Predicting landscape‐scale biodiversity recovery by natural tropical forest regrowth

• Published in: Conservation biology 2022-06, Vol.36 (3), p.e13842-n/a
• Main Authors: Prieto, Pablo V., Bukoski, Jacob J., Barros, Felipe S. M., Beyer, Hawthorne L., Iribarrem, Alvaro, Brancalion, Pedro H. S., Chazdon, Robin L., Lindenmayer, David B., Strassburg, Bernardo B. N., Guariguata, Manuel R., Crouzeilles, Renato
• Format: Article
• Language: English
• Subjects: Abundance; Algorithms; Animals; Anthropogenic factors; Biodiversity; bosque aleatorio; bosque secundario; Conservation of Natural Resources; Demography; Deviation; Ecosystem; Ecosystem disturbance; Ecosystem services; Environmental restoration; forest restoration; Forests; Growth; Humans; Invertebrates; Land use; Landscape; Machine learning; Meta-analysis; metaanálisis; modelos predictivos; Natural disturbance; natural regeneration; planeación espacial; Plants; predictive models; Provisioning; random forest; Recovery; regeneración natural; Regrowth; restauración forestal; Restoration; secondary forest; Soil; Soil chemistry; spatial planning; Species richness; Tropical Climate; Tropical forests; Vertebrates
• ISSN: 0888-8892; 1523-1739
• DOI: 10.1111/cobi.13842

Natural forest regrowth is a cost‐effective, nature‐based solution for biodiversity 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 biodiversity recovery, which is an indicator of conservation value and the potential provisioning of diverse 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 biodiversity 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 biodiversity 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 biodiversity recovery through natural forest regrowth. Predicción de la Recuperación de la Biodiversidad a Escala de Paisaje según la Regeneración Natural del Bosque Tropical Resumen La regeneración natural del bosque es una solución rentable para la recuperación de la biodiversidad basada en la naturaleza, sin embargo, los diferentes factores socioambientales pueden derivar en resultados variables. Cómo predecir la ubicación en donde la regeneración natural del bosque recuperará los niveles de biodiversidad, los cuales son un indicador del valor de la conservación y un suministro potencial de diferentes