Search

Solving many‐objective optimization problems (MaOPs) by means of evolutionary algorithms obtains considerable attention in the community of evolutionary computation. However, it is a difficult task to effectively handle MaOPs with both regular and irregular Pareto front (PF) shapes. In this paper, a novel two reference vector sets based evolutionary algorithm, referred to as TVEA, is proposed to solve both types of MaOPs. TVEA can be featured as i) two independent reference vector sets are used, where the regular reference vector set is used for the regular PF MaOPs and the irregular one is used for the irregular PF MaOPs; ii) according to the two reference vector sets, TVEA automatically detects the PF shape of a MaOP at hand; iii) a new delete‐and‐add strategy is proposed to adaptively update the irregular reference vector set; iv) a novel environmental selection strategy is developed to handle the irregular PF MaOPs. TVEA is extensively evaluated on 23 benchmark MaOPs with both regular and irregular PF shapes. The results indicate that TVEA can correctly detect the PF shape of MaOPs to be solved. Compared with other state‐of‐the‐art algorithms, TVEA obtains better or at least competitive results on both types of MaOPs. [ABSTRACT FROM AUTHOR]

Constrained many-objective optimization problems (CMaOPs) pose great challenges for evolutionary algorithms to reach an appropriate trade-off of solution feasibility, convergence, and diversity. To deal with this issue, this paper proposes a constrained many-objective evolutionary algorithm based on adaptive infeasible ratio (CMaOEA-AIR). In the evolution process, CMaOEA-AIR adaptively determines the ratio of infeasible solutions to survive into the next generation according to the number and the objective values of the infeasible solutions. The feasible solutions then undergo an exploitation-biased environmental selection based on indicator ranking and diversity maintaining, while the infeasible solutions undergo environmental selection based on adaptive selection criteria, aiming at the enhancement of exploration. In this way, both feasible and infeasible solutions are appropriately used to balance the exploration and exploitation of the search space. The proposed CMaOEA-AIR is compared with the other state-of-the-art constrained many-objective optimization algorithms on three types of CMaOPs of up to 15 objectives. The experimental results show that CMaOEA-AIR is competitive with the compared algorithms considering the overall performance in terms of solution feasibility, convergence, and diversity. [ABSTRACT FROM AUTHOR]

Striking a balance between convergence and diversity matters considerably for evolutionary algorithms in solving many-objective optimization problems. The performance of these algorithms depends on their capability of obtaining a set of uniformly distributed solutions as close to the Pareto optimal front as possible. However, most existing evolutionary algorithms encounter challenges in solving many-objective optimization problems. Thus, in this paper, an adaptive many-objective evolutionary algorithm with coordinated selection strategies, labeled ACS-MOEA, is proposed to balance the convergence and diversity. The coordinated selection strategies include three selection strategies, i.e., the selection based on shifted-dominated distance, the selection based on objective vector angle, and the selection based on Non-Euclidean geometry distance. The first is used in the mating selection process to select high-quality parents for the generation of good offspring. Both the second and the third selection strategies are employed in the environmental selection process to delete poor solutions one by one for preserving the elitist solutions of the next generation. The performance of ACS-MOEA is verified by comparing it with six state-of-the-art algorithms on several well-known benchmark test suites with up to 10 objectives. Experimental results have fully demonstrated the competitiveness of ACS-MOEA in balancing convergence and diversity. Moreover, the proposed ACS-MOEA has also been verified to be effective in solving constrained many-objective optimization problems. [ABSTRACT FROM AUTHOR]

In recent years, the dynamic multiobjective optimization problems (DMOPs), whose major strategy is to track the varying PS (Pareto Optimal Solution, PS) and/or PF (Pareto Optimal Frontier), caused a great deal of attention worldwide. As a promising solution, reusing of "experiences" to establish a prediction model is proved to be very useful and widely used in practice. However, most existing methods overlook the importance of environmental selection in the evolutionary processes. In this paper, we propose a dynamic multiobjective optimal evolutionary algorithm which is based on environmental selection and transfer learning (DMOEA-ESTL). This strategy makes full use of the environmental selection and transfer learning techniques to generate individuals for a new environment by reusing the experience to maintain the diversity of the population and speed up the evolutionary process. As experimental validation, we embed this new scheme in the NSGA-II (non-dominated sorting genetic algorithm). We test the proposed algorithm with the help of six benchmark functions as well as compare it with the other two prediction based strategies FPS (Forward-looking Prediction Strategy, FPS) and PPS (Population Prediction Strategy, PPS). The experimental results testify that the proposed strategy can deal with the DMOPs effectively. [ABSTRACT FROM AUTHOR]

The primary objective of multi-objective evolutionary algorithms (MOEAs) is to find a set of evenly distributed nondominated solutions that approximate the Pareto front (PF) of a multi-objective optimization problem (MOP) or a many-objective optimization problem (MaOP). This implies that the approximated solution set obtained by MOEAs should be as close to PF as possible while remaining diverse, adhering to criteria of convergence and diversity. However, existing MOEAs exhibit an imbalance between achieving convergence and maintaining diversity in the objective space. As far as the diversity criterion is concerned, it is still a challenge to achieve an evenly distributed approximation set with different sizes for a problem with a complicated PF shape. Furthermore, Pareto dominance has its own weaknesses as the selection criterion in evolutionary multiobjective optimization. Algorithms based on Pareto criterion (PC) can suffer from problems such as slow convergence to the optimal front and inferior performance on problems with many objectives. To effectively address these challenges, we propose a multi-objective bean optimization algorithm (MOBOA). Given that the selection of parent species, representing global optimal solutions, directly influences the convergence and diversity of the algorithm, MOBOA incorporates a preference order equilibrium parent species selection strategy (POEPSS). By extending the Pareto criterion with the preference order optimization criterion, the algorithm effectively enhances parent species selection pressure across multiple objectives. To balance convergence and diversity, MOBOA proposes a multi-population global search strategy explicitly maintaining an external archive during the search process. Leveraging the inherent multi-population advantages of bean optimization algorithm (BOA), the algorithm facilitates information sharing among the main population, auxiliary populations, and historical archive solution sets. Additionally, a diversity enhancement strategy is employed in the environmental selection stage, introducing the environmental selection strategy of the SPEA2 algorithm to generate a set of evenly distributed nondominated solutions. Experimental results on a series of widely used MOPs and MaOPs demonstrate that the proposed algorithm exhibits higher effectiveness and competitiveness compared to state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In multiobjective optimization, set-based performance indicators are commonly used to assess the quality of a Pareto front approximation. Based on the scalarization obtained by these indicators, a performance comparison of multiobjective optimization algorithms becomes possible. The R2 and the hypervolume (HV) indicator represent two recommended approaches which have shown a correlated behavior in recent empirical studies. Whereas the HV indicator has been comprehensively analyzed in the last years, almost no studies on the R2 indicator exist. In this extended version of our previous conference paper, we thus perform a comprehensive investigation of the properties of the R2 indicator in a theoretical and empirical way. The influence of the number and distribution of the weight vectors on the optimal distribution of μ solutions is analyzed. Based on a comparative analysis, specific characteristics and differences of the R2 and HV indicator are presented. Furthermore, the R2 indicator is integrated into an indicator-based steady-state evolutionary multiobjective optimization algorithm (EMOA). It is shown that the so-called R2-EMOA can accurately approximate the optimal distribution of μ solutions regarding R2. [ABSTRACT FROM AUTHOR]

Selection strategy, including mating selection and environmental selection, is a key ingredient in the design of evolutionary multiobjective optimization algorithms. Existing approaches, which have shown competitive performance in low-dimensional multiobjective optimization problems with two or three objectives, often encounter considerable challenges in many-objective optimization, where the number of objectives exceeds 3. This paper first provides a comprehensive analysis on the selection strategies in the current evolutionary many-objective optimization algorithms. Afterward, we propose a coordinated selection strategy to improve the performance of evolutionary algorithms in many-objective optimization. This selection strategy considers three crucial factors: 1) the new mating selection criterion considers both the quality of each selected parent and the effectiveness of the combination of selected parents; 2) the new environmental selection criterion directly focuses on the performance of the whole population rather than single individual alone; and 3) both selection steps are complement to each other and the coordination between them in the evolutionary process can achieve a better performance than each of them used individually. Furthermore, in order to handle the curse of dimensionality in many-objective optimization problems, a new convergence measure by distance and a new diversity measure by angle are developed in both selection steps. Experimental results on both DTLZ and WFG benchmark functions demonstrate the superiority of the proposed algorithm in comparison with six state-of-the-art designs in terms of both solution quality and computational efficiency. [ABSTRACT FROM AUTHOR]

With the aim of reducing cost, carbon emissions, and service periods and improving clients' satisfaction with the logistics network, this paper investigates the optimization of a variant of the location-routing problem (LRP), namely the regional low-carbon LRP (RLCLRP), considering simultaneous pickup and delivery, hard time windows, and a heterogeneous fleet. In order to solve this problem, we construct a biobjective model for the RLCLRP with minimum total cost consisting of depot, vehicle rental, fuel consumption, carbon emission costs, and vehicle waiting time. This paper further proposes a novel hyper-heuristic (HH) method to tackle the biobjective model. The presented method applies a quantum-based approach as a high-level selection strategy and the great deluge, late acceptance, and environmental selection as the acceptance criteria. We examine the superior efficiency of the proposed approach and model by conducting numerical experiments using different instances. Additionally, several managerial insights are provided for logistics enterprises to plan and design a distribution network by extensively analyzing the effects of various domain parameters such as depot cost and location, client distribution, and fleet composition on key performance indicators including fuel consumption, carbon emissions, logistics costs, and travel distance and time. [ABSTRACT FROM AUTHOR]

How to balance the satisfaction of constraints and the optimization of objective functions is one of the key issues to solve constrained multi-objective optimization problems (CMOPs), especially when the constraints are complex. Although many algorithms have been designed to handle this, most of them are still unable to effectively handle CMOPs with complex constraints. Based on the above issue, this paper proposes a framework for bi-directional search, which evolves two populations (P 1 and P 2). P 1 aims to search the constrained Pareto front (CPF) from the infeasible side of the objective space, and P 2 from the feasible side, aiming to achieve a more efficient and comprehensive bi-directional search for the CPF. To ensure the diversity, we adopt a preferred weight vector selection strategy to choose potential mating parents, which improves the search capability for the marginal CPF. Furthermore, to coordinate the interaction between the two populations, we propose an environmental selection strategy to select the offspring generated by P 1 and P 2 under the same weight vector with the better fitness to update the populations respectively, and the fitness is evaluated based on the different constraint relaxations of the two populations, to update them respectively. Extensive experiments indicate that our proposed algorithm has significantly better results or was at least competitive when compared to eight state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Viruses modulate the diversity and activity of microbial communities. However, little is known about their role for the structure of stream bacterial biofilm communities. Here, we present insights into the diversity and composition of viral communities in various streams draining three proglacial floodplains in Switzerland. Proglacial streams are characterized by extreme environmental conditions, including near-freezing temperatures and ultra-oligotrophy. These conditions select for few but well-adapted bacterial clades, which dominate biofilm communities and occupy niches via microdiversification. We used metagenomic sequencing to reveal a diverse biofilm viral assemblage in these streams. Across the different floodplains and streams, viral community composition was tightly coupled to that of the bacterial hosts, which was underscored by generally high host specificity. Combining predictions of phagehost interactions with auxiliary metabolic genes (AMGs), we identify specific AMGs shared by phages infectingmicrodiverse clade members. Our work provides a step towards a better understanding of the complex interactions among bacteria and phages in streambiofilm communities in general and streams influenced by glacier meltwaters and characterized by microdiversity in particular. [ABSTRACT FROM AUTHOR]

Abstract Solving many‐objective optimization problems (MaOPs) by means of evolutionary algorithms obtains considerable attention in the community of evolutionary computation. However, it is a difficult task to effectively handle MaOPs with both regular and irregular Pareto front (PF) shapes. In this paper, a novel two reference vector sets based evolutionary algorithm, referred to as TVEA, is proposed to solve both types of MaOPs. TVEA can be featured as i) two independent reference vector sets are used, where the regular reference vector set is used for the regular PF MaOPs and the irregular one is used for the irregular PF MaOPs; ii) according to the two reference vector sets, TVEA automatically detects the PF shape of a MaOP at hand; iii) a new delete‐and‐add strategy is proposed to adaptively update the irregular reference vector set; iv) a novel environmental selection strategy is developed to handle the irregular PF MaOPs. TVEA is extensively evaluated on 23 benchmark MaOPs with both regular and irregular PF shapes. The results indicate that TVEA can correctly detect the PF shape of MaOPs to be solved. Compared with other state‐of‐the‐art algorithms, TVEA obtains better or at least competitive results on both types of MaOPs.

As a unique ecosystem, Karst caves harbor an impressive diversity of specific fungi. However, the factors and mechanisms that shape fungal biodiversity in caves remain elusive. In this study, we explored the assembly patterns of fungal communities based on our previous research in eight representative Karst caves in Southwest China. Our results indicated that dispersal limitation plays a crucial role in shaping the overall fungal community as well as specific communities in rock, sediment, and water samples. However, "Undominated" processes contributed more than dispersal limitation in air samples. Interestingly, the dominant assembly processes varied between caves. Consistently, environmental selection had a minor impact on the assembly of fungal communities. Among the examined spatial and environmental variables, latitude, longitude, altitude, and temperature were found to significantly influence fungal communities irrespective of substrate type. These findings provide valuable insights into the ecological factors governing fungal community assembly in Karst caves. [ABSTRACT FROM AUTHOR]

The metacommunity concept provides a theoretical framework that aims at explaining organism distributions by a combination of environmental filtering, dispersal, and drift. However, few works have attempted a multitaxon approach and even fewer have compared two distant biogeographical regions using the same methodology. We tested the expectation that temperate (mediterranean‐climate) pond metacommunities would be more influenced by environmental and spatial processes than tropical ones, because of stronger environmental gradients and a greater isolation of waterbodies. However, the pattern should be different among groups of organisms depending on their dispersal abilities. We surveyed 30 tropical and 32 mediterranean temporary ponds from Costa Rica and Spain, respectively, and obtained data on 49 environmental variables. We characterized the biological communities of bacteria and archaea (from the water column and the sediments), phytoplankton, zooplankton, benthic invertebrates, amphibians and birds, and estimated the relative role of space and environment on metacommunity organization for each group and region, by means of variation partitioning using generalized additive models. Purely environmental effects were important in both tropical and mediterranean ponds, but stronger in the latter, probably due to their larger limnological heterogeneity. Spatially correlated environment and pure spatial effects were greater in the tropics, related to higher climatic heterogeneity and dispersal processes (e.g., restriction, surplus) acting at different scales. The variability between taxonomic groups in the contribution of spatial and environmental factors to metacommunity variation was very wide, but higher in active, compared with passive, dispersers. Higher environmental effects were observed in mediterranean passive dispersers, and higher spatial effects in tropical passive dispersers. The unexplained variation was larger in the tropical setting, suggesting a higher role for stochastic processes, unmeasured environmental factors, or biotic interactions in the tropics, although this difference affected some actively dispersing groups (insects and birds) more than passive dispersers. These results, despite our limitations in comparing only two regions, provide support, for a wide variety of aquatic organisms, for the classic view of stronger abiotic niche constraints in temperate areas compared with the tropics. The heterogeneous response of taxonomic groups between regions also points to a stronger influence of regional context than organism adaptations on metacommunity organization. [ABSTRACT FROM AUTHOR]

Background: Understanding the selection of environmental conditions by animals requires knowledge of where they are, but also of where they could have been. Presence data can be accurately estimated by direct sampling, sightings, or through electronic tag deployments. However, absence data are harder to determine because absences are challenging to measure in an uncontrolled setting. To address this problem, ecologists have developed different methods for generating pseudo-absence data relying on theoretical movement models. These null models represent the movement of environmentally naive individuals, creating a set of locations that animals could have been if they were not exhibiting environmental selection. Methods: Here, we use four different kinds of null animal movement models—Brownian motion, Lévy walks, Correlated random walks, and Joint correlated random walks to test the ability and power of each of these null movement models to serve as appropriate animal absence models. We use Kolmogorov-Smirnov tests to detect environmental selection using two data sets, one of simulated animal tracks biased towards warmer sea surface temperatures, and one of 57 observed blue shark tracks of unknown sea surface temperature selection. Results: The four different types of movement models showed minimal difference in the ability to serve as appropriate null models for environmental selection studies. Selection strength and sample size were more important in detecting true environmental selection. We show that this method can suffer from high false positive rates, especially in the case where animals are not selecting for specific environments. We provide estimates of test accuracy at different sample sizes and selection strengths to avoid false positives when using this method. Conclusion: We show how movement models can be used to generate pseudo-absences and test for habitat selection in marine organisms. While this approach efficiently detects environmental selection in marine organisms, it cannot detect the underlying mechanisms driving this selection. [ABSTRACT FROM AUTHOR]

Environmental selection of multiobjective evolutionary algorithms (MOEAs) is a key component that chooses promising solutions from a candidate set for later usage. Most environmental selection strategies choose solutions based on their function values. However, in real-world optimization problems, function evaluations can be time consuming. The necessity of a large number of function evaluations leads to the low efficiency of MOEAs. How to decrease the number of function evaluations is one of the main issues of MOEAs. The environmental selection can be regarded as a classification process. The selected solutions are the promising class, and the discarded solutions are the unpromising class. Based on this consideration, we propose a classification-assisted environmental selection (CAES) strategy in this paper to decrease the number of function evaluations in MOEAs. In the proposed method, solutions are divided into two classes. One is non-dominated solutions (i.e. promising class) and the other is dominated solutions (i.e. unpromising class). The classifier is built to classify the offspring solutions into these two classes. Only promising offspring are evaluated (unpromising ones are removed with no function evaluations). Therefore, the number of function evaluations is reduced. We integrate the proposed CAES strategy into six MOEAs. The effectiveness of the proposed CAES strategy is examined through computational experiments on various test suites and three real-world application problems. Our experimental results show that the proposed CAES strategy clearly reduces the number of function evaluations without severely degrading the search ability of the original MOEAs. [ABSTRACT FROM AUTHOR]

Aim: An important research question in ecology is how climate and the biodiversity of aboveground plants and belowground microbiomes affect ecosystem functions such as nutrient pools. However, little is studied on the concurrent role of above‐ and belowground species composition in shaping the spatial distribution patterns of ecosystem functions across environmental gradients. Here, we investigated the relationships between the taxonomic composition of plants, soil bacteria and soil fungi and spatial turnover in nutrient pools, and assessed how species composition–nutrient pool relationships were mediated by contemporary climatic conditions. Location: Qinghai‐Tibetan Plateau. Time period: Current. Major taxa studied: Plants, soil bacteria and soil fungi. Methods: We surveyed plant assemblages, sampled the taxonomic composition of soil bacteria and soil fungi, and measured plant‐ and soil‐mediated nutrient pools at 60 alpine grasslands on the Qinghai‐Tibetan Plateau. Using Mantel tests, structural equation models and general linear models, we investigated the relative importance of the taxonomic composition of plant, soil bacterial, and soil fungal communities on the spatial turnover of alpine grassland nutrient pools. Results: We found that the taxonomic composition of plant, soil bacterial, and soil fungal communities was associated with local climate. However, the effects of local climate on the spatial turnover of plant‐ and soil‐mediated nutrient pools were mainly indirect and mediated through plant and soil bacterial species composition, but not through soil fungal species composition. We further found that the replacement component of soil bacterial β‐diversity and the richness difference of plant β‐diversity were the direct predictors of nutrient pools in the alpine grasslands. Main conclusions: These results highlight that belowground bacterial composition together with aboveground plant species composition are related to spatial turnover in nutrient pools, perhaps even driving it. Conserving above‐ and belowground biodiversity may therefore safeguard against the impacts of local climate on the functions of climate‐sensitive alpine grasslands. [ABSTRACT FROM AUTHOR]

The concept of ecotypes is complex, partly because of its interdisciplinary nature, but the idea is intrinsically valuable for evolutionary biology and applied conservation. The complex nature of ecotypes has spurred some confusion and inconsistencies in the literature, thereby limiting broader theoretical development and practical application. We provide suggestions for how incorporating genetic analyses can ease confusion and help define ecotypes. We approach this by systematically reviewing 112 publications across taxa that simultaneously mention the terms ecotype, conservation and management, to examine the current use of the term in the context of conservation and management. We found that most ecotype studies involve fish, mammals and plants with a focus on habitat use, which at 60% was the most common criterion used for categorization of ecotypes. Only 53% of the studies incorporated genetic analyses, and major discrepancies in available genomic resources among taxa could have contributed to confusion about the role of genetic structure in delineating ecotypes. Our results show that the rapid advances in genetic methods, also for nonmodel organisms, can help clarify the spatiotemporal distribution of adaptive and neutral genetic variation and their relevance to ecotype designations. Genetic analyses can offer empirical support for the ecotype concept and provide a timely measure of evolutionary potential, especially in changing environmental conditions. Genetic variation that is often difficult to detect, including polygenic traits influenced by small contributions from several genes, can be vital for adaptation to rapidly changing environments. Emerging ecotypes may signal speciation in progress, and findings from genome‐enabled organisms can help clarify important selective factors driving ecotype development and persistence, and thereby improve preservation of interspecific genetic diversity. Incorporation of genetic analyses in ecotype studies will help connect evolutionary biology and applied conservation, including that of problematic groups such as natural hybrid organisms and urban or anthropogenic ecotypes. [ABSTRACT FROM AUTHOR]

Dominance resistance is a challenge for Pareto-based multi-objective evolutionary algorithms to solve the high-dimensional optimization problems. The Non-dominated Sorting Genetic Algorithm III (NSGA-III) still has such disadvantage even though it is recognized as an algorithm with good performance for many-objective problems. Thus, a variation of NSGA-III algorithm based on fine final level selection is proposed to improve convergence. The fine final level selection is designed in this way. The θ-dominance relation is used to sort the solutions in the critical layer firstly. Then ISDE index and favor convergence are employed to evaluate convergence of individuals for different situations. And some better solutions are selected finally. The effectiveness of our proposed algorithm is validated by comparing with nine state-of-the-art algorithms on the Deb-Thiele-Laumanns-Zitzler and Walking-Fish-Group test suits. And the optimization objectives are varying from 3 to 15. The performance is evaluated by the inverted generational distance (IGD), hypervolume (HV), generational distance (GD). The simulation results show that the proposed algorithm has an average improvement of 55.4%, 60.0%, 63.1% of 65 test instances for IGD, HV, GD indexes over the original NSGA-III algorithm. Besides, the proposed algorithm obtains the best performance by comparing 9 state-of-art algorithms in HV, GD indexes and ranks third for IGD indicator. Therefore, the proposed algorithm can achieve the advantages over the benchmarks. [ABSTRACT FROM AUTHOR]

In the multimodal multi-objective optimization problems (MMOPs), there may exist two or multiple equivalent Pareto optimal sets (PS) with the same Pareto Front (PF). The difficulty of solving MMOPs lies in how to locate more equivalent PS in decision space and maintain a promising balance between the diversity of Pareto optimal solutions in decision space and the convergence of Pareto optimal solutions in objective space at the same time. To address these issues, a grid search based multi-population particle swarm optimization algorithm (GSMPSO-MM) is proposed in this paper to handle MMOPs. Multi-populations based on the k-means clustering method is adopted to locate more equivalent PS in decision space, and a grid is applied to explore high-quality solutions in decision space in GSMPSO-MM. The environmental selection operator, including the removing inefficient solutions operator and the updating non-dominated solutions archive, aims to approach the true non-dominated solutions, where the updating non-dominated solution archive is responsible for developing the diverse solutions in both the decision and objective space, simultaneously. Besides, the purpose of removing inefficient solutions with inferior convergence in objective space is to maintain promising convergence solutions in objective space. GSMPSO-MM is compared with seven state-of-the-art algorithms on a well-known MMOPs benchmark function. Experimental results demonstrate the superior performance of our proposed algorithm in solving MMOPs. [ABSTRACT FROM AUTHOR]

The Pareto-dominance-based multi-objective evolutionary algorithms (MOEAs) have been successful in solving many test problems and other engineering optimization problems. However, their performance gets affected when solving more than 3-objective optimization problems due to lack of sufficient selection pressure. Many attempts have been made by the researchers toward improving the environmental selection of those MOEAs. One such attempt is selecting solutions using the reference-lines-based framework. In this paper, an efficient environmental selection and normalization scheme are proposed for this framework. The environmental selection operator is developed to equally prioritize solutions associated with different lines drawn from the origin and the reference points. A normalization scheme is also suggested in which the extreme point is used which gets updated on the designed rules. The framework is referred to as LEAF, and it is tested on 3-, 5-, 10-, and 15-objective DTLZ and WFG test instances. LEAF demonstrates its outperformance on almost all DTLZ instances and shows better performance on most of WFG instances over six MOEAs from the literature. [ABSTRACT FROM AUTHOR]

Dryland ecosystems cover more than 30% of the terrestrial area of China, while processes that shape the biogeographic patterns of bacterial and fungal α-diversity have rarely been evaluated synchronously. To compare the biogeographic patterns and its drivers of bacterial and fungal α-diversity, we collected 62 soil samples from a typical dryland region of northwest China. We assessed bacterial and fungal communities by sequencing bacterial 16S rRNA gene and fungal ITS data. Meanwhile, the α-diversity was decomposed into two components: species replacement (species turnover) and nestedness to further explore the bacterial and fungal α-diversity patterns and its causes. The results show that both bacterial and fungal α-diversity were derived almost entirely from species turnover rather than from species nestedness. Distancedecay relationships confirmed that the geographic patterns of bacterial and fungal α-diversity were significantly different. Environmental factors had the dominant influence on both the bacterial and fungal α-diversity and species turnover, however, the role of geographic distance varied across bacterial and fungal communities. Furthermore, both bacterial and fungal nestedness did not significantly respond to the environmental and geographic distance. Our findings suggest that the different response of bacterial and fungal species turnover to dispersal limitation and other, unknown processes may result in different biogeographic patterns of bacterial and fungal α-diversity in the drylands of northwest China. Together, we highlight that the drivers of α-diversity patterns vary between bacterial and fungal communities, and microbial α-diversity are driven by multiple factors in the drylands of northwest China. [ABSTRACT FROM AUTHOR]

There has been much debate over the relative importance of environmental selection and spatial variation on community organization in microorganisms. To assess the importance of environmental or spatial variables in diatom species assemblages in Gall Lake, northwest Ontario, 41 surface-sediment samples were collected in a two-dimensional gridded pattern along and across depth contours. A depth-constrained cluster analysis separated the diatom flora into three communities: a shallow-water benthic zone (B1); a deeper-water benthic zone (B2); and a planktonic zone (P). Redundancy analysis (RDA) confirmed that water depth was a major predictor of variation in the flora. Further RDAs and variation partitioning using orthogonal polynomials and Moran's eigenvector maps showed that spatial location had minimal effect on the diatom assemblages. Principal components analysis grouped the diatom flora not only by assemblage, but also by water depth, regardless of two-dimensional spatial separation, suggesting the importance of the environmental gradients associated with lake depth. These findings indicate that environment is a more important explanatory variable than spatial variables for diatoms within lakes, suggesting dispersal plays a limited role in intra-lake diatom distributions. [ABSTRACT FROM AUTHOR]

The ability for organisms to disperse throughout their environment is thought to strongly influence population structure and thus evolution of diversity within species. A decades-long debate surrounds processes that generate and support high microbial diversity, particularly in the ocean. The debate concerns whether diversification occurs primarily through geographic partitioning (where distance limits gene flow) or through environmental selection, and remains unresolved due to lack of empirical data. Here we show that gene flow in a diatom, an ecologically important eukaryotic microbe, is not limited by global-scale geographic distance. Instead, environmental and ecological selection likely play a more significant role than dispersal in generating and maintaining diversity. We detected significantly diverged populations (FST > 0.130) and discovered temporal genetic variability at a single site that was on par with spatial genetic variability observed over distances of 15,000 km. Relatedness among populations was decoupled from geographic distance across the global ocean and instead, correlated significantly with water temperature and whole-community chlorophyll a. Correlations with temperature point to the importance of environmental selection in structuring populations. Correlations with whole-community chlorophyll a, a proxy for autotrophic biomass, suggest that ecological selection via interactions with other plankton may generate and maintain population genetic structure in marine microbes despite globalscale dispersal. Here, we provide empirical evidence for global gene flow in a marine eukaryotic microbe, suggesting that everything holds the potential to be everywhere, with environmental and ecological selection rather than geography or dispersal dictating the structure and evolution of diversity over space and time. [ABSTRACT FROM AUTHOR]

Methane seeps are widespread seafloor ecosystems shaped by the emission of gas from seabed reservoirs. The microorganisms inhabiting methane seeps transform the chemical energy in methane to products that sustain rich benthic communities around the gas leaks. Despite the biogeochemical relevance of microbial methane removal at seeps, the global diversity and dispersion of seep microbiota remain unknown. Here we determined the microbial diversity and community structure of 23 globally distributed methane seeps and compared these to the microbial communities of 54 other seafloor ecosystems, including sulfate-methane transition zones, hydrothermal vents, coastal sediments, and deepsea surface and subsurface sediments. We found that methane seep communities show moderate levels of microbial richness compared with other seafloor ecosystems and harbor distinct bacterial and archaeal taxa with cosmopolitan distribution and key biogeochemical functions. The high relative sequence abundance of ANME (anaerobic methanotrophic archaea), as well as aerobic Methylococcales, sulfate-reducing Desulfobacterales, and sulfide-oxidizing Thiotrichales, matches the most favorable microbial metabolisms at methane seeps in terms of substrate supply and distinguishes the seep microbiome from other seafloor microbiomes. The key functional taxa varied in relative sequence abundance between different seeps due to the environmental factors, sediment depth and seafloor temperature. The degree of endemism of the methane seep microbiome suggests a high local diversification in these heterogeneous but long-lived ecosystems. Our results indicate that the seep microbiome is structured according to metacommunity processes and that few cosmopolitan microbial taxa mediate the bulk of methane oxidation, with global relevance to methane emission in the ocean. [ABSTRACT FROM AUTHOR]

The extent of genetic differentiation among 17 Ethiopian populations (249 individuals) of Phytolacca dodecandra (Endod) sampled along altitudinal gradients that varied from 1600 to 3000 m was investigated using random amplified polymorphic DNA (RAPD). The populations were classified into three altitude groups: lowland (1600–2100 m), central-highland (2101–2500 m) and highland (2500–3000 m). Seventy polymorphic loci scored from 12 RAPD primers, singly or in combination with ecogeographical variables (altitude, longitude, latitude, temperature and rainfall), were used for principal component, discriminant, correlation, and stepwise multiple regression analyses. Principal component analysis (PCA) clearly differentiated lowland and the central-highland populations from those of the highlands independent of their geographical regions. Canonical discriminant analysis separated the lowland plants from those of the highlands with the central-highland plants being intermediate. Classificatory discriminant analysis corrected classification of 92.8% of the 249 plants into their respective three altitude groups. Multiple regression analysis identified a strong association between some RAPDs and altitude, temperature and rainfall, while the variation in most RAPDs was explained by combinations of the different ecogeographical variables. It is hypothesised that the different altitude groups may be (1) chemical and/or physiological ecotypes produced as a result of complex interactions of altitude with climatic and/or edaphic factors, or (2) different in ploidy levels. The significant correlations obtained between population means from some RAPDs and altitude and temperature as well as the strong association of some RAPDs with the ecogeographical variables in the multiple regression analysis suggest that part of the RAPD polymorphism could be adaptive, and responsive to environmental selection. [ABSTRACT FROM AUTHOR]

In deserts, pond cladocerans suffer harsh conditions like low and erratic rainfall, high evaporation, and highly variable salinity, and they have limited species richness. The limited species can take advantage of ephippia or resting eggs for being dispersed with winds in such habitats. Thus, environmental selection is assumed to play a major role in community assembly, especially at a fine spatial scale. Located in Inner Mongolia, the Ulan Buh desert has plenty of temporary water bodies and a few permanent lakes filled by groundwater. To determine species diversity and the role of environmental selection in community assembly in such a harsh environment, we sampled 37 sand ponds in June 2012. Fourteen species of Cladocera were found in total, including six pelagic species, eight littoral species, and two benthic species. These cladocerans were mainly temperate and cosmopolitan fauna. Our classification and regression tree model showed that conductivity, dissolved oxygen, and pH were the main factors correlated with species richness in the sand ponds. Spatial analysis using a PCNM model demonstrated a broad-scale spatial structure in the cladoceran communities. Conductivity was the most significant environmental variable explaining cladoceran community variation. Two species, Moina cf. brachiata and Ceriodaphnia reticulata occurred commonly, with an overlap at intermediate conductivity. Our results, therefore, support that environmental selection plays a major role in structuring cladoceran communities in deserts. [ABSTRACT FROM AUTHOR]

Changes in species composition across communities, i.e., β-diversity, is a central focus of ecology. Compared to macroorganisms, the β-diversity of soil microbes and its drivers are less studied. Whether the determinants of soil microbial β-diversity are consistent between soil depths and between abundant and rare microorganisms remains controversial. Here, using the 16S-rRNA of soil bacteria and archaea sampled at different soil depths (0–10 and 30–50 cm) from 32 sites along an aridity gradient of 1500 km in the temperate grasslands in northern China, we compared the effects of deterministic and stochastic processes on the taxonomic and phylogenetic β-diversity of soil microbes. Using variation partitioning and null models, we found that the taxonomic β-diversity of the overall bacterial communities was more strongly determined by deterministic processes in both soil layers (the explanatory power of environmental distance in topsoil: 25.4%; subsoil: 47.4%), while their phylogenetic counterpart was more strongly determined by stochastic processes (the explanatory power of spatial distance in topsoil: 42.1; subsoil 24.7%). However, in terms of abundance, both the taxonomic and phylogenetic β-diversity of the abundant bacteria in both soil layers was more strongly determined by deterministic processes, while those of rare bacteria were more strongly determined by stochastic processes. In comparison with bacteria, both the taxonomic and phylogenetic β-diversity of the overall abundant and rare archaea were strongly determined by deterministic processes. Among the variables representing deterministic processes, contemporary and historical climate and aboveground vegetation dominated the microbial β-diversity of the overall and abundant microbes of both domains in topsoils, but soil geochemistry dominated in subsoils. This study presents a comprehensive understanding on the β-diversity of soil microbial communities in the temperate grasslands in northern China. Our findings highlight the importance of soil depth, phylogenetic turnover, and species abundance in the assembly processes of soil microbial communities. [ABSTRACT FROM AUTHOR]

Allozyme variation in the tetraploid wild emmer wheat, Triticum dicoccoides, the progenitor of all cultivated wheats, was studied for the proteins encoded by 42 gene loci in 1815 plants representing 37 populations - 33 from Israel and 4 from Turkey - sampled in 33 localities from 1979 to 1987. The results showed that: (a) 6 loci (14%) were monomorphic in all populations, 15 loci (36%) were locally polymorphic, and 21 loci (50%) were regionally polymorphic. These results are similar to those obtained earlier on 12 Israeli populations. All polymorphic loci (except 4) displayed high local levels of polymorphism (>/ 10%). (b) The mean number of alleles per locus, A, was 1.252 (range: 1.050-1.634); the proportion of polymorphic loci per population averaged 0.220 (range: 0.050-0.415); genic diversity, He, averaged 0.059 (range: 0.002-0.119). (c) Altogether there were 119 alleles at the 42 putative loci tested, 114 of these in Israel, (d) Genetic differentiation was primarily regional and local, not clinal; 70% of the variant alleles were common (>/ 10%) and not widespread, but rather localized or sporadic, displaying an 'archipelago' population genetics and ecology structure. The coefficients of genetic distance between populations were high and averaged D = 0.134; range: 0.018-0.297, an indication of sharp genetic differentiation over short distances, (e) Discriminant analyses differentiated Israeli from Turkish populations, and within Israel, between central and 3 marginal regions, as well as between different soil-type populations, (f) Allozymic variation comprised 40% within and 60% between populations, (g) Gametic phase disequilibria were abundant, their number being positively correlated (r = 0.60, P<0.01) with the humidity, (h) Multilocus organization was substantive, also positively correlated with humidity, (i) Allozyme diversity, overall and at single loci, was significantly correlated with, and partly predictable by, climatic and edaphic factors, (j) The distrubition of the significant positive and negative values and the absence of autocorrelations in the correlogram revealed no similar geographic patterns across loci, eliminating migration as a prime factor of population genetic differentiation. These results suggest: (I) during the evolutionary history of wild emmer, diversifying natural selection, through climatic and edaphic factors, was a major agent of genetic structure and differentiation at both the single and multilocus levels; (II) wild emmer harbors large amounts of genetic diversity exploitable as genetic markers in sampling and abundant genetic resources utilizable for wheat improvement. [ABSTRACT FROM AUTHOR]