Eco-climatic matching to guide foreign exploration and optimal release strategies for biological control agents of Rastrococcus iceryoides in Africa and Asia.
Rastrococcus iceryoides (Green) (Homoptera: Pseudococcidae) is a major invasive pest of several horticultural crops [in Africa and Asia, outside its native range in India], with damage levels ranging from 30% to complete crop failure. Due to lack of effective co-evolved parasitoids in the invaded regions, maximum entropy (MaxEnt) and genetic algorithm for ruleset production (GARP) were used to identify climatically suitable areas in India for foreign exploration. Based on the outcome of the predictive models, an extensive survey was conducted in 15 major mango growing regions in the state of Tamil Nadu, India. Thereafter, both models were used to identify climatic compatibility habitats in the invaded regions of R. iceryoides. Our results revealed ten host plants belonging to eight families with considerably low levels of infestation. The percentage parasitism established using mummified R. iceryoides was relatively high ranging between 16.7 ± 1.4 to 91.3 ± 3.7%. Both old and new host-parasitoid associations were recorded with eleven parasitoid species described. Eight of the parasitoids recorded were new records of R. iceryoides. Among these parasitoids, Praleurocerus viridis Agarwal, Anagyrus chryos Noyes & Hayat and Neoplatycerus tachikawai Subba Rao were the most dominant and widespread parasitoid species, highly specific to R. iceryoides with percent parasitism of 53.2 ± 5.4, 31.3 ± 2.7 and 8.8 ± 2.9%, respectively. Using the occurrence data of the parasitoids, both models successfully identified optimal suitable habitats in Africa and Asia. Both models showed optimal performances with the value of the average area under the curve (AUC) of 0.98 for MaxEnt and 0.95 for GARP. However, the percentage contribution of the predictor variables that influenced the current and future predictions in the native and invaded range varied considerably. These findings demonstrate the importance of predictive modelling as novel tools to support future classical biological control program targeting R. iceryoides in the invaded regions. Our results provide important information to guide strategic planning for future classical biological control programmes.