Avian Influenza Virus Dynamics in Australian Wild Birds

Avian influenza virus dynamics in Australian wild birds

Marta Ferenczi

MSc. (Hons)

Submitted in fulfilment of the requirements for the degree of

Doctor of Philosophy

Deakin University

May, 2016

Acknowledgements

I would like to express my special appreciation and thanks to my principal supervisor

Professor Dr. Marcel Klaassen. Marcel, you have been a great mentor for me. I would like to thank you for your continued support throughout my “PhD journey” and for believing that I would grow as a research scientist, even in hard times. I feel lucky that I could spend quite a lot of time with you in the field and I will never forget our desert expeditions. I would also like to thank my fantastic second supervisor Dr. Christa Beckman for her incredible help.

Christa, your advice on both research as well as my career have been priceless. You were always there for me if I needed help, either in writing or in statistics.

I would like to thank my other supervisors, Dr. David Roshier and Professor Bill Buttemer for their contribution to my thesis. David, if I had not met you in the Prairie in Russia, I probably would have never gotten this fantastic opportunity to do a PhD in Australia. Thank you for all your useful comments on the various chapters too. Bill, I always enjoyed the chats with you about science as well as those not about science. You gave really useful final comments on my thesis.

I would especially like to thank my fellow PhD students, Yaara Aharon-Rotman, Simeon

Lisovski, Meijuan Zhao and Alice Risely for their amazing support. Yaara, you are the one who broke the ice and gave advice about how to write a confirmation, annual review and a lot more. You also knew how to “handle” a supervisor. As a friend you made my life as PhD student much easier! Simeon, thank you for all your help with statistics, R scripts and graphs.

It has been a pleasure getting to know such a talented bloke as yourself. Mei and Alice, you two make me feel as though I do not want to leave the office (however you know how much I

don’t like being indoors)! We have had so much fun together and your friendship has made my PhD journey much more enjoyable. Mei, thank you so much for all your help in database handling, R programming and statistics. Your help is priceless. I am extremely lucky that my desk neighbour in the office was such a talented young woman. Alice, I am very glad that you joined our team, you bought lots of laughs and sarcastic humour when you moved into the office. Thank you also for the great times that we spent together outside the office surfing, swing dancing, partying etc. We will continue! I would also like to mention the other smiley faces from the corridor, the members of “Bird Angels”: Eliza Larson, Ondi Crino and Aida

Rodrigues, for their amazing emotional support during my PhD. You girls rock! Jacintha van

Dijk, while most of the time working on her PhD was far away from our group, the time that she spent here was lot of fun! Jacintha, you gave me really great advice about PhD life. We developed a great friendship in a short period of time and I hope we will meet again soon in some part of the world!

I am really glad that Beata Ujvari joined our group. Bea, your kindness, friendship and support means a lot to me. Speaking our mother tongue always warms my heart.

Kerry Petterson Fanson and Lee Ann Rollins always have been really helpful for me during the last four years. During our shared car lifts I learned heaps about science, American accents and a lot more. Thanks girls!

I would like to thank Peter Biro and Ben Fanson for the great discussions about statistics.

Pete, I really enjoyed the sessions with you. You even talk about stats and manage to make it interesting. Ben, thank you for your help in R and introducing me ggplot2. That R package made my graphs much nicer, I think.

I would like to thank Andrew Cerasuolo for his fantastic IT help. You have always been there for me if I had problems with different computer programs. I really enjoyed our discussions about birds and rock music too!

I would like to thank Natasha Kaukov for her great support and help in administrative tasks.

Natasha, it was always very nice to chat to you. Thank you for your kindness and great help.

I would like to thank to my mentor, friend and master goose catcher, Gerhard Muskens.

Gerhard you always believed in me and stuck up for me. Without you telling me that I should not be afraid to move to the other side of the globe to pursue my dreams, I would not be in my current position. Thank you Gerhard, I cannot wait to visit you in the Netherlands!

I would like to thank Kamilla, the person who has been my best friend for 25 years for her endless support. Even being so far from each other in the last few years, our relationship did not change. Thank you for being happy for me when I had success and thank you for crying with me in hard times. It means a lot to me!

Last but not least, a huge special thanks to my family. Words cannot express how grateful I am to my mother, Agi and my father, Zoli for all of the sacrifices that you have made on my behalf. I am extremely lucky to have such beautiful, lovely and caring parents. I know that you were very sad when I decided to move to Australia as we have never been away from each other before. But you were there for me when preparing for this huge journey and even came after me when I missed you so much. This PhD thesis is dedicated to you! I also thank my sister Dori and her family (Bali, Bibi and Edi) for their fantastic support over the last few years. The skype chats have helped me a lot to handle being homesick. Your love and smile always helped me to get going!

Lastly, I would like to express appreciation to my beloved husband, Gyula. You have not even hesitated for a moment when I told you that I would like to move to the other side to globe to do some crazy science stuff. You just said “let’s go, where ever you are, I’ll go there too!” You are an amazing person, always supportive, kind, understanding, caring and loving.

Doing a PhD was a challenging journey for both of us, but you did not doubt for a second that

I could do it. This PhD is as much as yours as mine.

Documents that have been accepted for publication resulted from this work

Marta Ferenczi, Christa Beckmann, Simone Warner, Richard Loyn, Kim O'Riley, Xinlong

Wang, Marcel Klaassen: Avian influenza infection dynamics under variable climatic conditions - viral prevalence is rainfall driven in waterfowl from temperate, south-east

Australia, Veterinary Research (VETR-D-15-00177)

Author’s affiliations

Marta Ferenczi ([email protected]) 1

Christa Beckmann ([email protected]) 1

Daniel Ierodiaconou ([email protected]) 1

Marcel Klaassen ([email protected]) 1

Simeon Lisovski ([email protected]) 1

Richard Loyn ([email protected]) 2

Kim O'Riley (Kim.O'[email protected]) 3

David Roshier ([email protected]) 1, 4

Xinlong Wang ([email protected]) 3

Simone Warner ([email protected]) 3

1 Centre for Integrative Ecology, School of Life and Environmental Sciences, Deakin University, Geelong, Locked Bag 20000, Victoria 3220, Australia

2 Arthur Rylah Institute for Environmental Research, Department of Sustainability and Environment, Heidelberg, Victoria, Australia; current address Director, Eco Insights, 4 Roderick Close, Viewbank, Victoria 3084, Australia.

3 Department of Economic Development, Jobs, Transport and Resources, Biosciences Research, AgriBio, Centre for AgriBiosciences, 5 Ring Road, Bundoora, Victoria 3083, Australia

4 Australian Wildlife Conservancy, Adelaide, Halifax Street, PO Box 6621, South Australia 5000, Australia

Contents

CHAPTER 1 – General introduction ...... 2

CHAPTER 2 – A description of the avian influenza reservoir community in wild Australian

birds and the role of phylogeny, ecology and eco-region ...... 24

CHAPTER 3 – Avian influenza infection dynamics under variable climatic conditions –

viral prevalence is rainfall driven in waterfowl from temperate, south-east

Australia ...... 57

CHAPTER 4 – Non-seasonal and long-term avian influenza virus (AIV) dynamics in

Australia’s arid interior suggest bird numbers, age structure and

immunocompetence influences AIV susceptibility ...... 100

CHAPTER 5 – Rainfall driven and wild-bird mediated avian influenza virus outbreaks in

Australian poultry ...... 133

CHAPTER 6 – Synthesis: Synthesis: Avian influenza virus dynamics in Australian wild

birds……………...... 159

Curriculum vitae ...... 176

CHAPTER 1

General introduction

Marta Ferenczi

Knowledge of infection dynamics is central to our understanding of zoonotic diseases, their impact on wildlife populations, and their potential to spillover into domestic or human populations (Paull et al. 2012). Pathogen prevalence can fluctuate dramatically and how prevalence is affected by different environmental factors is still poorly researched in some areas (Altizer et al. 2006, Gaidet 2015). Avian influenza virus (AIV) is one of the zoonotic diseases that cause major concern in respect to public and domestic animal health (Gilbert and Pfeiffer 2012, Liu et al. 2013, Gilbert et al. 2014). Since the first outbreaks of highly pathogenic avian influenza H5N1 in 2003 and H5N8 in 2014, these particular subtypes of the virus began to spread rapidly around the globe, resulting in high socio-economic costs

(Gilbert and Pfeiffer 2012, Liu et al. 2013). Not only domestic poultry but also wild birds were infected (Feare 2010, Kim et al. 2015, Ozawa et al. 2015). Further, between 2003 and

2015, 826 cases of high pathogenic H5N1 infection in humans were recorded, of which 440

(53.3 %) were fatal (Source: WHO/GIP, data as of 31 March 2015). Other avian influenza subtypes (i.e. H7N7 [in 2003] and H7N9 [in 2013]) were also associated with human deaths

(Fouchier et al. 2004, Gao et al. 2013).

The real threat is that H5N1 has pandemic potential; further evolution of the virus could result in serious outbreaks and death in humans and animals around the world (Li et al.

2004). As low pathogenic forms is thought to originate from wild birds and to evolve into high pathogenic AIV in poultry (Alexander 2000, Olsen et al. 2006) (BOX 1), rather than solely focusing on infections in poultry and humans we need to better understand the ecology and transmission of the virus in wildlife (Kuiken et al. 2006, Coker et al. 2011). To advance our knowledge in this respect we urgently need information globally on: (1) what the main

AIV reservoir species (i.e. long-term host of a pathogen of an infectious disease) are; (2) which ecological and environmental drivers influence AIV prevalence in different biomes;

and (3) what the role of wild birds is in AIV outbreaks in poultry (Olsen et al. 2006, Hoye et al. 2010, Gaidet 2015). These issues have received considerable research attention in the northern hemisphere, however the southern hemisphere remains largely ‘undiscovered’.

BOX 1: Avian influenza virus

Influenza viruses belong to a group of RNA viruses called the Orthomyxoviridae.

There are three types of influenza viruses: A, B and C (Webster et al. 1992). Type B and C only infect humans. Avian influenza virus (AIV) belongs to type A (Webster et al. 1992).

The AIVs can infect a wide range of host species, mammals and a variety of domestic and wild bird species and occasionally humans. Among these groups, wild birds are thought to be the main reservoirs of the AIVs in nature (Webster et al. 1992).

The viruses are classified based on their surface proteins. In birds, 16 hemagglutinin [HA] and nine neuraminidase [NA] surface glycoproteins have been recognised, which can form all possible combinations (HA, H1-16; NA, N1-9) (Webster et al. 1992, Tong et al. 2012, Tong et al. 2013). In addition, AIVs are classified based on their ability to cause disease in chickens. By definition, low pathogenic forms cause only mild or non-detectable clinical signs in chickens (i.e. termed LPAI). Occasionally high pathogenic forms (i.e. termed HPAI) evolve from H5 and H7 subtype LPAI, which are defined as viruses which cause severe clinical signs and induce up to 100% mortality in chickens (Alexander 2000). The LPAI viruses replicate in the gastrointestinal and respiratory tracts (Kida et al. 1980, Daoust et al. 2011). The HPAI viruses can replicate throughout the bird in clinical cases of the disease (Alexander 2000). LPAI is thought to be mainly transmitted by the faecal-oral route, with waterbirds shedding virus contaminated faeces in the water which are subsequently ingested by other animals (Webster et al. 1992).

Avian influenza dynamics in wild birds

Several studies conducted in the northern hemisphere have suggested significant seasonal fluctuations of infection in wild waterbirds (Munster et al. 2007, van Dijk et al.

2013). The single published study to date that (in part) conducted surveys for AIV in the southern hemisphere showed that although peaks of AIV prevalence in waterbird communities in Africa are seasonal, they are not as pronounced as in the northern hemisphere

(Gaidet et al. 2012). Whether the seasonal pattern of infection observed on northern hemisphere continents applies to Australia, as a key southern hemisphere example, remains unclear.

With the exception of the tropics the predominant feature of the Australian climate is the alternation of wet and dry periods that occur on other than annual cycles (Kingsford and

Norman 2002, Kingsford et al. 2010). These multi-year periods of wet and dry are accompanied by large changes in bird densities at local, regional and continental scales

(Kingsford 2000). As a consequence, the ecology of birds and other life on the Australian continent is quite different from that elsewhere. Thus, AIV dynamics and the ecological and environmental drivers that influence infection prevalence on this continent (and potentially elsewhere in the southern hemisphere) are likely to differ from the widely observed seasonal dynamics in the northern hemisphere. The key aim of my study is to advance our knowledge of the ecological and environmental drivers underlying the AIV dynamics in wild Australian birds

The ecological and environmental drivers of AIV dynamics

However the ecological and environmental drivers of avian influenza infection in wild bird populations are well understood in the northern hemisphere, the drivers are still less researched in the tropical regions (Gaidet et al. 2012, Gaidet 2015) and in the southern hemisphere. A range of factors have been hypothesized in the literature to explain AIV dynamics. The important hypothesized drivers of avian influenza prevalence in wild bird populations are summarized below.

Host specific factors at the community, population and individual level that are hypothesised to be related to avian influenza prevalence include:

(1) Studies in the northern hemisphere found that waterfowl (Anseriformes) and

shorebirds (Charadriformes) are the main reservoirs of AIVs (Hinshaw et al. 1985,

Olsen et al. 2006, Stallknecht and Brown 2007). Whether the main AIV reservoir

community in Australia is similar to that found in the northern hemisphere or if other

species endemic to Australia may be important contributors to Australia’s AIV’s

reservoir community remains to be clarified.

Australia’s avifauna is different from that in other parts of the world, due to

major differences in environmental conditions and relative isolation in space and

geological time between Australia and other continents. Therefore I hypothesise that

the composition of the AIV reservoir community might potentially also be very

different from that elsewhere in the world and notably the well-researched northern

hemisphere. Duck species in the genus Anas (i.e. dabbling ducks) have representatives

in all over the globe, including Australia. All have similar water surface feeding behaviour, which provides the potential mechanism for why they are the main reservoirs of low pathogenic avian influenza viruses in the northern hemisphere

(Munster et al. 2007); ducks shed the virus via faeces into water where it can be transmitted (via the fecal-oral route) to other waterfowl foraging in the same habitat.

Australia has another endemic duck species, which has a foraging behaviour that might be even more suited for such a transmission route: Pink-eared Duck

(Malacorhynchus membranaceus), the sole member of the genus Malacorhynchus.

The Pink-eared Duck is nomadic, unlike other duck species in the northern hemisphere and has evolved to feed in a highly specialised manner (i.e. water is sucked through the bill-tip, and then expelled through grooves along the side of the bill, filtering out tiny invertebrates) (Olsen and Joseph 2011). Judging from its foraging ecology, movement behaviour and wide distribution, Pink-eared ducks might play an important role in spreading avian influenza viruses in Australia.

The behaviour and ecology of endemic Australian shorebirds, for example the

Red-necked Avocet (Recurvirostra novaehollandiae) and Banded Stilt

(Cladorhynchus leucocephalus) are adapted to the climatic conditions of Australia. In contrast to most migratory shorebird species that are proven important reservoirs of

AIVs (Olsen et al. 2006), they are nomadic (Gosbell and Christie 2006). Similarly to nomadic ducks, the movements of these endemic shorebirds are mainly determined by the availability of ephemeral wetlands (Roshier et al. 2002). During drought they congregate in high numbers on the remaining wetlands together with other waterbird species. This may increase the likelihood of AIV infection.

Although bird species other than waterbirds have very low AIV prevalence in the northern hemisphere (Olsen et al. 2006), Australian ‘boom and bust’ species such

as the Budgerigar (Melopsittacus undulatus), which may occur in huge swarms during a “boom”, cannot be ruled out as potential temporary reservoirs. They can be exposed to infection as these species occasionally congregate in large numbers at temporary wetlands (Tischler et al. 2013), where nomadic waterbirds that are potential AIV reservoirs also co-occur.

(2) High bird density may increase AIV prevalence in the waterbird community, as the likelihood of becoming infected via increased contact rates is higher when birds congregate (Krauss et al. 2010, Altizer et al. 2011, Gaidet et al. 2012). Different factors, (i.e. migration or drought) may force birds to concentrate at staging sites (i.e. resting and feeding places) or remaining wetlands, increasing the potential of infection in the community (Klaassen et al. 2011, Gaidet et al. 2012, van Dijk et al.

2013).

(3) Increases in the abundance of immunologically naïve young birds has been shown to be an important driver in seasonal changes in AIV prevalence in temperate regions

(Hinshaw et al. 1980, van Dijk et al. 2013). Thus, age as a demographic factor is involved in AIV infection dynamics under the assumption that juvenile birds are more susceptible to virus infection than adults.

(4) Sex is another proven demographic factor in AIV infection, with male birds having higher AIV prevalence than females (Ip et al. 2008, Farnsworth et al. 2012).

This might result from males’ higher testosterone levels (Haase 1983) reducing immuno-competence and thus increasing susceptibility to infection during the breeding season (Peters et al. 2004). An alternative hypothesis is that males are more

mobile than females on the breeding grounds (Doherty et al. 2002). As a result, males

may meet more con-specifics that may carry different virus strains and subtypes to

which they are immunologically naïve (Ip et al. 2008).

(5) Low immune function and poor body condition – potentially resulting from

migration or low food availability – have been suggested as drivers of AIV prevalence

in the waterbird community. These conditions may make the birds more susceptible to

infection, resulting in higher prevalence (van Gils et al. 2007, Flint and Franson 2009,

Latorre-Margalef et al. 2009).

Environmental factors that are hypothesised to be related to avian influenza prevalence include:

(1) Low water temperature, often resulting from low air temperature, has been

suggested to be related to higher AIV prevalence in the waterbird community as AIV

persists longer at lower water temperatures (Brown et al. 2009, Roche et al. 2009,

Rohani et al. 2009).

(2) AIV persistence in different types of water plays an important role in transmission.

AIV can accumulate in various types of water with different persistence time.

Investigations of AIV survival in water revealed that persistence decreased going

from distilled water, saline water to fresh surface water collected from a natural lake

(Nazir et al. 2010, Keeler et al. 2013).

(3) Increased AIV persistence in lake sediment can be an important factor in AIV

transmission in a waterfowl community. Laboratory tests confirmed that AIV survival

was higher in lake sediments, compared to duck faeces and duck meat. Data were

analyzed by a linear regression model to calculate time required for 90% loss of virus

infectivity (i.e. T) and estimated persistence of the viruses. The T values in sediment

ranged from 5 to 11, 13 to 18, 43 to 54, and 66 to 394 days at 30, 20, 10, and 0°C,

respectively, which were 2 to 5 times higher than the T90 values of the viruses in the

faeces and meat. The hypothesis is that the faeces of infected waterbirds sink to the

lake bottom where viruses accumulate in the sediment. Subsequently, filter-feeding

birds can pick up the virus while feeding (Nazir et al. 2011).

(4) AIV accumulation in food sources is another potential factor influencing AIV

infection dynamics. The role of Zebra mussels (Dreissena polymorpha; an invasive

species and potential food source for many waterbird species throughout the northern

hemisphere in accumulation of AIV from freshwater has been tested under laboratory

conditions. Zebra Mussels were kept in infected water for 48 hours and then placed in

fresh, non-infected water. With quantitative real-time reverse transcriptase-PCR and

egg culture methods the presence of viruses in the mussels was detectable even after

two weeks (Stumpf et al. 2010). However AIV accumulation was not tested on other

mussel species nor the natural environment, it is possible that other species from all

around the globe can potentially contribute to AIV infection in wetland ecosystems.

Some of these hypothesised factors have never been tested, with only a few tested in laboratory conditions. The reason for this is simple: the investigation of these potential drivers of disease dynamics is greatly hampered by the difficulty in conducting controlled

experiments in the field. Research on the drivers of AIV prevalence in wild birds is therefore limited to the investigation of correlations with ecological and environmental factors; but even such studies are still rare (Gaidet et al. 2012). Although recent insights from studies conducted in Africa and Europe suggest that the ecological drivers on AIV dynamics may be the same between hemispheres, the effects of the various ecological drivers on AIV dynamics may vary between different geographical and ecological contexts (Roche et al. 2009, Gaidet et al. 2012, Gaidet 2015). This difference may, for example be due to differences in water temperature between continents (Brown et al. 2009, Stallknecht et al. 2010). At higher water temperatures the virus does not persist as long as at lower temperatures (Brown et al. 2009,

Roche et al. 2009, Rohani et al. 2009). In temperate regions, where water temperatures are cool, transmission through ingestion of AIV present in the water is likely the more common pathway for infection (Roche et al. 2009, Rohani et al. 2009, Gaidet et al. 2012). In contrast, the short residence time of AIV in the environment in the tropics – resulting from the shorter survival time of the virus in warmer water - suggests that the interactions between individuals may be more important in the transmission of AIV in these climates.

It is difficult to determine which of the potential drivers would be most applicable in the southern hemisphere as the ecological and environmental conditions are very variable in this biome. Moreover, the data to conduct such a study are also not available since temporal

AIV dynamics in Australia have thus far been only poorly described.

Aim of this study

In Australia to date, a high diversity of AIVs but a relatively low prevalence have been found, which contrasts with North America and Europe where both AIV diversity and prevalence are high (Olsen et al. 2006, Sims and Turner 2009, Grillo et al. 2015). It is largely unknown if the patterns seen in Australia result from (1) a wider range of reservoir species than is currently known; or (2) the different ecological and environmental drivers that influence the diversity and prevalence of AIV in Australia. Tracey (2010) suggested that the generally observed low prevalence of AIV in Australia was the result of the irregular movements and patterns of abundance in waterfowl (Tracey 2010).

Klaassen et al. (2011) hypothesized that the increase in densities of waterbird communities through recruitment and aggregation in the dry phase of weather cycles is a driver of AIV prevalence in wild birds in Australia. The association of drought and outbreaks of high pathogenic AIV in poultry suggests that such a mechanism is reflected in spillover into commercial poultry flocks (Klaassen et al. 2011). One of the goals of this thesis is to test

Klaassen at al.’s (2011) hypothesis and to elucidate whether the observed low prevalence reflects the real situation or if it is the result of random and/or patchy sampling activity

(Klaassen et al. 2011). In this thesis I thus aimed to identify the main AIV reservoir bird species in Australia. Additionally, I aimed to both describe the AIV dynamics and investigate the interactions between bird density and weather conditions on viral and antibody prevalence in Australian waterbird communities. I also endeavoured to elucidate the relationship between outbreaks in poultry and climatic factors, assuming that the ancestral viruses causing outbreaks spill-over from wild birds to poultry. Ultimately, given the contrasting temporal patterns and correlations between the proposed drivers for AIV dynamics in Australia and the

northern hemisphere, I anticipate that this study as a whole will also provide fundamental information on the global validity of some of the proposed drivers.

Thesis outline

In Chapter 2 I described the main AIV reservoirs and their prevalence in Australian bird communities, based on long-term surveillance data. I focused not only on avian groups that have been traditionally identified as important reservoirs but those which, based on their ecology, are potential hosts in the Australian ecosystem. Although my co-authors and I found that the reservoir composition and prevalence is very similar to that found on other continents, we identified additional reservoir species in the Australian environment.

Since Chapter 2 highlighted that dabbling ducks have the highest AIV prevalence among all investigated species, in Chapter 3 I investigated AIV prevalence of dabbling ducks in relation to biotic (bird numbers) and abiotic (weather and climate; i.e. temperature, rainfall and Southern Oscillation) drivers at a major permanent wetland in the temperate coastal area in Australia. My colleagues and I tested Klaassen et al.’s (2011) hypothesis that the non-seasonal weather patterns that influence the ecology of waterfowl also affect the temporal patterns of AIV prevalence in Australian waterbird communities. In relation to weather patterns we hypothesized that three of the above mentioned drivers - (1) bird density;

(2) abundance of immunologically naïve birds and (3) low immune function and poor body condition - are involved in the complex AIV infection ecology in Australian wild waterbirds.

In this chapter we showed that rainfall patterns importantly correlated with AIV dynamics.

These results were interpreted assuming that rainfall patterns determine breeding opportunities and are therefore linked to bird numbers (Loyn et al. 2014). Rainfall thus also

influences age structure in the duck community, which may subsequently affect AIV dynamics.

Building on the findings in Chapter 3, in Chapter 4 , I moved my focus on AIV dynamics into Australia’s interior, where I conducted a longitudinal study of AIV prevalence in an entire bird community in relation to an abiotic (i.e. extent of surface water) driver in

Australia’s interior. Although rainfall in Australian temperate coastal environments may vary non-seasonally (Chapter 2), desert wetland systems are even more extreme. These environments are typically characterized by boom and bust dynamics, where dry and wet periods alternate and may run over several years (Kingsford et al. 1999, Roshier et al. 2001,

Kingsford et al. 2010). Thus, this system provides one of the most extreme environments in which AIV dynamics can be studied and the drivers that were hypothesized in Chapter 2 evaluated. We found that AIV prevalence of wild birds in the Australian desert wetland system was systematically related to water availability. Moreover, we found that these patterns not only existed in ducks but could be identified in a wide range of bird species in this extreme environment.

As it has been suggested that wild birds are the main source of AIV outbreaks in poultry in northern hemisphere studies (Morgan and Kelly 1990, Burns et al. 2012), we assumed that wild Australian waterbirds may also be at the base of these outbreaks and that

AIV spills over from wild birds into poultry in Australia. In Chapter 3 and 4 we found that rainfall and water availability have a major effect on AIV dynamics and prevalence in wild

Australian waterbird communities. Therefore we can assume that the same driver (i.e. rainfall) that affects prevalence in wild birds would (indirectly) influence the likelihood of an outbreak in poultry. In Chapter 5 I investigated this hypothesis by examining the timing of

AIV outbreaks in Australian poultry in relation to temporal patterns in rainfall. My co-authors

and I found that, after including an appropriate time-lag, rainfall indeed showed a significant correlation with AIV outbreaks in poultry.

In Chapter 6 I synthesize the results of the thesis and stress what the significance and interpretation of these results are globally. In this chapter I discuss whether our findings of the main AIV reservoirs and their virus prevalence in Australia differ from what has been found on the rest of the globe. I discuss the potential role of endemic Australian bird species and boom and bust species in AIV infection. I highlight that not even highly related species may have a similar role as AIV reservoirs and that may be related to their foraging ecology. I address that however the main reservoir species (i.e. dabbling ducks) should be the most important targets to detect AIV, other taxonomic groups should be part of AIV surveillance programs too. In addition I discuss whether the determined key drivers for AIV dynamics in

Australia match up with the drivers in the temperate northern hemisphere and the Afro- tropical regions. I stress that identifying drivers of disease dynamics are problematic in general since possibilities for experimentation are limited because of the size of the systems and public and wildlife health concerns (i.e. ethical issues). Furthermore I discuss how the suggested drivers can affect AIV outbreaks in Australian poultry and highlight the problems in biosecurity systems, not only in Australia but other continents.

These chapters are written as stand-alone publications and thus can be some repetitive elements in the various chapters. Although working towards publication produced a more focused and concise thesis that with future publication allows to engage with the broader scientific community.

REFERENCES

Alexander, D. J. 2000. A review of avian influenza in different bird species. Veterinary microbiology 74:3-13.

Altizer, S., R. Bartel, and B. A. Han. 2011. Animal Migration and Infectious Disease Risk. Science 331:296-302.

Altizer, S., A. Dobson, P. Hosseini, P. Hudson, M. Pascual, and P. Rohani. 2006. Seasonality and the dynamics of infectious diseases. Ecology Letters 9:467-484.

Brown, J. D., G. Goekjian, R. Poulson, S. Valeika, and D. E. Stallknecht. 2009. Avian influenza virus in water: Infectivity is dependent on pH, salinity and temperature. Veterinary Microbiology 136:20-26.

Burns, T. E., C. Ribble, C. Stephen, D. Kelton, L. Toews, J. Osterhold, and H. Wheeler. 2012. Use of observed wild bird activity on poultry farms and a literature review to target species as high priority for avian influenza testing in 2 regions of Canada. The Canadian Veterinary Journal 53:158.

Coker, R., J. Rushton, S. Mounier-Jack, E. Karimuribo, P. Lutumba, D. Kambarage, D. U. Pfeiffer, K. Stärk, and M. Rweyemamu. 2011. Towards a conceptual framework to support one-health research for policy on emerging zoonoses. The Lancet infectious diseases 11:326-331.

Daoust, P.-Y., F. S. Kibenge, R. A. Fouchier, M. W. Van De Bildt, D. Van Riel, and T. Kuiken. 2011. Replication of low pathogenic avian influenza virus in naturally infected mallard ducks (Anas platyrhynchos) causes no morphologic lesions. Journal of wildlife diseases 47:401-409.

Doherty, P. F., J. D. Nichols, J. Tautin, J. F. Voelzer, G. W. Smith, D. S. Benning, V. R. Bentley, J. K. Bidwell, K. S. Bollinger, and A. R. Brazda. 2002. Sources of variation in breeding-ground fidelity of mallards (Anas platyrhynchos). Behavioral Ecology 13:543-550.

Farnsworth, M. L., R. S. Miller, K. Pedersen, M. W. Lutman, S. R. Swafford, P. D. Riggs, and C. T. Webb. 2012. Environmental and Demographic Determinants of Avian Influenza Viruses in Waterfowl across the Contiguous United States. PLoS ONE 7.

Feare, C. J. 2010. Role of Wild Birds in the Spread of Highly Pathogenic Avian Influenza Virus H5N1 and Implications for Global Surveillance. Avian diseases 54:201-212.

Flint, P. L., and J. C. Franson. 2009. Does influenza A affect body condition of wild mallard ducks, or vice versa? Proceedings of the Royal Society B: Biological Sciences 276:2345-2346.

Fouchier, R. A., P. M. Schneeberger, F. W. Rozendaal, J. M. Broekman, S. A. Kemink, V. Munster, T. Kuiken, G. F. Rimmelzwaan, M. Schutten, and G. J. van Doornum. 2004. Avian influenza A virus (H7N7) associated with human conjunctivitis and a fatal case of acute respiratory distress syndrome. Proceedings of the National Academy of Sciences of the United States of America 101:1356-1361.

Gaidet, N. 2015. Ecology Of Avian Influenza Virus In Wild Birds In Tropical Africa. Avian diseases.

Gaidet, N., A. Caron, J. Cappelle, G. S. Cumming, G. Balanca, S. Hammoumi, G. Cattoli, C. Abolnik, R. S. de Almeida, P. Gil, S. R. Fereidouni, V. Grosbois, A. Tran, J. Mundava, B. Fofana, A. B. O. El Mamy, M. Ndlovu, J. Y. Mondain-Monval, P. Triplet, W. Hagemeijer, W. B. Karesh, S. H. Newman, and T. Dodman. 2012. Understanding the ecological drivers of avian influenza virus infection in wildfowl: a continental-scale study across Africa. Proceedings of the Royal Society B-Biological Sciences 279:1131-1141.

Gao, R., B. Cao, Y. Hu, Z. Feng, D. Wang, W. Hu, J. Chen, Z. Jie, H. Qiu, and K. Xu. 2013. Human infection with a novel avian-origin influenza A (H7N9) virus. New England Journal of Medicine 368:1888-1897.

Gilbert, M., N. Golding, H. Zhou, G. W. Wint, T. P. Robinson, A. J. Tatem, S. Lai, S. Zhou, H. Jiang, and D. Guo. 2014. Predicting the risk of avian influenza A H7N9 infection in live-poultry markets across Asia. Nature communications 5.

Gilbert, M., and D. U. Pfeiffer. 2012. Risk factor modelling of the spatio-temporal patterns of highly pathogenic avian influenza (HPAIV) H5N1: a review. Spatial and spatio- temporal epidemiology 3:173-183.

Gosbell, K., and M. Christie. 2006. The Breeding of Banded Stilt and Red-Necked Avocet in the Coorong, South Australia: December 2005 – February 2006. Stilt:277 - 284.

Grillo, VL., KE. Arzey, PM. Hansbro, AC. Hurt, S. Warner, J. Bergfeld, GW. Burgess, B. Cookson, CJ. Dickason, M. Ferenczi, T. Hollingsworth, MDA Hoque, RB. Jackson, M. Klaassen, PD. Kirkland, NY. Kung, S. Lisovski, MA. O'Dea, K. O'Riley, D. Roshier, LF. Skerratt, JP Tracey, X. Wang, R. Woods, and L. Post. 2015. Avian influenza in Australia: a summary of 5 years of wild bird surveillance. Australian veterinary journal 93(11): 387-393.

Haase, E. 1983. The annual reproductive cycle in Mallards. Journal of Steroid Biochemistry 19:731-737.

Hinshaw, V., W. Bean, R. Webster, and G. Sriram. 1980. Genetic reassortment of influenza A viruses in the intestinal tract of ducks. Virology 102:412-419.

Hinshaw, V., J. Wood, R. Webster, R. Deibel, and B. Turner. 1985. Circulation of influenza viruses and paramyxoviruses in waterfowl originating from two different areas of North America. Bulletin of the World Health Organization 63:711.

Hoye, B. J., V. J. Munster, H. Nishiura, M. Klaassen, and R. A. M. Fouchier. 2010. Surveillance of Wild Birds for Avian Influenza Virus. Emerging infectious diseases 16:1827-1834.

Ip, H. S., P. L. Flint, J. C. Franson, R. J. Dusek, D. V. Derksen, R. E. Gill, Jr., C. R. Ely, J. M. Pearce, R. B. Lanctot, S. M. Matsuoka, D. B. Irons, J. B. Fischer, R. M. Oates, M. R. Petersen, T. F. Fondell, D. A. Rocque, J. C. Pedersen, and T. C. Rothe. 2008. Prevalence of Influenza A viruses in wild migratory birds in Alaska: Patterns of variation in detection at a crossroads of intercontinental flyways. Virology Journal 5.

Keeler, S. P., C. Lebarbenchon, and D. E. Stallknecht. 2013. Strain-related variation in the persistence of influenza A virus in three types of water: distilled water, filtered surface water, and intact surface water. Virology Journal 10:13.

Kida, H., R. Yanagawa, and Y. Matsuoka. 1980. Duck influenza lacking evidence of disease signs and immune response. Infection and Immunity 30:547-553.

Kim, H.-R., Y.-K. Kwon, I. Jang, Y.-J. Lee, H.-M. Kang, E.-K. Lee, B.-M. Song, H.-S. Lee, Y.-S. Joo, and K.-H. Lee. 2015. Pathologic Changes in Wild Birds Infected with Highly Pathogenic Avian Influenza A (H5N8) Viruses, South Korea, 2014. Emerging infectious diseases 21:775.

Kingsford, R. T. 2000. Ecological impacts of dams, water diversions and river management on floodplain wetlands in Australia [review]. Austral Ecology 25:109-127.

Kingsford, R. T., A. L. Curtin, and J. Porter. 1999. Water flows on Cooper Creek in arid Australia determine 'boom' and 'bust' periods for waterbirds. Biological Conservation 88:231-248.

Kingsford, R. T., and F. I. Norman. 2002. Australian waterbirds - products of the continent's ecology. Emu 102:1-23.

Kingsford, R. T., D. A. Roshier, and J. L. Porter. 2010. Australian waterbirds - time and space travellers in dynamic desert landscapes. Marine and Freshwater Research 61:875-884.

Klaassen, M., B. J. Hoye, and D. A. Roshier. 2011. Identifying crucial gaps in our knowledge of the life-history of avian influenza viruses - an Australian perspective. Emu 111:103-112.

Krauss, S., D. E. Stallknecht, N. J. Negovetich, L. J. Niles, R. J. Webby, and R. G. Webster. 2010. Coincident ruddy turnstone migration and horseshoe crab spawning creates an ecological ‘hot spot’for influenza viruses. Proceedings of the Royal Society of London B: Biological Sciences:rspb20101090.

Kuiken, T., S. Kennedy, T. Barrett, M. Van de Bildt, F. Borgsteede, S. Brew, G. Codd, C. Duck, R. Deaville, and T. Eybatov. 2006. The 2000 canine distemper epidemic in Caspian seals (Phoca caspica): pathology and analysis of contributory factors. Veterinary Pathology Online 43:321-338.

Latorre-Margalef, N., G. Gunnarsson, V. J. Munster, R. A. M. Fouchier, A. Osterhaus, J. Elmberg, B. Olsen, A. Wallensten, P. D. Haemig, T. Fransson, L. Brudin, and J. Waldenstrom. 2009. Effects of influenza A virus infection on migrating mallard ducks. Proceedings of the Royal Society B-Biological Sciences 276:1029-1036.

Li, K., Y. Guan, J. Wang, G. Smith, K. Xu, L. Duan, A. Rahardjo, P. Puthavathana, C. Buranathai, and T. Nguyen. 2004. Genesis of a highly pathogenic and potentially pandemic H5N1 influenza virus in eastern Asia. Nature 430:209-213.

Liu, D., W. Shi, Y. Shi, D. Wang, H. Xiao, W. Li, Y. Bi, Y. Wu, X. Li, and J. Yan. 2013. Origin and diversity of novel avian influenza A H7N9 viruses causing human infection: phylogenetic, structural, and coalescent analyses. The Lancet 381:1926- 1932.

Loyn, R., D. Rogers, R. Swindley, K. Stamation, P. Macak, and P. Menkhorst. 2014. Waterbird monitoring at the Western Treatment Plant, 2000–12: The effects of climate and sewage treatment processes on waterbird populations. Arthur Rylah Institute for Environmental Research Technical Report Series.

Morgan, I., and A. Kelly. 1990. Epidemiology of an avian influenza outbreak in Victoria in 1985. Australian Veterinary Journal 67:125-128.

Munster, V. J., C. Baas, P. Lexmond, J. Waldenstrom, A. Wallensten, T. Fransson, G. F. Rimmelzwaan, W. E. P. Beyer, M. Schutten, B. Olsen, A. D. M. E. Osterhaus, and R.

A. M. Fouchier. 2007. Spatial, temporal, and species variation in prevalence of influenza A viruses in wild migratory birds. Plos Pathogens 3:630-638.

Nazir, J., R. Haumacher, A. Ike, P. Stumpf, R. Boehm, and R. E. Marschang. 2010. Long- Term Study on Tenacity of Avian Influenza Viruses in Water (Distilled Water, Normal Saline, and Surface Water) at Different Temperatures. Avian diseases 54:720- 724.

Nazir, J., R. Haumacher, A. C. Ike, and R. E. Marschang. 2011. Persistence of Avian Influenza Viruses in Lake Sediment, Duck Feces, and Duck Meat. Applied and Environmental Microbiology 77:4981-4985.

Olsen, B., V. J. Munster, A. Wallensten, J. Waldenstrom, A. Osterhaus, and R. A. M. Fouchier. 2006. Global patterns of influenza A virus in wild birds. Science 312:384- 388.

Olsen, P., and L. Joseph. 2011. Stray Feathers: Reflections on the Structure, Behaviour and Evolution of Birds. CSIRO PUBLISHING.

Ozawa, M., A. Matsuu, K. Tokorozaki, M. Horie, T. Masatani, H. Nakagawa, K. Okuya, T. Kawabata, and S. Toda. 2015. Genetic diversity of highly pathogenic H5N8 avian influenza viruses at a single overwintering site of migratory birds in Japan, 2014/15.

Paull, S. H., S. Song, K. M. McClure, L. C. Sackett, A. M. Kilpatrick, and P. T. J. Johnson. 2012. From superspreaders to disease hotspots: linking transmission across hosts and space. Frontiers in Ecology and the Environment 10:75-82.

Peters, A., K. Delhey, A. G. Denk, and B. Kempenaers. 2004. TradeǦOffs between Immune Investment and Sexual Signaling in Male Mallards. The American Naturalist 164:51- 59.

Roche, B., C. Lebarbenchon, M. Gauthier-Clerc, C. M. Chang, F. Thomas, F. Renaud, S. van der Werf, and J. F. Guegan. 2009. Water-borne transmission drives avian influenza dynamics in wild birds: The case of the 2005-2006 epidemics in the Camargue area. Infection Genetics and Evolution 9:800-805.

Rohani, P., R. Breban, D. E. Stallknecht, and J. M. Drake. 2009. Environmental transmission of low pathogenicity avian influenza viruses and its implications for pathogen invasion. Proceedings of the National Academy of Sciences of the United States of America 106:10365-10369.

Roshier, D., P. Whetton, R. Allan, and A. Robertson. 2001. Distribution and persistence of temporary wetland habitats in arid Australia in relation to climate. Austral Ecology 26:371-384.

Roshier, D. A., A. I. Robertson, and R. T. Kingsford. 2002. Responses of Waterbirds to Flooding in an Arid Region of Australia and Implications for Conservation. Biological Conservation 106:399-411.

Sims, L. D., and A. J. Turner. 2009. Avian influenza in Australia. Avian influenza:239-250.

Stallknecht, D. E., and J. D. Brown. 2007. Wild birds and the epidemiology of avian influenza. Journal of wildlife diseases 43:S15-S20.

Stallknecht, D. E., V. H. Goekjian, B. R. Wilcox, R. L. Poulson, and J. D. Brown. 2010. Avian Influenza Virus in Aquatic Habitats: What Do We Need to Learn? Avian diseases 54:461-465.

Stumpf, P., K. Failing, T. Papp, J. Nazir, R. Boehm, and R. E. Marschang. 2010. Accumulation of a Low Pathogenic Avian Influenza Virus in Zebra Mussels (Dreissena polymorpha). Avian diseases 54:1183-1190.

Tischler, M., C. R. Dickman, and G. M. Wardle. 2013. Avian functional group responses to rainfall across four vegetation types in the Simpson Desert, central Australia. Austral Ecology 38:809-819.

Tong, S., Y. Li, P. Rivailler, C. Conrardy, D. A. A. Castillo, L.-M. Chen, S. Recuenco, J. A. Ellison, C. T. Davis, and I. A. York. 2012. A distinct lineage of influenza A virus from bats. Proceedings of the National Academy of Sciences 109:4269-4274.

Tong, S., X. Zhu, Y. Li, M. Shi, J. Zhang, M. Bourgeois, H. Yang, X. Chen, S. Recuenco, and J. Gomez. 2013. New world bats harbor diverse influenza A viruses. PLoS Pathog 9:e1003657.

Tracey, J. P. 2010. Risk-based surveillance of avian influenza in Australia's wild birds. Wildlife Research 37:134-144. van Dijk, J. G., B. J. Hoye, J. H. Verhagen, B. A. Nolet, R. A. Fouchier, and M. Klaassen. 2013. Juveniles and migrants as drivers for seasonal epizootics of avian influenza virus. Journal of Animal Ecology.

Webster, R. G., W. J. Bean, O. T. Gorman, T. M. Chambers, and Y. Kawaoka. 1992. Evolution and ecology of influenza A viruses. Microbiological reviews 56:152-179.

World Health Organisation http://www.who.int/influenza/human_animal_interface/EN_GIP_201503031cumulati veNumberH5N1cases.pdf. Accessed on 31 March 2016.

CHAPTER 2

A description of the avian influenza reservoir community in wild

Australian birds and the role of phylogeny, ecology and eco-region

Marta Ferenczi

Simeon Lisovski

David Roshier

Marcel Klaassen

AUTHOR’S CONTRIBUTIONS:

MF, SL, DR and MK designed the study, collected the virus samples, analysed the data, and drafted the manuscript. SL performed the ELISA tests. All authors read and approved the final manuscript.

ABSTRACT

Although virtually all birds may become infected with Avian Influenza Virus (AIV), globally species of the order Anseriformes (waterfowl) and Charadriiformes (shorebirds, gulls and terns) and notably species from the subfamily Anatinae (dabbling ducks), are considered to be the major AIV reservoir species. Since Australia has (i) a distinct bird community with many endemics, (ii) few migratory, yet, many nomadic bird species, (iii) highly erratic climatology resulting in multi-year boom and bust population dynamics in many species, the dynamics of AIV in wild birds may significantly differ over time, across species and across eco-regions compared to other parts of the world. Therefore we investigated AIV viral and antibody prevalence in wild birds in Australia, spanning six years. We sampled bird communities in the tropics, in the desert and in the coastal temperate regions, including the main AIV reservoir waterbirds as well as boom and bust species of other bird orders that are normally not implied as typical reservoir species. Additionally, for the comparison of the prevalence of eco-regions, AIV antibody prevalence data of shorebirds sampled in north

Western Australia by Curran et al. (2014) were included. As antibody prevalence was generally higher due to antibody’s longer detectability compared to virus, we suggest that the best tool for reservoir evaluation is serology. Especially when sample sizes are low, ecological conditions fluctuating and disease dynamics variable, as is the case under

Australia’s erratic climatic conditions. Our findings were largely consistent with results from other studies conducted elsewhere in the world, with waterfowl, notably dabbling ducks being the main reservoirs of AIV. However, also endemic, nomadic, non-Anatinae ducks with similar foraging ecology were found to be a prime reservoir. We found differential infection rates between eco-regions with highest prevalence in the temperate regions and

lowest in the tropics. As found in other studies globally, our results suggest that Australian ducks and shorebirds, which had the second highest prevalence, deserve our focal attention when planning on long-term AIV surveillance programs. However other potential reservoir species should not be ruled out.

Keywords: Pink-eared Duck, Grey Teal, Zebra Finch, Diamond Dove, disease dynamics, southern hemisphere

INTRODUCTION

Although predominantly found in birds, avian influenza viruses (AIVs) have been isolated worldwide from numerous vertebrate species (Webster et al. 1992). They can be classified based on their surface proteins and their pathogenicity. In AIVs found in birds 16 different hemagglutinin [HA] and nine different neuraminidase [NA] surface glycoproteins have been recognised which can form several combinations (Webster et al. 1992, Tong et al.

2012, Tong et al. 2013). By definition, low pathogenic forms of the virus cause only mild or non-detectable clinical signs in poultry (i.e. termed LPAI). Occasionally high pathogenic forms (termed HPAI) evolve from H5 and H7 subtype LPAI, which are defined as viruses which cause severe clinical signs and rapid death in poultry (Capua and Alexander 2002,

Ellis et al. 2004, Kuiken and Harder 2012). The HPAI forms of AIVs can cause massive losses in poultry (Alexander 2007), resulting in extensive socio-economic costs (Gilbert and

Pfeiffer 2012, Gilbert et al. 2014, Vergne et al. 2014). In several cases avian-to-human transmissions of both LPAI and HPAI have also occurred of subtypes H5, H7, H9 and H10

(World Health Organization 2012). Besides illness, certain strains (notably of subtype H5 and

H7) have in some cases also resulted in death (Cowling et al. 2013, Gao et al. 2013, To et al.

2013).

Wild birds are thought to be the main reservoirs of AIVs in nature (Alexander 2007),

HPAI viruses evolving in poultry after the alleged introduction of LPAI from wild birds

(Capua and Alexander 2002, Ellis et al. 2004, Kuiken and Harder 2012). Such virus spillover could occur when infected wild birds enter poultry premises. These wild birds could either directly infect the poultry or indirectly contaminate surfaces from where the virus is transmitted to the poultry by farm workers, pets or equipment (Morgan and Kelly 1990,

Burns et al. 2012). Accordingly, low bio-security farming, poor handling practices and unsafe transport of poultry have been identified as important factors in the spread of HPAI viruses in Asia (Morris et al. 2005, Kurscheid et al. 2015). Although wild birds may become infected by HPAI and may potentially have a role in spreading HPAI virus (Feare 2010,

Newman et al. 2012, Verhagen et al. 2015), it is still unclear how migratory birds may shed the virus on a long-distance movement without being severely affected by the disease (Olsen et al. 2006, Feare 2010). Although some studies showed that HPAI viruses are less pathogenic in experimentally infected ducks, while the viruses remained highly pathogenic in chickens (Hulse-Post et al. 2005, Sturm-Ramirez et al. 2005). Thus, because wild bird are known to be the reservoir of LPAI viruses and may also have the potential of spreading HPAI viruses, there is great interest in studying which species and individuals of wild birds act as the key reservoirs.

To identify the host community for AIVs in wild birds, hundreds of thousands of wild bird samples have been collected to evaluate avian influenza viral prevalence in Africa, Asia,

Europe, and North- and South-America (i.e. n=93,344 individual wild birds in Olsen et al.

[2006] and n=448,888 samples according to Influenza Research Database; http://www.fludb.org accessed on 2/12/2015). However, at least 80% of the samples in the

Influenza Research Database have been collected in the northern hemisphere (i.e. n=356,660 samples in Asia, Europe and North-America on 2/12/2015), with just 0.5% (i.e 2,422) of the samples collected in Australia. Apparently, however, not all AIV surveillance data from

Australia has been lodged with the Influenza Research Database. Tracey (2010) mentioned that 33,139 individual wild bird were sampled for AIV before 2010. Grillo el al. (2015) reported that as many as 50,684 oropharyngeal, cloacal or fresh environmental swabs for AIV detecting and 8,387 blood serum samples for AIV antibody detection were collected in

Australia between 2007 and 2012. Yet, Australia, as the whole of the southern hemisphere,

remains under evaluated in this perspective and a comprehensive overview of the AIV host reservoir community is as yet unavailable.

Many of the Anseriformes and Charadriiformes identified by Olsen et al. (2006) as key reservoir species for AIV are migratory, which may partly explain their major role as reservoirs since the migratory trait also includes pre-migratory aggregation, increasing contact rates. Unlike in the northern hemisphere, the majority of Anseriformes in Australia are nomadic rather than migratory, with movements mainly determined by the availability of ephemeral wetlands in the landscape (Roshier et al. 2002). However, these nomadic waterfowl do share many similarities in behaviour and ecology with their AIV reservoir counterparts in the northern hemisphere (Marchant and Higgins 1990, Briggs 1992, del Hoyo et al. 1992). Many Australian Anseriformes aggregate occasionally in the drought period in few remaining wetlands (Roshier et al. 2002), which may potentially qualify them as important AIV reservoirs too (Tracey et al. 2004). Among Anseriformes the subfamily

Anatinae (i.e. dabbling ducks) are thought to be the main reservoirs of low pathogenic avian influenza viruses in the northern hemisphere (Munster et al. 2007). The reason for this is their surface feeding behaviour. The ducks shed the virus via faeces into water where it can be transmitted (via the fecal-oral route) to other waterbirds foraging in the same habitat. The chance to get infected is much higher when birds forage at high densities within a relatively small area. In the Australian context this could for instance occur when birds congregate during a drought period (Roshier et al. 2002).

In the northern hemisphere, extensive monitoring programs for AIV prevalence in ducks identified a key role for both Northern Mallard (Anas platyrhynchos) and Common

Teal (Anas crecca), having the highest prevalence levels observed among all ducks (Olsen et al. 2006, Munster et al. 2007). Australian dabbling duck species that thus comes into focus as potential AIV reservoir species include the Pacific Black Duck (Anas superciliosa) which

shows strong similarities in its ecology and phylogeny with Northern Mallard. Similar parallels in ecology and phylogeny can be drawn with the Australian Grey Teal (Anas gracilis) and Chestnut Teal (Anas castanea) on the one hand with the European Common

Teal on the other (Marchant and Higgins 1990, Briggs 1992, Scott and Rose 1996).

Dabbling-duck-like foraging behaviour (i.e. highly specialized filter-feeding behaviour) is also seen in another typical Australian nomadic duck species, Pink-eared Duck

(Malacorhynchus membranaceus). Although this species does not belong to and is also not closely related to the Anatinae, AIV samples from this species have regularly tested positive

(Grillo et al. 2015) qualifying it as an AIV reservoir candidate. However, for the southern hemisphere, little is known about AIV prevalence in wild ducks (Olsen et al. 2006, Hoque et al. 2014) and whether similarities in phylogeny or ecology between northern and southern hemisphere duck species cause similarities in AIV susceptibility and prevalence, remains to be clarified.

Charadriiformes are the only major group of species in Australia that encompasses a large number of truly migratory species, migrating annually from Siberia, through South-east

Asia to Australia in the non-breeding season (Tracey et al. 2004). Other species of

Charadriiformes in Australia such as Banded Stilt (Cladorhynchus leucocephalus) and Red- necked Avocet (Recurvirostra novaehollandiae) are endemic and, similarly to Australian

Anseriformes species, also nomadic (Gosbell and Christie 2006). For example, Banded Stilts and Red-necked Avocets are opportunistic breeders and disperse in response to rainfall.

When not breeding, flocks of tens of thousands of birds congregate in coastal areas (Gosbell and Christie 2006). Whether the virus prevalence of these endemic and nomadic

Charadriiformes species are similar to other seasonally migrating Charadriiformes species, still needs to be clarified.

Although, in Australia, as elsewhere, AIV sampling to date has focused on

Anseriformes and Charadriiformes (Tracey et al. 2004, Turner 2004, Sims and Turner 2008,

Grillo et al. 2015), other species, particularly social species that aggregate on or near waterbird-rich wetlands, cannot be ruled out as potential hosts of AIVs (Hoye et al. 2010).

Therefore sampling these species in addition to those belonging to Anseriformes and

Charadriiformes is important in determining the AIV reservoir community. This is notably necessary in an ecosystem where the availability of water can change rapidly, forcing various bird species to the same, few remaining wetlands in periods of drought. Typical boom and bust species in Australia that thus come into focus as potential (temporary) AIV reservoirs are for instance Budgerigar (Melopsittacus undulatus), Zebra Finch (Taeniopygia guttata) and

Diamond Dove (Geopelia cuneata), which may occasionally congregate in large numbers at temporary wetlands (Tischler et al. 2013) along with waterbirds.

In the northern hemisphere, the drivers (e.g. breeding and migration) of AIV dynamics in wild birds occur in a yearly seasonal pattern (Munster et al. 2007, Altizer et al.

2011, van Dijk et al. 2013) and taking this into account long-term average AIV prevalence can thus be determined in a systematic fashion within few years. In contrast, when planning long-term AIV surveillance in Australia, the irregular wetland availability on this continent has to be taken into account. Although extreme climate anomalies are observed in both hemispheres (Kousky et al. 1984), in Australia erratic and less seasonal weather patterns are common place (Pittock 1975, Norman and Nicholls 1991).This erratic climate has been instrumental in the evolution of the life histories of many Australian bird species, determining breeding opportunities for nomadic waterfowl and the ecology of boom and bust species

(Kingsford et al. 2010, Loyn et al. 2014). These factors presumably result in significant temporal variations in AIV prevalence within and among species (Klaassen et al. 2011). Thus to identify the long-term average prevalence of different species in this extreme environment,

where AIV may only be detected during relatively short episodes, long-term surveillance is required to accommodate for the temporal fluctuations.

Although irregular water availability is characteristic over large parts of Australia

(Pittock 1975, Norman and Nicholls 1991), Australia’s climate is geographically very variable compared to other continents, resulting in a range of climatic zones. This variability in climatic zones has resulted in a particularly diverse range of species, with each climatic zone having its own uniquely adapted fauna. However these ecological features of Australia makes comprehensive AIV sampling a daunting task since each eco-region may potentially exhibit contrasting AIV prevalences within and among avian species. Indeed, Grillo et al.

(2015) found that the proportion of birds that were tested positive for influenza A varied significantly temporally and geographically. Thus besides running programmes for multiple years it is also important to run AIV surveillance at various distinct sampling sites across the nation to determine the suitability of species as AIV reservoirs.

Identifying viral prevalence has been the primary tool in AIV epidemiological studies in wild birds (Olsen et al. 2006, Munster et al. 2007). Birds shed the virus for up to six days

(Pasick et al. 2007, Kistler et al. 2012). Viral prevalence can therefore be notoriously low requiring large sample sizes to allow for sufficiently accurate estimates (Hoye et al. 2010).

Virus antibody testing (i.e. identifying antibody prevalence) can provide another useful tool to understand AIV epidemiology and enhance disease surveillance (Brown et al. 2009, Hoye et al. 2010). Antibodies can be detected from bloodserum for an extended period of time (i.e. in the order of months rather than days), thus AIV infection can be detected after virus shedding stopped (Kistler et al. 2012).

We investigated AIV viral and antibody prevalence in wild birds in Australia over a period spanning six years. We sampled representative species of bird communities in the

tropics (i.e. northern Australian coastal areas), in Australia’s interior (i.e. desert wetland system in Innamincka Regional Reserve) and in temperate regions (i.e. coastal areas in southeast Australia), including the supposedly main AIV reservoir (waterfowl and shorebirds) as well as boom and bust species of other bird orders that are normally not implied as typical reservoir species. This allowed us to evaluate the relative role of various groups of bird species as potential reservoirs in Australia and the potential differences of AIV prevalence between three eco-regions. Additionally, we compared AIV antibody and viral prevalence within species between the above mentioned different areas (i.e. tropics, interior and temperate). Only for the comparison of eco-regions, in addition to our data collected in the tropics, we included antibody prevalence data from Curran et al.’s (2014) study on shorebird species in northern Australia. As in our study for several shorebird species we were not able to collect satisfying numbers in the tropics, Curran et al.’s (2014) data were a good addition to be able to compare the AIV prevalence of different species between eco-regions, including the tropics.

METHODS

Surveillance and species selection

Our surveillance for AIV prevalence and prevalence for AIV antibodies in wild birds was conducted in Australia in seven states (i.e. New South Wales, Northern Territory,

Queensland, South Australia, Tasmania, Victoria and Western Australia) at 16 different sampling sites within three eco-regions between November 2010 and April 2015 (Figure 1).

Waterbirds (i.e. species belonging to orders Charadriiformes and Anseriformes), were the

prime target species, but other bird species that occurred in large numbers or potential AIV reservoir boom and bust species were also targeted. Timing of the collection events was mainly determined by the occurrence of these target species at the collection sites and included both boom and bust periods.

This research was conducted under approval of Deakin University Animal Ethics

Committee (permit numbers A113-2010 and B37-2013) and Wildlife Ethics Committee of

South Australia (permit numbers 2011/1, 2012/35 and 2013/11). Banding was done under

Australian Bird Banding Scheme permit (banding authority numbers 2915 and 2703).

Research permits were approved by Department of Environment, Land, Water and Planning

Victoria (permit numbers 10006663 and 10005726); by Office of Environment and Heritage

New-South Wales (permit number SL101252); and by Department of Environment, Water and Natural Resources South Australia (research permit numbers M25919-1,2,3,4,5).

Figure 1: Avian influenza virus sampling sites within three eco-regions. The different eco- regions are depicted using differently coloured symbols where red stands for temperate coast, green for desert and blue for tropics. One sampling site was not included in any of the eco-regions.

Catching techniques

We used three main catching techniques at the collection sites: baited funnel walk-in traps (McNally and Falconer 1953), cannon nets and mist nets. Baited funnel walk-in traps were deployed on land or in water shallow enough for foraging by dabbling ducks and coots.

Traps baited with seeds were set at dawn and operated during the day, and left open (so birds could enter and leave the traps freely) during the night (Whitworth 2007). Cannon nets to capture roosting ducks and waders (Whitworth 2007) were operated during day. To capture waterbirds at night, mist nets were erected on poles above the water surface. Small songbirds,

doves and parrots were caught during the day using mist nets. All trapping techniques were used in areas of high bird activity (Whitworth 2007).

Sample collection and analysis

Immediately after removal of birds from traps and nets, samples were collected for

AIV and antibodies against AIV. Oropharyngeal and cloacal swabs for AIV analysis were placed into vials containing 2 ml viral transport media (Johnson 1990). Prior to July 2013 oropharyngeal and cloacal swabs were stored and analysed separately, whereas they were stored and analysed pooled after that date. Immediately following collection swabs were stored in a fridge at 5 °C. Within seven days of collection the samples were moved to a temperature at or below -80, (i.e. into a dewar filled with liquid nitrogen at -195.95 °C, when still in the field, or into a -80 °C freezer after return to the laboratory at Deakin University), until analysis. Before analysis samples were subsampled and then pooled by three. If the pooled sample was identified as positive, then the original individual samples were analyzed one by one to identify the positive(s). An individual was considered positive for AIV if either the cloacal, the oropharyngeal or the pooled cloacal/oropharyngeal sample was positive.

Following swab collection, approximately 200 μl of blood was collected from the brachial vein for detection of antibodies against AIV. Blood samples were collected into 200

μl vials and immediately stored in a fridge at 5 °C. Within 7-24 hours of collection, blood samples were centrifuged at 13,000 RPM (revolutions per minute) for 15 min. Next, the serum samples were stored and transported as described above for swab samples. Birds were individually banded with a coded metal ring except for Diamond Doves, Budgerigars and

Zebra Finches.

The oropharyngeal, cloacal or pooled cloacal/oropharyngeal samples were tested using nested influenza A PCR targeting the highly conserved matrix gene (Fouchier,

Bestebroer et al. 2000). Nested PCR uses two sets of primers. The first primer set binds to sequences outside the target DNA. The second set of primers bind and amplify target DNA within the products of the first set. This method results in increase PCR specificity and sensitivity. Samples collected before 2011 were analyzed by the Australian Animal Health

Laboratory (AAHL). After this date samples were tested at the Victorian Department of

Economic Development, Biosciences Research Division. Samples were processed for RNA extraction using 5XMagMAX - 96 Viral Isolation Kit (Ambion Cat. No AMB1836-5) on a

Thermo KingFisher - 96 Robot. A volume of 35 μL was then run in a Superscript III

Platinum One-Step qRT-PCR Kit with ROX (Life Technologies 11745-100) using a Px2

Thermal Cycler PCR machine. A volume of 5 μL (diluted 1/100) was then transferred to 15

μL Fast SYBR Green mastermix (Life Technologies 4385612) solution and the PCR performed in an ABI 7500 Real Time PCR machine.

Serum samples collected before 2011 were analyzed by AAHL and the remaining samples were analyzed at Deakin University. At AAHL sera was tested using a nucleoprotein competitive enzyme-linked immunosorbent assay (NP c-ELISA) targeting influenza group

A–specific nucleoprotein antibodies using reagents supplied by AAHL. Optical densities were read at 450 nm (Multiskan EX Microplate Photometer, Thermo Labsystems), with test serum results calculated as the percentage inhibition of binding of the monoclonal antibody in the absence of any serum. Sera with >60% inhibition were interpreted as positive, 40%–60% as equivocal, and <40% as negative. At Deakin University the presence of antibodies to nucleoprotein in individual serum samples was tested using a commercially available blocking enzyme-linked immunosorbent assay (bELISA; MultiS-Screen Avian Inﬂuenza

Virus Antibody Test Kit) following manufacturer’s instructions. Assays were carried out in

duplicate, each using a 10 μl sera sample. Absorbance was measured at 620 nm using a

FLOUstar omega plate reader. All samples were run in combination with supplied positive and negative controls. Sample signal to noise ratios (the quotient of sample mean absorbance divided by negative control mean absorbance) lower than 0.5 were considered positive for the presence of antibodies to AIV. However serum samples collected before and after 2011 were analysed with two different ELISAs (i.e. c and b), several studies showing that there are no significant differences between the results of the two techniques (Kalis, Barkema et al. 2002,

Neves, Roger et al. 2009).

Statistical analysis

To investigate species-specific AIV and AIV antibody prevalence over the entire six year period of surveillance we only included species for which a minimum of 100 and 50 samples were collected, respectively. AIV and AIV antibody prevalence and their 95% confidence intervals were calculated for each species and for each order over the entire period of the surveillance. To examine the relationships of AIV or AIV antibody prevalence between taxonomic categories (i.e. species or order), we used generalized linear models (GLM). The

AIV or AIV antibody prevalence was used as the binomial response variable and taxonomic category (i.e. species or order) was used as factorial explanatory variable in the models. To compare means we used Tukey’s post-hoc multiple comparisons of means. Thus AIV or AIV antibody prevalence of each species was compared with the results from all other species and

AIV or AIV antibody prevalence of each order was compared with the results from all other orders.

To examine the spatial effects of AIV infection in different bird species, three eco- regions were considered. Samples from coastal sampling sites in Southeast Australia located in South Australia, Tasmania and Victoria were aggregated in one eco-region termed temperate coast. Samples collected in Australia’s interior, in northern South Australia, were aggregated in eco-region desert. Samples from sampling sites in coastal northern Western

Australia, Northern Territories, and north Queensland were aggregated in eco-region tropics.

Additionally, AIV antibody prevalence data of shorebirds sampled in north Western Australia by Curran et al. (Curran et al. 2013) were included in eco-region tropics. To examine the effects of eco-region on AIV antibody prevalence we calculated AIV antibody prevalence and their 95% confidence intervals for each species within each eco-region. AIV antibody prevalence of each species was used as the binomial response variable to test for the effect of eco-regions temperate coast, desert and tropics in a generalized linear model (GLM) using

Tukey’s post-hoc multiple comparisons of means. Analysis of the effects of eco-regions on

AIV prevalence in different species could not be conducted because of insufficient data. To analyse the variation in AIV antibody prevalence between eco-regions across all species, we ran a GLM using species as a random factor and a Tukey multiple comparisons to test for differences between the individual means for each eco-region. All analyses were conducted using R (RDevelopment 2008). It should be noted that as result of a characteristic of the post- hoc Tukey multi-comparison testing, any categories including zero positives are indistinguishable from any of the other groups in the comparison and that the interpretation of comparisons involving zero-categories should be conducted with care.

RESULTS

Virology

A total of 6,947 individuals from 17 species (representing five orders and six families) sampled for AIV over the entire period of the surveillance. Of these 180 individuals (2.59 %) from nine species (52.94 %; representing two orders and three families) were tested positive for AIV (Figure 2). Among the orders, Anseriformes had significantly higher AIV prevalence

(4.33 %) than the order Charadriiformes (1.38 %). The remaining orders, Passeriformes,

Gruiformes and Columbiformes, all revealed zero AIV prevalence, and were thus, as result of a characteristic of the post-hoc Tukey multi-comparison testing, indistinguishable from any of the other groups (Figure 2). At the species level, few significantly different average AIV prevalence were identified, only Red-necked Stint (Calidris ruficollis) having a significantly lower AIV prevalence than three Anseriformes and one other Charadriiformes species

(Figure 2).

Figure 2: Avian influenza viral prevalence with 95 % confidence intervals, percentages and sample sizes (top of the bar) of Australian avian orders and species sampled in order of decreasing prevalence. Colours of bars denote different taxonomic orders and families.

Letters above each bar identify groups that are not significantly different using multiple post- hoc comparisons. Scientific names for species are provided in the main text under

Results/serology.

Serology

A total of 6,302 individuals from 23 species (representing six orders and 11 families) were sampled for AIV antibodies over the entire period of the surveillance. Of these 1,863 individuals (29.56 %) from 18 species (78.26 %; representing five orders and nine families) were tested positive for AIV antibodies (Figure 3). Most orders were significantly different from one another with Anseriformes having the highest AIV antibody prevalence (57.18 %), followed by the orders Charadriiformes (20.64 %), and Passeriformes (7.34 %), Gruiformes

(1.37 %), Columbiformes (0.54 %) and Psittaciformes (0 %) (Tukey post hoc; Figure 3).

Five of the six sampled species of order Anseriformes (family Anatidae: Chestnut

Teal [Anas castanea], Australian Shelduck [Tadorna tadornoides], Grey Teal [Anas gracilis],

Pacific Black Duck [Anas superciliosa] and Pink-eared Duck [Malacorhynchus membranaceus]) had the highest and statistically indistinguishable AIV antibody prevalence of all species. The exception was the herbivorous Wood Duck (Chenonetta jubata), which had a near tenfold lower antibody prevalence than the other Anseriformes (Figure 3). A total of five species of order Charadriiformes (familiy Scolopacidae: Ruddy Turnstone [Arenaria interpres], Red-necked Stint, Sharp-tailed Sandpiper [Calidris acuminata]; family

Recurvirostridae: Red-necked Avocet [Recurvirostra novaehollandiae] and family Laridae:

Silver Gull [Chroicocephalus novaehollandiae]) formed the next group with second highest

AIV antibody prevalences among all species and no significant differences in their AIV antibody prevalences compared to each other. The remaining four species of order

Charadriiformes (familiy Scolopacidae: Curlew Sandpiper [Calidris ferruginea], Sanderling

[Calidris alba], family Haematopodidae: Pied Oystercatcher [Haematopus longirostris] and

family Sternidae: Whiskered Tern [Chlidonias hybrida]) and one species of Passeriformes

(family Estrildidae: Zebra Finch), one species of Gruiformes (family Rallidae: Common

Coot [Fulica atra]) and one species of Columbiformes (family Columbidae: Diamond Dove) had significantly lower prevalence than the aforementioned five Charadriiformes and five

Anseriformes species (Figure 3). Finally there was a group of five species of mixed orders

(Great Knot [Calidris tenuirostris], Greater Sand Plover [Charadrius leschenaultii], Bar- tailed Godwit [Limosa lapponica], Black-tailed Nativehen [Tribonyx ventralis] and

Budgerigar), which had zero positive cases and, as result of a characteristic of the post-hoc

Tukey multi-comparison testing, were thus indistinguishable from any of the other groups

(Figure 3).

Figure 3: Avian influenza antibody prevalence with 95 % confidence intervals, percentages and sample sizes (top of the bar) of Australian avian orders and species sampled in order of decreasing prevalence. Colours of bars denote different taxonomic orders and families.

Letters above each bar identify groups that are not significantly different using multiple post- hoc comparisons. Scientific names for species are provided in the main text under

Results/serology.

After accounting for species, AIV antibody prevalence across all species was significantly higher on the temperate coast than in both the desert (p<0.05) and the tropics

(p<0.001). However, when testing at the level of individual species, AIV antibody prevalence of Grey Teal and Silver Gull were significantly lower at the temperate coast than in the desert

(p<0.05), while for all remaining species for which comparisons were possible, no significant differences between the two eco-regions could be determined (Figure 4). Conform the pattern found across all species combined, AIV antibody prevalence at the species level for Sharp- tailed Sandpiper (p<0.05), Red-necked Stint and Curlew Sandpiper (p<0.001) were all significantly higher on the temperate coast than in the tropics (Figure 4).

Figure 4: Avian influenza virus antibody prevalence with 95 % confidence intervals, percentages (top of the bar) of Australia avian species sampled in northern Australian coastal areas (tropics; i.e. Broome, Darwin, Gladstone), in Australia’s interior (desert; i.e.

Innamincka) and coastal southeast Australia (temperate coast). Letters above each bar identify groups that are not significantly different using multiple post-hoc comparisons

(comparing areas within species only). Scientific names for species are provided in the main text under Results/serology.

DISCUSSION

Our findings are consistent with Olsen et al.’s (2006) global review identifying key avian reservoir species for AIV. Both their and our study showed that Anseriformes had the highest prevalence (Olsen et al. [2006]: 7.7 %; our data: 4.3 %) followed by the order

Charadriiformes (Olsen et al. [2006]: 1.2 %; our data: 1.4 %). However, in Australia, all the other orders revealed zero AIV prevalence, AIV antibody prevalence providing a finer scale for evaluating variation in AIV infection and yielding marked differences across orders and species. AIV antibody prevalence data continued supporting the patterns observed in AIV prevalence across the orders Anseriforems and Charadriiformes, although with higher prevalence of 57.2 versus 4.3% and 20.6 versus 1.4 % respectively. Yet, for the orders with zero viral prevalence (i.e. Passeriformes, Gruiformes and Columbiformes) the AIV antibody prevalence continued revealing further fine-scale differences with antibody prevalence of 7.3

%, 1.4 % and 0.5 %, respectively. Also the R2= 0.79 for the correlation between AIV prevalence and AIV antibody prevalence for species with both prevalence estimations (n =

17), is indicative of the close relationship between both indicators. These results suggest that serologic testing of wild birds can indeed provide supportive data to advance our knowledge of AIV infection history (Kistler et al. 2012). To this end, serological testing may be especially beneficial in circumstances where sampling at high temporal frequency is challenging and where sample size may be limited.

The AIV antibody prevalence at species level showed a clear pattern that all species of Anseriformes, except for the herbivorous Wood Duck, had higher prevalence than many species within the Charadriiformes, with AIV antibody prevalence of some other

Charadriiformes species and species of the other remaining orders showing lower prevalence

still. The surface feeding of dabbling ducks (subfamily Anatinae) has been suggested to facilitate fecal-oral infection rates, explaining these species’ top-ranking as AIV reservoirs

(Olsen et al. 2006). Remarkably, our top five ranking species were all Anseriformes and all surface feeding ducks, but not all Anatinae species. Pink-eared Duck and Australian Shelduck were here the exception. Moreover, not all Anatinae species had similarly high prevalence, with Wood Duck, being mostly a terrestrial herbivorous duck rather than an aquatic surface feeder, ranking only 13th of all species with an average AIV antibody prevalence of 7%.

Thus, among ducks, foraging ecology rather than phylogeny, seems to be determining their status as AIV reservoir species.

We found AIV antibody prevalence was generally low and AIV prevalence even zero, for boom and bust species such as Zebra Finch and Diamond Dove. Still these data do indicate that these species do become infected occasionally. Thus, together with the findings for Anseriformes and Charadriiformes the variation we found across the various taxonomic groups in Australia was in many aspects similar to what has been found elsewhere around the globe to date. They indicate that while possibly all birds can become infected with (certain strains of) AIV (Fuller et al. 2010, Vandegrift et al. 2010), waterbirds are the main carriers

(Olsen et al. 2006).

We found that among all eco-regions, the coastal area in south-east Australia (i.e. temperate coast) had the highest AIV antibody prevalence. This result is supported by the study of Grillo et al. (2015) that showed that the proportion of birds that were positive for influenza A was greater in coastal areas in Victoria and New South Wales than in the other states. On a global scale Herrick et al.(2013) attempted to create a model predicting AIV prevalence but it is rather course in its predictions, one of which was that that northern areas on our globe have the highest relative predicted risk of AIV infection that is related to low annual rainfall and low temperature (Herrick et al. 2013). Lisovski et al. (Lisovski et al. PhD

thesis 2015) questions whether intrinsic biological drivers (e.g. population dynamics), are not a more important driver of AIV dynamics and prevalence levels than extrinsic drivers (e.g. temperature and precipitation) as used by Herrick at al. (2013). But irrespectively, for

Australia, Herrick et al.’s (2013) analyses did not provide any marked differences across eco- regions, which may be considered confirmed by our data, showing only moderate differences between the three eco-regions.

Migration is often associated with pre-migratory aggregations, increasing contact rates between individuals, and potentially also immunosuppression as a consequence of exhaustion. Migration may thus be considered a trait that increases AIV prevalence among species. None of the Anseriformes sampled were migratory but all are nomadic to varying extend. The nomadic duck species (i.e. Pink-eared Duck and Grey Teal), did not stand out from any of the other species sampled. Similarly for the Charadriiformes, which counted many truly long-distance migratory species, the one nomadic resident species sampled, did not stand out and had similar prevalence levels to its migratory close relatives.

Our findings are consistent with the results from other studies from the rest of the globe that waterfowl, notably dabbling ducks and other ducks with similar foraging ecology are the main reservoirs of AIV in Australia and may therefore also be of socio-economic and public health concern. Klaassen et al. (2011) suggested that the likelihood of AIV outbreaks in poultry increases as temporary wetlands dry up. It was hypothesised that in these dry periods when wild waterfowl are concentrated on a few remaining waterbodies, including farm dams, where they may be in (indirect) contact with domestic birds (Marchant and

Higgins 1990, Loyn, Rogers et al. 2014), the likelihood of virus transmission between wild and domestic birds might increase. Our data in combination with Klaassen et al.’s (2011) hypothesis indeed indicate that Australian ducks, like ducks elsewhere on the globe, deserve our focal attention when it comes to AIV exchange between wild birds and livestock. At the

same time, we showed that there definitely is variation in the suitability of duck species to act as reservoir on source of infection of poultry. In that respect the largely terrestrial feeding and herbivorous Wood Duck stood out with particularly low AIV and AIV antibody prevalence, whereas it otherwise is one of the most common ducks in agricultural landscapes.

The second most important group of AIV reservoirs, waders, the group harbouring the only long-distance intercontinental migrants, may form the bridge between Australia and the rest of the world, notably south-east Asia. Although the chances of introducing AIV through this flyway seems relatively low, given that HPAI H5N1 still has not made it to Australia from southeast Asia, where it is endemic in many countries since 2003, there is some intercontinental exchange of viruses between Australia, North America and Eurasia (Vijaykrishna et al. 2013). Yet, in this context the millions of Procellariformes that roam the oceans between the continents should possibly also be considered for AIV sampling.

REFERENCES

Alexander, D. J. 2007. An overview of the epidemiology of avian influenza. Vaccine 25:5637-5644.

Altizer, S., R. Bartel, and B. A. Han. 2011. Animal Migration and Infectious Disease Risk. Science 331:296-302.

Briggs, S. V. 1992. Movement patterns and breeding characteristics of arid zone ducks. Corella 16:15-22.

Brown, J. D., D. E. Stallknecht, R. D. Berghaus, M. P. Luttrell, K. Velek, W. Kistler, T. Costa, M. J. Yabsley, and D. Swayne. 2009. Evaluation of a commercial blocking enzyme-linked immunosorbent assay to detect avian influenza virus antibodies in multiple experimentally infected avian species. Clinical and Vaccine Immunology 16:824-829.

Capua, I., and D. J. Alexander. 2002. Avian influenza and human health. Acta tropica 83:1-6.

Cowling, B. J., L. Jin, E. H. Lau, Q. Liao, P. Wu, H. Jiang, T. K. Tsang, J. Zheng, V. J. Fang, and Z. Chang. 2013. Comparative epidemiology of human infections with avian influenza A H7N9 and H5N1 viruses in China: a population-based study of laboratory-confirmed cases. The Lancet 382:129-137.

Curran, J. M., T. M. Ellis, and I. D. Robertson. 2013. Surveillance of Charadriiformes in Northern Australia Shows Species Variations in Exposure to Avian Influenza Virus and Suggests Negligible Virus Prevalence. Avian diseases 58:199-204. van Dijk, J. G., B. J. Hoye, J. H. Verhagen, B. A. Nolet, R. A. Fouchier, and M. Klaassen. 2013. Juveniles and migrants as drivers for seasonal epizootics of avian influenza virus. Journal of Animal Ecology 83: 266-275.

Ellis, T. M., R. Barry Bousfield, L. A. Bissett, K. C. Dyrting, G. S. Luk, S. Tsim, K. Sturm- Ramirez, R. G. Webster, Y. Guan, and J. M. Peiris. 2004. Investigation of outbreaks

of highly pathogenic H5N1 avian influenza in waterfowl and wild birds in Hong Kong in late 2002. Avian Pathology 33:492-505.

Feare, C. J. 2010. Role of Wild Birds in the Spread of Highly Pathogenic Avian Influenza Virus H5N1 and Implications for Global Surveillance. Avian diseases 54:201-212.

Fouchier, R. A., T. M. Bestebroer, S. Herfst, L. Van Der Kemp, G. F. Rimmelzwaan and A. D. Osterhaus (2000). "Detection of influenza A viruses from different species by PCR amplification of conserved sequences in the matrix gene." Journal of clinical microbiology 38(11): 4096-4101.

Fuller, T. L., S. S. Saatchi, E. E. Curd, E. Toffelmier, H. A. Thomassen, W. Buermann, D. F. DeSante, M. P. Nott, J. F. Saracco, and C. Ralph. 2010. Mapping the risk of avian influenza in wild birds in the US. BMC Infectious Diseases 10:187.

Gosbell, K., and M. Christie. 2006. The Breeding of Banded Stilt and Red-Necked Avocet in the Coorong, South Australia: December 2005– February 2006. Stilt:277 - 284.

Herrick, K. A., F. Huettmann, and M. A. Lindgren. 2013. A global model of avian influenza prediction in wild birds: the importance of northern regions. Veterinary Research 44:42.

Hoque, M., G. W. Burgess, A. L. Cheam, and L. F. Skerratt. 2014. Epidemiology of avian influenza in wild aquatic birds in a biosecurity hotspot, North Queensland, Australia. Preventive Veterinary Medicine.

Hoye, B. J., V. J. Munster, H. Nishiura, M. Klaassen, and R. A. M. Fouchier. 2010. Surveillance of Wild Birds for Avian Influenza Virus. Emerging infectious diseases 16:1827-1834.

Hoyo, J. d., A. Elliott, and J. Sargatal. 1992. Handbook of the Birds of the World: Vol 1: Ostrich to Ducks. American Library Association, Lynx.

Hulse-Post, D., K. Sturm-Ramirez, J. Humberd, P. Seiler, E. Govorkova, S. Krauss, C. Scholtissek, P. Puthavathana, C. Buranathai, and T. Nguyen. 2005. Role of domestic ducks in the propagation and biological evolution of highly pathogenic H5N1 influenza viruses in Asia. Proceedings of the National Academy of Sciences of the United States of America 102:10682-10687.

Johnson, F. B. 1990. Transport of viral specimens. Clinical microbiology reviews 3:120-131.

Kalis, C., H. Barkema, J. Hesselink, C. Van Maanen and M. Collins (2002). "Evaluation of two absorbed enzyme-linked immunosorbent assays and a complement fixation test as replacements for fecal culture in the detection of cows shedding Mycobacterium avium subspecies paratuberculosis." Journal of veterinary diagnostic investigation 14(3): 219-224.

Kingsford, R. T., D. A. Roshier, and J. L. Porter. 2010. Australian waterbirds - time and space travellers in dynamic desert landscapes. Marine and Freshwater Research 61:875-884.

Kistler, W. M., D. E. Stallknecht, T. J. Deliberto, S. Swafford, K. Pedersen, K. V. Why, P. C. Wolf, J. A. Hill, D. L. Bruning, and J. C. Cumbee. 2012. Antibodies to avian influenza viruses in Canada geese (Branta canadensis): a potential surveillance tool? Journal of wildlife diseases 48:1097-1101.

Klaassen, M., B. J. Hoye, and D. A. Roshier. 2011. Identifying crucial gaps in our knowledge of the life-history of avian influenza viruses - an Australian perspective. Emu 111:103-112.

Kousky, V. E., M. T. Kagano, and I. F. Cavalcanti. 1984. A review of the Southern Oscillation: oceanicǦatmospheric circulation changes and related rainfall anomalies. Tellus A 36:490-504.

Kuiken, T., and T. Harder. 2012. H5N1 HIGHLY PATHOGENIC AVIAN INFLUENZA. New Directions in Conservation Medicine: Applied Cases of Ecological Health:228.

Kurscheid, J., J. Millar, M. Abdurrahman, I. G. A. A. Ambarawati, W. Suadnya, R. P. Yusuf, S. Fenwick, and J.-A. L. L. Toribio. 2015. Knowledge and Perceptions of Highly Pathogenic Avian Influenza (HPAI) among Poultry Traders in Live Bird Markets in Bali and Lombok, Indonesia. PloS one 10:e0139917.

Lisovski, S. 2015. Wildlife infectious disease dynamics in the context of seasonality and bird migration. PhD thesis, Deakin University, Australia

Marchant, S., and P. J. Higgins. 1990. Handbook of Australian, New Zealand and Antarctic birds. Volume 1 Ratites to ducks. Oxford University Press, Melbourne.

McNally, J., and D. D. Falconer. 1953. Trapping and banding operations Lara Lake, 1952. Emu 53:51-70.

Morgan, I., and A. Kelly. 1990. Epidemiology of an avian influenza outbreak in Victoria in 1985. Australian Veterinary Journal 67:125-128.

Morris, R., R. Jackson, M. Stevenson, J. Benard, and N. Cogger. 2005. Epidemiology of H5N1 avian influenza in Asia and implications for regional control. Food and Agriculture Organization of the UN.

Munster, V. J., C. Baas, P. Lexmond, J. Waldenstrom, A. Wallensten, T. Fransson, G. F. Rimmelzwaan, W. E. P. Beyer, M. Schutten, B. Olsen, A. D. M. E. Osterhaus, and R. A. M. Fouchier. 2007. Spatial, temporal, and species variation in prevalence of influenza A viruses in wild migratory birds. Plos Pathogens 3:630-638.

Neves, C. G. D., M. Roger, N. G. Yoccoz, E. Rimstad and M. Tryland (2009). "Evaluation of three commercial bovine ELISA kits for detection of antibodies against Alphaherpesviruses in reindeer (Rangifer tarandus tarandus)." Acta Veterinaria Scandinavica 51(1): 9.

Newman, S. H., N. J. Hill, K. A. Spragens, D. Janies, I. O. Voronkin, D. J. Prosser, B. Yan, F. Lei, N. Batbayar, and T. Natsagdorj. 2012. Eco-virological approach for assessing the role of wild birds in the spread of avian influenza H5N1 along the Central Asian Flyway.

Norman, F. I., and N. Nicholls. 1991. The Southern Oscillation and variations in waterfowl abundance in southeastern Australia. Australian Journal of Ecology 16:485-490.

Olsen, B., V. J. Munster, A. Wallensten, J. Waldenstrom, A. Osterhaus, and R. A. M. Fouchier. 2006. Global patterns of influenza A virus in wild birds. Science 312:384- 388.

World Health Organization, 2012. Antigenic and genetic characteristics of zoonotic influenza viruses and development of candidate vaccine viruses for pandemic preparedness. Wkly. Epidemiol. Rec 87:97-108.

Pasick, J., Y. Berhane, C. Embury-Hyatt, J. Copps, H. Kehler, K. Handel, S. Babiuk, K. Hooper-McGrevy, Y. Li, and Q. M. Le. 2007. Susceptibility of Canada geese (Branta canadensis) to highly pathogenic avian influenza virus (H5N1). Emerging infectious diseases 13:1821.

Pittock, A. 1975. Climatic change and the patterns of variation in Australian rainfall. Search 6:498-504.

RDevelopment, C. 2008. TEAM: R: a language and environment for statistical computing. R Foundation for Statistical Computing, Austria.

Roshier, D. A., A. I. Robertson, and R. T. Kingsford. 2002. Responses of Waterbirds to Flooding in an Arid Region of Australia and Implications for Conservation. Biological Conservation 106:399-411.

Scott, D. A., and P. M. Rose. 1996. Atlas of Anatidae populations in Africa and western Eurasia. Wetlands International Wageningen.

Sims, L., and A. Turner. 2008. Avian influenza in Australia. In ‘Avian Influenza’.(Ed. DE Swayne.) pp. 239–250. Blackwell: Ames, IA.

Sturm-Ramirez, K., D. Hulse-Post, E. Govorkova, J. Humberd, P. Seiler, P. Puthavathana, C. Buranathai, T. Nguyen, A. Chaisingh, and H. Long. 2005. Are ducks contributing to the endemicity of highly pathogenic H5N1 influenza virus in Asia? Journal of virology 79:11269-11279.

Tischler, M., C. R. Dickman, and G. M. Wardle. 2013. Avian functional group responses to rainfall across four vegetation types in the Simpson Desert, central Australia. Austral Ecology 38:809-819.

To, K. K., J. F. Chan, H. Chen, L. Li, and K.-Y. Yuen. 2013. The emergence of influenza A H7N9 in human beings 16 years after influenza A H5N1: a tale of two cities. The Lancet infectious diseases 13:809-821.

Tong, S., X. Zhu, Y. Li, M. Shi, J. Zhang, M. Bourgeois, H. Yang, X. Chen, S. Recuenco, and J. Gomez. 2013. New world bats harbor diverse influenza A viruses. PLoS Pathog 9:e1003657.

Tracey, J. P. 2010. Risk-based surveillance of avian influenza in Australia's wild birds. Wildlife Research 37:134-144.

Tracey, J. P., R. Woods, D. Roshier, P. West, and G. R. Saunders. 2004. The Role of Wild Birds in the Transmission of Avian Influenza for Australia: an Ecological Perspective. Emu 104:109-124.

Turner, A. 2004. The role of wild aquatic birds in the epidemiology of avian influenza in Australia. Australian Veterinary Journal 82:713-713.

Vandegrift, K. J., S. H. Sokolow, P. Daszak, and A. M. Kilpatrick. 2010. Ecology of avian influenza viruses in a changing world. Pages 113-128 Year in Ecology and Conservation Biology 2010.

Vergne, T., M. C. Paul, W. Chaengprachak, B. Durand, M. Gilbert, B. Dufour, F. Roger, S. Kasemsuwan, and V. Grosbois. 2014. Zero-inflated models for identifying disease risk factors when case detection is imperfect: Application to highly pathogenic avian influenza H5N1 in Thailand. Preventive veterinary medicine 114:28-36.

Verhagen, J. H., S. Herfst, and R. A. Fouchier. 2015. How a virus travels the world. Science 347:616-617.

Vijaykrishna, D., YM. Deng, YC. Su, M. Fourment, P. Iannello, GG. Arzey, PM. Hansbro, KE. Arzey, PD. Kirkland, S. Warner, K. O'Riley, IG. Barr, GJ. Smith, and AC. Hurt. 2013. The recent establishment of North American H10 lineage influenza viruses in Australian wild waterfowl and the evolution of Australian avian influenza viruses. Journal of virology 87:10182-10189.

Webster, R. G., W. J. Bean, O. T. Gorman, T. M. Chambers, and Y. Kawaoka. 1992. Evolution and ecology of influenza A viruses. Microbiological reviews 56:152-179.

Whitworth, D. N. S. H. M. T. H. P. 2007. Wild birds and avian influenza: an introduction to applied field research and sampling techniques. Food and agriculture organization of the United Nations, Rome.

CHAPTER 3

Avian influenza infection dynamics under variable climatic conditions -

viral prevalence is rainfall driven in waterfowl from temperate, south-east

Australia

This manuscript has been accepted for publishing in Veterinary Research.

Marta Ferenczi

Christa Beckmann

Simone Warner

Richard Loyn

Kim O'Riley

Xinlong Wang

Marcel Klaassen

AUTHOR’S CONTRIBUTIONS:

MF, CB and MK participated in the design of the study, analyzed data, and drafted the manuscript. SW, KO and

XW collected the virus samples and performed the PCRs. RL participated in the bird data collection, helped to interpret the results and participated in finalizing the manuscript. All authors read and approved the final manuscript.

ABSTRACT

Understanding Avian Influenza Virus (AIV) infection dynamics in wildlife is crucial because of possible virus spill over to livestock and humans. Studies from the northern hemisphere have suggested several ecological and environmental drivers of AIV prevalence in wild birds.

To determine if the same drivers apply in the southern hemisphere, where more irregular environmental conditions prevail, we investigated AIV prevalence in ducks in relation to biotic and abiotic factors in south-eastern Australia. We sampled duck faeces for AIV and tested for an effect of bird numbers, rainfall anomaly, temperature anomaly and long-term

ENSO (El-Niño Southern Oscillation) patterns on AIV prevalence. We demonstrate a positive long term effect of ENSO-related rainfall on AIV prevalence. We also found a more immediate response to rainfall where AIV prevalence was positively related to rainfall in the preceding three to seven months. Additionally, for one duck species we found a positive relationship between their numbers and AIV prevalence, while prevalence was negatively or not affected by duck numbers in the remaining four species studied. In Australia largely non- seasonal rainfall patterns determine breeding opportunities and thereby influence bird numbers. Based on our findings we suggest that rainfall influences age structures within populations, producing an influx of immunologically naïve juveniles within the population, which may subsequently affect AIV infection dynamics. Our study suggests that drivers of

AIV dynamics in the northern hemisphere do not have the same influence at our south-east

Australian field site in the southern hemisphere due to more erratic climatological conditions.

Keywords: dabbling duck, infectious disease, virus prevalence, weather anomaly

INTRODUCTION

Knowledge of infection dynamics is central to our understanding of zoonotic diseases, their impact on wildlife populations and the potential of these diseases to spill over into domestic animal or human populations (Paull et al. 2012). Avian influenza virus (AIV) in its low pathogenic form, causing only mild or non-detectable clinical signs, occurs naturally in wild bird populations (Alexander 2000). Recently there has been increasing interest in AIV infection dynamics, largely in response to highly pathogenic AIV outbreaks in domestic poultry and the possibility of virus transmission to humans (Capua and Alexander 2002).

In the northern hemisphere, AIV prevalence shows marked seasonal fluctuations in wild bird communities, with a yearly peak in late summer / early autumn, followed by low prevalence in winter (Munster et al. 2007, van Dijk et al. 2013). However the degree of seasonality varies geographically, with seasonal amplitude or intensity, tending to be lower and longer-lasting at low latitudes. In a continent-wide comparison of North American AIV data from waterfowl, Lisovski et al. (unpublished data) found a relationship between the shape of the annual infection dynamics and the degree of seasonality. Overall seasonal intensity and duration were positively correlated with geographically corresponding amplitudes and durations of the infection peak. In contrast to the northern areas, in southern

North America, Lisovski et al. found much less pronounced seasonal variation in AIV infection dynamics than in comparable northern sites.

Extreme climate anomalies are observed in both hemispheres (Kousky et al. 1984).

These anomalies are primarily related to the El Niño Southern Oscillation (ENSO) (Kousky et al. 1984), which reflects fluctuating ocean temperatures in the east equatorial Pacific

(McBride and Nicholls 1983, Rasmusson and Wallace 1983). Although ENSO has a nearly

global effect on climate, it particularly affects precipitation patterns in the southern hemisphere with extreme rainfalls occurring in the central and eastern Pacific, Peru, Ecuador and Southern Brazil and droughts in Australia, Indonesia, India, West Africa and Northeast

Brazil (Kousky et al. 1984). In Australia ENSO drives the erratic and less seasonal weather patterns that are characteristic over large parts of the continent, particularly the south-east

(Pittock 1975, Norman and Nicholls 1991). As a consequence, AIV dynamics in the southern hemisphere may differ from the widely observed seasonal dynamics in the northern hemisphere.

While tens of thousands of individual wild birds have been sampled for AIV in the northern hemisphere (Olsen et al. 2006, Herrick et al. 2013), a comparatively smaller number have been sampled in Australia (Haynes et al. 2009, Grillo VL et al. 2015) and there remains a lack of information on AIV prevalence and temporal variation in wild birds from the southern hemisphere. Based on northern hemisphere studies, the main reservoirs of AIV belong to the family Anatidae (swans, geese and ducks) (Hinshaw et al. 1985, Olsen et al.

2006, Stallknecht and Brown 2007). The highest infection rates occur in the subfamily

Anatinae (dabbling ducks), with nearly all AIV subtypes being found in wild dabbling ducks

(Olsen et al. 2006, van Dijk et al. 2013). This may be due to their ‘surface-feeding’ behaviour, which makes dabbling ducks particularly prone to infection via the faecal-oral route (del Hoyo et al. 1992, van Dijk et al. 2013). Furthermore, some dabbling ducks often feed on land, including farm pastures, where they may mix with domestic birds (Marchant and Higgins 1990, Loyn et al. 2014a). As Australian dabbling duck species share many similarities in behavior and ecology with their northern hemisphere counterparts (Marchant and Higgins 1990, Briggs 1992, del Hoyo et al. 1992), we consider it highly likely that dabbling ducks and other Australian ducks with related ecologies, are potentially important reservoirs of AIV in Australia.

In the northern hemisphere the following three mechanisms have been suggested in maintaining the seasonality of AIV dynamics among waterbird communities: (i) the annual increase in abundance of immunologically naïve young birds results in a higher number of individuals susceptible to infection in the waterbird community (Hinshaw et al. 1985, van

Dijk et al. 2013), (ii) the seasonal congregation of migratory birds at staging and wintering sites increases contact rates and thereby infection rates (Gaidet et al. 2012, van Dijk et al.

2013), and (iii) migration influences an individual’s susceptibility to infection since long distance movements are energy demanding and may potentially impair immuno-competence

(Altizer et al. 2011). These three hypothesized, key drivers of AIV dynamics are all linked to the annual breeding cycle of waterfowl (van Dijk et al. 2013).

Water availability is an important factor in the ecology of waterfowl in Australia

(Woodall 1985, Halse and Jaensch 1989a, Marchant and Higgins 1990). Across much of the

Australian continent, climatic conditions are extreme and non-seasonal (Ummenhofer et al.

2009). Although regular rains fall seasonally in the tropics (summer) and the temperate southeast and south-west (winter-spring) regions, water availability varies largely non-seasonally across the rest of the continent. Inland areas, in particular, may lack water for longer periods.

Inland Australia contains extensive flood-plains and wetland systems that may be filled by water-flows from distant rain events (Roshier et al. 2001). In south-eastern Australia inter- annual variation in rainfall is very high, with marked effects on breeding waterfowl (Norman and Nicholls 1991). Wet and dry periods can persist for several years (Ummenhofer et al.

2009), occasionally creating extreme climate events, such as the ‘Big Dry’ phenomenon in south-eastern Australia between 1997 and 2009 (Gergis et al. 2012). These inter-annual and multi-year periodic climate changes are ENSO linked (Norman and Nicholls 1991, Power et al. 1998). Periods of drought across east, especially south-east Australia usually correlate with the El Niño phase of the ENSO, when the Pacific Ocean is warm and atmospheric

pressure is higher than average across Australia (Pittock 1975). The extreme rainfall events occur during the La Niña phase of ENSO, when the ocean is cooler and atmospheric pressure is below average (Pittock 1975).

These ENSO driven irregular climatic conditions strongly influence the movement and breeding biology of many Australian waterfowl at local, regional and continental scales

(Briggs 1992, Kingsford et al. 2010). After high precipitation, bird numbers increase at flooded areas where food resources become available, creating appropriate conditions for breeding (Briggs and Maher 1985, Roshier et al. 2002). During these wet periods bird numbers decrease at permanent wetlands, and the birds only return when the temporary wetlands begin to dry (Norman and Nicholls 1991). Klaassen et al. (2011) suggested that the non-seasonal and often multi-year alternations of wet and dry periods that influence the ecology of waterfowl might therefore also affect the temporal patterns of AIV prevalence on the Australian continent (Klaassen et al. 2011). Adopting the same three hypotheses mentioned above, but now applying them to the typical climatic conditions of the Australian continent, Klaassen et al. (2011) hypothesized that intense rainfall leads to breeding events and increased numbers of immunologically naïve juvenile birds. After breeding, when the temporary wetlands dry, increasing densities of (immunologically naïve) waterbirds returning to permanent water bodies might be driving AIV prevalence in wild waterfowl in Australia.

Another study in Australia showed a relationship between regional variation in rainfall and evolutionary dynamics of AIV that is possibly linked to waterbird movements and behavior

(Vijaykrishna et al. 2013).

To test Klaassen et al.’s (2011) hypothesis for south-east Australia, we investigated

AIV prevalence in faecal samples from dabbling ducks in relation to biotic (bird numbers) and abiotic (weather) drivers at a major permanent wetland, the Melbourne Water Western

Treatment Plant (WTP), 40 km from Melbourne, Australia, between 2006 - 2012.

MATERIALS AND METHODS

Faecal sampling and analysis

Faecal samples for AIV analyses were collected at the WTP waste-stabilization ponds, operated by Melbourne Water in Victoria, Australia (37°59'11.62"S, 144°39'38.66"E).

The WTP covers 10,851 ha and consists of a 1,820 ha array of sewerage ponds (Fig.1). This

Ramsar-listed pond system is one of the most significant sites for waterbirds in Victoria, providing habitat for numerous waterfowl species that often occur in flocks of up to tens of thousands of individuals (Hamilton and Taylor 2004, Murray and Hamilton 2010, Loyn et al.

2014a), with totals sometimes exceeding 100,000 (Loyn et al. 2014a, Loyn et al. 2014b).

We sampled faeces of roosting waterbirds at WTP’s 115 East Lagoon, between 2006 and 2012. This site was chosen as it offers several of the few land-based roosting sites for waterbirds in the area, is closed to the public and harbours large numbers of waterbirds at all times of the year. Samples were collected at irregular intervals, with two to nine (ܺഥ = 4.8) collection events per year. Each collection event covered one to three days. Prior to faecal sample collection (i.e. before flushing the birds), waterfowl species at the roost locations were identified, with species being recorded as present/absent. As waterfowl are commonly found roosting in mixed species flocks (M. Ferenczi and M. Klaassen, pers. obs.), it was not possible to positively link species to individual faecal samples. Three fresh faecal samples were collected into each vial containing 4ml viral transport media, with 13 to 365 (ܺഥ = 96.9) pooled samples (hereafter called “samples”) being collected per collection event. The viral transport media used was consistent with standards outlined in Johnson (1990) (Johnson

1990). Samples were immediately chilled and transported to the laboratory of the Department of Economic Development in Melbourne, within one hour drive from the study site. Samples

were then stored at 4°C until analysed for AIV presence, within 1-7 days after collection.

AIV presence was tested using an influenza A PCR targeting the highly conserved matrix gene. Samples were processed for RNA extraction using 5XMagMAX - 96 Viral Isolation

Kit (Ambion Cat. No AMB1836-5) on a Thermo KingFisher - 96 Robot. A volume of 35 μL was then run in a Superscript III Platinum One-Step qRT-PCR Kit with ROX (Life

Technologies 11745-100) using a Px2 Thermal Cycler PCR machine. A volume of 5 μL

(diluted 1/100) was then transferred to 15 μL Fast SYBR Green mastermix (Life

Technologies 4385612) solution and the PCR performed in an ABI 7500 Real Time PCR machine. AIV prevalence with 95% confidence intervals was calculated for each month within which a collection event took place, resulting in 34 prevalence estimates across the seven year period.

Bird counts

During the 2006 - 2012 study period regular waterbird counts were conducted as part of a longer-term study for Melbourne Water, with the total numbers of each waterbird species counted on each treatment pond (Loyn et al. 2014a). These counts were carried out over a period of one to six days typically during the months of January, February or March, May,

July, September and November. Additional counts were also conducted in June 2006 and in

October 2010.

For our comparison of bird counts with AIV prevalence, we combined count data from all ponds to give the total of all birds counted over the entire WTP area. A total of 94 waterbird species were observed. For our analysis, we excluded species observed fewer than ten times during the seven year study period. For the remaining 66 species we conducted a

principal component analysis (PCA) in R (RDevelopment 2008) in an attempt to reduce the number of variables (i.e. species) into a smaller number of principal components that could account for most of the variance in the observed species numbers. We also conducted a PCA across the 13 guilds (coots, dabbling ducks, diving ducks, filter-feeding ducks, fish-eaters, grazing ducks, grebes, gulls, waterhens, large wading birds, swans, terns and waders) to which these 66 species belong. Neither the species nor guild PCA resulted in a limited number of principal components explaining a large proportion of the variation in bird numbers. For our comparison of bird counts with AIV prevalence we thus changed strategy and focused on only those species that were both abundant and frequently observed at the faecal sampling sites.

At the faecal collection sites a total of 11 species were recorded across all collection events. Ducks, and notably dabbling ducks (Anatinae), are thought to be the main reservoir for AIV in the northern hemisphere (see Introduction), therefore we focused on duck species that were observed at the faecal collection sites: Australian Shelduck (Tadorna tadornoides)

[3.5 % of all birds recorded], Pacific Black Duck (Anas superciliosa) [32.6 % of all birds recorded], Pink-eared Duck (Malacorhynchus membranaceus) [5.8 % of all birds recorded] and Teal spp [33.7 % of all birds recorded]. As teal were observed as mixed flocks of Grey

(Anas gracilis) and Chestnut Teal (Anas castanea), both species were included in the analysis. Of these five species, two are not dabbling ducks (Australian Shelduck and Pink- eared Duck); however, these species show many similarities to dabbling ducks in their ecology, and oropharyngeal/cloacal samples from these two species have also regularly tested positive for AIV in other projects in Australia (Grillo et al. 2015). These five selected species contributed 70.58 % of the duck population at WTP at any one point in time. The

Australasian Shoveler (Anas rhynchotis) was also frequently observed in the area, but was excluded from analysis as it was rarely observed roosting at the locations where faecal

samples were collected (W. Steele pers. comm.), thus samples collected are unlikely to be from Australian Shovelers. Of the bird species considered, Grey Teal and Pink-eared Duck breed mainly on ephemeral wetlands in inland Australia whereas the other three have substantial local breeding populations in south-eastern Australia (Marchant and Higgins

1990, Norman and Nicholls 1991, Kingsford and Norman 2002). These differences produce contrasting patterns of variation in numbers of birds at permanent wetlands in south-eastern

Australia (Chambers and Loyn 2006, Loyn et al. 2014a). To allow matching of bird count and

AIV data, we used linear interpolation of the bird count data for the five species to obtain monthly bird counts.

Weather data

Monthly rainfall and temperature (anomaly) data were obtained from the Australian

Bureau of Meteorology for four geographic regions of progressively increasing size: WTP

(105 km2), Victoria (VIC) (227 600 km2), South-eastern Australia (SE) (723 333 km2) and

Murray-Darling Basin (MDB) (1 056 450 km2) (Figure 1). For all regions except WTP, anomalies were provided online and were calculated as departures from the 1961-1990 average reference values (Meteorology.). For WTP we calculated anomalies from downloaded rainfall and temperature data using departures from the reference values in VIC

(1961-1990) as rainfall and temperature data were not available for all years at WTP between

1961 and 1990.

The first three years of our study occurred during a period of drought, and the last four years during a wet period. To investigate the effect of these long-term, ENSO driven

weather effects, we included an additional “ENSO drought/wet” factor in our models, which was either “dry” (2006-2009) or “wet” (2010-2012).

Figure 1: Geographic regions. Monthly rainfall and temperature anomalies were calculated for the following geographic regions: Western Treatment Plant, Victoria, South-eastern

Australia and Murray-Darling Basin. Adapted from Australian Bureau of Meteorology.

Statistical analysis

Effects of biotic drivers (bird numbers) on AIV prevalence

To examine the effects of bird numbers (i.e. bird count data) on AIV prevalence, we used generalized linear models (GLM) weighted for total sample size (i.e. number of AIV samples collected at each collection event). A total of five GLMs were run, one for each

species, with monthly AIV prevalence as the binomial response variable and bird numbers as the explanatory variable.

Effects of abiotic drivers (weather) on AIV prevalence

The effects of weather are not always immediately expressed in ecological processes, thus there may be a cumulative effect and a ‘time-lag’ between changes in the weather and

AIV prevalence (Halse and Jaensch 1989b, Letnic and Dickman 2006). To investigate these cumulative and time-lag effects of rainfall and temperature on monthly AIV prevalence we calculated the average rainfall and temperature anomalies over the same month in which the prevalence estimate took place, and the preceding one to twelve months. Thus each monthly

AIV prevalence estimate was compared with the average rainfall and temperature data from the same month and the preceding month, the same month and the preceding two months, same month and preceding three months, etc. up to twelve months. These twelve ‘time-lag classes’ for rainfall and temperature anomalies were calculated for all four geographic regions. We tested for effects of rainfall anomalies, temperature anomalies, and “ENSO drought/wet” factor on AIV prevalence using GLMs. A total of 48 GLMs were run for all possible combinations of regions (4) and time-lag class (12) weighted for total sample size

(i.e. number of AIV samples collected at each collection event). Monthly AIV prevalence as the binomial response variable was analyzed in relation to rainfall anomaly, temperature anomaly and “ENSO drought/wet” factor as explanatory variables. Within each region the best fitting model(s) among the 12 time-lag classes were selected based on their Akaike information criterion (i.e. lowest AIC value as best fit and ΔAIC < 2) (Snipes and Taylor

2014). Combining the above analyses in a single GLM would result in an over- parameterization of the models and result in spurious outcomes.

Interaction of biotic and abiotic drivers affecting AIV prevalence

To understand how bird numbers and weather data might potentially interact in ultimately explaining AIV prevalence, we analyzed the relationship between bird counts and

(time-lagged) weather data in a similar fashion as outlined above using linear models (LM).

A total of 240 LMs were run for all possible combinations of geographic regions (4), species

(5), and time-lag class (12), in which monthly bird counts as the response variable were analyzed in relation to rainfall anomaly, temperature anomaly and “ENSO drought/wet” factor as explanatory variables. Within each region and species, the best fitting model(s) were selected based on their AIC value (Snipes and Taylor 2014), and the significance level of explanatory variables. Selected models had to have a ΔAIC < 2 and at least one of the explanatory variables had to be significant (i.e. p < 0.05).

We ran the above three clusters of models rather than running full-factorial models, combining all possible combinations of abiotic and biotic factors to explain viral prevalence, since the latter resulted in over-parameterization and underidentified models (Green et al.

1999). We Z-transformed all variables prior to statistical analyses to allow appropriate comparison of the effect sizes (in terms of odds ratios) of the explanatory variables. After

GLM and LM analyses, effect sizes of the parameter estimates were calculated as odds ratios

(OR) of the Z-transformed explanatory variables. An OR > 1 indicates a positive, whereas an

OR < 1 indicates a negative effect of the explanatory variable. All analyses were conducted using R (RDevelopment 2008).

RESULTS

A total of 3,295 pooled faecal samples were collected, of which 179 (5.43 %) tested positive for AIV. Over the seven year period AIV prevalence varied greatly, without any apparent seasonal pattern (explicit testing of seasonality was not prudent given the many time gaps in the data; Figure 2). Bird numbers also fluctuated greatly, with some species showing clear seasonal variation in numbers (termed ‘seasonal’ species such as Chestnut Teal,

Australian Shelduck and Pacific Black Duck) and others less so (termed ‘non-seasonal’ species such as Grey Teal and Pink-eared Duck) (Figure 2). The ‘seasonal’ species have substantial breeding populations in temperate south-eastern Australia in permanent wetlands, whereas the ‘non-seasonal’ species breed mainly inland where wetland availability is more erratic (Marchant and Higgins 1990). Rainfall and temperature data also showed high variability between years. In particular, rainfall increased in amount and frequency at the beginning of 2010 when the drought from the previous 10 years broke (Figure 2).

Effects of biotic drivers (bird numbers) on AIV prevalence

The relationship between AIV prevalence and bird numbers varied among the five bird species (Table 1). AIV prevalence was significantly positively related to Australian

Shelduck numbers, significantly negatively related to Grey Teal and Pink-eared Duck, and not correlated with Chestnut Teal and Pacific Black Duck numbers (Table 1).

Figure 2: Bird numbers of five waterfowl species, rainfall anomaly and temperature anomaly for four geographic regions, and AIV prevalence with 95% confidence intervals between

2006 and 2012. The data is subdivided into a drought (2006-2009) and wet (2010-2012) period. The bird species for which data are presented are Chestnut Teal (CT), Australian

Shelduck (ASD), Pacific Black Duck (PBD), Grey Teal (GT) and Pink-eared Duck (PED).

The four geographic regions are Western Treatment Plant (WTP), Victoria (VIC), South- eastern Australia (SE) and Murray-Darling Basin (MDB).

Species Seasonality (breeding area) Species number OR AIC

Australian Shelduck seasonal (south-eastern 1.91*** 322.11 Australia)

Chestnut Teal seasonal (south-eastern 1.09 383.60 Australia)

Grey Teal non-seasonal (inland) 0.40*** 272.49

Pacific Black Duck seasonal (south-eastern 0.94 384.07 Australia)

Pink-eared Duck non-seasonal (inland) 0.56*** 316.13

Table 1: Odds ratios (OR) and AICs for the best fitting generalized linear models of the effects of bird number of five waterfowl species on AIV prevalence. An OR > 1 indicates a positive, whereas an OR < 1 indicates a negative effect of the explanatory variable (i.e. OR > 1 means that AIV prevalence was greater when bird numbers were higher and OR < 1 means that AIV prevalence was greater when bird numbers were lower). Stars indicate significance level: ***

= p < 0.001.

Effects of abiotic drivers (weather) on AIV prevalence

Out of the 48 models that examined AIV prevalence in relation to rainfall anomalies,

temperature anomalies and “ENSO drought/wet” factor for the various regions and time-lag

classes, seven models were found to have a ΔAIC < 2. In these seven best models, rainfall

anomalies had a significant and substantial positive effect on AIV prevalence (Table 2). This

significant result also held true for most of the remaining models (OR: 1.43 – 2.48;

(Appendix S1)). Five of the seven best models in WTP, VIC and SE also showed a

significant positive effect of “ENSO drought/wet” factor on prevalence, wet years being

associated with a higher AIV prevalence (Figure 2, Table 2). This also held true for 18 of the remaining models (Appendix S1).

Temperature anomaly had a significant positive effect on AIV prevalence in only one of the seven best models (Table 2). In the remaining models, the effect of temperature anomalies on AIV prevalence had a mix of significant positive and negative effects (Appendix S1).

Investigating the time-lag classes of the models, we found that environmental conditions averaged over the preceding three months to preceding seven months provided the best model fits (Table 2).

Time-lag Region Rainfall anomaly Temperature “ENSO AIC class OR anomaly OR drought/wet” OR

3 WTP 1.55*** 0.88 1.49*** 228.27

5 VIC 1.67*** 0.97 1.35** 231.33

4 SE 1.59*** 0.88 1.37** 234.69

5 SE 1.64*** 0.95 1.36** 234.16

6 SE 1.74*** 1.07 1.32* 233.32

7 SE 1.80*** 1.04 1.23 235.01

6 MDB 2.48*** 1.34* 1.08 213.16

Table 2: Odds ratios (OR) and AICs for the best fitting generalized linear models describing the effects of rainfall anomaly, temperature anomaly and “ENSO drought/wet” factor on AIV prevalence. An OR > 1 indicates a positive, whereas an OR < 1 indicates a negative effect of the explanatory variable (e.g. OR > 1 means that AIV prevalence was greater when rainfall anomaly was higher and OR < 1 means that AIV prevalence was greater when rainfall anomaly was lower). Regions: WTP (Western Treatment Plant), Victoria (VIC), South- eastern Australia (SE) and Murray-Darling Basin (MDB). Stars indicate significance levels:

*** = p < 0.001; ** = p < 0.01; * = p < 0.05.

Interaction of biotic and abiotic drivers affecting AIV prevalence

The positive effect of these short and long-term rainfall patterns on AIV prevalence described above is likely mediated through a complex interaction with bird numbers. We found that numbers of ‘seasonal’ species that breed in south-eastern Australia were either positively (Australian Shelduck), or not (Chestnut Teal and Pacific Black Duck) related to

AIV prevalence (Table 1). Numbers of these duck species did not show any (consistent) patterns in numbers in relation to climatic variables (Table 3, Appendix S2). ‘Seasonal’ bird numbers were not affected by “ENSO drought/wet” factor and mostly unaffected by rainfall anomalies (with positive and negative effects in only a few cases). Temperature anomalies had mainly mixed positive and negative effects on ‘seasonal’ bird numbers (Table 3,

Appendix S2). These results were apparent across all time-lag classes (i.e. all 240 models tested; Appendix S2), including the selected best models (Table 3).

The ‘non-seasonal’ species (i.e. Grey Teal and Pink-eared Duck) that tend to move long distances and breed inland, were negatively related to AIV prevalence (Table 1). Duck numbers were negatively related to rainfall and temperature (Table 3, Appendix S2), with numbers at the WTP dropping when the wet period started (2010), suggesting they indeed left the area to breed inland (see Figure 2). For the ‘seasonal’ species, the best models were apparent across almost all time-lag classes, yet the best models for ‘non-seasonal’ species were restricted to nine, eleven and twelve month time-lag classes (i.e. conditions averaged over the preceding 9, 11 and 12 months; see Table 3).

An integrated summary of the results is presented in a schematic diagram in Figure 3, including an interpretation of the correlations found into possible direct and indirect effects.

Figure 3: Diagram summarizing the main relationships between climate factors, bird numbers and AIV prevalence. Arrows show the direction of the effects, including potential time-lags. The colour of the arrows indicates the direction of the correlations and whether the relationship is direct or indirect (white – direct negative effect; grey - mixed direct effects

[positive effect of one species and no effects for the two other species]; black - positive indirect effect). The close link between rainfall and ENSO drought/wet is reflected by their partial overlap in the diagram.

Time- lag Region Species Rainfall Temperature “ENSO AIC class anomaly OR anomaly OR drought/wet” OR

3 WTP Chestnut Teal 1.23 0.65* 0.83 95.25

4 WTP Chestnut Teal 1.22 0.63** 0.82 94.06

6 VIC Chestnut Teal 1.03 1.45* 1.02 100.44

7 VIC Chestnut Teal 0.97 1.48* 1.07 99.94

8 VIC Chestnut Teal 0.85 1.44* 1.17 100.34

- SE Chestnut Teal - - - -

12 MDB Chestnut Teal 0.49* 0.60* 1.40 99.68

1 WTP Australian Shelduck 1.09 0.64* 1.11 95.18

2 WTP Australian Shelduck 1.27 0.70* 1.04 95.20

3 WTP Australian Shelduck 1.25 0.69* 1.05 94.75

- VIC Australian Shelduck - - - -

- SE Australian Shelduck - - - -

4 MDB Australian Shelduck 1.94* 1.65* 0.99 96.13

5 MDB Australian Shelduck 1.81* 1.74* 1.05 95.18

4 WTP Pacific Black Duck 1.12 0.63* 0.85 96.20

5 WTP Pacific Black Duck 1.26 0.68* 0.81 96.24

6 WTP Pacific Black Duck 1.27 0.67* 0.81 95.87

6 VIC Pacific Black Duck 1.01 1.48* 1.02 99.89

7 VIC Pacific Black Duck 0.90 1.53* 1.11 98.52

8 VIC Pacific Black Duck 0.77 1.49* 1.24 98.63

- SE Pacific Black Duck - - - -

1 MDB Pacific Black Duck 0.77 0.58** 1.00 95.08

2 MDB Pacific Black Duck 0.83 0.56** 0.94 95.08

12 WTP Grey Teal 0.81 0.52*** 0.73 76.85

11 VIC Grey Teal 0.63 0.61** 0.97 84.59

12 VIC Grey Teal 0.74 0.58*** 0.85 83.29

11 SE Grey Teal 0.57* 0.60*** 0.99 84.38

12 SE Grey Teal 0.65 0.56*** 0.86 82.86

11 MDB Grey Teal 0.34*** 0.51*** 1.23 83.39

12 MDB Grey Teal 0.37*** 0.48*** 1.10 84.71

11 WTP Pink-eared Duck 0.60* 0.60*** 0.96 80.33

9 VIC Pink-eared Duck 0.42*** 0.66*** 1.32 72.29

9 SE Pink-eared Duck 0.38*** 0.70** 1.40 74.99

9 MDB Pink-eared Duck 0.32*** 0.69* 1.44 79.13

Table 3: Odds ratios (OR) and AICs for the best fitting linear models describing the effects of

rainfall anomaly, temperature anomaly and “ENSO drought/wet” factor on bird number of

five waterfowl species. An OR > 1 indicates a positive, whereas an OR < 1 indicates a

negative effect of the explanatory variable (e.g. OR > 1 means that bird numbers were higher

when rainfall anomaly was higher and OR < 1 means that bird numbers were higher when

rainfall anomaly was lower). Regions: WTP (Western Treatment Plant), Victoria (VIC),

South-eastern Australia (SE) and Murray-Darling Basin (MDB). Stars indicate significance

levels: *** = p < 0.001; ** = p < 0.01; * = p < 0.05. Empty cells indicate that the models

were not significant for that region and species, thus best models could not been selected.

DISCUSSION

We found AIV prevalence among wild dabbling ducks to be related to rainfall patterns. This was apparent at two temporal scales: as a positive effect of long term rainfall patterns (“ENSO drought/wet” factor) that was linked to the wet period 2010 – 2012, and as a more immediate positive response (albeit with time-lag effects of three to seven months; i.e. best results were obtained when considering rainfall in the preceding 3-7 months). ENSO and rainfall in SE Australia are closely linked (Norman and Nicholls 1991) and the apparent

“ENSO drought/wet” effect reinforces the importance of the indirect effect of rainfall on AIV dynamics rather, than an effect of ENSO per se. The occasional lack of an ENSO drought/wet effect on AIV prevalence at larger geographical scales (i.e. MDB and SE) is probably due to

ENSO’s effects being already explained by rainfall anomalies. AIV prevalence showed a clear positive relationship with duck numbers for only one ‘seasonal’ species (Australian

Shelduck) and was not related to numbers of the remaining two ‘seasonal’ species (Chestnut

Teal and Pacific Black Duck). Numbers of both ‘non-seasonal’ duck species (Grey Teal and

Pink-eared Duck) negatively affected AIV prevalence, thus AIV prevalence at the WTP was highest when these species were away breeding (i.e. when their numbers were low at the

WTP). As discussed below, rainfall patterns importantly determine breeding opportunities and are therefore linked to bird numbers (Loyn et al. 2014a). Thus rainfall can influence age structures within the duck community, which may subsequently affect AIV dynamics.

However, we cannot rule out that rainfall may also have a direct effect on AIV dynamics, as

AIV is generally highly persistent in water (Stallknecht et al. 1990).

In contrast to the northern hemisphere, a determinative feature of the southern hemisphere climate is the ENSO linked irregularity in both timing and location of wet and

dry periods (Kousky et al. 1984, Kingsford and Norman 2002, Kingsford et al. 2010). These erratic climate patterns may relax seasonality in breeding, where reproduction occurs during periods of higher rainfall and associated increases in food availability (Halse and Jaensch

1989a, Marchant and Higgins 1990, Briggs 1992). For some ‘seasonal’ Australian ducks this means that the annual time window within which breeding can take place is much wider in wet years than what is typically found in northern hemisphere birds (5-7 vs 3 months respectively) (e.g. Pacific Black Duck and Chestnut Teal (Marchant and Higgins 1990)). For other ‘non-seasonal’ species breeding may be completely opportunistic, and take place at any time of the year after intense rainfall, with multiple broods per breeding season possible (e.g.

Grey Teal and Pink-eared Duck (Marchant and Higgins 1990)). Pink-eared Ducks may begin to breed 8-28 days after intense rainfall (Marchant and Higgins 1990). Gonadal development in Grey Teal takes place ~ 60 days prior to breeding after intense rainfall (Braithwaite 1969,

Braithwaite 1976). In our data, the relationship with rainfall for these ‘non-seasonal’, inland breeding ducks is evident. However, the best models indicate that their numbers decrease at the WTP in relation to rainfall averaged over the preceding 9 months to preceding 12 months after inland rain (Table 3), with the remaining models also showing significant results with rainfall averaged over the preceding 2 to preceding12 months (Table S2).

We included temperature in our analysis as in the northern hemisphere AIV prevalence has been shown to increase during the colder months (Park and Glass 2007) and

AIV survival has generally been shown to be negatively related to temperature (Brown et al.

2007). However, we found no support for such an effect in our data; AIV prevalence was largely unrelated or, in one case only, positively rather than negatively related to temperature.

However, we did find a negative relationship between temperature and ‘non-seasonal’ bird numbers, suggesting that at higher temperature (with 9-11 month time-lag effects) ‘non- seasonal’ duck species leave the coastal area and travel inland to breed. Although not as

strong as the effect of rainfall (which may increase nutrient input into wetlands and also has a strong positive effect on total wetland area (for MDB during the period of our study r =0.68)

(Kingsford et al. 2013), relatively high temperatures may boost wetland primary productivity and thereby improve conditions for breeding (Serventy and Marshall 1957).

Our results indicate that AIV dynamics are not simply a function of bird numbers.

This is notably true for the ‘non-seasonal’ species (Grey Teal and Pink-eared Duck), which show a negative relationship between AIV prevalence and bird numbers. The negative relationship between AIV prevalence and ‘non-seasonal’ bird numbers overlapping in time

(i.e. overlapping time-lags) with the positive relationship between rainfall and AIV prevalence suggests that the highest AIV prevalence might not be observed when numbers of

‘non-seasonal’ species are at their highest. As we discuss below, age-structure is a key element of our hypothesis and the bird numbers per se do not reflect the ratio of adults and juveniles. This may be the result of different arrival time of juveniles and adults in the area

(i.e. after breeding, inland juveniles return earlier, while adults remain inland to have second clutches). In the northern hemisphere juvenile birds have been identified as possible drivers for AIV, having higher virus isolation frequency than adults (Hinshaw et al. 1985). Van Dijk et al. (2013) also showed that the AIV peak in a Mallard (Anas platyrhynchos) population was driven by juvenile birds in the summer as they shed more viruses and also antibodies against AIV were barely detectable in their blood, in contrast to adults. Although we lack data on the proportion of juveniles in the monitored populations, we suggest that an influx of juveniles that arrive from inland areas together with more locally hatched juvenile birds, is the likely a driver of AIV prevalence dynamics at our study site. This would be consistent with the time-lag of three to seven months between rainfall and AIV prevalence. Both Pink- eared Duck and Grey Teal have a ~ 26-28 day incubation period and typically require ~ 55 days to complete total body and feather growth of ducklings (Marchant and Higgins 1990).

Thus the first juvenile birds might be expected to arrive at the WTP three to five months after significant rainfall. In other waterfowl species, for example Pacific Black Duck and

Australian Shelduck, breeding is also related to rainfall. In these species there is typically a

60 – 90 day time-lag between intense rainfall and onset of laying (Halse and Jaensch 1989a), yielding increased juvenile numbers four to six months after rainfall. In summary, for all duck species combined, following rainfall it takes three to six months before fully grown juvenile birds appear in the population. This coincides with rainfall calculated over the preceding three to seven months being positively related to AIV prevalence in our data.

The positive relationship between resident Australian Shelduck numbers and AIV prevalence is possibly related to the increase in juveniles along with total numbers for this species. The negative relationship between numbers of inland breeding species and AIV prevalence possibly indicates that adults may remain breeding in inland areas engaging in multiple brooding, while birds present at WTP are mainly juveniles that are unlikely to breed in their first year. Young birds are known to form high proportions of the Victorian duck population in wet years (up to 80%, usually 50% or 0% in dry years) (Loyn 1989, 1991).

Unfortunately, no data are available on juvenile percentages in the various duck populations at the WTP, necessary to confirm our hypothesis.

Analyses of AIV dynamics in other areas of the world characterized by erratic climatic conditions are, to our knowledge, not available. Several studies, however, have highlighted the importance of climate variability in driving infectious disease prevalence in humans, domestic animals, and wildlife (Epstein 2001, Kovats et al. 2003, Anyamba et al.

2006). Viral disease outbreak cases have also been linked to ENSO driven weather anomalies

(Kovats et al. 2003). In some cases, the outbreaks were associated with drought conditions

(e.g. dengue fever, Watts et al. 1986) while in others heavy rains triggered elevated disease risk (e.g. West Nile virus) (Landesman et al. 2007). In Australia, studies focusing on viral

diseases also found strong links between rainfall patterns and disease risk. In south-eastern

Australia, heavy rainfall in summer and autumn increased disease risk of Murray Valley encephalitis (Nicholls 1986). Similarly, in south-eastern Australia Ross river viral disease was related to high summer and winter rainfalls (Woodruff et al. 2002). All of the above mentioned viruses are arboviruses for which mosquitos act as a vector, their ecology being directly related to weather factors, notably precipitation (Woodruff et al. 2002). Yet, besides breeding and survival of arthropod vectors, the population dynamics of potential host mammals and birds are also affected by ENSO driven weather anomalies (Halse and Jaensch

1989a, Woodruff et al. 2002, Letnic and Dickman 2006). Such climate driven changes in host population numbers, age structures and body condition may also play a role in the temporal patterns in disease dynamics (Plowright et al. 2008, Tabachnick 2010). This parallels our suggestion that intense rainfall events affect the breeding ecology of waterbirds and concomitantly AIV prevalence.

Our study highlights the importance of investigating disease dynamics in various regions of the world with contrasting climatic conditions. Such a comparative approach will allow us to better identify the role of hypothesized drivers. In addition, and notably for systems regularly experiencing extreme weather events, such studies may allow for an improved evaluation of the consequences of climate change on disease dynamics (Anyamba et al. 2006).

ACKNOWLEDGEMENTS

We thank Melbourne Water and Arthur Rylah Institute for Environmental Research for collecting and providing the waterbird count data and the Department of Economic

Development for collecting and analysing the AIV data. The project has been funded by the

National Institute of Allergy and Infectious Diseases, National Institutes of Health,

Department of Health and Human Services, under Contract No. HHSN266200400041C. The study was also supported by the Australian Research Council (DP 130101935). This works was funded in part by the National Avian Influenza Surveillance Program, which receives from the Australian Government Department of Agriculture and Water Resources and is administered by Wildlife Health Australia. We thank all individuals, organisations and laboratories which contributed to this analysis / research by providing logistic support, expertise and collection, analysis and submission of wild bird influenza virus data to the

NAIWB Surveillance Program. We thank Robert Swindley for his contribution of collecting the waterbird count data and Kasey Stamation for managing the waterfowl database. William

Steele commissioned the waterbird counts and made suggestions on the earlier version of the manuscript. We thank David Roshier for building the various collaborative relationships at the beginning of the project and his comments on the manuscript. Additional, much appreciated comments on the manuscript were received from Meijuan Zhao and Simeon

Lisovski. Peter Biro assisted with the statistical analysis. Australian Bureau of Meteorology is also thanked for providing all the weather data for analysis.

APPENDIX

Time lag Region Rainfall Temperature ENSO AIC class anomaly OR anomaly OR OR 1 WTP 1.15 0.74** 1.92*** 252.90 2 WTP 1.43*** 0.79* 1.63*** 234.89 3 WTP 1.55*** 0.88 1.49*** 228.27 4 WTP 1.61*** 0.95 1.45*** 231.59 5 WTP 1.94*** 1.15 1.27 230.74 6 WTP 1.89*** 1.27* 1.32* 238.80 7 WTP 1.94*** 1.21* 1.23 236.90 8 WTP 1.84*** 1.20* 1.25 241.84 9 WTP 1.80 1.23 1.27 246.03 10 WTP 1.76*** 1.28** 1.31* 244.87 11 WTP 1.59*** 1.26** 1.45** 253.35 12 WTP 1.48*** 1.20* 1.59*** 259.66 1 VIC 1.55*** 0.93 1.52*** 239.71 2 VIC 1.55*** 0.91 1.49*** 242.71 3 VIC 1.58*** 0.92 1.43** 238.21 4 VIC 1.55*** 0.89 1.43** 236.84 5 VIC 1.67*** 0.97 1.35** 231.33 6 VIC 1.66*** 1.06 1.36** 237.03 7 VIC 1.75*** 1.06 1.26 237.07 8 VIC 1.80*** 1.06 1.22 240.00 9 VIC 1.83*** 1.04 1.20 244.34 10 VIC 1.75*** 1.08 1.26 249.86 11 VIC 1.70*** 1.12 1.31* 250.96 12 VIC 1.70*** 1.17 1.34* 251.18 1 SE 1.64*** 0.88 1.42** 236.42 2 SE 1.55*** 0.87 1.43** 242.43 3 SE 1.60*** 0.89 1.38** 237.08 4 SE 1.59*** 0.88 1.37** 234.69 5 SE 1.64*** 0.95 1.36** 234.16 6 SE 1.74*** 1.07 1.32* 233.32 7 SE 1.80*** 1.04 1.23 235.01 8 SE 1.91*** 1.05 1.16 237.31 9 SE 1.84*** 1.01 1.19 244.04 10 SE 1.81*** 1.04 1.22 248.76 11 SE 1.85*** 1.11 1.22 247.44

12 SE 1.79*** 1.20* 1.30* 250.85 1 MDB 1.97*** 0.79* 1.13 218.37 2 MDB 1.94*** 0.78* 1.07 218.61 3 MDB 1.96*** 0.92 1.13 222.39 4 MDB 2.06*** 1.04 1.14 223.92 5 MDB 1.88*** 1.01 1.23 228.17 6 MDB 2.48*** 1.34* 1.08 213.16 7 MDB 2.29*** 1.20 1.09 220.62 8 MDB 2.11*** 1.08 1.12 229.31 9 MDB 2.03*** 1.06 1.15 234.09 10 MDB 2.22*** 1.11 1.07 229.20 11 MDB 2.30*** 1.24 1.09 231.42 12 MDB 2.14*** 1.30* 1.23 243.63

S1: Odds ratios (OR) and AICs for generalized linear models of the effects of rainfall anomaly, temperature anomaly and “ENSO drought/wet” factor on AIV prevalence. An OR

> 1 indicates a positive, whereas an OR < 1 indicates a negative effect of the explanatory variable (e.g. OR > 1 means that AIV prevalence was greater when rainfall anomaly was higher and OR < 1 means that AIV prevalence was higher when rainfall anomaly was lower).

Regions: WTP (Western Treatment Plant), Victoria (VIC), South-eastern Australia (SE) and

Murray-Darling Basin (MDB). Stars indicate the difference between the significance levels:

*** = p < 0.001; ** = p < 0.01; * = p < 0.05. Bold numbers indicate the best fitting models

(ΔAIC < 2).

Time Region Species Rainfall Temperatur ENS AIC lag anomaly e anomaly O class OR OR OR

1 WTP Chestnut Teal 0.92 0.77 1.01 103.29 2 WTP Chestnut Teal 1.09 0.69* 0.91 99.73 3 WTP Chestnut Teal 1.23 0.65* 0.83 95.25 4 WTP Chestnut Teal 1.22 0.63** 0.82 94.06 5 WTP Chestnut Teal 1.28 0.69* 0.83 96.65 6 WTP Chestnut Teal 1.25 0.70 0.84 98.03 7 WTP Chestnut Teal 1.37 0.81 0.80 101.38 8 WTP Chestnut Teal 1.34 0.94 0.81 103.99 9 WTP Chestnut Teal 1.04 1.00 0.99 105.45 10 WTP Chestnut Teal 0.86 1.05 1.15 105.11 11 WTP Chestnut Teal 0.71 1.00 1.32 103.97 12 WTP Chestnut Teal 0.85 0.90 1.13 104.72 1 VIC Chestnut Teal 1.01 0.99 1.00 105.46 2 VIC Chestnut Teal 1.16 1.03 0.92 105.01 3 VIC Chestnut Teal 1.27 1.15 0.88 103.58 4 VIC Chestnut Teal 1.14 1.27 0.96 102.99 5 VIC Chestnut Teal 1.10 1.40 0.97 101.15 6 VIC Chestnut Teal 1.03 1.45* 1.02 100.44 7 VIC Chestnut Teal 0.97 1.48* 1.07 99.94 8 VIC Chestnut Teal 0.85 1.44* 1.17 100.34 9 VIC Chestnut Teal 0.72 1.28 1.34 102.22 10 VIC Chestnut Teal 0.65 1.13 1.45 102.90 11 VIC Chestnut Teal 0.63 0.98 1.48 102.78 12 VIC Chestnut Teal 0.72 0.90 1.31 103.73 1 SE Chestnut Teal 1.06 0.88 0.95 104.87 2 SE Chestnut Teal 1.25 0.91 0.86 104.05 3 SE Chestnut Teal 1.39 1.03 0.82 103.39 4 SE Chestnut Teal 1.29 1.14 0.88 103.82 5 SE Chestnut Teal 1.25 1.31 0.92 102.44 6 SE Chestnut Teal 1.19 1.37 0.97 101.70 7 SE Chestnut Teal 1.09 1.40 1.02 101.38 8 SE Chestnut Teal 0.95 1.37 1.12 102.02 9 SE Chestnut Teal 0.81 1.20 1.25 103.85 10 SE Chestnut Teal 0.70 1.04 1.36 104.05 11 SE Chestnut Teal 0.65 0.90 1.42 103.07 12 SE Chestnut Teal 0.73 0.82 1.26 103.26

1 MDB Chestnut Teal 0.91 0.71 0.96 101.69 2 MDB Chestnut Teal 1.06 0.71 0.87 101.16 3 MDB Chestnut Teal 1.24 0.79 0.80 101.53 4 MDB Chestnut Teal 1.41 0.95 0.78 102.70 5 MDB Chestnut Teal 1.74 1.26 0.76 101.37 6 MDB Chestnut Teal 1.72 1.35 0.80 100.88 7 MDB Chestnut Teal 1.62 1.37 0.81 101.51 8 MDB Chestnut Teal 1.51 1.35 0.82 102.62 9 MDB Chestnut Teal 1.24 1.14 0.90 104.93 10 MDB Chestnut Teal 0.84 0.90 1.11 105.11 11 MDB Chestnut Teal 0.58 0.69 1.33 102.15 12 MDB Chestnut Teal 0.49* 0.60* 1.40 99.68 1 WTP Australian Shelduck 1.09 0.64* 1.11 95.18 2 WTP Australian Shelduck 1.27 0.70* 1.04 95.20 3 WTP Australian Shelduck 1.25 0.69* 1.05 94.75 4 WTP Australian Shelduck 1.22 0.75 1.08 98.10 5 WTP Australian Shelduck 1.13 0.86 1.16 101.87 6 WTP Australian Shelduck 1.09 0.93 1.20 102.84 7 WTP Australian Shelduck 1.09 0.98 1.20 103.16 8 WTP Australian Shelduck 0.96 1.06 1.32 103.18 9 WTP Australian Shelduck 0.77 1.09 1.58 102.21 10 WTP Australian Shelduck 0.71 1.04 1.67 101.67 11 WTP Australian Shelduck 0.67 0.96 1.74 101.00 12 WTP Australian Shelduck 0.79 0.90 1.51 102.12 1 VIC Australian Shelduck 1.28 1.19 1.14 100.66 2 VIC Australian Shelduck 1.30 1.37 1.11 97.94 3 VIC Australian Shelduck 1.26 1.33 1.13 98.80 4 VIC Australian Shelduck 1.13 1.29 1.22 100.41 5 VIC Australian Shelduck 1.06 1.29 1.25 100.77 6 VIC Australian Shelduck 1.04 1.24 1.27 101.58 7 VIC Australian Shelduck 1.05 1.14 1.25 102.63 8 VIC Australian Shelduck 0.93 1.07 1.35 103.11 9 VIC Australian Shelduck 0.81 1.00 1.51 102.78 10 VIC Australian Shelduck 0.74 0.93 1.63 101.87 11 VIC Australian Shelduck 0.72 0.95 1.66 101.88 12 VIC Australian Shelduck 0.80 1.03 1.54 102.69 1 SE Australian Shelduck 1.24 1.19 1.16 101.38 2 SE Australian Shelduck 1.34 1.37 1.11 98.77 3 SE Australian Shelduck 1.19 1.36 1.23 99.74 4 SE Australian Shelduck 1.19 1.36 1.23 99.74 5 SE Australian Shelduck 1.09 1.37 1.28 99.77 6 SE Australian Shelduck 1.08 1.32 1.29 100.54

7 SE Australian Shelduck 1.05 1.20 1.28 102.09 8 SE Australian Shelduck 0.90 1.10 1.41 102.83 9 SE Australian Shelduck 0.79 1.02 1.56 102.61 10 SE Australian Shelduck 0.71 0.95 1.68 101.83 11 SE Australian Shelduck 0.69 0.96 1.71 101.75 12 SE Australian Shelduck 0.80 1.04 1.55 102.67 1 MDB Australian Shelduck 1.39 1.05 1.04 101.28 2 MDB Australian Shelduck 1.65 1.28 0.97 99.15 3 MDB Australian Shelduck 1.80 1.42 0.97 97.82 4 MDB Australian Shelduck 1.94* 1.65* 0.99 96.13 5 MDB Australian Shelduck 1.81* 1.74* 1.05 95.18 6 MDB Australian Shelduck 1.69 1.69 1.11 95.46 7 MDB Australian Shelduck 1.48 1.44 1.11 99.35 8 MDB Australian Shelduck 1.20 1.22 1.20 102.33 9 MDB Australian Shelduck 0.97 1.05 1.33 103.20 10 MDB Australian Shelduck 0.81 0.94 1.48 102.85 11 MDB Australian Shelduck 0.76 0.92 1.54 102.61 12 MDB Australian Shelduck 0.83 1.01 1.49 102.75 1 WTP Pacific Black Duck 0.84 0.87 1.07 104.35 2 WTP Pacific Black Duck 0.93 0.73 0.99 102.30 3 WTP Pacific Black Duck 1.09 0.66* 0.88 98.42 4 WTP Pacific Black Duck 1.12 0.63* 0.85 96.20 5 WTP Pacific Black Duck 1.26 0.68* 0.81 96.24 6 WTP Pacific Black Duck 1.27 0.67* 0.81 95.87 7 WTP Pacific Black Duck 1.36 0.75 0.78 99.81 8 WTP Pacific Black Duck 1.34 0.86 0.79 103.36 9 WTP Pacific Black Duck 1.02 0.95 0.97 105.37 10 WTP Pacific Black Duck 0.86 1.02 1.11 105.18 11 WTP Pacific Black Duck 0.69 1.02 1.34 103.60 12 WTP Pacific Black Duck 0.82 0.95 1.14 104.88 1 VIC Pacific Black Duck 0.98 0.84 0.98 104.39 2 VIC Pacific Black Duck 1.07 0.90 0.94 104.98 3 VIC Pacific Black Duck 1.17 1.04 0.90 104.89 4 VIC Pacific Black Duck 1.09 1.22 0.96 103.88 5 VIC Pacific Black Duck 1.08 1.39 0.96 101.45 6 VIC Pacific Black Duck 1.01 1.48* 1.02 99.89 7 VIC Pacific Black Duck 0.90 1.53* 1.11 98.52 8 VIC Pacific Black Duck 0.77 1.49* 1.24 98.63 9 VIC Pacific Black Duck 0.63 1.33 1.47 100.14 10 VIC Pacific Black Duck 0.59 1.17 1.55 101.36 11 VIC Pacific Black Duck 0.56 1.01 1.59 101.32 12 VIC Pacific Black Duck 0.65 0.92 1.40 102.77

1 SE Pacific Black Duck 1.00 0.73 0.92 102.02 2 SE Pacific Black Duck 1.13 0.77 0.87 102.56 3 SE Pacific Black Duck 1.26 0.89 0.83 103.70 4 SE Pacific Black Duck 1.23 1.04 0.88 104.66 5 SE Pacific Black Duck 1.21 1.24 0.91 103.49 6 SE Pacific Black Duck 1.15 1.34 0.96 102.27 7 SE Pacific Black Duck 1.01 1.42 1.06 101.27 8 SE Pacific Black Duck 0.85 1.39 1.20 101.37 9 SE Pacific Black Duck 0.70 1.22 1.37 102.67 10 SE Pacific Black Duck 0.63 1.06 1.47 102.93 11 SE Pacific Black Duck 0.58 0.91 1.52 101.92 12 SE Pacific Black Duck 0.64 0.82 1.36 102.17 1 MDB Pacific Black Duck 0.77 0.58** 1.00 95.08 2 MDB Pacific Black Duck 0.83 0.56** 0.94 95.08 3 MDB Pacific Black Duck 0.95 0.61* 0.86 98.32 4 MDB Pacific Black Duck 1.11 0.74 0.82 101.59 5 MDB Pacific Black Duck 1.44 1.01 0.78 102.97 6 MDB Pacific Black Duck 1.53 1.15 0.79 102.79 7 MDB Pacific Black Duck 1.46 1.23 0.82 103.29 8 MDB Pacific Black Duck 1.32 1.22 0.86 104.23 9 MDB Pacific Black Duck 1.06 1.03 0.96 105.44 10 MDB Pacific Black Duck 0.76 0.83 1.14 104.58 11 MDB Pacific Black Duck 0.55 0.67 1.33 101.46 12 MDB Pacific Black Duck 0.44* 0.56* 1.45 97.76 1 WTP Grey Teal 0.90 0.93 0.73 100.19 2 WTP Grey Teal 0.79 0.87 0.79 99.01 3 WTP Grey Teal 0.65 0.80 0.86 95.85 4 WTP Grey Teal 0.60* 0.79 0.91 93.71 5 WTP Grey Teal 0.50** 0.73 1.04 89.39 6 WTP Grey Teal 0.47*** 0.70* 1.09 86.41 7 WTP Grey Teal 0.45*** 0.74* 1.18 83.97 8 WTP Grey Teal 0.46*** 0.76 1.21 84.84 9 WTP Grey Teal 0.58* 0.69* 1.03 87.28 10 WTP Grey Teal 0.68 0.63** 0.90 84.91 11 WTP Grey Teal 0.67 0.56*** 0.89 80.14 12 WTP Grey Teal 0.81 0.52*** 0.73 76.85 1 VIC Grey Teal 0.61* 1.13 0.94 93.07 2 VIC Grey Teal 0.56** 1.03 1.01 91.36 3 VIC Grey Teal 0.56** 1.00 1.01 91.35 4 VIC Grey Teal 0.53** 0.97 1.03 89.58 5 VIC Grey Teal 0.53** 0.89 1.04 89.20 6 VIC Grey Teal 0.53** 0.83 1.04 88.55

7 VIC Grey Teal 0.49** 0.82 1.14 87.23 8 VIC Grey Teal 0.47** 0.79 1.21 86.40 9 VIC Grey Teal 0.51** 0.72* 1.17 86.38 10 VIC Grey Teal 0.57* 0.66** 1.05 86.00 11 VIC Grey Teal 0.63 0.61** 0.97 84.59 12 VIC Grey Teal 0.74 0.58*** 0.85 83.29 1 SE Grey Teal 0.61* 1.12 0.97 93.26 2 SE Grey Teal 0.55** 1.02 1.04 91.58 3 SE Grey Teal 0.56** 0.98 1.02 92.06 4 SE Grey Teal 0.53** 0.96 1.05 90.19 5 SE Grey Teal 0.53** 0.88 1.04 90.07 6 SE Grey Teal 0.53** 0.83 1.01 89.57 7 SE Grey Teal 0.50** 0.82 1.12 88.55 8 SE Grey Teal 0.48** 0.78 1.18 88.60 9 SE Grey Teal 0.50** 0.72* 1.15 87.81 10 SE Grey Teal 0.54* 0.66** 1.07 86.53 11 SE Grey Teal 0.57* 0.60*** 0.99 84.38 12 SE Grey Teal 0.65 0.56*** 0.86 82.86 1 MDB Grey Teal 0.65* 1.22 1.00 93.55 2 MDB Grey Teal 0.56* 1.10 1.08 91.54 3 MDB Grey Teal 0.56* 1.02 1.04 92.22 4 MDB Grey Teal 0.48** 0.91 1.11 89.66 5 MDB Grey Teal 0.51* 0.87 1.04 91.88 6 MDB Grey Teal 0.52** 0.82 0.99 92.23 7 MDB Grey Teal 0.48** 0.81 1.08 91.14 8 MDB Grey Teal 0.47** 0.77 1.13 91.76 9 MDB Grey Teal 0.40** 0.67* 1.21 89.15 10 MDB Grey Teal 0.37*** 0.60** 1.25 86.25 11 MDB Grey Teal 0.34*** 0.51*** 1.23 83.39 12 MDB Grey Teal 0.37*** 0.48*** 1.10 84.71 1 WTP Pink-eared Duck 0.92 0.86 0.69 99.07 2 WTP Pink-eared Duck 0.77 0.92 0.79 98.34 3 WTP Pink-eared Duck 0.68 0.93 0.84 96.46 4 WTP Pink-eared Duck 0.63* 0.96 0.89 94.64 5 WTP Pink-eared Duck 0.55** 0.91 0.99 91.92 6 WTP Pink-eared Duck 0.51** 0.88 1.04 89.49 7 WTP Pink-eared Duck 0.48*** 0.90 1.14 87.27 8 WTP Pink-eared Duck 0.45*** 0.85 1.23 85.22 9 WTP Pink-eared Duck 0.48** 0.77 1.19 84.26 10 WTP Pink-eared Duck 0.57* 0.69** 1.02 84.29 11 WTP Pink-eared Duck 0.60* 0.60*** 0.96 80.33 12 WTP Pink-eared Duck 0.67 0.60*** 0.85 84.77

1 VIC Pink-eared Duck 0.72 1.24 0.85 95.09 2 VIC Pink-eared Duck 0.61* 1.21 0.95 91.60 3 VIC Pink-eared Duck 0.53** 1.14 1.02 88.40 4 VIC Pink-eared Duck 0.51*** 1.04 1.05 87.09 5 VIC Pink-eared Duck 0.50*** 0.92 1.06 85.97 6 VIC Pink-eared Duck 0.51*** 0.80 1.05 84.42 7 VIC Pink-eared Duck 0.48*** 0.74* 1.13 81.51 8 VIC Pink-eared Duck 0.45*** 0.69** 1.24 76.67 9 VIC Pink-eared Duck 0.42*** 0.66*** 1.32 72.29 10 VIC Pink-eared Duck 0.44*** 0.67** 1.29 75.81 11 VIC Pink-eared Duck 0.49** 0.64** 1.17 78.93 12 VIC Pink-eared Duck 0.56* 0.69* 1.06 86.94 1 SE Pink-eared Duck 0.70 1.36 0.91 91.77 2 SE Pink-eared Duck 0.60* 1.30 1.01 88.34 3 SE Pink-eared Duck 0.53** 1.22 1.07 85.49 4 SE Pink-eared Duck 0.50*** 1.11 1.10 85.68 5 SE Pink-eared Duck 0.49*** 0.97 1.08 86.33 6 SE Pink-eared Duck 0.49*** 0.86 1.06 86.25 7 SE Pink-eared Duck 0.46*** 0.79 1.15 83.38 8 SE Pink-eared Duck 0.40*** 0.73* 1.31 78.39 9 SE Pink-eared Duck 0.38*** 0.70** 1.40 74.99 10 SE Pink-eared Duck 0.38*** 0.72* 1.43 77.53 11 SE Pink-eared Duck 0.41*** 0.69** 1.30 81.56 12 SE Pink-eared Duck 0.49** 0.74* 1.13 89.48 1 MDB Pink-eared Duck 0.73 1.57** 0.98 86.73 2 MDB Pink-eared Duck 0.69 1.50* 1.01 85.31 3 MDB Pink-eared Duck 0.64* 1.38 1.04 84.74 4 MDB Pink-eared Duck 0.60* 1.25 1.06 86.76 5 MDB Pink-eared Duck 0.57* 1.11 1.02 89.48 6 MDB Pink-eared Duck 0.54* 0.97 1.01 90.85 7 MDB Pink-eared Duck 0.47** 0.86 1.11 88.91 8 MDB Pink-eared Duck 0.38*** 0.75 1.27 84.47 9 MDB Pink-eared Duck 0.32*** 0.69* 1.44 79.13 10 MDB Pink-eared Duck 0.32*** 0.72 1.46 79.96 11 MDB Pink-eared Duck 0.33*** 0.74 1.42 83.98 12 MDB Pink-eared Duck 0.46* 0.89 1.18 91.62

S2: Odds ratios (OR) and AICs for linear models describing the effects of rainfall anomaly, temperature anomaly and “ENSO drought/wet” factor on bird number for five waterfowl species. An OR > 1 indicates a positive, whereas an OR < 1 indicates a negative effect of the explanatory variable (e.g. OR > 1 means that bird numbers were higher when rainfall anomaly was higher and OR < 1 means that bird numbers were higher when rainfall anomaly was lower). Regions: WTP (Western Treatment Plant), Victoria (VIC), South- eastern Australia (SE) and Murray-Darling Basin (MDB). Stars indicate the different significance levels of the effects: *** = p < 0.001; ** = p < 0.01; * = p < 0.05. Bold numbers indicate the best fitting models (ΔAIC < 2 and at least one of the explanatory variable is significant).

REFERENCES

Alexander, D. J. 2000. A review of avian influenza in different bird species. Veterinary microbiology 74:3-13.

Altizer, S., R. Bartel, and B. A. Han. 2011. Animal Migration and Infectious Disease Risk. Science 331:296-302.

Anyamba, A., J.-P. Chretien, J. Small, C. J. Tucker, and K. J. Linthicum. 2006. Developing global climate anomalies suggest potential disease risks for 2006–2007. International journal of health geographics 5:60.

Braithwaite, L. 1969. Testis cycles of a native duck. Journal of reproduction and fertility 19:390-391.

Braithwaite, LW. (1976) Environment and timing of reproduction and flightlessness in two species of Australian ducks. Proceedings of the 16th International Ornithology Congress 486-501.

Briggs, S. V. 1992. Movement patterns and breeding characteristics of arid zone ducks. Corella 16:15-22.

Briggs, S. V., and M. T. Maher. 1985. Limnological studies of waterfowl habitat in south- western New South Wales. II. Aquatic macrophyte productivity. Australian Journal of Marine and Freshwater Research 36:707-715.

Brown, J. D., D. E. Swayne, R. J. Cooper, R. E. Burns, and D. E. Stallknecht. 2007. Persistence of H5 and H7 avian influenza viruses in water. Avian diseases 51:285- 289.

Capua, I., and D. J. Alexander. 2002. Avian influenza and human health. Acta tropica 83:1-6.

Chambers, L. E., and R. H. Loyn. 2006. The Influence of Climate Variability on Numbers of Three Waterbird Species in Western Port, Victoria, 1973-2002. International Journal of Biometeorology 50:292-304. van Dijk, J. G., B. J. Hoye, J. H. Verhagen, B. A. Nolet, R. A. Fouchier, and M. Klaassen. 2013. Juveniles and migrants as drivers for seasonal epizootics of avian influenza virus. Journal of Animal Ecology 83: 266-275.

Epstein, P. R. 2001. Climate change and emerging infectious diseases. Microbes and infection 3:747-754.

Gergis, J., A. J. E. Gallant, K. Braganza, D. J. Karoly, K. Allen, L. Cullen, R. D’Arrigo, I. Goodwin, P. Grierson, and S. McGregor. 2012. On the long-term context of the 1997– 2009 ‘Big Dry’in South-Eastern Australia: insights from a 206-year multi-proxy rainfall reconstruction. Climatic Change 111:923-944.

Green, S. B., J. G. Marquis, S. L. Hershberger, M. S. Thompson, and K. M. McCollam. 1999. The overparameterized analysis of variance model. Psychological Methods 4:214.

Halse, S., and R. Jaensch. 1989a. Breeding seasons of waterbirds in South-western Australia—the importance of rainfall. Emu 89:232-249.

Halse, S. A., and R. P. Jaensch. 1989b. Breeding seasons of waterbirds in south-western Australia - the importance of rainfall. Emu 89:232-249.

Hamilton, A. J., and I. R. Taylor. 2004. Distribution of foraging waterbirds throughout Lake Borrie ponds at the Western Treatment Plant, Victoria (Australia). Victorian Naturalist 122:68-78.

Haynes, L., E. Arzey, C. Bell, N. Buchanan, G. Burgess, V. Cronan, C. Dickason, H. Field, S. Gibbs, P. M. Hansbro, T. Hollingsworth, A. C. Hurt, P. Kirkland, H. McCracken, J. O'Connor, J. Tracey, J. Wallner, S. Warner, R. Woods, and C. Bunn. 2009. Australian surveillance for avian influenza viruses in wild birds between July 2005 and June 2007. Australian veterinary journal 87:266-272.

Herrick, K. A., F. Huettmann, and M. A. Lindgren. 2013. A global model of avian influenza prediction in wild birds: the importance of northern regions. Veterinary Research 44:42.

Hoyo, J. d., A. Elliott, and J. Sargatal. 1992. Handbook of the Birds of the World: Vol 1: Ostrich to Ducks. American Library Association, Lynx.Johnson, F. B. 1990. Transport of viral specimens. Clinical microbiology reviews 3:120-131.

Kingsford, R., G. Bino, J. Porter, and K. Brandis. 2013. Waterbird communities in the Murray–Darling Basin, 1983–2012. University of New South Wales, Sydney.

Kingsford, R. T., and F. I. Norman. 2002. Australian waterbirds - products of the continent's ecology. Emu 102:1-23.

Kingsford, R. T., D. A. Roshier, and J. L. Porter. 2010. Australian waterbirds - time and space travellers in dynamic desert landscapes. Marine and Freshwater Research 61:875-884.

Klaassen, M., B. J. Hoye, and D. A. Roshier. 2011. Identifying crucial gaps in our knowledge of the life-history of avian influenza viruses - an Australian perspective. Emu 111:103-112.

Kousky, V. E., M. T. Kagano, and I. F. Cavalcanti. 1984. A review of the Southern Oscillation: oceanicǦatmospheric circulation changes and related rainfall anomalies. Tellus A 36:490-504.

Kovats, R. S., M. J. Bouma, S. Hajat, E. Worrall, and A. Haines. 2003. El Niño and health. The Lancet 362:1481-1489.

Landesman, W. J., B. F. Allan, R. B. Langerhans, T. M. Knight, and J. M. Chase. 2007. Inter- annual associations between precipitation and human incidence of West Nile virus in the United States. Vector-Borne and Zoonotic Diseases 7:337-343.

Letnic, M., and C. R. Dickman. 2006. Boom means bust: interactions between the El Niño/Southern Oscillation (ENSO), rainfall and the processes threatening mammal species in arid Australia. Biodiversity & Conservation 15:3847-3880.

Loyn, R., D. Rogers, R. Swindley, K. Stamation, P. Macak, and P. Menkhorst. 2014a. Waterbird monitoring at the Western Treatment Plant, 2000–12: The effects of climate and sewage treatment processes on waterbird populations. Arthur Rylah Institute for Environmental Research Technical Report Series.

Loyn, R. H. 1989. The management of duck hunting in Victoria: a review. Wildlife Management Branch, National Parks and Wildlife Division, Department of Conservation, Forests and Lands.

Loyn, R. H. 1991. Assessing and managing the impact of duck hunting in Victoria--a new approach. Wildfowl 42:155-161.

Loyn, R. H., R. J. Swindley, and K. Stamation. 2014b. Waste water not wasted: The Western treatment plant as a habitat for waterfowl. Victorian Naturalist 131:147–149.

Marchant, S., and P. J. Higgins. 1990. Handbook of Australian, New Zealand and Antarctic birds. Volume 1 Ratites to ducks. Oxford University Press, Melbourne.

McBride, J. L., and N. Nicholls. 1983. Seasonal relationships between Australian rainfall and the Southern Oscillation. Monthly Weather Review 111:1998-2004.

Meteorology., B. o. http://www.bom.gov.au/climate/change/index.shtml#tabs=Tracker&tracker=timeserie s. Accessed on 1 Jan 2014.

Murray, C. G., and A. J. Hamilton. 2010. Perspectives on wastewater treatment wetlands and waterbird conservation. Journal of Applied Ecology 47:976-985.

Nicholls, N. 1986. A method for predicting Murray Valley encephalitis in southeast Australia using the Southern Oscillation. The Australian Journal of Experimental Biology and Medical Science 64:587-594.

Norman, F. I., and N. Nicholls. 1991. The Southern Oscillation and variations in waterfowl abundance in southeastern Australia. Australian Journal of Ecology 16:485-490.

Olsen, B., V. J. Munster, A. Wallensten, J. Waldenstrom, A. Osterhaus, and R. A. M. Fouchier. 2006. Global patterns of influenza A virus in wild birds. Science 312:384- 388.

Park, A. W., and K. Glass. 2007. Dynamic patterns of avian and human influenza in east and southeast Asia. The Lancet infectious diseases 7:543-548.

Pittock, A. 1975. Climatic change and the patterns of variation in Australian rainfall. Search 6:498-504.

Plowright, R. K., H. E. Field, C. Smith, A. Divljan, C. Palmer, G. Tabor, P. Daszak, and J. E. Foley. 2008. Reproduction and nutritional stress are risk factors for Hendra virus infection in little red flying foxes (Pteropus scapulatus). Proceedings of the Royal Society B-Biological Sciences 275:861-869.

Power, S., F. Tseitkin, S. Torok, B. Lavery, R. Dahni, and B. McAvaney. 1998. Australian temperature, Australian rainfall and the Southern Oscillation, 1910-1992: coherent variability and recent changes. Australian Meteorological Magazine 47:85-101.

Rasmusson, E. M., and J. M. Wallace. 1983. Meteorological aspects of the El Nino/southern oscillation. Science 222:1195-1202.

RDevelopment, C. 2008. TEAM: R: a language and environment for statistical computing. R Foundation for Statistical Computing, Austria.

Roshier, D. A., A. I. Robertson, and R. T. Kingsford. 2002. Responses of Waterbirds to Flooding in an Arid Region of Australia and Implications for Conservation. Biological Conservation 106:399-411.

Roshier, D. A., P. H. Whetton, R. J. Allan, and A. I. Robertson. 2001. Distribution and persistence of temporary wetland habitats in arid Australia in relation to climate. Austral Ecology 26:371-384.

Serventy, D. L., and A. J. Marshall. 1957. Breeding periodicity in Western Australian birds: with an account of unseasonal nestings in 1953 and 1955. Emu 57:99-126.

Snipes, M., and D. C. Taylor. 2014. Model selection and Akaike Information Criteria: An example from wine ratings and prices. Wine Economics and Policy 3:3-9.

Stallknecht, D., S. Shane, M. Kearney, and P. Zwank. 1990. Persistence of avian influenza viruses in water. Avian diseases 406-411.

Stallknecht, D. E., and J. D. Brown. 2007. Wild birds and the epidemiology of avian influenza. Journal of wildlife diseases 43:S15-S20.

Tabachnick, W. 2010. Challenges in predicting climate and environmental effects on vector- borne disease episystems in a changing world. The Journal of experimental biology 213:946-954.

Ummenhofer, C. C., M. H. England, P. C. McIntosh, G. A. Meyers, M. J. Pook, J. S. Risbey, A. S. Gupta, and A. S. Taschetto. 2009. What causes southeast Australia's worst droughts? Geophysical Research Letters 36.

Watts, D. M., D. S. Burke, B. A. Harrison, R. E. Whitmire, and A. Nisalak. 1986. Effect of temperature on the vector efficiency of Aedes aegypti for dengue 2 virus. The American Society of Tropical Medicine and Hygiene 36(1):143-152.

Woodall, P. F. 1985. Waterbird Populations in the Brisbane Region, 1972-83, and Correlates with Rainfall and Water Heights. Australian Wildlife Research 12:495-506.

Woodruff, R. E., C. S. Guest, M. G. Garner, N. Becker, J. Lindesay, T. Carvan, and K. Ebi. 2002. Predicting Ross River virus epidemics from regional weather data. Epidemiology 13:384-393.

CHAPTER 4

Non-seasonal and long-term avian influenza virus (AIV) dynamics in

Australia’s arid interior suggest bird numbers, age structure and

immunocompetence influences AIV susceptibility

Marta Ferenczi

Christa Beckmann

Daniel Ierodiaconou

Simeon Lisovski

David Roshier

Marcel Klaassen

AUTHOR’S CONTRIBUTIONS:

MF, SL, DR and MK designed the study, and collected the virus samples. MF, CB SL, DR and MK analysed the data, and drafted the manuscript. SL performed the ELISA tests. DI did all the work with the geographical information system and gave final comments on the manuscript. All authors read and approved the final manuscript.

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ABSTRACT

Our knowledge of Avian Influenza Virus (AIV) infection dynamics in wildlife is at best only partially understood in some species and biomes yet poorly understood in others. The seasonal variation in AIV dynamics observed in a number of mainly temperate, northern hemisphere studies have been attributed to (i) seasonal increases in bird density, (ii) seasonal influx of juveniles and (iii) seasonally related variations in energy-demand and concomitant changes in immunocompetence. In contrast to most northern biomes, ecological and environmental conditions in many southern biomes, such as central Australia, are less predictable and resources are more patchily distributed. Australian desert wetland systems thus provide an excellent opportunity to examine the robustness of the hypothesized drivers that influence AIV dynamics. In this four-year study, we investigated AIV dynamics in a free-living bird community in Australia’s interior from the peak of a wet period until severe drought conditions. Similar to other parts of the world, we confirm that Anseriformes and

Charadriiformes are the main carriers and possibly reservoirs of AIV. Across a range of waterbird species, we found that as the extent of local surface water decreased, AIV antibody prevalence increased, suggesting increased chances of an AIV epizootic when this desert system starts to dry out. Given the key role of water availability to bird productivity and condition in this biome, we suggest that the drivers responsible for long-term AIV dynamics in Australia’s arid interior are essentially the same as the suggested drivers of the annual seasonal dynamics observed in the temperate northern hemisphere.

Keywords: infectious disease, virus prevalence, antibody prevalence, rainfall, boom-and-bust cycle, Grey Teal, Pink-eared Duck, Zebra Finch

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INTRODUCTION

High pathogenic outbreaks of Avian Influenza Virus (AIV) in poultry and the possibility of transmission to humans have extensive socio-economic costs (Capua and

Alexander 2002, Rushton et al. 2005, Obayelu 2007). The precursors of high pathogenic forms of the virus are thought to circulate naturally in wild birds as low pathogenic forms

(Alexander 2000, Olsen et al. 2006). Therefore, understanding the dynamics of AIV infections in wild bird populations is increasingly important (Capua and Alexander 2002,

Arzey et al. 2012, Caron et al. 2012).

To date, the majority of research on AIV dynamics in wild birds has been conducted in temperate northern biomes. The pattern observed there is highly seasonal, with a yearly peak in late summer/early autumn, followed by low prevalence in winter (Munster et al.

2007, van Dijk et al. 2013). The only published study to date that included AIV surveys in the southern hemisphere showed that peaks of AIV prevalence in waterfowl communities in

Africa are not as pronounced as in the northern hemisphere. Nonetheless, a shallow seasonal peak was suggested for the southern hemisphere birds (Gaidet et al. 2012). In contrast, in a study from the temperate South-east of Australia, Ferenczi et al. (data accepted for publication) found that AIV prevalence was related to non-seasonal rainfall patterns. Water availability largely varies non-seasonally across Australia (Kousky et al. 1984, Ummenhofer et al. 2009). Inland desert wetland systems, in particular, may lack water for several years

(Kingsford et al. 1999, Roshier et al. 2001, Morton et al. 2011). As a consequence, AIV dynamics in desert wetland systems in the southern hemisphere are likely to differ from the widely observed seasonal dynamics found in the northern hemisphere.

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Three mechanisms have been suggested as drivers of the seasonal AIV dynamics among waterbird communities in the northern hemisphere: (i) the annual congregation of migratory birds at staging and wintering sites increases contact rates between individuals, and thereby infection rates (Krauss et al. 2010, Altizer et al. 2011, Gaidet et al. 2012), (ii) an increase in the abundance of immunologically naïve young birds results in a higher number of individuals susceptible to infection in the waterbird community (Hinshaw et al. 1980, van

Dijk et al. 2013), and (iii) variations in energy-demand, notably in relation to migration, potentially impairing immunocompetence (Altizer et al. 2011). These three hypothesized drivers of AIV dynamics are suggested to be linked to the annual breeding cycle of waterfowl in the northern hemisphere (van Dijk et al. 2013). To what extent these key factors influence

AIV dynamics in other areas of the globe, is largely unknown. Ferenczi et al.’s (data accepted for publication) study in coastal southeast Australia showed AIV prevalence among wild Australian dabbling ducks is related to local and regional rainfall patterns. This study, however, was conducted in temporal coastal areas where rainfall is unpredictable, but not as irregular as in Australia’s desert interior (Morton et al. 2011).

Across much of the Australian continent where the El Niño Southern Oscillation

(ENSO) has an extreme climatological impact, precipitation patterns can be unpredictable

(Kousky et al. 1984, Ummenhofer et al. 2009). Desert wetland systems in Australia are non- seasonal and typically characterized by boom and bust population dynamics, where dry and wet periods alternate and each may persist over several years (Kingsford et al. 1999, Roshier et al. 2001, Kingsford et al. 2010). Thus, weather patterns of the Australian desert wetland system provide one of the most extreme environments in which AIV dynamics can be studied and the hypothesized drivers evaluated.

These irregular climatic conditions strongly influence the movement and breeding biology of many Australian waterfowl species. During wet periods, bird numbers increase in

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flooded areas which can increase in size by orders of magnitude during severe weather events that flood the arid interior (Roshier et al. 2001). As boom turns to bust, some waterbirds aggregate on the remaining wetlands while others depart (Kingsford and Norman 2002,

Roshier et al. 2002, Kingsford et al. 2010). Applying these typical climatic and ecological conditions in Australia to the proposed drivers of AIV prevalence in northern biomes,

Klaassen et al. (2011) hypothesized that following breeding, increasing densities of water birds (of which many are juvenile and immunologically naïve) congregating at the remaining water bodies might be driving AIV prevalence in wild Australian waterfowl. Under these circumstances, food availability may become limited, reducing immunocompetence (Appleby et al. 1999). Ferenczi et al.’s (data accepted for publication) findings also support Klaassen et al.’s (2011) hypothesis that rainfall influences population dynamics and age structure within the duck community, which may subsequently affect AIV dynamics. Although evaluating the validity and importance of these proposed influences on AIV dynamics through experimental research is logistically challenging (Hoye et al. 2010a), a comparative approach may provide many insights.

In Australia and elsewhere, AIV sampling to date has mainly focused on waterfowl

(Anseriformes) and shorebirds (Charadriformes) (Tracey et al. 2004, Turner 2004, Sims and

Turner 2008), as these orders are thought to be the main reservoirs of AIV (Hinshaw et al.

1985, Olsen et al. 2006, Stallknecht and Brown 2007). However, AIV has been observed in most avian groups and other species, particularly social species that aggregate on or near waterbird-rich wetlands, cannot be ruled out as potential hosts of AIV (Hoye et al. 2010b). In the Australian interior, boom and bust species such as Budgerigar (Melopsittacus undulatus),

Zebra Finch (Taeniopygia guttata) and Diamond Dove (Geopelia cuneata) occasionally congregate in large numbers at temporary wetlands (Tischler et al. 2013).

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We studied AIV dynamics in Australia’s interior at the peak of a wet period until an almost complete drying out of the system, spanning four years. We sampled representative species of the entire bird community, including the presumed main AIV reservoir

(waterbirds) and boom and bust species. This allowed us to evaluate the relative role of various bird species as potential reservoirs in this extreme environment. Moreover, the significant decrease of water availability during the study period allowed us to investigate the hypothesized importance of water availability on AIV infection dynamics.

METHODS

Study site

Fieldwork was conducted in Innamincka Regional Reserve in South Australia (1,

354,206 ha; 27°32'28''S, 140°35'47''E) within a 8,200 km2 area commencing shortly after the millennium drought of 2001-2009, from November 2010 to March 2013. The reserve is located in Australia’s arid zone (see Figure 1) and contains a diverse range of habitats such as desert uplands, woodlands, gibber plains, dune fields and wetlands which are shaped by rainfall events and provide critical breeding habitat for more than 200 bird species

(Macdonald and McNeil 2012). The area is dominated by the Cooper Creek floodplain and

Coongie Lakes, which are listed on the Register of the National Estate as a ‘significant intact ecosystem’. The Coongie Lakes are listed under the Convention on Wetlands of International

Importance as Waterfowl Habitat (Macdonald and McNeil 2012).

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Figure 1: Map of study area showing the persistence of water bodies represented in % of time available during the study period. Derived from Landsat 5 TM, Landsat 7 ETM and

Landsat 8 OLI captured between March 2009 to November 2014, across 18 evenly spaced time steps, at approximately 3 month intervals using a Normalized Difference Water Index

(NDWI). Identification number of expeditions are included in Appendix S1.

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Blood samples were collected from wild birds and tested for both AIV antibodies

(termed as AIV antibody prevalence) and AIV (termed as AI viral prevalence). We conducted expeditions once every four months (excluding November 2013), each lasting approximately two weeks. As the landscape was changing dynamically during the study period due to flooding and intense drying, changes in water availability and thus bird distributions resulted in changes in catching locations between expeditions. Adapting to these circumstances, we located the most effective catching sites (i.e. remaining wetlands where waterbirds might be located) prior to each expedition by selecting sites based on satellite images provided by the

National Aeronautics and Space Administration using LANCE-MODIS data system and

Earth Observing System Data and Information System. As the possible catching sites could only be roughly identified based on the satellite images, final sites suitable for capturing birds were selected in the field, based on bird densities, water depth, structure of the shore and surrounding vegetation. On four expeditions (expedition numbers 2, 3, 5 and 6; see Figure 1 and S1) local conditions (e.g. heavy local rainfall resulting in impassable roads) did not allow us to approach sites identified via satellite images. In these cases we selected accessible catching sites that had high bird densities and habitat suitable for catching.

Capture sites near Lake Coongie (expeditions 1, 4, 7, 8, 9 and 10; see Figure 1 and

Appendix S1) were set up on the lakeshore, floodplains and small channels. Capture sites at

Gidgealpa and Innamincka (expedition numbers 2, 3, 5 and 6; see Figure 1 and Appendix S1) were on floodplains and water holes in the direct vicinity (within 5 km) of Cooper Creek or on the creek itself. See Appendix S1 for location coordinates of the 10 sampling sites.

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Species selection and capture techniques

Waterbirds, the main reservoirs of AIV (Hinshaw, Wood et al. 1985, Olsen, Munster et al. 2006, Stallknecht and Brown 2007) were the prime target species, but other bird species that occurred in large numbers, (i.e. Diamond Dove, Budgerigar and Zebra Finch) were also targeted. We used three catching techniques: baited funnel walk-in traps (McNally and

Falconer 1953), cannon nets, and mist nets. Baited funnel walk-in traps were deployed on land or in water shallow enough for foraging by dabbling ducks, coots and waders. Traps baited with seeds were set at dawn and checked hourly during the day, and left open (so birds could enter and leave the traps freely) during the night (Whitworth 2007). Cannon netting to capture roosting ducks (Whitworth 2007), was used on a single occasion, expedition 8. The cannon net (set on a dry upland) was operated during day and the birds in the catching area were continuously observed. To capture waterbirds at night, 9-15 large mesh size (45x45 mm) 12-18 m long mist nets were erected on poles above the water surface. During the day we caught small songbirds, doves and parrots using small mesh size (16x16 mm) 6-12 m long mist nets. Nets were deployed in areas of high bird activity (Whitworth 2007). Catching effort was at similar intensity during each expedition.

This research was conducted under approval of Deakin University Animal Ethics

Committee (permit numbers A113-2010 and B37-2013) and Wildlife Ethics Committee of

South Australia (permit numbers 2011/1, 2012/35 and 2013/11). Banding was done under

Australian Bird Banding Scheme permit (banding authority numbers 2915 and 2703).

Research permits were approved by Department of Environment, Water and Natural

Resources South Australia (research permit numbers M25919-1,2,3,4,5).

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Sample collection and analysis

Immediately after removal of birds from traps and nets, blood samples were collected for t AIV antibodies and AIV. First, approximately 200 μl of blood was collected from a brachial vein for detection of antibodies against AIV. Blood samples were collected into 200

μl vials and then subsequently stored in a refrigerator at 5 °C. Within 7-24 hours of collection, blood samples were centrifuged at 13,000 RPM (revolutions per minute) for 15 min. Next, the serum was separated and stored in a refrigerator at 5 °C. Within seven days of collection, the samples were transferred to a dewar filled with liquid nitrogen ( -195.95 °C) while in the field or into an -80 °C freezer after return to the laboratory at Deakin University, until analysis.

Following blood collection, oropharyngeal and cloacal swabs for AI viral analysis were placed into separate vials containing 2 ml viral transport media (Johnson 1990) on the first nine expeditions. On the last expedition (March 2014) the two swabs (belonging to the same individual) were collected (pooled) in one 2 ml vial. In the analysis of the swab samples from the first nine expeditions, the individual bird was counted positive for AIV if either one or both of the (oropharyngeal and cloacal) swabs tested positive. After collection, the swabs were stored and transported as described above for serum samples. Before analysis the oropharyngeal and cloacal samples were subsampled and then pooled for three individuals. If the pooled sample was identified as positive, then the original individual samples were analyzed singularly to identify which of the birds were positive(s). Birds were individually banded with a coded metal ring or, for Diamond Doves, Budgerigars and Zebra Finches, we

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cut a small portion of specific tail feathers to identify recaptures and avoid re-sampling on the same expedition.

Serum samples collected on the first two expeditions were analyzed by the Australian

Animal Health Laboratory (AAHL) but all other samples were analyzed at Deakin

University. At the AAHL sera were tested using a nucleoprotein competitive enzyme-linked immunosorbent assay (NP c-ELISA) targeting influenza group A–specific nucleoprotein antibodies using reagents supplied by the AAHL. Optical densities were read at 450 nm

(Multiskan EX Microplate Photometer, Thermo Labsystems), with test serum results calculated as the percentage inhibition of binding of the monoclonal antibody in the absence of any serum. Sera with >60% inhibition were interpreted as positive, 40%–60% as equivocal, and <40% as negative. At Deakin University the presence of antibodies to nucleoprotein in individual serum samples was tested using a commercially available blocking enzyme-linked immunosorbent assay (bELISA; MultiS-Screen Avian Inﬂuenza

Virus Antibody Test Kit) following manufacturer’s instructions. Assays were carried out in duplicate, each using a 10 μl serum sample. Absorbance was measured at 620 nm using a

FLOUstar Omega plate reader. All samples were run in combination with supplied positive and negative controls. Sample signal to noise ratios (the quotient of sample mean absorbance divided by negative control mean absorbance) greater and equal to 0.5 were considered negative for the presence of antibodies to AIV.

The oropharyngeal and cloacal samples were tested using influenza A PCR targeting the highly conserved matrix gene (Fouchier, Bestebroer et al. 2000). Samples collected on the first two expeditions were analyzed by the AAHL and the rest of the samples (eight expeditions) were tested at the Victorian Department of Economic Development, Biosciences

Research Division. Samples were processed for RNA extraction using 5XMagMAX - 96

Viral Isolation Kit (Ambion Cat. No AMB1836-5) on a Thermo KingFisher - 96 Robot. A

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volume of 35 μL was then run in a Superscript III Platinum One-Step qRT-PCR Kit with

ROX (Life Technologies 11745-100) using a Px2 Thermal Cycler PCR machine. A volume of 5 μL (diluted 1/100) was then transferred to 15 μL Fast SYBR Green mastermix (Life

Technologies 4385612) solution and the PCR performed in an ABI 7500 Real Time PCR machine.

Environmental conditions

The extent of surface water in the reserve, monthly total rainfall data for three geographic regions (i.e. Southern Australia, Northern Australia and Australia) and Southern

Oscillation Index (SOI) were calculated for every fourth month, starting 20 months prior to the first sample collection and covering the period of the field expeditions (March 2009 –

March 2014). Thus environmental conditions were determined from the break of the dry period (March 2009), through the peak of the wet (first collection in November 2010) and the subsequent decline of the extent of surface water in the area (November 2010 – March 2014).

To calculate water surface area, we obtained imagery from the Landsat archive targeting months March, July and November from 2009 to 2014 (n=16, Appendix S3). Table

S3 shows the Landsat 5 TM, Landsat 7 ETM+ and Landsat 8OLI time series obtained from the US Geological Survey (USGS) using the Earth Explorer Viewer. The Landsat data (Level

1 Terrain Corrected (L1T) product) were pre-georeferenced to UTM zone 54 South projection using WGS-84 datum. To prepare the input satellite images for processing, the radiometric calibration tool was applied to calibrate image data to radiance using ENVI version 5.1. Then we applied the Fast Line-of-sight Atmospheric Analysis of Hypercubes

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(FLAASH) tool for atmospheric corrections to generate surface reflectance. The Normalized

Difference Water Index (NDWI) was then used to extract the water surface area using the equation by McFeeters (1996) where; NDWI = (Green band − Near Infrared)/( Green band +

Near Infrared). The Landsat 7 imagery used in the time series required a gap correction due to the Scan Line Corrector (SLC) failure on the L7 sensor resulting in around 20% image loss. We used a majority filter in ARCGIS 10.2.2 (ESRI®) to post process the L7 NDWI output to extrapolate surface areas across data gaps. We then analyzed water extent across the study area to determine water availability across the time series. These indirectly assessed water extents were locally confirmed during the catching expeditions. Total monthly rainfall

(http://www.bom.gov.au/climate/change/index.shtml#tabs=Tracker&tracker=timeseries) and

SOI data (http://www.bom.gov.au/climate/current/soihtm1.shtml) were obtained from the

Australian Bureau of Meteorology.

Statistical analysis

To investigate species-specific AIV antibody and AI viral prevalence over the entire study period, we only included species for which a minimum of 20 samples were collected.

To analyze the temporal variation in AIV antibody and AI viral prevalence, we selected species for which samples were collected on at least five expeditions and for which at least five samples had been identified as positive on a minimum of two expeditions. To analyze the temporal variation in AIV antibody prevalence counted for taxonomic levels (i.e. species, family and order), we selected species for which samples were collected on at least two expeditions. AIV antibody and AI viral prevalence and their 95% confidence interval were calculated for each species and expedition.

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We used logistic regressions to examine the effects of the extent of surface water on

AIV antibody presence and AI viral infections, meaning that presence/absence and infected/non-infected data per expeditions were used as the response variable allowing the model to account for sample size. We used generalized linear models (GLM) for each of the selected species and a generalized linear mixed model (GLMM) for all selected species combined. The extent of surface water was used as an explanatory variable. In the GLMM, the extent of surface water and the taxonomy categories (species, family and order) were used as explanatory variables. The extent of surface water was a fixed factor and the three nested taxonomic categories (i.e. species, within families, within orders) were random factors in the model.

For all the models Akaike information criterion (AIC) was calculated. After GLM, effect sizes of the parameter estimates were calculated as odds ratios (OR). An OR > 1 indicates a positive, whereas an OR < 1 indicates a negative effect of the explanatory variable. All analyses were conducted using R stastical procedures (RDevelopment 2008).

RESULTS

At the beginning of 2010, the local drought broke and the study area was flooded by a large volume of water due to both heavy local rainfalls and rainfalls upstream in Queensland that inundated the area via the Cooper Creek catchment (Schmarr et al. 2013). A rapid drying period then commenced, resulting in a decrease in the extent of surface water (see Figure 1 and Appendix S3). Monthly total rainfall patterns within the three, large-scale geographic regions (i.e. North Australia, South Australia and Australia) as well as SOI index all showed decreasing rainfall trends between 2010 and 2014 (Figure 2).

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Figure 2: Top panels (a-i): AIV antibody prevalence (± 95% confidence interval) in selected bird species as a function of water availability in the area (% surface water). For significant cases only (cf. Table 2), the generalized linear model results have been drawn lines (± 95% confidence intervals). Bottom panels (j-l): percentage of the local surface water, rainfall in

Australia, North Australia and South Australia, and SOI between March 2009 and March

2014.

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A total of 2,306 individuals from 39 species of birds were sampled over the entire study period (Table 1 and Appendix S2). Of these, 562 individuals (24.4 %) representing 22 species (56.4%) tested positive for AIV antibodies, and 30 individuals (1.3 %) from four species (10.3%) tested positive for AIV (Table 1: species with sample sizes higher than 20;

Appendix S2: remaining species). We found that five of the six Anseriformes in Table 1 had relatively high (>20%) AIV antibody prevalence. One of these (Hardhead [Aythya australis]) is a diving duck while the remaining species are dabbling ducks (Anatinae; i.e. Grey Teal

[Anas gracilis] and Pacific Black Duck [Anas superciliosa]) or duck species with a similar ecology to dabbling ducks (i.e. Pink-eared Duck [Malacorhynchus membranaceus] and

Freckled Duck [Stictonetta naevosa]). The AIV antibody prevalence of Grey Teal and Pink- eared Duck was high, 63.08% and 54.33%, respectively (Table 1). The one perching duck species (Australian Wood Duck [Chenonetta jubata]) had low (4.88%) AIV antibody prevalence. Among Charadriiformes in Table 1, only Silver gull (Chroicocephalus novaehollandiae) and Red-necked Avocet (Recurvirostra novaehollandiae) had relatively high AIV antibody prevalence (54.8% and 28.6% respectively), the remaining three species

(Black-winged Stilt [Himantopus himantopus], Masked Lapwing [Vanellus miles] and Red- kneed Dotterel [Erythrogonys cinctus]) had much lower AIV antibody prevalences, ranging between 4.8 and 5.1%. The five remaining species in Table 1, belonging to the orders

Passeriformes, Psittaciformes, Gruiformes and Columbiformes all had low AIV antibody prevalences ranging between 0.9 and 5.3%. The four species for which positive AIV cases were recorded all belonged to Anseriformes with species-specific AI viral prevalences varying between 1.7 and 4.1%.

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Species Total AIV AI viral Family Order sample antibody prevalence prevalence size (%) (%)

Grey Teal 260 63.08 3.08 Anatidae Anseriformes (Anas gracilis)

Silver Gull 42 54.76 0 Laridae Charadriiformes (Chroicocephalus novaehollandiae)

Pink-eared Duck 462 54.33 4.11 Anatidae Anseriformes (Malacorhynchus membranaceus)

Pacific Black Duck 49 42.86 0 Anatidae Anseriformes (Anas superciliosa)

Freckled Duck 26 34.62 0 Anatidae Anseriformes (Stictonetta naevosa)

Red-necked Avocet 126 28.57 0 Recurvirostridae Charadriiformes (Recurvirostra novaehollandiae)

Hardhead 39 20.51 2.56 Anatidae Anseriformes (Aythya australis)

Zebra Finch 266 5.26 0 Estrildidae Passeriformes (Taeniopygia guttata)

Black-winged Stilt 39 5.13 0 Recurvirostridae Charadriiformes (Himantopus himantopus)

Masked Lapwing 20 5.00 0 Charadriidae Charadriiformes (Vanellus miles)

Australian Wood 123 4.88 1.63 Anatidae Anseriformes Duck (Chenonetta

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jubata)

Red-kneed Dotterel 21 4.76 0 Charadriidae Charadriiformes (Erythrogonys cinctus)

Budgerigar 110 4.55 0 Psittaculidae Psittaciformes (Melopsittacus undulatus)

Common Coot 68 4.41 0 Rallidae Gruiformes (Fulica atra)

Diamond Dove 326 2.15 0 Columbidae Columbiformes (Geopelia cuneata)

Black-tailed 232 0.86 0 Rallidae Gruiformes Nativehen (Tribonyx ventralis)

Table 1: Avian influenza virus antibody and avian influenza viral prevalence of species with

sample size > 20 and taxonomic categories of family and order.

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For the analysis of temporal variation in AIV antibody prevalence, nine species (1,677 individuals), and for the analysis of temporal variation in AI viral prevalence, two species

(722 individuals) were selected. We found that the AIV antibody prevalence of six out of nine species, including Red-necked Avocet, Zebra Finch and four species of dabbling ducks

(Freckled Duck, Grey Teal, Pacific Black Duck and Pink-eared Duck) were all significantly and negatively correlated with the extent of surface water (i.e. smaller surface water = higher prevalence) (Table 2, Figure 2).

Species OR AIC

Grey Teal 0.71*** 72.59

Pink-eared Duck 0.65*** 92.75

Pacific Black Duck 0.41*** 22.55

Freckled Duck 0.37* 14.82

Red-necked Avocet 0.53*** 26.68

Hardhead 1.40 19.09

Zebra finch 0.48*** 33.12

Australian Wood Duck 0.79 18.05

Diamond Dove 0.57 29.19

All species combined -*** 453.5

Table 2: Odds ratios (OR) and AICs for generalized linear (GLM) and generalized linear mixed models (GLMM) of the effects of the extent of surface water on AIV antibody prevalence of nine species (GLMs) and all species combined (GLMM). An OR > 1 indicates a positive, whereas an OR < 1 indicates a negative effect of the explanatory variable. Note

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that odd ratio was not calculated for GLMM. Stars indicate significance level: *** = p <

0.001, * = p < 0.05.

Also the GLMM on the effect of the extent of surface water on AIV antibody prevalence for all species combined showed a significant negative relationship with the extent of surface water (Table 2). For AI viral prevalence only Pink-eared Duck was significantly and positively correlated with the extent of surface water (Table 3 and Figure 3).

Species Odd ratio AIC

Grey Teal 1.12 23.85

Pink-eared Duck 2.07*** 43.78

Table 3: Odds ratios (OR) and AICs for generalized linear models of the effects of the extent of surface water on AI viral prevalence of two species. An OR > 1 indicates a positive, whereas an OR < 1 indicates a negative effect of the explanatory variable. Stars indicate significance level: *** = p < 0.001.

Figure 3: Avian influenza viral prevalence (± 95% confidence interval) in selected bird species as a function of water availability in the area (% surface water). For significant cases only (cf. Table 3), the generalized linear model results have been drawn lines (± 95% confidence intervals).

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DISCUSSION

We found that AIV antibody prevalence of wild birds in the Australian desert wetland system was systematically related to water availability. As the extent of local surface water decreased, AIV antibody prevalence increased in a range of waterbird species (dabbling ducks and one wader species) and in one non-waterbird species (Zebra Finch).

The availability of mostly temporary water resources drives the breeding biology of waterfowl in Australia (Kingsford et al. 2010). We accordingly hypothesized that water availability in the Australian landscape might also strongly influence AIV dynamics. We hypothesized that after a wet period and as the temporary wetlands dry, AIV prevalence in

Australia’s interior would increase and reach a maximum as a combined result of (i) increasing densities of waterbirds on the increasingly smaller wetlands, (ii) high proportion of juvenile and thus immunologically naïve individuals and (iii), reduced immune competence in the population as a result of reduced food availability. Although this hypothesis predicts an increase in AIV prevalence as the system dries up, it does not necessarily predict a peak in prevalence when the system is at its driest. Peak prevalence might be reached earlier.

Contrary to our hypothesis, AI viral prevalence in Pink-eared Ducks decreased as surface water decreased. However, this pattern importantly hinged on “outlier” results from a single expedition (expedition 5), where an atypically large number of infected individuals were caught. Moreover, rather than showing a decrease in AI viral prevalence over time, the pattern suggests a parabolic relationship (substantiated with a near-significant [P = 0.088] 2nd order effect of total water surface area) with a maximum at intermediate levels of total water

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surface area. This again supports our initial hypothesis of an increase in AIV infection risk and thus an increasing chance of an AIV epizootic as the system dries.

The importance of water availability’s influence on AIV prevalence of Australian waterfowl was also highlighted in Ferenczi et al.’s study (data accepted for publication) from south-east Australia. They suggested that AIV prevalence in a wild duck population in a permanent coastal wetland was related to rainfall-driven breeding events, which acted both on a long (ENSO-driven) time scale and shorter time scales. As our sampling started at the peak of the wet period, when conditions were appropriate for breeding, we assume that there was a strong influx of naïve juvenile birds during the later months, as the wetlands started to dry again, subsequently positively affecting AIV antibody prevalence in the community. As the desert wetland system dries, birds have been shown to congregate in high numbers on increasingly smaller wetlands (Kingsford and Norman 2002, Kingsford et al. 2010, Norman and Chambers 2010). Higher bird density increases contact rates and thereby infection rates, which, in turn, can increase AIV prevalence (Klaassen et al. 2011, Gaidet et al. 2012). That

AIV dynamics are a function of both bird densities and changes in age structure was suggested from northern hemisphere studies (Hinshaw et al. 1980, van Dijk et al. 2013). In addition, reduced food availability can influence the birds’ immunocompetence (Appleby et al. 1999) and therefore create an additional factor increasing AIV infection risk.

In the South Australian desert system, the flows of water across the floodplains are dependent on the upper catchment river flows that in turn are fed by distant cyclonic rains

(Roshier et al. 2001). If there is no resupply of water, the floodplains in the desert dry up over time (Roshier et al. 2001). Local water availability in these systems is thus a function of both regional and large-scale rainfall patterns. However, as may be expected for a system driven by broad scale tropical influences, over the study period temporal rainfall patterns in North

Australia, South Australia and Australia as a whole, were all highly correlated (Figure 2) and

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there was relatively great spatial consistency in water availability across the continent over this period. The SOI values, similarly to rainfall patterns, decreased over the years of the study (Figure 2).

For many of the species in this study, the number of samples collected was generally too low to make accurate inferences about variations in AI viral prevalence across species.

Yet, contrary to prevalence of acute infections, AIV antibody prevalence was generally much higher and provided a means to study species variation in AIV infection, yielding marked differences across species. We only recorded acute AI viral infections in ducks (1.9%; Table

1), which is consistent with Olsen et al.’s (2006) global review showing that ducks had the highest AI viral prevalence among all bird species investigated (9.5%), followed by representatives of the order Charadriiformes (terns: 1.7% gulls: 1.4%; and waders: 0.8%), with the remainder of the avian taxa generally showing lower prevalence. This pattern of viral prevalence across species is also supported by our AIV antibody prevalence data.

Within ducks Olsen et al. (2006) showed that AI viral prevalence in dabbling ducks was more than six times higher than in diving ducks. In our study dabbling ducks had three times lower average AI viral and two times higher average AIV antibody prevalence compared to diving ducks, although it should be noted that diving ducks were represented by only one species

(Table 1).

We found boom and bust species (Zebra Finch [order Passeriformes], Budgerigar

[order Psittaciformes] and Diamond Dove [order Columbiformes]) had no apparent role in

AIV infection dynamics. Although the AIV antibody prevalences in these three species were generally low (Table 1), these data indicate they do become infected occasionally. Thus, in all aspects the variation we found across the various taxonomic groups in Australia’s interior was similar to what has been found elsewhere around the globe to date. They indicate that

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while all birds can potentially become infected with (certain strains of) AIV (Fuller et al.

2010, Vandegrift et al. 2010), waterbirds are the main carriers (Olsen et al. 2006).

The suggested factors most influencing (i, ii, iii) for AIV dynamics in seasonal northern hemisphere environments, Africa and temporal south-east Australia, and now

Australia’s interior, appear to be consistent. However, evaluating the validity and importance of these putative drivers of AIV through experimental research is logistically challenging, if not impossible. The comparative approach to which our research contributes, where AIV dynamics are studied under widely different environmental and ecological conditions, may well be the best alternative approach. In this study we build a case that the seasonal and annual environmental dynamics that determine AIV dynamics across large areas of the globe are greatly overridden by non-seasonal, weather factors that seemingly drive AIV dynamics over multi-year periods. Yet, the underlying presumed drivers remain the same.

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ACKNOWLEDGEMENTS

We thank Australian Animal Health laboratory and the Department of Economic

Development for analysing the AIV data. The project has been funded by the National

Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of

Health and Human Services, under Contract No. HHSN266200400041C. The study was also supported by the Australian Research Council (DP 130101935). This work would be impossible without the invaluable and selfless local logistic support from Gidgealpa Station,

Innamincka Station, Parks South Australia, and Santos. This work further benefitted greatly from the administrative support by the Australian Bird and Bat Banding Scheme, Parks South

Australia and the Science Resource Centre, Department of Environment and Natural

Resources. We thank Jutta Leyrer, Jacintha van Dijk, Christophe de Franceschi, Renata

Hurtado, Olga Pokrovskaya, Patrick Mileto, Alex Miller, Meijuan Zhao, Sharon Woodend,

Alice Taysom, Anita Heim, Yaara Aharon-Rotman, Bethany Hoye, Dejan Stojanovic, Sven

Ihnken and Mateuzs Jochym for their fantastic field assistance. Peter Biro was a great help with the statistical analysis. Additional, much appreciated comments on the manuscript were received from Bill Buttemer. We thank Mohammad Ali Jalali for assistance with processing the Landsat Imagery used in this study. Australian Bureau of Meteorology is also thanked for providing all the weather data for analysis.

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APPENDIX

Number of Date Location Coordinates expedition

1 November Coongie – Tirrawarra 27°25'52.98"S, 140° 8'23.81"E 2010

2 March 2011 Gidgealpa 27°49'23.15"S, 140° 8'58.10"E

3 July 2011 Gidgealpa 27°49'23.15"S, 140° 8'58.10"E

4 November Coongie - Dimpoona 26°57'15.88"S, 140°16'44.06"E 2011 Waterhole

5 March 2012 Gidgealpa 27°49'23.15"S, 140° 8'58.10"E

6 July 2012 Innamincka 27°29'8.66"S, 140°30'27.34"E

7 November Coongie - Dimpoona 26°57'15.88"S, 140°16'44.06"E 2012 Waterhole

8 March 2013 Coongie - Lake Goyder 27° 0'36.54"S, 140°10'11.83"E

9 July 2013 Lake Goyder South 27° 0'50.82"S, 140°10'40.84"E

10 March 2013 Lake Sir Richard 27° 2'12.75"S, 140°23'12.86"E

S1: List of locations of expeditions

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Species Total AIV AI viral Family Order sample antibody prevalence prevalence size (%) (%)

Chestnut Teal 2 100 0 Anatidae Anseriformes (Anas castanea)

Hoary-headed Grebe 1 100 0 Podicipedidae Podicipediformes (Poliocephalus poliocephalus)

Royal Spoonbill 1 100 0 Threskiornithidae Pelecaniformes (Platalea regia)

Yellow-billed 2 100 0 Threskiornithidae Pelecaniformes Spoonbill (Platalea flavipes)

Black Swan 4 50 0 Anatidae Anseriformes (Cygnus atratus)

Whiskered Tern 6 16.7 0 Sternidae Charadriiformes (Chlidonias hybrida)

Australasian Grebe 7 0 0 Podicipedidae Podicipediformes (Tachybaptus novaehollandiae)

Australian Pratincole 2 0 0 Glareolidae Charadriiformes (Stiltia isabella)

Australian Shoveler 1 0 0 Anatidae Anseriformes (Anas rhynchotis)

Banded Lapwing 2 0 0 Charadriidae Charadriiformes (Vanellus tricolor)

Banded Stilt 3 0 0 Recurvirostridae Charadriiformes (Cladorhynchus leucocephalus)

Black-fronted Dotterel 19 0 0 Charadriidae Charadriiformes (Elseyornis melanops)

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Glossy Ibis 1 0 0 Threskiornithidae Pelecaniformes (Plegadis falcinellus)

Gull-billed Tern 1 0 0 Sternidae Charadriiformes (Gelochelidon nilotica)

Little Corella 5 0 0 Cacatuidae Psittaciformes (Cacatua sanguinea)

Nankeen Night Heron 7 0 0 Ardeidae Pelecaniformes (Nycticorax caledonicus)

Australian Painted 1 0 0 Rostratulidae Charadriiformes Snipe (Rostratula australis)

Pied Cormorant 3 0 0 Phalacrocoracidae Suliformes (Phalacrocorax varius)

Plumed Whistling 5 0 0 Anatidae Anseriformes Duck (Dendrocygna eytoni)

Red-capped Plover 16 0 0 Charadriidae Charadriiformes (Charadrius ruficapillus)

Sharp-tailed 5 0 0 Scolopacidae Charadriiformes Sandpiper (Calidris acuminata)

Straw-necked Ibis 1 0 0 Threskiornithidae Ciconiiformes (Threskiornis spinicollis)

White-faced Heron 2 0 0 Ardeidae Pelecaniformes (Egretta novaehollandiae)

S2: Avian influenza virus antibody and avian influenza viral prevalence of species with sample size < 20 and taxonomic categories of family and orde

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Date Source Surface Water % of total area (hectare)

24 March 2009 Landsat 5 TM 5948.4 0.7

07 August 2009 Landsat 7 ETM 8353.3 1.0

03 November 2009 Landsat 5 TM 7124 0.9

19 March 2010 Landsat 7 ETM 190327 23

25 July 2010 Landsat 7 ETM 56248 6.8

06 November 2010 Landsat 5 TM 45131 5.4

30 March 2011 Landsat 5 TM 44027 5.3

28 July 2011 Landsat 7 ETM 39751 4.8

03 December 2011 Landsat 7 ETM 24234 3.0

08 March 2012 Landsat 7 ETM 42582 5.1

14 July 2012 Landsat 7 ETM 27626 3.2

19 November 2012 Landsat 7 ETM 19730 2.4

11 March 2013 Landsat 7 ETM 13941 1.7

25 July 2013 Landsat 8 OLI 8690 1.1

14 November 2013 Landsat 8 OLI 6139 0.8

22 March 2014 Landsat 8 OLI 1951 0.3

S3: Surface water area between March 2009 and November 2014 using the Landsat archive.

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REFERENCES

Alexander, D. J. 2000. A review of avian influenza in different bird species. Veterinary microbiology 74:3-13.

Altizer, S., R. Bartel, and B. A. Han. 2011. Animal Migration and Infectious Disease Risk. Science 331:296-302.

Appleby, B., M. Anwar, and S. Petty. 1999. Short-term and long-term effects of food supply on parasite burdens in Tawny Owls, Strix aluco. Functional Ecology:315-321.

Arzey, G. G., P. D. Kirkland, K. E. Arzey, M. Frost, P. Maywood, S. Conaty, A. C. Hurt, Y.- M. Deng, P. Iannello, and I. Barr. 2012. Influenza virus A (H10N7) in chickens and poultry abattoir workers, Australia. Emerg Infect Dis 18:814-816.

Capua, I., and D. J. Alexander. 2002. Avian influenza and human health. Acta tropica 83:1-6.

Caron, A., M. de Garine-Wichatitsky, M. Ndlovu, and G. S. Cumming. 2012. Linking avian communities and avian influenza ecology in southern Africa using epidemiological functional groups. idea 1:13-14. van Dijk, J. G., B. J. Hoye, J. H. Verhagen, B. A. Nolet, R. A. Fouchier, and M. Klaassen. 2013. Juveniles and migrants as drivers for seasonal epizootics of avian influenza virus. Journal of Animal Ecology 83: 266-275.

129

Hinshaw, V., W. Bean, R. Webster, and G. Sriram. 1980. Genetic reassortment of influenza A viruses in the intestinal tract of ducks. Virology 102:412-419.

Hoye, B. J., V. J. Munster, H. Nishiura, M. Klaassen, and R. A. Fouchier. 2010a. Surveillance of wild birds for avian influenza virus. Emerging infectious diseases 16:1827.

Hoye, B. J., V. J. Munster, H. Nishiura, M. Klaassen, and R. A. M. Fouchier. 2010b. Surveillance of Wild Birds for Avian Influenza Virus. Emerging infectious diseases 16:1827-1834.

Johnson, F. B. 1990. Transport of viral specimens. Clinical microbiology reviews 3:120-131.

Kingsford, R. T., A. L. Curtin, and J. Porter. 1999. Water flows on Cooper Creek in arid Australia determine 'boom' and 'bust' periods for waterbirds. Biological Conservation 88:231-248.

Kingsford, R. T., and F. I. Norman. 2002. Australian waterbirds - products of the continent's ecology. Emu 102:1-23.

Kingsford, R. T., D. A. Roshier, and J. L. Porter. 2010. Australian waterbirds - time and space travellers in dynamic desert landscapes. Marine and Freshwater Research 61:875-884.

Klaassen, M., B. J. Hoye, and D. A. Roshier. 2011. Identifying crucial gaps in our knowledge of the life-history of avian influenza viruses - an Australian perspective. Emu 111:103-112.

Kousky, V. E., M. T. Kagano, and I. F. Cavalcanti. 1984. A review of the Southern Oscillation: oceanicǦatmospheric circulation changes and related rainfall anomalies. Tellus A 36:490-504.

130

Macdonald, J., and D. McNeil. 2012. Environmental and cultural values of South Australia’s outback water resources. Goyder Institute for Water Research Technical Report Series.

McFeeters, S. K. 1996. The use of the normalized difference water index (NDWI) in the delineation of open water features. International Journal of Remote Sensing 17:1425- 1432.

McNally, J., and D. D. Falconer. 1953. Trapping and banding operations Lara Lake, 1952. Emu 53:51-70.

Morton, S., D. S. Smith, C. Dickman, D. Dunkerley, M. Friedel, R. McAllister, J. Reid, D. Roshier, M. Smith, and F. Walsh. 2011. A fresh framework for the ecology of arid Australia. Journal of Arid Environments 75:313-329.

Norman, F. I., and L. E. Chambers. 2010. Counts of selected duck species at Corner Inlet, Victoria: changes in relation to local and distant meteorological variations. International Journal of Biometeorology 54:269-282.

Obayelu, A. 2007. Socio-economic analysis of the impacts of avian influenza epidemic on households poultry consumption and poultry industry in Nigeria: empirical investigation of Kwara State. Livestock Research for Rural Development 19:4.

Olsen, B., V. J. Munster, A. Wallensten, J. Waldenstrom, A. Osterhaus, and R. A. M. Fouchier. 2006. Global patterns of influenza A virus in wild birds. Science 312:384- 388.

RDevelopment, C. 2008. TEAM: R: a language and environment for statistical computing. R Foundation for Statistical Computing, Austria.

Roshier, D., P. Whetton, R. Allan, and A. Robertson. 2001. Distribution and persistence of temporary wetland habitats in arid Australia in relation to climate. Austral Ecology 26:371-384.

Roshier, D. A., A. I. Robertson, and R. T. Kingsford. 2002. Responses of Waterbirds to Flooding in an Arid Region of Australia and Implications for Conservation. Biological Conservation 106:399-411.

131

Rushton, J., R. Viscarra, E. Guerne Bleich, and A. McLeod. 2005. Impact of avian influenza outbreaks in the poultry sectors of five South East Asian countries (Cambodia, Indonesia, Lao PDR, Thailand, Viet Nam) outbreak costs, responses and potential long term control. World's Poultry Science Journal 61:491-514.

Schmarr, D., R. Mathwin, D. Cheshire, and D. McNeil. 2013. Aquatic ecology assessment and analysis of the Cooper Creek, South Australia.

Sims, L., and A. Turner. 2008. Avian influenza in Australia. In ‘Avian Influenza’.(Ed. DE Swayne.) pp. 239–250. Blackwell: Ames, IA.

Stallknecht, D. E., and J. D. Brown. 2007. Wild birds and the epidemiology of avian influenza. Journal of wildlife diseases 43:S15-S20.

Tischler, M., C. R. Dickman, and G. M. Wardle. 2013. Avian functional group responses to rainfall across four vegetation types in the Simpson Desert, central Australia. Austral Ecology 38:809-819.

Tracey, J. P., R. Woods, D. Roshier, P. West, and G. R. Saunders. 2004. The Role of Wild Birds in the Transmission of Avian Influenza for Australia: an Ecological Perspective. Emu 104:109-124.

Turner, A. 2004. The role of wild aquatic birds in the epidemiology of avian influenza in Australia. Australian Veterinary Journal 82:713-713.

Vandegrift, K. J., S. H. Sokolow, P. Daszak, and A. M. Kilpatrick. 2010. Ecology of avian influenza viruses in a changing world. Pages 113-128 Year in Ecology and Conservation Biology 2010.

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CHAPTER 5

Rainfall driven and wild-bird mediated avian influenza virus outbreaks in

Australian poultry

Marta Ferenczi

Christa Beckmann

Marcel Klaassen

MF and MK designed the study, analysed the data. MF, MK and CB drafted the manuscript. All authors read and approved the final manuscript.

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ABSTRACT

Globally, outbreaks of Avian Influenza Virus (AIV) in poultry continue to burden economies and endanger human, livestock and wildlife health. Wild waterbirds are often identified as possible sources for poultry infection. Therefore it is important to study the ecological and environmental factors that directly influence infection dynamics in wild birds and may therewith indirectly affect outbreaks in poultry. In Australia, where large parts of the country experience erratic rainfall patterns, intense rainfalls lead to wild waterfowl breeding events and increased proportions of immunologically naïve juvenile birds. It is hypothesized that after breeding, when the temporary wetlands dry, increasing densities of immunologically naïve waterbirds returning to permanent water bodies might strongly contribute to AIV prevalence in wild waterfowl in Australia. Because rainfall has been proven to be an important environmental driver in AIV dynamics in wild waterbirds in southeast Australia and wild waterbirds are identified globally to have a role in virus spillover into poultry, we assume that rainfall events have an indirect effect on AIV outbreaks in poultry in southeast

Australia. In this study we investigated this hypothesis by examining the timing of AIV outbreaks in poultry in and near the Murray-Darling basin in relation to temporal patterns in regional rainfall. Our findings support the initial hypothesis and suggest that an increased risk of AIV outbreaks in poultry exists after a period of high amounts of rainfall. This is presumably triggered by increased rates of waterbird breeding and consequent higher proportions of immunologically naïve juvenile waterbirds entering the population.

Keywords: waterfowl, infection dynamics, climatic forcing

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INTRODUCTION

High pathogenic outbreaks of Avian Influenza Virus (AIV) in domestic poultry and the possibility of transmission of AIV to humans can result in extensive socio-economic costs

(Gilbert and Pfeiffer 2012, Gilbert et al. 2014, Vergne et al. 2014). AIV in its low pathogenic form (defined as causing only mild or non-detectable clinical signs in poultry; termed LPAI) occurs naturally in wild bird populations (Alexander 2000). Occasionally high pathogenic forms of AIV (defined as causing severe clinical signs and rapid death in poultry; termed

HPAI) evolve in poultry after alleged exposure to LPAI from wild birds (Capua and

Alexander 2002, Ellis et al. 2004, Kuiken and Harder 2012). Virus spillover onto poultry farms could occur when infected wild birds enter poultry barns. These wild birds could either directly infect the chickens/ducks, or indirectly contaminate water and surfaces from where the virus is transmitted to the poultry, potentially assisted by farm workers, pets or transport of equipment (Morgan and Kelly 1990, Burns et al. 2012). Therefore we can assume that the same ecological and environmental factors that affect outbreaks of LPAI in wild birds would indirectly result in increased incidence of LPAI and HPAI outbreaks in poultry.

Due to HPAI outbreaks in domestic poultry there is increasing interest in the ecological and environmental factors that influence infection dynamics in wild birds and the possible virus transmissions between wild birds and poultry (Morgan and Kelly 1990, Arzey et al. 2012, Caron et al. 2012). To date, the majority of research on AIV dynamics in wild birds has been conducted in the northern hemisphere. The AIV pattern observed there is highly seasonal, with a yearly peak in late summer/early autumn, followed by low prevalence in winter (Munster et al. 2007, van Dijk et al. 2013). The only published study to date that included birds from the southern hemisphere showed that peaks of AIV prevalence in

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waterfowl communities are lower there than in the northern hemisphere. Nonetheless, a shallow seasonal peak was suggested in southern hemisphere birds (Gaidet et al. 2012).

Furthermore, recent studies, in temperate southeast Australia (Ferenczi et al. data accepted for publication) found that AIV prevalence was related to irregular, non-seasonal rainfall patterns.

Several ecological mechanisms have been studied as potential drivers of AIV dynamics in wild birds (Altizer et al. 2011, Gaidet et al. 2012, van Dijk et al. 2013). Among wild waterbird communities, three ecological mechanisms have been suggested as the primary drivers of the seasonal AIV dynamics in the northern hemisphere: (i) the annual congregation of migratory birds at staging and wintering sites increases contact rates between individuals, and thereby infection rates (Gaidet et al. 2012), (ii) an increase in the abundance of immunologically naïve young birds results in a higher number of individuals susceptible to infection in the waterbird community (Hinshaw et al. 1980, van Dijk et al. 2013) and (iii) increases in energy-demanding activities, notably in relation to migration, potentially impairing immunocompetence (Altizer et al. 2011). In general the ecological drivers for disease dynamics are importantly linked to seasonal variation in resources in the northern hemisphere (Hosseini et al. 2004, Altizer et al. 2006). Large parts of the globe, however, are far less seasonal (Chown et al. 2004).

In Australia, for instance, water availability is highly variable and an important factor in the ecology of waterfowl (Woodall 1985, Halse and Jaensch 1989a, Marchant and Higgins

1990). Across much of the Australian continent, climatic conditions are extreme and non- seasonal (Ummenhofer et al. 2009). Although regular rainfall occurs seasonally in the

Australian tropics (summer) and the temperate southeast and southwest regions (winter- spring), water availability is largely aseasonal across the rest of the continent (Norman and

Nicholls 1991, Ummenhofer et al. 2009). In southeastern Australia, inter-annual variation in

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rainfall is very high, with higher rainfall being positively related to waterfowl breeding

(Norman and Nicholls 1991). Wet and dry periods can each persist for several years

(Ummenhofer et al. 2009), occasionally creating extreme climate events, such as the ‘Big

Dry’ phenomenon in southeastern Australia between 1997 and 2009 (Gergis et al. 2012).

These irregular rainfall patterns strongly influence the movement and breeding biology of many Australian waterfowl species. During wet periods, bird numbers increase at flooded areas where food sources become available, creating appropriate conditions for breeding (Briggs 1992, Roshier et al. 2001). Afterwards, when flooded areas start to dry and reduce in size, waterbirds congregate on the remaining wetlands (Briggs and Maher 1985,

Kingsford and Norman 2002, Roshier et al. 2002). Klaassen et al. (2011) suggested that the non-seasonal and often multi-year alternations of wet and dry periods that influence the breeding ecology of waterfowl might, in turn, affect the temporal pattern of AIV prevalence on the Australian continent. Applying the previously mentioned ecological drivers (i.e. i, ii, iii) to the climatic conditions in the southern hemisphere, Klaassen et al. (2011) hypothesized that intense rainfall leads to breeding events and increased numbers of immunologically naïve juvenile birds. After breeding, when the temporary wetlands dry, increasing densities of immunologically naïve waterbirds returning to permanent water bodies might importantly influence AIV prevalence in wild waterfowl in Australia. In addition, the reduced food availability that accompanies the drying ephemeral wetlands can lead to reduction in birds’ immunocompetence (Appleby et al. 1999) and therefore further increase AIV infection risk.

Ferenczi et al.’s (data accepted for publication) findings from temperate southeast Australia also support Klaassen et al.’s (2011) hypothesis that irregular rainfall influences population dynamics and age structure within the duck community, which may subsequently affect AIV dynamics.

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As (1) rainfall is an important environmental driver in AIV dynamics in wild

Australian waterbirds (Ferenczi et al. data accepted for publication) and (2) wild waterbirds, especially ducks, are identified globally to have a role in virus spillover into poultry

(Keawcharoen et al. 2008, Kim et al. 2009, Tian et al. 2015), we suggest that rainfall events have an indirect effect on AIV outbreaks in Australian poultry. We investigated this hypothesis by examining the timing of LPAI and HPAI outbreaks in poultry in one of the most poultry-dense regions within Australia, the Murray-Darling basin and nearby locations in relation to temporal patterns in regional rainfall.

METHODS

Avian influenza virus outbreak data

We tabulated the LPAI and HPAI outbreaks on poultry farms in Australia based on literature obtained through the following search engines: Web of Science and Google

Scholar. The search included the following terms and patterns: “Avian Influenza Virus”,

“LPAI”, “HPAI”, “chicken”, “duck”, “poultry”, “outbreak” and “Australia”. Additionally, we obtained data through Wildlife Health Australia. We found that with one exception in

Tasmania, all the AIV outbreaks in poultry in Australia occurred in or in close proximity to the Murray-Darling basin. We therefore selected outbreaks from the Murray-Darling basin and sites within 100 kilometres of its boundary. We excluded one LPAI outbreak that occurred in 1994, as this outbreak was not publically available. Outbreaks that occurred in the same month but at different locations were included as one outbreak in the analysis.

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Weather data and statistical analysis

In order to investigate AIV outbreak events in relation to rainfall, we obtained monthly total rainfall data for Murray-Darling basin from the Australian Bureau of

Meteorology

(http://www.bom.gov.au/climate/change/index.shtml#tabs=Tracker&tracker=timeseries). The effects of weather are not always immediately expressed in ecological processes (Halse and

Jaensch 1989b, Letnic and Dickman 2006), thus there may be a cumulative effect and/or a time lag between rainfall, waterfowl breeding events and associated changes in the epidemiology of AIV within wild bird populations and AIV outbreaks in poultry. We therefore categorised rainfall as two separate variables that can potentially affect the AIV outbreaks in poultry: (1) the amount of rainfall over a given time period triggering waterfowl breeding (i.e. termed “rainfall period” from here on) and (2) the time lag between the rainfall- induced breeding period and the increased AIV prevalence after breeding that ultimately leads to an increased risk of AIV outbreaks in poultry (i.e. termed “time lag period” from here on). The rainfall categories were applied to all months when an outbreak occurred (i.e. termed “outbreak month(s)” from here on) and for all the other months between the “outbreak months” when an outbreak was not observed (i.e. termed “non-outbreak month(s)” from here on). We calculated the rainfall categories for these focal months for one up to 24 months for the “rainfall period” and for zero up to 22 months for the “time lag period”. For example, the rainfall category of two months “rainfall period” with zero “time lag period” means that rainfall was averaged over two months preceding a focal month (i.e. an “outbreak” or “non- outbreak month”). Another example for rainfall category of three months “rainfall period” with one month “time lag period” means that rainfall was averaged over the second, third and

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fourth month (i.e. skipping the first month) prior to the focal month. Within each rainfall category we compared the “rainfall periods” between “outbreak” and “non-outbreak months” with a Welch’s t-test. All analyses were conducted using Excel and R (RDevelopment 2008).

RESULTS

We selected seven HPAI and five LPAI outbreaks across the Murray-Darling basin and close vicinity between 1976 and 2013 to analyse AIV outbreak events in relation to rainfall (Figure 1 and Table 1). Between January 1974 and December 2014, 276 rainfall categories were calculated. For each rainfall category we calculated a t-test (i.e. total of 276 t- tests), showing the differences of the mean of rainfall between the “outbreak” and “non- outbreak months”. A t-test with ‘positive sign’ means that the rainfall of “outbreak months” was higher than for “non-outbreak months” for that rainfall category, while the reverse is true for t-tests with ‘negative signs’. Out of the 276 t-tests 41 were statistically significant

(p<0.05). The rainfall for two to 20 months “rainfall period” was significantly higher for the

“outbreak months” than for the “non-outbreak months” if a “time lag period” of four to 22 months was taken into consideration (Figure 2). A notable concentration of significant results

(71 %) was observed for rainfall periods of 7-15 and time lag periods of 8-16 months, where rainfall and time lag period added up to a relatively narrow period of 21 to 24 months (the analyses being restrained to a maximum total duration of 24 months for each focal month;

Figure 2).

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Figure 1: Locations of high and low pathogenic avian influenza outbreaks in poultry in the

Murray-Darling basin (shaded blue) and close vicinity: 1) Keysborough, 2 and 3) Bendigo,

4) Lowood, 5) Tamworth, 6 and 7) Sydney Basin, 8) North-west Melbourne, 9 and 11) Hunter

Valley, 12) Young.

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Included in Year Month State Location Affected HPAI / LPAI the analysis stock subtype (X) X 1976 January VIC Keysborough Chicken, HPAI H7N7 (Outer suburbs duck of Melbourne) X 1985 May VIC Bendigo Chicken HPAI H7N7

X 1992 August VIC Bendigo Chicken, HPAI H7N3 duck X 1994 December QLD Lowood (outer Chicken HPAI H7N3 area of Brisbane) 1994 ? VIC ? Duck LPAI H4N8

X 1997 November NSW Tamworth Chicken, HPAI H7N4 emu X 2006 October NSW Sydney Basin Chicken, LPAI H6N4 duck

X 2010 Mar NSW Sydney basin Chicken LPAI H10N7

X 2012 Jan VIC North west Duck LPAI H5N3 Melbourne X 2012 April NSW Hunter Valley Turkey LPAI H9N2

2012 April NSW North Coast Duck LPAI H4N6

X 2012 July QLD Chicken LPAI H10N7

X 2012 November NSW Hunter Valley Chicken HPAI H7N7

X 2013 October NSW Young Chicken H7N2

Table 1: High pathogenic (HPAI) and low pathogenic (LPAI) avian influenza virus outbreaks across the Murray-Darling basin and close vicinity between 1976 and 2013. Text for high pathogenic avian influenza outbreaks are bold and red. Outbreaks that are included in the analysis are marked with an X. More detail on the outbreaks, including their sources in

Appendix S1.

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Figure 2: Rainfall categories (i.e. “rainfall period” and “time lag period”) with calculated t- tests (i.e. the differences of the mean of rainfall between the “outbreak” and “non-outbreak months”). A t-test with ‘positive sign’ means that the rainfall of “outbreak months” was higher than for “non-outbreak months” for that rainfall category, while the reverse is true for t-tests with ‘negative signs’. Significant results are marked with black dots.

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DISCUSSION

We found that average rainfall was significantly higher prior to outbreaks when allowing for time lags of four to 22 months. These findings support our initial hypothesis and suggest that an increased risk of AIV outbreaks in poultry exists after (1) a period of intense rainfall over a period of two to 20 months presumably triggering increased waterbird breeding and increased numbers of immunologically naïve juvenile waterbirds, which (2) enter the population at gradually increasing densities over a time lag between four and 22 months, with a notable concentration between eight and 16 months. Remarkably, when combining the rainfall period with the time lag period, most of the significant relationships fell within a period of 21 to the maximum of 24 months for which our analyses allowed. Thus outbreak risks appear to be correlated with rainfall patterns over the preceding two years.

A dominant feature of Australian climate is the ENSO-linked irregularity in both timing and location of wet and dry periods (Kousky et al. 1984, Kingsford and Norman 2002,

Kingsford et al. 2010). These erratic climate patterns may relax seasonality in waterfowl breeding, where reproduction occurs after periods of higher rainfall and associated increases in food availability (Halse and Jaensch 1989b, Marchant and Higgins 1990, Briggs 1992).

Ferenczi et al. (data accepted for publication) indicated that after rainfall-triggered breeding events, the influx of juveniles that arrive from inland areas that mix with locally hatched juveniles, were likely drivers of AIV prevalence dynamics in two wild duck species on a coastal permanent wetland. Thus the time lag that is observed between breeding and the increased AIV prevalence in waterfowl populations after breeding (Ferenczi et al. data accepted for publication) is ultimately reflected in AIV outbreaks in poultry.

That the average rainfall is low directly before (i.e. up to six months) AIV outbreaks in poultry support Klaassen et al.’s (2011) hypothesis and suggests that the likelihood of AIV

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outbreaks in poultry increases as temporary wetlands dry up. This was also hypothesised in another study for an Australian desert wetland system by Ferenczi et al. (unpublished data) where the decreasing extent of local surface water resulted in an increased AIV antibody prevalence. As inland wetland systems contract with the onset of dry periods, associated wild waterbirds from these regions (Briggs 1992, Roshier et al. 2001), may be concentrated on a few remaining waterbodies, such as farm dams. In these situations, they may be in direct or indirect contact with domestic birds (Marchant and Higgins 1990, Loyn et al. 2014), which increases the likelihood of virus transmission between wild and domestic birds.

Recognizing the role of rainfall as a major driver of waterbird population dynamics,

Vijaykrishna et al. (2013) showed a decrease in AIV diversity during years when the rainfall across Australia was below average. To our knowledge, Vijaykrishna et al.’s (2013) rainfall driven evolutionary dynamics of AIV and Ferenczi et al.’s (data accepted for publication) rainfall driven viral prevalence in waterfowl are the only studies that support the idea that aseasonal rainfall patterns are a major driver of AIV dynamics in this part of the world..

Although rainfall is considered to be of less importance in AIV dynamics in the northern hemisphere, a few studies have found it to influence AIV prevalence in wild and domestic birds (Si et al. 2013, Hu et al. 2015). East et al.’s (2008) study of H5N1 HPAI infection risk analysis in Australia suggested that the areas of highest risk for introduction of AIV from wild birds into poultry were in eastern Australia where there are (1) higher densities of poultry farms; (2) more wetland habitats for waterbirds and (3) the climate is wetter (East et al. 2008).

Our study highlights the importance of investigating AIV dynamics in both wild and domestic birds in relation to different environmental and ecological factors, allowing for a better understanding of AIV transmission risks between them. Additionally, and notably for systems regularly experiencing extreme weather events, such studies may allow for an

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improved understanding of the climatic drivers of disease dynamics. Climatic forcing of disease dynamics is commonly assumed for both humans and wildlife (Hosseini et al. 2004,

Koelle and Pascual 2004). However, to evaluate causality of correlations between weather conditions and disease patterns is often hampered by high levels of seasonality, which notably prevail in the northern hemisphere. Disease-dynamics studies in regions of the world with less predictable climatic conditions, such as in large parts of Australia and many other areas of the southern hemisphere, may thus provide important insights in the true drivers of disease dynamics and the consequences of climate change on disease dynamics (Anyamba et al. 2006, Gilbert et al. 2008).

ACKNOWLEDGEMENTS

We thank Tiggy Grillo from Wildlife Health Australia for helping to research all the avian influenza virus outbreak data in Australian poultry. Additionally, much appreciated comments on the manuscript were received from Bill Buttemer. Australian Bureau of

Meteorology is also thanked for providing all the weather data for analysis.

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APPENDIX

Year Month State Location Affected HPAI / Outcome on Notes and Citations stock LPAI property Note – more detail in Table 5.2 National Dossier subtype 1976 January Vic Keysborough Chicken, HPAI All chickens on the Poultry – Chicken (meat chicken - broiler and egg layer); domestic duck duck H7N7 outbreak farm being slaughtered, LPAI (H7N7) was isolated on a duck farm, located opposite the infected chicken farm, the farm dis- during investigation of an HPAI (H7N7) outbreak in chickens in Victoria in 1976. The infected, spelled, ducks showed no signs of clinical disease and virological analysis confirmed the duck virus checked for as LPAI. residual virus using sentinel Information relating to wild birds: Serum collected from wild seagulls, ducks and chickens and sparrows captured on the farm was negative. In addition, no haemagglutination-inhibition restocked after the antibody to H7 were detected in approx. 300 sera collected from a single short-tailed prescribed shearwater rookery south of Melbourne soon after the outbreak (Westbury 1997, conditions were unpublished data).

met. References: x (Westbury 2003) x National Avian Influenza Surveillance Dossier

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1985 May Vic Bendigo Chicken HPAI All chickens on the Poultry - Chicken (multi-age chicken farm; broiler breeders, broilers, egg layers and H7N7 outbreak farm being started pullets; property also had a small abattoir) slaughtered, the farm dis-infected, spelled, Information relating to wild birds: Property had a large lake with waterfowl, first shed checked for residual infected was closest to the shed. Attempts to sample ducks near / on the property were virus using sentinel unsuccessful. One starling caught on the farm was positive for H7N7 – noted as a possible chickens and source of transmission. restocked after the prescribed References: conditions were met. x (Westbury 2003) x National Avian Influenza Surveillance Dossier 1992 August Vic Bendigo Chicken, HPAI All chickens and Poultry – Chicken (meat - broiler breeders) and domestic ducks duck H7N3 ducks on affected properties were Antibodies to H5, H7 and other subtypes of AI viruses were detected in commercial slaughtered, the farm domestic duck property, a neighbouring property, during investigation of an HPAI (H7N3) dis-infected, spelled, outbreak in chickens in Victoria in 1992. checked for residual virus using sentinel Information relating to wild birds: Ducks had access to small paddock, and a small dam, chickens and and it is likely had contact with wild waterbirds. In addition, personnel on the duck restocked after the property also visited the poultry property. No virus detected from domestic ducks. prescribed conditions were met. References: x (Westbury 2003) x (Selleck et al. 1997) x National Avian Influenza Surveillance Dossier 1994 Decemb Qld Lowood Chicken HPAI All chickens on the Poultry – Chicken (multi-age layer farm) er (outer H7N3 outbreak farm being area of slaughtered, the farm Information relating to wild birds: Unusually high numbers of waterfowl present in river Brisbane) dis-infected, spelled, adjacent to the property, which acted as a source of (untreated) water for chickens. Small checked for residual number of wild waterfowl caught did not have detectable antibodies or virus. virus using sentinel chickens and References:

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restocked after the x (Westbury 2003) prescribed x National Avian Influenza Surveillance Dossier conditions were met. 1994 ? Vic ? Duck LPAI Improved Poultry – Commercial multi-age domestic ducks H4N8 biosecurity, all-in-all out system Note: farm investigated due to suspect and then confirmed Reimerlla anatipestifer introduced infection. LPAI H4N8 incidental finding.

Information relating to wild birds: Farm had access to wild birds prior to incident.

References: x National Avian Influenza Surveillance Dossier ¾ Month and specific location could not be confirmed. 1997 Novemb NSW Tamwort Chicken, HPAI Poultry – Chicken (meat chicken breeders), Emu (swabs positive from 3-month old emus, er h emu H7N4 with no clinical signs, on a contract broiler grower farm).

The swabs form the emu results in a H7N4 virus with an identical HA cleavage site amino acid sequence to that of the viruses isolated from chickens. It is possible that emus were the source of the infection, although other sources and means of transmission cannot be excluded.

Information relating to wild birds: The water supply was filtered, chlorinated, reticulated town water. There were no dams in the immediate vicinity, and no direct contact with wild waterfowl could be identified. The negative results on samples from wild birds are not conclusive, since this survey was done some time after the outbreak and any potential carriers that were migratory may have left the area. Serology samples from wild water birds in the vicinity of the infected properties were tested (i.e. Pacific Black Duck, Wood Duck, Grey Teal, unidentified ducks), all were negative for antibody to H7 influenza A virus by HI. A more extensive survey (serum and cloacal swabs) of wild waterbirds was done approx. 6 weeks after the initial diagnosis of HPAI on IP1. No antibodies to H7 were detected and no virus isolated.

References:

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x (Selleck et al. 2003) x National Avian Influenza Surveillance Dossier 2006 October NSW Sydney Chicken, LPAI Affected birds were Poultry – Chicken (breeders) Basin Duck H6N4 sent for early processing. Chickens were seropositive to H6 in October 2006, with one cloacal and one tracheal pool positive H6N4.

Information relating to wild birds: Research into the prevalence of AI viruses in wild birds frequenting poultry farms in the area has not found evidence of AIV in any of the wild ducks sampled during the 12 month project.

References: x National Avian Influenza Surveillance Dossier x George Arzey (pers. comm – date) 2010 Mar NSW Sydney Chicken LPAI Poultry – Chickens (Breeders; Biosecure intensive poultry enterprise) basin H10N7 Information relating to wild birds: Surveillance of poultry flocks within a 2-km radius of the affected farm did not detect any serologic or virological evidence of subtype H10 infection. Ongoing surveillance of wild waterfowl in New South Wales reported influenza virus A (H10N7) in other areas in the previous year (K.E. Arzey, unpublished data); however, during 2007–2008, onsite surveillance detected no evidence of influenza A infection among wild waterfowl (G.G. Arzey, unpublished data).

References: x (Arzey et al. 2012) x NSW Animal Health Surveillance: http://www.dpi.nsw.gov.au/__data/assets/pdf_file/0003/388452/AHNewsJan- Mar2011.pdf; http://www.dpi.nsw.gov.au/__data/assets/pdf_file/0019/332146/AHS- Mar2010.pdf; http://www.animalhealthaustralia.com.au/wp- content/uploads/2011/05/AHSQ-Q1-2010.pdf

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2012 Jan Vic North west Duck LPAI Quarantined Poultry – Commercial free-range duck farm Melbourne H5N3 Depopulate Tracing surveillance In late January 2012, a low pathogenicity notifiable AI (H5N3) was detected in two Surveillance for isolated, but related, free-range duck farms in Victoria. Proof of Freedom on property Information relating to wild birds: Investigations indicated that wild birds were the source of the virus. Low pathogenicity strains of AI are detected in wild birds from time to time in Australia. This is the first time that an H5 subtype virus has been detected in domestic ducks in Victoria.

References: x World Animal Health Information Database (WAHID) Interface: http://www.oie.int/wahis_2/public/wahid.php/Reviewreport/Review/viewsummary ?fupser=&dothis=&reportid=11554 x Animal Health in Australia: http://www.animalhealthaustralia.com.au/wp- content/uploads/2011/01/AHIA-2012-full-report.pdf x Department of Agriculture: http://www.agriculture.gov.au/biosecurity/australia/reports-pubs/biosecurity- bulletin/2012/biosecurity-bulletin-march-2012 2012 April NSW Hunter Turkey LPAI Depopulate Poultry - Turkey Valley H9N2 Tracing Surveillance Two farms affected.

Information relating to wild birds: It is unclear whether the virus was transmitted somehow between the two farms or was acquired independently by each farm. The second farm was in a wetland area with plenty of opportunities for transmission of the infection directly or indirectly via wild ducks.

References: x NSW Animal Health Surveillance: http://www.dpi.nsw.gov.au/__data/assets/pdf_file/0008/447128/Animal-health- surveillance-2012-3.pdf

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2012 April NSW North Duck LPAI Quarantine Poultry – ducks of several age groups Coast H4N6 Follow up AI testing on property Infection was evident mainly in the ducks inside the shed. No obvious sickness or deaths were observed in chickens or geese housed 10 to 20 m away from the ducks, and infection with H4N6 was not detected in these species.

Information relating to wild birds: The source of H4N6 could not be identified, but there is a dam frequented by wild waterfowl on the property; the wild waterfowl could have been the source.

References: x NSW Animal Health Surveillance: http://www.dpi.nsw.gov.au/__data/assets/pdf_file/0008/447128/Animal-health- surveillance-2012-3.pdf ¾ The outbreaks from April 2012 were included as one in the analysis. 2012 July Qld Chicken LPAI Quarantine Poultry – Chickens H10N7 Information about wild birds: “The observed genetic differences between the chicken H10 viruses isolated from the NSW and Qld farms and their relatedness to wild waterfowl isolate sequences from 2009 and 2011 may suggest that the H10N7 viruses that caused the 2010 and 2012 incidents were independently introduced into chickens from waterfowl. However, considering the lack of clinical symptoms in ducks (farm) infected with the H10N7 virus and the absence of ongoing active influenza surveillance in poultry in Australia, the possibility cannot be excluded that domestic ducks are the source of infection of chickens. The occurrence of poultry outbreaks two years apart on farms that are approximately 1,000 km apart may indicate the endemic nature of the introduced North American H10 subtype viruses in Australian wild waterfowl. However, movements of domestic poultry are known to occur from the affected farm in NSW and the area in Qld, where the second farm is located. Therefore, on both farms it is not possible to rule out that the incursion of the virus was associated with presence of infection in domestic ducks and movements of domestic poultry.

References:

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x (Vijaykrishna et al. 2013) ¾ Specific location could not be confirmed. 2012 Novemb NSW Hunter Chicken HPAI Quarantined Poultry – er Valley H7N7 Depopulate Tracing surveillance Information about wild birds: H7 detected in wild birds in May 2012 aligned with the Surveillance for variant H7 HA gene sequence from the NSW outbreak when re-tested at AAHL (previously Proof of Freedom on tested negative at AAHL; T Grillo pers. comm.). property References: x NSW Animal Health Surveillance: http://www.dpi.nsw.gov.au/__data/assets/pdf_file/0003/458553/AHS-oct-dec- 2012.pdf

2013 October NSW Young Chicken H7N2 Poultry –

References: x OIE World Organisation for Animal Health: http://www.oie.int/wahis_2/public%5C..%5Ctemp%5Creports/en_fup_000001481 1_20140221_123501.pdf

S1: High pathogenic (HPAI) and low pathogenic (LPAI) avian influenza virus outbreaks across the Murray-Darling basin and close vicinity between 1976 and 2013. Text for high pathogenic avian influenza outbreaks are bold and red.

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REFERENCES

Altizer, S., R. Bartel, and B. A. Han. 2011. Animal Migration and Infectious Disease Risk. Science 331:296-302.

Altizer, S., A. Dobson, P. Hosseini, P. Hudson, M. Pascual, and P. Rohani. 2006. Seasonality and the dynamics of infectious diseases. Ecology Letters 9:467-484.

Appleby, B., M. Anwar, and S. Petty. 1999. Short-term and long-term effects of food supply on parasite burdens in Tawny Owls, Strix aluco. Functional Ecology:315-321.

Briggs, S. V. 1992. Movement patterns and breeding characteristics of arid zone ducks. Corella 16:15-22.

Capua, I., and D. J. Alexander. 2002. Avian influenza and human health. Acta tropica 83:1-6.

Chown, S. L., B. J. Sinclair, H. P. Leinaas, and K. J. Gaston. 2004. Hemispheric asymmetries in biodiversity—a serious matter for ecology.

154

van Dijk, J. G., B. J. Hoye, J. H. Verhagen, B. A. Nolet, R. A. Fouchier, and M. Klaassen. 2013. Juveniles and migrants as drivers for seasonal epizootics of avian influenza virus. Journal of Animal Ecology 83:266-275.

East, I. J., S. A. Hamilton, L. A. Sharp, and M. G. Garner. 2008. Identifying areas of Australia at risk for H5N1 avian influenza infection from exposure to nomadic waterfowl moving throughout the Australo-Papuan region. Geospatial Health 3:17-27.

Ellis, T. M., R. Barry Bousfield, L. A. Bissett, K. C. Dyrting, G. S. Luk, S. Tsim, K. Sturm- Ramirez, R. G. Webster, Y. Guan, and J. M. Peiris. 2004. Investigation of outbreaks of highly pathogenic H5N1 avian influenza in waterfowl and wild birds in Hong Kong in late 2002. Avian Pathology 33:492-505.

Gilbert, M., J. Slingenbergh, and X. Xiao. 2008. Climate change and avian influenza. Revue scientifique et technique (International Office of Epizootics) 27:459.

Halse, S., and R. Jaensch. 1989a. Breeding seasons of waterbirds in South-western Australia—the importance of rainfall. Emu 89:232-249.

Halse, S. A., and R. P. Jaensch. 1989b. Breeding seasons of waterbirds in south-western Australia - the importance of rainfall. Emu 89:232-249.

155

Hinshaw, V., W. Bean, R. Webster, and G. Sriram. 1980. Genetic reassortment of influenza A viruses in the intestinal tract of ducks. Virology 102:412-419.

Hosseini, P. R., A. A. Dhondt, and A. Dobson. 2004. Seasonality and wildlife disease: how seasonal birth, aggregation and variation in immunity affect the dynamics of Mycoplasma gallisepticum in house finches. Proceedings of the Royal Society of London B: Biological Sciences 271:2569-2577.

Hu, W., W. Zhang, X. Huang, A. Clements, K. Mengersen, and S. Tong. 2015. Weather variability and influenza A (H7N9) transmission in Shanghai, China: A Bayesian spatial analysis. Environmental research 136:405-412.

Keawcharoen, J., D. Van Riel, G. van Amerongen, T. Bestebroer, W. E. Beyer, R. Van Lavieren, A. D. Osterhaus, R. A. Fouchier, and T. Kuiken. 2008. Wild ducks as long- distance vectors of highly pathogenic avian influenza virus (H5N1). Emerging infectious diseases 14:600.

Kim, J. K., N. J. Negovetich, H. L. Forrest, and R. G. Webster. 2009. Ducks: the “Trojan horses” of H5N1 influenza. Influenza and other respiratory viruses 3:121-128.

Kingsford, R. T., and F. I. Norman. 2002. Australian waterbirds - products of the continent's ecology. Emu 102:1-23.

Kingsford, R. T., D. A. Roshier, and J. L. Porter. 2010. Australian waterbirds - time and space travellers in dynamic desert landscapes. Marine and Freshwater Research 61:875-884.

Klaassen, M., B. J. Hoye, and D. A. Roshier. 2011. Identifying crucial gaps in our knowledge of the life-history of avian influenza viruses - an Australian perspective. Emu 111:103-112.

Koelle, K., and M. Pascual. 2004. Disentangling extrinsic from intrinsic factors in disease dynamics: a nonlinear time series approach with an application to cholera. The American Naturalist 163:901-913.

Kousky, V. E., M. T. Kagano, and I. F. Cavalcanti. 1984. A review of the Southern Oscillation: oceanicǦatmospheric circulation changes and related rainfall anomalies. Tellus A 36:490-504.

Kuiken, T., and T. Harder. 2012. H5N1 HIGHLY PATHOGENIC AVIAN INFLUENZA. New Directions in Conservation Medicine: Applied Cases of Ecological Health:228.

156

Marchant, S., and P. J. Higgins. 1990. Handbook of Australian, New Zealand and Antarctic birds. Volume 1 Ratites to ducks. Oxford University Press, Melbourne.

Morgan, I., and A. Kelly. 1990. Epidemiology of an avian influenza outbreak in Victoria in 1985. Australian Veterinary Journal 67:125-128.

Norman, F. I., and N. Nicholls. 1991. The Southern Oscillation and variations in waterfowl abundance in southeastern Australia. Australian Journal of Ecology 16:485-490.

RDevelopment, C. 2008. TEAM: R: a language and environment for statistical computing. R Foundation for Statistical Computing, Austria.

Roshier, D. A., A. I. Robertson, and R. T. Kingsford. 2002. Responses of Waterbirds to Flooding in an Arid Region of Australia and Implications for Conservation. Biological Conservation 106:399-411.

Roshier, D. A., P. H. Whetton, R. J. Allan, and A. I. Robertson. 2001. Distribution and persistence of temporary wetland habitats in arid Australia in relation to climate. Austral Ecology 26:371-384.

Selleck, P., G. Arzey, P. Kirkland, R. Reece, A. Gould, P. Daniels, and H. Westbury. 2003. An outbreak of highly pathogenic avian influenza in Australia in 1997 caused by an H7N4 virus. Avian diseases 47:806-811.

Selleck, P., L. Gleeson, F. HOOPER, H. Westbury, and E. Hansson. 1997. Identification and characterisation of an H7N3 influenza A virus from an outbreak of virulent avian influenza in Victoria. Australian Veterinary Journal 75:289-292.

157

Si, Y., W. F. de Boer, and P. Gong. 2013. Different environmental drivers of highly pathogenic avian influenza H5N1 outbreaks in poultry and wild birds.

Tian, H., S. Zhou, L. Dong, T. P. Van Boeckel, Y. Cui, Y. Wu, B. Cazelles, S. Huang, R. Yang, and B. T. Grenfell. 2015. Avian influenza H5N1 viral and bird migration networks in Asia. Proceedings of the National Academy of Sciences 112:172-177.

Westbury, H. 2003. History of highly pathogenic avian influenza in Australia. Avian diseases:23-30.

Woodall, P. F. 1985. Waterbird Populations in the Brisbane Region, 1972-83, and Correlates with Rainfall and Water Heights. Australian Wildlife Research 12:495-506.

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CHAPTER 6

Synthesis: Avian influenza virus dynamics in Australian wild birds

Marta Ferenczi

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Notably after high pathogenic avian influenza H5N1 started its global rampage in

2003, not only infecting poultry but also wild birds (Ozawa et al. 2015, Feare 2010, Kim et al. 2015), the number of avian influenza virus (AIV) surveillance programs targeting wild birds increased dramatically (Olsen et al. 2006). These surveillance programs were mainly conducted in the northern hemisphere, partly because most of the high pathogenic AIV outbreaks occurred there (Alexander 2000, Munster et al. 2005, Olsen et al. 2006). The goal of most of these AIV surveillance programs was to identify new AIV strains and potential

AIV reservoir species among wild birds (Hoye et al. 2010). However, in order to investigate the possible ways of virus transmissions between wild birds and poultry (and vice versa), there has been an increasing need to understand the ecological and environmental drivers underlying AIV dynamics in wild birds. Although research on AIV in wild birds has seen considerable intensification in the northern hemisphere over the last decade, many knowledge gaps remain in the southern hemisphere (Klaassen et al. 2011). Therefore the aim of my thesis was to study and better understand the ecology and dynamics of AIV in Australian waterbird communities, including the ecological and environmental drivers of infection.

Not all birds are equally suitable as AIV host

In order to understand the ecology and epidemiology of AIV infection, we must first identify the key reservoir species. Based on mainly northern hemisphere studies, AIV has been isolated from many species, including pigs, horses, marine mammals, bats and humans

(Webster et al. 1992). Still, wild birds, particularly waterbirds of the order Anseriformes

(ducks, geese and swans) and Charadriiformes (gulls, terns and waders) are thought to be the main natural reservoirs of AIV (Webster et al. 1992, Tong et al. 2013). Although AIV was

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already described in wild birds in the 1960s (Easterday et al. 1968, Schild and Newman 1969,

Tumova and Easterday 1969), our understanding of the ecology, epidemiology and diversity of AIV in wild birds has only developed in the last couple of decades. One important basis for our current understanding of AIV dynamics is (1) the identification of the AIV reservoir community within which the virus is transmitted and maintained (Stallknecht et al. 2008), and (2) on which a range of potential ecological and environmental drivers act, in turn affecting virus dynamics.

A reservoir community consists of a range of different species that may vary greatly in their ecology, including their sensitivity to infection (Johnson and Hartson 2009). It was

Lourens Baas Becking, a Dutch botanist and microbiologist, who was instrumental in building our understanding of the importance of the environment in explaining the distribution of micro-organisms, including pathogens. It was his hypothesis that in the case of microorganisms "everything is everywhere, but the environment selects", where

“environment” also includes e.g. the interior of animals. By applying Johnson et al.’s (2009) findings on host species inequality and Lourens Baas Becking’s hypothesis (De Wit and

Bouvier 2006) on the importance of the environment on microorganism distributions, we can better understand how and why bird species are not equally susceptible to AIV infection. For example, among waterbirds species in the genus Anas (i.e. dabbling ducks) are likely to play a key role as reservoir hosts of AIV (Dijk et al. 2013). This genus has representatives all over the globe, including Australia, all exhibiting a similar water surface feeding behaviour. It is this behaviour that provides the potential key as to why they are the main reservoirs of AIV

(Munster, Baas et al. 2007); ducks shed the virus via faeces into water where it can readily be transmitted (via the so-called faecal-oral route) to other waterfowl foraging in the same habitat. We should, however, be cautious that even highly related species may play different roles in disease dynamics as a result of small differences in their ecology. We have for

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instance seen the example of the Wood Duck (Chenonetta jubata), which is a dabbling duck similar to other Anas species (e.g. Pacific Black Duck [Anas superciliosa] and Grey Teal

[Anas gracilis]) in Chapter 2; AIV prevalence in Wood Ducks was significantly lower than that of many other dabbling ducks, which may be explained by its differential foraging ecology. In contrast to the surface-feeding behaviour of most other dabbling ducks, Wood

Ducks primarily obtain food by grazing on land (Marchant and Higgins 1990) and therefore may be less exposed to the virus. On the other hand, species that are not highly related but share specific ecological features may play a similar role in AIV dynamics. As an example of this we introduced the Australian Pink-eared Duck (Malacorhynchus membranaceus) in

Chapter 2. This species neither belongs to nor is closely related to the subfamily of dabbling ducks. Yet, it has evolved into a highly specialised surface-feeder with a beak “designed” for filter feeding (Olsen and Joseph 2011). Completely in line with what may be expected from a duck exhibiting these anatomical and behavioural characteristics, the AIV prevalence of

Pink-eared Duck is similarly high to that of surface-feeding Anas dabbling duck species, both within and outside Australia.

Although a (relatively) high prevalence may be a good indicator for a species’ potential role in disease dynamics, it does not necessarily follow that other species that are infected only occasionally and/or have low prevalence do not play a role as (temporary) reservoirs, especially if such species (occasionally) co-occur with key reservoir species. A good example of this may be certain non-waterbirds, namely Australian ‘boom and bust’ species (described in Chapters 2 and 4) that congregate during droughts on the same remaining wetlands with key AIV reservoir waterbird species. We found that at least some of these species (e.g. Budgerigar [Melopsittacus undulatus]) may indeed become infected with

AIV also. Although AIV (antibody) prevalences found were very low and the likely role of

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these species in AIV dynamics minor, given the sheer numbers in which they occasionally occur warrants further study.

"Everything is everywhere, but the environment selects" (De Wit and Bouvier 2006) and thus specific AIV subtypes may exclusively occur in selected host species only. For example, the latest avian influenza viruses (i.e. H17N10 and H18N11) were not detected in birds but in fruit bats (Tong et al. 2012, Tong et al. 2013). Another example is the AIV subtype of H16 that was detected in Black-headed Gulls (Chroicocephalus ridibundus) and appears to be exclusive to this family (Fouchier et al. 2005). These discoveries of new strains constrained to specific taxonomic groups and the identification of new potential reservoirs in extreme environments suggests that we should continue venturing into unknown territory and not limit our efforts to the sampling of known key reservoirs (i.e. dabbling ducks).

Are key drivers of AIV essentially the same around the globe?

In Chapter 2 I determined what reservoir species to focus on and next I tried to identify the key ecological and environmental drivers for AIV dynamics in Australia in

Chapters 3 and 4. Identifying the drivers of disease dynamics in wildlife is problematic given possibilities for experimentation are limited because of the size of the systems (i.e. potentially up to a continental scale), the time scale of the dynamics and thus the period over which disease surveillance would have to take place (i.e. minimum one year) and for ethical considerations (i.e. catching and sampling wild animals). Adopting the comparative approach, by studying the same system under different environmental conditions, may offer

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an alternative avenue to investigate global commonality in the drivers of certain disease dynamics.

However, the necessity to run disease surveillance programs over long time periods remains, and is also particularly important given that many infectious diseases (e.g. cholera, malaria and dengue) typically vary over periods longer than one year (Koelle et al. 2005).

Disease-related amphibian declines that were linked to El Niño Southern Oscillation (ENSO) events were revealed only in a multi-decade study (Rohr and Raffel 2010) may serve as a good example as to the value of long-term surveillance.

In Australia, ENSO drives the erratic and less seasonal weather patterns that are characteristic over large parts of the continent (Pittock 1975, Norman and Nicholls 1991). As a consequence wetland availability is irregular, particularly in desert wetland systems, where dry and wet periods alternate and may run over several years (Kingsford et al. 1999, Roshier et al. 2001, Kingsford et al. 2010). It was hypothesized that these erratic climatic factors might result in significant temporal variations in AIV prevalence within and among species

(Klaassen et al. 2011). To test this hypothesis and identify potential AIV disease dynamics in this extreme environment, where AIV may only be detected during relatively short episodes, long-term surveillance was required to accommodate for temporal fluctuations.

Hitherto, the majority of studies on AIV dynamics in wild birds have been conducted in seasonal and temporal regions of the northern hemisphere, which contrasts sharply with the largely non-seasonal and more erratic dynamics in Australia. Some of these northern hemisphere long term surveillance studies showed a highly seasonal AIV prevalence pattern with a yearly peak in late summer / early autumn, followed by low prevalence in winter

(Munster et al. 2007, van Dijk et al. 2013). In tropical regions and the southern hemisphere

AIV infection dynamics in wild birds are less well studied, but potentially are very different

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from that observed in the northern hemisphere (Klaassen et al. 2011, Gaidet et al. 2012). This supposed difference in AIV dynamics may be due to differences in host ecology, climate and seasonality between the biomes (Gaidet 2015). For example, in contrast to the temperate northern hemisphere, in Afro-tropical regions, seasons are determined by rainfall rather than temperatures (Gaidet et al. 2012). These rainfall driven climatic patterns combined with the ecological characteristics of this biome (potential drivers explained below) may explain why peaks in AIV prevalence in waterfowl communities in Africa are not as pronounced and seasonally less variable than in the northern hemisphere (Gaidet et al. 2012).

One of the drivers thought to underlie AIV dynamics in both the temperate northern hemisphere and Afro-tropical regions is an increase in the abundance of immunologically naïve young birds that results in a higher number of individuals susceptible to infection in the waterbird community (Hinshaw et al. 1980, van Dijk et al. 2013, Gaidet 2015). Although the influx of juvenile waterbirds in the temperate northern hemisphere happens in a relatively short period of time after the breeding season (Hoyo et al. 1992), in the tropics the extended breeding period during the wet season results in continual influx of juveniles into the host community (Gaidet 2015). These differences in breeding ecologies of waterbirds between the two regions may partly explain the differences in dynamics of AIV prevalence (Gaidet 2015).

Another driver likely to influence and explain the above mentioned differences in

AIV dynamics in the temperate northern hemisphere and Afro-tropical regions is the annual congregation of migratory birds. Higher densities of birds result in increased contact rates between individuals and thereby infection rates (Krauss et al. 2010, Altizer et al. 2011, Gaidet et al. 2012). Due to the gradual drying of the wetlands in the tropics, the congregation of waterbirds is progressive in the Afro-tropical regions, while flocking at staging and wintering sites in the temperate northern hemisphere happens over a shorter time period (Gaidet 2015).

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Yet an additional mechanism suggested as driver of seasonal AIV dynamics among waterbird communities in the northern hemisphere is variations in energy-demand, notably in relation to migration, which potentially impairs immuno-competence (Altizer et al. 2011).

Considering (1) all the drivers that have been shown to play a role in driving AIV prevalence in the temperate northern hemisphere and Afro-tropical regions and (2) the marked differences in host ecology, climate and seasonality between these biomes (Gaidet

2015), one of the main aims of my thesis was to describe the ecological and environmental drivers of dynamics of AIV infections in Australian wild birds (Figure 1). I was notably keen to do so because in many regions in Australia weather patterns are erratic and less seasonal

(Pittock 1975, Norman and Nicholls 1991) and thus this continent provides one of the most extreme environments in which AIV dynamics can be studied and the generality of hypothesized drivers evaluated. Although there are other parts of the world, like the Antarctic where drivers of AIV could be studied under extreme circumstances, essentially nothing is known (Hurt, Vijaykrishna et al. 2014). I considered that my study would make an ideal

“comparative approach” case to learn more about the global drivers of AIV dynamics.

Similar to the Afro-tropical regions, Australia’s weather patterns are mainly characterized by rainfall. However, in contrast to the tropics where the alteration of wet and dry periods are seasonal (Gaidet 2015), Australia and particularly its desert wetland systems, are typically characterized by non-seasonal boom and bust dynamics (Kingsford et al. 1999,

Roshier et al. 2001, Kingsford et al. 2010). The erratic climate determines breeding opportunities for nomadic waterfowl and boom and bust species (Kingsford et al. 2010, Loyn et al. 2014), presumably causing significant temporal changes of AIV prevalence within and among species (Figure 1).

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I investigated two highly contrasting biomes in Australia, the temperate coastal regions (Chapter 3) and desert wetland systems (Chapter 4), which have in common erratic rainfall patterns. Rainfall is related to the breeding ecology of waterbirds, indeed turned out to be key in driving AIV prevalence in Australia. While my studies were not able to identify the biological mechanisms with absolute certainty, they suggest that the same key drivers as in the temperate northern hemisphere and in Afro-tropical regions apply, to both temperate coastal regions and desert wetland systems in Australia. To expand our knowledge of the global significance of different ecological and environmental drivers of AIV infection, it would be fascinating to further investigate AIV dynamics in other extreme environments, such as the tropics, high Arctic or Antarctica.

The AIV link between wild birds and poultry in Australia

Since the first outbreaks in 2003, high pathogenic H5N1 AIV has spread rapidly, causing social and economic issues in poultry industry, and serious impacts on wildlife and human health (Feare 2010, Alders et al. 2014, Kurscheid et al. 2015). It launched a major push for research to elucidate the dynamics of AIV in wild birds and the wild birds’ potential role in infecting poultry with H5N1 and other subtypes (including low pathogenic forms).

Having evaluated the key correlates of AIV dynamics in two eco-regions within

Australia in Chapters 3 and 4, in Chapter 5 I tested my findings on the drivers in relation to

AIV outbreaks in the Australian poultry industry. Indeed there appeared to exist a similar link between rainfall patterns and AIV outbreaks in poultry as I found between rainfall patterns and AIV prevalence in wild waterfowl (see Chapters 3 and 4). Thus, wild waterbirds in

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Australia, especially ducks, as elsewhere in the world, seemingly have a role in acting as an

AIV reservoir from which virus spills over into poultry (Keawcharoen et al. 2008, Kim et al.

2009). A risk analysis for high pathogenic H5N1 introduction into Australia suggested that there is only a low to medium risk for the introduction of H5N1 into Australian poultry via wild waterbirds (East et al. 2008b). Our results, however, suggest that the danger may instead come from within, and that there is a link between high AIV prevalence in wild waterbirds and outbreaks in poultry. This is also illustrated by the fact that outbreaks in Australian poultry were caused by high pathogenic AIV subtypes other than H5N1 in the last few decades (i.e. H7N7, H7N3, H7N4 and H7N2) (Selleck et al. 1997, Selleck et al. 2003,

Westbury 2003). Although highly pathogenic when isolated from poultry, these viruses were probably all of low pathogenicity when they spilled over from wild birds into poultry.

However some studies only focus on the spillover risk from wild birds to poultry of high pathogenic forms of AIV (East et al. 2008a, East et al. 2008b), the low pathogenic forms of

AIV should receive the same amount of attention, as the high pathogenic forms evolve from the low pathogenic forms in poultry (Alexander 2000). These points should not be taken to mean that H5N1 outbreaks will not occur in Australia. Grillo et al. (2015) showed that low pathogenic H5 subtypes are a predominant and widespread subtype in Australia. These subtypes may therefore represent a potential risk to the poultry industry, despite all previous high pathogenic incidents in Australian poultry being attributed to H7 (Selleck et al. 1997,

Selleck et al. 2003, Westbury 2003).

Our finding that drivers of AIV dynamics in wild birds may indirectly affect outbreaks in poultry assumes that there is direct contact between wild birds and poultry on farms or that the virus is indirectly transmitted between waterbirds and poultry via paratenic hosts or on fomites such as pets, farm workers or equipment (Morgan and Kelly 1990, Burns et al. 2012). Although everyone likes to claim that one always adheres to the highest safety

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standards (including biosecurity) when conducting their work, this is clearly not the case

(Cristalli and Capua 2007). It thus makes sense to pay attention to studies like ours that identify risk factors and critical periods during which increased vigilance is warranted. By making an extra effort during these periods to guarantee the provisioning of uncontaminated water and to guarantee wild bird proofing of poultry housing, a significant reduction in the risk of virus spillover from wild birds to poultry might be achieved (Alexander 1995, Capua et al. 1999, Tracey et al. 2004). It is also here at the interface between wild birds and poultry that I believe the most critical knowledge for controlling AIV outbreaks through advanced biosecurity measures can be discovered.

Figure 1: Suggested main drivers of avian influenza virus dynamics in Australian waterbird communities

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REFERENCES

Alders, R., J. A. Awuni, B. Bagnol, P. Farrell, and N. de Haan. 2014. Impact of avian influenza on village poultry production globally. Ecohealth 11:63-72.

Alexander, D. 1995. The epidemiology and control of avian influenza and Newcastle disease. Journal of comparative pathology 112:105-126.

Alexander, D. J. 2000. A review of avian influenza in different bird species. Veterinary microbiology 74:3-13.

Altizer, S., R. Bartel, and B. A. Han. 2011. Animal Migration and Infectious Disease Risk. Science 331:296-302.

Capua, I., S. Marangon, L. Selli, D. Alexander, D. Swayne, M. D. Pozza, E. Parenti, and F. Cancellotti. 1999. Outbreaks of highly pathogenic avian influenza (H5N2) in Italy during October 1997 to January 1998. Avian Pathology 28:455-460.

Cristalli, A., and I. Capua. 2007. Practical problems in controlling H5N1 high pathogenicity avian influenza at village level in Vietnam and introduction of biosecurity measures. Avian diseases 51:461-462. van Dijk, J. G., B. J. Hoye, J. H. Verhagen, B. A. Nolet, R. A. Fouchier, and M. Klaassen. 2013. Juveniles and migrants as drivers for seasonal epizootics of avian influenza virus. Journal of Animal Ecology 83: 266-275.

De Wit, R., and T. Bouvier. 2006. ‘Everything is everywhere, but, the environment selects’; what did Baas Becking and Beijerinck really say? Environmental microbiology 8:755- 758.

East, I. J., S. Hamilton, and G. Garner. 2008a. Identifying areas of Australia at risk of H5N1 avian influenza infection from exposure to migratory birds: a spatial analysis. Geospatial Health 2:203-213.

170

East, I. J., S. A. Hamilton, L. A. Sharp, and M. G. Garner. 2008b. Identifying areas of Australia at risk for H5N1 avian influenza infection from exposure to nomadic waterfowl moving throughout the Australo-Papuan region. Geospatial Health 3:17-27.

Easterday, B., D. Trainer, B. Tůmová, and H. Pereira. 1968. Evidence of infection with influenza viruses in migratory waterfowl.

Feare, C. J. 2010. Role of Wild Birds in the Spread of Highly Pathogenic Avian Influenza Virus H5N1 and Implications for Global Surveillance. Avian diseases 54:201-212.

Fouchier, R. A., V. Munster, A. Wallensten, T. M. Bestebroer, S. Herfst, D. Smith, G. F. Rimmelzwaan, B. Olsen, and A. D. Osterhaus. 2005. Characterization of a novel influenza A virus hemagglutinin subtype (H16) obtained from black-headed gulls. Journal of virology 79:2814-2822.

Gaidet, N. 2015. Ecology Of Avian Influenza Virus In Wild Birds In Tropical Africa. Avian diseases.

Hinshaw, V., W. Bean, R. Webster, and G. Sriram. 1980. Genetic reassortment of influenza A viruses in the intestinal tract of ducks. Virology 102:412-419.

Hoye, B. J., V. J. Munster, H. Nishiura, M. Klaassen, and R. A. M. Fouchier. 2010. Surveillance of Wild Birds for Avian Influenza Virus. Emerging infectious diseases 16:1827-1834.

Hoyo, J. d., A. Elliott, and J. Sargatal. 1992. Handbook of the Birds of the World: Vol 1: Ostrich to Ducks. American Library Association, Lynx.

171

Hurt, A. C., D. Vijaykrishna, J. Butler, C. Baas, S. Maurer-Stroh, M. C. Silva-de-la-Fuente, G. Medina-Vogel, B. Olsen, A. Kelso and I. G. Barr 2014. Detection of evolutionarily distinct avian influenza A viruses in Antarctica. mBio 5(3): e01098-01014.

Johnson, P. T., and R. B. Hartson. 2009. All hosts are not equal: explaining differential patterns of malformations in an amphibian community. Journal of Animal Ecology 78:191-201.

Kim, J. K., N. J. Negovetich, H. L. Forrest, and R. G. Webster. 2009. Ducks: the “Trojan horses” of H5N1 influenza. Influenza and other respiratory viruses 3:121-128.

Kingsford, R. T., A. L. Curtin, and J. Porter. 1999. Water flows on Cooper Creek in arid Australia determine 'boom' and 'bust' periods for waterbirds. Biological Conservation 88:231-248.

Kingsford, R. T., D. A. Roshier, and J. L. Porter. 2010. Australian waterbirds - time and space travellers in dynamic desert landscapes. Marine and Freshwater Research 61:875-884.

Klaassen, M., B. J. Hoye, and D. A. Roshier. 2011. Identifying crucial gaps in our knowledge of the life-history of avian influenza viruses - an Australian perspective. Emu 111:103- 112.

Koelle, K., X. Rodó, M. Pascual, M. Yunus, and G. Mostafa. 2005. Refractory periods and climate forcing in cholera dynamics. Nature 436:696-700.

Kurscheid, J., J. Millar, M. Abdurrahman, I. G. A. A. Ambarawati, W. Suadnya, R. P. Yusuf, S. Fenwick, and J.-A. L. L. Toribio. 2015. Knowledge and Perceptions of Highly

172

Pathogenic Avian Influenza (HPAI) among Poultry Traders in Live Bird Markets in Bali and Lombok, Indonesia. PloS one 10:e0139917.

Morgan, I., and A. Kelly. 1990. Epidemiology of an avian influenza outbreak in Victoria in 1985. Australian Veterinary Journal 67:125-128.

Munster, V. J., A. Wallensten, C. Baas, G. F. Rimmelzwaan, M. Schutten, B. Olsen, A. D. Osterhaus, and R. A. Fouchier. 2005. Mallards and highly pathogenic avian influenza ancestral viruses, northern Europe. Emerging infectious diseases 11:1545-1551.

Norman, F. I., and N. Nicholls. 1991. The Southern Oscillation and variations in waterfowl abundance in southeastern Australia. Australian Journal of Ecology 16:485-490.

Olsen, B., V. J. Munster, A. Wallensten, J. Waldenstrom, A. Osterhaus, and R. A. M. Fouchier. 2006. Global patterns of influenza A virus in wild birds. Science 312:384-388.

Olsen, P., and L. Joseph. 2011. Stray Feathers: Reflections on the Structure, Behaviour and Evolution of Birds. CSIRO PUBLISHING.

Ozawa, M., A. Matsuu, K. Tokorozaki, M. Horie, T. Masatani, H. Nakagawa, K. Okuya, T. Kawabata, and S. Toda. Genetic diversity of highly pathogenic H5N8 avian influenza viruses at a single overwintering site of migratory birds in Japan, 2014/15.

Pittock, A. 1975. Climatic change and the patterns of variation in Australian rainfall. Search 6:498-504.

Rohr, J. R., and T. R. Raffel. 2010. Linking global climate and temperature variability to widespread amphibian declines putatively caused by disease. Proceedings of the National Academy of Sciences 107:8269-8274.

173

Roshier, D., P. Whetton, R. Allan, and A. Robertson. 2001. Distribution and persistence of temporary wetland habitats in arid Australia in relation to climate. Austral Ecology 26:371-384.

Schild, G., and R. Newman. 1969. Immunological relationships between the neuraminidases of human and animal influenza viruses. Bulletin of the World Health Organization 41:437.

Stallknecht, D. E., J. D. Brown, and D. Swayne. 2008. Ecology of avian influenza in wild birds. Avian influenza 1:43-58.

Tong, S., X. Zhu, Y. Li, M. Shi, J. Zhang, M. Bourgeois, H. Yang, X. Chen, S. Recuenco, and J. Gomez. 2013. New world bats harbor diverse influenza A viruses. PLoS Pathog 9:e1003657.

Tracey, J. P., R. Woods, D. Roshier, P. West, and G. R. Saunders. 2004. The Role of Wild Birds in the Transmission of Avian Influenza for Australia: an Ecological Perspective. Emu 104:109-124.

Tumova, B., and B. C. Easterday. 1969. Relationship of envelope antigens of animal influenza viruses to human A2 influenza strains isolated in the years 1957-68. Bulletin of the World Health Organization 41:429. van Dijk, J. G., B. J. Hoye, J. H. Verhagen, B. A. Nolet, R. A. Fouchier, and M. Klaassen. 2013. Juveniles and migrants as drivers for seasonal epizootics of avian influenza virus. Journal of Animal Ecology 83: 266-275.

Webster, R. G., W. J. Bean, O. T. Gorman, T. M. Chambers, and Y. Kawaoka. 1992. Evolution and ecology of influenza A viruses. Microbiological reviews 56:152-179.

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Westbury, H. 2003. History of highly pathogenic avian influenza in Australia. Avian diseases:23-30.

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Curriculum vitae

Marta Ferenczi

Centre for Integrative Ecology School of Life and Environmental Sciences Deakin University Waurn Ponds Pigdons Road Geelong VIC 3217 Australia Tel.: +61423033166 Email.: [email protected]; [email protected]

Education and training

2002-2009 ELTE University, Faculty of Science, Budapest, Hungary, Biology studies

2009-2011 Debrecen University, Faculty of Science and Technology, Hungary, Ecologist, Master of Science

Thesis title: Effects of wetland-restoration on waterbird populations in Nyirkai-Hany

November 2010 – March Erasmus Fellowship, Wageningen University, Alterra Institute, the 2011 Netherlands Research thesis: Migration of Greater White-fronted Geese between Siberia and central Europe

October 2011 – International doctoral scholarship, Centre for Integrative Ecology, Deakin University, Australia PhD thesis: Avian influenza virus dynamics in Australian waterbirds

Scholarships and awards

1999 Ministry of Environment and Water of Hungary, Student Award

2000 Ministry of Environment and Water of Hungary, Student Award

2003–04 ELTE University, Hungary, Scientific Honors Program

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2004 “For Talented Students“ Award, Hungary

2006 Pro Renovanda Endowment, “Students for science” Award, Hungary

2008 Hungarian Scientific Competition for Students, Award (1. place)

2009 Hungarian Development Bank, Habilitas Award

2009 Pro Renovanda Endowment, “Students for science” Award, Hungary

2013 Stuart Leslie Bird Research Award, Birdlife Australia

2015 Stuart Leslie Conference Award, Birdlife Australia

2015 Best student talk award, Deakin University HDR Conference, Australia

Publications

Refereed scientific journal Horváth. Z., Ferenczi M., Ambrus. A., Forró. L., Szövényi. G., Andrikovics. articles S. Invertebrate food sources for waterbirds provided by the reconstructed wetland of Nyirkai-Hany, northwestern Hungary. Hydrobiologia, pp. 1-14., 2012.

Grillo. V., Arzey. K., Hansbro. P., Hurt. A., Warner. S., Bergfeld. J., Burgess. G., Cookson. B., Dickason. C., Ferenczi. M., Hollingsworth. T., Hoque. M., Jackson. R., Klaassen. M., Kirkland. P., Kung. N., Lisovski. S., O'Dea. M., O'Riley. K., Roshier. D., Skerratt. L., Tracey. J., Wang. X., Woods. R., and Post. L. (2015) Avian influenza in Australia: a summary of 5 years of wild bird surveillance. Aust Vet J, 93: 387–393. doi: 10.1111/avj.12379.

Ferenczi. M., Beckmann. C., Warner. S., Loyn. R., O'Riley. K., Wang. X., Klaassen. M. Avian influenza infection dynamics under variable climatic conditions - viral prevalence is rainfall driven in waterfowl from temperate, south-east Australia, Veterinary Research (in press)

Book chapters Pellinger. A., Ferenczi. M. Migration of Wood Sandpiper (Tringa glareola), Hungarian Bird Migration Atlas, Budapest, Issue 1., pp. 319–320., 2009.

Refereed international Ferenczi. M., Pellinger. A., Faragó. S., Winkler. D., Verhagen. J., Nechay. conference proceedings G. Bird flu (H5N1) monitoring on wild birds in the Kisalföld Region, Conservation Medicine - Field Programmes and the Role of Zoos Conference, Budapest, Hungary, pp. 69–71., 2011.

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Refereed conference Ferenczi. M., Pellinger. A., dr. Csörgő. T. Changing of waterbird abundance proceedings in Nyirkai-Hany, Forestry Scientific Conference, Sopron, Hungary, Conference Volume, pp. 101., 2007.

Pellinger. A., Ferenczi. M., dr. Winkler. D., dr. Faragó. S. Avian influenza monitoring (H5N1) in Hungary. Forestry Scientific Conference, Sopron, Hungary, Conference Volume, pp. 106., 2009.

Ferenczi. M. Goose catching Project in Hungary. Madártávlat, Hungary, Issue 1, pp. 17–18., 2011.

Research experience

2000–2012 Waterbird monitoring and shorebird banding project in National Park of Ferto- Hansag, Hungary - field assistant, bird bander

2001–2012 Research project of White Stork migration in Kisalföld region, Hungary - project leader

2002–2012 Waterbird and goose monitoring in National Park of Ferto-Hansag, Hungary – project leader

2003–2012 Research project of Mediterranean Gull migration in Kisalföld region, Hungary – project leader

2005–2012 Research project of Spoonbill migration in Kisalföld region, Hungary – project leader

2005–2012 CES bird banding project in National Park Ferto-Hansag, Hungary - bird bander

2006 White stork nests survey in Kisalföld region, Hungary - project leader

February 2007 Goose catching project, the Netherlands – volunteer

May 2008 Goose catching project, the Netherlands – volunteer

2009 Avian influenza monitoring project in Kisalföld region, Hungary – project leader

February 2009 Goose catching project, the Netherlands – volunteer

May 2009 Goose catching project, the Netherlands – volunteer

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2010 Avian influenza monitoring project in Kisalföld region, Hungary – project leader

May 2010 Goose catching project, the Netherlands – volunteer

May 2011 Goose catching project, the Netherlands – volunteer

2012 Birdlife Australia, Orange-bellied parrot survey – volunteer

2012 Geelong Field Naturalist Group, Geelong - birdwatcher, volunteer

2012 - 2015 Victorian Wader Study Group – volunteer

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