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

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

Land biomass inventories Forest census, Peru

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

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

a)Old-growth forest plots (RAINFOR network) 135 plots 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

The Model

Losses RAINFOR probability of mass loss m per hectare and year

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

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, Clauset A., Shalizi C.T. Newman M.E.J (2007) Power law distri- butions in empirical data. [ arXiv: v1 ‘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

Mathematical nature of RAINFOR mortality stats

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

Model for biomass gains

Simulator Results

Statistical significance of biomass gains E Amazon W Amazon All Amazon Exp. model Mixed model 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

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

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 / / / / / / Severity of disturbance and Return times

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 !

Thank you for your Attention ! Thank you for your attention

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

Main axis of forest biomass gains controls

Courtesy Oliver Phillips

Thank you for your attention