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

土地使用变化对生物多样性和生态系统功能有重大影响，因此是生物多样性和生态系统功能研究小组的一个重要方面。该提案将利用关于土地使用模型的大量初步工作，进一步制定一种动态方法，模拟人类土地分配，为创新的土地使用情景提供信息。这些情景考虑到各种决策标准、决策者的可变期望以及气候变化引起的输入数据的变化。为此，土地分配模型将整合HUMBOL-TD模型的数据。该项目使用不确定性空间来表示情景的输入数据，这些数据代表决策者的可变期望。作为一项重要的进步，新模式将考虑与土地使用活动无关的农场特征，如农场外收入。根据决策标准的选择，该项目将制定可能的土地使用情景，假设社会经济标准，模拟农民的偏好和需求，或创新的土地使用情景，考虑新的土地使用/土地覆盖类型以及生物多样性指标或生态系统服务作为决策标准。新的土地利用、土地利用的变化类型将包括农林业系统，通过关于其组成和管理的强有力的多标准技术进行优化。模拟的土地利用情景将导致随着时间的推移，土地分配到各种LULC类型的变化，这是不明确的空间。基于这些信息，我们将采用多标准优化技术，以生成基于网格的，空间上明确的信息，以前模拟的土地利用变化的山区雨林和干旱森林。预期的研究将分配某些LULC类型的变化（例如，扩大牧场面积），通过整合现有土地覆盖对新的LULC类型的适宜性。这种空间上明确的模拟将包括来自我们合作伙伴项目的信息。目前的建议将推进我们现有的建模方法，提供：1）优化程序的输入变更，考虑气候变化的响应。 2)山地干旱林的新模式。3)与土地使用无关的农场因素（例如，农业收入）。4)稳健、多功能和新的土地利用/土地利用变化类型和管理做法，如农林系统。5)关于土地利用变化的明确的空间信息。

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.