Innovative scenarios of land-use change - simulating shifts in land allocation under climate change at landscape and ecosystem scales

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Land-use change has a major impact on biodiversity and ecosystem functions, thus being an important aspect within the RESPECT research group. Utilizing extensive preliminary work on land-use models, this proposal will further develop a dynamic approach for simulating human land allocation to inform innovative land-use scenarios. These scenarios consider various decision criteria, variable expectations of decision makers and alterations of input data due to climate change. To this end, the land allocation model will integrate data from the HUMBOL-TD model. The project uses uncertainty spaces to represent the input data for the scenarios, which represent the variable expectations of decision makers. As an important advancement, the new model will consider farm characteristics not related with land-use activities, such as off-farm income. Depending on the selection of decision criteria, the project will develop likely land-use scenarios by assuming socio-economic criteria to model farmer preferences and needs or innovative land-use scenarios by considering new land-use/land-cover (LULC) types as well as biodiversity indicators or ecosystem services as decision criteria. New LULC types will include agroforestry systems, optimized by robust multi-criteria techniques concerning their composition and management. The simulated land-use scenarios will result in changes in the allocation of land to various LULC types over time, which are not spatially explicit. Based on such information, we will apply multi-criteria optimization techniques to generate grid-based, spatially explicit information of the previously simulated land-use changes for the mountain rain and dry forest. The intended research will allocate changes of certain LULC types (e.g., expanded pasture areas) by integrating the suitability of current land cover for the new LULC type. This spatially explicit simulation will include information from our partner projects. The current proposal will advance our existing modelling approaches by providing: 1) Responses of the optimization procedure to the input alterations, which consider climate change. 2) A new model for the mountain dry forest. 3) Farm factors unrelated to land use (e.g., off-farm income). 4) Robust, multi-functional and new LULC types and management practices, such as agroforestry systems. 5) Spatially explicit information on land-use changes.