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Carbon recovery dynamics following disturbance by selective logging in Amazonian forests

  1. Camille Piponiot  Is a corresponding author
  2. Plinio Sist
  3. Lucas Mazzei
  4. Marielos Peña-Claros
  5. Francis E Putz
  6. Ervan Rutishauser
  7. Alexander Shenkin
  8. Nataly Ascarrunz
  9. Celso P de Azevedo
  10. Christopher Baraloto
  11. Mabiane França
  12. Marcelino Guedes
  13. Eurídice N Honorio Coronado
  14. Marcus VN d'Oliveira
  15. Ademir R Ruschel
  16. Kátia E da Silva
  17. Eleneide Doff Sotta
  18. Cintia R de Souza
  19. Edson Vidal
  20. Thales AP West
  21. Bruno Hérault  Is a corresponding author
  1. UMR EcoFoG (Agroparistech, CNRS, Inra, Université des Antilles, Cirad), French Guiana
  2. UMR EcoFoG (Agroparistech, CNRS, Inra, Université des Antilles, Université de Guyane), French Guiana
  3. UMR EcoFoG (Agroparistech, Inra, Université des Antilles, Université de Guyane, Cirad), French Guiana
  4. Cirad, UR Forests and Societies, France
  5. Embrapa Amazônia Oriental, Brazil
  6. Wageningen University, Netherlands
  7. University of Florida, United States
  8. CarbonForExpert, Switzerland
  9. University of Oxford, United Kingdom
  10. Instituto Boliviano de Investigación Forestal, Bolivia
  11. Embrapa Amazônia Ocidental, Brazil
  12. Florida International University, United States
  13. Embrapa Amapa, Brazil
  14. Instituto de Investigaciones de la Amazonia Peruana, Peru
  15. Embrapa Acre, Brazil
  16. University of São Paulo, Brazil
Research Article
Cite this article as: eLife 2016;5:e21394 doi: 10.7554/eLife.21394
10 figures and 1 table

Figures

Figure 1 with 1 supplement
Post-disturbance annual ACS changes of survivors and recruits in 133 Amazonian selectively logged plots.

Data is available between the year of minimum ACS (t=0) and t=30 years. ACS changes are: recruits’ ACS growth (orange), recruits’ ACS loss (gold), new recruits’ ACS (red), survivors’ ACS growth (light green) and survivors’ ACS loss (dark green). Thick solid lines are the maximum-likelihood predictions (for an average plot, when all covariates are null), and dashed lines are the model theoretical behaviour. New recruits’ ACS, recruits’ ACS growth, and recruits’ ACS loss converge over time to constant values. A dynamic equilibrium is then reached: ACS gain from recruitment and recruits’ growth compensate ACS loss from recruits’ mortality. Survivors’ ACS growth and loss. decline over time and tend to zero when all initial survivors have died.

https://doi.org/10.7554/eLife.21394.003
Figure 1—figure supplement 1
Experimental sites location, each site being composed of permanent forest plots varying in logging intensities, census length (colour) and total area (size).
https://doi.org/10.7554/eLife.21394.004
Figure 2 with 1 supplement
Effect of covariates on the rate at which post-disturbance ACS changes converge to a theoretical steady state (in yr-1).

Covariates are : disturbance intensity (loss) , i.e. the proportion of initial ACS loss; mean site’s ACS (acs0), and relative forest maturity, i.e. pre-logging plot ACS as a % of acs0 (dacs); annual precipitation (prec); seasonality of precipitation (seas), soil bulk density (bd). Covariates are centred and standardized. Red and black levels are 80% and 95% credible intervals, respectively. The median rate is the prediction of the convergence rate for an average plot (when all covariates are set to zero). Negative covariate values indicate slowing and positive values indicate accelerating rates. (a) Survivors’ ACS growth. (b) New recruits’ ACS. (c) Recruits’ ACS growth. (d) Survivors’ ACS loss. (e) Recruits’ ACS loss.

https://doi.org/10.7554/eLife.21394.005
Figure 2—source data 1

Parameters posterior distribution.

Columns are the 2.5%, 10%, 50%, 90% and 97.5% quantiles of the posterior distribution of the model parameters (rows).

https://doi.org/10.7554/eLife.21394.006
Figure 2—figure supplement 1
Fitted vs observed values of cumulative ACS changes (Mg C ha-1).

(a) Survivors’ cumulative ACS growth. (b) New recruits’ cumulative ACS. (c) Recruits’ cumulative ACS growth; (d) Survivors’ cumulative ACS loss; (e) Recruits’ cumulative ACS loss. The closer the dots are to the x=y line, the better the prediction. Dot transparency is proportional to the observation weight: transparent dots are low-weight observations. Because mortality is a stochastic event, ACS loss has poorer predictions than ACS gain which is a more continuous process.

https://doi.org/10.7554/eLife.21394.007
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.

https://doi.org/10.7554/eLife.21394.008
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).

https://doi.org/10.7554/eLife.21394.009
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.

https://doi.org/10.7554/eLife.21394.010
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.

https://doi.org/10.7554/eLife.21394.011
Appendix 1—figure 1
Observed vs fitted values of net ACS accumulation (MgC ha-1).

(a) Fitted values from the all-in-one model. (b) Fitted values from the process-based model (right). Net ACS accumulation is the sum of cumulative ACS changes (gain and loss). Each combination of a colour and shape is specific to a site. The closer the dots are to the x=y line, the better the prediction.

https://doi.org/10.7554/eLife.21394.013
Appendix 1—figure 2
Predicted trajectories of net ACS accumulation (MgC ha-1) per site with (a) the all-in-one model and (b) the process-based model.
https://doi.org/10.7554/eLife.21394.014
Author response image 1
Time to recover 50% of initial ACS per region and disturbance intensity.
https://doi.org/10.7554/eLife.21394.015
Author response image 2
Effect of botanical indetermination on cumulative ACS changes in the 9 Paracou forest plots.

Wood density of all undetermined trees is set to 0.4 (lower bound), 0.9 (higher bound), or the plot average wood density (dashed lines): the latter is the method used in the study. Cumulative ACS changes are then calculated. Cumulative ACS changes (MgC/ha) are: survivors’ ACS growth (light green), survivors’ ACS loss (dark green), new recruits’ ACS (red), recruits’ growth (orange), recruits’ ACS loss (ocher).

https://doi.org/10.7554/eLife.21394.016

Tables

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).

https://doi.org/10.7554/eLife.21394.012
ModelParameterPriorJustification
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
Rrη𝒰[0,3]Rr(t=0)<3×Rr(t=)
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 M λlossM𝒰[-βM,βM]Avoid multicollinearity problems
All models M (λlM)lloss𝒰[βM4,βM4]Avoid multicollinearity problems
  1. t0.95=ln(20)β is the time when the ACS change has reached 95% of its asymptotic value.

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

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