Seasonal Variation Research Articles

Seasonal variation and fundamental characteristics of baroclinic tides in the Arabian Gulf

In the Arabian Gulf, baroclinic tides significantly impact physical and biological phenomena, shaping the region’s marine ecosystem. This study provides the first high-resolution investigation of the baroclinic tides using the three-dimensional, nonhydrostatic MIT general circulation model (MITgcm). The simulation outputs were validated against data from Oregon State University’s Tidal Inversion Software (OTIS), tidal stations, and mooring observations, showing high agreement and confirming the model’s accuracy. We further examined the characteristics of baroclinic and barotropic tides, identifying four key regions with high-intensity baroclinic tides based on energy budget analysis: 1) the narrow Strait of Hormuz; 2) the strait’s entrance along the trough, including the islands Farur, Siri, Abumusa, and Greater Tunb; 3) the central axial trough area with sea-bottom ridges; and 4) the northern area following a secondary ridge. Most baroclinic tides dissipate quickly due to interactions with complex bottom topography and do not propagate far from their sources. Significant seasonal variation shows that in winter, only the Strait of Hormuz and the area around the four islands showed significant baroclinic tidal energy, likely due to well-mixed upper layer seawater that hinders thermocline disturbances. The strong correlation between the simulation results and satellite images of internal waves suggests that intense baroclinic tides generate internal solitary waves in the Gulf.

Integrating Mixed Livestock Systems to Optimize Forage Utilization and Modify Woody Species Composition in Semi-Arid Communal Rangelands

Communally owned rangelands serve as critical grazing areas for mixed livestock species such as cattle and goats, particularly in the arid and semi-arid regions of Southern Africa. This study aimed to evaluate the nutritional composition and woody species composition of communal rangelands where cattle and goat flocks graze together and to investigate the influence of grazing intensity on vegetation dynamics. Vegetation surveys were conducted across varying grazing intensities to assess species richness, biomass, and dietary preferences, while soil properties were analyzed to determine their interaction with vegetation attributes. Stepwise regression and path analyses were used to explore the relationships between soil characteristics, vegetation structure, and livestock dietary choices. The results revealed that high grazing pressure significantly reduced grass biomass (p = 0.003) and woody species density (p = 0.007) while increasing shrub cover (p = 0.018). Nutritional analysis indicated that goats preferred woody shrubs, which contributed 42.1% of their diet compared to 27.8% for cattle (p = 0.008). Regression analysis further showed that soil organic carbon (p = 0.002) and tree height (p = 0.041) were strong predictors of shrub cover. Seasonal variation significantly affected forage availability and nutritional content, with higher crude protein levels recorded during the wet season (p = 0.007). These findings suggest that grazing management strategies should be tailored to the distinct forage needs of cattle and goats to maintain the productivity and ecological stability of communal rangelands. A holistic approach that considers livestock dietary preferences, vegetation composition, and soil health is essential for sustainable rangeland management in mixed-species grazing systems.

An examination of seasonal variation in taxonomic richness and community composition using eDNA on a tropical coral reef

An examination of seasonal variation in taxonomic richness and community composition using eDNA on a tropical coral reef

Seasonal Variation of Physicochemical Properties in the Lower River Benue, Nigeria

The study investigates the impact of seasonal changes on water quality in the River Benue, Nigeria. Conducted over eighteen months from January 2020 to June 2021, the research focuses on three locations: Ibi, Lau, and Mayo-Ranewo. Key physicochemical parameters such as temperature, pH, turbidity, conductivity, Biochemical Oxygen Demand (BOD), hardness, dissolved oxygen (DO), fluoride, and nitrate were measured to assess water quality. The findings reveal significant seasonal variations in these parameters, influenced by rainfall patterns, land use, and anthropogenic activities. Temperature ranged from 25.56°C to 30.70°C, remaining within acceptable limits for tropical waters, supporting stable biological processes. However, turbidity levels exceeded recommended values, indicating the presence of suspended solids that could impair water quality and harm aquatic life. Total dissolved solids (TDS) and conductivity showed potential contamination risks, likely due to agricultural runoff and urban discharges. Dissolved oxygen (DO) levels were generally adequate, especially during the wet season, enhancing oxygen content through increased photosynthetic activity and water inflows. However, BOD spiked with rainfall, signalling organic pollution and potential eutrophication risks. Seasonal shifts in pH and water hardness reflected changes in runoff and photosynthesis, with values largely within acceptable ranges for aquatic life. Fluoride and nitrate concentrations increased in the wet season, primarily due to agricultural runoff, highlighting the influence of land-based activities on water quality.

Forecasting heart failure: Seasonal alignment of heart failure outcomes in New York.

Seasonal variations have been observed in heart failure (HF) hospitalization. Numerous explanatory mechanisms have been proposed, but no prior studies have examined potential contributors directly. Our objective was to identify specific factors that could contribute to seasonal variability using a large longitudinal dataset of HF hospitalizations. Hospital discharge data were obtained for all hospitals in the state of New York from 1994 to 2007. Records with a primary diagnosis of HF by the International Classification of Diseases-9 Clinical Modification (ICD-9-CM) code (428.xx and 425.xx) were included. Year and month of admission were used as predictors to evaluate outcomes of in-hospital mortality, population-adjusted daily rate of hospital admissions and length of stay (LOS) using univariable regression including a sinusoidal model to assess the seasonality of HF outcomes. Observations were then adjusted for multiple medical covariables as well as the average local monthly temperature and humidity at each hospital using data from the Global Historical Climate Network to identify potential modifiers of seasonal variability. Among 949907 records, the median age was 76 [inter-quartile range (IQR) 65-84years old], and the cohort was 54% female (510945 records). The population-adjusted rate of HF admissions per day increased by 1.1 admissions/day/year between 1994 and 2007, whereas in-hospital mortality and LOS decreased by -0.3%/year and -0.3days/year, respectively (P<0.001 for all). Seasonal trends were identified for daily HF admissions (February peak, P<0.0001), mortality (January peak, P<0.001) and LOS (January peak, P<0.01). Cosinor analysis revealed significant periodicity for HF admission rate (amplitude=±0.9 admissions/day/100000 people, P<0.001), in-hospital mortality (amplitude=±0.47%, P<0.001) and LOS (amplitude=±0.23days, P<0.01). No other patient characteristics were significant modifiers of seasonality. Odds of mortality were highest in July rather than January when adjusted for average local temperature but not humidity. Adverse outcomes in patients hospitalized with HF were significantly worse in the winter months even when adjusted for patient characteristics and concurrent acute diagnoses such as pneumonia. Local ambient temperature was the strongest modifier of the observed seasonality. Given the increasing frequency of extreme weather events, additional work to determine the mechanisms of this and other environmental risk factors is urgently needed.

A Gaussian Process Regression and Wavelet Transform Time Series approaches to modeling Influenza A.

The global spread of Influenza A viruses is worsening economic and social challenges. Various mechanistic models have been developed to understand the virus's spread and evaluate intervention effectiveness. This study aimed to model the temporal dynamics of Influenza A using Gaussian Process Regression (GPR) and wavelet transform approaches. The study employed Continuous Wavelet Transform (CWT), Discrete Wavelet Transform (DWT) and Wavelet Power Spectrum to analyze time-series data from 2009 to 2023. The GPR model, known for its non-parametric Bayesian nature, effectively captured non-linear trends in the Influenza A data, while wavelet transforms provided insights into frequency and time-localized characteristics. The integration of GPR with DWT denoising techniques demonstrated superior performance in forecasting Influenza A cases compared to traditional models like Auto Regressive Integrated Moving Averages (ARIMA) and Exponential Smoothing (ETS) using Holt-Winter method. The study identified significant anomalies in Influenza A cases, corresponding to known pandemic events and seasonal variations. These findings highlight the effectiveness of combining wavelet transform analysis with GPR in understanding and predicting infectious disease patterns, offering valuable insights for public health planning and intervention strategies. The research recommends extending this approach to other respiratory viruses to assess its broader applicability.

Comparative Analysis for the Seasonal Variations of the IF2 and T Ionospheric Indices during Solar Cycles 23 and 24

In this work, a comparative analysis for the behavior and pattern of the variations of the IF2 and T Ionospheric indices was conducted for the minimum and maximum years of solar cycles 23 and 24. Also, the correlative relationship between the two ionospheric indices was examined for the seasonal periods spanning from August 1996 to November 2008 for solar cycle 23 and from December 2008 to November 2019 for solar cycle 24. Statistical calculations were performed to compare predicted values with observed values for the selected indices during the tested timeframes. The study's findings revealed that the behavior of the examined indices exhibited almost similar variations throughout the studied timeframe. The seasonal variations were adopted to examine the cross-correlation between the studied indices. The seasonal correlation between tested indices demonstrated that the two indices are highly correlated to each other, with determination coefficient (R2) values ranging from 0.991 to 0.998 during solar cycle 23 and from 0.996 to 0.998 during solar cycle 24. Furthermore, the results of the comparative analytical study revealed that the mathematical correlation equation between the tested indices could be described as a first-order polynomial equation. The proposed mathematical correlation formula for these two indices exhibited a high level of accuracy and good fit between observed values and generated datasets for all seasons during both solar cycles 23 and 24.

Seasonal variation in dietary intake and its association with obesity-related chronic diseases in northeast China

Abstract Objective The objective of this study was to assess seasonal changes in dietary and nutrient intake of residents (18-75 years old) in Northeast China during summer and winter, and to explore the associations between fatty acids, phytosterols, and the prevalence of obesity-related chronic diseases, particularly obesity, hyperlipidemia, and NAFLD. Methods A total of 4773 participants from the Internet-based Dietary Questionnaire for Chinese (IDQC) were included in this study. Dietary intake information was collected using a validated food frequency questionnaire. Student’s t-test or Mann-Whitney U-test was used to analyze continuous variables, while Chi-squared tests were used to compare categorical variables. Multivariable logistic regression was employed to assess the relationship between fatty acids, phytosterols, and obesity-related chronic diseases. Results The mean consumption of legumes, vegetables, fruits, nuts, dairy products, fish, condiments, energy, protein, fat, and carbohydrate differed significantly between summer and winter (P &lt; 0.05). Significant inverse associations were found between both fatty acids and phytosterols and obesity-related chronic diseases in multivariate adjusted models. Summer polyunsaturated fatty acid (PUFA) intake was negatively associated with the prevalence of hyperlipidemia (Q4, OR, 0.515; 95%CI, 0.283-0.921; P &lt; 0.05) and non-alcoholic fatty liver disease (NAFLD) (Q4, OR, 0.331; 95%CI, 0.176-0.599; P &lt; 0.001). Phytosterols intake was negatively associated with the prevalence of obesity (Q4, OR, 0.603; 95%CI, 0.414-0.873; P &lt; 0.05), hyperlipidemia (Q4, OR, 0.420; 95%CI, 0.233-0.731; P &lt; 0.001), and NAFLD (Q4, OR, 0.206; 95%CI, 0.111-0.360; P &lt; 0.001) during the summer. Conclusions Higher PUFA intake was associated with a lower prevalence of obesity, hyperlipidemia, and NAFLD. Phytosterol intake was inversely associated with the prevalence of hyperlipidemia and NAFLD. These findings suggest that the associations between PUFA and phytosterols and the prevalence of obesity-related chronic diseases may be influenced by seasonal differences in food intake.

Seasonal infestation patterns of ticks on Hokkaido sika deer (Cervus nippon yesoensis).

Ticks prefer specific feeding sites on a host that are influenced by host–tick and tick–tick interactions. This study focused on the spatiotemporal distribution of ticks in Hokkaido sika deer, an important tick host in Hokkaido, Japan. Tick sampling was performed on the sika deer in the Shiretoko National Park between June and October 2022. Ticks were collected from 9 different body parts of the deer to compare their attachment site preferences. Interspecific and intraspecific relationships among ticks were examined using co-occurrence analysis. The collected ticks were nymphal and adult stages of 4 species: Ixodes ovatus, Ixodes persulcatus, Haemaphysalis japonica and Haemaphysalis megaspinosa. Seasonal variations in tick burden were observed, with I. persulcatus and I. ovatus peaking in June and declining towards October; H. japonica showing low numbers in July and August and increasing from September; and H. megaspinosa appearing from September onwards with little variation. Attachment site preferences varied among species, with a significant preference for the pinna in I. ovatus and I. persulcatus. Haemaphysalis japonica was mainly found on the body and legs between June and August, and shifted to the pinna from September. Haemaphysalis megaspinosa showed a general preference for areas other than the legs. Co-occurrence analysis revealed positive, negative and random co-occurrence patterns among the tick species. Ticks of the same genus and species exhibited positive co-occurrence patterns; I. ovatus showed negative co-occurrence patterns with Haemaphysalis spp. This study revealed the unique attachment site preferences and distinct seasonal distributions of tick species in the Hokkaido sika deer.

Mapping coconut plantation in Western Agro-Climatic zone using object-based classification and machine learning technique

Coconut (Cocos nucifera), a key crop for over 10 million farming families in India, is vital in the agricultural economies of southern states like Tamil Nadu. However, traditional methods of monitoring coconut plantations are challenging due to the crop's geographical dispersion and seasonal variations. This study focuses on mapping coconut plantations in the Western Agro-Climatic Zone of Tamil Nadu using Object-Based Classification (OBC) and machine learning techniques. A ten-year time series of Landsat 7 optical satellite data (2012-2013 and 2022-2023) was employed, combined with ground truth surveys across the region. The study utilized Support Vector Machine (SVM) and Random Forest (RF) classifiers, with RF demonstrating superior accuracy. The RF classifier achieved an accuracy of 91.7% in 2012-2013 and 90.3% in 2022-2023, outperforming SVM, which hovered around 70%. The research also conducted a change detection analysis, revealing a net increase of 3,270 hectares of coconut plantations over the decade, with the Coimbatore district contributing the most significant growth of 2,560 hectares. This study underscores the effectiveness of integrating OBC and machine learning, mainly RF, for accurate and efficient mapping of coconut plantations using Landsat satellite data.

Initial Performance Outlook on the Sliding Particle Receiver (SlideRec)

The next generation of central receivers is expected to reach high outlet temperatures of 800 °C and above, maintain stable operation and react to a varying incoming flux magnitude caused by hourly and seasonal variations, endure high-temperature operation, and remain cost-effective. Particle-based central receivers are being considered due to their high-temperature durability and favorable thermal properties of particle materials such as bauxite particles. This paper describes a new particle-based central receiver concept and provides an initial exploration of its performance in comparison with the existing CentRec® technology. Discrete Element Method (DEM) simulations demonstrated that a stable falling and sliding particle film can be achieved inside the SlideRec. The residence time of particle flow within the SlideRec is estimated to be higher than a falling particle receiver and similar to that of an obstructed-flow receiver. The results of the thermal model (incorporating reflective, radiative, convective and conductive heat losses) indicate that the SlideRec demonstrates a higher thermal efficiency than the CentRec® under an incoming aperture flux of between 0.1 MW/m2 and 1.7 MW/m2. The concept therefore is promising and is recommended for further experimental exploration.

Role of intra-individual variation in the detection of thresholds for DFI and for misclassification rates: A retrospective analysis of 14,775 SCSA® tests.

Sperm DNA damage is associated with reduced male fertility after natural conception and intrauterine insemination. However, the impact on in vitro fertilization (IVF) and especially intracytoplasmic sperm injection (ICSI) treatments is still unclear. Few studies have focused on the intra-individual variation in DFI even though it may have an important role to play in terms of detection of thresholds and for misclassification rates. Results for Sperm Chromatin Structure Assay (SCSA®) tests performed for 70 European fertility clinics between January 1st, 2008 and December 31st, 2022 were examined. A small retrospective study included 406 couples receiving their first treatment with IVF or ICSI. These results were then used for a mathematical simulation to investigate the role of intra-individual variation. The large retrospective study included a total of 14,138 diagnostic tests and 637 tests from an IUI study. The distribution of DFI was assessed for the IUI cohort and cohorts of patients attending Sims IVF and Fertility Center Hamburg (FCH). Descriptive analysis of the data was performed regarding time of year, male age, and year. When DFI was above the thresholds of 15 and 25, a significant reduction in ongoing pregnancies after 12weeks of gestation was observed for IVF and ICSI treatments, respectively. For IVF treatments, the pregnancy rate was reduced from 45.1% to 24.6%, odds ratio=2.58 (p=0.004). For ICSI treatments, the pregnancy rate was reduced from 48.6% to 29.6%, odds ratio=2.00 (p=0.032). Intra-individual variation was significantly related to the misclassification rate and the sample size required to identify a threshold. The percentage of patients with a DFI below 15 was 64.8% for the IUI cohort and 51.7% and 41.6% for cohorts of patients attending Sims IVF and FCH, respectively. The median DFI for these cohorts differed significantly and was 11.6, 15.0 and 17.2, respectively. DFI shows a seasonal variation, and increases with male age. During the past 15 years, the median DFI has increased by 0.05% per year (p=0.02). Ongoing pregnancy rates are reduced significantly for both IVF and ICSI treatments when DFI is above the thresholds of 15 and 25, respectively. The misclassification rate and the required sample size increase with increasing intra-individual variation. Couples with a DFI above 15 are more likely to experience failed assisted reproductive technology(ART) cycles. DFI appears to have increased during the past 15 years.

Interpopulation and seasonal variations in habitat and microhabitat use of Vipera ammodytes (Linnaeus, 1758)

Despite the abundant data on habitat use of Vipera ammodytes, most studies are purely descriptive, merely listing the habitats in which the species is most often found. More complete studies evaluating the habitat preference of the species are lacking. The intraspecific variation (i.e., interpopulation or seasonal) in habitat and microhabitat utilization of the species also remains a poorly studied topic. In the current study, we assessed the general patterns of habitat and microhabitat use of V. ammodytes and their interpopulation and seasonal variations, based on habitat/microhabitat availability. To achieve that, we studied five different populations along a latitudinal gradient in western Bulgaria. In all of the studied areas, V. ammodytes showed a clear preference for various stony and rocky habitats and microhabitats, overgrown with herbaceous and shrub vegetation, while it avoided bare habitats, dark deciduous forests as well as cultivated agricultural lands. There were clear interpopulation and seasonal variations in habitat and microhabitat preference and spatial niche utilization. Our results suggest that habitat and microhabitat use of V. ammodytes depends on a combination of many factors such as season, locally specific characteristics like habitat structure and availability, population dynamics, food availability, physical and microclimatic conditions, and possibly on the extent of the interspecific competition

Spatiotemporal analysis of Zika virus transmission dynamics incorporating human mobility and seasonal variations using modified homotopy perturbation method

AbstractThis study employed a mathematical model to evaluate how seasonal variations, vector dispersal, and mobility of people affect the spread of the Zika virus. The model's positive solutions, invariant zones, and solution existence and uniqueness were validated through proved theorems. The equilibria points were identified, and the basic reproduction number was calculated. The model was semi-analytically solved using a modified homotopy perturbation approach, and an applied convergence test proved that the solution converges. The simulation results indicated that under optimal breeding conditions, the density of healthy mosquitoes peaked in the fourth month. Two months later, increased mosquito dispersal and human carriers facilitated by favorable weather led to a rise in mosquito infectiousness, peaking between the fourth and eighth months due to significant seasonal effects, resulting in high Zika transmission. To effectively control mosquito populations and reduce Zika transmission, it is recommended that public health interventions focus on the critical periods spanning the third to eighth months.

Seasonal variations in microplastics in a coastal wetland in southwest India as well as their risks to Sillago sihama and Gerres filamentosus.

Microplastics are minute plastic particles ranging from 1µm to 5mm in size. Mangroves are crucial ecosystems with roles in carbon sequestration, shoreline protection, and habitat for diverse species. Despite their significance, the extent of microplastic pollution in mangroves, especially in India, remains inadequately understood. To address this gap, we conducted a seasonal sampling in the Kota mangrove ecosystem at different water column depths. Our analysis revealed average microplastic abundances of 0.93 (monsoon), 3.71 (post-monsoon), and 2.92 MPs/L (pre-monsoon). The average microplastic abundances were 19.88 and 15.86 microplastics/individual for Gerres filamentosus and Sillago sihama, respectively. Fibrous microplastics smaller than 1mm were dominant. Transparent microplastics dominated the water column (28.57% in monsoon, 77.45% in post-monsoon, and 49.24% in pre-monsoon), and they were also prevalent in S. sihama (49.55%) and G. filamentosus (41.51%). This points towards greater bioavailability and suggests that transparent microplastics are often mistaken for prey. Anthropogenic influence is a major factor that governs microplastic distribution than season in Kota mangroves. Fourier transform infrared spectroscopy revealed that polypropylene was the dominant polymer in both water column as well as in S. sihama and G. filamentosus. We identified aquaculture, tourism, and local activities as probable sources of microplastic pollution. The monitoring data is crucial as it provides insights into microplastics pollution in two economically important fish species that are largely consumed by the local population. Exposure to microplastics from the consumption of these fish may cause serious health issues for human beings.

The sacred and the wild: Unpacking human-wildlife interaction in Indian part of Kailash Sacred Landscape

The negative interaction between humans and wildlife in the Indian Himalayas has increased in recent decades. Studies on human-wildlife negative interactions have primarily focused on protected areas. However, research on such interactions is limited in regions like the Indian part of Kailash Sacred Landscape (KSL), where wildlife and humans share a common space. This study examined the patterns of crop damage, livestock depredation, human fatalities, seasonal and temporal variations, location-specific factors, and critical variables contributing to negative humanwildlife interactions in KSL India. We examined 574 incidences of livestock depredation, 123 incidences of human injury/death between 1984-2016, and 560 crop damage incidences between 2002-2017. Most incidences of human injury/ death and livestock depredation were by common leopards, followed by Asiatic black bears. Maize was the highest raided crop (n=202), followed by wheat (n=190) and paddy (n=168). Maximum incidences of crop loss were reported in 2017 (n=76), and the lowest was in 2002 (n=5). The hotspot maps of crop damage and livestock depredation revealed that the negative interaction between humans and wildlife was highest in the southern part of the landscape. The result predicted 195 villages as hotspots in the Indian part of KSL. Villages were identified at the forest range level. We found that 11 villages in Askot, 12 in Munshiyari, 17 in Berinag, 15 in Dharchula, 36 in Didihat, 61 in Gangolihat, and 43 in Pithoragarh forest ranges are severely affected by negative interaction with wildlife.

Study of seasonal variation of accident-derived atmospheric radiocesium in Koriyama City, Fukushima Prefecture, Japan during 2011-2014.

This study measured the atmospheric concentration and deposition flux of radiocesium in Koriyama City, Fukushima Prefecture, Japan, from November 2011 to October 2014. The results show synchronous seasonal change in atmospheric concentration and deposition flux of radiocesium, which is high during winter to early spring and low during summer to autumn. These seasonal variations are similar to those observed in Fukushima City but differ from those in Namie Town, Fukushima Prefecture, comprising a larger contaminated forest area. The evaluation of the relationship between atmospheric 137Cs concentration or 137Cs specific activity in atmospheric particulate matter (PM) and deposition density of 137Cs in PM source area suggest that stronger winds blowing from areas with relatively large 137Cs deposition (west of Koriyama City) toward the study site affect the site's atmospheric 137Cs concentrations.

Methods and Evaluation of AI-Based Meteorological Models for Zenith Tropospheric Delay Prediction

Zenith Tropospheric Delay (ZTD) is a significant error source affecting the accuracy of certain space geodetic measurements. This study evaluates the performance of Artificial Intelligence (AI) based meteorological models, such as Fengwu and Pangu, in estimating real-time ZTD. The results from these AI models were compared with those obtained from the Global Navigation Satellite System (GNSS), the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis (ERA5), and the third generation of the Global Pressure–Temperature data model (GPT3) to assess their accuracy across different time intervals, seasons, and geographic locations. The findings reveal that AI-driven models, particularly Fengwu, offer higher long-term forecasting accuracy. An analysis of data from 81 stations throughout 2023 indicates that Fengwu’s 7-day ZTD forecast achieved an RMSE of 2.85 cm when compared to GNSS-derived ZTD. However, in oceanic regions and areas with complex climatic dynamics, the Fengwu model exhibited a larger error compared to in other land regions. Additionally, seasonal variations and station altitude were found to influence the accuracy of ZTD predictions, emphasizing the need for detailed modeling in complex climatic zones.

Seasonality and interannual variability of an arctic marine time series, IsA

The Arctic is warming twice as fast as anywhere else on the planet, in the European Arctic mainly driven by exalted Atlantic Water inflow. Microbial eukaryotes are diverse and essential to the functioning of marine ecosystems, thus environmental perturbations altering their communities influence the entire ecosystem. To study seasonal and interannual variation and the potential effects of Atlantification, a long-term marine time series has been established in Isfjorden-Adventfjorden (IsA), West Spitsbergen. We here present three years of high-resolution data including hydrography, nutrients, photosynthetic biomass, flow cytometry and community composition of microbial eukaryotes (0.45–10μm, based on Illumina metabarcoding of 18S rDNA and rRNA). The timing, magnitude and species composition of the spring bloom varied interannually, with the more Atlantic spring 2014 being distinctly different from the two other years. A strong recurring seasonal pattern was evident in biodiversity, cell abundances and community composition. Winter communities were characterized by high alpha diversity and very low cell numbers with a dominance of heterotrophic and parasitic taxa. Despite large intra- and interannual differences in communities during the productive time of the year, winter communities were always highly similar, suggesting that the polar night represents a strong environmental forcing that resets the microbial communities.

Assessing yellow fever outbreak potential and implications for vaccine strategy.

Yellow fever (YF), a vector-borne viral hemorrhagic fever, is endemic in tropical regions of Africa and South America, with large vaccination programmes being used for control. However, significant outbreaks have occurred in recent years. Data on infection rates and seroprevalence is often sparse, requiring robust mathematical models to estimate the burden of yellow fever. In particular, modelling is required to estimate the risk of outbreaks and inform policy decisions regarding the targeting of vaccination. We present a dynamic, stochastic model of YF transmission which uses environmental covariates to estimate the force of infection due to spillover from the sylvatic (non-human primate) reservoir and the basic reproduction number for human-to-human transmission. We examine the potential for targets identified by the World Health Organization EYE Strategy (50%, 60% or 80% vaccination coverage in 1-60 year olds) to achieve different threshold values for the effective reproduction number. Threshold values are chosen to reflect the potential for seasonal and/or climatic variation in YF transmission even in a scenario where vaccination lowers the median reproduction number below 1. Based on parameter estimates derived from epidemiological data, it is found that the 2022 EYE Strategy target coverage is sufficient to reduce the static averaged annual effective reproduction number R below 1 across most or all regions in Africa depending on the effectiveness of reported vaccinations, but insufficient to reduce it below 0.5 and thereby eliminate outbreaks in areas with high seasonal range. Coverage levels aligned with the 2026 targets are found to significantly decrease the proportion of regions where R is greater than 0.5.