The cassava green mites Mononychellus tanajoa and M. mcgregori are highly invasive species that rank among the most serious pests of cassava globally. To guide the development of appropriate risk mitigation measures preventing their introduction and spread, this article estimates their potential geographic distribution using the maximum entropy approach to distribution modeling. We compiled 1,232 occurrence records for M. tanajoa and 99 for M. mcgregori, and relied on the WorldClim climate database as a source of environmental predictors. To mitigate the potential impact of uneven sampling efforts, we applied a distance correction filter resulting in 429 occurrence records for M. tanajoa and 55 for M. mcgregori. To test for environmental biases in our occurrence data, we developed models trained and tested with records from different continents, before developing the definitive models using the full record sets. The geographically-structured models revealed good cross-validation for M. tanajoa but not for M. mcgregori, likely reflecting a subtropical bias in M. mcgregori’s invasive range in Asia. The definitive models exhibited very good performance and predicted different potential distribution patterns for the two species. Relative to M. tanajoa, M. mcgregori seems better adapted to survive in locations lacking a pronounced dry season, for example across equatorial climates. Our results should help decision-makers assess the site-specific risk of cassava green mite establishment, and develop proportional risk mitigation measures to prevent their introduction and spread. These results should be particularly timely to help address the recent detection of M. mcgregori in Southeast Asia.
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Baldwin RA (2009) Use of maximum entropy modeling in wildlife research. Entropy 11:854–866
Barve N, Barve V, Jiménez-Valverde A, Lira-Noriega A, Maher SP, Peterson AT, Soberón J, Villalobos F (2011) The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecol Modell 222:1810–1819
Bellotti AC, van Schoonhoven A (1978) Mite and insect pests of cassava. Annu Rev Entomol 23:39–67
Bellotti AC, Mesa N, Serrano M, Guerrero J, Herrera C (1987) Taxonomic inventory and survey activity for natural enemies of cassava green mites in the Americas. Int J Trop Insect Sci 8:845–849
Bellotti AC, Smith L, Lapointe SL (1999) Recent advances in cassava pest management. Annu Rev Entomol 44:343–370
Bellotti AC, Herrera Campo BV, Hyman G (2012) Cassava production and pest management: present and potential threats in a changing environment. Trop Plant Biol 5:39–72
Ceballos H, Fregene M, Pérez JC, Morante N, Calle F (2007) Cassava genetic improvement. In: Kang MS, Priyadarshan PM (eds) Breeding major food staples. Blackwell, Ames, pp 365–391
Chen Q, Lu F, Huang G, Li K, Ye J, Zhang Z (2010) General survey and safety assessment of cassava pests. Chin J Trop Crops 31:819–827
Elith J, Leathwick JR (2009) Species distribution models: ecological explanation and prediction across space and time. Annu Rev Ecol Evol Syst 40:677–697
Elith J, Graham CH, Anderson RP, Dudik M, Ferrier S, Guisan A, Hijmans RJ, Huettmann F, Leathwick JR, Lehmann A (2006) Novel methods improve prediction of species distributions from occurrence data. Ecography 29:129–151
Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE, Yates CJ (2011) A statistical explanation of maxent for ecologists. Divers Distrib 17:43–57
FAOSTAT (2014) Cassava. http://faostat.fao.org/site/567/default.aspx#ancor. Accessed 28 Nov 2014
Gutierrez A, Yaninek JS, Wermelinger B, Herren H, Ellis C (1988) Analysis of biological control of cassava pests in Africa. III. Cassava green mite Mononychellus tanajoa. J Appl Ecol 25:941–950
Herrera Campo BV, Hyman G, Bellotti AC (2011) Threats to cassava production: known and potential geographic distribution of four key biotic constraints. Food Sec 3:329–345
Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978
Jiménez-Valverde A, Peterson AT, Soberón J, Overton J, Aragón P, Lobo JM (2011) Use of niche models in invasive species risk assessments. Biol Invasions 13:2785–2797
Kearney M, Porter W (2009) Mechanistic niche modelling: combining physiological and spatial data to predict species’ ranges. Ecol Lett 12:334–350
Lebot V (2009) Tropical root and tuber crops: cassava, sweet potato, yams and aroids. CABI, Wallingford
Lu H, Ma Q, Chen Q, Lu F, Xu X (2012) Potential geographic distribution of the cassava green mite Mononychellus tanajoa in Hainan, China. Afr J Agric Res 7:1206–1213
Lu F, Chen Q, Chen Z, Lu H, Xu X, Jing F (2014a) Effects of heat stress on development, reproduction and activities of protective enzymes in Mononychellus mcgregori. Exp Appl Acarol 63:267–284
Lu H, Lu F, Xu XL, Chen Q (2014b) Potential geographic distribution areas of Mononychellus mcgregori in Guangxi province. Appl Mech Mater 522:1051–1054
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
Parsa S, Kondo T, Winotai A (2012) The cassava mealybug (Phenacoccus manihoti) in asia: first records, potential distribution, and an identification key. PLoS ONE 7:e47675
Peterson AT, Papes M, Eaton M (2007) Transferability and model evaluation in ecological niche modeling: a comparison of GARP and Maxent. Ecography 30:550–560
Peterson AT, Moses LM, Bausch DG (2014) Mapping transmission risk of Lassa Fever in West Africa: the importance of quality control, sampling bias, and error weighting. PLoS ONE 9(8):e100711. doi:10.1371/journal.pone.0100711
Phillips SJ, Dudík M, Schapire RE (2004) A maximum entropy approach to species distribution modeling. Proceedings of the 21st international conference on machine learning. ACM Press, New York, pp 655–662
Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Modell 190:231–259
Radosavljevic A, Anderson RP (2014) Making better Maxent models of species distributions: complexity, overfitting and evaluation. J Biogeogr 41:629–643
Reddy S, Davalos L (2003) Geographical sampling bias and its implications for conservation priorities in Africa. J Biogeogr 30:1719–1727
Vásquez-Ordóñez AA, Parsa S (2014) A geographic distribution database of Mononychellus mites (Acari, Tetranychidae) on cassava (Manihot Esculenta). ZooKeys 407:1–8
Venette RC, Kriticos DJ, Magarey RD, Koch FH, Baker RH, Worner SP, Raboteaux NNG, McKenney DW, Dobesberger EJ, Yemshanov D (2010) Pest risk maps for invasive alien species: a roadmap for improvement. Bioscience 60:349–362
Yaninek JS (1988) Continental dispersal of the cassava green mite, an exotic pest in Africa, and implications for biological control. Exp Appl Acarol 4:211–224
Yaninek JS, Hanna R (2003) Cassava green mite in africa: a unique example of successful classical biological control of a mite pest on a continental scale. In: Neuenschwander P (ed) Biological control in IPM systems in Africa. CABI, Wallingford, pp 61–75
Yaninek JS, Herren H (1988) Introduction and spread of the cassava green mite, Mononychellus tanajoa (Bondar) (Acari: Tetranychidae), an exotic pest in Africa and the search for appropriate control methods: a review. Bull Entomol Res 78:1–13
Yaninek JS, Herren H, Gutierrez AP (1989) Dynamics of Mononychellus tanajoa (Acari: Tetranychidae) in Africa: seasonal factors affecting phenology and abundance. Environ Entomol 18:625–632
We thank Rodrigo Zuñiga for his curatorial work at CIAT’s Arthropod Reference Collection (CIATARC). We also thank the International Institute of Tropical Agriculture (IITA) for generously sharing their M. tanajoa distribution database with us. Emmanuel Zapata and Julian Ramirez (CIAT) kindly helped with GIS methods. This research was supported by the Research Program on Roots, Tubers, and Bananas (RTB) of the Consultative Group on International Agriculture Research (CGIAR).
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Parsa, S., Hazzi, N.A., Chen, Q. et al. Potential geographic distribution of two invasive cassava green mites. Exp Appl Acarol 65, 195–204 (2015). https://doi.org/10.1007/s10493-014-9868-x
- Cassava green mite
- Manihot esculenta
- Mononychellus tanajoa
- Mononychellus mcgregori
- Pest risk map
- Species distribution modeling