Impacts of seasonal vegetation dynamics on water balance and groundwater recharge: a monthly leaf area index (LAI)-driven WetSpass-M application in the Sapgyocheon basin, South Korea

Accurate identification of pest species is fundamental to the development of effective integrated pest management (IPM) strategies. This study presents the first molecular identification of the dominant fruit fly species infesting chili (Capsicum annuum L.) in West Sumatra, Indonesia, using DNA barcoding of the mitochondrial cytochrome c oxidase subunit I (COI) gene. Adult specimens were collected between October 2024 and March 2025, and COI-specific primers were used to amplify the genomic DNA extracted from adult tissues. The resulting ~685 bp sequences showed 99.0–99.4% similarity and 96–100% query coverage with reference sequences of Atherigona orientalis (e.g., accession codes PQ483146.1, PQ483144.1, EU627707.1) based on BLASTn analysis. Phylogenetic analysis using the Neighbor-Joining method further confirmed species-level identification by clustering the specimens within the A. orientalis clade with strong bootstrap support. This study provides the first molecular evidence of A. orientalis infestation in chili crops in West Sumatra. The findings offer new insights into the pest status of A. orientalis within chili agroecosystems and emphasize the need for targeted pest management strategies. Moreover, these results establish a valuable baseline for future studies on the host range, dispersal patterns, and seasonal dynamics of this emerging pest to support more effective mitigation planning.

The family Chaetocerotaceae includes the genera Chaetoceros and Bacteriastrum, with Chaetoceros being one of the most cosmopolitan, abundant, and diverse diatom genera in the oceans. This study investigated the diversity and seasonal dynamics of the Chaetocerotaceae in surface waters of two coastal sites along the Catalan coast (Barcelona and Blanes Bay), from 2004 to 2019. A combination of field sample observations, morphological (light and scanning electron microscopy), and molecular (SSU and LSU rDNA gene sequences) analyses of monoclonal cultures, and V4 18S rDNA metabarcoding time-series data from Blanes was used for species identification. The seasonal dynamics of both genera were investigated during two annual cycles at both sites through monthly light microscopy observations and, at Blanes Bay, also through the metabarcoding dataset. A total of 60 species of Chaetoceros and 10 of Bacteriastrum were detected. Based on cell ultramorphology and SSU and LSU rDNA gene phylogenies, two species within the genus Chaetoceros are putatively new to science, although they are not formally described here. The most abundant and frequent taxa were C. tenuissimus, C. socialis, C. vixvisibilis, and the C. curvisetus complex. These species presented similar seasonal trends at both sites, with C. tenuissimus and C. vixvisibilis peaking in late spring and summer, whereas C. socialis was more prevalent in winter. The C. curvisetus complex did not show a clear seasonality although its components were less abundant in summer. These results highlight the value of integrating morphological and molecular approaches to assess the diversity and ecological dynamics of the dominant Chaetoceros species.

Remote sensing has revolutionized monitoring landscapes that are inaccessible or impractical to survey on the ground. Satellite platforms such as Sentinel-2 enable assessment of ecosystem changes over extensive areas with high temporal frequency, while Unmanned Aerial Systems (UAS) offer flexible, ultra-high-resolution observations ideal for site-specific analysis and sensitive environments. This study compares the performance of Sentinel-2 and Phantom 4 multispectral RTK data for monitoring vegetation dynamics in Mediterranean shrubland ecosystems, focusing on the Normalized Difference Vegetation Index (NDVI). Both platforms produced broadly consistent patterns in seasonal and interannual vegetation dynamics. However, UAS outperformed satellite data in capturing fine-scale heterogeneity, regeneration patches, and subtle disturbance responses, particularly in sparsely vegetated or heterogeneous terrain where satellite metrics may be insensitive. The comparison of NDVI across platforms accounted for standardized processing, harmonization, radiometric and atmospheric correction, and spatial resolution differences. Results show platform selection can be optimized according to monitoring objectives: satellite data are well suited for long-term monitoring of landscape-level vegetation dynamics, as both platforms capture consistent patterns when evaluated at comparable, spatially aggregated scales, while UAS data provide critical detail for localized management, early stress detection, and restoration prioritization by resolving fine-scale features. A combined approach enhances ecosystem disturbance assessments and resource management by binding the strengths of both wide-area coverage and precise spatial detail.

This study presents the results of a comprehensive investigation into the seasonal dynamics of protein and carbohydrate-lipid metabolism in 1.5-year-old Yakut horses (Equus caballus) differing in body condition. The research was conducted during late winter and spring, periods characterized by pronounced feed scarcity and metabolic energy stress. Serum concentrations of total protein, protein fractions, urea, free amino nitrogen, glucose, total lipids, triglycerides, cholesterol, β-lipoproteins, and free fatty acids were determined. It was established that the parameters of protein metabolism remained highly stable regardless of season and body condition, reflecting the compensatory capacity of the organism and the effectiveness of supplementary feeding in maintaining nitrogen balance. Total protein concentration was within the physiological range (80–83 g/L), with no statistically significant differences between fractions (p>0.05). However, urea levels were 16–22% higher in well-conditioned animals (p<0.01), indicating more intensive deamination processes and a higher metabolic rate. More pronounced differences were observed in carbohydrate-lipid metabolism. During the winter period, the concentration of total lipids in well-conditioned horses was 35% higher, and cholesterol — 49% higher (p<0.001), suggesting more active lipid metabolism and better energy supply. By spring, a nearly threefold decrease in total lipids was recorded, reflecting mobilization of fat reserves for thermoregulation and energy demands. The results confirm that the stability of protein metabolism, despite marked seasonal fluctuations in lipid and carbohydrate components, represents a key physiological mechanism underlying the adaptive resilience of Yakut horse young stock to the extreme conditions of the subarctic climate.

River water quality in monsoon-driven subtropical basins exhibits strong seasonal variability driven by hydroclimatic forcing and increasing anthropogenic pressure, posing challenges for reliable assessment and management. Despite advances in water quality modeling, most Water Quality Index (WQI) prediction frameworks require extensive sampling and lack interpretability, limiting rapid baseline assessment during critical periods. This study develops the first integrated Explainable Artificial Intelligence (XAI) framework combining Machine Learning (ML), Deep Learning (DL), and Physics-Informed Neural Networks (PINNs) to predict, interpret, and spatially characterize seasonal water quality dynamics in the Padma River Basin, Bangladesh. Forty-four surface water samples collected during winter and monsoon seasons were evaluated using WQI assessment, explainable modeling, probabilistic uncertainty analysis, and spatial regionalization. Results show that seasonal variability dominates over spatial variability (p < 0.0001), with winter low-flow conditions promoting solute concentration and localized degradation, while monsoon discharge drives basin-wide dilution and recovery. Model performance is strongly region-dependent: Deep Neural Networks achieve the highest accuracy in winter (R2 = 0.98; RMSE = 1.16), whereas Ridge Regression and Voting Ensemble models perform more robustly during the monsoon (R2 ≈ 0.97; RMSE ≈ 1.01). Explainable AI analysis identifies NO3- emerged as the dominant contaminant (24.0 ± 36.3mg/L winter, 47.5 ± 68.7mg/L monsoon, with isolated samples exceeding WHO limits), whereas pH and DO exhibit dual seasonal influences. PINN-based data augmentation improves model generalization under limited sampling while preserving hydrochemical consistency. Monte Carlo simulations quantify prediction uncertainty and reveal seasonal shifts in WQI probability distributions, while spatial autocorrelation analysis identifies localized winter degradation hotspots and widespread monsoon improvement. The proposed physics-informed and explainable AI framework enhances predictive reliability, interpretability, and decision relevance, offering a transferable approach for uncertainty-aware water quality assessment and adaptive management in monsoon-affected, data-limited river basins.

River ecosystems have emerged as important sources of greenhouse gases (GHGs), yet the dynamics of greenhouse gas (GHG) fluxes at the water-air interface (WAI) in dammed rivers affected by algal blooms (ABs) remain poorly understood. In particular, the influence of different algal bloom (AB) phases on carbon dioxide (CO2) and methane (CH4) emissions is not well characterized. Here we investigate spatiotemporal variations in CO2 and CH4 concentrations and fluxes in a subtropical dammed river reservoir in China during both the growth and decay phases of ABs. We found that the dominant algal taxa included Cyanobacteria, Bacillariophyta, and Chlorophyta, with cyanobacterial blooms prevailing throughout the period. Overall, the mean CO₂ and CH₄ concentrations across the whole reservoir were 163.12 ± 18.77 and 19.51 ± 3.59 μmol/L (mean ± SD), with corresponding fluxes of 9.44 ± 1.32 mmol/m²/h and 32.52 ± 27.45 μmol/m²/h, respectively. Notably, both gases exhibited consistently higher concentrations in the bottom waters than in the surface. Fluxes of both CO₂ (p < 0.01) and CH₄ (p < 0.05) were remarkably elevated during the non-AB phase relative to the AB phase. Chlorophyll-a (Chl-a), water temperature (Tw), and dissolved total nitrogen (DTN) may be the important factors influencing the pattern of CO2 concentration changes. DTN and dissolved organic carbon (DOC) were identified as key factors regulating CO₂ flux variations. They explained 91% and 80% of the variation in CO2 concentration and flux, respectively. During the AB phase, key environmental factors including pH, Chl-a, algal density (AD) and Tw exhibited significantly higher values (p < 0.01) compared to the non-AB phase. We propose that thermal stratification, shaped by seasonal warming and AB dynamics, plays a critical role in modulating GHG production and transport. These findings highlight how environmental transitions during distinct AB phases govern carbon cycling in dammed river ecosystems, and provide critical insights into the dynamic mechanisms of GHG flux under shifting AB regimes. Highlights During the growth phase of the algal bloom, CO₂ emissions from the reservoir decrease significantly. During the decline phase of the algal bloom, both CO₂ and CH₄ fluxes increase significantly at the water-air interface of the reservoir. Different phases of the algal bloom drive changes in environmental factors that affect the concentrations of CO2 and CH4. The concentrations of CO2 and CH₄ are higher in the bottom waters than in the surface. Values of emission factors estimated by the IPCC are lower than those observed in the field.

Abstract Olive mill wastewater (OMW), a by-product of olive oil production, is widely reused as an organic amendment in Mediterranean agriculture, yet its environmental behavior remains insufficiently characterized under realistic seasonal scenarios despite its growing agronomic relevance in many producing regions. OMW contains high concentrations of organic matter and polyphenolic compounds that can alter soil and groundwater quality. Understanding its fate in soil under seasonally variable climatic conditions is essential for evaluating environmental risks and valorization potential. OMW is widely reused as an organic amendment in Mediterranean agriculture, yet its environmental behavior remains insufficiently characterized under realistic seasonal scenarios despite its growing agronomic relevance in many producing regions. A laboratory lysimeter experiment was conducted to simulate OMW application to soil under semi-arid climatic conditions. Four seasonal phases two winters, one spring, and one summer were reproduced over 18 weeks. Leachate and soil properties were analyzed for soluble phenolic compounds (SPC), pH, electrical conductivity (EC), water drop penetration time (WDPT), and dissolved organic carbon (DOC) quality, specific ultraviolet absorbance at 254 nm (SUVA₂₅₄). Wet winter conditions enhanced OMW percolation, producing elevated SPC and EC levels in leachates, while moderate spring conditions promoted degradation processes, lowering SPC in leachates and reducing topsoil water repellency. Hot, dry summer conditions induced polymerization and accumulation of OMW-derived compounds at the topsoil, whereas the second winter simulation showed lower SPC values, indicating partial stabilization of the soil system after repeated exposure cycles Seasonal climatic variability thus exerts a strong control on OMW degradation and mobility in soil. These insights emphasize the need for season-specific guidelines for land application, particularly in semi-arid regions where rainfall distribution is highly irregular. The results provide a scientific basis for improving management strategies to minimize environmental risks and support the sustainable reuse of OMW as an organic soil amendment within integrated soil fertility programs.

Seasonal dynamics of Polydora infestation in eastern oysters (Crassostrea virginica) from a tidally restricted New England estuary.

Seasonal Dynamics and Coexistence Mechanisms of Plankton Communities in a Karst Plateau Reservoir: Integrating Network Analysis and Environmental Drivers

Bioconversion of heavy metals in river sediments: Seasonal dynamics, driving mechanisms, and BSAF-based assessment in the Liujiang River basin.

Influence of feeding zones and seasonal dynamics on metal bioaccumulation and human health risk assessment in riverine fish.

Assessing spatial and seasonal dynamics and source apportionment of microplastics in Deeporbeel wetland in Assam-India using the PCA-APCS-MLR receptor model.

Revealing groundwater depletion and seasonal dynamics in Northwest China by integrating GRACE with physically based and data-driven modeling

Seasonality plays a crucial role in the transmission dynamics of many infectious diseases, contributing to periodic fluctuations in disease incidence. The previously developed geographically dependent individual-level model (GD-ILM) has been effective in modeling infectious diseases, but does not incorporate seasonal effects, limiting its ability to capture seasonal trends. In this study, we extend the GD-ILM by introducing a seasonally varying transmission component, allowing the model to account for periodic fluctuations in infection risk. Our approach integrates a seasonally forced infection kernel to model periodic changes in transmission rates over time, leading to a novel spatiotemporal kernel. To facilitate efficient and reliable parameter estimation in this high-dimensional setting, we employ the Monte Carlo expectation conditional maximization algorithm. We apply our model to individual-level influenza A data from Manitoba, Canada, examining spatial and seasonal infection patterns to identify high-risk regions and periods, and thus informing targeted intervention strategies. The proposed model's performance is further validated through comprehensive simulation studies. Simulation results confirm that models omitting seasonal components lead to biased spatial parameter estimates under various disease prevalence conditions. To support reproducibility and practical application, we developed the SeasEpi R package publicly available on the comprehensive R archive network (CRAN), which implements the seasonal GD-ILM framework and provides tools for model fitting, simulation, and evaluation. The seasonal GD-ILM offers a more accurate framework for modeling infectious disease transmission by integrating both spatial and seasonal dynamics. It supports more accurate risk assessment and enhances public health responses by enabling timely and location-specific interventions based on seasonal transmission patterns.

Seasonal dynamics and effect of harvest time on the concentration and yield of non-structural carbohydrates in rhizomes and aboveground biomass of giant reed (Arundo donax L.)

Abstract Understanding ice dynamics across varying temporal scales is essential for accurately assessing the contribution of the Antarctic Ice Sheet to global sea level rise. Investigations of seasonal timescale ice dynamics illuminate how glaciers respond to environmental forcings and improve the accuracy of discharge‐based mass balance estimates. Here, we generated a high‐precision, monthly ice velocity field for Byrd Glacier by combining ITS_LIVE image‐pair products with ALOS‐2 offset tracking measurements. We then applied seasonal signal detection methods to systematically analyze the ice velocity variations. Our results reveal a distinctive dipole‐like seasonal flow pattern of Byrd Glacier: from austral spring through summer, ice velocities decrease by an average of ∼40 m/yr in the grounding zone, while flow speeds on the downstream ice shelf increase by ∼20 m/yr. Empirical orthogonal function (EOF) analysis indicates that these seasonal variations are primarily governed by physical processes operating at the grounding zone. We propose that the dipole signal is best explained by interactions between seasonal incursions of high‐salinity shelf water (HSSW) and the subglacial hydrological system. Although sea surface height anomalies have been suggested as a potential driver, their modeled amplitudes and in‐phase patterns indicate that they are unlikely to be the dominant contributors. In contrast, the HSSW–subglacial hydrology framework provides a consistent explanation for both the velocity magnitude and the out‐of‐phase behavior. Although further observations and modeling are needed, findings highlight the complexity of Antarctic glacier seasonality and the need for improved observations and coupled modeling to clarify mechanisms and implications for ice sheet mass balance.

Understanding the drivers of seasonal disease outbreaks remains a fundamental challenge in disease ecology. Periodic outbreaks can be driven by several seasonally varying factors, including pulses of susceptible individuals through births, changes in host behaviour and social aggregation and variation in host immunity. However, when these potential drivers overlap temporally, isolating their relative contributions to outbreak patterns becomes challenging. We studied Hendra virus, a zoonotic pathogen with seasonal spillovers from bats to horses and humans. Multiple seasonal factors have been hypothesized to drive Hendra virus transmission, including food shortages, birth pulses and changes in host aggregation, but their temporal overlap has made identifying primary drivers difficult. We conducted a 4-year longitudinal study of Pteropus bats to test whether seasonal birth pulses and the resulting influx of susceptible juveniles drive Hendra virus transmission. Using a Bayesian ageing model, we aged sexually immature bats and placed them into birth cohorts. We used our age predictions to model how viral shedding and antibody responses changed as bats aged. We tracked Bartonella spp. Infection-a bacterial pathogen requiring close contact for transmission-as an indicator of transmission opportunities within each cohort for comparison. We found no evidence that seasonal birth pulses of immunologically naïve juveniles drove Hendra virus transmission. Two out of three cohorts showed substantially reduced maternal antibody transfer compared to the 2018 cohort, with seroprevalence near zero at our earliest sampling timepoints and showed no clear evidence of synchronized seroconversion. Furthermore, Bartonella infection rates were consistent across cohorts, indicating that opportunities for pathogen transmission remained consistent across cohorts despite varying viral shedding patterns. Our findings demonstrate that birth pulses alone cannot explain observed patterns of Hendra virus outbreaks. These results highlight the importance of using multiple lines of evidence to evaluate competing mechanisms underlying seasonal disease dynamics, particularly when potential drivers coincide temporally.

Spatio-temporal dynamics and flux of microplastics in the lower Ganges-Brahmaputra-Meghna River system and estuary.

Seasonal dynamics of sedimentary dissolved organic matter in plateau lakes: Driving effects on microbial community and functional genes in elements cycling.