Intraspecific niche models for the invasive ambrosia beetle Xylosandrus crassiusculus suggest contrasted responses to climate change
- PMID: 38536504
- DOI: 10.1007/s00442-024-05528-9
Intraspecific niche models for the invasive ambrosia beetle Xylosandrus crassiusculus suggest contrasted responses to climate change
Abstract
Xylosandrus crassiusculus is an invasive ambrosia beetle comprising two differentiated genetic lineages, named cluster 1 and cluster 2. These lineages invaded different parts of the world at different periods of time. We tested whether they exhibited different climatic niches using Schoener's D and Hellinger's I indices and modeled their current potential geographical ranges using the Maxent algorithm. The resulting models were projected according to future and recent past climate datasets for Europe and the Mediterranean region. The future projections were performed for the periods 2041-2070 and 2071-2100 using 3 SSPs and 5 GCMs. The genetic lineages exhibited different climate niches. Parts of Europe, the Americas, Sub-Saharan Africa, Asia, and Oceania were evaluated as suitable for cluster 1. Parts of Europe, South America, Central and South Africa, Asia, and Oceania were considered as suitable for cluster 2. Models projection under future climate scenarios indicated a decrease in climate suitability in Southern Europe and an increase in North Eastern Europe in 2071-2100. Most of Southern and Western Europe was evaluated as already suitable for both clusters in the early twentieth century. Our results show that large climatically suitable regions still remain uncolonized and that climate change will affect the geographical distribution of climatically suitable areas. Climate conditions in Europe were favorable in the twentieth century, suggesting that the recent colonization of Europe is rather due to an increase in propagule pressure via international trade than to recent environmental changes.
Keywords: Biological invasion; Climate change; Genetic structure; Preparedness; Species distribution modeling.
© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
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