, Volume 172, Issue 2, pp 307–316 | Cite as

Inferring host specificity and network formation through agent-based models: tick–mammal interactions in Borneo

  • Konstans WellsEmail author
  • Robert B. O’Hara
  • Martin Pfeiffer
  • Maklarin B. Lakim
  • Trevor N. Petney
  • Lance A. Durden


Patterns of host–parasite association are poorly understood in tropical forests. While we typically observe only snapshots of the diverse assemblages and interactions under variable conditions, there is a desire to make inferences about prevalence and host-specificity patterns. We studied the interaction of ticks with non-volant small mammals in forests of Borneo. We inferred the probability of species interactions from individual-level data in a multi-level Bayesian model that incorporated environmental covariates and advanced estimates for rarely observed species through model averaging. We estimated the likelihood of observing particular interaction frequencies under field conditions and a scenario of exhaustive sampling and examined the consequences for inferring host specificity. We recorded a total of 13 different tick species belonging to the five genera Amblyomma, Dermacentor, Haemaphysalis, Ixodes, and Rhipicephalus from a total of 37 different host species (Rodentia, Scandentia, Carnivora, Soricidae) on 237 out of 1,444 host individuals. Infestation probabilities revealed most variation across host species but less variation across tick species with three common rat and two tree shrew species being most heavily infested. Host species identity explained ca. 75 % of the variation in infestation probability and another 8–10 % was explained by local host abundance. Host traits and site-specific attributes had little explanatory power. Host specificity was estimated to be similarly low for all tick species, which were all likely to infest 34–37 host species if exhaustively sampled. By taking into consideration the hierarchical organization of individual interactions that may take place under variable conditions and that shape host–parasite networks, we can discern uncertainty and sampling bias from true interaction frequencies, whereas network attributes derived from observed values may lead to highly misleading results. Multi-level approaches may help to move this field towards inferential approaches for understanding mechanisms that shape the strength and dynamics in ecological networks.


Acari Hierarchical model Biotic interaction Host specificity Multispecies model 



We thank the Economic Planning Unit Malaysia for research permission and Sabah Parks and Yayasan Sabah for various kinds of support during field work. We are especially thankful to all staff and people at the different field sites for their warm hospitality. In particular, we thank Alim Biun, Fred Tuh Yit Yu, Jickson Sankin, Awang Matamin, Suati Selimon, Aloysius Mail and Jadda Suhaimi. We thank Brigitte Fiala and K. Eduard Linsenmair for academic and logistic support. We appreciate the mentoring and friendship of the late Elisabeth K. V. Kalko; she sadly passed away, to our disbelief, while this study was being performed. Field work was supported by the German Academic Exchange Service (DAAD).

Supplementary material

442_2012_2511_MOESM1_ESM.doc (68 kb)
Supplementary material 1 (DOC 68 kb)
442_2012_2511_MOESM2_ESM.xlsx (59 kb)
Supplementary material 2 (XLSX 59 kb)


  1. Adler GH (1998) Impacts of resource abundance on populations of a tropical forest rodent. Ecology 79:242–254CrossRefGoogle Scholar
  2. Anderson RM, May RM (1991) Infectious diseases of humans: dynamics and control. Oxford University Press, OxfordGoogle Scholar
  3. Bersier LF, Banašek-Richter C, Cattin MF (2002) Quantitative descriptors of food-web matrices. Ecology 83:2394–2407CrossRefGoogle Scholar
  4. Blüthgen N, Klein A-M (2011) Functional complementarity and specialisation: the role of biodiversity in plant–pollinator interactions. Basic Appl Ecol 12:282–291CrossRefGoogle Scholar
  5. Blüthgen N, Menzel F (2006) Measuring specialization in species interaction networks. BMC Ecol 6:9. doi: 10.1186/1472-6785-6-9 PubMedCrossRefGoogle Scholar
  6. Clauset A, Moore C, Newman MEJ (2008) Hierarchical structure and the prediction of missing links in networks. Nature 453:98–101PubMedCrossRefGoogle Scholar
  7. Colwell RK, Futuyma DJ (1971) On the measurement of niche breadth and overlap. Ecology 52:567–568CrossRefGoogle Scholar
  8. Cumming GS (2002) Comparing climate and vegetation as limiting factors for species ranges of African ticks. Ecology 83:255–268CrossRefGoogle Scholar
  9. Cumming GS (2004) On the relevance of abundance and spatial pattern for interpretations of host–parasite association data. Bull Entomol Res 94:401–409PubMedCrossRefGoogle Scholar
  10. Durden LA (2006) Taxonomy, host associations, life cycles and vectorial importance of ticks parasitizing small mammals. In: Morand S, Krasnov BR, Poulin R (eds) Micromammals and macroparasites: from evolutionary ecology to management. Springer, Tokyo, pp 91–102CrossRefGoogle Scholar
  11. Durden LA, Merker S, Beati L (2008) The tick fauna of Sulawesi, Indonesia (Acari: Ixodoidea: Argasidae and Ixodidae). Exp Appl Acarol 45:85–110PubMedCrossRefGoogle Scholar
  12. Feinsinger P, Spears EE, Poole RW (1981) A simple measure of niche breadth. Ecology 62:27–32CrossRefGoogle Scholar
  13. Geevarghese G, Mishra AC (2011) Haemaphysalis ticks of India. Elsevier, LondonGoogle Scholar
  14. Gelman A, Hill J (2007) Data analysis using regression and multilevel/hierarchical models. Cambridge University Press, New YorkGoogle Scholar
  15. Gillespie T, Chapman CA (2006) Prediction of parasite infection dynamics in primate metapopulations based on attributes of forest fragmentation. Conserv Biol 20:441–448PubMedCrossRefGoogle Scholar
  16. Golicher DJ, O’Hara RB, Ruíz-Montoya L, Cayuela L (2006) Lifting a veil on diversity: a Bayesian approach to fitting relative-abundance models. Ecol Appl 16:202–212PubMedCrossRefGoogle Scholar
  17. Hoogstraal H (1964) Studies of Southeast Asian Haemaphysalis ticks (Ixodidea, Ixodidae). Redescription, hosts, and distribution of H. traguli Oudemans. The larva and nymph of H. vidua W. and N. Identity of H. papuana toxopei Warburton (new combination). J Parasitol 50:765–782PubMedCrossRefGoogle Scholar
  18. Hurlbert S (1978) The measurement of niche overlap and some relatives. Ecology 59:67–77CrossRefGoogle Scholar
  19. Ings TC, et al. (2009) Ecological networks—beyond food webs. J Anim Ecol 78:253–269PubMedCrossRefGoogle Scholar
  20. Klompen JSH, Black WC IV, Keirans JE, Oliver JH Jr (1996) Evolution of ticks. Annu Rev Entomol 41:141–161PubMedCrossRefGoogle Scholar
  21. Kohls GM (1957) Malaysian parasites. XVIII. Ticks (Ixodoidea) of Borneo and Malaya. Stud Inst Med Res Malays 28:65–94Google Scholar
  22. Krasnov BR, Stanko M, Morand S (2007) Host community structure and infestation by ixodid ticks: repeatability, dilution effect and ecological specialization. Oecologia 154:185–194PubMedCrossRefGoogle Scholar
  23. Lafferty KD, Dunne JA (2010) Stochastic ecological network occupancy (SENO) models: a new tool for modeling ecological networks across spatial scales. Theor Ecol 3:123–135CrossRefGoogle Scholar
  24. Lande R, Engen S, Sæther B-E (2003) Stochastic population dynamics in ecology and conservation. Oxford University Press, OxfordGoogle Scholar
  25. LoGiudice K, Ostfeld RS, Schmidt KA, Keesing F (2003) The ecology of infectious disease: effects of host diversity and community composition on Lyme disease risk. Proc Natl Acad Sci USA 100:567–571PubMedCrossRefGoogle Scholar
  26. Lunn D, Spiegelhalter D, Thomas A, Best N (2009) The BUGS project: evolution, critique and future directions. Stat Med 28:3049–3067PubMedCrossRefGoogle Scholar
  27. MacKenzie DI, Nichols JD, Lachman GB, Droege S, Royle JA, Langtimm CA (2002) Estimating site occupancy rates when detection probabilities are less than one. Ecology 83:2248–2255CrossRefGoogle Scholar
  28. Mason NWH, Mouillot D, Lee WG, Wilson JB (2005) Functional richness, functional evenness and functional divergence: the primary components of functional diversity. Oikos 111:112–118CrossRefGoogle Scholar
  29. Mouillot D, Krasnov BR, Poulin R (2008) High intervality explained by phylogenetic constraints in host parasite webs. Ecology 89:2043–2051PubMedCrossRefGoogle Scholar
  30. Olesen JM, Bascompte J, Elberling H, Jordano P (2008) Temporal dynamics in a pollination network. Ecology 89:1573–1582PubMedCrossRefGoogle Scholar
  31. Petney TN, Kolonin GV, Robbins RG (2007) Southeast Asian ticks (Acari: Ixodida): a historical perspective. Parasitol Res 101:S201–S205PubMedCrossRefGoogle Scholar
  32. Poulin R, Mouillot D (2005) Combining phylogenetic and ecological information into a new index of host specificity. J Parasitol 91:511–514PubMedCrossRefGoogle Scholar
  33. Rao CR (1982) Diversity and dissimilarity coefficients: a unified approach. Theor Pop Biol 21:2443CrossRefGoogle Scholar
  34. Robinson WS (2009) Ecological correlations and the behavior of individuals. Int J Epidemiol 38:337–341 [reprinted from (1950) Am Sociol Rev 15(3):351–357]Google Scholar
  35. Royle JA, Nichols JD, Kéry M (2005) Modelling occurrence and abundance of species when detection is imperfect. Oikos 110:353–359CrossRefGoogle Scholar
  36. Sehgal RNM (2010) Deforestation and avian infectious diseases. J Exp Biol 213:955–960PubMedCrossRefGoogle Scholar
  37. Sodhi NS et al (2010) Conserving Southeast Asian forest biodiversity in human-modified landscapes. Biol Conserv 143:2375–2384CrossRefGoogle Scholar
  38. Thébault E, Fontaine C (2010) Stability of ecological communities and the architecture of mutualistic and trophic networks. Science 329:853–856PubMedCrossRefGoogle Scholar
  39. Vázquez DP, Poulin R, Krasnov BR, Shenbrot GI (2005) Species abundance and the distribution of specialization in host–parasite interaction networks. J Anim Ecol 74:946–955CrossRefGoogle Scholar
  40. Vázquez DP, Melian CJ, Williams NM, Blüthgen N, Krasnov BR, Poulin R (2007) Species abundance and asymmetric interaction strength in ecological networks. Oikos 116:1120–1127Google Scholar
  41. Wells K, Pfeiffer M, Lakim MB, Kalko EKV (2006) Movement trajectories and habitat partitioning of small mammals in logged and unlogged rain forests on Borneo. J Anim Ecol 75:1212–1223PubMedCrossRefGoogle Scholar
  42. Wells K, Kalko EKV, Lakim MB, Pfeiffer M (2007a) Effects of rain forest logging on species richness and assemblage composition of small mammals in Southeast Asia. J Biogeogr 34:1087–1099CrossRefGoogle Scholar
  43. Wells K, Smales LR, Kalko EKV, Pfeiffer M (2007b) Impact of rain-forest logging on helminth assemblages in small mammals (Muridae, Tupaiidae) from Borneo. J Trop Ecol 23:35–43CrossRefGoogle Scholar
  44. Wells K, Lakim MB, Beaucournu JC (2011) Host specificity and niche partitioning in flea–small mammal networks in Bornean rainforests. Med Vet Entomol 25:311–319PubMedCrossRefGoogle Scholar
  45. Wenger SJ, Freeman MC (2008) Estimating species occurrence, abundance, and detection probability using zero-inflated distributions. Ecology 89:2953–2959PubMedCrossRefGoogle Scholar
  46. Wikle CK (2003) Hierarchical Bayesian models for predicting the spread of ecological processes. Ecology 84:1382–1394CrossRefGoogle Scholar
  47. Wilson DE, Reeder DM (eds) (2005) Mammal species of the world. A taxonomic and geographic reference, 3rd edn. Johns Hopkins University Press, BaltimoreGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Konstans Wells
    • 1
    • 2
    Email author
  • Robert B. O’Hara
    • 3
  • Martin Pfeiffer
    • 1
    • 4
  • Maklarin B. Lakim
    • 2
  • Trevor N. Petney
    • 5
  • Lance A. Durden
    • 6
  1. 1.Institute of Experimental EcologyUniversity of UlmUlmGermany
  2. 2.Sabah ParksKota KinabaluMalaysia
  3. 3.Biodiversity and Climate Research Centre (BiK-F)Frankfurt (Main)Germany
  4. 4.Department of EcologyNational University of MongoliaUlaanbaatarMongolia
  5. 5.Karlsruhe Institute of Technology, Zoological InstituteKarlsruheGermany
  6. 6.Department of BiologyGeorgia Southern UniversityStatesboroUSA

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