A new approach to the biological monitoring of freshwater systems: Mapping nutrient loading in two South African rivers, a case study. (15th March 2020) - Motitsoe, Samuel N.; Hill, Martin P.; Avery, Trevor S.; Hill, Jaclyn M.
Abstract: Excessive addition of nitrogen (N) has threatened aquatic ecosystems for decades. Traditional water quality and biological monitoring assessment tools are widely used for monitoring nutrient loads and ecosystem health, but most of these methods cannot distinguish between different types and sources of pollution. This is a challenge, particularly when dealing with non-point sources of anthropogenic nitrogen inputs into freshwater systems. Recent laboratory studies using stable isotopic ratios (δ 15 N and C/N) of aquatic macrophytes (duckweed: Spirodela spp.) have shown successful differentiation and mapping between different N-sources and further, showed abilities to act as early warning indicators for environmental N-loading. Therefore, the aim of this study was to field test the potential of stable isotopic values of transplanted Spirodela spp. to map temporal and spatial N-loading variation and determine the main sources of N-loading in two river systems in the Eastern Cape Province of South Africa, using previously grown, isotopically calibrated and transplanted Spirodela plants, collected over a 13-month sampling period. Nitrogen isotopic values (δ 15 N) of Spirodela plants traced environmental N-loading and identified pollution hotspots and sources through time and space over a wide range of nutrient gradients. δ 15 N isotopic values of Spirodela spp. provided detailed dynamics on N-loading, therefore supporting its utilisation in the biological monitoring of ecosystem health and the early detection of eutrophication in freshwater systems. Graphical abstract: Image 1 Highlights: δ 15 N and C/N ratios of transplanted Spirodela spp. traced N-loading in freshwater systems. Sewage and agricultural run-off (manure) were the main sources of ecosystem N-loading. Nutrient analyses largely complimented raw data and model predicted δ 15 N and C/N trends. Mapping pollution hotspots is a versatile tool for monitoring and assessment of ecosystem health.