Additive Partitioning Research Articles

Implicit-explicit Runge-Kutta for radiation hydrodynamics I: Gray diffusion

Radiation hydrodynamics are a challenging multiscale and multiphysics set of equations. To capture the relevant physics of interest, one typically must time step on the hydrodynamics timescale, making explicit integration the obvious choice. On the other hand, the coupled radiation equations have a scaling such that implicit integration is effectively necessary in non-relativistic regimes. A first-order Lie-Trotter-like operator split is the most common time integration scheme used in practice, alternating between an explicit hydrodynamics step and an implicit radiation solve and energy deposition step. However, such a scheme is limited to first-order accuracy, and nonlinear coupling between the radiation and hydrodynamics equations makes a more general additive partitioning of the equations non-trivial. Here, we develop a new formulation and partitioning of radiation hydrodynamics with gray diffusion that allows us to apply (linearly) implicit-explicit Runge-Kutta time integration schemes. We prove conservation of total energy in the new framework, and demonstrate 2nd-order convergence in time on multiple radiative shock problems, achieving error 3–5 orders of magnitude smaller than the first-order Lie-Trotter operator split at the hydrodynamic CFL, even when Lie-Trotter applies a 3rd-order TVD Runge-Kutta scheme to the hydrodynamics equations.

Optimization design of support structure based on 3D printing technology

Parts are often warped and deformed when they are molded using selective laser melting (SLM) technology. Thus, it is necessary to study the addition support modes of parts molded using SLM. Consequently, we designed dendritic, E-stage and conical supports, having different structural parameters and different partitions using Magics, and then, we analyzed their performances using the finite element software Abaqus. The structural parameters of the supports were optimized and finally tested using SLM molding technology. The maximum stress concentration was found for dendritic supports, followed by E-stage supports, and then conical supports. The stress concentration and deformation level of Scheme 2 were less than those of Scheme 1. The stress intensity and deformation levels for two partitions were less than those for three partitions. For parts molded by SLM, the deformation was maximum for conical supports, followed by dendritic supports, and then E-stage supports. When gradient supports of similar volumes were added, additional partitions did not effectively improve the molding quality. When supports of similar volumes were added, adding gradient supports did not effectively improve the molding quality. The results provide a basis for the application of SLM in molding high-precision parts.

Efficient Multi-View Clustering via Unified and Discrete Bipartite Graph Learning.

Although previous graph-based multi-view clustering (MVC) algorithms have gained significant progress, most of them are still faced with three limitations. First, they often suffer from high computational complexity, which restricts their applications in large-scale scenarios. Second, they usually perform graph learning either at the single-view level or at the view-consensus level, but often neglect the possibility of the joint learning of single-view and consensus graphs. Third, many of them rely on the k -means for discretization of the spectral embeddings, which lack the ability to directly learn the graph with discrete cluster structure. In light of this, this article presents an efficient MVC approach via u nified and d iscrete b ipartite g raph l earning (UDBGL). Specifically, the anchor-based subspace learning is incorporated to learn the view-specific bipartite graphs from multiple views, upon which the bipartite graph fusion is leveraged to learn a view-consensus bipartite graph with adaptive weight learning. Furthermore, the Laplacian rank constraint is imposed to ensure that the fused bipartite graph has discrete cluster structures (with a specific number of connected components). By simultaneously formulating the view-specific bipartite graph learning, the view-consensus bipartite graph learning, and the discrete cluster structure learning into a unified objective function, an efficient minimization algorithm is then designed to tackle this optimization problem and directly achieve a discrete clustering solution without requiring additional partitioning, which notably has linear time complexity in data size. Experiments on a variety of multi-view datasets demonstrate the robustness and efficiency of our UDBGL approach. The code is available at https://github.com/huangdonghere/UDBGL.

PFAS distribution in cascade derived foam at wastewater treatment plants: The role of non-linear drainage, collapse induced enrichment, and implications for removal

Per- and polyfluorinated alkyl substances (PFAS) enrichment in foam was investigated for the first time at a wastewater treatment plant cascade. A novel sampling device was utilized to allow spatial and temporal heterogeneity in PFAS concentrations and liquid content to be characterized. Concentrations of 8 PFAS compounds were normalized to liquid content and fit to a power law model revealing strong correlation (R2 = 0.91) between drainage induced enrichment and PFAS molar volume. Short chain PFAS such as perfluorobutanoate (PFBA) exhibited minor to no enrichment factors in foam (0.24–5.9) compared to effluent concentrations across the range of foam liquid contents (0.28–6.24 %), while long chain compounds such as perfluorooctane sulfonate (PFOS) became highly enriched with factors of 295–143,000. A conceptual model is proposed to explain higher than expected enrichment of more surface-active PFAS relative to liquid content, which combines continuous partitioning of PFAS to air bubbles during foam formation with additional partitioning during non-linear drainage and foam collapse, both controlled by their affinity for the air-water interface. Scoping calculations suggest the majority of PFOS and other long chain PFAS may be removed if foam is continuously collected with potential to reduce waste volume under economic barriers for current destructive technologies.

Mapping paleostress trajectories by means of the clustering of reduced stress tensors determined from homogeneous and heterogeneous data sets

It is easy to draw stress trajectories to investigate the present stress field by interpolating stress orientations determined at control points. However, challenges arise when we deal with the trajectories of paleostresses, because neighboring control points may have the stress orientations of different tectonic phases. We must choose coeval stresses to draw the trajectories. Recent stress inversion techniques can separate stresses from heterogeneous data from fault, dilational fractures, etc. Natural data sets from those structures are often heterogeneous, and age data are usually not enough to classify the stresses by age. As a result, an unsupervised classification problem of the inversion results must be solved to draw the trajectories. Here, we propose a simple and heuristic procedure for this problem. We assume smooth trajectories during each of the phases. The smoothness makes density-based clustering adoptable to solve the problem. The heterogeneity of data sets allows the additional partition of the clusters. As a worked exercise for this technique, the trajectories of minimum horizontal stress orientations were drawn based on the paleostresses determined from the attitudes of felsic dikes and quartz veins formed in mid Cretaceous orogeny in the North Kitakami Terrain, northern Japan. The orogen-parallel and orogen-perpendicular extensional stress fields delineated by the present technique were probably the manifestations, respectively, of the gravitational collapse of the orogen and of regional extensional tectonics in the Far East.

Additive partitioning of multispecies distributional aggregation of local assemblages.

The distribution of species is not random in space. At the finest-resolution spatial scale, that is, field sampling locations, distributional aggregation level of different species would be determined by various factors, for example spatial autocorrelation or environmental filtering. However, few studies have quantitatively measured the importance of these factors. In this study, inspired by the statistical properties of a Markov transition model, we propose a novel additive framework to partition local multispecies distributional aggregation levels for sequential sampling-derived field biodiversity data. The framework partitions the spatial distributional aggregation of different species into two independent components: regional abundance variability and the local spatial inertia effect. Empirical studies from field amphibian surveys through line-transect sampling in southwestern China (Minya Konka) and central-southern Vietnam showed that local spatial inertia was always the dominant mechanism structuring the local occurrence and distributional aggregation of amphibians in the two regions with a latitudinal gradient from 1200 to nearly 4000 m. However, regional abundance variability is still nonnegligible in highly diverse tropical regions (i.e. Vietnam) where the altitude is not higher than 2000 m. In summary, we propose a novel framework that shows that the multispecies distributional aggregation level can be structured by two additive components. The two partitioned components could be theoretically independent. These findings are expected to deepen our understanding of the local community structure from the perspective of both spatial distribution and regional diversity patterns. The partitioning framework might have potential applications in field ecology and macroecology research.

Microeukaryotic community dynamics and assembly mechanisms in shrimp aquaculture ponds

Microeukaryotes play a crucial role in maintaining microbial diversity and stability of aquaculture pond ecosystems. Despite their importance, our understanding of microeukaryotic community assembly and temporal dynamics throughout the aquaculture cycle remained limited. This study investigated microeukaryotic community dynamics in nine shrimp (Litopenaeus vannamei) farming ponds over 60 days using 18S rRNA gene sequencing. The results indicated that microeukaryotic community dynamics in the pond system can be divided into two stages: a fluctuating stage from day 0 to day 40 and a stable stage from day 40 to day 60. The alpha diversity of the microeukaryotic community in sediment increased in the first stage and decreased in the second stage, while it remained relatively stable in water. The community structure, network complexity, and stability of the microeukaryotic community in water were more stable in the second stage than in the first stage. Additive partitioning analysis indicated local diversity and within-habitat variation were the primary contributors to the total microeukaryotic metacommunity diversity of aquaculture pond system. Furthermore, the species pool in both water and sediment primarily originated from historical sources within the microeukaryotic metacommunity, and the dispersal route between habitats (water and sediment) was strengthened in the second stage compared to the first stage. Additionally, the assembly of microeukaryotic community was primarily governed by stochastic processes in the first stage and deterministic processes in the second stage. Specifically, increasing nitrogen loading influenced the relative abundance of dominant green algae and strengthened the homogeneous environmental selection of the microeukaryotic community. Overall, this study demonstrates the significant impact of historical species pool and nutrient loading in structuring the microeukaryotic metacommunity in the aquaculture pond system.

Combined Effects of Thermal Buoyancy, Wind Action, and State of the First-Floor Lobby Entrance on the Pressure Difference in a High-Rise Building

The stack effect in high-rise buildings, stemming from an inside/outside temperature difference, may produce a significant pressure difference on the elevator doors, potentially causing elevator malfunctions. This effect can also be influenced by wind action and human behaviors, e.g., opening/closing of building entrances. In this study, a wind tunnel test was conducted to determine the real wind pressure distribution on a high-rise building in northern China. A numerical simulation utilizing the Conjunction of Multizone Infiltration Specialists software (COMIS) was carried out to investigate the pressure difference of elevator doors under the effects of thermal buoyancy, wind action, and opening/closing of the first-floor lobby entrance. An alternative solution of a locally strengthened envelope is proposed and validated for the studied building zone. The study reveals that the opening of the first-floor lobby entrance increases the pressure difference regardless of the environmental conditions, and the increase of wind speed tends to increase the pressure difference in winter but decrease it in summer. The proposed countermeasure combination, involving using revolving doors instead of swing doors, increasing additional partitions, and strengthening the local building envelope, was found to be synergistic and effective in reducing the pressure difference inside the building. The research findings offer practical engineering solutions for mitigating elevator door pressure challenges in high-rise buildings.

Mycorrhizal type and tree diversity affect foliar elemental pools and stoichiometry.

Species-specific differences in nutrient acquisition strategies allow for complementary use of resources among plants in mixtures, which may be further shaped by mycorrhizal associations. However, empirical evidence of this potential role of mycorrhizae is scarce, particularly for tree communities. We investigated the impact of tree species richness and mycorrhizal types, arbuscular mycorrhizal fungi (AM) and ectomycorrhizal fungi (EM), on above- and belowground carbon (C), nitrogen (N), and phosphorus (P) dynamics. Soil and soil microbial biomass elemental dynamics showed weak responses to tree species richness and none to mycorrhizal type. However, foliar elemental concentrations, stoichiometry, and pools were significantly affected by both treatments. Tree species richness increased foliar C and P pools but not N pools. Additive partitioning analyses showed that net biodiversity effects of foliar C, N, P pools in EM tree communities were driven by selection effects, but in mixtures of both mycorrhizal types by complementarity effects. Furthermore, increased tree species richness reduced soil nitrate availability, over 2 yr. Our results indicate that positive effects of tree diversity on aboveground nutrient storage are mediated by complementary mycorrhizal strategies and highlight the importance of using mixtures composed of tree species with different types of mycorrhizae to achieve more multifunctional afforestation.

Development of a hard cider flavor wheel using free word sorting and multivariate statistical techniques

AbstractFlavor wheels are visual tools built from standardized sensory lexicons that are used in many different industries to improve communication, marketing, and quality discrimination among products. To date, flavor wheels have been developed for spices, coffee, wine, beer, and many other foods and beverages—yet no flavor wheel has been constructed for hard cider. While there is no single established method for constructed flavor wheels, most approaches are based on sensory descriptive analysis (DA) and free word sorting activities that investigate the semantic similarity and dissimilarity of descriptive terms. This research study utilized multiple DA studies for the generation of a sensory lexicon, followed by independent word sorting tasks with cider industry professionals (N = 40) and untrained consumers (N = 58) to establish two flavor wheels that are broadly understandable to a variety of industry stakeholders. Based on the results of DISTATIS and additive tree partitioning, this research showcases a workflow for developing and refining flavor wheels that incorporate both stakeholder and researcher input and can be built upon by other analysts.Practical ApplicationsThis research demonstrates an accessible methodology for developing flavor wheels that incorporates input from diverse parties and considers the semantic associations of terms used by cider consumers and industry professionals. The proposed methodology is a useful framework for other sensory scientists seeking to efficiently develop flavor wheels for multiple audiences. As a demonstration, this research also delivers two flavor wheels—one that highlights discrepancies in how industry consumers and producers semantically understand sensory experiences compared to trained sensory personnel, and a second wheel that showcases how hard cider can more objectively be described. Together, these flavor wheels are tools for improving sensory communication, education, and marketing in the US hard cider industry.

Reconsidering the Price equation: Benchmarking the analytical power of additive partitioning in ecology

The Price approach allows the partitioning of composite variables into a set of additive components and has become an important tool in evolutionary and ecological research. However, because such components are not mutually independent and might be constrained by the inherent data structure, comparing and interpreting Price partitions among different datasets is not straightforward and has contributed to controversy in ecology.Here we discuss and develop null model approaches that might be used as statistical standards to normalise partition values and reduce collinearity between partitions. We use a simulation approach to estimate Price partitions of artificial data and their randomisations. Using structural equation modelling we then reveal the degree of collinearity between raw and standardised partitions.We first demonstrate that the degree of collinearity between partitions strongly depends on the data structure and then provide a general framework for null model selection. Null models that require limited additional information on the possible distribution of species richness and abundance perform best. These null models consistently reduce, but do not fully eliminate, collinearity between partitions. They assign separate type I and II error levels to each partition that differed among partitions.We argue in favour of null models that strike a balance between information need (simplicity) and complexity of additional information, but we discourage the use of simple permutation approaches that have been successful for analysing other biodiversity measures such as species richness. We highlight that the interpretation of additive partitions of complex ecological data will benefit from analyses of the dependence among partitions.

Generalised scaled boundary isogeometric analysis – a method for structural analysis of naturally shaped timber structures

ABSTRACT Natural building materials, such as naturally shaped tree trunks, have been used in construction since ancient times. Wood is primarily processed as a long, thin product due to its growth and structure, leaving many parts unused. These neglected parts, like branches, can be used for load-bearing structures. However, they require a new parametric description due to material and geometric behaviour differences. It's crucial to transfer these geometries into the calculation model accurately. The surface is described using NURBS based on image data to realise this. Applying the Scaled Boundary Isogeometric Analysis (SBIGA) can directly use these geometries for structural analysis. This method combines the concepts from the scaled boundary finite element method (SBFEM) with the isogeometric analysis (IGA). In this technique, two-dimensional surfaces are scaled to a centre point. When used in the context of thin, long structures, obtuse-angled polyhedrons occur within a patch, leading to numerical condition problems. This motivates this contribution to modify the conventional SBIGA towards a generalised scheme. This work introduces a generalised SBIGA approach, employing a scaling centre line instead of a scaling centre point. This enables the analysis of slender, curved objects without additional structure partitioning, simplifying the preprocessing while improving the performance.

Diversity patterns along ecological succession in tropical dry forests: a multi‐taxonomic approach

Evaluating the diversity of multiple taxa is fundamental to understand community assembly and to assess the integrity and functionality of tropical secondary forests. In this study, we analyzed the natural regeneration of tropical dry forests (TDFs) in three regions of Brazil using Hill–Simpson diversity, abundance and β‐diversity of trees and five groups of insects (herbivores, fruit‐feeding butterflies, ants, culicid mosquitoes and dung beetles). Sampling was conducted in 39 0.1 ha plots using a chronosequence approach (13 plots in early, intermediate and old‐growth forests). We evaluated the contribution of three different levels to γ‐diversity: α (within plots), β1 (among plots) and β2 (among successional stages), and further determined the relative importance of turnover (species replacement) and nestedness (differences in species number among sites) to β2. Our results showed that, unexpectedly, the Hill–Simpson diversity was consistently higher in early than old‐growth stages for all regions, but varied more widely in the intermediate stages. For each group separately, the same trend was observed for butterflies, ants, dung beetles and herbivores and did not differ among stages for mosquitoes. Successional differences in abundance were only detected for trees (increasing along the gradient) and for mosquitoes (decreasing). According to our expectations, the additive partitioning analysis showed that β2‐diversity contributed more to γ‐diversity than β1‐diversity, when all taxa were considered together and for most of them separately (except for butterflies and dung beetles). Most of the β2‐diversity was due to species turnover, but this contribution varied among groups and regions, with the highest turnover for herbivores and the lowest for dung beetles. Our results suggest that the Hill–Simpson diversity and changes in species composition (as given by β2‐diversity) are better indicators of forest natural regeneration than raw species richness, corroborating previous studies with plants and animals.

Diversity patterns of echinoids (Echinodermata: Echinoidea) in shallow waters of the Mexican Pacific

The Mexican Pacific hosts a large diversity of echinoids occupying different habitats and ecological niches; nevertheless, the group remains scarcely studied in terms of their spatial patterns of biodiversity and variation in the region. This work evaluated the alpha, beta, and gamma diversity of echinoids living in the Mexican Pacific's shallow waters (0–200 m) at three spatial scales: Operational geographic units (OGUs), ecoregions, and provinces. Alpha diversity was evaluated through the standardized coverage values of 0D, 1D, and 2D, while the contribution of alpha and beta diversity components to gamma diversity was estimated with additive partitioning. Finally, we evaluated whether beta diversity resulted from overlap, species replacement, or richness differences. A total of 45 species and 2,232 incidences were registered in the 150 OGUs distributed throughout the Mexican Pacific. Cluster completeness in the ecoregions of the Warm Temperate Northeast Pacific (WTNP) province was higher (> 95 %) than in the ecoregions of the Eastern Tropical Pacific (TEaP) province. Standardized coverage values of the 0D, 1D, and 2D indices showed differences between the ecoregions Cortezian and Magdalena Transition in the WTNP province. At the ecoregion and province level, alpha diversity might be influenced by sampling techniques and habitat availability, which limits the distribution of regular and irregular echinoids. Gamma diversity is mainly determined by beta diversity at the ecoregion scale, where species overlap and richness differences were the major contributing components, probably resulting from substrate dependence, habitat availability, and mesoscale ocean circulation patterns.

Investigation of phytoplankton community structure and formation mechanism: a case study of Lake Longhu in Jinjiang.

In order to explore the species composition, spatial distribution and relationship between the phytoplankton community and environmental factors in Lake Longhu, the phytoplankton community structures and environmental factors were investigated in July 2020. Clustering analysis (CA) and analysis of similarities (ANOSIM) were used to identify differences in phytoplankton community composition. Generalized additive model (GAM) and variance partitioning analysis (VPA) were further analyzed the contribution of spatial distribution and environmental factors in phytoplankton community composition. The critical environmental factors influencing phytoplankton community were identified using redundancy analysis (RDA). The results showed that a total of 68 species of phytoplankton were found in 7 phyla in Lake Longhu. Phytoplankton density ranged from 4.43 × 105 to 2.89 × 106 ind./L, with the average density of 2.56 × 106 ind./L; the biomass ranged from 0.58-71.28 mg/L, with the average biomass of 29.38 mg/L. Chlorophyta, Bacillariophyta and Cyanophyta contributed more to the total density, while Chlorophyta and Cryptophyta contributed more to the total biomass. The CA and ANOSIM analysis indicated that there were obvious differences in the spatial distribution of phytoplankton communities. The GAM and VPA analysis demonstrated that the phytoplankton community had obvious distance attenuation effect, and environmental factors had spatial autocorrelation phenomenon, which significantly affected the phytoplankton community construction. There were significant distance attenuation effects and spatial autocorrelation of environmental factors that together drove the composition and distribution of phytoplankton community structure. In addition, pH, water temperature, nitrate nitrogen, nitrite nitrogen and chemical oxygen demand were the main environmental factors affecting the composition of phytoplankton species in Lake Longhu.

Long‐term organic farming and floral diversity promotes stability of bee communities in agroecosystems

Abstract Increasing bee diversity promotes pollination services on farms. Yet, given the high turnover in pollinator communities, without knowledge of how pollinator communities assemble, it is difficult to conserve or increase bee diversity. Thus, a mechanistic understanding of factors mediating pollinator community assembly could promote pollinator conservation measures. To assess the determinants of pollinator community assembly and structure, we surveyed bee communities and floral resources on 36 farms ranging from 0 to 43 years in organic production. We used niche‐based and stochastic species abundance models to characterise the mechanisms driving community assembly, and an additive partition of beta diversity to evaluate resource and species turnover (i.e. community structure). We then used statistical models to assess whether resource turnover or time in organic production altered community assembly and beta diversity, and a jackknife analysis to assess the sensitivity of top models to resource and species identity. We show that bee communities on farms that practiced organic methods for longer assembled by niche‐based rather than stochastic processes and had less turnover in bee species across years. Because our model of niche‐based processes assumes resource use, these results indicate bee communities reflect underlying species‐specific resource preferences (e.g. floral and/or nesting resources) and that longer periods of organic management reduced dissimilarity mediated by species replacement. Our jackknife approach then examined the role of species identity effects in beta diversity, showing changes in floral resources increased dissimilarity driven by bee species loss, but only in landscapes simplified by urbanisation. This jackknife analysis then indicated that landscape resource replacement which was not driven by particular landscape classes, mediated bee species replacement wherein dissimilarity was driven by a generalist native bee, suggesting bee life history (e.g. flexibility in resource use) and landscape complementarity, rather than identity, underlie patterns identified using the beta diversity equations. Our results show bee communities assemble by niche‐based processes, evidenced by collinearity in resource and bee species turnover. Because niche‐based assembly indicates ecosystem health, farmers who adopt crop diversification and practice organic methods for longer may promote pollinator diversity and stability leading to improved pollination in farms. Read the free Plain Language Summary for this article on the Journal blog.

The species richness and community composition of different growth forms and life forms of mosses are dominated by different factors along an elevational gradient of China

Understanding the elevational variation in species richness and community composition as well as their mechanisms is critical for biodiversity conservation. On Mt Jianfengling of China, we examined the elevational patterns of species richness and analyzed the important variables driving the variation of species richness and community composition using boosted regression trees for all mosses and two growth forms and three life forms of mosses, respectively. We assessed the contribution of elevation toward explaining the heterogeneity among sampling sites. Last, we compared the occurrence probability and proportional use of community type along the elevational gradient between two growth forms and three life forms of mosses. Although each taxonomic group displayed a negatively skewed (hump-shaped) diversity pattern and a high turnover rate of community composition along the elevational gradient, the leading drivers for the patterns of each group are different. The habitat complexity and the mid-domain effect (MDE) hypothesis were supported for the diversity patterns of acrocarpous mosses and turfs, whereas the habitat complexity hypothesis was supported for pleurocarpous mosses, mats, and wefts. Habitat composition dominated the community variation of acrocarpous mosses and turfs, whereas solar radiation was the leading factor for pleurocarpous mosses, mats and wefts. According to the additive diversity partitioning, α explained only 4.8–7.3% of the variation in species richness. For many low-lying sampling sites with sunny and dry conditions, extinction and dispersal limitation might be the primary processes producing low species richness pattern, which decreases the overall average diversity for each group. The variations of the occurrence probability and proportional use of community type along the elevational gradient in each group explain the contributions of elevation toward the heterogeneity among sampling sites for all mosses. With higher sensitivity to environmental changes, pleurocarpous mosses and wefts may have greater fluctuations in species richness when environmental conditions turn from unfavorable to optimal, therefore the elevation effect on explaining the heterogeneity among sampling sites was higher (18.9%) for pleurocarpous mosses as compared to 11.1% for acrocarpous mosses; and it ranged from a low of 11.0% in turfs to 19.9% in wefts. Although each group displayed similar biogeographic patterns along the elevational gradient, the underlying drivers are different. Therefore, the total measurements of species will veil the complex mechanisms for each growth form and life form. It is also suggested that conservation efforts should be paid to the top altitude for pleurocarpous mosses, mats and wefts, and the whole elevational range for acrocarpous mosses and turfs.

Genotypic stability in root system architecture and aboveground biomass revealed diverse adaptability of peanut (Arachis hypogaea L.) to moderate water deficit

AbstractMany crop species, including cultivated peanut (Arachis hypogaea L.), modify their above‐ and below‐ground growth to cope with water deficit stress. This acclimation to water deficit often triggers a biomass partitioning shift—allocating more biomass to the roots, to increase the accessibility of roots to water resources. However, additional carbon partitioning to roots may not always translate into increased water use and maintenance of aboveground biomass (ABM) and yield. Therefore, selecting an efficient root system architecture (RSA) should aim to sustain a high ABM production under a water deficit scenario. To better understand the associations of above and belowground biomass partitioning under moderate water deficit, this study evaluated the genotypic stability of 40 peanut genotypes in ABM and RSA in greenhouse experiments and further assessed genotypic differences in 4 site‐year field experiments. Our results suggested that higher ABM‐producing genotypes generally had high plasticity when subjected to water deficit whereas the low ABM‐producing genotypes had relatively high stability. Hierarchical clustering analysis further revealed that genotypes with a high root‐to‐shoot ratio potentially had increased genotypic stability in ABM underwater deficit. Interestingly, genotypes that maintained the highest ABM underwater deficit did not have the highest total root biomass and length. Instead, these genotypes had the highest root length in the top layer of soil (0–0.3 m) and relatively fewer roots in the deeper layer of soil (0.3–1 m). Greenhouse‐screened stable genotypes exhibited minimal yield reduction when subjected to mid‐season water deficit in some of the field validation experiments, but it also happened to some plastic genotypes, indicating that further validation of controlled environment screenings for genotypic water‐deficit tolerance in the field is necessary.

Temperature dissimilarity drives flower–visitor interaction turnover across elevation in the Mexican Transition Zone

AbstractAimMost biodiversity studies have considered species to be isolated entities, neglecting the fact that their biotic interactions and spatial variation are fundamental to their persistence across elevational gradients. Here, using a standardized sampling methodology, we evaluated how and why the composition of flower–visitor interactions (i.e. beta diversity) varies over an extensive elevational gradient. Specifically, we aimed to identify which biotic (species turnover) and abiotic factors (temperature, precipitation and primary productivity) inherent to elevational gradients can explain the distribution of floral visitor–plant interactions.LocationMexican Transition Zone.TaxonAngiosperms, Hymenoptera, Lepidoptera, Diptera, Hemiptera and Coleoptera.MethodsWe sampled ecological interactions between floral visitors and flowering plants at 10 sites along an elevational gradient from 4 to 3425 m.a.s.l. We measured the additive partitioning of the beta diversity of species interactions and used generalized dissimilarity modelling to assess how spatial and environmental factors can explain the observed dissimilarity.ResultsWe found that the composition of interactions between floral visitors and plant species differs from lowlands to highlands mainly due to differences in temperature across the studied elevation gradient, rather than geographical distance or other environmental factors (i.e. mean annual precipitation and net primary productivity). We also observed that the main component of the beta diversity of interactions was interaction turnover driven by the turnover of both plants and floral visitors along the elevation gradient studied, which may be influenced by both temperature and the biogeographical affinity of biotas.Main ConclusionsWe conclude that environmental filters play a crucial role in the establishment of novel interactions, as temperature can filter species and impact the behaviour and traits of floral visitors and plants across an elevational gradient. These findings underscore the importance of considering the role of abiotic factors in predicting and explaining the distribution of species interactions across different elevational gradients.

Effects of Vegetation Structure on Ant Diversity in Different Seasonal Periods in Impacted Fragments of Atlantic Forest

The destruction of forest habitats generates extremely fragmented areas and changes in vegetation structure. That changes the characteristics of microhabitats and the availability of resources and affects the diversity of animal species such as ants, which are a large group with an important ecological role. As ants forage on trees and shrubs, the vegetation structure can affect the presence of these organisms. Abiotic factors such as seasonality can also influence the diversity of the ant community. Based on that, this study aimed to investigate the distribution of the diversity of ant species that forage on plants in different seasonal periods. The study was carried out in eight secondary forest fragments of the Atlantic Forest. We used additive partitioning of diversity, partitioning of β diversity and canonical correspondence analysis to measure the effect of seasonality and vegetation structure on the distribution of ant species diversity. The rainy season positively influences the diversity of ants that forage on plants. Composition was affected by shrub leaf density in the rainy season and by tree circumference in the dry. Each fragment preserved a different pool of ant species, and the vegetation structure positively influenced ant diversity. These results highlight the importance of conserving the majority of forest fragments to better preserve the pool of ant species regionally.