COS 32-7 - Patterns and drivers of stability in long-term metacommunity data

Tuesday, August 13, 2019: 3:40 PM
M109/110, Kentucky International Convention Center
Nathan I. Wisnoski, Department of Biology, Indiana University, Bloomington, IN, Eric R. Sokol, Batelle, National Ecological Observatory Network (NEON), Boulder, CO, Riley Andrade, School of Geographical Sciences and Urban Planning, Arizona State University, Max C. N. Castorani, Department of Environmental Sciences, University of Virginia, Charlottesville, VA, Christopher P. Catano, Department of Biology, Washington University in St. Louis, St. Louis, MO, Aldo Compagnoni, German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Martin Luther University Halle-Wittenberg, Leipzig, Germany, Thomas Lamy, Marine Science Institute, University of California, Santa Barbara, Santa Barbara, CA, Nina K. Lany, Forestry, Michigan State University, East Lansing, MI, Luca Marazzi, Southeast Environmental Research Center (SERC), Florida International University, Miami, FL, Sydne Record, Biology, Bryn Mawr College, Bryn Mawr, PA, Annie C. Smith, Department of Forestry, Michigan State University, East Lansing, MI, Christopher M. Swan, Geography and Environmental Systems, University of Maryland, Baltimore County, Baltimore, MD, Jonathan D. Tonkin, School of Biological Sciences, University of Canterbury, Christchurch, New Zealand, Nicole M. Voelker, Geography & Environmental Systems, University of Maryland, Baltimore County, Baltimore, MD and Phoebe Zarnetske, Department of Integrative Biology, Michigan State University, East Lansing, MI
Background/Question/Methods

Metacommunity ecology focuses on the local and regional scale factors that influence community structure and dynamics. Although the ecological processes that confer community stability at the local scale are well characterized, a deeper examination of stability at the metacommunity scale has only recently begun. Stability at the metacommunity scale will depend on the relationship between temporal environmental variability, spatial heterogeneity, and species dispersal rates. Generally, combinations of these factors that reduce spatial synchrony in community dynamics tend to stabilize aggregate properties at the metacommunity scale, but drivers of compositional metacommunity stability are uncertain.

We used NSF Long-Term Ecological Research (LTER) data to quantify metacommunity variability across a variety of ecosystem and organism types using a novel, multi-scale stability metric focused on compositional variability. We used on-site measurements and remote sensing data to quantify the spatial and temporal variability at each LTER site. We used this environmental data, along with organismal and ecosystem type, to predict metacommunity variability across the LTER network.

Results/Conclusions

Metacommunity stability was influenced by temporal variability in temperature and productivity, but the relationship between environmental and metacommunity variability was context dependent. In terrestrial macroorganisms, higher temperature variability was associated with more stable local communities, but because it increased spatial synchrony, more variable temperatures were associated with lower stability at the metacommunity scale. Marine metacommunities showed a weaker relationship with temperature variability, but had higher stability when productivity was more variable. In contrast, more variable productivity was correlated with lower stability in metacommunities of freshwater diatoms. Freshwater and terrestrial metacommunities overall had lower stability at higher latitudes due to higher spatial synchrony, while marine metacommunities were less stable in the tropics.

Our work extends the study of aggregate metacommunity stability to include the spatial scaling of compositional stability. Our results suggest that, in natural systems, there appear to be general patterns of metacommunity stability that depend on species traits (e.g., body size), biome (e.g., marine, freshwater, terrestrial), trophic group (e.g., producers, consumers), and spatial/temporal environmental variation.