Environment Animals Wildlife Endangered Species German hairy snails are disappearing from London's River Thames Londoners are scouring riverbanks to save the endangered mollusk. Breakthroughs, discoveries, and DIY tips sent every weekday. Researchers believe that its signature hairs help the strange creature live in its damp, riverside environments by enabling it to sweat off moisture. By wicking off that excess moisture, the slime gets more sticky, so the snail can hold onto the slick riverside debris and the plants it eats. However, the snail needs some extra support.

Ch Supt Dan Card from the Met, local policing commander for north-east London, said the force was committed to finding Kaliyah, and were using drone technology and boats as part of their "thorough search over a wide area". "Specialist officers are supporting Kaliyah's family through this deeply upsetting time and our thoughts go out to all those impacted by what has happened." He added: "I'd like to thank the members of public, our first-responding officers, and colleagues from other emergency services, as they responded rapidly to carry out a large-scale search during a highly pressurised and distressing time." The force is appealing for witnesses. The search on Monday involved boats and helicopters from HM Coastguard, the Royal National Lifeboat Institution and London Fire Brigade.

By employing superstatistical methods and machine learning, we analyze time series data of water quality indicators for the River Thames, with a specific focus on the dynamics of dissolved oxygen. After detrending, the probability density functions of dissolved oxygen fluctuations exhibit heavy tails that are effectively modeled using $q$-Gaussian distributions. Our findings indicate that the multiplicative Empirical Mode Decomposition method stands out as the most effective detrending technique, yielding the highest log-likelihood in nearly all fittings. We also observe that the optimally fitted width parameter of the $q$-Gaussian shows a negative correlation with the distance to the sea, highlighting the influence of geographical factors on water quality dynamics. In the context of same-time prediction of dissolved oxygen, regression analysis incorporating various water quality indicators and temporal features identify the Light Gradient Boosting Machine as the best model. SHapley Additive exPlanations reveal that temperature, pH, and time of year play crucial roles in the predictions. Furthermore, we use the Transformer to forecast dissolved oxygen concentrations. For long-term forecasting, the Informer model consistently delivers superior performance, achieving the lowest MAE and SMAPE with the 192 historical time steps that we used. This performance is attributed to the Informer's ProbSparse self-attention mechanism, which allows it to capture long-range dependencies in time-series data more effectively than other machine learning models. It effectively recognizes the half-life cycle of dissolved oxygen, with particular attention to key intervals. Our findings provide valuable insights for policymakers involved in ecological health assessments, aiding in accurate predictions of river water quality and the maintenance of healthy aquatic ecosystems.