A fractional land use change model for ecological applications. (January 2022)

• Record Type: Journal Article
• Authors: Kapitza, Simon; Golding, Nick; Wintle, Brendan A.
• Is Part Of: Environmental modelling & software. Volume 147(2022)
• Publication Date: 2022-01
• Subjects: Land use forecasting -- Fractional land cover -- Continuous fields -- Agricultural expansion -- Socio-economic change -- Biodiversity conservation; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2021.105258
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: By mapping land use under projections of socio-economic change, ecological changes can be predicted to inform conservation decision-making. We present a land use model that enables the fine-scale mapping of land use change under future scenarios. Its predictions can be used as input to virtually all existing spatially-explicit ecological models. Our model maps the fractional cover of land use within each grid cell, providing higher information content than discrete classes at the same spatial resolution. The method accurately reproduced land use patterns observed in the Amazon, both in terms of the allocated fractional amounts and also the direction of predicted land use changes. A small case study showcases the application of our model to reproduce patterns of agricultural expansion and natural habitat declines. The model source code is provided as an open-source R package, making this new method available to bridge the gap between socio-economic, land use and biodiversity modelling. Highlights: A model to predict fine-resolution fractional land use change, based on future land use demands. Development version implemented as R package, making the model accessible to R-trained users. Validation shows high prediction accuracy in Amazon basin, but the model can be fitted anywhere to predict land use change.