Mapping the global distribution of invasive pest Drosophila suzukii and parasitoid Leptopilina japonica: implications for biological control

Abstract

Insect pest invasions cause significant damage to crop yields, and the resultant economic losses are truly alarming. Climate change and trade liberalization have opened new ways of pest invasions. Given the consumer preference towards organic agricultural products and environment-friendly nature of natural pest control strategies, biological control is considered to be one of the potential options for managing invasive insect pests. Drosophila suzukii (Drosophilidae) is an extremely damaging fruit pest, demanding development of effective and sustainable biological control strategies. In this study, we assessed the potential of the parasitoid Leptopilina japonica (Figitidae) as a biocontrol agent for D. suzukii using ecological niche modeling approaches. We developed global-scale models for both pest and parasitoid to identify four components necessary to derive a niche based, target oriented prioritization approach to plan biological control programs for D. suzukii: (i) potential distribution of pest D. suzukii, (ii) potential distribution of parasitoid L. japonica, (iii) the degree of overlap in potential distributions of pest and parasitoid, and (iv) biocontrol potential of this system for each country. Overlapping suitable areas of pest and parasitoid were identified at two different thresholds and at the most desirable threshold (E = 5%), potential for L. japonica mediated biocontrol management existed in 125 countries covering 1.87 × 107 km2, and at the maximum permitted threshold (E = 10%), land coverage was reduced to 1.44 × 107 km2 in 121 countries. Fly pest distributional information as a predictor variable was not found to be improving parasitoid model performance, and globally, only in half of the countries, >50% biocontrol coverage was estimated. We therefore suggest that niche specificities of both pest and parasitoid must be included in site-specific release planning of L. japonica for effective biocontrol management aimed at D. suzukii. This study can be extended to design cost-effective pre-assessment strategies for implementing any biological control management program.

Invasive Pest and Invasive Host: Where Might Spotted-Wing Drosophila (Drosophila suzukii) and American Black Cherry (Prunus serotina) Cross Paths in Europe?

“…Several case studies have determined their potential ranges in Europe [25][26][27][28]. For example, dos Santos et al (2017) predicted that extreme northern Spain and Portugal [29], France, Austria, Greece, Italy, Switzerland, Albania, and southern Germany comprised invasion hotspots for spotted-winged drosophila; these findings were largely consistent with those of Nair et al (2023) [30]. American black cherry's potential range in Europe has also received much attention [31][32][33].…”

“…For example, Gutierrez et al (2016) identified the potential range of spotted-wing drosophila in Europe and found that winter temperature played a key role in determining its potential range [25]. Recently, Nair and Peterson (2023) predicted the potential range of this pest in 17 European countries, including Ukraine, and found that precipitation in the driest month and precipitation in the driest quarter were the most important variables [30]. Climatic variables have also been shown to play a major role in the potential range of American black cherry in Europe.…”

An evaluation of the ecological niche of Orf virus (Poxviridae): Challenges of distinguishing broad niches from no niches

“…Principal components analysis transforms highly correlated environmental variables into a smaller set of orthogonal ( i . e ., non-correlated) axes, retaining most of the original information in the original, raw (untransformed) environmental data [ 48 , 67 ], summarizing environmental variability across a given geographic area [ 49 , 68 ]. In our analyses, multi-collinearity reduction via PCA identified 10 axes that together explained >95% of the variation in the original 15 environmental variables [ 48 , 68 ].…”