Nature Index of Norway – spatial predictive modelling of soft sediment reference conditions along the Norwegian coast

Soft sediments cover most of the ocean seabed and often contain benthic communities with high biological diversity. Sediment-dwelling organisms depend on the substrate that they are attached to or live in, and species composition varies with sediment type. Macrofauna composition and diversity in soft sediments are commonly used as ‘‘health indicators’’ in various pollution monitoring programmes worldwide, and this fauna component has also been selected as one of the main quality elements in the EU Water Directive. Several areas diverge from the reference condition, and spatial predictive modelling is an essential tool to get spatial maps of reference condition in soft sediments and to find areas in which actions have to be taken to gain good environmental status according to the directive. This paper shows how we have integrated GIS models on geophysical variables (such as depth, slope, wave exposure and terrain structures) and different infauna indices developed based on data collected for more than 30 years. We focus on the quality index NQI, an index intercalibrated within the EU. The model selection technique Akaike Information Criterion (AIC) was used to select the best statistical model from a set of candidate GAM models, which further was used to develop a spatial predictive NQI model for the Norwegian coast. The method and results from this study are considered as a great improvement over earlier approaches, where the same reference value was used in all regions and water types in Norway.