1Definitions of network parameters calculated for waterbird networks at Strandfontein wastewater treatment works. http://www.sajs.co.za/index.php/SAJS/article/downloadSuppFile/283/2624
As an overall measure of the connection strength of each species to the rest of the network, we considered the group of median interspecific association strengths belonging to a species and calculated the mean and standard deviation of this group. Median association strengths of zero were omitted from the calculations, and so the resulting mean represented the overall strength of interspecific associations actually realised by the species. Comparison with independent prevalence data The final step of the analysis was to test whether empirical data on the prevalence of avian influenza in our study species offered any support to the hypothesis that social interactions may influence influenza prevalence. We used a recently published independent data set on avian influenza prevalence in southern Africa. 21The majority of these data come from three sites: (1) Lakes Manyame and Chivero in Zimbabwe, (2) Barberspan (in the North West Province of South Africa), and (3) Strandfontein (the study site for the analysis presented here). The prevalence of avian influenza across all sampled species in the region is about 2.5%, 21and some of the species considered in this study (particularly the grebes and the diving ducks) are virtually impossible to catch. Recorded prevalence from Strandfontein was not feasible to use on its own because the total numbers of sampled birds and viruses were too low for several of the study species. However, by merging data from all three sites, we were able to obtain workable regional prevalence estimates for six of our study species (numbers following species names indicate the number of sampled birds and the avian influenza prevalence, as a ratio of virus detected to birds sampled): red-knobbed coot (498, 0.014), Cape teal (115, 0.009), Egyptian goose (738, 0.009), red-billed teal (762, 0.046), Cape shoveler (39, 0) and yellow-billed duck (310, 0.006).We compared these prevalence data to our network-derived measures of mean geodesic distance and actor degree centrality. Because the influence of sample size dominates prevalence estimates, we used partial correlations to compare prevalence to network measures with sample size corrected for. The analysis was run separately for terrestrial and aquatic interaction networks. ResultsIn total, 20 907 focal bird observations were made, resulting in 35 587 associations. Of these associations, 67% occurred in the water and the remaining 33% on land (Appendix Figure A1). The proportion of each focal species observed within different habitat types and water depth categories varied between species (Appendix Figures A2 and A3). Hypothesis testing: Matrix correlations For aquatic data, there was no significant correlation between pond abundances of individuals of a species and association strengths in ponds (Mantel test r = 0.041, p = 0.326). Therefore, species association strengths in a pond were not random. Similarly, there were no significant correlations between species association strengths in the water and species’ habitat (distance from reeds) or depth preferences (Mantel tests r = 0.076, p = 0.199 and r = 0.110, p = 0.194, respectively). Therefore, neither water depth nor the presence or absence of reeds were significant drivers of species associations. Network analysis General network properties Black-necked grebes, little grebes and Maccoa ducks were never involved in associations on land, resulting in a terrestrial network size of 7. The aquatic network included these three species, resulting in a network size of 10. The aquatic network had an overall density of 0.89, compared with the density of 0.67 for the terrestrial network, indicating that there were relatively more ‘missing’ associations between species in the terrestrial network than in the aquatic network (Figures 2 and 3). In both networks analysed, all species were either directly or indirectly reachable by others, meaning that no species was completely isolated from other species in the network. The network diameter of both networks was 2.00, further highlighting the cohesive (well-connected) structure of both networks. Network properties relating to intra-specific and inter-specific associations Differences between index groups were significant for both aquatic data (Kruskal–Wallis H = 1266.70; χ2 = 1190.16; df = 50; p < 0.001) and terrestrial data (H = 284.29; χ2 = 274.71; df = 20; p < 0.001). The medians representing the interspecific and intraspecific association strengths ranged between 0 and 2.22 in the aquatic network, and between 0 and 2.00 in the terrestrial network (Table 2). The thicknesses of lines linking nodes in the network diagrams (Figures 2 and 3) are proportional to the association strengths between species.
2Intraspecific and interspecific association strengths between waterbird species in aquatic and terrestrial networks at Strandfontein wastewater treatment works. http://www.sajs.co.za/index.php/SAJS/article/downloadSuppFile/283/2625
All birds on the water associated more with conspecifics than with heterospecifics (Table 2). Mean association strength, actor degree centrality and mean geodesic distance (Tables 3 and 4) provide an indication of a species’ gregariousness and its level of influence in the community in its potential to transmit AIV or contract the virus itself.
3Actor degree centrality, mean geodesic distance and mean association strength for each of the waterbird species in the aquatic network for Strandfontein wastewater treatment works. http://www.sajs.co.za/index.php/SAJS/article/downloadSuppFile/283/2626
4Actor degree centrality, mean geodesic distance and mean association strength for each of the waterbird species in the terrestrial network for Strandfontein wastewater treatment works. http://www.sajs.co.za/index.php/SAJS/article/downloadSuppFile/283/2627
Empirical comparison Correlations of prevalence data with actor degree centrality and mean geodesic distance yielded similar results, although the signs of the correlations were different. For the aquatic network, both metrics were significantly correlated with prevalence (partial correlation coefficient = –0.91 for mean geodesic distance and 0.91 for actor degree centrality; p < 0.02). For the terrestrial network the relationships were not significant at the 0.05 level (partial correlation coefficient magnitude = 0.58; p < 0.3).DiscussionWaterbird associations both in the water and on land at Strandfontein were clearly not random, supporting the hypothesis that community composition at a point-count or pond scale does not provide an adequate indicator of interaction strengths and hence of transmission capability. Typical waterbird count data alone are thus inadequate to infer the potential for influenza transmission. Water depth and the presence or absence of reeds could not be used to predict patterns of species associations. It is possible that other habitat variables (such as water pH, salinity and nutrient status) may be influencing patterns of association, but there is little spatial variation in water quality in the ponds at Strandfontein; fine-scale species associations appear to be driven largely by social choice. In other words, a bird may choose to associate with another bird belonging to a different species because it benefits in some way by doing so. 22,23,24 These ‘choice’ associations appear to override fine-scale ecological drivers of association. This conclusion must be assessed within the context of our network analysis. The small diameters of both networks (2.00) indicate that within each network, all species are easily ‘reachable’ by others. An indirect route of infection between two species would at most involve one extra species as a link in a chain. Therefore, should viral transmission simply depend on the presence or absence of associations between species and not be a function of association strength, AIV would have the potential to spread rapidly to all species present in the network. The higher network density of the aquatic network indicates that it is more cohesive than the terrestrial network, suggesting that there should be a faster rate of disease spread amongst birds on the water than on land. The greater size of the aquatic network further implies that a greater proportion of species in the wetland community we examined would be more immediately exposed to the virus, were it to be introduced. The time spent by a species in each habitat and the physical parameters affecting viral survival in water or in faeces on land would also play a role in determining the rate of AIV transmission in water and on land, respectively. The high intraspecific association strengths in both networks (between 1.01 and 2.20 for the aquatic network, and between 0.34 and 2.00 for the terrestrial network) suggest that AIV would spread faster within a species group than between species. Little grebes, red-knobbed coots and Maccoa ducks had the lowest intraspecific association strengths in the aquatic network, although this probably (for the first two species) reflects the fact that they are territorial when breeding and the study occurred during the breeding season. 25Red-knobbed coots showed the highest intraspecific association strength on land, suggesting that intraspecific viral transmission in coots may nonetheless be a potentially important route of infection throughout the year. Red-billed teal had the highest mean association strength in the aquatic network, portraying this species as potentially the most influential species (for disease transmission) in the network. This result is independently supported by the higher avian influenza prevalence in teal found by Cumming et al. 21If southern pochards are discounted because of their low abundance during the study, then Cape shovelers exhibit the highest mean association strength in the terrestrial network. Based on actor degree centrality, Egyptian geese and red-knobbed coots occupied the least influential positions in the aquatic and terrestrial networks, respectively. The relatively strong correlation that we found between avian influenza prevalence and measures of network membership is intriguing. We are hesitant to regard it as rigorous proof of the value of a network approach because of the relatively small number of bird species involved in the comparison, the generally low prevalence of avian influenza in the subregion, and the resulting small numbers of positive samples on which the analysis is based. However, the fact that there is a strong and significant correlation between actor location within the aquatic network and documented prevalence lends credence to the approach and assumptions presented here and suggests that this may be a fertile area for further research that links network analysis and epidemiology. It is also interesting that results were significant for the aquatic network but not the terrestrial network, as might be expected for a waterborne pathogen in a waterbird community; we speculate that in these relatively warm habitats, viral survival on the banks may be low and co-feeding rather than co-roosting may thus become the dominant mechanism driving transmission. The number and strength of associations in a species network (and the ways in which they change through space and time) are not the only factors that could influence the pattern of pathogen transmission. For instance, juveniles may show lower immunological competence, 8making breeding colonies more vulnerable. Differences in AIV susceptibility between species could also affect interspecies transmission, even in a fairly cohesive association network. The nature of interspecific interactions may further influence the potential for interspecies pathogen transmission, and the problem extends beyond the species that we considered in this study. Charadriiformes (wading birds), for example, having also been identified as potential reservoirs for AIV, 11,13 and by virtue of feeding in association with anatids, 23,26 warrant further investigation for their role in the broader avian influenza disease network. For diseases which cross the wildlife–domestic interface, further expansion of network analysis should attempt to identify the potential critical links in pathogen transmission between wild and domestic animals. For instance, in South Africa, outbreaks of both high-pathogenic and low-pathogenic AIV in semi-intensively farmed common ostriches (Struthio camelus) have been associated with large numbers of wild waterfowl frequenting ostrich camps on the affected farms. 27,28 As a result of the bidirectional nature of interspecific associations, wild birds are placed at risk by ostriches just as much as domestic poultry are placed at risk by wild birds. Our findings demonstrate that the scale at which interactions are recorded is of prime importance for epidemiological analysis. For health-care professionals who are seeking to evaluate wild bird risks, a combination of information from multiple scales may be required for the accurate depiction of pathogen transmission dynamics. Fine-scale species association data collected at the level of individual ponds revealed strongly non-random species interactions that differed from those interactions that might be inferred from broader-scale abundance data. Although broad-scale patterns are also relevant to understanding which species are present in a given area, transmission potential must be studied at a finer scale if we are to understand the dynamics of AIV in wild populations. Acknowledgements We thank Éva Plagányi-Lloyd who developed the association index formula. We also thank Morné du Plessis, Marna Sinclair, Birgit Erni and Morné Carstens and his team at Strandfontein for advice and assistance. This study was funded by the DST/NRF Centre of Excellence at the Percy FitzPatrick Institute of African Ornithology and the USAID-supported Global Avian Influenza Network for Surveillance (GAINS) initiative of the Wildlife Conservation Society’s Field Veterinary Program. Further financial support was provided by the Gordon Sprigg Scholarship Fund. 1.10.1098/rstb.2001.08891151637710884942.TaylorPBNavarroRAWren-SargentMHarrisonJAKieswetterSLTOTAL CWAC report: Coordinated water bird counts in South Africa, 1992–9719993.10.2307/15892913589604.10.1159/0001490144291435.KidaHYanagawaRMatsuokaYDuck influenza lacking evidence of disease signs and immune response19803054755374399945513466.Scientific report on migratory birds and their possible role in the spread of highly pathogenic avian influenza20063571467.BrownJDStallknechtDEBeckJRSuarezDLSwayneDESusceptibility of North American ducks and gulls to H5N1 highly pathogenic avian influenza viruses20061216631670172836158.WebsterRGBeanWJGormanOTChamberTMKawaokaYEvolution and ecology of influenza A viruses19925615217915791083728599.10.1071/MU0401710.10.1079/WPS20045111.AlexanderDJA review of avian influenza in different bird species20007431310.1016/S0378-1135(00)00160-712.10.1023/B:VERC.0000014125.49371.141453537613. StallknechtDEEcology and epidemiology of avian influenza viruses in wild bird populations: Waterfowl, shorebirds, pelicans, cormorants, etc.199747616914.Tulsa: StatSoft Inc.200415.McCuneBGraceJBAnalysis of ecological communities200216. 10.1080/0094965000881203517. McCuneBMeffordMJPC-ORD multivariate analysis of ecological data.199918.BorgattiSPEverettMGFreemanLCUCINET 6 for Windows: Software for social network analysis200219.BorgattiSPNetDraw: Graph visualisation software200220. HannemanRARiddleMIntroduction to social network methodsc20052007 Jan 1021. 10.1007/s10393-011-0684-z22. DawsonRMarsh sandpipers (Tringa stagnatilis) associating with feeding avocets (Recurvirostra avosetta) and other species19756829429523.PettetAMarsh sandpipers (Tringa stagnatilis) associating with feeding teal (Anas crecca)19756829524.LewisADwo commensal feeding associations observed in Kenya19891210210325.HockeyPARDeanWRJRyanPGRoberts – Birds of southern Africa. 7th ed.200526.ReynoldsJFFeeding association between marsh sandpiper and Hottentot teal197218927. 10.2307/15931071100701528. SinclairMBrücknerGKKotzeJJAvian influenza in ostriches: Epidemiological investigation in the Western Cape Province of South Africa200642512Appendix 1 Sampling distribution of species within habitat types (figures) and matrices used in data analyses (tables)
A1Symmetrical association distance matrix (Matrix Ad)a for aquatic data. Each element in the matrix represents the level of dissociation between study speciesa at Strandfontein wastewater treatment works. http://www.sajs.co.za/index.php/SAJS/article/downloadSuppFile/283/2628
A2TABLE A2: Habitat distance matrix (Matrix Hd)a for aquatic data recorded at Strandfontein wastewater treatment works. Each element in the matrix represents the dissimilarity between two species in terms of their distribution within each habitat type (open water or within 3 m of reeds). http://www.sajs.co.za/index.php/SAJS/article/downloadSuppFile/283/2629
A3Depth distance matrix (Matrix Dd)a for aquatic data collected at Strandfontein wastewater treatment works. Each element in the matrix represents the dissimilarity between two species in terms of their sampling distribution within different depth categories (< 0.5 m, 0.5 m 1.0 m, > 1.0 m). http://www.sajs.co.za/index.php/SAJS/article/downloadSuppFile/283/2630
A4 Proportional pond abundance matrix (Matrix P)a for each study species in the aquatic network at Strandfontein wastewater treatment works. Blank entries indicate the complete absence of the species from the pond over the entire study period. http://www.sajs.co.za/index.php/SAJS/article/downloadSuppFile/283/2631
A5Pond abundance distance matrix (Matrix Pd)a for aquatic data. Each entry represents the dissimilarity between two species in terms of their distribution across 15 ponds at Strandfontein wastewater treatment works. http://www.sajs.co.za/index.php/SAJS/article/downloadSuppFile/283/2632