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Background Microbial interactions shape the structure and function of microbial communities; microbial co-occurrence networks in specific environments have been widely developed to explore these complex systems, but their interconnection pattern across microbiomes in various environments at the global scale remains unexplored. Here, we have inferred an Earth microbial co-occurrence network from a communal catalog with 23,595 samples and 12,646 exact sequence variants from 14 environments in the Earth Microbiome Project dataset. Results This non-random scale-free Earth microbial co-occurrence network consisted of 8 taxonomy distinct modules linked with different environments, which featured environment specific microbial co-occurrence relationships. Different topological features of subnetworks inferred from datasets trimmed into uniform size indicate distinct co-occurrence patterns in the microbiomes of various environments. The high number of specialist edges highlights that environmental specific co-occurrence relationships are essential features across microbiomes. The microbiomes of various environments were clustered into two groups, which were mainly bridged by the microbiomes of plant and animal surface. Acidobacteria Gp2 and Nisaea were identified as hubs in most of subnetworks. Negative edges proportions ranged from 1.9% in the soil subnetwork to 48.9% the non-saline surface subnetwork, suggesting various environments experience distinct intensities of competition or niche differentiation. 2NmSq7Q5noeLTtkrbKwa4L Video abstract Conclusion This investigation highlights the interconnection patterns across microbiomes in various environments and emphasizes the importance of understanding co-occurrence feature of microbiomes from a network perspective.

QUESTIONS: What factors control broad‐scale variation in edge length and three‐dimensional boundary structure for a large region extending across two biomes? What is the difference in structure between natural and anthropogenic edges? LOCATION: Temperate and boreal forests across all of Sweden, spanning latitudes 55–69° N. METHODS: We sampled more than 2000 forest edges using line intersect sampling in a monitoring programme (National Inventory of Landscapes in Sweden). We compared edge length, ecosystem attributes (width of adjacent ecosystem, canopy cover, canopy height, patch contrast in canopy height, forest type) and boundary attributes (profile, abruptness, shape) of natural edges (lakeshore, wetland) with anthropogenic edges (clear‐cut, agricultural, linear disturbance) in five regions. RESULTS: Anthropogenic edges were nearly twice as abundant as natural edges. Length of anthropogenic edges was largest in southern regions, while the abundance of natural edges increased towards the north. Edge types displayed unique spectrums of boundary structures, but abrupt edges dominated, constituting 72% of edge length. Anthropogenic edges were more abrupt than natural edges; wetland edges had the most gradual and sinuous boundaries. Canopy cover, canopy height, patch contrast and forest type depended on region, whereas overall boundary abruptness and shape showed no regional pattern. Patch contrast was related to temperature sum (degree days ≥ 5 °C), suggesting that regional variability can be predicted from climate‐controlled forest productivity. Boundary abruptness was coupled with the underlying environmental gradient, land use and forest type, with higher variability in deciduous than in conifer forest. CONCLUSIONS: Edge origin, land use, climate and tree species are main drivers of broad‐scale variability in forest edge structure. Our findings have important implications for developing ecological theory that can explain and predict how different factors affect forest edge structure, and help to understand how land use and climate change affect biodiversity at forest edges.

1. Although anthropogenic edges are an important consequence of timber harvesting, edges due to natural disturbances or landscape heterogeneity are also common. Forest edges have been well studied in temperate and tropical forests, but less so in less productive, disturbance-adapted boreal forests. 2. We synthesized data on forest vegetation at edges of boreal forests and compared edge influence among edge types (fire, cut, lake/wetland; old vs. young), forest types (broadleaf vs. coniferous) and geographic regions. Our objectives were to quantify vegetation responses at edges of all types and to compare the strength and extent of edge influence among different types of edges and forests. 3. Research was conducted using the same general sampling design in Alberta, Ontario and Quebec in Canada, and in Sweden and Finland. We conducted a meta-analysis for a variety of response variables including forest structure, deadwood abundance, regeneration, understorey abundance and diversity, and non-vascular plant cover. We also determined the magnitude and distance of edge influence (DEI) using randomization tests. 4. Some edge responses (lower tree basal area, tree canopy and bryophyte cover; more logs; higher regeneration) were significant overall across studies. Edge influence on ground vegetation in boreal forests was generally weak, not very extensive (DEI usually < 20 m) and decreased with time. We found more extensive edge influence at natural edges, at younger edges and in broadleaf forests. The comparison among regions revealed weaker edge influence in Fennoscandian forests. 5. Synthesis. Edges created by forest harvesting do not appear to have as strong, extensive or persistent influence on vegetation in boreal as in tropical or temperate forested ecosystems. We attribute this apparent resistance to shorter canopy heights, inherent heterogeneity in boreal forests and their adaptation to frequent natural disturbance. Nevertheless, notable differences between forest structure responses to natural (fire) and anthropogenic (cut) edges raise concerns about biodiversity implications of extensive creation of anthropogenic edges. By highlighting universal responses to edge influence in boreal forests that are significant irrespective of edge or forest type, and those which vary by edge type, we provide a context for the conservation of boreal forests.

Aim Range shifts along elevational gradients are considered a major response of mountain species to climate change. However, empirical studies have so far mainly focused on leading edges or on species’ optima, and evidence of rear edge shifts remains scarce. Yet, the balance between leading and rear edge shifts has important consequences for conservation and co‐determines species’ extinction risk. Here, we present a comparative synthesis of range dynamics observed at both range limits. Location Global. Time period 1850–present. Major taxa studied Plants, invertebrates, vertebrates. Methods From the literature, we compiled elevational leading and rear edge shifts of 1,026 species observed at the same localities over the same time period. We used linear mixed‐effects models to analyse whether both range limits shifted upslope, whether leading edges shifted faster than rear edges and elevational range sizes have thus changed, whether observed shifts were linked to temperature changes, and whether shifts lagged behind temperature changes. Results Despite pronounced species‐specific variation, both range limits shifted upslope on average. Rates of shift did not differ between rear and leading edges, elevational range sizes thus did not change. Regional differences in temperature trends were only related to dynamics at rear edges. Yet, the stronger climate warmed regionally, the more species’ responses lagged behind expectations at both range limits. Main conclusions Our results demonstrate that extinctions at rear edges of mountain species have at least been as common as colonizations at leading edges. The drivers of observed range limit shifts are not deducible from our data, but weak relationships with temperature trends suggest that other factors than climate warming played an additional role. These results do not relax concerns about possible detrimental effects of environmental change on mountain biodiversity and point to the importance of refocusing monitoring towards a better representation of rear edge dynamics.

Edge Computing (EC) is a new architecture that extends Cloud Computing (CC) services closer to data sources. EC combined with Deep Learning (DL) is a promising technology and is widely used in several applications. However, in conventional DL architectures with EC enabled, data producers must frequently send and share data with third parties, edge or cloud servers, to train their models. This architecture is often impractical due to the high bandwidth requirements, legalization, and privacy vulnerabilities. The Federated Learning (FL) concept has recently emerged as a promising solution for mitigating the problems of unwanted bandwidth loss, data privacy, and legalization. FL can co-train models across distributed clients, such as mobile phones, automobiles, hospitals, and more, through a centralized server, while maintaining data localization. FL can therefore be viewed as a stimulating factor in the EC paradigm as it enables collaborative learning and model optimization. Although the existing surveys have taken into account applications of FL in EC environments, there has not been any systematic survey discussing FL implementation and challenges in the EC paradigm. This paper aims to provide a systematic survey of the literature on the implementation of FL in EC environments with a taxonomy to identify advanced solutions and other open problems. In this survey, we review the fundamentals of EC and FL, then we review the existing related works in FL in EC. Furthermore, we describe the protocols, architecture, framework, and hardware requirements for FL implementation in the EC environment. Moreover, we discuss the applications, challenges, and related existing solutions in the edge FL. Finally, we detail two relevant case studies of applying FL in EC, and we identify open issues and potential directions for future research. We believe this survey will help researchers better understand the connection between FL and EC enabling technologies and concepts.

As one key enabler of Intelligent Transportation System (ITS), Vehicular Ad Hoc Network (VANET) has received remarkable interest from academia and industry. The emerging vehicular applications and the exponential growing data have naturally led to the increased needs of communication, computation and storage resources, and also to strict performance requirements on response time and network bandwidth. In order to deal with these challenges, Mobile Edge Computing (MEC) is regarded as a promising solution. MEC pushes powerful computational and storage capacities from the remote cloud to the edge of networks in close proximity of vehicular users, which enables low latency and reduced bandwidth consumption. Driven by the benefits of MEC, many efforts have been devoted to integrating vehicular networks into MEC, thereby forming a novel paradigm named as Vehicular Edge Computing (VEC). In this paper, we provide a comprehensive survey of state-of-art research on VEC. First of all, we provide an overview of VEC, including the introduction, architecture, key enablers, advantages, challenges as well as several attractive application scenarios. Then, we describe several typical research topics where VEC is applied. After that, we present a careful literature review on existing research work in VEC by classification. Finally, we identify open research issues and discuss future research directions.

Most edges are anthropogenic in origin, but are distinguishable by their maintaining processes (natural vs. continued anthropogenic interventions: forestry, agriculture, urbanization). We hypothesized that the dissimilar edge histories will be reflected in the diversity and assemblage composition of inhabitants. Testing this “history‐based edge effect” hypothesis, we evaluated published information on a common insect group, ground beetles (Coleoptera: Carabidae) in forest edges. A meta‐analysis showed that the diversity‐enhancing properties of edges significantly differed according to their history. Forest edges maintained by natural processes had significantly higher species richness than their interiors, while edges with continued anthropogenic influence did not. The filter function of edges was also essentially different depending on their history. For forest specialist species, edges maintained by natural processes were penetrable, allowing these species to move right through the edges, while edges still under anthropogenic interventions were impenetrable, preventing the dispersal of forest specialists out of the forest. For species inhabiting the surrounding matrix (open‐habitat and generalist species), edges created by forestry activities were penetrable, and such species also invaded the forest interior. However, natural forest edges constituted a barrier and prevented the invasion of matrix species into the forest interior. Preserving and protecting all edges maintained by natural processes, and preventing anthropogenic changes to their structure, composition, and characteristics are key factors to sustain biodiversity in forests. Moreover, the increasing presence of anthropogenic edges in a landscape is to be avoided, as they contribute to the loss of biodiversity. Simultaneously, edges under continued anthropogenic disturbance should be restored by increasing habitat heterogeneity. We hypothesized that the dissimilar edge histories (edges maintained only by natural processes vs. edges repeatedly disturbed by anthropogenic activities) will be reflected in the diversity and assemblage composition of inhabitants. A meta‐analysis, testing this “history‐based edge effect” hypothesis showed that the diversity‐enhancing properties and filter function of the forest edges are significantly different according to their history.

Cloud-native computing principles such as virtualization and orchestration are key to transferring to the promising paradigm of edge computing. Challenges of containerization, operative models and scarce availability of established tools make a thorough review indispensable. Therefore, the authors have described the practical methods and tools found in the literature as well as in current community-led development projects, and have thoroughly exposed the future directions of the field. Container virtualization and its orchestration through Kubernetes have dominated the cloud computing domain, while major efforts have been recently recorded focused on the adaptation of these technologies to the edge. Such initiatives have addressed either the reduction of container engines and the development of specific tailored operating systems or the development of smaller K8s distributions and edge-focused adaptations (such as KubeEdge). Finally, new workload virtualization approaches, such as WebAssembly modules together with the joint orchestration of these heterogeneous workloads, seem to be the topics to pay attention to in the short to medium term.

Artificial Intelligence (Al) models are being produced and used to solve a variety of current and future business and technical problems. Therefore, AI model engineering processes, platforms, and products are acquiring special significance across industry verticals. For achieving deeper automation, the number of data features being used while generating highly promising and productive AI models is numerous, and hence the resulting AI models are bulky. Such heavyweight models consume a lot of computation, storage, networking, and energy resources. On the other side, increasingly, AI models are being deployed in IoT devices to ensure real-time knowledge discovery and dissemination. Real-time insights are of paramount importance in producing and releasing real-time and intelligent services and applications. Thus, edge intelligence through on-device data processing has laid down a stimulating foundation for real-time intelligent enterprises and environments. With these emerging requirements, the focus turned towards unearthing competent and cognitive techniques for maximally compressing huge AI models without sacrificing AI model performance. Therefore, AI researchers have come up with a number of powerful optimization techniques and tools to optimize AI models. This paper is to dig deep and describe all kinds of model optimization at different levels and layers. Having learned the optimization methods, this work has highlighted the importance of having an enabling AI model optimization framework.

Negative landscape‐scale fragmentation effects are often inferred from negative patch‐scale edge effects. I tested this cross‐scale extrapolation using two evaluations. First, I searched for studies that estimated the direction of both a patch‐scale edge effect and a landscape‐scale fragmentation effect. The directions were concordant and discordant in 55% and 45% of cases, respectively. Second, I extracted from the literature a sample of landscape‐scale fragmentation effects on individual species. Then, for each species I searched for studies from which I could calculate the slope of its patch‐scale edge effect. Species showing negative patch‐scale edge effects were nearly equally likely to show negative or positive landscape‐scale fragmentation effects, and likewise for species showing positive patch‐scale edge effects. The results mean that the efficacy of policies related to habitat fragmentation cannot be inferred from observed patch‐scale edge effects. Such policies require landscape‐scale evidence, comparing species' responses in landscapes with different levels of fragmentation.