A Diversified Population Migration-Based Multiobjective Evolutionary Algorithm for Dynamic Community Detection

Identifying communities in dynamic networks is essential for exploring the latent network structures, understanding network functions, predicting network evolution, and discovering abnormal network events. Many dynamic community detection methods have been proposed from different viewpoints. However, identifying the community structure in dynamic networks is very challenging due to the difficulty of parameter tuning, high time complexity and detection accuracy decreasing as time slices increase. In this paper, we present a dynamic community detection framework based on information dynamics and develop a dynamic community detection algorithm called DCDID (dynamic community detection based on information dynamics), which uses a batch processing technique to incrementally uncover communities in dynamic networks. DCDID employs the information dynamics model to simulate the exchange of information among nodes and aims to improve the efficiency of community detection by filtering out the unchanged subgraph. To illustrate the effectiveness of DCDID, we extensively test it on synthetic and real-world dynamic networks, and the results demonstrate that the DCDID algorithm is superior to the representative methods in relation to the quality of dynamic community detection.

Sex steroid hormones have been shown to alter regional brain activity, but the extent to which they modulate connectivity within and between large-scale functional brain networks over time has yet to be characterized. Here, we applied dynamic community detection techniques to data from a highly sampled female with 30 consecutive days of brain imaging and venipuncture measurements to characterize changes in resting-state community structure across the menstrual cycle. Four stable functional communities were identified, consisting of nodes from visual, default mode, frontal control, and somatomotor networks. Limbic, subcortical, and attention networks exhibited higher than expected levels of nodal flexibility, a hallmark of between-network integration and transient functional reorganization. The most striking reorganization occurred in a default mode subnetwork localized to regions of the prefrontal cortex, coincident with peaks in serum levels of estradiol, luteinizing hormone, and follicle stimulating hormone. Nodes from these regions exhibited strong intranetwork increases in functional connectivity, leading to a split in the stable default mode core community and the transient formation of a new functional community. Probing the spatiotemporal basis of human brain–hormone interactions with dynamic community detection suggests that hormonal changes during the menstrual cycle result in temporary, localized patterns of brain network reorganization.

Diversity has been shown to be pivotal in ecosystem stability and resilience. It is therefore important to increase our knowledge about the development of diversity. The aim of this study was to investigate the temporal dynamics of the bacterial community structure in the rhizosphere of wheat plants growing in a soil in which the initial conditions for bacterial re-colonization were modified by mixing different amounts of sterilized with native soil at ratios of 19:1, 9:1, 4:1 and 1:1. Additional treatments comprised sterilized soil or native soil. Plant dry weight at day 20 decreased with increasing percentage of native soil in the mix. The bacterial community structure in the rhizosphere was assessed by polymerase chain reaction-denaturing gradient gel electrophoresis (DGGE) at days 3, 14 and 20 after planting. The bacterial community in the sterilized soil had a lower diversity and evenness than the native soil. Both diversity and evenness increased with time in the sterilized soil. Community structure in the different mixes changed over time and the changes were mix-specific. Principal component analyses of the DGGE banding patterns showed clear differences between the treatments particularly at day 3 and day 14 and revealed changes in community structure within a few days in a given treatment. The results of the present study show that bacterial communities rapidly re-colonize sterilized soil. During re-colonization, the community structure changes rapidly with a general trend towards higher diversity and evenness. The changes in community structure over time are also affected by the amount of sterile substrate to be re-colonized.

11 - Effects of Elevated CO2 on Plant Species Dominance in a Highly Diverse Calcareous Grassland

Long-term monitoring (May 1990 – November 1994) of benthic macrofauna and sediment composition was undertaken to examine changes in community structure following the construction of a tidal barrage at the entrance to Sutton Harbour, Plymouth (South West England). The harbour has permanently anoxic sediments, below a depth of 3 cm, consisting mainly of silt with relatively high total metal concentrations in the m<63 μm fraction for Cu (96 – 222 μg g−1), Hg (0.7 – 2.0 μg g−1), Pb (93 – 297 μg g−1) and Zn (114 – 460 μg g−1). Polychaete worms, particularly of the family Cirratulidae, dominated the 93 taxa that form the macrofauna of the fine sediments. Multi-dimensional scaling (MDS) identified significant changes, surpassing all seasonal variability, in benthic community structure between pre-construction and construction phases and the similarities percentages procedure (SIMPER) isolated the species differentiating pre- and post-construction samples. Principal components analysis (PCA) revealed changes in heavy metal concentrations and sediment size distribution, primarily between surveys encompassing the start of construction. Rank correlations between the biotic and abiotic similarity matrices (BIOENV) were highest for sediment Pb and Zn concentrations, indicating that these variables offered a potential explanation of the changes in community structure. The results demonstrate the sensitivity of the benthic community to man-induced change and the need for ecological factors to be accounted for in harbour development at other locations.

This study evaluates changes in the microbial community structure and function of a pilot-scale denitrifying fluidized bed reactor during periods of constant operating conditions and periods of perturbation. The perturbations consisted of a shutdown period without feed, two disturbances in which biofilms were mechanically sheared from carrier particles, and a twofold step increase in feed nitrate concentration. In the absence of perturbations, nitrate removal was stable and consistently greater than 99%. The structure and dynamics of the microbial community were studied using cloning and sequencing techniques and terminal restriction fragment length polymorphism (T-RFLP) of the SSU rRNA gene. Under unperturbed operating conditions, stable function was accompanied by high constancy and low variability of community structure with the majority of terminal restriction fragments (T-RFs) appearing throughout operation at consistent relative abundances. Several of the consistently present T-RFs correlated with clone sequences closely related to Acidovorax (98% similarity), Dechloromonas (99% similarity), and Zoogloea (98% similarity), genera recently identified by molecular analyses of similar systems. Significant changes in community structure and function were not observed after the shutdown period. In contrast, following the increase in loading rate and the mechanical disturbances, new T-RFs appeared. After both mechanical disturbances, function and community structure recovered. However, function was much more resilient than community structure. The similarity of response to the mechanical disturbances despite differences in community structure and operating conditions suggests that flexible community structure and potentially the activity of minor members under nonperturbation conditions promotes system recovery.

In recent years, community structure has emerged as a key component of complex network analysis. As more data have been collected, researchers have begun investigating changing community structure across multiple networks. Several methods exist to analyze changing communities, but most of these are limited to evolution of a single network over time. In addition, most of the existing methods are more concerned with change at the community level than at the level of the individual node. In this paper, we introduce scaled inclusivity, which is a method to quantify the change in community structure across networks. Scaled inclusivity evaluates the consistency of the classification of every node in a network independently. In addition, the method can be applied cross sectionally as well as longitudinally. In this paper, we calculate the scaled inclusivity for a set of simulated networks of United States cities and a set of real networks consisting of teams that play in the top division of American college football. We found that scaled inclusivity yields reasonable results for the consistency of individual nodes in both sets of networks. We propose that scaled inclusivity may provide a useful way to quantify the change in a network's community structure.

Climate-mediated changes in zooplankton community structure for the eastern Bering Sea

In the last decade, community detection in dynamic networks has received increasing attention, because it can not only uncover the community structure of the network at any time but also reveal the regularity of dynamic networks evolution. Although methods based on the framework of evolutionary clustering are promising for dynamic community detection, there is still room for further improvement in the snapshot quality and the temporal cost. In this study, a dynamic community detection algorithm based on optional pathway guide pity beetle algorithm (DYN-OPGPBA), which is a novel dynamic community detection method based on the framework of evolutionary clustering, is proposed. We propose an improved PBA for community detection of the network at the first time step, including a discrete search strategy based on adjacent nodes, a closeness-based community modification strategy and a crowded community split strategy. Compared with many representative static community detection methods, the proposed method has some superior detection accuracy. A neighbour vector competition-based individual update strategy and an external population size restriction mechanism are also proposed for community detection at subsequent time steps. Results show that DYN-OPGPBA has a better balance between snapshot quality and temporal cost than two representative dynamic community detection methods.

Dissimilatory arsenate-respiring bacteria (DARB) reduce arsenate to arsenite and may play a significant role in arsenic mobilization in aquifers and anoxic sediments. Many studies have been conducted with pure cultures of DARB to understand their involvement in arsenic contamination. However, few studies have examined uncultured DARB in the environment. In order to investigate uncultured DARB in anoxic sediments, genes encoding arsenate respiratory reductases (arr) were targeted as a genetic marker. Degenerate primers for the alpha-subunit of arr genes were designed and used with PCR amplification to detect uncultured DARB in the sediments collected from three stations (upper, mid and lower bay) in the Chesapeake Bay. Phylogenetic analysis of putative arrA genes revealed the diversity of DARB with distinct community structures at each of the three stations. Arsenate reduction in sediment communities was confirmed using enrichment cultures established with sediment samples from the upper bay. In addition, terminal restriction fragment length polymorphism analysis of the putative arrA genes showed changes in the community structure of DARB in the enrichment cultures while reducing arsenate. This was also confirmed by cloning and sequence analysis of the arrA genes obtained from the enrichment cultures. Thus, we were able to detect diverse uncultured DARB in sediments, as well as to describe changes in DARB community structure during arsenic reduction in anoxic environments.

Most coral reefs differ from those visited by explorers in the 15th century and described by ecologists in the 1950s, and reports of degraded reefs and hypotheses regarding the implications of the changes abound. Tests of these hypotheses require decadal-scale ecological analyses, yet data to support these efforts remain rare. In this study, 25 yr of time-series analyses from 3 habitats in St. John, US Virgin Islands, revealed changes in coral community structure that are spatially and temporally heterogeneous, only loosely coupled with local disturbances, and equivocal in terms of the coral community structure that can be projected from the recent past. In a near-shore habitat at 7 to 9 m depth, coral cover remained ~4% between 1992 and 2011, and variation in benthic community structure was driven mostly by Agaricia. In an Orbicella annularis (formerly Montastraea annularis)-dominated habitat at 9 m depth, coral cover declined from 45% in 1987 to 7% in 2011, and varied among decades. In a second O. annularis-dominated habitat at 14 m depth, coral cover increased from 32% in 1987 to 49% in 2002, but then declined to 29% in 2011. The density of small corals also changed between 1994 and 2011, beginning with a density (colonies per 0.25 m2) of 3.5 in 1992, rising to 4.9 in 2005, but declining to 3.0 in 2011. Each genus of small corals responded in dissimilar ways over time, with genera waxing and waning in abundance on multiple occasions and in asynchronous patterns. The reefs of St. John have changed markedly since 1987, but the present results show how adjacent habitats and sympatric coral taxa translate similar environmental signals into discordant community trajectories that suggest that these coral communities will not disappear in the short term.

Forest vernal pools experience strong environmental fluctuations, such as changes in water chemistry, which are often correlated with changes in microbial community structure. However, very little is known about the extent to which these community changes influence ecosystem processes in vernal pools. This study utilized experimental vernal pool microcosms to simulate persistent pH alteration and a pulse input of nitrate (NO3 -), which are common perturbations to temperate vernal pool ecosystems. pH was manipulated at the onset and microbial respiration was monitored throughout the study (122 days). On day 29, NO3 - was added and denitrification rate was measured and bacterial, fungal, and denitrifier communities were profiled on day 30 and day 31. Microbial respiration and both bacterial and fungal community structure were altered by the pH treatment, demonstrating both structural and functional microbial responses. The NO3 - pulse increased denitrification rate without associated changes in community structure, suggesting that microbial communities responded functionally without structural shifts. The functioning of natural vernal pools, which experience both persistent and short-term environmental change, may thus depend on the type and duration of the change or disturbance.

Community detection and evolution has been largely studied in the last few years, especially for network systems that are inherently dynamic and undergo different types of changes in their structure and organization in communities. Because of the inherent uncertainty and dynamicity in such network systems, we argue that temporal community detection problems can profitably be solved under a particular class of multi-armed bandit problems, namely combinatorial multi-armed bandit (CMAB). More specifically, we propose a CMAB-based methodology for the novel problem of dynamic consensus community detection, i.e., to compute a single community structure that is designed to encompass the whole information available in the sequence of observed temporal snapshots of a network in order to be representative of the knowledge available from community structures at the different time steps. Unlike existing approaches, our key idea is to produce a dynamic consensus solution for a temporal network to have unique capability of embedding both long-term changes in the community formation and newly observed community structures.

Effects of nitrogen addition on vegetation and soil and its linkages to plant diversity and productivity in a semi-arid steppe.

In spring lakes, which have homogeneous environmental characteristics, it is expected that there will be no significant changes in the composition and structure of communities over time, and fluctuations will generally be related to the ecological attributes of the species. We studied the fish community in a small subtropical spring lake, Lake Zacapu, for two different decades to analyse its structural characteristics. Sampling was performed every 3 months at four sites from January to October 1995 and from May 2019 to May 2020. We determined the temporal variation (seasonal and decadal) in the fish community structure with respect to composition, abundance, diversity and dominance. We evaluated the association between species and water quality. Our results showed spatial homogeneity in most physicochemical variables that remained in the same range across decades, although some nutrients (NO3) recently increased. All species identified in the early samples were present in the recent surveys (eleven native and two nonnative species), indicating that the fish community composition had significant qualitative stability. However, the dominant littoral cyprinid in 1995 was replaced by the more limnetic atherinopsid in 2019–2020, and the diversity and abundance of sensitive fish decreased in recent samplings. Consequently, the current status of critically endangered species is supported for some restricted and microendemic fish. The changes in the community structure attributes, which show a tendency for reduction in native species, emphasise the need to implement efforts to conserve freshwater fish diversity in small spring lakes.