Drivers of Genetic Diversity in Keystone Forest Species: Contrasting Responses of Rare and Common Saproxylic Beetles to Habitat Location and Naturalness

Abstract

Understanding how forest management shapes genetic diversity is pivotal for the conservation of saproxylic beetles, key indicators of ecosystem integrity. However, the relative influence of spatial isolation versus anthropogenic modification on their genetic structure remains poorly understood across Central Europe. We investigated whether geographic location (forests spanned over 600 km W‐to‐E and 500 km S‐to‐N) and forest management type (primaeval vs. commercial) drive genetic differentiation across 12 beetle species with varying abundance (common/rare) and pest status (pest/not‐pest). Using a multi‐species double digest restriction‐site associated DNA (ddRAD) approach, we analysed single nucleotide polymorphism (SNP) variation in taxa differing in abundance and mobility. We used generalized linear mixed models (GLMMs), distance‐based redundancy analysis (dbRDA) and analysis of molecular variance (AMOVA) to evaluate the landscape and ecological factors driving genetic diversity and differentiation. Furthermore, population structure, isolation‐by‐distance (IBD) and genetic admixture were explicitly resolved using ADMIXTURE and discriminant analysis of principal components (DAPC) frameworks. Analyses revealed geography, likely driven by post‐glacial recolonization and IBD, as the main determinant of differentiation. Forest management exerted a secondary but significant effect, especially among rare specialists. In these sensitive species, populations from primaeval protected sites exhibited distinct genetic signatures compared to commercial stands, indicating that intensive management isolates ancient forest specialists, while pests remained unaffected. Our results highlight that regional connectivity and the preservation of primaeval refugia must be prioritized to maintain gene flow. By integrating high‐resolution genomic data with management context, this study offers actionable insights for incorporating genetic monitoring into sustainable forest biodiversity management.

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