Combining long-term land cover time series and field observations for spatially explicit predictions on changes in tropical forest biodiversity
Combining spatially explicit land cover data from remote-sensing and faunal data from field observations is increasingly applied for landscape-scale habitat and biodiversity assessments, but without modelling changes quantitatively over time. In a novel approach, we used a long-term time series including historical map data to predict the influence of one century of tropical forest change on keystone species or indicator groups in the Kakamega–Nandi forests, western Kenya.