Potential geographic distribution of two invasive cassava green mites
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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.
KeywordsCassava green mite Manihot esculenta Mononychellus tanajoa Mononychellus mcgregori Pest risk map Species distribution modeling
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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