Drylands cover 40% of the global terrestrial surface and provide important ecosystem services. While drylands as a whole are expected to increase in extent and aridity in coming decades, temperature and precipitation forecasts vary by latitude and geographic region suggesting different trajectories for tropical, subtropical, and temperate drylands. Uncertainty in the future of tropical and subtropical drylands is well constrained, whereas soil moisture and ecological droughts, which drive vegetation productivity and composition, remain poorly understood in temperate drylands. Here we show that, over the twenty first century, temperate drylands may contract by a third, primarily converting to subtropical drylands, and that deep soil layers could be increasingly dry during the growing season. These changes imply major shifts in vegetation and ecosystem service delivery. Our results illustrate the importance of appropriate drought measures and, as a global study that focuses on temperate drylands, highlight a distinct fate for these highly populated areas.

Desertification is an escalating concern in global drylands, yet assessments to guide management and policy responses are limited by ambiguity concerning the definition of “desertification” and what processes are involved. To improve clarity, we propose that assessments of desertification and land transformation be placed within a state change–land‐use change (SC–LUC) framework. This framework considers desertification as state changes occurring within the context of particular land uses (eg rangeland, cropland) that interact with land‐use change. State changes that can be readily reversed are distinguished from regime shifts, which are state changes involving persistent alterations to vegetation or soil properties. Pressures driving the transformation of rangelands to other types of land uses may be low, fluctuating, or high, and may influence and be influenced by state change. We discuss how the SC–LUC perspective can guide more effective assessment of desertification and management of drylands.

any, machine learning model and covariate set might be optimal for predicting soil classes across 23 different landscapes. 24Our objective was to compare multiple machine learning models and covariate sets for predicting soil 25 taxonomic classes at three geographically distinct areas in the semi-arid western United States of 26 America (southern New Mexico, southwestern Utah, and northeastern Wyoming). All three areas were 27 the focus of digital soil mapping studies. Sampling sites at each study area were selected using 28 conditioned Latin hypercube sampling (cLHS). We compared models that had been used in other DSM 29 studies, including clustering algorithms, discriminant analysis, multinomial logistic regression, neural 30 networks, tree based methods, and support vector machine classifiers. Tested machine learning models 31 were divided into three groups based on model complexity: simple, moderate, and complex. We also 32

Restoration and rehabilitation of native vegetation in dryland ecosystems, which encompass over 40% of terrestrial ecosystems, is a common challenge that continues to grow as wildfire and biological invasions transform dryland plant communities. The difficulty in part stems from low and variable precipitation, combined with limited understanding about how weather conditions influence restoration outcomes, and increasing recognition that one-time seeding approaches can fail if they do not occur during appropriate plant establishment conditions. The sagebrush biome, which once covered over 620,000 km of western North America, is a prime example of a pressing dryland restoration challenge for which restoration success has been variable. We analyzed field data on Artemisia tridentata (big sagebrush) restoration collected at 771 plots in 177 wildfire sites across its western range, and used process-based ecohydrological modeling to identify factors leading to its establishment. Our results indicate big sagebrush occurrence is most strongly associated with relatively cool temperatures and wet soils in the first spring after seeding. In particular, the amount of winter snowpack, but not total precipitation, helped explain the availability of spring soil moisture and restoration success. We also find considerable interannual variability in the probability of sagebrush establishment. Adaptive management strategies that target seeding during cool, wet years or mitigate effects of variability through repeated seeding may improve the likelihood of successful restoration in dryland ecosystems. Given consistent projections of increasing temperatures, declining snowpack, and increasing weather variability throughout midlatitude drylands, weather-centric adaptive management approaches to restoration will be increasingly important for dryland restoration success.

. 2011. Cross-system comparisons elucidate disturbance complexities and generalities. Ecosphere 2(7):art81.doi:10.1890/ES11-00115.1Abstract. Given that ecological effects of disturbance have been extensively studied in many ecosystems, it is surprising that few quantitative syntheses across diverse ecosystems have been conducted. Multi-system studies tend to be qualitative because they focus on disturbance types that are difficult to measure in an ecologically relevant way. In addition, synthesis of existing studies across systems or disturbance types is challenging because sufficient information needed for analysis is not easily available. Theoretical advances and improved predictions can be advanced by generalizations obtained from synthesis activities that include multiple sites, ecosystems, and disturbance events. Building on existing research, we present a conceptual framework and an operational analog to integrate this rich body of knowledge and to promote quantitative comparisons of disturbance effects across different types of ecosystems and disturbances. This framework recognizes individual disturbance events that consist of three quantifiable components: (1) environmental drivers, (2) initial system properties, and (3) physical and biological mechanisms of effect, such as deposition, compaction, and combustion. These components result in biotic and abiotic legacies that can interact with subsequent drivers and successional processes to influence system response. Through time, a coarse-scale quasi-equilibrial state can be reached where variation in drivers interacting with biotic processes and feedbacks internal to the system results in variability in dynamics. At any time, a driver of sufficient magnitude can push the system beyond its realm of natural variability to initiate a new kind of event. We use long-term data from diverse terrestrial ecosystems to illustrate how our approach can facilitate cross-system comparisons, and provide new insights to the role of disturbance in ecological systems. We also provide key disturbance characteristics and measurements needed to promote future quantitative comparisons across ecosystems.