Eigenvector Maps Research Articles

A global dataset of tree hydraulic and structural traits imputed from phylogenetic relationships

We present a dataset of plant hydraulic and structural traits imputed for 55,779 tree species based on TRY plant trait dataset observations and phylogenetic relationships. We collected plant trait values for maximum stomatal conductance (gsMAX), xylem pressure at 12%, 50%, and 88% conductance loss (P12, P50, P88), maximum observed rooting depth (rdMAX), photosynthetic Water Use Efficiency (WUE), maximum plant height (height), Specific Leaf Area (SLA), and leaf Nitrogen content (LeafN). We demonstrated that each of these traits exhibited remarkably large phylogenetic signals across all land plants. Based on the strength of this signal we then developed random forest (RF) models trained on TRY trait data to impute the traits of previously unstudied tree species using Phylogenetic Eigenvector Maps. We quantified imputed trait uncertainty by fitting RF model test dataset residuals to skew exponential power distributions accounting for heteroscedasticity, demonstrating encouraging lack of biases in the imputed dataset. The resulting dataset of imputed trait values can support global analyses of plant trait variations and species-level parameterization of earth systems models.

Eigenvector spatial filtering enhancing natural hazards vulnerability assessment in a susceptible urban environment: A case study of Izmir earthquake in Turkey

The increasing risk of earthquakes in urban areas has made it crucial to develop accurate vulnerability models for city infrastructure and systems. We aimed to assess and compare the effectiveness of different models and vulnerability analysis techniques in predicting earthquake vulnerability in the specific context of Izmir, Turkey. One central hypothesis in this research aimed to determine whether integrating Eigenvector Spatial Filtering (ESF) into both regression models and machine learning algorithms would yield a comparable enhancement in model performance. We performed earthquake vulnerability modeling (EVM) by considering (ⅰ) only seismic-related variables (SRV) and (ⅱ) integrating ESF by using Moran's eigenvector maps (MEMs). For each approach, we evaluated the predictive performance of two simple regression-based models; generalized linear model (GLM) and generalized additive model (GAM), and two complex machine learning ones; generalized boosting model (GBM), and random forest (RF). The study utilized five primary indicators encompassing geotechnical, physical, structural, social, and facilities data. The predictive performance of the models was assessed using evaluation metrics including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and adjusted R2. The results indicated that the optimal candidate model consisted of five key variables: altitude, building height, distance to safety gathering places, Peak Ground Acceleration (PGA), and population density. We found that decision-tree-based methods performed better than regression-based methods for both modeling schemes. RF exhibited the highest predictive performance for the training data (RMSE = 0.59, adjusted R2 = 0.71), while GBM outperformed other models for the test data (RMSE = 0.79, adjusted R2 = 0.78). However, incorporating ESF to the EVM analysis revealed that regression-based methods, particularly the GLM, obtained highest improvement in accuracy (RMSE 0.94 vs 0.76 and adjusted R2 0.56 vs 0.71 for the SRV and SRV + MEMs modeling approach). Significant differences were observed between GLM-GBM and GLM-RF comparisons, as well as GAM-GBM and GAM-RF comparisons. The findings of this research are expected to be helpful for informed decision-making, targeted risk reduction, and the development of effective policies and strategies to enhance preparedness and resilience in the face of seismic events in highly susceptible urban systems.

Spatial analysis of dengue transmission in an endemic city in Brazil reveals high spatial structuring on local dengue transmission dynamics.

In the last decades, dengue has become one of the most widespread mosquito-borne arboviruses in the world, with an increasing incidence in tropical and temperate regions. The mosquito Aedes aegypti is the dengue primary vector and is more abundant in highly urbanized areas. Traditional vector control methods have showing limited efficacy in sustaining mosquito population at low levels to prevent dengue virus outbreaks. Considering disease transmission is not evenly distributed in the territory, one perspective to enhance vector control efficacy relies on identifying the areas that concentrate arbovirus transmission within an endemic city, i.e., the hotspots. Herein, we used a 13-month timescale during the SARS-Cov-2 pandemic and its forced reduction in human mobility and social isolation to investigate the spatiotemporal association between dengue transmission in children and entomological indexes based on adult Ae. aegypti trapping. Dengue cases and the indexes Trap Positive Index (TPI) and Adult Density Index (ADI) varied seasonally, as expected: more than 51% of cases were notified on the first 2months of the study, and higher infestation was observed in warmer months. The Moran's Eigenvector Maps (MEM) and Generalized Linear Models (GLM) revealed a strong large-scale spatial structuring in the positive dengue cases, with an unexpected negative correlation between dengue transmission and ADI. Overall, the global model and the purely spatial model presented a better fit to data. Our results show high spatial structure and low correlation between entomological and epidemiological data in Foz do Iguaçu dengue transmission dynamics, suggesting the role of human mobility might be overestimated and that other factors not evaluated herein could be playing a significant role in governing dengue transmission.

Response of Chironomids (Diptera, Chironomidae) to Environmental Factors at Different Spatial Scales.

Factors responsible for species distribution of benthic macroinvertebrates, including responses at different spatial scales, have been previously investigated. The aim of the present research was to review the most relevant factors explaining chironomid species distribution focusing on factors operating at different spatial scales, such as latitude, longitude, altitude, substrate, salinity, water temperature, current velocity, conductivity, acidity, dissolved oxygen, nutrient content etc. acting at regional levels and at a large or small water basin level. Data including chironomid species abundances from different lentic and lotic waters in Italy and other surrounding countries were analyzed using partial canonical correspondence analysis (pCCA) and multiple discriminant analysis (DISCR). Spatial analyses, including univariate Moran's I correlograms, multivariate Mantel correlograms and Moran's eigenvector maps (MEMs), were thereafter carried out. The results showed that habitat type, including different types of lotic waters (i.e., kryal, crenal, rhithral, potamal) and different lake types (i.e., littoral, sublittoral, profundal zones), is the most significant factor separating chironomid assemblages, while spatial factors act only as indirect influencers.

Attention non-negative spectral clustering

Deep spectral clustering is an advanced unsupervised deep learning method with widespread applications. However, when applied to large-scale datasets, it may confront problems such as an unstable training process, limited scalability, and diminished interpretability. To mitigate these limitations, a novel unsupervised deep learning algorithm named Attention Non-negative Spectral Clustering (ANSC) is proposed. Specifically, a network architecture called the Spectral Attention Network (SAN) is designed to serve as the backbone network for ANSC. This design aims to improve the stability of the training process and the model’s scalability. This attention-based network architecture includes a feature extraction network, an eigenvector mapping network, and an orthogonalization module. Furthermore, to enhance the interpretability of ANSC, the objective function incorporates a cluster indicator matrix by introducing non-negative constraints. Additionally, the ℓ2,1-norm is employed as regularization to approximate the solution of the objective function. An analysis of a lower bound for the network size of ANSC is conducted based on the VC dimension. Extensive experiments validate that ANSC outperforms state-of-the-art clustering methods in terms of clustering accuracy, normalized mutual information, and adjusted rand index.

Living with a tumor: A case of osteosarcoma involving the medullary region in Phrynops cf. P. geoffroanus (Testudines: Chelidae).

The individual Geoffroy's side-necked turtle, Phrynops cf. P. geoffroanus, was diagnosed postmortem with osteosarcoma associated with the forelimb through morphological and histological analysis. Osteosarcoma stands as the most prevalent primary malignant bone tumor in tetrapods. The tumor presents itself as a large mass in the distal epiphysis, characterized by spicular outgrowths and a rugose external texture. Histologically, the afflicted humerus displayed a high degree of vascularity and exhibited an extensive bone resorption process involving the medullary and endosteal regions. Notably, a clear transition between the bone marrow and cortical bone was absent, indicative of a remodeling process featuring Haversian bone system apposition. Additionally, the diaphyseal region displayed the progression of neoplastic bone tissue along the bone. For comparative purposes, we describe a humeral thin section from a healthy specimen revealing compact primary bone interrupted by cyclical growth marks which differs from the continuous growth observed in the neoplastic humerus. To assess the neoplastic bone growth rate at the mid-diaphysis level, phylogenetic eigenvector maps (PEM) were employed, utilizing osteocyte density and vascular density as explanatory variables. The findings indicated that the osteosarcoma exhibited a slow-growing nature, suggesting that the turtle had to live with this condition for years. As the neoplasia continued to expand, it likely led to disadvantages for the pathological Phrynops individual due to humeral deformity. Furthermore, malignancy was associated with angiogenesis and the invasion of the medullary region by neoplastic bone tissue, raising the likelihood of metastasis as an additional factor contributing to the individual's sickness. The presence of numerous vascular canals in the diaphyseal thin section suggested a low-grade central osteosarcoma. It is worth noting that osseous neoplasms are rarely documented in Testudines, making this case of osteosarcoma in a South American freshwater chelid specimen a unique and rare occurrence.

Geography and ecology shape the phylogenetic composition of Amazonian tree communities

AbstractAimAmazonia hosts more tree species from numerous evolutionary lineages, both young and ancient, than any other biogeographic region. Previous studies have shown that tree lineages colonized multiple edaphic environments and dispersed widely across Amazonia, leading to a hypothesis, which we test, that lineages should not be strongly associated with either geographic regions or edaphic forest types.LocationAmazonia.TaxonAngiosperms (Magnoliids; Monocots; Eudicots).MethodsData for the abundance of 5082 tree species in 1989 plots were combined with a mega‐phylogeny. We applied evolutionary ordination to assess how phylogenetic composition varies across Amazonia. We used variation partitioning and Moran's eigenvector maps (MEM) to test and quantify the separate and joint contributions of spatial and environmental variables to explain the phylogenetic composition of plots. We tested the indicator value of lineages for geographic regions and edaphic forest types and mapped associations onto the phylogeny.ResultsIn the terra firme and várzea forest types, the phylogenetic composition varies by geographic region, but the igapó and white‐sand forest types retain a unique evolutionary signature regardless of region. Overall, we find that soil chemistry, climate and topography explain 24% of the variation in phylogenetic composition, with 79% of that variation being spatially structured (R2 = 19% overall for combined spatial/environmental effects). The phylogenetic composition also shows substantial spatial patterns not related to the environmental variables we quantified (R2 = 28%). A greater number of lineages were significant indicators of geographic regions than forest types.Main ConclusionNumerous tree lineages, including some ancient ones (>66 Ma), show strong associations with geographic regions and edaphic forest types of Amazonia. This shows that specialization in specific edaphic environments has played a long‐standing role in the evolutionary assembly of Amazonian forests. Furthermore, many lineages, even those that have dispersed across Amazonia, dominate within a specific region, likely because of phylogenetically conserved niches for environmental conditions that are prevalent within regions.

Principais preditores na ocorrência do fitoplâncton em sistemas lóticos

Abstract: Aim Our goal was to relate the phytoplankton metacommunity to its possible determinants in a micro watershed: (I) determinants related to landscape-scale filtering, (II) determinants referring to local microhabitat filtering, (III) determinants referring to previous colonization, and (IV) determinants representing three different dispersal routes. Methods Eight sampling stations were selected along the Cascavel River watershed, located in the state of Paraná, Brazil. Samples were collected quarterly for three years. All phytoplankton samples were quantitatively analyzed to determine the density of the metacommunity. In addition, it was characterized the landscape in terms of land use and occupation, and environmental characterization in terms of physical and chemical variables of the water. All data underwent relevant statistical analysis, where variance partitioning was carried out using partial RDA models, with prior selection of predictor variables, to estimate the relative role of each predictor in the community. We also compared three possible dispersal routes: “Asymmetric Eigenvector Map” (AEM), “Overland” and “Watercourse”. Results It was found that the metacommunity was best explained by “asymmetric eigenvector mapping” (AEM), indicating that because it is a small spatial scale the high connectivity between the sampling stations enables species to disperse overland as well. The different filters act together and depend on rainfall variation. Besides fluctuating temporally, the influence of these mechanisms is subject to which dispersal hypothesis is being considered. Conclusions At the watershed scale, we argue that small-scale processes should be considered, since they homogenize the landscape and consequently leave the environmental gradient similar between sampling stations. In addition, the connectivity of colonization patches is essential to understand the behavior of microalgae that have a high dispersal capacity and are not restricted only to the river course.

The cost of living in Notosuchia (Crocodyliformes, Mesoeucrocodylia)

Limbs of terrestrial notosuchian crocodyliforms are characterized by a permanent parasagittal position similar to that of mammals and dinosaurs. Thus, we expect high maximal rates of oxygen consumption (i.e., high aerobic capacities). To test this hypothesis, we inferred mass-independent maximal metabolic rates (MMRs) in seven notosuchian species using the femur blood flow rate (Q̇; cm3 s−1) correlated with the maximal metabolic rate as a proxy. We employed a phylogenetic eigenvector maps (PEMs) inference model based on 20 extant tetrapod species. Generally, ectothermic amniotes have lower maximal metabolic rates than similarly sized endotherms. However, certain anatomical features of the cardiorespiratory system in varanid lizards enable them to develop a more active lifestyle than other ectothermic sauropsids. We found that the retrodicted mass-independent aerobic capacity values for Notosuchia are significantly lower than those measured in mammals and varanid lizards sampled but significantly higher than those quantified in Crocodylia. Notosuchians exhibit Q̇ values on the femoral shaft higher than Crocodylus porosus, suggesting greater oxygen consumption during intense exercise, but significantly lower than those measured in varanid lizards. This condition probably allowed notosuchians to engage in prolonged strenuous locomotion activities, such as hunting prey, defending themselves from predators or avoiding competitors. The evolution of Mesoeucrocodylia metabolism involves at least two phenotypes, a relatively high (Notosuchia) and a low (Neosuchia, Crocodylia) MMR. Significance statementWe modeled three-dimensional (3D) objects from Notosuchia femora and conducted measurements on the nutrient foramina size. These values were used as a proxy to infer the blood flow rate (Q̇) and provided insights into the maximal metabolic rate in these terrestrial crocodyliforms. Our findings suggest that Notosuchia were likely ectotherms, but exhibited a more active lifestyle than extant Crocodylia. However, they had aerobic capacities significantly lower than those measured in varanid lizards.

Trait‐based and multi‐scale approach provides insight on responses of freshwater mussels to environmental heterogeneity

AbstractOur understanding of the factors driving the distribution of metacommunities at different scales can be obscured by high variation in species composition between sites and a lack of fine‐scale distribution data. Trait‐based approaches have long been used to better identify and examine ecological patterns. Most recent studies of riverine metacommunities examining trait‐based patterns have focused on shorter lived organisms. Here we focused on a group of longer lived, sedentary riverine organisms, unionid freshwater mussels. The objective of this study was to examine how (1) the distribution of mussels with different life history strategies (trait‐based approach) and (2) the relative importance of environmental and spatial factors (as a proxy for dispersal) would differ with spatial scale and position in the river; and to (3) further compare this with patterns derived from a taxonomic approach. Fine‐scale distribution data of mussels and environmental factors were collected every 100 m in spatially extensive surveys in an upstream and downstream segment (200 sites/20‐km segment) of a semiarid river, making them some of the most spatially intensive surveys documented to date. A combination of redundancy analysis, asymmetric eigenvector mapping, and variation partitioning analyses revealed that more variation was explained by environmental factors where more environmental differences occur between sites. Where environmental heterogeneity was lower, the amount of variation explained by smaller scale spatial factors was higher, likely mostly associated with stochastic rather than dispersal processes. A higher amount of unexplained variation at the taxonomic level suggests that stochasticity may also play an important role in determining species composition. In contrast, different life history groups had a highly predictable distribution pattern driven by environmental heterogeneity, especially between river segments and mesohabitat, which was associated with different flow conditions. The role we predict for environmental heterogeneity and stochasticity in shaping the distribution of mussels in our study river likely also applies to other taxa and ecosystems at a spatial scale at which neither dispersal limitation nor mass effects occur. Thus, understanding the magnitude and extent of dispersal relative to the amount of environmental heterogeneity may be key for predicting metacommunity structure and dynamics for different organisms.

DeepSTI: Towards tensor reconstruction using fewer orientations in susceptibility tensor imaging.

Susceptibility tensor imaging (STI) is an emerging magnetic resonance imaging technique that characterizes the anisotropic tissue magnetic susceptibility with a second-order tensor model. STI has the potential to provide information for both the reconstruction of white matter fiber pathways and detection of myelin changes in the brain at mm resolution or less, which would be of great value for understanding brain structure and function in healthy and diseased brain. However, the application of STI in vivo has been hindered by its cumbersome and time-consuming acquisition requirement of measuring susceptibility induced MR phase changes at multiple head orientations. Usually, sampling at more than six orientations is required to obtain sufficient information for the ill-posed STI dipole inversion. This complexity is enhanced by the limitation in head rotation angles due to physical constraints of the head coil. As a result, STI has not yet been widely applied in human studies in vivo. In this work, we tackle these issues by proposing an image reconstruction algorithm for STI that leverages data-driven priors. Our method, called DeepSTI, learns the data prior implicitly via a deep neural network that approximates the proximal operator of a regularizer function for STI. The dipole inversion problem is then solved iteratively using the learned proximal network. Experimental results using both simulation and in vivo human data demonstrate great improvement over state-of-the-art algorithms in terms of the reconstructed tensor image, principal eigenvector maps and tractography results, while allowing for tensor reconstruction with MR phase measured at much less than six different orientations. Notably, promising reconstruction results are achieved by our method from only one orientation in human in vivo, and we demonstrate a potential application of this technique for estimating lesion susceptibility anisotropy in patients with multiple sclerosis.

Environmental factors and spatial dependence explain half of the inherent variation in carbon pools of tropical paddy soils

The current study examined macro-environmental (climate and topography), micro-environmental (soil chemical properties) drivers, and spatially derived parameters that are significantly associated with soil carbon pools (0–15 cm) across tropical paddy-growing areas in Sri Lanka using the data from 987 sampling sites across the country. Redundancy analysis was performed to identify the relationships between the explanatory variables and the variation in different soil carbon pools i.e., total carbon (TC), Microbial Biomass Carbon (MBC), Permanganate Oxidizable Carbon (POXC), and Dissolved Organic Carbon (DOC). The spatial patterns in soil carbon pools were evaluated using Moran's eigenvector maps. Results indicated that macro, micro-environmental drivers and spatial variables explained 47% of the inherent variation of the TC, MBC, POXC and DOC. Micro-environmental drivers had a larger unique fraction relative to macro-environmental drivers (4% and 1% of the total variation, respectively). Most of the variation explained by macro-environmental drivers was shared by micro-environmental drivers (11% out of 15%). Among macro-environmental drivers, rainfall and enhanced vegetation index were more strongly related to the soil carbon pools compared to the topography-related factors. In terms of micro-environmental drivers, total N, available K, Ca, and soil pH (H2O) were the best explanatory variables of soil carbon pools. Spatial patterns in soil carbon pools were largely induced by the environmental predictors that are spatially structured. Our findings provide insights into improving the reliability of spatial estimation of the soil carbon by incorporating important soil carbon preditors and quantifying the impacts of environmental changes on soil carbon pools.

Spatial influence of the Asian Summer Monsoon on pollen assemblages of the Tibetan Plateau and its potential implication for the interpretation of fossil pollen records

The direction and intensity of Asian Summer Monsoon (ASM) circulation in the Tibetan Plateau (TP) region strongly influence pollen dispersal patterns; however, this spatial effect has been rarely quantified. In this paper, we explore the relative importance of this directional spatial effect. Specifically, we construct a wind connectivity diagram for modern pollen sample sites according to the prevailing monsoon direction, and extract latent spatial variables representing the directional process of the ASM, using the method of asymmetric eigenvector maps. We fit redundancy analysis models using both spatial variables and local climate factors, and conduct variation partitioning to quantify the effect of directional spatial effect by the ASM. Taking into account the joint effect with local climate, the directional spatial process induced by the ASM significantly explains the variation of pollen assemblages on the TP. The effect is detected in pollen groups with contrasting functional types and dispersal modes; for example, the composition of arboreal pollen and non-arboreal pollen are both significantly affected by the ASM, yet best explained by models assuming different monsoon directions. Anemophilous pollen shows a higher percentage of variation explained by the ASM than non-anemophilous pollen. Our results indicate that directional processes induced by the monsoon is an important influence on pollen assemblages in the TP and support the idea that deposition patterns of pollen, especially arboreal pollen is a useful proxy for studying monsoon evolution. A similar approach can also be applied to paleomonsoon simulations using fossil pollen records. The role of directional dispersal in affecting fossil pollen composition and abundance by prevailing winds deserves further investigation when undertaking Quaternary paleoenvironmental reconstructions using pollen.

Drivers of the spatiotemporal patterns of the mangrove crab metacommunity in a tropical bay.

Revealing community patterns and driving forces is essential in community ecology and a prerequisite for effective management and conservation efforts. However, the mangrove ecosystem and its important fauna group such as the crabs, still lack multi-processes research under metacommunity framework, resulting in evidence and theorical application gaps. To fill these gaps, we selected China's most representative mangrove bay reserve in tropical zone as a stable experimental system and conducted a seasonal investigation (July 2020, October 2020, January 2021, and April 2021) of mangrove crabs. We performed a multi-approach analysis using both pattern-based and mechanistic method to distinguish the processes driving the mangrove crab metacommunity. Our results showed that the crab metacommunity exhibits a Clementsian pattern in the bay-wide mangrove ecosystem but is influenced by both local environmental heterogeneity and spatial processes, thus representing a combined paradigm of species sorting and mass effect. Moreover, the long-distance spatial constraints are more pronounced compared to the local environmental factors. This is reflected in the greater importance of the broad-scale Moran's Eigenvector Maps, the distance-decay pattern of similarity, and the difference in beta diversity dominated by the turnover component. This pattern changes throughout the year, mainly due to changes in dominant functional groups caused by the stress of changes in water salinity and temperature induced by air temperature and precipitation. This research provides multi-dimension research data and relevant analysis, offering clear evidence for understanding the patterns and related driving forces of crab metacommunity in tropical bay mangroves, and verifies the applicability of some general laws in the system. Future studies can address more diverse spatiotemporal scales, gaining a clearer understanding to serve the conservation of mangrove ecosystems and economically important fishery species.

Neutral‐based processes overrule niche‐based processes in shaping tropical montane orchid communities across spatial scales

Abstract Tropical montane forests (TMF) are characterised by high endemism, species richness and turnover across elevations. A key question is how niche‐based processes, via adaptation of species to local environmental conditions, and neutral‐based processes from dispersal limitation shape community composition at different spatial scales across human‐modified landscapes. We expect that (1) communities are highly distinct even within the same habitat type and (2) niche‐based processes play the main role in compositional turnover. To address these expectations, we investigated the compositional turnover of orchids, one of the most prominent floristic elements of TMFs. We sampled orchids in 332 plots spanning over 270 km in the eastern Colombian Andes. Plots ranged between elevations of 1160–3750 m. We used two different spatial extents (whole gradient and two elevational bands), two grains of analysis for the first expectation (regional and local) and two spatial grains for the second expectation (broad and fine scales based on Moran's Eigenvector Maps [MEMs]). We found 331 orchid species in 171 (51%) plots. We found a strong pattern of high compositional turnover across scales (>72% of total beta diversity is given by species turnover), with 87.5% of the total species pool occurring in fewer than five plots, supporting our first expectation. Contrary to our second expectation, we found that community composition is mainly driven by geographical distance, while the relative influence of elevation, environmental variables and their combined fractions were negligible across habitats and spatial scales, rejecting niche‐based processes. Synthesis. High compositional turnover, even across habitats with the highest degree of human intervention, suggests that both forest‐dwelling and open‐habitat species do not easily disperse across habitats. Species dispersal is the major force of orchid community turnover and might be strongly dependent upon macroevolutionary processes and species life‐history traits over multiple scales. Dispersal limitation also draws attention to the importance of preserving habitat connectivity to halt biodiversity losses.

Intra- and inter-annual variations in metal concentrations in the superficial water of a highly polluted urban basin of Argentina.

To protect ecosystems impacted by human activities and prevent their degradation, it is imperative to evaluate variations in the concentration of environmental pollutants over time. Here, we evaluated the intra- and inter-annual variations of several metals from 15 sites in the Matanza Riachuelo River basin (one of the most polluted in the world) and determined the physicochemical and meteorological parameters associated with these changes from 2008 to 2015. For this, in each site, we used Asymmetric Eigenvector Maps and Redundancy Analysis. The results highlighted temporal patterns of metal concentrations and several factors associated with them, perhaps related to the actions performed in the area since 2008. Additionally, we found that the effects of physicochemical and meteorological factors on metal concentrations were site-dependent, possibly related to the presence of different local sources of pollution or characteristics of the river in each site, such as its sediments. This approach could be applied to different scenarios (aquatic and terrestrial) and could provide a tool to help decision-makers address the harmful consequences of the continuous advance of human activities on human and ecosystem health.

Co-occurrence patterns and assembly processes of abundant and rare bacterioplankton in plain river network areas of eastern China

In the context of anthropogenic impacts on riverine ecosystems globally, understanding the response of bacterioplankton to anthropogenic stress is important for human and environmental health. Bacterioplankton communities are critical for maintaining ecosystem stability, but little is known about their co-occurrence networks and assembly processes in intense human-impacted plains river networks. By applying cooccurrence networks, variance partitioning, and null model analysis, we investigated the mechanisms of interaction and assembly of abundant (>1% relative abundance) and rare (<0.01%) taxa at 32 sites in river networks along an urbanization gradient (urban, suburban, and agricultural areas). Our results show that interactions between the bacterioplankton communities were more complex in urban areas than in other areas, and that environmental factors (primarily fluoride, nitrate nitrogen, and dissolved organic carbon) as well as spatial factors (i.e., Moran’s Eigenvector Maps) explained most of the variation in bacterioplankton communities. Abundant taxa showed stronger spatial turnover than rare taxa, indicating that spatial factors played a greater role in the assemblage of abundant taxa. Land use types, especially impervious surfaces, had a unique influence on rare taxa, but contributed less to community change than other factors. In addition, rare taxa were mainly influenced by deterministic processes, whereas abundant taxa were more influenced by stochastic processes, especially in agricultural areas. These findings suggest that the shares of abundant and rare taxa mediate disturbances in local environmental conditions in anthropogenic river network areas, while deterministic processes play a greater role in shaping bacterioplankton communities. Overall, our study provides important insights for environmental monitoring and management in areas of intense human activity, emphasizing the need to consider both abundant and rare taxa when assessing the impact of human activities on riverine ecosystems.

Collaborative Governance and Environmental Regulation Measures for Pollution Reduction and Carbon Reduction in the Yangtze River Basin under the “Double Carbon” Goal

The Yangtze River Basin occupies an important position in the overall layout of China’s economic development. However, due to the increasing water pollution, the environment of the Yangtze River Basin continues to deteriorate, which hinders the long-term development and sustainable development goals of the Yangtze River Economic Belt. Therefore, this study started from the perspective of the reduction of coordinated pollution carbon. Then, through empirical analysis, the impact of environmental regulation measures in the Yangtze River Basin on industrial pollution control was studied. At the same time, the spatial eigenvector mapping (SEVM) method was used to explore the spatial impact of environmental regulation on carbon emissions. The results showed that the increase in the intensity of environmental regulation would lead to the expansion of the hidden economy, which would lead to the effect of environmental regulation weakening. There was an inverted “U” relationship between per capita real gross domestic product (GDP) and environmental pollution indicators. In addition, the expansion of foreign trade in the Yangtze River Economic Belt hada less inhibitory effect on ecological environmental protection than a promotional one. At the same time, command-type environmental regulation had a “green paradox” effect on carbon emissions in the Yangtze River Basin. The carbon emission reduction effect of implicit environmental regulation was different under different levels of incentive-type environmental regulation. The research showed that the multi-agent governance model could be further constructed from the interaction between the environmental regulation system and the administrative management system. It had a good effect on the coordinated treatment of pollution reduction and carbon reduction under the “double carbon” goal.

Dispersal ability is associated with contrasting patterns of beta diversity in African small mammal communities

AbstractAimSpatial processes and environmental filtering are important factors shaping community composition, but their effects are rarely tested across all aspects and components of beta diversity. We investigate both of these factors to explain patterns of taxonomic, functional and phylogenetic beta diversity of small mammals across Sub‐Saharan Africa. We predict that groups with poorest dispersal ability will experience the highest taxonomic and phylogenetic turnover but a nesting of functional diversity independent of dispersal.LocationAfrica.TaxaRodents, bats, shrews.MethodsWe amassed a continent‐spanning dataset of 97 bat assemblages, 166 rodent assemblages and 153 shrew assemblages comprising a total of 183, 225 and 109 species, respectively, from six bioregions of Sub‐Saharan Africa. We calculated three aspects of beta diversity: taxonomic, functional and phylogenetic. For each of these, we first calculated total beta diversity based on the Sørensen index (βsor) and then decomposed this into turnover (βsim) and nestedness (βnes) components. We then used Moran's Eigenvector Maps to examine the relationships between each aspect of beta diversity and environmental gradients (environmental filtering) and geographical distance between sites (spatial processes).ResultsWe found consistent patterns across the three taxa in all aspects of beta diversity, with taxonomic beta diversity being greatest and phylogenetic beta diversity being lowest. The turnover component was typically greater than the nestedness component for taxonomic and functional beta diversity, but not for phylogenetic beta diversity, for all three groups. Beta diversity was also linked with the dispersal ability of the three groups, with the highest levels of beta diversity found in shrews, intermediate levels in rodents and the lowest levels in bats.Main ConclusionsSpatial processes, which are linked to the dispersal abilities of the three taxa of small mammal, dominate environmental drivers in structuring African small‐mammal communities with phylogenetic comparisons suggesting a relatively permeable continent.

Non‐local low‐rank constraint‐based self‐consistent PMRI reconstruction using eigenvector maps

AbstractEigenvector‐based SPIRiT (ESPIRiT) can estimate multiple sets of coil sensitivity maps from the calibration matrix constructed from the auto‐calibration data. Recently, the L1 norm and total variation were combined with the ESPIRiT model to improve the reconstruction quality of magnetic resonance (MR) images. To further improve the reconstruction performance, the non‐local low‐rank regularisation term is incorporated into the ESPIRiT model (NLR‐ESPIRiT) is proposed. The proposed NLR‐ESPIRiT model takes full advantage of the non‐local self‐similarity features of MR images. The resulting optimisation problem can be transformed into a gradient problem and a denoising problem with low‐rank constraints using the operator splitting technique. The weighted nuclear norm (WNN) is applied as a surrogate of the rank. Then the denoising subproblem with the WNN can be effectively solved by using the alternating direction method of multipliers technique. For practical applications, a parameter‐selecting method is proposed to obtain almost optimal parameters for the same kind of MR images. Simulation experiments on in vivo data sets demonstrate that the proposed NLR‐ESPIRiT outperforms all competing traditional model‐based algorithms in terms of three objective metrics and visual comparison.