A socio-ecological approach for identifying and contextualising spatial ecosystem-based adaptation priorities at the sub-national level

Climate change adds an additional layer of complexity to existing sustainable development and biodiversity conservation challenges. The impacts of global climate change are felt locally, and thus local governance structures will increasingly be responsible for preparedness and local responses. Ecosystem-based adaptation (EbA) options are gaining prominence as relevant climate change solutions. Local government officials seldom have an appropriate understanding of the role of ecosystem functioning in sustainable development goals, or access to relevant climate information. Thus the use of ecosystems in helping people adapt to climate change is limited partially by the lack of information on where ecosystems have the highest potential to do so. To begin overcoming this barrier, Conservation South Africa in partnership with local government developed a socio-ecological approach for identifying spatial EbA priorities at the sub-national level. Using GIS-based multi-criteria analysis and vegetation distribution models, the authors have spatially integrated relevant ecological and social information at a scale appropriate to inform local level political, administrative, and operational decision makers. This is the first systematic approach of which we are aware that highlights spatial priority areas for EbA implementation. Nodes of socio-ecological vulnerability are identified, and the inclusion of areas that provide ecosystem services and ecological resilience to future climate change is innovative. The purpose of this paper is to present and demonstrate a methodology for combining complex information into user-friendly spatial products for local level decision making on EbA. The authors focus on illustrating the kinds of products that can be generated from combining information in the suggested ways, and do not discuss the nuance of climate models nor present specific technical details of the model outputs here. Two representative case studies from rural South Africa demonstrate the replicability of this approach in rural and peri-urban areas of other developing and least developed countries around the world

Summary of natural and socio-economic features included, and data sets which were used to map these, for ecosystem priority areas mapping.

<p>Features shown in <b>bold</b> were specific to the Namakwa District municipality only; Features shown in <i>italics</i> were specific to the Alfred Nzo District municipality only. Further details on all of the mapping methods for each individual layer and for the composite maps of each category can be found in the vulnerability assessment technical reports for each pilot site [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0155235#pone.0155235.ref044" target="_blank">44</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0155235#pone.0155235.ref045" target="_blank">45</a>].</p

Map showing the location of the two case study sites.

<p>Map showing the location of the two case study sites.</p

Diagram illustrating the integration method used to develop composite maps for each category of information making up the final EbA priority areas maps.

<p>This particular example shows all the individual layers which made up the composite map for natural features supporting resilience to climate change impacts at a landscape scale in the Namakwa District Municipality, South Africa. Specific spatial data on corridors, gradients, diversity, endemism, refugia, and unfragmented landscapes were overlaid to produce composite maps for each category of natural feature with the potential to contribute to climate change adaptation. These were then overlaid to produce a single map, at the District municipality scale, which summarises all the climate resilient natural features identified, showing in the darker areas where there are high levels of overlap (i.e. where many climate resilient natural features are present). Reprinted from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0155235#pone.0155235.ref044" target="_blank">44</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0155235#pone.0155235.ref045" target="_blank">45</a>] under a CC BY license, with permission from CSA, original copyright 2015.</p

Summary of the analysis and integration process.

<p>Reprinted from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0155235#pone.0155235.ref044" target="_blank">44</a>] under a CC BY license, with permission from CSA, original copyright 2015.</p

Maps of biome impacts.

<p>Modelled current biomes in the NDM (4a), projected changes in climatic suitability for the biomes in the NDM, medium term MIROC (4b), projected changes in climatic suitability for the biomes on the NDM, long term MIROC (4c), modelled current biomes in the ANDM (4d), projected changes in climatic suitability for the biomes in the ANDM, medium term MIROC (4e), projected changes in climatic suitability for the biomes on the ANDM, long term MIROC (4f). Reprinted from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0155235#pone.0155235.ref044" target="_blank">44</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0155235#pone.0155235.ref045" target="_blank">45</a>] under a CC BY license, with permission from CSA, original copyright 2015.</p