6533b7d7fe1ef96bd12678e8

RESEARCH PRODUCT

Combining long-term land cover time series and field observations for spatially explicit predictions on changes in tropical forest biodiversity

subject

description

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. Four time steps of land cover data between 1912/13 and 2003, derived from Landsat satellite imagery, aerial photography and old topographic maps, formed the basis for extrapolating species abundance data on the army ant Dorylus wilverthi, the guild of ant-following birds and three habitat guilds of birds differing in forest dependency. To predict the species' spatio-temporal distribution, we combined spatially explicit geographical information system (GIS)-based modelling with statistical modelling, that is, ordinary least square (OLS) ...