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.Fil: Strassburg, Bernardo B. N.. Pontifícia Universidade Católica do Rio de Janeiro; Brasil. Universidade Federal do Rio de Janeiro; BrasilFil: Iribarrem, Alvaro. Pontifícia Universidade Católica do Rio de Janeiro; BrasilFil: Beyer, Hawthorne L.. The University of Queensland; Australia. University of Queensland; AustraliaFil: Cordeiro, Carlos Leandro. Pontifícia Universidade Católica do Rio de Janeiro; BrasilFil: Crouzeilles, Renato. Universidade Federal do Rio de Janeiro; Brasil. Pontifícia Universidade Católica do Rio de Janeiro; BrasilFil: Jakovac, Catarina C.. Pontifícia Universidade Católica do Rio de Janeiro; BrasilFil: Braga Junqueira, André. Pontifícia Universidade Católica do Rio de Janeiro; BrasilFil: Lacerda, Eduardo. Pontifícia Universidade Católica do Rio de Janeiro; Brasil. Universidade Federal Fluminense; BrasilFil: Latawiec, Agnieszka E.. University of East Anglia; Reino Unido. Pontifícia Universidade Católica do Rio de Janeiro; BrasilFil: Balmford, Andrew. University of Cambridge; Estados UnidosFil: Brooks, Thomas M.. University Of The Philippines Los Banos; Filipinas. Institute For Marine And Antarctic Studies; Australia. International Union For Conservation Of Nature And Natural Resources; SuizaFil: Butchart, Stuart H. M.. University of Cambridge; Estados UnidosFil: Chazdon, Robin L.. University Of The Sunshine Coast; Australia. University of Connecticut; Estados UnidosFil: Erb, Karl-Heinz. Universitat Fur Bodenkultur Wien; AustriaFil: Brancalion, Pedro. Universidade de Sao Paulo; BrasilFil: Buchanan, Graeme. Royal Society For The Protection Of Birds; Reino UnidoFil: Cooper, David. Secretariat Of The Convention On Biological Diversity; CanadáFil: Díaz, Sandra Myrna. Universidad Nacional de Córdoba; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: Donald, Paul F.. University of Cambridge; Estados UnidosFil: Kapos, Valerie. United Nations Environment Programme World Conservation Monitoring Centre; Reino UnidoFil: Leclère, David. International Institute For Applied Systems Analysis, Laxenburg; AustriaFil: Miles, Lera. United Nations Environment Programme World Conservation Monitoring Centre; Reino UnidoFil: Obersteiner, Michael. Oxford Social Sciences Division; Reino Unido. International Institute For Applied Systems Analysis, Laxenburg; AustriaFil: Plutzar, Christoph. Universitat Fur Bodenkultur Wien; Austria. Universidad de Viena; AustriaFil: de M. Scaramuzza, Carlos Alberto. International Institute For Sustainability; BrasilFil: Scarano, Fabio R.. Universidade Federal do Rio de Janeiro; BrasilFil: Visconti, Piero. International Institute For Applied Systems Analysis, Laxenburg; Austri

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Carbon sequestration potential of second-growth forest regeneration in the Latin American tropics

Regrowth of tropical secondary forests following complete or nearly complete removal of forest vegetation actively stores carbon in aboveground biomass, partially counterbalancing carbon emissions from deforestation, forest degradation, burning of fossil fuels, and other anthropogenic sources. We estimate the age and spatial extent of lowland second-growth forests in the Latin American tropics and model their potential aboveground carbon accumulation over four decades. Our model shows that, in 2008, second-growth forests (1 to 60 years old) covered 2.4 million km2 of land (28.1%of the total study area).Over 40 years, these lands can potentially accumulate a total aboveground carbon stock of 8.48 Pg C (petagrams of carbon) in aboveground biomass via low-cost natural regeneration or assisted regeneration, corresponding to a total CO2 sequestration of 31.09 Pg CO2. This total is equivalent to carbon emissions from fossil fuel use and industrial processes in all of Latin America and the Caribbean from1993 to 2014. Ten countries account for 95% of this carbon storage potential, led by Brazil, Colombia, Mexico, and Venezuela. We model future land-use scenarios to guide national carbon mitigation policies. Permitting natural regeneration on 40% of lowland pastures potentially stores an additional 2.0 Pg C over 40 years. Our study provides information and maps to guide national-level forest-based carbon mitigation plans on the basis of estimated rates of natural regeneration and pasture abandonment. Coupled with avoided deforestation and sustainable forestmanagement, natural regeneration of second-growth forests provides a low-costmechanism that yields a high carbon sequestration potential with multiple benefits for biodiversity and ecosystem services. © 2016 The Authors

Biodiversity recovery of Neotropical secondary forests

Old-growth tropical forests harbor an immense diversity of tree species but are rapidly being cleared, while secondary forests that regrow on abandoned agricultural lands increase in extent. We assess how tree species richness and composition recover during secondary succession across gradients in environmental conditions and anthropogenic disturbance in an unprecedented multisite analysis for the Neotropics. Secondary forests recover remarkably fast in species richness but slowly in species composition. Secondary forests take a median time of five decades to recover the species richness of old-growth forest (80% recovery after 20 years) based on rarefaction analysis. Full recovery of species composition takes centuries (only 34% recovery after 20 years). A dual strategy that maintains both old-growth forests and species-rich secondary forests is therefore crucial for biodiversity conservation in human-modified tropical landscapes. Copyright © 2019 The Authors, some rights reserved

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Dataset for Strong floristic distinctiveness across Neotropical successional forests

sThis dataset is part of the 2ndFOR network on secondary forests (www.2ndfor.com). It contains the list of tree species (dbh>=5cm) found in secondary forests younger than 21 years after abandonment, located in 75 landscapes across the Neotropics. All secondary forests were previously used as pasture and/or agriculture. This data supported the study Jakovac et al 2022. Strong floristic distinctiveness across Neotropical successional forests. Science Advances. More information on the data collection can be found in the publication. File "dataset_sppList.csv" contains for each site (called "landscape" as in the publication) the list of tree species found in secondary forest plots. The number of plots and area of plots sampled in each site varies. Such information is available upon request. File "dataset_predictors" conatains for each site (called "landscape" as in the publication) the values for the environmental predictors used in the final analyses. All values are averages of predictor values extracted for each vegetation plot wihtin a site. The description of the variables headings is below: [1,] "landscape" = site [2,] "lat_mean" = latitude of the site [3,] "long_mean" = longitude of the site [4,] "Age_mean" = average age of the secondary forests sampled in each site. Age is defined as the time since abandonment, and was derived from field intervirews or remote sensing. [5,] "CWD_mean" = climatic water deficit (J. Chave, Long term climatic water deficit, p. ., (available at https://chave.ups-tlse.fr/pantropical_allometry.htm). [6,] "HFP2009_mean" = human footprint for the year 2009 (O. Venter, E. W. Sanderson, A. Magrach, J. R. Allan, J. Beher, K. R. Jones, H. P. Possingham, W. F. Laurance, P. Wood, B. M. Fekete, M. A. Levy, J. E. M. Watson, Global terrestrial Human Footprint maps for 1993 and 2009. Sci. Data. 3, 1–10 (2016).) [7,] "MAP_mean" = mean annual precipitation from the Chelsea database [8,] "TS_mean" = temperature seasonality from the Chelsea database [9,] "bdod_mean" = soil bulk density from the SoilGrids database (kg/dm³) [10,] "cec_mean" = soil cation exchange capacity from the SoilGrids database (cmol(c)/kg) [11,] "clay_mean" = soil clay content from the SoilGrids database (%) [12,] "N_mean" = soil nitrogen content from the SoilGrids database (g/kg) [13,] "phh2o_mean" = pH in water from the SoilGrids database (pH) [14,] "sand_mean" = soil sand content from the SoilGrids database (%) [15,] "silt_mean" = soil silt content from the SoilGrids database (%) [16,] "elev_mean" = elevation from SRTM (J. J. Danielson, D. B. Gesch, “Global multi-resolution terrain elevation data 2010 (GMTED2010)” (2011), , doi:10.3133/ofr20111073.) [17,] "human_mod_mean" = human modification index (C. M. Kennedy, J. R. Oakleaf, D. M. Theobald, S. Baruch‐Mordo, J. Kiesecker, Managing the middle: A shift in conservation priorities based on the global human modification gradient. Glob. Chang. Biol. 25, 811–826 (2019).) [18,] "for_cover_5000_mean" = percentage of forest cover within 500m. Forest cover maps from Copernicus Global Land Cover Layer collection 3. M. Buchhorn, M. Lesiv, N.-E. Tsendbazar, M. Herold, L. Bertels, B. Smets, Copernicus Global Land Cover Layers—Collection 2. Remote Sens. 12, 1044 (2020)

Correction to: Soil erosion as a resilience drain in disturbed tropical forests

In the original version of this article, the following text must be added in the acknowledgement. M.H., B.M.F. and R.S.O. acknowledge the project grant from Instituto Serrapilheira/Serra-1709–18983.</p

Soil erosion as a resilience drain in disturbed tropical forests

Background: Tropical forests are threatened by intensifying natural and anthropogenic disturbance regimes. Disturbances reduce tree cover and leave the organic topsoil vulnerable to erosion processes, but when resources are still abundant forests usually recover. Scope: Across the tropics, variation in rainfall erosivity – a measure of potential soil exposure to water erosion – indicates that soils in the wetter regions would experience high erosion rates if they were not protected by tree cover. However, twenty-first-century global land cover data reveal that in wet South America tropical tree cover is decreasing and bare soil area is increasing. Here we address the role of soil erosion in a positive feedback mechanism that may persistently alter the functioning of disturbed tropical forests. Conclusions: Based on an extensive literature review, we propose a conceptual model in which soil erosion reinforces disturbance effects on tropical forests, reducing their resilience with time and increasing their likelihood of being trapped in an alternative vegetation state that is persistently vulnerable to erosion. We present supporting field evidence from two distinct forests in central Amazonia that have been repeatedly disturbed. Overall, the strength of the erosion feedback depends on disturbance types and regimes, as well as on local environmental conditions, such as topography, flooding, and soil fertility. As disturbances intensify in tropical landscapes, we argue that the erosion feedback may help to explain why certain forests persist in a degraded state and often undergo critical functional shifts.</p