Community Structure Information Research Articles

Community Detection Using Deep Learning: Combining Variational Graph Autoencoders with Leiden and K-Truss Techniques

Deep learning struggles with unsupervised tasks like community detection in networks. This work proposes the Enhanced Community Detection with Structural Information VGAE (VGAE-ECF) method, a method that enhances variational graph autoencoders (VGAEs) for community detection in large networks. It incorporates community structure information and edge weights alongside traditional network data. This combined input leads to improved latent representations for community identification via K-means clustering. We perform experiments and show that our method works better than previous approaches of community-aware VGAEs.

Deep graph clustering by integrating community structure with neighborhood information

Deep graph clustering approaches employ deep graph neural networks to encode node embeddings and subsequently partition nodes based on these representations. Recent one-step methods have demonstrated progress; however, they often encounter issues during the representation learning phase. A primary challenge is that the encoding networks fail to capture the high-order structural information of the graph for effective clustering. We believe that combining the two distributions comprehensively characterizes the real node distribution in terms of neighborhood proximity and community aggregation to achieve better clustering. Additionally, the prevalent representation learning paradigms based on adjacency matrix reconstruction are computationally intensive and increase optimization challenges. This paper presents a novel deep graph clustering model, termed Deep Integration of Community structure and Neighborhood information (DICN). The model incorporates a graph self-attention mechanism to aggregate neighborhood-level node information, and utilizes a probabilistic generative model for edge community detection to captain community-level node information. These two-level embeddings are employed to compute explicit node membership distributions for the clustering loss. This ensures that the clustering objective effectively integrates both low-order and high-order node characteristics. The comprehensive learning process of DICN optimizes not only the node embeddings but also the clustering results by integrating the two-level losses from representation learning and the clustering loss with a weak guidance loss for clustering. Experiments on some benchmark datasets illustrate that DICN model has superior performance over existing state-of-the-art methods.

CSIM: A Fast Community Detection Algorithm Based on Structure Information Maximization

Community detection has been a subject of extensive research due to its broad applications across social media, computer science, biology, and complex systems. Modularity stands out as a predominant metric guiding community detection, with numerous algorithms aimed at maximizing modularity. However, modularity encounters a resolution limit problem when identifying small community structures. To tackle this challenge, this paper presents a novel approach by defining community structure information from the perspective of encoding edge information. This pioneering definition lays the foundation for the proposed fast community detection algorithm CSIM, boasting an average time complexity of only O(nlogn). Experimental results showcase that communities identified via the CSIM algorithm across various graph data types closely resemble ground truth community structures compared to those revealed via modularity-based algorithms. Furthermore, CSIM not only boasts lower time complexity than greedy algorithms optimizing community structure information but also achieves superior optimization results. Notably, in cyclic network graphs, CSIM surpasses modularity-based algorithms in effectively addressing the resolution limit problem.

Recovery of 1887 metagenome-assembled genomes from the South China Sea

The South China Sea (SCS) is a marginal sea characterized by strong land-sea biogeochemical interactions. SCS has a distinctive landscape with a multitude of seamounts in its basin. Seamounts create “seamount effects” that influence the diversity and distribution of planktonic microorganisms in the surrounding oligotrophic waters. Although the vertical distribution and community structure of marine microorganisms have been explored in certain regions of the global ocean, there is a lack of comprehensive microbial genomic surveys for uncultured microorganisms in SCS, particularly in the seamount regions. Here, we employed a metagenomic approach to study the uncultured microbial communities sampled from the Xianbei seamount region to the North Coast waters of SCS. A total of 1887 non-redundant prokaryotic metagenome-assembled genomes (MAGs) were reconstructed, of which, 153 MAGs were classified as high-quality MAGs based on the MIMAG standards. The community structure and genomic information provided by this dataset could be used to analyze microbial distribution and metabolism in the SCS.

Depth-dependent microbial metagenomes sampled in the northeastern Indian Ocean

The northeastern Indian Ocean exhibits distinct hydrographic characteristics influenced by various local and remote forces. Variations in these driving factors may alter the physiochemical properties of seawater, such as dissolved oxygen levels, and affect the diversity and function of microbial communities. How the microbial communities change across water depths spanning a dissolved oxygen gradient has not been well understood. Here we employed both 16S rDNA amplicon and metagenomic sequencing approaches to study the microbial communities collected from different water depths along the E87 transect in the northeastern Indian Ocean. Samples were collected from the surface, Deep Chlorophyll Maximum (DCM), Oxygen Minimum Zone (OMZ), and bathypelagic layers. Proteobacteria were prevalent throughout the water columns, while Thermoproteota were found to be abundant in the aphotic layers. A total of 675 non-redundant metagenome-assembled genomes (MAGs) were constructed, spanning 21 bacterial and 5 archaeal phyla. The community structure and genomic information provided by this dataset offer valuable resources for the analysis of microbial biogeography and metabolism in the northeastern Indian Ocean.

From biomarkers to community composition: Negative effects of UV/chlorine-treated reclaimed urban wastewater on freshwater biota

The use of urban wastewater reclaimed water has recently increased across the globe to restore stream environmental flows and mitigate the effects of water scarcity. Reclaimed water is disinfected using different treatments, but their effects into the receiving rivers are little studied. Physiological bioassays and biomarkers can detect sub-lethal effects on target species, but do not provide information on changes in community structure. In contrast, official monitoring programs use community structure information but often at coarse taxonomic resolution level that may fail to detect species level impacts. Here, we combined commonly used biomonitoring approaches from organism physiology to community species composition to scan a broad range of effects of disinfection of reclaimed water by UV-light only and both UV/chlorine on the biota. We (1) performed bioassays in one laboratory species (water flea Daphnia magna) and measured biomarkers in two wild species (caddisfly Hydropsyche exocellata and the barbel Luciobarbus graellsii), (2) calculated standard indices of biotic quality (IBQ) for diatoms, benthic macroinvertebrates, and fishes, and (3) analysed community species composition of eukaryotes determined by Cytochrome Oxidase C subunit I (cox1) metabarcoding. Only the UV/chlorine treatment caused significant changes in feeding rates of D. magna and reduced antioxidant defenses, increased anaerobic metabolism and altered the levels of lipid peroxidiation in H. exocellata. However, inputs of reclaimed water were significantly associated with a greater proportion of circulating neutrophils and LG-PAS cells in L. graellsii. Despite IBQ did not discriminate between the two water treatments, metabarcoding data detected community composition changes upon exposure to UV/chlorine reclaimed water. Overall, despite the effects of UV/chlorine-treated water were transient, our study suggests that UV-light treated is less harmful for freshwater biota than UV/chlorine-treated reclaimed water, but those effects depend of the organizational level.

A hybrid approach for enhanced link prediction in social networks based on community detection

Community detection and link prediction are interdependent to a high degree. Knowing the community structure beforehand improves the identification of missing links, whereas clustering on networks with newly introduced missing links improves community detection. In this work, we examine the effectiveness of employing community structure information to predict links in static networks by combining local, quasi-local, and global similarity features to compensate for the weaknesses of each approach. Moreover, we formally defined two classes of links, called relevant links, based on the network's community structure. These links are important because they connect communities or distant nodes within communities. To solve these issues, we developed two hybrid link prediction algorithms based on network communities. To evaluate the effectiveness of the proposed hybrid algorithms, we conducted a comprehensive computational campaign using both real-world and synthetic data-sets. Experiments show that adding information on communities and relevant links enhances the accuracy of link prediction.

Threshold Selection for Brain Connectomes.

Introduction: Structural and functional brain connectomes represent macroscale data collected through techniques such as magnetic resonance imaging (MRI). Connectomes may contain noise that contributes to false-positive edges, thereby obscuring structure-function relationships and data interpretation. Thresholding procedures can be applied to reduce network density by removing low-signal edges, but there is limited consensus on appropriate selection of thresholds. This article compares existing thresholding methods and introduces a novel alternative "objective function" thresholding method. Methods: The performance of thresholding approaches, based on percolation and objective functions, is assessed by (1) computing the normalized mutual information (NMI) of community structure between a known network and a simulated, perturbed networks to which various forms of thresholding have been applied, and by (2) comparing the density and the clustering coefficient (CC) between the baseline and thresholded networks. An application to empirical data is provided. Results: Our proposed objective function-based threshold exhibits the best performance in terms of resulting in high similarity between the underlying networks and their perturbed, thresholded counterparts, as quantified by NMI and CC analysis on the simulated functional networks. Discussion: Existing network thresholding methods yield widely different results when graph metrics are subsequently computed. Thresholding based on the objective function maintains a set of edges such that the resulting network shares the community structure and clustering features present in the original network. This outcome provides a proof of principle that objective function thresholding could offer a useful approach to reducing the network density of functional connectivity data.

A novel network core structure extraction algorithm utilized variational autoencoder for community detection

Community detection technologies have the general research significance in complex networks, in which the topology information of network is worthy to be the focus for its widely application. It is the definition of community structure that the connection of nodes in the community is dense with the connection of nodes outside the community is sparse, which is corresponding to the core structure in the complex real networks is represented by a compact and dense set of connected nodes. While all the notes in the network are considered by the traditional topology, it is hard to extract the core structure with the continuous, exponential growth of community networks. In this paper, a novel network core structure extraction algorithm utilized variational autoencoder for community detection(CSEA) is proposed for finding the community structure more accurately. Firstly, the K-truss algorithm is used to find the core structure information in the network, and the similarity matrix is generated by similarity mapping combined with local information. Secondly, the variational autoencoder is used to extract and reduce the dimension of the similarity matrix containing the core structure of the network, and the low-dimensional feature matrix is obtained. Finally, the K-means clustering algorithm is utilized to obtain the community structure information. We compare CSEA algorithm with 18 different types of community detection algorithms using 4 evaluation metrics on 19 complex real networks. By extensively evaluating our algorithm on large real-world datasets, we show that CSEA algorithm has an excellent community division effect in dense complex real networks, especially in small and medium-sized networks, and it can accurately divide the complex real networks with unknown community structure. Simultaneously, CSEA algorithm also reveals some efficiency advantage in its on-line test.

Improving Network Representation Learning via Dynamic Random Walk, Self-Attention and Vertex Attributes-Driven Laplacian Space Optimization.

Network data analysis is a crucial method for mining complicated object interactions. In recent years, random walk and neural-language-model-based network representation learning (NRL) approaches have been widely used for network data analysis. However, these NRL approaches suffer from the following deficiencies: firstly, because the random walk procedure is based on symmetric node similarity and fixed probability distribution, the sampled vertices’ sequences may lose local community structure information; secondly, because the feature extraction capacity of the shallow neural language model is limited, they can only extract the local structural features of networks; and thirdly, these approaches require specially designed mechanisms for different downstream tasks to integrate vertex attributes of various types. We conducted an in-depth investigation to address the aforementioned issues and propose a novel general NRL framework called dynamic structure and vertex attribute fusion network embedding, which firstly defines an asymmetric similarity and h-hop dynamic random walk strategy to guide the random walk process to preserve the network’s local community structure in walked vertex sequences. Next, we train a self-attention-based sequence prediction model on the walked vertex sequences to simultaneously learn the vertices’ local and global structural features. Finally, we introduce an attributes-driven Laplacian space optimization to converge the process of structural feature extraction and attribute feature extraction. The proposed approach is exhaustively evaluated by means of node visualization and classification on multiple benchmark datasets, and achieves superior results compared to baseline approaches.

Study on the dynamic changes of oral microbiota in type 2 diabetes patients with periodontitis after initial periodontal therapy

Objectives: To clarify the effect of initial periodontal therapy on the dynamic changes of oral (saliva, dorsal tongue and subgingival plaque) microbiota in periodontitis patients with or without type 2 diabetes mellitus (T2DM). Methods: A total of 14 patients with chronic periodontitis (CP group) and 14 CP patients with T2DM (CP-T2DM group) were included from Department of Periodontology, School and Hospital of Stomatology,Cheeloo College of Medicine, Shandong University. The microbial samples were collected from saliva, dorsal tongue and subgingival plaque of first molars at baseline, 1.5 and 3 months after initial periodontal therapy, and were detected by 16S rRNA (V3-V4 region) gene sequencing. The sequencing data were analyzed to obtain microbial distribution and community structure information. The same professional periodontist evaluated the periodontal status of patients according to periodontitis detection indices before and after initial periodontal therapy. Meanwhile, patients' blood samples were collected and related metabolic indices were evaluated. Results: After initial periodontal therapy, the glycosylated hemoglobin levels [(7.46±1.69)%] in CP-T2DM group were significantly improved than that at baseline [(7.65±1.34)%] (t=0.52,P=0.610). The probing depth of the sampling sites [CP group: (2.94±0.46) mm, CP-T2DM group: (2.95±0.35) mm] and bleeding index (CP group: 1.91±0.42, CP-T2DM group: 1.67±0.49) at 3 months after treatment were significantly decreased than the probing depth [CP group: (3.99±0.77) mm, CP-T2DM group: (3.80±0.76) mm] (F=25.61, P<0.001; F=17.63, P<0.001) and bleeding index (CP group: 3.03±0.52, CP-T2DM group: 2.54±0.65) (F=28.43, P<0.001; F=20.21, P<0.001) at baseline. The flora analysis showed that the α and β diversity indices of the same sites in the CP and CP-T2DM groups did not change significantly before and after the initial therapy, but the bacterial abundance at each site changed. There were commonalities and differences in the microbial composition of each site in the CP and CP-T2DM groups. Among them, the relative abundance of Proteobacteria in saliva and dorsal tongue samples of the two groups after treatment was basically consistent with the change trend in the subgingival plaque microbes. In the subgingival plaque of the CP group, the relative abundance of Proteobacteria showed a gradual increase with the prolongation of initial periodontal therapy; while in the CP-T2DM group, it showed a trend of first increase and then decrease. Syntrophy, Dethiosulfate, Methanobacteriaceae and TG5 in CP and CP-T2DM groups were all significantly dominant bacteria in subgingival plaque at baseline (P<0.05). Moreover, in the CP-T2DM group Spirochetes also showed a significant advantage. At 1.5 months after treatment, Rhizobacteria, Alcaligenes, Comamomons, Delftia, Blautella, etc. were dominant in subgingival plaque (P<0.05). Firmicutes, Clostridia/Clostridiales, Enterococci and Ruminococci showed significant differences at 3 months (P<0.05). Conclusions: Plaques in saliva and tongue dorsal could reflect the effects of initial periodontal therapy on the dynamic changes of microorganisms to a certain extent. CP and CP-T2DM patients had differences in microbial composition and responses to initial periodontal therapy.

Characterizing the reputation of evaluators using vectors in the object feature space

Evaluation is a frequent occurrence in our daily lives, especially in online systems. Establishing ways to characterize the reputation of an evaluator is therefore becoming an important problem in online systems, and this has attracted much attention. Most existing evaluation methods use the rating information in user–object bipartite networks to evaluate the reputations of evaluators. These methods use a scalar quantity to represent the reputation of each user, which is applied to try to identify malicious ratings and spamming attacks. In this paper, we suggest that the reputation should be characterized by a vector, and propose a reputation–evaluation algorithm based on the classification of objects. More specifically, the objects are classified into several categories according to the community structure information obtained from a one-mode projection onto the objects of a bipartite network. The reputation vector of an evaluator then represents his/her reputation for the objects in different categories. The evaluator is also assigned an appropriate attribute according to his/her reputation vector using K-means clustering. The results from both artificial and real rating data have shown that the presented method is better than a correlation-based ranking method in terms of both accuracy and robustness.

Environmental Difference and Spatial Distance Affect the Fidelity of Variation Source of Microbial Community Structure in Air-Dried Soils.

Air-dried soil archives are important for microbial ecology research, although the process of air-drying preservation inevitably destroys the original microbial information in soils. Only upon fully understanding the limitations of air-dried soil can it play a greater role. The value of air-dried soil depends on the fidelity of microbial community structure information in the air-dried soil relative to that in fresh soil. To evaluate this, high-throughput sequencing was applied to investigate the microbial community of fresh soils and 227 days air-dried archives from typical farmland under a large spatial scale, and PERMANOVA was used to analyze the explanation proportion (EP) of the spatial factor on the microbial community structure in any paired-fresh or air-dried soils. The results show that for any paired soils, the value of EP ranged from 42.4% to 97.9% (p < 0.001). Importantly, taking fresh soil as a reference, the value of EP declined in air-dried soils (effect size r = 0.79, p < 0.001). Furthermore, the standardized difference in EP between fresh and air-dried soil (NDEP) was used to characterize the fidelity of variance source of microbial community structure in air-dried soils, and correlation tests showed that NDEP was negatively correlated with spatial distance (r = −0.21, p < 0.01) and with environmental difference (r = −0.37, p < 0.001). Further analyses show that larger NDEP was observed at a spatial distance <25 km or an environmental difference <0.58. Variance partitioning analysis showed that 28.0% of the variation in NDEP could be explained, with environmental difference constituting 14.0% and the interaction between the environmental difference and spatial distance constituting the remaining 14.0%. Soil texture was the most important factor for predicting NDEP, followed by soil pH and annual average temperature. This study not only emphasizes the possible decline in EP when using air-dried soils to reveal microbial community patterns, but also implies that air-dried soil is more suitable for addressing scientific questions under a large spatial scale or environmental differences.

Variation in woody leaf anatomical traits along the altitudinal gradient in Taibai Mountain, China

As important indicators of leaf traits, leaf anatomical traits are sensitive and adaptable to environmental changes. However, how leaf anatomical traits at species and community levels vary along the altitudinal gradients is still unclear. In this study, a total of 60 woody plants from four typical vegetation zones along the 2000 m-elevation gradients in the northern slope of Taibai Mountain were selected to measure the leaf thickness (LT), epidermal thickness (ET), palisade tissue thickness (PT), spongy tissue thickness (ST), and palisade-spongy tissue thickness ratio (PST). Based on the data of the relative dominance of each species within a community, we calculated the community weighted means (CWM) of leaf traits. We aimed to investigate the variation of leaf anatomical traits at both species and community levels along the altitudinal gradients, and to explore their influencing factors. Our results showed that different elevational patterns of leaf anatomical traits occurred between the species and community levels. At the species level, LT, ET, PT, and ST increased with the altitudinal gradient, which were controlled by both of phylogenetic and environmental factors. Differently, the community-level leaf anatomical traits increased first and then decreased with elevation, and were mainly influenced by climatic factors. These results indicated that, different from species-level traits, community-level traits, which included community structure and species information, could better reflect the real plant community and their variation along the elevation. Furthermore, both of environmental factors and phylogenetic differences should be taken into account when exploring the variation in leaf anatomical traits.

Foraging guild structure and niche characteristics of wintering waterbirds at Lake Shengjin, lower reaches of the Yangtze River

集团结构和生态位特征是量化动物群落组织过程的重要指标，鸟类集团结构及其食物资源分割的分析对于理解群落中不同鸟种生态适应性具有重要意义.越冬水鸟集团结构和生态位特征取决于食物资源的丰富度和可获得性.本文通过对升金湖越冬水鸟的觅食生境和觅食行为的观察，对水鸟群落的集团结构进行划分，采用Levins指数和Pianka指数计算生态位宽度和生态位重叠度.结果表明，升金湖越冬水鸟可被划分为4个觅食集团，即G1浅水啄食集团、G2草滩啄食集团、G3广食性集团和G4深水潜水觅食集团.在浅水和中水区的觅食集团物种组成比深水区的水鸟集团丰富.G3广食性集团的生态位宽度远高于其余3个特化集团.比较而言，所有水鸟物种的觅食行为均较特化，但在觅食生境方面却较宽化.同一集团内物种间的生态位重叠远高于集团间的生态位重叠.G3广食性集团内物种的生态位重叠度低于其它特化集团内物种的生态位重叠度.研究结果表明，升金湖越冬水鸟群落集团结构主要受食物资源空间配置及其可利用性的影响.水鸟群落结构和生态位特征的信息对监测升金湖湿地的质量和制定有效的湿地保护策略都具有重要意义.;Guild structure and niche characteristics are important indicators to quantify the assemble process of animal community. It is of great significance to analysis bird guild structure and food resource partitioning for understanding ecological adaptability of different bird species in a community. The guild structure and niche characteristics of wintering waterbirds depend on the abundance and availability of food resources. In this study we divided the guild structure of waterbird community based on the data of foraging habitat and foraging behavior of wintering waterbirds collected at Lake Shengjin, and calculated the niche width and niche overlap using Levin's index and Pianka index. The results showed that the waterbirds at Lake Shengjin can be divided into four different foraging guilds, namely the G1, pickers in shallow-water, G2, pickers in grassland, G3, generalists and G4, divers. The species composition of foraging guilds in shallow and middle-depth water is richer than that in deep water. The niche width of G3 generalists is much higher than that of the other three specialized species guilds. In comparison, the foraging behavior of all waterbird species is more specialized, but the foraging habitat is more generalized. The niche intra-guild overlap of waterbird species is much higher than that of inter-guilds. The niche overlap of species in G3 generalist is lower than that in other specialized guilds. The results show that the guild structure of wintering waterbird community at Lake Shengjin mainly affected by the spatial allocation and availability of food resources. The information of water bird community structure and niche characteristics is of great significance for monitoring the quality of Lake Shengjin wetland and developing effective wetland protection strategies.

Composition of marine nematode communities across broad longitudinal and bathymetric gradients in the Northeast Chukchi and Beaufort Seas

Benthic communities are responding to rapid environmental change in the Arctic, yet the ecologically important meiofauna remain poorly studied, leaving little baseline information for evaluating such changes. We investigated the community structure of meiofaunal nematodes (> 63 µm) on the Northeast Chukchi Sea (NEC) continental shelf (< 50 m), and along a broad longitudinal and bathymetric gradient in the Beaufort Sea (BEAU; 20–1200 m). In total, 139 nematode genera representing 32 families were identified. Nematode communities differed between the NEC and BEAU, but the opportunistic genera Sabatieria (Comesomatidae) and Daptonema (Xyalidae) were abundant in both seas. Despite the relatively small sampling area of similar depth across the NEC, spatial variation was reflected by the high abundance of Sabatieria at a subset of sites with higher proportion of fine sediment. Nematode communities in the western BEAU offshore of the Colville River showed little difference among depths, in contrast to areas further east where shelf and upper slope communities were more distinct. Diversity indices were higher in the BEAU than in the NEC, with maximum values in the easternmost area (Banks Island); lower evenness was recorded in the western BEAU (Colville Plume), which was dominated by Sabatieria at all depths. Organic matter quality and quantity influenced community structure in the BEAU. This study provides the first genus-level characterization of nematode communities across this environmentally heterogeneous region. With increased exploration for natural resources and reduced ice coverage, the baseline community structure information provided here supports evaluation of ecosystem change in the Arctic.

Complex network graph embedding method based on shortest path and MOEA/D for community detection

As one of the main applications of graph embedding, community detection has always been a hot issue in the field of complex network data mining. This paper presents a complex network graph embedding method based on the shortest path matrix and decomposition multi-objective evolutionary algorithm (SP-MOEA/D) for community detection, which can better reflect the network structure at the level of network community structure. Firstly, by calculating the shortest path matrix between nodes in the network, the node relationship matrix is obtained by adding the node similarity. Next, aiming at the problem of community detection in disconnected networks, a decomposition-based multi-objective optimization method is proposed to assign distances to unrelated nodes. Then, the network similarity matrix is calculated based on the relationship matrix of network nodes, and the low-dimensional vector representation of nodes is obtained by random surfing strategy and multi-dimensional scaling method. Finally, the community structure of the network can be detected based on the obtained node representation structure. Starting from the essence of network structure and the tightness between nodes, this method can reflect the relationship characteristics of network nodes more effectively, and then obtain the vector representation of nodes which can more accurately reflect the information of community structure in networks. The test results on 11 networks show that the node vector representation results obtained by this method can better reflect the community structure information in complex networks.

Dynamic Immunization Node Model for Complex Networks Based on Community Structure and Threshold.

In the information age of big data, and increasingly large and complex networks, there is a growing challenge of understanding how best to restrain the spread of harmful information, for example, a computer virus. Establishing models of propagation and node immunity are important parts of this problem. In this article, a dynamic node immune model, based on the community structure and threshold (NICT), is proposed. First, a network model is established, which regards nodes carrying harmful information as new nodes in the network. The method of establishing the edge between the new node and the original node can be changed according to the needs of different networks. The propagation probability between nodes is determined by using community structure information and a similarity function between nodes. Second, an improved immune gain, based on the propagation probability of the community structure and node similarity, is proposed. The improved immune gain value is calculated for neighbors of the infected node at each time step, and the node is immunized according to the hand-coded parameter: immune threshold. This can effectively prevent invalid or insufficient immunization at each time step. Finally, an evaluation index, considering both the number of immune nodes and the number of infected nodes at each time step, is proposed. The immune effect of nodes can be evaluated more effectively. The results of network immunization experiments, on eight real networks, suggest that the proposed method can deliver better network immunization than several other well-known methods from the literature.

Information content of absorption spectra and implications for ocean color inversion.

The increasing use of hyperspectral optical data in oceanography, both in situ and via remote sensing, holds the potential to significantly advance characterization of marine ecology and biogeochemistry because, in principle, hyperspectral data can provide much more detailed inferences of ecosystem properties via inversion. Effective inferences, however, require careful consideration of the close similarity of different signals of interest, and how these interplay with measurement error and uncertainty to reduce the degrees of freedom (DoF) of hyperspectral measurements. Here we discuss complementary approaches to quantify the DoF in hyperspectral measurements in the case of in situ particulate absorption measurements, though these approaches can also be used on other such data, e.g., ocean color remote sensing. Analyses suggest intermediate (${\sim}5 $∼5) DoF for our dataset of global hyperspectral particulate absorption spectra from the Tara Oceans expedition, meaning that these data can yield coarse community structure information. Empirically, chlorophyll is an effective first-order predictor of absorption spectra, meaning that error characteristics and the mathematics of inversion need to be carefully considered for hyperspectral data to provide information beyond that which chlorophyll provides. We also discuss other useful analytical tools that can be applied to this problem and place our results in the context of hyperspectral remote sensing.

Effect of strip harvesting on bacterial diversity of forest soils in the Daxing'an Mountains

AbstractStrip harvesting can improve the management of low‐quality forests, but the practice affects forest soil properties and microbial communities. The effects of different strip widths on the physicochemical properties of the forest soil and the bacterial diversity were determined. The design consisted of four strips with the same length and different widths of 6 m (ZM6), 10 m (ZM10), 14 m (ZM14), and 18 m (ZM18) and an unharvested strip (ZM0) as the control. Illumina high‐throughput sequencing was used to determine the sequence information of the soil microbial community structure in the five strips. The dominant bacterial phyla were Proteobacteria and Acidobacteria, while the dominant bacterial genera were AD101, Rhodoplanes, and Bradyrhizobium. The strip width exhibited a highly significant positive correlation with Proteobacteria and Acidobacteria (p &lt; .001). The order of plots based on soil bacterial diversity (Chao1 and the abundance‐based coverage estimator [ACE]) was ZM18 &gt; ZM10 &gt; ZM6 &gt; ZM14 &gt; ZM0, and there was a highly significant positive correlation between the indices and the strip width (p &lt; .001). Strip harvesting significantly affected soil pH, total nitrogen (TN), total potassium (TK), soil respiration (SR), soil temperature (ST), and soil moisture (SM) (p &lt; .05). The results of a principal component analysis (PCA) showed that strip harvesting explained 23% of the bacterial variation. The 10‐m‐wide strip exhibited the most desirable effects because it had relatively rich bacterial diversity and moderate soil fertility.