Predicting natural enemy efficacy in biological control using ex-ante analyses

Massive losses in agricultural and natural systems accrue globally due to invasive species, and yet the success rate of natural enemy introductions to control them is low. The high failure rate is due to the unknown efficacy of the introduced natural enemies. Furthermore, reviews of prior biological control efforts have not led to the development of assessment methods to predict their pre-release efficacy. To demonstrate a potential solution, we deconstructed the biological control of the invasive cassava mealybug (CM) and cassava green mite (CGM) in Africa using weather-driven metapopulation tri-trophic physiologically based demographic models (PBDMs). Bioeconomic analysis of the simulation results enabled parsing the contributions of the introduced natural enemies and endemic fungal pathogens to the control of CM and CGM and to the recovery of cassava yield across the vast ecological zones of Africa. The analysis shows that ex-ante pre-release analyses of natural enemy efficacy would have correctly predicted the biological control of the two pests. PBDM analyses of other biological control programs explained their success and/or failure. The results suggest well-parameterized mechanistic models can predict pre-release the efficacy of natural enemies and become an important instrument in increasing global food security.