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After the preparation of 2D electronic flat band (EFB) in van der Waals (vdW) superlattices, recent measurements suggest the existence of 1D electronic flat bands (1D‐EFBs) in twisted vdW bilayers. However, the realization of 1D‐EFBs is experimentally elusive in untwisted 2D layers, which is desired considering their fabrication and scalability. Herein, the discovery of 1D‐EFBs is reported in an untwisted in situ‐grown two atomic‐layer Bi(110) superlattice self‐aligned on an SnSe(001) substrate using scanning probe microscopy measurements and density functional theory calculations. While the Bi–Bi dimers of Bi zigzag (ZZ) chains are buckled, the epitaxial lattice mismatch between the Bi and SnSe layers induces two 1D buckling reversal regions (BRRs) extending along theZZdirection in each Bi(110)‐11 × 11 supercell. A series of 1D‐EFBs arises spatially following BRRs that isolate electronic states along the armchair (AC) direction and localize electrons in 1D extended states alongZZdue to quantum interference at a topological node. This work provides a generalized strategy for engineering 1D‐EFBs in utilizing lattice mismatch between untwisted rectangular vdW layers.

Tundra dominates two‐thirds of the unglaciated, terrestrial Arctic. Although this region has experienced rapid and widespread changes in vegetation phenology and productivity over the last several decades, the specific climatic drivers responsible for this change remain poorly understood. Here we quantified the effect of winter snowpack and early spring temperature conditions on growing season vegetation phenology (timing of the start, peak, and end of the growing season) and productivity of the dominant tundra vegetation communities of Arctic Alaska. We used daily remotely sensed normalized difference vegetation index (NDVI), and daily snowpack and temperature variables produced by SnowModel and MicroMet, coupled physically based snow and meteorological modeling tools, to (1) determine the most important snowpack and thermal controls on tundra vegetation phenology and productivity and (2) describe the direction of these relationships within each vegetation community. Our results show that soil temperature under the snowpack, snowmelt timing, and air temperature following snowmelt are the most important drivers of growing season timing and productivity among Arctic vegetation communities. Air temperature after snowmelt was the most important control on timing of season start and end, with warmer conditions contributing to earlier phenology in all vegetation communities. In contrast, the controls on the timing of peak season and productivity also included snowmelt timing and soil temperature under the snowpack, dictated in part by the snow insulating capacity. The results of this novel analysis suggest that while future warming effects on phenology may be consistent across communities of the tundra biome, warming may result in divergent, community‐specific productivity responses if coupled with reduced snow insulating capacity lowers winter soil temperature and potential nutrient cycling in the soil.

Understanding whether soil microbial respiration adapts to the ambient thermal climate with an enhanced or compensatory response, hence potentially stimulating or slowing down soil carbon losses with warming, is key to accurately forecast and model climate change impacts on the global carbon cycle. Despite the interest in this topic and the plethora of recent studies in natural ecosystems, it has been seldom explored in croplands. Using two recently published independent datasets of soil microbial metabolic quotient (MMQ; microbial respiration rate per unit biomass) and carbon use efficiency (CUE; partitioning of C to microbial growth vs. respiration), we find a compensatory thermal adaptive response for MMQ in global croplands. That is, mean annual temperature (MAT) has a negative effect on MMQ. However, this compensatory thermal adaptation is only half or less of that found in previous studies for noncultivated ecosystems. In contrast to the negative MMQ‐MAT pattern, microbial CUE increases with MAT across global croplands. By incorporating this positive CUE‐MAT relationship (greater C partitioning into microbial growth rather than respiration with increasing temperature) into a microbial‐explicit soil organic carbon model, we successfully predict the thermal compensation of MMQ observed in croplands. Our model‐data integration and database cross‐validation suggest that a warmer climate may select for microbial communities with higher CUE, providing a plausible mechanism for their compensatory metabolic response. By helping to identify appropriate representations of microbial physiological processes in soil biogeochemical models, our work will help build confidence in model projections of cropland C dynamics under a changing climate.