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\"At the convergence of the land and sea, coastal environments are some of the most dynamic and populated places on Earth. This book explains how the many varied forms of spatial analysis, including mapping, monitoring and modelling, can be applied to a range of coastal environments such as estuaries, mangroves, seagrass beds and coral reefs. Presenting empirical geographical approaches to modelling, which draw on recent developments in remote sensing technology, geographical information science and spatial statistics, it provides the analytical tools to map, monitor and explain or predict coastal features. With detailed case studies and accompanying online practical exercises, it is an ideal resource for undergraduate courses in spatial science. Taking a broad view of spatial analysis and covering basic and advanced analytical areas such as spatial data and geostatistics, it is also a useful reference for ecologists, geomorphologists, geographers and modellers interested in understanding coastal environments\"-- Provided by publisher.

Rivers are representative of the overall contamination found in their catchment area. Contaminant concentrations in watercourses depend on numerous factors including land use and rainfall events. Globally, in Mediterranean regions, rainstorms are at the origin of fluvial multipollution phenomena as a result of Combined Sewer Overflows (CSOs) and floods. Large loads of urban-associated microorganisms, including faecal bacteria, are released from CSOs which place public health - as well as ecosystems - at risk. The impacts of freshwater contamination on river ecosystems have not yet been adequately addressed, as is the case for the release of pollutant mixtures linked to extreme weather events. In this context, microbial communities provide critical ecosystem services as they are the only biological compartment capable of degrading or transforming pollutants. Through the use of 16S rRNA gene metabarcoding of environmental DNA at different seasons and during a flood event in a typical Mediterranean coastal river, we show that the impacts of multipollution phenomena on structural shifts in the particle-attached riverine bacteriome were greater than those of seasonality. Key players were identified via multivariate statistical modelling combined with network module eigengene analysis. These included species highly resistant to pollutants as well as pathogens. Their rapid response to contaminant mixtures makes them ideal candidates as potential early biosignatures of multipollution stress. Multiple resistance gene transfer is likely enhanced with drastic consequences for the environment and human-health, particularly in a scenario of intensification of extreme hydrological events.

Water quality monitoring is relevant for protecting the designated, or beneficial uses, of water such as drinking, aquatic life, recreation, irrigation, and food supply that support the economy, human well-being, and aquatic ecosystem health. Managing finite water resources to support these designated uses requires information on water quality so that managers can make sustainable decisions. Chlorophyll-a (chl-a, µg L−1) concentration can serve as a proxy for phytoplankton biomass and may be used as an indicator of increased anthropogenic nutrient stress. Satellite remote sensing may present a complement to in situ measures for assessments of water quality through the retrieval of chl-a with in-water algorithms. Validation of chl-a algorithms across US lakes improves algorithm maturity relevant for monitoring applications. This study compares performance of the Case 2 Regional Coast Colour (C2RCC) chl-a retrieval algorithm, a revised version of the Maximum-Peak Height (MPH(P)) algorithm, and three scenarios merging these two approaches. Satellite data were retrieved from the MEdium Resolution Imaging Spectrometer (MERIS) and the Ocean and Land Colour Instrument (OLCI), while field observations were obtained from 181 lakes matched with U.S. Water Quality Portal chl-a data. The best performance based on mean absolute multiplicative error (MAEmult) was demonstrated by the merged algorithm referred to as C15−M10 (MAEmult = 1.8, biasmult = 0.97, n = 836). In the C15−M10 algorithm, the MPH(P) chl-a value was retained if it was > 10 µg L−1; if the MPH(P) value was ≤ 10 µg L−1, the C2RCC value was selected, as long as that value was < 15 µg L−1. Time-series and lake-wide gradients compared against independent assessments from Lake Champlain and long-term ecological research stations in Wisconsin were used as complementary examples supporting water quality reporting requirements. Trophic state assessments for Wisconsin lakes provided examples in support of inland water quality monitoring applications. This study presents and assesses merged adaptations of chl-a algorithms previously reported independently. Additionally, it contributes to the transition of chl-a algorithm maturity by quantifying error statistics for a number of locations and times.

The health of an ecosystem and the quality of water can be determined by the clarity of the water. The Persian Gulf and Oman Sea have a unique ecosystem, and monitoring their water clarity is necessary for sustainable development. Here, various criteria such as hue angle, chlorophyll-a, Forel-Ule index, organic carbon (OC), precipitation, sea surface salinity (SSS), Secchi disk depth (SDD), and sea surface temperature (SST) were analyzed from 2002 to 2018 using MODIS-Aqua Imagery, statistical tests, and deep learning (DL) models to monitor the water clarity of the Persian Gulf and the Oman Sea. The study found differences in criteria across different regions, with coastal areas showing higher Forel-Ule index and chlorophyll-a values. Positive trends in the Persian Gulf and the Oman Sea were attributed to the Forel-Ule index and OC, while negative trends were seen in SSS and SST in the Persian Gulf. The convolutional neural network (CNN) model was found to perform better than long short-term memory (LSTM) in predicting water clarity. Interpretation techniques were used to determine the importance of criteria in monitoring water clarity, with the Forel-Ule index, hue angle, and OC showing the greatest interaction. Sensitivity analysis revealed that chlorophyll-a and SSS had the most significant impact on water clarity prediction. Overall, this study using DL models and MODIS-Aqua Imagery can help improve water quality and protect the environment.

Background Despite the increasing number of cases of scrub typhus and its expanding geographical distribution in China, its potential distribution in Fujian Province, which is endemic for the disease, has yet to be investigated. Methods A negative binomial regression model for panel data mainly comprising meteorological, socioeconomic and land cover variables was used to determine the risk factors for the occurrence of scrub typhus. Maximum entropy modeling was used to identify the key predictive variables of scrub typhus and their ranges, map the suitability of different environments for the disease, and estimate the proportion of the population at different levels of infection risk. Results The final multivariate negative binomial regression model for panel data showed that the annual mean normalized difference vegetation index had the strongest correlation with the number of scrub typhus cases. With each 0.1% rise in shrubland and 1% rise in barren land there was a 75.0% and 37.0% increase in monthly scrub typhus cases, respectively. In contrast, each unit rise in mean wind speed in the previous 2 months and each 1% increase in water bodies corresponded to a decrease of 40.0% and 4.0% in monthly scrub typhus cases, respectively. The predictions of the maximum entropy model were robust, and the average area under the curve value was as high as 0.864. The best predictive variables for scrub typhus occurrence were population density, annual mean normalized difference vegetation index, and land cover types. The projected potentially most suitable areas for scrub typhus were widely distributed across the eastern coastal area of Fujian Province, with highly suitable and moderately suitable areas accounting for 16.14% and 9.42%, respectively. Of the total human population of the province, 81.63% reside in highly suitable areas for scrub typhus. Conclusions These findings could help deepen our understanding of the risk factors of scrub typhus, and provide information for public health authorities in Fujian Province to develop more effective surveillance and control strategies in identified high risk areas in Fujian Province.

The Ocean Health Index (OHI) is a framework to assess ocean health by considering many benefits (called 'goals') provided by the ocean provides to humans, such as food provision, tourism opportunities, and coastal protection. The OHI framework can be used to assess marine areas at global or regional scales, but how various OHI goals should be weighted to reflect priorities at those scales remains unclear. In this study, we adapted the framework in two ways for application to Canada as a case study. First, we customized the OHI goals to create a national Canadian Ocean Health Index (COHI). In particular, we altered the list of iconic species assessed, added methane clathrates and subsea permafrost as carbon storage habitats, and developed a new goal, 'Aboriginal Needs', to measure access of Aboriginal people to traditional marine hunting and fishing grounds. Second, we evaluated various goal weighting schemes based on preferences elicited from the general public in online surveys. We quantified these public preferences in three ways: using Likert scores, simple ranks from a best-worst choice experiment, and model coefficients from the analysis of elicited choice experiment. The latter provided the clearest statistical discrimination among goals, and we recommend their use because they can more accurately reflect both public opinion and the trade-offs faced by policy-makers. This initial iteration of the COHI can be used as a baseline against which future COHI scores can be compared, and could potentially be used as a management tool to prioritise actions on a national scale and predict public support for these actions given that the goal weights are based on public priorities.

Characteristics of mosquito larval habitats are important in determining whether they can survive and successfully complete their developmental stages. Therefore, data on the ecological factors affecting mosquito density and abundance especially the physicochemical properties of water of their breeding sites, can possibly be helpful in implementing larval management programs. Mosquito larvae were collected using a standard 350 ml dipper from fixed habitats including: artificial pool, river edge, creek and etc, in 30 villages of 16 counties from May-December 2014. Water samples were collected during larval collection and temperature (°C), acidity (pH), turbidity (NTU), electrical conductivity (μS/cm), alkalinity (mg/l CaCO3), total hardness (mg/l), nitrate (mg/l), chloride (mg/l), phosphate (mg/l) and sulphate (mg/l) were measured using standard methods. Spearman correlation coefficient, Kruskal-Wallis test of nonparametric analysis, Chi-square (χ2) analysis, regression analysis and C8 interspecific correlation coefficient were used for data analysis. A total of 7,566 mosquito larvae belonging to 15 species representing three genera were collected from fixed larval breeding places. Culex pipiens was the dominant species except in four villages where An. maculipennis s.l. and Cx. torrentium were predominant. There was a significant positive correlation between the density of Cx. pipiens and electrical conductivity, alkalinity, total hardness and chloride, whereas no significant negative correlation was observed between physicochemical factors and larval density. The highest interspecific association of up to 0.596 was observed between An. maculipennis s.l/An. pseudopictus followed by up to 0.435 between An. maculipennis s.l/An. hyrcanus and An. hyrcanus/An. pseudopictus. The correlations observed between physicochemical factors and larval density, can possibly confirm the effect of these parameters on the breeding activities of mosquitoes, and may be indicative of the presence of certain mosquito fauna in a given region.

Shoreline armoring is prevalent around the world with unprecedented human population growth and urbanization along coastal habitats. Armoring structures, such as riprap and bulkheads, that are built to prevent beach erosion and protect coastal infrastructure from storms and flooding can cause deterioration of habitats for migratory fish species, disrupt aquatic–terrestrial connectivity, and reduce overall coastal ecosystem health. Relative to armored shorelines, natural shorelines retain valuable habitats for macroinvertebrates and other coastal biota. One question is whether the impacts of armoring are reversible, allowing restoration via armoring removal and related actions of sediment nourishment and replanting of native riparian vegetation. Armoring removal is targeted as a viable option for restoring some habitat functions, but few assessments of coastal biota response exist. Here, we use opportunistic sampling of pre- and post-restoration data for five biotic measures (wrack % cover, saltmarsh % cover, number of logs, and macroinvertebrate abundance and richness) from a set of six restored sites in Puget Sound, WA, USA. This broad suite of ecosystem metrics responded strongly and positively to armor removal, and these results were evident after less than one year. Restoration responses remained positive and statistically significant across different shoreline elevations and temporal trajectories. This analysis shows that removing shoreline armoring is effective for restoration projects aimed at improving the health and productivity of coastal ecosystems, and these results may be widely applicable.

Despite the environmental risks posed by microplastic pollution, there are presently few standardized protocols for monitoring these materials within marine and coastal habitats. We provide a robust comparison of methods for sampling microplastics on sandy beaches using pellets as a model and attempt to define a framework for reliable standing stock estimation. We performed multiple comparisons to determine: (1) the optimal size of sampling equipment, (2) the depth to which samples should be obtained, (3) the optimal sample resolution for cross-shore transects, and (4) the number of transects required to yield reproducible along-shore estimates across the entire sections of a beach. Results affirmed that the use of a manual auger with a 20-cm diameter yielded the best compromise between reproducibility (i.e., standard deviation) and sampling/processing time. Secondly, we suggest that sediments should be profiled to a depth of at least 1 m to fully assess the depth distribution of pellets. Thirdly, although sample resolution did not have major consequence for overall density estimates, using 7-m intervals provides an optimal balance between precision (SD) and effort (total sampling time). Finally, and perhaps most importantly, comparing the minimum detectable difference yielded by different numbers of transects along a given section of beach suggests that estimating absolute particle density is probably unviable for most systems and that monitoring might be better accomplished through hierarchical or time series sampling efforts. Overall, while our study provides practical information that can improve sampling efforts, the heterogeneous nature of microplastic pollution poses a major conundrum to reproducible monitoring and management of this significant and growing problem.

Mangrove ecosystems provide numerous benefits, including carbon storage, coastal protection and food for marine organisms. However, mapping and monitoring of mangrove status in some regions, such as the Red Sea area, has been hindered by a lack of data, accurate and precise maps and technical expertise. In this study, an advanced machine learning algorithm was proposed to produce an accurate and precise high-resolution land use map that includes mangroves in the Al Wajh Bank habitat in northeastern Saudi Arabia. To achieve this, high-resolution multispectral images were generated using an image fusion technique, and machine learning algorithms were applied, including artificial neural networks, random forests and support vector machine algorithms. The performance of the models was evaluated using various matrices, and changes in mangrove distribution and connectivity were assessed using the landscape fragmentation model and Getis-Ord statistics. The research gap that this study aims to address is the lack of accurate and precise mapping and assessment of mangrove status in the Red Sea area, particularly in data-scarce regions. Our study produced high-resolution mobile laser scanning (MLS) imagery of 15-m length for 2014 and 2022, and trained 5, 6 and 9 models for artificial neural networks, support vector machines and random forests (RF) to predict land use and land cover maps using 15-m and 30-m resolution MLS images. The best models were identified using error matrices, and it was found that RF outperformed other models. According to the 15-m resolution map of 2022 and the best models of RF, the mangrove cover in the Al Wajh Bank is 27.6 km 2 , which increased to 34.99 km 2 in the case of the 30-m resolution image of 2022, and was 11.94 km 2 in 2014, indicating a doubling of the mangrove area. Landscape structure analysis revealed an increase in small core and hotspot areas, which were converted into medium core and very large hotspot areas in 2014. New mangrove areas were identified in the form of patches, edges, potholes and coldspots. The connectivity model showed an increase in connectivity over time, promoting biodiversity. Our study contributes to the promotion of the protection, conservation and planting of mangroves in the Red Sea area.