Validating GIS tool to assess eelgrass potential recovery in the Limfjorden (Denmark)

Ecological Modelling 338 (2016) 135–148

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Ecological Modelling journal homepage: www.elsevier.com/locate/ecolmodel

Review

Validating GIS tool to assess eelgrass potential recovery in the Limfjorden (Denmark) Paula Canal-Vergés a,∗ , Jens K. Petersen a , Erik K. Rasmussen b , Anders Erichsen b , Mogens R. Flindt c a

Danish Shellfish Centre, Institute of Aquatic Resources, Danish Technical University, Øroddevej 80, DK-7800 Nykøbing Mors, Denmark DHI, Agern Alle 5, DK-2970 Hørsholm, Denmark c Institute of Biology, University of Southern Denmark, Campusvej 55, DK-5230 Odense M, Denmark b

a r t i c l e

i n f o

Article history: Received 27 November 2015 Received in revised form 8 April 2016 Accepted 26 April 2016 Available online 3 June 2016 Keywords: GIS Eelgrass reestablishment Environmental conditions Water quality

a b s t r a c t Eelgrass is a key indicator for the water quality in Europe (WFD, European Union, 2000). However, although water quality has been improved in most Danish water bodies, the eelgrass population does not seem to be recovering. In this study, we validate and further develop a GIS tool designed by Flindt et al. (2016), to evaluate the potential of eelgrass reestablishment in Danish waters. The GIS tool was tested in two large broads of the Limfjorden, Løgstør and Lovns broad (Denmark), where two scenarios are run. The first scenario was set up including modelled data, whereas the second scenario included both monitored and modelled data. All scenarios were validated with monitored data collected over a 5 years period in the two broads. The developed GIS tool highlights areas with eelgrass potential, both for vegetative growth and sexual reproduction, in accordance with those found in situ in the period 2009–2013, in the two investigated broads. A combination of modelled and monitored data was found to be optimal to achieve accurate predictions for eelgrass development in the Limfjorden using this GIS tool. In order to implement the current model or to use this GIS tool in other locations, it is needed to have detailed knowledge of the area in focus, especially on the controlling ecosystem parameters and pressures. This eelgrass GIS tool is been proven to be especially beneficial as site selection tool for marine spatial planning e.g. in relation to the implementation of the WFD and the ICZM directives (WFD, ICZM), to help assessing anthropogenic/targeted environmental impacts e.g. assessing mussel fisheries impacts and is as well a powerful tool to optimize monitoring cost efficiency. Finally, the described GIS tool, originally set for Odense fjord (Denmark) by Flindt et al. (2016), has been validated with data from Limfjorden, corroborating the efficiency of the studied tool in Danish waters. © 2016 Elsevier B.V. All rights reserved.

Contents 1. 2.

3.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 Materials and methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 2.1. Limfjorden . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 2.1.1. Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138 3.1. GIS layers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138 3.1.1. Model vs Monitored . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138 3.1.2. Løgstør broad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 3.1.3. Lovns broad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 3.1.4. Løgstør vs Lovns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 3.2. Fisheries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142

∗ Corresponding author. Tel.: +45 26606275. E-mail address: [email protected] (P. Canal-Vergés). http://dx.doi.org/10.1016/j.ecolmodel.2016.04.023 0304-3800/© 2016 Elsevier B.V. All rights reserved.

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4. 5.

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3.3. GIS tool results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 3.4. Impact of blue mussel fisheries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 Appendix 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

1. Introduction The first records of eelgrass (Zostera marina Linnaeus, 1753) distribution in Denmark are from 1900 when eelgrass covered approximately 1/7 of all Danish marine waters corresponding to ∼6726–7000 km2 (Petersen, 1901, 1914). In the same years, Limfjorden eelgrass coverage was estimated to be 345 km2 (Ostenfeld, 1908). During the 1930s the Danish eelgrass population suffered great losses caused by the world wide wasting disease, and by 1942 the eelgrass coverage in Denmark was reduced to ∼7% (∼540 km2 ) of the distribution in the beginning of the century (Rasmussen, 1977). In Limfjorden the eelgrass population was reduced to 84 km2 (1994), corresponding to about 5% of the estuary (Ærtebjerg et al., 2003). After this dramatic eelgrass die off, the Danish eelgrass populations initially experienced a small recovery, followed by a further reduction in many cases attributed to eutrophication (Sand-Jensen et al., 1997; Krause-Jensen et al., 2000). Eelgrass is a marine species that remains central in coastal water management. The constant focus on this species is due to the ecosystem services it provides (Duarte, 2000; Larkum et al., 2006; McGlathery et al., 2012), making eelgrass key indicator species for marine water quality in Europe (WFD, European Union, 2000). Therefore to a certain extent, this species evolution, and presence is central for marine coastal management. Different methods, tools and models have been used to predict/assess eelgrass presence and development over time. These methods from simple correlations between e.g. eelgrass depth limit and nitrogen loading (Nielsen et al., 2002) or light availability (Olesen, 1996; Lathrop et al., 2001; Greeve and Krause-Jensen, 2005) to statistical multi-parameter approaches (Carstensen et al., 2013; Krause-Jensen et al., 2011), geo-statistical and geospatial techniques (Short et al., 2002; Bekkby et al., 2008; Barrell and Grant, 2013; Schubert et al., 2015; Flindt et al., 2016) and a wide range of ecological/dynamic models (Bach, 1993; Bocci et al., 1997; Rasmussen et al., 2009a; Carr et al., 2012; Yang et al., 2013; Kuusemäe et al., 2016). When using GIS tools to describe or predict eelgrass development, a wide variation of parameters can be included in the models. Some of the parameters are documented to be critical for eelgrass distribution, e.g. light conditions, oxygen, salinity or nutrient regime (Krause-Jensen et al., 2000; Carstensen et al., 2013). Others have recently been recognized as important and included in existing prediction models, e.g. wave and current exposure or sediment characteristics (Short et al., 2002; Rasmussen et al., 2009b; Yang et al., 2013; Kuusemäe et al., 2016). Recently, more stressors like the ballistic impact from drifting macro-algae (Canal-Vergés et al., 2009, 2014a,b), burial of seeds and seedlings by the bioturbation generated by lugworm (Valdemarsen et al., 2011), substrate competition from pacific oyster (Tallis et al., 2009) or diverse anthropogenic activities (Neckles et al., 2005) has been proved to affect eelgrass distribution and performance. The purpose of the present study is to test a 9 parameters (layers) site selection GIS tool developed by Flindt et al. (2016), using modelled layers and a mix of modelled and monitored layers. Here we further develop the existing GIS tool to also include two extra layers, oxygen concentrations and blue mussel fisheries,

which are relevant stressors for the eelgrass community in the Limfjorden.

2. Materials and methods 2.1. Limfjorden The Limfjorden is located in the northern part of Jutland (Denmark) covering about 1500 km2 . It extends in east from Thyborøn Channel on the North Sea coast to Hals on the Kattegat coast (Fig. 1). Hydrodynamically, it is a long and complex canal with numerous broads, islands and sills. As for many other Danish estuaries, the nutrient loading to Limfjorden increased dramatically during the 70–80s, impoverishing the fjord’s water quality (Ærtebjerg et al., 2003). A sustained high nutrient loading stimulated the excess growth of phytoplankton leading to a reduction in the benthic light availability, severely affecting the distribution of eelgrass and benthic vegetation in general. From 1985 to 2003, nutrient loading to Limfjorden was reduced with 69 and 20% for phosphorus and nitrogen respectively. As a result of the reduction in external loading, a decrease in nitrogen and phosphorus availability was observed in the water column leading to a reduction of phytoplankton concentrations (18%) as well as an improvement in the Secchi depth (8%) (Markager et al., 2006). However, neither the seagrass population nor the oxygen concentration improved (Nielsen et al., 2014; Canal-Vergés et al., 2014b). The stratification of the water column occurs frequently in some areas of the Limfjorden, leading to oxygen depletion. From 1997 to 2003 the area subjected to oxygen depletion increased from ∼30 km2 to 60 km2 even though nutrients were reduced. In the same period the seagrass depth limit was reduced from a range of 2.5–5.0 m to 1.9–2.5 m (Markager et al., 2006). Due to occasional occurrence of weak haloclines, anoxic and hypoxic bottom water events are frequent in the late growth season (June–October) (Markager et al., 2006; Canal-Vergés and Petersen, 2015). Thus, anoxia might occur in eelgrass areas and in areas with potential for eelgrass recolonization, even at shallow waters during wind driven mixing events. Furthermore, the long history of eutrophication has led to organically enriched sediments with low critical shear stress (easy to resuspend) and poor anchoring capacity for eelgrass. Frequent resuspension events reduce light penetration and affect eelgrass expansion and recolonization. At present, the average Secchi depth in the fjord is 1.5–3 m (Carstensen et al., 2013). However in 2013 eelgrass depth limit in the Limfjorden was estimated to be around 3–4 m (as reported by Carstensen et al., 2013). An additional important stressor in the Limfjorden is the potential effects caused by dredging activities in relation to mussel fishery. Fishery in the Limfjorden targeted previously a number of different species, such as flatfish, eel, herring, cod, blue mussels and oysters (Dyekjær et al., 1995). However, in the past decades fishing activities has been reduced to a small quota of herring catchment, whereas mussel dredging has become the main fishery in the Limfjorden.

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Fig. 1. Bathymetry over Lovns and Løgstør broads, in the Limfjorden, Denmark. The blue gradient represents the bathymetry. The black dots represent the positions for the monitored stations. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

This investigation is focused on two separated areas of the Limfjorden, Lovns and Løgstør broads as case studies. Løgstør and Lovns broads are designated as Natura 2000 areas and are located in the central part of the Limfjorden (Fig. 1). As part of the Limfjorden, both broads are subjected to a high nutrient loading, highly organic sediments, low light availability and blue mussel fisheries. However there is a size difference (changing fetch), and an eutrophication gradient between the two broads, which makes their biology and main characteristic differ from one another. Furthermore, Løgstør and Lovns broads are considered as different water bodies, sensu EU Water Framework Directive (European Union, 2000), due to difference in nutrient loading and water exchange rates. Hence, in the recent River Basin Management Plans they are subject to different targets and different requirements for actions (Erichsen et al., 2014). Due to the National regulations both areas have been extensively monitored since 2009. The main characteristics and differences between these two broads are summarized in Table 1. 2.1.1. Tool The GIS tool used in this study was developed by Flindt et al. (2016). It is a site selection tool that highlights areas with potential for eelgrass recovery. The tool is explained in more detail by Flindt et al. (2016). In summary, the GIS tool is based on 9 Table 1 Main characteristics of Løgstør and Lovns broads.

Extension (km2 ) Area below 3 m depth (%) a Nutrient loading 2009 (tonnes N year−1 ) a Nutrient loading 2009 (tonnes P year−1 ) b Retention time 200–2009 (days) Exposure Sediment organic content Area muddy sediments (%) a b

Løgstør

Lovns

350 24 180 7 8.7 High Moderate–high 38

68 18 338 11 25.1 Moderate High 70

Windolf et al. (2013). Unpublished data from the Limfjorden hydrodynamic MIKE model (DHI).

parameters, which affect the eelgrass recovery potential: physical exposure from waves and currents ( wc ), organic content in the sediment (S), frequency of resuspension events (R), light availability at the sea bed (BL), oxygen conditions (O), presence of opportunistic macroalgae (OM), presence of non-opportunistic loosely attached macroalgae (NOM), presence of lugworm (L) and presence of eelgrass beds (E), creating 9 GIS layers to be incorporated into the GIS tool. Each parameter is classified in 5 ranges, 5 represent an optimal condition and 1 represents a very bad condition for eelgrass growth and recovery. In this study we tested the GIS tool using two kinds of input data, modelled and monitored parameters. The modelled parameters consist of data maps extracted from a hydrodynamic and ecological model set up for the Limfjorden, similar model system (MIKE3 FM from DHI) has been set up on other lagoon system (Rasmussen et al., 2009a,b; Kuusemäe et al., 2016, Appendix 1). The monitored parameters/layers used in the GIS tool, were based on the interpolation of 259 and 172 points measurements for Løgstør and Lovns broads respectively (Fig. 1). Each point measurement was based on video analyses of a 100 m transect running parallel to the coast sampled in 2013. For some of the parameters such as sediment characteristics, or species diversity, video data was validated with point sample collection. The interpolation method used was spline with barriers (using depth contour as barriers) and the result files were stored in a 30 m square raster mask. The monitored oxygen layer was based on point interpolation of data collected by the yearly campaigns of the national centre for environment and energy (DCE) for the years 1993–2013. Finally, the light availability was based on a light survey performed in five broads of the Limfjorden during the growth season of 2013. The results from the video monitoring and MIKE model simulations are presented in Table 2. All modelled/monitored parameters were extracted from the model simulation/video analyses and ranged in five categories using the reclassify function of ArcGIS, to match the thresholds established by Flindt et al. (2016) (Table 2). In total 9 GIS layers were generated from the model simulation data.

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Table 2 Classification ranges for modelled (Model) and monitored parameters/layers introduced in the GIS tool for prediction of areas with potential for eelgrass recovery.

The GIS layers generated with monitored data were ranged with equivalent thresholds to those generated with modelled data. In order to ensure close equivalences, for some of the parameters, e.g. sediments organic content, the observations collected from video recordings were correlated with a subset measured stations (LOI%, data from 2013, data not presented). BL and O were constantly measured in several areas, following a depth gradient (2, 4, 6 and 8 m) during the growth season (data from 2014, data not presented); samples of OM, NOM and E were collected at different coverages to establish DW biomass ranges (data from 2013 and 2014, data not presented). There are three GIS layers ( wc , R and L) with no monitored data. In order to keep a comparative approach, these three layers were added with model layer data when running the GIS model with monitored parameters. All measurements were then used to calibrate and reclassify the monitored parameters into comparative ranges as those described by Flindt et al. (2016), generating 6 layers based on monitored data. The described modelled and monitored 9 GIS layers constitute respectively the modelled and monitored scenarios (Model and Monitored), even though 3 layers are identical between the scenarios. The mussel fishery (dredging) in the Limfjorden was examined for 2008–2012, and it was concluded that 2009 was a representative year in terms of dredging intensity and locations. Since the MIKE model simulation cover the year 2009, the impacts from blue mussel fisheries in Løgstør and Lovns broads were calculated for the same year. Blue mussel fisheries were evaluated both for the entire broad and also with special focus on the predicted areas for eelgrass transplantation and seedling recovery. This evaluation was based on direct and indirect impacts. Direct impacts were calculated as number of days with fishing activities and total area dredged (assuming an average of 3 dredging nets summing a total width of 4.5 m, for each boat). The indirect impacts are caused by reduced light condition at the bottom due to resuspension of sediment while dredging. Through field experiments, 300 m of distance was estimated to be the impact range for each individual dredge (Canal-Vergés et al., in prep.). Indirect impacts were therefore calculated and evaluated as 300 m buffers around the digitalized route

of each mussel dredging activity. Information on fishing activities is based on compulsory whereabouts reporting to food safety authorities. 3. Results The GIS tool was set and tested on two different scenarios for the each of the researched broads (Lovns and Løgstør). A GIS model scenario (9 GIS layers with modelled data) and a GIS monitored scenario (6 GIS layers with monitored data and 3 GIS layers with modelled data). 3.1. GIS layers 3.1.1. Model vs Monitored The exposure layer was introduced to the tool as maximum shear stress in 2009 (N m−2 ). The shear stress is a combination of both current and wave-induced stress. Since the selected parameter is the maximum shear stress of 2009, it is a proxy for “exposure worst case scenario”, highlighting areas with the highest exposure in stormy situations during the simulation. Shear stress is only modelled and is identical in both scenarios (Fig. 2, A1 and B1). The sediment layer was introduced to the GIS tool as a proxy for eelgrass anchoring capacity and is specified as organic matter content (loss of ignition, LOI%). The model and monitored sediment data differ substantially from each other. In general, the model data suggest lower LOI values where the biggest differences can be found in the deeper waters (Fig. 2, A2, A3, B2 and B3). The resuspension layer is a proxy for frequent physical disturbances, where eelgrass seedling would potentially be uprooted and the benthic light reduced. Resuspension is only modelled and thus identical in both scenarios. The benthic light intensity within the growth season was incorporated as a proxy for light availability in the seabed for eelgrass growth and maintenance (␮E m−2 s−1 ). There is a good agreement between modelled and monitored data sets (Fig. 2, A5, A6, B5 and B6).

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Fig. 2. GIS layers for Løgstør and Lovns broads included in the GIS tool. (A) GIS layers for Løgstør broad from a HD-ECO 3D MIKE model run for 2009 (Model) and the layers created from monitored data collected in 2013 (Monitored). (A1) Max. shear stress (N m−2 ); (A2) modelled sediment characteristics (LOI%); (A3) monitored sediment characteristics (sand-mud categories); (A4) resuspension (frequency); (A5) modelled benthic light (␮E m−2 ); (A6) monitored benthic light (light level); (A7, A8) anoxic events (frequency); (A9) lugworm (g WW m−2 ); (A10) model opportunistic macroalgae (g C m−2 ); (A11) monitored opportunistic macroalgae (distribution); (A12) model non-opportunistic macroalgae (g C m−2 ); (A13) monitored non-opportunistic macroalgae (distribution); (A14) model eelgrass coverage (g C m−2 ); (A15) monitored eelgrass (distribution). (B) GIS layers for Lovns broad. B numbering follows the description of A.

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Fig. 2. (Continued )

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The oxygen layer was used to highlight the areas where anoxia is so frequent during the growth season that eelgrass recovery is hindered or delayed. The quantification of anoxic events in the model and monitored layers is not the same, and therefore cannot be directly compared (Table 2). However no mayor differences were found in Løgstør broad, whereas there were big differences in Lovns broad (Fig. 2, A7, A8, B7 and B8). Lugworm, Arenicola marina is included as a layer due to its sediment reworking activity creates loss of the seed bank and uproots eelgrass seedlings (Valdemarsen et al., 2011). Lugworm is only based on modelled data and is identical for both scenarios (Fig. 2, A9 and B9). Opportunistic macroalgae and non-opportunistic macroalgae were introduced due to the substrate competition and the ballistic impacts generated by loosely attached or unattached macroalgae drifting along the sediment at low current velocities. Opportunistic macroalgae were very scarce both in both data sets in Løgstør and Lovns broads. However the biomass of opportunistic macroalgae was higher at specific shallow locations within the GIS layer based on monitored data in both broads (Fig. 2, A10, A11, B10 and B11). Eelgrass coverage was introduced as a proxy for seed bank abundance and as physical protection of the existent beds. The distribution and extension of the existing beds varied between the data sets, however areas with good or optimal conditions for eelgrass were in all cases very limited (Fig. 2, A14, A25, B14 and B15). 3.1.2. Løgstør broad In Løgstør broad the highest exposure was found in the Easter areas and mostly affecting shallow waters (<3 m) (Fig. 2A1). Sandy sediments were primarily distributed in all shallow waters with a higher expansion in the eastern area of the broad. Muddy sediments were mostly found covering an important area of the deeper waters in the monitored scenario (>3 m), whereas the same areas were covered by sand mud mixes in the modelled scenario (Fig. 2, A2 and A3). Frequent resuspension was found in all shallow waters of the broad, reaching deeper waters (up to 4–5 m depth) in the Eastern part (Fig. 2A4). Good and optimal light conditions are restrained to the shallower areas (<2 m) (Fig. 4, A5 and A6). The oxygen conditions found in Løgstør broad were good or optimal for its entire extension (Fig. 2, A7 and A8). A. marina did not create very poor or poor conditions at any location of the broad (Fig. 2, A9). There was no areas of conflict generated by opportunistic macroalgae in the modelled scenario, however two small areas in the north and east part of the broads were found in the monitored scenario (Fig. 2, A10 and A11). Non-opportunistic macroalgae generated conflicts both in the modelled and monitored scenario; however the distribution of these conflicted areas differed (Fig. 2, A12 and A13). The modelled layer showed higher concentration of non-opportunistic macroalgae in the northeast and northwest of the broad (Fig. 2, A12); whereas the modelled scenario showed the highest concentration in the east of the broad, as well as the middle-south west (Fig. 2, A13). All areas with problematic concentrations of macroalgae were restrained to the shallower waters (<3 m). The eelgrass distribution in the monitored layer was restrained to a few locations in the north of the broad, whereas some more locations in the east and south of the broad appeared in the modelled layer (Fig. 2, A14 and A15). 3.1.3. Lovns broad Once again the highest exposure of the broad appeared to be found in the Eastern coast of the broad (Fig. 2, B1). On shallow water the largest difference between modelled and monitored sediment conditions are found in the south and east part of Lovns broad, where monitored layer indicates better conditions than modelled layer for the shallower waters (Fig. 2, B2 and B3). Sediment characteristics differed substantially between the model and

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the monitored scenario in Lovns. On shallow water the largest difference is found in the south and east part of Lovns broad, where monitored layer indicates better conditions than modelled layer for the shallower waters (Fig. 2, B2 and B3). On the other hand the monitored sediment layer shows a large extension of muddy sediments (poor or very poor conditions) in depths >3 m whereas no muddy sediments are found in the modelled layer (Fig. 2, B2 and B3). In Lovns broad, resuspension is very frequent in shallow waters, especially in the eastern coast (Fig. 2, B4). Good and optimal light conditions are restrained to the shallower areas (<2 m) (Fig. 4, B5 and B6). Oxygen depletion appears on a few of isolated deep areas in the modelled scenario, whereas a big area of the deep distribution of the broad appears highly impacted by oxygen depletion in the monitored scenario (Fig. 2, B7 and B8). A. marina did not create very poor or poor conditions at any location in Lovns (Fig. 2, B9). There were just a few small locations in the northeast and southwest part of the broad with high concentration opportunistic macroalgae in the monitored data. There were no areas with high concentrations of opportunistic macroalgae in the modelled layer (Fig. 2, B10 and B11). On the other hand some concentrations of non-opportunistic macroalgae were found in several shallow locations of Lovns for the modelled layer, whereas just a single location showed up in the monitored layer (Fig. 2, B12 and B13). Finally, the eelgrass distributions was restrain to a few small areas in the south coast of Lovns, in the modelled layer, where the monitored layer showed a higher coverage both in the north and south distribution of the broad (Fig. 2, B14 and B15). 3.1.4. Løgstør vs Lovns Areas with poor, very poor or threshold exposure conditions covered ∼33 and ∼16% of the total area in Løgstør and Lovns respectively (Fig. 3), reaching higher depths in Løgstør broad. In Løgstør broad there are no areas with poor or very poor sediment conditions in the model data, whereas bad or very bad sediment condition cover ∼40% of the broad in the monitored data (Fig. 3). Likewise, in Lovns broad 17 and 70% of its area is classified as bad or very bad sediment condition in the model and monitored data sets respectively (Fig. 3). The areas with good or optimal sediments conditions were mostly confined to shallow areas covering just between 34 and 20% of the broad’s area in the model and monitored data sets for both Løgstør and Lovns. Resuspension has a higher impact for eelgrass in Løgstør broad, where the bad and very bad conditions are extended to most of the E and NE of the broad, covering ∼60% of the total area, whereas it covers ∼24% of the total area in Lovns (Fig. 3). Furthermore, areas with low frequency or no resuspension only account for 6% of the total area in Løgstør, whereas it accounts for ∼70% of the area in Lovns (Fig. 3). High resuspension frequency primarily affects the shallower areas in both broads (Fig. 2, A4 and B4). The monitored data suggests slightly larger areas with good or optimal benthic light condition ∼18 and ∼26% compared to modelled data of ∼16 and ∼36% for Løgstør and Lovns respectively (Fig. 3). Oxygen conditions in Løgstør were good or optimal (Fig. 2, A7 and A8). Likewise, in Lovns broad, most of the modelled data from resulted in areas with good or optimal conditions (∼98%). However, just ∼11% of the area was considered having good or optimal oxygen conditions in the monitored data. Differences in data type and processing account for some of these differences (see Section 4). Lugworm did not generate areas with bad or very bad conditions, however, just 35 and 37% of the areas of Løgstør and Lovns were considered in good or optimal condition (Fig. 3). Løgstør and Lovns had ∼98 and ∼99% of their total area under good or optimal conditions for opportunistic macroalgae (Fig. 3). High biomasses of macroalgae (bad/very bad eelgrass conditions) were found just in the GIS layer based on monitored data.

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Fig. 3. Percentage with bad, threshold and good conditions for eelgrass recovery in Løgstør and Lovns broad. Parameters included in the GIS layer: shear stress at the sea bed ( wc ), sediment characteristics (S), resuspension events (R), benthic light (BL), anoxic events (O), lugworm (L), opportunistic macroalgae (OM), non-opportunistic macroalgae (NOM) and eelgrass distribution (E). (A) Areas with bad, threshold and good conditions for eelgrass recovery in Løgstør with respect to the diverse parameters from an HD-Ecological 3D MIKE model run for 2009 (Model) and created from monitored data collected in 2013 (Monitored). (A1, B1) Areas with bad condition for eelgrass recovery; (A2, B2) areas with threshold condition for eelgrass recovery; (A3, B3) areas with good or very good condition for eelgrass recovery.

Nevertheless, up to ∼94 and 99% of Løgstør and Lovns broads, presented good or very good conditions for eelgrass (Fig. 3). For Løgstør, the modelled data showed a more disperse distribution with a slightly higher coverage of good or optimal areas (4%) when compared with the monitored (2%, Fig. 3). For Lovns, GIS layer based on monitored data had a wider distribution and coverage of eelgrass, with a total good or optimal area of ∼8%, compared to a modelled area covering ∼1% (Fig. 3).

3.3. GIS tool results The GIS tool generates two types of results: (1) areas with potential for survival of transplanted eelgrass beds (or vegetative growth of existing beds) and (2) areas with potential for survival of eelgrass seedlings (sexual reproduction). All scenarios were validated with data collected between the years 2009 and 2013 (mean value for all years at each location). The validation data were extracted from field campaigns carried out from 2009 to 2013 where eelgrass development was followed by yearly video transect observations

3.2. Fisheries During 2009, fishery activities took place for a period of 40 and 20 days in Løgstør and Lovns. In 9 and 10 of those days, dredging took place in the eelgrass growth season in Løgstør and Lovns. Assuming an average of 3 dredges per vessel, the estimated area directly impacted was 0.78 and 0.27% of the total area for Løgstør and Lovns. Respectively 15.8 and 15.7% of the total area of Løgstør and Lovns broads was impacted indirectly by the dredging due to the resuspension created by the activities (Table 3).

Table 3 Blue mussel fishery description in Løgstør and Lovns broads, 2009. Fishing activities 2009 Broad

Frequency −1

Direct impact

Indirect impact

Name

Days year

Days GS

Area%

Area%

Løgstør Lovns

40 20

9 10

0.78 0.27

15.76 15.69

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Fig. 4. Predicted areas with potential for eelgrass transplantation and eelgrass seedling survival in Løgstør and Lovns broads by the GIS modelled scenario and the GIS monitored scenario. The averaged data collected from 2009 to 2013 is represented with colour points, as validation for all scenario predictions.

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Fig. 5. Predicted areas with potential for eelgrass transplantation and eelgrass seedling survival in Løgstør and Lovns broads predicted by Model and Monitored scenarios.

Fig. 6. Evaluation of potential fisheries impacts on eelgrass beds in 2009.

(see materials and methods) in 259 and 172 points in the broads of Løgstør and Lovns, see Fig. 1. The data is presented as average situation for eelgrass presence from the studied period. The areas with potential for transplantation differed between the scenarios in the location and percentage of the total area covered. In Løgstør broad, the GIS model scenario predicted more disperse and smaller areas with transplantation potential than the GIS monitored scenario (Fig. 4, A1 and A2). According to the data used for validation, the amount and location of areas designated as suitable for transplantation in the GIS model scenario in Løgstør were slightly overestimated whereas fitted quite well the distribution in the GIS monitored scenario (Fig. 4, A1 and A2). In Lovns broad, amount and location of areas designated as suitable for transplantation in the GIS model scenario were slightly underestimated in several areas, whereas the GIS monitored scenario overestimated eelgrass presence in the most eastern part of the broad (Fig. 4, B1 and B2). In all, highlighted transplantation locations estimated from the GIS monitored scenario covered 1 and 15% more area in Løgstør and Lovns respectively than in the GIS model scenario (Fig. 5). The areas with highest potential for seedling survival were almost non-existent in all scenarios besides the GIS monitored scenario in Lovns (Fig. 4, B1, B2, B3 and B4). The total area with seedling potential in Løgstør was 0.02 and 0.09% for the model and monitored scenarios. The areas with potential for seedling growth in Lovns were higher, 0.3 and 8% for the GIS model scenario and the GIS monitored scenarios respectively (Fig. 5).

Table 4 Blue mussel fisheries impact in Lovns broad, 2009. Direct impacts

Model Monitored

Indirect impacts

Transplanting %

Seedlings %

Transplanting %

Seedlings %

0.94 0.37

0.00 0.58

19.27 11.14

24.80 11.63

3.4. Impact of blue mussel fisheries The total impact of blue mussel fisheries in predicted eelgrass areas in Løgstør (both transplantation and seedling areas) was 0%. The dredging activities, although intense, were far from the areas where the GIS scenarios predicted potential for eelgrass recovery, as they are performed in deep waters (Fig. 6). The fishery activities in Lovns took place closer to the coast (in shallower waters), although less intensive, the fishery had a higher potential impact in predicted eelgrass areas (Fig. 6). The reported direct impacts in eelgrass transplantation areas were 0.94 and 0.37% of the total predicted eelgrass area in the model and monitored scenarios respectively. Seedling areas were not directly disturbed in the model scenario, whereas 0.58% of the seedling areas from the GIS monitored scenario were affected by direct fishing impact (Table 4). The indirect fishery impact was greater in all scenarios. Eelgrass transplantation areas had indirect impact in 19 and 11% of their area, for the model and monitored scenarios respectively. Whereas

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the indirect impact in the seedlings areas accounted for 25 and 12% of the predicted areas in the model and monitored scenarios respectively.

4. Discussion The present study validate a 9 parameters site selection GIS tool developed by Flindt et al. (2016), using two different input data sets. The GIS tool successfully predicts areas with potential for eelgrass vegetative and sexual growth in two broads in the Limfjorden (validated results). The presented GIS tool provides a module building opportunity. The core of the tool is applicable to most coastal environments, and can be refined with increasing or decreasing number of stressors depending on the available data, specific stressors and specific characteristics of the area subjected to the study. Our study emphasizes the importance of the local conditions when predicting patchy populations of eelgrass in highly disturbed environments. Scaling is in this connection crucial not just to the basin level, but to specific areas within the basin. Each subarea might combine the included parameters differently within relatively small distances. This micro scale increases in importance when parameters with patchy distribution, such as macro algae, lugworm, or mussels are included in the tool. Patchy distributed parameters should therefore be included in the GIS tool in a relevant scale (gridding size representing the specific patchiness). Other parameters such as hydrodynamic conditions can be slightly up-scaled as long as the fundamental considerations are accounted for. For instance, depth distribution on the deeper areas of the fjord (>6 m) does not need to be so detailed, since no light is available there and therefore there is not chance for plant growth (unless e.g. a channel or similar is present affecting the general hydrodynamic conditions). However, a correct depth distribution of the shallow areas becomes important when calculating the correct shear stress, or light at the seabed. Therefore a sufficient number of grids have to be allocated to describe the models bathymetry. This may sometime be a bottleneck due to lack of time or insufficient input data. Our results also highlight the importance of a multifactorial approach when estimating eelgrass dynamics/distribution. Removing important stressors will radically change the end results (location and size of areas with eelgrass potential). Our results, argue therefore, against the uses of single parameters as eelgrass indicators, e.g. Secchi depth as a proxy for depth limit. The quality of the input data used in the GIS tool will affect the accuracy of the results. Therefore, it is important to critically evaluate the available input data, in order to understand the quality and accuracy of the obtained results. When evaluating the input data introduced in this tool, it should be consider for instance: (1) the spatial resolution of the bathymetry both for the monitored and modelled GIS layers, (2) the number of locations monitored and interpolation method used to generate the monitored GIS layer, (3) availability of data and (4) resources needed to collect and process required missing data for generating model and monitoring GIS layers. The level of detail of these points will have a direct impact on the quality and accuracy of the results. In the present study, the mentioned points are especially important due to the organism in focus is eelgrass, which have a very patchy distribution (large patches to patches down to less than 1 m2 ) and the scenarios were run for large areas (350 and 68 km2 for Løgstør and Lovns respectively). The bathymetry of the MIKE model could have benefit of a finer resolution on shallow waters (smaller grid cells), when compared with the actual bathymetric distribution. In areas with a steep bathymetric slope, one grid cell in the model might cover a depth range from 3 to 4 m and assign it a value of 4 m. Since light penetration was an important parameter for eelgrass presence, a 4 m depth grid will appear as non-viable for eelgrass recovery. If the

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grid definition was more accurate, the correspondent area to the 3 m depth would appear as viable for eelgrass recovery in the GIS modelled scenario. In the north and southwest distribution of Lovns there is probably habitat loss generated by the mentioned grid simplification. However, when modelling large areas, there is always a tradeoff between level of detail (grid cell sizing) and computational power. This kind of GIS tool can then be used as feedback mechanism to decide the detail needed for each local area when re-running the model for a specific organism. For instance, in this case, looking at our GIS results it would be advisable to increase the grid detail in the shallower areas >5 m and reduced it in the deeper areas. Other possibility could be to re-run the model for smaller but more detail subareas. The number of monitored locations (points) and the interpolation method are also fundamental when creating GIS layers, especially in relation to large areas. Different parameters might have different accuracy demand to represent reality. For instance, sediment distribution was less heterogeneous distribution than e.g. the eelgrass and lugworm distributions and thus requiring fewer sampling points in order to get the representative resolution. In general, a high number of sampling points will give more accurate results, but resource constraints normally limit monitoring efforts. This study shows that all parameters do not require equal efforts. When using monitored input data alone, some important layers may not be available (R,  wc ) or accurate enough. In such cases, there is a need to evaluate the consequences of excluding these layers or including them from other sources, e.g. hydrodynamic models. Most of these decisions have to be based on the scientific knowledge of the focus organism and the local knowledge of the studied area. For instance, in our study, exposure and frequency of sediment resuspension, which were expected to have a big influence in the eelgrass distributions of both Lovns and Løgstør broad, were only available as modelled data (due to the lack of measurements). The consequence of mixing input data sources in our GIS tool (e.g. monitored scenario) was a decrease on the resolution (different grid sizes in both data types) which ultimately affected the scenario result. However, if no modelled layers were introduced, important parameters would be lost, and the results would be even less accurate. In this study, it was evaluated that with a small loss of location accuracy (lower grid resolution), it was relevant to include the two modelled layers. Overall predictions based on different input data (monitored and modelled scenarios) resulted in the same general picture with regard to location and depth of areas suitable for eelgrass. However the accuracy was not the same. Predictions based on only input from modelled data resulted in general in a more patchy and scarce distribution of suitable areas in both of the studied broads (especially in Lovns). The predictions based on the GIS monitored scenario (modelled and monitored data) were closer to the general dynamics and the observed distribution in 2009–2013. The data used for validation is not quite straight forward. For instance, the yellow points (seedlings) cannot be considered stable zones of expansion. The presented data is a mean value of a 5 years period. Meaning, if an area has successful recolonization, the seedlings that were found 1 year (yellow points) should become a small patch over a period of 3–5 years (light green points), if the average value remained “yellow”, it would mean that seedlings appear and disappear randomly over the years. Light green points on the other hand indicate eelgrass expansion areas, the proximity of an eelgrass bed, or areas for future development. Dark green points in the validation represent areas with consistently medium/high eelgrass coverage. Most light green and green points from the validation coincide with areas highlighted by the GIS model as transplantation potential in the monitored scenario. Fewer coincidences can be observed for the highlighted areas with seedling potential, especially in Løgstør broad. This result is not unreasonable, due to the higher sensitivity

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of seedlings to stressors, makes areas with small eelgrass patches or even big beds unsuitable for eelgrass seedling survival. Meaning that, the only possibility for eelgrass survival in those areas, is the presence and protection of the existing (or transplanted) beds, but not the result of sexual reproduction (seed/seedling dispersal). For both scenarios and in both broads benthic light was one of the main factors regulating eelgrass distribution, confining the eelgrass to the shallower parts of the broads. Depth limit was found to be ∼3 m depth, which roughly covered 20 and 30% of the area of Løgstør and Lovns respectively. However, eelgrass distribution did not fulfil the expected depth limit potential. Just 3 and 16% of the areas in Løgstør and Lovns had potential for eelgrass transplantation success (out of the 20 and 30% of the area with enough light for eelgrass recovery respectively). The absence of initial big eelgrass beds can be a limiting factor because just a maximum of 4 and 8% of Løgstør and Lovns broads contained beds of eelgrass that could contribute to the seed pool and physical protection. Further, sediment characteristics were found to be suitable in only a limited part of the total area in both broads (≤30%). Muddy sediments associated with loss of anchoring capacity, increase of water turbidity and potential for anoxic conditions have previously been recognized as a key parameter when modelling eelgrass survival (Flindt et al., 1999; Short et al., 2002; Krause-Jensen et al., 2011). In the present study, sediment organic content was as stated before proxy for anchoring capacity. Resuspension events, benthic light and oxygen conditions were included independently to add complexity and environmental resolution to the GIS tool making it possible to separate and weigh the three independent stressors. Resuspension events do not only depend on the sediment characteristics, bathymetry, meteorology and hydrodynamic forcing also determine resuspension. With prevailing westerly winds, these parameters make the impact of resuspension higher on the eelgrass distribution on the eastern shores of both broads (2009 data). The impact of resuspension was much higher in Løgstør broad, where just 6% of the broad, was sheltered. Maximal shear stress was as described before a proxy for uprooting occurring in the areas exposed to wave action during stormy events. This parameter had low impact area wise, however, a high impact on the eelgrass distribution because the areas most affected by storms, are restricted to the shallowest part of the broads. Extreme and sustained anoxic events (>8 h) are reported to generate eelgrass die offs (Pulido and Borum, 2010), and have been observed in e.g. Lovns broad. Oxygen conditions were in the scenarios only problematic in Lovns broad. The modelled and monitored oxygen layers used in the scenarios are not directly comparable. Where the oxygen layer in the GIS model scenario corresponds to the oxygen conditions in 2009, the GIS monitored scenario summarizes oxygen depletion over a 20 year period. This accumulated data gives the monitored layer a more robust prediction potential, and can be used as a constant layer when building other scenarios in the Limfjorden; unless strong changes such as significant load reductions, climate changes or changes in the fjord’s hydrodynamics are introduced in the system, changing the general pattern. Although anoxic events at ∼4 m have been observed during the summer period in parts of Lovns broad (monitored data 2015), most of the impact of anoxic stress is confined to deeper waters where eelgrass recovery is already limited by low light intensities. Extreme anoxic conditions might be expected in global change scenarios with increasing summer temperatures, and it is expected to vary from year to year. Effects of lugworm and drifting macroalgae have previously been proven to have a damaging effect in eelgrass beds (Valdemarsen et al., 2010; Canal-Vergés et al., 2014a) where they were considered to contribute significantly to the eelgrass lack of recovery in Odense fjord (Valdemarsen et al., 2010; Flindt et al., 2016). It was decided that the available monitored data for

lugworm did not have sufficient quality to be included as a layer in the monitored scenario. A better estimate of this parameter might potentially improve the scenario results especially in Løgstør broad where sandy sediments are more widely spread. In contrast to the results from Odense fjord, drifting opportunistic and nonopportunistic macroalgae did not represent a threat for eelgrass in any of the scenarios (due to low macroalgae biomass). However, in a nutrient reduction scenario (leading to the improvement of benthic light intensity), it can be expected a faster recolonization of macroalgae over eelgrass. In such scenario, macroalgae might become an increasing threat (if growing unattached or loosely attached to the substrate). Limfjorden’ s substrate is characterized by areas with small amounts of larger, stable stones and extended presence of mussel beds/shells and gravel. Large areas with unstable substrate, which might become highly mobile if colonized by larger macroalgae, characterize the hard substrate in Lovns and specially in Løgstør Broad. Dense mussel beds might as well confine eelgrass distribution, through competition for substrate. This phenomenon has been observed before in oyster beds (Tallis et al., 2009). On the other hand, other studies demonstrate increment of water clarity around mussel communities due to their filtering capacity, which might improve eelgrass survival chances (Petersen et al., 2014). Limfjorden in general and Lovns broad in particular, are highly eutrophic systems, still out of balance. The frequent phytoplankton blooms boost the growth of the mussel populations. Furthermore, mussel spawning might occur several times every year, and the survivors will concentrate in the areas with the best oxygen conditions, which tend to be the shallower areas. Therefore eelgrass and blue mussels may compete for available space in several locations, e.g. the East coast of Lovns. A potential competition for space between mussels and eelgrass may modify the simulated results. Mussel beds are therefore suggested to be included in a further development of the present GIS tool. Regarding the impact of mussel fisheries on eelgrass beds, this study highlights the importance of the specific location where the dredging activities takes place (e.g. specific depth) on the potential impacts on eelgrass communities. For instance, although in 2009, fisheries took place in a relatively greater area of Løgstør compared to Lovns, the actual direct and indirect effect on eelgrass was zero in Løgstør. In Lovns with a lower total and relative area affected, dredging contributed to a higher direct (max. 0.94%) and indirect (max. 24.8%) impact on the areas with potential for eelgrass transplantation and sexual reproduction. This kind of assessment where accurate dredging position and intensities are reported will contribute to a more clear impact assessment. Furthermore, the described GIS tool can be used to plan fishing activities forehand. In this context, the described tool might help reducing impacts in the eelgrass communities (both in the existing eelgrass population, but also in areas where eelgrass expansion can be expected) and contribute to optimize fisheries effort.

5. Conclusions Over all, the use of this GIS tool emphasizes the importance of the relevant knowledge on the local conditions when predicting patchy populations of eelgrass in highly disturbed environments. About spatial resolution, we do not only refer to a broad size scale, but to the specific areas environmentally defined as sub-habitats within each broad. Each sub-habitat will combine the included environmental parameters differently both towards depth, but also over relatively short distances within the same depth. The importance of model grid resolution becomes pronounced, especially when predicting or implementing parameters with patchy distribution, such as eelgrass, macroalgae, lugworm, or mussels. For

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instance, the resolution in shallow areas becomes fundamental when calculating the correct critical shear stress, or benthic light intensities. The correct definition of the shallow waters becomes fundamental when investigating eelgrass in areas where the depth limit (minimum light requirement) restricts eelgrass to the shallow parts of the estuary. Therefore, in eelgrass distribution models, special attention should be given to the definition of coastal zones. Our results also highlight the importance of a multifactorial approach when estimating eelgrass recovery and dynamics, and demonstrate that uses of single parameters such as eelgrass depth limit or Secchi depth have limited value. Light is still a key factor for eelgrass growth, but light alone does not ensure eelgrass recover. The environmental pressure on the eelgrass recovery needs multiparameter analytical tools including the true 3D-environmental state. Minor improvements of the GIS tool and a continuous flow of adequate monitoring data and modelled data (verified by monitored data) will ensure the validity and maintenance of the tool and its predictions over time. At present, the results of the presented GIS tool suggest that it can be successfully used to predict eelgrass evolution in the Limfjorden and similar systems. Furthermore, the presented GIS tool or similar tools, might contribute to a more cost efficient guidance for planning of future monitoring activities, as well as other marine activities (e.g. fisheries, marine remediation programs). In fact, this kind of site selection tool has already been used before to optimize sampling efforts as well as to select optimal sites for eelgrass restoration efforts (Short et al., 2002). In the Limfjorden, the presented tool can be used to locate areas for eelgrass restoration and to highlight the most effective measure for it e.g. eelgrass beds transplantations vs eelgrass seed dispersion to improve eelgrass recolonization. Finally, this GIS tool is proven to be a useful tool as well, when planning other coastal uses, facilitating the implementation and decision making in coastal management. For instance blue mussel fishery activities and its consequences for eelgrass in the Limfjorden were successfully and more accurately evaluated. In the future, this kind of studies/tool, could be used forehand, when planning e.g. fishery effort/areas or any other coastal uses in the Limfjorden or other coastal areas. Acknowledgments This study was possible due to the data and knowledge generated by the projects REELGRASS (09-063190/DSF) and NOVAGRASS (0603-00003DSF), as well as the monitoring efforts performed by the Danish Shellfish Centre (DTU Aqua) and the modelling efforts carried out by DHI. We would like to thank the numerous staff members, both technicians, students and academics of Danish Shellfish Centre, DHI and SDU that have contributed to this project, both with hard work in the field, laboratories and data analyses. This work was financially supported by the European Fishery Fund and the Danish Ministry of Environment and Food. Appendix 1. A.1. Short MIKE model description This 3D MIKE model system is a coupled 3D hydrodynamic and ecological model. The model simulates benthic vegetation including eelgrass, opportunistic macroalgae, non-opportunistic attached macroalgae and microbenthic algae. The eelgrass biomass and coverage is affected by a complex of processes and feedback mechanisms regulating the net growth and seed dispersion. The ecological model includes state variables and processes for a C, N and P cycling in the pelagic and in the sediment as well as oxygen and light penetration in the water column (Rasmussen et al., 2009a,b; Kuusemäe et al., 2016). Pre-calculated wave properties make it possible to

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include wave and current generated shear stress,  wc , which is used to simulate resuspension and transport of fine organic- and inorganic sediment. These properties are needed to estimate the light condition at the bottom. The Mike model system was calibrated and validated on data of water level, temperature, salinity, oxygen, chlorophyll, total N and P, inorganic N and P (Erichsen et al., 2014). Initial maps of sediment organic matter and pools of N and P was generated by using the average and maximum yearly shear stress to create a map of sediment types consisting of erosion, transport and deposition areas. Sediment properties from 125 sediment profiles from Danish coastal was subdivided into area types and water depths and allocated to the map of sediment types for the Limfjorden. Benthic monitoring data of sediment properties and vegetation coverage presented in Fig. 1, has however not been used in the calibration or validation of the model. The MIKE model was run for 10 years (2000–2010) period, where data from 2009 (8 years hot-start) was extracted to be used in the GIS analysis. In total the model set up includes 17,870 triangular grids of different size. The vertical resolution consists of five 0.2 m thick sigma layers below which there were up to 15 1 m layers (Erichsen et al., 2014; Kuusemäe et al., 2016). The 9 parameter/layers used in the GIS tool, were extracted for the deepest model layer (closest to the seabed). Although the model was run for the entire Limfjorden water body, all extracted layers, were restricted to the broads of Løgstør and Lovns. The MIKE model is continuously under development, therefore future improvements of the parameters and rate constants described in this paper are expected in future publications.

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