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Intensification of the global hydrological cycle, ranging from larger individual precipitation events to more extreme multiyear droughts, has the potential to cause widespread alterations in ecosystem structure and function. With evidence that the incidence of extreme precipitation years (defined statistically from historical precipitation records) is increasing, there is a clear need to identify ecosystems that are most vulnerable to these changes and understand why some ecosystems are more sensitive to extremes than others. To date, opportunistic studies of naturally occurring extreme precipitation years, combined with results from a relatively small number of experiments, have provided limited mechanistic understanding of differences in ecosystem sensitivity, suggesting that new approaches are needed. Coordinated distributed experiments (CDEs) arrayed across multiple ecosystem types and focused on water can enhance our understanding of differential ecosystem sensitivity to precipitation extremes, but there are many design challenges to overcome (e.g., cost, comparability, standardization). Here, we evaluate contemporary experimental approaches for manipulating precipitation under field conditions to inform the design of 'Drought-Net', a relatively low-cost CDE that simulates extreme precipitation years. A common method for imposing both dry and wet years is to alter each ambient precipitation event. We endorse this approach for imposing extreme precipitation years because it simultaneously alters other precipitation characteristics (i.e., event size) consistent with natural precipitation patterns. However, we do not advocate applying identical treatment levels at all sites - a common approach to standardization in CDEs. This is because precipitation variability varies >fivefold globally resulting in a wide range of ecosystem-specific thresholds for defining extreme precipitation years. For CDEs focused on precipitation extremes, treatments should be based on each site's past climatic characteristics. This approach, though not often used by ecologists, allows ecological responses to be directly compared across disparate ecosystems and climates, facilitating process-level understanding of ecosystem sensitivity to precipitation extremes.

Climate change is intensifying the hydrologic cycle and is expected to increase the frequency of extreme wet and dry years. Beyond precipitation amount, extreme wet and dry years may differ in other ways, such as the number of precipitation events, event size, and the time between events. We assessed 1614 long-term (100 year) precipitation records from around the world to identify key attributes of precipitation regimes, besides amount, that distinguish statistically extreme wet from extreme dry years. In general, in regions where mean annual precipitation (MAP) exceeded 1000 mm, precipitation amounts in extreme wet and dry years differed from average years by ~40% and 30%, respectively. The magnitude of these deviations increased to >60% for dry years and to >150% for wet years in arid regions (MAP99th percentile of all events); these occurred twice as often in extreme wet years compared to average years. In contrast, these large precipitation events were rare in extreme dry years. Less important for distinguishing extreme wet from dry years were mean event size and frequency, or the number of dry days between events. However, extreme dry years were distinguished from average years by an increase in the number of dry days between events. These precipitation regime attributes consistently differed between extreme wet and dry years across 12 major terrestrial ecoregions from around the world, from deserts to the tropics. Thus, we recommend that climate change experiments and model simulations incorporate these differences in key precipitation regime attributes, as well as amount into treatments. This will allow experiments to more realistically simulate extreme precipitation years and more accurately assess the ecological consequences.

Invasions have increased the size of regional species pools, but are typically assumed to reduce native diversity. However, global-scale tests of this assumption have been elusive because of the focus on exotic species richness, rather than relative abundance. This is problematic because low invader richness can indicate invasion resistance by the native community or, alternatively, dominance by a single exotic species. Here, we used a globally replicated study to quantify relationships between exotic richness and abundance in grass-dominated ecosystems in 13 countries on six continents, ranging from salt marshes to alpine tundra. We tested effects of human land use, native community diversity, herbivore pressure, and nutrient limitation on exotic plant dominance. Despite its widespread use, exotic richness was a poor proxy for exotic dominance at low exotic richness, because sites that contained few exotic species ranged from relatively pristine (low exotic richness and cover) to almost completely exotic-dominated ones (low exotic richness but high exotic cover). Both exotic cover and richness were predicted by native plant diversity (native grass richness) and land use (distance to cultivation). Although climate was important for predicting both exotic cover and richness, climatic factors predicting cover (precipitation variability) differed from those predicting richness (maximum temperature and mean temperature in the wettest quarter). Herbivory and nutrient limitation did not predict exotic richness or cover. Exotic dominance was greatest in areas with low native grass richness at the site- or regional-scale. Although this could reflect native grass displacement, a lack of biotic resistance is a more likely explanation, given that grasses comprise the most aggressive invaders. These findings underscore the need to move beyond richness as a surrogate for the extent of invasion, because this metric confounds monodominance with invasion resistance. Monitoring species' relative abundance will more rapidly advance our understanding of invasions.

Many serious ecosystem consequences of climate change will take decades or even centuries to emerge. Long-term ecological responses to global change are strongly regulated by slow processes, such as changes in species composition, carbon dynamics in soil and by long-lived plants, and accumulation of nutrient capitals. Understanding and predicting these processes require experiments on decadal time scales. But decadal experiments by themselves may not be adequate because many of the slow processes have characteristic time scales much longer than experiments can be maintained. This article promotes a coordinated approach that combines long-term, large-scale global change experiments with process studies and modeling. Long-term global change manipulative experiments, especially in high-priority ecosystems such as tropical forests and high-latitude regions, are essential to maximize information gain concerning future states of the earth system. The long-term experiments should be conducted in tandem with complementary process studies, such as those using model ecosystems, species replacements, laboratory incubations, isotope tracers, and greenhouse facilities. Models are essential to assimilate data from long-term experiments and process studies together with information from long-term observations, surveys, and space-for-time studies along environmental and biological gradients. Future research programs with coordinated long-term experiments, process studies, and modeling have the potential to be the most effective strategy to gain the best information on long-term ecosystem dynamics in response to global change.

In terrestrial ecosystems, climate change forecasts of increased frequencies and magnitudes of wet and dry precipitation anomalies are expected to shift precipitation–net primary productivity (PPT–NPP) relationships from linear to nonlinear. Less understood, however, is how future changes in the duration of PPT anomalies will alter PPT–NPP relationships. A review of the literature shows strong potential for the duration of wet and dry PPT anomalies to impact NPP and to interact with the magnitude of anomalies. Within semi‐arid and mesic grassland ecosystems, PPT gradient experiments indicate that short‐duration (1 year) PPT anomalies are often insufficient to drive nonlinear aboveground NPP responses. But long‐term studies, within desert to forest ecosystems, demonstrate how multi‐year PPT anomalies may result in increasing impacts on NPP through time, and thus alter PPT–NPP relationships. We present a conceptual model detailing how NPP responses to PPT anomalies may amplify with the duration of an event, how responses may vary in xeric vs. mesic ecosystems, and how these differences are most likely due to demographic mechanisms. Experiments that can unravel the independent and interactive impacts of the magnitude and duration of wet and dry PPT anomalies are needed, with multi‐site long‐term PPT gradient experiments particularly well‐suited for this task. [ABSTRACT FROM AUTHOR]

Ongoing intensification of the hydrological cycle is altering rainfall regimes by increasing the frequency of extreme wet and dry years and the size of individual rainfall events. Despite long‐standing recognition of the importance of precipitation amount and variability for most terrestrial ecosystem processes, we lack understanding of their interactive effects on ecosystem functioning. We quantified this interaction in native grassland by experimentally eliminating temporal variability in growing season rainfall over a wide range of precipitation amounts, from extreme wet to dry conditions. We contrasted the rain use efficiency (RUE) of above‐ground net primary productivity (ANPP) under conditions of experimentally reduced versus naturally high rainfall variability using a 32‐year precipitation–ANPP dataset from the same site as our experiment. We found that increased growing season rainfall variability can reduce RUE and thus ecosystem functioning by as much as 42% during dry years, but that such impacts weaken as years become wetter. During low precipitation years, RUE is lowest when rainfall event sizes are relatively large, and when a larger proportion of total rainfall is derived from large events. Thus, a shift towards precipitation regimes dominated by fewer but larger rainfall events, already documented over much of the globe, can be expected to reduce the functioning of mesic ecosystems primarily during drought, when ecosystem processes are already compromised by low water availability.Climate change is expected to increase the variability of precipitation, with uncertain consequences for ecosystem functioning. We experimentally removed variability in growing season rainfall patterns across a large gradient of total precipitation amounts in native grassland. On comparing levels of primary productivity with the removal of rainfall variability to productivity under naturally high rainfall variability using long‐term observational data, we discover that rainfall variability reduces ecosystem functioning primarily during dry conditions, and that the mechanism underpinning this is the presence of large rainfall events. [ABSTRACT FROM AUTHOR]

This article is a response to Zang et al., 26, 322–324. [ABSTRACT FROM AUTHOR]

Many serious ecosystem consequences of climate change will take decades or even centuries to emerge. Long-term ecological responses to global change are strongly regulated by slow processes, such as changes in species composition, carbon dynamics in soil and by long-lived plants, and accumulation of nutrient capitals. Understanding and predicting these processes require experiments on decadal time scales. But decadal experiments by themselves may not be adequate because many of the slow processes have characteristic time scales much longer than experiments can be maintained. This article promotes a coordinated approach that combines long-term, large-scale global change experiments with process studies and modeling. Long-term global change manipulative experiments, especially in high-priority ecosystems such as tropical forests and high-latitude regions, are essential to maximize information gain concerning future states of the earth system. The long-term experiments should be conducted in tandem with complementary process studies, such as those using model ecosystems, species replacements, laboratory incubations, isotope tracers, and greenhouse facilities. Models are essential to assimilate data from long-term experiments and process studies together with information from long-term observations, surveys, and space-for-time studies along environmental and biological gradients. Future research programs with coordinated long-term experiments, process studies, and modeling have the potential to be the most effective strategy to gain the best information on long-term ecosystem dynamics in response to global change. [ABSTRACT FROM AUTHOR]

This article is a response to Korell et al., 26, 325–327; See also the Commentary on this article by Muller et al., 26, e4–e5; See also the response to this Letter to the Editor by Korell et al., 26, 328–329. [ABSTRACT FROM AUTHOR]