A socio-ecological perspective of urban green networks: the Stockholm case

The term ‘green networks’ refers to green structure as well as surface and ground water occurring in the urban landscape that link to the surrounding landscapes. Green networks are defined as a set of networks with social and ecological functions, linked into a spatially coherent entity through flows of organisms, and interacting with the landscape matrix (Xiu et al. 2016). These networks represent an integration of three categories of networks in practice: river (or blue) network (serving as edges), green space network (serving as nodes) and transport greening network (greenery along the roads, served as corridors and edges as well). Green-blue spaces in an urban environment are layered for portray potential habitat patches and dispersal links. The ultimate aim is to combine these three networks into an overall ‘green network’ (serving as matrix and network) (Fig. 2). Both from a social and ecological perspective, the three networks play an important role since they provide habitats where flora and fauna live, move, reproduce, etc., and where human beings move and recreate. Furthermore, green-blue connections between the three networks provide crucially interactive habitats and stepping stones for wildlife and preferred places for humans.

Applying graph theory to green-blue space planning, habitat patches are linked by connectivity zones and species dispersal, which compose a network in reality. Habitat patches are normally those varied scaled plant communities in green spaces (Cook 2002), road greenery and water ways serve as linkages (although sometimes waterways can be great barriers to habitat movement) (Xiu et al. 2016). The most common issue in graph theory is about network flow between nodes or the cost flow. It deals with shortest or maximum path of moving within a network for dedicated users. Since Urban and Keitt (2001) introduced graph theory at the landscape level it has been commonly recognised that habitat patches can be represented as nodes that are connected to some extent by edges (Minor and Urban 2008; Urban and Keitt 2001; Zetterberg 2011; Zhang and Wang 2006). Between nodes, edges provide the ecological flux for example through genetic movements, species migration, etc. Then landscape topology can be visualised as a graphed network of patches linked via connectivity zones for propagule dispersal or material flow (Fig. 3).

After geographical selection and visualisation of green networks, the question emerges how to choose crucial node sources (habitat locations) when considering both sociological and ecological functions, which means selecting habitats that can work for both wildlife shelters and human recreation. As widely known for the case of complex networks, the structure is different in terms of different users. For example, one person’s social network may be quite different from another person’s, although they may work or live in the same area. One species’ dispersal network differs from another species, although they may mutually related and dependent. In the analysis of ecosystems and their management, specific species are often used as indicators since they are believed to indicate fluctuations of other species (Simberloff 1998). These specific species are considered as targets and their habitat requirements are used to analyse the habitat criteria of landscape support (Billeter et al. 2008). This approach also respond to a reality in which it is unrealistic to monitor and manage every species in landscape connectivity research. Hence, it is necessary to select one or several species as representative indicators of habitats and even of the entire community ecosystem (Simberloff 1998). A series of approaches and criteria have emerged, such as selecting umbrella, flagship, keystone or focal species in ecological perspective (Caro 2010; Zetterberg 2011). However, in this paper we argue that in addition to ecological functions, indicator species play a significant role in expressing physical contexts of landscape, such as land uses, or different layers of green networks that discussed above (green or blue spaces). In order for indicator species to gain acceptance within planning, design and decision making, these species should also provide some information on social values. Therefore, species selection should take one extremely important species into consideration – human beings as the issue of landscape fragmentation is not only about ecological segmentation for wildlife but also very much about humans. Even more crucially, people are the driving forces behind landscape fragmentation and they will continue to influence how this issue is being identified, now and in the future (Spangenberg 2007).

Therefore, we use two types of geographical reference mapping analysing and showing wildlife and human activities: biotope mapping and sociotope mapping. Biotope was first defined in Germany in the 1970s as an area providing a living place for a specific assemblage of plants and animals (Sukopp and Weiler 1988). Biotope mapping is the identification and specification of biotopes in maps. Two methods of biotope mapping exist: selective biotope mapping for certain biotopes and dedicated species worthy of protection, and comprehensive mapping for all biotopes in the city or research areas (Sukopp and Weiler 1988). Nowadays many cities have created comprehensive biotope maps according to own ecological contexts, aimed at marking biotopes and maintaining biodiversity in urbanised areas (Qiu et al. 2010). The concept of “sociotope” has emerged in Sweden by adapting the biotope concept to a social value context within city planning, assessments and decision-making. Sociotope mapping thus focuses on the social values of open space, related to the frequent use of an area by citizens, their opinions and preferences, and is measured by public participation through interviews and questionnaires answered by urban planners and the general public (Ståhle 2006). Sociotope maps are now included in many Swedish municipal planning processes, for example in Stockholm, Uppsala, Malmö and Gothenburg. It has proved to be a useful tool to visualise social values of landscapes.

This paper focuses on those human recreation places that are most frequently used in cities, using procedure similar to that for selecting important biotopes in cities. Locations of plant communities for species’ breeding, gene migration, winter-summer hibernation etc. were chosen as ecological habitats. Overlaid valuable biotopes and sociotope locations are subsequently merged. The geographical location of certain points coincided, which would suggest that they are functional from both a sociotope and biotope perspective. These dual-important habitats can fulfil both human recreational and ecological values for wildlife in green networks. Although these two functions almost always conflict in cities, identifying locations that can fulfil both values is still a holistic perspective and to balance different interests of humans and other life forms in urban environments (Xiu et al. 2016). It has to be noted that, however, based on different purposes of planning and design, species and valuable patches may differ as well, depending on the priorities of the specific city context.

The optimum edges between nodes in a network are those that minimise risks of crossing low-qualified areas and facilitate species dispersal (Teng et al. 2011). When searching for effective edges, impedance cost created by the mosaics of land uses from one node to another need to be considered. Cost distance is the main measure to decide costs in terms of different landscapes. Among the determining factors for costs are land cover, human disturbance, and slope of landscape. The least-cost-path (LCP) uses a raster-based algorithm to calculate the minimal route of theoretical cumulative-cost (Adriaensen et al. 2003). A number of tools are provided in GIS software for least-cost-path analysis, for example the tools “cost-distance” and “least-cost-path” in ArcGIS Spatial Analyst (ESRI 2015).

Stockholm, capital city of Sweden, is located in northern Europe. Stockholm was awarded the European Union’s ‘Green Capital Award’ in 2010 and is considered good example of green space planning. However, Stockholm is also a fragmented city due to its topography, continued urban development and from an ecological perspective (Löfvenhaft et al. 2004). Based on the Stockholm City Habitats report published by Stockholm Municipality in 2012, Stockholm has been facing challenges in terms of biodiversity loss, green space loss and conflicts between green/blue and grey spaces since 1998.

The test area for this study, with an area of 215 km², covers the City of Stockholm with its approximately 900,000 residents (Fig. 4). The city of Stockholm comprises of 14 islands of districts on the coast of Lake Mälaren and Baltic Sea. It includes narrow valleys with lakes, remnant forests hills (50–90 m in altitude), grassland and agricultural fields, constructed buildings and roads.

Although green-blue spaces (including parks and other green zones) account for 57% of Stockholm’s total land area, this is a steep decrease from the past. From 1998 to 2009 the reduction of green-blue spaces in Stockholm has been 1. 92 km², leading to a corresponding increase of impermeable surfaces such as roads and buildings. This trend is going to continue because of densification and construction in the city. Stockholm is one of the five fastest growing cities in Europe according to Stockholm Municipality. By 2022 the population will reach 1 million which requires continuous infrastructure development and which will make conflicts between different land uses, especially green-blue and grey spaces, even more prevalent. The city is and will continue to have more isolated and fragmented landscapes. Thus we test the green network framework in Stockholm to see how a socio-ecological framework of green networks can benefit current green and blue space planning, and how green/blue spaces in a fragmented city can be connected as a functional network and can eventually be used in practical work of urban planners.

By carrying out the LCP analysis in ArcMap, ‘ideal’ green network maps for the three indicator species were generated. These maps represent the effective paths-connections for wildlife movement of crested tit and common toad, and citizen’s connection to nature. Insight into these connections can be an important foundation for planning and design. Taking into consideration the different cost values we propose three separate maps of ideal green networks (Fig. 7).

Figure 7a shows the most cost-saving paths for crested tit and may potentially be relevant for other local birds as well. There are 1711 corridors varying from 0.9 to 36.8 km in length, 26,537 km in total. This network allows crested tit to disperse with less resistance among different habitats. Therefore these corridors should be prioritized for conservation of crested tit and similar species as well. The most effective green network paths are distributed in and around the south-eastern part of the city – the Royal National City Park, where crucial habitats are located, such as forest and parks with oak trees. Starting from these important nodes, the LCP corridors travel across residential neighbourhoods with intensively built areas to downtown, connecting the entire city. However, several links pass through the big water barrier of Lake Mälaren.

Figure 7b indicates the ideal green network for common toad and possible other amphibians. It contains 1711 corridors which cover 22,249 km, with single length of a corridor ranging from 0.9 to 26.3 km. These corridors were generated to examine the connectivity needs of toads and possible local amphibians and their conservation. Most paths follow the river, bogs and forests in the Royal National City Park, as well as locations in the north-western and north-eastern parts of Stockholm. Ideal green network maps for crested tit and common toad show somewhat similar pattern: intense network paths are connecting the City Park to the north-west part of the city. Some paths are crossing water bodies when necessary.

Figure 7c illustrates local human dwellers’ opportunities of recreation to have effective connectivity to green-blue spaces in Stockholm. This map consists of 1711 corridors (22,138 km in total) ranging from 0.8 to 33.5 km in length. This map demonstrates a more even distributed network within the whole city. The cost-saving corridors always run along roads, squares and other open spaces, targeting areas of high recreational value or places that offer contact with nature. Remarkably dense paths are located on the both sides of Lake Mälaren, where places highly preferred by residents in Stockholm are located.

Our main goal has been to develop a comprehensive green network plan which can combine multiple essential parameters. Both ecological and social requirements, wildlife and human recreation should be taken into consideration in integral green network map. However, for suggesting a comprehensive green networks plan it is problematic to just merge the three separate ideal maps presented in the previous section as every map contains 1711 paths and 5133 corridors in total - although some of the paths in these three maps share the same routes (Fig. 8). In Fig. 8 high and medium level density of effective corridors are shown. These areas are going to be crucial dispersal zones for wildlife and humans, as these will help in more effectively connecting different parts of Stockholm. It is clear to see that ideal paths of the highest value link the Royal National City Park in the north-eastern part with north-western and southern part of the city.

When comparing our data with current land use conditions (see background map in Fig. 8), the lack of important green networks in some areas can be clearly seen. They are specifically needed for improvement of connectivity, and this is especially the case for two corridors that can link up the Royal National City Park in the north-eastern part of Stockholm. As one of the most crucial large habitat areas in the city, the National City Park provides a variety of ecosystem services (Löfvenhaft et al. 2004). Thus the linkage between this City Park and the rest of city becomes extremely important. From our density analysis of the green network map we suggest two potential paths which are located on two sides of the park. One path can go from the upper north part of the National Park all along the densely built places in the downtown area until it meets the green wedges in the western part of the city (through Bromma to Rinkeby-Kista and Hässelby-Vällingby districts, connecting the green wedges in these areas). The other path starts from the lower south side of the Park and extends across the water body trying to converge all of the green wedges in the southern part of Stockholm (through Enskede-Årsta-Vantör to Farsta, and to Hägersten-Liljeholmen and Skärholmen districts, meeting the green wedges as well). These two corridors passing through compact infrastructures in the city centre will allow for sufficient migration to important habitat patches.

Zooming in to the neighbourhood of Hjorthagen in Norra Djurgårdsstaden, we justify effective corridors by offering a LCP model. After recalculation of LCP and line density through ArcGIS we merged the two separate effective corridors into one vector layer. Since optimal routes for crested tit and human recreation are quite close in location, combining these paths in consideration of construction expenses. The length of offered corridors varies from 196 to 566 m. These two locations of ideal paths-corridors can tie together current habitat patches and green-blue spaces with developed areas. Based on this map we can offer suggestions for potential improvement of the local green network (Fig. 9a). We suggest leaving space for these two corridors in order to link current habitat patches in Hjorthagen to its neighbouring National City Park since they incorporate different wildlife habitats and water bodies and should be prioritized by municipal planners. One possible street greening plan integrating all three layers of green networks can be proposed, taking into account the three user groups (Fig. 9b). Linking up small streams and ponds in street greening balances the requirements of three different species, and the common toad in particular. As a result of the additional street greening with small ponds in the middle, the potential dispersal probability and net production would increase, as well as the dispersal flux over a larger region.

This study has confirmed that graph theory is a useful method in support of green-blue spaces planning, as also discussed by e.g. Zetterberg et al. (2010), Teng et al. (2011), Kong et al. (2010), and others. Its network analysis framework in combination with the corresponding spatial extents of the city can offer an integral approach to planning. In this paper we highlighted that different layers of green-blue spaces play different roles. Linear river and transport greening are regarded as links/barriers for the dispersal of (most) species. Green spaces are usually habitats that used by wildlife for living and reproduction. All of these spaces are also used and perceived by humans. The three layers of green-blue spaces offer indispensable values for wildlife and people, and combining these three layers can assist in the creation of integrated green networks.

One of the major issues of the visualizing network approach in green-blue space planning is choosing nodes. Nodes can then be the habitat patches in the green space layers that provide important functions of wildlife living and reproduction, or they can be fragile patches that need to be protected and preserved in front of urban densification and city’s development, or they also can be preferred sites for human recreation, contact with nature, etc. How to integrate both ecological and social functions of nodes leads to biotope and sociotope maps, since they provide valuable references on current habitat patches in both perspectives. In order to propose a detailed integration framework of green networks, this paper selected node sources overlapping in two maps since both ecological and social value properties are taken into consideration as the comprehensive planning context. Although one possible issue concerns the conflict of habitats between human and wildlife needs, in the ‘real’ city environment both groups live and share the habitats. Therefore, identifying their locations as fulfilling both perspectives is crucial for dealing with complex urban ecosystems.

Selection of nodes also concerns network users for which species are proposed to use the graphic network. It relates to species indicators as a result of different criteria, such as focal species, umbrella or endangered species, and so forth. In all, 596 habitats were selected as important nodes for further analysis in Stockholm (Fig. 5b). Suggested maps also show potentially crucial habitats that are already used by wildlife and human. For example we identified several patches in densely built downtown – the closed construction areas (Fig. 5a). These patches are located in the dense buildings in the city core and isolated from the rest of habitats in the city, but they provide substantial social and ecological values to the whole city. Hence, although no habitat expansion can be realized because of special regulations in this area, understanding their importance and preservation and protection is important.

Apart from nodes selection, the LCP analysis needs another layer, namely that of the cost raster. The latter contains the different friction costs of movement that across each cell. Knappen (1992) first proposed using the relative number to decide the cost of values response to patch accessibility based on simulated dispersers. Some researchers have applied a similar way of simplifying the decision of cost values with different variables (Adriaensen et al. 2003; Graham and Marvin 2001; Teng et al. 2011), for example the transitions per day under constant energy constraints and prediction of species’ presence/absence in the proximity of source patches. A variety of factors can affect the dedicated cost values, such as land use, vegetation cover, human activities, elevation, etc. The more elements considered the more accurate or comprehensive cost values can be reached. Setting cost through the LCP model is not only a valuable way to consider ecological and social aspects but also integrates economic feasibility into sustainable green-blue spaces planning. In the present study relative energy expenditure was used to decide a range of costs according to species indicators, including three main characters (land use, slope and human disturbance) to decide specific costs and quality weight. Then the final costs value could be set for the LCP analysis and could be achieved by calculating the most effective routes between any other two nodes crossing any possibility of cost pixel.

Although the LCP model represents a potential method for development of green-blue space planning on the bases of current land use, there is still a problem related to the cost assumption of least cost – whether the plants, animals and human choose to use the ideal paths. Literally low-cost corridors of the landscape for an organism would be the optimal movement routes, but for particular species this does not always apply since they cannot use the effective paths for their movement. This calls for further research on better models of actual and ideal organism migration routes as well as the corresponding spatial extents of the connectivity zones.

Another issue of using graph network analysis for green-blue spaces planning is to call for improvement potentials for movement facilitation. One of the major advantages of the graph network approach is the convenience of zooming in and out between city and neighbourhood scale while using the same approach and model. The proposed green network framework and its test in Stockholm demonstrate how to improve landscape connections in response to the current structural complexity. In this paper we used the LCP model to identify the ideal green network for different species as well as the overall density analysis. This aimed to generate a network intensity map that allows for planning prioritisation. However, this should be integrated with the specific context of cities. For example, this paper tested the framework in one of the greenest cities in Europe – Stockholm. But as a result of its current spatial planning strategy, with a Green Wedges approach at the regional scale, the proposed green corridors are dedicated to enhance landscape connectivity at the city level on the one side, and link the connection between city to regional level on the other. Zooming in on the local neighbourhood scale requires differed strategies for each particular case. At the local scale two corridors and street greening plan were suggested for linking local and neighbouring habitats which are at the moment are separated by built areas. However, proposed suggestions will have to be treated within their respective local contexts, such as planning priorities and research questions.

Our results reveal how multiple functions can be integrated properly into an operational network plan. Having said this, more research is needed on suitable development without substantial impact on biodiversity and ecosystem services. One potential lies in looking at effective green network routes. The LCP model could provide an accessible way of connecting habitat patches as a well-linked network. Leaving spaces for effective corridors in dense areas is extremely important. This links to another potential area of study looking into network resilience when nodes are removed and its influences on the network. As for green network paths, the question matters on how these connectivity zones would be realized and how wide the paths should be for serving as corridors-connectors.

Ecological influxes into physical planning and design on the regional, city and local scales are quite complex processes which are not always addressed. For example the surrounding nature of the Stockholm city, addressed in regional planning, is an important provider of ecosystem services and component of larger-scaled green networks. This calls for future studies that integrate Stockholm as well as other municipalities into a wider range of green network analysis. More attention should be given to regional planning approaches for ecological structures which can place neighbouring land and water into a regional context, after all species move not only within the city but also to and within neighbouring areas. Another possible study in the following step would select specific plant as species indicators as well. For example investigating possible dispersals for selected seeds of trees, shrubs or grasses. But this paper only touch the potential that with the migration of bird and amphibian, some plant species would be spread correspondingly.