Diversity and ecological assembly process of aerobic anoxygenic phototrophic bacteria in a low irradiation area, Three Gorges Reservoir

Aerobic anoxygenic phototrophic bacteria (AAPB) are significant bacterial groups in aquatic ecosystems, known for their rapid growth and photoheterotrophic characteristics. However, the distribution and ecological assembly process of AAPB in low irradiation freshwater basins remain unclear, warranting further investigation. In this study, we present the diversity, abundance, spatial variations, ecological process, and community interaction of AAPB in sediment of Three Gorges Reservoir (TGR) under low irradiation. Our findings demonstrate the dominant genera of AAPB community that exist in the TGR area also are appeared in different waters, with some regional preference. Moreover, the concentration of pufM gene, an indicator for AAPB, maintains a consistently high numerical level ranging from (2.21 ± 0.44) × 104 to (9.98 ± 0.30) × 107 gene copies/g. Although solar irradiation is suggested as the major factor affecting AAPB, it remains unclear whether and how AAPB differ between regions due to varying solar irradiation levels. Our results show spatial differences between total bacteria and AAPB communities, with significant differences observed only in AAPB. Geographical and environmental factor contributed less than 10% to the spatial difference of community, with sediment type and environmental factors being the key factors influencing microbial community structure. The stochastic process plays a dominant role in the aggregation and replacement of AAPB communities, among which the most contribution is dispersal limitation. For AAPB network, Yoonia and Gemmobacter are the hubs for modules. Those results valuable insights into the AAPB communities in TGR with low irradiation.

Economic impact disharmony in global biological invasions

A dominant syndrome of the Anthropocene is the rapid worldwide spread of invasive species with devastating environmental and socio-economic impacts. However, the dynamics underlying the impacts of biological invasions remain contested. A hypothesis posits that the richness of impactful invasive species increases proportionally with the richness of non-native species more generally. A competing hypothesis suggests that certain species features disproportionately enhance the chances of non-native species becoming impactful, causing invasive species to arise disproportionately relative to the numbers of non-native species. We test whether invasive species with reported monetary costs reflect global numbers of established non-native species among phyla, classes, and families. Our results reveal that numbers of invasive species with economic costs largely reflect non-native species richness among taxa (i.e., in 96 % of families). However, a few costly taxa were over- and under-represented, and their composition differed among environments and regions. Chordates, nematodes, and pathogenic groups tended to be the most over-represented phyla with reported monetary costs, with mammals, insects, fungi, roundworms, and medically-important microorganisms being over-represented classes. Numbers of costly invasive species increased significantly with non-native richness per taxon, while monetary cost magnitudes at the family level were also significantly related to costly invasive species richness. Costs were biased towards a few 'hyper-costly' taxa (such as termites, mosquitoes, cats, weevils, rodents, ants, and asters). Ordination analysis revealed significant dissimilarity between non-native and costly invasive taxon assemblages. These results highlight taxonomic groups which harbour disproportionately high numbers of costly invasive species and monetary cost magnitudes. Collectively, our findings support prevention of arrival and containment of spread of non-native species as a whole through effective strategies for mitigation of the rapidly amplifying impacts of invasive species. Yet, the hyper- costly taxa identified here should receive greater focus from managers to reduce impacts of current invasive species.

Resistance of aerobic granular sludge microbiomes to periodic loss of biomass

Granular sludge is a biofilm process used for wastewater treatment which is currently being implemented worldwide. It is important to understand how disturbances affect the microbial community and performance of reactors. Here, two acetate-fed replicate reactors were inoculated with acclimatized sludge and the reactor performance, and the granular sludge microbial community succession were studied for 149 days. During this time, the microbial community was challenged by periodically removing half of the reactor biomass, subsequently increasing the food-to-microorganism (F/M) ratio. Diversity analysis together with null models show that overall, the microbial communities were resistant to the disturbances, observing some minor effects on polyphosphate-accumulating and denitrifying microbial communities and their associated reactor functions. Community turnover was driven by drift and random granule loss, and stochasticity was the governing ecological process for community assembly. These results evidence the aerobic granular sludge process as a robust system for wastewater treatment.

Resolving inter-regional communication capacity in the human connectome

Applications of graph theory to the connectome have inspired several models of how neural signaling unfolds atop its structure. Analytic measures derived from these communication models have mainly been used to extract global characteristics of brain networks, obscuring potentially informative inter-regional relationships. Here we develop a simple standardization method to investigate polysynaptic communication pathways between pairs of cortical regions. This procedure allows us to determine which pairs of nodes are topologically closer and which are further than expected on the basis of their degree. We find that communication pathways delineate canonical functional systems. Relating nodal communication capacity to meta-analytic probabilistic patterns of functional specialization, we also show that areas that are most closely integrated within the network are associated with higher order cognitive functions. We find that these regions' proclivity towards functional integration could naturally arise from the brain's anatomical configuration through evenly distributed connections among multiple specialized communities. Throughout, we consider two increasingly constrained null models to disentangle the effects of the network's topology from those passively endowed by spatial embedding. Altogether, the present findings uncover relationships between polysynaptic communication pathways and the brain's functional organization across multiple topological levels of analysis and demonstrate that network integration facilitates cognitive integration.

Microbial community assembly and co-occurrence relationship in sediments of the river-dominated estuary and the adjacent shelf in the wet season

In the estuarine ecosystem, microbial community plays a vital role in controlling biogeochemical processes. However, there is currently limited comprehensive study on the deterministic and stochastic processes that drive the microbial community assembly in the estuaries and adjacent shelves. In this study, we systematically investigated the co-occurrence relationship and microbial community assembly in the sediments along a large river-dominated estuary to shelf in the northern South China Sea during the wet season. The sampling sites were divided into estuary, transection, and shelf sections based on their salinity values. The microbial co-occurrence networks, hierarchical partitioning-based canonical analysis, null model, neutral community model, and the Mantel test were used to investigate the community assembly. Results suggested that microbial community in the estuary section exhibited more interactions and a higher positive interaction ratio than those in the transition and shelf sections. Stochastic processes dominated community assembly in the study, with homogenizing dispersal contributing the most. The estuary exhibited a higher degree of heterogeneous selection than the transition and shelf sections, whereas homogeneous selection showed an opposite trend. Only the estuary section showed dispersal limitation and undominated processes. The river inflow and the resulting environmental heterogeneity were believed to be the key regulators of the community assembly in the studied area. Our study improved the understanding of how microbial community assembly in estuaries and adjacent shelves.

The backbone network of dynamic functional connectivity

Temporal networks have become increasingly pervasive in many real-world applications, including the functional connectivity analysis of spatially separated regions of the brain. A major challenge in analysis of such networks is the identification of noise confounds, which introduce temporal ties that are nonessential, or links that are formed by chance due to local properties of the nodes. Several approaches have been suggested in the past for static networks or temporal networks with binary weights for extracting significant ties whose likelihood cannot be reduced to the local properties of the nodes. In this work, we propose a data-driven procedure to reveal the irreducible ties in dynamic functional connectivity of resting-state fMRI data with continuous weights. This framework includes a null model that estimates the latent characteristics of the distributions of temporal links through optimization, followed by a statistical test to filter the links whose formation can be reduced to the activities and local properties of their interacting nodes. We demonstrate the benefits of this approach by applying it to a resting-state fMRI dataset, and provide further discussion on various aspects and advantages of it.

Whether warming magnifies the toxicity of a pesticide is strongly dependent on the concentration and the null model

How global warming changes the toxicity of contaminants is a research priority at the intersection of global change biology and ecotoxicology. While many pesticides are more toxic at higher temperatures this is not always detected. We studied whether deviations from this general pattern can be explained by concentration-dependent interaction effects and by testing the interaction against the inappropriate null model. We exposed larvae of the mosquito Culex pipiens to three concentrations of the pesticide chlorpyrifos (absence, low and high) in the absence and presence of 4 °C warming. Both the low and high chlorpyrifos concentration were lethal and generated negative sublethal effects: activity of acetylcholinesterase (AChE) and total fat content decreased, and oxidative damage to lipids increased, yet growth rate increased. Warming was slightly lethal, yet had positive sublethal effects: growth rate, total fat content and metabolic rate increased, and oxidative damage decreased. For four out of seven response variables the independent action model identified the expected synergistic interaction between chlorpyrifos and warming. Notably, for three variables (survival, AChE and fat content) this was strongly dependent on the chlorpyrifos concentration, and for two of these (AChE and fat content) not associated with a significant interaction in the general(ized) linear models. For survival and fat content, warming only potentiated chlorpyrifos (CPF) toxicity at the low CPF concentration, while the opposite was true for AChE. Our results highlight that taking into account concentration-dependence and appropriate null model testing is crucial to improve our understanding of the toxicity of contaminants in a warming world.

Disentangling the influence of aridity and salinity on community functional and phylogenetic diversity in local dryland vegetation

One of the key hypothesized drivers of community assembly and dynamics is environmental filtering, where environmental stress limits species migration and survival as a result of functional trait convergence. Whereas most such studies focus on large-scale variation in functional traits along a single-factor environmental gradient, the mutual effects of small-scale multiple environmental filtering remain unclear. Furthermore, it has rarely been tested whether the combined effect of aridity and salinity on local dryland vegetation constrains the patterns of functional traits and phylogenetic structures. Across an 8-km long transect in the arid northwest of China, we assessed the role of environmental filtering in shaping community assemblages by testing the hypotheses that aridity and salinity stresses, interspecific competition and phylogenetic structures constrained functional diversity in the local dryland vegetation. Our results showed that aridity significantly increased convergence of the maximum plant height, specific leaf area, leaf area and leaf nitrogen concentration. However, salinity significantly promoted the convergence of only leaf area and leaf nitrogen concentration. In addition, interspecific competition increased the convergence of the maximum plant height and leaf area. Leaf area converged significantly due to phylogenetic history. Aridity filtering, but not salinity filtering, obviously increased the clustering of phylogenetic structure. Interspecific competition and phylogenetic structure had weak effects on functional diversity in local dryland vegetation. In conclusion, compared with salinity filtering, aridity filtering was more important in reducing phylogenetic diversity in dryland vegetation.

Statistical methods for constructing disease comorbidity networks from longitudinal inpatient data

Tools from network science can be utilized to study relations between diseases. Different studies focus on different types of inter-disease linkages. One of them is the comorbidity patterns derived from large-scale longitudinal data of hospital discharge records. Researchers seek to describe comorbidity relations as a network to characterize pathways of disease progressions and to predict future risks. The first step in such studies is the construction of the network itself, which subsequent analyses rest upon. There are different ways to build such a network. In this paper, we provide an overview of several existing statistical approaches in network science applicable to weighted directed networks. We discuss the differences between the null models that these models assume and their applications. We apply these methods to the inpatient data of approximately one million people, spanning approximately 17 years, pertaining to the Montreal Census Metropolitan Area. We discuss the differences in the structure of the networks built by different methods, and different features of the comorbidity relations that they extract. We also present several example applications of these methods.

A unifying framework for fast randomization of ecological networks with fixed (node) degrees

The Curveball algorithm is an efficient and unbiased procedure for randomizing bipartite networks (or their matrix counterpart) while preserving node degrees. Here we introduce two extensions of the procedure, making it capable to randomize also unimode directed and undirected networks. We provide formal mathematical proofs that the two extensions, as the original Curveball, are fast and unbiased (i.e. they sample uniformly from the universe of possible network configurations).

•

We extend the Curveball algorithm to unimode directed and undirected networks.

•

As the original Curveball, extensions are fast and unbiased.

•

We provide Python and R code implementing the new procedures.

Quantitative prediction of shrimp disease incidence via the profiles of gut eukaryotic microbiota

One common notion is emerging that gut eukaryotes are commensal or beneficial, rather than detrimental. To date, however, surprisingly few studies have been taken to discern the factors that govern the assembly of gut eukaryotes, despite growing interest in the dysbiosis of gut microbiota-disease relationship. Herein, we firstly explored how the gut eukaryotic microbiotas were assembled over shrimp postlarval to adult stages and a disease progression. The gut eukaryotic communities changed markedly as healthy shrimp aged, and converged toward an adult-microbiota configuration. However, the adult-like stability was distorted by disease exacerbation. A null model untangled that the deterministic processes that governed the gut eukaryotic assembly tended to be more important over healthy shrimp development, whereas this trend was inverted as the disease progressed. After ruling out the baseline of gut eukaryotes over shrimp ages, we identified disease-discriminatory taxa (species level afforded the highest accuracy of prediction) that characteristic of shrimp health status. The profiles of these taxa contributed an overall 92.4% accuracy in predicting shrimp health status. Notably, this model can accurately diagnose the onset of shrimp disease. Interspecies interaction analysis depicted how the disease-discriminatory taxa interacted with one another in sustaining shrimp health. Taken together, our findings offer novel insights into the underlying ecological processes that govern the assembly of gut eukaryotes over shrimp postlarval to adult stages and a disease progression. Intriguingly, the established model can quantitatively and accurately predict the incidences of shrimp disease.

NULL MODELS FOR THE NUMBER OF EVOLUTIONARY STEPS IN A CHARACTER ON A PHYLOGENETIC TREE

Random trees and random characters can be used in null models for testing phylogenetic hypothesis. We consider three interpretations of random trees: first, that trees are selected from the set of all possible trees with equal probability; second, that trees are formed by random speciation or coalescence (equivalent); and third, that trees are formed by a series of random partitions of the taxa. We consider two interpretations of random characters: first, that the number of taxa with each state is held constant, but the states are randomly reshuffled among the taxa; and second, that the probability each taxon is assigned a particular state is constant from one taxon to the next. Under null models representing various combinations of randomizations of trees and characters, exact recursion equations are given to calculate the probability distribution of the number of character state changes required by a phylogenetic tree. Possible applications of these probability distributions are discussed. They can be used, for example, to test for a panmictic population structure within a species or to test phylogenetic inertia in a character's evolution. Whether and how a null model incorporates tree randomness makes little difference to the probability distribution in many but not all circumstances. The null model's sense of character randomness appears more critical. The difficult issue of choosing a null model is discussed.

A null model of guild proportionality, applied to stratification of a New Zealand temperate rain forest

Much ecological theory assumes that the number of species that can coexist (by 'species packing') is limited, because competitive exclusion occurs when any pair of species within a guild is too similar - 'species saturation' or 'niche limitation'. If such niche limitation occurs, the proportion of species in each guild should be relatively constant - 'guild proportionality'. This concept is applied to the guilds represented by strata in a forest. A method is produced, and used to examine a New Zealand temperate rain forest. Most strata showed no deviation from a null model of no niche limitation, i.e. no tendency to guild proportionality. The proportion of lianes was more variable than in the null model, tending to be inversely related to the proportion of epiphytes, Canopy tree proportion was significantly more constant than in the null model, but this could be interpreted as a limit caused by the size of a canopy tree individual.