Integrating environmental and neighborhood factors in MaxEnt modeling to predict species distributions: A case study of Aedes albopictus in southeastern Pennsylvania

Autoři: Daniel Wiese aff001;  Ananias A. Escalante aff003;  Heather Murphy aff002;  Kevin A. Henry aff001;  Victor Hugo Gutierrez-Velez aff001
Působiště autorů: Department of Geography and Urban Studies, College of Liberal Arts, Temple University, Philadelphia, Pennsylvania, United States of America aff001;  Department of Biostatistics and Epidemiology, College of Public Health, Temple University, Philadelphia, Pennsylvania, United States of America aff002;  Department of Biology, College of Science and Technology, Temple University, Philadelphia, Pennsylvania, United States of America aff003
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0223821


Aedes albopictus is a viable vector for several infectious diseases such as Zika, West Nile, Dengue viruses and others. Originating from Asia, this invasive species is rapidly expanding into North American temperate areas and urbanized places causing major concerns for public health. Previous analyses show that warm temperatures and high humidity during the mosquito season are ideal conditions for A. albopictus development, while its distribution is correlated with population density. To better understand A. albopictus expansion into urban places it is important to consider the role of both environmental and neighborhood factors. The present study aims to assess the relative importance of both environmental variables and neighborhood factors in the prediction of A. albopictus’ presence in Southeast Pennsylvania using MaxEnt (version 3.4.1) machine-learning algorithm. Three models are developed that include: (1) exclusively environmental variables, (2) exclusively neighborhood factors, and (3) a combination of environmental variables and neighborhood factors. Outcomes from the three models are compared in terms of variable importance, accuracy, and the spatial distribution of predicted A. albopictus’ presence. All three models predicted the presence of A. albopictus in urban centers, however, each to a different spatial extent. The combined model resulted in the highest accuracy (74.7%) compared to the model with only environmental variables (73.5%) and to the model with only neighborhood factors (72.1%) separately. Although the combined model does not essentially increase the accuracy in the prediction, the spatial patterns of mosquito distribution are different when compared to environmental or neighborhood factors alone. Environmental variables help to explain conditions associated with mosquitoes in suburban/rural areas, while neighborhood factors summarize the local conditions that can also impact mosquito habitats in predominantly urban places. Overall, the present study shows that MaxEnt is suitable for integrating neighborhood factors associated with mosquito presence that can complement and improve species distribution modeling.

Klíčová slova:

Census – Housing – Mosquitoes – Neighborhoods – Permutation – Population density – Urban areas – Urban environments


1. Rochlin I, Ninivaggi D, Hutchinson M, Farajollahi A. Climate Change and Range Expansion of the Asian Tiger Mosquito (Aedes albopictus) in. PLoS One. 2013;8(4).

2. Higa Y. Dengue vectors and their spatial distribution. Tropical medicine and health. 2011;39(4SUPPLEMENT):S17–S27.

3. Chan YC, Ho BC, Chan KL. Aedes aegypti (L.) and Aedes albopictus (Skuse) in Singapore City: 5. Observations in relation to dengue haemorrhagic fever. Bulletin of the World Health Organization. 1971;44(5):651–7.

4. Tsuda Y, Suwonkerd W, Chawprom S, Prajakwong S, Takagi M. Different spatial distribution of Aedes aegypti and Aedes albopictus along an urban–rural gradient and the relating environmental factors examined in three villages in northern Thailand. Journal of the American Mosquito Control Association. 2006;22(2):222–8. doi: 10.2987/8756-971X(2006)22[222:DSDOAA]2.0.CO;2

5. Bonizzoni M, Gasperi G, Chen X, James AA. The invasive mosquito species Aedes albopictus: current knowledge and future perspectives. Trends in parasitology. 2013;29(9):460–8. doi: 10.1016/ 23916878

6. Faraji A, Egizi A, Fonseca DM, Unlu I, Crepeau T, Healy SP, et al. Comparative host feeding patterns of the Asian tiger mosquito, Aedes albopictus, in urban and suburban Northeastern USA and implications for disease transmission. PLoS neglected tropical diseases. 2014;8(8):e3037. doi: 10.1371/journal.pntd.0003037 25101969

7. Li Y, Kamara F, Zhou G, Puthiyakunnon S, Li C, Liu Y, et al. Urbanization increases Aedes albopictus larval habitats and accelerates mosquito development and survivorship. PLoS neglected tropical diseases. 2014;8(11):e3301. doi: 10.1371/journal.pntd.0003301 25393814

8. Waldock J, Chandra NL, Lelieveld J, Proestos Y, Michael E, Christophides G, et al. The role of environmental variables on Aedes albopictus biology and chikungunya epidemiology. Pathogens and global health. 2013;107(5):224–41. doi: 10.1179/2047773213Y.0000000100 23916332

9. Misslin R, Telle O, Daudé E, Vaguet A, Paul RE. Urban climate versus global climate change—what makes the difference for dengue? Annals of the New York Academy of Sciences. 2016;1382(1):56–72. doi: 10.1111/nyas.13084

10. Leisnham PT, Juliano SA. Impacts of climate, land use, and biological invasion on the ecology of immature Aedes mosquitoes: implications for La Crosse emergence. Ecohealth. 2012;9(2):217–28. doi: 10.1007/s10393-012-0773-7 22692799

11. Igarashi A. Isolation of a Singh’s Aedes albopictus cell clone sensitive to Dengue and Chikungunya viruses. Journal of General Virology. 1978;40(3):531–44. doi: 10.1099/0022-1317-40-3-531 690610

12. Grard G, Caron M, Mombo IM, Nkoghe D, Ondo SM, Jiolle D, et al. Zika virus in Gabon (Central Africa)–2007: a new threat from Aedes albopictus? PLoS neglected tropical diseases. 2014;8(2):e2681. doi: 10.1371/journal.pntd.0002681

13. Chouin-Carneiro T, Vega-Rua A, Vazeille M, Yebakima A, Girod R, Goindin D, et al. Differential susceptibilities of Aedes aegypti and Aedes albopictus from the Americas to Zika virus. PLoS neglected tropical diseases. 2016;10(3):e0004543. doi: 10.1371/journal.pntd.0004543 26938868

14. Kraemer MUG, Sinka ME, Duda KA, Mylne AQN, Shearer FM, Barker CM, et al. The global distribution of the arbovirus vectors Aedes aegypti and Ae. albopictus. eLife. 2015;4:e08347. doi: 10.7554/eLife.08347 26126267

15. Hawley WA, Reiter P, Copeland RS, Pumpuni CB, Craig GB Jr. Aedes albopictus in North America: probable introduction in used tires from Northern Asia. Science. 1987;236:1114+. doi: 10.1126/science.3576225 3576225

16. Brady OJ, Golding N, Pigott DM, Kraemer MU, Messina JP, Reiner RC Jr, et al. Global temperature constraints on Aedes aegypti and Ae. albopictus persistence and competence for dengue virus transmission. Parasites & vectors. 2014;7(1):338.

17. Juliano SA, Philip Lounibos L. Ecology of invasive mosquitoes: effects on resident species and on human health. Ecology letters. 2005;8(5):558–74.

18. Armstrong PM, Andreadis TG, Shepard JJ, Thomas MC. Northern range expansion of the Asian tiger mosquito (Aedes albopictus): Analysis of mosquito data from Connecticut, USA. PLoS neglected tropical diseases. 2017;11(5):e0005623–e. doi: 10.1371/journal.pntd.0005623 28545111.

19. Trpiš M. Dry season survival of Aedes aegypti eggs in various breeding sites in the Dar es Salaam area, Tanzania. Bulletin of the World Health Organization. 1972;47(3):433. 4539825

20. Estallo EL, Ludueña-Almeida FF, Visintin AM, Scavuzzo CM, Lamfri MA, Introini MV, et al. Effectiveness of normalized difference water index in modelling Aedes aegypti house index. International journal of remote sensing. 2012;33(13):4254–65.

21. Messina JP, Kraemer MU, Brady OJ, Pigott DM, Shearer FM, Weiss DJ, et al. Mapping global environmental suitability for Zika virus. Elife. 2016;5:e15272. doi: 10.7554/eLife.15272 27090089

22. Sallam MF, Fizer C, Pilant AN, Whung P-Y. Systematic Review: Land Cover, Meteorological, and Socioeconomic Determinants of Aedes Mosquito Habitat for Risk Mapping. International journal of environmental research and public health. 2017;14(10):1230. doi: 10.3390/ijerph14101230 29035317.

23. Buckner EA, Blackmore MS, Golladay SW, Covich AP. Weather and landscape factors associated with adult mosquito abundance in southwestern Georgia, USA. Journal of Vector Ecology. 2011;36(2):269–78. doi: 10.1111/j.1948-7134.2011.00167.x

24. Diez Roux AV. Investigating neighborhood and area effects on health. Am J Public Health. 2001;91(11):1783–9. doi: 10.2105/ajph.91.11.1783 11684601.

25. Rochlin I, Turbow D, Gomez F, Ninivaggi DV, Campbell SR. Predictive Mapping of Human Risk for West Nile Virus (WNV) Based on Environmental and Socioeconomic Factors. PLoS ONE. 2011;6(8):e23280. doi: 10.1371/journal.pone.0023280 21853103

26. Whiteman A, Delmelle E, Rapp T, Chen S, Chen G, Dulin M. A Novel Sampling Method to Measure Socioeconomic Drivers of Aedes albopictus Distribution in Mecklenburg County, North Carolina. International Journal of Environmental Research and Public Health. 2018;15(10). doi: 10.3390/ijerph15102179 30301172

27. Little E, Biehler D, Leisnham PT, Jordan R, Wilson S, LaDeau SL. Socio-Ecological Mechanisms Supporting High Densities of Aedes albopictus (Diptera: Culicidae) in Baltimore, MD. Journal of medical entomology. 2017;54(5):1183–92. Epub 06/12. doi: 10.1093/jme/tjx103 28605549.

28. Witten IH, Frank E, Hall MA, Pal CJ. Data Mining: Practical machine learning tools and techniques: Morgan Kaufmann; 2016.

29. Yackulic CB, Chandler R, Zipkin EF, Royle JA, Nichols JD, Campbell Grant EH, et al. Presence-only modelling using MAXENT: When can we trust the inferences? Methods in Ecology and Evolution. 2013;4(3):236–43. doi: 10.1111/2041-210x.12004

30. Phillips SJ, Anderson RP, Schapire RE. Maximum entropy modeling of species geographic distributions. Ecological modelling. 2006;190(3–4):231–59.

31. Peterson AT, Soberon J., Pearson R.G., Anderson R.P. Martinez-Meyer E., Nakamura M. and Araujo M.B. Ecological Niches and Geographic Distributions. Monographs in Population Biology: Princeton University Press; 2011. 328 p.

32. Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE, Yates CJ. A statistical explanation of MaxEnt for ecologists. Diversity and distributions. 2011;17(1):43–57.

33. Manson S, Schroeder J, Van Riper D, Ruggles S. IPUMS National Historical Geographic Information System: Version 12.0 [Database]. Minneapolis: University of Minnesota. 2017.

34. Peel MC, Finlayson BL, McMahon TA. Updated world map of the Köppen-Geiger climate classification. Hydrology and earth system sciences discussions. 2007;4(2):439–73.

35. Yarnal B. Climate. In: Miller EW, editor. A Geography of Pennsylvania: University of Pennsylvania Press; 1995. p. 44–55.

36. Daly C, Halbleib M, Smith JI, Gibson WP, Doggett MK, Taylor GH, et al. Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. International Journal of Climatology: a Journal of the Royal Meteorological Society. 2008;28(15):2031–64.

37. Daly C, Neilson RP, Phillips DL. A statistical-topographic model for mapping climatological precipitation over mountainous terrain. Journal of applied meteorology. 1994;33(2):140–58.

38. Guillera‐Arroita G. Modelling of species distributions, range dynamics and communities under imperfect detection: advances, challenges and opportunities. Ecography. 2017;40(2):281–95.

39. Phillips SJ, Dudík M, Elith J, Graham CH, Lehmann A, Leathwick J, et al. Sample selection bias and presence‐only distribution models: implications for background and pseudo‐absence data. Ecological applications. 2009;19(1):181–97. doi: 10.1890/07-2153.1

40. Boria RA, Olson LE, Goodman SM, Anderson RP. Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecological Modelling. 2014;275:73–7.

41. Kramer‐Schadt S, Niedballa J, Pilgrim JD, Schröder B, Lindenborn J, Reinfelder V, et al. The importance of correcting for sampling bias in MaxEnt species distribution models. Diversity and Distributions. 2013;19(11):1366–79.

42. Hijmans RJ, J. van Etten, J. Cheng, M. Mattiuzzi, M. Sumner, J. A. Greenberg, O. P. Lamigueiro, A. Bevan, E. B. Racine, and A. Shortridge. Package ‘raster’. R package2016.

43. Medeiros MC, Boothe EC, Roark EB, Hamer GL. Dispersal of male and female Culex quinquefasciatus and Aedes albopictus mosquitoes using stable isotope enrichment. PLoS neglected tropical diseases. 2017;11(1):e0005347. doi: 10.1371/journal.pntd.0005347 28135281

44. Team RC. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2013.

45. Liu C, White M, Newell G. Selecting thresholds for the prediction of species occurrence with presence‐only data. Journal of biogeography. 2013;40(4):778–89.

46. Fawcett T. An introduction to ROC analysis. Pattern recognition letters. 2006;27(8):861–74.

47. Warren DL, Seifert SN. Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecological applications. 2011;21(2):335–42. doi: 10.1890/10-1171.1 21563566

48. Vargas REM, Phumala-Morales N, Tsunoda T, Apiwathnasorn C, Dujardin J-P. The phenetic structure of Aedes albopictus. Infection, Genetics and Evolution. 2013;13:242–51. doi: 10.1016/j.meegid.2012.08.008 22985681

49. Paupy C, Delatte H, Bagny L, Corbel V, Fontenille D. Aedes albopictus, an arbovirus vector: from the darkness to the light. Microbes and Infection. 2009;11(14–15):1177–85. doi: 10.1016/j.micinf.2009.05.005 19450706

50. Honório NA, Castro MG, Barros FSMd, Magalhães MdAFM, Sabroza PC. The spatial distribution of Aedes aegypti and Aedes albopictus in a transition zone, Rio de Janeiro, Brazil. Cadernos de Saúde Pública. 2009;25:1203–14. doi: 10.1590/s0102-311x2009000600003 19503951

51. Müller GC, Xue R-D, Schlein Y. Differential attraction of Aedes albopictus in the field to flowers, fruits and honeydew. Acta Tropica. 2011;118(1):45–9.

52. Kuemmerle T, Perzanowski K, Chaskovskyy O, Ostapowicz K, Halada L, Bashta A-T, et al. European Bison habitat in the Carpathian Mountains. Biological conservation. 2010;2010 v.143 no.4(no. 4):pp. 908–16. doi: 10.1016/j.biocon.2009.12.038

53. Harrigan RJ, Thomassen HA, Buermann W, Cummings RF, Kahn ME, Smith TB. Economic conditions predict prevalence of West Nile virus. PLoS One. 2010;5(11):e15437. doi: 10.1371/journal.pone.0015437 21103053

54. Brown HE, Childs JE, Diuk-Wasser MA, Fish D. Ecologic factors associated with West Nile virus transmission, northeastern United States. Emerging infectious diseases. 2008;14(10):1539. doi: 10.3201/eid1410.071396 18826816

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2019 Číslo 10