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This dataset contains soil volumetric water content data collected starting in 2011 for a long-term precipitation and nutrient manipulation experiment at the Jornada Basin LTER site in southern New Mexico, U.S.A. This experiment uses precipitation shelters and irrigation treatments to manipulate water inputs, and fertilization treatments to alter nitrogen input to 2.5 x 2.5 meter plots in a desert grassland. Soil sensors are installed at surface and deep soil layers in each plot and collect hourly averages of volumetric water content using a time-domain reflectometry method. This dataset contains daily averages. This is an ongoing study and the dataset will be updated yearly. 

This dataset contains cover and biomass data collected starting in 2006 for a long-term precipitation and nutrient manipulation experiment at the Jornada Basin LTER site in southern New Mexico, U.S.A. This experiment uses precipitation shelters and irrigation treatments to manipulate water inputs, and fertilization treatments to alter nitrogen input to 2.5 x 2.5 meter plots in a desert grassland. Plant cover measurements are made annually in each plot, from which biomass or net primary production are derived. This is an ongoing study and the dataset will be updated yearly. 

This dataset contains cover and biomass data collected starting in 2012 for a long-term precipitation variability manipulation experiment at the Jornada Basin LTER site in southern New Mexico, U.S.A. The study was designed to assess the effect of interannual variability in precipitation on average aboveground net primary productivity (ANPP) in Chihuahuan Desert grasslands. The study began in 2009, has five annual precipitation treatments, and contains 50 plots (10 per treatment). This experiment uses precipitation shelters and irrigation treatments to manipulate water inputs to 2.5 x 2.5 meter plots in a desert grassland. There are high, low, and ambient (control) precipitation variability treatments. Ambient plots receive natural precipitation each year, while variability treatments alternate between 20% and 180% (high variability), or 50% and 150% (low variability) of ambient precipitation each year. Plant cover measurements are made annually in each plot, from which biomass or net primary production are derived. This is an ongoing study and the dataset will be updated yearly. 

This dataset contains perennial grass tiller and stolon counts collected starting in 2012 for a long-term precipitation variability manipulation experiment at the Jornada Basin LTER site in southern New Mexico, U.S.A. The study was designed to assess the effect of interannual variability in precipitation on average aboveground net primary productivity (ANPP) in Chihuahuan Desert grasslands. The study began in 2009, has five annual precipitation treatments, and contains 50 plots (10 per treatment). This experiment uses precipitation shelters and irrigation treatments to manipulate water inputs to 2.5 x 2.5 meter plots in a desert grassland. There are high, low, and ambient (control) precipitation variability treatments. Ambient plots receive natural precipitation each year, while variability treatments alternate between 20% and 180% (high variability), or 50% and 150% (low variability) of ambient precipitation each year. Perennial grass tiller and stolon counts were made annually in each plot from 2012-2014. This is an ongoing study and the dataset will be updated as needed. 

This dataset contains soil moisture data from a study at the Jornada Experimental Range (JER) in southern New Mexico. The study was designed to assess the effect of interannual variability in precipitation on average aboveground net primary productivity (ANPP) in Chihuahuan Desert grasslands. The study began in 2009 and has five precipitation treatments (see Methods). While the study began in 2009, contains 50 plots (10 per treatment) and is ongoing, these data have only been collected since July 2011 in a subset of 20 plots (4 per treatment). This dataset is intended to provide information about the amount of water in surface and deep soil layers, as well as verify that experimental precipitation manipulations are effective. This is an ongoing dataset that will be updated yearly. 

This dataset contains soil volumetric water content data collected starting in 2011 for a long-term precipitation and nutrient manipulation experiment at the Jornada Basin LTER site in southern New Mexico, U.S.A. This experiment uses precipitation shelters and irrigation treatments to manipulate water inputs, and fertilization treatments to alter nitrogen input to 2.5 x 2.5 meter plots in a desert grassland. Soil sensors are installed at surface and deep soil layers in each plot and collect hourly averages of volumetric water content using a time-domain reflectometry method. This dataset contains daily averages. This is an ongoing study and the dataset will be updated yearly. 

This dataset contains perennial grass tiller and stolon counts collected starting in 2012 for a long-term precipitation and nutrient manipulation experiment at the Jornada Basin LTER site in southern New Mexico, U.S.A. This experiment uses precipitation shelters and irrigation treatments to manipulate water inputs, and fertilization treatments to alter nitrogen input to 2.5 x 2.5 meter plots in a desert grassland. Tillers and stolons of perennial grasses were counted in each plot in 2012, 2013 and 2014. This is an ongoing study and the dataset will be updated as needed. 

This dataset contains cover and biomass data collected starting in 2006 for a long-term precipitation and nutrient manipulation experiment at the Jornada Basin LTER site in southern New Mexico, U.S.A. This experiment uses precipitation shelters and irrigation treatments to manipulate water inputs, and fertilization treatments to alter nitrogen input to 2.5 x 2.5 meter plots in a desert grassland. Plant cover measurements are made annually in each plot, from which biomass or net primary production are derived. This is an ongoing study and the dataset will be updated yearly. 

1. Climate change is projected to cause shifts in precipitation regimes globally, leading to intensified periods of precipitation and droughts. Most studies that have explored the influence of changing precipitation regimes on ecosystems have focused on changes in mean annual precipitation, rather than the variance around the mean. Soil fungi are ubiquitous organisms that drive ecosystem processes, but it is unknown how they respond to long-term increased interannual precipitation variability. 2. Here, we investigated the influence of long-term increased precipitation variability and host type on soil fungal diversity and community composition in a dryland ecosystem. We collected 300 soil samples from two time points and different host type substrate types at a long-term precipitation variability experiment at the Jornada Long Term Ecological Research site. Next, we used amplicon sequencing to characterize soil fungal communities. 3. Soil fungal alpha diversity and community composition were strongly affected by host type and sampling year, and increased precipitation variability caused a modest, statistically insignificant, decrease in soil fungal evenness. Furthermore, results from our structural equational model showed that the decrease in grass-associated soil fungal richness was likely an indirect result of host decline in response to increased precipitation variability. 4. Synthesis. Our work demonstrates effects of increase in interannual precipitation variability on soil fungi, and that plant hosts play a key role in mediating soil fungal responses. 

Abstract Climate forecasts project change not only in the mean of climate variables but also in their variance. If these dual changes interact, then future ecological dynamics will be difficult to predict using current experimental approaches, which typically change the mean or impose a single extreme event, such as drought. We designed a new field experiment to factorially reduce mean precipitation and increase its interannual variability. Across 4 years, drier, more variable precipitation additively reduced aboveground primary productivity by 48%–69% and interactively reduced the dominant plant species, but had no effect on the plant species predicted to dominate in the future, which could lead to state transition. Drier, more variable precipitation also interactively reduced biodiversity more than either climate factor alone, with 37%–42% fewer plant species than under ambient conditions, a pattern that matched declining richness during the past 20 years of ongoing climate change. Drier, more variable precipitation restructured the composition and spatiotemporal variation of the plant community. Altered precipitation mean or variance affected 14% of plant species, with eight species sensitive to the mean × variance interaction. Results suggest that future forecasts of plant community structure may be inadequate if they fail to incorporate climate mean × variance interactions.