Original Paper

Biodiversity and Conservation

, Volume 16, Issue 13, pp 3781-3802

First online:

Systematic landscape restoration in the rural–urban fringe: meeting conservation planning and policy goals

  • Neville D. CrossmanAffiliated withPolicy and Economic Research Unit, CSIRO Land and WaterSchool of Earth and Environmental Sciences, University of Adelaide Email author 
  • , Brett A. BryanAffiliated withPolicy and Economic Research Unit, CSIRO Land and Water
  • , Bertram OstendorfAffiliated withSchool of Earth and Environmental Sciences, University of Adelaide
  • , Sally CollinsAffiliated withSouth Australian Urban Forest Biodiversity Program

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Many landscapes that straddle the rural/urban divide suffer from low levels of species diversity following extensive clearing and fragmentation of native vegetation communities and conversion of land to agriculture. Further pressures are placed on remnant vegetation by encroaching urban expansion. These landscapes now exhibit a mosaic of small, patchy vegetation remnants that are under considerable pressure from housing and light-industrial development. Furthermore, agriculture in these landscapes tends to be of high economic value from uses such as intensive horticulture. Concerted and well-planned efforts are needed to balance the many conflicts of interest and competing demands for land with the need to restore landscapes for the protection of biodiversity. There has been a recent move in Australia toward regional biodiversity planning and goal setting, however specific detail on how to plan for achieving targets in complex landscapes is lacking. This paper applies a systematic landscape restoration model to a mixed-use, peri-urban landscape on the northern fringes of Adelaide, South Australia. The region contains fragments of remnant vegetation amongst a mosaic of high-value horticulture, light industry and urban development. Models produce maximally efficient solutions that meet comprehensive, adequate and representative conservation targets. Further constraints are added to the model to take into account the value of agricultural output, the biodiversity value of remnants, and property size and tenure. The effects on solution efficiencies as the number of constraints increase are investigated. This paper demonstrates the flexibility found in applying a systematic landscape restoration methodology. The process we present can be transferred to any rural–urban fringe region.


Integer programming Geographic Information Systems Spatial optimisation Urban landscapes Landscape restoration