• Figure 3.
    Download figureOpen in new tabFigure 3. Predicted effect of disturbance intensity on ACS changes along time in an Amazonian-average plot.

    (a) Survivors’ ACS growth. (b) New recruits’ ACS. (c) Recruits’ ACS growth. (d) Survivors’ ACS loss. (e) Recruits’ ACS loss. (f) Net ACS change. The net ACS change is the sum of all five ACS changes. ACS changes were calculated with all parameters set to their maximum-likelihood value and covariates (except standardized disturbance intensity loss) set to 0. Time since minimum ACS varies from 0 to 30 year (i.e. the calibration interval) and disturbance intensity ranges between 5% and 60% of initial ACS loss.

    DOI: http://dx.doi.org/10.7554/eLife.21394.008

    Figure 4.
    Download figureOpen in new tabFigure 4. Predicted cumulative ACS changes (Mg C ha1) over the first 10 year after losing 40% of ACS.

    Extrapolation was based on global rasters: topsoil bulk density from the Harmonized global soil database (Nachtergaele et al., 2008), Worldclim precipitation data (Hijmans et al., 2005) and biomass stocks from Avitabile et al. map (Avitabile et al., 2016). Cumulative ACS changes are obtained by integrating annual ACS changes through time. We here show the median of each pixel. Top graphs are ACS gain and bottom graphs are ACS loss. (a) ACS gain from survivors’ growth. (b) ACS gain from new recruits. (c) ACS gain from recruits’ growth. (d) ACS loss from survivors’ mortality. (e) ACS loss from recruits’ mortality. Black dots are the location of our experimental sites. Survivors’ ACS changes (a and d) show strong regional variations unlike to recruits’ ACS changes (b,c and e).

    DOI: http://dx.doi.org/10.7554/eLife.21394.009

    Figure 5.
    Download figureOpen in new tabFigure 5. Predicted net ACS recovery over the first 10 year after losing 40% of pre-logging ACS.

    (a) median predictions. (b) coefficient of variation (per pixel). Four areas were arbitrarily chosen to illustrate four different geographical behaviours: (1) the Guiana Shield and (2) northwestern Amazonia are two areas with high ACS recovery; the Guiana Shield has higher initial ACS and slower ACS dynamics whereas northwestern Amazonia has lower initial ACS and faster ACS dynamics. (3) central Amazonia has intermediate ACS recovery. (4) southern Amazonia has low ACS recovery.

    DOI: http://dx.doi.org/10.7554/eLife.21394.010

    Figure 6.
    Download figureOpen in new tabFigure 6. Predicted contribution of annual ACS changes in ACS recovery in four regions of Amazonia (Figure 5).

    The white line is the net annual ACS recovery, i.e. the sum of all annual ACS changes. Survivors’ (green) and recruits’ (orange) contribution are positive for ACS gains (survivors’ ACS growth, new recruits’ ACS and recruits’ ACS growth) and negative for survivors’ and recruits’ ACS loss. Areas with higher levels of transparency and dotted lines are out of the calibration period (0–30 year). In the Guiana Shield and in nothwestern Amazonia, high levels of net ACS recovery are explained by large ACS gain from survivors’ growth. Extrapolation was based on global rasters: topsoil bulk density from the Harmonized global soil database (Nachtergaele et al., 2008), precipitation data from Worldclim (Hijmans et al., 2005) and biomass stocks from Avitabile et al. (Avitabile et al., 2016) map.

    DOI: http://dx.doi.org/10.7554/eLife.21394.011

  • Table 1.

    List of priors used to infer ACS changes in a Bayesian framework. Models are : (Sg) survivors’ ACS growth, (Sl) survivors’ ACS loss, (Rr) new recruits’ ACS, (Rg) recruits’ ACS growth, (Rl) recruits’ ACS loss. λloss is the parameter relative to the covariate loss (logging intensity).

    DOI: http://dx.doi.org/10.7554/eLife.21394.012

    SgαjSg𝒰[25,250]On average 100 survivors/ha storing 0.25 to 2.5 MgC each
    SgβjSg𝒰[0.015,0.04]75<t0.95Sg<200 yr
    SlβjSl𝒰[0.006,βSg]t0.95Sg<t0.95Sl<500 yr
    RrαiRr𝒰[0.1,1]Range of observed values in TmFO control plots
    RrβjRr𝒰[0.006,0.6]5<t0.95Rr<500 yr
    RgαiRg𝒰[0.5,3]Range of observed values in Amazonia (Johnson et al., 2016)
    RgβjRg𝒰[0.006,0.15]20<t0.95Rg<500 yr
    RlβjRl𝒰[0.003,0.06]50<t0.95Rl<1000 yr
    All models MλlossM𝒰[-βM,βM]Avoid multicollinearity problems
    All models M(λlM)lloss𝒰[βM4,βM4]Avoid multicollinearity problems
    • t0.95=ln(20)β is the time when the ACS change has reached 95% of its asymptotic value.

    • M is one of the five models: either Sg, Sl, Rr, Rg, Rl.

  • The following dataset was generated:

    Piponiot C, Sist P, Mazzei L, Peña-Claros M, Putz F, Rutishauser E, Shenkin A, Ascarrunz N, de Azevedo C, Baraloto C, França M, Guedes M, Honorio Coronado E, d'Oliveira MVN, Ruschel AR, da Silva KE, Doff Sotta E, de Souza CR, Vidal E, West TAP, Hérault B, 2016,Data from: Post-disturbance carbon recovery in Amazonian forests, http://dx.doi.org/10.5061/dryad.rc279, Available at Dryad Digital Repository under a CC0 Public Domain Dedication