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Precipitation (PPT) is a primary climatic determinant of plant growth and aboveground net primary production (ANPP) over much of the globe. Thus, PPT–ANPP relationships are important both ecologically and to land–atmosphere models that couple terrestrial vegetation to the global carbon cycle. Empirical PPT–ANPP relationships derived from long-term site-based data are almost always portrayed as linear, but recent evidence has accumulated that is inconsistent with an underlying linear relationship. We review, and then reconcile, these inconsistencies with a nonlinear model that incorporates observed asymmetries in PPT–ANPP relationships. Although data are currently lacking for parameterization, this new model highlights research needs that, when met, will improve our understanding of carbon cycle dynamics, as well as forecasts of ecosystem responses to climate change.

Drylands exert a strong influence over global interannual variability in carbon and water cycling due to their substantial heterogeneity over space and time. This variability in ecosystem fluxes presents challenges for understanding their primary drivers. Here we quantify the sensitivity of dryland gross primary productivity and evapotranspiration to various hydrometeorological drivers by synthesizing eddy covariance data, remote sensing products and land surface model output across the western United States. We find that gross primary productivity and evapotranspiration derived from eddy covariance are most sensitive to soil moisture fluctuations, with lesser sensitivity to vapour pressure deficit and little to no sensitivity to air temperature or light. We find that remote sensing data accurately capture the sensitivity of eddy covariance fluxes to soil moisture but largely over-predict sensitivity to atmospheric drivers. In contrast, land surface models underestimate sensitivity of gross primary productivity to soil moisture fluctuations by approximately 45%. Amid debates about the role of increasing vapour pressure deficit in a changing climate, we conclude that soil moisture is the primary driver of US dryland carbon–water fluxes. It is thus imperative to both improve model representation of soil water limitation and more realistically represent how atmospheric drivers affect dryland vegetation in remotely sensed flux products. Soil moisture is the primary driver of variability in dryland carbon and water cycling, according to a synthesis of eddy covariance, remote sensing and land surface model data from the western United States.

Climate change forecasts of more frequent climate extremes suggest that such events will become increasingly important drivers of future ecosystem dynamics and function. Because the rarity and unpredictability of naturally occurring climate extremes limits assessment of their ecological impacts, we experimentally imposed extreme drought and a mid-summer heat wave over two years in a central U.S. grassland. While the ecosystem was resistant to heat waves, it was not resistant to extreme drought, which reduced aboveground net primary productivity (ANPP) below the lowest level measured in this grassland in almost 30 years. This extreme reduction in ecosystem function was a consequence of reduced productivity of both C 4 grasses and C 3 forbs. However, the dominant forb was negatively impacted by the drought more than the dominant grass, and this led to a reordering of species abundances within the plant community. Although this change in community composition persisted post-drought, ANPP recovered completely the year after drought due to rapid demographic responses by the dominant grass, compensating for loss of the dominant forb. Overall, these results show that an extreme reduction in ecosystem function attributable to climate extremes (e.g., low resistance) does not preclude rapid ecosystem recovery. Given that dominance by a few species is characteristic of most ecosystems, knowledge of the traits of these species and their responses to climate extremes will be key for predicting future ecosystem dynamics and function.

In water-limited ecosystems, evidence suggests that photovoltaic (PV) arrays may alleviate water stress, potentially mitigating the loss of key ecosystem services such as forage production. This hypothesized aridity mitigation potential (AMP) of PV arrays posits that negative effects of reduced sunlight on photosynthesis and aboveground net primary production (ANPP) are offset by improved water relations, maintaining or enhancing productivity in arid and semi-arid ecosystems. Field tests of this hypothesis are lacking, however. Here we test the AMP hypothesis in a semi-arid Colorado (USA) grassland by contrasting ANPP responses to a single-axis tracking PV array in years with above- and below average precipitation. At all sites within and outside the PV array, precipitation inputs strongly limited ANPP. But in dry years, ANPP was increased by ∼20% in the array relative to adjacent open grassland, with ANPP in some array locations (near PV panel edges) increased by ∼90%. This AMP effect was muted with average precipitation and disappeared in wet years. We conclude that with climate change projections of increased aridity and drought, PV arrays that reduce plant water stress could provide a unique opportunity to reduce greenhouse gas emissions and mitigate some of their ecosystem consequences in water-limited grasslands.

Why some species are consistently more abundant than others, and predicting how species will respond to global change, are fundamental questions in ecology. Long-term observations indicate that plant species with high stoichiometric homeostasis for nitrogen ( H N ), i.e., the ability to decouple foliar N levels from variation in soil N availability, were more common and stable through time than low- H N species in a central U.S. grassland. However, with nine years of nitrogen addition, species with high H N decreased in abundance, while those with low H N increased in abundance. In contrast, in climate change experiments simulating a range of forecast hydrologic changes, e.g., extreme drought (two years), increased rainfall variability (14 years), and chronic increases in rainfall (21 years), plant species with the highest H N were least responsive to changes in soil water availability. These results suggest that H N may be predictive of plant species success and stability, and how plant species and ecosystems will respond to global-change-driven alterations in resource availability.

In contrast to pulses in resource availability following disturbance events, many of the most pressing global changes, such as elevated atmospheric carbon dioxide concentrations and nitrogen deposition, lead to chronic and often cumulative alterations in available resources. Therefore, predicting ecological responses to these chronic resource alterations will require the modification of existing disturbance-based frameworks. Here, we present a conceptual framework for assessing the nature and pace of ecological change under chronic resource alterations. The \"hierarchical-response framework\" (HRF) links well-documented, ecological mechanisms of change to provide a theoretical basis for testing hypotheses to explain the dynamics and differential sensitivity of ecosystems to chronic resource alterations. The HRF is based on a temporal hierarchy of mechanisms and responses beginning with individual (physiological/metabolic) responses, followed by species reordering within communities, and finally species loss and immigration. Each mechanism is hypothesized to differ in the magnitude and rate of its effects on ecosystem structure and function, with this variation depending on ecosystem attributes, such as longevity of dominant species, rates of biogeochemical cycling, levels of biodiversity, and trophic complexity. Overall, the HRF predicts nonlinear changes in ecosystem dynamics, with the expectation that interactions with natural disturbances and other global-change drivers will further alter the nature and pace of change. The HRF is explicitly comparative to better understand differential sensitivities of ecosystems, and it can be used to guide the design of coordinated, cross-site experiments to enable more robust forecasts of contemporary and future ecosystem dynamics.

1. Plant traits can provide unique insights into plant performance at the community scale. Functional composition, defined by both functional diversity and community-weighted trait means (CWMs), can affect the stability of above-ground net primary production (ANPP) in response to climate extremes. Further complexity arises, however, when functional composition itself responds to environmental change. The duration of climate extremes, such as drought, is expected to increase with rising global temperatures; thus, understanding the impacts of long-term drought on functional composition and the corresponding effect that has on ecosystem function could improve predictions of ecosystem sensitivity to climate change. 2. We experimentally reduced growing season precipitation by 66% across six temperate grasslands for 4 years and measured changes in three indices of functional diversity (functional dispersion, richness and evenness), community-weighted trait means and phylogenetic diversity (PD). Specific leaf area (SLA), leaf nitrogen content (LNC) and (at most sites) leaf turgor loss point (πTLP) were measured for species cumulatively representing ~90% plant cover at each site. 3. Long-term drought led to increased community functional dispersion in three sites, with negligible effects on the remaining sites. Species re-ordering following the mortality/senescence of dominant species was the main driver of increased functional dispersion. The response of functional diversity was not consistently matched by changes in phylogenetic diversity. Community-level drought strategies (assessed as CWMs) largely shifted from drought tolerance to drought avoidance and/or escape strategies, as evidenced by higher community-weighted , πTLP, SLA and LNC. Lastly, ecosystem drought sensitivity (i.e. relative reduction in ANPP in drought plots) was positively correlated with community-weighted SLA and negatively correlated with functional diversity. 4. Synthesis. Increased functional diversity following long-term drought may stabilize ecosystem functioning in response to future drought. However, shifts in community-scale drought strategies may increase ecosystem drought sensitivity, depending on the nature and timing of drought. Thus, our results highlight the importance of considering both functional diversity and abundance-weighted traits means of plant communities as their collective effect may either stabilize or enhance ecosystem sensitivity to drought.

Agrivoltaic (AV) systems are designed to coproduce photovoltaic (PV) energy on lands simultaneously supporting agriculture (food/forage production). PV infrastructure in agroecosystems alters resources critical for plant growth, and water‐limited agroecosystems such as grasslands are likely to be particularly sensitive to the unique spatial and temporal patterns of incident sunlight and soil water inherent within AV systems. However, the impact of resource alteration on forage production, the primary ecosystem service from managed grasslands, is poorly resolved. Here, we evaluated seasonal patterns of soil moisture (SM) and diurnal variation in incident sunlight (photosynthetic photon flux density [PPFD]) in a single‐axis‐tracking AV system established in a formerly managed semiarid C3 grassland in Colorado. Our goals were to (1) quantify dynamic patterns of PPFD and SM within a 1.2 MW PV array in a perennial grassland, and (2) determine how aboveground net primary production (ANPP) and photosynthetic parameters responded to the resource patterns created by the PV array. We hypothesized that spatial variability in ANPP would be strongly related to SM patterns, typical of most grasslands. We measured significant reductions in ANPP directly beneath PV panels, where SM and PPFD were both low. However, in locations with significantly increased SM from the shedding and redistribution of precipitation by PV panels, ANPP was not increased. Instead, ANPP was greatest in locations where plants were shaded in the afternoon but received high levels of PPFD in the morning hours, when air temperatures and vapor pressure deficits were relatively low. Thus, contrary to expectations, we found relatively weak relationships between SM and ANPP despite significant spatial variability in both. Further, there was little evidence that light‐saturated photosynthesis (Asat) and quantum yield of CO2 assimilation (ϕCO2) differed for plants growing directly beneath (lowest PPFD) versus between (highest PPFD) PV panels. Overall, the AV system established in this semiarid managed grassland did not alter patterns of ANPP in ways predictable from past studies of controls of ANPP in open grasslands. However, our results suggest that the diurnal timing of low versus high periods of PPFD incident on plants is an important determinant of productivity patterns in grasslands.

Water availability is the primary constraint to aboveground net primary productivity (ANPP) in many terrestrial biomes, and it is an ecosystem driver that will be strongly altered by future climate change. Global circulation models predict a shift in precipitation patterns to growing season rainfall events that are larger in size but fewer in number. This “repackaging” of rainfall into large events with long intervening dry intervals could be particularly important in semi-arid grasslands because it is in marked contrast to the frequent but small events that have historically defined this ecosystem. We investigated the effect of more extreme rainfall patterns on ANPP via the use of rainout shelters and paired this experimental manipulation with an investigation of long-term data for ANPP and precipitation. Experimental plots (n = 15) received the long-term (30-year) mean growing season precipitation quantity; however, this amount was distributed as 12, six, or four events applied manually according to seasonal patterns for May-September. The long-term mean (1940-2005) number of rain events in this shortgrass steppe was 14 events, with a minimum of nine events in years of average precipitation. Thus, our experimental treatments pushed this system beyond its recent historical range of variability. Plots receiving fewer, but larger rain events had the highest rates of ANPP (184 ± 38 g m⁻²), compared to plots receiving more frequent rainfall (105 ± 24 g m⁻²). ANPP in all experimental plots was greater than long-term mean ANPP for this system (97 g m⁻²), which may be explained in part by the more even distribution of applied rain events. Soil moisture data indicated that larger events led to greater soil water content and likely permitted moisture penetration to deeper in the soil profile. These results indicate that semi-arid grasslands are capable of responding immediately and substantially to forecast shifts to more extreme precipitation patterns.

Community-scale surveys of plant drought tolerance are essential for understanding semi-arid ecosystems and community responses to climate change. Thus, there is a need for an accurate and rapid methodology for assessing drought tolerance strategies across plant functional types. The osmometer method for predicting leaf osmotic potential at full turgor (πo), a key metric of leaf-level drought tolerance, has resulted in a 50-fold increase in the measurement speed of this trait; however, the applicability of this method has only been tested in woody species and crops. Here, we assess the osmometer method for use in herbaceous grassland species and test whether π₀ is an appropriate plant trait for understanding drought strategies of herbaceous species as well as species distributions along climate gradients. Our model for predicting leaf turgor loss point (πTLP) from π₀ (πTLP = 0.80πo–0.845) is nearly identical to the model previously presented for woody species. Additionally, π₀ was highly correlated with πTLP for graminoid species (ptlp = 0.944π₀ –0.611; r² = 0.96), a plant functional group previously flagged for having the potential to cause erroneous measurements when using an osmometer. We report that π₀, measured with an osmometer, is well correlated with other traits linked to drought tolerance (namely, leaf dry matter content and leaf vulnerability to hydraulic failure) as well as climate extremes linked to water availability. The validation of the osmometer method in an herb-dominated ecosystem paves the way for rapid community-scale surveys of drought tolerance across plant functional groups, which could improve trait-based predictions of ecosystem responses to climate change.