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M. Gloor, O.L. Phillips, J. Lloyd, S.L. Lewis, Y. Malhi, T.R. Baker, G. Lopez-Gonzalez, J. Peacock S. Almeida, E. Alvarez, A.C. Alves de Oliveira, I. Amaral, S. Andelman, L. Arroyo, G. Aymard, O. Banki, L. Blanc, D. Bonal, P. Brando, K.-J. Chao, J. Chave, N. Davila, T. Edwin, J. Espejo, A. di Fiore, T. Feldpausch, A. Freitas, R. Herrera, N. Higuchi, E. Honorio, E. Jiménez, T. Killeen, W. Laurance, C. Mendoza, A. Montegudo, H. Nascimento, D. Neill, D. Nepstad, P. Núñez Vargas, J. Olivier, M.C. Penuela, A. Peña Cruz, A. Prieto, N. Pitman, C. Quesada, R. Salamão, M. Schwarz, J. Stropp, A. F. Ramírez, H. Ramírez, A. Rudas, H. ter Steege, N. Silva, A. Torres, J. Terborgh, A. R. Vásquez, G. van der Heijden Acknowledgements Bruce Nelson, Laurens Poorter, Fernando Santo-Espirito, Aaron Clauset, C.T. Shalizi Does the disturbance hypothesis explain the biomass increase in basin-wide Amazon forest plot data? A simple data-based analysis

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Outline 1. Introduction on large-scale forest census results 2. Hypothesis why trends wrongly interpreted 3. Rainfor and Blowdown data as basis for simulator 4. Implications

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Land biomass inventories Forest census, Peru

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Possibly large-scale response of tropical rainforests to a changing atmospheric environment and climate Implications on: - global carbon budget and greenhouse warming - future feedbacks of forests on climate Relevance

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Positive Growth trends are artefact of 1.‘Slow in rapid out’ effect which biases results 2.Basin wide catastrophe responsible for observed growth trends Hypothesis

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a)Old-growth forest plots (RAINFOR network) 135 plots 226.2 ha 11.3 years /plot Fairly good coverage of main axis of known aboveground biomass gains controls b) Blow-down data from Bruce Nelson estimated from Landsat images (thanks again!) The Data

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The Model

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Losses RAINFOR probability of mass loss m per hectare and year

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e.g. for, similarly for n year census interval

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Blow-downs (Nelson et al. 1994) Goldstein M. L., Morris S.A., Yen G. G. (2004) Problems with fitting to the power law distribution. Eur. Phys. J. B 41, 225-258. Clauset A., Shalizi C.T. Newman M.E.J (2007) Power law distri- butions in empirical data. [http://physics,data-an] arXiv:0706.1062v1 ‘power law - fat tail’ =3.1 Maximum Likelihood Estimator (Ordinary Least Squares not appropriate method) Bootstrapping: power law plausible distribution for Nelson blow down data

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Mathematical nature of RAINFOR mortality stats

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Models for biomass losses Mixed exponential - power law model Pure exponential model

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Model for biomass gains

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Simulator Results

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Statistical significance of biomass gains E Amazon W Amazon All Amazon Exp. model 0.19 0.19 0.14 Mixed model 0.41 0.41 0.30 N number of censuses Pooling of results from different census intervals: exploit that variance grows linearly with observation period -> permits to scale variances to one-year periods - then use common rule to combine independent estimates

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Severity of disturbance and Return times Probability of mass loss event with loss > m where Return time of such an event Biomass loss associated with a given return time

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Percentile Return time Mortality loss (%) (yr) (t ha -1 yr -1 ) (%) (t ha -1 yr -1 ) (%) (W / E) (W / E) All Amazon Exponential Model Mixed Model 95 20 12.5 / 13.6 5.0 / 3.6 12.8 3.4 99 100 19.1 / 20.9 7.6 / 5.5 25.4 6.7 99.9 1000 28.8 / 31.4 11.5 / 8.3 96.4 25.4 Severity of disturbance and Return times

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Summary Statistical power of RAINFOR network forest census data is sufficient to detect a positive above ground biomass signal; ‘Slow in - rapid out’ effect is covered by the network; Large-scale disturbances are just really, really rare Highly likely there is indeed an Amazon wide forest response growth response to exterior forcing Exciting !

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Thank you for your Attention ! Thank you for your attention

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Observed and predicted decrease in variance with increasing census interval according to exponential model n (yr): census interval

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Main axis of forest biomass gains controls

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Courtesy Oliver Phillips

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Thank you for your attention

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