Species-genetic diversity correlations depend on ecological similarity between multiple moorland plant species

Species diversity (SD) and genetic diversity (GD) are fundamental aspects of biodiversity and are dependent because they are sympatrically affected by biotic and abiotic factors. Species-genetic diversity correlations (SGDCs) range from positive to negative among multiple focal species even across same communities. As determinant mechanisms of SGDCs, previous studies identified neutral processes related with habitat size and connectivity, environmental filtering and interspecific interactions. Theoretically, similar responses of SD and GD to these mechanisms can lead to positive SGDCs. The quantitative evaluation and comparison of similarity in each mechanism have not been performed. Here, we tested whether alpha- and beta-SGDCs could be determined by similarity in ecological characteristics (ecological similarity), such as environmental responses (responses of GD to moorland area, altitude, pH, electric conductivity and spatial autocorrelation based on regression analysis), dispersal ability (seed dispersal and pollination mode) and functional traits associated with competitions (leaf height, leaf size and specific leaf area (SLA)). We investigated SD and GD of 11 species based on single nucleotide polymorphisms across 20 moorland plant communities in northern Japan, and evaluated correlations between SGDCs and principal components of the ecological characteristics across the focal species. alpha-SGDCs varied from positive to negative, which was explained by ecological similarity in the response of GD to pH, seed dispersal and pollination mode, leaf height and SLA. In contrast, beta-SGDCs were almost consistently positive, which resulted from spatial autocorrelation and could not be explained by ecological similarity. We emphasized that not only environmental filtering and neutral process via dispersal limitation but also competitions can contribute to SGDCs. The incorporation of trait-based approaches is effective for understanding spatial patterns of combinations of SD and GD. Evaluating SGDCs and spectrums of functional traits highly related to community assembly may lead to spatial predictions of GD in whole members of communities.