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Abstract Background Assessing the relationships between spatial and temporal structures and functions of plant communities is an effective way to understand the changing dynamics of plant communities in specific environments. In this study, we investigated the response of structural and functional stabilities of plant communities to stocking rate in the desert steppe over a 16-year grazing period as the research background. Methods We used classical statistical methods to investigate the quantitative characteristics of plant communities over time (2014–2019) and space (2017–2019) at four stocking rates (control, CK, 0 sheep·ha–1·month–1; light grazing, LG, 0.15 sheep·ha–1·month–1; moderate grazing, MG, 0.30 sheep·ha–1·month–1; heavy grazing, HG, 0.45 sheep·ha–1·month–1) in the Stipa breviflora desert steppe of Inner Mongolia. We then examined the relationship between structural and functional stability of plant communities. Results On the spatial scale, the structural stability of plant community was the highest in the LG treatment and the lowest in the MG treatment. The functional stability of plant community was the highest in the MG treatment and the lowest in the HG treatment. On the temporal scale, the structural stability of plant community was the highest in the MG treatment and the lowest in the LG treatment. The functional stability of plant community was the highest in the LG treatment and the lowest in the HG treatment. Affected by the stocking rate, the structural stability of plant community fluctuated more widely on the spatial scale and its functional stability varied more widely on the temporal scale. Nonetheless, the functional stability of the plant community is more responsive to the stocking rate. Conclusions Our findings suggest that influenced by the disturbance of stocking rate, the structural stability of plant community is more significant than the functional stability in the desert grassland ecosystem, which lays a solid foundation for the study of ecosystem stability.

Abstract Background Grazing exclusion is an efficient practice to restore degraded grassland ecosystems by eliminating external disturbances and improving ecosystems’ self-healing capacities, which affects the ecological processes of soil-plant systems. Grassland degradation levels play a critical role in regulating these ecological processes. However, the effects of vegetation and soil states at different degradation stages on grassland ecosystem restoration are not fully understood. To better understand this, desert steppe at three levels of degradation (light, moderate, and heavy degradation) was fenced for 6 years in Inner Mongolia, China. Community characteristics were investigated, and nutrient concentrations of the soil (0–10 cm depth) and dominant plants were measured. Results We found that grazing exclusion increased shoots’ carbon (C) concentrations, C/N, and C/P, but significantly decreased shoots’ nitrogen (N) and phosphorus (P) concentrations for Stipa breviflora and Cleistogenes songorica. Interestingly, there were no significant differences in nutrient concentrations of these two species among the three degraded desert steppes after grazing exclusion. After grazing exclusion, annual accumulation rates of aboveground C, N, and P pools in the heavily degraded area were the highest, but the aboveground nutrient pools were the lowest among the three degraded grasslands. Similarly, the annual recovery rates of community height, cover, and aboveground biomass in the heavily degraded desert steppe were the highest among the three degraded steppes after grazing exclusion. These results indicate that grazing exclusion is more effective for vegetation restoration in the heavily degraded desert steppe. The soil total carbon, total nitrogen, total phosphorus, available nitrogen, and available phosphorus concentrations in the moderately and heavily degraded desert steppes were significantly decreased after six years of grazing exclusion, whereas these were no changes in the lightly degraded desert steppe. Structural equation model analysis showed that the grassland degradation level mainly altered the community aboveground biomass and aboveground nutrient pool, driving the decrease in soil nutrient concentrations and accelerating nutrient transfer from soil to plant community, especially in the heavily degraded grassland. Conclusions Our study emphasizes the importance of grassland degradation level in ecosystem restoration and provides theoretical guidance for scientific formulation of containment policies.

The desert steppe is unique as a transition zone from the steppe to the desert of the Eurasian Steppe. The spatial distribution of shrubs and perennial plants has changed due to long-term disturbance caused by overgrazing in the ecosystem. This study aimed to determine the degree of patchiness of shrubs and perennial plants under grazing disturbance, which is of great significance to better understand the succession mechanism of plant community in the desert steppe. This study investigated the spatial distribution of shrubs and perennial plants under four stocking rates (no grazing, CK, 0 sheep·hm−2·half year−1; light grazing, LG, 0.93 sheep·hm−2·half year−1; moderate grazing, MG, 1.82 sheep·hm−2·half year−1; heavy grazing, HG, 2.71 sheep·hm−2·half year−1) in the desert steppe of Inner Mongolia, primarily using geostatistical methods. The density of perennial grasses increased with increasing stocking rate, while its height decreased (except for HG). The density and height of the shrubs and the perennial forbs showed an exponential or gradual decline. The spatial distribution of shrubs and perennial plants is mainly affected by structural factors such as topography, soil parent material, and climate, and the density of shrubs and perennial plants is most sensitive to changes in stocking rate. An appropriate stocking rate increases the spatial heterogeneity of shrubs and perennial plants, but heavy grazing reduces the spatial heterogeneity of shrubs and perennial plants.

Community stability plays a vital role in ensuring the consistent provision of ecosystem services despite climatic changes. It is presumed that future changes to annual precipitation will impact the ecological stability of many systems, particularly the ‘fragile’ desert steppe. However, most studies of ecological stability are inferred from short-term field precipitation manipulation experiments. There is still extensively to learn regarding how the desert steppe reacts to long-term changes in precipitation. We conducted a 7-year experimental study monitoring the aboveground biomass of major plant functional groups (perennial grasses, perennial forbs, annual herbs, and semi-shrubs) under four experimentally manipulated precipitation gradients [reducing natural precipitation by 50% (-50%), natural precipitation (CK), increasing natural precipitation by 50% (+50%), and increasing natural precipitation by 100% (+100%)] to evaluate how changes in precipitation affect the biomass stability of plant communities. We did observe higher levels of species asynchrony, resistance, resilience, and temporal stability of community and functional groups in experimental plots receiving more precipitation. Interestingly, the contribution of species richness to community stability was not observed in our experiment. The increase in soil water content in the 20–30 cm soil layer caused by increased precipitation promoted the resilience of perennial grasses but inhibited the resistance of perennial forbs. At the same time, the decrease in soil inorganic nitrogen caused by increased rainfall inhibited the resilience of perennial grasses. Notably, community resilience predominantly hinged on the resilience of perennial grasses, while community resistance was primarily dictated by the resistance of perennial forbs. Moreover, species asynchrony emerged as the primary regulator of community temporal stability. Our long-term experimental evidence underscores the transformative potential of precipitation in reshaping grassland stability across both functional and structural dimensions. Importantly, sustaining the high stability of perennial herbs emerges as a strategic avenue for enhancing the ecological stability of the desert steppe in the face of evolving precipitation patterns.

Abstract Optoelectronic properties of semiconductors are significantly modified by impurities at trace level. Oxygen, a prevalent impurity in organic semiconductors (OSCs), has long been considered charge-carrier traps, leading to mobility degradation and stability problems. However, this understanding relies on the conventional deoxygenation methods, by which oxygen residues in OSCs are inevitable. It implies that the current understanding is questionable. Here, we develop a non-destructive deoxygenation method (i.e., de-doping) for OSCs by a soft plasma treatment, and thus reveal that trace oxygen significantly pre-empties the donor-like traps in OSCs, which is the origin of p-type characteristics exhibited by the majority of these materials. This insight is completely opposite to the previously reported carrier trapping and can clarify some previously unexplained organic electronics phenomena. Furthermore, the de-doping results in the disappearance of p-type behaviors and significant increase of n-type properties, while re-doping (under light irradiation in O2) can controllably reverse the process. Benefiting from this, the key electronic characteristics (e.g., polarity, conductivity, threshold voltage, and mobility) can be precisely modulated in a nondestructive way, expanding the explorable property space for all known OSC materials.

Forests are the largest carbon storage and carbon sinks on land and are closely related to global climate change. Using remote sensing images to monitor global deforestation has become the mainstream method, mainly because of their long time-series observation period and low cost. Although researchers have proposed several excellent deep-learning models to improve deforestation monitoring accuracy, few studies pay attention to designing an online deforestation detection system with deep-learning methods for nonexpert users or researchers, due to the complicated development process or excessive cost investment. In this study, we aimed to develop an open-sourced and user-friendly online system for monitoring large-scale deforestation on high-resolution remote sensing imageries, named OpenForestMonitor. The OpenForestMonitor system was the first online deforestation detection system developed for high-resolution remote sensing imageries with deep learning methods. To validate the performance of the OpenForestMonitor system, we selected four large-scale study areas in China as the study areas, and they are Liuyang City, Cangwu County, Sanya City, and Xianyang City. The final experiment results indicated that the F1-score of these study areas is 0.719, 0.854, 0.792, and 0.650, respectively. Furthermore, we also tested the performance of the OpenForestMonitor system on median-resolution Landsat remote sensing imageries and low-quality remote sensing images in complicated regions, and the final F1-score in Liuyang City and Cangwu County is 0.784 and 0.741, respectively. Compared with commercial software such as ArcGIS Pro 3.0, the OpenForestMonitor also indicated an obvious efficiency advantage. The OpenForestMonitor system was open-sourced, and it can also inspire researchers to develop other landcover, change detection, and object extraction online systems conveniently.

Forest is the largest carbon reservoir and carbon absorber on earth. Thus, mapping forest cover change accurately is of great significance to achieving the global carbon neutrality goal. Accurate forest change information could be acquired by deep learning methods using high-resolution remote sensing images. However, deforestation detection based on deep learning on a large-scale region with high-resolution images required huge computational resources. Therefore, there was an urgent need for a fast and accurate deforestation detection model. In this study, we proposed an interesting but effective re-parameterization deforestation detection model, named RepDDNet. Unlike other existing models designed for deforestation detection, the main feature of RepDDNet was its decoupling feature, which means that it allowed the multi-branch structure in the training stages to be converted into a plain structure in the inference stage, thus the computation efficiency can be significantly improved in the inference stage while maintaining the accuracy unchanged. A large-scale experiment was carried out in Ankang city with 2-meter high-resolution remote sensing images (the total area of it was over 20,000 square kilometers), and the result indicated that the model computation efficiency could be improved by nearly 30% compared with the model without re-parameterization. Additionally, compared with other lightweight models, RepDDNet also displayed a trade-off between accuracy and computation efficiency.

Change detection on high-resolution remote sensing imagery using end-to-end deep learning methods has attracted considerable attention in recent years. Nevertheless, the performance of end-to-end models on complicated scenarios still is limited. Interactive deep-learning models have proven to be a valuable technique for enhancing model performance with minimal human interaction. For instance, the clicks-based interactive models have attracted much attention recently, however, their performance on large regions or complex areas still can be further improved, because they cannot provide accurate semantics or shape prior information of the change regions for the interactive models, as we know that the shape and semantic features of changed regions in remote sensing imagery are typically irregular and complex. Scribble-based interactive form, which can accurately represent the shape or semantic features of the changed regions, thus it is quite suitable for change detection tasks in remote sensing imagery. Therefore, we proposed a novel interactive deep learning model called ScribbleCDNet in this manuscript, which pioneered the use of scribble as an interactive form for detecting change in bi-temporal high-resolution remote sensing imageries. Compared with the widely used clicks-based interactive deep learning models, the proposed ScribbleCDNet acquired superior results on four open-sourced change detection datasets. Last but not least, we also developed an interactive change detection tool with a user-friendly graphical interface, and it can aid researchers in conducting change detection or generating training samples conveniently. Moreover, the proposed ScribbleCDNet can also inspire researchers to develop other interactive deep-learning models related to semantic segmentation, landcover classification, or object extraction in high-resolution remote sensing imageries.

Abstract Organic field‐effect transistors (OFETs) show great application potential in organic electronic and optoelectronic fields due to their excellent mechanical flexibility, low cost, and solution processing. However, grain boundaries (GBs) disrupt the aggregation state of organic semiconductor (OSC) films and hinder electrical performance and stability, which limits the application of OFETs. Besides, the sensitive nature of GBs is widely used in sensing, but detailed descriptions of the GBs are scarce. This review aims to fill this knowledge gap. The role of GBs and their effect on the performance and stability of OFETs are analyzed, followed by a detailed summary of the characterization of GBs. Then, strategies for suppressing the negative effects of GBs and utilizing the sensitive nature of GBs for application are proposed. Finally, potential research directions for GBs in OFETs are discussed.

The quality of optical remote sensing images is largely affected by clouds and haze. In addition, the mosaicking image of multiple remote sensing images, due to objective factors such as acquiring time or climate conditions, will lead to large spectral differences in the area around the seamline. The aforementioned scenarios will seriously affect the accuracy of change detection models based on deep learning. However, there is still a lack of methods to address such issues. To solve these problems, from the perspective of training samples, this article proposed a simple but effective data augmentation method to improve the generalization ability of the deep change detection model in the region of haze cover and the seamline. First, from the characteristics of the optical remote sensing image itself, two image simulation methods are designed to conduct data augmentation, named mosaic simulation and haze image simulation. Then, the newly augmented training samples are mixed with the original training samples and then input into a deep learning model for model training. Finally, the change detection results indicate that the proposed data augmentation method can effectively improve the generalization ability of the change detection model in the region of haze cover and seamline, which has high practical value for improving the deep learning model's performance in real-world scenarios and also provides a simple but effective algorithm reference for other intelligent interpretation tasks from the perspective of training data.

Abstract Cleistogenes songorica, as a clustered grass, is the main grassland flora of the Stipa breviflora desert grassland. Some studies have shown that the constructive species of S. breviflora (sparse cluster type) is prone to cluster fragmentation; however, research on C. songorica is relatively rare. Then will the C. songorica plant population (dense cluster type) also have cluster fragmentation under the influence of intense grazing? To answer this question, we used variance analysis and geo‐statistical methods. The spatial distribution of C. songorica in S. breviflora desert steppe in Inner Mongolia was measured under four grazing intensities (no grazing, CK, 0 sheep·ha−1·half year−1; light grazing, LG, 0.93 sheep·ha−1·half year−1; moderate grazing, MG, 1.82 sheep·ha−1·half year−1; and heavy grazing, HG, 2.71 sheep·ha−1·half year−1) and four scales (10 cm × 10 cm, 20 cm × 20 cm, 25 cm × 25 cm, 50 cm × 50 cm). We then analyzed C. songorica whether fragmentation was present. The results showed that increased grazing intensity is associated with increased density and decreased height, coverage, and standing crop of C. songorica. The spatial distribution of C. songorica was affected by structural factors, and spatial heterogeneity decreased with increased spatial scale. With increased grazing intensity and spatial scale, the patch area of C. songorica gradually increased and tended toward band distribution. In summary, increased grazing intensity and spatial scale led to weakened heterogeneity of C. songorica spatial distribution and increased consistency.

BackgroundChanges in rainfall induced by climate change will likely influence the utilization of water resources and affect the nutrient cycle in plants in the water-limited desert steppe. In order to understand the response of nitrogen and phosphorus resorption characteristics of plant leaves to precipitation changes, this study compared the nitrogen (N) resorption efficiency, phosphorus (P) resorption efficiency and influencing factors of plants in a desert steppe through water treatment experiments.MethodsA 4-year field experiment was performed to examine the response and influencing factors of nitrogen (N) and phosphorus resorption efficiency of five dominant plants in Stipa breviflora desert steppe to simulated precipitation change in Inner Mongolia, with four simulated precipitation gradients including reducing water by 50%, natural precipitation, increasing water by 50%, increasing water by 100%.ResultsCompared with natural precipitation, increasing water by 100% significantly increased soil moisture, and significantly increased the aboveground biomass of S. breviflora, C. songorica, A. frigida, decreased the N concentrations in green leaves of S. breviflora, Cleistogenes songorica, Artemisia frigida, Kochia prostrata, decreased the N concentrations in senesced leaves of C. songorica, decreased the P concentrations in green leaves of K. prostrata and Convolvulus ammannii, decreased the NRE of S. breviflora. NRE was significantly negatively correlated with N concentration in senesced leaves, and PRE was significantly negatively correlated with P concentration in senesced leaves.ConclusionsIncreasing water indirectly reduces NRE by reducing plant leaf green leaves nitrogen concentration, and decreasing water indirectly reduces PRE by reducing soil moisture.

Abstract Background Grazing disturbance plays an important role in the desert steppe ecosystem in Inner Mongolia, China. Previous studies found that grazing affected the spatial distribution of species in a community, and showed patchiness characteristics of species under different grazing treatments. Artemisia frigida is the dominant species and semi-shrub in desert steppe, and whether grazing interference will affect the spatial distribution of A. frigida is studied. In this study, geo-statistical methods were mainly used to study the spatial distribution characteristics of A. frigida population in desert steppe of Inner Mongolia at two scales (quadrat size 2.5 m × 2.5 m, 5 m × 5 m) and four stocking rates (control, CK, 0 sheep·ha–1·month–1; light grazing, LG, 0.15 sheep·ha–1·month–1, moderate grazing, MG, 0.30 sheep·ha–1·month–1, heavy grazing, HG, 0.45 sheep·ha–1·month–1). Results The results showed that the spatial distribution of A. frigida tended to be simplified with the increase of stocking rate, and tended to be banded with increased spatial scale. The density and height of A. frigida increased with increasing scale. With increased stocking rate, the density of A. frigida population decreased linearly, while its height decreased in a step-wise fashion. The spatial distribution of A. frigida was mainly affected by structural factors at different scales and stocking rate. The density of A. frigida was more sensitive to change in stocking rate, and the patchiness distribution of A. frigida was more obvious with increase in scale. Conclusions Stocking rate has a strong regulatory effect on the spatial pattern of A. frigida population in the desert steppe. Heavy grazing reduced the spatial heterogeneity of A. frigida in the desert steppe. The smaller dominant populations are unfavourable for its survival in heavy grazing condition, and affects the stability and productivity of the grassland ecosystem.

Aggregate state instability is a major cause of device degradation in organic field-effect transistors. Here, authors elucidate the origin of such instability and provide a strategy to overcome it, such that devices with ultra-long lifetimes can be obtained.

Abstract Phase‐change semiconductor is one of the best candidates for designing nonvolatile memory, but it has never been realized in organic semiconductors until now. Here, a phase‐changeable and high‐mobility organic semiconductor (3,6‐DATT) is first synthesized. Benefiting from the introduction of electrostatic hydrogen bond (S···H), the molecular conformation of 3,6‐DATT crystals can be reversibly modulated by the electric field and ultraviolet irradiation. Through experimental and theoretical verification, the tiny difference in molecular conformation leads to crystalline polymorphisms and dramatically distinct charge transport properties, based on which a high‐performance organic phase‐change memory transistor (OPCMT) is constructed. The OPCMT exhibits a quick programming/erasing rate (about 3 s), long retention time (more than 2 h), and large memory window (i.e., large threshold voltage shift over 30 V). This work presents a new molecule design concept for organic semiconductors with reversible molecular conformation transition and opens a novel avenue for memory devices and other functional applications.

Abstract Organic field‐effect transistors (OFETs) are of great significance in next‐generation optoelectronics and have made remarkable advances during the last decade. For the commercialization of OFETs, power consumption is one of the main issues needed to be addressed. To this end, OFETs with low‐voltage operation are constructed, and several strategies have been successively developed, which have attracted intensive attention for researchers to achieve this goal. In this review, we summarized the recent progress of various approaches for realizing low‐voltage OFETs, including adopting high‐k dielectrics, ultra‐thin dielectric, electrolyte dielectric, and interface engineering. Besides, the potential applications of low‐voltage OFETs in organic electronics are introduced. Finally, we give a brief outlook of this area for the future direction.

Dominant species play a principal role in controlling and maintaining ecosystem stability. Stipa breviflora is the dominant species in desert steppe. Changes in the stability of a plant population will further affect the stability of the broader habitat, such as the desert steppe. In the desert steppe ecosystem, it is not clear what level of grazing intensity is best for improving the grazing tolerance and stability of the vegetation. And, the study of this question should involve a multi-dimensional, comprehensive analysis. This study will utilize variance analysis, plant population stability, and trade-off index to study S. breviflora, the dominant species in the desert steppe in Inner Mongolia, and its performance under four grazing intensities (control, CK, 0 sheep·ha−1·half year−1; light grazing, LG, 0.93 sheep·ha−1·half year−1; moderate grazing, MG, 1.82 sheep·ha−1·half year−1; and heavy grazing, HG, 2.71 sheep·ha−1·half year−1) over six scales (5 cm × 5 cm; 10 cm × 10 cm; 20 cm × 20 cm; 25 cm × 25 cm; 50 cm × 50 cm; and 100 cm × 100 cm). The characteristics of the population stability of S. breviflora were explored. The results showed that the response of S. breviflora’s stability to heavy grazing was multidimensional. Heavy grazing reduced the population stability of S. breviflora. Across different dimensions, base coverage was the first of the population stability metrics of S. breviflora to destabilize, followed by projection coverage, density, and height. Heavy grazing also affected the trade-offs of S. breviflora’s population stability across different dimensions. In general, the trade-off degree decreased as the grazing intensity increased, and it increased as the scale increased.

We report a phase diagram mapping for solid hydrogen based on several thousands of Raman spectroscopic data points obtained at simultaneous high-pressure and -temperature (PT) experiments. Information about phase behavior in the range of 300 K up to the melting line and 0–300 GPa for hydrogen has remained almost unknown to date; therefore, revealing it is very desirable. A network analysis of the isothermal and isobaric dependencies for various Raman modes has been used; discontinuities in these dependencies, plus the mode appearance/disappearance provide evidence for phase transitions. These transition data show self-consistency and have been used to outline possible phase boundaries for hydrogen in this PT region. Evidence we found shows hydrogen may experience a gradual molecular dissociation process before its metallization.

To examine the effect of litter on ANPP and species composition (using ground cover) in a Desert Steppe community by removing or adding litter, during plant dormancy, in a single event in either fall or spring. Litter was removed or added in three intensity treatments (heavy, moderate, control—undisturbed) as the main plot and season (fall or spring) as the secondary treatment in a split-plot design with five replications. The experiments were repeated in each of 5 years while three of those were resampled twice. The year effect was analyzed by classifying them into high or low precipitation categories and including those in the statistical model. We found few treatment effects one year after treatment and no persistent effect. Therefore, we focus our examination on the first year only. The total ANPP of individual plant types, or their proportions, were not affected by litter treatment or its interaction with season of treatment or precipitation category. Only the ground cover of selected species was influenced by the treatment. The ground cover of Stipa breviflora was greater with heavy litter removal in fall but unaffected by litter removal in spring while Neopallasia pectinata had a greater cover with moderate or heavy removal in years when precipitation was low. Litter addition resulted in a greater ground cover of Neopallasia pectinata and reduced the cover of Convolvulus ammannii in years of low precipitation. The marginal effectiveness of litter treatments on the plan community in the Desert Steppe suggests that it need not be a factor for consideration in grazing management.

Controlling temperature-depending charge transport in organic semiconductors is key to tailoring their electronic properties. Here, the authors report a potential barrier engineering strategy for modulating thermally-activated charge transport in organic semiconductors.

Forests play a critical role in global carbon cycling, and continuous forest destruction together with other factors has accelerated global warming. Furthermore, continued decline of forest areas will critically hinder the accomplishment of carbon neutrality goals. Although the geographical location of deforestation can now be rapidly and accurately detected with remote sensing technology, current forest change products are still not fine-grained, especially from the perspective of carbon trading. Here, we used a deep learning method to detect deforestation in large regions based on 2 m high-resolution optical remote sensing images. Firstly, we proposed a new deforestation detection dataset, which was generated from 11 provincial regions in the Yangtze River Economic Zone of China, containing a total number of 8330 samples (the size of each sample being 512 × 512 pixels). Then, a new deforestation detection model, SiamHRnet-OCR, was designed, based on this dataset. Compared with other deep learning models, SiamHRnet-OCR achieves better results in terms of precision, F1-score, and OA indicator: 0.6482, 0.6892, and 0.9898, respectively. Finally, two large-scale scenarios of deforestation experiments in Southern China were further tested; the deforestation detection results demonstrate that SiamHRnet-OCR can not only detect deforestation effectively but also capture the accurate boundary of the changing area.

Abstract Understanding how grazing activity drives plant community structure or the distribution of specific species in a community remains a major challenge in community ecology. The patchiness or spatial aggregation of specific species can be quantified by analyzing their relative coordinates in the community. Using variance and geostatistical analysis methods, we examined the quantitative characteristics and spatial distribution of Stipa breviflora in a desert steppe in northern China under four different grazing intensities (no grazing, NG, light grazing, LG, moderate grazing, MG, and heavy grazing, HG) at three small spatial scales (10 × 10 cm, 20 × 20 cm, 25 × 25 cm). We found that grazing significantly increased cover, density, and proportion in standing crop of S. breviflora, but decreased height. The spatial distribution of S. breviflora was strongly dependent upon the sampling unit and grazing intensity. The patchiness of S. breviflora reduced with sampling scale, and spatial distribution of S. breviflora was mainly determined by structural factors. The intact clusters of S. breviflora were more fragmented with increasing grazing intensity and offspring clusters spread out from the center of the parent plant. These findings suggest that spatial aggregation can enhance the ability of S. breviflora to tolerate grazing and that smaller isolated clusters are beneficial to the survival of this dominant species under heavy grazing.

Macroscopically large supercrystals are very difficult to assemble from metallic nanoparticles. Here, the authors use a binary solvent diffusion method to form sub-millimeter gold nanoparticle supercrystals with rare hcp symmetry, and discover that they exhibit facet-dependent optical properties.

Abstract Background The reasons that clonal plants shift between sexual and clonal reproduction have persisted as a knowledge gap in ecological literature. We hypothesized that clonal plants’ shifts between sexual and clonal reproduction in different environments are driven by the relative costs of sexual and clonal reproduction. Moreover, we hypothesized plants prioritize sexual reproduction over clonal reproduction. To test these hypotheses, we determined the costs of sexual and clonal reproduction, and proportions of sexual and clonal reproduction of Caragana stenophylla along a climatic aridity gradient (semi-arid, arid, very arid and intensively arid zones) in the Inner Mongolia Steppe using several complementary field experiments. Results The cost of sexual reproduction increased while the cost of clonal reproduction decreased as climatic drought stress increased from the semi-arid to the intensively arid zones. The changes in the costs of these reproductive modes drove a shift in the reproductive mode of C. stenophylla from more sexual reproduction in the semi-arid zone to more clonal propagation in the intensively arid zone. However, because of the evolutionary advantages of sexual reproduction, sexual reproduction still held priority over clonal production in C. stenophylla, with the priority of sexual reproduction gradually increasing from the semi-arid to the intensively arid zones. Conclusions Our study suggested that sexual reproduction has relatively high priority in propagation of C. stenophylla. However, if the costs of sexual reproduction are too high, C. stenophylla likely chooses clonal reproduction, and the ratio between sexual and clonal reproduction could be mediated by reproductive cost. These reproductive strategies reflect optimal resource utilization, and allow the persistence of both reproductive modes across stressful conditions depending on their evolutionary advantages.

Gold nanostructures have shape-dependent properties, making synthetic control over their morphology critical. Here, the authors use dynamic compression to obtain a variety of gold nanoarchitectures, which are formed at very fast timescales by the controlled coalescence of spherical particle arrays.

Nature Communications 8: Article number: 14778 (2017); Published: 16 March 2017; Updated: 24 May 2017 In Fig. 5 of this Article, the image in the centre column for [111] incorrectly replicates the image above. The correct version of Fig. 5 appears below as Fig. 1.