Local Environmental Conditions Research Articles

Variability of trace elements in bodies of scrapers (Ephemeroptera) and predators (Plecoptera) from mountain rivers of Dzungarian Alatau (Kazakhstan) and Western Carpathians (Slovakia).

Bioaccumulation of trace elements in aquatic environments can be influenced by local environmental conditions such as temperature fluctuations, pH levels, sediment composition, dissolved organic matter content, and the presence of other chemical substances. We analyzed the differences in trace elements accumulation (S, Cl, K, Ca, Ti, Cr, Mn, Fe, Cu, Zn, Rb, Sr, Mo, Ba, and Pb) between two trophic guilds-scrapers (Ephemeroptera) and predators (Plecoptera)-of freshwater benthic macroinvertebrates collected from mountain streams in Kazakhstan and Slovakia. Trace elements in dried insect bodies were analyzed using an X-ray spectrometer, and physicochemical parameters of stream water were investigated at each sampling site. Our results showed significant differences in Fe, Ti, and Sr levels in predators from Kazakhstan and Cu levels in predators from Slovakia. Despite some trace elements showing higher concentrations in one group over another, the overall differences between regions were more pronounced. Principal component analysis (PCA) revealed that the primary factors influencing trace elements variability were associated with environmental conditions such as temperature, oxygen levels, and total dissolved solids (TDS). PCA components indicated a higher load of trace elements in the warmer, less oxygenated streams, particularly in Kazakhstan. These findings suggest that both biotic (feeding strategies) and abiotic (geographical and environmental conditions) factors significantly influence trace elements dynamics in freshwater ecosystems.

Soil Bacteria in Archaeology: What Could Rank Abundance Functions Tell Us About Ancient Human Impacts on Microbial Communities?

Metagenomic analysis of soil bacterial communities based on 16S rRNA reflects a typical community composition containing a low number of high-abundance types and a very high number of low-abundance types. Here, the formation of characteristic rank order functions of bacterial abundance is investigated by modelling the dynamics of soil bacterial communities, assuming a succession of different bacterial populations that grow rapidly and decay more slowly. We found that the characteristic shape of typical rank order functions is well reflected by simulations. In addition, our model allowed us to investigate strong disturbances in the soil, which could be expected in cases of strongly changing local environmental conditions in soil, e.g., after translocation and covering of soil material. Such events could lead to the formation of shoulders in the rank order functions. Abundance rank orders observed in cases of some archaeological soil samples do indeed show such a shoulder and could be well interpreted by simulated rank order functions. As a result, it can be concluded that the investigations herein support our hypothesis that abundance rank orders contain information about the temporal order of developing bacterial types in changing communities and thus store information about local environmental conditions in the past, including ancient humans’ impact on soil. This information can be used for interpretation of archeological findings and for reconstruction of different former human activities, as well as knowledge on the translocation of soil material in the past.

A Design and Safety Analysis of the “Electricity-Hydrogen-Ammonia” Energy Storage System: A Case Study of Haiyang Nuclear Power Plant

With the development of modernization, traditional fossil energy reserves are decreasing, and the power industry, as one of the main energy consumption forces, has begun to pay attention to increasing the proportion of clean energy generation. With the deepening of electrification, the peak-valley difference of residential electricity consumption increases, but photovoltaic and wind power generation have fluctuations and are manifested as reverse peak regulation. Thermal power plants as the main force of peak regulation gradually reduce the market share, making nuclear power plants bear the heavy responsibility of participating in peak regulation. The traditional method of adjusting operating power by inserting and removing control rods has great safety risks and wastes resources. Therefore, this paper proposes a new energy storage system that can keep the nuclear power plant running at full power and produce hydrogen to synthesize ammonia from excess power. A comprehensive evaluation model of energy storage based on z-score data standardization and objective parameter assignment AHP (analytic hierarchy process) analysis method was established to evaluate energy storage systems according to a multi-index system. With an AP1000 daily load tracking curve as the input model, the simulation model built by Aspen Plus V14 was used to calculate the operating conditions of the system. In order to provide a construction basis for practical engineering use, Haiyang Nuclear Power Plant in Shandong Province is taken as an example. The system layout scheme is proposed according to the local environmental conditions. The accident tree analysis method is combined with ALOHA 5.4.1.2 (Areal Locations of Hazardous Atmospheres) hazardous chemical analysis software and MARPLOT 5.1.1 geographic information technology. A qualitative and quantitative assessment of risk factors and the consequences of leakage, fire, and explosion accidents caused by hydrogen and ammonia storage processes is carried out to provide guidance for accident prevention and emergency rescue. The design of an “Electric-Hydrogen-Ammonia” energy storage system proposed in this paper provides a new idea for zero-carbon energy storage for the peak shaving of nuclear power plants and has a certain role in promoting the development of clean energy.

A fingerprint of source-specific health risk of PM2.5-bound components over a coastal industrial city

The influence of specific local land-use activities (continuously redistributing elements across environments) and environmental conditions (altering the chemical composition of airborne particulate matter) on the intrinsic health risk of PM2.5 exposure is sparsely reported. To fill this gap, we employed a novel integrated approach to address the influence of short-term changes in source-specific PM2.5 composition on the exposure-response risk, while controlling for weather conditions. We combine receptor-based source apportionment with conditional logistic regression in a space-time-stratified case-crossover design. This approach is different from previous studies as it: i) controls the impact of spatiotemporal variations in air pollution and human mobility using multilocation-specific fixed and disjointed space-time strata ii) addresses the spatial heterogeneity of personal exposure separating its variable effect from other predictors by allowing different baseline hazards for each space-time stratum; iii) aligns case/control periods with strong/regular episodes of source-specific PM-multipollutant fingerprint contributions rather than health outcomes. This enabled comprehensive examination of the association between source-specific PM2.5-bound species and cardiorespiratory disease hospitalizations. The epidemiological findings were that primary anthropogenic emissions [industrial (ORs 2.5 – 4.8)] were associated with higher 1-day moving average PM-induced risks. Natural-related sources [fresh / aged sea salt aerosol, dust, soil resuspension] and secondary sulfate formation were consistently associated with higher health risks (ORs 1.0 – 1.54) after 1 to 5-days since exposure. The results emphasize the importance of source-specific air quality management in complex areas and our research provides an adaptable universal tool to support targeted place-based policy interventions to mitigate air pollution impacts on health.

Moths versus Bees: Contrasts in Habitat Preferences Across Barrens of the Northeastern USA.

Bees and moths are globally important pollinators. Xeric barrens in the largely mesic northeastern USA support high levels of pollinator diversity, including rare bees and moths. We investigated the response of bee vs. moth communities to abiotic and vegetation drivers in barrens across the region. We sampled local environmental conditions, vegetation, bees, and moths for 2-4 years in 19 preserves. Employing random forest analysis, we tested the role of 29 abiotic and vegetation predictors of bee vs. moth abundance, species richness, Shannon-Wiener Index, evenness, and species composition. Variables related to climate, canopy cover, and soils were the most important predictors of abundance, diversity, and species composition for both bees and moths. Vegetation variables, such as species richness of shrubs and hostplants, were also important for bees. The direction of these relationships contrasted sharply between bees and moths: bees were more abundant and species rich in more open, sandy sites and moths the opposite. Habitat preferences for a subset of moth xeric specialists were much more similar to bees than to other moths, with a preference for open, sandy conditions. Contrasts between bees and moths in habitat preferences likely stemmed from differences in their life histories: bees rely on flowers for feeding and porous substrates for nesting, whereas most moth adults feed on flowers, but many moth caterpillars use woody plants as hosts. In sharp contrast to the results for abundance and richness, bees and moths responded similarly for the Shannon-Wiener Index, which raises important general questions about the conservation value of these two metrics. Our results suggest that, because of differences in habitat preferences among pollinators, barrens management for both open and more closed habitats is likely to promote the highest abundance and diversity of local bee and moth pollinator communities jointly.

Revenue assessment of damage aware wind farm control for secondary frequency regulation provision

Abstract In this paper, a case study of a wind power plant participating in the balancing market is presented and any potential revenue benefits are estimated. To make a representative performance assessment, an existing case for the frequency regulation market is selected, based on the pay-for-performance scheme of the US regional transmission operator Pennsylvania-New Jersey-Maryland (PJM). In this implementation, the wind power plant is being rewarded or penalized based on its signal-tracking capabilities, which are quantified by proper performance metrics. The fast response characteristics of the wind turbines allow for tracking even the faster automatic regulation signal (RegD from PJM). However, one of the significant challenges associated with providing such type of active power reference tracking on the wind farm scale is that different turbines have different responses and structural damage accumulation due to the local environmental conditions and the coupling due to wakes. The in-house developed, numerically efficient, wind farm simulation model “regfarm” is employed, allowing for a thorough analysis and revenue assessment under various operating regimes and weather conditions. Numerous time-domain simulations and historical pricing time series are used to quantify the expected revenue from market participation. Under the regulation market pay-for-performance compensation scheme, the simulated wind farm earned a lower revenue when compared to maximizing energy production. Depending on the configuration, the wind farm experiences between a 2.14% and a 2.38% decrease in revenue when providing 10% of the estimated available power to the regulation market. The wind farm is operated as both damage-ignorant and damage-aware, with no significant comparative loss in revenue when the damage distribution is considered.

Promising Anti-Inflammatory Properties of Ursolic Acid Isolated from Atylosia goensis.

Indigenous plants are plant species that are native to a specific region and have evolved naturally and adapted to local environmental conditions over a long period. This study aimed to explore the anti-arthritic effects of Atylosia goensis, an indigenous plant species in the Western Ghats of India. An ethanolic extract of Atylosia goensis was obtained using the Soxhlet extraction method, which revealed a diverse array of phytochemicals through liquid chromatography-mass spectroscopy (LC-MS). Key compounds, including fatty acids, sterols, and potential health-beneficial compounds, were identified, and one prominent phytoconstituent, ursolic acid, was spectroscopically characterized using 1H NMR, 13C NMR, and mass spectrometry. The research also examined the anti-inflammatory activity and in-vitro and in-vivo anti-arthritic activity of ethanolic extract. The ethanol extract exhibited notable inhibition of Cox-2, indicating potential anti-inflammatory effects. The in vivo anti-arthritic activity of ursolic acid was evaluated at different doses (200 and 400 mg/kg) over a 24-day period. Ursolic acid significantly reduced joint edema, particularly at higher doses, thereby emphasizing its anti-inflammatory properties. Biomarker analysis revealed dose-dependent attenuation of disease-associated biomarker levels, supporting the potential therapeutic efficacy of ursolic acid in arthritis management. Moreover, the hepatoprotective potential of ursolic acid was evident in biochemical parameters, including SGPT, SGOT, and ALP levels. Both doses of ursolic acid effectively mitigated liver dysfunction induced in the disease control group, demonstrating its protective role in liver health. Histopathological assessments corroborated these findings, indicating a reduction in inflammatory areas following ursolic acid treatment, especially at higher doses. This experimental work provides valuable information on the therapeutic potential of Atylosia goensis and ursolic acid, emphasizing their roles in anti-inflammatory and hepatoprotective applications. This study contributes to the understanding of plant-derived compounds for potential pharmaceutical use in the management of inflammatory and arthritic conditions.

Ocean alkalinity enhancement approaches and the predictability of runaway precipitation processes: results of an experimental study to determine critical alkalinity ranges for safe and sustainable application scenarios

Abstract. To ensure the safe and efficient application of ocean alkalinity enhancement (OAE), it is crucial to investigate its impacts on the carbonate system. While modeling studies reported a sequestration potential of 3–30 Gt carbon dioxide (CO2) per year (Oschlies et al., 2023), there has been a lack of empirical data to support the applicability of this technology in natural environments. Recent studies have described the effect of runaway carbonate precipitation in the context of OAE, showing that calcium carbonate (CaCO3) formation was triggered if certain Ωaragonite saturation thresholds were exceeded. This effect could potentially lead to a net loss of the initially added alkalinity, counteracting the whole concept of OAE. The related precipitation can adversely affect the carbon storage capacity and may in some cases result in CO2 emissions. Experiments at the Espeland marine biological station (Bergen, Norway) were conducted to systematically study the chemical consequences of OAE deployment. The experiments lasted for 20–25 d to monitor the temporal development of carbonate chemistry parameters after alkalinity addition and the subsequent triggered carbonate precipitation process. Identified uniform patterns before and during the triggered runaway process can be described by empirical functional relationships. For approaches equilibrated to the CO2 concentration of the atmosphere, total alkalinity (TA) levels of up to 6500 µmol kg−1 remained stable without loss of total alkalinity (TA) for up to 20 d. Higher implemented TA levels, up to 11 200 µmol kg−1, triggered runaway carbonate formation. Once triggered, the loss of alkalinity continued until the Ωaragonite values leveled out at 5.8–6.0, still resulting in a net gain of 3600–4850 µmol kg−1 in TA. The non-CO2-equilibrated approaches, however, only remained stable for TA additions of up to 1000 µmol kg−1. The systematic behavior of treatments exceeding this level allows us to predict the duration of transient stability and the quantity of TA loss after this period. Once triggered, the TA loss continued in the non-CO2-equilibrated approaches until Ωaragonite values of 2.5–5.0 were reached, in this case resulting in a net loss of TA. To prevent a net loss of TA, treated water must be diluted below the time-dependent critical levels of TA and Ωaragonite within the identified transient stability duration. Identified stability and loss patterns of added TA depend on local environmental conditions impacting the carbonate system, such as salinity, temperature, biological activity, and particle abundance. Incorporating such stability and loss patterns into ocean biogeochemical models, which are capable of resolving dilution processes of treated and untreated water parcels, would, from a geochemical perspective, facilitate the prediction of safe local application levels of OAE. This approach would also allow an accurate determination of the fate of added alkalinity and a more realistic carbon storage potential estimation compared to the assessments that neglect carbonate system responses to OAE.

Design and Use of a Stratum-Based Yield Predictions to Address Challenges Associated with Spatial Heterogeneity and Sample Clustering in Agricultural Fields Using Remote Sensing Data

Machine learning (ML) models trained with remote sensing data have the potential to improve cereal yield estimation across various geographic scales. However, the complexity and heterogeneity of agricultural landscapes present significant challenges to the robustness of ML-based field-level yield estimation over large areas. In our study, we propose decomposing the landscape complexity into homogeneous zones using existing landform, agroecological, and climate classification datasets, and subsequently applying stratum-based ML to estimate cereal yield. This approach was tested in a heterogeneous region in northern Morocco, where wheat is the dominant crop. We compared the results of the stratum-based ML with those applied to the entire study area. Sentinel-1 and Sentinel-2 satellite imagery were used as input variables to train three ML models: Random Forest, Extreme Gradient Boosting (XGBoost), and Multiple Linear Regression. The results showed that the XGBoost model outperformed the other assessed models. Furthermore, the stratum-based ML approach significantly improved the yield estimation accuracy, particularly when using landform classifications as homogeneous strata. For example, the accuracy of XGBoost model improved from R2 = 0.58 and RMSE = 840 kg ha−1 when the ML models were trained on data from the entire study area to R2 = 0.72 and RMSE = 809 kg ha−1 when trained in the plain area. These findings highlight that developing stratum-based ML models using landform classification as strata leads to more accurate predictions by allowing the models to better capture local environmental conditions and agricultural practices that affect crop growth.

Linking housing, socio-demographic, environmental and mental health data at scale

Mental disorders are a growing problem worldwide, putting pressure on healthcare systems and wider society. Anxiety and depression are estimated to cost the global economy US$1 trillion per year, yet only 2% of global median government healthcare expenditure goes towards mental health. There is growing evidence linking housing, socio-economic status and local environmental conditions with mental health inequalities. The aim of this paper is to link several open-access datasets at the local area level (N = 32,844) for England to clinical mental health metrics and describe initial statistical findings. Two mental health metrics were used: Small Area Mental Health Index (SAMHI) and diagnosed depression prevalence. To demonstrate the utility of the longitudinal mental health data, changes in depression prevalence were investigated over two study periods (2011–19, i.e. austerity; and 2019–22, i.e. COVID-19). These data were linked to housing data (energy efficiency, floor area, year built, type and tenure) from Energy Performance Certificates (EPCs); socio-demographic data (age, sex, income and education deprivation, household size) from administrative records; and local environment data (winter temperature, air pollution and access to green space). The linked dataset provides a useful resource with which to investigate the social and environmental determinants of mental health. Practice relevance Initial observations of the data revealed a non-linear relationship between home energy efficiency (EPC band) and the mental health metrics, with depression prevalence higher in local areas where the mode EPC bands were C and D, compared with B and E. Researchers can further investigate this relationship using the dataset through robust statistical analysis, adjusting for confounding variables. National and local governments may use the dataset to help allocate resources to prevent and treat mental health conditions. Practitioners can map and interrogate the data to describe their local areas and make preliminary conclusions about the relationships between the built environment and mental health. This preliminary analysis of the data demonstrated a gradient in SAMHI and depression prevalence with income and employment deprivation at the local area level.

Effects of Various Herbicide Types and Doses, Tillage Systems, and Nitrogen Rates on CO2 Emissions from Agricultural Land: A Literature Review

Although herbicides are essential for global agriculture and controlling weeds, they impact soil microbial communities and CO2 emissions. However, the effects of herbicides, tillage systems, and nitrogen fertilisation on CO2 emissions under different environmental conditions are poorly understood. This review explores how various agricultural practices and inputs affect CO2 emissions and addresses the impact of pest-management strategies, tillage systems, and nitrogen fertiliser usage on CO2 emissions using multiple databases. Key findings indicate that both increased and decreased tendencies in greenhouse gas (GHG) emissions were observed, depending on the herbicide type, dose, soil properties, and application methods. Several studies reported a positive correlation between CO2 emissions and increased agricultural production. Combining herbicides with other methods effectively controls emissions with minimal chemical inputs. Conservation practices like no-tillage were more effective than conventional tillage in mitigating carbon emissions. Integrated pest management, conservation tillage, and nitrogen fertiliser rate optimisation were shown to reduce herbicide use and soil greenhouse gas emissions. Fertilisers are similarly important; depending on the dosage, they may support yield or harm the soil. Fertiliser benefits are contingent on appropriate management practices for specific soil and field conditions. This review highlights the significance of adaptable management strategies that consider local environmental conditions and can guide future studies and inform policies to promote sustainable agriculture practices worldwide.

Multivariate genetic analysis and diversity assessment in lotus (Nelumbo nucifera) accessions

The present study was carried out during 2019 to 2022 at the Floriculture Research Station (Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu), Thovalai, Kanyakumari, Tamil Nadu to access the genetic variability in lotus accessions (Nelumbo nucifera) collected from 6 diverse locations. The research presents significant insights into the growth and flowering characteristics of these extraordinary aquatic plants and their adaptability to various environmental conditions. The analysis of mean performance showcases noteworthy variations in plant height, leaf dimensions, flowering time and the number of leaves, and flowers/plant across different collection sites, highlighting the profound influence of local environmental conditions on lotus growth. Lotus traits with significant genetic diversity and heritability, presenting opportunities for targeted selective breeding were identified. Principal component analysis uncovers key trait influencers, while cluster analysis categorizes accessions based on similarities, streamlining research and cultivation. Correlation and path analysis unveil relationships among lotus traits, enhancing our understanding of growth factors. D2 cluster composition reveals distinct groupings; Cluster I includes KNn1 and KNn5, Cluster II features KNn2, Cluster III comprises KNn6, Cluster IV contains KNn3, and Cluster V houses KNn4. Genetic divergence analysis identifies the number of flowers/plant as the primary driver of genetic diversity, emphasizing its importance for breeding. It underscores the need to consider both genetic and environmental factors in lotus cultivation and highlights the importance of targeted breeding efforts to unlock the full potential of lotus.

Nitrogen fixation in the North Atlantic supported by Gulf Stream eddy-borne diazotrophs

Mesoscale oceanic eddies contribute to the redistribution of resources needed for plankton to thrive. However, due to their fluid-trapping capacity, they can also isolate plankton communities, subjecting them to rapidly changing environmental conditions. Diazotrophs, which fix dinitrogen (N2), are key members of the plankton community, providing reactive nitrogen, particularly in large nutrient-depleted regions such as subtropical gyres. However, there is still limited knowledge about how mesoscale structures characterized by specific local environmental conditions can affect the distribution and metabolic response of diazotrophs when compared with the large-scale dynamics of an oceanic region. Here we investigated genetic diazotroph diversity and N2 fixation rates in a transect across the Gulf Stream and two associated eddies, a region with intense mesoscale activity known for its important role in nutrient transport into the North Atlantic Gyre. We show that eddy edges are hotspots for diazotroph activity with potential community connectivity between eddies. Using a long-term mesoscale eddy database, we quantified N2 fixation rates as up to 17 times higher within eddies than in ambient waters, overall providing ~21 µmol N m−2 yr−1 to the region. Our results indicate that mesoscale eddies are hotspots of reactive nitrogen production within the broader marine nitrogen cycle.

Microclimate monitoring in commercial tomato (Solanum Lycopersicum L.) greenhouse production and its effect on plant growth, yield and fruit quality

IntroductionHigh annual tomato yields are achieved using high-tech greenhouse production systems. Large greenhouses typically rely only on one central weather station per compartment to steer their internal climate, ignoring possible microclimate conditions within the greenhouse itself.MethodsIn this study, we analysed spatial variation in temperature and vapour pressure deficit in a commercial tomato greenhouse setting for three consecutive years. Multiple sensors were placed within the crop canopy, which revealed microclimate gradients.Results and discussionDifferent microclimates were present throughout the year, with seasonal (spring – summer – autumn) and diurnal (day – night) variations in temperature (up to 3 °C, daily average) and vapour pressure deficit (up to 0.6 kPa, daily average). The microclimate effects influenced in part the variation in plant and fruit growth rate and fruit yield – maximum recorded difference between two locations with different microclimates was 0.4 cm d-1 for stem growth rate, 0.6 g d-1 for fruit growth rate, 80 g for truss mass at harvest. The local microclimate effect on plant growth was always larger than the bulk climate variation measured by a central sensor, as commonly done in commercial greenhouses. Quality attributes of harvested tomato fruit did not show a significant difference between different microclimate conditions. In conclusion, we showed that even small, naturally occurring, differences in local environment conditions within a greenhouse may influence the rate of plant and fruit growth. These findings could encourage the sector to deploy larger sensor networks for optimal greenhouse climate control. A sensor grid covering the whole area of the greenhouse is a necessity for climate control strategies to mitigate suboptimal conditions.

A New Dawn for Protist Biogeography

ABSTRACTAimBiogeographers have believed for a long time that the geographical distributions of protists are only determined by environmental conditions, because dispersal is not limited. During the past two decades, the field has come a long way to show that historical and spatial factors also significantly contribute to shaping protist distributions, calling for a reappraisal of our understanding of protist biogeography.MethodsWe review the current state‐of‐the‐art on the field of protist biogeography, highlighting several outstanding questions and opportunities. Our review brings together insights from different disciplines, ranging from morphology‐based research to environmental, population and speciation genomics.ResultsProtist communities harbour cosmopolitan and geographically restricted species and are shaped by both local environmental conditions and historical processes, yet the relative contributions of these patterns and processes likely differs depending on the geographic scale, protist lineage and the habitat that is being investigated. The field is ready to move beyond the decades‐long ubiquity versus (moderate) endemicity discourse and to instead ask why and where specific protist species and clades are more prone to widespread or restricted distributions. With the advent of next‐generation sequencing technologies, from whole‐genome sequencing to environmental and ancient DNA surveys, it is now possible to integrate insights from multiple lines of evidence and investigate protist communities, species and populations at an unprecedented scale and detail.OutlookTo further advance the field, the protist community needs to focus on understudied habitats and protist lineages, study the impact of protist traits on biogeographical patterns, perform targeted field and experimental work to disentangle the processes that underlie protist biogeographies and expand and develop databases with sequence, trait, distributional and phylogenetic information of protists. Given that a good understanding of species boundaries is central to unravelling protist biogeography, it remains crucial to invest in polyphasic taxonomic research.

Global meta‐analysis reveals the drivers of gut microbiome variation across vertebrates

AbstractShifts in gut microbial diversity and structure are one route by which vertebrate hosts adapt to local environmental conditions. However, recent studies have mostly been limited to a single species, small sample sizes, or restricted geographic ranges. Therefore, drawing a global picture of vertebrate gut microbiome diversity, community structure, and determinants for their adaptive shifts remains to be elucidated. We here collected 6508 samples from 113 vertebrate species covering diverse classes, feeding behaviors, and host habitats based on 16S rRNA gene sequencing. The results showed that host diet pattern had a significant impact on gut microbiome variation, which might drive taxonomic and functional contents of gut microbiome across vertebrates. Of note, the phylum Fusobacteria were enriched in carnivorous vertebrate gut while herbivorous vertebrate gut selectively increased the abundance of Verrucomicrobia. Also, climate factors were strongly associated with gut microbiome variation across vertebrates. Interestingly, we found that the abundance of microbiota belonging to Bacteroidetes increased gradually while the members from Proteobacteria showed a decreasing trend from high‐ to low‐latitude zones, potentially contributing to vertebrate adaptation to local climate condition. Additionally, we comprehensively deciphered the common antibiotic resistomes and their potential mobility between terrestrial vertebrate gut microbiome (n = 487) and their sympatric soil biological environment samples (n = 203) by integrating metagenomic sequencing datasets. Particularly, potential horizontal antibiotic resistance genes (e.g., bacA) transfers were detected between vertebrates gut microbiome and their sympatric soil biological environment. Together, our findings provide new evidence of how external environmental factors affect vertebrate gut microbiome variation.

You can bring plankton to fecal indicator organisms, but you cannot make the plankton graze: particle contribution to E. coli and MS2 inactivation in surface waters.

Organisms that are associated with feces ("fecal indicator organisms") are monitored to assess the potential for fecal contamination of surface water bodies in the United States. However, the effect of the complex mixtures of chemicals and the natural microbial community within surface water ("particles") on fecal indicator organism persistence is not well characterized. We aimed to better understand how particles, including biological (e.g., potential grazers) and inert (e.g., minerals) types, affect the fecal indicator organisms Escherichia coli K-12 ("E. coli") and bacteriophage MS2 in surface waters. A gradient of particles captured by a 0.2-µm-pore-size filter ("large particles") was generated, and the additional particles and dissolved constituents that passed through the filter were deemed "small particles." We measured the ratio of MS2 and E. coli that survived over a 24-h incubation period for each condition (0%-1,000% large-particle concentration in raw water) and completed a linear regression that included large- and small-particle coefficients. Particles were characterized by quantifying plankton, total bacterial cells, and total solids. E. coli and MS2 persistence was not significantly affected by large particles, but small particles had an effect in most waters. Small particles in higher-salinity waters had the largest, negative effect on E. coli and MS2 survival ratios: Significant small-particle coefficients ranged from -1.7 to -5.5 day-1 in the marine waters and -0.89 to -3.2 day-1 in the fresh and estuarine waters. This work will inform remediation efforts for impaired surface water bodies.IMPORTANCEMany surface water bodies in the United States have organisms associated with fecal contamination that exceed regulatory standards and prevent safe recreation. The process to remediate impaired water bodies is complicated because these fecal indicator organisms are affected by the local environmental conditions. For example, the effect of particles in surface water on fecal indicator concentrations are difficult to quantify in a way that is comparable between studies and water bodies. We applied a method that overcomes this limitation to assess the effects of large particles, including natural plankton that could consume the seeded fecal indicator organisms. Even in environmental water samples with diverse communities of plankton present, no effect of large particles on fecal indicator concentrations was observed. These findings have implications for the interpretation and design of future studies, including that particle characterization of surface water may be necessary to assess the fate of fecal indicators.

A global database of soil seed bank richness, density, and abundance.

A soil seed bank is the collective name for viable seeds that are stored naturally in the soil. At the species or population level, the ability to form a seed bank represents a strategy for (re)colonization following a disturbance or other change in the local environmental conditions. At the community level, seed banks are thought to buffer local diversity during periods of environmental change and are often studied in relation to the potential for passive habitat restoration. The role that seed banks play in plant population and community dynamics, as well as their importance in the agricultural sector, means that they have been widely studied in ecological research. This database is the result of a comprehensive literature search, including all seed bank studies from the Web of Science from which data could be extracted, as well as an additional search of the Russian language literature. The database contains information on the species richness, seed density, and/or seed abundance in 3096 records from at least 1929 locations across the world's seven continents, extracted from 1442 studies published between 1940 and 2020. Records are grouped into five broad habitat categories (aquatic, arable, forest, grassland-including shrubland-and wetland), including information relating to habitat degradation from, or restoration to other habitats (total 14 combinations). Sampling protocols were also extracted for each record, and the database was extensively checked for errors. The location of each record was then used to extract summary climate data and biome classification from external published databases. The database has several potential uses. The large geographical spread relative to many other global biodiversity datasets is relevant for investigating patterns of diversity in biogeographical or macroecological contexts. Habitat type and status (intact, degraded, and restored) can be used to provide insights for biodiversity conservation, while the potential effects of sampling method and effort can be used to inform optimized data collection for future seed bank studies. This database is released under the CC-BY license.

The biogeography of the Amazonian tree flora

We describe the geographical variation in tree species composition across Amazonian forests and show how environmental conditions are associated with species turnover. Our analyses are based on 2023 forest inventory plots (1 ha) that provide abundance data for a total of 5188 tree species. Within-plot species composition reflected both local environmental conditions (especially soil nutrients and hydrology) and geographical regions. A broader-scale view of species turnover was obtained by interpolating the relative tree species abundances over Amazonia into 47,441 0.1-degree grid cells. Two main dimensions of spatial change in tree species composition were identified. The first was a gradient between western Amazonia at the Andean forelands (with young geology and relatively nutrient-rich soils) and central–eastern Amazonia associated with the Guiana and Brazilian Shields (with more ancient geology and poor soils). The second gradient was between the wet forests of the northwest and the drier forests in southern Amazonia. Isolines linking cells of similar composition crossed major Amazonian rivers, suggesting that tree species distributions are not limited by rivers. Even though some areas of relatively sharp species turnover were identified, mostly the tree species composition changed gradually over large extents, which does not support delimiting clear discrete biogeographic regions within Amazonia.

Freshwater Mussel (Unio pictorum) Shells Reveal Hydrological and Environmental Change From 1300 BC to the Present Day.

Preserved biological communities can provide baseline data about the historical ecosystems and environmental conditions that preceded recent anthropogenic alteration. Freshwater mussel shells show particularly good preservation, and the shell assemblages commonly found during archaeological excavations can offer insights into past ecosystems. We studied assemblages of Unio pictorum mussel shells from palaeochannel silts associated with the Late Bronze Age site of Must Farm in eastern England (c. 850 BC), on an ancient tributary of the modern-day River Nene. We compared archaeological shells from two sediment horizons (broadly 1300-700 BC) to live individuals collected from two analogous sites on the present-day Nene. Size and growth rate, interannual growth variability and stable isotope (δ18O and δ13C) composition were compared between the populations. Size and the von Bertalanffy growth parameter L∞ differed among all four populations. Mean lengths and L∞ were higher in the two modern populations (mean lengths 77.3 ± SE 0.8 and 73.8 ± SE 1.1 mm, L∞ 91.8 ± 5.4 and 79.0 ± 8.1 mm) than the ancient populations (mean lengths 58.1 ± SE 1.6 mm and 68.4 ± SE 0.9 mm; L∞ 71.5 ± 16.9 and 76.8 ± 6.2 mm). Modern individuals also showed greater variation in age-corrected year-to-year growth. δ13C was lower in modern shells (-11.8‰ for modern shells, -9.03‰ and -9.02‰ for ancient shell populations), potentially reflecting altered hydrological and nutrient regimes. δ18O and δ13C were positively correlated for all but one sampled ancient shell, but not modern shells. These results reflect changes in local environmental conditions, particularly the transition from a shallow, slow-flowing tributary to a deeper, canalised river with faster flow, as well as effects of anthropogenic nutrient enrichment. The findings demonstrate the importance of long-term data in studying anthropogenic ecosystem alteration and avoiding shifting baseline syndrome in conservation planning.