Effects of topographic variability and forest attributes on fine-scale soil fertility in late-secondary succession of Atlantic Forest

Understanding how soil fertility changes due to topographical conditions and forest attributes is an essential premise for local-scale forest management practices. We evaluated the effects of topographic variables and forest attributes on soil fertility along a local topographical gradient in a Brazilian Atlantic Forest. We hypothesised that soil fertility is positively affected by topographic variability and forest attributes (structure and diversity). We used tree species richness, composition, abundance, and aboveground biomass as forest attributes. We analysed two 1-ha forest patches with contrasting topographical conditions. We used different linear mixed effects models (LMMs) to test the main effects of different forest attributes and topography variables on soil fertility. The results showed that higher topographic variability determines soil fertility along a fine-scale gradient. The first two axes of the PCA explained 66.8% of the variation in soil data, with the first axis (PCA1) explaining 49.6% of the variation in soil data and positively correlating with fertility-related soil properties. The second axis (PCA2) explained 17.2% of the variation in topographical data and positively correlated with convexity (the elevation of a plot minus the average elevation of all immediate neighbour plots) and elevation. Our best models showed that topographic variables (elevation and convexity) are the main predictors that affect fine-scale soil fertility. Our study demonstrates that the topographic variability, mainly elevation and convexity, determines fine-scale soil fertility in an Atlantic Forest. These results advance our understanding that context-dependent conditions based on topography and soil properties have a high variability at a fine scale, which can influence variations in forest attributes (i.e., species distribution, diversity and structure of tree communities). In addition, the information generated in this research may be important for planning forest restoration activities (passive and active) based on the high variability of environmental variables at a fine scale.