Bayesian reconstruction of past land‐cover from pollen data: model robustness and sensitivity to auxiliary variables
Abstract
Realistic depictions of past land cover are needed to investigate prehistoric environmental changes, effects of anthropogenic deforestation, and long term land cover‐climate feedbacks. Observation based reconstructions of past land cover are rare and commonly used model based reconstructions exhibit considerable differences. Recently Pirzamanbein, Lindström, Poska, and Gaillard (Spatial Statistics, 24:14‐‐31,2018) developed a statistical interpolation method that produces spatially complete reconstructions of past land cover from pollen assemblage. These reconstructions incorporate a number of auxiliary datasets raising questions regarding the method's sensitivity to different auxiliary datasets.
Info
Journal Article, 2019
UN SDG Classification
DK Main Research Area
Science/Technology
