Glob Chang Biol. 2016 Jan;22(1):92-109. doi: 10.1111/gcb.13087. Epub 2015 Nov 18.

Toward an integrated monitoring framework to assess the effects of tropical forest degradation and recovery on carbon stocks and biodiversity.

Bustamante MM¹, Roitman I¹, Aide TM², Alencar A³, Anderson LO^4,^5,⁶, Aragão L⁶, Asner GP⁷, Barlow J^8,⁹, Berenguer E⁸, Chambers J¹⁰, Costa MH¹¹, Fanin T¹², Ferreira LG¹³, Ferreira J¹⁴, Keller M^15,¹⁶, Magnusson WE¹⁷, Morales-Barquero L¹⁸, Morton D¹⁹, Ometto JP²⁰, Palace M²¹, Peres CA²², Silvério D¹, Trumbore S²³, Vieira IC⁹.

Author information

1: Department of Ecology, University of Brasília, Brasília, DF, CEP 70910900, Brazil.
2: Department of Biology, University of Puerto Rico, San Juan, PR, 00931-3360, Puerto Rico.
3: Amazon Environmental Research Institute - IPAM, SHIN CA5 Bl J2 Sala 309, Brasilia, DF, Brazil.
4: National Center for Monitoring and Early Warning of Natural Disasters - CEMADEN, Parque Tecnológico de São José dos Campos, Estrada Doutor Altino Bondensan, 500, São José dos Campos, SP, 12247-016, Brazil.
5: Environmental Change Institute, ECI, University of Oxford, South Parks Road, Oxford, OX1 3QY, UK.
6: Instituto Nacional de Pesquisas Espaciais, São José dos Campos, SP, 12247-016, Brazil.
7: Department of Global Ecology, Carnegie Institution for Science, 260 Panama Street, Stanford, CA, 94305, USA.
8: Lancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ, UK.
9: Museu Paraense Emilio Goeldi, C.P. 399, Belém, Pará, CEP 66040170, Brasil.
10: Geography Department, University of California, Berkeley, CA, 94720, USA.
11: Department of Agricultural Engineering, Federal University of Viçosa, Viçosa, MG, 36570-900, Brazil.
12: Faculty of Earth and Life Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
13: Instituto de Estudos Sócio-Ambientais - IESA, Federal University of Goiás, Goiânia, Brazil.
14: Embrapa Amazonia Oriental, C. Postal 48 66017-970, Belem, PA, Brazil.
15: USDA Forest Service, International Institute of Tropical Forestry, San Juan, Puerto Rico.
16: EMBRAPA Monitoramento por Satélite, Campinas, São Paulo, Brasil.
17: Instituto Nacional de Pesquisas da Amazônia (INPA), Caixa Postal 2223, Manaus, AM, 69067-971, Brazil.
18: School of Environment, Natural Resources and Geography, College of Natural Sciences, Bangor University, Bangor, Gwynedd, LL57 2UW, UK.
19: Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Code 618, Greenbelt, MD, 20771, USA.
20: Earth System Science Centre (CCST), National Institute for Space Research (INPE), Av dos Astronautas, 1758, São José dos Campos, SP, 12227-010, Brazil.
21: Earth System Research Center, Institute for the Study of Earth, Oceans, and Space, UNH, Norwich, UK.
22: School of Environmental Sciences, University of East Anglia, Norwich, NR47TJ, UK.
23: Max Planck Institute for Biogeochemistry, Jena, Germany.

Abstract

Tropical forests harbor a significant portion of global biodiversity and are a critical component of the climate system. Reducing deforestation and forest degradation contributes to global climate-change mitigation efforts, yet emissions and removals from forest dynamics are still poorly quantified. We reviewed the main challenges to estimate changes in carbon stocks and biodiversity due to degradation and recovery of tropical forests, focusing on three main areas: (1) the combination of field surveys and remote sensing; (2) evaluation of biodiversity and carbon values under a unified strategy; and (3) research efforts needed to understand and quantify forest degradation and recovery. The improvement of models and estimates of changes of forest carbon can foster process-oriented monitoring of forest dynamics, including different variables and using spatially explicit algorithms that account for regional and local differences, such as variation in climate, soil, nutrient content, topography, biodiversity, disturbance history, recovery pathways, and socioeconomic factors. Generating the data for these models requires affordable large-scale remote-sensing tools associated with a robust network of field plots that can generate spatially explicit information on a range of variables through time. By combining ecosystem models, multiscale remote sensing, and networks of field plots, we will be able to evaluate forest degradation and recovery and their interactions with biodiversity and carbon cycling. Improving monitoring strategies will allow a better understanding of the role of forest dynamics in climate-change mitigation, adaptation, and carbon cycle feedbacks, thereby reducing uncertainties in models of the key processes in the carbon cycle, including their impacts on biodiversity, which are fundamental to support forest governance policies, such as Reducing Emissions from Deforestation and Forest Degradation.