Oceans and Human Health: A Rising Tide of Challenges and Opportunities for Europe

The European Marine Board recently published a position paper on linking oceans and human health as a strategic research priority for Europe. With this position paper as a reference, the March 2014 Cornwall Oceans and Human Health Workshop brought together key scientists, policy makers, funders, business, and non governmental organisations from Europe and the US to review the recent interdisciplinary and cutting edge research in oceans and human health specifically the growing evidence of the impacts of oceans and seas on human health and wellbeing (and the effects of humans on the oceans). These impacts are a complex mixture of negative influences (e.g. from climate change and extreme weather to harmful algal blooms and chemical pollution) and beneficial factors (e.g. from natural products including seafood to marine renewable energy and wellbeing from interactions with coastal environments). Integrated approaches across disciplines, institutions, and nations in science and policy are needed to protect both the oceans and human health and wellbeing now and in the future

Predictive performance of deep-learning-enhanced remote-sensing data for ecological variables of tidal flats over time

Tidal flat systems with a diverse benthic community (e.g., bivalves, polychaetes and crustaceans) is important in the food chain for migratory birds and fish. The geographical distribution of macrozoobenthos depends on physical factors, among which sediment characteristics are key aspects. Although high-resolution and high-frequency mapping of benthic indices (i.e., sediment composition and benthic fauna) of these coastal systems are essential to coastal management plans, it is challenging to gather such information on tidal flats through in-situ measurements. The Synoptic Intertidal Benthic Survey (SIBES) database provides this field information for a 500m grid annual for the Dutch Wadden Sea, but continuous coverage and seasonal dynamics are still lacking. Remote sensing may be the only feasible monitoring method to fill in this gap, but it is hampered by the lack of spectral contrast and variation in this environment. In this study, we used a deep-learning model to enhance the information extraction from remote-sensing images for the prediction of environmental and ecological variables of the tidal flats of the Dutch Wadden Sea. A Variational Auto Encoder (VAE) deep-learning model was trained with Sentinel-2 satellite images with four bands (blue, green, red and near-infrared) over three years (2018, 2019 and 2020) of the tidal flats of the Dutch Wadden Sea. The model was trained to derive important characteristics of the tidal flats as image features by reproducing the input image. These features contain representative information from the four input bands, like spatial texture and band ratios, to complement the low-contrast spectral signatures. The VAE features, the spectral bands and the field-collected samples together were used to train a random forest model to predict the sediment characteristics: median grain size and silt content, and macrozoobenthic biomass and species richness. The prediction was done on the tidal flats of Pinkegat and Zoutkamperlaag of the Dutch Wadden sea. The encoded features consistently increased the accuracy of the predictive model. Compared to a model trained with just the spectral bands, the use of encoded features improved the prediction (coefficient of determination, R2) by 10-15% points for 2018, 2019 and 2020. Our approach improves the available techniques for mapping and monitoring of sediment and macrozoobenthic properties of tidal flat systems and thereby contribute towards their sustainable management

Enhancing the predictive performance of remote sensing for ecological variables of tidal flats using encoded features from a deep learning model

Tidal flats are among the ecologically richest areas of the world where sediment composition (e.g. median grain size and silt content) and the macrozoobenthic presence play an important role in the health of the ecosystem. Regular monitoring of environmental and ecological variables is essential for sustainable management of the area. While monitoring based on field sampling is very time-consuming, the predictive performance of these variables using satellite images is low due to the spectral homogeneity over these regions. We tested a novel approach that uses features from a variational autoencoder (VAE) model to enhance the predictive performance of remote sensing images for environmental and ecological variables of tidal flats. The model was trained using the Sentinel-2 spectral bands to reproduce the input images, and during this process, the VAE model represents important information on the tidal flats within its layer structure. The information in the layers of the trained model was extracted to form features with identical spatial coverage to the spectral bands. The features and the spectral bands together form the input to random forest models to predict field observations of the sediment characteristics such as median grain size and silt content, as well as the macrozoobenthic biomass and species richness. The maximum prediction accuracy of feature-based maps was close to 62% for the sediment characteristics and 37% for benthic fauna indices. The encoded features improved the prediction accuracy of the random forest regressor model by 15% points on average in comparison to using just the spectral bands. Our method enhances the predictive performance of remote sensing, in particular the spatiotemporal dynamics in median grain size and silt content of the sediment thereby contributing to better-informed management of coastal ecosystems

Red knots

The booklet visualizes a selection of recent monitoring results of the entire trilateral Wadden Sea to illustrate that continued, harmonised and effective trilateral monitoring and assessment programmes, based on sound scientific evidence, are necessary to expand our knowledge on the on-going and ever-changing interactions between ecological and socio-economic drivers within this region