Behavioral Ecology and Sociobiology

, Volume 67, Issue 12, pp 1951–1959 | Cite as

Network structure and prevalence of Cryptosporidium in Belding’s ground squirrels

  • Kimberly L. VanderWaal
  • Edward R. Atwill
  • Stacie Hooper
  • Kelly Buckle
  • Brenda McCowanEmail author
Original Paper


Although pathogen transmission dynamics are profoundly affected by population social and spatial structure, few studies have empirically demonstrated the population-level implications of such structure in wildlife. In particular, epidemiological models predict that the extent to which contact patterns are clustered decreases a pathogen’s ability to spread throughout an entire population, but this effect has yet to be demonstrated in a natural population. Here, we use network analysis to examine patterns of transmission of an environmentally transmitted parasite, Cryptosporidium spp., in Belding’s ground squirrels (Spermophilus beldingi). We found that the prevalence of Cryptosporidium was negatively correlated with transitivity, a measure of network clustering, and positively correlated with the percentage of juvenile males. Additionally, network transitivity decreased when there were higher percentages of juvenile males; the exploratory behavior demonstrated by juvenile males may have altered the structure of the network by reducing clustering, and low clustering was associated with high prevalence. We suggest that juvenile males are critical in mediating the ability of Cryptosporidium to spread through colonies, and thus may function as “super-spreaders.” Our results demonstrate the utility of a network approach in quantifying mechanistically how differences in contact patterns may lead to system-level differences in infection patterns.


Social networks Cryptosporidium Ground squirrels Pathogen transmission Infection patterns Clustering Wildlife disease 



We thank Jennifer Dike and Katryna Fleer for their assistance in data collection and Allison Heagerty for her comments and contributions to data analysis. We also thank two anonymous reviewers for their constructive comments on an earlier version of this manuscript. This work was conducted under the auspices of the Bernice Barbour Communicable Disease Laboratory, with financial support from the Bernice Barbour Foundation, Hackensack, N.J., as a grant to the Center of Equine Health, University of California, Davis.

Ethical standards

The experiments described herein comply with the current laws of the USA.


  1. Ames GM, George DB, Hampson CP, Kanerek AR, McBee CD, Lockwood DR, Achter JD, Webb CT (2011) Using network properties to predict disease dynamics on human contact networks. Proc R Soc Lond B 278:3544–2550CrossRefGoogle Scholar
  2. Anderson RM, May RM (1992) Infectious diseases of humans. Oxford University, OxfordGoogle Scholar
  3. Atwill ER, Camargo SM, Phillips R, Alonso LH, Tate KW, Jensen WA, Bennet J, Little S, Salmon TP (2001) Quantitative shedding of two genotypes of Cryptosporidium parvum in California ground squirrels (Spermophilus beecheyi). Appl Environ Microbiol 67:2840–2843PubMedCrossRefGoogle Scholar
  4. Atwill ER, Phillips R, Pereira MGC, Li X, McCowan B (2004) Seasonal shedding of multiple Cryptosporidium genotypes in California ground squirrels (Spermophilus beecheyi). Appl Environ Microbiol 70:6748–6752PubMedCrossRefGoogle Scholar
  5. Badham J, Stocker R (2010) The impact of network clustering and assortivity on epidemic behaviour. Theor Popul Biol 77:71–75PubMedCrossRefGoogle Scholar
  6. Bansal S, Grenfell BT, Meyers LA (2007) When individual behaviour matters: homogeneous and network models in epidemiology. J R Soc Interface 4:879–891PubMedCrossRefGoogle Scholar
  7. Benarska M, Bajer A, Kulis K, Sinski E (2003) Biological characterisation of Cryptosporidium parvum isolates of wildlife rodents in Poland. Ann Agric Environ Med 10:163–169Google Scholar
  8. Burnham KP, Anderson DR (2002) Model selection and multimodal inference: a practical information-theoretic approach. Springer, New YorkGoogle Scholar
  9. Cohen S, Doyle WJ, Skoner DP, Rabin BS, Gwaltney JMJ (1997) Social ties and susceptibility to the common cold. JAMA-J Am Med Assoc 277:1940–1944CrossRefGoogle Scholar
  10. Corner LAL, Pfeiffer DU, Morris RS (2003) Social-network analysis of Mycobacterium bovis transmission among captive brushtail possums (Trichosurus vulpecula). Prev Vet Med 59:147–167PubMedCrossRefGoogle Scholar
  11. Croft DP, James R, Krause J (2008) Exploring animal social networks. Princeton University, PrincetonGoogle Scholar
  12. Croft DP, Madden JR, Franks DW, James R (2011) Hypothesis testing in animal social networks. Trends Ecol Evol 26:502–507PubMedCrossRefGoogle Scholar
  13. Drewe JA (2009) Who infects whom? Social networks and tuberculosis transmission in wild meerkats. Proc R Soc Lond B 277:633–642CrossRefGoogle Scholar
  14. Fenner AL, Godfrey SS, Bull CM (2011) Using social networks to deduce whether residents or dispersers spread parasites in a lizard population. J Anim Ecol 80:835–843PubMedCrossRefGoogle Scholar
  15. Ferrari N, Cattadori IM, Nespereira J, Rizzoli A, Hudson PJ (2003) The role of host sex in parasite dynamics: field experiments on the yellow-necked mouse Apodemus flavicollis. Ecol Lett 6:1–7CrossRefGoogle Scholar
  16. Godfrey SS, Bull CM, James R, Murray K (2009) Network structure and parasite transmission in a group living lizard, gidgee skink, Egernia stokesii. Behav Ecol Sociobiol 63:1045–1056CrossRefGoogle Scholar
  17. Godfrey SS, Moore JA, Nelson NJ, Bull CM (2010) Social network structure and parasite infection patterns in a territorial reptile, the tuatara (Sphenodon punctatus). Int J Parasitol 40:1575–1585PubMedCrossRefGoogle Scholar
  18. Grear DA, Perkins SE, Hudson PJ (2009) Does elevated testosterone result in increased exposure and transmission of parasites? Ecol Lett 12:528–537PubMedCrossRefGoogle Scholar
  19. Hawley DM, Etienne RS, Ezenwa VO, Jolles AE (2011) Does animal behavior underlie covariation between hosts’ exposure to infectious agents and susceptibility to infection? Implications for disease dynamics. Integr Comp Biol 5:528–539CrossRefGoogle Scholar
  20. Holekamp K (1984) Dispersal in ground-dwelling Sciurids. In: Murie JO, Michener GR (eds) The Biology of Ground-dwelling Squirrels. University of Nebraska, Lincoln, pp 295–320Google Scholar
  21. Hou L, Li X, Moeller R, Palermo B, Atwill ER (2004) Neonatal-mouse infectivity of intact Cryptosporidium parvum oocysts isolated after optimized in vitro excystation. Appl Environ Microbiol 70:642–646PubMedCrossRefGoogle Scholar
  22. Keeling MJ (1999) The effects of local spatial structure on epidemiological invasions. Proc R Soc Lond B 266:859–867CrossRefGoogle Scholar
  23. Keeling MJ (2005) The implications of network structure for epidemic dynamics. Theor Popul Biol 67:1–8PubMedCrossRefGoogle Scholar
  24. Keeling MJ, Eames KTD (2005) Networks and epidemic models. J R Soc Interface 2:295–307PubMedCrossRefGoogle Scholar
  25. Lloyd-Smith JO, Schreiber SJ, Kopp PE, Getz WM (2005) Superspreading and the effect of individual variation on disease emergence. Nature 438:355–359PubMedCrossRefGoogle Scholar
  26. McCallum H, Barlow N, Hone J (2001) How should pathogen transmission be modelled? Trends Ecol Evol 16:295–300PubMedCrossRefGoogle Scholar
  27. McLean IG (1984) Spacing behavior and aggression in female ground squirrels. In: Murie JO, Michener GR (eds) The biology of ground-dwelling squirrels. University of Nebraska, Lincoln, pp 321–335Google Scholar
  28. Michener GR (1984) Age, sex, and species differences in the annual cycles of ground-dwelling Sciurids: implications for sociality. In: Murie JO, Michener GR (eds) The biology of ground-dwelling squirrels. University of Nebraska, Lincoln, pp 79–107Google Scholar
  29. Murie JO, Michener GR (1984) Biology of ground-dwelling squirrels. University of Nebraska, LincolnGoogle Scholar
  30. Newman MEJ (2003) Properties of highly clustered networks. Phys Rev E 68:1–6Google Scholar
  31. Nunes S, Muecke EM, Anthony JA, Batterbee AS (1999) Endocrine and energetic mediation of play behavior in free-living Belding’s ground squirrels. Horm Behav 36:153–165PubMedCrossRefGoogle Scholar
  32. Otterstatter MC, Thomson JD (2007) Contact networks and transmission of an intestinal pathogen in bumble bee (Bombus impatiens) colonies. Oecologia 154:411–421PubMedCrossRefGoogle Scholar
  33. Perkins SE, Ferrari MF, Hudson PJ (2008) The effects of social structure and sex-biased transmission on macroparasite infection. Parasitology 135:1561–1569PubMedCrossRefGoogle Scholar
  34. Perkins SE, Cagnacci F, Stradiotto A, Arnoldi D, Hudson PJ (2009) Comparison of social networks derived from ecological data: implications for inferring infectious disease dynamics. J Anim Ecol 78:1015–1022PubMedCrossRefGoogle Scholar
  35. Porphyre T, McKenzie J, Stevenson MA (2011) Contact patterns as a risk factor for bovine tuberculosis infection in a free-living adult brushtail possum Trichosurus vulpecula population. Prev Vet Med 100:221–230PubMedCrossRefGoogle Scholar
  36. Turner J, Bowers JA, Clancy D, Behnke MC, Christley RM (2008) A network model of E coli O157 transmission within a typical UK dairy herd: the effect of heterogeneity and clustering on the prevalence of infection. J Theor Biol 254:45–54PubMedCrossRefGoogle Scholar
  37. Valente TW (2010) Social networks and health: models, methods, and applications. Oxford University, OxfordCrossRefGoogle Scholar
  38. Wasserman S, Faust K (1994) Social network analysis: methods and applications. Cambridge University, CambridgeCrossRefGoogle Scholar
  39. Wu X, Liu Z (2008) How community structure influences epidemic spread in social networks. Physica A 387:623–630CrossRefGoogle Scholar
  40. Zu S-X, Fang G-D, Fayer R, Guerrant RL (1992) Cryptosporidiosis: pathogenesis and immunology. Parasitol Today 8:24–27PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Kimberly L. VanderWaal
    • 1
    • 2
  • Edward R. Atwill
    • 3
    • 4
  • Stacie Hooper
    • 5
  • Kelly Buckle
    • 6
  • Brenda McCowan
    • 2
    • 3
    Email author
  1. 1.Animal Behavior Graduate GroupUniversity of California—DavisDavisUSA
  2. 2.International Institute for Human–Animal NetworksUniversity of California—DavisDavisUSA
  3. 3.Department of Population Health and Reproduction, School of Veterinary MedicineUniversity of California—DavisDavisUSA
  4. 4.Western Institute for Food Safety and SecurityUniversity of California—DavisDavisUSA
  5. 5.Department of Ecology and EvolutionUniversity of California—DavisDavisUSA
  6. 6.School of Veterinary ScienceThe University of QueenslandGattonAustralia

Personalised recommendations