubrobacter 9 other phyla 13 other classes 23 other orders 27 other families 17 other genera 3 species The table above contains the taxa identified in both Davis fecal samples (N=2). Core taxa present in all samples (N=11; Table 6) were only included in this table when displaying Davis core taxa at lower levels. Table contents are listed alphabetically.

Variable Microbiota All sites returned taxa found only at that location, termed the variable microbiota. The Critter Creek microbiota possessed the largest relative abundance of variable microbiota (42.1%), followed by Fresno (19.3%), and Davis was comprised of only 3.0%.

Variable fecal microbiota of Fresno American Crows. The variable microbiota of Fresno crows, taxa only identified in the Fresno fecal samples, included four phyla, 10 classes, 18 orders, 30 families, 56 genera, and 27 species (Table 11). The genus Fusobacterium, commonly present in carnivorous and omnivorous avian fecal microbiota, was present only in Fresno.

Variable fecal microbiota of Critter Creek American Crows. The Critter Creek fecal microbiota possessed two unique phyla, six classes, six orders, 10 families, 36 genera, and 10 species (Table 12). The notable human pathogens Rickettsia and Brachyspira were present in Critter Creek crow feces. 44 44 Table 11. Fresno American Crow Variable Fecal Microbiota Phylum Class Order Family Genus Species Acidobacteria S035 Actinobacteria Acidimicrobiia Acidimicrobiales EB1017 Iamiaceae Actinobacteria Actinomycetales Actinomycetaceae Actinopolysporaceae Actinopolyspora Actinosynnemataceae Saccharothrix Cellulomonadaceae Actinotalea Geodermatophilaceae Geodermatophilus obscurus Modestobacter Intrasporangiaceae Terracoccus Jonesiaceae Jonesia Kineosporiaceae Kineococcus Micrococcaceae Microbispora rosea Nocardiaceae Nocardia Pseuonocardiaceae Saccharopolyspora hirsuta Sporichthyaceae Streptosporangiaceae Nonomuraea Sphaerisporangium 4 other phyla 9 other classes 18 other orders 22 other families 45 other genera 24 other species The table above contains the taxa identified only in Fresno fecal samples which comprises the variable microbiota. These taxa are underlined. Table contents are listed alphabetically.

Table 12. Critter Creek American Crow Variable Fecal Microbiota Phylum Class Order Family Genus Species Candidatus Actinobacteria Acidimicrobiia Acidimicrobiales Microthrixaceae Microthrix parvicella Actinobacteria Actinomycetales Cellulomonadaceae Demequina Microbacteriaceae Cryocola Propionibacteriaceae Bacteroidetes Cytophagia Cytophagales Cyclobacteriaceae Flavobacteriia Flavobacteriales Flavobacteriaceae Aequorivita Gelidibacter Myroides Winogradskyella Sphingobacteriia Sphingobacteriales Sphingobacteriaceae Parapedobacter Chlamydiae Chlamydiales Chlamydiales Criblamydiaceae FBP Firmicutes Bacilli Bacillales Bacillaceae Natronobacillus 4 other phyla 9 other classes 18 other orders 22 other families 46 other genera 24 other species The table above contains the taxa identified only in Critter Creek fecal samples which comprises the variable microbiota. These taxa are underlined. Table contents are listed alphabetically. 45 45

Variable fecal microbiota of Davis American Crows. A single class, four orders, seven families, 10 genera, and fives species were unique to the Davis crow fecal microbiota (Table 13). The Davis variable microbiota contained the family Chlamydiaceae and the genera Treponema and Citrobacter, which contain potential human pathogens.

Table 13. Davis American Crow Variable Fecal Microbiota Phylum Class Order Family Genus Species Acidobacteria Acidobacteria-6 iii1-15 mb2424 Chloracidobacteria PK29 Actinobacteria Actinobacteria Actinomycetales Micromonosporaceae Actinoplanes Coriobacteriia Coriobacteriales Coriobacteriaceae Adlercreutzia Bacteroidetes Bacteroidia Bacteroidales Prevotellaceae Prevotella Flavobacteriia Flavobacteriales Weeksellaceae Riemerella Chlamydiae Chlamydiales Chlamydiales Chlamydiaceae FBP Firmicutes Bacilli Bacillales Bacillaceae Bacillus halodurans longiquaesitum Lactobacillales Lactobacillaceae Lactobacillus paralimentarius No unique phyla 3 other orders 4 other orders 6 other genera 2 other species The table above contains the taxa identified only in Davis fecal samples which comprises the variable microbiota. These taxa are underlined. Table contents are listed alphabetically.

Human Pathogen-Containing Taxa Several human pathogen-containing taxa (22 orders, 44 families, 52 genera, 13 species) were identified in the fecal microbiota at all collection sites (Table 14). Taxa labeled as pathogen-containing at higher taxonomic levels are rather removed from the OTU to which it is assigned than those at lower levels and are highly inclusive, likely including many non-pathogenic containing taxa. Genus level is useful in comparing sites, as genera contain fewer possible organisms to with the OTU could have originated, making it more likely the organism was a pathogen. Genera are also well represented with 40 in Fresno, 40 in Critter Creek, and 28 in Davis. Critter Creek and Davis microbiota harbored larger percentages 46 46 of pathogen-containing orders (42% and 38.5%) than Fresno (22.5%). All sites processed similar percentages of pathogen-containing families and genera (Table 14), and Davis had the lowest relative abundance of pathogenic species (9.7%).

Table 14. Number of Pathogen-Containing Taxa in American Crow Microbiota Taxonomic Fresno Critter Creek Davis All sites level Number of pathogen-containing taxa (% of total taxonomic level) Order 16 (22.5%) 21 (42.0%) 20 (38.5%) 22 (25.9%) Family 39 (27.2%) 40 (39.2%) 31 (32.6%) 44 (27.0%) Genus 40 (21.6%) 40 (29.9%) 28 (27.5%) 52 (21.8%) Species 8 (14.3%) 6 (17.6%) 3 (9.7%) 13 (16.7%) Table displays the number of human pathogen-containing taxa identified at each taxonomic level, starting from order, at each collection site and the percent the pathogenic taxa represents of said level (e.g. 27% of all the genera identified in Critter Creek were pathogen-containing). See Appendix B for complete microbiota (to species level).

We identified 10 genera containing species capable of causing conditions listed on the list of National Notifiable Diseases [134] including Corynebacterium, Mycobacterium, Streptococcus, Rickettsia, Legionella, and Yersinia. All pathogen- containing genera were present at low relative abundance, the highest originating from Streptococcus with 6.8% (CCW5) and Yersinia with 4.4% (FW7). The other genera ranged between 0.1% and 0.001% relative abundance. Noteworthy genera containing human pathogens included enteric pathogens such as Helicobacter and Yersinia (both present in 50% of Fresno samples, 33% of Critter Creek, and 50% of Davis) and non-enteric pathogens including Brachyspira (33% of Critter Creek samples) and the family Mycoplasmataceae (67% Fresno, 100% Critter Creek and Davis). Listeriaceae (the family containing the intracellular enteric pathogen Listeria monocytogenes [135]) was present in the majority of Critter Creek samples (67%) and half of Fresno samples. Mycobacterium (genus containing tuberculosis species[91]) was present in 50% of Fresno and Critter Creek and all of Davis samples. The order Chlamydiales 47 47

(containing the zoonotic, intracellular respiratory pathogen C. psittaci [35]) was identified in 83% of Fresno samples, 33% of Critter Creek and 100% of Davis. Staphylococcus was identified in Fresno (50%), Critter Creek (100%) and Davis (50%). C. perfringens, which causes food poisoning (diarrhea and abdominal cramps) [41] and gas gangrene [136], was identified in Fresno (33%) and Critter Creek (66%) but the genus (Clostridium) was present in all sites (66.7% in Fresno and 100% in Critter Creek and Davis). Treponema, genus containing causative agents of syphilis, pinta, and yaws [137], was present in one sample (DW1) albeit at low relative abundance (0.007%).

Diversity Analyses of Fecal Microbiota The diversity of the microbiota community from each collection site was compared using Shannon’s Diversity Index, unweighted UniFrac, and weighted UniFrac analyses (Table 15). Resulting P were adjusted (q-value) using the Benjamini-Hochberg correction [128] to account for increased false positive rates produced from multivariate analyses. In cases when only two data sets are compared at a time (roosting period, diet fluctuation, and when Davis is the focus of the comparison), the P and q values are equal.

Alpha Diversity Comparison Between All Collection Sites We compared the alpha diversity, that is biodiversity within each collection site, using Shannon’s Diversity Index (Table 15), and analyzed the variance between the indexes using Kruskal-Wallis test. The Kruskal-Wallis test is advantageous because it is nonparametric, relatively insensitive to unequal sample sizes and does not assume a normal distribution. The P were adjusted using the 48 48

Benjamini-Hochberg correction [128]. No significant difference was observed between any of the pairwise Kruskal-Wallis analysis (Table 15).

Table 15. Summary of Microbiota Diversity Statistical Analyses Alpha Diversity Pairwise Adjusted P (q-value) Shannon’s Diversity Index Kruskal-Wallis Benjamini-Hochberg correction [128] Critter Creek vs. Davis P=0.248 q=0.372 Critter Creek vs. Fresno P=0.121 q=0.364 Davis vs. Fresno P=0.739 Roosting Period P=0.102 Dynamic vs. Consistent diet P=0.828 Beta Diversity Unweighted UniFrac Pairwise Adjusted P (q-value) PERMANOVA Benjamini-Hochberg correction Critter Creek vs. Davis P=0.094 q=0.141 Critter Creek vs. Fresno *P=0.012 *q=0.036 Davis vs. Fresno P=0.475 Roosting Period P=0.089 Dynamic vs. Consistent diet *P=0.010 Weighted UniFrac Pairwise Adjusted P (q-value) PERMANOVA Benjamini-Hochberg correction Critter Creek vs. Davis P=0.513 Critter Creek vs. Fresno P=0.109 q=0.327 Davis vs. Fresno P=0.297 q=0.446 Roosting Period P=0.702 Dynamic vs. Consistent diet P=0.153 Alpha Diversity was investigated by Shannon’s Diversity Index and compared between collection sites, roosting periods, and diet fluctuation types using pairwise Kruskal-Wallis analysis. P were adjusted in multivariate analyses. Beta Diversity was compared by pairwise PERMANOVA on resulting UniFrac analyses, both unweighted and weighted.

Beta Diversity Comparison Between All Collection Sites The unweighted UniFrac analysis, which checks for the absence or presence of taxa, (Figure 6) returned one statistical significance comparison of the 49 49 collection sites. After pairwise PERMANOVA (Table 15) analysis, with Critter Creek as the focal for the comparison, Critter Creek and Fresno (P=0.012, q=0.036) exhibited a statistically significant difference in beta diversity. No significant differences were observed between Critter Creek and Davis (P=0.094, q=0.141) or between Davis and Fresno (P=0.475). When relative abundance of taxa was used to weight the analysis (weighted UniFrac), no significant differences were observed between any of the collection sites (Creek vs. Davis P=0.513; Critter Creek vs. Fresno P=0.109, q=0.327; Davis vs. Fresno P=0.297, q=0.446).

Roosting Periods and Diet Fluctuation Effect on Microbiota There was no significant difference between the microbiota identified from early roosting samples and those identified from late roosting samples in alpha diversity (Shannon’s Diversity Index; Kruskal-Wallis P=0.102; Table 15) or beta diversity (Unweighted UniFrac: P=0.089, Weighted UniFrac: P=0.702, both pairwise PERMANOVA). When the free-range, urban roosting crows (Fresno and Davis) were grouped and compared to the Critter Creek crows that do not forage in an environment as dynamic as the urban crows, a significant difference between the microbiota was observed. When analyzed using unweighted UniFrac (Figure 6), the dynamic and consistent diet samples were significantly different (PERMANOVA P=0.010). When relative abundance was used to weight the analysis in weighted UniFrac (Figure 7), no significance was observed (PERMANOVA P=0.153). There was no significant difference (Kruskal-Wallis P=0.828) in alpha diversity between the consistent and dynamic diet microbiota

(Table 15).

50 50

Figure 6. Unweighted and weighted UniFrac analysis of beta diversity between collection sites. Graphs above display bet diversity comparison between collection sites using unweighted (top) and weighted (bottom) UniFrac analysis. After pairwise comparison and P adjustment (q), unweighted UniFrac returned Critter Creek vs. Davis P=0.094, q=0.141; Davis vs. Fresno P=0.475; Critter Creek vs. Fresno *P=0.012, *q=0.036. Weighted UniFrac analysis returned Critter Creek vs. Davis P=0.513; Davis vs. Fresno P=0.297, q=0.446; Critter Creek vs. Fresno P=0.109, q=0.327.

51 51

Figure 7. Unweighted and weighted UniFrac analysis of beta diversity between dynamic diet samples (Fresno and Davis) and consistent diet samples (Critter Creek). Graphs above display bet diversity comparison between dynamic diet samples (Fresno and Davis) and consistent diet samples (Critter Creek) using unweighted (top) and weighted (bottom) UniFrac analysis. After pairwise comparison, unweighted UniFrac returned *P = 0.010 and weighted analysis returned P=0.153.

52 52 American Crow Resistome

Antibiotic Resistance Genes (ARGs) Identified from Metagenome The most abundant single antibiotic resistant gene identified in the Fresno resistome was an unnamed multidrug transporter gene at 182 reads per million and was a member of the multidrug efflux pump group, the most common group found in the resistome (Table 16; 950.71 reads per million; 66.67%). The next most abundant resistance genes were for MLSs (macrolide-lincosamide-streptogramin B) at 12.24%. The majority of the resistome contained efflux pumps (66.67%), followed by genes that coded for modifications to the drug target (16.12%), and genes coding for enzymes that modified (e.g., phosphorylation, acetylation) the antimicrobial (10.8%).

Table 16. Antibiotic Resistance Genes Identified in Fresno Resistome Method of Total reads Percent of Group of Drug Drug target resistance per million resistome Multidrug N/A EP 950.71 66.67 Macrolide-lincosamide- streptogramin (MLS) 50S TM 174.58 12.24 Unclassified N/A N/A 90.08 6.32 Bacitracin Cell wall DM 59.29 4.16 Vancomycin Cell wall TM 34.49 2.42 Aminoglycoside 30S DM 34.09 2.39 Tetracycline 30S DM 32.04 2.25 β-lactams Cell wall DM 20.51 1.44 Polymyxin Membrane TM 11.38 0.80 Kasugamycin 30S TM 9.43 0.66 Rifamycin RNA polymerase DM 4.29 0.30 Fosfomycin Cell wall DM 2.35 0.16 Chloramphenicol 50S DM 1.45 0.10 Drugs less than 0.1% DM, EP, alone Metabolite, 50S TM 1.25 0.08 Table above displays antibiotic to which resistance was identified, the target of the drug, the method of resistance (EP, efflux pump. DM, drug modification. TM, target modification), the frequency of the ARG group per one million reads, and the percent of the resistome each ARG group contributed. See Appendix C for a complete list of resistance genes. 53 53 Minimal Inhibitory Concentrations (MICs)

Critter Creek. The three organisms obtained from isolation agar showed a degree of antibiotic resistance (Table 17). Aeromonas veronii showed resistance to spectinomycin (100 µg/mL), streptomycin (41.7 µg/mL ± 11.8), ampicillin (133.3 µg/mL ± 47.1), and kanamycin (16.7 µg/mL ± 5.9). Staphylococcus saprophyticus was resistant to spectinomycin (200 µg/mL) and polymyxin B (100 µg/mL). Corynebacterium falsenii showed minimal resistance to spectinomycin (25 µg/mL) and polymyxin B (25 µg/mL).

Table 17. Minimum Inhibitory Concentrations (µg/mL) of Critter Creek Isolates. Antibiotics (mean Aeromonas veronii Staphylococcus Corynebacterium µg/mL ± SD) saprophyticus falsenii Cefotaxime 6.3 6.3 6.3 Spectinomycin 100 200 25 Gentamycin 8.3 ± 5.9 6.3 6.3 Polymyxin B 6.3 100 25 Streptomycin 41.7 ± 11.8 6.3 6.3 Ofloxacin 6.3 6.3 6.3 Ampicillin 133.3 ± 47.1 6.3 6.3 Kanamycin 16.7 ± 5.9 6.3 6.3 Chloramphenicol 6.3 6.3 6.3 Meropenem 6.3 6.3 6.3 Rifampicin 6.3 6.3 6.3 MICs were performed in triplicate (n=3). No SD is presented if all trails returned same minimal inhibitory concentration.

Fresno. Organisms from crow feces in Fresno, California survived high concentrations (50-400 μg/mL) of a variety of antibiotics (Table 18). B. pumilus was isolated from wells containing ampicillin (400 μg/mL) and meropenem (50 μg/mL), and the potential human pathogens K. pneumoniae, E. pallens, and S. hominis were isolated from wells containing cefotaxime (400 μg/mL), hygromycin (200 μg/mL), and erythromycin (50 μg/mL) respectively (Table 19). 54 54 Table 18. Minimal Inhibitory Concentration (µg/mL) Observed in Fresno, California Fecal Microbiota Antibiotic MIC (μg/mL) Antibiotic MIC (μg/mL)

Ampicillin >400 Kanamycin 200 Apramycin 200 Meropenem 100 Cefotaxime >400 Methicillin >400 Chloramphenicol >400 Novobiocin >400 Erythromycin 100 Polymyxin B >400 Gentamicin >400 Tetracycline >400 Hygromycin 400 Vancomycin 200

Table 19. Antibiotic Resistant Organisms Isolated from Fresno Crow MIC Antibiotic (μg/mL) Organism Apramycin (100) Bacillus pumilus strain m414 Cellulosimicrobium sp. X6-36 Cefotaxime (400) Klebsiella pneumoniae strain FWX7 Erythromycin (50) Pantoea vagans strain A17 Staphylococcus hominis strain PbT3 Hygromycin (200) Enterococcus pallens C8 Meropenem (50) Bacillus pumilus strain W-N-5-3-2 Staphylococcus sp. 4b-2 Vancomycin (100) Pantoea sp. AHM32

CSU, Fresno - Farm Animals. Crows have been observed feeding in the outdoor agricultural pens at CSU, Fresno (personal communication, Dr. Paul Crosbie). Therefore, we wanted to determine whether it was possible crows could be consuming pathogenic and/or AR bacteria from this location, which could alter their gut microbiota. The isolates obtained from livestock at CSU, Fresno displayed low resistance levels relative to Fresno. Escherichia fergusonii showed resistance to spectinomycin (100 µg/mL), gentamycin (14.6±7.8 µg/mL), streptomycin (25 µg/mL), and kanamycin (20.8±5.9 µg/mL) (Table 20). Shigella sonnei was resistant to spectinomycin (100 µg/mL), gentamycin (12.5 µg/mL), 55 55 streptomycin (25 µg/mL), and kanamycin (25 µg/mL). Aeococcus viridans only showed resistance to spectinomycin at a high concentration of 400 µg/mL.

Table 20. Minimum Inhibitory Concentrations (µg/mL) of CSU, Fresno Farm Animals Isolates Antibiotics (mean Escherichia Shigella sonnei Aeococcus viridans µg/mL ± SD) fergusonii Cefotaxime 6.3 6.3 6.3 Spectinomycin 100 100 400 Gentamycin 14.6±7.8 12.5 6.3 Polymyxin B 6.3 6.3 6.3 Streptomycin 25 25 6.3 Ofloxacin 6.3 6.3 6.3 Ampicillin 6.3 6.3 6.3 Kanamycin 20.8±5.9 25 6.3 Chloramphenicol 6.3 6.3 6.3 Meropenem 6.3 6.3 6.3 Rifampicin 6.3 6.3 6.3

DISCUSSION

American Crow Microbiota Our analysis returned a total of 1,638 phylotypes with a maximum of 79 phylotypes per sample which is lower than the Hird et al. (2015) study investigating the avian microbiota where the average phylotype per sample was 201 [138]. This discrepancy could be explained by the large sample size (n=112) and the variety of birds (8 orders) used in the Hird study [138] relative to this study. Examination of emu Dromaius novaehollandiae [139], kakapo Strigops habroptilus [140], and penguins [141] (gento Pygoscelis papua and little penguin Eudyptula minor) returned a similar number of phylotypes (39, 28, 53, and 50 respectively), indicating the number of phylotypes we generated is comparable to other species specific fecal microbiota studies.

Core Microbiota The core microbiota of the American Crow studied in Fresno, Davis, and Critter Creek, contained the phyla Proteobacteria (44.46% relative abundance), Firmicutes (38.59%), Actinobacteria (10.70%), and Plantomycetes (1.0%), and the orders Lactobacillales (22.2%), Enterobacteriales (21.9%), Pseudomonadales (13.1%), and 14 others (Figure 4, p. 36). Cyanobacteria and Chloroflexi, identified in all sites, are photosynthetic bacteria so it is likely these are transient members of the fecal microbiota, sourced from the crows’ diet. Our results resemble the Passeriformes (songbirds order, including corvids) fecal microbiota with 48% Proteobacteria, 37% Firmicutes, 5% Bacteroidetes, and 3% Actinobacteria [138]. The notable difference between the crow fecal microbiota and the fecal microbiota of Passeriformes as a whole, was the under representation of Bacteroidetes and the increased representation of Actinobacteria in the crows. Bacteroidetes was found 57 57 in 91% of the samples but only comprised 1% of the total microbiota, suggesting the function, if any, they serve does not require large abundance in crows. Avian orders that contain less Bacteroidetes maintain diets including seeds, nuts, and fruit [Passeriformes (songbirds), Apodiformes (hummingbirds), and Columbiformes (doves)] while orders with higher abundances tend to be carnivores, eating insects, eggs, fish, and small vertebrates [Caprimulgiformes (nightjars), Cuculiformes (cuckoos), Sphenisciformes (penguins) and Struthioniformes (ostriches)] [138]. On the avian species level, this Bacteroidetes pattern may be lost as turkey vultures Cathartes aura would be expected to have a larger abundance than observed (~5%) [39] while the reverse would be expected to in emus (~57%) [139]. Following the order level patterns, we could hypothesize the crows sampled in this study feed more on plant-based food sources than animal-based sources. Fusobacteria (found only in Fresno at 0.001%) is commonly found in the feces of carnivores, including turkey vultures (~10%) [39] and gentoo penguins (55%) [141], and omnivores such as gulls (0.7%). Proteobacteria is present in multiple avian fecal microbiotas at varying relative abundance. The avian orders Passeriformes (songbirds), Psittaciformes (parrots), Piciformes (woodpeckers), Procellariiformes (fulmars), Coraciliformes (bee-eaters), Trogoniformes (trogons), Apodiformes (hummingbirds), and Anseriformes (waterfowl) display fecal microbiota with Proteobacteria at the highest relative abundance [138]. At the avian species or genus level, relative abundance of Proteobacteria ranges from minor members (chickens Gallus gallus domesticus 3.22% [142]), to moderate abundance (gentoo penguin Pygoscelis papua 18%, little penguin Eudyptula minor 30% [141], geese Branta species 23% [143], gull Larus species 23% [144] and emus 24% [139], to the most abundant member (kakapos 48% [140]). 58 58

Firmicutes were observed in varying abundances in the Fresno microbiota, two samples (FW10 and FW3) had high abundance (92% and 82% respectively), two (FW2 and FW7) were in lesser abundance (21% and 24% respectively), and the last two samples (FW4 and FW8) displayed medium abundance (51% and 57%). Critter Creek and Davis contained lower levels of Firmicutes. Critter Creek ranged from 11% (CCW5) to 35% (CCW7) and Davis included 14% (DW1) and 22% (DW2). This spread in relative abundance shows Firmicutes may be the most abundant phyla and it is not until Davis and Critter Creek are included that Proteobacteria establishes itself as the major phyla. The Critter Creek (N=3; 172,235 sequences) and Davis (N=2; 81,289 sequences) microbiota contained fewer samples and total sequences than the Fresno (N=6; 327,840 sequences) microbiota. If the sample sizes were increased, Critter Creek and Davis may develop the same pattern discovered in Fresno. Large inter-sample variation is a factor encountered by other fecal microbiota studies [62, 138, 140], giving credit to the functional core hypothesis that the microbiota requires specific functional roles to be filled and they may be achieved by a variety of microbes, leading to distinctly different relative abundance between individuals [62].

Environment Over Host Species Studies of avian and human fecal microbiota suggest environment drives differences between microbiota more than the species (C. brachyrhynchos or H. sapiens) [60]. We discovered this when comparing the urban samples (Fresno and Davis) to the consistent diet Critter Creek samples (Figure 7; p. 50, Unweighted UniFrac P=0.010), and when comparing Fresno and Critter Creek (Figure 6; p. 37, Unweighted UniFrac P=0.036). Fresno and Davis were not significantly different (Figure 6; Unweighted UniFrac P=0.475) likely due to similar environments, and 59 59 the small sample size of Davis (N=2). At present, no studies have characterized the fecal microbiota of American Crows, but gulls may serve as a useful comparison. Gulls are similar to crows in their diet and adaptation to urban living, and display many similarities in fecal microbiota relative abundance including moderate abundance of Bacilli (class within Firmicutes phylum) (crow 50%, gull 37% [144]), Actinobacteria (crow 10%, gull 6.4% [144]), but differ in relative abundance of Proteobacteria (crow 44%, gull 23% [144]). The difference in Proteobacteria can be explained by analyzing the Fresno microbiota, arguably the more representative because of sample size (N=6), as the relative abundance in Fresno’s microbiota is 28%. Though these birds are representatives of different avian orders (crows are Passeriformes, gull are Charadriiformes), their similar environments and interactions with urban environments produce comparable fecal microbiotas.

Variable Microbiota Each site microbiota had unique taxa that were not identified in the other two collection sites, comprising the variable microbiota (Tables 11-13, pp. 44-45). Critter Creek possessed 36 unique genera which composed 42.1% of the microbiota and contained Brachyspira (associated with human gastroenteritis [145]), Rickettsia (the causative agent of spotted fever diseases [146]), among other pathogenic and nonpathogenic taxa (Table 12, p. 44). The relatively large variable microbiota of Critter Creek (42.1% vs. 19.3% in Fresno and 3.0% in Davis) is likely the factor that resulted in Critter Creek’s divergence from the more urban microbiota based on their controlled diet. Furthermore, the crows in Critter Creek Wildlife Station may have originally come from anywhere in the San Joaquin, providing the microbial variety that may not be present in the Fresno or 60 60

Davis populations. The Fresno roost had the most diverse variable microbiota (57 unique genera), likely because it had the largest sample size (N=6), but the variable component only contributed 19.3% of the total microbiota. Fresno’s variable microbiota consisted of genera such as Tsukamurella (a rare human pathogen that may cause bacteremia [147]), Rubrobacter (a radiation-resistant bacterium [148]), Exiguobacterium (may cause bacteremia in immunocompromised patients [149]), among other pathogenic and nonpathogenic taxa. The Davis roost had the fewest unique genera (10) likely because it had the smallest sample size (N=2).

Effect of Low Abundance Taxa Few phyla were high in abundance (Firmicutes, Proteobacteria, and Actinobacteria) but there were others that were present in all samples, just in fewer numbers (Cyanobacteria 1.6%, Bacteroidetes 1%, Chloroflexi 0.7%, and Tenericutes 0.3%). Since all crows have a relatively high abundance of Firmicutes, Proteobacteria, and Actinobacteria, the rare and less abundant taxa are what drive the differences between microbiota of different crow populations (Figures 6 and 7, pp. 50-51). When the rare taxa are given less weight in the overall analysis (weighted UniFrac), there is no significant difference between any of the collection sites (Critter Creek vs. Davis P=0.513, Davis vs. Fresno P=0.446, Critter Creek vs. Fresno P=0.327). When the rare taxa are weighted evenly with the rest of the taxa (unweighted UniFrac), we observed significant differences (P=0.036) between the Critter Creek and Fresno variable microbiota (42.2% and 19.3% of microbiota respectivley), but no other microbiota were statistically different (Critter Creek vs. Davis P=0.094. Davis vs. Fresno P=0.475). The difference between Critter Creek and Davis was greater with unweighted than 61 61 weighted UniFrac (P=0.094 vs. P=0.513 respectively), but the low sample size of Davis (N=2) and the small percentage of variable microbiota (3.0%) limited the statistical power to detect a significant difference, if it existed. This pattern is observed again in the diet fluctuation comparison (Fig. 7). By combining Fresno and Davis (N=9), the power of the analysis increased, and the numerous variable microbiota of Fresno resulted in a significant difference when compared to Critter Creek (unweighted UniFrac P=0.010). When the variable microbiota was weighted, therefore having less statistical power, no significant difference was observed. But the difference in relative abundance of the extensive taxa (Constant diet: 21% Firmicutes, 60% Proteobacteria. Dynamic diet: 45% Firmicutes, 40% Proteobacteria) and the power gained from increased sample size (N=9) resulted in further difference in the diet fluctuation comparison (weighted UniFrac P=0.153) than either Critter Creek vs. Fresno (P=0.327) or Critter Creek vs. Davis (P=0.513).

Roosting Period There was no significant difference between the microbiota composition between the early roosting season and the late roosting season (Table 15; p. 48, Unweighted UniFrac: P=0.089. Weighted UniFrac: P=0.702). It is possible the microbiota has the resilience to changes in diet [62] and can maintain the structure of the microbiota over the roosting period. Alternatively, the diet of the crows may not have dramatically changed over the roosting season, as changes in diet generally result in an observable shift in gut microbiota [150]. Anthropogenic sources provide stable, relatively unchanging food sources including food waste, pet food, and landfills, when compared to invertebrate lifecycles or crops seasons. Urban diets may relay negative effects to the crows due to the subordinate 62 62 nutritional benefits [10], possibly creating populations with lesser fitness, members of which may harbor an increased number of microbes if their immune systems are impaired.

Pathogens At least eight taxa that contain potential human pathogens were identified in the core microbiota including Enterococcus (genus), Actinetobacter (genus), and Enterobacteriaceae (family) (Tables 7 and 14, pp. 40, 46). Members of the genus Enterococcus, like E. faecium and E. faecalis, may cause urinary tract infections, wound infections and blood infections [92]. Vancomycin resistant strains have been identified in American Crow fecal microbiota sampled in Kansas (0.7% crows sampled), New York (2.3% of crows), and Massachusetts (6.5% of crows), but none were identified in California [54]. Vancomycin resistance genes were identified in the Fresno resistome, though it cannot be confirmed that it originated from Enterococcus. Multiple members of the family Enterobacteriaceae may be pathogenic to humans including Salmonella, Klebsiella, and Escherichia coli (identified in 82% of samples) and can cause a range of diseases like pneumonia and bloodstream infections [53]. Many organisms from Enterobacteriaceae are drug resistant like carbapenem-resistant Enterobacteriaceae (Klebsiella and E. coli), extended spectrum β-lactamase (EMBL) producing Enterobacteriaceae, drug resistant typhoidal and non-typhoidal Salmonella, and drug resistant Shigella [92]. Plasmid-Mediated Quinolone Resistant Enterobacteriaceae has been isolated from 33% of American Crows sampled in the United States and Enterobacteriaceae resistance to ciprofloxacin ranging from 43% in California and 81% in New York [53]. Members of the genus Acinetobacter can cause pneumonia and blood infections and has been deemed a 63 63 serious public health risk because many strains are resistant to at least three different drug classes (multi-drug resistant) [92]. Like the fecal microbiota of waterfowl [44], shorebirds [45-48], pigeons [49], and songbirds [50-52], the genus Campylobacter was identified in 33% of Critter Creek and 50% of Davis samples and the Campylobacteraceae family in 16.7% of Fresno samples. Campylobacter species cause campylobacteriosis, marked by abdominal pain and diarrhea and is one of the four main causes of diarrheal diseases globally [151]. American Crow fecal microbiota sampled in Yolo County by Weis et al. (2014) revealed between 53.8% (2012) and 92.9% (2013) contained Campylobacter species, 96% of which were C. jejuni, which has been known to exhibit multidrug resistance conferred by the AR cassette cmeABC [73]. The cmeB segment of the cmeABC operon was identified in the Fresno resistome (0.01% of resistome) [73]. The order Chlamydiales was identified in all sites (Fresno 83%, Critter Creek 33%, Davis 100%) and the family Chlamydiaceae in DW1. Chlamydia has been identified in shorebirds and waterfowl [35], poultry chicken [35], and Psittacine birds [42] such as parrots. C. psittaci is the causative agent of the respiratory infection Psittacosis which is usually transferred from Psittacine birds to pet owners [110] or between poultry and workers [111]. C. psittaci is spread by ingestion or inhalation of desiccated secretions such as urine and feces [152], which may be possible with urban crows roosting in high traffic areas as with the Fresno and Davis crows. A single sample, Davis W1, contained the genus Treponema which contains T. pallidum which possesses subspecies which may cause syphilis (subsp pallidum) and yaws (subsp pertenue) and T. carateum which causes pinta [137]. More samples need to be analyzed to determine in Treponema was a transient 64 64 member of the fecal microbiota or a resident. With the information present, it is likely a transient member as it was only present in one sample (DW1) and at low relative abundance (0.007%). Though possibly Treponema is a resident member as the genus has been identified in the oral and GI track of swine [153] and humans [137]. The family Clostridiaceae was identified in all samples (N=11), with 82% identified to the genus level (Clostridium). Clostridium includes pathogens such as C. botulinum, C. difficile, C. tetani, and C. perfringens (identified in 36.4% of samples). Fresno and Critter Creek, like poultry fecal microbiota [41], contained C. perfringens which may cause food poisoning and gas gangrene [41]. C. botulinum has been identified in the feces of shorebirds and waterfowl [34] and may have been present, but not identified to the species level in the crows. Clostridial species may be transferred to humans as the endospores produced by Clostridium can withstand harsh environments [154], possibly allowing them to colonize a host.

American Crow Resistome Many antibiotic resistance genes (ARG) were found in the Fresno (Table 20), microbiome (Table 16), although the specific bacteria from which they originated is unclear. Multidrug efflux pump ARGs belonged to the Major Facilitator Superfamily [155] (such as rosA, 0.46% of Fresno resistome) and the resistance-nodulation-cell division systems [73] (including acrAB, 1.6%) comprised the majority of the Fresno microbial resistome (67%). The most abundant single gene (12%) returned from the resistome analysis was emrB-qacA which encodes multidrug efflux pumps that eject dicationic biocides [156], nalidixic acid, thiolactomycin, [157] and uncoupling antibiotics [158]. ARGs 65 65 coding for multidrug resistance are particularly concerning because fewer classes of drugs are effective against these organisms, leading to longer, more costly hospitalizations or pan-resistant, deadly organisms. We also found genes encoding resistance (12%) to drugs that disable the 50S ribosomal subunit: macrolides, lincosamides, and streptogramin B (MLS). [78] Resistance can result from ribosomal modification as seen with the erm genes which transcribe for enzymes that methylate the 23S rRNA component of the 50S ribosomal subunit, providing partial or complete resistance to MLSs [77]. The incidence of beta-hemolytic streptococci resistant to macrolides and lincosamides has been increasing globally [159]. Finding resistance to fosfomycin, even at a low level, is concerning as it was classified as a reserve, or “last resort,” drug by the World Health Organization (WHO) [160]. For now, fosfomycin is effective against ESBL and AmpC-producing Enterobacteriaceae [161]. The organisms (E. fergusonii, S. sonnei, and A. viridans) isolated from CSU, Fresno livestock displayed medium to high resistance to spectinomycin (100-400 µg/mL) and low resistance to gentamycin (12.5-14.6±7.8 µg/mL), streptomycin (25 µg/mL), and kanamycin (20.8±5.9-25 µg/mL). The moderate levels of AR could be due to CSU, Fresno only exposing the livestock to antibiotics when necessary instead of dosing them regularly (personal correspondence, Dr. Art Parham). Gut microbes of the livestock are not selected for AR and maintain susceptibility to the drugs. Crows feeding at these outdoor agricultural pens are exposed to less AR organisms than were present in the existing Fresno resistome. 66 66 Future Directions To gain insight into what specific functions the members of the microbiota play in the American Crow, it is critical to identify specific metabolites produced by specific members of the microbiota using metabolomics [162]. The results could further determine if there are multiple organisms serving a single functional role, compiling a functional core and identifying a potential mechanism for explaining the potential resilience of the microbiota community. In this way, different organisms may fill the same role, and as long as one taxon remains in the microbiota, the functional core is maintained. By learning about the functions the taxa perform, we can determine if the few phyla composing the core microbiota may be responsible for all the essential functions microbiota fill for the host [62]. Alternatively, some members of the variable microbiota may fill multiple functional roles. Linking microbiota composition and abundance to behavioral and ecological data potentially allows broader scale question to be addressed including how the microbiota develops over time, how pathogen load relates to foraging locations, a number of mates, and roosting location. If we radio collared multiple birds, we could involve data about the crows’ daily territories, commuting habits, and roosting fidelity. With field observations we could also estimate the diet of some members of the roost.

CONCLUSION

Urban American Crow populations have been increasing since the 1950s and it is now common to find legions of crows in urban roosts where they may serve as a mechanisms of transmitting AR pathogens to humans. The crow fecal microbiota of possesses pathogen-containing taxa such as Enterococcus, Campylobacter, Clostridium, Chlamydia, among others. The unweighted UniFrac analysis was shown to produce significant results between Fresno and Critter Creek (P=0.012) and between consistent and dynamic diet (P=0.010), due to the contribution of the variable microbiota. The Fresno, California crows displayed antibiotic resistance genes for multiple drug efflux pumps, lacrolide-lincosamide- streptogramin (MLS), vancomycin, β-lactams, and more. Critter Creek displayed resistance to spectinomycin (25-200 µg/mL), streptomycin (41.7 µg/mL ± 11.8), ampicillin (133.3 µg/mL ± 47.1), kanamycin (16.7 µg/mL ± 5.9), and polymyxin B (25-100 µg/mL). The spread of ARG and pathogens may be increased by the common urban wildlife, the American Crow.

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APPENDICES

APPENDIX A: QIIME2 SYSTEM DETAILS AND COMMANDS 86 86

System Details

System versions

Python version: 3.5.4

QIIME 2 release: 2017.10

QIIME 2 version: 2017.10.0

q2cli version: 2017.10.0

Installed plugins

alignment 2017.10.0 longitudinal 2017.10.0

composition 2017.10.0 metadata 2017.10.0

dada2 2017.10.0 phylogeny 2017.10.0

deblur 2017.10.0 quality-control 2017.10.0

demux 2017.10.0 quality-filter 2017.10.0

diversity 2017.10.0 sample-classifier 2017.10.0

emperor 2017.10.0 taxa 2017.10.0

feature-classifier 2017.10.0 types 2017.10.0

feature-table 2017.10.0 vs.earch 2017.10.0

gneiss 2017.10.0

Application config directory

/home/qiime2/.config/q2cli

87 87

Commands for Microbiota Analysis qiime tools import --type 'SampleData[PairedEndSequencesWithQuality]' - -input-path manifest.csv --source-format PairedEndFastqManifestPhred33 - -output-path demux.qza qiime demux summarize --i-data demux.qza --o-visualization demux.qzv qiime dada2 denoise-paired --i-demultiplexed-seqs demux.qza --o-table table --o-representative-sequences rep-seqs --p-trim-left-f 13 --p-trim-left-r 17 --p-trunc-len-f 251 --p-trunc-len-r 250 qiime feature-table summarize --i-table table.qza --o-visualization table.qzv qiime feature-table tabulate-seqs --i-data rep-seqs.qza --o-visualization rep- seqs.qzv qiime alignment mafft --i-sequences rep-seqs.qza --o-alignment aligned- rep-seqs.qza qiime alignment mask --i-alignment aligned-rep-seqs.qza --o-masked- alignment masked-aligned-rep-seqs.qza qiime phylogeny fasttree --i-alignment masked-aligned-rep-seqs.qza --o- tree unrooted-tree.qza qiime diversity core-metrics-phylogenetic --i-phylogeny rooted-tree.qza --i- table table.qza --p-sampling-depth 27398 --m-metadata-file CrowMicrobiota-metadata.tsv --output-dir core-metrics-results qiime diversity alpha-group-significance --i-alpha-diversity Shannon_vector.qza --m-metadata-file CrowMicrobiota-metadata.tsv --o- visualization Shannon_vector-group-significance.qzv qiime diversity beta-group-significance --i-distance-matrix weighted_unifrac_distance_matrix.qza --m-metadata-file CrowMicrobiota- 88 88 metadata.tsv --m-metadata-category “CollectionSite” --o-visualization weighted-unifrac-collection-site-significance.qzv --p-pairwise qiime diversity alpha-rarefaction --i-table table.qza --i-phylogeny rooted- tree.qza --p-max-depth 27398 --m-metadata-file CrowMicrobiota- metadata.tsv --o-visualization alpha-rarefaction.qzv wget -o “gg-13-8-99-515-806-nb-classifier.qza” “https://data.qiime2.org/2017.12/common/gg-13-8-99-515-806-nb- classifier.qza” qiime feature-classifier classify-sklearn --i-classifier gg-13-8-99-515-806- nb-classifier.qza --i-reads rep-seqs.qza --o-classification taxonomy.qza qiime metadata tabulate --m-input-file taxonomy.qza --o-visualization taxonomy.qzv qiime taxa barplot --i-table table.qza --i-taxonomy taxonomy.qza --m- metadata-file CrowMicrobiota-metadata.tsv --o-visualization taxa-bar- plots.qzv qiime taxa collapse --i-table table.qza --i-taxonomy taxonomy.qza --p-level 2 --o-collapsed-table TaxaFeatureTable.qza qiime feature-table heatmap --i-table TaxaFeatureTable.qza --m-metadata- file CrowMicrobiota-metadata.tsv --m-metadata-category CollectionSite -- p-method weighted --p-color-scheme spring_r --p-cluster features --o- visualization Heatmap

APPENDIX B: MICROBIOTA DETAILS 90 90

Full Fecal Microbiota of American Crows All samples containing the named taxa contain an “X” in the corresponding column. Taxa identified as containing human pathogens were bolded (Table 21).

Table 21. Full Microbiota of American Crow Feces

FW2 FW3 FW4 FW7 FW8 FW10 CCW4 CCW5 CCW7 DW1 DW2 K.: Archaea X X X X X X X P.: Crenarchaeota X X X X X X X C.: Thaumarchaeota X X X X X X X O.: Nitrososphaerales X X X X X X X F.: Nitrososphaeraceae X X X X X X X G.: Candidatus Nitrososphaera X X X X X X X S.: Gargensis X X X S.: SCA1145 X X X X X X X S.: SCA1170 X P.: Euryarchaeota X X X C.: Methanobacteria X X X O.: Methanobacteriales X X X F.: Methanobacteriaceae X X X G.: Methanobacterium X G.: Methanobrevibacter X X X G.: Methanosphaera X X K.: Bacteria X X X X X X X X X X X P.: Acidobacteria X X X X C.: Acidobacteria-6 X X X O.: iii1-15 X X X F.: mb2424 X C.: Chloracidobacteria X O.: PK29 X C.: S035 X P.: Actinobacteria X X X X X X X X X X X C.: Acidimicrobiia X X X X X X X X X X X O.: Acidimicrobiales X X X X X X X X X X X F.: AKIW874 X X F.: C111 X X X X X X F.: EB1017 X X F.: Iamiaceae X F.: Microthrixaceae X X X X G.: Candidatus Microthrix X S.: parvicella X

91 91

C.: Actinobacteria X X X X X X X X X X X O.: Actinomycetales X X X X X X X X X X X

F.: Actinomycetaceae X X F.: Actinopolysporaceae X G.: Actinopolyspora X F.: Actinosynnemataceae X G.: Saccharothrix X F.: Bogoriellaceae X X X X X G.: Georgenia X X X X X F.: Brevibacteriaceae X X X X X G.: Brevibacterium X X X X X S.: aurem X X X F.: Cellulomonadaceae X X X X X X X X X X G.: Actinotalea X G.: Cellulomonas X X X X X X X X X X G.: Demequina X F.: Corynebacteriaceae X X X X X X X X G.: Corynebacterium X X X X X X X X F.: Dermabacteraceae X X X X X X G.: Brachybacterium X X X X X X F.: Dermacoccaceae X X X X G.: Dermacoccus X X X X F.: Dietziaceae X X X X X X X G.: Dietzia X X X X X X X F.: Frankiaceae X X F.: Geodermatophilaceae X X X X X X X X X X X

G.: Geodermatophilus X X X X S.: obscurus X X G.: Modestobacter X X X X X F.: Glycomycetaceae X X G.: Glycomyces X X F.: Gordoniaceae X X X X G.: Gordonia X X X X F.: Intrasporangiaceae X X X X X X X X X X X

G.: Phycicoccus X X X X X X X G.: Terracoccus X F.: Jonesiaceae X G.: Jonesia X F.: Kineosporiaceae X X X X X X G.: Kineococcus X F.: Microbacteriaceae X X X X X X X X X X X

G.: Agromyces X X G.: Cryocola X X X G.: Curtobacterium X X X X X X X X X

92 92

G.: Leucobacter X X X X X X X X X G.: Salinibacterium X X X F.: Micrococcaceae X X X X X X X X X X X

G.: Arthrobacter X X X X X X X G.: Kocuria X X X X G.: Microbispora X X X X S.: rosea X X X X G.: Micrococcus X X X S.: luteus X X X G.: Nesterenkonia X X X G.: Rothia X X X X X X S.: mucilaginosa X X X X X X F.: Micromonosporaceae X X X X X X X X G.: Actinocatenispora X G.: Actinoplanes X G.: Catellatospora X X X F.: Mycobacteriaceae X X X X X X X G.: Mycobacterium X X X X X X X F.: Nakamurellaceae X X X F.: Nocardiaceae X X X X X X X X X X X

G.: Nocardia X X G.: Rhodococcus X X X X X X X X X X X

S.: fascians X X X X X X F.: Nocardioidaceae X X X X X X X X X X X

G.: Aeromicrobium X X X X X X X X G.: Nocardioides X X X X X X F.: Nocardiopsaceae X X X X F.: Promicromonosporaceae X X X X X X G.: Cellulosimimicrobium X X X X X X G.: Xylanimicrobium X X F.: Propionibacteriaceae X F.: Pseudonocardiaceae X X X X X X X G.: Actinomycetospora X X X G.: Pseudonocardia X X X X X G.: Saccharopolyspora X X S.: hirsuta X F.: Sanguibacteraceae X X X X X X X X X X G.: Sanguibacter X X X X X X X X X X F.: Sporichthyaceae X X X X F.: Streptomycetaceae X X X X X X X X X X G.: Streptomyces X X X X X X X X F.: Streptosporangiaceae X X X X X G.: Nonomuraea X X X X G.: Sphaerisporangium X

93 93

F.: Thermomonosporaceae X X X X G.: Actinoallomurus X X X S.: iriomotensis X X X G.: Actinocorallia X G.: Actinomadura X X X S.: vinacea X X X F.: Tsukamurellaceae X X G.: Tsukamurella X X F.: Williamsiaceae X X X X X G.: Williamsia X X X X X F.: Yaniellaceae X X G.: Yaniella X X C.: Coriobacteriia X X X O.: Coriobacteriales X X X F.: Coriobacteriaceae X X X G.: Adlercreutzia X C.: MB-A2-108 X X X O.: 0319-7L14 X X X C.: Nitriliruptoria X X X X O.: Euzebyales X X X F.: Euzebyaceae X X X G.: Euzebya X X X F.: Nitriliruptoraceae X G.: Nitriliruptor X C.: Rubrobacteria X X X X O.: Rubrobacterales X X X X F.: Rubrobacteraceae X X X X G.: Rubrobacter X X X X C.: Thermoleophilia X X X X X X X X X X X O.: Gaiellales X X X X X X X X X X X

F.: AK1AB1_02E X X X X X F.: Gaiellaceae X X X X X X X O.: Solirubrobacterales X X X X X X X X X X X

F.: Conexibacteraceae X X X X X X F.: Patulibacteraceae X X X X X X X X G.: Patulibacter X X X X X X X X F.: Solirubrobacteraceae X X X X X X X X G.: Solirubrobacter X X X X X X P.: Bacteroidetes X X X X X X X X X X X

C.: Bacteroidia X X X X O.: Bacteroidales X X X X F.: Porphyromonadaceae X X X X G.: Dysgonomonas X X X F.: Prevotellaceae X

94 94

G.: Prevotella X C.: Cytophagia X X X X X O.: Cytophagales X X X X X F.: Cyclobacteriaceae X X F.: Cytophagaceae X X X X G.: Hymenobacter X G.: Sporocytophaga X C.: Flavobacteriia X X X X X X X X O.: Flavobacteriales X X X X X X X X F.: Flavobacteriaceae X X X X X X X G.: Aequorivita X X X G.: Flavobacterium X X X X X S.: Succinicans X X X G.: Gelidibacter X X X G.: Myroides X X G.: Winogradskyella X F.: Weeksellaceae X X X X X X X G.: Chryseobacterium X X X X X G.: Riemerella X G.: Wautersiella X X C.: Saprospirae X X X X X O.: Saprospirales X X X X X F.: Chitinophagaceae X X X X X G.: Flavisolibacter X C.: Sphingobacteriia X X X X X X X X X O.: Sphingobacteriales X X X X X X X X X F.: Sphingobacteriaceae X X X X X X X X X G.: Parapedobacter X G.: Pedobacter X X X X X G.: Sphingobacterium X X X X X X S.: faecium X X X X X S.: multivorum X P.: BRC1 X X X X C.: PRR-11 X X X X P.: Chlamydiae X X X X X X X X C.: Chlamydiia X X X X X X X X O.: Chlamydiales X X X X X X X X F.: Chlamydiaceae X F.: Criblamydiaceae X F.: Parachlamydiaceae X X X X X X X G.: Candidatus Protochlamydia X X X X P.: Chloroflexi X X X X X X X X X X X

C.: Anaerolineae X X X X O.: Caldilineales X X X

95 95

F.: Caldilineaceae X X X C.: Chloroflexi X X X X X O.: AKIW781 X X X X O.: Chloroflexales X F.: FFCH7168 X O.: Roseiflexales X F.: Kouleothrixaceae X C.: Ellin6529 X X X X X X C.: Gitt-GS-136 X X X X X C.: Ktedonobacteria X X X X O.: Ktedonobacterales X X X X F.: Ktedonobacteraceae X X X X C.: Thermomicrobia X X X X X X X X X X X

O.: AKYG1722 X O.: JG30-KF-CM45 X X X X X X X X X X X

O.: Sphaerobacterales X C.: TK10 X X X X O.: AKYG885 X X F.: Dolo_23 X X O.: B07_WMSP1 X X P.: Cyanobacteria X X X X X X X X X X C.: 4C0d-2 X X O.: SM1D11 X O.: YS2 X C.: Chloroplast X X X X X X X X X X O.: CAB-I X X X X X X O.: Chlorophyta X X X X X O.: Stramenopiles X O.: Streptophyta X X X X X X X X X X O.: UA01 X C.: ML635J-21 X X X C.: Nostocophycideae X X O.: Nostocales X X F.: Nostocaceae X X C.: Oscillatoriophycideae X O.: Oscillatoriales X F.: Phormidiaceae X P.: FBP X P.: Firmicutes X X X X X X X X X X X C.: Bacilli X X X X X X X X X X X O.: Bacillales X X X X X X X X X X X

F.: Alicyclobacillaceae X X X X G.: Alicyclobacillus X X X X F.: Bacillaceae X X X X X X X X X X

96 96

G.: Bacillus X X X X X X X X S.: badius X X X S.: cereus X X S.: clausii X X X S.: endophyticus X S.: firmus X S.: flexus X X X X X X S.: halodurans X S.: longiquaesitum X S.: muralis X X S.: selenatarsenatis X G.: Natronobacillus X X X G.: Virgibacillus X X X X X F.: Exiguobacteraceae X X X G.: Exiguobacterium X X X F.: Listeriaceae X X X X X G.: Brochothrix X X X X X F.: Paenibacillaceae X X X X X X X X G.: Ammoniphilus X X X G.: Brevibacillus X G.: Paenibacillus X X X X X X S.:amylolyticus X S.:chondroitinus X X F.: Planococcaceae X X X X X X X X X X X

G.: Planomicrobium X X X X X G.: Rummeliibacillus X G.: Solibacillus X X G.: Sporosarcina X X X X X X X X X F.: Staphylococcaceae X X X X X X X G.: Jeotgalicoccus X X X S.: psychrophilus X X X G.: Salinicoccus X X X G.: Staphylococcus X X X X X X X S.: sciuri X X F.: Thermoactinomycetaceae X X X X G.: Planifilum X X X G.: Shimazuella X X O.: Lactobacillales X X X X X X X X X X X

F.: Aerococcaceae X X X X X G.: Facklamia X X X X F.: Carnobacteriaceae X X X X X X X X X G.: Carnobacterium X X X X X X X X X S.: viridans X X X X X X X X X G.: Desemzia X X

97 97

G.: Trichococcus X X X X F.: Enterococcaceae X X X X X X X X X X X G.: Enterococcus X X X X X X X X X X X

G.: Vagococcus X X X X X X F.: Lactobacillaceae X X X X X X X X G.: Lactobacillus X X X X X X X S.: agilis X X X S.: brevis X S.: paralimentarius X S.: salivarius X X G.: Pediococcus X F.: Leuconostocaceae X X X X G.: Leuconostoc X X S.: mesenteroides X X G.: Weissella X X F.: Streptococcaceae X X X X X X X X X G.: Lactococcus X X X X X X X S.: garvieae X X X G.: Streptococcus X X X X X X X X S.: Luteciae X X X X S.: Minor X O.: Turicibacterales X X X F.: Turicibacteraceae X X X G.: Turicibacter X X X C.: Clostridia X X X X X X X X X X X O.: Clostridiales X X X X X X X X X X X

F.: Christensenellaceae X F.: Clostridiaceae X X X X X X X X X X X

G.: Alkaliphilus X G.: Candidatus Arthromitus X X X X X X X X X X X

G.: Clostridium X X X X X X X X X S.: bowmanii X X X X X S.: perfringens X X X X G.: Proteiniclasticum X X G.: SMB53 X F.: Eubacteriaceae X G.: Acetobacterium X F.: Gracilibacteraceae X G.: Gracilibacter X F.: Lachnospiraceae X X X X X X X G.: Anaerostipes X G.: Butyrivibrio X X G.: Clostridium X X X S.: piliforme X X X

98 98

G.: Coprococcus X X X F.: Mogibacteriaceae X X G.: Anaerovorax X G.: Mogibacterium X F.: Peptococcaceae X G.: Desulfotomaculum X F.: Peptostreptococcaceae X X X X X X X X G.: Peptostreptococcus X G.: Proteocatella X S.: sphenisci X G.: Tepidibacter X X X X F.: Ruminococcaceae X X X X X G.: Ruminococcus X X X F.: Symbiobacteriaceae X G.: Symbiobacterium X S.: thermophilum X F.: Tissierellaceae X X X X G.: Gallicola X G.: Peptoniphilus X X G.: Tepidimicrobium X F.: Veillonellaceae X X X X G.: Sporomusa X X X X O.: Halanaerobiales X F.: Halanaerobiaceae X O.: Natranaerobiales X F.: Anaerobrancaceae X O.: OPB54 X X X X C.: Erysipelotrichi X X X X X X X X X O.: Erysipelotrichales X X X X X X X X X F.: Erysipelotrichaceae X X X X X X X X X G.: Erysipelothrix X X X X G.: L7A_E11 X P.: Fusobacteria X C.: Fusobacteriia X O.: Fusobacteriales X F.: Fusobacteriaceae X G.: Fusobacterium X P.: Gemmatimonadetes X X X X C.: Gemm-3 X C.: Gemm-5 X X X P.: MVP-21 X P.: Nitrospirae X C.: Nitrospira X O.: Nitrospirales X

99 99

F.: 0319-6A21 X P.: OD1 X X C.: ABY1 X P.: Planctomycetes X X X X X X X X X X X

C.: Phycisphaerae X X O.: WD2101 X X C.: Planctomycetia X X X X X X X X X X X

O.: Gemmatales X X X X X X X X X F.: Gemmataceae X X G.: Gemmata X X F.: Isosphaeraceae X X X X X X X X O.: Pirellulales X X X X X X X X X X X F.: Pirellulaceae X X X X X X X X X X X

G.: A17 X G.: Pirellula X X G.: Rhodopirellula X X X S.: baltica X X X F.: Planctomycetaceae X X X X X G.: Planctomyces X X X X X P.: Proteobacteria X X X X X X X X X X X C.: Alphaproteobacteria X X X X X X X X X X X O.: Caulobacterales X X X X X X X X X X X F.: Caulobacteraceae X X X X X X X X X X X

G.: Brevundimonas X X X S.: Diminuta X X X G.: Mycoplana X X X X X X X X G.: Phenylobacterium X O.: Rhizobiales X X X X X X X X X X X

F.: Aurantimonadaceae X X X X X F.: Beijerinckiaceae X X X X F.: Bradyrhizobiaceae X X X X X X X X G.: Balneimonas X X X X X X X G.: Bosea X X S.: genosp X X G.: Bradyrhizobium X X X X F.: Brucellaceae X X X X X X X G.: Ochrobactrum X X X X X F.: Hyphomicrobiaceae X X X X X X X X X X G.: Devosia X X X X X X X X X X G.: Hyphomicrobium X G.: Pedomicrobium X X G.: Rhodoplanes X X F.: Methylobacteriaceae X X X X X X X X X X G.: Methylobacterium X X X X X X X X X

100 100

S.: adhaesivum X X X X X X F.: Methylocystaceae X X G.: Methylopila X F.: Phyllobacteriaceae X X X X X X X X X X X

G.: Aminobacter X G.: Mesorhizobium X X X X G.: Phyllobacterium X X F.: Rhizobiaceae X X X X X X X X X X X

G.: Agrobacterium X X X X X X X X X X G.: Kaistia X X X G.: Rhizobium X G.: Sinorhizobium X F.: Rhodobiaceae X X G.: Afifella X X F.: Xanthobacteraceae X X X X G.: Labrys X O.: Rhodobacterales X X X X X X X X X X X F.: Rhodobacteraceae X X X X X X X X X X X

G.: Amaricoccus X X X X G.: Paracoccus X X X X X X X X S.: marcusii X X X G.: Rhodobacter X X X X G.: Rubellimicrobium X X X X X O.: Rhodospirillales X X X X X X X X X X F.: Acetobacteraceae X X X X X X X X G.: Roseomonas X X F.: Rhodospirillaceae X X X X X G.: Azospirillum X G.: Skermanella X X X X X O.: Rickettsiales X X X X X X X X X X X

F.: mitochondria X X X X X X X X X X G.: Pythium X S.: ultimum X G.: Trebouxia X S.: aggregata X F.: Rickettsiaceae X G.: Rickettsia X O.: Sphingomonadales X X X X X X X X F.: Erythrobacteraceae X X G.: Lutibacterium X F.: Sphingomonadaceae X X X X X X X X G.: Kaistobacter X X X X X G.: Novosphingobium X X X G.: Sphingobium X X

101 101

G.: Sphingomonas X X X X X X X S.: echinoides X S.: wittichii X G.: Sphingopyxis X X X S.: alaskensis X C.: Betaproteobacteria X X X X X X X X X X X O.: Burkholderiales X X X X X X X X X X X

F.: Alcaligenaceae X X X X X X G.: Achromobacter X X G.: Alcaligenes X S.: faecalis X G.: Denitrobacter X F.: Comamonadaceae X X X X X X X G.: Comamonas X X X X X G.: Variovorax X X S.: paradoxus X X F.: Oxalobacteraceae X X X X X X X X G.: Janthinobacterium X X X X X X X S.: lividum X O.: Neisseriales X X X X X F.: Neisseriaceae X X X X X G.: Kingella X X G.: Vitreoscilla X X X O.: Rhydocyclales X F.: Rhodocyclaceae X C.: Deltaproteobacteria X X X X X X X X X X X

O.: Bdellovibrionales X X X X X F.: Bacteriovoracaceae X X X X F.: Bdellovibrionaceae X X X X G.: Bdellovibrio X X X X S.: bacteriovorus X O.: Desulfovibrionales X F.: Desulfovibrionaceae X G.: Desulfovibrio X O.: GMD14H09 X X X O.: MIZ46 X X X X X O.: Myxococcales X X X X X X X X X X F.: Cystobacterineae X X F.: Myxococcaceae X X X X X G.: Anaeromyxobacter X G.: Corallococcus X S.: exiguus X G.: Myxococcus X X X F.: Nannocystaceae X

102 102

G.: Nannocystis X F.: OM27 X X X X X F.: Polyangiaceae X X G.: Sorangium X S.: cellulosum X O.: Spirobacillales X X O.: Syntrophobacterales X F.: Syntrophobacteraceae X C.: Epsilonproteobacteria X X X X X X O.: Campylobacterales X X X F.: Campylobacteraceae X X X G.: Arcobacter X S.: cryaerophilus X G.: Campylobacter X X F.: Helicobacteraceae X X X X X G.: Helicobacter X X X X X C.: Gammaproteobacteria X X X X X X X X X X X

O.: Aeromonadales X X X X X F.: Aeromonadaceae X X X X G.: Oceanisphaera X X X O.: Alteromonadales X F.: Alteromonadaceae X G.: Marinobacter X F.: Shewanellaceae X X G.: Shewanella X X O.: Cardiobacteriales X X X F.: Cardiobacteriaceae X X X O.: Enterobacteriales X X X X X X X X X X X F.: Enterobacteriaceae X X X X X X X X X X X

G.: Citrobacter X G.: Erwinia X X X X X X G.: Escherichia X X X X X X X X X S.: coli X X X X X X X X X G.: Morganella X X X X S.: morganii X X G.: Proteus X G.: Providencia X X G.: Serratia X X X S.: marcescens X G.: Yersinia X X X X X O.: Legionellales X X X X X X X X X X X

F.: Coxiellaceae X X X X X G.: Aquicella X X F.: Legionellaceae X X X X X X

103 103

G.: Legionella X X O.: Oceanospirillales X X F.: Halomonadaceae X X G.: Halomonas X O.: Pasteurellales X X X F.: Pasteurellaceae X X X G.: Avibacterium X X X S.: gallinarum X X X O.: Pseudomonadales X X X X X X X X X X X F.: Moraxellaceae X X X X X X X X X X X G.: Acinetobacter X X X X X X X X X X X

S.: lwoffii X X X S.: rhizosphaerae X X G.: Alkanindiges X X X G.: Psychrobacter X X X X X X X S.: pulmonis X X X X F.: Pseudomonadaceae X X X X X X X X X X X G.: Pseudomonas X X X X X X X X X X X

S.: fragi X X X X X X X X X S.: stutzeri X X X S.: veronii X S.: viridiflava X X X X X O.: Xanthomonadales X X X X X X X X X X F.: Sinobacteraceae X G.: Steroidobacter X F.: Xanthomonadaceae X X X X X X X X X X G.: Dokdonella X X X G.: Ignatzschineria X X X G.: Luteibacter X S.: Rhizovicinus X G.: Luteimonas X X X X G.: Lysobacter X X X G.: Rhodanobacter X X G.: Stenotrophomonas X X X X X X X X X X S.: acidaminiphila X S.: maltophilia X X X X X X X X X G.: Thermomonas X X S.: fusca X G.: Wohlfahrtiimonas X C.: TA18 X O.: CV90 X P.: Spirochaetes X X C.: Brachyspirae X O.: Brachyspirales X 104 104

F.: Brachyspiraceae X G.: Brachyspira X C.: Spirochaetes X O.: Spirochaetales X F.: Spirochaetaceae X G.: Treponema X P.: SR1 X P.: Tenericutes X X X X X X X X X X C.: CK-1C4-19 X C.: Mollicutes X X X X X X X X X X O.: Anaeroplasmatales X F.: Anaeroplasmataceae X O.: Mycoplasmatales X X X X X X X X X F.: Mycoplasmataceae X X X X X X X X X G.: Mycoplasma X X X X S.: gallisepticum X X C.: RF3 X O.: ML615J-28 X P.: Thermi X X X X C.: Deinococci X X X X O.: Deinococcales X X X X F.: Deinococcales X X X X G.: Deinococcus X X X X S.: aquatilis X F.: Trueperaceae X X X G.: B-42 X X X G.: Truepera X P.: TM6 X X C.: SJA-4 X X P.: TM7 X X X X C.: SC3 X X C.: TM7-1 X C.: TM7-3 X P.: Verrucomicrobia X X X X X X X X X X C.: Opitutae X C.: Spartobacteria X X X X X X X X X O.: Chthoniobacterales X X X X X X X X X F.: Chthoniobacteraceae X X X X X X X X X G.: Candidatus Xiphinematobacter X X G.: Ellin506 X G.: heteroC45_4W X X C.: Verrucomicrobiae X X X X X O.: Verrucomicrobiales X X X X X F.: Verrucomicrobiaceae X X X X X G.: Luteolibacter X X X G.: Prosthecobacter X S.: debontii X P.: WPS-2 X X

105 105

Fresno Phyla Relative Abundance

Table 22. Complete Relative Abundance of Phyla in the Fresno Crow Microbiota Relative abundances of Fresno crow microbiota Phyla W2 W3 W4 W7 W8 W10 Firmicutes 20.71 82.09 51.29 24.02 56.76 91.84 Proteobacteria 54.17 7.60 42.91 53.03 6.49 3.41 Cyanobacteria 4.97 0.17 0.13 3.20 0.70 2.87 Actinobacteria 15.68 8.75 3.99 17.91 12.77 0.88 Tenericutes 0.12 <0.01 <0.01 0.17 0.75 0.86 Chloroflexi 0.76 0.24 0.44 0.72 1.04 0.06 Bacteroidetes 0.04 0.87 0.84 0.56 <0.01 0.03 Verrucomicrobia 0.07 0.04 0.05 0.06 0.07 0.02 Planctomycetes 0.09 0.03 0.13 0.07 0.07 0.01 Chlamydiae 0.02 0.02 <0.01 0.06 0.01 0.01 Gemmatimonadetes 0.03 <0.01 <0.01 0.01 <0.01 0.01 WPS-2 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 Unclassified 2.96 0.07 0.05 <0.01 21.09 <0.01 Crenarchaeota 0.28 0.09 0.14 0.16 0.22 <0.01 [Thermi] 0.05 0.01 <0.01 <0.01 <0.01 <0.01 TM7 0.01 0.02 <0.01 <0.01 <0.01 <0.01 BRC1 0.01 <0.01 <0.01 0.01 0.02 <0.01 Euryarchaeota 0.01 <0.01 0.01 <0.01 <0.01 <0.01 Nitrospirae 0.01 <0.01 <0.01 <0.01 <0.01 <0.01 OD1 0.01 <0.01 <0.01 <0.01 <0.01 <0.01 Fusobacteria 0.01 <0.01 <0.01 <0.01 <0.01 <0.01 TM6 0.01 <0.01 <0.01 <0.01 <0.01 <0.01 Acidobacteria <0.01 <0.01 0.01 0.01 <0.01 <0.01 MVP-21 <0.01 <0.01 <0.01 0.01 <0.01 <0.01

106 106

Critter Creek Phyla Relative Abundance

Table 23. Complete Relative Abundance of Phyla in the Critter Creek Crow Microbiota Relative abundances of Critter Creek crow microbiota Phyla W4 W5 W7 Proteobacteria 69.08 75.45 29.19 Firmicutes 15.73 11.07 35.34 Actinobacteria 6.83 11.02 25.99 Chloroflexi 0.55 1.07 2.47 Bacteroidetes 5.24 0.43 2.82 Unclassified 0.37 0.33 0.03 Planctomycetes 0.12 0.27 0.19 Verrucomicrobia 0.13 0.21 0.20 Tenericutes 0.31 0.08 0.02 [Thermi] 0.02 0.04 <0.01 Cyanobacteria 1.58 0.02 3.52 Gemmatimonadetes <0.01 <0.01 <0.01 BRC1 <0.01 <0.01 0.05 Chlamydiae <0.01 <0.01 0.12 FBP <0.01 <0.01 0.03 SR1 0.01 <0.01 <0.01 Spirochaetes 0.01 <0.01 <0.01 TM6 <0.01 <0.01 0.01 TM7 0.01 <0.01 0.01 WPS-2 <0.01 <0.01 0.02

107 107

Davis Phyla Relative Abundance

Table 24. Complete Relative Abundance of Phyla in the Davis Crow Microbiota Relative abundances of Davis crow microbiota Phyla W1 W2 Proteobacteria 81.14 66.59 Firmicutes 13.97 21.69 Actinobacteria 4.17 9.64 Chloroflexi 0.19 0.24 Tenericutes 0.19 1.14 Acidobacteria 0.09 0.01 Chlamydiae 0.09 0.01 Crenarchaeota 0.08 0.05 Bacteroidetes 0.03 0.12 Planctomycetes 0.03 0.02 Spirochaetes 0.01 <0.01 Euryarchaeota <0.01 0.05 Unclassified <0.01 0.02 Cyanobacteria <0.01 0.38 Verrucomicrobia <0.01 0.04

APPENDIX C: RESISTOME RESULTS VIA ARGS-OAP 109 109 Table 25. Complete List of Antibiotic Resistance Genes Identified by ARGs-OAP. Reads per Antibiotic resistance genes million aminoglycoside__aac(3)-IV 0.05 aminoglycoside__aac(3)-VI 0.05 aminoglycoside__aac(6')-I 32.99 aminoglycoside__aac(6')-II 0.25 aminoglycoside__ant(9)-I 0.30 aminoglycoside__aph(3')-I 0.25 aminoglycoside__aph(3'')-I 0.10 aminoglycoside__aph(6)-I 0.10 Total aminoglycoside 34.09 bacitracin__bacA 59.09 bacitracin__bcrA 0.20 Total bacitracin 59.29 beta-lactam__ACT-13 0.10 beta-lactam__ACT-14 0.20 beta-lactam__ACT-16 0.05 beta-lactam__ACT-19 0.05 beta-lactam__ACT-20 0.05 beta-lactam__ACT-23 0.20 beta-lactam__ACT-5 0.10 beta-lactam__ampC 0.30 beta-lactam__CFE-1 0.05 beta-lactam__class A beta-lactamase 0.25 beta-lactam__class B beta-lactamase 0.10 beta-lactam__class C beta-lactamase 12.13 beta-lactam__fmtC 0.25 beta-lactam__KPC-10 0.05 beta-lactam__mecA 1.50 beta-lactam__metallo-beta-lactamase 0.30 beta-lactam__MIR-6 0.05 beta-lactam__MOX-4 0.05 beta-lactam__OXA-12 0.05 beta-lactam__OXA-192 0.10 beta-lactam__OXA-9 0.30 beta-lactam__PBP-1A 0.85 beta-lactam__PBP-1B 1.10 beta-lactam__PBP-2X 0.10 beta-lactam__penA 1.70 beta-lactam__SHV-5 0.10 beta-lactam__SRT-1 0.20 110 110 beta-lactam__TEM-157 0.10 beta-lactam__TEM-205 0.15 Total beta-lactam 20.51 chloramphenicol__cat_chloramphenicol acetyltransferase 0.85 chloramphenicol__catA 0.05 chloramphenicol__catB 0.20 chloramphenicol__chloramphenicol exporter 0.35 Total chloramphenicol 1.45 fosfomycin__fosA 0.45 fosfomycin__fosB 0.05 fosfomycin__fosX 1.85 Total fosfomycin 2.35 fosmidomycin__rosA 6.59 fosmidomycin__rosB 15.97 Total fosmidomycin 22.56 kasugamycin__kasugamycin resistance protein ksgA 9.43 macrolide-lincosamide-streptogramin__erm(38) 0.05 macrolide-lincosamide-streptogramin__erm(39) 0.05 macrolide-lincosamide-streptogramin__ermC 0.05 macrolide-lincosamide-streptogramin__ermO 0.10 macrolide-lincosamide-streptogramin__ermT 0.60 macrolide-lincosamide-streptogramin__ermX 0.10 macrolide-lincosamide-streptogramin__lmrB 0.05 macrolide-lincosamide-streptogramin__lmrP 0.10 macrolide-lincosamide-streptogramin__lnuA 0.05 macrolide-lincosamide-streptogramin__lsa 21.56 macrolide-lincosamide-streptogramin__macA 7.39 macrolide-lincosamide-streptogramin__macB 19.36 macrolide-lincosamide-streptogramin__mgtA 0.10 macrolide-lincosamide-streptogramin__mphB 0.10 macrolide-lincosamide-streptogramin__mphC 0.20 macrolide-lincosamide-streptogramin__msrC 124.42 macrolide-lincosamide-streptogramin__srmB 0.05 macrolide-lincosamide-streptogramin__vatB 0.25 Total macrolide-lincosamide-streptogramin 174.58 multidrug__acrA 5.64 multidrug__acrB 39.18 multidrug__acrF 1.90 multidrug__adeB 4.39 multidrug__adeC 115.34 multidrug__adeJ 0.55 111 111 multidrug__adeK 0.35 multidrug__amrB 0.95 multidrug__bicyclomycin- multidrug_efflux_protein_bcr 7.69 multidrug__bpeF 1.25 multidrug__ceoB 0.45 multidrug__cmeB 0.15 multidrug__emrA 8.68 multidrug__emrB 14.27 multidrug__EmrB-QacA family major facilitator transporter 174.53 multidrug__emrD 10.98 multidrug__emrE 0.40 multidrug__emrK 2.99 multidrug__major_facilitator_superfamily_transporter 6.74 multidrug__marR 0.50 multidrug__mdfA 6.59 multidrug__mdtA 9.18 multidrug__mdtB 34.69 multidrug__mdtC 22.96 multidrug__mdtD 6.24 multidrug__mdtE 9.23 multidrug__mdtF 27.45 multidrug__mdtG 5.49 multidrug__mdtH 6.34 multidrug__mdtK 8.23 multidrug__mdtL 11.28 multidrug__mdtM 5.64 multidrug__mdtN 7.14 multidrug__mdtO 13.97 multidrug__mdtP 9.98 multidrug__mexA 3.19 multidrug__mexB 1.90 multidrug__mexC 0.45 multidrug__mexD 3.14 multidrug__mexE 6.64 multidrug__mexF 36.23 multidrug__mexI 0.70 multidrug__mexT 10.78 multidrug__mexW 30.69 multidrug__mexX 7.99 multidrug__mexY 0.30 112 112 multidrug__multidrug_ABC_transporter 8.33 multidrug__multidrug_transporter 182.12 multidrug__norA 1.85 multidrug__omp36 4.79 multidrug__ompF 12.53 multidrug__ompR 18.37 multidrug__opmD 0.05 multidrug__oprC 0.60 multidrug__oprJ 0.05 multidrug__oprM 5.04 multidrug__oprN 5.59 multidrug__sdeY 0.20 multidrug__smeB 0.20 multidrug__smeD 9.18 multidrug__smeE 0.55 multidrug__smeF 1.50 multidrug__TolC 16.37 multidrug__ykkD 0.05 multidrug 950.71 polymyxin__arnA 11.38 puromycin__puromycin resistance protein 0.05 quinolone__norB 0.05 quinolone__qepA 0.20 quinolone__qnrB 0.30 Total quinolone 0.55 rifamycin__ADP-ribosylating transferase_arr 2.20 rifamycin__rifampin monooxygenase 2.10 Total rifamycin 4.29 sulfonamide__sul1 0.35 tetracenomycin_C__tcmA 0.30 tetracycline__otrA 0.10 tetracycline__tet32 0.05 tetracycline__tet34 0.45 tetracycline__tet35 0.05 tetracycline__tet41 0.55 tetracycline__tet43 1.15 tetracycline__tetA 1.25 tetracycline__tetB 0.15 tetracycline__tetC 0.10 tetracycline__tetL 0.50 tetracycline__tetM 18.62 tetracycline__tetO 1.20 113 113 tetracycline__tetP 0.05 tetracycline__tetR 0.05 tetracycline__tetracycline_resistance_protein 5.59 tetracycline__tetS 0.70 tetracycline__tetU 0.60 tetracycline__tetV 0.55 tetracycline__tetW 0.15 tetracycline__tetZ 0.20 Total tetracycline 32.04 unclassified__bacterial regulatory protein LuxR 2.30 unclassified__cAMP-regulatory protein 7.94 unclassified__cob(I)alamin adenolsyltransferase 2.25 unclassified__DNA-binding transcriptional regulator gadX 7.39 unclassified__DNA-binding_protein_H-NS 1.60 unclassified__rpsD_(ramA_or_sud2) 1.85 unclassified__sdiA 2.94 unclassified__transcriptional regulatory protein CpxR cpxR 8.14 unclassified__truncated putative response regulator ArlR 55.70 unclassified 90.08 vancomycin__vanA 0.10 vancomycin__vanC 0.30 vancomycin__vanD 3.09 vancomycin__vanH 0.20 vancomycin__vanR 19.56 vancomycin__vanS 9.53 vancomycin__vanT 0.65 vancomycin__vanX 0.25 vancomycin__vanY 0.80 Total vancomycin 34.49