Synthesis of pattern and process in biodiversity conservation assessment: a flexible whole-landscape modelling framework
To describe a general modelling framework for integrating multiple pattern- and process-related factors into biodiversity conservation assessments across whole landscapes. Location
New South Wales (Australia), and world-wide. Methods
The framework allows for a rich array of alternatives to the target-based model traditionally underpinning systematic conservation planning and consists of three broad modelling components. The first component models the future state (condition) of habitat across a landscape as a function of present state, current and projected pressures acting on this state, and any proposed, or implemented, management interventions. The second component uses this spatially explicit prediction of future habitat state to model the level of persistence expected for each of a set of surrogate biodiversity entities. The third component then integrates these individual expectations to estimate the overall level of persistence expected for biodiversity as a whole. Results
Options are explored for tailoring implementation of the framework to suit planning processes varying markedly in purpose, and in availability of data, time, funding and expertise. The framework allows considerable flexibility in the nature of employed biodiversity surrogates (species-level, discrete or continuous community-level) and spatial data structures (polygonal planning units, or fine-scaled raster), the level of sophistication with which each of the three modelling components is implemented (from simple target-based assessment to complex process-based modelling approaches), and the forms of higher-level analysis supported (e.g. optimal plan development, priority mapping, interactive scenario evaluation). Main conclusions
The described framework provides a logical, and highly flexible, foundation for integrating disparate pattern- and process-related factors into conservation assessments in dynamic, multiple-use landscapes.