Global Change Models Research Articles

Understanding predicted shifts in diazotroph biogeography using resource competition theory

Abstract. We examine the sensitivity of the biogeography of nitrogen fixers to a warming climate and increased aeolian iron deposition in the context of a global earth system model. We employ concepts from the resource-ratio theory to provide a simplifying and transparent interpretation of the results. First we demonstrate that a set of clearly defined, easily diagnosed provinces are consistent with the theory. Using this framework we show that the regions most vulnerable to province shifts and changes in diazotroph biogeography are the equatorial and South Pacific, and central Atlantic. Warmer and dustier climates favor diazotrophs due to an increase in the ratio of supply rate of iron to fixed nitrogen. We suggest that the emergent provinces could be a standard diagnostic for global change models, allowing for rapid and transparent interpretation and comparison of model predictions and the underlying mechanisms. The analysis suggests that monitoring of real world province boundaries, indicated by transitions in surface nutrient concentrations, would provide a clear and easily interpreted indicator of ongoing global change.

Convergence of terrestrial plant production across global climate gradients.

Variation in terrestrial net primary production (NPP) with climate is thought to originate from a direct influence of temperature and precipitation on plant metabolism. However, variation in NPP may also result from an indirect influence of climate by means of plant age, stand biomass, growing season length and local adaptation. To identify the relative importance of direct and indirect climate effects, we extend metabolic scaling theory to link hypothesized climate influences with NPP, and assess hypothesized relationships using a global compilation of ecosystem woody plant biomass and production data. Notably, age and biomass explained most of the variation in production whereas temperature and precipitation explained almost none, suggesting that climate indirectly (not directly) influences production. Furthermore, our theory shows that variation in NPP is characterized by a common scaling relationship, suggesting that global change models can incorporate the mechanisms governing this relationship to improve predictions of future ecosystem function.

Land management trumps the effects of climate change and elevated CO2 on grassland functioning

Summary Grasslands cover ˜30% of the Earth's terrestrial surface and provide many ecosystem services. Many grasslands are heavily managed to maximize these services for human benefit, but the outcome of management is anticipated to be increasingly influenced by various aspects of climate change and elevated atmospheric CO2. The relative importance of global change vs. land management on grasslands is largely unknown. A meta‐analysis is used here to examine drivers at both scales primarily targeting services provided by grasslands relating to plant productivity (above‐ and below‐ground biomass) and soil processes (nutrients and soil respiration) in 38 manipulative experiments published in the last decade. We specifically target effects of (i) single and combined land management practices (LMs), (ii) single and combined factors relating to broad‐scale climate change and elevated CO2, and (iii) combined management practices and changes to climate and CO2. Collectively, this examines the general efficacy of global change models in predicting changes to grassland functioning. We found that combinations of management practices had approximately double the explanatory power for variation in grassland services compared with individual or interactive effects of factors associated with climate change and CO2. These interacting management practices such as nutrient additions and defoliation predominantly influenced functions associated with productivity or biomass both below and above ground. The effects of interacting factors of climate and CO2 influenced a wider range of ecosystem functions, but the magnitude of these effects was relatively smaller. Interactions between management practices or between climate change/CO2 factors always had higher explanatory power than any factor in isolation indicating that multivariate synergistic models of environmental change can better describe impacts on ecosystem function in plant communities (e.g. relative to univariate climate‐based models). Given that the magnitude and direction (positive or negative) of the interactions varied widely, this also implies that the outcomes of these multivariate interactions can vary spatially, temporally or by immediate context (e.g. management prescriptions). Synthesis. Although our work confirms how climate change and CO2 can affect many ecosystem‐based functional attributes, it suggests that combinations of LMs remain the dominant set of factors in determining the performance of grassland plant communities. Land management may thus be critical for influencing projected responses to future climate change and elevated CO2 in models of grassland function at least for factors relating to primary production.

Comparing three global parametric and local non-parametric models to simulate land use change in diverse areas of the world

This paper compares one global parametric land use change model, the artificial neural network – based Land Transformation Model, with two local non-parametric models: a classification and regression tree and multivariate adaptive regression spline model. We parameterized these three models with identical data from different regions of the world; one region undergoing extensive agricultural expansion (East Africa), another region where forests are re-growing (Muskegon River Watershed in the United States), and a third region where urbanization is prominent (South-Eastern Wisconsin in the United States). Independent training data and testing data were used to train and calibrate each model, respectively. Comparisons of simulated maps from the three kinds of land use change patterns were made using conventional goodness-of-fit metrics in land use change science. The results of temporal and spatial comparison of the data mining models show that the artificial neural network outperformed all other models in a short-time interval (East Africa; 5 years) and for coarse resolution data (East Africa; 1 km); however, the three data mining models obtained similar accuracies in a long-time interval (Muskegon River Watershed; 20 years) and for fine resolution data with large numbers of cells (Muskegon River Watershed; 30 m). Furthermore, the results showed that the probability of agriculture gain was more likely in locations closer to towns and large cities in East Africa, urbanization was more likely in locations closer to roads and urban areas in South-Eastern Wisconsin and the probability of forest gain was more likely in locations closer to the forest and shrub land cover and farther away from roads in Muskegon River Watershed.

Different mechanisms drive the performance of native and invasive woody species in response to leaf phosphorus supply during periods of drought stress and recovery

The effects of drought stress and leaf phosphorus (Pi) supply on photosynthetic metabolism in woody tropical species are not known, and given the recent global environmental change models that forecast lower precipitation rates and periods of prolonged drought in tropical areas, this type of study is increasingly important. The effects of controlled drought stress and Pi supply on potted young plants of two woody species, Anadenanthera colubrina (native) and Prosopis juliflora (invasive), were determined by analyzing leaf photosynthetic metabolism, biochemical properties and water potential. In the maximum stress, both species showed higher leaf water potential (Ψl) in the treatment drought +Pi when compared with the respective control −Pi. The native species showed higher gas exchange under drought +Pi than under drought –Pi conditions, while the invasive species showed the same values between drought +Pi and −Pi. Drought affected the photochemical part of photosynthetic machinery more in the invasive species than in the native species. The invasive species showed higher leaf amino acid content and a lower leaf total protein content in both Pi treatments with drought. The two species showed different responses to the leaf Pi supply under water stress for several variables measured. In addition, the strong resilience of leaf gas exchange in the invasive species compared to the native species during the recovery period may be the result of higher efficiency of Pi use. The implications of this behavior for the success of this invasive species in semiarid environments are discussed.

Microbial communities respond to experimental warming, but site matters.

Because microorganisms are sensitive to temperature, ongoing global warming is predicted to influence microbial community structure and function. We used large-scale warming experiments established at two sites near the northern and southern boundaries of US eastern deciduous forests to explore how microbial communities and their function respond to warming at sites with differing climatic regimes. Soil microbial community structure and function responded to warming at the southern but not the northern site. However, changes in microbial community structure and function at the southern site did not result in changes in cellulose decomposition rates. While most global change models rest on the assumption that taxa will respond similarly to warming across sites and their ranges, these results suggest that the responses of microorganisms to warming may be mediated by differences across the geographic boundaries of ecosystems.

Biodiversity, photosynthetic mode, and ecosystem services differ between native and novel ecosystems

Human activities have caused non-native plant species with novel ecological interactions to persist on landscapes, and it remains controversial whether these species alter multiple aspects of communities and ecosystems. We tested whether native and exotic grasslands differ in species diversity, ecosystem services, and an important aspect of functional diversity (C3:C4 proportions) by sampling 42 sites along a latitudinal gradient and conducting a controlled experiment. Exotic-dominated grasslands had drastically lower plant diversity and slightly higher tissue N concentrations and forage quality compared to native-dominated sites. Exotic sites were strongly dominated by C4 species at southern and C3 species at northern latitudes with a sharp transition at 36-38°, whereas native sites contained C3:C4 mixtures. Large differences in C3:C4 proportions and temporal niche partitioning were found between native and exotic mixtures in the experiment, implying that differences in C3:C4 proportions along the latitudinal gradient are caused partially by species themselves. Our results indicate that the replacement of native- by exotic-dominated grasslands has created a management tradeoff (high diversity versus high levels of certain ecosystem services) and that models of global change impacts and C3/C4 distribution should consider effects of exotic species.

Challenges and conceptions of globalization

Purpose– This paper, which is conceptual in both nature and approach, builds on a recent contribution to the theorization of “globalization” and seeks to utilise the framework developed therein to help promote a more complex conceptual understanding of the potential implications of how business operates and responds to these challenges in a global environment. The paper aims to discuss these issues.Design/methodology/approach– The paper draws primarily on a heuristic framework developed by O'Byrne and Hensby that reviews eight models of global change. In this paper, the authors review and give consideration to the relationship between these models and business practice and contend that this relationship is far more complex than the majority of the current literature in the business and management field represents. Within the paper, the authors explore and discuss the dynamics of the eight models of “globalization” and assess the potential implications for business practice of working within these often conflicting and contradictory paradigms of “globalization”. As part of this review, the authors consider the strategic implications of “globalization” for business practice and propose a conceptual model with eight strategic options which are aligned to the eight models of global change.Findings– The paper presents a tentative heuristic framework seeking to align the eight models of global change with strategic options that companies might peruse in response to the global forces for change. The paper concludes by advocating a more integrative and complex understanding of globalization than is currently the case and identifies potential for further research in this area.Originality/value– The paper develops a conceptual framework for assessing the challenges that processes of globalization present to business. The paper places a particular emphasis on considering the strategic implications of the various models of global change and offers a tentative framework for further debate and discussion.

A Combined Model of Global Cultivated Area Change and Prediction for Future: 1961–2020

As a basic condition for food safety, cultivated area fluctuates in recent years. So, it has important political and economical significance to understand the future change trend of global cultivated area. Based on historical data in the past 50 years, deterministic time series analysis model, random time series analysis model, and combined model were established by using time series analysis method. By comparison, the combined model has the highest fitting and prediction accuracy, and it is suitable for the prediction of global cultivated area change trend. The global cultivated area will rise slightly in fluctuation in the near future driven by a combination of deterministic factors and random factors.

Land cover change or land‐use intensification: simulating land system change with a global‐scale land change model

Land-use change is both a cause and consequence of many biophysical and socioeconomic changes. The CLUMondo model provides an innovative approach for global land-use change modeling to support integrated assessments. Demands for goods and services are, in the model, supplied by a variety of land systems that are characterized by their land cover mosaic, the agricultural management intensity, and livestock. Land system changes are simulated by the model, driven by regional demand for goods and influenced by local factors that either constrain or promote land system conversion. A characteristic of the new model is the endogenous simulation of intensification of agricultural management versus expansion of arable land, and urban versus rural settlements expansion based on land availability in the neighborhood of the location. Model results for the OECD Environmental Outlook scenario show that allocation of increased agricultural production by either management intensification or area expansion varies both among and within world regions, providing useful insight into the land sparing versus land sharing debate. The land system approach allows the inclusion of different types of demand for goods and services from the land system as a driving factor of land system change. Simulation results are compared to observed changes over the 1970-2000 period and projections of other global and regional land change models.

Predicting community and ecosystem outcomes of mycorrhizal responses to global change

Mycorrhizal symbioses link the biosphere with the lithosphere by mediating nutrient cycles and energy flow though terrestrial ecosystems. A more mechanistic understanding of these plant-fungal associations may help ameliorate anthropogenic changes to C and N cycles and biotic communities. We explore three interacting principles: (1) optimal allocation, (2) biotic context and (3) fungal adaptability that may help predict mycorrhizal responses to carbon dioxide enrichment, nitrogen eutrophication, invasive species and land-use changes. Plant-microbial feedbacks and thresholds are discussed in light of these principles with the goal of generating testable hypotheses. Ideas to develop large-scale collaborative research efforts are presented. It is our hope that mycorrhizal symbioses can be effectively integrated into global change models and eventually their ecology will be understood well enough so that they can be managed to help offset some of the detrimental effects of anthropogenic environmental change.

Clinal adaptation and adaptive plasticity in Artemisia californica: implications for the response of a foundation species to predicted climate change

Local adaptation and plasticity pose significant obstacles to predicting plant responses to future climates. Although local adaptation and plasticity in plant functional traits have been documented for many species, less is known about population-level variation in plasticity and whether such variation is driven by adaptation to environmental variation. We examined clinal variation in traits and performance - and plastic responses to environmental change - for the shrub Artemisia californica along a 700km gradient characterized (from south to north) by a fourfold increase in precipitation and a 61% decrease in interannual precipitation variation. Plants cloned from five populations along this gradient were grown for 3years in treatments approximating the precipitation regimes of the north and south range margins. Most traits varying among populations did so clinally; northern populations (vs. southern) had higher water-use efficiencies and lower growth rates, C:N ratios and terpene concentrations. Notably, there was variation in plasticity for plant performance that was strongly correlated with source site interannual precipitation variability. The high-precipitation treatment (vs. low) increased growth and flower production more for plants from southern populations (181% and 279%, respectively) than northern populations (47% and 20%, respectively). Overall, precipitation variability at population source sites predicted 86% and 99% of variation in plasticity in growth and flowering, respectively. These striking, clinal patterns in plant traits and plasticity are indicative of adaptation to both the mean and variability of environmental conditions. Furthermore, our analysis of long-term coastal climate data in turn indicates an increase in interannual precipitation variation consistent with most global change models and, unexpectedly, this increased variation is especially pronounced at historically stable, northern sites. Our findings demonstrate the critical need to integrate fundamental evolutionary processes into global change models, as contemporary patterns of adaptation to environmental clines will mediate future plant responses to projected climate change.

Interactive Effects of Fire, Rainfall, and Litter Quality on Decomposition in Savannas: Frequent Fire Leads to Contrasting Effects

One of the many ecological processes expected to undergo alteration due to global change is the decomposition of organic matter, with little known concerning the effects that changing disturbance regimes may have. Fire, a critical process in many habitats, is expected to become more common. We measured the decomposition rates of four grass species that differed in litter quality, investigating them under different fire regimes across a savanna rainfall gradient in South Africa. We also collected data on the abundance and activity of fungus-growing termites and recorded measurements of temperature and canopy cover. Overall, decomposition rate followed global models, increasing under warmer and wetter conditions. Litter quality was also significant with higher quality grasses decomposing faster; however, this effect was less pronounced than expected. Fire regimes did not have a consistent effect on decomposition rate along the rainfall gradient. In the most arid savanna type examined, fire had no effect, whereas in the intermediate rainfall savanna burning increased decomposition rate under higher levels of fungus-growing termite activity. In the wetter savannas, fire slowed decomposition, possibly through modification of vegetation structure and potential effects on other invertebrates. Our results demonstrate that grass decomposition in African savannas varies significantly along precipitation gradients, with different factors becoming influential in different habitats. Importantly, we demonstrate that fire does not always act to slow decomposition and that it interacts with other factors to influence the process. These findings have important implications for decomposition in the light of global change models that predict wetter climates and a higher frequency of fires for southern African savannas.

Innovative approaches to integrated global change modelling

Integrated models are important tools to investigate the interactions between planetary processes and the growing impacts of human populations – in short: global change. Current models still have significant shortcomings, notably in their representation of socio-economic processes and the feedbacks between these and the environmental system. They are also often not designed with sufficient transparency to enable participation of interested parties or effective communication with stakeholders and policy makers. These deficiencies are discussed and possible directions for improvement are identified. This Thematic Issue provides a collection of papers that offer a number of innovative ideas for remedying these shortcomings using novel methods and approaches.

Response of switchgrass yield to future climate change

A climate envelope approach was used to model the response of switchgrass, a model bioenergy species in the United States, to future climate change. The model was built using general additive models (GAMs), and switchgrass yields collected at 45 field trial locations as the response variable. The model incorporated variables previously shown to be the main determinants of switchgrass yield, and utilized current and predicted 1 km climate data from WorldClim. The models were run with current WorldClim data and compared with results of predicted yield obtained using two climate change scenarios across three global change models for three time steps. Results did not predict an increase in maximum switchgrass yield but showed an overall shift in areas of high switchgrass productivity for both cytotypes. For upland cytotypes, the shift in high yields was concentrated in northern and north-eastern areas where there were increases in average growing season temperature, whereas for lowland cultivars the areas where yields were projected to increase were associated with increases in average early growing season precipitation. These results highlight the fact that the influences of climate change on switchgrass yield are spatially heterogeneous and vary depending on cytotype. Knowledge of spatial distribution of suitable areas for switchgrass production under climate change should be incorporated into planning of current and future biofuel production. Understanding how switchgrass yields will be affected by future changes in climate is important for achieving a sustainable biofuels economy.

Deep-time evidence of a link between elevated CO2 concentrations and perturbations in the hydrological cycle via drop in plant transpiration

The physiological effects of high CO 2 concentrations, i.e., [CO 2 ], on plant stomatal responses may be of major importance in understanding the consequences of climate change, by causing increases in runoff through suppression of plant transpiration. Radiative forcing by high [CO 2 ] has been the main consideration in models of global change to the exclusion of plant physiological forcing, but this potentially underestimates the effects on the hydrological cycle, and the consequences for ecosystems. We tested the physiological responses of fossil plants from the Triassic–Jurassic boundary transition (Tr–J) succession of East Greenland. This interval marks a major high CO 2 -driven environmental upheaval, with faunal mass extinctions and significant floral turnover. Our results show that both stomatal size (expressed in fossil material as SL, the length of the stomatal complex opening) and stomatal density (SD, the number of stomata per mm 2 ) decreased significantly during the Tr–J. We estimate, using a leaf gas-exchange model, that the decreases in SD and SL resulted in a 50%–60% drop in stomatal and canopy transpiration at the Tr–J. We also present new field evidence indicating simultaneous increases in runoff and erosion rates. We propose that the consequences of stomatal responses to elevated [CO 2 ] may lead to locally increased runoff and erosion, and may link terrestrial and marine biodiversity loss via the hydrological cycle.

ON GENERATING DIGITAL ELEVATION MODELS FROM LIDAR DATA – RESOLUTION VERSUS ACCURACY AND TOPOGRAPHIC WETNESS INDEX INDICES IN NORTHERN PEATLANDS

Global change and GHG emission modelling are dependent on accurate wetness estimations for predictions of e.g. methane emissions. This study aims to quantify how the slope, drainage area and the TWI vary with the resolution of DEMs for a flat peatland area. Six DEMs with spatial resolutions from 0.5 to 90 m were interpolated with four different search radiuses. The relationship between accuracy of the DEM and the slope was tested. The LiDAR elevation data was divided into two data sets. The number of data points facilitated an evaluation dataset with data points not more than 10 mm away from the cell centre points in the interpolation dataset. The DEM was evaluated using a quantile-quantile test and the normalized median absolute deviation. It showed independence of the resolution when using the same search radius. The accuracy of the estimated elevation for different slopes was tested using the 0.5 meter DEM and it showed a higher deviation from evaluation data for steep areas. The slope estimations between resolutions showed differences with values that exceeded 50%. Drainage areas were tested for three resolutions, with coinciding evaluation points. The model ability to generate drainage area at each resolution was tested by pair wise comparison of three data subsets and showed differences of more than 50% in 25% of the evaluated points. The results show that consideration of DEM resolution is a necessity for the use of slope, drainage area and TWI data in large scale modelling.

GPU-CA model for large-scale land-use change simulation

Land-use change simulation for large-scale regions (i.e. provincial regions or countries) is very useful for many global studies. Such simulation, however, is affected by computational capability of general computers. This paper proposes a method to implement cellular automata (CA) for land use change simulation based on graphics processing units (GPUs). This method can be applied to large-scale land-use change simulations by combining the latest GPU high-performance computing technology and CA. We carried out the experiments by simulating land-use change processes at a provincial scale. This involves a lot of sophisticated techniques, such as model mapping, and computational procedure of GPU-CA model. This proposed model has been validated by land-use change simulation in Guangdong Province, China. The comparison indicates that the GPU-CA model is faster than traditional CA by 30 times. Such improvement is crucial for land-use change simulations in provincial regions and countries. The outputs of the simulation can be further used to provide information to other global change models.

The role of environmental microorganisms in ecosystem responses to global change: current state of research and future outlooks

The effects of human activities have dramatically altered our natural environment. Greenhouse gas production, nutrient loading, land-use change and water consumption, to name a few, can dramatically affect ecosystem processes by changing the dynamics of global biogeochemical cycles. Currently, one of the most crucial scientific objectives is to gain an understanding of how drastically anthropogenic changes have altered our planet and what those changes mean for the future. In order to predict accurate scenarios of how global change will affect terrestrial ecosystems, the effects and controls over biogeochemical pools and fluxes, as incorporated into predictive global change models, must be carefully examined. This is not only necessary to guide scientific endeavor, but also to inform policymakers and to serve as a basis for advocacy of social change. By examining and understanding the dynamics of carbon, nitrogen, and other nutrient transformations scientists can take the pulse of an ecosystem and predict changes into the future. Many of these biogeochemical cycles are catalyzed by abundant and diverse microorganisms, the ‘‘gatekeepers’’ that populate every ecosphere. However, most global change models treat ‘‘microbes’’ as a single pool, responsible for a single rate of flux (ToddBrown et al. 2011; Treseder et al. 2011). Advances in microbial molecular techniques, and increasing integration between microbiological and ecological disciplines have provided overwhelming evidence that microbial communities are far more diverse than could ever have been imagined (e.g. Roesch et al. 2007; Fierer and Jackson 2006; Schloss and Handelsman 2006; Gans et al. 2005). With these insights comes a whole new body of evidence that microorganisms are not simple bags of enzymes, the abundance of which directly relate to the rate of a chemical reaction in the environment. On the contrary, we find that microorganisms are dynamic catalysts with a rich evolutionary history spread across all three domains of life. This life history shapes what metabolic capabilities microorganisms have, and how they respond to a diverse array of ecological constraints, including nutrient and dispersal limitation, competition, predation, cooperation and disturbance. Microorganisms themselves are affected by the global changes that occur, and shifts in microbial communities are inevitably linked, through the biogeochemical cycles they mediate, to the entire ecosystem. How K. M. Docherty (&) Department of Biological Sciences, Western Michigan University, 1903 West Michigan Ave, Kalamazoo, MI 49008, USA e-mail: kdochert8@gmail.com

The use of high-performance and high-throughput computing for the fertilization of digital earth and global change studies

The study of global climate change seeks to understand: (1) the components of the Earth's varying environmental system, with a particular focus on climate; (2) how these components interact to determine present conditions; (3) the factors driving these components; (4) the history of global change and the projection of future change; and (5) how knowledge about global environmental variability and change can be applied to present-day and future decision-making. This paper addresses the use of high-performance computing and high-throughput computing for a global change study on the Digital Earth (DE) platform. Two aspects of the use of high-performance computing (HPC)/high-throughput computing (HTC) on the DE platform are the processing of data from all sources, especially Earth observation data, and the simulation of global change models. The HPC/HTC is an essential and efficient tool for the processing of vast amounts of global data, especially Earth observation data. The current trend involves running complex global climate models using potentially millions of personal computers to achieve better climate change predictions than would ever be possible using the supercomputers currently available to scientists.