Abstract
ABSTRACT: While plant community theory tends to emphasize the importance of abiotic heterogeneity along niche axes, much empirical work seeks to characterize the influence of the absolute magnitude of key abiotic variables on diversity. Both magnitude (as reflected, e.g., by a mean) and heterogeneity (variance) in abiotic conditions likely contribute to biodiversity patterns in plant communities, but given the large number of putative abiotic drivers and the fact that each may vary at different spatiotemporal scales, the challenge of linking observed biotic patterns with the underlying environment remains acute. Using monitoring data from a natural resource agency, we compared how well statistical models of the mean, heterogeneity, and both the mean and heterogeneity combined of 17 abiotic factor variables explained patterns of forb species richness in Northeast Ohio, USA. We performed our analyses at two spatial scales, repeated in spring and summer across four forest types. Although all models explained a great deal of the variance in species richness, models including both the mean and heterogeneity of different abiotic factors together outperformed models including either the mean or the heterogeneity of abiotic factors alone. Variability in forb species richness was mostly due to changes in mean calcium levels regardless of forest type. After accounting for forest type, we were able to attribute variation in forb species richness to changes in the heterogeneity of different abiotic factors as well. Our results suggest that multiple mechanisms act simultaneously according to different aspects of the abiotic environment to structure forb communities, and this underscores the importance of considering both the magnitude of and heterogeneity in multiple abiotic factors when looking for links between the abiotic environment and plant community patterns. Finally, we identify novel patterns across spatial scales, forest types, and seasons that can guide future research in this vein. OPEN RESEARCH BADGES: This article has earned an Open Data Badge for making publicly available the digitally-shareable data necessary to reproduce the reported results. The data is available at https://doi.org/10.5061/dryad.kp3cb17.