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

Landscape fragmentation threatens habitats, biodiversity and other ecosystem services. In tackling this threat, the dynamic processes of social-ecological systems should be recognised and understood. Although network analysis based on graph theory has been recognised as an efficient way of spatially understanding landscape or habitat connectivity, only few studies have offered specific approaches or suggestions for integrating detailed social-ecological values into geographical distributions. As a contribution to bridging this gap, this paper introduces a social-ecological network model for the issue of landscape or habitat fragmentation applied to the case of Stockholm, Sweden. Graph theory was used in combination with sociotope and biotope maps for simple visualisation of network situations in two-dimensional maps. The European crested tit (Lophophanes cristatus), European common toad (Bufo bufo) and human beings were selected as indicator species, based on a landscape ecology analysis in Stockholm Municipality in 2009. Slope, land use and human disturbance maps were assessed in order to decide cost values of travelling from node to node. Lease-cost-path accumulation was used to create ideal reference maps of green networks. Three separate maps were then developed for suggesting efficient routes for three indicators in city scale that mainly connect from the Royal National City Park to the other parts of the city. The model in Hjorthagen neighbourhood to highlight two practical paths that link this fragmented community to its neighbouring park areas. Findings make it possible to address two scales of network improvement strategies, namely first for the city-scale green network that connects geographical habitats (nodes) and the regional green wedges of the city, and the second that of neighbourhood links between habitats in detailed layers of green networks. Strategic improvement potentials are presented based on ideal reference maps of green networks correspondingly.