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

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

Présentation de la mycorhization

National audienc

Atelier d'observation 3D des micro-organismes du sol, des champignons, des bactéries...

National audienc

Arbuscular mycorrhizal fungus Rhizophagus irregularis expresses an outwardly Shaker-like channel involved in potassium nutrition of rice ( Oryza sativa L.)

Abstract Potassium (K + ) plays crucial roles in many physiological, molecular and cellular processes in plants. Direct uptake of this nutrient by root cells has been extensively investigated, however, indirect uptake of K + mediated by the interactions of the roots with fungi in the frame of a mutualistic symbiosis, also called mycorrhizal nutrient uptake pathway, is much less known. We identified an ion channel in the arbuscular mycorrhizal (AM) fungus Rhizophagus irregularis . This channel exhibits the canonical features of Shaker-like channel shared in other living kingdoms and is named RiSKC3. Transcriptionally expressed in hyphae and in arbuscules of colonized rice roots, RiSKC3 has been shown to be located in the plasma membrane. Voltage-clamp functional characterization in Xenopus oocytes revealed that RiSKC3 is endowed with outwardly-rectifying voltage-gated activity with a high selectivity for K + over sodium ions. RiSKC3 may have a role in the AM K + pathway for rice nutrition in normal and salt stress conditions. The current working model proposes that K + ions taken up by peripheral hyphae of R. irregularis are secreted towards the host root into periarbuscular space by RiSKC3. Significance Statement Mutualistic symbiosis with arbuscular mycorrhizal (AM) fungi are beneficial for about 80% of land plants thanks to an exchange of nutrients. The AM pathway responsible for potassium (K + ) nutrition of the plant is not known. Here we uncovered a key step of this phenomenon, by functionally characterizing the first transport system in the AM fungus Rhizophagus irregularis , and we univocally demonstrated that RiSKC3 is an K + outwardly-rectifying voltage-gated Shaker-like channel

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