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Abstract
Grassland landscapes are important ecosystems in East Africa, providing habitat and grazing grounds for wildlife and livestock and supporting pastoralism, an essential part of the agricultural sector. Since future grassland availability directly affects the future mobility needs of pastoralists and wildlife, we aim to model changes in the distribution of key grassland species under climate change. Here we combine a global and regional climate model with a machine learning-based species distribution model to understand the impact of regional climate change on different key grass species. The application of a dynamical downscaling step allows us to capture the fine-scale effects of the region’s complex climate, its variability and future changes. We show that the co-occurrence of the analysed grass species is reduced in large parts of eastern Africa, and particularly in the Turkana region, under the high-emission RCP8.5 scenario for the last 30 years of the 21st century. Our results suggest that future climate change will alter the natural resource base, with potentially negative impacts on pastoralism and wildlife in East Africa.
In East Africa, the distribution and co-occurrence of seven grass species are projected to change noticeably under the high-emission scenario for the last 30 years of the 21st century, according to an analysis that combines a regional climate model with machine-learning-based grass species data.
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1 University of Bern, Climate and Environmental Physics, Bern, Switzerland (GRID:grid.5734.5) (ISNI:0000 0001 0726 5157); University of Bern, Oeschger Centre for Climate Change Research, Bern, Switzerland (GRID:grid.5734.5) (ISNI:0000 0001 0726 5157); Delft University of Technology, Geoscience and Remote Sensing, Faculty of Civil Engineering and Geosciences, Delft, The Netherlands (GRID:grid.5292.c) (ISNI:0000 0001 2097 4740)
2 University of Bern, Centre for Development and Environment, Bern, Switzerland (GRID:grid.5734.5) (ISNI:0000 0001 0726 5157); University of Bern, Institute of Geography, Bern, Switzerland (GRID:grid.5734.5) (ISNI:0000 0001 0726 5157)
3 University of Bern, Institute of Plant Sciences, Bern, Switzerland (GRID:grid.5734.5) (ISNI:0000 0001 0726 5157)
4 University of Bern, Climate and Environmental Physics, Bern, Switzerland (GRID:grid.5734.5) (ISNI:0000 0001 0726 5157); University of Bern, Oeschger Centre for Climate Change Research, Bern, Switzerland (GRID:grid.5734.5) (ISNI:0000 0001 0726 5157); University of the Basque Country, Department of Physics, Leioa, Spain (GRID:grid.11480.3c) (ISNI:0000 0001 2167 1098)
5 University of Bern, Centre for Development and Environment, Bern, Switzerland (GRID:grid.5734.5) (ISNI:0000 0001 0726 5157)
6 ETH Zurich, Institute for Atmospheric and Climate Science, Zurich, Switzerland (GRID:grid.5801.c) (ISNI:0000 0001 2156 2780)
7 University of Bayreuth, Department of Plant Systematics, Bayreuth, Germany (GRID:grid.7384.8) (ISNI:0000 0004 0467 6972)
8 University of Nairobi, Land Resource Management and Agricultural Technology Department, Faculty of Agriculture, Nairobi, Kenya (GRID:grid.10604.33) (ISNI:0000 0001 2019 0495)
9 University of Bern, Climate and Environmental Physics, Bern, Switzerland (GRID:grid.5734.5) (ISNI:0000 0001 0726 5157); University of Bern, Oeschger Centre for Climate Change Research, Bern, Switzerland (GRID:grid.5734.5) (ISNI:0000 0001 0726 5157)