Abstract
Understanding what governs grassland biodiversity across different
spatial scales is crucial for effective conservation and management.
However, current evidence often focuses on single sampling grain sizes,
leaving the mechanisms of biodiversity drivers and their
scale-dependency unclear. Here, we investigated the impact of climate,
soil properties, abiotic disturbance, and land use on plant diversity
across fine spatial scales in various grassland types. We collected
spatially explicit data on species presence, relative cover, and total
community cover at two grain sizes (α- and γ-diversity) to assess the
mechanisms driving scale-dependent diversity patterns (β-diversity). In
our study, the most influential factors of plant diversity at both
scales (grain sizes) were climate variables, followed by soil humus
content, litter cover, and soil pH. The effects of soil and litter were
primarily driven by the response of rare species, while climate and
grazing effects were driven by locally common species. The strength of
most of these effects varied between spatial scales and therefore
affected β-diversity. We identified three key mechanisms through which
these drivers affect the scale-dependency of biodiversity: total plant
cover, species relative cover (commonness or rarity of species and
species evenness in the community), and species intraspecific
aggregation. Climate effects operated through changes in species
relative cover and intraspecific aggregation. Soil humus influenced
β-diversity by altering the total cover of the plant community and by
increasing intraspecific aggregation, resulting in stronger effects of
soil productivity on plant diversity at larger than smaller spatial
scales. Microhabitat patchiness by litter altered distributions in the
relative cover of species due to reduced asymmetric competition, and
affected the total cover of the plant community. Our results underscore
the importance of incorporating the scale-dependency of biodiversity
drivers in conservation efforts, management strategies, and analyses of
global change impacts, which would enhance our ability to predict
potential biodiversity change.