Legacy effects of precipitation and disturbance on biomass production (ANPP) (Sala et al. 2012) provide an interesting way to understand the biology of ecosystems through simple monitoring or experimental data. Many different mechanisms could lead to legacy patterns, including litter-soil feedbacks (Levine and Rees 2004), community composition shifts (Haddad, Tilman and Knops 2002), root stocks of perennial species etc. A new Bayesian framework for quantifying ecological memory (Ogle et al. 2015) provides a robust way to combine both current and antecedent variables to understand controls on ANPP. This working group will deploy this framework to examine long term experiments across grassland sites in the LTER network to see how ecological memory differs across different kinds of perturbations to ecosystems and across different climatic and rainfall regimes. Specifically, we will use this approach to understand how fertilization and varying precipitation regimes interact to determine ANPP, with the hypothesis that fertilization will result in a closer temporal coupling of ANPP to precipitation.
This approach has the potential to give us new insight into biological mechanisms underlying aggregate ecosystem measures at large scales and across decadal time scales, an area of great strength across US LTER sites (e.g. Hallett et al 2013). This working group will develop ideas on ecological memory and perturbations in grasslands and identify sites, experiments, and collaborators with data that can contribute to answering these questions. We will also engage with the methodological questions arising from connecting among and comparing across long term datasets collected using different protocols; we will explore the effectiveness of using short term standardized data collection (e.g. the Nutrient Network) across these sites. We see this group as laying the grounds for cross-site synthesis efforts and possible publications. The second session will provide time for interested collaborators to begin working on pulling datasets together.