A couple of days ago, the regional newspapers Smålandsposten and Barometern published a four-page spread on the duck’n’flu fieldwork we do at Ottenby and some plans we have for the future. It is in Swedish, but the photos are great. Enjoy.
Mallards on the wing (Photo by Flickr user Bengt Nyman used under a CC-BY 2.0 license)
Wildlife disease studies are challenging. That’s a fact. If you want an easy science life you should choose another path with more instant results. However, challenging is also the opposite of boring, and the rewards of getting your results are even more exhilarating when lots of toil, sweat and tears have been invested. As readers of this blog are aware, wildlife disease studies are what we do, and I have repeatedly written about our ongoing work on influenza A virus ecology and epidemiology in wild migratory Mallards. This week another study from our study site was published, entitled Capturing individual-level parameters of influenza A virus dynamics in wild ducks using multistate models, which can be found on early view in the Journal of Applied Ecology.
The challenges of studying wildlife disease dynamics are that you want to capture a dynamic process influenced both by the host and the pathogen, which in turn is compounded by variation in the environment – both biotic factors, such as food abundance and the occurrence of other potential hosts, and abiotic factors, such as weather and climate. Disentangling these interconnected effects is a little like making a cube out of mercury. In most wildlife disease studies the available data is at the population level, usually in the form of prevalence rates at specific time points. This type of data is ‘fairly easy’ to collect – you head out into the field, sample all animals you can lay your hands on and then use this snapshot in time as a proxy for the true disease dynamic in your system. The more times you are out collecting data, the better your model becomes. However, disease is driven by factors operating at the level of individuals, such as infection risk and recovery rate, and that type of data can only be acquired by repeated sampling of individuals across a suitable timescale. This is rarely achieved because of logistical, practical and monetary reasons.
Mallards on the wing (Photo by Flickr user Bengt Nyman used under a CC-BY 2.0 license)
We, however, sit on a huge collection of Mallard and flu data gathered at the same study site with similar methods over a period of close to 15 years. Our latest paper, headed by Alexis Avril and with collaboration with colleagues in France, utilizes this dataset to develop individual-based influenza A virus epidemiological models. This proved to a monumental task that stretched over several years and burned the processors of a good number of computers. Part of the difficulty can be attributed to the data itself – capture and disease histories for 3500 individuals collected over 7 seasons, where at each capture occasion axillary data on bird age, sex, condition, infection status and weather were included. But also the patchy nature of recapture probability and the short duration of most influenza virus infections contributed significantly to extensive data crunching.
The conceptual framework in the multistate CMR model.
The method we used was multistate capture-mark-recapture models, which are extensions of models originally developed to investigate mortality rates from census data, but where one can include the infection state – i.e. infected or not with influenza virus – as a factor in the analyses. Interested readers should head over and read the publication, as I will spear the rest of you any hardcore statistics and model lingo. Parts of the abstract serves as a good summary:
For most years, prevalence and risk of influenza A virus (IAV) infection peaked at a single time during the autumn migration season, but the timing, shape and intensity of the infection curve showed strong annual heterogeneity. In contrast, the seasonal pattern of recovery rate only varied in intensity across years. Adults and juveniles displayed similar seasonal patterns of infection and recovery each year. However, compared to adults, juveniles experienced twice the risk of becoming infected, whereas recovery rates were similar across age categories. Finally, we did not find evidence that infection influenced the timing of emigration from the stopover site.
Our study provides robust empirical estimates of epidemiological parameters for predicting IAV dynamics. However, the strong annual variation in infection curves makes forecasting difficult. Prevalence data can provide reliable surveillance indicators as long as they catch the variation in infection risk. However, individual-based monitoring of infection is required to verify this assumption in areas where surveillance occurs. In this context, monitoring of captive sentinel birds kept in close contact with wild birds is useful. The fact that infection does not impact the timing of migration underpins the potential for mallards to spread viruses rapidly over large geographical scales.
Our findings corroborate much of the earlier works done on IAV in birds from population level data or from infection experiments, but with higher robustness of the conclusions. Importantly, we provide estimates of the most crucial infection parameters and show how they vary in relation to age in different seasons and years. And from a model point of view, we show that MS-CMRs are a potent method for disease dynamic inferences. We hope this paper will be read and cited by people in the IAV field and in general disease dynamic research, and that it will be useful for stakeholders interested in the contribution of wild birds in the epidemiology of IAV in poultry.
Link to the paper:
Avril, A., Grosbois, V., Latorre-Margalef, N., Gaidet, N., Tolf, C., Olsen, B. & Waldenström, J. 2016. Capturing individual-level parameters of influenza A virus dynamics in wild ducks using multistate models. Journal of Applied Ecology, online early.
There was light snow this morning, but it has since melted away, leaving small puddles on the streets. Unfortunately, the sun seems to have lost today’s battle with the fog and the low clouds – it is, in essence, an ordinary wet, cold and gloomy February day. But if I peer out through the window, ignoring the construction works in the foreground, there is water on the horizon. And where there is water, there are ducks. And where there are ducks, there is flu. One cannot ask for more.
Over the years I have thought much about ducks and flu. (Some would say too much, but they don’t know what they miss). Although my research group has already produced four PhD theses on this topic, there is so much more that I would like to know. Some of it is highly specialized knowledge, of interest for a limited set of like-minded scientists with acquired duck disease tastes. Other things are quite basic, but hard to study, such as the question whether ducks infected with flu suffer from infection or not. That is a pretty important question also for a broader audience, as it has relevance for how well virus can spread with individuals in the environment; especially how ducks may spread virus long distances during migration. So, do they suffer from infections, or not?
Actually, there has been some controversy on this topic – partly stemming from different methods of quantifying disease effects. A field ecologist and a veterinarian have different scales in their toolboxes, one could say. In the latter case, disease signs are determined in experimental infections in animal house facilities, where individuals can be followed over time. Such experiments in Mallards have not been associated with strong disease signs – as long as we consider the low-pathogenic avian influenza viruses that are naturally occurring in wild avian populations (highly pathogenic AI is a completely different story). Infected Mallards shed viruses, but are otherwise apparently healthy, or only display a very short increase in body temperature. But, the ecologist argues, the artificial environment with plenty of food, controlled temperature and absence of predators is not really mimicking the situation in the wild, where even small reductions in vigilance and movement capacity may end in the death from a raptor’s claw. Absence of overt disease is not equal to absence of ecological costs, the ecologist would conclude.
The field studies so far have been a mixed bag, ranging from large effects to negligible effects depending on study and the species considered. The largest effect was seen in a study of Bewick’s swans in the Netherlands, where infected birds had poorer condition and migrated slower than uninfected swans. Such large effects have not been seen in other species, and one can not conclusively rule out other underlying factors, as the swan study was based on a limited number of birds. When it comes to Mallards – the most glorious of all avian influenza reservoir species – previous population studies from our group have suggested infected birds to weigh on average less than uninfected birds at capture.
Averages and populations are all and well, but to get to a mechanistic understanding one is better off with experiment conducted on a set of individuals. However, a problem is that we can not infect birds and release them in the field; in fact, we are not allowed to do so – there is a reason infection experiments are conducted in biosafety labs, after all. What to do, then? Well, we approached this question via GPS and accelerometer loggers attached to two groups of birds caught during the ongoing surveillance at our study site: one group of 20 Mallards with natural avian influenza infection at the time of capture, and another group of 20 Mallards that were negative for influenza at the time of capture.
The benefit of these data loggers is that they record such a wealth of information. From the GPS fixes we can follow the birds in the landscape and quantify their movements at spatial and temporal scales; from the accelerometer we can get metrics that describe activity, defined as movements in the x, y, z-dimensions. We predicted that infection would significantly hamper movement, and that with time the difference between infected and uninfected birds would level off (see figure below); hence the analyses need to take time in to account, too.
Theoretical predictions of the influence of infection on movement metrics. If infection affects spatial behaviour, infected (blue) and uninfected (red) birds should behave differently at the time of release. We postulate that, at this time, movement metrics for infected birds should be lower than for uninfected birds, which would be revealed as different intercepts of the regression of the movement metrics against time for uninfected (β0) and infected birds (β0+βInf). As infected birds recover with time, their movement metrics will approach and eventually meet the values for uninfected birds. This happens when the slope of the regression of the movement metrics against time for infected individuals (βT.aft.Rel*inf) reaches the slope for uninfected birds (βT.aft.Rel), which is expected to be null.
The full paper is freely accessible at Royal Society Open, and I hope readers with a more heavy interest in movement ecology download and read it. There is a lot of data crunching and statistics there that most of you are likely not that interested in – if you are, go read the original publication – but remember even easy questions may be hard to answer. Okay, with that said, what where the results?
Well. There were no effects of infection on the movement parameters measured, at all. Yes, there were differences among individuals, and between night and day, but infection status did not explain much of the variation in movement metrics. This means that under the natural situation in this study, conducted during stopover in autumn migration, infected ducks moved as much as uninfected ducks. This also means they likely are not impaired by infection during active migration, and could therefore carry LPAI viruses on the wing as they depart the stopover site.
Is this, then, the last nail in the ‘cost of infection’ coffin for low-pathogenic influenza in ducks? Probably not, because one could argue that non all viruses behave the same (in fact, there should be a variation for virulence), and that some viruses may have adapted to infect non-mallard-birds, and hence be spillover infections in Mallards (and then potentially be at less than optimal virulence). Moreover – and perhaps a stronger argument – there may be differences in outcome depending on whether it is a primary infection, or subsequent infection; where the first infection in a naïve bird could be believed to carry a larger cost. Or there may be effects seen only at certain environmental conditions.
All these ‘but, or, perhaps, mayhaps’ are classic scientist disclaimers… My personal belief, these days, is that also the ecological costs of infection are slim. But I am happy to be proven wrong – out you go now and study.
There is water at the horizon still. And questions aplenty.
Link to the article:
By Jonas Waldenström
Today we published a major article in a well-respected journal. The reason why I write major is not to brag (although I am very pleased). No, the reason for that epithet is that the paper is based on such a huge long-term effort. In fact, in this paper, ten years of fieldwork, laboratory work, and statistical analyses are boiled down into nine glossy pages!
As frequent readers of this blog probably know, mallards and flu is our main study system. Through repeated captures, samplings and recaptures of ducks at a migratory stopover site we have built very large datasets that we now can analyze for long-term patterns in virus-host interactions. The title of the current paper is: “Long-term variation in influenza A virus prevalence and subtype diversity in migratory mallards in northern Europe”
Influenza A virus prevalence was in part determined by peaks of mallard migration. Photo by Sergey Yeliseev under a CC BY-NC-ND 2.0 license.
What we did was to screen all 22,229 samples collected in the period 2002-2010 for the presence of influenza A virus RNA. Positive samples were then inoculated in eggs in order to obtain virus isolates. After this process, we had a virus bank consisting of 1081 viruses of 74 different subtypes, ranging from H1N1 to H12N3. As you can see from the figures above, influenza virus research is time-consuming and costly, and the travel from sample to RRT-PCR-positive to characterized virus could be described as a negative logarithmic function. It is all about big numbers! You need a lot of samples to get the statistical power to say something about virus ecology and epidemiology at the level of subtypes. You also need to be stubborn as a mule.
There are three major results that I would like to share with you.
First, we were able to fit a model of how influenza A virus varied with season in the sampled mallard population. The resulting figure very neatly shows how the virus starts low in spring, becomes more or less absent during the breeding season, and how it suddenly increases in frequency in August when the first wave of migrating mallards arrive at Ottenby. The August peak is followed by a second peak in October-November, likely consisting of mallards with a Finnish or Russian origin. Actually, the plot looks like a camel!
Influenza A virus prevalence showed two distinct peaks in autumn, one in August and one in October-November.
However, plotting prevalence rates over time has been done before. The strength with our analysis is that it includes and accounts for the variation in prevalence induced by year effects. Mallards are migratory birds, but their timing of migration is rather flexible. In years characterized by mild autumns they arrive late at our study site, and in years with harsh autumns they are early. The final model accounted for approximately half of the variance in prevalence, which is pretty good all considered.
Second, I would like to stress the incredible diversity of subtypes! The two surface proteins hemagglutinin (16 variants) and neuraminidase (9 variants) sit on two different RNA-segments in the genome and can theoretically be combined in 144 different ways, or subtypes as we call them. We found 74 different HA/NA subtypes. In addition, some subtypes are likely not functional, or would have to include a hemagglutinin (like H14 or H15) that is restricted to areas outside Europe. This plethora of genotypes is a world record from a single site. Or to put it in perspective: more than half of the possible subtypes have been found in mallards trapped in our little duck pond on the southern point of the island Öland, in the SW part of the Baltic Sea, in Northern Europe. A speck in the ocean, but a global diversity of viruses.
Further, the 1081 viruses were not evenly distributed on subtypes. Rather, some subtypes were very common, such as the H4N6, the H1N1, or the H2N3 subtypes. Others were rare, including the famous combinations H5N1 and H7N9, both which were only found once, and not in the pathogenic forms known from elsewhere. Interestingly, the high frequency of certain combination, and a low frequency of other combinations despite the HA and NA being common in other virus constellations suggests that some subtypes have low fitness. Consider for instance H4N3 that was found only 5 times, while the H4 hemagglutinin was found in 291 viruses, and the N3 neuraminidase in 116 viruses.
A cute mallard couple. Photo by Chuq Von Rospach under a CC BY-NC-ND 2.0 license
Third, and perhaps most interestingly, we found a heterosubtypic effect at the virus population level. By grouping viruses in classes depending on their HA relatedness we could see that the different virus classes peaked at different times within an autumn. The virus type that was common in early autumn was rare in late autumn and vice versa. Understanding how individual and herd immunity processes affect influenza A virus dynamics in nature is highly warranted, as that would aid our capacity to predict how the virus population could change over time. Viruses in wild birds remain an important pool from which genotypes could be seeded in domestic animals, and even humans.
Finally, I would like to say how incredibly fortunate I am to have had the opportunity to work in such a hard-working and persistent research group. The work we presented today has been collected by a small army of duck trappers, a score of laboratory staff, a handful PhD-students, a couple of postdocs and a quartet of PIs from Kalmar, Uppsala and Rotterdam. And the most important of all was Dr Neus Latorre-Margalef, who carried this publication from start to finish! Well done!
Link to the article:
Latorre-Margalef, N., Tolf, C., Grosbois, V., Avril, A., Bengtsson, D., Wille, M., Osterhaus, A.D.M.E., Fouchier, R.A.M., Olsen, B. & Waldenström, J. 2014. Long-term variation in influenza A virus prevalence and subtype diversity in migratory Mallards in Northern Europe. Proceedings B, online early.
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A good day in the duck trap! Duck trapper Gabriel Norevik is herding the ducks. Photo by Ville Fagerström
By Jonas Waldenström
It is time to do a field season wrap-up. There are still a few weeks of fieldwork to do, but now it is mainly the everyday routine trapping of ducks that remains. And when I say routine, I mean it. We have run our Mallard disease-monitoring scheme at Ottenby Bird Observatory, Sweden, since 2002. A full dozen years with daily sampling during the field seasons! That is truly remarkable!
If you don’t think 12 years is a long time, then you are likely not a scientist, at least not one working with animals in the wild. The truth is that long time series are rare in biological systems. Very rare. The few that are still running (some for 50+ years) have produced fantastic data, such as the Darwin Finches at the Galapagos island Daphne Mayor, run by Peter and Rosemary Grant since 1973, the St Kilda Soay Sheep project in the UK, the Great Tit population in Wytham Woods outside Oxford, or the Collared Flycatchers of Southern Gotland, Sweden. For flu, there are the sampling schemes of shorebirds at Delaware Bay, and the long-running duck sampling in Alberta by St Jude’s Children Research Hospital – two programs that have shaped our view of flu. But why then, you may ask, are long time series rare? That my friend is an excellent question!
One failed grant application can put a grinding halt to a time series.
To start with, funding typically favors shorter projects, roughly 2-4 years long. No research body says ‘cool project, let’s fund it for the next 20 years’, unless it is mega-large projects like CERN (in France/Switzerland), the International Space Station (in orbit), or the Human Genome project (finished). For us mortal researchers, a long time series rely on successful applications in grant cycle, after grant cycle, after grant cycle. This is a major hurdle for long projects. For instance, the Swedish Research Council, one of the main funding bodies in Sweden, turned down 84 % of the proposals in 2013. Thus, it only takes one year with bad luck to put a grinding halt to a time series.
And even if it is the senior researcher(s) who fund the project, it is often the PhD and postdoctoral students that actually run it. The length of a PhD varies (in Sweden it is 4 years) but are fairly short, and postdocs even shorter. When the student has graduated, chances are that the continued fieldwork simply dies, especially if techniques/skills were not shared between staff, or that if no suitable replacement was found. Also, similar to old land-owning dynasties (where a drunken playboy lost the manor house and the estate playing dice), a wrong recruit may effectively spoil a time series.
The staff is always important in any project…
Another pitfall is curiosity. Researchers are by and large driven by curiosity, and sometimes the allure of greener pastures elsewhere seems more compelling to pursue, than to dig where you stand yet another year. Consequently, there is a risk that the leading scientist leaves a project that could have grown into an important long-term data series because he/she started to grow bored and restless. Thus, funding is scarce, time changes, people move and priorities shift. And as a result few time series reach a decade.
So how come the Ottenby data series is still running after twelve years?
The project was started by professor Björn Olsen (birder, physician, and the chair of Infectious Diseases at Uppsala University) in 2002. Björn had worked with tick-borne infections, such as Lyme Disease, and gastrointestinal bacteria such as Salmonella and Campylobacter, and when a move to Kalmar Hospital brought him close to Ottenby Bird Observatory it was like pieces of the puzzle just came together. For what can be more of a perfect match for a physician interested in birds than the avian zoonotic pathogen influenza A virus? And to have a field site at the best birding spot in Sweden! Fabulous!
The dismantled duck trap from an earlier trapping period 1960s – 1980 was resurrected and hopes were high that ducks would start to appear. Which they did, but only after a few months of very low trapping numbers, which made everyone wondering whether we would have to cancel the whole thing. Furthermore, funding was initially modest. Agencies thought there were more pressing research fronts – one review of a proposal actually dismissed the value of birds as hosts for influenza at all, as he/she believed minks were the most important hosts… But the work was done, the publications started to come out and brick by brick the flu house was constructed. Funding came in more steadily, and in the last decade we have had grants from the regional councils (FORSS, Sparbanksstiftelsen Kronan), national councils (including the Swedish Research Councils VR and FORMAS), and international councils (EU-FP6, NIAID), plus authorities such as the Swedish Board of Agriculture and the European Commission. So far the grants have come when we needed them, and never too late. It has been close sometimes, but so far so good. Another reasons to why we still are in the business are great collaborations! Already from the start we collaborated closely with Albert D. M. E. Osterhaus and Ron Fouchier from Erasmus MC in Rotterdam – a collaboration that has continued ever since. Other long term research friends are Johan Elmberg in Kristianstad, Åke Lundqvist in Stockholm, Vladimir Grosbois and Nicolas Gaidet at CIRAD, France, and Martin Wikelski at Max Planck in Constance, Germany, as well as Calle Nyqvist and Kalmar Surveillance AB! And many, many more!
A tipping point was when the highly pathogenic avian influenza H5N1 crossed Eurasia and hit Europe with force in the winter 2005/2006. This was almost like a deus ex machina moment, and suddenly the things we did were what everyone wanted. We were at the center – the eye of a hurricane – and delivered data to national and European authorities, helped with risk assessments, answered billions of news reporters, and through out it all continued to do good science and publish quality papers. In those days, Björn could be seen in three, four major newspapers, and national TV in the same day! Crazy times!
Throughout there has been a great team that made it all possible. The Ottenby project has so far directly involved seven PhD students:
And five postdocs:
Lab work has been immense and a large number of hands have helped out during longer and shorter times. Among others: Abbtesaim Jawad, Sara Larsson, Maria Blomqvist, Diana Axelsson-Olsson, Lovisa Svensson, Petra Griekspoor, Jenny Olofsson, Jorge Hernandez, Oskar Gunnarsson, Lorena Grubovic, Anna Schager, and many, many more.
And in the field we talk about more than 40 duck trappers and >33,000 duck trap/retrap occasions! The two most frequent are Stina Andersson and Frida Johnsson that have made several seasons in the duck trap! The list is too long to post here – but I promise to return soon with a ‘best of’ post with statistics of duck trapping and trappers! Simply, without the trappers no science – incredibly important people!
Who knows what the future may bring? Painting by French artist in 1910 imagining what life would be in 2000.
To sum up a long post: we have done well because of fortunate timing, a good study site, great staff in the field and in the lab, good collaborators, and lastly great science! A few weeks ago we learned that we have funding for another three years – hopefully we can get this virus-host time series through adolescence and into adulthood! Time will tell.
In the mean time – shout it out for the ducks! Quack, quack, quack!
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If you enjoyed this post, or other posts on this blog, why not follow the blog via email, Feedly or get updates via Twitter by following @DrSnygg?
By Jonas Waldenström
Home of the Ebola
Unlike most organisms, viruses are often named after the site they were first found. Sometimes these names give a flare of dark jungles, full of mosquitoes, and the eyes of unknown animals that stare at you in the dark. Surely, the name Ebola is enough to spark fears in any man. This virus, named after a river in Congo, is one of the ugliest viruses we know of: a hemorrhagic fever virus where you literally bleed to death. I think of the Heart of Darkness, that dark Joseph Conrad novel. It makes me shiver, every time. A related virus is Marburg virus, named after a city in Germany where a laboratory worker accidently infected himself and caused the first human case. Although from Germany and not a deep African jungle, a Marburg virus still sounds like a vicious killer, especially if said with a thick German accent.
Chikungunya to the right. To the left is the cat Shigella.
Another favorite is the chikungunya virus. I can’t decide whether it is the name of a fluffy rabbit, or something very deadly. Mixed feelings for that name. The virus is pretty bad though. A mosquito borne illness first discovered in Tanzania, but which name does not denote the place of origin, but the local name for “that which bends up“. That prosaic word stems from the contorted posture of patients suffering joint pain and arthritic symptoms. However, although interesting (read: horrific) diseases, neither Ebola, nor Chikungunya virus are to be found in Mallards – my pet model species. But there is a city virus that does infect Mallards: Newcastle disease virus!
This week in the Virology Journal we published an article on the occurrence of Newcastle disease virus in Mallards sampled at Ottenby. Newcastle disease virus, or NDV for short, should not be mistaken for Newcastle Brown Ale. The latter is a pretty nice beer, the former an infectious disease of birds. In fact, NDV – or avian paramyxovirus type 1 which it is also called – can be a devastating disease in poultry. It is fairly rare in Northern Europe, but occasionally there are outbreaks in poultry. To complicate matters even more, there are three different classes, or pathotypes, of NDV depending on their severity of infection. Lentogenic viruses are fairly benign, and are not associated with severe disease, while mesogenic and velogenic strains are real killers. The difference between the pathotypes is related to genetic differences in the F protein of the virus, a key player in the fusion of the virus with the host cell. In order to infect the cell, the F protein must be cleaved by host proteases at the F0 cleavage site, and velogenic strains can be cleaved by many more types of proteases than lentogenic strains thereby causing a more systemic, less local, disease.
The Newcastle Disease Virus has a distinct circular capsid with small spikes.
So what did we do? Given that outbreaks are rare in Sweden, and occur mainly in late autumn, we wanted to know how prevalent NDV was in our Mallard population and whether this species could be involved in transmission. We screened roughly 2300 samples collected from migratory Mallards for the presence of NDV RNA. A molecular typing method is like a fishing expedition: you need to have the right equipment and the right bait to get the fish. Or, in our case, positive amplification of NDV RNA of the F gene in real-time-PCR assays. It took a lot of time and effort to optimize the protocols.
And what did we find? Twenty of the samples, some from the same individuals sampled more than once, were positive for NDV. This makes NDV pretty rare in Mallards, at least compared to influenza A virus that can be found in a prevalence of 10-30% at Ottenby in autumn. I had expected to see more infections, and the NDV was one of the viruses I had thought to include in coming viral pathogen assemblage studies. At the moment it feels a bit to rare to work efficiently with, but we will see. Paramyxoviruses are interesting and besides the NDV there is nearly two handfuls of other avian paramyxoviruses to screen for. The phylogenetic analysis, and the sequencing of the F cleavage site placed the Mallard viruses in the lentogenic group. And the risk for poultry from the Mallard viruses should be negligible.
NDV phylogeny from the study. Original picturee from http://www.virologyj.com/content/10/1/285
It is always nice to get a paper out. In the old days you could actually feel the glossy paper between your fingers – those days are more or less gone, with online-only and printed pdf. But it still feels good. And it feels even better today, after a few pints of microbrewery beer in Aberdeen – this is the closest I have been to Newcastle in years!
The full paper: Tolf, C., Wille, M., Haidar, A-K., Avril, A., Zohari, S. & Waldenström, J. 2013. Prevalence of avian paramyxovirus type 1 in Mallards during autumn migration in the western Baltic Sea region. Virology Journal 10:285.
By Jonas Waldenström
At this time of the year the air is full of migrating birds. Some, as cranes or geese with their conspicuous formations are easily spotted with the naked eye, while other birds, including most smaller songbirds, fly at altitudes where you need a scope to see them. But you can often hear them; each species has its own tune, and an experienced ear can tell them apart on call alone.
The question “how do they find their way” is as old as the field of ornithology itself. Generally, migration wouldn’t be possible without some sort of compass; a way of telling the bird in which direction to move. It has been shown that birds may use the sun, the stars, and the earth’s magnetic field for assessing their heading. And in some species also visible cues, a sort of map sense from previous travels, or even olfactory cues (a posh word for smelling where home is). As the vast majority of birds migrate without the guidance of their parents (which seems reserved to some flock-living species), a juvenile bird must be born with not only the tools to assess where it is, but also a sense of where it should go.
One of the pioneering fathers of ornithology was the Dutch professor Albert Christiaan Perdeck. He made one of the first real tests on how birds can sense where they are going, and how they can adjust the course if they get out of track. In order to test this he wanted to do a displacement study, where birds should be experimentally transported to a novel site, far from the catching site. As this study was conducted in the 1950s, in the pre-gadget era of ornithology, he needed a species that he could catch in large quantities, and where ring recovery data could be collected. His choice of study animal was the European Starling Sturnus vulgaris, a common farmland bird in most of Northern Europe. Starlings in autumn can aggregate in huge flocks, sometimes consisting of several thousand individuals, and was thus a good target species for Perdeck.
With a remarkable enthusiasm, the team caught and ringed thousands of starlings. Some were released at the ringing site in the Hague, while the other half were transported with airplanes to Switzerland and released. After some time the ring recoveries started to come in, and the results were extremely interesting. It seemed as the young starlings had a vector compass, as the birds that were transported south stayed on the same heading as they had when they were caught. But instead of ending up in Holland, the young starlings ended up way south, sometimes even on the Iberian peninsula. I wrote ‘young’ deliberately, as there was a clear age effect. Where the juvenile birds continued on the same vector, the adult starlings compensated for the displacement, changed course and headed to the original winter quarters. Adult birds are more experienced, and in the starling case they were able to adjust to the circumstances and get back on the right track. A quite remarkable feat – some of my colleagues cant find their way to the university canteen without a helper…
Spurred by the old studies (classics, you could say) and the advancement of new tracking tools we conducted a similar experiment with Mallards. The study was a collaborative effort with scientists from Sweden, Germany, the UK and Denmark (with the lead from Professor Martin Wikelskii at the Max Plank Institute for Ornithology, in Constance, Germany). Today’s gadgets can do stuff Perdeck could only dream about. During two autumn seasons, we caught juvenile Mallard females at Ottenby – our beloved duck field site – and equipped a total of 76 birds with satellite GPS transmitters. Half of the ducks were released at Ottenby, and the other half were transported in a private airplane to Lake Constance in southern Germany and released there. The tags had solar panels and, in the best of circumstances, had the potential to send data for at least two years; providing highly accurate GPS fixes at several times a day.
However, the best of circumstances is not often met in nature. The tags on the birds in Ottenby had problems with the lack of sunshine during Swedish late autumn and winter, and many of them just went offline. But a fair number of tags delivered data on movements both in autumn/winter and in spring, when birds headed to their breeding grounds. Contrary to the Perdeck’s starlings, our displaced Mallards did not continue migration in autumn; they stayed in the Lake Constance region. Of the Mallards released at Ottenby, some continued migration to the general wintering area of our study population, that is Denmark and Germany, south to The Netherlands.
After the winter: “most of the translocated ducks headed straight north-north-east, as if heading towards Ottenby, with one duck going as far as northern Sweden. Three of the transported ducks, however, first headed in a more easterly direction and turned northwards when reaching the longitudes of the area the control birds migrated to. It is unclear how these birds decided when to turn north, but the movement trajectories could be interpreted as if individuals had noticed that they were in the wrong place and then corrected for the southward translocation. Based on the observation that this second group of transported ducks ended up in their potential natural breeding grounds, and the first group had a more northerly heading than the control group, we conclude that mallards, just like the starlings from Perdeck’s original experiment, can correct for translocation during the spring season following the experiment.”
Thus, there was quite large differences between individuals in the translocated group, from those that seemed to take the shortest route north to Ottenby in spring, to those that followed a eastern direction (and then going north), more in the direction of what they should have had if the stayed in the normal wintering grounds: a flexibility in continental navigation and migration.
The article is open access and can be found here.
By Jonas Waldenström
“Bam-bam, bam-bam”!!!
A week ago, this year’s duck hunting season started. Or harvesting, as some hunters would say. And it is really a harvest: it is estimated that around 180,000 Mallards are shot in Sweden alone. This in a country with only 10 million inhabitants. On top of that you may add Eurasian Teals and diving ducks such as Tufted Ducks. And the occasional Garganey, Gadwall and other more rare species – collateral damage in the cannonade.
Should we worry? Should we be angry? No, at least not today, and at least not in NW Europe. The image of a hunter as a trigger-happy mooron isn’t accurate – actually many hunters are conservationists. Biodiversity doesn’t come from nothing, it requires landowners that preserve or maintain land. In this case good breeding and stopover areas for ducks. Without the incentive for hunting, a lot of nice duck habitat wouldn’t exist today. And you can combine bird watching and bird hunting, two sides of the same coin.
On the other hand, hunting has consequences. For the individual (a dead duck can not reproduce) and for the population. Our data from Ottenby show that roughly 10 % of the Mallards we equip with a band will be shot. With a migratory species, a population that is breeding in some part of Europe may be harvested in another region. In our case, a fair proportion of the Mallards shot in E Sweden originate from Finland, Russia and the Baltic states and may continue migration to Denmark, Germany and the Netherlands. Each country with their sets of riffles.
For Mallards, it has been shown that hunting is not an additive mortality, but a compensated mortality at the population level. In other words, the death toll imposed by hunting reduces the population number, but this is in turn compensated by less mortality from other sources, or by higher breeding output of the remaining birds. Density-dependent population regulation, you can say.
It should be noted, as well, that a chunk of what is shot is released Mallards. You know the put-and-take industry in recreational fishing? This is something similar: estates where Mallards are breed and released, and where visiting hunters pay for hunting. Roughly 100,000 ducks in Sweden per year, and more than a million in France! Per year! Some of the domestic origin birds survive the onslaught and live to reproduce. As they have a different stock than wild Mallards, some researchers fear that they could affect the population genetic structure of Mallards in Europe. Some things can be observed already today: Mallards tend to be heavier now than 40 years ago, and have fewer lamellae in the upper mandible, presumably an adaption to a more coarse diet. Whether this really has to do with released ducks isn’t settled yet, but similar trends are not seen in Eurasian Teals – a species not reared for hunting purposes.
But one thing is clear: the start of the hunting season is also a major push for birds to start migrate in earnest. Especially evident is the movements of Eurasian Teals which tend to start the same morning as the hunting season starts. But, little ducky, you may run, but you can’t hide – hunting will continue along the migratory route and on the win
By Jonas Waldenström
Migratory animals are per definition mobile, performing regular movements between areas. Sometimes such movements are small, as in up or down a mountain. Other times they involve crossing 11,000 km over open sea, as the Bar-tailed Godwits do on their migration from Siberia to New Zealand.
Not exactly a rocket
Our model species is the Mallard. It is not exactly a rocket or a Godwit. No, it is a bulky and rather heavy bird, not designed for enduring intercontinental flight. But it does fly, and fairly decent distances. From band recoveries and analyses of stable isotope contents in feathers, we know that the breeding areas are for Mallards passing Ottenby in autumn can be roughly outlined as the Baltic States, Finland and parts of Eurasian Russia. Winter areas are more easily depicted, as a lot of ducks are harvested by hunters and the number of bands reported back during non-breeding is high.
But a dead duck is an endpoint, and doesn’t tell us much about its behavior before (or after) it was shot. As Mallards are an important reservoir host for influenza A viruses we want to know more about what movements actually mean for the epidemiology of disease. Does infection impair movements? Can infected birds transport viruses along migration to other sites? How does that affect local and global transmission?
A few years ago we started to collaborate with Martin Wikelski and his research group at Max Plank Institute of Ornithology in southern Germany. His group is a leading group on research in movement ecology, experts in animal movements. It is really a cutting-edge discipline, as new techniques for following animals are constantly developed. A lot of new cool gadgets!
Together with our German colleagues, we have carried out a number of studies with tagged Mallards, equipped either with satellite transmitters or with GPS loggers. There are a few articles in the tube, and Daniel Bengtsson, one of my PhD students, has Mallard movements as his subject area. The very first article on Ottenby Mallards appeared recently in Movement Ecology. Actually in the very first issue of the journal!
In this study, Kamran Safi gathered movement data from nine different species of birds (including our Mallards) and used it to analyze how the effect of wind support during migration best should be modeled. Completely still air is rare, and migrating birds need to adjust migration to wind strength and wind direction. A tail wind component can be extremely beneficial, and headwinds detrimental. With the modern tags birds can be followed at high sampling frequencies (at the scale of minutes and hours) during active flight, and their heading and speed can be examined in conjunction with global weather databases. But it is crucial that you used the right models, otherwise you may end up with the wrong conclusions.
Schematic representation of the calculated measures, where α represents the vector of a bird’s movement relative to the ground. Its length is vg. Wind support (ws) is the length of the wind vector in the direction of c and cross-wind (wc) the length of the perpendicular component. Finally, airspeed (va) is the speed of the bird relative to the wind and can be calculated as given above, or modeled as the intercept of a model with vg as a function of ws and wc.
Perhaps not surprising, Safi et al found that wind was a strong predictor of bird ground speed, but with variation among species. However: determining flight direction and speed from successive locations, even at short intervals, was inferior to using instantaneous GPS-based measures of speed and direction. Use of successive location data significantly underestimated the birds’ ground and airspeed, and also resulted in mistaken associations between cross-winds, wind support, and their interactive effects, in relation to the birds’ onward flight.
It is rather complex paper if you are not into the field, but it feels good that our flu-carrying little duckies can contribute with some pieces of the puzzle in the making of next generation migration models. We will return to Mallards and movements in this blog in the future, as the Mallard flies and the papers become published.
Links to the papers:
The only thing constant in flu epidemiology is that it is always changing. New subtypes appear, old ones retreat; like a play where actors constantly change masks and costumes. Names are put forward in the press, such as the Mexican flu, which changed to swine flu, which changed to the new flu A/H1N1 (but, of course, the swine flu label stuck). The current evildoers in humans are H1N1 and H3N2. These are seasonal flu viruses, meaning that they circulate predominantly in humans, and only occasionally give infections in other animals. Both of them made the leap from another animal reservoir before becoming human flu viruses, and both, in turn, have once been avian influenza viruses.
Most readers will also remember the ‘bird flu’ virus H5N1. First of all: it still exists, endemic in parts of Asia, and in Egypt. It hasn’t left the scene. This virus is a highly-pathogenic avian influenza virus that cause rare, but often fatal infections in humans. The highly-pathogenic prefix means that it is an efficient poultry killer – with close to 100% mortality in infected chicken flocks. That’s like tossing in a mini nuke, closing the barn door and wait for the explosion. A mean virus, for a chicken.
However, the norm among avian influenza viruses is to be low-pathogenic, only causing mild infections in their hosts. For domestic poultry that equals a mild cold, in wild ducks even less so. Recently, yet another flu actor entered the scene: H7N9. This virus has caused a number of human infections and deaths in China, but contrary to H5N1 has not been associated with die-off of domestic poultry. New costume, new play, but still a deadly mix.
So, there is H1N1, H3N2, H5N1 and H7N9 out there – all with the capacity of infecting humans. Earlier flu pandemics have been caused by yet other viruses, and from studies of poultry workers and veterinarians we know that there are viruses with other H and N letters that can infected humans, but without leading to severe symptoms. Even if the list seems long, it is nothing compared to the total diversity of influenza A viruses. The H and the N are shorts for the two surface proteins hemagglutinin (responsible for attachment to cells, and to invasion) and neuraminidase (responsible for letting new virus progeny leave an infected cell). There are 16 H variants, and 9 N variants and as they are encoded on different genome segments, they can end up in any of 144 possible combinations, or subtypes. More than 100 of these subtypes have been found in ducks, and more than 70 of them have been found in our study population of Mallards at Ottenby, in SE Sweden. Thus, there are many, many more flu viruses out there lurking in the shadows.
But why are there so many viruses? And especially, why so many in Mallards?
In study published last week in PLOS Pathogens, we returned to this question and analyzed infection histories of more than 7,000 Mallards sampled at Ottenby during 8 years! Together these ducks were caught and sampled more than 18,000 times! The repeated capture and recaptures of ducks is a major benefit of our trapping scheme, as it allows us to follow the course of natural infections in different birds. This is a gospel I have been singing in two previous posts on individuals and reassortment, and a topic I am likely to return to. Predictable fellow, yes, yes. But let’s turn back to the subject.
What did we do? Well, we analyzed all cases where we had at least two characterized virus isolates from the same bird in the same season. Then we used this data to investigate how frequent reinfection with a particular subtype was given the first detected subtype and how this depended on time. This sounds rather simple, doesn’t it? In truth it was a rather large statistical undertaking, as the 25 supplementary files tells. The devil is in the detail – in this case in dealing with potential pseudo replication and test assumptions. Anyway, we leave the finer details of the stats for now and instead take a look at the table below. It is a contingency table, where rows and columns relate to H subtype at first and second infection, respectively. This means that the diagonal shows cases where the same subtype was isolated at both occasions. The colors highlight combinations that were either overrepresented (blue), or had a deficiency of cases (red) compared to the expected. The first thing to note is the diagonal, where very few cases of reinfections were noted. In other words, a bird infected with, let’s say, an H4 virus, will have a low probability of being infected with the same subtype again the same autumn. This is called homosubtypic immunity, and not different from what we want to achieve with vaccination in humans. Once you have had it you are immune (at least for some time…).
However, we also found a great degree of heterosubtypic immunity, meaning that an infection with one H subtype made reinfections with other related subtypes less frequent than expected. If you check the figure again, you can see that there are patterns to these cases of heterosubtypic immunity. In fact, they follow higher order clustering of hemagglutinin gene relationships, as can be seen in the next figure. H1, H2, H5 and H6 viruses belong to the H1 Clade, and a primary infection with any of these will make it less likely to be reinfected with other viruses of the same clade. The pattern was similar for other clades and was actually also detectable at the H Group level (the highest level of structure).
But what does this mean?
It is actually big business. It gives a very strong case for existing selection pressures for hemagglutinin gene diversification. Subtypes as a term predates the genomic area and is based on immune reactions. Typically, a subtype is defined as a group of viruses recognized by the same antisera (antibodies towards a particular virus). Subtypes are well resolved for hemagglutinin and neuraminidase, both in phylogenetic relationships and in responses to antisera. Things match. You would be tempted to think that virus subtypes have diversified until their antigenic properties are different enough for the immune system of the host animal to be unable to treat them with the same set of weapons. For instance, antibodies to an H1 virus shouldn’t interfere with an H2 virus infection.
Here, we show that heterosubtypic immunity is strong for hemagglutinin (but absent for neuraminidase), and that it follows genetic relationships. This means that there is ongoing warfare among hemagglutinin subtypes. If an individual is infected with one subtype, it then becomes harder for other related subtypes to enter and cause reinfections. The strength of this response, and its longevity, will be extremely important for infection dynamics at the population scale and drive which viruses that peak at different times. This is especially interesting in a migratory species like the Mallard, where viruses need to follow their hosts, not in only time, but also in geography. And it means that H subtypes are still diverging. The pace of this divergence would be very interesting to tackle, but will require good time series of influenza genomes (rest assured, we are sequencing like crazy and will return to this subject).
To conclude, our study provides evidence from the field on how natural selection in influenza A virus is driven by host immune processes and that it is evident for the most antigenic protein. The question ‘why’ is therefore dependent on disruptive selection. It also raises a bundle of additional questions. Is the diversification we see in influenza A virus the result of geographic allopatric processes, or through separation in different host species, or is there sympatric diversification going on?
More to do, more to do. This virus will keep us busy for sure.
Jonas Waldenström
Link to the article:
http://www.plospathogens.org/article/info%3Adoi%2F10.1371%2Fjournal.ppat.1003443
Zoonotic Ecology and Epidemiology 