LAUSR

Abstract Forest decline is mostly attributed to factors related to climate change and human activities. However, it is not well-known how topographic factors indirectly shape the forest decline. In this study, we analyzed the topographic patterns of growth decline in Pinus densiflora forests in the Mengshan Mountains of eastern China. A forest decline event occurring in 2009–2014 was identified using the Normalized Difference Vegetation Index (NDVI) and tree-ring widths. A decision tree model was developed to extract the topographic pattern of the forest decline. Boosted regression tree model indicates that the relative influence of elevation is 93.6%, and the remaining relative influence includes aspect, slope, and landform. We conclude that topography, elevation in particular, indirectly shapes the vulnerability of sites to forest decline and can thus be used to predict the spatial decline risks. Our findings provide new insights into the role of topography in forest decline and hold implications for predicting decline risk and protecting forests in a rapidly changing world.