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.
- Develop an adaptable indicator-based framework that embeds landscape ecology theory and a triage approach to inform cost-effective, targeted restoration under various decision-making contexts.
- Develop an attractive, powerful and user-friendly front-end for framework in the form of an R shiny online web application. In this work we aim to develop applications to enable users to generate and explore the following data:
- Habitat patch and network metrics reflecting coherence and resilience
- Ecological networks
We have two operating prototype applications available for you to test:
The issue of lack of evidence of where land management actions will have the largest return on investment manifests at multiple scales, from the development of long-term, nationwide habitat restoration strategies and associated distribution of incentives to local decisions on the prescription of management actions by land owners and managers.
Local ‘patch’ scale indicators of habitat quality are often compared in isolation from important information on the composition and spatial configuration of the surrounding landscape. With our approach we aim to take account of a wide range of spatial indicators representing both the coherence and resilience of targeted habitat types in a landscape.
We have based these metrics on the Lawton Principles (Lawton, 2010) which promote approaches to land management that enhance patch/network reslience and coherence through increasing patch size (bigger/better), patch shape and degree of connectivity within the landscape.
Another important component of BioCoRe is the generation of ecological networks to represent the spatial extent to which a targeted habitat type or species utilises the landscape for dispersal or source habitat.
In BioCoRe we aim to develop an adaptable indicator-based framework that embeds landscape ecology theory and a triage approach to inform cost-effective, targeted restoration under various decision-making contexts. The triage approach enables the prioritisation of areas for protection and restoration.
Using R the open source programming environment R we aim to develop an attractive, functional, and user-friendly front-end for our framework in the form of an R shiny application which will enable users to analyse their own data and adapt model parameters. The final product provides an easy-to-use yet powerful tool to assist the development of cost-effective land management decisions.