Seasonal Dynamics Research Articles

Investigation of Seasonal Dynamism of Peroxidase from Strains of Aspergillus sp.

Investigation of Seasonal Dynamism of Peroxidase from Strains of Aspergillus sp.

Ecosystem functional types of the circumpolar Arctic tundra based on the seasonal dynamics of vegetation productivity

Abstract Biodiversity, when viewed through the combined lenses of compositional, structural, and functional attributes, provides for a holistic understanding of the complexities found within community assemblages and ecosystems. However, advancement in our understanding of how ecosystem functional diversity interacts with structural and compositional diversity metrics is lacking, in part because universally applied methodologies to derive ecosystem functional classifications are still under development and vary widely across scales, extents and biomes. This study presents a methodology to construct ecosystem functional types (EFTs), or areas of the land surface that function similarly, using the MODIS NDVI record, for the terrestrial circumpolar Arctic. EFTs were derived from the seasonal dynamics of NDVI, over the Arctic tundra at 250m resolution and compared to bioclimate subzones and to structurally and compositionally defined vegetation units of the Circumpolar Arctic Vegetation Map (CAVM). Correspondence analyses of CAVM EFTs to previously delineated CAVM bioclimatic subzones, physiognomic (vegetation) units and floristic provinces revealed a general congruence, indicating convergence across composition, structure, and function; yet also demonstrated substantial functional variability even within bioclimate subzones and vegetation units. Strong latitudinal gradients in ecosystem function are present, with EFT richness ranging from low (34) in northernmost regions to high (45) in southernmost regions. Locally, the mountainous regions of northern Alaska, and eastern and western Siberia had high spatial variability in ecosystem functioning. Aside from these generalities, we found that EFTs varied widely within individual mapped vegetation units, successfully capturing the functional dimension of biodiversity across the circumpolar Arctic tundra.

Annual and seasonal precipitation dynamics in the South of Russia in the context of climate change

Annual and seasonal precipitation dynamics in the South of Russia in the context of climate change

Normalised difference moisture index in water stress assessment of maize crops

Remote sensing is a promising technique for better management of water resources in agriculture through improvement of dynamic control and operational scheduling on irrigated croplands. The main goal of this study was to identify the possibilities of application of the normalised difference moisture index (NDMI) to water stress monitoring in maize crops, and to determine the relationship between the index and soil moisture content. The study was carried out in 2019–2021 in the experimental fields of disturbed maize, cultivated on dark-chestnut soils in the Southern Ukraine at the NAAS Institute of Climate-Smart Agriculture. The crop cultivation technology was common for the conditions of the steppe zone of Ukraine. Actual soil moisture content was determined by gravimetric method in the pre-sowing and post-harvest period. The NDMI values were calculated using cloudless aerospace images from the satellites Landsat 8, Sentinel-2, and MODIS with 250 m resolution. It was revealed that the seasonal NDMI dynamics perfectly reflected the water-supply conditions of the disturbed maize, and could be used for operational monitoring and scheduling of irrigation. The parameters of the water-supply conditions were determined in 2021, which was the wettest year of the study: the cumulative seasonal NDMI reached 1.71, while the highest water stress was recorded in the driest year, 2020, – the cumulative NDMI was 0.15. Additionally, there was a moderately strong negative correlation between NDMI and soil moisture content, and the coefficient of determination was 0.62. The linear regression models, developed to predict soil moisture content in the 0–100 cm layer depending on the NDMI values, had good fitting quality and reasonable accuracy, but they required further calibration and extension of the initial dataset to provide more robust and reliable results for practical implementation. Based on the results of the study, spatial NDMI could be considered a good and reliable tool for improving irrigation water management. Further studies should focus on the practical implementation of the NDMI-based model of moisture-content estimation, as well as on the possibilities of the index usage for mapping irrigated lands.

Seasonality Affects Low-Molecular-Weight Organic Acids and Phenolic Compounds’ Composition in Scots Pine Litterfall

Background and Aims: Secondary plant metabolites, including organic acids and phenolic compounds, have a significant impact on the properties of organic matter in soil, influencing its structure and function. How the production of these compounds in foliage that falls to the forest floor as litterfall varies across tree age and seasonality are of considerable interest for advancing our understanding of organic matter dynamics. Methods: Monthly, we collected fallen needles of Scots pine (Pinus sylvestris L.) across stands of five different age classes (20, 40, 60, 80, and 100 years) for one year and measured the organic acids and phenolic compounds. Results: Seven low-molecular-weight organic acids and thirteen phenolic compounds were detected in the litterfall. No differences were observed across stand age. Significant seasonal differences were detected. Most compounds peaked during litterfall in the growing season. Succinic acid was the most prevalent organic acid in the litterfall, comprising 78% of total organic acids (351.27 ± 34.27 µg g− 1), and was 1.5 to 11.0 times greater in the summer than all other seasons. Sinapic acid was the most prevalent phenolic compound in the litterfall (42.15 µg g− 1), representing 11% of the total phenolic compounds, and was 39.8 times greater in spring and summer compared to autumn and winter. Growing season peaks in needle concentrations were observed for all thirteen phenolic compounds and two organic acids (lactic, succinic). Citric acid exhibited a definitive peak in late winter into early spring. Conclusions: Our results highlight the seasonal dynamics of the composition of secondary plant metabolites in litterfall, which is most different at the onset of the growing season. Fresh inputs of litterfall at this time of emerging biological activity likely have seasonal impacts on soil’s organic matter composition as well.

Seasonal antigenic prediction of influenza A H3N2 using machine learning

Antigenic characterization of circulating influenza A virus (IAV) isolates is routinely assessed by using the hemagglutination inhibition (HI) assays for surveillance purposes. It is also used to determine the need for annual influenza vaccine updates as well as for pandemic preparedness. Performing antigenic characterization of IAV on a global scale is confronted with high costs, animal availability, and other practical challenges. Here we present a machine learning model that accurately predicts (normalized) outputs of HI assays involving circulating human IAV H3N2 viruses, using their hemagglutinin subunit 1 (HA1) sequences and associated metadata. Each season, the model learns an updated nonlinear mapping of genetic to antigenic changes using data from past seasons only. The model accurately distinguishes antigenic variants from non-variants and adaptively characterizes seasonal dynamics of HA1 sites having the strongest influence on antigenic change. Antigenic predictions produced by the model can aid influenza surveillance, public health management, and vaccine strain selection activities.

Growth and mortality rates of picophytoplankton in the Baltic Sea Proper

Picophytoplankton (<2 µm diameter), a diverse group of picocyanobacteria and photosynthetic picoeukaryotes, are significant contributors to primary production. Predatory mortality controls picophytoplankton biomass and thereby energy transfer in the marine food web. The 2 major pathways of picophytoplankton mortality are grazing and viral lysis. Grazing passes carbon directly to higher trophic levels, while lysis products are passed into the viral loop. Picophytoplankton are abundant in the Baltic Sea but little is known about their predatory mortality. Using a modification of the dilution approach, we calculated growth and mortality rates of picophytoplankton and studied the effect of predation on community structure during late August and September. The experiments were conducted coinciding with the peak in picophytoplankton abundance (∼105 cells ml-1) at the Linnaeus Microbial Observatory in the Baltic Sea Proper. The results showed that grazing is an important controller of picocyanobacteria and photosynthetic picoeukaryote populations, while no significant viral lysis effect was detected. Grazing on picocyanobacteria was proportional to growth rates, while grazing on photosynthetic picoeukaryotes exceeded growth. Selective grazing of phylogenetically distinct picocyanobacterial clades had a significant effect on community structure, suggesting that grazing has an impact on the seasonal dynamics of co-occurring clades. Picocyanobacteria had a higher carbon transfer contribution to higher trophic levels than photosynthetic picoeukaryotes at the time of the experiments. The study shows that picophytoplankton are important contributors to carbon cycling in the Baltic Sea microbial food web and should be considered for future ecological models.

Seasonal dynamic of the benthic food web in subtidal sandbanks

Submarine sandbanks are prevalent worldwide but, paradoxically, these ecosystems and their dynamics remain largely unknown. As submarine sandbanks are targeted by a large variety of human activities, there is an urgent need for sound scientific knowledge for environmental impact assessments (EIAs) and the appropriate management of biodiversity in these areas. To our knowledge, the present study is the first to investigate the seasonal dynamics of the benthic food web in sandbank areas. We performed a stable isotope analysis in the French part of the southern North Sea. This area is typified by numerous sandbanks and by massive phytoplankton blooms in spring. We found a very simple food web structure that is heavily dependent on organic matter particles in seawater. Primary consumers, i.e. deposit feeders and, to a lesser extent, suspension feeders, dominate the benthic biomass. Small predator-scavengers such as annelids, shrimps and crabs prey upon them. Fish predators such as Echiichthys vipera represent a very restricted proportion of the biomass. We observed that the general structure of the food web is relatively well preserved over seasons. We thus propose that the functioning of the ecosystem is resilient to natural disruptions—such as dune migrations—and, probably, to anthropogenic disturbances.

Harnessing eDNA metabarcoding to investigate fish community composition and its seasonal changes in the Oslo fjord

In the face of global ecosystem changes driven by anthropogenic activities, effective biomonitoring strategies are crucial for mitigating impacts on vulnerable aquatic habitats. Time series analysis underscores a great significance in understanding the dynamic nature of marine ecosystems, especially amidst climate change disrupting established seasonal patterns. Focusing on Norway's Oslo fjord, our research utilises eDNA-based monitoring for temporal analysis of aquatic biodiversity during a one year period, with bi-monthly sampling along a transect. To increase the robustness of the study, a taxonomic assignment comparing BLAST+ and SINTAX approaches was done. Utilising MiFish and Elas02 primer sets, our study detected 63 unique fish species, including several commercially important species. Our findings reveal a substantial increase in read abundance during specific migratory cycles, highlighting the efficacy of eDNA metabarcoding for fish composition characterization. Seasonal dynamics for certain species exhibit clear patterns, emphasising the method's utility in unravelling ecological complexities. eDNA metabarcoding emerges as a cost-effective tool with considerable potential for fish community monitoring for conservation purposes in dynamic marine environments like the Oslo fjord, contributing valuable insights for informed management strategies.

Cracking the dynamic code of the deep: Unexpected seasonal patterns of active protistan-bacterial microbiomes in the mesopelagic zone of the South China Sea

Disentangling microbial dynamics in the mesopelagic zone is crucial due to its role in processing sinking photic production, affecting carbon export to the deep ocean. The relative importance of photic zone processes versus local biogeochemical conditions in mesopelagic microbial dynamics, especially seasonal dynamics, is largely unknown. We employed rRNA gene transcript-based high-throughput sequencing on 189 samples collected from both the photic and mesopelagic zones, along with seasonal observations, to understand the South China Sea’s protistan-bacterial microbiota diversity, drivers, and mechanisms. Mesopelagic communities displayed unexpectedly greater seasonal but less vertical dynamics than photic counterparts. Temperature, dissolved oxygen, nutrients, and bacterial abundance drove mesopelagic communities vertically. Photic zone processes (using net community production and mixed layer depth as proxies) of past seasons, coinciding with strong monsoon periods, shaped seasonal fluctuations in mesopelagic communities, indicating a time-lag effect. Furthermore, certain microbes were identified as indicators for beta diversity by depth and season. This investigation deepens our understanding of how and why mesopelagic communities vary with season and depth. Recognizing the time-lagged effect of photic zone processes on mesopelagic communities is crucial for understanding the current and future configurations of the ocean microbiome, especially in the context of climate change and its effect on carbon export and ocean storage.

Seasonal Dynamics and Ecological Risks of Organochlorine Pesticides in Kafrelsheikh-Egypt: Implications for Aquatic Ecosystems and Public Health

Kafrelsheikh, situated in the middle of the Nile Delta, significantly contributes to Egypt's aquaculture and agriculture sectors. However, the local use of organochlorine pesticides (OCPs) in agriculture is often a source of OCP residues in associated drainage waters, which may threaten aquatic ecosystems. These pesticides persist in local water bodies, jeopardizing aquatic ecosystems and potentially impacting human health. This comprehensive study assessed the seasonal variations of 17 OCPs in both water sources and Nile tilapia across eight fish farming locations in Kafrelsheikh. Using gas chromatography coupled with mass spectrometry (GC-MS), we detected OCP concentrations in water that varied seasonally: winter recorded the highest levels at 22.5 ng/mL. In comparison, summer showed the lowest levels at 6.2 ng/mL. Despite water samples occasionally showing risk quotients (RQs) greater than 1, indicating a potential threat to aquatic life, the OCP levels in Nile tilapia were sufficiently low, with target hazard quotients (THQ) below 1, suggesting no significant risk to human consumers. The research highlights a pressing need for regular environmental monitoring and stricter regulatory control over pesticide use to protect aquatic life and ensure the safety of aquaculture products. These findings provide a critical basis for policy-making, aimed at balancing agricultural productivity with environmental sustainability in regions heavily dependent on both.

Seasonality and vegetation shape the taxonomic and functional diversity of microorganisms in islands of fertility of a semi-arid region in the Colombian tropics

The islands of fertility or resource islands play a crucial role in combating desertification, as they harbor diverse microbial communities essential for their establishment. The objective of this study was to determine the taxonomic and functional diversity of soil microorganisms in a semi-arid region of the Colombian tropics using a metagenomic approach. To achieve this, we sampled three species of nurse trees as well as bare soil during both the dry and wet seasons. Total DNA was extracted and sequenced extensively. Taxonomic diversity was established by comparing against the SILVA database, while functional diversity was determined using the DIAMOND program and the KEGG database. The analysis unveiled seasonal microbial dynamics influenced by vegetation cover. Islands of fertility exhibited higher diversity and nutritional value compared to bare soil. Wet season conditions boosted microbial diversity, narrowing the gap between vegetated and unvegetated soils. Under drought conditions, Proteobacteria predominated in the resource islands, while Actinobacteria prevailed in the bare soil. Both islands of fertility and the rainy season are significant drivers of microbial communities in these semi-arid environments. The presence of unique genera indicated specialized adaptations, revealing a under-explored microbial diversity. Although complete methanogenesis was absent, methane oxidation pathways were detected. Bacteria demonstrated notable adaptations for nitrate reduction, even under organic substrate limitation. Organic matter and anaerobic microenvironments could play a crucial role in nitrate reduction and denitrification. Sulfate reduction prevailed during the dry season, whereas thiosulfate oxidation was more prominent during the wet season. These findings underscore the remarkable adaptability of microbial communities in challenging environments and provide key insights for addressing desertification in semi-arid zones.

Effects of nitrogen and water addition on ecosystem carbon fluxes in a heavily degraded desert steppe

Changes in global precipitation patterns and increased nitrogen (N) deposition have important effects on the dynamics of the carbon (C) cycle in grassland ecosystems. Long-term overgrazing caused heavy degradation of grasslands and more than 60 % of China’s natural grasslands experienced moderate to severe degradation. To elucidate the effects of increasing precipitation and N deposition on C fluxes in a desert steppe ecosystem, experiments with nitrogen and water addition were conducted with four treatments, i.e., control (CK), N addition (N), water addition (W), combined addition of N and water (NW), in a Stipa breviflora desert steppe that had been heavily degraded. The ecosystem CO2 fluxes, including gross ecosystem productivity (GEP), net ecosystem CO2 exchange (NEE), and ecosystem respiration (ER), were determined using the closed chamber method during the growing seasons (May–October) of 2019 and 2020. Our results showed that water and nitrogen addition did not change the patterns of the seasonal dynamics of the ecosystem CO2 fluxes, indicating that water is the main limiting factor of CO2 fluxes in the studied desert steppe ecosystem. Precipitation increase (both W and NW treatments) stimulated all three components of the ecosystem CO2 fluxes, but N addition alone enhanced NEE and GEP while had no significant effects on ER, indicating water-induced increase of GEP was caused by both NEE and ER while N-induced increase of GEP was mainly caused by increases of NEE. Water addition can increase the effectiveness of N addition, but there was little interaction between the two factors. Soil moisture was more significantly correlated than soil temperature with ecosystem CO2 fluxes, and water addition could further enhance the correlation of ecosystem CO2 fluxes with soil temperature and moisture. Overall, ecosystem CO2 fluxes are more sensitive to the precipitation increase than to the N addition. These findings indicate that water supply will play more important role in ecosystem productivity of the desert steppe under the predicted scenarios of precipitation increase and increasing N deposition, which also provides scientific evidence for the restoration of those degraded desert steppe induced by overgrazing can not be achieved simply by increasing soil N availability.

Seasonal dynamics of Arctic soils: Capturing year-round processes in measurements and soil biogeochemical models

Seasonal dynamics of Arctic soils: Capturing year-round processes in measurements and soil biogeochemical models

Atlas of phytoplankton phenology indices in selected Eastern Mediterranean marine ecosystems.

Phytoplankton is a fundamental component of marine food webs and play a crucial role in marine ecosystem functioning. The phenology (timing of growth) of these microscopic algae is an important ecological indicator that can be utilized to observe its seasonal dynamics, and assess its response to environmental perturbations. Ocean colour remote sensing is currently the only means of obtaining synoptic estimates of chlorophyll-a (a proxy of phytoplankton biomass) at high temporal and spatial resolution, enabling the calculation of phenology metrics. However, ocean colour observations have acknowledged weaknesses compromising its reliability, while the scarcity of long-term in situ data has impeded the validation of satellite-derived phenology estimates. To address this issue, we compared one of the longest available in situ time series (20 years) of chlorophyll-a concentrations in the Eastern Mediterranean Sea (EMS), along with concurrent remotely-sensed observations. The comparison revealed a marked coherence between the two datasets, indicating the capability of satellite-based measurements in accurately capturing the phytoplankton seasonality and phenology metrics (i.e., timing of initiation, duration, peak and termination) in the studied area. Furthermore, by studying and validating these metrics we constructed a satellite-derived phytoplankton phenology atlas, reporting in detail the seasonal patterns in several sub-regions in coastal and open seas over the EMS. The open waters host higher concentrations from late October to April, with maximum levels recorded during February and lowest during the summer period. The phytoplankton growth over the Northern Aegean Sea appeared to initiate at least a month later than the rest of the EMS (initiating in late November and terminating in late May). The coastal waters and enclosed gulfs (such as Amvrakikos and Maliakos), exhibit a distinct seasonal pattern with consistently higher levels of chlorophyll-a and prolonged growth period compared to the open seas. The proposed phenology atlas represents a useful resource for monitoring phytoplankton growth periods in the EMS, supporting water quality management practices, while enhancing our current comprehension on the relationships between phytoplankton biomass and higher trophic levels (as a food source).

Temporal Dynamics of Canopy Properties and Carbon and Water Fluxes in a Temperate Evergreen Angiosperm Forest

The forest–atmosphere exchange of carbon and water is regulated by meteorological conditions as well as canopy properties such as leaf area index (LAI, m2 m−2), photosynthetic capacity (PC μmol m−2 s−1), or surface conductance in optimal conditions (Gs,opt, mmol m−2 s−1), which can vary seasonally and inter-annually. This variability is well understood for deciduous species but is poorly characterized in evergreen forests. Here, we quantify the seasonal dynamics of a temperate evergreen eucalypt forest with estimates of LAI, litterfall, carbon and water fluxes, and meteorological conditions from measurements and model simulations. We merged MODIS Enhanced Vegetation Index (EVI) values with site-based LAI measurements to establish a 17-year sequence of monthly LAI. We ran the Community Atmosphere Biosphere Land Exchange model (CABLE-POP (version r5046)) with constant and varying LAI for our site to quantify the influence of seasonal canopy dynamics on carbon and water fluxes. We observed that the peak of LAI occurred in late summer–early autumn, with a higher and earlier peak occurring in years when summer rainfall was greater. Seasonality in litterfall and allocation of net primary productivity (FNPP) to leaf growth (af, 0–1) drove this pattern, suggesting a complete renewal of the canopy before the timing of peak LAI. Litterfall peaked in spring, followed by a high af in summer, at the end of which LAI peaked, and PC and Gs,opt reached their maximum values in autumn, resulting from a combination of high LAI and efficient mature leaves. These canopy dynamics helped explain observations of maximum gross ecosystem production (FGEP) in spring and autumn and net ecosystem carbon loss in summer at our site. Inter-annual variability in LAI was positively correlated with Net Ecosystem Production (FNEP). It would be valuable to apply a similar approach to other temperate evergreen forests to identify broad patterns of seasonality in leaf growth and turnover. Because incorporating dynamic LAI was insufficient to fully capture the dynamics of FGEP, observations of seasonal variation in photosynthetic capacity, such as from solar-induced fluorescence, should be incorporated in land surface models to improve ecosystem flux estimates in evergreen forests.

Machine Learning-Based Sales Prediction and Inventory Management for Grocery Stores

In today's competitive grocery retail environment, effective inventory management coupled with precise sales prediction is pivotal for success. This research undertakes an in-depth exploration into a machine learning framework tailored for "Sales Forecasting and Inventory Optimization in Grocery Retail." Utilizing historical sales records, meteorological data, and consumer behavior metrics, the proposed model endeavors to predict nuanced seasonal demand fluctuations. The primary value proposition of this approach lies in its capacity to curtail wastage, ensuring the balance between overstocking and inventory stockouts. By recognizing and adjusting for seasonal dynamics, the system offers enhanced demand fulfillment during high-demand periods. The subsequent refinement of the replenishment cycle fosters superior operational efficacy. As an outcome, businesses witness cost reductions, augmented profit margins, and elevated consumer contentment due to consistent product availability. As the broader grocery sector undergoes transformation, embedding sophisticated machine learning strategies can be a linchpin for sustained adaptability and competitiveness. This research further underscores the role of emergent technologies, such as machine learning within the IoT spectrum, in reshaping supply chain and communication paradigms. The endeavor here is to curtail lifecycle expenses in the supply chain by refining inventory practices. The research introduces a Deep Inventory Management (DIM) technique, employing the LSTM paradigm of deep learning. Through DIM's innovative approach, time-series challenges are reimagined as supervised learning tasks, enabling efficient model training. Preliminary tests indicate DIM's prediction accuracy hovers around 80%, translating to an impressive 25% reduction in inventory expenses when juxtaposed with prevailing methods, alongside rapid anomaly detection in inventory activities

Climate Variability of Air Temperature and Its Warming Trends in the Arabian Gulf

AbstractOver a 41-year period (1979–2019), the climate of the Arabian Gulf (Gulf) exhibited notable variability. This variation, influenced by mesoscale and globalscale meteorological and climatic determinants, showed temperature fluctuations, predominantly in the Northern Gulf (N Gulf). The N Gulf recorded an average temperature of 25.9 °C, a noticeable 3 °C higher than the broader Gulf average. This period marked extreme temperature records, with a peak at 53.8 °C in July 2016, paralleling the highest known temperatures in Asia. Data reveals a warming trend (0.1–0.6 °C per decade) in the region, with the N Gulf’s rise surpassing global trends. The years 2014–2019 were notably warmer, impacting overall trends, and 1997 was the transitional year shifting from cooler to warmer climates. The N Gulf’s seasonal dynamics are also transforming, with extended summers and shortened winters. In this study, we employed empirical orthogonal function (EOF) decomposition analysis to examine the interannual to multidecadal fluctuations in air temperature within the Gulf from 1979 to 2019, using hourly air temperature data from the Watch Forcing Dataset (WFDE5) generated from the fifth generation (ERA5) European Centre for Medium-Range Weather Fore-casts (ECMWF). Through this method, we discerned three principal EOF modes, which together accounted for 82% of the air temperature variability in the Gulf, highlighting external influences such as North Atlantic Oscillation (NAO), El Nino-Southern Oscillation (ENSO), and Atlantic Multidecadal Oscillation (AMO). The data for these external influences were sourced from the National Oceanic and Atmospheric Administration (NOAA) Physical Sciences Laboratory. These findings stress the pressing need for detailed climate studies and mitigation strategies for a rapidly warming Gulf region, especially the N Gulf.

Impact of Hillslope Agriculture on Soil Compaction and Seasonal Water Dynamics in a Temperate Vineyard

Major losses of agricultural production and soils are caused by erosion, which is especially pronounced on hillslopes due to specific hydrological processes and heterogeneity. Therefore, the aim of this study was to assess the impact of agricultural management on the compaction, infiltration, and seasonal water content dynamics of the hillslope. Measurements were made at the hilltop and footslope, i.e., soil water content and potential were measured using sensors, wick lysimeters were used to quantify water flux, while a mini-disk infiltrometer was used to measure the infiltration rate and calculate the unsaturated hydraulic conductivity (K_unsat). Soil texture showed differences between hillslope positions, i.e., at the hilltop after 50 cm depth, the soil is classified as silty clay loam, and from 75 cm onward, the soil is silty clay, while at the footslope, the soil is silt loam even at the deeper depths. The results show a higher K_unsat at the footslope as well as higher average water volumes collected in wick lysimeters compared to the hilltop. Average water volumes showed a statistically significant difference at p < 0.01 between the hilltop and the footslope. The soil water content and water potential sensors showed higher values at the footslope at all depths, i.e., 8.0% at 15 cm, 8.4% at 30 cm, and 27.3% at 45 cm. The results show that, even though the vineyard is located in a relatively small area, soil heterogeneity is present, affecting the water flow along the hillslope. This suggests the importance of observing water movement in the soil, especially today when facing extreme weather (e.g., short-term high-intensity rainfall events) in order to protect soil and water resources.

Porewater microbiomes in buried wetland soils: Synergic effects of water chemistry and redox gradients associated with hydrological processes

Abstract Interstitial water or pore water occupies the space between soil particles and provides “hotspots” and “fluvial networks” for microbial activities in floodplain soil. However, to date, we know very little about the microorganisms living in pore water and how they respond to environmental changes. This study aimed to understand microbial distribution and assemblage in riparian pore waters, and how they respond to water chemistry and redox gradients associated with hydrological processes. We analysed the annual changes of porewater microbial communities from the east and west banks of the White Clay Creek, a site at the Christina River Basin – Critical Zone Observatory, Pennsylvania, USA. Microbial abundances were quantified by epifluorescence microscopy and detailed community structures were characterised by high‐throughput sequencing. Water chemistry and redox gradients were also monitored and recorded, and their interactions with porewater microbiomes were analysed using correlations and multivariate analyses. Abundance of microbial cells increased during summer and late autumn. Wetland porewater microbiomes mainly contained Acidobacteria, Bacteroidetes, Nitrospirae and Proteobacteria, and microbiome structures were easily distinguishable from those in the underlying hyporheic gravel layer. Seasonal dynamics of bacterial community structure in the east and west wetlands were distinct, responding to floodplain topography and associated hydrological/geochemical processes. Iron (Fe)‐cycling bacteria (mainly Gallionellaceae and Rhodoferax spp.) dominated the porewater microbiome, and their relative abundance was significantly higher in the east than the west wetland. Furthermore, Fe‐oxidising bacteria (Gallionellaceae) were negatively correlated with Fe‐reducing bacteria (Rhodoferax spp.) at the east wetland. Microbial abundances (cell density) in pore waters showed similar seasonal patterns across stream banks, but microbial community structure did not. Microbiome assembly in pore water is correlated with water chemistry and redox gradients primarily associated with local hydrological processes. As a consequence of their significance for carbon (C) mineralisation and Fe reduction at terrestrial–aquatic interfaces, microbiomes in riparian pore waters and associated microbial activity play an essential role in C and mineral dynamics. These findings will inform future studies of the response of freshwater ecosystems to hydrological dynamics influenced by global climate change.