Biomass resilience of Neotropical secondary forests

Journal name:
Nature
Volume:
530,
Pages:
211–214
Date published:
DOI:
doi:10.1038/nature16512
Received
Accepted
Published online

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.

At a glance

Figures

  1. Relationship between forest biomass and stand age using chronosequence studies in Neotropical secondary forest sites.
    Figure 1: Relationship between forest biomass and stand age using chronosequence studies in Neotropical secondary forest sites.

    a, AGB (N = 44); b, AGB recovery (N = 28). Each line represents a different chronosequence. The original plots on which the regression lines are based are indicated in grey (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. See Extended Data Fig. 4 for the same figure with plots colour-coded by forest type.

  2. AGB after 20 years.
    Figure 2: AGB after 20 years.

    a, In relation to annual rainfall; b, in relation to CWD for Neotropical forest sites. Lines indicate predicted AGB at 20 years based on a multiple regression including 1/rainfall, CWD, and rainfall seasonality (R2 = 0.59). Other variables were kept constant at the mean across sites (two-sided P < 0.005 for 1/rainfall; P = 0.03 for CWD). The third, less significant factor (rainfall seasonality) is shown in Extended Data Fig. 2. N = 43 sites (one site was excluded because no climatic data were available).

  3. Potential biomass recovery map of Neotropical secondary forests.
    Figure 3: Potential biomass recovery map of Neotropical secondary forests.

    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 (red is low recovery; green is high recovery). The 44 study sites are indicated by circles (the symbol on the ocean belongs to an island) and 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). See Extended Data Fig. 5 for a colour-blind-friendly map.

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

  5. AGB recovery after 20 years in relation to abiotic factors, forest cover, and land use.
    Extended Data Fig. 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).

  6. Uncertainty map of potential biomass recovery of Neotropical secondary forests.
    Extended Data Fig. 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.

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

  8. Potential biomass recovery map of Neotropical secondary forests.
    Extended Data Fig. 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).

  9. AGB of secondary forest.
    Extended Data Fig. 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.

Tables

  1. Overview of the sites included in the study
    Extended Data Table 1: Overview of the sites included in the study
  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
    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

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Author information

Affiliations

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

    • Lourens Poorter,
    • Frans Bongers,
    • Catarina C. Jakovac,
    • Madelon Lohbeck,
    • Marielos Peña-Claros &
    • Danaë M. A. Rozendaal
  2. PO Box 23360, Department of Biology, University of Puerto Rico, San Juan, PR 00931-3360, Puerto Rico

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

    • Angélica M. Almeyda Zambrano &
    • Eben N. Broadbent
  4. Instituto de Investigaciones en Ecosistemas y Sustentabilidad, Universidad Nacional Autónoma de México, CP58190, Morelia, 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, 13418-900, Piracicaba, 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, 04103 Leipzig, Germany.

    • Dylan Craven
  10. Institute for Biology, Leipzig University, Johannisallee 21, 04103 Leipzig, 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, Campeche, CP 24500, México

    • Ben H. J. de Jong,
    • Susana Ochoa-Gaona &
    • Edith Orihuela-Belmonte
  13. Department of Ecology and Evolutionary Biology, Tulane University, New Orleans, Louisiana 70130, 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, South Carolina 29634, 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, Alberta T6G 2E3, Canada

    • Sandra M. Durán &
    • Arturo Sanchez-Azofeifa
  19. Departamento de Biologia Geral, Universidade Estadual de Montes Claros, Montes Claros, Minas Gerais, CEP 39401-089, 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, 6700 AK Wageningen, The Netherlands

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

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

    • André B. Junqueira
  25. Department of Physical and Environmental Sciences, Colorado Mesa University, 1100 North Avenue, Grand Junction, Colorado 81501, 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, New York 10027, USA

    • Robert Muscarella,
    • Naomi B. Schwartz &
    • Maria Uriarte
  32. Section of Ecoinformatics and Biodiversity, Department of Bioscience, 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, Minnesota 55108, USA

    • Jennifer S. Powers
  36. Department of Plant Biology, University of Minnesota, Saint Paul, Minnesota 55108, 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, California 93106, USA

    • Jorge Ruíz
  39. Cr 5 No 14-05, PO Box 412, Cota, 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, Singapore 138610

    • Michiel van Breugel
  43. Department of Biological Sciences, National University of Singapore, 14 Science Drive 4, Singapore 11754

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

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

    • Hans F. M. Vester
  46. Bonhoeffer College, Bruggertstraat 60, 7545 AX Enschede, The Netherlands

    • Hans F. M. Vester
  47. Museu Paraense Emilio Goeldi, CP 399, CEP 66040-170, Belém, Brazil

    • Ima C. G. Vieira
  48. Department of Biological Sciences, Louisiana State University, Baton Rouge, Louisiana 70803-1705, USA

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

    • Danaë M. A. Rozendaal

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.

Competing financial interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to:

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.

Author details

Extended data figures and tables

Extended Data Figures

  1. Extended Data Figure 1: Relative recovery of AGB after 20 years in relation to abiotic factors, forest cover, and land use. (222 KB)

    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).

  2. Extended Data Figure 2: AGB recovery after 20 years in relation to abiotic factors, forest cover, and land use. (155 KB)

    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).

  3. Extended Data Figure 3: Uncertainty map of potential biomass recovery of Neotropical secondary forests. (402 KB)

    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.

  4. Extended Data Figure 4: Relationship between forest biomass and stand age using chronosequence studies in Neotropical secondary forest sites. (259 KB)

    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.

  5. Extended Data Figure 5: Potential biomass recovery map of Neotropical secondary forests. (425 KB)

    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).

  6. Extended Data Figure 6: AGB of secondary forest. (162 KB)

    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 Tables

  1. Extended Data Table 1: Overview of the sites included in the study (381 KB)
  2. 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 (52 KB)

Supplementary information

PDF files

  1. Supplementary Information (234 KB)

    This file contains Supplementary Text, Supplementary References and 2 Supplementary Tables.

Additional data