Landscape-scale biogeographic distribution analysis of the whitefly, Bemisia tabaci (Gennadius, 1889) in Kenya

Abstract

Understanding the drivers of habitat use and the suitability of landscape patches by invasive insect species is crucial in their control and management. This simplifies the comprehension of the processes driving invasive insect population dynamics, their functioning, and potential disturbance within their introduced ecosystems. The whitefly, Bemisia tabaci (Gennadius, 1889), is ranked among the world’s 100 most invasive insect pests and is a major threat to many important cash and staple food crops. In this study, we identified levels and areas at risk of the invasive B. tabaci at a landscape scale in Kenya using elevation, land surface temperature, land cover, rainfall, and temperature of the present and future (the year 2050 of the community climate system model version 4 (CCSM4)), and using a maximum entropy (MaxEnt) model. Our results show that ~14% of Kenya’s land area is currently at risk of B. tabaci invasion. This area is likely to increase to 15% and 16% because of climate change using the representative concentration pathways (RCP) i.e. RCP 2.6 and RCP 8.5 of the year 2050, respectively. Land cover, particularly croplands, provided the highest permutation importance together with precipitation variables in determining the occurrence of the pest. A wide preference range within elevation, precipitation, temperature, and plant hosts was observed suggesting a great potential for B. tabaci to establish in many areas in Kenya and potentially in other countries with similar conditions in Africa. However, the predicted increases in global temperature could reduce the pest’s preferred environment, but this also imposes limitations on the productivity of many of its host crops. Therefore, our results can be used in adaptive management to control the pest and to prevent the introduction and spread of B. tabaci in areas where it is yet to establish.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

References

  1. Abd-Rabou S, Simmons AM (2015) Infestation by Bemisia tabaci (Hemiptera: Aleyrodidae) and incidence of whitefly-transmitted viruses after the application of four biorational insecticides in some crops in Egypt. International Journal of Tropical Insect Science 35:132–136. https://doi.org/10.1017/S1742758415000168

    Article  Google Scholar 

  2. Ajene IJ, Fathiya KM, Asch B Van, et al (2020a) Distribution of Candidatus Liberibacter species in eastern Africa, and the first report of Candidatus Liberibacter asiaticus in Kenya. Scientific Reports

  3. Ajene IJ, Khamis F, Van Asch B et al (2020b) Habitat suitability and distribution potential of Liberibacter species ( “Candidatus Liberibacter asiaticus ” and “ Candidatus Liberibacter africanus ”) associated with citrus greening disease. 1–14. https://doi.org/10.1111/ddi.13051

  4. Arthur FH, Morrison WR, Morey AC (2019) Modeling the potential range expansion of larger grain borer, Prostephanus truncatus (Coleoptera: Bostrichidae). Sci Rep 9:1–10. https://doi.org/10.1038/s41598-019-42974-5

    CAS  Article  Google Scholar 

  5. Azrag AGA, Pirk CWW, Yusuf AA, Pinard F, Niassy S, Mosomtai G, Babin R (2018) Prediction of insect pest distribution as influenced by elevation: combining field observations and temperature-dependent development models for the coffee stink bug, antestiopsis thunbergii (gmelin). PLoS One 13:1–18. https://doi.org/10.1371/journal.pone.0199569

    CAS  Article  Google Scholar 

  6. Beck J, Böller M, Erhardt A, Schwanghart W (2014) Spatial bias in the GBIF database and its effect on modeling species’ geographic distributions. Ecological Informatics 19:10–15. https://doi.org/10.1016/j.ecoinf.2013.11.002

    Article  Google Scholar 

  7. Biber-freudenberger L, Ziemacki J, Tonnang HEZ, Borgemeister C (2016) Future risks of pest species under changing climatic conditions. PLoS One 11:e0153237. https://doi.org/10.1371/journal.pone.0153237

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  8. Bonato O, Lurette A, Vidal C, Fargues J (2007) Modelling temperature-dependent bionomics of Bemisia tabaci (Q-biotype). Physiol Entomol 32:50–55. https://doi.org/10.1111/j.1365-3032.2006.00540.x

    Article  Google Scholar 

  9. Booth TH (2018) Why understanding the pioneering and continuing contributions of BIOCLIM to species distribution modelling is important. Austral Ecology 43:852–860. https://doi.org/10.1111/aec.12628

    Article  Google Scholar 

  10. Boykin LM, De Barro PJ (2014) A practical guide to identifying members of the Bemisia tabaci species complex: and other morphologically identical species. Front Ecol Evol 2:1–5. https://doi.org/10.3389/fevo.2014.00045

    Article  Google Scholar 

  11. Bradshaw CD, Hemming D, Baker R, Everatt M, Eyre D, Korycinska A (2019) A novel approach for exploring climatic factors limiting current pest distributions: a case study of Bemisia tabaci in north-West Europe and assessment of potential future establishment in the United Kingdom under climate change. PLoS One 14:1–18. https://doi.org/10.1371/journal.pone.0221057

    CAS  Article  Google Scholar 

  12. CABI (2020) Invasive species compendium: detailed coverage of invasive species threatening livelihoods and the environment worldwide. https://www.cabi.org/isc/datasheet/8927.

  13. CGIAR-CSI (2019) SRTM. http://srtm.csi.cgiar.org/. Accessed 31 Mar 2019

  14. Csillag F, Kummert Á, Kertész M (1992) Resolution, accuracy and attributes: approaches for environmental geographical information systems. Comput Environ Urban Syst 16:289–297. https://doi.org/10.1016/0198-9715(92)90010-O

    Article  Google Scholar 

  15. De Barro PJ, Liu SS, Boykin LM, Dinsdale AB (2011) Bemisia tabaci: a statement of species status. Annu Rev Entomol 56:1–19. https://doi.org/10.1146/annurev-ento-112408-085504

    CAS  Article  PubMed  Google Scholar 

  16. Degbelo A, Kuhn W (2018) Spatial and temporal resolution of geographic information: an observation-based theory. Open Geospatial Data, Software and Standards 3. https://doi.org/10.1186/s40965-018-0053-8

  17. Delatte H, Duyck PF, Triboire A, David P, Becker N, Bonato O, Reynaud B (2009) Differential invasion success among biotypes: case of Bemisia tabaci. Biol Invasions 11:1059–1070. https://doi.org/10.1007/s10530-008-9328-9

    Article  Google Scholar 

  18. Dormann CF, Elith J, Bacher S, Buchmann C, Carl G, Carré G, Marquéz JRG, Gruber B, Lafourcade B, Leitão PJ, Münkemüller T, McClean C, Osborne PE, Reineking B, Schröder 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 36:027–046. https://doi.org/10.1111/j.1600-0587.2012.07348.x

    Article  Google Scholar 

  19. ESA (2020) CCI Land cover - S2 prototype land cover 20m map of Africa 2016. http://2016africalandcover20m.esrin.esa.int/.

  20. Fick SE, Hijmans RJ (2017) WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int J Climatol 37:4302–4315. https://doi.org/10.1002/joc.5086

    Article  Google Scholar 

  21. Gangwar RK, Charu G (2018) Lifecycle, distribution, nature of damage and economic importance of whitefly, Bemisia tabaci ( Gennadius ). Acta Scientific Agriculture 2:36–39

    Google Scholar 

  22. Gaudreau J, Perez L, Harati S (2018) Towards modelling future trends of Quebec’s boreal birds’ species distribution under climate change. ISPRS International Journal of Geo-Information 7: https://doi.org/10.3390/ijgi7090335

  23. GBIF (2020) GBIF occurrence download https://doi.org/10.15468/dl.rehypu

  24. Gilioli G, Pasquali S, Parisi S, Winter S (2014) Modelling the potential distribution of Bemisia tabaci in Europe in light of the climate change scenario. Pest Manag Sci 70:1611–1623. https://doi.org/10.1002/ps.3734

    CAS  Article  PubMed  Google Scholar 

  25. Hijmans RJ (2020) Raster: geographic data analysis and modeling. R package version 3.3–7. https://CRAN.R-project.org/package=raster

  26. IPCC (2014) Climate change 2014: synthesis report. Contribution of working groups I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change

  27. Janzen DH, Hallwachs W (2019) Perspective: where might be many tropical insects? Biol Conserv 233:102–108. https://doi.org/10.1016/j.biocon.2019.02.030

    Article  Google Scholar 

  28. Jozani HJ, Thiel M, Abdel-rahman EM et al (2020) Investigation of maize lethal necrosis ( MLN ) severity and cropping systems mapping in agro-ecological maize systems in Bomet, Kenya utilizing RapidEye and Landsat-8 imagery. Geology, Ecology, and Landscapes 00:1–16. https://doi.org/10.1080/24749508.2020.1761195

    Article  Google Scholar 

  29. Kanakala S, Ghanim M (2019) Global genetic diversity and geographical distribution of Bemisia tabaci and its bacterial endosymbionts. PLoS One 14. https://doi.org/10.1371/journal.pone.0213946

  30. Kinzner M-C, Gamisch A, Hoffmann AA, Seifert B, Haider M, Arthofer W, Schlick-Steiner BC, Steiner FM (2019) Major range loss predicted from lack of heat adaptability in an alpine Drosophila species. Sci Total Environ 695:133753. https://doi.org/10.1016/j.scitotenv.2019.133753

    CAS  Article  PubMed  Google Scholar 

  31. Kotir JH (2010) Climate change and variability in sub-Saharan Africa: a review of current and future trends and impacts on agriculture and food security. Environ Dev Sustain 13:587–605. https://doi.org/10.1007/s10668-010-9278-0

    Article  Google Scholar 

  32. Kramer-Schadt S, Niedballa J, Pilgrim JD, Schröder B, Lindenborn J, Reinfelder V, Stillfried M, Heckmann I, Scharf AK, Augeri DM, Cheyne SM, Hearn AJ, Ross J, Macdonald DW, Mathai J, Eaton J, Marshall AJ, Semiadi G, Rustam R, Bernard H, Alfred R, Samejima H, Duckworth JW, Breitenmoser-Wuersten C, Belant JL, Hofer H, Wilting A (2013) The importance of correcting for sampling bias in MaxEnt species distribution models. Divers Distrib 19:1366–1379. https://doi.org/10.1111/ddi.12096

    Article  Google Scholar 

  33. Kriticos DJ, De Barro PJ, Yonow T et al (2020) The potential geographical distribution and phenology of Bemisia tabaci Middle East/Asia minor 1, considering irrigation and glasshouse production. Bull Entomol Res 110:567–576. https://doi.org/10.1017/S0007485320000061

    CAS  Article  PubMed  Google Scholar 

  34. Kumar R, Kranthi S, Nagrare VS, Monga D, Kranthi KR, Rao N, Singh A (2019) Insecticidal activity of botanical oils and other neem-based derivatives against whitefly, Bemisia tabaci (Gennadius) (Homoptera: Aleyrodidae) on cotton. International Journal of Tropical Insect Science 39:203–210. https://doi.org/10.1007/s42690-019-00027-4

    Article  Google Scholar 

  35. Kyalo R, Abdel-Rahman EM, Subramanian S et al (2017) Maize cropping systems mapping using RapidEye observations in agro-ecological landscapes in Kenya. Sensors 17:2537. https://doi.org/10.3390/s17112537

    Article  Google Scholar 

  36. Labou B, Brévault T, Sylla S, Diatte M, Bordat D, Diarra K (2017) Spatial and temporal incidence of insect pests in farmers’ cabbage fields in Senegal. International Journal of Tropical Insect Science 37:225–233. https://doi.org/10.1017/S1742758417000200

    Article  Google Scholar 

  37. Landmann T, Dubovyk O, Ghazaryan G, Kimani J, Abdel-Rahman EM (2020) Wide-area invasive species propagation mapping is possible using phenometric trends. ISPRS J Photogramm Remote Sens 159:1–12. https://doi.org/10.1016/j.isprsjprs.2019.10.016

    Article  Google Scholar 

  38. Lebouvier M, Laparie M, Hullé M, Marais A, Cozic Y, Lalouette L, Vernon P, Candresse T, Frenot Y, Renault D (2011) The significance of the sub-Antarctic Kerguelen Islands for the assessment of the vulnerability of native communities to climate change, alien insect invasions and plant viruses. Biol Invasions 13:1195–1208. https://doi.org/10.1007/s10530-011-9946-5

    Article  Google Scholar 

  39. Leroy B, Meynard CN, Bellard C, Courchamp F (2016) Virtualspecies, an R package to generate virtual species distributions. Ecography 39:599–607. https://doi.org/10.1111/ecog.01388

    Article  Google Scholar 

  40. Macfadyen S, Paull C, Boykin LM, et al (2018) Cassava whitefly, Bemisia tabaci ( Gennadius ) ( Hemiptera : Aleyrodidae ) in east African farming landscapes : a review of the factors determining abundance. 61:565–582. https://doi.org/10.1017/S0007485318000032

  41. Mahadav A, Kontsedalov S, Czosnek H, Ghanim M (2009) Thermotolerance and gene expression following heat stress in the whitefly Bemisia tabaci B and Q biotypes. Insect Biochem Mol Biol 39:668–676. https://doi.org/10.1016/j.ibmb.2009.08.002

    CAS  Article  PubMed  Google Scholar 

  42. Makori D, Mutanga O, Irungu J et al (2017) Predicting spatial distribution of key honeybee pests in Kenya using remotely sensed and bioclimatic variables: key honeybee pests distribution models. ISPRS Int J Geo Inf 6:66. https://doi.org/10.3390/ijgi6030066

    Article  Google Scholar 

  43. Marchioro CA, Krechemer FS (2018) Potential global distribution of Diabrotica species and the risks for agricultural production. Pest Manag Sci 74:2100–2109. https://doi.org/10.1002/ps.4906

    CAS  Article  Google Scholar 

  44. Masocha M, Dube T (2017) Modelling Opuntia fulgida invasion in Zimbabwe. Transactions of the Royal Society of South Africa 72:217–224. https://doi.org/10.1080/0035919X.2017.1301593

    Article  Google Scholar 

  45. McCullough DG, Work TT, Cavey JF et al (2006) Interceptions of nonindigenous plant pests at US ports of entry and border crossings over a 17-year period. Biol Invasions 8:611–630. https://doi.org/10.1007/s10530-005-1798-4

    Article  Google Scholar 

  46. Merow C, Smith MJ, Silander JA (2013) A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography 36:1058–1069. https://doi.org/10.1111/j.1600-0587.2013.07872.x

    Article  Google Scholar 

  47. Mesgaran MB, Cousens RD, Webber BL (2014) Here be dragons: a tool for quantifying novelty due to covariate range and correlation change when projecting species distribution models. Divers Distrib 20:1147–1159. https://doi.org/10.1111/ddi.12209

    Article  Google Scholar 

  48. Midega CAO, Pittchar JO, Pickett JA, Hailu GW, Khan ZR (2018) A climate-adapted push-pull system effectively controls fall armyworm, Spodoptera frugiperda ( J E Smith ), in maize in East Africa a climate-adapted push-pull system effectively controls fall armyworm, Spodoptera frugiperda ( J E Smith ), in maize in. Crop Prot 105:10–15. https://doi.org/10.1016/j.cropro.2017.11.003

    Article  Google Scholar 

  49. Moshobane M, Mukundamago M, Adu-acheampong S, Shackleton RT (2019) Development of alien and invasive taxa lists for regulation in South Africa. Bothalia:1–12

  50. Mpakairi KS, Tagwireyi P, Ndaimani H, Madiri HT (2019) Distribution of wildland fires and possible hotspots for the Zimbabwean component of Kavango-Zambezi Transfrontier conservation area. S Afr Geogr J 101:110–120. https://doi.org/10.1080/03736245.2018.1541023

    Article  Google Scholar 

  51. Mtengwana B, Dube T, Mkunyana YP, Mazvimavi D (2020) Use of multispectral satellite datasets to improve ecological understanding of the distribution of invasive alien plants in a water-limited catchment. South Africa African Journal of Ecology. https://doi.org/10.1111/aje.12751

  52. Mudereri BT, Dube T, Adel-Rahman EM, Niassy S, Kimathi E, Khan Z, Landmann T (2019) A comparative analysis of PlanetScope and Sentinel-2 space-borne sensors in mapping Striga weed using guided regularised random Forest classification ensemble. ISPRS - international archives of the photogrammetry. Remote Sensing and Spatial Information Sciences XLII-2(W13):701–708. https://doi.org/10.5194/isprs-archives-XLII-2-W13-701-2019

    Article  Google Scholar 

  53. Mudereri BT, Abdel-Rahman EM, Dube T, Landmann T, Khan Z, Kimathi E, Owino R, Niassy S (2020a) Multi-source spatial data-based invasion risk modeling of Striga (Striga asiatica) in Zimbabwe. GIScience & Remote Sensing 57:553–571. https://doi.org/10.1080/15481603.2020.1744250

    Article  Google Scholar 

  54. Mudereri BT, Mukanga C, Mupfiga ET, Gwatirisa C, Kimathi E, Chitata T (2020b) Analysis of potentially suitable habitat within migration connections of an intra-African migrant-the blue swallow (Hirundo atrocaerulea). Ecological Informatics 57:101082. https://doi.org/10.1016/j.ecoinf.2020.101082

    Article  Google Scholar 

  55. Muposhi VK, Gandiwa E, Chemura A, Bartels P, Makuza SM, Madiri TH (2016) Habitat heterogeneity variably influences habitat selection by wild herbivores in a semi-arid tropical savanna ecosystem. PLoS One 11. https://doi.org/10.1371/journal.pone.0163084

  56. Muscarella R, Galante PJ, Soley-Guardia M, Boria RA, Kass JM, Uriarte M, Anderson RP (2014) ENMeval: an R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods Ecol Evol 5:1198–1205. https://doi.org/10.1111/2041-210x.12261

    Article  Google Scholar 

  57. Naimi B, Hamm NAS, Groen TA, Skidmore AK, Toxopeus AG (2014) Where is positional uncertainty a problem for species distribution modelling? Ecography 37:191–203. https://doi.org/10.1111/j.1600-0587.2013.00205.x

    Article  Google Scholar 

  58. Niang I, Ruppel OC, Abdrabo MA, et al (2014) Africa. In: Barros, V.R., C.B. Field, D.J. Dokken, M.D. Mastrandrea, K.J. Mach, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea and LLW (eds. . (ed) Climate Change 2014: Impacts, adaptation, and vulnerability. Part B: Regional aspects. Contribution of working group II to the fifth assessment report of the Intergovernmental Panel on Climate Change. Barros, Cambridge university press, Cambridge, United Kingdom and New York, NY, USA, pp 1199–1265

  59. Otieno B, Nahrung H, Steinbauer M (2019a) Where did you come from? Where did you go? Investigating the origin of invasive Leptocybe species using distribution modelling. Forests 10:115. https://doi.org/10.3390/f10020115

    Article  Google Scholar 

  60. Otieno MHJ, Ayieko MA, Niassy S, Salifu D, Abdelmutalab AGA, Fathiya KM, Subramanian S, Fiaboe KKM, Roos N, Ekesi S, Tanga CM (2019b) Integrating temperature-dependent life table data into insect life cycle model for predicting the potential distribution of Scapsipedus icipe Hugel & Tanga. PLoS One 14:1–27. https://doi.org/10.1371/journal.pone.0222941

    CAS  Article  Google Scholar 

  61. Parry H, Kalyebi A, Bianchi F, Sseruwagi P, Colvin J, Schellhorn N, Macfadyen S (2020) Evaluation of cultural control and resistance-breeding strategies for suppression of whitefly infestation of cassava at the landscape scale: a simulation modeling approach. Pest Manag Sci 76:2699–2710. https://doi.org/10.1002/ps.5816

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  62. Pathania M, Verma A, Singh M et al (2020) Influence of abiotic factors on the infestation dynamics of whitefly, Bemisia tabaci (Gennadius 1889) in cotton and its management strategies in North-Western India. International journal of tropical insect science 1. https://doi.org/10.1007/s42690-020-00155-2

  63. Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190:231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026

    Article  Google Scholar 

  64. Phillips SJ, Dudík M, Elith J, Graham CH, Lehmann A, Leathwick J, Ferrier S (2009) Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecol Appl 19:181–197. https://doi.org/10.1890/07-2153.1

    Article  PubMed  Google Scholar 

  65. Plant RE (2012) Spatial data analysis in ecology and agriculture using R. CRC Press, Taylor and Francis Group, Califonia

    Book  Google Scholar 

  66. Qin A, Liu B, Guo Q, Bussmann RW, Ma F, Jian Z, Xu G, Pei S (2017) Maxent modeling for predicting impacts of climate change on the potential distribution of Thuja sutchuenensis Franch., an extremely endangered conifer from southwestern China. Global Ecology and Conservation 10:139–146. https://doi.org/10.1016/j.gecco.2017.02.004

    Article  Google Scholar 

  67. R Core Team (2020) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/

  68. Ramos RS, Kumar L, Shabani F, Picanço MC (2018) Mapping global risk levels of Bemisia tabaci in areas of suitability for open field tomato cultivation under current and future climates. PLoS One 13:1–20. https://doi.org/10.1371/journal.pone.0198925

    CAS  Article  Google Scholar 

  69. Ramos RS, Kumar L, Shabani F, da Silva RS, de Araújo TA, Picanço MC (2019) Climate model for seasonal variation in Bemisia tabaci using CLIMEX in tomato crops. Int J Biometeorol 63:281–291. https://doi.org/10.1007/s00484-018-01661-2

    Article  PubMed  Google Scholar 

  70. Rodenburg J, Riches CR, Kayeke JM (2010) Addressing current and future problems of parasitic weeds in rice. Crop Prot 29:210–221. https://doi.org/10.1016/j.cropro.2009.10.015

    Article  Google Scholar 

  71. Saghafipour A, Zahraei-Ramazani A, Vatandoost H, et al (2020) Relationship between some environmental and climatic factors on outbreak of whiteflies, the human annoying insects. Journal of arthropod-borne diseases 14:78–87. https://doi.org/10.18502/jad.v14i1.2714

  72. Sango I, Godwell N (2015) Climate change trends and environmental impacts in the Makonde communal lands, Zimbabwe. South African journal of science 111:1–6. https://doi.org/10.17159/sajs.2015/20140266

  73. Serdeczny O, Adams S, Baarsch F, Coumou D, Robinson A, Hare W, Schaeffer M, Perrette M, Reinhardt J (2016) Climate change impacts in sub-Saharan Africa: from physical changes to their social repercussions. Reg Environ Chang 15:1585–1600. https://doi.org/10.1007/s10113-015-0910-2

    Article  Google Scholar 

  74. Shekede MD, Murwira A, Masocha M, Gwitira I (2018) Spatial distribution of Vachellia karroo in Zimbabwean savannas (southern Africa) under a changing climate. Ecol Res 33:1181–1191. https://doi.org/10.1007/s11284-018-1636-7

    Article  Google Scholar 

  75. Sokame BM, Subramanian S, Kilalo DC, Juma G, Calatayud PA (2020) Larval dispersal of the invasive fall armyworm, Spodoptera frugiperda, the exotic Stemborer Chilo partellus, and indigenous maize Stemborers in Africa. Entomologia Experimentalis et Applicata 168:1–10. https://doi.org/10.1111/eea.12899

    CAS  Article  Google Scholar 

  76. Stansly PA, Naranjo SE, Brown JK, et al (2010) Bemisia: Bionomics and management of a global pest

  77. Støa B, Halvorsen R, Mazzoni S, Gusarov VI (2018) Sampling bias in presence-only data used for species distribution modelling: theory and methods for detecting sample bias and its effects on models. Sommerfeltia 38:1–53. https://doi.org/10.2478/som-2018-0001

    Article  Google Scholar 

  78. Tay WT, Evans GA, Boykin LM, de Barro PJ (2012) Will the real Bemisia tabaci please stand up? PLoS One 7:7–11. https://doi.org/10.1371/journal.pone.0050550

    CAS  Article  Google Scholar 

  79. Teng X, Wan F, Chu D (2010) Bemisia tabaci biotype Q dominates other biotypes across China. Fla Entomol 93:363–368. https://doi.org/10.1111/j.1365-2338.2004.00729.x

    Article  Google Scholar 

  80. Tonnang HEZ, Balemi T, Masuki KF, et al (2020) Rapid acquisition, management, and analysis of spatial Maize (Zea mays L .) phenological data — Towards ‘Big Data’ for agronomy transformation in Africa. Agronomy 10: https://doi.org/10.3390/agronomy10091363

  81. Venables WN, Ripley BD (2002) Modern applied statistics with S. Fourth edition. Springer, New York ISBN 0-387-95457-0

  82. Wan Z, Hook S, Hulley G (2015) MOD11C2 MODIS/Terra land surface temperature/emissivity 8-day L3 global 0.05Deg CMG V006 [data set]. NASA EOSDIS land processes DAAC. https://doi.org/10.5067/MODIS/MOD11C2.006

  83. Yu H, Wan FH, Guo JY (2012) Different thermal tolerance and hsp gene expression in invasive and indigenous sibling species of Bemisia tabaci. Biol Invasions 14:1587–1595. https://doi.org/10.1007/s10530-012-0171-7

    Article  Google Scholar 

Download references

Acknowledgments

We gratefully acknowledge the financial support for this research by the following organizations and agencies: UK’s Foreign, Commonwealth & Development Office (FCDO); Swedish International Development Cooperation Agency (Sida); the Swiss Agency for Development and Cooperation (SDC); Ethiopian and Kenyan Governments. “B.T.M” was supported by a German Academic Exchange Service (DAAD) In-Region Postgraduate Scholarship. The views expressed herein do not necessarily reflect the official opinion of the donors.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Bester Tawona Mudereri.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Mudereri, B.T., Kimathi, E., Chitata, T. et al. Landscape-scale biogeographic distribution analysis of the whitefly, Bemisia tabaci (Gennadius, 1889) in Kenya. Int J Trop Insect Sci 41, 1585–1599 (2021). https://doi.org/10.1007/s42690-020-00360-z

Download citation

Keywords

  • Biogeography
  • Climate change
  • Ecological niche
  • Entomology
  • Invasive species