Large-scale mapping of predicted tick communities (Ixodidae) identifies spatial patterns of tick assemblages

Tick-borne pathogen systems are inherently multi-species networks, yet most predictive frameworks model vector distributions independently, thereby overlooking community structure. Here, we develop a stacked approach to infer and map tick communities from species distribution models (SDMs), leveraging habitat suitability to reconstruct patterns of co-occurrence across large spatial extents. We applied this framework to eight tick species across the Iberian Peninsula using high-resolution climatic and vegetation data (1 km resolution; years 2010-2025). Species-level predictions were integrated and clustered using Gaussian Mixture Models (GMMs) to identify statistically supported communities, followed by downstream analyses of dominance and specialization. Climatic variables alone achieved high predictive performance, with limited statistical gains observed from the inclusion of vegetation descriptors. Species distributions exhibited extensive spatial overlap, revealing that most of the study area supports multi-species assemblages rather than isolated populations. Community reconstruction identified a structured gradient extending from Atlantic systems dominated by Ixodes ricinus to Mediterranean assemblages characterized by Hyalomma lusitanicum and Rhipicephalus bursa. Transitional communities formed a continuous ecological spectrum, demonstrating strong environmental filtering within Mediterranean assemblages. Our results show that SDM-derived stacked models can be leveraged to infer biologically meaningful community structures, providing a practical and scalable alternative to data-intensive joint modelling approaches. This framework offers a robust tool to integrate community ecology into tick surveillance and to improve the spatial risk assessment of tick-borne diseases.