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

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1 Not to be cited without prior reference to the author ICES CM 2011/G:08 Nature Index of Norway spatial predictive modelling of soft sediment reference conditions along the Norwegian coast Hege Gundersen, Trine Bekkby, Karl Norling, Eivind Oug, Brage Rygg and Mats Walday Norwegian Institute for Water Research, Gaustadalléen 21, NO-0349 Oslo, Norway. Phone: , fax: Contact author: Trine Bekkby, Abstract 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 biological quality elements in the European Water Framework Directive (WFD). Spatial predictive modelling is an essential tool to get spatial maps of reference condition in soft sediments and to find areas were natural conditions create habitats of either reduced or enhanced environmental status. This should be used when actions are planned in order to gain good environmental status according to the WFD in all Norwegian waters. 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 calculated from grab sample data on macroinvertebrates (abundance and composition) collected between 1974 and We focus on the quality index NQI, an index intercalibrated within phase 1 of the WFD. The model selection technique Akaike Information Criterion (AIC) was used to select the best statistical model from a set of candidate Generalised additive models (GAMs), which was further used to develop a spatial predictive NQI model of reference conditions for the Norwegian coast. The method and results from this study are considered as a great improvement over earlier approaches, where the same reference values were used in all regions and water types in Norway. Keywords: biodiversity, marine infauna, macroinvertebrates, Norway, NQI, reference conditions, spatial predictive modelling, sensitivity. Background The project Naturindeks for Norge ( Nature index of Norway, aims at giving an overview of the development of biodiversity in Norway and measure whether the political goal of stopping the loss of biodiversity has been met (Nybø, 2010; Nybø et al., 2011). The Nature Index calculates the state of biodiversity from a large set of selected indicators, including naturally occurring species, diversity measures or surrogates representing the potential for diversity. Each indicator is scaled in relation to a specific reference state, which is specified as low impacted nature or ecologically sustainable state of the indicator. The Nature Index is an average of scaled indicator values combined for ecosystems or geographical regions, and takes values ranging from 1 (reference state) to 0 (very poor state). The data used are compiled from environmental assessments studies and monitoring programmes. The basic principles for scaling indicator values are rather similar to those used for assessing environmental status for biological quality elements in the European Water Framework Directive (WFD, Directive 2000/60/EC). For aquatic systems, biological quality elements developed for the WFD are included in the Nature Index. 1

2 The present study focuses on setting reference conditions for macroinvertebrate infauna diversity in soft bottom sediments in Norwegian coastal waters. Soft sediment fauna is used as a biological quality element by the WFD. Soft sediments cover most of the sea bed (Snelgrove, 1999) and contain communities of high biodiversity (Pearson and Rosenberg, 1978). As pollution sooner or later ends up in the seabed sediments and benthic macroinvertebrate communities in relatively stable sediment environments change little over time (Gray 1990; Rosenberg et al., 2004), species composition and biodiversity are often used as health indicators in different monitoring programs (see discussion in Borja et al., 2004, 2007). In Norway, the reference condition for soft sediment is based on several indices (Molvær et al., 1997, 2009). Five indices of species diversity and sensitivity to pollution have been developed for the WFD. Two of the indices are included as indicators of coastal ecosystems in the Nature Index. Presently, the reference state for all indices has been set to the same value for all regions and water types in Norway. Clearly, this is an oversimplification, as the natural condition of soft sediment species communities vary with sediment type (Pearson, 1970; Gray, 1974; Snelgrove and Butman, 1994) and thereby also with depth, water movement, salinity etc. This may result in indicator values that show either too good or too bad state if the true reference values are higher or lower than the fixed values. If managers and planners are to decide on areas to be subjected to actions for obtaining good environmental status (which is required by the WFD), methods for getting better estimates are needed. This paper presents an approach to model the reference conditions of soft sediments at a spatial resolution of 25 m for the whole of the Norwegian coast based on the geophysical GIS models available at a national scale. This approach has been applied in several projects (e.g. Guisan and Zimmermann, 2000; Elith et al., 2006; Bekkby et al., 2008). The models are developed into maps available for managers and planners, as described by the Gundersen et al. (2010) report. Methods Data Through the Norwegian Coastal Monitoring Programme (1990-, Norderhaug et al., 2011) and other projects, NIVA (the Norwegian Institute for Water Research) has quantitative data on soft sediment infauna macroinvertebrates (from grabs) with high spatial and temporal coverage from a period of more than 30 years (Figure 1). The total dataset (from ) consist of samples from stations. In 2009, an initial screening was carried out to integrate data, ensure the compatibility and quality-check the georeferencing. At the same time, the stations were classified as either reference condition (622 stations) or polluted/fresh water influenced (537 stations) (Figure 1). Stations defined as reference conditions were deeper than 5 m, with limited fresh water influence and distant from pollution sources. Indices Five indices were calculated at station (Table 1). All indices are included in the WFD monitoring system for Norwegian coastal waters. Class limits for ecological status have been developed according to the WFD (Table 2, Molvær et al., 2009), and the Norwegian quality index, version 1 (NQI1) has been intercalibrated in phase 1 within the North East Atlantic Geographical Intercalibration Group (NEA-GIG), and is one of the primary method in the Norwegian reports to the EU. The Shannon-Wiener index (H ) and the Norwegian quality index version 1 (NQI1) have both been included as indicators in the Nature Index. 2

3 Figure 1. All stations available to the project, classified into reference conditions (green dots) or polluted/fresh water influenced (red dots), for the ecoregions Skagerrak, North Sea, Norwegian Sea and Barents Sea. Note that some stations may be hidden behind others. Table 1. The indices calculated for all stations. AMBI (the AZTI Marine Biotec Index), the index being part of NQI1 and NQI2, is presented in Borja et al. (2000). Index Symbol Formula/description Reference Shannon-Wiener diversity index H H =-Σ(p i )*(log2p i ), where p i is the proportion of the individuals in the sample belonging to species i. Shannon and Weaver (1963) Hurlberts diversity index (100) ES 100 Expected number of species per 100 individuals. Hurlbert (1971) Indicator Species Index (a sensitivity index) ISI Average of the sensitivity values (ES 100 min s ) for the species in the sample. Rygg (2002) Norwegian quality index, version 1 NQI1 0.5*(1-AMBI/7)+0.5*(SN/2.7)*(N/(N+5), where SN=ln(number of species)/ln(ln(number of individuals)), N=number of individuals Molvær m.fl. (2009) Norwegian quality index, version 2 NQI2 0.5*(1-AMBI/7)+0.5*(H /6) Molvær m.fl. (2009) 3

4 Table 2. The classes of ecological status for the different indices and multimetric methods according to the European Water Framework Directive (Molvær et al., 2009). H =Shannon-Wiener diversity index, ES100=Hurlberts diversity index (100), ISI=Indicator Species Index (a sensitivity index), NQI1=Norwegian quality index, version 1, NQI2=Norwegian quality index, version 2. Index Reference value Ecological status for the indices Very good Good Moderately Bad Very bad H 4.4 > <0.9 ES > <5 ISI 9.0 > <4.2 NQI > <0.31 NQI > <0.20 Predictor variables The indices were analysed against the following geophysical variables: Depth - an interpolated digital bathymetric model based on data from the Norwegian Hydrographic Service. Slope - calculated from the digital bathymetric model as the maximum rate of change from each cell to its neighbours (in degrees). Wave exposure - calculated from data on fetch (distance to nearest shore, island or coast), wind strength and direction (Isæus, 2004). The model has been validated in the Stockholm archipelago (Isæus, 2004) and has been applied in several studies (e.g. Eriksson et al., 2004; Sandström et al., 2005; Bekkby et al., 2009). Basin and curvature basin calculated using the Fill function in ArcGIS 9.2, curvature as the difference between the depth in a given point and the mean depth in a specified neighbourhood (described in more detail by Bekkby et al., 2008). All models had a 25 m spatial resolution. Statistical analyses and predictive modelling The Norwegian coast is long and complex, and the environmental conditions vary. Consequently, we analysed the data for the three regions separately (Skagerrak, North Sea and Norwegian Sea+Barents Sea, the two latter combined due to limited number of samples in the Barents Sea). This added up to 15 analyses (5 indices, 3 regions). To avoid pseudoreplication due to repeated data sampled at the same station, the analyses were based on average values for the given station (first for measures within the same year, second for repeated measures for different years). Some of the response variables (i.e. the indices) were transformed (quadrate or log 10 ) to gain optimal distribution. Some of the predictors were also log 10 transformed to gain a more optimal distribution and to avoid that single observations got a disproportionately large influence on the models. The statistical analyses were carried out in R version (R Development Core Team, 2008) using Generalised Additive Models (GAMs) and the statistical tool GRASP (Lehmann et al., 2002, 2004). AIC (Akaike Information Criteria, Burnham and Anderson, 2001) was used as a model selection method. Spatial predictive models (at a spatial resolution of 25 m) of the different indices were 4

5 developed from GIS layers of the predictors. The prediction was limited to the coastal zone, i.e. within 1 nautical mile from the base line, and was not run for areas with environmental conditions not covered by geophysical data. The models were validated using cross-validation. Results The statistical analyses and predictive modelling resulted in raster maps showing the different index values for each 25x25 m pixel, i.e. what would be expected under natural conditions. Table 3 shows the final GAM models and the validation results (correlation coefficients) for the three different regions. Figure 2 shows the reference conditions prediction of the Norwegian quality index, version 1 (NQI1) for the Norwegian coast. The other models are presented in Gundersen et al. (2010), where index values also have been averaged for municipalities, counties and ecoregions for use by managers. Table 4 shows an example of how reference values differ (ecoregions as an example). Table 3. The final GAM models, selected using AIC, sample size (n) and correlation coefficients. H =Shannon- Wiener diversity index, ES100=Hurlberts diversity index (100), ISI=Indicator Species Index (a sensitivity index), NQI1=Norwegian quality index, version 1, NQI2=Norwegian quality index, version 2. Ecoregion and index Model selected n COR cvcor Skagerrak North Sea H Curvature+basin+wave exposure ES 100 Depth+slope+curvature+wave exposure ISI Depth+curvature+basin+wave exposure NQI1 Curvature+basin+wave exposure NQI2 Curvature+basin+wave exposure H Slope+curvature+basin+wave exposure ES 100 Curvature+basin+wave exposure ISI Depth+slope+basin+wave exposure NQI1 Depth+slope+curvature+basin+wave exposure NQI2 Depth+slope+curvature+basin+wave exposure Norwegian Sea and Barents Sea H Depth+slope+wave exposure ES 100 Depth+slope+wave exposure ISI Depth+basin+wave exposure NQI1 Depth+curvature+basin+wave exposure NQI2 Depth+slope+curvature+basin+wave exposure

6 Barents Sea North Sea Norwegian Sea Skagerrak Figure 2. The predicted reference condition (with 25 x 25 m spatial resolution) of the Norwegian quality index, version 1 (NQI1) for the Norwegian coast. The colour coding coincides with that of Table 2, indicating that what was previously called Very good, Good, Moderately, Bad or Very bad is now the reference condition. White areas indicate lack of digital depth model coverage or that the environmental conditions were not covered by data (and a prediction consequently not carried out). NQI1 has been intercalibrated in phase 1 within the North East Atlantic Geographical Intercalibration Group (NEA-GIG), and is one of the primary method in the Norwegian reports to the EU. Table 4. Estimated reference values for the indices averaged for each ecoregion (average±standard deviation, sd). H =Shannon-Wiener diversity index, ES100=Hurlberts diversity index (100), ISI=Indicator Species Index (a sensitivity index), NQI1=Norwegian quality index, version 1, NQI2=Norwegian quality index, version 2. Ecoregion H' ES 100 ISI NQI1 NQI2 Skagerrak 3.84± ± ± ± ±0.06 North Sea 4.35± ± ± ± ±0.07 Norwegian Sea 4.32± ± ± ± ±0.08 Barents Sea 4.44± ± ± ± ±0.05 Discussion and conclusion The Nature Index 2010 gives the most up-to-date overall knowledge on the state of biodiversity in Norway. At the same time, it documents large gaps of knowledge. The present version of the Nature Index must therefore be regarded as a step towards improving knowledge about biodiversity (Nybø et al., 2011). On a general basis there is a need to describe and depict variations in reference conditions on smaller geographical scales to ensure that the Nature index illustrates geographical variation in 6

7 biodiversity as correctly as possible. For coastal waters in particular, there is presently an insufficient number of available indicators, which is partly due to difficulties in determining the reference states for several potential indicators (Nybø, 2010). The present work describes a modelling approach developed to determine reference states for soft sediment species diversity. By combining data from unperturbed environments with GIS layers on ecological factors, spatial variation can be documented and depicted both regionally and within small geographical areas. For the Nature Index, the knowledge on spatial variation will improve the reliability of the indicators for soft bottom fauna and ensure that changes in the biodiversity status will be more readily observed and recognised. Further, the detailed spatial maps produced can be used in environmental management and planning, for instance as requested by the WFD. The spatial modelling indicates that lower reference values should be applied to Skagerrak than to West and North Norway (Table 4). This is in accordance with observed results (Molvær et al. 2009). However, the data sample was not equally good in all regions. The Barents Sea had a low number of soft sediment stations. Still, the models in this region showed a good agreement with the observed data. There are several factors that will influence the indices used, e.g. oxygen levels, current speed level. However, these factors have not been available at GIS layers covering the whole of the Norwegian coast, and could therefore not be included. A possible improvement of the models could be a further differentiation between different types of water bodies as well as developing GIS layers of other relevant explanatory variables. The models developed in this project are of high relevance to managers and planners. However, it is important to remember that the quality of the output model reflects the quality of the input models. Even though the spatial resolution of our models is quite good (25 m), we only have one depth value per 625 m 2 (=25 m * 25 m). This will have a significant impact in areas of high terrain variability. However, as our stations are located in flatter terrain, the resolution is not expected to be a problem. An extremely important source of data for this project has been the Norwegian Coastal Monitoring Programme, with 21 years of data sampling (1990-) (Norderhaug et al., 2010). However, in 2011 the project funding was drastically reduced and the future existence of the program is uncertain. The quality of the input to the Nature Index of Norway, the ability to define reference conditions and monitor changes and the ability to fulfil the requirement of the WFD all require that time series like the Coastal Monitoring Program continue in all water types and regions defined in the national typology. The large potential of the reference conditions modelling is related to the comparison with field measured values of the indices, i.e. comparing the present situation with the expected (i.e. modelled). Such data is at present available for some regions, and may reveal areas in which actions have to be taken to gain good environmental status according to the WFD. Acknowledgement This paper is based on a report to the Directorate for Nature Management (DN). We wish to thank DN and the project Naturindeks for Norge ( Nature index of Norway ) for funding, NIVA for strategic funding through the SoftMod project and to all NIVA partners and projects for making data available. 7

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