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Biomass resilience of Neotropical secondary forests

Nature volume 530, pages 211–214 (2016)Cite this article

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Abstract

Land-use change occurs nowhere more rapidly than in the tropics, where the imbalance between deforestation and forest regrowth has large consequences for the global carbon cycle1. However, considerable uncertainty remains about the rate of biomass recovery in secondary forests, and how these rates are influenced by climate, landscape, and prior land use2,3,4. Here we analyse aboveground biomass recovery during secondary succession in 45 forest sites and about 1,500 forest plots covering the major environmental gradients in the Neotropics. The studied secondary forests are highly productive and resilient. Aboveground biomass recovery after 20 years was on average 122 megagrams per hectare (Mg ha−1), corresponding to a net carbon uptake of 3.05 Mg C ha−1 yr−1, 11 times the uptake rate of old-growth forests. Aboveground biomass stocks took a median time of 66 years to recover to 90% of old-growth values. Aboveground biomass recovery after 20 years varied 11.3-fold (from 20 to 225 Mg ha−1) across sites, and this recovery increased with water availability (higher local rainfall and lower climatic water deficit). We present a biomass recovery map of Latin America, which illustrates geographical and climatic variation in carbon sequestration potential during forest regrowth. The map will support policies to minimize forest loss in areas where biomass resilience is naturally low (such as seasonally dry forest regions) and promote forest regeneration and restoration in humid tropical lowland areas with high biomass resilience.

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Figure 1: Relationship between forest biomass and stand age using chronosequence studies in Neotropical secondary forest sites.
Figure 2: AGB after 20 years.
Figure 3: Potential biomass recovery map of Neotropical secondary forests.

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Acknowledgements

This paper is a product of the 2ndFOR collaborative research network on secondary forests. We thank the owners of the secondary forest sites for access to their forests, all the people who have established and measured the plots, and the institutions and funding agencies that supported them. We thank J. Zimmerman for the use of plot data, and the following agencies for financial support: Australian Department of Foreign Affairs and Trade-DFAT, CGIAR-FTA, CIFOR, Colciencias grant 1243-13-16640, Consejo Nacional de Ciencia y Tecnología (SEP-CONACYT 2009-129740 for ReSerBos, CONACYT 33851-B), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq: 563304/2010-3, 562955/2010-0, 574008/2008-0 and PQ 307422/2012-7), FOMIX-Yucatan (YUC-2008-C06-108863), ForestGEO, Fundação de Amparo à Pesquisa de Minas Gerais (FAPEMIG CRA APQ-00001-11), Fundación Ecológica de Cuixmala, Heising-Simons Foundation, HSBC, ICETEX, Instituto Internacional de Educação do Brasil-IEB, Instituto Nacional de Serviços Ambientais da Amazônia -Servamb-INPA, Inter-American Institute for Global Change (Tropi-Dr Network CRN3-025) via a grant from the US National Science Foundation (grant GEO-1128040), Motta Family Foundation, NASA Terrestrial Ecology Program, National Science Foundation (NSF-CNH-RCN grant 1313788 for Tropical Reforestation Network: Building a Socioecological Understanding of Tropical Reforestation (PARTNERS), NSF DEB-0129104, NSF BCS-1349952, NSF Career Grant DEB-1053237, NSF DEB 1050957, 0639393, 1147429, 0639114, and 1147434), NUFFIC, USAID (BOLFOR), Science without Borders Program (CAPES/CNPq) grant number 88881.064976/2014-01, The São Paulo Research Foundation (FAPESP) grant 2011/06782-5 and 2014/14503-7, Silicon Valley Foundation, Stichting Het Kronendak, Tropenbos Foundation, University of Connecticut Research Foundation, Wageningen University (INREF Terra Preta programme and FOREFRONT programme). This is publication number 683 in the Technical Series of the Biological Dynamics of Forest Fragments Project BDFFP-INPA-SI. This study was partly funded by the European Union’s Seventh Framework Programme (FP7/2007-2013) under grant agreement number 283093; Role Of Biodiversity In climate change mitigatioN (ROBIN).

Author information

Authors and Affiliations

  1. Forest Ecology and Forest Management Group, Wageningen University, PO Box 47, Wageningen, 6700 AA, The Netherlands

    Lourens Poorter, Frans Bongers, Catarina C. Jakovac, Madelon Lohbeck, Marielos Peña-Claros & Danaë M. A. Rozendaal

  2. Department of Biology, PO Box 23360, University of Puerto Rico, San Juan, Puerto, PR 00931-3360, Rico

    T. Mitchell Aide

  3. Department of Geography, Spatial Ecology and Conservation Lab, University of Alabama, Tuscaloosa, 35487, Alabama, USA

    Angélica M. Almeyda Zambrano & Eben N. Broadbent

  4. Instituto de Investigaciones en Ecosistemas y Sustentabilidad, Universidad Nacional Autónoma de México, Morelia, CP58190, Michoacán, México

    Patricia Balvanera, Miguel Martínez-Ramos, Francisco Mora & Jorge Rodríguez-Velázquez

  5. Department of Ecology and Evolutionary Biology, Brown University, Providence, Rhode Island, 02912, USA

    Justin M. Becknell

  6. Department of Ecology and Evolutionary Biology, University of Connecticut, Storrs, Connecticut, 06269, USA

    Vanessa Boukili, Robin L. Chazdon & Danaë M. A. Rozendaal

  7. Department of Forest Sciences, Luiz de Queiroz College of Agriculture, University of São Paulo, Avenida Pádua Dias 11, Piracicaba, 13418-900, São Paulo, Brazil

    Pedro H. S. Brancalion & Ricardo G. César

  8. SI ForestGEO, Smithsonian Tropical Research Institute, Roosevelt Avenue, Tupper Building – 401, Balboa, Ancón, Panamá, Panamá

    Dylan Craven, Jefferson S. Hall & Michiel van Breugel

  9. German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, Leipzig, 04103, Germany

    Dylan Craven

  10. Institute for Biology, Leipzig University, Johannisallee 21, Leipzig, 04103, Germany

    Dylan Craven

  11. Departamento de Botanica, Universidade Federal de Pernambuco, Pernambuco, CEP 50670-901, Brazil

    Jarcilene S. de Almeida-Cortez & George A. L. Cabral

  12. Department of Sustainability Science, El Colegio de la Frontera Sur, Unidad Campeche, Av. Rancho Polígono 2A, Parque Industrial Lerma, Campeche, CP 24500, Campeche, México

    Ben H. J. de Jong, Susana Ochoa-Gaona & Edith Orihuela-Belmonte

  13. Department of Ecology and Evolutionary Biology, Tulane University, New Orleans, 70130, Louisiana, USA

    Julie S. Denslow

  14. Smithsonian Tropical Research Institute, Roosevelt Avenue, Tupper Building – 401, Balboa, Ancón, Panamá, Panamá

    Daisy H. Dent

  15. Biological and Environmental Sciences, University of Stirling, Stirling FK9 4LA, UK

    Daisy H. Dent

  16. Department of Biological Sciences, Clemson University, 132 Long Hall, Clemson, 29634, South Carolina, USA

    Saara J. DeWalt

  17. Centro de Investigación Científica de Yucatán, AC, Unidad de Recursos Naturales, Calle 43 No. 130, Colonia Chuburná de Hidalgo, CP 97200, Mérida, Yucatán, México

    Juan M. Dupuy & José Luis Hernandez-Stefanoni

  18. Earth and Atmospheric Sciences Department, University of Alberta, Edmonton, T6G 2E3, Alberta, Canada

    Sandra M. Durán & Arturo Sanchez-Azofeifa

  19. Departamento de Biologia Geral, Universidade Estadual de Montes Claros, Montes Claros, CEP 39401-089, Minas Gerais, Brazil

    Mario M. Espírito-Santo, Yule R. F. Nunes & Maria D. M. Veloso

  20. Fondo Patrimonio Natural para la Biodiversidad y Areas Protegidas, Calle 72 No. 12-65 piso 6, Bogotá, Colombia

    María C. Fandino

  21. Biological Dynamics of Forest Fragments Project, Environmental Dynamics Research Coordination, Instituto Nacional de Pesquisas da Amazonia, Manaus, Amazonas, CEP 69067-375, Brazil

    Catarina C. Jakovac, Paulo Massoca, Rita Mesquita, Alberto Vicentini, Tony Vizcarra Bentos & G. Bruce Williamson

  22. Centre for Crop Systems Analysis, Wageningen University, PO Box 430, Wageningen, 6700 AK, The Netherlands

    André B. Junqueira

  23. Knowledge, Technology and Innovation Group, Wageningen University, PO Box 8130, Wageningen, 6700 EW, The Netherlands

    André B. Junqueira

  24. Coordenação de Tecnologia e Inovação, Instituto Nacional de Pesquisas da Amazônia, 2936 – Aleixo, Avenida André Araújo, 69060-001, Manaus, Brazil

    André B. Junqueira

  25. Department of Physical and Environmental Sciences, Colorado Mesa University, 1100 North Avenue, Grand Junction, 81501, Colorado, USA

    Deborah Kennard

  26. Department of Environmental Studies, Purchase College (State University of New York), Purchase, New York, 10577, USA

    Susan G. Letcher

  27. Instituto Boliviano de Investigación Forestal (IBIF), FCA-UAGRM, Casilla 6204, Santa Cruz de la Sierra, Bolivia

    Juan-Carlos Licona & Marisol Toledo

  28. World Agroforestry Centre (ICRAF), PO Box 30677 - 00100, Nairobi, Kenya

    Madelon Lohbeck

  29. Department of Geography, University of Wisconsin-Madison, 550 North Park Street, Madison, Wisconsin, 53706, USA

    Erika Marín-Spiotta

  30. Departamento de Ecología y Recursos Naturales, Facultad de Ciencias, Universidad Nacional Autónoma de México, México, 04510 DF, México

    Jorge A. Meave, Francisco Mora, Rodrigo Muñoz, Eduardo A. Pérez-García & I. Eunice Romero-Pérez

  31. Department of Ecology, Evolution and Environmental Biology, Columbia University, New York, 10027, New York, USA

    Robert Muscarella, Naomi B. Schwartz & Maria Uriarte

  32. Department of Bioscience, Section of Ecoinformatics and Biodiversity, Aarhus University, Aarhus, 8000, Denmark

    Robert Muscarella

  33. Departamento de Ecologia, Instituto de Biociências, Universidade de São Paulo, Rua do Matão, travessa 14, No. 321, São Paulo, CEP 05508-090, Brazil

    Alexandre A. de Oliveira

  34. Universidade Federal do Sul da Bahia, Centro de Formação em Ciências Agroflorestais, Itabuna-BA, 45613-204, Brazil

    Daniel Piotto

  35. Department of Ecology, Evolution, & Behavior, University of Minnesota, Saint Paul, 55108, Minnesota, USA

    Jennifer S. Powers

  36. Department of Plant Biology, University of Minnesota, Saint Paul, 55108, Minnesota, USA

    Jennifer S. Powers

  37. School of Social Sciences, Geography Area, Universidad Pedagogica y Tecnologica de Colombia (UPTC), Tunja, Colombia

    Jorge Ruíz

  38. Department of Geography, 4841 Ellison Hall, University of California, Santa Barbara, 93106, California, USA

    Jorge Ruíz

  39. PO Box 412, Cota, Cr 5 No 14-05, Cundinamarca, Colombia

    Juan G. Saldarriaga

  40. 4007 18th St Northwest, Washington, DC 20011, USA

    Marc K. Steininger

  41. Department of Biology, University of Maryland, College Park, Maryland, 20742, USA

    Nathan G. Swenson

  42. Yale-NUS College, 12 College Avenue West, 138610, Singapore

    Michiel van Breugel

  43. Department of Biological Sciences, National University of Singapore, 14 Science Drive 4, 11754, Singapore

    Michiel van Breugel

  44. Departamento de Agricultura, Sociedad y Ambiente, El Colegio de la Frontera Sur - Unidad Villahermosa, Centro, 86280, Tabasco, México

    Hans van der Wal

  45. Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam, PO Box 94248, GE Amsterdam, 1090, The Netherlands

    Hans F. M. Vester

  46. Bonhoeffer College, Bruggertstraat 60, AX Enschede, 7545, The Netherlands

    Hans F. M. Vester

  47. Museu Paraense Emilio Goeldi, CP 399, Belém, CEP 66040-170, Brazil

    Ima C. G. Vieira

  48. Department of Biological Sciences, Louisiana State University, Baton Rouge, 70803-1705, Louisiana, USA

    G. Bruce Williamson

  49. Department of Biology, University of Regina, 3737 Wascana Parkway, Regina, S4S 0A2, Saskatchewan, Canada

    Danaë M. A. Rozendaal

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  1. Lourens Poorter
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  2. Frans Bongers
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  3. T. Mitchell Aide
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  4. Angélica M. Almeyda Zambrano
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  5. Patricia Balvanera
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  6. Justin M. Becknell
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  7. Vanessa Boukili
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Contributions

L.P., F.B. and D.R. conceived the idea and coordinated the data compilations, D.R. analysed the data, L.P., F.B., E.N.B. and R.C. contributed to analytical tools used in the analysis, E.N.B. and A.M.A.Z. made the map, L.P. wrote the paper, and all co-authors collected field data, discussed the results, gave suggestions for further analyses and commented on the manuscript.

Corresponding author

Correspondence to Lourens Poorter.

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Competing interests

The authors declare no competing financial interests.

Additional information

Plot-level AGB data of 41 sites are available from the Dryad Digital Repository: http://dx.doi.org/10.5061/dryad.82vr4, and for four sites they can be requested from L.P.

Extended data figures and tables

Extended Data Figure 1 Relative recovery of AGB after 20 years in relation to abiotic factors, forest cover, and land use.

a, Annual precipitation; b, CWD; c, rainfall seasonality; d, CEC; e, percentage forest cover in the surrounding matrix; f, previous land use (SC, shifting cultivation, N = 17; SC & PA, some plots shifting cultivation, some plots pasture, N = 2; PA, pasture, N = 9; means ± s.e.m. are shown). Relative recovery is expressed as the ratio of AGB after 20 years over median AGB of old-growth forest (as a percentage). Regression lines are shown while keeping the other variable constant at the mean value across sites (P = 0.040 for 1/rainfall, P = 0.027 for CEC, R2 = 0.23, N = 28 Neotropical forest sites).

Extended Data Figure 2 AGB recovery after 20 years in relation to abiotic factors, forest cover, and land use.

a, Rainfall seasonality; b, CEC; c, percentage forest cover in the surrounding matrix; d, previous land use (SC, N = 19; SC & PA, N = 9; PA, N = 15; means ± s.e.m. are shown). For rainfall seasonality, the regression line is shown based upon the multiple regression model that also includes rainfall and CWD, and where these variables were kept constant at the mean value across sites (two-sided P = 0.003, see Fig. 2 for these models for rainfall and CWD).

Extended Data Figure 3 Uncertainty map of potential biomass recovery of Neotropical secondary forests.

The uncertainty is based on the 95% confidence interval of the mean predicted AGB after 20 years (see Fig. 3 and Methods). It is expressed as a percentage of the predicted AGB: 100 × (0.5 × 95% confidence interval of the mean)/predicted AGB. In general the uncertainty is low: 80.32% of the mapped area has an uncertainty less than 20%, and 10.2% of the mapped area has an uncertainty between 20% and 30%. Because it is a relative uncertainty, it is highest in the driest areas, which have a low predicted biomass.

Extended Data Figure 4 Relationship between forest biomass and stand age using chronosequence studies in Neotropical secondary forest sites.

a, AGB (N = 44); b, AGB recovery (N = 28). The same as Fig. 1 but with plots and regression lines coloured by forest type: green, dry forest (<1,500 mm rainfall per year); light blue, moist forest (1,500–2,499 mm yr−1); dark blue, wet forest (≥2,500 mm yr−1). Each line represents a different chronosequence. The original plots on which the regression lines are based are shown (N = 1,364 for AGB, N = 995 for AGB recovery). AGB recovery is defined as the AGB of the secondary forest plot compared with the median AGB of old-growth forest plots in the area, multiplied by 100. Significant relations (two-sided P ≤ 0.05) are indicated by continuous lines, non-significant relationships (two-sided P > 0.05) are indicated by broken lines. Plots of 100 years old are also second-growth.

Extended Data Figure 5 Potential biomass recovery map of Neotropical secondary forests.

The same as Fig. 3 but with colour-blind-friendly colour coding. The total potential AGB accumulation over 20 years of lowland secondary forest growth was calculated on the basis of a regression equation relating AGB with annual rainfall (AGB = 135.17 − 103,950 × 1/rainfall + 1.522 × rainfall seasonality + 0.1148 × CWD; see Methods). The colour indicates the amount of forest cover recovery (purple, low recovery; green, high recovery). The 44 study sites are indicated by circles; the size of the symbols scales with the AGB attained after 20 years. The grey areas do not belong to the tropical forest biome. The map focuses on lowland tropical forest (altitude <1,000 m).

Extended Data Figure 6 AGB of secondary forest.

a, AGB 10 years and b, 20 years after land abandonment. Predicted mean AGB is given for three different forest types (dry (<1,500 mm rainfall), moist (1,500–2,499 mm), wet (≥2,500 mm)) using three different allometric equations (indicated by different colours). These allometric equations are ordered from left to right as ref. 34 (blue), ref. 33 (red), and ref. 32 (grey). Means ± s.e.m. are shown.

Extended Data Table 1 Overview of the sites included in the study
Full size table
Extended Data Table 2 Overview of the modelling results of absolute (N = 43, one site was excluded because of missing climatic data) and relative (N = 28) AGB recovery after 20 years in relation to rainfall, CEC, land use, and forest cover in the landscape matrix
Full size table

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Poorter, L., Bongers, F., Aide, T. et al. Biomass resilience of Neotropical secondary forests. Nature 530, 211–214 (2016). https://doi.org/10.1038/nature16512

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