Low-Cost Approach to an Instream Water Depth Sensor Construction Using Differential Pressure Sensors and Arduino Microcontrollers.

Accurate hydrological data with high spatial resolution is important for flood risk and water resource management, particularly under the context of climate change. The cost of monitoring networks, as well as the characteristics of the hydrological environment itself, can be a barrier to meeting these data requirements, however. This study covers the design and testing of a low-cost, "build-it-yourself", instream water depth sensor providing an assessment of its potential in future hydrological monitoring projects. The low-cost sensor was built using an Arduino microcontroller, a differential pressure sensor and a thermistor, a real-time clock, and an SD card module. The low-cost logger was deployed in tandem with a factory-calibrated Solinst®LevelLogger® 5 Junior for 6 months in the River Wissey, UK. We found the mean absolute error of the Arduino-based logger relative to the commercial setup to be ±0.69 cm for water depth and ±0.415 °C for water temperature. Economically, the Arduino-based logger offers an advantage, costing a total of £133.35 (USD 168.26 at time of publication) comparative to the industrial comparison's cost of £408 (USD 514.83 at time of publication). This study concludes that the low cost of the Arduino-based logger gives a strong advantage to its incorporation in hydrological data collection, if the trade-offs (i.e., time investment and accuracy) are considered acceptable and appropriate for a project.

Addressing Challenges in Port Depth Analysis: Integrating Machine Learning and Spatial Information for Accurate Remote Sensing of Turbid Waters.

Bathymetry estimation is essential for various applications in port management, navigation safety, marine engineering, and environmental monitoring. Satellite remote sensing data can rapidly acquire the bathymetry of the target shallow waters, and researchers have developed various models to invert the water depth from the satellite data. Geographically weighted regression (GWR) is a common method for satellite-based bathymetry estimation. However, in sediment-laden water environments, especially ports, the suspended materials significantly affect the performance of GWR for depth inversion. This study proposes a novel approach that integrates GWR with Random Forest (RF) techniques, using longitude, latitude, and multispectral remote sensing reflectance as input variables. This approach effectively addresses the challenge of estimating bathymetry in turbid waters by considering the strong correlation between water depth and geographical location. The proposed method not only overcomes the limitations of turbid waters but also improves the accuracy of depth inversion results in such complex aquatic settings. This breakthrough in modeling has significant implications for turbid waters, enhancing port management, navigational safety, and environmental monitoring in sediment-laden maritime zones.

A Convolutional Neural Network with Spatial Location Integration for Nearshore Water Depth Inversion.

Nearshore water depth plays a crucial role in scientific research, navigation management, coastal zone protection, and coastal disaster mitigation. This study aims to address the challenge of insufficient feature extraction from remote sensing data in nearshore water depth inversion. To achieve this, a convolutional neural network with spatial location integration (CNN-SLI) is proposed. The CNN-SLI is designed to extract deep features from remote sensing data by considering the spatial dimension. In this approach, the spatial location information of pixels is utilized as two additional channels, which are concatenated with the input feature image. The resulting concatenated image data are then used as the input for the convolutional neural network. Using GF-6 remote sensing images and measured water depth data from electronic nautical charts, a nearshore water depth inversion experiment was conducted in the waters near Nanshan Port. The results of the proposed method were compared with those of the Lyzenga, MLP, and CNN models. The CNN-SLI model demonstrated outstanding performance in water depth inversion, with impressive metrics: an RMSE of 1.34 m, MAE of 0.94 m, and R2 of 0.97. It outperformed all other models in terms of overall inversion accuracy and regression fit. Regardless of the water depth intervals, CNN-SLI consistently achieved the lowest RMSE and MAE values, indicating excellent performance in both shallow and deep waters. Comparative analysis with Kriging confirmed that the CNN-SLI model best matched the interpolated water depth, further establishing its superiority over the Lyzenga, MLP, and CNN models. Notably, in this study area, the CNN-SLI model exhibited significant performance advantages when trained with at least 250 samples, resulting in optimal inversion results. Accuracy evaluation on an independent dataset shows that the CNN-SLI model has better generalization ability than the Lyzenga, MLP, and CNN models under different conditions. These results demonstrate the superiority of CNN-SLI for nearshore water depth inversion and highlight the importance of integrating spatial location information into convolutional neural networks for improved performance.

High-Resolution Monitoring of Scour Using a Novel Fiber-Optic Distributed Temperature Sensing Device: A Proof-of-Concept Laboratory Study.

Scour events can severely change the characteristics of streams and impose detrimental hazards on any structures built on them. The development of robust and accurate devices to monitor scour is therefore essential for studying and developing mitigation strategies for these adverse consequences. This technical note introduces a novel scour-monitoring device that utilizes new advances in the fiber-optic distributed temperature sensing (FO-DTS) technology. The novel FO-DTS scour-monitoring device utilizes the differential thermal responses of sediment, water, and air media to a heating event to accurately identify the locations of the interfaces between them. The performance of the device was tested in a laboratory flume under flow conditions with water velocities ranging from 0 m/s to 0.16 m/s. In addition, the effect of the measurement duration on the device's measurement accuracy was also investigated. The FO-DTS scour-monitoring device managed to detect the sediment-water and water-air interfaces with average absolute errors of 1.60 cm and 0.63 cm, respectively. A measurement duration of fewer than 238 s was sufficient to obtain stable measurements of the locations of the sediment-water and water-air interfaces for all the tested flow conditions.

A Low-Cost Radar-Based IoT Sensor for Noncontact Measurements of Water Surface Velocity and Depth.

We designed an out-of-water radar water velocity and depth sensor, which is unique due to its low cost and low power consumption. The sensor is a first at a cost of less than USD 50, which is well suited to previously cost-prohibited high-resolution monitoring schemes. This use case is further supported by its out-of-water operation, which provides low-effort installations and longer maintenance-free intervals when compared with in-water sensors. The inclusion of both velocity and depth measurement capabilities allows the sensor to also be used as an all-in-one solution for flowrate measurement. We discuss the design of the sensor, which has been made freely available under open-hardware and open-source licenses. The design uses commonly available electronic components, and a 3D-printed casing makes the design easy to replicate and modify. Not before seen on a hydrology sensor, we include a 3D-printed radar lens in the casing, which boosts radar sensitivity by 21 dB. The velocity and depth-sensing performance were characterised in laboratory and in-field tests. The depth is accurate to within ±6% and ±7 mm and the uncertainty in the velocity measurements ranges from less than 30% to 36% in both laboratory and field conditions. Our sensor is demonstrated to be a feasible low-cost design which nears the uncertainty of current, yet more expensive, velocity sensors, especially when field performance is considered.

Physiological, Epigenetic, and Transcriptome Analyses Provide Insights into the Responses of Wheat Seedling Leaves to Different Water Depths under Flooding Conditions.

Flooding stress, including waterlogging and submergence, is one of the major abiotic stresses that seriously affects the growth and development of plants. In the present study, physiological, epigenetic, and transcriptomic analyses were performed in wheat seedling leaves under waterlogging (WL), half submergence (HS), and full submergence (FS) treatments. The results demonstrate that FS increased the leaves' hydrogen peroxide (H2O2) and malondialdehyde (MDA) contents and reduced their chlorophyll contents (SPAD), photosynthetic efficiency (Fv/Fm), and shoot dry weight more than HS and WL. In addition, FS increased catalase (CAT) and peroxidase (POD) activities more than HS and WL. However, there were no significant differences in the contents of H2O2, MDA, SPAD, and Fv/Fm, and the activities of superoxide dismutase (SOD) and POD between the HS and WL treatments. The changes in DNA methylation were related to stress types, increasing under the WL and HS treatments and decreasing under the FS treatment. Additionally, a total of 9996, 10,619, and 24,949 genes were differentially expressed under the WL, HS, and FS treatments, respectively, among which the 'photosynthesis', 'phenylpropanoid biosynthesis', and 'plant hormone signal transduction' pathways were extensively enriched under the three flooding treatments. The genes involved in these pathways showed flooding-type-specific expression. Moreover, flooding-type-specific responses were observed in the three conditions, including the enrichment of specific TFs and response pathways. These results will contribute to a better understanding of the molecular mechanisms underlying the responses of wheat seedling leaves to flooding stress and provide valuable genetic and epigenetic information for breeding flood-tolerant varieties of wheat.

Monsoon-induced response of algal chlorophyll to trophic state, light availability, and morphometry in 293 temperate reservoirs.

Eutrophication management is one of the greatest environmental challenges for lacustrine systems worldwide. The empirically predicted models between algal chlorophyll (CHL-a) and total phosphorus (TP) provide a basis for managing eutrophication in lakes and reservoirs, but other environmental factors influencing the empirical relations must be considered. Here, we tested the impacts of morphological and chemical variables, as well as the effect of the Asian monsoon, on the functional response of CHL-a to TP using two-year data of 293 agricultural reservoirs. This study was based on the approaches of empirical models (linear and sigmoidal), CHL-a:TP ratio, and trophic state index deviation (TSID). Algal CHL-a exhibited a strong log-linear relation with TP on the basis of 2-year average data (R2 = 0.69, p < 0.001), whereas it had a more sigmoidal relation in terms of monsoon-seasonal averages (R2 = 0.52, p < 0.001). The linear segment of the CHL-a-TP relation aligned with the gradient of TP (10 mg/L < TP < 100 mg/L) from mesotrophic to eutrophic conditions. The transfer efficiency of TP to CHL-a based on the 2-year mean CHL-a:TP was high (0.6 <) across all assessed agricultural systems. CHL-a:TP showed insignificant correlations with reservoir morphological variations, but it decreased (<0.5) in eutrophic and hypereutrophic systems during the monsoon season (July-August). Because TP and total suspended solids (TSS) have become increasingly abundant, light conditions become insufficient for algal growth during and after the monsoon season. Light-limited conditions become more prevalent in hypereutrophic systems with shallow depth and high dynamic sediment ratio (DSR) because of the intense rainfall inputs and wind-induced sediment resuspension of the post-monsoon season. TSID reflected the degree of phosphorus limitation and the reduction in underwater light corresponding to changes in reservoir water chemistry (ionic content, TSS, and TN:TP ratio), trophic state gradient, and morphological metrics (mainly mean depth and DSR). Our findings suggest that monsoon-induced changes in water chemistry and light attenuation, which are also associated with anthropogenic pollutant runoffs and reservoir morphology, are critical factors that influence the functional response of algal CHL-a to TP in temperate reservoirs. Modeling and assessing eutrophication should therefore take into account monsoon seasonality along with individual morphological features further.

Estimation of Water Depth on Road Surfaces Using Accelerometric Signals.

The paper presents an experimental study conducted to evaluate the feasibility of using accelerometers as an indirect means to estimate water depths on road surfaces. It makes use of the vibration of the vehicle's wheel arch due to water droplets projected by a tire rolling on a wet road surface. A trailer equipped with a wheel and towed by a van was used. The test setups to spread water on the road surface and before the test wheel, measure the water depth and visualize the water spray are described. The test program, conducted on a test track closed to the traffic, includes three surfaces and two speeds. Visualization of water flows by means of high-speed cameras makes it possible to choose a suitable location for the accelerometers. It turns out that signals provided by the accelerometers are affected by the trailer's movement; a filtering method has been successfully developed to remove noises. Results show a tight relationship between the mean amplitude of accelerometric signals and actual water depths. Discussions are made in terms of effects of the vehicle speed and the road surface texture. Perspectives for using the developed system to improve passenger safety under autonomous driving conditions are presented.

A Smart Multi-Sensor Device to Detect Distress in Swimmers.

Drowning is considered amongst the top 10 causes of unintentional death, according to the World Health Organization (WHO). Therefore, anti-drowning systems that can save lives by preventing and detecting drowning are much needed. This paper proposes a robust and waterproof sensor-based device to detect distress in swimmers at varying depths and different types of water environments. The proposed device comprises four main components, including heart rate, blood oxygen level, movement, and depth sensors. Although these sensors were designed to work together to boost the system's capability as an anti-drowning device, each could operate independently. The sensors were able to determine the heart rate to an accuracy of 1 beat per minute (BPM), 1% SpO2, the acceleration with adjustable sensitivities of ±2 g, ±4 g, ±8 g, and ±16 g, and the depth up to 12.8 m. The data obtained from the sensors were sent to a microcontroller that compared the input data to adjustable threshold values to detect dangerous situations. Being in hazardous situations for more than a specific time activated the alarming system. Based on the comparison made in the program and measuring the time of submersion, a message indicating drowning or safe was sent to a lifeguard to continuously monitor the swimmer' condition via Wi-Fi to an IP address reachable by a mobile phone or laptop. It is also possible to continuously monitor the sensor outputs on the device's display or the connected mobile phone or laptop. The threshold values could be adjusted based on biometric parameters such as swimming conditions (swimming pool, beach, depth, etc.) and swimmers health and conditions. The functionality of the proposed device was thoroughly tested over a wide range of parameters and under different conditions, both in air and underwater. It was demonstrated that the device could detect a range of potentially hazardous aquatic situations. This work will pave the way for developing an effective drowning sensing system that could save tens of thousands of lives across the globe every year.

Elucidating phytoplankton limiting factors in lakes and reservoirs of the Chinese Eastern Plains ecoregion.

Knowledge of phytoplankton limiting factors is essential for cost-efficient lake eutrophication management. Herein, we propose a statistical framework to explore site-specific phytoplankton limiting factors and their dependence on water depth (WD) in 54 lakes in the Chinese Eastern Plains ecoregion. First, the maximal chlorophyll a (Chla) response to total N (TN) or P (TP), representing a region-specific "standard" model where phytoplankton were primarily N- or P-limited, was quantified using a 95% quantile regression. Second, site-specific limiting factors were identified using analogical residual analysis. N- or P-limitation was inferred if FractionTN (i.e. fraction of Chla observed and predicted by the "standard" model for a given TN) > 0.95 or FractionTP >0.95; if both FractionTN and FractionTP <0.95 in a specific environmental condition (e.g. high non-algal turbidity), light limitation was suggested. As a result, 5%, 7%, 4%, 36%, 16%, 2%, and 30% of the sampling sites were limited by N, P, N+P, light availability, rapid flushing, abundant macrophytes, and unmeasured factors, respectively. Bloom control suggestions in the short run are proposed considering these actual limiting factors. Furthermore, the maximal FractionTN or FractionTP response to WD was explored, reflecting the effect of WD on FractionTN (or FractionTP) without significant confounders. The results indicated that phytoplankton in the studied freshwaters would be potentially light-limited, N-limited, N+P-co-limited, or P-limited depending on WD (<1.8, 1.8-2.1, 2.1-5.2, or >5.2 m, respectively), because N will gradually become surplus with increasing WD, while at very shallow depths, strong sediment re-suspension induces light limitation. This finding implies that long-term nutrient management strategies in the studied freshwaters that have WDs of 0-2.1, 2.1-5.2, and >5.2 m can entail control of N, N+P, and P, respectively. This study provides essential information for formulating context-dependent bloom control for lakes in our study area and serves as a valuable reference for developing a cost-efficient eutrophication management framework for other regions.

Response of community composition and biomass of submerged macrophytes to variation in underwater light, wind and trophic status in a large eutrophic shallow lake.

Light climate is of key importance for the growth, community composition of submerged macrophytes in lakes and, they, in turn, are affected by lake depth and the degree of eutrophication. To test the relationships between submerged macrophyte presence and the ratio of Secchi disk depth (SDD) to water depth, i.e. SDD/depth, nutrients and wind, we conducted an extensive sampling campaign in a macrophyte-dominated area of the eastern region (n = 36) in 2016 in Lake Taihu, China, and combined the data gathered with results from extensive physico-chemical monitoring data from the entire lake. We confirmed that SDD/Depth is the primary factor controlling the community composition of macrophytes and showed that plant abundance increased with increasing SDD/Depth ratio (p < 0.01), but that only SDD/Depth > 0.4 ensured growth of submerged macrophytes. Total phosphorus and total nitrogen also influenced the growth and community composition of macrophytes (p < 0.01), while Chla was an indirectly affecting factor by reducing underwater light penetration. Wave height significantly influenced plant abundance (p < 0.01), whereas it had little effect on the biomass (p > 0.05). The key to restore the macrophyte beds in the lake is to reduce the nutrient loading. A decrease of the water level may contribute as well in the shallow bays but will not bring plants back in the main part of the lake. As the tolerance of shade and nutrients varied among the species studied, this should be taken into account in the restoration of lakes by addition of plants.

Designing a Smart Bath Assistive Device Based on Measuring Inner Water Temperature for Bathing Temperature Monitoring.

Today, taking a bath is not only a means to keep clean, but also to reduce fatigue and stress. However, taking a bath with hot water for a long time can also be dangerous, leading to scalding or even a heart attack. To prevent these risks, several studies based on measuring bio-signals have been conducted, but due to high prices, difficulty of use, and restricted functions, these studies' recommendations cannot be easily adopted by the public. Therefore, developing accurate methods to measure bathing temperature and bathing time should be the most direct approach to solve these problems. In this study, a smart bath assistive device based on an inner water temperature measurement function is proposed. Prior to development of the device, a bathing environment was emulated with six temperature sensors affixed to different depths to find the optimal depth for measuring bathing temperature. According to the measurement results, the device was designed in a mushroom shape with the cap part floating on the water's surface and housing the electronic components, and temperature sensors within the stem part were immersed in the water approximately 5 cm below the surface to measure the inner water temperature. Due to the low-power consuming Advanced RISC Machine (ARM) processor and waterproof design, the device is able to float in hot water and monitor the bathing temperature variation over a long period of time. The device was compared alongside a commercial analog bathing thermometer to verify the performance of temperature measurements. In addition, a compensation algorithm was developed and programmed into the device to improve the accuracy of measurements. Processed data is transmitted by Bluetooth to a dedicated Android app for data display and storage. The final results show that the proposed device is highly accurate and stable for monitoring bathing temperature.

Sex-specific plasticity of reproductive allocation in response to water depth in a clonal, dioecious macrophyte.

PREMISE OF THE STUDY: Sex-specific differences in reproductive investment contribute to sexual dimorphism in dioecious plants. Along environmental gradients, males and females may plastically adjust reproductive allocation differently because of contrasting reproductive costs. In dioecious macrophytes, variation in water depth is likely to influence reproductive allocation but has not been investigated in detail. METHODS: Vallisneria spinulosa was grown in aquatic mesocosms at water depths of 50, 100 and 150 cm for 14 weeks. Plasticity in allocation was measured to investigate whether sexual dimorphism in reproductive allocation and vegetative growth changed in response to varying water depths. KEY RESULTS: Females invested a higher fraction of resources to sexual reproduction than males across all water depths and decreased proportional allocation to sexual structures in shallow and deep water compared to intermediate water depth. In contrast, males maintained similar sexual allocation across all water depths. Females displayed larger vegetative size than males, despite greater sexual investment, but decreased vegetative biomass more than males in shallow or deep water. The sexes invested similarly in clonal propagation by tubers at all water depths, but a trade-off with sexual reproduction was only evident in females. CONCLUSIONS: Our results suggest that females of V. spinulosa have mechanisms to compensate for the costs of sexual reproduction in heterogeneous environments. Compared to males, females expressed greater plasticity in biomass allocated to sexual reproduction and vegetative growth in response to water depth variation. Environmental variation in underwater light availability probably caused the sex-specific allocation strategies found in V. spinulosa.

Multisensor Capacitance Probes for Simultaneously Monitoring Rice Field Soil-Water- Crop-Ambient Conditions.

Multisensor capacitance probes (MCPs) have traditionally been used for soil moisture monitoring and irrigation scheduling. This paper presents a new application of these probes, namely the simultaneous monitoring of ponded water level, soil moisture, and temperature profile, conditions which are particularly important for rice crops in temperate growing regions and for rice grown with prolonged periods of drying. WiFi-based loggers are used to concurrently collect the data from the MCPs and ultrasonic distance sensors (giving an independent reading of water depth). Models are fit to MCP water depth vs volumetric water content (VWC) characteristics from laboratory measurements, variability from probe-to-probe is assessed, and the methodology is verified using measurements from a rice field throughout a growing season. The root-mean-squared error of the water depth calculated from MCP VWC over the rice growing season was 6.6 mm. MCPs are used to simultaneously monitor ponded water depth, soil moisture content when ponded water is drained, and temperatures in root, water, crop and ambient zones. The insulation effect of ponded water against cold-temperature effects is demonstrated with low and high water levels. The developed approach offers advantages in gaining the full soil-plant-atmosphere continuum in a single robust sensor.

Testing of the 4SM Method in the Gulf of California Suggests Field Data Are not Needed to Derive Satellite Bathymetry.

Satellite-derived bathymetry methods over coastal areas were developed to deliver basic and useful bathymetry information. However, the process is not straightforward, the main limitation being the need for field data. The Self-calibrated Spectral Supervised Shallow-water Modeler (4SM) method was tested to obtain coastal bathymetry without the use of any field data. Using Landsat-8 multispectral images from 2013 to 2016, a bathymetric time series was produced. Groundtruthed depths and an alternative method, Stumpf's Band Ratio Algorithm, were used to verify the results. Retrieved (4SM) vs groundtruthed depths scored an average r² (0.90), and a low error (RMSE = 1.47 m). 4SM also showed, over the whole time series, the same average accuracy of the control method (40%). Advantages, limitations and operability under complex atmosphere and water column conditions, and high and low-albedo bottom processing capabilities of 4SM are discussed. In conclusion, the findings suggest that 4SM is as accurate as the commonly used Stumpf's method, the only difference being the independence of 4SM from previous field data, and the potential to deliver bottom spectral characteristics for further modeling. 4SM thus represents a significant advance in coastal remote sensing potential to obtain bathymetry and optical properties of the marine bottom.

Development and Sensing Properties Study of Underwater Assembled Water Depth-Inclination Sensors for a Multi-Component Mooring System, Using a Self-Contained Technique.

Prototype monitoring techniques play an important role in the safety guarantee of mooring systems in marine engineering. In general, the complexities of harsh ocean environmental conditions bring difficulties to the traditional monitoring methods of application, implementation and maintenance. Large amounts of existing mooring systems still lack valid monitoring strategies. In this paper, an underwater monitoring method which may be used to achieve the mechanical responses of a multi-point catenary mooring system, is present. A novel self-contained assembled water depth-inclination (D-I) sensor is designed and manufactured. Several advanced technologies, such as standalone, low power consumption and synchronism, are considered to satisfy the long-term implementation requirements with low cost during the design process. The design scheme of the water resistance barrel and installation clamp, which satisfies the diver installation, are also provided in the paper. An on-site test has previously been carried out on a production semisubmersible platform in the South China Sea. The prototype data analyses, including the D-I value in the time domain (including the data recorded during the mooring retraction and release process) and spectral characteristics, are presented to reveal the accuracy, feasibility and stability of the sensor in terms of fitting for the prototype monitoring of catenary mooring systems, especially for in-service aging platforms.

Clonal integration in homogeneous environments increases performance of Alternanthera philoxeroides.

Physiological integration between connected ramets can increase the performance of clonal plants when ramets experience contrasting levels of resource availabilities in heterogeneous environments. It has generally been shown or assumed that clonal integration has little effect on clonal performance in homogeneous environments. However, a conceptual model suggests that integration could increase performance in a homogeneous environment when connected ramets differ in uptake ability and external resource supply is high. We tested this hypothesis in a greenhouse experiment with the amphibious plant Alternanthera philoxeroides. Ramets in clonal fragments containing three rooted and two unrooted ramets were either left connected or divided into a basal part with two rooted ramets and an apical part with the other ramets. To simulate realistic, homogeneous environments of the species with different levels of resource supply, plants were grown at 0, 20, or 40 cm of water depth. Water depth had a positive effect on most measures of growth, indicating that resource supply increased with depth. Connection had negative to neutral effects on total growth of fragments at a water depth of 0 cm, and neutral to positive effects at 20- and 40-cm depths; effects on the apical part were generally positive and larger at greater depth; effects on the basal part were generally negative and smaller at greater depth. Results largely supported the hypothesis and further suggest that clonal integration of allocation and reproduction may modify benefits of resource sharing in homogeneous environments.

Re-assessment of the Conservation Status of the Atlantic Humpback Dolphin, Sousa teuszii (), Using the IUCN Red List Criteria.

The Atlantic humpback dolphin (Sousa teuszii) is an obligate shallow water dolphin that is endemic to the western coasts of Africa, ranging from Western Sahara to Angola. The species occurs exclusively in a limited number of near-shore habitats, a tendency that routinely exposes it to a suite of lethal and deleterious anthropogenic threats. These include habitat degradation, accidental capture in artisanal fishing nets, and hunting for use as food and bait. The species also competes with rapidly expanding human populations for coastal resources in some of the poorest countries on Earth. Data for most aspects of the species' ecology are sparse, but S. teuszii is considered by most qualified observers to be rare and greatly threatened. A lack of appropriate survey data precludes a quantitative assessment of population trends and status. Most populations for which any data are available are considered to be extremely small, numbering in the tens or low hundreds of individuals. The available published estimates suggest that the total population likely falls below 3000 individuals. Declines in abundance have been observed or are suspected for each population and will continue, given projected expansions of identified threats that affect most of the species' known range, and a corresponding lack of appropriate management actions. The apparent scale of threats, the presumed isolation of most populations, and a lack of directed conservation efforts in most areas suggest that the species qualifies for a listing of Critically Endangered (under criteria A3cd) on the IUCN Red List.

A Review of the Geographical Distribution and Habitat of the Atlantic Humpback Dolphin (Sousa teuszii).

Understanding of the distributional ecology of the Atlantic humpback dolphin (Sousa teuszii) has been hampered by a lack of systematic and consistent sampling effort. The only comprehensive species distribution review was published in 2004; since then a considerable amount of novel information has emerged. We compiled 853 sighting, capture and specimen records of the species, and produced global and regional distribution maps. Of the 830 records where year was available, 63.1% dated from ≥2005 and confirm a contemporary occurrence in six marine ecoregions and 11 countries: Western Sahara, Mauritania, Senegal, Gambia, Guinea-Bissau, Guinea, Benin, Cameroon, Gabon, Congo Republic and Angola. Additionally, Togo is a recently confirmed range state. Group sizes ranged from 1 to 45 animals, with small groups of 1 to 10 animals comprising 65% of the sightings. Similarities were noted in the regions inhabited by Atlantic humpback dolphins across their range, particularly an occurrence in relatively shallow (predominantly ≤20 m) depths, in warm waters (average SSTs of 15.8-31.8°C) and in dynamic habitat strongly influenced by tidal patterns. These conditions occur in various habitats occupied by the species, including estuarine systems, open coasts, archipelagos, tidal mud-flats and sheltered bays. Sightings were recorded at distances of 13 m to 12.8 km (mean of 573 m) from land, indicating that the species occurs several kilometres from shore when suitable shallow habitat is present. The Atlantic humpback dolphin may be a 'nearshore' species based on oceanographic definitions incorporating water depth, wave action and sedimentation rather than on spatial distance from the coast.

Water depth affects submersed macrophyte more than herbivorous snail in mesotrophic lakes.

Introduction: Water depth (WD) and snail abundance (SA) are two key factors affecting the growth of submersed aquatic plants in freshwater lake ecosystems. Changes in WD and SA drive changes in nutrients and other primary producers that may have direct or indirect effects on submersed plant growth, but which factor dominates the impact of both on aquatic plants has not been fully studied. Methods: To investigate the dominant factors that influence aquatic plant growth in plateau lakes, a one-year field study was conducted to study the growth of three dominant submersed macrophyte (i.e., Vallisneria natans, Potamogeton maackianus, and Potamogeton lucens) in Erhai Lake. Results: The results show that, the biomass of the three dominant plants, P.maackianus, is the highest, followed by P.lucens, and V.natans is the lowest. Meanwhile, periphyton and snails attached to P.maackianus are also the highest. Furthermore, WD had a positive effect on the biomass of two submersed macrophyte species of canopy-type P.maackianus and P.lucens, while it had a negative effect on rosette-type V.natans. Snail directly inhibited periphyton attached on V.natans and thereby increasing the biomass of aquatic plants, but the effect of snails on the biomass of the other two aquatic plants is not through inhibition of periphyton attached to their plants. Discussion: The dominant factors affecting the biomass of submersed macrophyte in Erhai Lake were determined, as well as the direct and indirect mechanisms of WD and snails on the biomass of dominant submersed macrophyte. Understanding the mechanisms that dominate aquatic plant change will have implications for lake management and restoration.