Targeting and evaluating biodiversity conservation action within fragmented landscapes: an approach based on generic focal species and least-cost networks

The focus of biodiversity conservation is shifting to larger spatial scales in response to habitat fragmentation and the need to integrate multiple landscape objectives. Conservation strategies increasingly incorporate measures to combat fragmentation such as ecological networks. These are often based on assessment of landscape structure but such approaches fail to capitalise on the potential offered by more ecologically robust assessments of landscape function and connectivity. In this paper, we describe a modelling approach to identifying functional habitat networks and demonstrate its application to a fragmented landscape where policy initiatives seek to improve conditions for woodland biodiversity including increasing woodland cover. Functional habitat networks were defined by identifying suitable habitat and by modelling connectivity using least-cost approaches to account for matrix permeability. Generic focal species (GFS) profiles were developed, in consultation with stakeholders, to represent species with high and moderate sensitivity to fragmentation. We demonstrated how this form of analysis can be used to aid the spatial targeting of conservation actions. This ‘targeted’ action scenario was tested for effectiveness against comparable scenarios, which were based on random and clumped actions within the same landscape. We tested effectiveness using structural metrics, network-based metrics and a published functional connectivity indicator. Targeting actions within networks resulted in the highest mean woodland area and highest connectivity indicator value. Our approach provides an assessment of landscape function by recognising the importance of the landscape matrix. It provides a framework for the targeting and evaluation of alternative conservation options, offering a pragmatic, ecologically-robust solution to a current need in applied landscape ecology.