Publication
Predicting landscape‐scale biodiversity recovery by natural tropical forest regrowth
Publisher:
Wiley
Date:
20-04-2022
DOI:
10.1111/COBI.13842
Abstract: Natural forest regrowth is a cost‐effective, nature‐based solution for bio ersity recovery, yet different socioenvironmental factors can lead to variable outcomes. A critical knowledge gap in forest restoration planning is how to predict where natural forest regrowth is likely to lead to high levels of bio ersity recovery, which is an indicator of conservation value and the potential provisioning of erse ecosystem services. We sought to predict and map landscape‐scale recovery of species richness and total abundance of vertebrates, invertebrates, and plants in tropical and subtropical second‐growth forests to inform spatial restoration planning. First, we conducted a global meta‐analysis to quantify the extent to which recovery of species richness and total abundance in second‐growth forests deviated from bio ersity values in reference old‐growth forests in the same landscape. Second, we employed a machine‐learning algorithm and a comprehensive set of socioenvironmental factors to spatially predict landscape‐scale deviation and map it. Models explained on average 34% of observed variance in recovery (range 9–51%). Landscape‐scale bio ersity recovery in second‐growth forests was spatially predicted based on socioenvironmental landscape factors (human demography, land use and cover, anthropogenic and natural disturbance, ecosystem productivity, and topography and soil chemistry) was significantly higher for species richness than for total abundance for vertebrates (median range‐adjusted predicted deviation 0.09 vs. 0.34) and invertebrates (0.2 vs. 0.35) but not for plants (which showed a similar recovery for both metrics [0.24 vs. 0.25]) and was positively correlated for total abundance of plant and vertebrate species (Pearson r = 0.45, p = 0.001). Our approach can help identify tropical and subtropical forest landscapes with high potential for bio ersity recovery through natural forest regrowth.