A socio-ecological model for predicting impacts of land-use and climate change on regional plant diversity in the Austrian Alps

Climate and land-use change jointly affect the future of biodiversity. Yet, biodiversity scenarios have so far concentrated on climatic effects because forecasts of land use are rarely available at appropriate spatial and thematic scales. Agent-based models (ABMs) represent a potentially powerful but little explored tool for establishing thematically and spatially fine-grained land-use scenarios. Here, we use an ABM parameterized for 1,329 agents, mostly farmers, in a Central European model region, and simulate the changes to land-use patterns resulting from their response to three scenarios of changing socio-economic conditions and three scenarios of climate change until the mid of the century. Subsequently, we use species distribution models to, first, analyse relationships between the realized niches of 832 plant species and climatic gradients or land-use types, respectively, and, second, to project consequent changes in potential regional ranges of these species as triggered by changes in both the altered land-use patterns and the changing climate. We find that both drivers determine the realized niches of the studied plants, with land use having a stronger effect than any single climatic variable in the model. Nevertheless, the plants' future distributions appear much more responsive to climate than to land-use changes because alternative future socio-economic backgrounds have only modest impact on land-use decisions in the model region. However, relative effects of climate and land-use changes on biodiversity may differ drastically in other regions, especially where landscapes are still dominated by natural or semi-natural habitat. We conclude that agent-based modelling of land use is able to provide scenarios at scales relevant to individual species distribution and suggest that coupling ABMs with models of species' range change should be intensified to provide more realistic biodiversity forecasts.