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

土地利用变化对生物多样性和生态系统功能有重大影响，因此是RESPECT研究小组的一个重要方面。利用土地利用模型的广泛初步工作，本提案将进一步发展一种动态方法来模拟人类土地分配，为创新的土地利用情景提供信息。这些情景考虑了各种决策标准、决策者的可变期望以及由于气候变化而导致的输入数据的变化。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场景的输入数据，这些数据表示决策者的可变期望。作为一个重要的进步，新模型将考虑与土地利用活动无关的农场特征，如非农收入。根据决策标准的选择，该项目将通过假设社会经济标准来模拟农民的偏好和需求，或通过考虑新的土地利用/土地覆盖（LULC）类型以及生物多样性指标或生态系统服务作为决策标准，制定可能的土地利用情景。新的土地利用资源组合类型将包括农林业系统，在其组成和管理方面采用强大的多标准技术进行优化。模拟的土地利用情景将导致土地分配到不同土地利用价值类型的变化，这种变化在空间上并不明确。基于这些信息，我们将应用多准则优化技术生成基于网格的、空间显式的山地雨旱林土地利用变化模拟信息。拟进行的研究将通过综合当前土地覆盖对新的土地利用潜力类型的适宜性来分配某些土地利用潜力类型的变化（例如，扩大的牧场面积）。这个空间显式模拟将包括来自我们合作伙伴项目的信息。目前的建议将通过提供：1)考虑气候变化的优化过程对输入变化的响应来推进我们现有的建模方法。2)山地干旱林的新模式。3)与土地利用无关的农业因素（如非农收入）。4)稳健的、多功能的、新型的LULC类型和管理实践，如农林复合系统。5)土地利用变化的空间显性信息。

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.