Search for: All records

This article is a Commentary onCurasiet al. (2023),239: 562–575. 

Abstract In our changing world, understanding plant community responses to global change drivers is critical for predicting future ecosystem composition and function. Plant functional traits promise to be a key predictive tool for many ecosystems, including grasslands; however, their use requires both complete plant community and functional trait data. Yet, representation of these data in global databases is sparse, particularly beyond a handful of most used traits and common species. Here we present the CoRRE Trait Data, spanning 17 traits (9 categorical, 8 continuous) anticipated to predict species’ responses to global change for 4,079 vascular plant species across 173 plant families present in 390 grassland experiments from around the world. The dataset contains complete categorical trait records for all 4,079 plant species obtained from a comprehensive literature search, as well as nearly complete coverage (99.97%) of imputed continuous trait values for a subset of 2,927 plant species. These data will shed light on mechanisms underlying population, community, and ecosystem responses to global change in grasslands worldwide. 

Abstract. Future global changes will impact carbon (C) fluxes andpools in most terrestrial ecosystems and the feedback of terrestrial carboncycling to atmospheric CO2. Determining the vulnerability of C in ecosystems to future environmental change is thus vital for targeted land management and policy. The C capacity of an ecosystem is a function of its C inputs(e.g., net primary productivity – NPP) and how long C remains in the systembefore being respired back to the atmosphere. The proportion of C capacitycurrently stored by an ecosystem (i.e., its C saturation) provides informationabout the potential for long-term C pools to be altered by environmental andland management regimes. We estimated C capacity, C saturation, NPP, andecosystem C residence time in six US grasslands spanning temperature andprecipitation gradients by integrating high temporal resolution C pool andflux data with a process-based C model. As expected, NPP across grasslandswas strongly correlated with mean annual precipitation (MAP), yet Cresidence time was not related to MAP or mean annual temperature (MAT). We linksoil temperature, soil moisture, and inherent C turnover rates (potentiallydue to microbial function and tissue quality) as determinants of carbon residence time. Overall, we found that intermediates between extremes in moisture andtemperature had low C saturation, indicating that C in these grasslands maytrend upwards and be buffered against global change impacts. Hot and drygrasslands had greatest C saturation due to both small C inputs through NPPand high C turnover rates during soil moisture conditions favorable formicrobial activity. Additionally, leaching of soil C during monsoon eventsmay lead to C loss. C saturation was also high in tallgrass prairie due tofrequent fire that reduced inputs of aboveground plant material.Accordingly, we suggest that both hot, dry ecosystems and those frequentlydisturbed should be subject to careful land management and policy decisionsto prevent losses of C stored in these systems. 

Abstract Grassland and other herbaceous communities cover significant portions of Earth's terrestrial surface and provide many critical services, such as carbon sequestration, wildlife habitat, and food production. Forecasts of global change impacts on these services will require predictive tools, such as process‐based dynamic vegetation models. Yet, model representation of herbaceous communities and ecosystems lags substantially behind that of tree communities and forests. The limited representation of herbaceous communities within models arises from two important knowledge gaps: first, our empirical understanding of the principles governing herbaceous vegetation dynamics is either incomplete or does not provide mechanistic information necessary to drive herbaceous community processes with models; second, current model structure and parameterization of grass and other herbaceous plant functional types limits the ability of models to predict outcomes of competition and growth for herbaceous vegetation. In this review, we provide direction for addressing these gaps by: (1) presenting a brief history of how vegetation dynamics have been developed and incorporated into earth system models, (2) reporting on a model simulation activity to evaluate current model capability to represent herbaceous vegetation dynamics and ecosystem function, and (3) detailing several ecological properties and phenomena that should be a focus for both empiricists and modelers to improve representation of herbaceous vegetation in models. Together, empiricists and modelers can improve representation of herbaceous ecosystem processes within models. In so doing, we will greatly enhance our ability to forecast future states of the earth system, which is of high importance given the rapid rate of environmental change on our planet. 

We use the Multiple Element Limitation (MEL) model to examine the responses of twelve ecosystems - from the arctic to the tropics and from grasslands to forests - to elevated carbon dioxide (CO2), warming, and 20% decreases or increases in annual precipitation. The ecosystems respond synergistically to elevated CO2, warming, and decreased precipitation combined because higher water use efficiency with elevated CO2 and higher fertility with warming compensate for the responses to drought. The response to elevated CO2, warming, and increased precipitation combined is additive. We analyze changes in ecosystem carbon (C) sequestration based on four nitrogen (N) and four phosphorus (P) attribution factors of the ecosystem: (1) changes in total N and P in the ecosystem, (2) changes in the distribution of N and P between vegetation and soil, (3) changes in vegetation C:N and C:P ratios, and (4) changes in soil C:N and C:P ratios. In the combined CO2 and climate change simulations, all ecosystems gain C. The relative contribution of changes in these four N and P attribution factors to changes in ecosystem C storage varies among ecosystems because of differences in the initial distributions of N and P between vegetation and soil and the openness of the ecosystem N and P cycles. The net transfer of N and P from soil (low C:N and C:P) to vegetation (high C:N and C:P) dominates the C response of forests. For tundra and grassland ecosystems, the C gain is also associated with an increase in soil C:N and C:P. In ecosystems with symbiotic N fixation, gains in C resulted from the accumulation of N and sometimes P. Because of differences in the openness of the N versus P cycles and the distribution of organic matter between vegetation and soil, changes in the N attribution factors do not always parallel changes in the P attribution factors. These findings highlight how differences among ecosystems in C-nutrient interactions and the amount of woody biomass interact to shape ecosystem C sequestration under simulated global change. By using a single model framework across multiple ecosystems, we suggest that a better understanding of the factors influencing the openness of the N and P cycles, controls on N and P distribution within ecosystems, and controls on ecosystem stoichiometry is needed to improve the representation of nutrient effects on C sequestration in ecosystems and their responses to elevated CO2 and climate change. 

Plants are subject to tradeoffs among growth strategies such that adaptations for optimal growth in one condition can preclude optimal growth in another. Thus, we predicted that a plant species that responds positively to one global change treatment would be less likely than average to respond positively to another treatment, particularly for pairs of treatments that favor distinct traits. We examined plant species abundances in 39 global change experiments manipulating two or more of the following: CO2, nitrogen, phosphorus, water, temperature, or disturbance. Overall, the directional response of a species to one treatment was 13% more likely than expected to oppose its response to a another single-factor treatment. This tendency was detectable across the global dataset but held little predictive power for individual treatment combinations or within individual experiments. While tradeoffs in the ability to respond to different global change treatments exert discernible global effects, other forces obscure their influence in local communities.