1921
Volume 93, Issue 4
  • ISSN: 0002-9637
  • E-ISSN: 1476-1645

Abstract

Abstract.

The Lone star tick ( L.) is the primary vector for pathogens of significant public health importance in North America, yet relatively little is known about its current and potential future distribution. Building on a published summary of tick collection records, we used an ensemble modeling approach to predict the present-day and future distribution of climatically suitable habitat for establishment of the Lone star tick within the continental United States. Of the nine climatic predictor variables included in our five present-day models, average vapor pressure in July was by far the most important determinant of suitable habitat. The present-day ensemble model predicted an essentially contiguous distribution of suitable habitat extending to the Atlantic coast east of the 100th western meridian and south of the 40th northern parallel, but excluding a high elevation region associated with the Appalachian Mountains. Future ensemble predictions for 2061–2080 forecasted a stable western range limit, northward expansion of suitable habitat into the Upper Midwest and western Pennsylvania, and range contraction along portions of the Gulf coast and the lower Mississippi river valley. These findings are informative for raising awareness of -transmitted pathogens in areas where the Lone Star tick has recently or may become established.

Loading

Article metrics loading...

The graphs shown below represent data from March 2017
/content/journals/10.4269/ajtmh.15-0330
2015-10-07
2019-09-19
Loading full text...

Full text loading...

/deliver/fulltext/14761645/93/4/875.html?itemId=/content/journals/10.4269/ajtmh.15-0330&mimeType=html&fmt=ahah

References

  1. Parmesan C, , 2006. Ecological and evolutionary responses to recent climate change. Annu Rev Ecol Evol Syst 37: 637669. [Google Scholar]
  2. Chen IC, Hill JK, Ohlemueller R, Roy DB, Thomas CD, , 2011. Rapid range shifts of species associated with high levels of climate warming. Science 333: 10241026. [Google Scholar]
  3. Parmesan C, Yohe G, , 2003. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421: 3742. [Google Scholar]
  4. Root TL, Price JT, Hall KR, Schneider SH, Rosenzweig C, Pounds JA, , 2003. Fingerprints of global warming on wild animals and plants. Nature 421: 5760. [Google Scholar]
  5. Deutsch CA, Tewksbury JJ, Huey RB, Sheldon KS, Ghalambor CK, Haak DC, Martin PR, , 2008. Impacts of climate warming on terrestrial ectotherms across latitude. Proc Natl Acad Sci USA 105: 66686672. [Google Scholar]
  6. Paaijmans KP, Heinig RL, Seliga RA, Blanford JI, Blanford S, Murdock CC, Thomas MB, , 2013. Temperature variation makes ectotherms more sensitive to climate change. Glob Change Biol 19: 23782380. [Google Scholar]
  7. Gage KL, Burkot TR, Eisen RJ, Hayes EB, , 2008. Climate and vectorborne diseases. Am J Prev Med 35: 436450. [Google Scholar]
  8. Githeko AK, Lindsay SW, Confalonieri UE, Patz JA, , 2000. Climate change and vector-borne diseases: a regional analysis. Bull World Health Organ 78: 11361147. [Google Scholar]
  9. Sutherst RW, , 2004. Global change and human vulnerability to vector-borne diseases. Clin Microbiol Rev 17: 136173. [Google Scholar]
  10. Altizer S, Ostfeld RS, Johnson PTJ, Kutz S, Harvell CD, , 2013. Climate change and infectious diseases: from evidence to a predictive framework. Science 341: 514519. [Google Scholar]
  11. Epstein PR, , 2005. Climate change and human health. N Engl J Med 353: 14331436. [Google Scholar]
  12. Harvell CD, Mitchell CE, Ward JR, Altizer S, Dobson AP, Ostfeld RS, Samuel MD, , 2002. Climate warming and disease risks for terrestrial and marine biota. Science 296: 21582162. [Google Scholar]
  13. Rohr JR, Dobson AP, Johnson PTJ, Kilpatrick AM, Paull SH, Raffel TR, Ruiz-Moreno D, Thomas MB, , 2011. Frontiers in climate change-disease research. Trends Ecol Evol 26: 270277. [Google Scholar]
  14. Rosenthal J, , 2009. Climate change and the geographic distribution of infectious diseases. EcoHealth 6: 489495. [Google Scholar]
  15. Patz JA, Campbell-Lendrum D, Holloway T, Foley JA, , 2005. Impact of regional climate change on human health. Nature 438: 310317. [Google Scholar]
  16. Jongejan F, Uilenberg G, , 2004. The global importance of ticks. Parasitology 129: S3S14. [Google Scholar]
  17. Randolph SE, , 2001. The shifting landscape of tick-borne zoonoses: tick-borne encephalitis and Lyme borreliosis in Europe. Philos Trans R Soc Lond B Biol Sci 356: 10451056. [Google Scholar]
  18. Kuehn BM, , 2013. CDC estimates 300,000 U.S. cases of Lyme disease annually. JAMA 310: 11101110. [Google Scholar]
  19. Bock R, Jackson L, De Vos A, Jorgensen W, , 2004. Babesiosis of cattle. Parasitology 129: S247S269. [Google Scholar]
  20. McLeod R, Kristjanson P, , 1999. TickCost Project—Economic Impacts of Ticks and Tick-Borne Diseases to Livestock in Africa, Asia, and Australia. Final report of joint esys/ILRI/ACIAR. Nairobi, Kenya: International Livestock Research Institute. [Google Scholar]
  21. Needham GR, Teel PD, , 1991. Off-host physiological ecology of Ixodid ticks. Annu Rev Entomol 36: 659681. [Google Scholar]
  22. Sauer JR, Hair JA, , 1986. Morphology, Physiology, and Behavioral Biology of Ticks. Chichester, United Kingdom: Ellis Horwood Limited, 510. [Google Scholar]
  23. Sonenshine DE, Roe RM, , 2014. Biology of Ticks. Oxford, United Kingdom: Oxford University Press. [Google Scholar]
  24. Brownstein JS, Holford TR, Fish D, , 2003. A climate-based model predicts the spatial distribution of the Lyme disease vector Ixodes scapularis in the United States. Environ Health Perspect 111: 11521157. [Google Scholar]
  25. Ogden NH, Bigras-Poulin M, O'Callaghan CJ, Barker IK, Lindsay LR, Maarouf A, Smoyer-Tomic KE, Waltner-Toews D, Charron D, , 2005. A dynamic population model to investigate effects of climate on geographic range and seasonality of the tick Ixodes scapularis. Int J Parasitol 35: 375389. [Google Scholar]
  26. Eisen L, , 2008. Climate change and tick-borne diseases: a research field in need of long-term empirical field studies. Int J Med Microbiol 298: 1218. [Google Scholar]
  27. Jaenson TGT, Jaenson DGE, Eisen L, Petersson E, Lindgren E, , 2012. Changes in the geographical distribution and abundance of the tick Ixodes ricinus during the past 30 years in Sweden. Parasit Vectors 5: 115. [Google Scholar]
  28. Ogden NH, Maarouf A, Barker IK, Bigras-Poulin M, Lindsay LR, Morshed MG, O'Callaghan CJ, Ramay F, Waltner-Toews D, Charron DF, , 2006. Climate change and the potential for range expansion of the Lyme disease vector Ixodes scapularis in Canada. Int J Parasitol 36: 6370. [Google Scholar]
  29. Ogden NH, Lindsay LR, Morshed M, Sockett PN, Artsob H, , 2009. The emergence of Lyme disease in Canada. CMAJ 180: 12211224. [Google Scholar]
  30. Randolph SE, , 2010. To what extent has climate change contributed to the recent epidemiology of tick-borne diseases? Vet Parasitol 167: 9294. [Google Scholar]
  31. Leger E, Vourc'h G, Vial L, Chevillon C, McCoy KD, , 2013. Changing distributions of ticks: causes and consequences. Exp Appl Acarol 59: 219244. [Google Scholar]
  32. Ogden NH, Mechai S, Margos G, , 2013. Changing geographic ranges of ticks and tick-borne pathogens: drivers, mechanisms and consequences for pathogen diversity. Front Cell Infect Microbiol 3: 46. [Google Scholar]
  33. Medlock JM, Hansford KM, Bormane A, Derdakova M, Estrada-Pena A, George JC, Golovljova I, Jaenson TGT, Jensen JK, Jensen PM, Kazimirova M, Oteo JA, Papa A, Pfister K, Plantard O, Randolph SE, Rizzoli A, Santos-Silva MM, Sprong H, Vial L, Hendrickx G, Zeller H, Van Bortel W, , 2013. Driving forces for changes in geographical distribution of Ixodes ricinus ticks in Europe. Parasit Vectors 6: 111. [Google Scholar]
  34. James A, Burdett C, McCool M, Fox A, Riggs P, , 2015. The geographic distribution and ecological preferences of the American dog tick, Dermacentor variabilis (Say), in the USA. Med Vet Entomol 29: 178188. [Google Scholar]
  35. Porretta D, Mastrantonio V, Amendolia S, Gaiarsa S, Epis S, Genchi C, Bandi C, Otranto D, Urbanelli S, , 2013. Effects of global changes on the climatic niche of the tick Ixodes ricinus inferred by species distribution modelling. Parasit Vectors 6: 271. [Google Scholar]
  36. Leighton PA, Koffi JK, Pelcat Y, Lindsay LR, Ogden NH, , 2012. Predicting the speed of tick invasion: an empirical model of range expansion for the Lyme disease vector Ixodes scapularis in Canada. J Appl Ecol 49: 457464. [Google Scholar]
  37. Ogden NH, St-Onge L, Barker IK, Brazeau S, Bigras-Poulin M, Charron DF, Francis CM, Heagy A, Lindsay LR, Maarouf A, Michel P, Milord F, O'Callaghan CJ, Trudel L, Thompson RA, , 2008. Risk maps for range expansion of the Lyme disease vector, Ixodes scapularis, in Canada now and with climate change. Int J Health Geogr 7: 24. [Google Scholar]
  38. Guisan A, Thuiller W, , 2005. Predicting species distribution: offering more than simple habitat models. Ecol Lett 8: 9931009. [Google Scholar]
  39. Elith J, Leathwick JR, , 2009. Species distribution models: ecological explanation and prediction across space and time. Annu Rev Ecol Evol Syst 40: 677697. [Google Scholar]
  40. Pearson RG, Dawson TP, , 2003. Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Glob Ecol Biogeogr 12: 361371. [Google Scholar]
  41. Hutchinson GE, , 1957. Population studies—animal ecology and demography - concluding remarks. Cold Spring Harb Symp Quant Biol 22: 415427. [Google Scholar]
  42. Grinnell J, , 1917. Field tests of theories concerning distributional control. Am Nat 51: 115128. [Google Scholar]
  43. Araujo MB, Guisan A, , 2006. Five (or so) challenges for species distribution modelling. J Biogeogr 33: 16771688. [Google Scholar]
  44. Araujo MB, Peterson AT, , 2012. Uses and misuses of bioclimatic envelope modeling. Ecology 93: 15271539. [Google Scholar]
  45. Thomas CD, , 2010. Climate, climate change and range boundaries. Divers Distrib 16: 488495. [Google Scholar]
  46. Araujo MB, New M, , 2007. Ensemble forecasting of species distributions. Trends Ecol Evol 22: 4247. [Google Scholar]
  47. Heikkinen RK, Luoto M, Araujo MB, Virkkala R, Thuiller W, Sykes MT, , 2006. Methods and uncertainties in bioclimatic envelope modelling under climate change. Prog Phys Geogr 30: 751777. [Google Scholar]
  48. Hair JA, Howell DE, , 1970. Lone star ticks: their biology and control in Ozark recreation areas. Okla State Univ Agr Expt Sta Bul B-679: 47. [Google Scholar]
  49. Bolte JR, Hair JA, Fletcher J, , 1970. White-tailed deer mortality following tissue destruction induced by Lone star ticks. J Wildl Manage 34: 546552. [Google Scholar]
  50. Barnard DR, , 1985. Injury thresholds and production loss functions for the Lone star tick, Amblyomma americanum (Acari, Ixodidae), on pastured, preweaner beef cattle, Bos taurus. J Econ Entomol 78: 852855. [Google Scholar]
  51. Childs JE, Paddock CD, , 2003. The ascendancy of Amblyomma americanum as a vector of pathogens affecting humans in the United States. Annu Rev Entomol 48: 307337. [Google Scholar]
  52. Goddard J, Varela-Stokes AS, , 2009. Role of the Lone star tick, Amblyomma americanum (L.), in human and animal diseases. Vet Parasitol 160: 112. [Google Scholar]
  53. Mixson TR, Campbell SR, Gill JS, Ginsberg HS, Reichard MV, Schulze TL, Dasch GA, , 2006. Prevalence of Ehrlichia, Borrelia, and Rickettsial agents in Amblyomma americanum (Acari: Ixodidae) collected from nine states. J Med Entomol 43: 12611268. [Google Scholar]
  54. Paddock CD, Yabsley MJ, , 2007. Ecological Havoc, the Rise of White-Tailed Deer, and the Emergence of Amblyomma americanum—Associated Zoonoses in the United States. Wildlife and Emerging Zoonotic Diseases: The Biology, Circumstances and Consequences of Cross-Species Transmission. Heidelberg, Germany: Springer Berlin, 289324. [Google Scholar]
  55. Savage HM, Godsey MS, Lambert A, Panella NA, Burkhalter KL, Harmon JR, Lash RR, Ashley DC, Nicholson WL, , 2013. First detection of Heartland virus (Bunyaviridae: Phlebovirus) from field collected arthropods. Am J Trop Med Hyg 89: 445452. [Google Scholar]
  56. Springer YP, Eisen L, Beati L, James AM, Eisen RJ, , 2014. Spatial distribution of counties in the continental United States with records of occurrence of Amblyomma americanum (Ixodida: Ixodidae). J Med Entomol 51: 342351. [Google Scholar]
  57. Dennis DT, Nekomoto TS, Victor JC, Paul WS, Piesman J, , 1998. Reported distribution of Ixodes scapularis and Ixodes pacificus (Acari: Ixodidae) in the United States. J Med Entomol 35: 629638. [Google Scholar]
  58. Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A, , 2005. Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25: 19651978. [Google Scholar]
  59. Gray J, Dautel H, Estrada-Peña A, Kahl O, Lindgren E, , 2009. Effects of climate change on ticks and tick-borne diseases in Europe. Interdiscip Perspect Infect Dis 2009: 593232. [Google Scholar]
  60. Eisen RJ, Lane RS, Fritz CL, Eisen L, , 2006. Spatial patterns of Lyme disease risk in California based on disease incidence data and modeling of vector-tick exposure. Am J Trop Med Hyg 75: 669676. [Google Scholar]
  61. Thornton PE, Thornton MM, Mayer BW, Wilhelmi N, Wei Y, Devarakonda R, Cook RB, , 2014. Daymet: Daily Surface Weather Data on a 1-km Grid for North America; Version 2 Oak Ridge, TN: Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center. [Google Scholar]
  62. Thornton PE, Thornton MM, Mayer BW, Wilhelmi N, Wei Y, Devarakonda R, Cook R, , 2012. Daymet: Daily Surface Weather on a 1 km Grid for North America, 1980–2008. Oak Ridge, TN: Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center for Biogeochemical Dynamics (DAAC). [Google Scholar]
  63. Moore SM, Eisen RJ, Monaghan A, Mead P, , 2014. Meteorological influences on the seasonality of Lyme disease in the United States. Am J Trop Med Hyg 90: 486496. [Google Scholar]
  64. Taylor KE, Stouffer RJ, Meehl GA, , 2012. An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93: 485498. [Google Scholar]
  65. Knutti R, Masson D, Gettelman A, , 2013. Climate model genealogy: generation CMIP5 and how we got there. Geophys Res Lett 40: 11941199. [Google Scholar]
  66. Gent PR, Danabasoglu G, Donner LJ, Holland MM, Hunke EC, Jayne SR, Lawrence DM, Neale RB, Rasch PJ, Vertenstein M, Worley PH, Yang ZL, Zhang MH, , 2011. The community climate system model version 4. J Clim 24: 49734991. [Google Scholar]
  67. Giorgetta MA, Jungclaus J, Reick CH, Legutke S, Bader J, Bottinger M, Brovkin V, Crueger T, Esch M, Fieg K, Glushak K, Gayler V, Haak H, Hollweg HD, Ilyina T, Kinne S, Kornblueh L, Matei D, Mauritsen T, Mikolajewicz U, Mueller W, Notz D, Pithan F, Raddatz T, Rast S, Redler R, Roeckner E, Schmidt H, Schnur R, Segschneider J, Six KD, Stockhause M, Timmreck C, Wegner J, Widmann H, Wieners KH, Claussen M, Marotzke J, Stevens B, , 2013. Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations for the Coupled Model Intercomparison Project phase 5. J Adv Model Earth Sy 5: 572597. [Google Scholar]
  68. Collins WJ, Bellouin N, Doutriaux-Boucher M, Gedney N, Halloran P, Hinton T, Hughes J, Jones CD, Joshi M, Liddicoat S, Martin G, O'Connor F, Rae J, Senior C, Sitch S, Totterdell I, Wiltshire A, Woodward S, , 2011. Development and evaluation of an Earth-System model-HadGEM2. Geosci Model Dev 4: 10511075. [Google Scholar]
  69. Voldoire A, Sanchez-Gomez E, Melia DSY, Decharme B, Cassou C, Senesi S, Valcke S, Beau I, Alias A, Chevallier M, Deque M, Deshayes J, Douville H, Fernandez E, Madec G, Maisonnave E, Moine MP, Planton S, Saint-Martin D, Szopa S, Tyteca S, Alkama R, Belamari S, Braun A, Coquart L, Chauvin F, , 2013. The CNRM-CM5.1 global climate model: description and basic evaluation. Clim Dyn 40: 20912121. [Google Scholar]
  70. Bi DH, Dix M, Marsland SJ, O'Farrell S, Rashid HA, Uotila P, Hirst AC, Kowalczyk E, Golebiewski M, Sullivan A, Yan HL, Hannah N, Franklin C, Sun ZA, Vohralik P, Watterson I, Zhou XB, Fiedler R, Collier M, Ma YM, Noonan J, Stevens L, Uhe P, Zhu HY, Griffies SM, Hill R, Harris C, Puri K, , 2013. The ACCESS coupled model: description, control climate and evaluation. Aust Meteorol Oceanogr J 63: 4164. [Google Scholar]
  71. Moss RH, Edmonds JA, Hibbard KA, Manning MR, Rose SK, Van Vuuren DP, Carter TR, Emori S, Kainuma M, Kram T, , 2010. The next generation of scenarios for climate change research and assessment. Nature 463: 747756. [Google Scholar]
  72. Riahi K, Rao S, Krey V, Cho C, Chirkov V, Fischer G, Kindermann G, Nakicenovic N, Rafaj P, , 2011. RCP 8.5—A scenario of comparatively high greenhouse gas emissions. Clim Change 109: 3357. [Google Scholar]
  73. Thomson AM, Calvin KV, Smith SJ, Kyle GP, Volke A, Patel P, Delgado-Arias S, Bond-Lamberty B, Wise MA, Clarke LE, , 2011. RCP4. 5: a pathway for stabilization of radiative forcing by 2100. Clim Change 109: 7794. [Google Scholar]
  74. Willett KM, Jones PD, Gillett NP, Thorne PW, , 2008. Recent changes in surface humidity: development of the HadCRUH dataset. J Clim 21: 53645383. [Google Scholar]
  75. Held IM, Soden BJ, , 2000. Water vapor feedback and global warming. Annu Rev Energy Environ 25: 441475. [Google Scholar]
  76. Wood S, , 2006. Generalized Additive Models: An Introduction with R. Boca Raton, FL: CRC press. [Google Scholar]
  77. Dormann CF, Elith J, Bacher S, Buchmann C, Carl G, Carre G, Marquez JRG, Gruber B, Lafourcade B, Leitao PJ, Munkemuller T, McClean C, Osborne PE, Reineking B, Schroder B, Skidmore AK, Zurell D, Lautenbach S, , 2013. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36: 2746. [Google Scholar]
  78. Morisette JT, Jarnevich CS, Holcombe TR, Talbert CB, Ignizio D, Talbert MK, Silva C, Koop D, Swanson A, Young NE, , 2013. VisTrails SAHM: visualization and workflow management for species habitat modeling. Ecography 36: 129135. [Google Scholar]
  79. Elith J, Leathwick JR, Hastie T, , 2008. A working guide to boosted regression trees. J Anim Ecol 77: 802813. [Google Scholar]
  80. McCullagh P, Nelder JA, , 1989. Generalized Linear Models. London, United Kingdom: Chapman and Hall. [Google Scholar]
  81. Friedman JH, , 1991. Multivariate adaptive regression splines. Ann Stat 19: 167. [Google Scholar]
  82. Phillips SJ, Dudík M, , 2008. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31: 161175. [Google Scholar]
  83. Breiman L, , 2001. Random forests. Mach Learn 45: 532. [Google Scholar]
  84. Dormann CF, Purschke O, Marquez JRG, Lautenbach S, Schroder B, , 2008. Components of uncertainty in species distribution analysis: a case study of the great grey shrike. Ecology 89: 33713386. [Google Scholar]
  85. Liu CR, White M, Newell G, , 2013. Selecting thresholds for the prediction of species occurrence with presence-only data. J Biogeogr 40: 778789. [Google Scholar]
  86. Manel S, Williams HC, Ormerod SJ, , 2001. Evaluating presence–absence models in ecology: the need to account for prevalence. J Appl Ecol 38: 921931. [Google Scholar]
  87. Fielding AH, Bell JF, , 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv 24: 3849. [Google Scholar]
  88. Allouche O, Tsoar A, Kadmon R, , 2006. Assessing the accuracy of species distribution models: prevalence, Kappa and the True skill statistic (TSS). J Appl Ecol 43: 12231232. [Google Scholar]
  89. Jiménez-Valverde A, Lobo JM, Hortal J, , 2008. Not as good as they seem: the importance of concepts in species distribution modelling. Divers Distrib 14: 885890. [Google Scholar]
  90. Marmion M, Parviainen M, Luoto M, Heikkinen RK, Thuiller W, , 2009. Evaluation of consensus methods in predictive species distribution modelling. Divers Distrib 15: 5969. [Google Scholar]
  91. Barrett AW, Noden BH, Gruntmeir JM, Holland T, Mitcham JR, Martin JE, Johnson EM, Little SE, , 2015. County scale distribution of Amblyomma americanum (Ixodida: Ixodidae) in Oklahoma: addressing local deficits in tick maps based on passive reporting. J Med Entomol 52: 269273. [Google Scholar]
  92. Diuk-Wasser MA, Gatewood AG, Cortinas MR, Yaremych-Hamer S, Tsao J, Kitron U, Hickling G, Brownstein JS, Walker E, Piesman J, Fish D, , 2006. Spatiotemporal patterns of host-seeking Ixodes scapularis nymphs (Acari: Ixodidae) in the United States. J Med Entomol 43: 166176. [Google Scholar]
  93. Diuk-Wasser MA, Vourc'h G, Cislo P, Hoen AG, Melton F, Hamer SA, Rowland M, Cortinas R, Hickling GJ, Tsao JI, Barbour AG, Kitron U, Piesman J, Fish D, , 2010. Field and climate-based model for predicting the density of host seeking nymphal Ixodes scapularis, an important vector of tick-borne disease agents in the eastern United States. Glob Ecol Biogeogr 19: 504514. [Google Scholar]
  94. Fish D, Howard C, , 1999. Methods used for creating a national Lyme disease risk map. MMWR 48: 2124. [Google Scholar]
  95. Estrada-Peña A, , 2002. Increasing habitat suitability in the United States for the tick that transmits Lyme disease: a remote sensing approach. Environ Health Perspect 110: 635. [Google Scholar]
  96. Berger KA, Ginsberg HS, Gonzalez L, Mather TN, , 2014. Relative humidity and activity patterns of Ixodes scapularis (Acari: Ixodidae). J Med Entomol 51: 769776. [Google Scholar]
  97. Berger KA, Ginsberg HS, Dugas KD, Hamel LH, Mather TN, , 2014. Adverse moisture events predict seasonal abundance of Lyme disease vector ticks (Ixodes scapularis). Parasit Vectors 7: 181. [Google Scholar]
  98. Vail SG, Smith G, , 1998. Air temperature and relative humidity effects on behavioral activity of blacklegged tick (Acari: Ixodidae) nymphs in New Jersey. J Med Entomol 35: 10251028. [Google Scholar]
  99. Perret JL, Guigoz E, Rais O, Gern L, , 2000. Influence of saturation deficit and temperature on Ixodes ricinus tick questing activity in a Lyme borreliosis-endemic area (Switzerland). Parasitol Res 86: 554557. [Google Scholar]
  100. Stafford KC, , 1994. Survival of immature Ixodes scapularis (Acari, Ixodidae) at different relative humidities. J Med Entomol 31: 310314. [Google Scholar]
  101. Yoder JA, Benoit JB, , 2003. Water vapor absorption by nymphal lone star tick, Amblyomma americanum (Acari: Ixodidae), and its ecological significance. Int J Acarol 29: 259264. [Google Scholar]
  102. Hair JA, Sauer JR, Durham KA, , 1975. Water balance and humidity preference in three species of ticks. J Med Entomol 12: 3747. [Google Scholar]
  103. Sauer JR, Hair JA, , 1971. Water balance in Lone star tick (Acarina-Ixodidae)—effects of relative humidity and temperature on weight changes and total water content. J Med Entomol 8: 479485. [Google Scholar]
  104. Lancaster JL, McMillan HL, , 1955. The effects of relative humidity on the Lone star tick. J Econ Entomol 48: 338339. [Google Scholar]
  105. Kollars TM, Oliver JH, Durden LA, Kollars PG, , 2000. Host associations and seasonal activity of Amblyomma americanum (Acari: Ixodidae) in Missouri. J Parasitol 86: 11561159. [Google Scholar]
  106. Brown HE, Yates KF, Dietrich G, MacMillan K, Graham CB, Reese SM, Helterbrand WS, Nicholson WL, Blount K, Mead PS, Patrick SL, Eisen RJ, , 2011. An acarologic survey and Amblyomma americanum distribution map with implications for tularemia risk in Missouri. Am J Trop Med Hyg 84: 411419. [Google Scholar]
  107. 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: 136148. [Google Scholar]
  108. Bishopp FC, Trembley HL, , 1945. Distribution and hosts of certain North American ticks. J Parasitol 31: 154. [Google Scholar]
  109. Yabsley MJ, Wimberly MC, Stallknecht DE, Little SE, Davidson WR, , 2005. Spatial analysis of the distribution of Ehrlichia chaffeensis, causative agent of human monocytotropic ehrlichiosis, across a multi-state region. Am J Trop Med Hyg 72: 840850. [Google Scholar]
  110. Wimberly MC, Baer AD, Yabsley MJ, , 2008. Enhanced spatial models for predicting the geographic distributions of tick-borne pathogens. Int J Health Geogr 7: 115. [Google Scholar]
  111. Yabsley MJ, Dugan VG, Stallknecht DE, Little SE, Lockhart JM, Dawson JE, Davidsoni WR, , 2003. Evaluation of a prototype Ehrlichia chaffeensis surveillance system using white-tailed deer (Odocoileus virginianus) as natural sentinels. Vector Borne Zoonotic Dis 3: 195207. [Google Scholar]
  112. Dugan VG, Yabsley MJ, Tate CM, Mead DG, Munderloh UG, Herron MJ, Stallknecht DE, Little SE, Davidson WR, , 2006. Evaluation of White tailed deer (Odocoileus virginianus) as natural sentinels for Anaplasma phagocytophilum. Vector Borne Zoonotic Dis 6: 192207. [Google Scholar]
  113. Centers for Disease Control and Prevention, 2010. Summary of notifiable diseases—United States, 2008. MMWR 57: 194. [Google Scholar]
  114. Centers for Disease Control and Prevention, 2011. Summary of notifiable diseases—United States, 2009. MMWR 58: 1100. [Google Scholar]
  115. Centers for Disease Control and Prevention, 2012. Summary of notifiable diseases—United States, 2010. MMWR 59: 1111. [Google Scholar]
  116. Centers for Disease Control and Prevention, 2013. Summary of notifiable diseases—United States, 2011. MMWR 60: 1118. [Google Scholar]
  117. Centers for Disease Control and Prevention, 2014. Summary of notifiable diseases—United States, 2012. MMWR 61: 1122. [Google Scholar]
  118. Ostfeld RS, Brunner JL, , 2015. Climate change and Ixodes tick-borne diseases of humans. Philos Trans R Soc Lond B Biol Sci 370: 20140051. [Google Scholar]
  119. Scott JD, Anderson JF, Durden LA, , 2012. Widespread dispersal of Borrelia burgdorferi-infected ticks collected from songbirds across Canada. J Parasitol 98: 4959. [Google Scholar]
  120. Guilbert J, Betts AK, Rizzo DM, Beckage B, Bomblies A, , 2015. Characterization of increased persistence and intensity of precipitation in the northeastern United States. Geophys Res Lett 42: 18881893. [Google Scholar]
  121. Rawlins M, Bradley RS, Diaz H, , 2012. Assessment of regional climate model simulation estimates over the northeast United States. J Geophys Res Atmos 117: D23112. [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.4269/ajtmh.15-0330
Loading
/content/journals/10.4269/ajtmh.15-0330
Loading

Data & Media loading...

Supplementary PDF

Supplementary Table

  • Received : 04 May 2015
  • Accepted : 09 Jun 2015
  • Published online : 07 Oct 2015

Most Cited This Month

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error