Integrating natural gradients, experiments, and statistical modeling in a distributed network experiment: An example from the WaRM Network.

A growing body of work examines the direct and indirect effects of climate change on ecosystems, typically by using manipulative experiments at a single site or performing meta-analyses across many independent experiments. However, results from single-site studies tend to have limited generality. Although meta-analytic approaches can help overcome this by exploring trends across sites, the inherent limitations in combining disparate datasets from independent approaches remain a major challenge. In this paper, we present a globally distributed experimental network that can be used to disentangle the direct and indirect effects of climate change. We discuss how natural gradients, experimental approaches, and statistical techniques can be combined to best inform predictions about responses to climate change, and we present a globally distributed experiment that utilizes natural environmental gradients to better understand long-term community and ecosystem responses to environmental change. The warming and (species) removal in mountains (WaRM) network employs experimental warming and plant species removals at high- and low-elevation sites in a factorial design to examine the combined and relative effects of climatic warming and the loss of dominant species on community structure and ecosystem function, both above- and belowground. The experimental design of the network allows for increasingly common statistical approaches to further elucidate the direct and indirect effects of warming. We argue that combining ecological observations and experiments along gradients is a powerful approach to make stronger predictions of how ecosystems will function in a warming world as species are lost, or gained, in local communities.

Plant removal across an elevational gradient marginally reduces rates, substantially reduces variation in mineralization.

The loss of aboveground plant diversity alters belowground ecosystem function; yet, the mechanisms underpinning this relationship and the degree to which plant community structure and climate mediate the effects of plant species loss remain unclear. Here, we explored how plant species loss through experimental removal shaped belowground function in ecosystems characterized by different climatic regimes and edaphic properties. We measured plant community composition as well as potential carbon (C) and nitrogen (N) mineralization and microbial extracellular enzyme activity in soils collected from four unique plant removal experiments located along an elevational gradient in Colorado, USA. We found that, regardless of the identity of the removed species or the climate at each site, plant removal decreased the absolute variation in potential N mineralization rates and marginally reduced the magnitude of N mineralization rates. While plant species removal also marginally reduced C mineralization rates, C mineralization, unlike N mineralization, displayed sensitivity to the climatic and edaphic differences among sites, where C mineralization was greatest at the high elevation site that receives the most precipitation annually and contains the largest soil total C pool. Plant removal had little impact on soil enzyme activity. Removal effects were not contingent on the amount of biomass removed annually, and shifts in mineralization rates occurred despite only marginal shifts in plant community structure following plant species removal. Our results present a surprisingly simple and consistent pattern of belowground response to the loss of dominant plant species across an elevational gradient with different climatic and edaphic properties, suggesting a common response of belowground ecosystem function to plant species loss regardless of which plant species are lost or the broader climatic context.