Landscape Ecology

, Volume 34, Issue 10, pp 2435–2449 | Cite as

Habitat amount, quality, and fragmentation associated with prevalence of the tick-borne pathogen Ehrlichia chaffeensis and occupancy dynamics of its vector, Amblyomma americanum

  • Dylan T. Simpson
  • Molly S. Teague
  • Joanna K. Weeks
  • Brent Z. Kaup
  • Oliver Kerscher
  • Matthias LeuEmail author
Research Article



Tick-borne diseases are becoming increasingly prevalent world-wide. This is likely due in part to land-cover change, particularly forest fragmentation, but this evidence is largely limited to Lyme disease. It is unknown whether this is generalizable to other, emergent tick-borne pathogens.


Motivated by hypotheses regarding landscape context and vertebrate hosts, we asked how landscape context, namely habitat amount, quality, and fragmentation, is related to the distribution of Ehrlichia chaffeensis, a tick-borne pathogen of increasing concern, and the interannual occupancy dynamics of its vector, the lone star tick (Amblyomma americanum).


We collected nymphal ticks from 130 plots in southeastern Virginia, U.S., for 5 years and tested for E. chaffeensis via targeted PCR. We derived metrics of landscape context from Landsat data and related these to pathogen prevalence and tick turnover using hierarchical Bayesian models.


Landscape context was associated with both pathogen prevalence and tick turnover. Pathogen prevalence was negatively associated with total forest landcover, coniferous forest landcover, and forest edge density. Tick turnover was positively associated with coniferous landcover and with an interaction between total forest landcover and edge. This interaction was such that turnover was predicted to be lowest in small contiguous forests, and highest in small fragmented forests.


Landscape context affects E. chaffeensis prevalence and occupancy dynamics of its tick host, though these processes appear decoupled. We hypothesize that pathogen prevalence may be more driven by reservoir host movement and social behavior and tick dynamics are more driven by host population density.


Ehrlichia chaffeensis Amblyomma americanum Tick-borne disease Forest fragmentation Edge effect 



We thank Andrew Lewis, Julia Moore, Joseph Thompson, Alan Harris, Richard Cannella, Matt Feresten, Ann Marie Rydberg, Christopher Tyson, Stephanie Wilson, James Woods, and Nora Wicks for help collecting ticks. We thank Phillip D’Addio for his help with molecular analyses. We are grateful to the following landowners for providing access: Colonial National Historical Park, Colonial Williamsburg, Newport News Park, Waller Mill Park, Freedom Park, Greensprings Trail Park, York River State Park, Joint Base Langley-Eustis, and the Virginia State Department of Forestry. This work was supported by William & Mary’s Commonwealth Center for Energy and the Environment, Charles Center, and Environmental Science and Policy Program, and the Strategic Environmental Research and Development Program (RC-2202).

Supplementary material

10980_2019_898_MOESM1_ESM.pdf (4.1 mb)
Supplementary material 1 (PDF 4194 kb)


  1. Allan BF, Goessling LS, Storch GA, Thach RE (2010) Blood meal analysis to identify reservoir hosts for Amblyomma americanum ticks. Emerg Infect Dis 16:433–440CrossRefGoogle Scholar
  2. Allan BF, Keesing F, Ostfeld R (2003) Effect of forest fragmentation on Lyme disease risk. Conserv Biol 17:267–272CrossRefGoogle Scholar
  3. Anderson BE, Sims KG, Olson JG, Childs JE, Piesman JF, Happ CM, Maupin GO, Johnson BJB (1993) Amblyomma americanum: a potential vector of human ehrlichiosis. Am J Trop Med Hyg 49:239–244CrossRefGoogle Scholar
  4. Beasley JC, Devault TL, Rhodes OEJ (2007) Home-range attributes of raccoons in a fragmented agricultural region of northern Indiana. J Wildl Manage 71:844–850CrossRefGoogle Scholar
  5. Berger BW (1989) Dermatologic manifestations of lyme disease. Rev Infect Dis 11:S1475–S1481CrossRefGoogle Scholar
  6. Beyer HL (2014) Geospatial Modeling Environment.
  7. Bishopp FC, Trembley HL (1945) Distribution and hosts of certain North American ticks. J Parasitol 31:1–54CrossRefGoogle Scholar
  8. Brownstein JS, Skelly DK, Holford TR, Fish D (2005) Forest fragmentation predicts local scale heterogeneity of Lyme disease risk. Oecologia 146:469–475CrossRefGoogle Scholar
  9. Campbell TA, Laseter BR, Ford WM, Miller KV (2004) Feasibility of localized management to control white-tailed deer in forest regeneration areas. Wildl Soc Bull 32:1124–1131CrossRefGoogle Scholar
  10. Cornicelli L, Woolf A, Roseberry JL (1996) White-tailed deer use of a suburban environment in southern Illinois. Trans Illinois State Acad Sci 89:93–103Google Scholar
  11. Côté IM, Gross MR (1993) Reduced disease in offspring: a benefit of coloniality in sunfish. Behav Ecol Sociobiol 33:269–274CrossRefGoogle Scholar
  12. Davidson WR, Lockhart JM, Stallknecht DE, Howerth EW, Dawson JE, Rechav Y (2001) Persistent Ehrlichia chaffeensis infection in white-tailed deer. J Wildl Dis 37:538–546CrossRefGoogle Scholar
  13. de Sá-Hungaro IJB, de Almeida Raia V, da Costa Pinheiro M, Ribeiro CCDU, Famadas KM (2014) Amblyomma auricularium (Acari: Ixodidae): underwater survival of the non-parasitic phase of feeding females. Braz J Vet Parasitol 23:387–392CrossRefGoogle Scholar
  14. Estrada-Peña A, Vatansever Z, Gargili A, Ergönul Ö (2010) The trend towards habitat fragmentation is the key factor driving the spread of Crimean-Congo haemorrhagic fever. Epidemiol Infect 138:1194–1203CrossRefGoogle Scholar
  15. Gaff H, Schaefer E (2010) Disease transmission modelling. In: Michael E, Spear RC (eds) Modeling parasite transmission and control. Lands Bioscience and Springer, New York, pp 51–65CrossRefGoogle Scholar
  16. Gaughan CR, DeStefano S (2005) Movement patterns of rural and suburban white-tailed deer in Massachusetts. Urban Ecosyst 8:191–202CrossRefGoogle Scholar
  17. Ginsberg HS, Ewing CP (1989) Comparison of flagging, walking, and collecting from hosts as sampling methods for northern deer ticks, Ixodes dammini and lone-star ticks, Amblyomma americanum (Acari: Ixodidae). Exp Appl Acarol 7:313–322CrossRefGoogle Scholar
  18. Glueck TF, Clark WR, Andrews RD (1988) Raccoon movement and habitat use during the fur harvest season. Wildl Soc Bull 16:6–11Google Scholar
  19. Habib TJ, Merrill EH, Pybus MJ, Coltman DW (2011) Modelling landscape effects on density-contact rate relationships of deer in eastern Alberta: implications for chronic wasting disease. Ecol Modell 222:2722–2732CrossRefGoogle Scholar
  20. Hasapes SK, Comer CE (2016) Adult white-tailed deer seasonal home range and habitat composition in Northwest Louisiana. J Southeast Assoc Fish Wildl Agencies 3:243–252Google Scholar
  21. Heitman N, Scott Dahlgren F, Drexler NA, Massung RF, Behravesh CB (2016) Increasing incidence of ehrlichiosis in the United States: a summary of national surveillance of Ehrlichia chaffeensis and Ehrlichia ewingii infections in the United States, 2008–2012. Am J Trop Med Hyg 94:52–60CrossRefGoogle Scholar
  22. Hölzenbein S, Marchinton RL (1992) Integration of maturing-male white-tailed deer into the adult population. J Mammal 73:326–334CrossRefGoogle Scholar
  23. Kilpatrick HJ, Labonte AM, Stafford KC (2014) The relationship between deer density, tick abundance, and human cases of lyme desease in a residential community. J Med Entomol 51:777–784CrossRefGoogle Scholar
  24. Kollars TMJ, Oliver JHJ, Durden LA, Kollars PG (2000) Host associations and seasonal activity of Amblyomma americanum (Acari: Ixodidae) in Missouri. J Parasitol 86:1156–1159CrossRefGoogle Scholar
  25. Liu Y, Lund RB, Nordone SK, Yabsley MJ, McMahan CS (2017) A Bayesian spatio-temporal model for forecasting the prevalence of antibodies to Ehrlichia species in domestic dogs within the contiguous United States. Parasit Vectors 10:138CrossRefGoogle Scholar
  26. Lovely KR, McShea WJ, Lafon NW, Carr DE (2013) Land parcelization and deer population densities in a rural county of Virginia. Wildl Soc Bull 37:360–367CrossRefGoogle Scholar
  27. Mackenzie DI, Hines JE (2016) PRESENCE.
  28. MacKenzie DI, Nichols JD, Hines JE, Knutson MG, Franklin AB (2003) Estimating site occupancy, colonization, and local extinction when a species is detected imperfectly. Ecology 84:2200–2207CrossRefGoogle Scholar
  29. Manangan JS, Schweitzer SH, Nibbelink N, Yabsley MJ, Gibbs SEJ, Wimberly MC (2007) Habitat factors influencing distributions of Anaplasma phagocytophilum and Ehrlichia chaffeensis in the Mississippi Alluvial Valley. Vector-Borne Zoonotic Dis 7:563–574CrossRefGoogle Scholar
  30. McGarigal K (2015) FRAGSTATS HELP v4. University of Massachusetts, Amherst.
  31. McGarigal K, Cushman SA, Ene E (2012) Fragstats v.4: spatial pattern analysis program for categorical and continuous maps. University of Massachussets, Amherst.
  32. Mooring MS, Hart BL (1992) Animal grouping for protection from parasites: selfish herd and encounter-dilution effects. Behaviour 123:173–193CrossRefGoogle Scholar
  33. Nair ADS, Cheng C, Jaworski DC, Willard LH, Sanderson MW, Ganta RR (2014) Ehrlichia chaffeensis infection in the reservoir host (white-tailed deer) and in an incidental host (dog) is impacted by its prior growth in macrophage and tick cell environments. PLoS ONE. Google Scholar
  34. Oregon State University (2018) PRISM. In: Northwest Alliance Computer Science Engineering. Accessed 20 Apr 2018
  35. Ostfeld RS, Jones C, Wolff J (1996) Of mice and mast: ecological connections in eastern deciduous forests. Bioscience 46:323–330CrossRefGoogle Scholar
  36. Ostfeld RS, Levi T, Keesing F, Oggenfuss K, Canham CD (2018) Tick-borne disease risk in a forest food web. Ecology 99:1562–1573CrossRefGoogle Scholar
  37. Plummer M (2017) JAGS: a program for analysis of Bayesian graphcial models using Gibbs sampling.
  38. R Core Team (2019) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Google Scholar
  39. Rand PW, Lubelczyk C, Lavigne GR, Elias S, Holman MS, Lacombe EH, Smith RP Jr (2003) Deer density and the abundance of Ixodes scapularis (Acari: Ixodidae). J Med Entomol 40:179–184CrossRefGoogle Scholar
  40. Robinson SJ, Samuel MD, Lopez DL, Shelton P (2012) The walk is never random: subtle landscape effects shape gene flow in a continuous white-tailed deer population in the Midwestern United States. Mol Ecol 21:4190–4205CrossRefGoogle Scholar
  41. Rosatte R, Ryckman M, Ing K, Proceviat S, Allan M, Bruce L, Donovan D, Davies JC (2010) Density, movements, and survival of raccoons in Ontario, Canada: implications for disease spread and management. J Mammal 91:122–135CrossRefGoogle Scholar
  42. Rubenstein DI, Hohmann ME (1989) Parasites and social behavior of island feral horses. Oikos 55:312–320CrossRefGoogle Scholar
  43. Saïd S, Servanty S (2005) The influence of landscape structure on female roe deer home-range size. Landsc Ecol 20:1003–1012CrossRefGoogle Scholar
  44. Semtner PJ, Barker RW, Hair JA (1971) The ecology and behavior of the lone star tick (Acarina: Ixodidae). II. Activity and survival in different ecological habitats. J Med Entomol 8:719–725CrossRefGoogle Scholar
  45. Simpson DT, Teague MS, Weeks JK, Lewis AD, D'Addio PM, Moore JD, Thompson JA, Harris AC, Cannella RT, Kaup BZ, Kerscher O, Matthias L (2019) Broad, multi-year sampling effort highlights complex dynamics of the tick-borne pathogen Ehrlichia chaffeensis (Rickettsiales: Anaplasmatacae). J Med Entomol 56:162–168CrossRefGoogle Scholar
  46. Skuldt LH, Mathews NE, Oyer AM (2008) White-tailed deer movements in a chronic wasting disease area in South-Central Wisconsin. J Wildl Manage 72:1156–1160CrossRefGoogle Scholar
  47. Stanek G, Wormser GP, Gray J, Strle F (2012) Lyme borreliosis. Lancet 379:461–473CrossRefGoogle Scholar
  48. Stein KJ, Waterman M, Waldon JL (2008) The effects of vegetation density and habitat disturbance on the spatial distribution of ixodid ticks (Acari: Ixodidae). Geospat Health 2:241–252CrossRefGoogle Scholar
  49. Stuber EF, Gruber LF, Fontaine JJ (2017) A Bayesian method for assessing multi-scale species-habitat relationships. Landsc Ecol 32:2365–2381CrossRefGoogle Scholar
  50. Su Y-S, Yajima M (2015) R2jags: Using R to Run “JAGS”. R package version 0.5-7.
  51. U.S. Census Bureau (2011) Transportation geodatabase. In: TIGER Products.
  52. Virginia Department of Game and Inland Fisheries (2015) White-tailed Deer Management Plan, 2015-2024. RichmondGoogle Scholar
  53. Virginia Geographic Information Network (2011) Virginia LIDAR. In: Virginia GIS clearinghouse.
  54. Wimberly MC, Yabsley MJ, Baer AD, Dugan VG, Davidson WR (2008) Spatial heterogeneity of climate and land-cover constraints on distributions of tick-borne pathogens. Glob Ecol Biogeogr 17:189–202CrossRefGoogle Scholar
  55. Wright CL, Gaff HD, Hynes WL (2014) Prevalence of Ehrlichia chaffeensis and Ehrlichia ewingii in Amblyomma americanum and Dermacentor variabilis collected from southeastern Virginia, 2010-2011. Ticks Tick Borne Dis 5:978–982CrossRefGoogle Scholar
  56. Yabsley MJ (2010) Natural history of Ehrlichia chaffeensis: vertebrate hosts and tick vectors from the United States and evidence for endemic transmission in other countries. Vet Parasitol 167:136–148CrossRefGoogle Scholar
  57. Yabsley MJ, Dugan VG, Stallknecht DE, Little SE, Lockhart M, Dawson JE, Davidson WR (2003) Evaluation of a prototype Ehrlichia chaffeensis surveillance system using white-tailed deer (Odocoileus virginianus) as natural sentinels. Vector-borne zoonotic Dis 3:195–207CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Biology DepartmentWilliam & MaryWilliamsburgUSA
  2. 2.Department of SociologyWilliam & MaryWilliamsburgUSA
  3. 3.Graduate Program in Ecology & Evolution, Department of Ecology, Evolution, and Natural ResourcesRutgers UniversityNew BrunswickUSA
  4. 4.Department of GeographyUniversity of Wisconsin-MadisonMadisonUSA

Personalised recommendations