General Landscape Connectivity Model (GLCM): a new way to map whole of landscape biodiversity functional connectivity for operational planning and reporting

Abstract

<p>Graph-theoretic approaches are commonly used to map landscape connectivity networks to inform environmental management priorities. We developed the new General Landscape Connectivity Model (GLCM), as a operationally practical way of evaluating and mapping habitat networks to inform conservation priorities and plans. GLCM is built on two complementary metapopulation ecology-based measures: Neighbourhood habitat area (Ni) and habitat link value (Li). Ni is a measure of the amount of connected habitat to each location considering its cross-scale connectivity to neighbouring habitat. The remaining Ni across a region can be reported as an indicator of Ecological Carrying Capacity for wildlife (plants and animals). Li at any location is its contribution to the landscape connectivity of the study region (i.e. which is reported as summed Ni across a region) by virtue of providing the 'least-cost' linkages between concentrations of habitat. Mapped Li provides valuable insights into the pattern of a region's habitat network, highlighting functioning habitat corridors and stepping-stones, and candidate areas for conservation and restoration. Due to its foundations in ecological theory and its parsimonious design, GLCM addresses a number of criteria we list as important, while addressing criticisms often levelled at graph-theoretical approaches. We present results for three south-east Australian casestudies using continuous-value ecological condition surfaces as input. However, a simple habitat/non-habitat binary surface approximating a threshold ecological condition can also be used. GLCM has been designed to specifically address the need for generic landscape connectivity assessment at regional scales, and broader. It incorporates connectivity analyses across a range of spatial scales and granularities relevant to broad ranges of taxa and movement processes (foraging, dispersal and migration). Successively finer spatial scales are more intensively sampled based on a simple scaling-law. This approach allows analysis resolutions to be determined by data-driven ecological relevance rather than by processing limitations. The operational advantages of GLCM means that landscape connectivity assessments can be readily updated with refined or changed inputs including time-series remote sensing of land cover, or applied to alternative scenarios of land use, ecological restoration, climate projections or combinations of these.</p>