2018 ESA Annual Meeting (August 5 -- 10)

COS 42-10 - Understanding spatial structure in herbaceous layer plant communities

Tuesday, August 7, 2018: 4:40 PM
356, New Orleans Ernest N. Morial Convention Center
Samantha Catella, Biology, Case Western Reserve University, Cleveland, OH, Kristen Buse, Mentor High School, OH and Karen C. Abbott, Department of Biology, Case Western Reserve University, Cleveland, OH
Background/Question/Methods

The herbaceous layer is an integral part of temperate deciduous ecosystems; yet most of the empirical research concerning understory plant communities has been spatially implicit. Much of this research shows that plants are patchily distributed across almost all scales investigated (e.g. from local pit-mound topography, to regional comparisons across forests), and that this patchiness can be attributed to both environmental conditions as well as biotic processes. What we lack – and what may clarify some of the pattern diversity encountered in the herbaceous layer – is the ability to determine what drives spatial patterns across different spatial and temporal (e.g. seasonal) scales. By employing multivariate spatial statistics to observed point pattern data, we characterized community patterns in a spatially explicit way, and then randomized local neighborhoods from small to large block sizes, pinpointing at which neighborhood scale spatial characteristics in the real neighborhoods departed from the randomized neighborhoods. We then incorporated fine-scale environmental data using heterogeneous Poisson process models to simulate null communities structured by known relationships between environmental variables and plant community responses, and then analyzed this “abiotic null” via the same random block analysis as for the real data.

Results/Conclusions

Our results show that four distinct spatial patterns – richness (the total number of species in a neighborhood), proportion of conspecifics (a measure of within-species clumping), predictability (related to evenness, e.g. the relative abundance of species), and composition – changed in different ways across four distinct spatial scales. Moreover, by comparing results from the same neighborhood block randomization for both the real and the abiotic null data, we were able to link patterns to abiotic conditions at various spatial scales. In terms of forest restoration, management practices usually revolve around tree and animal species, even though oftentimes it is the herbaceous layer that fails to recover after severe anthropogenic disturbances (such as clearcutting), even more than 100 years later. If we wish to successfully restore and manage both tree and understory communities, we need to understand what structures herbaceous layer communities across different spatial and temporal scales. Identifying scale-dependent patterns and comparing them to patterns generated with knowledge of abiotic conditions allowed us to make hypotheses about the processes shaping the herbaceous layer at remaining spatial scales, which will be used to inform future research and, hopefully, be applied to more comprehensive and informed forest management practices.