Rise of toxic cyanobacterial blooms is promoted by agricultural intensification in the basin of a large subtropical river of South America.

Toxic cyanobacterial blooms are globally increasing with negative effects on aquatic ecosystems, water use and human health. Blooms' main driving forces are eutrophication, dam construction, urban waste, replacement of natural vegetation with croplands and climate change and variability. The relative effects of each driver have not still been properly addressed, particularly in large river basins. Here, we performed a historical analysis of cyanobacterial abundance in a large and important ecosystem of South America (Uruguay river, ca 1900 km long, 365,000 km2 basin). We evaluated the interannual relationships between cyanobacterial abundance and land use change, river flow, urban sewage, temperature and precipitation from 1963 to the present. Our results indicated an exponential increase in cyanobacterial abundance during the last two decades, congruent with an increase in phosphorus concentration. A sharp shift in the cyanobacterial abundance rate of increase after the year 2000 was identified, resulting in abundance levels above public health alert since 2010. Path analyses showed a strong positive correlation between cyanobacteria and cropland area at the entire catchment level, while precipitation, temperature and water flow effects were negligible. Present results help to identify high nutrient input agricultural practices and nutrient enrichment as the main factors driving toxic bloom formation. These practices are already exerting severe effects on both aquatic ecosystems and human health and projections suggest these trends will be intensified in the future. To avoid further water degradation and health risk for future generations, a large-scale (transboundary) change in agricultural management towards agroecological practices will be required.

Las floraciones de cianobacterias tóxicas vienen aumentando drásticamente a nivel mundial con efectos negativos en los ecosistemas acuáticos, los usos del agua y la salud humana. Los principales mecanismos promotores de las floraciones son la eutrofización, la construcción de represas, la contaminación con residuos urbanos, la pérdida de vegetación natural y el cambio y la variabilidad climáticos. Los efectos relativos de cada determinante aún no se han abordado adecuadamente, particularmente en las grandes cuencas fluviales de América del Sur. En este trabajo, realizamos un análisis histórico de la abundancia de cianobacterias en un gran e importante ecosistema de América del Sur (el Río Uruguay, c.a. 1.900 km de largo, cuenca de 365.000 km2). Evaluamos las relaciones entre la abundancia de cianobacterias y el cambio en los usos del suelo, el caudal de los ríos, la contaminación urbana, la temperatura y la precipitación desde 1963 hasta el presente. Nuestros resultados evidencian un aumento exponencial en la abundancia de cianobacterias durante las últimas dos décadas, de forma congruente con el aumento en la concentración de fósforo en agua. Fue identificado además, un cambio brusco en la tasa de aumento de la abundancia de cianobacterias después del año 2000, lo que resultó en niveles de alerta por encima de riesgo para la salud pública desde 2010. Los análisis estadísticos indicaron una fuerte y positiva correlación entre las cianobacterias y el área de cultivo en la cuenca, mientras que la precipitación, la temperatura y el flujo de agua fueron insignificantes. Estos resultados contribuyen a identificar que las prácticas agrícolas con alto aporte de nutrientes y el enriquecimiento de nutrientes son los principales impulsores de la formación de floraciones tóxicas. Estas prácticas ya están teniendo graves efectos en los ecosistemas acuáticos y la salud humana y las proyecciones sugieren que se intensificarán en el futuro. Para evitar una mayor degradación de la calidad del agua y el incremento de los riesgos para la salud de las generaciones futuras, se requerirá un cambio a gran escala (transfronterizo) en la gestión agrícola hacia prácticas agroecológicas.

Machine learning methods for imbalanced data set for prediction of faecal contamination in beach waters.

Predicting water contamination by statistical models is a useful tool to manage health risk in recreational beaches. Extreme contamination events, i.e. those exceeding normative are generally rare with respect to bathing conditions and thus the data is said to be imbalanced. Modeling and predicting those rare events present unique challenges. Here we introduce and evaluate several machine learning techniques and metrics to model imbalanced data and evaluate model performance. We do so by using a) simulated data-sets and b) a real data base with records of faecal coliform abundance monitored for 10 years in 21 recreational beaches in Uruguay (N ≈ 19000) using in situ and meteorological variables. We discuss advantages and disadvantages of the methods and provide a simple guide to perform models for a general audience. We also provide R codes to reproduce model fitting and testing. We found that most Machine Learning techniques are sensitive to imbalance and require specific data pre-treatment (e.g. upsampling) to improve performance. Accuracy (i.e. correctly classified cases over total cases) is not adequate to evaluate model performance on imbalanced data set. Instead, true positive rates (TPR) and false positive rates (FPR) are recommended. Among the 52 possible candidate algorithms tested, the stratified Random forest presented the better performance improving TPR in 50% with respect to baseline (0.4) and outperformed baseline in the evaluated metrics. Support vector machines combined with upsampling method or synthetic minority oversampling technique (SMOTE) performed well, similar to Adaboost with SMOTE. These results suggests that combining modeling strategies is necessary to improve our capacity to anticipate water contamination and avoid health risk.

Deoxynivalenol in barley samples from Uruguay.

A survey of the natural occurrence of deoxynivalenol (DON) in barley harvested in Uruguay from 1996 to 2002 was conducted. A total of 292 samples were analyzed for DON by an immunochemical method using inmunoaffinity columns and fluorimetric detection. Between 26 and 100% of the samples were positive for DON while mean DON contents varied between the quantification limit (500 mug/kg) to 6349 mug/kg. Annual maximum levels in individual samples ranged from 1900 mug/kg to 10,000 mug/kg. The mean DON contents were similar from 1996 to 1999 increasing markedly from 2000 to 2002. The percentage of the samples with DON were highest in 1997, 2000, 2001 and 2002 (67, 90, 100 and 100%) as was the accumulated precipitation during the flowering period. A positive correlation between DON levels and precipitation was seen. These results suggest that monitoring for DON barley crops, particularly in years with heavy rainfall during the flowering period, must be regularly performed.