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Tropical forest monitoring networks

Tropical forest monitoring networks. Yadvinder Malhi University of Oxford. Tropical forests are variable in soils, climate, faunal and floral composition, disturbance history and biogeographical context. RAINFOR Campaigns 2001-2010, permanent plots. Besotes 2010. Barinas 2009.

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Tropical forest monitoring networks

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  1. Tropical forest monitoring networks Yadvinder Malhi University of Oxford

  2. Tropical forests are variable in soils, climate, faunal and floral composition, disturbance history and biogeographical context

  3. RAINFOR Campaigns 2001-2010, permanent plots Besotes 2010 Barinas 2009 PtoNare 2010 Rio Grande 2004, 9 San Rafael 2010 Carbonera 2009 Mabura Hills 2006, 10 El Dorado 2004, 9 San Sebastian 2010 Nouragues 2008 Pibiri 2006, 10 Iwokrama 2010 Araracuara 2010 Bogi 2002,7,10 San Carlos de Rio Negro 2004,6 Braganca 2002 Jari 2003 Tiputini 2002,7,10 Lorena 2004,6 Zafire 2005,6,8 Jatun Sacha 2002,7,10 Tapajos 2003 Mocambo 2003 Manaus 2002,5,6 Caxiuana 2002,4,5,6,7,8,9,10 Iquitos 2001,5,6,8,9,10 Agua Pudre 2004,5,6 Jenaro Herrera 2005,6,7,8 Alta Floresta 2002,8 Jurua 1999,2009 Acre 2003,9 Pasco 2008,10 Sinop 2002 DoisIrmaos 2003,6,9 Porongaba 2003,6,9 Tanguro2009,10 Mabet 2010 Los Amigos 2008 El Tigre 2009 Cusco Amazonico 2003,6,8 Nova Xavantina 2008,10 BEEM 2006,10 Andes Transect 2003,6,7,8,9,10 Sacta 2006,9 Tambopata 2002,3,5,6,7,8,9,10 Noel Kempff 2001,6,7,9

  4. Measured biomass carbon sink Amazonia 0.45±0.12 Mg C ha-1 year-1 Phillips et al 2009 Science Africa 0.63±0.40 Mg C ha-1 year-1 Lewis et al. 2009 Nature

  5. Drought Sensitivity of the Amazon Forest Phillips et al. Science, 2009

  6. Tree alpha diversity in the Amazon Basin: ATDN 2012

  7. The Global Ecosystems Monitoring Network (GEM) gem.tropicalforests.ox.ac.uk

  8. Gross Primary Productivity Photosynthesis Global vegetation models Flux towers Optical remote sensing Carbon sequestration Standing Biomass Forest dynamics models Forest inventories Biomass remote sensing Malhi, 2012, J. Ecology

  9. Gross Primary Productivity Photosynthesis Net Primary Productivity Woody Productivity Residence time Mortality rate Standing Biomass Malhi, 2012, J. Ecology

  10. The Carbon Cycle of a Forest GPP NPP VOC NPPleaves,flowers,fruit R leaf NPPwood (Branch + Stem) DFine litterfall R stem R soil R CWD DCWD R roots NPPcoarse roots NPPfine roots DRoot R soil het Fdoc

  11. NPPTotal = 15.14±0.83 NPPAG= 9.96±0.41 NPPBG= 5.18±0.72 GPP= 36.15±3.97 Example results The carbon cycle of a forest at Tambopata. Peru NPP litterfall= 5.61±0.32 NPP herbivory = 0.76±0.05 R leaf=8.86±2.78 NPP branch turnover = 0.95±0.10 Dfine litterfall 5.61±0.32 R stem = 5.85±2.50 NPP ACW= 2.64±0.24 Rcwd Rsoil=12.98±0.82 D CWD 3.59±0.26 Rcoarseroot 1.23±0.62 R rhizosphere 5.07±0.86 NPP coarse roots = 0.51±0.05 NPP fine roots = 4.67±0.72 D root 5.18±0.72 Rsoilhet= 7.14±0.49 Malhi et al, Plant Ecology and Diversity, 2013

  12. The GEM Protocol Stem respiration Climate Dendrometers Soil respiration Litterfall, and components Ingrowth Cores Rhizotron Leaf respiration and photosynthesis Soil respiration partitioning

  13. Conclusions • Tropical forests are variable in soils, climate, faunal and floral composition, disturbance history and biogeographical context • Networks of forest plots are much greater than the sum of the parts • Standardization of measurement protocols are important for robust comparison • But the most important point is to reach out and build the networks

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