Spatial Conservation Prioritisation as Part of a General Land Use Planning Process

CONTEXT. Land use decisions are essential for reaching of biodiversity conservation targets. Usually, conservation is planned separately from other land use, using specialised approaches such as spatial conservation planning and prioritisation (SCP). This separation of processes makes it dicult to optimise between competing land uses or to plan for land sharing solutions. OBJECTIVES: We present a real-life planning case where SCP was integrated to regional planning process from early on. The aim is (i) to present the process and its results, and (ii) based on the experiences, to evaluate and discuss the potential and challenges of integrating SCP to a general land use planning. METHODS: We present the regional planning of the Helsinki-Uusimaa region in Finland where SCP was integrated as part of the general land use planning process between 2014 and 2018. We applied Zonation software and a diverse collection of spatial biodiversity data and carry out various spatial prioritizations guided by planners and environmental experts. We compared the priority areas to future plans (Uusmaa 2050). RESULTS: We show high spatial variation of biodiversity in Uusimaa region and SCP is able to highlight sites of high importance for biodiversity aware planning. Roughly 70% of biodiversity is outside protection by the Uusimaa 2050 plan draft. CONCLUSIONS: While SCP is relatively well-known by ecologists and nature conservationists, its concepts, framework, and tools are usually not familiar to general land use planners. Integration of SCP can yield to better decisions, but new practices require sucient resourcing and tight collaboration between the parties.


Introduction
Urban growth combined with the decline of biodiversity have increased the importance of biodiversity conservation in general land use planning (Ricketts and Imhoff 2003;Joppa and Pfaff 2009;Seto et al. 2012;Newbold et al. 2016). Currently, the international agreements for biodiversity conservation are being revised (the so called 'post-2020 targets'), with the intention of improving integration of the needs of both nature and people (Bhola et al. 2020). The actual planning and execution of conservation is left to bodies at the national or regional levels, in a process that is often separate from other governance processes such as land use planning: conservation areas are delineated by environmental experts to restrict land use. The land use on remaining areas is then planned with other things in mind, although nature values should also be considered in all planning (Bottrill and Pressey 2012; Cai and Pettenella 2013; Rose et al. 2018). Such a stepwise approach simpli es the land use planning process, but the planning outcomes may be far from optimal in protecting biodiversity and ensuring the availability of nature-based solutions (Theobald et  Conservation planning differs from other land use planning processes by having more expert driven and globally applied methodological frameworks, such as Systematic Conservation Planning, in place. The SCP framework evolved in late 1990s (Margules and Pressey 2000) and has been applied in conservation planning by environmental planning authorities worldwide (Sinclair et al. 2018). As a framework, it derives from spatial ecology, mathematics and decision theory, but uses its own vocabulary and analytical approaches (Kukkala and Moilanen 2013). Spatial Conservation Prioritisation (SCP) is one practical approach for Systematic Conservation Planning. SCP refers to approaches that value the study area based on biodiversity and other values usually using a computational software package such as Marxan (Ball and  There are many reasons for the slow adoption, stemming from the different traditions and processes. The aim of general land use planning is to nd compromise solutions for spatial allocation of competing land uses; from housing, industry, and tra c to recreation. This is complex and involves a lot of stakeholder interaction and balancing between different values (Albrechts 2004;Berke et al. 2006;Stokes et al. 2010). Conservation planning has traditionally been a more top-down process leaning on expert modelling with high ecological detail, even if stakeholder interaction has been emphasised in practical planning cases (Knight et al. 2011 Operationalisation of spatial prioritisation requires that the analyses are truly useful for their implementers: that they cover relevant areas, are based on relevant data, and the results are provided in an understandable form and that the results are trusted by the participants of planning process (Pierce et al. 2005;Knight et al. 2011).
In this paper, we have summarised our experiences from the regional planning case of the Helsinki-Uusimaa region, Finland, where an attempt was made to integrate spatial conservation prioritisation into the general regional planning process. The project was executed from 2013 to 2019 as a collaboration between university researchers and regional planners. The Ministry of Environment of Finland funded part of the work, with the intention of testing if the approach would be useful in other regional planning cases. Here, the conservation planning approach was used as part of the general land use planning procedure to 1) strengthen the network of biodiversity-rich areas in the Helsinki-Uusimaa region, 2) to identify areas requiring special attention in general land use planning (i.e. ecological impact avoidance) and 3) to quantify the impact of future urban developments on biodiversity until 2050. A special emphasis was placed on the advancement of transparency and stakeholder participation in the process. In this article, we have presented the outcomes of our SCP analyses, their visualisation and discuss about the experiences gained during the process.

SCP with Zonation software
We utilized a spatial conservation and land use planning framework and a software package called Zonation 4.0 (Lehtomäki and Moilanen 2013; Moilanen et al. 2014). Zonation takes in large amounts of spatial biodiversity data (species, habitats, ecosystem services, etc.), and potentially land use and cost information, and ranks the entire landscape from low to high importance for biodiversity based on these data. The prioritising attempts to balance between input features, as it follows the principle of complementarity, and connectivity may be taken into account ( (Table 1). Table 1 Examining the difference between scholarly driven and planning orientated use of spatial conservation prioritization. The table borrows from Jasanoff (1990)  To political decision-makers, civil servants, stakeholders, landowners The Helsinki-Uusimaa planning case and the process In Finland, the regional planning process, coordinated by the 18 Regional Councils, takes into consideration and reconciles international, national, regional and local interests, and guides the more detailed municipal planning processes. Regional planning is guided by the national Land Use and Building Act (2003). The process is widely collaborative and different stakeholders are heard during the process. In the Uusimaa region, the previous full regional plan came into force in 2006 (Regional Council of Uusimaa, 2007) Thereafter, the Regional Council updated the plan in separate phases, each phase focusing on a different aspect of the regional development. Our work started during the fourth phase, which concentrated on developing the competitiveness of the region and safeguarding conditions for wellbeing (Regional Council of Uusimaa, 2017), with a speci c focus on green areas and their role in supporting ecological sustainability and recreation. The topics of the phase plans were later integrated to the comprehensive plan. This Uusimaa 2050 plan draft (Regional Council of Uusimaa, 2018) was announced in October 2018 and the nal plan is ready to be accepted during 2020.
Uusimaa is an administrative region of 9,600 km 2 in size and located in the Southern Finland, including the rapidly growing capital region (Helsinki Metropolitan area). The region has 1.7 million inhabitants and it is the most populous of the 18 regions in Finland. The Uusimaa region is expected to grow by more than half a million new residents and 290 000 new workplaces by 2050 (Regional Council of Uusimaa, 2018). The region consists of 26 municipalities. The area is under heavy anthropogenic impact including urban areas and many tra c corridors, as well as forestry and agriculture (Jalkanen et al. 2020a). Some relatively natural areas still exist, including old-growth forests, mires and coastal habitats. The region has three national parks, several Natura 2000 areas and other smaller protected areas.
In this article, we followed the process and the role of SCP-informed knowledge production during the planning, until acceptance of the nal plan and preparation during the planning of the phase plan, its acceptance and the following preparation of the comprehensive plan. Spatial conservation planning was used to create three main inputs for the regional planning process: 1) Information on the distribution of the biodiversity across the Uusimaa landscape, 2) Identi cation of top priority areas to be included in the plan and 3) Impact assessment of the 2050 plan. Additionally, the connectivity of the broad ecological network was assessed (Jalkanen et al. 2020a). The SCP approaches were integrated into the planning processes all the way as a collaborative effort of a range of actors (politicians, stakeholders, environmental experts, planners at the Regional Council, private consultants, researchers at the University). Integration of SCP into regional planning was supported by the Ministry of Environment development fund, led by the planners at the Regional Council of Uusimaa, and in practice executed jointly by the planners of the Regional Council and the researchers at the University of Helsinki. The outcome of the planning has been presented for stakeholders in a report in Finnish (REFERENCE WILL BE INSERTED AFTER REVIEW).

Materials And Methods Used In Scp
Datasets used in the prioritisation The biodiversity data included information on 31 features (represented as raster layers) that described distributions of habitats (20 layers) or species or species groups (11 layers). The features were produced from multiple sources including national, regional, and municipal authorities and environmental NGOs. A feature was included in the prioritisation if it met the following criteria: (i) it included ecologically relevant information (e.g. distribution of a species or quality of a habitat), (ii) it covered the entire study area, (iii) it was of good quality and up to date, (iv) we were able to access metadata on the production chain of the data, (v) its resolution/scale was detailed enough for the analysis, and (vi) with other data, it constituted diverse data that would broadly represent biodiversity for the purpose of planning. See Supplementary material (Tables S1-S2) for the full list of biodiversity layers and their data providers.

Expert elicitation
Expert elicitation was required in several phases of the analysis process. The Regional Council of Uusimaa formed an expert group of around 20 representatives of environmental experts of major municipalities, SYKE: the Finnish Environment Institute, other relevant authorities and NGOs to give their opinion for different steps of the analysis. The group met with the planners and the researchers every second month. During these meeting, we rst trained the group to understand the principles of SCP and use of Zonation as a tool. Later, the expert group participated through facilitated group discussions on selecting 1) the features included in the analyses, 2) weighting of species or habitats and 3) values for connectivity decay distances for different features. After the analyses were carried out, the expert group 4) provided feedback on the visualisations and 5) evaluated the outcome and suggested changes to the analysis structure and amendments to the source data.
Data pre-processing Zonation analysis necessitates that all input data are in raster format, with the same extent and pixel size. As the biodiversity data were originally diverse and heterogeneous, including point, polygon, and raster type data, with varying values describing presence/absence or abundance of species, or the biodiversity value of habitats, each input data layer was pre-processed separately. We converted all data to raster format with a spatial resolution of 100 metres and having the same projection and extent using ArcGIS 10 software (ESRI, Redlands, CA, USA). First, we buffered point data, mainly observations of rare/endangered species, with a species-speci c radius de ned by the experts (see Supplementary S7). Buffered areas represent the habitats of the species in a suitable manner, especially when using condition layer (see more detailed description below) that "cuts off" or lowers the value of known unsuitable habitat areas within the buffers around the species observation sites.
We determined raster cell values in four ways, depending on the type of the original data. If the data were based on eld inventories (e.g. great cormorant Phalacrocorax carbo), we used abundance-based continuous values in the raster data. If the values were based on continuous indices (e.g. forest layers that were calculated as a function of stem volume and tree age) we used them as they were. If the data were observation-type presence-only data (e.g. otter Lutra lutra), we used binary values 1 and 0. If the data included some kind of earlier classi cation (e.g. valuable esker habitats that had been classi ed as nationally, regionally, or locally important), we used hierarchical categories determined by expert decision. In addition to the cell values, Zonation considers the distribution of each input layer. Cell of a rare species weigh proportionally more compared to cells of widely occurring species.

Zonation analyses
We produced a set of prioritisations with Zonation, which allowed planners to assess the importance of the same areas from different ecological perspectives. The 'basic' analysis included all the species and habitat layers as input features. Layers were assigned with individual weights that were de ned by expert elicitation. Habitat layers were given an aggregate weight of 200 (a weight that was divided for different habitat layers based on their relevance for conservation), and species layers an aggregate weight of 100. See Supplementary Table S1-S2 for full list of the weights used.
We then developed multiple versions of the prioritisation analyses. We made them with and without considering connectivity between species distribution and habitat patches. For those analyses accounting for connectivity, the connectivity distance values were de ned separately for individual habitat types and species with the expert group (see "Expert elicitation"). When connectivity is accounted for, higher priorities are given to areas that are well connected (spatially aggregated), even if the local habitat quality in some grid cells would not be as high as in some other areas (Lehtomäki et al. 2009).
We applied so called hierarchical prioritisation analysis (Mikkonen and Moilanen 2013) to examine how well the existing protected areas contribute to biodiversity protection. Additionally, we used the hierarchical analysis to identify the most e cient expansions of a protected area network.
Furthermore, we ran variants with and without a condition layer that can be used to modify the species or habitat data with other data sources that give additional information about habitat quality (Moilanen et al. 2011b). The basic use of the condition layer is to reduce habitat quality in locations that are known to be impacted by human activities. We used Corine Land Cover (2006) data as the basis for our landscape condition layer, complemented by some local data sources that described e.g. areas that had been built up after the production of the Corine or the species or habitat data sets (Supplementary material, Table S4). Highest condition values (1.0 = untransformed) were given to natural areas such as forests (in Corine) and lowest (0.001-0.1 = heavily degraded) to heavily-modi ed areas such as mineral extraction sites and industrial areas.
All different versions, or variants in Zonation language, were made with and without water areas and aquatic species, and solely for the region of the Helsinki-Uusimaa or the region plus a 15 km buffer around the region to make sure the results were not in uenced by any edge effects.
Visualising and comparing the prioritisation results Zonation outputs two main data products: a priority ranking map and performance curves of that prioritisation for every input feature. On the priority map, the pixels of the entire study area are ranked based on their importance for all input features. The rank ranges linearly from 0.0 (pixels with lowest value) to 1.0 (pixels with highest value). The performance curves report how large a proportion of each input feature (from the initial distribution of species and habitats) is included in a certain priority We tested various visualisations for these two output types with the expert group and chose a visualisation through which the rank maps and the performance curves are coloured with the same green to sand colour palette. The performance curve background was coloured with the map colours to facilitate the comparison of landscape fractions in each priority bin intuitively (adopted from Pouzols et al. 2014). The map presentations were always shown with performance curves, accompanied with information on the data sets included in the analysis and a checkbox listing of factors that had been considered in the respective analysis. This standard layout was used to report the result of each analysis version. In addition to individual version variations, we produced difference maps for comparing different analysis versions.
Identifying important biodiversity areas for the plan Continuous priority rasters produced by Zonation can inform the planners and stakeholders, but the actual plan must be made with distinct symbols (polygons, lines, points). To mark ecologically important areas in the new plan, the planners implemented a new planning zone called LUO (an abbreviation of the Finnish word for nature, luonto) to guide more detailed planning. For this, we identi ed areas in the top 10% priority ranks of Helsinki-Uusimaa that also included cells that belonged to the highest 5% priority fraction (see Supplementary S8). Considering both the ecological values and the scope of the regional plan, the planners selected those areas that were over 50 ha in size for further investigation and generalized the areas consisting of raster cells to smooth lined polygons using ArcGIS 10.0. To produce quantitative metrics of the biodiversity found in the LUO areas, a landscape identi cation analysis (LSM) was done in Zonation (Moilanen et al. 2005(Moilanen et al. , 2014. Site descriptions included mean priority rank of the LUO area's cells and list of noteworthy biodiversity features from the LSM analyses. In addition, a feature density index was developed to compare the aggregated biodiversity values across sites of different sizes. The feature density index for the site j is calculated as where SDS j is the sum of feature distribution proportions of the site j (received from the LSM analysis), A j is the area of the site j, SDS t is the sum of feature distribution proportions in the entire study landscape (in our case, Uusimaa), and A t is the area of the study landscape. In other words, the feature density index compares the aggregation of biodiversity features in a site to the average distribution of biodiversity features in the study landscape. Finally, a descriptive "information card" was made for each LUO area. These included a map and basic information on the characteristics of the site, the biodiversity value and the feature density. Some LUO areas were also checked on-site by local municipalities to verify their importance.

Impact assessment of the strategic Uusimaa 2050 plan
The renewal of the regional plan entity started in 2017. The ecological impacts of the newly proposed plan draft (called the Uusimaa 2050 plan; Regional Counil of Uusimaa, 2018) were assessed with Zonation using the previous results as the starting point, particularly the one presented in Fig. 1 (analysis including connectivity and land use effects, but no protected areas). The plan draft included polygon symbols for future developments with high biodiversity impacts including residential, industrial, and commercial zones. Low biodiversity impact land use symbols included protected areas (including new areas to be implemented by the state) and recreational areas, as well as forestry areas that may have a varying impact on biodiversity depending on the management actions. Line-type symbols were used for roads, railways, and point-type symbols for small commercial centres. For the impact assessment, all data were converted to polygons and then rasterised for the use in Zonation. Linear features were buffered to be polygons with the width of the actual symbols in the Uusimaa 2050 plan (300-600m). Point-type small commercial zones were transformed into polygons with a 300m buffer radius, as suggested by the regional planners. We used the same Zonation post-processing methods and feature density index as in the LUO examination, to compare the planned land uses to the current biodiversity priorities.

Distribution of biodiversity priority areas across the Helsinki-Uusimaa region
For purposes of planning and related discussions, we produced multiple variants of the prioritisations (see Supplementary material for a broader description of the versions). Figure 1 shows one of the prioritisations: the ranking of the landscape for the Helsinki-Uusimaa region, based on all the selected biodiversity data for terrestrial areas, taking into account connectivity between habitats and the harmful land uses. The map and the curve together show that the areas falling into the top 20% of the landscape in terms of their priority for biodiversity (dark green) are crucial for balanced preservation of biodiversity: As seen from the average performance curve, on average almost 80% of the distributions of the biodiversity features could be covered in those areas alone. On the other hand, the lowest-priority areas (grey and sand) harbour hardly any ecological values. Not surprisingly, top-priority areas cover old-growth forests and mires whereas the lowest priorities are found in built urban areas and intensively managed agriculture elds. Figure 2 presents a hierarchical prioritisation allowing the evaluation of the effectiveness of the current protected area network. The jump on the performance curve at 5% shows that the currently protected areas, covering 5% of the Helsinki-Uusimaa region, are holding 30% of the biodiversity features on average. This means that the current protected area network is relatively well delineated. Furthermore, the result shows that a large proportion of biodiversity features are still unprotected in the Helsinki-Uusimaa region. Areas belonging to the top 20% fraction outside the currently protected areas are the priority sites for conservation or less impactful land uses.
Comparing the maps in Fig. 1 (analysis without masking the current protected areas) and Fig. 2 (analysis with the protected area mask) gives site-speci c information: Higher priority of a given site in Fig. 2 compared to Fig. 1 shows that the site harbours some biodiversity features that are currently poorly protected, and therefore the site could be important for complementing the protected area network. On the other hand, if the priority is lower in Fig. 2 compared to Fig. 1 the site contains some features that are already covered elsewhere by the protected area network. These areas might, however, be important for local ecological networks and the planners should examine the options carefully.
Top-priority areas for land use planning (LUO) Larger areas outside the protected area network that require special attention were marked in the phase plan proposal using the new zoning category "LUO". Figure 3 shows that the LUO areas are distributed across the Helsinki-Uusimaa region and how they overlap with the original top priority areas of the Zonation analysis. A special emphasis was placed on the clarity of the presentation of these areas as the LUO categories were anticipated to cause dissent. Figure 4 shows a descriptive site information card developed in collaboration with the planners for each LUO area, to facilitate the discussions about the planning process. These were created for and used in the discussions with the stakeholders. During the evaluation phase of the plan proposal, all LUO zonings were dropped from the plan due to opposition by some stakeholders. The information cards were passed on to the municipal level planners for consideration in more detailed planning.

Impacts of the strategic Uusimaa 2050 plan
Our results demonstrate the ecological impacts of the Uusimaa 2050 plan draft in two ways: by quantifying the biodiversity distributions overlapping with zones enabling new development, and by visually identifying key con ict areas between urban expansion and biodiversity. Table 3 shows how the developments suggested in the Uusimaa 2050 plan draft overlap with biodiversity features. The new development directly threatens a total of 7% of the distributions of biodiversity features. Most of the impact is caused by the residential zones, which are spatially the most extensive zone type. Planned protected areas cover 27% of biodiversity distributions on average and they are in areas of high ecological value. Roughly 70% of biodiversity is outside protection by the Uusimaa 2050 plan draft. The "white areas", with primary focus of forestry, agriculture, and other natural resources extraction, cover 34% of biodiversity feature distributions. These overlaps were visualised for the planners using maps (Fig. 5), to support planning actions that could minimise the ecological damage (REFERENCE WILL BE INSERTED AFTER REVIEW).

Discussion
Our study demonstrates a case in which expert-driven spatial conservation prioritisation framework was integrated into a general regional planning process. The results, and their use in the process, demonstrate that scholarly driven quantitative planning tools may support decision-making in a spatial planning process, but some aspects of the process require particular attention.
Aiming at local detail and regional extent A common dilemma in conservation planning is the selection of the target study area: administrative borders are rarely aligned with ecologically boundaries ) but important for implementation. Administrative borders usually override the ecological justi cation if prioritization is to be implemented in practice (Botts et al. 2019). In the case of Helsinki-Uusimaa, we mitigated that problem by extending the analysis over the Uusimaa border with a 15 km buffer. Doing analyses with a buffer means that all the input data should cover the surrounding areas as well, emphasising the need for good national data sources that extend over administrative borders. The utility of high-quality, high-resolution, standardized and easily available biodiversity data cannot be overemphasized.
Data questions were central in the process overall. In the case of Uusimaa, it was considered important for the legitimacy and relevance of the SCP project for all types of biodiversity data that are being used in the Finnish environmental administration to be included in the SCP analyses whenever possible. Inclusion of a diverse set of input datasets works as a heuristic to account for biodiversity as broadly as possible, but the heterogeneity of input data requires extra effort in analysis design (like weights) and pre-processing of the data into a format that is meaningful in the analysis. Sourcing datasets from many institutional sources (Supplementary S1-S4) often required a lot of time, negotiations and non-trivial bureaucratic effort.

Versioning and visualization
To be able to balance between the needs of many interests, land use planners should have the tools for assessing the importance of same areas from multiple perspectives (Theobald et al. 2000). In the ecological realm, this can be achieved by doing several versions of the SCP analyses with alternative features and settings (Lehtomäki et al. 2016). In our case, a series of complementary prioritisations were considered to be the outcome of the analysis, instead of nding one ' nal' prioritisation.
To support operational planning, spatial conservation prioritisation outcomes must be presented in an easily understandable manner ( what was included in the speci c analysis, without the need to revisit prioritisation reports. In addition to products like priority maps produced by SCP, even more quantitative and detailed information is sometimes useful in land use planning. The Landscape identi cation tool in Zonation (Moilanen et al. 2005(Moilanen et al. , 2014 is rarely mentioned in the scholarly literature, but we found it useful for operational planning. We used the tool for summarising the biodiversity found in top-priority (LUO) areas, as well as for assessing the ecological impacts of the Uusimaa 2050 plan draft. The feature density index (Eq. 1) allowed quick comparison of 'biodiversity concentration' between sites of different sizes, which was useful both for demonstrating the high value of LUO areas for regional biodiversity as well as for assessing the general biodiversity impacts of the Uusimaa 2050 plan draft.

From scienti c analyses to real-life planning
The use of spatial conservation prioritisation varies from purely academic research to operational planning (Sinclair et al. 2018; Table 3 In implementation-oriented analyses, the focus is on the relevance and legitimacy of the outputs, not just their methodological or scienti c credibility (Cash et al. 2003). In our case, the complexity of the modelling tool led to (at least) two outcomes in the process: Firstly, the priority maps were questioned for being too vague and suggestive instead of providing a clear delineation guideline for the plan. Despite the efforts to explain and visualise the process in a new way, their production process was sometimes intentionally seen as a "black box" by some stakeholders, providing an easy argument to neglect the ndings of the analysis. On the other hand, others might have taken the results too much for granted, without remembering or acknowledging that the prioritisation process involves many, sometimes subjective choices, that may considerably alter the outcome of the prioritisation (Kujala et al. 2018). Despite these challenges, the outputs of SCP work during the planning were considered to provide a unique opportunity 1) to take into account and quantify high nature values in planning, especially outside the current conservation network and 2) to evaluate the impacts of plans (Uusimaa 2050 plan draft) from the viewpoint of biodiversity. The collaboration between researchers and planners was considered fruitful, and both parties learned from the process. From the planning perspective, the effort to compile existing ecological data and analysing it systematically increased the existing understanding of the biodiversity values in Uusimaa and brought systematic approach to planning. Planners continue to use Zonation outputs in municipal planning in collaboration with the researchers (Jalkanen et al. 2020a).
Integrating SCP into the regional planning process required more time and human resources than the planning organisation had anticipated. To avoid underestimation of resources, we propose a list of key questions to be asked before starting a project using SCP in operational planning (Fig. 6). Furthermore, it was evident that a well-managed stakeholder engagement in the process improved the acceptance of both the process and the nal results (Pierce et al. 2005), and increased the overall relevance of ecological questions in the land-use decision-making (Dewulf et al. 2020).

Conclusions
Regional plans typically aim to nd a compromise between allocation to competing land uses: from housing, industry, and tra c to recreation and biodiversity conservation. Scholarly-driven analytical tools like Zonation may mediate the challenge if integrated well to the planning system. However, the complexity of scienti c models is a challenge from the viewpoint of the collaborative planning process and decision-making (Theobald et al. 2000) as we also encountered. Development of new ways of sharing the process between various stakeholders and communicating the results in a transparent manner are needed to ensure the legitimacy of the process and its results. Developing new practices requires su cient resourcing and novel technological solutions, but also tight collaboration between the parties and new types of capacity building: The participants in the planning process need to have some literacy of the scienti c modelling processes at hand, while scientists would bene t from improved understanding about the planning and decision-making processes.