Monitoring the Spatial and Interannual Dynamic of Zostera noltei.

Seagrass is a vital structural and functional element of the marine environment worldwide and is highly valued for its ecological benefits. Monitoring the evolution of the seagrass habitat is essential to understand how this coastal ecosystem changes, and to develop good environmental management practices. For the present study, two remote sensing methods were used to map and monitor Zostera noltei Hornemann, 1832 (Z. noltei), in the Merja Zerga lagoon from 2010 to 2020. These methods which are the random forest algorithm and the object-oriented classification, were convenient to provide significant results. The first approach employed Sentinel-2 images from 2018 to 2020, which were used to extract information on changes in Z. noltei (commonly called dwarf eelgrass) distribution and aboveground biomass estimation. The second involved three orthophotography (orthophoto) mosaics from the years 2010, 2016, and 2018, which were analyzed to map the distribution of the species. It was revealed that Z. noltei coverage has increased by 212 ha since 2010, with most of the growth occurring in the center and upstream part of the lagoon. The mean aboveground biomass of dwarf eelgrass in the lagoon was 78.5 DW/m² in 2018, 92.6 DW/m² in 2019, and 115.2 g DW/m² in 2020. The approach used in this study has provided important insights into the dynamic and mean biomass of Z. noltei in the Merja Zerga lagoon. It is therefore a valuable, non-destructive method that uses freely-available Sentinel-2 satellite data.

Neonatal Screening for Sickle Cell Disease in Western Andalusia: Results and Lessons Learnt after 3 Years of Implementation.

OBJECTIVE: The aim of this study was to present the results obtained in the Newborn Screening Program (NSP) for sickle cell disease (SCD) in western Andalusia and the autonomous city of Ceuta in the first 3 years of implementation, and to describe the discrepancies found in the diagnosis of hemoglobinopathies between the screening method and the confirmatory tests. STUDY DESIGN: A descriptive and retrospective study was carried out, and the findings obtained in the newborns included in the NSP between November 2018 and December 2021 were analyzed. RESULTS: A total of 111,205 samples were screened by high-performance liquid chromatography (HPLC). The birth prevalence of SCD, sickle cell trait, hemoglobin C carriers, and the compound heterozygosity Hb C/ß-thalassemia was 1/12,356, 1/467, 1/1,278, and 1/55,602 newborns, respectively. Although there was a correlation between the first-line HPLC screening technique (VARIANTnbs HPLC analyzer, Bio-Rad) and the confirmatory tests in most cases, major discrepancies were found in detecting carriers of G-Philadelphia, D, E, and O-Arab hemoglobin variants, with the former having an incidence of 1/10,110 and the others 1/22,241. The carrier status of Hb G-Philadelphia produced an FAD pattern on the screening method that could be mistaken as Hb D, while Hb O-Arab was identified as an FA5 pattern. Hb D was initially recognized as Hb D in two cases. CONCLUSION: An NSP requires at least two different combined methods in order to identify the hemoglobin variant with sufficient certainty. Furthermore, even though software solutions for HPLC suggest a pattern, it must be confirmed with another technique to obtain a correct interpretation of the chromatograms. KEY POINTS: · The NSPs are an essential activity in preventive medicine.. · At least two different combined methods are required to correctly identify hemoglobin variants.. · Different variants can produce a similar or identical pattern by a single method..

Classifying and Mapping Cultural Ecosystem Services Using Artificial Intelligence and Social Media Data.

Quantifying and mapping cultural ecosystem services are complex because of their intangibility. Data from social media, such as geo-tagged photographs, has been proposed for mapping cultural use or appreciation of ecosystems. However, manual content analysis and classification of large numbers of photographs is time-consuming. The potential of deep learning for automating the analysis of crowdsourced social media content is still being explored in CES research. Here, we use a new deep learning model for automating the classification of natural and human elements relevant to CES from Flickr images. This approach applies a convolutional neural network architecture to analyze over 29,000 photographs from the Lithuanian coast and uses hierarchical clustering to group these photographs. The accuracy of the classification was assessed by comparison with manual classification. Over 37% of the photographs were taken for the landscape appreciation class, and 28% of the photographs were taken of nature, of animals or plants, which represent the nature appreciation class. The main clusters were identified in urban areas, more precisely in the main coastal cities of Lithuania. The distribution of the nature photographs was concentrated around particular natural attractions, and they were more likely to occur in parks and natural reserves with high levels of vegetation and animal cover. This approach that was developed for clustering the photographs was accurate and saved approximately 100 km of manual work. The method demonstrates how analyzing large numbers of digital photographs expands the analytical toolbox available to researchers and allows the quantification and mapping of CES at large geographical scales. Automated assessment and mapping of cultural ecosystem services could be used to inform urban planning and improve nature reserve management.

Harnessing LSTM and XGBoost algorithms for storm prediction.

Storms can cause significant damage, severe social disturbance and loss of human life, but predicting them is challenging due to their infrequent occurrence. To overcome this problem, a novel deep learning and machine learning approach based on long short-term memory (LSTM) and Extreme Gradient Boosting (XGBoost) was applied to predict storm characteristics and occurrence in Western France. A combination of data from buoys and a storm database between 1996 and 2020 was processed for model training and testing. The models were trained and validated with the dataset from January 1996 to December 2015 and the trained models were then used to predict storm characteristics and occurrence from January 2016 to December 2020. The LSTM model used to predict storm characteristics showed great accuracy in forecasting temperature and pressure, with challenges observed in capturing extreme values for wave height and wind speed. The trained XGBoost model, on the other hand, performed extremely well in predicting storm occurrence. The methodology adopted can help reduce the impact of storms on humans and objects.

Ecology and extent of freshwater browning - What we know and what should be studied next in the context of global change.

Water browning or brownification refers to increasing water color, often related to increasing dissolved organic matter (DOM) and carbon (DOC) content in freshwaters. Browning has been recognized as a significant physicochemical phenomenon altering boreal lakes, but our understanding of its ecological consequences in different freshwater habitats and regions is limited. Here, we review the consequences of browning on different freshwater habitats, food webs and aquatic-terrestrial habitat coupling. We examine global trends of browning and DOM/DOC, and the use of remote sensing as a tool to investigate browning from local to global scales. Studies have focused on lakes and rivers while seldom addressing effects at the catchment scale. Other freshwater habitats such as small and temporary waterbodies have been overlooked, making the study of the entire network of the catchment incomplete. While past research investigated the response of primary producers, aquatic invertebrates and fishes, the effects of browning on macrophytes, invasive species, and food webs have been understudied. Research has focused on freshwater habitats without considering the fluxes between aquatic and terrestrial habitats. We highlight the importance of understanding how the changes in one habitat may cascade to another. Browning is a broader phenomenon than the heretofore concentration on the boreal region. Overall, we propose that future studies improve the ecological understanding of browning through the following research actions: 1) increasing our knowledge of ecological processes of browning in other wetland types than lakes and rivers, 2) assessing the impact of browning on aquatic food webs at multiple scales, 3) examining the effects of browning on aquatic-terrestrial habitat coupling, 4) expanding our knowledge of browning from the local to global scale, and 5) using remote sensing to examine browning and its ecological consequences.

Alarming coastal vulnerability of the deltaic and sandy beaches of North Africa.

The arid coasts of North Africa, extending over 4633 km from the Gulf of Tunis to the Nile Delta, are undergoing pronounced shoreline retreats and coastal floodings that are reported as a consequence of the ongoing sea level rise resulting from global warming. Of particular interest are the abnormal shoreline dynamics for deltaic and sandy beaches, which are severely impacted by abrupt decadal variabilities in both climatic and anthropogenic drivers resulting in their increased vulnerability to disturbances from coastal hazards. Unfortunately, the evolution, distribution and impacts of these drivers remain largely unquantified, let alone understood, for these extensive arid coasts that harbor the major portion of North Africa's population as well as unique and fragile marine ecosystems. To address this deficiency, we use GIS-based multi-criteria approaches combined with analytic hierarchy process to map the Coastal Vulnerability Index and the Socioeconomic Vulnerability Index along these coasts to investigate the amplitude and extent of shoreline deterioration resulting from sudden fluctuations in sediment transport to the coastline. We use the western bay of the Gulf of Tunis, the coasts of Tripoli and the Nile Delta as three validation sites for our vulnerability assessment. The statistical Integrated Coastal Vulnerability Index map reveals that 47% of arid North African coasts are characterized by high to very high vulnerability. In particular, we observe that the densely populated deltaic coasts in both Tunisia and Egypt are 70% more vulnerable than any others coast in the eastern Mediterranean Basin. These abnormally high-vulnerability extensive areas are also correlated with significant deterioration of coastal aquifers and hence in crop production, compromising local food security and resulting in increasing outflow migration trends. Both Tunisia and Egypt observed dramatic increases in the net population outflow migration by respectively 62% and 248% between 2000 and 2016, mostly from coastal areas. Our source analysis of the amplitude and extent of these high coastal vulnerabilities suggests that they result from the anthropogenic drivers of damming and rapid urban growth over the last few decades rather than the effects of global warming.

Climatological influences on major storm events during the last millennium along the Atlantic coast of France.

This paper reviews the climatological influences on major past storm events in the North-east Atlantic. Analyses are based on a millenary record of sedimentological and historical impacts affecting coastal societies. The effects of 20 past storms have been found from sedimentary deposits from the last 1,000 years. Historical archives confirmed these events. This paper highlights five major storms that have markedly impacted coastal populations. They date back to 1351-1352, 1469, 1645, 1711 and 1751 AD. The 1351-1352 AD event is defined as a millennium storm that was "likely apocalyptical", provoking serious damage and long lasting floods on much of the European coast. Major storm impacts have mostly been recorded during positive North Atlantic Oscillation phases. Four decreasing temperature phases are concomitant with 1300-1355, 1420-1470, 1560-1590 and 1690-1715 AD periods, during which much of the northern Atlantic coast of France underwent severe storm damages.

Heavy metal and polycyclic aromatic hydrocarbons in Ebrié lagoon sediments, Côte d'Ivoire.

Surface sediments throughout Ebrié lagoon, Côte d'Ivoire were collected in 2001 and analyzed for their heavy metal and polycyclic aromatic hydrocarbons (PAH) contaminant content. Geochemical maps of heavy metals (Cd, Cu, Zn, Fe, and Mn) in the surface sediment were produced based on geographical information system (GIS) technology. Heavy metals and PAH were detected at high concentration and provide evidence for several anthropogenic inputs to the lagoon. A significant spatial relationship was found for Fe, Zn, and Cu in the sediment using a GIS-based analysis, suggesting that these metal contaminants in the sediments of the Biétri bay had common sources.

Heavy metal concentrations in marine molluscs from the Moroccan coastal region.

Mussels (Mytilus galloprovincialis), clams (Venerupis decussatus) and oysters (Crassostrea gigas) were sampled seasonally during 2004-2005, from different coastal environments of Morocco in order to measure their accumulated heavy metal concentrations. The concentrations of Hg and Pb were determined by AFS and ICP-MS methods, respectively, whilst the remaining metals (Cd, Cr, Cu, Mn, Zn and Ni) were quantified by AAS. The soft tissue concentrations of the mussels were on average 7.2 mg kg(-1) (Cd), 9.6 mg kg(-1) (Pb), 0.6 mg kg(-1) (Hg), 26.8 mg kg(-1) (Cu), 8.8 mg kg(-1) (Cr), 292 mg kg(-1) (Zn), 20.8 mg kg(-1) (Mn) and 32.8 mg kg(-1) (Ni). The highest tissue heavy metal concentrations were recorded in the south from the industrial area of Jorf Lasfar. The relationships between metal concentration and season in each species showed very similar annual profiles with a peak observed around spring-summer. Statistical analysis indicated that different species showed different bioaccumulation of metals depending on study site and season.

Environmental risk assessment of the Moroccan Atlantic continental shelf: the role of the industrial and urban area.

The present research presents the first large-scale analysis of heavy metal assessment in the Moroccan Atlantic shelf. This work provides scientific basis for future studies on environmental research and fills the gap in knowledge on the worldwide continental platforms. Metal distributions identified three different zones, mainly influenced by industrial and urban sewer (northern areas), agriculture runoffs (central zone), and estuarine discharges (southern areas), respectively. In the north part of the shelf, metal enrichments are observed near industrial and urban sewer mouths (Casablanca and Mohammedia cities). In the south and central areas, the probable absence of human impact on sediments is attributed to effective trapping in the estuary (Oum Er Rbia) and coastal zones, as well as dilution with less contaminated sediments and shelf sediments and removal with fine fractions due to estuary discharges.

Evaluation of the anthropogenic influx of metal and metalloid contaminants into the Moulay Bousselham lagoon, Morocco, using chemometric methods coupled to geographical information systems.

Superficial and cored sediment samples from the Moulay Bousselham lagoon and sub-watershed were analyzed for Al, Fe, Cu, Zn, Pb, Mn, Ni, Cr, As, Hg, and Cd. The temporal and spatial distributions of the main contamination sources of heavy metals were identified and described using chemometric and geographic information system (GIS) methods. Sediments from coastal lagoons near urban and agricultural areas are commonly contaminated with heavy metals, and the concentrations found in surface sediments are significantly higher than those from 50-100 years ago. The concentrations of these elements decrease sharply with depth in the sediment column, and the elements are preferentially enriched in the <2-µm-sized fraction of the sediment. The zones of enhanced risk of heavy metals were detected by means of GIS-based geostatistical modeling. According to sediment pollution indices and statistical analysis, heavy metals (Pb, Cu, Ni, Zn, Cr, and Hg) that pose a risk have become largely enriched in the lagoon sediments during the recent period of agricultural intensification.

Biomonitoring of heavy metals using Mytilus galloprovincialis in Safi coastal waters, Morocco.

Heavy metal concentrations of mercury, cadmium, lead, zinc, cooper, nickel, manganese, and chromium in Mytilus galloprovincialis were investigated to provide information on pollution of Safi coastal area, since these metals have the highest toxic potential. The concentration of Hg and Pb was determined by AFS and ICP-MS methods, respectively, whilst the remaining metals (Cd, Cr, Cu, Mn, Zn, and Ni) were quantified by AAS. High lead, cadmium, chromium, and mercury levels were registered in tissue samples collected from two stations near the Jorf Lihoudi and Safi city, while elevated concentration of manganese and zinc (14.70-25.30 mg kg(-1) and 570-650 mg kg(-1) dry wt, respectively) were found in mussel specimens from Cap Cantin. The high levels of nickel found respectively in the areas near the industrial area being of concern in terms of environmental health need frequent monitoring. The metal concentrations recorded at the clean stations may be considered as useful background levels to which to refer for comparison within the Atlantic coast. M. galloprovincialis are suitable biomonitors to investigate the contamination levels of heavy metals pollution face a different human activity in this coastal area of the Atlantic coast.