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Aeolian sediment transport occurs as a function of, and with feedback to ecosystem changes and disturbances. Many desert grasslands are undergoing rapid changes in vegetation, including the encroachment of woody plants, which alters fire regimes and in turn can change the spatial and temporal patterns of aeolian sediment transport. We investigated aeolian sediment transport and spatial distribution of sediment in the surface soil for 7 years following a prescribed fire using a multiple rare earth element (REE) tracer‐based approach in a shrub‐encroached desert grassland in the northern Chihuahuan desert. Results indicate that even though the aeolian horizontal sediment mass flux increased approximately three‐fold in the first windy season in the burned areas compared to control areas, there were no significant differences after three windy seasons. The soil surface of bare microsites was the major contributor of aeolian sediments in unburned areas (87%), while the shrub microsites contributed the least (<2%) during the observation period. However, after the prescribed fire, the contribution of aeolian sediments from shrub microsites increased considerably (∼40%), indicating post‐fire microsite‐scale sediment redistribution. The findings of this study, which is the first to use multiple REE tracers for multi‐year analysis of the spatial and temporal dynamics of aeolian sediment transport, illustrate how disturbance by prescribed fire can influence aeolian processes and alters dryland soil geomorphology in which distinct soils develop over time at very fine spatial scales of individual plants.

 

This paper addresses the problem of training a robot to carry out temporal tasks of arbitrary complexity via evaluative human feedback that can be inaccurate. A key idea explored in our work is a kind of curriculum learning—training the robot to master simple tasks and then building up to more complex tasks. We show how a training procedure, using knowledge of the formal task representation, can decompose and train any task efficiently in the size of its representation. We further provide a set of experiments that support the claim that non-expert human trainers can decompose tasks in a way that is consistent with our theoretical results, with more than half of participants successfully training all of our experimental missions. We compared our algorithm with existing approaches and our experimental results suggest that our method outperforms alternatives, especially when feedback contains mistakes. 

Many grass‐dominated ecosystems in dryland regions have experienced increasing woody plant density and abundance during the past century. In many cases, this process has led to land degradation and declines in ecosystem functions. An example is the Chihuahuan Desert in the southwestern United States, which experienced different stages of shrub encroachment in the past 150 years. Among a wide variety of mechanisms to explain the grass–shrub transitions in this dryland system, soil erosion (both wind and water) and fire are particularly well studied. Here, we synthesize recent developments on the drivers and feedback in the process of shrub encroachment in the Chihuahuan Desert through the intercomparison of two Long Term Ecological Research (LTER) sites, namely Jornada and Sevilleta. Experimental and modeling studies support a conceptual framework, which underscores the important roles of erosion and fire in woody plant encroachment. Collectively, research at the Jornada LTER provided complementary, quantitative support to the well‐known fertile‐islands framework. Studies at the Sevilleta LTER expanded the framework, adding fire as a major disturbance to woody plants. Conceptual models derived from the synthesis represent the general understanding of shrub encroachment that emerged from research at these two sites, and can guide management interventions aimed at reducing or mitigating undesirable ecosystem state change in many other drylands worldwide.

 

Trigger-action programming (TAP) is a programming model enabling users to connect services and devices by writing if-then rules. As such systems are deployed in increasingly complex scenarios, users must be able to identify programming bugs and reason about how to fix them. We first systematize the temporal paradigms through which TAP systems could express rules. We then identify ten classes of TAP programming bugs related to control flow, timing, and inaccurate user expectations. We report on a 153-participant online study where participants were assigned to a temporal paradigm and shown a series of pre-written TAP rules. Half of the rules exhibited bugs from our ten bug classes. For most of the bug classes, we found that the presence of a bug made it harder for participants to correctly predict the behavior of the rule. Our findings suggest directions for better supporting end-user programmers.