Nestedness of ectoparasite-vertebrate host networks.
Determining the structure of ectoparasite-host networks will enable disease ecologists to better understand and predict the spread of vector-borne diseases. If these networks have consistent properties, then studying the structure of well-understood networks could lead to extrapolation of these properties to others, including those that support emerging pathogens. Borrowing a quantitative measure of network structure from studies of mutualistic relationships between plants and their pollinators, we analyzed 29 ectoparasite-vertebrate host networks--including three derived from molecular bloodmeal analysis of mosquito feeding patterns--using measures of nestedness to identify non-random interactions among species. We found significant nestedness in ectoparasite-vertebrate host lists for habitats ranging from tropical rainforests to polar environments. These networks showed non-random patterns of nesting, and did not differ significantly from published estimates of nestedness from mutualistic networks. Mutualistic and antagonistic networks appear to be organized similarly, with generalized ectoparasites interacting with hosts that attract many ectoparasites and more specialized ectoparasites usually interacting with these same "generalized" hosts. This finding has implications for understanding the network dynamics of vector-born pathogens. We suggest that nestedness (rather than random ectoparasite-host associations) can allow rapid transfer of pathogens throughout a network, and expand upon such concepts as the dilution effect, bridge vectors, and host switching in the context of nested ectoparasite-vertebrate host networks.
Graham
Sean P
SP
Department of Biological Sciences, Auburn University, Auburn, Alabama, USA. grahasp@auburn.edu
Hassan
Hassan K
HK
Burkett-Cadena
Nathan D
ND
Guyer
Craig
C
Unnasch
Thomas R
TR
D000818:Animals; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D006790:Host-Parasite Interactions; D008954:Models, Biological; D009010:Monte Carlo Method; D010271:Parasites / Q000378:metabolism*; D011157:Population Dynamics; D012044:Regression Analysis; D013559:Symbiosis; D014714:Vertebrates / Q000378:metabolism*