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How we manage priority habitats within increasingly fragmented landscapes is a critical conservation issue. Practitioners and policy makers are often faced with the dilemma of deciding where to focus limited resources, but evidence on where particular actions will have the largest return on investment is lacking. To aid this decision making process, we have developed a spatial framework in the open source programming environment R for measuring and combining indicators of habitat biodiversity, coherence and resilience.
We have two operating prototype applications available for you to test:
Information on where land management actions are likely to most effective in supporting biodiversity is often lacking. This limits our ability to act strategically at various scales of decision making, from the development of long-term, nationwide habitat networks and the distribution of associated incentives, to more local decisions by land owners and managers on the prescription of resources. If we are to meet national and international targets to reduce biodiversity decline and to restore ecosystems with the limited resources available, scientifically grounded, accessible landscape decision-making tools are required.
Local ‘patch’ scale indicators of habitat quality or condition are often compared in isolation from important information on the composition and spatial configuration of the surrounding landscape. BioCoRe uses spatial-environmental data to measure both patch- and network-scale indicators of habitat Biodiversity, Coherence and Resilience according to the Lawton Principles of ‘more, bigger, better and joined’ habitat (Lawton, 2010; see examples in table below). It allows users to create ‘least cost’ ecological networks based on the distances between patches and the permeability of the intervening habitat (Watts et al., 2010). Users can then map and compile these indicators and apply a triage approach to identify areas where restoration and conservation actions are likely to be most cost effective.
In recognition of the fact that priorities, goals, evidence and data availability vary between decision-making contexts, our suite of interactive BioCoRe Shiny Apps enable end users to analyse their own data and to explore the impact of altering model parameters on the output. Users can also run patch- and network-level analyses independently or in combination to suit different circumstances.
We work with a range of practitioners to apply the BioCoRe framework. Recent examples include: