Potential geographic distribution of two invasive cassava green mites

Abstract

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.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

References

  1. Baldwin RA (2009) Use of maximum entropy modeling in wildlife research. Entropy 11:854–866

    Article  Google Scholar 

  2. 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

    Article  Google Scholar 

  3. Bellotti AC, van Schoonhoven A (1978) Mite and insect pests of cassava. Annu Rev Entomol 23:39–67

    CAS  PubMed  Article  Google Scholar 

  4. 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

    Article  Google Scholar 

  5. Bellotti AC, Smith L, Lapointe SL (1999) Recent advances in cassava pest management. Annu Rev Entomol 44:343–370

    CAS  PubMed  Article  Google Scholar 

  6. 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

    Article  Google Scholar 

  7. 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

    Chapter  Google Scholar 

  8. 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

    Google Scholar 

  9. Elith J, Leathwick JR (2009) Species distribution models: ecological explanation and prediction across space and time. Annu Rev Ecol Evol Syst 40:677–697

    Article  Google Scholar 

  10. 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

    Article  Google Scholar 

  11. 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

    Article  Google Scholar 

  12. FAOSTAT (2014) Cassava. http://faostat.fao.org/site/567/default.aspx#ancor. Accessed 28 Nov 2014

  13. 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

    Article  Google Scholar 

  14. 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

    Article  Google Scholar 

  15. 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

    Article  Google Scholar 

  16. 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

    Article  Google Scholar 

  17. Kearney M, Porter W (2009) Mechanistic niche modelling: combining physiological and spatial data to predict species’ ranges. Ecol Lett 12:334–350

    PubMed  Article  Google Scholar 

  18. Lebot V (2009) Tropical root and tuber crops: cassava, sweet potato, yams and aroids. CABI, Wallingford

    Google Scholar 

  19. 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

    Google Scholar 

  20. 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

    CAS  PubMed  Article  Google Scholar 

  21. 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

    Article  Google Scholar 

  22. 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

    Article  Google Scholar 

  23. 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

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  24. 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

    Article  Google Scholar 

  25. 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

    PubMed Central  PubMed  Article  Google Scholar 

  26. 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

    Google Scholar 

  27. Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Modell 190:231–259

    Article  Google Scholar 

  28. Radosavljevic A, Anderson RP (2014) Making better Maxent models of species distributions: complexity, overfitting and evaluation. J Biogeogr 41:629–643

    Article  Google Scholar 

  29. Reddy S, Davalos L (2003) Geographical sampling bias and its implications for conservation priorities in Africa. J Biogeogr 30:1719–1727

    Article  Google Scholar 

  30. Vásquez-Ordóñez AA, Parsa S (2014) A geographic distribution database of Mononychellus mites (Acari, Tetranychidae) on cassava (Manihot Esculenta). ZooKeys 407:1–8

    PubMed  Article  Google Scholar 

  31. 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

    Article  Google Scholar 

  32. 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

    Article  Google Scholar 

  33. 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

    Google Scholar 

  34. 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

    Article  Google Scholar 

  35. 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

    Google Scholar 

Download references

Acknowledgments

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).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Soroush Parsa.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

Keywords

  • Cassava green mite
  • Manihot esculenta
  • Mononychellus tanajoa
  • Mononychellus mcgregori
  • Pest risk map
  • Species distribution modeling