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Tundra is experiencing more intense warming than any other ecosystem on earth. While warming is the most direct effect of climate change on tundra, warming leads to a cascade of environmental changes such as permafrost thaw, altered precipitation regimes, and increased wildfires. This chapter will first focus on how climate change is changing the environment of Arctic and subarctic tundra and then focus on how climate change is altering tundra's carbon, nitrogen, and phosphorus cycles with a focus on soils. Overall, tundra soils are shifting from being a carbon sink into a carbon source as rising temperatures increase microbial activity—a positive feedback to climate change. However, those rising temperatures are also increasing nutrient mineralization rates, which could increase ecosystem carbon storage via enhanced plant productivity as well as increase emissions of nitrous oxide, a powerful greenhouse gas. There is currently a disconnect between the large soil carbon losses measured in many in situ experiments and the strong plant carbon gains predicted by models. Ultimately, more research is needed on the interplay between tundra soils, nutrients, and plants to determine the magnitude of tundra's feedback to climate change.

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.

Ecological communities are being impacted by global change worldwide. Experiments are a powerful tool to understand how global change will impact communities by comparing control and treatment replicates. Communities consist of multiple species, and their associated abundances make multivariate methods an effective approach to study community compositional differences between control and treated replicates. Dissimilarity metrics are a commonly employed multivariate measure of compositional differences; however, while highly informative, dissimilarity metrics do not elucidate the specific ways in which communities differ. Integrating two multivariate methods, dissimilarity metrics and rank abundance curves (RACs), have the potential to detect complex differences based on dissimilarity metrics and detail the how these differences came about through differences in richness, evenness, species ranks, or species identity. Here we use a database of 106 global change experiments located in herbaceous ecosystems and explore how patterns of ordinations based on dissimilarity metrics relate to RAC-based differences. We find that combining dissimilarity metrics alongside RAC-based measures clarifies how global change treatments are altering communities. We find that when there is no difference in community composition (no distance between centroids of control and treated replicates), there are rarely differences in species ranks or species identities and more often differences in richness or evenness alone. In contrast, when there are differences between centroids of control and treated replicates, this is most often associated with differences in ranks either alone or co-occurring with differences in richness, evenness, or species identities. We suggest that integrating these two multivariate measures of community composition results in a deeper understanding of how global change impacts communities.

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.