Global priority areas for ecosystem restoration.

Extensive ecosystem restoration is increasingly seen as being central to conserving biodiversity1 and stabilizing the climate of the Earth2. Although ambitious national and global targets have been set, global priority areas that account for spatial variation in benefits and costs have yet to be identified. Here we develop and apply a multicriteria optimization approach that identifies priority areas for restoration across all terrestrial biomes, and estimates their benefits and costs. We find that restoring 15% of converted lands in priority areas could avoid 60% of expected extinctions while sequestering 299 gigatonnes of CO2-30% of the total CO2 increase in the atmosphere since the Industrial Revolution. The inclusion of several biomes is key to achieving multiple benefits. Cost effectiveness can increase up to 13-fold when spatial allocation is optimized using our multicriteria approach, which highlights the importance of spatial planning. Our results confirm the vast potential contributions of restoration to addressing global challenges, while underscoring the necessity of pursuing these goals synergistically.

Predicting landscape-scale biodiversity recovery by natural tropical forest regrowth.

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 servicios ambientales, es un vacío de conocimiento importante en la planeación de la restauración forestal. Buscamos predecir y mapear la recuperación a escala de paisaje de la riqueza de especies y la abundancia total de vertebrados, invertebrados y plantas en bosques tropicales y subtropicales de segundo crecimiento para guiar la planeación de la restauración. Primero, realizamos un metaanálisis mundial para cuantificar la medida a la que se desvió la recuperación de la riqueza y la abundancia total de especies en los bosques de segundo crecimiento de los valores de biodiversidad en los bosques antiguos referenciales en el mismo paisaje. Después, utilizamos un algoritmo de aprendizaje automático y un conjunto integral de factores socioambientales para predecir espacialmente la desviación a escala de paisaje para después mapearla. Los modelos explicaron en promedio el 34% de la varianza observada en la recuperación (rango de 9-51%). La recuperación de la biodiversidad a escala de paisaje en los bosques de segundo crecimiento pudo predecirse espacialmente con base en los factores socioambientales del paisaje (demografía humana, uso y cobertura del suelo, alteraciones naturales y antropogénicas, productividad del ecosistema, tipo de topografía y de suelo); fue significativamente más alta para la riqueza de especies que para la abundancia total de vertebrados (desviación media pronosticada ajustada al rango de 0.09 versus 0.34) e invertebrados (0.2 versus 0.35) pero no para las plantas (las cuales mostraron una recuperación similar para ambas medidas [0.24 versus 0.25]); y tuvo una correlación positiva para la abundancia de especies de plantas y vertebrados (Pearson r =0.45, p=0.001). Nuestra estrategia puede ayudar a identificar los paisajes de bosques tropicales y subtropicales con un potencial alto para la recuperación de la biodiversidad por medio de la regeneración natural del bosque.

A metric for spatially explicit contributions to science-based species targets.

The Convention on Biological Diversity's post-2020 Global Biodiversity Framework will probably include a goal to stabilize and restore the status of species. Its delivery would be facilitated by making the actions required to halt and reverse species loss spatially explicit. Here, we develop a species threat abatement and restoration (STAR) metric that is scalable across species, threats and geographies. STAR quantifies the contributions that abating threats and restoring habitats in specific places offer towards reducing extinction risk. While every nation can contribute towards halting biodiversity loss, Indonesia, Colombia, Mexico, Madagascar and Brazil combined have stewardship over 31% of total STAR values for terrestrial amphibians, birds and mammals. Among actions, sustainable crop production and forestry dominate, contributing 41% of total STAR values for these taxonomic groups. Key Biodiversity Areas cover 9% of the terrestrial surface but capture 47% of STAR values. STAR could support governmental and non-state actors in quantifying their contributions to meeting science-based species targets within the framework.

Author Correction: Strategic approaches to restoring ecosystems can triple conservation gains and halve costs.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Strategic approaches to restoring ecosystems can triple conservation gains and halve costs.

International commitments for ecosystem restoration add up to one-quarter of the world's arable land. Fulfilling them would ease global challenges such as climate change and biodiversity decline but could displace food production and impose financial costs on farmers. Here, we present a restoration prioritization approach capable of revealing these synergies and trade-offs, incorporating ecological and economic efficiencies of scale and modelling specific policy options. Using an actual large-scale restoration target of the Atlantic Forest hotspot, we show that our approach can deliver an eightfold increase in cost-effectiveness for biodiversity conservation compared with a baseline of non-systematic restoration. A compromise solution avoids 26% of the biome's current extinction debt of 2,864 plant and animal species (an increase of 257% compared with the baseline). Moreover, this solution sequesters 1 billion tonnes of CO2-equivalent (a 105% increase) while reducing costs by US$28 billion (a 57% decrease). Seizing similar opportunities elsewhere would offer substantial contributions to some of the greatest challenges for humankind.

Ecological restoration success is higher for natural regeneration than for active restoration in tropical forests.

Is active restoration the best approach to achieve ecological restoration success (the return to a reference condition, that is, old-growth forest) when compared to natural regeneration in tropical forests? Our meta-analysis of 133 studies demonstrated that natural regeneration surpasses active restoration in achieving tropical forest restoration success for all three biodiversity groups (plants, birds, and invertebrates) and five measures of vegetation structure (cover, density, litter, biomass, and height) tested. Restoration success for biodiversity and vegetation structure was 34 to 56% and 19 to 56% higher in natural regeneration than in active restoration systems, respectively, after controlling for key biotic and abiotic factors (forest cover, precipitation, time elapsed since restoration started, and past disturbance). Biodiversity responses were based primarily on ecological metrics of abundance and species richness (74%), both of which take far less time to achieve restoration success than similarity and composition. This finding challenges the widely held notion that natural forest regeneration has limited conservation value and that active restoration should be the default ecological restoration strategy. The proposition that active restoration achieves greater restoration success than natural regeneration may have arisen because previous comparisons lacked controls for biotic and abiotic factors; we also did not find any difference between active restoration and natural regeneration outcomes for vegetation structure when we did not control for these factors. Future policy priorities should align the identified patterns of biophysical and ecological conditions where each or both restoration approaches are more successful, cost-effective, and compatible with socioeconomic incentives for tropical forest restoration.