Incidental nutrient transfers: Assessing critical times in agricultural catchments using high-resolution data

AbstractManaging incidental losses associated with liquid slurry applications during closed periods has significant cost and policy implications and the environmental data required to review such a measure are difficult to capture due to storm dependencies. Over four years (2010–2014) in five intensive agricultural catchments, this study used high-resolution total and total reactive phosphorus (TP and TRP), total oxidised nitrogen (TON) and suspended sediment (SS) concentrations with river discharge data to investigate the magnitude and timing of nutrient losses. A large dataset of storm events (defined as 90th percentile discharges), and associated flow-weighted mean (FWM) nutrient concentrations and TP/SS ratios, was used to indicate when losses were indicative of residual or incidental nutrient transfers. The beginning of the slurry closed period was reflective of incidental and residual transfers with high storm FWM P (TP and TRP) concentrations, with some catchments also showing elevated storm TP:SS ratios. This pattern diminished at the end of the closed period in all catchments. Total oxidised N behaved similarly to P during storms in the poorly drained catchments and revealed a long lag time in other catchments. Low storm FWM P concentrations and TP:SS ratios during the weeks following the closed period suggests that nutrients either weren't applied during this time (best times chosen) or that they were applied to less risky areas (best places chosen). For other periods such as late autumn and during wet summers, where storm FWM P concentrations and TP:SS ratios were high, it is recommended that an augmentation of farmer knowledge of soil drainage characteristics with local and detailed current and forecast soil moisture conditions will help to strengthen existing regulatory frameworks to avoid storm driven incidental nutrient transfers

Forest biodiversity, ecosystem functioning and the provision of ecosystem services

Forests are critical habitats for biodiversity and they are also essential for the provision of a wide range of ecosystem services that are important to human well-being. There is increasing evidence that biodiversity contributes to forest ecosystem functioning and the provision of ecosystem services. Here we provide a review of forest ecosystem services including biomass production, habitat provisioning services, pollination, seed dispersal, resistance to wind storms, fire regulation and mitigation, pest regulation of native and invading insects, carbon sequestration, and cultural ecosystem services, in relation to forest type, structure and diversity. We also consider relationships between forest biodiversity and multifunctionality, and trade-offs among ecosystem services. We compare the concepts of ecosystem processes, functions and services to clarify their definitions. Our review of published studies indicates a lack of empirical studies that establish quantitative and causal relationships between forest biodiversity and many important ecosystem services. The literature is highly skewed; studies on provisioning of nutrition and energy, and on cultural services, delivered by mixed-species forests are under-represented. Planted forests offer ample opportunity for optimising their composition and diversity because replanting after harvesting is a recurring process. Planting mixed-species forests should be given more consideration as they are likely to provide a wider range of ecosystem services within the forest and for adjacent land uses. This review also serves as the introduction to this special issue of Biodiversity and Conservation on various aspects of forest biodiversity and ecosystem services

Crowdsourcing digital health measures to predict Parkinson’s disease severity: the Parkinson’s Disease Digital Biomarker DREAM Challenge

Consumer wearables and sensors are a rich source of data about patients' daily disease and symptom burden, particularly in the case of movement disorders like Parkinson's disease (PD). However, interpreting these complex data into so-called digital biomarkers requires complicated analytical approaches, and validating these biomarkers requires sufficient data and unbiased evaluation methods. Here we describe the use of crowdsourcing to specifically evaluate and benchmark features derived from accelerometer and gyroscope data in two different datasets to predict the presence of PD and severity of three PD symptoms: tremor, dyskinesia, and bradykinesia. Forty teams from around the world submitted features, and achieved drastically improved predictive performance for PD status (best AUROC = 0.87), as well as tremor- (best AUPR = 0.75), dyskinesia- (best AUPR = 0.48) and bradykinesia-severity (best AUPR = 0.95)