Biomass resilience of Neotropical secondary forests (original) (raw)

References

  1. Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013)
    Article ADS CAS PubMed Google Scholar
  2. IPCC. 2006 IPCC Guidelines for National Greenhouse Gas Inventories (Institute for Global Environmental Strategies, 2006)
  3. Grace, J., Mitchard, E. & Gloor, E. Perturbations in the carbon budget of the tropics. Glob. Change Biol. 20, 3238–3255 (2014)
    Article ADS Google Scholar
  4. Chazdon, R. L. Second Growth: The Promise of Tropical Forest Regeneration in an Age of Deforestation Ch. 11 (Univ. Chicago Press, 2014)
  5. Food and Agriculture Organization of the United Nations. Global Forest Resources Assessment. FAO Forestry Paper 163 (FAO, 2010)
  6. Banks-Leite, C. et al. Using ecological thresholds to evaluate the costs and benefits of set-asides in a biodiversity hotspot. Science 345, 1041–1045 (2014)
    Article ADS CAS PubMed Google Scholar
  7. Hirota, M., Holmgren, M., Van Nes, E. H. & Scheffer, M. Global resilience of tropical forest and savanna to critical transitions. Science 334, 232–235 (2011)
    Article ADS CAS PubMed Google Scholar
  8. Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011)
    Article ADS CAS PubMed Google Scholar
  9. Saatchi, S. S. et al. Benchmark map of forest carbon stocks in tropical regions across three continents. Proc. Natl Acad. Sci. USA 108, 9899–9904 (2011)
    Article ADS PubMed PubMed Central Google Scholar
  10. Lohbeck, M., Poorter, L., Martínez-Ramos, M. & Bongers, F. Biomass is the main driver of changes in ecosystem process rates during tropical forest succession. Ecology 96, 1242–1252 (2015)
    Article PubMed Google Scholar
  11. Rozendaal, D. M. A. & Chazdon, R. L. Demographic drivers of tree biomass change during secondary succession in northeastern Costa Rica. Ecol. Appl. 25, 506–516 (2015)
    Article PubMed Google Scholar
  12. Chazdon, R. L. et al. Rates of change in tree communities of secondary Neotropical forests following major disturbances. Phil. Trans. R. Soc. B 362, 273–289 (2007)
    Article PubMed Google Scholar
  13. Becknell, J. M., Kucek, L. K. & Powers, J. S. Aboveground biomass in mature and secondary seasonally dry tropical forests: a literature review and global synthesis. For. Ecol. Mgmt 276, 88–95 (2012)
    Article Google Scholar
  14. Becknell, J. M. & Powers, J. S. Stand age and soils as drivers of plant functional traits and aboveground biomass in secondary tropical dry forest. Can. J. Forest Res. 44, 604–613 (2014)
    Article CAS Google Scholar
  15. Jakovac, C. C., Peña-Claros, M., Kuyper, T. W. & Bongers, F. Loss of secondary-forest resilience by land-use intensification in the Amazon. J. Ecol. 103, 67–77 (2015)
    Article Google Scholar
  16. Zarin, D. J. et al. Legacy of fire slows carbon accumulation in Amazonian forest regrowth. Front. Ecol. Environ. 3, 365–369 (2005)
    Article Google Scholar
  17. Marín-Spiotta, E., Cusack, D. F., Ostertag, R. & Silver, W. L. in Post-Agricultural Succession in the Neotropics (ed. Myster, R. W. ) 22–72 (Springer, 2008)
    Book Google Scholar
  18. Martin, P. A., Newton, A. C. & Bullock, J. M. Carbon pools recover more quickly than plant biodiversity in tropical secondary forests. Proc. R. Soc. B 280, http://dx.doi.org/10.1098/rspb.2013.2236 (2013)
  19. Brienen, R. J. W. et al. Long-term decline of the Amazon carbon sink. Nature 519, 344–348 (2015)
    Article ADS CAS PubMed Google Scholar
  20. Rutishauser, E. et al. Rapid tree carbon stock recovery in managed Amazonian forests. Curr. Biol. 25, R787–R788 (2015)
    Article CAS PubMed Google Scholar
  21. Bongers, F., Chazdon, R., Poorter, L. & Peña-Claros, M. The potential of secondary forests. Science 348, 642–643 (2015)
    Article ADS CAS PubMed Google Scholar
  22. Toledo, M. et al. Climate is a stronger driver of tree and forest growth rates than soil and disturbance. J. Ecol. 99, 254–264 (2011)
    Article Google Scholar
  23. IPCC. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC, 2014)
  24. Quesada, C. A. et al. Basin-wide variations in Amazon forest structure and function are mediated by both soils and climate. Biogeosciences 9, 2203–2246 (2012)
    Article ADS Google Scholar
  25. Davidson, E. A. et al. Recuperation of nitrogen cycling in Amazonian forests following agricultural abandonment. Nature 447, 995–998 (2007)
    Article ADS CAS PubMed Google Scholar
  26. Fischer, R., Armstrong, A., Shugart, H. H. & Huth, A. Simulating the impacts of reduced rainfall on carbon stocks and net ecosystem exchange in a tropical forest. Environ. Model. Softw. 52, 200–206 (2014)
    Google Scholar
  27. Mascaro, J., Asner, G. P., Dent, D. H., DeWalt, S. J. & Denslow, J. S. Scale-dependence of aboveground carbon accumulation in secondary forests of Panama: a test of the intermediate peak hypothesis. For. Ecol. Mgmt 276, 62–70 (2012)
    Article Google Scholar
  28. Chazdon, R. L. Beyond deforestation: restoring forests and ecosystem services on degraded lands. Science 320, 1458–1460 (2008)
    Article ADS CAS PubMed Google Scholar
  29. Birch, J. C. et al. Cost-effectiveness of dryland forest restoration evaluated by spatial analysis of ecosystem services. Proc. Natl Acad. Sci. USA 107, 21925–21930 (2010)
    Article ADS PubMed PubMed Central Google Scholar
  30. Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005)
    Article Google Scholar
  31. Nachtergaele, F., van Velthuizen, H., Verelst, L. & Wiberg, D. Harmonized World Soil Database Version 1.2 (FAO and IIASA, 2012)
  32. Pearson, R., Walker, S. & Brown, S. Source Book for Land Use, Land-Use Change and Forestry Projects (World Bank, 2005)
  33. Chave, J. et al. Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 145, 87–99 (2005)
    Article ADS CAS PubMed Google Scholar
  34. Chave, J. et al. Improved allometric models to estimate the aboveground biomass of tropical trees. Glob. Change Biol. 20, 3177–3190 (2014)
    Article ADS Google Scholar
  35. Clark, D. B. & Clark, D. A. Landscape-scale variation in forest structure and biomass in a tropical rain forest. For. Ecol. Mgmt 137, 185–198 (2000)
    Article Google Scholar
  36. Mitchard, E. T. A. et al. Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites. Glob. Ecol. Biogeogr. 23, 935–946 (2014)
    Article PubMed PubMed Central Google Scholar
  37. Chave, J. et al. Towards a worldwide wood economics spectrum. Ecol. Lett. 12, 351–366 (2009)
    Article PubMed Google Scholar
  38. Zanne, A. E. et al. Data from: Towards a worldwide wood economics spectrum. http://dx.doi.org/10.5061/dryad.234 (Dryad Digital Repository, 2009)
  39. Chave, J. et al. Regional and phylogenetic variation of wood density across 2456 Neotropical tree species. Ecol. Appl. 16, 2356–2367 (2006)
    Article PubMed Google Scholar
  40. Poorter, L. et al. Diversity enhances carbon storage in tropical forests. Glob. Ecol. Biogeogr. 24, 1314–1328 (2015)
    Article Google Scholar
  41. Poorter, H. et al. How does biomass distribution change with size and differ among species? An analysis for 1200 plant species from five continents. New Phytol. 208, 736–749 (2015)
    Article PubMed PubMed Central Google Scholar
  42. R Core Team. R: a language and environment for statistical computing (R Foundation for Statistical Computing, 2014)
  43. Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51, 933–938 (2001)
    Article Google Scholar
  44. Broadbent, E. N. et al. Integrating stand and soil properties to understand foliar nutrient dynamics during forest succession following slash-and-burn agriculture in the Bolivian Amazon. PLoS ONE 9, e86042 (2014)
    Article ADS CAS PubMed PubMed Central Google Scholar
  45. Peña-Claros, M. Changes in forest structure and species composition during secondary forest succession in the Bolivian Amazon. Biotropica 35, 450–461 (2003)
    Article Google Scholar
  46. Toledo, M. & Salick, J. Secondary succession and indigenous management in semideciduous forest fallows of the Amazon basin. Biotropica 38, 161–170 (2006)
    Article Google Scholar
  47. Kennard, D. K. Secondary forest succession in a tropical dry forest: patterns of development across a 50-year chronosequence in lowland Bolivia. J. Trop. Ecol. 18, 53–66 (2002)
    Article Google Scholar
  48. Steininger, M. K. Secondary forest structure and biomass following short and extended land use in central and southern Amazonia. J. Trop. Ecol. 16, 689–708 (2000)
    Article Google Scholar
  49. Piotto, D. Spatial Dynamics of Forest Recovery after Swidden Cultivation in the Atlantic Forest of Southern Bahia, Brazil. PhD thesis, Yale Univ. (2011)
  50. Vieira, I. C. G. et al. Classifying successional forests using Landsat spectral properties and ecological characteristics in eastern Amazonia. Remote Sens. Environ. 87, 470–481 (2003)
    Article ADS Google Scholar
  51. Williamson, G. B., Bentos, T. V., Longworth, J. B. & Mesquita, R. C. G. Convergence and divergence in alternative successional pathways in Central Amazonia. Plant Ecol. Divers. 7, 341–348 (2014)
    Article Google Scholar
  52. Madeira, B. G. et al. Changes in tree and liana communities along a successional gradient in a tropical dry forest in south-eastern Brazil. Plant Ecol. 201, 291–304 (2009)
    Article Google Scholar
  53. Cabral, G. A. L., Sampaio, E. V. S. B. & de Almeida-Cortez, J. S. Estrutura espacial e biomassa da parte aérea em diferentes estádios successionais de caatinga, em Santa Terezinha, Paraíba. Rev. Bras. Geogr. Fís. 6, 566–574 (2013)
    Article Google Scholar
  54. Junqueira, A. B., Shepard, G. H. & Clement, C. R. Secondary forests on anthropogenic soils conserve agrobiodiversity. Biodivers. Conserv. 19, 1933–1961 (2010)
    Article Google Scholar
  55. Vester, H. F. M. & Cleef, A. M. Tree architecture and secondary tropical rain forest development - a case study in Araracuara, Colombian Amazonia. Flora 193, 75–97 (1998)
    Article Google Scholar
  56. Ruiz, J., Fandino, M. C. & Chazdon, R. L. Vegetation structure, composition, and species richness across a 56-year chronosequence of dry tropical forest on Providencia Island, Colombia. Biotropica 37, 520–530 (2005)
    Article Google Scholar
  57. Saldarriaga, J. G., West, D. C., Tharp, M. L. & Uhl, C. Long-term chronosequence of forest succession in the upper Rio Negro of Colombia and Venezuela. J. Ecol. 76, 938–958 (1988)
    Article Google Scholar
  58. Powers, J. S., Becknell, J. M., Irving, J. & Perez-Aviles, D. Diversity and structure of regenerating tropical dry forests in Costa Rica: geographic patterns and environmental drivers. For. Ecol. Mgmt 258, 959–970 (2009)
    Article Google Scholar
  59. Chazdon, R. L., Brenes, A. R. & Alvarado, B. V. Effects of climate and stand age on annual tree dynamics in tropical second-growth rain forests. Ecology 86, 1808–1815 (2005)
    Article Google Scholar
  60. Letcher, S. G. & Chazdon, R. L. Rapid recovery of biomass, species richness, and species composition in a forest chronosequence in northeastern Costa Rica. Biotropica 41, 608–617 (2009)
    Article Google Scholar
  61. van Breugel, M., Martínez-Ramos, M. & Bongers, F. Community dynamics during early secondary succession in Mexican tropical rain forests. J. Trop. Ecol. 22, 663–674 (2006)
    Article Google Scholar
  62. Mora, F. et al. Testing chronosequences through dynamic approaches: time and site effects on tropical dry forest succession. Biotropica 47, 38–48 (2015)
    Article Google Scholar
  63. Orihuela-Belmonte, D. E. et al. Carbon stocks and accumulation rates in tropical secondary forests at the scale of community, landscape and forest type. Agric. Ecosyst. Environ. 171, 72–84 (2013)
    Article Google Scholar
  64. Lebrija-Trejos, E., Bongers, F., Pérez-García, E. A. & Meave, J. A. Successional change and resilience of a very dry tropical deciduous forest following shifting agriculture. Biotropica 40, 422–431 (2008)
    Article Google Scholar
  65. Dupuy, J. M. et al. Patterns and correlates of tropical dry forest structure and composition in a highly replicated chronosequence in Yucatan, Mexico. Biotropica 44, 151–162 (2012)
    Article Google Scholar
  66. van Breugel, M. et al. Succession of ephemeral secondary forests and their limited role for the conservation of floristic diversity in a human-modified tropical landscape. PLoS ONE 8, e82433 (2013)
    Article ADS CAS PubMed PubMed Central Google Scholar
  67. Denslow, J. S. & Guzman, S. Variation in stand structure, light and seedling abundance across a tropical moist forest chronosequence, Panama. J. Veg. Sci. 11, 201–212 (2000)
    Article Google Scholar
  68. Marín-Spiotta, E., Ostertag, R. & Silver, W. L. Long-term patterns in tropical reforestation: plant community composition and aboveground biomass accumulation. Ecol. Appl. 17, 828–839 (2007)
    Article PubMed Google Scholar
  69. Aide, T. M., Zimmerman, J. K., Pascarella, J. B., Rivera, L. & Marcano-Vega, H. Forest regeneration in a chronosequence of tropical abandoned pastures: implications for restoration ecology. Restor. Ecol. 8, 328–338 (2000)
    Article Google Scholar

Download references

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

Authors

  1. Lourens Poorter
  2. Frans Bongers
  3. T. Mitchell Aide
  4. Angélica M. Almeyda Zambrano
  5. Patricia Balvanera
  6. Justin M. Becknell
  7. Vanessa Boukili
  8. Pedro H. S. Brancalion
  9. Eben N. Broadbent
  10. Robin L. Chazdon
  11. Dylan Craven
  12. Jarcilene S. de Almeida-Cortez
  13. George A. L. Cabral
  14. Ben H. J. de Jong
  15. Julie S. Denslow
  16. Daisy H. Dent
  17. Saara J. DeWalt
  18. Juan M. Dupuy
  19. Sandra M. Durán
  20. Mario M. Espírito-Santo
  21. María C. Fandino
  22. Ricardo G. César
  23. Jefferson S. Hall
  24. José Luis Hernandez-Stefanoni
  25. Catarina C. Jakovac
  26. André B. Junqueira
  27. Deborah Kennard
  28. Susan G. Letcher
  29. Juan-Carlos Licona
  30. Madelon Lohbeck
  31. Erika Marín-Spiotta
  32. Miguel Martínez-Ramos
  33. Paulo Massoca
  34. Jorge A. Meave
  35. Rita Mesquita
  36. Francisco Mora
  37. Rodrigo Muñoz
  38. Robert Muscarella
  39. Yule R. F. Nunes
  40. Susana Ochoa-Gaona
  41. Alexandre A. de Oliveira
  42. Edith Orihuela-Belmonte
  43. Marielos Peña-Claros
  44. Eduardo A. Pérez-García
  45. Daniel Piotto
  46. Jennifer S. Powers
  47. Jorge Rodríguez-Velázquez
  48. I. Eunice Romero-Pérez
  49. Jorge Ruíz
  50. Juan G. Saldarriaga
  51. Arturo Sanchez-Azofeifa
  52. Naomi B. Schwartz
  53. Marc K. Steininger
  54. Nathan G. Swenson
  55. Marisol Toledo
  56. Maria Uriarte
  57. Michiel van Breugel
  58. Hans van der Wal
  59. Maria D. M. Veloso
  60. Hans F. M. Vester
  61. Alberto Vicentini
  62. Ima C. G. Vieira
  63. Tony Vizcarra Bentos
  64. G. Bruce Williamson
  65. 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.

Corresponding author

Correspondence toLourens Poorter.

Ethics declarations

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, _R_2 = 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

Supplementary information

PowerPoint slides

Rights and permissions

About this article

Cite this article

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

Download citation