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Climate-driven redistribution of Hyalomma marginatum (Ixodida: Ixodidae): robust ecological niche modeling reveals
Seyma S Celina1, Jiří Černý1, Abdallah M Samy2,3,4
1Center for Infectious Animal Diseases, Faculty of Tropical AgriSciences, Czech University of Life Sciences Prague, Prague, Czech Republic.
Abstract:
Hyalomma marginatum (Ixodida: Ixodidae) is the principal vector of Crimean-Congo hemorrhagic fever virus (CCHFV) and an emerging public health concern because climate change may facilitate its establishment in regions where it is currently absent. We used ecological niche modeling to characterize the current climatic suitability of H. marginatum and project its future redistribution across the Old World under CMIP6 climate scenarios. Models were calibrated using a comprehensive dataset of spatially filtered occurrence records and bioclimatic predictors within a biologically informed accessible area (M). To evaluate projection robustness, we implemented 4 alternative accessible-area hypotheses (M1-M4), including an Eastern Hemisphere-constrained scenario, compared MaxEnt projections with independently fitted Boosted Regression Trees (BRT), and quantified prediction uncertainty using pixel-wise variance decomposition. Current projections identified the Mediterranean Basin, North Africa, the Middle East, and western to central Asia as the principal regions of climatic suitability. Future projections consistently indicated a marked northward redistribution of suitable conditions into Central and Northern Europe, particularly under the high-emissions SSP5-8.5 scenario, while localized reductions in suitability were projected across parts of southern Europe and North Africa. Accessible-area definition and modeling algorithm contributed more to overall projection uncertainty than differences among climate models or emissions scenarios, highlighting the importance of explicitly evaluating methodological uncertainty in ecological forecasts. The broad pattern of northward redistribution was consistent across accessible-area hypotheses, modeling algorithms, and climate projections, although quantitative uncertainty remained substantial. These findings provide a stronger evidence base for climate-informed surveillance and preparedness strategies for Crimean-Congo hemorrhagic fever (CCHF).
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