Medfly in a warming world: using models to understand shifting range dynamics

Abstract

Background: Climate is the principal factor defining the potential distribution of poikilotherms, and hence climatic changes are expected to influence the ranges of a range of pests, including insects, vector-borne diseases, and weeds. As these organisms spread beyond their native ranges, they may
threaten natural and productive ecosystems, often triggering substantial expensive management responses.

Methods: We explore the apparent range expansion of Medfly, Ceratitis capitata, into areas that were hitherto too cold to support persistent populations, with a focus on Europe and California. We reviewed and refined a published CLIMEX model for C. capitata. We used the refined model to assess the climatic suitability of a range of recent occurrence locations spanning the previously modelled range limits in Europe. To assess the meaning of the new European distribution records for C. capitata we used the CRU TS4 climate time series dataset to explore the temporal patterns of climate suitability from 1970 to 2019.

Results: At selected indicator sites in Europe, we found statistically significant trends in increasing climate suitability, as well as a substantial northward expansion in the modelled potential range for persistent populations. Recent geographical records in Italy and France appear to represent a mixture
of established and ephemeral populations, which is consistent with reports of the seasonal range dynamics of C. capitata in Europe. Over the same period in California, we also found a statistically significant trend of northward and altitudinal expansion of areas suitable for C. capitata persistence, and a general trend toward improved conditions for population growth

Conclusion: These statistically significant trends in suitability for C. capitata are attributable to a warming climate. These findings have implications for both pest risk analyses and pest management of invasive fruit flies and other mobile pests. The modelling revealed the need to use a climatology that is temporally concordant with species distribution data.