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 Carbon Cycle And Ecosystems: People
Marcos  Longo's Picture
4800 Oak Grove Drive
Pasadena, CA 91109
+1 (818) 393-8231
Curriculum Vitae:

Marcos Longo

Tropical forests are the most biodiverse terrestrial ecosystem and contain a large fraction of land carbon, yet the role of tropical forests in the global carbon cycle is still uncertain. My main research interest is to understand how changes in climate and land cover and land use in tropical regions affect the carbon stocks, structure and functioning of tropical forests. My current research focus on integrating airborne lidar, satellite data (e.g. Landsat, MODIS, ECOSTRESS), and the Ecosystem Demography Model (ED-2) to quantify and predict the impact of human disturbances - fires, selective logging, fragmentation - on the productivity, evapotranspiration, and flammability of degraded forests. I am also interesting in understanding how extreme events such as droughts affect the dynamics of tropical forests.

  • Ph.D. (2014), Earth and Planetary Sciences, Harvard University, Cambridge, MA, USA
  • M.Sc. (2003), Atmospheric Sciences, University of São Paulo, São Paulo, SP, Brazil
  • B.Sc. (2000), Atmospheric Sciences, University of São Paulo, São Paulo, SP, Brazil

Research Interests
  • Tropical forests and the carbon cycle
  • Biosphere-Atmosphere interactions
  • Land Cover and Land Use Change

Professional Experience
  • Post-Doctoral Research Associate, Brazilian Agricultural Research Corporation (Embrapa) (2014-2017)
  • Graduate Research Associate, University of São Paulo (2003-2005)

Selected Publications
  1. Longo, M., R. G. Knox, D. M. Medvigy, N. M. Levine, M. C. Dietze, Y. Kim, A. L. S. Swann, K. Zhang, C. R. Rollinson, R. L. Bras, S. C. Wofsy, and P. R. Moorcroft (2019), The biophysics, ecology, and bio- geochemistry of functionally diverse, vertically and horizontally heterogeneous ecosystems: the Ecosystem Demography model, version 2.2 - part 1: Model description, Geosci. Model Dev., doi:10.5194/gmd-2019- 45, in press.
  2. Longo, M., R. G. Knox, N. M. Levine, A. L. S. Swann, D. M. Medvigy, M. C. Dietze, Y. Kim, K. Zhang, D. Bonal, B. Burban, P. B. Camargo, M. N. Hayek, S. R. Saleska, R. da Silva, R. L. Bras, S. C. Wofsy, and P. R. Moorcroft (2019), The biophysics, ecology, and biogeochemistry of functionally diverse, vertically and horizontally heterogeneous ecosystems: the Ecosystem Demography model, version 2.2 - part 2: Model evaluation for tropical South America, Geosci. Model Dev., doi:10.5194/gmd-2019-71, in press.
  3. di Porcia e Brugnera, M., F. Meunier, M. Longo, S. Moorthy, H. De Deurwaerder, S. A. Schnitzer, D. Bonal, B. Faybishenko, and H. Verbeeck (2019), Modelling the impact of liana infestation on the demography and carbon cycle of tropical forests, Glob. Change Biol., doi:10.1111/gcb.14769, advance online publication.
  4. de Almeida, C. T., L. S. Galvão, L. E. d. O. C. e. Aragão, J. P. H. B. Ometto, A. D. Jacon, F. R. d. S. Pereira, L. Y. Sato, A. P. Lopes, P. M. L. A. Graça, C. V. d. J. Silva, J. Ferreira-Ferreira, and M. Longo (2019), Combining LiDAR and hyperspectral data for aboveground biomass modeling in the Brazilian Amazon using different regression algorithms, Remote Sens. Environ., 232, 111,323, doi:10.1016/j.rse.2019.111323.
  5. Scaranello, M. A. S., M. Keller, M. Longo, M. N. dos Santos, V. Leitold, D. C. Morton, E. R. Pinagé, and F. D. B. Espírito-Santo (2019), Estimation of coarse woody debris stocks in intact and degraded forests in the Brazilian Amazon using airborne lidar, Biogeosciences, 16, 3457-3474, doi:10.5194/bg-2019-75.
  6. Farinosi, F., M. E. Arias, E. Lee, M. Longo, F. F. Pereira, A. Livino, P. R. Moorcroft, and J. Briscoe (2019), Future climate and land use change impacts on river flows in the Tapajós basin in the Brazilian Amazon, Earth's Future, 7(8), 993-1017, doi:10.1029/2019EF001198.
  7. Pinagé, E. R., M. Keller, P. Duffy, M. Longo, M. N. dos Santos, and D. C. Morton (2019), Long-term impacts of selective logging on Amazon forest dynamics from multi-temporal airborne LiDAR, Remote Sens., 11(6), 709, doi:10.3390/rs11060709.
  8. Longo, M., and M. Keller (2019), Not the same old(-growth) forest, New Phytol., 221(4), 1672-1675, doi:10.1111/nph.15636.
  9. Hayek, M. N., M. Longo, J. Wu, M. N. Smith, N. Restrepo-Coupe, R. Tapajós, R. da Silva, D. R. Fitzjarrald, P. B. Camargo, L. R. Hutyra, L. F. Alves, B. Daube, J. W. Munger, K. T. Wiedemann, S. R. Saleska, and S. C. Wofsy (2018). Carbon exchange in an Amazon forest: from hours to years. Biogeosciences, 15(15): 4833-4848. doi:10.5194/bg-15-4833-2018.
  10. Longo, M., R. G. Knox, N. M. Levine, L. F. Alves, D. Bonal, P. B. Camargo, D. R. Fitzjarrald, M. N. Hayek, N. Restrepo-Coupe, S. R. Saleska, R. da Silva, S. C. Stark, R. P. Tapajós, K. T. Wiedemann, K. Zhang, S. C. Wofsy, and P. R. Moorcroft (2018). Ecosystem heterogeneity and diversity mitigate Amazon forest resilience to frequent extreme droughts. New Phytol., 219(3): 914-931. doi:10.1111/nph.15185.
  11. Leitold, V., D. C. Morton, M. Longo, M. N. dos-Santos, M. Keller, and M. Scaranello (2018). El Niño drought increased canopy turnover in Amazon forests. New Phytol., 219(3): 959-971. doi:10.1111/nph.15110.
  12. Rappaport, D., D. Morton, M. Longo, M. Keller, R. Dubayah, and M. N. dos-Santos (2018). Quantifying long-term changes in carbon stocks and forest structure from Amazon forest degradation. Environ. Res. Lett., 13(6): 065013. doi:10.1088/1748-9326/aac331.
  13. Becknell, J. M., M. Keller, D. Piotto, M. Longo, M. N. dos Santos, M. A. Scaranello, R. B. d. O. Cavalcante, and S. Porder (2018). Landscape-scale lidar analysis of aboveground biomass distribution in secondary Brazilian Atlantic Forest. Biotropica, 50(3): 520-530. doi:10.1111/btp.12538.
  14. Hayek, M. N., R. Wehr, M. Longo, L. R. Hutyra, K. Wiedemann, J. W. Munger, D. Bonal, S. R. Saleska, D. R. Fitzjarrald, and S. C. Wofsy (2018). A novel correction for biases in forest eddy covariance carbon balance. Agric. For. Meteorol., 250-251: 90-101. doi:10.1016/j.agrformet.2017.12.186.
  15. Fisher, R. A., C. D. Koven, W. R. L. Anderegg, B. O. Christoffersen, M. C. Dietze, C. Farrior, J. A. Holm, G. Hurtt, R. G. Knox, P. J. Lawrence, J. W. Lichststein, M. Longo, A. M. Matheny, D. Medvigy, H. C. Muller-Landau, T. Powell, S. P. Serbin, H. Sato, J. Shuman, B. Smith, A. T. Trugman, T. Viskari, H. Verbeeck, E. Weng, C. Xu, X. Xu, T. Zhang, and P. Moorcroft (2018). Vegetation demographics in Earth System Models: a review of progress and priorities. Glob. Change Biol., 24(1): 35-54. doi:10.1111/gcb.13910.
  16. Freitas, S. R., J. Panetta, K. M. Longo, L. F. Rodrigues, D. S. Moreira, N. E. Rosário, P. L. Silva Dias, M. A. F. Silva Dias, E. P. Souza, E. D. Freitas, M. Longo, A. Frassoni, A. L. Fazenda, C. M. Santos e Silva, C. A. B. Pavani, D. Eiras, D. A. França, D. Massaru, F. B. Silva, F. C. Santos, G. Pereira, G. Camponogara, G. A. Ferrada, H. F. Campos Velho, I. Menezes, J. L. Freire, M. F. Alonso, M. S. Gácita, M. Zarzur, R. M. Fonseca, R. S. Lima, R. A. Siqueira, R. Braz, S. Tomita, V. Oliveira, and L. D. Martins (2017), The Brazilian developments on the Regional Atmospheric Modeling System (BRAMS 5.2): an integrated environmental model tuned for tropical areas, Geosci. Model Dev., 10(1), 189-222, doi:10.5194/gmd-10-189-2017.
  17. Longo, M., M. M. Keller, M. N. dos Santos, V. Leitold, E. R. Pinagé, A. Baccini, S. Saatchi, E. M. Nogueira, M. Batistella, and D. C. Morton (2016). Aboveground biomass variability across intact and degraded forests in the Brazilian Amazon. Global Biogeochem. Cycles, 30(11): 1639-1660. doi:10.1002/2016GB005465.
  18. Morton, D.C., J. Rubio, B. D. Cook, J.-P. Gastellu-Etchegorry, M. Longo, H. Choi, M. O. Hunter, and M. Keller (2016). Amazon forest structure generates diurnal and seasonal variability in light utilization. Biogeosciences, 12(7): 2195-2206. doi:10.5194/bg-13-2195-2016.
  19. Levine, N. M. , K. Zhang, M. Longo, A. Baccini, O. L. Phillips, S. L. Lewis, E. Alvarez, A. C. S. de Andrade, R. Brienen, T. Erwin, T. R. Feldpausch, A. L. M. Mendoza, P. N. Vargas, A. Prieto, J. E. S. Espejo, Y. Malhi, and P. R. Moorcroft (2016). Ecosystem heterogeneity determines the resilience of the Amazon to climate change. Proc. Natl. Acad. Sci. U. S. A., 113(3): 793-797. doi:10.1073/pnas.1511344112.
  20. Swann, A. L. S., M. Longo, R. G. Knox, E. Lee, and P. R. Moorcroft (2015). Future deforestation in the Amazon and consequences for South American climate. Agric. For. Meteorol., 214-215: 12-24. doi:10.1016/j.agrformet.2015.07.006.

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